[
  {
    "path": ".gitignore",
    "content": "*.bak\n*.bak.*\n*.ckpt\n*.old\n*.pyc\n.DS_Store\n.ipynb_checkpoints\n.vscode/\ncheckpoint\nlogs/*\ntf_logs/*\nimages/**/*.png\nimages/**/*.dot\nmy_*\nperson.proto\nperson.desc\nperson_pb2.py\ndatasets/flowers\ndatasets/lifesat/lifesat.csv\ndatasets/spam\ndatasets/titanic\ndatasets/words\n\n"
  },
  {
    "path": "02_end_to_end_machine_learning_project.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 2 – End-to-end Machine Learning project**\\n\",\n    \"\\n\",\n    \"*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.*\\n\",\n    \"\\n\",\n    \"*This notebook contains all the sample code and solutions to the exercices in chapter 2.*\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note**: You may find little differences between the code outputs in the book and in these Jupyter notebooks: these slight differences are mostly due to the random nature of many training algorithms: although I have tried to make these notebooks' outputs as constant as possible, it is impossible to guarantee that they will produce the exact same output on every platform. Also, some data structures (such as dictionaries) do not preserve the item order. Finally, I fixed a few minor bugs (I added notes next to the concerned cells) which lead to slightly different results, without changing the ideas presented in the book.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"mpl.rc('axes', labelsize=14)\\n\",\n    \"mpl.rc('xtick', labelsize=12)\\n\",\n    \"mpl.rc('ytick', labelsize=12)\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"end_to_end_project\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Get the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import tarfile\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"DOWNLOAD_ROOT = \\\"https://raw.githubusercontent.com/ageron/handson-ml/master/\\\"\\n\",\n    \"HOUSING_PATH = os.path.join(\\\"datasets\\\", \\\"housing\\\")\\n\",\n    \"HOUSING_URL = DOWNLOAD_ROOT + \\\"datasets/housing/housing.tgz\\\"\\n\",\n    \"\\n\",\n    \"def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\\n\",\n    \"    os.makedirs(housing_path, exist_ok=True)\\n\",\n    \"    tgz_path = os.path.join(housing_path, \\\"housing.tgz\\\")\\n\",\n    \"    urllib.request.urlretrieve(housing_url, tgz_path)\\n\",\n    \"    housing_tgz = tarfile.open(tgz_path)\\n\",\n    \"    housing_tgz.extractall(path=housing_path)\\n\",\n    \"    housing_tgz.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fetch_housing_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"def load_housing_data(housing_path=HOUSING_PATH):\\n\",\n    \"    csv_path = os.path.join(housing_path, \\\"housing.csv\\\")\\n\",\n    \"    return pd.read_csv(csv_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>median_house_value</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>-122.23</td>\\n\",\n       \"      <td>37.88</td>\\n\",\n       \"      <td>41.0</td>\\n\",\n       \"      <td>880.0</td>\\n\",\n       \"      <td>129.0</td>\\n\",\n       \"      <td>322.0</td>\\n\",\n       \"      <td>126.0</td>\\n\",\n       \"      <td>8.3252</td>\\n\",\n       \"      <td>452600.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>-122.22</td>\\n\",\n       \"      <td>37.86</td>\\n\",\n       \"      <td>21.0</td>\\n\",\n       \"      <td>7099.0</td>\\n\",\n       \"      <td>1106.0</td>\\n\",\n       \"      <td>2401.0</td>\\n\",\n       \"      <td>1138.0</td>\\n\",\n       \"      <td>8.3014</td>\\n\",\n       \"      <td>358500.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-122.24</td>\\n\",\n       \"      <td>37.85</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>1467.0</td>\\n\",\n       \"      <td>190.0</td>\\n\",\n       \"      <td>496.0</td>\\n\",\n       \"      <td>177.0</td>\\n\",\n       \"      <td>7.2574</td>\\n\",\n       \"      <td>352100.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>-122.25</td>\\n\",\n       \"      <td>37.85</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>1274.0</td>\\n\",\n       \"      <td>235.0</td>\\n\",\n       \"      <td>558.0</td>\\n\",\n       \"      <td>219.0</td>\\n\",\n       \"      <td>5.6431</td>\\n\",\n       \"      <td>341300.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-122.25</td>\\n\",\n       \"      <td>37.85</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>1627.0</td>\\n\",\n       \"      <td>280.0</td>\\n\",\n       \"      <td>565.0</td>\\n\",\n       \"      <td>259.0</td>\\n\",\n       \"      <td>3.8462</td>\\n\",\n       \"      <td>342200.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\\\\n\",\n       \"0    -122.23     37.88                41.0        880.0           129.0   \\n\",\n       \"1    -122.22     37.86                21.0       7099.0          1106.0   \\n\",\n       \"2    -122.24     37.85                52.0       1467.0           190.0   \\n\",\n       \"3    -122.25     37.85                52.0       1274.0           235.0   \\n\",\n       \"4    -122.25     37.85                52.0       1627.0           280.0   \\n\",\n       \"\\n\",\n       \"   population  households  median_income  median_house_value ocean_proximity  \\n\",\n       \"0       322.0       126.0         8.3252            452600.0        NEAR BAY  \\n\",\n       \"1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \\n\",\n       \"2       496.0       177.0         7.2574            352100.0        NEAR BAY  \\n\",\n       \"3       558.0       219.0         5.6431            341300.0        NEAR BAY  \\n\",\n       \"4       565.0       259.0         3.8462            342200.0        NEAR BAY  \"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing = load_housing_data()\\n\",\n    \"housing.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 20640 entries, 0 to 20639\\n\",\n      \"Data columns (total 10 columns):\\n\",\n      \"longitude             20640 non-null float64\\n\",\n      \"latitude              20640 non-null float64\\n\",\n      \"housing_median_age    20640 non-null float64\\n\",\n      \"total_rooms           20640 non-null float64\\n\",\n      \"total_bedrooms        20433 non-null float64\\n\",\n      \"population            20640 non-null float64\\n\",\n      \"households            20640 non-null float64\\n\",\n      \"median_income         20640 non-null float64\\n\",\n      \"median_house_value    20640 non-null float64\\n\",\n      \"ocean_proximity       20640 non-null object\\n\",\n      \"dtypes: float64(9), object(1)\\n\",\n      \"memory usage: 1.6+ MB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"housing.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<1H OCEAN     9136\\n\",\n       \"INLAND        6551\\n\",\n       \"NEAR OCEAN    2658\\n\",\n       \"NEAR BAY      2290\\n\",\n       \"ISLAND           5\\n\",\n       \"Name: ocean_proximity, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing[\\\"ocean_proximity\\\"].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>median_house_value</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20433.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"      <td>20640.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>-119.569704</td>\\n\",\n       \"      <td>35.631861</td>\\n\",\n       \"      <td>28.639486</td>\\n\",\n       \"      <td>2635.763081</td>\\n\",\n       \"      <td>537.870553</td>\\n\",\n       \"      <td>1425.476744</td>\\n\",\n       \"      <td>499.539680</td>\\n\",\n       \"      <td>3.870671</td>\\n\",\n       \"      <td>206855.816909</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>2.003532</td>\\n\",\n       \"      <td>2.135952</td>\\n\",\n       \"      <td>12.585558</td>\\n\",\n       \"      <td>2181.615252</td>\\n\",\n       \"      <td>421.385070</td>\\n\",\n       \"      <td>1132.462122</td>\\n\",\n       \"      <td>382.329753</td>\\n\",\n       \"      <td>1.899822</td>\\n\",\n       \"      <td>115395.615874</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>-124.350000</td>\\n\",\n       \"      <td>32.540000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.499900</td>\\n\",\n       \"      <td>14999.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>-121.800000</td>\\n\",\n       \"      <td>33.930000</td>\\n\",\n       \"      <td>18.000000</td>\\n\",\n       \"      <td>1447.750000</td>\\n\",\n       \"      <td>296.000000</td>\\n\",\n       \"      <td>787.000000</td>\\n\",\n       \"      <td>280.000000</td>\\n\",\n       \"      <td>2.563400</td>\\n\",\n       \"      <td>119600.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>-118.490000</td>\\n\",\n       \"      <td>34.260000</td>\\n\",\n       \"      <td>29.000000</td>\\n\",\n       \"      <td>2127.000000</td>\\n\",\n       \"      <td>435.000000</td>\\n\",\n       \"      <td>1166.000000</td>\\n\",\n       \"      <td>409.000000</td>\\n\",\n       \"      <td>3.534800</td>\\n\",\n       \"      <td>179700.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>-118.010000</td>\\n\",\n       \"      <td>37.710000</td>\\n\",\n       \"      <td>37.000000</td>\\n\",\n       \"      <td>3148.000000</td>\\n\",\n       \"      <td>647.000000</td>\\n\",\n       \"      <td>1725.000000</td>\\n\",\n       \"      <td>605.000000</td>\\n\",\n       \"      <td>4.743250</td>\\n\",\n       \"      <td>264725.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>-114.310000</td>\\n\",\n       \"      <td>41.950000</td>\\n\",\n       \"      <td>52.000000</td>\\n\",\n       \"      <td>39320.000000</td>\\n\",\n       \"      <td>6445.000000</td>\\n\",\n       \"      <td>35682.000000</td>\\n\",\n       \"      <td>6082.000000</td>\\n\",\n       \"      <td>15.000100</td>\\n\",\n       \"      <td>500001.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          longitude      latitude  housing_median_age   total_rooms  \\\\\\n\",\n       \"count  20640.000000  20640.000000        20640.000000  20640.000000   \\n\",\n       \"mean    -119.569704     35.631861           28.639486   2635.763081   \\n\",\n       \"std        2.003532      2.135952           12.585558   2181.615252   \\n\",\n       \"min     -124.350000     32.540000            1.000000      2.000000   \\n\",\n       \"25%     -121.800000     33.930000           18.000000   1447.750000   \\n\",\n       \"50%     -118.490000     34.260000           29.000000   2127.000000   \\n\",\n       \"75%     -118.010000     37.710000           37.000000   3148.000000   \\n\",\n       \"max     -114.310000     41.950000           52.000000  39320.000000   \\n\",\n       \"\\n\",\n       \"       total_bedrooms    population    households  median_income  \\\\\\n\",\n       \"count    20433.000000  20640.000000  20640.000000   20640.000000   \\n\",\n       \"mean       537.870553   1425.476744    499.539680       3.870671   \\n\",\n       \"std        421.385070   1132.462122    382.329753       1.899822   \\n\",\n       \"min          1.000000      3.000000      1.000000       0.499900   \\n\",\n       \"25%        296.000000    787.000000    280.000000       2.563400   \\n\",\n       \"50%        435.000000   1166.000000    409.000000       3.534800   \\n\",\n       \"75%        647.000000   1725.000000    605.000000       4.743250   \\n\",\n       \"max       6445.000000  35682.000000   6082.000000      15.000100   \\n\",\n       \"\\n\",\n       \"       median_house_value  \\n\",\n       \"count        20640.000000  \\n\",\n       \"mean        206855.816909  \\n\",\n       \"std         115395.615874  \\n\",\n       \"min          14999.000000  \\n\",\n       \"25%         119600.000000  \\n\",\n       \"50%         179700.000000  \\n\",\n       \"75%         264725.000000  \\n\",\n       \"max         500001.000000  \"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure attribute_histogram_plots\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x1080 with 9 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"housing.hist(bins=50, figsize=(20,15))\\n\",\n    \"save_fig(\\\"attribute_histogram_plots\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# to make this notebook's output identical at every run\\n\",\n    \"np.random.seed(42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# For illustration only. Sklearn has train_test_split()\\n\",\n    \"def split_train_test(data, test_ratio):\\n\",\n    \"    shuffled_indices = np.random.permutation(len(data))\\n\",\n    \"    test_set_size = int(len(data) * test_ratio)\\n\",\n    \"    test_indices = shuffled_indices[:test_set_size]\\n\",\n    \"    train_indices = shuffled_indices[test_set_size:]\\n\",\n    \"    return data.iloc[train_indices], data.iloc[test_indices]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"16512 train + 4128 test\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_set, test_set = split_train_test(housing, 0.2)\\n\",\n    \"print(len(train_set), \\\"train +\\\", len(test_set), \\\"test\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from zlib import crc32\\n\",\n    \"\\n\",\n    \"def test_set_check(identifier, test_ratio):\\n\",\n    \"    return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32\\n\",\n    \"\\n\",\n    \"def split_train_test_by_id(data, test_ratio, id_column):\\n\",\n    \"    ids = data[id_column]\\n\",\n    \"    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))\\n\",\n    \"    return data.loc[~in_test_set], data.loc[in_test_set]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The implementation of `test_set_check()` above works fine in both Python 2 and Python 3. In earlier releases, the following implementation was proposed, which supported any hash function, but was much slower and did not support Python 2:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import hashlib\\n\",\n    \"\\n\",\n    \"def test_set_check(identifier, test_ratio, hash=hashlib.md5):\\n\",\n    \"    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want an implementation that supports any hash function and is compatible with both Python 2 and Python 3, here is one:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test_set_check(identifier, test_ratio, hash=hashlib.md5):\\n\",\n    \"    return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing_with_id = housing.reset_index()   # adds an `index` column\\n\",\n    \"train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \\\"index\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing_with_id[\\\"id\\\"] = housing[\\\"longitude\\\"] * 1000 + housing[\\\"latitude\\\"]\\n\",\n    \"train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \\\"id\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>index</th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>median_house_value</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"      <th>id</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-122.26</td>\\n\",\n       \"      <td>37.84</td>\\n\",\n       \"      <td>42.0</td>\\n\",\n       \"      <td>2555.0</td>\\n\",\n       \"      <td>665.0</td>\\n\",\n       \"      <td>1206.0</td>\\n\",\n       \"      <td>595.0</td>\\n\",\n       \"      <td>2.0804</td>\\n\",\n       \"      <td>226700.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"      <td>-122222.16</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>-122.26</td>\\n\",\n       \"      <td>37.85</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>2202.0</td>\\n\",\n       \"      <td>434.0</td>\\n\",\n       \"      <td>910.0</td>\\n\",\n       \"      <td>402.0</td>\\n\",\n       \"      <td>3.2031</td>\\n\",\n       \"      <td>281500.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"      <td>-122222.15</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>-122.26</td>\\n\",\n       \"      <td>37.85</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>3503.0</td>\\n\",\n       \"      <td>752.0</td>\\n\",\n       \"      <td>1504.0</td>\\n\",\n       \"      <td>734.0</td>\\n\",\n       \"      <td>3.2705</td>\\n\",\n       \"      <td>241800.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"      <td>-122222.15</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>-122.26</td>\\n\",\n       \"      <td>37.85</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>2491.0</td>\\n\",\n       \"      <td>474.0</td>\\n\",\n       \"      <td>1098.0</td>\\n\",\n       \"      <td>468.0</td>\\n\",\n       \"      <td>3.0750</td>\\n\",\n       \"      <td>213500.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"      <td>-122222.15</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>-122.26</td>\\n\",\n       \"      <td>37.84</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>696.0</td>\\n\",\n       \"      <td>191.0</td>\\n\",\n       \"      <td>345.0</td>\\n\",\n       \"      <td>174.0</td>\\n\",\n       \"      <td>2.6736</td>\\n\",\n       \"      <td>191300.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"      <td>-122222.16</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    index  longitude  latitude  housing_median_age  total_rooms  \\\\\\n\",\n       \"8       8    -122.26     37.84                42.0       2555.0   \\n\",\n       \"10     10    -122.26     37.85                52.0       2202.0   \\n\",\n       \"11     11    -122.26     37.85                52.0       3503.0   \\n\",\n       \"12     12    -122.26     37.85                52.0       2491.0   \\n\",\n       \"13     13    -122.26     37.84                52.0        696.0   \\n\",\n       \"\\n\",\n       \"    total_bedrooms  population  households  median_income  median_house_value  \\\\\\n\",\n       \"8            665.0      1206.0       595.0         2.0804            226700.0   \\n\",\n       \"10           434.0       910.0       402.0         3.2031            281500.0   \\n\",\n       \"11           752.0      1504.0       734.0         3.2705            241800.0   \\n\",\n       \"12           474.0      1098.0       468.0         3.0750            213500.0   \\n\",\n       \"13           191.0       345.0       174.0         2.6736            191300.0   \\n\",\n       \"\\n\",\n       \"   ocean_proximity         id  \\n\",\n       \"8         NEAR BAY -122222.16  \\n\",\n       \"10        NEAR BAY -122222.15  \\n\",\n       \"11        NEAR BAY -122222.15  \\n\",\n       \"12        NEAR BAY -122222.15  \\n\",\n       \"13        NEAR BAY -122222.16  \"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_set.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>median_house_value</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20046</th>\\n\",\n       \"      <td>-119.01</td>\\n\",\n       \"      <td>36.06</td>\\n\",\n       \"      <td>25.0</td>\\n\",\n       \"      <td>1505.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1392.0</td>\\n\",\n       \"      <td>359.0</td>\\n\",\n       \"      <td>1.6812</td>\\n\",\n       \"      <td>47700.0</td>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3024</th>\\n\",\n       \"      <td>-119.46</td>\\n\",\n       \"      <td>35.14</td>\\n\",\n       \"      <td>30.0</td>\\n\",\n       \"      <td>2943.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1565.0</td>\\n\",\n       \"      <td>584.0</td>\\n\",\n       \"      <td>2.5313</td>\\n\",\n       \"      <td>45800.0</td>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15663</th>\\n\",\n       \"      <td>-122.44</td>\\n\",\n       \"      <td>37.80</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>3830.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1310.0</td>\\n\",\n       \"      <td>963.0</td>\\n\",\n       \"      <td>3.4801</td>\\n\",\n       \"      <td>500001.0</td>\\n\",\n       \"      <td>NEAR BAY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20484</th>\\n\",\n       \"      <td>-118.72</td>\\n\",\n       \"      <td>34.28</td>\\n\",\n       \"      <td>17.0</td>\\n\",\n       \"      <td>3051.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1705.0</td>\\n\",\n       \"      <td>495.0</td>\\n\",\n       \"      <td>5.7376</td>\\n\",\n       \"      <td>218600.0</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9814</th>\\n\",\n       \"      <td>-121.93</td>\\n\",\n       \"      <td>36.62</td>\\n\",\n       \"      <td>34.0</td>\\n\",\n       \"      <td>2351.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1063.0</td>\\n\",\n       \"      <td>428.0</td>\\n\",\n       \"      <td>3.7250</td>\\n\",\n       \"      <td>278000.0</td>\\n\",\n       \"      <td>NEAR OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\\\\n\",\n       \"20046    -119.01     36.06                25.0       1505.0             NaN   \\n\",\n       \"3024     -119.46     35.14                30.0       2943.0             NaN   \\n\",\n       \"15663    -122.44     37.80                52.0       3830.0             NaN   \\n\",\n       \"20484    -118.72     34.28                17.0       3051.0             NaN   \\n\",\n       \"9814     -121.93     36.62                34.0       2351.0             NaN   \\n\",\n       \"\\n\",\n       \"       population  households  median_income  median_house_value  \\\\\\n\",\n       \"20046      1392.0       359.0         1.6812             47700.0   \\n\",\n       \"3024       1565.0       584.0         2.5313             45800.0   \\n\",\n       \"15663      1310.0       963.0         3.4801            500001.0   \\n\",\n       \"20484      1705.0       495.0         5.7376            218600.0   \\n\",\n       \"9814       1063.0       428.0         3.7250            278000.0   \\n\",\n       \"\\n\",\n       \"      ocean_proximity  \\n\",\n       \"20046          INLAND  \\n\",\n       \"3024           INLAND  \\n\",\n       \"15663        NEAR BAY  \\n\",\n       \"20484       <1H OCEAN  \\n\",\n       \"9814       NEAR OCEAN  \"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_set.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x114f7c828>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing[\\\"median_income\\\"].hist()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: in the book, I did not use `pd.cut()`, instead I used the code below. The `pd.cut()` solution gives the same result (except the labels are integers instead of floats), but it is simpler to understand:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"# Divide by 1.5 to limit the number of income categories\\n\",\n    \"housing[\\\"income_cat\\\"] = np.ceil(housing[\\\"median_income\\\"] / 1.5)\\n\",\n    \"# Label those above 5 as 5\\n\",\n    \"housing[\\\"income_cat\\\"].where(housing[\\\"income_cat\\\"] < 5, 5.0, inplace=True)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing[\\\"income_cat\\\"] = pd.cut(housing[\\\"median_income\\\"],\\n\",\n    \"                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\\n\",\n    \"                               labels=[1, 2, 3, 4, 5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3    7236\\n\",\n       \"2    6581\\n\",\n       \"4    3639\\n\",\n       \"5    2362\\n\",\n       \"1     822\\n\",\n       \"Name: income_cat, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing[\\\"income_cat\\\"].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x12b945588>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing[\\\"income_cat\\\"].hist()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import StratifiedShuffleSplit\\n\",\n    \"\\n\",\n    \"split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\\n\",\n    \"for train_index, test_index in split.split(housing, housing[\\\"income_cat\\\"]):\\n\",\n    \"    strat_train_set = housing.loc[train_index]\\n\",\n    \"    strat_test_set = housing.loc[test_index]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3    0.350533\\n\",\n       \"2    0.318798\\n\",\n       \"4    0.176357\\n\",\n       \"5    0.114583\\n\",\n       \"1    0.039729\\n\",\n       \"Name: income_cat, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"strat_test_set[\\\"income_cat\\\"].value_counts() / len(strat_test_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3    0.350581\\n\",\n       \"2    0.318847\\n\",\n       \"4    0.176308\\n\",\n       \"5    0.114438\\n\",\n       \"1    0.039826\\n\",\n       \"Name: income_cat, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing[\\\"income_cat\\\"].value_counts() / len(housing)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def income_cat_proportions(data):\\n\",\n    \"    return data[\\\"income_cat\\\"].value_counts() / len(data)\\n\",\n    \"\\n\",\n    \"train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\\n\",\n    \"\\n\",\n    \"compare_props = pd.DataFrame({\\n\",\n    \"    \\\"Overall\\\": income_cat_proportions(housing),\\n\",\n    \"    \\\"Stratified\\\": income_cat_proportions(strat_test_set),\\n\",\n    \"    \\\"Random\\\": income_cat_proportions(test_set),\\n\",\n    \"}).sort_index()\\n\",\n    \"compare_props[\\\"Rand. %error\\\"] = 100 * compare_props[\\\"Random\\\"] / compare_props[\\\"Overall\\\"] - 100\\n\",\n    \"compare_props[\\\"Strat. %error\\\"] = 100 * compare_props[\\\"Stratified\\\"] / compare_props[\\\"Overall\\\"] - 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Overall</th>\\n\",\n       \"      <th>Stratified</th>\\n\",\n       \"      <th>Random</th>\\n\",\n       \"      <th>Rand. %error</th>\\n\",\n       \"      <th>Strat. %error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.039826</td>\\n\",\n       \"      <td>0.039729</td>\\n\",\n       \"      <td>0.040213</td>\\n\",\n       \"      <td>0.973236</td>\\n\",\n       \"      <td>-0.243309</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.318847</td>\\n\",\n       \"      <td>0.318798</td>\\n\",\n       \"      <td>0.324370</td>\\n\",\n       \"      <td>1.732260</td>\\n\",\n       \"      <td>-0.015195</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.350581</td>\\n\",\n       \"      <td>0.350533</td>\\n\",\n       \"      <td>0.358527</td>\\n\",\n       \"      <td>2.266446</td>\\n\",\n       \"      <td>-0.013820</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0.176308</td>\\n\",\n       \"      <td>0.176357</td>\\n\",\n       \"      <td>0.167393</td>\\n\",\n       \"      <td>-5.056334</td>\\n\",\n       \"      <td>0.027480</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>0.114438</td>\\n\",\n       \"      <td>0.114583</td>\\n\",\n       \"      <td>0.109496</td>\\n\",\n       \"      <td>-4.318374</td>\\n\",\n       \"      <td>0.127011</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    Overall  Stratified    Random  Rand. %error  Strat. %error\\n\",\n       \"1  0.039826    0.039729  0.040213      0.973236      -0.243309\\n\",\n       \"2  0.318847    0.318798  0.324370      1.732260      -0.015195\\n\",\n       \"3  0.350581    0.350533  0.358527      2.266446      -0.013820\\n\",\n       \"4  0.176308    0.176357  0.167393     -5.056334       0.027480\\n\",\n       \"5  0.114438    0.114583  0.109496     -4.318374       0.127011\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"compare_props\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for set_ in (strat_train_set, strat_test_set):\\n\",\n    \"    set_.drop(\\\"income_cat\\\", axis=1, inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Discover and visualize the data to gain insights\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing = strat_train_set.copy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure bad_visualization_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing.plot(kind=\\\"scatter\\\", x=\\\"longitude\\\", y=\\\"latitude\\\")\\n\",\n    \"save_fig(\\\"bad_visualization_plot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure better_visualization_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing.plot(kind=\\\"scatter\\\", x=\\\"longitude\\\", y=\\\"latitude\\\", alpha=0.1)\\n\",\n    \"save_fig(\\\"better_visualization_plot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611). Thanks to Wilmer Arellano for pointing it out.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure housing_prices_scatterplot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x504 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing.plot(kind=\\\"scatter\\\", x=\\\"longitude\\\", y=\\\"latitude\\\", alpha=0.4,\\n\",\n    \"    s=housing[\\\"population\\\"]/100, label=\\\"population\\\", figsize=(10,7),\\n\",\n    \"    c=\\\"median_house_value\\\", cmap=plt.get_cmap(\\\"jet\\\"), colorbar=True,\\n\",\n    \"    sharex=False)\\n\",\n    \"plt.legend()\\n\",\n    \"save_fig(\\\"housing_prices_scatterplot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading california.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('./images/end_to_end_project/california.png',\\n\",\n       \" <http.client.HTTPMessage at 0x7fcc40087150>)\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Download the California image\\n\",\n    \"images_path = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", \\\"end_to_end_project\\\")\\n\",\n    \"os.makedirs(images_path, exist_ok=True)\\n\",\n    \"DOWNLOAD_ROOT = \\\"https://raw.githubusercontent.com/ageron/handson-ml/master/\\\"\\n\",\n    \"filename = \\\"california.png\\\"\\n\",\n    \"print(\\\"Downloading\\\", filename)\\n\",\n    \"url = DOWNLOAD_ROOT + \\\"images/end_to_end_project/\\\" + filename\\n\",\n    \"urllib.request.urlretrieve(url, os.path.join(images_path, filename))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure california_housing_prices_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x504 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.image as mpimg\\n\",\n    \"california_img=mpimg.imread(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png')\\n\",\n    \"ax = housing.plot(kind=\\\"scatter\\\", x=\\\"longitude\\\", y=\\\"latitude\\\", figsize=(10,7),\\n\",\n    \"                       s=housing['population']/100, label=\\\"Population\\\",\\n\",\n    \"                       c=\\\"median_house_value\\\", cmap=plt.get_cmap(\\\"jet\\\"),\\n\",\n    \"                       colorbar=False, alpha=0.4,\\n\",\n    \"                      )\\n\",\n    \"plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\\n\",\n    \"           cmap=plt.get_cmap(\\\"jet\\\"))\\n\",\n    \"plt.ylabel(\\\"Latitude\\\", fontsize=14)\\n\",\n    \"plt.xlabel(\\\"Longitude\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"prices = housing[\\\"median_house_value\\\"]\\n\",\n    \"tick_values = np.linspace(prices.min(), prices.max(), 11)\\n\",\n    \"cbar = plt.colorbar()\\n\",\n    \"cbar.ax.set_yticklabels([\\\"$%dk\\\"%(round(v/1000)) for v in tick_values], fontsize=14)\\n\",\n    \"cbar.set_label('Median House Value', fontsize=16)\\n\",\n    \"\\n\",\n    \"plt.legend(fontsize=16)\\n\",\n    \"save_fig(\\\"california_housing_prices_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"corr_matrix = housing.corr()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"median_house_value    1.000000\\n\",\n       \"median_income         0.687160\\n\",\n       \"total_rooms           0.135097\\n\",\n       \"housing_median_age    0.114110\\n\",\n       \"households            0.064506\\n\",\n       \"total_bedrooms        0.047689\\n\",\n       \"population           -0.026920\\n\",\n       \"longitude            -0.047432\\n\",\n       \"latitude             -0.142724\\n\",\n       \"Name: median_house_value, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"corr_matrix[\\\"median_house_value\\\"].sort_values(ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure scatter_matrix_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# from pandas.tools.plotting import scatter_matrix # For older versions of Pandas\\n\",\n    \"from pandas.plotting import scatter_matrix\\n\",\n    \"\\n\",\n    \"attributes = [\\\"median_house_value\\\", \\\"median_income\\\", \\\"total_rooms\\\",\\n\",\n    \"              \\\"housing_median_age\\\"]\\n\",\n    \"scatter_matrix(housing[attributes], figsize=(12, 8))\\n\",\n    \"save_fig(\\\"scatter_matrix_plot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure income_vs_house_value_scatterplot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing.plot(kind=\\\"scatter\\\", x=\\\"median_income\\\", y=\\\"median_house_value\\\",\\n\",\n    \"             alpha=0.1)\\n\",\n    \"plt.axis([0, 16, 0, 550000])\\n\",\n    \"save_fig(\\\"income_vs_house_value_scatterplot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing[\\\"rooms_per_household\\\"] = housing[\\\"total_rooms\\\"]/housing[\\\"households\\\"]\\n\",\n    \"housing[\\\"bedrooms_per_room\\\"] = housing[\\\"total_bedrooms\\\"]/housing[\\\"total_rooms\\\"]\\n\",\n    \"housing[\\\"population_per_household\\\"]=housing[\\\"population\\\"]/housing[\\\"households\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: there was a bug in the previous cell, in the definition of the `rooms_per_household` attribute. This explains why the correlation value below differs slightly from the value in the book (unless you are reading the latest version).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"median_house_value          1.000000\\n\",\n       \"median_income               0.687160\\n\",\n       \"rooms_per_household         0.146285\\n\",\n       \"total_rooms                 0.135097\\n\",\n       \"housing_median_age          0.114110\\n\",\n       \"households                  0.064506\\n\",\n       \"total_bedrooms              0.047689\\n\",\n       \"population_per_household   -0.021985\\n\",\n       \"population                 -0.026920\\n\",\n       \"longitude                  -0.047432\\n\",\n       \"latitude                   -0.142724\\n\",\n       \"bedrooms_per_room          -0.259984\\n\",\n       \"Name: median_house_value, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"corr_matrix = housing.corr()\\n\",\n    \"corr_matrix[\\\"median_house_value\\\"].sort_values(ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"housing.plot(kind=\\\"scatter\\\", x=\\\"rooms_per_household\\\", y=\\\"median_house_value\\\",\\n\",\n    \"             alpha=0.2)\\n\",\n    \"plt.axis([0, 5, 0, 520000])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>median_house_value</th>\\n\",\n       \"      <th>rooms_per_household</th>\\n\",\n       \"      <th>bedrooms_per_room</th>\\n\",\n       \"      <th>population_per_household</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16354.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"      <td>16354.000000</td>\\n\",\n       \"      <td>16512.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>-119.575834</td>\\n\",\n       \"      <td>35.639577</td>\\n\",\n       \"      <td>28.653101</td>\\n\",\n       \"      <td>2622.728319</td>\\n\",\n       \"      <td>534.973890</td>\\n\",\n       \"      <td>1419.790819</td>\\n\",\n       \"      <td>497.060380</td>\\n\",\n       \"      <td>3.875589</td>\\n\",\n       \"      <td>206990.920724</td>\\n\",\n       \"      <td>5.440341</td>\\n\",\n       \"      <td>0.212878</td>\\n\",\n       \"      <td>3.096437</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>2.001860</td>\\n\",\n       \"      <td>2.138058</td>\\n\",\n       \"      <td>12.574726</td>\\n\",\n       \"      <td>2138.458419</td>\\n\",\n       \"      <td>412.699041</td>\\n\",\n       \"      <td>1115.686241</td>\\n\",\n       \"      <td>375.720845</td>\\n\",\n       \"      <td>1.904950</td>\\n\",\n       \"      <td>115703.014830</td>\\n\",\n       \"      <td>2.611712</td>\\n\",\n       \"      <td>0.057379</td>\\n\",\n       \"      <td>11.584826</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>-124.350000</td>\\n\",\n       \"      <td>32.540000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>0.499900</td>\\n\",\n       \"      <td>14999.000000</td>\\n\",\n       \"      <td>1.130435</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.692308</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>-121.800000</td>\\n\",\n       \"      <td>33.940000</td>\\n\",\n       \"      <td>18.000000</td>\\n\",\n       \"      <td>1443.000000</td>\\n\",\n       \"      <td>295.000000</td>\\n\",\n       \"      <td>784.000000</td>\\n\",\n       \"      <td>279.000000</td>\\n\",\n       \"      <td>2.566775</td>\\n\",\n       \"      <td>119800.000000</td>\\n\",\n       \"      <td>4.442040</td>\\n\",\n       \"      <td>0.175304</td>\\n\",\n       \"      <td>2.431287</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>-118.510000</td>\\n\",\n       \"      <td>34.260000</td>\\n\",\n       \"      <td>29.000000</td>\\n\",\n       \"      <td>2119.500000</td>\\n\",\n       \"      <td>433.000000</td>\\n\",\n       \"      <td>1164.000000</td>\\n\",\n       \"      <td>408.000000</td>\\n\",\n       \"      <td>3.540900</td>\\n\",\n       \"      <td>179500.000000</td>\\n\",\n       \"      <td>5.232284</td>\\n\",\n       \"      <td>0.203031</td>\\n\",\n       \"      <td>2.817653</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>-118.010000</td>\\n\",\n       \"      <td>37.720000</td>\\n\",\n       \"      <td>37.000000</td>\\n\",\n       \"      <td>3141.000000</td>\\n\",\n       \"      <td>644.000000</td>\\n\",\n       \"      <td>1719.250000</td>\\n\",\n       \"      <td>602.000000</td>\\n\",\n       \"      <td>4.744475</td>\\n\",\n       \"      <td>263900.000000</td>\\n\",\n       \"      <td>6.056361</td>\\n\",\n       \"      <td>0.239831</td>\\n\",\n       \"      <td>3.281420</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>-114.310000</td>\\n\",\n       \"      <td>41.950000</td>\\n\",\n       \"      <td>52.000000</td>\\n\",\n       \"      <td>39320.000000</td>\\n\",\n       \"      <td>6210.000000</td>\\n\",\n       \"      <td>35682.000000</td>\\n\",\n       \"      <td>5358.000000</td>\\n\",\n       \"      <td>15.000100</td>\\n\",\n       \"      <td>500001.000000</td>\\n\",\n       \"      <td>141.909091</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>1243.333333</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          longitude      latitude  housing_median_age   total_rooms  \\\\\\n\",\n       \"count  16512.000000  16512.000000        16512.000000  16512.000000   \\n\",\n       \"mean    -119.575834     35.639577           28.653101   2622.728319   \\n\",\n       \"std        2.001860      2.138058           12.574726   2138.458419   \\n\",\n       \"min     -124.350000     32.540000            1.000000      6.000000   \\n\",\n       \"25%     -121.800000     33.940000           18.000000   1443.000000   \\n\",\n       \"50%     -118.510000     34.260000           29.000000   2119.500000   \\n\",\n       \"75%     -118.010000     37.720000           37.000000   3141.000000   \\n\",\n       \"max     -114.310000     41.950000           52.000000  39320.000000   \\n\",\n       \"\\n\",\n       \"       total_bedrooms    population    households  median_income  \\\\\\n\",\n       \"count    16354.000000  16512.000000  16512.000000   16512.000000   \\n\",\n       \"mean       534.973890   1419.790819    497.060380       3.875589   \\n\",\n       \"std        412.699041   1115.686241    375.720845       1.904950   \\n\",\n       \"min          2.000000      3.000000      2.000000       0.499900   \\n\",\n       \"25%        295.000000    784.000000    279.000000       2.566775   \\n\",\n       \"50%        433.000000   1164.000000    408.000000       3.540900   \\n\",\n       \"75%        644.000000   1719.250000    602.000000       4.744475   \\n\",\n       \"max       6210.000000  35682.000000   5358.000000      15.000100   \\n\",\n       \"\\n\",\n       \"       median_house_value  rooms_per_household  bedrooms_per_room  \\\\\\n\",\n       \"count        16512.000000         16512.000000       16354.000000   \\n\",\n       \"mean        206990.920724             5.440341           0.212878   \\n\",\n       \"std         115703.014830             2.611712           0.057379   \\n\",\n       \"min          14999.000000             1.130435           0.100000   \\n\",\n       \"25%         119800.000000             4.442040           0.175304   \\n\",\n       \"50%         179500.000000             5.232284           0.203031   \\n\",\n       \"75%         263900.000000             6.056361           0.239831   \\n\",\n       \"max         500001.000000           141.909091           1.000000   \\n\",\n       \"\\n\",\n       \"       population_per_household  \\n\",\n       \"count              16512.000000  \\n\",\n       \"mean                   3.096437  \\n\",\n       \"std                   11.584826  \\n\",\n       \"min                    0.692308  \\n\",\n       \"25%                    2.431287  \\n\",\n       \"50%                    2.817653  \\n\",\n       \"75%                    3.281420  \\n\",\n       \"max                 1243.333333  \"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Prepare the data for Machine Learning algorithms\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing = strat_train_set.drop(\\\"median_house_value\\\", axis=1) # drop labels for training set\\n\",\n    \"housing_labels = strat_train_set[\\\"median_house_value\\\"].copy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4629</th>\\n\",\n       \"      <td>-118.30</td>\\n\",\n       \"      <td>34.07</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>3759.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>3296.0</td>\\n\",\n       \"      <td>1462.0</td>\\n\",\n       \"      <td>2.2708</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6068</th>\\n\",\n       \"      <td>-117.86</td>\\n\",\n       \"      <td>34.01</td>\\n\",\n       \"      <td>16.0</td>\\n\",\n       \"      <td>4632.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>3038.0</td>\\n\",\n       \"      <td>727.0</td>\\n\",\n       \"      <td>5.1762</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17923</th>\\n\",\n       \"      <td>-121.97</td>\\n\",\n       \"      <td>37.35</td>\\n\",\n       \"      <td>30.0</td>\\n\",\n       \"      <td>1955.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>999.0</td>\\n\",\n       \"      <td>386.0</td>\\n\",\n       \"      <td>4.6328</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13656</th>\\n\",\n       \"      <td>-117.30</td>\\n\",\n       \"      <td>34.05</td>\\n\",\n       \"      <td>6.0</td>\\n\",\n       \"      <td>2155.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1039.0</td>\\n\",\n       \"      <td>391.0</td>\\n\",\n       \"      <td>1.6675</td>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19252</th>\\n\",\n       \"      <td>-122.79</td>\\n\",\n       \"      <td>38.48</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>6837.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>3468.0</td>\\n\",\n       \"      <td>1405.0</td>\\n\",\n       \"      <td>3.1662</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\\\\n\",\n       \"4629     -118.30     34.07                18.0       3759.0             NaN   \\n\",\n       \"6068     -117.86     34.01                16.0       4632.0             NaN   \\n\",\n       \"17923    -121.97     37.35                30.0       1955.0             NaN   \\n\",\n       \"13656    -117.30     34.05                 6.0       2155.0             NaN   \\n\",\n       \"19252    -122.79     38.48                 7.0       6837.0             NaN   \\n\",\n       \"\\n\",\n       \"       population  households  median_income ocean_proximity  \\n\",\n       \"4629       3296.0      1462.0         2.2708       <1H OCEAN  \\n\",\n       \"6068       3038.0       727.0         5.1762       <1H OCEAN  \\n\",\n       \"17923       999.0       386.0         4.6328       <1H OCEAN  \\n\",\n       \"13656      1039.0       391.0         1.6675          INLAND  \\n\",\n       \"19252      3468.0      1405.0         3.1662       <1H OCEAN  \"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\\n\",\n    \"sample_incomplete_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"Empty DataFrame\\n\",\n       \"Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\\n\",\n       \"Index: []\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sample_incomplete_rows.dropna(subset=[\\\"total_bedrooms\\\"])    # option 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4629</th>\\n\",\n       \"      <td>-118.30</td>\\n\",\n       \"      <td>34.07</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>3759.0</td>\\n\",\n       \"      <td>3296.0</td>\\n\",\n       \"      <td>1462.0</td>\\n\",\n       \"      <td>2.2708</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6068</th>\\n\",\n       \"      <td>-117.86</td>\\n\",\n       \"      <td>34.01</td>\\n\",\n       \"      <td>16.0</td>\\n\",\n       \"      <td>4632.0</td>\\n\",\n       \"      <td>3038.0</td>\\n\",\n       \"      <td>727.0</td>\\n\",\n       \"      <td>5.1762</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17923</th>\\n\",\n       \"      <td>-121.97</td>\\n\",\n       \"      <td>37.35</td>\\n\",\n       \"      <td>30.0</td>\\n\",\n       \"      <td>1955.0</td>\\n\",\n       \"      <td>999.0</td>\\n\",\n       \"      <td>386.0</td>\\n\",\n       \"      <td>4.6328</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13656</th>\\n\",\n       \"      <td>-117.30</td>\\n\",\n       \"      <td>34.05</td>\\n\",\n       \"      <td>6.0</td>\\n\",\n       \"      <td>2155.0</td>\\n\",\n       \"      <td>1039.0</td>\\n\",\n       \"      <td>391.0</td>\\n\",\n       \"      <td>1.6675</td>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19252</th>\\n\",\n       \"      <td>-122.79</td>\\n\",\n       \"      <td>38.48</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>6837.0</td>\\n\",\n       \"      <td>3468.0</td>\\n\",\n       \"      <td>1405.0</td>\\n\",\n       \"      <td>3.1662</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       longitude  latitude  housing_median_age  total_rooms  population  \\\\\\n\",\n       \"4629     -118.30     34.07                18.0       3759.0      3296.0   \\n\",\n       \"6068     -117.86     34.01                16.0       4632.0      3038.0   \\n\",\n       \"17923    -121.97     37.35                30.0       1955.0       999.0   \\n\",\n       \"13656    -117.30     34.05                 6.0       2155.0      1039.0   \\n\",\n       \"19252    -122.79     38.48                 7.0       6837.0      3468.0   \\n\",\n       \"\\n\",\n       \"       households  median_income ocean_proximity  \\n\",\n       \"4629       1462.0         2.2708       <1H OCEAN  \\n\",\n       \"6068        727.0         5.1762       <1H OCEAN  \\n\",\n       \"17923       386.0         4.6328       <1H OCEAN  \\n\",\n       \"13656       391.0         1.6675          INLAND  \\n\",\n       \"19252      1405.0         3.1662       <1H OCEAN  \"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sample_incomplete_rows.drop(\\\"total_bedrooms\\\", axis=1)       # option 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4629</th>\\n\",\n       \"      <td>-118.30</td>\\n\",\n       \"      <td>34.07</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>3759.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>3296.0</td>\\n\",\n       \"      <td>1462.0</td>\\n\",\n       \"      <td>2.2708</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6068</th>\\n\",\n       \"      <td>-117.86</td>\\n\",\n       \"      <td>34.01</td>\\n\",\n       \"      <td>16.0</td>\\n\",\n       \"      <td>4632.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>3038.0</td>\\n\",\n       \"      <td>727.0</td>\\n\",\n       \"      <td>5.1762</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17923</th>\\n\",\n       \"      <td>-121.97</td>\\n\",\n       \"      <td>37.35</td>\\n\",\n       \"      <td>30.0</td>\\n\",\n       \"      <td>1955.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>999.0</td>\\n\",\n       \"      <td>386.0</td>\\n\",\n       \"      <td>4.6328</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13656</th>\\n\",\n       \"      <td>-117.30</td>\\n\",\n       \"      <td>34.05</td>\\n\",\n       \"      <td>6.0</td>\\n\",\n       \"      <td>2155.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>1039.0</td>\\n\",\n       \"      <td>391.0</td>\\n\",\n       \"      <td>1.6675</td>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19252</th>\\n\",\n       \"      <td>-122.79</td>\\n\",\n       \"      <td>38.48</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>6837.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>3468.0</td>\\n\",\n       \"      <td>1405.0</td>\\n\",\n       \"      <td>3.1662</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\\\\n\",\n       \"4629     -118.30     34.07                18.0       3759.0           433.0   \\n\",\n       \"6068     -117.86     34.01                16.0       4632.0           433.0   \\n\",\n       \"17923    -121.97     37.35                30.0       1955.0           433.0   \\n\",\n       \"13656    -117.30     34.05                 6.0       2155.0           433.0   \\n\",\n       \"19252    -122.79     38.48                 7.0       6837.0           433.0   \\n\",\n       \"\\n\",\n       \"       population  households  median_income ocean_proximity  \\n\",\n       \"4629       3296.0      1462.0         2.2708       <1H OCEAN  \\n\",\n       \"6068       3038.0       727.0         5.1762       <1H OCEAN  \\n\",\n       \"17923       999.0       386.0         4.6328       <1H OCEAN  \\n\",\n       \"13656      1039.0       391.0         1.6675          INLAND  \\n\",\n       \"19252      3468.0      1405.0         3.1662       <1H OCEAN  \"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"median = housing[\\\"total_bedrooms\\\"].median()\\n\",\n    \"sample_incomplete_rows[\\\"total_bedrooms\\\"].fillna(median, inplace=True) # option 3\\n\",\n    \"sample_incomplete_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: Since Scikit-Learn 0.20, the `sklearn.preprocessing.Imputer` class was replaced by the `sklearn.impute.SimpleImputer` class.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"try:\\n\",\n    \"    from sklearn.impute import SimpleImputer # Scikit-Learn 0.20+\\n\",\n    \"except ImportError:\\n\",\n    \"    from sklearn.preprocessing import Imputer as SimpleImputer\\n\",\n    \"\\n\",\n    \"imputer = SimpleImputer(strategy=\\\"median\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Remove the text attribute because median can only be calculated on numerical attributes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing_num = housing.drop('ocean_proximity', axis=1)\\n\",\n    \"# alternatively: housing_num = housing.select_dtypes(include=[np.number])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"       strategy='median', verbose=0)\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imputer.fit(housing_num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\\n\",\n       \"        408.    ,    3.5409])\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imputer.statistics_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check that this is the same as manually computing the median of each attribute:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\\n\",\n       \"        408.    ,    3.5409])\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_num.median().values\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Transform the training set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = imputer.transform(housing_num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing_tr = pd.DataFrame(X, columns=housing_num.columns,\\n\",\n    \"                          index=housing.index)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4629</th>\\n\",\n       \"      <td>-118.30</td>\\n\",\n       \"      <td>34.07</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>3759.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>3296.0</td>\\n\",\n       \"      <td>1462.0</td>\\n\",\n       \"      <td>2.2708</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6068</th>\\n\",\n       \"      <td>-117.86</td>\\n\",\n       \"      <td>34.01</td>\\n\",\n       \"      <td>16.0</td>\\n\",\n       \"      <td>4632.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>3038.0</td>\\n\",\n       \"      <td>727.0</td>\\n\",\n       \"      <td>5.1762</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17923</th>\\n\",\n       \"      <td>-121.97</td>\\n\",\n       \"      <td>37.35</td>\\n\",\n       \"      <td>30.0</td>\\n\",\n       \"      <td>1955.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>999.0</td>\\n\",\n       \"      <td>386.0</td>\\n\",\n       \"      <td>4.6328</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13656</th>\\n\",\n       \"      <td>-117.30</td>\\n\",\n       \"      <td>34.05</td>\\n\",\n       \"      <td>6.0</td>\\n\",\n       \"      <td>2155.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>1039.0</td>\\n\",\n       \"      <td>391.0</td>\\n\",\n       \"      <td>1.6675</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19252</th>\\n\",\n       \"      <td>-122.79</td>\\n\",\n       \"      <td>38.48</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>6837.0</td>\\n\",\n       \"      <td>433.0</td>\\n\",\n       \"      <td>3468.0</td>\\n\",\n       \"      <td>1405.0</td>\\n\",\n       \"      <td>3.1662</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\\\\n\",\n       \"4629     -118.30     34.07                18.0       3759.0           433.0   \\n\",\n       \"6068     -117.86     34.01                16.0       4632.0           433.0   \\n\",\n       \"17923    -121.97     37.35                30.0       1955.0           433.0   \\n\",\n       \"13656    -117.30     34.05                 6.0       2155.0           433.0   \\n\",\n       \"19252    -122.79     38.48                 7.0       6837.0           433.0   \\n\",\n       \"\\n\",\n       \"       population  households  median_income  \\n\",\n       \"4629       3296.0      1462.0         2.2708  \\n\",\n       \"6068       3038.0       727.0         5.1762  \\n\",\n       \"17923       999.0       386.0         4.6328  \\n\",\n       \"13656      1039.0       391.0         1.6675  \\n\",\n       \"19252      3468.0      1405.0         3.1662  \"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_tr.loc[sample_incomplete_rows.index.values]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'median'\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imputer.strategy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>-121.89</td>\\n\",\n       \"      <td>37.29</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1568.0</td>\\n\",\n       \"      <td>351.0</td>\\n\",\n       \"      <td>710.0</td>\\n\",\n       \"      <td>339.0</td>\\n\",\n       \"      <td>2.7042</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>-121.93</td>\\n\",\n       \"      <td>37.05</td>\\n\",\n       \"      <td>14.0</td>\\n\",\n       \"      <td>679.0</td>\\n\",\n       \"      <td>108.0</td>\\n\",\n       \"      <td>306.0</td>\\n\",\n       \"      <td>113.0</td>\\n\",\n       \"      <td>6.4214</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-117.20</td>\\n\",\n       \"      <td>32.77</td>\\n\",\n       \"      <td>31.0</td>\\n\",\n       \"      <td>1952.0</td>\\n\",\n       \"      <td>471.0</td>\\n\",\n       \"      <td>936.0</td>\\n\",\n       \"      <td>462.0</td>\\n\",\n       \"      <td>2.8621</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>-119.61</td>\\n\",\n       \"      <td>36.31</td>\\n\",\n       \"      <td>25.0</td>\\n\",\n       \"      <td>1847.0</td>\\n\",\n       \"      <td>371.0</td>\\n\",\n       \"      <td>1460.0</td>\\n\",\n       \"      <td>353.0</td>\\n\",\n       \"      <td>1.8839</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-118.59</td>\\n\",\n       \"      <td>34.23</td>\\n\",\n       \"      <td>17.0</td>\\n\",\n       \"      <td>6592.0</td>\\n\",\n       \"      <td>1525.0</td>\\n\",\n       \"      <td>4459.0</td>\\n\",\n       \"      <td>1463.0</td>\\n\",\n       \"      <td>3.0347</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\\\\n\",\n       \"0    -121.89     37.29                38.0       1568.0           351.0   \\n\",\n       \"1    -121.93     37.05                14.0        679.0           108.0   \\n\",\n       \"2    -117.20     32.77                31.0       1952.0           471.0   \\n\",\n       \"3    -119.61     36.31                25.0       1847.0           371.0   \\n\",\n       \"4    -118.59     34.23                17.0       6592.0          1525.0   \\n\",\n       \"\\n\",\n       \"   population  households  median_income  \\n\",\n       \"0       710.0       339.0         2.7042  \\n\",\n       \"1       306.0       113.0         6.4214  \\n\",\n       \"2       936.0       462.0         2.8621  \\n\",\n       \"3      1460.0       353.0         1.8839  \\n\",\n       \"4      4459.0      1463.0         3.0347  \"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_tr = pd.DataFrame(X, columns=housing_num.columns,\\n\",\n    \"                          index=housing_num.index)\\n\",\n    \"housing_tr.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's preprocess the categorical input feature, `ocean_proximity`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17606</th>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18632</th>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14650</th>\\n\",\n       \"      <td>NEAR OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3230</th>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3555</th>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19480</th>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8879</th>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13685</th>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4937</th>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4861</th>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      ocean_proximity\\n\",\n       \"17606       <1H OCEAN\\n\",\n       \"18632       <1H OCEAN\\n\",\n       \"14650      NEAR OCEAN\\n\",\n       \"3230           INLAND\\n\",\n       \"3555        <1H OCEAN\\n\",\n       \"19480          INLAND\\n\",\n       \"8879        <1H OCEAN\\n\",\n       \"13685          INLAND\\n\",\n       \"4937        <1H OCEAN\\n\",\n       \"4861        <1H OCEAN\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_cat = housing[['ocean_proximity']]\\n\",\n    \"housing_cat.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: earlier versions of the book used the `LabelEncoder` class or Pandas' `Series.factorize()` method to encode string categorical attributes as integers. However, the `OrdinalEncoder` class that was introduced in Scikit-Learn 0.20 (see [PR #10521](https://github.com/scikit-learn/scikit-learn/issues/10521)) is preferable since it is designed for input features (`X` instead of labels `y`) and it plays well with pipelines (introduced later in this notebook). If you are using an older version of Scikit-Learn (<0.20), then you can import it from `future_encoders.py` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"try:\\n\",\n    \"    from sklearn.preprocessing import OrdinalEncoder\\n\",\n    \"except ImportError:\\n\",\n    \"    from future_encoders import OrdinalEncoder # Scikit-Learn < 0.20\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.],\\n\",\n       \"       [0.],\\n\",\n       \"       [4.],\\n\",\n       \"       [1.],\\n\",\n       \"       [0.],\\n\",\n       \"       [1.],\\n\",\n       \"       [0.],\\n\",\n       \"       [1.],\\n\",\n       \"       [0.],\\n\",\n       \"       [0.]])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ordinal_encoder = OrdinalEncoder()\\n\",\n    \"housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\\n\",\n    \"housing_cat_encoded[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\\n\",\n       \"       dtype=object)]\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ordinal_encoder.categories_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: earlier versions of the book used the `LabelBinarizer` or `CategoricalEncoder` classes to convert each categorical value to a one-hot vector. It is now preferable to use the `OneHotEncoder` class. Since Scikit-Learn 0.20 it can handle string categorical inputs (see [PR #10521](https://github.com/scikit-learn/scikit-learn/issues/10521)), not just integer categorical inputs. If you are using an older version of Scikit-Learn, you can import the new version from `future_encoders.py`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<16512x5 sparse matrix of type '<class 'numpy.float64'>'\\n\",\n       \"\\twith 16512 stored elements in Compressed Sparse Row format>\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"try:\\n\",\n    \"    from sklearn.preprocessing import OrdinalEncoder # just to raise an ImportError if Scikit-Learn < 0.20\\n\",\n    \"    from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"except ImportError:\\n\",\n    \"    from future_encoders import OneHotEncoder # Scikit-Learn < 0.20\\n\",\n    \"\\n\",\n    \"cat_encoder = OneHotEncoder()\\n\",\n    \"housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\\n\",\n    \"housing_cat_1hot\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"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:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 0., 0., 0., 0.],\\n\",\n       \"       [1., 0., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., 0., 1.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 1., 0., 0., 0.],\\n\",\n       \"       [1., 0., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., 1., 0.]])\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_cat_1hot.toarray()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, you can set `sparse=False` when creating the `OneHotEncoder`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 0., 0., 0., 0.],\\n\",\n       \"       [1., 0., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., 0., 1.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 1., 0., 0., 0.],\\n\",\n       \"       [1., 0., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., 1., 0.]])\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cat_encoder = OneHotEncoder(sparse=False)\\n\",\n    \"housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\\n\",\n    \"housing_cat_1hot\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\\n\",\n       \"       dtype=object)]\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cat_encoder.categories_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a custom transformer to add extra attributes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\\n\",\n       \"       'total_bedrooms', 'population', 'households', 'median_income',\\n\",\n       \"       'ocean_proximity'],\\n\",\n       \"      dtype='object')\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n    \"\\n\",\n    \"# get the right column indices: safer than hard-coding indices 3, 4, 5, 6\\n\",\n    \"rooms_ix, bedrooms_ix, population_ix, household_ix = [\\n\",\n    \"    list(housing.columns).index(col)\\n\",\n    \"    for col in (\\\"total_rooms\\\", \\\"total_bedrooms\\\", \\\"population\\\", \\\"households\\\")]\\n\",\n    \"\\n\",\n    \"class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\\n\",\n    \"    def __init__(self, add_bedrooms_per_room = True): # no *args or **kwargs\\n\",\n    \"        self.add_bedrooms_per_room = add_bedrooms_per_room\\n\",\n    \"    def fit(self, X, y=None):\\n\",\n    \"        return self  # nothing else to do\\n\",\n    \"    def transform(self, X, y=None):\\n\",\n    \"        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\\n\",\n    \"        population_per_household = X[:, population_ix] / X[:, household_ix]\\n\",\n    \"        if self.add_bedrooms_per_room:\\n\",\n    \"            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\\n\",\n    \"            return np.c_[X, rooms_per_household, population_per_household,\\n\",\n    \"                         bedrooms_per_room]\\n\",\n    \"        else:\\n\",\n    \"            return np.c_[X, rooms_per_household, population_per_household]\\n\",\n    \"\\n\",\n    \"attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\\n\",\n    \"housing_extra_attribs = attr_adder.transform(housing.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, you can use Scikit-Learn's `FunctionTransformer` class that lets you easily create a transformer based on a transformation function (thanks to [Hanmin Qin](https://github.com/qinhanmin2014) for suggesting this code). Note that we need to set `validate=False` because the data contains non-float values (`validate` will default to `False` in Scikit-Learn 0.22).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import FunctionTransformer\\n\",\n    \"\\n\",\n    \"def add_extra_features(X, add_bedrooms_per_room=True):\\n\",\n    \"    rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\\n\",\n    \"    population_per_household = X[:, population_ix] / X[:, household_ix]\\n\",\n    \"    if add_bedrooms_per_room:\\n\",\n    \"        bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\\n\",\n    \"        return np.c_[X, rooms_per_household, population_per_household,\\n\",\n    \"                     bedrooms_per_room]\\n\",\n    \"    else:\\n\",\n    \"        return np.c_[X, rooms_per_household, population_per_household]\\n\",\n    \"\\n\",\n    \"attr_adder = FunctionTransformer(add_extra_features, validate=False,\\n\",\n    \"                                 kw_args={\\\"add_bedrooms_per_room\\\": False})\\n\",\n    \"housing_extra_attribs = attr_adder.fit_transform(housing.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>housing_median_age</th>\\n\",\n       \"      <th>total_rooms</th>\\n\",\n       \"      <th>total_bedrooms</th>\\n\",\n       \"      <th>population</th>\\n\",\n       \"      <th>households</th>\\n\",\n       \"      <th>median_income</th>\\n\",\n       \"      <th>ocean_proximity</th>\\n\",\n       \"      <th>rooms_per_household</th>\\n\",\n       \"      <th>population_per_household</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>-121.89</td>\\n\",\n       \"      <td>37.29</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>1568</td>\\n\",\n       \"      <td>351</td>\\n\",\n       \"      <td>710</td>\\n\",\n       \"      <td>339</td>\\n\",\n       \"      <td>2.7042</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"      <td>4.62537</td>\\n\",\n       \"      <td>2.0944</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>-121.93</td>\\n\",\n       \"      <td>37.05</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>679</td>\\n\",\n       \"      <td>108</td>\\n\",\n       \"      <td>306</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>6.4214</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"      <td>6.00885</td>\\n\",\n       \"      <td>2.70796</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-117.2</td>\\n\",\n       \"      <td>32.77</td>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>1952</td>\\n\",\n       \"      <td>471</td>\\n\",\n       \"      <td>936</td>\\n\",\n       \"      <td>462</td>\\n\",\n       \"      <td>2.8621</td>\\n\",\n       \"      <td>NEAR OCEAN</td>\\n\",\n       \"      <td>4.22511</td>\\n\",\n       \"      <td>2.02597</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>-119.61</td>\\n\",\n       \"      <td>36.31</td>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>1847</td>\\n\",\n       \"      <td>371</td>\\n\",\n       \"      <td>1460</td>\\n\",\n       \"      <td>353</td>\\n\",\n       \"      <td>1.8839</td>\\n\",\n       \"      <td>INLAND</td>\\n\",\n       \"      <td>5.23229</td>\\n\",\n       \"      <td>4.13598</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-118.59</td>\\n\",\n       \"      <td>34.23</td>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>6592</td>\\n\",\n       \"      <td>1525</td>\\n\",\n       \"      <td>4459</td>\\n\",\n       \"      <td>1463</td>\\n\",\n       \"      <td>3.0347</td>\\n\",\n       \"      <td>&lt;1H OCEAN</td>\\n\",\n       \"      <td>4.50581</td>\\n\",\n       \"      <td>3.04785</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  longitude latitude housing_median_age total_rooms total_bedrooms population  \\\\\\n\",\n       \"0   -121.89    37.29                 38        1568            351        710   \\n\",\n       \"1   -121.93    37.05                 14         679            108        306   \\n\",\n       \"2    -117.2    32.77                 31        1952            471        936   \\n\",\n       \"3   -119.61    36.31                 25        1847            371       1460   \\n\",\n       \"4   -118.59    34.23                 17        6592           1525       4459   \\n\",\n       \"\\n\",\n       \"  households median_income ocean_proximity rooms_per_household  \\\\\\n\",\n       \"0        339        2.7042       <1H OCEAN             4.62537   \\n\",\n       \"1        113        6.4214       <1H OCEAN             6.00885   \\n\",\n       \"2        462        2.8621      NEAR OCEAN             4.22511   \\n\",\n       \"3        353        1.8839          INLAND             5.23229   \\n\",\n       \"4       1463        3.0347       <1H OCEAN             4.50581   \\n\",\n       \"\\n\",\n       \"  population_per_household  \\n\",\n       \"0                   2.0944  \\n\",\n       \"1                  2.70796  \\n\",\n       \"2                  2.02597  \\n\",\n       \"3                  4.13598  \\n\",\n       \"4                  3.04785  \"\n      ]\n     },\n     \"execution_count\": 71,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_extra_attribs = pd.DataFrame(\\n\",\n    \"    housing_extra_attribs,\\n\",\n    \"    columns=list(housing.columns)+[\\\"rooms_per_household\\\", \\\"population_per_household\\\"],\\n\",\n    \"    index=housing.index)\\n\",\n    \"housing_extra_attribs.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's build a pipeline for preprocessing the numerical attributes (note that we could use `CombinedAttributesAdder()` instead of `FunctionTransformer(...)` if we preferred):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"\\n\",\n    \"num_pipeline = Pipeline([\\n\",\n    \"        ('imputer', SimpleImputer(strategy=\\\"median\\\")),\\n\",\n    \"        ('attribs_adder', FunctionTransformer(add_extra_features, validate=False)),\\n\",\n    \"        ('std_scaler', StandardScaler()),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"housing_num_tr = num_pipeline.fit_transform(housing_num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\\n\",\n       \"        -0.08649871,  0.15531753],\\n\",\n       \"       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\\n\",\n       \"        -0.03353391, -0.83628902],\\n\",\n       \"       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\\n\",\n       \"        -0.09240499,  0.4222004 ],\\n\",\n       \"       ...,\\n\",\n       \"       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\\n\",\n       \"        -0.03055414, -0.52177644],\\n\",\n       \"       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\\n\",\n       \"         0.06150916, -0.30340741],\\n\",\n       \"       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\\n\",\n       \"        -0.09586294,  0.10180567]])\"\n      ]\n     },\n     \"execution_count\": 73,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_num_tr\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: earlier versions of the book applied different transformations to different columns using a solution based on a `DataFrameSelector` transformer and a `FeatureUnion` (see below). It is now preferable to use the `ColumnTransformer` class that was introduced in Scikit-Learn 0.20. If you are using an older version of Scikit-Learn, you can import it from `future_encoders.py`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"try:\\n\",\n    \"    from sklearn.compose import ColumnTransformer\\n\",\n    \"except ImportError:\\n\",\n    \"    from future_encoders import ColumnTransformer # Scikit-Learn < 0.20\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"num_attribs = list(housing_num)\\n\",\n    \"cat_attribs = [\\\"ocean_proximity\\\"]\\n\",\n    \"\\n\",\n    \"full_pipeline = ColumnTransformer([\\n\",\n    \"        (\\\"num\\\", num_pipeline, num_attribs),\\n\",\n    \"        (\\\"cat\\\", OneHotEncoder(), cat_attribs),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"housing_prepared = full_pipeline.fit_transform(housing)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\\n\",\n       \"         0.        ,  1.        ],\\n\",\n       \"       ...,\\n\",\n       \"       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\\n\",\n       \"         1.        ,  0.        ]])\"\n      ]\n     },\n     \"execution_count\": 76,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_prepared\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(16512, 16)\"\n      ]\n     },\n     \"execution_count\": 77,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_prepared.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For reference, here is the old solution based on a `DataFrameSelector` transformer (to just select a subset of the Pandas `DataFrame` columns), and a `FeatureUnion`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n    \"\\n\",\n    \"# Create a class to select numerical or categorical columns \\n\",\n    \"class OldDataFrameSelector(BaseEstimator, TransformerMixin):\\n\",\n    \"    def __init__(self, attribute_names):\\n\",\n    \"        self.attribute_names = attribute_names\\n\",\n    \"    def fit(self, X, y=None):\\n\",\n    \"        return self\\n\",\n    \"    def transform(self, X):\\n\",\n    \"        return X[self.attribute_names].values\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's join all these components into a big pipeline that will preprocess both the numerical and the categorical features (again, we could use `CombinedAttributesAdder()` instead of `FunctionTransformer(...)` if we preferred):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"num_attribs = list(housing_num)\\n\",\n    \"cat_attribs = [\\\"ocean_proximity\\\"]\\n\",\n    \"\\n\",\n    \"old_num_pipeline = Pipeline([\\n\",\n    \"        ('selector', OldDataFrameSelector(num_attribs)),\\n\",\n    \"        ('imputer', SimpleImputer(strategy=\\\"median\\\")),\\n\",\n    \"        ('attribs_adder', FunctionTransformer(add_extra_features, validate=False)),\\n\",\n    \"        ('std_scaler', StandardScaler()),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"old_cat_pipeline = Pipeline([\\n\",\n    \"        ('selector', OldDataFrameSelector(cat_attribs)),\\n\",\n    \"        ('cat_encoder', OneHotEncoder(sparse=False)),\\n\",\n    \"    ])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import FeatureUnion\\n\",\n    \"\\n\",\n    \"old_full_pipeline = FeatureUnion(transformer_list=[\\n\",\n    \"        (\\\"num_pipeline\\\", old_num_pipeline),\\n\",\n    \"        (\\\"cat_pipeline\\\", old_cat_pipeline),\\n\",\n    \"    ])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\\n\",\n       \"         0.        ,  1.        ],\\n\",\n       \"       ...,\\n\",\n       \"       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\\n\",\n       \"         0.        ,  0.        ],\\n\",\n       \"       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\\n\",\n       \"         1.        ,  0.        ]])\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"old_housing_prepared = old_full_pipeline.fit_transform(housing)\\n\",\n    \"old_housing_prepared\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The result is the same as with the `ColumnTransformer`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 82,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(housing_prepared, old_housing_prepared)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Select and train a model \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\\n\",\n       \"         normalize=False)\"\n      ]\n     },\n     \"execution_count\": 83,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LinearRegression\\n\",\n    \"\\n\",\n    \"lin_reg = LinearRegression()\\n\",\n    \"lin_reg.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predictions: [210644.60459286 317768.80697211 210956.43331178  59218.98886849\\n\",\n      \" 189747.55849879]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# let's try the full preprocessing pipeline on a few training instances\\n\",\n    \"some_data = housing.iloc[:5]\\n\",\n    \"some_labels = housing_labels.iloc[:5]\\n\",\n    \"some_data_prepared = full_pipeline.transform(some_data)\\n\",\n    \"\\n\",\n    \"print(\\\"Predictions:\\\", lin_reg.predict(some_data_prepared))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare against the actual values:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Labels: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Labels:\\\", list(some_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.15604281,  0.77194962,  0.74333089, -0.49323393, -0.44543821,\\n\",\n       \"        -0.63621141, -0.42069842, -0.61493744, -0.31205452, -0.08649871,\\n\",\n       \"         0.15531753,  1.        ,  0.        ,  0.        ,  0.        ,\\n\",\n       \"         0.        ],\\n\",\n       \"       [-1.17602483,  0.6596948 , -1.1653172 , -0.90896655, -1.0369278 ,\\n\",\n       \"        -0.99833135, -1.02222705,  1.33645936,  0.21768338, -0.03353391,\\n\",\n       \"        -0.83628902,  1.        ,  0.        ,  0.        ,  0.        ,\\n\",\n       \"         0.        ],\\n\",\n       \"       [ 1.18684903, -1.34218285,  0.18664186, -0.31365989, -0.15334458,\\n\",\n       \"        -0.43363936, -0.0933178 , -0.5320456 , -0.46531516, -0.09240499,\\n\",\n       \"         0.4222004 ,  0.        ,  0.        ,  0.        ,  0.        ,\\n\",\n       \"         1.        ],\\n\",\n       \"       [-0.01706767,  0.31357576, -0.29052016, -0.36276217, -0.39675594,\\n\",\n       \"         0.03604096, -0.38343559, -1.04556555, -0.07966124,  0.08973561,\\n\",\n       \"        -0.19645314,  0.        ,  1.        ,  0.        ,  0.        ,\\n\",\n       \"         0.        ],\\n\",\n       \"       [ 0.49247384, -0.65929936, -0.92673619,  1.85619316,  2.41221109,\\n\",\n       \"         2.72415407,  2.57097492, -0.44143679, -0.35783383, -0.00419445,\\n\",\n       \"         0.2699277 ,  1.        ,  0.        ,  0.        ,  0.        ,\\n\",\n       \"         0.        ]])\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"some_data_prepared\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"68628.19819848922\"\n      ]\n     },\n     \"execution_count\": 87,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"\\n\",\n    \"housing_predictions = lin_reg.predict(housing_prepared)\\n\",\n    \"lin_mse = mean_squared_error(housing_labels, housing_predictions)\\n\",\n    \"lin_rmse = np.sqrt(lin_mse)\\n\",\n    \"lin_rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"49439.89599001897\"\n      ]\n     },\n     \"execution_count\": 88,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import mean_absolute_error\\n\",\n    \"\\n\",\n    \"lin_mae = mean_absolute_error(housing_labels, housing_predictions)\\n\",\n    \"lin_mae\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\\n\",\n       \"           max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n       \"           min_impurity_split=None, min_samples_leaf=1,\\n\",\n       \"           min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n       \"           presort=False, random_state=42, splitter='best')\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeRegressor\\n\",\n    \"\\n\",\n    \"tree_reg = DecisionTreeRegressor(random_state=42)\\n\",\n    \"tree_reg.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.0\"\n      ]\n     },\n     \"execution_count\": 90,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_predictions = tree_reg.predict(housing_prepared)\\n\",\n    \"tree_mse = mean_squared_error(housing_labels, housing_predictions)\\n\",\n    \"tree_rmse = np.sqrt(tree_mse)\\n\",\n    \"tree_rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Fine-tune your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\\n\",\n    \"                         scoring=\\\"neg_mean_squared_error\\\", cv=10)\\n\",\n    \"tree_rmse_scores = np.sqrt(-scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Scores: [70194.33680785 66855.16363941 72432.58244769 70758.73896782\\n\",\n      \" 71115.88230639 75585.14172901 70262.86139133 70273.6325285\\n\",\n      \" 75366.87952553 71231.65726027]\\n\",\n      \"Mean: 71407.68766037929\\n\",\n      \"Standard deviation: 2439.4345041191004\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def display_scores(scores):\\n\",\n    \"    print(\\\"Scores:\\\", scores)\\n\",\n    \"    print(\\\"Mean:\\\", scores.mean())\\n\",\n    \"    print(\\\"Standard deviation:\\\", scores.std())\\n\",\n    \"\\n\",\n    \"display_scores(tree_rmse_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Scores: [66782.73843989 66960.118071   70347.95244419 74739.57052552\\n\",\n      \" 68031.13388938 71193.84183426 64969.63056405 68281.61137997\\n\",\n      \" 71552.91566558 67665.10082067]\\n\",\n      \"Mean: 69052.46136345083\\n\",\n      \"Standard deviation: 2731.674001798348\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\\n\",\n    \"                             scoring=\\\"neg_mean_squared_error\\\", cv=10)\\n\",\n    \"lin_rmse_scores = np.sqrt(-lin_scores)\\n\",\n    \"display_scores(lin_rmse_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note**: we specify `n_estimators=10` to avoid a warning about the fact that the default value is going to change to 100 in Scikit-Learn 0.22.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n       \"           max_features='auto', max_leaf_nodes=None,\\n\",\n       \"           min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"           min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\\n\",\n       \"           oob_score=False, random_state=42, verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 94,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestRegressor\\n\",\n    \"\\n\",\n    \"forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\\n\",\n    \"forest_reg.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"21933.31414779769\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_predictions = forest_reg.predict(housing_prepared)\\n\",\n    \"forest_mse = mean_squared_error(housing_labels, housing_predictions)\\n\",\n    \"forest_rmse = np.sqrt(forest_mse)\\n\",\n    \"forest_rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Scores: [51646.44545909 48940.60114882 53050.86323649 54408.98730149\\n\",\n      \" 50922.14870785 56482.50703987 51864.52025526 49760.85037653\\n\",\n      \" 55434.21627933 53326.10093303]\\n\",\n      \"Mean: 52583.72407377466\\n\",\n      \"Standard deviation: 2298.353351147122\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\\n\",\n    \"                                scoring=\\\"neg_mean_squared_error\\\", cv=10)\\n\",\n    \"forest_rmse_scores = np.sqrt(-forest_scores)\\n\",\n    \"display_scores(forest_rmse_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"count       10.000000\\n\",\n       \"mean     69052.461363\\n\",\n       \"std       2879.437224\\n\",\n       \"min      64969.630564\\n\",\n       \"25%      67136.363758\\n\",\n       \"50%      68156.372635\\n\",\n       \"75%      70982.369487\\n\",\n       \"max      74739.570526\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring=\\\"neg_mean_squared_error\\\", cv=10)\\n\",\n    \"pd.Series(np.sqrt(-scores)).describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"111094.6308539982\"\n      ]\n     },\n     \"execution_count\": 98,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVR\\n\",\n    \"\\n\",\n    \"svm_reg = SVR(kernel=\\\"linear\\\")\\n\",\n    \"svm_reg.fit(housing_prepared, housing_labels)\\n\",\n    \"housing_predictions = svm_reg.predict(housing_prepared)\\n\",\n    \"svm_mse = mean_squared_error(housing_labels, housing_predictions)\\n\",\n    \"svm_rmse = np.sqrt(svm_mse)\\n\",\n    \"svm_rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n       \"           max_features='auto', max_leaf_nodes=None,\\n\",\n       \"           min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"           min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\\n\",\n       \"           oob_score=False, random_state=42, verbose=0, warm_start=False),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=None,\\n\",\n       \"       param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\\n\",\n       \"       scoring='neg_mean_squared_error', verbose=0)\"\n      ]\n     },\n     \"execution_count\": 99,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"param_grid = [\\n\",\n    \"    # try 12 (3×4) combinations of hyperparameters\\n\",\n    \"    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\\n\",\n    \"    # then try 6 (2×3) combinations with bootstrap set as False\\n\",\n    \"    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\\n\",\n    \"  ]\\n\",\n    \"\\n\",\n    \"forest_reg = RandomForestRegressor(random_state=42)\\n\",\n    \"# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \\n\",\n    \"grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\\n\",\n    \"                           scoring='neg_mean_squared_error', return_train_score=True)\\n\",\n    \"grid_search.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best hyperparameter combination found:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'max_features': 8, 'n_estimators': 30}\"\n      ]\n     },\n     \"execution_count\": 100,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n       \"           max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n       \"           min_impurity_split=None, min_samples_leaf=1,\\n\",\n       \"           min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n       \"           n_estimators=30, n_jobs=None, oob_score=False, random_state=42,\\n\",\n       \"           verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 101,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_search.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the score of each hyperparameter combination tested during the grid search:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"63669.05791727153 {'max_features': 2, 'n_estimators': 3}\\n\",\n      \"55627.16171305252 {'max_features': 2, 'n_estimators': 10}\\n\",\n      \"53384.57867637289 {'max_features': 2, 'n_estimators': 30}\\n\",\n      \"60965.99185930139 {'max_features': 4, 'n_estimators': 3}\\n\",\n      \"52740.98248528835 {'max_features': 4, 'n_estimators': 10}\\n\",\n      \"50377.344409590376 {'max_features': 4, 'n_estimators': 30}\\n\",\n      \"58663.84733372485 {'max_features': 6, 'n_estimators': 3}\\n\",\n      \"52006.15355973719 {'max_features': 6, 'n_estimators': 10}\\n\",\n      \"50146.465964159885 {'max_features': 6, 'n_estimators': 30}\\n\",\n      \"57869.25504027614 {'max_features': 8, 'n_estimators': 3}\\n\",\n      \"51711.09443660957 {'max_features': 8, 'n_estimators': 10}\\n\",\n      \"49682.25345942335 {'max_features': 8, 'n_estimators': 30}\\n\",\n      \"62895.088889905004 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\\n\",\n      \"54658.14484390074 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\\n\",\n      \"59470.399594730654 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\\n\",\n      \"52725.01091081235 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\\n\",\n      \"57490.612956065226 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\\n\",\n      \"51009.51445842374 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cvres = grid_search.cv_results_\\n\",\n    \"for mean_score, params in zip(cvres[\\\"mean_test_score\\\"], cvres[\\\"params\\\"]):\\n\",\n    \"    print(np.sqrt(-mean_score), params)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>mean_fit_time</th>\\n\",\n       \"      <th>std_fit_time</th>\\n\",\n       \"      <th>mean_score_time</th>\\n\",\n       \"      <th>std_score_time</th>\\n\",\n       \"      <th>param_max_features</th>\\n\",\n       \"      <th>param_n_estimators</th>\\n\",\n       \"      <th>param_bootstrap</th>\\n\",\n       \"      <th>params</th>\\n\",\n       \"      <th>split0_test_score</th>\\n\",\n       \"      <th>split1_test_score</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>mean_test_score</th>\\n\",\n       \"      <th>std_test_score</th>\\n\",\n       \"      <th>rank_test_score</th>\\n\",\n       \"      <th>split0_train_score</th>\\n\",\n       \"      <th>split1_train_score</th>\\n\",\n       \"      <th>split2_train_score</th>\\n\",\n       \"      <th>split3_train_score</th>\\n\",\n       \"      <th>split4_train_score</th>\\n\",\n       \"      <th>mean_train_score</th>\\n\",\n       \"      <th>std_train_score</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.060251</td>\\n\",\n       \"      <td>0.001252</td>\\n\",\n       \"      <td>0.004077</td>\\n\",\n       \"      <td>0.000397</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 2, 'n_estimators': 3}</td>\\n\",\n       \"      <td>-3.837622e+09</td>\\n\",\n       \"      <td>-4.147108e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-4.053749e+09</td>\\n\",\n       \"      <td>1.519609e+08</td>\\n\",\n       \"      <td>18</td>\\n\",\n       \"      <td>-1.064113e+09</td>\\n\",\n       \"      <td>-1.105142e+09</td>\\n\",\n       \"      <td>-1.116550e+09</td>\\n\",\n       \"      <td>-1.112342e+09</td>\\n\",\n       \"      <td>-1.129650e+09</td>\\n\",\n       \"      <td>-1.105559e+09</td>\\n\",\n       \"      <td>2.220402e+07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.195211</td>\\n\",\n       \"      <td>0.001845</td>\\n\",\n       \"      <td>0.010179</td>\\n\",\n       \"      <td>0.000394</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 2, 'n_estimators': 10}</td>\\n\",\n       \"      <td>-3.047771e+09</td>\\n\",\n       \"      <td>-3.254861e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.094381e+09</td>\\n\",\n       \"      <td>1.327046e+08</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>-5.927175e+08</td>\\n\",\n       \"      <td>-5.870952e+08</td>\\n\",\n       \"      <td>-5.776964e+08</td>\\n\",\n       \"      <td>-5.716332e+08</td>\\n\",\n       \"      <td>-5.802501e+08</td>\\n\",\n       \"      <td>-5.818785e+08</td>\\n\",\n       \"      <td>7.345821e+06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.589625</td>\\n\",\n       \"      <td>0.003846</td>\\n\",\n       \"      <td>0.030403</td>\\n\",\n       \"      <td>0.003266</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 2, 'n_estimators': 30}</td>\\n\",\n       \"      <td>-2.689185e+09</td>\\n\",\n       \"      <td>-3.021086e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.849913e+09</td>\\n\",\n       \"      <td>1.626879e+08</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>-4.381089e+08</td>\\n\",\n       \"      <td>-4.391272e+08</td>\\n\",\n       \"      <td>-4.371702e+08</td>\\n\",\n       \"      <td>-4.376955e+08</td>\\n\",\n       \"      <td>-4.452654e+08</td>\\n\",\n       \"      <td>-4.394734e+08</td>\\n\",\n       \"      <td>2.966320e+06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.098321</td>\\n\",\n       \"      <td>0.001313</td>\\n\",\n       \"      <td>0.003398</td>\\n\",\n       \"      <td>0.000078</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 4, 'n_estimators': 3}</td>\\n\",\n       \"      <td>-3.730181e+09</td>\\n\",\n       \"      <td>-3.786886e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.716852e+09</td>\\n\",\n       \"      <td>1.631421e+08</td>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>-9.865163e+08</td>\\n\",\n       \"      <td>-1.012565e+09</td>\\n\",\n       \"      <td>-9.169425e+08</td>\\n\",\n       \"      <td>-1.037400e+09</td>\\n\",\n       \"      <td>-9.707739e+08</td>\\n\",\n       \"      <td>-9.848396e+08</td>\\n\",\n       \"      <td>4.084607e+07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0.322361</td>\\n\",\n       \"      <td>0.002503</td>\\n\",\n       \"      <td>0.010031</td>\\n\",\n       \"      <td>0.000395</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 4, 'n_estimators': 10}</td>\\n\",\n       \"      <td>-2.666283e+09</td>\\n\",\n       \"      <td>-2.784511e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.781611e+09</td>\\n\",\n       \"      <td>1.268562e+08</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-5.097115e+08</td>\\n\",\n       \"      <td>-5.162820e+08</td>\\n\",\n       \"      <td>-4.962893e+08</td>\\n\",\n       \"      <td>-5.436192e+08</td>\\n\",\n       \"      <td>-5.160297e+08</td>\\n\",\n       \"      <td>-5.163863e+08</td>\\n\",\n       \"      <td>1.542862e+07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>0.964709</td>\\n\",\n       \"      <td>0.003208</td>\\n\",\n       \"      <td>0.026985</td>\\n\",\n       \"      <td>0.000617</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 4, 'n_estimators': 30}</td>\\n\",\n       \"      <td>-2.387153e+09</td>\\n\",\n       \"      <td>-2.588448e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.537877e+09</td>\\n\",\n       \"      <td>1.214603e+08</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-3.838835e+08</td>\\n\",\n       \"      <td>-3.880268e+08</td>\\n\",\n       \"      <td>-3.790867e+08</td>\\n\",\n       \"      <td>-4.040957e+08</td>\\n\",\n       \"      <td>-3.845520e+08</td>\\n\",\n       \"      <td>-3.879289e+08</td>\\n\",\n       \"      <td>8.571233e+06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>0.132895</td>\\n\",\n       \"      <td>0.003371</td>\\n\",\n       \"      <td>0.003408</td>\\n\",\n       \"      <td>0.000064</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 6, 'n_estimators': 3}</td>\\n\",\n       \"      <td>-3.119657e+09</td>\\n\",\n       \"      <td>-3.586319e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.441447e+09</td>\\n\",\n       \"      <td>1.893141e+08</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>-9.245343e+08</td>\\n\",\n       \"      <td>-8.886939e+08</td>\\n\",\n       \"      <td>-9.353135e+08</td>\\n\",\n       \"      <td>-9.009801e+08</td>\\n\",\n       \"      <td>-8.624664e+08</td>\\n\",\n       \"      <td>-9.023976e+08</td>\\n\",\n       \"      <td>2.591445e+07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>0.443695</td>\\n\",\n       \"      <td>0.005027</td>\\n\",\n       \"      <td>0.010141</td>\\n\",\n       \"      <td>0.000682</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 6, 'n_estimators': 10}</td>\\n\",\n       \"      <td>-2.549663e+09</td>\\n\",\n       \"      <td>-2.782039e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.704640e+09</td>\\n\",\n       \"      <td>1.471542e+08</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>-4.980344e+08</td>\\n\",\n       \"      <td>-5.045869e+08</td>\\n\",\n       \"      <td>-4.994664e+08</td>\\n\",\n       \"      <td>-4.990325e+08</td>\\n\",\n       \"      <td>-5.055542e+08</td>\\n\",\n       \"      <td>-5.013349e+08</td>\\n\",\n       \"      <td>3.100456e+06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>1.343336</td>\\n\",\n       \"      <td>0.004695</td>\\n\",\n       \"      <td>0.027562</td>\\n\",\n       \"      <td>0.001805</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 6, 'n_estimators': 30}</td>\\n\",\n       \"      <td>-2.370010e+09</td>\\n\",\n       \"      <td>-2.583638e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.514668e+09</td>\\n\",\n       \"      <td>1.285063e+08</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-3.838538e+08</td>\\n\",\n       \"      <td>-3.804711e+08</td>\\n\",\n       \"      <td>-3.805218e+08</td>\\n\",\n       \"      <td>-3.856095e+08</td>\\n\",\n       \"      <td>-3.901917e+08</td>\\n\",\n       \"      <td>-3.841296e+08</td>\\n\",\n       \"      <td>3.617057e+06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>0.169927</td>\\n\",\n       \"      <td>0.001055</td>\\n\",\n       \"      <td>0.003458</td>\\n\",\n       \"      <td>0.000110</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 8, 'n_estimators': 3}</td>\\n\",\n       \"      <td>-3.353504e+09</td>\\n\",\n       \"      <td>-3.348552e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.348851e+09</td>\\n\",\n       \"      <td>1.241864e+08</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>-9.228123e+08</td>\\n\",\n       \"      <td>-8.553031e+08</td>\\n\",\n       \"      <td>-8.603321e+08</td>\\n\",\n       \"      <td>-8.881964e+08</td>\\n\",\n       \"      <td>-9.151287e+08</td>\\n\",\n       \"      <td>-8.883545e+08</td>\\n\",\n       \"      <td>2.750227e+07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>0.570730</td>\\n\",\n       \"      <td>0.004385</td>\\n\",\n       \"      <td>0.010258</td>\\n\",\n       \"      <td>0.000573</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 8, 'n_estimators': 10}</td>\\n\",\n       \"      <td>-2.571970e+09</td>\\n\",\n       \"      <td>-2.718994e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.674037e+09</td>\\n\",\n       \"      <td>1.392720e+08</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>-4.932416e+08</td>\\n\",\n       \"      <td>-4.815238e+08</td>\\n\",\n       \"      <td>-4.730979e+08</td>\\n\",\n       \"      <td>-5.155367e+08</td>\\n\",\n       \"      <td>-4.985555e+08</td>\\n\",\n       \"      <td>-4.923911e+08</td>\\n\",\n       \"      <td>1.459294e+07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>1.724103</td>\\n\",\n       \"      <td>0.005320</td>\\n\",\n       \"      <td>0.027100</td>\\n\",\n       \"      <td>0.000618</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>{'max_features': 8, 'n_estimators': 30}</td>\\n\",\n       \"      <td>-2.357390e+09</td>\\n\",\n       \"      <td>-2.546640e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.468326e+09</td>\\n\",\n       \"      <td>1.091647e+08</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>-3.841658e+08</td>\\n\",\n       \"      <td>-3.744500e+08</td>\\n\",\n       \"      <td>-3.773239e+08</td>\\n\",\n       \"      <td>-3.882250e+08</td>\\n\",\n       \"      <td>-3.810005e+08</td>\\n\",\n       \"      <td>-3.810330e+08</td>\\n\",\n       \"      <td>4.871017e+06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>0.093079</td>\\n\",\n       \"      <td>0.001465</td>\\n\",\n       \"      <td>0.004737</td>\\n\",\n       \"      <td>0.000253</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\\n\",\n       \"      <td>-3.785816e+09</td>\\n\",\n       \"      <td>-4.166012e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.955792e+09</td>\\n\",\n       \"      <td>1.900966e+08</td>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>0.000000e+00</td>\\n\",\n       \"      <td>0.000000e+00</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>0.309217</td>\\n\",\n       \"      <td>0.002585</td>\\n\",\n       \"      <td>0.011903</td>\\n\",\n       \"      <td>0.000335</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\\n\",\n       \"      <td>-2.810721e+09</td>\\n\",\n       \"      <td>-3.107789e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.987513e+09</td>\\n\",\n       \"      <td>1.539231e+08</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>-6.056477e-02</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-2.967449e+00</td>\\n\",\n       \"      <td>-6.056027e-01</td>\\n\",\n       \"      <td>1.181156e+00</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>0.124233</td>\\n\",\n       \"      <td>0.002787</td>\\n\",\n       \"      <td>0.004212</td>\\n\",\n       \"      <td>0.000185</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\\n\",\n       \"      <td>-3.618324e+09</td>\\n\",\n       \"      <td>-3.441527e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.536728e+09</td>\\n\",\n       \"      <td>7.795196e+07</td>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-6.072840e+01</td>\\n\",\n       \"      <td>-1.214568e+01</td>\\n\",\n       \"      <td>2.429136e+01</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>0.408420</td>\\n\",\n       \"      <td>0.003067</td>\\n\",\n       \"      <td>0.012991</td>\\n\",\n       \"      <td>0.000480</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\\n\",\n       \"      <td>-2.757999e+09</td>\\n\",\n       \"      <td>-2.851737e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.779927e+09</td>\\n\",\n       \"      <td>6.286611e+07</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-2.089484e+01</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-5.465556e+00</td>\\n\",\n       \"      <td>-5.272080e+00</td>\\n\",\n       \"      <td>8.093117e+00</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>0.154712</td>\\n\",\n       \"      <td>0.002472</td>\\n\",\n       \"      <td>0.004148</td>\\n\",\n       \"      <td>0.000244</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\\n\",\n       \"      <td>-3.134040e+09</td>\\n\",\n       \"      <td>-3.559375e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-3.305171e+09</td>\\n\",\n       \"      <td>1.879203e+08</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>0.000000e+00</td>\\n\",\n       \"      <td>0.000000e+00</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>0.512536</td>\\n\",\n       \"      <td>0.001934</td>\\n\",\n       \"      <td>0.012041</td>\\n\",\n       \"      <td>0.000309</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\\n\",\n       \"      <td>-2.525578e+09</td>\\n\",\n       \"      <td>-2.710011e+09</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-2.601971e+09</td>\\n\",\n       \"      <td>1.088031e+08</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-1.514119e-02</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-0.000000e+00</td>\\n\",\n       \"      <td>-3.028238e-03</td>\\n\",\n       \"      <td>6.056477e-03</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>18 rows × 23 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\\\\n\",\n       \"0        0.060251      0.001252         0.004077        0.000397   \\n\",\n       \"1        0.195211      0.001845         0.010179        0.000394   \\n\",\n       \"2        0.589625      0.003846         0.030403        0.003266   \\n\",\n       \"3        0.098321      0.001313         0.003398        0.000078   \\n\",\n       \"4        0.322361      0.002503         0.010031        0.000395   \\n\",\n       \"5        0.964709      0.003208         0.026985        0.000617   \\n\",\n       \"6        0.132895      0.003371         0.003408        0.000064   \\n\",\n       \"7        0.443695      0.005027         0.010141        0.000682   \\n\",\n       \"8        1.343336      0.004695         0.027562        0.001805   \\n\",\n       \"9        0.169927      0.001055         0.003458        0.000110   \\n\",\n       \"10       0.570730      0.004385         0.010258        0.000573   \\n\",\n       \"11       1.724103      0.005320         0.027100        0.000618   \\n\",\n       \"12       0.093079      0.001465         0.004737        0.000253   \\n\",\n       \"13       0.309217      0.002585         0.011903        0.000335   \\n\",\n       \"14       0.124233      0.002787         0.004212        0.000185   \\n\",\n       \"15       0.408420      0.003067         0.012991        0.000480   \\n\",\n       \"16       0.154712      0.002472         0.004148        0.000244   \\n\",\n       \"17       0.512536      0.001934         0.012041        0.000309   \\n\",\n       \"\\n\",\n       \"   param_max_features param_n_estimators param_bootstrap  \\\\\\n\",\n       \"0                   2                  3             NaN   \\n\",\n       \"1                   2                 10             NaN   \\n\",\n       \"2                   2                 30             NaN   \\n\",\n       \"3                   4                  3             NaN   \\n\",\n       \"4                   4                 10             NaN   \\n\",\n       \"5                   4                 30             NaN   \\n\",\n       \"6                   6                  3             NaN   \\n\",\n       \"7                   6                 10             NaN   \\n\",\n       \"8                   6                 30             NaN   \\n\",\n       \"9                   8                  3             NaN   \\n\",\n       \"10                  8                 10             NaN   \\n\",\n       \"11                  8                 30             NaN   \\n\",\n       \"12                  2                  3           False   \\n\",\n       \"13                  2                 10           False   \\n\",\n       \"14                  3                  3           False   \\n\",\n       \"15                  3                 10           False   \\n\",\n       \"16                  4                  3           False   \\n\",\n       \"17                  4                 10           False   \\n\",\n       \"\\n\",\n       \"                                               params  split0_test_score  \\\\\\n\",\n       \"0              {'max_features': 2, 'n_estimators': 3}      -3.837622e+09   \\n\",\n       \"1             {'max_features': 2, 'n_estimators': 10}      -3.047771e+09   \\n\",\n       \"2             {'max_features': 2, 'n_estimators': 30}      -2.689185e+09   \\n\",\n       \"3              {'max_features': 4, 'n_estimators': 3}      -3.730181e+09   \\n\",\n       \"4             {'max_features': 4, 'n_estimators': 10}      -2.666283e+09   \\n\",\n       \"5             {'max_features': 4, 'n_estimators': 30}      -2.387153e+09   \\n\",\n       \"6              {'max_features': 6, 'n_estimators': 3}      -3.119657e+09   \\n\",\n       \"7             {'max_features': 6, 'n_estimators': 10}      -2.549663e+09   \\n\",\n       \"8             {'max_features': 6, 'n_estimators': 30}      -2.370010e+09   \\n\",\n       \"9              {'max_features': 8, 'n_estimators': 3}      -3.353504e+09   \\n\",\n       \"10            {'max_features': 8, 'n_estimators': 10}      -2.571970e+09   \\n\",\n       \"11            {'max_features': 8, 'n_estimators': 30}      -2.357390e+09   \\n\",\n       \"12  {'bootstrap': False, 'max_features': 2, 'n_est...      -3.785816e+09   \\n\",\n       \"13  {'bootstrap': False, 'max_features': 2, 'n_est...      -2.810721e+09   \\n\",\n       \"14  {'bootstrap': False, 'max_features': 3, 'n_est...      -3.618324e+09   \\n\",\n       \"15  {'bootstrap': False, 'max_features': 3, 'n_est...      -2.757999e+09   \\n\",\n       \"16  {'bootstrap': False, 'max_features': 4, 'n_est...      -3.134040e+09   \\n\",\n       \"17  {'bootstrap': False, 'max_features': 4, 'n_est...      -2.525578e+09   \\n\",\n       \"\\n\",\n       \"    split1_test_score       ...         mean_test_score  std_test_score  \\\\\\n\",\n       \"0       -4.147108e+09       ...           -4.053749e+09    1.519609e+08   \\n\",\n       \"1       -3.254861e+09       ...           -3.094381e+09    1.327046e+08   \\n\",\n       \"2       -3.021086e+09       ...           -2.849913e+09    1.626879e+08   \\n\",\n       \"3       -3.786886e+09       ...           -3.716852e+09    1.631421e+08   \\n\",\n       \"4       -2.784511e+09       ...           -2.781611e+09    1.268562e+08   \\n\",\n       \"5       -2.588448e+09       ...           -2.537877e+09    1.214603e+08   \\n\",\n       \"6       -3.586319e+09       ...           -3.441447e+09    1.893141e+08   \\n\",\n       \"7       -2.782039e+09       ...           -2.704640e+09    1.471542e+08   \\n\",\n       \"8       -2.583638e+09       ...           -2.514668e+09    1.285063e+08   \\n\",\n       \"9       -3.348552e+09       ...           -3.348851e+09    1.241864e+08   \\n\",\n       \"10      -2.718994e+09       ...           -2.674037e+09    1.392720e+08   \\n\",\n       \"11      -2.546640e+09       ...           -2.468326e+09    1.091647e+08   \\n\",\n       \"12      -4.166012e+09       ...           -3.955792e+09    1.900966e+08   \\n\",\n       \"13      -3.107789e+09       ...           -2.987513e+09    1.539231e+08   \\n\",\n       \"14      -3.441527e+09       ...           -3.536728e+09    7.795196e+07   \\n\",\n       \"15      -2.851737e+09       ...           -2.779927e+09    6.286611e+07   \\n\",\n       \"16      -3.559375e+09       ...           -3.305171e+09    1.879203e+08   \\n\",\n       \"17      -2.710011e+09       ...           -2.601971e+09    1.088031e+08   \\n\",\n       \"\\n\",\n       \"    rank_test_score  split0_train_score  split1_train_score  \\\\\\n\",\n       \"0                18       -1.064113e+09       -1.105142e+09   \\n\",\n       \"1                11       -5.927175e+08       -5.870952e+08   \\n\",\n       \"2                 9       -4.381089e+08       -4.391272e+08   \\n\",\n       \"3                16       -9.865163e+08       -1.012565e+09   \\n\",\n       \"4                 8       -5.097115e+08       -5.162820e+08   \\n\",\n       \"5                 3       -3.838835e+08       -3.880268e+08   \\n\",\n       \"6                14       -9.245343e+08       -8.886939e+08   \\n\",\n       \"7                 6       -4.980344e+08       -5.045869e+08   \\n\",\n       \"8                 2       -3.838538e+08       -3.804711e+08   \\n\",\n       \"9                13       -9.228123e+08       -8.553031e+08   \\n\",\n       \"10                5       -4.932416e+08       -4.815238e+08   \\n\",\n       \"11                1       -3.841658e+08       -3.744500e+08   \\n\",\n       \"12               17       -0.000000e+00       -0.000000e+00   \\n\",\n       \"13               10       -6.056477e-02       -0.000000e+00   \\n\",\n       \"14               15       -0.000000e+00       -0.000000e+00   \\n\",\n       \"15                7       -2.089484e+01       -0.000000e+00   \\n\",\n       \"16               12       -0.000000e+00       -0.000000e+00   \\n\",\n       \"17                4       -0.000000e+00       -1.514119e-02   \\n\",\n       \"\\n\",\n       \"    split2_train_score  split3_train_score  split4_train_score  \\\\\\n\",\n       \"0        -1.116550e+09       -1.112342e+09       -1.129650e+09   \\n\",\n       \"1        -5.776964e+08       -5.716332e+08       -5.802501e+08   \\n\",\n       \"2        -4.371702e+08       -4.376955e+08       -4.452654e+08   \\n\",\n       \"3        -9.169425e+08       -1.037400e+09       -9.707739e+08   \\n\",\n       \"4        -4.962893e+08       -5.436192e+08       -5.160297e+08   \\n\",\n       \"5        -3.790867e+08       -4.040957e+08       -3.845520e+08   \\n\",\n       \"6        -9.353135e+08       -9.009801e+08       -8.624664e+08   \\n\",\n       \"7        -4.994664e+08       -4.990325e+08       -5.055542e+08   \\n\",\n       \"8        -3.805218e+08       -3.856095e+08       -3.901917e+08   \\n\",\n       \"9        -8.603321e+08       -8.881964e+08       -9.151287e+08   \\n\",\n       \"10       -4.730979e+08       -5.155367e+08       -4.985555e+08   \\n\",\n       \"11       -3.773239e+08       -3.882250e+08       -3.810005e+08   \\n\",\n       \"12       -0.000000e+00       -0.000000e+00       -0.000000e+00   \\n\",\n       \"13       -0.000000e+00       -0.000000e+00       -2.967449e+00   \\n\",\n       \"14       -0.000000e+00       -0.000000e+00       -6.072840e+01   \\n\",\n       \"15       -0.000000e+00       -0.000000e+00       -5.465556e+00   \\n\",\n       \"16       -0.000000e+00       -0.000000e+00       -0.000000e+00   \\n\",\n       \"17       -0.000000e+00       -0.000000e+00       -0.000000e+00   \\n\",\n       \"\\n\",\n       \"    mean_train_score  std_train_score  \\n\",\n       \"0      -1.105559e+09     2.220402e+07  \\n\",\n       \"1      -5.818785e+08     7.345821e+06  \\n\",\n       \"2      -4.394734e+08     2.966320e+06  \\n\",\n       \"3      -9.848396e+08     4.084607e+07  \\n\",\n       \"4      -5.163863e+08     1.542862e+07  \\n\",\n       \"5      -3.879289e+08     8.571233e+06  \\n\",\n       \"6      -9.023976e+08     2.591445e+07  \\n\",\n       \"7      -5.013349e+08     3.100456e+06  \\n\",\n       \"8      -3.841296e+08     3.617057e+06  \\n\",\n       \"9      -8.883545e+08     2.750227e+07  \\n\",\n       \"10     -4.923911e+08     1.459294e+07  \\n\",\n       \"11     -3.810330e+08     4.871017e+06  \\n\",\n       \"12      0.000000e+00     0.000000e+00  \\n\",\n       \"13     -6.056027e-01     1.181156e+00  \\n\",\n       \"14     -1.214568e+01     2.429136e+01  \\n\",\n       \"15     -5.272080e+00     8.093117e+00  \\n\",\n       \"16      0.000000e+00     0.000000e+00  \\n\",\n       \"17     -3.028238e-03     6.056477e-03  \\n\",\n       \"\\n\",\n       \"[18 rows x 23 columns]\"\n      ]\n     },\n     \"execution_count\": 103,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.DataFrame(grid_search.cv_results_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n       \"           max_features='auto', max_leaf_nodes=None,\\n\",\n       \"           min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"           min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\\n\",\n       \"           oob_score=False, random_state=42, verbose=0, warm_start=False),\\n\",\n       \"          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\\n\",\n       \"          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1210939e8>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x121093710>},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=42, refit=True,\\n\",\n       \"          return_train_score='warn', scoring='neg_mean_squared_error',\\n\",\n       \"          verbose=0)\"\n      ]\n     },\n     \"execution_count\": 104,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"from scipy.stats import randint\\n\",\n    \"\\n\",\n    \"param_distribs = {\\n\",\n    \"        'n_estimators': randint(low=1, high=200),\\n\",\n    \"        'max_features': randint(low=1, high=8),\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"forest_reg = RandomForestRegressor(random_state=42)\\n\",\n    \"rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\\n\",\n    \"                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\\n\",\n    \"rnd_search.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"49150.657232934034 {'max_features': 7, 'n_estimators': 180}\\n\",\n      \"51389.85295710133 {'max_features': 5, 'n_estimators': 15}\\n\",\n      \"50796.12045980556 {'max_features': 3, 'n_estimators': 72}\\n\",\n      \"50835.09932039744 {'max_features': 5, 'n_estimators': 21}\\n\",\n      \"49280.90117886215 {'max_features': 7, 'n_estimators': 122}\\n\",\n      \"50774.86679035961 {'max_features': 3, 'n_estimators': 75}\\n\",\n      \"50682.75001237282 {'max_features': 3, 'n_estimators': 88}\\n\",\n      \"49608.94061293652 {'max_features': 5, 'n_estimators': 100}\\n\",\n      \"50473.57642831875 {'max_features': 3, 'n_estimators': 150}\\n\",\n      \"64429.763804893395 {'max_features': 5, 'n_estimators': 2}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cvres = rnd_search.cv_results_\\n\",\n    \"for mean_score, params in zip(cvres[\\\"mean_test_score\\\"], cvres[\\\"params\\\"]):\\n\",\n    \"    print(np.sqrt(-mean_score), params)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([7.33442355e-02, 6.29090705e-02, 4.11437985e-02, 1.46726854e-02,\\n\",\n       \"       1.41064835e-02, 1.48742809e-02, 1.42575993e-02, 3.66158981e-01,\\n\",\n       \"       5.64191792e-02, 1.08792957e-01, 5.33510773e-02, 1.03114883e-02,\\n\",\n       \"       1.64780994e-01, 6.02803867e-05, 1.96041560e-03, 2.85647464e-03])\"\n      ]\n     },\n     \"execution_count\": 106,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"feature_importances = grid_search.best_estimator_.feature_importances_\\n\",\n    \"feature_importances\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0.3661589806181342, 'median_income'),\\n\",\n       \" (0.1647809935615905, 'INLAND'),\\n\",\n       \" (0.10879295677551573, 'pop_per_hhold'),\\n\",\n       \" (0.07334423551601242, 'longitude'),\\n\",\n       \" (0.0629090704826203, 'latitude'),\\n\",\n       \" (0.05641917918195401, 'rooms_per_hhold'),\\n\",\n       \" (0.05335107734767581, 'bedrooms_per_room'),\\n\",\n       \" (0.041143798478729635, 'housing_median_age'),\\n\",\n       \" (0.014874280890402767, 'population'),\\n\",\n       \" (0.014672685420543237, 'total_rooms'),\\n\",\n       \" (0.014257599323407807, 'households'),\\n\",\n       \" (0.014106483453584102, 'total_bedrooms'),\\n\",\n       \" (0.010311488326303787, '<1H OCEAN'),\\n\",\n       \" (0.002856474637320158, 'NEAR OCEAN'),\\n\",\n       \" (0.00196041559947807, 'NEAR BAY'),\\n\",\n       \" (6.028038672736599e-05, 'ISLAND')]\"\n      ]\n     },\n     \"execution_count\": 107,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"extra_attribs = [\\\"rooms_per_hhold\\\", \\\"pop_per_hhold\\\", \\\"bedrooms_per_room\\\"]\\n\",\n    \"#cat_encoder = cat_pipeline.named_steps[\\\"cat_encoder\\\"] # old solution\\n\",\n    \"cat_encoder = full_pipeline.named_transformers_[\\\"cat\\\"]\\n\",\n    \"cat_one_hot_attribs = list(cat_encoder.categories_[0])\\n\",\n    \"attributes = num_attribs + extra_attribs + cat_one_hot_attribs\\n\",\n    \"sorted(zip(feature_importances, attributes), reverse=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"final_model = grid_search.best_estimator_\\n\",\n    \"\\n\",\n    \"X_test = strat_test_set.drop(\\\"median_house_value\\\", axis=1)\\n\",\n    \"y_test = strat_test_set[\\\"median_house_value\\\"].copy()\\n\",\n    \"\\n\",\n    \"X_test_prepared = full_pipeline.transform(X_test)\\n\",\n    \"final_predictions = final_model.predict(X_test_prepared)\\n\",\n    \"\\n\",\n    \"final_mse = mean_squared_error(y_test, final_predictions)\\n\",\n    \"final_rmse = np.sqrt(final_mse)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"47730.22690385927\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"final_rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can compute a 95% confidence interval for the test RMSE:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy import stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([45685.10470776, 49691.25001878])\"\n      ]\n     },\n     \"execution_count\": 111,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"confidence = 0.95\\n\",\n    \"squared_errors = (final_predictions - y_test) ** 2\\n\",\n    \"mean = squared_errors.mean()\\n\",\n    \"m = len(squared_errors)\\n\",\n    \"\\n\",\n    \"np.sqrt(stats.t.interval(confidence, m - 1,\\n\",\n    \"                         loc=np.mean(squared_errors),\\n\",\n    \"                         scale=stats.sem(squared_errors)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We could compute the interval manually like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(45685.10470776014, 49691.25001877871)\"\n      ]\n     },\n     \"execution_count\": 112,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\\n\",\n    \"tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\\n\",\n    \"np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, we could use a z-scores rather than t-scores:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(45685.717918136594, 49690.68623889426)\"\n      ]\n     },\n     \"execution_count\": 113,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"zscore = stats.norm.ppf((1 + confidence) / 2)\\n\",\n    \"zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\\n\",\n    \"np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Extra material\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A full pipeline with both preparation and prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\\n\",\n       \"       189747.55849879])\"\n      ]\n     },\n     \"execution_count\": 114,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"full_pipeline_with_predictor = Pipeline([\\n\",\n    \"        (\\\"preparation\\\", full_pipeline),\\n\",\n    \"        (\\\"linear\\\", LinearRegression())\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"full_pipeline_with_predictor.fit(housing, housing_labels)\\n\",\n    \"full_pipeline_with_predictor.predict(some_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model persistence using joblib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"my_model = full_pipeline_with_predictor\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#from sklearn.externals import joblib # deprecated, use import joblib instead\\n\",\n    \"import joblib\\n\",\n    \"\\n\",\n    \"joblib.dump(my_model, \\\"my_model.pkl\\\") # DIFF\\n\",\n    \"#...\\n\",\n    \"my_model_loaded = joblib.load(\\\"my_model.pkl\\\") # DIFF\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example SciPy distributions for `RandomizedSearchCV`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import geom, expon\\n\",\n    \"geom_distrib=geom(0.5).rvs(10000, random_state=42)\\n\",\n    \"expon_distrib=expon(scale=1).rvs(10000, random_state=42)\\n\",\n    \"plt.hist(geom_distrib, bins=50)\\n\",\n    \"plt.show()\\n\",\n    \"plt.hist(expon_distrib, bins=50)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"Question: 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). Don't worry about what these hyperparameters mean for now. How does the best `SVR` predictor perform?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 5 folds for each of 50 candidates, totalling 250 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.5min\\n\",\n      \"[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:  9.4min\\n\",\n      \"[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 15.4min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"       estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\\n\",\n       \"  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=4,\\n\",\n       \"       param_grid=[{'kernel': ['linear'], 'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0, 10000.0, 30000.0]}, {'kernel': ['rbf'], 'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0], 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]}],\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring='neg_mean_squared_error', verbose=2)\"\n      ]\n     },\n     \"execution_count\": 118,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"param_grid = [\\n\",\n    \"        {'kernel': ['linear'], 'C': [10., 30., 100., 300., 1000., 3000., 10000., 30000.0]},\\n\",\n    \"        {'kernel': ['rbf'], 'C': [1.0, 3.0, 10., 30., 100., 300., 1000.0],\\n\",\n    \"         'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"svm_reg = SVR()\\n\",\n    \"grid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\\n\",\n    \"grid_search.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best model achieves the following score (evaluated using 5-fold cross validation):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"70363.90313964167\"\n      ]\n     },\n     \"execution_count\": 119,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"negative_mse = grid_search.best_score_\\n\",\n    \"rmse = np.sqrt(-negative_mse)\\n\",\n    \"rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's much worse than the `RandomForestRegressor`. Let's check the best hyperparameters found:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'C': 30000.0, 'kernel': 'linear'}\"\n      ]\n     },\n     \"execution_count\": 120,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The linear kernel seems better than the RBF kernel. Notice that the value of `C` is the maximum tested value. When this happens you definitely want to launch the grid search again with higher values for `C` (removing the smallest values), because it is likely that higher values of `C` will be better.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Question: Try replacing `GridSearchCV` with `RandomizedSearchCV`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 5 folds for each of 50 candidates, totalling 250 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.9min\\n\",\n      \"[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed: 12.1min\\n\",\n      \"[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 19.6min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\\n\",\n       \"  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False),\\n\",\n       \"          fit_params=None, iid='warn', n_iter=50, n_jobs=4,\\n\",\n       \"          param_distributions={'kernel': ['linear', 'rbf'], 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13a243278>, 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13a243e48>},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=42, refit=True,\\n\",\n       \"          return_train_score='warn', scoring='neg_mean_squared_error',\\n\",\n       \"          verbose=2)\"\n      ]\n     },\n     \"execution_count\": 121,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"from scipy.stats import expon, reciprocal\\n\",\n    \"\\n\",\n    \"# see https://docs.scipy.org/doc/scipy/reference/stats.html\\n\",\n    \"# for `expon()` and `reciprocal()` documentation and more probability distribution functions.\\n\",\n    \"\\n\",\n    \"# Note: gamma is ignored when kernel is \\\"linear\\\"\\n\",\n    \"param_distribs = {\\n\",\n    \"        'kernel': ['linear', 'rbf'],\\n\",\n    \"        'C': reciprocal(20, 200000),\\n\",\n    \"        'gamma': expon(scale=1.0),\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"svm_reg = SVR()\\n\",\n    \"rnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,\\n\",\n    \"                                n_iter=50, cv=5, scoring='neg_mean_squared_error',\\n\",\n    \"                                verbose=2, n_jobs=4, random_state=42)\\n\",\n    \"rnd_search.fit(housing_prepared, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best model achieves the following score (evaluated using 5-fold cross validation):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"54767.99053704408\"\n      ]\n     },\n     \"execution_count\": 122,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"negative_mse = rnd_search.best_score_\\n\",\n    \"rmse = np.sqrt(-negative_mse)\\n\",\n    \"rmse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now this is much closer to the performance of the `RandomForestRegressor` (but not quite there yet). Let's check the best hyperparameters found:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 123,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}\"\n      ]\n     },\n     \"execution_count\": 123,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This time the search found a good set of hyperparameters for the RBF kernel. Randomized search tends to find better hyperparameters than grid search in the same amount of time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the exponential distribution we used, with `scale=1.0`. Note that some samples are much larger or smaller than 1.0, but when you look at the log of the distribution, you can see that most values are actually concentrated roughly in the range of exp(-2) to exp(+2), which is about 0.1 to 7.4.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"expon_distrib = expon(scale=1.)\\n\",\n    \"samples = expon_distrib.rvs(10000, random_state=42)\\n\",\n    \"plt.figure(figsize=(10, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.title(\\\"Exponential distribution (scale=1.0)\\\")\\n\",\n    \"plt.hist(samples, bins=50)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.title(\\\"Log of this distribution\\\")\\n\",\n    \"plt.hist(np.log(samples), bins=50)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The distribution we used for `C` looks quite different: the scale of the samples is picked from a uniform distribution within a given range, which is why the right graph, which represents the log of the samples, looks roughly constant. This distribution is useful when you don't have a clue of what the target scale is:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"reciprocal_distrib = reciprocal(20, 200000)\\n\",\n    \"samples = reciprocal_distrib.rvs(10000, random_state=42)\\n\",\n    \"plt.figure(figsize=(10, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.title(\\\"Reciprocal distribution (scale=1.0)\\\")\\n\",\n    \"plt.hist(samples, bins=50)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.title(\\\"Log of this distribution\\\")\\n\",\n    \"plt.hist(np.log(samples), bins=50)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The reciprocal distribution is useful when you have no idea what the scale of the hyperparameter should be (indeed, as you can see on the figure on the right, all scales are equally likely, within the given range), whereas the exponential distribution is best when you know (more or less) what the scale of the hyperparameter should be.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Question: Try adding a transformer in the preparation pipeline to select only the most important attributes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n    \"\\n\",\n    \"def indices_of_top_k(arr, k):\\n\",\n    \"    return np.sort(np.argpartition(np.array(arr), -k)[-k:])\\n\",\n    \"\\n\",\n    \"class TopFeatureSelector(BaseEstimator, TransformerMixin):\\n\",\n    \"    def __init__(self, feature_importances, k):\\n\",\n    \"        self.feature_importances = feature_importances\\n\",\n    \"        self.k = k\\n\",\n    \"    def fit(self, X, y=None):\\n\",\n    \"        self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)\\n\",\n    \"        return self\\n\",\n    \"    def transform(self, X):\\n\",\n    \"        return X[:, self.feature_indices_]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: this feature selector assumes that you have already computed the feature importances somehow (for example using a `RandomForestRegressor`). You may be tempted to compute them directly in the `TopFeatureSelector`'s `fit()` method, however this would likely slow down grid/randomized search since the feature importances would have to be computed for every hyperparameter combination (unless you implement some sort of cache).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's define the number of top features we want to keep:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"k = 5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's look for the indices of the top k features:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  1,  7,  9, 12])\"\n      ]\n     },\n     \"execution_count\": 128,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"top_k_feature_indices = indices_of_top_k(feature_importances, k)\\n\",\n    \"top_k_feature_indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(['longitude', 'latitude', 'median_income', 'pop_per_hhold',\\n\",\n       \"       'INLAND'], dtype='<U18')\"\n      ]\n     },\n     \"execution_count\": 129,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.array(attributes)[top_k_feature_indices]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's double check that these are indeed the top k features:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0.3661589806181342, 'median_income'),\\n\",\n       \" (0.1647809935615905, 'INLAND'),\\n\",\n       \" (0.10879295677551573, 'pop_per_hhold'),\\n\",\n       \" (0.07334423551601242, 'longitude'),\\n\",\n       \" (0.0629090704826203, 'latitude')]\"\n      ]\n     },\n     \"execution_count\": 130,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sorted(zip(feature_importances, attributes), reverse=True)[:k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looking good... Now let's create a new pipeline that runs the previously defined preparation pipeline, and adds top k feature selection:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"preparation_and_feature_selection_pipeline = Pipeline([\\n\",\n    \"    ('preparation', full_pipeline),\\n\",\n    \"    ('feature_selection', TopFeatureSelector(feature_importances, k))\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 132,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(housing)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the features of the first 3 instances:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 133,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.15604281,  0.77194962, -0.61493744, -0.08649871,  0.        ],\\n\",\n       \"       [-1.17602483,  0.6596948 ,  1.33645936, -0.03353391,  0.        ],\\n\",\n       \"       [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499,  0.        ]])\"\n      ]\n     },\n     \"execution_count\": 133,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_prepared_top_k_features[0:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's double check that these are indeed the top k features:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 134,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.15604281,  0.77194962, -0.61493744, -0.08649871,  0.        ],\\n\",\n       \"       [-1.17602483,  0.6596948 ,  1.33645936, -0.03353391,  0.        ],\\n\",\n       \"       [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499,  0.        ]])\"\n      ]\n     },\n     \"execution_count\": 134,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"housing_prepared[0:3, top_k_feature_indices]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Works great!  :)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Question: Try creating a single pipeline that does the full data preparation plus the final prediction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prepare_select_and_predict_pipeline = Pipeline([\\n\",\n    \"    ('preparation', full_pipeline),\\n\",\n    \"    ('feature_selection', TopFeatureSelector(feature_importances, k)),\\n\",\n    \"    ('svm_reg', SVR(**rnd_search.best_params_))\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 136,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('preparation', ColumnTransformer(n_jobs=1, remainder='drop', sparse_threshold=0.3,\\n\",\n       \"         transformer_weights=None,\\n\",\n       \"         transformers=[('num', Pipeline(memory=None,\\n\",\n       \"     steps=[('imputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"       strategy='median', verbose=0... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False))])\"\n      ]\n     },\n     \"execution_count\": 136,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"prepare_select_and_predict_pipeline.fit(housing, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try the full pipeline on a few instances:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 137,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predictions:\\t [203214.28978849 371846.88152572 173295.65441612  47328.3970888 ]\\n\",\n      \"Labels:\\t\\t [286600.0, 340600.0, 196900.0, 46300.0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"some_data = housing.iloc[:4]\\n\",\n    \"some_labels = housing_labels.iloc[:4]\\n\",\n    \"\\n\",\n    \"print(\\\"Predictions:\\\\t\\\", prepare_select_and_predict_pipeline.predict(some_data))\\n\",\n    \"print(\\\"Labels:\\\\t\\\\t\\\", list(some_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well, the full pipeline seems to work fine. Of course, the predictions are not fantastic: they would be better if we used the best `RandomForestRegressor` that we found earlier, rather than the best `SVR`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Question: Automatically explore some preparation options using `GridSearchCV`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 138,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 5 folds for each of 48 candidates, totalling 240 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.5min\\n\",\n      \"[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:  9.7min\\n\",\n      \"[Parallel(n_jobs=4)]: Done 240 out of 240 | elapsed: 19.8min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"       estimator=Pipeline(memory=None,\\n\",\n       \"     steps=[('preparation', ColumnTransformer(n_jobs=1, remainder='drop', sparse_threshold=0.3,\\n\",\n       \"         transformer_weights=None,\\n\",\n       \"         transformers=[('num', Pipeline(memory=None,\\n\",\n       \"     steps=[('imputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"       strategy='median', verbose=0... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False))]),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=4,\\n\",\n       \"       param_grid=[{'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'], 'feature_selection__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}],\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring='neg_mean_squared_error', verbose=2)\"\n      ]\n     },\n     \"execution_count\": 138,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid = [{\\n\",\n    \"    'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'],\\n\",\n    \"    'feature_selection__k': list(range(1, len(feature_importances) + 1))\\n\",\n    \"}]\\n\",\n    \"\\n\",\n    \"grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=5,\\n\",\n    \"                                scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\\n\",\n    \"grid_search_prep.fit(housing, housing_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 139,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'feature_selection__k': 15,\\n\",\n       \" 'preparation__num__imputer__strategy': 'most_frequent'}\"\n      ]\n     },\n     \"execution_count\": 139,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_search_prep.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best imputer strategy is `most_frequent` and apparently almost all features are useful (15 out of 16). The last one (`ISLAND`) seems to just add some noise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Congratulations! You already know quite a lot about Machine Learning. :)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {\n   \"height\": \"279px\",\n   \"width\": \"309px\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "04_training_linear_models.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 4 – Training Linear Models**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 4._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/04_training_linear_models.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"mpl.rc('axes', labelsize=14)\\n\",\n    \"mpl.rc('xtick', labelsize=12)\\n\",\n    \"mpl.rc('ytick', labelsize=12)\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"training_linear_models\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linear regression using the Normal Equation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"X = 2 * np.random.rand(100, 1)\\n\",\n    \"y = 4 + 3 * X + np.random.randn(100, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure generated_data_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.axis([0, 2, 0, 15])\\n\",\n    \"save_fig(\\\"generated_data_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_b = np.c_[np.ones((100, 1)), X]  # add x0 = 1 to each instance\\n\",\n    \"theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [2.77011339]])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theta_best\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [9.75532293]])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_new = np.array([[0], [2]])\\n\",\n    \"X_new_b = np.c_[np.ones((2, 1)), X_new]  # add x0 = 1 to each instance\\n\",\n    \"y_predict = X_new_b.dot(theta_best)\\n\",\n    \"y_predict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(X_new, y_predict, \\\"r-\\\")\\n\",\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.axis([0, 2, 0, 15])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The figure in the book actually corresponds to the following code, with a legend and axis labels:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure linear_model_predictions\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(X_new, y_predict, \\\"r-\\\", linewidth=2, label=\\\"Predictions\\\")\\n\",\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 2, 0, 15])\\n\",\n    \"save_fig(\\\"linear_model_predictions\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array([4.21509616]), array([[2.77011339]]))\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LinearRegression\\n\",\n    \"lin_reg = LinearRegression()\\n\",\n    \"lin_reg.fit(X, y)\\n\",\n    \"lin_reg.intercept_, lin_reg.coef_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [9.75532293]])\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lin_reg.predict(X_new)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `LinearRegression` class is based on the `scipy.linalg.lstsq()` function (the name stands for \\\"least squares\\\"), which you could call directly:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [2.77011339]])\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theta_best_svd, residuals, rank, s = np.linalg.lstsq(X_b, y, rcond=1e-6)\\n\",\n    \"theta_best_svd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This function computes $\\\\mathbf{X}^+\\\\mathbf{y}$, where $\\\\mathbf{X}^{+}$ is the _pseudoinverse_ of $\\\\mathbf{X}$ (specifically the Moore-Penrose inverse). You can use `np.linalg.pinv()` to compute the pseudoinverse directly:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [2.77011339]])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linalg.pinv(X_b).dot(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note**: the first releases of the book implied that the `LinearRegression` class was based on the Normal Equation. This was an error, my apologies: as explained above, it is based on the pseudoinverse, which ultimately relies on the SVD matrix decomposition of $\\\\mathbf{X}$ (see chapter 8 for details about the SVD decomposition). Its time complexity is $O(n^2)$ and it works even when $m < n$ or when some features are linear combinations of other features (in these cases, $\\\\mathbf{X}^T \\\\mathbf{X}$ is not invertible so the Normal Equation fails), see [issue #184](https://github.com/ageron/handson-ml/issues/184) for more details. However, this does not change the rest of the description of the `LinearRegression` class, in particular, it is based on an analytical solution, it does not scale well with the number of features, it scales linearly with the number of instances, all the data must fit in memory, it does not require feature scaling and the order of the instances in the training set does not matter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linear regression using batch gradient descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"eta = 0.1\\n\",\n    \"n_iterations = 1000\\n\",\n    \"m = 100\\n\",\n    \"theta = np.random.randn(2,1)\\n\",\n    \"\\n\",\n    \"for iteration in range(n_iterations):\\n\",\n    \"    gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)\\n\",\n    \"    theta = theta - eta * gradients\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [2.77011339]])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21509616],\\n\",\n       \"       [9.75532293]])\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_new_b.dot(theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"theta_path_bgd = []\\n\",\n    \"\\n\",\n    \"def plot_gradient_descent(theta, eta, theta_path=None):\\n\",\n    \"    m = len(X_b)\\n\",\n    \"    plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"    n_iterations = 1000\\n\",\n    \"    for iteration in range(n_iterations):\\n\",\n    \"        if iteration < 10:\\n\",\n    \"            y_predict = X_new_b.dot(theta)\\n\",\n    \"            style = \\\"b-\\\" if iteration > 0 else \\\"r--\\\"\\n\",\n    \"            plt.plot(X_new, y_predict, style)\\n\",\n    \"        gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)\\n\",\n    \"        theta = theta - eta * gradients\\n\",\n    \"        if theta_path is not None:\\n\",\n    \"            theta_path.append(theta)\\n\",\n    \"    plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"    plt.axis([0, 2, 0, 15])\\n\",\n    \"    plt.title(r\\\"$\\\\eta = {}$\\\".format(eta), fontsize=16)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure gradient_descent_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"theta = np.random.randn(2,1)  # random initialization\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10,4))\\n\",\n    \"plt.subplot(131); plot_gradient_descent(theta, eta=0.02)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.subplot(132); plot_gradient_descent(theta, eta=0.1, theta_path=theta_path_bgd)\\n\",\n    \"plt.subplot(133); plot_gradient_descent(theta, eta=0.5)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"gradient_descent_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Stochastic Gradient Descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"theta_path_sgd = []\\n\",\n    \"m = len(X_b)\\n\",\n    \"np.random.seed(42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure sgd_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 50\\n\",\n    \"t0, t1 = 5, 50  # learning schedule hyperparameters\\n\",\n    \"\\n\",\n    \"def learning_schedule(t):\\n\",\n    \"    return t0 / (t + t1)\\n\",\n    \"\\n\",\n    \"theta = np.random.randn(2,1)  # random initialization\\n\",\n    \"\\n\",\n    \"for epoch in range(n_epochs):\\n\",\n    \"    for i in range(m):\\n\",\n    \"        if epoch == 0 and i < 20:                    # not shown in the book\\n\",\n    \"            y_predict = X_new_b.dot(theta)           # not shown\\n\",\n    \"            style = \\\"b-\\\" if i > 0 else \\\"r--\\\"         # not shown\\n\",\n    \"            plt.plot(X_new, y_predict, style)        # not shown\\n\",\n    \"        random_index = np.random.randint(m)\\n\",\n    \"        xi = X_b[random_index:random_index+1]\\n\",\n    \"        yi = y[random_index:random_index+1]\\n\",\n    \"        gradients = 2 * xi.T.dot(xi.dot(theta) - yi)\\n\",\n    \"        eta = learning_schedule(epoch * m + i)\\n\",\n    \"        theta = theta - eta * gradients\\n\",\n    \"        theta_path_sgd.append(theta)                 # not shown\\n\",\n    \"\\n\",\n    \"plt.plot(X, y, \\\"b.\\\")                                 # not shown\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)                     # not shown\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)           # not shown\\n\",\n    \"plt.axis([0, 2, 0, 15])                              # not shown\\n\",\n    \"save_fig(\\\"sgd_plot\\\")                                 # not shown\\n\",\n    \"plt.show()                                           # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.21076011],\\n\",\n       \"       [2.74856079]])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\\n\",\n       \"       eta0=0.1, fit_intercept=True, l1_ratio=0.15,\\n\",\n       \"       learning_rate='invscaling', loss='squared_loss', max_iter=50,\\n\",\n       \"       n_iter=None, n_iter_no_change=5, penalty=None, power_t=0.25,\\n\",\n       \"       random_state=42, shuffle=True, tol=-inf, validation_fraction=0.1,\\n\",\n       \"       verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import SGDRegressor\\n\",\n    \"sgd_reg = SGDRegressor(max_iter=50, tol=-np.infty, penalty=None, eta0=0.1, random_state=42)\\n\",\n    \"sgd_reg.fit(X, y.ravel())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array([4.16782089]), array([2.72603052]))\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sgd_reg.intercept_, sgd_reg.coef_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Mini-batch gradient descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"theta_path_mgd = []\\n\",\n    \"\\n\",\n    \"n_iterations = 50\\n\",\n    \"minibatch_size = 20\\n\",\n    \"\\n\",\n    \"np.random.seed(42)\\n\",\n    \"theta = np.random.randn(2,1)  # random initialization\\n\",\n    \"\\n\",\n    \"t0, t1 = 200, 1000\\n\",\n    \"def learning_schedule(t):\\n\",\n    \"    return t0 / (t + t1)\\n\",\n    \"\\n\",\n    \"t = 0\\n\",\n    \"for epoch in range(n_iterations):\\n\",\n    \"    shuffled_indices = np.random.permutation(m)\\n\",\n    \"    X_b_shuffled = X_b[shuffled_indices]\\n\",\n    \"    y_shuffled = y[shuffled_indices]\\n\",\n    \"    for i in range(0, m, minibatch_size):\\n\",\n    \"        t += 1\\n\",\n    \"        xi = X_b_shuffled[i:i+minibatch_size]\\n\",\n    \"        yi = y_shuffled[i:i+minibatch_size]\\n\",\n    \"        gradients = 2/minibatch_size * xi.T.dot(xi.dot(theta) - yi)\\n\",\n    \"        eta = learning_schedule(t)\\n\",\n    \"        theta = theta - eta * gradients\\n\",\n    \"        theta_path_mgd.append(theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.25214635],\\n\",\n       \"       [2.7896408 ]])\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"theta_path_bgd = np.array(theta_path_bgd)\\n\",\n    \"theta_path_sgd = np.array(theta_path_sgd)\\n\",\n    \"theta_path_mgd = np.array(theta_path_mgd)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure gradient_descent_paths_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 504x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(7,4))\\n\",\n    \"plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], \\\"r-s\\\", linewidth=1, label=\\\"Stochastic\\\")\\n\",\n    \"plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], \\\"g-+\\\", linewidth=2, label=\\\"Mini-batch\\\")\\n\",\n    \"plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], \\\"b-o\\\", linewidth=3, label=\\\"Batch\\\")\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=16)\\n\",\n    \"plt.xlabel(r\\\"$\\\\theta_0$\\\", fontsize=20)\\n\",\n    \"plt.ylabel(r\\\"$\\\\theta_1$   \\\", fontsize=20, rotation=0)\\n\",\n    \"plt.axis([2.5, 4.5, 2.3, 3.9])\\n\",\n    \"save_fig(\\\"gradient_descent_paths_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Polynomial regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import numpy.random as rnd\\n\",\n    \"\\n\",\n    \"np.random.seed(42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"m = 100\\n\",\n    \"X = 6 * np.random.rand(m, 1) - 3\\n\",\n    \"y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure quadratic_data_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.axis([-3, 3, 0, 10])\\n\",\n    \"save_fig(\\\"quadratic_data_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-0.75275929])\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n    \"poly_features = PolynomialFeatures(degree=2, include_bias=False)\\n\",\n    \"X_poly = poly_features.fit_transform(X)\\n\",\n    \"X[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-0.75275929,  0.56664654])\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_poly[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array([1.78134581]), array([[0.93366893, 0.56456263]]))\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lin_reg = LinearRegression()\\n\",\n    \"lin_reg.fit(X_poly, y)\\n\",\n    \"lin_reg.intercept_, lin_reg.coef_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure quadratic_predictions_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xd8VFX+//HXSQgklCAoTaQpSFlYC4gbXAEVFQviihVZKSoqFlaXn4qCICqsgqvuKrrsV9DVxY4gKCCwoKhRmggoTQULRUDpBAjJ+f1xCCGQMpPM3Htn5v18POYxmXbvuTOZ+dzTPsdYaxEREQmaJL8LICIiUhgFKBERCSQFKBERCSQFKBERCSQFKBERCSQFKBERCSQFKBERCaSIBihjzB3GmAXGmH3GmJeOeOw8Y8wKY8weY8xsY0yDSO5bRETiS6RrUOuBR4Gxh99pjDkOmAAMBqoDC4A3IrxvERGJI+UiuTFr7QQAY0wb4ITDHroC+Npa+9bBx4cCW4wxzay1KyJZBhERiQ8RDVDF+B3wVd4Na+1uY8x3B+8/KkAZY/oCfQEqVarUulmzZh4VU0REImH3bli5EqwFY6BpU6hUyT22cOHCLdbaGiVtw6sAVRnYfMR924EqhT3ZWjsGGAPQpk0bu2DBguiWTkREImrECBg8GHJyICkJbrgBBg50jxljfghlG16N4tsFpB9xXzqw06P9i4iIhzp2hPLlITnZXXfsGP42vKpBfQ30zLthjKkEnHTwfhERiTMZGTBrFsyZ44JTRkb424hogDLGlDu4zWQg2RiTChwA3gVGGmO6Ae8DDwFLNEBCRCR+ZWSULjDliXQT3yAgC7gf6HHw70HW2s1AN+AxYCtwJnBthPctIiJxJNLDzIcCQ4t4bCag4XgiIonsjdCnwHrVBxU1O3bsYNOmTWRnZ/tdFPFISkoKNWvWJD39yHE3IhJon3wC110X8tNjOkDt2LGDX375hbp165KWloYxxu8iSZRZa8nKymLdunUAClIisWLPHujTx02MClFMJ4vdtGkTdevWpWLFigpOCcIYQ8WKFalbty6bNm3yuzgiEqrBg2H1amjZMuSXxHSAys7OJi0tze9iiA/S0tLUrCsSKz77DJ56yk2KGjcu5JfFdIACVHNKUPrcRWJEVlZ+096990KbNiG/NOYDlIiIBNiQIS4pX/Pm8NBDYb1UAUpERKIjMxOefNIl4xs3DlJTw3q5AlSca9myJUOHDj10u2HDhowaNapM2+zYsSN33HFHGUsmInEtKwt69YLcXBgwAM48M+xNKED5oFevXhhjMMaQkpLCiSeeyIABA9i9e3fU9z1//nz69esX0nNfeuklKleufNT9EyZMYMSIEZEumojEk0GDYNUqaNECHn64VJuI6XlQsaxTp0688sorZGdnM3fuXG666SZ2797N888/f9Rzs7OzSUlJich+a9QocQmWElWvXj0CJRGRuDV3bv6ovZdfDrtpL49qUD6pUKECtWvXpl69enTv3p3rr7+eiRMnMmfOHIwxfPDBB7Rt25by5cszffp0ACZPnkzr1q1JTU2lUaNGPPjgg+zfv//QNjdt2kTXrl1JS0ujQYMGjB079qj9HtnEt337dm677Tbq1KlDamoqzZs354033mDOnDn07t2b3bt3H6rt5TUVHtnEt3XrVnr27Em1atVIS0ujU6dOfP11fqL6vJrYrFmzaNmyJZUqVeKcc85hzZo1h57z008/0bVrV6pXr07FihVp1qwZr7/+esTebxHxyO7d0Lu3G7U3cGBYo/aOFH81KL+GH4cxO7owR87rue+++3jyySdp3LgxVapUYfr06Vx//fU888wztG/fnh9//JFbb72Vffv2HQo4vXr14ocffmDmzJlUrFiRu+++m7Vr1xZTZMvFF1/M1q1bGTduHCeffDIrV65k7969tGvXjqeffpoHHniA7777DqDQ5r68/a5cuZJJkyZRrVo1HnzwQTp37syqVasOzVPbt28fI0aMYOzYsaSmptKzZ09uvfXWQ8G3X79+7N27l9mzZ5Oens7KlSvL9H6KiHcyMw9bVmP8/fDdd/D737vJuWVhrQ30pXXr1rYo33zzzdF3ulDh/SUMPXv2tJdccsmh21988YU99thj7dVXX21nz55tAfv2228XeM3ZZ59thw0bVuC+d99911aqVMnm5ubalStXWsB+8sknhx5fu3atTUpKskOGDDl0X4MGDezIkSOttdZ++OGH1hhT+PtorR03bpytVKnSUfd36NDB3n777dZaa1etWmUB+9FHHx16fNu2bTY9Pd3++9//PrQdwK5YseLQc1599VVbvnx5m5uba621tlWrVnbo0KFFv2mFKKrcIuKdzz6zNi3N2uRkay8qP9P9HpYrZ+2iRUW+BlhgQ/j9j78aVBlrMl6ZNm0alStX5sCBA2RnZ9O1a1f++c9/8s033wDQ5ohq8cKFC5k3bx6PP/74oftyc3PJyspi48aNLF++nKSkJNq2bXvo8QYNGnD88ccXWYYvv/ySOnXq0Lx581IfR95+Mw5b9KVq1aq0atXq0LGAa9Js2rTpodvHH388+/fvZ+vWrVSvXp3+/ftz6623Mm3aNM477zz+9Kc/0bp161KXS0S8MWcO7N8PlXK280JOb3fnkCFw2mll3rb6oHzSvn17Fi9efKhJbcKECdSsWfPQ45UqVSrw/NzcXIYMGcLixYsPXZYsWcLq1asLDHwIUoaFw8tSrly5Qh/Lzc0F4MYbb2TNmjX07t2bVatW0a5duwLD40UkmPKWdv+H6U99fmJni7Zw//0R2bYClE8qVqxI48aNadCgQUgj9E4//XRWrFhB48aNj7qUK1eOZs2akZuby7x58w695scff2T9+vVFbvO0005jw4YNLF++vNDHy5cvT05OTrHlat68Obm5uWRmZh66b8eOHSxdupQWLVqUeFyHO+GEE+jbty9vvvkmw4YNY8yYMWG9XkS8l5EBix6aSE/7MrnlU6ky4T9QLjKNcwpQMeKhhx5i/PjxPPTQQyxbtowVK1bw9ttvc++99wLQtGlTOnfuzC233EJmZiaLFy+mV69exSbTPe+88zjzzDPp1q0b06dPZ82aNcyYMYOJEycCbsTf3r17mTFjBlu2bGHPnj1HbaNJkyZ07dqVW265hblz57J06VJ69OhBeno63bt3D/n4+vfvz7Rp0/j+++9ZvHgx06ZNCzvAiYgPNm2i2d/7ApA08nE4rCm/rBSgYsSFF17I+++/z+zZs2nbti1t27blb3/7G/Xr1z/0nJdeeolGjRpx7rnn0qVLF7p3707Dhg2L3GZSUhJTp07lrLPOokePHjRv3pz+/fsfGrrerl07br31Vq677jpq1KjBE088Ueh2xo0bR9u2bbnsssto27Yte/bsYdq0aWFlms/NzeXOO++kRYsWnH/++dSqVYuXX3455NeLiA+shVtugc2b4ZxzIMIZZowN+KCCNm3a2AULFhT62PLly8vUwS+xTZ+/iM/GjXOZytPTYckSaNAgpJcZYxZaa0ucIKUalIiIhG/NGrjrLvf3s8+GHJzCoQAlIiLhycmBG26AXbvgyiuhR4+o7EYBSkREwjNqFHzyCdSpAy+8ELUMPgpQIiISusWL81MYjR0Lxx4btV3FfIAK+iAPiQ597iI+2LMHuneH7Gzo1w86d47q7mI6QKWkpJCVleV3McQHWVlZEVuCRERCdO+9sHw5NGsGI0dGfXcxHaBq1qzJunXr2LNnj86oE4S1lj179rBu3boCqaFEJMo++ACeew5SUmD8eKhYMeq7jOlksenp6QCsX7++wFIVEt9SUlKoVavWoc9fJN4VWM4io6RnR8GmTW6NJ4BHH41IIthQxHSAAhek9EMlIvEqMxPOO89lDC9fHmbN8jhIWQs33uiCVMeO8Ne/erbrmG7iExGJd3nLWeTkuOs5czwuwOjRMGUKHHMM/Oc/bhl3jyhAiYgEWN5yFsnJ7rpjRw93vmxZfo1pzBioV8/DncdBE5+ISDzLyHDNemXtgwq7HysrC667Dvbt45cuNzL226vomOlt86IClIhIwGVklC0whNuPlZkJKffcS5tly8iqfzK/m/EM2z7wvg9MTXwiInEunH6szEwY1XEKbT5/lv2k8NQZr7Etu5IvfWCqQYmIxLm8fqy8GlRx/VgLJq3jhf1uSPmDZgS7apwe8msjzdMAZYxpCIwGMoB9wNvAX6y1B7wsh4hIIgm5Hysnhxtm9KAqW/iQCxhd4W5m3uCmPb3zDnTrFt99UKOBTUAd4BhgBtAP+IfH5RARSSgh9WONGEHVRXPYX70Wy2/5DzO7uF6gv/zF1aDmzoVWreK3D6oR8Ka1dq+1diMwDfidx2UQEYk7mZkwYoS7LpW5c2HIEADKv/4K/YfXIiPD33lYXtegngauNcbMAaoBFwGDj3ySMaYv0Begfv36XpZPRCTmlDnbxG+/uSzlublw331w/vmHHgqn/yrSvK5BfYyrMe0AfgYWABOPfJK1doy1to21tk2NGjU8LqKISGwpUy3HWujTB37+Gc48Ex55pMDDef1XjzzifZolzwKUMSYJ16Q3AagEHIerRT3uVRlEROJRmbJNPPMMTJoEVavCa6+5bOVHyMiAgQO9T1TrZQ2qOlAfeNZau89a+yswDrjYwzKIiMSd0tZylrw4n5wB97obY8dCo0Zh77vMfV/F8KwPylq7xRizBrjNGDMKqAz0BJZ4VQYRkXgVbraJeR9uo8bN15Bss3k++Q5OrXMF4VaQop1p3es+qCuAzsBm4FsgG7jb4zKIiCQ2a0m/5yYa2TUs5HT+akeVanRetEf4eTqKz1q7GOjo5T5FROQIzz1Hs6/fYQdV6J70BlSoUKrRedEe4adURyIiiWT+fLjnHgA2PvJ/9EpuXOos6ZHKtF4UBSgRkUSxdStcdRVkZ8Mdd3DyoKsZWMZNljXTenGUzVxEJBHk5kLPnvDDD3DGGTBqlN8lKpEClIhIInjySZg8GapVgzffhAoV/C5RiRSgRETi3dy5bqYtwH/+Aw0b+lqcUClAiYjEs40b4eqr3Vjwe++FSy/1u0QhU4ASEYlXBw7ANde4INWxIzz2mN8lCosClIhIvHrgAfj4Y6hTB15/HcrF1sBtBSgRkXg0YQKMHOkyyL75JtSq5XeJwqYAJSISb1auhF693N8jR8If/+hrcUpLAUpEJJ7s3Al/+pO7vuoqt157jFKAEhGJF3mLDy5fDi1auCU0jPG7VKWmACUiEi9GjYK334b0dHj3Xahc2e8SlYkClIhIPJg1C+6/3/39yitw8sn+licCFKBERGLd2rVuvlNuLgwaBJdd5neJIkIBSkQklu3ZA5dfDr/+ChddBEOH+l2iiFGAEhGJVdbCjTfCV19BkyYwfryb9xQnFKBERGLVqFEuQ0TlyjBxIhxzjN8liigFKBGRWDR9esFBES1aeF6EzEwYMcJdR0NsJWYSEYlxmZkRWCJ91ar8QRGDB7s+KI9lZsJ558H+/VC+vBtEqCXfRURiVER+1Ldvd6P0tm93gemIQRERCYAhmDPHHUdOjrueM0cBSkQkZpX5Rz0nB667zuXaa9XKNe0l5ffUeFGrydOxo9tH3r46doz8PhSgREQ8UuYf9YEDYepUOPZYmDTpqEwRXtRq8mRkuAAYzdqaApSIiEfK9KP+n/+4zOTlyrl0Ro0aHfUUL2o1h8vIiG4zogKUiIiHSvWj/umncPPN7u9//KPIyONFrcZLClAiIkG2dq1bPmP/frjjDrjttmKfHu1ajZc0D0pEJKh27oQuXWDzZrjgAnjqKb9L5CkFKBGRIMrJge7dYdkyaNoU3njD9T8lEAUoEZEgGjAApkyBatVg8uS4S2MUCgUoEZGgGT0ann4aUlLcwoNNmvhdIl8oQImIBMm0aXDXXe7vf/8bOnTwtzw+UoASEQmKZcvg6qtd/9ODD0LPnn6XyFcKUCIiQbBhA1xyiRu5d801MGyY3yXynQKUiIjfdu2CSy+FH390k5jGjSuQYw+iv7RFEHk+ZtEYcy0wBKgPbAR6WWvnel0OEZFAOHDA1ZgWLYKTTnI59tLSCjzFyySwQeJpDcoYcz7wONAbqAK0B773sgwiEn9itnZhLdx5J3zwgUsAO3Uq1Khx1NMKSwKbCLyuQT0MDLPWfn7w9jqP9y8icSbItYsS12Z64gl44QWoUAHee6/I4eReJ4ENCs8ClDEmGWgDvGeM+RZIBSYC/89am3XEc/sCfQHq16/vVRFFJAZ5ucREOEoMnP/9b8El29u1K3Jb8ZYENlReNvHVAlKAK4GzgVOB04BBRz7RWjvGWtvGWtumRiHVXRGRPHm1i+TkYNUuim2WmzkTevd2fz/1FFx1VYnby8hwy0ElSnACbwNUXi3pn9baDdbaLcDfgYs9LIOIxJm82sUjjwSrea/IwLl4MVxxBWRnwz33wF/+4mMpg82zJj5r7VZjzM+APfxur/YvIvEriEtMFNos98MPcPHF+XOdRo70uZTB5vUgiXHAncaYaUA2cDcwxeMyiIh4okDgzFsyY8MGF7FefvmouU5SkNcB6hHgOGAVsBd4E3jM4zKIiHhr1y6XJWLVKjjlFJg40Y3ck2J5GqCstdlAv4MXEZG4UeSQ8v37XZ/T/PnQqJGb61S1qk+ljC0h1S+NMS8YY6wx5vhCHmtqjNlvjPlH5IsnIhJ8eUPKBw9214cmDOfmQq9eMGOGm4A7fTrUqeNnUWNKqA2geW9320IeewrYgUtfJCKScA4fUr5vHwwdCpmfWbdsxmuvQeXKruaUoOs6lVaoASov80OBAGWMuQS4CHjIWrs1kgUTESkrr1Ig5Q0pT0pylaaZM2FOhyHw3HPugUmToHXr6BYiDoUaoFYBv3FYgDLGpODmMS0D/hX5oomIlF6RzW5RkDekvFMnF6Tuyn2KgQceIdckwRtvwLnnHlW2mMwd6LGQBklYa60x5nPgLGOMsdZaoD9wMtDJWpsTzUKKiITL6xRIGRmuaa/+7Jd5KvceAL5/4EUaX355gecFOXdg0IQzCP9zoCrQ1BhTExgMTLTWzopKyUREysCPFEgZ695mTE4fANbe9XcaP9rrqOckamby0ghnmPnhAyXaAxWAv0a8RCIiEeB5gtUPPoDu3TG5uTB4MA2H3V3o0xI1M3lphBOg5gG5wE3AWcBIa63WchKRwPIsBdKcOdCtW35+vYcfLrZMiZiZvDRCDlDW2h3GmG9wmcg3ogwQIiLwxRfQpQvs3Qt9+8KoUWBMsS8JYu7AIAo3EdS8g9cDrbU7I10YEZGYsmgRXHihS2V0/fUwenSJwUlCF3KAOjisvCOwAHg5WgUSEYkJS5bA+efD9u0uldFLL7kRGRIx4fRBDQAaAdcfHGYuIpKYvvnGTXr67TfXvPfaa1DO69zb8a/Yd9QYUx24EPg98P+Av1trPy/uNSIicW3VKjeRafNm17z31ltuOJ5EXEkh/0JgPLAJl3Pv/qiXSEQkqFavhnPOgY0bXXaId9/VshlRVGyAsta+BrzmUVkCr8h0+iIS/1avdl/+9euhQwd47z1IS/O7VHFNjaYhUnoSkQT27beu5pQXnN5/HypV8rtUcU/rDYdI6UlEElRezWndOmjfXsHJQwpQIfIjr5eI+GzFCldjWrcOzj5bwcljauILkdKTiCSYr7927fq//OK+9FOmkLmkkn4DPKQAFQalJ5FElXADhJYscfOcNm9215MmkflVRfVDe0wBSkSKFasDhEodVBcuhAsucJNwO3eGCRMgLc3z9aVEAUpEShCLP8ylDqqffgoXXww7dsCll7pJuKmpgJbJ8IMGSYhIsWJxgFCpRt3OmuVqTjt2wFVXwTvvHApOkN8P/cgjsVOLjHWqQYlIsWJxgFDYtZ0pU8jtdiVJ+/ex6eKe1Bz/f4Xm1lM/tLcUoESkRLH2wxxWUB0/ntwbepKUc4DnzW0M+N+zzJyfFFPHG6/UxCcicSkjAwYOdH+PGOH6pY4yejT06EFSzgFGmnvpZ59jX3ZSgSbBzMxiXi9RpRqUiMStIgdLWAuPPQaDBwPww21/Y8hL95F8RJNgrI5gjBeqQYlI3Cp0sERuLvz1ry44GQNjxtBg9H2FDoBQijN/JWQNKuEmHYokqCMHS5xz1n74c28YPx5SUuDVV+Hqq4HC+9k0tNxfCRegVGUXiQ+hnGgePlji3La7OHP4lTB9OlSu7NZy6tSpxO0//TT8+qtOaP0QdwGqpH/aWJx0KCIFhXOimZEBGY03u4m38+ZBjRowdSq0bh2R7Uv0xHQf1JGja/L+qQYPdteFjbqJxUmHIlJQWH1D334L7dq54NSokcsWUUxwCnv7EjUxW4Mq7AwnlNpRLE46FJGCQu4b+uILV3PasgVOP90tl1G7duS2L1EVswGqsGAU6j9VrE06PJwGeIiEeKI5eTJccw1kZbmkr2++CVWqRG77EnW+BChjTBNgKfC2tbZHsU/evbvQuwsLRvH+T6V2cZF8xZ5oPvss9O/vhpT37g3/+pcbtScxxa8a1HPA/JCeuXKlO/M5OBQ0T1HBKJZrRyXRAA+REuTkuDlOzzzjbg8Z4i7GhLUZnQwGg+cByhhzLbAN+AxoXOILrIVrrmHOi99SYchAMtrl/6PFczAqjNrFRYqxaxd07+6a9lJS4MUX4c9/LtWmdDIYDJ6O4jPGpAPDgHtKeF5fY8wCY8yC7ZWPJRdDxw8f5Nv2ffj84/3eFDaAlO5fpAg//wwdOrjgVK0azJhR6uAEGu0bFMZa693OjHkGWG+tfdwYMxRoXFIf1AkntLFtNwzildzrqcQefmzYnvrz34HjjvOkzCIScPPmweWXw4YNcNJJbqRe06Zl3qwGJEWPMWahtbZNSc/zrAZljDkV6AQ8Fc7rqlSBaRUu55ykj1nP8dRf+zG0bQvLlkWnoCISO15/3dWcNmxw1198EZHgBPnZ0BWc/ONlE19HoCHwozFmIzAA6GaMWVTciypVcs1Zf3q0NRsmzYM2bWDNGvdfM3ly9EstIsGTmwsPPQTXXQd798LNN8OHH8Kxx/pdMokgz5r4jDEVgfTD7hqAC1i3WWs3F/W6Nm3a2AULFuTfkZUFffq4MydjYPhwuO++sEfpiEiM2rHD9S+99x4kJcGTT7oh5VH4DVAzX3SE2sTn2Sg+a+0eYE/ebWPMLmBvccGpUGlpLhNxy5YwaJCrgy9aBOPGueqWiMSvb7+Frl3hm2/gmGPcieqFF0ZlVxpq7j/fcvFZa4eWOEm3KMbAgw/CpEmuk+qtt9x/zvffR7iUIhIY06bBGWe44NSiBcyfH7XgBMrHFwQxnSyWyy5zI3hOPhmWLnX/vNOn+10qEc/F9bLkubnw6KNw8cWwbZurQX3+OTQueRplWWiouf88HWZeGkf1QRVm+3bo0QOmTHG1q4cfdjWspNiOvyKhiNemqMxMyJy6jV6zb6D6J5ML/W5Hu49IfVDREbg+qGhx/0BV6Xj/JDLaPubSmjz0kBtu+sorbtKeSBzzM+tBtH7AMzOh/zlLeHXflVRnNQeqVKPcG/+Fiy4q8JxoB+ZEy1YTNDEdoAr+gyYxa9ZgMs44A66/3k3Wa93a9U8VsfaLzo4kHviVAiuaAeLXJ8fx0b5+pLGXxZxC5i0TuO2iEws8R+mI4l9Mt4EV2onZuTMsXOiC0po1bqGy0aNdTr/DhLK4oUgs8CsFVlQGEezZA336cOk7fUhjL2NNH85NzeTUK0486qnqI4p/MV2DKvLMsWFD+OQTl9V49Gi4/Xa2TPiY4yaMgXQ3FUtnXxJP/GiKinjNbcUKt2rB0qWQmsq394zml8q9eb9j4ccW78vrSBwMkiiumS4zE57v8DrPZd9MFXaRdUJj0ia+Dq1bx23HsoiXItJMbi28/DLcfrurQTVp4prmTzklgiWVIAl1kETMB6jijBjhmvBOylnJm1zNKSxxafgffxz+8hcyPzc6+xLx086d0K8fvPqqu929O7zwQsgr30psClyyWD/kNUF8l9yUjqlfsLHb7ZCdDffcA126kNF4c5mSQcb13BORaJs/3/UVv/oqVKwIY8e6vxWc5KCY7oMqScE26lRqZzwLEzu5XH7vvw+//71LkdS5c9jbVhOhJKKINOnl5MDIka5548ABaNXKpSxq0SKCJZV4ENc1KCgkZf7ll8NXX0H79rBxo5tXcdddLgltGIoawaRalcSriIx8/fln6NTJfSkPHHBJXufNU3CSQsV9gCpUvXrwv/+5SFKuHPzzn24Zjy+/DHkThQ1x1dB1iWdlGlZuLfz3vy7J85w5ULMmfPABPP00pKZGp8AS8xIzQAEkJ5PZ4X7G3fI5WfVPdgko27Z1Ob8OHCjx5YXNPVFyydJRrTM2lHre0ZYtbvh4jx4uLVmXLrBkSYGsECKFstYG+tK6dWsbDZ99Zm1amrXJydYem7rLrr/yDmvdeZ61Z5xh7fLlZdpmWpq7LcXTexZbPvvM2uHDw/ic3nvP2tq13feqcmVrX3zR2tzcqJZRgg9YYEP4/U/YGtThtZ1t2ZV46fR/8v5fZrAptZ4bXXTaaa4jNycn5G36NaM/lqnWGVtCXgZ961a44Qa34sDGjXD22a7W1KePFheVkCVsgDqyuWLbNrj06U402buUcfRyy0jfe69LlfTNNyFvN+QvcII6sjlP6WoiJzBNpZMnw+9+55I1p6bC3/8Os2dDo0Y+F0xiTijVLD8v0Wris7Zgc8UFF+S38IG1D572vrV167ob5ctb++ij1u7fH7WylKbMsaao5rxYPqagCERT6S+/WHvttflforPOsnbVKh8KIkGHmvhKdnhtp1u3go/Vv/Vi+PpruPlm1/Y0aJCbVPjFF/4UltgfJVhUc55qnWXna1NpXqqi5s3dfKaKFV2t6aOPXNoikVJK6AB1uL594V//ggsucNd9+wJVq8KYMTBjBpx4oktimZHh5k3t3Ol5GWO9v0bNedHj23u7erX70vTqBb/9BuefD8uWwd13u8KIlEFc5+KLqD17YNgwGDXKRYi6dd0cjm7dPOv0jYfsFVqDK3o8fW/37XM5LYcPd39Xrw5PPQV//rMGQUiJlCzVvamMAAASA0lEQVQ2Wr76yjX7zZ/vbnfu7Cb6Nm7sye71Ay++mznTZR5ftcrd7tULnngCatTwtVgSOxSgoiknB/79b9dxsm0bVKjgRvzdf79rfxeJRz/+6BItv/OOu928OTz/PHTo4G+5JOYom3k0JSfDrbfCypVurse+fW7yU7Nmbh2bgAd9kbDs3QuPPeb+v995x52EjRgBixcrOElUKUCVRc2abvTS3LluYu9PP7mULuee6768IjGmwFwqa90JV/PmbhRrVhZcc407Mbv/ftcRGql9iRQirpfb8Mwf/+j6pF58ER54wHUSnX469O7tcvvVqeN3CUVKdPggnDPLLWRqs7tJ/2que7BlS3jmGXfyFeF9xeqAH4k+1aAiJTnZjU1ftSp/iO3YsW4eyLBhsHu3zhgl0ObMgTr71vJSTg8+3dfGBafjjnMr3H75ZcSCU96+YnnKhHhDASrSqld3kxS/+Qb+9CfYvRuGDGF//ZN4vcPzPDwo+6hJtkEOXEEum0TQb7/Rc+kAvsltSg/+yz7Ks777APj2W7jlFrcsTQRpTpyEJJR0E35eopnqyBNz5ljbtu2h9C8raWKvNa/b4Y/mWGsDkqKmCEEum0TIzp0ujVfVqof+R5ee2sMufGdNqTcZauoqpbhKXISY6kh9UNHWoQN8/jkrh79D0uAHONmu5jV7LbtfGg6tHmHOsi7s328KNHWE0xYfzXlRhTXDqJ8gTuzd65ruhg+HzZvdfZ06weOP0/L000u92XD6ljIy9P8kxVMTXxQc1SxmDE0fvJJfP/qaqV1fYF+NulT6dgl07cqdr57JpclTSU6yYTd1FJebLxJNc141w8RrM2Igj2vvXjex/KSTXF/p5s0uSvzvfy6lVxmCE6hvSSIslGqWn5dYa+ILqVksK8vap5+2tlatQ80q6+u2scsfnxTWYm7Dh7v9gLsePjyMMoRxPNFshonXZsTAHdeePdY+84y1derkZxs/5RRrp0yJ6AKCgTtuCSSUzdwfIZ1BpqZC//7w3XduUcSaNamzbgHN7uvqzmDfeCOkZeeLquFE8iw22pnG4/WMOzDHtX27q8Y1bOj+5zZsgFNPhXffhUWL4JJLIpo7T4t2SkSFEsX8vMRlDepIu3e7GtXhZ7cnnmi/GzDaPvHwnmK3UVgNJ5bOYmOprOHw/bjWr7d24EBr09Pz/6dat7Z2Uni1dJFoIMQaVNzn4vMjuWqp97l3r8tMMXKkq10Bm6jBv8v144KJ/TjjkprRL4MPYqms4YjkcYW8rWXL4MknYfx4V3UDOOccVw3u1EmZxiUQQs3F51lNCKgAvAj8AOwEFgMXlfS6stSgfD+LLa0DB+yEa9+wCzj90NlvdrkK1vbpY+2SJX6XTjxW4v9xTo61kycXXBbaGGuvuMLazExfyixSHALYB1UO+AnoAFQFBgFvGmMaRmuHgekHCFdyMrXvupqzUxdwXtJspiRdRnLOfpeZ4ve/Z8dpHVw/VXa23yWNa4WNwvNjZF6R/8dbt7pJ4U2aQJcu8OGHLpHrHXe4hQTfeQf+8Icy7TuQIxElYXg2D8pauxsYethdU4wxa4DWwNpo7DNvEEHenIxYmq2ekQGz/meYM6cjx3bsyOKfVvN593/QI+cl0hd/DNd+DLVrw003wY03uk5wiZjC5vOAP/njCvwfp1guPfZz6PUvePNNl8AV3Od/++3Qp4/LZhIBypcnfvNtFJ8xphZwMvB1IY/1NcYsMMYs2Jw3ibAUYn1E0eEj6KZ914Q7+SfHs547zHNsqdEcNm50yWhPPNEtu/3WW27pDymzwmotftXIMzLgo3e2MPWif7C57im0uqWd66vMynJLrE+a5FISDRgQseAEMdwCIfEjlHbASF+AFGAm8K+Snhtro/hKI5S5Rkf1Q3yaa+3s2dZ2725thQr5fQ/Vq1vbr5+1X3yh0VplUFi/j+d9mvv3W/vee64vKSUl/zOuUcPa++6zdvXqqO4+ZvtwJfAI6ig+Y0wSMB5IB7paa4vtSAnkiroRFE4zSpEjuX77DV591S33sWRJ/v3NmsF117lLkyZRPApveTXqr7D9RH3fubnw6aduFN5bb8Gvv7r7k5LYemZn5jTsRZ1bu/KH9mVbiylU8TrCUvwVuFF8BwOhAcYBs4G0UF4T7zWoorJBlNrixdbefbe1NWvmn3GDtaefbu0TT1j73XcRKXcerxN+xuVZfU6OO5C777a2Xr2Cn1uLFtY+/ridP2ld/B23JCwCmiz2eaA50Mlam+XxvgMp4gM5TjnFjex6/HFXHXv99fysAYsWwb33uudccQVcfjm0alXquTF+dKLHTQLb/fvh44/hvffc5/Pzz/mP1asH3bu7y8HPZ8aIODlukTB4FqCMMQ2AW4B9wEaT/6N4i7X2v16VI2jyBnJEvBklJQU6d3aXF16AqVPh7bdh8mT46it3GTIE6teHSy91l44dIS0t5F34ESxieWQmv/zihoK//777PHbsyH+sXj248kp3+cMfIKng+KWyHLea6SRWxX0mCa/EzI/Avn0uIk6YAFOmuB/NPBUqwNlnuxGBF1zgzt6Tih7o6dcw5KLe68B9BllZrlCzZsG0aa4Ge7iWLd38pa5doW3bEmuypTk+DRWXIAq1D0oBKgJi9kcgNxcWLnSBasqUo39Aq1d361l17OiuW7Z0mWkPE8qPpheBIxCfwe7dMG8efPIJzJ4Nn31WcNh/aqp7Ezp3doHpxBOjXqQRI9xyLDk57qN75BE3dUHET6EGKC1YGAEx2y+SlARnnOEuDz/s1gaaOdM1Q82c6fpF3n3XXQCqVIEzz4R27dx169ZkZNQqthbjVeDw/DOw1s09mj/fXT79FL788ugs9KecAueeCxdeCO3bh9WEGgkx3SQqCU8BqgShnP0H4UcgIrWUGjXyh6VbC2vW5M9Q/eQTd3vmTHfJc8IJ0Lo1nHoqKyv8nr4Pt2Jl9omUq5B8qG/Ni8AR1c9g715YuRIWL87vv/vyS5dq6HBJSW65lLPOcjXODh3guOMiWJDwRa2PU8QDauIrRkTmKHnAs+atDRvczj77zNUaFi2CXbuOetoe0lhJUyq0akqVM5ry0CsnszrnRNaVb8T4WbXIaBedjNpl+gxycuCnn1wW+e++c7WjFStg+XL4/nvXHHqk2rVd7bNtW1ej/MMfXC1TRIqlJr4ICOfsPyPDv7NTz5q36tRxw9OvuMLdzs2FVatcP9bSpWz9eAl7MpdQl3WcxmJYuhiWuolvAOwFOqW5EWt16+Zfatd2NY0aNdz1McdAejpUreoibogKfAbWuv6fbdvcZetWN6H5l19g0yZ3Wb/eBaWff3Z/F7VIZFKSm+h8yin5l1NPdbVHLV8hEjUKUMUIQtNdKHwrZ1KSy1bRrBkA1YAVmfDG1G2cX38lrVJWuKax1ath7VrXRPjrry6orVoV2j7Kl3f9NmlpbpBB3hLCSUnuYq0LLAcOuOzue/fCnj3ukpMT3vEcfzycdFL+pWlTaN7cBacKFcLbloiUmZr4ShC4octFiJVysmOHq7WsXw/r1rnLpk1ugMbmzbBli1umfMcOd11UrSYUKSmuNlatWv51rVpQs6a7rl3b1ebq1XPBKTU1cscpIkXSMHOJCk8DYV4zXVaWqxllZblqYm6uu+Tk8NXSJL5YWI4zMspx2hnlXE2rYkV3nZJS5iL43bcYEycdImFSH5REnOdzjYxxtZoiajaZmXBe34Pl+Vfky+Pn3Cq/53UpOEoQ+LYelMSeoK0PFO3y+Hm8fu47LzgOHuyutZqu+EUBSkKWNxgjOTkYg0aiXR4vjreoJdX9fK+DdiIiiUtNfBKyoE36jHZ5or394prx/HyvY2X0qsQ/DZIQ8UmQ8+SpD0qiSYMkRAIuyDUVPyeei+RRgBLxSdCaTEWCRgFKxEeqqYgUTaP4REQkkBSgJOqKGkotem9EiqMmPokqZUQomt/vTUmC/N5JYlCAkqjyc7XhoAeAUN8bPwJF0N87SQxq4pOoUkaEooXy3viVdijo750kBtWgJKqUEaFoobw3ftVAg/7eSWJQJgmJa7Hej+J3RvVYfu8kuLQeVEAl8pc+kY+9LPS+SbxRqqMAitbZcCz8gKnTvfQ0mVcSlQZJeCgaHc+xsnaPOt1FJFwKUB6Kxoi2WPnhD9paUiISfGri81A0RrTFymireE6MGgtNrCKxSIMk4kC8/EDG4nGob00kfBokkUDioRM91B/6oAUxPzNliMQ7BSgJhFB+6INYW4mVJlaRWKQAJYEQyg99EGsr8dy3JuI3BSgJhFB+6INaW4mHJlaRIFKAkogqSx9RST/0qq2IJBZPA5QxpjrwInABsAUYaK0d72UZJHq86CNSbUUkcXg9Ufc5YD9QC7geeN4Y8zuPyyBREiuThkUkNngWoIwxlYBuwGBr7S5r7SfAe8CfvSqDRJeyRYhIJHnZxHcycMBau+qw+74COhz5RGNMX6DvwZv7jDHLPChf0ByHawaNMVUqQXqVrKwdO9u127m7lBuJ0WMvMx13YknU4wZoGsqTvAxQlYEdR9y3Hahy5BOttWOAMQDGmAWhzDiON4l63JC4x67jTiyJetzgjj2U53nZB7ULSD/ivnRgp4dlEBGRGOFlgFoFlDPGNDnsvlOArz0sg4iIxAjPApS1djcwARhmjKlkjDkL6Aq8UsJLx0S9cMGUqMcNiXvsOu7EkqjHDSEeu6fZzA/OgxoLnA/8CtyveVAiIlKYwC+3ISIiiUkr6oqISCApQImISCDFRIAyxrxqjNlgjNlhjFlljLnJ7zJ5wRhTwRjzojHmB2PMTmPMYmPMRX6XywvGmDuMMQuMMfuMMS/5XZ5oMsZUN8a8a4zZffCz7u53mbyQSJ/x4RL8ex3Wb3lMBChgBNDQWpsOXAY8aoxp7XOZvFAO+AmXbaMqMAh40xjT0McyeWU98ChuUE28S9QclYn0GR8ukb/XYf2Wx0SAstZ+ba3dl3fz4OUkH4vkCWvtbmvtUGvtWmttrrV2CrAGiPvgbK2dYK2diBvtGbcSOUdlonzGR0rw73VYv+UxEaAAjDGjjTF7gBXABuADn4vkOWNMLVxOQ01ujh9F5ahMhBqUkHjf63B+y2MmQFlr++Hy9p2Nm/C7r/hXxBdjTArwX+Bla+0Kv8sjERNyjkqJP4n4vQ7nt9z3AGWMmWOMsUVcPjn8udbanINNICcAt/lT4sgJ9diNMUm4jBv7gTt8K3CEhPOZJwDlqExQ8fa9Dkeov+W+L/lure1YipeVIw76oEI5dmOMwa1CXAu42FqbHe1yRVspP/N4dShHpbV29cH7lKMyzsXj97qUiv0t970GVRJjTE1jzLXGmMrGmGRjzIXAdcAsv8vmkeeB5kAXa22W34XxijGmnDEmFUgGko0xqcYY30+oIq0MOSpjXqJ8xkVIuO91qX7LrbWBvgA1gI+Abbi2+qXAzX6Xy6Njb4Ab5bIX1xSUd7ne77J5cOxDyR/lk3cZ6ne5onSs1YGJwG7gR6C732XSZxzV407I73VpfsuVi09ERAIp8E18IiKSmBSgREQkkBSgREQkkBSgREQkkBSgREQkkBSgREQkkBSgREQkkBSgREQkkBSgREQkkBSgRDxijEkzxvxsjPnRGFPhiMf+zxiTY4y51q/yiQSNApSIR6xLCjoEqAf0y7vfGDMCuBG401r7uk/FEwkc5eIT8ZAxJhm3Ym5N4ETgJuApYIi1dpifZRMJGgUoEY8ZYy4FJgP/A84BnrXW3uVvqUSCRwFKxAfGmEXAacDruOU17BGPXw3cBZwKbLHWNvS8kCI+Ux+UiMeMMdfgVs0F2HlkcDpoK/As8KBnBRMJGNWgRDxkjLkA17w3GcgGrgJaWWuXF/H8y4GnVYOSRKQalIhHjDFn4pZ3/xS4HhgE5AIj/CyXSFApQIl4wBjTAvgAWAVcbq3dZ639DngR6GqMOcvXAooEkAKUSJQZY+oD03H9ShdZa3cc9vAjQBbwhB9lEwmycn4XQCTeWWt/xE3OLeyx9UBFb0skEhsUoEQC6OCE3pSDF2OMSQWstXafvyUT8Y4ClEgw/RkYd9jtLOAHoKEvpRHxgYaZi4hIIGmQhIiIBJIClIiIBJIClIiIBJIClIiIBJIClIiIBJIClIiIBJIClIiIBNL/Bz0jvuH4XFJLAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_new=np.linspace(-3, 3, 100).reshape(100, 1)\\n\",\n    \"X_new_poly = poly_features.transform(X_new)\\n\",\n    \"y_new = lin_reg.predict(X_new_poly)\\n\",\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.plot(X_new, y_new, \\\"r-\\\", linewidth=2, label=\\\"Predictions\\\")\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n    \"plt.axis([-3, 3, 0, 10])\\n\",\n    \"save_fig(\\\"quadratic_predictions_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure high_degree_polynomials_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXd8U9X7x983SRejlpZN2UNZyqhAWRbBhQtBwA0i4vqJipOvAi0o7q04ERH3REXAUayKlA0CgmiZbRlllu6s8/vjNM1o0iZtkjblvH3xSnLHuefWNp/7jPM8mhAChUKhUChqG7qanoBCoVAoFO5QAqVQKBSKWokSKIVCoVDUSpRAKRQKhaJWogRKoVAoFLUSJVAKhUKhqJUogVIoFApFrcSvAqVp2v9pmrZe07QSTdPed9k3XNO0fzRNK9Q07VdN09r689oKhUKhqFv424I6ADwOvOe4UdO0xsDXwAwgFlgPfObnaysUCoWiDmHw52BCiK8BNE1LAOIddo0G/hZCfFG6Pxk4qmnaWUKIf/w5B4VCoVDUDfwqUBXQHfjL9kEIUaBp2q7S7eUEStO0KcAUgPr16/c966yzANh5bCctG7SkYUTDSi94KP8QZquZ+Oh4t/s3HdrE2c3ORq/pq3A7CoVCUbv499i/5JXk0bdlX7f79+Xuo15YPZrUawLA/tz9RBoiaVq/aaVjV3SsVVjZfGgzfVr0ASDPmMeBvAPER57Jzp0gBGganHkm1K8vz9mwYcNRIUSTSi8shPD7P6Sb732Hz/OBp1yO+ROYWNlYffv2FTaS3k8SK3avEN7wzMpnxAM/PuBxf8O5DUVuca5XYykUCkVtJ+n9JEEyHvdP+W6KeHPdm2Wf7/rhLvHqmle9GvvOJXeK19a85nZfgbFARD0eVfY5bU+aGLpgqJg7Vwi9XgiQr3Pn2s8B1gsvtCRYWXz5QLTLtmggL0jXVygUijqNqGWFv5OSIDwc9Hr5mpTk+xjBcvH9DUywfdA0rT7QsXS7QqFQKKqJQAqUEAJN02p4NpCYCKmpkJYmxSkx0fcx/CpQmqYZSsfUA3pN0yIBM/AN8KymaWOAH4CZwBahEiQUCoXCL1iFFZBCpVHzAgVSlKoiTDb8bUE9Bsxy+HwDkCKESC4Vp9eAD4E1wDVVvYjJZCIrK4vi4mKPxwypPwRrlJUdO3a43f/FsC/I3JWJTqs9a5UjIyOJj48nLCyspqeiUChCjDKBEoJaok/Vxt9p5slAsod9vwBn+eM6WVlZNGzYkHbt2nk0ZQ/lH8JkMdH6jNZu9xcdLOKsZmeh19WOLD4hBMeOHSMrK4v27dvX9HQUCkWI4WhB1RWCFYPyK8XFxRWKUyiiaRpxcXEcOXKkpqeiUChCECcLqpayciVs3er98SEpUECdEicbdfGeFApFcKjtFlRhIdx8M2RkeH9O7QnAKBQKhaLK2Cyn2mpBzZghxalHD+/PUQJVRYqLi+nXrx/nnHMO3bt3Z9YsmRuyZ88e+vfvT6dOnRg/fjxGoxGAkpISxo8fT6dOnejfvz979+6twdkrFIq6RmUWVE1aVqtWwYsvyjVRCxZ4f54SqCoSERHBihUr+Ouvv9i8eTPLly9n9erVPPzww9x3331kZGTQqFEj5s+fD8D8+fNp1KgRGRkZ3HfffTz88MM1fAcKhaIu4U0MqibCCEVFMGmSLHn00EOQkOD9uUqgqoimaTRo0ACQae8mkwlN01ixYgVXX301ABMmTGDx4sUAfPvtt0yYINcqX3311aSmptZaU1yhUIQetTUG9d57sHMndO0KM2f6dm7IJkn4g+r+j7RYLPTt25eMjAzuuusuOnbsSExMDAaD/LHGx8eTnZ0NQHZ2Nq1by5R3g8HAGWecwbFjx2jcuHH1bkKhUCiovVl8d9wBViv06weRkb6dWycESkvxv9kqZlX+P1mv17N582ZOnjzJVVddxT//qMIYCoWiZrAJVG1Dp4O7767auXVCoNyJSWULdTcd3ETPZj0x6Kr/I4iJiWHYsGGkp6dz8uRJzGYzBoOBrKwsWrVqBUCrVq3IzMwkPj4es9lMbm4ucXFx1b62QqFQQO1z8b37LlxwAbStRu90FYOqIkeOHOHkyZMAFBUV8fPPP9O1a1eGDRvGl19+CcDChQu58sorAbjiiitYuHAhAF9++SXnn3++WvekUCj8Rm1y8eX+ezZTpkDv3lD6NVkl6oQFVRMcPHiQCRMmYLFYsFqtjBs3jssuu4xu3bpxzTXX8Nhjj9G7d29uueUWAG655RZuvPFGOnXqRGxsLJ9++mkN34FCoahLlFUzr2ELqqhQx875DyME3HknxMRUfSwlUFXk7LPPZtOmTeW2d+jQgbVr15bbHhkZyRdffBGMqSkUitOQmrSgLPv78eSTsq3G2292oDinFWef7XvWnitKoBQKhaIOUFMxqDWrdRgXLGWGFQwGKClphaY38/77BsLDqze2ikEpFApFHaAyCypQltXvv+nAHI7FAiUlclubK96nd+/qj60ESqFQKOoA3lhQgWhkOPQ8KxiM6ErVpG2nAtpc+rFfxlYuPoVCoagD1FQMqv8AK+E3jyS5XRotWoChzd+8s9/il7GVQCkUCkUdoCbXQenbrGX6dPn+t71FsN8/4yqBUigUijpATVhQQsCdt4VjKb4yIOOrGFQVyczMZNiwYXTr1o3u3bvz8ssv1/SUFArFaUxNWFDvvw8LFxgwfv0mx475f3wlUFXEYDDw/PPPs337dlavXs3rr7/O9u3ba3paCoXiNEUIgYYWNAtqzx6YOlW+D7tsGoGo3KYEqoq0aNGCPn36ANCwYUO6du1aVrlcoVAogo1VWNHr9EGxoCwWuOkmyM+Hq0ab0ff6JCDXqRMCpWnl/7Vo2Jw2Ma15+237cW+/bd/fp2VvwvQGp3Oqyt69e9m0aRP9+/ev/s0oFApFFbAKK3pNHxQL6rnnYOVKaNECXn7dWK3vz4qoEwJVk+Tn5zNmzBheeukloqOja3o6CoXiNMVmQQWazZthxgz5/r33CIhrz0adECghyv87mHeI/SczmTLFftyUKfb9Gw9swmQxO53jKyaTiTFjxnD99dczevRo/92QQqFQ+IhVWNFpOo8uPn+5/sxmiI+XhWAvvtgvQ3pEpZlXESEEt9xyC127dmXatGk1PR2FQnGaYxVWwnXhFbr4/NHiJyFBWlGGIKhHnbCgaoI///yTRYsWsWLFCnr16kWvXr1YunRpTU9LoVCcpgQ6SaIgN6LsfXQ01KsXkMs4oSyoKjJ48OBa0RhMoVDUfdLTIS1NtrNITHR/TCCTJApPNuDxqdexdzy8+CJERFR+jj9QAqVQKBS1mPR0GD4cjEYID4fUVPciJRAVxqCqihDw68s3kHe8Hjt2BMe1Z0O5+BQKhaKWUmwuJnWFBaNRrj0yGqUl5Y4yF5+fLah582Dfup5ENSzmgw9AH/hEwTKUQCkUCkUt5dHUR8lr+T3h4VIYwsOlm88dZS4+P1pQ27bB/ffL99dOT6N1a78N7RVKoBQKhaKWcqL4BHFd/iU1FebMqcC9V2o16TSdRwvq8D8dWPreOaSne3ftoiK45hrZhLBtvy0cyTzD63P9hYpBKRQKRS2l2FxMsbmYxPM8J0eAfQ2UpmluLaj0dFg24wGsZgPL35NCVxHp6fC//8Hff0Pr1pC1+Sz2rzfwy/ueRTIQKAtKoVAoaik2gaqMMoHyUCw2LQ2sZj3CqqswjgX2pIw//pBuxX79vD/X3yiBqiKTJk2iadOm9OjRo6anolAo6ihF5iKKTEWVHleZBZWUBDqDGZ3eWmEcC6QA2ZIyAJo0Ab3B4tW5/iaoAqVpWjtN05ZqmnZC07RDmqa9pmla8NyMycl+G2rixIksX77cb+MpFAqFK/6yoBIT4ZLZzzPq9g0VuugsFsjJwSkp46abYNCtX9AlIZOXXgqeew+Cb0HNA3KAFkAv4DzgzqBdPSXFb0MNHTqU2NhYv42nUCgUrhSZiii2eCdQGppHCwqg6Vm7GDlpS4UCM3cuvPQSnHOOPSkD4M93xrJzXWvuvZegJkoEO0miPfCaEKIYOKRp2nKge7VG9FBbqnklx/eubFxVJUKhUNQw3lpQAoHIHMCJ7EvZ2CmCLpf4fq0//rA7mWbPhgsukO+ffBIsLjGoYFlRwRaol4BrNE1LAxoBlwAzXA/SNG0KMAWgTZs2wZyfQqFQ1BqKzEVeCdTqdI3i95ZQbA7n5l90tF3hm4gcPw7XXQdWKzz8sF2cQMac9AYLVotGeLiu7saggN+RFtMpIAtYDyx2PUgI8bYQIkEIkdCkSZOKR3TXa0MIDuUdJPPkfsr10yh9v+nARswWk8fzFQqFoqbx1oL6/XcdmMNBGDCZNJ8y7YSASZMgKwv695euPUcSE+GKJ17h0ilrgppiDkEUKE3TdMBy4GugPtAYaUU9Haw5KBQKRShRZPIui2/QEBMYjKAzExYmfLJyXnkFvv0WzjgDPvkEwsLKH9O86x4umrgxqOIEwbWgYoE2yBhUiRDiGLAAGBm0Gcya5behrr32WhITE9m5cyfx8fHMnz/fb2MrFAoFeG9B9e1nJPrW0TS59BXmf7XPayHZvaU5770n37/3HrRv7/sc16zWYfrtgYAkTwQtBiWEOKpp2h7gDk3TngMaABOALcGagz/TzD/55BO/jaVQKBTu8CXNPKLdRhp32885CRd5NfahHe357tErsZpl+4wWLXyfX3o6XHpRBObimQxfWZr1V4VxPBHsGNRo4GLgCJABmID7gjwHhUKhqPVYrBZMVpNv66AqSDN3RAjI3NwFs0mPxSLbuFelQoRtUS/CEJAqE0HN4hNCbAaSgnlNhUKhCEVswuStQGma5nGhLuAkXK+/Dv/+ei56gxVh0VW5QkRSklzMW1RsIjw8jKQkMPo+jEdCttRRXexmWxfvSaFQVI0is0yO8GodlBBeWVCaprFuHUybBiezm3PxpHUVVkmvjMRE+OHHEgwj5gQkwy8kBSoyMpJjx47VqS90IQTHjh0jMjKypqeiUChqAcXmYuqH1S8TqoqorNSRjYJT4YwdCyYT9LzsNy66eQPTp1dPWPoPsBJ23nMByfALyXYb8fHxZGVlceTIEY/H5JbkYrVayY/Kd7v/SO4Rdp7YiU6rPRodGRlJfHx8TU9DoVDUAopMRTSKakROQU6lx3oTgxJWjfkzh7JvH5x7LvSd/A3Qyc+z9i8hKVBhYWG0ryQf8tk/nyWnIIdnL3zW7f6BTw9k19RdxEapenoKhaL2UWwuJjoimuxT2WUC5AlvLKht317EX7+3oVEj+PxzeG67OVBT9xu1x3wIMhrua/gpFApFbaDIXES9sHpEGCIoMZdUeGxlFtT27bBh0RgAPvgA2rULxIz9T0haUAqFQlHXKTYXE2mIJNIQSbG5mKiwKI/HVmZBde0K/SZ9ShO6c9llvQI5bb+iBEqhUChqIcXmYqIMUWUCVRGVWVCaBt0uS2VwmxJkp6PQ4LR18SkUCkVtpshUVGZBVZbJ52hBOfLqq5CREchZBhZlQSkUCkUtxObW89aCsomTzcX39dcwdSrExcHu3QGfbkBQFpRCoVDUQorM0oKKMkRVKlAC54W6O3fCxIly36OPQnQ0XpVAqm0ogVIoFIpaSJViUGjk52lcdRXk5cHYsXDvvfbjQi17WQmUQqFQ1EIcY1DeChRoPH5/B3bsgG7dZAsNLbQ0yQkVg1IoFIpaiGuaeUXYBOrQjzeSuTSO6Gj45hto0CBIkw0QyoJSKBSKWkiRuchnF5+hXj7hEVYWLYIuXYI00QCiBEqhUChqIY4WVGVt320C1fy87/js9w1ccUWQJhlglEApFApFLcSWZl5ZFl9BAezJCC/L4mvcvOKySKGEEiiFQqGohXiTJCEE3HILTLqsB7nb+1fabiPUOK0Fqi79j1QoFHWLYkvlaebPPguffQZCaETGHvW65XuocNpm8WmhnHupUCjqPJVZUD/+CNOny/ePvbCTZWH7g25BpafDT6kGLJn9AjL+aStQCoVCUROkp0NaGiQlVdzJtqJSR//+C+PHg9UKM2fCoAuP8GOadIh5bFjoZ+FKT4fhw8FoDMOiW0r6RNXyXaFQKEIW25f6jBnyNT3d87G2UkeuxWJzc+GKK+TrqFEwa5ZzJYltGxry5JPux/an5ygtDYxGsFg0MIeRlua3octQFpRCoVAECfuXunxNS/NsddhKHUWFOWfxrVsHe/dCz56waBHodHaBOpFxFg881wuzCcLDITXV/1aNjaQkeQ2jUWDRmUhKCvP7NZQFpVAoFEHC9qWu18vXpCTPx3qKQY0YAb/9Bt9+a68UYROo3H96YTJqTgIYKBITpQDOSDYRfvPIgAihsqAUCoUiSNi+1L2NQTkK1KlTsio5QP/+zsfaBCrmrL8ICxdlFlRFAugPEhPhnAQzTz+zNiDjK4FSKBSKIJKY6J3bzTFJImtbW9pNhnfegTFjyh9bJlCdd/DMoo0U/HdupQIYCiiBUigUilqILUki91Aj0p9/EOMpaXl5Eiit9L+uvU9ywdVBn25AUAKlUCgUtZBiczHmoigev6M/xlMNuPBCePFF98e6NiysK6gkCYVCoQggB/IOUGgq9Pm8wpISbr85mr3/NiCq+T4++wwMHkwKxzTzulQhRwmUQqFQBJDrv76egfMHsj93v9fnCCEwLpvLsqV6omPMtL/zHmJiPB9fJlDKglIoFAqFN5gsJtZlr2NM1zEMeHcA6ZkVrMx1YGdGCWy4lbAweHlBJsT9V+Hx3lhQoShcSqAUCoUiQPx1+C/aN2rPjPNm8O4V73Llp1fy4ZYPKz2vWXwRDW4fyaJFMGSo1euGhZVZUBqhVYP0tBaoUHyiUCgUoUN6ZjqJ8TLXe2Tnkfw64VfuWnoXh/MPuz3eZJKvReYiGrT9l/Hj8amjropB1RFC7UlCoVCEHquyVjGw9cCyz92bdic+Op6jhUfLHXvwIPToAR99ZC9zBPjUUTdUYlDx0NKb405bgVIoFIpAsyrTWaAAYiJjOFF8wmlbfj5cdpmsUj5vHhSUyCoSQFktvvR0PBaBtQormqbVXgsqOdn+XgiaQQtvTgu6QGmado2maTs0TSvQNG2XpmlDgj0HhUKhCDTZp7IpMBbQObaz0/aYyBhOFp8s+2w2y9YZGzdCx46weDEYrUVlAhWhj6BoTy+GDxceq6ALUbvWQU1cvNd5Q0oKLF0Kd94Jbdt6PU5QF+pqmnYB8DQwHliLlyqqUCgUFeFtj6Vgkp6VTmLrxHItLhwFSgi4+2753R0XB8uWQZMm8O9+WeYIQK/To9t3foVV0Gs6BvVoqgketX+++dv9kJ0tb2zJErnx0kt9HjfYlSRSgNlCiNWln7ODfH2FQlHHsDfOC3yLCZ/mlZlOq5NX8+STzsLZKLJRmUA98wy8+SZERMB330HnUmPLVubIRmSn1Yg/7ffoWgS2pmNQj/5qlt0TN2yg7YcL5Mb4ePcHT54M777r1bhBEyhN0/RAAvCdpmkZQCSwGHhQCFHkcuwUYApAmzZtgjVFhUIRgvjSYymY/PjbKTJevKFcb6aYyBhOFJ3g6FF46il57KJFMNAhVOWYJAFQv8MWFiw5wZY1sW6tREcLKmgkJ8P996Nf/oP8HB0NBQW0cz3ussukFeVo2XkpUMGMQTUDwoCrgSFAL6A38JjrgUKIt4UQCUKIhCZNmgRxigqFItTwpcdSsCg2F/PvhpaYTbpyvZlsLr7GjeH33+Htt2HsWOfzbb2gbEQaIunRJ4/p092Lr1VY0ZV+nQfKxXfuO6VCtGcPvPqqjCtFRxMx7lq5vaAAgFM9z5SfCwulKH3/fbmxDsNBb64ZTIGyWUmvCiEOCiGOAi8AI4M4B4VCUcew9ViaM6f2uPc2HtxI+177CA/XyglnlIgrc/H17Am33lr+fFurDRuVrYXyxsXns3DZMu/MZjr+fZB+7y6D7t2hQweYOlXu0zQsiQPk+y1bwGpl03dvyc9R9vkza5bT0FlwwJspBM3FJ4Q4oWlaFjj99Go+3UShUIQ83vZYCharMldxwXkNuH6Uc/LGvn2QfPV42lxZBFd6Pr/IXESk3m5BubZ9d8XbJAnXhA0nkpPtonTihLSQMjJg2TKmHT8ut2/f7nyOEOjTS1MKevYs27zgyjbc7Dp2FQh2mvkC4G5N05pqmtYIuA9YEuQ5KBQKRUBJz0pnYOuBJCZS5pY7cgQuvBCOHqrH7t8TsVg8nx8IC8od/d5ZKt8IIQXpueekmtpCKx99BDZxcsRmEQlBobGAJ4Y52zrvj2rn9RwqItgCNQdYB/wL7AA2AU8EeQ4KhUIRMIQQrMpcVVbiCORC3EsvlQtxu3QrpM2Ue9DrPY9ha/duwxuB8mah7jlvfFP2Xm+ySLfdvffa0wcffBB++w1X9fzh2gTbzcl/LhbRE8PDPN9MNQiqQAkhTEKIO4UQMUKI5kKIqUKIiotMKRQKRQhgq/Twzc+HAGgX0w6QCRKjR8O6ddC+Pbz/RQ55WlaFYxWZipyy+CoTKI8NC12E5Jy3FsP778PVV/PUtfPlxpdfhl27nAd8+OHSgaUgLb2+X/mLusSVAoFXMShN094EbgNaCSEOuOw7E9gKvCmEmOr/KSoUCkXtxnEtls7QhMRHJ6BpGlYrTJwIP/8svWY//ghx8dFOlSTcUWwuJjoiuuxzpCGSIrPnenw2F9+4z7djneUgUCkpMGoULFnCzA+WyW03y+hQlJtxnFLBn37aadfayZfgJFNVjCv5grcWlK2whhsZ5UXgFBB4OfUztbJmlUKhCDnkWiyBxQImIxT+/CDp6TIp4qefoEEDWSWic2eIjojmVMkprMLqcbxyC3W9jEGN/XI7uuISue7o9tvlzt69YcYMOv3nXKB2xZVnyzc2t50rLhbSult9rwRRXbwVKFvlByeB0jTtUuASYKYQ4kS5s2oxFWazKBSKOkFFBVb9ybkDC0BvBM0CQs/GP+MYPhwOHYI//pBLgfr2lccadAbqhdUjryTP43iuSRJRBg9ZfKVWTL3Dxxm0dBsAYwbcDJdfDm+95XRo+qDSGnilgvT1FJcyqK4uuyBYSJXhrUD9CxzHQaA0TQtDrmPaBrzl4TyFQqGoEWxuN08FVv1FZm4m/7e1LyPnvMDw4aDTSdeebXFu167lFw9HHTqfJ+ZaPc6pQgsqOVmWFVqzRrrwmjdnyqg5jH01FQCdpdQys13UYgEheOP+oeWus3byJfYPtUCQXPFKoIT0ha0GEjS76XEP0AW4VwhRQcKkQqFQBB93JZD8jdlq5pqvruGGs2/gu4emM2e2HkNpZF+nc1/VIj0djrzxCc/PjfEonE6ljpKTiTREYs09CV99JUWpRQsYULpA9vBhjFHhbBvYCYCvfnlVWkm//mqfSCl/3TbK6Tprb63ddRJ8Wai7Gln14UxN044DM4DFQojUgMxMoVAoqoGtBJKnAqv+IDktmQbhDfjfkP8BsoC3rSvunXe6XzyclgbCHIYQmvvagcnJFJ1ZakHt2gUpKdzVpy0dtmSBudQWyMlxGjO8yMihjs3osSqDoiYx9h0ObjuB4K87ruKc6t920PBFoBwTJYYCEcD9fp+RQqFQ+AFbCaRAteFI3Z3Kgs0L2DhlIzpNx9KlcN110niZOVMaOu5ISgJdmAUsOsLDdSRlvAtMljvNZkhJ4cZL2jJ8xt2wKxOAzhv3eZ6IEMz9Yy55JXmcKjnlnPzl4rYLtU7ivgjUWsCK/EkOAp4VQuwOyKwUCoXCDwSqBNLh/MPctPgmFo5aSLMGzUhLgzFjpPU0bVrF4ZzERHhiyGTWd72X+6/rS+LAW+H8KJl5t3w5AGOXeRCkWbOk8tlEqDTiYmtY+M0153Ch/26zxvF6oa4Q4hSwHVmJPAdVAUKhUJyGCCGY+O1EJp4zkREdRrBmjUyaKy6GKVNktaByScIuLc8fSf2Qe0QyiQ+VZtLdcAN8+imcdFkf9Zhs9vBy+kvcs3RqeeUrdeHZ0syBWtFR11/4WklibenrdCGE5xxJhUKhqKPsPbmXTQc3kZyUDED9+vLf9dfDvHml4uQqJCkpckHU1Kmyrzsw5PUlsHKl83F33w1Az3k92HpoiyzRjksWn2M6eOl1arqjbqDw2sVXmlaeBKwHFgZqQgqFQlGbWbl/JUPbDiVML+vP9eghM75btcJeXy8lRS6UdWx5ftFFngd1FJVXX5WljmzroGbNkgJlcUgzd6GmO+oGCl8sqAeA9sDdoi5JtEKhUPjAyv0r6WS5gvfft29ruyAZg17Axo0we7bc2KIF3HILfPON8wCTJgEw4ZubPFZwcCoWW5pm7o92G6FGhRaUpmmxwEXA2cCDwAtCiNUVnaNQKBR1mV/W7ee253dycyHERRZweb1UaTE9/7wsW+7KyJHSknIUjvfes9fjc1PBoeiZV6tU6qiuWVCVufguAj5GJkW8CDwS8BkpFApFbSQ5mTWjp2J+cTYPFfbjvLgN9Ju4AkpK5P78fGjZEi67TPZxz8+XwSkolzWx954JnCjaXTauK04LdSktFmuqvFhsXbOgKnTxCSE+EUJoQohmQogH61rFCF+fNIJV10uhUPiXAmMB571/Hkv+9aE/qk04LBZYtQpSUmjQ5zz25cuKb/2PLUOziZONAwekaw/s4gTlrKTcR+7zWNFcCEGRybnUkTcddcv6QfnwvVbbxSzYDQtrDb4uWAtWXa9gcbzoOCXmksoPVCjqALN/m41BZ+DW729l/sb57g9ytGROnZJuuwkToHlzGDQIgO6WbeXPc+guW9bMr5LCqzGRMR4FymQ1YdAZ0OvsHQ196gflQXRct4fCol1fFuqe1rir6xWIBYDBYsLiCew+sZuFoxaS0DKhpqejUASMrYe3smDzArbesZXcklwu/vBiDuYf5NFUE5pjuYeUFIiJkaXHf/9dbvvgA88DO37hu5aNqKTwaqOoRh4FyrWbLvgQg6rEggq1Lg6nrQXlK7a6Xnp94Op6BZOdR3cyvvt4Lv34UmasmIHRYqzpKSkUfscqrNy25DbmDJtDswbN6PLqx6y6ZRVf7fgKbfZsWVD1/vvhrLPDLlF7AAAgAElEQVTkCffdBytWyJJDDhy5cyYA9WY2odBYUP5CPnaXbRDegEJTIWarudw+pxTzUnxKkqjlbjtfUALlJba6XnPmyNdQtp7MVjP7cvfx0KCH2HzbZjYf3kz/d/tXGIRVKEKRTbddiUBwa99b5YaUFJp/8zNrfm4vP59/PrzwAuzc6XziQw/J11K3XZPXpYXUwXQlLz9Xj/RJ7zgf72OrCp2mIzoimtzi3HL7AmlBhRpKoHwgMRGmTw9tcQLYn7ufZvWbEWmIpEXDFnx3zXdYrBb+PvJ3TU9NUUsJmQQhB6HIKcih7ztLeL/jA+iefgYGD5Y7brqJ8K9c1iZNnChfSwVpy/Wy3fnrr9sPmXfJnex84XUZh/5kcrV/Fp7iUEXmIqcMPvC+5buyoBSsylxFoamwpqdRZXYd30Wn2E5lnzVN48zGZ/Lfsf9qcFaK2kpIJQilpMiieMuX88mIu3mSRzg+7Dn5ZPnnn+UOv3jRRVKUFiwo27ZhAwwbBsnM4ocfZG9AgBcb9MVqMvitv5SnOJQ7C8pjR91SlAWlKOPuZXezcv/Kyg+spWQcz3ASKIDOsZ3JOJ5RQzNS1GaC0fivytgspgMH4N135fuGDUm/JIXpqxcwgzkMJ5V0BsDYsXK/LdsO+bDpuGD2zz+l1+/4cdhwWTLffCP7/ZmtZg7EfUx4hOa3OHRMZAwnik+U265iUHaUQFWBnIIcjhUeq+lpVJmM4xl0bNTRaVun2E78d1xZUIry1LoEIVvL8w0bpMXUsqUshHdraZzJbCaNJIyEY8GAUR9F2tx0+Pxz53FmzWJY+2Fla6NShyRz4YUyw3zsWNm8NiJCHrrl8Bba9jjAilTNb3FoTy4+dxaUQWfAKqxukyrA2YKqSyiB8hEhhBSootAVqF0ndrm1oJRAKdxR4wlCNispPx++/VaKUnw8JJQujzh4EKKiZM8LYM6HU/h1bjjhUQb0mAgP1+yi6lIJfPRZo/l6x9f8+CNccgkUFsrXjz+WYmxj5f6VDG4z2K9x6JgIzzEoV4HSNI1IQ6THtYtWYS0TJ+XiO43JLcnFaDGGvAVVTqDiOqsYlMIjQU0QcsyI27tXCtIll0DjxjBqlNx+8KDzOUVF0KcPAK8dXMyHU++Sonr+b86i6pJtd/mZl5O6J5WcY8WYTLIiUVoarFvnPPwf+/9gSJshfrpBSUUxKNckCajYzWdrWKhcfKc5OQU5ACFrQVmFld0ndtOhUQen7c3qN6PEUsKJovI+cYUioDiKhsUiBWn6dOjZE9qXpoMvX26veedKaUzJPPMx3rq0Oc+MeIam9ZtKUU0dAXjOQIyNiqVfq378uC4DvV4O5RpnS/ujhCXze3LGkYv9crs2YiJj3P69uXPxQcWZfN4kSYSiZaUqSfhIqAvUwbyDREdE0zCiodN2TdPKEiXObXVuDc1OcVqQnOwsSikp0LWr7Ju0bJnc9tRTbk99/ZLG3LXsKF1fO4v9ufspeLSQrq93xWw1U2gqpPv4nvx4zk1lx9syEI1G6bKzWVNCwNy5EBkJo4eOZnHWl4SH9yg7zuYSTE+Hiy7UYzQ+yjW/6/3q4oyJjOFg3sFy24tM5dPMoWILyop3SRKhFqNSAuUjOQU5RBmiQtbF5869Z8OWKKEESuF3HEUpJQWuvVYKkq2Z3zXXuD/vscfg8ccxW0z0mNeDVy55BTpdxI67dpBXksfh4w/x1bi7CdOFYdAZaBXdyqmcj7sMxP794YEH4MUXZYbeb+vG8D/dmSz/6VH+/COMpCS7CMnzNbDq/V7iLCYyhpMl3iVJQMWp5nU1zfy0FKj0dChcMZV1fQxcOty3c3MKcjiz8Zkha0FlHM+gY2xHt/s6x6o4lMJPuLOShgyxC5KttJArd90lV8c6WgGPP87HWz+mSf0mXNDhgrJEh4YRDWn43Bs0q2AatgxEm2U0aBDceKNMgggLgw8/hMF9mtJtczcKm6cyfbqzG6/XgJOgD0evRTknW/iBRpHuY1BF5vJp5lCJBaXSzOsGNpO/4MdHuPryRj4vOjycf5iujbuGrAW168QuOjVyb0F1jlOZfAo/kZICR47IYqvjxsltI0bASy85HzdmjHy1rU167bVyQ1lmPEbKbynMGTZHWkelwudNdQvHDMTvv5duvY8/hgYNZA9B29RGnzWaj7Z+5HRuejq8+eU/JEz6iDlzNL9nMFYlBqUsqBAnPV2a4dmxLYlqn1Nuv83kRxgwGYXPJntOQQ5dG3flh/9+8NOMg0vG8QyuOusqt/s6x3bmzfVvBnlGijqBrcXE1q12K6lpU8/Hm81yYRWUa+bnWnh1wai2tNvWjqR2SWXbPMWW3JGYCJ06yT6Ca9dCkyYy1NW3r/2YSb0n0fONnvy+73eGth1aNn5RcQLhEefyygr/ZzB6LHVkKqJeWL1y25UFFWK4PkE5lmR5+55xHNjettw5NpMfzURYFRYd5hTm0CWuCwXGAkwWU3VvIehUFINSFpTCa2zuu6IiaYrYWlWccw48+qjzsZdcIl8dKjiUiRNU2DupxFzCnN/nMGfYHKdDfK1uUVIC2dkyKfDPP53FCWTK97xL5zH5u8kUmgpJS4MSowBhwGLSBaR6hi8LdaHirrpVbVhY2wlZgXJXH8zxl9Zs0pG5pUO582wmf/2Ln+HL70/4/FSUU5BD8wbNaRTViONFx/1zM0FCCMGuE7s8xqCa1GuC2WoOuftSBAmbcGRnS0G64gqIi4NLL5XbT52CZs3gllvk57w8KUhLlzqPU0kzP0fmb5pP9ybdGdh6oNN2X6tbxMfDTz9Jcerc2f0xV5x5BX1b9mXWr7NISgKdwYyms/g99mTD0zqoqsSg1DqoWoa7JyjHX1pDmJXWZ+92e25iItQ//xUS+vtuAeUU5NC0flPiouJqJFGiOlWljxYeRa/piY2Kdbtf0zSZyacSJRRgFw6rVfrGUlLkYtj4eLn9+++lBeXI4cP2/Q0a2Le7VHDwlrc3vM0jgx8pt92b6ha2mJONbt3s3dg98crFr7BoyyJ0rdfQ7M7ruP2hAwGrnhFliMJsNZerDuExi6+Ctu8qBuVHNE3rDGwFvhRC3FCVMVyzc2ypoampthjU50S13+fHWUsO5x+WAlUvLuiJEr743d1RkXvPhm0tVP/4/tWcrSLkcMy8y8uTgrR/P/zwA+SUxnM3bSp/3qxZ8tiKntx97JcEkFeSx3/H/2NA/AC3+xMTPf/+v/Ya3HOP1NZBg+C887y7ZpP6TXj54pe58tMrCWMo8Q3jfZ63t2iaVubma9bAnovo0cWnVzGoYPE6sK7So4B//oEvvii/3dMTlK0kS9seB/w4XYnRYiTPmEejqEY1YkFVt6p0Re49G6om32mEq2ikpMArr8CFF0rXHcg2FDnlk40Ae0zJnfhUQZBcWX9gPb2a9yJcH175waVYLHDvvXD33VKcZs2CoUN9u+647uPoUXIrh+d9xMyZWkBbjLhz87nrBwXVz+ILReEKukBpmnYNcBJI9eb4ggKZCnrRRXDyv25O+4LdQPBo4VEa12uMTtMRVy8u6LGa6laVzjie4THF3IZKlKjDuBOkP/6Ahx+G7t3ltnvugZ9/BpOL+3umbHnulOjgiI8tz71hddZqBrRybz25Iz8frroKXn5ZrnH64AN5y65JgpWhaRrna7Oxmv3X+8kT7hIlqpVmXlklCV9/GDVMUAVK07RoYDYwrZLjpmiatl7TtPUNG0of908/weYnX+GvNWcEYabuySnIoVl9aYrHRsYG3cVX3arS3rr4VAyqDuG6WPbECfjkE7j+erlt6FB45hnYvt35vAcflK82QUpJcd7vQ6JDVVmTvcaje8+VAwekG+/776FRI6mxN95Y9WsPG6YRHu6/3k+ecNcTyl0/KPC+Fl9dItgW1BxgvhAiq6KDhBBvCyEShBAJ0dFR6GyztOp5+sGuHD0a+Im6w5YgAcgYVA0kSVTHavTKxVdqQYWiO0DhhpQU2LEDnn1Wfo6Lg+uuk6tVHbmptH6dTZCeeab8WFVMdKgKQghWZ632OhYaHi61t2NH6Y7zNubkiWC1GPG3BQWhWRTWE0FLktA0rRcwAujty3kNG8ruljL2IjiUFUW/fjKpqHHjwMzVE7YECYC4qDj2nNgT3AlUE28sqLioOIQQHC86Tly9uCDNTOE3kpPlE8zvv9sXzHZzcI3bHjwuvFC6JRwfRD74wHmsIFhJntiXuw+dpqN1dOsKjxNCuvAaN5YFz+Pi7OGz6lJREoa/cFfuyFMMKiosyuNDcV1NkghmFl8S0A7YX+oHbQDoNU3rJoTo4+mk+vXtmXkfH34U06+PMKRftN9+CX2hNlhQVSW3OJciU1GZi9ITmqaVWVFKoEIAW+bd4cP2BbNz55aPIdk4eRLOKHWTV1LBIZiC5MrqrNUMiB/gMWZitcrp5efDCy/IbV26BG9+/sKfFpSGWqhbHd4GOgK9Sv+9CfwAXFTZiTa3VuPe6bz06SZef93+t3X0aMXZrf7ESaBqaB1UVbF10fUmSKriULUYm2gIAZs3S0EaMEAu8Jk0Se4zmWQvpUdK1w+ZzfY/kjMcYri1SJBcsQmUO06dkskQc+bIhIi//w7cPKqz7tAb3NXjqygG5XGhLmqhbrUQQhQKIQ7Z/gH5QLEQ4ogv40REWstaMefmwuDBMH68zPbzeU4+PmnkFNqTJGpiHVR1qKiKuSsq1byWYROOwkIpSLffDm3aQO9Sb/maNeWf0rZuhYgI+d5WVqgWC5IrnhIkMjLkA+t338nKSkuX2hMQ/Y27ajX+xu9ZfMqC8g9CiOSqLtK1sX27zN754gv5S7vbfeEIt1Ql2yWULajdJ3bTIaZ86Sd3dI6Ti3UVNYSjcGRmSlG67DJ7cOWttyDLTZ6RTYAc1ycFMbHBX5SYS9hyeAt9WzgXzFu+HM49V/7dd+sm27JfVKn/pepUd92hNzSKbFSuJ1RF66AqzeJTFlTtITFRJkt06SIfGBMS4McfA3c9pySJUgsqVH4Zsk9lEx/t3ap4W+NCRZBwFA6rVQrSY49Br17SUgJZzaHY5enZYW1S+irBkxHJpONidQRJlPz5d7D50Ga6xHWhfnj9sm1ffAEjR8oQ2pVXwurVskJ5IKnuukNvUBZUxYS0QIHse7Z2rXzAPHFCFk6eM0f+nVeVY4XHWJW5qtx2Rwsq0hBJmD6MfGN+1S8URLLzsmkV3cqrYzvFdmLX8V0BntFpjLsFs19+CRMnQvPmctsTT8Bff7k/32VtkpMryvB7wOIl7jBajFz/9fXctfQuv41pW6DrGP+54AJZ5HX2bPj6a5ndC4GNEQUj1dw1BmW2mrEKKwZd+fw1rzrqVmBBhaJwhbxApafDvHkyHjx7ttw2cyZkrG9X5TFX7FnBvcvvddomhHASKAgtN192XjatGnonUHFRcRgtRk6VnArwrE4T3AlSRoZs3jdihNw2diwsXCib/Dlia11RQQUHJ1eUCAtY1QNXCk2FnDfnEdZ+OpxFP2R4bAXhK6uzV9P42OWcf749/rNjh8wJmTGDsnWRwYgRBbpaTUxkDEcL7Qs7i83FRBmi3CYz+cOCCrWFvCHdsNBd8dRzz5W+4rh+e8mpJHFi/Zowtq61F5q1UWgq5K/Df2G0GMvqgOUZ8wjXhztl19jcfO1i2vn93vxN9invLShN02gX0459J/fRs1nPAM/sNCAlRQrNqlX2tUmeej7ccQe88YazGD3xhP29m0SHpPTyhZMDTW5xLkPnPMzfz70MlnCE/jpeGPQnj14/vNpjr8law/Ff3irzaNriP64i4S5GFKyyZ/6iY2xHLMLCxoMb6dOij0f3HvhQ6sgaepaSJ0LagnL3C3rxxfDUU/Zj/vqr/N87gHFvX8ZeHuv26avQVIjRYmRbzrayba7WE4SOBWWxWsgpyKFFg0p6DTjQLqYde0/uDdyk6jrJyXKFua1iQ8OGUjmee875uKtKuxvbLKR588qPVUmiQ7CqHtjIK8lj2MJh1MseCZZwLBYNLOF89H31CzTvyTlM9qI5/LQkGpDLSTyJbjBiRIHGoDNwR8IdvLZWtrr3lGIOXqyDUg0LaxeV/YKajXouuwzuvFP6sE85eKxMuwdi8pChU2gqBGBdtr3gumOChI1QSTXPKcihUVQjwvRhXp/T9oy2SqB8ITlZCsz27bJMUEqKzLqz1bwrKe35Y1MPo1Ee//XX5ceqQjp4MAsn/7L7F2IiY3h+yuVl9eoiwjX2NVpYrQLK//wDw4ZEYdxwPZGR8L//SePRk+gGW5gDxeQ+k/nmn284Wni0UgvKkxtVNSyshVT2C5qTEV/WKSA1VaambtggP4d1WEWYB3ErMhfRKLIR6w+st48VwhaUL/EnG8qCqgSbaJSUyJJBKSmyEFz37rI6OMiS2rYY03//SUFatcq+z0YIrU8CmcSQ1C6JgQM1h78/jZHnN+Kr7V9VacyFC2Ub9n3/RhMbf5TVq6U4VSa6we5oEAga12vMqLNGMX/jfI8p5qCy+EKSin5BM7d0wGKxf87Olse9+CKEtd3AF98fdytuhaZChrQdwvqDXghUCFhQvsSfbLSLacfe3L2BmVAo4igahw5JQRo9WlpJtsU4e1xqM5pM8Msv8r1jTnSICZIrjotoHf/+ru95PR9t/cjn8YSQWfSFhdB8YCpvfruec87x96xrN/937v8xb/088o35Hi0orzrqhlg7jcoIeYGqiNZn7y5zAUZFwZgx8jtj2jTI/fgNEvqb3IpboamQAa0GsPPozjKT2rHVho28XT348f0+QU3rrQrKgqoijmWFUlJkmmi/fva+4d98476EieuC2RAXJEfMVjMbDm6gX6t+5fZd0ukStuZsJTM307uxzPJV0+Dtt+HDDwUlV4xjSJde/pxySNC3ZV9aNWzFF39/UeUYVFk1c+XiCw1adtvn5AL88kvp8m/UCCK6/eTxvEJTIbFRsZzZ+Ey2HN4ClLeg0tPhjbvHkL5oZEA7bvqD7FNVE6h9J/cFaEa1FEfhKCiQojRlCsSXLnCeNUuWL3DFXQUHT+OGOB8v3UNUego7NsWU2xdhiGBM1zF8su2TCsewWGQi04AB9rXHMTEwcORe6oVF0bxB80BMvdbzf/3+j3c2vuPRgorQR2C0GN0KkHLxhSiuLsCrrpJLUKJ6f1N2TFoaFDnEHgtNhdQLq0dCi4SyONThAuckibQ0MJt0YNU7JVkEurhkVfBlka6NxvUaU2QuIq8kL0CzqgW4W580b54sWWArK/TOO7KeliP/+598rajleQA6zNY06ekw+ep2HPnhHo8PZdf1vK5CN19WlgzLTZ8u48HLltn3bTi4gb4t+3o8t65zdberaRDewGMMStM0wvXhlFhKyu1TpY7qELGx9vdbt8rU9IQE2LRJbisyF0mBaplQFodytaCSkiAsXIDOXJZkEYyFg1WhKi6+srVQuXXIinInSOnpco3S2WfLbXfdJb81S8p/CZQJkuO6JBt1yI3nibQ0MLl5KHNkaNuhHC867rREA+SP7aOPoEcPeV7TprLQqy3LHmD9gfUktEgI5C3UasL14dzW9zanEk+ueMrk88aCCkXhOi0Fysa61QbeeUf+sWzfLsMLjz8O+cXFdoE64F6gEhNh0eKDNLrkxbIki2AUl6wKVUmSgOClmgfN6kxJkSXwv/gCJkyQ2wYOlP2Ttm51Pvb+++VrBRUcnKiDguRKUhJoeiN6vfC47kin6Rh91miW/LukbNuxYzBuHNxwg/zxX345bNkiy5I5sv7A+tPaggKYPmQ6z13wnMf9nuJQ3lpQoZZEcdoKlGlfAuOuiGXePNlTaswYGbSdMQPSk5/jyP44ejTtwa7juygwFsgkiQbOSRIjhtbHMvjxMvdhbV04WBULCoKTKBFQq9MmGv/9J1M3QQYgx40r3z32xhvlq02QXBfUnoaC5ErX3ieJvOUyUmZbK1x3NKz9MNL2ppV9/uEHGf9t0ADmz4dvv4VmLn0zhRDSxdfi9BaoSEMkLRp6XlBfkUDVxYaFIV3qqDoYdw3EZNTKrJ2+faF1a1l1Im9PV6ZcaiVqkY7uTbuz/sB6cktyiY2KdRrjjMgzKDAWYLKYCNOHla3LSksrXz6ppsg35mO0GImJLB/UroxgCJTfy9UkJ0uX3cqV0mJ69VVZ0cGG7elyxAiZAm612rtfLlrkPFYItqoIJGuz13JufzOPTtRXeNzQtkO58cuby/4ubrwRdu2StXDbt3d/zu4Tu2kQ3qDcQ6DCGU+p5qphYR0jvOMqwsJFmbVz8qSs3SlDDwKLBfr0gYQWCSzLWEZcVFxZGqcNnaajUVQjp9XztW3h4IG8A7Rq2KpKpn0gBMrVnVdtq9MmHEePSoFJSYHGjeH88+X248el1XTddfLzsWNSpH7+WX52/LmEuJUUaFfpmqw1DGjlvsutI3/+Eovx5S18vVK2utU0+b/FkziBTJBIaHn6xp+8xVNPqLqaxXfaWlDh7Tbw/nfH2bauMUlJ5b+Lep9bQseOUSTkJvDS6pcJ2zYJk8m5AADYq0kE68kvPd03C62q8Sfwv0C5K+7rs9WZnGwvK7Rtm/zm+/lnObjtyfGUSxX2EyfsxVkdM2RCXJAckT9bUfqz1QJS+md19mom957scX9ODtxzD3z6KUBrXn71MOPP827s0z1BwlsMOgNmq7nc9rqaxXfaChRAQn8Tl5dWohkzRlassTHuWiMQRULLBLYtGwBL5tJ3o8w67t/fflww6/F5+oKviKrGnwC/Z/F5cufZ/rnFJkggF82kpMiWFEuWwP79crutfJA7KvpjDWFBcmXpTwUUFUeAMASksrcQgjVZa3jn8nfc7JMhvWnTpMFarx5cM3UrmV1nAMvKD+aG9QfW89Cgh/w34TqKQWfAYrWU215XLajT1sXnypQpspP2hRdCxKipTJkiXT/dmnQjrMl+GjQ7zNat8o9+6lTIK10eFMx6fL/+KjAahU9ZglVZpGujSb0mFBgL/NaU0Wt3nqNwpKTAu+/CqFH2tUnz5tnFyRHXBbMVjVuHKDYX853xfvRhFtBMhIcLvyfo7Dqxi3ph9WjZsKXT9j175N/MxIlSnC64QBq2z85sxersPzFZTJWObRVWNh7ceNonSHiDXtOfVhaUEigHpkyB5csF5t5vlC2WC9OH0XdQLhPefJ6HH5bN0l59Fbp2lZlJsUGqxyeE4BfxGDqD2ad4TVUW6drQNI22MW39VlHCY3Ffdy3Pk5Pl4jSAW2+VqV+Fhc4DOrQ8d7tgtg4ulnXFKqxM+nYSZ/Y6zm+/htFt/Gfc/sqX/nfvZa0uq7/ndH2rzEeJjZUFX3/8UcaaYqNi6Rjb0angsid2Hd/FGZFn0KR+E/9Oug6i1+mxCGVBnbaYrCY0TXNqTTGo9SA6NmvBU0/J1e/nnisLz44dC0fXDg+KBfXx1o9Zp3uFXg894FN7geq4+MD/cajERJhekuw895QUWLwYJk+GVq3s22yl523cd598dWl57sRplnk389eZ7D25l4WjFjJooI73nu3M1/kPemW5+MKarDX0byV926tX2w3Ujh3lsrJ//oGbbnLOOUlqm+SUbu4JlSDhPZW6+JQFVbcpMskqEo48OfxJpvafCsA555TW4XsDBg+GARdkl1lQgfq9OJR/iGk/TeOb8d+wI+o97n/I6PUTcnWSJADaneEHgXJXwWHvXnjtNVnGA2RJgfnzZaVwV2yC9MIL5ffVoUQHX/l+5/d8uu1Tvr3m27ICo/3j+9Muph2f//25X6+1Ons17bShXH21fMhYuNC+77LLoIkb4yepXRJp+9IqHXv9gfXKveclFbn4KmtYGIqWlRIoF2x1+BwJ04eh19nXfuj1cPvt8Pvv0DS6EceKjpGTI8Xriy/8K1RCCO744Q4m957MiA4j6BLXhbXZa70+35ZmXlX8YkGlpMhV0CtXwiOPyG3t28Pdd0ufkCO33SZfVQWHSvns7894aNBD5VxjDw96mGdWPeO3J+lTBUY2f34pNw5P4KuvZBKEuyLurgxpO4T0zPRKrTllQXmPJxefY8PCitBQlSRCGncC5QlNsydJzJsnq+WMGyeX4Gze7J/5fPb3Z/x77F9mnifjLcPaDePXPb96da5VWDmUf6jClemVUaW+UDbROHnSlnMsW54PGQJPP+187JVXylebIL35pvN+JUhusQorP+36iQs7Xlhu38WdLkYIwfKM5T6P67iWSgj5wNW1K5h/SaaoSGP8eNi5U5YsrIzK4lDp6TB3rpW1q/XKgvISvaav0MUHoVlzzxNKoFzwRaDAnmY+Y4b8bo2Lk9l1ffrALbfAwYNVn0tOQQ73Lr+XBVcuIMIQAZQK1F7vBCqnIIeYyBjC9eFVnoPXFpRtbdLOndJiatdOLpC99lq539ZXwZajX1Iij1+8uPxYp1kcqSr8degvGkU1ol1Mu3L7NE3joUEP8fSfT5c/sQJcy07Nni0fuA5khhPdej+pqfJ5w9Z9xBs8xaHKrjVTo2j+9/z7V5xPcz1dMegMKknidMHd/8hCU6HHcvfusFlQer30Tv37r4zl6/Xw3ntyfehHpd0HfF3p/+b6N7nyzCudmsMNaTuEtdlrPTYuc6S68SeooC+UTTiMRpmxkZICXbrAWWfJ7fv2yR/CsGHy886dUpBWr5afwx1EU1lJPvPjrh+5qONFHveP7z6ejOMZ7Diyw+sxXdep6fUyznrRPV9z34IPyopz+IKnOJTtWlaLBpbwWlNYubaj16k089MCT75YW6sNb3FdqBsbK2P527fLuH9BgRSpioqiuhMuIQQfbf2ISb0nOV0vOiKa7k27szprdaVzq24GH8CuLU058fNt/H7zG/aNR45IQRo7VpYVGlG62jkjw/lkiwWGDpXvu3Sxb1eCVG08ufdshOnDGNFhBL/v+92r8Y4fly5qi8W+Tm34cBln1febT3ZzhSkAACAASURBVK9WPao0T09xKLkmzrldjaJyKnPxKQuqjuOzi6/Ugvpt729Of4SdO8vuvdu2yTYejk+nJSXwa6mXzpNwrT+wHquwum2t7W0cqjqLdElOJj0dRozQMP8yi4vfn0D6be/LFC5bKeovv7SvWHbFcW2SEiS/km/MZ92BdSS1S6rwuMFtBrMyc2XFY+XL9lYdOsAnpY1wr7rKvoxB02Dr4a2c3exsr+fn+MAVGxVL35Z9+eAv5+rxiYkwY/5vtB71Dr+u0Nea2pW1nUrXQSkLqm7jq0BFGCJ4ZsQzPPDzAzR9rilXf361U3C6e3f5mpQEhtLCUlardP99950UKnc9pD7c8iE39LzBbVaOYxyqIrehz4t0XSo4pL21E2OxRZbPIYy0t3c6L4KxcZpWcKgp0vamkdAygQbhDSo8bnCbwazc716giotlceQOHeCxx2SfpgsugI0bZWKETTBOFp/kRPEJt7Eud7h74HpmxDPM+HUGp0qcayT+UPQYz89urMTJByqtxacsqLqNrwIFcF/ifay7dR3/3PUPF3e6mPEvvsQTc61OopGYKGuajholvWK7dskEtg8/lC4Vx+oQJouJT//+lOvPvt7t9Qa1GcTGgxv59Y/iCt2Gyxb0onhPb88Td7c+6a234IorAEhaOJFwUYIeE+GYSCJNHjd9unz1UwWHoDUsDDKBuq+fdv1UYfzJxplxZ5JvzCfrVFa5fRMnyljpkSPyd3PFClmLsrfLr8u2nG10b9K9XCV/T7irt3huq3O5sOOFPPnHk2XHrclaQ3ZeNld1vcrjWIryVOriUxZU3cbXJAlHmjVoRveSyeS98w0zZ2rlRGPIEPjmG8jMlE+vzZrBjh3yD7l1aylgiYnwy+5faB/Tnk6xndxep0F4A85udjYffpfl1vqyPcVu/PgqXrhjpH0O7gRp7VpZMsj2zXT77fD99wAksppUhnPHWW+TynASRWnu8dy55SdVxcy7gDYsrEECeV8/7vqxwviTDU3TGNxmMH/u/5OiIueM0jvvlOv2liyBP/+057K4svXwVno27en13DzVW5w7fC7vbHyHPSf2APB8+vPc2/9eDLrTul61z+g1zy6+utiwUAmUC+4qSfhCWhpgCcdq0TwWdI2MlG0Jdu2CZ5+VLedHjYJBg+T+RZs/4dpuN1Z4nWHthmFpk+r2y8D2FItVj9mks88hJUXGjL7+GiaVJl/07y+L47lbuCUEiSKdwV80JhGXpAw/xZXcPXHXBQJ1X3tP7uVE0Ql6Ne/l1fF9G53PS89F0a4d3HGHffvQobBpE1x6qXN5Ile2HN5Cz2beC5SneostG7bkvgH38eDPD7LnxB5S96SWSwBSVI6nUkfeNCwMRctKPb64UBUXnyO2J8gSo5nwcEOF2Un168MDD8inWaNRbss35rP48was2nA7WWPk+tYLLihfd29Y+2HM2jeL1NTbyvVSss2huMgk59A5G175Su6MiZFBMFeuuw4+/tgeQ3L41jq72dm8cnEjpjoe76e4km2uthYidSWbK1D39dOun7ig4wWVutwOHpRFjV957Q4K8uSfeXa2rLdbr/TX25selltztjKu+zif5uipfcq0xGl0fb0rExZP4Jbet9AwoqFP4yq8SDOvxIKqSuPSmqTOW1C+xgGqK1CJifDzL4KoC5/k0++OeBUArldP6gbA4n8W0zBjIvv26HnuOWmoDB0KP/zgfM7A1gP569Bf9Oybb+/gWyoaieeaWfr0Gp5gJr+EjyRxbLw02cAuTsOH2z8LYV+sZcPBQuoS14X/DTGWC3L7A48VzkMcf9+X7ff4k2V7K4w/ZWbCzTfLddJPPgkFeQZ0HX7jmyUFrF1rFydvEEKwLWebTxZURUSFRfHMBc+QnpVeVttS4RsVufjKLKg65OILmgWlaVoEMA8YAcQCu4DpQgjvOppVgQPb2zL8f741+Cs0FdK4XuNqXXfIIANXTt5OTuy3gOcOpO74cMuHvPj+zXz/VL+yKkFms0yomDABHnxQroWtF1aPPi36cOiB2+n0yodyIUtKilwpvHw5SSdOkASQC5xxhizK+tlnMireuPT+NM1zy3MHC0mv09OjaQ+2HN7C4DaDff+BVEKFDQtDGH/dl71RpcCie4xpAwo9HqvXy2cNsxlGj5a/L9N3JhN55sNo2sU+XTfzVCZRYVHV/ntwZGy3sfRp0Yf4aB/KUSjK0OvKJ0nYXHdlxWJD0JXniWBaUAYgEzgPOAN4DPhc07R2gbpg5pYOPscBCk2FZZWhq8OlnS/lh/9+qPxA5NNPemY6036cxtrstYzqdjlTp0JUlOw/pdPJe3jvPXh99C/SGhSCcbqedHr1I2li2cpJf/KJbHHuSG6uvcJDY4cvGx/iSL2a92LzIT8VGAwR3FnfNZFxaI9nyaoL29bJ/4cnTshF4cOHy98PgJYt5e/Jf//BV1/BgAEwuLXndPOK2Hp4K61zx/n1fjVN85j8o6gcd2nmjnX4lAVVRYQQBUCyw6YlmqbtAfoCewNxzdZn7/Y5DuBrJQlPXNTxIu5aehcl5pKyOnquCCFI+S2F+ZvmEx0RzdhuY1k9eTX1wuqVuYjSHvuFpMdHkJkJ118veGNHEvOHGklteh3/d6A0rvTHH+XGXnrduYz8eJ3fWp73at7Lq+ZzdQW71WK3vqH8tmBYfmUxxRILeoOVuDiZJv7551BUJI9ZvlwmPADccIPz+YPbDObJlU/iK0tWHGPTM8+y0Rzc+1V4xp2Lz0mglAXlHzRNawZ0Af52s2+KpmnrNU1bf+TIkSpfo2W3fT7HAaobg7LRpH4TujXpVmGpmXc3vsvifxaz/Prl/H3n3yQnJdPl1Y/L9icmwvQVF5D4zwJ2zf4QYbZgwYDRrJF2oLPbMUuK5dqkewecLL+zGokNp5sF5S4Lr6YyDhMTpQA1PnsdjZsZue022Y+pqEgm0Hz7rb2tltvzWyey/sB6jBajT9dNXxmBxWSocxmWoYw7F5+rBVWXqBGB0jQtDPgIWCiE+Md1vxDibSFEghAioYm7Tmg+kJiIPYnAC/wlUCDdfEv/W+p2X8bxDKanTuej0R9xanq63Y2SkiLzf+fMsVf+njSJpL9fJxyj06LZvTfOACA83IxWata3bAk3Tj7F8QMxCFtLdD/Qs2lPth/Z7vdOrbUVd+t5PK3xCRQWh++h/okmju5rzqHMBjRpAg8/LN14P/0k11Xr9Z7HiY6IpktcFzYc2OD5IDfktfg+qPerqBx31cxta6BsKBdfNdA0TQcsAozA/wX7+o64M4X9KVAjO4/kmi+v4cWLX3Tabp01kxta/8SMoTM4tas7w99rj1FnJVwzk8oAEvv0KTdWIqtJHfc2aZ8fJmnVkyQmlgYFFs1h4qJH6XHoKd54YhbHc+DD+dFc+sIItPtkS/T8fGhQcVWcSqkfXp82Z7Rh57Gd9GhatcKh/iI9nXKp9b6y6eAmDuUf4uJOF7t96ixzsbpcx902f2K1wqpVMuP/q69k6aFWrWDT4Q00HbSB8xvexe2322vweout7FFia+8mbbKYOBDzFct/mk/6yoiA3a/CN9x11LWtgQLl4qsWmvwmmA80A8YIIWrscdyTKVydShKu9G7em3xjPv8d+89pu272HDrkh3H3pjDSbl6IkXAsVh1Gi0aazL2TXHONfC0tKZT42b1M5ymnL4rs+yaz6ugSpk6FOw4ns3kzdL9uIdcM71Z2zPnnQ9++clHw7t1Vvx93bj5/JA3sz93Pws0LmbB4An3e6sOqzFUej/VHhYbP//6ciz68iEdSH+GcN8/h460fu11b4s769tUi9warVd7HtGkyPXzIEHjjDcjJgWWlOa4Ll/zLsZ9v5fPPpTvP1/v2pnCsIzuP7aTtGW1JGhLh9/tVVB1vXHx1yYIKtovvDaArcLkQoijI1/YKfyVJgPxlGdl5JFvuHM0HGxaw6osX2HHbGAA+vm8lujvvImnnm+Vcd2Vrk2zlpR1xybxr/tybZOdlcyj/EADdepjI6nlPWSmcY8dkK6aNG+Ghh6BjR+jVSzaj27LFt/b0rgJVXbEwW82M/GgkCW8nsDRjKYnxiUxLnMaoT0d5bGtfnTiQEIKnVj7FAz89wM83/szm2zbz9IineXP9/7d339FRV2kDx783HZAuxUgUaaKIghAkSiSABAIiFpSm6L4CLiiuL6su+MJCREFFF+HoqrAI0lTWgkiVBIMLBE4ILugKghQRQRRWCDVt7vvHzSSTZCZ1ym9mns85c3Ba5v5mnN8ztz3P21z7xrWVHgJzh7w8k/n+1lth1iyzjykmxgzh7dplil4CpG7Mx1aN+SB7D8pZFgJnKptBQnhHdRZJ+GPg8uY+qKuBx4Bs4BeHHsxjWuulLp/oZW4Z4ps61Vyyspj235u4Yvl8sleMIjKn9MkhbkAjUj/vRdr0dBISwom7dZvrvUn2v+0gNCSU7ld358tDXzK0/VC2Hd1Gi/otaFyrMWAq/J44YX6Jf/SRSbO3a5e5TJliJtgLcsOiddnZBTo07cCrW18tvO4sWFTml/akjZPI1/kc//NxQkOKJlHqR9VnwPsDWDNsDZ2ii5cCr2qGhjxbHmNXjyXjWAbpj6YXZnlPap1EUuskln2zjEH/HETm6Ewa1GhQ8YOohBMnzJxRairMmwfh4SbD/XXXQW4uDBpkLl27mq0Fdrn5ufzccCmRkY9UKTOFGRKNps6pPnz9y9d0ju5c7nMqm4NPeEd1l5m7qoNnVd5cZv4jWP/dqWqASn/0H6S1GklCq6PEJSfD5s3w1VdckWtGMSNz8s34zYABJgfNpUsQaZafxylFXEGC8KrkuOt5TU82HtrI0PZDnVZajYoyNX7uucfUokpNNen4NmwoniT00UfNL/jERHNp3774idLeg9Jao5SqVjqfld+v5P1v3ydzdGax4ATQv01/5t45l/7L+rP+wfXc1PSmwvtczQ1B2XNTr219je9Pfc9Xj3zlNMXOsPbDyPg5g4dXPMxnQz6rcPbusly8aNqUmmpW4e3cWXTfww8XvfeLF5tMIq5+HGQez6T1TSd5K1VVev7Lcbm8ClvIP1q+T+cx5Qeo3b/uZmTHym0yF55X7hBfgM1BSS6+EiocoOy9pNxc0ud+Q693h5FDHhE0MAsd7BtnHB0+bEruQmFwAqqcCdyu5zU9mb19NmAyXc/sPdPlYyMjoV8/c3HsMWlt0in9+iukpJjhwAYNoHt3c0Ls3x9atmxKWEgYR7OOElM3psxg4ahk4Dj4+0FGrhzJyqErXWYpGNh2ILm2XPot68eex/dQJ7JO4X3OMjQ427dkf8zxs8eZuXUm20ZuKzP/28u9X6b7wu7M3DKTv3T7i8vHuWKvRgsm713LluYHgV1UlHkP+vYt2jcNUL9+2X837XAaCc0TqpSZwrGXG0IYX6Rmw5iyn5OTn8OWI1tYMHBB5V5MeFy5Q3wBNgclAaoEl5kk7AEJzMROcjLs3Qvr1pF2Zgw5TDN7lNCkkVCU/XvKFPNYN22YdaZdo3acyznHjmM72HdqH7fG3Fqh5zn+YlfKVP9NSTHDUCkpcPSoKQ/y6aemw/fss6YXtWb7PmLyYujUqXSwKBmMSgaONeuzGf/dICbfPpmuzbqW2b5B1w9i7f61PL/peV5NfLXMx5Y13DghdQIjbx5ZbgaDiNAIlg9aTuy8WOJi4rj9atdL5bQ2Fe4zMsxlyxbz2pkF01jR0aacSoMGpqfUp49ZeVejCutv0g6n8cfOf6z8Eyk5JKr4pdGHnM0eWmag3vrTVlo3bF04TCysw1k2c+lBBYnc/Fy01oSHhBcGpPR0SPtSk5C8nrgaNUwBna0Fq8w+/BCABNKIIIccdNFCB8f/SZKTi7+QmyvMKqW4Mecxhjy1i/btRhMRGlGlv9OoEQwdai5aw6FDRRtUe/Y0j+nQtAPL3w9l47vmerNmZoVghw7mRDhtmplPsfdiSgaOSe+m0Lp/a57oUrEdBtN7TeeGt25g5M0jaXt5W5ePczXcuP3odlIOprD38VLb7ZyKqRvDwrsXMvTjoWx7dBsxdWOK3f/FF/Dyy2arWsmMUmFhpppJ7dom4O/dW7WA5Cg3P5etP21l2X3Lyn+wE8V7uYpJ+2HTj5u4s82dLp+zZv8aklolVbHFwpOcZTOXHlQwmDqVixPHUzO8plmCnpxMet2+9Hr2ZnLyQoggldQJvUrXRQLipvQhNbnEQgdHlawwW1np6bDp+b+SmwNHVmrSE6u/LFgpUw68RYui0lFgAtSG8O+4/fYEdu40vayjR81iC0f2XsyPP5phL60hLNzGtzXf5NOk9yq8473JZU14rttz/Gndn1g3fJ3L5zkbbrRpG+PWjmNGrxnllnbIzzfzbwcOwJEDfWmd+SntFv5AowvRPDkutDAZ/IULpvosQNOmEBsLXbqYPdVdu8KhC7uZv3k+3Zt35+62d1PdhbKZxzNpUb9FtRZuOPZyE/MTWf/D+jID1Nof1jJvwLwqv57wHFdDfPbvhfSgAkjtGa/BjL+ZK8nJXHrwHh79WpnVBEDa+M/IoXPpobsJE+Cll4r1kuKSk6u10KE60tIgPy8UtMKWpyu9mq4yOjbtyOnr+5H59lhsNpM8PTMTvvkGNm2CHTvM2xIRYXpVzz1X9NzsSyHY5n9Gt5RwrrzSLHXvVpAcfc8eOHLE5LKtVw/q1DFJ2CMi4IkuTzBv5zxWfr+SgW0HumxbyeHGBTuWkJd1OZ0jHiQ93SR8P3HCzLNlZRUvDHz99eZYinQB4Cywa3ce9q9KfDysXGmOrVkzE8i11mz6cRMPrHyZ3Sd284cOf+ClzS8x+cvJPNftOQbfMLjKlWPt80/u0qdVHwZ/NNjl/T+d+YnjZ48TGx3rttcU7hMaUjpAaa2lBxWI6rw0CwYNN8N2QOPWHTA5H1YADkN3ShGhc0nYOgPiCpJuvvRS8T9WzYUO1ZGQAJERkJ1jIyIixKMpaVo1aMWJcyc4c+kMdaPq0rZt8Ql/xzmoVq3Mptb9+yFzzymOHA4l93w99u0zwcBxAcGiRaXfUjABqm3bcGZ/MpvHVj1Gn1Z9iI+L4uJFs8JQa7OXKC/PDC3+9a+mx/f7xd/586u7OLN8De2edn4skycXDcFFR5uMGy1bFl1at7Gx4Oiz/N7kKPm2pYSGhNKwoVmIaZfxcwbjvxjPb+d/45lbn2HF4BVEhkUyrcc0NhzcwIv/epHpm6ez5X+2UC+qXqXf7+rMPzlzY5MbOX3pNIdPH6Z5veal7l/3wzoSWyaWWlkprEHmoALZ1KmmQE5qKq8sL0im2tnJktv+/WH1auJ0Oqn2E+5zvYrSC4HXe0llMcNbirQ05fGUNKEhobRv0p7dJ3YTf3W807Y4vv706ZCVncV1b97I5gc+5vo6XfnpJ7PKzTGjU4sWcMcdcPKkqQ6SlWX+zckxgad3y950aNqBGf+awXffJXPBRUmkkyfNL8rHVj3GbW16k9HIrJKrV8/826QJNG5s/nXMdZeS4iyfXQh3571I36V9eejThxjefjidojvR9LKmHDt7jImpE9lwYAMv9nyRETeNKHZSV0qR2DKRxJaJPL76cR5f8zhL763cdr+z2WdJP5pe5fknZ0JUCL1b9GbDgQ2M6jSq1P1rf1jLPW3vcdvrCfdylupI5qD8VO/FWyARMxGyerVZrPDCC5Cfz3Anj8/c/xV//HI8GaNWFS5xKzzhZpeoYurDgOSMt4r+pacD/5rIouz9xI8tHaCcmZo2laRWSYWr9tq1MxdHo0aZiyOtTS8rpyAJ95ykOXSZ14VZH9/Brc3iyc83Q4uZmebYY2PNxuRFuxax5+QeMqYsImpaxY7LVbLVnRmR3H5kLYfqLeD1C6+TeSyTGuE1yM7LZnSn0Xz/xPflzm/NTJzJze/czAfffsCQG4ZUrEHA80s2ELPrTb7/dwO3fraJLRP5fN/npQJUTn4OqYdSefvOt933YsKtnA3xlexBueKPPavAClD2peD5+UR/c5jEJemw60ZzFrOz2eCWW3il/nc8u+6suV5QWfZcmK1oD5SFekhWYV8ynp0zgG3LLjG43Xnu6F6rzOd8ffxrluxewn/GlqqqUi6lzN6hqChzvVmdZiy/fzn3fngvm2M3c2pfG0aPNgHsnXfMIglb3QM8veFpNo7YSFRYVFUOs1DREvkoIiLGkJo6hq4Pag6fPkxEaERhNory1AyvyZJ7l9BvaT+6XdWtQtVkt27VvDYmCZUfSa/P3FuLqXeL3jy17inybfnFen1bjmyhTcM2NK7V2C0JeYX7lTfEB2UHIn8rx+GzelBuYw8cZ86YHtKIEdCkCcPH/N3c7hicwPws376dUFvBh2j/wKZMKb5JVwJSKfYl47Z8hcqP4OWl28t8/Lmccwz5eAiz+86mUa3qlU2x63ZVN17s+SIDPxjIupRLxZawp27MZ/gnw5kUP8kteeSc7a1SSnFN/WsqHJzsOkd3ZlyXcTyy4hFs2lbu499bcRidF47NFuL2WkxX1L6CmLoxpQpQrv1hLf1a9XNLQl7hGcE2xOd3Aar56wuLruzfb4JSz55FpcwXLzYbaUuy94gKMoPP6lOXrAn/W3T/1KluLbURiBzrIUVGKDIiZnL6kpPCiJhfcWNWjyH+qniGth/q1naM6jSKHs17kGqbTESEJjRUExaez46IV6kXVY9xt4xzy+u4u/7TxPiJnM89z5ztcwpvc5UN/mC9BYRHaI/VYkpskcj6A+uL3bb2h7UktU7yWWFGUb6KDPH541CeK9Yf4jt2rPA/Q/NsXDNnEYRdblbe2dcFf/llqadt+cMd3LYgpcwNs1kTx1PH4bo7S20EouJ7jUKY+2tTZqXPIrlHcqnHvrfrPXYe30nGqAyPtOX1vq+TdCqJ3Ae7U/f4XTTvcJjLr73ECz0XuiWPHpSd968qwkLCWHzPYuLmx9H96u5cOtzRaXqmX879QkbIHFate4bM9HCPDLM90O4B+izpw/7/7md81/FcXvPywuXlOqHqORaFZ4Uq57n47HNPgdaDsn6AOn7crEFetYoVq7aY2/72N9ePLwhIW7fMNAHKUTkbZt1ZaiNQOS7GaPL7ZGLnxfLkLU/SsGbDwsfs+W0Pz2x4hrSH0zz2fkaERpDyUAr6Ie22gOSMuxeftGrQitl9ZzP4o8EMO/0NOTmRpdIzzd85n/uvv5/EhNokJrjvtR3FXhnLwT8dZG7mXO764C7CQ8ILl5e7OzAL93FWUVcKFvraww/DP//JZRdLlKuYbEqe24ftSvriwRLfrHLmlWSIr3Ja1G/BfdfdV1iC47fzv7F091LuXX4vM3rNoF3jduX8hepRSnk0OHnKsPbDiL8qnu3hL5caQsyz5fFO5juMjR3r8XbUi6rHs7c9y8EnDzK913QmdJtQeJ8nCjOK6pNURxb28R1Xcl/Kz8WD0TSHdcQlekgbHrqNxEr8fQlQlTfp9kl0eLsDKYdS2H9qPz2u6cGE2yYw4qYRvm6apc1JmkPs0Vj+/FZnah7rV9hT+WzvaprVaUbHKzp6rS3hoeGVWv4ufMfVEF+g9qD8I0AVvOFvvNfDBChHbszgIAGq8q6qexVL7l1CrfBaxMXEVTlRbbCpFVGLDwd9SM9FPZnUYxIfnTnC9Pf3sf3odmb3ne3r5gmLcjbEJz0oCzn05AiucbzBjcvBL+ReoH5UOcV5RCn9WvfzdRP8Uvsm7Xm7/9us3r+aaxteS/zV8czsPbPMrO0iuJU7xCc9KC+74opiVw8/9UjxAOVGF3NlkYQnyKZP16LP3Efr7+7j9gSIk7gkylHuEF8ZPSh/7FlZP0BFR3vtpS7kyRCfu5VV6dZbr2/V4Ojr96Y8Vn7vglV190GVlQrJiqwfoLxI5qDcr6xKt55m9QBQ0ffGF4HC6u9dsCo3m3mAzUH53xpdD5IA5X7uzsZQGVbPiFCR98ZXaYes/t4FK1epjqRgYRC4kHuBGuGSScKdfLnp01UZeKuoyHvjqx6o1d+7YCUFC4OYLJLwDG+VAnH2ulbPiFDee+OrQOEP710wqkjBwkAS1AGqZFfYG0N8wTzx7Itj91VwdBdfBgp/f+8CUXnZzME/6z65ErQBylldFE8HKE9NPPtD0JNJ96qTQCHsyl3FF2BDfLJIwoGnA5QnJp79pXaPTLoLUX3BlupIApQDT5fb8MSKNn858ftyNZ8QgUJSHQUxT5fb8MR8gr+stgrkSXd/GGIVgaE6qY78sWclAapAni2PPFuex5Oduns+wZ9O/OUduz+e6GVuTXhTdQsWOpt7tzIJUAXsS8z97QOEwJhEr+iJ3mpBzJeZMkTwCbaChRKgCkgWCd+qyIneir0VfxliFYFBChYGKU8vkBBlq8iJ3oq9FX8aYhX+L9hW8UmAKuDpBRKibBU50Vu1txIIQ6zCPwTbPigJUAVkiM89qjNHVN6JXnorIthVJNWR9KCqSCnVAJgPJAIngYla62XebIMrEqCqzxtzRNJbEcEsRIWg0cWCUiD3oLy9UfdNIAdoAgwH3lJKtfNyG5ySAFV9/rJpWAh/VnIeKpB7UF4LUEqpWsB9wGSt9Tmt9WZgJfCQt9pQkuMvDSm1UX2SLUIIzyu51LxYPahK9qCs3ttS3oq2SqmOwBatdU2H254GumutB5R47GhgdMHVG4BvvdJIa7kcMwzqZ2rXgjq1IessnD1fxT/ip8debXLcwSVYjxvgWq117fIe5M05qMuArBK3nQFKNVJrPReYC6CU2qG17uz55llLsB43BO+xy3EHl2A9bjDHXpHHeXMO6hxQp8RtdYCzXmyDEEIIP+HNALUPCFNKtXa47SbgP15sgxBCCD/htQCltT4PfAI8r5SqpZS6DRgILC7nqXM93jhrCtbjhuA9djnu4BKsxw0VPHavLZKAwn1Q7wK9gVPABKvsgxJCCGEtXg1QQgghREVJo7rXIwAABD9JREFURV0hhBCWJAFKCCGEJflFgFJKLVFKHVdKZSml9imlRvq6Td6glIpUSs1XSv2olDqrlPq3UirJ1+3yBqXUE0qpHUqpbKXUQl+3x5OUUg2UUp8qpc4XfNbDfN0mbwimz9hRkH+vK3Uu94sABcwAmmut6wB3AS8opTr5uE3eEAb8BHQH6gKTgOVKqeY+bJO3HANewCyqCXSWzVHpYcH0GTsK5u91pc7lfhGgtNb/0Vpn268WXFr6sEleobU+r7WeqrU+rLW2aa1XAYeAgA/OWutPtNYrMKs9A5YVc1R6S7B8xiUF+fe6UudyvwhQAEqpvyulLgB7gePAGh83yeuUUk2ANsjm5kDSBsjTWu9zuG0XEAw9KEHwfa8rcy73mwCltR6LydsXj9nwm132MwKLUiocWAq8p7Xe6+v2CLepcI5KEXiC8XtdmXO5zwOUUipNKaVdXDY7PlZrnV8wBNIMGOObFrtPRY9dKRWCybiRAzzhswa7SWU+8yAgOSqDVKB9ryujoudyn5d811onVOFpYQTAHFRFjl2ZQi/zMRPo/bTWuZ5ul6dV8TMPVIU5KrXW+wtukxyVAS4Qv9dVVOa53Oc9qPIopRorpYYopS5TSoUqpfoAQ4FUX7fNS94CrgMGaK0v+rox3qKUClNKRQGhQKhSKkop5fMfVO5WjRyVfi9YPmMXgu57XaVzudba0hegEbAJOI0Zq/8GGOXrdnnp2K/GrHK5hBkKsl+G+7ptXjj2qRSt8rFfpvq6XR461gbACuA8cAQY5us2yWfs0eMOyu91Vc7lkotPCCGEJVl+iE8IIURwkgAlhBDCkiRACSGEsCQJUEIIISxJApQQQghLkgAlhBDCkiRACSGEsCQJUEIIISxJApQQQghLkgAlhJcopWoopY4qpY4opSJL3PcPpVS+UmqIr9onhNVIgBLCS7RJCjoFiAHG2m9XSs0AHgXGaa0/8FHzhLAcycUnhBcppUIxFXMbAy2AkcAsYIrW+nlftk0Iq5EAJYSXKaXuBD4HNgI9gDe01k/6tlVCWI8EKCF8QCm1E+gIfIApr6FL3P8A8CTQATiptW7u9UYK4WMyByWElymlBmOq5gKcLRmcCvwOvAH8n9caJoTFSA9KCC9SSiVihvc+B3KB+4H2Wus9Lh5/N/C69KBEMJIelBBeopS6BVPefQswHJgE2IAZvmyXEFYlAUoIL1BKXQ+sAfYBd2uts7XWB4D5wECl1G0+baAQFiQBSggPU0pdBazHzCslaa2zHO6eBlwEXvFF24SwsjBfN0CIQKe1PoLZnOvsvmNATe+2SAj/IAFKCAsq2NAbXnBRSqkoQGuts33bMiG8RwKUENb0ELDA4fpF4EeguU9aI4QPyDJzIYQQliSLJIQQQliSBCghhBCWJAFKCCGEJUmAEkIIYUkSoIQQQliSBCghhBCWJAFKCCGEJf0/dN+06XGTgWAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"for style, width, degree in ((\\\"g-\\\", 1, 300), (\\\"b--\\\", 2, 2), (\\\"r-+\\\", 2, 1)):\\n\",\n    \"    polybig_features = PolynomialFeatures(degree=degree, include_bias=False)\\n\",\n    \"    std_scaler = StandardScaler()\\n\",\n    \"    lin_reg = LinearRegression()\\n\",\n    \"    polynomial_regression = Pipeline([\\n\",\n    \"            (\\\"poly_features\\\", polybig_features),\\n\",\n    \"            (\\\"std_scaler\\\", std_scaler),\\n\",\n    \"            (\\\"lin_reg\\\", lin_reg),\\n\",\n    \"        ])\\n\",\n    \"    polynomial_regression.fit(X, y)\\n\",\n    \"    y_newbig = polynomial_regression.predict(X_new)\\n\",\n    \"    plt.plot(X_new, y_newbig, style, label=str(degree), linewidth=width)\\n\",\n    \"\\n\",\n    \"plt.plot(X, y, \\\"b.\\\", linewidth=3)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\")\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.axis([-3, 3, 0, 10])\\n\",\n    \"save_fig(\\\"high_degree_polynomials_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"def plot_learning_curves(model, X, y):\\n\",\n    \"    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=10)\\n\",\n    \"    train_errors, val_errors = [], []\\n\",\n    \"    for m in range(1, len(X_train)):\\n\",\n    \"        model.fit(X_train[:m], y_train[:m])\\n\",\n    \"        y_train_predict = model.predict(X_train[:m])\\n\",\n    \"        y_val_predict = model.predict(X_val)\\n\",\n    \"        train_errors.append(mean_squared_error(y_train[:m], y_train_predict))\\n\",\n    \"        val_errors.append(mean_squared_error(y_val, y_val_predict))\\n\",\n    \"\\n\",\n    \"    plt.plot(np.sqrt(train_errors), \\\"r-+\\\", linewidth=2, label=\\\"train\\\")\\n\",\n    \"    plt.plot(np.sqrt(val_errors), \\\"b-\\\", linewidth=3, label=\\\"val\\\")\\n\",\n    \"    plt.legend(loc=\\\"upper right\\\", fontsize=14)   # not shown in the book\\n\",\n    \"    plt.xlabel(\\\"Training set size\\\", fontsize=14) # not shown\\n\",\n    \"    plt.ylabel(\\\"RMSE\\\", fontsize=14)              # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure underfitting_learning_curves_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lin_reg = LinearRegression()\\n\",\n    \"plot_learning_curves(lin_reg, X, y)\\n\",\n    \"plt.axis([0, 80, 0, 3])                         # not shown in the book\\n\",\n    \"save_fig(\\\"underfitting_learning_curves_plot\\\")   # not shown\\n\",\n    \"plt.show()                                      # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure learning_curves_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"polynomial_regression = Pipeline([\\n\",\n    \"        (\\\"poly_features\\\", PolynomialFeatures(degree=10, include_bias=False)),\\n\",\n    \"        (\\\"lin_reg\\\", LinearRegression()),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"plot_learning_curves(polynomial_regression, X, y)\\n\",\n    \"plt.axis([0, 80, 0, 3])           # not shown\\n\",\n    \"save_fig(\\\"learning_curves_plot\\\")  # not shown\\n\",\n    \"plt.show()                        # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Regularized models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure ridge_regression_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import Ridge\\n\",\n    \"\\n\",\n    \"np.random.seed(42)\\n\",\n    \"m = 20\\n\",\n    \"X = 3 * np.random.rand(m, 1)\\n\",\n    \"y = 1 + 0.5 * X + np.random.randn(m, 1) / 1.5\\n\",\n    \"X_new = np.linspace(0, 3, 100).reshape(100, 1)\\n\",\n    \"\\n\",\n    \"def plot_model(model_class, polynomial, alphas, **model_kargs):\\n\",\n    \"    for alpha, style in zip(alphas, (\\\"b-\\\", \\\"g--\\\", \\\"r:\\\")):\\n\",\n    \"        model = model_class(alpha, **model_kargs) if alpha > 0 else LinearRegression()\\n\",\n    \"        if polynomial:\\n\",\n    \"            model = Pipeline([\\n\",\n    \"                    (\\\"poly_features\\\", PolynomialFeatures(degree=10, include_bias=False)),\\n\",\n    \"                    (\\\"std_scaler\\\", StandardScaler()),\\n\",\n    \"                    (\\\"regul_reg\\\", model),\\n\",\n    \"                ])\\n\",\n    \"        model.fit(X, y)\\n\",\n    \"        y_new_regul = model.predict(X_new)\\n\",\n    \"        lw = 2 if alpha > 0 else 1\\n\",\n    \"        plt.plot(X_new, y_new_regul, style, linewidth=lw, label=r\\\"$\\\\alpha = {}$\\\".format(alpha))\\n\",\n    \"    plt.plot(X, y, \\\"b.\\\", linewidth=3)\\n\",\n    \"    plt.legend(loc=\\\"upper left\\\", fontsize=15)\\n\",\n    \"    plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"    plt.axis([0, 3, 0, 4])\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8,4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_model(Ridge, polynomial=False, alphas=(0, 10, 100), random_state=42)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_model(Ridge, polynomial=True, alphas=(0, 10**-5, 1), random_state=42)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"ridge_regression_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1.55071465]])\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import Ridge\\n\",\n    \"ridge_reg = Ridge(alpha=1, solver=\\\"cholesky\\\", random_state=42)\\n\",\n    \"ridge_reg.fit(X, y)\\n\",\n    \"ridge_reg.predict([[1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.49905184])\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sgd_reg = SGDRegressor(max_iter=50, tol=-np.infty, penalty=\\\"l2\\\", random_state=42)\\n\",\n    \"sgd_reg.fit(X, y.ravel())\\n\",\n    \"sgd_reg.predict([[1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1.5507201]])\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ridge_reg = Ridge(alpha=1, solver=\\\"sag\\\", random_state=42)\\n\",\n    \"ridge_reg.fit(X, y)\\n\",\n    \"ridge_reg.predict([[1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure lasso_regression_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import Lasso\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8,4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_model(Lasso, polynomial=False, alphas=(0, 0.1, 1), random_state=42)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_model(Lasso, polynomial=True, alphas=(0, 10**-7, 1), tol=1, random_state=42)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"lasso_regression_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.53788174])\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import Lasso\\n\",\n    \"lasso_reg = Lasso(alpha=0.1)\\n\",\n    \"lasso_reg.fit(X, y)\\n\",\n    \"lasso_reg.predict([[1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.54333232])\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import ElasticNet\\n\",\n    \"elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5, random_state=42)\\n\",\n    \"elastic_net.fit(X, y)\\n\",\n    \"elastic_net.predict([[1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure early_stopping_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"m = 100\\n\",\n    \"X = 6 * np.random.rand(m, 1) - 3\\n\",\n    \"y = 2 + X + 0.5 * X**2 + np.random.randn(m, 1)\\n\",\n    \"\\n\",\n    \"X_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10)\\n\",\n    \"\\n\",\n    \"poly_scaler = Pipeline([\\n\",\n    \"        (\\\"poly_features\\\", PolynomialFeatures(degree=90, include_bias=False)),\\n\",\n    \"        (\\\"std_scaler\\\", StandardScaler()),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"X_train_poly_scaled = poly_scaler.fit_transform(X_train)\\n\",\n    \"X_val_poly_scaled = poly_scaler.transform(X_val)\\n\",\n    \"\\n\",\n    \"sgd_reg = SGDRegressor(max_iter=1,\\n\",\n    \"                       tol=-np.infty,\\n\",\n    \"                       penalty=None,\\n\",\n    \"                       eta0=0.0005,\\n\",\n    \"                       warm_start=True,\\n\",\n    \"                       learning_rate=\\\"constant\\\",\\n\",\n    \"                       random_state=42)\\n\",\n    \"\\n\",\n    \"n_epochs = 500\\n\",\n    \"train_errors, val_errors = [], []\\n\",\n    \"for epoch in range(n_epochs):\\n\",\n    \"    sgd_reg.fit(X_train_poly_scaled, y_train)\\n\",\n    \"    y_train_predict = sgd_reg.predict(X_train_poly_scaled)\\n\",\n    \"    y_val_predict = sgd_reg.predict(X_val_poly_scaled)\\n\",\n    \"    train_errors.append(mean_squared_error(y_train, y_train_predict))\\n\",\n    \"    val_errors.append(mean_squared_error(y_val, y_val_predict))\\n\",\n    \"\\n\",\n    \"best_epoch = np.argmin(val_errors)\\n\",\n    \"best_val_rmse = np.sqrt(val_errors[best_epoch])\\n\",\n    \"\\n\",\n    \"plt.annotate('Best model',\\n\",\n    \"             xy=(best_epoch, best_val_rmse),\\n\",\n    \"             xytext=(best_epoch, best_val_rmse + 1),\\n\",\n    \"             ha=\\\"center\\\",\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.05),\\n\",\n    \"             fontsize=16,\\n\",\n    \"            )\\n\",\n    \"\\n\",\n    \"best_val_rmse -= 0.03  # just to make the graph look better\\n\",\n    \"plt.plot([0, n_epochs], [best_val_rmse, best_val_rmse], \\\"k:\\\", linewidth=2)\\n\",\n    \"plt.plot(np.sqrt(val_errors), \\\"b-\\\", linewidth=3, label=\\\"Validation set\\\")\\n\",\n    \"plt.plot(np.sqrt(train_errors), \\\"r--\\\", linewidth=2, label=\\\"Training set\\\")\\n\",\n    \"plt.legend(loc=\\\"upper right\\\", fontsize=14)\\n\",\n    \"plt.xlabel(\\\"Epoch\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"RMSE\\\", fontsize=14)\\n\",\n    \"save_fig(\\\"early_stopping_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.base import clone\\n\",\n    \"sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True, penalty=None,\\n\",\n    \"                       learning_rate=\\\"constant\\\", eta0=0.0005, random_state=42)\\n\",\n    \"\\n\",\n    \"minimum_val_error = float(\\\"inf\\\")\\n\",\n    \"best_epoch = None\\n\",\n    \"best_model = None\\n\",\n    \"for epoch in range(1000):\\n\",\n    \"    sgd_reg.fit(X_train_poly_scaled, y_train)  # continues where it left off\\n\",\n    \"    y_val_predict = sgd_reg.predict(X_val_poly_scaled)\\n\",\n    \"    val_error = mean_squared_error(y_val, y_val_predict)\\n\",\n    \"    if val_error < minimum_val_error:\\n\",\n    \"        minimum_val_error = val_error\\n\",\n    \"        best_epoch = epoch\\n\",\n    \"        best_model = clone(sgd_reg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(239,\\n\",\n       \" SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\\n\",\n       \"        eta0=0.0005, fit_intercept=True, l1_ratio=0.15,\\n\",\n       \"        learning_rate='constant', loss='squared_loss', max_iter=1,\\n\",\n       \"        n_iter=None, n_iter_no_change=5, penalty=None, power_t=0.25,\\n\",\n       \"        random_state=42, shuffle=True, tol=-inf, validation_fraction=0.1,\\n\",\n       \"        verbose=0, warm_start=True))\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_epoch, best_model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"t1a, t1b, t2a, t2b = -1, 3, -1.5, 1.5\\n\",\n    \"\\n\",\n    \"# ignoring bias term\\n\",\n    \"t1s = np.linspace(t1a, t1b, 500)\\n\",\n    \"t2s = np.linspace(t2a, t2b, 500)\\n\",\n    \"t1, t2 = np.meshgrid(t1s, t2s)\\n\",\n    \"T = np.c_[t1.ravel(), t2.ravel()]\\n\",\n    \"Xr = np.array([[-1, 1], [-0.3, -1], [1, 0.1]])\\n\",\n    \"yr = 2 * Xr[:, :1] + 0.5 * Xr[:, 1:]\\n\",\n    \"\\n\",\n    \"J = (1/len(Xr) * np.sum((T.dot(Xr.T) - yr.T)**2, axis=1)).reshape(t1.shape)\\n\",\n    \"\\n\",\n    \"N1 = np.linalg.norm(T, ord=1, axis=1).reshape(t1.shape)\\n\",\n    \"N2 = np.linalg.norm(T, ord=2, axis=1).reshape(t1.shape)\\n\",\n    \"\\n\",\n    \"t_min_idx = np.unravel_index(np.argmin(J), J.shape)\\n\",\n    \"t1_min, t2_min = t1[t_min_idx], t2[t_min_idx]\\n\",\n    \"\\n\",\n    \"t_init = np.array([[0.25], [-1]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure lasso_vs_ridge_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def bgd_path(theta, X, y, l1, l2, core = 1, eta = 0.1, n_iterations = 50):\\n\",\n    \"    path = [theta]\\n\",\n    \"    for iteration in range(n_iterations):\\n\",\n    \"        gradients = core * 2/len(X) * X.T.dot(X.dot(theta) - y) + l1 * np.sign(theta) + 2 * l2 * theta\\n\",\n    \"\\n\",\n    \"        theta = theta - eta * gradients\\n\",\n    \"        path.append(theta)\\n\",\n    \"    return np.array(path)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12, 8))\\n\",\n    \"for i, N, l1, l2, title in ((0, N1, 0.5, 0, \\\"Lasso\\\"), (1, N2, 0,  0.1, \\\"Ridge\\\")):\\n\",\n    \"    JR = J + l1 * N1 + l2 * N2**2\\n\",\n    \"    \\n\",\n    \"    tr_min_idx = np.unravel_index(np.argmin(JR), JR.shape)\\n\",\n    \"    t1r_min, t2r_min = t1[tr_min_idx], t2[tr_min_idx]\\n\",\n    \"\\n\",\n    \"    levelsJ=(np.exp(np.linspace(0, 1, 20)) - 1) * (np.max(J) - np.min(J)) + np.min(J)\\n\",\n    \"    levelsJR=(np.exp(np.linspace(0, 1, 20)) - 1) * (np.max(JR) - np.min(JR)) + np.min(JR)\\n\",\n    \"    levelsN=np.linspace(0, np.max(N), 10)\\n\",\n    \"    \\n\",\n    \"    path_J = bgd_path(t_init, Xr, yr, l1=0, l2=0)\\n\",\n    \"    path_JR = bgd_path(t_init, Xr, yr, l1, l2)\\n\",\n    \"    path_N = bgd_path(t_init, Xr, yr, np.sign(l1)/3, np.sign(l2), core=0)\\n\",\n    \"\\n\",\n    \"    plt.subplot(221 + i * 2)\\n\",\n    \"    plt.grid(True)\\n\",\n    \"    plt.axhline(y=0, color='k')\\n\",\n    \"    plt.axvline(x=0, color='k')\\n\",\n    \"    plt.contourf(t1, t2, J, levels=levelsJ, alpha=0.9)\\n\",\n    \"    plt.contour(t1, t2, N, levels=levelsN)\\n\",\n    \"    plt.plot(path_J[:, 0], path_J[:, 1], \\\"w-o\\\")\\n\",\n    \"    plt.plot(path_N[:, 0], path_N[:, 1], \\\"y-^\\\")\\n\",\n    \"    plt.plot(t1_min, t2_min, \\\"rs\\\")\\n\",\n    \"    plt.title(r\\\"$\\\\ell_{}$ penalty\\\".format(i + 1), fontsize=16)\\n\",\n    \"    plt.axis([t1a, t1b, t2a, t2b])\\n\",\n    \"    if i == 1:\\n\",\n    \"        plt.xlabel(r\\\"$\\\\theta_1$\\\", fontsize=20)\\n\",\n    \"    plt.ylabel(r\\\"$\\\\theta_2$\\\", fontsize=20, rotation=0)\\n\",\n    \"\\n\",\n    \"    plt.subplot(222 + i * 2)\\n\",\n    \"    plt.grid(True)\\n\",\n    \"    plt.axhline(y=0, color='k')\\n\",\n    \"    plt.axvline(x=0, color='k')\\n\",\n    \"    plt.contourf(t1, t2, JR, levels=levelsJR, alpha=0.9)\\n\",\n    \"    plt.plot(path_JR[:, 0], path_JR[:, 1], \\\"w-o\\\")\\n\",\n    \"    plt.plot(t1r_min, t2r_min, \\\"rs\\\")\\n\",\n    \"    plt.title(title, fontsize=16)\\n\",\n    \"    plt.axis([t1a, t1b, t2a, t2b])\\n\",\n    \"    if i == 1:\\n\",\n    \"        plt.xlabel(r\\\"$\\\\theta_1$\\\", fontsize=20)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"lasso_vs_ridge_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logistic regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure logistic_function_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"t = np.linspace(-10, 10, 100)\\n\",\n    \"sig = 1 / (1 + np.exp(-t))\\n\",\n    \"plt.figure(figsize=(9, 3))\\n\",\n    \"plt.plot([-10, 10], [0, 0], \\\"k-\\\")\\n\",\n    \"plt.plot([-10, 10], [0.5, 0.5], \\\"k:\\\")\\n\",\n    \"plt.plot([-10, 10], [1, 1], \\\"k:\\\")\\n\",\n    \"plt.plot([0, 0], [-1.1, 1.1], \\\"k-\\\")\\n\",\n    \"plt.plot(t, sig, \\\"b-\\\", linewidth=2, label=r\\\"$\\\\sigma(t) = \\\\frac{1}{1 + e^{-t}}$\\\")\\n\",\n    \"plt.xlabel(\\\"t\\\")\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=20)\\n\",\n    \"plt.axis([-10, 10, -0.1, 1.1])\\n\",\n    \"save_fig(\\\"logistic_function_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn import datasets\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"list(iris.keys())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \".. _iris_dataset:\\n\",\n      \"\\n\",\n      \"Iris plants dataset\\n\",\n      \"--------------------\\n\",\n      \"\\n\",\n      \"**Data Set Characteristics:**\\n\",\n      \"\\n\",\n      \"    :Number of Instances: 150 (50 in each of three classes)\\n\",\n      \"    :Number of Attributes: 4 numeric, predictive attributes and the class\\n\",\n      \"    :Attribute Information:\\n\",\n      \"        - sepal length in cm\\n\",\n      \"        - sepal width in cm\\n\",\n      \"        - petal length in cm\\n\",\n      \"        - petal width in cm\\n\",\n      \"        - class:\\n\",\n      \"                - Iris-Setosa\\n\",\n      \"                - Iris-Versicolour\\n\",\n      \"                - Iris-Virginica\\n\",\n      \"                \\n\",\n      \"    :Summary Statistics:\\n\",\n      \"\\n\",\n      \"    ============== ==== ==== ======= ===== ====================\\n\",\n      \"                    Min  Max   Mean    SD   Class Correlation\\n\",\n      \"    ============== ==== ==== ======= ===== ====================\\n\",\n      \"    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n\",\n      \"    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n\",\n      \"    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n\",\n      \"    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n\",\n      \"    ============== ==== ==== ======= ===== ====================\\n\",\n      \"\\n\",\n      \"    :Missing Attribute Values: None\\n\",\n      \"    :Class Distribution: 33.3% for each of 3 classes.\\n\",\n      \"    :Creator: R.A. Fisher\\n\",\n      \"    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n\",\n      \"    :Date: July, 1988\\n\",\n      \"\\n\",\n      \"The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\n\",\n      \"from Fisher's paper. Note that it's the same as in R, but not as in the UCI\\n\",\n      \"Machine Learning Repository, which has two wrong data points.\\n\",\n      \"\\n\",\n      \"This is perhaps the best known database to be found in the\\n\",\n      \"pattern recognition literature.  Fisher's paper is a classic in the field and\\n\",\n      \"is referenced frequently to this day.  (See Duda & Hart, for example.)  The\\n\",\n      \"data set contains 3 classes of 50 instances each, where each class refers to a\\n\",\n      \"type of iris plant.  One class is linearly separable from the other 2; the\\n\",\n      \"latter are NOT linearly separable from each other.\\n\",\n      \"\\n\",\n      \".. topic:: References\\n\",\n      \"\\n\",\n      \"   - Fisher, R.A. \\\"The use of multiple measurements in taxonomic problems\\\"\\n\",\n      \"     Annual Eugenics, 7, Part II, 179-188 (1936); also in \\\"Contributions to\\n\",\n      \"     Mathematical Statistics\\\" (John Wiley, NY, 1950).\\n\",\n      \"   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n\",\n      \"     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n\",\n      \"   - Dasarathy, B.V. (1980) \\\"Nosing Around the Neighborhood: A New System\\n\",\n      \"     Structure and Classification Rule for Recognition in Partially Exposed\\n\",\n      \"     Environments\\\".  IEEE Transactions on Pattern Analysis and Machine\\n\",\n      \"     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n\",\n      \"   - Gates, G.W. (1972) \\\"The Reduced Nearest Neighbor Rule\\\".  IEEE Transactions\\n\",\n      \"     on Information Theory, May 1972, 431-433.\\n\",\n      \"   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\\\"s AUTOCLASS II\\n\",\n      \"     conceptual clustering system finds 3 classes in the data.\\n\",\n      \"   - Many, many more ...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(iris.DESCR)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = iris[\\\"data\\\"][:, 3:]  # petal width\\n\",\n    \"y = (iris[\\\"target\\\"] == 2).astype(np.int)  # 1 if Iris-Virginica, else 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"log_reg = LogisticRegression(solver=\\\"liblinear\\\", random_state=42)\\n\",\n    \"log_reg.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x10bfe7cf8>]\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\\n\",\n    \"y_proba = log_reg.predict_proba(X_new)\\n\",\n    \"\\n\",\n    \"plt.plot(X_new, y_proba[:, 1], \\\"g-\\\", linewidth=2, label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.plot(X_new, y_proba[:, 0], \\\"b--\\\", linewidth=2, label=\\\"Not Iris-Virginica\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The figure in the book actually is actually a bit fancier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure logistic_regression_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\\n\",\n    \"y_proba = log_reg.predict_proba(X_new)\\n\",\n    \"decision_boundary = X_new[y_proba[:, 1] >= 0.5][0]\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 3))\\n\",\n    \"plt.plot(X[y==0], y[y==0], \\\"bs\\\")\\n\",\n    \"plt.plot(X[y==1], y[y==1], \\\"g^\\\")\\n\",\n    \"plt.plot([decision_boundary, decision_boundary], [-1, 2], \\\"k:\\\", linewidth=2)\\n\",\n    \"plt.plot(X_new, y_proba[:, 1], \\\"g-\\\", linewidth=2, label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.plot(X_new, y_proba[:, 0], \\\"b--\\\", linewidth=2, label=\\\"Not Iris-Virginica\\\")\\n\",\n    \"plt.text(decision_boundary+0.02, 0.15, \\\"Decision  boundary\\\", fontsize=14, color=\\\"k\\\", ha=\\\"center\\\")\\n\",\n    \"plt.arrow(decision_boundary, 0.08, -0.3, 0, head_width=0.05, head_length=0.1, fc='b', ec='b')\\n\",\n    \"plt.arrow(decision_boundary, 0.92, 0.3, 0, head_width=0.05, head_length=0.1, fc='g', ec='g')\\n\",\n    \"plt.xlabel(\\\"Petal width (cm)\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Probability\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"center left\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 3, -0.02, 1.02])\\n\",\n    \"save_fig(\\\"logistic_regression_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.61561562])\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"decision_boundary\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 0])\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg.predict([[1.7], [1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure logistic_regression_contour_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = (iris[\\\"target\\\"] == 2).astype(np.int)\\n\",\n    \"\\n\",\n    \"log_reg = LogisticRegression(solver=\\\"liblinear\\\", C=10**10, random_state=42)\\n\",\n    \"log_reg.fit(X, y)\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(\\n\",\n    \"        np.linspace(2.9, 7, 500).reshape(-1, 1),\\n\",\n    \"        np.linspace(0.8, 2.7, 200).reshape(-1, 1),\\n\",\n    \"    )\\n\",\n    \"X_new = np.c_[x0.ravel(), x1.ravel()]\\n\",\n    \"\\n\",\n    \"y_proba = log_reg.predict_proba(X_new)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 4))\\n\",\n    \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"bs\\\")\\n\",\n    \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"g^\\\")\\n\",\n    \"\\n\",\n    \"zz = y_proba[:, 1].reshape(x0.shape)\\n\",\n    \"contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"left_right = np.array([2.9, 7])\\n\",\n    \"boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\\n\",\n    \"\\n\",\n    \"plt.clabel(contour, inline=1, fontsize=12)\\n\",\n    \"plt.plot(left_right, boundary, \\\"k--\\\", linewidth=3)\\n\",\n    \"plt.text(3.5, 1.5, \\\"Not Iris-Virginica\\\", fontsize=14, color=\\\"b\\\", ha=\\\"center\\\")\\n\",\n    \"plt.text(6.5, 2.3, \\\"Iris-Virginica\\\", fontsize=14, color=\\\"g\\\", ha=\\\"center\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.axis([2.9, 7, 0.8, 2.7])\\n\",\n    \"save_fig(\\\"logistic_regression_contour_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='multinomial',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='lbfgs',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = iris[\\\"target\\\"]\\n\",\n    \"\\n\",\n    \"softmax_reg = LogisticRegression(multi_class=\\\"multinomial\\\",solver=\\\"lbfgs\\\", C=10, random_state=42)\\n\",\n    \"softmax_reg.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure softmax_regression_contour_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x0, x1 = np.meshgrid(\\n\",\n    \"        np.linspace(0, 8, 500).reshape(-1, 1),\\n\",\n    \"        np.linspace(0, 3.5, 200).reshape(-1, 1),\\n\",\n    \"    )\\n\",\n    \"X_new = np.c_[x0.ravel(), x1.ravel()]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"y_proba = softmax_reg.predict_proba(X_new)\\n\",\n    \"y_predict = softmax_reg.predict(X_new)\\n\",\n    \"\\n\",\n    \"zz1 = y_proba[:, 1].reshape(x0.shape)\\n\",\n    \"zz = y_predict.reshape(x0.shape)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 4))\\n\",\n    \"plt.plot(X[y==2, 0], X[y==2, 1], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n    \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n    \"\\n\",\n    \"from matplotlib.colors import ListedColormap\\n\",\n    \"custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\\n\",\n    \"\\n\",\n    \"plt.contourf(x0, x1, zz, cmap=custom_cmap)\\n\",\n    \"contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)\\n\",\n    \"plt.clabel(contour, inline=1, fontsize=12)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"center left\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 7, 0, 3.5])\\n\",\n    \"save_fig(\\\"softmax_regression_contour_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"softmax_reg.predict([[5, 2]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6.38014896e-07, 5.74929995e-02, 9.42506362e-01]])\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"softmax_reg.predict_proba([[5, 2]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 11.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 12. Batch Gradient Descent with early stopping for Softmax Regression\\n\",\n    \"(without using Scikit-Learn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by loading the data. We will just reuse the Iris dataset we loaded earlier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = iris[\\\"target\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need to add the bias term for every instance ($x_0 = 1$):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_with_bias = np.c_[np.ones([len(X), 1]), X]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And let's set the random seed so the output of this exercise solution is reproducible:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(2042)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The easiest option to split the dataset into a training set, a validation set and a test set would be to use Scikit-Learn's `train_test_split()` function, but the point of this exercise is to try understand the algorithms by implementing them manually. So here is one possible implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"test_ratio = 0.2\\n\",\n    \"validation_ratio = 0.2\\n\",\n    \"total_size = len(X_with_bias)\\n\",\n    \"\\n\",\n    \"test_size = int(total_size * test_ratio)\\n\",\n    \"validation_size = int(total_size * validation_ratio)\\n\",\n    \"train_size = total_size - test_size - validation_size\\n\",\n    \"\\n\",\n    \"rnd_indices = np.random.permutation(total_size)\\n\",\n    \"\\n\",\n    \"X_train = X_with_bias[rnd_indices[:train_size]]\\n\",\n    \"y_train = y[rnd_indices[:train_size]]\\n\",\n    \"X_valid = X_with_bias[rnd_indices[train_size:-test_size]]\\n\",\n    \"y_valid = y[rnd_indices[train_size:-test_size]]\\n\",\n    \"X_test = X_with_bias[rnd_indices[-test_size:]]\\n\",\n    \"y_test = y[rnd_indices[-test_size:]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The targets are currently class indices (0, 1 or 2), but we need target class probabilities to train the Softmax Regression model. Each instance will have target class probabilities equal to 0.0 for all classes except for the target class which will have a probability of 1.0 (in other words, the vector of class probabilities for ay given instance is a one-hot vector). Let's write a small function to convert the vector of class indices into a matrix containing a one-hot vector for each instance:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def to_one_hot(y):\\n\",\n    \"    n_classes = y.max() + 1\\n\",\n    \"    m = len(y)\\n\",\n    \"    Y_one_hot = np.zeros((m, n_classes))\\n\",\n    \"    Y_one_hot[np.arange(m), y] = 1\\n\",\n    \"    return Y_one_hot\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's test this function on the first 10 instances:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 1, 1, 0, 1, 1, 1, 0])\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_train[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 0., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [0., 0., 1.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [1., 0., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [1., 0., 0.]])\"\n      ]\n     },\n     \"execution_count\": 70,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"to_one_hot(y_train[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looks good, so let's create the target class probabilities matrix for the training set and the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"Y_train_one_hot = to_one_hot(y_train)\\n\",\n    \"Y_valid_one_hot = to_one_hot(y_valid)\\n\",\n    \"Y_test_one_hot = to_one_hot(y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's implement the Softmax function. Recall that it is defined by the following equation:\\n\",\n    \"\\n\",\n    \"$\\\\sigma\\\\left(\\\\mathbf{s}(\\\\mathbf{x})\\\\right)_k = \\\\dfrac{\\\\exp\\\\left(s_k(\\\\mathbf{x})\\\\right)}{\\\\sum\\\\limits_{j=1}^{K}{\\\\exp\\\\left(s_j(\\\\mathbf{x})\\\\right)}}$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def softmax(logits):\\n\",\n    \"    exps = np.exp(logits)\\n\",\n    \"    exp_sums = np.sum(exps, axis=1, keepdims=True)\\n\",\n    \"    return exps / exp_sums\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are almost ready to start training. Let's define the number of inputs and outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = X_train.shape[1] # == 3 (2 features plus the bias term)\\n\",\n    \"n_outputs = len(np.unique(y_train))   # == 3 (3 iris classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now here comes the hardest part: training! Theoretically, it's simple: it's just a matter of translating the math equations into Python code. But in practice, it can be quite tricky: in particular, it's easy to mix up the order of the terms, or the indices. You can even end up with code that looks like it's working but is actually not computing exactly the right thing. When unsure, you should write down the shape of each term in the equation and make sure the corresponding terms in your code match closely. It can also help to evaluate each term independently and print them out. The good news it that you won't have to do this everyday, since all this is well implemented by Scikit-Learn, but it will help you understand what's going on under the hood.\\n\",\n    \"\\n\",\n    \"So the equations we will need are the cost function:\\n\",\n    \"\\n\",\n    \"$J(\\\\mathbf{\\\\Theta}) =\\n\",\n    \"- \\\\dfrac{1}{m}\\\\sum\\\\limits_{i=1}^{m}\\\\sum\\\\limits_{k=1}^{K}{y_k^{(i)}\\\\log\\\\left(\\\\hat{p}_k^{(i)}\\\\right)}$\\n\",\n    \"\\n\",\n    \"And the equation for the gradients:\\n\",\n    \"\\n\",\n    \"$\\\\nabla_{\\\\mathbf{\\\\theta}^{(k)}} \\\\, J(\\\\mathbf{\\\\Theta}) = \\\\dfrac{1}{m} \\\\sum\\\\limits_{i=1}^{m}{ \\\\left ( \\\\hat{p}^{(i)}_k - y_k^{(i)} \\\\right ) \\\\mathbf{x}^{(i)}}$\\n\",\n    \"\\n\",\n    \"Note that $\\\\log\\\\left(\\\\hat{p}_k^{(i)}\\\\right)$ may not be computable if $\\\\hat{p}_k^{(i)} = 0$. So we will add a tiny value $\\\\epsilon$ to $\\\\log\\\\left(\\\\hat{p}_k^{(i)}\\\\right)$ to avoid getting `nan` values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 5.446205811872683\\n\",\n      \"500 0.8350062641405651\\n\",\n      \"1000 0.6878801447192402\\n\",\n      \"1500 0.6012379137693314\\n\",\n      \"2000 0.5444496861981872\\n\",\n      \"2500 0.5038530181431525\\n\",\n      \"3000 0.47292289721922487\\n\",\n      \"3500 0.44824244188957774\\n\",\n      \"4000 0.4278651093928793\\n\",\n      \"4500 0.41060071429187134\\n\",\n      \"5000 0.3956780375390374\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"eta = 0.01\\n\",\n    \"n_iterations = 5001\\n\",\n    \"m = len(X_train)\\n\",\n    \"epsilon = 1e-7\\n\",\n    \"\\n\",\n    \"Theta = np.random.randn(n_inputs, n_outputs)\\n\",\n    \"\\n\",\n    \"for iteration in range(n_iterations):\\n\",\n    \"    logits = X_train.dot(Theta)\\n\",\n    \"    Y_proba = softmax(logits)\\n\",\n    \"    loss = -np.mean(np.sum(Y_train_one_hot * np.log(Y_proba + epsilon), axis=1))\\n\",\n    \"    error = Y_proba - Y_train_one_hot\\n\",\n    \"    if iteration % 500 == 0:\\n\",\n    \"        print(iteration, loss)\\n\",\n    \"    gradients = 1/m * X_train.T.dot(error)\\n\",\n    \"    Theta = Theta - eta * gradients\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And that's it! The Softmax model is trained. Let's look at the model parameters:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 3.32094157, -0.6501102 , -2.99979416],\\n\",\n       \"       [-1.1718465 ,  0.11706172,  0.10507543],\\n\",\n       \"       [-0.70224261, -0.09527802,  1.4786383 ]])\"\n      ]\n     },\n     \"execution_count\": 75,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's make predictions for the validation set and check the accuracy score:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9666666666666667\"\n      ]\n     },\n     \"execution_count\": 76,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits = X_valid.dot(Theta)\\n\",\n    \"Y_proba = softmax(logits)\\n\",\n    \"y_predict = np.argmax(Y_proba, axis=1)\\n\",\n    \"\\n\",\n    \"accuracy_score = np.mean(y_predict == y_valid)\\n\",\n    \"accuracy_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well, this model looks pretty good. For the sake of the exercise, let's add a bit of $\\\\ell_2$ regularization. The following training code is similar to the one above, but the loss now has an additional $\\\\ell_2$ penalty, and the gradients have the proper additional term (note that we don't regularize the first element of `Theta` since this corresponds to the bias term). Also, let's try increasing the learning rate `eta`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 6.629842469083912\\n\",\n      \"500 0.5339667976629505\\n\",\n      \"1000 0.5036400750148942\\n\",\n      \"1500 0.49468910594603216\\n\",\n      \"2000 0.4912968418075476\\n\",\n      \"2500 0.48989924700933296\\n\",\n      \"3000 0.4892990598451198\\n\",\n      \"3500 0.4890351244397859\\n\",\n      \"4000 0.4889173621830818\\n\",\n      \"4500 0.4888643337449303\\n\",\n      \"5000 0.4888403120738818\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"eta = 0.1\\n\",\n    \"n_iterations = 5001\\n\",\n    \"m = len(X_train)\\n\",\n    \"epsilon = 1e-7\\n\",\n    \"alpha = 0.1  # regularization hyperparameter\\n\",\n    \"\\n\",\n    \"Theta = np.random.randn(n_inputs, n_outputs)\\n\",\n    \"\\n\",\n    \"for iteration in range(n_iterations):\\n\",\n    \"    logits = X_train.dot(Theta)\\n\",\n    \"    Y_proba = softmax(logits)\\n\",\n    \"    xentropy_loss = -np.mean(np.sum(Y_train_one_hot * np.log(Y_proba + epsilon), axis=1))\\n\",\n    \"    l2_loss = 1/2 * np.sum(np.square(Theta[1:]))\\n\",\n    \"    loss = xentropy_loss + alpha * l2_loss\\n\",\n    \"    error = Y_proba - Y_train_one_hot\\n\",\n    \"    if iteration % 500 == 0:\\n\",\n    \"        print(iteration, loss)\\n\",\n    \"    gradients = 1/m * X_train.T.dot(error) + np.r_[np.zeros([1, n_outputs]), alpha * Theta[1:]]\\n\",\n    \"    Theta = Theta - eta * gradients\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because of the additional $\\\\ell_2$ penalty, the loss seems greater than earlier, but perhaps this model will perform better? Let's find out:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1.0\"\n      ]\n     },\n     \"execution_count\": 78,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits = X_valid.dot(Theta)\\n\",\n    \"Y_proba = softmax(logits)\\n\",\n    \"y_predict = np.argmax(Y_proba, axis=1)\\n\",\n    \"\\n\",\n    \"accuracy_score = np.mean(y_predict == y_valid)\\n\",\n    \"accuracy_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Cool, perfect accuracy! We probably just got lucky with this validation set, but still, it's pleasant.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's add early stopping. For this we just need to measure the loss on the validation set at every iteration and stop when the error starts growing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 4.7096017363419875\\n\",\n      \"500 0.5739711987633519\\n\",\n      \"1000 0.5435638529109128\\n\",\n      \"1500 0.5355752782580262\\n\",\n      \"2000 0.5331959249285545\\n\",\n      \"2500 0.5325946767399382\\n\",\n      \"2765 0.5325460966791898\\n\",\n      \"2766 0.5325460971327978 early stopping!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"eta = 0.1 \\n\",\n    \"n_iterations = 5001\\n\",\n    \"m = len(X_train)\\n\",\n    \"epsilon = 1e-7\\n\",\n    \"alpha = 0.1  # regularization hyperparameter\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"Theta = np.random.randn(n_inputs, n_outputs)\\n\",\n    \"\\n\",\n    \"for iteration in range(n_iterations):\\n\",\n    \"    logits = X_train.dot(Theta)\\n\",\n    \"    Y_proba = softmax(logits)\\n\",\n    \"    xentropy_loss = -np.mean(np.sum(Y_train_one_hot * np.log(Y_proba + epsilon), axis=1))\\n\",\n    \"    l2_loss = 1/2 * np.sum(np.square(Theta[1:]))\\n\",\n    \"    loss = xentropy_loss + alpha * l2_loss\\n\",\n    \"    error = Y_proba - Y_train_one_hot\\n\",\n    \"    gradients = 1/m * X_train.T.dot(error) + np.r_[np.zeros([1, n_outputs]), alpha * Theta[1:]]\\n\",\n    \"    Theta = Theta - eta * gradients\\n\",\n    \"\\n\",\n    \"    logits = X_valid.dot(Theta)\\n\",\n    \"    Y_proba = softmax(logits)\\n\",\n    \"    xentropy_loss = -np.mean(np.sum(Y_valid_one_hot * np.log(Y_proba + epsilon), axis=1))\\n\",\n    \"    l2_loss = 1/2 * np.sum(np.square(Theta[1:]))\\n\",\n    \"    loss = xentropy_loss + alpha * l2_loss\\n\",\n    \"    if iteration % 500 == 0:\\n\",\n    \"        print(iteration, loss)\\n\",\n    \"    if loss < best_loss:\\n\",\n    \"        best_loss = loss\\n\",\n    \"    else:\\n\",\n    \"        print(iteration - 1, best_loss)\\n\",\n    \"        print(iteration, loss, \\\"early stopping!\\\")\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1.0\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits = X_valid.dot(Theta)\\n\",\n    \"Y_proba = softmax(logits)\\n\",\n    \"y_predict = np.argmax(Y_proba, axis=1)\\n\",\n    \"\\n\",\n    \"accuracy_score = np.mean(y_predict == y_valid)\\n\",\n    \"accuracy_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Still perfect, but faster.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's plot the model's predictions on the whole dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x0, x1 = np.meshgrid(\\n\",\n    \"        np.linspace(0, 8, 500).reshape(-1, 1),\\n\",\n    \"        np.linspace(0, 3.5, 200).reshape(-1, 1),\\n\",\n    \"    )\\n\",\n    \"X_new = np.c_[x0.ravel(), x1.ravel()]\\n\",\n    \"X_new_with_bias = np.c_[np.ones([len(X_new), 1]), X_new]\\n\",\n    \"\\n\",\n    \"logits = X_new_with_bias.dot(Theta)\\n\",\n    \"Y_proba = softmax(logits)\\n\",\n    \"y_predict = np.argmax(Y_proba, axis=1)\\n\",\n    \"\\n\",\n    \"zz1 = Y_proba[:, 1].reshape(x0.shape)\\n\",\n    \"zz = y_predict.reshape(x0.shape)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 4))\\n\",\n    \"plt.plot(X[y==2, 0], X[y==2, 1], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n    \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n    \"\\n\",\n    \"from matplotlib.colors import ListedColormap\\n\",\n    \"custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\\n\",\n    \"\\n\",\n    \"plt.contourf(x0, x1, zz, cmap=custom_cmap)\\n\",\n    \"contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)\\n\",\n    \"plt.clabel(contour, inline=1, fontsize=12)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 7, 0, 3.5])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now let's measure the final model's accuracy on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9333333333333333\"\n      ]\n     },\n     \"execution_count\": 82,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits = X_test.dot(Theta)\\n\",\n    \"Y_proba = softmax(logits)\\n\",\n    \"y_predict = np.argmax(Y_proba, axis=1)\\n\",\n    \"\\n\",\n    \"accuracy_score = np.mean(y_predict == y_test)\\n\",\n    \"accuracy_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our perfect model turns out to have slight imperfections. This variability is likely due to the very small size of the dataset: depending on how you sample the training set, validation set and the test set, you can get quite different results. Try changing the random seed and running the code again a few times, you will see that the results will vary.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {},\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "05_support_vector_machines.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 5 – Support Vector Machines**\\n\",\n    \"\\n\",\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 5._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/05_support_vector_machines.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"mpl.rc('axes', labelsize=14)\\n\",\n    \"mpl.rc('xtick', labelsize=12)\\n\",\n    \"mpl.rc('ytick', labelsize=12)\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"svm\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Large margin classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\\n\",\n       \"  kernel='linear', max_iter=-1, probability=False, random_state=None,\\n\",\n       \"  shrinking=True, tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn import datasets\\n\",\n    \"\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = iris[\\\"target\\\"]\\n\",\n    \"\\n\",\n    \"setosa_or_versicolor = (y == 0) | (y == 1)\\n\",\n    \"X = X[setosa_or_versicolor]\\n\",\n    \"y = y[setosa_or_versicolor]\\n\",\n    \"\\n\",\n    \"# SVM Classifier model\\n\",\n    \"svm_clf = SVC(kernel=\\\"linear\\\", C=float(\\\"inf\\\"))\\n\",\n    \"svm_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure large_margin_classification_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x194.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Bad models\\n\",\n    \"x0 = np.linspace(0, 5.5, 200)\\n\",\n    \"pred_1 = 5*x0 - 20\\n\",\n    \"pred_2 = x0 - 1.8\\n\",\n    \"pred_3 = 0.1 * x0 + 0.5\\n\",\n    \"\\n\",\n    \"def plot_svc_decision_boundary(svm_clf, xmin, xmax):\\n\",\n    \"    w = svm_clf.coef_[0]\\n\",\n    \"    b = svm_clf.intercept_[0]\\n\",\n    \"\\n\",\n    \"    # At the decision boundary, w0*x0 + w1*x1 + b = 0\\n\",\n    \"    # => x1 = -w0/w1 * x0 - b/w1\\n\",\n    \"    x0 = np.linspace(xmin, xmax, 200)\\n\",\n    \"    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\\n\",\n    \"\\n\",\n    \"    margin = 1/w[1]\\n\",\n    \"    gutter_up = decision_boundary + margin\\n\",\n    \"    gutter_down = decision_boundary - margin\\n\",\n    \"\\n\",\n    \"    svs = svm_clf.support_vectors_\\n\",\n    \"    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\\n\",\n    \"    plt.plot(x0, decision_boundary, \\\"k-\\\", linewidth=2)\\n\",\n    \"    plt.plot(x0, gutter_up, \\\"k--\\\", linewidth=2)\\n\",\n    \"    plt.plot(x0, gutter_down, \\\"k--\\\", linewidth=2)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12,2.7))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(x0, pred_1, \\\"g--\\\", linewidth=2)\\n\",\n    \"plt.plot(x0, pred_2, \\\"m-\\\", linewidth=2)\\n\",\n    \"plt.plot(x0, pred_3, \\\"r-\\\", linewidth=2)\\n\",\n    \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n    \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 5.5, 0, 2])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_svc_decision_boundary(svm_clf, 0, 5.5)\\n\",\n    \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\")\\n\",\n    \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 5.5, 0, 2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"large_margin_classification_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Sensitivity to feature scales\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure sensitivity_to_feature_scales_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\\n\",\n    \"ys = np.array([0, 0, 1, 1])\\n\",\n    \"svm_clf = SVC(kernel=\\\"linear\\\", C=100)\\n\",\n    \"svm_clf.fit(Xs, ys)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12,3.2))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \\\"bo\\\")\\n\",\n    \"plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \\\"ms\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf, 0, 6)\\n\",\n    \"plt.xlabel(\\\"$x_0$\\\", fontsize=20)\\n\",\n    \"plt.ylabel(\\\"$x_1$  \\\", fontsize=20, rotation=0)\\n\",\n    \"plt.title(\\\"Unscaled\\\", fontsize=16)\\n\",\n    \"plt.axis([0, 6, 0, 90])\\n\",\n    \"\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"scaler = StandardScaler()\\n\",\n    \"X_scaled = scaler.fit_transform(Xs)\\n\",\n    \"svm_clf.fit(X_scaled, ys)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \\\"bo\\\")\\n\",\n    \"plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \\\"ms\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf, -2, 2)\\n\",\n    \"plt.xlabel(\\\"$x_0$\\\", fontsize=20)\\n\",\n    \"plt.title(\\\"Scaled\\\", fontsize=16)\\n\",\n    \"plt.axis([-2, 2, -2, 2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"sensitivity_to_feature_scales_plot\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Sensitivity to outliers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure sensitivity_to_outliers_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA1gAAAC7CAYAAAB4pz8ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XdYVNfWwOHfAQTELiCI3diuNQqW2HtFnUk0pphY8qUnN90UY9QbY9qNxnRjTIzRNK+ZsXfFrhF71yh2RcCGjbq/P44cGJozw9DX+zzzwJyyzz4ksliz915HU0ohhBBCCCGEECLn3PK7A0IIIYQQQghRVEiCJYQQQgghhBAuIgmWEEIIIYQQQriIJFhCCCGEEEII4SKSYAkhhBBCCCGEi0iCJYQQQgghhBAuIgmWEEIIIYQQQrhIniVYmqZ5aZo2XdO0k5qmxWqatkvTtD7ZHP+KpmkXNE27pmnaj5qmeeVVX4UQQhQ9EoeEEELkhbwcwfIATgOdgHLAu8CfmqbVTH+gpmm9gLeAbkANoDYwPq86KoQQokiSOCSEECLXaUqp/Lu4pu0Bxiul5qbb/itwQin1zp333YDZSqnAfOimEEKIIkrikBBCCFfLtzVYmqYFAPWA/ZnsbgTsTvN+NxCgaZpvXvRNCCFE0SdxSAghRG7wyI+LappWApgN/KyUOpTJIaWBq2nep3xfBohJ19ZTwFMApUqVCm7QoIHrOyyEEKJA2L59e7RSyj+n7eRWHHJ3dw+uWrUq5cqVo0SJEjnqY2xsLEeOHEl7HcqUKUP58uUpX758jtsXQgjhGHtjUJ4nWJqmuQG/APHAC1kcdh0om+Z9yvex6Q9USn0PfA8QEhKiwsPDXddZIYQQBYqmaSdd0EauxSFN09TJkyfRNI127dphMpkwm83Url3bqb6eOHECq9WKxWJhw4YNXLt2jWvXrnH69GlOnjxJtWrVnGpXCCGE4+yNQXm6BkvTNA34EagJ9FVK3criuF+BCKXU6DvvuwK/3m3uuyRYQghRtGmatl0pFZKD83M1DtWsWVM1atSIlStXEh8fb2xv2rSpkWw1a9YMvRuOiYqKYsGCBVgsFs6fP0/aePfggw/SoEEDTCYTzZs3d6p9IYQQ2bM3BuV1gvUdcC/QXSl1PZvjegMzgK7AOeAv4G+l1FvZtS8JlhBCFG0uSLDyJA5du3aNJUuWYLVaWbRoEbGxqQNfNWvWxGw2YzKZaNeuHe7u7g7fR3JyMm5u+jLqY8eOUadOHWNfjRo1MJlMmEwm2rdvj4dHvqwGEEKIIqfAJViaptUATgBxQGKaXU8D64EDQEOl1Kk7x78KvAmUBOYCzyil4rK7hiRYQghRtOUkwcqvOBQXF8fq1auxWq3MmzePyMhIY5+/vz8DBgzAZDLRvXt3vL29Hb6v+Ph4wsLCsFgsWK1WLly4YOzz9fVl+fLltGjRwuF2hRBC2CpwCVZekARLCCGKtpyOYOW2u8WhpKQktmzZgsViwWKxcPz4cWNf6dKl6dOnD2azmb59+1KuXDmHr5+cnMzWrVuNdVtnzpwhOjoaHx8fAKZMmYKfnx/9+vWjfPnyjt+gEEIUY5JgCSGEKHIKe4KVllKKffv2GSNPO3fuNPaVKFGCrl27YjabGTBgAJUrV3a4L0opzp49S9WqVQF9JM3f35/Y2Fg8PDzo0qULZrOZgQMHEhQU5HD7QghR3EiCJYQQosgp6AlWkyZN1N69e506N6VioNVqZf369SQnJwN6efY2bdpgNpsxm802660ccfPmTaZPn47FYmHdunUkJSUZ+1q3bs2kSZNo27atU20LIURxYG8MyrcHDQtR0C1fvpw+ffrg6+uLt7c39erV48033+Ty5ctOtff555/z119/Zdg+bty4DBW/NE1j3LhxTl1HCJF/9u3bR9OmTRk7diy7du3CkQ8xa9asycsvv0xYWBgXLlxg+vTp9O/fH09PTzZv3syoUaOoW7cujRs3ZsyYMezYscOh9n18fHjxxRdZvXo1kZGRzJgxg4EDB+Lt7c3WrVttpgyuWbOGbdu2OdS+EEIInYxgCZGJiRMnMnr0aEwmE48//jgVK1Zk+/btfPzxx5QpU4Y1a9Y4/PyZmjVr0r59e2bNmmWzfdy4cYwfP97mD5ktW7ZQtWpVY2qPEEJX0Eew3N3dVcrIE+j/7lPKs3fo0MGp8unXr19n6dKlWK1WFi5cyNWrqc8/rl69utG+sxUDb9y4wdq1a+nTp4/Rv5CQELZv307VqlWNioQdO3aUhxsLIYo1mSIohJPWrFlDt27deOmll5g8ebLNvoiICIKDg2nWrBlr1qxxqF1HEixXiouLw8vLK1faFgVfYCCkKVpnCAiANMXmCo2CnmAFBwerDz74AIvFYlMxsE6dOhw5csRIYOLj4/H09HS4/bQVA+fNm8f58+eNfb6+vvTv3x+z2UyPHj0oWbKkU/eQlJTESy+9hNVq5ezZs8b2ChUq0L9/f1544QVatmzpVNtCiOKnKMUhmSIohJM++eQTKlasyIcffphhX61atXjrrbcICwtj69atnDhxAk3TmDFjhs1xYWFhaJpGWFgYoCdXJ0+eZPbs2WiahqZpDB8+PMs+ZDZFcPfu3QwYMIAKFSpQsmRJ2rVrx/r1622OGT58OFWrVmXz5s20bduWkiVLMmrUKGd+DKKIyCyoZbdd5IymafTu3ZupU6dy7tw5Nm7cyOuvv86zzz5rJFenT5/Gz8+PBx98kF9//dVmROpuPD096dmzJ99++y1nzpyxmToYExNjTPvz8/PjgQceYNasWQ5Pa3Z3d+err77i1KlTbN26lbfffpsGDRpw+fJlZs6cyYkTJ4xjT506xaVLlxxqXwhRvBTHOCQJlhBpJCYmsnbtWnr06JHl82gGDBgAwOrVq+1u12KxEBgYSK9evdi8eTObN29mzJgxdp+/Y8cO2rZty6VLl5g2bRpz587F19eX7t27s337dptjr169ykMPPcTDDz/MkiVLeOSRR+y+jhDCddzc3Gjbti2ffvopr776qrF93bp1xMbGMmfOHB599FH8/f2NpCztiJQ97bdp04aPP/6Yw4cPs3//fiZMmEBwcDA3b97kr7/+4rHHHqNSpUr06NGDb775xmZEyp72W7VqxcSJEzl48CAHDx7kww8/pHfv3sYxY8aMoVKlSnTr1o2vvvqKM2fO2N2+EEIUVfJ4dyHSiImJ4datW9SsWTPLY1L2nT592u52mzdvjpeXF35+frRp08bhfr3xxhtUr16d1atXG9OKevXqRePGjXn//fexWq3GsdevX2fWrFkMHDjQ4esIIXLfo48+Srt27WwqBi5btoxly5bx8ssvExMTYzy3yl6aptGwYUMaNmzI6NGjOX36tPEsrHXr1rFy5UpWrlzJ888/T+vWrY11W/Xr17f7Gg0aNOCtt96y2Xbr1i1A/8Bp9erVvPjii4SEhGA2mxk8eDB169Z16D6EEKIokBEsIQq4W7dusXbtWgYPHoybmxuJiYkkJiailKJ79+6sW7fO5vgSJUoQGhqaT70VQtgjfcXAH3/8kf79+9OvXz8juUpMTKRDhw68++67bN++3aF1mtWqVcu2YmDKtL+GDRvyzjvvOF0x8M8//+TixYvMnDkTs9lMyZIlCQ8PZ/To0TbrTW/fvk3a4h9CCFGUSYIlRBopJdnTrjFIL2Wfo1UEnXXp0iWSkpJ4//33KVGihM3rq6++4vLlyzZ/uPj7++Pu7p4nfRNC5Jy/vz8jRoxg/vz5zJkzx9i+bt06NmzYwAcffEBISAg1a9bkpZdeIiwsjMTERLvb9/X1ZdiwYVitVqKjo42pg+XLlzem/bVq1Yrq1avzwgsvsGrVKhISEuxuv2LFijz22GP89ddfREdHY7VaGT58OIMGDTKO+eKLL6hWrRrPPfccK1ascKh9IYQobCTBEiINDw8POnXqxIoVK7h9+3amx8yfPx+Arl27Guu04uPjbY6JiYlxWZ/Kly+Pm5sbL774Itu2bcv05eaW+k/ZmTLQougKCHBse4rAQNC0jK/AQNf3UaRK+++3ffv2LF++nGeffZbKlStz6tQpvvjiC7p06UJgYKBD05RTlCpVCrPZzMyZM7l48aIxbbBKlSqcOXOGr7/+mu7duxMQEMDjjz+OxWLh5s2bdrfv4+PDwIED+emnn2jSpImxffPmzZw7d45vv/2Wnj17UqlSJYYOHcrcuXO5ceOGw/chhCg8imMckgRLiHRef/11YmJieOeddzLsi4iI4OOPP6Zjx460bt2agIAAvLy82Ldvn81xixYtynCul5eXsV7BEaVKlaJDhw7s3r2bFi1aEBISkuElRFYuXAClMr7uVhq3OFZ9Kmg8PT2N4hRnzpxhy5YtvPnmm9SrV4/SpUvbPCfvnXfecbhiYIkSJYziFKdOneLvv/+2qRj4yy+/cP/99+Pn54fJZOLnn392umLgX3/9ZUwdbNSoEVeuXGH27NkMGjSIkSNHOtWmEKJwKI5xSIpcCJFO9+7dGT9+PGPHjuXEiRM8/vjjVKhQgR07dvDRRx9Rrlw5fvnlF0D/tHnIkCFMnz6devXqUb9+fRYtWmSUZ0+rYcOGrF+/noULFxIYGIifn1+2xTTSmjRpEh07dqRXr1488cQTVK5cmejoaHbs2EFSUhIfffSRC38CQoiCxs3NjdatW9O6dWs+/PBDYmJijNGu8+fPG4+V8PDwoHPnzpjNZgYOHEiVKlXsbr9ly5a0bNmSiRMncujQIaNIxt9//828efOYN28e7u7udOrUyXj4sL1TpTVNIzg4mODgYCZMmMDRo0exWCxYrVZMJpNx3NKlS/noo48wm82YTCZq1Kjh4E9KCCEKAKVUkXkFBwcrIVxlyZIlqmfPnqp8+fLK09NT1alTR73++usqJibG5rjLly+roUOHKl9fX1WhQgX19NNPq4ULFypArVmzxjju4MGDqn379qpkyZIKUMOGDVNKKTV27Fil/1NMBaixY8fabDtw4IAaMmSI8vf3V56enqpKlSqqf//+atGiRcYxw4YNU1WqVHHpz0EUT5l/3qi/8rdfhKsCEG+yeuVHHLp8+bL68ssvVdeuXZW7u7sCjFfr1q3Vvn37ctT+6dOn1ddff626d++uPDw8bNoPCQlREyZMUPv371fJyck5vpf/+7//s2m/RYsW6v3331d79+51SftCiMKjIMYhe2OQppyoGlRQhYSEqPDw8PzuhhBCFHrZLeXLz7Chadp2pVSBnReb33EoJiaGhQsXYrVaWbZsGQkJCVy8eJEKFSoA+jP5qlatSkhIiFPrNS9fvmy0v3TpUpv1WfXq1TNGnlq1amWzNtReV65cYfHixVgsFpYsWWKzPqtv376ZTr8WQhRNBTEO2RuDJMESQgiRQUEMbCAJliNu3LjBjh076NChAwBJSUkEBQVx8eJFqlSpYjwLq2PHjpQoUcLh9m/evMmKFSuwWq3Mnz/fZn1WUFAQAwcOxGQy0blzZ+P5fY64ffs2K1euxGKxMH/+fJ588kkmTpwI6NVcP/roI0wmE127dnWqfSFEwVYQ45AkWEIIIZwWGJj5QuKAgLsvTM5NkmA579q1a7zzzjtYrVbOnj1rbK9QoQKhoaG88cYbNpX/HJGYmMj69euNdVtpKxyWK1eO0NBQzGYzvXr1onTp0k61f/v2bePcyZMn8+qrrwJQtmxZ+vXrh8lkok+fPpQpU8apexBCFCwFMQ5JgiWEuKukpCR5ZlYB42xAyctAlJ9BTxKsnFNKER4ejsViwWKxcOjQIQC2bt1Kq1atADh48CABAQFUrFjRqfZ37NhhJFv79+839nl7e9OjRw/MZjP9+/fHz8/PqXs4dOgQv/32GxaLhb179xrbPT096d+/P3PmzJFHVgjhhMIQg/LjeikkwRJCZGvPnj20adOGl156ifHjx8sUmwLC2SkReTmVIj+nbUiC5XqHDh1iyZIlvPTSS8a6qc6dO7NhwwanKgamd/ToUSPZ2rx5s7Hdzc2NDh065Lhi4LFjx4z2N23aRP/+/Zk3bx6gP6Pw22+/ZcCAAdSqVcup9oUoTgpDDMqP66VeVxIsIUQWEhISaNy4MUeOHMHHx4egoCDmzJnDvffem99dK/YKQ3CTBCtrRSEOJSUlERoaysqVK0lMTDS2BwcHYzabefjhh6ldu7ZTbZ8/f5558+ZhtVpZvXo1CQkJxr7mzZtjNpsxm800atTIqRGoCxcucO3aNerVqwfA8uXL6dWrFwDNmjUz2m/SpImMcAmRicIQg/LjeqnXlQRLCJGF9957j88++8ymAljJkiV5+eWXGTdunIxm5aPCENwkwcpaUYpDly9fZtGiRVgsFpuKgT/++CMjRowAIDY2llKlSuWoYqDVamXx4sU2FQPvueceIxlq06aNU+0DhIeH89///pfFixcTGxtrbK9duzYmk4kPPvgAb29vp9oWoigqDDEoP66Xel0XJ1iapvkA9wKVAJvfdEqpv5zppKsVpcAmRG45e/YsNWrUICkpKcM+Gc3Kf4UhuEmClbWiGodu3brFihUrsFgsfPLJJ/j7+wPw/PPPY7VaGThwIGazmU6dOrmkYmB0dLSxLyAgwGjf2YqBcXFxrFq1CovFwrx584iKiuKee+7h6NGjxkjW+vXradWqFV5eXg63L0RRURhiUH5cL/W6LkywNE3rDvwG+GayWymlCsQq+aIa2IRwpaSkJEaPHs0XX3zBrVu3Mj1GRrPyT2EIbpJgZa24xaG2bdvarKtKqRhoMpno3bu3UxUDk5KS2Lhxo1GE4+TJk8a+smXL0rdvX8xms9MVA5OSkti8eTOXL1+mf//+AJw5c4Zq1apRunRpo/2+fftStmxZh9sXojArDDEoP66Xel07Y5A9TyMG9gMzgCB7js+mnReAcCAOmJHNccOBJOB6mlfnu7UfHBzsxDOZhSietm/frmrXrq18fHwUkOHl4+Oj6tSpo3bu3JnfXS2wAgIyf8J8QIDzbWb35Prsrufmlvk+N7esr+XMObl13/YCwlUBjUGqGMah5ORkFR4ert59913VqFEjm98hr732mkva37lzp3rvvfdUkyZNbNr39PRUffv2VdOmTVORkZE5us7ff/+tmjVrZtN+iRIlVO/evdXUqVNVbGxsju9FiNzg6t/HeRmDlCp8ccjeGGTvCNYNoKlS6thdD86+nfuBZKAXUFIpNTyL44YD/6eUau9I+8Xtk0Mhcio+Pp4xY8bw5Zdf5mg0SylVLBeM58YnaLnxY8yqLwXxIY53k5MRrNyOQSBxKKVioNVq5ZNPPqFdu3YATJ06lV9//dWoGFizZk2n2k9fMTDlbxg3NzfatWtnPDzZ2YqBERERRvsbNmxAKUWJEiWIjo42RrNiYmLw9c1sQo8Qec/Vv8fzMgbd7XoFMQ65eorgcuBzpdRiF3VuAlBVEiwhCoYdO3YwePBgLly4YFP4IkV2a7Pmzp3LmDFj2LVrV7GbTigJVt5zxRTB3IpBIHEoK71792bZsmXG++bNmxvJUOPGjZ36gCYyMpL58+djsVhYtWoV8fHxxr5mzZoZ7Tdt2tSp9i9evMiCBQs4ffo048aNA/QHHgcGBlK5cmWjCMe9995bLD9gEgWDJFh5K8cJlqZpLdK8rQlMACYBe4GEtMcqpXY42Dl7gtvXwC3gEvAL8KFSKjGTY58CngKoXr16cNq52kII+9k7mpX2uVlRUVHUqVOH27dvM2HCBN5444087nX+kgQr7+VhgmVXDLpzvMShu8iuYuDw4cP56aefctT+tWvXWLJkCRaLJUPFwFq1ahnJVtu2bXP0cPWDBw/Spk0brl27ZmyrUaOG0X779u3l4e0iT0mClbdckWAlo89DvtuPWikHi1zYEdxq37n2SaAR8Afwi1Lqw+zalU8Ohcg5R0azxowZw7Jly0hISKBUqVJEREQY1cWKA0mw8l4eJVhOxSCQOGSP27dvGxX95s+fz/vvv8/TTz8NwJYtW/jpp58wmUx07drVqYp+cXFxrF692qgYePHiRWOfv78/AwYMwGw2061bN6dKtMfHx7NmzRqj/QsXLhj7wsPDCQ4OBorv1GmRtyTByluuSLBq2HsxpZRDH9fdLbhlcvxDwBtKqeDsjpPAJoRr2DOa5e3tjZubm5GEeXp68sgjj+T4k+jCRBKsvJcXCVYmx9sVg0DikKOSkpJITEw0EqmXX36ZKVOmAK6rGLhlyxajIuHx48eNfaVLl6ZPnz5GxcBy5co53H5ycjJbt27FarUSHh7OypUrjaQqpYqi2WymX79+lC9f3uH2hbgbSbDylqvXYHUENqWfHqFpmgfQVim1zsHOORrchgBvKqVaZHecBDYhXOtuo1nplSxZkq1bt9KkSZM86F3+CwyEyMiM2wMCIM2H2g7JLtgEBGR9PXC8L+7ukJyccbubG2TymLQCIZ8SLLtiEEgcyql9+/YxZ84crFYre/bsMbZ7enoybNgwvv/+e6fbVkqxd+9eo4jFrl27jH0lSpSgW7dumEwmBg4cSGBgYI7uIyYmhkqVKpF85x+Yh4cHXbp0wWw2M3DgQIKCgnLUvhApXB2H8jIGQeGLQ/bGIHsfjb4GqJjJ9nJ39tnbKQ9N07wBd8Bd0zTvO0la+uP6aJoWcOf7BsAYYJ691xFCuEaLFi04ePAgL7zwAiVLlrzr8bdv3+bJJ5/Eng9u8kNgoB480r+c/VvqwoXMi9neLai5u2feD3f37ArkZt9mVFTm2yMjs77npKTMr1MQg5orSAwq+Bo3bsz48ePZvXs3//zzD5999hnt27cnISHBZjrfxYsXmTRpEhEREXa3rWkaTZs25b333mPnzp0cP36cyZMn07FjR5KSkli6dCnPPPMMQUFBtGvXjk8//ZR//vnHqfvw9fXl+PHjTJkyhc6dO5OcnMyKFSt47rnnqFKlCgsXLnSqXVG4uToGgevjkJuba2NQVFT2911U45C9I1jJQIBSKird9nro9eDtehKfpmnjgLHpNo8HfgQOAA2VUqc0Tfsv8BhQGogEZgHvK6USyIZ8cihE7rF3NKtUqVLMnj2bgQMH5mHv7FNQpiLkxoMcnVFA8+Bs5bBM+zhyMQaBxKHcEhkZSWJiIlWqVAFg2rRpPPXUUwA0bdrUqOjnbMXAqKgoFixYgMViYcWKFcTFxRn7GjdubJSXb968uVPtR0dHs3DhQiwWC2vWrOHEiRNUrKh/bv3BBx9w8+ZNzGYzwcHBsm6rCCsoMQic60teTx8siFwyRVDTtPl3vu0HrER/OGMKd6AxcFAp1TsHfXUZCWxC5K6zZ89Sq1YtEhKy/zuzcuXKnDhxosCVbS8owU0SLOe5YopgbpI4lDfWrVvHN998k2nFwEGDBvHxxx87najExsaybNkyLBYLCxcuzLRioMlkon379nh4ZBgAvau4uDhjzVlSUhJVqlQh8s7cqmrVqhntd+zY0an2RcFVUGIQSILlLFdNEYy589KAy2nexwBngO+AoTnrqhCisHjmmWfsOu7q1at8/vnnudwbIURx1bFjR37//XeioqJYvHgxTz75JJUqVSIiIoLt27cbyVVycjJLlizh9u3bdrddpkwZBg0axOzZs4mKimLZsmU888wzBAYGcvLkSaZMmUKXLl0IDAxk5MiRLFiwIMtiQJlJXxnxl19+4bnnniMoKIjTp0/z5Zdf0q1bNwICAvjzzz/tblcIUXDYO0VwLPBfpdSNux6cj+STQyFyz5w5cxg+fLhdxS6AAlm2vaB8eigjWM6TESyRlZSKgUop2rfXnxG9adMm2rVr5/KKgRaLhaNHjxr7SpUqRe/evXNUMTA5OZlt27YZ7R8+fJjNmzfTpk0bABYvXkx0dDShoaHG9EJRuBSUGAQyguUsl1YRLCwksAmRezp16sTmzZvx8fFBKcXNmzdJTMz0uatAwSzbXlCCmyRYzpMESzhi5cqVjBo1ip07dxrb0lYMHDFihFNTmZVSHDhwwEiGtm/fbuxzVcXAQ4cOUa9ePdzc9MlGXbp0ISwsDHd3dzp37mysC0tZlyYKvoISg0ASLGe54jlYEegPWrwrpVRtx7qXOySwCZF7lFLExMRw4sQJTpw4QUREBIcPH+bw4cOcOHGCixcvommaMf3l5s2bJCUlsWfPHvz9d3P8+Gji4k7h5VWd2rU/ICDg0Wyvlxsl0LNrM7Pt2bnzN0+W5WWz25fZ9rv1JSBAr8bk6PWyu46zP8f8JAmWcMaJEyewWq1YrVbWr19PcnIygYGBnD171khgzp0753QydOrUKaP9devWkZSmBFrr1q2NIhz16tVz+h6+++47/ve//xEWFmbTfsuWLRk1ahSDBg1yum2ROVfHIWdjUFZxI7fiUGacjUFubuDv7/p4nl9ckWC9luZtaeBV4G9g851t9wGtgM+UUv/JWXddQwKbEPknswTsyJEjPPxwLTw8PiA5OXVqoZubD/Xrf59tkpXXn/QV9sJdhe1TQGdJgiVyKqViYFxcHM8++yygP7cqICCAf/3rXzmuGBgTE8OCBQuwWq0sW7bMZv1XSvs5qRh46dIlFi1ahMViYenSpdy6dYsffviBJ554AoAjR45w5coVQkJCjORROCcv45DEoMLB1Q8angEcUUpNTLf9baCRUqpAFLqQwCZEwbN5c03i4k5m2O7lVYP77juR5XmSYDlGglvBIHGocFq3bh0DBgzg6tWrxraUioFms5l27do5VdHvxo0bNhUJr1y5YuyrWrWq0b6zFQNv3rzJ8uXL6dChA76+vgC8+OKLfPXVV1SpUoWBAwdiNpvp1KkTJUqUcLj94k4SLPtJDEp3nJ0J1jWghVLqn3Tb6wA77H0OVm6TwCZEwRMW5kbms401OnfOeo6CJFiOkeBWMEgcKrzi4+MJCwvDYrEwb948zp8/D+gPKD5//jwBAQGAXozCmZGhhIQE1q5di8ViwWq1cu7cOWNfxYoVCQ0NxWw207NnT3x8fJy+jwkTJvD9999z+vRpY1v58uUJDQ3l8ccfp0ePHk63XdxIgmU/iUG27P0NcQPonMn2zoB9JcWESGvGDP23SefOGffVrKnvCwtzvN1x4/Rzhw/PSe/3agxmAAAgAElEQVSEC3l5VXdouxBC5AdPT0969uzJt99+y5kzZ9i8eTOjRo1i6NChRnKllKJRo0ZGGfe0I1J3U6JECbp3787XX3/N6dOn2bp1K2+99Rb169fn0qVLzJw5E7PZjJ+fH2azmZkzZ3Lp0iWH7+Pdd9/l5MmTbNu2jdGjR9OwYUOuXLnCrFmzWLJkiXFcbGws0dHRDrcvhLg7exOsycDXmqZ9p2na8Duv74Av7+wr+oYPzzohEMVDyv8D48bld08Kldq1P8DNzfbTWDc3H2rX/iCfeiSEENlzc3OjTZs2fPzxx8ycOdPYfuDAAQ4dOsTcuXMZOnQo/v7+9OzZk2+++YazZ8861H6rVq348MMPOXToEAcPHmTixIm0bNmSW7duYbVaGTZsGJUqVaJbt2589dVXnDlzxu72NU0jJCSECRMmsH//fg4fPsxHH33EY489Zhwze/ZsAgMD6dKlC1988QWnTp2yu30hRPbsSrCUUp8AjwFNgEl3Xk2AYUqpj3Ove6LIKlcO6teH6jKKUdQFBDxK/frf4+VVA9Dw8qpx1wIX+nmObc9rbm6plZMc3ZfdvTm7TwiR+xo1asTJkyf54osv6NKlC0opVqxYwfPPP0/VqlVtyrU7okGDBrz99tv8/fffnD59mq+++opu3boBsHr1al588UWqVatGy5YtmThxIgcPHnSo/Xr16vHmm2/SvHlzY9uZM2fQNI2wsDBeeuklatSoQXBwsJGUiYLzO9fZWONMHMrqHIlBDlJKFZlXcHCwyjXDhikFSnXqlHvXELoaNfSf9Zo1jp87dqx+7rBhru2TUqn/D4wd6/q2RZ4ICND/E6Z/BQRkv8+Z9pztR27cW1EChKsCEG+yeuVqHBIFTnR0tPrpp5/UwIEDVfXq1VViYqKx77nnnlPvvPOO2rZtm0pOTnaq/ZiYGDVz5kxlNptVyZIlFfqCVgWo+vXrqzfffFNt2bJFJSUlOdX+5cuX1axZs9QDDzygfHx8jLZ79uxpHJOUlOR0+8KWq2PQ3drMjfPyqr2Cyt4YJPU7hRDFRlbPGYmMzH6fM+052w9n5UabQojs+fr6Mnz4cKxWK8ePH8fd3R2Aq1evMm3aNGPaX/Xq1XnxxRdZvXo1CQkJdrdfsWJFHnvsMf766y+io6ONqYMVK1bk8OHDfPzxx7Rp04Zq1arx3HPPsWLFCofaL1++PI8++ij/+9//iI6OZv78+YwYMcJmKuHGjRupUqUKzzzzDMuWLSM+Pt7+H5Cw4eoYdLc2c+O8vGqvsMsywdI07ZqmaX53vo+98z7TV951twDq3FlflzNjBly7BqNGwT33QMmSULs2vPcepHkGBqtWQa9e4OcHpUpBx46wfn3mbact2JCcDJMnQ7Nm+nm+vjBgAPz9d/b9u3ZNb6dZMyhdWn81bQpjx0KacrQZrF0LgwZB1arg6alP6atbF0wmmDo149PkYmPh/fchOBjKlNHPCQqCkBB44w3Yt8/2+OyKXKR16hT83/9BtWrg7Q21asHrr2ff97vZsAEeeki/Ny8v/WfZvTv89lvxKYMjhBDCpVKSK4BSpUqxePFinnvuOYKCgjhz5owx7S8gIIBVq1Y53L6Pjw8DBw5kxowZREZG2kwdPHfuHN9++y09e/akUqVKDB06lLlz53Ljxg272y9ZsiT9+/fnxx9/ZOjQ1KfvrFq1igsXLjB16lR69+6Nv78/jzzyCHPmzOH69esO34chMREiImDjRlizRv8aEaFvF6Kwy2poCxgGeN35fvid95m+7Bkqy4tXvkwR7NRJ3z5pklL16+vflyqlVIkSqeOj/fvrx379tVKappSbm1Jly6bu9/RUasOGjNdMme72+ONKmc369x4eSpUrl3quu7tSv/+eeZ+PHk2dbgdK+fjor5T31asrdeRIxvOmTrUd3/Xx0e8p7bZbt1KPv3JFqYYNU/e5uSlVoYL+NWXbm2/aXuOnnzL/eSqV2udp05Ty99e/L11aKW/v1Pbq1FHq3Lmsf2ZZTREcNcr2PsqW1f+bpLx/6CGlspoKIVMEC73Mpi/Y83KmPWf7kRv3VpQgUwRFIZOUlKS2bt2q3nrrLVW/fn0FqDNnzhj7Z8yYoX7++WcVExPjVPvJyclq27ZtavTo0aphw4Y20wi9vb3VgAED1I8//qiioqKcbn/Hjh1qzJgxqnHjxjbt33PPPY5Pf0xOVmrPHqXmztVff/6Z+krZtmePflwR4+oYdLc2c+O8vGqvoLI3BmU5gqWU+lkpFXfn+xl33mf6yo3Er9AZP17/un49XL+uv6ZNAw8PWLBAH915+WV46y2IidFHYE6cgPvug/h4eOWVrNueNw/mz4dJk/QRqStX4J9/oEcPSEqCESPg2DHbc+Lj4YEH4ORJffRn+fLUfq1cqReXOHUKzGaIi0s97+ZNeO01/fuRI/VjbtzQz4uJgSVL4OGHbVdBTpkCBw6Avz8sXKi3d+mSPnJ35Ah89JE+queo11/XR87Wr9dHyG7cAKtVH/375x8YNsyx9qZMgU8+0Vdjfv+9/nO8elVv9/ffITBQ//qx1G0RQgjhGukrBh4/fpwqVaoA+ofc48ePNyoGdu/e3SUVA1OmDt6+fZv58+czcuRIAgIC6NKlC1OmTOHkyYwPf8+u/ebNm/Of//yHvXv3cvToUT799FPatm1L37590e48wCkqKoouXbrw+eefc+LEicwbU0ofqTp6VP/7JSnJdn/KtqNH9eOUsrufQhQo9mRhwDvAfYCHPcfn1ytfR7A8PPQRo/RGjkxN4UeMyLj/xInUEZSTJ233pYzGgFITJmQ899at1FGzJ56w3Tdzpr69RAml9u7NeO6+famjbNOnp27fulUZo3BpFutmq08f/ZyPPrLveKXsG8Hy9s78Z7p6derPZf16231ZjWBdvpw6CrZrV+Z92rRJ/29RoYJScXEZ98sIVqEnI1iFHzKCJYqQhIQE9dVXX6nu3bsrDw8Pm9GhkJAQtXTp0hy1f/bsWfXtt9+qnj17Zmi/RYsW6v3331d79+51ughH2uIX06dPt2n/3nvvVePHj1e7d+9ObT9l5CrtqFVWr5SRrCJERrAKP3tjkL1FLvoAa4DLmqYt1zTtHU3T2mqa5uHKZK9QGzwY6tTJuL1799Tv33474/4aNVLPS79OKYWPjz76lZ63d+po09y5tp/0/O9/+teBA6Fx44znNmqkr7EC+PPP1O1ly+pfExL0ESt7pJxz56n3LvPgg5n/TLt0gbZt9e9T7vNu5s7VR+G6d9fXo2Xmvvv0NV6XL4OTpXYLi8BAfQlc+ldgYH73LOeyuzdnS6dnxdmStblR6lbK5wpR+Hh4ePD888+zYsUKLl68yMyZM7n//vvx8fEhPDzcZl3Xtm3b2LJlC8np10BnIygoyChOERUVxezZsxk0aBClSpVix44djBkzhiZNmlCvXj1GjRrFpk2bHGrfLc0v1UGDBvHbb7/x4IMPUrp0aXbt2sXYsWNp1qwZDRo0IP7mzdSRKyDwyf5oDw7O8Ap8sr/eYMpIViFbk5WXMSi7/XkdhyQG2bL3OVgdgAqAGdiKnnCtQk+4luVe9wqRJk0y316pkv7V2zvzZAFS/++7fDnz/SEhemGLzHTqpH+9ckVfHJpixw79a5cuWfe5a1fbY0EvZFG3rj7F8L779MIahw5lP0zft6/+9Ysv4LHH9GmEsbFZH2+v7ApgpNx32r5nZ9Mm/evq1fpvuaxep0/rx6V8LaKKcrWf7O4tq78bkpPhwoXMP3u7cCHrazlzTk7Oy+s2hRB5p0KFCjz22GPMnTuXqKgo5s2bR6eUWAf85z//4b777rOpGOhIRb/y5csbxSmioqJYsGABI0eOxM/Pj3/++YdPP/2Udu3aOV0xsGzZsjz00EP88ccfREVFsWjRIv7v//4Pf39/goKC8Lzzyzk5OZlXf/6ZyKurgbgM7URe9bbdUMjicV7GICg4cUhikC27y7QrpW4ppVYCXwHfAHMBL6BDLvWtcKlcOfPtKZ8+BQToH2Fkd0xWpVXvzNW+676oqIzfZ3du1ar615iY1ATK3R1+/VU/7/hxePVV+Ne/9HVPgwfra8HSJ1uPPw5PPaVvnzVLT7jKl4fmzfUqis6ObNlz32nvOTspfbh5M7UeamavlP8GN28612chhBAih3x8fBgwYAAlSpQwtv3rX//KsmLgunXrHGq/ZMmShIaGMn36dM6fP09YWBgvv/wyNWrUyLJiYKwDH5x6e3vTt29fpk2bxvnz5/n999/h3DlISmLbsWNMXrQI6Af4AUOA34FMilInJennCVHI2JVgaZr2oKZp32iadhA4DjwJHAV6oI9siYIqbYl4e4WE6MPys2bpyVPt2nrRiv/9T59y2K9fxoWpU6fqUxzfe08fefLygl279OIedevCihUuuR2npXxs9NJL9k15Hj48X7srhBBCpPXJJ59w8uRJwsPDGT16NI0aNeLq1avMnj2bnTt3GsdFRkYSHR1td7seHh506tSJyZMnExERwY4dO3jvvfdo0qQJ165dM6b9+fv7G0nZxYsX7W7f3d2dgIAAfWYMEFShAuMGDwaaAdeBP4GHAX+gL5Dug1N7n+slZd9FAWLvCNbvwAPAj4C/UqqrUmq8UmqtulNpUOSi7D69SbvP3z/j96dOZX1uSpUiX9+Mo2slS8Kjj8LPP+sVCo8f19eQaZo+BfC77zK216iRXk1xzRp9yuKCBfrUyRs39Ip/Djz8MMO9ZbUv7T1nJ2UaZnY/j7uZMUNPvsaNc74NIYQQwkmaphEcHMyECRPYt28fR44c4eOPP+b+++83jvn8888JCAigc+fOTlcMHD9+PHv27DEqBrZr1474+Hhj2l/lypXp2LGjkZTZxdMTgGp+fowdPBjYBRwDJqFPhkoAdgC+xil/bNrEsbslc0rB3r36DJudO/W/D6Kj9a87d+rb9+7NfqmDEC5mb4L1FLAceBE4p2naAk3TXtM0rYWmZTXvTbhMeHjWU9bWrtW/li+vF2hI0aKF/nXNmqzbXb3a9tjs1KoFEyfCkCG2182KpyeEhsKcOfr78+f1UTFHZHeNlH329B309WQAYWFw65Zj/RBCCCEKoLp16zJq1CiqVatmbLty5Qpubm6sXbuWl19+mZo1axpJ2cGDBx1qv06dOrz++uts2LCBc+fOMXXqVPr06YO7uzvr16/n1VdfpXbt2tx7771GUqaySmSCglKXRBhqA68A64ALwP9I+dP08vXrDP3yS+oMGULTpk0ZO3YsO3futG1fKSn7Lgoke4tc/KCUekwpVR0IBqxAS2AzYPc4tKZpL2iaFq5pWpymaTPucuwrmqZd0DTtmqZpP2qa5mXvdYqcGzf0ZzilFxenPxsL9IqAaXPdlAqBS5bon+Ckt39/agW+Bx9M3X63Ba0lS6Ze255zUo5Pf449/vhDHzlLb906/Rcl6OvC7DF4sF4o5PJl+M9/sj82q2IjRUhRrvaT3b0V5fsWdycxSBQH3377bZYVAydPnmwcl5CQ4FDFwMDAQJ566ikWL15MdHS0TcXA3bt3M27cOJo1a0adOnV47bXX2LBhA0lpE540SSBAQLn0SxgqAe2N7ddu3WLwffdRtmxZ9u7dy3/+8x9atGhB7dq1eeWVVzh37py+NOHixYyJVXpJSfpxWVVrdiGJQQIcKHKhaZqbpmmtgUHAg0AooAFHHLjeOWAC+lTD7K7VC3gL6AbUQP+IY7wD1ylaypWDMWP0JCtl9OX4cX091MGDeoXCt96yPWfIEGjaVP/eZNIfLpzyyc2qVXohioQEfVrfo4+mnrd4sT7aM22a/pDiFDdv6ttmz9bf9+qVuq97d/j3v/XEJ+3o0P79qWuZKlfOutJiVjw9oU+f1AqAycn6tMOU5LFHD2jXzr62fH3hww/17z/6CJ58Un8Icopbt/QHGj/7bGoJ+PQ6d9aT2CKwPis3qv24u2demjbDB5Y5PAeyL4Ob3b1lty+rNrPqY1EoaV8MSQwSxULaioHR0dFGxcCHH37YOOaPP/4gKCiIp59+mqVLlzpdMTA6OtqmYuDx48eZNGkSHTp0ICgoiCeffJLFixcTl5Skr8m+8wv+wrQFqD/nZHhdmLYAgBqBgfz6zTdcvHiRJUuW8PTTTxMYGMiJEyeYMmUKbsnJxsjVgTNncHswNNOy7+5D7vzNkEXZd1fHIXBtDEopcixxqHCx6zlWmqYtAdoCJYHtQBj6pNkNSqkb9l5MKfXXnfZCgKrZHDoMmK6U2n/n+PeB2egBr/gZOFAve/7yy/DGG/pIzJUr+j53d/jpJ7jnHttzPD31Zz91764nSj166M/TgtTphtWrw19/6QUp0tqyRX+BPgLl7a1fLyVB69tXrxqY4to1+PJL/eXmpieEt26lFtjw8YFffgEPBx+b9t//wjvv6ElU6dL6L8eUBK5OHX19mCNefBGuXtULcfzwg/4qVUr/WV29mloIo2ZNx9oVQPblZ115DuROmfmszs2qL0WhpH1xIzFIFEfe3t6EhoYSGhpqs339+vVERkby/fff8/3331O2bFn69euHyWSiT58+lClTxq72vby86Nu3L3379uW7775j8+bNWCwWLBYLERER/PDDD/zwww+ULl2avn37Yv7Xv+hbpw5l0//tkZa7u/6Ym8aN8dI0evfuTe/evfnmm2/YunUr4eHhBN5Z162Uos/EiSjGoD9FyIxeobAcAMkq3UqW06dtllQUlDjkTHsShwoue0ewdqGPWlVQSt2nlHpbKbXMkeTKQY2A3Wne7wYCNE3zzeL4ok3T9LVMkybpJdPj46FCBX2N06ZN8NBDmZ9Xpw7s3q0nFGkfNty4sT4itmcP1Ktne07XrnoyNGyYPuLk46Mnd76+epI2c6Y+ipQ2WfrhB724RZcuetKWkgQ1aAAvvKAPyXfr5vh916mjrz8bOVJP2pKS9OTntdf07VmVxs/Ou+/qP5OnntI/SUtO1qdgVq6sj8p98ok+kiWEKM4kBoki77vvvsu0YuCQIUMyJGP2cnd3p3379nz22WccO3bMZurg9evX+fPPP3l4/Hj8Roygz4cf8v2qVUSmfGAM+t8W7u56fG7XLkMBLjc3N+677z5efPFFo+x7dGwsvmXKADfQ13A9il6RsBfwHTYrWaTsu8gjWpaLEXPzopo2AaiqlBqexf5jwPNKqaV33pcA4oFaSqkT6Y59Cr0IB9WrVw92pFpOgTdunJ64DBumV7ATogDLrtxNVr9mnDknJ+dlx5lyPbJeOu9pmrZdKRWSwzZcFoPu7C+6cUgUG//88w/z5s3DYrFgNpt57bXXANi1axf//ve/MZlMmM1maqUtqOWAiIgIrFYrFouFDRs2GMUqNE3jvoYNMXfqhHnwYO5p396+GS9r1ujVAu/QHmyFXiLAAqwHUoag1qD+1Eu/34yLw6dqVX3Kf8p5BSQOOVsyTuJQ3rI3Btm9BiuPXQfKpnmf8n2Gp9wppb5XSoUopUL87S3ZLYQQQmTN7hgEEodE0ZC2OMWrr75qbLdYLKxfv57XXnvN/oqBmahVqxavvPIK69at48KFC/zwww/069ePEiVKsGn/ft745hvqdOlCk+bNee+99zJWDEzvTtn3VDWAl9BXsUSiL7V8CGhvHDFo0iQajRjBu+++y/bt2x3qvxCOKKgJ1n70J9ClaAZEKqVi8qk/Qgghig+JQaJYS/sEntdee43ff/+dIUOGUKZMGZtpf61bt3YqSalUqRJPPPEECxcuJDo6Wp86+PDDlC1bln379vH+++/TokULatWqxcsvv8zatWttKxJCFmXfU/gBI4DfSCk3EJ+YyI6ICA6cOMEHH3xASEgINWrUAP4NrAHkgcTCdfI0wdI0zUPTNG/AHXDXNM1b07TMxoFnAk9omtZQ07TywLvAjDzsqhDCQW5Z/DbJaruz50DulLrN6tys+iJldQsfiUFCOK5s2bIMGTKE33//naioKBYvXsyTTz5JpUqVaNiwoZGMxcbG8vTTT+sVAx14LEuZMmUYPHgwv/76K1FRUSxdutSoGHjy5EmmTJlC586dCQwMZOTIkSxYsIDbt29nKPvupmWe6KVs9/Tw4PTUqSxfsoRnn32WypUrc/r0aeBLoCswzfa8PI5DUt69aMnTNViapo0DxqbbPB59HPcA0FApderOsa8Cb6JXLpwLPKOUyvZfbEhIiAoPD3d1t/OPrMESQggbOVmDldsxCIpgHBIiC0lJScTGxlK+fHkA/vzzT4YMGQKQWjHQbKZv376ULVs2u6YylZyczNatW42KhP/884+xr1SpUvTp0wdTcDD9qlShvLf33RtMKZ5x55ExycnJbNu2DYvFgtVqZcWKFcYDm8eNG8eePXswm82EhoZSoUIFh/sviiZ7Y1C+FLnILRLYhBCiaHNFkYvcJHFIFFfHjx9n1qxZWCwWdu3aZWz39PSke/fuWCwWPDOsm7KPUooDBw4YydaOHTuMfR7u7nRt3BhTy5YMDAkhqGLFjA2klH3PpDJhZurVq8fRo0f19j086Ny5M2azmYEDB1KlShWn7kEUDTlOsDRNiwXsyr6UUo5/NJELJLAJIUTRJgmWEAVfSsVAq9XKhg0baNGiBdu2bQP0ZOn777+ne/fu3JP+GZ53k5gIp09zascOrGFhWDZuZN3u3SSneWhVm7p1MbdqhallS+pVr66X2atbV39EjZ2l+k6fPm1UVEy//mvixIm8/fbbjvVbFBmuSLCG2XsxpZSDT3zNHRLYhBCiaJMES4jC5eLFi5w/f55mzfS6Mfv376fxnWdzNm7cGLPZjNls5t5777UprmFDKf2ZmndGlUiT8ETfuMHC8HAs+/ezfPNmbqdZ/9Wwbl3MgwZhuv9+goODs24/G5cuXWLhwoVYLBaWLVuG1WqlZ8+eAMyfP58tW7ZgMpkICQnB7W4Lt0ShJ1MEhRBCFDmSYAlRuB04cIAJEyawaNEirl27ZmyvUaMGJpOJsWPH2q55Ugo2boSLF20Sqwzc3blRpgzLrl3DYrWycOFCrqR5iHG1atWMZ3l16NABD3uetZXOzZs38fT0NM41mUzMmzcPgCpVqmAymTCZTHTq1IkSJUo43L4o+CTBEkIIUeRIgiVE0RAfH8+aNWuwWCzMmzePCxcuUK5cOS5evGis1dq2bRtNNA3vU6eyT65SpClkkZCQQFhYmDFV8dy5c8ZhFStWpH///pjNZnr06IGPj49T97Bu3TrmzJmD1WrlzJkzxvby5cszatQomUpYBLk0wdI0zRMYDTwMVAds0nKlVFYPIshTEtiEEKJokwRLiKInpWJgREQEjzzyCKCPFvn5+eGmFH3uvRdTy5b0a9GCuIo7OF7qN+LcYvBK9qX2jYcJiOuQ2pi7OwwYAGlGqFIqBlqtViwWC4cPHzb2+fj40KtXL8xmM/369aNiZkUy7kIpRXh4uNH+wYMHmTRpEq+88goAhw8fZsuWLYSGhuLr6+vkT0kUBK5OsD4GhgAfApPRnwlSE/0R2WOUUlNz1FsXkcAmhBBFmyRYQhQPR44c4ZEHHmD7vn3GNg93N5o3V7Rrr+jWDUqXBjflSf3Yp1OTLHd3aN4catXKsu2DBw8a5dlTim/op7obFQNNJpPTFQMPHz5MxYoV8ff3B2D06NFMnDgRd3d3OnbsaFQkrF69ulPti/zj6gQrAnhWKbX0TnXBe5VSxzRNexboppQalPMu55wENiGEKNokwRKiGNm4kVN79mD9+28s27ax7uB+UgoGzp4NQUH69wlXK9Ij/rvU84KC9JLsdkipGGi1WgkLC7OpGNiyZUujCEeDBg2cvo05c+Ywbdo01qxZQ2JiorE9ODiY4cOH88ILLzjdtshbrk6wbgINlFKnNE07D4QqpbZrmlYL2C1l2oUQQuQFSbCEKEbWrIHoaOPtvBIPsnkLHDkC//536mHDh0OppKpGefbg1q3RunRx+HLpKwbeunXL2Fe/fn0j2XK2YuDly5dZtGgRVquVJUuWcPPmTUaOHMn06dMBiI2NZf/+/bRq1UoqEhZQrk6wDgHDlVJbNE1bDyxRSk3UNO0RYLJSKiDnXc45CWxCCFG0SYIlRDGycSOkKU6xueJzxLlH2xxy5Qo8/phG7PXUv2erVaqEacgQTCYTHTt2dLpi4PLly7FYLCxYsIDLly8b+6pUqcLAgQMxm81OVwy8desWK1asoFq1ajRv3hyAX3/9lUcffZTKlSsb7Xfu3NnpBzQL13N1gvUhcF0p9YGmaYOA34AzQBXgU6XU6Jx22BUksAkhRNEmCZYQxUhEBOzcaVQQjPRaz+EyU0nW4omLAy8vfQ1W7cv/x6GdFbH8/TfW8HDOXbpkNLFgwQJCQ0Nz1I2EhATWr19vrNtKXzEwNDQUs9lMr169KFWqlNPX+fnnn3nvvfc4deqUsa1cuXL069eP+++/nwceeCBH9yFyLlfLtGua1hpoBxxRSi10on+5QgKbEEIUbZJgCVGMJCbC/Pk2Jdojvdbzzeaf+Hradf6a5Uv9+EdsqggmaxrhQUFYFixg+fLlbNiwgZIlSwIwYsQIrly5gtlsJjQ01OmKgdu3b8disRgVA1N4e3vTs2dPzGYz/fv3d6pioFKKnTt3GhUJ990p8tG6dWu2bNliHBMTE4Ofn5/D7YuccfUIVkdgk1IqMd12D6CtUmqd0z11IQlsQghRtEmCJUQxs3cvHD1qJFmJSUnUeO45oq5d44sRI3imZ8/UY9M8Byu9+Ph4/Pz8iI2NvXOoXjEw5eHAVatWdap7hw8fNpKhrVu3pumKOx06dDAqEjpbMfCff/7BarVSpUoVHn74YQB2795NixYtaN++vdF+zZo1nWpfOMbVCVYSUFkpdTHddl/gojwHSwghRF6QBEuIYkYpfS3WxYuQlMTMtWt5fvp0rt++jV+ZMpydOhVPDw89uapUSSDH6bQAACAASURBVK8eqGmZNnX69Gnmz5+PxWLJUDFw+vTpjBw5MkddPXfuHPPmzcNisWSoGNiiRQujSEbDhg3RsuijPX755ReeeOIJEhISjG333nuv0X7jxo1z1L7ImqsTrGQgQCkVlW57PSBcqggKIYTIC5JgCVEMKQX79pF46BA1nn6ac3cKTpTy8uK/w4bxTK9e+shV48ZZJlfpXbp0iUWLFmGxWFi6dCk7d+6kfv36AEydOpWIiAjMZjMtW7Z0umLg4sWLsVgsRsXAFHXr1sVkMmE2m2ndurVT7V+9etWm/evXrwNQoUIFIiMjjcIbSilJtlzIJQmWpmnz73zbD1gJxKXZ7Q40Bg4qpXrnoK8uI4FNCCGKNkmwhCi+Zv70E8+/8ALX0yQrfhUqcPbMGTx9fJxu99atW8Y6LYDmzZuza9cuAIKCgoxkKCcVA1euXInFYmH+/PnExMQY+wIDA42KgV26dHGqYuDt27dZtWoVVquVMmXKMGnSJEAv+96kSRNjXVjXrl3x8vJyuH2RylUJ1k93vh0G/AncSrM7HjgBTFNKRVMASGATQoiiTRIsIYqnxMREatSowbk0ZdsBSpUqxX//+1+eeeYZl11r9erVWVYM/Oijj3j66aedbjsxMZGNGzcaRTIyqxhoMpno06cPpUuXztF9zJs3D5PJZLwvU6YMffv2xWw206dPH8qWLRAT0AoVV08RHAv8Vyl1wxWdyy0S2IQQomiTBEuI4mnmzJk8//zzxlS4tPz8/Dh79qzLnxeVUjEwpYjFgQMHmDt3Lvfffz8AmzZt4siRI4SGhjpV0U8pxa5du4xkK6ViIICXlxc9evTAZDIxYMAA/P39nWp/z549Rvt79uyxaf/MmTNSidBBuVKmXdO0EOAeYKFS6oamaaWAuPTVBfOLBLbCJTJyNsePjyYu7hReXtWpXfsDAgIeze9uCSEKMEmwhKtIDCo8shq9SpEbo1iZOXLkCFWrVsXnznTEoUOHMnv2bNzc3OjYsaPLKgZaLBY2b95Myt/obm5uLqkYePz4caxWK1arldu3b/P3338DeiI2dOhQoxBH7dq1nWq/OHD1CFYAMA9oBSigrlLquKZpU4HbSqmXctphV5DAVnhERs7m8OGnSE5OnUft5uZD/frfS4ATQmRJEizhChKDCpfsRq9S5NYoVnZ++eUXZs2axerVqzNUDHzhhRcYMWKE021fuHDBqHi4atWqTCsGmkwmmjRp4lQRi/j4eONndeDAARo1amTsa9q0qdF+s2bNpEhGGvbGIHvLlkwGIgFf4Gaa7XOAnpmeIUQ2jh8fbRPYAJKTb3L8+Oh86pEQQojiQmJQ4ZGYmMjbb7+dbXIFeiGJH3/8MY96pXvsscdYtmwZUVFRzJ49m0GDBlGqVCl27NjB6dOnjePOnj3L5s2bSU5OtrvtwMBAnnrqKZYsWUJUVBS//fYbDz74IKVLl2bXrl2MHTuWZs2aUadOHV5//XU2btxoU3b+btImojVq1OD333/noYceokyZMuzZs4fx48fTvHlz7rnnHg4fPmx3u0Jn7whWJNBNKbVP07RYoNmdEaxawD6lVKnc7qg95JPDwiMszA19MDQ9jc6d7f8FJIQoXmQES7iCxKDCw57RqxT5MYqVXkrFwCZNmhhT+T788EPeeecdl1cMnDdvHlFRqU9QqlSpktG+sxUD4+LiWL16tdH+zZs3iYqKMtr65ptvqF69Ot27d8fb29vh9gs7V08RvAaEKKWOpEuwWgFLlFK+Oe9yzklgKzw2b65JXNzJDNu9vGpw330n8r5DQohCQRIs4QoSgwqHu629Ss/b25vJkyfn+losR33xxRdMmjSJkydT/59LqRj40EMP0b9/f6faTUpKYtOmTca6rYiICGOfKyoGJiUlcezYMerVqwfAzZs38fPz49atW5QuXZo+ffpgNpvp27cv5cqVc+oeChtXTxFcBwxP815pmuYOvAmscrx7orirXfsD3Nxsn1nh5uZD7dof5FOPhBBCFBcSgwqHCxcu4OnpSUBAAAEBAVSoUMEoMJHC3d3d2F+uXDmbSnwFxb///W8iIiLYsWMHY8aMoUmTJly9epVff/2VWbNmGcfFxcXZjEjdjbu7Ox06dOCzzz7j2LFjNlMHY2Nj+eOPP3jooYfw9/enb9++TJs2jcjISIfaT0muABISEnj77bdp3rw5169fZ86cOTzyyCP4+/vTu3dvdu/ebXfbRZ29I1gNgbXALqATsBBoBJQD2imljtl1MU2rCExHX7cVDbytlPo1k+PGAaOxfbBxU6XU8ezal08OC5fsKjhJdSch/r+9ew+Pqrr3P/7+hoQQLgICBsJVECiBiiCKSoEoAVRAElEL1VKrHiwq0lKreAWkKvLrRfSIVIvFAyKepxpULkIRAkiRq1iLF6gIFPCEixQFQrit3x8zGTLJJJmZTDKT5PN6nnk0+7Jm7W0yH9fstb9bAinrFSzlkORTBlU+a9asYdCgQRw5csS3LDU1la1bt0axV+HJrxjYrVs3+vXrB8C7775LZmZmxCsGfvjhh76KhGbGVVdd5Wu/Xbt2YbW/c+dO3nnnHbKysli9ejVnz55l+/btXHTRRQB8+OGHJCcn0759+7Daj1URL9NuZs2A0UB3PFe+NgMvOue+CaFTb3j3vRO4BFgIXOWc21pou4nARc6524JtGxRsVYWqO4lIcSIwwFIOSYmUQbGrKg2wAvnDH/7A+PHji60YePHFF4fV7v79+30VCZctW8bJkyd96374wx+SmZlJZmZm2BUDDxw4QHZ2NjfffLNvWZcuXdi6dSudO3f2td+tW7dKX5GwXJ6DVRbeZ2YdBro457Z5l80G9jrnxhfadiIKtmpLc+NFpDhlGWAphyQYyqDYVdUHWADfffcdixYtIisri0WLFvmKe3Tu3Nlv+uPZs2eJiwv2Tp9zvv/+exYvXkxWVhYLFy7k+++/961r06YNGRkZZGZm0qtXL2rUqBHWMeTl5XHnnXeyYMECv/9WrVq1IiMjg7vvvpvU1NSw2o62iNyDZWa1zexFM9trZvvNbK6ZhfvI5w7A6fxQ8/oEz1TDQIaY2bdmttXMRpfQx1FmttHMNoYyb1ViV17e7pCWi4gESTkkpVIGSTSdd955DB8+nDfffJMDBw6wcOFC7rrrLu644w7fNl999RUpKSmMGjWKRYsWkZeXV0KL/urVq8ctt9zCG2+8wYEDB1i8eDGjRo0iOTmZnTt38txzz9G3b1+aNm3qGySdOHEipGNITExkzpw57N+/nyVLljB69GiaNWvG7t27ef7559m+fbtv23379pGbmxtS+5VBaUPfSXiKWywE5gH9gZfCfK+6wHeFlh0B6gXY9n+BTkAT4L+AJ8xsRKBGnXMvO+d6OOd6NGnSJMyuSSxJTAz8BPTilouIBEk5JKVSBkmsqFWrlq84xbhx43zLly5dSk5ODq+88gqDBg2iSZMmDB8+nHnz5vHdd4U/4oqXmJjItddey5/+9Cf27dvHmjVreOCBB2jXrh0HDx7k1VdfZciQITRu3Jibb76ZuXPn+l2RKk3NmjUZMGAA06dPZ8+ePaxdu5bx48czYMC5R+iOGzeOxo0bM2zYMObMmcPhw4eDbj+WlThF0My+Ah51zs3z/nw5sAao5ZwL/mlmnn27AWucc7ULLPs1kOacK7E+pZmNBy5zzg0raTtNzagaNP9dRIpTximCyiEplTIodlWHKYLBcM7xj3/8w1eevWD1vgYNGrB//34SEhLK1P7WrVvJyspi/vz5bN682bcuISGBq6++mszMTIYOHUqzZs3K9D7p6eksX77ctyw+Pp60tDTffWcpKSlht18eIlWmvSWwOv8H59x64DQQztFuA+LNrGA5ka5AMH8VDqjcd8VJ0JKTb6Vjx5dJTGwNGImJrRVsIhIJyiEplTJIYp2Z0bVrVyZMmMCWLVv46quv+P3vf0/v3r1JT0/3Da7y8vIYOHCgr4x7KO136dKFxx9/nE2bNvlNHTxz5gxLly5l9OjRpKSkcOWVVzJ16lS/aX+hvM8HH3zgmzp49dVX45xj2bJl3Hvvvbz22mu+bU+fPh1y+9FU2gCrBnCy0LLTQHyob+ScOwa8DTxpZnXMrBcwFJhdeFszG2pmDc3jcuB+4J1Q31MiJyfnddaubUN2dhxr17YhJ+f1oPbbsiWd7GzzvbZsSS9zm5Huo4hUD8qhyi3SOVQemaEckmho27Yt48aNY9WqVcybN8+3fPny5SxdupQHHniAiy66iIsvvpgJEybw8ccfU9IMtsJat27N2LFjyc7OJicnxzd1MDExkY8++oiHHnqIDh060KVLFx577DE2bdoUUvstW7ZkzJgxLF++nJycHGbNmsXQoUPJzMz0bfPMM8+QmprKo48+yoYNG0JqPxpKmyJ4Fvgb/s8BuQ7PM7F8186dczcE9Wae54+8iuderkPAeOfcXDPrDSx2ztX1bvcGnmeUJAJ7gOnOuedLa19TM8pHuNMltmxJ5z//Kfoc6gYN+tGs2c+LbRMI+f00pUOkeojQc7CUQ5VMpHMoKSmVvLydEcugsvRRgqcpgqHJrxg4f/58Fi5c6Hd/Vps2bdiwYQONG4dbuw6OHj3KkiVLyMrKKlIxsGXLlr6KhL179yY+PuRrM36uvvpqsrOzfT+3aNHCNwjr06dPmaZEhiIiZdrN7C/BvJlz7uch9K3cKNjKR7gla7Ozi59Nk5jYutg2gZDfT2V1RaqHsg6wyptyqHyURw4FEm4GlaWPEjwNsMKXl5fHihUryMrK4p133qF+/fp88cUXvudSTZo0iUsvvZT09HRq1aoVcvsnT55k5cqVvvu2vvnm3GNyGzVqxJAhQ8jIyGDAgAEkJSWF3P6pU6dYtWqVr/29e/f61t1xxx3MnDkz5DbDEXPPwaoICrbykZ0dh+f2g8KMtLSzJexXUrBZsW16hPZ+4fZRRCoXDbCqp/LJoUDCyyDPeymHypsGWJFx9uxZ9u7dS8uWLQHYtWsXbdq0AaBOnTpcd911ZGZmMmjQIOrXrx9W+xs2bCArK4usrCy2bTv3ZIzatWtz7bXXkpGRweDBg2nYsGFY7W/cuNFX5GPy5MncdNNNACxYsICZM2eSmZnJ4MGDOf/880NuvySRKnIhUi4la0tqM5z3U1ldEZGqq6I+48PNoJLWK4ck1sTFxfkGV+ApB//kk0/SvXt3jh07xl//+lduvfVWmjRpwsCBA9mxY0fI7ffs2ZMpU6bwxRdf8Nlnn/HUU0/Ro0cPjh8/zttvv83IkSO54IIL6N+/P9OnT/e7IhVM+5dffjlPP/00n3/+OcOGnSvu+uabbzJ//nx+9rOfccEFF9CvXz/++7//m3//+98hHUNZaYAlpWrb9ini4mr7LYuLq03btk+VuF+DBv2KXV5Sm+G8X7h9FBGR2BfpHEpKSo1oBpWljxKakycL116TskpOTi5SMTAtLY0zZ86wcuVKLrjgAt+2CxYs8LsiVRozo1OnTjzyyCNs2LCB3bt388ILL3DNNdf4VQxs0aKF36AsFPnTHAGmTJnCiy++SHp6OmbG8uXLGTNmDK1atWLkyJEhtVsWmiIoQSl8o3CDBv245JJlpa5bt64zubmf+dYlJaXSs6fnUn5Ozuvs2PEoeXm7SUxsRdu2T/luBN627R727XsZOAPUICVlFB06TC+xjyW1JyJVg6YIVl+RzqFIZxAoh8pbTk4O9913n98gq2/fvn4P4ZXIOXjwIB9//DH9+/cHPPdBJScnc/jwYVJTU8nMzCQzM5Pu3bv7DXKCdejQIRYuXEhWVhZLliwhNzfXt+4HP/iB71lYl112WVjtHz582Nf++++/z4MPPsiECRMA+Ne//sUrr7xCZmYml19+OXFxwV1z0j1YEjGeoHmpyPKUlNEcP74trEqBqsQkIuHQAKt6qsgcUgaJBHbw4EF++ctfFlsxcOzYsbRr1y6sto8fP86SJUuYP38+7733HocPH/ata968ORkZGWRkZNC3b9+wKgbm5uZy8uRJ3z1lU6ZM4eGHHwagWbNmvoqEaWlp1KxZs9h2NMCSiMnOjsfzLV5hNYpZ7lFSpUBVYhKRcGiAVT1VZA4pg0RKVlzFwC1bttC1a1cAvvrqK1JSUspUMXD+/PnMnz+fPXv2+NY1bNiQwYMHk5GRwcCBA6lTp05Yx7Bx40Zmz55NVlaW3/1Z9evXZ8SIEbz0UtEvdEBFLiSiiguv4kMNIC9vd0jLy7qfiIhUVRWXQ8ogkZLVrFnTV5xiz549fPTRR/z2t7/l4osv9m1z22230bhxY2688UZmz57td0WqNAkJCfTr148XXniB3bt3s379eh5++GE6derE4cOHmT17NsOGDaNJkyZkZGTw2muvcejQoZCOoUePHkybNo1du3axceNGHnvsMTp37syRI0f82srNzeUvf/kLBw8eDKl9XcGSUukKlojECl3Bqp50BUuk8sjLy6NPnz6sX7/etyw+Pp60tDQyMjIYNmwYTZs2DavtL7/80nflbN26db7lNWrUoE+fPmRmZjJ06FBatQqveuf27ds5deoUqampALz33nvccMMNxMXF0bt3b1auXKkrWBIZKSmjil0ebqXAkqgSk4iIFFSROaQMEimbxMRE1q1bF7Bi4H333ccHH5y7ZzIvLy+ktjt27Mj48eP56KOP2LNnDy+++CL9+/fHzFixYgX3338/rVu3pkePHjz11FN89tlnhHIxqX379r7BFUC9evUYMGAAcXFxrFy5Muh2NMCSUnXoMJ2UlNF4vikET0Wl0XToMJ1LLllWJNzyqzclJ99Kx44vk5jYGjASE1sHdZNwuPuJiEjVVJE5pAyKnKVLl3LdddfRqFEjatWqRYcOHXjooYdCmi5W0HPPPcfbb79dZPnEiROLVJkzMyZOnBjW+0hktGzZ0jeg2r9/P6+99ho33ngjgwYN8m1z77330qlTJx5++GHWr1/P2bPBP5S7efPm3HPPPSxdupT9+/cze/ZsbrzxRmrXrs2mTZt80/46duzIQw89xEcffRRS+wBpaWksWbKEAwcOsHDhwqD30wCrisrJeZ21a9uQnR3H2rVtyMl5vdR9tm27h+zseLKzjezseLZtu8e37vDhlZybhnHG+7PHf/6zyq+dgj9//vl/eadaOPLydvH55//lW7dmTXPve3lea9Y0D+tY84VzzCIiEnnhfh5XZA5FOoNAOVTQ008/zcCBA6lVqxZ//vOfWbJkCb/4xS+YNWsWl112WVgPfi1ugBXI2rVrueuuu0J+Dykf559/PiNHjuStt96iQYMGADjnWLNmDV988QVTpkyhZ8+etGrVivvuu49ly5Zx6tSpoNtv2LAht912G2+99RYHDx7knXfe4fbbb6dRo0Zs376dqVOncuWVV9KiRQtGjx7N0qVLQ3qmWoMGDbj++uuD3l73YFVB4ZSYLakE7uHDK/2eIZIvKSmV3NztQKA/gAQgHsgNsC6JhISGnDq1r+heCSlcdNFUldUVkYB0D1bsC/fzuGJzKLBwMwiUQwWtWLGCfv36MXbsWP74xz/6rfv666+59NJL6dq1KytWrAip3TZt2vCjH/2IOXPm+C2fOHEikyZNCmkqWCjy8vJITEwsl7aru1OnTrF69WrffVUFKwY+8cQTTJo0qUztnz59mg8//NDX/u7d54rV1K9fn0GDBpGZmcm1115L3bp1S21PVQSrsR07HvX7gAc4e/Y4O3Y8Wuw+ngcqBl4eKNQA7/Livl04RfGhlhtwcAVw6tS+sPofzj4iIhJ54X4eV2wOFbNHmBkEyqGCpk6dyvnnn88zzzxTZN2FF17I+PHjyc7OZt26dezcuRMzY9asWX7bZWdnY2ZkZ2cDnsHVrl27eP311zEzzIzbb7+92D4EmiL4ySefcMMNN9CwYUOSkpLo1asXq1ev9tvm9ttvp0WLFqxdu5arrrqKpKQkHnzwwXBOgwQhISGBa665xlcxcMOGDTzyyCN06tSJwYMH+7Z76aWXwqoYmF9cY9q0aezcudM3dbBLly4cOXKEuXPncvPNN9O4cWOGDBnCq6++yoEDB8p8XBpgVUHhlZgNrwRueVBZXRGRyiv8z+PYyCE9YqRsTp8+zcqVK+nfvz+1atUKuM0NN9wAwPLly4NuNysri6ZNmzJw4EDWrl3L2rVrefzxx4Pef/PmzVx11VV8++23vPLKK7z11ls0atSI9PR0Nm3a5LftkSNHGD58OCNGjGDx4sX85Cc/Cfp9JHxm5lecokePcxeK5s2b55v2l5yc7BuUhTLV1Mzo3r07kydP5tNPP2Xbtm2+qYMnT55kwYIF3HnnnTRt2pS+ffvy3HPPsXPnzrCORQOsKigxMXBpyuKWe9QIcXn5Caf/4R2ziIhEWvifx7GRQ+H2XznkcejQIXJzc2nTpk2x2+SvC+V/jrt160ZiYiKNGzfmiiuu4IorrqBdu3ZB7/+b3/yGVq1asXz5cm666Sauv/56srKyaNu2LZMnT/bb9ujRozz//POMGTOGtLQ0evbsGfT7SOQULFwyd+5cpk+fXqRiYKtWrfj1r38dVvvt27fnN7/5DX//+9/Zu3cvM2bMYODAgdSoUYNVq1bxq1/9igsvvJDu3bvz5JNP8umnnwbdtgZYVVA4JWZLKoGblJQacJ1neUIxLSYAxT29O4mEhJTAeyWkqKyuiEglFu7nccXmUDF7hJlBoByKZbm5uaxcuZKbb76ZuLg4Tp8+zenTp3HOkZ6ezqpV/kVSEhIS/KanSfQ1b97cV5wiv2LgsGHDqF27Nj/84Q99261ZsyasioHNmjXj7rvv5v333+fAgQO+qYN169bl448/ZsKECX4PUi5NfEhHJ5VC/s20O3Y8Sl7ebhITW9G27VO+5Tk5rxdZ16HDdCB/DvwZPCVwR/mWr1vX2W8OfFJSKj17bgUgO9u/NCpAWtpJ77oaQMFf8DjS0jxz1Nesae53L1ZCQgq9eu31/Vxc/8M5ZhERqRjhZFBy8q0VnENnIppBwRx3dZFfkr2kqVX561q2bFkhffr22285c+YMkydPLnK1Kt/Zs2eJi/Ncd2jSpAk1alT8DB4JTn7FwNtuu43cXP/7LOfMmcOMGTOYOnUqzZo1Y+jQoWRmZpKWlkbNmjWDar9+/fqMGDGCESNGcOLECT744AOysrJ49913g74/SwOsKio5+daAH+qFqxzl5e3iyy893xp26DDdF2SF5YdYYQVL6AZeXvjbg7Ns23YPHTpM9wuyYPtfknD2ERGRyAsng/IHWRWVQ5HOoLLsV5XEx8fTt29f/va3v3HixImA92G9++67AFxzzTW+9YVLZodSyKA0DRo0IC4ujnvvvZeRI0cG3CZ/cAUUeaaWxK6kJP+r1CNHjiQxMZGsrCx2797NjBkzmDFjBvXr1+cXv/gFU6ZMCan9WrVqMWjQIAYNGsSZM2eIjw9u6KQpgtVMpKsclVT1qaR1IiJS/ZRHpT3lUOx54IEHOHToEI888kiRdV9//TXPPvssffr0oWfPniQnJ5OYmMg///lPv+0CPdQ1MTGxyBWLYNSpU4fevXvzySef0L17d3r06FHkJVXDlVde6StOUbhi4Jkz5wrmfPPNNyFXDAzlqqauYFUzka9yFE7Vp4qvTCgiItFXPpX2lEOxJj09nUmTJjFhwgR27tzJyJEjadiwIZs3b2bKlCnUr1+f2bNnA56rRT/+8Y+ZOXMmHTp0oGPHjixcuNBXnr2g1NRUVq9ezYIFC2jatCmNGzcusZhGQX/4wx/o06cPAwcO5M4776RZs2YcPHiQzZs3c+bMmZCvbEhsy68YmF81cPv27X5XU9966y3GjBlDXFwcP/rRj8jMzCQjIyPo36fS6ApWNRP5KkclVX2KjYpQIiISG8qn0p5yKBY98cQTLF68mGPHjvHzn/+cAQMGMH36dEaOHMnGjRtp1ercf/Np06Zx4403MnHiRH784x9z4sQJXnjhhSJtPvPMM3Ts2JFbbrmFyy67rMhzrkrSvXt3NmzYQKNGjbj//vsZMGAAY8eO5dNPP6VPnz6ROGSJYe3bt/e75+/CCy8MWDGwW7duPPvss2V+Pyuvp15HQ48ePdzGjRuj3Y2YFuknzW/bdg/79r1UZHlKymiAYtcVN8deRKQkZrbJORez83mUQyWLdAaBckhEwnfkyBEWLVpEVlYWixcv5ujRo1x33XUsWrQI8DzXbf369VxxxRXExcUFnUEVOkXQzM4HZgIDgIPAw865uQG2M2AKcJd30Z+B8a4qjQajJNJVjkqr+lTaOhGRiqQciq7yqLSnHBKRcAWqGFivXj3f+tWrV3PNNdeQnJzse0B2MCr0CpaZvYFnWuKdwCXAQuAq59zWQtvdDYwD+gEO+BvwvHNuRknt65tDEZGqraxXsJRDIiISrKysLMaNG1fwsQNBZVCF3YNlZnWAYcDjzrmjzrkPgXeBnwbY/GfA751ze5xze4HfA7dXVF9FRKTqUQ6JiEgoMjMz2bFjB1u2bGHatGlB71eRRS46AKedc9sKLPsE6Bxg287edaVtJyIiEizlkIiIhMTM6Nq1K/fff3/Q+1TkPVh1ge8KLTsC1Ctm2yOFtqtrZlZ4/ruZjQJGeX/MMzP/BylIYzz3GYiHzkdROidF6Zz4i6Xz0boM+yqHKl4s/e7ECp2TonRO/Ol8FBUr5ySoDKrIAdZR4LxCy84Dvg9i2/OAo4FuLnbOvQy8DGBmG2O5ulQ06Jz40/koSuekKJ0Tf1XofCiHKpjOR1E6J0XpnPjT+Siqsp2TipwiuA2IN7P2BZZ1BbYG2Hard11p24mIiARLOSQiIuWuwgZYzrljwNvAk2ZWx8x6AUOB2QE2/x9gnJk1N7MU4NfArIrqq4iIVD3KIRERqQgVeQUL4B4gCdgPvAGMds5tNbPeZna0wHZ/At4DPgX+MH6uvgAACKRJREFUiaeM7p+CaP/lCPe3KtA58afzUZTOSVE6J/6q0vlQDlUsnY+idE6K0jnxp/NRVKU6JxX6HCwREREREZGqrKKvYImIiIiIiFRZGmCJiIiIiIhESJUYYJnZ+WaWZWbHzGyXmf0k2n2KJjO7z8w2mlmemc2Kdn+izcwSzWym93fjezPbYmbXRbtf0WZmc8zsGzP7zsy2mdld0e5TLDCz9mZ2wszmRLsv0WZm2d5zcdT7+jLafYpVyiF/yiF/yqHAlEOBKYfOqaw5VCUGWMCLwEkgGbgVeMnMOke3S1G1D/gt8Gq0OxIj4oF/A32B+sBjwP+aWZso9ikWPAO0cc6dB9wA/NbMLo1yn2LBi8CGaHcihtznnKvrfXWMdmdimHLIn3LIn3IoMOVQYMohf5Uuhyr9AMvM6gDDgMedc0edcx8C7wI/jW7Posc597Zzbj5wKNp9iQXOuWPOuYnOuZ3OubPOuQXA10C1/hB3zm11zuXl/+h9tYtil6LOzIYD/wE+iHZfpPJQDhWlHPKnHApMOVSUcqhqqPQDLKADcNo5t63Ask+A6vzNoZTAzJLx/N5U+4eGmtl0MzsOfAF8AyyKcpeixszOA54ExkW7LzHmGTM7aGZrzCwt2p2JUcohCYly6Bzl0DnKoWJVuhyqCgOsusB3hZYdAepFoS8S48wsAXgdeM0590W0+xNtzrl78Pyt9MbzANa8kveo0iYDM51ze6LdkRjyENAWaI7nGSTvmVm1/na5GMohCZpyyJ9yyI9yqKhKmUNVYYB1FDiv0LLzgO+j0BeJYWYWB8zGc5/EfVHuTsxwzp3xTmlqAYyOdn+iwcwuAdKBP0a7L7HEObfOOfe9cy7POfcasAa4Ptr9ikHKIQmKcigw5ZByqDiVNYfio92BCNgGxJtZe+fcdu+yruiyuxRgZgbMxHMD+vXOuVNR7lIsiqf6zn1PA9oAuz2/KtQFaphZqnOuexT7FWscYNHuRAxSDkmplENBUQ4ph0pTKXKo0l/Bcs4dw3NJ+Ukzq2NmvYCheL4hqpbMLN7MagE18Pxx1jKzqjCYLouXgE7AEOdcbrQ7E21mdoGZDTezumZWw8wGAiOovjfVvown1C/xvmYAC4GB0exUNJlZAzMbmP/5YWa3An2A96Pdt1ijHCpKORSQcqgA5VARyqFCKnMOVfoBltc9QBKwH3gDGO2cq87fHD4G5ALjgdu8//5YVHsURWbWGrgbzwfW/xV4lsKtUe5aNDk80zD2AIeB3wG/dM69G9VeRYlz7rhz7v/yX3imfJ1wzh2Idt+iKAFPme0DwEFgDJBRqJCDnKMc8qccKkA5FJByqADlUECVNofMORftPoiIiIiIiFQJVeUKloiIiIiISNRpgCUiIiIiIhIhGmCJiIiIiIhEiAZYIiIiIiIiEaIBloiIiIiISIRogCUiIiIiIhIhGmCJxCAzu93MjpayzU4ze6Ci+lQSM2tjZs7MekS7LyIiUjbKIJGy0QBLpBhmNsv7ge3M7JSZ7TCz35lZnRDbWFCe/axoVfGYRERijTIosKp4TFL1xEe7AyIxbhnwUzxPE+8N/Bmog+fp8yIiIuVJGSRSCekKlkjJ8pxz/+ec+7dzbi7wOpCRv9LMUs1soZl9b2b7zewNM2vqXTcR+BkwqMC3kGnedVPM7Eszy/VOs5hqZrXK0lEzq29mL3v78b2ZrSw4XSJ/yoeZ9TOzf5rZMTNbYWYXFmrnYTPL8W77P2Y2wcx2lnZMXq3N7G9mdtzMPjOz/mU5JhGRak4ZpAySSkgDLJHQ5OL5JhEzawasAv4JXA6kA3WBd8wsDvgd8L94voFs5n393dvOMeAOoBNwDzAceDTcTpmZAQuB5sBgoJu3b8u9/cyXCDzsfe8rgQbAjALtDAcmePvSHfgcGFdg/5KOCeAp4HmgK7ABmGdmdcM9LhER8aMMUgZJJaApgiJBMrPLgZ8AH3gXjQY+cc49VGCbkcC3QA/n3Hozy8X7DWTBtpxzkwv8uNPMngYeAB4Ps3tXA5cATZxzud5lj5vZEDzTS6Z6l8UD9zrnvvT293fAq2ZmzjkHjAVmOef+7N3+GTO7Gujg7ffRQMfkyVYA/uice8+77BFgpLdfH4Z5XCIigjLI229lkFQKGmCJlOxa81RSisfzreE7wBjvukuBPha40lI7YH1xjZrZTcAvgYvwfONYw/sK16VAbeBAgaABqOXtS768/GDz2gfUBBriCeUfAK8Uansd3nALwj8KtQ1wQZD7ioiIP2WQMkgqIQ2wREq2ChgFnAL2OedOFVgXh2dKRKAytTnFNWhmVwDzgEnAr4D/ADfgmfoQrjjve/YOsO67Av9+utA6V2D/SPCdH+ec8watpiKLiIRHGRQaZZDEBA2wREp23Dn3r2LWbQZuAXYVCr2CTlL0W8FewN6CUzTMrHUZ+7kZSAbOOud2lKGdL4DLgFcLLLu80DaBjklERCJPGaQMkkpIo3qR8L0I1AfeNLOeZtbWzNK9VZTqebfZCXQxs45m1tjMEoBtQHMzu9W7z2hgRBn7sgxYg+fm5uvM7EIzu9LMJplZoG8UizMNuN3M7jCz9mb2INCTc98yFndMIiJSsZRByiCJURpgiYTJObcPzzeBZ4H3ga14Ai/P+wLPXPLPgY3AAaCX9wbc/wc8h2e+eH/giTL2xQHXA8u97/klnkpLHTk3Dz2YduYBk4EpwMdAFzwVnk4U2KzIMZWl7yIiEjplkDJIYpd5/iZERAIzsywg3jk3JNp9ERGR6kUZJJWR7sESER8zq42n9O/7eG5GHgYM9f5TRESk3CiDpKrQFSwR8TGzJOA9PA+JTAK2A8865+ZGtWMiIlLlKYOkqtAAS0REREREJEJU5EJERERERCRCNMASERERERGJEA2wREREREREIkQDLBERERERkQjRAEtERERERCRCNMASERERERGJkP8PQaNTEfHFWUAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x194.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\\n\",\n    \"y_outliers = np.array([0, 0])\\n\",\n    \"Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\\n\",\n    \"yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\\n\",\n    \"Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\\n\",\n    \"yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\\n\",\n    \"\\n\",\n    \"svm_clf2 = SVC(kernel=\\\"linear\\\", C=10**9)\\n\",\n    \"svm_clf2.fit(Xo2, yo2)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12,2.7))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \\\"bs\\\")\\n\",\n    \"plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \\\"yo\\\")\\n\",\n    \"plt.text(0.3, 1.0, \\\"Impossible!\\\", fontsize=24, color=\\\"red\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.annotate(\\\"Outlier\\\",\\n\",\n    \"             xy=(X_outliers[0][0], X_outliers[0][1]),\\n\",\n    \"             xytext=(2.5, 1.7),\\n\",\n    \"             ha=\\\"center\\\",\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n    \"             fontsize=16,\\n\",\n    \"            )\\n\",\n    \"plt.axis([0, 5.5, 0, 2])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \\\"bs\\\")\\n\",\n    \"plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \\\"yo\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf2, 0, 5.5)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.annotate(\\\"Outlier\\\",\\n\",\n    \"             xy=(X_outliers[1][0], X_outliers[1][1]),\\n\",\n    \"             xytext=(3.2, 0.08),\\n\",\n    \"             ha=\\\"center\\\",\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n    \"             fontsize=16,\\n\",\n    \"            )\\n\",\n    \"plt.axis([0, 5.5, 0, 2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"sensitivity_to_outliers_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Large margin *vs* margin violations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the first code example in chapter 5:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\\n\",\n       \"     penalty='l2', random_state=42, tol=0.0001, verbose=0))])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn import datasets\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.svm import LinearSVC\\n\",\n    \"\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = (iris[\\\"target\\\"] == 2).astype(np.float64)  # Iris-Virginica\\n\",\n    \"\\n\",\n    \"svm_clf = Pipeline([\\n\",\n    \"        (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"        (\\\"linear_svc\\\", LinearSVC(C=1, loss=\\\"hinge\\\", random_state=42)),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"svm_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.])\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"svm_clf.predict([[5.5, 1.7]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's generate the graph comparing different regularization settings:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\\n\",\n      \"  \\\"the number of iterations.\\\", ConvergenceWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\\n\",\n       \"     penalty='l2', random_state=42, tol=0.0001, verbose=0))])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"scaler = StandardScaler()\\n\",\n    \"svm_clf1 = LinearSVC(C=1, loss=\\\"hinge\\\", random_state=42)\\n\",\n    \"svm_clf2 = LinearSVC(C=100, loss=\\\"hinge\\\", random_state=42)\\n\",\n    \"\\n\",\n    \"scaled_svm_clf1 = Pipeline([\\n\",\n    \"        (\\\"scaler\\\", scaler),\\n\",\n    \"        (\\\"linear_svc\\\", svm_clf1),\\n\",\n    \"    ])\\n\",\n    \"scaled_svm_clf2 = Pipeline([\\n\",\n    \"        (\\\"scaler\\\", scaler),\\n\",\n    \"        (\\\"linear_svc\\\", svm_clf2),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"scaled_svm_clf1.fit(X, y)\\n\",\n    \"scaled_svm_clf2.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Convert to unscaled parameters\\n\",\n    \"b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\\n\",\n    \"b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\\n\",\n    \"w1 = svm_clf1.coef_[0] / scaler.scale_\\n\",\n    \"w2 = svm_clf2.coef_[0] / scaler.scale_\\n\",\n    \"svm_clf1.intercept_ = np.array([b1])\\n\",\n    \"svm_clf2.intercept_ = np.array([b2])\\n\",\n    \"svm_clf1.coef_ = np.array([w1])\\n\",\n    \"svm_clf2.coef_ = np.array([w2])\\n\",\n    \"\\n\",\n    \"# Find support vectors (LinearSVC does not do this automatically)\\n\",\n    \"t = y * 2 - 1\\n\",\n    \"support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\\n\",\n    \"support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\\n\",\n    \"svm_clf1.support_vectors_ = X[support_vectors_idx1]\\n\",\n    \"svm_clf2.support_vectors_ = X[support_vectors_idx2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure regularization_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(12,3.2))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf1, 4, 6)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"$C = {}$\\\".format(svm_clf1.C), fontsize=16)\\n\",\n    \"plt.axis([4, 6, 0.8, 2.8])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\")\\n\",\n    \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf2, 4, 6)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"$C = {}$\\\".format(svm_clf2.C), fontsize=16)\\n\",\n    \"plt.axis([4, 6, 0.8, 2.8])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"regularization_plot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Non-linear classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure higher_dimensions_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\\n\",\n    \"X2D = np.c_[X1D, X1D**2]\\n\",\n    \"y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.grid(True, which='both')\\n\",\n    \"plt.axhline(y=0, color='k')\\n\",\n    \"plt.plot(X1D[:, 0][y==0], np.zeros(4), \\\"bs\\\")\\n\",\n    \"plt.plot(X1D[:, 0][y==1], np.zeros(5), \\\"g^\\\")\\n\",\n    \"plt.gca().get_yaxis().set_ticks([])\\n\",\n    \"plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n    \"plt.axis([-4.5, 4.5, -0.2, 0.2])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.grid(True, which='both')\\n\",\n    \"plt.axhline(y=0, color='k')\\n\",\n    \"plt.axvline(x=0, color='k')\\n\",\n    \"plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \\\"bs\\\")\\n\",\n    \"plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \\\"g^\\\")\\n\",\n    \"plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n    \"plt.ylabel(r\\\"$x_2$\\\", fontsize=20, rotation=0)\\n\",\n    \"plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\\n\",\n    \"plt.plot([-4.5, 4.5], [6.5, 6.5], \\\"r--\\\", linewidth=3)\\n\",\n    \"plt.axis([-4.5, 4.5, -1, 17])\\n\",\n    \"\\n\",\n    \"plt.subplots_adjust(right=1)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"higher_dimensions_plot\\\", tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.datasets import make_moons\\n\",\n    \"X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\\n\",\n    \"\\n\",\n    \"def plot_dataset(X, y, axes):\\n\",\n    \"    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\")\\n\",\n    \"    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\")\\n\",\n    \"    plt.axis(axes)\\n\",\n    \"    plt.grid(True, which='both')\\n\",\n    \"    plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n    \"    plt.ylabel(r\\\"$x_2$\\\", fontsize=20, rotation=0)\\n\",\n    \"\\n\",\n    \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\\n\",\n       \"     penalty='l2', random_state=42, tol=0.0001, verbose=0))])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.datasets import make_moons\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n    \"\\n\",\n    \"polynomial_svm_clf = Pipeline([\\n\",\n    \"        (\\\"poly_features\\\", PolynomialFeatures(degree=3)),\\n\",\n    \"        (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"        (\\\"svm_clf\\\", LinearSVC(C=10, loss=\\\"hinge\\\", random_state=42))\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"polynomial_svm_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure moons_polynomial_svc_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_predictions(clf, axes):\\n\",\n    \"    x0s = np.linspace(axes[0], axes[1], 100)\\n\",\n    \"    x1s = np.linspace(axes[2], axes[3], 100)\\n\",\n    \"    x0, x1 = np.meshgrid(x0s, x1s)\\n\",\n    \"    X = np.c_[x0.ravel(), x1.ravel()]\\n\",\n    \"    y_pred = clf.predict(X).reshape(x0.shape)\\n\",\n    \"    y_decision = clf.decision_function(X).reshape(x0.shape)\\n\",\n    \"    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\\n\",\n    \"    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\\n\",\n    \"\\n\",\n    \"plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"moons_polynomial_svc_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\\n\",\n       \"  kernel='poly', max_iter=-1, probability=False, random_state=None,\\n\",\n       \"  shrinking=True, tol=0.001, verbose=False))])\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"\\n\",\n    \"poly_kernel_svm_clf = Pipeline([\\n\",\n    \"        (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"        (\\\"svm_clf\\\", SVC(kernel=\\\"poly\\\", degree=3, coef0=1, C=5))\\n\",\n    \"    ])\\n\",\n    \"poly_kernel_svm_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\\n\",\n       \"  decision_function_shape='ovr', degree=10, gamma='auto_deprecated',\\n\",\n       \"  kernel='poly', max_iter=-1, probability=False, random_state=None,\\n\",\n       \"  shrinking=True, tol=0.001, verbose=False))])\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"poly100_kernel_svm_clf = Pipeline([\\n\",\n    \"        (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"        (\\\"svm_clf\\\", SVC(kernel=\\\"poly\\\", degree=10, coef0=100, C=5))\\n\",\n    \"    ])\\n\",\n    \"poly100_kernel_svm_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure moons_kernelized_polynomial_svc_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"plt.title(r\\\"$d=3, r=1, C=5$\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"plt.title(r\\\"$d=10, r=100, C=5$\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"moons_kernelized_polynomial_svc_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure kernel_method_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAxAAAAEYCAYAAADMNRC5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd4FFXbx/HvSSNACEkEQpDeFem9CKGoIFVUBASFV0EFFfHR50EFRRFBrCCIIgiIgihNpKsQBOlNIKAI0iGIQCAJpG3O+8ckIQkpm2R3Z8v9ua692J2ZnflNNmH37JxzH6W1RgghhBBCCCGs4WV2ACGEEEIIIYTrkAaEEEIIIYQQwmrSgBBCCCGEEEJYTRoQQgghhBBCCKtJA0IIIYQQQghhNWlACCGEEEIIIawmDQghhBBCCCGE1aQBIYQQQgghhLCaNCCEEEIIIYQQVvMxO4AjlSpVSleuXLnQ+4mLi6N48eKFD2QjzpYHnC+Ts+UB58vkbHnA+TK5c57du3f/q7UubZOdOTlbvRfk5c8//8RisXDnnXfa/VhmcLa/B1ty13Nz1/MCOTdbsfa9wKMaEJUrV2bXrl2F3k9ERATh4eGFD2QjzpYHnC+Ts+UB58vkbHnA+TK5cx6l1Emb7MgF2Oq9IC/h4eFER0c75FhmcLa/B1ty13Nz1/MCOTdbsfa9QLowCSGEEEIIIawmDQghhBBCCCGE1aQBIYQQQgghhLCaNCCEEEIIIYQQVpMGhBBCCCGEEMJq0oAQQgghhBBCWE0aEEIIIYQQQgirSQNCCCGEEEIIYTVpQAghhBBCCCGsJg0IIYQQQgghhNWkASGEEEIIIYSwmjQghBBCCCGEEFaTBoQQQgghhBDCag5tQCilnlVK7VJKJSil5uSx7UilVJRS6ppS6kulVJEM6yorpTYopa4rpf5QSnWye3ghhBB2Z6v3CXdzPuY87ea0Iyo2yuwoQgjh8CsQ54C3gS9z20gpdR8wCugIVAKqAm9m2GQBsBe4DXgNWKSUKm2PwEIIIRzKVu8TbmXcr+PYfGoz4zaOMzuKEEI4tgGhtV6itV4GXMpj08eBWVrrSK31FWAcMAhAKVUTaAS8obW+obVeDBwAHrRfcpGX60nX0+9/uPVDvj75NbP3zmbn2Z0kpySbmEzk5vBhmDnz5uPISFizBhITzcskPJst3ifczfmY88zeN5sUncLsfbPlKoQQwnQ+ZgfIQR3ghwyPfwdClVK3pa77W2sdk2V9nex2pJQaCgwFCA0NJSIiotDhYmNjbbIfWzErT0xSDKujVrP2wlr8vPyY3mg6ADP2zODPmD+ZdWIWAIE+gXQL68aQqkMcnjGNs71mYE4miwV27w6hcePLeHvDwoUV+OyzapQpsxkvr1hmzjzJd99VYMWKzfj5pXDhQhFCQhLx9dUOzZnG2V43yeNUcnyf0Frf0viwx3tBXqKjo7FYLIU+1kdHPiLZYnwRk2RJ4ukFT/NCjRdskLBw3Pn3z13PzV3PC+TcHE5r7fAbxuXpObmsPwZ0zvDYF9BAZWAgsC3L9uNz21/arXHjxtoWNmzYYJP92Iqj81xPvK7fjHhTB04I1IxFt5jZQn+89eP09SkpKXrdL+v0kX+P6IUHF+qBSwbqj7Z+pLXW2pJi0Uf+PeLQvFo732umtTmZvv9ea9B6+XLj8cWLWp8+rXVKipHn0iWtd+++uf0992jdsqWx3gzO9rq5cx5glzbh/SCnW2HeJ/Lat63eC/LSrl07Xb9+/ULt49y1c9r/bX/NWNJvRd8uqs/HnLdRyoJztr8HW3LXc3PX89Jazs1WrH0vcNYrELFAYIbHafdjslmXtj4G4RBf/f4Vb0S8Qa/avRjbbiz1y9bPtF4pha+XLzVuq0GN22rQp06f9HXzfp/HE8ufYEzbMbzW9jV8vJz1V9B9XL4Mx49D48bQuzf88AN07mysK1Uq87YhIcYtzciREBcHSoHWcPEilCnjuOxC5CK39wm3Me7XcaTolEzLLNrCuI3jmNZ1mkmphBCezlnLuEYCGT+V1gcuaOOydCRQVSlVIsv6SAfm8zhaa05EnwDgiUZPsHnwZpY+svSWxkNeutbsSt+7+jJ241ipKOIgDz0EDz4ISUng5QU9eoCvr3XP7dLFeD7A999D7drGOAkhnEBu7xNuY+uZrSRaMg9KSrQksuXMFpMSCSGEg8dAKKV8Uo/pDXgrpfyBZK111lG2XwFzlFLfYFTkGA3MAdBaH1FK7QPeUEqNBroA9ZBB1HaTaEnkieVPsOboGg4NO0Tp4qVpXbF1gfZVqlgpvu79NV1rdOXJH5+k6RdNWdFvRb4bIsJ6n34K169b32jISePG0Lcv1Kplm1xCZMcW7xPuZO9Te82OIIQQt3D0FYjRwA2M0nsDUu+PVkpVVErFKqUqAmit1wCTgA3AKeAk8EaG/fQFmgBXgInAQ1rriw47Cw9yPek6PRb04Ov9XzOi+QhKFSuV95Os0K9uP377v9/w9fLl9LXTNtmnuGnGDHj9deN+7drQqFHh91mtmtEY8fGB2FiYPt3o1iSEjdnqfUIIIYSdOPQKhNZ6LDA2h9UBWbb9EPgwh/2cAMJtl0xkJz45ngcWPsBPx35iZveZPNHoCZvuv0HZBvzx7B/4efsBEJMQQ4kiJfJ4lsiL1rBjB5w7B8nJxgd+W5s92xgf0aoV1JeLR8KGbPU+IYQQwn6cdQyEcALjfx3PumPrmNVjls0bD2nSGg9LDi+h2pRqHLhwwC7H8RRaGwOeZ8wwBkvbo/EA8OyzsG2bNB6EEEIITyQNCJGj19q+xqr+qxjccPAt6+69916UUixevDjTcq01gwYNon379owaNcrqYzUKa4Svty+dv+nMuZhzhc7uiSIijCsC588bg6Wzjnmw5WumFDRpYtzfssUYYC2EEEIIzyANCHGLVX+tIjo+Gn8ff7rU6JLtNu+99x5eXl6MGTMGi8WSvvyll15i7ty5dOvWjYkTJ1p9zMpBlVn96GquJVyj57c9M81sLaxz44ZxBaJo0ezX2/o1S/PWWzB2rMxeLYQQQngKaUCITHac3UGvb3sx6ufcv4muX78+AwcO5PDhw8ybNw+Ad955hw8//JA+ffowcuTIfB+7Xmg95veez+5zu3lm5TMFyu/JunSBrVshKCj79fZ4zQDmz4fNm8HPr6DJhRBCCOFKpAEh0l2+cZk+3/chrEQY73R8J8/tx40bh7+/P2+++SZTp07ltdde47777mPevHl4eRXsV6t7re6MDR9L5ZKVb5k8SWTv5ZdhwQLjvlK5b2uP1ywkBIKDISUFZs2CDBc3hBBCCOGGpAEhAEjRKTy+7HHOxZzj+4e/J6RoSJ7PqVChAi+88AInTpzgueeeo1WrVixZsgS/LF9FT5gwgaZNmxIYGEjp0qXp3r07Bw8ezHG/r7d7nTfbv4mXkl/PvMTHG4OZ91pZKt7a12zatGnUq1ePwMBAAgMDadmyJStXrsx13z/9BE8+CUuXFvRshBBCCOEK5BOaAGDytsmsOLKCD+79gGa3N7P6eaVLl06/P2vWLIoVK3bLNhEREQwbNowtW7awfv16fHx86NSpE5cvX8513+uPr6fr/K4kJCdYfyIext8fNmyA8eOtf441r1n58uV599132bNnD7t27aJDhw706tWL/fv357jfe+81sjwoUzoKIYQQbk0aEAKAh+s8zISOE3i22bNWP2f+/Pm89NJLlC1bFoDJkydnu93atWsZPHgwd911F3Xr1mXevHlcvHiR3377Ldf9xybGsuqvVbzyyyvWn4iHsFiMRkNsrFGq1dpZpq19zXr27EmXLl2oXr06NWvWZPz48ZQoUYKtW7fmuG+lIDzc+DcqyhjULYQQQgj3Iw0ID5eiU9BaUz6wPKPajELl1Yk+1apVqxg0aBB33XUX+/fvp1atWsycOZM///wzz+fGxMSQkpJCcHBwrtv1qNWDYU2G8fG2j9l6OucPrp5o0yYYMwbWrbP+OQV9zSwWC99++y2xsbG0atUqz+Ncvgx168Jrr1mfTQghhBCuQxoQHu6T7Z/QaV4nYhJirH7O5s2beeihhyhfvjxr166ldOnSvP322yQnJ/O///0vz+ePGDGCBg0a0LJlyzy3ndhpIuUDyzPkxyEkWqROaJrwcDh4EB54wLrtC/KaHThwgICAAIoUKcLTTz/N0qVLqVu3bp7HCgmB0aNh6NB8nJAQQgghXIY0IDzY31f+5tX1r1LUpygBfgFWPWffvn1069aNkiVL8tNPPxEWFgbAQw89RJMmTfjhhx/YtGlTjs9/8cUX2bx5M4sXL8bb2zvP45UoUoJPu35K5MVIFhxYYN2JuTGt4fhx4/6dd+ZddQkK/prVqlWLffv2sX37dp555hkef/zxXAe/ZzRiBNSubfVpCSGEEMKFSAPCQ2mtGfLjEHy8fPis22dWdV06evQonTt3RinF2rVrqVatWqb1EyZMAODll1/O9vkjR45kwYIFrF+/nqpVq1qdtVvNbqx/bD2P1X/M6ue4q/nzoVYt2LHDuu0L85r5+flRvXp1GjduzIQJE2jQoAEfffSR1VmTk+H55+G996x+ihBCCCFcgI/ZAYQ5vj/0PeuPr2d61+mUDyxv1XOqV69OVFRUjus7deqE1howKi9lNGLECBYuXMiGDRuoXYCvpttXaQ/A+ZjzhAaEemyJ13vugVdfhcaNrdu+MK9ZVikpKSQkWF8Ry8cHzp83KkUJIYQQwn1IA8JDfbztYxqUbcCQRkPsfqzhw4czb948li1bRnBwcPoH2oCAAAICrOs6BXDo4iGaz2zO590+p3/d/vaK69TKlIGxY+1/nFGjRtG1a1cqVKhATEwM8+fPJyIiIs+5ILJauBAKOD+dEEIIIZyUvLV7qHUD1/H9w9/j7ZX3OITC+vTTT4mJiaFjx46EhYWl395///187ad2qdrUCKnB/37+H9eTrtsprXOKjDTmVzh71jHHi4qKYsCAAdSqVYuOHTuyc+dOVq9eTZcuXfK1n7TGw+HDsHmzHYIKIYQQwuHkCoSHuXzjMgF+AQT4BVA9pLpDjpnWRaawvJQXH3f+mHZz2vHBlg8Y026MTfbrCg4dgl27oEgRxxxvzpw5NtuX1tCvnzFXxY4d1g38FkIIIYTzkisQHuapFU/RYmYLLCkWs6MUSNtKbXnwjgeZ+NtEzl5z0NfxTuDhh+HoUShVyuwk+acUfPUVrFoljQchhBDCHUgDwoNsOrmJRYcW0at2L4d0XbKXSfdMwpJiYeVf+euP74oSE292/bF2tmlnVK8elC5t3Le4ZttVCCGEEKmkAeEhtNaM+mUU5UqU46VWL5kdp1CqBlfl7xF/M7Sx+89U9uWXcPfdsHOn2UkKLykJunc3ZtAWQgghhOuSMRAeYsWRFWw5vYXPu31OMd9iZscptHIlygFwIvoElYMqmxvGjh57DEqUgCZNzE5SeL6+ULEilC1rdhIhhBBCFIZcgfAQCw4uoHpIdQY3GGx2FJtZ9dcqqk6uysYTG82OYjfFisGjj7rP2IFp04zJ5YQQQgjhuqQB4SG+7v01vzz2C77eLtyRPov2ldsTViKM19a/ZrNKT87iyhXo1Mk9ui5lpTX8/LMxKFwIIYQQrkcaEG4u0ZLIlRtX8FJeVCxZ0ew4NlXUtyij7x7Nb6d/Y83RNWbHsanjx42bo8q2OlJ0NPTqBR99ZHYSIYQQQhSENCDc3Mw9M6k6pSono0+aHcUunmj0BFWCqvDa+tdI0Slmx7GZRo3gyBGjepG7CQ42rkB8+KHZSYQQQghRENKAcGMJyQm8s+kd7ipzl9tdfUjj5+3H2PCxHP73MJH/RJodxyZ27zZKnXq7bqXdPLVoYVxdcbOeZ0IIIYRHkAaEG5u9bzZnY87yRrs3UO4yCjcbj9Z9lL+f/5u6oXXNjlJoUVHQujW8/rrZSezv4EFo2BD27zc7iRBCCCHyQ8q4uqlESyITNk+gZfmWdKzS0ew4duXt5U1YiTC01kTHRxNcNNjsSAVWujTMmweNG5udxP5uv924ChEdbXYSIYQQQuSHXIFwUz8d+4lTV0/xervX3frqQ0aDfhhEp3mdXLoik7c3PPwwVK1qdhL7Cw6G7duhbVuzkwhno5QKUUotVUrFKaVOKqX657BdEaXUZ0qpC0qpy0qpH5VStzs6rxBCeBppQLiprjW7svepvdxX7T6zozhMu0rt2HN+D6uPrjY7SoFMngxffGF2CsdLSoKN7juVhyiYaUAiEAo8CkxXStXJZrsRQEugHlAOuAJ84qiQQgjhqaQB4YYSLYkANCjbwGOuPgAMqDeAiiUrMu7XcS53FUJrWLXKqE7kad59Fzp0gL//NjuJcAZKqeLAg8AYrXWs1nozsBwYmM3mVYC1WusLWut4YCGQXUNDCCGEDckYCDdjSbHQ8POGDKw3kFFtRpkdx6H8vP0Y1XoUw1YNY8OJDXSo0sHsSFZTCtasgevXzU7ieEOGQIMGUKWK2UmEk6gJJGutj2RY9jvQLpttZwGTlVLlgGiMqxXZXoJUSg0FhgKEhoYSERFhy8zZio6OxmKxOORYZoiNjZVzczHuel4g5+Zo0oBwM8v+WMahi4eoHlLd7CimGNxwMON+HcfUHVNdpgFx44ZxBaJYMShe3Ow0jhcaCt26mZ1COJEA4FqWZVeBEtls+xdwGjgLWIADwLPZ7VRrPQOYAdCkSRMdHh5uo7g5CwoKIjo6GkccywwRERFybi7GXc8L5NwcTbowuRGtNZO2TKJacDUeqP2A2XFM4e/jz8r+K/m699dmR7HaZ59BpUpw4YLZScw1dSq8847ZKYQTiAUCsywLBGKy2XYaUAS4DSgOLCGHKxBCCCFsRxoQbmTTqU3sOLuD/7T8D95ebjwLWR4ahjWkmG8xlxkH0aoVDB1qfBPvyXbtgi1bZHI5wRHARylVI8Oy+kB2M0U2AOZorS9rrRMwBlA3U0qVckBOIYTwWNKAcCPvb3mf0sVKM6jBILOjmG7bmW3c+emd/H3F+UfmNm8O48ebncJ8n38OK1YY40GE59Jax2FcSXhLKVVcKdUa6AnMy2bzncBjSqmSSilfYBhwTmv9r+MSCyGE55EGhBuZ3HkyXz3wFUV9i5odxXQVS1bk2OVjfLj1Q7Oj5EhrmDYNLl40O4lzKFLE+DcmBuLjzc0iTDcMKAr8AywAntFaRyql7lZKxWbY7iUgHmMsxEXgfsAz+28KIYQDObQBkY/JgVYrpWIz3BKVUgcyrD+hlLqRYf06x52F86oSXIXO1TubHcMplCtRjgH1BvDl3i/597pzfhl59GgAzz4Ly5ebncR5nDwJFSrA3LlmJxFmSu2S1EtrXVxrXVFrPT91+SatdUCG7S5prR/VWpfRWgdprdtorXeYl1wIITyDo69AWDU5kNa6i9Y6IO0GbAG+z7JZ9wzb3Gv35E7sYsJFeizowR///mF2FKfyn5b/4UbyDabvnG52lGzVqBHLoUMwYIDZSZxHxYrwzDPQrJnZSYQQQgiRE4c1IPI5OVDG51UG7ga+sndGV7X4zGJW/bUKfx9/s6M4lTpl6nB/jfv5ZMcnJFgSzI6TrTvuuNl1RxjjHyZMgIYNzU4ihG2djzlPuzntiIqNMjuKS5KfnxDOxZHzQORncqCMHgM2aa1PZFn+jVLKC9gLvKy1/j27J9tj8iBnmtAjNjmW5eeW0650O07sO8EJTpgdCXCen1H3gO40q9yM+OvxTpEnzfvv16RIkQpAhNlR0jnLawbw779+/PZbKTp2dJ5M4Fw/I3C+PCJn434dx+ZTmxm3cRzTuk4zO47LkZ+fEM7FkQ2I/EwOlNFjwNtZlj0K7AEUMAJYq5SqrbWOzvpke0we5EwTery7+V1upNzg/V7v0zDMeb62dZafUThGBmfJA8bg6YUL4dq1k06TCZzrZ/TRRzBlCjRpcplu3VqYHSedM/2MwPnyiOydjznP7H2zSdEpzN43mzHtxlA2oKzZsVyG/PyEcD6OHAORn8mBAFBKtQHKAosyLtda/6a1vqG1vq61ngBEY3Rz8ihJliSm7JhC46DGTtV4cDY3km4w98Rc1hxdY3YUwOimM306DBly3OwoTuvJJ+Gvv+D226Uck3B9434dR4pOAcCiLYzbOM7kRK5Ffn5COB9HNiDyMzlQmseBJVrr2Fy2AdAYVyM8SlJKEsOaDKNfxX5mR3Fqvt6+rLmwhnG/mv+mExMDx46ZncL5lSgBVauanUKIwksqksTsfbNJtCQCkGhJZPa+2dKX30ppVx/k5yeEc3FYAyKfkwOhlCoK9AHmZFleUSnVWinlp5TyV0q9DJQCfrPrCTihYr7FeK3tazQObmx2FKfm4+XDg7c/yJbTW9h5dqepWWbPhho1jG/XRe60NsaKvPKK2UmEKLgLtS+kf3ueRr5Ft17Gqw9p5OcnhPkcXcbV2smBAHphdE3akGV5CWA6cAU4C3QGumitL9k1uZOJ/CeShQcXkmRJMjuKS+hStgsl/EowZccUU3M89BB8+qnRiBC5U0pmpRauLy4kLv3b8zSJlkS2nNliUiLXsvXMVvn5CeGEHDmIGq31ZYyGQdblmzAGWWdctgCjkZF120ignr0yuor3trzHokOLZOI4KxX3Kc7gBoOZvms6kzpNIqxEmCk5ypWDp5825dAu6T//OUJ4eDmzYwhRYLU21GLfvn1mx3BZe5/aa3YEIUQ2HH0FQtjAP3H/sODgAgY1GERJ/5Jmx3EZzzV/js7VOxObmNeQGvuYOBF2yBy5BfLHH2CxmJ1CCCGEECANCJf0xe4vSLQk8myzZ82O4lKqh1Rneb/l1LjN8f2HrlyBSZNg7VqHH9rlbdxoTLi3cqXZSYQQQggBDu7CJAovyZLEp7s+5d5q91K7VG2z47ikY5ePcenGJZrd3sxhxwwOhlOnICUl721FZq1bwwcfQMuWZicRQgghBEgDwuWcunqKwCKBPN/sebOjuCStNb0W9sLP249dQ3ahHDBKNyUFvLwgICDvbcWtfHzgxRfNTiGEEEKINNKFycVUC6nGoWGH6FKji9lRXJJSiuFNh7Pn/B62nHZMFY+pU+Huu405IETBbdwIn39udgohhBBCSAPChVyIvUBcYhxKKbyUvHQFNbDeQIL8g5i8fbJDjhccDBUqGJOjiYKbNw8mTIDkZLOTCCGEEJ5NPoW6kP/9/D/umHYHySnyCaowivsV58mGT7Lk8BJOXz1t9+MNHAjz59v9MG5v4kSjGpOPdLwUQgghTCUNCBeRVrq1Z62e+HjJJ6jCGt5sOH7efmw9s9Wux9m2TQZO20qpUuDvb8xQrbXZaYQQQgjPZXUDQilV2p5BRO5m7J4hpVttqHJQZS68dIE+dfrY7RhHjhiVgz7+2G6H8DhnzkCzZvDDD2YnEUIIITxXfq5AnFVKLVJKdVGOKF0j0iVZkpi+azr3VbuPWqVqmR3HbZQoYgxKuBp/1S77r1IFvv0WHn3ULrv3SGXLQkiIUdVKCCGEEObIz9twVyARWAycUkqNU0pVs08skdEvx3/hXMw5nm8upVtt7akfn6LN7DZoO/SJ8fWFRx6B0FCb79pj+fgYk/H16GF2EiGEEMJzWd2A0Fr/pLXuD5QDJgJdgCNKqfVKqUeVUv72CunpOlfvzK4hu+hcvbPZUdxOi/ItOPjPQTac2GDT/S5cCDNmyPgHe0lOhj17zE4hhBBCeKZ8dwTQWkdrradprZsAzwOtgHnAOaXURKWUTJdlB43LNZbSrXbQr24/ShUrxSc7PrHpfhctgtmzpauNvbzyCrRpA5cumZ1ECCGE8Dz5/nijlApTSo1SSv0BvAt8C7QDngE6A8tsG9GzPb3iaV5Y84LZMdyWv48/QxsNZfmfyzkRfcJm+/3uO1i50ma7E1kMGQILFkBQkNlJhBBCCM+TnypMvZVSK4CTQB9gCnC71nqQ1nqT1noh0Btoa5+onudC7AVm75uNJcVidhS39kzTZ1AoPt9lm2mOk5NBKWOwr7CPmjWhZ0/w9jY7iRBCCOF58nMFYjZwBmiptW6ktf5Ua521fM15YLzN0nk4Kd3qGOUDy/Njvx959e5XC72vI0egYkXYuNEGwUSuEhJgyhT4+WezkwghhBCeJT8zkoVpra/ntoHW+gbwZuEiCYBESyLTd02nc/XOUrrVAbrU6GKT/SQlQdOmULu2TXYncuHtDR9+CN27Q6dOZqcRQgghPEd+rkDEKKXKZF2olLpNKSV9bGxs8aHFnI89z/PNpHSroyw9vJRHFj1SqJKudeoYk5xJ6Vb78/GBXbvgE9uOfxdOQCkVopRaqpSKU0qdVEr1z2XbRkqpX5VSsUqpC0qpEY7MKoQQnig/DYicJo8rgjE/hLChZrc34/W2r3Nf9fvMjuIxLt+4zHeR3/HryV8L9Pzdu+HyZRuHErkqVcr4V8rlup1pGO8rocCjwHSlVJ2sGymlSgFrgM+B24DqwDoH5hRCCI+UZwNCKfWiUupFQANPpz1Ovb0MfAb8Ye+gnqZaSDXebP+mlG51oP51+xNSNKRAJV21hv79oXdvOwQTuVq8GCpXhitXzE4ibEEpVRx4EBijtY7VWm8GlgMDs9n8RWCt1vobrXWC1jpGa33YkXmFEMITWTMG4rnUfxXwJJCxu1IicAJ42raxPNu0HdOoX7Y+bSq2MTuKRynqW5QhjYbw/pb3OXX1FBVLVrT6uUoZH2RjY+0YUGSrenVo1gyuXYPgYLPTCBuoCSRrrY9kWPY7RrnwrFoAB5RSWzCuPmwHhmutT9k/phBCeK48GxBa6yoASqkNQG+ttXzPZ0cXYi/w4roXGdpoqDQgTPBMk2d4b8t7TN85nQmdJuTruXfdZadQIlf16xsT9wm3EQBcy7LsKlAim23LA42Ae4ADwCRgAdA664ZKqaHAUIDQ0FAiIiJslzgH0dHRWCwWhxzLDLGxsXJuLsZdzwvk3BzN6ipMWuv29gwiDFK61VyVgirxaptXaRTWyOrn/PUXTJ1qzI5ctqwdw4lcRUVBdLRUwHIDsUBglmWBQEw2294AlmqtdwIopd4E/lVKlcxaZlxrPQOYAdCkSRMdHh5u69y3CAou48spAAAgAElEQVQKIjo6GkccywwRERFybi7GXc8L5NwcLdcGhFJqCvCK1jou9X6OtNZSLqiQpHSrcxjXYVy+tt+2Db780mhACHNoDW3aQJUq8NNPZqfxXEqpdRhXAx7SWi/OsFxhzCX0OPCu1npULrs5AvgopWporf9KXVYfiMxm2/0Y4/PSFLyEmhBCCKvlNUK3LuCber9e6uPsbtJ5wwakdKvzuHT9El/s/sKqkq4DB8L583L1wUxKwfTpMG2a2Uk83stACjBOKZVxnvD3MRoPM/JoPKC1jgOWAG8ppYorpVoDPYF52Ww+G3hAKdVAKeULjAE2ZzPJaWbx8TLqXgghCiHXKxAZuy1prcPtnsbDxSfH06ZiGynd6gQWH17MUyue4o7Sd+Q6FiUhAYoUgYAAB4YT2brnHrMTCK3170qpeRiNhYHAHKXUqxjVkr4DnrFyV8OAL4F/gEvAM1rrSKXU3cBqrXVA6vHWp+5/JVAM2AzkOGdEuhMnICwMevWCQYOMXx5v77yeJYQQIpVVNUKVUr5Kqajs6nAL2xnccDCbBm+S0q1O4NG6jxLkH5RrSdeUFGjYEF5/3YHBRK6OH4fhw+Fq7t8/C/saA8QDbyilngXGA2uBgVprq2bs0Fpf1lr30loX11pX1FrPT12+Ka3xkGHb6Vrr27XWwVrr7lrr03keIC7OaP0vXAhdukDFijBqFByWCrDCM6xatQqlFEuXLs12/cGDB/Hx8eGnQvQJ/eGHH/Dz8+Ovv/7Ke2Phcqz6pKq1TgKSkP6ldrP73G4sKTKht7Mo7lecJxo+weJDizlz7Uy22yQkQPfuRiNCOIcrV4zxKDt2mJ3Ec6V+gP8YqAx8AmzBqOCXacJRpdRwpdR+pdS11NtWpVRXh4QsVizz43Pn4N134c47oXlzoz+cdHESbuzAgQMA1K1bN9v1L774Iq1bt+aeQlza7dmzJ3Xr1uV///tfgfchnFd+vur+BHhFKWV15SZhnajYKFrOaskbEW+YHUVkMLzpcFJ0Cp/t+izb9UWLGp85HnjAwcFEjho1MqoxSXcm013McP8JrfX1bLY5A/wPowxrE2A9sEwpVc/u6e64A/bvh//8B8qUybxuxw4YNswY1PSMtT2uhHAtBw4coFixYlStWvWWdVu3buWnn37ixRdfLPRxRowYwdKlS4mMzK4GgnBl+WlA3I0xkO2sUuoXpdTyjDc75fMIM3bPICklicfqP2Z2FJFBleAq9KjVg5NXT96y7uRJ2LXLhFAiTyVLGv8mJ5ubw1MppfpjDJqOSl00IrvttNY/aK1Xa62Paq2PaK1fwyjV2tIhQevWhfffhzNn4Mcf4cEHwc/v5vrERBkXIdzWgQMHqFOnDl5et34M/PTTTylVqhT3339/oY/Tu3dvihUrxmefZf9FnHBd+WlA/AssBlYBpzAGtmW8iQJISE5IL91a87aaZscRWXz/8PfMe+DW4i8ffWSUDb182YRQIk9DhkDPnman8DxKqfuBOcBBjMp9fwJPKqVyrUutlPJWSvXFmERui71zZuLrC926GbMRnjtnTOrSpImxbtCgW7cfPdoo9yV//MLFREZG8sYbbxAcHMz+/fvZtWsX5cuXZ/z48enbJCcns2zZMjp16oSvr2/68l27duHn54dSimLFivHnn3+mrxs9ejRKKZRStGrViuQM394EBARw9913s0hm+3Q7VjcgtNaDc7vZM6Q7+y7yO6Jio3ih+QtmRxHZ8PU2/gM9F3MuU0nXt96C5cshJMSsZCI39etD06bG/BDCMZRSbYBFGF2T7tNaXwRGY1T7ezeH59RVSsUCCcBnwANa6wMOinyr224zRuHv3Al//AGNG2def+kSTJoEzz5rVHF6+GFYuVIudwmnt3r1apo2bcrp06fp06cPAAMHDiQsLIzRo0fz8ccfA7B7925iY2Np1qxZpuc3adIkvaFx48YNHn/8cSwWC9u3b2fixImAMXHiggUL8PHJ3NO9ZcuWREVF8ccff9j7NIUDSbkfky37cxm1S9Xm3mr3mh1F5GDN0TVU+KgC285sS18WGAj3ykvmtJ59FsaONeaHEPanlGoArACuAvdorc8DaK0XAbuAnqklWLP6E2gANAemA3OVUs4xr1CtWrf+Ai1YAElJxv3EROOqRbduUL48vPwyHDzo+JxC5OHs2bM88sgj1KlTh+nTp1O/fn3AGJ+wdu1afH1907sYHTp0CIBq1ardsp+XXnqJe1Pf+LZv387YsWPTGxIAX3zxBZUqVbrleWn7knEQ7iVfDQil1GCl1Dql1B9Kqb8z3uwV0N1999B3rB2wFiWfdJxWm4ptKOFXgk92fILFAgMGwObNZqcSedEafvsNrl0zO4l7U0pVB9ZgVOm7T2t9LMsmaXO0v5f1uVrrxNQxELu11q8A+4CRdg1cGP37G92XmjbNvPzCBWM8Rd26RvenqVONqxVCOIH333+fmJgYvvjiC4oUKcKePXvw8/PjrrvuIiQkhHr16nH6tFH9+OJFo/5BSDaX15VSfPXVV4SGhgLw9ttvp3dlGjp0KA899FC2x7/tttsA+Oeff2x+bsI8VjcglFIvAx8AuzHK8y3D6OcagjHhj8in5JRkvL28qViyotlRRC4C/AIY3GAw3x/6nu0HL7Bxo1HpRzi3/fuNcSrzspu/WNhMagOgbOo8DPuzWf+z1lpprVtYsTsvoIjtU9pISIhRoWnHDoiMhP/+1+jKlNHu3fDcczB0qDkZhchi8eLF1K5dmwYNGgBGN6W77roLv9SiAdevXyc4OBgg/ctMnUP/z9DQUObMmZNpWc2aNdO7QGUnbV/yRal7yc8ViCHA0NRviZKAqVrrHhiNiluvWYlcnYw+SYWPKrD26FqzowgrDG82HEuKhbWXP+X4cSnd6grq14fvv89+HKwwn1JqolLqbqVU5dSxEBOAcOAbk6NZ5847jTrOp07BqlXQp0/mKk6PZVNV78IFx+UTAuNb/9OnT9MwdcKixMREIiMjaZw6vufq1ascO3Ys/XHp0qUBuJxLkYCDWbrqRUVFEZXLt2pp+0rbt3AP+WlAlAfSpme6AQSm3l8APGjNDpRSIUqppUqpOKXUydRyf9ltN1YplaSUis1wq5phfQOl1G6l1PXUfxvk4zycwrSd07gYd5E7St9hdhRhheoh1bm3Qm9m7f0SL+8Uqe7oIh56CIoXNzuFyEFZ4GuMcRC/AE2BLlrr1aamyi8fH2M264ULjUuT06dD587GsowSE6FOHWOykilT4N9/zckrPMql1K50AQHGBO7Hjx8nKSkpvcHw3XffkZiYmN796K67jCFIOc0evXv3bl599VWA9MHS165do1+/fpmqL2V09OjRTPsW7iE/DYgooFTq/ZPcrNVdHetnqJ4GJAKhwKPAdKVUnRy2Xai1Dshw+xtAKeUH/IDxxhMMzAV+SF3uEuIS4/hizxf0vqO3dF9yIYEbZnLb18dAS+0BV7JiBbzySt7bCcfSWg/SWlfSWhfRWpfRWnfSWrv2JdngYHj6aVi9OvPVCDCqNV26BHv3wogRUK6ccSnzhx9uDswWwsbKlSuHl5cXmzdvJiUlJX3MQqNGjTh9+jSvvPIKderUoW/fvgA0bNiQwMBAtm3bdsu+YmNj6devH0mpv6/z5s2jY8eOgDGoesyYMdlm2LZtG6GhodSqlWs1Z+Fi8vNJaD3QI/X+LOBDpdQGYCGwJK8nK6WKY1ypGKO1jtVabwaWAwPzF5lwjLKAH2utE7TWUwAFdMjnfkzz1e9fER0fzYjm2c6vJJzUg92CGNDPj2zm3RFObNcuo1hOXJzZSYRHO34cimQY3pGUBMuWQa9ecPvtMHIk/P67efmEWypZsiR9+/bl8OHD9O7dm40bNwLw448/0rRpU4oUKcKSJUvS53zw9vamd+/e/PLLLyQkJGTa17Bhw9KvTPTv35++ffsyd+7c9PETkyZNYv369ZmeExsby6ZNm3j44YftfarCwVROA2Vu2VApL8BLa52c+vgRoDVwBPhca53rVyhKqYbAb1rrYhmWvQS001p3z7LtWIxKHBbgPMZ4i+mp60YC92qtu2TYfgWwQWv9QTbHHQoMBQgNDW387bffWnW+uYmNjU2/HJhfWmsG7RpEUe+iTG843SaDigqTx16cLZOt8py/cZ73jrzHk1We5M7AO50ik604Wx6wTaaEBC98fGzT7czZfka2zNO+ffvdWusmNtmZk2vSpIne5YCp5MPDw4mOjmbfvn3Gguhoo6vTnDmQzTe8gDF459VXjTEVTi4iIoLw8HCzY9iFO51bXFwcI0eOZNGiRVy5cgWlFKGhofTs2ZM333wzvapSmh07dtC8eXMWLVrEgw8aPdS/+eYbBgwYAED58uU5cOAAQUFBgNEN6pFHHgEgLCyM/fv3U6qU0WFl7ty5DBo0iAMHDti9C5M7vWZZOfLclFLWvRdorR1yA+4GorIsGwJEZLPtnUA5wBtohdGI6Je6bgzwbZbtvwHG5pWhcePG2hY2bNhQ4OempKToTSc36Y0nNtokS2Hz2IuzZSpMnsRErb/+WusbN7SOSYjRQROD9MPfPWxqJntwtjxa2zZTcrLxGhaGs/2MbJkH2KUd9H5g9s1W7wV5adeuna5fv372K//4Q+tXXtH69tu1NqoO37xNneqQfIXlbH8PtuSO55aUlKR9fX314MGD89z2vvvu023atCn0MRs2bKgfeOCBQu/HGu74mqVx5LlZ+16Qa2cMpVQja29WNGpiuTnwOk0gEJNNo+aQ1vqc1tqitd4CTAbSCgxbvR9npJSiTcU2tK3U1uwowkpr1hhzP/z8s1HS9anGT7H48GJORJ8wO5qwUkwM1K4NH31kdhIhUtWqBe+8AydPwtq10K8f+PsbYydS+6On0xreftsYPyHTq4sCOnLkCElJSVZdCfjggw/YunUr69atK/Dxli1bxsGDB3n33WwnohcuLq/e3LuAnan/5nbbacWxjgA+SqkaGZbVB6yZmlBjjHMgdft6KnPfn3pW7sdUf/77J8NWDuN8zHmzo4h86NYNNmy4WVTl2WbP4qW8mLxtsrnBhNVKlICePY3eIUI4FW9vY1r7+fONKk4//gipE2+l27ULxowxKjg1aGC0hGVSLpFPaeVXrWlA1KlTh+Tk5PSZpwuiV69eJCYmUqNGjbw3Fi4nrwZEFaBq6r+53armtIM0Wus4jMHWbymliiulWgM9gVumeVJK9VRKBStDM+B5jMpLABEYYyOeV0oVUUo9m7p8fdb9OJsp26cwa+8svL2kBqgrUQrCw0nvQ18+sDyP1HmEmXtncjX+qqnZhPXefx/uv9/sFELkomRJozGR1ezZN+/v3w8vvmgMvO7RA5YsMUrECpGH/DQghMiLT24rtdYnbXy8YRizVv8DXAKe0VpHKqXuBlZrrdNGA/ZN3a4IcAZ4V2s9NzVTolKqFzATmAgcBnpprZ36f9ArN64w9/e5PFr3UcoUL2N2HGGlYcOM0u3Dh2de/nKrl2kU1ggfr1z/hISTiYuD5cuNHiIyKapwGX37Gv3wFi+GGzeMZcnJxtWKtCsW/fsbsyY2bCi/3CJbb731Fh06dKBcuXJmRxFuINdPP6ljG/ZprVPyGuegtd6T18G01peBXtks3wQEZHjcL4/97AUa53U8Z/L57s+JS4qT0q0uJDnZqLwYEnLruvpl61O/rPSHcTXffgtPPglVq0Lz5manEcJKbdsat2nTjOnV58yBzZtvrr90CT75xLh98IFxhUIIIewor69Pd2HMFvpP6v2MYxEy0hgVk0Q2EpITmLx9MvdUvUc+dLoQHx9jPqiUlOzXJ6ck8/X+rwkLCOO+6vc5NpwokP79jbGrzZqZnUSIAggMhCeeMG5Hj8LcufDVV3Dq1M1tuna99XnJycZ/aEIIYSN5/Y9SBbiY4b4ogLikOLrX7E7fu/rmvbFwCteuGQ2HoCBynDjOS3nxzqZ3CPIP4t5q99pkTg9hX0WLQps2ZqcQwgaqV4dx4+DNNyEiwrgqce6c0ULO6PRpaNwYHnnE6OLUqJF0cRJCFFqug6i11idTa8Km3c/x5pi4rimkaAgzus+gQxWXmSzb402ZAhUrwr//5ryNl/JiZIuR7Dy3k99O/+a4cKLQ3nkHXn/d7BRC2ICXF3ToYFyJ+OmnW9fPmwcXL8LUqdCkCdSta1QUiIpyfFYhhNvIqwpTJkopv9R5Hzorpe7PeLNXQFe34+wOtp7eanYMkU89ehgfMFMn08zRY/UfI6RoCB9u/dAxwYRN/P230QNESuoLt5LdlYUNGzI/joyEl1+G8uWNGtWLFkFCgmPyCY8WGxvL4sWLGTVqFG3atCEoKIgPP5T3TldldadIpdQ9GCVXsyshJGMgcvDSupc4EX2Cv0f8LRV7XEi9esYtL8X9ivN046eZsHkCRy4doeZtNe0fThTa55/fLMsrhFtbuxY2bjS6OC1aBNevG8stFli50rgFBxsT2Y0YATXl/zBhH9999x3Dhw8nISGB1M4trFy5khdl0L9Lys8ViGnACoyxEMWAohluxWwfzfVtP7OdTac2MbLFSGk8uIiUFKN7y+nT1j/n+ebP0/T2ply+cdl+wYRNpTUeLl40SrsK4ba8vKB9e2PAdVSUMadEu3aZt7lyBT79FM6eNSej8Ah33nknfn5+6Y0HgP3795uYSBRGfhoQYcA7qWMe4rXWCRlv9groyt7b8h4li5TkyUZPmh1FWGn/fqPr0q+/Wv+c0IBQtj+5nRblW9gvmLC506ehUiWYMcPsJEI4SIkSxkDqiAg4dgzeeAMqVzbWVap0a8MiNhYWLoT4eAcHFe6oXr16XE+7Apbq2rVrXL4sX765ovw0IFYArewVxN0cvXyUJYeXMKzpMEoUKWF2HGGlBg2M/vF9+uT/uVfjr7L+uNNPiC5SVagAY8dmX/VSCLdXtarxB3DsmNGg+PjjW0vOLV5sTGIXFgbPPAPbt8vAIVFgxYoVo2zZspmW+fv7s3fvXpMSicLITwPiaaCvUuojpdQTSqnHMt7sFdBVHb54mLIBZXmu2XNmRxFWsliMfytWBF/f/D//xbUv0vPbntKVyYX897/S5dsZKaVClFJLlVJxSqmTSqn+eWzvp5Q6rJQ646iMbsPLy7jy0OuWOV6NcRMA0dHw2WfQogXceSdMnCjdnUSBNG6ceQ7g+Ph49uzJcx5i4YTy04C4D+gIjAAmY4yJSLtNtX0019a9VndOjTxFWIkws6MIKz3wAIwcWfDnv9DiBWITY5m2Y5rtQgm7+/tvGDMm5wkDhSmmAYlAKPAoMF0pVSeX7V/m5pxFwha0NsrDVskyBdQff8Arr0DFitT773+N6d1v3DAno3A5bdq0wc/PL/1xYmIimzPOqi5cRn4aEO9jNBRKaK0DtNYlMtwC7ZTPJUX+E0mKTpGB0y7EYjG+ia5UqeD7qBtal241uzF5+2TiEmVkrqvYvh0mTYIDB8xOIgCUUsWBB4ExWutYrfVmYDkwMIftqwADgAmOS+kBlDJa1kePGlWcBg+G4sVvrk9JIWTnTqN6U1iY8YckRB4aNWqEv79/pmW7d+82KY0ojPx8wg0CPtNayyejXFy+cZkWs1owtNFQPrjvA7PjCCt5extzKxXWqNajaDO7DbP2zuL55s8XfofC7h5+GMLDjc9AwinUBJK11kcyLPsdaJfD9p8ArwK5fg2ulBoKDAUIDQ0lIiKi8EnzEB0djcViccix7O6xx/B++GFK/forZdeuJThDv/XkpCS2XLlCijucZ6rY2Fj3eN2yMPu84uLibhlIHRUVxerVqylatGih9m32udmTM55bfhoQi4FOwDE7ZXELU7ZPITYxlkENBpkdRVjp+HGji2/DhoXfV+uKrbm74t3si9pX+J0Jh/Dxudl4SE42HgtTBQDXsiy7CtxSjUIp9QDgrbVeqpQKz22nWusZwAyAJk2a6PDwXDe3iaCgIKKjo3HEsRymSxeYMAFOnOD4W29RZeNGfNq3p23nzpm3++UX49LeoEHG+IpCfjh0tIiICPd63VI5w3kFBQXx77//pj8uXrw4QUFBtGzZEq0158+fp1SpUpm6OlnDGc7NXpzx3PLzVvk3MF4p1RbYDyRlXKm19vjpBK8lXGPy9sn0qt2LuqF1zY4jrDRhAsyfD+fOQaANOuOtGbCGYr4yNYqreeYZOHkSVq0yO4nHiwWy/iUGAjEZF6R2dZoE3O+gXCKjypU5+dhjVJk1yyj3mtXs2bBunXELDIRHHjEaEy1bZj9jtvAY9evX55dffkl/nJiYyJgxY4iNjSUyMpLY2FjmzZvHgAEDTEwp8pKfBsT/YfwH3opby7lqwOMbEFN3TCU6PprRd482O4rIh/feM8q22qLxAKQ3Hk5dPUX5wPJ4qfwMNRJmqVMHbrvNGEydtZqlcKgjgI9SqobW+q/UZfWByCzb1QAqA5uU8YHUDyiplIoCWmitTzgmrodTyphfIqMbN2DZspuPr12DL74wbjVqGA2JgQONWsrCIyQmJnLo0CH27t3L1atXUUqlTygXHx/P+vXr0x8XLVqUVq1k1gBnZ3UDQmtdJe+tPJfWmuV/LqdL9S40Ltc47ycIp1GyJHTqZNt97jy7k1ZftuKb3t/Qp04BJpUQDvfss2YnEABa6zil1BLgLaXUk0ADoCe3fnF1EMj4CbQVRqGPRkhFJnMVLQqHDsG8eUYp2KNHb6776y947TUYPRo6djQaE717u1wXJ2G9PXv20Lx5c/z9/dFaExd361DajLNTBwUFUbVqVUdGFAUg37PZiFKKzf+3mTm95pgdRVjp/HmjO29k1u81baBRWCNqhNTgrY1vkaKlPqgr2boVDh40O4XHGwYUBf4BFgDPaK0jlVJ3K6ViAbTWyVrrqLQbcBlISX1sMS+6AIwJdV57DY4cgd9+gyFDMl/m1Rp+/hkeewyuXDEvp7C7evXqUb9+feLj47NtPGSklKJ79+4OSiYKI9cGhFJqSmo/07T7Od4cE9c5JSQncD3pOj5ePpQpXsbsOMJKR44YHxTt8cWXt5c3Y9qOIfJiJIsPLbb9AYRdxMdDjx7w9ttmJ/FsWuvLWuteWuviWuuKWuv5qcs3aa0DcnhOhNa6vGOTijwpBa1awYwZxrc233wD99xzcxzEvfdCuXKZn3P+PJw65fiswi58fHz48ccfCQjI9k83kxIlStCzZ08HpBKFldcViLqAb4b7Od3usldAV/DFni+oMrkK52LOmR1F5EO7dnDiBNjrSmmfOn2oXao2b258U65CuAh/f1ixAmbONDuJEG6oWDHo398YWH3yJLzzDowYcet2H38MlSsbfUu//hry+NZaOL+wsDCWLFmSZ6nW+Ph42rXLqWKzcCa5NiC01u211tEZ7qffgHuA7qmPOzgirDNKSE7g3d/epeZtNQkLkELyruLgQeMKure3/Y6RdhXiz0t/suf8HvsdSNhU8+YQEGD8fggh7KRCBWNG66zlX5OTjbETWhulYAcONOosP/EEbNokf5gurH379owaNYpixXKuUtigQQOKZ5ywUDitPMdAKKU6KqX6ZFk2CqPUXrRSao1SKsheAZ3dF3u+4My1M7ze9nWUlKZzCWfPQpMmMG6c/Y/1SJ1H+Ou5v2hSron9DyZs5tgxo9fFtm1mJxHCw1y6BPXqZS71GhMDX34JbdsaVZzGjTOuYAiXM3r0aJo1a5btHA/+/v706SNFR1yFNYOoRwHp/UqVUs2Ad4B5wH8xyuu9Zpd0Tu560nXGbxpP20pt6VTVxmV8hN2UKQPTpxtj9+zN28ubykGVAbgaf9X+BxQ2ERoKSUkytlMIhwsNhTVrjDEQEyZArVqZ1x87Bq+/bnRx6tABrsr/q67Ey8uLxYsXExR06/fO3t7edOnSxYRUoiCsaUDUBTZmePwwsEVrPSR18rjngR72COfslhxeQlRsFOM7jJerDy7E1xcGDzbefxxl5JqRNJvZjOSUZMcdVBRYQADs3GlU6RJCmKB8eRg1Cg4fNkqjPfWUUXM7o3//td0EPsJhQkJCWLly5S3jIYoUKcIdd9xhUiqRX9Y0IIIwSumlaQ2syfB4J3C7LUO5ikfrPsrOITtpU7GN2VGElcaNg4ULHX/cdpXbceTSEebum+v4g4sCUcqYVO7nn81OIoQHUwpatIDPPjOqM337rTFuwsvLmEMi65d38+fDm2/C8eOmxBXWadKkCZMmTco0HuL++++XL2NdiDUNiPNANQClVBGgIbA1w/oSQILtozm3+OR4lFLSt92FJCXBDz8YJckdrWetnjS/vTljN44lPjne8QFEgcyda1Sc3LTJ7CRCCIoWhUcegdWr4fRp+L//u3WbDz6AsWON8nrt2xsT2cXGOjqpsMLw4cO599578ff3JzAwkAceeMDsSCIfrGlArAYmKaU6AO8CcUDGt9N6wNHsnuiuriZdpdLHlZj3+zyzo4h88PWFHTuMbrWOppTinY7vcObaGT7b9ZnjA4gC6dfP+MKzdWuzkwghMilXDrL2o9+/H/ZkqHgXEWH0Vy1b1rhaERFhXFYUTkEpxbx58wgNDSUuLo6OHTuaHUnkgzUNiNeBeOBn4P+AIVrrxAzr/w/4yQ7ZnNbXJ7/mYtxFGoU1MjuKsNLFi0WIjzeueptVIa5DlQ50rNKRz3Z9JvNCuAh/f+MLTy9r/qcUIoOyZWHjxgh+/30fSpF+K1vW7GRurGZNo4/q/fdn/qONizMuJ7ZvD9WqwRtvGIOxhekCAgJYs2YNw0cOp8fSHkTFRpkdSVgpz7dFrfW/Wuu2QDAQrLVemmWTh4G37BHOGf195W+WnVvG4AaDqVOmjtlxhBW0hvHj76BtW/NLiM/sMZNtT27DS8knUlfy44/GnFbJydI/V1jnwoX8LRc24O8PffrAypVGF6dJk+DOOzNvc+IEvPWWcVnRYhhYtnoAACAASURBVDElpsisdu3aJIUnsfnUZsZtdEB9dWETVn+K0Vpf1Vrf8temtb6c5YqEW3v1l1fxVt681d5j2kwuTykYOPAEo0bdOt7O0SoHVSbIPwhLioXrydfNDSPy5epVuHz51trlQggnVK4cvPyyMWvojh0wbBgEB99c37//rTOJXrokXZxMcD7mPLP3zSZFpzB732y5CuEi5GvQfDgZfZJFhxbRp3wfbg/0yMJTLqtx42h69zY7hcGSYqHlrJZMPTbV7CjCSt26GZ9BypTxuHoRQrg2paBpU5g2zaji9P330LWrMSYiq8GDoUoVeP11/M+edXhUTzXu13Hp3Xot2iJXIVyENCDyoVJQJX5/+nf6VuhrdhRhpW++gYkTwWJxnq4n3l7e3F3xbtZEreH3qN/NjiOskNZ/PT7eiw0bzE4jhCiQIkXgoYdgxQpjtuuMLlyAVauMCezGjaPFgAHGzNezZsG1a+bk9QBpVx8SLUZHlkRLolyFcBHSgLBSbKJRBq5OmToU8ymWx9bCWWzcaPRf9/IyefBDFqPbjqaETwn+s+4/aLMHZgirzZhRlfvvh4sXzU4ihLCpyMhbJ6rbtAmefNIY+T5wIPzyi3RxsrGMVx/SyFUI1yANCCskJCfQeEZjXvn5FbOjiHyaMQPWrTN/7ENWwUWDGVhpIL8c/4VVf60yO46wUr9+p1m7FkqXNjuJcHahoflbLkzWoQOcOweLF0P37uiMVZxu3ICvvzYqKVSuDG+/bVpMd7P1zNb0qw9pEi2JbDmzxaREwlo+ZgdwBR9v+5gjl44QXjnc7CjCSmfOgI+P8cWRWWVb89KzXE/WXlnLrL2z6Fqzq9lxhBVKl06gbVvjvtbO1zAVziMqCsLDw4mOjmbfvn1mxxHWKFIEeveG3r3ZsmQJrU+ehNmz4cCBm9ucPp35sSiUvU/tNTuCKCC5ApGHs9fOMu7XcfSs1ZP7qt9ndhxhpREjoEkTSHTi+mC+Xr6seXQNCx9aaHYUkU+ffgrh4dKbQQh3lRQSAiNHwu+/G5PTPf883HabsTK7Adhz5sBPP0lpWOExHNqAUEqFKKWWKqXilFInlVL9c9juZaXUQaVUjFLquFLq5SzrTyilbiilYlNv6+yV+b8//5fklGQ+vO9Dex1C2MGECTBlCvg5edXNaiHV8PX25VrCNS7GScd6V1Hy/9u78/go6vOB458nJyEcCWeAGEAIqKCgYEVQiRdQqIoHHqCiVkEQxdpqLZYWwatWUasgxwsFAbHUH3jhLYJVPADlLBAQEQMEEAgkAXLsfn9/fDfkBHYhOzNJnvfrta/szszuPJPZmdlnvld9aNQIcnLcjkQpFVYicPbZ8MILtorT22/D5ZeXXubwYbj/fujVy1ZxeuQRSE93JVylnOJ0CcQEIB9oCgwCXhaRikZjE+BW7OB1fYARIlK266MrjDF1Ao9e4Qh2V+4uFqQv4KEeD3Fq4qnhWIWqZEXtkdu1wzPdth5Pvi+fzpM6c+8H97odigrSwIG2qnS9em5HopRyTEwMXHmlrR9b0ttv24FiwNaffeIJaN/eDlY3ZUrxPKWqEccSCBGJB64FRhtjcowxXwLvALeUXdYY87Qx5ntjTKExZgPwNtDDqViLNIlvwoYRG3j4goedXrU6QY8/DkOHVq1S5JjIGG7tdCv/XvtvPv9J+witCoraPuzYAVOnuhuLUsplnTvbEohGjUpPX7LEXpCSkuxdh48/rloXJ6WOwckSiHZAoTGmZLneSqCiEogjRESAC4G1ZWbNFpHdIvKxiHSq3FBh9c7V+I2fpnWaUjtau22tKg4dgoMHyw8w6nV/7vFnWie05p737ynXI4XyrokTbdVoHXNKqRqsfXt47jl7InjrLejfv3QpxeHDMGcO9O4NAwa4F6dSlcjJXpjqAGVHY9kP1D3O+8ZgE51XS0wbBHyPreo0EvhIRE4zxmSVfbOIDAGGADRt2pRFixYdN9Dth7Zzx7I7uD75eu5ofUe5+Tk5OUF9jlO8Fg+4F9Pll9tqTGVXfSLxbNiwgREjRlBYWEhsbCxTpkwhJSUFgGnTpjFr1iwAOnTowAsvvEBkiFlL2ZjuSr6LUWtGMWzWMG5pWa5gLuz0e3R8ZePp3j2CqVNj2bjxEBs3uh+PUspFMTFw1VX2sXu3TRqmT4cfSvQ01Ldv+ff5fFXvrpeq8ZxMIHKAsjWG6wHZR3uDiIzAtoW40BiTVzTdGPNVicWeFJHB2FKKd8t+hjFmCjAFoGvXriYtLe2YQRpj6D2rNzFRMTxx7RMk10sut8yiRYs43uc4yWvxgPMxffyxLSUuO7joycSTlpbGgQMHeOihh8jLy2PixIl89dVXLFu2jDlz5gCQkJDAggULaNmyZcgxl40pjTRWmpXsKdxDz549EYf7CNXv0fEdK57t26F5c+/Eo5RyUePGtnjyvvtsT04zZsC8eeVLIIyxVaA6drS9O112mSYTqkpwsgpTOhAlIqklpnWifNUkAETkDuBh4FJjTMZxPttgSyNO2uzVs/lk8yc8eemTFSYPynuMgT/9Ce6+u7gRdWX505/+RK9eto3+t99+y5gxYxg8eDC+QD3WqVOnnlDycDSvXvUq82+Y73jyoE7Oq69Cmzawfr3bkSilPKdTJxg/Hn76qfxo1199BWvWwBtvQJ8+kJICDz8M69a5E6tSQXIsgTDG5ALzgLEiEi8iPYCrgJlllxWRQcATwOXGmM1l5qWISA8RiRGRWoEuXhsBX5X9nFD9evBX/vDRH+iW3I27u959sh+nHCICCxfCzJmVP7CXiPDaa6/RNDB87GOPPcaGDRsAGDJkCNddd12lri8uOg4RYUvWFt7dUK5ATXnUb39rxx4J1HBTSqnyKrpAffZZ6dfbt8M//gFnnAHdusGkSbBvnzPxKRUCp7txHQ7EAbuAOcAwY8xaEblQREr2qP4Y0BBYWmKsh0mBeXWBl4F9wDZsN6+/NcbsOdngMg5kkFgrkalXTCUyQosQq4Kff7alDo0a2TvA4dC0aVOmT59ealq7du14/vnnw7NC4I8f/5GB8wbyc9bPYVuHqjxJSfDUU1Bb+1tQSoXi73+3VZweeACaNCk979tvYdgwaNYMbrgBFi92J0alKuBoAmGM2WuM6W+MiTfGpBhjXg9M/68xpk6J5VobY6JLjPNQxxhzd2DeWmPMWYHPaGiMudQYs6wy4uuc1Jl196yjY5OOlfFxKsz27LGjTT/0UPjXtWbNmlKvMzMzyczMDNv6xveyAxfe/vbt+I0Od1xV/PijHaFax5BSSgXtrLPg2WftGBLvvgvXXgvR0cXz8/Jg7tzyvYMo5SKnSyA8KeNABqMXjiavME9LHqqQxET4299g8ODwrmf58uWMGjUKgKhA13wHDhzgpptuorCwMCzrbJnQkhf6vMDnWz7nua+fC8s6VOWrXdvWQNi61e1IqjYRaSAi80UkV0R+FpGBR1nuQRFZIyLZIvJToEqrUlVTdDT87nfw5pt2kJkXX4QuXYrn33pr+fe8+Sbs3etcjEoF1PgEwm/83P727Tz3zXNsy9bO3KsKvx8iIuDee23nFeGSk5PDTTfdREFBAQAzZ87k0ksvBWyj6tGjR4dt3bd3vp3+p/Vn1MJRrN65OmzrUZWnWTPb9vGyy9yOpMqbAOQDTbHddr8sIhWNGSTYnvoSsdVZR4jIjY5FqVS4NGwII0bAsmWwejW89BK0bl16mR9/tL06NWtm/y5YAGG6qaVUWTU+gZjw3QQ+3fwpz/Z6llMTT3U7HBWE9HTo0MGeV8Nt+PDhbAx08D9w4EBuvPFGZsyYQWJiIgBPP/00CxcuDMu6RYQpv5vCPefeQ6uEVmFZh6p8RT0wzpplO1hRoRGReOBaYLQxJscY8yXwDlBucBRjzNPGmO+NMYXGmA3A20APZyNWKsw6doR77ik//bXX7N/8fFsS8bvfQXIyPPig7dlJqTBychwIz1m+fTl/+uRP9Evtx5AuQ9wORwUpL89WX0pKCu96Zs+ezcyZtpOw5ORkJkyYAECLFi2YNGkSN9xwA36/n5tvvplVq1bRqFGjSo+hcXxjxve27SEKfAVER0Yf5x3KCw4dgjFj4LzzoIf+nA1VO6DQGFOyJclKoOex3iS27+MLgclHmR/yoKInKysrC5/PV20H+6vOAxlWhW1rYgzJp51GvZL9R+/cCc88A888Q3a7dmT26cPOSy6hMNB9bFXYrhOl2+asGptA+I2f296+jSbxTZjRf4b2u1+FnHmmvbMb7l02aNAgBg0aVOG866+/nuuvvz68AZTwy/5f6DO7D+MuHsc1p1/j2HrViYmLs10Lt2jhdiRVUh3gQJlp+7E98B3LGGyp+qsVzQx1UNHKkJCQQFZWVrUd7K86D2RYJbYtLQ3GjoX//c8OVDdzpm07EVA3PZ266emkvvwyvPACDBtWNbbrBOm2OavGVmGKkAhev+Z1/u/6/6Nh7YZuh6OCsGCBvatbWBj+5MFrmsQ3IT46ntvfvp1Neze5HY4KQkqKrc6UnQ0ffeR2NFVKDlCvzLR6QPbR3iAiI7BtIfoZY/LCGJtS3nPGGXbsiK1b4f33bZevsbHF8wsK7GB2SlWiGplAbNxj67Sf2fRMftPiNy5Ho4L16afw9tv2XFjTxEbF8p8B/yFSIrlu7nUcKjjkdkgqSI88AldfbWsWqKCkA1EiklpiWidgbUULi8gdwMPApcaYDAfiU8qboqLsqJZvvGFLIl5+2dajTE2F888vvWxWFnTvbnt6+vVXd+JVVVqNSyC+3PolHSZ24KXvXnI7FBWi8ePtODpxcW5H4o6WCS2Zdc0sVu5cyYj3R2CMcTskFYSxY+HDDyEwmLk6DmNMLjAPGCsi8SLSA7gKmFl2WREZBDwBXG6M2exspEp5WGIi3H03fPMNLF1avth+7lz4+mu47z5o3hyuuQbeeadm3qFTJ6RGJRD5vnyu+fc1tE5szc1n3ex2OCoIxtixHjIy7PmvXtmKDTVM39S+PHLhI6zetZqDBQfdDkcFISEBLrrIPl+xwnaYoo5rOBAH7ALmAMOMMWtF5EIRySmx3GNAQ2CpiOQEHpNciFcp7wo0oC7l9deLnxcUwPz5cNVVtuHWAw/Y0bGVOoYalUBs2ruJfF8+79z4Dgm1EtwORwVh40Z4/nl7blPW2IvHsvi2xcTHxLsdigrBli22NsHjj7sdifcZY/YaY/obY+KNMSnGmNcD0/9rjKlTYrnWxphoY0ydEo+73YtcqSrirbdg0iTo1q309N274bnnoHNnOPts2/haqzipCtSoBOJQ4SHmDphL+0bt3Q5FBaldO1i71o6nEy4+nw+fzxe+FVSyCIkgLjqO/Yf3c8ObN7Bml/b3XRW0amWrJI8c6XYkSqkaLyEBhg611ZjWr4e//KV8t3ErVsD999su5ZQqo0YlEKfUO4VebXq5HYYKwuLFth0YwCmnhLfXpbvuuovExESGDh3Kt99+W2XaFmTnZ/PFz19wxZwr2JW7y+1wVBDuuAMaNLAjqX//vdvRKKUU0L49PPEE/Pyz7TLuppugVi07LyEBrryy9PKFhVrFSdWsBKJJfBO3Q1BBGj8eHnss/PXF8/PzmTt3LtnZ2UybNo3LLruM5ORkMjK835lLcr1k3rnxHXbm7KTPrD7sP7zf7ZBUkB57zHaK8uOPbkeilFIBkZHQq5dtH5GZCVOm2EaIRclEkY8/tlWcOne2dYx36Q2smqhGJRCq6vj3v+05KiYmvOv58MMPiYiwh4HP5yMnJ4c9e/aQkFA12sic2+Jc5t0wjzW71nDFnCu0YXUVMWKErc7Upo3bkSilVAXq14e77oI//KH8vOnT7d+VK+38Fi2gf3/bWFF7iagxNIFQnpGRAcOHw+HD9oZH8+bhX+fkyZPJzi49PlXv3r2pU6fOUd7hPX3a9mHm1TPZvG8z2w5sczscFYQGDWx1JrClEGu0GYtSqiowBurWLV0qUVhoB2m65hp74R45En74wS6rqi1NIJRnfPGFLTnd5NBAy9nZ2Xz22WelptWtW5chQ4Y4E0AluqHjDWwYsYHUhqkYYyj0F7odkgqCMTBwoB04tgq141dK1VQiMG2areI0dSr06FF6/p498K9/wTnn2NGvly93J04VdppAKNcV3aQYONAmDx07OrPeefPmER0dXSYWQ69eVbOhfVG3rg998hDXzr2WvMI8lyNSxyMCr71mOwyIjHQ7GqWUClL9+nDnnfDll5CeDn/9q+3xpKR168pPU9WGJhDKVZs3w7nnwqpV9nWjRs6te8qUKeTkFI9JFRERwYABA8olFVVN68TWvLPhHa564yptE1EFtG8PZ55pn0+ZojfslFJVTGoqjBtnB7v59FO4+WaIi4O+faFJmc5rVq+Ge++1Jzqt4lSlaQKhXBUZaQfBPHTI2fXu3LmT5WV+qdWuXZs7iiqmV2HDzx3OtCun8fGPH9N3dl/2HdrndkgqCAcPwlNP2XGblFKqyomIgEsvhZkzbRWnik5mr7wCL70EXbvaOyfPPGOXVVWOJhDKFT/+aG8+tGxp21qdd56z63/jjTeO9L5UpFatWnTv3t3ZQMLkjrPvYPY1s1nyyxIuee0SfH6tYO91tWvDV1/ZUgiwibVSSlVJ9erZ0TNLKiiA2bOLX69dCw8+CMnJ8LvfwZtvQp5Wva0qNIFQjlu+HE4/HSZPtq8jXPgWTp48mUMlij2io6MZPHhwuaSiKrvpzJv45JZPGHXBKCIjtIJ9VdCsme3cJD8fLrvMlkgopVS1EBlp+2i/9VZ7x6SIzwcLFsCAAfYkeM89sHSpVnHyuOrza0lVGWefDWPG2J5n3LBp0yZ++umnUtOio6O57bbb3AkojHq26smADgMAmL1qNq+tfM3liFQwjIHWre1DKaWqhYgIuPhimDHDVlt65RW46KLSy+zbBxMnwm9+Axs2uBOnCoomEMoRO3bAbbfBr7/ac8ioUZCY6My6J06cyMKFCzl40DYonjlzJn6/v9QyTZs2paNT3T+5wG/8zFw1k8FvDWbIu0M4XHjY7ZDUMcTG2rGaipLsjz+G9etdDUkppSpP3bpw++2weLHtfvFvf7N1mot06QKnnVb6Pbm5dqAo5QmaQChH7Nhhx5lZtsz5dU+YMIGnnnqKhg0bcs011zB58mTyS4yWGRsby5133ul8YA6KkAjeG/geoy4YxdTvp9J9Wnc27XVowA11UgoLbYn+Pfe4HYlSSoVBmzbw6KO2W8bPP4fBg+Huu8sv9/LLtorT8OHw3XdaxcllmkCosDl4ED74wD4/5xzYuhX69HE+jjZt2lBQUMDhw4eZP38++/aV75Xo5ptvdj4wh0VFRPH4pY/z3k3vsSVrC50ndWZH9g63w1LHERVlb9K98op9nZ1tx2pS3pWUZMf4WLx4EStXrkDEvk5KcjsypTwsIgLS0mzxa9mbesbY6VlZNpE47zzo0AGefhq2b3chWOfsyN7ByBUjyczxVm9VmkCosHniCbjiCntTAWyJpRtOK1MMWrL0AaCwsJCJEyeybt06J8NyTb92/Vg9bDXP9nqWZnWbAZCdl+1yVOpYmjcvLt0fNcoOtnjggLsxqaPbuTO06Uqp49i9296VLGndOvjzn+1gdb/9LY0XLqyWVZzGfTGO1ftXM27xOLdDKUUTCFWp9uyBX36xzx980I4pc+qp7sbUtm1bYmNjjzrf5/Mxfvx4unTpQmpqKkuXLnUwOne0qNeCoV2HAvBtxre0GN+CWT/P0rYRVcCdd8Jf/mJ7SQRNJJRSNUCTJratxOLFtu1EfHzxPL8fPvyQDuPG2SpOd98N+/e7F2sl2pG9g1dXvIrB8OqKVz1VCqEJhKo0Pp8tVSwai61+fVsa6bZWrVoRFRV1zGWKqjj9+uuvJDrVutsjmtVtRu+2vZm2ZRodJnbg3Q3vYrRuqWd16gT33Wefb91amxYtbPsipZSq1iIibK9Nr7xie3GaMQMuuaT0MllZ8N57UKeOOzFWsnFfjMNvbKcvPuPzVCmEJhDqpPh8tocYY2wXz+PHw/PPux1Vaa1atSrX61JZERERNGzYkO+++462bds6FJk3pNRP4T8D/sMzZz1DTGQMV75xJf1e76dJRBVQu3YhN94I559vX2dmVssSfKWUKq1OHTuexGefwU8/wdixHGre3M675Rb7g6SkRYtgzhwoMf6T1xWVPuT7bLXrfF++p0ohNIFQJ2XOHOjd2x7DAFdeads1eUlKSgp5xxjdMjIykiZNmrB06VJSU1MdjMxbuiR2YdXdq5jYdyK92/RGRPAbP19t/UqTCY9q1CifqVNt6T7A0KG2+3TdXUqpGqNVKxg9mm9nzYIvvoBhw8ov8+STMHCgreI0dCh8/bXnT5QlSx+KeKkUQhMIFZL8fHjmGXjnHft6wACYN698KaKX1KpVi7i4uArnRUVF0axZM5YuXUqrVq2cDcyDoiOjGXbuMEZ2GwnABxs/4IJXL6Dr1K68vvp1CnwFLkeojuW+++Chh2yPP8bAhAmwbZvbUdU8TZuGNl0pVQlE4MILISWl9PSMDPjkE/t8/36YMgW6d7fjTDz5pJ3vQV9nfH2k9KFIvi+fJRlLXIqoNE0g1HEZU3x8RUfDtGnw0Uf2dWwsXH21rZroZQ0bNiw3LTo6muTkZJYuXUpycrILUXnfJa0vYVK/SeTm5zJo3iBSnk/h4U8fJic/x+3QVAUuvRSKeiROT4d774W33rKvCwvtQ4VfZqY9b/bsmUanTp0xxr7O9EbNA6Vqlrg4GDcOylZPTk+33dqlpECvXvD66+V7enLRD0N/wPzdYP5u+Lzn50ee/zD0B7dDAzSBUEG4806brPv9NsH/9lt7Z/Oo8vLswseoNuS05kV1IwNiYmJo3bo1S5cuJUk7Zz/qPouLjmNo16H8757/8d5N73Fu83OZu3YucVG2RGfxlsVsz67efXBXVe3bw8aNdkwmsIlESortyEQppWqMhg3hkUdswvDll/ZHTcl+5Y2xJRSDBtlB7Qq0pD0Yx+6aRtVI779vu2D96itISLB3NC+6yDaYjogo7j6yFJ/P9sn80UewejVcfLGtZ3jmmbaRxOmnl2/U5KCWLVvyzTffAHbk6dTUVP773/+SkJDgWkyuC2GfRUgE/dr1o1+7fhwuPExkRCR+4+fG/7uRnTk76X5Kd/qm9qVXm16cnXQ2kRHu7WtVrE2b4ufNm9ubbK1b29cvv2x3//PPe78EUSmlTpoI9OhhHy+8APPn28HpPvusuD3E5ZfbqhYl+f16kqyA/kcU69bZY+b77+3rhg0hOdmO2wL2d+XgweWPqSMOHYIXX4R//tOOGpeSAjEx9u/mzXb6iy+62vtB8+bNiYuLo1atWnTs2JElS5bU7OThJPZZrahagE0qPh/8OY+mPUpuQS6PLHyEc6eey4OfPAhAga+AtbvW4vP7HN00VbHu3e21siiP//lnWLtWr4tKqRqodm1b4vDJJ/Zk+PjjkJoKt91WftkhQ+Cyy2DWLMjNdTxUr9ISiBoiL89271i/Pvz6K/TrZxtcDhoEDRrYEVL37rXLnndecRuH4/L5YPJkWLHC9oQgUjwvIgIaN4ZGjez8yZNtpWwXSiKSkpI4dOgQ559/Pp9++im1a9d2PAbPqMR9dlqj0xjdczSje45mV+4uPt38Ke0btgdg1c5VdJ3alboxdenavCu/afEbujbvysWtLqZh7fJtUpSznnrK852QKKWquaSkikdob9rUwTZDp5xi20L85S/l5+XkwBtv2MThs89s1acBA2yiccEFpa+fNYyj955EpIGIzBeRXBH5WUQGHmU5EZF/iMiewOMfIsV7SUQ6i8hyETkY+Ns53LEnJdnviQhcfHHakeduVZ8/XjxTptiqSGB/LyYm2h8MYBOGBg1sAg72QF21yibYIVu3zhZdtGx55EDaQTYjfxlLJoGGtiJ2/g8/2OUdtiN7BzPyZ3DX8LtYuHChJ5KHHdk7GLlipDv9OYdpnzWJb8LAMwfSpXkXAFoltOK1/q8xuNNgcgtyee6b5xjwnwGs3LkSgIU/LeS6udcxeuFopn0/jc82f8amvZso9NuWvq7+j0qoasd+KLx67ausa4Wq+kp+30s+tOla9VBR8nCs6WFV9OUqacmS0g2rs7PtQHYXXWRLLMaNsyUYNZDTJRATgHygKdAZWCAiK40xa8ssNwToD3QCDPAJ8BMwSURigLeB54GJwFDgbRFJNcbkEyZOf8kLCmypQdFgikuX2mnduwcXzz//aUsS+va1N4+ffBLOPtvOi4iADz6opEA/+shm5CUOunF8wepDGxhHHSbQz04UsRvz0UfQsWMlrTw4474YxzrfOtL6plGrVi1H1300474Yx+r9qxm3eBwT+h2rRXoYOLTPGtZuyC2dbuGWTrcAkFeYx5pda2jfyJZQ7Dm4h9W7VjN//fxSfV1vuncTbRq0YdC8Qazav4qe03tyRbsraBrflKZ1mnJTx5uIjowmMyeTQwWHqBdbj3qx9YiOPFodu5PjqQvcMdbrVjxhctLXCgdjVWFUQ77vyqt69YKtW2HmTFsHND29eN6PP8Lf/mYfF18Mt99uB7GrIcSpAaJEJB7YB3Q0xqQHps0EthljHi6z7BJgujFmSuD174G7jDHdRKQX8CqQbALBi8hWYIgx5sNjxRAX18yce24HIiIKOXgwmdzcNjRq9CUiPnJzW5GT04YmTRYiYsjJaUtOTluSkuxHLl686Kif27NnGvv2nc2hQ6fQvLkdIGH37os4eDCFli1nAbBjRz8OHkyhTZuXAdi6dSCHDjWnfftnAEhPf4C8vCaceab9V6xa9TSFhfGcc849AKxY8RzGRHL22fcFFU9BQV2iorLDe4fR77cHU4kf5XlxhXx33Xb8UYaIQuG8N1sQc6hE9ZfDh6FdO8cqXufF5PFdt+/wmodiwgAAFGdJREFUR/qJ8EVw3jfnEZMf48i6PRmTB/eZX/zkxeaRVyuPw7UO02RnEwqiC/j2/G8xEQYMiF8wkfZcdeGiC4kwEWxM3cj25OIeoCJ8EUQVRNHt624IwpZWW9iXuI8If8SRR3RBNO3S2wGQmZRJbnwuYuTII7ogmhbbWgCwu/Fu8mLz+HHj/eCPBH8UHGwM6/vbFbb9gNPOecA+D5xGYwpiSNyXCMCeBnvwRfoQig/CmPwY6u+vD8DeBnvxR/hLvT82L5a6ObZ3kL2JezFiZxR9RkxeDMvfXxZYf/nTXdfU0cQfjMdg2NdgX7n5tQ7Vovah2vjFT1ZiVnFceTEsf3/5cmNM1+PsrrCrrGvFsdZRt25d06VLl7DEX9KKFSsoLCyka1fX/61hkZWVFfa2ZMe71oWLE9vmBq9tV2Xu37BvmzGckZ1Nn8xMLtm1izq+0u37vk9I4IFOncKyaif32+LFi4O6FjhZAtEOKCy6IASsBHpWsGyHwLySy3UoMW+VKZ35rApML3dFFZEh2LtUQBf27i0gKmo/u3ZdyY4dD9CxY3ciIw+yc2cXMjPvJTr6bSIiCsnMPJedO4dSq9Ybx92wrKwstm07j6ysPtSu/RoAO3d2JDu7O/XrvwTA3r1JHDzYjoYN7UU7N1fIy4slKysrEOcGoqJ2H3ldr95cjIk58jop6TGg8Mjr48UDx1+uUiQklPphmXHODvyBHz1+MaR3ySH5+2bFy9eqBQcOOBMbkNEpAz/2R5ofP+nN0kle6e6YD67H5OF9VotaHOAAGZ0yike/9kPiz4k0X9ucwthCDuTaWOI3xnPKzlPwRfnwR/vxRfswYtiftR+A/Nx8/PF+CiMLMZEGf4yfyKjII8fQztSd7E/aD4JNVARicmKIXxsPwNaOW8lpnAOp9xYHuKNzcQJx8d9Z32J9qfjjf42n7U+2r/GNXTeSV7d0t7h1M+ty6s+nArC+23oK4kp3F1g/oz6tlrYCYO0Fa/FHlx6FtMGWBsUvBvaDiNLzt2xqRIvtLfBH+ll98epy/98m65vQbEczCmIL+F/a/45MT/jFOz8oqLxrRSklrwXR0dFBnUtPVmFhIcYYR9blBp/P5+q2hXPdbm9buFSl7Qo1Tie2bQmwpEkTHm/UiLT9+7ly3z66ZWcTCcyrW7fc+rvm5LA9OprtsbEntV4v7jcnSyAuBP5jjEkqMe0uYJAxJq3Msj6ggzFmfeB1KpCObbPx18C8G0ssPxvYaIwZc6wYzjijq1m1ahlRUbaHocxMOOMMW8Vnzx7buDg11f622rfPDlhYVE38WHfyjbGd1fh8xVWOwu148TgiL892+5mSAhER7CCbU/kXh6V4tKo4E8VmRpJEHXv3+5dfYNIkOwJdmO3I3sGp/zqVw4WHi+OJimPzyM0k1XGnAq3rMXl8n4F7/yNjDH7jP9IFbW5+Lvm+fBo08kFEIYgPTATkBJKrer+QvvkwJlB8YIwhLjqOlPp2FNRNezeRV5hXan6dmDq0TrT9qK7dtZZ8X36p+YlxiZyaaBOM5duXU+gvPDIfoHHtxrRtGOibNfmbctvw0+okWiW0wuf38d2278rNb1GvBSn1U8j35bN8+/Ij0xvVbkS7Ru28UgJRKdcKc4yLW9euXc2yZcvCEX4paWlpZGVlsWLFirCvyw2LFi0iLS0trOtw61rnxLa5wWvbVZn717Vt27YNZs+G4cNL/wj0+22/2Vu3QlqabXh97bUn9EPRyW0TEc+VQOQAZUcQqAdkB7FsPSDHGGNEJJTPKaV2bYgKbHHjxvZRpGFD+yiSmGgfwYqLC37ZaiM21o4ZsHkzNG7MOL7AT+kj3odhHIttvfo9e2xdeod+iI77YlypuvUAPuNzp92BV2Ly+D4D9/5HIkKkFFfdio+JJ554ONrApAdOIfUYnUm1bdD26DOBDk0qvFF+RFFj9KPKKF9Lp1WgICEyIpLzTzn/qG+NiYw55nyXVcq1IkyxKaUqUdOmR++Fqcpo0QIeeqj89EWLbPJQ9HzRIrjnHrjuOptMXHRRle5H28nI04GowB2iIp2Aso3iCEzrdJTl1gJnlelp46yjfE6lOdqX2a0vuWfi6d3b9kpgDF+TQb6UrhOYLz6WkGFvJeTk2OUd8nXG1+T7Srerz/flsyRjiWMxlOWJmDy8z8Aj/6MSPHOsHWe9VeqCe2yVda1Q1UAN+L7XaJmZ9lJT9uFYF67hVLu27cmmZJKQmwszZthG123awJgx9oZeFeRYCYQxJldE5gFjReRObM8aVwHdK1j8NeABEXkf27zwj8CLgXmLAB9wn4hMAu4KTF8YxvBLfZm9UATomXhOPx3OOQdWrOCHlkMg0NhzUfv2pG3YYJcxxnZzdvbZdnmH/DD0hyPPvbDPwCMxeXifgUf+RyV45lgL8Fo8la0SrxWqGqgWPyRVzdStGyxYANu32ypO06fD/4rbnrFlCzz6qH3cfrvtHrYKcbrsZDgQB+wC5gDDjDFrReTCQNWkIpOBd4HVwBpgQWAaga5a+wO3YlsK3wH0D2cXruoYIiNtnfrOne3BsHu3rfcH9u/u3XZ65852ORcGkVNl6D5T3nfS1wqllPKE5s3hwQdhzRr47jvbVqJsHfnU1Irf62GOjgNhjNmL/fFfdvp/gTolXhvgocCjos/5AQh/H3wqOHFxdrTidevsmAFr1tiiuV9+sfXne/e2d7H1h6h36D5THlZZ1wqllPIMETj3XPsYPx7efdeWSnzyScXjR9xwA5x2Gtx6q+OhBsPpgeRUdRUZaX94duxoe/r56itHe+5RJ0D3mVJKKeW82FjbmPq662y3n2VLJNavh7lz7fOxY+l81lkwciQMGGAHgvWAqtv8W3lXbKxtNKQ/RKsO3WdKKaWU8yrq8vP110u9TFi1Cn7/e0hKsiUSCxcWVz12iSYQSimllFJKecUjj8Cbb8IVV5SuSnzwIMycCZdeaseYGD0aNm1yJURNIJRSSimllPKK2Fg76Nw778C2bWwaNsxWNy5p61Z47DF4/nlXQtQEQimllFJKKS9q2pSM66+HVatg+XLbAUrJkY9vu638e1auBJ+v/PRKpAmEUkoppZRSXiZix3D617/s2BLz5tmRrbuU6ZR0zx7b01OrVrYqVHp6WMLRBEIppZRSSqmqIiYGrr4aXnrJJhYlzZkDBQWQkQFPPAHt20OPHjBlCuzfX2khaAKhlFJKKaVUdeDzQaNGpactWWIHhk1KgoED4eOPT7qKkyYQSimllFJKVQcjR8K2bTB/Plx1FUSVGPLt8GFbQtG7N7RsWa672FBoAqGUUkoppVR1ERMD/fvDW2/ZZOK556Bz59LLbNsG8fEnvApNIJRSSimllKqOmjSB+++HH36wj/vvt1WcGjeGvn1LL5ufH/THagKhlFJKKaVUdde5sy2N2L4dPv8coqNLz1+wIOiPEmNMJUfnXSKyG/i5Ej6qEfBrJXxOZfFaPOC9mLwWD3gvJq/FA96LqTrH09IY07iSPsvTKvFaEAyvfWcqk25b1VNdtwt02ypLUNeCGpVAVBYRWWaM6ep2HEW8Fg94LyavxQPei8lr8YD3YtJ4VKiq8z7Sbat6qut2gW6b07QKk1JKKaWUUipomkAopZRSSimlgqYJxImZ4nYAZXgtHvBeTF6LB7wXk9fiAe/FpPGoUFXnfaTbVvVU1+0C3TZHaRsIpZRSSimlVNC0BEIppZRSSikVNE0glFJKKaWUUkHTBEIppZRSSikVNE0gTpKIpIrIYRGZ5YFYZonIDhE5ICLpInKni7HEisg0EflZRLJFZIWI/NateErENUJElolInohMdymGBiIyX0RyA/+fgW7EUSIe1/8nZeLx3HfHS8dWSV46/9RkwR7TYv1DRPYEHv8QEXE63mCFsF0PisiawPH6k4g86HSsoQr1PCwiMSKyTkQynIrxRIWybSJyjoh8ISI5IrJTREY6GWsoQvg+xorIpMD27BWRd0WkhdPxhiKU67CI/EFEMgPXo1dEJNahMEvRBOLkTQCWuh1EwJNAK2NMPeBK4DER6eJSLFHAL0BPoD7wV2CuiLRyKZ4i24HHgFdcjGECkA80BQYBL4tIBxfj8cL/pCQvfne8dGyV5KXzT00W7DE9BOgPdALOAq4AhjoV5AkIdrsEuBVIBPoAI0TkRseiPDGhnocfBHY7EVglCGrbRKQR8CEwGWgItAU+djDOUAW7z0YC52OPsebAPuBFp4I8QUFdh0WkN/AwcCnQEjgVeDTs0VVAE4iTEDhBZgGfuR0LgDFmrTEmr+hl4NHGpVhyjTFjjDFbjDF+Y8x7wE+Aqz+6jDHzjDFvAXvcWL+IxAPXAqONMTnGmC+Bd4Bb3IgH3P+flOXF746Xjq0iXjv/1FQhHtODgWeNMRnGmG3As8BtjgUbglC2yxjztDHme2NMoTFmA/A20MPZiIMX6nlYRFoDN2NvJHhaiNv2APCRMWa2MSbPGJNtjFnnZLzBCnG7WmO3a6cx5jDwb8DNm3THFcJ1eDAwLXBN2geMw6VziCYQJ0hE6gFjsQegZ4jIRBE5CKwHdgDvuxwSACLSFGgHrHU7Fpe1AwqNMeklpq3E4yc3N3nlu+OlY8ur558aKpRjukNg3vGW84ITOlcFqmRdiLfP9aFu24vAKOBQuAOrBKFsWzdgr4gsEZFdgao+KY5EGbpQtmsa0ENEmotIbWxpxQcOxOiEis4hTUWkodOBaAJx4sZhs0BP1Yc0xgwH6mJP4POAvGO/I/xEJBqYDcwwxqx3Ox6X1QEOlJm2H7vPVBle+u547Njy5PmnhgrlmK4TmFdyuToebQdxoueqMdjfFq+GIabKEvS2icjVQKQxZr4TgVWCUPZbMvaO9kggBVvSOyes0Z24ULZrI7Ya7LbAe07H3nCpDio6h4ALvyE0gaiAiCwSEXOUx5ci0hm4DHjOKzGVXNYY4wsU7yUDw9yMR0QigJnYeosjwhFLqDG5LAeoV2ZaPSDbhVg8zcnvTrCcOLaOx43zjzqmUI7pssvWA3KMN0d0DflcJSIjsG0h+pWo8udFQW1boNrM08B9DsVVGULZb4eA+caYpYGqPo8C3UWkfphjPBGhbNcEIBbbriMee8OnupRAVHQOARd+Q0Q5vcKqwBiTdqz5InI/0ArYGrhxVAeIFJEzjDHnuBHTUUQRpnrawcQTuKs2Ddvgqa8xpiAcsYQSkwekA1EikmqM2RiY1glvF/c7zunvzgkI27EVhDQcPv+oYwrlmF4bmPfdcZbzgpDOVSJyB7Zx50VVoGQs2G1LxR5r/w0cazFAfRHJBLoZY7Y4E25IQtlvq7DtuYp4MZEtEsp2dQYeMcbsBRCRF4GxItLIGPOrM+GGTdE5ZG7gdSdgpzHG8TaMWgJxYqZgfzx0DjwmAQuA3m4FJCJNRORGEakjIpGBlvo34W4Dy5exRYdXGGM8UXdURKJEpBYQif3RVUtEHEukjTG52LshY0UkXkR6AFdh77S7wu3/yVF45rvjwWPLc+efmizEY/o14AERaSEizYE/AtMdCzYEoWyXiAwCngAuN8ZsdjbS0IWwbWuAUyg+1u4Edgae/+JcxMEL8fv4KnC1iHQOVBcdDXxpjNlfwbKuCnG7lgK3ikj9wHYNB7Z7OXkI4Tr8GvB7ETlDRBKwvRROdzDUYsYYfZzkA1vnc5bLMTQGFmN7ZTkArAbucjGelti7GYexRW5Fj0Ee2FemzGOMwzE0AN4CcoGtwMCa/j/x8nfHa8fWUfafq+efmv442jGNbS+TU2I5wVaJ2Rt4PA2I2/FXwnb9BBSUOV4nuR1/ZWxbmfekARlux16Z24atirkN29Xpu8Apbsd/stuFrbo0G9gVOG9/CfzG7fiPs20VXoexbVNygJQSyz6ATWQPYJPAWDdilkAwSimllFJKKXVcWoVJKaWUUkopFTRNIJRSSimllFJB0wRCKaWUUkopFTRNIJRSSimllFJB0wRCKaWUUkopFTRNIJRSSimllFJB0wRCKaWUUkopFTRNIJRSSimllFJB0wRCqTARkY9FxIjItWWmi4hMD8x7yq34lFJKhZ9eC1R1pCNRKxUmItIJ+B7YAJxpjPEFpj+LHYp+ijFmqIshKqWUCjO9FqjqSEsglAoTY8xKYCZwOnALgIiMwl4w5gLD3ItOKaWUE/RaoKojLYFQKoxE5BQgHcgEngVeBD4CrjTG5LsZm1JKKWfotUBVN1oCoVQYGWN+AZ4HWmEvGEuAa8peMETkIhF5R0S2BerD3uZ4sEoppcIihGvBX0RkqYgcEJHdIvKuiHR0PmKljk0TCKXCb3eJ5783xhysYJk6wBpgJHDIkaiUUko5KZhrQRowEegOXAIUAp+KSIPwh6dU8LQKk1JhJCIDgVnATiAJmGSMOWZ9VxHJAUYYY6aHP0KllFLhdiLXgsD76gD7gf7GmHfDG6VSwdMSCKXCRET6AtOxJQtnYXvguFNE2rsZl1JKKeec5LWgLva32r6wBajUCdAEQqkwEJELgDeBDKC3MWY38FcgCviHm7EppZRyRiVcC14AVgBfhy1IpU6AJhBKVTIR6Qy8hy12vtwYswPAGPMmsAy4SkQudDFEpZRSYXay1wIRGQ9cAFxbNHaEUl6hCYRSlUhE2gIfAgZ7t+nHMov8JfD3n44GppRSyjEney0QkeeAm4BLjDGbwxaoUidIG1Er5THaiFoppWouEXkBuAG42Bizzu14lKpIlNsBKKWO9LTRNvAyAkgJFH/vNcZsdS8ypZRSThGRCdjRqvsD+0QkKTArxxiT415kSpWmJRBKeYCIpAGfVzBrhjHmNmejUUop5QYROdqPskeNMWOcjEWpY9EEQimllFJKKRU0bUStlFJKKaWUCpomEEoppZRSSqmgaQKhlFJKKaWUCpomEEoppZRSSqmgaQKhlFJKKaWUCpomEEoppZRSSqmgaQKhlFJKKaWUCpomEEoppZRSSqmg/T8VRoX62evIZwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def gaussian_rbf(x, landmark, gamma):\\n\",\n    \"    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\\n\",\n    \"\\n\",\n    \"gamma = 0.3\\n\",\n    \"\\n\",\n    \"x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\\n\",\n    \"x2s = gaussian_rbf(x1s, -2, gamma)\\n\",\n    \"x3s = gaussian_rbf(x1s, 1, gamma)\\n\",\n    \"\\n\",\n    \"XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\\n\",\n    \"yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.grid(True, which='both')\\n\",\n    \"plt.axhline(y=0, color='k')\\n\",\n    \"plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\\\"red\\\")\\n\",\n    \"plt.plot(X1D[:, 0][yk==0], np.zeros(4), \\\"bs\\\")\\n\",\n    \"plt.plot(X1D[:, 0][yk==1], np.zeros(5), \\\"g^\\\")\\n\",\n    \"plt.plot(x1s, x2s, \\\"g--\\\")\\n\",\n    \"plt.plot(x1s, x3s, \\\"b:\\\")\\n\",\n    \"plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\\n\",\n    \"plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n    \"plt.ylabel(r\\\"Similarity\\\", fontsize=14)\\n\",\n    \"plt.annotate(r'$\\\\mathbf{x}$',\\n\",\n    \"             xy=(X1D[3, 0], 0),\\n\",\n    \"             xytext=(-0.5, 0.20),\\n\",\n    \"             ha=\\\"center\\\",\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n    \"             fontsize=18,\\n\",\n    \"            )\\n\",\n    \"plt.text(-2, 0.9, \\\"$x_2$\\\", ha=\\\"center\\\", fontsize=20)\\n\",\n    \"plt.text(1, 0.9, \\\"$x_3$\\\", ha=\\\"center\\\", fontsize=20)\\n\",\n    \"plt.axis([-4.5, 4.5, -0.1, 1.1])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.grid(True, which='both')\\n\",\n    \"plt.axhline(y=0, color='k')\\n\",\n    \"plt.axvline(x=0, color='k')\\n\",\n    \"plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \\\"bs\\\")\\n\",\n    \"plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \\\"g^\\\")\\n\",\n    \"plt.xlabel(r\\\"$x_2$\\\", fontsize=20)\\n\",\n    \"plt.ylabel(r\\\"$x_3$  \\\", fontsize=20, rotation=0)\\n\",\n    \"plt.annotate(r'$\\\\phi\\\\left(\\\\mathbf{x}\\\\right)$',\\n\",\n    \"             xy=(XK[3, 0], XK[3, 1]),\\n\",\n    \"             xytext=(0.65, 0.50),\\n\",\n    \"             ha=\\\"center\\\",\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n    \"             fontsize=18,\\n\",\n    \"            )\\n\",\n    \"plt.plot([-0.1, 1.1], [0.57, -0.1], \\\"r--\\\", linewidth=3)\\n\",\n    \"plt.axis([-0.1, 1.1, -0.1, 1.1])\\n\",\n    \"    \\n\",\n    \"plt.subplots_adjust(right=1)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"kernel_method_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Phi(-1.0, -2) = [0.74081822]\\n\",\n      \"Phi(-1.0, 1) = [0.30119421]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1_example = X1D[3, 0]\\n\",\n    \"for landmark in (-2, 1):\\n\",\n    \"    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\\n\",\n    \"    print(\\\"Phi({}, {}) = {}\\\".format(x1_example, landmark, k))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\\n\",\n       \"  max_iter=-1, probability=False, random_state=None, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False))])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rbf_kernel_svm_clf = Pipeline([\\n\",\n    \"        (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"        (\\\"svm_clf\\\", SVC(kernel=\\\"rbf\\\", gamma=5, C=0.001))\\n\",\n    \"    ])\\n\",\n    \"rbf_kernel_svm_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure moons_rbf_svc_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x504 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"\\n\",\n    \"gamma1, gamma2 = 0.1, 5\\n\",\n    \"C1, C2 = 0.001, 1000\\n\",\n    \"hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\\n\",\n    \"\\n\",\n    \"svm_clfs = []\\n\",\n    \"for gamma, C in hyperparams:\\n\",\n    \"    rbf_kernel_svm_clf = Pipeline([\\n\",\n    \"            (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"            (\\\"svm_clf\\\", SVC(kernel=\\\"rbf\\\", gamma=gamma, C=C))\\n\",\n    \"        ])\\n\",\n    \"    rbf_kernel_svm_clf.fit(X, y)\\n\",\n    \"    svm_clfs.append(rbf_kernel_svm_clf)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 7))\\n\",\n    \"\\n\",\n    \"for i, svm_clf in enumerate(svm_clfs):\\n\",\n    \"    plt.subplot(221 + i)\\n\",\n    \"    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n    \"    gamma, C = hyperparams[i]\\n\",\n    \"    plt.title(r\\\"$\\\\gamma = {}, C = {}$\\\".format(gamma, C), fontsize=16)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"moons_rbf_svc_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Regression\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"m = 50\\n\",\n    \"X = 2 * np.random.rand(m, 1)\\n\",\n    \"y = (4 + 3 * X + np.random.randn(m, 1)).ravel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\\n\",\n       \"     random_state=42, tol=0.0001, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import LinearSVR\\n\",\n    \"\\n\",\n    \"svm_reg = LinearSVR(epsilon=1.5, random_state=42)\\n\",\n    \"svm_reg.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\\n\",\n    \"svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\\n\",\n    \"svm_reg1.fit(X, y)\\n\",\n    \"svm_reg2.fit(X, y)\\n\",\n    \"\\n\",\n    \"def find_support_vectors(svm_reg, X, y):\\n\",\n    \"    y_pred = svm_reg.predict(X)\\n\",\n    \"    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\\n\",\n    \"    return np.argwhere(off_margin)\\n\",\n    \"\\n\",\n    \"svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\\n\",\n    \"svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\\n\",\n    \"\\n\",\n    \"eps_x1 = 1\\n\",\n    \"eps_y_pred = svm_reg1.predict([[eps_x1]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure svm_regression_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_svm_regression(svm_reg, X, y, axes):\\n\",\n    \"    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\\n\",\n    \"    y_pred = svm_reg.predict(x1s)\\n\",\n    \"    plt.plot(x1s, y_pred, \\\"k-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n    \"    plt.plot(x1s, y_pred + svm_reg.epsilon, \\\"k--\\\")\\n\",\n    \"    plt.plot(x1s, y_pred - svm_reg.epsilon, \\\"k--\\\")\\n\",\n    \"    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\\n\",\n    \"    plt.plot(X, y, \\\"bo\\\")\\n\",\n    \"    plt.xlabel(r\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"    plt.legend(loc=\\\"upper left\\\", fontsize=18)\\n\",\n    \"    plt.axis(axes)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(9, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\\n\",\n    \"plt.title(r\\\"$\\\\epsilon = {}$\\\".format(svm_reg1.epsilon), fontsize=18)\\n\",\n    \"plt.ylabel(r\\\"$y$\\\", fontsize=18, rotation=0)\\n\",\n    \"#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \\\"k-\\\", linewidth=2)\\n\",\n    \"plt.annotate(\\n\",\n    \"        '', xy=(eps_x1, eps_y_pred), xycoords='data',\\n\",\n    \"        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\\n\",\n    \"        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\\n\",\n    \"    )\\n\",\n    \"plt.text(0.91, 5.6, r\\\"$\\\\epsilon$\\\", fontsize=20)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\\n\",\n    \"plt.title(r\\\"$\\\\epsilon = {}$\\\".format(svm_reg2.epsilon), fontsize=18)\\n\",\n    \"save_fig(\\\"svm_regression_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"m = 100\\n\",\n    \"X = 2 * np.random.rand(m, 1) - 1\\n\",\n    \"y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: the default value of `gamma` will change from `'auto'` to `'scale'` in version 0.22 to better account for unscaled features. To preserve the same results as in the book, we explicitly set it to `'auto'`, but you should probably just use the default in your own code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\\n\",\n       \"  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVR\\n\",\n    \"\\n\",\n    \"svm_poly_reg = SVR(kernel=\\\"poly\\\", degree=2, C=100, epsilon=0.1, gamma=\\\"auto\\\")\\n\",\n    \"svm_poly_reg.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\\n\",\n       \"  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVR\\n\",\n    \"\\n\",\n    \"svm_poly_reg1 = SVR(kernel=\\\"poly\\\", degree=2, C=100, epsilon=0.1, gamma=\\\"auto\\\")\\n\",\n    \"svm_poly_reg2 = SVR(kernel=\\\"poly\\\", degree=2, C=0.01, epsilon=0.1, gamma=\\\"auto\\\")\\n\",\n    \"svm_poly_reg1.fit(X, y)\\n\",\n    \"svm_poly_reg2.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure svm_with_polynomial_kernel_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\\n\",\n    \"plt.title(r\\\"$degree={}, C={}, \\\\epsilon = {}$\\\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\\n\",\n    \"plt.ylabel(r\\\"$y$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\\n\",\n    \"plt.title(r\\\"$degree={}, C={}, \\\\epsilon = {}$\\\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\\n\",\n    \"save_fig(\\\"svm_with_polynomial_kernel_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Under the hood\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = (iris[\\\"target\\\"] == 2).astype(np.float64)  # Iris-Virginica\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"\\n\",\n    \"def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\\n\",\n    \"    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\\n\",\n    \"    X_crop = X[x1_in_bounds]\\n\",\n    \"    y_crop = y[x1_in_bounds]\\n\",\n    \"    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\\n\",\n    \"    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\\n\",\n    \"    x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n    \"    xs = np.c_[x1.ravel(), x2.ravel()]\\n\",\n    \"    df = (xs.dot(w) + b).reshape(x1.shape)\\n\",\n    \"    m = 1 / np.linalg.norm(w)\\n\",\n    \"    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\\n\",\n    \"    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\\n\",\n    \"    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\\n\",\n    \"    ax.plot_surface(x1s, x2, np.zeros_like(x1),\\n\",\n    \"                    color=\\\"b\\\", alpha=0.2, cstride=100, rstride=100)\\n\",\n    \"    ax.plot(x1s, boundary_x2s, 0, \\\"k-\\\", linewidth=2, label=r\\\"$h=0$\\\")\\n\",\n    \"    ax.plot(x1s, margin_x2s_1, 0, \\\"k--\\\", linewidth=2, label=r\\\"$h=\\\\pm 1$\\\")\\n\",\n    \"    ax.plot(x1s, margin_x2s_2, 0, \\\"k--\\\", linewidth=2)\\n\",\n    \"    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \\\"g^\\\")\\n\",\n    \"    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\\\"k\\\")\\n\",\n    \"    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \\\"bs\\\")\\n\",\n    \"    ax.axis(x1_lim + x2_lim)\\n\",\n    \"    ax.text(4.5, 2.5, 3.8, \\\"Decision function $h$\\\", fontsize=15)\\n\",\n    \"    ax.set_xlabel(r\\\"Petal length\\\", fontsize=15)\\n\",\n    \"    ax.set_ylabel(r\\\"Petal width\\\", fontsize=15)\\n\",\n    \"    ax.set_zlabel(r\\\"$h = \\\\mathbf{w}^T \\\\mathbf{x} + b$\\\", fontsize=18)\\n\",\n    \"    ax.legend(loc=\\\"upper left\\\", fontsize=16)\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(11, 6))\\n\",\n    \"ax1 = fig.add_subplot(111, projection='3d')\\n\",\n    \"plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\\n\",\n    \"\\n\",\n    \"#save_fig(\\\"iris_3D_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Small weight vector results in a large margin\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure small_w_large_margin_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\\n\",\n    \"    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\\n\",\n    \"    y = w * x1 + b\\n\",\n    \"    m = 1 / w\\n\",\n    \"\\n\",\n    \"    plt.plot(x1, y)\\n\",\n    \"    plt.plot(x1_lim, [1, 1], \\\"k:\\\")\\n\",\n    \"    plt.plot(x1_lim, [-1, -1], \\\"k:\\\")\\n\",\n    \"    plt.axhline(y=0, color='k')\\n\",\n    \"    plt.axvline(x=0, color='k')\\n\",\n    \"    plt.plot([m, m], [0, 1], \\\"k--\\\")\\n\",\n    \"    plt.plot([-m, -m], [0, -1], \\\"k--\\\")\\n\",\n    \"    plt.plot([-m, m], [0, 0], \\\"k-o\\\", linewidth=3)\\n\",\n    \"    plt.axis(x1_lim + [-2, 2])\\n\",\n    \"    plt.xlabel(r\\\"$x_1$\\\", fontsize=16)\\n\",\n    \"    if ylabel:\\n\",\n    \"        plt.ylabel(r\\\"$w_1 x_1$  \\\", rotation=0, fontsize=16)\\n\",\n    \"    plt.title(r\\\"$w_1 = {}$\\\".format(w), fontsize=16)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12, 3.2))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_2D_decision_function(1, 0)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_2D_decision_function(0.5, 0, ylabel=False)\\n\",\n    \"save_fig(\\\"small_w_large_margin_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.])\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn import datasets\\n\",\n    \"\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, petal width\\n\",\n    \"y = (iris[\\\"target\\\"] == 2).astype(np.float64) # Iris-Virginica\\n\",\n    \"\\n\",\n    \"svm_clf = SVC(kernel=\\\"linear\\\", C=1)\\n\",\n    \"svm_clf.fit(X, y)\\n\",\n    \"svm_clf.predict([[5.3, 1.3]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Hinge loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure hinge_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 360x201.6 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"t = np.linspace(-2, 4, 200)\\n\",\n    \"h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(5,2.8))\\n\",\n    \"plt.plot(t, h, \\\"b-\\\", linewidth=2, label=\\\"$max(0, 1 - t)$\\\")\\n\",\n    \"plt.grid(True, which='both')\\n\",\n    \"plt.axhline(y=0, color='k')\\n\",\n    \"plt.axvline(x=0, color='k')\\n\",\n    \"plt.yticks(np.arange(-1, 2.5, 1))\\n\",\n    \"plt.xlabel(\\\"$t$\\\", fontsize=16)\\n\",\n    \"plt.axis([-2, 4, -1, 2.5])\\n\",\n    \"plt.legend(loc=\\\"upper right\\\", fontsize=16)\\n\",\n    \"save_fig(\\\"hinge_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Extra material\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training time\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x12d8542e8>]\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYQAAAD/CAYAAADxL6FlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJztvX10HNWZ5/99uiVZWmQnCxYyBDAwixlwBstYJz+YZKzswm9DJpOXCcSA7cRMQmyszRnOwM5Z+QwsJJ3dedlszi+ZIX6ZWH4Tv8GEQAK2mT0xmVh4sNkxkWQiZzABx16ikhGGYEmW1VL33T+6b+l21b310l39qudzTh9b3dVVt6q6nufe55WEEGAYhmGYWLkHwDAMw1QGrBAYhmEYAKwQGIZhmCysEBiGYRgArBAYhmGYLKwQGIZhGACsEBiGYZgsrBAYhmEYAKwQGIZhmCx15R5AGObPny+uvPLKcg+DYRimqnjllVfeEUK0+G1XVQrhyiuvxJEjR8o9DIZhmKqCiE4G2Y5NRgzDMAwAVggMwzBMFlYIDMMwDABWCAzDMEwWVggMwzAMgAgVAhHNIaKtRHSSiEaJqJ+IPumx/Z8R0TARnSWibiKaE9VYGCYMCxYARO7XggXlHhnDlJYoVwh1AP4PgA4AHwDwEIAniehK54ZE9AkAXQBuAbAQwNUAvh7hWBgmMKdPh3ufYWqVyBSCEGJcCPGoEOLXQoi0EGIPgBMAlmk2XwNgqxBiUAjxHoAEgHuiGgvDMAwTnqL5EIioFcAiAIOajxcDGFD+HgDQSkQXFWs8DMMwjDdFUQhEVA/gcQA7hBD/qtmkGcD7yt/y/3M1+1pLREeI6MjIyEj0g2UYhmEAFEEhEFEMwC4ASQBfM2w2BmCe8rf8/6hzQyHEFiFEuxCivaXFtxQHwzAMkyeRKgQiIgBbAbQCuF0IMWXYdBDAEuXvJQBOCyHORDkehglCa2u49xmmVom6uN1GANcBuFUIMeGx3U4A24nocQBDyEQkbY94LAwTiOHhco+AYSqDKPMQFgJYB6ANwDARjWVfq4joiuz/rwAAIcQ/AvgbAP8E4BSAkwAeiWosTHXC+QAMU14iWyEIIU4CII9Nmh3bfxvAt6M6PlP9cD4Aw5QXLl3BMAzDAGCFwDAMw2RhhcAwDMMAYIXAMAzDZGGFwFQM+eQDlCsyiSOimFqEFUIVU2tCaXgYEML98soTKFdkEkdEMbUIK4QqhoUSwzBRwgqBYVB7qy2GyQdWCAwDXm0xDMAKoWTwDJRhmEqHFUKJ4BmomUKUZbkqlXKFVKYWibraKVNkFizwVyLVJpQKUZalqFRKjgpdra1cIZWpTVghVBleQlKI0o1jNsOrOqZWYZMRw6D6VlUMUwx4hcAw0JuAnKYihql1eIVQItgJyTBMpcMrhBLBTkgzra16uzwrS4YpLbxCqDL8VhrVmO8QpIZROc6LV3XMbCNShUBEXyOiI0Q0SUTbPba7h4hSSt/lMSL6eJRjqVX8hGcl5DsUQ3h7nVcxlIQpvJdDTplaJmqT0RCAbwL4BIAmn20PCSE+FvHxmQqg3EopiuOU+xwYphxEukIQQjwthPgRgDNR7pcpDfnM7HXfYRimOimnD2EpEb1DRMeJ6GEiYgd3mclnVswzZoapHcqlEHoBfBjAxQBuB3A3gD/XbUhEa7N+iSMjIyMlHCJT61SjA55hiklZFIIQ4k0hxAkhRFoI8SqAbwC4w7DtFiFEuxCivaWlpbQDrRDCCK5ajYwJO/4gAr5S/ASsmMJjjVro2N6B4TH28EdJpYSdCgBsfTYQRnDl04ayVLS25i/8dOcVVEnkI+C99h21sK4UxVRNJHoTOHjqIBIHEuUeSk0RddhpHRE1AogDiBNRo843QESfJKLW7P9/F8DDAH4c5ViY4iIFuxc6pRSl8HMqiSjxG4/6Oc/wS4s1amFb/zakRRrb+rfxKiFCol4hPARgAkAXgNXZ/z9ERFdkcw2uyG53C4CjRDQOYB+ApwH894jHwoQkjLmp2kpwF1NA8wy/tCR6E0iLNAAgJVK8SogQElVUM7m9vV0cOXKk3MMoOV4z8XLdPq8xeZWiiLJ8d5DeEMVEjjdoqK0pqa0S72+lYo1auPq7V+P89Hn7vaa6Jrx5/5tY0MxLMhNE9IoQot1vu0rxITA1RDFmzDqzTLXNwOV4nefCBEddHUh4lRAdrBCqAD8H52ywVxdb+BfLF6Ej6LmY7vts9lkceusQkqlkznvJVBIvvfVSmUZUW3AyWBWgmhlMM8pqmy07qTSfgx+lGK8pMmw2+yz61vWVewg1Da8QZjGmmWaQWadJIMZC/KL8wmLV8RUb9Vy9nOu68VabMmMYE7xCmCXk64A1fcc0e81HeJfbOSyRYwibs+Hcnv0CTLXCK4RZQrEEbhQO0lIog9bW0s3kazVbnKl9WCEwBRFF5FApOH16JpHNiygctH7Z4kEUQ60pDy41UR2wQigBUUaFVNLss9ColkowE5lwjq3Qe6h+P8h5e5mtivUbKGb0EpeaqA44Ma0EVELiUaEzcV1SVa3bytV7U+g9DHutytGZrVi/UzWZjJPIygMnpjGRUsmz+Uog6tl1sVqDlgMuNVE9sEKYJZTLJl1rtnATXrkBUQnyalTKshCdTCZLppJckK6CYYUwS5COTi8BXQzhXYgQK7cyCRo95fd5KQR5pWYvc6mJ6oIVQp5U6gPoRzXMMmUC2PBw+ZVCtVCp2ctBSk1wBFLlwIlpeRLmAfSq/mnClKxVTGejn/BQZ8Iyrj9qgVOok133/Vp3fkdFPr9TP4KUmlAjkB771GP5H4wpGF4hlIB8upgFVThBVirFKAGhxvVHVRgun74LTHSUo9seN7upLFghVDlBFEe1CFU1siYf01uYOkrVSC2azzgCqbKo8UeocimFD6LS/Rle5KPE0mn9OdeKIK2EvthRwhFIlQcrhDJRCiegnHEHISqhWW7hqzOpeV3Tco83Kiopgz0oHIFUeUSqEIjoa0R0hIgmiWi7z7Z/RkTDRHSWiLqJaE6UYyk21fgA6lDtxGHHrlvRBKkXVEr8FGy1mNP8KIf9v1C42U3lEXWU0RCAbwL4BIAm00ZE9AkAXQD+Q/Y7zwD4eva9qqDYD5op4iMWK17UjDynMPvXrUKiVIrFiGQqGs0WcMddwFO7gTG37SpoKYzZAje7qTwiXSEIIZ4WQvwIwBmfTdcA2CqEGBRCvAcgAeCeKMdSTEph/zfN+NJp/+8GpbVVfy6FEpUAr7q+yR0J4IqDwHK3ycOpJKttJcnMDsrlQ1gMYED5ewBAKxFdVKbxhKJSk4DCMjxcvDHXssDTZnw3W0DbNiCWBpZuw/wrhz1NNyaFXyumSKY6KZdCaAbwvvK3/P9c54ZEtDbrlzgyMjJSksGVglp/8IOUylBR21MWQqmun8tX0pEAKLt8oxTeuT4/x2g1+gKY2qFcCmEMwDzlb/n/UeeGQogtQoh2IUR7S0tLSQZXCorx4OfjFC4W0oTmtwJRy1REge9+mi3gng6gOf8DukyDcnVQl3WQ1iWBpdtAc4e1JsVqLXvC1D7lUgiDAJYofy8BcFoI4ed7YDyotgifssx8Pez8niiKRE2gs/dJDucOpVzHkNejVkyOTO0RddhpHRE1AogDiBNRIxHpIpl2AvgKEV1PRB8E8BCA7VGOpZIpZIbotQqIujxFNeJ5PR12/lCrBC9FctmhmdWBpC4JXO4On5zt94epbKJeITwEYAKZ8NHV2f8/RERXENEYEV0BAEKIfwTwNwD+CcApACcBPBLxWIpGofb/QmaITlNTNWMS3lH5AVzX02Hnb7pNv0rwcxi7FMnmPuBR4X5tjiasslATU5Bqos5t2Kw1O4k67PRRIQQ5Xo8KIU4JIZqFEKeUbb8thGgVQswTQvyJEGIyyrEUE3b8RY8qvNXrGxk6O3/bNlijw9r7aCuFZgtYuyxjAgK0pqBik88EQhXol96dQO+Jg7jkzoSx6KFzm6jNWvmUuOay2KWHS1dECM+q3JQ7aso+vsbOP3E+hUvuTGjvl63cb+kC5lpA3VTm76zDuBCndDFw/vZswe1Y3Zwenxl3kG2iQi1xXazvsAIpHFYIeWAS/Ows1FM2E1ezhWv/qgPW6DDaPh3czm9nXzdbwJLHAafdv0irhEKUp/E35jCTaccdZJsCyKfEdT7fyUfpMLmwQsiDShHwpVh5xGJFElRR4BdC2pEREFf/SQL99+Vh5+9IzJiKVAyKpFDU6KVIMscN4bA5wtWwTZQroHxKXCd6E0ilM9d+Oj3t+x3uqxANrBBKRJAHPKzgLYViSqejb4RjQme6aW2FWfB7Rf40W8DSjICYuDYPAScFpXq/ppqAb1mROowLxfM3ZQiHlWYyr22iWiXkU+JafmcqnTHTTaWnfL9TzL4Ks8kUNesUQiXZ+avNKR1F57UwSk92ZWu6TSP4fSJ/1j+RQMOcEGYQp9IpsqAsCUHCYUOEzALhJy35lLhWVweSiekJbNi/Qbt9sfsqFGKKqjZlMusUQiXa+YMqKed2pSbfa+T0s8RiAVYbWQFNCwYyM3yn4PeyezfnCohAZhDnasMgKNs+81LgbOdiOtQDrdKChMOGCJnNZ9KST4nrQ28dslcHKs8df067fTH7KhRqiqo2v8asUwiVSFAlFZXSKnf+QqCKrVJAf36VW/D72L2bbnMLCM/ZvXO10ToATM6bMQ8pr751fYGynVtb8+sxUankex596/ogHhGul1fp630r96GxrtH1/nhyXCuQi9lXoRBTVDX6NVgh5IFXYlrUtvYozDRVhyqgLx50C/5bNrjMOQ2NKXQ+kYAQwLW3ugWEpyPYudr4/CqjwG+5Oli2s1Te5SonUsgxy51jo5vxAxkhrxPI+SidIBRqiqrGftGsEBSCmm5KmZhWKRFNQQmqDD1NXzr7vf3FFLBor8uco84IVQHhG1GkW21cPGgU+O9cn1+IZpTVbWtl1WFCN+MHgDRKO9MuxBRVrf2iWSEoFMO/UKllrvM+fgTVQj334RTQToVRlwTOXqa1e/ff1xc+QMBP+egc2QFDNIudo+L32zLe4yjuYRGRCn19+3rUx+pzPivlTLsQU1S19ouedQqh1AK60spcyPMMUxPJVdnTw34eSBjf2gUs7M2YfpzoBPR0A/C/OwPXCAolZHXOY3muToFfIZFHTnOU6bdl+nz9E3lWfC0xOudyKXsu71u5D8sXLof1oBXaFFWt/aJnnUKoNAENFK6k/LaL7DwDVAtVhXFM9+tqtoDfy2b/3rDLvY+QYZAFI6Ns/mU9kNYMWBX4pR5bEZCmDNM9LPfKVUV1LjfVNdmCuVS9mAuJECqWX6PYzDqFUInolNTQ2UzZhSDL+jBC3uQnCUTIEgeplGYFcmsXEMvGmMdS7lXC5j7gW0PAr5fnRvkUKxGs2QK+ctOMonOiCvwiVzUtlCA+MNWUoTriC5kweMXaFxKHX06nbDVGCEUBK4QKRc5OTCWawxDElu1LCPu5UeGoqwPAvErwy0CO0v7dkQAuexmIO2b+qpkqhMAvZ1lyPz9Fvo5OP6HuNZPOd5YdtVM2rGKqxgihKGCFoFApDmB1doI2n4SqUhGF/VxdHUicqwQ/s1S+Hc90qOUpnKuDbBKaFPCVZErJByLgd76cn6PTS6h7zaQLmWWHdcoWorRM5+SnjKotCzkIrBAUyulfcNamP38+8zBMnA8mdIsusKKwn1+zV1M5FMAiJQPVJwM5SA6Aei08r4t6LIfj2mnvDbuyiuJ+RD1BmbgovKPTT6h7zaQLmWV7OWV1gtgk8K1RCzd9/yZs6wuumIIqo2rLQg4CK4QKwVWbPmBoYz5RQybU77uEThT287OXe7/vd+4BfBhSgUuh0f8rw8NfhCqf6jWLosmPKdP59Ok8a28p9zCoo9NLqHvNpAs1+fSt68P69vVoiDcAABriDehs70Tfuj6XIPZSWoneBF7+zctIppPac9ARJEIoKh9Dpa0yWCFUGiFMM7I8gs6ZWCim1VJB9vHH9wFTjpIEU03A489n/u917iEFuBQaXfu7UL9W43OIMIS0GCtJqQiC5i8Uozijn1D3mkkXmtQlZ/XOYw8MD7gEsVNpdf2kCx3bOzAwPIDuvm4AsD8PopiCRAhF5WOotFVGpAqBiC4komeIaJyIThLRSsN2jxLRVLbPsnxdHeVYCiXfqqgFV1P1MM04S2NIZRBFNnNJbOR+QtjLLBVCgPdb/dh0ZBPSIo2eoz2YvvRFt3O+yCGkhZYcCatcipHR7ifUvWbShSZ1qbN69dirnl7lEv5OpdXzag9ePPkiVj29ClMpd5G8Qp3EUTm8KzGSqS7i/T0GIAmgFUAbgL1ENCCEGNRsu1sIsTri40dGvpmlBWekephgTmNGwMjVQRSCQJ31x+P64nOxWCaM1I/WVv2YWluB0yYhvGQH0Puwt/lp3dLAAnz1M6shkDmpVDoFELI9ER4GxhagtRUY+fu+YEX28qTQ+0JUfke2n1AvRky9nScBuJRRMpXEsZFj9r1NppLYcXSHNpsZAAZHdGKn8AQxL0X52Kcey2s/+Xy/GESmEIjoAgC3A/iwEGIMwEEiehbAFwF0RXWcYhHVTLtUBB2rFPZeM9Ugs1gpPL1WOlJJmViwoM897k91Ass2Z2b6+zwehgC+itbWzOogRxDYIa7T9jHyuc9eik7ScrWFdzruAp7aDaBwG065f4/lSKLKyZOIN+DepffaQrJzbye29m11KSldqWwdzv15YY1auOuHd2H3HbuxoDn3XkaRhWxaZTzc8bDreKUkSpPRIgDTQojjynsDABYbtv80Eb1LRINEtD7CceRFuR++cs8Gg+J1nfzMHMPDmYQ726YfMGrID9WEtvoZw6Kzbir0MVSzj0kZqOf8zvXVURKitVVj2pxrgf6kAzR3uGwNo/xMMaaidwSC9aCFoQeGtGWzJWGEtpdtP4os5EqtdRSlQmgGcNbx3vsA5mq2fRLAdQBaAHwVwH8lort1OyWitUR0hIiOjIyMRDjcwonSmVtLtfNNWKMWlm1ZNmPTD5j5HPS6WKOW0Uzgdwzd8cKYB63RaJRbKdCaGh35HcWeIEnH8c1bbw7kpAZyBbEagVQfrzc6smV0UhihXQrbfqXWOopSIYwBmOd4bx6AUeeGQohjQoghIURKCPESgO8AuEO3UyHEFiFEuxCivaWlJcLhFk6+D43J8Rx2f36zuFIrGD8Hetf+LlhjFgQERFs35tzU7R01lM1KPj0ePLs05vWTDug0zidaKNGbX1nsvGnODVecf1UBGdx5rtQKLUvx8m9exuG3DgdyUjuPq1tJ9J7sjUTIliJLuVJrHUWpEI4DqCOia5T3lgDwmLLZCLhTliqSfIWs+r2oZl+nT3snL5WrYJ96frbym2thZ//j9vvnk5OuCJD6OdPA2htz8w4CmGCkItryv3qRhsZTPLKoqHWHbEdohDkNvnTkmjS+8LcJxK46aNcmCruvfJRZIWUpZDgoAHT3d2N4bDiwkDStJDoWdhQsZKu1j0FURKYQhBDjAJ4G8A0iuoCIPgrgswB2Obclos8S0b+lDB8B8KcAfhzVWKImn6zlUmU7e2VXR91tLZ/92Mrhlq6MsJHEhEt4T6WngLlWbt5BiFlr6s3l7hWCAJCuL2ofZDWz3KaYq4RmC1g6Y9LQxeaH2lceCXqFlqVQJwOmTmgmStUyU1IJtv1SEXViWieAJgBvA/gHAOuFEINE9AdENKZsdxeAXyFjTtoJ4K+FEDsiHksoKqWOUZQU2nylkGQ0aU6wncdLHnevAafrbfvu0ANDmaQ1grtNZlDheoVmhUAALj6W2Z9htVGI4iZC6ctidyTQMGfGpOGMzU8cSAT/PeeZoJevWUWuDtT7lBZpe5UQhELNLV6mrkq17ZcKEuUqzZgH7e3t4siRI+Uehk2YGbNqwomyP7LX7cv3OLp9ht3X+j2d2PzKZqRfvg8gAbRv1BoF2xa0oW9dHzr3dmLj4a0ZQTpdn1kZqIXwppqA77wJjHk4Tj7VidhHNrv78U43ZIRcPDWzHwjgjrsw/2e7MXJCv8+KDEVutoD7rwbqz5u3mW4C/j/lWjVbrnO1z23dUuCSftcu6kbaMPV3egFrjVq4+rtX4/z0zBia6prw5v1v+oZMdu7txOYjm12KO0Yx3LfsvpLE4Xfuzfw2S3W8SoCIXhFCtPttx6UrCiDM6qGYgqWgHgfFoDk7CxRpYGk3cOVP9R4iqw3WI30aG/xUrnkJ8J+1Zk0fuubsqEvOKBe5n44EsPAgVvxdCFNAHqW3C5lv5fy+ZN+GdUvd18aF41plO9S9s2Smqqxtatykn22rysA5o9aZVabT04FWCYfeOqT186RFOtAsXB1LPk7tSswOriRYIRSAzn4fBG0nMQWnucZv+V+KEMFQgrAjgcmprI04ngQgAOe1ydYwOn1aL2C0vZS9TDA604cA8OqKGVNUdj+NN3ej8aZtAHkLBb/QzCAQ+d9vHXJFKX8D659IAJe/DMw9nVGYXqjXytGhjuYOh84x6Nrfhd6TvdiwfwOsUQs7B3Zqk8MOnDxgFNBy0tJ/X24DpNaN4Uw+do2qn3Rh2ZZlePHki6Hs+7O1z0FQWCGUAb+SCc4onSBJUVGhUz6JXm9BmKMQmy2grXumv0AsDcw/ril7PTOLNSUcwWoLXlnV1Bv5d3/sUhTJVNK3+qVLaBaQROe831Lhe6HeW2dUTmNdo6vPr/rKuVaaDnVhJhDWqIXHX81Eh+06ugsbXtiAiekJdLZ35iSCxSiGxS2LjVFHOcdUFGvYscjZ/a6ju+wQ5qAz/dkeQRQEVggViozn96qNFLVpSKdkQvfg7UgA8QClBJRZbN86R1/jkJ3KpOlj6IEhd5RR3aRLUaSR9q1+qV0dRJRnIO9dUIJG5chZuE3QDnUedO3vsmsDpUQKuwZ22eaWDS9ssK9jWqTx5LEn/U0xBShWdXavmp2CzvRnewRREFghlJCwAryYpqCgjYC69ndhcnoSQMAevFf0uruPqec81TTTK1kV+E5BcfFAjpkqSNRM1/4urbNSzmaXL1yONUvW2BmuEl+hECY0M4IWn+o5hYnKcf1ePDrUBanIq64O7GNnxzGdnkbP0R7tys7zevooVpNfwDm7Vwk605/tEURBYIUQMdUcpupECgS1uqTvg3dqeSaqx4Rpdu0UFLevss0KsVhG8fQN9eMDf/lBDAwf1Sqk544/59ptGmkcOHnAtj3vPb43vFAIEJpp3/coW3zCvTqQBIrdD9KhTkE23rE7932pK1MtVsNUespeOejG1t3XnVOWAoBRsXp1PpMKQl2N6Agy0/cLV620ZjXlgBVCxETRuawgAs5Qg/RqUM0FEt8HT2fLV3E4h1tblTpAqqC4eNBeLfzm/cy5rH5mNd6ffB8rf+hus2GNWjg76Syllall035pu217Hp8a19rfrUf6zBFahjwDtefy8DDM5hDdPfG4T6o5cONzBUTlmEJ0TZ3r4Fhl6BRKQJKpZE5ZCgBGxerV+UwqiD3H9+j9TMrxCp3pV1qzGpVSKStWCLVGHjNUk2lq7+t7Xe/5PXitP1JabX5raKZD2nTTjCDe1JcjSHPqADmhTBOUZZuX2YXrBkcGcfT00ZzNEr0J7Yw1mUpix0t77Exik0LzNM852odK89Pzq57P2azpNoM5RHdPgtynZguYnDdjYsu+ZGSOb1TOqeW5Ppmw5Ts8FIcfUolJwd7aCqNilb8nV+ez/V22gjg3dQ7WgxbaFrRpjyfzWfKl0sNRS6WsWCGEoOBuaHkQygQVUTlpIPOAjE+N57zXVJcR6l4PnjNUsqExW9t+jnllcegtQ4QQANQl0XO0Bz8f/nnOx1/4wRfc+9BxejHQMG7vv9DIktZW/cNpjVoQOj/DxQPue+K8T60D+tWCQWn4+pZkzoJyDGt0OPyq1dRHO9uT2SScAdjOfamAh4fNOQ/7Vu7TtszsOdpjm6zkfpwVT6WPqNCicJUcjlpKZcUKIQQFd0Mz4FegLvBDXEAkjFPZ6erzhC1REDTEz1TW2D4u3DP/42eO56wSZFN214z41HLX6iPsAy9Epo/D8m0d+F/9+rpB+lyKrC/EeU+c9+nzq9yCP4Ryd0UXdSSAy17O5oAgxywTJfK+6foQyBVCEAXs1TJTNr9x7ieokAxiaqn0cNRSKitWCCjuzD/IDN+rQF1g8ixSJnEpNc3yPmyDkaAhfupDa8xJ0CBXCdaohWWbl2HTkU1uAVrgeajnc/DUQW3dIFOyFuqSQMuxnHtiJ8UZ/CU5lV49lLuxbLrMAyHMRHvVJbHplU22AvWM2MojSkqrDBW8hJhXy0yv/QQVkkFMLZUcjlpqZcUKAcWb+QPBZviRKKQ8i5QZUcwF+RQQCxPipz606mph6IEhxCluPMbxt9+wVzM/t34Ouy6Xet4as0e+JZHTIo3BkUHXw6kma6nXan37ejTU5/b7VZPiXFC2/HfrQEao56PcDXkg6XQaS76+EgsWeE9Amm4L74PyU+JeCtjZMlNeQ505Su4nqJAMuoqo5HDUUisrLm4H79wA9fIE3S7sMbywZZzf9w1FymC1+ToRTf2CvbaPKktaLZTmLJC25pk12Hl0p/nLU03A3x8C1v4/mQQ052eGYnhCBC9cN/8qC/X/aRnOTJzRCr2GeAOSU6lMfL8czx/+aaav8qpP6u+JFwLAO9cCF72em88x3QD8/F7vvtN+he/SBHx7yHVN5P103ouJvzIXE4xCbPRb/bhxy412WDMwUyRveHQYy/5+GdIi7fpd6Hor6/olq9uF6adcSSzdvBT9w+7fUFgnetDidnXhhsdULCGyenUUO2HO1LRcTXyTMx/50OqinHKQNvr4pP6z5QmjAPU6B/UafeHJP8VTv7SM2yZTSSUTODuell9mju1zT5ru6ERqiaNpPAGY/1r4Wk6AfpWYzhoBYulMXwjNNZHXwnkvmm5LYOIp9/XTmZysUQt/vPuPQUR45s5nfKueWqMWPtr90RxlII+bOJDAgZMHXCYh+bsIMqOv1Cb2YSl1BzU2GYWgkJ4Jpeq3kK8duNiYInN0iW8DwwPo2N7hOmq/AAAgAElEQVTh/+DWJYGWQX2sfAT9CKxRC0/98inX+9K0YTtTlYgorT/AwMRFHqYWKSfDlPLQhXXG0jm+BNO4dPcCbbnRSV6+LV1LTC+69nfh3PQ51/vJVBL739yf0xvbaRIK0g8hXx+W7u/ZBCuEEBTi/I3EcWzAtb+Is2ULwRq1cPP3b7bLYasPtinxbdXTq3Dw1EFjS0TxiMjUPRLQKoMYYmj6RadWgMZiwVZD1qiFts36sEo5G/V0pgYp1z05D9aD1kxzIPu7mDmvWLaEuJdykROAx5/P9Zf8y3p31rhhXJd++X5XVnJQW7WpJabX9mpJDAJhzZI19r2tj9e7vnN++jw27N/get9Evj4s3d+zCVYIqI5uaUHHaGf9RpCLEBadc/zSuxM4/NZhu/yCKmRMiW/HRo55dtHqt/qBZZtyBadCGmlce+tLWgXsV2lW0rW/C2+Pv53zXgwx3HTZTXYuhtaZqq4WvK5/VmknDiS8E/OATPiol3JxTgCkgriiN3gnt2ufdV3LZCqJAycPmI+bJWxLTOdEQECg52iP3ePg2Mgx13cEhLY0iYmgXdWcjueC2pHWAKwQUNzZexCCCPugY8wRLqZ6OxGiKgGXXV4JgXTGpQ8MD7gS3wDg2ouuBWWn8CbBsvqZ1QBlbSpOn8i0f/KcH7qibkDmHFSTiBQ6YWfjao7Btv5t6D2pEdwqsTSw0CCYc/IVuoGv3DzTLvRkx0zGeLb/gNb01NrvcsrHKQ4Cof2SdjSs6wDNHc7c57kW6E867L4KXsX3pOlPFar9Vr82UEBmJid6E9oVAgCcmzoXuYB2hq/qwopnE5EqBCK6kIieIaJxIjpJRO6iM5ntiIj+mojOZF9/TZRvHE71E0YhmUJU5cO68VBujHvT78/YgYFo8i1UxWJ0zjZbwNpl2hBI54On8tqZ13JKKjtXCf1Wf8a+rJZ0VvDKiPal2UL92kwhNVPhNgCumaNXWQbtbFxx/k6cT2Fwb0du+W8ngoAhQ4CI6kiOJ4HLDgM39OSuELPd4ZpuM1yXz692vZVKpyAgsKO/B1MLXtSW4ZDNjXTF985Pn8ftT96OF0++iBs332hfr9XPuI8l2Xt8r2cIa9QCWud4doYVd/eZzV+16GuIeoXwGIAkgFYAqwBsJKLFmu3WAvgcgCUAbgDwaQDrIh5LyShWYptuv54RPpook4nzKVxyZ8K3t4LXKiWvldOtXcBcy10KG5kH7Y333giUgOZcJXgJFLm9tBOHemCbLVzy6DJMX/oieo72eG7qFEzDwzCXeXDOxuWqyZlj0KqUuHBCAlikibhyJiPG0tmENKVd6C1dmW0obTuJXfu4+Jjb9KZGTsWEMsbuHL+GqSUmALzx3hsQELDGLHTt7zKagyTjU+N4ftXzdkmKNTeswZz4HPvzfJKyvH4Dfgl18piFJL1VG5EpBCK6AMDtAB4WQowJIQ4CeBbAFzWbrwHwP4UQbwkhfgPgfwK4J6qxlBq/Jjb5KobQ4Z0es1S/fXmtUkIrPGdjFolAprbQt4bwkQ99xHaoOsseqKRF2rZjewmUtgVtLjtxqAf21i67A5dzddAQb8hJkEumkvjeoW0zZhRC4MiuptsSiNU7ZtSyfIVDmTfEG2ZWDA1j+lpHOt+D6sdY0gPZg9lWZOpYOxJAymGiEZp9yTHGldaoa2/E86uez0kkbIjpS5/3HO3Bhhc22OYg0jh/nAXtel7tca0+wq4SvH4DQbLiZfl0J5VeDC9folwhLAIwLYQ4rrw3AEC3Qlic/cxvu5rATxibBG5ogs5SPdDNqEJncnck3I1ZgIxwaTkG3LLBfkj9Zmn1sXp0LOwAAK19uSHegBXXr8CJ907k1DYyPbDalVCzBdzg9hlIkqmk24Tk9A94RHapCvbqf9/rnlHXJYEL39CW2LBXDDrHsl+pcTnOutx6QHP+aMPMWE2tR52oIbVA5t+5Vk7kT6I3YczCTokUegZmGuo48w/k+PYc35Njw3deK12+gWkF4Ce0TY5ntZ5WQ7zB/v2pVHIxvEKIUiE0A3AWpH8fwFzDtu87tmvW+RGIaC0RHSGiIyMjI5ENtpIoZme0sBS8DJZmDJNCS9cBS3rsh7T3ZK/nLG0qPWULAFMo4Y9f+7GrT4LpgXWuhFpbkTFvkUPgp+N2uW5tVU9nI/uAkV3LFy6fKd433TCTY/DfJnLKWw89MJRjLtGGn2YnALqCgDaO+zCdnsb0dRkfQ9Pvb4P1rednHOOPikxme1Ao02dZRgdt/flWz811RQrjNHOdhx4YwvjUuDEjXJa1cOYbmH6v+QjtIGUxKr0YXiFEqRDGAMxzvDcPwGiAbecBGBOaOhpCiC1CiHYhRHtLS0tkg2VysZ3Sh8Mtg7U9lb1CKOumckwYHQs73L2UvzUETGWEYWNdo913oG9dn92LQAqRvrV9mExlImRkn4QwtW5O/9FNevMWpWy797w58+zjaVdeAavMutpAGkJTbWdt2mFaik9mah05tjeZPnSmOLXTmVZIylWmTjFolLzch9fqwAtTwTonXoI5LdI5xfvUz8IK7SAJbZVcDK9QolQIxwHUEdE1yntLAAxqth3Mfua3HRMhvmGnHQkA4WZUruJ9JjOG1TbTMCcrWJKpJLr7uxVHpRIVE58xczgfRnVG6HQyr/zhysAPbKI3WybaYN569rVn/VdMJiexphe0sUS2Rsj3nux1bxsTGUe9Q+HoFKV4RGDiLyZyTCFOf40qJF2/jcf32UrZDzt0NgCLWxa7FJUcg59N30swp0XauEKUTKenfX/TQRLaKrkYXqFEphCEEOMAngbwDSK6gIg+CuCzAHZpNt8J4AEi+hARXQrgQQDboxpLMdHZ+0uNGvkT5juqucSFI2Il72Ww048hY+Aff167ekimkjOOSjsqptu2Vauhp06b8P43ckscAJlVwk9P/FT7wB44ecC2N9tllw3JbQDw3vhZbDyUybD+3uHstXA6jzXVRRsaU+h8IoGhsxau/asZ+7ZW4NUltUJ++cLlwLTG2UvQKpxCyzw7TWnrn9A4vw1Mp6fx7sS7WLNkTY7tXZb3WL5wOfrX9WP5wuVov7TdOAbVpu9V7RTQrLaQ20lPd61V86OJIAltQZPeqpGow047ATQBeBvAPwBYL4QYJKI/IKIxZbvNAJ4D8CqAXwDYm32v4vGy9/slmEXmPIamKYoPvqGiGmEdyTK4I4HYVQfR+UQCbZ92rx7SwlFr54Yel4CVqwSnTXjFUyu0h6yL1Wkf2OULl+PgqYOZlpxbls2UaphuAEYWuZPcYqmZJjPIXIuc8tDNFnDDdleoaDKVxPeeewmX3p1A74mDuOTOBFquzpie+tf1z5SpmGqcWTE5TEeZLnLOaCT574xZSlYplYpy45GNrvai6j6DzGx1yWZeTKWnYI1Z6Dnak2Oi6e7vxv3/eD96T/ZixVMrjL2RdY5i1UwnVzdq21KTeUmuEnTNe5rqmlxtT5lcuPx1SMpRAjsKnGNzjcVQPrttQRv67zPPfNSENxdKOWa1hHHn3k5sfmUzrpt/HV5/9/VcAWGoT3TtRdfi5PsncX7aUNpZobGuERN/MZHznlraOY6428FpOG7OfuMZwXI+lTmfFYtXYMfADsQohrRIZ0phH7oX6H0IuPPzwII+oH4yUxb7FysQu3EXrpt/HQat1zPKT61EKstb9z4E3HEX+v/yu2j7u5sypax1Y8uW9xajmev5/Z9/3/Y5LG5ZjF90/sL3Opno3NuJzUc2+yqEhngD7l58N3Yf2629LzHEXPtwlrI2Hv+Vzbhv2X12hVPne6ay0ATC0IND2lLZBMKXlnwJ2z+33e8S1BxBy1/XdOmKcvRAzhddDkCU+K5KNvfZzl210UvQZbB2daSsOtTuYnI2e2zkmL4WkOPcG+INqIvVuWaE9bF6Oz+AQBi4b8C2nTvJKe2siXYJwmRq0nacpkQKuwYy1lA5rmQqCdy4NVM64rLDiils2o6sGhwZzE0kc1YivWUDsDDTmQ00PXNNnGRXCfJ6qg7owbcHQVe9kFNiIgxeyWYqyVQSe17fY3QE6/bht+rUhYrq3pMtU53RVfXxenv/TrOSgMCOgR3GFRRT4wqhmJ3Qag5HfZ2wvgOnDXrorIXGm90+iQ0vbLAFSH283tVlrG1Bm0sAmjKb1YgZAYEVP9CbkJylnbUQMo5v6feYvMC1iYDIEf5aQRifzCaDQRH2M5FVnlAK8Rt7AJKKY9q8bTbsNdGbcFUpBQCs+AJwxYvA2htxejz3XvZb/fjgX31QKxhVc4023DbLoosWacNE4xRHfUxfiwjw903pQkVN4aN+JjCTWcn0O2FqXCGUiyh9BcXE6URsaIwu0cbkwHTamZ3CweSwm/iLiZwZobo6kLx25jW88OYLrkQlXZltiRoHLzYpYaT1E0DfmtxIG6c+0c7coRf+Qe5/nSYBToegTKG6zZmKq67wVALQ9N5MVNItXTkfr35mtStvQyZ4bXhhQ0470y/d8CVtVnF9rN54j13jceDVl8AZKtrd343uvm7tb8bPuWuKWnrtzGs1kTNQDFghhCRIZdJKWYEErW7qF7Md5JydGaOm2Zuu/0E+CUPq6kDl9idvd0XbeHVeS4kU1j23zh77/KuUJDOng9tLqAvlXz/hryakyRyMvjXAtLmER84xUpnOZ62twL6V+zz7ToMALOmx74tdHBCZqJz2Le0YHhtGojeBF09m6jipppm9r+/VrqyOjRzzTSo0Yco2VleP6rb5lq+Q4bg5CX5ZuvZ3ab7BsEIISTFKZRerH0NQxeQXtx/knJ1hj7rZm18oYdgx6nh/8n2X/VlXZltl7+t77bF/4W9nVkqIp9zF5qYaM/WYnBiqr2qtVHVJtH3mpRmzWiyN+I09aJjjc35qjaL2TTgtjuLSL3XpTUbqOCiFS1Z3YcECR96GAF4ZegWXrO7CxkPbMnWc0jNJa137u4zXjohcDYy8TEySxS2LjdnGe4/vdSmYtEj7lq/wItGb0Cot2X+ByYUVQokJo0w88wbywVCArdBEm6CFvgqJ3w5SiExFtT9rBaZj27RIo7uvG9v6tnkfJz4J/JsRd+8DE4SMArFDTZuAb2X6NTht47rjti1o05emoDRw+0rgmr3+K5LsKuF0kyNvQ+ZgLNk1Y+ZSkgZ7jvYYr51adFCyb+W+nBBP60HLty6Q+tsZnxpH/7p+XNJ8CQhk+5d0SXdBgx1+euKn2hVOKTKLq7E8dk0rhGrohFZSlAJs6jUIKqhNvpHf+XLxC305x+g3G5Vmr96Tvb42bfU7vuUXYgJoflvJTzAw1TiTnHdquSJwp3Oig1QlIIWo8x4YO7O1HMtUQQVyTVY6KAWsMDhTKe3OeYC3P0An3J0KrusnXb7lI1y5JT9YYVedldua+nEHEbZ1sTrjZ8XOLK7G8tg1rRDK3QmtolCiiJp+fxv6fxX+ImhNUM0WJq4tfaGvvnV9vkohJVJov6Tds7y2Shppo1nqkuZLcOHu/plEMkOuhC3Mv5kNfbUzwLOCtW4KWJqJtgraw1hVhuvb1c5sNKOYVJOVTikQgMbf6k88j4AHU+a3+jvYcXSH5znqvnP83ZliydPp6Zxy2OrvKoiwVf0lElXpFjOzuFrLY9e0QigXxViZFLxPTU5AJBQrw9mAOjMMUurAK05eh1pVc/6OIdvUY41aePc/3OlZuE87G+1IuKOO4hN47rXnXLNvLzOdNWrh5u/fnGlmn5PHYFgSpLNSXnVgfyONoQeGvJ3QWXSOWAC4qOkiV+a3Vxlzr3P08wtNpadyzFa6XBYvYatrplSqInTVWh6bFUIRKMbKxGufvkohqjpFOjTF7IpZ6Ms0M1SVw9ADQ6gfWg5s7MeZs/pyyibUsb+zpCvjMwAys+j5r3n2H3jjvTfcb16mKUERy+Q0OO3tpibwMgLn8G8Oa9tVuiDMKApHsb1Eb8Ic2irzMB4V+PLSL2vzCWRfY6dQNkUcEciuY+Q8xyB+IdVspctl8Qph1TVTKkURumouj82lK2oQV7vMT3UCS7fmCLOGeAPiA/di4qnHXN+XhfCcFFK2IwrU8hNeJRA693Zi4//eDJy5Bph/HCD94BriDbh36b12eQTnsS79H5dnIo0kjpBSr+8D2fswPlO+QyWGGOridUimktr9WKMW7vrhXbjqg1dh58BOxCgWLEdBx3QD8OrdqG89gUVLzrjMKJkDttnlvOdfZWHsK1dry1HIsQoIu1xGfaweX73xqzllJmS5iIZ4A6658Br88p1f5pSicNK5txMbj2z0PZX6WD3SIp1zLXS/BXUMzrGbxhAV5Ty2CS5dUWOEKcPhXE3oisolU0lMXKSfKVVKHoWTIMtwu4ppLJ2Z0RuUAeA9W+za3+Uui63JoPaabQ4P5yb8qaSR9nW2yrwAta1nQ7wBa5asmfGLTDXOJM9NNelDYuuSuOj39yD1oYM5oaKyd3FneyfEpj779/KFv/XuS/DCiRdyymVMpadcZSbUcxscGfQ17xx665DxOqro8k90v4Vylqiu5vLYvEKoEooxO/faZ2trcMVgWlF4IWfAu+/Y7VnoTN1erg4kvjNDOaMXAPrXAD/eDiDY9Zr/N/NxZuKM/sN0DJ0f0c925Xl997bv4r499+Ho20dxbuqc7/HUGaTuXFXiFEeMYhmBrCuQty93XENn3SsrIYRxtWUqHCdZ3LIYx88cz/EPyFWCgHDNjnXnqCPIzNo0trYFbTVRfrpY8AqByY9sroKz/o3K0FkLy7d1wBodzts34vQF+IUR6hyQ56fP52ScukI51cibJT3G1pbOY/sms8UyPRpu3nqza7zyvFY9vQqHf3MY56bOBXLiqjPI3/lyAufPm81DOeGgugJ5ynm2tiInF0M2ifFabcmSFQCwZskaV7bvsZFjLmex7DXg5Rfws6UHmVnXci+CSoAVQoVQSBJLpFVdPZrFSwqNr9ZFifjtUycsBASefe3ZnHEZo1YoBXzi/kDno92PY1WRnE7i8FuHXbHx3X3dM1VNs3jZ/tsWtOUINWvUwsS13dq8gCDI5jzS/NP3uuUy7+jqA3X3decoxMdffRxAJqN3wwsbXM5sp5KTvQacIbLOhDqviBsW9uWHFUKFUIiQjayqa5Bm8c2Fx1e7EpgMseYqzigiKZDOTp717kgmIQAf/gHmX+nuq+w8tjERTEGWU+jum1kpJHoTwaKAMKMInMIu0evuwBYGZ36ALlN7cnrSNU61Ven9z9+f03d518CunPIRqk9DUml2fCY/WCHkSZSzcmt0prn99w5tA80dLknvBle4apBm8R2FxVfrnI66WHMv1OqlKZHChv0bAMwoDWPCGgnE/1ObZ6asrPLpLLkg20E6k9ySqcxKoWt/V+AuY16du3pP9rrrJwVEjlPND9BVQxUQrnGmkSlFYY1aeOqXT7k+8yOZSmLHwI5AlWt5xl+5sELIkyh7LSR6Z5rbOwVxMSN+1GgkZ/+CHHu0rIHUOgC0FRZf7Vcy2W+fqjlDsuvoLpcgMimF0+OnPTNlt/Vvw8DwgNuk0t+Ntk1tmE7l9iiQwlIt6+3HdHoaN26+0XWO1qiFdyfedfdSDog0/Wzrm1nxbP/sdpd5R5b81tUZuv/5+737Riio5q717esxMT1RNQlYjJ7IFAIRXUhEzxDROBGdJKKVHts+SkRTRDSmvK6OaizVhB0mqRPEJURrN5fKSfoVPr+q4KzkoMlIpn3qehuoqwTJvpX7jNm20l5uUk6rnl7lzrCdTuLtc29jWuib1qREKrAglT2InefYtb8L1piVt/8AyK3HlBIp3PnUndrrtfbZtVpl+OzxZ137BID5TfONM33VdyJ7OufjE/P7TjUWi6s2olwhPAYgCaAVwCoAG4lIExRts1sI0ay83oxwLFWDpyAuIVpBXZfE4k8dsEs04+Jj2nyGLc+bbcLOhzhIkTovO7Opt8Fzx5/L+dvLni/t5SYb96/e/ZXrngRtOO9k0YWL7CzdoQeGcPEFF9ufydVIx/YODAwPuFY+LgLoG7UeUzKVxGtnXtNu99y/7sX5825lOJma1G5/2QcuMx4z0ZuwFaiAwMofrszLJ+b3nbCRaUx4IslDIKILALwH4MNCiOPZ93YB+I0QwtWJgogeBfDvhBDuYiMeVFIeQlR5AcaYbyVzVIj8cgaC5ge4MpuV739+qxIbbohzl2PUoWuYXghecej7Vu6zcwBu2nqTMY4fmMlh+C8/+S/YdXQXBEROFq6aaVtHdTg37Z9LYIJAWN++HmPJMew8utN+vz5Wj0UXLcIv3/klrpt/nT6DGJm6QiZBLc9lxeIV2DGwAzGKBavdFKSRD/xzB6xRC1d95yrX+BpiDUimk54Z5c79eGWh6z7/xoFvRPrbqmWC5iFEpRCWAvhnIcS/Ud77zwA6hBCf1mz/KIA/A5ACYAH4OyGEb956LSqEoPvzEtqFVm81HrvZQmOXI0Fqqgn4zpvAWO4DrjvnoKUmvAiSwKaWedh1dBeum38dXn/3dU/TVEO8AXd/+O6MQ1sxqTTGM07j8ymzMsmHxnhjpg9zwJWGbhzqNXSWh0ilU8bwVpm0RX+klDBRlfunOhH7yGZc2Hgh3pl4x/h9Fec1Nykhp0Ix3U/n+TiVkPPzuz98N3YP7i7otzWbKHViWjOAs4733gcw17D9kwCuA9AC4KsA/isR3a3bkIjWEtERIjoyMjIS0XALJ+qKpn77i6JgXuhezx2FmbOiqPgYxPTgbP94bOSYr58imUri2deedQnRQD0R8uB86nwos5NuHPIa9lv92HhkY479Xy1tIau1OvMb1AKHtq/qyv3Ask12gxpXP4a1fTjx3gkcPX00Zyzyvjz72rOeKxJnkICpt4FXMbgoItOYYARSCET0MyIShtdBAGMA5jm+Ng/AqG5/QohjQoghIURKCPESgO8AuMOw7RYhRLsQor2lpSX4mRWZqCualqJ3Q+iIpcv0fgVc7h9HHkXFxyBljqVDU42Nr4/X20JR1utRkfV7nO8D3j0RSoluHPIa3vXDu4zfM13nRK+7TLndNCdb70knWFc/sxrvT76PlT/MxIioZbjTIo2zk2dBPrYnv7LVfi1cC41MY4ITSCEIIT4uhCDD62MAjgOoI6JrlK8tAaA3imoOgbzadDBFZfOMA9ju/vWosH0bXvg95EEIssLQ9cxVBUTvyV63g1ik8cKJF1zlKdQy1LqcAz/BFxVximtLTwOZkFWTo1hiShJzle6uSwJN7+W0zVQFq9pgZnBkEEdPH0WiN5FThjtIdJXMUVDLVp+fPm9Hhpmc+9sHtmNgeAA7B3YWFJnGBCcSk5EQYhzA0wC+QUQXENFHAXwWwC7d9kT0WSL6t5ThIwD+FMCPoxhLNRFpyYkyojN3RdWn2WuFoa4OnKRECjduvhHtl7a7yic0xBtQF6sLPSuVK4/17euN45ax+UEazpvwal0ZpB2o7jr3revLNPz59XLgW1ZGsWsqo6rXwNlgZsUPVmQa9CA36qqprglrlqxxJfKpOQrnps7l5GoICDt/xBl5Jq/vualzWPX0KkxMT+T0V76g/oJA58yEJ7Jqp0R0IYBuAP8vgDMAuoQQ/3/2sz8A8LwQojn79z8A+I8A5gB4C8D3hBDf9TtGJTmVo6DU/QU8/QUaVId1qccapPKljGDyMvHMb5qvdZQ21jVqo5DaFrTBeqQPpz+3FLhEH80EIFDFTa+qpXNiczCZnonMcUYSOR3Im1/ZjC/+3hfxxOATroiexrpGnLj/hK9TVY34emj5Q/jQtz+kVaZtC9qw7TPbsHTLUs/9Sepj9S5FpauqquOeJfdg2+e22X+bopY4sqgwgjqVzR2oQyKEeBfA5wyfvYiM41n+rXUgM+Wj0qqgB1lh6MxBQCb2/9TZUzg/fd52lDpDGL0il+g+6M1izRbm/W3wkt1exfZUZQDAJQDlTP2h5Q/Z9vqeV3ugm8DJvAovAem0349PjaM+Xu+6xnGK4/lVz+PWnbf6np9Et2qR4xcQngpblz+iMw+lRApdP+nC7mO7kRZpbHplE9a1r8MNrTcEHifjD5eumEUUEhlVjD7RXgSpg7N84XKtOag+Xu/pe5BRSbryEUaaLWDtMvSeeBGX3JkIZOILkpVtQio/NekrJVLaSKW0SPuaS5z+mD3H9xgF74b9G/TtQB00xBuw5oY1Wt+KLLKXU45cg2zJCXibAJOpJHpe7bHPIS3StqObiQ5WCLOIQiKZShEFFRbTKkINOzWFMAoIWGOWq+SFkVu6gLlWplexo7TI6dP6rNm+dX24qOmiwOcjBat0bu9buc9eHUgaYg246bKb3OGhHgXjdP6Yc1PnYD1o2X0PVHYd3YUT958IlFG+5/U9qI9nHOBO38Hyhct9I7acfhsv5ZESqZzPpaObiQ5WCEzRKHZpAd0qYn37eltASbycxc7CeFqaLWDJ40rDnSlXLoYpX+LyD1we+HzkzFiOV10dSJLpTHXVMKsbU8RX1/4ubbmMlEjh/n33e5YckQ7e8alxo/INskJSzYCH3joUuB6UhFcJ0cItNMtIMbOPK4Goy1YEwa+0hc7B6XRsuhzon10DtO3MDYxOE7CpH3j7hpyM7nyyZk3tQa/4wBWeIaafWfQZ/Hbytzk+DZ1/xHRNLmq6yNgmdE58DqbSU573Tuf4JxC+tORL2P657b7n7cSrTeonH/+k9hwIhKEHhzhL2QduoVkFVKIZJiqCJJUVAy/fg8nJ61wl5PhFnKsDCQng9uzstMAeEaYZfF2szuUjUXn2+LN48eSLOcfTrVS8ZvcmJqcns/05ukH33oyWq9z3z9TFbu9xfQFCP7xyV5w9KiT18XrOP4gQVghVTqXmMkRRtiJqTCYM5/hURb3+iQQQ09QIImSqv17s3SMiiNnM5At54703fE0uAsI+Xhgl7NluVCWeBC47jHeud98/Z4qbnwwAAAzBSURBVBc7mcg3PjWe1wTAL7KsGB3YuGJqLqwQqpwoG/VERZDaNOV4CL0a55iEyqG3Dpl3KADc7t0jIkgtJqdgleWyJ/5iQjsrdjKdnrZ9DkGVsK99X66IYunM/5d2B1Yw+U4A9q3cZysVNWtcOsyL0YGt0P7gtQYrBCZygtSmKddDKE0Psp6RswCcLlJofft6xHSPSkyAWvU9Il566yXfGbvueM5rE8QxO5WesjulBa0dZRKu+Jf1QFpzrvGk8X5FUbcKKP2qslxmzUqGFQITOV5L+3I/hF7HNymq3pO9xkqlRITFLYtdZRucPgtd20zn8fqtfmw6silnbKrg9gph9aqOGoorDD2dY+b7FUXdqqiUShgq0axZblghMJET1LFbjofQdHwvRbF84XLj/pzlttW+zKqAk20zu/Z3GY+3+pnVrtBTuW3H9g7PSBpTddTQ9vVTyzO9EgBXhzbT/YrCth+FUglDORRQNcAKgSkZ5X4IvY7vpahMfoT5TfO1iV3T6WmsenqV1mnbc7RHe7z7n78/p2Oac2wHTx1Ex8KOnHwLp9mrUPu6NWplku6kCcwRWWUS8lHY9ovhMPai1AqoWuA8hCqnmnIZghSsKyZrnlljt8tUj3/34rux+9hubfy7c1bu7ALXWNeI986/5zqWqXgeAKxZssbu9iUhkCspy9QZTC0YF2W3sHLfn1Lila9SiJO6Uil5cTumPFSa0Pei1LNAJ3tf3+sSurL8gmm26BSETr/A2clMo0C15aUqpHXJVjsGdrj6HZjq9+w5vse1clELxpnGGRQ1ka3c96eU1KLQjwJWCEzJKOdDaI1adiKWc1atmy3qBKHT5KSWlUimkraJRZaFOPHbE7jqg1dpTUd+fQ3U1YFq4pL9CJxmr4c7Hs5rlaA6tnV9k/949x+DiDA8NlzR2cCqYhNC+PbhZvSwD4GpGIqZn+DlIwhqA/dK5lKdurLn74snX8Te43u1YaMEsuPsjYXjjrtXLn7RRGGuoawumhZpdPe58wwSvQm8/JuXcfitwxVvW1cVG+cW5A8rBKZiKNaDHJUzO0w5a9laUm1cryaZqSUXTArp8g9c7jqeXzRRmGuY6E3YrTBlTwWJVBaS7n7vxLRyokZsyXwMzi3ID1YITMFEMbMvZn5CVBElquAO2iLT2WA+jFIyKQrda9/Kfbjp+zcFFob26iCbX5FG7ipBVRZyvFEq6ihXg+r9VVdQHDUUHlYITMFEMbMvZn5CIc5Sk+AyCWu1po88zrb+bTkN5iV+5xlGaErzTlBh6BT4cqxSeanKAsjkW4RZJfiNParVoFPROk13vEoIRyQKgYi+RkRHiGiSiLYH2P7PiGiYiM4SUTcRzYliHEzpiWJmX+z8hELi5MMKLtNqRNehzE8pBT22at4JKgx12ddppHHg5AGtspD7DHMdTGOPajVojVpYtmUZUmlN8cEsvEoIR1QrhCEA3wTQ7bchEX0CQBeAWwAsBHA1gK9HNA6mxEQxs6/UJKF8BJdpNXL5By73VUrqrDps5VKdAPe6hqb2o+2XtGPnwM68W3XK8/Aae1SrwURvAtaY5RmxVaths8UikrBTIcTTAEBE7QAu89l8DYCtQojB7HcSAB5HRkkwVYRpZh82BLJS4991gssv3t+vlaVXOKQ6qw6aa2ALX40A97qGpmu+5/U9mJieQGd7Z965DV7XLarfjNwPYE4iZMJTDh/CYgADyt8DAFqJKHjzWaYiKIazNqqyxoVSDDNWUDNKd383uvu6Ax1bdw/U3sama+jVNKeY5r+ofjPlrolVq5RDITQDeF/5W/5/rm5jIlqb9U8cGRkZKfrgmOBU6sw+CqI2Y4UxoyRTSZcJqJiF5XRjKJb5L4rxlrsmVi3jazIiop8B6DB8/M9CiI+FPOYYgHnK3/L/o7qNhRBbAGwBMrWMQh6LKSK1nP4ftbILY0bRJb95FZaLglKZ//rW9eXUTMqnVpKX0qm1mkulxlchCCE+HvExBwEsAfBk9u8lAE4LIfTdvpmqwc9GXk1Eqez8hK3J7FPKonJRCVm/6xaF4qnllWm5iSrstI6IGgHEAcSJqJGITMpmJ4CvENH1RPRBAA8B2B7FOJjywiUD9ERtRilGiY9SCdkoTHGV6HOqFaIqbvcQgEeUv1cjE0r6KBFdAeAYgOuFEKeEEP9IRH8D4J8ANAH4oeO7TBXitJHnW2ytnBRrhRPEjBIGVfFGtYLQFbaT1yJKeHZf2XA/BCYSCrULVwKdezux+ZXNuG/ZfRU7dmc/hmKFW1bDtWCCE7QfApeuYAqmFqI+yt3rWY7BzxRUinDLSrgWTHlghcAUTKVmGoehEuLaTT4YqSicfZqLpXgr4Vow5YEVAlMw1W4XroQVjtesXCoKXZ/mqAV2JVwLpnywQmAKptqjPiphhWOalauK4tjIsaIr3kq4Fkz54BaazKyn3Cscr9h8VUDXx+uL7qwv97VgygtHGTGMgVIl2qkRWhK1p/L56fP2+1zIjckHjjJiappi9l+WlCrRzlh5VNNTmc03TDFhhcBUJcUW1qUMvQzTU5nNN0wxYYXAVB2lENaVEHpZzc76UqzgmOhhhcBUHcUW1hx6WThc16o6YYXAVBWlENYcelkYnOlcvbBCYKqKUghrDr0sjEowtzH5wXkITFVRCmFdDTb6SiWqRjtMeWCFwFQVLKwrG+5mVt2wyYhhmMhgc1t1wysEhmEig1dw1Q2vEBiGYRgArBAYpqbhBDEmDKwQGKaG4QQxJgyRKAQi+hoRHSGiSSLa7rPtPUSUIqIx5fXxKMbBMMwMnCDGhCWqFcIQgG8C6A64/SEhRLPy+llE42AYJkvQBDE2KzGSSBSCEOJpIcSPAJyJYn8MwxRGmBIfbFZiJOXyISwloneI6DgRPUxExvBXIlqbNUcdGRkZKeUYGaZqCVrig81KjEo5FEIvgA8DuBjA7QDuBvDnpo2FEFuEEO1CiPaWlpYSDZFhqpugCWJcd4hR8W2hSUQ/A9Bh+PifhRAfU7b9JoDLhBD3BB4A0V0A/lwIscxvW26hyTDRYY1auPq7V3OLzllAZC00hRAfF0KQ4fUxv+8HQACgCPbDMEwIuMw34ySqsNM6ImoEEAcQJ6JGk1+AiD5JRK3Z//8ugIcB/DiKcTAMExyuO8Q4iaqW0UMAHlH+Xg3g6wAeJaIrABwDcL0Q4hSAWwBsJ6JmAKcB9AD47xGNg2GYgHDdIcaJrw+hkmAfAsMwTHgi8yEwDMMwswNWCAzDMAwAVggMwzBMFlYIDMMwDIAqcyoT0QiAkz6bzQfwTgmGUynw+dY+s+2c+XyjZ6EQwrfUQ1UphCAQ0ZEg3vRagc+39plt58znWz7YZMQwDMMAYIXAMAzDZKlFhbCl3AMoMXy+tc9sO2c+3zJRcz4EhmEYJj9qcYXAMAzD5AErBIZhGAZADSoEIppDRFuJ6CQRjRJRPxF9stzjKjZE9LVsq9FJItpe7vFEDRFdSETPENF49t6uLPeYikmt308ns/G5JaIeIrKI6Gy2nfC95R5TVOWvK4k6AP8HmS5vpwD8IYAniej3hBC/LufAiswQgG8C+ASApjKPpRg8BiAJoBVAG4C9RDQghBgs77CKRq3fTyez8bn9SwBfEUJMZnvD/IyI+oQQr5RrQDW3QhBCjAshHhVC/FoIkRZC7AFwAoBvi85qRgjxtBDiRwDOlHssUUNEFyDTf/thIcSYEOIggGcBfLG8IysetXw/dczG51YIMSiEmJR/Zl+/U8Yh1Z5CcJLtzrYIQK3OJGcDiwBMCyGOK+8NAFhcpvEwRWa2PLdE9D0iOgfgXwFYAPaVczw1rRCIqB7A4wB2CCH+tdzjYfKmGcBZx3vvA5hbhrEwRWY2PbdCiE5kfsd/AOBpAJPe3yguVacQiOhnRCQMr4PKdjEAu5CxO3+tbAOOgKDnXMOMAZjneG8egNEyjIUpIrX03AZFCJHKmkEvA7C+nGOpOqeyEOLjftsQEQHYiowD8g+FEFPFHlcxCXLONc5xAHVEdI0Q4vXse0tQ4+aE2UatPbd5UAf2IRSFjQCuA/BpIcREuQdTCoiojogaAcQBxImokYiqTuHrEEKMI7Oc/gYRXUBEHwXwWWRmkjVJLd9PD2bNc0tEFxPRXUTUTERxIvoEgLsBvFDWgQkhauoFYCEy3vrzyJga5GtVucdW5PN+FDORCvL1aLnHFeH5XQjgRwDGkQlLXFnuMfH9jPR8Z9VzC6AFwAEAv0XGP/YqgK+We1xcy4hhGIYBULsmI4ZhGCYkrBAYhmEYAKwQGIZhmCysEBiGYRgArBAYhmGYLKwQGIZhGACsEBiGYZgsrBAYhmEYAKwQGIZhmCz/Fx9AYsCieBVLAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\\n\",\n    \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\")\\n\",\n    \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[LibSVM]0 0.1 0.8537578582763672\\n\",\n      \"[LibSVM]1 0.01 0.7340240478515625\\n\",\n      \"[LibSVM]2 0.001 0.7935328483581543\\n\",\n      \"[LibSVM]3 0.0001 1.4816207885742188\\n\",\n      \"[LibSVM]4 1e-05 2.543147087097168\\n\",\n      \"[LibSVM]5 1.0000000000000002e-06 2.138460159301758\\n\",\n      \"[LibSVM]6 1.0000000000000002e-07 2.3709118366241455\\n\",\n      \"[LibSVM]7 1.0000000000000002e-08 2.3185129165649414\\n\",\n      \"[LibSVM]8 1.0000000000000003e-09 2.213902235031128\\n\",\n      \"[LibSVM]9 1.0000000000000003e-10 2.2198519706726074\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x12d840898>]\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"tol = 0.1\\n\",\n    \"tols = []\\n\",\n    \"times = []\\n\",\n    \"for i in range(10):\\n\",\n    \"    svm_clf = SVC(kernel=\\\"poly\\\", gamma=3, C=10, tol=tol, verbose=1)\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    svm_clf.fit(X, y)\\n\",\n    \"    t2 = time.time()\\n\",\n    \"    times.append(t2-t1)\\n\",\n    \"    tols.append(tol)\\n\",\n    \"    print(i, tol, t2-t1)\\n\",\n    \"    tol /= 10\\n\",\n    \"plt.semilogx(tols, times)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Linear SVM classifier implementation using Batch Gradient Descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Training set\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, petal width\\n\",\n    \"y = (iris[\\\"target\\\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1.],\\n\",\n       \"       [0.]])\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.base import BaseEstimator\\n\",\n    \"\\n\",\n    \"class MyLinearSVC(BaseEstimator):\\n\",\n    \"    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\\n\",\n    \"        self.C = C\\n\",\n    \"        self.eta0 = eta0\\n\",\n    \"        self.n_epochs = n_epochs\\n\",\n    \"        self.random_state = random_state\\n\",\n    \"        self.eta_d = eta_d\\n\",\n    \"\\n\",\n    \"    def eta(self, epoch):\\n\",\n    \"        return self.eta0 / (epoch + self.eta_d)\\n\",\n    \"        \\n\",\n    \"    def fit(self, X, y):\\n\",\n    \"        # Random initialization\\n\",\n    \"        if self.random_state:\\n\",\n    \"            np.random.seed(self.random_state)\\n\",\n    \"        w = np.random.randn(X.shape[1], 1) # n feature weights\\n\",\n    \"        b = 0\\n\",\n    \"\\n\",\n    \"        m = len(X)\\n\",\n    \"        t = y * 2 - 1  # -1 if t==0, +1 if t==1\\n\",\n    \"        X_t = X * t\\n\",\n    \"        self.Js=[]\\n\",\n    \"\\n\",\n    \"        # Training\\n\",\n    \"        for epoch in range(self.n_epochs):\\n\",\n    \"            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\\n\",\n    \"            X_t_sv = X_t[support_vectors_idx]\\n\",\n    \"            t_sv = t[support_vectors_idx]\\n\",\n    \"\\n\",\n    \"            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\\n\",\n    \"            self.Js.append(J)\\n\",\n    \"\\n\",\n    \"            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\\n\",\n    \"            b_derivative = -C * np.sum(t_sv)\\n\",\n    \"                \\n\",\n    \"            w = w - self.eta(epoch) * w_gradient_vector\\n\",\n    \"            b = b - self.eta(epoch) * b_derivative\\n\",\n    \"            \\n\",\n    \"\\n\",\n    \"        self.intercept_ = np.array([b])\\n\",\n    \"        self.coef_ = np.array([w])\\n\",\n    \"        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\\n\",\n    \"        self.support_vectors_ = X[support_vectors_idx]\\n\",\n    \"        return self\\n\",\n    \"\\n\",\n    \"    def decision_function(self, X):\\n\",\n    \"        return X.dot(self.coef_[0]) + self.intercept_[0]\\n\",\n    \"\\n\",\n    \"    def predict(self, X):\\n\",\n    \"        return (self.decision_function(X) >= 0).astype(np.float64)\\n\",\n    \"\\n\",\n    \"C=2\\n\",\n    \"svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\\n\",\n    \"svm_clf.fit(X, y)\\n\",\n    \"svm_clf.predict(np.array([[5, 2], [4, 1]]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0, 60000, 0, 100]\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\\n\",\n    \"plt.axis([0, svm_clf.n_epochs, 0, 100])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-15.56761653] [[[2.28120287]\\n\",\n      \"  [2.71621742]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(svm_clf.intercept_, svm_clf.coef_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-15.51721253] [[2.27128546 2.71287145]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"svm_clf2 = SVC(kernel=\\\"linear\\\", C=C)\\n\",\n    \"svm_clf2.fit(X, y.ravel())\\n\",\n    \"print(svm_clf2.intercept_, svm_clf2.coef_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[4, 6, 0.8, 2.8]\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"yr = y.ravel()\\n\",\n    \"plt.figure(figsize=(12,3.2))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \\\"bs\\\", label=\\\"Not Iris-Virginica\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf, 4, 6)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"MyLinearSVC\\\", fontsize=14)\\n\",\n    \"plt.axis([4, 6, 0.8, 2.8])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \\\"g^\\\")\\n\",\n    \"plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \\\"bs\\\")\\n\",\n    \"plot_svc_decision_boundary(svm_clf2, 4, 6)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"SVC\\\", fontsize=14)\\n\",\n    \"plt.axis([4, 6, 0.8, 2.8])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-14.06195929   2.24179316   1.79750198]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[4, 6, 0.8, 2.8]\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 396x230.4 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import SGDClassifier\\n\",\n    \"\\n\",\n    \"sgd_clf = SGDClassifier(loss=\\\"hinge\\\", alpha = 0.017, max_iter = 50, tol=-np.infty, random_state=42)\\n\",\n    \"sgd_clf.fit(X, y.ravel())\\n\",\n    \"\\n\",\n    \"m = len(X)\\n\",\n    \"t = y * 2 - 1  # -1 if t==0, +1 if t==1\\n\",\n    \"X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\\n\",\n    \"X_b_t = X_b * t\\n\",\n    \"sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\\n\",\n    \"print(sgd_theta)\\n\",\n    \"support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\\n\",\n    \"sgd_clf.support_vectors_ = X[support_vectors_idx]\\n\",\n    \"sgd_clf.C = C\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(5.5,3.2))\\n\",\n    \"plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \\\"g^\\\")\\n\",\n    \"plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \\\"bs\\\")\\n\",\n    \"plot_svc_decision_boundary(sgd_clf, 4, 6)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"SGDClassifier\\\", fontsize=14)\\n\",\n    \"plt.axis([4, 6, 0.8, 2.8])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 7.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 8.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn import datasets\\n\",\n    \"\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris[\\\"data\\\"][:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = iris[\\\"target\\\"]\\n\",\n    \"\\n\",\n    \"setosa_or_versicolor = (y == 0) | (y == 1)\\n\",\n    \"X = X[setosa_or_versicolor]\\n\",\n    \"y = y[setosa_or_versicolor]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LinearSVC:                    [0.28474532] [[1.05364923 1.09903601]]\\n\",\n      \"SVC:                          [0.31896852] [[1.1203284  1.02625193]]\\n\",\n      \"SGDClassifier(alpha=0.00200): [0.319] [[1.12072936 1.02666842]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC, LinearSVC\\n\",\n    \"from sklearn.linear_model import SGDClassifier\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"\\n\",\n    \"C = 5\\n\",\n    \"alpha = 1 / (C * len(X))\\n\",\n    \"\\n\",\n    \"lin_clf = LinearSVC(loss=\\\"hinge\\\", C=C, random_state=42)\\n\",\n    \"svm_clf = SVC(kernel=\\\"linear\\\", C=C)\\n\",\n    \"sgd_clf = SGDClassifier(loss=\\\"hinge\\\", learning_rate=\\\"constant\\\", eta0=0.001, alpha=alpha,\\n\",\n    \"                        max_iter=100000, tol=-np.infty, random_state=42)\\n\",\n    \"\\n\",\n    \"scaler = StandardScaler()\\n\",\n    \"X_scaled = scaler.fit_transform(X)\\n\",\n    \"\\n\",\n    \"lin_clf.fit(X_scaled, y)\\n\",\n    \"svm_clf.fit(X_scaled, y)\\n\",\n    \"sgd_clf.fit(X_scaled, y)\\n\",\n    \"\\n\",\n    \"print(\\\"LinearSVC:                   \\\", lin_clf.intercept_, lin_clf.coef_)\\n\",\n    \"print(\\\"SVC:                         \\\", svm_clf.intercept_, svm_clf.coef_)\\n\",\n    \"print(\\\"SGDClassifier(alpha={:.5f}):\\\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the decision boundaries of these three models:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Compute the slope and bias of each decision boundary\\n\",\n    \"w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\\n\",\n    \"b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\\n\",\n    \"w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\\n\",\n    \"b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\\n\",\n    \"w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\\n\",\n    \"b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\\n\",\n    \"\\n\",\n    \"# Transform the decision boundary lines back to the original scale\\n\",\n    \"line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\\n\",\n    \"line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\\n\",\n    \"line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\\n\",\n    \"\\n\",\n    \"# Plot all three decision boundaries\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"plt.plot(line1[:, 0], line1[:, 1], \\\"k:\\\", label=\\\"LinearSVC\\\")\\n\",\n    \"plt.plot(line2[:, 0], line2[:, 1], \\\"b--\\\", linewidth=2, label=\\\"SVC\\\")\\n\",\n    \"plt.plot(line3[:, 0], line3[:, 1], \\\"r-\\\", label=\\\"SGDClassifier\\\")\\n\",\n    \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\") # label=\\\"Iris-Versicolor\\\"\\n\",\n    \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\") # label=\\\"Iris-Setosa\\\"\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"upper center\\\", fontsize=14)\\n\",\n    \"plt.axis([0, 5.5, 0, 2])\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Close enough!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 9.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"try:\\n\",\n    \"    from sklearn.datasets import fetch_openml\\n\",\n    \"    mnist = fetch_openml('mnist_784', version=1, cache=True, as_frame=False)\\n\",\n    \"except ImportError:\\n\",\n    \"    from sklearn.datasets import fetch_mldata\\n\",\n    \"    mnist = fetch_mldata('MNIST original')\\n\",\n    \"\\n\",\n    \"X = mnist[\\\"data\\\"]\\n\",\n    \"y = mnist[\\\"target\\\"]\\n\",\n    \"\\n\",\n    \"X_train = X[:60000]\\n\",\n    \"y_train = y[:60000]\\n\",\n    \"X_test = X[60000:]\\n\",\n    \"y_test = y[60000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"rnd_idx = np.random.permutation(60000)\\n\",\n    \"X_train = X_train[rnd_idx]\\n\",\n    \"y_train = y_train[rnd_idx]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\\n\",\n      \"  \\\"the number of iterations.\\\", ConvergenceWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\\n\",\n       \"     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\\n\",\n       \"     verbose=0)\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lin_clf = LinearSVC(random_state=42)\\n\",\n    \"lin_clf.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.86275\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"\\n\",\n    \"y_pred = lin_clf.predict(X_train)\\n\",\n    \"accuracy_score(y_train, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Wow, 86% accuracy on MNIST is a really bad performance. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"scaler = StandardScaler()\\n\",\n    \"X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\\n\",\n    \"X_test_scaled = scaler.transform(X_test.astype(np.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\\n\",\n      \"  \\\"the number of iterations.\\\", ConvergenceWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\\n\",\n       \"     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\\n\",\n       \"     verbose=0)\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lin_clf = LinearSVC(random_state=42)\\n\",\n    \"lin_clf.fit(X_train_scaled, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.923\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = lin_clf.predict(X_train_scaled)\\n\",\n    \"accuracy_score(y_train, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's much better (we cut the error rate in two), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default).\\n\",\n    \"\\n\",\n    \"**Warning**: if you are using Scikit-Learn ≤ 0.19, the `SVC` class will use the One-vs-One (OvO) strategy by default, so you must explicitly set `decision_function_shape=\\\"ovr\\\"` if you want to use the OvR strategy instead (OvR is the default since 0.19).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\\n\",\n       \"  max_iter=-1, probability=False, random_state=None, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"svm_clf = SVC(decision_function_shape=\\\"ovr\\\", gamma=\\\"auto\\\")\\n\",\n    \"svm_clf.fit(X_train_scaled[:10000], y_train[:10000])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9475833333333333\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = svm_clf.predict(X_train_scaled)\\n\",\n    \"accuracy_score(y_train, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 10 candidates, totalling 30 fits\\n\",\n      \"[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\\n\",\n      \"[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\\n\",\n      \"[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\\n\",\n      \"[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\\n\",\n      \"[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\\n\",\n      \"[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.2s\\n\",\n      \"[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\\n\",\n      \"[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.3s\\n\",\n      \"[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\\n\",\n      \"[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.2s\\n\",\n      \"[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\\n\",\n      \"[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\\n\",\n      \"[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\\n\",\n      \"[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\\n\",\n      \"[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\\n\",\n      \"[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\\n\",\n      \"[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\\n\",\n      \"[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\\n\",\n      \"[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\\n\",\n      \"[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\\n\",\n      \"[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\\n\",\n      \"[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\\n\",\n      \"[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\\n\",\n      \"[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.2s\\n\",\n      \"[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\\n\",\n      \"[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.1s\\n\",\n      \"[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\\n\",\n      \"[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.2s\\n\",\n      \"[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\\n\",\n      \"[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.1s\\n\",\n      \"[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\\n\",\n      \"[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.2s\\n\",\n      \"[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\\n\",\n      \"[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.2s\\n\",\n      \"[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\\n\",\n      \"[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\\n\",\n      \"[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\\n\",\n      \"[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\\n\",\n      \"[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\\n\",\n      \"[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\\n\",\n      \"[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\\n\",\n      \"[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\\n\",\n      \"[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\\n\",\n      \"[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\\n\",\n      \"[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\\n\",\n      \"[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\\n\",\n      \"[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\\n\",\n      \"[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.2s\\n\",\n      \"[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\\n\",\n      \"[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.2s\\n\",\n      \"[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\\n\",\n      \"[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.3s\\n\",\n      \"[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\\n\",\n      \"[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.2s\\n\",\n      \"[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\\n\",\n      \"[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.2s\\n\",\n      \"[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\\n\",\n      \"[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.3s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   55.2s finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv=3, error_score='raise-deprecating',\\n\",\n       \"          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\\n\",\n       \"  max_iter=-1, probability=False, random_state=None, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False),\\n\",\n       \"          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\\n\",\n       \"          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10f38c828>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10f38c320>},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=None, refit=True,\\n\",\n       \"          return_train_score='warn', scoring=None, verbose=2)\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"from scipy.stats import reciprocal, uniform\\n\",\n    \"\\n\",\n    \"param_distributions = {\\\"gamma\\\": reciprocal(0.001, 0.1), \\\"C\\\": uniform(1, 10)}\\n\",\n    \"rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2, cv=3)\\n\",\n    \"rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\\n\",\n       \"  kernel='rbf', max_iter=-1, probability=False, random_state=None,\\n\",\n       \"  shrinking=True, tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search_cv.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.865\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search_cv.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set (run this at night, it will take hours):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n       \"  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\\n\",\n       \"  kernel='rbf', max_iter=-1, probability=False, random_state=None,\\n\",\n       \"  shrinking=True, tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.99965\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\\n\",\n    \"accuracy_score(y_train, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ah, this looks good! Let's select this model. Now we can test it on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.971\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\\n\",\n    \"accuracy_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: train an SVM regressor on the California housing dataset._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"\\n\",\n    \"housing = fetch_california_housing()\\n\",\n    \"X = housing[\\\"data\\\"]\\n\",\n    \"y = housing[\\\"target\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Split it into a training set and a test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Don't forget to scale the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"\\n\",\n    \"scaler = StandardScaler()\\n\",\n    \"X_train_scaled = scaler.fit_transform(X_train)\\n\",\n    \"X_test_scaled = scaler.transform(X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train a simple `LinearSVR` first:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\\n\",\n      \"  \\\"the number of iterations.\\\", ConvergenceWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\\n\",\n       \"     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\\n\",\n       \"     random_state=42, tol=0.0001, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import LinearSVR\\n\",\n    \"\\n\",\n    \"lin_svr = LinearSVR(random_state=42)\\n\",\n    \"lin_svr.fit(X_train_scaled, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's see how it performs on the training set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.954517044073374\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"\\n\",\n    \"y_pred = lin_svr.predict(X_train_scaled)\\n\",\n    \"mse = mean_squared_error(y_train, y_pred)\\n\",\n    \"mse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the RMSE:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.976993881287582\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sqrt(mse)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 10 candidates, totalling 30 fits\\n\",\n      \"[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\\n\",\n      \"[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.4s\\n\",\n      \"[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.3s remaining:    0.0s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\\n\",\n      \"[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\\n\",\n      \"[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\\n\",\n      \"[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\\n\",\n      \"[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.3s\\n\",\n      \"[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\\n\",\n      \"[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.2s\\n\",\n      \"[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\\n\",\n      \"[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.1s\\n\",\n      \"[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\\n\",\n      \"[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\\n\",\n      \"[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\\n\",\n      \"[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\\n\",\n      \"[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\\n\",\n      \"[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\\n\",\n      \"[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\\n\",\n      \"[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.7s\\n\",\n      \"[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\\n\",\n      \"[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\\n\",\n      \"[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\\n\",\n      \"[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\\n\",\n      \"[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\\n\",\n      \"[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\\n\",\n      \"[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\\n\",\n      \"[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.2s\\n\",\n      \"[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\\n\",\n      \"[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\\n\",\n      \"[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\\n\",\n      \"[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\\n\",\n      \"[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\\n\",\n      \"[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\\n\",\n      \"[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\\n\",\n      \"[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.9s\\n\",\n      \"[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\\n\",\n      \"[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\\n\",\n      \"[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\\n\",\n      \"[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   4.9s\\n\",\n      \"[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\\n\",\n      \"[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\\n\",\n      \"[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\\n\",\n      \"[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\\n\",\n      \"[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\\n\",\n      \"[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\\n\",\n      \"[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\\n\",\n      \"[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.8s\\n\",\n      \"[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\\n\",\n      \"[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.9s\\n\",\n      \"[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\\n\",\n      \"[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   5.1s\\n\",\n      \"[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\\n\",\n      \"[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.8s\\n\",\n      \"[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\\n\",\n      \"[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.8s\\n\",\n      \"[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\\n\",\n      \"[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.7s\\n\",\n      \"[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\\n\",\n      \"[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   5.0s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  3.5min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv=3, error_score='raise-deprecating',\\n\",\n       \"          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\\n\",\n       \"  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False),\\n\",\n       \"          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\\n\",\n       \"          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2e390>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2eac8>},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=42, refit=True,\\n\",\n       \"          return_train_score='warn', scoring=None, verbose=2)\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVR\\n\",\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"from scipy.stats import reciprocal, uniform\\n\",\n    \"\\n\",\n    \"param_distributions = {\\\"gamma\\\": reciprocal(0.001, 0.1), \\\"C\\\": uniform(1, 10)}\\n\",\n    \"rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, cv=3, random_state=42)\\n\",\n    \"rnd_search_cv.fit(X_train_scaled, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\\n\",\n       \"  gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\\n\",\n       \"  tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search_cv.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's measure the RMSE on the training set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5727524770785359\"\n      ]\n     },\n     \"execution_count\": 70,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\\n\",\n    \"mse = mean_squared_error(y_train, y_pred)\\n\",\n    \"np.sqrt(mse)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looks much better than the linear model. Let's select this model and evaluate it on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5929168385528734\"\n      ]\n     },\n     \"execution_count\": 71,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\\n\",\n    \"mse = mean_squared_error(y_test, y_pred)\\n\",\n    \"np.sqrt(mse)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {},\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "06_decision_trees.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 6 – Decision Trees**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 6._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/06_decision_trees.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"mpl.rc('axes', labelsize=14)\\n\",\n    \"mpl.rc('xtick', labelsize=12)\\n\",\n    \"mpl.rc('ytick', labelsize=12)\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"decision_trees\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Training and visualizing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\\n\",\n       \"            max_features=None, max_leaf_nodes=None,\\n\",\n       \"            min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"            min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\\n\",\n       \"            splitter='best')\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.datasets import load_iris\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"\\n\",\n    \"iris = load_iris()\\n\",\n    \"X = iris.data[:, 2:] # petal length and width\\n\",\n    \"y = iris.target\\n\",\n    \"\\n\",\n    \"tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\\n\",\n    \"tree_clf.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.tree import export_graphviz\\n\",\n    \"\\n\",\n    \"def image_path(fig_id):\\n\",\n    \"    return os.path.join(IMAGES_PATH, fig_id)\\n\",\n    \"\\n\",\n    \"export_graphviz(\\n\",\n    \"        tree_clf,\\n\",\n    \"        out_file=image_path(\\\"iris_tree.dot\\\"),\\n\",\n    \"        feature_names=iris.feature_names[2:],\\n\",\n    \"        class_names=iris.target_names,\\n\",\n    \"        rounded=True,\\n\",\n    \"        filled=True\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure decision_tree_decision_boundaries_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib.colors import ListedColormap\\n\",\n    \"\\n\",\n    \"def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\\n\",\n    \"    x1s = np.linspace(axes[0], axes[1], 100)\\n\",\n    \"    x2s = np.linspace(axes[2], axes[3], 100)\\n\",\n    \"    x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n    \"    X_new = np.c_[x1.ravel(), x2.ravel()]\\n\",\n    \"    y_pred = clf.predict(X_new).reshape(x1.shape)\\n\",\n    \"    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\\n\",\n    \"    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\\n\",\n    \"    if not iris:\\n\",\n    \"        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\\n\",\n    \"        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\\n\",\n    \"    if plot_training:\\n\",\n    \"        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n    \"        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n    \"        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n    \"        plt.axis(axes)\\n\",\n    \"    if iris:\\n\",\n    \"        plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"        plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"    else:\\n\",\n    \"        plt.xlabel(r\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"        plt.ylabel(r\\\"$x_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"    if legend:\\n\",\n    \"        plt.legend(loc=\\\"lower right\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"plot_decision_boundary(tree_clf, X, y)\\n\",\n    \"plt.plot([2.45, 2.45], [0, 3], \\\"k-\\\", linewidth=2)\\n\",\n    \"plt.plot([2.45, 7.5], [1.75, 1.75], \\\"k--\\\", linewidth=2)\\n\",\n    \"plt.plot([4.95, 4.95], [0, 1.75], \\\"k:\\\", linewidth=2)\\n\",\n    \"plt.plot([4.85, 4.85], [1.75, 3], \\\"k:\\\", linewidth=2)\\n\",\n    \"plt.text(1.40, 1.0, \\\"Depth=0\\\", fontsize=15)\\n\",\n    \"plt.text(3.2, 1.80, \\\"Depth=1\\\", fontsize=13)\\n\",\n    \"plt.text(4.05, 0.5, \\\"(Depth=2)\\\", fontsize=11)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"decision_tree_decision_boundaries_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Predicting classes and class probabilities\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.        , 0.90740741, 0.09259259]])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tree_clf.predict_proba([[5, 1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tree_clf.predict([[5, 1.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Sensitivity to training set details\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[4.8, 1.8]])\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X[(X[:, 1]==X[:, 1][y==1].max()) & (y==1)] # widest Iris-Versicolor flower\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\\n\",\n       \"            max_features=None, max_leaf_nodes=None,\\n\",\n       \"            min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"            min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"            min_weight_fraction_leaf=0.0, presort=False, random_state=40,\\n\",\n       \"            splitter='best')\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"not_widest_versicolor = (X[:, 1]!=1.8) | (y==2)\\n\",\n    \"X_tweaked = X[not_widest_versicolor]\\n\",\n    \"y_tweaked = y[not_widest_versicolor]\\n\",\n    \"\\n\",\n    \"tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\\n\",\n    \"tree_clf_tweaked.fit(X_tweaked, y_tweaked)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure decision_tree_instability_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"plot_decision_boundary(tree_clf_tweaked, X_tweaked, y_tweaked, legend=False)\\n\",\n    \"plt.plot([0, 7.5], [0.8, 0.8], \\\"k-\\\", linewidth=2)\\n\",\n    \"plt.plot([0, 7.5], [1.75, 1.75], \\\"k--\\\", linewidth=2)\\n\",\n    \"plt.text(1.0, 0.9, \\\"Depth=0\\\", fontsize=15)\\n\",\n    \"plt.text(1.0, 1.80, \\\"Depth=1\\\", fontsize=13)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"decision_tree_instability_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure min_samples_leaf_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.datasets import make_moons\\n\",\n    \"Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\\n\",\n    \"\\n\",\n    \"deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\\n\",\n    \"deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\\n\",\n    \"deep_tree_clf1.fit(Xm, ym)\\n\",\n    \"deep_tree_clf2.fit(Xm, ym)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\\n\",\n    \"plt.title(\\\"No restrictions\\\", fontsize=16)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\\n\",\n    \"plt.title(\\\"min_samples_leaf = {}\\\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"min_samples_leaf_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAgIAAADgCAYAAABxVg5sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAH6ZJREFUeJzt3X2UVPWd5/H3FxoaBREfsBFjS9yVGJgMOILuGiHOuKvH7Obo0YT4uOREx6we7Uyyc1ZJzGp0ckBPxolGY5bRRMyznsXEGNfszmwcfDgRNZGJzUyaiIBIaEEFGsVGmu/+UVVYXV11q+r2faq6n9c5dex761bxvS36+97v78ncHREREcmnMWkHICIiIulRIiAiIpJjSgRERERyTImAiIhIjikREBERyTElAiIiIjmmREBERCTHUk8EzOwaM3vezAbN7P46137BzLaa2S4z+46ZdSYUpoiISFtKPREAtgB/A3wn6CIzOxu4HjgTOA44Hvhq7NGJiIi0sdQTAXdf6e4/Bd6oc+li4D5373X3t4BbgM/EHZ+IiEg7Sz0RaMJsYE3Z8Rqgy8yOSCkeERGRlteRdgBNmATsLDsu/XwIVaoJZnYlcCXAxIkHn3ziif829gAl27Zv38a77+5lcHA8dOzDxu/jsEMPY+LBE9MOTUQkUr974Xfb3X1qI9e2UiKwG5hcdlz6eaDaxe6+HFgOMG/eHF+9+pfxRieZt2LFt1i79jXWr+9m/9TX6Zi+jU//p0WcetKpaYcmIhKp7rHdGxu9tpW6BnqBOWXHc4B+d683tkBERERqSD0RMLMOM5sAjAXGmtkEM6tWqXgAuNzMZpnZFOAG4P4EQxUREWk7qScCFBr0PRSmBl5a/PkGM+s2s91m1g3g7o8DtwG/AjYBG4Eb0wlZRESkPaQ+RsDdbwJuqvH2pIprbwdujzkkERGR3MhCRUBERERSokRAREQkx5QIiIiI5JgSARERkRxTIiAiIpJjSgRERERyTImAiIhIjikREBERyTElAiIiIjmmREBERCTHlAiIiIjkmBIBERGRHFMiICIikmNKBERERHJMiYCIiEiOKREQERHJMSUCIiIiOaZEQEREJMeUCIiIiOSYEgEREZEcUyIgIiKSY0oEREREckyJgIiISI4pERAREckxJQIiIiI5pkRAREQkx5QIiIiI5JgSARERkRxTIiAiIpJjSgRERERyTImAiIhIjmUiETCzw83sYTN728w2mtnFNa67yczeM7PdZa/jk45XRKSV9Q/086kVn+L13a+nHYpkQCYSAeBuYC/QBVwC3GNms2tc+xN3n1T2Wp9YlCIibeCOJ+9g9auruWPVHWmHIhmQeiJgZhOBC4CvuPtud38KeAS4LN3IRETaT/9APw+teQh356E1D6kqIOknAsBMYJ+795WdWwPUqgh8wszeNLNeM7uq1pea2ZVm9ryZPb9t2xtRxisi0rLuePIO3B2A/b5fVQHJRCIwCdhVcW4ncEiVax8EPgxMBf4S+B9mdlG1L3X35e4+z93nTZ16RJTxioi0pFI1YO/QXgD2Du1VVUAykQjsBiZXnJsMDFRe6O5r3X2Luw+5+zPAHcAnE4hRRKTllVcDSlQVkCwkAn1Ah5mdUHZuDtDbwGcdsFiiEhFpM7/Z/JsD1YCSvUN7eWHzCylFJFnQkXYA7v62ma0EbjazK4C5wLnAaZXXmtm5wCpgBzAf6AG+lGC4IiIta8VFK7hm5TXcfcHdHDXpqLTDkYzIQkUA4GrgIOB14EfAVe7ea2YLzGx32XUXAn+g0G3wAHCru69IPFoRkRakaYNSTSYSAXd/093Pc/eJ7t7t7j8snn/S3SeVXXeRux9RXD/gRHe/M72oRURaRytMG9RCR+nIRCIgIiLxCpo2mJUGWBWLdCgREBFpc/WmDWahAW6FikW7UiIgItLmgqYNZqUB1kJH6VEiICLS5oKmDWahy0ALHaVLiYCISJt7/MrH2fSVTSNeKy5akWiXQe/WXmbfNpu1/WuHnddCR+lSIiAiklNJdxn0PNzDwOAAPSt7hp3XQkfpSn1BIRERSUezXQZf+/jXgEIpv9mFiXq39rJu+zoA+rb3sbZ/LbO6ZgGFioWkRxUBEZGcSrLLoOfh4VWAyqpAlLIyHbJVKBEQEZFhou4yKK8GlJSqAnHIwnTIVqJEQEREhgk7y6CWymrAgfMxVAWyMh2ylWiMgIiIDFOrz75/oJ/T7zp9RJfB5xd+PnCswKYdm6qe37hj4+iDrRD12IY8UCIgIiINCeoyKDW21axbsq7me1GqtR5BKVEp7zIIijdv1DUgIiINyfo0v1ZYQTGLVBEQEZGGZH2aX9jpkHmnREBERFIRdZ991GMb8kJdAyIiGdbOc+KTmuanJYyDKREQEcmwdp0Tn2SffdbHNqRNXQMiIhlV2ViWl7JbfSpckn32WR/bkDZVBEREMipo8Z5WrhRo2+FsUSIgIpJBQY1lq0yFqzW+QX322dJQImBmB5nZZjPbZGadFe/da2ZDZnZhPCGKiORPUGMZVCnI0uDCWlWLsH32Wbq3dtJQIuDue4AbgWOBq0vnzWwpcDlwrbv/OJYIRURyqFZj+eymZyPfGbCeMA1wUNWi1q6H9fryW7k7JMua6Rq4H+gFlpjZJDP7K+B64EZ3/1YcwYmI5FWtxvKU7lMSXz0vTAMcZnOiIK3SHdKKGk4E3H2IQsM/FfgZcDvwTXe/OabYRESkQtidAcOW1cM0wHEMBow6sZD3NTVY0N0fBX4L/AXwE+Dz5e+bWaeZ/b2ZrTezATPrM7NrowtXRCTfalUKVly0IpYugzDJRdjBgLW+T7MM4tVUImBmnwbmFA8HvPLfdGFdgq3AWcChwCLgBjNbNNpARUQkXOMbtqxerwGOejBgre+LOrGQ4RpOBMzsLOAB4GHgx8BnzezD5de4+9vu/hV3/4O773f3F4FHgNOjDFpEJGlZaVTCNL5hy+phk4swgwGDvi/qxEKGa3T64KnASuBp4BLgBmA/sLTO58YBC4B/Hl2YIiLpiqpROXn6dLrHHkv39Dfo/vJH6D76LbrHHsvJ06fX/WyYxrdel0GQJMcjBH1f1ImFDFc3ETCzWcBjQB9wnrsPuvvLwH3AuWb20YCP3wUMUKgkiIi0pCgblW39Yws/nH8pdO6ECy4efj5AUGPZu7WX2bfNZm3/2pqfKWm0KpDUeAQNLkxX4F4DZtYN/BJ4CzjH3XeVvX0LsBi4DRiRDJjZ7cC/B/7C3fdWvi+SNnenf3s//dv60w5FMm7ZqmXs378fgKH9Qyz7P8u4bsF1Ib/tWOh6EY7qBaPwz6P+GV7/08BP1WosS/sP9Dzcw8DgAD0re/iHq/7hwOfi2HAnKLnoWdDT9P4IQd8XZv+Ber8rGS4wEXD3TRQWEar23hbg4Grvmdk3gDMpJAHbRxukJKe/fyUbNixlcPA1OjuPYcaMJXR1nZ92WJHo6Oigs3MfZkMMvT0e9jq/+vU/8U+rV6UdmmTYHt/DY/4L9lNIBN7b/x4P9z7M4NpBJthBIb7xJ4VqQLkLLoZ7XgIKT/aLHljEQ4sfYlbXrAOXBDWWF//Zxazbvg6Avu19rO1fe+CzcWy402yXQakxL68UlDfwUScrUScW7S7y3QfN7E4K0wv/3N23NfiZwyl0NZwFbAeWuPsPq1xnwDLgiuKpe4Hrq8xekBD6+1eybt1fs3//HgAGBzezbt1fA7RFMjBx4iFMnDiW2bPfoq9vKns2jWHosDcZsvT/+vzve+9g8J1DR5zvPHgn51zx+SqfkKS8dPAavJPC03vRfnd+924fc9/5d81/YXk1AIZXBTgs1JP9rzf+etj5ys9GrVZy0T/Qz+l3nV71SbxUIahWKYg6WdG2w82JNBEws+OAa4FB4JVCuw3Ak+5+TsBH7wb2Al3AXOAXZrbG3XsrrrsSOI/CFEYH/i/wCvDtyG4ixzZsWHogCSjZv38PGzYsbYtEYMyYMYwfP54pUyYAY3jzzRP4k9mzDpR80/TTKkkAwOA7h3LWiZ9JNpgqrrvuNAYGxo84f8ghe7n11mdSiCg5z7/xND40/O+I2372TRzirO7PNP19P62sBpRccDG9W7/e9JN979Zezvn74f97rfxsUoKexB3XtsMZFWki4O4bGZY312dmE4ELgD9x993AU2b2CHAZhZUMyy0G/tbdNxc/+7fAX6JEIBKDg681db7VdXaO5+yzF6YdRl3nnHNG2iFw9dXVzw8MjA8V3/z5sL1Kp+GRR8JzzzX9dbF9J8A5rAv/4Wpee3nk/yUNOPxleh7uGXa6kSf7ys80+tla/fWjEbQ/wsa3NqrPPqMi7xoIYSawz937ys6tAT5W5drZxffKr5td7UvN7EoKFQS6u4+JJtI219l5DIODm6ueF4lStQY76Hwa3xlXYnFn16O8+GIfQ1O20tH9Gh0dHSw+bzE7bWeoJ/tNOzZVPb9xx8bAOGr1149GrSfxLz32JTa8uWHYOfXZZ0dTKwvGZBKwq+LcTuCQGtfurLhukpX1QZS4+3J3n+fu86ZOPSKyYNvZjBlLGDNm+OCnMWMOYsaMJSlFJJKeOJKVIEFP9kHWLVlXdYrfuiW1KxlJz7EP02eflQWc8iALFYHdwOSKc5MprD9Q79rJwG4NFoxGaRxAu84aEMmysE/2YUr8QSP74xCmzz6OioVUl4VEoA/oMLMT3L2Uws6hsOVxpd7ie6vrXNf24prm19V1vhr+FBx5ZO0ytORD0BN8kGYbzFaYY19ZschSbO0o9a4Bd3+bwvLFN5vZxOJKhecC36ty+QPAF83sGDObDvw34P7Egs2I0jS/Qn++H5jm19+/Mu3QJKTnnoNXXhn5Gk1fdJRqJSRKVNIVpsQ/mpUGk6JVAZOVeiJQdDVwEPA68CPgKnfvNbMFZra77Lr/Cfwc+B3wEvCL4rlcCZrmJxKHqBOVOBKLPCYrYRrMrM+x15bDyctC1wDu/iaF9QEqzz9JYYBg6diB/1585VaS0/zaeaVBSU8clY6ov7PZ7pqoZxmcPH161f0HpnYN8cKWLaFL/FmfY69VAZOXiURAmpPUNL92X2lQJEizjXfUswxqbUJUOt+uDWbWKxbtSIlAC5oxY8mwBhrqT/N78cVF7Nr15IHjyZMXMHfug4F/TruvNChSzQc/WPu9V15JLo562rXBzHrFoh0pEWhBzU7zq0wCAHbtepIXX1wUmAzkbaVBSU+rNL5ZogZToqJEIEZx9q83M82vMgmod75EKw2KiLQ/JQIxiat/va/verZu/T4wBIxl2rRLmTlz2ajirJWshOmCEGlnlZWL0S43LJIFSgRiEkf/eiEJWFF2ZujAcdhkIChZ0UqDkgXVug3MYP365GOpVD4QMOpFoaZ2DdWcNSASJSUCMYmjf71QCah+/p131tccDDh58oIa3QDj6yYrWmmwNRx/PFRbaDsrDWbUwi4qHsfvqVqiEkWl4IUtW0b3BRHp3drLogcW8dDihxLf1jjv4tghspqsLCjUdmr1o4+uf73Wk8BQzcGAAHPnPsjkyQuGvV84fq/qtw0OvkZ//0qefXY+q1ZN59ln52vVwoyr1TBmZReO+fMLDWbla/78ZONI6vcU18ZEaeh5uIeBwYG6mx9J9MqXj46TKgIxCdO/Xr//fyy1k4GRypODarMDnn12ftXBgB0dU7R+gEQqjp384uoyqDZLIWhWQzvr3drLuu2FPRAa2RJZopPkfguqCMSkq+t8Tjjh63R2fgAwOjs/wAknfL1mQ/p+/3+poS/0//f1XX/gmmnTLm06jqAn+1rbDrujJYwlUdWWLw4zbTArFZB2Ubk1cnlVQNsENy7M7yrJ/RaUCMSoq+t8Tj31ORYu3MKppz4X+DQd1P9fMnPmMqZNW0yhMgCFqsHiwBiCNieqlawMDe2o+l1aP0DikIUug3qysF/BydOn0z322BGvk6dPj+XPK68GlJSqApBc2bodNPu7Snq/BXUNZEbt/v/Vq8/g3Xd/f+DMhAkf4pRTnjhwXDlQ8H3hBgMWZglo/YB2lvXBhaUuA7Pmn/Kj7DKot39Akl0G9ZYcjlplNeDA+ZU9/ODSH2ib4AaFKfEnvXy0EoHMqN3/X54ElI5Xrz7jQDIwd+6DVZcQ3rXrqarfV+/JXusHtJ5aDaZZ9eujHjQX9YY7JbUa72Yb4NJ9Nft7qje2odkpg4UE7EzgzGHnl1/rbNw3MvlO06Ydm6qe37hjY9WydSvvbxCnML+rpJePViKQoKDFe6ZNu7RijYBglclBM4MB6z3Zh1k/QLsUpivtp/iwDWaS4pjm1+xnaydgNTKRFK1bsq7q+f6Bfk6/6/Smdz1sV0FT/Fplh0iNEUhIaaXBWv31Yfr/V606llWrji57HTvs/VqDARt5sm9mfEO9exN57rloBgOW1HqCb9ZoZy1Uvo4/Ppq4siyobB2kXQcXBvX/h/1dJU2JQEKCVhosmTlzGQsXbmbhwj+ycOHmBlYL3DfiuDwZaHbmAgTPMhjNvUl+RTkYsLQeQeWT9ZFHZmNzotHOWmiFxjJs2TrqwYVZ+F1V9v9XxtIqO0SqayAhYVcanDDhQyO6AYINTw6aWRkw7P4I2qVQmhW2jz2p9QjS2kOgvLGs7EfOypLDYcrWccyJD/pdhRFmFb96/f+tskOkKgIJCbvS4CmnPMGECR8adq7yuNKqVR+s6DJobGRV2Cf7eFZRlDjVKq3XOh/XyoC1ugzCNsJRdxmEmTYY9vdU7+nyhS1b2DT06ohXVpYiDhL1nPh6v6uwMWZ5il+clAgkZDT99aec8kSxu6DwKp86WN27I44bSQbCPtmP5t4kHevXV2+Aaw06bGQwYBbUuq+wohrbUD4dshozD2wss1AGDyuOBjMLiUWr9P83QolAQsL01wdrtlenMjkYKeyTffT3Jq0m6sGAUUvyyT7I+vVw553/yGc/ew+Lv3gjl3/jCj5313/lZ79/NLCxbOXFe6JuMLOSWLRK/38jNEYgQVHu5Ldw4avFgYHlYwI6GDmA8H3PPHMS+/Ztff/qjmmcdtpvDxyPZv0A7VIoWVarq6HZ9QhGs9BRkO/+5rs1G8ueBT0tvXhP2AazVp991IvttMoUvzgpEWhhCxe+OuLcqlVH17y+PAkoHT/zzEkHkoEw6weIBGl2MGDU35f1hY5KXnr9pZqNZasv3hO2waw1GDDqJ/GkV/HLIiUCLSh4l8IJNNINULJv31YtBiSxiXrkfbPf1woLHQGsOH8FHzj6AyPO53XxnqBZBlE/ibdTiT8sJQIt5v1dCkuGDhwX1iF4pTgwsDwZCE4OtOWw1BP1k31WRNVlUBL17ymvT6tJVkHaqcQflhKBFlNvl8JalYKgLoN6GxOJpDGnvhVF/XvK49Nq2D77rAizHkHalAi0nNq7FAZVCjo6po0YIxBEiwFJHOLqs49aViogeXxabfUqSNQLHSVB0wdbTnNbjpYqBaed9ls6OqYNe6+jY1pxyt9IWgxI4hDHyoBxiHqhI2lcK1dB4ljoKAmqCLSYZncpLK8glE8VLKlcVhi0GJC0j6w82UvjWrkK0qozPFQRaDG1dymsVSkIriAktTGRSD2tsISxtJ6kVmVs5SWHVRFoQTNnLqu6M2G1SsG0aZfW/b4kNiYSCSMrXQatMrYhS7IyaC6pPvtWHtugikCbqFUpqL+VcXO05bDkUauMbciSLCyLnGSffSuPbUi9ImBmhwP3AWcB24El7v7DGtfeBHwZGCw7/afuXmOdr3ypVSmIkrYcltHIygI+Eq84th0OQ+sRNCYLFYG7gb1AF3AJcI+ZzQ64/ifuPqnspSQgQdpyWEYj65sTSTSi3h0wjFbus09aqomAmU0ELgC+4u673f0p4BHgsjTjktq05bCIBMlKA9xO2wTHLe2KwExgn7v3lZ1bAwRVBD5hZm+aWa+ZXVXrIjO70syeN7Pnt217I6p4c09bDkscak3n0zS/1pOVBriV++yTlvYYgUnAropzO4FDalz/ILAc6AdOBf6Xme1w9x9VXujuy4vXMm/enAg3DBVtOSxRy/rIe61H0LisNMCt3GeftFgTATN7AvhYjbefBq4FJlecnwwMVPuAu68tO3zGzO4APgmMSARERKKS9UQlS9QAt55YEwF3PyPo/eIYgQ4zO8Hd1xVPzwF6G/0jAAsfoYiISL6lOkbA3d8GVgI3m9lEM/socC7wvWrXm9m5ZnaYFZwC9AA/Sy5iERGR9pL2YEGAq4GDgNcplPivcvdeADNbYGa7y669EPgDha6DB4Bb3b2ZhfdFRKSOpJbllWxIe7Ag7v4mcF6N956kMKCwdHxRUnGJiORVK26lK+FloSIgIiIZ0apb6Up4SgREROSALKwKKMlSIiAiIkB2VgWUZCkREBERIDurAkqylAiIiAiQnVUBJVmpzxoQEZFs0KqA+aSKgIiISI4pERAREckxJQIiIiI5pkRAREQkx5QIiIiI5JgSARERkRxTIiAiIpJjSgRERERyTImAiIhIjikREBERyTElAiIiIjmmREBERCTHlAiIiIjkmBIBERGRHFMiICIikmNKBERERHJMiYCIiEiOKREQERHJMSUCIiIiOaZEQEREJMeUCIiIiOSYEgEREZEcUyIgIiKSY0oEREREcizVRMDMrjGz581s0Mzub+D6L5jZVjPbZWbfMbPOBMIUERFpW2lXBLYAfwN8p96FZnY2cD1wJnAccDzw1VijExERaXOpJgLuvtLdfwq80cDli4H73L3X3d8CbgE+E2d8IiIi7a4j7QCaMBv4WdnxGqDLzI5w90YSCRE6OzuZPHkK7713EAcfPCXtcCSHDj30EA49dDI+ZQ/jDt9Dx7gOxo0bl3ZYkmOtlAhMAnaWHZd+PoQqFQUzuxK4sni4e+zYo38fb3iBjgS2p/jnpy2z93/99bH/EZm994To/hu4/6UsTSCUVOjff3r3f1yjF8aWCJjZE8DHarz9tLuf3uRX7gYmlx2Xfh6odrG7LweWN/lnxMLMnnf3eWnHkZY833+e7x10/7p/3X8r3H9siYC7nxHxV/YCc4AHi8dzgH51C4iIiISX9vTBDjObAIwFxprZBDOrlZw8AFxuZrPMbApwA3B/QqGKiIi0pbSnD94A7KEwLfDS4s83AJhZt5ntNrNuAHd/HLgN+BWwCdgI3JhG0CFkoosiRXm+/zzfO+j+df/51hL3b+6edgwiIiKSkrQrAiIiIpIiJQIiIiI5pkQgJs3uo9BuzKzTzO4zs41mNmBmL5rZOWnHlRQz+76Z/bG4L0afmV2RdkxpMLMTzOxdM/t+2rEkycyeKN737uIrzXVMUmFmF5rZv5jZ22b2spktSDumJJT9Oy+9hszsm2nHFaSVFhRqNaV9FM4GDko5ljR0AK9SWEtiE/Bx4EEz+4i7b0gzsIQsBS5390EzOxF4wsx+6+4vpB1Ywu4Gnks7iJRc4+73ph1EGszsPwK3Ap8GVgNHpxtRctx9UulnM5sEbAUeSi+i+lQRiEmT+yi0HXd/291vcvcN7r7f3R8FXgFOTju2JBT3xBgsHRZf/ybFkBJnZhcCO4B/TDsWSdxXgZvd/dfF//5fc/fX0g4qBRcArwNPph1IECUCkggz6wJmUlgYKhfM7Ftm9g7wr8AfgcdSDikxZjYZuBn4YtqxpGipmW03s6fN7Iy0g0mKmY0F5gFTzewPZrbZzO4yszxWRhcDD3jGp+cpEZDYmdk44AfACnf/17TjSYq7X01hL4wFwEpgMPgTbeUWCruFbk47kJRcR2Gr9GMozCX/uZnlpSLUBYwDPknh7/5c4CSKa8TkhZkdR6FrdEXasdSjREBiZWZjgO8Be4FrUg4nce4+5O5PAR8Arko7niSY2VzgPwB/l3YsaXH3Z919wN0H3X0F8DSFcTJ5sKf4z2+6+x/dfTtwO/m5/5LLgKfc/ZW0A6lHgwUlNmZmwH0UnhA+7u7vpRxSmjrIzxiBM4AZwKbCXwEmUVhCfJa7/1mKcaXJAUs7iCS4+1tmtpnCPR84nVY8KfovwLK0g2iEKgIxaXIfhXZ1D/Bh4BPuvqfexe3CzI4qTp2aZGZjzexs4CLyM2huOYWkZ27x9W3gFxRm0LQ9M5tiZmeX/ps3s0uAhcDjaceWoO8C1xb/WzgM+ALwaMoxJcbMTqPQLZTp2QIleWuYknQDw/dCuJTCSNqbUokmYcX+sc9R6BffWnwyBPicu/8gtcCS4RS6Ab5NIdneCPyVuz+SalQJcfd3gHdKx2a2G3jX3belF1WixlGYOnwiMERhsOh57t6XalTJugU4EugD3qWwa+zXUo0oWYuBle4+kHYgjdBeAyIiIjmmrgEREZEcUyIgIiKSY0oEREREckyJgIiISI4pERAREckxJQIiIiI5pkRAREQkx5QIiIiI5JgSARERkRxTIiAioZnZQcX95jeZWWfFe/ea2ZCZXZhWfCJSnxIBEQmtuJnUjcCxwNWl82a2FLgcuNbdf5xSeCLSAO01ICKjYmZjgTXAUcDxwBXA3wE3uvvNacYmIvUpERCRUTOz/wz8HPh/wJ8Dd7l7T7pRiUgjlAiISCTM7DfAScCPgYu94n8uZrYI6AHmAtvdfUbiQYrICBojICKjZmafBuYUDwcqk4Cit4C7gC8nFpiI1KWKgIiMipmdRaFb4OfAe8CngI+4+7/UuP484BuqCIhkgyoCIhKamZ0KrASeBi4BbgD2A0vTjEtEGqdEQERCMbNZwGNAH3Ceuw+6+8vAfcC5ZvbRVAMUkYYoERCRpplZN/BLCv3+57j7rrK3bwH2ALelEZuINKcj7QBEpPW4+yYKiwhVe28LcHCyEYlIWEoERCQRxYWHxhVfZmYTAHf3wXQjE8k3JQIikpTLgO+WHe8BNgIzUolGRABNHxQREck1DRYUERHJMSUCIiIiOaZEQEREJMeUCIiIiOSYEgEREZEcUyIgIiKSY0oEREREckyJgIiISI79fwh0gcG4WCIiAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"angle = np.pi / 180 * 20\\n\",\n    \"rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\\n\",\n    \"Xr = X.dot(rotation_matrix)\\n\",\n    \"\\n\",\n    \"tree_clf_r = DecisionTreeClassifier(random_state=42)\\n\",\n    \"tree_clf_r.fit(Xr, y)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 3))\\n\",\n    \"plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1], iris=False)\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure sensitivity_to_rotation_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(6)\\n\",\n    \"Xs = np.random.rand(100, 2) - 0.5\\n\",\n    \"ys = (Xs[:, 0] > 0).astype(np.float32) * 2\\n\",\n    \"\\n\",\n    \"angle = np.pi / 4\\n\",\n    \"rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\\n\",\n    \"Xsr = Xs.dot(rotation_matrix)\\n\",\n    \"\\n\",\n    \"tree_clf_s = DecisionTreeClassifier(random_state=42)\\n\",\n    \"tree_clf_s.fit(Xs, ys)\\n\",\n    \"tree_clf_sr = DecisionTreeClassifier(random_state=42)\\n\",\n    \"tree_clf_sr.fit(Xsr, ys)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"sensitivity_to_rotation_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Regression trees\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Quadratic training set + noise\\n\",\n    \"np.random.seed(42)\\n\",\n    \"m = 200\\n\",\n    \"X = np.random.rand(m, 1)\\n\",\n    \"y = 4 * (X - 0.5) ** 2\\n\",\n    \"y = y + np.random.randn(m, 1) / 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\\n\",\n       \"           max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n       \"           min_impurity_split=None, min_samples_leaf=1,\\n\",\n       \"           min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n       \"           presort=False, random_state=42, splitter='best')\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeRegressor\\n\",\n    \"\\n\",\n    \"tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\\n\",\n    \"tree_reg.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure tree_regression_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeRegressor\\n\",\n    \"\\n\",\n    \"tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\\n\",\n    \"tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\\n\",\n    \"tree_reg1.fit(X, y)\\n\",\n    \"tree_reg2.fit(X, y)\\n\",\n    \"\\n\",\n    \"def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\\\"$y$\\\"):\\n\",\n    \"    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\\n\",\n    \"    y_pred = tree_reg.predict(x1)\\n\",\n    \"    plt.axis(axes)\\n\",\n    \"    plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"    if ylabel:\\n\",\n    \"        plt.ylabel(ylabel, fontsize=18, rotation=0)\\n\",\n    \"    plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"    plt.plot(x1, y_pred, \\\"r.-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_regression_predictions(tree_reg1, X, y)\\n\",\n    \"for split, style in ((0.1973, \\\"k-\\\"), (0.0917, \\\"k--\\\"), (0.7718, \\\"k--\\\")):\\n\",\n    \"    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\\n\",\n    \"plt.text(0.21, 0.65, \\\"Depth=0\\\", fontsize=15)\\n\",\n    \"plt.text(0.01, 0.2, \\\"Depth=1\\\", fontsize=13)\\n\",\n    \"plt.text(0.65, 0.8, \\\"Depth=1\\\", fontsize=13)\\n\",\n    \"plt.legend(loc=\\\"upper center\\\", fontsize=18)\\n\",\n    \"plt.title(\\\"max_depth=2\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_regression_predictions(tree_reg2, X, y, ylabel=None)\\n\",\n    \"for split, style in ((0.1973, \\\"k-\\\"), (0.0917, \\\"k--\\\"), (0.7718, \\\"k--\\\")):\\n\",\n    \"    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\\n\",\n    \"for split in (0.0458, 0.1298, 0.2873, 0.9040):\\n\",\n    \"    plt.plot([split, split], [-0.2, 1], \\\"k:\\\", linewidth=1)\\n\",\n    \"plt.text(0.3, 0.5, \\\"Depth=2\\\", fontsize=13)\\n\",\n    \"plt.title(\\\"max_depth=3\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"tree_regression_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"export_graphviz(\\n\",\n    \"        tree_reg1,\\n\",\n    \"        out_file=image_path(\\\"regression_tree.dot\\\"),\\n\",\n    \"        feature_names=[\\\"x1\\\"],\\n\",\n    \"        rounded=True,\\n\",\n    \"        filled=True\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure tree_regression_regularization_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAxAAAAEYCAYAAADMNRC5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXmcU9Xd/98n67AM2wCCWEQQUHBcEYmKjLVKn9a2KH3aR620PtZBra2tbV3a8lTLr0XpZhdrpVXU1qWtoFYtlWoZEI0iKjoKBUVBEYFhH2Amk5mc3x/n3uQmc5PJbEkm+b5fr7xyc3Ny78lM7j3nc76b0lojCIIgCIIgCIKQDZ58d0AQBEEQBEEQhJ6DCAhBEARBEARBELJGBIQgCIIgCIIgCFkjAkIQBEEQBEEQhKwRASEIgiAIgiAIQtaIgBAEQRAEQRAEIWtEQAhCHlFKjVJKaaXUpE4eRyulPt9V/RIEQegOSuVepZS6Vyn1ZE8+l1KqWin1vlIqppS6uauPL/RsREAIBY11Y9RKqTkp+6us/YPz1bd0KKVuVkq9mWXzD4DhwJosj51uoBgOPJHlOQVBEPKF3Kt6AEqpgcAdwE+BEcDPOnCMs5RSf1dKfWiN119xaaOsMXOrUqpBKVWjlJrY6S8gdDsiIISeQCPwXaXUkO4+kVLK393ncJwroLVu0Vpv01o3d+ZY1jEiXdU3QRCE7kDuVT2GIwEf8KTW+iOt9YEOHKMv8CZwLdCQps31wLeBrwOnAjuAfymlyjtwPiGHiIAQegLLgE3AnEyNrNWOl5RSjUqp7UqpXyqlAhna21aMTymlVimlmoDp1nufUUq9Yh3rPaXUj53HUkpdqJR6w1ox2a2UWq6UOsxaYfkhMNE6dnzVxdr+mlJqsVLqIPATNxcmpdQx1qrNPqXUAaVUWClVaZmQvwx82nHsKsexP+84RqVS6hlH/+5VSvV3vH+vUupJpdS11urQHqXUQqVU75S/54tWH/ZZf6Pj2vxvCYJQElirxXcqpX5u3WfqrHtKUCl1h1Jqr+UCc6njM/F7leP+N1Mp9S+l1CGl1Fql1LlZnt+vlPq1tXodUUp9oJS61fH+l5RSLyul6pVSO5RSf1NKjXC8b48B/2Xd7xuUUs8ppY5QSk1TSr1u3f+eVEpVOD5n3z9/YI01B6z7Z68MfVVKqeuVUhut89Qqpb6U0ub/lFKbre+yTSl1f3b/iQ6d61al1Hrr/U1KqflKqTLrva8Ar1lN37X+RqPa2w+t9T+01t/TWj8CxNz6CXwTuFVrvUhr/SZmjCsHLm7v+YTcIgJC6AnEgBuBK5VSY9waWIPCEsxN7yTgcuAiYF4Wx78N+AFwDPCSUmo68ADwW2Ai8L/A54GfWOcaBjwM3AccC5wF/Mk61l+AnwPrMab64dY+mx8C/wAqMebh1O9xOLAS0MC5wMlWOy/GhPxX4BnHsV9wOUYf4GngADAZuAA4HbgnpelU4DjgE8AXrXbXWsfwAY9bfTkBOA24HWhx+wMKglCyXALUY+4Rt2LuE48BG4BJmPvkH5VSwzMc48fArzH3mpeBh5VSfbM49zcw963/AcZi7mPrHe8HMPfcE4DzgcHAQy7HuQUzkT0NGIi5Z/8fUA1UYcaBm1M+M8067jnATOA8zFiSjv+HGZe+BkzAjE13KaU+DaCUmgl8B7ja+i7nA6syffmOnsviIGZsO9Y65/8A37fe+wvwSWt7Mmas+UApNdUSS5ke32tHP48ChgFL7R1a6wZgBWbMEgoZrbU85FGwD+BejAkVjCXiYWu7CjPJHmy9/jHwNuBxfPYrQATonebY9jFmpuxfAcxJ2TcDMyFXmEm9Bo5Mc9ybgTdd9mvgNyn7Rln7Jzm+x2Yg0Nbfw+XYn7e2rwD2AeUu3/Vox3E+ALyONn8AnrG2B1ntp+X7NyAPecijMB9ADRB2vFZAHfB3xz4/0OS4PznvVfb9b7aj/Qhr35lZnP/XwLOAyrK/x1jHPsJ6bd8XpzvaXGPtO9mxL+mebt0/9wJ9Hfu+ZI03fRxt7LGrD8aFZ2pKf24H/mFtX4cRP/4O/B/ada40x7gSeMfxepL1dxjl2NcLOLqNx6A0xz8AfCVl3+nWOUam7L8HeDrfv295ZH74EISeww1AWCn1U5f3jgVe1Fo7zaQrMStQRwNvZDju6pTXpwCTlVI3OPZ5MDfPYcDrGCvAm0qppdb2I1rruiy+Q+q5UjkJWKm1bsriWOk4FnhDa13v2PcCxpIzAXjH2rdWa+20KGzFrMChtd6tlLoXeFop9SxmkH5Ea/1+J/olCELxEb+3aq21UmoHUOvYF1VK7QGGZnMMzH2INtrb3Av8C9hg3Yv/ASyxxwGl1MkYC8SJmEURZX1uJLAlzfm3W8+1KftS+/OGTo4LCGPGmzG0Hm8mAGXAP5VS2rHfj3HPBfgbxgL8nlLqaeCfGCHW3niRbM6F5Ub2Tcz42Bdj5fZmOrA21oF3MrURSgdxYRJ6DFrrVcAiYH57P9rG+wdTXnswJu0THY/jMWblOmvSfZ71eANjKn5bKXVCFn1JPVeucf4toi7vxe8JWuvLMIJiBfBZYL3l3iUIgmDjdh/JeG/JdAxtLUG30d5u+yrGinGT1f4+TACux+HKeQi4FBOga7vlpMbGOfurrWOn7uvMfMn+7GdIHlcmYsYRtNYfAOOB2cB+jCvsK9b36NJzKaWmYNxwn7banYRx482YRKQbXJi2Wc+Hpew/zPGeUKCIBULoaXwPWEtiILBZB3xBKeVxWCHOxJjON7bzHK8Cx2it0660WINcGGMR+RHwFsb/9nXrnBlXcjLwGvAlZTI0uVkhsjn2OuB/lVLlDivE6ZiBZV17OqO1fh3znW5TSi3BBLg93Z5jCIIgdBfWPe4R4BHLavoiZlW9HBPz8D2t9Xtgkl904akrlVJ9tNb2otAU0o83azHuTUdqrf+d7oBa60bgKeApKxh8G3AGjhiBLMjmXGcAH2qt59o7lFJHZnHs1RgxkondWfXS8B7mO56LiX3BCuSeCny3HccR8oAICKFHobV+Rym1ACvY18HvMObY3ymlfgWMxgT0/VZrfaidp/kR8KRSajMmaLkZE2w8WWt9vbV68wnMRHo7ZvXmY5gbNxgz8ZGW+fx9oL4dZujfYXxR/6qU+jGwB7Nytk5rvcY69n8ppcYDu4B9KStlYALAbwHuV0r9HyYo8C5gcSZR5EQpdRRmJezvwIeYv+fxwJ1Zfg9BEIRuRSl1HfARpo5OFJO5Zz/GPakPZiJ9jVLqDoxr59w0h+oIPuAeawHpcMx48weHoIijta5XSv0M+JmVeWgFxm1oChDTWi+wMh/5gJcw8QJftL7T2+3pVDbnwgS4j1BKXYJZCJuOSTrS1rHb5cJkBcIfbb30ACOVUicCu7XW71sub7cD31NK/cfq1w8w3//BbM8j5AdxYRJ6Ij/CTOrjaK0/BP4LM5lfgwnCeghjsWgXWuungU8DZ2OyYKzCZIGy/f/3YVZwnsTc3H8OzNVa/9l6fxHGF/dZTEBhmzfmlO9xFsbEvgxjkfg6ie/7B4wVYbV17DNcjnEIMyD0s/r+OGaQ+N9s+4Ex+4/D+OVuwLgGPEDmLCOCIAi5pB6zUr0KYzk+EfgvrfUhKybty5gEGGsxsRDXdeG5l2Msz8uAR4F/Y2oapGMOJhj7O9bn/oXJ3vSe9f5ejDvsc5jaCTOBC23rSTvJeC6t9ROYAnG3Y9xwz8VknepqJmHGsNcwMYS3WNs/crSZD/wSk21wNSbj03kpMXxCAaIS7oaCIAiCIAhCJixXqcFa6/Pz3RdByBdigRAEQRAEQRAEIWtEQAiCIAiCUHAopX6fIdvP7/Pdv1yglBrZRtajkfnuo1CaiAuTIAiCIAgFh1JqKCaWy439WusduexPPlBK+TCpatOxSWvdnOF9QegWREAIgiAIgiAIgpA1RZvGdfDgwXrUqFGwdSt89BEMHQo7doDHAyedBEBkyw7YuxcGDCR4xJD8dlgQBCGHvPLKKzu11kV744uPARk4eBDq66G8HPq0t1yXIAhCD6azY0DRCohRo0axevVq+N73YN48mD0b5s4FrxdWr2bLV37AiFd+bBpvr+fdL3+fMbdV57fTgiAIOcKqc1K0xMeANITDcM450NQEu3fDs89CKJTDDgqCIOSRzo4BxR9E3WQV8y0vN9aHaBSiUQJLHkMBymqmFi/KVw8FQRCEHFNTY4aHlhbzXFOT7x4JgiD0HIpfQESsAsDBIPTqZbYbGuDE5Grs+sKZOe6YIAhCNxIOw5VXsu+ks/jwY6ex8YYF+e5RQVFVBYGAMUoHAua1IAhCsREOG0eccLhrj1u0LkxxnAKid2/j9NrQwNDPnQ5LHwBg+2evEPclQRCKh3AYpk5Ft7TQHyuNzfxVbAS511mEQsZtqabGiAdxXxIEodhwumoGAl3rqln8FgjbhckWEACHDhm7tcWwL0zLQ8cEQRC6iZoaaGlJuGjaz+KqmUQoBDfdJOJBEITipDtdNYtfQNgWiEAgrYCIiwxBEIRioKoKlJENzkTd4qopCIJQOnSnq2bxuzA5LRB2DMShQ9DsqLsSjea+X4IgCN1FKAQnnABr1hBTHrw6xrYLrxb3JUEQhBKiO101i19ApMZAgAmidlogREAIglBsWAsm3sEVUFfH8EvOyXOHBEEQhFwTCnWPm2bpCIhAIG6N2HTZzZRVjmOY3UYERNEQiUTYvXs39fX1tDhFotCKQCDA4MGD6d+/f767InQH9n2trMw8y/UAmKBCCZwWBEHoHMUvIGwXpnfeQb+8GgWMencZze+uSLQRAVEURCIR3n//fQYOHMioUaPw+/0opdr+YAmitaahoYEtW7YQDAYpsyeZQvFgu2na/1un22aJ0p0ZSQRBEEqJ0gmirq1F60Q4oQdxYSo2du/ezcCBAxk8eDCBQEDEQwaUUvTu3ZvBgwdTV1eX7+4I3YFYIFohxeMEQRC6huIXELYF4rTT0H5/PCNJDG+ijQiIoqC+vp5+/frluxs9ivLychobG/PdDaE7SLVAiICQ4nGCIAhdRF4EhFLqGqXUaqVURCl1bxttv6WU2qaU2q+UukcpFWzXyWwLxKmn4l1eg/aYr7z34450hpLGtShoaWnB7/fnuxs9Cp/PR7O4thQnqRaIAvo/53QMcGBnJJk7V9yXBEEQOkO+LBBbgf8H3JOpkVJqOnAjcA5wJDAauKVdZ3IGUYdCeKzMJIPHDUq0EQtE0SBuS+1D/l5FTGFbIHI3BqQgxeMEQRA6T14EhNZ6sdb6MWBXG02/DNyttX5La70HmAt8pV0nc9aBAPBZceO2sAAREIIgFB8FbIHI6RggCIIgdDmFHgMxEXjd8fp14DClVIVbY6VUtWUWXx0PDHXWgYCEgHD6fYuAEAShpxMO8/4Vc7n/qjDhMMUSRN35MUAQBKFECIdh3jzz3N0UehrXvsA+x2t7uxyXlSut9QJgAcCkSZN07YIwE+p2mnDpNWtg+nSxQAiCUHyEw8SmnsXIlma+wI/55D3LeCbYbG7wPVtAdGoMkJoPgiCUCrlOU13oFogDgDOtjr1d39YHG+oOcszsqXh1DIDYZz5n/roiIIQiZf369dx8882sX78+310Rck1NDZ4W46IUoIkzojXEItZ9zYr7KiQXpnbQ4THg4EEzmM6ZY55zsSInCIKQL3KdprrQBcRbwAmO1ycA27XWbfnN0ry3Hm9SrQfrrykuTEIRorXm8ssv5xe/+AVXXHFFUs0ToQRw5CON4eF5fxU+CjqIOls6PAbU10vNB0EQSodcp6nOVxpXn1KqDPACXqVUmVLKzZ3qfuBypdQEpdQA4AfAvdmcwzegHI3JMKMB/NZf080CIWlchR7O3XffzaZNm1izZg0bN27kvvvuy3eXhFzisFNvnPBZ5tWE8DQXbhB1LsaA8nKp+SAIQumQ6zTV+bJA/ABowKTn+5K1/QOl1Eil1AGl1EgArfU/gfnAMuB9YDPww2xO0GtIH6LDRgKw8+P/jWf5MvPX7AILRC6DVAShLXbu3MlNN93EwoULGT16NAsXLuSGG25g9+7d+e6akAfGTx9F6LQYxIz7JoGAeS4sC0S3jwF9+nTvYCrjgCAIhUYu01TnJYhaa30zcHOat/umtP0F8IuOnKes5RAAQx74NQwbZnZ2MgYi10EqgtAWgwcPxplx5rzzzmP79u157JGQV8rKEtYGny9xzysgAZGrMSAU6p77s4wDgiCUOoWehalz7LLcZCscGf86aYFwC1KRgUMQhIKhV6/EPc3vNz48UFAuTDlh/XoYM8bc85ubs3+uqIBTToFZs9Le3GUcEASh1CleAdHcbEz4/fubQdSmkwLCDlKxV57Er1YQhLzjtKh6PAmx4PcXpAUiJxw4YB7t5d134eWXYeFCWLbMVRnIOCAIQqlT6FmYOs6GDQA0NmhqFzicVDvpwpTrIBVBSMeVV16JUoqtW7e2em/9+vUEAgG+8Y1v5KFnQs7Zvz+xHY0m7mk+X+laIDpLhtRNMg4IglDqFK+AaGgAINi0n7Gzz06ICNsa0Yk6ELkMUhGEdISsH+CqVatavfetb32Lfv36ccstt+S6W0I+cAqIpqZkF6ZStUBgMvA5H+2iDdOCjAOCIJQyxevCZKEAP03sWlQD1WmyMEkaVyEbCqys7ZQpUwAjIGbMmBHf/9RTT7FkyRLuuOMOBg4cmK/uCbkkVUA4g6hL1ALRWDaAtY3DiRAkSIRBw4IMHxiBYNAsILk9b9tmHhMnwh/+UBDXuSAIQiFS1ALCXnGKEqBiZpV5IZWoSw+l8t0DdzpZ7G3cuHEMGjQoyQIRjUa57rrrOO6445g9e3Zneyj0FBwCYudr7zPYLYi6xCwQLaPGcOrm1YlMSYtheFt64K674Mor4YwzRDwIgtCjyPUaZ/EKiN692T9uEhsDE/BfPovKauuv6XP5yiIghB6IUoopU6bw/PPPo7VGKcWvfvUrNmzYwDPPPIPXnjgKRc+mv61ilLU94N+LWf/ATMZDSbsw2XUg2jWg2i6uMiYIgtCDyEdq6eIVEMceS//Vyzk5db8IiNKjkyv9QMEmfp8yZQr/+Mc/WL9+PYMGDWLu3LnMmDGDc845J99dE3LIoRWvxLc9xNj3Tyvmq4RdmKADdSDsonvi1ioIQg8iH6mli1dApEMEhNAR7LQrBRQDAcmB1CtWrCASifDzn/88z70Sck2/k0ZDrdmO4WHgOSfDC5S0BaJDiIAQBKEHko/U0iIggOZde3l/7HT0hTMZc1t1Hjol9Ai6q6xtJ5g8eTIej4c//vGPPP/883z3u99l9OjR+e6WkGOOmDwC7jfbByZVMfazE2AupV1Irh3YvsMXKr9x/ZJFJUEQehD5WOMUAQH4Du7nqHeWwvylbH7qMZZPncPYWaFCmysKQiv69evHhAkTeO655xg2bBjf//73890lIR9YaasBBozsn1wHQiwQGVmwAK65xvx5XvYFWAy8s66JunDBrRcIgiCkJddrnMVbByIdbi5MmHSvCjjyrSX89+8/zk1VYcJh16aCUFBMnjwZgHnz5lFeXp7n3gjdzZt3PkfN9HnJBTIdAiIpjWsJZ2HKhnAYvvY1o7diMTgYNS5Mm9Y3cc45yBggCELBEQ7DvHn5vz+VvAUiRmsVFaCJM6I11NSIFUIobKLRKDU1NUyaNIkvf/nL+e6O0M3857bHmXjjDDSKxqVl1PKsyTDnFBBSiTpramqMcLCJYrIw+YjmLBBREAQhWwopn0vJWyCaKCN1Xa4ZH8/7q3IShCIIneFnP/sZ7733Hr/5zW9QhVrvQugygn/4DQrwoBMFMiGzBUJcmNJSVWXqx3k85s/0+YuNBSJIU84CEQVBELLFLdtSvih5C8Tmm++hZdFjTKj9a3zfC+fezLxbxPogFCa7d+/m6aef5o033uCnP/0p1113XbwqtVDcBE+phI3PAikFMlMFhFshObFAtKJV4KE/AA/AqBFNPPs3sT4IglBY5CPbUjpKXkCMv3gSnPMxmJoQEGef0QQycAgFytNPP83FF1/M0KFD+da3vsWtt96a7y4JOeLwTx4P1q1q4++eThTIzOTCJBaIjNiBh+Ew3H2/n8uB4RXRtqtWC4Ig5JhCyihf8gICr5f/rKzjGMeu2Nz/h+e882T5SShILrroIi666KJ8d0PIB470opWfG5PYL0HUncL2Kx4dCXA5cGhfE73z3SlBEAQXCiWjfMnHQODzsW3ZOhxxdKiW5vw6lgmCILhx6FBi+6OPEtuNjYntNWvY/7/fiG+zfr3ZFhemtNh+xQ0xEwMRqZdCcoIg9ExylaWp9CwQfn/ya6+Xipln07Q0QJAmFKC9PpREzwmCUGg4BMT+qvMJlPeibM53ky0QQL96Iy70xo2o664zO8UCkRbbrzgW8UMMevulkJwgCIWLXfwy1Y0pl1maxALh9VJZHeLtu2rYMex4ADxXzm71H/nogqt49bSrknOvC4Ig5JING+Kb/Q5so+yj99BXXw3vvuvaXEHC8iAWiLTYfsXfuiGRhSmVQsm9LghCaWOLhDlzaFWvJpdZmkrPAuHiwgSYYMTN58NP3oDDDku8Hw4Tm3oWw1qaGQZEVi2klmWJ4EVBEIQuZOMNC1CLF6EvnMmY26pT3tzo/qG6uqSX2rGtfD4TOyEWiIyEQhAaH4B5mJHXQSHlXhcEobhJZ12wcRMJdrtcZmkSAWEHGAKUlZnnSCSxr6YG1dKMnWE/nntdBIQgCF3Mxut/z+ifXmVezF/KRkgWEYMHAwmBEK/80acP1NfHm+0ZOBrvkUfQf8oEOPVUuPxyERDZYLu4pgiITAO2IAhCV5HNYkUmkZDLLE15ERBKqUHA3cB5wE7gJq31gy7tgsCvgAsAP/A8cKXW+sMOnzyTgAgGzbNTQKTIt6Tc60LBobWWgmrtQGvddiMhZwQfvi8uCjSgFi8Cp4Do1w+A/SefTeT9bQzduQ5VWZkkHgAG/eX3cO655sWaNea5wFyY8joOpCNgXJic2a6gsHKvC4JQJDz8MPz4x8aC7PVCSwsTD3nZ2NBCC168DS30me6F3i3x9/F6CbW0sKePl6inBX8vL8ELWr8f8nrhV8n7U58nkJSAtN3kywJxB9AEHAacCDyllHpda/1WSrtrMRUZjgf2AQuA3wAXdvjMaVyYAHcBEQqheveOBy++fZe4LxUqXq+XaDRKwJ4ECG3S3NyML/WaEPKGZ8pk+ODF+Gt94czkBtZ9qP+NV8HIkTBlilk1Twmi3vBCHeMs/VDAaVzzNw6kw2mB0BqsxYhCyr0uCEIREA6DSzr2ftYjTr31SCFoPTjY8S70gj4d/3QegqiVUn2AmcAcrfUBrfVK4O/ApS7NjwKe1lpv11o3An8BJnaqA+21QEDCtQmopJb9E05j/cQLJKC6wCgvL2f//v357kaPor6+njLH71vIL4fPOC2+ve/kqtYxEHYWpt69YcQIs/3mm8S270hq9rGbv5q4P9n3vAKyQOR9HEiHx5O2cncoBDfdlCg6JwHVgiB0mCIoFZCPLEzjgGat9QbHvtdxHxDuBs5QSh2ulOoNXAIsSXdgpVS1Umq1Ump1XUpQYZxsBIQzpzpALFElQs+eTfm6VYxb+xjjZ09rt4hY+6t/8drk2dQueKFdnxPaZtCgQezZs4edO3fS1NQk7jkZ0Fpz6NAhdu7cyZAhQ/LdnZJkw80P8M74T7Htc9WJmeiuXfH3B5w4uvWHnALivffMdlMTHisqwv7F++xYLShUC0S3jANZjQFtkcaNySZTBpRsEPEhCILTD1KTnPiivegMj+4kH74LfYHUZeJ9QLlL27eBD4APgRagFrgm3YG11gsw5m0mTZrk/rdrbxA1JAXUOb3rfUTbFVBduyDMxG9Ox4Mm8vK91FIj7lBdSDAYZOTIkezevZtNmzbRUlgTpoIjGAxy2GGHiQUiD6z91b+YcMuXANAbILbkfjzLlyUJiKSicTa2q1KvXrBsmeuxW1DJsVqFKSC6ZRzIagxoi0DA/J2bmoxQS6EzAdWSzUkQBABCIaL9B+Pft5MNjOUQfRg3KkKfAUEzBw1m/7zzwwhb6oL4idBEkAARogTbPF7DmjWdcIDKj4A4QIqLl/XaxcuLOzBuXhUYT6/rMStPp7m0zQ6HgNCAevHFxB08nQtT6muLFnztCqjetagmvlLob6f4ELIjGAwyfPhwhg8fnu+uCEJa9i9aGt9WgI5aM9G2BITTAlFVBT4f2uFq04yXFyZcwaBrZyUWJwrQhYk8jwMZ0ySmycRk05mAasnmJAiCTVOzwg9MZSW7vUOZW23cJNvLO46FCZ8PLrsMZs2CPm3cW9Yq9Z8OddwiHy5MGwCfUmqsY98JQGrgHJjAunu11ru11hFM4NxkpdTgjp58++KVSa9jZ01L2JLdBERLS9qVuw+uu71dFgSn2IjhkWxOglCiDDzn5Pi2BvBbM9EMAmLjDQtoXrvevFi/3sw8V6xAzZjBoVHHsmHCDP5z13NMe+vO5PtSYVog8jYOtOmC1IYLkx1QPXdu+y0ItvjweiWbkyCUOkGPWdTRHl9W94N07o/Oe9KyZXDnnblZmMi5BUJrfVAptRj4kVLqq5jB4XPA6S7NXwZmKaVqgEPA1cBWrfXOjp6/ZU1tfNtUaY0mloHcBESaVSiA0Wcf2a5zV1aHYLbZ3n/mp8V9SRBKlPEXHgc3m+3IsCMpu+V7cP/9RJ9Ygt9udOgQhMPsvHE+vPoqow+8H3ehjF18CZ4jjjD3rUcfpQ8wPt3JLAtEdFsdTUdNiJu0x8KY7vp+bZHPcaBNK4AtIDLc+0Ohjg3Qks1JEAQbnzaLFDfO8XP69ESChtT7QzgM998P99xj7ltu7o9t3ZPaKk7Xof53zWHazdXAPcAOYBdwldb6LaXUVGCJ1rqv1e47wK8xPrAB4E1MLvAO03DJFej5q+Ovtc+PsmWfWxB1hkEkNfd6exg0YViHP5sRa8IReXcrjRdf3jqLiyAIueexx6i78xGWHHU1Y798OqFgYnW77PAKuPpqdEtL0g25ae3b+M6cSkXMWA6c8VfKufDRBmsfeJUJgK+lCd+MqpYIAAAgAElEQVSmdcZ1E+gHA7rgm3WGvIwDbbog2a5ep5xixoTmZiPCsnnu2xfOOAO+9rW0/5uOio/20h0TBkEQOk6ra9Kycn77Bh/0co+RArOvsdFklobCib3Ki4DQWu8GZrjsfw4TXGe/3oXJuNFljLmtmo1A2QN3UzbmcCpuvT7xl3QLok4T/wB0SkDQHcXOwmFijgkH81e1rmQrCEJuCYfRF1zAEOCL/I3pC2v4zW8Vlfb727dDSwupdwTP/r2oWPJ+Oyo4aeGjDXYuWR0XDYVEvsaBjFaAcBi2bDHbe/a0/+A7d8KmTfDII8aXIE8zdwnWFoTCwvWatBcrrLgrN+soJMrSgJk6FkrsVT5iIPLOmNuqGbHlJSqWP5r8V8zWhancShRy4EDHO9EdAqKmJj7hsI+uFi/q+vMIgpA9NTXx69FHlDOiNax52eFfn5LYIZ46KNi6IGKTtxe7z5qBZ8XyrEeAgV84lyjenKX26wk4azok0VW52Z2jfx5INxERBCE/tLoml+lEXJoVp+YWI+XcFwzC7NmFE3slJWidWAKi8e332XvBVQy7fhYMHdq63ciR8NZbSRaI2gVhdv3t31T898ezi23oDgHh8qtoVclWEITc4rguY3h53l/FxZWJAOn63U30xYj+/cdP5dDOgwzf+iq+xtZZmIKf/yzBhx9u1+krq0PU8hyB2+dzRMP6eAzE/nXr9nbwGxUvVVW0eH14WszKYEfu0hrQvgCePEZIdyZTlCAIXU/qNXn2VHOPiXl93HariltD3ayjnY2b6q7YKxEQDtYvfovxQFlkP4c99ntiTy3Ec9/C1g37WdkHLQtE7YIw42dPw0+UyDNBalnWtojwdIPxJxRC2b64QN0nLxX3JUHIN4679foTvsC8O0NM3PfP+L7y+o8AaC4fQP/XV9D/0CHo0ydhs3bSwZodldUhqH40ad/bSm3s0MGKmDAhbvKu4NqW+YxnPSPHBunrMznTD+6NsL8xSL8y99zqDdv20GvbZrZzGP+jHmUeIfLlNSTB2oJQWKRek1NOMFboxhY/c+YkuxqmXq9dETfVHbFXIiAcbH/mjXgmk3hu9hdaV4zWL60yK1P19fDMM4y4cTYBzI8hQCS7+g7dYYGApEnH0Aunds85BEHoEMedNwJCwBOtU4TuDwxmEGQWCbabpdAt1NTAypYQy3kUrxfmXmZcnVr5Lz/YejC+feZqblp8Kh8ygpUtobzXeMhVsLYgCNmRdE3uNwu9zfh6bF2YkoyBSMfAL06nydJUxldYs++5N+Kv49hByu++C5/8JIP2vBt/SwFqSEXbJ+suAeHM9R6Ldc85BEHoGM40Gin4h1n3DY/HVJp2Q6qGdytOX2GvF95/P5E5JVNMQTgMf/u7EXdBIvh84jYkCEIGrAxMUfw9ti6MCAgHldUhPrj+jnjGEi/Q7/UVAEkBiNprZWrfssW1OJOu29VqXyu6w4UpVTCIgBCEwsSlSFn5KEddNH+8GgQxr8NQLAKiW7HdDK64wqzx/OEPxvJQUZE5CLGmBhpiCQFx2WU9ayVREIQcY7malw/0pS1Kma5wXKEgLkwpjBmwixgKZdkcbDtBdPDhvDX6sxx+OAw7+1i49lqi6zcm/QEVKRWmU2sy3HpF93Y+VcyIgBCEwsQtu9vbb8dHCr1/fyKTmhXQC4iAyAGhkBEEzc0Ji8OuXa1jCpw53auq4F5/ECJQpiLMmpV8TKnJIAhCEtYikvb5Xd/uCamYRUCkUlWF9vvR0SYTB4ERBsExH+PkF+8EYMtXfsARgD9mJgHa0a5x9AQTsBgOEzvjTCq0NYmfv4qNLc2J0q/NjklBVyECQhB6Bi4WCL1hA+rss+Gyy9J/TgRETnDLYuT0X3Yb3B94JAifgWH9G/mYY6DvCRMBQRByjDUGbNvpaxVEDd1Xu6ErERemVEIhvMtr2HP6pwFHGr/DDos3aXr+5aSYCAV4rJiGPl6rhkRNDUrHkmoyeB511GRwmUB0GhEQgtAzsFefHLsUxC0TMa/PvWaDCIicYLsypXMtcBvcJ51p/jf+WKTNtoIglDjWInKT9rveG7qrdkNXIgLCjVCIQb+6OXmfI/uJXVshqTBTwBR9an53M9suuAoqKlrnED///MS2CAhBKF0sodCSWuAtEIBZs/A+t4JdZ81g6xGT+fCS6xOfEwGRM9IWmyPN4G6NEbHGSJLfck+YCAiCkGOsOWCzcg+ibmsRoxAQF6Z0DBqU/DqQqAo75rZqNgJlD9xN3yFl9J8ygY3lJzD6p1fha2myakgEk9TZ3snnctSN/wO/vs7scPOB7iwiIAShZ2ANHnvO+Twf1A/kyLJtVEwYBrNmxUeKwcutug2vvAIPzDfbIiAKAtc6C1YQtacpwpwfaAJBFR/4pSaDIAhJWBaIkaN9zL3c/d5Q6KmYRUCkY+DA5Ncp+dfH3FYNjiJtH0yfx2hrO15Dwnm4CSNMwSEbsUAIQulhp3G1rv8hk0Yx5NZbM3/GKRqkDkTB0Gpw93ho8fjwxprxxKI0NQXifsuFPhEQBCHHWGNAn/5+bropz33pIOLClI7+/ZNrNTgsEG5UzKwi6qghgT+l/c6dyVYHERCCULrY9wK/ewaOJJw1IcQCUdhYAq+3JyLuSoIgpMdOpOPruev4IiDS4fFA377xlweXPp8xGa9dQwIgOnAonscfTW6wc2eyBUJcmAShdLEXELIREE7RIAIi57QnF7u3lxEQN9/YWLB+y4IgFADtGQMKFBEQ6QiH0fX18Ze9360lNu3sjKPImC+cAkDgqCPgxBOT3mt4fQPvPPRyYkeWFoj/3PY4m0afzY5PfqntESxVQOhWOVwEQSgE7AWENiybgAiIPGKnYJ0zxzy3KSKs/883r4okiYfOFIQq9GJSgiB0AHsO2IMtED23591NSq69eFxDpmS8tpJsaor/OFoCQbxNEcoadnPEvKsTbbMQELULwky4cQZeQL8HsX8/gmf5svTnFwuEIBQezuvQ3m7P6pO4MOWNdudit2NUHNbmztSBkBoSglCk2C5MYoEoQqqqiPkDySkW/W04tdqridFofILQ5OkVLzLnwyEasnBh2rWoJv4PUgDRNpKIi4AQhMLDuVhgb7dHQDgDpyWIOqe0OwWri4DoTB0IqSEhCEVKEbgwiQUiHVZBuY/m38/+9dvoP34Yw66flXn5x8UC4elfDo17AWjBhw9LOGRhgaiYWYVa6jx+GyOYCAhBKDycVeft7fa4MHkc6zyqVXUZoRtpdwpWFwHhVtU6WzrzWUEQCpgiCKLuuT3PBaEQwx8NMTzb9vZkwCEggoP70Vxfju9QPXWzvs0R988D4NBHe+ndxuEqq0Mw22zHlMrsvgTJExUQASEI+WbBAvYu+AsD7NcdsUA4eGfR6xx98sld1j2hbdqVgtUWEI2NSZ93ihAwMQ3ZCBKpISEIPZ9w2OUabucY4HqMPCMCoitxcWHC78c3oBwO1eNtOBhvWvbB29QuCBuRkAWesrK2fzVigRCEwuGXv0Rfdx39HbsiDy1i1XsjOO7gVgZCVhaI2gVhKq3tET+5mtojj8n6viHkGDtGxZlxj4QI6UhMg9SQEISeS+o1f/vtsGsXzGxuZhxkZYEo1FgoiYHoSlxcmPD740GQ0ZdfizdVaHYtqsn+2NmYuURACELhcM89KKz4JYvgob2c8e8fUf7SM2ZHFqtPuxbV0GLdqn1E23ffEHKLiwuTE4lpEITSwnnNRyLwta+ZrG63zs3eAlGo9w2xQHQlaSwQ9qrUgH2b4001ioqZVZmP50zD6vW2fX4REIJQOEyYAG++2Wq3kQLWtZrF4FExs4rI0iB+mogSaPu+IeSPQ4fM83XXmft3MGhmDdbzV/cH+WxLhAhByloijFwYhD8l3mfIEPO7mdVGvJ0gCD0CZxyTx2OmabEYqFj2aVwLNRYqbwJCKTUIuBs4D9gJ3KS1fjBN25OB24GTgYPAT7TWv8pVX7OmDQtEvz3vx5s29erXthuCM9A6m5oOIiAEoXCYODHtWxoPEMvKhamyOkQtz7JrUQ0VM6uKxn2p6MaAcDhRrGHt2lZva2Cw9bBRb6c0WrcOVqyAhQthWRsxb4IgFDzOOKaKCvj6183ULuBpNutIWSwiFWosVD4tEHcATcBhwInAU0qp17XWbzkbKaUGA/8EvgU8AgSAI3Lc1+ywfwipFghnHncLT2rAsxuOQLxW4sANERCCkBfeu/aXqCf+Tst/X8SY26rNzv3707Z/hUlMZlXWAXSV1SEoEuHgoLjGgJqajPfcduXPyqrohCAIhUKmIGdnDJSdSM+v2ldIrhBjofIiIJRSfYCZwHFa6wPASqXU34FLgRtTml8HPK21fsB6HQHW5ayz7cHjMa5GLS3Q0GD2+f2uqRc90UYOHjWBPieOh+uvd/9lOAVENoJDBIQg5Jz3L7+ZUffcYl7Mr2EjGBGRQUCM4l2zsXQpfPKT3d/JAqMoxwCnn4EL6WzIrsKikPwUBEHISLZBzjU1ZiqnNSgpJNdhxgHNWusNjn2vA242/ynAbqXUC0qpHUqpJ5RSI90OqpSqVkqtVkqtrqur64ZuZ4HtknDQyriUYoGIesz7XlrovWkd+rHHYNq0hOnbiTMQL4MFIhw2aQHfekMEhCDkmuBTi5OCpdXiRWYjg4AYyk4A9C9/CQsWdG8HC5PiGwNCITNDuPJKOOssOPZYOPHE+LM69lgOjj2RrQOPZQ0nshbzfHCU1W64lTD8mGPa7b5kjwFuw4ggCN1LtkHOVVUJg4NdWHhrXc8VEPlyYeoLpI6u+4Byl7ZHYPxezwVqgfnAQ8AZqQ211guABQCTJk3KImigG/D7jfXBDqbz+5MCoJt69cd3sC551SkadTdXZ2GBcCrfFd4WljjfzCZuQhCETuE5vhL+VRt/rS+caTYyCIgkFi2C6upu6FlBU5xjQBt+Bn2B38wzWVhaWszQMLcabroJeOghuPhiIybaKR4KMcWjIJQK51eEGcz9jGMtQ1rqWidHsJ5DwSAb/BF2RoKMZBMADa+0TrTRU8iXgDgA9EvZ1w+od2nbADyqtX4ZQCl1C7BTKdVfa72ve7vZAdwsEHZqP4ChQ4m+twc/zSiMWVv5/e7maqcFQmtjUfAkG42cyldrsUAIQq4Z8vFK+JfZ3nnuxVnFQCTNbGfO7La+FTDFOwa0QdqMKn36mGd78SlL3FY/RUAIQo4Ih6n8ehXHtSRcF1slR7DQwMesh83ot5caK3QPXETKlwvTBsCnlBrr2HcC8JZL2zdIHm8Le1ndFhBOC4RdXAjoc/ThrL9rBRuO/RxguT2kM1c7LRDg6sZkD0ZeLwR9IiAEIefsS8xhh3x6cmJ/BgERA/b0OQJ11109cuDoAop3DGgDO6PK3Lkp1oLevc3zwYNpP+uGcwyQ0AlByDGWgrfdWDMlS1ApD3sfixZ1Zw+7jbxYILTWB5VSi4EfKaW+isnA8TngdJfmC4FFSqlfYwaXOcDKgl15sgNi0sRAUF5uZVR5zFgmmprglFPcj+UmIFICbpzpvWb4W+C7jjdFQAhC9+MQEEmpl9MIiBY8RAjy4S/+yqDiy6yUFUU9BmSBq6eTLSDaaYEo1BSPglASVFWZRDlZuIy7tVDQY63Q+UzjejVwD7AD2AVcpbV+Syk1FViite4LoLX+t1Lqe8BTQG9gJXBxnvrcNm1YICh3uPjaAiISSW5jk1rNNE0cRHww+rtYIAQh5zgFRFMThMPsvHE+Azd/gFv5x1dD11D2lf8pmnoOnaA4x4CO0kEBAYWZ4lEQSoJQCE44AdasgcpKM08LBjm4N8L+xiD9yiL0GWBiIJRj/wD20GtQH7j22h5rhc6bgNBa7wZmuOx/DhNr5tx3J3BnjrrWObKwQMSxxYYlFFrlEc7ChSnj+yIgBKH7cQqId98lNuf/qIilv1ZP/dM3YMyYHHSssCnaMaCjWAJi95ZDrA+LIBCEHoM9x/v97+H001snNngwcT33sR7FQL5iIIoXtyDqTBYIgEgk/oObM8c8h8NkbYGIIwJCEHKPU0C8/TYq1pLZF7ZPsQwfQlfyyjojIA7tOpQYAwRBKHzsuZo1/8s2rWtPRwREV9OWBaKvY2HNFhBNTe4/OLFACEJ+CIf5aMaVvHraldQuaGMm5xQQgwfHs6vZj1aIgBBcWLHaCIjeHCrqSYcg9BSyrq9iCwhrTlcqiQ2yEhBKqd8rpbRS6nCX98YrpZqsADehDQtE3b/fSLR1WCBcf3CpAkIsEILQ/YTDxKZOY9jjd3HSqrsYO/vsjCKiacuOxAtLHMS8PnYOOZZ9p3y8VfvaB17v8i4LhU9bk5HQOQkBUcyTDkHoCbh6haShYb9J4fraWjOnS5tpjeIq+pitBcL+qpNd3vslpiDQD7ukRz0d2wLhCKLe/njilzL42b+w8Qar8qxDQLj+4FJdmMQCIQjdT00NqiUad0MK0MSuRTWuTWsXhPHv2R5/vb92MwDeIYMZsmMtAx6+q9Vnxlx1XttWDaGoyGYyMmVaEK0UZUR4dmmLxEAIQh7J1g0pHIa6LWaudtGXA/FrOxQyBSJTxUO2oqQnkK2AeNF6ThIQSqlPA/8F/J/Wek9XdqzH4mKBaFlTm9RELV6U3NYSCq1+cC++mPS5rC0QduVrERCC0H6qqtCeRP6kZvxUzKxybbrrb/9OinVofv9Ds2EvDtiZdRz4MwgSoTjJajKiFMr6vYROSGRiKqYVS0HoKWTrhlRTAwFt5nAHokHXa9u+hu+/v7hiI7LNwrQB2I1DQCil/MAvgDeB1stspYqLBaLhkivQ81fHm+gLrZy/jhiIVixZYn5tDtY/uJrx3z8q/bltAeHzmW0REILQfkIhPJ85Hx5/HIAPrv9N2pSrh00/AZ5JvA72D5o7pXVtr/3rm0yw3rPjIVrwpRUkQnGStvp0Kr17m8WnQ4egvLx1NpdnJTuTIOSCbOurVFUZKzWADgRbXdvOa9jnS6zvFoObYlYCQmutlVIvAmcopZTWWgPXAuOAT2it2/CtKSFSLRCBAGNuq2YjxvKgL5zJmNusnL8OF6ZWPPBAq117/xGG7/93+nPbAsLvN8fMorCJIJQ6G29Y0Pra7N8//v6Y89KnXD12+pFJxRv72PkSrGt7x5JXOAZj6lVYFahnXCY1IEqMrIu92bOLU06B3r05pi7Kaw1+fERpbvAz9FNRGOI3BQv9bTwPGACTJ8NXviKqQxAy0CqFvkU29VVCIWgpi0AjLH4ywGkp7Z3WR4ArroCRI4uj6GN76kC8CHwKGK+U2o2pBvqY1vrZbulZT8WtkByYiYk9ObFxERBv3VGD747bGf3BcvwkVi0VMPDcRMXq9T96mLKFd9Jr/McY+sOvWb9ih4AAsUAIQhts+dINjH5gvnkxfykbsa5Vp6hvaEh/gLq65Nf19ebZSpxQMbOKpqUBgtYKFf4gw6+f1TWdF3oUbU5GwmHYts1sf2hc4QZajzh7rUe2rFkD990Hy5b1/NmKIHQDXWHl80bNeHHaWcFW76VaH2fNKp5LsT0CwhlIfRYQBL7d5T3q6ew1d/fYnj0mwCQchquvdm+bEgNRuyDMsdecg4/kiX/U35tA9BDjLjgu3m7iDy/CA+hNEPv3I3iWLxMBIQjZEg7z0fz7GfDEg/EYBo0Vn3RbdXIGtEwCYoeVgcnnMzFKBw6Y19biQGV1iFpqiN59P4cfDsOuL6LRQ0hLuhXNjHSXQ7TtbC2/O0FIIhyGm282U7BYrIOXSktLsvt4CllbH3sg7REQqzAW+K8CZwA/1Vq/2y296qmEw+il/0KRiE7Xf/4zaupU91LlKTEQuxbV4E0RDwoIlHkhSvxHuv+hp+LHV4CORvjwC99EnR7icBABIQiZWLkSPfUshrlUaYjHJ2UrIGwLxIgRsHlzwgIRTKxEVVaHQFyWSoYOr2hWVYHPh3ZJlpFalDCTc2qrAobF4GwtCF2MfZ3a4sHj6eClYsewBoOg3MuHZuMK1RPJWkBorfcrpdYCU4FtwI+7rVc9lZoatNatb+CLFmUWEJYFouKCqbDU7HK6LjFokJmYvPYa3HEHlW8ke40p4PAtq2j+66tmhwgIQUjPk0+iXKZg22ZcmYiBcLowOcVEOGySG6xdC3V1RLbtJgg0RL30gkSmtGBrU7ZQGrhlXMpq8hAKwYoV7LpxPjtWrCdCkCARRo4N0tcXMb+piHnesy3C5m1BAkRoIvEcb9+wE7ZsgVGj4MEHi3P2IgidwL5ObfHwiU8Ya0R7L5VVz0WYDDT7gu1akS8G2vt9VwHHATdpreu7oT89m6oqtN+PjqZkVZo50719ioCo/MxRcBU0+XrRcNyp9A80wuWXw8KFZnVz9mx0Swv9Ug5jCxYv1uRFBIQgpGfSJNfdwz9/ZuKFmwUiHIazzkpKp2zLhLKtKcZYR/FIobTIOuOSG6EQg5c/ytsOF6i+KROa+Mqpx0x8zj8fnnjCCBavF+ZeBjdVPgmf+QxMnCjiQRBcSL1OOyIewmG46HMRNgF7DgZ4J1xal1vWAsJK21oFrAbu664O9WhCIbzLa/ho/v00vbaWQX0aKb/2cnfrAyRiICwT2MZfP8UYgL596f+7WxO/RDuda0tLknXDaaXQQAwvHlpEQAgCsHn2j+n12EP0DUbp7YvCcceZQivHHef+ATtzGrgLiJqatmux2IgFomTpjM+zM3bippvc2zhXTpWCYcNcBMs+a2jP9vcqCEVIplikrohNqKkhPn+LECy5UKP2WCC+AxwFXGKlcRXcCIUY/miWvyCHBaJ2QZgJ868EwL+3jti0s01gdCjUKjDH+cfff+JZlL++Eo+OsWf6RQx5+s8iIISSZuMNC6j49RyObNyR/MZ778E//wl//KP7B50Cwi0LU1WVmbFZt7+MN0ERECVNR3yes42dcMvqMmtWykToGREQQmmzYAF87WtmGhQMul9PnY1NqKqCB/wRiECTal0DotjJKCCUUoOA6cDxmGznv9Bav5jpM0I7cAiIAw88hteakpjAaIfzrJ0bfNgw2LaNhiPGsoeBNF58ufHZPuYYWL+eIf3tVJEiIITS5N1v/5bRv/h6+gbRKLz0kvt7bVkgQiE44QSTGrOyEtXczIHmIPsaAsTOnc7HFs5NfEYEhNBOso2dSLdymtTWJwJCKF3CYbjmmsTPPxLpnkRkoRD8+e4IfAmGjQwwuoSsD9C2BWI68CCwA/glcGO396iUcAiIw045AlaYlxrA73CeTRkMev9kDr0vvTRxHKvo1c5/v85gEAEhlCy9H/yjq5sfWLFCfj8cf7z7h+0UrJA+C5N9Td13H5x0En2BvmCCqp0CQmIghHbSntgJt5XTJHcNERBCCVNTk8isCiZWqLusAydOMAu3vQeU3qJRRgGhtX4IeChHfSk9HGlcR0/sDcCuirFEp56TnC/etkDYExzn5CQcRq9+BQVU7FwPwP4Gvwm0FgEhlBje446Fba/HhYMGGkeMpffWd4zr0ZNPJgR2CquXHyRqB8Gly8K0Z495HphU3isRz2QjFgihnXQ2dsLp/vTS7V4qQQSEUJJUVSWSlnm98Nvftn09dah2CyTGihK855da1qnCwp503HsvkSZNEFDHjGfYo3cmt7NXk+yJTK9eifdqauJCwV553b3fZwSEhKoIJcaQM8bDM1BffjjRkyZTcev19A6FYPhwU+V34kSorXX9bO1LB/naOZavbDoLhC0gBgxI/nCqKCnBwUToHB2ewNDa/emV131GQDiXYQWhRGivGO9UNWpbQKQuIpUAIiDyycsvA6C3b8f+6Q18/kk23rAgkY8eEhYIG6cFoqoK7fWhWqLxXQOG+OFdxAIhlB47dwLQ78c3wtcdsRB9+pjngwcThX9S6K0PJnzP3QSEXWlaKeiXkkxZLBBCJ+jUBIbW7k8nnSouTEJp054A6Q7XboHkQnIlhqftJkK38Z//AMZy4PTbVosXJbdLLY/utECEQniuTQ4aHTBEYiCEEsUSEAwenLw/CwHRl4PG9/zM5uRrxxYQe/ea5wEDjFOtk1QLhMRACO3AbQLTHuwV17lzzfMJp4iAEIRssQW419uB2i3iwiTkhdmz4aqrWqWD1BemFJ7LZIEAOPHE5NcSRC2UKp0QEMcddZBnH4BQZWPS/sizK4mOO4m+Mat2ZnOzWTJ2LlGJBULoBJ0qPmeRtOK6TgSEUKLceiv8+c8m4140auZD6Z7rzT09VF7OrgFRDjb56ROI0usriTYN9Y795S7HsBeW1q5tPS4UOSIg8smVV4LHg7r7bg7saWJfQyCRmtVJJgsEtHanEAEhlCp1deY5k4BwBkg7GLr1NQZ99TS4/ItJ+wNN9QTeXoPGshTW18PZZ8OyZYnBQmIghE7QFUWtkpAsTEIp8p3vwM9/3v7PffQRvYD4zOqjxFvp9rdi06bW40KRkzcXJqXUIKXUo0qpg0qpzUqpi9toH1BKrVNKbclVH3NCdTW89BJ9N7zGiA9eai0eoG0LhJXGNc6mTeZZBIRQYkQ3bwXgP09vTn7DFhCHDqW1QPSK7KPv2lXob38bgJjyxEVDqpthKz+TFAvE9sfDHf0KJYOMAcmEQqb6dEfnHuEwzJtnnkVACCXJkiX5PX9TE5vvr0lch0VOPmMg7gCagMOAS4A7lVITM7T/LlCXi44VHKkWiBQB8camZAuEXrXKbIiAEEqI2gUv4NtnXJhG3nQxtQscd/AsXJggWSQ0Y4S7djzipPqZrFqV9P7Qpxay8YYF7f8SpYWMAV2EHYQ9Z455fuV1ERBC6VE34gQgzT07A7odj0zEfAG+fE9V/DosdhGRFxcmpVQfYCZwnNb6ALBSKfV34FJcitUppY4CvgRcB/whl30tCFItECkuTC+82Q/X0lgiIIQSYt/D/4wLAD9N7FpUA9XWcm4bAqIJH36akywNfh2lBQ/bB46n/xSHVCQAACAASURBVOAgfX0RGDIEJkyAWbOSl4prahIuThZq8SJwsygKMgZ0MalB2M+/5OMUEAEhlBRrAqdxLg+xjcPYxnCOGBJhyAirIETQ5XnPHlCKg/4BvP92hAhBgpjP1H1oXgeI0HdQkMH9IvQZkOZY1rjwZ2ax8g+hjmVz6oHkKwZiHNCstd7g2Pc6MC1N+98A3wMa0rwPgFKqGqgGGDlyZBd0s0BowwJxysf7g+X2l6SQRUAIJcTQacfAMnMNRAlQMbMq8WYbMRDr71rOsO//L4N3ro+LAAV4iPH2qZdS9fRNmU9upVPWLYkJW6tkCIITGQO6kNQg7NCZ1qKT1IEQSohxo8zi0IPqUuaU/ZRnH4chWUzg+wL7HHVYHq8x1ryWFrN+O/c7xr2wLcaGIXBf55Ih9CTy5cLUF9ifsm8fUJ7aUCl1AeDVWj/a1kG11gu01pO01pOGDBnSNT0tBNqIgTj1nIQL0/7Bo1Hf/755IQJCKCHGTR0GwL4Bo9h417NUVjtGjjYsEJXVpzPk7wtRPl+SuboZf7IQSUcohPe5Few6awZbj5jMu9ff5R7PJNjIGNCFhEJw++3GbeL22+HUkLgwCaXHkYebelihs/ztrqXijEHqaFrX1HTKxWx9gPxZIA4AKamD6AfUO3dYZu75wKdy1K/CxCkggkFTyMpB7X2vmqqjQHDnh7ztGcdYEAEhlBYfmRQZA6afxoDqlDu3LSB+9zsaYz5aVWmw0++tWIGaP5+Da9azpfd4mq69PlmIZCIUYvDyNue4gkHGgC4kHIZvftNo4+eegxNH+5gMIiCE0iJqBMTp0/yQ4bZtV32vqIBdu1pnPutMVrT2FLDr6eRLQGwAfEqpsVrrt619JwBvpbQbC4wCnlNm0hwA+iultgFTtNabctPdPON0YUpN4QrsWpTwv/bSzN5nXjFvxGJmAJk1C5YvJ9IYo7mxmeajj6H/7+eXzq9cKA22bTPPw4a1fm/NGgD0tm2txQMQm3Y2nuVW+r1HH6UPML7bOiogY0CXkhoDsfx5dwERDndhqlhBKDRs63JqXR4HdsKBSMRMkTwesy6bajEoJSHQUfLiwqS1PggsBn6klOqjlDoD+Bzwp5SmbwIfA060Hl8FtlvbH+Sux3nGaYFwqXBbMbOKRnrRjJcoAfqfe6p5Q2u49lp46CHYupXg7m30ObSTfm+sJHbWtOJPESCUFHVPG+H80fsuWZY2GFd71fodQ7QD5X+FDiNjQNeS6nIx9ezWLkypmZrk9i8UGx9uNhaIzVv9advYYtt20IjFOlb9XchvGterMfU5dgAPAVdprd9SSk1VSh0A0Fo3a6232Q9gNxCzXpdOdJjDAtG8Z1+rO39ldYh37nqWlefNZeNdzzLuiyebN2IxeOaZVodTgGqOyhUjFA21C8IM/NdfAah49O7kFK5gijaSnGQgKTWfvwQi3goPGQPaQVKdhxRSfa+nnNlaQKRaKeT2LxQT4TA8+rBZPLrjD/60AtkW2x5r9uvxlEbAc3eQt0rUWuvdwAyX/c9hAuzcPlMDHNG9PSs8dte8ziBr2xtpIHbWNDwrlifZ1yqrQ4mUlf/5j3mOxWDkSNiwIWnipADt86PkihGKhF2LavBg5pNempNTuALMng1KoW6/HdatA2DDlEs5GOvD4YfDsOtnib06x8gYkD229cDO7uIWoJnscuExsXJax/00UjM1ye1fKCZqaqB/zFggGpoDaVOoOuMb0sVACNmRNwEhZE/j5u1JFXGxrQfpfvG2tI7FYJCRHmr0aA54+9H77TUmPeXSp+WKEYqGiplV6KUeIEYzPvfMSdXVZqQYb6Ibxl8zHS65JJfdFIQO4WY9aPP27fOZoNLmZggEOhUYKgiFTlUVrPNEocUskGYSyBLf0DXk04VJyJKGS64AHC4XPn/m5SOngNiyxWwvXEjfDa/hKbeyJJ58cnd1VxC6npUr4bzz4OijOThqArsrjmb31M/G/Tkqq0McOsb8prdeOz995qShQxPbGQLtBKGQ6FBaSTt2zuHG5ExVKQg9mVSXvlAIzp9uXJi++V2//MZzgFggegBjbqtmI1D2wN2UjTmciluvzzwC2AJi40bzANixwzzbkyaXXPgAb91RQ93jz1Px+Y9TWR2idkGY6N33d6+bRzjMzhvnE3l3K40XXy7584VkwmGYOjX+so/10Cs3Ejvrn3F3vvKKIABHzcwgjvv3T2xLmmOhh9Ah64EVO/fz+S2cPj37W3dqpqZcZW6SDFFCtqRz6Rs6wLgwHT1BFodygQiIHsKY26ohy4n1q2s8OKdQGlAXXwwjRmQUEG/95lkmfuMTaKDhX71YueynnP7wNfHMNbGnFiZSXXYV4TCxM6dSEbPiIeevYiOIiBASpIn2bOXOZ1eYDgbTH8tRQ2XLE69yxBe/2FW9FIRupb1uF83Khw+YN7eZQ/OzK2yVOjG7/fZEfYl0sRddQTYxHoJgk9alz6oDgT99FiYbEaydR1yYipDwS8n/VgXmwqqpSVxY1oXmNAM2/ekv8fZ+mhiw5EE8OGIvuiPV5T/+gYq1JM4BqMWLuvYcQo+hdkGYmunzkrMoOfw1kjInkeLO19honjMICOdxhz7wy9bZmgShSGhqMeuDKtacMeuScwxInZgtWpSbzE2SIUpwkinjGGRw6bMXRtsQEJLSuGsQC0QRMuX0hICwJ1rKb0207rnH7GhqarXqs/QzlfCyeTtKgKbTq2DJC4njdEeqy+OPb5WbX184s2vPIfQIaheEGT97Gj6iNC7tRS3PmliG006Lt1HHHsuB5iBlG9/EF2vG89CDieUj2wLhUivFZteiGmIoPGgUsdbZmgShSPD18sEhCHqa08ZNuFkcnJmaZs40la27O3OTZIgSbLLNOObq0mdbINqIb+tQUgKhFSIgipBTTk0IiOiAwQSqzoTrrbgJhwtT6kX0Ye+x8c9tvOtZTj5Ow5KfAHBwTCV9/3RX119l48Ylvdz6P9eJ+1KJsmtRDQHMABAkkpjcHzpkGvTuDWvXmvye48eb4nCVlYkDZGGBqJhZRePSMvw0ESXgnq1JEIqAQC8zvN/4nWZOmeF+604dA3btaj0xq6zsflcPyRAl2GQ7uXd16cvSAiGCtWsQAVGMeBICInDpRfDrXyfec7gwpV5EJxwTiTervHwyrFgRf913+tTuuasfPJj0csRXP9X15xB6BBUzq2Cp2U5KxXrggHnu0yfR2C2WJwsLRGV1iFqeZdeiGipmVqXP1iQIPR0riPqaK5vhKPcmbhOp1IlZrlJeSmpNATo5uc8yBkIEa9cgAqIYcQSKtppMOSZeqRfRMRvr481eWhHhtEhCUKTL2tRpUgSEZMYpXSqrQzDbbO+47MbE5N7+jbQlILKwQMTPI8JBKHZ8ratRp5JuIiUBpkK+6NTkPksXJvs88tvuHCIgihGHBSKTgIDki+jdxfWMtprN/FQDz9wc4Rj7c7kSEC0t3XMeoUfxsbOPTrywfyN9HcWJU5IBAFlZIAShZMhCQEDriZRkRBLyTYcn91m6MAldg2RhKkYyCQi3iZfF5tr9iUM0NbJuTQ4sELZ7io1YIIRUsrFAaJ1dGldBKBVcCsllg2REEnos7UjjKnQeERDFSDssEE7GDku4MJX7GzlurIuACIfhqqvYd9I06oZOYNe0CyAcpnZBmFdPu5JtF1zZvpxoJWqBcE1XKiTQOrGdjYBwrjx55LYmCHELRDvvqdlWvXZLtdlW+k0hgfytuoF2uDAJnUdcmIqRDgqII/olLBAP/LGBsY0pAiIchmnT0NEo/ez9detoOfMJjo2BFzNQxZ66N/uCcyUYA1G7IMzY2WczkSYal5Yl0pWWGlYFcr1uPYERQ+g/ZYJ7O7cg6lRLmlgfBCGZLF2YUsnGB93NzQnE9SlbxE0sQZfG24gLU04RAVGMZCMgXFyYqE9YIE48phFeShEQNTUQjbaq2+Cxqkjb+3U0Q+61VFJdmErAArFrUQ2VmL9tUrrSUmLFCvS0aVTYr+vWodesSPy22muBsAOoJf5BEAwdFBDQtg96Ojcnya2fHVKHwNDlQkpcmHKKCIhiJJsYiFQLRDhM5Imnia/fNjQkVnXt9lVVxq7d0pIoUAfElBd0DK+9tz0F50rQAvH/2zvzOLfKev+/n2RmMtPpPt2gFQqllLaUTS4aCnQqi4r6olKveOFSvKBTQETQC4I/FxSl0quIIhc6WKsFlauUgrJoETqlYBCQbUoLLaULlK7TdaadJDN5fn885yQnmZPkZLLN8n2/Xnllzsk5yXOeZM7zfJ7v5kxXGsPXP2sR/O53XYRoEk4h6RZEbQmId79xN0Pu+A11R1iv5WCBkEwzQp+mGwLC6/9EulSbklvfG1KHwFAIIZX0m7XnNR5dmGQMyA8REH2RXF2YQiFiZ5xJIJaYtH3wyyUcftLoxDGRiPkP++Qn4bHHODRuIlVbN1HRGcG/+Dfs+9mvGfLKcvPxjzzs/b+xH8ZAONOV7v3Ef/RL96WW1VupgyQhqklYsZIsZC4WiL2b9jEUOGr9U7Deca7TcpEBcSEQ+jw5Cohc/ifSuTlJbn1vSB0C83vbvDnxM+2OkEr9ze4LRKkETxYIGQPyR6IN+yK5ujA99ljcDSn+FitXJFsg7OMHm+iHAT/+HhUnWVWAjzuOIWMdq8OnnOK9rf3QAuFk5ImHl7sJJae5McTQ5x4HoBPFgYBxZFJHHx0/pvWORt4fdxoHx02E20w1dNaujb9+aEuLOcfxADi4+5CnoETJNCP0eXIUELn+TwSDcPPNXYvOpe4T3OnPfWVP3u+7z6z5fPnLXSfwXoLMU3+z0YNmnvLiq9kFhIwB+SMWiL5Iri5MkybF/4yv5P7bv3V1YYJkX3P7vdvbk98vBxGw741NDAFifj++zs5+YYFIwuOKeSlpbgwVtVJzy5ImfNi/ER/vT/wYU1b9CSZOhHffBWDg2lcYmHrik09CYyM0NBA4ehx88EqX965u38PN9SHmNQW75YIhCH0GO77s+uvNfTUQMPf0NM9f7QhwQWeYMAGGdu5hzC8U/HFoxnNcn6uq4IoroKHBUzPFjaTnUezvxDl5BzjiiO7VInHexysqgLCZh5w/q4q/PNM9NzzBOyIg+iK5ujCNHWt2DakjGoXagy2MuXA6vP564hj7+EOHEu/rFBBOseFRBDQ3hpjyygoAlH1OP7NA9LTrtTNETSFKeFmgKBmi6mbXo5cpFJooldSefiKs+lNSEH9aliyBhgaGnzAOnjO7nK5PCpgebaIpi4AQFwKhTxMKwYsvmr9XrfJ0ykBgsmNbbQO2dfPz7c/OIiLEjaTnUYrvJNvk3WtshPM+vnkzVN5rLBBtkcqs8RQyBuSPCIi+iHKEp3pxYdq1y7x07kyqhg0zdsVUUZDNAuE81uOkOHkl2qK/WSB6mIBwZoiqKlKGqGkNQTq+MQRf6142/M9DTDlOQyOwf3/ScU7bTPwXPXu2eXYEyanKSrSVHUwBe3x1fLo+ezu6Xe1UEHo6TU3dsm5mTGyQK5bYz4RkI+p5lOI7sSfve+Y3MuPVO6m9+BAMTVi7rt0b5hOdAaoIE+kMcGxjGP7obvUKBgIEw2FaOwJUYtz1TvO9TH396Z7aIb+37iMCoi/iFBCpWWncXJgsAcGIEYmJmVsWJvDmwuRRBDhXojXmuadNqItOD3NhSs4Q5S9ahqiKqLFkTbnmbHjOMiWkCIiW4ROpGVxB7VAXtwhnkNxddxG94VtUHdgNwE86r2N98zQZGYT+i3OJt1zYYj8D4kbS8yjVdxJc8WN45GbX12qBkxzbamP297NTbGjgqY563m5eIWNAkREB0RfJJCDcXJh27jTPI0cmRIMXC4T93t20QExrCBL+7ocIbN/MoWOmUfvOG2KBKDPODFE7Lrm+OBmiwmHzqKw0vyFbiKa4MI345ffhP/7D/T2cafo+8hHWjT+Xqc3/B0Alkf5ZW0MQbIJBs3S8eDGsXm3u8bnEMezZY8aRoTnGQGzbZs699FJPMRDiRtLzKNl38qtfZXw5V2uY0421gqiMASWgbAJCKTUcWAicB+wCbtZa/97luBuAy4AjreP+V2v9P6Vsa68mNR9yBhcmRozgvddb+BDQ/vN7qR47InFMqoCoqcnbAgEQqDQT6NqJY6E/CogeZoFwMu4j4/J7A63ZcvF/U7PsUQJDAtSefTpcfjkcc4x5fcgQM0mxhWiKBSKpcFwqzt/1gAHErrmW9rmP4KeDKFX9s7ZGL0PGgCKTh39Gt4Nor7wSFiyA07O7j9iIG0nPo5DfSdrf0vjxsH59YT6EZJfXDiplDCgB5bRA3A1EgNEYa9XjSqnXtdZvphyngDnAG8AEYJlS6j2t9YMlbW0vZc0f32Dyfx+R2JHiwvTW7Y9yzD33UQFsf+oN/vWEERDV2zaht21KrALkEAOx9g8v0/7neYw6soYxN87JfCey07ha6WF72op80elp1+v4HjetbuXIUIhtty8i/NpqRtBC7cnHwY03ehpdPvjC1xn7xzvNxm7Qv1qNuv9+uP9+s8/+zu3fUap4HDAg/Zs7XZgGDGBaw+k0s7yo2aOEgiNjQBHxKgJSj8sriNb6v9zwdoQH54lVoTfj/F1A9wRlxt/S+PHmecwYMwbkau1KeVaBAG17w7w/YBKRr90oY0AJKIuAUErVArOB47XWrcBzSqk/A5cCNzmP1VrPd2y+rZR6FJgOyOCRhubGEFaFBsbf8HmaBzsy6ThcmN686xmm3DQrLhJGPbaQ40nk4k8yIWYSEOFwkgVi4vf/0xQGexVijy/Ct2J5+ruOnWpwyBDzLBaIsvLS8lb+zfo7dN8qxt33XUZ3dsR/C3rTW6jHH4cV2f1LK5qeStpWYH4nK0zmrS4CIhWvFgjruGkNQTFZ9xJkDCguXkWA23F5BdFa/5cLfhnlJ1oyK/VWnL8Lv98Yijs6cv8+M/6WbIvznXfCRRcVpN21wKSsRwmFolyF5I4FOrTWax37XgemZjpJKaWAM4HUFSr79Qal1MtKqZd32n79/ZCWJU10WlO+Ctsf3Ma6wUceeJDhN17Rxc8wQgDXKW00aia7HtK4On0RiWao0BKNmoffn3ivUqzIh0Js/exVbBo/gwNTP2JqC5SLHmaBeOmZRBzCMZ1voxziAezvNOqp6o46wchY+/ekwfz+JlvJIrMJiEwWiBQXJqHXIWNAEfFSJCsUgltuMbdu53F2EK3f340gWuv/0t8Z6fEFurwUKuuvOH8/0Wj3C65l/C3ZMW/2OCD0OsrlwjQQSHF4Zh8wKMt5t2BEzyK3F7XWjZiEkJx66qk9a2m3hNTNrie8rJpKIl39wa27ZdWenYzBDLDOjgrceB3Ny1ZywmsPdH3jaNRbGlcLDVCZfgTa+LU7GA90VAao8PvNzmJbIEIhYmeexZjORHVWPfdFMzH2WPiooMRiRS/clgvTT0gIiHZVA1oBOv4bUWAq9niYVYw87xT4+4NEK2qo6jiEGjsW/vSnRNB+PhYIJ6lxPkJvQMaAIpItk469whwOmzUMny9xnDOItq4uMWH0tOps/S/W+CP4dXYBUq4icj2t/kRPK6bn/P2kWiByEZQZA7JtC4QIiF5LuQREK5D6qxkMpK0kpZS6BuMHe6bWuutsVYgzrSFIM0+7T0rfTCzc2SvLbb5B6OMmM+hrVzChoQHGd8LVLgIiHE4ICGf2nNQgaovo0BFUPfFn1zvi6p88zpR7jKeCr/0gO1dtZyQUf0W+qanLqroGTznLi8Hu1duYtGAGfjqLVrgtF048OvEvOLn2PSK146je/h7bxpzEqJY1+KNhUyfE/k5DIVi8mH0vrKZ1VzvtF1/BhNutfrSsVVVnBeGZZ+LlRnef8RmGA4dee4uaUAiOP969MZksC84kACrXfB1CD0DGgCKSLZOOvcIci5l/n6OPhhtuSBxnP+c8ybYExBcvjuA/LvOEuJyT+J5Uf6KniRkwn3/nnWZYnD0bpk3LLnDSiaB0AdkHt+1nAPD6hsGcOL3glyCUgHIJiLVAhVJqotZ6nbXvRNKbpS/H+MWepbV+v0Rt7NWk9QdvaEBfeSWQEBC1sQO8++krGGRPoLdvT6ruG//bDniurDTLEnb2nIMHkyd0FlWHj0p7t4k8+HD8bwUcXGd9rcW2QNTXW5+ok67RS87yYqBWNVOF6bsekX7UjkkBhrduMtM84LCXH4PPfQ5eeAGOPdbsDIVgxgx0NMpgrNng/BdZD0ZE2O5udXXm+eBB3vzF35n6/GMAVL//DrEZM/H9fZl7WzJZIDo60r8m9AZkDCgymTLp2CvMtgXi3XfhuuvMRNE+p1uTbCuIetyoCDe7p/iPU85JfE+qP9GTxIxNKGR+D5EIrFxpRE2m7zNXERQKwdgN+zkCuOhLg1g0ofzXLOROWWIgtNZtwMPAD5RStUqp6cAFwP2pxyqlLgFuA87VWr9b2pb2QebORS1YQHvl4ORKvw8vSWx8/OPE/BVokt2b4ibHmhrznCZ/f5yDB9M2YyCtSds1k480fxTbAhEMoo6ZAECnzwx26itfKY/7ElBdm/gX7BHpRx3fpV3ZOQZmhuG0OIEZ6RwVoOOxL/ZvyUVAHHjob0nvTzRiCsm5WRG8WiCEXoeMAeXFtlCcc45xX4rFuvq3dysWwi1NeBrq6xPuMX5/aSfx9vXfemv5V/zzijkpEk5R095uyol4Pd5LnERTEwzSZj7REh3cY+NkhMyUK4ga4GqgBtgB/AG4Smv9plLqTKWUc3b5Q6AOeEkp1Wo97i1De/sODQ18cL1Jox4PcL3QsQIfDOJf+SwtZ83ig3Gn0TFoqNlvCwh7Imk/79vn/jn2BDKF5sYQ4/+VECwaxahTrFSzbhaIlSvZfcZnWH3CRTQ3hmhuDNH08Xk0N3Yz+s2arFYca4QE55zTvfcpADXDE5Pk9QvK674EuIpBBcTOPjfxfdoCor4+3pdOoRn/LbkIiLoZx8ePj8fIzJzpHgfhTNWaigiIvoCMAWUkGDRB1IGA++S1W5Nst0KlGbDXDbx4IaYGPecbBB0MmlX1cq989yQxY1Nfb0LdwORO+fWvM/dzriKofoZmsBUCFa4a1CNEk5A7ZasDobXeDcxy2b8SE2Bnbx9Vynb1Fybc3sB6zGqxvnB2wm/dJhhkxIql5u/DD4cDe1n2/RDnQVcBkVoAzCbFAhEKwbrFIT782C34SQgFH9rceaCrBSIUIjZzJsM7OxkOROY+jMZHJRFiyxSbf34eR/zqe7ndde2JrR28Vc7JqCONa9nFA8BTJvWq071LAToaSYgLW0AEgzBpErz1Fp0DBlFx8AC7Pvb5LjEQTgEx8dyj4IfQOnAMbefMStQJqa5OKzhdcbgwNTeGekbfCTkhY0D5yRYr4XSD8hTo60FA2O+zebP5N9baPGdy3Ul1kbnzTvjqV82tu7KyZ7j95EOPKqYXCjHxpvm8qd5mHwGqCBONBBh3QRjGutdgCAYC7BwdZn97gMHVYWqvzlyzIVhRAcToVD6e//nLTCt1wUOhIJSzkJxQZibc3gCpwiGVUAi9bRsKOPuxawHo2L2PilAou4BwTAhDIfhh/VM8EjkfRVcrw/ZXtjAaulogmppQjn0VdMTdZXxoPrT6b8RmNCXVmtjU8EMGLP09tYEoAyqiMHUqfPvbiTuMLWzs2hM9RECUnb/9DX6fKASc1LLKKhg7FlavTggIiP9d8alPwJ/+xMhzTkq85iIg7N/KoLNOYdDSexLHulggMgmD9zdGsetkT5h7dtmDzwWht+Jl8hoKGUOhPYFfnq60TxYBkVpfoKLC3AJ9vsRtwo1UF5mFCxMfEYkYFxtne2Ri2U1CIWJnnEldrJMuX8dO65GGWuuRC34dY9o1M2Bacl0hL99fTww+72+U04VJ6A00NaGtSa5lI8Dfus+MJu9a7sivvOJ+bkdHfHLe1ATfjnyXSjqocKk0Mfyx35o/Ui0Q9fVonz++2UkF2pFDKbXWxPtf/DZH3vcdRu5aw4At78CmTfDEEzBjRsIGawsI2wJRzoDcniQg/vrX+J8mzBw2DZjM9llXGoE2zpqyOwXErl3m2e01W0AMG2Z8FMJh2LPH7EtN3WcH5DuYMPfstG5q67cPImb9DipTa50IglBQFi82/75am+dUn3jbnWjtBsvtMI2AcAqBzk745CeNeOjsNEG76dxkUl1kDj88fVvtieV3vmOepc5DDjQ1oWKdSXFtqTFuBSelrpDX7y/XuAuh8IiAEDJTX482TkZx7IrC+367NPv5K1aw6ap5+F8MUeG0PKQ4vfrs11ItEMEgvgs/G9/cfOPd7Dry1Ph2aq2JwBMP44p9k4rFEpPcQYMSr5ULp2AqkZhIG0Ny4ommGdYjQoADP1vImKX3JNyMIDkWorXV+BCMGpXYZ2MfV1OTCIjevt0829YfG5drzyQMhv/72bRTTRR/zwg+F4R+inPC973b3IOobYFRV5csBMaMMf/6bkHcTlLjBG680aw5KGWe58xJHCsTS2+4xpA4ghG041FUKiuTPnfxYjOMZPv+emLweX9DXJiEzASDbL3kvxn7u/nJIqKqitZd7Qwm88pE7PxP86FohGuoppkTEuePHg3bthGhEkUMjQ8/UfcsTCNGxP+c8Mlj4a3DYJPZ7hw4hIplT8ZtlxXjx8HONV0Fj32TchbCyyFjSNFwCqZYLBELUiSaG0McO7eeSqK0L6tOdv057jgAosNGsmribCqvmJPsFpSahcm2PowYkcjMlUlAtLXB1q1mn9MCEQoZS5GF/d11UpFWGGSsdSIIQkGZM8cE0toxB+km7Id0Vxcmt/iFlpbEhO+3v/WWTjXV1Wr5cnc3l56UorWnktb9JxhE1dTAoUPsGjGJmmE1DKxIH8uQ9/OkSUYNWl9gKASLFiXWkzJl58oWvyMUHxEQQlbGPXA768dOoPp3VKcD1gAAIABJREFUCxk4spohH50Cc+bQ/kgzzH8x47m+qKn3FKCdYezu8vr6+UvY/vdVTBq0hcOW3O2ehclpDg+Hk4KzK0YOT7pzDPvQIHgJ9gyfQFXdIAZuWm3O/81vzHH2pHfAgESWn1ILCOdqu/PaotGiC4iWJU0EMJ8ZoD257oRVTbzq+OM45dl7up7sFBDLltF21depBaJtYSo/+CDxmo2bBWLbNvPsFBBNTWYp0eoXO3XsPyf/FzMyCIO0tU4EQSgowaD5N802YY/5qyBC0n0t1SLQ0pJcU6C7k8B0sRsyscxOxtoT1nc38oPmzJnwKHysSVNTwqNYKbj88szv26OCz/shIiAET7gFXE8IBtm4fzfj781SMQjQ+NitRoBdM8qaaE7+0hlMvuEzcMcdsAR3C4Rzgh8OJwra2dsAjz4Kt91G579exQ/snnszx9x2BXz0o/DPf8L48eY4W3w4BUSpYyCcIsluf4naUTe7Hqy6bTH8ySv8dltc4hGAhIB45x1i3/0etTFzHRX7dxP76R3GH9KZScmrgKivh8pKtGPSESHA8Oscy5yCIJSVTBN2u2rxl4+vgjtIEhDZLAKFngRKAHV20n4n0agZn/x+T+Kh0EHMqe2aI0NAj0YEhJAXB04+y9Nx6yd/iknDOuEf1g57cmlPSn1WOE5nJxuu+zm1Dy5kUFWYmm9/I7uACIVglskGaa/fHzHvKprHT2HaUKuGhV2rwjmptRNdl9oC4RRJzhX7EgiIaQ1BmGv+3l9/QbLrjz3opxMQtpvS+vWoWEIEKUiIou5YIKzlTbV4MS2rt7GpfUxX9ylBEHokzqrFHU2VfAaSBERGi8Avf2l8ViKRxKxx3z7aI7CPIQysilA7xDGjzPLcti9C7XtVzCJClCraPuTh/JEjTQnuOXP6jeJI+504XXyzUIwK2mI96l2IgBDyomXJCmKolDDrrkw6+wjYuDGxw17ttm9UluvO3n+sYvzLv4jHVei5c1Enn5x8nrO+RDjsGmVVQdS45wy1gnX37jXPbhaIVAFx990cuGsRB/dGGFgdofbkyUl+mnnjtEA4V+yLICCaG0Np4wTqjhmWfLBXC8SoUdh5muKFCCsqUB3R9ALCurbYmrfwAVsf/SeHffGLiWOtZcg6YG0I/tYErSEZQAShp+OcSB60YyBS7qmuVobrrzemCxeqgWreMxvveW9LLTgi7axzs52/di08/7wRMo78tH3FkpHuOly/E+c9OwvdjTXJ1q/2PntY781939cRASHkRd3seiLLKuN+9WkDqltbu5YbtdNoQNwCoddv6Poe77+f+DvVAhGJwFkJK4g9oe2g0rjn/Guj2WFbILK5MF1/PfrOOxkEDLLfc9PbqMcfhxUrCnM3cwqIQlogGhsTCdLDYfbs7GDqrnfMx6QGTEOy+5Rje9f+Ku6b53KDtwXEsGGo8UfCxo1sPPpj1JxwLGPqjzPLkO3tEAqx66b5DNu5y1iE/u//oLkZAJ821pcxj9zL+m+e3KWAoeT2FoTehXMiqSuqIAxteyP8wu0e4uTPfy5hKz3gWEbvK/ehUMh8B54L7lkCIuyv4Y4s3182a4GbUPDSr32l7/sDksZVyItpDUHWLWji1dOuJDIifXLu/a+uT7gp2TjNpJYFomqoy8qHnSIUulogIhE4ySpe5vNxcPxk1k6ZxdsLVpjJ8pAUC4RzhcXNAvGXv3QRMMo+plD5AN0CxVPbkSs/+xnMnQsvvgivvYZes4ahu9bhs5LwVnOIff/v9uSUfWkExJPLA+45uJ1pXC0Rd9RzD5g0r6ecYl7bsYPYGWdQ9+wjcatU7PrrXdO0qoeXdNknKRgFoXfhTLF676+NBWLzO5H4PaSx0SVdKCRqx6SgXR650O30o45l9L5yH1q82BJ22jxnqrUBxMfHTTtqPNXRCAZNQHw6EZD6Hl76ta/0fX9ALBBC3sSz4ZzwPOz6wPWYga8/x351Jknlw5xmUktA1A5MzkKkrrsOVq2CN980O1ItEJCohD1kCLUbVjPJ+VpqDITTAuEWA3HccbB+fXzwsQuqqZRc1XmRTkDkY4FIqeyUKoJ8wPRdj/KVsxqJ3+tTiz1ZAuJQLECndvFrtQXEwYMmlQokUuzar+3YgYrFkj5fWelpdcp16wtnd7kMScEoCL2PuDvMO0ZAVOoInZ3mlnLNNSbsq8tq8ujR5vnoo01MlJXeU+3Zw6GwYi9DGVwdpnZobqlBVS6pRHfsgA8+gGOOSSpn3VfvQy+9ZCb0aVf1LQFxUNfkFduQLj7CS7/21b7vi4iAEArHmDFxVxVN8iRWoWn/oCVZQDgtELZ1whkTAHDuufDqq4nt1lYz0fb7zfltbQnrgpvfpm2B+N//hT/8gcjuA1QBh15dTY2dmckpIKZOhccfJzJyHHt9wxi9vRnl9xfOfQmKIyA+/GF47bX4Zpc6GBYXdDhW/dNYIDp8Afy43Lzt72vrVjMjGDo0YcWxX2ttjYuueFsqKlF3/xL16qvse2E1rbvaab/4ii7uSyBBdILQq7Fq6wRUBL/PeKh2diYXiov/T9sptRcsgHPOSXqbGutRDJJca7Y+DLNnw/HHJ91s+sp9aM4cE9ph3+q128KQE9uFSdXg93V/Ap9OBHjp177S9/0BERBC4bBXlICY8uPTZqJsT2AHhFPqQLi4MCW5J4G58zkn+Hv2WG9mxTC0tSWsC24Cwp5U798P+/dTZX/0lneJ3XOv8eFzTtytVfnATdcz+tprzWfEYiYdbIFY89sXmez2Qj4CIhg08Q8jRsC4cahwmNaOANGdexi6d1P8O3i0YjYf77DyuKZaIKztWZ+vYt+0DDEQL70EQPuhGOsaQ8YCZb924AAAnQMGsad2HL7Jk6j7cSIAfYj1yHYpMmgIQi/EWlAYPTzKrd8wlaevu87cxn0+sx1n507zPHJkyZqX6l//8o9qmQJdrdoU9z5UqgDtYNDEhS9ebAoBdnZmEQVWTN5xJ1dz6+cyty/TNWQSAV76VcaA3oEICKFwOIqg+X90K2zezNZlzRz27vMoYOC+rcnHOyf86SwQqQJityVCamsTAdiZLBDvvuvaVJN61EzYt70fZdlVIWbQxJE7NpsDqqqMi1N1tbmptrd7ykyRjo3X/ISahxbTOXAIx67/h/tB+QgIu98+/3m4+24ABtqvnXkmPPccq068hEvvaYDTrTyuaVyYDj8qkFToKc7TT5tny4ISCO9n4tyZNLOcaed/KOnQitM/wsinnur+9QiC0PuwLBBVOpJ0D/nKV8xt47rrTMbUYJDkSvYlItW15l9vJQREsSf19vvboqpUQcL2ZHzOHA/XZ40jQ0bXuI8BFl4CnUUE9H1EQAiFwxkkffzxcPPNvP3x2zjs3efdj/dqgXBMdPe/up7BQMf+NioG15qdmQTERRfBM88AXYPqtL8C1dnBK4++xyWxM1BotM9nxIWdyrS21oiH1tZuC4j3//ObjP/dfPOZ2zNkqspHQDhjO1I55RR47jmmffFUcN7Q07gwpU3j+sorSZsKqCRi0uVe+OXkY52B74Ig9A8sAcG+fTBlCgQCfHZLmH/rCFBJmOihAOMuCMPYQKImzLp1MHZsTh/T3cl+qmvNCcFaaIS2nW1FzfzjnHD7fBncuoqIpwm9xzSuxagBIfQ+REAIhcNhgbADlOtmzySyrIIqXCbHbjEQqZPaSCTJAjH4jefMRx08gD5o/O3jLkxuxW8aLD/7hQtRkQhte8PsYiSBk6YwZmod/OhHfDj2In5MelFtF3mzB8KBA03AcGtrt03tA558OP53WvEA+WVhynTjt/e5WXfcttMJiM99Dp56KkmIRaky6XJT+97hziYIQj/BXmTo7IQ1awAYYT3i7LQeNuedl1OMWT5pPlNda06sM4tQ4d1tRZ0QOyfcWpvhTqkeGCTsUUBIoLMAIiCEQuK0QFgCYlpDkA2rf8JRP7+u6/EuWZi6kOrCZJFUxixTDAQYEWEJiVrrAcADDwAQw/HZPh/EOpMFBLj6yHql4vARsPud7Ae6WSCamth7/S2Et+6m9bJrXAOPgcwWCHty396eFMAdXv0OHUdNofakSaZQni0g7GtPxepDtXAh+yLVrK+akqgYndL2Xf/aQOkcEwRB6BE839XanHHRBBIpsj3O2PNd/U5aid9iRoOBqq2oE+LUCfedd5p1Ka8WlGK4V7m+p0cBkSnGoa8U4BOyIwJCKBxOEWBn5wGO+tyH4eddD29/dQ3VIavccGqNCJs0AkKDWcLROrMLUyasNtbVAVZWUnXGdHj22cQkutaSG62tub23RXNjiKM2uMdhdCFVQIRC6JkzsRLRMmr+XNaDu4iwBUQ2C4SjL6uibVRtXIPeuMYUyjv/fPNCOgsExMXYEOAU5/6XXkrKvFX37COs/2ZjesEjCELfY+ZMs3jkwR0zUcW+El8OM/aCrn5b9/eqcGvRM/9cdpl5njMnt/cvRmG1tO+ZQyVqN5coKQLXvxABIRQOFwsE4O5aBAS2biA2Yya+FcvTWyBSXJgAOvyV7PrMFYxZ/7xJG5ungKjqcLj22BYHexJtb3dDQDQ3hpg09yx39y03Ugddlwo66uEl4DYpt2/8bhaINAIiaWUwGk1U/M4kINKRS1sFQeibBINmAWb+fHj77bQ1GFo7AmxeF+ZtJvFz343MI4jXeWZB03zaC0RtbQQ/qgkGs9pLciZ1Uj1nTtfXM12Lq8WFUNY+zvR8zJYwzx9KxKWMP38PDFXxLEy89Va3rlViI/oXIiCEwpHGApFOQChAR627zJQp7u/pYoGomHCUqX78sY+ZHdlcmNJht9EuRAfxNKSFcGFqWdJEhUM8pNbG6MIbbySuCVyX1tyKrwGZLRBOFyYrIL1LnYjKShg+3OzojoCoryfmr8DX6bjedG0VBKHvEgzC0qUZD7lrnqlS3NkJ/s483ZDyobLSPKJRM9akGavyIdOk2suKfarF5dN1IZNZL109IQ90iUvZaz1snnrKlBBvyG0BSGIj+hdp/EYEoRuks0CkmdhrgErrLpNqgXAGVae6MNkBuvZEt7sWCLuN2jGdtsVIAVyY6mbXoy3JYD4hy+qWs2AewEknJZ1x4MQz07sE5WiB6Bg0jLWTZ6F9Vr//8Y+mGix0T0AEg/hXPkvLWbP4YNxpvHvjAnFfEgTBFXui6ff3gImmwwpRDDJdq5u4SMW2uNx6q3metmt5XuIBzEiU+ujCkiVuezOS2laxPvRtxAIhFI4cLBAd/ip2feZyxtxoOYQ+8UTyAYMHG2FQTAHhbKONbY2wBYQ9qHz/+7R9+0fsbw8wuDqcCDx23iH/8Q+YNw/WraMtWkVduJq2YeMYvOc99kz/NMN3rTMm53SkWmF2JxfeGzw1udZCEl5jICwLROWQWiatXgonn2yK7Y0blz2IOhvBICNWZF55FARB6FHVhu2FpPPPh+3bTWzd0KFpXa8O7Q1TMzTAwIrMbkJte8Psbw9wQnWYHeMc512ROO7qwEjqmMJi3xxeqQqmFVJJFpe200rRK6ZCdzeQ+g/9h7IJCKXUcGAhcB6wC7hZa/17l+MU8GPgS9auXwE3aa1T0/oL5SaHGIiKI8YaNySbVAuELSBcYiD2vbPTVDN25hxP8zkZySYgQiF4/HEA9Lp1DADs9f144LGdfjAUgunT429T6zgWYPiPvwnf+lZmAXHMMfDMM+z+7p3s37KfgUePSjYzZ7KCZLJAOF2Y7L60+86u17BjR/Y0roJQQGQM6N90Z6JZ8Aw/oVCioN2LL2Y8VOPI4rczs0uqhqTxAqzCnjuTjxvCGr7Ms1zuX8SaO5czzbqojNc5frx5rqkxf3cjBqLL8549CeFUVQVXXJGz+5LQ/yinBeJuIAKMBk4CHldKva61fjPluAZgFnAi5v/yKWADcG8J2yp4waUOBOC+Kj50aPJ2ahamQYPMs4sFYvBrK0yGH3ui290YiAqXn78tIAIB4wdqmYpdBwpn+sHHHuvyctI5gUD29q1ejf7e9xiuNcMBvTHl9UwCIlMaVxcLRFw82dac7dsTr4mAEEqDjAGCZ4qS4cfNZygNuYRX53psRSzCtJYmIJj9OnfsMM8nnAAvvJDDJwlCYSlLDIRSqhaYDXxHa92qtX4O+DNwqcvhlwE/1Vq/r7XeAvwU+GLJGit4xykCnKv7bhPSIUOSt90sEGAmvVZxt6TA34eXxF2XOt/fYna+9lpu7XWzQNiZkGxnVWulXqc8bA7evYido6YQXrAovi/1mPj7uU3unaxalRSPER+E7PO8WCA8ujDFLRCWgIh+7Rt0rPyH2ffXv2ZupyDkiYwBQq6kixcIhYznaCjUjTd13OOzkToGaJf92Y5NiyM4ImtchC0gbOtxgcirH4V+SbksEMcCHVrrtY59rwMzXI6dar3mPG6q25sqpRowq1UcccQRhWmp4J10Fgi/P5HpwmL/3hiD050LCQFhTZpjPj8q1hm/EVd95CRif/gpPsBv7dUPPYTKJXOEm4Cwqaoyyz5NTbB4MWr1ato272R/e4AR+9dTefAAGqjZso7UKbsaM4ZwuJPAHoe9OhDILiAmTkzajJvIx42DtWu7b4Fwc2Gyr33zZrN5YE/ic+fPR02YICZsoZjIGNBP6a4bkluGn7ytEo57PKtXw6ZNaWMglEsMhJ2ONkyAAGGOmGj2K0cMxODqMLVDXdyG9u+Hd981i2lPPhlveNZMRkUQEFK/QegO5RIQA4H9Kfv2AYPSHLsv5biBSimV6gOrtW4EGgFOPfVU8Y8tNeksEGAmsQ4BMeC152luDJkqxqnnQsKFyQpi9tVUs/4rd6AeXoK+cDYThrYQsywTSSxZUjgBAUmOunH/1zPOgOefT2+mPuEEAvX1JubB+X7ZXJjGj0cNGBAXA/b7t8VqzOdaAmLDtXcw4I+/YWAgQm1F1FyHJQR48004+ujk981kgdi5092XN5d+FITckTGgH5LPRNUt8HrevALUHcghGGOg9bBJSkfrh1v/C26+2bwWHy/ScfCgyQB14AB885vmHq4UwaFD2Tk6IT70ZQF2OgO395jFnt1vbmHBvMLEg0j9BqE7lEtAtELyArS1fcDDsYOBVgmg64Gks0CAmcQeSP56W5Y0gS0gslggqKw0aUHt1KChELqy0tSRcJJL5gi3GAibTHEAc+bA88+7mqWV3YbUzFFeLBDRaKKQj4MB71iLr62tbLpqHuPv/VZ68TJ7diKw28YWEG5B1F/4Aixf3vVaupmBQxA8ImNAPyTfiWrqXL/cdQfy+vzXrft6LAYrVya9ZIsP7dhODdwe9sJf2fjPRs6ubsjbYlDufhR6J+USEGuBCqXURK31OmvfiUBq8BzWvhOBF7McJ5SbdFmYIClDUgd+IlRRN7ve/VxIxEg4BEQSwSD+FU1snb+YyKurGV7bzqCv5Zg5wosFwg3rM9TChbTuiSRM2sMc2Svuvz/5HC9B1AcOxOM9XGltpXrpHzIH6DkDu22svo98sIstS1/jKEhce0MDCth/50La9kQYOLwq934UhNyRMaAfUuiJarnTweb1+R4CuLMFY39WL2FhpMGTEMvkOlbufhR6J2UREFrrNqXUw8APlFJfwmTguAA43eXwxcDXlVJPYAT4N4C7StZYwTvp6kBAkoD458ybGfyF8xPuS6nnAtvX7WU0JOowuE32g0EOW5rHna67AgLMBLuhoYtJO86gFE8ML0HUe/d22RUP1FMKFYlQNf5w2N7ctZK0TWVll1F59f81MwWojLZx+M9vTLTHcS2DGxq6LAcLQrGQMaB/4nWimkucRKHqDnQ3NqPbn+9UU2lIZ+W29y9Vsz0JMS+uY1K/QciVcqZxvRr4NbADaAGu0lq/qZQ6E3hSa23PyxYARwPN1vavrH1CTyOTBcKx+j79kRsSLkpu5wLDHnvA/JHOAlEIMmVtyvfzUq/PiwViz54uuw7VDGfrV+cx4e6vQ1sbA/5lTN0HBh2Ob8yo5GJGk1yK2wE7Hn+RyVjpArGyTBWjPwUhN2QM6Idkm6iWI6C3LEHEHgK40wVu7ztUxTszrmD81Aaers/eVolxEIpB2QSE1no3Jrd36v6VOBZ1LT/XG62H0JPxaIFwnbymWCD8mPoLsV0t+ICO7TupCIUKe9d74w33AGK/v2tMRq6kCojKyuwWCBcBMeCc6UyYNQ09vw0FBDpMgPXAgzvw/fYhT/1R97mPoZ9SKDQxfPiJdb/atCAUCBkDBDdSJ7uLFxfftaaUE+xkS0f2ZX+nlTtJ6DwMT3/FWzslxkEoBmWpAyH0A1JjGuwqx0Dzb17OenwnZgLv0yYmwN9+kNhZMwqbpPrss4n5K7rm6S5EITWnC1NlJfh8bP/bq5nPcXFhoqbG1VdWdXZ4LoI0rSHIvo+cB0BnRSDRJkEQhBLipdaAPdm113EWLTKZjs4+u3g1CpyfWcwJti0Auns9WWtEpMF2Hfvyl+Gyy3JttSC4IwJCKBzpAoBDIfRriTTuE68+l+bGlDtnyor/nk9f2sXPX3VEc6ocmpVgEP/KZ2k5axatgw5L7C/E6rzDAtHpM4a+g2s2ZS4oZFsgnEX2qquhvj5J6GhAV3SNdchEZ8BYP6o7TEzJvqVPd/0OBEEQioTXybM92b31Vrj8clPbM9cJc644P7PQ7ktO0dRdAWCTr9D57W/hvvvMeVddJUXjhPwQASEUjnQCoqkJ5xp/JRGTwtWJ0wJRW8vob30p70mzJ4JBRqxYyqCvJ7IOdbYdzPvO+uZDa+J/+8KHaG4MEZv9eSBDVVLbAjFiRGJfTU2S0Nk1cjK7z5qF79kVOY1yhzbtTPrcwe07mDh3pogIQRBKQi6T52DQ1FOYM6c0lgH7M+vrTbsKNbFOFU11dfldTz5CJ7X/FyworlVH6PuUM4ha6GukExD19cQqqvB1mGwT0dQUrpBsgRg1Kj5p3nXTfPSat/FNnkTdj7sGCBeKXctfx562+6IRYmfNyHmS7mTnYy8kbbcsaWLa325mPXDET6+lsjPc9aS1pihvZPcB4jYQO/DaEjrdJXLRpTD/uXjMh8Ih5Bokmk4QhOLSHT/8UqYXLUYgdeqkvaUl/fWEQibeA4xwSvfZ3c2WZPd/eztobR4SUC3kgwgIoXCkExDBIP5nTc2GrR9A5RVzklO4QrIFYtSo+Hn5TJpzIfrO5vjfCqDDpZ5CDtTNngnLnNv1AKYY3h/nwcaNac+t3LMjsZEtc5NHJtzewPtb1nP47/4H2wbiKuQEQRCKQHfFQKnSixYjkNpNNLldTyhkXrMzui5aBMuXF/a67f5fvNi8f0eHBFQL+SECQigcmYqgWTUbDkv3usMCsfftbbzXGOoqMorIwf+ci54/N76tKypRedxZpzUEwXo7ZW/bZKqATUpWKGf2qjwZ98Dt8JVZmYWcIAhCkfAiBrpbjyFfipGpyKtoamoyNUBtimUZsPt/zhwpGifkjwgIoXBkEhDZeDWRoWjI3k1Uz51JM8tLNsGdcHsD64Hq3y2kesLhhXGXGjAADh7sut9FQKTGRcRFRIEsEHGyCTlBEIQyUZZ6DBbFcpfyIprq601iPNsCUYp4DxEOQr6IgBAKRz4Corm57P75E25vgNsbsh/oldpaVwFxMOwntSLE6mkXMeLABtqppnrasYz+y6/MC4UWEIIgCD2Uchc8K+XEOtXSYteUg8wxEILQUxABIRSOfATEeecR++FtmQOtexu1tbBzZ9KuUAgCGwOcknLo1Ms/Ctc9aDY6O6HCEhAFdGESBEHoyfSXgmfpLC0iGoTehKRxFQqHzljlIDNWoPW2WVfy6mlXsm5B6dyXikZtbZddTU3wDB8jVWptfe6dxIYzI1WWeAlBEIS+QjHrMfQknJaWcBhuuUXSqQq9D5mdCIUjHwsE9D3//IEDu+yqr4ebKy/k6sjdVBGmwpISdUsaaW68JHN2KkEQhD5Of1iJty0t4bAZNv/+d1i5sm+LJqHvIbMToXDkKyD6Gg4LhF2wLRiEeU1BHrryad4edw4xK1xaEetaXA9EQAiCIPRinJWobWxLyznnmFt8LFbcStuCUAxkdiIUDhEQSbR0DI7/PWHu2UkiYs49QWLfuYV2qoniTx/zoVTXfYIgCEKPJ7USdaqIuOUWCARKU2lbEAqNCAihcIiASGKNf1o81iGeVcrBtIYg6xc8zfPn3cr6BU+7xny8t/ydLvsEQRCEno9bVikn2WI+3KwXgtBTkBgIoXA4BERziQvB9USGfOGTtC//CZVE0loYpjUEu6SqbW4MMc36e9TC22g+7bx+35eCIPQOylUIrifiJatUupiPctbEEAQviIAQCsau17cwwvp7wtyzacZ9Vb2/MK0hSDNP07KkibrZ9Z77omVJE50o/Gh8dJa8HoYgCEJ3kElvMvkUpyt3TQxByIYICKFgbGupjAuIchSC64m4WRiyUTe7nvCy6oyWC0EQhJ6GTHq70t2sUv2lJobQexEBIRQMffXVhOc+hI8OmfjmQXctF4IgCOVEJr2FIx/rhSCUAhEQQsGY1nA6zSyXiW8B6I7lQhAEoZzIpLew9IeaGELvRQSEUFBk4isIgtB/kUmvIPQPJI2rIAiCIAiCIAieEQEhCIIgCIIgCIJnSi4glFLDlVJLlVJtSqlNSqmLMxx7g1JqlVLqgFJqg1LqhlK2VRAEQSgsMgYIgiD0fsoRA3E3EAFGAycBjyulXtdav+lyrALmAG8AE4BlSqn3tNYPlqy1giAIQiGRMUAQBKGXU1ILhFKqFpgNfEdr3aq1fg74M3Cp2/Fa6/la61e01h1a67eBR4HppWuxIAiCUChkDBAEQegblNoCcSzQobVe69j3OjAj24lKKQWcCSzIcEwD0GBthpVSq/Joa19hBLCr3I0oM9IHBukHg/SDYVIZPlPGgNIjv3eD9IP0gY30gyGvMaDUAmIgsD9l3z5gkIdzb8GIOG5iAAAIrUlEQVRYTBalO0Br3Qg0AiilXtZan9q9ZvYdpB+kD2ykHwzSDwal1Mtl+FgZA0qM9INB+kH6wEb6wZDvGFBQFyalVJNSSqd5PAe0AoNTThsMHMjyvtdg/GA/pbUOF7LNgiAIQmGQMUAQBKF/UFALhNa6PtPrlv9rhVJqotZ6nbX7RMAteM4+53LgJuAsrfX7hWqrIAiCUFhkDBAEQegflDSIWmvdBjwM/EApVauUmg5cANzvdrxS6hLgNuBcrfW7OX5cY16N7TtIP0gf2Eg/GKQfDCXvBxkDyoL0g0H6QfrARvrBkFc/KK11oRri7QOVGg78GjgXaAFu0lr/3nrtTOBJrfVAa3sDMA5wmqwf0FpfWdJGC4IgCAVBxgBBEITeT8kFhCAIgiAIgiAIvZeSV6IWBEEQBEEQBKH3IgJCEARBEARBEATP9FoBoZQarpRaqpRqU0ptUkpdnOY4pZS6XSnVYj1utwoS9Qly6IcblFKrlFIHlFIblFI3lLqtxcRrPziOr1JKrVFK9amsLrn0g1LqFKXUs0qpVqXUdqXU10rZ1mKRw/9EQCl1r3Xtu5VSf1FKjS11e4uFUuoapdTLSqmwUuo3WY69Xim1TSm1Xyn1a6VUoETN7DYyBhhkDDDIGGCQMcAg40Dxx4BeKyCAu4EIMBq4BLhHKTXV5bgGYBYmVeAJwGeAuaVqZAnw2g8Kk0d9GPAJ4Bql1BdK1sri47UfbG4AdpaiYSXGUz8opUYAf8VU9a0DjgGWlbCdxcTrb+FrQBBzXzgc2APcVapGloAPgB9iApbTopT6OCZN6tnAkcDRwPeL3rr8kTHAIGOAQcYAg4wBBhkHij0GaK173QOoxfwwjnXsux/4scux/wAaHNtXAC+U+xpK3Q8u5/4CuKvc11COfgCOAtYAnwTeL3f7y9EPmNSY95e7zWXug3uA+Y7tTwFvl/saitAnPwR+k+H13wO3ObbPBraVu90F/J5lDHA/V8YAGQP63BjQjX7o8+NAscaA3mqBOBbo0Fqvdex7HXBTl1Ot17Id1xvJpR/iWOb7M8lQvKmXkWs/3AV8CzhU7IaVmFz64aPAbqXUP5RSOyyz7RElaWVxyaUPFgLTlVKHK6UGYFapnixBG3sabvfI0UqpujK1xwsyBhhkDDDIGGCQMcAg40BudGsM6K0CYiCwP2XfPmBQmmP3pRw3sI/4wObSD05uwXz3i4rQpnLguR+UUp8F/FrrpaVoWInJ5fcwDrgMY749AtgA/KGorSsNufTBOuA9YIt1zmTgB0VtXc/E7R4J2e8j5UTGAIOMAQYZAwwyBhhkHMiNbo0BvVVAtAKDU/YNBg54OHYw0KotO00vJ5d+AExQDcYP9lNa63C643oZnvpBKVULzAeuLVG7Sk0uv4dDwFKt9Uta63aMv+PpSqkhRW5jscmlD+4GAhj/31pMheT+tvIE7vdIyHAf6QHIGGCQMcAgY4BBxgCDjAO50a0xoLcKiLVAhVJqomPfibibY9+0Xst2XG8kl35AKXU5VqCM1rovZZ7w2g8TgfHASqXUNsyN4jAr88D4ErSz2OTye3gDcE6g+sJkCnLrg5MwfqG7rYnUXcBpVnBhf8LtHrlda91SpvZ4QcYAg4wBBhkDDDIGGGQcyI3ujQHlDu7IIyjkQYy5rRaYjjG5THU57kpMsNRYTIT9m8CV5W5/GfrhEmAbMLncbS5XPwAVwBjH40JMloIxGJN22a+jhL+Hj2GyTZwEVAI/A1aWu/0l7oNFwBJgiNUH3wK2lLv9BeyHCqAamIcJIKwGKlyO+4R1b5gCDAWewUMQbrkfMgbk3A8yBsgY4Dyuz44BOfZDnx0Hij0GlP0C8+iY4cAjQBuwGbjY2n8mxjxtH6cwJsvd1mM+oMrd/jL0wwYgijFV2Y97y93+UvdDyjn19KEMHLn2A3AVxu9zD/AX4EPlbn8p+wBjsv4dsAPYCzwHnFbu9hewH27BrCo6H7dg/J1bgSMcx34d2I7xAV4EBMrd/gJ+zzIGaBkD0pwjY0AfHANy6Ye+PA4UewxQ1omCIAiCIAiCIAhZ6a0xEIIgCIIgCIIglAEREIIgCIIgCIIgeEYEhCAIgiAIgiAInhEBIQiCIAiCIAiCZ0RACIIgCIIgCILgGREQgiAIgiAIgiB4RgSEIAiCIAiCIAieEQEhCIIgCIIgCIJnREAIgiAIgiAIguAZERCCUACUUjVKqfeVUpuVUoGU136llOpUSn2hXO0TBEEQiouMA0J/QgSEIBQArfUh4HvAh4Cr7f1KqXnAFcBXtdYPlql5giAIQpGRcUDoTyitdbnbIAh9AqWUH3gdGAUcDXwJ+BnwPa31D8rZNkEQBKH4yDgg9BdEQAhCAVFKfRr4C/AMMBP4pdb62vK2ShAEQSgVMg4I/QEREIJQYJRSrwAnAw8CF+uUfzKl1OeBa4GTgF1a6/Elb6QgCIJQNGQcEPo6EgMhCAVEKXURcKK1eSB10LDYA/wS+H8la5ggCIJQEmQcEPoDYoEQhAKhlDoPY7b+CxAF/h2YprVek+b4WcCdsvIkCILQN5BxQOgviAVCEAqAUuojwMPA88AlwLeBGDCvnO0SBEEQSoOMA0J/QgSEIOSJUmoK8ASwFpiltQ5rrdcDC4ELlFLTy9pAQRAEoajIOCD0N0RACEIeKKWOAP6G8Wf9pNZ6v+PlW4FDwPxytE0QBEEoPjIOCP2RinI3QBB6M1rrzZiiQW6vfQAMKG2LBEEQhFIi44DQHxEBIQglxio0VGk9lFKqGtBa63B5WyYIgiCUAhkHhN6OCAhBKD2XAosc24eATcD4srRGEARBKDUyDgi9GknjKgiCIAiCIAiCZySIWhAEQRAEQRAEz4iAEARBEARBEATBMyIgBEEQBEEQBEHwjAgIQRAEQRAEQRA8IwJCEARBEARBEATPiIAQBEEQBEEQBMEzIiAEQRAEQRAEQfDM/weD5rViGPAp+QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"tree_reg1 = DecisionTreeRegressor(random_state=42)\\n\",\n    \"tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\\n\",\n    \"tree_reg1.fit(X, y)\\n\",\n    \"tree_reg2.fit(X, y)\\n\",\n    \"\\n\",\n    \"x1 = np.linspace(0, 1, 500).reshape(-1, 1)\\n\",\n    \"y_pred1 = tree_reg1.predict(x1)\\n\",\n    \"y_pred2 = tree_reg2.predict(x1)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.plot(x1, y_pred1, \\\"r.-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n    \"plt.axis([0, 1, -0.2, 1.1])\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$y$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.legend(loc=\\\"upper center\\\", fontsize=18)\\n\",\n    \"plt.title(\\\"No restrictions\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(X, y, \\\"b.\\\")\\n\",\n    \"plt.plot(x1, y_pred2, \\\"r.-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n    \"plt.axis([0, 1, -0.2, 1.1])\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.title(\\\"min_samples_leaf={}\\\".format(tree_reg2.min_samples_leaf), fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"tree_regression_regularization_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## 7.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: train and fine-tune a Decision Tree for the moons dataset._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"a. Generate a moons dataset using `make_moons(n_samples=10000, noise=0.4)`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Adding `random_state=42` to make this notebook's output constant:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import make_moons\\n\",\n    \"\\n\",\n    \"X, y = make_moons(n_samples=10000, noise=0.4, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"b. Split it into a training set and a test set using `train_test_split()`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"c. Use grid search with cross-validation (with the help of the `GridSearchCV` class) to find good hyperparameter values for a `DecisionTreeClassifier`. Hint: try various values for `max_leaf_nodes`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 294 candidates, totalling 882 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    1.4s\\n\",\n      \"[Parallel(n_jobs=-1)]: Done 882 out of 882 | elapsed:    3.5s finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score='raise-deprecating',\\n\",\n       \"       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\\n\",\n       \"            max_features=None, max_leaf_nodes=None,\\n\",\n       \"            min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"            min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\\n\",\n       \"            splitter='best'),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=-1,\\n\",\n       \"       param_grid={'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'min_samples_split': [2, 3, 4]},\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring=None, verbose=1)\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"params = {'max_leaf_nodes': list(range(2, 100)), 'min_samples_split': [2, 3, 4]}\\n\",\n    \"grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1, verbose=1, cv=3)\\n\",\n    \"\\n\",\n    \"grid_search_cv.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\\n\",\n       \"            max_features=None, max_leaf_nodes=17,\\n\",\n       \"            min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"            min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\\n\",\n       \"            splitter='best')\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_search_cv.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"d. Train it on the full training set using these hyperparameters, and measure your model's performance on the test set. You should get roughly 85% to 87% accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default, `GridSearchCV` trains the best model found on the whole training set (you can change this by setting `refit=False`), so we don't need to do it again. We can simply evaluate the model's accuracy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.8695\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"\\n\",\n    \"y_pred = grid_search_cv.predict(X_test)\\n\",\n    \"accuracy_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: Grow a forest._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"a. Continuing the previous exercise, generate 1,000 subsets of the training set, each containing 100 instances selected randomly. Hint: you can use Scikit-Learn's `ShuffleSplit` class for this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import ShuffleSplit\\n\",\n    \"\\n\",\n    \"n_trees = 1000\\n\",\n    \"n_instances = 100\\n\",\n    \"\\n\",\n    \"mini_sets = []\\n\",\n    \"\\n\",\n    \"rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances, random_state=42)\\n\",\n    \"for mini_train_index, mini_test_index in rs.split(X_train):\\n\",\n    \"    X_mini_train = X_train[mini_train_index]\\n\",\n    \"    y_mini_train = y_train[mini_train_index]\\n\",\n    \"    mini_sets.append((X_mini_train, y_mini_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"b. Train one Decision Tree on each subset, using the best hyperparameter values found above. Evaluate these 1,000 Decision Trees on the test set. Since they were trained on smaller sets, these Decision Trees will likely perform worse than the first Decision Tree, achieving only about 80% accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.8054499999999999\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.base import clone\\n\",\n    \"\\n\",\n    \"forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\\n\",\n    \"\\n\",\n    \"accuracy_scores = []\\n\",\n    \"\\n\",\n    \"for tree, (X_mini_train, y_mini_train) in zip(forest, mini_sets):\\n\",\n    \"    tree.fit(X_mini_train, y_mini_train)\\n\",\n    \"    \\n\",\n    \"    y_pred = tree.predict(X_test)\\n\",\n    \"    accuracy_scores.append(accuracy_score(y_test, y_pred))\\n\",\n    \"\\n\",\n    \"np.mean(accuracy_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"c. Now comes the magic. For each test set instance, generate the predictions of the 1,000 Decision Trees, and keep only the most frequent prediction (you can use SciPy's `mode()` function for this). This gives you _majority-vote predictions_ over the test set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)\\n\",\n    \"\\n\",\n    \"for tree_index, tree in enumerate(forest):\\n\",\n    \"    Y_pred[tree_index] = tree.predict(X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.stats import mode\\n\",\n    \"\\n\",\n    \"y_pred_majority_votes, n_votes = mode(Y_pred, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"d. Evaluate these predictions on the test set: you should obtain a slightly higher accuracy than your first model (about 0.5 to 1.5% higher). Congratulations, you have trained a Random Forest classifier!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.872\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_score(y_test, y_pred_majority_votes.reshape([-1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {\n   \"height\": \"309px\",\n   \"width\": \"468px\"\n  },\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "08_dimensionality_reduction.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 8 – Dimensionality Reduction**\\n\",\n    \"\\n\",\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 8._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/08_dimensionality_reduction.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"mpl.rc('axes', labelsize=14)\\n\",\n    \"mpl.rc('xtick', labelsize=12)\\n\",\n    \"mpl.rc('ytick', labelsize=12)\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"unsupervised_learning\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Projection methods\\n\",\n    \"Build 3D dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(4)\\n\",\n    \"m = 60\\n\",\n    \"w1, w2 = 0.1, 0.3\\n\",\n    \"noise = 0.1\\n\",\n    \"\\n\",\n    \"angles = np.random.rand(m) * 3 * np.pi / 2 - 0.5\\n\",\n    \"X = np.empty((m, 3))\\n\",\n    \"X[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * np.random.randn(m) / 2\\n\",\n    \"X[:, 1] = np.sin(angles) * 0.7 + noise * np.random.randn(m) / 2\\n\",\n    \"X[:, 2] = X[:, 0] * w1 + X[:, 1] * w2 + noise * np.random.randn(m)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## PCA using SVD decomposition\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the `svd()` function returns `U`, `s` and `Vt`, where `Vt` is equal to $\\\\mathbf{V}^T$, the transpose of the matrix $\\\\mathbf{V}$. Earlier versions of the book mistakenly said that it returned `V` instead of `Vt`. Also, Equation 8-1 should actually contain $\\\\mathbf{V}$ instead of $\\\\mathbf{V}^T$, like this:\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{V} =\\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \"  \\\\mid & \\\\mid & & \\\\mid \\\\\\\\\\n\",\n    \"  \\\\mathbf{c_1} & \\\\mathbf{c_2} & \\\\cdots & \\\\mathbf{c_n} \\\\\\\\\\n\",\n    \"  \\\\mid & \\\\mid & & \\\\mid\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_centered = X - X.mean(axis=0)\\n\",\n    \"U, s, Vt = np.linalg.svd(X_centered)\\n\",\n    \"c1 = Vt.T[:, 0]\\n\",\n    \"c2 = Vt.T[:, 1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"S = np.zeros(X_centered.shape)\\n\",\n    \"S[:n, :n] = np.diag(s)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(X_centered, U.dot(S).dot(Vt))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W2 = Vt.T[:, :2]\\n\",\n    \"X2D = X_centered.dot(W2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X2D_using_svd = X2D\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## PCA using Scikit-Learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With Scikit-Learn, PCA is really trivial. It even takes care of mean centering for you:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.decomposition import PCA\\n\",\n    \"\\n\",\n    \"pca = PCA(n_components = 2)\\n\",\n    \"X2D = pca.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.26203346,  0.42067648],\\n\",\n       \"       [-0.08001485, -0.35272239],\\n\",\n       \"       [ 1.17545763,  0.36085729],\\n\",\n       \"       [ 0.89305601, -0.30862856],\\n\",\n       \"       [ 0.73016287, -0.25404049]])\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X2D[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1.26203346, -0.42067648],\\n\",\n       \"       [ 0.08001485,  0.35272239],\\n\",\n       \"       [-1.17545763, -0.36085729],\\n\",\n       \"       [-0.89305601,  0.30862856],\\n\",\n       \"       [-0.73016287,  0.25404049]])\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X2D_using_svd[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"Notice that running PCA multiple times on slightly different datasets may result in different results. In general the only difference is that some axes may be flipped. In this example, PCA using Scikit-Learn gives the same projection as the one given by the SVD approach, except both axes are flipped:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(X2D, -X2D_using_svd)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Recover the 3D points projected on the plane (PCA 2D subspace).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X3D_inv = pca.inverse_transform(X2D)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Of course, there was some loss of information during the projection step, so the recovered 3D points are not exactly equal to the original 3D points:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(X3D_inv, X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can compute the reconstruction error:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.01017033779284855\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.mean(np.sum(np.square(X3D_inv - X), axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The inverse transform in the SVD approach looks like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X3D_inv_using_svd = X2D_using_svd.dot(Vt[:2, :])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The reconstructions from both methods are not identical because Scikit-Learn's `PCA` class automatically takes care of reversing the mean centering, but if we subtract the mean, we get the same reconstruction:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(X3D_inv_using_svd, X3D_inv - pca.mean_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `PCA` object gives access to the principal components that it computed:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-0.93636116, -0.29854881, -0.18465208],\\n\",\n       \"       [ 0.34027485, -0.90119108, -0.2684542 ]])\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pca.components_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare to the first two principal components computed using the SVD method:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.93636116,  0.29854881,  0.18465208],\\n\",\n       \"       [-0.34027485,  0.90119108,  0.2684542 ]])\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Vt[:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice how the axes are flipped.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's look at the explained variance ratio:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.84248607, 0.14631839])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pca.explained_variance_ratio_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first dimension explains 84.2% of the variance, while the second explains 14.6%.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By projecting down to 2D, we lost about 1.1% of the variance:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.011195535570688975\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1 - pca.explained_variance_ratio_.sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here is how to compute the explained variance ratio using the SVD approach (recall that `s` is the diagonal of the matrix `S`):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.84248607, 0.14631839, 0.01119554])\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.square(s) / np.square(s).sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, let's generate some nice figures! :)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Utility class to draw 3D arrows (copied from http://stackoverflow.com/questions/11140163)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib.patches import FancyArrowPatch\\n\",\n    \"from mpl_toolkits.mplot3d import proj3d\\n\",\n    \"\\n\",\n    \"class Arrow3D(FancyArrowPatch):\\n\",\n    \"    def __init__(self, xs, ys, zs, *args, **kwargs):\\n\",\n    \"        FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)\\n\",\n    \"        self._verts3d = xs, ys, zs\\n\",\n    \"\\n\",\n    \"    def draw(self, renderer):\\n\",\n    \"        xs3d, ys3d, zs3d = self._verts3d\\n\",\n    \"        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\\n\",\n    \"        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\\n\",\n    \"        FancyArrowPatch.draw(self, renderer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Express the plane as a function of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"axes = [-1.8, 1.8, -1.3, 1.3, -1.0, 1.0]\\n\",\n    \"\\n\",\n    \"x1s = np.linspace(axes[0], axes[1], 10)\\n\",\n    \"x2s = np.linspace(axes[2], axes[3], 10)\\n\",\n    \"x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n    \"\\n\",\n    \"C = pca.components_\\n\",\n    \"R = C.T.dot(C)\\n\",\n    \"z = (R[0, 2] * x1 + R[1, 2] * x2) / (1 - R[2, 2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot the 3D dataset, the plane and the projections on that plane.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure dataset_3d_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAEKCAYAAABHZsElAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXd4HOW9PX5mtmvVe+/FlmS5G9vUJBhwCDiU+Npw4dqmfCmJQwlwww8SciGJwUBCCRhIsDEh4fJAQhIeTAlcYhOMjcE2uIEl7aprJa229zK/P5Z3PDM723dlWcx5nn0sb5m2O+95P5/3fM6HYhgGEiRIkCBBwnQDfbIPQIIECRIkSBCDRFASJEiQIGFaQiIoCRIkSJAwLSERlAQJEiRImJaQCEqCBAkSJExLSAQlQYIECRKmJeQxXpc06BIkSJAgIdOgxJ6UIigJEiRIkDAtIRGUBAkSJEiYlpAISoIECRIkTEtIBCVBggQJEqYlJIKSIEGCBAnTEhJBSZAgQYKEaQmJoCRIkCBBwrSERFASJEiQIGFaQiIoCRIkSJAwLSERlAQJEiRImJaQCEqCBAkSJExLSAQlQYIECRKmJSSCkiBBggQJ0xISQUmQIEGChGkJiaAknNJgGAaBQAAMI3WGkSBhpiFWPygJEqYlGIaB3++H3++H2+0GRVGgaRoymQwymQw0TbMPiqJAUaLtZiRIkDCNQcWYeUrTUgnTCsFgEIFAAH6/n33O5/OxfzMMExZNxSIvCRIknHSI3ogSQUmY9mAYBsFgEH6/H8FgEECIdILBIAYHB2GxWJCVlcU+NBoNj3gIaZEH9zUucRHyIhGXRF4SJEwZJIKScGqBrC/5/X4esXi9XvT398NgMKC8vBw5OTlwu91wOp1wOp1wu90AALVajaysLGi1Wpa85HI5b/vkX5Iy1Ov1aG5u5kVdcrlcShdKkJBZiN5U0hqUhGkHQhZE/EDIwm63Q6/Xw2q1ora2FsuXLwdFUfB6vWGkEQwGeaRlMpngdDoRCASgUCh4EZdWq4VKpYJMJoPD4QBN0+w2AoEAvF4vb9tiUZeULpQgIf2QIigJ0wbC9SUy4E9OTkKv1yMYDKK+vh7FxcXsawzDiBJUNPh8PjidTjgcDl7URVEU3G43qqureQQmk8nYz3LXuLj3DsMwLHHRNC1FXRIkJAYpxSdh+oEM+D6fj7e+xDAMRkdH0dfXB61Wi4aGBuTk5Ih+PlGCigSfz4f9+/ejoaGBR17BYBBKpZIXcWVlZUGpVPKIkns+QkhRlwQJUSGl+CRMH5D1pUAggL6+PshkMlRVVcHv92NgYADDw8MoLS3F/PnzoVarp+SYCHGUlJSgpKSEd6xer5clrImJCTgcDni9XtA0zYu2yIOkCcnnCQn7fD643W4YDAbU1dWBoqiIxCWRl4RvOiSCkjClEFtfoigKHo8Hx44dw+TkJKqrq7F06VKeoOFkgqIoqFQqqFQqFBQU8F4LBAJwuVxwOBxwOBwYHx+H0+kEwzBQqVQ8gQaJugDA5XKxJCaUzRNwSUuKuiR8EzE9RgAJMx5i60s0TcNsNmNwcBB+vx+tra2YNWvWSRuASWoxEchkMmRnZyM7O5v3PMMw8Hg8bNRlMBjgdDrZmi2/34/+/n6WwNRqdVjURa6ZmDRerK5LirokzDRIBCUhY+DKtwOBAIATwoexsTHo9XoolUqUlJRALpejvLz8ZB5uWkFRFNRqNdRqNQoLC3mvWa1W9Pb2QqVSwWq1YnR0FC6XCwzDQKPRhKULFQoF+1lCoFLUJeGbAImgJKQdkQprA4EAhoaGMDg4iMLCQsyZMwdZWVkYGRmBy+U6yUeNKRvEaZqGQqEII2SGYVhpvMPhwMjICJxOJ/x+P+RyeVhNl1qtDitIFkZdPT09aGpqYomKqy6Uoi4J0x0SQUlIG8QKa8n6Un9/P8bGxlBZWYklS5bwogKaplki+6ZAjBQoioJGo4FGo0FRURHvNSKNdzqdMJvNGBoaYqXx3KhLq9VCo9Gw63cWiyXqWpdkAyVhOkMiKAkpg2vcCpwY9Gw2G/R6Pex2O1tYy11nIaBpWnIjjwGFQoG8vDzk5eXxnicFyUQWLyxIdrvdGBoaYglMpVLFjLoIJPNdCScbEkFJSArR1peMRiP0ej0oikJ9fT0KCwujDmjEV09C4uDK3LkgsvZPP/0UNE3DaDRiYGAAHo8HNE2zURdJGXKjLvJ5ICTm4JrxAlLUJWHqIBGUhIQQaX2JYRgMDQ1hYGAAOTk5aGtrEy2sFcM3McWXaVAUBYVCAblcjsrKSt5rwWCQTReSui5SkKxSqcJsoBQKRcyoy2QyQSaTIT8/XzLflZA2SAQlIS4Q2bTX62UHLIqi4PP5MDAwgJGREZSVlWHBggVQqVQJbTsZebeE2BCm7Ahomo4ojScFyaSmq6+vL6wgmRt1ERsop9PJ1niRqIu7f8l8V0IykAhKQlRw03gGgwF2ux3Nzc1wOp3Q6/Uwm82orq7GsmXLeJ51iSDZCMrj8aCvrw8Gg4Gd+ZPBU2zm/01DoqQfqyCZRFw2mw0Gg4GVxqvVavh8PmRnZ7OpQzFpvGS+KyFRSAQlQRRihbUymQxOpxP79++Hz+dDfX09Zs+enfJAkihBEVdzm82G2tpaLFq0CH6/nzfz1+v18Pl8kMlkYfJsYb+omYx0nadMJkNOTk5Y2pZI43t7e1n/RIfDAb/fL3rtxQqSySRI2HiSW5AsRV3fTEgEJYFFtPUlg8GA7u5uMAyDrq6uMDVZKog3xWcymaDT6RAIBFBfX4+Ojg4AYIlIbObPJS6LxcLWXHHl2dwBdCYhUoovnSDXUa1WIz8/nyePF7v2wl5d3OsvJtIQi7pMJhNycnLYFKMUdc1cSAQlIax+CThRWDs4OIihoSEUFRWhpaUFRqMxreQERI+gGIZhXSdUKhWampp4+49FbHK5HLm5ucjNzeU9HwwG4XK52AHUaDSyfx88eFA0XZguTNV621QQFEEwGAwrIYh27bm9uoaGhlhpvFwuD/MvFBYkj42NQalUQi6XiyoMJfPdmQOJoL7BiGbc2tfXh4mJCV5hrdVqZSXl6YQYQQUCAQwPD2NgYAD5+fms60Sk80h08KFpGlqtFlqtludcvnfvXsyaNYs1fzUYDLyUFZe0xAbPeDEVg+VUCk8S+Q4iSeMBfq8uk8kkWpDsdDrh8Xig1WrDenUBkg3UTIJEUN9ARDJutVqt0Ov1cDqdqK2tRUtLC29WnCk5ODfF5/P50N/fj9HRUZSVlWHRokWsOmwqwBUKCD30uG4OwsFTmK4Sttw4GSDrOFMBsQgqGUQrSCYR79jYGNsrTNiri3wH3F5dgGS+e6pCIqhvCCI1BgSAiYkJ6PV60DSNhoYGFBQURJQnZ4KgaJqGz+fD0aNHMTk5iZqaGixdujRpVWCmEG3wJMQlbLkhXGvRarUn6egzi0ynE7kR7+DgINra2qBUKtnfNHHSECtIFqYME4m6iA+iWq2Woq6TAImgZjgiCR+CwSCGh4fR39+P3NxczJ49O6wuRgiZTJb2FJ/NZkNvby/sdjsaGhoSbrdBZrlTud4iRLS6Iu5ay8jICNvoMBAI4Msvv+QRl3DWnypO9hpUJvdFSIaiKCiVSiiVyoi9usQmDsKyBCKNF0ZdIyMjUKlUKCkpEY26JPPdzEIiqBmKSMat3BRaeXl5Qim0dEVQDMNgcnISOp0OFEWhrq4ODodjRrXbACKbv9psNvT396OsrEx01i9WEJvM4D+VBDUdyTDeXl1jY2NwOBy8sgTyIGtdYgrDWGtdkg1U6pAIaoYhGAzC4/Gw/yfrSw6HA3q9HhaLBTU1NUkV1spkspQIKhgMwmAwoK+vD1qtlrVD+ia6SNA0jfz8fOTn5/Oej1YQK3QtF0qzhZiOpJEupHJe0Xp1+f1+tkOyzWbD5OQkO5lSq9Vh6UKhulMy300vJIKaARAat+7evRvLly8HEKoZ0ev1CAQCqKurQ3t7e9I3Q7Kmrn6/n+0DVVxcjHnz5kGtVvO2KyGEaAWxJF0llGYrFIowdSFZn5kqTHW0linI5XLe9fd6vaisrEROTk5YulbYqyuaulMy300OEkGdwoi0vgQAIyMj6O/vh0ajQXNzc1gtSjJI9Gbxer2sFZFYHygJ8YOrFORCKBIg6yyksNXv97MRayZdNKY6gpoqBAIByGSytPTqIg9h1MuNuj777DMsWLCAfU1oA0XI7JsSdUkEdQoi0vpSIBDAwMAAnE4nLBZLWKQyVeCmE6P1gfqmIp0RQDSRgMViYQucI7locGf9qagmg8HglA2YUzkwE4KKhmR7dUUSaZD9CaOub6L5rkRQpxAiNQZ0uVzo6+uD0WhEVVUV8vLy0NTUNKX1QwBgNpuh0+lYn75U0okzHVNxXWiahlKpDBOfCF00JicnRdttcNOFsTCVNVdTiXgIKhKiFSQT13iuSMbtdsPtduPw4cNhHZLFpPHxmO8yDIOJiQnU1NQkdQ4nGxJBnQIgaTxuY0CKotgZssvlQl1dHVpbW0HTNEwmU0YcH8RAbgCdTgeFQoGGhoawhf9M75+0NddqtTNykEwW0dptiLlokHYbZMZvMBjgdDpZdVs0F42pWoOaakFNKgQVDSTq5d4rTqcTPT09qKur45EXmTwolcowkYZYQTLXfLe/vx/33HMP/v73v6f9HKYCEkFNU0RbXyJu3XK5nCUE7o80E/VKQgSDQYyMjKCvrw95eXno6OiY0iLUYDDIugloNBowDMMrjhUOptHUbjMViZJGNBcNv9/PS1UJXTQ8Hg/Gx8cz7qIx1WtdUxkZEi/CeHt1kbXGaL26zGbzlE4Y041v3l07zRFpfSkYDGJwcBCDg4PIz8+PSgg0TWeMoBiGQW9vL0ZGRlBaWoqFCxcm3KAwFQQCAbZzb3FxMebPn8/K30nBLjfvz1W7kRloLBPYk1n0m06k8zzkcnlUFw2LxcIOnC6Xi00XCicKqYpkZqoYAzjhWiGGeHt12e12tjThxz/+MTt5e/7559HW1oZZs2aFiTymMySCmiYQM26laRoejwf9/f0wGAyoqKiIq7A2ExGU2+1GX18fHA4HaJrGaaedNqVRCUlXjIyMoKKigqcI5Mp2uWorLsTSV8QEljhoa7Va+P1+eDweqFSqU56kpiIdRlw0SHqXu2+Px8Neb6EsW0hc8V7vbypBRUOk0oRdu3Zh27Zt2LdvH+x2O1566SXYbDa8+OKLYdt48sknsW3bNnzxxRdYu3Yttm3bFnF/v/nNb/Dggw/C6XTi8ssvx9NPP81OUvV6PdavX489e/agtrYWTz75JM4999yEz4lAIqiTjEjGraQpn9VqTVgJl06Cstvt0Ol0sNvtqK+vR15eHqqqqtJOTpHsiggxkoXeZDv3xjKBJe7lgUAAR48eDWt2mKp7+cnCyTpWbjGsmCw7lndeJBeNTK0JTQckS1CRQIQSixYtwsaNG6O+t7KyEvfccw/efvttuFyuiO97++23sWnTJrz//vuorKzEJZdcgp///OfYtGkTAGDt2rVYtmwZ3nzzTbz55pu4/PLLcfz4cd5aZyKQCOokIJpx6+TkJPR6PYLBINuUL9FBJlWCYhiGbQ7IMAzq6+tRVFQEiqIwNDSUMUdzrseaw+GATqeDzWZDXV1dmLN6OqFQKFhXh5GREcydOxc0TfMa7omtu0QbSOM530xjuqYqudebC+Kd53A4eKkq7roi+W2nezAXw0wQZJjNZtTW1sZ836WXXgoA2LdvHwYHByO+74UXXsA111zDNgu99957ceWVV2LTpk346quv8Nlnn+Gdd96BRqPBZZddht/+9rd47bXXcMMNNyR1/BJBTSHI+pLX60V3dzdaWlrYyIEIDrRaLVpbW8PC9USQLEGRzrl9fX1Qq9VoaWkJK/DNlACD+PyRiM3r9aKhoSEpgk4F3NYfkRrucQdSrh0RAF6XXjGJ8FRiuhJUJETzziPripOTk3C5XDh48GBUF410nPdUR2t+vz/tXZ3NZjPmzp2btu0dPnwYq1atYv8/d+5cGAwGGI1GHD58GI2Njbyxa+7cuTh8+HDS+5MIagogtr5kNBrR2NiIwcFBDA8Po6SkBPPnz09LYW2iJEKEB4ODgygoKEBXV1fYGg5BJlpukOuzf/9+VpkoXAieKsTTfj7SQMotzHQ4HJiYmGAFA6TthlarZRWamcZM8TjkrisSwmhtbY3qokGUbcL0bCJR7skgqHRHhRaLJa33kt1u5wllyN82my3sNfL60NBQ0vuTCCqDiLS+5HQ64Xa7sXfvXlRXV6ddcCCTyXiGsZHg9XoxMDDACg/iEWCkk6BIO3edTgePx4O2trakFEbTJUrgyn2F9UXEx83hcMBsNsNqtWLv3r282hYymKbLDupUi6DiQSKtNsj1tlqtPBcNsTIEMSKaCQRlNpvTSlDZ2dmwWq3s/8nfOTk5Ya+R11PJBkkElWYIjVuBE4W1ZrMZer0eHo8HMpkMy5cvz8gAEiuCcjqd6Ovrg8lkSlh4kKqjOQBeL6qCggLMnTsXx44dixi1TSXiiaCS2SbXxy0vLw+jo6NobW3l1baIKQtT7Rc104pnE2m1IaZsE9oPcV00hJMFLhlOBfx+f0bWoNJJUB0dHTh48CBWr14NADh48CDKyspQVFSEjo4O9Pb2wmazsdf94MGDuOKKK5Len0RQaUK0wtqxsTHo9XoolUq2sPajjz7K2OARiaCsVit0Oh3cbjfq6+sTbg4IpFZj5ff7MTg4iKGhIZSWlvIitkwQw3RHtNoWYkDqcDhgNBrR39/Ppq6EtVyRlIVTFUFNdbPCVPYVyX5IWIYwNjYGi8UCt9uNTz/9lBdtRbvmqYAU6qYTFoslTLUqBmKhFggEEAgE4Ha7IZfLw47n6quvxrp163DllVeisrISDzzwANatWwcglHadN28efvGLX+CBBx7Ajh078Pnnn+O1115L+vglgkoR8RTWFhQUYM6cOaI3RaYjKIZhYDQaodPpIJPJUl7fSSbFR1zNx8bGUFVVJZrSzFQ7+UQxXYgykgEpURaS4tjh4eEwZSE3AphpasFAIJARMhQrQzAajTCbzaz1kMPhCLvm3JbyqYpiMpHi83q9ca1rP/DAA/jFL37B/v+Pf/wjfv7zn2PDhg1ob2/HkSNHUFtbiwsuuAB33nknvvWtb8HlcuGyyy7jfe7ll1/GunXrUFBQgNraWrz66qtJS8wBiaCSRiTjVo/Hg76+PoyPj6OiogKLFy8WXVMgJJIJqaxMJoPf78fw8DD6+vqQk5MTV0v3eJAIkbhcLuj1ephMJtTW1mLZsmURB5dUCSpdg+R0X7OJpCwkjg5kIB0bG4PVagXDMLBarXGtuSSLk9XuPdMga1DRrjlRczqdTp4oJhkXjXRfx0QmWvfddx/uu+8+0dfsdjvv/7fddhtuu+020ffW19fjgw8+iHu/sSARVAKItr5ks9mg1+tht9tRW1uL5ubmqD82uVyekRmT3+/H6OgoxsbGoFKp0qYMJIhHIWiz2aDT6eB0OtHQ0BBXKpGm6WkRuQCnpvqNODpwJyEjIyPwer0oKSkRXXMhgyh3IE3m9ziVfnXBYHDKHExiiSS46VYuhC4ao6OjomuL5JpzXTTSOUEiv+PpPumKBomg4kC09SWj0Qi9Xg+KolBfX4/CwsK4fhCEoNIFbuRWWlqKvLw8tLW1pW37BNEiHVLcGwwG0dDQEPe1AJLv1ptuTFWKb6pSb9HWXIRWRMRJg9QWcYkrmrpzKntBTWW0lqyKL5aLBrf4e3BwkHXRIBkHbrowlXN1Op1pr6uaakgEFQWR1pcYhmFVaDk5OWhra0tYSpmugldiiWSz2djILRAIYP/+/SlvWwxCguK221AqlWhqagpbN4l3u4kSA3ffPp+PHVQz3T32VEKk8480iHJri4j5q16v57Xc4F5jlUo15aQxlftKdwfoaGuL+/btg0ajYVO0QheNRN35T3Unc0AiKFFwbYi4xq3cuqGysjIsWLAgaSfvVCMok8kEvV4Pv98vaomUKTdzmUwGr9fLa3eRk5OTcruNRCIo4nih1+uRnZ2NWbNmIRAIoLu7GyMjI/B4PPB4PPD5fGzqi0iOc3Jy2FQWt4029zgyHUFNVQoxmXW5aLVF3Nn/5OQk66FHMgxT1Vp+qtegpgpKpRJlZWW856K583NdNLiRLrnu6ZaYnwxIBMUBt7D2448/Zhf1nU4n9Ho9zGYzqqurkzYs5SIZgmIYBuPj49DpdFCpVGhsbBSNVjK9nmM0GjE0NMS2u0jHGlc8IgluD6qCggK2pb3T6cTx48fZdRXupIHc4EajEYODg3C5XGwHUhJBcKOCvr4+1iSWEBi3rTb3OfKYru7a6V4bijT7J6kqbmt5t9sNALzBMx1pq1MhxZfsvsSiokju/AB4NXTCUoQtW7ZAo9HAarXi+PHjaGhoOCV7op16R5xmRFtfIoW1Pp8PdXV1mD17dtpmhYmk+LiFrfn5+aKS9UyDtLsYGBhAVlYWr91FOhCNVLk9oEpKSnj1Uy6XC1999RV8Pp/odxOt/QZpsU1siUjk5XQ6kZ2dzRJYrJoXiqJYEiOEFYnI5HI5HA4H3G43vF4v+1omMFWRWqzW8tx0IWkqKfQsjFdZOFMJKpkiXbGuvEDouK+55hq88cYb6Onpwd133w2dTofq6mq8/vrrYduZnJzENddcg3feeQfFxcX49a9/LVpcu3LlSuzatYv9v9frRVtbG7744gsAIQWfwWBgz2P58uV45513EjonIb6xBCVcXwJOEBOp6O/r64sYpaSKeCIon8/HSynGY0WUbgjbXbS3t8NkMqU9Ny+W4vP7/RgYGMDw8DDKy8vDSNHhcKCnpyepVCmXuLhpkJ6eHpSUlLCGsCaTiY0GVCoV1Go1+zlCXMKSg2j7ZBgGNpsNJpOJN0ERI7R4HiSym46FutFUbi6Xi5cuFCoLueTFnflP9RrUVBJUuiIcmUyGZcuWobe3FzU1NbjjjjsARJ6w3HzzzVAqlTAYDDhw4AAuvPBCzJ07l3UsJ9ixYwfv/+eccw6+/e1v8577xz/+kVL/JyG+cQQlZtxKURQCgQDrclBYWIjs7Gx0dHSkfSAmkMvlEfuuuFwu9PX1wWg0oqamBkuXLp1yR+xI7S6EA2u6wE3xeb1etkljVVWV6PnbbDb09vam/VhomoZCoUBubi6PuIjqzeVywe12w2w2w+12g2EYqFQqlrS4xEVA/o4W0ZAK/kTA3S43aiOkZTAYoFarwTBMWBSXzjRlMq3lSeqvuLiYtx3i5uBwOHhNDsl6C2nFoVAooFAoMkrApypBEZhMJpSWlrL/F7tWDocDr732Gg4dOoTs7GycccYZuPjii/Hiiy+yPZ7EoNfr2YaImcQ3hqAiGbdyI4TKykp2lr5//374fL6MEZRYio9bP1RfX4+2traUbsBkZtAWiyVqu4tMOT5QFAWv14tjx47BaDRGLewlx5ju44iVxiPpPi4IcbndbrhcLtYeh3i7EcEA+Wy6Zv9C0gsGgwgGg7zuwmazOe5OtYmmKbnPETFKOs4pUlNJQlyTk5OYnJzE8PBwmLJQrK4oFUz1GlS692WxWNDa2hr1PV999RXkcjnvfXPnzsW//vWvqJ/bvn07zjzzTNTX1/Oev/LKKxEMBjF//nxs3rw55VYfM5qgIjUGpCgKVqsVer0eDodDtCFeuuuUhCDbZxgGk5OT0Ol0CddSRQNZ04lnO9xjoGk6qh1SJgiKFDM6nU60tbWhtbU14oBH1IuZMHRNZptc4uKuBZBowOVyhREX+W6MRiNLXsk0O4zneOP9HQkzC4nAZDLB5XLBbrfHlZaMFMVFO1ay3qJQKNDS0sJGG36/n1W4CeuKhNZPiSoLT/UIKh4Vn91uD3PIyMvLg81mi/q57du345577uE999JLL2HBggVgGAaPPfYYzj//fBw7diwlqfuMJCiyvhQIBMKEDxMTE9Dr9aBpOioZyOVy3mw03ZDJZLBardizZw+ysrKSqqWKtX2/3x91zYrb7iLeY0iHmzmB3W5Hb28vXC4X8vLyUFxcjIqKiojvJ0qlTJNTOrZP0zTUajVUKlUYcZlMJhiNRvh8PlitVl7EJVzjEg6QiZBToueRLEmTiVCkNGW8k5pIURv3MTk5CYvFwhqZEsVlTk5OWGt4ssZls9kwOjrKriUK28pnZWWJThCm0iEj1r2aDOLpBZVMi4wPP/wQo6OjuPzyy3nPn3766ezfP/3pT/HCCy9g165duOiii5I4+hBmHEGRlIuwsHZoaAj9/f3Izc2Ny5dOoVBkJIIia11koF2yZElarYgIohGJWLuLeFtdpOJmTmCxWNDb2wu/34/GxkYUFhbCYDDA6XRG/MzY2FjUVtTJIlPtNQBxgqAoCgqFAmq1mqd6I5E+ibjGxsYSIi4xJHJeqVyHaE4SiUTcYmlKIcbGxtDf3w8g/JhJmlIYqcnlchQWFrJRmtfrhcfjYb3zPB4PKIoKK0KeSsurTHXTjeVk3traCr/fj+PHj6OlpQVAqEWGUCDBxQsvvIBLL7005hiajntrxhEUISXyQyQquPLy8oRUcOmOoIijt8FgQGVlJebPn49jx45lhJyA0MAgJNho7S4S2W6yEZTJZEJPTw9omkZjYyMvsohWqDsyMoKRkZGk9hkJkUgk1ZuKpO+SiV5IGourGiWpN9LwcHx8nDUkVSgULGEJO86Sz8a7/pTKOUeKNDLpUC92zPGqKbmgaRoajQYMw8DpdMJsNsPn88HtdsNkMmF4eJitlSMF39nZ2VCpVLw1u3jSlNGQiXRiPASl1Wpx6aWX4mc/+xl+//vf48CBA/jb3/6Gjz76SPT9LpcLr7zyCv7617/yniclKIsXL0YwGMQTTzyBiYkJXlSVDGYcQQGhNQ2dTgeLxZJwQz4ChUKRFoLiFvnW1tZi+fLlbBSS6TUuriouVruLeJHooCO0QoqURoxUBzU4OIixsbGkjjUSYkU4yQ7WyZJTNBDhgFKp5F03MhiTiMtoNMLlcrEOAxqNBi6XK6opcSLpwniOk4tMkRMh3UxEvaSMgGyb3LPkOhsMBvTf8nKaAAAgAElEQVT19bHXWSaThak3lUolj7RI2lJIZELVpdvtTns6MV4vvqeeegobNmxAaWkpioqK8PTTT6OjowO7du3CypUreW7mr7/+OvLz8/Gtb32Ltw2bzYYbb7wRPT09UKvVmDdvHnbs2JFUh2wuZiRBjYyMoKSkBO3t7UnPaKLJwOMBVw1XX18fVuSb6f5HMpmMtUaJp91FItuNJ8UntCOKZYUkFkENDAzAaDTyUrWpDkzpHJSF283E9xmJ9EiqkEjiCYQRl81mg9lsZp0KuANqKr2LuBDaD2Xyt51JlxQu8XFrI8WuMwCWuEjZgcvlYhWNwpQsl/jE0N3dDYfDwaZvEyE4rVYbdl+TfcVzvxcWFooW8J555plhrTbWrl2LtWvXhr23o6MDn3/+ecx9JYoZSVAtLS0p3yDJpPi40YJCoWC754ohk/UbNpsNRqMRExMTaGlpSapzbiTE2o7Qjije9S3uoMYwDNtHiiDazc1N60YrWOVuP9q2pkpcEM92E/0dcwfUrKws5Ofns5EXSV253W5MTk7C5XKxDgbCSCCR8gpuKnGq03qZ2nY8jhVyuVy0rTzpSOtyuWC1WjExMcFGSFx3Eo1Gw0riSUSWaJqyoKAgoqAh0v1wKmFGElQ6vpRERBJkUCYijFSNU5MFaXcRCASQl5eHkpKSMPuZTEFoR7Rw4cKEjHS5kUJvby8sFktcnwsGgxgbG4PNZuMNsmIDrNiiuvB1mqbZB8CfSUeKZDJFTqluV/h5hUIBpVKJ3Nxc3mvcSIBIxiMRl1wuD7tuZA0q0+Q0lcSXiikttzaLu+1gMMgWe5PeXB6PB0CoXc7o6Civbi7WOJaXl4e6ujrR17xeb8ZqOKcSM5Kg0oF46qC4VjylpaUpuZsnC+EaD2l3kYlCVjHEsiOKFxRFwe/3o6enJ0z2KoZgMAiDwYCxsTEUFBSgqKgIbrebNSvlDrBZWVnsrJV7bGKL7MSbUXjtxMiCKCXJQMIdUCKtlcRbu5QO0hMjU7HnI0UCJFXocrnCUlhc4vL5fMjKyso4gWTCuinStU6HpZKQsLnXjQuGYXD48GFotVo2XejxeFgFJ1cIQ1KAubm5aGhoiHg9TCZTRizaphozkqDSFUFFSvFx3SdSER2QWWEyN0KsdheJmNEmA6EdUSrCCyB0k/b19aGqqirq+wKBAAwGA8bHx1FcXIzOzk6W3MQGWGL+yo0MuGsx5MbnHns85CAmt0+EjISDrdC2iButpbL+JiTPRLYhl8vDuvQCYH0KSQGyxWKB2WxmxRnkupK+Rancj8IIJJ1CgmgTgVT3lUg0SSJ3Yc0SKfYmkwRSeqBWq1FfX4/u7m6eZyF38jUTWm0AM5Sg0gGxCMput0On08Fut4u6TyS7j0Sk3txUWrR2F8SCJt0gtj779u1jFZKpDhp+vx+9vb1wOBwR3xMIBDAyMgKj0YiSkhJ0dnayKZhIA4FCoWBbbAv3RwZYshZDRASEdMhsVyzNk2oqSyy64hJTLOIDxNOTwu2TuqB0C0NkMhlLXGTb+fn5yMrK4q29jI2Nse0fuKIBkoKNRVyRrlE6ECtKTUX2na5UJ9f6iURDWq0WTU1NPAcNYm5NJl/bt2+HSqWC2+3G8PAwKioqTtm1qBlJUOn4Mrg3tdlsRm9vLxiGQX19PYqKitKyD+6AGAuk3cXIyAgqKipiptJkMhlbOZ8OELm8xWKBTCbD0qVL0zJYeL1edHd3s8arQvj9fgwPD8NkMqGsrAxz5syJa7/RBqBIKS3iHh8MBkVl22q1GtnZ2VAqlWmvWUlGvh/r/1zbolhrb+Q9kdKU0ST55DXu2gsXpO1GNOIi/5KGe5HWhdJ1b8ci62QjqGTIKd7UZVZWFpqamlj1nphnoc/nw6pVq/DWW2/BYDDg2muvxcjICFpaWvDKK6+EbTPeVhv33XcffvnLX/KWMD7//HM0NjYCAA4cOIBrrrkGR48exezZs/GHP/wB8+bNS+g6iGFGElQ6QCr79+7dC7VajZaWljCZaaogdkTRIGx3EW9NV7pSfMSOyOl0orGxEbNnz8bHH3+c8naBUDR2/PhxdrDi3tg+nw8jIyMwmUwoLy+PSkzpqsEhIoKcnBx2xsqtN3K73TAYDGyhrNDhIdlmfCejoDXS/+NJUwpTk8FgkHVQF/tcNOIiEZfdbsf4+DjbI0vY2kSpVKYlxRfv+l4yIolkv8d41rs0Gg1LTtGgUCjwrW99CwaDAdXV1bj77rsBgG3SKUS8rTYA4D/+4z/wxz/+Mex5r9eLVatW4ZZbbsFNN92EZ555BqtWrcLx48dTtm+SCEoAkkIbHBxEIBDIaHPAaEKMSO0u4kWqBCVmRySUEqcyWLjdbhw/fpxNQ5KBw+v1Ynh4GFarNSYxiSHVwV5M6adQKKBSqXjbJcTldDpFrYmE6rdI55BJd/hMSrKF14KIS+L9PMBPFXLBjbi4zSTJfkZGRsJk2vHuN95rkqhIIpXvMVY6UaVSoampKaE1XuEalBhRJNtqQ4gPPvgAfr8ft9xyCyiKwsaNG/Hwww/j/fffxwUXXBD3dsQwIwkqmTQA1xapoqICixYtwv79+zPaIFAsgorV7iKRbSdDUMSOiKIoNDU1idZxpTqoOp1OdHd3887d5/PB6XTi2LFjqKysRF1dXcLnnanBXmy7hLiELdBJ5M1NaQmJCwB77pkiJ3Is6YbYtUhUXRcpYuMSiJjajdQUqdVqdv3Q6/WCYZiwAmSlUskTmyT620hkApbq7y4aQSmVSjQ3NyesjLVYLKitrY36nkRbbfzjH/9AYWEhKioq8MMf/hA33ngjAODw4cPo6uri/Qa6urpw+PBhiaBShcvlYotChSm0aDYx6QBZg2IYfsuNxsbGlBU4iRCUUKoey9U8FUdzu92Onp4e9tjcbjeGhobgcDggk8kwZ86cpAg5nQvT3EEz0e1G89Qj7TdMJhMcDgeOHj3KdpGN1vAw0eMn++P+Px0QuxbvvivDU0+1YnJSibIyBtdd58WKFYlPjOKNbhQKBQoKCsKaSZICZG7EBYSiD1JmQCIuLvFEKuwm30us40rH744IdIRQKBRobm5OapIcj5N5Iq02Vq9ejeuvvx5lZWXYs2cPLrvsMuTn52Pt2rWw2+1hkvZ4WnbEgxlJUPHclFarFTqdDm63G/X19aJuC5lyNCcg7QP0en3aW27EQ1AME7Ij0ul0olL1SEjW0dxqtaK3t5dN4QwNDcHtdqOyshINDQ04dOhQ0uTk8/nSKo4h201XhMNVZBGH7ZqaGjCMeKdeADziInVc0c5RSE7prBuKRE6bN6vg8YT2YTBQ2LxZBcCTEEnFS06R0m4URbHXiTsoE2d0koblFsbGuraBQCBiYTdXJQlAdO0tkdIAsfOSy+Vobm5Ouq7SbDbH7MOUSKuN9vZ29u/ly5fjxz/+MV599VWsXbs2qZYd8WJGEhQQeXHYaDRCp9NBJpOxVkSRbuJM9YQi7S70ej00Gk1C7S7iRTSCEtoRzZs3L6H9JzNwm81m6HQ6OBwODA0Nwev1orq6mr3+wpuei1gz2Ey3oM/kdikqeqderhxebHAlUQGxyRGmz9JBUJGuxXPPKVlyIvB4KDz3nBIrVsTnY5nIulCiMnMi3FCpVGERF3dSYDKZWBUpubZOpxNKpZJ3DYVkFe33EalIWuw5ohQlKUmZTIampqaUOh1kqtUG99jJOXZ0dOCRRx7hXavPP/8cN998c9LHTzBjCYoLblFrdnZ2XP2ggPR31RW2u2hubobH40k7OQHiBJWqHRFBooO30WjE0aNHMTAwgEAggOrqaja1wB1UY804xQpbyfHQNM0jOjF5dSKYqrWsSOASF3dwffddGR57TAGbLTRQZ2cH8IMf9GPJEmOYZDsdBBXtmA0G8W2LPf/uuzI895wSBgPFpgLPOy+Y0PeTrnWheCYFFosFJpMJExMTbLqPXFetVsuuccULsfMkz5FojSgHGxsbUxZmxZPiS6TVxt/+9jecddZZyM/PxyeffILHH38cv/rVrwAA55xzDmQyGR5//HHccMMNeO655wAA3/72t1M6B2AGExRFUfD5fCwhRCtqjYR0tdyI1O5iYmIianFqKuDeoMSOaGhoKK4aqlhIZH2rp6cHe/bsAcMwqKqqSkmqH2kGS9YMookDohW2crcvdMsWG1iTWWMRmuEmQxzvvivDpk0q+P0nPmu3K/Diiw2oqqrEGWfYWZ83o9EIu92Or776ircGw601SuSYxV8HxF4WjttiqcCHHtIAcCV0LeONoJKNfrnEZTKZUFFRwRI9WT90u90YGRlJSrEZCYFAgP1cY2NjWnw8bTZbXFZH8bbaePnll7FhwwZ4PB5UV1fjrrvuwn/9138BCAk5Xn/9dVx77bX47//+b8yePRuvv/56WgRmM5ag9Ho9BgYGUFlZmbQNT6oRFFeAIdbuIpN2RCSK6O7uZu2Ili5dmhbBRzwDgMlkwt69e2E0GlFdXR1XxJrO/QsRb30PN4p7910ZHnpIBa839J0ZDMBDD2lAUR6cd16A3Y4wchNuO13pwueeU/LICaAAMPD7SVqNr3z78ssv0dDQwNZxcWuNYrk7xHPMkV4WPh+eCqTh9QYjpgIjTQoiiQm4SNe15irryPqhsGWGkLiEis14a+TIvhoaGtJyn5AJWzx1XPG22vjzn/8cdTvz58/Hp59+mvjBxsCMJaji4mJUV1enVKujUCiSinBsNht0Oh2cTmdEAQaQ/hQigcfjgV6vh8PhgEqlSosdEReRBgGiRuzp6YHZbIZWq0VJSUna9htp32TgjdRZNh6IpQefe04JrzdEAgReL/Dss3Kce+6JyDpSOpHriMD1xOM6psdDnAT81Bn/uCKl22iaRlZWVljKiBTJOp3OMHeHrKwsdi0mmp9eWRkjut+yMv458N9DAwhGPOZowovOTibq95rOdUNhOlFsrYwrfOFCWGogrJET9ooKBoNoaGhIuxHAqWpvxMWMJaicnJyUo5NECYTb7kJY3JqO7ccC146orq4OWq0WNTU1ads+gVBmTmTqvb290Gg0yM3NzYg0X7gwbbPZMDg4CIqiUFhYCK/Xi4mJCZ7Tg7A+JtJitRgxJLLGEs/xAidmt2KO6WKf5/5dVkbBYCCDJoPQYB867tJS8QlDpN8fIaL//d//xfr163mvORwOUVsi4bW87jo+mQCAShWKeLg4QWQnyIk8v3XrVt7+owkvfvObyFZH6Ra1cCOQRIueo5UakOJut9uN8fFxuFwu5OXlYXx8HB6Ph3Xc0Gq1Sd9DmWgff7IwYwkqHYiHQIQ1RKTdRTzIpB0RRVHQ6/Upb1sMRDnHMAzGx8fR29uL7OxsdHZ2Ynx8HJOTk2nfJ3eQsNlsGBgYAE3TqK2tRVZWVpjMnMxiicSYm37h1saQBW9uhEP2E2+EIIZ0DJjCdNJ117nx61+rceInQzzwgrj++hOkwD0X0n1VbNuHDh3Ctm3bsHjxYnR2drLHLGZLJHQwHx0dRWWlD1deWYy//KUSZrMSwCgKC/+KFSvW8T573XVePPSQBl4v93q4sHDhft7+geiTgkgiiUwoLgm5p7vUQFjcXVtbi8HBQTQ2NsLn88HhcGBkZAQOhwOBQABKpZJHWllZWTHXjy0WS9qjsZOFGUtQ6Qhvo4kkYrW7iAepRlDR7IiA1Np5RANFUTCZTGyDxrlz50KtVifUaDDR/TEMA6vVisHBQchkMjZCBCKvJ5FZLLcehCi1SHrLZDKx8m21Wo1AIACVSgWPxxN3hCBEpiTq550XBODB448rYLWSYzKDoh5DRcVKAKFB/vnnn8f69eujRmmffPIJ7rrrLgDAbbfdht/+9rdhriVCpaRcLmebHZLvpK3NhwULDmDv3r3Yvn07RkaAZ57x4Oyzz2YjreXLtViy5AN8+GEXgHIAowB+hzfffJvd/6OPPorOzs6okwKx33ImfQwzuW0AqKmpQVFREfr6+qDRaJCTk8OThpNJlsPhgMPhwOjoKOtarlAoeMTFbbcxU1ptADOYoNIBMQKJt91FPBBbg4gHXDuiaK4TJEJLF0ERUtbpdNBoNOy5B4PBuBsNJgIyQFosFpaY6uvrU5Lgcos6hcTldrsxOjoKt9uN/v5+VFZ6ccUVRfj732tgNMohl0/i1lvVOPdcILQGFA7uoJYuBSA5boZhsGKFH4ODz2Hbtm3sa34/cPPNb2DdunVYvHgxG5UQ6b0QW7du5X3e4/HgxhtvxLp163jptmjSaPIvqTXiZg3+9Kc/oby8HOeeey5bw3X66TZ8+OHFolkDj8eDm2++GRdfvA7XXXdtxEmBkKAyTSCZ8jIEgMrKShQXFwOInJLjTrKE97jX62WJi9tu4+GHH4ZCoYDZbMbOnTvR3t7O7udURHqn1tMI6YiguIW6Pp8PPT092L17N3w+H5YsWYK2traUiukSAUmn7d27F319fWhra8PChQujzpTSlUIMBoMYHBzExx9/DJvNhsbGRpSUlLARR3d3d9rIiVuJb7VacfjwYYyOjqK+vh5tbW0RySkRw1DufrjPazQaZGdno6CgAC0tLejo6MBVV5XgxRfNWLPmUfj9FyA//2McOXIEx44dQ19fH8bHx2Gz2eD3+3lrTmSxP7RmFFo72rxZhXffFV8b2Lp1a9Rj5h7v+vXr8bvf/Y43qP3kJz/B4sWL8aMf/QhAKCrp6enhEVH450PzU5pW4bvf3YKzzrom9gUUwaOPPoonnngi7Ll169ahuLgYFosFjzzyKAAgGAxd/3PP/SEoSv71+clRXr4CRuNVKCycxMaNNpSUhK6jWm3FnXf6vo4ewZJuph3ggcwRVHl5OcrKykT3GS8IaVVXV6OtrQ0LFizAkiVL8MQTT2Dp0qVQqVR49dVXsWbNGqxatUp0G5OTk7jkkkug1WpRV1eHP/3pT6Lv27x5Mzo7O5GTk4OGhgZs3ryZ93p9fT1772RnZ+O8885L6FyiQYqgooAM8F9++WXC7S7SBWJHpNfrkZ2dnVAqMVWCIsQ0MDCA0tJSLFq0iLXm93q98Pv96O7uhtPpTHofXJCI0m63Y2BgAHK5POWISQyxBh7u66RVxPe//328/PLL6O7uxlVXXcVrwTE5OQm3282mXjQaDbZsaY642L9kCX9AEq4FcRFpgb6zsxNXXXUVS0C/+c1veN+1x+PBww8/DACi29VourBgwb345JOf46yzfgSNpgsvv0xhzRo/mpoSG/hPP/0/0N39K3BVhStXXoEdO/6EBx98EG+++Sb7PMOEMhL79rlRVvZtjI6+g7lzL8KBA39FU9P38dFHLbjoogHcf78LW7ZsgV6vR2vrExgfz2IdzbkTgXjr2+K1HkpUEJEoSktLUVFRkbHtl5WVobKyEmeddRbuvffeqO+Nt9UGwzDYvn07urq60NPTg/POOw81NTVYs2YN+55//OMfODeUWkgYX9e3UczXF56iKPZvKYKKAIfDgUOHDsHpdCInJwfLli1DbW1tRshJ7IYIBoMYGhrC7t27YTKZMHfuXHR2dia0zpWsqWsgEIBer8fu3bvh9XqxZMkStLS0sIV3NE3D4/Ggu7sbLpcrYiopmf0ePXoUw8PDqK+vR2tra9Lk9O67MqxercHZZ2dh9WoN/vlPeVyDT6TzqKiowLx58/D2229j69atbNPDkpISNrprb29HQ0MD8vLyMDEh/jsxGChec8aDBw/illtuARCKeg4dOsQ7lkjHK0zTCSciFCUDub1vuYW/XQB47LGtqKn5DurrW6HROJGTA+TkMNi5M7Eh4ZFHtuKFF34JLjkBwI4dobqZ9957D8uW3QmaJr8dOc4441n4fKdhdPQd5OfX4uDBfwAA/v3v27B7dx8CgZDQZ+XKlTCbzRgbG0MgEGDLJ7744gscP34cAwMDGB8fZwUFQoUkeZDnhYXe3AdN02z3YWI3xI3W0oHi4mJUVVXxnssEGZpMppg+fKTVxv333x/WakOIO++8EwsWLIBcLkdbWxtWrVqFf//73ykfp8C3kOE8z/49YwkqWVgsFhw4cACHDx9GWVkZtFotKisr0y40IBCmKgKBAPr7+7F79244HA4sXLgQs2fPTsoOKZ6GiFz4/X7odDrs3r0bwWAQp512mqjVP2nR7nQ6RW9+rq+esO6H3PTkeYqiYLFYcOjQIfj9ftTW1kZN5UWC0P2Bn16T4cEHVXjnnfi+w0iDxvnnn4+BgQFs27aNHfC5JEJUWrm5udBqxZ2cNZoTFjoPPvggNm7cyKaRyVrMI49sjUmmixcvjipDZpgAiKTb5zuxXSDUHuHgwefhdB7GokUbkZ29EACg1YIjY48PhYXXoajodrEjYM9p9+6H0NgYSvvMm3cWBgbG4XT+PwCA2dzPRlWAB0ND1+GBB7ahp4dm6/f279+PsrIyaDQatLa2or29HXV1dcjNzYXf74fBYMCXX36Jw4cP4/jx4xgcHITRaITT6Yw4QSPRFPdBCM3v97P3JXmeCzFy45IZ9zdOHoWFhaIlH5kQMcVjcxSp1cbhw4ejfo5hGOzatSssyrryyitRUlKC8847DwcPHox5jGSM2LdvH/7whz+AoqhqAKAo6nSKor5PUdQsQErxAThRYCrW7qK7uzsjPyICIsRgGCatdkRA/BGU3+9HX18fRkdHYzpOuN1u6HQ6VvkmBuHgGmmxnajyFAoFmpqa0NfXx7Y44K4DCNcESIon0iDOr6UJFbR6vYjLxDQaMZSWlrJ/33rrrazyTQy33qrEgw8G4POdiKQUigBuv12N8vJyOBwO3HnnnWhvPx+PPHI7GMYPmlZi7txfwmDowgcfDKG1lUJWVlaYy4MwejoBUmdEobb2XAwMvP81UalwzjmPw+3uwquvvoennvo5AOCddzbiggseR2npbACAwwGUlSUWcX/44fMwGv8g8krouqtUKnznO49Bo+mCWq3B8LACo6P/X4StKXDGGb9DWVkndu5ksH59Hrq6uvDhhx9CLpdj6dKl7OAfq6WJWGlBpJYmwu88lrAoWnG12G8nPz8/Ym+mTLTzSXerDS7uu+8+BINBnpjmpZdewoIFC8AwDB577DGcf/75OHbsWNQojqIo/P3vf8fTTz+N/v5+AHiIoqjXAHwXwDIAAYqirp2xBBVPyolhGIyNjUGn00Vsd0EIJFONC2maRk9PD0wmU1rtiMi2o61B+Xw+6PV6jI2NoaamBkuXLo2awiSNBgOBQNIL1FxiUiqVrL0LIRwya41204s5N5BBhqZpGAzkHChwHRcMhuQXwIWk4PV6cdNNN4Up3wiIWu93v/PBZFJCrbbiJz9RY8WKAMzmE8c8MbEIy5f/BP/+9yacc86dqKs7AxYLgy+/VKGzcxI2my2sWPa73/0uOjo6cPfdd8Pn84GmaVxwwVM4ciQAvf5mdHb+J8bHf4SyspcwOvoYWlvvQ339HHz88e/xzjsnyCQY9ODNN/8furo2oKXlWthsFC68MLE1y9NPXw+7fT5GR28H4AFFqVBb+yQ0mk9x7NgW/PKXv0RhYQdefplCW9utcLlo1NZ+CwMDN7DkmZX1YzidD2Hp0jvQ3NyJYPBEJHfGGWfgySefxLZt21BcXIzZs2dHPBaus4NQocklLtLShGEYtiaO23YjnRPS3Nxc1NfXRxyP/H5/2pcN4pGZJ9Mi48knn8T27duxa9cunnvG6aefzv7905/+FC+88AJ27dqFiy66SHQ7ZNJ5//3348Ybb0RlZSVWrly5BsCXAB4G4AbwAIB7ZixBAZFnw6TdRX9/P/Lz86O2uyC1UOkmKJJPj+TTlw6QhohCeL1e6HQ6TExMxL1vbqNBMYeEWGAYhpWLq1QqNDY2ss4O3AXvZHLywgLdsrLA1yTFP8aysnD3bGHdj3Ddgbx/w4YNWLJkCTZu3Ai/3w+VSoVHH30UIyNzsXq1uJR8xYoAVqwANm36OXbs2IGKit+B1CoRGAw0Ghq+C6NxHyorz0IwyCA7GxgfV6GoqIj3Xm6xbEVFBX784x/j4YcfxgUXXACbrRnNzTQ8niXIzs7D4CBAUReiru5vaGgIAKCxZMk1yM8/DZ98cuPXLR5UWLHiMTDMXOTkBHHhhYGEBRJnnunHZ5/NRjD4O4yNXYuamicRDHahoKAUdXU7AABNTUGsWePHzp00KArIypqDZct+hY8+ugvNzU+CYebAbp/ErFnfA8CP5Lhqt8ceewwNDQ1hgo9YiEVcxNmBtDQh98zw8HDcvbjEkJOTg8bGxqifi8dfMFHEE0El2mrj+eefx6ZNm7Bz505UV1dH3Xas+5hcj+7ublx99dXc89/CMIzh6/dsANAzowlKCNLuYnBwEGVlZawqLRoyZUdkNptRX1+PiooKFBUVZSSFKIyg3G439Ho9JicnUVdXh5aWlrj2y200CCQmvhASU1NTEzsZEP6Q06Wguv56Px58kPraRy+ESAW2YpFapALXjo4OPP7447jpppvw6KOPYnR0Hh56SA2vFwitdQGbN9OgaR/r1ffFF1/grbdCBak/+tFtuOCCx3D++TUgFoVlZUHYbHIsXXr/1zNLwG4XT7XJZDJWyguECj0//PBD+P1+VFUFYbEAbW3/g6EhJQIBPxhGC6VyM3JzC+DzeeByKTBrVgfmzbsOW7Zswf33/wLLls0GEL3wOBoaG4O45JJhHDjQgV27roVSOQft7QGsWlWAv/1tMf75z6N4773TUVYWxFlnhR4vvyxHTs6Z6OragJKSThw5QmHOnGsQDDJwOACbjUJhIYPvfpeBw3E+gLkAfgef723cfPPNEaPWREF6RQm99MxmM8xmMzQaTVgvLmFLE5KSFiI7OzsmOQGZSfHFE0El0mrjpZdewt13343/+7//Q2NjI++1/v5+DAwMYPHixQgGg3jiiScwMTHBi6rEwDAMFAoF5HI5udfeJ+T09eseiqLyZjRBkQGPtKugpDMAACAASURBVLtIxtU7XV11uXZEDQ0NrB0RKbDLBIhIwuVyQafTwWw2o6GhAW1tbXHPBkmjQSGRxCIohmFgNpsxNDQEtVrNIyayDSEZpaO2haIonHuuDwwTZItkgRGsXevEihXJy3vJ9ero6MC6devQ2dmJ//kfBbxecg5EFAA884wM3/mONywtGEqp3YDDh9fhppsuQnU1hbPPZvDnP4dSPFotYLczsNlofO978bV5uf7667F9+3bMmtWLjz6ai6IiBrW1wPAwhf37g6ioqEAg4MTYmAtWqwuzZ4+grCwkMrBarXC5XEm3mCedjGtrPfjOd7y4/farAIQG8p4eGnZ7J4LBf6G62gGbLQsvvyzHmjV+Npqqqrrua+IKQKcL1YqVlQVRWMjghRfU8HoJcVQCuAc0LccTT3w34QhKDNEmQyT1F6m1PLeliTc0O+EZwBYVFaGhoSGuyd/JWoMC4m+1cc8998BoNGLx4sXsZ//zP/8TW7Zsgc1mw4033oienh6o1WrMmzcPO3bsCIv+hXA6nVi4cCGGh4eJ7P4W7usURS0EYJ/RBOVyudDb28um0ZYvX55wpJJqV12r1Yqenh74/X40NDSgqKiINxhksuVGIBDAyMgIRkZGeB598WJychJ9fX1hNzLp3ioGQkyDg4PQaDRobm4OK2aORERk0HjnHRpbtsgwOgqUlwM33BBgCzXF4HQ6MTw8DLlczg4S554bWu9xOp245JLVmJg4F8AdUc9369at+P73vx8xDUieJ7P3WGay69evx9jYUrz11o8QDPogkylw/vlPgqbbsGePG0uXMmhuZrBmjQ87d9JfD9AMLrzQh8bGcOUYOQYiImEYBo2NjVCrtdi79xDs9vkwGBhotUBLSwANDYPw+WpgMOSgqiqItWuDaGxsgNfrxYIFy7FnTw927jwHeXlmzJtnRlNTkNc7KlJ0AJz4Drl9uLjYuZNGTk47LJYP4HYfQ07OAgAhGfv69eG1Vj6fDDt2yGAwyEFRYg4rGmi1d6CzM/VyhliRejyt5bkgxOV0OuHz+RAIBPDZZ5+xhrxcOyLhZCATBEU8/GIh3lYbOp0u4jY6Ojrw+eefJ3yMGo0GTz31FPLy8sj3wdZBUBSlADAfwP/OaIIaGxtDQUFBxHYX8SDZFJ/JZEJvby8ARLUjykTLDYfDgZ6eHlgsFuTk5GDu3LkJn//4+DgGBgZEX6MoKoxUGYaByWTC0NAQsrKy0NLSIuqyEavT6XvvKfD44zK43aHjHR0FNm0KRRhCknI6nXjlFRtefbUUk5MdKC72Yc2aCSxYMMxLyZx22ml47733cP311yM3N1f0Whw8eBDbtm3DrFmzUFdXxzsmcn5chJwfLkVods8H10yWYebivPOewFtv3YClS/8DpaVz4HS6YTAo2GvR1ISYaz/CVCT5f2+vDF7vHASD+9DY6ITLpYLNRuGccxi4XJPo7KwEcOK7oigZhoezYLUuRCDwBmprPfB4yvHJJ2WornYgP9/KRgcejyesb1RWVhaUSiX7HUYSFBgMNIqLq2C358Ll6kZ29oKIMnZ+iw0aDCN+LWw2LYDUisLjSSMnKpIgxEUcSAjhBAKhCZLD4YDFYsHw8DDcbjdommYJy+l0Iisri6dWTQXxFCOfDExMTOCTTz7BypUr2evb0NDAvk6Kc7/+1wfg98AMl5nX19enHJ0oFIqokmouGIbvbN7S0hLTVVgul8e9/Viw2Wzo7e2Fx+NBY2MjKisrMTExkfAPf3R0FMPDwxFfF4oS4iEmIJ4OrTS2bs1iyYnA7aawZYuMJSin04nBwUF8+GE2XnyxFh6PHEAQExNyPPdcGe64Ix8rVgTYme2KFSvwr3/9C6+//joWLVrEzmzJoKvT6XDbbbcBAH72s5/h7rvvRmVlZVTF3/r167F//w4cPHghAO6M2oXW1p0AzgBA1pi6MGfOFWhuvvHr46dRXOxNi1XPzp00srO7YLV+DLf7KHJy5gGg8K9/UViyRFwB+cEHcuTkdMBsfgNO50Hk54dac+/Zo8H69aHSBq7zAokOuA0PZTIZ23fL7/fD5/PxyiJC502hvPx6yOUhYUIkGfuJsgB+Ow4hCgpSu0/iXeMkhsGJQKVSobm5mRcNyWQy5OTkhCnjAoEA66PncDhgs9kwOjoKmUyGrKwsZGdnswQWb/djAm4pxnTCZ599htWrV+Pjjz9GR0eHWORIIVSjy/uCZjRBpQNyuTxm00KGY0ek1Wqn1I4I4KcRm5qaWEdks9mc8LaHhoZgMBiivofc6JOTk3ERExDf+hJFURgfF5fcjo6GUraDg4Pwer2orq7GG28Uw+Phq/WIndCKFS52ZnvGGWegqqoKn332GYLBIK6++mpWufX73/+el+bwer247777cPz4cVx33XVRb/THH1+J++/fg3/+sxUhl24D1q1zYP36M9j3EFFAa+sPwTAhAYTdTuOMM2wAEhsExRCKVGrh87WDptWgKBpabRAGQ2R3D4OBRklJHny+LlBU6DsTRjfcgY7bEZZ8h0RRaDab4fV6WVd9uVyOrKwsdHXl4o03CpGfX4ScHApWKwO7ncL3vhfKFhBbq2AwKNorKhwunH76QQBzkrpOiQhwEo2glEqlaEF7JMhkMuTm5iI3Nxd2ux3FxcUoKChge0XZ7XYYjUb09/ezkwFumjA7OztiCs9ut0eVip8s1NfXo6qqCpdddhleffVVdHZ2wufzsY4dDMMEKYpqBJDLMMwB8rkZTVDpmEXEarkxMjKCvr6+mHL1SEglxWc2m9HT0wNAPI2YKPkR65hoIBGT2+2G2WxGa2trzNlmvOIHiqJQUhLA+Hj4z7KoKGTWW1NTw6bpRkfFBzXh2hBFUVi5ciWrViLedDk5Obj11luxYsUK3HbbbfB4PJDJZPjJT36C2bNn4+jRo2AYJqx1N3dt5t57F0Ovvwbd3d047bTTcNVVv+LtmyuxDq0xAWeeaUVZWfLKOS5CkQqNsrL//HrtJhiz4JZENyUlJ7zUYn1G+B0SRSEQIiuSFvX5fHC73VAqnfj2twfx8ccafPmlEiUlfpxzjht5eTI4nSEVHJH0l5ZSGBtjwHeIpwD4ESKuEQBP4o033kZx8Tps2LAhoTRWourQeNulA2CLzJMtQ+FGEqSdiTDr4vf72WjLaDSir68PPp8PcrmcR1xAaLIay+boZKC1tRWvvPIKVq9ejdWrV+O1115ja9oGBwdRU1PzQwC3A9gN4AqKouQMw/glq6MYiNRyg9gR2e12LFy4EO3t7UnZESVDUCaTCfv27UNvby+am5sjuprHS1AMw6Cvrw9GozGiHRHDMDAajfjiiy9gNpvZWqZo5PT22zQuuUSFpUtluPRSRUyrIZqmcfXVNqjV/MFEoQhgwwYXOjo62EVVmqZRXi4+oIqlgurr69m/hZ53nZ2dePTRkNt2V1cXdu+ehw8+aIFK1Yn29nZUVlZCo9GwqcUjR47g6NGj0Ol0MBgM2LBhA+rq6qDRaPDVV1+F7bupKYj16/24+24/1q/3or4+fWuOZ50VIhubjUYgEITNFpJpn3VWeM1X+GeAYBC8z4gh2gSDFEcDobWkK6/Mxfe+V4Yf/agebnc17rgjH5s2abBxoxqdnSr4fD6eNVFPTw8uv3wESmUAISUkeQSg1QYgk/0cwEVQKv8Pd999N9avXx+mKBWzHeI+yDnEi3hb1MjlcjQ1NaXU0SCe7rdyuRx5eXmorKxES0sL5s+fjyVLlmDOnDkoKyv7OvMwjhtuuAGrVq3CgQMHsHHjRjzzzDPYvXu36DbjdTJnGAZ33XUXioqKUFRUhLvuuot3/Q8cOICFCxciKysLCxcuxIEDByJup6urC9u3b0cgEMC1116Lv/zlL7j33ntx5plnAsDjAFwA/kI+AgCy++67L9q1ifriqYBU8/zBYBBjY2OoqKhgLYGOHDmCrKwstLe3o7S0NCUVjt/vx8TEBMrLy6O+j6TUDh06BLvdjpaWFtTX10e9OUj/psrK8IV87nZ1Oh1MJhPPl4y7DaPRiO7ubjAMg4aGBpSWlrLXRDhAAKFB4513ZPj1rxWwWkMzY7udwscfU6ioAJqaxAdOu92OxsYg6uoUOHKEgcsVmj1ff70Na9bks9unaRrPPvssvvOdBdi1KwCG4d7gLrjdDwDoxvz58wGEBA2EgIDQoEActsl7bLZyfP65D6Wlfmg0NIDZ+OwzGaqqgNJSOdvKvrCwkJXlqlQq1sF8cHAQNE3DZDKhpKQEXq8XDMd0lDvIE+fzdKRiCgsZVFcDIyMMxsZoFBYybMHt+Pg4z56J+5mqKgajo1TYZ4SIFf16PB54PB7s3VuIzZtVsFhC/ocOB4U9e2QoLw+iqSl0HdRqNdvOpKSkhPXXq6vzIj/fhSNHVPD5Qp8HKPh8MshkZyIYHMDmzTegqqoqpnya+xvmlkOIKTO5v1cukU1OTiIvLw8KhSIiUclkMjQ3N6fstD88PIyysrKk3CTINc3NzUVRURF+8IMfYPbs2fD7/VizZg0MBgMOHz6Ms88+O+yzGzZsAE3T2LVrF04//XRcccUVuPjii8N+L88++yy2bt2K3bt346abbsI999wDuVyORYsWwev1Yvny5bj++uvx2muvwe/3Y+PGjbj55pvDzodc66qqKmg0Grz00kt44403cPToUbS3t6O7u/tGAHcwDPPp10KJIDDDU3zpABFJdHd3J1VHFQuR3B4IiPCit7cXGo0G7e3tbGolFmJFUMFgEDqdTrQLLneNKTs7G7NmzWLTGNyC1kjYskXOqREKwe2m8fTTNC64gJ8y5Q4mIdulEbzwQjUoisKqVZfCaLwEwK0AQgPIwYMH8fzzz+OZZ07DvffOxeOPezA5qQAwCrn8WTz22MW8Wpn169dj8eLFuPXWW+H1elkXCO57du6UYcGCm2G3b4PXexjZ2RcDoLFzJx1x4CaplZKSErS3t+PIkWMYHBzCtm0lKCigMW/eJIqLQ0pIIoEn7enTpbSiaRqNjQEI6idjbr+pKRhTORhPapas1/D9D0PgrgcK8c9/KvDMM3KMjWlYB47sbBkcDv42/H451Oo7oFQeg8ViYZtlEpGLXC4XTeXHOvZIqkiGYdjeXmKKOEJmjY2NaWkDk26ZucViQUVFBc4++2xRYgJOOJkfOnQozMl806ZNvPe+8MILuP3221n3iNtvvx3PPfccbrjhBnzwwQfw+/245ZZbQFEUNm7ciIcffhjvv/8+LrjggrD9fvLJJ3jooYewY8cOyOVydjL39NNPo66u7hmA32oDmOFu5qmuQRFistlsUKlUWLZsGerr69P6g4qU4iM+gXv27IHBYMCcOXPQ1dUVNzkB0QmKNBoUkhM3lWexWDBr1iw0NjaK5tgjDYI0TWN0VGxwYGAwhLdDINd5bGwMGo0Gc+bMQWFhIQoLC3HOOefgrbfewrPPPguapvH5/8/el4e5UV3ZH+2tlnpf1It6V3trum1DvAEGEsAECEMyySQsM8TYTsxiCIFhEhLmF2DCNgww2CxmIGAYSPKRzRMIJCzBZgtgZjAGL03v3WotLXWrW/ta9ftDvOdXpZJUWtqAzfk+f3YvLlWVSu+8e++55+7bhyuv3AIAuOyyq/Deex/hvvtUePDB9wD8He6772zJRs7jjjsO9957LwCkkFPSv08Bg4GHXr8MPB9AODySk7u3UrkcHBcHwMPrPQSPpwyvvdYGna4XixYtQmtrK4xGIyKRCGZmZuByuXDgwAEMDw/DbrdTsUEuxJVtES7k+ZdbNySbi2w9YSxeflmDO+/UYGpKOMwx3THC4Qq0t7ejqqqKZiwIWbHDI6empuDz+TKOupeDTCIJhUJB/SOLATZFWgzIcZHIxcl8//79WLp0qeTv7d+/H319fYLnrK+vT/I4GzduxKpVq/DHP/4R3/nOd7Br1y48/vjj8Hq9uOmmm6BQKCTrI19EUBJgnRfa29thMBgkrfKLATGJEEXgyMgIysvL8xJeEGQyqBwaGhKoEwkx2Ww2lJWVCSKmdMeW6t0gkVBDQ7LRVgw2kxmNRjE5OQmfz4fm5mYYDAZB4yfP8/i7v/s7vPzyy9ixYwfcbjeee+455pyjeO65TTh0aBN++tP11OFB7K9H0NfXh/Xr16O3t1dQOE/69yXrMiUl3TAav4uSks60lkNiDA0p8ac/LUZjoxFKZQwazf9idHQN2tuB3bs5dHYKveBKSkqon56UhJuYwrJSePGmSK4qMh/k4uhBngGTiZckGLYnjBz74YfVAhsqAJ9EXwkAqakug8FH06WsIICAHR5J3EuSXoMaaktE7qUcMkgnklAoFGhvb/9MquQIPB5PVq+8XJzM/X6/wDG+oqICfr8fPM+n/CzTcSYnJ7F+/Xr867/+K8xmMzQaDZYtW4ZwOIwtW7YAwL0KhWILf3j2CoCjnKBy/YCmsyMaHR2dnxPE4XMk9aLR0VFUVVVh+fLl8zJOPhaL0UGDQO7EREAWMfYDzy76l12WwB13qAQ9TSUlPC67LIFoNAqbzQav14umpia0f+L27HK5UiI+9lxeeuklnHTSj/G3v90DjotCqVTjnHMegl7fi927Oaxfv55eE4E4IiFFdrJzJQvxqady+NWvNCgp0UKlaoTfr4LPp8B558WpJDpd2ue115SIx1WIx5dBp3sTHNcErZaHywVotel34umcCVhTWNJjxi64rPt2ugU33xRirnZT5Bn43veiTLNtEmL/Q3Ifp6bSfS6V0GhSR5T88IdacJwv7bWS4ZEVFRWCmhMhrlAohKmpKTp6Q6vVChqPxe4O6TZebW1tKQvyZw1zc3Po7c0sxc/FyVz8u16vF0ajEQqFIqfjPPvss1AoFHSjRe7x5s2bEYvFcNVVV30fgE+hUNzI8zxVOR3VBAXIk5hmsyMCpB/aYoDjOESjUbz99tuoqanBCSeckHOToFxEo1EMDAzQ0dlutxt2ux3l5eWyiYmAXcikGlpJU+1DDynhdAJarQf//M8GLFw4gUOHknnytrY2wT0lCxgAPProo+B5/hPHhiQikQjefPMOWCznYXDwWZxyyhWor+/9ZDxDbu+N2E+ws5PDBRfE8PLLHMbHVbBYEjj3XA4dHRzIr6WzQJqaUqGiQgGv9ySUlCxFPN4KjUaB2Vng+OMT9LrkkobYFJa8djweRzgcRiAQSJl1xEZbOp0uL4LKxwuRfC6SDu4Rgf9hT887OPPM5KBC1sYqHZK1qBgdUVJVFcWVVyZd4T2ezKkw8bkrFMnhkWSAJHu+7OiNubk5OnqDtBOQ+8y2E7S0tMjyt8sF8+H4MDc3l1VmnouTeU9PDz744AOsXLky5fd6enpw9913C9bGZAr+ypTjiHvE2M/9li1bcNVVV/0IwJ0AtgEYJz876gkqE3KxIyq2LT7HcbBarZiYmADHcbKc1QtBOBzG4OAgIpFIQcREQBaETG4L69ZxWLeOwyOPPILHH38cOt2/Qa/vRUtLS1qvM47jcNttt+G5557Df/3Xf2H16tW46qqrqLjhzDPvh17fC6PRhM7OiwAke3iyiCBTXkfqfLu6ODQ0BOF2uwV2R+lAjlFfn0A0ymN4uBocVwOtlkMgwEOtBk45JZ6y6LNNsFKbHjZaI/dYoUimCTUajWCHyvM8IpEIXXCJRZFCoUA0GoXD4cgqKADyN+plF6fkeJEQlTD/3/8BH33URHf0PM/jkUe0EPY7EXA4++xhnHlmI848E3j88QcFjuWZpN+5nDu5j1KjN8h95DgOk5OT1OGlvb2dprWMRmNGj8JcIEdiniuK7WR+ySWX4J577sE555wDhUKBu+++G1dddRUA4LTTToNKpcLWrVtx2WWX4ZFHHgEAfOUrX8n5vHmev0uhUDQBcLPfP+YIKl87ItIYVygSiQSdnGsymbBy5Urs2bOn4Om5mRAMBvHxxx/D6XTCbrejoqIib2IiENdwpBCPx2Gz2ehYj/7+fpx++ukZj/nrX/+V1pkuv/wq/OQn27Bt2zZs3rwZW7duhV5/HH75SwW6uzeB40DHM5x7rrzeIjkRda4726RbhBKdncDUFIe5OQXUah7f/W6qKSo5B/I66V5LbFnDphnZ7wOgBMSCCE+IoMDhcNDOfTbaItN6CxlAKSYOm81G77N48nCmSHfHjgsAJEdpiMdppBMTFMMBH0jeTzL3yW63o6urCwDQ2NgIo9EIv98v8NPLxd0hHeZr1AZxkskEuU7mmzdvxvDwMN1kbNq0CZs3bwaQTL/v3LkTmzZtwo9//GMsXrwYO3fuzHtd4Xn+h+LvKbJ8GD97roM5IhaL0SmtrB1RZ2enbDuiffv2oaOjo6DiaDwep8TU1NSE1tZW+nC+++67WL58+byQ1KuvvopIJAKHw0Gb/YrxOsPDwzCZTJL3MB6Pw263Y2ZmBo2NjaitrcUNN9yAjz76CDt37kz7+tdddwv+9rc/p3z/61/fiOpqHps2bQIADA4mveYcDsBkAo4/fhYazRg4jhPUFsjiSyCHnILBIJxOp8DIUg6Gh1XYvfvwyIhTTkkv4yZOHJ+MGciKfBbhWCyGkZERLFy4kH5PoVBQOx0yMoL0ZGm1WlqPkarLpIPdbodWq6XjFdKNo1+/fj2mpqbw/POXQcpgF7BBofgmbrhhK846a0nKT51OJ1QqFWpra+n3ikVOLHiep705jY2NafsTWVuiQCAAv99PN7Gsl57BYEj7vPv9foyPj2PJktTrzRenn346du3aVRQJ/BGG5MN21EdQJFwvxI4ok91RNsRiMYyPj8PhcKTtoSJKvmISFMdxGBgYQH9/Px2VXazjk4VLLGgg4z2mp6dhMpnQ29tLd73f+MY38Prrr+O1117DyMgIJRuCRx99VJKc2tvPQ03N97Bx4+GFyGLh0dXF0UGIer0ejY1dNK0VDAYxNzcHu91Od6lk1AFZfIsp7U3Xi1SsY+e7CIsJmUQhpL5Fjs3zPGKxGCWuubm5lAF9LOGTlKM4ygMO95xdc801iMVitOeM47hPDHkTAG6E2GAXeAA8H8Ntt10Omy11ICERNxTjvmQCEX2YTKaMzfPpbIlisRglLKfTSee9abXaFOKajwgqEonkrfr9LOKoJ6jh4WHEYrGCxAf52BGRIYnJxtMWrF69Om2+uZgjN8g4+/379yMcDqOsrKxoURMgVO+RBSoTMRGsXLkSDQ0NeOqpp9Df349Vq1YJ1EabNm3CRx+txHvvXQ2OSyq/Vq++BosWXZAiV/d6vZiYmIBWq6WDEBOJBBKJBEpLS1N2j8Q9mkRHRFwgNUYi19qCnIXyo48+wt69e7Fs2bKchu0VsghnixTFIhetVgutVptSlyED+rxeL5xOp0AGTybOiu9bX18f7rvvPlxxxRW499578e677zJR1V8++ftKAA1QKFzg+W2ffF+L5uYHcMopiyD2WGTrnfNFTuR1ampqMrqvZIJGo0FlZaXkaHlCXJOTkwgEAohGo1AoFBgeHqakVVpamvfmab6EXJ8mjnqC6u7uLsrIDbkEEo1GMTIyArfbjdbWVqxZsybrA1cMgiLENDY2Bq1Wi+rqaqjVampRJJZKA7nXWtiFQalUIh6PY3JyEm63G/X19ZLExP7f1atXU+fwq6++Glu3bqUkpVAosGDBEvD8v2PPnmvQ0NCImZnz8e67CixYkDxPkhJRqVTo6OiQlcYg5yPe7ZLFl/Qhud1uOh2VOFqk60GSuh/p8NFHH+Haa6+l4yjuueceNDc3yzrvQhbhTO9tLua95B6wdQ1WBk8IzOFwUCfz0tJStLe347vf/S56enrQ09ODpqZVuOOOqz5pZt4FnU4LjjOhvf0czMysxfT0n9HdvQUq1VL8+tc8LrwwDouFEzyzxMVhvsgJSPbxFDs9xgoz2PvocDhoT5Lf74fb7UYwmJx3pdfrBRGXXq+XTT5HE0kd9QRVjDdLzlTdcDiM0dFRzMzMoK2tjQoD5KCQkRskhTk+Po76+np0dnbCbrcDOLyjiscPK8kyLVzZFGXsH9JY2tjYmJGYCMRjLSKRCDZv3owNGzbge9/7Hniex9q1cXz0UR+qqi7AokUO+Hz/B7d7LRyOBF56aRRmcwhtbW2ya4fZhiOSxZdVPQUCAVitVigUCszMzCAUCtEJpWy0VVpaKji21+vF5ORkyp+WlhZaB43FYti7d29WgirGIpxuN12MY7My+HA4jMrKSpSVldG6TDAYhNfrxZo1a7B//37odDocOtSBFSuuwTvv/Afa238M4BxMTyswMQFotc0wm/8b1dUBmEwJaDQ8du8GnSpM1IxqtVpygq9US0imr9OhqqoKNTU1sFqt+d+cHJBIJKDX61FbWyuorXEch1AoJJgVFQqF6BwzlrhYRWEoFJqX3slPE0c9QRUDmYYKEteJubk5tLe3Y+HChTmTYj4RlJiYVq5cSb3zWOTqCpAJiUQCDocDU1NTKCkpQWtrK0wmk+RxxJHapk2bsGrVKmzZsoXWJrZt24be3l76exYLj8rKOKLRaxGJbINe/y46OnqgUnEYGDDjzDPTP67ie57vQqxSqaBWq1FXVye4nmg0Crvdjg8//JCKXVwuF9xuN1wuFziOQ3NzM8xmM8xmM5YtW4Zzzz0XXq8Xr7zyCjWVXbZsWVGiGzko1j3JBLYORXqOxJFqJBLB7KwOjY2nob19J1pa1LDbAygt1WJ6WoOqKg4m049RW2tEdTX/SV+bcMOTqaVBDgFlI7WKigq0tbVhbm6u6NLvdIjH42mnThMCYs1b2Qm9Ho8HVquVjoh5/vnnaSuM2+0WEN7nGUc9QRUjgpISSQSDQQwPD8Pn86Gzs5O6TuSDXAiK7Z8ixKTRaGCz2eCQ8BZSKpUFpTgViuR4d6fTCafTibq6OvT29mJqaorubKUgtWj09fXhgQcewPe//31s27YNfX199DVIGjIWU+KEE2JwuZYgGv0A8TVt0wAAIABJREFU9fU8SkqqkRxTJW9xLaSnZ3p6Gv39/ejv78fk5CSsViuNhpRKJSWg5uZmLFu2DHV1dTSdGgqFUqTc//3f/41/+Id/QGlpKa1BzczMFPW8012LuAm60GO/9JKKNuISg9fu7sO2QFLvOZFvt7aq4fNV49RTH4dCoUJnJ4/Z2QQGBqIwm0PQamvAcQm43W5EIjpUVirh80VQWlqaNnLKBZlIrby8nDaNz0dvUjrkKpJIN6GXNBa//PLL8Hg8uOCCC+B2u9HZ2Ynf//73aY52GDMzM9i4cSNefPFF1NbW4vbbb8dFF10k+bt33XUXnnjiCYyNjaG2thZXXHEFrr/+evrz9vZ2qrgEgBNPPBEvvvii7GsU46gnqGKAJZBAIIChoSGEQiF0dHSgp6enYBKUk+JjiYn0TxHhg9VqxdTUVNpjF6ICs9vtcDqdOHCgFc888yU4nQo0NAAXXmjE2rWZJw0DwIsvKrF9e9KXr6EBuOyyPmzYsAG9vb0p5xWNRlFWFoTVGkVDwxro9cmGP78/+ZpsFpHdubP9RHJcrKempijpsARks9lQUlKC2tpadHV1obm5GWvXrkVzczOam5tRWVkp614Si529e/diaGgIl1xyCVQqFXQ6HSYnJwXp1vks/Bfz2C+9pBJYGTmdCtx1lw7r15fj61/PHsWQycKACgYDEAgoEApp8J3vJPDmm2XQ6YwwGACfj0cwyGHVqmnMzHipzVMsFoNSqURZWVlOMvhsMBqN6OjoEChTjxRBFeu11Go1TjrpJCgUCvj9fmzfvh0AJKcUSOHKK6+EVquF0+nE3r17ce6552Lp0qWSzhI8z+PJJ59EX18fhoaGsG7dOrS0tOCCCw4Pv3z22WdxxhlnFHxdwBcEJQsajQahUAgffPABIpEIOjs7Je2Q8gXZfUshEzEBoIMG00Fs6SMHHMdRt4nq6mrYbH145BEt9dVzOIAHH6xCIpFAdbWYgBLU5ujFF5UCPz6HA7j9djVuuOH7+GTcC4DDprEHDhyAUumDTncR4nGA4/hPGnGBr32NA8elLoJiVwYS1TkcjhQSslqttFG5qamJRkKnn346zGYzmpqaoFarMTExAYvFInidXBZ54g23c+dObNiwAX19fQKngunpaYTDYXi9XoEijiy8xVBcyiVsuUg3TuO3v23AN76RfSFMnSzM0RlULS08830e550HdHVVQamsoe9nf38/ysrKEI1GqT0RAGrzRFoIclFiGgwGdHV1Ceqnn+UIKhs8Ho+gnirHNzCX0RsA8C//8i/03wsXLsT555+PN998U0BQxcRRT1CFkojX68XAwAC8Xi+WL18uq0s7V6Sb2mu1WmG1WtHQ0IBVq1YJHmae5zE6OgqPx5P12HJTfISYHA4HKisr0dPTA7VajR/8QCMwfQWASESJxx6rRjwuJKA77kh+uNet47B9u0r0/xSIRHhs367CunVJ0YDNZsPMzAzeeustPPPMM1Cr1Xj44QtpI25DA3DeeRwsFiE5ERsfco+sVivGx8cxOTkJp9OJ2tpaGvk0Nzdj6dKlaGpqotNx04GkcsWODWw9Tc4ztXfvXkxOTuLss8+mxyFOBYSsSKMukcDPzs4iGAzSepW46TiXZ5mcZ7GiMmkHCAWmp9PbJ4mRbgaV1PdZ/0Jy/MrKSgF5sDJ4VompVCoFDubihm0g6b4hJifg801Qcnz4xEg3emP37t1Z/y/P83j99depswTBxRdfDI7jsHz5ctx1112CcR254qgnqHwxOzuLoaEhAEBHRwf6+/vnhZwAYYovGzEBmQcNiiHHPYGNmGpqalKaeqXGZgCA36+CuAE8HFZQAhL+PwWIMYnDAYyPj8Pj8UChUODee++F1WpFLBZDIpFAc3MQGzfqEYlEaOTz7rtWARm53W6YTCZaE2pqasLxxx+PlpYWNDY2FhSFiC2I2H+zxCW2HmJJ7Be/+AXWr1+fcQEi0Y14hAQxhiWNs06nk0bYrDFsaWkpbZyVOnYxYTD4EAiwTanJ97OkZK7oryXlGi9ldZTODZ6o4KQatktLS1FZWZnWa7HYDfOZUGyCkuPDJ0YuozfEuOmmm8BxnKCp+umnn8bxxx8Pnudx33334ayzzsKhQ4dyJk6Co56gco2gPB4PhoaGoFQqYbFYaJg8H87DBETGPjo6isnJSTQ2NkoSE5D88A0NDcl6gIAk+aVLH7KO5hUVFTjuuOMkd48NDelJSgrkdw8vaofJCQBKSuag0WgwNDSEbdu2CRSSCoUCV155JaanpzE7O4vGxkZKQp2dnTjllFNgNpvR0NAAtVpNF3mO4+gk1GIhE7mLe8nY39uzZw9mZmZw5plnphyPgNxndtEVK+IqKioEaRo2YhDPjyLSdzJWPZFIFPVe/PCHWtx5JxmFkXw/NZoE/vEfZ6FQ5Lf4SKEQpR4Bq4JjQTZAJpMJTqcTw8PDtGGbSLdDodARswkqdrQ2NzeHTpGdyWmnnZY2GjrppJOwbds22SMzWNx///148skn8frrrwsMEE466ST67xtuuAFPPPEEXn/9dZx33nm5Xg6AY4CggOxRBM8nx5sPDQ1Bq9Vi4cKFR2woGSvdNhqNaYmJ/O7g4KBg0GA2sI4PBGJiWrx4MXQ6Xdp0kNRsJ5UqBr1eAb8/9Vyrq6MAFLj++lL8/Oc82OylWh3Hv/xLKf7t3/4Ze/fuTfm/CoUCJ510Es4++2w0NDTkNGKhWCCKsXw2JSR62rBhQ8riwx5PztRXKZk4u/CSCC4WiwnmR1mtVsTjcfA8D6vVKpgflY20Hn/88RSbISDpVK5QANu2JTA7q6ajMFpbZ6BQFGcMRSZnfPbn+cJgMKC7u1tgmURI3+/3w+/3w+PxwOPxUM9OYgQr7jkqFop5PCmj2F27dmX8P8SKSc7oDYLHHnsMd9xxB1577bWswxHz/RwRHBMElQ5koR4eHoZer8eSJUuKNso5G1hX8/r6elRUVKTsfliQhyhdNJQOrMycEPHk5KRgOGG2hZ6IHogYArAjkXgAixZtxHvvdUCY5uMxM7MTjz46jU2bNsHjmcVjj5XA5ytFVVUUP/iBGuvWARbLtfjzn/+MXbt2YXp6GolEgjYU63S6rFYz4nOORCIIBoNUllwIpEhdLv72t78hHA6nHTmQy7HlRBE8z9PGWXZgn9frxczMDMrLy2mqiwgLWE/C0tJSPPXUU7j00kvxl78cwI4dO3Do0IlYtKgnxfD2zDM5nHFGAo8//l+UxA4eTD8ePRdkI6dCodFoYLFYUpy22TRhXV0dIpEImpqaYDQaEQgEJHuOCGGRv49USjAb5ubmck7x5TJ6A0im8H7yk5/g1VdfTVmvxsfHMTExgRUrVoDjOGzbtg1ut1sQVeWKY4KgpEwzp6amMDIyAqPRiN7e3qxhPVnoCw3Jiau5zWZDU1MTVq1aBYVCgT179qT9P9FoFIODg3SByQWkvjU9PZ1CTED6KCRVHp7A738fw6uvvoqf/vSnAID33tuCVBNiBaqrv47zz3fjgw8+wIknVuNb3zLg8ce3f2IQm3wti8WCLVu2YMuWLRgfH8fLL7+MP/3pT7Db7RgfH0cmsOccjUZhtVrh9/uh1+ths9kEzg+5RA+Z7occ8DyPxx57DBs3bpw3F4d0EPe7kZqNVOMsqc94vV68+eab2LFjB1wu4E9/ehoA8O67V0Gnux82Wx8uvDCOrq6E4DMkjrAKjQLmm5zUajUsFossL07yGSeSdqmeI+JgzprB6nS6lIir2LW5bMinBgWkH70BIGX8xo033ojp6WmsWLGC/v9//Md/xPbt2+Hz+XD55ZdjaGgIJSUlWLZsGV544QXqdJ8PjgmCIiAjN0ZGRlBeXp6TszlR2uVLUGJiYs1jMzW8krk+pA+EvZZsvno8z8Pn88Hj8UClUqXMgMpETmJ5+B13qPDqq69i9+6fMr+Z6iIBADMzJQiFQliyZAndXYrdy1m0trZiw4YN2LBhA1588cWM4wfIOZNZU7Ozs2hubkZ7ezutxwAQuHOT8Rasuosd4kdQ6EL72muvQaFQ4OSTT0573vOBV17RYPt2NaamDjfQrlyZPk1Grv+jjz7C3XffAwD405920N/huAh27/4e2touxO9/fxEuvjhAjXVJmkvcBJzJbkiOvdZ8kZNKpUJXV5dsC6Bsm1C1Wp3WDJYQ18TEBAKBAHieR2lpqYC0iBqz0MZjKcidBSVGdXW1wIaMxdq1ayk5AcDIyEja4/T09GDfvn05v34mHBMExfM8bDYbRkdHUVVVheXLl+fsWUXcJHJ1RI/H4xgfH4fdbqfjNsQfgHQPaigUouQkhXSd+zzPY3Z2lo6iMBqNdAAbS2rpFsxUeXhSndfffwYefrgaV1999SfCBicA8UwjJUymOFpbWyWPnQ0NDQ1p89pJp4kYdU5vbGyk7uCJRIKOjiC/W1ZWhvLycrqgsuouj8dDm0A1Gg1dPLRabV5EwnEcHnvsMVx22WVHxGKI4JVXNLjjDg2iUWED7ZVX6nHCCemFNOnmNgGAUqnDV7+6FTU1vXA6eZSUJC12yLRetncrHo/LHubJ3hcp6yGxcq9QEHLKRfSQT5aENYNlo4VMnnrk3s3MzOQ17FAKgUDgiJUojhSOCYIaGRlBJBI5oiM35BBTJhDHilxek+d5mi8vLS2FxWKBWq1Gf39/Sh8Pqxhj/wYAh0OJw6k7nv7tcAC9vb3YunUrNm/ejK99bQwvvFCLRILk4JVQqSK4/PL5cVOenJzE1NQUtVsi10PSWeRDzpraiolBp9Ol9BTF43EEAgGEQslx5cFgEAcPHpSchZQOr7zyCkpLS7F69WrB9+c7rbd9u5qSE0EkosCTT5bhS19ypf2/l156KaamVuPPf/4BOC4ChUIFk+l6OBx3oLPzftTV9cDvV6KpKYGqqipB6ogl+ng8jsHBQZpWZSNUcVpVHFVlK6Cz0RVxMmfBHkN8nOSMLvlDSQmKqazL5Kk3PT1NSX9sbIy63bPRlsFgkF1PZTdmRxOOCYLq6uoqeOSGHEdzILnYjY2NCQYU5vrA+3w+DA8Pyz5nnufp8L6SkhJ0d3fTCJHjOHocqXSKVIqwoYGDw5FKMg0NyWP09fXhoosuQn39IBKJZ1FV9TN4PDoANiQS2/Dee99CZ2cvLJbCF2Zxj1ZPTw+1bxIvXOkiUfK7UsRFoisSbZFBjwsXLqSjONheGtJAyy7CHMdhx44duPbaa4vuf5cOJCKcmpK+ZrdblTWFxPNLsW7dVvz5z5txwglfx+zsOaipcYPj+hAI8PD5eJx7bur5swuvy+XCokWLBL1bUqIMtulYo9HIEouQnxPJvNS9FB+DvJ8dHR15RRNS/VbFhkqlopkNopwDIJgZZbfbEQgEkEgkUFJSQonLaDRCr9enPcejadQGcIwQVLEMYzNFMywxmc3mnImJfAB9Ph9GRkZkLWw8z9PhfTqdjg7vYyFevOWkUKRk5SUlPBYufBlzc8thtVrR0tKCu+++G0AcXu9fkRQ/JInw+ef/Crf7IVxxRQ8sFl4ySiPnkq6OxvNJ41a73Y7y8nIsWbKEmoaSBUtcC0kHqQ8zqQGw5JVIJDA7O0t/TiICcbQlXoTfeustGAwGNDU1wev10lrNfJITuVcazTRisVTnaoViGmNjWmQSQ5pMHHy+XixdugHHHbcJMzMKfPzxJsTjChiNcZxzTvqx9VLnlKl3i4xHn5qaQjwep2ku1u0h3eeFDMiUi7a2tpTm088apJp0ydBINlol948Ql8vlokpeUt+y2+0pLhtHC44JgioG0qX4CiUm9vgulws2m00WiRBi0mg06OzslJVnl5vfF8rKeahUbpxwQhy7d/dg9+4aaDRALPYbAMn7kUgII0uOi+HttzchHN6EBx/ckFXIIf6a1M8MBgN1tWCjHrJY5VuvINJs8jcAwWtaLBb6c3GaUKFQwGg0oqysDApFctbWzTffjOuuuw4ajQazs7N08ixxLtDr9XTkfDqylgtxWuxHPzLi1lvj4HlW7BHH0qUG7NmjwJo16Y9FDFwtlk3gOECj4dHaqsQFF0RkE5Oc82XdHsj5E0PdUCgEt9uNUChENwVsdEoiVLkE1dramrdrwZGEXBcJ9v6JZ0YR0n/jjTfw0ksvYWRkBCeffDKOO+44LF26VLIeKkYuTuY33XQTbr31VkGZZN++fVRuvnfvXmzcuBEHDx7E4sWL8Ytf/ALLli2TczvS4pggqGJFUGwPUiwWw9jYGJxOJ1paWmRNzs2EQCCA4eHhrDUyr9cLq9UKtVota6psviqpdes4rFvH4frrr8ebbxrx5ps3AkguMrFYLdTqmxGPKwG8AIVCA4ADzycjKKVSg69+9UEoFL2QOyKDXFs4HIbb7aYNlTzPC/zx0hXapYru7HWzdQo2veT3++n9tFgskuIZ1n1cTFrPPfcczGYzjj/+eCiVStTV1dHojDTQBoNBWiAn56ZWq2m9Rq2W52cnVbM588wEnn1WjYMH/YhGS6HRBLB6tR4mUwQuV+aPt9jAtaEBOPfcaE7klMtzxZ4/MdRlZdxEDRcMBhEOhzEzM4NIJEKbmh0OR8Z6YGtr67zZkRUbhdocKZVKmvK7/vrrcf755+O2227D448/jv3792NoaEjWM5WLkzkAfOc738FTTz2V8v1oNIrzzz8f11xzDa644go8/PDDOP/88zEwMFCQAOSYIKhigERQxSYmAJiamoLT6cw4aZUQk0qlQnt7u6yIqRBiDoVCePXVV/Hmm28C+CMIORHE41rodNchEnkB7e0/Ac+3YHQ0KSU/55wHodf3Qm6WJRAIYHx8nKZ9yI6MrZ1lr6fIs8Yh5BQKhaiKr62tDaWlpZLpxkw1iXA4jKeffhq33HILlVqzdUqFQkFrB+QaeJ6H3W6nbuZOp5Oq4FiPPbGQI5OgYPnyBCwWLd544/s4++wHAXBwuZSoq4sj20ecGLXO9yh1OY4CrBqOBanJqNVqSW89vV4Pi8VScOQ0n9cvRiEtK1IgRrHl5eVYs2YN1mQKnT9Brk7mmbBr1y7E43Fcc801UCgUuPrqq/Ef//Ef+Otf/4qvfvWr+V7WsUFQxSockumpxSImALDb7bDb7VCpVJIpRJ/Ph4mJiZyICUgt0JN6TzaEw2FYrVa8//772LZt2yffbRAfHQCHSKQcFRVV6OpyY2DgXFRUbILJBOj1vfD5FDjvvMwf+FAohImJCcTjSVm6wWDAwYMH4XQ6qey7WB9iQjTEgDYYDMJsNgu8FtPJ9qX+DQB//OMfsWDBAvT19dF6FpsaTKckVKvV0Ov1aGhooMdloy3WHJYUyDNFWyRVd/LJD4LjgEAA8PuVOO20IIDsqtVCBjzKQTHIT6vVpkyJJfesrKwM4XAY77//PniepylVEmHIbdIm79+RQCKRyFtRLAXxqA05yMfJ/Nlnn0V1dTUaGxuxZcsWXH755QCA/fv3088BQV9fH/bv3/8FQc0niImrw+GAWq3G6tWri6byYQcNiocW+v1+TExMQKFQoK2tLSe5rHhBILvXTB9S1pHhtddewy9/+Uvmpw4ApNqeJCcA0Gii+PKXr8bs7G4sXTqLwcHvIxrlUV7OS47IIIhEIrBarQiFQmhpaaHqOY7jYDabMTc3R1NiJAdP6hK5SG8JiADCarVidnYWTU1NaG9vz3njwi7IoVAITz31FO666y4Ah3ffbLQnlljH43HY7XbMzs6itbVV0LeVLtqKRCIIBALwer2YmppCNBoVTOwtLS1FR4ceF1wAwaylE0+cQ2trdtVpoeSR7R4Wg5zS1aA0Gg11ryfgeZ6ORff5fDRaValUAtKSsij6PI/aOBJO5t/+9rfx/e9/HyaTCe+88w6++c1vorKyEhdeeCH8fn/K/Cm5ruiZ8AVBpUE0GsXY2BimpqbQ2tqKE044AQcPHiwaOY2Pj8PtdtOviXSadKLzPI+WlpacpbJSCwIhP6lzj0ajsNls8Hq9aG5uRkdHB/r6+nDqqafiyiuv/CSqewDAjQAMOFxTiqKjI4KysjVQq+tgNOqxciUHlwvYuFF6QSKDCX0+H8xmMzo6OgAIUytkASFIJBIpzbXxeBxarZZGWWIBAguO4+B0OuFyudDQ0ICenp6ivIe/+93v0NfXRyXWUmAbol0uF5xOJ+rr6wXnwKoI2XtBviZKwpqaGrrJiMViVBk3NTWFcDgMjuNwyimHZyCFQiHwfObrLJQ8sm16ipU2TEdQ9fX1AnICDhO9uPeI9LoRJaHf76cWRYSwiDv+kcCRIKhiO5mzDi8nnngifvCDH+C3v/0tLrzwQhiNxrxc0bPhmCCoXHbK0WgUo6OjcLlcaGtro6k8Mna6UPC89KDBRCKByclJqNVqtLS05PXGpusVkVooWKugxsZGtLW1Ce6TXt+HE064E++8cx36+rTYt+82AJcDaIBKNQOTqRpzc1r4fAZUV38JQHLybYM4G8i8lsfjQVNTE53FI24WlgIx52RJi0QWpEufFNNZGx+ySLtcLkH/VDHg9/vxq1/9Cvfff3/GNBerSKysrKRSeRbpJPDkbxL9sa9DrpNEW6SuRkiLkHk8HofH40mpbSmVyqJFNuneu2LWtKTqgGQgpVyo1WpJCTyJUP1+PxwOB3w+H/bs2UPvL/mTy6ReOZiPYYVig+X5cDJnwdYVe3p6cPfddws2Lfv27cOVV16Z45UIcUwQFJC9SJuOmAiK8YHjeR4jIyN01DdJR0xMTCAYDKKiooJGFbki0/WxRqKJRIJOsSVWQVKL5O7dCrS2noTp6ROg0UwDeAvACzjuuPWorR1HWdmPsWePHv39CqxaRUazC+tOHMfBbrfD7XajoaEhxf1Bbh+T1LUSCTK7ayQ9Sm63G5OTk1AoFFCpVPD7/eA4jpJXoWMTfvOb32DlypUZ3yu/34/x8XHodDosWLAgp3oDqWOJ3xdxw7G4kVulUqGiogKVlZVQKpVQqVSoqqqiEajL5aLRFmmeJdEnuSe52A2lE5AUW3AhJsLq6mq0tLQUfFz2OaqpqUFFRQWmpqZgsViohJt1Mler1QLSMhgMeW96ip1OzCfFl6uT+f/8z//glFNOQWVlJfbs2YOtW7fitttuA5CM1lQqFbZu3YrLLrsMjzzyCACkdfWXi2OGoNIhGo1iZGQE09PTaGtrg8VikfzQFbp74jgOw8PDNAwOBoN0bo/ZbKYFX/FrpbOKYb/OtiAQAYbNZsPU1BQli0zpDIcDqKsD2tu/hlde+TdyJHi9vWhvr0ZlJYfu7gDsdg1cLuFo9qTDQVKZWFdXR0lQyv2hmCBkX1pait7eXipTJ7vkQCAAl8tFxyawKUK5ggyv14vf/OY3ePjhhyV/Hg6HMTExQdWBuVrtZNtoiCHlkhGLxeDz+aiqjdTuCGEnEgkagfp8Ptq3xQ4+JBEXuSdSTdZS5zQfakBWvFBZWZm3z2M2sE7m4qgdSNajiSGszWajTg9iUYZYgSmF+Yig5tvJ/Ne//jU2bNiASCQCs9mMH/3oR/jud78LIJmK3rlzJzZt2oQf//jHWLx4MXbu3Fmwx+AxQ1DiD76YmLq7u+ct/5xIJDA0NAS/349QKASr1YpoNCpQkc3OzgrmNhFk282SSEwqGiELVjgcxsDAABobG9HX1yfrOk0m4O23H8WBA4+yV4Lx8etQXb0J9fWV0Ok8OOmkODZvPtw4OzXlgt1uR3V1tYCYcnV/yAWkbqdSqdDZ2Slw0xDvkglYRwiimuN5XuAlV1paKkjt8DyPX//611i7dm3KDj4Wi2FychJ+vx8tLS0pBWM5kCPFFoN9L8nGYGpqCiaTiV4vURIqlUpEo1EAh1NehMRI07FU86x4zDy5J2IiyvYs5hqhsdelUqlQXl6el7hFLrJFNRqNJsWXkHy+yMBD8iwROyg22mIX61zdMbIh31EbuTiZ/+pXv8p4rOXLl+N///d/cz6HTDhmCIogEolgdHQU09PTaG9vz4mYyIcylweLmGnOzMxgYmICkUiEKtfEvm25+gVKSckJSGHe4XBQiXpNTQ1dJFg3BnYBJj//8pcBm+17aGhYhd27tyCRiEChUONLX3oIPT298PmAYFCNk0+Og+c1NBXC2hKxaaj5ICYiiY/FYjkLStRqdcqsJI7jaB2HjSyI/14ikcAf/vAHPProYdImE5FJylRcy5OLfMiJgOd5KiCpqqqSrLcplUpqMZRJAs9GW6ySkKQJ3W43jba0Wi0dM2EwGNI+i+m+l0nCz/4+z/MoLy9HZ2fnvJETkL+TOTvwkD0WqW253W6Mjo4iFotBq9XCaDTSaKxYc6PyjaA+6zhmCIoM/ZuZmUF7ezsWLFiQ88NOmnXlhq2xWAz79u3D0NAQlVRXVFRIvq5arc6JoNKlUnieh8uVjGLIYuVIjsGlPweQdTHp6uJw0UUK7N7dh3D4Abz11iYsW9aLJUva4HYrYTIBS5d6UFurwf79Luj1eixatIjaEs0nMbHRitlsLpq1DZviEr9eKBTCQw89hBUrVtCx4EByw1NVVZVznYlFIeRE2hG0Wm3acyDPSrqFUJwmlOrbEkdbiUQCHo8HLpeLbr5ItMUKMjLV++RmCkpKSuY1ciIoZl2IRHxiGTchdKfTSWvPpHeLjbbk9m4ReL3ez4XFU644ZghqamoK5eXlWLhwYd4Pei4ENTs7i127dmFubo4uotnUanIJSoqceD5prmqz2VBRUSEYFig3OhPbA1ksPCyWBIAluP/+i2Cz2dDR8RY2b14Hn8+H/n4HpqdL6UA4dnFj60zscdldca7vQzweh8PhgMfjKShayQU8z0Or1WJubg67du3CE088AY1GQ0ea1NbWIhwOY3h4mO6Q2RRhSUlJxh1yvuQUiUQwMTGBWCxGm5ylIKcmJLe2xdYQAdDUX0tLC70OYlUUCoVS5kdJ1baywWAwUAf0+QaZDTafIGawWq1xt8HwAAAgAElEQVSWyrbJ+BK/34+5uTnYbDbau8WSltFoTFu34jiuqDWtzwqOvitKg9bW1pxmK0khm6M5kEw7HThwAAcOHEBDQwP98GaDXIKSSuuR9Jp4nDt77GyLlJicxNiyZQueeuopvPPOO2hubqY7xIaGBuh0OsmISWwbJPV6Uk3EYsshtrYi7iOaT5BUKM/zeOqpp3D66adjenoaGo1GMNKE/X2yQBOpdzgcpmkgstiyjhC5khPb7Jtt41OIYCFbtEUW0traWsFzyyoJgcPRFqltzczMwGq1phjDSkVber0eXV1dGBoaOiLv95Fq1BW/Dju+xGQ6PKWa7d1yOp0YGhpKGb8Rj8dTHDaOJhwzBFUMZJoJRXbRTqcTALB48eKcdvdyohx2wSGLBFGtLVy4MG2KiUyiTYds5AQkr6+lpQUHDhzA88+HEI2uREnJLJYvn8PSpbGUIjB77Gy9QuLXZr/HzoI67rjjBB9s9v4SKTIrYS8EwWAQpaWl4HkeExMTeOGFF3DLLbdkjFZYLzm2HkAWaNKzRdJh7AJtMBiypsOIMlIOSc+Xt14kEsH4+DhUKhU115UTbZHIqba2Nmu0RdoHzGYzdRg52ghKTrSTqXeLiDKeeOIJPPvss3C73Vi/fj16e3vR29uLM844I+O15OJifvbZZ+P111+nX0ejUSxcuBAffvghAKC9vR1Op5O+3oknnogXX3xR1r3Ihi8IKgdIjdyIRCIYHh6Gx+OByWRCVVVVXgtDtg8gu+CQURtarVZyJy+GSqWiw+PEyEZOxAIpEAigrOx4AP8Hn+9dNDevgNdbhpdfLoFK5UZtrYOmuEikkG3RTQfS4Do5OYmysjI6ckN8nulk9+LXyxSdSf0fnuexadMmKJVKXHDBBXjjjTdw9tlnY9WqVTldB4G42ZiIbSKRCI22pqenEQ6H6W6alb8Tx3VWgJIJ80FO8XicuoC0tbUJGsnl1LYI0bCQiraI4tNkMsHj8WBiYgJ+v5/2CZL7SMx9i4kjRVCFGMWyqtTa2lr87Gc/w9VXX42LLroI1157Lfbt24ddu3Zh3bp1GY+Ti4v5Cy+8IPj6tNNOS+lvevbZZ3HGGWfkdU2ZcMwQVDEeZjaCikQiGBkZwczMDDo6OtDU1CR70GCuIAsOMY6VO2qDIJ3DRCZyIkIEr9cLs9mM9vZ2/OIXKhgMqxEIPItodAKVla1QqVT4+ONmrF3bkJLiYnfFbKSQqeeIXKNOp0s7/iJXZCI1KTIj0enc3BzuuecexGIx3HHHHWkJMBewKU2y0LAjIojUOxAIwG630745o9EIpVIJn8+XUXyQ7r3OF2zk1tDQQF1A5CCf2pZWq8XixYvp9SmVSuzbt4+m6NmhfcTWiG2eLaSGdCQJqpj1Io/Hg5qaGixduhRLly7N+vuFuJiPjo7i9ddfx44dO4p09plxzBBUMUBmQvX391OZ+sKFCzE3N4fh4eG8lViZoFAo4PP5MD4+DoUid+NYQLq+lS71JnaaIE2RPM/D4QBqao5HMPgXhEIHUFLSCqMRcDoP15N0Op2kwwNZdKempgTKJUJcSqUSdrsdQDJlIJd8iwmSUrTZbPR7pG/oZz/7GSwWC2688UaYzWZJF4VsYhA5NSe1Wg2dTgeXy4V4PI5FixbBYDAIptKKByKy5F9MkBQyidyIQrMQZIq21Go1Ojs7odVqKclGo1Hq4lBWVkbJnKTE2UmzIyMjAn89OSPSWXxeCSrXHqh8XMwJnnzySaxduxbt7e2C71988cXgOA7Lly/HXXfdJYso5eCYIahCI6hoNIqpqSl4PB4sXLiQ9k9NT09jfHy8aOTELmjsOIp8/fkA4YA+8rV4l00GwrlcLphMJvT29tLdODmnhgbA59OjufkaqNXJD4TfD5hMQp848b2QGk5Heo7m5uYwNjZGZyKVlJTA7XbTBTdXuW2+8Hg8sNlsMBqNWLJkScprhsNhHDp0CC6XC2azWfI6s4lB0n2P9d6z2+2YmZlBU1MTOjo6BDUcvV6f0mwcCASojdHIyIjgd0mqNddu/nA4jLGxMSgUChrFFiKFlwMiPCFN1jzPw+l0YmRkBA0NDQKVKPsZIc9JQ0MDfc6j0Sh8Ph/tQQoGg1AoDvsXsv56LD4tkUShILOg5CJXF3MWTz75JG688UbB955++mkcf/zx4Hke9913H8466ywcOnSoKLL3Y4ag8gUZtzE1NYXa2lrodDpqUulyuTA5OQmgsF4WAkIc8Xg8pam30OOSCEpMTmxDb21trcD9gQgPyJ9TT03gV79SA6iG0Zg0h/X5gK997bCCT+49SCQS1FWaHdOdTgVHdsLFnhPFeuYRuTy5LwQ6nQ719fX493//97w94NKJQMjfpEWgrq5OIIDIVD8jSsrKykpBfxuJtsjYEtJszKZYpaIKts7U2tpKn7v5JielUgmLxULJye/34+OPP4ZOp8MJJ5yQQiSEpMgzSt4rVklaVVWVEm2RCHR6eppuikjjrNFoRDQaPSKbofmIoFgyKLaLOcEbb7wBh8OBb33rWynHJLjhhhvwxBNP4PXXX8d5552X66Wk4JghqFwfPJaYiHms3+/H2NgYAMDhcAhSQeLXyWT1Qr6WwvDwMFXMpWvqzRVEZi5WARKFXGVlJXUfyNRka7EAF14Yx+7dKjidCphMPL72tQQsFvmLWCKRgN1uT9vLJKWCI2NIxClCYkuUT6RAXCii0ahkSpGIYXQ6Hc4++2xcffXVBfuKScHr9WJ8fBxGo1EgBiHIVj8Ty/TJrCgiyCCbDFaQMTs7S2s45B5Go1HMzc3RtG4uaclCoFAo0NXVhdLSUsRiMepXuWDBgrR2UYRY2U0KS1LihmPyPJOUsslkEkRbRBEXiUTw/vvvA0BKbauY730uzf5yIE7xzZeL+RNPPIG///u/z+rYUsxn5pghKEDejYvH4xgbG4PD4UiZnEv6oCYnJ6mcXIxMBJQuBUTmJIVCIdTV1Qlyw8V4o8mHkew8xX1Tubg/WCz4pHlXeA3ZzjPfXiZSKE+XIiSRgt1uT2mUlUoRslFCc3Nz2tw9qfHccsstgh1isRAKhWhdsaurS+AfKBeZNjziXjmtVgutVpvSn0QiN5VKBbVaDafTCY/HI0gRzlealVy7wWDA5OQkxsfH0dbWlpfLC3mW0jnAZ4q2KisrUVVVBafTiRUrVtANUbpoy2AwoKysjNZOc0U8Hi9qjXV2djanKQi5upgDyef1mWeewR/+8AfB98fHxzExMUHv27Zt2+B2u4v2mTmmCCoTMhETgVqthtVqLdruIBaLwWazUbeJRCIBg8GQ9vhyojOp/0vUh6TordfrsXDhQur2XYgtUTZJM0lfEeslOTJpgkzExyoDSaMizyedvMlEVTZFSHbooVCIRgni2hyLdevW4ZJLLoHZbJZ1rnLB2jSxabRcUegulRAkACxcuJCmNklUQZwNZmZmEAqFqPs7K8ogEYyUIEQOOjo6kEgksGfPHlRWVmLFihVFd0OQE22RNDcAqtIVR1sABNHW+Pg4AoEAAOQ8O6oQmbkU8vHhy8XFHAB27tyJyspKfPnLXxYcx+fz4fLLL8fQ0BBKSkqwbNkyvPDCC4JaaSFQZHnI5y+2/xQQi8UkB/eNj4/DZrOhpaUFZrNZ8uHheR5jY2PYtWsX+vr6CjoP8QC/uro6KBQKDA8Po6ampuCaEyAkM5/Ph/3796Oqqgqtra0oKSlBIpEQ2BLls0PORE5Eqm21WmE0GtHc3JyTBLhYaQIik7bb7dDr9VCr1XSsCbHqyVdMkAvYyb5NTU3UuDcfFHJvyLPn9Xpzcl0nNRz2TyKRgE6nExCXVqsVEJc4VUi+19jYCJfLRZs+i61AlItAIID+/n6UlJSgs7MTGo2GRlpi4iV1P3JN5PkntS3yh9T8xLOjCNEdPHgQZrO54GmzBNdccw0uvfRSnHzyyUU53qcEyQ/DMRtBJRIJjI+PY3JyEmazGWvWrEm7q+F5HsPDw5ibmyv4Ne12O6anp+lMJlL3IQ9/oXZM7DkTNRbHcdBoNLBYLDTKAITElK65NZ1kOhM5EQNT8pq59jIVi5zINFuj0Yienh6ayiRFc5Ii9Hq9VEyQLUWYK3iex8zMDGw2G6qrqwue7JvvvWH7mUwmE3p6enK6LpVKlZJmJa4GrJIw26wtMjpjZGQEXV1d1FniSCMej2NkZISqclmizrW2RYY/1tfXS0ZbExMTgmjL7/fD5/PRtGuh15/vqI3PA445gkokEpiYmIDVakVzczNWr16dMa0gHjSYD1gJd319PR0WKF7kc3U0T4dwOIzJyUkqtigrK8PevXsRCASg0+mgVCol04SZvgYOkxYRXYjJLRgMUsl9JkugTCgGObHzoViCJORE/pZyLicqQqkUIauCk5OKIqmgkpISmlItBPneG3E/U7HSaGyzMQt21hYralEqlXRsRl9fn6zBfsUGIerh4WG0tLTAYrFkPAc5tS2WtOLxOD0e65LBRlv79++nvVvFmNQ7NzcnaPY+mnBMEZTD4cDHH3+MpqamrMQECAcNspCbaxdPlmWn2EpFIIVa1BBbomAwiObmZror5DgODQ0NdHQ1sdNh6wlyi71SnoGEEMlIETZFmUtkVig5RSIRqswTz4diySkTxGIC4HB6i0zkTaci1Gg0UCgUVCEYj8eL2nSc670Jh8O0zlQsVw45EM/aCofDGB0dRX19Pbq7uxGJRHDo0CFEIhFoNBoamZEFer5899h0npR8PRdkqm2xKUKpaEupVKKzs5OuP2Q2lN/vx+TkJAKBAG1kZ4krnXvI0ToLCjjGCKqsrEwWMQGHBw0Gg0HB9wmJZNrhcByX1eRUiohUKlVGU9d0YMUWLS0ttMubzfmbTCbqlCw1TRaApOO21LUTsPUMcWMpgdzIjBxbitBYIpPaHLDn0dzcnOLwLZec0iFdeiscDiMQCNAUYSQSoYtTXV0dmpqa8lLnFYp86kzzISdnz2PFihVYtGhRyu+wTbVEeMDzPF2UyX0vhEwypfOKiWzRViwWw8jICH0WyWed53mUlZXR8yKfBXYEx+TkZEq0FYvFUF1djVgslvcsss86jimRRCKRkFXjicViGBgYkDRYPXjwILq6uiQ/MOLeoqamppRFPtNCMDMzQ9Vdcq+H1LQaGxvpRE+2215uCoWkH8ifQCBAB9CRnhC9Xg+tVotEIgGn0wm3242GhgYq8sgXhdRViPCAvX7yM3LMQsgpl/MgzdylpaW0LlNIijCf8yBN1yaTCfX19Vnfl0LJO90xiXyduJKQ5nY5IDJvn89HySsajUKn09FIS47Mm03nmc1m6gDyacDlcmFwcJCeB9tYzfoTsmCb5Ml1stHWzp078fTTT8PpdOIrX/kK+vr60NfXh69+9asZCf3+++/Hjh078OGHH+LCCy/M6qt377334s4770QwGMS3vvUtPPTQQ5QQR0dHcemll+Kdd95Ba2sr7r///nxNYyXfmC8ISoRIJILBwUFEIhHJnw8MDKC5uVmQtiGF8MnJSZSXl6dVrGVL4c3NzcHj8dAIKB3ENS2TyUQFFmLlVCEgUQLZyQUCAYRCISqHr6+vL1hIkA85sdL16upqNDQ0pFVeiutl7OuS38lXJs3zPBViVFZWorGxUZJ42HEbhPzZFCGrgMv3PpKG37KyMjQ3N8siwPkgJxIJlZaWwmw2o7GxsShSfSLIIAIDn89HLYwIYZG/NRqNIJ1nsVjmVaGZCeFwGP39/VAqlbKmLqerbRGQ94wl5lNOOQW/+c1vsG/fPnzwwQe48cYbM77O73//eyiVSvzlL39BKBTKSFB/+ctfcMkll+Cvf/0rmpqa8I1vfAOrV6+mprJr1qzBmjVrcOutt+L555/Hxo0bMTAwINgsysQXBMVxXMYUWjgcxsDAQMbfGR4eRm1tLcrLy+kCNTExAaPRCLPZnPaDIKe+RIxAu7q60p4/mzpsbGykD+p8jVgn6UiiRKuoqEBtba1AvSWua5EoIVstIR9yIgX/bNL1QhbfTGRG/g4GgxgbG4NGo0FLS0vOKRY2RUiiVrElkZyJvGydibQQyH39YpJTLBaj9lxEIFNTUyM7G5AvEokEjSgIcZENQH19PWpra+dtPEcmcByH8fFxOBwOdHd3F9QXJFXbIu9bf38//umf/gmDg4M5H/fGG2+E1WrNSFAXXXQR2tvbcdtttwEAXnnlFVx88cW0nt/b2wu3203T32vXrsXFF1+Myy67LNfT+UJmngnBYBCDg4NZIyyitBM3vWZaoOSKH9INLWQjNNaWiH1gi01MQHJhJhECGYpICLi0tDTFsZwstna7naZH09W1ciUnohAUK/OkUOjim6luFolEaD2ALMT51M0UCgX1xGNBmmQDgYDAkkicIgSQVz8TOadikRPrENLc3Izq6mooFAraczffIDOlysvLqZlzZ2cnamtrKWk5HA4Eg0E6l4sVZcxHqnV2dhb9/f2oq6vDypUrCxZ9SNW2YrEYHnjgATzzzDP44Q9/WNDxM2H//v04//zz6ddLly6F0+nE9PQ09u/fj87OTkFtdunSpdi/f3/RXv+YIqhMCzgZ/heLxQR/4vE44vG44OuxsTGUlpbKsqjJRZknlpmLIzSxLRHbk1RssITAmqhmOnfx9E82tTU9PU0nyebitk2UeYQQsvmAzUfailwL8RAkFkniyIo9Bxbsc5eNzLRaLXQ6Xdr76Ha7MTc3h2g0Cr1eTwdkRiIRWSnCYt4fskljN01AUl6dy9yoQpFOnVdaWor6+nr6e2SelM/ng81mg9/vp88jmyLMN2UdjUYxODiIcDiM3t7eeRsZs2fPHlx//fU466yz8Pbbb8+rOtPv9wueRfJvUhsUb4wqKiqogXYxcEwRVCao1Wqo1eq0hDM3N4eBgQE0NjZi8eLFaG9vFxCXmMxIPSgX2Tg7t4lMzdXpdDR3TYgJmB9SApIWODabDbFYDGazOSshZIJ4kiyQXJBJHcbr9Qo89NgUoVqtht1ux+zsbAohpMN8FfyJ8KC+vh7HHXdcwY27mb5mv0deh4wrIbW36upqNDY20qjV5/PRGVGZUoTFuj+Z5Ovl5eWSas75QCKRoNOsFyxYkHW8g1qtRmVlpeD3SLrW7/fD6/XS/kHiBMHK3zM18tvtdoyNjaGjowMmk2lert/r9eLmm2/GoUOHsGPHDixZsiTld7I5mb/xxhs5vabRaBT0gJJ/k/uSjyt6LviCoLLA5/NhYGAAPM+ju7ub7mTlNKGSmhdLYOkiM7J4xGIxHDx4EEqlEh0dHdDr9QJiIoqeYkcIrGEt20NVTJBoUpza4nle0CBLFgmtVouKigrah5SprjUf5ETSm2VlZUVtcJULci3hcJhGn+SZAJLmxeTfZEEUp1rZFgKixJRqIZADEkXOzs7CbDanEILRaDwi5CRW561YsaIgkY7BYIDBYKBtGEAyhUaiBKvVCr/fD57nUVpaSlOEZWVliEaj6O/vh9FonBcvQSB5vX/84x9x++234+qrr8YDDzyQ9nOQzck8V/T09OCDDz7At7/9bQBJ13OTyYSamhr09PRgeHgYPp+PktIHH3yAiy66qGivf0wRVC4Psd/vx+DgIGKxGCwWC623EHKRA6VSScdHZMPc3BwOHTqEuro62mVPpomyhMZ6hBUD8Xicpq5ID9V8LDCZUp0KRXLUt9/vh8fjoRECz/Mpiy2p3ZBFhdjoFJOccql3zSfIe0PMhDNFCOTayYwotlma2DoRd25CdiUlJfQ+so3G4roZWwOtr6/HkiVLUhZIkvKer8iegKTz0s2KKhY0Gg2qq6sFDg2kFcPn82F6ehoHDx5ENBqlzcVTU1MoKysT+O4VCqvViuuuuw7l5eV46aWXBCSaL8hakkgk6LNBMkhiXHLJJVi/fj0uvvhiNDU14ec//znWr18PAFiwYAGWLVuGm2++GT//+c/xwgsvYN++ffjd735X8DkSHFMqPiAZKWS65mAwiKGhIQSDQVgslhT1DTFAzTY7RS5CoRCGhoYQCATQ3d2NgwcPYvXq1YJFQkwY4mgsFoshkUikRGuZbJPIwEAyQVdOz0y+yFaHI+nM0tJSNDc3Z1x0pCTbgND41WAw5GRMSxCLxagTR2tra1FTFbmA7aerr68XeLzlAylBClERsn1vUoMNAVC7ptbWVqjV6pS6mV6vR3d397xOo801nTefcLlcGBoaQnNzM8xmM422WBUhiczY2lYuZBqPx/Hwww/jl7/8Je644w6sW7euaJ/Pm266CTfffLPgez/72c9w0003YXx8HEuWLMGBAweoyOWee+7BnXfeiVAohG9+85vYvn27oA9q/fr1tA/qgQce+KIPqhCkI6hwOIyhoSH4fL6MJpaBQAADAwNYtmxZQecRiUQwPDyM2dlZ+no8z+Pdd9+FwWBAeXk5TSPk+8En3epiQpucnMTY2Biqqqpov0KxTGrFyEROwWAQExMTUCgUaGlpydl1gaT1SNc9WWiDwaBkXSudVUwikYDD4cDMzAwaGxsLchrPFWLy8Pl8GBsbo20LhaaMclVLxmIxOgvJ7XanzEESqzFLSkrQ3d09b+nPz1KzbS49TRzHpcjfieMDmyKUkr+///77uO6663Daaafh//2//zdvYovPGL4gKCB15AZLFJ2dnVkjiUgkgg8//BBf+tKX8n790dFRuFwuWlAFhA16REBAcuAcxwnksaQZMReQXfnw8DCqq6vR3t4uOAZbL2OFHlLRmlykIyfiGRgKhfKOVLLVnEhdi420otEonThLIoRQKAS73Y7a2lo0NDTMe4oq3fmTOlMikUBbW1tRLJLybYIm/pGNjY2ora2lGwDSqB0IBJBIJFBWVoaenh5UV1ejrKws7QYgX7DpvO7u7k+t2bZYPU2k2Zh8rkmzsVKpxB/+8AeUlpZiaGgIVqsV27dvL3isz+cMXxAUcJigotEoRkZGMD09jY6ODjQ0NMg2gH3nnXewZs2anF43kUhgbGwMdrsdra2taGpqon1P2dwfxKTl8/mQSCRowZZEW+k+wB6PB0NDQygtLUVnZ2fBNRWpdKJYAJJIJBCNRgX/Lx6Pw+FwSEq1c0EhgggiIvB4PHC73QAgmGlEUoTzSVTs+edSZ8oF+ZATid7Ky8slbbpYqNVqtLS00AXX5/NRYQu7mcrnXiYSCYyMjGBmZuZTT+eRnqba2lq0t7fPSxqTpPN27txJp3b7fD6ceuqp2Lp1a9Ff7zOKLwgKSNZ8hoeH4XQ60dbWRokiF7z11ls48cQTZf0ux3GwWq2YmJhAc3MzWlpaaGRRyMBAIo9lSYv0xhDSUiqVGBsbo8X+T2MoXDweRyQSwfj4OMbGxmAymVBbWysguWz1MhaFqvWIRJrnkyNB9Hq9oK5FogRWRECIK5+6VrrzZ11BilFnYpErOUUiEUxMTCAej8uK3tRqddoUFzF/JVGC3+8X2BGRmozUvfwspfNYP85FixbNW5rNZrPh+uuvh0ajwX/+53+iqakJQHLdmJ6ezscy6POKLwgKSHrpAYDZbM57QZBDUDzPw2azYXR0FCaTie6+CiWmbK8ZDofhdrtpc6tarU6JtI7UHB7SQzQ8PIy6ujq0tbWl3ZWTelk28QfHcXnVy+LxOJULy3Fe4DiOigik6lqEuHJJaxFyIr55BoOhKHUmFrmQE8dxsNvtmJmZgdlsljWyQaVSYcGCBTlF4YlEQmD+6vP5EI/H6aj0srIyqNVqjIyM0JrWp5XOO1I9TYlEAo888gieeOIJ3HrrrTj33HM/NTL+jOALggKQ0249HTIRFNkFDg0NoaamBh0dHYIx0kDxiYmApC3n5ubQ2dlJi/2RSIRGWl6vF6FQiM7hIaQlZdlTCGZnZzEwMACDwYCurq6ijgPIpV7GWvGQmkq668y2uJMaAqt8YzcBhLik/PNITSyXSCVX5OJa4vF4YLVaUVNTI7v2plQq0d3dXZRogud5hEIhqor1+/3QaDQoKSkR1FrnO93Kwu/3o7+/nz6zxYiYpfDhhx/i2muvxerVq3HLLbd8auPuP2P4gqAA+SM3MuFvf/sbVq1aJfjgkC7/wcFBlJWV0UX5SBBTPB7H+Pg4pqam0NbWJquexqZivF4vLday6sF8hscFAgEMDg6C53lYLJaCnCgKAYneBgYGaF8Vib7E9TISqeWbNiTKN0JcYv88vV4Pr9db9DoTC7nkFAqFMDY2RmtIcjcOSqWyqGli8v4MDQ0J0nlkVDqbJkznWF4ssBL2RYsWCXrIiolAIIDbb78db7/9Nh544AEsX758Xl7nc4ovCAooDkG999576Ovro2kIj8eDwcFB6HQ6WCwW6PX6I0JMpL41OTkJs9mM5ubmgnabpDhLSItdHLLJ3oka0u/3CxqbPw0QWyrSPCp3ERZHX/n0lxEQ9wtiYKpSqaiVFmv8WoyFVg45scMD29raclJOKhSKom42clXnpUsRknor+ZOPh564p2k+Pqc8z+Oll17CTTfdhA0bNuCKK6444q4knwN8QVBA9pEbcrB37150d3eD4zh8/PHHUCgU6O7uhtFoPCLExPM8HA4HxsbGUF9fTxso5wNknAFLWqzs3WAwwOPxUDXkfDb8ZkM4HMbg4CCi0Si6u7vnrdFWTr1sZmYGQ0NDMBgMdD4TW9fy+/0IBoOIx+NU+UaIK5e6VjZyIpE9GR6Yz/vT1dVVlKiimOo8kiJkJdts6jpbijDXOU35wuFw4Ec/+hHi8Ti2bdtWlNlYRym+ICigeARFzGC7u7tRUVFxxIhpenoaw8PDqKioQEdHx6dSTOY4Dj6fDxMTE3C73VCr1VCpVLRzPpvsvdiIx+MYHR3F9PQ0urq6jmijrRhkplg8HseCBQug1+sz1stIrxYhfzI9lvjsEeKSqmtlIye/34+xsbGCxBgdHR0FpyRZscx8RirAYQ891tUBgMDVYXZ2FksiQU4AACAASURBVC6Xq+A5TZmQSCSwY8cOPPLII7j55pvx9a9//VgXQWTDFwQFHC5W5wOyQ3e5XGhra0NHR4dggNh8EROQFB0MDQ1Bp9PJGvMxX2AXG9IbolarZcnei93MyXEcbDYbJiYm0NLSklfLQLGQSCRoA7bFYkFtbW1exyBkFgwGMTs7S//4fD7BOA6j0YiSkhLJdCs7PLCtrS1vUUNbW5vAiy4ffBaabYmrg9PphM1mg1KppOnWQlOEUjhw4ACuvfZaLFu2DLfeeuunZpn1OcMXBAXkR1DRaJQWUbu6uuD1emEwGFBXVzfvxPRZER0Ah2s7er1e1owoIntnSSscDlO7l0Jk7263myolCUl+GiDp1tHRURodzBdJJhIJeh89Hg+dC6XT6VBSUgKdTofZ2Vk4HA40NjaivLw8b8VqS0tLXiTLnutnpdmW7WlauHAhDAYDfTbZaCsUCtHRJvkIhUKhEO6880689tpr2LZtG1asWDHPV3ZU4QuCIohEIrJ+Lx6PY2Tk/7d37mFRVesf/27ulwG5qAMo95uKyEUsyLtpx9STaUeRTDLrSeuYlWWmP0RUVKzEzGupJ+p4K7PSOnXUMlOzFJCLgoBcBBEZENRhYIC5rN8fnLXdAzMwyFwQ9ud55nlk2M5es5nZ71rv+r7ftwRVVVUqbhO3b99GUVER+vTpAzs7O/Tp06fdfjEPA/UGlEql8PX1NarogHYbVigU7F5bV+DK3qndi7ayd9r+xMLCwqgrSaAlYBcUFLCqTX3JktuDuozcvn0bt2/fbrM6oCstMzMzlT0zdeIPmi4cMGCASqO/zqBJnWcMHqamSV2KkBDSrtUYIQRnzpxBXFwc5s6dizfffNMon4VHHD5AUToKUAqFAmVlZaioqIC7uzv7JePuM9EbA1c8wP0g0xttZ4OWTCZDSUmJSutqY33B6cpRLBbDz8+vy+mejs6lTvZOr6WVlRUqKyshlUrZfT9jwRVjBAQEGHVVK5VKUVBQAADsnpemlauFhUUbAQH3s0Vl+A+bhmtoaEB+fj7bndpYxbaAbmuauN91rvHrvn37YG1tjRs3bgAA9u3bBy8vL928gd4HH6AomhzNlUolbt26hbKyMri5ucHDw6NTtkQ01y0Wi9so3rgybXXpKBoUKysr4enpCVdXV6MFJjoWkUgELy8vvVXTd4RcLsf9+/dRWlqK+/fvw9zcnO10qgu3986ii30mXY/lzp078Pf312ryQCcBNHBxJwHclFZnr2d3Sudxa5oCAwP1NpFRKBTYsWMHfvjhBwwYMADNzc24ceMGgoKCcODAAZ2ea/v27UhJScGVK1cQExODlJQUjcdu2bIFmzZtQkNDA/7xj39g165delMo6hg+QFFaByiaCrhx4wb69evH7mnoQpnHNXqlNwauTFsgEEAikaCiogJubm6sV58xoPZMNEAbeyw0PcPd2+lI9v6wbu8djcVQ+0zajIX61eliLHK5nJ1UUcl2Rykt7lha1xEZ67oAhqlpAoD8/Hy88847CAwMxMaNG1UCslQq1Xna+dtvv4WJiQlOnDgBqVSqMUCdOHECsbGxOH36NNzc3DBjxgxERkYiKSlJp+PRE3yAolALHO4XzNHRET4+PgaxJaIrrVu3bkEkEsHExIRdGXDTg4bKY3fUisPQ1NbWorCwEH369GH/Ju3ROt1KCzmptJhe04dJOXH3mXx8fIyatqqrq0NBQQGsra3h5+ent7Foup5cRSb1zqPF6cacpdOaJoZhEBgYqLexNDY2YvPmzTh16hQ+/vhjREVFGTSzEBcXh/Lyco0B6vnnn4eXlxc2bNgAAPj1118xd+5cVFZWGmyMXUDthey15czUlsjW1hahoaGwsrJSMSLVpzKPtr+wt7dHVFQUa4lEZdrUy4+21KAtvPURtO7fv4/CwkJYWVkhJCTEaO3NgQfNIBmGwdChQ7WWR3PTVNQNmit7r62tRWlpqYrsnd5oNcneuftMgwcPNuo+k0wmY5tpBgYG6s2Kh6LpelLvvJKSEtTX18Pc3ByEEJSWlrLXU10DPn2hVCpx8+ZN3L59W681TYQQnD9/HitWrMCsWbNw/vx5o05UNJGTk4Pp06ezP4eEhEAkEqGmpkZv10bf9MoAVVhYiLq6OvYmaKjAJBaLUVhYCHNz8zY3YBMTEwgEAggEApWbAp3J0toj6gLd1YJYqVSKwsJCyGQyBAQEGLVWg4ox6urqdGaTRFtu29rawtXVFQBUpMVisRi3bt1qI3u3sbFBVVUVqqur2+2sbAgIIbh16xZu3rwJLy8vBAYGGrXYkxb+urm5sek8bj+o6upqne1rdQS3T9OIESP0tg9ZU1ODuLg4iEQiHDlyBL6+vno5jy6QSCQqe27033V1dXyAepTw9fVl03iGCEz19fUoKiqCXC7vlAUP9cETCAQqN1m6MqipqUFJSQlkMhm70uooncV1PKeuC8aCdiq9ffu2QW7ADMPA2toa1tbWKjJqKnu/ffs264xhbW2N2tpayOVyvbi9d8S9e/dQUFAAR0dHjBgxwqjebVSdZ25ujrCwMJUUmqWlJSwtLVUEI1xPR+pUru2+Vkdwa5qGDh2qNydwpVKJw4cPY+vWrVixYgXmzJlj1P01bRAIBBCLxezP9N+PcqFwrwxQFy9eRN++fTFgwACYmprq7cbDNVD19fXViUxb08pAKpWqTWfRoCUQCFBZWYnKykp4eXkhICDAqCsDkUjE9sp67LHHDKbEU0djYyNu3LgBOzs7jBo1ChYWFpDJZOz+S+uVAfea6vqmxbVKCgoKMmorhodV55mZmcHR0VFlJcwVC9EUNjcbQB+aUq6G6tMEtGRYli5dCi8vL/z+++96La/QJUFBQcjKysLs2bMBAFlZWRAKhY/s6gnopQEqNTUVp06dws2bN+Hm5obQ0FCEhYVh+PDhOrHLkclkKC0txZ07d+Dt7Y1BgwbpfWVAjUZdXFwAqAat8vJy1lHb3t4eDQ0NuHPnDrsHY0honyiBQIDw8HCj5vLb22cyNzeHs7OzypebuzIoKytjfd50IXtXKpUoLS2FSCSCr6+vUTupcu2s3NzcEBER0eXvBDftxz0PNc69d+8ea89EU670QU2ZbW1tERERoTcBT3NzMz7++GP8+OOPSE5OxujRo7uFfx5tEaNQKKBQKNDY2AgzM7M2q+rY2FjMnz8fc+fOhZubGxITEzF//nzjDFpH9EoVH4W2q0hLS0NaWhouX76M8vJyDBgwQCVoubq6avUFVSgU7Kath4eH1v9PH1BjWapQ9Pb2hpmZmUoBp1gsRlNTE6ysrFTSg7r0y6NIpVJcv35dZ24UXUGhUKC0tBRVVVVd3mfqquydKiiLiorg4uLC1t4ZC246z9/f3yjqPJpyFYvFqKysRGNjI2xsbODo6NilXmWaIITgr7/+wnvvvYfp06dj+fLl3ap2KCEhAWvWrFF5bvXq1ViwYAGGDBmC3NxceHh4AACSk5OxadMmSKVSPPfcc9i9e3e3ei/twMvMtYEqg2jQSk9PR0VFBQYOHIjQ0FCEh4cjPDxcpZBWJpNBJBLh5s2bcHV1hbu7u1FTVlSMoY0dEO0SS28INGhZWlqqqAcf1kiT64zh5+dn1HQDTS2WlJTotc5Lk0y79T6hTCZDQUGBUYMBhVv4GxAQYFRrLeBBTRP9O9F6LW4jQ6DFpZybcu3s6uru3btYvXo1bty4gR07diAwMFAfb4enY/gA9bDQ9AsNWOnp6aisrMSAAQPg4OCAtLQ0rF27FlOmTDFqykoqlaKoqAhNTU3w9/d/aDkyN2jRm2xrtRu1H2rPVYM2U/Tw8ICbm5tR0yVisZhNE/n6+hr870TTWXV1dWy7h+bmZtjb28PJyUmvq9eOxsVN5xm72LYzNU20nrB1I0PuvpamNLZSqcTRo0fx0Ucf4d1338W8efO6vQiih8MHKF1y/vx5LF26FPb29uwyWyQSwd3dHeHh4exqyxA2QdxVij77IXFXWtT92cLCos1Kixb99uvXD56enkZVoDU1NaGwsBBNTU1G983jbvTTFDCVadNrqiu3d23oDuk8iq5qmrgTAfqgGYE7d+6gvLwcHh4e2LFjB1xcXPDRRx8Zdb+Ph4UPULrk5MmT8PT0VEkJKJVKlJSUqOxpVVVVwcPDgw1aYWFhOgta3D0vY/n3cW+wtbW1EIvFMDU1Rf/+/dk9A0MWb1K4foLGrmcCHjhS2Nvbd+iO0Z7bO7de62Fn/N0tnUcl9bR1iq7T4zQjkJGRgU8++QRXrlyBiYkJK5B6/fXXMWjQIJ2eE2hxRHn55Zdx8uRJ9O3bFxs3bsTzzz/f5riEhASsX79eZYKQnZ0NHx8fnY+pG8MHKGOgVCpRXFyM1NRUpKen4/Lly6iuroaXlxfCwsLYoNWZVtxcb7jusOdF1XB0lWJpaamSHuT22aGrLX0FLa5XnbH9BIEWZdj169e7vILjyt5pGwiuMs7e3l4r4QB3b8fY6Tx1fZr0RWpqKt59911MnjwZ//d//wcrKyvcv38fmZmZ8PPzw4ABA3R+zpiYGCiVSuzbtw+ZmZmYOnUqLly4gKCgIJXjEhISUFhYiP379+t8DI8QfIDqLiiVShQVFbFBKz09HTU1NfD29kZYWBj7UDfrp8o8Y7Z8p9BW63fu3OlwlaLOSZvK3jvqAaUtxt5n4sJNWfn4+KBfv346D8hc2TtXOKBO9t6d0nmGrGm6f/8+1qxZg/z8fOzYsQNDhgzRy3laU19fD0dHR1y9ehUBAQEAgHnz5mHAgAFtzFv5AAWAD1DdG6VSievXr6sIMe7evQtvb2+EhobCyckJR44cwapVqzB8+PCHbuOtC7iu5wMHDsSAAQMeaiZOm8PRfS0atFqvtDp6bbrP1NjYaHTbJuCBzyPdgzPk6ra17J3uaxFCIBQKIRQKDWpE3Bpd9mlqD0IIjh8/jo0bN2LJkiVYsGCBQVeLGRkZGDlyJBoaGtjnPvroI/z+++/44YcfVI5NSEjAli1bYGpqCldXVyxevBivvfaawcbaTeAD1KOGQqHA2bNnER8fj5s3b8LX15et3eGutJycnAy2v0JvvvpyPecGLZrKokGLBi5bW1u29QbdZ9LXKqUz0OaBDMPA39/fqN1+gQfpPFdXVzg6Oqq01VAne9fnipPrSqHPPk0AcPPmTbz77ruwt7dHcnIyhEKh3s6liXPnzmHWrFkqTuJ79uzBgQMHcObMGZVjc3Nz4eDgAKFQiIsXL+K5555DcnIyYmJiDDxqo8IHqEeR9evXIywsDE8//TQYhoFCoUBBQYHKnhb11eMGLUdHR53erCUSCa5fvw5TU1OD33xpKovbs4i2LHd0dISXlxfs7OyMtp9Cb741NTVaNw/UJ9qk87hqN3pdm5ub9VK03bqmSV+TCLlcjt27d+PgwYPYtGkTnnrqKaNNWNStoDZv3owzZ860WUG1JikpCampqTh69Ki+h9md4ANUT0WhUCA/P18laInFYvj7+7Ny99DQUDg4OHT6C9vU1ISioiLU19cjICDAqK3WgZZ9puvXr8PS0hJCoZC1c5JIJKwjPE0P6sMrjwu38LcrqU5d0VV1Xmu399aydxq0tJW9c2uaAgIC9NrKJSMjA++88w7GjRuH+Ph4o6bAgQd7UDk5OfD39wfQYkXk5ubWYQPBTZs24eLFi/j2228NMdTuAh+gehNyuRx5eXnsntbly5dRV1cHf39/hIWFsUGrT58+am823PSZt7d3p1SG+kCbfSaFQtFmpQWgjdJNF3tCtHmgjY2N0QUZgH7Vea1rtTqSvRuqTxPQ8ndITExEVlYWtm/fjmHDhuntXJ1lzpw5YBgGe/fuRWZmJqZMmaJWxXfs2DGMGTMGDg4OSE1NxYwZM7Bhwwa8+OKLRhq5UeADVG9HLpfj2rVrbJ1WRkYG6uvr4e/vj/DwcISFhSE4OBhfffUV+vbti8cee8zoMm2uierD7DNR0QAVYrSndNMG2jxQIpEgICBA780DO8JY6jx1e4UmJiawsLCARCKBk5MT/P399SqC+Pnnn7Fu3TosWrQIr776qlFLLdRRW1uLBQsW4NSpU3B2dkZSUhKef/55nDt3Dk8//TT7WYyJicHJkyfR1NSEgQMH4vXXX8eSJUuMPHqDwwconrbI5XLk5uYiLS0Nx48fx5kzZ+Dn5wcvLy8MHz4cYWFhCAkJgb29vcEteKqqqlBSUqJzE1V1SjcArMErDVzcGx4hBOXl5SgvL4eXlxdcXFyMuqLsbsW2MpkM+fn5qK+vh7OzM5qamro8GdBERUUFli1bBnNzc3z88cdsg0+eRxo+QPFo5vPPP8fx48exadMmeHt7Izc3l93TyszMhFQqRWBgoMqelkAg0MtNmqbPrK2t4evra5BVAfV14xbDUldyMzMz1NbWwtnZGb6+vka1bgK6V7Ett6ZJXeDW5PbONXnVVvYul8uxd+9efPHFF1i/fj2mTp3aLdph8OgEPkDxaEahULQ7s5XJZMjJyWGDVlZWFqRSKQYNGsQGrZCQkC4FLSrIkEql3aKeqaGhAXl5eWhqaoJAIIBUKlW5udIbrKECllQqRV5eXrcotgVahAB5eXmdrmniur3TwNWR7D07OxtLly5FVFQU1q5da9RGjjx6gQ9QPLqlubkZV69eZX0HMzMz0dTUhEGDBrFy95CQkA4dImjr98rKym4hyOAKRPz8/FTamatrpaFQKGBra6uSHtTl3kt3S+fpo6aptcmrWCzG0aNHcenSJVhZWaGiogJbtmzB5MmT9fbZ0NY7jxCC999/H3v37gUAvPLKK0hKSuJXc12DD1Dasn37dqSkpODKlSuIiYlBSkqKxmNTUlLw8ssvq9QF/fjjjxg3bpz+B9oNaW5uxpUrV1j1YFZWFtuxlhu0bGxsQAhBRkYGZDIZhEIhPDw8jLrRzW090Zl9L0II286cG7RarwgeJmhxi22NLVgBgDt37qCwsFDvNU2EEJw8eRIbN25EWFgY+vXrh8zMTNy8eRMpKSkICwvT+Tm19c779NNPkZycjF9//RUMw2DSpElYsmQJFi1apPMx9SL4AKUt3377LUxMTHDixAlIpdIOA9TevXtx/vx5ww3wEaOpqalN0KJpHX9/fyxduhShoaFGcT2n1NfXIz8/H5aWlvDz8+ty+owGLe5KSyaTsUGLBi5N8nSpVIr8/HyYmZl1i3SeIWuaKisrsXz5csjlcmzbtg0DBw5U+T0hROefk8545z3xxBOYP38+Xn31VQDAvn37sGfPHvz11186HVMvQ+0f1Li7vd2UmTNnAgDS0tJQXl5u5NE8+lhaWiIiIgIRERFQKBR4/fXXkZ+fjwULFkAikeCrr77CypUroVAoEBQUxO5pBQcH660XEkUul6O4uBj37t3TqQUPwzAQCAQQCARwdXUF8CCNJRaLUVNTg5KSEjZocW2cKioquk06z5A1TQqFAikpKdizZw/WrFmDZ599Vu3fXh+fh4KCApiZmbHBCQBCQkLw+++/tzk2JycHISEhKsfl5OTofEw8fIDSCRkZGejbty+cnJwwb948rFixwuhKr+6KqakpXnrpJTz++ONtbjSNjY3Izs5Gamoq9u/fj+zsbCiVyjZB62Hbz3Np3TzQ399f76s3hmFga2sLW1tblaBF3TBu3bqFmpoa1uX97t27kMvlGrvC6pv79+8jPz8fzs7OGDFihF7Tr7m5uXj77bcRFhaGP/74w+ACGYlE0qamrU+fPmwJQutjuROZPn36QCKRaL2yay1I0seKsKfA30W7yJgxY3D16lV4enoiJycH0dHRMDMzw4oVK4w9tG5LZGSk2uetrKzw2GOP4bHHHgPw4OadnZ2NtLQ0pKSk4OrVqwCAoKAgdk8rODi4U55x3OaBERERRnP2BlqCFsMwqKyshKmpKUaOHAkLCwtIpVK2PXxZWZmKTx63e7E+kMlkKCwsRENDA4KCgvSqmJNKpdi0aRPOnj2Lbdu2YcSIEXo7V3sIBAKIxWKV58RisdpA2fpYsVisVr166dIlyGQyjBw5EkDLapRhGDY4FRcXw93d3aifv+6OcXdcjcC4cePYm0Lrx6hRozr9ej4+PvD29oaJiQmCg4MRHx+Pb775Rqv/u337dkRERMDS0hLz58/v8PgtW7bAxcUF9vb2WLBgAZqamjo93kcJhmFgY2ODyMhILF68GCkpKUhNTcXZs2exaNEimJqa4l//+hcmTZqEsWPH4vXXX8eePXuQlpaGxsbGNq/X1NSEnJwcFBUVYfDgwQgMDDTqzUGhUKC4uBjZ2dnw8PBQCbQ2NjYQCoXw9/fH8OHDERkZiUGDBkEgEOD+/fvIzc3Fn3/+iYyMDBQWFqKqqoptq/Gw0FVlWloaHBwcEB4errfgRAjB6dOn8eSTT6J///44d+6c0YITAAQEBEAul+P69evsc1lZWW0EEkDL5CgrK6vd43777TdERkairKwMQMv7NTExAcMw+OmnnzB48GC88sormDhxotb3i95Ir1tBtba61zUMw2h9k3Bzc0NcXBwrxmiPEydOICkpCadPn4abmxtmzJiB1atXd2g82dOgN++oqChERUUBeLC3k5mZibS0NOzduxc5OTnspCE4OBh5eXlobGzEmjVrjN6WA1BV540YMaJDdR7DMLC2toa1tTXbPoK2MqfqwVu3brHmrq1XWh29X1rTZGNjo/dVZVVVFVauXIm6ujocO3YMnp6eejuXttja2mLmzJmIj49nvfOOHTuGCxcutDk2NjYWycnJmDJlChiGwebNm/HGG28AeJC+y83NhUAgwPjx4wGA7USwdu1attB44sSJWLVqFQ4fPoyhQ4fqpe38o06vC1DaIJfLIZfLoVAooFAo0NjYCDMzM7X7Sj///DPCw8MhFAqRl5eHdevWYdasWVqdpzNijC+++AIvv/wyO1NbtWoV5s6d2+sClDro3s7IkSPZdApV0X322WdITk6Gh4cHmpqaMGvWLAwbNozd0xoyZIhB93eoOs/U1BRhYWFdOjfDMLCysoKVlRX69+8PQDVo1dXVqQQtbiNIGrQM2adJqVRi//792LFjB+Lj4/Hcc88ZXTbPZefOnViwYAH69+8PZ2dn7Nq1C0FBQW288xYuXIji4mIEBwcDaKmDio2NBQD2/WRlZcHHx0flejY0NKCoqAiff/45G7jeeustTJs2zeju690VPkCpITExEWvWrGF/3r9/P1avXo2EhASUlZVhyJAhyM3NhYeHB3799VfMnz8fEokEQqEQL7zwAlauXKnzMeXk5GD69OnszyEhIRCJRKipqdGrsupRhd58r1y5ggsXLsDd3Z0NWpcvX0Z6ejp27dqF3NxcmJqaIiQkRCVo6dqdXKFQoLS0FNXV1XrtGaUuaAFQWWndvn0bUqkUDMOgqakJzs7OGDJkiF73mvLy8vDOO+9g0KBBOHfuHBwcHPR2rofFyckJ33//fZvnR48ezQYnoOUaf/DBB/jggw8AAMnJyfDx8cGmTZswYcIEuLu7o6ioCAMHDmTrIwkhsLOzw3vvvQcfHx8QQqBUKuHg4AAzM7NOiSx6E3wdVDcgLi4O5eXl7dZb+fr6YseOHZg8eTKAlo1sCwsLlJSUwMvLyzAD7YEQQiCRSNiglZ6ejtzcXJibmyMkJIQVYgwePPihgxYtbu0uxba0pkmpVEIoFLI9oGgbDW56sKu1aY2Njdi8eTNOnTqFjz/+GFFRUT3uJrxp0yZ89dVXyMzMRFhYGFauXIlXXnkFb7/9NuLj4yGTydqkTJVKJUxMTHD69GksXrwYV69eZffCeyl8HZQhGTdunNoaCgAYOXJkpwt71SmHAGglx9XWwgUAEhISsH79epXUU3Z2Nnx8fDo13kcFhmFgZ2eHsWPHYuzYsQBaglZdXR0uX76MtLQ0bNu2DdeuXYOFhQVCQ0MRGhrKBq329mq46bzQ0FC9Frdqg1KpRHl5OSoqKtpYOFGam5vZ9KBIJFLp/UQDlzZBixCCc+fOYeXKlZg1axbOnz9v9J5ZuoaueJYvX45ly5Zh7dq1+PLLLzFv3jyYmJiwExH6GeGukOjv0tPTERYWxv6cl5eHgIAAo09iugt8gNITuhZjUOXQ7NmzAbTkuIVCoVbpvX/+85+wsLCASCRiLVxCQkLUKpQAIDo6Gvv379fp+B8lGIaBvb09xo0bx1pWEUIgFouRkZGB1NRUbN26FdeuXYOVlRUbtMLDwxEYGIjm5mb8+OOP8PLy6hYt4IEHNU1OTk7t1jRZWFigb9++KsGrubmZdcSorq5GQ0MDW6vFLTCmN9+amhrExcVBJBLhyJEj8PX1Nch7NDT0/dLVUHx8PN566y3ExMTgxIkTOHToEKqrq/Huu++2sYWiYoq0tDTMnDkThBDMnTsXFy9exPHjxzV+N3sbfJg2InK5HI2NjSpiDLlcrvbY2NhY7Nu3D7m5ubh37x4SExO1kqbX19fj6NGjWLduHQQCAUaNGoVnnnkG//73v3X8bno2DMOgT58+GDduHJYtW4ZDhw4hIyMDJ06cQHR0NMRiMTZv3oywsDAMHToU3333HfLz83H79m2Nf1NDIJPJcO3aNRQWFiIoKAh+fn6dLri1sLCAs7MzvL29MWzYMERGRiI0NBT9+vVDc3MziouLcfDgQURGRmLGjBmIiorCkCFDcPz4cYMGp9raWsyYMQO2trbw9PTEwYMHNR6bkJAAc3Nz1u1DIBCguLj4oc7L7STs4OCAwYMHo3///ggJCcG2bdsQGRmJXbt24datWwBaJjumpqaQSqWoqKjATz/9BHd3d0ilUmRmZvLBiQMfoIxIYmIirK2tkZSUhP3798Pa2hqJiYkAgLKyMggEAraOYvLkyXjvvfcwfvx4eHh4wNPTU0XIoQlNFi7tWbP88MMPcHJyQlBQEHbt2tXFd9lzoUFr/PjxWLZsGUxMTBAZ6nD5UgAADIpJREFUGYnffvsNb775Ju7fv48PPvgAo0ePxsSJE/HOO+9g//79yM3N1XvQ0ndNk7m5OZycnODl5YVhw4bh8ccfh1AohL29PRYuXIjs7GxERkbiww8/1Nk5O4KbKThw4ABee+21dj/n0dHRkEgk7KOraWwzMzMoFAr8/vvvmDhxIg4ePIjvvvsOPj4++Oc//4lJkybhzp077EqqtLQUf/zxBy5fvoy9e/fiu+++g52dHRQKRZfG0ZPgRRI9nHPnzmHWrFmorKxkn9uzZw8OHDigNg2Zm5sLBwcHCIVCXLx4Ec899xySk5MRExNjwFE/mpSVlcHDw6PN84QQ3Lt3D+np6axhbkFBAQQCgYoQIyAgQCcWWdyaJj8/P73WNDU1NWHr1q344YcfsGXLFowePVollUXTX/qmM2avQMsKqrCwUOep7OrqagwdOhSLFi1iJ5DNzc34+uuvMWrUKBVBEyEEBw4cwAsvvMD+TAt6eyG8SKIn0pEYY9u2bVpbuADAkCFD2H8/8cQTePPNN/HNN9/wAUoL1AUnoGWl5ejoiIkTJ2LixIkAWm5Gd+/eRXp6OlJTU5GUlITr16/Dzs5ORYgREBCgdUrOkDVNhBD8+eefeP/99/HMM8/gwoULamu6DHWz7YzZK4VmClxdXbF48WK89tprXR7HvXv3UF1dzdp1AS0pUhqEuD58DMOwz8vlcpiZmfVmFZ9a+AD1iNORGKO+vp61cPH39weg2cJFHR05Y/BN3h4OhmHg5OSESZMmYdKkSQBarlFtbS0btDZs2IDr16+jT58+KkIMdftI3D5NEREReg0Md+/exerVq3Hjxg0cOHAAgYGBejuXtnTG7BUAZs+ejVdffVUlU+Dg4NDliRj1iuS6nXPRNNngzaU1QJeVGh48PYDo6GgyZ84cIpFIyPnz54m9vT25evWq2mO///57UltbS5RKJbl48SJxc3MjKSkpGl97zpw5ZPbs2aSuro6cO3dO42vv3r2bBAQEkJs3b5Ly8nIyePBgsmvXLp29x56KUqkk1dXV5Oeffybr1q0jM2bMIMHBwWT06NHkjTfeIJs3byZjxowh+/btIzU1NaS+vl5vj7q6OpKSkkKGDh1KUlJSiEKhMNh1GDt2LEHLlkObx8iRI8nly5eJtbW1yv/56KOPyLRp07R6/Y0bN5KZM2d2aYwKhYLMmTOHDB48mNy7d8+g16cHoDYG8QGqF1BTU0OmT59ObGxsiLu7Ozlw4AD7u7NnzxJbW1v25zlz5hAnJydia2tLAgMDydatWzW+rkQiIebm5iQ/P5997oUXXiDLly9vc2xUVBT59NNP2Z/37t1LHn/88a6+tV6JUqkkFRUV5NVXXyWurq5k6tSpJDg4mIwZM4YsWbKEfP755yQzM5PU1dXpLDjl5OSQv/3tbyQ2NpZUVVUZ+xK0gX4WCwoK2OfmzZun9rOojqSkJDJjxowujyMjI6PLr9FLURuD+HVlL0CThQvQ1sbl0KFDWr8u3+TNODAMg2vXrkEoFKKoqAjW1tZsu/q0tDSkpaXhu+++Q3FxMZydndnUYGhoKHx8fDqV/pPJZNi+fTuOHj2KDz/8EBMmTOiWadnOmL0CwLFjxzBmzBg4ODggNTUVn3zyCTZs2NDlcYSGhgJ4sKfE0zX4K8jz0BiyyRuPKhMmTMCECRPYnxmGQf/+/TFlyhRMmTIFQEt2pKqqCmlpaUhNTcU333yDGzduwNnZmVUOhoaGsu1iWnPp0iUsW7YMkydPxoULF4zuhNERmsxeAbQxfD18+DDbsmbgwIFYvnw5XnzxRZ2NhQ9OuoG/ijwPjT6avLWHtoKM3mbXpAmGYSAUCjF16lRMnToVQEvQEolEbND6+uuvcePGDfTr148NWv7+/vj888+Rl5eHL774QkXZ2Z3RV6aAx3jwAYqnQ1q3qKZwm7x1pBCkVk1UftsZJSGlM5ZNvd2uSRMMw8DFxQXTpk3DtGnTAKgW9aalpWH9+vWIiYnBzp07e2tNDk83gS/U5WkXiUSCgIAABAUF4dSpU21+P2fOHDAMw+b9p0yZggsXLrQJGrt378bWrVvxyy+/gGEYTJo0CW+88QYWLVqk1Tg6U4ipryJMHh4evaE2lcJPj3jaxcbGBtHR0bh48SJKSkrY5xsaGrB8+XI0NTXhzp076N+/P2JiYlSavAkEAvb4hQsX4u9//zuCg4MxdOhQTJ06FQsXLtR6HJ21bOLtmnh4egCa5H2El5n3emgdx9GjRwnDMGTHjh2EEEIuXbpEnnzySWJlZUUWLlxISkpK9D6Ws2fPEqFQqPLcZ599RsaOHdvm2JycHHLr1i0il8vJH3/8QVxcXMjBgwf1PkYe7dm2bRsZPnw4sbCwIC+++GKHxycnJxOhUEjs7OzISy+9RBobG/U/SB5DojYG8SsoHo3Q/YeIiAi4u7vj3Llz2LdvH2bOnIny8nIcOnQIu3fvhpeXV7tuE7qgM4KMIUOGwM3NDaampip2TTzdBzc3N8TFxWHBggUdHnvixAkkJSXh119/RWlpKYqLi7F69WoDjJLH2PABiqddCCHw8PBAZGQkfvzxRyxevBijRo3Cf/7zHzz77LPsMVSNp1Ao9OLUzRVkULQVWnRk19Sa7du3IyIiApaWlh22NNmyZQtcXFxgb2/PypZ5OmbmzJl49tlntepn9sUXX+Dll19GUFAQHB0dsWrVqna7T/P0HPgAxdMhJ06cwMmTJ1FfX4/Y2FgcOnRIpc8PwzCQyWQAWrzG9FEDwi3ErK+vxx9//IFjx45h3rx5bY49duwY7t69C0IILl26hE8++QTTp0/X+lzazu75mb1hUFfkLRKJUFNTY8RR8RgCPkDxaKS2thZr167FzJkz4e3tDU9PTyiVSgBQWZEQQnD48GFMnDgRo0ePxsaNG/WS8tu5cyekUmmHgozDhw/Dz88PdnZ2iI2N7XQRpraze35mbxjUFXkD0GgEy9Nz4AMUj1qysrIQHR2NNWvW4O2338Yvv/yCiIgInDp1Cg0NDSoFto2NjXBxccHs2bNhY2ODL7/8stNpNW2ghZj19fUoKytji3TVFWHW1NRAIpEgLy8PS5Ys0ek4KPzMXj3jxo0DwzBqH6NGjer066kr8gagsWUMT8+BD1A8bVi1ahVGjBiBwsJC/Pe//0ViYiKcnJwQGhqKsrIynD59GsCDVZS1tTUmTZqE6OhoeHt744knngAAdrXVU+Fn9uo5c+aMRmXw+fPnO/16tMibkpWVBaFQqNX+Fc+jDR+geFRQKpUIDg7GW2+9hYsXL+Kpp55if0ftcjIyMgCAXUXRQCWRSJCTk4Pw8HADj9o46Gpmr60oIyUlBaamphAIBOyjo35g3RW5XI7GxkYoFAooFAo0NjZqFNfExsZi3759yM3Nxb1795CYmNiheIWnZ8AHKB4VTExMMHv2bHzwwQfo37+/yu9CQ0MxYcIEHDlyRCWlRgNVVVUVKisrWTujnm6To6uZfWck11FRUZBIJOxj3LhxnR12tyAxMRHW1tZISkrC/v37YW1tjcTERABAWVkZBAIBysrKAACTJ0/Ge++9h/Hjx8PDwwOenp5sO3Weng3vxcfTBoVCAYZh1AaYX375ReP/Ky8vh0wmw7BhwwDgkXUpl8vlkMvlKrN7MzOzNurE2NhYzJ8/H3PnzoWbm9tDz+xnzpwJAEhLS0N5ebku3kK3JyEhAQkJCWp/5+HhoTIBAoClS5di6dKlBhgZT3eiZ09xeR4KU1NTtcGJEAKFQqH2/8jlcly7dg39+vWDpaWlxuMeBTTN7rvDzD4jIwN9+/ZFQEAA1q1bp5eaMx6e7gJvFsvTJSQSCWpqamBnZ4f4+HiYmJjgk08+MfawHkni4uJQXl6uUapeXFwMhmHg6emJnJwcREdHY968eVixYoVhB8rDo3t4s1ge3SMSiRAbG4u+ffti586d+Pnnn/HZZ59BJBIZe2g9Dh8fH7a5YHBwMOLj43kLJ54eTUcrKB4erWAYxhZAMICnATwJYAkh5LJxR/VowTBMIoCBhJD5Wh4fDWA5IaR3yCZ5eh28SIKnSzAtSgiGEFIP4K//PXi/n07AMIwZWr6LpgBMGYaxAiAnhMhbHfc0gMuEEBHDMIMArAJwxOAD5uExEHyKj6dL/M8qXwkADMOYMAxjxjBM2/a7PO0RB0AK4H0AL/zv33EMw3gwDCNhGMbjf8c9CSCbYZh6AD8B+BbABmMMmIfHEPApPh4eHh6ebgm/guLh4eHh6ZbwAYqHh4eHp1vCBygeHh4enm7J/wOYnKbpq7CcqQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x273.6 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(6, 3.8))\\n\",\n    \"ax = fig.add_subplot(111, projection='3d')\\n\",\n    \"\\n\",\n    \"X3D_above = X[X[:, 2] > X3D_inv[:, 2]]\\n\",\n    \"X3D_below = X[X[:, 2] <= X3D_inv[:, 2]]\\n\",\n    \"\\n\",\n    \"ax.plot(X3D_below[:, 0], X3D_below[:, 1], X3D_below[:, 2], \\\"bo\\\", alpha=0.5)\\n\",\n    \"\\n\",\n    \"ax.plot_surface(x1, x2, z, alpha=0.2, color=\\\"k\\\")\\n\",\n    \"np.linalg.norm(C, axis=0)\\n\",\n    \"ax.add_artist(Arrow3D([0, C[0, 0]],[0, C[0, 1]],[0, C[0, 2]], mutation_scale=15, lw=1, arrowstyle=\\\"-|>\\\", color=\\\"k\\\"))\\n\",\n    \"ax.add_artist(Arrow3D([0, C[1, 0]],[0, C[1, 1]],[0, C[1, 2]], mutation_scale=15, lw=1, arrowstyle=\\\"-|>\\\", color=\\\"k\\\"))\\n\",\n    \"ax.plot([0], [0], [0], \\\"k.\\\")\\n\",\n    \"\\n\",\n    \"for i in range(m):\\n\",\n    \"    if X[i, 2] > X3D_inv[i, 2]:\\n\",\n    \"        ax.plot([X[i][0], X3D_inv[i][0]], [X[i][1], X3D_inv[i][1]], [X[i][2], X3D_inv[i][2]], \\\"k-\\\")\\n\",\n    \"    else:\\n\",\n    \"        ax.plot([X[i][0], X3D_inv[i][0]], [X[i][1], X3D_inv[i][1]], [X[i][2], X3D_inv[i][2]], \\\"k-\\\", color=\\\"#505050\\\")\\n\",\n    \"    \\n\",\n    \"ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], \\\"k+\\\")\\n\",\n    \"ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], \\\"k.\\\")\\n\",\n    \"ax.plot(X3D_above[:, 0], X3D_above[:, 1], X3D_above[:, 2], \\\"bo\\\")\\n\",\n    \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n    \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n    \"ax.set_xlim(axes[0:2])\\n\",\n    \"ax.set_ylim(axes[2:4])\\n\",\n    \"ax.set_zlim(axes[4:6])\\n\",\n    \"\\n\",\n    \"# Note: If you are using Matplotlib 3.0.0, it has a bug and does not\\n\",\n    \"# display 3D graphs properly.\\n\",\n    \"# See https://github.com/matplotlib/matplotlib/issues/12239\\n\",\n    \"# You should upgrade to a later version. If you cannot, then you can\\n\",\n    \"# use the following workaround before displaying each 3D graph:\\n\",\n    \"# for spine in ax.spines.values():\\n\",\n    \"#     spine.set_visible(False)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"dataset_3d_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure dataset_2d_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = fig.add_subplot(111, aspect='equal')\\n\",\n    \"\\n\",\n    \"ax.plot(X2D[:, 0], X2D[:, 1], \\\"k+\\\")\\n\",\n    \"ax.plot(X2D[:, 0], X2D[:, 1], \\\"k.\\\")\\n\",\n    \"ax.plot([0], [0], \\\"ko\\\")\\n\",\n    \"ax.arrow(0, 0, 0, 1, head_width=0.05, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n    \"ax.arrow(0, 0, 1, 0, head_width=0.05, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n    \"ax.set_xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"ax.set_ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"ax.axis([-1.5, 1.3, -1.2, 1.2])\\n\",\n    \"ax.grid(True)\\n\",\n    \"save_fig(\\\"dataset_2d_plot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Manifold learning\\n\",\n    \"Swiss roll:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import make_swiss_roll\\n\",\n    \"X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure swiss_roll_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"axes = [-11.5, 14, -2, 23, -12, 15]\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(6, 5))\\n\",\n    \"ax = fig.add_subplot(111, projection='3d')\\n\",\n    \"\\n\",\n    \"ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=t, cmap=plt.cm.hot)\\n\",\n    \"ax.view_init(10, -70)\\n\",\n    \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n    \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n    \"ax.set_xlim(axes[0:2])\\n\",\n    \"ax.set_ylim(axes[2:4])\\n\",\n    \"ax.set_zlim(axes[4:6])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"swiss_roll_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure squished_swiss_roll_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.scatter(X[:, 0], X[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n    \"plt.axis(axes[:4])\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$x_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.scatter(t, X[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n    \"plt.axis([4, 15, axes[2], axes[3]])\\n\",\n    \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"squished_swiss_roll_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure manifold_decision_boundary_plot1\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAFgCAYAAADuCe0ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXl8XFd99/++d2Y0Gu27ZHmRLcubvMSJnaQJEAJJWErZQluahqUU+sDzFCjwomwPNAmF8gtQ1jxtytIaCA2UJgRKQ8KeFJPETkJsJ45X2bJkWdvMSLPP3c7vj9EdzT4jaSSNrfN+vfzy6C7nnnPnzvnc7/d8z/coQggkEolEIqk01OWugEQikUgkuZACJZFIJJKKRAqURCKRSCoSKVASiUQiqUikQEkkEomkInEW2S9D/CQSiUSy2Ci5NkoLSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkUqAkEolEUpFIgZJIJBJJRSIFSiKRSCQViRQoiUQikVQkzuWugOTSRQiBaZrJvxVFyfqca5tEIpGAFChJGRFCYFkWlmVhmiZCCAzDSBOpYiiKkhQqwzCIx+PU19enCVrqMZmfU8sp9H/mZ4lEUnlIgZLMCyEEQJYg2aQKh6qqRcUg9VybcDjM6OgomzdvznuMvW2+YlNIyAoJYSnCJ0VRIlkYUqAkJSGESP4LBoNUV1fnFKSFCkUqtrCpavmHSvOJXeb2fMedO3eO+vp6mpub59zmUsQQSGt36uf5iqNEcrEhBUqSE7uzti0ky7KSnfXTTz/Ntddem1c4hBBEo1F8Ph8+nw8hBKqq4nA4UFW15M/xeBzDMDAMA4fDUdbONldZ8xEap7P0n9B8RXGuVmIoFELTNFpaWtLqmk8YUz9L16mkkpACJQHSBck0TSzLSu7LfKPPtJSEEITDYXw+H36/n2AwSE1NDc3NzaxatQpFUZIBE6kuQcuykuNMmdtN00TTNKLRKIcOHUqrTyaFhG6uopj6udyWWzlEsRQ0TSMUCtHe3p5TFDPvZTHXqRCiLFai/X88Hqeqqir50iFdp5J8SIFaodiClCoaNsXcdbabzxakUChEbW0tLS0t9Pb2UldXlzxX07R5dXAAwWCQ4eFhtm3bVrAdqcJW7LMdtJFqGeY7PlfHDSStO6/Xy+joaE5hm49YLsRFmo+lEkWbUqzEU6dO0d3dTX19fdZxC6lb5v2TrtOLHylQK4TUjnxqaiqtc5iLIPl8PsLhMKdPn6alpYW+vj5qa2vznmu/gS8WiqLgcDhwOByLdo1U7Ps4MDBAXV0djY2NOa2/1G26rpckovnuk93GUsUvHA4nBbTQ8csliOX+zsrtOp2cnASgra2t6LWl63RxkQJ1iZIr5BvANE2ef/55rr766rwPvGVZBAIB/H4/Pp+PWCyWDAjYsmUL0WiU3bt3L2VzKobUztXlclFTU7Oo18tl6eYTQvuzrutomsbU1FTB4wsJ4kLdo7mE07YSF2op5apvKdtKxTAMgIICutSu07vuuou9e/fyile8oqTjLxWkQF0ClBryne9tzLIspqenky67eDxOQ0MDLS0tbNu2LasTXqo3uZX4xpiJ/b3NZTxsfHycSCTC+vXr53y9XGORC3WbRjWDsZDJUMBgTb1Co6oRCASSAmC3r9h44HzGFefzDNlBPYVYatfp1NQUmqYtWvmVihSoi5DUkO9c7qFiLjvLspLuOr/fj67rSUFavXo11dXVS9WUoiyme1CSTaogziVCMRUhBFNRg0FfhLPeKBeicYQqCLgNDscEr+yOs3Xjeurq6gDyCls+60/TtILCmWtcNZNC4hYOh3E4HJimOWercbFEyjTNeX8fFzMrr8UXIalvtdPT09TW1mYJUqE3PsMwmJqaSrrs7Amwzc3NrF27FrfbvRTNkFzCGJbgwnSMs94o5/xRpqM6AK21Vexe08D6Fg++iM4jJ70IEUvryBcjYrIQmRZipsiZppmcQlAo0jTX53zM11UaCAQ4ffo0fr+f4eFhzp49i8fjoa6ujtra2iW7Z8uFFKgKpFDI9+HDh7nmmmuKCpItRvY4RFNTEy0tLaxbt46nnnqK/v7+BddRuuBWNhHNZNAXZdAXYcgfQzctnrsQYltXLddvamNdi4eG6tkuxhdJiJbC8j43xYI0QqEQVVVVdHV1leV6mePBxT4bhpHcdvr0aX7wgx9w9uxZhoaGeOihh4hGo2zfvp3Pf/7zZalfJSMFqgJYSMg3gK7rSUHy+/0oipIUpN7eXlwu11I0o+wsdgSgZG4IIZgIaQz6opz1RpkIxQGodTvZ3FFLT4sHBbhsTSM7uutzFDBbTiW/3CxGEEdqYM1c6O3t5WUvexkf/OAHueWWW7j++uuLnnPXXXexb98+jhw5wi233MK+ffsAOHv2LBs2bEizvD784Q/ziU98Yk51WkqkQC0DmRka5ipImqbh9/vx+/1MTU2hqipNTU20trbS19e36L7qxYjEuhipVPEsZ700w2J4Kpa0lM56I5gWXL62gavXN9PT4qG11pURRp2nXjP/V/pjU4nf61zGoLq7u/n4xz/Oww8/TDQazdo/NTV10YxnXRy1vMjJdNfNNYddPB5PWkfhcJhnnnmGlpYWOjo62Lx585LNAZLMUunivJD6TUf1GUGKcn4qhiUEVU6Vtc0eTEvgdqrcvHtVznMF5HXg2c99JQpAJpX2/drpvkrh5ptvBuDJJ59keHh4Mau16EiBKjP5Qr6npqYYGRlh+/btRQUpFoslo+wCgQAul4vm5ma6urqYnp5m7969yypKS+V6u9RdfLo+ysDA2+jt3YfL1bls9TAtwWggzqAvwtELIbxhjfpqJ00eF7tW19PTUkNXgxuHqvDfz44T1ox5XSdpQVF5ApBKJXoHLMsqm9XT09ODoijcdNNNfO5znytpQvJyIQVqgZQa8p065yPzfDuxqt/vJxAI4Ha7aW5uZvXq1fT392elZlnuTrvSfrwXKyMjdxIKPcbIyJ309HxhQWWlil0pRDSTIX+Us74oQ/4ommGhKgrBuIFhCW69cjWNnuzxEoEoHOQgirvwKv3xWe7fVy7mYkHlo62tjYMHD7J79268Xi9//dd/za233srDDz9cplqWHylQc6RQlm8oHvIthCASiaQlVq2urk5G2NXX1y9pyO18qcQf8cWEro/i9X4XsPB676G7+8MFrah0ARJZlleq2LndH8o6XwiBN6xz1hdh0BtlPKghENRUOdjYlghwWNNUzWNn/AxMRnKKU776pNa7kIDZT0wlWiiZVFr9DMNYsAVVV1fH3r17Aejs7OSuu+5i1apVBIPBrLyIlYIUqCLMJct3vvPD4TBjY2N4vV72799PbW0tzc3NrF+/Pm212FKoBLfXUmaSWO62LhYjI3cC9rNkFbWiUgUIRJrllSl2nZ1vQ1Ea0c1EgMM5X8JSCscNzvmiOFSF113Wxer6EIHxv2Ljhn24XIlsIYbhZ2LiHnT9f+cUTDFjIeWy/mbT+ORuw8XyVVaigC7GRF27jYUmNC83UqAyyHTXTU1NEYlE6OrqKimgITPTdzgcpq6ujpqaGurq6rjiiisWnLG5EjrtSqjDxYotKEIkUtcIoeH13kN7+9sYGvpQllWSLkDfmenoZy2vVLEL69X8fvBevNqbiJ0bwrQELofK2uZqenqaGJiM4I/oXNnTxODgHYTD6SLj9d2Hpp0tKJiWGZiT9TdLMs684gQglUp8tuciUPYaavak41gshtPp5KmnnqKpqYlNmzbh9/t573vfy/XXX09jY+Mi137+rHiBKhbyba+tk89KsiwrTZAikQj19fW0tLSwadOmZKbvUCjE6dOnF/zDrASBquTO5WIg3XqysRgYeDvx+AlGRu6ku/tDSRda6vFC6GnnnBu6jYELj3IhtI3RyGrOBTbgVEx6OwJcsamLnhYPqxqrcaqJ72x4Koqi5HYxgiAY+A3QUVB4pqZ+Ci3Z1l+xp9J+bC+Gx6fSnvG5CNSnPvUp7rjjjuTf99xzD7fddhtbtmzhYx/7GOPj4zQ0NHDTTTdx7733LlaVy8KKFih7UTybXBZSZlCCnenbjrKLx+NJQdq6dSsejydvIslyCEul/HCWWyQrjblE5IXDB5LWk40QGvH4cUDg9d6DZUUIhR7j/Pnb8PnuTzneQjOrGIusYizczdhpHc28HlUxaPWM43bEWVt/jpeu+SY7Nn49bx1yuRgTI0ikbcu0ogxjmnD4cURzuvXX3f1hVEcHUJoAVcpznItKdPHNJUji9ttv5/bbb8+575ZbbiljrRafFS1QkL3IWSZ2lN3p06fx+XxomkZjYyPNzc1s374dj8dT8nXK1akvtzjIMahs5hKR19+/P2vbmTPvxOf7HgBCmPh83ydh3fw7QjgJ6g2MhVczGl6NL9aGJVTcjjhdtcN01ZynveYCVQ6dn+l1eJxBDONczmsbxhTjY//KpHoPMCsyk5PfBFzA7uS2XFaUf+onKDmsv5GRO+ns+lsmJ7+LsebPgaasaye/yQr/TivxmZPJYlcgucTJXtDPdtnFYjFUVaWzs7MiMn1XSqddCXWoFOYakZfrfJ/vP5jtwnVMS2UyuorRyGpGw91E9Doiei0T0S6uXvUIW1qeo8ntQ1UEHs9O+vufBeCoOUy1GaWlJXcCYJ/vAeLaIKBn7BGAhhAKSrIe6VaUro8SCj6BID0pqhAa4fATjIx8Dk2L4vX+O30/+zfGI+Npx7nN7bQ6ruWeF99a8r1ZLirNgjJNc0VOyF/RAgWzmb5tQbIsi8bGxmTYdyQS4cKFC6xalXvmfKmU08W33OKwlBN1LwbmGpGXyfDwbYBJ1PAwFu5mNLya8WgXpuXEoRp0eEZ5+eUfpL62j18cn+R1l72H7sb0FyXbxWiaXwCcOe+dro8SDP4WhVqyx8ASCBQUxc74kBCe1HZaQqe+/jr27LkPgL67+2aE6AiIozQab+Rfhv6LuGM8V/GE9TCKUtnfbSW6+KQFtQIJBAI888wzycSq69evp6qqKu2YaDR6yQjLxUil37N8EXmlzGs6depWvJF6Bv0xLgRfwWCgF4di0lF7gXX1A3TVjtDmGcOpmrgj78TT/Ju85dkuxsD0r6htfFneY2xhUpQqWlvfQjD4W+LxY8lt9fXXETA1tm3bn4woTG0nvIBQ6DF0/RZcrs4sKylBvu+ssjr9fFSiQJVjHtTFSOXPCF1EGhoauOaaa9i2bRudnZ1Z4gSVZ/lUgtDNpQ6V9kO30fVRjh9/Jbo+Nq9zA4G/xDQn8kbkJbZnoxkWpyfC3PfEt/nP59by0MkujnnX41BNqhxx1jUM8LKeH7O740m6akdwqgl3Wjx+DMOYzFsf28UYCj+FENN5jxEk0hQlxp2+nRQne1swuB9NO8PAwNtT5l2lipuCkrd9xb5rxb5QxT4XlcpKtaBWtECVQqUJSyUI1FxY6JyvxSJ94uvcGB6+DcN4Cr//s3kj8rzef0+K31RE55nhAD86PMo3HxviwWfPcPLCSdo9o+zt2s8fbvhPrlvzczpqRvG4IiiKSuZPU1FcjI/9S962pLoYo9GH8h8jFGYtnFxLiFuAmBEui8nJbxII/CZpJQoUQMfrvaeAuOd+PhVRPXP9yqYSLSg5BiXJSaUJSyUI1FzqsNC6LkZbE9bEPcw3zVAioAEikR+xa9exrHMHzryf48MPcvbJfyPqfDPTUZ3zUzFMC157WSfOyL9A/X+iKvlWYM0eHxJCIxJ9Bki3UzJdjGASjz+Gaf4hsDZ5XC4hzYUQmXaQ4PTpt+SoU34rMfUbU0UDLmstLrEGt7kNS4kk2lBhApBKpQrUSrSgVl6L58ilJCySBCMjdyYnvAqhzSnNkGWFIRnFZjI8fBsbNtxNRDM554tyeuICTx6LoJsvwaEOcsXmCLu6OxmaijEejLN3TZwjR76OyBInN92rb6OjoZU929pz1mPIH+X5I+lWSy4Xo4JFOHw38LXkNju0feLoOFMRg7a2rpmsFMVFy7Kmkp/t5TRev1/Dr38j40gFBDiop9rchVOsxSESWQpMZRJNPUW9c03R60myMU3zosjRWW5WtECV8pZUaQJVCUJXCXWYL6nWU4L0TAqZk22z0wylh2f7fN+noe0TfP/3ie3x8M9YUzdIZ80gHbV+Ops99Kz+At5wYn+qOGbUjOmpBzFCQ+h9Hyk5TD3TMhJCAQwM45k5nZc8n9Qw81ne8JgDn2ZSZySm84adaSfhEG24rA24xRYcZivVVVHuvPFD9LbWsKGthjq3k8fP+Pn9cADIPZZWKVSiBQWVbXUuFitaoEqh0oTlYhaHubIYbc0lELYVlZmE1T5+Ns2QRvb4iskjR75OOP5m3rTXzcjAHUAsuXdW/BI/tXD4ALlDvC2i0aM4XOc5fLifbdseoaZmR9H2ZE76fdYYptaK0Npak3Wsro9yfuSzeBr+V9p5s6HiUGOCQ8A/DEKzC+6/NnGMT7MtPjvQQcUp2nGJtbistSh4QCgI4nzlD9/LTdva8LjSx0xs66tSBcCmUutXiXVabKRAFaHSBKES6lMJdZgvodDvyDWeEgw+gqYNkWlRpY/v5G5zOPwkTTV/iRb4RxTFykiUYI/V/F8gdxYJSIjHL87Zy2ToDAy8lR07nsp5bPH1lnIfMDJyJ7HYKTTxM2Brcnt6qPhsEIU/ay6viiqaUUU9DaIFBTcCA0MZQVeHMJigwXwdG1o9WeJUrO6pIplKR00Hp951Kk9LJZc6UqCKUGmWTyWIw8X8JldXdy3x+Ok095aiVAEOcuWmyzehNRWnM7Eiab6IvnD4iaK/tJGRO0HMXiseP0kk8myaFVXS157nmKSrUlxFOHwAXR8ryY14834nIW01NWINTqsbt7UZQZyw4zl0ZQhDuQAz42mKSGSveMm9L0VTT2SJi11/IQSbv7Y5zxyqdEo5ptxUqgW1EpECVYRLLcmrzUJ/hHO5J/O91mKIcbFErfbfXu89uN3rc47TeDw7qan5LjU1NXR0dBAf8PHchVBe6whg/IQ3777Zca4b0yLoCllRc2VO2S6EE5e1AZe1DkvvogYHghi6ehZQMJUJoo7Hcpxo1z5xH7PFZXYxw+UQnlKRAlU5SIEqQiVYLKmUoz6L/eOzLIvp6WkmJyeZnJzENM3ksveqquJwOJL/7L9Tt9ufITGDPhgM5jx+PuQSkcHB9+eIaLOoq3sh27cfyFnO2bNn53X9XCTGxYys7bmsqNJIfz6ys12YWeH1ivDMhILvxCk6UKnBUsLE1ZPoyhCmMgmKwCFaEXmtynSBAmj4QkPSkrIXO5RISkUKVBEuRYGChb0l5qpDLBZLClIoFKKpqYm2tjbWrFmTtnKnvRCkvZhars+6rmOaJoZhoGkaQ0NDWcflWgU0l8iV8jkYfDy/a65ECn0luj7K+fOfxqz6Pzn3JwInsgUKYGDg7ezYkVmPAtn3sVe1nT0mLdAjGaVnceLMF9BrPkadcRMOkQhtV3FjKKOEnA9h4iuaHKKjpiOnpZTKeGScvrv7ULUdRKKtBFw/KFzoMlNpFlQl9T9LjRSoIlSiQC13GYqiYFkWXq+XyclJvF4vLpeLtrY2Nm7cSF1dXfIa9uqeqqqiquqcJhuapkkwGKS/v7/osakrIRcSQVv44vF4cp/T+W8oSvY5oZDgwIFZC0pRlKSoxWIxXC4XPp+P8xMGPr/B2bNWThH0+T5JNHaC6cgviMU2JPfZ2fT7+/fz9NMdOdulaWdm21jynUvHdmsKAVGjhrHIKn45eAMRy017u5/6qiZ8+iF0dYhq8zJU6jEVX57SZoMoAh8IAAkrqRjjkXGqzSmqaJlnK5aOShMom0qs02IjBaoIlSZQsPA3qvm2KRKJJK0kn89HW1tbUpSWe5a73dmrqorL5VqUa9irL5umyeDgIG63m6amJgbjU1RHw9TU1CQFTtO0GWtwlGDwB8BlRKPPcPz4Y1hWU1IEZ/kJlmUghIqiVKUJ3PHjx3E4HIyGTaamYly4AFaoKqdVqOs6lpooWwjBprs34Y0YuKzLcIm1uM1dKDhweqb4/h9/kA2tHv7Pdd9M1qLrs69awB0qMRffHOio6VjyCL9K+72vZKRAFaHSBKqcLr5imKaJz+dLClJ1dTVtbW00NTWxYcMGGhsbF1yPiwl7HM3hcOByuaiurqahoYH6eoOaIHR0dMykRfqrZFqko0ffSmokYFPTf+UNTjipnKet1sWuzS1pFp8tikGiOJ0Ja9QWwUy36cRkGMulcyju5+fPnicavIY6kVhew1DG0dTTWEQIWf+F6vsAw9MORlIELtVCKkS7pz2Z3WDWzZcQIJH3/NLKhnTxyWehLWagxUq0ViqRFS1QS5lJolwsZpCEEIJwOMzk5CQTExPouk5LSwvt7e1s3rw5GbgQCoUq6p5UEplpkQxjNGWvUTT3n6IoOJ3OnBZpkCh1dTpdnZ10NqQvSGhagvNTMSKnzzEZDuPHRXNTAyZ+Yo7DGMoIQolTa1yPQuLczZs3Z4lgcRTqnLX84A/+k2eeeQbLsrh3z70IIbjpf15b5Mz8tFW3MRmbzTAxHhlPBlhkkfLoZYpXOayqSnXxrURWtECVwqX4oKaKnGEYeL1eJiYmmJ6epqamhra2Nnbs2JF3OfulXLDwYhLCYmmREsx9QUObzHuhmxZD/hgDkxHO+iJ8/vEvo8dXo6tnCDt+ja6MgDN38AUw59WhAx8I8L0nR2iqcXJ5f7ZwtD/ZTjwIhdeDyr0vVZxSSVpJdhqK1KLyHH/mzJmCwTG59qX+zi+mZ+5SRwrURcZCO20hBIZhcObMmeQKwq2trXR3d9Pf378iE1LOl9nvIdG5padF0smXlXwu0YE2fXf34Qs5qTVeym1PHMYhGnCKbupcjfztH7yPDa01jFsPUqNeg66eRVfPzbNVhZmJEcy576m3PcXV//wRIgXnNmc/u7kjAUnJjZSxvcg7Y11dXd6AmHxBNPZ3qSgK4XCYp59+uqCglbIv9bM9RiqZG1KgLjLmI1CapiWDGwKBALqu43a72b17N263u3gBOeqw0sm8B6YVzEiLlJlh3MWqVR9hz55vZZVlBwHUG3+EqfiI/PR3AEn31kR4GpdYg8fcRbXVj4IbU/GhqwOMW0P8xR98HoeqYPz3eTCLPRuFx4Ea3Y0E49mWn12XhPsrf+nv3fsePnHg53mv/akXf4rtjndzy1O3JEUpS5xKtJby0d6eOxt8qTz55JNcfvnlaUExhQTOMIyix9hRpqnYka2FhO/RRx9lenoawzD47//+b2pra6mpqWHbtm3U19cvqJ0XA1KgLjJKESghBNPT00xMTOD1elEUhdbWVnp6emhoaODpp5+mq6trXuKUeo3F5mJy8QUDj0JdIdNBEAj8GtiWtSeX9aAID1OhJlzWWhpEB4le2oGhTBJ2PIKunkp23A51ftFxufjCDV8krBn86RXdcy6zlK9KQeGPH/9j/Lo/4+ScBwMFLKxFwh6DWuwFAovNC7SDUOyXyieeeIJwOEwkEuE973lPSdMvLnZWvEBdTJ0g5K9v5kTZxsZG2tvbWb9+fc6w64W0+WK7Z0uBpg8XXFtJYKBphd1uinDjFGuoEy/DIRL5/Uxlmpj6LLo6hGrVUMv1WGr6BNrMYIJc34w9Z+nHh8cwLIubd6/KU8/ZdES59+c3aESOT+kooDArTuke0rwF5wp6KGXu1UJYCi+BbUEV4tZbbyUWi/HLX/6ST37ykyWVe9ddd7Fv3z6OHDnCLbfcwr59+5L7fvnLX/LXf/3XnDt3jquvvpp9+/bR09OzkGYsKiteoC5WLMvC7/cnQ8CdTmfOibK5WEoX3UpxB7a3/y/27Pl03v3BE16cvkjaNiEE3rCO29yZSDNk7UQQJeZ4hpiamDhrKYHk8apSm7f8YhaG3aHXGi+loaqZm3f/WynNykm+r9R+aWmpbmFMG8x5zMd+81FwUbI45bP08llV9vELnTtVSc/tXFfT7e7u5uMf/zgPP/ww0Wg0uX1ycpKbb76Zb3zjG7z61a/mE5/4BG984xt5/PHHF6PaZUEK1EVEJBJhamqKaDTKmTNnaG5untdE2YVaQEsZxXepIYRgLKgxMBnhjDfCdFSn2tqBoUygK0No6gAR568XepWCewNaIO++cuTL++kbH6SvPSGmDV9oSCSfFatxm/2YyszqvCVcw7b6clFMZPIJdiUnqc2HYRhzcjfefPPNQGIsbXh4OLn9/vvvZ/v27fzJn/wJALfffjttbW0cO3aMrVu35ixruZECVcGkTpT1+/243W5UVaWzs5Pe3t4ly0YuWRiWEHjDGgOTUb594DzhuIGiKKxpqmb3mgY+evCHCCVGvfFqUOILutbtL7qDFjXKNT0NbLl3S97j8lkYqxyv4dPXfS7veUKQ1wWY+kSZluCcP0qNeS1OazUKLlTRiKVEcp4ryY9pmmUZD3vuuee47LLLkn/X1tayceNGnnvuOSlQkuKkTpSdnJxE0zSam5vTJsqeO3cua97GXClHLj4pcIUxZibO2pbSsdEg0zGDazY00bu+iZ4WD9Uzi/q11zYwHolllWG7q4olY50P+SyJQDw4j4TLabBRAAAgAElEQVRECSxLMB01ODA4xW9OetEMC6e1Cl0dRFPOUqX04aRt/pVeoczVgspHKBTKinBsbGwkGAwuuOzFQgrUEpM5S92eKDs5OcnU1FRyouz27dvzTpRdqlRHkrmhmxbnfFEGvBEGvVE006LKodLT6sHjUoloJq/cnj2mYrurvnvwPB31Vdy0NTtMOjGGVJp0FD6q9HRDuUgEUaT8LQRjwTgnxyMcPj/NifEQqgJ71jWxqaOWjxz8ISiJ6EZF9JW46qIklbmOQeWjrq6OQCDdbRoIBCo6XF0K1BJiZwFPtZJM06S1tZVVq1axbdu2olE95Up1dDGMQV08CE5PRPjXx4YwLUG1y8HG9lp622pY3VSNU1X4zQkvZ33F3VuFIuhK4fb/uQNNPU3sd0/PswSF9/z83bzt17NzmVIDCxJfu2AypHFyIsypiTDBmIFDVWivd7OxrZbX7+5ic0fdTHFWWtmp4hj4QGBO0Xj53JKZdbzUKJcFtX37dr71rdl5eOFwmNOnT7N9+/YFl71YSIFaAjRNw+v1Eo1Geeyxx6ivr6etrY3LLrtsznORbJFbCJdi8MHyouBQFbZ11dPb5qG7sRq1zPe4o6YDX6gcJc3dgrJFYTqqM+SPMjAZ4fh4ODmOduW6Jja01RCI6kyGNJxlzEZSSJRy1dGm0Nypxi805kxoW6kiN1cLyl7ixp5LFYvFcDqdvP71r+dv//Zvue+++3jVq17FJz/5SXbt2lWx408gBWpRsCfK2laSPVHW7XZz1VVXLXg5iOV28UkLKsFsehxY3+rhur4WdH2Ukyduprd3Hy5X50x287dhWHcBVfO+1ql3nWJgMsJDR8f50yu+QltddlnlmhuU+s0qwoPL6uEHT19gIhTnnC9KT2sN1/W10ttWQ02VI+u8QtqcP9N5buYbdXfqXafy3o98dajUCL+5Bkl86lOf4o477kj+fc8993Dbbbdx++23c9999/Hud7+bN73pTVx99dV873vfW4wqlw0pUGUiHo8ns4DbE2Xb2tpYt24dVVWJzsTO6rAQKmXBwpWOfQ90fZThoTuwxHsAO5v57zh69EX09/9PMru5L3YfOG5JnjMw8LakiNkIAYbp5/jxN2ftKy+FREJBEQ6qrD5c1nqcoh17VOva3mYcqsLmjjp2dOcft8j7dAj53MwHwzDmZEHdfvvt3H777Tn33XjjjRw7dqxMNVt8pECVSGZwg2VZTE1NMTExgc/nw+FwFJ0oq6rqso8flauMlWJB2WKydu1nOXfufQgBPT1fwu//3yiKyejoONHoagL6o+h6y0w2c4FhjHLu3IeYnv4pYBEM/BoaXg2kL8mRmdXc73uA9prEvu7uD+UUsvlgzyla9dk35D5AOBMLGlpbcYhGHLRgKQFi6hF0dZA/ueJuAA4Np0d8pbrgHKKVOuPlfPzAb2ipM3K4yxYWoLFSKVeQxMXIymx1CqV01vYxsViMiYkJJicniUajNDU1zXmibCWIy0LrsVIsKF0f5ejR6zCMMQYG3k48nnjzPHbsRoSIphzZTSRyiOHh3yPE7JpKU1MPAAnXjJgRKV1vTlmSI31tKNMMEAk+CjWJfZYVzitk+awwZq5WEsKBS3TjsnpwidWAAwUPujJC0PkgFlNZ5lBmFF+xCbHpY0GzAmWH0BfLCCFJWFArdZWBFS9QhbAnysZiMR577DHcbjdtbW1s3ryZ2traOXfUlWL9lENgLmULyu78VbUhueCgLU5AhjjZ2yx8vv8AUhf9E4C9HpNJOPw0w8OHmc10nr421HTgl1Qnl+swZ8qbFTKYdatlWmG6PkpLlQtDz/5uUzt7Rago1OMxr8FlrUHBhSBGXD2Nrpyl2twNisCyMz6QLRZ5H5+kC2/22Ui1oh58bpxQzGCD9UGuvPLKrP2p1pi9YGGpXMqCZprmgsetL1akQKUghCASiSStJHuirMvlYu/evQvK/g2VFVwgLaj8DA/fRii0f45nCdLFKRcmPt99yeOE0FLERxAJP4nbbQva7JIXQsQZHr4N6r+AaYY4efKPcYvHsBdGDIcPousj3HeNwS8v7GVT6wfZ1vxFwuHv09b2l6xb94+MTMc4OR6m2rqchKWkoqvn0JWzGMo4KDPPg6WQulRIZrqh0h6bAgcVeHTmEqSQOYl5roJWqMxKw85qvhJZ8QJlGEYyuGFqagqPx0NbWxv9/f3U1NQAcODAgbI8IJVkQckxqNzo+uiM5bIYWGSLmDWz0KEgf8cu8Pm+T131xwgG9xN1P0OVOyFgQuhEo4eSR0Yiz2A19xEK3c9UvIlnjx3BOn+MqOHBqapUuTSC5iBB5+wE2rlQKJt5sSnCC31kMsWyHFGLhfL9VQpyDGoFc+rUKYQQBSfKlsvyqRRxkVF8uUmMOb2A4pbQfMm+b6kr7AqxoUCItsmF0X8kErEtLCvj/wQxw83zF37KSeWVhPQaHKrCRveD3Nj/Lja0emitfSdVTpX37/+f3OtQUY4whtwlFFvKYznIJXLNrmYGr8ydjX050HV90demqlRWpt2YwtatW9myZQstLS15raRKE6hycClbULo+yvHjr0TXx0rabjM8fBuGMbGga7e1vYM9ewK0tb2dXD8vp7OLXbtOsmvXCerqXsCuXSfp799Pf/9+1q69k42938Xj2Zmz7EDgUXItIx/Waznh7+dX517J6ek+hoJ11LgCXN7xBK9Y/wN2NNzJhuYQLkeiPqY5Pe85P0KQNgiV7hbLt3323IU8vn13983/5DmQtZjiMmNZlrSgJPkp59jRckfgwaWf6sgeQxoevo01a25PhomfOPEaTNPLuXMfZuPGfcBsQERX14fw+e5d8LVtaygxhpUpJonwc9ully9Cr79/P7o+yuHDO4HU7OYatnUSM6o5H1rHcHA9vlgiAWuLZ4KumvNc1n6QyzsPppxXlbyOAKb8PynQguxIu/QWpEfxpQY5nJ+K8aPDo7xm1xdZ05Q7j+RCXq8WMpF2rmmVUo9d7gwT5Up1dDEiBaoEymlBVUJdLkUXXep8JXsMyef7HtPTP8c0Jzl9+q2YpheAqakf8vzzQ/T1/XtSzE6dev28r60ooCgO2trekRSburoXEIudJjXYwcbr/c7MeEx6qHlqhoOEiKWfq5supuLNHBx9ASG9ASEUGt1+trc9w+q6QWpdYaZirVQ50lf2ff1+Db/+DeAb1BkvRxAv+Mv/2iu/xqt3diYtzlLnYdn1L+TGK/TolbK0e+bqwUvBcmeYWMljUCvexVcKlebiW24rrBIFzg67Pn36rcyOIVmY5gQg0LTUN2BBJHKQ8+dvSwmImEvAgAu3+zXAbFSnECZe73d4/vkbiESOzMx1yhanxLF6yj47SCId2wIzLCdDwR4eH7mO/xm+idHwGqJ6DVuan+WGnp/w0nU/ZXPzUWpd4eS5qmMta9eeSroZ/bmrkQeFCxc+g66PpYWyz1a+0Iq6MyUU2F9IvE6961RJQQvjkfF5RfxdrEgLSlKQShIoWH4X31zqMF8xsy0ieF/WtrVrP8vQ0IfS8t3Zk1/ThagwXu/3KC5MucIGdOLx7PEgIeJEIgcZGHh72oTdbKyUc1JDzRMYlsBvvpznRjsZCXdiWk48zghrG4apr93Gu1/+TzS6/Rw5smtmXKeanTuP4HJ1cjAySL0riqYdZXLyX3PUvTjx+ADDw7fh99/Pzb+zktYXQIP+J2jqaWo8Rzj5zqM5Lau806SWOIvEcrvmysVKtqBWZqtTKKUDrSSBqpRcfKW2IxKJEAwGcblcOByOtH+FQvftt3dFaQVenrYtkdXhRHJcJfGGP58M78XPURQXra1vobv7QzOCEENRqlHVNkxzKuc58fhx5iYMFsPn72Qq8h4mQxpnfBHOX1BQrTbW1Z9hTf1ZWqsnGAmt4/feTSiKktFmK22MCcDr/UCeOihFheIfnof/e/LfCx7j0/Sc42f5MCyBL6zT5Fm6Lqccc6MqgXKtqHsxsuIFqhQqTaAqwQorVG4oFGJsbIyJiQmcTicejwfLspLp/+1/mcuGKIoyI1x+TPM7JDrghzh58gkUxc/0dMIisLM6TE5+E7f7Rrze7yKEllWX8rQnEQaeKQgu1x66uh4kHr8TvM9ktMOFEArpAQ65ygZfrI3h0HomhqoYiARp9rjY1d3ATVs/ypqmahzqbFJa39PvQ1GcxKLPp7U50wozzXMYRmmWw69fPPv5JY+UcsasRZmZqinTxZdYzFDj+FiIkxNhDg8HeUFvU9ErlDIWVU5sS6uQmGXuW0rrzDAMmUlCkp9LTaDKYUGliosQgqmpKcbGxvB6vdTW1tLR0cHevXtxOBxomlbSRGfLsrAsi6GhD+DzJbdiWd8iGv0t2RaB4Pz5P2d+1lNxNmz4FbW1O7GsCZ5//oo0QYjFHiAev2XGtbglvVZCI5+jq7p6J509v55d7M8ycNQpXLauhqrRINtX1XPD1uxl0UdG7iQWfRZNa2Jw8H1UZ02ytcey/oZo5OtQna9V6S7L6uqtxGLH0vaX7oqzGB6+DU0bpLd3H4JEJx6Omzx1bppjYyGmozpOVaW3rQYFcDlVFKPw82d3/Itp/Sx0gu5SCqi0oCQFudQECsozD2piYoLx8XH8fj+NjY10dnayadOmtB9TrsUVUxOdgkgbV0pE4d1LIqQaFMUgHP4BQuSzRhZHnADOnHk7Tue3MYzPIYSRNvgvhMn4+N+gqvnGmpx4PG+gufkOnE4nJ/UAcS3GgSkPgdFzOBwq61o8XLmukd62WqqcKhOheNJiSkXXR5mcvAdBFwBx7RTuqnSLUQiNUGg/pvl2LDGatq/ZRc5AiWYX1NZeQTx+FiFiJd0TJUXghNDw+b4PWJwb/ixD+kc5PhYmHDepdTtY1VjNFWsb2dhWQ5VT5YfPjKIqpT13heY8LZaFtdSWW6nIIAlJQSptHtRCmW977LRQw8PDRKNROjs76erqKmmp+lTSo8NEMvpO004xMPBWMqPf8otTKg4SHWc5Bess/f1dnDx5hmjUSNujKAaKMkq+SD3QmY4c41wkyBl/nMOjUQIxi1VVcdbXKXTVgFP3ExyyODSUeC7ODRqEPQpNocG0sTpd/xy2YCcQeDyvp7398zgcDrzeTxAI3IPHczXT07+iMWMK0g9fUIWq1mGaPn51Dmpc8AerEvumph4i/Z4VyyWRKlDgjbVwLtDLyEAItXaEqKbRpnyd11z+IVrru9LOFAg+9JsPc8F8AB5NLzXTZVZIKIq54+bLqXedwjAMWr7SUvayF4KcqCspyEqeB6VpGhMTE4yNjRGLxWhra6O1tRWn08mGDRtKLic1Am92uYnvzLRlNvouHp+vXz+3JePx7GTTpvs4fHhb3mPyoSguRkbupL9/f1obenv3MTqq4XQ6WbVqFfoZP4HBERTlAWK6YCTcB03fYjycWKiyvbWeK+sMDNPif72wJ+/1jolhVje62b2pJTlOF4+PcPr0f5EpGtHojwgE3o1pGgSD/wEI/P57icX+DMWTGV2oYRjNKMoLmRUjF07na7GsIwjhYy4owkObspOfD24grNfhVHXW1J+jo/knOIxW1np+TMin0lqfHkAhBATi0zl7nUqxXCrhBTITaUFJCrLSXHyxWIyxsTHGx8cxTZOOjg42b95MXV0dACMjI8Tjua0aTbvAiRNvZvPme6iq6kr+7XavT0bg2Z1kYj5QYYtnPulxFKWK1ta3JCPMzpx5J8XESVGqs9xcqXnyIN3yU9X3pxwnGJw4iBV8IRORNgQuuoyHuXrrm+lrq6WpxsVvTngZ9GUv05GjIknLCWB8/C5ml+yYOQQAC0X5f7jdzYRCIjnxVwgj2Z6dO49gu1DXrLmT48dvBF4yU4qOaf6E1asfxbIMRkZeQrMrjp5+KSDhCvz+HzgZCa3jR6d6qXasoqN2lBrnGF8bOoLXHALFwGmdpdZ8MZ9zmjRUfYNfv+rPcbk6k+0JhkKUI9NfOaynvrv7LpoQdBkkISlIpQnUYtQjFAoxPj7O+Pg4DoeDjo4Odu7cSXV13tH2JJp2gWPH3oiiQHX1ZoLB3zE8/Bl6e7/M+fP/H+HwYzMdvZW2rlJpYd5zbBzpUW26PpYnhZGTtra/SK6ldOrUrUSjh1KiAd3s2vVsMkItda6V13sPLS1vwulM+MlMa4rRqUmaWmrY1Pw8q+sGaXT/gF2r/hSXq3jUWiHC4QMkBT1jn9//ExRFTamzzqz7zUxzoZ458w6EMGfStdolWcRiXwMEiiK4/1r41TmodQmuXpV4OZiMdnIu2MsvhvfS0vpX1NQHuWZDM3tbvkwseB+fOK2lxIPMfll+HTTtm3g8f8e191+LN+6lznh5wbYeOHBg3vdpruSz2Er9fS7l5F/TNEv6HV6KSIEqgUoSKCjfRN3p6WnGxsaYnJykurqajo4OrrjiCqqqqko632Z4+DMzHSmEQk8BFhMT36al5bWMj3+D0tZKKjeJqLZA4Bd59htJERsZuZNI5GDGfj1tMcCjR69LmXxrEQ7fjdt9BwA+772srRvlZT0/TgqqoiTy37W3/wXHj/8hceePgbl3aqnuxeHf/VHGXg0h8v2EdSYnvzNTHyvn3KxEYMVvZ4IkZiIUUYgZ1Rz17mIo2Aeua+lofQUvbK9la1ctABtbNeLB7+QN7bejACOR+9i48Xa8cW/WvlxcddVVs3/8Nu9hZeO5557LmpsnhKDZ1ZwzYWy7p53jf3Uch8OxpNlU5IKFkoJUkkAtpAzLsvD7/YyNjRGNRmlubqazs5Pe3t45DcKm/jg17QJjY99K2Wsvxmdy4sStlMOlU7w+7qxACrvz1bSzec8TwmR4+LaZRQQzsZic/A7d3R+eyXI+mnKeRiz2Q0zz3cAqYrFjOFRPRpRfwj0YDP4WywowOvYVqPvUvNuYfzLyrE8u+7HQU7Y5SbgAFRTFya5dJ3G5OhkcfD/x+ACa6WIktI6T/m0Ylos19YN01Fygp+FfuHHPa6ipbk2W6vPeS7UzRlvbO7AzTEB2GqPX74/h/82mlC2ld+pzjaizw8bn4v5bv3591ty8WCzGf1z1H3R2dmbtM02Tw4cPY5pm8jdo/x5VVcXhcOB0OlFVFafTmSV+xf6pqppT+OQYlKQgF7NAmaaJ1+tlbGyM6elpmpubqaurY9WqVaxbt27e9bDrMDz8GXJFsgmhYZr5Js+qJDpb+//54gRMWlvfnDOjwZkz78yY45OJzvT0QxSKxEvP15eKRTD4T8Dd9PR8Ba8aYM+er6UdEYkc5vnnX5goSR9G6BeANQVblK8LD4cPzIzZkeKiS/y1a9cJTp68mUjkSFYdU9syi2Bk5E7WrvtHTo+d5sTElVwIr8G0HJiWgzX1Z3n5+h/hcUZRlComxj6byFIhBKYZIhD4Bd0tCVdnIbJD2wtHCC511ofa2tqsbbFYjF337cpp6SkoTH9gOmu7ECLnRPTMf5qmFdxvWVaa8A0NDfH5z38eRVGorq7mkUceoa6ujk2bNvE3f/M35b8hFciKF6iLLdVRKRiGkRxPCofDtLa2smbNGnbs2IGiKAwODi5ohWD7nk1N/ZKxsa8VOToXuRfbK4bD0YTHs53e3n0MD/9dcmwpM6MBlLoyrhvLCheohzUThp3LPamjab8vWPrAwDvS/vZ6/wO4skidZkpPiRh0uTrp79/PhHYbjGZaFUoy0lAIwYHIIHXOC6jqm7EsP9niqxA1XDx6/BD68DFi5ldwNzu4YUst6vSt/PZsgMZqPx5nIqAjM1AkGNxPiyff92f/lpYvEm6hc5mEEHndkPm22xlQym3l7Nmzh5tuuokvfelLdHd3c8MNNxAKheZ0neuvv57HH3886SFZvXo1x48fL2s9F5MVL1ClUEkCla+MeDzO+Pg4Y2Nj6LpOe3s7GzdupK6uLkuEy5Us9vjxW+ZxphtFEQihJaPturs/xOHDWyjesamEQo9lWTV2QECqFTU8fBvFx730LLdYap1On76VcPhwxv5EdJwdZj5bifRyIpHDGQEhYBjjBAKP0NDwYoqRGjFotysceQZYlXGkRSj027QqaPH/wnKnd9KaWcVwcD0n/dswhcra+mFWGZ/mhsv/gY2da3CqCvBTzlnnaa2rYs+29qw6afoo+47/Hj86cQekz82C4gJVnvV6M0kNWLAj8/ru7isoVPmCHCopzFxVVerr66mqqqK7u5udO3MvZFmMu+66i3e84x3FD6xApECVQCUt0Jdal0gkkgwHB+jo6KC/v5+ampqSy5grmnaBkZE/o6rqHVjWfNLFxJOiYEfbWVZkJn/dbIdnC8X4+MtoaPgya9d+lmPHbiARRZeZhVzPsqKmpx8uoS7ZllNq7r1w+CDZK9JYWWHmuci0nmxOnnwdNTVX0Nf372kRggMDb8M0vwBUZ0UM2u2qqdmNokymlacoVdTVvTD5t2mG0I1EYIWghsbVB3n08KcZ9FtYQkWg0FN/hpvW/5hqZ4xqrQOnOivsgvxuxpGRzxK1PbPzpjy/o2Kpii6WEPJSkKmOJAWpFAtKCEE4HCYcDvPYY4/hcrno7Oxk9+7duN3u4gWUgeHhzxCLHSAWe65MJVpMTz+UFRFmC4WijGXNn8otLOlWVFXVaqLRXBNQE2/xqUtUZJIYO7ou57XsetXXF25V/uAMk0jkYFpdbWtpevpX0PrGvJnKo9FnESLdssnlgqty1HJk4gqGQxtRz/8YJ128dPdb2LF6DQ8/P0GLJ4JH+3EiE0QO92g+XvJf3wH+mPmLjAIlpjpaLirlRTQVXdcXlEniox/9KB/5yEfYsmULn/70p7n++uvLV7lFRgpUCSynQNmJWMfHx5Ph4Kqqsnfv3nmHnmYmey0VTbvA+Pi3SXRQoXldOxMhNKqqVtPffzZrn66PcujQDrLnT+VCT+uo7fDsVAYH3z+TvUIjtePPJGH9JO5P5qRfm6GhoYK1ueKKWffS4OD7Yfxo2n47QhBE0loKhw8QjW7HG86dqby391uc0SfZvfurNHrSv/uIZvLc+UGOjkaoc+2ho2aMrtrzrKv/Zzprz9NRHaCtLtEGn+8BOuuyBdAm37CsTzNoLNjqwi6+QosVzoXFnINUiQK1kFRHd955J/39/VRVVfG9732PV7/61TzzzDNs3LixzLVcHKRAlcBSC5RlWfh8PsbGxtISsfb19WGaJocOHVrQvIj5zuFIROwVErbcYwz5OvlizGWdJ49nZ05RsrHdZrk6/lTrIXPsSAiNyclv0t7+l9TU7JhT/VOvC7sy9sQ5evRFNDS8hNk2Ci6MfolVHXkylVd/Mm2raQkGfVGOjYUY9EXx+n4OWPQ1Hefa1b/G7ZgNvbfbapoBoqFHEbW578Mnf/v3TBtniTz4uxytKebbW9wgiYVmIC8VJU9G93IJ7FxZSJj51Vdfnfz81re+lXvvvZcHH3yQ97znPeWq3qIiBaoElkKg7ESs4+PjBAIBWlpaciZiTQ1FXQhzLcO2ngqvu5Qn+inDDVUKdseuKDly78xQTJRSyS122dZD7rEjwcDA29mxI70NpdzDkZE7ybe6rmGMJrOBJ8u0wnndncLpZWLi2zxx6DlcTf/AgK+KuGFSW+XkstUNiNr7iUU38E9nh/jkmcxUVDFaH9vFB/o+TlXW9zR7H0J6CJHXDbdQAcr9ApMqPPmCG5Yqc4MQgsdf+Tjbtm1bkuuVQjmTxVbSeHopSIEqgXIKVKprLVci1p6eHhoaGvJaOeWOBExNU7RlSyLpqJ1LL/VzcespP21tb6en54tzOieXoMzXEgN7HlHujt9G10dnMi5kE48f4/nnb0gGN5RqhSYybOSbZwW57qnbvS1LDMNxg+/t/wonJmsJxzppbfw5l298A1s761jTXI2qKJjWQxwKDRIcuiWR3D0DbzxKLH6WKkdmtvhSXyBKFaj8+zMtk0yrZLmDG4QQS5olohTma0FNTU3xxBNP8OIXvxin08n3v/99Hn30Ub785S8vQi0XBylQJTDfMZtcaJrG4OBgMhFre3t7WiLWUuqyUFLLSE1TlBAhkcyll/o5GHw8p/Xk8fSze/fTABw6dBWRyOGsY+zy85E558c+p5igzIVSLK2RkTuzoglnUbKCG2b35P9O+vv3c/ToC0qup6IkVgzW9TEURwdnJiMcHw9xdnKSE8NeVKWRnW1P0df8n+ztez0u1+zaGoJEFF8hvnRsDEOB+qHE8hazFssReKiBel5Tcl1z1D5Zj5wIJStEsPTFEVcupmnOy4LSdZ2Pf/zjHDt2DIfDwdatW3nggQfYvHnzItRycZACVQILtVrC4TBjY2MMDw8D0NPTw44dO/B4PEXOzE25XHzh8KG0ibZjY/uSudvGx7+FvRTGxMS3ufzy56mq6mJg4L2Mje0DEvOYGhpelDz/sssOMDDwXsbH92GvKtvaeis9Pf+vYF1yzfmxBeXgwYNceWVpk1sXSi5RnCXxgjIb3FA6/f37GXd6cfmi7Np1J0eO7MrKnG6TWAa+iwcO7GNauQXNsKhzO1nneYDmrt/wvG8nPY0DVDmiOcUyGCzN5Wm70eY3qXUOFpQAp+ikSvThtrZhKBPzuF42hVyBhaywYuddShZUe3s7Bw9m5pi8uFjQjIZLgcXIJGEnYj1x4gS/+93vOHbsGFVVVaxfv561a9fS09Mzb3Fa6BymZ5+9EdOcQAjByZN/kXFEPJlOJ9FR259Nhoc/kxLFNzvAPjHxbcLhwzz77I2Ew4czxqkEXu+96PpY3jplzvkpdOxi09+/nz17AuzZE6Ct7e0oSq6kufqM+3EWw/AxWWLd8wV+RPQaJiJdHJq4kkeGXsLx8QAbWmp49c5O/uxyB2tcX8LjSrWOErkCU6+p66NEo4klNuZP8cm0+UeoZn9LiqjGbfZTb76GWvMGnFYXlhJiLvn4CpFPWIsJbrHzKlGg5IKFkoKUIgpCiGQiVp/PR11dXXfExxAAACAASURBVFYi1gsXLmAY+Qf9y1WXfCRcdfuJRP6Iurp/IBp9PsdR2XONbCEyzey0QEKYHD/+JuLxExw5ch3Z2RtMzp+/jQ0b/iVnnfLN+Vlu8ltTCSFtbX0zqppwR3q996JpQyXVPbVcw3JyIbyGc4Fe/PouIs4/ZkNrDa+7rCu5TDrA4ODHki8O6egZ86k+hxClu6LnnvuuSMctQBV11Bh/gJMOQMVQxog5DqErQ9QbrwFl8bPa52pXMcsq33nzKafcyGSxkoLkE4XMRKxNTU10dnayZcuWnLnuljOCJnUOk2mOMz39vjmdL4TJ1NRPc44LxeMnZj7ndlulZnVIHW+y5wAVC/1ejrda28WYPnfKxiIc/meamm5H10cJBH6BEFtKmvS6bdtvuRCIc2w0xOnJCLpq0dLl4prOWp46N82Wzjq2daWPR6auCZWOlRyTS1ii9wKvpd4J8byPWRELSeSfTJu0kDL2K8JDldWLx7yaKmsDpjpN3HEcTT2NpQTSSliuPH3lWrF3OVb+ne8YVLnRNI1YLEZtbS0Oh4NoNIphGLjd7qJL9MyX5W/1RUCqsBiGkYy8y5WItdRyFlKXuZC6om16uPPcJtraE2ovu+xJjh69Bafzk+zYcR0nT76DycnUrNbqTKDBbJizZUXQ9TFcrs608aZEZ1U89Hs5yResoevPAKS0AwrVPawZnPNH+e7BEQIxHZdDZWNbDVu76ljV4EZRFI5eyP2dbNp0H0eOpM+jysyEkRrO/g87BZuaAVy85JFCEYQLQCg4xSqqrD5cohtQEUTR1XMcfd8/01Cd3rX03d1HNDsROJBtuSyHlVLJVIIFZZomX/3qV3nwwQe55ZZb+KM/+iP+7u/+jgceeIAtW7Zw9913s3379rJfVwpUCRiGwfT0NE899RSaptHW1kZvby/19fVzEoylsqBSl1233XrB4GMUSp6qKFV0dLyN3t7CIagDA+8lGn0Cl+vraNomJiczV6u1stxRdhqi7u4PpY03ud0byhqptxjki/4bHh7GNMdnLMDEmkeZFqBmWJyejHBsLMRvT40w5j/Mjq5rubKnm962GlyO0oaAZ92gqc9auhgmrKxM97FOS5UTn5ayvYCFlKCQlaOgCCdVZi9uZSuqqEUQI64+j6aexilW4TGvnEk8m86pd51i3+NDdNWqrFWn2L59e16X2nJYKZXMco5BWZaFqqr8/d//PQcPHmT16tV88IMf5NFHH+UNb3gDb37zm/n0pz/Nxz72Mb72ta/R2Vk8ZdZckAKVh0gkkpYdvKqqih07dhRNxFqI8gmUl2efvZHNm++hqqora29ClH7H4ODH8Xr/k1JWtBVCmxGx/KS6CXX9x5w5Y+UpN9Mq0pMJWFPHm+rqXsD27bnFyHYFKsr7K3LgGiAU+ieyx+Qsnj7+VYLO9zEwGeELB75M0LiAIqoBlR//+r3A3KyEUkLuN226j98fuiLtGEWp5uQ7Z62sUsZYct5l21oyt+G2tqKqjcSVZ4k5nkZXzoNiJY9LJTNirkG/GV0dIuo4SMf/LGzi7XyX1VjochzLgWEYy7airt1f3X///dx1111cd911rF69mle96lW89rWvBeCBBx7gqquuYmRkhM7OzrL+XqVAzSCEIBQKMTY2xsTEBC6Xi46ODnbv3k0wGMTr9S5InFKvs3C+k5yflGnxJELHvw4IJifvRVGKvaU72LPndE6hyyR9sq6F3/+TkmqrKNWsX383x47dUHS8ycZ2BUIbcGNJ11lqNO2ZZHs0s2pmmfQN6KisXhVhc0ctY9YPMR2TeKyrcFqrk+fm6yQzf9apnbzLghoTvnoe2ms7OPWuWesuXfyLuxxLQRE1VFkbqbI2olKDEDqGMkHE8Sgxx6G85636ahfttY052jhrnRUTiWJi2lHTkTP1UbHz7JeCpV4ccSFUgosvFoslx5kaGhqSc6k0TaO6uppwOIxplj8AZsULVDwe5+TJk3i9XjweD52dnVmJWEOhUNkySSwUTbsAPIQ9P2nNmo+miUsidNyuq5k3zc4sZk6hy3Xd9BByHSFKbY+VkY18dnuuDjQ19Bx+iq6P4XZnroO0/LS3P0B3dzfGGT/7vcOEqhvYuaqarZ11rG/14HKomOpk8YLykH9NI5G1PRw+gJVhZb1+v4Zf/wapy7IXD1RQcFgt1IgXz4wtgaFcIKo+iYmPBvN1WEqkaN3zCVC5HNz5ys9nIZUrVdJSpVxKZTmDJOw+q7m5ORmB/NGPfpS1a9cCJPtJIURyRYVyejtWvEApikJzczN9fX1531IqZbkNSLdi7PlJtriEw4fyhI7buEi45NKFYnz8W0mhSx2/ShW+xHXTxU5RXFnjVrmySQihoWlnSx5vSrUGFMXiwoXPsn793FIlLSWqqrChrYa3XL2aWnfpP6nMt/h6/fUY6nmijtyZN4plrNAMi6ej56h1hent7cb/SE/JdVFELVVWL25rBxYRNPU0cfU5GmoDTMfOzhxTPXN04hnOFoNSUiGVbwzWvn+p7tJS3aZzcfUtVZLafCznelC22PzZn/0ZqqoihOAtb3lL2v6f/exnbNq0iebm5rJff8ULVFVVFR0dhd+KKkWgbCvGTqBqz0+yxSV74m0muSO6hNCSQmePX2VaVYlUR9k53DLHrS677EDGMQJN00p+q8rMOg4GPt93Wb36IyWtWbRc1LkdcxKnXJTrvbNQOWn7hIpTdOO2+nCKhIUqiBJTnyXsfJiO2va0Dj8cN/jWE8O8uK+V7d2JBbHSRFbYqY6WdkXdXEJTLGNEppAtd5LaQiyHBaVpGlVVVcnf7fvfn3+Bzuuuu44XvehF804+UIgVL1CLkUliscrJlbDVtqLWrPkI0WjhNZMUxZ0W/j2LRTD4WEoQRLb7cNu2H/H009sQIoaiVFNdfT+7d7903m3JR65MC0JUVuh5pSKS/xd+xhThptq8jCqrFwUPFhFi6rNo6gD1xiuw1ElQRFYnbpf6uvtfx5hWKIVOsWzo+Ql8IFCW8aG5Zpqw2zo+Pk4kEmH9+vULrkO5MAxjyQXqi1/8Im9605tYvToxbloo8KG6ujrn9nKw4gWqFCpFoHIlbLWtmOHhzxRIdGofG2fXrgNo2louXLhAf39/2v6BgfeSz32Y7lq00PVvAvkFyrIsJv9/9t47PLKzvPv/nDNNGrXVqmv7arVF621er9frgjfBeV/zgsH276UaQmITAiEhwQECwY6BgEt4gVACJhhiY2PT3Cg2YMB9u2Vv0zatdtXbaCRNn9Oe3x9TNOVMU9nV2vO9Ll2S5jznOc+Mjp7vue/7e9+3y8XAwAA+nw9JkrBYLPEvWZ5AUW6jvPw/sNsb469PTu42eQ/zS3oeQz5/y7lRjU3jHhIyNrEokrdEC5rcjyoNoMidaNJAkvQ89pOZCw1gPOQ2LZI2/3SWhWM+KkbPhwX19a9/nfb2dr71rW9RX19v+pkEg8E5sZoSUSSoPDBfCCrmPtu1axeXX3550rGDBy/N0aspghMn3s+KFc+nvZ4qgkh0H4JIEUgoaNqvUZShpDhVrNzT4OAg4+Pj1NTUsHz5ckpKSpAkCV3X41/9/Z8iGHwVXb8fu/3f0HWdcDhMefnD6LqOpmnouo7b7cbpdOL3i7TCl1arFYvFEv+e6Wez18wqfRSCfDex2Mb+s5fu4dTwCa5dHnn9z9L/BAUjzS0lbFRp78Tm6KR93S+ASOmhiBIvYi1JlKDIx/Faf4PIQ+wAiXXqco3M9Zlkd/HF3GnnUwo+HwnqfKj47rnnHv7u7/6OYDDIvffeS3Nzc/xYOBzmwIEDfPjDH+bZZ5/NGSKZCYoElQfmC0FNIT0PKjH2E6k4fh9m5XHC4ZOEw8cQIrl5t7n7UIu33TBT4MUsLLf7FKdP/xWK8hkqK5fT3NxMW1tb/P2Gw2EkScJqtWK1WqPlgSK9p3y+n7Ny5R3YbIswQ3t7Oxs3bkx7gjQMI052MTJL/TkQCKS9Hvue+HcQQiRZd/kQXjgcRpZlNE2Lz5VtcwuFT+WhqMwfZpt4TEThUb2cGQvRZLmeQMgJGHFrSRDCWeqlxF7OSMCMoPIpFju9hoZSlrkThQiZhA7nQho+HwnqfCTqfvCDH6SyspKbb76Zm266iR/96EfU1dVx6NAhvvCFL/D0009jt9vnfF1FgsoD84+gMudBQcQVmK25YH//RykpeTjtnHT3oRoXQaRbZyou1/MMDe3Cav0Wut5OY+PvaWn5Vtr1Uv/hI3EmNT7PdOJLsiwjy/KsJDAKIeKEl4nsVFUlFArFX/d6vRiGgdvt5sSoQr/bYP/+kfh7TSe4f8TpNKir+3csFgu1JVfhCplJ0DPfH8/f9ALPnxrjL7d/m3KHNW3DlkU5VqORMvEmnj09wW077mRdYzlrG8opjwo4fri7l1W1Tt7UWgMUtunPjrxh+rNks6wqv1b5ui2RdD4sKE3TuOGGG2hoaOCtb30r73nPe1iyZAmPP/44JSUlfPzjH+e2225j4cKFc7qOIkHlgfnUJjlXHhRMWVN79iwwLeCqqt04HML0HEUZjIshZLmUdet+Ge8DFevzJIQVXf/fLFnyDaqrBYcPP00kMfghliz5XNak30DgEC7XDxJeMeI9lsxUeufis0+Mj+Vb9LK/vx+ARYsWoXdP4O2eYNu2ZfH1ppLcKb+bCT2S7KhpGs++9dmk49uf3p7zmp2dnYyOanSeClJRGl2nkLGJJdiNFqzGIqyilqDUz87lZaxfXo/Nai3QnZnZypn6O0y3JfzMLJNcSbaJ5JWLzMxQ76znhbe/MO8sKJjd3KJ8YLVa8fv9LFmyhMsuu4zf/e537N69m3e9611885vfnFO3XtI6zslVLnDMJwsqWx5UKi6++Jhp+/ZQyEl3d3de8/f0fBm7/ZZ4k8LI+9CwWv9Afb2Vvr47814PQFfXh0xenZ4VNV+R6M6MJS+WV6iUq1YaGzOQ99PZ56x31rN48RLOhN00NNQSCKuU6FuisSUHhuQjJB9GQiJkfRV8o3QcdaW5M3t7VZi0UBXow2KxICHl1dW28muVyKKMCu0dpu3k5wvMCs/mG88aCYzMSxff+VjTqVOn+N73vsfXv/51nE4n119/Pbt372ZwcBBFyR3rni0UCSoPzBeCypUHlYrEnCYS2rfX1X0p6/yJQomRkQcoKTFrxGdEa/393FRYkZj0u2jR3fT1/QtLlvwH4bCZFN7A53tpOh/J6wb1znpCk+mbUKLb6mC/B68q8UJPiDG/gsNYgyr1RZV4Q0g4KBERZWZLSwtVVVVp8x1Se1hZW8raZZUR8irI5ZbvJnlu86Cy4UKruzdfcPHFF6NpGm95y1v43Oc+x44dO9i7dy833HAD11xzDU8//TQrVqyY83UUCSoPzBeCypYHlWq1JOY0jYw8QEzoMDr6IxYs+Ie0dQgh6Oy8Iy2QL8ug6y8Ts56moEb7Q6VXLk9N+j1z5kOEwyej5Y7MYKO8/ErTI/PJvTqX6PxIJ/+zu5eVtU6ujsaHYnD7FTqGfLxwyk2Xy09zVQmXrajGcfRlJoO9UwOjH1OlNVtcScIiW+KWXTbEhAtTVkm6Cy/ZQslAYMIWdUE2EJKPpB2utlWzf/9+hBDIspxTgTmXiK0hhum2lp9NnA+L7qqrruIf/uEfeMtb3gJEhBrbt2/nmWee4W1vextXX301v//971m7du2crqNIUHlgvhBUtjyoVCS76pSE8Tqjo19DiL9FCIHX62VwcBCXy4XVuofUahOxPlA1Nf8fw8P3UVq6lmDwOLL8Dmy2E2jamOl6EgkyZjWZW08Qq3ZexBRU3aDLFaBj0MegJ4QsSdRX2pGkct6ztYkyh5XOjx5NOieg6Ny/p5eltsy9vnJbTIVZOdlKHcliAQ6jFZuxAklYkSkznXpcHefNL745YRaJoY8OmaovZ9qROhfC4TA2my0uTJhua/kLHU899RQw1W4jRtrr16/nj3/8I9deey1XXXUVQ0NDc/rQ8IYnqAuhkkTMVbZu3S/p67uToaEfIklaxh5O6YVdk9u3j48/jK6/hd27/ZSWltLc3ExrayuynFxDL3G+9vZ1gEEw2BGZ0XiKsrJ3Egp1Rq8jUVv7flpbvw8kJ/3mhoXW1sdMj8y3eMBcwxPSeLHTzYkRH4pmUFVqY8eKatY0lNPlCvBC59h5/EzyqLUniIg29NVYRQOgo8hnUaSTlOtvydGLKjaFyJoAOpd5UmNjYwghGB0dxTCy378nTpzIK+cu9vNMc+/OJVKJKRErV67k+eef5y/+4i/m/D294QkqH5xvgkrt75QrBmXmCkyEEBqS9BDbtj2Ul0zbfD492qww5hIUuFwPs2zZv5Oe2JsLelaRxOvdxafqBqdHAxwe8GKVJVbWOmmpc9LWWEFzlSOJkL554Ft8dv+DCClZnVnvrOfQzSeAHJEiAdn4LVuuUjZIwoHdWI7DWAs6UdHGqyhyF0IKJ/SKEvHrFBb/mkKqay2f1hz5ElpjYyN2u53GxkZW3bsq59hCcu9SCU+W5azEdvjwYdxuN5IksX//fiorK6moqKC2tjYvF+1MkIt4mpqaeOmll+b8YalIUHngfBJUan+n1BozZjEoM1dgMjRkuSMvckq3xqbmSIdOd/ftWCylZCJISbJTXf1/GR9/LEkCn6031OsVLl8ktnQyai1pusHGRVW8e2szpTZzt4lf9Zv+1yZuwJlUX6vuXUVo8moU+TQhS3uGVUUIKrFIarYN3iJqsBursRvLsBpNGFIIv+X5tPJJqbQ5XXIqFGaxomyEJoTg8scuz5CjlowlP1iS1/UyXSdX7t3Y2BiHDx/G5/Pxwx/+EK/Xi9fr5dZbb2Xnzp05rzHXWLBgwZxfo0hQeeB8BupT+zultrxIjUEZhkFT068ZGBggEAhQX19PU1MTZWVl8TGBQIATJ07kdf1c1lgqxsefwuFYlJEghVCYnPxt2pxChOnru4MVK+7N+1rzDYLcOjdVN+gcDdAx6GXYG8YiS7TUltHWVM5vbaOsrHFSarOYBuftRiulbJv2+kYCI+SbljsSGGHVvauSKn+P+RV++soAt++9DpuxHIexGouoRaASljtRpG7sYhma3D/tNU4H2XpAmZFFtvFCiLzIKRPytdTyyb17//vfjxCCXbt2ce+90/u/cLvd3HLLLfz+97+ntraWu+66i/e9733Tmut8oEhQeeB8WVBm/Z1kuRTDeIhLL702HpwUQuB2uxkYGGBiYoLa2lpWrFhBZWWl6ZN0IevIbY0lQ4hAPLk3trbUdhsdHVcQDB5OPTNKXNNf63yGJ6Ry2uXngT19KLpBtdPOFS0LWV1fxoYfrImQh3ojqtxD0HIgx2zmn0fTt5qo1G4kaNlH1cFJTn/09IzWnLrZtn73IqrU91ImvRkhhTAkD0HLARSpCySNEn1TFu9gPr2ippCpUK0ZClXTZRt/9uzZguaa7/jYxz6G3W5neHiY1157jbe+9a1s2rSJ9evXn++l5YUiQeWB80VQZv2dIjLwH2EY/wu/38/g4CCjo6MsWLCApqYm1q9fn9MvXIjfOLW/E4Cqqhw48H7gV+Qre1fVITo7b0KSoLX1sbgr78yZv8XtfgQAwwigqsOvGzefqhucGvHTMeRj79lxvCGdq1fV0NZUTmPlVGwp/4B/PsVYIxgNjmYZl/89WPnVSiyiDodYTbl+LVZRhyKfJWjZjSYNTaM4RGH/R+daLfd6eBiKwe/38+ijj3LkyBHKy8u58sorefvb386DDz7I3Xfffb6XlxeKBEVu4pitQGAhBKUog6b9nYRQMIwj7Nu3j7KyMpqamqIKvMLUNDP5R1TVYSKlD9Jdf5lk7wMD9xAI7I//vGzZ11DVIdzunyWcm10sMd+Q6TMc8YbpGPRxatSPqhssdNppa6xANwRvXlubZcZzoc7Ls+K4sGIXy7Abq7GIagRhFPkUIAha9qPJQ5nPzXrd+U0AryeCOnnyJFarldWrV8df27RpE88/Pwul9M8RigR1DlEIQZn3d7IhSW/FMD7GJZdcnCbFzdSufSbrMMPAwN2kbjSZJO8QsZ5crofiv8cEEX19d5AcU1PTxBLz1cWXui7dEBwd8HJs2IfLp2CVZVrqnKxvKqehwsFzp9z0uIPnaHXT/7wk7NiMVuzGaiTs6NI4QcteFOksMhXYRLY28tNX5l1IOJ/tQAqBz+ejsjI56lhVVYXX6z1PKyocRYI6h5h57EeltLQLi6XENDkuU7t2s3VA/oQWG3vixLsRAgzDi1lCr5nlBDA4+B9J4yMt5u9Isp6mjl1YVhQAQtAx5EOWoLbcwVUtC1ndUI7Den7zXlLjOPXOesKTJlaOAKtowm6sxqGvQZOGCVoPEJZOokujceNHEvlYeNktqOnSVyEVHWZS/UEIQW1JralQIvH8+dwiPoby8nI8Hk/Sax6Ph4qKivO0osJRJKjzBDNySOxCGwj8p6kCD2D//t9y4sS1rFnzcPzcbO3azSCEyJvQFGWQQ4d2oKoRt059/d8QCn2Xyy67LG1cap8qRRmMWk+J7kADt/unpCoSI7jwqkpIssTiBSXcsLmJpkrHnOeGFIrYRtr5kU6+91I3G5orufaJZZESRGIlDqMVWVQiCKJJI/gtLxPKKdQwQ+b3LU2x3DTmLayF+3SqPxRKaueqzJFhGNO+n1avXo2maZw6dYrW1lYADh48eMEIJMC0cXMRc4XEG22KHO7E7XZz5MgRdu3ahdvtZuXKlezYsYNVq1bFySm2+SvKEIbxAD7fnqgEfGq+1Kri2dZhGKNJhKYoZjGFCLq7b4+TE8DIyIMIMZY2Lrk4beK61LSx5uQEpaUbaGt7OWmt89HFl4pqpy1J+FAIZvLUXei5QoAvrFGqX0Kldj2l+lYECgHLy3gsT6LJI5HEWlPMJI40vXNn2yKp/FqlaQJuNlLLlbA7l9A0bdpNAcvKyrjxxhv5t3/7N/x+Py+//DJPPvkkH/jAB2Z5lXOHogV1HhAOD8TJYWjofny+G1m0aENWBV5iNQnD+A2JllJq5YZcVc4jBPUAkpS7TUbEAnok9R0APwLemjTOzILz+faSKY8qkYxUdYiurr9m5cr7M35ur1fEnsZ/uLuXz7z0wbzPS326r/pqZovZEII137kK3fNWBCHscguK3I0in0SX3JFBebd0zzZw9h4mErvsziYKjR+NBEbOW0NEXddn1LX2O9/5DjfffDP19fXU1NTw3e9+94KyoIoEdQ4RCAQIh8O88so/IUkRC0KWobz8cWpqdmY8L3Hzj5BF1J8ft5TSW7JnJ50h4Om8CK27+3bSrR0BPI2iDMXHZ+pTtXHjXjo7/x6X63+orb05Y2xpYOAefL7dF178aZaxwFHFUB71UPPdvCMliFp4aF8/Id9F2HEQlo/is/7OxFKaqdJufrk2ZxvnQxgx0266Cxcu5IknnpjFFZ1bFF18c4xwOEx3dzd79uyho6MDSXIjy78n5vaK9VxKdbEluvSSqznoxMoMxc71eF7Ku8o5wODg3aSLHLQ0t6C59TQ1f2y8WR+pmNtQUQYZG/sxYDA29lBUop4MVR2Kj3G5fkggkNyS4UJw8c0W7vs/P8BzqycHAQkqv1aZ9jUlRBBYRDWl+mVUatdTYmymqtSK3/ICinwCVe7O4sabCc6/zPx8uuPmArqun/N27/MJRQtqDqBpGsPDwwwODqJpGk1NTWzZsgWHw8GuXf9BurWjpFk7qQVis5UOqqy8is2bM9VWS4fX+zKpG4UQahqhRQjIPFYkScTHZ+tTJYSRcMwwtZAGBu5JGtPVdQsXXbQ3ep0L56lc5FPrKNf5WZFjciFjEVU49Yh4RaCiyKepKBvnHRsb0f7QB3q2i0Tmry5ZwJBZ2HBGqyuMoFJjT9nKEyXiQpB/F4KZWlAXOooENUswDIPR0VEGBwcJBAI0NDTQ1taG0+lMGXnUhGwMPJ4X479lculluDIjI7lVe4moqLiSYPAUiQVfZbmUdet+mTTO692TcQ4h7PHxmfpUeTwvEAqdSbKsXK4fUld3M07nRcCU9ZR4fjh8jEDgSHxMEdkhiVLsxipK9Db+bNF7aXIovH3LMrasqDORuptbOfXOekb8EWn1eGjcvK27mI0YlPnxXC7LuY79zNfcppnGoC50vHHfeQKmqxSL1cAbHByM18BbuXIlFRUVWZ78v8+OHZfT1fVxRkbuRwgFSbJTWXlVfES6Sy8bZIQI5pSKx6Aog4yOPkhqNXLD0NLmWLfuSU6e/AAOx3LGxn6WQkJGfLxZOSSI9IQKhVLrwSVbSMnWU+K5U2OKMIEgXoLIZiwBJDRpjNX15Xz/2M18u++1gqaLbM65PP65COrCsHbrnfUZZeW5cK477BYtqCIKQqwL7cDAAC6Xq6AaeDFkitmYKfISIculXHTR8xw6dBURJR3ENveRkQfysqIyVydX04QSMTdjMHgsbT2SpGWMccWQqdBsOHw8XnPP79+XdcyFIjM/ZxASNmMFdrEyWoJIISyfRJFPAoKqUiuT2mQWrsmn2sNMRBIm5wqwGs3IInOCaCHFYWeC2Py5ekhlOvdcd9gtWlBF5AXDMOjs7GRkZASn00lTUxOrV6+eVkfJbDEbM0Ve4phIAVmzGnghurtvo7X1vqzXzladPFF9l+hmNIwAW7eeTSK/Xbt2sWnT5VmvtWnTvng1856eWxkbezBqMdrisai2tpfp7v5E/FgMsTHwoazXeD3C7CldEmU49NWRhoBI6PJYtARRN0QbWMqizGS2QpCvBZSZwJKOCAt2sQKHsRaLUYNV1CIQdLyrg7aftZmeP9dutphkfDqYLrHNBG90kURRxZcF4XCYs2fPsmfPHoLBIKWlpVx66aVs3ryZhoaGabc7zhSz8Xp35yAQJdp+wzyC7XI9kjXhFiKksWNHCCFaTOc3Ez4kJv7G1IXgznqdRKTGmYRQkhR9Kmfk3gAAIABJREFUPt/Lpp/HhVZRYjYgkbBJC7AajTi1N1GpvR2bWIYh+fFZ/oTP8jSKfDpOTrN39eiFZzKLcODQN0QTgS9FoBKI1vObaxXfXJYaOtfkBEUL6o37zjMgpsAbGBjAMAwaGxvZsmULr7zyCs3NzbOiKssUs0lFYpwqBkmyYbW+nfLyciYmHk7Z2PW8rKgI7kaSPhDvaltb+/74edlckDG3H9QAb0ub1ayEU6QWX6rVN6XoKy+/glDopGmeVEfHi/T0/BOtrQ/O2zYc0izFXr6y9z9wa0dAtkZLEK2OliAKEZaPIpAoMdajy6MZjJ1cKr+ZrjCHTk+UYRNLqNSuB2RUqZ+wfBxdGkGmHIyLATJaT7OBQtvBz3e80WNQRQuKyFNKrKHXvn37CIVCrF+/nu3bt7Ns2TIcDsecxUL8/oPs21eP338o7VgmS0vXDxEI7De1tCYmns7zyg9Ge0tFkGh9ZXJBdnffFnf7xRJ1U2FW7sgszhSzkBJzoMzypBTlBwSDe6PuvvmDubgf/IqG3WilUruBUv2SqOWxG4/1CUKWQwhy5y7dtXs2+vyYvy8pIc8qcajVaKBM24lT34ZVLESRu/Baf03A+gK6PJLCa8V4YiF4o7v4ihYUkb4pQFYF3lwR1KlTf4Wuezh16q/ScpkyWVodHR3RIrIh2tvXxa0giDT9S6zwYAa//yCRZoOJ72fK+spEjOPjT5GYr5Sq+kstd9TU9GnGxyXg+5SVyVitViwWC1arNf7zmTOfj+ZKgRAG/f13s2zZ15AkCVUdQtd/A4i0NhyvFxhC0O0OcmTAi8NYh46bgHV3tARRer3DCKYrcpi9ZFlJyFjFskh8SVQjCKHIZwGNoGX/nF47F6ar0JsJ5sq1qGkaNpttTua+EFAkKGDdunUYhrkwIYY5eWJOaOkeDHbg9x+irGxj3udnE1tkk5xHhBbp78XleoRly75kSoyKMhglwwhxSZJmqvqLrccwNNrbP0Fd3ZdYvHgxEPln03UdTdPQNI1gsA+v9+dAjAwjsanR0WuBhUjSfwI6khR5X0eP/gulpZ9NI7lMP8/nJ8+gqnN8yMeRQS/ekEaZw4omDRKU2wlazNWRuR2J5iM8t3oim7Y/W5fdqfMz3eW/eNMqnjltYVnlKlzBKwhppVTYJ1m1YC+3HjlDKLwdq6graG2pmI2N/lyR01zVCkyEruvTjnW/HlAkqDwxFwSV2tLdzIrKtpZsYotMiHXqNQ+lZSa3XGQYCPQxPPwAU2SjYrH8nmXLvoEkmccBzpz5UsL42HsT1Nf/jubmT3P48DMIERMBqBjGU9TV3QYsjJOcpmmEQqE08tN1Pe2hI0ZgmUgt07F8N4h87g6XT+HwgJeTIz50Q9BUVcLlK6pZXuPkc88NIyRzgcxM0fmRTiq/uqCAlSZDFmWcnLiSEf1tVMhQ6fgSW+r30uAcQJLg8StLGHH8Fy5/Ke+/9KGkc7PFgc7FJm+G+ZqYm4qiSKKIvDCbBCWEIBA4FLeeYsjXioqtJWbpdHV9nOHh+2ho+JucybrmnXqn4PXuNhU6ZCLDiYmXOHToED7fl+LV0ROP9/ffxeLF/8/0WpOTvyO95FIkNmWewGvg9X5nWsVkhRDoup5EYomkpigKgUAgjeQ0TUv7u1ssFjRNQ5IkgsEg/WMaExMqA/392G3WNMKTZAv9E0EOD3jwKxpWWWZNfTkXNVdQW24v+L3k8W6ndZZZzyaLUYvDWItNLOHkiBuL9jLb6nqokZ9NS9x2u59AKnlvxtlnsrbZxkwl4+eqMaGqqvPaEzDXKBJUnphtCyrVekp8PZsVpSiDeL23sGDB94GaghoVpqrzIJL8u2XLMUDESamv7860RoaJbr9wOMyuXb/G4bgHWb6bJUuW0NNzlkAgVf5u4PG8ZLoWVR3CMAIASFIJGzYcToovdXRcMavSc0mS4qThcDimNUdkDRGiGxgYQFEU6urq6FcmsY5H2mjHiE7TNHwhlc6xMJ1uldMTKooOWys8LK2QcXhkev0WBhMsNafViZLd05zrXZq+OrUR5+kqEhI2Y0k0vlQXTQbu4MpFHtqH/UjKHoQj/W8TDp3EXpI+Xb2zHpc/JvAQSa9fiDiXVl/RgioiL+n4bBFUbJ5Q6Izp8VCoK+v5fX13oWntuFx34nIN4XAsx6zNhRm6u29HiGQlWGKCcGJx2lTCi3X77evrQ1EUrNaHkaQjVFY+SXX1n1NdPWXNJZdwujLperG+T3b7UhIFF6lFZGN9ok6cOEFjYyNVVVVZP5dzhRjR2Ww2JEmiqqqKykkoLzdoXrQIiwTD3jCHB7ycngxgWMrYsLqUFkUnqOq897IlQCTxO2ah6brOpgc3EdauBjn5HltoX4hbieWczXEpIWHFImpw6lcgpCCG5CNoOYAidVFt1yixNEWGCR8gUVt7S9LfbOzYKKO+dMu88yOduHwKP2sfYGP1J1lVV05jY351IzNhpi3XLxT5eVHFV0RemG2CuuyyiYLPnbKWBF7v40RcXruJ1etLLZmU6qaLSNDTXWqxoq6x4rSSFHnSNgyNgwe3U1HxCC6XQU1NDa2trTgcPg4ceIZIodoH8PleZe3an2HWOHFk5EEaGj4FiCgxLcPn2wXsSVr3ha7SMwScGPLRMeRj1BfGbpG5qLmCi5oqWOC08ezJMXrGg/Hxsiwjy3JcoTUaHMVsy5wip7mDJEpxGKux62uxGc2E5Q4ClhdRpT6e3Tl1vwz5YwSpE1NWNjV9On5/zbCYe0HIVnJoruNL57J5YdGCKiIvzDZBgXlSazaYF5FNLiabahElli7SdX/S2Jh7r6/vzoSirnpCfpSKpg2jKP/Fjh0/ij/JdXV9jimrTcHv35elTJMejSeJBGISpus2a8VxIbTbEELQMegFBDVlDt60qobV9WXY0yqJn28kx4FksQCHsQ67sQyQUKUhFPk0AeteVLk3fpbFsgpd702fRWgcOvRJhPhHADqHNHyqxEG7K01wMhkWeL1+gg4Dv1/C4/EkxelkWZ61v3WMOAqxkmJuu1dffZWrn7065/hzJbB4oyfqFgkqT8wFQSUmteYSN5jFj8yQahElVoDIlHybrd8UgN//BLo+isXSGF+HFC+xM1WstqRkhWncyOd7iXD4LGbENAXVNL5kGC56e/+GwUErLS0Pz0sLS5YlmqtKuG5DA0urS2ew0Z4DMhZgETWUaX+OVTQiUAnLp1Dk4wg0bFpTygkWqqq2MT7eYzKZhiz/ng0bvorN1sDY0RHG/Apr19alqSrf/OtrUfyX4rc8jyb3x2dYaF/IozseLVhxmQ+mY0nNt8LERQuqiLww2wSVKm5oaLiFM2duzWhNZa5CPmUJxc5LbHORjYQi/Zly9ZuCRPl5pnVEGie+ic2bX015XdDZ+fcmbTcAHNEcpzCSVEJr62NpIxTlPlT1VcJh5nU7+IVlNhYtmAk5ZYfnVg+v9k7y9of/hWkl6goZm7ESu2jFYtSiSr2E5NdQ5E4W2BVCKkiiJOn8ahuAzuTk7xBCQZj2g0qIH0pgkWUcDkeaEMUdHqfcZG1uxc3WrVuTl5qguGz7YRujwVz5W8nYvz+SKPzI1kfipHblH67Meo7X68VqtWIYBnWldQVfc65QjEEVkRdmm6D6+5OLsZ469VcEg8czWlOFViFPbhI4FVcyO9d8T01unRCzxDKvw1xFONXy3azArcrUR5oulFDVIVR1qonihR6nmikEUGYrYzLjbWhSAUU4sButOIzVSMIJCMLyEfyW5yCaFvBYtCh9SIOnz8CmeliZoEmx2xfR1naWZ/ZeH73K1AISlZVCkOFeKgyt32udkQtt27Zt0fWIuBXHH7Kf09fXh6ZpBAIBfnLJT+Kvv/nFN2c8p7u7O2c+3UwfWHRdL1aSKCI3ZpOgzEgkGOwAyCgVj8m8Dx68lEAguW5fpirkU0iMK6WuB5zOjUkycrMitTESjI3btetdyPJvTcektq7PZPklvm4mlIjErtSkMR0dV9HW9uLriqRW3bsq5xhVHaKn53Y+tuWfuGXHJzl2dCM37goznqE1uywqIvlLxgokrGjSAGF5D+gGmjQYJ6fqhL1PRAkukYASLduWloc5FR5h48ZvUFdhLtWfyXY8GyWKElV8iakFubD96e0AVNuqOfOxM1PJ2S9mPqekpCSeKJ4pvy4RkiTllRgeIzaXy4XP5yv8Q3gdoUhQeWI286DMq3tHkEsqvmnTPs6cOYPD4aC5uTnteGYLpxVJuge7/S7WrfsJr77azeWXm/dzyq9CRXrrerMqFj7fXtP1WCwLMQxfWrLnwMA9NDd/mtOnb4rWDEx2J2naEH19d7Bixb2ma78Qkc+mXPOt1Tj0NkqMb/G59p+CZPLAIUAWTkr0rciUAAaKfIawfBxD8oCwRm+7qc/0MZNbQMrkwosdz2AVZP3vyKNd/GwIDxL7PU1HaTeujif9nimOVe+sp6GhsIekxNSCVCLTNA1VVeM/ezwe7rjjDsbHx5mYmODJJ58EoKGhgaeeeqqg617IKBJUnphNC8rvN69EDslS8XyUfanYtGlfQt28EELYWbDgKZYu3czIyL8yPPxKlCDfnXWO3Ii0rk9ETJWYWKx248a9KIqStql1dFxBMHg46bXEKhJ+v1nB0Qjc7p+yePEXzrsVlXo/RH4/F6rDlPtQyNjEEkr0LdiNFoRsELTsR5FPIaSQ+RQkW08Zr5Tkwst+/2d/+9kJKh8rslBMl/AS79XZlJKnphbkwlNPPcWPf/xjfD4fn/zkJ2d8/Z07d7Jnz564Rblo0SJOnDgx43nnEkWCyhOzSVBr1jyP0+kEsrvTMllRmdYS62XV03MrMbWcLENJyc+x21uSRBnwF2nnK8ogx4+/G0mCNWt+VjBBFqJKjCXipkJVhzh8OFfBXHNJ+rnEvJC/CxsOowW7WIMsyhCoqHI/fsufUC1n48OqbRFLSdElftMFG2phVXXyVJJkZ+HCD9Ic/jvWtdbQ1pS5PXvG5SAy9saqdawi7HdgRlBzmTQby1kqBPPibxvFbMvMv/3tb/OhD104XaqLBJUn5kJmDvm60yKIWShO590IEU2OFAKPx0NfXx8TExPU1spI0u8QQo3PNTr6o2gO1JQoAx4kteFgX99d+P374j/nIpnUteVbcikbEmvwSZIdm+0dNDR8ir6+N5HYVuT1IJgoPOYSjREJZySx1mhBwoYmDRO0HMDAT4X+f0DSySOVJwlCKAQCB7KPia0iy/6deEwIQc94iPaeST605r8Y9Sn8sOvcu6cuhKKwmaBpWlFm/kbH+Sh1FEO+3XVhykIxjO9QUXE7Z8+eZXBwkNLSUhYvXkxbWxudnX9jUs5Ii8rJpyo3xBoOxkhEUQaj1cgjGBl5oCCSMWsRv3LlN+KfrRAi5+ds1hpeUX6J222QHrNLj41cCJiJEECmApuxhEpxHUgCVe6JdqyNVJuQRaRaucgY55kqBltauiHNivWENPbt68v5d8p0NHZbG0JwejTAq72TuPwK5Q4rlyytomPIh+iaX3lG8x26rmO3z15B4c9+9rN85jOfYc2aNXz5y19m586dszb3XGC+pbrPW8wVQeWLRAvF5/sFPT2vAbB161Y2b95MbW0tkiRFmwqmxkZU0hNkjaSut5GfkxVzicfzWVtqi/hYx918XSaZKpgHAn+a1cKxcwVVHebEibekdQWO4a5ddxZOTgKsxiLKtGso1bcjiwrC8kk81l8SsOyKk1MiqrI8dtps9axd+8eMLtasS8lx2+qGYHAyxCMHBnjm+CiaIfiz1bXctG0RaxrLscjzx3WWCdW26tyDziFmM1H3nnvuoauri/7+fj784Q9z3XXXcfq0WX7i/EGRoPLE+SSoYDDI0aOfwTC06ByCBQt+xfLly5OerhRlMF4hXJZL2br1LDt2hHA602M6kqTF3YhT1lMiOUTq7Jm1dU9Ftl5RhcCsNTyoWK1NbN3qYetWD7W1twAytbUfmtYmO5cYHPwKPt/upPb0qjrEiRNvQdcm8CoFSIaFBbvRSoX+Nsr0q5EpIyydJCwfJ2R5FSEF4kOrbfDs1fDoDvjgMvjmFvMpS0vXU1d3c+ZLRu/LjBZS9HvqA4eqG7zW52FX1zgnR/zYLBL/e10d77mkmXVRYpq65Wf2P5QpxjVT1Dvr8dzq4ReX/WJO5p8u8nXx7dy5E0mSTL+uvDKSpLx9+3YqKipwOBx88IMf5Iorrpj3isCiiy9PnGuCMgyDkZERentfQ9P+FehMKC+kEgg8iqLcmeSCy+RmM3Mj7t69m40bL0s4Lz2hJmZFZYpFxdx202mcaAYzwjl9+jQLFkRcVzEXIBjzKgYlAbruY2zsESxS8toGBu7B59tFj+szQGnuuYSNEn0jdqMVCQe6NEbA8jLl9h7uWtdGx1g171gFsgQLF74vSW4/6g1zQhvEavkwF120Pq2aQ1DV2bW7d8ZbfOz8kKpzeMDLoX4vYU2n1G6htb6Md25pmjWhQSFS8ZmILc5F4dfpwDCMvAjqueeeK3juuWjCOtsoElSeOFd/TJ/PR19fHy6Xi7q6Oiorn8Tt7iDd2DWSyCOTmy1bHEkIgaoOMToa2fTTYWQUa8A/oShP4nA0FRRHmw5in3uyC3B+xaC83pehIvZwEKa///MsWnRHlFAFhjFONoKSRRU2sQSbsRhNHkKV+ilxHOPxK0bjwoMTKd48t/sncyK3z8Qtsb9DQNHoGPJydNCHqhssr3Fy8ZJK9p6dwDCyu3SrHQtwqfmXESqEOC6ULrmFYLZUfBMTE+zdu5err74aq9XKT3/6U1544QW+8Y38hVDnA0WCyhNzaUFpmsbQ0BD9/f1YrVYWL17M6tWr0bRh2tsfiY5KJRA1iTxytWTPtI6+vrswjCANDX+bNC6x0noqIl15D9PXdxctLd8s6P0nvYNob6iVK+/PuMnGNjszAcV8saI0zU0weDiyNgkirSh+ghBGxgoeRIZhFQ04jHVYRTMWsYCwfBRLyVM8caU3jysXRtKx2266to03rHF2LMjP2gdxWC2sqneyZXFVvCtwYh5UqhjEajRRpv8ZNqeg+6+7qa6eivVks3wSj0lIpgKQmJWVSmYXSs+nbJitGJSqqtx2220cP34ci8XC2rVreeKJJ1i9evUsrHLuUCSoPDHbBCWEYHJykr6+PiYnJ2lsbGTTpk2UlEy1JE0kHUmyU1//16xc+Q0GBgYIh8OsWLEiPjaTm83jeZEjR65JK0KbWHLJTBqeKacpsSfV6OiDLFnyr9OSkwNR99fuvDbZTAKKc2FFpRJpxOq8iYqKrwBLcbkeIj22ouN2P2IyG9HE2mXRjrXVCILY7Ad5TzO0LjjJRbVT5CRJJchyKbqeWOFg6lqJQpGZ3p2Zbm+XT+HVvkn2dE3g8insbF3IFS0LqSpNTziNkV+6JRM5Mq6MM11kUidmsprytajmc2dfXdenyi7NAHV1dfEiuhcSigSVJ2aLoAzDYGBggGPHjlFWVsbixYtZv359mlskm8vObC2pFSQSez0ND99nakn199+NWcwqF3El9qQqNF8qhkLjSWYCinOh5FPVITo63oSmDcXrAA4M3IOiHMDv/y7wfYKhYwhRSS7bxGmxoOjrsRurkSlFlyYIWPZQbj/Lz68w+NXpNpOzjIzzmknF80G+8aEhT4hXejx0uwPYLDKrG8qoKLFkJKdz2bAwH8zXuFIhKBaLLSIvzISghBDxdunj4+M0Njaybdu2rDdeNped0/lZ07WkiiQytW+PYAyX6yFTAswktphOnCsRiZZIrnhSbKzV+kWEqDinij1VHaKz8yYkCWy2RWhaRMmoaUP09n463pk4FHoCVf0SK5Z/l36tF3gS0NLm8ynlnJlsZVvFKlZWOah3DrJqwR7qnYM5q38LocSriRvdE0x0T7B163eR56DagSBiRQ17whwb8jEwGaLEZmHbsgVsaK7gzFiQMX962arECTIUzSex1NElP7lk3rSzmO94ozcsLMrM88R0CCoQCHDq1Cl27drF6OgoK1euZNGiRdTX1+d8KspXGacogxw5cg1+/yHTNhtTybnJsm8hIhZS8vwRUsuU0zRTOXnMpdfXd4dpPCkxfyg2Nhj8Xl5zF4KY9DtTvlJf3x0EAvvx+/czMfFE0rHx8ccT4kpGXFLu9b5MqhJyLFjL3sGr+EPPdQz7m6hyTPDnS5/iikXP0lCWTk4izf6wFSSnj9+eeSRFp8IQgrNjATqGvDzfOcZkSOOKloV84NJFbFu2gBJbfptkZhn4FEEVySl/FCtJFDGrlSQMw2B4eJi+vj6EECxevJjLLrss/hQ0NDSU1zzZlHHDw8PxOWKxolOn/opsbTZSrR0hjgDpBBixDsxJaCZy8kSXntv9U8xUiTErKnFsOPwkun4rUJdz/lyCixiyxb5UdSi6vqxXi393uX6AUvkBFKUXMDCExKBvCZ0Ta3GHarFbwqyu7qDR2cdEuIYqx0SOuZOvM5suTLM8J90QnBzx0947ydBkCN2AbcsWcM3aOqwpibW586RyO/kyV7mYGVIFEdOpZD4fka/M/PWKN+47LxC5CMrr9dLX18fY2Bj19fW0tbVRVlZmOs9srSUxVhQMHiNXmDzRXWez3c/GjRux2+1xxd6KFV/jyJE/y0hCsTjXyZMfwO//Zy6++Jq8/ePJLUZ0UitbJMaTUt1/ExPforHxu1nnz1dwYRb7AhEnt76+O0gn+mwQDAzcjdX2Zk5PrKHb04JfLafM5mNT3QGWVnZhlTXah7cXMGcEmWJMs+HcU3WDjiEfr/V58Ic1asvsXL26hhKbhVV1ZWnklHT9jDL0qWPnW/L9epGbv9FdfEWCyhOSJGEYyRuXpmkMDg7S39+P3W5n8eLFrFmzJqvqZjbEFokS8SmVny2u8oPcjQ0jv6dbYWaS80TExkpSLXBNXutNlYjHUFNzE5JUgsv1P9TW3pxkPU2NjSQlq+rnM1pGuQQXuWJfIPD5dtPff0ce1lM6FKWbEyM6gfIt1DqHuai2ncayfmQp8985NckW4JVgD63N5WxdubDgNUDuYq4C0HTBsWEve7snCKk6TVUl/FlrDUuqS3AHVPadzWzhFdJoPma9xCybuaoA8XrHbJY6uhDxxn3nBSJRHj4xMUFfXx9er5fGxka2bNmSlrWfa56ZQlWPMTZ2H1NihmQXXq7kWXMrLHtX38SxQkSKzdpsS3Ku1VwiTpRU7CQSi9lYIbK31zAjnebmTyeRUoyAxsYeJebajMTpHoxu6AZjYz8xXWcuSJJMndPFVYt/T70zvyf3ycm5KzFjdnsFFJ29Zyc41O9hPFDKJcsWcPGSSpqqStLOy0glM8qjyt2wsIh0FC2oIvKCpmmMjY0xMDBAeXk5ixcvprq6umCX3WxZUD7fZykkMddsDigswTd5bIQIWlu/nfNa5jX2Yoi9bkQbFZrX48sUi8mUwGsY/rggY3z8MSIE9NO09wrhhA09H3KSTcZpOC39LCxJL9yafR4zzK6lMRlUea3Pw/FhH0HFoKrUxl+sq+PylbNfFDXRxZeOIkFNB8UYVBEZYRhGXB7u8/koLy/PKQ/PhdkgKF0fwTDOpL1eaP27VNl44jwjIw/g873K2rU/AwQnTrwbn+8gU4SiMjb2EMuW3ZZVYj45OYnV+j+UlwtkeRyP5zognDYuRiwbNhyOJ8N2df01dvuXcDoXJ7XXzuyyi82l43b/jHRBRpaqDnmjcAvLDHb7ommfm087jPGAxuEuD6dG/UjA2oZyWuudPHlomAWl2f/tc7V0z3w8c8PCIqaHogVVRBoCgQB9fX2MjIxQU1NDa2srfr8fv98/46S52SCosbH/JPKnU5MqTBS6jqGhr5Bpw42IFvZFrSaBz7ePXPUA468aBkNDQ/T09FBSUkJjYyOyLDM8/I2M14tcU+PgwX9GiH9Ekv4T2IVhfBVZvhW3243NZsNqtRII3EkwuIuuri+gKO2mFtcU0gUZuWHBYlmIrs++HHq6ybX5QeAL67zqVTn16hAldiubFlWwcVEl5Q4r3lB6jlZBs+dx3+Z2KJwbC2o+V4coBMUYVBFA5EYYHh6mv78fgMWLF9PS0hJ/egkEAue0mnkmKMogHs/PiG3ChSbLJq7D79+fxfUWIZKRkQeY2lRSLZVkiy0UCtHb28vw8DANDQ1s3ryZkpISFEVBCMHAwEFSc4WSoeF0nqG1dQmHDz+DEAKL5Rlqaj7JwoWL0DSNcHiAsbHHiZDmzykrexKLZQGapqFpGro+giS9H0nK9L7ygR4nJ0kqobr6BtzunyJJNlKbQWaDxbKQzZvPAjB5woV9IkRb2+KM43NJsDPdNkII+iZC/Omki2NDXuosgp1LK9m8tJrShPylnBbQTO9vk9Njaj6LUYMkrGmvzwU8t3qmdd58rOxdtKCKwOfzceDAAerr61m/fj1OpzNtzGxWM5/JPH19dyFE9phRYqHXbKS1evVzplL4rq6PMzJyP0IoSQSWaK21t7fT1taGw+HA7XbT3d2NoigsWbKEyy+/3FTJ2Nb2Mt3dn2Bs7MG0eWtq/jIuguju/gSJsa5Q6L+prLw3euzfkSQRjXcISkt/kSSe6O7+BC7X7G00QqhRN6GRhZyixVJTX52BGy8fCCE4MxbklZ5JRn1hdAOWVJeyvUrjkqVV2PJMrs37etHv2VSCqYeef88RDvRMcLDPQ1A1uGHZR2hbtZzy8vIZdRd+o6BoQRVBWVkZl19+eVbf/mwWi50JvN49mCXYplY2Nyv0mowxOjs/wbp1jySRWHpcaooME601IQT9/f2MjIxQVlbGkiVlDA19itrah7LK7HPV1DOTmfv9j6KqXyBSJTx7RXO/fx/ZrbRCkewetFpX09j4G9xuN6FQP5L0JUpKvoTV6sBq+QgIHV3Xo0IWwf79+7FarXSPGrhCcOpUEKvVavqlqhqGrhNJghaVAAAgAElEQVTrs5VxRYbgVDS5diKoUlVq4+rWGipLrPzq8DAy5pXQ829IWOhnNIXYuf0TIfZ3TzAwGcJpt7B5cSXd7iBCqPH31vmRzlmvOD4T116uz/18oEhQRSDLcu7A83lu+R7Dpk37cLlOcurUNiAcLwobI5lshV4Toao/RNP2ppGYmaovEYahcfjwpxkf/0vKysrYunUrdrudrq6P50GK5k0JE5GtanlkC81e0by19VEOH96IEKG0uR2OdWjaMLpeiNouGZp2koqKUaCO0dGvEg6/RknJc9TXv5+NG5dityaTs2EYEffx8VFUd5Da2oVomoaqqmiaRjAYRNd1VFVlbMxHlzqGY+JM0mZpsViwWq2cHjM4Najyzd9OEtJhYZmdrc3ltNSVYLcJXIEQuq6DdW422jiBZTouwO1XeeLgUJyYrmhZSFtjOadHA/RNhOZcQjGT6hFFgpp/eOO+8wIxXwgKwOX6GmbFXCFzV91EKMogmvZrzEjMrJxRMlTgCAsXLmTp0qXxShTZSLGQ95xLZp6rovnAwD1k6sEUDh8DSkyPFYKurltYuPA+wuEnAYPJyd+hyzegqkOcPvUBJAlaWh7GZmtAlmVkWcZut+MoMZL6IKWi0d3NquZKtiVIwIUQBMIqh/omOXpmFFdYZ0ulk7Y6Bw1OCV3XmRgfj/QU84QZHQ3SXBWmvb09Pocsy1itVkKGjMul0FsapCRUmmS92Ww2fEEdTdMRhmG6WcfzpEw28f6JEAf7PYQ1nU2LqriyZSHrGsuxWSKEPfXXF2z80cY5qcf3ehFGJKIYgyoiL8wmQaVWpCgEijLIxMRPSBRJjIzcj8/3Ki0t386r2ng2EktM8FVVlf7+fgYGDiNJ7ydmsW3Y8BRHjx6hs/NtrF37cF6kmC9SLaze3l6sVitNTU1Zz4tJz1V1hOwuvhC1tR8yTfrt6LiCYPBwzjWGw8eZnPx/JMbJvN6XGRw8QSAQ6bkz3T5ViXt/QNE52O/h6IAXRTeorSih0ungL69sMT23cjJER2iIMtnFxRdvjj9563rE7TjmC1ExNkhlZTlOpw1VVVEUBb/fj6ZpjHjDuFxBjh1z4yqZutclScJqtdLlgVGXTmengtMRUVS6goJ3/+Y2gooTm7EcRT7FfWcfA0mPE8ZIYAS70UKpvh2P9QmEFCj4c0nFdIUQ2TAfLahisdgi8sJ8saDMRRJh/P59pgVjzQQUEWvHXAWoKIMcO/Y+ZPnzeL02mpubqal5HLc7QnqGoXHo0A50fQuGsYfu7ttwuX5OYnWG6agKsyGfzytWLcLhyN0hNFP/qWT3oMS6dS8zOvqDNFEHWAkEfslUpXiVYPAgrrFHscnp11DVIfoH7sRwfDSv9+sJabzW5+HYkBfDgJV1Ti5eXEnXWID2nsI3ZovFgsViwVkq4XA4WLCgivr68rRxVZ4whwODXHRRA8sWTrWnF0KgaRpKzzg9ygS1tTWMeEK0n/ExMBnGr0iELQcw8KLLbpAin4u5AGLm/0NzZSnNR4IqJuoWkRfmC0GZiSRiMCsYayagMCOx3t47KS//HGfOfAIh9lBZ+WN27PgeqjrEK6/8NGFeFVUdBIYBI9rSY/oVLXIhnw0jufr58QyjLETyuFQydeJNjn8JurpuQZatOfKsIjGZyEPD1DghlHjJpY6ONxEKLscf+hOwLuP7CCo6r/ZM8lqfBwlY01DOlsWVLHBGcu+6xgJ5FZrIFiOaDiRJwmazYbc7CAuZF3oVRr0qZfYy3npxE3ed/CVIOg4j28PB7Gz8C+0L+fXOX9PZ2ZlRbBL7slgs845wCkXRxVdEXpgvBLVp0z58Ph8dHR/GMH6d9mRvs9WyceNuU+tFUQYZHU0v2iqEwvDwHwmH3wn8lliOkap+ke7u2zFPdE2sTJ6MQitazBSZav0lYyph10z9Z1bQNhw+xrp1u3A6L0qaydwVmPo3jdQX1DRXtOHhMgKBV1DV4aRrdnX9NeV13+fQoJ1DA16aq0q4tq2OzYsjybVJV8hx28SOT//+Mlf5CSHonwzxfKebE8M+6ssdXNmykLamCtb+d2vcYopSdV7XmA5cf++K57slfimKQiAQMD0WgyRJcbFJauzNYrFgs9kQQqDrOsFgME5ws9FufSYodtQtIi/MF4ICokq0X5NuSUWsm+7u22htvS/tvL6+u+LVyicn/yquHlu8eDFNTU309NyKx5McS4r0h8oPmapaKMoQXV0fZOXKB0wrkufq5ZTt88pUKT03kq2oTCTX1XULF12UXAewre1lRkZGCAQCLF++nNED/w6uAZN1K0kNDyPJypFrCiFoP/Et9naV4DvxFPW117KoqoRr1tZyZcv0qpknwsxyyJWom77+CDHt755kcDKEP6yztLqU925rjicAJ7vxpCzJxjOzZOqd9djtdux2+7TOj5FPTD2Z+hUOh+NfnZ2d8dcT7z0zgsv1lY9COBuKFlQReWE2E3VnipGRr5LtSdTleoRly75kmt8EBsPD96MoO1m+/GKWLl2KJEmm7dxHRh5Ii3dlQ6b40+Dg3fh8ezIKB7L1csr1z53NeoolAPv9e9MsnlT1X6aCtuHwMQKBI2lWVCJCwQ6gyuRIegNJl+shwo5/4tW+AEdOT+KwVNJa/SBv23IdDx4QaTL1c4mp21vQNxGME1OZ3cpVLQsJaQb7uyewytI0kmxnVix2ps0HY0KPbPGccDiMoihs2LAh7ZgQAsMwTMlN0zRCoZDp67o+5WFIXEOq9Rb7HjsWCoWQZbkoMz/fC7hQMJ8sKL9/P9mValMxIEUZ5Pjxm1DVGgxDQ5IiFRjKyx+nsnInqjrEyZMfwOFYjlkpo0KLo5qJMlyu7L2asvVyyoVsldJjJJRP7btMVS7A3IpKxPIV36NX6wF+BZmSZJEZDy/kD93/C73/t5Q7FDbX72dJ+QmsFiuuka8A/5hzndnoOneppByJukLgCWk8c9xFWDPixLSuqQKrLPFKz2TkfEl6XVaAyPZ/GXMRWiyWvFvrpCIbwamqSjAYjP/+m9/8hl/+8pcMDAxw2WWXxV2Nn/nMZ3jnO985retfiCgSVJ6YTwS1bt2LdHR0cPHFFwMREmhvX5eUnDoy8iPKyz/K2bNfRNd3AxKSNNU7SlGeRFX/lb6+7+D17iIQOGay0Rcuh0+NP3V3355QIihdnGDWy6kQeXYq+RTS+j0Vma2o4wQCh+nt/XTavKo6RE/PbXg8m6E8/fPSDAs93pW8NnIJ7lAd9c4httT+hjrHHiSCQCwm9iCjo+X0GANcsvjL5q7Ogt5N/oi58p455uLEsI9SWxV/vromTkxT189OcNljUNO3oM5VftNcq/hi+XD5uCnb2tr41Kc+xVVXXcUrr7yCLMtxN2W++Pa3v83999/P4cOHee9738v999+fdPyPf/wjH/vYx+jp6WH79u3cf//9LFu2rNC3NacoEhT5+eTnE0FB8tOeuTJP5ezZz2EYfySyKaQH8UdGvozP9ysideYCbN16Nsk1l1iTb2r9dkpKVhEMHqO6+oOsXXsvmRCxnh6JXztVnJCpl1OiFVVo3li+rd/NkEh2idaUJNno6rqFcPhk0ryGMUpHx00EAwsIhyuSPidFt3HW00rnxFrCWgk2i8KyytO8dXUv5eWXMjb2cpLoQQgVVR1CCZ+Ydg5VDPnusbEiswe6Jxn0hNAMwdLqUt6xsYHlNen1KGNQ1aFss2a/Zpbj9c76GbvyZoL5KDOHqf0p5iLMF83Nzdx222387ne/IxgMJh1zuVzceOON3HfffVx33XXcfvvtvPvd72bPnj2zuvaZ4vxKVC4gzCeCSp3DvPqDhiTtIbMVpOL3/yF+POaaS4TZvEIocTn7xMQjKEryZqUogxw5cg2KMsTZs7eRqvITwqC//y5UVc2Q02XQ3383hmEQDg/gcr0fVT3OiRNvQVWHs3wq6e7CXONzzZNInBH5evK8fv9/RhV6EYJZu/ZZrCU7CVa9xlHtVcbtP2Hzmnv46LX/yTsu/RfaWv6F9etfzmCpTX0OLteD01p7LhVfYjWH3vEgTxwc5leHh/GENa5qWcj1GxtpqHRgtZhv0u978n3ctftu6r+9NsMCsmzuWY7VO+vx3Oo5r+T0esSNN97I9ddfT01NTdqxxx57jPXr1/POd76TkpISPv/5z3Pw4EGOH8+UpnF+UCSoPDEfCUoIwejoKJr2XSyWF1ix4izbtwfYsSPE1q1n0HVf0kYoy6Vs3XqWHTtCLFjwB4QIpFWdSCScTZv2sWNHKOmroeHDxNw1QqhppBYrVNvb++UMCkCFQGA/FouFYPAA6UrESNxI13W6u7+Eqr7C5OSnoy3bI8QW+1IUJcmP399/dwLhRYgOIoQTI7jEnzMhu2w94obU9REU5TdTq9YtPLn36zx2tIEXjz3D4gWl/N8tTVy3oYFFC5LLK7W1vczWrZ74V23tLST/KyrRNaQj3wd8UxWfEEwGNf50YixOTG9aVcNN2xaxYVElFjn75OOhidhMma6a8ViiozAV84WY5qsFNRc4evQomzZtiv9eVlZGS0sLR48ePY+rSkfRxZcn5hNBxYqM7t69m6qqKlavXk1FRUXSmIh1kiykSBQwBIP/nbaOXAm2EaXfAyTGjEZGHoir9sLhgbhScGTkR2mbqSSVsmnTkbgbccOGdHeCEIKxsTGOHHkew4h089X1yAbmdj9Mc/NnsFrr42Nj31V1CLf7YRIrWrjdP6a+/p8ZHv5KnOB03Y/P9zK9vbezePG348HnxI0pl/BibOwhSkr645+DAPq8S7BKnSyvOs3qhX/i8tbrsdlyB9Mj1tpDJBOiwOV6MC1Pq7f3CxjiH+K/Z4q1meUx9U2EeO7kGCdHfFzUXMn/XlfH2sbyDDGmTJt07k6+hR6dT/Xz3kgE5fP5qKurS3qtqqoKr9dc5HO+ULSg8sR8IKhIgm5HvBDotm3bWL9+fRo5AXg8L2GmyvN4XuTIkWvQtFfI1bYjFeakp9DbeyeaptHb++WEa6ppY0FnYOBu07l1Xae/v599+/YxMjJCVdUvTawFg+Hhr2Cz2aKVDSIBZ4fDkVRAd2ptOiMjX2Z8PFLtYmzsx0xM/ByA8fGfYxguJElKskgNw6C19TnWrTtKWdnlLFjwHmL9nqbm1QgGfxv/XZIEjWX9vHnpb7i4YS8V9sm4mzIXIsVtzRSZyVbUwMA9BIPH8HhfiP8ei7VlghARV97jB4f41eFhvGGNpdWlvG1DPRc1JwsgCkLW0/IXSZzvmNOFjJ07d8bv3dSvK6+8Muf55eXleDzJZbM8Ho/pXnI+UbSg8sT5yoMyDIPR0VF6enqQZZmlS5fS2tpKe3t71gzzysorCYU6o0H+qQTaU6c+hMv1EA7HO1i8+Ce43Z/I2dgwBjPSAwOP50XC4Yic3KyPVAxCKPh8yXJtRVHo6+tjeHiY2lqJ8vLbWbbsq3R0/MQ0/jU6+hDNzZ9JW6/PZ14FPeJmjK1FYWqD1Bka+ndaWv7b9L0ODX0dv38PFstx0t2Q6a3TS61BSm2B+Dpj1lvMutF1HSOaKBoZI5BlGZ9vH+buRMHY2MM0N/9L9OcfA2sJBA4SCBzOKs2PEdMrvR6GPGHKHFbetKqG2nI7j702iC1DdQQh4JsHvsXn9j2BLidXG49YOrlUkebM5bnVQ3vvJHvOjLPN+Tds2bxpXub2XEgW1HPPPTej89evX88DDzwQ/93v93P69GnWr18/w5XNLooWVBTzrR+Uoih0dXWxe/du3G43bW1tbN26lbq6urjkNPO56Um3o6M/wu8/FFXVQTj8a8bG7o73cMoHlZVXIkmp1oSVYLCVgwf/mfQ2FzaczvexbFkXLS09rFkzwMqVf0RRFHw+H8eOHaO9vR273c6ll16KzfYIfv8eTp++mcwxIHMr7KKLdrN5cyeSlOhWc6Dr/gTiSv7MxsZ+kibygFhJqIjbTdcDlJVdyqJFTzFlSeWziRm4XF/DbrfHEzFlWY7n0litViRJYs2a52lr60CSUtuAWBAiSH//XSliEoOurpvjv8dEJboeaZUxEVDZO6RHLSadN62q4V2bLdi870Lorpyr9qt+09dHAiNR11+2ezf9eL2zPiJjnwihG/Mj0T0T5ksifgyzEQoIhULxavaxZGKAG264gSNHjvDoo48SCoX44he/yMaNG1m7NoMA5jyhSFB54lwR1OTkJIcPH+bAgQNYrVa2b9/OunXrklqz55ojU0HYEyduYkpVp+PzPU6sh1PiRp2oxIucG3F/man6JEmjrOwsTudZ0pOHVRSlnUAggNvtprf3IEeOXMOuXb9m3759jI2NRdfbR3v7H+Lxq1DoeNYYUGQd6e9/YODuFHdZ2GRNidDp7f0303mmPr9IpfiBgb9PeM826uv/hksv9XPppX5KSlaZrtPv3xcnJdkS6QsVc0/GXJSZ3JORv1PEchofj8XWJEAnHD7BlFWnMDb2Y04OvMYjL/0/DvUMEVR8LLd/kRvWC9bUlTA0cDc+326Ghr4ZKfejTZX7CQb7OH78WkKhAf784T/P8llBPrX2fn7DL/Dc6sHz/7d35tFtVHff/0qyJUuWLclrvMRLvMROQhayOGnok3DYC4EmLaSUJT3wwjl9oPR92gdSEiAOpJQ0p6ElQLqEEiBhKYSGt0ATCEshCQlbdrJ4k7zJlrVZ+zr3/UOe8UgayZZjW3JyP+f4WJ7RjK6s0f3O73d/y69s6P+ffnx8ywm8eUQPrcmFhgoNRupVHC9S0YIa6Zg2bNgAuVyOp556Cjt27IBcLseGDRsAAPn5+di1axfWrl0LjUaDw4cP4/XXXx/NYY8KqWdnpyhjKVAMw6CnpwcdHR2QSqUoKytDTk7OiC/MWOHhXm9TxDOZgdf349tva3DJJQeQnp6P48cXwe/vRWfnk6io+COYgQZ206Yd4PzcicAwDAwGAyyWxwAcR3HxPtTWPs8bG0Fb2y9h5G7w06FU3gK1eh1XO43/2+MJ4PDhQVdhSASsCAZfhpALMh5W656wv1nrKdLqIqSVN95wV2PVlL+hmzFj/vw/Q5Ym4c7T0vIz+Hw9Q7pPw92TkdeYH7EuO0KAPvcknDHPgaVlF3wBGwoV57B8divSvLthNedAXrSaW4OzWP4JQu5AmkSCum11EdUg6pDGFCFTcAwJQBgwDINeuw+HtFbo+z3IykjD1fX5qCnIxBFTaooAMLFcfMOhsbERjY2NMfdfeeWVKRdWHgkVqGEyFgLl8XjQ0dGB3t5eFBQUYObMmZDL5UOcYeix8JsOsrBrT8IEQAhw9uztyMpawCViGgwvY9KkhyCVThqRMAUCAXR3d6OrqwsaDQOR6INQqHP/P+DzPcZN3IP5S4NWgdO5C9XV6yGVhme2sxN/VdVL3PEMw6Ct7ZcwmaLXhkLEvvMPBNSctRoKfX8ShASGEc4dcjVWVPyR28L/SLq7Q+7TyOcIMWNGKDDla3cbqouCEPU3YLAqiNBaXkiYTpsvgdmdD0W6EzPz9kImceHLHqDfug+5cgZG404wjJt3DgKn8yDS0hKtozdcRLA4ffjX8R60W0It379XoUJdYSYkYhF8Xi+XHsCun/KvKf7jZFQRv9AE6kKAClQS8Hq9OHr0KDweDyZPnoxFixYlVLF4JF+i4VQl93rPwesdjKoiJAi9fuOQE2wkrPAajUYUFxdj/vz56Oz8Nez2wRYd/Ik73KWGqOfwRUlo4heLxQM5VbGsJX532PBQd0IIPB4P2tvb0dfXh4yMM2CYWELHOyPxobf3YxgMX0JrB3qNDM6cdSNTlg6x2AqT6RWE3KevIDv7fni9GQjGaKXOx2h6DcWS8LU8kUiK/PxVqM15FK1Hd+HzLjtMbg3kaW4sKFVjRokCVosOekceCAlCJGIDMQIwmV4H69ZliB8u13FcGdOVNzAuUbwbMeF9YpKNLHEdDulsqCnIxGU1ebhkIFKQYRjo9Xp0dnaiqKiIK/XDuo7Zx3wCgYBAy3nC3ZwJpQckW+Aoow8VqGFyvndWwWAQer0eOp0Ofr8fs2fPhlqtHqXRDY1UWoJAwDSMZ/IneX/MqDkWvnh4vZnQ6XRwuVwoKytDVVUVxGJxlNss0kUmFIHHj/hjRamj4zGYTG8hlGf1Sti4WCuEj1b7S/T1vRxx7kHhY4XJbDajtLQUNTU1EIuPxDyWFQq+YDMMA7HOgi7GiKJJ+ZCAQW/v77n/IyFBaLVPQG+4H3q7H19+2Rs2GfMrWxuNbiiVJ1CsCV83YxgfzunP4rBJh9M9bswukGJ2wZcoy2pFmkQKi5lg0PokvM9QaP2NwOS2jHD1Ofo7ICIKFKZfhl9fuhkiALNKVZhbroIsTQJCCHp7e6HVapGXl4f58+cPq7cRPzyfL2BCYsbfzidWzTq+uAHhZYQSqXNHGR+oQI0xLpcL7e3tMJlMmDRpEqZNmwatVjuu4gREu/3a2tpASBN6e2+Mexwhgbhuqq6up2C3H8DRo7+GVPogysvLoVarwwR9KAtJSFxY+BF1IWtgMGQ83rjYOoBCwmezfYHvvvsOdrsd5eXlqK6u5oSUFduhRJMlFPiQhvR0KVQqFcTEgLa2tzEoDn4EArtQVPQLSFSlaGgo450vvEeRoqMDubm/AZh9ALwgRAQ7eQ0nDYXQ95th8/8TkzI9uKr8XxBzhX+9iMWKg4CFp1ESIoIyEAQkI3VVh/o9OR90wuUL4pt2K052hxI7ZxRnYW6ZGgppSJiMRiNaW1uRnZ2NOXPmJFQB/HytHyGBE9rGPmat6M7OTqhUKi4VQAhqvY0vVKDGALYagk6nQzAYRFlZGWprayEWi+FyuZI+NoZhkJGRgba2UFWC+MahHxbLAWRnm7k7fbYRW1fX8YGKEQQi0R5Mm/YHSKWaqDPEmuzt9v04ffqasPWkSMLFjX+HG21FRR7HMG5kZNTD620ZeH0pJJIb4PP9EkVF+aivr48SUtZ9yBdNrfaXMBj+joKCuzlB5IsZMLhu2NHxmIBoEPT0bIE4+4mwraFCuH3QakPnkcvl8Pt3ARKCXmcRzpgvgcX7GTIkDBaW+WFyHIXONoUTJ/bckbDvyBIvgHGEKNOzcajNgmOd/QgyBHWTsjC/XI2sjNBUYrVa0dLSAplMhksuuWRYa6qjjZBAxHKhBwIBtLW1wWw2o7q6GhrN4PUbz3qL556MZ4klInCpFvaeDKhAjSKhenBd6OrqQnZ2tmAJomQl/LLCxN5J5ufnQ6frjRkhxpKWVgeV6g0YjUZugdvhcMDn80Eu/zMkkkFX1vHjvwCgR1bWJkilkzgxKyp6lxM3dptIJOIm/liWUHREXdQzBI/lW10ez+mw5zPM+5g1axOk0vyYx/Ddj+HbX4HTeQw1Na+FiRnS1nPnCUUFRv9Tfb5OpAftYa/X0vIzSKVl3HmCgbugNX+LJmYpzJ48KNIdmJ2/F2XZbRCLRDA7ZkEkSkNu7k9hMr2OgoK7UVy8enB9zp6NdlEvZs9+FoXZMuA/mVHjiM9gtYdjy4+FRU+6vT58qPXhpInB7gMnMTlbghkFUuT4HOhs6wMhBFarFSKRCMXFxVAqlfD7/SCEhH3mqQLDMOju7kZHRwfKyspQXV09KuNL1D0ZuQ0YFLhvv/0WBsOF13crEahAjQIOhwPt7e2wWCwoKSmJ62sfT4FiXycYDHKP+dF48+cPZ00qhMvlgk6ng9frRWVlJfLyRDhx4mMQwgYU+MEwewEEQchLkMsbuZqBdrudK/A62ErbCJHoZYhEobp9VuuNSE8vDBMxm+1xxO/oy8Bu3x+1VdilOHiMkKhFWmrd3U+huHg1Tp26jEtAZnObOjoeg9m8C6yYifNDjQZ9vh4wTMhCFonkyMlZDrP5LYSqeUhgte7B6dO/4gV7HADwBQhhcFK7D99q5yKNmYFyVTPmFBweEKaI90EYbh2ur28HgkEn7PaDqP7zDPS7NcgMLsWaL/ciKBr+ZxtJrjwP1dWh3K4gQ/Cd3o4zOityCgO4fZoc36vMQY4iDYFAAA6Hg7suCgsLIZVK4ff7YTKZwj7vSLcZ3xrnP471ezTbnhuNRrS0tCA3Nxfz588f1aoWo+GeNJlMePzxx9HS0oJt27aN0sgmJlSgBkhUOAghMBgMYSWIIl1Go/E6I4G1lvh3biMJEw+FhPdDq9Vyrsq6ujrO+hFOLgVstjcxZUojpNLSmOcOBSGEQqbZDr8FBb+NyHf6FtFlhvjjS4PbXYujR4/yXI8WWCyvxDxOuNyScBCH32+K6H0Uer8m02sA2AkziN7ev8JoVKCzsxuDwRGesDUzQgJwuo7Abj/ACRwhBL2uwpArz5MPl6cXcwtPYcnkD6OFCcDjpwGfj8EfOlkXkhvAq9zjNOSw7zLm/yweufJc/GrmGqycWwyGEJzrdeJLrQU2TwDF6gxcO70AxapQxQuv14u2tjbYbDZUVVUllLdHCAkTLv5vr9cLp9MZtY9vmYjF4mGJGl8ARSIRHA4Hzp07B6lUilmzZiEjI7J6R3IJBAJ48cUX8cILL2D16tX429/+dtGvZ1GBShC2dpxer0dOTg6mTZsWVuVhKMZSoCLdePwCkonAr/+XkZGBKVOmIDs7O+w5wrXvWIYOYIgUBIvlVZSVrUV29uB6UmnpN9zjkycXweU6HnYekSgAubwV1dVT4fP50Nvbi76+zUhLi5zc0yESXQ9CfgmGYeBwAIcOHeJKDjHMZkSWaSLED4vl7Vj/IQwKjw/9/R/C650Ds/mfkEr40XSRn3Nom9H4OnpdxThjngaLJxeKdAfmFHwBgEGpmsSstuAIRJatTRAy6MLj43wwVN6o1ejE+yd7oTW58OGZPpidfuRnSbGsthBlGjlEIhH8fj90Oh2MRiMqKysxderUhK8vkUjEWcojWaMKlXWKFrdAICAobn6/H16vFwzDQCaTgWEYnD17NqaYjaX1JlhG0YsAACAASURBVAQhBIcPH8bDDz+M//qv/8L+/fujvm8XK1SghonNZoPb7cbXX3+N0tJSNDQ0jMg1MBYCFUuYEoWfWJubm4sZM2bEnEBqa9/mBQkQHD06HaHSQsBwAhiEKq3HE7VYkX7BYBCdnZ3o7u5GQUEBlMp2eDzRJZfk8lbMmLGA93qh/5nf78fZs+cQDEYeM3QuFG8UEb9j0+MsHrCYWGEadOUd7VuAgF+PoapfjCZsuwtCCLqtXpzSO+DyBVGiluPa6QWoylNwIdgdHR3Q6/UoKyvDggULknZ3z9YzHCoyMBgMor29Hb29vaivr0dBQei9DmW9RW6PtN6GI2qR1lssent7sW7dOvT09ODFF1/EtGnTRuefdIFABSoOkSWI0tPTsWjRovNaTB1NgWJF6XzceIBwYu1Q4hsWJACC6Jyb2IIjbH0xA9XShYmsIOH3+9He3g6DwRAx5uG1rBaJRNxEN2tWtMvv2LEZvGoOw4MIFpFVgZC/w+LZjhbrVLgDiihh4o7nLBwxVhxkoqLwMgZeJc674kYy1H7WagIAfb8Hh9os6LC4celkFRZN0WBaURbEolCSbVdXFzo6OlBcXIwFCxaMuUVxvhBC0NPTA51Oh6KioigxHQvrjbXSItfd2OKs/Nc+ffo03n33XXi9Xhw7dgzXX3897rzzTng8iV1vFwNUoASIVYLo4MGDSY9EYu/8g8Eg13KDH1gQ67HQ3a7dbucSaydPnswl1g5FZMSbTFYCoRp4QgEMwKA1xE+GFYmkyM6O3cdmMFn3twgGf8El1zY0NIz6nXz8IItB5PJ6eDxtMfcTAhjcOWj3HMFZyzQESFrs4IcwosUpxOhce7nyUAtwo8OHw1oL2owuyKUSLKnJxfTiLKQNVMvv6emBVqtFfn7+qAcTjBVWqxVNTU3IysrCpZdeylWtGC2Ga70JEWqs6R+oS2lBTU0NVq1aBbfbjd7eXmRmZuLSSy8d1fFOdFL/ihsnCCGwWCzQ6XQjLkE0HEZqQUW68RYsWBDmY+ffxbFuCv72yPBWn88HkUgEtVqNvLw8BAIBGAyGKDdFenp6lChH5yalI1SagD/pipGVFS444blDJG51icjjWEEM9bL6FpdeumtYPayEXlvo/Px9sdbXFIqZUflRHk9L1PMIwcAaUyj4QZF2DlNUXgAiVKhaws4nl8/Akn+9CosfUPkBTwfgHcVLTpOuwVsL3wIAmNwMDugJ5pfI8MIH36DLQSBLk+CSokzMKJJBkeGHw2aDw+FAZ2cn1Gr1mEzyY4HL5UJzczMYhkl4XXi86Onpwdq1a2G32/HCCy+gtrY22UNKeahADfDdd9/B7/ejoqICKpVqzCylRASKnwUfub7ERjINNxKJrYfW0dGBrKwsFBUVIT09PUzkPB5PlLCFQsL5pXn6wTAvg99aPTzXiHvFKJddtFswdnUJFrvdjnPnHuSCGESiAHy+o8MqwhrrtYVCzNl9xcWrIZFkY/bslgSrkIeEKXqN6QtMLcyEUfb/0GnxYMGCnWHn+OabyaOeUMtv3d75fzu57ed67fjIqMVJRxqyZWIsrMpAXZ4UYhJEIOBDT48ZJpMJYrEYGRkZsNlsXPdmIGQ98K1yIYudv2081qj8fj/a2tpgtVpRXV2NnJycoQ8aZ3w+H7Zu3Yo33ngD69evx4033ph0T8xEgQrUANOmTRuX/KThCBS//fj5ri/xO9YWFBSc1x0xIQStrQ/AFJViI7T2EQoBP3QotCYkEpkhEoXaYRgML0MsLo1Zhsjv98PhcECr1YIQIxjmfQyucYVELV4QRiSxknCF9rF5RYlUIZd19qPlRC904keh6zeB8TtwZX0hvn/JFEjTQ1+xj872CZ5DJisFYB7yPQwy9DWaJ89D2y+6uL9dviC+bbfiaEc/ClUZuHSyGt+r0iBTGhqb3W5Hc3MzxGIx5s6dC6VSGf2qEWWZ+L99Pl9Y9JxQcAEbMRkvJJz/WCwWx73e2bWxzs5Orst0qk36hBB8+umneOyxx7Bs2TJ88cUXSamsMZGhAjXAcC2b8y3JH+/YyPylkYaJA4M1AK1WK0pLS0dlcVskEg1UDY90fwn93wLIzGzDjBkLAQBtbQ+gr2/w+ZmZ34NG80FUOLDT6cT+/SHLSyaTQSJ5AUBQoByTF01NjyI//7dRE51EIolTCzDUpNDn03EJs/xcJZPpDQgJWTwYhkFrjxk+qw9Lpk7C9y+5hBOmoaitfRv4d3TDw5Hw7V3f4t+nDFg5rxgA4AswONLRj6Od/QgwBHVFWVhQoUZ2RiiJ3OVyoaWlBT6fD9XV1VCpVDHPLRKJOKspUfgRk0IBBm63O2obv1xQZN6T3++H1WpFVlYWKioqIJPJ4HA4Yn7+yaCjowNr1qwBwzB48803MWXKlKSOZ6JCBSoBWBEb7YtfKEx8JO4RNrFWp9MhEAigrKxsRHkq8RAK9xaKehOJ5Kit/Se332gM7/fkcLyFqqpGSKWTwVa9bm9vh1KpxPTp07k1hJMnfw2XSyjkm8Dp3AWZ7B4Eg6owkQuf3CwAwl2SbAuKc+cehNv9Hs+S4/vaot2NkbCh19+d6UKZSo7/uXYOVIrEFs+F2tfHRoRYgRIFigLuNiEYJDjS0Y9v2q3w+BlU5WeioUKNnMyQ5ez1etHa2gq73Y6qqirk5uYmNOZE4UdMjgRW3Pr7+9HW1gaJRIKKigoAgNvths1mi/n5szlXw7Xezrckk8fjwTPPPIN33nkHv/3tb3HdddclXSwnMlSgEmA0L7R460sjORdb1SIjIwOVlZXjmug3VLXyWPu7un4HqfQhdHR0QKPRYObMmVFrakIRfywiEUFa2quoro4tIuGW2+BrA4DLtRux+k4Q4oPB8DLM5hsglU6KWluxWq2wWq3IyxMhU/EXqNP+N8xqiheUwX9OyL04PN7+0dtoMjjwfxa/JLj/XK8DBrsXbx3pBiBCWY4cCys1KMgKiabf74dWq4XJZEJlZSVXFSTV8fv9aGlpgdvtRn19fULXNsMwMcPCXS6X4JorC99qjLXWlpaWhq6uLqjVahw9ehQbNmzALbfcgkOHDo0o0o8SDhWoAYbzRRUN5IWcz+Iv68u32WzcovNIXRJsYm13dzdycnLiJtaOJUO1poi132D4BHl5D+CSS4rR3n4PxOKXAAhP5sNtfxGJ0/kVYpdLGqwIEXv/vSgt3QdCNPB6vVyIsFKpRG5uLpzOJ+HxnERv3wf48ksnZJLQ5ygW/wnAAZw48RCUyrXo0ftgcRPo9YMTXl/fhiFqDQ4PhhA0GZz49JwRgSBBbqYM/1WTgxJ16FpgE1Z7enrC+nSlOsFgEDqdDgaDAVOmTEF+fn7C3xOxWAypVDqidVe2JJOQiPFrTD700EPQ6/UwGo2YNGkS3nrrLezatQt79uxBfn7+0C9EiQkVqAQ4nyRbvhsvPz8fbW1tgovJ8RaO2R82gdhsNqO4uBhz584dViO4sSJePyf+/sjk2pKSEqSlpUGr/eWQgQn81xiOdRLruNgJuEJt4f0IBHphNv8JYvH/QK/Xo7i4GNOnT4dEIhk43/sAyiAStWD27J9ClVkMr1eP48c/GPjM34NGswZyqxxir4cLKPB69XA6XwUnnsO4rCxmM1yuAPr7+7k7+45+P77S9cPk9CFPKcW93y9AeY6cu5liAwkmSpItMJhoq9VqUVJSkrSqFfySTEK4XC5s3rwZNpsNW7ZswRVXXMEJKMMwE8I6TXWoQCXASARKaH2ppqYm5nPZSKnIuza32w2j0Qir1Qq/3w+ZTAaxWIyenh709PSEfZkiBU1I7MZzoorsXMtPro0XYReLeCHjQx0Xy2ISidLDuuXyxayv72Xk598RNcGH934i0Ov/AFX1H6DX81+Hgcv1Z6jUa2EnMpSXTwYAaLVb4HKFQtOHi8/vh9vlCZWjsnpwrMcDoyuAzHQR6jRilPhFMLbpYe0YrDSuVCpRXFwMmUwGq9Uq2PYklbBYLGhqaoJKpcK8efOSeuMVC4Zh8N577+HJJ5/EnXfeiYMHD0ZZaBPBQp0IUIFKgEQi/YDYbS7inZ+9M2ZddWzzQ4PBAJFIhGnTpkGj0USdi+9rFxK3yGi5yBDgWOImJHDDndRcLhe0Wi0cDkfMUGChNhfxRGckgsYSr8AtP1FYIsnHmTNrwTABiESAWAyIxTsgkcwLG0co4o/t6ROE0fganEWrBBOQA/L7wBYrihyLJh2wxC7aDiAUBFFYUIBuXz/O+TLR4XUjd5IE11VoUDdJyZUlYlusK5VKTJ4cEkP2GhBuezJ4fcZabxmLYIJIXC4XmpqaACAsSCbVOHfuHH7zm98gPz8fe/bsQVFRUbKHdEFDBSoBhhKo0WpzAQwm1nZ2diIrKwtTp06N+6Udqa9dKL+Fn7gbOanx+/qwk1qkmAWDQZhMJgSDQZSUlKCyslJwXLHaXMQTnUQFjU9kFYjIoAsgiDNn1sLpXA6JZDdEokDMcel0DyKqOCxh0NJyF4QCQszmXYD0NsGxdC4IfQ7P/UeLBRVqLKiI7kpscvrw2ledaDY4kZWRju9X53BliYCQ5dHS0gK5XI7Zs2cnvBYZa71luMEEQ+U1xUoD8Pv9aG1tRX9/f8om2gKhnm+bNm3CZ599hk2bNuH73/9+ylmfFyJUoBIglkCNVpsLIPSF7ezsRE9PDwoKCjBnzpwxLTVzPvktkRFSFosFer0ehBCoVKqBpoM2mEymqNyWkKvsjwJtLgJoaWlEcfHGqHJLIxG0WMQKuggEjqKwMBMmE4lwv4WLodX6XtQ5CfHB620TPK/Hcw7iEXyMVrcfX2mtOGdwQJomwaqFkzGrVAVpWkiY+Em2dXV1gkm2w2Go9ZZ4CEXKsY8jq4Pzw8B9Ph/8fj8UCgUyMzPR29sLs9kcU+yGk8A72jAMg3/+85/YtGkT7rnnHhw4cGBC1CS8UKD/6QSIFKjRanMBjE1i7VjDth6w2WzQarWQSqWYMWNGVJv7SNj/26lTzYKtMVyuL9Hd3R020YXyz/4IIBCWtEtIAGfOrEVOznpB12SsCMkZM74IayFRXFyM0tJSSCQSnDy5KG7EoNN5TMBVKMLMmSehUhYLvmf72T50WoauVs2O1OkN4CudFd/p7RCLRZhTqsKlZSpkpIeuCTbJ1u/3o6qqKm6S7ViTiPVOCEFfXx9aW1tRUlKCsrKyKPf0YMNKT9T2yATe4VhsI113/e6777B69WqUl5dj3759XLuO8cDr9eK///u/sW/fPpjNZlRVVeF3v/sdrrvuOgDARx99hPvuuw/t7e1oaGjA9u3bUV5ePm7jGy+oQCUAGxk1Wm0ugFD15bFMrB0rIpNr6+vrh71uwCZuzpwZP0Q8kpMndQJJu34Eg8dACIl7tw6ET2gejwculwsqlQplZWWQSqWw2+1IT09Hbe1/YlaAB4Dm5jsFthJ0dj0B1dStCb2nSLxBBgdbzDjeZQNDCKYXZWFeuRqZstBX1ePxoK2tDQ6HA1OmTBnzJNvRxGazoampCRkZGZg9e3ZYzttIcoaEWl/Eqk4htO4aKWYAsHfvXiiVSnzyySc4e/YsNmzYgCuvvHLcu+8GAgFMnjwZ//nPf1BWVob3338ft9xyC06cOAGlUokVK1Zg27ZtWLZsGR599FGsXLmSKyt2ISEaYtF/7IvTpQiRd2eREEJw7NgxKJVKKBQKpKencz2iEi2MyU+slclkKC8vT+odcCLwi85qNBqUl5enXOvsWPh8Pi4fKDc3FxqNhqtSIBRcwv9uDFpk/XC5rgs7b6u1Fsf65uGG6o/RMPesoNX20YAFtWrhZMGxef1BrH/vLNIkYhRmyzC1UIn55Wqo5CGXGz/JdqQ5QcnC4/GgpaUFHo8HtbW1Q1rYYw2/9BL/87bb7di4cSOam5shl8tRVFQEq9UKi8WCf/3rX8jLy0vquGfOnIl169bBZDJh+/btOHjwIADA6XQiLy8PR44cQV1dXVLHeB4IXszUghoCvhtv8uTJ6O/vh8PhiJrQhHKZhNxN/f39MJlMUKvVnNUxESaaQCCArq4urnPtRGnDACDKlZdoGxV+AEFn5//C5RJ+HhPMxdGjR8MCCFirTWcIwuQBWlp8YW4nkSQN54xeHNc7oZSloa5IiSU1ecgdKEvET7ItLy9HdXX1hLhegNDYtVot+vr6UFVVhby8vJQYO7/0EntzdeLECTz00EOor6/Hu+++m3QxiqS3txfnzp3D9OnTsXXrVsyaNYvbl5mZiaqqKpw6dWoiC5QgVKAG4H9xYpUhUqvVUKvVcc8jFA3ldDrR29sLh8MBhUKBrKws+Hw+nD59OipwIF6IN/9nvApixu5cm/pECtNI1/X4AQQ+3xHB56SnF2LuvC+gkIafn71T7xYZ4LO4kZ2dHVpf8fpwvNOK43oXHN4A8jKAuWpA5TGj6UQ7mgbG7/V6oVQqkZOTA6/Xi66uLsHIyVTKaSKEQK/XQ6fTJTXRdjhYLBZs2LABp06dwtNPP425c+cme0hR+P1+3HbbbVi1ahXq6urgcDiiKlSoVCrY7fYkjXDsmBgzzTgxGm0u+JOZ3W6HXq/nOtYWFhbG/KLGC/eOzGGJDPNl79KHm8s0nPcTL7k21RktYRJCqGpGRpcNxqaoHiQAQp+NTCZDhjwDcg+Ql5eH5j4nDndaYXXJMWWyGgsrNSjVDOa9sVUUCgoKMGnSJK4TKz+vLbJAqlDY91D5THzLfrQwm81obm6GWq1O2URbIHSN7Ny5E8899xx+9atf4bnnnkvJ65thGNxxxx2QSqV49tlnAQBKpRI2my3seTabLemu07GACtQAu3fvht/vh1qtRk5ODvcjk8kSqmDOJta2t7dDJBKhvLxcMLE2kvMJ9+YvFvN/Irvrsj/814yctAghsFqt8Hq9KCoqwowZM1J2kolkLIUpHsO6MghgdvrwxjfdMDpCZYmun1GIilw5Fx1qNBrR2toKjUaDuXPnjtiFKpS0nUjwQLybnVhJ206nE01NTRCJRJgxYwYUCsWIxj7WEEJw5MgRrF69GnPnzsVnn30GjSY67ywVIITg7rvvRm9vL95//33uezh9+nS89NJgwWCn04mWlhZMnz49WUMdM6hADeBwONDS0gKz2QyLxQKTyQSLxQKfbzCcODs7GxqNBhqNBjk5OdxjjUYDpVKJgwcPory8HHPmzEFtbe24rS+x/vREI6H4E1l/fz+6urrg8/m49+T3+2PWDIznfozcPtYikSxhGi5dVjeOdfWj3exBnlKGq+vzUVMweG1YLBY0NzcjMzMTs2bNOu+gk/FM2iaEwOv1gmEYZGZmQi6XQ6fTDWnJJ+PzMZlMWL9+PVpaWrB161bMnDlz3MeQCD//+c9x+vRp7Nu3Lyzxevny5XjwwQexa9cuXH/99Xj88ccxc+bMC279CaBRfMOCdfvZbDYYjUaYzWaYzaH22GazGXv37sXhw4dRV1cHlUoFs9kMj2cw5yUrKytMzPgixwod+1sul59X2HqiWK1WtLW1AQAqKiqGdTcZr+qA0F37WK2zxcpjGm9OdNnwnyYT7vpeWdgalMHuxaE2C9rNbihlEswvD5UlkohD74lNspVIJKiqqkrZ8j5CMAyDjo4OdHd3o6KiAgUFBWF1JGOV3Yp3TcSz1sICSxL8bgQCAWzfvh3btm3D6tWrceutt467O+/ZZ5/F9u3bceLECdx6663Yvn07AECr1aKysjLss1+9ejXuvPNOrhkj36vyl7/8Bbfddhv27duH+++/HzqdjsuDYntkTVBoFN9IYaN+WGGJZPHixaivr49ya7BrWmw1Bb6wmUwmNDc3c9ssFgvMZjPcbjd3fGZmZpSgRVpvrMgpFIphCxvrhmSTa6urqxPyX4+06sBQ62w2my1mOR3+OptEIoHL5YLD4UBubi6qqqogk8ng9XqjXE/JwOz04bDWgpY+F+TpYlxWFSpLlC4JTYqsSyYQCKC6unpce3edL/xE24KCgjBrVSKRjMhqiwz1Zz9/r9cLh8MRJW6R9QOFxOzs2bNwuVywWq14/vnnsWTJEnz++edJS+coLi7GI488gr1794Z9x1nYQr584hkPV155Jc6cOTPq40w1qAWVgrDC5nA4ODHjW2x8oWOFzTUQ+0wIgUKhEHRDqtVqfPfddwgEAli2bBkqKyu50N9UiQCLBRvR1tHRgb6+Pmg0GmRnZwuuv0VOYsOx1th9I7mzZi2oH88pwkm9HWd7HUiXiDG7NBuzeWWJPB4PWltb4XQ6UVVVlbJ152Jhs9lw7tw5KBQK7qYgmURa8vzP/6233sLevXu56FM2mvbHP/4xHn744aSN+ZFHHkFnZ2eUBeX3+ydMdOwYQS2oiQIrGNnZ2cjOzkZlZeWwjmOFzel0RllsX375JX7/+99zuTR//OMfYTab4XA4uGPlcrmgxSb0ODs7e9yEje/KKykpSSiPaTgBA/wfoeTcodbZgsEg2s0u/OPbbkglYsweKEskHyhLxK7lWSwWVFZWor6+PuVvCPh4PB40NzfD5/Nh6tSpKRMtJmTJ+/1+bNu2DW+88QYeffRR/OhHP0rJ6LxIysvLIRKJcNVVV2HTpk0pl4eVLKgFdZHQ09OD9PR0wdI47DXgcrnChI2/3sZ3Q5pMprCcC6lUGuVyVKvVUW5JVtiGW/AzUphKSkrGbY0p3t155Dary4+DXV5oMkSoz5FAKRt0O7lcLrjdbuTm5iIvLy9p+WwjIRAIQKfTpVyirRCEEOzfvx9r167F1VdfjbVr16bkml6kBeVwOHDmzBnMnj0bJpMJ9913H+x2O/bu3ZvcgY4/ghcWFSjKiGGvHbfbDYvFEiVorHuSFTaz2QybzcZZeunp6VHramq1GpmZmTh8+DBqamqwZMkSlJWVQaPRpPRkzqYisDlLrLDm5uZCrVbHbESZaD6bUIj3WLyX7u5utLe3o7S0FCUlJSlthej1eqxduxZ2ux1PP/00amtrkz2kmEQKVCQ9PT0oKiq6YPOa4kBdfJTRhZ0gFQoFFAoFSkpKhnUcK2wej4cL6WeF7IMPPsDu3bsxZ84cpKen4+jRozCbzejv7+fC3NPS0jgxi1xn4//NisN4CBsrTmwFhcLCQixcuDChdYVYASRssECksI3FOpvJZEJLSws0Gk1KJ9oCodqKW7duxRtvvIHGxkbcdNNNKXsDM1z4LeMpVKAoSYD9EsrlcsjlchQXD7aomDdvHjZv3izYcI+dkH0+HydsRqORe2w2m9Ha2hr2t9Vq5b7sEokkzPXIPo4V8s+Ky1CTHhvZ1tbWdl5Jtmw+W6Kc7zobK0I2mw0SiQSTJk1CZmYmHA5H0nOXhCCE4NNPP8Vjjz2GZcuW4Ysvvki4QeN4w3YwDgaDCAaD8Hg8SEtLwzfffAO1Wo2amhpYLBY88MADWLp06YQpHj3WUBcf5YKHvcb9fn+UxcaPkuSvtVmtVq7qBitsrMXGt9x6enrQ3NyMVatWoaysDIWFhdyEn+p384QQuFwutLW1wW63o7i4GFKpNGZeW7yKE0Mlao/W/6KjowNr1qxBMBjE5s2bMWXKlFE5byLEymkCYvdpamxsxPr168POs27dOkydOhVr1qyBwWBAdnY2rrrqKvz+97/HpEmJNeC8AKBrUBRKorC5W5Fi1tnZiWeeeQZpaWmYP38+3G43J3rsupJIJIJKpRIM+ee7IVnRY8O2x0PYGIZBe3s79Ho9KisrUVhYmFA5r1juyETX2eK5JfnrbB6PB1u2bME777yDDRs24LrrrkvaDcDbb78NsVjM5TSxAmU0GlFVVRXWp+nzzz+/IPs0jQFUoCiU0YIQgjNnzqC+vj7mfoZhYLVaBauPROax8ctqsSkGsaqPRLok2dJIw03SNhgMaGtrQ2FhIcrKysbVdZeIsPn9fq4/k81mg0ajwaWXXor8/Hxcc801uOGGG8Zt3EJEBjz89a9/xfYLr0/TeEGDJCiU0UIkEsUUJ3a/RCJBbm5uQl1vWWFj+4ZFRkSeOXMmLOSfLavFihO/rFak1WYwGHDmzBncdtttXFmu8SaRdTa20sm8efPw5JNPIisri3vPqZjkfOrUqYumT9N4QQWKQkkhWGFjLaThEllWi199pLe3Fxs3boTD4cCCBQuwceNGWCwWruQOIYTrOTWcepGJlNUaCS6XC08//TT27t2Lp556CldccQX3WgUFBWPymqPBxdSnabygAkWhXACwgsEGc1RVVXH7CCH43ve+h4ULF0Ydxwqb3W6PstjYqMivv/46zGpz8VoKs2W1Yokaf5tSqYwrbAzD4L333sOTTz6JO+64AwcPHpwwXZuB0e3TxH4uqZx/Nh5QgaJQLnBEIpGgOLH72GAOlUo17Ki4WGW12PU2nU6HI0eOhLWvcTqd3LH8slo5OTlIS0vDwYMHUVtbi3//+99hqQcThZH2aXrnnXfCQsuDwSAX+ej1eke9qeREggoUhUJJGFbYsrKykJWVNexWD5FltVhx02q1qK+vxz333JPy4fmxcpoS6dNECIHb7cY111yDQCCAG2+8kdvHitETTzyBTz75BLm5uVi5ciV+8IMfpGwjyDGDvROK8UPhsWXLFjJ37lwilUrJqlWrovbv27ePTJ06lcjlcrJ06VKi1WrHf5AUCmXELFmyhMhkMpKZmUkyMzNJbW1t1HPWrVtHEIpw5n7WrVtHCCHkww8/JFOnTiUZGRlkyZIlpK2tLexYhmG4x0ajkahUKrJ582ZCCCGBQIAQQsiJEydIQ0MDaWhoIJ9//jm55557yHXXXUd27NgxNm86NRDUoIvbwZkgbE+Xu+66K2qf0WjEihUr8MQTT8BsNmPevHlYuXJlEkZJoVDOh2effRYOhwMOhwNnz56N2t/Y2Bg1kTY2NgIY7NPkdrvx6aefhlmWDMOEWYfHjh2D3+/nXICs5fThhx9i1qxZOHToEC677DI899xz3I5mFgAACcBJREFUCAaDMBqNAOL3ibrQoAKVACtWrMAPf/hDwbDht99+G9OnT8fNN9+MjIwMNDY24tixYxdFUzEKhRKfTz75BEqlEj/+8Y/xxRdfAABaW1uRnp7OFbdlhefmm2/Gz372MwChdaz09HQoFAp0d3cDSP0KJaMJFahRIl4ORLJZunQpMjIyoFQqoVQqMXXq1GQPiUJJWR5++GHk5eVh8eLF+PTTT0flnEVFRVi5ciU+/PBDXHHFFXj66aexa9cuVFdXo6KiAsFgkBOe0tJSLFq0CAC4liFdXV34wQ9+wJ3vYrGiqECNEg6HIyrxMZVyIIZyW1AoFGDjxo1obW1FV1cX7r33XixbtgwtLS3ndU5CCOrq6vDiiy/i7bffxuWXX44HH3wQ+/fvR3FxMYxGIyQSScwK5k1NTfB6vWFtRPjloy5kqEANsHTpUi4yKfLnsssuG/L40cyBoFAoyaGhoQFZWVmQyWRYtWoVFi9ejPfff/+8zsl3yV1xxRV477338MQTT8DpdOLrr7/Gvffei+bmZi7nibWOWME6cOAA1Go1ioqKcPr0aVx77bX461//el5jmihQgRrg008/jRnNuH///iGPnz59Oo4dO8b9PdwciPFiLNwW54vZbMby5cuRmZmJ8vJyvPrqq8keEoUSBtvna7RgRWf69OnIysrC4sWL8fHHH2PevHlYu3Yt+vv7o9aYTp8+jXnz5qGxsRELFizAnDlzcN99943amFKaWJMyoWHmUfj9fuJ2u8lvfvMbcvvttxO32038fj8hhBCDwUCys7PJW2+9RdxuN3nooYdIQ0NDkkcc4tChQ8RmsxGPx0O2b99OlEolaW5uTvawyE9+8hNyyy23ELvdTj7//HOSnZ1NTp48mexhUS5SLBYL2bNnD/e93rFjB1EoFOTs2bOj/lq/+MUvSE1NDXE4HOTo0aPkmmuuISKRiKjVanL48GHueW63m1RVVRGRSERuuOEG0tnZye0LBoOjPq4kIqhBVKASIF7+AyFD50CMBUuWLIkaE/uzePFiwWOuueYa8swzz4z52OLhcDhIenp62Jf/9ttvJ6tXr07iqCgXMwaDgcybN48olUqiUqlIQ0MD+eCDD0b9dXw+H7n88svJVVddxW1zu93k9ddfJ6+99hq3jc2ZWrNmDdmzZw+3PRAIhOVTXSBQgaKEuPbaa8mf/vSnpI7h22+/JXK5PGzbpk2byA033JCkEcVnOAmclNTBZDKRH/7wh0ShUJCysjKyc+fOZA+Jw+FwkMLCQvLoo4/GfI6QADEMwyXzXoDQRN2LEavVir1798Lj8SAQCGDnzp347LPPcO211yZ1XA6HA9nZ2WHbUinqUQgaCTlxuO+++yCVStHb24udO3fi5z//eUqkfABAe3s7DAYDLr/88pjPiVyHIoRwle4vJmgtvgscv9+PRx55BGfOnIFEIkFdXR12794dFrKaDGjUI2WscDqd2LVrF06ePAmlUonLLrsMN954I1555RU89dRTyR4edu7ciby8PFRWVnLCMxQXU3IuH2pBXeDk5+fjq6++gt1uh9VqxaFDh3DVVVcle1iora1FIBBAU1MTt+3YsWMpE/UoRCpGQgIhy27evHmQyWRcBQKWjz76CHV1dVAoFLj88suh0+mSM8hx5Ny5c0hLSwu7CZs1a1bKWFB33303DAYDKioqLlrhGS5UoChJITMzEytWrMBjjz0Gp9OJAwcO4J133sEdd9yR7KEJMhYJnKNFrBqRF2t9yFR3H1dWVgK4eJJtzwcqUJSk8fzzz8PtdqOgoAC33nortm7dmrIW1FgkcI4WsWpEXqz1ISeK+zgtja6wDAX9D1GSRk5ODnbv3p3sYYyI0U7gHAvi1YcU6lF0ocB3H9fU1ABIffcxRRhqQVGSxk9+8hPceeednKuDzbLv7u7Gl19+CavVmszhcaRqJORQpHp9yLFiormPKbGhAkVJGoWFhdixYwf2798fynkQi9HZ2YlbbrkFCxcuxLvvvpvsIQIYjITMz89HXl4etmzZkhKRkEMxUVxdY8FEch9TYkNdfJSk8cgjj+Cll17Cxo0bsXTpUjQ3N+OOO+5Aa2srPv74YyxdujTZQwQwGAk50Zg+fTpeeukl7u9Uqw85lkxk9zFlEGpBUZJGXl4eVq1ahQ8//BA7duzAT3/6U7S3t+PNN9/E0qVLo9Z4GIaJ2ZLgYiYQCMDj8SAYDCIYDHKuyOXLl+PkyZPYtWsXPB4PHn/8ccycOfOCXn+iXFiIhljoTe1VYMqEp6mpCYsWLQLDMCguLsbf/vY3LFq0CMFg8KLLmh8pjY2NWL9+fdi2devWobGxEfv27cP9998PnU6HhoYGbN++PawNOYWSIggmhFGBoiSVd955B8uXL4dCocBzzz2HVatWwe/3Iz09HUDILbVt2za8/PLL6Orqws0334wtW7YMOwOfQqFMCAS/zNTFR0kar732Gu6//35UVlYiNzeXy/TnW06BQAAVFRV44IEHsHjxYrS1tQEAdfVRKBcBVKAoSWH79u246667MH/+fOzZswdz587Fzp070dHRwXUWBUJh0TfddBN+9KMfobCwEHl5eUkcNYVCGU+oQFHGneeffx533303VqxYge3bt6OmpgY33XQT9Ho9/vGPfwCItpCCwSAsFgtycnKSMWQKhZIEqEBRxpU1a9bg/vvvx7333ou///3vXM20ZcuWYebMmXj22WfR1tYWZkUBoVwkm80WVc6HQqFcuFCBoowra9euRUtLCzZv3gyZTMY1JsvJycHDDz+MadOmQa/XAwi3onw+HxwOB3XxUSgXETRRlzKuZGZmctWcgcE+N4QQrFy5MqzaNt+K8ng8cDqd0Gg04zdYCoWSVKgFRUkJRCIRgsFgVAsCv9/PPQ4EAlx5IZojRaFc+FCBoqQMEokkqgXBgQMHUFxcjOrqahw/fhxXXHEF5syZg/b29iSNkkKhjBc0UZeS8jAMA4vFArvdjq6uLvT19eHqq6+GQqFI9tAoFMroQCtJUCgUCiUloZUkKBMfNuqPQqFc+NAoPsqEgtbfo1AuHqgFRaFQKJSUhAoUhUKhUFISKlAUCoVCSUmoQFEoFAolJaECRaFQKJSUhAoUhUKhUFISKlAUCoVCSUmoQFEoFAolJaECRaFQKJSUhAoUhUKhUFISKlAUCoVCSUmoQFEoFAolJaECRaFQKJSUhAoUhUKhUFISKlAUCoVCSUmoQFEoFAolJaECRaFQKJSUZKiOurR9KYVCoVCSArWgKBQKhZKSUIGiUCgUSkpCBYpCoVAoKQkVKAqFQqGkJFSgKBQKhZKSUIGiUCgUSkry/wH92ICrfdhaGQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure manifold_decision_boundary_plot2\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 360x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure manifold_decision_boundary_plot3\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure manifold_decision_boundary_plot4\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 360x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib import gridspec\\n\",\n    \"\\n\",\n    \"axes = [-11.5, 14, -2, 23, -12, 15]\\n\",\n    \"\\n\",\n    \"x2s = np.linspace(axes[2], axes[3], 10)\\n\",\n    \"x3s = np.linspace(axes[4], axes[5], 10)\\n\",\n    \"x2, x3 = np.meshgrid(x2s, x3s)\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(6, 5))\\n\",\n    \"ax = plt.subplot(111, projection='3d')\\n\",\n    \"\\n\",\n    \"positive_class = X[:, 0] > 5\\n\",\n    \"X_pos = X[positive_class]\\n\",\n    \"X_neg = X[~positive_class]\\n\",\n    \"ax.view_init(10, -70)\\n\",\n    \"ax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], \\\"y^\\\")\\n\",\n    \"ax.plot_wireframe(5, x2, x3, alpha=0.5)\\n\",\n    \"ax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], \\\"gs\\\")\\n\",\n    \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n    \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n    \"ax.set_xlim(axes[0:2])\\n\",\n    \"ax.set_ylim(axes[2:4])\\n\",\n    \"ax.set_zlim(axes[4:6])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"manifold_decision_boundary_plot1\\\")\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(5, 4))\\n\",\n    \"ax = plt.subplot(111)\\n\",\n    \"\\n\",\n    \"plt.plot(t[positive_class], X[positive_class, 1], \\\"gs\\\")\\n\",\n    \"plt.plot(t[~positive_class], X[~positive_class, 1], \\\"y^\\\")\\n\",\n    \"plt.axis([4, 15, axes[2], axes[3]])\\n\",\n    \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"manifold_decision_boundary_plot2\\\")\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(6, 5))\\n\",\n    \"ax = plt.subplot(111, projection='3d')\\n\",\n    \"\\n\",\n    \"positive_class = 2 * (t[:] - 4) > X[:, 1]\\n\",\n    \"X_pos = X[positive_class]\\n\",\n    \"X_neg = X[~positive_class]\\n\",\n    \"ax.view_init(10, -70)\\n\",\n    \"ax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], \\\"y^\\\")\\n\",\n    \"ax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], \\\"gs\\\")\\n\",\n    \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n    \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n    \"ax.set_xlim(axes[0:2])\\n\",\n    \"ax.set_ylim(axes[2:4])\\n\",\n    \"ax.set_zlim(axes[4:6])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"manifold_decision_boundary_plot3\\\")\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(5, 4))\\n\",\n    \"ax = plt.subplot(111)\\n\",\n    \"\\n\",\n    \"plt.plot(t[positive_class], X[positive_class, 1], \\\"gs\\\")\\n\",\n    \"plt.plot(t[~positive_class], X[~positive_class, 1], \\\"y^\\\")\\n\",\n    \"plt.plot([4, 15], [0, 22], \\\"b-\\\", linewidth=2)\\n\",\n    \"plt.axis([4, 15, axes[2], axes[3]])\\n\",\n    \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"manifold_decision_boundary_plot4\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure pca_best_projection\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"angle = np.pi / 5\\n\",\n    \"stretch = 5\\n\",\n    \"m = 200\\n\",\n    \"\\n\",\n    \"np.random.seed(3)\\n\",\n    \"X = np.random.randn(m, 2) / 10\\n\",\n    \"X = X.dot(np.array([[stretch, 0],[0, 1]])) # stretch\\n\",\n    \"X = X.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]]) # rotate\\n\",\n    \"\\n\",\n    \"u1 = np.array([np.cos(angle), np.sin(angle)])\\n\",\n    \"u2 = np.array([np.cos(angle - 2 * np.pi/6), np.sin(angle - 2 * np.pi/6)])\\n\",\n    \"u3 = np.array([np.cos(angle - np.pi/2), np.sin(angle - np.pi/2)])\\n\",\n    \"\\n\",\n    \"X_proj1 = X.dot(u1.reshape(-1, 1))\\n\",\n    \"X_proj2 = X.dot(u2.reshape(-1, 1))\\n\",\n    \"X_proj3 = X.dot(u3.reshape(-1, 1))\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8,4))\\n\",\n    \"plt.subplot2grid((3,2), (0, 0), rowspan=3)\\n\",\n    \"plt.plot([-1.4, 1.4], [-1.4*u1[1]/u1[0], 1.4*u1[1]/u1[0]], \\\"k-\\\", linewidth=1)\\n\",\n    \"plt.plot([-1.4, 1.4], [-1.4*u2[1]/u2[0], 1.4*u2[1]/u2[0]], \\\"k--\\\", linewidth=1)\\n\",\n    \"plt.plot([-1.4, 1.4], [-1.4*u3[1]/u3[0], 1.4*u3[1]/u3[0]], \\\"k:\\\", linewidth=2)\\n\",\n    \"plt.plot(X[:, 0], X[:, 1], \\\"bo\\\", alpha=0.5)\\n\",\n    \"plt.axis([-1.4, 1.4, -1.4, 1.4])\\n\",\n    \"plt.arrow(0, 0, u1[0], u1[1], head_width=0.1, linewidth=5, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n    \"plt.arrow(0, 0, u3[0], u3[1], head_width=0.1, linewidth=5, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n    \"plt.text(u1[0] + 0.1, u1[1] - 0.05, r\\\"$\\\\mathbf{c_1}$\\\", fontsize=22)\\n\",\n    \"plt.text(u3[0] + 0.1, u3[1], r\\\"$\\\\mathbf{c_2}$\\\", fontsize=22)\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$x_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"plt.subplot2grid((3,2), (0, 1))\\n\",\n    \"plt.plot([-2, 2], [0, 0], \\\"k-\\\", linewidth=1)\\n\",\n    \"plt.plot(X_proj1[:, 0], np.zeros(m), \\\"bo\\\", alpha=0.3)\\n\",\n    \"plt.gca().get_yaxis().set_ticks([])\\n\",\n    \"plt.gca().get_xaxis().set_ticklabels([])\\n\",\n    \"plt.axis([-2, 2, -1, 1])\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"plt.subplot2grid((3,2), (1, 1))\\n\",\n    \"plt.plot([-2, 2], [0, 0], \\\"k--\\\", linewidth=1)\\n\",\n    \"plt.plot(X_proj2[:, 0], np.zeros(m), \\\"bo\\\", alpha=0.3)\\n\",\n    \"plt.gca().get_yaxis().set_ticks([])\\n\",\n    \"plt.gca().get_xaxis().set_ticklabels([])\\n\",\n    \"plt.axis([-2, 2, -1, 1])\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"plt.subplot2grid((3,2), (2, 1))\\n\",\n    \"plt.plot([-2, 2], [0, 0], \\\"k:\\\", linewidth=2)\\n\",\n    \"plt.plot(X_proj3[:, 0], np.zeros(m), \\\"bo\\\", alpha=0.3)\\n\",\n    \"plt.gca().get_yaxis().set_ticks([])\\n\",\n    \"plt.axis([-2, 2, -1, 1])\\n\",\n    \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"pca_best_projection\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# MNIST compression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import urllib.request\\n\",\n    \"try:\\n\",\n    \"    from sklearn.datasets import fetch_openml\\n\",\n    \"    mnist = fetch_openml('mnist_784', version=1, as_frame=False)\\n\",\n    \"    mnist.target = mnist.target.astype(np.int64)\\n\",\n    \"except ImportError:\\n\",\n    \"    from sklearn.datasets import fetch_mldata\\n\",\n    \"    mnist = fetch_mldata('MNIST original')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"X = mnist[\\\"data\\\"]\\n\",\n    \"y = mnist[\\\"target\\\"]\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pca = PCA()\\n\",\n    \"pca.fit(X_train)\\n\",\n    \"cumsum = np.cumsum(pca.explained_variance_ratio_)\\n\",\n    \"d = np.argmax(cumsum >= 0.95) + 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"154\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pca = PCA(n_components=0.95)\\n\",\n    \"X_reduced = pca.fit_transform(X_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"154\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pca.n_components_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9504334914295708\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sum(pca.explained_variance_ratio_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pca = PCA(n_components = 154)\\n\",\n    \"X_reduced = pca.fit_transform(X_train)\\n\",\n    \"X_recovered = pca.inverse_transform(X_reduced)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_digits(instances, images_per_row=5, **options):\\n\",\n    \"    size = 28\\n\",\n    \"    images_per_row = min(len(instances), images_per_row)\\n\",\n    \"    images = [instance.reshape(size,size) for instance in instances]\\n\",\n    \"    n_rows = (len(instances) - 1) // images_per_row + 1\\n\",\n    \"    row_images = []\\n\",\n    \"    n_empty = n_rows * images_per_row - len(instances)\\n\",\n    \"    images.append(np.zeros((size, size * n_empty)))\\n\",\n    \"    for row in range(n_rows):\\n\",\n    \"        rimages = images[row * images_per_row : (row + 1) * images_per_row]\\n\",\n    \"        row_images.append(np.concatenate(rimages, axis=1))\\n\",\n    \"    image = np.concatenate(row_images, axis=0)\\n\",\n    \"    plt.imshow(image, cmap = mpl.cm.binary, **options)\\n\",\n    \"    plt.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure mnist_compression_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 504x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(7, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_digits(X_train[::2100])\\n\",\n    \"plt.title(\\\"Original\\\", fontsize=16)\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_digits(X_recovered[::2100])\\n\",\n    \"plt.title(\\\"Compressed\\\", fontsize=16)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"mnist_compression_plot\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_reduced_pca = X_reduced\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Incremental PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"....................................................................................................\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.decomposition import IncrementalPCA\\n\",\n    \"\\n\",\n    \"n_batches = 100\\n\",\n    \"inc_pca = IncrementalPCA(n_components=154)\\n\",\n    \"for X_batch in np.array_split(X_train, n_batches):\\n\",\n    \"    print(\\\".\\\", end=\\\"\\\") # not shown in the book\\n\",\n    \"    inc_pca.partial_fit(X_batch)\\n\",\n    \"\\n\",\n    \"X_reduced = inc_pca.transform(X_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_recovered_inc_pca = inc_pca.inverse_transform(X_reduced)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 504x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(7, 4))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_digits(X_train[::2100])\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_digits(X_recovered_inc_pca[::2100])\\n\",\n    \"plt.tight_layout()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_reduced_inc_pca = X_reduced\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare the results of transforming MNIST using regular PCA and incremental PCA. First, the means are equal: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(pca.mean_, inc_pca.mean_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"But the results are not exactly identical. Incremental PCA gives a very good approximate solution, but it's not perfect:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(X_reduced_pca, X_reduced_inc_pca)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using `memmap()`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create the `memmap()` structure and copy the MNIST data into it. This would typically be done by a first program:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"filename = \\\"my_mnist.data\\\"\\n\",\n    \"m, n = X_train.shape\\n\",\n    \"\\n\",\n    \"X_mm = np.memmap(filename, dtype='float32', mode='write', shape=(m, n))\\n\",\n    \"X_mm[:] = X_train\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now deleting the `memmap()` object will trigger its Python finalizer, which ensures that the data is saved to disk.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del X_mm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, another program would load the data and use it for training:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"IncrementalPCA(batch_size=525, copy=True, n_components=154, whiten=False)\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_mm = np.memmap(filename, dtype=\\\"float32\\\", mode=\\\"readonly\\\", shape=(m, n))\\n\",\n    \"\\n\",\n    \"batch_size = m // n_batches\\n\",\n    \"inc_pca = IncrementalPCA(n_components=154, batch_size=batch_size)\\n\",\n    \"inc_pca.fit(X_mm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"rnd_pca = PCA(n_components=154, svd_solver=\\\"randomized\\\", random_state=42)\\n\",\n    \"X_reduced = rnd_pca.fit_transform(X_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Time complexity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's time regular PCA against Incremental PCA and Randomized PCA, for various number of principal components:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"n_components = 2\\n\",\n      \"    PCA: 1.8 seconds\\n\",\n      \"    IncrementalPCA: 13.8 seconds\\n\",\n      \"    PCA: 1.7 seconds\\n\",\n      \"n_components = 10\\n\",\n      \"    PCA: 2.3 seconds\\n\",\n      \"    IncrementalPCA: 14.3 seconds\\n\",\n      \"    PCA: 2.3 seconds\\n\",\n      \"n_components = 154\\n\",\n      \"    PCA: 4.3 seconds\\n\",\n      \"    IncrementalPCA: 23.0 seconds\\n\",\n      \"    PCA: 5.3 seconds\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"for n_components in (2, 10, 154):\\n\",\n    \"    print(\\\"n_components =\\\", n_components)\\n\",\n    \"    regular_pca = PCA(n_components=n_components)\\n\",\n    \"    inc_pca = IncrementalPCA(n_components=n_components, batch_size=500)\\n\",\n    \"    rnd_pca = PCA(n_components=n_components, random_state=42, svd_solver=\\\"randomized\\\")\\n\",\n    \"\\n\",\n    \"    for pca in (regular_pca, inc_pca, rnd_pca):\\n\",\n    \"        t1 = time.time()\\n\",\n    \"        pca.fit(X_train)\\n\",\n    \"        t2 = time.time()\\n\",\n    \"        print(\\\"    {}: {:.1f} seconds\\\".format(pca.__class__.__name__, t2 - t1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's compare PCA and Randomized PCA for datasets of different sizes (number of instances):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'PCA and Randomized PCA time complexity ')\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAZAAAAEdCAYAAAAikTHKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xmc1fP3wPHXoWiXRNEuhYqKsYQIP0tI0hcp+5KSfSdLyL5TSSKihaikbKHIrmhFm4pKu9JMe3N+f5zP5Hbdmbkz3WXuvef5eNzHzP0s93M+d+58zn2/35/3+y2qinPOOVdUOyU7AOecc6nJE4hzzrli8QTinHOuWDyBOOecKxZPIM4554rFE4hzzrli8QTiCiQi80Xk/5IdB4CIqIjsF4fX7Ssi98T4NS8Rka9i+ZqxJCIfisjFyY4jEUSklYgsjMHrdBKRT2IRU7rwBJIAwUV4vYhki8hSEXlNRCqErD9FRL4UkbUislxEvhCRM8Neo1VwAb098WcQWXAem4LzWiUiY0XkgGTHVVSq2kVVH0zU8USkbvC3zA4e80XkjpD1IiLXich0EckRkYUiMkxEDgp7nR7B6xxRyPF6iMiboctUtbWqvh7bM0tvqjpIVU/Oex6vLzSpxBNI4rRR1QrAIUAWcDeAiPwPGAYMBGoC1YB7gTZh+18MrAIuSlTAUXo8OK8awCLglSTHk0oqB+/d+cC9InJqsPw54HrgOqAK0BAYCZyet6OICPZZKImfCZcpVNUfcX4A84H/C3n+BDAaEOAP4NZC9i8PrAU6AJuArAK23T147eXA38HvNUPWjwceBL4OXvMToGrI+guBBcBKoHt47GHHeg3oGfL8NCAn5Hl94PPgtVYAg7CLZuj7cgswFVgDvAWUCVl/K/AXsBi4DFBgv2DdbljSXR7EezewU7DukuD8ngFWA78DRwXL/wSWARdHOg/gfSA75JELXBKsOwAYi120ZwLnhrzGHsAo4B/gh+A9/iqf961ucC6lQpb9GLwXDYCtwOGFfCaOBdYDnYL3d5d8tjs1+MxsDs5nSsjn4Ipivl+7Ak9in92lQF+gbAGxXgn8in3efgEOCZYfGMSxGpgBnBn2N+kDfBjE/TVQHXgW+1z/BjQP+yzdGbz+38AAgs8S0ApYGLLtPsC7wWdnHnBdyLoPgKdCng8FXg15n74Kfv8y+BvmBPGdB0zHvijm7Vsa+9w3z++9SfVH0gPIhAchF2GgVvDP8iB2QVKgXiH7X4hdSHfGLnAvFLDtHkB7oBxQESvdjAxZPx6Yi32rLRs8fzRY1yj4Zzg2uEg8DWwhigSCJbk3CC5QwbL9gJOC19oz+Kd7Nux9+SH4h64SXGS6BOtOxS5OTYLXHsz2CWQg8F5wjnWBWcDlwbpLgrgvDd6zntjFrncQy8nYxaxC+HmEnV9rLHnVCmL4M3jNUkDz4OLQKNh2KPB2sF0TrDRWaALBvkQcDawDTgS6AAui+Ey9EhyvNJZA2hewbQ/gzbBl49k+gRTl/XoGS5ZVgvf/feCRfI59TvBeHBac635AnSDuOcBdwC7ACcEx9g/5m6wADgXKYF9E5mGlrbwYx4V9lqYHf6sqWMLJ+2y2IkggWK3LJKyUvwuwL5YwTwnWV8cS5glYcv4dqBjyPn0Vcsxtn8fg+W3AWyHP2wLTkn39iecj6QFkwiP4cGdj37QWYN+sygYXDiXkW3c++39KcOHFqjuWA6WjPHYz4O+Q5+OBu0OeXw18FPx+LzA0ZF157NtrQQlkQ3BeucE/+MEFxHIW8HPY+3JByPPHgb7B768SJLbgecO8f9jgArKJ4OIdrL8KGB/8fgkwO2TdQcG+1UKWrQSahZxHz7BYGwYXkmOC5+cBE8K2eQm4L4hnM3BAyLqHKTyBrMa+Lf9K8C0YK/V9V8jftBxW0jkrJI73Cti+B4UnkKjeLywJ5AD1Q9a1AOblc+yPgesjLG8JLCEoNQbLhgA9Qv4mL4esuxb4NSzG1WGfpS4hz08D5ga/t+LfBHIE8EdYLHcCA0Ket8e+LKzI+/uHvE8FJZB9sCRYKXj+DnBbNP+nqfrwNpDEOUtVK6tqHVW9WlXXY/+UAHvnt5OI1AKOx6p/wL51lyGkPjxs+3Ii8pKILBCRf7Bv/ZVFZOeQzZaE/L4OyGvQ3wf7xwFAVXNCYszPk6paGbsorgf2D4mlmogMFZFFQSxvAlXD9o8qFizx5qmKfYNdELa+RsjzpSG/rw/OJ3xZBSIQkd2w9/luVc27k6oOcISIrM57YN9Qq2Olq1IFxJufqqq6u6oeqKrPB8tWUsDnIdAOKzF8EDwfBLQWkT2jOGZ+on2/9sQS2KSQ9+GjYHkktbASb7h9gD9VNTdkWWF/w8L+fuHv/z4RjlsH2Cfs73gX1vaY533sS8HMkL9/oVR1MVbyaS8ilbES7KCC90ptnkCSayb2oW9fwDYXYn+n90VkCVakLoM1qkdyM3YRP0JVK2HVUWDfHAvzF/YPbzuIlMOqxAqlqn9gDb/PiUjZYPHD2Le0g4JYLogyjv/EAtQO+X0F9o2/Ttj6RVG+dr5EZCesumycqvYLWfUn8EXwJSDvUUFVu2Ilwi0FxFsUnwE1RSSrgG0uxi6efwSfiWFYQu2Yz/ZazFgiWYFdvBuHvA+7qd0MEMmfWFtYuMVAreD9zrOjf8Pw939xPvHMC/s7VlTV00K2eQgrFe4tIucXMYbXsc/5OcC3qrrDn8mSzBNIEqmVc28C7hGRS0WkkojsJCLHiEjexeti4H6s+iDv0R44TUQiXdwrYv/gq0WkClbFEq13gDOC4+8CPEARPiOqOhb7p+0cEks2sEZEamCN4tF6G7hERBoFiWzbeajq1mD9QyJSUUTqYO/jm5Ffqkgewqrurg9bPhpoKCIXikjp4HGYiBwYxDMc6BGUABuRf4IvkKrOxqo4hwS3bu8iImVEpIOI3BG8jycCZ/Dv56Ep8Bj53421FKgbdrEulqDE8DLwjIjsBSAiNUTklHx26Q/cIiKHBrcn7xf8vb7HSpy3Be9lK+zOw6E7EF43EakZfO67YzdlhPsBWCsit4tIWRHZWUSaiMhhwbkci7UFXYT9DV8I3vNIlmJtKKFGYndaXo+106U1TyBJpqrvYPXrl2EX36VYA+F7InIk9i27t6ouCXmMwhogI307ehZrX1kBfIdVL0QbywygG/YN/C+sfr6oHbCewC4Ku2KJ7xDsDqsx2EU22lg+xM7lc+xcPw/b5FqsLv534Ksg5leLGGsk5wNHAn+H9NPopKprscbkDtjfaQl20d412O8arFSwBKu/H7ADMVwH9MIasVdjVUDtsKqVC4HJqvpJ6GcCeB44WESaRHi9YcHPlSLy0w7Eled27G/yXVA1+SkhVZehVHUYlpQHY+0DI4EqqroJSxitsc9qH+AiVf1tB+IajN1V+Dv2nvWMEM9W/k2+84Jj9wd2E5FK2EX/GlVdpKoTsJsVBgS3TYfrAbweVIWdG7z+euwOr3oU4fOeqiRo7HHOuZQlIvOxmwI+LQGx3As0VNULkh1LvJVKdgDOOZcuguqzy7GSYtrzKiznnIsBEbkSa6T/UFW/THY8ieBVWM4554rFSyDOOeeKJa3aQKpWrap169ZNdhjOOZdSJk2atEJVi9wRNa0SSN26dZk4cWKyw3DOuZQiItGMnPAfXoXlnHOuWDyBOOecKxZPIM4554olrdpAItm8eTMLFy5kw4YNyQ4locqUKUPNmjUpXbp0skNxzqWptE8gCxcupGLFitStW5fIw9mkH1Vl5cqVLFy4kHr16iU7HOdcmkr7KqwNGzawxx57ZEzyABAR9thjj4wrdTmXiQYNgrp1Yaed7OegBM5AkvYlECCjkkeeTDxn5zLNoEHQuTOsW2fPFyyw5wCdOsX/+GlfAnHOuXTVvfu/ySPPunW2PBE8gSTAzjvvTLNmzWjSpAlt2rRh9erVAMyfP5+yZcvSrFkzGjVqRJcuXcjNtRk+Z82axWmnnUaDBg045JBDOPfcc1m69N8ZPW+44QZq1KixbXvnXOb544+iLY81TyBh4lGfWLZsWSZPnsz06dOpUqUKvXv33raufv36TJ48malTp/LLL78wcuRINmzYwOmnn07Xrl2ZPXs2P/30E1dffTXLly8HIDc3lxEjRlCrVi2++OKLHQ/QOZdSNmyAG26A/MbCrV3cCZWLyBNIiLz6xAUL7A+TV58Yy0apFi1asGjRf6dJLlWqFEcddRRz5sxh8ODBtGjRgjZt2mxb36pVK5o0scnmxo8fT+PGjenatStDhgyJXXDOuRJvxgw44gh47jk45RQoV2779eXKwUMPJSaWjGhEz3PDDTB5cv7rv/sONm7cftm6dXD55fDyy5H3adYMnn02uuNv3bqVzz77jMsvv/w/69atW8dnn33GAw88wNixYzn00EPzfZ0hQ4Zw/vnn07ZtW+666y42b97s/T2cS3Oq0KcP3HILVKoEH3wArVvbF9zu3a3aqnZtSx6JaEAHL4FsJzx5FLY8WuvXr6dZs2ZUr16dpUuXctJJJ21bN3fuXJo1a8bRRx/N6aefTuvWrQt8rU2bNvHBBx9w1llnUalSJY444gg+/vjjHQvQOVeiLV8OZ54J11wDxx8PU6da8gBLFvPnQ26u/UxU8oAMK4EUVlKoW9eqrcLVqQPjxxf/uHltIOvWreOUU06hd+/eXHfddcC/bSChGjdunG/bxscff8zq1as56KCDACu5lC1bljPOOKP4ATrnSqyPP4aLL4bVq+H55y2JlJS79BNaAhGRKiIyQkRyRGSBiHQsYNtDRORLEckWkaUicn2843voofjWJ5YrV47nn3+ep556ii1btuS7XceOHfnmm28YM2bMtmVffvkl06dPZ8iQIfTv35/58+czf/585s2bx9ixY1kXfi+fcy6lbdwIN94Ip54KVavCjz/CtdeWnOQBia/C6g1sAqoBnYAXRaRx+EYiUhX4CHgJ2APYD/gk3sF16gT9+lmJQ8R+9usX2yJh8+bNOfjggwts/C5btiyjR4/mhRdeoEGDBjRq1Ig+ffpQsWJFPvroI04//fRt25YvX55jjjmG999/P3ZBOueS6pdfrKH82WetxPHjjxBUOpQoCZsTXUTKA38DTVR1VrDsDWCRqt4Rtu3DQC1VvbAox8jKytLwCaV+/fVXDjzwwB2KPVVl8rk7l4pUoW9fuOkmqFgRBgyAkO+LcSMik1Q1q6j7JbIE0hDYkpc8AlOA/5RAgCOBVSLyjYgsE5H3RSTinc0i0llEJorIxLx+Es45l2qWL4ezzoKrr4ZWrayhPBHJY0ckMoFUAP4JW7YGqBhh25rAxcD1QG1gHhCxzkdV+6lqlqpm7blnkaf0dc65pBs7Fg4+GD76yKqtxoyB6tWTHVXhEplAsoFKYcsqAWsjbLseGKGqP6rqBuB+4CgR2S3OMTrnXMJs3Ag33wwnnwxVqlhbx/XX20gYqSCRYc4CSolIg5BlTYEZEbadCoQ2ziSmocY55xLk11/hyCPh6aehWzeYONFKIakkYQlEVXOA4cADIlJeRI4G2gJvRNh8ANBORJqJSGngHuArVV2TqHidcy4e8hrKDz0UFi6E99+HXr2gbNlkR1Z0iS4oXQ2UBZZhbRpdVXWGiLQUkey8jVT1c+AuYEyw7X5Avn1GnHMuFaxYAe3aQdeu0LKlNZSnch/ghCYQVV2lqmepanlVra2qg4PlE1S1Qti2L6pqDVXdXVXbqOqfiYw1lkKHcz/nnHO2dfpbsmQJHTp0oH79+hx66KGcdtppzJr1701qzz77LGXKlGHNGi94OZfqPv3Uqqg+/NCqrT78EPbeO9lR7ZgUaapJkOrVrQdh+GMHb4cIHc59l112oW/fvqgq7dq1o1WrVsydO5dJkybxyCOPbDfnx5AhQzjssMMYPnz4jp6Zcy5JNm60ARBPOgkqV4bvv7ce5qnSUF6QNDiFGAq5eEe1vBhatmzJnDlzGDduHKVLl6ZLly7b1jVt2pSWLVsCNshidnY2PXv29CHbnUtRv/0GLVrAU09ZtdXEiTaCd7rIqMEUCx3PvSCtWkVeXoTx3Lds2cKHH37IqaeeyvTp0wscsn3o0KF06NCBli1bMnPmTJYuXUq1atWKEbhzLtFUbRikG2+08fTee89G0003XgJJgLzh3LOysqhdu3bE+UDCDRkyhA4dOrDTTjvRvn17hg0bloBInXM7auVKOPts6NIFjjkGpk1Lz+QBmVYCKaykUNAwlzswnnteG0ioxo0b884770Tcftq0acyePXvbvCGbNm2iXr16XHPNNcWOwTkXf599BhddZMOSPPWUVXqkQ1tHftL41Eq2E044gY0bN9KvX79ty6ZOncqECRMYMmQIPXr02DZk++LFi1m8eDELIk1W4pxLuk2b4LbbrKG8UiVrKL/ppvROHuAJZHv5tTHEoe1BRBgxYgSffvop9evXp3Hjxtx5551Ur16doUOH0q5du+22b9euHUOHDo15HM65HTNzpjWUP/EEdO4MkyZB8+bJjioxMqsKqzBLlsTlZbOzsyMu32effXj77bf/s/z333//z7Knn3465nE554pPFfr3t2qqsmVhxAgbTTeTeAnEOeeKaOVKaN/eShwtWliP8kxLHuAJxDnnimTcOGjaFEaPtmqrTz6BffZJdlTJkREJJFGzLpYkmXjOzsXTpk1wxx1w4olQoQJ89531ME/3hvKCpP2plylThpUrV2bUBVVVWblyJWXKlEl2KM6lhVmz4Kij4LHH4MorraH8kEOSHVXypX0jes2aNVm4cCGZNt1tmTJlqFmzZrLDcC6lqcKrr8J110GZMjB8uI2m60zaJ5DSpUtTr169ZIfhnEsxq1ZZI/m778IJJ8DAgVCjRrKjKlnSvgrLOeeKavx4aygfNQoef9zmLPfk8V+eQJxzLrB5M9x5p5U4ypWDb7+FW2/N7IbygqR9FZZzzkVj9mzo2NGGXL/iChs6r3z5ZEdVsnledc5ltLyG8ubNYe5ceOcdePllTx7R8ATinMtYf/8N550Hl18Ohx9uPcrbt092VKnDE4hzLiN98YU1lI8YAY8+ag3lfud70XgCcc5llM2boXt3OP5469vxzTdw++2w887Jjiz1JDSBiEgVERkhIjkiskBEOuazXQ8R2Swi2SGPfRMZq3Mu/cyZY7MEPvwwXHop/PQTHHZYsqNKXYm+C6s3sAmoBjQDxojIFFWdEWHbt1T1goRG55xLS6rw+utw7bVQqhQMGwb/+1+yo0p9CSuBiEh5oD1wj6pmq+pXwCjgwkTF4JzLPH//DR06WInj0EOtodyTR2wksgqrIbBFVWeFLJsCNM5n+zYiskpEZohI1/xeVEQ6i8hEEZmYaeNdOecK9uWX1lA+fLhVW332GdSqleyo0kciE0gF4J+wZWuAihG2fRs4ENgTuBK4V0TOj/SiqtpPVbNUNWvPPfeMZbzOuRS1eTPcfbc1lO+6qzWU33mnN5THWiITSDZQKWxZJWBt+Iaq+ouqLlbVrar6DfAc4IVO51yh5s6Fli3hoYfg4ovh55+9oTxeEplAZgGlRKRByLKmQKQG9HAKSFyics6lhbyG8mbNYOZMePtt62FeoUKyI0tfCUsgqpoDDAceEJHyInI00BZ4I3xbEWkrIruLORy4DngvUbE651LL6tVw/vlwySU20dOUKXDOOcmOKv0luiPh1UBZYBkwBOiqqjNEpKWIZIds1wGYg1VvDQQeU9XXExyrcy4FTJhgDeXvvGPVVp9/DrVrJzuqzJDQfiCqugo4K8LyCVgje97ziA3mzjmXZ8sWeOABSxr16llD+eGHJzuqzOLDuTvnUs7vv0OnTvDdd1Zt9fzzUDHS/ZwurjyBOOdShiq8+SZ062aTPA0daqPpuuTwwRSdcylhzRordVx0kd1pNWWKJ49k8wTinCvxvv7aksbbb8ODD8K4cVCnTrKjcp5AnHMl1pYt0KMHHHssiMBXX1kPc+9RXjJ4G4hzrkSaNw8uuMDurrrwQujVCyqFj2XhksoTiHOuxBk0CK6+2n4fPNg6CbqSp8hVWCKym4j4sCLOuZhbs8ZKHRdcAAcdZA3lnjxKrqgSiIiUEpEHRGQlsBKoFyx/SESujGeAzrnM8M031lA+dCjcfz+MHw916yY7KleQaEsg3YHzsaFINoYsnwxcHuugnHOZY8sWSxh5DeUTJsC999rMga5kizaBXAhcpapvAbkhy6cB+8c8KudcRpg/H1q1sjutzj8fJk+GFi2SHJSLWrQ5vgYwN8LynYBdYheOcy5TDB4MXYO5RgcNgo4dkxuPK7poSyC/AsdEWN4e+Dl24Tjn0t0//9htuZ06QZMmVurw5JGaoi2B9AT6i0h1LOmcKSL7A5dhc3o451yhvv3WEseCBVZt1b27t3WksqhKIKo6HLgEOBcoDTwJHAb8T1U/ilt0zrm0sHWrDUHSsqUNiDhhAtx3nyePHVa9ut15EP6oXj0hh4/6z6eqo4BRACIiqqpxi8o5lzYWLLB+HV99ZaWP3r1ht92SHVWaWLq0aMtjrMj5P+hEKKGdCVU1t4BdnHMZauhQ6NIFcnPhjTcskbj0EW1Hwhoi8paILAO2AJvDHs45t83atXDxxXZrbqNG1lDuySNGtm6F0aOhdetkRxJ1CeRNYHfgHmAp4NVXzrmIvv/e7qqaP986BN5zj7d1xMTKlfDqq/DiizbS5N57JzuiqBPIYcARqjojnsE451LX1q3wyCN2d1XNmvDFF3BMpJv/XdFMnGgNR0OHwoYN1mX/0UehXTvYJbnd8KJNINOByvEMxDmXuv74w6qoJkywaqs+faCyXzGKb8MGGDbMEsf330P58lYn2K2bjTKZp1q1yA3m1aolJMxoE0gX4GkReQxLJtu1e6jqslgH5pxLDW+9BVddZQ3lAwdaIvHxuotpwQLo2xf694cVK6BhQ3juOUsekW5dW7Ik8TGGiDaBbMDaQD4IWy5Ye0hU84OJSBXgFeBkYAVwp6oOLmD7XYApQEVVrRllrM65BFi7Fq69Fl5/HY44woYm2XffZEeVglTh00+ttPH++7asTRsrbZx4IuxUcieOjTaBvAFkYx0Jd6QRvTewCagGNAPGiMiUAtpWbgWWAxWLeTznXBz88IM1lM+bZ1PM3nsvlC6d7KhSzJo1ln379IGZM6FqVbj9divOpciE79EmkMZAc1WdWdwDiUh5bOysJqqaDXwlIqOwkX7viLB9PeAC4Cbg5eIe1zkXO1u3WvvtffdBjRo2Z0fLlsmOKsVMn26ljTfegJwcK74NHAjnnANlyiQ7uiKJNoFMAmoBxU4gQENgi6rOClk2BTgun+1fAO4C1hf0oiLSGegMULt27R0IzzlXkD//tPaNL7+E886zqnpvKI/S5s0wYoQlji+/hF13tbsNunWDrKxkR1ds0SaQZ4Fngkb0afy3Ef2XKF6jAvBP2LI1RKieEpF2wM6qOkJEWhX0oqraD+gHkJWV5f1TnIuDYcOgc2eb/Om11+Cii7yhPCp//QX9+tlj8WKbYvGxx+Cyy6zKKsVFm0CGBT8HBj/zLtRFaUTPBiqFLasErA1dEFR1PQ6cFmVszrk4yc6G666DAQPg8MNt3o799kt2VCWcqg381bs3vPuuZd1TT4WXXrLe4ztHdc9RSog2gRwYg2PNAkqJSANVnR0sawqEN6A3AOoCE4LhtnYBdhORJcCRqjo/BrE45wrx44/WUD53rg27ft993lBeoOxsy7C9e8O0aVa/d+21NmtWgwbJji4uokogO9J4HvIaOSIyHHhARK7A7sJqCxwVtul0rL0lz1FAL+AQ7I4s51wcbd0KTzxhQ5Dsvbc1lB97bLKjKsFmzbI7qV57ze6satoUXn7Z2jjKl092dHGVbwIRkdOAsaq6Ofg9X6oa3j8kP1cDrwLLgJVAV1WdISItgQ9VtYKqbgG29Y4RkVVArqomt8eMcxlg4UKbLXD8eDj3XGso3333ZEdVAm3dCmPGWGnjk0+saPa//1mj+FFHZUwDkeQ3rYeI5ALVVXVZ8Ht+VFVLRKVeVlaWTpw4MdlhOJeS3n0XrrwSNm2CF16ASy7JmOtg9FasgFdesQENFyywe5mvusreuARN4hQPIjJJVYt8O1hBVVhlVXVj3u/FC8s5V9JlZ8MNN9h18bDDrBo/Tavsi++HH6y08dZbsHEjtGoFTz0FZ56Z0Q1D+SaQkOQB1tg9SVW3hm4jIjsBWcAP8QnPORdPEydaQ/mcOXDnnXD//Rl9Pdzehg2WMHr3tjsKKlSAyy+Hq6+Gxo2THV2JEO1dWN8Ce2NtF6F2D9aViCos51x0cnOtofzuu63m5fPP7Uu1wyYyefFFK5KtXAkHHGB1ehddBJXCeyJktmgTSF5/j3C7A+tiF45zLt4WLrRr4bhx1u770ktQpUqyo0qy3FwYO9ZKG6NHW+NP27ZwzTVw/PHeGJSPAhOIiLwd/KpAfxEJrdbaGava+i5OsTnnYmz4cLjiCmsof+UVuPTSDL82rl5tt9/26QOzZ8Oee8Jdd1nDeK1ahe6e6QorgeS1eQiQG/IcbIyqQcCLcYjLORdDOTnWUN6/vw29NHhwhjeUT5lipY1Bg2DdOmjRwnpK/u9/Nk6Vi0qBCURVzwcQkflAT1XNSURQzrnYmTTJGspnz4Y77rCG8iTPhJocmzZZEax3bxtqpEwZe2O6dYNDDkl2dCkp2p7od8Y7EOdcbOXmwpNPWkP5XnvBZ59ZdX7GWbTo3wENlyyxWa+efNLq7zK+8WfHRNuI7pxLIYsW2Syon30GZ59tI2tk1LVS1YZN79XLhlHPzbWBDLt1s4ENS/Asf6nEE4hzaWbkSOuusGGDJY7LL8+ghvLsbJuoqXdvmDHDxmG58Ubo0gXq1092dGnHE4hzaSInB266yWpqDjnEGsr33z/ZUSXIb7/9O6Dh2rX2BrzyCnToAOXKJTu6tOUJxLk08PPPNvjrrFlw223w4IMZ0FC+ZYv12ejVy+rqdtnFpoW95hqbJjZjil3JE1UCEZFz81mlwAZgjqr+GrOonHNRyc2Fp5+2rgt77gmffgonnJDsqOJs2TK7H7lvX5tnt1Yymbd5AAAgAElEQVQteOgh6+Cy117Jji6jRFsCGYT1BQlvecoNliMi3wNnqOqq2IXnnMvP4sXWUP7pp9CunbV37LFHsqOKE1X4/ntr23j7bbsl98QT4bnnoE0bKOWVKckQ7a0IrYGfgBOxuc0rBL9PBM7AJn2qADwVhxidc2Heew8OPhi+/tqGInn33TRNHuvX23y6hx1mnf3ee88mZ//ll38zpyePpIn2nX8W6Kyq34QsGyciNwP9VLWxiNwIvBbrAJ1z/1q3zhrKX3oJmje3hvIDDkh2VHHw++82oOGrr8KqVdCokTWSX3ABVKyY7OhcINoEUh9YE2H5P8C+we9zgHT8DuRcifDzz9Zx+rff4JZboGfPNBt1IzcXPv7Yqqk++MD6arRrZ303jjvOG8VLoGgTyE/A4yJykaquBBCRPYBHgUnBNvsBi2IfonOZLTcXnnnG5uuoWtUGjf2//0t2VDH0999WTdWnD8ydC9WqWff5zp2hZs1kR+cKEG0CuQIYBSwKxsUCqAP8CZwZPN8dSyjOuRj56y9rKB871kYX79/fkkha+PlnK20MHmxtHUcfbcWqs8/OgHuQ00O0Y2H9KiIHYA3meV2TfgPG5M1SqKrvxCdE5zLT++/DZZdZB8G+fe0LecrX4mzaBO+8Y4njm2+sk98FF1g1VdOmyY7OFVHUty8EieK9OMbinMMaym+5xdqQmzWzL+gHHpjsqHbQwoXW8t+vn/Xj2G8/68ByySU23IhLSVEnEBFpBpwA7EXY7b+qeluM43IuI02ZYj3Kf/0Vbr7Z+selbEO5Kowfbz3F33vPGnPOOMNKGyed5AMapoFoe6JfCzwHLAQWs/30tpGmus3vdaoArwAnAyuAO1V1cITtbgSuBaoC2cBbwK2quiXaYzmXSnJzrU/cHXdYf45PPrFrbEpauxYGDrRG8V9+sRO6+WYb0LBevWRH52Io2hLIrdgFfEc7CvYGNgHVgGbAGBGZoqozwrYbBQxQ1dVB0nkHuA54egeP71yJs2SJ1eR8/DGceaaNAZiSDeW//GJJ4/XXbVTcrCwb3PDcc6Fs2WRH5+Ig2gRSGRixIwcSkfJAe6CJqmYDX4nIKOBC4I7QbVV1buiu2JAp++3I8Z0riUaPtoby7Gxr87jqqhRrKN+yxaqneveGcePs7qnzzrMBDQ8/PNnRuTiLthJyGLCjd543BLao6qyQZVOAxpE2FpGOIvIPVtXVFHgpn+06i8hEEZm4fPnyHQzRucRYv96usW3awD772LSzXbqkUPJYutRuua1Xz+YRnzsXHnnEGssHDvTkkSGiLYHMBB4UkcOBacDm0JWq2ieK16iA9VwPtQaIOC5B0DYyWEQaABcBS/PZrh/QDyArKyvq9hjnkmXqVOtRPmOGzXX0yCMp0lCuCt9+a43i77wDmzdbQ02vXtY4vvPOyY7QJVi0CeRGrBrp9OARSoFoEkg2UClsWSVgbUE7qepsEZkRHOPsqKJ1rgRSheefh9tvh8qVrc3j5JOTHVUU1q2DIUMsUUyeDJUqwdVXQ9euGTRjlYsk2o6Ee8fgWLOAUiLSQFVnB8uaAuEN6JGUwsbjci4lLV1qDeUffWRf1l991ebvKNHmzLGGmQEDbLiRgw6yHo2dOkGFCsmOzpUACRsHWVVzRGQ48ICIXIHdhdUWGwp+O8H6Uaq6TEQaAXcCHycqVudiacwYuPRSu7u1Vy/78l5i2zq2brUs17s3fPihDZV+9tnWYHPMMSU4cJcM+SYQEXkcuD+48D9e0IsUoSPh1cCrwDJgJdBVVWeISEvgQ1XN+1pzNPCQiFQAlmON+PdEeQznSoT162162V697Mv7uHHQOOItIyXAypVWLHrxRZg3D/beG3r0gCuvtFZ+5yIoqATSEigd8nt+om64DmYrPCvC8glYI3ve80ujfU3nSqJp06yhfPp0uP56ePRRKFMm2VFFMGmSlTaGDIENG+DYYy3Ydu2gdOnC93cZLd8EoqotIv3unMufKrzwgpU8Kle2WqBTT012VGE2boRhwyxxfPcdlC9vQ/5262ZFJeei5HNBOhcjS5daW8eHH8Jpp1nb8157JTuqEH/8YY3g/fvD8uXQsKGNn3LxxbDbbsmOzqWgogym2BabBz3SYIrnxjgu51LKBx9Y8lizxkog3bqVkPZmVfjsMyttjBply9q0sQBPPNEHNHQ7JNrBFB8GbgO+wQZT3BrPoJxLFRs2WL+O55+HJk3sWt2kSbKjAv75x8ak6tPH5sCtWtXq1bp0gTp1kh2dSxPRlkAuBS6KNHKuc5lq+nRrKJ82Da67Dh57rAQ0lM+YYaWNgQNtJqrDD7ffzzmnBATn0k20CWQX4Pt4BuJcqlC1a/Qtt1jTwQcfQOvWSQxo82YYOdKC+uILGxfl/POtmiorK4mBuXQXbQJ5BTgPeDiOsThX4i1bZqPnjhljSWPAAKhWLUnB/PUXvPyyzfS3eDHUrWvFoMsuS9Hx4F2qiTaBlAJuFZETgan8dzBFn5HQpb2PPrLhSFavtjaPa65JQkO5Knz9tfVOfPddG0791FMtibRu7QMauoSKNoG0AH4DygFHhq3zEXBdWtuwwWYKfO4560k+dmwSukvk5MCgQVZNNXWqdTK59lob0LBBgwQH45yJdjBF70joMtKMGdZQPnWqlTgefzzBk+vNnm13Ug0YYPcIN21q1Vbnn28dAJ1LIu9I6FwEqnbdvuUWqFjRZg48PXwig3jZutVa5nv3tjHfS5e2SZu6dYOjjiohHUycK3gwxbeBK1T1n+D3fHlHQpdOli+3dujRo615YcAAqF49AQdescImRO/bF+bPhxo14IEHbEDDhATgXNEUVALZyr/tG95x0GWETz6xkT1WrYJnn7Vmhrh31v7xRyttDB1q41S1agVPPAFt2/qAhq5EK2gwxfMj/e5cOtq4Ee68E555Bho1spqjgw+O4wE3bIC337a7qX780SZouvxymyykxI757tz2vA3EZbxffrGG8ilT7Pr95JNxbCifP9+qqF55xaqsDjjABs+66CKbKta5FFKUwRSPAjoAtbGe6duo6mkxjsu5uFO1a/lNN1kBYNQoG2cw5nJz4dNPrZpq9Ghb1rat3dZ1/PHeKO5SVlS1uyLSERgP1AJaA5uAuth0tIviFJtzcbNiBZx1lpU4jj3WbtONefJYvdo6jxxwAJxyCnz7rdWTzZ8Pw4fDCSd48nApLdrmwTuB61W1HZY8bgIaA28DS+IUm3MxM2iQjfSx00429Ej9+taz/Omnbf6OvfeO4cGmToWrrrK7qG64wYYVefNN+PNP6NkTatWK4cGcS55oq7D2BT4Mft8ElFdVFZFngM/x+cpdCTZoEHTuDOvW2fNly+yLf8+ecOONMTrI5s1WqujdGyZMsJFvO3a0vhuHHBKjgzhXskRbAvkbqBj8vghoFPy+G+DdYV2J1r37v8kjjyr06xeDF1+8GHr0gNq1oUMHWLTIWuEXLbKGck8eLo1FWwL5CjgBmAa8CzwnIq2AU7ASiHMl0ty5sGBB5HV//FHMF1W1UkavXjBihPUcb93aShunnuqz/LmMEe0n/TpgZPB7T+BFoD7wAXB5tAcTkSoiMkJEckRkQdA4H2m7W0VkuoisFZF5InJrtMdwDuDvv+Hmm+HAA/Nvp65du4gvmp1tt20dfDAcd5zdWXX99TZe1ZgxNhG6Jw+XQQotgYhIKeAMLFmgqluB+4t5vN5YG0o1oBkwRkSmqOqM8MMCF2FDx9cHPhGRP1V1aDGP6zLE5s12je/Rw5LIZZdB8+Y2m2toNVa5cvDQQ1G+6MyZNjDWa6/ZVLHNm1v1VIcO9kLOZSpVLfQBrAPqRLNtAa9RHkseDUOWvQE8GsW+zwMvFLbdoYceqi4z5eaqvveeasOGqqB6wgmqkyf/u/7NN1Xr1FEVsZ9vvlnIC27ZojpypOr//Z+9YOnSqp06qX7zjR3MuTQCTNRiXNejbQP5AWgK5FObHJWGwBZVnRWybApwXEE7iYgALYGX8lnfGegMULvIdRIuHUyebJ0Bx42D/feH99+3kXNDq646dbJHoZYvh/79rRjzxx9Qs6bdrnXFFUmcetC5kinaBNILeEpE9gEmATmhK1X1lyheowLwT9iyNfx7d1d+emBtNQMirVTVfkA/gKysLJ/cKoMsXgx33201S1WqWJt2587FGH9QFX74wW7Bfest2LQJTjzRRlNs0wZK+Yg/zkUS7X9G3nDufYKfeRdqCX6PZh7NbCB8sJ9KwNr8dhCRa7C2kJaqujHKWF2ay8mBp56y6b83b7bG8u7dbZK+iKpXh6VL/7t8r73sRXr1gkmTbOKPzp2te/qBB8b1HJxLB9EmkFj8N80CSolIA1WdHSxrCoQ3oAMgIpcBdwDHqurCGBzfpbjcXOvQfddd1s3if/+DRx+1XuUFipQ8wHoUXnqpDb/bpw9ccIElEedcVApMICLyKjaEycwdPZCq5ojIcOABEbkCuwurLTaeVvhxOwEPA8er6u87emyX+saPt5LGTz/BYYfZ1BnHHBODFx43zm7J9TGpnCuywm5avxiI5cDWVwevtwwYAnRV1Rki0lJEskO26wnsAfwoItnBo28M43ApYvZsaNfOBq1dvtyGJfnuuxglD7DJmzx5OFcshVVhxfQ/S1VXAWdFWD4Ba2TPe14vlsd1qWfVKnjwQWueKFPG+mzceGMR5+mYNMnqu5xzcRFNt1m/s8klzKZNNgL6fvvB889bR8DZsy0PRJ08fv3VGkiysiyJOOfiIpoEskREthb0iHuULu2pwsiRNpvrDTfYtX/yZHjpJbuJKioLFlijeJMmNiftfffB77/n33/D+3U4t0OiuQurM7A63oG4zPXTT9YR8Isv7O7ZDz6wMQmjbppYutTquPr2tbGobrwR7rjD5uEAWOJT1jgXD9EkkPdVdVncI3EZZ9Ei678xcKBd6/v0gSuvLEK/vdWr4YknrMPfxo1w+eVwzz3We9w5F3eF/at6+4eLuexsu+4/8YSNhH7bbTbT6267RfkCOTnwwgvWCXD1ajj/fLj/fmjQIK5xO+e2l9C7sFxm27rVShvdu8Nff8F558Ejj0C9aO+527QJXn7Zbs9auhTOOMPGqWraNK5xO+ciKzCBqKpPbuBi4vPPrSPg5Mlw5JHw7rvQokWUO2/dal3Qe/SA+fOt49/w4XDUf/qgOucSyBOEi6uZM6FtWxub8O+/rQf5N99EmTxULVEcdBBccgnssYfdXTVunCcP50oATyAuLlauhOuusztqx42zMat++82qrQq9u0oVxo6Fww+H9u3t+TvvwI8/wskne89x50oITyAupjZuhKefto6AvXvbNBpz5sDtt1uP8kJ99x2ccIIlimXLYMAAmDbNEoknDudKFE8gLiZUrV2jcWNr62jRAqZOhRdftFHTCzVtmtV1tWgBv/xi3dBnzbKqK5+Pw7kSyROI22E//gjHHmujh5QpAx99ZJ0BGzeOYuc5c2yqwKZNrSfhQw9Z7/Frr4Vdd4177M654vME4ortzz/hwgutqWLWLBt2ZPJkOOWUKHZetAi6dLGu5yNHWs/xefNs0Kvy5eMeu3Nux3ndgCuy7Gzrw/fkk1Z1dddd1sZRKXy+yUhWrPh3FsCtWy2JdO9ehAGvnHMlhScQF7WtW23+8bvvtuGlOnaEhx+GOnWi2HntWnjmGcs6OTlWdLnvviL0InTOlTSeQFxUPv3UGsenTrUuGCNHwhFHRLHjhg3Wkv7ww1b6OPts60neqFHcY3bOxZe3gbgC/fqrjRhy0klWiBg2DL76KorksWWLDTvSoIENtdu8Ofzwg92q5cnDubTgCcRFtHw5XHONdQKfMMEGPsybp6nA7hi5udbdvFEj6NzZRsb9/HP45BObzNw5lza8CsttZ+NGG+i2Z09rLO/SxZoq9tyzkB1V7d7d7t1hyhTLPKNGWfHFOwA6l5a8BOIAu/4PG2Z31d56KxxzjPXt69UriuTx5ZfQsqUli+xsGDTI7udt08aTh3NpzBNIBho0COrWtcn76ta1qTSOOQbOPRcqVrRhqEaPtmRSoJ9+sqkDjzvO+nD07Wv1XB072os759KaV2FlmEGDrGli3Tp7vmCBjZJeqRL0728jh+y8cyEv8ttvNvPfO+9AlSrWQNKtG5QtG+fonXMlSUK/JopIFREZISI5IrJARDrms93xIjJORNaIyPxExpjuunf/N3mE2m03mxG2wOSxYAFcdpmNUfLRR3DvvTbsyC23ePJwLgMlugTSG9gEVAOaAWNEZIqqzgjbLgd4FRgC3JXYENPXunWWAyJZuLCAHZcutX4cfftam8b119sctIU2jjjn0lnCSiAiUh5oD9yjqtmq+hUwCrgwfFtV/UFV3wB+T1R86WzTJhtavX79/LepXTvCwtWrrdt5/fr2AhddBLNn23jtnjycy3iJrMJqCGxR1Vkhy6YA0YzZmi8R6SwiE0Vk4vLly3cowHSTNwf5/vtbn46GDa3WqVy57bcrV84Gwd1m3Tobr2rffW3FGWfYEOsvvwy1aiX0HJxzJVciE0gF4J+wZWuAijvyoqraT1WzVDVrT/9WDNgtuSNGwMEHw8UX/zsT7PjxdsdVv342fpWI/ezXz0ZUZ9Mm6NPHShx33GFzc/z8s3UMbNgw2aflnCthEtkGkg2Ej9daCVibwBjSmqqNWXXXXTBxIhxwgN0odfbZId0xqlen09KldMrbaQFwAdBtN7ujat4869MxbJjd2+ucc/lIZAlkFlBKRBqELGsKhDegu2L49tsoZ4JdujTyC6xZA5Urw4cf2sROnjycc4VIWAlEVXNEZDjwgIhcgd2F1RY4KnxbEdkJ2AUobU+lDJCrqpsSFW+qmDrV2rnff9+mjn3+eevnUazJ/CZO9A6AzrmoJfpqcTVQFliG3aLbVVVniEhLEckO2e5YYD3wAVA7+P2TBMdaooT3Hn/6aWu3aNbMBjt8+OEoZoJdvbrgg3jycM4VgahqsmOImaysLJ04cWKyw4i58N7jeUqXtj58t94Ku+9ewAtMmmRzcgwZErkXYZ40+iw456InIpNUNauo+/lXzhSQX+/xatWs5BExeaxbZw0hhx8OWVmWPDp1irChc84VjyeQEkwVPvss/97jixZFWDhzJtx4I9SoYcOOZGfb+OyLF9v9utWqRX6x/JY751w+fDDFEmjjRiswPPOMNZLvtJPN0xRuW+/xzZtt7o0XX7SMU7q03bvbtSsce+z2t2EtWZKQc3DOpT8vgSRJeKP4oEE2C+CDD1rnvksvtaTx6quQXaE6ivzn8duqvWy2pzp1bKrA2bOt5/iff1rnv+OO8/k4nHNx443oSRCpUbxUUBbcsgVat7ZpxE88Mbj+F5QERGyHrl3tZ6FjsTvn3PaK24juVVhJEKlRfMsWqFABfvghiomcQs2dC/XqxTQ+55yLhldhxUikKqn8/PFH5OU5OWHJY8sWGDOm4AN78nDOJYmXQGIg0ix/nTvb7+F3zmZn29xLkW7L3dYo/vvv1vgxYIDdPeWccyWQl0BiIFKV1Lp1tjy0ZLLPPja0+tx1kRvFZy2rbA0f9evDI49A8+Y2rK5zzpVAnkCKKLyqqn///PtpLFhgd1MtWGB9Ov76ywoU1Yk8oOEu69fYaLg9e1o91+jRcNZZ3nfDOVcieRVWEUSqqrryyoL32by5iAeZM+e/Y1J53w3nXAnkJZAiyG9IkUqVIs/y9xeRq6oK5AMaOudShF+tiiC/u6fWro08y19+VVXOOZcOPIEUQc2akZfXrm13W82fb73H58/3cQudc+nPE0iUcnMh0pTr5crBr6urW9Ej/FFU3ijunEshnkCi9OCD8NNPcOGF/62qKrumGFVVqv99eGO5cy6F+F1YURg1Cnr0gL/LVKfyGyHJYgFwQbKics655PISSD5C+3ucdZaNGFJ5Q4waxb2qyjmXBrwEEsH6ytXptGYp27WDzyvmi6XRaMfOORfKSyARFKtNwznnMkzGJ5DwoUkGDIjhi3tVlXMujSU0gYhIFREZISI5IrJARDrms52IyGMisjJ4PCYS+6n11leuTqcLhPkLhFy1n5deVszD+F1VzrkMk+g2kN7AJqAa0AwYIyJTVHVG2HadgbOApoACY7FWiL6xDCZmVVVe0nDOZaCElUBEpDzQHrhHVbNV9StgFHBhhM0vBp5S1YWqugh4CrgkUbEWyEsazjkHJLYKqyGwRVVnhSybAjSOsG3jYF1h2yEinUVkoohMXL58ecyCjchLGs45t00iE0gF4J+wZWuAivlsuyZsuwqR2kFUtZ+qZqlq1p6RxhopLi9pOOdcgRKZQLKBSmHLKgFro9i2EpCt6p0qnHOupEhkApkFlBKRBiHLmgLhDegEy5pGsd2O8Zn+nHOu2BKWQFQ1BxgOPCAi5UXkaKAt8EaEzQcCN4lIDRHZB7gZeC3mQS1Z4lVVzjlXTInuSHg1UBZYBgwBuqrqDBFpKSLZIdu9BLwPTAOmA2OCZc4550qIhPYDUdVVWP+O8OUTsIbzvOcK3BY8nHPOlUAZP5SJc8654vEE4pxzrlg8gTjnnCsWSaeuFSKyHJsnsDiqAitiGE4q8HPODH7OmWFHzrmOqha5J3ZaJZAdISITVTUr2XEkkp9zZvBzzgzJOGevwnLOOVcsnkCcc84ViyeQf/VLdgBJ4OecGfycM0PCz9nbQJxzzhWLl0Ccc84ViycQ55xzxeIJxDnnXLFkfAIRkSoiMkJEckRkgYh0THZM4UTkmmDa3o0i8lrYuhNF5DcRWSci40SkTsi6XUXkVRH5R0SWiMhNidg3Rue8q4i8EvxN1orIZBFpnQHn/aaI/BUcf5aIXJHu5xxynAYiskFE3gxZ1jH4DOSIyEgRqRKyrsD/3XjtG6NzHR+ca3bwmJmS56yqGf3AhpV/CxsN+Bhs+tzGyY4rLMazsVGMXwReC1leNYj3HKAM8ATwXcj6R4AJwO7AgcAS4NR47xujcy4P9ADqYl90zsBmr6yb5ufdGNg1+P2A4PiHpvM5h8TxSRDHmyHvxVrgWOz/czAwNJr/3XjuG6NzHQ9ckc/fP2XOOekXx2Q+sIvUJqBhyLI3gEeTHVs+8fZk+wTSGfgm7HzWAwcEzxcDJ4esfzDvQxHPfeN4/lOB9ply3sD+wF/Auel+zkAH4G3sS0NeAnkYGByyTf3g/7ViYf+78do3huc7nsgJJKXOOdOrsBoCW1R1VsiyKVg2TgWNsXiBbbM+zgUai8juwN6h69n+3OKyb0zOKgIRqYb9vWbEK/aSct4i0kdE1gG/YQnkg3jFXRLOWUQqAQ8AN4WtCj/2XIKLIIX/78Zr31h6RERWiMjXItIqznHH5ZwzPYFUAP4JW7YGy9ipoAIWb6i8+CuEPA9fF899Y05ESgODgNdV9bdCjp/y562qVwev2RKbBnpjIcdO9XN+EHhFVReGLS8s7oL+d+O1b6zcDuwL1MA6AL4vIvULOXaJO+dMTyDZQKWwZZWwusBUUFD82SHPw9fFc9+YEpGdsKL2JuCaKI6fFuetqltV9SugJtC1kGOn7DmLSDPg/4BnIqwuLO6C4orXvjGhqt+r6lpV3aiqrwNfA6fFMe64nHOmJ5BZQCkRaRCyrClWTZIKZmDxAiAi5bG6yxmq+jdW/dE0ZPvQc4vLvjE5q39fV4BXgGpAe1XdHM/YS8p5hykVcox0POdW2I0Rf4jIEuAWoL2I/BTh2PsCu2L/t4X978Zr33hRQOIYd3zOOZYNYan4AIZidyeUB46mZN6FVQq7A+YR7Nt4mWDZnkG87YNlj7H93TWPAl9gd9ccgF0o8u6uidu+MTzvvsB3QIWw5Wl53sBeWGNyBWBn4BQgBzgzjc+5HFA95PEk8E5w3MZYtUtL7P/zTba/qyjf/9147huDc64c/G3z/o87BX/nhql2zkm/OCb7AVQBRgZ/wD+AjsmOKUKMPbBvKKGPHsG6/8MaW9djd3bUDdlvV+DV4IOxFLgp7HXjsm+MzrlOcJ4bsOJ13qNTup43dtH8AlgdHH8acGW840723zrCZ/3NkOcdsf/LHOA9oErIugL/d+O1b4z+zj9i1UOrsS9JJ6XiOftgis4554ol09tAnHPOFZMnEOecc8XiCcQ551yxeAJxzjlXLJ5AnHPOFYsnEOecc8XiCcS5FCIirURERaRqsmNxzhOIc865YvEE4pxzrlg8gbi0FUwb2kdEHg7mXVgmIk8GI/wWtu/ZIjJVRNaLyCoR+SKYkwQRqS8i74lN/5ojIj+JyBlh+88XkXtF5DWxKXn/FJHzRKSyiAwNpjGdLSInh+yTVz11htgUvhtEZJKIHFpIrEcF8a0TkUUi8mIwx0be+mNF5LvgmGtE5AcRaVL0d9S57XkCcemuE7AFOAobDv4G4LyCdhCR6tjAc69jU7weiw1imacC8CFwEjaC6bvAcBE5IOylbgB+AA7BZtt7HZsq9AOgGfAl8KaIlAnb70lsvogs4HdgtIiUyyfWg7CpYEcFsZwdvParwfpS2LhGXwXrjwCeBbYW9B44Fw0fC8ulLREZj80v3iJk2VhggapeUcB+hwCTsEEDF0R5rO+A0araM3g+H/hWVc8PnlfABs97QVWvC5bVBeYBh6nqxGBWunHABao6KGS/hcAtqto/ZJs9VXWFiAwENqvq5SGxNAN+xobB3wKsBFqp6hfRnItz0fISiEt3U8OeL8aGTS/IFOBTYLqIvCsiXUVkz7yVIlJeRB4XkV9E5G8RycZKC7XzO7aqZgPrsBF28ywNfobH823YftOARvnEeihwQVA9lR3E8nWwrr6qrgJeAz4WkTEicpOIhMfpXLF4AnHpbnPYc6WQz72qbgVODh5TgcuB2SKSN+HOk8A5wD3AcViV0Q/ALlEce3PYcwqLpxA7Af2DGPIeTYEGwOTgfC7Fqq6+xOYWmSkip8/M9KkAAAFbSURBVOzAMZ0DPIE4F5Gab1X1fuAwrOSS13ZyDDBQVd9V1alYFVP9GB7+yLxfghkAmwC/5rPtT9ikQHMiPNaHnM8UVX1MVVthc3pcHMN4XYYqlewAnCtpRORIbBKlj7FqpuZALeCXYJNZQDsReQ8rUdyHzS4XK3eLyHIsad2LzQc/OJ9tHwO+E5G+wEtYO8sBQBtVvUpE6gFXYY3si4B9gYOBF2MYr8tQnkCc+6812JSf12LTj/4JPKiqbwbrb8Lmap8A/I3d1RTLBHIH8BSwPzZX9RmqmhNpQ1WdKiLHAj2x2Qx3xu7cGhFssg6bKnUYUBVLiIOwxOPcDvG7sJwrIcLvsEpyOM4VyttAnHPOFYtXYbmMIyItsY6AEalqhQSG41zK8iosl3FEpCxQI7/1qjongeE4l7I8gTjnnCsWbwNxzjlXLJ5AnHPOFYsnEOecc8XiCcQ551yx/D8IChxO/hFawwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"times_rpca = []\\n\",\n    \"times_pca = []\\n\",\n    \"sizes = [1000, 10000, 20000, 30000, 40000, 50000, 70000, 100000, 200000, 500000]\\n\",\n    \"for n_samples in sizes:\\n\",\n    \"    X = np.random.randn(n_samples, 5)\\n\",\n    \"    pca = PCA(n_components = 2, svd_solver=\\\"randomized\\\", random_state=42)\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    pca.fit(X)\\n\",\n    \"    t2 = time.time()\\n\",\n    \"    times_rpca.append(t2 - t1)\\n\",\n    \"    pca = PCA(n_components = 2)\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    pca.fit(X)\\n\",\n    \"    t2 = time.time()\\n\",\n    \"    times_pca.append(t2 - t1)\\n\",\n    \"\\n\",\n    \"plt.plot(sizes, times_rpca, \\\"b-o\\\", label=\\\"RPCA\\\")\\n\",\n    \"plt.plot(sizes, times_pca, \\\"r-s\\\", label=\\\"PCA\\\")\\n\",\n    \"plt.xlabel(\\\"n_samples\\\")\\n\",\n    \"plt.ylabel(\\\"Training time\\\")\\n\",\n    \"plt.legend(loc=\\\"upper left\\\")\\n\",\n    \"plt.title(\\\"PCA and Randomized PCA time complexity \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now let's compare their performance on datasets of 2,000 instances with various numbers of features:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'PCA and Randomized PCA time complexity ')\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"times_rpca = []\\n\",\n    \"times_pca = []\\n\",\n    \"sizes = [1000, 2000, 3000, 4000, 5000, 6000]\\n\",\n    \"for n_features in sizes:\\n\",\n    \"    X = np.random.randn(2000, n_features)\\n\",\n    \"    pca = PCA(n_components = 2, random_state=42, svd_solver=\\\"randomized\\\")\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    pca.fit(X)\\n\",\n    \"    t2 = time.time()\\n\",\n    \"    times_rpca.append(t2 - t1)\\n\",\n    \"    pca = PCA(n_components = 2)\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    pca.fit(X)\\n\",\n    \"    t2 = time.time()\\n\",\n    \"    times_pca.append(t2 - t1)\\n\",\n    \"\\n\",\n    \"plt.plot(sizes, times_rpca, \\\"b-o\\\", label=\\\"RPCA\\\")\\n\",\n    \"plt.plot(sizes, times_pca, \\\"r-s\\\", label=\\\"PCA\\\")\\n\",\n    \"plt.xlabel(\\\"n_features\\\")\\n\",\n    \"plt.ylabel(\\\"Training time\\\")\\n\",\n    \"plt.legend(loc=\\\"upper left\\\")\\n\",\n    \"plt.title(\\\"PCA and Randomized PCA time complexity \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Kernel PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.decomposition import KernelPCA\\n\",\n    \"\\n\",\n    \"rbf_pca = KernelPCA(n_components = 2, kernel=\\\"rbf\\\", gamma=0.04)\\n\",\n    \"X_reduced = rbf_pca.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure kernel_pca_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.decomposition import KernelPCA\\n\",\n    \"\\n\",\n    \"lin_pca = KernelPCA(n_components = 2, kernel=\\\"linear\\\", fit_inverse_transform=True)\\n\",\n    \"rbf_pca = KernelPCA(n_components = 2, kernel=\\\"rbf\\\", gamma=0.0433, fit_inverse_transform=True)\\n\",\n    \"sig_pca = KernelPCA(n_components = 2, kernel=\\\"sigmoid\\\", gamma=0.001, coef0=1, fit_inverse_transform=True)\\n\",\n    \"\\n\",\n    \"y = t > 6.9\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"for subplot, pca, title in ((131, lin_pca, \\\"Linear kernel\\\"), (132, rbf_pca, \\\"RBF kernel, $\\\\gamma=0.04$\\\"), (133, sig_pca, \\\"Sigmoid kernel, $\\\\gamma=10^{-3}, r=1$\\\")):\\n\",\n    \"    X_reduced = pca.fit_transform(X)\\n\",\n    \"    if subplot == 132:\\n\",\n    \"        X_reduced_rbf = X_reduced\\n\",\n    \"    \\n\",\n    \"    plt.subplot(subplot)\\n\",\n    \"    #plt.plot(X_reduced[y, 0], X_reduced[y, 1], \\\"gs\\\")\\n\",\n    \"    #plt.plot(X_reduced[~y, 0], X_reduced[~y, 1], \\\"y^\\\")\\n\",\n    \"    plt.title(title, fontsize=14)\\n\",\n    \"    plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n    \"    plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"    if subplot == 131:\\n\",\n    \"        plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"    plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"kernel_pca_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure preimage_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(6, 5))\\n\",\n    \"\\n\",\n    \"X_inverse = rbf_pca.inverse_transform(X_reduced_rbf)\\n\",\n    \"\\n\",\n    \"ax = plt.subplot(111, projection='3d')\\n\",\n    \"ax.view_init(10, -70)\\n\",\n    \"ax.scatter(X_inverse[:, 0], X_inverse[:, 1], X_inverse[:, 2], c=t, cmap=plt.cm.hot, marker=\\\"x\\\")\\n\",\n    \"ax.set_xlabel(\\\"\\\")\\n\",\n    \"ax.set_ylabel(\\\"\\\")\\n\",\n    \"ax.set_zlabel(\\\"\\\")\\n\",\n    \"ax.set_xticklabels([])\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_zticklabels([])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"preimage_plot\\\", tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_reduced = rbf_pca.fit_transform(X)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 4))\\n\",\n    \"plt.subplot(132)\\n\",\n    \"plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot, marker=\\\"x\\\")\\n\",\n    \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"plt.grid(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score='raise-deprecating',\\n\",\n       \"       estimator=Pipeline(memory=None,\\n\",\n       \"     steps=[('kpca', KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto',\\n\",\n       \"     fit_inverse_transform=False, gamma=None, kernel='linear',\\n\",\n       \"     kernel_params=None, max_iter=None, n_components=2, n_jobs=None,\\n\",\n       \"     random_state=None, remove_zero_eig=False, tol=0)), ('log_reg', LogisticRe...ty='l2', random_state=None, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False))]),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=None,\\n\",\n       \"       param_grid=[{'kpca__gamma': array([0.03   , 0.03222, 0.03444, 0.03667, 0.03889, 0.04111, 0.04333,\\n\",\n       \"       0.04556, 0.04778, 0.05   ]), 'kpca__kernel': ['rbf', 'sigmoid']}],\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring=None, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"clf = Pipeline([\\n\",\n    \"        (\\\"kpca\\\", KernelPCA(n_components=2)),\\n\",\n    \"        (\\\"log_reg\\\", LogisticRegression(solver=\\\"liblinear\\\"))\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"param_grid = [{\\n\",\n    \"        \\\"kpca__gamma\\\": np.linspace(0.03, 0.05, 10),\\n\",\n    \"        \\\"kpca__kernel\\\": [\\\"rbf\\\", \\\"sigmoid\\\"]\\n\",\n    \"    }]\\n\",\n    \"\\n\",\n    \"grid_search = GridSearchCV(clf, param_grid, cv=3)\\n\",\n    \"grid_search.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'kpca__gamma': 0.043333333333333335, 'kpca__kernel': 'rbf'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(grid_search.best_params_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"rbf_pca = KernelPCA(n_components = 2, kernel=\\\"rbf\\\", gamma=0.0433,\\n\",\n    \"                    fit_inverse_transform=True)\\n\",\n    \"X_reduced = rbf_pca.fit_transform(X)\\n\",\n    \"X_preimage = rbf_pca.inverse_transform(X_reduced)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"32.78630879576608\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"\\n\",\n    \"mean_squared_error(X, X_preimage)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# LLE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=41)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.manifold import LocallyLinearEmbedding\\n\",\n    \"\\n\",\n    \"lle = LocallyLinearEmbedding(n_components=2, n_neighbors=10, random_state=42)\\n\",\n    \"X_reduced = lle.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure lle_unrolling_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.title(\\\"Unrolled swiss roll using LLE\\\", fontsize=14)\\n\",\n    \"plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n    \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"plt.ylabel(\\\"$z_2$\\\", fontsize=18)\\n\",\n    \"plt.axis([-0.065, 0.055, -0.1, 0.12])\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"lle_unrolling_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# MDS, Isomap and t-SNE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.manifold import MDS\\n\",\n    \"\\n\",\n    \"mds = MDS(n_components=2, random_state=42)\\n\",\n    \"X_reduced_mds = mds.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.manifold import Isomap\\n\",\n    \"\\n\",\n    \"isomap = Isomap(n_components=2)\\n\",\n    \"X_reduced_isomap = isomap.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.manifold import TSNE\\n\",\n    \"\\n\",\n    \"tsne = TSNE(n_components=2, random_state=42)\\n\",\n    \"X_reduced_tsne = tsne.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\\n\",\n      \"  warnings.warn(\\\"Variables are collinear.\\\")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\\n\",\n    \"\\n\",\n    \"lda = LinearDiscriminantAnalysis(n_components=2)\\n\",\n    \"X_mnist = mnist[\\\"data\\\"]\\n\",\n    \"y_mnist = mnist[\\\"target\\\"]\\n\",\n    \"lda.fit(X_mnist, y_mnist)\\n\",\n    \"X_reduced_lda = lda.transform(X_mnist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure other_dim_reduction_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"titles = [\\\"MDS\\\", \\\"Isomap\\\", \\\"t-SNE\\\"]\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11,4))\\n\",\n    \"\\n\",\n    \"for subplot, title, X_reduced in zip((131, 132, 133), titles,\\n\",\n    \"                                     (X_reduced_mds, X_reduced_isomap, X_reduced_tsne)):\\n\",\n    \"    plt.subplot(subplot)\\n\",\n    \"    plt.title(title, fontsize=14)\\n\",\n    \"    plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n    \"    plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n    \"    if subplot == 131:\\n\",\n    \"        plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n    \"    plt.grid(True)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"other_dim_reduction_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def learned_parameters(model):\\n\",\n    \"    return [m for m in dir(model)\\n\",\n    \"            if m.endswith(\\\"_\\\") and not m.startswith(\\\"_\\\")]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Extra Material – Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Introduction – Classification _vs_ Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import load_iris\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(['setosa', 'versicolor', 'virginica'], dtype='<U10')\"\n      ]\n     },\n     \"execution_count\": 74,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data = load_iris()\\n\",\n    \"X = data.data\\n\",\n    \"y = data.target\\n\",\n    \"data.target_names\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure classification_vs_clustering_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x252 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9, 3.5))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(X[y==0, 2], X[y==0, 3], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n    \"plt.plot(X[y==1, 2], X[y==1, 3], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n    \"plt.plot(X[y==2, 2], X[y==2, 3], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(fontsize=12)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.scatter(X[:, 2], X[:, 3], c=\\\"k\\\", marker=\\\".\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.tick_params(labelleft=False)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"classification_vs_clustering_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A Gaussian mixture model (explained below) can actually separate these clusters pretty well (using all 4 features: petal length & width, and sepal length & width).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.mixture import GaussianMixture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_pred = GaussianMixture(n_components=3, random_state=42).fit(X).predict(X)\\n\",\n    \"mapping = np.array([2, 0, 1])\\n\",\n    \"y_pred = np.array([mapping[cluster_id] for cluster_id in y_pred])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(X[y_pred==0, 2], X[y_pred==0, 3], \\\"yo\\\", label=\\\"Cluster 1\\\")\\n\",\n    \"plt.plot(X[y_pred==1, 2], X[y_pred==1, 3], \\\"bs\\\", label=\\\"Cluster 2\\\")\\n\",\n    \"plt.plot(X[y_pred==2, 2], X[y_pred==2, 3], \\\"g^\\\", label=\\\"Cluster 3\\\")\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"upper left\\\", fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"145\"\n      ]\n     },\n     \"execution_count\": 79,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sum(y_pred==y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9666666666666667\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sum(y_pred==y) / len(y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## K-Means\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by generating some blobs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import make_blobs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"blob_centers = np.array(\\n\",\n    \"    [[ 0.2,  2.3],\\n\",\n    \"     [-1.5 ,  2.3],\\n\",\n    \"     [-2.8,  1.8],\\n\",\n    \"     [-2.8,  2.8],\\n\",\n    \"     [-2.8,  1.3]])\\n\",\n    \"blob_std = np.array([0.4, 0.3, 0.1, 0.1, 0.1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X, y = make_blobs(n_samples=2000, centers=blob_centers,\\n\",\n    \"                  cluster_std=blob_std, random_state=7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's plot them:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_clusters(X, y=None):\\n\",\n    \"    plt.scatter(X[:, 0], X[:, 1], c=y, s=1)\\n\",\n    \"    plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n    \"    plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure blobs_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"plot_clusters(X)\\n\",\n    \"save_fig(\\\"blobs_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Fit and Predict\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train a K-Means clusterer on this dataset. It will try to find each blob's center and assign each instance to the closest blob:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import KMeans\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"k = 5\\n\",\n    \"kmeans = KMeans(n_clusters=k, random_state=42)\\n\",\n    \"y_pred = kmeans.fit_predict(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each instance was assigned to one of the 5 clusters:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4, 0, 1, ..., 2, 1, 0], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 88,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred is kmeans.labels_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And the following 5 _centroids_ (i.e., cluster centers) were estimated:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-2.80389616,  1.80117999],\\n\",\n       \"       [ 0.20876306,  2.25551336],\\n\",\n       \"       [-2.79290307,  2.79641063],\\n\",\n       \"       [-1.46679593,  2.28585348],\\n\",\n       \"       [-2.80037642,  1.30082566]])\"\n      ]\n     },\n     \"execution_count\": 90,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans.cluster_centers_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that the `KMeans` instance preserves the labels of the instances it was trained on. Somewhat confusingly, in this context, the _label_ of an instance is the index of the cluster that instance gets assigned to:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4, 0, 1, ..., 2, 1, 0], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 91,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans.labels_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Of course, we can predict the labels of new instances:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 1, 2, 2], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 92,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_new = np.array([[0, 2], [3, 2], [-3, 3], [-3, 2.5]])\\n\",\n    \"kmeans.predict(X_new)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Decision Boundaries\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the model's decision boundaries. This gives us a _Voronoi diagram_:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_data(X):\\n\",\n    \"    plt.plot(X[:, 0], X[:, 1], 'k.', markersize=2)\\n\",\n    \"\\n\",\n    \"def plot_centroids(centroids, weights=None, circle_color='w', cross_color='k'):\\n\",\n    \"    if weights is not None:\\n\",\n    \"        centroids = centroids[weights > weights.max() / 10]\\n\",\n    \"    plt.scatter(centroids[:, 0], centroids[:, 1],\\n\",\n    \"                marker='o', s=30, linewidths=8,\\n\",\n    \"                color=circle_color, zorder=10, alpha=0.9)\\n\",\n    \"    plt.scatter(centroids[:, 0], centroids[:, 1],\\n\",\n    \"                marker='x', s=50, linewidths=50,\\n\",\n    \"                color=cross_color, zorder=11, alpha=1)\\n\",\n    \"\\n\",\n    \"def plot_decision_boundaries(clusterer, X, resolution=1000, show_centroids=True,\\n\",\n    \"                             show_xlabels=True, show_ylabels=True):\\n\",\n    \"    mins = X.min(axis=0) - 0.1\\n\",\n    \"    maxs = X.max(axis=0) + 0.1\\n\",\n    \"    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\\n\",\n    \"                         np.linspace(mins[1], maxs[1], resolution))\\n\",\n    \"    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n    \"    Z = Z.reshape(xx.shape)\\n\",\n    \"\\n\",\n    \"    plt.contourf(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\\n\",\n    \"                cmap=\\\"Pastel2\\\")\\n\",\n    \"    plt.contour(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\\n\",\n    \"                linewidths=1, colors='k')\\n\",\n    \"    plot_data(X)\\n\",\n    \"    if show_centroids:\\n\",\n    \"        plot_centroids(clusterer.cluster_centers_)\\n\",\n    \"\\n\",\n    \"    if show_xlabels:\\n\",\n    \"        plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelbottom=False)\\n\",\n    \"    if show_ylabels:\\n\",\n    \"        plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelleft=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure voronoi_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"plot_decision_boundaries(kmeans, X)\\n\",\n    \"save_fig(\\\"voronoi_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Not bad! Some of the instances near the edges were probably assigned to the wrong cluster, but overall it looks pretty good.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Hard Clustering _vs_ Soft Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Rather than arbitrarily choosing the closest cluster for each instance, which is called _hard clustering_, it might be better measure the distance of each instance to all 5 centroids. This is what the `transform()` method does:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[2.81093633, 0.32995317, 2.9042344 , 1.49439034, 2.88633901],\\n\",\n       \"       [5.80730058, 2.80290755, 5.84739223, 4.4759332 , 5.84236351],\\n\",\n       \"       [1.21475352, 3.29399768, 0.29040966, 1.69136631, 1.71086031],\\n\",\n       \"       [0.72581411, 3.21806371, 0.36159148, 1.54808703, 1.21567622]])\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans.transform(X_new)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can verify that this is indeed the Euclidian distance between each instance and each centroid:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[2.81093633, 0.32995317, 2.9042344 , 1.49439034, 2.88633901],\\n\",\n       \"       [5.80730058, 2.80290755, 5.84739223, 4.4759332 , 5.84236351],\\n\",\n       \"       [1.21475352, 3.29399768, 0.29040966, 1.69136631, 1.71086031],\\n\",\n       \"       [0.72581411, 3.21806371, 0.36159148, 1.54808703, 1.21567622]])\"\n      ]\n     },\n     \"execution_count\": 96,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linalg.norm(np.tile(X_new, (1, k)).reshape(-1, k, 2) - kmeans.cluster_centers_, axis=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### K-Means Algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The K-Means algorithm is one of the fastest clustering algorithms, but also one of the simplest:\\n\",\n    \"* First initialize $k$ centroids randomly: $k$ distinct instances are chosen randomly from the dataset and the centroids are placed at their locations.\\n\",\n    \"* Repeat until convergence (i.e., until the centroids stop moving):\\n\",\n    \"    * Assign each instance to the closest centroid.\\n\",\n    \"    * Update the centroids to be the mean of the instances that are assigned to them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `KMeans` class applies an optimized algorithm by default. To get the original K-Means algorithm (for educational purposes only), you must set `init=\\\"random\\\"`, `n_init=1`and `algorithm=\\\"full\\\"`. These hyperparameters will be explained below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's run the K-Means algorithm for 1, 2 and 3 iterations, to see how the centroids move around:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"KMeans(algorithm='full', copy_x=True, init='random', max_iter=3, n_clusters=5,\\n\",\n       \"    n_init=1, n_jobs=None, precompute_distances='auto', random_state=1,\\n\",\n       \"    tol=0.0001, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_iter1 = KMeans(n_clusters=5, init=\\\"random\\\", n_init=1,\\n\",\n    \"                     algorithm=\\\"full\\\", max_iter=1, random_state=1)\\n\",\n    \"kmeans_iter2 = KMeans(n_clusters=5, init=\\\"random\\\", n_init=1,\\n\",\n    \"                     algorithm=\\\"full\\\", max_iter=2, random_state=1)\\n\",\n    \"kmeans_iter3 = KMeans(n_clusters=5, init=\\\"random\\\", n_init=1,\\n\",\n    \"                     algorithm=\\\"full\\\", max_iter=3, random_state=1)\\n\",\n    \"kmeans_iter1.fit(X)\\n\",\n    \"kmeans_iter2.fit(X)\\n\",\n    \"kmeans_iter3.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And let's plot this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure kmeans_algorithm_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x576 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 8))\\n\",\n    \"\\n\",\n    \"plt.subplot(321)\\n\",\n    \"plot_data(X)\\n\",\n    \"plot_centroids(kmeans_iter1.cluster_centers_, circle_color='r', cross_color='w')\\n\",\n    \"plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\\n\",\n    \"plt.tick_params(labelbottom=False)\\n\",\n    \"plt.title(\\\"Update the centroids (initially randomly)\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(322)\\n\",\n    \"plot_decision_boundaries(kmeans_iter1, X, show_xlabels=False, show_ylabels=False)\\n\",\n    \"plt.title(\\\"Label the instances\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(323)\\n\",\n    \"plot_decision_boundaries(kmeans_iter1, X, show_centroids=False, show_xlabels=False)\\n\",\n    \"plot_centroids(kmeans_iter2.cluster_centers_)\\n\",\n    \"\\n\",\n    \"plt.subplot(324)\\n\",\n    \"plot_decision_boundaries(kmeans_iter2, X, show_xlabels=False, show_ylabels=False)\\n\",\n    \"\\n\",\n    \"plt.subplot(325)\\n\",\n    \"plot_decision_boundaries(kmeans_iter2, X, show_centroids=False)\\n\",\n    \"plot_centroids(kmeans_iter3.cluster_centers_)\\n\",\n    \"\\n\",\n    \"plt.subplot(326)\\n\",\n    \"plot_decision_boundaries(kmeans_iter3, X, show_ylabels=False)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"kmeans_algorithm_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### K-Means Variability\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the original K-Means algorithm, the centroids are just initialized randomly, and the algorithm simply runs a single iteration to gradually improve the centroids, as we saw above.\\n\",\n    \"\\n\",\n    \"However, one major problem with this approach is that if you run K-Means multiple times (or with different random seeds), it can converge to very different solutions, as you can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_clusterer_comparison(clusterer1, clusterer2, X, title1=None, title2=None):\\n\",\n    \"    clusterer1.fit(X)\\n\",\n    \"    clusterer2.fit(X)\\n\",\n    \"\\n\",\n    \"    plt.figure(figsize=(10, 3.2))\\n\",\n    \"\\n\",\n    \"    plt.subplot(121)\\n\",\n    \"    plot_decision_boundaries(clusterer1, X)\\n\",\n    \"    if title1:\\n\",\n    \"        plt.title(title1, fontsize=14)\\n\",\n    \"\\n\",\n    \"    plt.subplot(122)\\n\",\n    \"    plot_decision_boundaries(clusterer2, X, show_ylabels=False)\\n\",\n    \"    if title2:\\n\",\n    \"        plt.title(title2, fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure kmeans_variability_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_rnd_init1 = KMeans(n_clusters=5, init=\\\"random\\\", n_init=1,\\n\",\n    \"                         algorithm=\\\"full\\\", random_state=11)\\n\",\n    \"kmeans_rnd_init2 = KMeans(n_clusters=5, init=\\\"random\\\", n_init=1,\\n\",\n    \"                         algorithm=\\\"full\\\", random_state=19)\\n\",\n    \"\\n\",\n    \"plot_clusterer_comparison(kmeans_rnd_init1, kmeans_rnd_init2, X,\\n\",\n    \"                          \\\"Solution 1\\\", \\\"Solution 2 (with a different random init)\\\")\\n\",\n    \"\\n\",\n    \"save_fig(\\\"kmeans_variability_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inertia\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the best model, we will need a way to evaluate a K-Mean model's performance. Unfortunately, clustering is an unsupervised task, so we do not have the targets. But at least we can measure the distance between each instance and its centroid. This is the idea behind the _inertia_ metric:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"211.5985372581684\"\n      ]\n     },\n     \"execution_count\": 101,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can easily verify, inertia is the sum of the squared distances between each training instance and its closest centroid:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"211.59853725816856\"\n      ]\n     },\n     \"execution_count\": 102,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_dist = kmeans.transform(X)\\n\",\n    \"np.sum(X_dist[np.arange(len(X_dist)), kmeans.labels_]**2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `score()` method returns the negative inertia. Why negative? Well, it is because a predictor's `score()` method must always respect the \\\"_great is better_\\\" rule.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-211.59853725816856\"\n      ]\n     },\n     \"execution_count\": 103,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans.score(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Multiple Initializations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So one approach to solve the variability issue is to simply run the K-Means algorithm multiple times with different random initializations, and select the solution that minimizes the inertia. For example, here are the inertias of the two \\\"bad\\\" models shown in the previous figure:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"223.29108572819035\"\n      ]\n     },\n     \"execution_count\": 104,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_rnd_init1.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"237.46249169442845\"\n      ]\n     },\n     \"execution_count\": 105,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_rnd_init2.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, they have a higher inertia than the first \\\"good\\\" model we trained, which means they are probably worse.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When you set the `n_init` hyperparameter, Scikit-Learn runs the original algorithm `n_init` times, and selects the solution that minimizes the inertia. By default, Scikit-Learn sets `n_init=10`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"KMeans(algorithm='full', copy_x=True, init='random', max_iter=300,\\n\",\n       \"    n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\\n\",\n       \"    random_state=11, tol=0.0001, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 106,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_rnd_10_inits = KMeans(n_clusters=5, init=\\\"random\\\", n_init=10,\\n\",\n    \"                              algorithm=\\\"full\\\", random_state=11)\\n\",\n    \"kmeans_rnd_10_inits.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, we end up with the initial model, which is certainly the optimal K-Means solution (at least in terms of inertia, and assuming $k=5$).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"plot_decision_boundaries(kmeans_rnd_10_inits, X)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### K-Means++\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Instead of initializing the centroids entirely randomly, it is preferable to initialize them using the following algorithm, proposed in a [2006 paper](https://goo.gl/eNUPw6) by David Arthur and Sergei Vassilvitskii:\\n\",\n    \"* Take one centroid $c_1$, chosen uniformly at random from the dataset.\\n\",\n    \"* Take a new center $c_i$, choosing an instance $\\\\mathbf{x}_i$ with probability: $D(\\\\mathbf{x}_i)^2$ / $\\\\sum\\\\limits_{j=1}^{m}{D(\\\\mathbf{x}_j)}^2$ where $D(\\\\mathbf{x}_i)$ is the distance between the instance $\\\\mathbf{x}_i$ and the closest centroid that was already chosen. This probability distribution ensures that instances that are further away from already chosen centroids are much more likely be selected as centroids.\\n\",\n    \"* Repeat the previous step until all $k$ centroids have been chosen.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The rest of the K-Means++ algorithm is just regular K-Means. With this initialization, the K-Means algorithm is much less likely to converge to a suboptimal solution, so it is possible to reduce `n_init` considerably. Most of the time, this largely compensates for the additional complexity of the initialization process.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To set the initialization to K-Means++, simply set `init=\\\"k-means++\\\"` (this is actually the default):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\\n\",\n       \"    n_clusters=8, n_init=10, n_jobs=None, precompute_distances='auto',\\n\",\n       \"    random_state=None, tol=0.0001, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 108,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"KMeans()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"211.5985372581684\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"good_init = np.array([[-3, 3], [-3, 2], [-3, 1], [-1, 2], [0, 2]])\\n\",\n    \"kmeans = KMeans(n_clusters=5, init=good_init, n_init=1, random_state=42)\\n\",\n    \"kmeans.fit(X)\\n\",\n    \"kmeans.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Accelerated K-Means\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The K-Means algorithm can be significantly accelerated by avoiding many unnecessary distance calculations: this is achieved by exploiting the triangle inequality (given three points A, B and C, the distance AC is always such that AC ≤ AB + BC) and by keeping track of lower and upper bounds for distances between instances and centroids (see this [2003 paper](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf) by Charles Elkan for more details).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To use Elkan's variant of K-Means, just set `algorithm=\\\"elkan\\\"`. Note that it does not support sparse data, so by default, Scikit-Learn uses `\\\"elkan\\\"` for dense data, and `\\\"full\\\"` (the regular K-Means algorithm) for sparse data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"83.7 ms ± 1.93 ms per loop (mean ± std. dev. of 7 runs, 50 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit -n 50 KMeans(algorithm=\\\"elkan\\\").fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"103 ms ± 2.66 ms per loop (mean ± std. dev. of 7 runs, 50 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit -n 50 KMeans(algorithm=\\\"full\\\").fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Mini-Batch K-Means\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Scikit-Learn also implements a variant of the K-Means algorithm that supports mini-batches (see [this paper](http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf)):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import MiniBatchKMeans\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"MiniBatchKMeans(batch_size=100, compute_labels=True, init='k-means++',\\n\",\n       \"        init_size=None, max_iter=100, max_no_improvement=10, n_clusters=5,\\n\",\n       \"        n_init=3, random_state=42, reassignment_ratio=0.01, tol=0.0,\\n\",\n       \"        verbose=0)\"\n      ]\n     },\n     \"execution_count\": 113,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"minibatch_kmeans = MiniBatchKMeans(n_clusters=5, random_state=42)\\n\",\n    \"minibatch_kmeans.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"211.93186531476775\"\n      ]\n     },\n     \"execution_count\": 114,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"minibatch_kmeans.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If the dataset does not fit in memory, the simplest option is to use the `memmap` class, just like we did for incremental PCA:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"filename = \\\"my_mnist.data\\\"\\n\",\n    \"m, n = 50000, 28*28\\n\",\n    \"X_mm = np.memmap(filename, dtype=\\\"float32\\\", mode=\\\"readonly\\\", shape=(m, n))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"MiniBatchKMeans(batch_size=10, compute_labels=True, init='k-means++',\\n\",\n       \"        init_size=None, max_iter=100, max_no_improvement=10, n_clusters=10,\\n\",\n       \"        n_init=3, random_state=42, reassignment_ratio=0.01, tol=0.0,\\n\",\n       \"        verbose=0)\"\n      ]\n     },\n     \"execution_count\": 116,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"minibatch_kmeans = MiniBatchKMeans(n_clusters=10, batch_size=10, random_state=42)\\n\",\n    \"minibatch_kmeans.fit(X_mm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If your data is so large that you cannot use `memmap`, things get more complicated. Let's start by writing a function to load the next batch (in real life, you would load the data from disk):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def load_next_batch(batch_size):\\n\",\n    \"    return X[np.random.choice(len(X), batch_size, replace=False)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can train the model by feeding it one batch at a time. We also need to implement multiple initializations and keep the model with the lowest inertia:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"k = 5\\n\",\n    \"n_init = 10\\n\",\n    \"n_iterations = 100\\n\",\n    \"batch_size = 100\\n\",\n    \"init_size = 500  # more data for K-Means++ initialization\\n\",\n    \"evaluate_on_last_n_iters = 10\\n\",\n    \"\\n\",\n    \"best_kmeans = None\\n\",\n    \"\\n\",\n    \"for init in range(n_init):\\n\",\n    \"    minibatch_kmeans = MiniBatchKMeans(n_clusters=k, init_size=init_size)\\n\",\n    \"    X_init = load_next_batch(init_size)\\n\",\n    \"    minibatch_kmeans.partial_fit(X_init)\\n\",\n    \"\\n\",\n    \"    minibatch_kmeans.sum_inertia_ = 0\\n\",\n    \"    for iteration in range(n_iterations):\\n\",\n    \"        X_batch = load_next_batch(batch_size)\\n\",\n    \"        minibatch_kmeans.partial_fit(X_batch)\\n\",\n    \"        if iteration >= n_iterations - evaluate_on_last_n_iters:\\n\",\n    \"            minibatch_kmeans.sum_inertia_ += minibatch_kmeans.inertia_\\n\",\n    \"\\n\",\n    \"    if (best_kmeans is None or\\n\",\n    \"        minibatch_kmeans.sum_inertia_ < best_kmeans.sum_inertia_):\\n\",\n    \"        best_kmeans = minibatch_kmeans\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-211.70999744411483\"\n      ]\n     },\n     \"execution_count\": 120,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_kmeans.score(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Mini-batch K-Means is much faster than regular K-Means:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"43.5 ms ± 1.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit KMeans(n_clusters=5).fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"25.4 ms ± 2.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit MiniBatchKMeans(n_clusters=5).fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's *much* faster! However, its performance is often lower (higher inertia), and it keeps degrading as _k_ increases. Let's plot the inertia ratio and the training time ratio between Mini-batch K-Means and regular K-Means:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 123,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from timeit import timeit\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100/100\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"times = np.empty((100, 2))\\n\",\n    \"inertias = np.empty((100, 2))\\n\",\n    \"for k in range(1, 101):\\n\",\n    \"    kmeans = KMeans(n_clusters=k, random_state=42)\\n\",\n    \"    minibatch_kmeans = MiniBatchKMeans(n_clusters=k, random_state=42)\\n\",\n    \"    print(\\\"\\\\r{}/{}\\\".format(k, 100), end=\\\"\\\")\\n\",\n    \"    times[k-1, 0] = timeit(\\\"kmeans.fit(X)\\\", number=10, globals=globals())\\n\",\n    \"    times[k-1, 1]  = timeit(\\\"minibatch_kmeans.fit(X)\\\", number=10, globals=globals())\\n\",\n    \"    inertias[k-1, 0] = kmeans.inertia_\\n\",\n    \"    inertias[k-1, 1] = minibatch_kmeans.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure minibatch_kmeans_vs_kmeans\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10,4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(range(1, 101), inertias[:, 0], \\\"r--\\\", label=\\\"K-Means\\\")\\n\",\n    \"plt.plot(range(1, 101), inertias[:, 1], \\\"b.-\\\", label=\\\"Mini-batch K-Means\\\")\\n\",\n    \"plt.xlabel(\\\"$k$\\\", fontsize=16)\\n\",\n    \"#plt.ylabel(\\\"Inertia\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"Inertia\\\", fontsize=14)\\n\",\n    \"plt.legend(fontsize=14)\\n\",\n    \"plt.axis([1, 100, 0, 100])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(range(1, 101), times[:, 0], \\\"r--\\\", label=\\\"K-Means\\\")\\n\",\n    \"plt.plot(range(1, 101), times[:, 1], \\\"b.-\\\", label=\\\"Mini-batch K-Means\\\")\\n\",\n    \"plt.xlabel(\\\"$k$\\\", fontsize=16)\\n\",\n    \"#plt.ylabel(\\\"Training time (seconds)\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"Training time (seconds)\\\", fontsize=14)\\n\",\n    \"plt.axis([1, 100, 0, 6])\\n\",\n    \"#plt.legend(fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"minibatch_kmeans_vs_kmeans\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Finding the optimal number of clusters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"What if the number of clusters was set to a lower or greater value than 5?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure bad_n_clusters_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_k3 = KMeans(n_clusters=3, random_state=42)\\n\",\n    \"kmeans_k8 = KMeans(n_clusters=8, random_state=42)\\n\",\n    \"\\n\",\n    \"plot_clusterer_comparison(kmeans_k3, kmeans_k8, X, \\\"$k=3$\\\", \\\"$k=8$\\\")\\n\",\n    \"save_fig(\\\"bad_n_clusters_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ouch, these two models don't look great. What about their inertias?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"653.2167190021553\"\n      ]\n     },\n     \"execution_count\": 127,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_k3.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"119.11983416102879\"\n      ]\n     },\n     \"execution_count\": 128,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_k8.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"No, we cannot simply take the value of $k$ that minimizes the inertia, since it keeps getting lower as we increase $k$. Indeed, the more clusters there are, the closer each instance will be to its closest centroid, and therefore the lower the inertia will be. However, we can plot the inertia as a function of $k$ and analyze the resulting curve:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(X)\\n\",\n    \"                for k in range(1, 10)]\\n\",\n    \"inertias = [model.inertia_ for model in kmeans_per_k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure inertia_vs_k_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x252 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 3.5))\\n\",\n    \"plt.plot(range(1, 10), inertias, \\\"bo-\\\")\\n\",\n    \"plt.xlabel(\\\"$k$\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Inertia\\\", fontsize=14)\\n\",\n    \"plt.annotate('Elbow',\\n\",\n    \"             xy=(4, inertias[3]),\\n\",\n    \"             xytext=(0.55, 0.55),\\n\",\n    \"             textcoords='figure fraction',\\n\",\n    \"             fontsize=16,\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.1)\\n\",\n    \"            )\\n\",\n    \"plt.axis([1, 8.5, 0, 1300])\\n\",\n    \"save_fig(\\\"inertia_vs_k_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, there is an elbow at $k=4$, which means that less clusters than that would be bad, and more clusters would not help much and might cut clusters in half. So $k=4$ is a pretty good choice. Of course in this example it is not perfect since it means that the two blobs in the lower left will be considered as just a single cluster, but it's a pretty good clustering nonetheless.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYYAAAERCAYAAAB/4wAeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXd4FOX2xz+TUHaXkoY0gdARFEITA9GgcKWIoOKVWC4oIoioNBsimoCIXvkRMCIgiCBYIFhBvKAXlVANSPOKlZLQRAlJgOymQOb3xzLj7GR2d3azLcl8n4dnk5l33vfMAue87ynfI4iiiAEDBgwYMCAhLNgCGDBgwICB0IJhGAwYMGDAgAMMw2DAgAEDBhxgGAYDBgwYMOAAwzAYMGDAgAEHGIbBgAEDBgw4wDAMBgwYMGDAAYZhMGDAgAEDDjAMgwEDBgwYcEC1YAvgDSIjI8XGjRsHWwwDQHFxMYcOHaJa9Wo0v6pNsMWpdMg5/Re5f56hXfuW1DTVCLY4Bio4Duz9+Ywoile4G1chDUPjxo1ZuXJlsMUwAAwbNgyAmR8s4uoeXYIsTeXD/dcOAODrXe8jCEKQpTFQ0dG41nVZesYZriQD5cLhw4cBDKPgZxhGwUAgYRgGA16jpKQEgOrVKuTB04ABA05gGAYDXuO5554D4J+PPxhkSQwYMOBLGIbBgNf45ptvAEga/1CQJTFgwIAvYRgGA15DFEUEQSA8PDzYohgwYMCHMAyDAa+wZMkSAK7te0OQJTFgwICvYRgGA17h7bffBuDZN18NsiQGqhpyzuSxYO5Kcs7kBVuUSgvDMBjwClJGUrXq1YMsiYGqhtUr1zFz2nxWr1wXbFEqLYw8QwMe46uvvgKgSZsWQZbEQFVE0vDBDp8GfA/jxGDAY7z88ssAzP50WZAlMVAVEVMvknGThhNTL9Iv8xuuKsMwGPAC586dA6B2nTpBlsSAAd/DcFUZhsFr5OXlsWLFCvLyqtau4tSpUwBY6hpGwUDw4M9dfdLwwUyb+ViVdlUZhsFLrF27lrS0NNauXRtsUQKKxx9/HIBpS+cEWZKqgdLSUoAq7dbQgj939f52VVUEGMFnLzFkyBCHz6qCo0ePAnBNj67BFaSKoMhWCNgV4bhJw4MsTejACED7F8aJwUtERkYyYsQIIiOrzq7i4sWLAFSrVt1g+wwQappNQMVUgP509xi7ev/CMAwGdGPq1KkA3PnY/UGWpOogLMz+X7QiKkAjiOsfBCJrynAlGdANiTTv3okPB1kSAxUBhrvHP5AMLuA396JxYjCgGxJpXlhYGPlnc/l40Qryz+YGW6wqgYqYV2+4e/yDQGRNGYbBR6js6asLFiwA4Lp+vQHYlL6OZbNeY1O64SYIBAyXjAEJgTC4hivJDfLy8li7di1DhgxxGWiW0lcBRowYESjxAoYVK1YAMGWRnTSv77DBDp8G/IuqnldvILAwDIMGlMZAr8JXp6/qNSgVBVJGktR7ISI6iqFjK58BDFUYqaoGAgm/GAZBEN4F+gK1gD+AV0VRfMvJ2EnAM4AF+BB4RBTFIn/IpRdKY6C3XkFKX9Wao6KfIP773/8C0MwgzQs4rOcvBFsEA1UQ/joxvAyMEkWxSBCEq4BvBUHYK4ri98pBgiD0B6YAfYCTwCfA9MvXAg5pl9+7t92PLu32vVHslakA7qWXXgLg1U8M0rxAo9Bqo25E7WCLYaCKwS/BZ1EUf1Ts+sXLf1ppDL0fWHp5fC7wIvCAP2TSA2mXv3nz5nIXr1WmArjz588DUKsC8SNVpqypqJiK/2/IUxgMp8GF37KSBEFYIAiCFfgZOAV8oTHsamC/4vf9QANBEGL8JZer7KEhQ4Ywfvx4hgwZojmusmceaeHPP/8EoFZE3SBL4hmMrKnAwF8KXMrVnzhmumEcggC/GQZRFMcBdYAbgI8BrbhBbSBf8bv0c5mtqSAIYwRB2C0Iwu7cXO93ga7I75S7/NWrV5OWlsbq1avJyspiwoQJvPPOO2WedWcsKroxGTt2LADPLpkdZEk8Q99hgxk5dYKRNeVnlKe62ZVRSRo+mL79e7Fp43a/p+kap5Oy8GtWkiiKl4CtgiD8C3gESFMNuQAot6LSz+c15loMLAbo0KGD6K1M7nz/UpxBUuSFhYWkpqaybds2SkpKGD16NDabjby8PCIjI90GmSt6EDo7OxuATtd1C7Ik2sg/m8um9HX0HTaYiOgo+bqRNeUaOWfyWL1yHUnDB5crH7481c2uKnhj6kUyb3GyLKM/EYhK4oqGQKWrVkM7xvAjEAekX/49DjgtimKOvwTRCiZrpaf26NFDvt+yZUtKSkp45JFHeOutt9i2bRsAZrOZ3r17Y7PZHIyFcr6KHISWKJ+re0ia50xZ+wOSywgwDIEH8JUylIqt3EHLELkzKnrnLi8M6o6y8LlhEAShPvYso88BG/AP4J7Lf9RYASwXBOE97FlJ04DlvpbJHbTSU3v37s3mzZux2WwsWbKE8ePHs337drZt20bTpk3Jy8tjyZIlbNmyBYC9e/cC8PDDD5c5JVTEkwLA008/DcA/H3/Qo+cCqayNQjvvEGhlqGWIAqX43SFU5Agl+OPEIGJ3Gy3CHsPIAiaKorhWEIRmwEGggyiK2aIobhAE4VXgG8AMfAQk+0Eml1Du6pUnihEjRpCVlcXBgwfp3bs3GzZsAODYsWM0atQI+NsgOJuvImPz5s0A3DtpjHxNz2kgkMracBl5h0ArQ2NXXrHgc8MgiuJfQG8n97KxB5yV11KBVF/LUV5I7iCbzca2bdvo0KEDAHfddReHDh2iYcOGdO3alZKSEn744QciIiLo1asXYHdXSW6pilz5LJHmKaHnNGAo66oLZ7ELY1desWCQ6KGdqaR0B40fPx6AJUuWcPLkSfbs2cPatWvZs2cPZ8+epX79+uTn55OWliZnIUlZTRWh9adW5tQbb7wBQK9b+jqMNbJ9DLiC0YOhcqBKGwZJIXbp0oWEhAS6dOkiK8jevXuTkJDAgAEDZJfQ6NGjmTx5MsOHD5ddSSdOnKCw0N5+URAE2aAUFhbSo0cPcnNzQz5VVcswrly5EoApC//tMFY6Dfg7qKyGvwrWKlMhXCggEJTQBvyPKk2iJynEhIQEOR01MzOTb7/9ltzcXI4dO0bLli3JyMggKytLPjlkZGRw6tQpGjduTIMGDbjrrrt48803eeCBB/jhhx+46667+Pbbbzl27BiZmZn88ssvzJo1K2RdSloxEYk0L1Tgr4C2kdWkDW/TWd25jHyVJmvAv6jShkGZgdStWzdyc3PJzMzkwIEDANSoUYP//Oc/nDlzhpo1a9KlSxdSU1PJysoiIiKCkydP0rBhQz799FOysrJ45ZVXOHnyJHXr1uXcuXPUrFmToqIiMjMzWbt2bchmJ6lTeKVObc3atQyWSGXgbUDbXbC8qmc1OVPU/srtL8+8hlEJHKq0YcjPz+f777+nd+/ejBgxgry8PEwmE9999x0HDhyguLiYM2fOAFBUVERaWhqCINClSxdat27NmjVr2LNnDwDVqlXj5MmTAJw7d46IiAjy8/Pp2rUr1apVo3fv3hWGinvGjBkAvJKuSYgbFHgb0HZ3IqhqgXK1cnWmqP2VRaRn3kAbK1drVlVUacMgVTQDTJ8+nbVr15KUlMSAAQN46aWX+Pnnn7FarQDUqVMHQRDYs2cPCQkJPPLII0RGRvLBBx8giiIFBQXUqFGDGjVqcOHCBYYMGUJUVJRcByGlflaEKmiJNK9OVIRf1/G0EM6bwrm+wwZTaLVhs1rJP5sb8NhIqEGtXJ0pal9nESkVr3Je6Xq/QYl8uT6DpOGDA26swKh+VqPKGQblrn3y5MkATJ482SELyWazsWfPHmrX/juz9sYbb+Tpp5+mbt261KtXj7CwMFJSUnjkkUf4+OOPmT9/Pnl5eRQXFxMZGUmfPn3o2LEjWVlZ7N+/n9zcXG6//XYgtOsbcnLsRee1I/1rFMBz/7438YCI6ChMFjPLZr2G2WKpUqcDLaiVa6DSSJ0pXun6ji172LRxu6aMEvwpq1Fn4YgqZxjUVcnSSUHKTGrdujWzZ9sJ4y5cuEC1atVo3LgxU6ZMoU2bNpSUlAB/00U0aNCARx99lIEDB3LPPfeQn59PXl4eCxcuJD4+HpvNRmZmJpmZmURFRYX0SQHgoYceAuCp11/y+1qe+ve9jQdU9TiCEsGqJ3CmeKXf+w1KpOcNXWVXTqBlNOosHFGp01W18vOlNFTJ55+cnExaWprMgfTCCy9w7NgxeXybNm1YtWoVsbGxrF+/vswaoiiyfv16YmNjWbduHa+//joJCQk0atSItLQ08vPzGT16NKNHj6Z3796yPKHKuiq9e9fePf2+lqvUV600Um9TZSOio+g7bDCb0tcZaakBgpqx1FkDe+l6VLT/T6iu5DPgiEptGLTy8zdv3sy2bdvYvHkza9euZdu2bSQkJDB58mQSEhLIy8ujWrW/D1IDBw6kdevWbNiwgZEjR/LCCy8AUL16dQCSk5N56KGH2LhxI02bNiU+Pp7XXnuNU6dOAXD06FGSkpIwm81s2LBBlscV/XewIIp20lrp3YIJX/dTMPozBBbqQjd3ilgav2xRujzOW+Wt5zmjEM81KrUrSSs/39m1yMhIJk+eTElJCXl5efz666+A3d1Us2ZNBg4cyKhRo1i6dCkWi4XU1FQmT57M0qVLGTVqFP3798dms1FUVMQPP/xAbGwsp06d4l//+hfJycls27aN0aNHy42A1PIEEs6yoyZNmgTAsPEPBVwmNXzt/tE7nxTg7nFzIplfZTgNdAeSQbYiQu06chfclcZZCwrlcYBXAWE9gWQjpuAaldowKPPz1VTY0s9SmuqKFSvkeADYYwdms5krrriCgoICwJ7GabFYeP3113n99dcBePzxx5ky5e8W1dHR0Q7U3KtXr5ZPJUlJSQ6KOFjxBmc9IiSZ754QeMOgR9GWRxnrTUuVThY/7Pye3V9vBbQD3UZhnGuoffZ6KbZzzuRhqWVyGOep8taj9I2YgmtUasOghFIZgmPaqLIHQ9euXdmzZw+JiYlYLBZMJhMXL17k4sWLVK9endTUVNkogD3lNTc3l5KSEsLDwyktLeW+++6TSfcGDBhAt27dQqp2QevUJIqiJmmeN/BGgasVrfr3/LO5zJuc4lJZ+wLSiaLHzYl0jO/m9IRhBLQ9g5Yi1qodUI/TSm11V2tgKP3yo8oYBrUylJrrZGVlYbPZ6NGjB5mZmTRt2pThw4djMpn46quvKCkp4dKlSwiCQElJiZziKmHy5MnyieHSpUsAzJkzh27dujFmzBjNxkDBhpZMkrG7YUi/cs/vzW5arWiVn0qj0L3P9R4pY0+NlPJkUScqgvXL0xERufWBpJDvEKelOANVuOXNOp7WDkjjrQU2LLXMRjGaH1FlDINaGZrNZtLS0ti/fz+ZmZkMHz6c06dPk5WVxeHDh5k+fTr79+/n5MmTNGjQgNLSUmbMmMHixYt5/PHH5RjD66+/jtVqZfr06QiCwOnTpzl06BCHDh0iIiICs9kcUqcFZ3j//fcBeDKt/GmqWrtpdwparWiVv3+8aIVsFCampnjkRlK6hrx59oN5iwGCUgNx+Cd7nCu+V2dd47UUbaAKt/SsozYenvr5teIQxsnAP6gyhkGJvLw8bDYbo0ePprCwUI4rdO7cmby8PJKSkuSx7777LlOmTGHDhg0sXryYUaNGMWXKFHJzc5kyZQpWq5WlS5fSs2dPBg4cyLvvvgsgu5IqQqWzKIoyaZ7aleROoWvd19pNl8cnrzQ0nsYW+g4bLMcLNqWvc1jb2bspA9CFVhsiYlBcRgX59gr0cZP+pWu8lqINVJBVzzpq4+FNW1B1HMKgsvAPKr1h0MrAWbt2rdyuMykpCZPJJJ8cAObPn09RURFZWVmEhYVx8uRJ+vXrx1tvvcXAgQMd5p8xYwY9e/ZkwIABZGdns2TJEsBOx92xY0eGDx/Ozp076d27NxERESHJlZSRkQFAbLvWZe65U+h6FX55fPLlcdtEREcxMTVFNgBKOJNdef3eyQ97tW4woKVoA+Vv17OOM+PhTrm7MigL5q4MyumhshukSm8YtDJw1K08zWYzmZmZhIeHc+nSJbKysigqKiI2Npbi4mKGDBnCBx984GAUpApogFtvvZWTJ0+SlJTE+fPnCQsLIz8/nxdeeIG2bduSmZnJv/9t72uQmZmJzWbj4YdDR+EkJ9u7qc5ctbDMPXcKXa/C91a5+yIt1NnazmR3dt1IUbXD15TcelNZtU4jru75U3lXdm6lSl3gBnblr64dkOIN0q59yJAhJCQkcOnSJSIjI0lOTiYhIYHU1FRatWpFdnY2ffv2Zc6cOZw+fdph/pycHF599VWGDBlCdnY2YWFhjBkzBpPJRF5eHu3atSMhIYF27drJJ5JQw4ULFwCIjIkuc89dtbG31ch6G+Q4K0zzRYMdZ7I7u24UydmhpzjMWZGZ1nV3zX2cVU27u+fPIrbK3pCo0p8Y9GQFRUZGypxJvXv3ZvPmzUyfPp3IyEjZ515YWEhqaiqpqan06dOHS5cu0bx5c/bu3cuBAweoVasWYOdQ+uGHHygsLCQhIYH7778fsNczSNlOyhhGsKm4JdK8OtGBXbu8Lqhg1BGEcopqIF0b3sQTXF1X1jAsmLuyXO+g/B78GV+p7Cmxld4waEFSxpIRkJTyiBEjWLFihYPr6ejRow7Pdu3alVatWrFkyRJsNpvc1Kdv377s3buX+Ph47r77blq2bMkvv/xCfn4+mzdvlmMaaiPlrNgsUJBI8ybNnRHQdcvrggqGkg7FFFUJgUzlLE88od+gRHZs2UO/QYllnlEyrc5bnOyV/GrD443yruzxAz2okoZBUsbff/+9XO2rFX/Iy8sjKiqKGjVqUKtWLXJz7W6LXr16sX//fvLz8wHo1KkT48ePd9jx//LLL3JsYdasWQ5zK6FVbBZISKR5196U4PGzgahEdrZWKCvpYCDUUjmdGY9P079k08btxHXtwJPTRpfZ4Uv026tXrvNKfl+cEip7/EAPqqRhULf0lFhP1cp59erVbNy4EYDi4mIA9uzZw4QJEzh37pw87vTp06xevVp2Ea1evZrCwkIA2rVr59KdFcwCOClFtXr1Gl49H0h3jkFB4RquKCX8Ae931aLDp1oJz1ucLM/r6Vq+2ukbPEpV1DAolbHafQQ4/KxEw4YNKSgokI1Cw4YN+eOPPzh9+jRLlizBbDYDyCmryhhDMOEsjiFVcd89aZRX83rjzsk/m+u0mtjXa3mCYGUc+Xpdd24eXylPb3fVI8cOk11d4L5xUM6ZPCaOmS438XG1liTT5k3fcW18HCPH3qXrHdXfiS/jBxXVLVUlDYMarlhYwR54llxDADVr1mTAgAHcfvvtLFy4kNjYWCIjIx3oNoAypHnBgrM4xo4dOwAY9ph3pHneuHO8rSYur+vInUEqT4V0eRDok5Cv3CTe7qrVStedEl69ch2bNm6nb/9ebtdSuqK2fLMLSy2T23f0xPB4A0++71AyIlXOMGjtntXuHOXPSUlJrF27lmeeeYYNGzawfv16Tp48yZ49e6hbty6ZmZnExcXJ44YMGRJSNQqgbfguXbokk+YFcrcs9WD2dzWx+p3cGSRXFdL+RKCD6L5yk5T3ZKJXCSrldacsY+pFMm9xMssWpQOCrnf0xPB4A0++71CKbYSnpKQEVQBv8MYbb6QMHTrUq2fT09NJS0sjMjKSuLg4p+Py8vJIT09n3759LFy4kIYNGzJq1Cj+/PNPDhw4wLlz5zCZTJw4cYJu3bpx+PBhh3ml52NjYzGZTC7XcDXGFzCZTMTFxTmskZaWxoEDB7jpzlvIPX2GZbNeIyI6ivbdHb+T/LO5fPHOGhq3bIbpsqvMGfSMNZnNdOzZnU49u7udTw+crfnFO2sc3qlxy2bUNJm4Jr4rA+4bKo+Vnm95dTvi+98od3tTzuXJd+ApTGYz7bvHac775/FTbFqzjpEP/5OYer4x2BaLiWt7xmGx2P8t5JzJY/mba2jZJla+5gssf3MNM6fNJ6ZeJNf2LPv/zN19LXn1yGqxmOiV2I1eid10vU/LNrHE1IvkyWljHAyPr74X9fetR5ak4YN9+nehxJxZb51KSUlZ7G5clTMMSrePK4WdnJzMhx9+SLdu3bj++uvl8Y0aNeLo0aP06tWLhg0bct1115GUlETDhg357bffOHbsGJ06deLLL790a4D0Gil/YOLEiZSWlvLaf97nylaxmgoRyipYV/BkrK+gXLNxy2ayAm95dTuHd3JmkD5euIKVsxdQ02Ti2j7Xaypprffyp7GQ4AvD4E7B6VXQniIqJpLsoye4856BfLrmyzLrS0qwR6/OPDP+FTp1aU90jOOJQC27UtaWbWL9qrj99b14I4svodcwVDlXkp4sIGXLT3WcYPPmzWRmZlK9enW2bdsmp6muXbuWvXv3AvYeDdOnTwdcp6EGK1VVSZoXFhbm0n/vztWhdNnocYv4wm3lbE21v179Tlpri5ezY6RP5RiwxwA69OhM9z7X0+Pmv3PvQz1LSnLVWAsKSX35LcBzuglP11K6e75cn8GmjdspLi5hyze72LzpOxYsn1mm78LwoZNk//7Kj+c6zKt2rShl9bfbpapnJlW5E4MeSKeKsWPHlgkeR0ZGcuLECR566CHq16/PxYsXad26Ne3atUMURUwmE0899RQNGzYs475RQ8vFEwhs376dDRs20Lxda24ZcZfLsa5cHeC4m+5yQ7zLserxzk4V7nbjztZs3LKZfEooshWWmUNr7dh2rYiIjpLdS8oxR378lWWzXiP3zxx2f72V+lc2kp9TrhWKJwZpx9vzhi70vKEbJSUl1G9Yj1Ur1jnssn2xS9XaXUsngjp1a7N75wGyj57U3H03a34lO7fupVOXq+jc/WrNU4XkWrFYTLRsE8vqlevoNyiRK5s28JvbJRC792DAODGUA65OFZs3b2bbtm1069ZN7ulgNpsZMWIEEyZMCLCk3mHq1KkApLw3381I9/A0eKpn/Prl6XwwbzGFVpsmu6mzOdQ9HNQ7eq3n1KclrTFa3dxCvcBOa3e9f89Pfsm+0dpdK+sqzGYTIMr3lSeMzO37OHLoGEcOHaNJs0ZuM5ZCKUBbmWEYBg/hLrW1IkDqYR1T/4pyz+WpgtQzXu3e0TuH0g3U4+ZEftj5vYP7R8/a0ilAndrapFVzN2+mjWDVRyiVqqSQ+w1KJK5re6wFheScydOdEukqg8hddpE9sDva4ZpSuScNH4y1oBCl4XAFZy6e33/NYvqUeSS/MpHWbWN1vVd5EEqppf6AYRjcwBXJXSi27XSHs2fPAlAnJnRpo299IAmzxaJ5qtCKAShTUqVTAsDur7fSMb6bx0rdl53bfB2L8EYhKY2EpZaZmdPm68rxl+Bql66+p0c+dQqq2nDofRclpk+Z5zRWoUSwC/wqCgzD4Abq4rBgk96VFw8++CAAE1OTgyyJcyh39lr1CErlr1S6Wm4gb+oDPK21cHUq8HWdQnkVkifNcqRrEuGds34I1gKbfArRI59WdfOyRWsAkZFjh3mlsJNfmejw6Qy+IOqDyh+cNgyDG6hdR0OGDMFms2Gz2cjLs3PKq08UwabSdoXjx48D0OOmG/wyv17XiatxynvqHbcz5a81nzPj4g4R0VEedW5zdSrwdSyivArJk2Y5rpS80pAoTyF65FMbodUr18mZU5ZaZq8MXuu2sS5PChKU1dHLFq2RZfbUQCi/x8roVvK5YRAEoSawAPgHEA0cAp4VRfE/GmMfAJYCNsXlW0VR/NbXcnkLtbtI6vgmBZ2hbF/nUD1VSF3nqtfwjjTPHfLP5jJvcgq7v94KuHaduFKmSnqKUc9PAv42BFrB4k3p6yi02mT3j6v5tOguyhsHCGT1sr/6AHjaL1odJ5A+pSwiT1p1ehpnKA/+ro5ew/aM79m5bW+ZVFpPURndSv44MVQDjgG9gWzgFiBdEISOoige1Ri/QxTF6/0gh9/gLgAdbCptZ5Cypv711Fi/zL8pfR27v95K9z7XOyhJteLNP5tLodXG3RNHa7bPLLTa6Hz9dXKMQI+BuXviaEZOnaA5n81qJe7yfBLdhatTiafwV4bSpYuXPBpfnp2rp/2i1cbAXTMepYzWgkImPzuKfoMS5cY8I8fexeqV68g9m+/33XdMvUgstUzs3GavO9ryzS6vab6hcrqVfG4YRFEsAFIUlz4XBOEI0A046uv1ggFX3Epa94MFtUtr165dAAx92D+Mr8qds3LnrVa8UnC3e5+y+wHp3t0TR9MlMd7tLtzZmmA3CrMfe479W7/jlhF3Ub1GdTlLSSmTP3b8vshGWjjtFQDqN4zRNd6ZQvaFq8OTOdydNFJffotpMx/jy/UZsryA7Pv3F6GdWkZrQSE2ayFmS81yKfXK2M3N7zEGQRAaAG2BH50M6SIIwhngLLASeFkUxYsa84wBxoCd7tqAeyhdWvfcc49MmucvuOu21uPmRD5etEJOJd399VbWL08H7KmpvW8bIJ8ket82gMyvMrxeE+zKf//W7wA4eTibfVu/k7OU1AZFOkV8vGiFpjI/fugoS1+cy6jnJ+nKctI6hXhqLGwXrPZ3jKzrdiw4V8i+cHWo53A1p96Thvpazl957P3+Rx6Z8K+AuJQ8yYYKNgIdx/CrYRAEoTrwHvCOKIo/awzJAK4BsoCrgdXAReBl9UBRFBcDiwE6dOigneDuJ4RyMNkVlC6t+fPt/4l7D73F692st89JildZdDYxNYVN6euwWa2smmfvX7H50w2cOnqMkVMnkPlVhsfuHbV8fYcNJi/nLEd+/JX7nhzrcALRMiiuXEpLX5wrx06Sl7+GO2idQvxNo+FMIfvC1eGsf4KnDXXUMQilvL/+fJidW/dSq5bZZ8qvsgSGAx3H8JthEAQhDPsJoBh4TGuMKIqHFb/+IAjCDOApNAxDMBGqwWR3ULq0Vq9eDcDE/0vmsyXveaWgyqvYnO3Sf959gH1bv+PU0WNyfOJ8bn6ZAjVwbZzU8kVERxEZE82+rd/RJTHeLXcQ5x+SAAAgAElEQVSSK5eSFASXPt1By/AEo091eaFUrL6qSnY2bsIzD5J15AQTnnnQZ/JXlsBwoOMYfjEMgt1fsRRoANwiimKJzkdFwH++Di8RqsFkvVCS5oWHh3utoMqr2Jy5fdp3j6PF1W0xmc0MemCYXK+w++uttOnUAZPFrFnHoEfxupJZy5A4M3hNWjXXdVJwhWDRaJRHOWo962wXLvntrQU2l5XVzpRc5vZ9/P5rFpnb99H9uo4eyekMlSUwHOg4hr9ODAuB9sA/RFG0ORskCMJAYI8oiqcFQbgKeB5Y4yeZvEaoBJO9xfbt9mBei6vbAq4VlKsdufo5Pa4ld2OkYPPIqRM0OYvycs7ywbzF2KxW7ps8toyiV8/vSR1BRdrBO1PGnlYaewotZe/M0EjZPvaaBuf1CL50eemh5NBay1cupsriqlIjzNcTCoIQCzwMdAb+EAThwuU/9wmC0Ozyz80uD+8LHBAEoQD4AvgYmOVrmao6nn32WQCSl2v3slZC2kVvSl8nX5OCsvlnc92O1TOfEn2HDebuiaMptNoc5pdcO4d//BWAYlsRHy9aAfy9u1fOP29yShn53EEyGr7kMHL2XZUXkjJevXIdOWfyWDB3pUOl8eqVzv8OJOXojeKSlH3qy0vlNZKGD2bazMecVkI7u6dnLU/llN5/QeoKhg+dxO+/Znn0nKvvLZDzhBr8ka6ahWt3UG3F2CeBJ30tgwFHWK327JaYBu5J8zwJmurZcbsbExEdhdliYdms1zBZzA7zS1lF3ftcTw1zTQcZpJOCMsMpkC05ncFfAWZnvQj8sct2tbY3hHp2ygt7u82RY+/y6c5akm3zpky2fGPvya63Alr5qYaSDuTL9RlOv6vK4qpSw6DEqOSQSPOuiKqja7wnQVNpV+/KVaTHr+5sfknpj3p+EnWiIhyI9ZQKWMpwckaNEUj4yz2lxZaqVVymB8sWpZP68lKsBTaenDYGcG0slGssmLvSabxi2aI1pL78FtaCQodUUHvtwlIAj8j79ECSrd+gRJld1RdQciq5qquojDUM4AdXkoHQwsiRIwEYN+8lzftq14eWK8SVy8WZq+j4oaNMf2ACxw8ddSqbtBagOX/mVxns/normV9llJGhx82JZbqqqeXR69bxpfvHH+4pNdy5XJSuJm0Iqk/9LhHXriJR9fn3M5OfHcXkZx/StbPWkt/VO+WcyePL9RnMW5ysm3Lb3ftK75n8ykSvXWMVGcaJoRIjLy+PEydOAND9pgTNMVpVyZ4UZjnbIevJ+1dXH6vXcLX7loxG607t+f3AT+z+eqvMiDr04RHYrFa54Y/yXbTgrjFQRYO7LKSRY+9yILwD/RlFrnbItw/rz66dB7BZixzmsBeTjdHtwvKU0E/tWtOzhjsXkPI9W7ct34mgIgaoDcNQifHhhx8CEB6mfTDU4izytDDLmavIVd5//tlcPl++mmJbkby21hp6MooKrTaZn6nQZuOTN1fS+frr2Lf1O6f8SWq4awxU0aBWemrFpOyuJnEV6ckockbNLVFmg8CWb3ax5ZtdxFzhfZ2Dp4R+zuIvrtYIpAuoItZSGIahEmPHjh0A3PPEI5r3lami0i69vIVZytOFdFLQ6qkgVTtLa6tpM/SmwAJynYNEr9Hi6rZylbMel46rxkD+hj9iInpJ7bRYTsH5LtrZTl6izJbcRWqWVHVfByV5nt4UU1eKXHmv36BEdmzZI69VXiiNIeBVyrCz7zWUTxKGYajEOHDgAABJj2tXkupV+Hq4hMCRdltJca3VU8FmtSIglKGoeC91EavmLZHrFpQwh9eg+IKV9FffZO37a+T5JNkA7nh4OCazWbeizT+by8cLV3Dox5/pcXOi22d8rcj9TZMBzhWT+rq7XbSznbxEme2syY7aoLgKYGvBEwX6afpGNm3cTlzX9k7dV57Mp5Qd0GVg1fCkB0aowDAMlRTFxcWIokiYC9I8Tytx3SkxqVq5SavmDumjagMUER3loPSVyla4HBCV6hb6Jd1Gp2ZtuNISTc3wahTWthE3ex4vPZfMuRqXyBMuUSqKcpxAciNJqa9ailxNuf3xm/YA+NIX57qtbva1Ivc0i6m8rT31XPdkHj1kdHp4llxBT/tQqedz09jGl58SNJ91dk2v7Oqf7TTiNt2BdXdzhwoMw1BJsWDBAgBuvHOQz+ZUunveS12EgCBTWKjvZ36VoVvZKZXtoAeGYbKYsVmtbHr3E6aOmUCTOvXlsTXMJkqKS4iOjKJJ7VqU1gxnb85ROT7Q4uq2tO8eh81qlQ2AWpGrg975Obkc+vFnXTxIzhR5eQgG3aX8KhHKu0xnUBsUT7ufSYpTckFZCwpl95U0j9Tz+YabrnXIInJ1ynEXaNeSXStukvryUqbNfMwnPTBCBeEpKSnBlsFjvPHGGylDhw4NthghjQkTJiCKIolD+nNlq1hMl7vNeYP8s7l88c4aWl7dji43xPPf9LW8O3sB/9v5PRHRUbTvHgeAyWymffc46kZH0r57nLzmF++sYdms1xzGSnM2btmMlle3kxWkNKblVW149pGJNGrUiHVr19KuXTsEQaD0UinFxcVUr1GDL7/6ims6dKCeqS7Vm0ZTNzqSxNv689816/jyg0/lOWuaTJSUlBDbrhUms5nGLZs5rNfymnYUXrByVfdObr8n6R3V47TeUS+cPfvJ4ncpLLDyxHN/78hbtomVGUotFpNH64Qilr+5hpnT5hNTL5L4XtdSLcyEgIDI302KLBYT1/aMY9UKu1HseUMX+g5IcPgOOnVpT/bRE8yY/QT9b02Ur0vPKr8ri8XEvu8P8u8ZbxJTL5Jre+r/+8o5k8fyN9fQsk0sFoupwv19zJn11qmUlJTF7sYZJ4ZKCFEUuXTJ/h/rnVdeJywsrFxuDz0xAgn5Z3NZvzxd7q+Q+VUGHXp0pnuf6+nQozPvp74p7+6lWIIy8CvFMLrEtqNJnfp89uln3P3PYTw6/jFmz5lDsa2Ii8UlTJs+jSWLF/NB+mpuH3o7nZq1ofZlam9lF7mI6ChMFjPLZr2G2WKRawyUu3R3rT/1oDyFbZ486+9dpjeuqvIEUe8ecRu9ruvDjYl9qVHtb8VaSgm2izkUXDyNVBehLuxTQtnz2Rf8Uc6CzuoTWyjv+ssDwzBUQkikec3bt+amOwaVO9vGXYxACWXtgFRf0L3P9XJNg/R5z8QxjJw6gUKrTTY6gKygN37+BQBDbhvCo+Mf4420+YQTRurcuaQkJ7Nk8WJGjRpF/379ALjSEs3h839qdnTTynhSu5PKS6tRHuZUf7Kueqq03bmqtObz1r11LreQOkJL/nFjY8JUKdVhVKdWtYaYwqPIKz7EJbFYtxLWI4+7uZwFnT2JC4Ry1pE7GIahEkIizZv29jwaXNmo3PN5orik00Dc9T0Y9fwkOsZ3o8fNifJnszYtOfTjzyTe1p8mrZqTfzYXEZFCq43E2/rzw87vOXP4OGElpVADBEFg9pw5hBNGWlqa3Bdj/PjxpLw4g5pm+y6zZng1zOE1wAXDqrJRkNqAKGk1KhM8VdrepKx6F0QVMItNCBdqkp7+IUlJdzl0FxRFkc8+Xctttw8hskYrcop+BsQyylZL+foiqOssNuHJCaEixoMkGIahEkIizfOFUfAUypqAiOgouQ2m9FnTbGL/1kwyPtvIvZMfLkOiNzE1hUPb9lG9Zg0KC2zUMJsIDwtj7ty5slEAmDt3LiXiJYeitOILVj5+532nQVytRkESnKXkBpt7qbzwVEl6krLqrImPHtSq1gBzw/qkp3/Iv+4bTuZ33zEn9f8QwsIQS0t5YvKTpKXN58MP07n9jtuoVa0BBRf/cNliVFn17EoevZ3mXAWd9cwVyllH7mBwJVUy/PXXXwBExHinxLzlDXLFe6ScU6vKWEm9fT43nwvWAooLi7BeKKDYVkipKDJpkmPG0KRJk7h06RKFBTZKS+1z/Wf1Jyyb9RqfL1+tKZ8eBa/mftJDLR7KKA/ltrv5PKWcVvIdmavFEBYWRlLSXYwf/xhpafN5YvLTCGI4T0x+mrS0+Ywf/xi33T6E0tJSCs4K5JzJK8PVpPxdSx4tjiVfUmW7msvX330gYZwYKhlGjRoFwKR5M7x63ts8fVfPKbmItKqMlaeG3w4cZPfXWxk77H7qWGpRrUZ1nnriCd5Im8/48eNJnTuXyZMmkZaWRnFJMdOemwZAafUwMr78BkCuhXAnn5axkBhdJXK+4DbzCW2KDm/rESIjYmg/7kbA7iqck/p/oOEqnJP6KoIgYLUWIpaGs3Htdu598JYydB6uXFq+c31poyKfClzBMAyVDCdPngSgW+9eXj3vLf2Fq85qylOCs7x9ZYC4Y3w3tu/NpM+1CWz8aANvpM3n4bFjeeaZZzh3Lp9nnnmG4pJiFi1cREJCArffOZS5c+fKvRsGPTBM13tpGQuJnK9jfDeatGoetHacAHl/nSE8PHiHek+6oylpLz5N34hW7wVJed7+zwEAlJaWYrMVYbGYNV2FonAJRBGzuabDc6Ct8KW0UaXMnipuTwPGRlaSgZDHhQsXADBVr+71HJ4oQrViVTfZke7d+kASYN/JOys6U67bpFVzLuTmYy0p4o67/skqU01u7vuPy+ypYKldi9mpc2Sj8PPBn5g1YyZNWjV3mm6qFUNQnw7ANycEX8UlRBGaNW/sfqCfoCd4KilSa4GN1JeXOvQvAByqoiUlGi7UAMBmK+LC+QKKi0t4ccaLDvNOmjRJPjGEhYVRq5aZwmom/vzrT9kAWQtsWAsKHYrUnKWTKk8Yzt4r50weE8dMd9l/oarAMAyVCE888QQAD854KiDrKZWoWhmqA71KV5FUYaxUvurna0dFkBVeQLR4kdtuv43SUpGwsDBqmE2EhQkUFtjoc1MfDh86xJ13DKXelQ157q05bgPGSqMEOJwOwDepo4HgPwoE9Oy2JSU7+dmHmDbzMfoNSqS4uIQt3+zCmSvsklhMKSWYzTUpKipmyjNTWLp0KePHj2fu3LlMuuwqhFJ7QFoQKKWES2Kxg1K31DJfZoM1uXUT6el6t3rlOjZt3E7f/r0qnWvIU+gyDIIg/BN4D2h7uXUngiC8BtwK9BJF8bT/RDSgF3v27AFg4H13BnxttTJUK1hlrUDH+G5O4xB5OWeJjIm2G43oKHb+9RvNa1/BlZZoTLX+rjYurR7Gln2ZnKsNff91h4MRcKWYtU4E6tPB8UNHWfriXEY9P0k2GJ4gkHEJf+bK63GTaBWdLVg+06E4TC3vskVr6H39zQzsN4Rvv/2apUuX8vjjjzIn9VVE4RJzUl8FSklLm09iYiK333Ebtos5ZdZTy+BKZrWcesY4Q0WuT9ALQRTdB7gEe4LxLmCvKIqjBUF4EngaSBBF8Tc/y1gGHTp0EFeuXBnoZUMaRUVFJCQkECYIfJa1OyBrSnUB90wcg4hIka0Qk9lM4m39Za4k5Q6+Q4/OrE5bWkbh5p/NZfZjU9m/NVMmwRs5dULZIPHgARza8yOdboqnWm3n1BXK6mvJjeWJa2f6AxPkwjx3pHr+xOBm3WjRqgnbDnzkdIzEVDpt5mMVwvUhyWsymfjltx9p3OhKuV5BqmMoLS3Fai3kyy83cscdt1NKsVzHoAVv+lD74h2U2VAVxUg0rnXd96Iodnc3TteJQRRFURCEqcB6QRAOAVOBvqIo/iYIQlNgJVAfuAi8KIrimnLIbsALLFq0CIA+/wycb1zaEdusVlbNWyJXOEuZReDYFa5Jq+YcP3TUwXUDdqW9f2smTVo1574nx8q9FJT3pYro3V9vdTAaWlDTYAAuO8Wp4arJUKghEFkxvlSuSppum3CcUq7g9jtucxhjsxVRcMFK//4DKL5oY8HiVAbcdr3TtfV2d/OV4VT2fPCmUrwiQHeMQRTFLwVB2AXMBAaLorjr8q2LwERRFPcJgtAQ+F4QhC9EUSzwg7wA/PTTT8THx/t0znr16lGjRg2fzimhWbNm9O7d2y9z16xZk/79+/P+++8DMO6VqV7P5alvXBnQNVssDhXOHeO7ObhUJKUucRipM5qk+wcz9zl1/6jnVcJVjEM5j553bNKqeZmTQqgWuvkqK8ZVQxpfKleJpjvnTB7vLl/D3SOKadqgrb2uAXvShNlck4KCcxRc/IPFC94kZcpc8s9pn4jstNeFTH52lGYDIH8Yzi/XZ7Bp43Z63tDVq0rxigDdhkEQhD5AHHaiczmmIIriKeDU5Z//EAThDBAN+M0wCIJAtWrhPpvv0qWL/PHHHz6bT43s7Gy2bt3qt/lfeOEF+efq5chI8tY3rs4okj6VGUBqygmpoQ/YFbTy/vFDR1n0/Ku0vLotdz5yv4MBcgZ3/aOl521WK/dMHONxOm5lCSg7g6TArAU29u/5ySEzxx/KVa0ws//4mY1rt3P7PwdQJ8IEdYsBuPOeAVy8WOJS8aa+/BbTZj7Gl+szNFNY9Spkvbt7V/EK9RwVtc5Bb/A5DvgEeBwYBLwM9NcY1w0IF0XxmC+FVOOqDh1Zkf6lP5eoMDh2LIv1n6WT8c1Gfvv5f5w+doIGTa/0ai5f5+w7S2dVM6Cqd+PzJqewf+t37N/6HZEx0bJSn/3Yc+zf+h2FVhv3Tn7YYS2lUVMW1CnHSS1FR06dIMvh6gQgyS8xySp7Y1c2SO4Rm62oTGaOM+WqrF34cn2GR+4StcKUDEVefo7T3g3O5pHSVm8f1s9hTk/hi37RlYV91a1hEAQhFvgPMEcUxbcFQcgEDgiCcKMoit8qxkUDKwDX7ZwM+BRNm8Yy9rGnGDn6ca7v2pyXxjxJ2n8+CLZYgPMTiDqV9f3UNx0U+ajnJ1FYYEUEucbAHof4DoCDu/eRfzbXQaErjZoW7YZ6XT0nAGl8odVWpjc2hK57yRtI7pG4ru3loKretpfK2gW9SlCtMPXsrKUubcmvTKR121h5Hq20VW/gL/K9igiXhuGyst8ArBNFcQaAKIr/EwRhDfZTQ8/L42oCnwKviKK43dl8BvyHmjVNXNWhEz//eIAL585Tu24dn6/hqSJ0RkynPpmoFXmTVs25tu8NLJv1GplfZdCkVXP6DhvM6eMn2bruS/ZvzeSjhe9w7LcjmimlatoNpdzSunrcZkr5TRZzmbGVyb2kN1VT65l+gxKJ69q+TLGZJ9CqWlZD6tIGyL0X1LJL0OsWUsdWyouKekJQw6VhEEXxLNBe43qS9PPlVNblwNeiKBo5pEHE3AXvMvDGTiydkcqE/0v2+fz+4FGCsooctHtA/Hn8FOdy82nSqjmHf/xVPkGoA8Vqw+Ou0todnI0NLo+Sb+GNQlMqcxBIffmtcu3a3blykl+Z6PDpSna9biFnfRe8eYeKmoGkBV9UPicASdjdS7dfvjZcFMUffDC3AQ9Q74r6XNGgEV9/+DnjZ7/gwG/vC3irCLWoJ5TQUrzKa9KO/9YHhvFH1nEmzEmmdkRduQjNH3LrOR0Fk0cpVPB35fMoB9ZTCZ4oS3duGGWXNnfQ69JxVzDnycmjMtFplJuhSxTFraIohomi2Fnxp9IZhbzcHFa+/QZ5uTnBFsUlnnn+FUpLS/l44Qqfzy0pQk/96RIxXeZXGeSfzeW91EW8n/qmnGWkpOXWov2WdvyfL0/n+KGjHMzcJ6eU6qlM9kZuX9Nte0tnrgUtKulgzSPRXo8cO0yTYloyHBPHTC+zzu+/ZjF86CR+/zUL0E9TrUduPXOplb7WM3opuisbnYbBlaQT6z5ZRdocO9HX8AcfDbI0ztH7pv6YzRbWzF/OnePuL9dcvgquqoO+q+YtAcBkMTsUwElQu32k5zv06AzgcPLwRkblM6BdFe1rN5Ev4xG+yo33dh5PspGShg+Wg9MTx0xn3uJkeayzmIGrNV2R4HkKPfN4c/Ko6G4kgPCUlJRgy+Ax3liwKOWOuwJ7VItt3oqoqBgG33E3JrMloGt7itLSUnZs/Zq4hGupX44ubl+8s4Zls14jIjqK9t3jytzPP5vLF++soXHLZpjMzikqTGYz7bvHYTKbadyyGTVMNekY353+9w2Vr0lFaS2vbkdEdBQ9bk7kv+lr5Xvtu8eR8dlGvlixhvpXNqJxy2Z88c4aftq1n5WzF2jKKMlXJypCnstkNju815Eff9V8R6XMvoDyHfXO+cHcxURF12XUI0kO11u2iZX9+xaLyWuZvJ1n+ZtrmDltPtlHT/DOko+IqRfJtT3L/vsAsFhM9O4bz68/HWbTxu0OYzt1aU/20RMkvzKR6BjXylRaU5JX+jx5/DQTRk+nU5f2budQQ8/7Wywmru0Z5/b70Tsu2Jgz661TKSkpi92N08WVFGrocE2caNQxOEdpaSnxna6kSf0YFuza6PU87nbjEleSO4oKd/No3deaW6vg7O6Jox0C18p5pDkkqo7ufa5nYmqKwzj1M6GUgqqHK8lX8CQW4E39gqvK6vLIN3zoJNmFozf+oBfefCehfmLQy5VknBh0IC83hzUfLCO2eauQPy2AvTL8919/Yv+B/QwaPgyTxbtdr6tdc/7ZXA7u2uew83cGaYde02Tit/0HHU4Y+WdzmTc5hS9WrHHYtWvtsNUnj4joKBJu6cuJw1m0vLqdbCykeaQxg0fezeljJ9n99VYioqPockO8PI/6Hd2dkvwBZycvZycGf0C5I1fv/nPO5LH8zTW0bBOLxWKSd8fRMZFud8nSs9fEtaN33+uwWEwu13IGZztyZ6cOtczewBM5vXknf8Dde+s9MRiGQQfWfLCMtDkvEhUVQ1yXHgFbtzy4rteNrFg6n5/3HuDmYbdpjtHrCtIavyl9He/OXsC1fa6nyw2ueaskBV1SUsK7sxeQ/ethuiTGy26dL1asoXuf67l38sOaRkAL0v3/pq+VFbm0y5fcUC2vbkeXG+KpGx1Jl8R4B0Pj7N29cfmUF86MkS8NgzuF4cqt4o3Sk9bbteMA/56xyOFZX7nCAKJjIhmaNKCMG8kXitoTOX35TuWBu/fWaxh8Hny+XOy2APgHds6kQ8Czoij+x8n4ScAzgAX4EHhEFMUiX8tVHgy+426Hz7zcHNZ9sorBd9xNZFSMx/OV93k9qFs3ghat2nIwcx/FhUXUMNUsM0ZvQFRyr0gsquA+OKt2yUiFYr8f+IndX29lU/o6ho4dUaYK2lOonx86doTsQlK+l7rYTqpmVr97MFJQXX2XebnnvS4aU8JdoNVVHYM31byu0lgDUQTmicxSjwgQGTl2mPxdeyJnqBS2+ary2ucxBkEQagFPYS96ywZuAT4AOoqieFQ1tj92Go0+wEnsfEw7RVGc4mqNQMYYlEocIP39t9m/N5PMHVsY/8TzXmUorXz7DdLmvOj183px/FgWdwy4jtseupeHXniizH29/nRl3wWpAtidEncWf8g/m8vny1cjIDDogWF+8eO7ei9JLmVsItRiCxIGN+sG4JNeC4H2gZd3vUDKK/VXgLLfdUWJHeiFT/sxeILLdNspikufC4JwBOgGHFUNvx9YKorijwCCILyIvVOcS8MQSCjTVAGWLJgDQEJiX9lYeAr1CcRfaNI0loiIKD5fnq5pGNztjiVlKaWHKpWmO0XqbBesbPMppau6g7dUHFroO2zw5d7Rou6ub8FEVHSET/LiA72j1bOeK6WrdcLRGu9sDk8L66QeEa5agrrre11ZjAcEoI5BEIQGQFvgR43bVwOfKX7fDzQQBCFGFMWQqCRTK3GbzQoiDLvvQa/dQJFRMQGrhXjquZeY9vQ4vlr9GTcnaccanMGVsnSnSN0pZ+VneeTwBJKBERFZNW8JZovFI+6kYCAyqk6lUTZquFK6apeIs8piJV24pZa5TA8J9XUtSD0itOBJ32ut96io8Gu6qiAI1bEzsx4SRfFhjfuHgEdFUdygGF8MtNBwO40BxgA0bNSk27r/BqZ9pQR1XEDtYvJ3zMBbiKLI9V2bU8dcg+U/fOvRs6526oF0vehZS88YPS6xUHEp5Z05y/CuN9OhY2v+u/O9oMmhhfLQbXubtiq5e/r27+VQJCfNZy0olPsyjJs03Ol1f6AinRiC5kqSIAhCGPaWn8XAY06GXQDqKn6Xfj6vHiiK4mJgMdhjDL6T1BHOAsOSS+n7XdtJmZXm8HuHazqzZMEc+V4oGQdBELhj2HBWv/sWhw/+SssObXU/K2XnaCnKQAZpnbG0KuGsB4MS6lOBsi90qLmUsn4+BMCcBdMCuq4eJecN3bZaUUvP6FXW7iqLbx/WD0stU5keEjln8hyuu5LNW8UeKoFnX6LcXElauMy4uhRoANwpimKJk6E/Yu8KJyEOOO0PN5JeriNJ4a/7ZJXD9cF33E1CYl+2ZWwi/f23yT17hqbNWrAtYxOI0CM+0X7vvbfJy81h8fzZLH5jtrxeMLmWJj8zA4BXH/U8dONrzqDywJUsznowKKHkTNqUvo4P5i1m1bwlDvP1HTaYkVMnhIxLqXadwNbN6OEGkviRkl+ZqEmcJ0HJafS3u0V0+czvv2aRdOtjvDj1dQcuJGfcR9K8X67P0LyvhzNJLx+SP+Ar3itfw18nhoXY6br/IYqizcW4FcByQRDew56VNA17NpPPocV1pHU6kFxDiTf1Z/H82SDAsHsfBKDD1Z3pcE1n8nLPsuaDZYA9CD3svgcpLLSRuTODwkIb6z5ZxZKF9iA1IpgtFmxWq3wt0FxLYWFhxF9/Ezu3foO1oABLrVq6n5WYUTv06Oy245kEZ+4Yret6r4HzOIBETHfPxDEMemCYLlnUQWgJVZ0xVQrEWgtsTtNklTvk1m2Hy8pNveNW+t71cglNnzKPLd/sYss3u4i5wv1O3NP0TK3TQTCb64RqfMIfdQyxwMNAEfCHgvr5YWALcBDoIIpitiiKGwRBeBX4BjADHwG+bySAdiaQlrGIvMyHlDJ1vP00cBkH/7ePbRmbSEjsi9Vqb2fdpLWjz8sAACAASURBVFkLUmalAfDLz3ZC2V9++h9PT5uFzWq1d8cG0ua8yOhxTzD+ief9nonkDMkz5zHwxjheHfcsKe+k6Xom/2wuS1+cK/dmVvZodgVn7hjp+g87v2diaoq8c1826zUKrTbZ7698vsfNiTK9dpNWzTXXVrbsVBstrTUlY+GvdNmKDHtHNNPljmhmXcrKmXKT2oX2G5So292S/MpEiotLuKZTO12K2tPsJy1Zg+kKCtWOb/5IV81CVomaqK0anwqk+loONbQygZTGQnl6WPfJKrZlbKJHzxvslc4ibMvYRGRUDNsyNjF85DiqV69Ou6s6AnYDk7ljC7EtWpO5M4OMbzYy5rGnAPupxGy2BD0wXe+KBjS6sil7vt3OpUuXCA8Pd/vMpvR1MsfQqOcn0TG+my4Xi6uWnj/s/F6zwM1mtcrGQPn8vMkpskFKXv6a5gnAVUaR1pqhEkcIVXiqrJyNl9qF9ryhK63bOu8Zrdy9t24by+rP55cZq+dZZ3B2cgkFhGp8okpTYpjMFuK69MBktjjQXgy+426iomJ4ZPwUEhL/Qas2V3H491/47Zcf6dEzkSZNYjGZzHywcgmIIsPufZCoqBjGjHuSiMgoLpaU0KrNVbJbyZ1RCBQXU/ce1/PR6ncICw+jY3w3t+Mleoh7Jz9M/Ssb6WYbdUZnYTKby1BTSGNj27VyoLVo3z2OIlshJw5nUdNsZkzKk9SNjtSkj3BFn6Fe88zJ06xbvoruNyYQVi2c2HatXFJkBAuns0/y9Uef8+DYuzxmDXUFPRxCnjKFOhsv0UT0G5TIqhXryqxZHtoKT55V0lVI4/W8my/4lkINQaPEqKhwVnQmnSRGPTwJq7WAE8eOkrkjg4hI+2517SeruO3O++STBsCShXMwW+wK3lkPB/UJJRC9Htq064ClVm0+nL+ceyaOcTvel/52rb7L6r4IamxKX8cnb65k5NQJckMeb+oNlO8xb3IK+7dmknPqT44fOirXMlSVU4SvfNqu0k6V98ZNGu5QWaxcszy7d6Wbyh283ZWHqv8/EKjShkEdfB7+4KPk5ebI8YUtm//LmT//4Fj2EZo0a8Hx7CMA1KpVm/y8XEwmM/l5uUwaN5wu3a9j7cerGDL0bsY/8TyJN/Xns4/ep0v3eE6dPM7i+bMdiuKUxiBQldAAj096jn/PfJadX24mvl/vMvf9lcevpXiV1yTuImW6qZYRKK+xklqBJo0fxcHMfWXWCJVsJH/BV64UZcpqXNf2pL68FLArULVCdbZmedwo7txUvkCouZ0CiSrdj0GLs2jxG7NZsmAOTZu14NhlQ1C3biTnzv2dTla7Tl3u+ddovtu+mQP77YV2NU0migoLubJJM+5Mut8hC0mCcp1AEOk5Q8+4ptSJjmDF7rK9GvT2WPAU7rKPPl++mlXzlnD3xNHcN3msz9atqNi/dRfT7h3Lln3ptGoT67d1vM3hV1YiT372IblWQH1i8LTwTW/RnCf0GAb+RtAL3EIdebk52KxWRo97gsSb+rPy7TfsO/bLdjI+oTdFRYX8efoUJRf/LsMID6/Gs8mv8tmH73Ng/25q1KhJcXERRYWFREbF0OuGPnIW0vCR49i35zsEQSCuy7UO6wSSFkONmwcO4T/rPuLY70do2rqFw73y7pydnTi0dvrKa7c+kOTQcMdAYPA3dUShg3J3h5h6kcxbnKypiJ2dBPRwI+ktmtNaI1RcP5XBQFVZw5D+/tssWTiH0eOeIOObjbJbp/+gOzj44z4Q4c/TpwCwXU5PBWjX/hoWpf1bPk0UF9sZwps2a8GTU2fy3vI3ueuekSDaA58/7P8egBv7DnRYJ5h9o5NfSuM/6z5i3qRk5qxb4XDPW1eNZBCc0Vm7gz/qB0KF3iKUIblJrAU2j5Wqp64gPdxI/QYl0vOGrl7VJYSK6ydUDFR5UGUNQ6HVCkDe2RyIgtHjnnBIVW3arEWZZ8LDq3Hwf/sAuzvpwvlzANRv0Ihj2Ud4ZuJDFBbaOH36JFlHfqdHfCLDH3gEk8XyNwmf1YrNZiUvNydo6avh4eHEdenB/r2ZlBQXU71GjXLPqWy1qadyWK20fcWHpCUTILur/En3XRHhSB1hLpdSdbdTdqW41UVzeqFWwqGgiEPFQJUHfqHEqAgwXc4ayso6bM8iupwmarNa6do9nmPZR6hZ097cplWbqwgPD+fSpYvy82Fhf9cBREZGA1BYaCMyKobkmfNISOxL5s4MDh/+Va6clrOWFswpQ7nhLbyl2ki+rDBTJz7v0XMSX5FUbSxBopK49YEkmXbCFdT0FnqoNzyl51DSW0hFcB/MW+xTeg9n30dFgzPqCE8oG9xRS7ijp/CGHkKi5wglJayHhiPUUWVPDMPufRCz2ULiTf3J+GYjnbtex/13D+Tk8Wzq1LFz+RUV2d1EJ09kc+nSJYfnz+XbFUFEZJQcmK5pMvPWys+IbdGaSU9P5/ixLLZlbCJl6nhatmzLyuULGT7yEZ9WQHub6lqnTl0stWqz/YuvEUURRYW6SzhL6/TUFeQsG8jVScPT+IdSpr7DBmOzWhEQfBrHqOxprp64RVztlN2dJrRotfU8o/e+9C7eBLWrIqqsYZCoL9Lfe5vCQhsvPPs4J49nA3D+/DnCq1Xj0kX7CcF22e2kRHh4Na6o34A/Tp0gKiqGsPBwOnftwayUJ7m6YxdMJgtZR34ntkVrtmVsoqTEHsD+5acfeXraLJ9lJHmb6rruk1VYCy4AsHbp+9z20H26nvNVWqfakOhtGuRtx7WI6CjumzxW93N6x1X2NFdPlL2rmIM7A7N65To2bdxO3/69HBS5u2f03gd0GbjKEB/wBaqEYXBFpa1MKa1TN4JGjZtw4fx5Tp7Iplq1aly8bBwEIQxRLJXHXrp0kbzcswAcPfI7AN9t3wzAnt07ueuekYx/4nk6d72OpW/OZdTDk6hevTrbMjbx6sypZO7MYOe2zbz0fwvLZRy8zW6SDMmi11/lvf9bpNswBItkTr0z92SnrlTyep/TO66yk+6VR9kr4czAKNNUpft6ye08ve+OHFDPnFUFVYISQ0l3Edelh3w9tnkrfvnpB04cz6ZJsxac+fMPWrVuz7XXJZB7NkdW/HaIhIWFIYoitWrVxmQyYbUWYG87Yc9xrXdFA8LCwykuLqJ2nTo8Mn4Kc19NZlvGJmJbtOLhx57CVNPMieNHOXXyOCeOZ5WRKVCQ6EBEUWTntm/pemMv6jVqEHA59EKi55CoNNS/q6GkuJCUvJJyw9lzztYLNPxFieEJ3FFCKKkm3FFGOKPNkKgtrmzagHGThjvcd0fN4cl9i8XEvu8P8u8Zb7qk0fCUDqSiQS8lRpUocHNVTJaXm8OKpfP58Ye9gH23D9Dh6jh++/UnSkqKsdSqjbXgArVq16HgwnnuuucBfjn4Awf2f0+PXons35NJnTp1OfPXnwwfOY5ffv4fsbEtOfT7L+zZvYMe8YnyyUAqqusRn0hc12sZdq/3LUJ9AVEUie90JTEN6/P2zvV+WyfQqaPKQj1nzYZ8BX+8W6AK3NTQYiJVd03zxdzBKEoz4gdGgRvgaBCccRUl3tSfjG+/sqeX9ryBrt17smf3Dg7+uB+wB5fjE27kf/u/58TxbJo2a0HOmb84sP976jdoRElxCUWFhRQVFtKkWQtuu/NeNq7/xMFFdfFisfyzMiYQCp3eBEGg1w192Lr5v/x18g+uaNzQL+vodc34Sskqff/+dvdUpgC0molUKjhbvXKdbp+7syrmYFNehyqTaSiiUhsGdcaO0hhILp6d278l68jvNG7SjHZXXUNhYSF7du+gw9VxnD9/jmPZR9i4/hNatbkKgGPZRzh5wh6k/vP0KfrfcofMp3T88tj9+3Y5yLFn907WfbKK4Q8+GtSKZ2d4OXUJN3RrwWtPzWDmewv8sobeIK1WfwZf7/J9vcMP9QC0JztldUMdZXWz3nmdVTHr9d8bO/vgo1IbBnXGjrJPs9R0p2WrdmTu2EKTps1ZuWwhXbr3BODMmT/58/QpatSsSXFREUcO/SbPK6WudrimMyNGPcqIUY+S/v7b5OWeZf26NZw8nk3jK5tR74r6HNi3mx49E4PWoEcPTCYzrdq0Z/+W7ygtLSUsTH95i14l66p/tBJa/Rm82YW72sX7eocf6gFoT4LE6l21q132skXppL68FGuBjSen/c3W66yKWe+O3deZQYah8RyV2jCod+fKtp3dru0l/x4VHSPXMxw98ht7d++Q6TCKi4owmcwUFv7dobRBwyvpN3AIhYWFPPfkI9z3wMPs37uL49lH5dPEyRPZJCT2wWSy0O6qqwP1yl4j5eXXGf7Pf7Bo2iuMmzVV93OeKFk9YyUlm382t1zcSe6a9zi7Vxnhv0wbQf5UK19XVczuCPN8La+Rguo5qkTw2R2ULqZJ44ZzLPsIDRs3oVHjJggI7Nm9A7Cns54/l0/X7vFc3bELK5ctBOxxiPw8e8Fb4yubUVp6iT9OnaBr93g5mK1kVlWu6SwgHgzm1Zuv74C14AIf/b5D9zOeuGUM7iL9+L8J09j8yX/47uAnNI1tHGxxNKEVqJ428zG3ylfqz9C3fy82bdyu6xnlej16dea1f79N8isTad3WfWBebbSq8gnCCD57AMnF9NnHH3As+whNm7Xgxr4DuOkfg1g0/98MGXoPe3fvlO9Jyl5CfK8bOfi/fXS4pjMH/7ePkyeyiYyKoUnT5uzZvZMe8WVdSa4qlgPVuEeNyVNe5IVnHuXrj9bT585Bup7xxI0S6i4XJYJtxA5stcepQtUogKNrSM8uX12zILma+g1KZMHclW4VtWR8WreN5fdfswBY+fFcj+RUzgPGCcIZqrRhUJ4Udm7/lswdW+gU142cnL9YuWwh327awLHsI0RGxZCXm0Nsi9b0iL+eY9lHaNXmKqpVr0G7dldjsljY+MUnhIWHcyz7CCazhbzcHP7446RMfyHt/JVrgnbFciAb9ygx8NY7mTFtIsuT/63bMFRWVKZMIy2Ud9fsSdWzBC2F3Lqt8w5vakhGR3li8AaBKmKryCeTKm0YlDvzuC49yNyxhdzcs5y4TI0R36s3YeHhZB35ncioGLKO/E5MzBV06d6TXw7+T27S8/RzszCbLXKV87B7HyT9/beZ9PR0Ylu0drqms9NAMDOX7v7XQ7y7bCHHD2fRpGXg8udDDRK3UqHVJhPkVSY3WHl3zd4870whe3LakJSsnpOCMwQqbbUin0yqtGFQ78wl5b7o9VepVac2G9Z/QqPGTeRYQWyL1nK8QULmjgwyvtkoU3anzEq73LCnr641Qw3jxj/Lu8sWMu/+x/i/Lb5jIZUQbBeNXkRER2G2WFg26zVMFnvlc2U6QXi6a1Yr5vI+r4QeRb1s0RpSX34La0EhT04brWvNYKMi02tUScOgDO5KCl0qgsvLzSE+oTfvLJ3P+fP5nP8ln67de9KjZyINGzYmIiISUYQWLVtz7FgW13TsIs+hJy4QinUMSlSvUYOu1/Ziz67tnM/Lp05khE/n97eLxpeGRyt7qbJkMpWnyY4UbPbERVL+3bOo+gx9VOSCuippGJRKHHBQ6NK9IUPvZtOX60EUadI0lrWq/glmi5m9u3dwNucvbvrHLdhsVkY/Ym8Tunj+bBCg/y13yKeJUKhy1ovZacvo27Mdrz/94v+3d+fhUVdXA8e/JwuZLGQBZCchiGDdgrIUpEYpbWltqaIWEaQVLEiQUiOKGxSCCFWJYCqLgJSWqhRfiYptpa8IBiEYg4CArzsSAwIKWckkJHDfPyYzzsQEkpDkN8v5PE8ekslkcjLP8Dtz7zn3Xh5evqBJH7u5W0VrSzyNTRY1i+X+MFJoLPd3v005jVRf4yaNPOdBQr48p+9tAjIxuE/nFBUWsPO97d8rBg8fMYrj337DtqxNHDlymAmTp5H77nZ27XRMJVVVVtK5SzwHD3zGnx76A/l5B5iQMs21ohrgw327XZ978yihpujoGDp3iSd745Ymf+zm7kyqLfH4eyG5JTS0A+lsP18fTVXcVo0TkInBfTrHeZRn3/5XMzaxp+t7hQXH6dGjF5WVlUx/ZB4xsY53mt0SEnj7rY28n7uDK5L6cfhQHqcqyhl7Rwrl5Xa2ZW3iqn4D6TtgMMOuH+GxkM6XPPrEEu4c8yvWLFjC2PsmWx1OvdWWeAJtQVtza4kpkpoX+fqMBnx5Tt/bBOTRnu7HYQ4fMYqp02aSPGSYxxGZ615YxZrVS0m6sj8JiT1Z98IqVixJZ1fuuxQVFpCQ2JOkq/oDjj2TPv54Hzabo0jZt/9gRo4e75PTSE5X9OlHVOtoXlv+gtWhnDdnsvDmYrfyVPPIznMdGwrnf6RmY44W9VcBOWKoWSgeO/5ulj/zJCuWpmMvK2PilPu/X+uq/nf4DTdTUVHGzbeOIzQsgv17d/N+bja9L76ckWPGEx4R0aBitDe7a8p00ufP4N033+aHP7nW6nBUAKk5KmmJ0YBORX0nIBNDrS2j4vnvyDHjKbeXsWf3e3x18AumTLmb+1In0a1bPMHBwQBUVZ3hxXXrWf7scn4+/BaPKSpvb0utj1G3/56FT8xixX1p/HD3W1aHowJYS0xf6VTUdwJyKsl5AXef4hk5ejxTp81k5OjxrnbWjz/az5FDX9KzWzTxndsQFRlBRfVmesacofKUnYQubZn9pwdp3/6Cc/4OX3TDTaM5eqKIE8e+sToUSxWdKGD9sr+7FrtZqaWnPJr69x3/tpAFc1ewYO5y12N6wzTO+U5F+ZOATAzgWWcAz4L07IenkpH+KD17Xcz69Zn0SOzB669vwBhDZWUlxpyh3G6ntLSEl9atIzQkiB5dYhA522/0TffcPwuAhaNTLI7EWs7Opk3rmn7RX0PVnG9v7otqfeb3G/p4T81fyVPzn3M9pvN33DMxTef4vUBATiVB3VtTOLuUBgxKZkC/K+jUqSOZmesZO3YsEydOZNasWVSeOkWYzcaDDz7IsmVLCQuzccONN9I+LoKjJ8qs+pOaRURkFD+4LInd+/ZQYS8nLNw/z8I911oHb+psqjnl0VRz43V1/jT1FMutY4dTdrIcMB6P3ZjT4lTzCNjEUFcNwPl1wfFvSf7R1YSGtuKmm29h27ZtLFu2FJvNxsKFC0lNTWXZsqVM+cNUfn3DDQC0jbH5XWIASM/4G9f/uA+LH57HvQvnWB1OszjXWgdv2hm2KQqztSWBuhJMU8/vt20X+71tLdxPi6vvbquq+QRsYqhrawrn7S+vfY4OHToQGhpK5akKZs6cQatWoWRkZJCRkQHA1KlTeeLJdKR6DikkJIhWoUGcqjzTon9Lc7ugQ0fatmvP25n/IfWpNNff60+8aUTQUI25cNeWBKwuvjr/jvrutmolf19lHbA1hnMZcfMoIiOjCI+IwBYeTnR0DAsXeu7ouHDhwu8dgxkS7J9P6azHMjhz5gyvrXrR6lCaRaCtdai5TgCap/haV/3jbHWR2mLzNk1dd/E2TX4VE5EpIpIrIhUisvos97tDRE6LSKnbx3VNHU9j2SJaEx4RiUgQIkGEtgojNTXV4z6pqamcPl2FvewkxjhGCVWn/Wu04DToR9cRHhHJPxcssToU1QRaqgOnrgvo2S6sjYmtpbuafCF5nY/meHt7GJgLrKrHfbONMVFuH1uaIZ5GOVV5hqoqx0XeGMN90+4lIyODqVOnUlVVxdSpU8nIyCD1nnsoLS2h3G6nquqM300jubtt7ARKTtrZ/U6O1aEoH1HXBbSpL6wt/Q7e31tbmzwxGGPWG2NeAY439WO3tPwjx7GXneTVVzN55pm/cOedd5KWNoeSkhIeeOABJk2axLJlS9m8eTO28HDyj3i2wPqbSX94ABFhVXULq1LnUtcFtKkvrP7+Dr6lWT0hfqWIfCsin4jITBGpsxguIhOrp6hyC06caPbACguOM2niePbv38fPfvoz/rluHQsWpANQVlZGq1atmDdvPitXruRXvxpOZZVh+bPLyUh/lA01tuj2FyLC0GHDOXDoGCUF2mvuq7xhMVlT8/d38C3NysSQBVwGtAduBm4D7q/rzsaY5caYfsaYfnFt2jRLQO6L3jZkrmXzm/8hfdEzEBTKjTfeRER1MToyMorIqGhEhJtvvgWCQvniUBE/H36L64znuh7X101/ZD4Ai3/rOzuuKk/+XjhV58+ydlVjzBduX+4VkTk4EsP8pv5d7ie2nW2LCueiN7vdsRZhQso0Ro4Zz+eHSmgfd5q2MTZCQoIIj4jEXnaSAwe+YNv2d7nkqmuJjmlTZwusP2yo5xTXpi2JPS5i+wefUFVZSUhoqNUh+aWThcXNtpLe6rZU5f28aR2D4but7JpUfS/Mznf69rIyVixNZ+q0mQA8+8yTFBac4LNPP+K664Yy6vZxHD9+grvGjeSrvAN0i09k4ZI1JCT2POvj+vKGeu4eX/QcI3+dzIq0dFLmPmh1OH7pVOUp2rVvnpGxLx85qVpGkyeG6jpBCBAMBIuIDagyxlTVuN8vgPeNMUdF5GJgJvBSU8cD9b8wux/SA2C3l7nOYXDalZtNTk42PS7sxVd5BwD4Ku8AC5+YRer0NNe/dSUJf5B4YS+iY2L57wuvMOnRB/xywZs3iI6OsjqEevH3xV6BqDlqDDMAO/AgcHv15zNEJL56rUJ89f2GAh+IyEng38B6YF4zxFPnTqfuc//unxcVFvDfN15lxZJ0yu1lXNVvIL16X0rHTl25Iqkv27I2sTNnu+txYmLjuPOuVNexngufmOXx+OteWOV3Renpj8ynqqqSd15/0+pQlMW0ZuF/mnzEYIyZDcyu49tRbve7D7ivqX//ubjXG5xTTDvf284ll/VxjQx2vredgwc+IyGxJzZbBO/n7nD9fHz3Hoy9I4VXMx0rgMPCbBQVFrDsmceZNOUBKisr6dy5G8sXPwnAiiXpjL0jhQGDkik4/i2FBcd9fitugJ9dfyNzZvyRFQ89yjXDf2p1OA1yrg3zVMNozcL/eFONoUW4CsxlZSBwZb+BbMvaRI8evVwdRclDhgGQOj0NcCQKu72M4qICcrKzAENxUSGto2PoFt+dD/ftISd7q+MXGOGltasBGDAwmQkp00AgJzuLnOwsbOERrlPefDlBiAjDbxrNy2tX88me/fRKutTqkOrtXBvmqYZp2y6WW8cO1+kkPxJwicFVYLaXsWJJOgMGJQNgi4hwFaaLCr87jGXjvzN5Pzfb9XW3+EQ6duoKQOvoGD7ct4cuXeM5Y3AlhyuS+mGLiCAnO4uBg69l+IhRlNvtfPzRXsrLHYVt8P0OpfsffoyX//k3/pr6CPPfesXqcOrNlzfM81Z6LObZ+VodJuASg3uBOTw8guQhw8javNGjMO2sFXz5xadUVZ0GIDKqNSdLS/gq7wAFBY4Fdofz8+jYqSuH8vMACLOFU1FuJzgklKQr+5PUpz/JQ4axIXMttvBwcrK3knTlgFrXOvii4OBgBgz8ETnZW7GXlREeEWF1SPXiTVto+wudTjo7X0ucVq98towzQSQk9nS9c3cWn1Onp9EtPpFD+XkcPXIIgJOlJa6RQmlJEcEhjpzq3F01OiaOinI7CYk9uewKR70iPCKCrM0bHa2yBtfRof5w5KfTn+Y6pmQen/SAxZE0DW86wtOX1LbyuLYV1v646ro+fG3LjoBNDDU5aw8bMteSkNiTn//yJsBxFkFU62gA2rT57mJ+uqqKLl3jiY2No3PXeIqLChicPJT0v6zGZotg7LjJ2MvKSB4yzJEQxvhXQnDq0LEzXbolsCtrB2fO+P4Ggt50hKevq61bKVA7mHxty46Am0qqydml5Cw4O6d4Ro4ZT7m9jFfXv0hpSTHd4hMJDnGs8o2KimbEb25n39732VXdsXRVv4FUnqrk1ZdfYM1flzA4eSjbsjYR7la7qO8KbF/zp8ee5q7f3siLC59lzDTfPhta6w9Np7bpJZ1y8g0Bnxhqdimte34VI8eMB2DLW29QXOwY8lZU2DmUfxCA0tJiPv5oH6erHGv2IiNb88nHH1JaUkz+V19yRVJfSoqLGDsuxaOW4E9bY7i7qu9AIiKjWL9sDaPvneTTC960/tB0althrauufUNAJgb3d+41u5Sc9uzOca1sDgltxbGjR1zfC7PZqttWvytKA9jCIzh8KI/DhxzF6NbRMRQVFjD74amkTk/zWIHtPlJxFr99eRQxddpM/jznAfZsy6HPj35odThKqfMQkImh5jt3Z5eSs6W0sPCEq/W0S9d4V9eRU0V5OWFhNioqyjlVUQFA+w6dGHj1tex8bzuH8vPo0jWezl268cdJozmUn0fel1+wcMkawNEO6+x82vnedrZlbXLF4qtG/GYsTz72MM9MeYiVu99q9OPo4jOlrBeQiWH4iFHYy8ooOHGc5c88ycgx44mNa+tqKc3/6qDrvh06duaypH5sefPfVFSU07FTV4KDg1zJorLyFABdunbnteotLwYMSiapT3/XegVw7Kf0xGMPkZO9lR3bt5CTvZXByUNJnZ5G3/5X+3z7alBQED/9xY288frLHPriIF16JDTqcXTxmVLWC8jEEBvXlvCICI8LNwI7tr0NONYndO4Sz5kzZ3g/dwcDBiZTUVGOzRbOka/zAYiOjqW4uJBWrcK4+AeX07NXb3btdCyE6937UledorDwBB99uJfQ0BASuvckJ3srCd17EhrayrXZ3lg/2XBvxpx03nj9ZRamzmLBq6sb9Rha/FXKegHbrpo8ZBgDBiUz9o4UEMeeRnv35ALQsVNXDh/KcyWBwoLjRMfEUl5ud/38oGuGEB0Tx6lTFXywJxcQOnep3h9QHMln4pT76dS5K3v35PJ+7g4OHvicCSnTiI1rw7asTWRt3tjSf3azCguz8YNLr+DjXXs5VV7RqMdwFn91Gkkp6wRsYsjavJGc7CziCD++OQAADmZJREFU2rZj5Ojxrq0xYmLjXOsVWoXZAPjk4/0UFzm6ky64oAMA27Peorjou0VQW7f811V0Li8v556UMRw88BnDR4xiQso0BgxKJmdHFuEREYwcPd5vVj/XNP+plQBkTJ9jcSRKqcYKyKkk+P4ZDb17X8r/7dtNUWEBHTp2pnPXeKKiWgPwyUf7XT/3zTdHASgpKcJxrpAB4MjXjhXS0TGxbN3yvxz5Op8DX3xK127dmTRlOuXVp8IlDxlW50lv/qBL13jad+jEOxveZNrTc326dVWpQBWwicH94rxm1WLWrF7q+t7Xh/KrL/yOC33dHEkhrk07usV35+jRrzn69SGKiwodrav5eRzOz+PokcMcPPAZ4Bip+EtNoS73PzKP+6eO49WVz3PjhNutDkcp1UABmxjAc9WzvayMo0cP8/Zbb7h2V20dHUOHjl1c00h1KTjxLQUnvqV7D8cFPzg42HXAT0hoK8b87i6e/9syel98uWuEUlhwnHXPrwKBkaPH+/QahpquG/oLwsJs/DN9qSYGpXxQQNQY3E9nc+dcz5C1eSMTp9xP98SeFBUWEBPrKHyWFBfRo2dvWrUKA3D9W5NzuuSrg18CcPr0aWJi43h80XMsXrmOzz/9iJzsrcS1aetKABsy17JiaTorlqT71cluTr+fPI3SsnI+2bP/3HdWSnmVgBgx1LUVRc06w/ARo9ix7W1ydmS5Frbt2LaZU6ccHTaxcXG0u6Aj3xw7wjfHjnB5Ul/CIyLJyc4iKCiI06eraN+hE8eOfk1C9wspKiyodR8m5+fObTj8sQh9+x0pLFk0n6cn3MviHP/qvlLK3wVEYqiZAAD27s4lbcY9zJq7yPUuPjauLUlX9idnRxZ9B1xNaWkJRYUFrlXOx44ecW2NERMbR+r0NEpKivhgd66ruHztkGHkvPsOH+zOZdof7nDVFmoWm53trP4qJCSEH137E7Zu+S/ffn2Mdp3aWx2Sz3CuppeAGM8rbxQQLz1nodl9Hj9txj0cPPAZaTPu8bjvyDGOVtIjhw+7ag2OFc9duDypL70uvgxwbGux6MnZPHJ/CuX2MkJDW3Flv4EgwsEDn9G5SzwHD3zGgEHJfjkiqI9HH18CwFOpMy2OxLfs27ELgIdmT7Y4Eu8XqOc7NLeASAy1mTV3EQmJPZk1d5HH7c4kMn3GPAYMuoYr+vQDHO2oQ35yPZFRUa77fvvNMUpLigHH1hi7qhexAXTs1BmApD79/aqw3BCRUVF0T7yIvdtzqaw4ZXU4Pufiy/y7e60pBOr5Ds0tIKaSanN5n378z+vv1Pn9hMSeLF75kqN76IVVFJ44zjtvv0mF3bH6Ocxm446JU8lct4b8vC+5Onko7S/oAAJJV/Vn2PUjvndkaCB6fNFKbr3hWv46L4OJafdZHY7yM+d7voOvncXcUgJ2xNAQ4eERHDz4Be/nZvPh/j3ExrWlorycJYvm83/791BSUkR8fCK28AjW/HUpe3blEBMb55cntjVUj569iWvTjo3/eMnqUJQfOt+T0Zp7xOGrU11+lRjqakut7XvuX9f1OXzX0dS796Vc1W8QV/YbSNr8DLrFJ1JYcJzW0dUvSKn+AHKyt7Ihc+1Z4wkkd6c+wqnKKv679hWrQ1HKQ3OfxeyrU11+NZXkOo3NXkZ4eITH4Tc1W1bXvbCKFUvSXfd1fg/wuF/ykGHsfG87N9wyhri27chIf5Rrrv0pP//VTaxYkk5JcSEJiT0Zdv0I1/oHjKMDyl9PbGuoG266jT+nTeeFxxbxs1E3Wh2OUi7NfaKcrx5l6leJwXUaW1nZ9y7I32tZNbj+dd6WPGQYG/+VyYTJ01y3ZW3eyLasTVxyaR8QmJAyzXW/seMms++DXezamc3Gf2Uyccr9TLz7uxbU2tpkA9Vtv53ImlWLyfv0APEXJVodjlItwlePMvWrxODsKCosOE54RITHBbnmxnUjx4wnPCKC5CHDXMd8OlcjT502k9i4thQWHMdeVsaEydMAx9bcg5OHUm4vY83qpXSNT2TQ4Osc5zDUslecP2+W11AT776PNauW8PT4qaRv9a1htVKBxq8Sg1N9LsjO+6xZtdg1uhg+YhR2exn2sjLXPkrORDF8xCg+3LebbVmbqKysBCA/7wAHu3T12y20m5LNFs5V/Qbyfm42J4tLiIxubXVISqk6+GViaAj3aaQNmWvBwIql6R4jDmetYva8DDZkruXCiy7m0Fdf0qbtBUx/ZD4JiT1dhWb3uobyNH/hCoZdcxnpf5zJn/666Nw/oLyetnv6J7/qSmoM58gha/NGx8hBcI0AalsxDfD86mc5lJ9HdEwsCdVbaDsLzf64IV5TadOmHR07dSX3rXeoqh51Kd/mq1036uwCfsTgVLNQ7Kw7uCcF58V/7LgUQluFkjo9rc6fV7Wb99Ryxt92Peuf/Tsjp9xpdTjqPPlq1406O00M1Woe3FOf3Vg3ZK5leGwcsXFttdBcT5dfcRVRUdGse3qVJgY/4KtdN+rsAn4qqTbDR4yqtaDsPrWkU0eNN/6ue6ioKGfbfzZZHYpSqhaaGGpRV23BXfKQYQxOHuo6a0HV3+3jUggODuYfM/9sdShKqVpoYmgk58K3rM16CE1DiQi//PVvyD92gm8OH7U6HKVUDU2eGERkiojkikiFiKw+x31TReSIiBSLyCoRqf3sTC9U13STqp9pDz0GwGO/v9fiSJRSNTXHiOEwMBdYdbY7icgw4EFgKJAA9ADSzvYz3qQ+002qbhGRkVxy2ZV8vu8jTpVXWB2OUspNkycGY8x6Y8wrwLm2FP0d8JwxZr8xpgB4FLijqeNR3uuxBUsB+MsDcy2ORCnlzsoaw6XAHrev9wAdRKTWt+AiMrF6iiq34MSJFglQNa+u3brTtu0FZL22kTNnzlgdjlKqmpWJIQoocvva+Xmtm+gYY5YbY/oZY/rFtWnT7MGpljHn8SWcOX2a/133qtWhKKWqWZkYSoFot6+dn5dYEMtZ6YE7zWfAoGsIC7OxKm2h1aEopapZmRj2A0luXycBR40xXnf11cVszev2cZMpO3mSvTtyrQ7FKxQe97r/AirANEe7aoiI2IBgIFhEbCJS29YbfwfuFJFLRCQWmAGsbup4moK2pjaviXffR1BQEM/dO8vqULzCmieWANCp8wUWR6ICVXOMGGYAdhytqLdXfz5DROJFpFRE4gGMMW8ATwCbgTzgIOCVVwZtTW1eQUFBDE7+CZ/nH6HwuDYWVFVWIgLh4TarQ1EBqjnaVWcbY6TGx2xjTJ4xJsoYk+d236eMMR2MMdHGmHHGGG1oD1BzHl8MwOMpD1gciVJKt8RQXiEqqjUXXnQx+9/dxemqKqvDUSqgaWJQXiNt3jMYY1g5J93qUJQKaJoYlNfofcllRMfE8sY/MjHGWB2OUgFLE4PyKg/PXkBVVSXb/qVnNShlFU0Myqv8+Ke/JCQklKUPzbc6FKUClvjikF1EvsHR3nq+2gHfNsHj+CN9bs5On5+66XNTN6ufmwRjzDkXyPhkYmgqIpJrjOlndRzeSJ+bs9Pnp2763NTNV54bnUpSSinlQRODUkopD4GeGJZbHYAX0+fm7PT5qZs+N3XziecmoGsMSimlvi/QRwxKKaVq0MSglFLKgyYGpZRSHjQxACLyDxH5WkSKReQTEfm91TF5AxEJE5HnROSgiJSIyG4R+YXVcXkLEZkiIrkiUiEiq62Ox2oi0kZEMkXkZPVrZrTVMXkLX3ut1HayWiCaD9xpjKkQkYuBLSKyyxiz0+rALBYCfAVci+MwpeuBdSJyuTHmSysD8xKHgbnAMCDc4li8wWLgFNAB6AP8S0T2GGP2WxuWV/Cp14qOGABjzH63Q4JM9ceFFobkFYwxJ6sPWfrSGHPGGPM6cADoa3Vs3sAYs94Y8woQ8Ic0i0gkcDMw0xhTaox5B3gNGGttZN7B114rmhiqicgSESkDPgK+Bv5tcUheR0Q6AL0AfQeoauoFVBljPnG7bQ9wqUXxqPOgiaGaMWYy0Bq4BlgP6DGjbkQkFHge+Jsx5iOr41FeJwoornFbEY7/U8rH+H1iEJEtImLq+HjH/b7GmNPVQ+CuQIo1Ebec+j43IhIErMExfzzFsoBbUENeNwqAUiC6xm3RQIkFsajz5PfFZ2PMdY34sRACoMZQn+dGRAR4DkdB8XpjTGVzx+UNGvm6CWSfACEicpEx5tPq25LQaUef5PcjhnMRkfYiMkpEokQkWESGAbcBeoSYw1LgB8BwY4zd6mC8iYiEiIgNCAaCRcQmIn7/Zqs2xpiTOKZg54hIpIgMBm7AMdIMeL72Wgn4xICjAykFyAcKgAXAPcaY1yyNyguISAJwF47WwyMiUlr9Mcbi0LzFDMAOPAjcXv35DEsjstZkHK2Yx4AXgRRtVXXxqdeKbqKnlFLKg44YlFJKedDEoJRSyoMmBqWUUh40MSillPKgiUEppZQHTQxKKaU8aGJQSinlQRODUkopD5oYlGoEEbml+jSuBLfbnhaRz6u3J1fKZ+nKZ6UaoXpzwfeAXcaYCSJyHzAdGOy2iZxSPslrN3FSypsZY4yIPIzj+MrPgYeBoc6kICKZwHXAJmPMLdZFqlTD6YhBqfMgItuBATh2n/2P2+3X4Tik5neaGJSv0RqDUo0kIj/GceaAAEfdv2eM2YIeUqN8lCYGpRpBRJKATOAPwCvAfGsjUqrpaI1BqQaq7kT6D5BujFklIjnAByJyXfVIQSmfpiMGpRpARNoAbwAbjDFzAIwx+4CX0FGD8hM6YlCqAYwxJ3AcdVrz9lstCEepZqFdSUo1AxF5E0dhOhI4AfzGGJNtbVRK1Y8mBqWUUh60xqCUUsqDJgallFIeNDEopZTyoIlBKaWUB00MSimlPGhiUEop5UETg1JKKQ+aGJRSSnn4fzY6mUAUPBi7AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_decision_boundaries(kmeans_per_k[4-1], X)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Another approach is to look at the _silhouette score_, which is the mean _silhouette coefficient_ over all the instances. An instance's silhouette coefficient is equal to $(b - a)/\\\\max(a, b)$ where $a$ is the mean distance to the other instances in the same cluster (it is the _mean intra-cluster distance_), and $b$ is the _mean nearest-cluster distance_, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes $b$, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the silhouette score as a function of $k$:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 132,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import silhouette_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 133,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.34507909442492657\"\n      ]\n     },\n     \"execution_count\": 133,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"silhouette_score(X, kmeans.labels_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 134,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"silhouette_scores = [silhouette_score(X, model.labels_)\\n\",\n    \"                     for model in kmeans_per_k[1:]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure silhouette_score_vs_k_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 3))\\n\",\n    \"plt.plot(range(2, 10), silhouette_scores, \\\"bo-\\\")\\n\",\n    \"plt.xlabel(\\\"$k$\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Silhouette score\\\", fontsize=14)\\n\",\n    \"plt.axis([1.8, 8.5, 0.55, 0.7])\\n\",\n    \"save_fig(\\\"silhouette_score_vs_k_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, this visualization is much richer than the previous one: in particular, although it confirms that $k=4$ is a very good choice, but it also underlines the fact that $k=5$ is quite good as well.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An even more informative visualization is given when you plot every instance's silhouette coefficient, sorted by the cluster they are assigned to and by the value of the coefficient. This is called a _silhouette diagram_:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 136,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure silhouette_analysis_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x648 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import silhouette_samples\\n\",\n    \"from matplotlib.ticker import FixedLocator, FixedFormatter\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11, 9))\\n\",\n    \"\\n\",\n    \"for k in (3, 4, 5, 6):\\n\",\n    \"    plt.subplot(2, 2, k - 2)\\n\",\n    \"    \\n\",\n    \"    y_pred = kmeans_per_k[k - 1].labels_\\n\",\n    \"    silhouette_coefficients = silhouette_samples(X, y_pred)\\n\",\n    \"\\n\",\n    \"    padding = len(X) // 30\\n\",\n    \"    pos = padding\\n\",\n    \"    ticks = []\\n\",\n    \"    for i in range(k):\\n\",\n    \"        coeffs = silhouette_coefficients[y_pred == i]\\n\",\n    \"        coeffs.sort()\\n\",\n    \"\\n\",\n    \"        color = mpl.cm.Spectral(i / k)\\n\",\n    \"        plt.fill_betweenx(np.arange(pos, pos + len(coeffs)), 0, coeffs,\\n\",\n    \"                          facecolor=color, edgecolor=color, alpha=0.7)\\n\",\n    \"        ticks.append(pos + len(coeffs) // 2)\\n\",\n    \"        pos += len(coeffs) + padding\\n\",\n    \"\\n\",\n    \"    plt.gca().yaxis.set_major_locator(FixedLocator(ticks))\\n\",\n    \"    plt.gca().yaxis.set_major_formatter(FixedFormatter(range(k)))\\n\",\n    \"    if k in (3, 5):\\n\",\n    \"        plt.ylabel(\\\"Cluster\\\")\\n\",\n    \"    \\n\",\n    \"    if k in (5, 6):\\n\",\n    \"        plt.gca().set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\\n\",\n    \"        plt.xlabel(\\\"Silhouette Coefficient\\\")\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelbottom=False)\\n\",\n    \"\\n\",\n    \"    plt.axvline(x=silhouette_scores[k - 2], color=\\\"red\\\", linestyle=\\\"--\\\")\\n\",\n    \"    plt.title(\\\"$k={}$\\\".format(k), fontsize=16)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"silhouette_analysis_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Limits of K-Means\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 137,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X1, y1 = make_blobs(n_samples=1000, centers=((4, -4), (0, 0)), random_state=42)\\n\",\n    \"X1 = X1.dot(np.array([[0.374, 0.95], [0.732, 0.598]]))\\n\",\n    \"X2, y2 = make_blobs(n_samples=250, centers=1, random_state=42)\\n\",\n    \"X2 = X2 + [6, -8]\\n\",\n    \"X = np.r_[X1, X2]\\n\",\n    \"y = np.r_[y1, y2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 138,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_clusters(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 139,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\\n\",\n       \"    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\\n\",\n       \"    random_state=42, tol=0.0001, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 139,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"kmeans_good = KMeans(n_clusters=3, init=np.array([[-1.5, 2.5], [0.5, 0], [4, 0]]), n_init=1, random_state=42)\\n\",\n    \"kmeans_bad = KMeans(n_clusters=3, random_state=42)\\n\",\n    \"kmeans_good.fit(X)\\n\",\n    \"kmeans_bad.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure bad_kmeans_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 3.2))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_decision_boundaries(kmeans_good, X)\\n\",\n    \"plt.title(\\\"Inertia = {:.1f}\\\".format(kmeans_good.inertia_), fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_decision_boundaries(kmeans_bad, X, show_ylabels=False)\\n\",\n    \"plt.title(\\\"Inertia = {:.1f}\\\".format(kmeans_bad.inertia_), fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"bad_kmeans_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using clustering for image segmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 141,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading ladybug.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('./images/unsupervised_learning/ladybug.png',\\n\",\n       \" <http.client.HTTPMessage at 0x7f910041fbd0>)\"\n      ]\n     },\n     \"execution_count\": 141,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Download the ladybug image\\n\",\n    \"images_path = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", \\\"unsupervised_learning\\\")\\n\",\n    \"os.makedirs(images_path, exist_ok=True)\\n\",\n    \"DOWNLOAD_ROOT = \\\"https://raw.githubusercontent.com/ageron/handson-ml2/master/\\\"\\n\",\n    \"filename = \\\"ladybug.png\\\"\\n\",\n    \"print(\\\"Downloading\\\", filename)\\n\",\n    \"url = DOWNLOAD_ROOT + \\\"images/unsupervised_learning/\\\" + filename\\n\",\n    \"urllib.request.urlretrieve(url, os.path.join(images_path, filename))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 142,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(533, 800, 3)\"\n      ]\n     },\n     \"execution_count\": 142,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib.image import imread\\n\",\n    \"image = imread(os.path.join(\\\"images\\\",\\\"unsupervised_learning\\\",\\\"ladybug.png\\\"))\\n\",\n    \"image.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 143,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = image.reshape(-1, 3)\\n\",\n    \"kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\\n\",\n    \"segmented_img = kmeans.cluster_centers_[kmeans.labels_]\\n\",\n    \"segmented_img = segmented_img.reshape(image.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 144,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"segmented_imgs = []\\n\",\n    \"n_colors = (10, 8, 6, 4, 2)\\n\",\n    \"for n_clusters in n_colors:\\n\",\n    \"    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(X)\\n\",\n    \"    segmented_img = kmeans.cluster_centers_[kmeans.labels_]\\n\",\n    \"    segmented_imgs.append(segmented_img.reshape(image.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 145,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure image_segmentation_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10,5))\\n\",\n    \"plt.subplots_adjust(wspace=0.05, hspace=0.1)\\n\",\n    \"\\n\",\n    \"plt.subplot(231)\\n\",\n    \"plt.imshow(image)\\n\",\n    \"plt.title(\\\"Original image\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"for idx, n_clusters in enumerate(n_colors):\\n\",\n    \"    plt.subplot(232 + idx)\\n\",\n    \"    plt.imshow(segmented_imgs[idx])\\n\",\n    \"    plt.title(\\\"{} colors\\\".format(n_clusters))\\n\",\n    \"    plt.axis('off')\\n\",\n    \"\\n\",\n    \"save_fig('image_segmentation_diagram', tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using Clustering for Preprocessing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's tackle the _digits dataset_ which is a simple MNIST-like dataset containing 1,797 grayscale 8×8 images representing digits 0 to 9.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 146,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import load_digits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 147,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_digits, y_digits = load_digits(return_X_y=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's split it into a training set and a test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 148,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 149,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's fit a Logistic Regression model and evaluate it on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 150,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 151,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='ovr',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 151,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg = LogisticRegression(multi_class=\\\"ovr\\\", solver=\\\"liblinear\\\", random_state=42)\\n\",\n    \"log_reg.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 152,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9666666666666667\"\n      ]\n     },\n     \"execution_count\": 152,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Okay, that's our baseline: 96.7% accuracy. Let's see if we can do better by using K-Means as a preprocessing step. We will create a pipeline that will first cluster the training set into 50 clusters and replace the images with their distances to the 50 clusters, then apply a logistic regression model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 153,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 154,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(memory=None,\\n\",\n       \"     steps=[('kmeans', KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\\n\",\n       \"    n_clusters=50, n_init=10, n_jobs=None, precompute_distances='auto',\\n\",\n       \"    random_state=42, tol=0.0001, verbose=0)), ('log_reg', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='ovr',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False))])\"\n      ]\n     },\n     \"execution_count\": 154,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline = Pipeline([\\n\",\n    \"    (\\\"kmeans\\\", KMeans(n_clusters=50, random_state=42)),\\n\",\n    \"    (\\\"log_reg\\\", LogisticRegression(multi_class=\\\"ovr\\\", solver=\\\"liblinear\\\", random_state=42)),\\n\",\n    \"])\\n\",\n    \"pipeline.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 155,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9822222222222222\"\n      ]\n     },\n     \"execution_count\": 155,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 156,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.4666670666658673\"\n      ]\n     },\n     \"execution_count\": 156,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1 - (1 - 0.9822222) / (1 - 0.9666666)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"How about that? We almost divided the error rate by a factor of 2! But we chose the number of clusters $k$ completely arbitrarily, we can surely do better. Since K-Means is just a preprocessing step in a classification pipeline, finding a good value for $k$ is much simpler than earlier: there's no need to perform silhouette analysis or minimize the inertia, the best value of $k$ is simply the one that results in the best classification performance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 157,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 158,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 98 candidates, totalling 294 fits\\n\",\n      \"[CV] kmeans__n_clusters=2 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=2, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=2 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=2, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=2 ............................................\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[CV] ............................. kmeans__n_clusters=2, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=3 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=3, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=3 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=3, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=3 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=3, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=4 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=4, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=4 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=4, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=4 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=4, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=5 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=5, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=5 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=5, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=5 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=5, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=6 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=6, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=6 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=6, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=6 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=6, total=   0.1s\\n\",\n      \"[CV] kmeans__n_clusters=7 ............................................\\n\",\n      \"[CV] ............................. kmeans__n_clusters=7, total=   0.1s\\n\",\n      \"<<522 more lines>>\\n\",\n      \"[CV] kmeans__n_clusters=94 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=94, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=94 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=94, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=95 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=95, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=95 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=95, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=95 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=95, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=96 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=96, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=96 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=96, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=96 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=96, total=   0.9s\\n\",\n      \"[CV] kmeans__n_clusters=97 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=97, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=97 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=97, total=   0.9s\\n\",\n      \"[CV] kmeans__n_clusters=97 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=97, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=98 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=98, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=98 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=98, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=98 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=98, total=   0.9s\\n\",\n      \"[CV] kmeans__n_clusters=99 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=99, total=   0.8s\\n\",\n      \"[CV] kmeans__n_clusters=99 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=99, total=   0.9s\\n\",\n      \"[CV] kmeans__n_clusters=99 ...........................................\\n\",\n      \"[CV] ............................ kmeans__n_clusters=99, total=   0.9s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done 294 out of 294 | elapsed:  2.4min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score='raise-deprecating',\\n\",\n       \"       estimator=Pipeline(memory=None,\\n\",\n       \"     steps=[('kmeans', KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\\n\",\n       \"    n_clusters=50, n_init=10, n_jobs=None, precompute_distances='auto',\\n\",\n       \"    random_state=42, tol=0.0001, verbose=0)), ('log_reg', LogisticRegression(C=1.0, class_weight=None, ...lty='l2', random_state=42, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False))]),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=None,\\n\",\n       \"       param_grid={'kmeans__n_clusters': range(2, 100)},\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring=None, verbose=2)\"\n      ]\n     },\n     \"execution_count\": 158,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid = dict(kmeans__n_clusters=range(2, 100))\\n\",\n    \"grid_clf = GridSearchCV(pipeline, param_grid, cv=3, verbose=2)\\n\",\n    \"grid_clf.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 159,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'kmeans__n_clusters': 90}\"\n      ]\n     },\n     \"execution_count\": 159,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_clf.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 160,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9844444444444445\"\n      ]\n     },\n     \"execution_count\": 160,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid_clf.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The performance is slightly improved when $k=90$, so 90 it is.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Clustering for Semi-supervised Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Another use case for clustering is in semi-supervised learning, when we have plenty of unlabeled instances and very few labeled instances.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the performance of a logistic regression model when we only have 50 labeled instances:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 161,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_labeled = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 162,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.8266666666666667\"\n      ]\n     },\n     \"execution_count\": 162,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg = LogisticRegression(multi_class=\\\"ovr\\\", solver=\\\"liblinear\\\", random_state=42)\\n\",\n    \"log_reg.fit(X_train[:n_labeled], y_train[:n_labeled])\\n\",\n    \"log_reg.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It's much less than earlier of course. Let's see how we can do better. First, let's cluster the training set into 50 clusters, then for each cluster let's find the image closest to the centroid. We will call these images the representative images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 163,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"k = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 164,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"kmeans = KMeans(n_clusters=k, random_state=42)\\n\",\n    \"X_digits_dist = kmeans.fit_transform(X_train)\\n\",\n    \"representative_digit_idx = np.argmin(X_digits_dist, axis=0)\\n\",\n    \"X_representative_digits = X_train[representative_digit_idx]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's plot these representative images and label them manually:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 165,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure representative_images_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x144 with 50 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 2))\\n\",\n    \"for index, X_representative_digit in enumerate(X_representative_digits):\\n\",\n    \"    plt.subplot(k // 10, 10, index + 1)\\n\",\n    \"    plt.imshow(X_representative_digit.reshape(8, 8), cmap=\\\"binary\\\", interpolation=\\\"bilinear\\\")\\n\",\n    \"    plt.axis('off')\\n\",\n    \"\\n\",\n    \"save_fig(\\\"representative_images_diagram\\\", tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 166,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_representative_digits = np.array([\\n\",\n    \"    4, 8, 0, 6, 8, 3, 7, 7, 9, 2,\\n\",\n    \"    5, 5, 8, 5, 2, 1, 2, 9, 6, 1,\\n\",\n    \"    1, 6, 9, 0, 8, 3, 0, 7, 4, 1,\\n\",\n    \"    6, 5, 2, 4, 1, 8, 6, 3, 9, 2,\\n\",\n    \"    4, 2, 9, 4, 7, 6, 2, 3, 1, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we have a dataset with just 50 labeled instances, but instead of being completely random instances, each of them is a representative image of its cluster. Let's see if the performance is any better:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 167,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9244444444444444\"\n      ]\n     },\n     \"execution_count\": 167,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg = LogisticRegression(multi_class=\\\"ovr\\\", solver=\\\"liblinear\\\", random_state=42)\\n\",\n    \"log_reg.fit(X_representative_digits, y_representative_digits)\\n\",\n    \"log_reg.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Wow! We jumped from 82.7% accuracy to 92.4%, although we are still only training the model on 50 instances. Since it's often costly and painful to label instances, especially when it has to be done manually by experts, it's a good idea to make them label representative instances rather than just random instances.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"But perhaps we can go one step further: what if we propagated the labels to all the other instances in the same cluster?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 168,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_train_propagated = np.empty(len(X_train), dtype=np.int32)\\n\",\n    \"for i in range(k):\\n\",\n    \"    y_train_propagated[kmeans.labels_==i] = y_representative_digits[i]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 169,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='ovr',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 169,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg = LogisticRegression(multi_class=\\\"ovr\\\", solver=\\\"liblinear\\\", random_state=42)\\n\",\n    \"log_reg.fit(X_train, y_train_propagated)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 170,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9288888888888889\"\n      ]\n     },\n     \"execution_count\": 170,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We got a tiny little accuracy boost. Better than nothing, but we should probably have propagated the labels only to the instances closest to the centroid, because by propagating to the full cluster, we have certainly included some outliers. Let's only propagate the labels to the 20th percentile closest to the centroid:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 171,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"percentile_closest = 20\\n\",\n    \"\\n\",\n    \"X_cluster_dist = X_digits_dist[np.arange(len(X_train)), kmeans.labels_]\\n\",\n    \"for i in range(k):\\n\",\n    \"    in_cluster = (kmeans.labels_ == i)\\n\",\n    \"    cluster_dist = X_cluster_dist[in_cluster]\\n\",\n    \"    cutoff_distance = np.percentile(cluster_dist, percentile_closest)\\n\",\n    \"    above_cutoff = (X_cluster_dist > cutoff_distance)\\n\",\n    \"    X_cluster_dist[in_cluster & above_cutoff] = -1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 172,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"partially_propagated = (X_cluster_dist != -1)\\n\",\n    \"X_train_partially_propagated = X_train[partially_propagated]\\n\",\n    \"y_train_partially_propagated = y_train_propagated[partially_propagated]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 173,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='ovr',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 173,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg = LogisticRegression(multi_class=\\\"ovr\\\", solver=\\\"liblinear\\\", random_state=42)\\n\",\n    \"log_reg.fit(X_train_partially_propagated, y_train_partially_propagated)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 174,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9422222222222222\"\n      ]\n     },\n     \"execution_count\": 174,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"log_reg.score(X_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Nice! With just 50 labeled instances (just 5 examples per class on average!), we got 94.2% performance, which is pretty close to the performance of logistic regression on the fully labeled _digits_ dataset (which was 96.7%).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is because the propagated labels are actually pretty good: their accuracy is very close to 99%:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 175,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9896907216494846\"\n      ]\n     },\n     \"execution_count\": 175,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.mean(y_train_partially_propagated == y_train[partially_propagated])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You could now do a few iterations of _active learning_:\\n\",\n    \"1. Manually label the instances that the classifier is least sure about, if possible by picking them in distinct clusters.\\n\",\n    \"2. Train a new model with these additional labels.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## DBSCAN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 176,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import make_moons\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 177,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X, y = make_moons(n_samples=1000, noise=0.05, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 178,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import DBSCAN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 179,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DBSCAN(algorithm='auto', eps=0.05, leaf_size=30, metric='euclidean',\\n\",\n       \"    metric_params=None, min_samples=5, n_jobs=None, p=None)\"\n      ]\n     },\n     \"execution_count\": 179,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dbscan = DBSCAN(eps=0.05, min_samples=5)\\n\",\n    \"dbscan.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 180,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  2, -1, -1,  1,  0,  0,  0,  2,  5])\"\n      ]\n     },\n     \"execution_count\": 180,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dbscan.labels_[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 181,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"808\"\n      ]\n     },\n     \"execution_count\": 181,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(dbscan.core_sample_indices_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 182,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  4,  5,  6,  7,  8, 10, 11, 12, 13])\"\n      ]\n     },\n     \"execution_count\": 182,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dbscan.core_sample_indices_[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 183,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-0.02137124,  0.40618608],\\n\",\n       \"       [-0.84192557,  0.53058695],\\n\",\n       \"       [ 0.58930337, -0.32137599]])\"\n      ]\n     },\n     \"execution_count\": 183,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dbscan.components_[:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 184,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-1,  0,  1,  2,  3,  4,  5,  6])\"\n      ]\n     },\n     \"execution_count\": 184,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.unique(dbscan.labels_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 185,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DBSCAN(algorithm='auto', eps=0.2, leaf_size=30, metric='euclidean',\\n\",\n       \"    metric_params=None, min_samples=5, n_jobs=None, p=None)\"\n      ]\n     },\n     \"execution_count\": 185,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dbscan2 = DBSCAN(eps=0.2)\\n\",\n    \"dbscan2.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 186,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_dbscan(dbscan, X, size, show_xlabels=True, show_ylabels=True):\\n\",\n    \"    core_mask = np.zeros_like(dbscan.labels_, dtype=bool)\\n\",\n    \"    core_mask[dbscan.core_sample_indices_] = True\\n\",\n    \"    anomalies_mask = dbscan.labels_ == -1\\n\",\n    \"    non_core_mask = ~(core_mask | anomalies_mask)\\n\",\n    \"\\n\",\n    \"    cores = dbscan.components_\\n\",\n    \"    anomalies = X[anomalies_mask]\\n\",\n    \"    non_cores = X[non_core_mask]\\n\",\n    \"    \\n\",\n    \"    plt.scatter(cores[:, 0], cores[:, 1],\\n\",\n    \"                c=dbscan.labels_[core_mask], marker='o', s=size, cmap=\\\"Paired\\\")\\n\",\n    \"    plt.scatter(cores[:, 0], cores[:, 1], marker='*', s=20, c=dbscan.labels_[core_mask])\\n\",\n    \"    plt.scatter(anomalies[:, 0], anomalies[:, 1],\\n\",\n    \"                c=\\\"r\\\", marker=\\\"x\\\", s=100)\\n\",\n    \"    plt.scatter(non_cores[:, 0], non_cores[:, 1], c=dbscan.labels_[non_core_mask], marker=\\\".\\\")\\n\",\n    \"    if show_xlabels:\\n\",\n    \"        plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelbottom=False)\\n\",\n    \"    if show_ylabels:\\n\",\n    \"        plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelleft=False)\\n\",\n    \"    plt.title(\\\"eps={:.2f}, min_samples={}\\\".format(dbscan.eps, dbscan.min_samples), fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 187,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure dbscan_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9, 3.2))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_dbscan(dbscan, X, size=100)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_dbscan(dbscan2, X, size=600, show_ylabels=False)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"dbscan_diagram\\\")\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 188,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dbscan = dbscan2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 189,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.neighbors import KNeighborsClassifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 190,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n       \"           metric_params=None, n_jobs=None, n_neighbors=50, p=2,\\n\",\n       \"           weights='uniform')\"\n      ]\n     },\n     \"execution_count\": 190,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"knn = KNeighborsClassifier(n_neighbors=50)\\n\",\n    \"knn.fit(dbscan.components_, dbscan.labels_[dbscan.core_sample_indices_])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 191,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 0, 1, 0])\"\n      ]\n     },\n     \"execution_count\": 191,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_new = np.array([[-0.5, 0], [0, 0.5], [1, -0.1], [2, 1]])\\n\",\n    \"knn.predict(X_new)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 192,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.18, 0.82],\\n\",\n       \"       [1.  , 0.  ],\\n\",\n       \"       [0.12, 0.88],\\n\",\n       \"       [1.  , 0.  ]])\"\n      ]\n     },\n     \"execution_count\": 192,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"knn.predict_proba(X_new)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 193,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure cluster_classification_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(6, 3))\\n\",\n    \"plot_decision_boundaries(knn, X, show_centroids=False)\\n\",\n    \"plt.scatter(X_new[:, 0], X_new[:, 1], c=\\\"b\\\", marker=\\\"+\\\", s=200, zorder=10)\\n\",\n    \"save_fig(\\\"cluster_classification_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 194,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-1,  0,  1, -1])\"\n      ]\n     },\n     \"execution_count\": 194,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_dist, y_pred_idx = knn.kneighbors(X_new, n_neighbors=1)\\n\",\n    \"y_pred = dbscan.labels_[dbscan.core_sample_indices_][y_pred_idx]\\n\",\n    \"y_pred[y_dist > 0.2] = -1\\n\",\n    \"y_pred.ravel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Other Clustering Algorithms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Spectral Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 195,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import SpectralClustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 196,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SpectralClustering(affinity='rbf', assign_labels='kmeans', coef0=1, degree=3,\\n\",\n       \"          eigen_solver=None, eigen_tol=0.0, gamma=100, kernel_params=None,\\n\",\n       \"          n_clusters=2, n_init=10, n_jobs=None, n_neighbors=10,\\n\",\n       \"          random_state=42)\"\n      ]\n     },\n     \"execution_count\": 196,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sc1 = SpectralClustering(n_clusters=2, gamma=100, random_state=42)\\n\",\n    \"sc1.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 197,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SpectralClustering(affinity='rbf', assign_labels='kmeans', coef0=1, degree=3,\\n\",\n       \"          eigen_solver=None, eigen_tol=0.0, gamma=1, kernel_params=None,\\n\",\n       \"          n_clusters=2, n_init=10, n_jobs=None, n_neighbors=10,\\n\",\n       \"          random_state=42)\"\n      ]\n     },\n     \"execution_count\": 197,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sc2 = SpectralClustering(n_clusters=2, gamma=1, random_state=42)\\n\",\n    \"sc2.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 198,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.04251990648936265\"\n      ]\n     },\n     \"execution_count\": 198,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.percentile(sc1.affinity_matrix_, 95)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 199,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_spectral_clustering(sc, X, size, alpha, show_xlabels=True, show_ylabels=True):\\n\",\n    \"    plt.scatter(X[:, 0], X[:, 1], marker='o', s=size, c='gray', cmap=\\\"Paired\\\", alpha=alpha)\\n\",\n    \"    plt.scatter(X[:, 0], X[:, 1], marker='o', s=30, c='w')\\n\",\n    \"    plt.scatter(X[:, 0], X[:, 1], marker='.', s=10, c=sc.labels_, cmap=\\\"Paired\\\")\\n\",\n    \"    \\n\",\n    \"    if show_xlabels:\\n\",\n    \"        plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelbottom=False)\\n\",\n    \"    if show_ylabels:\\n\",\n    \"        plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelleft=False)\\n\",\n    \"    plt.title(\\\"RBF gamma={}\\\".format(sc.gamma), fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 200,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9, 3.2))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_spectral_clustering(sc1, X, size=500, alpha=0.1)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_spectral_clustering(sc2, X, size=4000, alpha=0.01, show_ylabels=False)\\n\",\n    \"\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Agglomerative Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 201,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import AgglomerativeClustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 202,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = np.array([0, 2, 5, 8.5]).reshape(-1, 1)\\n\",\n    \"agg = AgglomerativeClustering(linkage=\\\"complete\\\").fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 203,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['children_', 'labels_', 'n_components_', 'n_leaves_']\"\n      ]\n     },\n     \"execution_count\": 203,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"learned_parameters(agg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 204,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 1],\\n\",\n       \"       [2, 3],\\n\",\n       \"       [4, 5]])\"\n      ]\n     },\n     \"execution_count\": 204,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"agg.children_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Gaussian Mixtures\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 205,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X1, y1 = make_blobs(n_samples=1000, centers=((4, -4), (0, 0)), random_state=42)\\n\",\n    \"X1 = X1.dot(np.array([[0.374, 0.95], [0.732, 0.598]]))\\n\",\n    \"X2, y2 = make_blobs(n_samples=250, centers=1, random_state=42)\\n\",\n    \"X2 = X2 + [6, -8]\\n\",\n    \"X = np.r_[X1, X2]\\n\",\n    \"y = np.r_[y1, y2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train a Gaussian mixture model on the previous dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 206,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.mixture import GaussianMixture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 207,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\\n\",\n       \"        means_init=None, n_components=3, n_init=10, precisions_init=None,\\n\",\n       \"        random_state=42, reg_covar=1e-06, tol=0.001, verbose=0,\\n\",\n       \"        verbose_interval=10, warm_start=False, weights_init=None)\"\n      ]\n     },\n     \"execution_count\": 207,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm = GaussianMixture(n_components=3, n_init=10, random_state=42)\\n\",\n    \"gm.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the parameters that the EM algorithm estimated:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 208,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.20965228, 0.4000662 , 0.39028152])\"\n      ]\n     },\n     \"execution_count\": 208,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.weights_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 209,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 3.39909717,  1.05933727],\\n\",\n       \"       [-1.40763984,  1.42710194],\\n\",\n       \"       [ 0.05135313,  0.07524095]])\"\n      ]\n     },\n     \"execution_count\": 209,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.means_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 210,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[ 1.14807234, -0.03270354],\\n\",\n       \"        [-0.03270354,  0.95496237]],\\n\",\n       \"\\n\",\n       \"       [[ 0.63478101,  0.72969804],\\n\",\n       \"        [ 0.72969804,  1.1609872 ]],\\n\",\n       \"\\n\",\n       \"       [[ 0.68809572,  0.79608475],\\n\",\n       \"        [ 0.79608475,  1.21234145]]])\"\n      ]\n     },\n     \"execution_count\": 210,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.covariances_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Did the algorithm actually converge?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 211,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 211,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.converged_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Yes, good. How many iterations did it take?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 212,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4\"\n      ]\n     },\n     \"execution_count\": 212,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.n_iter_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can now use the model to predict which cluster each instance belongs to (hard clustering) or the probabilities that it came from each cluster. For this, just use `predict()` method or the `predict_proba()` method:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 213,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2, 2, 1, ..., 0, 0, 0])\"\n      ]\n     },\n     \"execution_count\": 213,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.predict(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 214,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[2.32389467e-02, 6.77397850e-07, 9.76760376e-01],\\n\",\n       \"       [1.64685609e-02, 6.75361303e-04, 9.82856078e-01],\\n\",\n       \"       [2.01535333e-06, 9.99923053e-01, 7.49319577e-05],\\n\",\n       \"       ...,\\n\",\n       \"       [9.99999571e-01, 2.13946075e-26, 4.28788333e-07],\\n\",\n       \"       [1.00000000e+00, 1.46454409e-41, 5.12459171e-16],\\n\",\n       \"       [1.00000000e+00, 8.02006365e-41, 2.27626238e-15]])\"\n      ]\n     },\n     \"execution_count\": 214,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.predict_proba(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is a generative model, so you can sample new instances from it (and get their labels):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 215,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 2.95400315,  2.63680992],\\n\",\n       \"       [-1.16654575,  1.62792705],\\n\",\n       \"       [-1.39477712, -1.48511338],\\n\",\n       \"       [ 0.27221525,  0.690366  ],\\n\",\n       \"       [ 0.54095936,  0.48591934],\\n\",\n       \"       [ 0.38064009, -0.56240465]])\"\n      ]\n     },\n     \"execution_count\": 215,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_new, y_new = gm.sample(6)\\n\",\n    \"X_new\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 216,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 2, 2, 2])\"\n      ]\n     },\n     \"execution_count\": 216,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_new\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that they are sampled sequentially from each cluster.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also estimate the log of the _probability density function_ (PDF) at any location using the `score_samples()` method:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 217,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-2.60782346, -3.57106041, -3.33003479, ..., -3.51352783,\\n\",\n       \"       -4.39802535, -3.80743859])\"\n      ]\n     },\n     \"execution_count\": 217,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.score_samples(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's check that the PDF integrates to 1 over the whole space. We just take a large square around the clusters, and chop it into a grid of tiny squares, then we compute the approximate probability that the instances will be generated in each tiny square (by multiplying the PDF at one corner of the tiny square by the area of the square), and finally summing all these probabilities). The result is very close to 1:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 218,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9999999999217851\"\n      ]\n     },\n     \"execution_count\": 218,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"resolution = 100\\n\",\n    \"grid = np.arange(-10, 10, 1 / resolution)\\n\",\n    \"xx, yy = np.meshgrid(grid, grid)\\n\",\n    \"X_full = np.vstack([xx.ravel(), yy.ravel()]).T\\n\",\n    \"\\n\",\n    \"pdf = np.exp(gm.score_samples(X_full))\\n\",\n    \"pdf_probas = pdf * (1 / resolution) ** 2\\n\",\n    \"pdf_probas.sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's plot the resulting decision boundaries (dashed lines) and density contours:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 219,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib.colors import LogNorm\\n\",\n    \"\\n\",\n    \"def plot_gaussian_mixture(clusterer, X, resolution=1000, show_ylabels=True):\\n\",\n    \"    mins = X.min(axis=0) - 0.1\\n\",\n    \"    maxs = X.max(axis=0) + 0.1\\n\",\n    \"    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\\n\",\n    \"                         np.linspace(mins[1], maxs[1], resolution))\\n\",\n    \"    Z = -clusterer.score_samples(np.c_[xx.ravel(), yy.ravel()])\\n\",\n    \"    Z = Z.reshape(xx.shape)\\n\",\n    \"\\n\",\n    \"    plt.contourf(xx, yy, Z,\\n\",\n    \"                 norm=LogNorm(vmin=1.0, vmax=30.0),\\n\",\n    \"                 levels=np.logspace(0, 2, 12))\\n\",\n    \"    plt.contour(xx, yy, Z,\\n\",\n    \"                norm=LogNorm(vmin=1.0, vmax=30.0),\\n\",\n    \"                levels=np.logspace(0, 2, 12),\\n\",\n    \"                linewidths=1, colors='k')\\n\",\n    \"\\n\",\n    \"    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n    \"    Z = Z.reshape(xx.shape)\\n\",\n    \"    plt.contour(xx, yy, Z,\\n\",\n    \"                linewidths=2, colors='r', linestyles='dashed')\\n\",\n    \"    \\n\",\n    \"    plt.plot(X[:, 0], X[:, 1], 'k.', markersize=2)\\n\",\n    \"    plot_centroids(clusterer.means_, clusterer.weights_)\\n\",\n    \"\\n\",\n    \"    plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n    \"    if show_ylabels:\\n\",\n    \"        plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\\n\",\n    \"    else:\\n\",\n    \"        plt.tick_params(labelleft=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 220,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure gaussian_mixtures_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"\\n\",\n    \"plot_gaussian_mixture(gm, X)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"gaussian_mixtures_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can impose constraints on the covariance matrices that the algorithm looks for by setting the `covariance_type` hyperparameter:\\n\",\n    \"* `\\\"full\\\"` (default): no constraint, all clusters can take on any ellipsoidal shape of any size.\\n\",\n    \"* `\\\"tied\\\"`: all clusters must have the same shape, which can be any ellipsoid (i.e., they all share the same covariance matrix).\\n\",\n    \"* `\\\"spherical\\\"`: all clusters must be spherical, but they can have different diameters (i.e., different variances).\\n\",\n    \"* `\\\"diag\\\"`: clusters can take on any ellipsoidal shape of any size, but the ellipsoid's axes must be parallel to the axes (i.e., the covariance matrices must be diagonal).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 221,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GaussianMixture(covariance_type='diag', init_params='kmeans', max_iter=100,\\n\",\n       \"        means_init=None, n_components=3, n_init=10, precisions_init=None,\\n\",\n       \"        random_state=42, reg_covar=1e-06, tol=0.001, verbose=0,\\n\",\n       \"        verbose_interval=10, warm_start=False, weights_init=None)\"\n      ]\n     },\n     \"execution_count\": 221,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm_full = GaussianMixture(n_components=3, n_init=10, covariance_type=\\\"full\\\", random_state=42)\\n\",\n    \"gm_tied = GaussianMixture(n_components=3, n_init=10, covariance_type=\\\"tied\\\", random_state=42)\\n\",\n    \"gm_spherical = GaussianMixture(n_components=3, n_init=10, covariance_type=\\\"spherical\\\", random_state=42)\\n\",\n    \"gm_diag = GaussianMixture(n_components=3, n_init=10, covariance_type=\\\"diag\\\", random_state=42)\\n\",\n    \"gm_full.fit(X)\\n\",\n    \"gm_tied.fit(X)\\n\",\n    \"gm_spherical.fit(X)\\n\",\n    \"gm_diag.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 222,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compare_gaussian_mixtures(gm1, gm2, X):\\n\",\n    \"    plt.figure(figsize=(9, 4))\\n\",\n    \"\\n\",\n    \"    plt.subplot(121)\\n\",\n    \"    plot_gaussian_mixture(gm1, X)\\n\",\n    \"    plt.title('covariance_type=\\\"{}\\\"'.format(gm1.covariance_type), fontsize=14)\\n\",\n    \"\\n\",\n    \"    plt.subplot(122)\\n\",\n    \"    plot_gaussian_mixture(gm2, X, show_ylabels=False)\\n\",\n    \"    plt.title('covariance_type=\\\"{}\\\"'.format(gm2.covariance_type), fontsize=14)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 223,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure covariance_type_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"compare_gaussian_mixtures(gm_tied, gm_spherical, X)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"covariance_type_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 224,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"compare_gaussian_mixtures(gm_full, gm_diag, X)\\n\",\n    \"plt.tight_layout()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Anomaly Detection using Gaussian Mixtures\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Gaussian Mixtures can be used for _anomaly detection_: instances located in low-density regions can be considered anomalies. You must define what density threshold you want to use. For example, in a manufacturing company that tries to detect defective products, the ratio of defective products is usually well-known. Say it is equal to 4%, then you can set the density threshold to be the value that results in having 4% of the instances located in areas below that threshold density:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 225,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"densities = gm.score_samples(X)\\n\",\n    \"density_threshold = np.percentile(densities, 4)\\n\",\n    \"anomalies = X[densities < density_threshold]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 226,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure mixture_anomaly_detection_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 4))\\n\",\n    \"\\n\",\n    \"plot_gaussian_mixture(gm, X)\\n\",\n    \"plt.scatter(anomalies[:, 0], anomalies[:, 1], color='r', marker='*')\\n\",\n    \"plt.ylim(top=5.1)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"mixture_anomaly_detection_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model selection\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We cannot use the inertia or the silhouette score because they both assume that the clusters are spherical. Instead, we can try to find the model that minimizes a theoretical information criterion such as the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC):\\n\",\n    \"\\n\",\n    \"${BIC} = {\\\\log(m)p - 2\\\\log({\\\\hat L})}$\\n\",\n    \"\\n\",\n    \"${AIC} = 2p - 2\\\\log(\\\\hat L)$\\n\",\n    \"\\n\",\n    \"* $m$ is the number of instances.\\n\",\n    \"* $p$ is the number of parameters learned by the model.\\n\",\n    \"* $\\\\hat L$ is the maximized value of the likelihood function of the model. This is the conditional probability of the observed data $\\\\mathbf{X}$, given the model and its optimized parameters.\\n\",\n    \"\\n\",\n    \"Both BIC and AIC penalize models that have more parameters to learn (e.g., more clusters), and reward models that fit the data well (i.e., models that give a high likelihood to the observed data).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 227,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8189.74345832983\"\n      ]\n     },\n     \"execution_count\": 227,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.bic(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 228,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8102.518178214792\"\n      ]\n     },\n     \"execution_count\": 228,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"gm.aic(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We could compute the BIC manually like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 229,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_clusters = 3\\n\",\n    \"n_dims = 2\\n\",\n    \"n_params_for_weights = n_clusters - 1\\n\",\n    \"n_params_for_means = n_clusters * n_dims\\n\",\n    \"n_params_for_covariance = n_clusters * n_dims * (n_dims + 1) // 2\\n\",\n    \"n_params = n_params_for_weights + n_params_for_means + n_params_for_covariance\\n\",\n    \"max_log_likelihood = gm.score(X) * len(X) # log(L^)\\n\",\n    \"bic = np.log(len(X)) * n_params - 2 * max_log_likelihood\\n\",\n    \"aic = 2 * n_params - 2 * max_log_likelihood\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 230,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(8189.74345832983, 8102.518178214792)\"\n      ]\n     },\n     \"execution_count\": 230,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bic, aic\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 231,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"17\"\n      ]\n     },\n     \"execution_count\": 231,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"n_params\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There's one weight per cluster, but the sum must be equal to 1, so we have one degree of freedom less, hence the -1. Similarly, the degrees of freedom for an $n \\\\times n$ covariance matrix is not $n^2$, but $1 + 2 + \\\\dots + n = \\\\dfrac{n (n+1)}{2}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train Gaussian Mixture models with various values of $k$ and measure their BIC:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 232,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"gms_per_k = [GaussianMixture(n_components=k, n_init=10, random_state=42).fit(X)\\n\",\n    \"             for k in range(1, 11)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 233,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"bics = [model.bic(X) for model in gms_per_k]\\n\",\n    \"aics = [model.aic(X) for model in gms_per_k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 234,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure aic_bic_vs_k_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 3))\\n\",\n    \"plt.plot(range(1, 11), bics, \\\"bo-\\\", label=\\\"BIC\\\")\\n\",\n    \"plt.plot(range(1, 11), aics, \\\"go--\\\", label=\\\"AIC\\\")\\n\",\n    \"plt.xlabel(\\\"$k$\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Information Criterion\\\", fontsize=14)\\n\",\n    \"plt.axis([1, 9.5, np.min(aics) - 50, np.max(aics) + 50])\\n\",\n    \"plt.annotate('Minimum',\\n\",\n    \"             xy=(3, bics[2]),\\n\",\n    \"             xytext=(0.35, 0.6),\\n\",\n    \"             textcoords='figure fraction',\\n\",\n    \"             fontsize=14,\\n\",\n    \"             arrowprops=dict(facecolor='black', shrink=0.1)\\n\",\n    \"            )\\n\",\n    \"plt.legend()\\n\",\n    \"save_fig(\\\"aic_bic_vs_k_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's search for best combination of values for both the number of clusters and the `covariance_type` hyperparameter:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 235,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_bic = np.infty\\n\",\n    \"\\n\",\n    \"for k in range(1, 11):\\n\",\n    \"    for covariance_type in (\\\"full\\\", \\\"tied\\\", \\\"spherical\\\", \\\"diag\\\"):\\n\",\n    \"        bic = GaussianMixture(n_components=k, n_init=10,\\n\",\n    \"                              covariance_type=covariance_type,\\n\",\n    \"                              random_state=42).fit(X).bic(X)\\n\",\n    \"        if bic < min_bic:\\n\",\n    \"            min_bic = bic\\n\",\n    \"            best_k = k\\n\",\n    \"            best_covariance_type = covariance_type\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 236,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 236,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_k\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 237,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'full'\"\n      ]\n     },\n     \"execution_count\": 237,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_covariance_type\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Variational Bayesian Gaussian Mixtures\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Rather than manually searching for the optimal number of clusters, it is possible to use instead the `BayesianGaussianMixture` class which is capable of giving weights equal (or close) to zero to unnecessary clusters. Just set the number of components to a value that you believe is greater than the optimal number of clusters, and the algorithm will eliminate the unnecessary clusters automatically.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 238,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.mixture import BayesianGaussianMixture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 239,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"BayesianGaussianMixture(covariance_prior=None, covariance_type='full',\\n\",\n       \"            degrees_of_freedom_prior=None, init_params='kmeans',\\n\",\n       \"            max_iter=100, mean_precision_prior=None, mean_prior=None,\\n\",\n       \"            n_components=10, n_init=10, random_state=42, reg_covar=1e-06,\\n\",\n       \"            tol=0.001, verbose=0, verbose_interval=10, warm_start=False,\\n\",\n       \"            weight_concentration_prior=None,\\n\",\n       \"            weight_concentration_prior_type='dirichlet_process')\"\n      ]\n     },\n     \"execution_count\": 239,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bgm = BayesianGaussianMixture(n_components=10, n_init=10, random_state=42)\\n\",\n    \"bgm.fit(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The algorithm automatically detected that only 3 components are needed:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 240,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.4 , 0.21, 0.4 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ])\"\n      ]\n     },\n     \"execution_count\": 240,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.round(bgm.weights_, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 241,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 5))\\n\",\n    \"plot_gaussian_mixture(bgm, X)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 242,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"BayesianGaussianMixture(covariance_prior=None, covariance_type='full',\\n\",\n       \"            degrees_of_freedom_prior=None, init_params='kmeans',\\n\",\n       \"            max_iter=1000, mean_precision_prior=None, mean_prior=None,\\n\",\n       \"            n_components=10, n_init=1, random_state=42, reg_covar=1e-06,\\n\",\n       \"            tol=0.001, verbose=0, verbose_interval=10, warm_start=False,\\n\",\n       \"            weight_concentration_prior=10000,\\n\",\n       \"            weight_concentration_prior_type='dirichlet_process')\"\n      ]\n     },\n     \"execution_count\": 242,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bgm_low = BayesianGaussianMixture(n_components=10, max_iter=1000, n_init=1,\\n\",\n    \"                                  weight_concentration_prior=0.01, random_state=42)\\n\",\n    \"bgm_high = BayesianGaussianMixture(n_components=10, max_iter=1000, n_init=1,\\n\",\n    \"                                  weight_concentration_prior=10000, random_state=42)\\n\",\n    \"nn = 73\\n\",\n    \"bgm_low.fit(X[:nn])\\n\",\n    \"bgm_high.fit(X[:nn])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 243,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.52, 0.48, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ])\"\n      ]\n     },\n     \"execution_count\": 243,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.round(bgm_low.weights_, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 244,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.01, 0.18, 0.27, 0.11, 0.01, 0.01, 0.01, 0.01, 0.37, 0.01])\"\n      ]\n     },\n     \"execution_count\": 244,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.round(bgm_high.weights_, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 245,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure mixture_concentration_prior_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9, 4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_gaussian_mixture(bgm_low, X[:nn])\\n\",\n    \"plt.title(\\\"weight_concentration_prior = 0.01\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_gaussian_mixture(bgm_high, X[:nn], show_ylabels=False)\\n\",\n    \"plt.title(\\\"weight_concentration_prior = 10000\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"mixture_concentration_prior_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the fact that you see only 3 regions in the right plot although there are 4 centroids is not a bug. The weight of the top-right cluster is much larger than the weight of the lower-right cluster, so the probability that any given point in this region belongs to the top right cluster is greater than the probability that it belongs to the lower-right cluster.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 246,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_moons, y_moons = make_moons(n_samples=1000, noise=0.05, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 247,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"BayesianGaussianMixture(covariance_prior=None, covariance_type='full',\\n\",\n       \"            degrees_of_freedom_prior=None, init_params='kmeans',\\n\",\n       \"            max_iter=100, mean_precision_prior=None, mean_prior=None,\\n\",\n       \"            n_components=10, n_init=10, random_state=42, reg_covar=1e-06,\\n\",\n       \"            tol=0.001, verbose=0, verbose_interval=10, warm_start=False,\\n\",\n       \"            weight_concentration_prior=None,\\n\",\n       \"            weight_concentration_prior_type='dirichlet_process')\"\n      ]\n     },\n     \"execution_count\": 247,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bgm = BayesianGaussianMixture(n_components=10, n_init=10, random_state=42)\\n\",\n    \"bgm.fit(X_moons)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 248,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure moons_vs_bgm_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x230.4 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9, 3.2))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plot_data(X_moons)\\n\",\n    \"plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"$x_2$\\\", fontsize=14, rotation=0)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plot_gaussian_mixture(bgm, X_moons, show_ylabels=False)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"moons_vs_bgm_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Oops, not great... instead of detecting 2 moon-shaped clusters, the algorithm detected 8 ellipsoidal clusters. However, the density plot does not look too bad, so it might be usable for anomaly detection.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Likelihood Function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 249,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.stats import norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 250,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"xx = np.linspace(-6, 4, 101)\\n\",\n    \"ss = np.linspace(1, 2, 101)\\n\",\n    \"XX, SS = np.meshgrid(xx, ss)\\n\",\n    \"ZZ = 2 * norm.pdf(XX - 1.0, 0, SS) + norm.pdf(XX + 4.0, 0, SS)\\n\",\n    \"ZZ = ZZ / ZZ.sum(axis=1) / (xx[1] - xx[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 251,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure likelihood_function_diagram\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x324 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib.patches import Polygon\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 4.5))\\n\",\n    \"\\n\",\n    \"x_idx = 85\\n\",\n    \"s_idx = 30\\n\",\n    \"\\n\",\n    \"plt.subplot(221)\\n\",\n    \"plt.contourf(XX, SS, ZZ, cmap=\\\"GnBu\\\")\\n\",\n    \"plt.plot([-6, 4], [ss[s_idx], ss[s_idx]], \\\"k-\\\", linewidth=2)\\n\",\n    \"plt.plot([xx[x_idx], xx[x_idx]], [1, 2], \\\"b-\\\", linewidth=2)\\n\",\n    \"plt.xlabel(r\\\"$x$\\\")\\n\",\n    \"plt.ylabel(r\\\"$\\\\theta$\\\", fontsize=14, rotation=0)\\n\",\n    \"plt.title(r\\\"Model $f(x; \\\\theta)$\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(222)\\n\",\n    \"plt.plot(ss, ZZ[:, x_idx], \\\"b-\\\")\\n\",\n    \"max_idx = np.argmax(ZZ[:, x_idx])\\n\",\n    \"max_val = np.max(ZZ[:, x_idx])\\n\",\n    \"plt.plot(ss[max_idx], max_val, \\\"r.\\\")\\n\",\n    \"plt.plot([ss[max_idx], ss[max_idx]], [0, max_val], \\\"r:\\\")\\n\",\n    \"plt.plot([0, ss[max_idx]], [max_val, max_val], \\\"r:\\\")\\n\",\n    \"plt.text(1.01, max_val + 0.005, r\\\"$\\\\hat{L}$\\\", fontsize=14)\\n\",\n    \"plt.text(ss[max_idx]+ 0.01, 0.055, r\\\"$\\\\hat{\\\\theta}$\\\", fontsize=14)\\n\",\n    \"plt.text(ss[max_idx]+ 0.01, max_val - 0.012, r\\\"$Max$\\\", fontsize=12)\\n\",\n    \"plt.axis([1, 2, 0.05, 0.15])\\n\",\n    \"plt.xlabel(r\\\"$\\\\theta$\\\", fontsize=14)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.text(1.99, 0.135, r\\\"$=f(x=2.5; \\\\theta)$\\\", fontsize=14, ha=\\\"right\\\")\\n\",\n    \"plt.title(r\\\"Likelihood function $\\\\mathcal{L}(\\\\theta|x=2.5)$\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.subplot(223)\\n\",\n    \"plt.plot(xx, ZZ[s_idx], \\\"k-\\\")\\n\",\n    \"plt.axis([-6, 4, 0, 0.25])\\n\",\n    \"plt.xlabel(r\\\"$x$\\\", fontsize=14)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.title(r\\\"PDF $f(x; \\\\theta=1.3)$\\\", fontsize=14)\\n\",\n    \"verts = [(xx[41], 0)] + list(zip(xx[41:81], ZZ[s_idx, 41:81])) + [(xx[80], 0)]\\n\",\n    \"poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\\n\",\n    \"plt.gca().add_patch(poly)\\n\",\n    \"\\n\",\n    \"plt.subplot(224)\\n\",\n    \"plt.plot(ss, np.log(ZZ[:, x_idx]), \\\"b-\\\")\\n\",\n    \"max_idx = np.argmax(np.log(ZZ[:, x_idx]))\\n\",\n    \"max_val = np.max(np.log(ZZ[:, x_idx]))\\n\",\n    \"plt.plot(ss[max_idx], max_val, \\\"r.\\\")\\n\",\n    \"plt.plot([ss[max_idx], ss[max_idx]], [-5, max_val], \\\"r:\\\")\\n\",\n    \"plt.plot([0, ss[max_idx]], [max_val, max_val], \\\"r:\\\")\\n\",\n    \"plt.axis([1, 2, -2.4, -2])\\n\",\n    \"plt.xlabel(r\\\"$\\\\theta$\\\", fontsize=14)\\n\",\n    \"plt.text(ss[max_idx]+ 0.01, max_val - 0.05, r\\\"$Max$\\\", fontsize=12)\\n\",\n    \"plt.text(ss[max_idx]+ 0.01, -2.39, r\\\"$\\\\hat{\\\\theta}$\\\", fontsize=14)\\n\",\n    \"plt.text(1.01, max_val + 0.02, r\\\"$\\\\log \\\\, \\\\hat{L}$\\\", fontsize=14)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.title(r\\\"$\\\\log \\\\, \\\\mathcal{L}(\\\\theta|x=2.5)$\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"likelihood_function_diagram\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 8.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## 9.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Load the MNIST dataset (introduced in chapter 3) and split it into a training set and a test set (take the first 60,000 instances for training, and the remaining 10,000 for testing).*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The MNIST dataset was loaded earlier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 252,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = mnist['data'][:60000]\\n\",\n    \"y_train = mnist['target'][:60000]\\n\",\n    \"\\n\",\n    \"X_test = mnist['data'][60000:]\\n\",\n    \"y_test = mnist['target'][60000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Train a Random Forest classifier on the dataset and time how long it takes, then evaluate the resulting model on the test set.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 253,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"\\n\",\n    \"rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 254,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"rnd_clf.fit(X_train, y_train)\\n\",\n    \"t1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 255,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 3.98s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training took {:.2f}s\\\".format(t1 - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 256,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9492\"\n      ]\n     },\n     \"execution_count\": 256,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"\\n\",\n    \"y_pred = rnd_clf.predict(X_test)\\n\",\n    \"accuracy_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Next, use PCA to reduce the dataset's dimensionality, with an explained variance ratio of 95%.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 257,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.decomposition import PCA\\n\",\n    \"\\n\",\n    \"pca = PCA(n_components=0.95)\\n\",\n    \"X_train_reduced = pca.fit_transform(X_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Train a new Random Forest classifier on the reduced dataset and see how long it takes. Was training much faster?*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 258,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"rnd_clf2 = RandomForestClassifier(n_estimators=10, random_state=42)\\n\",\n    \"t0 = time.time()\\n\",\n    \"rnd_clf2.fit(X_train_reduced, y_train)\\n\",\n    \"t1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 259,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 10.29s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training took {:.2f}s\\\".format(t1 - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Oh no! Training is actually more than twice slower now! How can that be? Well, as we saw in this chapter, dimensionality reduction does not always lead to faster training time: it depends on the dataset, the model and the training algorithm. See figure 8-6 (the `manifold_decision_boundary_plot*` plots above). If you try a softmax classifier instead of a random forest classifier, you will find that training time is reduced by a factor of 3 when using PCA. Actually, we will do this in a second, but first let's check the precision of the new random forest classifier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Next evaluate the classifier on the test set: how does it compare to the previous classifier?*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 260,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9009\"\n      ]\n     },\n     \"execution_count\": 260,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_test_reduced = pca.transform(X_test)\\n\",\n    \"\\n\",\n    \"y_pred = rnd_clf2.predict(X_test_reduced)\\n\",\n    \"accuracy_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It is common for performance to drop slightly when reducing dimensionality, because we do lose some useful signal in the process. However, the performance drop is rather severe in this case. So PCA really did not help: it slowed down training and reduced performance. :(\\n\",\n    \"\\n\",\n    \"Let's see if it helps when using softmax regression:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 261,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\\n\",\n      \"  \\\"of iterations.\\\", ConvergenceWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"\\n\",\n    \"log_clf = LogisticRegression(multi_class=\\\"multinomial\\\", solver=\\\"lbfgs\\\", random_state=42)\\n\",\n    \"t0 = time.time()\\n\",\n    \"log_clf.fit(X_train, y_train)\\n\",\n    \"t1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 262,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 17.35s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training took {:.2f}s\\\".format(t1 - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 263,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9255\"\n      ]\n     },\n     \"execution_count\": 263,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = log_clf.predict(X_test)\\n\",\n    \"accuracy_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Okay, so softmax regression takes much longer to train on this dataset than the random forest classifier, plus it performs worse on the test set. But that's not what we are interested in right now, we want to see how much PCA can help softmax regression. Let's train the softmax regression model using the reduced dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 264,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\\n\",\n      \"  \\\"of iterations.\\\", ConvergenceWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"log_clf2 = LogisticRegression(multi_class=\\\"multinomial\\\", solver=\\\"lbfgs\\\", random_state=42)\\n\",\n    \"t0 = time.time()\\n\",\n    \"log_clf2.fit(X_train_reduced, y_train)\\n\",\n    \"t1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 265,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 4.87s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training took {:.2f}s\\\".format(t1 - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Nice! Reducing dimensionality led to a 4× speedup. :)  Let's check the model's accuracy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 266,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9201\"\n      ]\n     },\n     \"execution_count\": 266,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = log_clf2.predict(X_test_reduced)\\n\",\n    \"accuracy_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A very slight drop in performance, which might be a reasonable price to pay for a 4× speedup, depending on the application.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So there you have it: PCA can give you a formidable speedup... but not always!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Use t-SNE to reduce the MNIST dataset down to two dimensions and plot the result using Matplotlib. You can use a scatterplot using 10 different colors to represent each image's target class.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The MNIST dataset was loaded above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Dimensionality reduction on the full 60,000 images takes a very long time, so let's only do this on a random subset of 10,000 images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 267,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"m = 10000\\n\",\n    \"idx = np.random.permutation(60000)[:m]\\n\",\n    \"\\n\",\n    \"X = mnist['data'][idx]\\n\",\n    \"y = mnist['target'][idx]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's use t-SNE to reduce dimensionality down to 2D so we can plot the dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 268,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.manifold import TSNE\\n\",\n    \"\\n\",\n    \"tsne = TSNE(n_components=2, random_state=42)\\n\",\n    \"X_reduced = tsne.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's use Matplotlib's `scatter()` function to plot a scatterplot, using a different color for each digit:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 269,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(13,10))\\n\",\n    \"plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap=\\\"jet\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Isn't this just beautiful? :) This plot tells us which numbers are easily distinguishable from the others (e.g., 0s, 6s, and most 8s are rather well separated clusters), and it also tells us which numbers are often hard to distinguish (e.g., 4s and 9s, 5s and 3s, and so on).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's focus on digits 3 and 5, which seem to overlap a lot.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 270,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9,9))\\n\",\n    \"cmap = mpl.cm.get_cmap(\\\"jet\\\")\\n\",\n    \"for digit in (2, 3, 5):\\n\",\n    \"    plt.scatter(X_reduced[y == digit, 0], X_reduced[y == digit, 1], c=[cmap(digit / 9)])\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's see if we can produce a nicer image by running t-SNE on these 3 digits:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 271,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"idx = (y == 2) | (y == 3) | (y == 5) \\n\",\n    \"X_subset = X[idx]\\n\",\n    \"y_subset = y[idx]\\n\",\n    \"\\n\",\n    \"tsne_subset = TSNE(n_components=2, random_state=42)\\n\",\n    \"X_subset_reduced = tsne_subset.fit_transform(X_subset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 272,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9,9))\\n\",\n    \"for digit in (2, 3, 5):\\n\",\n    \"    plt.scatter(X_subset_reduced[y_subset == digit, 0], X_subset_reduced[y_subset == digit, 1], c=[cmap(digit / 9)])\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Much better, now the clusters have far less overlap. But some 3s are all over the place. Plus, there are two distinct clusters of 2s, and also two distinct clusters of 5s. It would be nice if we could visualize a few digits from each cluster, to understand why this is the case. Let's do that now. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"*Exercise: Alternatively, you can write colored digits at the location of each instance, or even plot scaled-down versions of the digit images themselves (if you plot all digits, the visualization will be too cluttered, so you should either draw a random sample or plot an instance only if no other instance has already been plotted at a close distance). You should get a nice visualization with well-separated clusters of digits.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a `plot_digits()` function that will draw a scatterplot (similar to the above scatterplots) plus write colored digits, with a minimum distance guaranteed between these digits. If the digit images are provided, they are plotted instead. This implementation was inspired from one of Scikit-Learn's excellent examples ([plot_lle_digits](http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html), based on a different digit dataset).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 273,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import MinMaxScaler\\n\",\n    \"from matplotlib.offsetbox import AnnotationBbox, OffsetImage\\n\",\n    \"\\n\",\n    \"def plot_digits(X, y, min_distance=0.05, images=None, figsize=(13, 10)):\\n\",\n    \"    # Let's scale the input features so that they range from 0 to 1\\n\",\n    \"    X_normalized = MinMaxScaler().fit_transform(X)\\n\",\n    \"    # Now we create the list of coordinates of the digits plotted so far.\\n\",\n    \"    # We pretend that one is already plotted far away at the start, to\\n\",\n    \"    # avoid `if` statements in the loop below\\n\",\n    \"    neighbors = np.array([[10., 10.]])\\n\",\n    \"    # The rest should be self-explanatory\\n\",\n    \"    plt.figure(figsize=figsize)\\n\",\n    \"    cmap = mpl.cm.get_cmap(\\\"jet\\\")\\n\",\n    \"    digits = np.unique(y)\\n\",\n    \"    for digit in digits:\\n\",\n    \"        plt.scatter(X_normalized[y == digit, 0], X_normalized[y == digit, 1], c=[cmap(digit / 9)])\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    ax = plt.gcf().gca()  # get current axes in current figure\\n\",\n    \"    for index, image_coord in enumerate(X_normalized):\\n\",\n    \"        closest_distance = np.linalg.norm(np.array(neighbors) - image_coord, axis=1).min()\\n\",\n    \"        if closest_distance > min_distance:\\n\",\n    \"            neighbors = np.r_[neighbors, [image_coord]]\\n\",\n    \"            if images is None:\\n\",\n    \"                plt.text(image_coord[0], image_coord[1], str(int(y[index])),\\n\",\n    \"                         color=cmap(y[index] / 9), fontdict={\\\"weight\\\": \\\"bold\\\", \\\"size\\\": 16})\\n\",\n    \"            else:\\n\",\n    \"                image = images[index].reshape(28, 28)\\n\",\n    \"                imagebox = AnnotationBbox(OffsetImage(image, cmap=\\\"binary\\\"), image_coord)\\n\",\n    \"                ax.add_artist(imagebox)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try it! First let's just write colored digits:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 274,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_digits(X_reduced, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well that's okay, but not that beautiful. Let's try with the digit images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 275,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 2520x1800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_digits(X_reduced, y, images=X, figsize=(35, 25))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 276,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1584x1584 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_digits(X_subset_reduced, y_subset, images=X_subset, figsize=(22, 22))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*Exercise: Try using other dimensionality reduction algorithms such as PCA, LLE, or MDS and compare the resulting visualizations.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start with PCA. We will also time how long it takes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 277,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PCA took 0.3s.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.decomposition import PCA\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_pca_reduced = PCA(n_components=2, random_state=42).fit_transform(X)\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"PCA took {:.1f}s.\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_pca_reduced, y)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Wow, PCA is blazingly fast! But although we do see a few clusters, there's way too much overlap. Let's try LLE:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 278,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LLE took 118.6s.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAv0AAAJCCAYAAABTWni0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xt8XHWd//HXdyZNmqQXodPW0jaZqsu2I1cFxUURRUVZDO5KlKQFadkW5SJ4303YHyokqygKCgotpgJNqqbuamTLahWqUpH71ZSL2iS9pDRToJfcmmS+vz/Omclczsn91uT9fDz6mM6Zc/lOGvRzvufz+XyNtRYREREREZm8AuM9ABERERERGV0K+kVEREREJjkF/SIiIiIik5yCfhERERGRSU5Bv4iIiIjIJKegX0RERERkklPQLyIiIiIyySnoFxERERGZ5BT0i4iIiIhMcgr6RUREREQmOQX9IiIiIiKTnIJ+EREREZFJTkG/iIiIiMgkp6BfRERERGSSU9AvIiIiIjLJKegXEREREZnkFPSLiIiIiExyCvpFRERERCY5Bf0iIiIiIpOcgn4RERERkUlOQb+IiIiIyCSnoF9EREREZJJT0C8iIiIiMskp6BcRERERmeQU9IuIiIiITHIK+kVEREREJjkF/SIiIiIik5yCfhERERGRSU5Bv4iIiIjIJKegXzwZQ6kxNBhDzH0tHe8xiYiIiMjQGGvteI9BJhg3wN8AmKTNFlhhLTXjMyoRERERGSoF/ZLBGLqBoMdHFngMOAXIdrddbS23jdXYRERERGTwssZ7ADIhBVkCnAbkA63ANmAPBnjHeA5MRERERAZPOf2SaQnwbmAGToLPDOCdwCwALgLuGK+hiYiIiMjgKeiXTKeR+QzoDcC5YC0/BV4Z+0GJiIiIyFAp6JdM+YPcLiIiIiITmoJ+ydQ6yO0iIiIiMqEp6JdMjwPdadu63e0iIiIictRR9x7JtMN9Te7e83jSdhERERE5qijoF287UJAvIiIiMkkovUcGxRhCQF7SpnxjCLnbRURERGQC0oq8ksEYhvRLYS1mpMciIiIiIsOn9B7xdFxJI8sqnye3oI32pjye++zJ7PvVovEeloiIiIgMgWb6JcPC0kZ78ronyMrvSWzrbg3yzOq3s7umULP5IiIiIkcZ5fRLhmWVz6cE/ABZ+T0sq3x+nEYkIiIiIsOhoF8y5Ba2DWq7iIiIiExsCvolg7XeGTx+20VERERkYlPQLxmM8a7z8NsuIiIiIhObgn7J0N6ZN6jtIiIiIjKxKeiXDNvvPoHuWDBlW3csyPa7TxinEYmIiIjIcCjolwx7Pl1I0wOFxGIGayEWMzQ9UMieTxeO99BEREREZAgU9EuG40oaKXhXI4GAxRgIBCwF72rkuJLG8R6aiIiIiAyBgn7JoD79IiIiIpOLgn7JkFvg06e/oA1j+PUYD0dEREREhklBv2Rob/Lp3uNs/9CYDkZEREREhk1Bv2TYXnYC3a2p3XtaG3L587nvAcAYOo3Bun+uGo8xioiIiMjAKeiXDHs2FvJM+dtpa8nDWmhryeO58rfR+tKs+C7Z4zk+ERERERkcBf2SaQm8t+Ah7mq5kl82f5K7Wq7kjCWPQiLm545xHJ2IiIiIDJKCfslQcnk16z6+hvAxjQSMJXxMIxtXL6ekrDq+yyvjOT4RERERGRwF/ZKh8qPl5GendvDJz26j8qPlAHZcBiUiIiIiQ6agXzIUvKGpr+1mTAcjIiIiIsOmoF8yNL1SMKjtIiIiIjKxKeiXDGVfr6C1PbVXf2t7HmVfrxinEYmIiIjIcCjolwwbf7Gc1V9YS8POQmIxQ8POQlZ/YS0bf7F8vIcmIiIiIkOQNd4DkImpKXchm+edw5wFUfZ3hWicvjD+UQxIfgyQbwwhAGuJjvU4RURERKR/xlo1Y5FU7y7dai9fdzvT8zsT23bWL+ILb/1Bn8dZqyJfERERkYlI6T2SobTy3pSAHyAn78g4jUZEREREhktBv2SYU5CZpTMvvI+f9FwAODP6Xn/GepwiIiIiMjAK+iXD/qbQoLaLiIiIyMSmoF8y1JRdTEdrTsq2jtYcasouHqcRiYiIiMhwqHuPZNi28WzAye2fUxBlf1OImrKL49tV+S0iIiJylFH3HklhDKVAdV/7KH9fRERE5OiioF9SGEMDUNjXPgr6RURERI4uCvolhTHEoO+gXkG/iIiIyNFFhbySrmm8ByAiIiIiI0tBv6Qr6+dzPRoSEREROcoo6JcU1lLTzy4/GJOBiIiIiMiIUU6/ZDDGfzZf+fwiIiIiRx/N9IuIiIiITHIK+iWF26dfRERERCYRBf2S4Ab868Z7HCIiIiIyspTTLwlamEtERERkclLQLwlamEtERERkclJ6jyTTwlwiIiIik5CCfklWBrSN9yBEREREZGRljfcAZOKwlhrjJO9Uj8f1TT2nA9cBJwFzgWwgCjwC3GQjPDwe4xIRERE52imnXzKM1+Jcpp5LgfU+H3cDZ9oIj47W9UVEREQmK6X3yETyMnAZsASYDkSAx93PskBrCIiIiIgMhdJ7ZFDMKkqBSqAAp/C3zFZRMxLnthG2AduSNm039dwDnOa+7xqJ64iIiIhMNUrvkQy+6T1LgLNpA/KStrYBq0cq8E+MoZ4s4Hjgbpyg/xBwho1QP5LXEREREZkKlN4jA+fMt+elbc3DmfkfMaaeBpxZ/b+4V20GPqCAX0RERGRoFPTLwOX7flIwyldeAGw29Zw0ytcRERERmZQU9Iuj3JRSbhooN7EdV4YpiXh07Wz1PXpEF/WyEcI47TqXApvczXOAG0byOiIiIiJThXL6xQn4YQP4t+NseL2QJfc3JHL6S3ZVU7m9nIL2Jl6ddsyhUNernyZqRzSvH8DUcwrwlPv2BRth2UhfQ0RERGSyU/ceAScnv8/++4GuHna8FqawrjEvOm0Os7oPkWOPABDqenVmtwmuzwoZBhv4m/qUbkCHgLuAW4BXgEXAV5J2/9tgzi0iIiIiDs30C5SbGB5Bf8xCwN3a+fA0cp7tt2NmI1EbHuhl3YB/HZnFwV5agffZCI8N9PwiIiIi4lBOv4BPTn7MBp3XLkPOCwNqkT/Ygt5KvAP+GE73nnbgReBO4FQF/CIiIiJDo/QeASgjbca9ozub6VlO+o55ycKRAZ1nsAW9fjcJxkbIHuS5eg82HguI2ZFdR0BERETkaKKZfoEKWwOsBhoB23CgkJ0HFwMQs4Y9zxzX7ynaArng3DwMht9NwpC7AbkB/zqgECdlqRBY524XERERmZIU9IujwtZQYcNU2MBdv1rFPxzr1Mx27szhf/ZdQKtNzcLpMNNomTaHGIbG3AJbvaj09iF07ynDWdE3WRuDv3lI5pUyNOILiImIiIgcTVTIK6lCprT7fcHqrON7nPebobUpj/Udn+L87M2Eg432cDB//2dPuJX1hZfNIZ4+UzW09Jm07j3OuSJDT8UxBs+iZMBaq5tcERERmZoU9EuqAtNEKYsJAq8DP3U2N/QUsuT1Bqztu7XneDOGBpyUnnSN1hIe29GIiIiITAwq5J3KnEW5UmbZWcoigu7nz/fuWhAY0UV3R1NGUTLDTxkSEREROaop3WGqcgL+jIJX3orz6KcTp1mmqyk22G6c48Pt0pMoSnZfV6t7j4iIiExlmumfurwLXuNbXgS6nb+22jzK2irGbmTD5Ab4CvJFREREXJrpn7r8pu5t6bc30PBQITFraOgpZPXhtWw8snxMByciIiIiI0eFvFNVuWnAp+DVVFqv7QATvpBXRERERDJppn/qGo0e+SIiIiIyASnon6rSVuF1X1e720VERERkElF6j2QwBt9fiomQ3jPSC3qJiIiITHYK+iXDRA763YDfqw//agX+IiIiIt4U9EuGCR70N+C34m5EK+6KiIiIeFFOvxxt/FqNHh2rh4mIiIiMAwX9crRpGuR2ERERkSlPQb8cbdRqVERERGSQFPRLCmMoLYlUs+PKMD3/EWDHlWFKItXjPawEt1g3o9WoinhFRERE/KmQV1JccuI9bT/88Gdy87N7J9Nbj+SxevNaNtYvH/dCXhEREREZPAX9kqLhqrANz27s3fAy8CjYw9DYU0g42LicqBbwEhERETmaKOgXKDcNeLfBdByCpOSZNmC1An8RERGRo4dy+qV/R1Le5eGshisiIiIiRwnN9EuKWJmxgfSs/YeBZ1O2WKJWN4wiIiIiRwkFbpKiqTsty6cLeCFzt7EZjYiIiIiMBAX9kqLscAUdsezeDX8lPb1HPfFFREREjjIK+iXFxuhydsYWA2AtvJrzBlry5hBzOnU2oiLeITP1lJp6Gkw9Mfe1dLzHJCIiIlODcvolxemnPWYfO/cdAPy680N8+NVfQzfwENi/j2yP/lqKSnGKggtwUobKiqmblDcUboC/DqcQOq4NLSwmIiIiY0Az/ZLis8d9L/H3Ww9fA4eBh4AdI3sdN+Bfh9Mq1Liv69ztk1ElqQE/qBOSiIiIjBHN9EuvcjO/szt7b07WEV7cfzxL73wBkib3R3I13lqKGvBeG6CxmLrwSF1nojD1xMDz52dtRDffIiIiMroUbExF5aaUctNAuYm5r/HZ9ctzspyq3e8/fjXeMeoQhUwpIdNAyMQImYbFm3YV+Ozpt33cDTMn36/jkTohiYiIyKjTTP9U4wT4Xrnlnwa+CSx4vWM2i76/i9auGSmHDnmmP5R5ze7pAfv4LSebnRcu4omnT+Phx97N3xr+gVdfPzbW3p7fDjQA9wE32SpeHdJ1R9Bwc/KV0y8iIiLjSTP9U49fbvl3gAUAf3r+XTyffwI9xwbY8YYwJdnVzl5ps/VuMD+ka2Z1xMyJN7xgAf7vgfP5w8PvZ3fzYtrb8wNAPvBW4CvA42YVs4f2VUfUsHLy3cB+NU4HJOu+KuAXERGRMZE13gOQMeeXPjOHCmtKc6rtuhlryA+2ARAONrJuxhr+qWMbpM5UFwLrCBkG0MLT85p5u9sBGqdldRW+791bDr15ycvfuOveK74DnAxsAhYBS4DLcG5KxtOw05HcAF9BvoiIiIw5pfdMNeWmAZ8CWipsuOGYsA0HGzM+7LZBskyP1xkbidpwn9cM9XHNqA2bVcy0VRxK/sCs4ovAt9y3d9o68wfS2nuO5XoBpp4GfL6DjRAeq3GIiIiIDIXSe6aeMpxc8mSJVXYLAt51pUE8A34Y2Ex3n9dMD/hd0+N/+dC+Xx+DR3vPQaQXjYQ+v4OIiIjIRKagf6qpsJ655e52mmLeMXwPQb8z9t99JupzTZ+ZerOKBcBV7tu2O5759D8xzj3ulZMvIiIiRzOl90iKRE6/6Z3UbrV5rO/4FFfl/rANj+4zI5lmY1axGPgNsBSIAZ+0deZn+PS4J2p14yoiIiLSDwVMkmLjkeWsPryWhp5CYtbQ0FPI6sNrubrtBzCI2fqhMKtYCmzDCfi7gYttFZtQj3sRERGRYdFMv6QwhtRfiCXAaThNNA2NQJmtGvmUFrOK04D7gRDOE4RiW8VmwLPPPyP0lMHUczpwHXASMBfIBqLAI8BNNsLDwzm/iIiIyESgoF9SpAT9S4B3k97Y1VlQapiBv1lFKb3dePYBs4BcYD9wvq3izykHOIH/iHfvMfVcCqz3+bgbONNGeHS41xmwkCkCPgucCswGDgNPA7cRtZvGbBwiIiIyqSjolxTGYI8raWRZ5fPkFrTR/moe239+AnseSelW2WirMttUpgXyTfg8FXD3S5+578vvbRVnD/KrDIip50zgH4EHgGbgTcA9OM83AG61Ea4djWtnCJlPAj/pY4+VRO2Px2QsIiIiMqkop19SHFfSyMnrniAv3IYJQF6ojZMvfYLj3pnSuz+jxY9ZxW3ABtLaaroBfjqv1W3HhY2wzUaoshEabIROG2E7TtAf1zWGw1mZ9Pev4iRVXZW07dNjOBYRERGZRLQir6RYVvk8WfmpPfmzcnpY9vHnk2f7Uwpo3cD+CjI77MTbaqbP9vv19re2avxuRE09WcDxwCXupkP4p/6Mhu6kv/+UqG0jZGqA29xtE+JGSURERI4+mumXFLkF6etPudvnJLZ7LUhViXdLTfAO8CdcNx53xd0u4C84qT3NwAdshPoxHMZanDalABcRMnmQ8qRk8xiORURERCYRBf2Sor3JezK5fX8exBekyszT72tVXq9A/mhY3XYBsNnUc9KYXTFq64AinCcM1wOtOLP87cB3gf8cs7GIiIjIpKKgX1JsLzuB7tbU1Xe7O4Js//kJfR3mN0Nv8Qjk3ZuGzNVtR6EV6EDZCGGcdp1LgXiXnDnADWM2iJA5CycVambaJznAMpwbEREREZFBU/ceSTGA7j0ZLTt9uvFYWtjCffwjyd187PgF9gNl6jkFeMp9+4KNsGxMLhwyTwGnuO8uwbn5eBfO+gXZwNNE7aljMhYRERGZVBT0S4qUPv3FwAzP3TJadma063yR+/gTK/FYUCsR+I9S7/3+mPqUsR4C7gJuAV4BFgEVwEXu7v9rI5w/2mMCIGQ6cGb124navKTtzwHxRy1zidromIxHREREJg0F/ZIiJei/FL/y3H677BhDA07bznSN1hL2WmU3FjRHnrj5pIMNKwrm4N4EFFM3ojcBbsA/0DUCWoH32QiPjeQYUqTe+PTQ21HrEqAWZ6b//3Bm+ruA2URt+6iNR0RERCYlBf2SYqgz/R7nieF9y2CtJUDINOBxU9C6KJfNT38g/rYNWD2Sgb/bpcfrZiSGE3R349xwbAVuthFeHqlrZ/C48enH7UTtVf3vJiIiIpJKffrFXxNO+Whm6H7fAI/2Cq7jRb+eHX/ydqdMYvv1+R8Ov05DxkbIHsHrDITfImUdODc8s93X7cC9wA/Gbmip0lKinPqMyMSvzxARERGHuvdIhpJINTuuDNPz/gA75oUpmV6dvstActz7a8vp2fGnbWFu+qa+2oEOxURaI8Dvu+UQtXOI2iyidhZR+06i9jaiNuaz/6hKSolKXW253nO1ZREREZmAFPRLipJINevOW0N4diMBYwlnNbJu9pr0wD8zWA2ZUkKmgZCJETINdo4Br7acvd17Mm4KunODPHfd0pTThjc07U8+r5sSMxwTaY2AiXQD0hevJxLxpzAiIiJyFFBOv6RouCpsw7MbM7d3F7KkpSH+NjWn3zs3vQ1Y3Wc3nqQi1u684P4nvn3izKZPLM6Jf1zws52dp1/9jAn02OS0m/7P24+0VJX97uZE8fCYpa0M9ec2gkw9pwPXAScBc3EKhqPAI8BNNsLDpr6P+oyIJg5ERESOBgr6JUWsLGADJvN3ImYNwb0x8OjT71eUCzQSteGBXruWopS88X8p2Jyf1dYTGu55/fh08nG+39gG/mPetjTO1HMpsN7n427gTOBn+HViivRd0C0iIiITg4J+SdFwbdiG831n+huBsoyVc0PGdyaYqE2ZCc7o5+91viGcdyj66OQzZYJZU8+ZwD8CDwDNwJuAe4DT3F1uBR5lvG+OREREZFj0aF5SlB2soDWWmr7dGsujrKUCW0XYJ0AfUG560sq9qQWhq3wLQkc7592vkHaki4cnLBthm41QZSM02AidNsJ2nKA/rssN7DPrMxTwi4iIHDUU9E815aaUctNAuYm5rykB90a7nG+FPsfBmflY4ODMfL4V+hwb7fK+zjrQ4tjBFoSOdtHt0VJIOyZMPVmmngjOwmDgrFa8HsBGqLERwjZCwH1VwC8iInIUUdA/lZQbz5n25MD/zFO2cs57txAMOt0hg8EY57x3C2eestX/vE4OevJMcBQnON+Q1nFncDPrmedtZBhFrqaeUlNPg6kn5qb23MfE6eQzrtyfRxfwF5zUnmbgAzZC/XiOS0ZRszmNZlNLs9lLszlCs9lHs3mQZvPh8R6aiIiMPOX0TyXlfRTcVjiFsb954Gz7/j88RFZXT+LD7mlBHjjr3Xzo/Vu98utT+a8yG1118l2sL7zMszC3vxV+h6uPot31OOsOjNuiUxNh4Suf+ob9wPtthGfHciwyBprNxTi/+0GPTytYYK8b4xGJiMgoU9A/lZT3URhb4RTGtt6UZ/MPtHvs4itxwwD01cmHbhM8svKUKrth8SU5SZszuwGNgolatDshOgj1jmUaTiHvjcCF7uY6G+GCsRyHjLJmczzwLJAD7ASuAP7gvj8NiLHA/nr8BigiIqNB6T1TS5857MZQmje4gB+cvO9kvkWwWbYn+7vPf+4I6QWhoxzw9zOu8S7anTALX9kIXTbCi0BF0ubjx3ocMuo+ixPgA6xkgb2PBfYgC2wLC+z9CvhFRCYnBf1TS3+FsZWvdM33PHBv13yosIYKa4CvJn10d9qufRbBHtv12kycNp2BProBjYaJWrQ75jcjabUNB0w9N5t6Fpt6sk09bwK+krT730ZrHDJuznFfu4BzaTY7aDadNJvtNJuraDb9p/GJiMhRR0H/VFLhUxhbkSiMLfj8/95Meywn5bD2WA6f/9+bMasozV51pGFPz4KvAnTZrE7grrSreN1YJDTlFsA4zGIz+p2AhmpMb0aS0onixdyzgM+71+vECfIvcndvBb42GuOQcRW/oZwGfAkI46zEvBT4PvCN8RmWiIiMpqzxHoCMrq+ZzBVfr7e+q9nGNtYvDwJUvq+cgllNNB0soOzBCja2LwdYd+H0TXnHBZsBqGpbFfz0wTvPsyTN1kdtDSEDcKvF/ZurNZhH2bIKGIeUGhuhxjh9aIZcMGvqOR24DjgJmIsTKEWBR4CbbISHhzC0Mrxz+kfrZsQrnQggBvTgrMLbBGwFbrYRXh6lccj4mZb09/uB5Ti1HA8CM4Ev0GxuZoHdNx6DExGR0aFC3knMDfg9i0Svt5ltL43B/5ehGJgBD885gzOyHyFmDZGWel7sWerbeeeuU1cdvHTXPTODtoceE+SOgjVcffIPYAy69YwGU8+luH3rPXQDZ9oIjw7hvGPWvcfU41vMbSN68jclNJs9wAL33cdYYH/pbv8f4GPu9g+ywP52HEYnIiKjRDP9k1tfRaJeQWUP3i38IB9Om/YYZ2Q/AsCWIx/kxZ6l4DdrHzKll5pgTpZ1Wn9m2R5W7rqbR449o3PD4kuGNItdS1FGcFxM3Vh2uHkZuAx4AKeP/ZtwVq89Dee/pVIYfNDvBvhjWdvg1cVovGsbZOw8gdOmti++KXoiInJ00sze5DbYIlHvgB/Awmfzvpd4e2vrNfG/xnyOqMyyPdnJG/J72vjhs1ccGkrxrhvwZyws5m4fEzbCNhuhykZosBE6bYTtOEF/XNdYjWUYJmptg4ymZlNKs2mg2cSAdyZ9cjnN5hiazdvpLfB9DXhyzMcoIiKjSkH/5Da8ItElOGk9l8K84Ct8IvdnALzYfTz3d34kvpff75DnjcWMntY5A7p2pgnT2hLA1JNl6okAl7ibDuGf+pN+bMrKwG56z5hwnypkFHOP9ZoAMoaaM1binovzVA/gI8CrwOM4+fwWuJYFtmMcRioiIqNI6T2T26CLREsi1b1FvD0FlB2qYGPHci7Pv5MccwSA77deTVJaeOIGwqzqzU3fOX1hbFHHbq8nB0NNIxlWa8uRTA3yWOirGfiYjVA/gGPT6ywKgXWmPhGQj7oxTieS8ed1wxzEmdHfC7wZ6MApSP8mC+zvxnZ4IiIyFjTTP4m5xboZs7peRbwApZENsXXnrSE8u5GAsYSzGlk3ew0rpt/Np/PuAOD12Gx+3H5p/JDEDYQb8CdmE78c+WawNZibfonhpJH43iz0l+IzBqlBC4DNpp6TBrDvhHpiIVOC343xG1hgIyywOSyws1lgP6SAX0Rk8lL3HknYdfWi7kWzMmfnW3pCzA1GAfju4Wv5/KHvWuJdZtz8fLMqY/abkl3V3FT/lZ5FHbsD8f2Jet9w9McN0Dfg3XmmsZi6cB/HZoxtIMf1xdQzDaeQ90bgQndznY1wQT/HqXuOjK1m04DP7z8LfNv3iojIJKP0Hkk4buYez0LeOYH9mGb35rCVHvtTz9+bjNnEjYuWs3HR8iD0tgId6i1mMXU1tRRV+3zcX4rPiK96ayN0AS+aeiroDfqPH8Ch6p4jY22s14IQEZEJSDOLktB00DsGbupxt3cDu7jD7/D0DSW7qtmxJUxPXcDs2BIuXLHznio3DWioerw2Lq7dZQmZBkIm5r6WJhfL7u+a49dhqN9AO63o9oCp52ZTz2JTT7ap503AV5J2/9sAvkOf3XPGs8hXJqkFPitxLxjaUzcRETk6KeiXhLIHK2g9kppu3hrLo+xQBRwG/sbtdhtX+R1OUjBbsquadc+sIdzeSABLuL2RO579TM7Kxh/dOowhZvy+Lt60i9M+/2yApHz97kBw/Ypf31MV37bhlU8FO2PZ6Yf2O9OZVHQbP/cs4PM4NwudOEH+Re7urcDX+vsCfXXP8bhevMhXgb8MzwJbwwIbZoENuK8K+EVEphjl9EuCMdiU7j0HCyh7sIKN9csBsNYzF733+KTuPTu2hE24vTFjn24TJMv2JGoCBprj7+b030PaWgLnnfJb8ne1Z+zfML+QJbUNifdnztrKivl398yZtj9RX9Bf9x6PLj1xMZynDt3uubYCN9sILw/kuwzheo02cvStYCwiIiITh4J+STCGNiCj5U6cV9CfHOiTVNwbCwVsoP8M/jZgdX+Bf1L3ncRjiMWbdnHijS+Qt6vd804kZgzB32dk9QyqWHasi25V5CsiIiKjRYGEJJs2mJ3T23QST0dZRWlbMC86gFMMtFVlSpvLxZt2cdrnniXfJ+AHaJrnWZ8w2GLZ4S1uNgj9pPA0Je+nnH8REREZLAX9kmyw3Zx8e87P6Gm9ptsEjwzgHAPpoJOyz4k3vkBWu2dNLwBtWblcd9kN3embGXy3kj6LbvszyAC9Ep9ZfpKKfFHOv4iIiAyBgn7xtgQoBi51X5d47uXfCjNqa7Jsz0p6C1b9ovSBzJqn7JO3OzOHH/ciDbmF/NtJ66juubiHI0RJK5YdwLV6z9dH0W1/x/oE6Bv6uAHwvflJup4W9hIREZEhUdAvmZYA7wZm4ISrM5z3Hu02U4Lx5BadhEwDAFEbJmoDwCUMfdaNYJ5BAAAgAElEQVQ8Zca9baF32UFjbiFLPtjAxkXLYTs5fI9WGyFgI4QHG/DH2Qg17vGDPY9XgG7wn6H3u/nZD4mbCK8iXxjGegMiIiIyNSjol0ynkZno47xPn1FOBOPpLTpxA1tCxglsoz69wgfQvcftspM49i9f+cdD3bmp64h15wb58fkr0g8d1WC4n/Sd/q6dPkNfhtMGNN0sU89tOE8N/GhhLxEREemTuvdIgjFuu51L8c4uB2urUm8U4917GrYUFha2e8aejURt2N33fOATwOnAG3EKhxuA+4CbbBWvDmSctRQ1LN60q/DEG18gb3c7bQtzee66pTx59qlc+eUfpVzbVo1sq0s3sI93K4LUn1QbvT33G/CfmY9L6cpj6mkBQh779ZDWqtTnmqXAt4EFOO1Ee3Bu7KPAI8BNNsLD/YxJREREJiEF/VNJuclor0lF70x7IugvxknpyeQfRIeMb7tJN70Hs4r/A871Gd0O4FRbxYH+vkYtRZ7XilnDRZf9Mv7WCYarhpbW4yUpTz89bSdZo40QHsy+Sef3+xn2ZXnawl59Xa8bONNGeHSQ1xAREZGjnNJ7pgon4M/o/OJuT/U4TniYzHnvmX9vVlG6a/rCjKb4ruTp/07gB8DbcdYDOAPY5X62BLgMgJApJWQaCJmY+9pnLUHca68d20Nywe0IBvwurzz9dAXgWQScfnftVc/gl6bjd2fe2E+RL8AenH9RcJK01OlHRERkClLQP3UMvPPLDuAh4DBOuHkY+BOHvILoeK/+L0e+GWwNpp6+NZjHqpPvyk8qAF5hq7jSVvGkraLDVvEIcGvSIce7AX7mzUlq4O/ZSnPOsfsvsVUEbBXhUQj4YWA1An0V5PbXTcjre/XgPfvfTepNg9/YFuCsZBzX5bOfiIiITGIK+qcO//aaXnYAtcCP3de/8Wmf4yuBvI2LlrP65LU05BYSw9CQW8jqk9eyvvCyEFBtVtECfNTj+OlJf9/JAG5O0gt73dfV7vbR1F/BbGL23qNlZwjne6zopwtQctB/CP9c/iygMl5EjHNr5qUZp3NS/Hzr+/kOIiIiMgkpp3+qKDcNeBeWNlLhFtoa3zQSrPXONTerBpWHnpJnb1axAHgKmO9+ttTWmUaf8yVqA8aLT958/GfWBJTFg/k+CnlT8viTznsHMDNtX8vgc/z9NAMfUz7/IDV71MEs6L/jlIiIyESjmf6pI5E60pY9nVVf+gFFFT+jqOJnhUXUPt7Psf66nD7yXpL69rNjS5iSXdV5OLP+DWYVVwEP4AT8MeBTtoqd+M+mD70tZf81AgPis1jXCp8e/r5PVpLafLaYeg4C1WQG/DByAT84aT6bTT0npVxgcKsGZw5wmMdPaM0+qWbNQ/v9ERERGU+a6Z9K3O49d3x0VeHmMz6c/MkTdRSfNtiZfmMo5U1UlYSrcypfKqegvYmm3ALKllUAsO6ZNeT39GartAbzWH3yWmfxrN5Z7G6cgN8JmHtz+pNn09sYYE//DEM4Xy1Fidndjtj0/VXNq9l64INzSJvN70sfM/0jOXs/VI301gN4/mwGuerwkI6f8Jr7eDq2wHk6JiIicrRQ0D/FFFF7BrANaAfy3c2JoP+4kkaWVT5PbkEb7U15bC87gT0bC/2C/oaS7OrCdTPXkE9qcN8WyGVuV+ZDgLr55/OJ02rpDE4nr7uVDU+u2Pcve3/xuZQAPOSRUjGUgN85VwN+gVs0M3BzA/6UQLYjlsOde65k28GzYYBBbR+pQOMd8A9ERgqSl8GkMI27kCkCPgucCszGqYF4GriNqN3keUxzH21oF4xvqpmIiMhgKeifQoqonQY8CZwAfA74rvvRE3UUn7awtNGevO4JsvJ7Esd0twZ5ZvXb2V1T6BX0x3a8IWzCwcaMa3lFtw+E3sfHTv8Fh6bNYk5nlPsePZ8zXnsEhjOT358BrB+Q7N6OT7RMn97htUCWry/87Xt7dnaGF5CZ159+81LgM5aJJmXRMD99rCswoOPHTMh8EvhJH3usJGp/nLFVM/0iIjKJTJz/Y5ax8GWcgP+/gV+kf7is8vmUgB8gK7+HZZXP+52vqSDgnWrvFQl+/fj/x6FpswDYnxPiXe/5M6bIYopsnimy1WYVWwf6RQZhwDUCZhWlOTmdgwr4AXps1nEk5XzH89rd4L+MCRLwB3D+bc+ctZXb33IZP1l2Abe/5TLOnLU1fdeB1k+MfP3F6FiZ9Pev4jzhuippm19nKs/WsPisVyEiIjKRZY33AGRsFFH7D8B1wAGcgCcnfZ/cgvT4pu/tQNmu2KINBcGd3jPpaUFubHxi3jK88869ArfK6P4Qc0Mtvif7RH1d/K/xxxvpM8Hx9qIDXSV3QE68r5pzbiln9t4mDryxgN9dW8Fz5y/33X+aOULMBgiaHkLTWojkP8eHjtnM9tYI7zvmAaYHOgGYm93C5cfdDpCcvjTQoLbPn63Hk44B1UOMguSl5n5K1LYRMjXAbe4273+fBbaGZgPq3iMiIpOAgv6p406cnvjX1FHcXERtOH2H9j155C3KDPDb9+TBoswTWkvNuhkf+qflgZor8kx7ckTvmbv+88c+TmvWDMLtmelAODn2Zw/wuwxc1NYQ8gjcvFOJCmp+fjGXX3o703M6Exu7YllMC6QvUdznQl3xzwaygm+/Tryvmo9ev4bsDuff5g3NjXz0+jUAvoH/LW/+DHOzM29eFufsJGhSF0+eHuikdN69bDt4diODCMxthBpTD3gE9h43PPGnIIxD4L8W+AjOk82LCJmbSF2ZeLPvkU6AryBfRESOesrpnwKKqD0H+C3wEnARTlB+HPC/7i71wPKn73jbUyesfIasnKSc/s4gz9zxdnZfk5rT766yWwkUrGz80f7vPX8NM3pa4x1uPFNZYhhWvO3ejK4+jGZO/yCYVU5h6pnv3Erpx+9lzpwo+7tCPHX4bXzo2F8D8Hr3G5gRPERrzwz7YtvS9p+2rMjb2emV9k0jI5TSc+0HwryhOfNG6fUFhdzy2wbPY36y7AICJvO/bWvBeIzIWviEqRuxRzF9FfniPAkY2ycAIfPPwEZSW6O246yP8BWiVisVi4jIpKac/qkhHugcj1PI+xS9AT9ABHjqnXmPsrbis/yi/JPcddMVnPHHR3jmx29nz9OpsZsb8Cf6l68vvCw0858P55kiu8LtiOOZ092UW0DTxxey7Run07ooF2ugOy8YZQIE/K4yoG3bI2dz5Zd/xEWX/ZIrn/2Rfbl9aWKHN2S9TpbpYXbWAfOOWY/kVS75Am+e/rLXuTzvBIZi9l7vFHm/7QD7u7xLE2I+/8lHu+Yywj32+1oBOqP3/aj29w+Zs3Bm69PXQsgBluGsYSAiIjKpKb1nkiqiNjmfOjqQY1bm/phFPbsBmHcgyjWbf8jzB05lY2tG/OqVtpLIZccj17s1mMePz1/B5ZfezoGcWWxe+YHk4yj2GtBItu4cAFtFjVkFKdfMpWDetL1Mf6ydd379SY596nV2nryI/73tXOYv2UtO4AiXzP9Rx/WN35jucUrDCLTpPPDGAs+Z/gNv9M8wqtl3MZcfd3sidx+c1qMPvvb+lJz++PaafRcD3DuC6TdNeN/4xOj7d2c03ArMcv9+CbAJeBdwP/Bh4Jc4rTxliqul6FJgfR+7LCum7oUxGo6IyIjSTP8k5Ab8ybOpc3FSaJbXUWzqKDbAkqRDnvj+VV/kvKd/k3Ke/EAblTPLIW293tJdGwrSVtqNf+REoU5gvvpwMD8aw9CYW8Cak+6MzbuhOSVX3hUP+FKFfFZDHeJqugNlq6ixVYRtFQFbRfi9b/jd4at//z0+8q8PMu/hV8nqiLHkkSaWF/80ccyS3L97BfzJ4iv4xvrZL8WJ91Vz7QfCzG5uJH2ZhCPT8/jdtRW+x247eDZ37rmSliNziVlDy5G53LnnSta/8hnP7W4RbwCoNvUcHIGZd7/ON37/m9NXjcRwLXNf24nae4nadqL2AZx0N4BTCJlBd20SERE5mmimf3LqbyY+w+JZOz1PVBBsgh1JG0KmdF0gl7xYOwDh9kbWPeMUlW5ctDyRb2KKbPyacYEL5tT6jdcr4Bv0dxgNn5xXnX9q5XNktae2Mp3e0XvzYm2fE/lNNkLY1HMb/q0hM6QX74J1A3/LgQWF/XbvASfwd4P5AW1PMhNYP5xZf78iX/e91xOAkW3zmfqUKP6Pl0vIXAzU4sz0H+9u7wJaR/T6ctQrZuRqXEREJgIF/ZNTX/nUANRR3EBS2knDwS/Z8OzMFJKmgxmnqsyLpXTqIb+njW9s/w+7cdHy5FaPGUF7H+0wvQK+fr/DgA0yTchdlbcSKJiThXnqWycyd9t+Fm7eS35TG62L83i64q2J/be3RfxOZYECU89BMvPJ+3TOLeVJAb/DYH2Ld0u2VFO5tpyCfU00zSugbE0FGz/of1Nw5qytlM67lznTnGLlmn0Xp98IZDPMGyz3hiHlePdGYKAtVIem9ylR/BrJ/zt3j/sn2Vqitn3Eri8iIjIBKeifnPzyqX1nU8serGDdeWvIz+4NNFuP5FH2YEYKiWfQvbh9J7YqJcDL2M+rHSZeAV/fKTyDmxHODADjaUJ4Bf5uwJ/Y3xhoXZzH3uvn89z1mcH94Z4ZbNi3MmO7K35zNKiAHwZXvFuypZp1N60hv9P5twu/0si6m9ynLx6B/5mztqbk+8/NbuGzC7/D1Qu/w/6uuck3AIWmnngLoChwzXDz/ftq8zmc86bxa5XagfP7Ntt93Q7cC/xgBK8tk0QtRXuBOcB+YCtwYzF1visViohMdMrpn5wGvZLoxvrlrN68loYDhcSsoeFAIas3r2VjfUbQ6BmNGne7WUWp2/oy49H4tkfO5s4fX8n+V+f0WIvFyXVfXUxdesBX6XW87f1ug9FXmtCA9s+JdrJ40y5m/PUwWYe6CHT2kNfYxo7n3syX/nYruzpHPh3dr0jXa3vl2vJEwB+X39lG5dpyz3OUzrs3pZAXnJubgOldrMtjld4QTr6/NfV0u+lKQ2Ij1NgIYRsh4L6OdLqW3z9IDlE7h6jNImpnEbXvJGpvI2oHVWshU8Z8nImx+cAngUdrKTp9fIckIjJ06tM/SaV172kCyuoo9g2ujKHPXwQbryQNmduAK0gKytsCuXb1KWupWbRiP06XlOwBDtOZPa5KC/pCJoZnn38IFtnltoqaAafs+JwLsERtxk1vLUWe+y+q3cVJFS+Qt7udffPn2u//89VbKj71n+9mBBbf8pKZ0+8U7/7qa2szcvl73hsg4PHfcQwI/iFzu18P/2QtR+Zy5V9/1N8wDwGfHqdVdv2FTAN+awQ4LWVlApiInXJqKXo3cALOuia7gMU43Z8+4u7yu2LqPuBzuIjIhKb0nknKDfBHNBhb97Z/u215IPeK5Jz+GIaqxZeamkUrwJkNHowQsM6sIj01yDM9qSm3EKDSXWF3oCk7g0118tz/qQtOsf/01ifj39sA78YJWM73Of+wxAP7c24pZ/beJrrfcCzn5MIN37qYprvLU3L2m+YVEH7Fa5VjQ8mW6owUn/1dIc/VepPNmTagLq8zcWb/7wA6cVIhxmaxrb5ltIxlpOsGZFIqpu4h4KGkTS/XUrQa5wYA4IyxH5WIyMhQeo8MiFlF6bktv7kivYg3gOX8fZuHc2qvVJuy1mDqBHprMI+yZRXgzOwPJmVnYKlOIVNKyDRcGPpVwXkn/9Yu3rQr8VFnLNvW7LskffY/DyfgL8MJeEfcc+cv55bfNvDSd+/lP3LbeX/nfgLWJnL2S7Y4rVLL1lQQ83iYEcB6pvjU7LuYjliO5zX/2v4Wbmoq58qX7xrMUGfi3MDFW6tuGE76z7C5LWPpbZXayMRZAE48FFNnPP6MeT/8Woq8/j/R+vxdROSoovQeAfpJ73knEIGeugABj91iGIJFfaZF9wDBPj63tir1BnTVOT9q+X8v3RAqaG+iKbeAsmUVbFy0HKDR1pkCBpCy4/aaryz9zYaCb97x77GF0d0B45UKlFnsS1dukCe+c5LdWbyo6Xu7Pl/w0MGzM69ngS6iZHs+4WgHpvuMsz/xH7IB2FEc9pzJb5hfyJLaBgB6zjKed/DWwHueejCjRWe8e09oWgsWJ58fYOvr7+cHe64dwpA9v8OKCZf6M0WYek4HrgNOwlmnIxsnne4R4CYb4eFxHB6Qmt4znu0xk7t14RR7/xK4HmjAuYlNTu+5v5i688ZhmCIiw6agXwAn6C/JrqYyr5yCQBNNsQLK2irYeOpyZ2kjAzu2hAm3ewSfuYUs+WCD36mP4ATUK/HPf2+0VYRTxrOKjEAcZ4Z+ta0zfr3eEznbbsDvfXxSIGrqKd35r4vuWRTdnXFT0jRvsS3c1LQCv97yB3AqGEY5XPHN2TeG4O+dmy2/G4PWRbn84slz+eGea3x78ye373z04BnU7f8X/tqxdCSG3mgjqf+uMjZMPZfiny/fDZxpIzw6diPKlJbT/wrj0CknvVtXP14D3lNM3V9Gd1QiIqND6T0CQEl2NetmrCEcbCRgLOFgI+tmrKFkRnUiqC1bVkEfaTd+DtoqrsJJt/BKFPfMtXZz/DNSNNztA0nZ6TcFKH5jcNz+PZ5PIRa17DLu/pnX6wL+iDOfPwxnztrK7W+5jJ8su4Db33KZV9ccmuZ5N6OJby/ZUk1+++GMZzDduUGeu24p2YEerl74Hd9rbDt4Nlf+9UdctP2XfGf3f4xUwA+ju8rupGPqKTX1NJh6Yu7rcFZFfhm4DGfl7elAhN61tbNg2Csuj7Tx6pTj1961G6dQ/QjO8oR3Aqco4BeRo5lm+gWAhmPCNhzsfxa/ZFc1ldvLKWhvYmfuYsqWVdiaRSv6m+tuBMpsFTXuDH5qj/b07j0D0U/3HlNPShee5NnsgLGNQNkn6usqgcJ+0mesjRAw9ZRygGpmAQeBP8LKc37Ih972awLEsEBnbDr/8N9/4603vsiMPW39LpJ1/UPX8cUffJf83W20LczlueuW8vK/vpk791yZMiuf3ocfoDUnj9VfXgtAVeVKpvd0JT6zwJFjp/FU5QnsvHBRxnVj1vnBRFN78o8GzfQP0ECfTA3zGlcD33PffttG+NJInHeoJkKnHL9uXYAtpk6TYiIyqSjoFwBicwLWq41jP/n6UaCVgXWvcQKYPgL8kWzhZ+oT+bgZi1HFx/O9XZ/Pfejg2aavoHrjB5cnAld3/YFCgJVX/pAPv+1+TFK4sHjTLk773LNktfd4nSdlfCVbqrn7259iWtK+3blBHv/uSTxZdGqiXWZipd1XGokFggRiPYnXpvmFHHswyqz21ozv33HMNH718of7/TmN8g3AiCzoNRUk/76mGfaNk6knCzgeuBs4DWcG+wwboX445x0NtRQtpLdTTmsxdTNG+XoN+Pzci6kLj+a1RUTGmmYyJqNyU0q5aaDcxNzXfh/lN8V8UkiyCvz7VVhCdHMsziPw/qSm1jiLeLWYVVj3T8vvt71vJNvhJVJyvBajAvKWz787Bs6qtau/vJaG+YXEjKFhfmE8UE9PGUqc80On/jol4Ac48cYXUgJ+SFoky+L8lNyfZeXa8pSAHyCrvYcTb3wh0S4zfjMSfqWRAJAV68G4rwGclXdnegT8ADmvdXluX7xpF+ed8lsunPsrzjvltxT+fBem70W5BsqSmb7ltGQdXprKVOGXCjWsFCn3ZqIL+AtOwN8MfGAiBPwTpFPOoBcyFBE5WqlP/2RTntGJxulhX26gwr9lYVlbBetmrCHfJM122zzKXqtwsluneRxkgCxmEqObADH6v4ksgESR7npSF/EK/eWFE//tvWc+CKR18+hN5akH0/T3FQX3PXHLyeeTlNqTvqqvjVBjnLCmcs60qOeTiGOz9gdw/g8+b+MHl8dn4+OBxn73dYOpd/L63fQkOIFbAyaW0bEnb7d3gn/BvibnZxX/GVp3m4e83e3s73JO7bXSbnoewmBqiNOfROTvaue0zz0LwM4LFzE90EnpvHuHOtu/H+epT/rPJX6zNylm+0dxQanBricxVAuAzaae99sIz47wufuV3imnliKvTjlxfxzt8RRTV1NLEaSlCnqsEi4ictTTTP/kM5ge9gAYQ2nTxxey7cbTaV2UizWwd/p8Vh9ey8Yjy+FPdNPdxxUDZOHxu1Syq5odW8L01AXYsSXMysYfxQPpStyAP3mfb/3lS5kFtb3tNAtx+8AX/Hz3lYs37Uq877GB9W4wATirERdR2/DRSO2Gj0Zq6TFBz5WmjKGJ5GLhtiNRrvvlfr78c+jomkNq73lnxvqLwIfJi3n8p9O2MNfzx9O+cLozg25I/Nk5b7Hnvq0L86jZdzHgf2MwEB3H5BCzhoPdMzkSc36sXk8i4k8X4ga4KJeXWYzSbPUUMSozzm5qUDawFNjkbp4D3DCc8w5GLUWltRQ1uPnzG+j9bzkXuAh4EWeti5fozed/Dcam5qCYuppi6sLF1AXcVwX8IjIpKeiffAYdeJ29csutl6+7nQOfmcXmpz/AppaP8vI3C/nO3C/Qc2yAHa+Gs0oeqIbDDPiBe8muatY9s4ZweyMBLOH2RtY+e/ksN4gv8NondKQ34KylaG8tRV11L3zo3j+ve1vegaUzE5+lB6pBE8vuiE2/FZyAn7SbhL+zZLbPsO+zEWpshDBv/doKTv+vPP7n6RDXnGOYPs1rMa5K90/eb149l/RymOeuW0p3bup9S7yDzmcXfoe7ji9JbP/Vu/45Y0Gtrtwg377ic2w7eDYlW6qJmYH955n+3TqnZXPZVU43nn97qZof7rmGliNzfZ9EJG+/c8+VA7mkV+5QNuBX/DHSs9UTwkguKOXWPWR2qxqBeggboctGeBFIbrN1/HDPOxBJLTHj/z16PZxSpxwRkTGg9J7JZ9BpAsXX/yQ0Pb83533xpl2c9u/PktXRAwanfWf7GvglbLxgOSWv93bwSVo46xDOyqwAVG4vJ78ndeIyy/Zk4wTN+4GQ1z5J5gN0hnLY+S8L2XPuGzn7gm0c+9QBIDOVJsd0xNNKMp50vHvTn6adfuMz5O1uT3TJcbvanA9clXxcSfazVF57MgX7drLvjfN4tnwpr31yNvu7Qmzcd3HBQ27qy0vty/gw96eMId4p58QbX8i4lgFmBlv59bNnccYXn2RmR2si+mm4aBGP3XYqAG/lWX5GEUTgf65x1gA694wHmfXXw4ATDSZHTa05eaz/yKc4/+HNFOxrSukYlNyxaH9XiFfeOJ83Nr+S8YOOP6HoiOXwp4Nn+f17JPNK9gJnAbY2MjvQKD96ANwAf8hBfnwxOpyb6kPAXcAtOD3wFwFfSdr9b0Mf6aD4tcRMFiymbtZYDEZEZCpT0D/5lOHd+s838JqzODWlw7Mg1bRRmVcOf4J1R9aQH3OC9XB7I+ueWcO0WBf3FFya2L+g3fsewzoByRGvfWbsaONtX3yWeb9vsf/32Dl5wOJ5f2h5Zt9Zc3N78oI8d90y3vvxPztfKC2VJto1N14hkPJE46xNf+Tdn3vML489ed+CkuxnWTfjV+S/4kxkv7H5FUJfivJ41kkELrSsOe52A9iHDp5N6bx7Mwp53XOmtMmMF87m7W6nOy9AVmv6/P7AdQeC/PjDlxw697Et+QujuwPJAf7Vafumdyyam93Cy/+vkGM+9xo5Hb111925QZ4tX8rB7pms37uaTjt9iKMDnJWXV5PeknWSdu+ppWgv47CglBePlp+zgM+7f9K1Al8bo6ENJLVrUj4JEhGZaBT0TzYVtoZyA+k97Pso4t2/M8TcwpbEe9+C1EATla3l5AdTZ+fze9r4+ovXz0wO+ptmFRA+mNn7vjG30AA5AE25BSkr/IYeeZXQI69yOJi//xOr6l4ACj7Tdttr7zvrN7kA+99+DNCbMhPXEcuhtuWi6BULnUuT9KTjkhs3+uax77xwUXKw0VSZ97vCfNPlty/TA52UzLvXPHTwbN/c98WbdiVm+o8cM42sQ90Eu5wEnGmtvq1PE4pDv/L9LGBjrD58QSe1P5pJoO9bB6+ORXuL5/On2NtZduPfmb/3FdoW5rLt39/B19/1Vba9dHbGOdKfFAygpWcQ5/fuPpynKAVApalPzGL7MvWcDlwHnATMxbmFiwKPADfZCA/3+YX9z5u5LsTI3YTMT3r9JFBUS9F7i6l7bITOPxh+M+oxnJuxbpzvvxW42UZ4eYzG5ffkMU5PgkRExohy+iejCltDhQ1TYQPua59Bzsayi21Ha07ivV9B6s7YYgoC3pNyKbP2S6Hsygpac9JW781KXb03fYVfa6DbBI/cu3D57B1bwoU9dQFz7V9vOTb+uQEOT8+P/td1X+56suhUYtbQcmQu65qv6Nx64IPXEDKlPy24OP8Xcz/BXadcwVmb/kho9368uDc2Ke04CwIHvPfd1c55p/yWxZt2MWdalJIt1XzklN8l2l4u3uS0FY93x8nf1Y6xkPNqVyLgHwlN8wrgM+8N0eyMs2RLNTuKw/S8N8CO4jAlW6oT+/rdlLR8IsSF9/+EwO8tM2raOPekrZ6BfPxJwdzsFgLGprf07OtLFQJXklRTwcDadr4VKALCQD5OCtEC4GPAH0w97+jn+AxJs9+DHUtf/gp8BvgHnELU4yGR55UL/Ncwzj0cfjPqxkbIthHybISlNsKnxzDgB+8CZUtS3YIKZ0VExoYW5xKMwZ5ZspXSynuZUxDlmDsP8J7r/0xuLHUxK3pTNzJm7ixwyal3d25YfEkOq4HZSQtLxXPNV1Sw8eXURaquz7mOz2+7hZnNrfz+v99lX/3bMd1n3vnItLkNr9K6OI+nK97K3g/GJ1S5v5i68zxnb88ykJbW1JGbTVfOtNjM11szbm6784LRaf/XfU3yebrPChQEsb5T6N25Qf5+0SLe9JNdKU8P4otqnXjjC+Tv8n5K0pfknP6cVzo4ct2zyGgAACAASURBVGw22a8eYd6f9rPs5peZ/cIhpwn+7Dlcc/WtbPynj1Pyp5+z7lu+C4px+1suY252i+f1Wo7MTSz+FZf+b/XSf4Y5cFFmmrV77CGcWf3+crWT9bnIlKnnTOAfgQdwesm/CbgHp7c8wK02wrWDuN6ILniV1moypa3jWC8o5WU0F/carr5+diIiMnYU9AvGZM7clmRXU5lXTkGgiYCxjUAZUVvjdt/ZgEcXjr3Z8zsWfHjvNL5AMP3T3pVle4t/mz6+kMsvvZ3pORkLZ2WY9toRTin/y5cf+8Gpu/EKIEKmAe+gJ4oTnKbUOKz758vWr/nKXSuTt/ecZfp99BULGgI9HisXBw2mxw4pXz856E8XbOtJKWDuyg3y+HdO5q03vsis3ZkLczXML2RJbQNnztrKZxd+x7PuIGYNF23/ZeK914rE8RuZ5PqEpGMtsAKfG0AfyccMKNXG1HM18D337bdtZHAtHE09Mby7xVgbGfhTzqQONOl1MqvdPu/HAbvd7YeLqZuZfo7R5pHTnxjjZK2pEBGRwVHQL55Bv4erreU2AELGM5iKAcEiS3ymP84rqGwN5rHtG6dzYGXqbHLBT3fy6tuPoWN+DrHsALnNHczf2sKy7/6VQEuMx24+mb0l85MPcYKv0K88b0TwCTbNH2xGwLrvoyHmHvBOB0o+2VALcf1E33ksB5bNZP7vW8jd00HbwtyUJxzzft+SKGAGaF2US95uJ4UoXcwYgr93agfuOn45s7IOZezTYwMYbCJPf8O5lxJ+JbP+onVRLpuf/kDKNnemPzF73McMc8bXxOPmC4+g1NSThZM2czfOTP8h4Iz4KrIDnTkeqdnvWopayFx0DJz0lA/iLCgV7y9/fzF15w303CNplOsXRETkKKegX4YS9HfjpHek6DZBpn2021kG6FwSjR13FIcHHFSed8pv+0yR8ToGaHQLYD0DPKI2nL4xfRa4ZEs1VZUrmd7j1YJ+7LW/cTr3Pf9BAIKHu/nXcG97UGucuguvn1N3IEjAxmiaV0D11Z9sf/PlLwaDJpZY+dhaKPj5Lk75j+fJea33u3reLRnY1PLRxPuOWA7rmq/o/OOB962KB5OmntuAK3xOEdfm/vEMnJMDcI9AvRn4mI3wKPQ/65584pGY/XavV93vjo7XgPeov7yIiExEKuSVofD8vQnYHmd13ZsC7PiXMCW/rHZ6dPqsLOvVJei565bSPT1zYd6+jsGZ2RzsiqYpg6pcWz5uAb93FUHvfVj6x/H+/+kLgVkgK9ZDwFrCrzRy7Q235b68NmJajswlZg09NkDBz3dx+tVPM/21ruQFgj3tX3As8WNbjsylqnlNNC3gLwVWnjlrq7n9LZfxk2UXcPtbLuM9sx48gjOzHy/WXI93wA/9t3RcAGw29Zzkvh/witMjtOCV70rW7jm1oJSIiBwV1LJzijNmSJ1MfNrwmUQLzvDBRtbdsgYehD05x/Us6tidEcm/Mns+HZ05KTn9fz3vTfbXt3+E8r9802TFetIP8ess1OTWG0B6q9Joaucis8pNgVhKAediS7ZWm8q15RR6PIkAN/Q2zl/8coeGm+7zUM07mPun/SzcvJf8prZEAXNc6OHelKN4u9J4rn3khpeYsaeNmAmQ/vPK72zj4u/VTFvyngYAfrLsAk688YUBdRTqzg3y0n+GefzQaZw283FC01q4YuH3uWLh92+tdWo6ms6efXV+l52Wl74ewOrjfpCNYeYfD7xvBcB7Zj9YddHcDX6tP1NuvmyEsKlnGk4h743AhTi98G8ALmCQK04Pd8GrPq4HsEIFqSIicrRQ0C/rj7umkWXlz5MbaqM9msf2ihPYc2ufadpl3Sa43l1hF4AYhkBallB+TxvVT64gOm1OEOjE7c8P0BrM5fNvupmmHy+k9OP3MmdOlP17QrbmSxebbVvPZs5Fr3HFljtSztk+PYcny05MvF+8aRcn3vCCzdvdXgCmASfID3uOOGRus3BFzI3RD92Xzz2/uYSV2+9OqTVIZ4MGE7POq0cB70jk97e/cTrPXR/huesjGZ9Ne+0IJ321Hgu0Lcy1z/3nUhMP+F/+1zfzxTO+w7aDZ9PzXu+HdslPWfZ3hXzXYIDeG5z4k4TdFy7kw/Z+/j97Zx4fVXm24eudySSZCRAhEyCQDVegbFZcWqtilaoosVWiEhAFBBWk2E0t0c/SCrYuVauggmIVEtqG1jbwaSugqKVfUZRNA1Urk2QgYibsmckyM+/3x5mZnJk5Z5YsQOBcv58Oc3L22e73eZ/nfoSAg2VO9t/7ib3J1UqmCS4eaCn41ZN7+c8PzonqB5BuauaW7BVp7x+6fOGYzLUZ03KWpKkHBXcOWATAxsNjNGdi5FBagf+IKhagiH5QcvyhHR2nO4je8VyG4DcwMDAw6E4Yov8UZ8Dc6tRzn/gAU+CdYMt2c+4THwDoCn9RJJla/fLhhz/7pT3PU0utNU+3A68Aslsbgv90oURta8oHlqxZmTvpOjaRv3HTmBreJ4MvlBSQiallTF3/apjgl4BslQy/bxffmf0Bzb0tWA57MXtDyTEFwFLsgsjoPnbxHDBbLdB7+RuZtfX5mPltEkJOPcInu6SIF2DYo7twXj+ABp0C5oBQr35j25XzCMxkNPnTG5bW3d1z4+ExSqOzvvmadRM1fdsC1V//Pgdp0h68ALg16iWCgn/f7VvxeZXtDvlhbW0r4+78mKH4cRaHO/zkrXIy7pF13LXnlYKjA2xUPXR2mAtQuqmZqf2XsvHwmFdQmnetQCnWfQl4GtgH5AL3q3b738Bj0h2nO4je8eZ20fEMDAwMDAy6BKOQ9yQgkKIT7tohE0tpuMb9Z2mxRXeKbW0y8fcZNyqJHABjeVW+xe1Lv3nHc1fVvzUr1+MUQevNlbmT3PVvZvnsrfsTsSpss/8Mvwb/xNQysdBWSoGpWtNqMkGiC3d1Co/1kCgRfi1rzq5Eb1AhgYem//KtBbc9dA7q1CWFhUCBpkNSmo2ZP3tRln9vstD6uxpfquDD342KsugE+DL77zS5ousdMk0wY3j4QCHYoEyrj4F631LCc3t/1Pz+ocvTiE8jcLkcyoegFNc2+dOfSRNNdldrNhX1t7g2HBo7t6ucahJxCwqljak/g8sM5xwDAwMDgxMHQ/R3cwKCX9uhJAHhP15WSC2BLSWsuacYFgcWPEDLkr13LJ30Zvksm98T2qLRbGPGyCUIKf1Ltt9pyvDpp8qoaAEOE4j6A/OmHX3pmWczfmjPEAltHwuJS4YH8O1a5pb6xLLE7KpofyzB/9Z5V3D1U+si/xRyoQk6EakbbH3dvy/bSwdz4OZM9nuz/D/45hum/nX7NPff0sfCloXDNAU/QJVYrXve/5MFq1xtDj967ksx7D9Dz3ualV4Ebp8Nk/DLFOE95PFnpKB06A1ZUJ5onvQBwa99PobwNzAwMDA4QTBEfzdHiBhe5DK+F3nCon8e1P4515frii7IdVgLGDTWwURnGQt3llLgqU5WGLubZKovXbR0RlOjtki/0kgsZgOpSLEdjEoPe2QXPTTEa1eI/liCv+q2Mxk2/XO9TasDha8OVNd4ca8NqItrASZkr9YexETYcmqxx7yaQ9GTQWSaYG4f2PT8uaEBQzLHUTcJ0zrnZn8qL+y9R130G7MrNMep+6yYFuMzuOz4dsM1MDAwMDAIYlh2dmdKRYljdkGB7+cmds8uZOLQMDvxeFaITEl7zf3yubP4a/bNvDRqFpeueh/ph2ZXKi2uVDL8h9pW9kBqfbPZ5c+K2k8wn39l7iQGjXUkZPofga2TBH9bbrci+JcSp3GUN8NMY64VCfhNYPb4GDF/p6+lT+pb7lRr2KV0VZQ/Focv6smiM6fzr0/P49DEHvguM7G7uJCJa8sA8isoKnlt8E0ZQbvMi3ttoKTv8qji2pbeFs396y0PkrfKyXdtoZYLISzAFVYQEoY/siu0XMddSXN5Q2ubi6fWOaeZWijpu1y9KGjNGdvBp06UUCcc1Al/4LE9DlXJkJSj0ImGqOJ8UcXfRBW7RRVHRRUtooq9oorXRRXfOt7nZ2BgYGDQORiFvN2VUkXUFmQqgrsws5ql42YCsLJqEsRzM7GLkud72KwZTiWdpq/TxU/ueZbeWw5yx8LXotd/CvpRD4DMCpe+fmFiorOMlbmTmOgswy/MmGS03WZn0yxSkXAkXbb0INqiU8vPPYqURh8pjUpE3xSIZtvqmsy2uqbvvHXeFVz50duhguJkBH/QCUcCpjijoFjOQOf/eDv2D/Yz6A/OUJ584b5qlj42k3PSq2ganFaWbmoCFGecOQN/q30MnXMILs9b5WT4I7uw7fGE3HtqJ+Qy/JFdZKQp4n69RynizTQpgn94urKt2hFox4ODNXP6dzw4OOy4fim8f6if7CPg6JRlcWmen8byYM68toNPnYhMtVEKvOsE5MiuSrU51o5Cnc03gKKIZTnA94HrRBUXB5ujGRgYGBh0X4xIf/clStRmpLpZeHkpJOZmsjAyf1544fJX30v6RFKkj6XbZvLstlks3TaTlC4W/BLwIbDIFtwpGRnAURTRVYZd+LALP3Ei/EFiNKiyjf1ovYi0IU2GVfXj4w4UpID/TsmParQVJMXj44zXasJENCge/D9+/mkRGR03CeW/SNTddyOXB4tvM5xKHUOG08OFd29hgn01tkCK0/B0uLc3PJylPAYFP4RH8Wsn5LL5qRHK7IlQcvk/enJEa+2E3LALMAnJUNsnLxFonHXA20fzTaOeDQgQLGLWa8SWcPOuTiTZxnAnGp8D04FBQDowFNgc+FsKtKuXh4GBgYHBCYaR099dKRV+NLSqXwrMj/onxS3itWtvD/D1/Xbu+Nli3B4b63dfK1EVUQbSZl5Dww3HK8xdLvi7CxKlwFWvsDVyvbxVTi68a0tSDcAiffVrJ+SSt8rJufM+IXW/IvKbe1vY+ugwLpi9VdONyG8WeHLSY55jLKQIz+nXoBroA2ilb7mKqcyGkENOWDGsXk7/n4YWoeveU6f7vpbkyC4Lcpxs7j2iijnA7wJPn5BD+dnxPB8DAwMDg45jRPq7L5qpAyYhqxO069ROPegB9oMNeBvN7Jw+DDkUkxxKYcgVRUmf0XzfmHUE/6k4rGy1Kbdox4OD8cf6lAX+VjshF3eudj683myBgFBkfvSPtjPqZ9s5/4dbSdvfGpq9SD/Qyvlztup68wufjNmwKxZSwBe3F8QU/MVUFqIt+AFCYfyABeYMApF/oPqfhy5btPHwmNBzAoIfWJpuarILoaQ0zRr4rC2wHPRTaro01UYuo1wuo1AuwxR47JaCX1SRIqoYCkwJLDoCvHIcT8nAwMDAoJMwcvq7Lx1tUjTPbxZlYdHfFOACqO9hZ9uM89i7sgCRrhHB1Mlh9p2Ckf7g3YsU5ilNfvJWOamdkMuFs7bo78Cvyqd3epACTfebeATTgLSi+eZWqTvwCg404kX6g9sH6w/cuW2zC6BbE7Am5j4liJ2UBAeUAeEfEsvFveGF3tyj3qYCHOin75Rz7Jt3nTREukABdcD35VCqjs8ZGRgYGBh0Jkakv7uyQEZFRoEZgeXxccnytbnfRfYIPO8BXAqNBTZ+tOIp9q4sUDJ82xxwROBx6dL86WvcpnBnm0azjRfyZ9JoDtdjjWYbLku0448eWuL0RJ4p0KkHwOSH836ynbxVTtCwu1QTyqdHEfzxrlfv73rR/OB5RhIssN3x4GDdmoIg7lwrq1zj+fO+61jlGs8bW68ME/yRNQGjf7Sd3Arn7DL/DX69DMIjvp4ASwO++4kS2yknR+dz0XVFvCczOcAboooRx/tEDAwMDAw6jpHTf6pSKkqavZYVaSmtIT3ol7B4893MWRsw5y9GGQxEU13iXJG/YOeDIt9THXLrqbEWsKbvOK77+g3yPTUEO/YCrPj4VtRFsWv6XcufBtzEh6edz1dp/Wk1WSh0O7hu3xru++Ix+rQe6LprP0ZIwGc1RxXhqvGT/Mjbr9MtWG+53j4+WDQqOlLv9EDEbINWR101yTTkCiIl/H3/NYzuuRm7pR4hCNr0ZNF2W6K631ZQ5EDHEz+QSpQQiXTZPeWoEyUt0rLwS//p+fd5HvOsbi0KjuAr5VCuP67nZmBgYGDQYQzR380poiJKvFSW3kTksqgZgFJRjyqnOki9O4u+Twf01+3oJZRLoGais6xg6baZqLvwBjv0rsydFLbBs9tmMbv6+dDurr7oTf7R92rNnQ9q/JIt755LpvdwjCs/8UnE1z9Z73+v1czuW3LDLDwB/GbR8uXkPDFoZa3F3BL/M63VLCsszUgnjScRrrroHXp9cTRUpKyFX4JPpmAxeePtrhmYFhTkWgW/BAp8ExXtnbGPk44Iq9Ot3pGce2Rr8K+75FCGHK9TMzAwMDDoHIz0nm5MQPCHpd+M2fLuMp8wvUJESk7A119NlOAHsFsb2p406h66BlizcGdpmOAHyPC5WbizNGqDf2VdTLNoa/GU5mtm1u5FfPTuN/GsSeff711IrqcWgN0Zp/Ny/nTdg59MJCr4JUrkfPNTI9j6+IgwW0yvzez66MkRh7c8OTLlw2dG+Zv7WJCBbfSKiCObZYWl6AAmn8QXSP9JRvCHnbNZ/+oEJCL4QfHxfyb4RKvgl+TF+vGw9TyhEFWUiCocogq/qOLQiuaS52v9ubYWaeFL3yB+03S/evX/Hq/zNDAwMDDoPIxIfzemiAoHEakOLz0+i74HNRsdVbNAFoaelWqXi0oJpkcDfxrCdi7iTKIjoq8Ad/gqTWlaPvZ+BOai8ET23WsLKfRUh54fMfegp+9o2DpPnPETfvaNJwC40/ECL2y/W+sUO52/N8JHTXBOKtzQQ9vnvj10VgffOLaYLpTXJ/QaqYtqm0+zYGn0oo7+a6XrtCdFJ8gE+2pdS1G9SH87mNRZUfgKinRtPYupPOkDIYEaisiZDk0yOEojPS6QQ/mw68/MwMDAwKArOel/4E5yoooa7QcbtNbTWldzZODyhIpu35JVjESrKBKuA9JqrNo1lVrL8z3hjomRgh+gydTW8SkvEPXvTLRGOa0SNjWBF/i0BVydaD7UKYKfcFvMvFVOxo1ax4Ts1YwbtY6ClbVpRAh+dVFt+oFWkNDcxxJqlhUh+FsAXdvOROw89axGyYE8tzPha43D0kBaTmfQZbaeFRSVVFDkqKDIX0GR48t7C57DLhzYhT/weCI0uoqa6Rht/pC+Yh8WWrDi5hzTLu5MfYEPel2wxxD8BgYGBicHhmVn9ybKOtN1WpZepF8RNEqaz0KUgslIWrJtDVPVPv8Bv/GwCKuYxgqAeUMWoJXTHyzenegsY+HOUvI9NfiFCVMMO8+6tP48N0hxZ7R5G5lS+5ruuu1FS4RbBIxIVQR/Xgr0iW1i0+lozQYEByfBxlqRLjnBXP4Mp4dv3rejp98iQusMf2RXVOGwuVXSZEuh8jPNGoqlwGz3QKtmpD8yDUiLNz6+gjRXM303NjDkyc/J3HUE0kHMheFHdlFra196UAQ2lDSf8k4owk3K1jPR40XWCuStchbk/3nPbNUqSqqdXQT7XRwvokblZ5k+Y0PPMUR06XYD9x2zszIwMDAw6FKMSH/3Zh7KD3OIFVfe3OwTppaI9RRBUyoiawDwS4GUcLQlwwVMTdDyswZgZe4kZoxcgsNagB+Bw1oQKuKd6Cxj6baZFHqqMSFJkT5dq8na9Fy+++232ZfeH5P08eqW28hr6rQIcVx+0BMezILbMiGlk1J7kqE1w9yWgy9AwKJVrvFy9edXh6XgaAn6FI+P4Y/sApRBgU3Hb9/m9Cj2oRE0NaXfDBzRsu0MWnrGxSRo7ptO7Q8Gsv6tS9h/ZSb8ArgWbP72Nf6KJDDDYZd2IceNXLcib5UzrGYlmVmAZOoCVEI+keOFRdC1Xi9OjNqBqBmNla2TmOP+nQvD6tTAwMDgpMXI6e/mJOXeUyocaNgdOg4VMGiRww3MSKSbr5hGyaTa5b9/ZNdDFrU1p9qxJzKHP4hXmDFLXyi6vavHOXzvoreoteWT4m/l1S23UbJnZWj9zsqL12L1UfhGKvRPgXShpPrU++DzVrg8brZz5xBpnSkl/ptEpVnLmnJC9mrNxl3BnH/1LIAWWrn8UgpaW81HUlO9PSMbbG0vHYyzOLEofXazi/o0pTa8b3M9l+3/NwCNJitv9ItdExCPyBkOnWvxoWPz2RGSsQiNrBXQe70AiUset4CLTk6/8vkf2j07CRsYGBgYxMcQ/acSpUKzgNEvBeZH/QDVUlKot7mYpnTnLXGuyF+6daZQR3HVVp0TnWWUfTxZU6wHT0AAmzPP45qL3sSVlo3N20jF5mLGff1mx64xCXY0wepGaFUtswDjM2B4ut5WnY/XambzE8OpvTkv5npFZ/+dtP2tUcub+1jw2lLidtWF6MLcelc29qx6RAdHVjmevdRZBwBg9nu5Yd+b+BB8mDmqw+k97SgyDtlvdjQVKJmi38gBgt55HzVnuHpee7RRfU6BNLrEsYuo60omZSgg/MM7bRuC38DAwOCkxsjpP1WItuwMUXM4lOKr1+00KPiXArYFOx+MSttQW3Uu3TYzRnReIJC8bb+c75//V45YepHV7GLNB9dx0YFNCV9OZxAU9us9cMgPmSa4wnpsBT8oKTr9/umKK/r1xudSJlZwC+Href2CNFtTwucZC6Hz786gHUXGNmBhBUUQHtEOpuag8v2/HcWNSo+9wACN5VpFv2G1AjseHBw9QyHMLXePWNyTNsvcAmCpmBaqn4mPXURG6oO1At9GKbKPOxAICHxD5BsYGBicQhii/9RhITpR/nnvLAg+jeVeEspXjnTiCVLgqdaN8INi5Rm0+Pzl2f/DEUsvABrS7Hzrkn+HrXuZawMb/nV5jNPpHIanHxuRHy9NSZ0GUmxfDUCTSD2yuv4qgJ4AaQejo/wAaQdaae5tUZx64qAuzDULSS/bkbjbJEKrKTX0b3uL4iBlRiZdyBuZYrTjwcG0s8g4n9h+/IkK3t8CvySBot/AzAKB/efXTsit6fvPhjWnr6gJCfGZI17MWJE3JbJHRuxzqhOjgfuBS4A+vIeZzzGxDNgYto9ZtL3NTpSiYQMDAwODEwRD9J866ETxJSurJkEM95LI7Wus+Zr5+rFErSJ6T/xUsq6oIfCbBfilXn63Lmmypee5P9nu2/LkCABd8SsAy1EfPovA3Kp/EAlhhbkdTelRE8znt/hbGHGkKrQ8mUJeLXei0T/artmBOIEi42DRrRaan4ViKjXvSAVFdSSYIhRY3va3p4GnuSf49JVp+DU20z0n6sStKDMRbVXWvYELgK2oRT9Ev3WTHeAYGBgYGJzEGKL/1CHK3hOgVkntqQbmxSniDW2vZdUZD4FSxJsSsO08FlH89tDZgl8K+GDRKLI27efMV6p1m1ipqfx0LC19Uknd30L2vxrMY679J/YPDtB8mgVfqghrtBXE3OoHcHltZsxun13rOC19LO3urqvGvtFF2v5WDg3pSVP/dLxWM1bpIaf5a4Yc/SJM6LtN8S0/g+i5Ew1Y+zU7Hhq8/ezFX45QzwB04FqS8uOPEvIdQ/NziNY51YmzUdJ4zEAtSiT/PcbwCUPI0x0+hKObsmdgYGBgcGphiP5TB01v8oLMmhlSUj5fiJL5AgeqaObDMiwtILR90KXn0Z3zZJ6nVghkQmLZJH00mm1JDRa6MzLwv+GP7GLv2L74zWDyhQ8sJHDwnIww4d/cLz306PzBQOqu6s+Y6zfSZ8shfBahOxshBfbNvx0hz5+zNSri70sVbFk4rMPXpOeks/vXuQz6rpMUVMsxs6NnApafAWLl7n8x8/QRX8w8vf0n3oYE1mj9oYKir1D6VzQAG4BHiqn8pDMOqiKZHgE/BNIC/55KjlwPQIN4gH9G7UPvbdHhhmMGBgYGBicHhk9/N0QISoTAIQT+wGN8j3LFfz+6u+4CWT5fRPn3FwBLA8uBUJFhaPuVuZOqC8dWN5iL/FRb9bIowqkJ+Pgrvv7aHXJPFoIKTKCkqZz5SjVmX7QqE4C1vgXbbg/f/Ol2rjl/PTcM/F+uvuBt+q/dB4DPZmbHg0MApdGWNGsPsdwDrQx/ZJfQSvFpzUjh8xvO6PB16UbjH/+azZkjaDRZkShWnZszRySVz6+Xo59Ig7AkEMAdOj77/VACIf2Am4EPKig6vzMPHvk5CjzO0CnivSLw2ApcRZ3YTZ1oZgcP8VsqIvaxmIieHcRP2TMwMDAwOIUwLDu7GQGBH4ryTRxaxq8v/7nM61WLECpP/iSYL7T9+4Hqh6Us1D0XJT9ZBBtxxYrg+4EGSxZZrftpsPQBwN6qFHweh35YnYZeR91krklvfU//dNZ8MhYA81EvNxS+GVqfdBAq452gb/2Fd2/R9fK/6z/PHLmyzzob6vzwJInVK2BV/fj27hZI2I+/s3AVU5ldQdF3gGHAOuBiYAEwULXe+mIqO9ZooL3UiUaiC5HVPEaOvD/0rIM2ngYGBgYGJzdGpL/7EXIkmTi0jKXjZpKfWSuEaIvQx7LnpFSUUCoclAp/4LEE/bxfZbldlGAXDuzCH3gM7l+jM6/i0qPGD/hECtmtDZiQZLc2kN3aEIqEd3f8glBHXUj+mgSKaI5Ghq0T+ncOiF+AzFG2a8y1hoRxrGj5Fb3X9aQDgj+4n2SWJ0PthFw2PzWCxlxr1HV1AXaAYir/WUzlCyilsYsJF/wA30lmpxUUlVRQ5PiTLPK/0DrVe4lzg19U4Qj44ieLRfXvN4E+wGggaLn0E+pE39AaLlmOSxbikqbAoyH4DQwMDAxCGKK/+xES6AsvLyUjNSq6HnTsiKZUO43HYqFB51g1Kk/w0DZ+RNmi8+72o4gQyWBYOX8Sg/7uwLxaMvm65YEBgMBhLaDBkoVFett/xScwzb0teAKCn0seWQAAIABJREFUtyMDmH+WX8CuOWdw5IwM/BbBkdMz2PzUyNDf7f8XeInSgbnAtSDeAvcnSoOqoDDe8eBgvNZwXR90uukMt55Y++8Maifk8sbWK1lVPz7suhKl1Z8C4KIt7SUmFRQFvwO17D0BUjWW6e0r9FkRApFlaTDPHLBIXNxrQwFNrSvEdX/2CzHfIcT8RAcALtW/XyRHHiBHfgSsDywzAyMSPT8DAwMDg1Mbo5C3+xFy/8jvpVujpxe51/Qtv3os7tVv4Ea7uDBqGxOSu6tfFBuzvtNz5ZWT4CraYpKZwHDgPSBQl5nVqjem6N74LAJLozchf/x4ePqns+Phoex4eGjU3ywHWhj+iyrIIST4g0RaYgZFcqTXfWdFy7t6/+0hmKHox8T6A2Ply1l3Zwf/VkFRPW2NsMKooEgCLRUU/QX9z4xQrR+vu2/UZyXd1ExJ3+VsPDxGMPcK+N9PlGZcYj5SPhwvEv8RSrOtWJwaVfEGBgYGBh3GyOnvZqhz+nfPLqQwUzOYWc0CjVz8UuFHx/hl/kImEyFoHpayHLvuNjisBQz6k0MR+gEmri1j6WMzyWhu0yLqplwnAxJw51pJcXtJ299xwQ+w93t9cV4/gIbzetPULw1/qglrXRP9NtRz1vNfkjqzhfRx0cdqNFl5o9/xSTk/EWn2p8o0U8tkVcfd54DZHdjl3mIqB6qi+JED4xmqY2l+VqQEV2s25ftuZWP+u8HF1VI+XAhAXVgufnCEnIUS6Q8OYN4EJs1yL5r1G+v9v+opjoqD/kz/2Yc/m/b1kL6vduD6DAwMDAxOEQzR3w0JCP+FJUNX5C+9diY2i0ctNNwEXHmiNizVL9jVHCQA2HW3wY/A/K4/TObsLi6kcF/0QOR4C//ObLoVLFrVK2rt6L6/uiSLXl+6Q5H0vWP7kv/WXlL3tiIyUS7mMMj+8MUDBWwpGRGWuqPV1fZ4RuI7AYkybxSrqFVNdTGVhQAVFDnQb9IVDw9wR6DTrt5+XMVUZidyrCZfKi/eOoCNK7MApJQPmwKCP3IwEUIqJSNRaZh+Kbjd/XuWt0xxAzPkUKMBl4GBgYFBbIyc/m6IlJRLSWHZp5NNNotnMho2nDqbziMyHeAzmnmZDI0iXQCW5k9fE1mYG6TGmg+Hw5flf62dciSQ1Fuy8COot2Rx2NzjmA4BBMlbhOqt7+mf7gPk0QEZHTspDYSEXl+6Q3ntOx4czKA/OEnb06oMMA4Ch5STE3Uw6AEneaucoRSXoANOhtODkG1dbfNWOTv9XDvIEaA5wXVrCLe5jEc+hNJxkhX8wZrsagKCX71PDexB+8/z7t22ZtzIdXJC9mrGjVoXdc/TzS2ULNwbfBr8oOjVEgAgwNQiUjic0gMfJlpECh/I0Yw9upblLVMgVg2PDsFi4wqK/IHH9hQZdw51ooQ64aBO+AOPx+9cDAwMDE5yjEj/qUapKpVgFw28Ry9kWLGiMlMQcP4Q03A8u21WwazqF8Ii9Y1mGzNGLiEyp18v0h+2TaC5l69SnFCjzg8Hj+aRKQ+y/YwR1PfOpsWSiv2giws/3cR9Kx/jW5/+O7iqC5g76cHlK5Y8fqdQpzIFaa+TD4TbX44btY4Mp3bTqiB+s+CDRaMAuGD2Vky+6M90Y65S8Hu88UkTGw5eseiF3nPuqaDoOSmZHafAOCyFBvTTaCJwAb1IohBXRWimQHVMB/oDiOpi++qopltadqN+P9xiPk+JzsuHy6nTT58LIoFVOW12qPUt2cz+4uWwVeTQxD5KiaQpHTO0ZzmU758cw3nIwMDAoLM5kTSXwbFggSxngSxkgTTxLo0Rgh+iI4f5c0YuZvI3wx15QuJ9F/APQtHneZMX0JiiHbjM8LlZuLM09LwmwaZex4pPC79B5Xeux5EziMb0HrSaU6nLGsBfL/0Blz77Hh8MCfVpsgNL01qbG2bctwRHP8Wq1GsyI1FSmTpiR+qzml0EZmT0utSqMfkk58/Zyvk/1Bb8ie6nq/FLeG7PvbxYN2eqqKKkqSn9Zi3BHxGHCBtRBUSrP3KbvFVOxo1ahyrKbqd9gh+0o/qxmlwVeG3mZ4iI2Kd4fAx/ZFfYigf2WnwEBb9C3I65blO4HWqWxRW5SjJddzWL+UlytqCTOJHOxcDAwOCkxxD9pwAxOvjG9udXCHnxDxrrwFzkZ9BYRyhaDyjCfynwJKz8fBIzRizRzcHI97Tpk3lDFkR5+ncW7Zm/Osv5OUsfn85fPr+WssE38NszZnF6+ucSwJtiofzKsMwD2++emcvKsZPcgyocmN+TTCl9FZ/JHLd2wW/SPz8pIMXtm0sgnSVR/3tzq8Tcon/cTu5qmzRSwj/2X8PGw2MgIOzS0po0XXUisANLKygq+fLegufGjVy3YkL2arM6faYdKU1H9P4QIEpEB6LgUWo7iNnj07yWiMGWOyu3dUqEa090yp0KL2Z29Ay3Q21oDTtUsl13E/nMHyu69FxEFSWBHgkd6ZVgYGBgcNJgiP7uinaTrShUbj9qb/4yIZBPeH5yQGfvatGjJUrcBITTRGcZu9cW4qs0sXttIROdZazMnUS1ThS/xqr8nk90lrFwZykiIH/X9LuWKee+ypDLq+h99X56jDvCsDE7eGDIo+y39E7ghkRcN8kL/4s/+Rd3rF7G9TPfZGK/15l5/u/lOW/tCkWVLd5w95weTY1ZR4oyfP7LBJ7rU1n+6ymk+H1R+1UjgQ8Wn8sXUwuiGnJJAbtvyTuCS5YX21dTbF9NhtMjtRt3JU5n+ui3Bynh7/uv4ZV9d6sX57sasjXX14j+2wpX1DyTv8o5K2OPR0QK++GP7Arr4gvaUfYA1cVU9kLfw1+iL6LnoiPQE2ha5geswMKwHHoljUVdr+AK/Ce9wuz6KHN4c62tLT3IJ00tFfW3qPsQJFvEqzcrkMxsQWfRZecSEPhRPUkM4W9gYHAqY4j+7ohOky0d4a9bKPh/3m+tRlvQh0SPXEakKKkOPO8x0VnG0m0zKfRUY0JS6Klm6baZTHSWMW/IAppFeHZFq0ghw3sUX6Vgxce3UuipDsX5nxt0D8vzprCr5xAOpvamMaUHn/Yaxm/OeoDRl27mUEqv5O5R4Ma0ZybB5JP4TGaqLYViX98BZoCejYeZ+sYrYetJED0OunsKCekHWjG3RmWdaFI7IZetj49g0/PnhnWf3fT8uXz07Ki7IhuiCakMCCTQ3MeCP4lL8ptFV3a1jYtPmvjdnh+HCf6Le23g+bOm+u1Z9ZGpPFHPgwx94jN7SlP4lQeFvV7qks3piSyoVb+3tQazElissuAMK3gNrDND61hxmpZJlO/a0Gc1RvFsIzCXHGlK6e/NrrHlTUP12TML/9R3Bo7NlkMxyaEUtsO1R28Qn8xsQWfRledipA4ZGBgYRGAU8nZH9K03QREI84IOPkIQq1BwjswS+4nw5w8W8cZCTMOxe21hQaEnOmDqsBYwb8gClm2ZSrpsi47Hss28/vy/ktvkZHrNyww9UsW2XiOZcP4qnNY8AJ785Mf8+Mun4p1WFMGLT0b6F/5xN9U5haHn/Vxf8ceHbuKyT99Xr9YuF9A4BbWK/aOOTWpw22A6izq67bMIEISl+GgVknYVejahLX7LkRfr7kl9/9DlaaAI/rsGPEeaqSVqm41zL6B28kB6WaKzb/TsURMpmPZazWx5dJjLMTl/bkRBsG6zrRgFr68As7QOWbiixnX+vdsaJeS7B1rZ8dBgEePeK8XCSRSzimlEnW9gUJ4UCTQZO3aE9yhQvn86oYhXVOl+7yVc9GxgYGBwsmGI/u6IfpOtICGvfiFwoD9AmCMlz7XnFMQ0SnyVpjKt/HU/ghprPloDAj2OmHvQ03c0bNkTZ/yEn33jCQDudLzAC9vv1to0JvWZWWQdatD8lZdAs9lCui88bSdS9AOkHm3irz/9PldV/QM/ZlKIncajRRwRLgGlqZROQzS1q4+WyAZVp9wBVhlHdHYWctTPtoszf18dJsrV1+qTppYX984+vOHQ2Kznz5rqz7I0mLUGLk2WNH4z/yfy9Ds+E+mmNjfPJn8aY0e+T/+6fR05z2pcOr0o1CizLAuloECnx4EPMGtvrLg6oYjYeFXqsphKE3UxemfkKOdbQVFJU1P6M2eu/q/9G7/6Dz32uqmx5vPQ4F82r8ibMq09wv9kR1Tpfu9Vy6EUHtuzMTAwMDgxMER/dyR2pD9INQtkobqDr8Y67Rb9AEf79ajv4WuMKmD0YULg73A47ZGzSnloyCPKv3eWUvp5cjPzsaLAEnh53NQj73zzuz1+88ID/oGuPSahTAyYAVrNKXw54HQevOMRVl1eDMDAHbX854fnkOFLzAnHL/C2ZlpMqYdaTQk0yXqrmMqrAN2GaEnYbvqAF4CbUQphu4y8VU554V1bNF03I87XR1uKi64VqdM+0HfLuhXmkr7LybK4aGi1U/71rZy98r+uZb+5w0acBl0xpl8kLhn7LWmPjrq3Y7bEjZK7H28WKBjpjx7g/S/wDFCH9NrMDR89MbynNIm0yEFSo9nGnGG/cy1bP127OOIURpXTHzWDYjQyMzAwOFUxRH93pDR2F88AkgWKyAl28EWZQj8MZAbW6ZDoD4ikV9CwRuxoB9y6tP6ce9kW9qX3x+ZtZNfbg8lr6uQGU66IElkN0ffh2ee1XvDSZgvAGTWf88Xks3V357MIvD1SZOqBVnwBsVZzU15avNPIW+Vk+K92SdseD0JJcVgDTKXj4nMGsILOa0YcRt4qp25fAAifmYgkRrqONL0nI7vvKmLtUvFtdFJrVNvr/TF+pD+xwVasSH+i67T54kdG+v8X+AXQ1Lay12rGm24i/UD4jBRAtTWfgtrqrrHA6uYEhH94OpQh+A0MDE5hjNzG7siCMMcPPUIOGMEOvlJiAp7utPNQcv8Pa/1Jq4g20eFlbXou3/322+xL749J+nh1y22agr/Dw1W7KDma26Pen22S1d8okENe+/Slm/+nfMPu/oXO5pRU+VnuWXumPLR8c3D1wc7/aO5GogjDjx8f4ar8/OrJwiVNr9eMa0xU8I/+0XYy9niEaCv0nAq84rWZXcEi33bk5geLFrvElSV43nqCH2LbhOr9TUR331U71FxHnAFMSx8L3nRT5EklWhyqaRWpKhR2o8yg6FpsBjBprKPu9KtuhBVezPoMYYIflILlNA3BD5DnqY1a1uGOuydJl1w5lPJAsXN7i54NDAwMTiqMSH93RzvqH8rpj1xdCH4BPBx42rFIP6CXfw5KrkyNtYB8Tw011nzW9B3H7OrnY6q2XT3O4XsXvUWtLZ8UfyuvbrmNkj0rNfYtkAjM0X2aEqXVazLLFL8vNEtR8CcHNf21s6Yy3Ed5+97v+i7Y9aFWBDcqipxg19hYHXerK1zjIX4aVyzk+gNjF1+a+c5si8nbnu2DswXfBmar/xCvU7AUsOn5c3UHKlo5/X6zaDH55FTdQvIY7zVQIuIfPTmiNftfDUtOX1FzHYkWp9tDxaSa9zoQ6a8mUPCqKoSNXUyfaLGsuph1RMjFNgy9GYyj5gxXj31HQ+k9He64q1FY7G61yhmvL6F82+QaYN6flhWR8LV1JXZRBPwQOBdl9vIosBV4DpdcdczPx8DAwOAExxD9JwOlGg4YGoIfEhT9do396QsxBzrix2EtYNBYR9iy3WsLdQt8N2eexzUXvYkrLRubt5GKzcWM+/rNqPX8CBYX3MWs6uc7darq0UkP8JfLbmDz4PP9gM/s88oz9vzXcvmWd8RP/vgkZzm/0NpMEcYR9ydg8Rh1XyILcG1Oj3bNgYCKr8dredaHrQYIPeccoPqmqkpeOntSQa+UeP2ooggKbOX1j0gT0kvPCZ77F7cXsPXxEUip6bsPQF6F0z98wS6TbY8Hn9XsSnH75sYR5w407qkE3LlWPr3/nCPVE/PuSkqAaqR0qdFz/oH4AruCottR0t/0GFJMZXgzAZ1rbO5jwezxhw2SvMLckiJ9YYMkvfcdwRqCeOgUFn/l7sdfMq/FnlUPRL2miQ8q1CT4PaOZpnOp8AF/iLH3qbjk75M6HwMDA4OTHCO952RggSxngSxkgTSxQBaKhZKIDryThMAuBHbCBUqGarlChEc8wR4Adu0p/qX509c0CUvU8maRyrwhC6KWzxuygEZztL5623453/3227jSsslqdrH+/64IE/wSRew7rAVM/uZy5oxcTI1OAzAJNPW24EtNLtX552W/ZtOdFwIIOZRU7+WWuv9MHixeePLuSMHvQ92zQFuoRnmQa3WO1YtbuwdaExH8i4vtqyddMHtrS2Q32vw/1TYHziG/h/lozB1F4AJaiPCVB9ZGnp8WfjOsf/wS/8ePjaTelc3f119DU7N2llNtca7pja1XulfVj5+UUuPNTsAqVtPXXcCkjK1uccHELb3aEXHW7GMRTNna/NQIHJPzQ12B1esEjhWVipTEOVRppN9EXaPfLFq2/89Q1+anRkh3TrpPBo4VKfgDdLTLreZ6/Wz7yLbXI4R28zSgLKlUogS/Z/SabFX3y1ena/0CyADuUS27K6HzMDAwMDiFMCL93ZyoKNgq1vCwUgQ6nDKuoBSJ4Bl2x9yPlAH5qR+5D6WwRPiF+yc6y8zP7JiLvbUBAJcli7nDn2Fl7iTNY010lqFeXwBjvv0O79rH6J7fZa4NDGjaE7bPYHOwDF+bRmo023j8hh/R91d1nPvOVr7xyH/ouacx4UpWR78CBlU4XHIo2dIu/ELLOhOkiOcEQygSXBZ8rpcSE5m6oVW0e7DMydelu/DWeEjJt9J3wWBOm5RbXWxfDRqvl9dmdqXUeLNFFY5FZ04vyE6tj3e6oGST79PaH4qgXYMipsx5q5xy9I+2i7DIs9VMzY0DF51xuPpfqCLgF1+4gZIJr0l7H5fQGcgkFoWG5GahEttfXHvUdp0noI70F1MpAsvip9904Bq7KtLfaLLyRr+EnKMSi/on8D0D+tabb/14rGfs5nXBkecQXHIXdtEb2B9YtgOXHJHICRsYGBicKhiR/m7GfCFK5gvhmC+Ev9TWo35E5WvLUEfBrmMW4xTBP56ZnBYKQiZMzEhhQPCrI2/mlbmTmDv8GaqtBUgEjSk94h7E5vck3TRr4c7SsOcrcycxY+QSHNYC/Ahq03P9M0e+KOc3PyJn3/cy337zIzLPO4pPaBupRN6VxjQb82YuAD+niSpKPDnpmgUDessjCQifUC6TXufYANV6RbsHy5zUzdyOt9oDErzVHupmbufgCmc+Oq9XituXBXBnzrNr0kyeqLG9X+IFDkRs9o7e/oD8YirvKaYypZhKcdGEj001Nw5c5M5J90kB7px0X82NAxed/nT1PZFdnDduGlM9+2fLJgvNbHVl37FuTBguWY5LFgbsN38ITMMuGrALL3ZxELvYgF1MSHh/ULOjCZ4+APMblMcdTbozGYmfpz7xO8Wqr1F5TGZQ09Eut1HbezGzo+fgRI+faNfbRGckNNd77oZ70iFU0HMLdmED1LMEbyRwDgYGBganFEakvxsxX0TnH7ek21g9fwk7rpvExN1lLNxaSr67ht/+zkTjUc0GUtUPyxjWhXEicGJadORNL+I+Y+QSzWh/rLz+WPgRmIti6u1g4eltwPeCC5/dNiuqgFgCb2VdwTnmL8ivr6GhZx8QkHV4PzV98/nl7Q+5Zg5alKUVzd781Ah50YSPNQfMkV1T77zt2TVXXLZ2FiB0venTB/pync4UvSjtZwXr8NZEb5cyMN1X2tTk1NoGpRB4HvGtXYMcAC5BMY1sf6Q4Bh2OQquxi5tpR053ZDday7fe+8+n/z70PXWZc4qAIXMK8D0TFShud6QfZQYlC0jRWV1p1tUJdLjjbqCwWEryaw/lsavP6eJQv17JnIIMHFf3+M1npR5OO9DaM3LD4AxV8HnMJluXitnASkC9Hw+Kw9L9uKS25ZGBwXFACLKBB4GLgFG0WV133FDDwCBBjEh/9yIqSpja5OaKp0uZuLuMpZtmUuiuxoTUE/wQP1oZFelrTk+VT7wwJ7+ICseAC6ujtl+4szRM8ANk+NxRkfnQCXja5yJZY40baA1GGa9QL5wzcjGLCu7GK8xIwCvMLCq4m6svXsegCgeTH1yOrdlD9qEGTFJSuK+aZ5/+od3c5G/Y/NQIGnOtqKPwn99wZoOowiGq8F++Z239cv9N9RUU+Zc33VR/ybfeCZt5efHVOVODqVM7HhyM1xo+69BotnH/0F8HP4fzgGb131tazbTWas8QePc0mYgd2dXMVwe8wBGU3P3dwIvAqGIqP42zv47Smfueqvr3L4iR0x20sPy3+5v+cfvWlU2oW10wbt86ked2Fny6p2VspK+RV8LO179ur+2nHv3QF/zQidaqxVSWF1NZWEylKfCoLvS9vYIiGeO/weTIcnJkoRggTQWP10z+864bjmjEhuJFi9Q5+CsqKAqJmgqKSrY8OqxH5GfBazWzZeGwyP1ovmd+vnzha0A54YIfIA0YAuTEOT8Dg2PNQJTZyQvQ6G1jYHAsMER/90JT9WZ+VcPCreHCO1MnMNezZxyPS1dbDwAJsn5glnz26TvFexMuEUDBqKkfiQEXhkfp9UR8gaea3WsLmegsY6KzjN1rC/FVmvCL5N92EkKFwRf/YAOLFk/nDy9fzz/GjqFuY398labgsQpQNUa6+MINLHpsOtmb97Bk521csuAdLOO9zBm5WFnhMCxcUkpGc8SgpdnNN3+2PS3SFaf6xjzv0rq7ewIFF/faIKblLLGnm5rsgEhPb7LPmLI47eILN6h3Zdt/IMsHUDshF/Ug4vCADGaMXEJ57mT1DQzLeBJC0NS7n95tqVG/XkQXF+uNkszFVPYqpjKtmMrTi6m8q5jKGuiU4lRdOnnfaq3+R1zSDWE+7DZoy6HPczsLRh/aLjICaWUZfg+jD23H6/RoZph5lYFWR8/zC+Bu4CyULr1nA9s01uusQVWn86dlRVx52TpzRC2GBBYDk9DuRxB5TwUwS1Xku7B2Qq7QGlA7Judnhe1sKJrvmYVLS8cDwW+5KSiv9xUo74urDw3u+Xm7+xQYGHQNB4GngFtQZqMMDI45RnpPN2K+0E69OZhTwJNTazCpgm87PoHVb0CrShpZUuC6ccgRf4tfhApQRIXm8dwuG+vvuzb0PF66TpOwIBCkyZbQsmQ79tZbsuh7jYuLf7CBO69dRLqpWdPrvTHFxoy5S1hZNImpfZ7n6v5vhrmNNDWn8eLvZ7Nx0xhlwWDwPW7CpBPKjCyw3fTkKHn/t58QGw+PYdGZ09EqkK13ZTP7vpdDz79z4Qb5wzt/G9ZlVko4cqQnr/7xjuahZ3/y0hWXrb0OHevTAy/XuOru2GZDo/jzYamf7x0jncYFNJJE+keHU0Y6G8Wj/XWUwMV84DGU6H8wovwbXPKB4D0Yt28dGf7oGZOnLhAcdmp+B8ZOg4tBrHtVQdFAQN1prppjeC+1iovjrO8gRkqWxrXmo//RDm4Tq99CYilUdtGEEtX34JJt6Y5nptakHmzNA/jbf75HS1YaxCsurtMonM7pQHF4d8W4D8eMTu+XY2CQIEakv3uhYednbnl31kOuWlte2IrDh8H4cW0R/8xeyvMRw5JKI9CMFFuzwoN7ejacQdJla5jgh+hf/FhDz0azjbnDnwGgZOxy0k1KBsyon38SJvgBMrxuFq4o5eLMDVwVIfgB0tOaKblxeduCXVDTUzsgHnmOKR4foxZWiZK+yvZZFpfmdllZ4QOBf24aE+wy6wqOLYSAXr2OMHv60+YrLlt7BzGacPWenp+FKtqZMjDdN2D5udahcvzCOFFMrdSIZpQIaZgFYqz9qBxnEt4mETrUOdYlK4EilDSlh1EGMc/50k3ys7tOZ9Xea28J7C8flMJxLa54QCKs7e7gG0UC90p9rKOR6TcnIDELbiNTiYidphTcl946klj33S5KsAtHwHEpOJtnxS5uxS7SsYvLzY3ePAC/ReC1hbKp9IuL63SsQ7tpF+J2Y9wHA4NTAkP0dyMCUd2wqW6Tzzf14wenZxe4a6Km2ocPg3vvgYfnKY/Dh9GCxo+qmEaJmIZDTMMfeCwB8DaZG7TOw9MQLvDVLjrtnTfSC/tJ4JXc20IFwVlpitDOW+Uk7YB2nV7+1zX8zz9/wXXnrmNC9mrGjVpH3qq24GpWVrhYn3fmAtwmbd/5SGx7PCGx39Bq11xHoKQVBXDTFsltjByEmEwyBSViGYuaoXI8Q+X4jKFyvDjbOdZ82uRcdb60pmjWSac5QnQ+aTzHlfiOM0nS4YGEXVyKRk63qcUven5+hPSvm4L9BRoA3df3jAlWmVmSu5iI9JFYMyhxeAade1VB0VnAS6rl77fzGJ1CBUVfVVDUGnj8QwVFUQn16At0veXz0B/D16jW0UoLWqw7AIr29VfXR7yGUsD7trlVOfSXUwrwW83krXIybtQ6JmSvLggMGCLfX53+3u6mGPfBwOAUwBD93YyHpSx/WMrCh6U0BR6VH8kFoQGBbgUvcDiyU6+GBWcBsFRMo+ST8nPxNkcU2zWb2fnnaG2wMncSg8Y6qNZpmJUIWkpBAFOdrzLRqdjdNzQrQnv4I7t0BwoNPfvw3Z/9k8iGVUHh39AQLtZX5k7iR/1u58kDwj+/AZ48YGJ7k/a+3QOtNLRmSYDyr2/Fr3HSQhCcTagGZgQsLCFOEbVKoKgHKm4Uf/ylgNYoI+h8qimaNSKxWRr7iHduHW34pEVHRcYzqHK6/1JzTc2Gv3wLf4qJnPX1XHzrh8H90eRPY0fPwXiJeC9jZrctf+3cl6rv0fxMJUng3muPBJXX5zPgmsDzA8DP2nOcRNEbzKsIFhf3A24GPqig6PyIdZIqvg6I9sVEf5xD2+gMRicXU3kP+mg3UTPR2nyaBb9Z0NojhYZvnsbHvx7GlkeHRTXDQ7sBWOcjGqtzAAAgAElEQVS8t+vEaOpEBXXiK+pEC3Xia+rEO9SJq5Paz/FD73oLjGi/gcHJgyH6TyYUQT8lxhpagk9XfNX+c1DWtt+fh9tlQ0oll3/b789j7yZ9Ya+V6tMkLDSL+GYFHlI1hb/aCah87a00+dN0Pe8lgCAq7SfF42P4I7uQEsr/fGvY34b/twz7zlc56ldqHY76/axpVPzaw/YtYMsDw+T6XVetBao3Hh4j9QYe2fZ6KZdRGBT8YholDfuzdIuotbr1jr53mzzv3m2vANeRmO1mIqI52chte7eJR0fF1pDAoweXXO6zpeTVX2rn6BkZAPTecZjUhmaArGV1M10fp5zLh71G0GiyKt12TVY2Z45gZ8+zv9OJhZ6x7r1E2y2pS9AbzJdV3FZIdHFxsPW1FXhUvZ/2FF8HxPvkWNvEchjSQft9IbFUfnE1f953HX91XMPbb13Cf+8YBCbB8Ed2RX0PEP0Z6fh7u07cCvwbmIAygLIA2cAY4DsJ7+f4Eut6jTQfA4OThFgWcgbdkQWynFLxDNoRR60v9ljiq2bvpoICtcif6Czj0Z2XkuepxZmeKx8Y+qgIpt5MdJaxcGcpVp8brzBjkj5qrAUh152FO0vJ99TQYOnDaa0HsURMSpiERErtVJ+gQ9DG18dAb7h+wJv02tMYtZ6rVxZZh/dHLQclNefv669pK+INcMVHpaRGWI62Aus9MDxdtVDCA4ceExufHvMdAhH8CnR9xEP3OijAVlTcZr7z9kWkpylFyGpnIHOjN3qg0uQXp6+oue6jp0cmE3VMxJI15N2ft8rJsF/9B9seT8FRc4/6Hr7GuRrNoLT8/jvqOFNDnPsWRXin2uDNsmIXt5prx9X22Xwgv8d/lfeEKqe7ZsOhsfM2HBq7dNGZ023OfrmRew2KwM7Iq4917ycf49x9zcH839688fa/Ft9YqFr2eQVFM2grLr4ockeB807q3NuzTRw03y86TdSAmM3w1K9Tx97bdeLswPZmoBaYBbyHkrI3GuK4pZ04xOrp0ZmfEQMDg+OIEek/OZlL4lPysSJdYVP7ShOuGRR4FKeg/KZaUfbxZFxv9PY/t+1uuXTbTAo91ZiAFOnDY7Yxb8gCVuZOCqX/mIv89L3GxUHLaVEHTJet+HW654Y8+gfDxp5juGvaizSmhf8+NZptzL30GWr6amuvfX378Ur53VHLMxu1b8GhiJ/ramtBcMCgjhZGpT80NaexeNmcDFU6xULAtnHTGF78/WwyXzkcFdXXq0+gzUkjUUSsothg5LapKd2VW7GH8+7dTo89bkxIevga7TKdMhaJenVkr4tsPJPz7I+T031D3hv5Y37wf5hblBctkNPtBuYFbR/tlminJYC8Vc78UIGodt53oui9Tq7jUKyr+SEQwq+1XOr8+0RC8/1S9dOztavpoVrItm7YEbS9Tjk6lreJu9b8kLaanKnkyDXkyMPkyHpy5JvkyH8kuJ/jS9t90KOgm6YtHTeEoEQIHELgDzxOEgK7ENgJH1xlqJYbGHQpRqT/5MVN2xeLC5gbmc8fQDfSJZdRLqYBgehqsymd0Zd+JL5K60+ryUKh28F1+9Zw3xePmWZXvxAlFoJpOequvMHZAHurZo0wJumj0WyL6u4bnC3gEpi4oYyFS0qxNrvxmsyY/D5q+hUwb8YCVl44CUbB0sdnhnnvN6bZ+PGMJ2F79DEPZeRzWmO0PshUDYnDzkFBLZ7cUir378jRnrxSPoONm8bYUWojwtbduGkMZ691REX19dKEjpozGhYvm5MxbdIS0tOaI/+s53wazO9HS2wWU1kuZrFw99pCu6Up4jyagBewcwNLqRNBMdDpkdtiKssrKILEbUD1Go01obxfM/0pouXQkJ5mR0m+5YtphWH7k0Mpr1D2ERYtzlvlZPS921AtD+Z9B3tWJGNXqvdZmhvjVnQVNUDBxRduoOTG5WRluWhosNPUnN5aQe39KHanDpTrfUa13XEtLtbFJcuxC4h4HRyT8yF2pD5+FF95j7fXsjbYCLAVuIo68RIwAPgSWAQsIqeb+GLnyHLqRNRnRIU6bWkj8PdjdGbdDiFC6XXB917QWEBraurXgf8gOSdrA4OkMXz6TwZKw9IeGlAcTdSOMG5gho7oD6afhP2gqYpPg+v8HbhKa/tBjV+y5d1zyfQejvqbH4G5SIm+KjMFM6O696pxBNKBgqlANdb80GwBwMQRZZqCfsZ9S1g5NjC4kDBxnTIwyP+6hpq++cybuYCVF0xSvnZVjP+qknmfzmPEkU9JF9Ai4SsfbGpOoa+tl7zMdEBEnkOA6j8tK4oSeVF9AJQBV29UDcN8laawngpBovoCCHPL1FHL5Iq8KWlB8WbPctHcnOZKT28KCslYP9K6nudiGn5fpUlonQeC4OCompz2edV3OopNY9QPol8IzO/6q2mL6OuicgwKvV7jRq6TGXs0G3RV45KFWtsQw/f9ROlnIKZRcsm33lk2Y8riNPVgUUqkELrC4gBwSVfWGmjR0XsWc/vwlDBl9jI6fS2R/Wu/B+pW66XEBHmMHHl/Msc7rrRZd6qvSQJPAr9CnbbUXWYxjgNC6KZ9xiTYvd3AoKswRH93p1TzS1qLaha0X8CJafxtWvXLl892LOo59EgV23qNZML5q3Balf4AT37yY3785VNR2zl6FTBojAOI38Sr0WxjxsglkeI6xPD/ljHlX7fR6PWRaYIrrG05945+BQyqcLStHKmgW4F/ALvaFt2054/88aNbdM/np0Mf/+TJM396NtEWl83AtD8tK9IU3JHNuSLRuw/NfSx4bSnY9njwWc2umWe9yCsF07WmfKvlMgqDT2I0O5LFVGqm8IlpOHavLSzQfD1ygLeU7clJrJFbl2PXbkynet3dwIwEhX9IBE6wr84XOvcOlzTFa06VzCUca5Y33VSfnt6k9f5pBPagvNJpgX+/BSwMdmY+ViQ7qDoexHwP1K0egBIBB6UgehJwOvAOSvDFBwwgR36dxPGO78BREf4v0GaHu5AcWXrMjq9HnRgN3A9cAvRB6XD7KfAbcuQJNesgBLrfyVIaadUGxw/jzdf90Ut7iCSxYtBSUUKpcFAq/IHHYH7z5Je33XHX4CO7ZLq/mQsPfsDcL9uyAj7rcTb+iO+4xjQb8364AH4CzGgrxo1EokT44wn+8Rtn0uhV0lEO+WG1ymGnYF81u4sLmbi2rG2jQ4GdHyJK8ANMrXkl9O+Hz/kFtnGNzB7e1hhxQt2qYUQLfoAjchnlUmrf08g+AJG8et0k6U2PsI+0mtmycBhvbL1Srqofvyilxpv9SsH0RO012+NAMu+hwb9sjmqqlo46GeWYCsA4ROV0N6bZmDczlHKVkN1npGuMiH/vusKu9JiQnt6k9/6xFVN5TjGVvYqpTCum8vRiKu861oI/QHfwh4/1HlB/2F8kRx4gR34ErA8sMwMjEj1QVzXCSwol3WlP4FkrYKFO7KZONFMndlIn7qEusuNIF9P9HJK6wvHMwKDDGKK/m1FERUkRFY4iKvxFVDhk4uIj/pdNaVSxpJKHWCpK5DKO4JLlM0Yt4bC5BxJoMrVZ2/Rr/orF4+7C0a9AqVrqV6Ck3HxvkrKnTHQLbKutBQwa69AV/BDbYYfAyRbuq2bpYzMV4X84cCVPBh4jBD+A19RW0vLHATfjSbFRPrDtt9Xm1U1DsotpPNfn1YNSw1c/qg+Amosv3MDpT38mNj89gsZcK6v7XUvJxSs48/uf89133vffcsdfWm6aVjlGTOPXtDmqRBL5WiZXFAvIZZSvyJsybc6w37nqM7OQAiXu+wvg2vjbH3NcsnzptdNfcdoH+tTvr5obB7LozOn8Ycj1LDpzekE7xNE8r8kc1i468Dx47bo/3vOFKJkvhGO+EP7A44lma9gdhEd3GFTFuo8fJbC9fj5jNJ0/CKoTJdQJB3XCH3hM5H0avP8WlH4ShSgBkMHAs7TloHc90Q5J44FMoC8wjhOzDiXp72QDg2OBIfq7EUVURIlyV6ZeMC+MRL9s4v7gpPmaG8z4+SqtP88NUnrp2LyNpPhamXP/YgZVODC/6w+l2uwuLsR3mYndxYWs+fY4TcediAJZTRJ12MlodrNwSWlCPwNLCmbiC3wEbtnzB6xeNyV72mbR3+g3Tnfbic6y2d/5+QemyAZguRVO7Fn1PP/EVN9l315/JHK7khuXk57WTO2EXN7YeiUPTf8lK7MmUd04CI8nw+T3p6QB30CZxk4jgR+O9rrryGWUL1s/PTv7vy4htjOJt6jm2nY5mHQ5ooqSmfe/NDXvL05z8P1Vc+NA7hywiOzUekxCkp1aD0lGRcV7kqk/XybVg9WpP18mxXuhtEfNH++9d2wLNkwLGyCfYMK/OwiP7jAwCbuPeW4n4/atkxPqVucDF6rWu5M60Zs6cR5tBb4HgI+TOFbnDoLqdAI58YW/RfXvN1HSaUajdPQG+Al1om+7zil5up1DkpRoficHlhsYHDeMnP5uRBEVDiJySy/d+j5zXn9Rpnlb1NOtLSh558GczFjuPW2U6hRLSoH5K78EaurfzOrjMdt6fu9bb7Gr5xBM0scfN9/M6IObGfQnhxJ/ASauLWPpY9EFt69ccxvX/eMNzSLdWNz7ZiGnfaXtsHNv74jzFQLz+PDRQNA1KPK44/b9Lys/mkgvb5s+d5utvFBwF/cP/Q1ekwUt9PLyG3OtvLH1SgB8PlPL4mVz5fv/d3moqPqPL1+PEG2fucd+V0qf3i4uv2QtP//l07ejzEmsAoKG8itQclh1i6xPBURVdGHcojOnB4V+JAnn22vtN7gPOVSpm9DKsa4Sq/UKqKsflidI8TNdmB/eCQWyqvM7oXP6oe0+5rmd+aMPbSMFv/p70gdoeQ1L4HZy5GtJHOM1nX21r4akTrsWhnhF+nViL8rcH8D3yZF/Cyx/Hfh+YPlYcuS6pM8pWerETpQZhlbgaaCY7uqQZGBwnDFEfzeiiApNUX7p1vflTyueraHtB3gNMBWNH1I94V9ERcmy39z1mv3w/qgfHIe3gEH1DgA+fXsIV1/0D2pt+aT4W3l1y22U7FmpuPTc51f8fSxKhL9wX7QodmQXMOhbjiSvHIafU8b4X8wktaltEGEWguttMryBFgQag/lD4h6Icg1qNNt48vQfM3f376Jch/wmqL14ID++6En+8p+bNc9H14FHwKr68aHnrv12/6yfLtuP0g255rXFN2Woiys9HitWqydY/Ku8RsoP2uOBVV6Uy7gr7g06ThyrokNRFV0Y94ch12MSmt9fugXMiew3uA85VH8mdL7QHiAD8mHZycXPdRoC+3jOwtg1zQOU9277hf9xdzxKCH0RfQD4CjgDxUZ2E0qB6XqNdaPQGfwEaf8gqE7/fRqzSL9OrEbpBK6s29a3pZg20X8xOfJfSZ9TstSJRk4mhyQDg+OIIfq7EVqR/gDVlRQXhp6VxojuqB18AtE6CfmugVlsmnKeuPLIBtJb21KcG/02ZhxawsomJRrfp6WB/alZ2LyNVGwuZtzXbwJQm57rz/9erYnBwDjwXW7CpPHeUlt4xmUwSoy7l/J0+P+WccXTpWR+VcOh/vm4rxjHb99+NWw2IdK0p9Fsw22ykq3RF6BZWEiTSlOsX034OWf8ehcDtn/FJbdswtziZ/83TqOo5PWoDr6QWKQflFmSW6b/TXGWWUa51o97U3Ma77z/XUaP2ow9q54/vD754Otrbgp2L3tQLiN+/pMGiVixdoT2RGnbK/COZ6Rfi/lC/zPWqZF+bQtFRWAfL+Gv46REwOb02J7MMaa9IjoOMRyCfMCUYirL2zX4SybSH77/I4S+eUM0oXzFWlEGOQPIkU0xj98Z1IkWOtkhycDgVMXI6T/BURcLjupxa0af196N7ND0/+ydeXxcZb3/389M9q6QSSFdkgCKUCkUrCvLLZZNhbiGe5u2QAcK2PKzKFfRhmuM2ipcF1CKtIUUoU2R4Ba4oCJSNgW1CBQKbjRJUwaaCW3SZs/M8/vjOWfmzJxzZs6smbTzeb3ymsxZnznr5/k+n+/na6XRja8LNVQ4FSAq9vaI827dzu+nLGTfdA9BBG9PqYgg/ADvFJVz9Iifx/+0KET4B1yl8jczLvwJMMDrgLBP2g1V1o2Hk1CjBtPU9hCw8+Il3Pr7dppeCXLr79t59TuX8uz330//7FKkgDHhMr2NJwUGbAuBFWmEf8BdSvm336Zw+hjd53g4dMIkAI5+9QDej26yXHfNyWuJdr4ZK3Wz86aTIqZpSb2hvAhdf9/tryAoBd3+Cl7/x3u4aNGjVHi6OdB7FH946vzpAC5XYBg13J8wNMJv0vIaqgSnAwklHaboTGLSp9/fvXQ4IF0jUcslqlm31b0L0VQvRFO7EE1B7bPeyXrxdvjG9dW3D8wrHZMVQg7MKx174/rq22MsnovuNhMh+TZTyFQOgt2xcxkIfzLafGfXqXn70YQflLdXKYr4X58Vwq+QVoekPPI4kpEn/TkMLSkw9CB29w97arw/EeV3/8EPyEV//YP/Z03LBtoaLt0SZa/p5MVkIhMlgyN84L4XuPe4xfj/r5yK7/tZ91ADi7u2RmzknSIPHz77OUStRNRKJl08IK6ef9engc1AgD5Yc/VaU9JuEEH1YAejDxUQaBPsfqyGxV1bWdy1ld2P1RBoc4WmcTaRqWRROHPqdq6ZuZ7e/5rKIy+ex4Pdl+DG4QhCFMoCg5zx+N9wDQWoeNrP5H/3q/YWCqbO6rVcZ9vsJaw4bSPtpdUEEbw1/Rj+dMv72PO52aFlhoaLafn5Mv1rtfASFF7aL/W2seord3f815W/puXnyzj1lJcRAvzvePjm/36b3r6jECLAqitv7ZPN7EnqR2WHLCZK/pJuk+a/H5EY93TvuV63CC4nwQTmeNsFVvDeJrDqNGnEv1FKy/W06bZ44/rq26t+vndVmW/ILSSU+YbcVT/fuyoG8c9Fgj0Rkm8zhUwlR8c7ppb3TlfvrHtjduQrra9TixECp9bPoLT8SQUjkkS6HZLyyOOIRV7ek8OIKSFYg6kaLPqwv4L1PF3Tb1PhVAKjhW6KRgOhaf3uMo4/7w32FR/jrOFalH7xdq0q7tsdgLDUwA+JQgSCYhkO2A64SrlqzSZl92kDK2nHx+f/nkldg6ZluwvLKQsOxqwEbIV/XVnD7778UVOhLb06bnm5n54eDy0/X8azzy+0nW6BAVQHafn6W64sq/B0s9c3i29//5v0vFOB2z3Gqitv5awPPeVYmx4N4Y2hVW9OT2c/0cJVyRQRGy8I0dSOnexHNtYku92BeaVjZb4hU97MQGVJoGznYIFxWiu19Z94+7F7y4Lm5RnPaslp1vRPOGQgxyKuVM5GVhSUAvdNwZB8MKGdRv4Op7772bnuItvmR3nyg7W8J3tSozzymOAoiL9IHuOIWFE++6jpWllDg4DoF1NkEm8nFqRGQAThByWRef6pD3Lc+e2OGr3491tZd2cDVQc76ZxRRc/Ucir6rCU2JZrEJuJHBAdZt7EhJukvLzQXwNp500ks+OLLFAxGdlhWz1NFxO594XIKCJjWG3SVMFJcxJSRgwRK3fSdOJmOS2fz98uPp9zdzc/uriUYdLFz1ykcX72bKVMOopemqfB0c80V6wF49vmFdiTf9BNRSXIrPOXdW/+9+12s+2EjBw9No7hoiC+uvJkzTt0BqUVOLc9vituMhl3H0y7qmY02pQtpibCbKgC/ZUngKY3qCOgk8OUpJ7sX9L4cfd2Or+2mX7bgsXi+HCaEP27eiSL4af2tdbS1tFJLjP1a3judB6ogPFrmvE3WuSLxkJ7rLl6nydy2CsIOSR8D3jFsLXmpUa4lyOeRRxaQj/TnMOJE+u2iM5K1DhLKrKN1toiVgGu0w+wpPJqpYwcjIvfRybWO9icE7ift5Tp2SZzTNvdx4tfbrSxB/bJNlFs1Rf9txkj98FAxJSVDGOtOSgl2dSg1950EfyWBL1z9v+5N965icKiMKZP7uHH1tzjxhL+DOmxLL/W2QRLJuAZNvylymIFkXkeJuRPFnhHSE+m3+r12o1HaPbJEJ87GUZQ5A13MO/g6ZcFBBl0lgbLg0GXjQU40aVPktfjmN4ieNpGJU85eoxYkvX+kjBW/2si2l5ZAoiN49gm+RgyjEnrLSde5dZKYniGHJAftGEGVdUzf780jjxxDnvTnMAyaftMDsnENtj7hEQ49saCI/9a4ywHtWtXcaCzu2mqyw0wH2o+pDhX4soKu6S9xhfOah4aL2fjTVfKZ5xZaUfP1sk1cjFW0rHSOrD6/M2KdbZs+hdvtPEdAc+lxvLyOqVMO0Hdweqz58jv/c4OIkgz5gdXxyHum3HsS3q7B032szN3zt3Wn0L60KvRidUqmhJeLgUuB9wPHorI+2lEWtbfI5ogIYErQCK51p0k2Omqvlfxp/pdf5l2bO+w6wSH3m1yTQlkdj6WfeXF4862/FgUFssiw6Pg6C6WIRCVrWYVP1Hf1zrp35tQ33Z0Hqljz2Fqd8AN0yGZ7tymLbdldX2C06Ez3eXTiJpQhhySH7TAiuWvZJxagiiuejSpqdgB4FdVB+U3Cbc0jjzQiL+/JYTRK2dIkzMPojVK2aPKdRKQVZrJWK9fINtFBnIdfrKq5615rcET47aL9Vpr+fncZa5bauFRqr4NnexeChPpj7qO80E/PqIeWjmU827fwEOGiZEZcjI0c5bcVF2wG/hMI+ee7XIklBcug4P67P8mhg5NBwOTJh+jp8fDXFxewYP5fbfX/t999PX1/tyf9M4/dKyo8akTDICXyPPv8wq3Cy23EIP/adNsXVizybhfBtxhB0F2BsGxH1IhSwUDA8/7rXxp4//UvLU1CDnIdytPJiPdqf5cKL6fLZqwzrxOElI0tQjRB9PFxSPg1REiB5jzYxXH3d8Ua9TIun2tSKJOc8Fs3PlFcUGAKGiUuNckt5GLitEKlbJnTACT43LeB3fWVac2+k+ObjWvfyflU17LPQspm1xHwiWWofC2jXK8CWAg8C+RJfx7jinykfyKjwUKTaFN8y07usfHFqzav6Lw7opDXsCiir2AK5aPvxK2aa1ekKhrdU8vpL51M1dsdBF1uXMEAnaXVoc6EqVrueUtChb5CGNM+jV3V6N7EKPBbVF3bSKjhb0PU+VDJpJ4vrL6NzZ+4spyDBHkSN6+rRN0vXP0DWymPecP2sp/oebon/7ln/4GS4shRig33rIrICVh/y5XohN+IKClRUpKdWPKfB5prsZt3qbfNdoTJMtKYRk934eXXQBdwN7ALOI3I6sU3yGZ+4Ghbuyw6PHPTS1Sjo8Z20h4DjJH+5GUmaaqWa4QQTaboa6DrG7is467pi8hmGeMe6Xdw7tIygjde9R/sI+wSWEqlbInRts2o4E3qUjJnkX69XYOYZbAHUfKnsBQI/gq8DBQDe4CVwFPa9wVAkEr526Tam0ceaUKe9B8hEN4YRYjaxBp0Iuye1PP5U++YsmXOZcVxNikBYVekyoj+4jJWfGUj287XOg72xDwSxuJcfagOgJMMhF7UKyMSEaRUI32RL5ZROLNzO9dcsD6CkNtBSpBS4HIldg8FAi5L6VB0XsD9d1tXnLWQEiU2tE/s60Ej/aZ5Q0Ml/stWPuAxrwLYaYptXKL0fZEAIRVepshmDkZN+28SrF5see71zlMaiX80cf9cxUNYFxAO7994LJIqZJYhZx2rHIfdz/+QmjmWAyvj5yyUIsZV059tV6Srxe08w7V046aCAGdxJxvldWnfjxGK0G/B+plglPhEX/v2VeaTIf4+x9JWPYE4HoaBJ4ELtO/nJZVrkEceGcaEjMYcEWgQ9Zr3fjDKgz9Z2A+r+mULflmDX7omv32oYsucy7xxtjUA3AF0NJz8bTngKrWlMmMud5jwSxQhtyP8J6EMR28g0nj0+9pnaZxW6TCXlbEa/ja7HxVC/fn3xSL8v+t5pzygF9X60cYvJZ6hjL10qLw80pFIK+xlgsX0iHMrvNQLL+16XQAbH+9Yw+yW84qLh+0IPyTn3a4KDHmcXdvRhF9DieF/pzUNslHDIFSIDc0jffDYErN1lEIAC2JXR1tLHW01dbS5tE8n5CZTv83kT/8/N587PDYmUi2OllOIPmckUfshBWTlugRUB+MXLGcfbiSwDze/YLnTezEufKIen2jHJ4Lap9pubIIefu5UyhYqZQ2V0qV1BC4mncdGtcNsARcJiXOOVAws0v4fBS7EJ3bjE8P4xGv4xHX4nI4d55FH5pCP9OciGmJEfKzkOw5kPjEj/RZR4hjLA/g3vnjVz1Z03q0Ptfb0uSZ5pgT7IziwKcJvHYFX0CvwGuU80SMCK1AVeuNB7aeDGMPfYpe1j/39J1tH19ESKKNlMXYSnFhwGuk/84PbueaK9XFlQIBfNisfa6euPTHO774Pv/+ZSZ1d1ZMO9B7FWMDNDM8+zjj1L5z5waf4yjd+ZPWT7CVGikQ0o16KdkhY6qP9hkrgb8AxWhtOclLMzO7cA1LOzWAgxCNuB1ZZzFmPP00RVvuRFYk/NcnNkeDeM67I4Lmz2Fc7aZLdmRBPOuQT3RjypyL2bTdClInkXme2pcmYz9nhFirljWnaVh55JIV8Im9uIlbEJ/KFau4gqOhpgyCK+CfqqW61PACLu7Z66ve2GMmLxy0k66s/z8X7HlHa/BlVrLlmbaSk52mbPYF1Bd5C4KOEJT6DKF1/PE3/n/A7kLtYJov1jHioKLYk8Z2gklWFGgdZB1S99Opphxad8/sp6dD0G6r3AoSIvcOCXzqcXjt218Nbf/rLWREl7ffsrWbP3mp+84dP2O0zXk5BvJdmwkmSwssc4Hcowh8ELk+gevF4JcleHGN6umQVGfttWhJz1HluxDwtjySRzesykwnL9s8glRRrZbQwQuwRovQfG9UBgXCnNYEt87gAACAASURBVIhZyiNIH/H/b3zi+1TKfWnYVh55JIV8pD8X0RAjqhHtwd8QI2ITZd2ZaAKYYflILa+Njj/C1jNaj/80sTX8N2D3iyOnjwEjSEqBUXp4lWkcT2FoP88wzBzu4tTIhK9orbadrvuayh9vXnTUY5baUashfrvEP43sd/x79wl/nzq19/zyo3uEgbQP/MdHHg9c+qmWKQmQ+VgI6ekTqcRrcT08DHiB0jNOe/7AyuU/Gnu7u7L8e7evCe7vPdpO1xo7n8A+ohixjUSii8LLSSjCPwd1RVyeSCJjtjT9JmQjknukV8udyMjmuctspN/+Orcn734qZYXFdH2bmU88jt3uHqxHJ4wIEl8O1It6K+ZHxfIYF+RJfy4iASJv2UH4J/Bn4JDBbznBl4bwcjtwLRZJTIE2Yflkk4BExHX8MSJU2GtIGx242jA6YIcR/HJ+hJwlTFwv4GFOtU74siH+6+oLt1TdXPbV4Cyx1yUEnW+UVj28Y/ppEZ2GGAWn4vqpW3W2tGUSrYhphxDxTlTGpSOWLAiYSThZNhqxawbETuQN78N5Mu8C4FHUC3gAqJPNPOJk3YjtZMG9x4RMEq3I/aTdvccIS5lPYlamedghw+cuaj+Z6WDE8uInRlHJeDKdTFfQjVdDIHL/0b9hBOXFf3oCe9TJV74DkEfWkCf9uYhENP3RHYR/okzCxiKWMj/MtZeLhKq9JbOCN879rqtl9lKdkH4Ea+0xi7u2suWFZXFtOvvdZaw4bWNM4r+4ayub/+aN8OgfLixi+VebYxP/fyP5JVuwKtK0knrKmGOxVoeca0F6U4wg2UX6h4ZK/MtKHrCPXGEbaTd2NuxekEZE6OmTrcQbq7MA3AV8y7Ct6I5KLE2/3Xb1bcckNVHHaB8qSlaKirxdLJt5zm7dnMNhEIVPR9GyPHIEmepgxHqmWowcaxh/x6dE3gWRHQA9wu9HefInCz+wetzIf76o2BGBPOnPVTj14I/uIGwFDllusQO/rBFeLn5v3ys3jomCM98uPkaMugqpGWjnwn2/pb20il/MqhtAkSpLsunEolNH38xJPPrSIlv5yr5HPVSM9pjW655WzoyHYhgr/Iwh9kS4toQxDbgMq7RR6yRNJxUiY6CV2tulZJVRty8l/O6Ji0bv3rLyilQq4AovfVjrX8FQNdOUpJyEj3csWRCKbOvJsu8Q9sU3ws6nP2mia9OBiYUnZTMLHS47PkiSaLVSewXKp9wOJ9fRFs8EN2VYWXdq6JCysSbT+89jgsAuKj9e9QGctzM68PJ3lCuPG+WydSeVWtK99W9xIvGJhQHgGdt9ZgrWRcV0rKVS3pTR/eeRNeRJ/+EAYwdhQ4zS6n7pEl5+g7mqKQBzBjrZUxY7j8tpMS4AKeDB7ksAa9eZYJuwZZmup6R1MS4YYBOv0cvz2BVpWogqhRIJu0h/Sq4QdpF+zYknGf/8WEPIobZZeuKnANtIfxEBRnAjgI/RzRt4eN1ZzkAISRLdOA5SVsh90p8kcoj0h+4X92QX5+46gdI5oQz8HW3Ume+8PIDDRBaVjmhwpmU6ThHfZcjObesgSvpqN2qRCazPGPH3iRPJFxU7YpB37zkcoEYA1ENzg23kWnc5GF65ez1Xdt7N3IO7eGnqaXzu/Q/SVTonLuEH6CytchzpH5gVNtYvKR6m/rP36aQ/vhtCL2G3H2NC8DRW0MtDUZ7tzwsvt6Hpzt/10j8Cj918gbtqn8oT+J+rvjm85cLL7JwhLBPLunpnBec0UO8gUm950DTP/YScMBKIbGfCzcPs5lMAjODGBXwMOJkK3q31+Mz00r5NfsP1mRjsjl/aOz0TCXW0xbx3MpyvELpfTv7ODCPhzyMGLGRR1cAmIZqYMMTfOhpcgQqzPAs4I/2VST8P0o14Tmd2hf6m4KxyzBjqWKXD+WcVPqF3QPSOd7o6TF8gPDa+PKqo2KMpbjuPHMMR++I8jGEqokOkNefS9Tuv6zij92+UBIf54IE/s/qN26K3YRvKX3PyWvrdkc9JabHCWKmbnTedFDHNUHxKAPgLyy334S88Wj1SX9f+wgW6OuRcWuIVaVr63BZq3u7AJSU1b3ew+TteIc+xfe6ajlf/SBlf+e3NbmCTTWErIyzJrlZAK1FybvUSikZGih9pnRtjUaJgyB71U8DJ2oKFCM4xne5MFWRKpuDXEQ2DM1E16j5T5HJX3OvYKdYAA9M/WErN549i7JB1obk8TIhFMOPDJxbgE634xFv4xAg+sQ+feAKfuCjdDbXZ/4mo68qNigZfghJTzgA+TmxD5lxFPNvSWJV4y1DkOxYKMBP+dEgrXITv7eZQ4bPkkS8qdgQhT/oPN6ioqqmipC6n0AhzBNEdckXI40dR1XYjU4E1bJu9hBWnbaS9tDpE9gXhJ5sERkrd/PWHp7Lnc5HS7+hKsqvn3caQiIwUDolCVs/7kb4pI2yJpVak6TqA0sCA9HZsjnhYFwQDRVi9XD2innmsYx6lY+e5CT4M7furWfGrjWx7aQk4eymvCQRcEVVJh4aLuf+XS4ft2hsDsUYGQucylTyBaBir9wK3AdNRp9NFCXApcHzUSirLIOL6SmebDIjXgc19pL+yNneNLQ1sk5+Wd40tDdw6/Pk/tlJ7imF20uSyltb6Wlrba2kNap+mtkrZ2FIwxXXt/M0zR4Vb8M+13fujlxG7qBe7aBe7CGqf6epwTGQk74uvIuzPAZ9D5dYUEo6wn5We5sVFdDT4YSplH5Wym0r56ASVf8QLKthV0Nbhwvx8iod0E+hi1HM7FejXYCHwZaAGKEIZb/8Y+G6K288jh5An/RMNkSSiW/uLJBR+2YJf1uCXLu0zkpAZOgZvFlfK247/gj5HAl+SzVwHXI4idCZsm72ENSevBcxPMAG4hiT/vOSEiOlWxae2zV6C9/TNtJdWE0TQXlqN9/TNRsefuMRSK9L0B7QiTT994XLmDHVZNTvy5RpOMK0GRMHbAQZvKmPN9Wt1wm+9XhTqaGtxu4PLh4ZK/FIqLX/z1qv9T//pXG8SRNjuJdQhm3HJZmrSTfiJjAp7MNY8HkINcn/P8Hc/IOjU2mLZJmNHQvt0Rvo8oh6PaMcjgnhEu1QqFlMHNkMdjPSjQVhG3VMl/tMK+lwFIsC0gj7XrOK9Hw5I145Wat+vzU6KXGoE39RWK+L/sb6TqqacXFwI/OLk7xxzhnFeFkYaJiqSG7XKnQj74RgNjhdUuDPO+p2o51O8zkGmEa9+QDwYI2+PonI1FkBoRP0GfGJGivvII0eQT+SdSLC28jTC2tbTBoaqpiehhir/UzbzYNQypkTXxV1b2fTS1UwKWAc5JHD22iesKskmUtkwbhKsZZGmNmFvCWf0QrexkowoMOawHelCsnabKeyvnUQT0WYT5L9YZqcRT/o3HAZ2liYkUm8jDlqpPeuet6789Y6D7z/6nTEP5YV+rjhmE2dM2aEv8ngdbeeJXTHqNFglsmuopdV2vTbqagzLvRuV9DeMEn0VA7u12Tse2lXnSWb/hzuStjqNTCY9L0pvnT34RD+xpYe3UClvzFZz0oZ4ScXq+H8ec4DUmPAbrxZJIuhAOQhZ1sixRaWM3H8iydI+8SZQqX37FJXy19r0X6LEnQDnUyl/77g9eeQs8om8EwvxNN/GJKSYsKlq+qDFoqZE13WvNdgSfoCAcPPs8wujLTrXo6zQrAhBdGcgroTDtkiTinlYvVyjt2cZ+awajAi8JSUlcZpI2UptxHIPNLPmUm/bCtO6mYtsx5cWnAT8BwEm40I4SgqNlxyX7vVskQNuKclLOqJQR9szl77DUfr3t0ZmssF3HRumLNcnfUj7NCdkO7uOnbZ1Ayp/ZnUbdb5aWmsS2U70NU+MwneHE6RsbBGiCRK/HqMj7HehiuW9gXqmrqcyK5G76GjwEpTw7wmU4O8GfOL7VMp9WWhL+hAvqVg55lwXh0TbVRlOFMb7VLcNPYS9bbMOlSynOihWnYVqYNMbB6o/YlN0coe2v3hty+MwQJ70Tyw4IQtxl0mwqqmJREQR4whIYECUsLhrqy7TkcAdspnrhJc/Rm9L2/9mIr2RI4iuhe/8FpTGdArRRZr8sgWPgPgWkZYP6jdLZgZQUR1TO5wQFoO8IdKlYxcYybK2LdNyDzTXrqijrSa6XRlC/JeVSqZ2oyK7TjogyRLdtBFkyBm3FLvjm3Aiciu1LkbaeiiyHcqf1Ept+wNzWXPpLouOY3z3nrhtraV1EXAu8A/gL7W0zkcRUB2l00veeat/ZHLlaLDItB27a76VWo4U4k/iHdhovbUOXW89B2WhmWn4CUeDN1Ap9wM78InHUdFgN3AqkPZo8LhUzzYiftTcqqPtBH6gP2K7CsZtxSP8I8DqGPaiOsqOGe5eCYg5A128r+/l6gIZ2CoRW0VkQvI1+MRTqA6d3uHcD7zg+FflkdPIa/onFpyQBdMyURrrt1AevB4UYV4Ug/Bbubr4O0vteZgApgb72fTS1dTv2RIElmo5Albb0jXa19lpxC1059XA1wg/DMuBPwkvMvRXK6+OmdOgYNJzjpW68X3Xs/+B5tql0e0wEJYIrbI23QiniZSpuXmkB1aaVjs4TRhL1nEn3U49uXp8HY8etVJb30pteyu1wUDANbi6+OajKsVe3IxybNGbfG1OU/QqquM4txY5lxo5F5f26YQgOWmrfs+diCIBfwP+zzB/7tnHP1557NS9EYnthu3kwjmZaMgVvfWO+IukPxo8bjkiPlGPT7Rr0p0t0fuPcMyptDTPsHKYM2IAVX23hkrp0j5bcObgFjDsZ5O2TizCD0BZcFDMGejiA70vUigDRgMOIw/8GKoA419R97sErqdSDsXbfh4TA3lNf64hViXeJDT9mahqevl59/TdsXPVlFgSH4BD7kn+yW8fiihLnujwflK6c6dFmjyifqzMfZt7MOAZmFXKzptO0h2HBoAVxnbZFeHS0IH2O8Qu+8q2xorArdTaLldHW9Y641GjKD0oUmG7f9lsbnOTCF+zIwWTeh7+8B1TXn7XZcaayFnX9BuLSEX/BCkbsxfscFpZOwoWUfFE0JHMaJGWtBvR1jbqWqKmx9Uu7zlQveHFNz9wEVGR2Vy55icUktFbp6v4VeR2/CjHILCW9+wHZqabHCabo5ISrIt2mfYfs2J7bJ1/B3bnxFl+gCoc6aydIfS7VFmBScHBWIuNEq4tIIC3gS+NS/G0PDKCvLwnl2Am9brbhyrAtVa20CA+Qli3F0QND07GnlA4iRw4hvBST9UVRaOuQta91kDVYCcCafmUmhzojzDiT3J4PzSssLhrq7bPDoLCTYEMSBKo8GqCX7b8ktp1mN0PrLTksWQmod8BbU4lHWmTfjiBhURKj+BGT9uSyHY1wh86p0Vj/Z5PPeMdcQdH/X878cpyLGRSlm2qlZ0bX7xq84rOuyNkXikk8Wb1+NpibRzNsD0s79uxMTcjo0UUFoxSUDCGjWdKUpKoNupMbTW4+pg6Y1qHoAZDIm8bdQuYjjJ+NSM3zkk2kToBT0xvbSaCemSahPZr3k4FKsLsJhwN1pHJaHBaZX8O4eSdGW//dtd67M6C0/yA8HXl6N0ugT53GceO9sRbNLra3jHAVnziPuAnGasKnEfWkCf9uYWYyYxt88Xt/97Nyr4+xLSpsGghrnmn4AaWxogeWj6c6ru2yK0vLBvE/DLfGKd+yDqgeNvsJSFrzdGHCiiQlq5l0cVLbH+fIsyWIwCdQHW0Y5BL7S883OoRJEkQnb5U4j2M9fNkm0jZSu0VqPwFO4yQAQ96i9EeVdRFHb8iw7RNqGi/nW78oDbyEjpH37A4py4ZKPrks1f1v/DMlRXmTdi36er5dy2/ev5d6XIqSjahNVdQNefBLuZ9+3XK9g6ij0R1fHYOV1z9MwDW33IlFZ5uq3XTSaLTmWA90c9JYkiWgJsj7Dqc6K3Tdb6stuPW9vcWcALK1Pd54OYMugqNR0fRSYci3v6dXeueqE7hZ3iYJpYTm8wLwzqOIIBjRnsYcJXGi/TbwYWqCnwZcG0+8j9xkR9SzS3YEtAmIep3vqoIP0BvHzz0COx8xV4TG8sf/eZdXw1i8XLo8sy6N45e0tRGlzXhB/P1FYtg2+nl1wADcRyDUtEFO9WSO9G/V2n6aXPegjNddaa8rq1e4MWECb8OfZlhi20EteUjzpFMPhKXUX23ljRpPg/Zde9JGjVbOnsWfPFlJnUNIiRM6hpkwRdf5qh7ekPL3P/LpcPRheFIP4mOeX7bqGtvo05ofwtibUjrxJvOyWGcxJv4NR7uKOj3mR5hB2d663RFxu2Wn06lnEulLKZSTqNSXpBhG9HxKNAXj9DH37+1zn9FBFn2mM51Nb9gOY1sNqxnBz3w4hgC6HeVDKYo6J5CdE5DHhMK+Uh/biFWVGPd2FgkKRwdg8e3w7xTbB/Q67DR0M4a2mvZ4ZvZ86YbC7eZWG3sLK2mZtCyjpdTSYtlBwRYJ5upEV6oGuzcarUDA5Id7nUUkamjrcUwGmEX8e+E0HGLR2Q6LLZTSAoWlTGQyLEpB5aiknb1iL8ebTTJoA6WzQpMHdhr5Scd74WU8WH7JN1ScgKnr3mFgsHIznTBYID53341yDUI/oPg05PPLXK/NdbjrdxEiWuofP/dnT1vrX4F2R/Y0oRYB6xplClH5NIaadUI/oQ8J0kgmWs81Qh7us5XTkix5FxaxC4gu+49Vu8EnSs7l2jFswO16xT+govZqEmAfKId+/OQkGuQAGaM7S+Ou2B8lAF34RP3ErYHDUtt86MAOY18pD+3ECuqYfmi6O0D7B/Eti8XYbNO54wqiB2NMkU41py8ln636bkzsOkTVz4sdtEudhEUu2h/fP/5D2P+fRL7IiRVoFx/XEjLXoWx6XHmWyKR6GMdbS2XetvW3NH8//xDw6ZnZ6LRp2xqVRM5Np2ymRbZTIVsRmh/FajOgAmPLfiuVSl6J8ci7ghL0pV9M4moqsFatC7tKBgIWB7vil6/4GIGmYIbgdjee77nstcfKLvvczPu8F31UpnsD3gwjMRoORc0CVHfJER7kxBB7TNmu4VoqheiqX3Hkq6qwEAwOjh4+Epy0otkHKlSjbCnKzI+HhF2S8i5tCThRpU8rKP0S6NcduzhEwvwiVZ84i18YgSf2IdPPIFPXBS1pJN3gNV5kNoy6yBiVKADiKfdSRfnKyXyvW3tbJRpOD/WeWjIk/7cg/EG9xN247F8UUydisT+QdwJsLhkK7sraggc62J3RQ3LS+/uweJh0l9cxpqr1+pfLR9Imt7aqDNl2+wlrDhtIx2lVUiQh0om+S//2ubA1TfetWr5MT+p3nbyp8QfXz2j+vvn3rjqcxUPlX1i3mOBOQ92ScxFuSLQ7a8QBrIXS16T0suojraWOtpq6mhzaZ+WD3Vdh779mfM9G+5ZRbe/AikFQ0MlfhKXKdiNsmYikmZ17IZROQRGxDqOlu3aecLSTiw6Td9YLjEQ9reFl6eFl9eEl/3CyyHUC2PUbv82Vq2bxpX4Ww3Hq3wSU5t00ixEU1D7TLTdlsd7r2eW5ajYu5/5zbVW04F1hmTriHbbEX9DjYPqN1v6xItXvSkGOkekVFZvHWhJvAn+niMRyRDn1KxrnchKsrmdiYpK2WJhpxkfPrEMeA74HCoJthAl0VoInBW1tNNzbbyG9Hemfh8vR0XXXVqC8FXEl6FmEmWoxF+JTwTxiT7tsz3tnYHEjnUeGvKWnbkCazvOkAVntEsKQGEBfPxCDs4/jWutEnmFl/qlJfc23znt88WTXOHnwJh0jxSIwHI2QJdn1r0ze950d86oYs3Va9l2/hJ9sbAlmpZsJKG6s7SKr528LpTEa8LJDPMxBC6Klh/zEy46+lGqft7Fgi++HCFXGC11s+OHp+oWmSYMDRez4Z5VelVfZfnYFlF0K4ihiFYKLi+OEcM+tEM2x7ePc5DIOwRcmQmNs1P3HrskWm39ZlQugI5hwBu9ToI2sWOoDkDE/lM91hmBx3aovQN/2JHDojAYHH/qMB/5xEEKi2I6Ghn2Zfk8WHLTfaUtFyw1dZQbT3EhrJ/lshB6Rq2TszsapdlJRIimduyOvWw0LZ9HDCTq3mNtw6jeA0cK4Z6o8IkTgZdRz8g9wEpUTZxiVG2FIJXyt6Hl49kUO7fkjHQEUuvdiXVhr4M207MJP6pGQfLXc6LHOo8Q8qQ/V9AgdPJjh46mdawpLeHOwSGmaO49zDsFsPDn13Hoa5O7J7v6LV/4rJU1FhVkQ9uTc2nZdMZVty/pallZFhwMEY1+dxkrTttoTfxXANPUv9tO/hRuEeTj83/PpC7zqGP/7FIeefG8iGlSgr+ngpafL9MJf6i940b2NAhvDA/+5vijZq3UngWcgqpa2QV8HliLGioFeKWOtnlpam5aoRH5zUQm/44Ayy1IfzvWpNGHch45DXgQ0Ht8N8hmfhC1jZSOdUbgsfXQlvhlqE0m0nzcKXDWJVAQkTfttHZBBGEUT0nLnJIvfXR2YOrb5tyKQvDbEH4A2Sil6VgK0RQ8c3GPqF/3JuVVI/R0FtGyZibPbivPbo2DIxXp8tk/nJGLxyiyKu55jhKcLe7xUADLXs8fDeXbb90e3d47gOoI/JHk63+kG4PAVUnWkEj8WOcB5BN5cwmxCD/AwUYVm51sMc/Wkm2yq99SG4yul7dKlHqZh/kdtwnYurv795RFWXxNCgyw7rUGa9I/NfyvS3Ps/Pe/B/nDAPQGYZoLFpXCvBIo22vuCPh7Klj1lbtt2zvOSCm5rY62Z4BnDJN+2ErtA6gOAMBxqTUvo1iH2e2nCOvrzu5cHSubVRKi8HIb8L/a9BMtls2JREKLfTtpU+TvX7AomvCDExtFv0UioLpXTZ30f5510eb3/fzuaKu/gYsmwVOD6t5z0G4AFi7393h/3OkpmaQCQhU1I1yzqYPCEhnX5DuPNCB+AujhCydkPl21CNIP3UZ1FLgQn7gLmAm8AawH1lMZFWW1usfDcPrOs34mKk99s6++L2LEPFOOcU5QipIC6SYdiYwAJH6s8wDymv5cQrxE1Z9i78YD9g+IuLpBeY4I/R08d/KkJXffdw1adLBq0Hr1qsFOzvzgdtbfciX33/1J1t9yJWd+cDv0hZcJ4uLA1i4e6g+Tjt4gPNQPO4dgYFZpxDaHR4rkA7+qtytf3plIMmIrtfWt1La3UhvUPtOhJ0wpua2VWqv7Tdr8n2tIJPHYiVa1xPD/HotlM55IKLxcLLzca8w1EF5eEV6+K7wcnUKbIn//pGl2TUi4I2tnCdv24F3XWU0/o4TyRaXmijvad8tj6f3RHnTCr6NkksT7I6vTlMeEgk/Ua/rqzOisU4HZrtQuMTRzdr+pHR/9fi4EvgzUoAIjJwE/Br6bYGucBDgSfyYa8xVUZWX9mXGQ8X0HeVCdgKAWyY+FdB/rIwZ5eU+uQGn6ozXTOoaBY1EezfalvddaVPqLkytgpSs0ynd6H57M1GC/abP9hSX8ov1iSorDlu5Dw8VsfGzl6DNzzi2kEJYf8xNOOOMGxjrNEf2pbvjw/aez53OzoyU9I6gHj/E4DFzyzFWb3/dPcyRz+pVVm2fedVpkFVcFy9+cql7eShsfS6KhdTb05YeAXwONEJLA3Iby32aff8ZL133lrulOt51NJKKxt9H0h+Qswksl8DdU8tUAcJJsNhP/RI91ohBefgNcaDN7N3C6bKY3Ymqs4Xh9u9Ga/rrVMNmyRG3mJWtaHsLOIXh8MDzadk4p/jMOScviaa3UWsqYJMgtLOvE8NvzCb0TCLmeL2AvZ4nWrNvL7KxkLs73n9rx8YkRwv1rvXqxEUGgkkq5L4X2jKBCa6HcoLSeu/BIixNZUbZgHgGIPNaPojovxwNPoHIWAsBMx8f6CEKe9OcSGkQ31vrbg6yVU2PMl8SqyttgQVT0ZW1IwanHlLPoMT9jC924pVkbEBTw8+5LTNP973iCK+9qFpyNYCo0zhMIq0tMwNygWr/bb5L0+IF+Y3u/sVlYPogK5pTKEzvPM74ABrQ/q+MUAC4zEv8oUm6sBJwytG070k+Ojhb0f/WbP3Dt2VtjHP6Ir/vOEoQXo4bSiPWy2TyEbEfYhZc5wO9QEZkg8J+ymQcz13Ib+ET9Rfc8etcJR/+7dNnp9/mOP+qNrx3znX2vEyfXwCk04q9+/7vm93DmJVNxuYwan4TOrTaqFXE8HXnwx0sWtEArte1Y3GuHKJO/5LPR91reyWeiwCmpHi84JfPJ/o540qFUj49PvAlUxlnqfCrl7+NuK7zN8cld8MUMQo43/Chpuh5J+RSV8tcA+MQvgU9p0xM71kcI8vKe3IKd/t5Kx2/EIVvCD7BWtrBW1rBWurRP47JVO4cwSXCe8/Uw7/+24rIg/IA1kQeOPqrHxdkInga+j+wts1YwFFQpbjs0XEzLz5dFzy6XzdTIZlzaZws2UoixrsHol0QZ9omLbsKVfo2k3KoScDpgNQwNyrHmICpqsxvY8MWGO/ZHEX5IY4XaNODiRKZrXv8R51B4OQl4FkX4x4BlaSf8Tnz0tQha6+K60vW11/GhOc9XzpjcfYdcK05AjbzosMo1cAQpG1ukbKyRstEl//nJClyu5URXB06M8Du23IyA38Z6MbbblUnGNIZb/o3Tre61XLk+JzayU/8htdogmZcGpVIdPbbMxZl0KNXaKTscLJOYnWay1qHpwXjq/WPBQ5jwA1xts9x4WpfmLPKkP7cQ76GXbKfADP0lA+LxQbNh+iiw6NYGAi77/OI5D3aZpvWMepR7z4XASYgnTm86OOoujegiiFKXnPHtk2TPO+UBgy2nEVbHwfLY6J2HBFBGmNhlThuqYPeycNfRNrWOtuI62o6vo+3aff5jZyW4jWwjpRei8LIAeBqYg3oYfzLtIxjOffTXAWVT4jO1VAAAIABJREFUig8Zp+nnPV6uQVKw6gQlsHpq16lftuCXNfilS/uMuW+rgnV/4kO0c7zV4rlyfeYGkikWlED9hxSRfA0A53r7VOCMzCdXQ8DJPZT48TF2hOCDMfYPamTzhTjLZB7OOm9Wxg25io/jE5vwifcRTvDdTy4c6xxE3r0nt7AGsy3iGLBGk+jYIfKhFE93HDXkb+PswbS3OrnTey2rfvUTU5dfAAu++DJAyGt/KFhMyz4tal8InA0vvX7FlKCrkEU7GpjW30nvpNny3zMvuKNt6V3XxdB+W0Vs1kQvK0pdcsbak6yiEX5tOTtZjUeL5me6Km4iDjS56FZjhOP2WUh7tgBfQGkte4CLZTPPZaCNsV7sEaNbViu/2VdZRdjtYgC4N90NTBLZrN4MhIh/6JjdR2s7uX19poQIOZYuR5ONiXVKVbGgzURqufViQc8Cv7Ha18GjCydNFqNOrttU22x6huI8EdTpvZU8KmVLlLOMvZwlcYcjp9VvnR8fs+beMkfGgA1UyqG4Lc0knDsfJfNsiVlsM8O4SvvTcX3WjrVPLABuBM4GjgYOAK8CN1Mpf5OVNiSAfKQ/9xAtnNErWtk590RW5LWKGgk2D322uC+4xiU7bqyW75QedR+GB9s0m6tg8lQXK4+5k4OnTrJM6S8YDDD3m/8gKAXdIxVseHMVz/YtDC+g2XfuPGEJt17aTtPyILde2ikeOuuuiyFU3dcUsbGKgmr65Yhlp9XPvmP6ktlWkaHV2rIB7LEOjbCMUsDP+Qz3sYz7WMZDXBw98JEsEhmGzrhbTYpw1D6bSroNhAvClAN/El6k4W97mtrolBybiOqeA7P5j7ueHEMlFweBy62Si8cJqVVpTRFNQtTPn7xs0hmuSzmlZiVHbX1an5VL12fSMFYgxlj9OZEqyqpY0CYU4d8DXIIa85wBfBw1ymW5rzJG7eSItsQrqTanVmU3Ox3PzMlZ4t9DiR8fO/lmNIaAdVTKlY5bmzk4HTV08mzxE3mslqLsMnMBP8UnAg5cgFLDBKwKnE/kHU+YE2wnYaFHHywq9peODJdjR/rXGpKc7KqGTkbltwNyQ+SGdE2/kekWFELtx0LFv0zr6AgicG8PWncfg2CTBinn/3Pz107u+MXXZ+97rqx4tI+houm9L5+wlCdOb5o6WjjZsVNLrERcbd5Wm1VV8jNs+jPvL/s7J4VmFDO0u5VlllqGRJFIonCm3WpShZP2xXD5iYUnZTMLU26gw4q50dGu17vfw/nNj8muvjkCNbJ2eS4dd6tq3GhJtI6SedO870BZEXt+5PX3XPnR1YdDEm9aKhA7LBZkta/d039IjbvXavHI6zbdbU4EuZ4EbIdwFPZ8QmUjQ0jNucg+8diI9ByfVBN647vyRCdLxzOhsD92uecAJIE7tLoF6cMErQqcJ/3jBWsrTUvoZ8jm6RJp1WlfNRSu0T63AociZ+0cgscGCByUuCZPFfKCk6Rr3tvacpNRPYJhTOgqmRWY89sut+0YxPfNk+e8/czAZb+9oKwwYLby3FPxYe752HYC7qK0ONe0UmvneATQ0UHVX57inM8VMMZY2M18Rxt1C1LZ75EKq0q6Z35wO/WfvY/ycj89PR5a2/7L/4T3/HhD4YkjEaca7cX0l64FVRfd8xv5zmC5S1u2TjbzSNrbliKSdu9Jfb/t2JC9RpnDZC8BCNFkX/3ZaQVin3gNlaA+CtwK1GFRLMhqX4uLXmbT5IeYJCIGGGM6LKWlzYkg1+0+rWAtt9LRC6xMqe3OKuamZiOq9pOqlagTrmHunER2NPTCfM6tQtX6dkG38cbLVMrTUtrCBK0KnJf3jB+cDg0SFC47wh8p7VGwHpYzpvp+AGRUNse8Ega+dDSXNUrpuuFCKeZ1EO4YHAJGIPrRNVBUKm/87++6EDYymlH8WHQVLvrzF0OEv2VRG+uW9rHjRCXHm9P9Jz7w2o8hfQm1q7HJ4g8iql/itM+CYIzCL6ZhX3lEXX9nfnA711yxngpPNy4hqfB0412y0ZNGh6Qw4jjVCC/1wku78BIUDXKdaJBbPvCTvxzSCH8PsCgXCT8oeVujlDWNUrq0z+TJSmJOMVnPJxgHpEM+5bRYkGmb20ZO5f/1fyxaKhHPYSm7kq/UpEHZR3y51X+moe1WksdopON8pGo4EY9rWMv0IqVWFdpfIrKrjzhs33jgVHxCan9OioFZIboq8G58YhifeA2fuA6fyEn3ozzpHz9YvjRffgVuvR2a1qnPHa+7ETa2mQAWVp3mB1EB8AHD93eD/8xyukpmBbB6yTxPkLGorUoIFgjaS6sJImifUc1VN24SLRcsFYD7zKnbWf+uK7n/5E+y/l1Xcva0J4YpYjXKmjKEkuEDzPL/FYCeqe/mH1WXMFI4hT+fHB55m/+vn8Y8RokgyokkAq/yXnqZLmbRNQD8KtV95QFEXX/1n70vooAboH/PjNWjjVONZa6BDGYr1yB3YJHz0y8Lt3pLPtltowfPCrkUouloIZpuFaKpQ4imYSGa3hSiqVmIpjnp3I8N0pFPYyx6/CgqoW8B4effDfjEDLt9bR4+Y3UiDktpanNiGF/7yETxBcIe88uplA9TKfuolN1UykfTIrswd4SiZRPm85Gc7WmqHe9Yy2Wy8/b5DGwzExDAKkMnwGkuwISsCpwn/eMH00tz5yvw8CPQ26e+9/bBw48I/viGrSOn+cUbHe0swB84W4zx7vAi/cEyvnTMD4bnXNB1meVLpt/6uhAjkuPOb8f9UJDjHmxn2/kqSeDMqdu5ZuZ6Koq0aG5RNytn3iYemFsLUTajBYH4CfUVB16lYGzI+vclgTraWupoq8HwUO5jCjuZRyEjfIjnEvb9zMMa0cnZ5eV+u0WzHSk2R7vEEfn4Mx2HSWKUr5c+6cGQCNpKbX0rte0z7zu9SpS64pOZFCBE0zSUu81q1HVRhCpytBx4XoimjGqDNccbs6FAYu49xgt9A5VyP5VyB6AP+buBU9O0r3S1+XBGdqKwxo6QyhGzHwlJ3vY01Y53j810v6nzpjol3QYC3I1PNBhsaMc0Uizxibdt266mT9QHrItwJyDWKIDTjn5OIW/ZOX4w2YM9vh1GoyPsQ2P84UnBgvcUUTI6Ypxj/+L1R9qZuc8St8s/spJhpRKShchzKp+6674XLlPLmC0+e7DQwXeWau/eqZHT62fcR4krMprrFsEiwg451aCkHos/cy/7nywm8NYw5X3/5MTOh2ivXMgHXgvfVy4ZpHTkncGDBTPTHbUKteU5PkSAAhbwHGUMHha2gwC1tE4BdhGuKpv1/ASN+LcA3DdU3F1SMmSVU5HtY27dyZAS7vmm/i3hJMhcT762gOVxqHL1giYZaKUWtGfT9KWzQcC+r70ux/YMQmbyCb4OoUz6W4CbUbYDP0KR/++j3DEyBo0sp/KbdmBfwE7HQJr2RTq3c1jBnESqR2F16FHYOagE3/RVvY1vI5qs7amVlSjAJHyiPsW8hGjd/nQieaEH+LbN2jOItvtMLonXD/QTfjblkixGHwVYhTkh2E+4ArPq6MMOfOJxVFVgN3AqkFNVgSdqT2ziY21ERB4IR/ijEXzrELd/6hq6p5VLaYwixKrCq8Mj6nmdFWIYIVBX8OTRAbGi8+6rNG2vVfRhKlFa/H53GQ0nf1sNYUa1s7wwZjR3DTCga7tnVPiZ0fSe0AL1j9eyZstU3vePuyJWnDT41vUZIE9rgAEfx/I2xzKVXsrpGfon79oAzDUsV1pL6/xaWo9OZicR2nH1mYnqmnb4DmHCP+4oKRmyyqkYD6tH607G8ADUrYYrvg51q6vtzpXVObWxJ92U5fOdKCyPQ2cwZGxSRRQ5mb5kNid2nifmyks69XwCIZrqhWhqF6IpqH0m9ZuFaBLA5drXAeB/pGx8R8rGH6OSYAE+KUTTUclsP6OwL8x0DT5xVL5YUJYRGUk34iXsorDZKTqmIzmZTlhGFP2i9eCsrXZFPT2oRGf9t3uwDwQHgH0W08O5BfbHPxaGgdVaEvEd5Bbhj4beAdAjlOmvwJwF5N17cgENynHn1tutif9wtYdX2u/Qv3a0UVdjuy1z1N7SBlTf1iH3pEmTA/1W8429705gjaiVqgd/Eqrirja4tf5dV1JR1G25/TraaoSX+p98b/m95Uf3hFwUerftpeeWfzG86yByRL4J/Br4NHAsKnV4WqOMlcyQHFqprW+n+tanOceJe8zyNuruSWT7MQqOpexEFA+1tH4IJZMYRJ13yAEnokRsSzMFy/MS0IbV3Kb3nB9YrZ+vGOd0AOt7q0M2U5OWhqcbFg5H/bKQFYcuYdvIqQAdD8gdVdg4w9TR5jJ4xJuv8QTlJUI0HQ/8W/v6spSNpxnmtaESMAEWSdn4h0S2nVFYO6IEsHaKkcAVVMpcKfZ2eMLeTWcflfIYbZlfoqKwoGw877JZJ/1WpKnantqv76dS2r/PnLkMxUMsE0HlUJT4fvwowq+PEoxhff9MBHQApwPHA0+gcsX2AzPHvSBbFPLyntxAJ1C9aCE89EikxKegEGo+cyzX/+9KPAd68E8vr+bApfWWUX7zC706Vok8CVVlgQG72R6M9lx+2YKXLQC8ri1xNjAVWtqXsfKEH40VuseM11MomiubaWmlZ4tx49MWz2La4lmgiMSsJiFOBK7VZm9PN+GvpVUjnsuqMEdMEkYMIpvsEG5KqKW1EHXuXcBNwA8zta9EEV3ddTwgm2kRXgDWIWUV/b1QUCgomWS1uNK3e/kISrJh9SKLVfG5KmdlP37ZgkdwSBbeVsaopzM4jTUDi3TCr9+zdsPz+ihBOq/xYwz/R5vVG7/nmjbW6hi4US/6t4ATUEWZnkdV5pwQdn4THHYRcztCPBBjnUzkHCVfEVl1Mu0ItSck87GSKtnLgxLBQW19K86oPxdSPWYTlfCDOjfvGL5LslkVOAHkSX9uYA2wad4p6qZ8fLuK+E+bCosWwimlryAOqAVnHPADNNMgrJx7TC+iWGNle0tmBcdEgbtm0GRsY1xdDXd6BNTKkCae1wmR/2dZSOFZowduKfoyc7/3D0/Z3kECpe6BgoEA+JXHeMGskuDYm0PugqpSjrq6iuK5Uyj7yNG4ytx7mqaIswkP7Unge3GPWALQCH90ufQBYIVeXKiW1hpgtzY/ZnRcI/ymUuZKC902XhaHXwFOAX6BciKKJP3mEaA1DhxCDiuEcw0EQtxWzxVfj+UhXYYqtpLMcHMPFteH8IbaML7wy5bJ0KJF7CM7JrKxxajpN6w1AKxpEqL+eqZVT6OXXqbxOIvYyan6Mum+xnN5qN/ut06nUiYlC8wjZVjmogFD+MRRqChstNwq/E6LRPpzjhQph0TzBxSR3xxn6/fhEx9BJb8bnzt6sK2H1Ej/VJvpxk6L3bG0gwdVOVeXMNmNlE1EtOXqyF6e9OcC1soWGtTDYN4pVOtVcGOgeMTtvrPIEFUTXuoDiGqXyTXMFsM3zv1ukUSw6aWrmRSIKT0r6/LMupcbcNGHKij/euQCM+9/u/z9L7wUqrZVMBDwAJvaStSDaGzvkBtgrGOQ7qZ/wEionVWoKnY6GhulfNKuIUlKRdIdfY+1vey9RDTU0vpuVHS/F7iOsFWdgsUIEHpH7ggj/jqkbGwR3rgJZ/FIpx9zxF+/kbI+2pMo7BJB62jTiX/EfbZLPASwaboWgJ9OL5fwEIBO/JO5xt82/D89ap6RaFjpiccT2b3P05VserhCHZ8pNnNLsYvC+kTy0fdkED/Z1wrrUI5WseDCOkihf7eT+DrFS8C52j6+ZdjuDYbr0OpYxhIbgOKgW/GJXC3ilSziJfWPG/KJvLmAhogHuiMUBgJTtAh2SHPcWRp7dd1IeEgUHfSedtfBltlLxbbZS9g8+3KCcfjNzJ433QgE01B6/pMi59+866tBLIjOv0a41jR9RCKKXVKUuHpRlmr7gd8CH2uU8lt2bTBE2COSrhwUeoobfW+jrr2NOqH9xdPAx9pe9v2zYQNQAnyljTqfxfxYnZQjGU6K69hhAGUxaS5YZJ84N2EKWuk2t3W0ubRPS+laEaMsUq6UyV7juwlbCr5LiCYjuXmv9jkG/C2JbWcS2bvPs5tsOlGxjuhgh0I/8BowAvQBjwHnh6KwE6PomNPnRiZHxuYB/0AR/kHD9H+F/rM+lnegjv2RhpwdschH+scbDY5KZNtBjxyuA8rWnLw2ZtReInDXBvXoZKjnf/G+R4g3QtA5w/DcKUTp+cPR/oFZQ3stve4PSuuLXw4HaZQyOrIXD8lG7B1H5RyOJNhuL0I7ngU9dy2ti1ARmH8Af6mldT4w07BI6Run1FRVdHUz5UB/9OoThoRmAoZzdRvmSFisCFUHkec04tzGGEGY6NawltfLNBX5d5zEayEreh5VJbUU+JYQTd9FeZ4fr63yaykb96fW9DQjWalGchiXPKFUYScfy9Du7J5lZVTKuTbzFJKLvmce4dGdXJC5uQg/I61GNhWij6X6DVdluG25iMB4N8AO+Uj/+MPqgR4XfWVTIPygqwLYNnsJK07byJiw7mR2lc6WqGhIxP6q4tjU9xeXsebqtZET1cB7KDIibAjNFGF78SdDgJLVyzuKyiUwkmC/PY+ol21inWwTVbJNdMo2kekETn1I+0SURvVvwP8Z5s+9fvst4s8XWQ5eTHQSmjJkMy2ymQqUL3x0hMrqHC+RzdTEOafjMdqTDVheL0LVN0iE8EffYwuBN7VFvoKSYvxI+/4WcEPyTc4gslehdrzyhJKGzXnelKy1qwNkpXJ0XNhV3E20Em9y9pfZRhA7G1q9yBdsxXoE5nDHnePdADvkSf/4w/bBbVXXG2DM5WLTJ66A8AMt9GDbNnsJl53+U/rdkf2IAVep3Fi14jEsdI+xZEFjLjcrvrIxVH03BEGHbMZlIECWROddRdxpNR2HBEivDNpKbRD1kLFCzAe7lqxrGsLVk3gNcCSD0SL/pu3VeZTmmehheE96h+Fraa2vpbW9ltYgsNHJOkGXK3qI9XAgoWmDRv5rDNf0dVhVPHXQgYuuSpzIujmOdHRm7O6xAIrod6Ikf28B9wAfkLLR1mngCEFuENrEkG1J4fh3tO1lWLfbTI/1XkgqGGiAHX1IJ4SWF2Hs0HTjEwMosp9qHkEuQxIpc9IRANYbCnjlHPI+/eONBtGNxc0x4i44WBgYm2I1rtdbOpllNzWH3GesfMSX7rl3+Ccvrzw4OdAfst0UtdJKxsCPX1rJqo6f2BjwwmX3/HR4y/GXRffWlZf5XAORsXGIaRLm6U4qelq45FhhAFiRDt93rWNh608edwOeGD7M/vR4Pls4EYETJ6K8e08eaUCy97IOIZps7zEpG/NBKCtY1wQYIPe05yGMy3ke72Rne596O1cae39+VfAtlqxHYu9WpLYbeTyIs71k0EF67EAnGg4C1+bqvRcPeU3/eKLB1nFgJOByTS6yEcZMHTwEBpJnpSPfMueyNfc9dlkLhBJ912HT87543yO2TwMxGTY/5xUuGTx47wlXGNuqvMx3QYj4+621kRopME0Pe+eHH9JR0Xe7aEcANUoV173HwT6MSNWRIxvD8Mnpe23OTR7OkLO++2mCU/213b2cALLubjXhkd38gXQh++d5/LX5ds95u6TOWO+FePaXuge/vfOQ8XioDoAx6OcHfoZymdGvKbuifFbQ93MbE5vwWx2HXL+3UkI+0j+eaLCNDByUYBnl19DBWmeRY5tqohF48QHBE4PQG4RpLlhUCvNKtJkfBd4NXaWzAnM+02UZrZBzE6w+6hH1g2XFtxUPDnv8szzce9Ninvrc2RAVsU418u4kKm6EzciC85GE7ET6bY9JG3X5SGkGMJ5VlrOBdFbYzaV95TF+iHue0xWVH+/ofmRb2klfpD/Wezs8ypPO3x+7oq5e0DJcsFMhl602dXJ72BP5RJAnCeMLU09/5ytw6+1M+eY6uPV29d0I7SpOVUMLwOKurTz+Kw//168IP6jPh/ph5xAq/ebdavrMwTeTiVaYoXnGlw4Me1wSZnT5ue6LGzjnwafBrPlMVcuakK7UVqvvXDqUDV3pRNT3TnRkW5+cbWTt92nE3pzvkCf8hxVinud0WZDmnpWp3fPfKq8N4Ch84gl84iLTHLP9ZQArS9H0JpPbtX8JlbJC+zPuJ5eef3oOQweqvUJra6aT7Ccc8vKe8UXEEN7OV+ChR2B0TH3v7VPfASIKdpkr8caCJSlf3LWVTS9dzabeAUaj5o0Cjw/CvI+Hp+0pmxPrNyQCE8EoGRzhsm9v06P9xvamWjglYbmNRvCTe0D4ZQsei2H49Grns1tMJg+YgO4pCSKrv8+uKFgesSG8XAxcCrwfOBZlntwOPAzcIpsjClCNO2Kc5+QkipnbTnoQS4blEy7g81FrTEU5Vz0L/MYmal+TncaTjIxsPJ9/AeDOXE6YzVXkI/3ji4ie9ePbw4Rfx+iYmq5DqJ5sGA2ingbRToMIap/RUQ5LUr7utQYmBQZCEf5o9AYJRfkH3KXya/MtO/WSBMmmlNYPCs9evT5PuL1piLxnPyruly34ZQ1+6dI+0/ryScCJKI/04XAfXTncf9/hguuAZajSiNOBSagCZjcCfxVepo1j2xJBujqZudcZt4q8+8SJgFdbYg9wCTANmIGqT/F0Wkc/wk46Y44tQmO13x7Zfj50aBF8QaUsyBP+5JCP9I8n1soWGsRHgGsBd2+f9WJR08Mk21zYSz0oGoRxNGAN0EyUV67uzT/NhSXxnzTVDQQk0Lnigxurth23xLwQhiReh9gbnBKc7T5okgr5Z5WDRcQ6pcj7YRoV1wh+nuRnD4fldWRAVn5flos1HY4YRtWPuBvYBZwGPAjMBo4DrgR+MG6tc450JflOlKTwLxB+/y6nUj5umPcooOvpUxu1MOcB6O/ZamALPvGREFGOHFXQI25hvb4zOUwmnHskqjDf4fy8HVfkI/3jCUXal6PdnNOmWi9mmO6PkvbE1eJqiYYHo7epe/MvKlVjxEYUlsK98++RrJUu1sqaluOWpi0SeOPAea5+GbnHodIi7rq2DtIcsc5HxfNIBw5j330g8zr7Vmrr7zv0qe6fBXZsXb97Z/WZi3uyUazpcMRS2cwq2cwLspkh2czzKPcUHSeOV8MSRLpyn8bfm98ZFmmfo8CF+MRufGIYn3gNn7gOnxAkM2oRXfArtpOOAFZq60SPKni0v8RGGFTHYDP29XN0DKISgcOa+2jFQhidFvkMkXkMeaSEvHvPeEER/nsxZPVHa/oBCgvgko/DvFO0jH0j6W+w9fJVhF2D8GJabnHXVra8ugzXsGTnkNLw9wZh2tFw6o3lLHrd3yGblSuP2BXDvSTBSL8QTe2Li16uXlf2OFWuXjqD01gzsIhtI6d2SNlYk8i28sgjlyBE09HA14FPozTXPcBvgEYpG/eMZ9si4BMLUJKQs4GjgQPAq8DNVMrfpHNXVo5YQ/2CDSuqeXZbOahqvjXp3OeRBOHlJuBb2tebZDNrYy2fMzgc3Xvs4BP9xI6G3wL8J3bOb1a6/tjuPrEQAPYTv3CWn0pZEXMJ+zbofMP+fEzA2hOHC/KkfzxgluWEsPMVpeHv7VMR/kULYd4pBP4587jHb1h183sw+s03XLoOuweFwdJTeGm3Wu7HrpWsfO5OXG9LRVFWQ//5ZVx870Oj23d/9HnUg0EljBWzn/cylQ8zhVJtaD4G4dc6CpFD+XNpySXLvlZqr0BFKuxwch1tr2epOXlMYAjRNA14DqW3joYP+HBOVJb1iWWoa97KjWstlfKmdO6uldp2LJ493e1FrDpuHuSLciUN4aUS+BtwDOoZepJsJnc6l3ko+MQI4QH1R1GR7uOBJ1B1egKofI3vE/leHAH6sJLdxLbXTBeWxCTg9m2wtyKNXD/3O2yHIfIP2/GBrY3mvFPg+uvwN65h4PrrQq497jndey8458WnI5J8frvgow/jbHhzDTCwuGsrux+rIdDmYvdjNfyx80yWrriPjierCP5W0P6Raq7+1Ybg9t0f9QFnYUwYG2Y2LzCV9bTzPU5zQPhNSUliF/V5y748DlN8nTDhvwX1ov6C9r0S9UIfX6iEwk0owm+dUJh+WMoTyqtG9H9zTX89ISC8zAH+gCL8QeDyPOHPWfgN/2+gUu6nUu4AdG2/G/gXke9FXQ5jJ7vJRrJyPEvO1BKp02s3modD5CP94wF7WQ4B4RoZKCktmDLYb+qQ7Zvu4aov38E5Lz7NZY9to+KAHxFdNONJHub1qOpyftmy6Yyrbl/S1bKyLDgY2m+/u4wVp21k22xTkm4A9UL5KuaEMYAbZLN9wti5ex/rrqu431Ne6Kdn1EPLvmU827cQEijkpckCIqIACbj2OIIx0l9HW7pLlOdxhECIJgF0o+7BAeAoKRtHtHn/RkX1xoAZUjbuH7eG+sTtwCrt23lRCYUZQZxIf74oVxIQXk4CfgfMQV1Xlx8u+SWHI9547qgXjq8+cDqA/52SYPlRQ0IIOlHPjAXaYmdSKf8YWileFN1+vh/oJ7HqunaQVEr7wHCqkf48xgX5SP/4wDK6FYTgbZ/9vJw0OGB5XjwHejjnxae57lcbmKEIP6hIQBmwlA2s4XWWo0XYdw5R/cP9bGkSInjwxbuv/ffAYMRDYFJggHWvNVjtyg2cmEzCWCu19d7KjZ6Kom5cQlJR1M01M9dz5tTt4DACYNABR4wUaNPzyCPXcByK8AP8Syf8Gl7VPguA07PaKjOcJBSmG6Zky6F+Qes3K/3kCX/CEF4WoEZk5qCO6yfzhD8HoSXZyjdFcMrk4fn6ZM/RQy4hQu80nfDvB16I2kK8KLpdEvPqUPRcyYgCNtvxEx5VsEvEjTcKN1ESqfMwIE/6xweWN8uPP33tO9tP/49i//Ryq3XwTy/nsse2UTI6Ej1Ld+yHMtObAAAgAElEQVQJyYZ2DqnKun1BBCAOStyhSrsG6NadoJJ7dflP+2PV1Vr1XB0lhv9jDSOvK3ENR0wocQ1TP+M+yFAl3XSgldq3Wqkd1T7vb6X2lPhr5ZEHoCQWOnqj5hm/z8hCW2JBJwyFwJeBGqAIJUv6MfDddO/QqtZGySS55InmH1fkNOGPdkYZpyqvwku98NIuvASFl7eAp1CBnh5gkWzmkfFo14RHJs6vcZuwBagWAlFRPijiCCqup1JGvZnj1M5w4nCj/r+M+J2Dn2jbiF4mNnnPu+xMSORJ/3hgrfXN8viCj5YD3Hv+YoYKiyJWGSos4t7zF+M50BO9NR1VGKIDjw9iW2nXCN26U6/QWzPYgQtJ9WAnA0WlWzZdftXtWsKYXghjAOU6FKsdJngK/ZDBSrppwDGoaOwxKCeFP7dS+/4M7i+PIwO5JBszeuU+inLuWUDY0vcGfCLtHZM62lrqaKupo82lfeY2KUhXoaQUIbym3KhjgFJtdjnwJ+FFGv62Z7N9ExaZOL/mbUbc9zHG0CSV0up9Gj+K7kQTH4+Y+0K24cYWSmCzI/Ke1+VPOORJ/3hhrWxhrazRvfC1jkAnwFPzz+b2T13Dvukeggj8U48OPHnaWeufmn92h90owKHgpJ6uklmhYbqYlXY19LvLaHzPNw4Cw3qFXiPKRgbFGTt3rKSYv+I8YcwyQjEsi/0J2Htmq0Lov1Cl0d+NepmeiF4sRX3/Tpr3l8fhibcN/0+PmmesvrHPOCMqituukbxMwklC4akZbsNEQNZHGhNoRx6pIxPnN9lzZf1OS2cUPTYxt2q3AC5OeD95TAjkK/LmFkIV7p6afzZPzT8bNDvLNupaLoTrOHCpyfJyTLpHPt97x5TAXLd700tXMykwYFtpd4oggOrsdU4KDKz56e+vaLnXS33VYOfW6GVfn/wePv2eXwuGmUk4YexBp7/BMG2gxDW0OpnjYNwGadYK1tH2DPCMYdI/W6ldAXRp3z+Uzv1NZNTSakqszhc5C2E3Sm5RDrxLiKYig67/vdrnGMpeEYiI4kZU0xbeUDGwTGAH8V/m0dHFIxHjMdKYyP6kbM4H7FJAJs5v3HWDQXBFnrXY7zRFzu087tNldZkr13oeWUKe9OcKGkR9G6yTUBYULoQM8s7UowM7Tpy/+cJPPx6+odfKFhoEGG76q3s3TNoydJlH99ZZ91oD5w518NCAIBApJhw4KFmBP/IBIZtpwSMjPP//Ou19fOxDj+IvrqCoaJiRkeJPOtGP1tHW0kotwDopqXpnf3lwa+vlpc88v3BdaF8JbIPMuve46miL7hpJm/+PWGiE30RQa2nlSCX+Wr0J4/X5PMr2shT4lhBN30WVkz9eW+XXUc49saKN6TumkQTBGOm/Bp94SmufnuBrlVCYdlgcuzU5pu/vxNqVJNv2oqm340j3Qrf6/Zk5v3bbBGBgsEBuvn8+n7zw78FZlQddmntP4ufCXNRKlyaR5HnNlWs9jywhb9k5TmgS4YdRYSE9n/j/7d1/fFxlmffxzz350SalTUtTpAXSoKKILb9d1h9g14oKQh70Ma5tcZVCBWsVfNbFh0YZ8+wmropKpSJQbF2EdN3sshhQEKyLsrqLC4i0Irq6NC00CAklbZO0aTL388c5k5yZOWfmzO9J+n2/Xn2lc2bmzJnJ5Jzr3Oe6r+s9zD5tKTN8HmqBm+mw63zuA4I77l7x2Kd4avBlBmNQA/2H4eqo9d8xbDrzio2XvfSdT1SPjPOTxr/gkjfdw/6aOTTU7OUzf9PJ5177laxyk31GMiHehKuM1SaSSoEeBL4PRGGigdkG4AL34fe30nNhGTazorTQvZOA0mw9tDaXdmvKL02DuVeART5PeQH4c29zLr+/WVfhRnH9u16O49+YywIfDcgvLphCN+cryglEhXQLzXsfWiHvo2yC3/8WnDz2gn0um244a+Oq9z+1tr5uLDk3Hgp5slXoMplH+nfkCKSgvwx6Tjcbt/+GtWNjkwf9mmq4+MKJZlzJLHCpm/cPJHW83U+Mn1KF2zs2Pik3KUff+UPuT/1Djh9cfrRsWf1bv/ZL3tv0Q37auCzdW/ip3UzaBwR1AQZ67eZwtfoLzVMKNEzu5V7g3FZ6fpPxkTlyP/fEgKUCy++10B0YoPbQesSlGRjTvhP/7/Zu4F+BS3Aacg0ADwDXWxtNmAdTkr+P4ABhL86JyGtwTnwfBb5Uirr9aT67XmujzVmuK+jvuR+4Oq/gv0JGyPPaRxzpddTTvX9nxL8gv9/493DFJU/Vd163jabjBnluzxz7o5++9uY1n3k8cLAuJ32BPX7S19RPv86K+K5LaSjoL7U2s/LrG7lz377UP9yGOXBN8C6ilw5nR+3peDt5sDsM/Ah4Bp59qJnmkd7UFdQ10Xx+by9JB454APLWcx7mytXf5O+/dj1P/25puncRJugv/khmloIaBeHkW48AM4DncRrfdLbSU7RLnJV6JcSPRvoTGdMe/N220VDf7ZL8/osRIOSpEJ+dZ107CU6pUOOvCvz9l1SJ3n8hT2QzOtJP5CRvyukvvU6/gB9gcF/a53kn1qTmA9cAb2ecZ4g0jezyXf8JI7vBf8JgE8DPH10GwCcuv5H58/sZGGhkQeNLq3LMpa/EXMGgyUlVrfTMCbivWEqT010YJZlYPYXk/d22m+kyq4HiXumpxL/BQm5TusmG5flbqqBR0wNDNQNHzTrc6HPX9MzXTv3sB3B6GiQLfP85povlPhk2+++L9sWSl+l/tl95mhoCwsug5S7vjsp/ZzKbiN1MJIJNHeZnsiY/qeXJJtb980eX8Ylrv82HLv8+n7j22715TJ6txG59pSoFGsaUqZrgTtZNKR93pE7ipUDfbbuZLruZZruZiPuz0J9nef8G/RsgFXKbMv3dlvZvqULq+4MTvH78s++dPTRck7B8bMyMMh0DRP/PfjaQ3Mky8LvmSRdL+P25y9MJd1xJ/XvY6Pd6ab8vaogleVLQX3q7li9zcvi9aqph+TL/JxysqWXD+66a5VZRgaCdzD6smzaQcmAdqqrnvmMuDOq4W/DgwA1gUnZOZU5dqaQTkUo6Acmoh9auHlqbe2iNuD+P2IOMO/KX+t2utFSScgYIAQGw3fMF/LYp28/OmPajgd+SvsJWqf+WKqW+P0DnnXefPmPNZy5m5+4GYjHYubuBj1178b5pGiD6ffYzgH1k+P4b077STdG5y2cdYX5/mY8r/n8Pa3N6PTXEkjwop7/U2pw//u07qN/2sJPS0zAH3rEMTvWZxGuBr7Z+MqFm/71Pt0KMLUSonXjgZE6/U0+/x/DczOPuWHRwT9WuuibuO+ZCLnvuHxIm946ZqtFqO34Z/bZrqkwqzVdS9Z6ilAINYyrl9ItkrYi5x8a0NwD/CZyc5mElyen37k8+0HdvUOPVkufQF3LuxJSQY/5+msngCevI9JllTAsK/nvwfb0jYs6FlIWC/nJwAv9Oa1lsMURM8O/gxbmNXPE3N3sX9fbQ2my+xku8mUbm4IxlPALx6j1Av93MAu9k2hfvb2TB4QG/l+ilXxOAyuFIOdGSI1ARJ1Ea0/5V4P+4N78MfAn4GvARd1nRAn5vcLfssv6BK2/rnVNV7Qy+XPinHzMrNuL3tLxPdLwlnnH3FUHll93t3Em2k0sraD5C1nI8ycwwGXxiHXlPyA3+e/B9PU3KlWLRRN5y6LBdZjX0Lmi6s6l6d+CO4GBNLXecvyJ5sZOnuoP57Ah8hfjkpV3A4hXP3UWjf8A/uT4pOTfAnxoHVZHsFGUSsTHthsTg/vNu9+OPGtN+Lk6zsVrg/nxeJ+C1E0aFW6/va6zyHEG3zz6Zswefoppx79OGNx26/L6PPc1OvCf3p4T/u3cD/pSGTO3GkCbwz27CZ+GbPhVMyMm1uU5wzXT8K1T6Z9DfgyXxZKDc895kmtMlpPLp3PvMbnPjRmjvhBs3wnY3iLfAYP1sNl5yZTytxyveQTbMwXM9MNz527Z0QwyhDsLdtKzspmVnNy0x92fJJ6eJyJRRrPkzJwLz3f//wQ344+I9NaqBM/J8HT8JeePzmxLniO6uP57HGk5lKFIHbg75pkOXb/nY8O2XkTw51Cm7nNPrutLmfucw76SS5iNMCD25Nvf5K+mOf4WcqxP093AzmpQrJaSR/jJZ+sc7m+77BYyNwdVrYe7cyfsM0DC8n89038Rnum/iT3MXsOZvvhm/O36ith5n4pGffpgsC9g0ssv3ce4QQ8aDsE9Tq8XApm5aKEc+vOQvbGqRWc1FwAeBNwHH4hSH3QncB3zZbublUm2zTCELbRd9BgqfLvIqz/8Hk+7z3j4mz9fxkzAqPLCrlgXNqYH/7vrje1vpaQZwR/jzLc2bU6UvN1gt6muUQPjSxs53K9vvV9AVgsKmh6X/eyhsAy+RNDTSXybnP/Z/Y2Nj4R47MmOm9+YumEgN+Sap1SsOAVfHb9jNdEWw/X7rNTOAK+l05xikU5GjQJIbzyTixNGz1b6jj+uAD+NMmpwLzALeCHwWeMyspqEkGy1TT55VRuJVVYxpj7k/M+2n0uZM57C+ZAmjwl3rF3FwKOUlk69mFCKYLkWlr0qtJlbUk5GMV0T6zNn0mW76zAv0mVH6zIv0mX+jz7wn6xdT1R2pABrpL5PZw89PnHBtuDn1/re/DZad5/z/J2e8HYCYNaMRYycOKHYz68xqfkG6EVunLOfslBeIAG8F4rmbbQY6AndClToKJLnJpjHYIZxL0N8GngZOA/4ZOB4n1eJynEmUUsFybDpUNj5VVRa7t9s8D5ub9DRvp5MXw6zPmHay+BwSRoV/vnU+kSoOrfnWrv0zj4rNx78aWLi5Dekn0ZaiIVOlNn0qeoO5wCsifebDwBagyrN0AbAM+DnwQKG2QaRUFPSXiQnemRGJwFluRuroYXjwrOWMxar6qyPjVyfXRw8xGbQTp15xohrgpIlbmS43V2Jnz5Jz+yQkHJinaL36bE7iLrWb2e+5/ahZzQbgK+7t1xV0y6TgChTwllrQiek1OJ1W5wOvNaa91pPX/0b35xjwq5DrC51mY220y5j2+LqagF2P3Dl//c+++438OqhmmEQbtbar3aSmhqSr3pO14qVj5as8JyN95nXu61YBu3Fq6v8M51h6NpNz60SmFAX95eO3MwPglJNhtjs2/+un4B/rPxq21JejMWHUyP+5h1KWpBu1r9RRoJJxA/6UA3ML3UzBwD/0SVxSwB/nzTfbXaiNkqLJO+Atg6D90WKcFIz5QB3wkjHtn8G5bvlq9zGjwAUkvreCXK10A/8HgOuB9wFbjGm/AWfUN2ptNOHvwZ5Cl3kacPfHJzQ8O7Bk4a+ojozf2eIsW98T4vfjBvjF/V3llhNfVH4nWpTmKtWnmBwsu4yFdpvnvoJXhhIpFQX9JZZUb3mgro76kREwBuItE855k/PTWnj6t84MytAaJ0eNth+EbSMwGIOGCCyvg6XxcO2olGcGjtq30tPVTQsk7XiPsEm8UzFwCpLzSZxZzUImJ54NA3cUfOuk0KZiel7gldCk5XOA25Luryf1SkbQ+gJrGftxG4P9nMTGYAuBy4D3GNP+Zmujvd7nuOU5u5IHDs4bfmTxR/Z33Wmd3b+fSv79lEyWE5ILZbn78zDwbvrM7cAi4H9w5tJ9k4VqciRTj4L+EvKpt9w4dhje3+LcuPeHsGABHH+cc/vZnbB0iVOJJ97QC2g6EJs18Kl9G9gycvlEHqknj78TN+C/d8jZY4ET+N875Px/yVFg/mxyuw5V19qHT3/bfe8O2O6CdLFty9iRsJeOim5IMhUDJ1/xqk5k2RjMrOYE4EGcCiox4CN2s0b6p4CpmJ4XeCU0pOQT8vW8eul3OOsdNcxqgKFBeGwbPLtjjjHtK7MYOb6eyYA/3hhsFfANnOD/q8AHAp47MXBw3vAjrBu8lZmMpruKW8m/n+kuvl+vAf7Gs/xk4CbgBJxiBiJTijryllC78Q98G+bANeucOv3V1fAG95DyPzs5/OpmPgq8BSencOIAcfD3Nex/dA7zh1/mubrj7Y8WvOvmNU/cvo5Gp/PfjXudQD/ZrOoq5n9uBRfWPcCCwQH6587njvNX8LPTzx0G1iSnqviU6wS3pFlWgX/moH8HHXZp6PWVWAvdOwno+NhDa3Npt6b0zGpOxgn4T8DJmf6IugdPDT45/RCiLGHoyb+NPpNQ+/PPBXdfP6gscRjW2mgE3IpVY4fvorpm8t6xUfj3e+HZHaE6rrqNwV7CSS0aBubF5xPMmb3+T/sPzDimunqcvidv2N149Mj/Tc6Hb6F7oivr7X9ayzEx36JqccOUuWa7Me1HM5nGdCzOVRHfNKZpp8+M4gT84KTzrMJJH/s3nMIY48AiFtoX/VcgUpk00l9avqPCg/ucn0uXJCwedwN+SAr4+W+Y+chhZo45V6abRnabVc91raXx27/AHdXzC/gBDozHeOC6D/GvtS3JdwWlqhQmrcVvFL/NRIEvuLf+IfS6yuOInddgVnM2zoGvEec9t9rN/LDQr5OU+lb4yYpHqFzyokNP/m0MmITaaMg38He3+w4Sq6dkwztS3pkQ8ANU18LZy+HZHWGv1vk3BuszK887Z+X8H2x7HWNjVTz19LEnvONtz/p1s5244tIYS5tV1EuZJ9HmksZUhG0oaMWpLNfXj/N+AW5lod0LPE6f2QZcgvOdPBX4ca7bI1IOqtNfWr6XaxvmOKP8//Ho5LI//g8/d0todpI8GfeXOGOtHvWxEeM+dj0w3BDwmx08tom6muTGgBP8Dn5Zp7W0G7Oy3Zid7cbE3J+p9bDbTA1wpXtrCLg9aH2VwL0CklLPeQpO4s3IrGalWc1Os5qYWc0LOFUrGnFG+pYXMeBP6R3g+92RrFkb7bI22mxtNOL+zPS9Ddubo9g9PG7J8Xnh6uXPaoDwaTRBjcE6580dmTgxebF/Fvh/BhNdWfsj8wnQWyE13JPTmObjTG6FyTSmogndibd463s8xGoDD6QilUpBf2mtr65ObKZVUw0nvRZ+8AAsOcVZdvAg3P19zus53WzE72B1IHD9TfTbrk1Nl285reFomzSuxejMerZd08HI4cA0Wb+DX1ZNW7II3lqZHEm5gw77StBGVYoeWrt6aG3uoTXi/iz3gbngfBp3vQqnSgo4B/7/MKuxnn8PF+il1QCusoQ92S9286R1OBMnx91F4zhpZn65MRa/BksO//3Y0KA99baF97XQvfNi+0+x85/rGjtu1dezbd7VZK1van7CZ+AdOLhj9gp7iNrk3NqKuHLopjF9xL05DHze2ujL1kZvwpnICvC/jGmfV8TNKPT+IPP6+sxK+sxO+kwMOMfzuCvpM/PoM2cxOcF3L/BEjtsiUjYK+ksoam3XxRc6I/vg/Lz4QvjvPziBf7xM55NPwfAI/PFZ1uJXXSK18k7cLoCPnX77Re+4ZMD843l38srcxVhjeGXhYu6N3sb2967it79bYmNjJrkfcNABZ2J0KsRjIfzOOj5qZHEmwUll8Pv9lcK0mSg9TYQ92S96J1dro+usjVZbGzXuz3fjXB1MZoBdAVcyUvdjY4ftSe987KHFa+ZdBiw2xpi642uqTrttkVm0Yo7fSPCfPP+fG+/w27u7wezbP9kK5ZjGiU1L+QziAwefqf9GZAajl5J05bDQI/w5diH2T2Ny/Mb9WQ2cUchtTVLo/UH69fWlDFYtGBubOJG7AHgZeAwnn98C17DQHsxxW0TKRjn9JXbqEnaduiRxQujdPTD4NOx4OvGx+/ZhBmJzj57JKLMik8cr+yYY+1kVNePjE8vGbNVotRmPB+JNANtfs4rtr1k1ucLfAdfBHhabqstir5y++bEhMjSayqFcZ+addZt5E5MjKQ/RYZ8JeI6UXtDvz9rNRR0kmIoVZqazsHNYyjXXJaug0LdiVXXN+pM/tjflJLd6VoQ3dB7Dnq37kucuPctkY7DX477v6764nB3PHOM8t3qcM5b0QZjPoMh18fNoyhaUxpR8+3vGtE9WkCts7fxC7w8yrS/1e1BtOTBUzYzaGNXVMYzBAjuATyfV7ReZMhT0l956kipSNMyZnMybvHyeGYxc+sp36ZzdRlPVLoZtff/WoQ9972evnHfF39ZfP6MpsotdsSY+P/z/7J2jfxXPHfLfwXnGxnZvOXH+rs0nLgizwW6AH3aHHmZn/SnP/zeEXK+URrmC7yN2onQlCj35t9920ejTyTXkJF43nSyr0rGurL+nft3LW+BOv8fWNU0kRzYZ077Ts32PAhcCte4/tt5z6uT63vU75s09WPaJuK5i9Bbx5jE1uj+L0eG50PuDTOvzPVmcVT/mbaJggNeQeFIkMqWoZGcpTdbaTzhYbd/h1Og/7Em4qal2Un9mn7yYE1/a6X14L1sgeR3x+6yl2axmI/CJhHss8FucQ5bnsTm/lwA+vQjALT8XtbaLNvMqnINnLfB74GQ69CWsFJ6c/tTyjkUu0anqPQXUZ87GqSN+LnA08ApOasaXWGgfKOemxSV/1xad08sbPrDd1h09gjHBVx8h9zKkyYJK8Q7vHGXbiX8AZ8/pDXSHcT7LRWlWayld59hAxrRPlAhNMlHKNOB5rwb+6N7cbm30VM99PcDFAU8NVfo0rCJU79kIXIVTeWccuMWdMwJ9GUtKezmTrUWmIAX9pdLmGwwDTtB//0MwMuLcrpsJF7wLXn1KPWsGb2PrwVXeh1s36A/YmRMxq9mJdwd2Ms6hfw6wB/gGh7iP1f9ki9Nl1xu8zZ5N7J1/QeTUJe4IILwWaHcf+kk67MZ8XsvtcpnwHqbjBNtSymP0VSpBn/kwsAX/UpcdLLSfK8VmZAravPupte/fwPVv+jsWxAboj8znjtkr+Fm9f++QsOsPI7lLLsDYUIxfr9nDnq0+l18du4G5OPnd6WR9ElJI7hWKhEB2xSVP8eXPPTR+/KL9EeJXZZKuSCT1IxgB5sbz+o1p/yNOvfoJi1bM4Q2dx1DXVIOJmF4qaB+c9B2BxONmPPjZddtXeu5bs+qJy/B8D2xwq2TLQqv5kDIlKegvlYDmVD94AB5LqgFQUw3nvX82N8//VnLAD0kj/YtW9PKGzh3UNQ1zcE/deN3xI3917+rWO4nv3E4G3s1kmxGAMUavPOGmTcvnPZSwkyOXpltB/E9yhoFRnAPmIHA8HTa4FlEGfgds9zWmZSlNkYz6zOuAp4AZOMHpWpySqzOAs4EYC+2Pir0ZYUbizWpigFlx2l1s/uDlzOTQxAMPUsvGhiv5Wf25RW9+Fx84sNY2HXxuzD792T9F0gT84ASLlxKuY3BBR7+zkfw7WHHJU2y6oYdZ9Qk1HIaBNWbRFyDxBOo3OGlM4JTs/Huc95xQdGHRijmctmkR1bMSYuCK2AcHfAdZcclTdF63jabjBtn1fAPrv7icrfecOnzbV3q2rLhk+1/W1x1u3PV8A7PqR1kwf8Rv1RrplylLQX+ptJmUS63bdziTeP0capx78IsX743hc9B0g/5Ni1b01p+26XGqZ417nzr85Oazh3f/+4lOvuUaoCF1/d866bLx+TUDfiOBva30NId6T+lk7sB7Ix320/m8xJHeJVckRZ/xpva9s1wTDv1GmV0TQXB8pP/ZzzTTPC+1z9OLkUaueNXNtofWrEZV87n6l2a7k/UD3wMucl/Hf0w4QypNsXlHunc/9tXY8Yv2p+zzDwzV9M8+qa2e1GNNpjQmlj/7Wuqba/3uKs8+uG/yKvNze2bHrv2786u8cy6cE597mVV/eGLZ0HANaz5zMVvvObXX2mhz/DNb+b5fN2264V7q68aS07vK2ilZJB+6RFU6KRPMtj0c/OAZ/a/M/Pr71v6BpJJudjNd1tIFrDnly9vHkwJ+gPolK38F8fJ0c/zXP6/65aAul4UqkRhYBYYOa/IN+DO8hso8ypEqXkf8MPBu+syz9JlD9Jnf0mfW0ReQsFB4Yf421wPDTXP95966XWuzmkDuufqX0CfEXR5G2H1HI3AFTkpRBGf/7Kes1ae8TdnclJ4U9XWHG/Gf8DuOM7K/C+f79AJJXWI8E56TNeVYLjR3SWU3j1+0v2rTDfey4pKnJh7Sed22hIAfYFb9YTqv2wbu7z7+md11992R+rqxopdVFSklBf2ls57JHEK27/Cv2BPXMAeuevzbpz5w8fL77GYidjPN3rxqa+mqO37E9/dXPXN8PvHusQGvsXfs6JSzBVehDlJFr99dotfIWwvds1vo3t1Ct3X/PVbubZJpKx601gB/AzTjTJo/GbgJJ02jFDL+bbr7szV79i3y3RcNRI62ZF+tJd+mTtnsO2Z41pttP5Ny8H1vu5/3uRTsON7a6NXWRhdbG621NroQmOV9wMiuw75PHDsQG6CAHXVDSvndewJ6AJqOS65AmrA89fNZaLvcDsmRCumULJIXBf2l0jG5s4hX60ln+TKYeXiUs37/5FVpHhZ4YLWb6bKbaaaBVfgcjJ48cOYtfssp3EGqFAfBqXCgBfgicHy5N0KOCN6h1/txKvecDex3l/01feaYEmxHqL9Nu5mu4xue/6vkxx6i1j4x47Sbc8gLz/fqn992Z3w9d56CM9AS3BW43Hx/J+1ff7tfd2MI0aH9t+tfZGwolvyY4R2f6oPSd9j2/R17A/2gE5zn9szJ5QRTZMpR0F8KbWalm+NuwEnrOZzcD9fj7DNh6RLn/0fv2xuUhgOenfi8ux5hSfNazox8kNOP+vAst4IO9hR8D0a3zPvkOr/lBZnEC/GTnJT1e09+8uVta+99jVwnkBXjcnQL3X8OfBz/DqIiheYN4G5lod3LQvs4EB/urAJOTX1aYWUVBC9M3VfMYPTSd8/dti6Hlw599c+Y9o3GtI8Z027dnxsDtjsoKE5YrzeVJqArcNYKuk/y+ZyBNVv+8cyrCT94kvV0SrYAACAASURBVHDisGfrPp66qu/Q2IFYv3edu7cMzvd5LuSaetlnzqbPdNNnXqDPjNJnXqTP/Bt95j2eR/n+7ve8MHs8vm0P/uw13yTpvQ6PVNsf/fS1N1fYCZpIUWgib7H5VLFpTzPW8f6WyYAfoH/O0eONnx3wbaJmnmblCQ3Pbnjrj7/ZeNLam6ganuyWHplheM/7qjn71YcZr63qrx4dv7qQAfd0kaGkW14l91rorgGeAJYAnwa+7t71eA+tZ+e2xVJqLXTPBp5m8mpNRf7+2o1ZeelWbn/N26kD+M/b+NqfR+1fA9Bn/hW4xH3oW3HSfhIbak3B1IXksp2n3rbwvsVr5vlWJfMOBrg12xN7mTgetDb6bp/X2ILbjMvjELDab/9QiHKihepFEGa7stnejKVYnfvvwL9kbPbVjMKWoO1LPdbiN/HWM9mXKfzdF8mFOvIW3waSLnOm68DrDfgt8PToyb+5wacaxb1PtwJs2j14Yv2x1/1zQsAPEDtk+bfvH+ZNn4bq0fHGmDFbIm0GBf6Tgkq6eeTbvfJanID/buAeJoN+mVoqOz2rz6x8YisbaupofPxOeM3bncWNJ3H1za83z6x9mCeYnOC7FzgJuJnJ7/1iYBN9hqkU/Pj8/S5+6mN9lwFbFq+ZF6+qE1S9Jyht8l3GtK/0BrGe7sQbmOxC2w9cnSbgT9gucutYW9COuiG2K9Q60z3W8xp+AXr2qZdOCdr4+vxL0MYttF30+XSHTv5OO7enzPdcpJA00l9Mzij/XcmL03Xg9Qb9AM+NLbJXdWw4HKm23lGm4SefP3t496BTljO6JIIJ+D0mXTnopaO49YWnUrOskOX5ciq510L3STj10g8Bb8A5SD3r3l2RI8WSyk3P+jlOk6L4JMbK+f25o5s3von6weedRa23wSkX+T7aAh8F/h8B5TSnUv3xMGVB0zw33YEvzPMDR7vz2a6k18ipo26a9eW8XWGvBKR5jXHgr7K+QlEhJWhFpgvl9BfXBr+FS5c4AX6DW06zYQ5c5BPwAyyq7jPxgP+8f36E209fyz0L/rL+vy48p3HFQ875xOCxwWmSSWVBi1rKsgDl8kotzOeRayWgW4GZwLU9tPbluA4pIzc9axPOfjLrLrYlKlnYCdQP7plc8C9r4aG/g5d+D2NOv6t9wEPA+Sy0dzB9St3m8z6CqpcBLE73+/KMZgdVpgm1XUnfj5fcf97vSqGrk+X0eQW837vc7U3+jILWFckxJalSStCKTAtK7ymuxqA7li5JDPLHTQRsShUE+huc+VDn/fMjrPv0rcwccdJ4XvX8S2z68scA2HZNB+//7KW+Q0JJaUTFLmVZ0MvRJbCL9CP9OVUCaqF7OfAXwO+B/2qh+3QSm9zUuct29dD6crbrl5IJSs862m0MF3g1q4ApHpk0ATQsgvhIf2wMfnGz8w/ojdqU0fug731FlboNIZ/3cQv+Of1x6X5fmfZzGbfL5/vhPVYsdu/7d1Ibf+VTnSzXz8vv/YKzzXcZ076ByVSnQn+3kkvQxsVL0J4AfDbHdYsccTTSXyGMjXGwJnGe2HCsjlv3/hlLmtcy1HoTt+wZZfvByftnHRqm87Y2tl+0ChvQEDI+DhIzZpTilySbaiOIfiXsLPmX3Jvt/nwdzkTeXwE/8Nx/irusJYd1Swm46VmfAwaB5Coyi8l8NSvfevFh7QJYfh3U1KXcF6oCS4bHVrKc34e10XXAgxkeVo8T1CaP+gftzxa7KTkn+Nx3KGm7ggJp72ufT2LAb4EteZw05vp5Zdp/NzJ5paPQ361KKUErMi0o6C+udKXeEh84t5GNl1zJi3MbiWF4saGRT9/xUQ595SfM6HVWMxiDe4dICPybXtwFFgz+KarWwlhtVX/E2suSJ/F207Kym5ad3bTE3J/5ph9MiWZZcQHl+S7NpeReC90rW+je2UJ3DLitOFssxeRNt3j534efIjg9K3m/6RfMl+oEeD0wvPT9cPFXoOE4wEBNPf3Amqj1mZgbULpxKk3ihYC/37dctIXLop1mNTGzmp1mNYH7NLdKzyqCu+nGJafvpNufGfyPq8kTW8N8D5JHcgzgP1sjBM/n5T0uhelJEGb/XQ90FqFfQUWUoBWZLjSRt5icibzfwR2t2L7DybEf3Ofk8S9f5qT4jBnDjR9Yx89OPxdwau4v+r//yIznXvIdv2+IwDXznOD/R4eqODAeI0aEKuubpup3eR83wPctb5ZrrX5PTn/acnnTTZj33UJ3M5rIW7G86RaN75jFm7ct5sDvD9k/fnWg7bRbF92Pk571gzSrsD20TgR7hZrMGYpKEALgBvj+JS43p08vDDmpv9faaHOIql9B+q2NLsji9fzkNIk3LpcyoFm+X0uOJUp99Zl7mTzRuYSF9vvu8sQStAvtL/J+LZEjgEb6i8/CZMWeeI794D7n9vYdMFR3VELAv/hjtzIzIOAHZ8R/+0HoGYahsXGMtVTZcb+x/nSXVQueflDoZllTSKlSOaR4Jn6H1bOd3eJRr5thTrt1USep6Vl+kkdDS5dCs9B2sdA2s9BG3J/T/e8tSD5/h2E68S42pv0QcAPwX8BzEHCJ1V9j/GrBxfTcdw1fs1G+wDV8naU8lfzYoPXme9U0688o4ApBEL+JzdnpMyvpMzvpMzHgHM89V9Jn5tFnziKxBO0TOb2OyBFIE3mLawNuQxe/LryHx5zln1pyYGLZceu3ptTcT9YQgQdHI4wlTfw1wLipImJjDM463v5x0bu29Pz+9qAAIHT6QTZlON3lR1rQkfGz7KF1J+7leiet6rs78XyeBeuELDlZtHJO0xs6jqGuqYbR/jTtsv2lBPOe2u55NWiaCoxpPxq4HngfcCwwADwARK2N7i7hpmTepzX6XBXpt11Jv690I/C1wEL3HziBfwOT83gy2dRiznrLWTxxGe7+YC6DtNBDhPH9v+aMo9ztug/wazKW70lj1mlnnnKd83EC/xlkfr+5FXBIbbC1AKfSUhVwAeAtfGCBa1hoDyIioSi9p1iSavR7u/BevRbmzg1+6iuvwIab/e8br6+l97YrOfHDG31r81tjaP/7GDwDQK/dTLPferpp2UlA+kErPRPPOVJTdrLhVnLx/Sx7aG32LihGWpXkp4XulePDsTur6iMTF9fGhmL8es0e9mzd12tttDkpPetZnKukFd+LIh9harMb094A/CdONZVkfcCbrY1mypkvCLOanQSlVG2m2Q34/Tu29k9eHUmTznKv++9m8hgwu4avM5dBv7sSUjEL0dU3WbZpZ2nSgbbgpN0kVxfyyj4Vqc8Ebd9e4AXgNcBB4FHgS6rbL5IdjfQXz0SYv32HU0Un7PnVIZ+BfguMLm7k+Y4VDHzovNEFn/qXmtkv70nZ2Q4e2wTnEg/6000WWx/DbIkw2fQrhhmNYJNHkqZaGc5yWI//gdFvVE6fZ+Xp9Ab8ANWzIrzh74+xe7bu8/sdvjzd52RkUXL0eiYD/i8DX8KZHPsNnNHwrwIfKMlGZ/47DPW3FzDqfwjnfTxPnsfNBv+AH5L212G75Lq/K99uwT4nDtleQQj6zC6KnySkOZHIJRUp6Jg1l4X26BzWJyIeCvqLpwkmc/m9Ab/fKP7b3lXL8rOdaP/JZ6rw9o45XDuTXRuv2P/KmvOOAnZFsOt/P/+Ctywd3vqJ2oOTaaijM+vZdk0HzJlYFLjT/S4f5kT+x57Ok8xiiCFm8SSn22d5Na3uY8xqVl70bRYHtD+p1DKcJddDa1cL3RAuBWqqlTU9Evh+9nUn1EwEuN70rCNExgDZmHYDfMS9bxj4vLXRUeAmY9qvAV4N/C9j2udZG91b7A22m+kyqye2fXJ0fHISb+i/PTdg/k/gj+6i31kbHTWmPbD3SliDNASN9O8C3xH++5gcVZ8Y8fcJ9uPi9fNvx7kiNcNdvhhYi5sJ6t434N53pzHtnaReTQjzmWUz6JHJdOkhIVKRFPQXTwyo8svlTxaJwJ+93gn4LTCnaSbm2AixFw6wv37R+O8+9OHfHPrfb33jTDtiDu6vO+GZvUtuee6e1qN23rsstnzD5yINL+xi8Ngmtl3TwfaLVuEeTzLtdDuf5dUznuXV3mUzcA/q8UoYIwP11Df6zm/TTtgji7kMOqhl0EL3bOBp4Hh3UWC1o6D5JtnMQyHgd2KMKenvpBjpHHkIE+ydiJPnDfAHN+CP+w1O0F8NnAH8pOBb6MMN8LP6PT83Pjt2glNfP/kzf5XnYbXuiHbetrGci7mXWg57Fw8D6wOusHibiMWvuLyF1BH7ZKmdGyZPXKuAUZz5CPFa+Itxgv87ic93CLG/KvD8lUKeQIhIEgX9xROBlI64vk45GWa706IM8ObmIc64spZbL7qyv/E7PHHtI7e/a8Hrv0T/cfO543MrIjXvH50d22PYfvGlZvvFlyau7DDwH+7l3fRl6jId1DuB+t/+yxJO++jjVM9IKAeqnXDuKu6glk2QXSJf9GxLIJ/5JvFGWckBUXw5AYF/2X8nJezgG1aYk1NvUJw8dO29XSnNk1J+z0O2mmuHz4/X0E/3mb+OE5dEOHs5zGqAn3wPdv0up43Y7paVX842GhjkMDX993MBv+LMO3EHi+KPXXHJU3Ret42m4wbZ9XwD67+4nK33nFoPXEVq7f9s1fosi58UxLsCbyFEOlDYVKSMFtou+gyoBK1IUSjoL55dwOKGOZkD/3PelLps5uFRPrrxzsYZPz78rpkHDwFwzHP9rPv0rZzc+zveNP4rFgz2s6u+ibbT/y7WdeKlBthFDevt/ynIiHMTwJ5HnYe84X/voG7+MO7Ivybx5qiVnq5upxFvwkGtzJN4QwXZpdBC958DHweGgFkZHh6UguIXEAXOm8gyPatYKm2uR74nQpWXCtVvu2icDCifG58du3b4/Kqtowm9nbyf+Z8mltbMiPC2i6HajZP9J2hZQr7v7Zw6EfwDc5k8FicE/JtuuJdZ9c4VgeYTBtl0w70AbL3n1HwD/jDqcdKK1lDKK1BOgK/ji0gRKOgvnvXApuXLqL+7J/hBixbC8cf53zfn4QOYpGJkM0dGuXDjg0TcCsjNw73c9uiVkbt+8eFVyR13w2wfwQf1iZOCPY8ungj+cSphaIecBzfAr4jPMMsgu9jbUoPznYwAnwO+nuEpQVerggKiwHkTFVBqtqLmeoRM2fiT5//J9cjmeP7/YlE2Mhf9kwGlm9LjJ/6ZP4uT8z6f2DgYz9fqlZecnyYCTunkQzjpMmFLd3r5Hoc7r9s2EfDHzao/TOd129h6T8ma0C4GKHhDOREpCzXnKhYnAF+zdAnU+WVWuv4sXRLFAf/Fkf2Jt2eND/Nc3XF3mKeDW84nC9FIq3TNhaQsfILscrsWWALcDdwT4vFBOfe+ranTPL4SBG1b2bbZ2miXtdFma6MR92fySVE8KAZ4rTHt3nSRN7o/x3Cam1WioM82ZpwTgmdxSkPC+Bg88RM4NAJPPwr73XnJTa8H2E+42vVZaTrOv8pP0PIiyr3RlohUFAX9JXDB+f7LZ82CN77B+X//ANy40an2AzBuIqOmOqAD4lGpixaN7KkCNmUb+PfQ2txDa8T9OVm2zhnNTzkp0Cj/tJJtkF00LXSfhHPiMQisC/m0oBPTWwKWV/IJ65Q4yTamfaUx7TtTgmJn0ujfGtM+z5j2T8JEhYDvl6JyT46CuvBWMdlZdhlOjXjY8Qvo+jI8+oDzqLqj4NRzD+C7R87frucbsloe0m6y6yIM6i4uMm0o6C+uDQBLl8DZZ6beedYZUO1e2P3lY07u/9098Pdfhy/Xv7Nmww1XMV4VSajab6uBP0td1676Jshx59xC98oWune20B1zf64EJ/C3m2m2m4m4PxXwTxM5BtnFdCswE7i2h9a+ME9Ic7VqXcDyiv3+uqPoqSfZFdTB1zPZeDGJQfEe9yHX4nRM/YZ7+wXgr4u1LfGTD/dn1iPRPp+53xWieuAA8171FLPmOKXW6o6C154OF3wEdvxiFkWav7D+i8sZGq5JWDY0XMP6Ly7PdZUj1kab3IZZ2badVklhkWlAHXmLyLYZ6z0a/OABeOwJ5/+RCFzzCadqz8GD8LWb4LAnfTPeefe0mtihj3/m9v0zRw41vnRcI7/88Jm8c//DzDw8eS4wVFXPmnNuY+uJqwDsF95oLiUpFzdq/fP91XH3yNRC92+AU3CCnV3A13BPUsmzek+7MSmlJ4O+f+62LAd+DPwe+JC7TYuAH7gPeRqn4dOuHlpfznW7JD9pmjDtBv4VuASnIdcA8ABwvbXR3UXYjsB9VrqTpEwlUd2rF34BvLU2GjEnff8lznh7I7MaYGgQHtsGz+4oxFsKFFC9J59VrnLnazwOJA9FDbv//HoR+HbsFZGpRRN5C63NrAQ22KQd5/Yd8Lgns9VbpvPJpxIDfoCq4VFec813uPS4GTNmjhyacbBuRv+d6/9y9sMffPuMZ558PX/10FYaXxlgz1GLuPbUL8UDfs64+9sD+JT+azeGgMCr0qqGSJG10N2BE/DD5Ijtlz0PqWuh+3RyCLLdgD+b7x9M5kK/DnjC5/5TcPLCLwO+k832SEEFjfYeb230auDqEm1H1vuskCVR01c0+8OT8/nDk/lteZa23nNqoSft3uFO0H6ayaA/fuIfTyWrqJLCIlI4CvoLyQn4NwMzvMNFfl15dzzt/EvH9u/nGOvM2q0bPtR49Se/NVo1Ot6/7dJ3zP/JqcsGnuo7c/bzg80zPE8ZvqDzasjugFhRVUOkJNb4LPN+j/IJskMHZEkNtKTyVUpjuVz2WWG+l6ErmiUJXaqzAlThvEfv5OqV1kb/MX6jgI22RKTCKOgvrE4SgycAwnTl9dOQNOOiajxWe/U1twxdfem3FhABM0jK5erakaE7A1YXdEDM+UBegU2dpqQsu8cW4nUWFHrdHqECskxpZS10N+NMFAV9rypF2ZuYuXLZZ2X8XoYoUxr0/rfg1LOfbCYFd1K5JwL1JKb2JJRULVijLRGpOAr6C8v3wJKuOVcsYrD1M4gcOJhwhKgBlvuX+pw8SJ2S2nK+3TlgZXNAzOdAXjFNnaaqNF1lg7rHFup10sknyA4bkCmtLIRMeeilFLJ2fyn47bMscF+a54T6XqYLeDO8/4TJ8Ma0B+2HK0X86FLJJVVFpMAU9BdWyoFle6Z5XtVV9N7iZFsc17aV2l0DHF40b3x5ZLRq6bBvof5MI/BZBfGZupG20L2RyQ6n48AtPbSuq6SmTlNcqYJfv9fxGmEyEMhH2O9f2pHXHlp3UrkjpSURMg+9pCphFNgNvt8CrGXyO2KAy4xp/0XAZ1OQqxRZvP/1OFcAajM9sMwquaSqiBSYSnYW1nogYUrutofTPyEyOsZxbVs9S5zE/0cueYs9NLM2ubRSxoOUO1kypfRfuuopQfX63YD/E0x2OK0CPtFC983k0NQpqDToEa5UcyrSra8XuKKHVuP+yzmVJovvX8U1o6pA6U4Ij3QXkXpSGPjZlLokqrvey4qx7gIqWklVEalMKtlZaG1mH57OjO0hD8/JM8HG62upu2IZVzz8uG18fgATzxXtDw7eC62F7jEmA36vGE7AfzfOQSNj7nWhSoOWKv+9VFro3ol/GkBvD63NU+11wlKp2MwylZAs9fZUkqny2RjTXokH2FdwmvEVpaSqiFSuitk5TgtO9Z6EVuwNc8I9NfnoVTU8yqFvPMjXB41Z/aWrBui3zbkG/HmMsPsF/OB8b7Jt6pT3qKUnUPQ2B9o0xa8YlKoTa0V1fE3TWEsB/yRdDQmmzyY3vdZG51kbvUwBv8iRR0F/YaUEsMuXQSTHzGQDzOjt54RPbW50659nzS9QPvqOn26+/qiZL7UbE2s3Zmeadft1qIwL3TnVVYg0lmmX7lCq4LcSg+ygtDKZUFEnahVmqnw2veXeAI9K/HxEpISU3lNIbSblkvP2H8P3f5k+eg6pN2ptc7ZPSk7rmHfXIyz+2K1UDY96HzaMT961J6c/2SvAO8iic2oh0kta6A68pN9Dq05gZdqppOo9lWYqfDYB3YPLoZcK/HxEpLRUvaewUqr3bHu8IAE/5D6xM+F5x7VtTQ74IaBajFulByar98Rz+eeSfefUQlTPqJTmQCIlUQnVcipVJXw2mU48PGU+7yr0a791xQArO/cwv2mUgV21dK1fxM+3zvd9rLXR5kK/vohMPRodLayUOtGDBYr458whPmcgWwkBce2ugaDHBZ1U/AJ4DmdUP/DJmRQovaSwl/QbzUoazU4aTcz9OZXnBohICXlG8RPmGLnLJ7gnAQVN83nrigGu3NTLguZRIhFY0DzKlZt6eeuKnHfRInIEUNBfWBclL6gNmc+fLsmqphreuQxDbrnrCYHyaJP/SBA+o+U+8wEWuOtaFS/vCJzoecrj7vLv+L1AvjncBc1LdwL8hAP2wbrauzbc+fGXpvjEYBEpjWzmGAUNWKyyNmqAg9m88MrOPcyclXjUmDnLsrJzTzarEZEjjNJ7CithtHz7DhgNOWUi6Nygrg4uOB+WLkldfxjJzbf6Pv+BgcVX3jbHjMe8TWOCRssrrmuqG+Anzz3IpYxnynubOTLKihv+uXHbpe8oeEdcqVztxiwFosDbgTnAbuB7QGfU2qFybptUtNDFCUJ0M76cLFKA5jelpGimXS4iAgr6C20AaIzfyNSYKx1j4H0XTwT7cTnlricEype30n7FLSlBckDzrowHNW/n1HhpUEpYQ9+n3nu8jGemoN33vTU+PwBlPrGR0mk35s3ANhK7Eb8G5yT4L9qNWRa1VpGU+MlqjlGIOQjJ7VoCDeyqZUFz6tdyYJdvA+D9YdYpItOfgv5C8anRP7gv99VZmxLw55S77jsK7gT4YQLa0Ae1PILvfOV6NcL3vfUfN5H+VOiOuFKZvsFkwN8CPAx8DbgCeDPwSeCrZdkyqXSFKE4Q10nIgB+ga/0irtzUm5Dic3DI0LV+kd/DD+WwPSIyDSmnv3A6gRneBTmW5weg+qjq/STlrtORXXOuAjSz8stDtUCTT5OvctXQz63+/2pmcSUk/zvmvf30tH2QzV+6KlbYzZRK027MXCDeQfq/o9beG7V2P7DR87CPlH7LZCpwR+5T5hjlWBYzq0GGn2+dz61rFvPSzlpiMXhpZy23rlkcVL0ncCKXiBxZNNJfOE3g5PFvezi/Uf7x+lp23rj6EJd/qznPbcpqFNzvqgDOQS2+DCbPZZJH8gvRfCsXuZXxrGEITypWshmHD1XUjDjlnRfFzBCPeWO7MTOj1mY10VKODAUsGxq0Hwv0863zA0t0+qxbRERBf0G4pTR/8AA85le9PgvWQP9HljFw+TsaW+i2OLXxDbnlyIcOxIPSc3Cq4zQHNNfynkCUq4Z+bpfYOyYbncVPdj60rbtp5U+6DcDskaFvFHxLc6S886L5E/ACcCxwUrsxF+Ok96zzPCYCzAOy6T4tki2//Vgh1y0iovSeAuncvgOTb8APYCzM/WHCiiJkSM2JT6BtoTuWlHYTFHD7Lc+UnpPpBCLvGvpp3kegQpTx7KG1q6ftgyet/En3C+6iIeD2sM8vgeS88zlMbl8871yyFHXakUc9i3qAfTj5/F6HS7ZRckQKSBUqyARcdeEVkTjjHPckL20mduNGTD4pPV7WGJ6IfS/o7t4eWpvjN1ro3gisJXEKwTDOAQT8R4/6gau9gXEL3fErCimb00NrJGCkH9z27kymAMW79mZ1ZcLnSsPE+yhJ6Uznak28ZN636LBri/6aIbh553vdm/8dtfZ17vLTgCfd5duj1p5aju2bDtqNWQFci9NRuh/4PvA+nCsAB4CGqLWa4yEl5Wn+ldfov9sHQEREI/0FMlCogB/SNtACz4i7GygnB/zgjtB7RsH7k+5vxHPVwP0ZFNTEWzwGjeTfR+Jk4SpghOxTkco1ETjuU+5PizOyXilC550XfUumqai1W6PWnhG1dkbU2uOAG4FXuXc/rIBfysFn9F9EJC8K+gtjRsOc7J90di1UJ4Xr4/W1PN+xAoB5dz3Ckua1nBn5S5Y0r2XeXY8AxDxpL+nKvDXBRPqL30TPeqDTM8JeFbCe2S10rwxKo8HpQlyIYL1cE4GhzbwJOMe99RAd9pmiv2Z48bxzcPPO242ZjX/euWTQbszKdmN2thsTc39e127MJe3GHNNuTH27MecC/4Lzd2WBG8q7xXIkszbaZW202dpoBFiFgn8RyYMm8hbG7OXL4N4fwuGxcE84uxbeOxuaDsKDoxH2j1lGm+bzfMcK9q46l3l3PcLij91K1bAzP3NGbz+LP3YrQNXeVefe1UJ3pu6NEw2zCK4K0YT/CLvXDPcxXQHdcO9Ms+5slGsiMEyO8gNsKMHrhRa11rYbEwVudRf1BDxUeecZtBvjN1k9SlKpXY9o1NqflmLbRDLxdPW9ncRJ/emMF2+LRGSq0Uh/gSxdAhdfCGFH/H89Cu0DcPcQjI/FePa769ix82b2rjoXgOPatk4E/HFVw6Mc17Y19DZ5RvGD7CJccJ7uMdlMFk4n74nAOWkzrwI+6N76PXB/UV8vB1FrbwNW4uTwjwJ7gG8xeQXgAPByebZuSvE7wZ0BHMS5onIYZ/7Ej4ALotb+bWk3TyQ9d+S/3s3TX0X6yb4xa6Ma2BORCdohFNDSJc6/7Tvg7qDxWJd3WHbEQvPqbwFMBP21vclp+KRd7mOc9KP48YC6k8z1odMF8AXpStlDa1cL3ZDcPbhQk3idibqJPQicZmdXAvHe9TfRUZkz26PWbgUmzvjajXkdcJV7U3nn4QSdvM6IWntsSbdEJE/eHgHupN+E/Zuq9ohIMgX9hbEfmB2/se3h7FcQGR3juLatE0E/VREY94njqkJfnLkFZ5JvkDWeQDtdhYi0AXyYYN1Nq0i4P2pTuwv7pQ8VRJtvWscm2kwVk4HzIPCdLi9iNAAACvhJREFUgr92jpI+sxdwAv4v4YzqnwXcjPLOs1XOFDKRoilgkzARmcZUsrMQ2sw+PEF/e671Zgx8/NRGGp8f4G/7/X8vFnjC/lPy4v04AW0Vzgj/LT20rktXZjOp7Kc3wIxX65lPAUbbffKowS3F6Rf4F0Wb2Yn/59DPZFfeG+mwny7J9mQQ8JkFuV5pKOFUxHdRRESkTDTSXxgTAf8PHsh9JXMicMxzTvpOQwQGfQb6x+bPTl50GLgqIDAPlXpTtBF2R7pSnKUKtILSOubTYSuxhnVQWlYM56TuAPBL4MaotXl8444sUWu72o2BEFedREREphsF/flqMyu373BSevKt1f8OTw2R5XXw/aHU0gtV+4aZd9cjk2lAcFvQSHw2efJhU3ByUL5SnJOmWlpH0GdjotbWBtwnIbjfaQX5IiJyxFHQn482s/LX29n8g/vDl+pMx1tMf+lMuH/I6XLlFTk8npj779TJX0eAMKP4AaUMN7UbQwEC/0oIuAsy2biEKuEzExERkWlEJTvz0/lvP2VGIQJ+gEdH4Ma9TinPG/emBvxxtbsGvDcLMWJezG645SnF6eVU6UltLNZRsWkd5f/MREREZFrRSH9+mvJN6fHaZ5not+iXzx832jTfe7MQo78ZU3ByTf+pmDzqjqmT1lExn5mIiIhMG6rek482s/PGG1k8mDwmW0Sxmip2blnL3lXncvQdPz3UtPb2/VVDhyYq7eQSGLYbM4ZT+SfZeNTa6mJXPSnifAIRERERQek9+Xmc3y2vhpoSvmTsqJmxvavOtfO//ZP+5tXfMlVDhxpx6rXH8/BX5rDaoO9BfHnR0n88JxSLyf99iIiIiIgPBf35+C3Ll9bAxbOgrkSFH6v3DpkeWiPNV9wyZMZjyZVccg3Eg1KE4suLWYGnmPMJRERERAQF/fkZclJils6E2tJVe4+1GxPDv7oL5BaIZ5o4mumkIB+VUNJTREREZFpT0J+PWUxMt0038bbAqnDSYAJlmxrj5s+nVLfx5NUXs5pMMU8oRERERARV78ldm1nJOVh+Bow56T0jlTEn2pBDt9t0TYuKXE1mqtXQFxEREZlyVL0nV21mJ7CY/wZ+CV/uDa6rXwY2au2UuYqj6j0iIiIixaWgP1dtJoYnzaa9sqad9katbS73RoiIiIhIZZgyo8GVzpRuIq/XIWA0aZlSY0REREQkgYL+XLSZlSRNpi3BBZN+YH/S7dXAZQRPwBURERER0UTeHCUk82zfUfTXexB4G4mTXesh/QRcERERERHQSH+uEmrI3/9gQdY5BgQV/lyOGliJiIiISI4U9OdmwHtj5GBe64oB34xaW0Nw/f2qgOVqYCUiIiIiGSm9p7ySq+zswr/T7jj+gb8aWImIiIhIRhrpz838AqzDr8pOUOfbWwKWq0qPiIiIiGSkoD83+SX0OFV4UqrsuLfXkFqNZ13Ack3gFREREZGM1JwrF20m4UPLsjHXg1Fr313Q7RERERERSUMj/dlyavRPCFuu0z1LWKWAX0RERERKTUF/9rbE/7N9B9z7w3BPeva7n7RKxxERERGRclDQnw1nlL82fnPbw3B4LPPTTE2EvZeeq0o7IiIiIlIWCvqzs8F7Y3BfuCctf3+dRZV2RERERKRMFPRnp9F7o2FO5iecfSa85TVD9NCq1B4RERERKQsF/Xk46bXp76+tgfe+B4yaaImIiIhIGakjbw6274D7H4SRDNX6L7oAgFGU2iMiIiIiZaSgP0vxij1hJvAuXQLAPjpUtUdEREREykfpPWG59fnDVuzxmF+MzRERERERCUtBf3idEL5iT83kNRTl84uIiIhIWSnoD68JwlXsAbj4QgAOoXx+ERERESkzBf1ZWr4sYRQ/kJvPv1r5/CIiIiJSbgr6Q7JgwAnm3VH8QO7VgG8q4BcRERGRSqCgPwfuKH6g5cuADruuFNsiIiIiIpKJSnbmKJo+U39/iTZDRERERCQjjfQXR2+5N0BEREREJE4j/WG0mZf8Fm/f4dTtH9wH71oObz5n4q5/KNWmiYiIiIhkYqy15d6GytdmsvmQhoDj6bCvFGtzRERERESyofSewrtDAb+IiIiIVBKl92TJm9LTMMep1JNUzecbZdkwEREREZEAGunPwg8egLt7nIAfnJ+PPZHwkAfpsM+UYdNERERERAJppD+TNjNsLfzwRykBPgBnnpFwc0OJtkpEREREJDSN9GdgLXU7fuMf8M+aBW98w8TNV4D7S7dlIiIiIiLhKOgP4f4H/ZefdQZUT14r+TwdKoUkIiIiIpVHQX86bWYlwMjB1LsiETjbTe0ZH8cC3yndhomIiIiIhKegP70txvjfccrJMHu28/+qKjbQYQ+UbrNERERERMJT0J9eLUBdXeodO56Gzq+4Nzrsp0u4TSIiIiIiWVHQH8IF50NVVeKyqiq4+AIAAjL+RUREREQqg0p2hhBvvuXblKvDvruMmyYiIiIikpGC/pCWLknpvCsiIiIiMiUovSdIm+lNd7dbnPOpkmyLiIiIiEgeFPQHa8r4iA57Wgm2Q0REREQkLwr6cxRUylNEREREpNIo6M+dqvaIiIiIyJSgoD9XqtojIiIiIlOEgn4/beZH5d4EEREREZFCUdDv710Z7lfVHhERERGZMhT050JVe0RERERkClHQLyIiIiIyzakjby7ajHX/10uHbS7npoiIiIiIZKKR/vzsL/cGiIiIiIhkoqA/WebKPV/w/P8firglIiIiIiIFoaA/VabKPVe6P4eA24u8LSIiIiIieVPQn539wEL3/3fQYV8p58aIiIiIiIShoD87T7s/LfCNcm6IiIiIiEhYCvq92szGDI84x/35EB32mWJvjoiIiIhIISjoT7Q25OM2FHUrREREREQKSEF/IhPiMb8H7i/2hoiIiIiIFIqC/uzdRIe1mR8mIiIiIlIZFPRnZxD4Trk3QkREREQkGwr649rMyhCP2kKHPVD0bRERERERKaDqcm9ABUk/ObfDhsn3FxERERGpOBrpn9SY5r7+km2FiIiIiEiBKegP5+pyb4CIiIiISK4U9E8KGs3fT4ftKumWiIiIiIgUkIL+SVcDh5KWHQKuKsO2iIiIiIgUjIL+OGc0fzXQC1j352qN8ouIiIjIVGes+kyJiIiIiExrGukXEREREZnmFPSLiIiIiExzCvpFRERERKY5Bf0iIiIiItOcgn4RERERkWlOQb+IiIiIyDSnoF9EREREZJpT0C8iIiIiMs0p6BcRERERmeYU9IuIiIiITHMK+kVEREREpjkF/SIiIiIi05yCfhERERGRaU5Bv4iIiIjINKegX0RERERkmlPQLyIiIiIyzSnoFxERERGZ5hT0i4iIiIhMcwr6RURERESmOQX9IiIiIiLTnIJ+EREREZFpTkG/iIiIiMg0p6BfRERERGSaU9AvIiIiIjLNKegXEREREZnmFPSLiIiIiExzCvpFRERERKY5Bf0iIiIiItOcgn4RERERkWlOQb+IiIiIyDSnoF9EREREZJpT0C8iIiIiMs0p6BcRERERmeYU9IuIiIiITHMK+kVEREREpjkF/SIiIiIi05yCfhERERGRaU5Bv4iIiIjINKegX0RERERkmlPQLyIiIiIyzSnoFxERERGZ5hT0i4iIiIhMcwr6RURERESmOQX9IiIiIiLTnIJ+EREREZFpTkG/iIiIiMg0p6BfRERERGSaU9AvIiIiIjLNKegXEREREZnmFPSLiIiIiExz/x86rdeV54INwAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.manifold import LocallyLinearEmbedding\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_lle_reduced = LocallyLinearEmbedding(n_components=2, random_state=42).fit_transform(X)\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"LLE took {:.1f}s.\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_lle_reduced, y)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That took a while, and the result does not look too good. Let's see what happens if we apply PCA first, preserving 95% of the variance:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 279,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PCA+LLE took 38.1s.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"pca_lle = Pipeline([\\n\",\n    \"    (\\\"pca\\\", PCA(n_components=0.95, random_state=42)),\\n\",\n    \"    (\\\"lle\\\", LocallyLinearEmbedding(n_components=2, random_state=42)),\\n\",\n    \"])\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_pca_lle_reduced = pca_lle.fit_transform(X)\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"PCA+LLE took {:.1f}s.\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_pca_lle_reduced, y)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The result is more or less the same, but this time it was almost 4× faster.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try MDS. It's much too long if we run it on 10,000 instances, so let's just try 2,000 for now:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 280,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MDS took 119.8s (on just 2,000 MNIST images instead of 10,000).\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.manifold import MDS\\n\",\n    \"\\n\",\n    \"m = 2000\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_mds_reduced = MDS(n_components=2, random_state=42).fit_transform(X[:m])\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"MDS took {:.1f}s (on just 2,000 MNIST images instead of 10,000).\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_mds_reduced, y[:m])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Meh. This does not look great, all clusters overlap too much. Let's try with PCA first, perhaps it will be faster?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 281,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PCA+MDS took 121.2s (on 2,000 MNIST images).\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"pca_mds = Pipeline([\\n\",\n    \"    (\\\"pca\\\", PCA(n_components=0.95, random_state=42)),\\n\",\n    \"    (\\\"mds\\\", MDS(n_components=2, random_state=42)),\\n\",\n    \"])\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_pca_mds_reduced = pca_mds.fit_transform(X[:2000])\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"PCA+MDS took {:.1f}s (on 2,000 MNIST images).\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_pca_mds_reduced, y[:2000])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Same result, and no speedup: PCA did not help (or hurt).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try LDA:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 282,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\\n\",\n      \"  warnings.warn(\\\"Variables are collinear.\\\")\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LDA took 1.9s.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_lda_reduced = LinearDiscriminantAnalysis(n_components=2).fit_transform(X, y)\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"LDA took {:.1f}s.\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_lda_reduced, y, figsize=(12,12))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This one is very fast, and it looks nice at first, until you realize that several clusters overlap severely.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well, it's pretty clear that t-SNE won this little competition, wouldn't you agree? We did not time it, so let's do that now:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 283,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"t-SNE took 199.5s.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.manifold import TSNE\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_tsne_reduced = TSNE(n_components=2, random_state=42).fit_transform(X)\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"t-SNE took {:.1f}s.\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_tsne_reduced, y)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It's twice slower than LLE, but still much faster than MDS, and the result looks great. Let's see if a bit of PCA can speed it up:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 284,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PCA+t-SNE took 124.8s.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 936x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pca_tsne = Pipeline([\\n\",\n    \"    (\\\"pca\\\", PCA(n_components=0.95, random_state=42)),\\n\",\n    \"    (\\\"tsne\\\", TSNE(n_components=2, random_state=42)),\\n\",\n    \"])\\n\",\n    \"t0 = time.time()\\n\",\n    \"X_pca_tsne_reduced = pca_tsne.fit_transform(X)\\n\",\n    \"t1 = time.time()\\n\",\n    \"print(\\\"PCA+t-SNE took {:.1f}s.\\\".format(t1 - t0))\\n\",\n    \"plot_digits(X_pca_tsne_reduced, y)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Yes, PCA roughly gave us a 25% speedup, without damaging the result. We have a winner!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "09_up_and_running_with_tensorflow.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 9 – Up and running with TensorFlow**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 9._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/09_up_and_running_with_tensorflow.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions. In particular, the 1st edition is based on TensorFlow 1, while the 2nd edition uses TensorFlow 2, which is much simpler to use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    # %tensorflow_version only exists in Colab.\\n\",\n    \"    %tensorflow_version 1.x\\n\",\n    \"except Exception:\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"def reset_graph(seed=42):\\n\",\n    \"    tf.reset_default_graph()\\n\",\n    \"    tf.set_random_seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['axes.labelsize'] = 14\\n\",\n    \"plt.rcParams['xtick.labelsize'] = 12\\n\",\n    \"plt.rcParams['ytick.labelsize'] = 12\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"tensorflow\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Creating and running a graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"x = tf.Variable(3, name=\\\"x\\\")\\n\",\n    \"y = tf.Variable(4, name=\\\"y\\\")\\n\",\n    \"f = x*x*y + y + 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'add_1:0' shape=() dtype=int32>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"42\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sess = tf.Session()\\n\",\n    \"sess.run(x.initializer)\\n\",\n    \"sess.run(y.initializer)\\n\",\n    \"result = sess.run(f)\\n\",\n    \"print(result)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sess.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    x.initializer.run()\\n\",\n    \"    y.initializer.run()\\n\",\n    \"    result = f.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"42\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"result\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    result = f.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"42\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"result\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"42\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sess = tf.InteractiveSession()\\n\",\n    \"init.run()\\n\",\n    \"result = f.eval()\\n\",\n    \"print(result)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sess.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"42\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"result\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Managing graphs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"x1 = tf.Variable(1)\\n\",\n    \"x1.graph is tf.get_default_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"graph = tf.Graph()\\n\",\n    \"with graph.as_default():\\n\",\n    \"    x2 = tf.Variable(2)\\n\",\n    \"\\n\",\n    \"x2.graph is graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x2.graph is tf.get_default_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10\\n\",\n      \"15\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w = tf.constant(3)\\n\",\n    \"x = w + 2\\n\",\n    \"y = x + 5\\n\",\n    \"z = x * 3\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    print(y.eval())  # 10\\n\",\n    \"    print(z.eval())  # 15\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10\\n\",\n      \"15\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    y_val, z_val = sess.run([y, z])\\n\",\n    \"    print(y_val)  # 10\\n\",\n    \"    print(z_val)  # 15\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linear Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using the Normal Equation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"housing = fetch_california_housing()\\n\",\n    \"m, n = housing.data.shape\\n\",\n    \"housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]\\n\",\n    \"\\n\",\n    \"X = tf.constant(housing_data_plus_bias, dtype=tf.float32, name=\\\"X\\\")\\n\",\n    \"y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\\\"y\\\")\\n\",\n    \"XT = tf.transpose(X)\\n\",\n    \"theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    theta_value = theta.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-3.7185181e+01],\\n\",\n       \"       [ 4.3633747e-01],\\n\",\n       \"       [ 9.3952334e-03],\\n\",\n       \"       [-1.0711310e-01],\\n\",\n       \"       [ 6.4479220e-01],\\n\",\n       \"       [-4.0338000e-06],\\n\",\n       \"       [-3.7813708e-03],\\n\",\n       \"       [-4.2348403e-01],\\n\",\n       \"       [-4.3721911e-01]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theta_value\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare with pure NumPy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[-3.69419202e+01]\\n\",\n      \" [ 4.36693293e-01]\\n\",\n      \" [ 9.43577803e-03]\\n\",\n      \" [-1.07322041e-01]\\n\",\n      \" [ 6.45065694e-01]\\n\",\n      \" [-3.97638942e-06]\\n\",\n      \" [-3.78654265e-03]\\n\",\n      \" [-4.21314378e-01]\\n\",\n      \" [-4.34513755e-01]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X = housing_data_plus_bias\\n\",\n    \"y = housing.target.reshape(-1, 1)\\n\",\n    \"theta_numpy = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\\n\",\n    \"\\n\",\n    \"print(theta_numpy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare with Scikit-Learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[-3.69419202e+01]\\n\",\n      \" [ 4.36693293e-01]\\n\",\n      \" [ 9.43577803e-03]\\n\",\n      \" [-1.07322041e-01]\\n\",\n      \" [ 6.45065694e-01]\\n\",\n      \" [-3.97638942e-06]\\n\",\n      \" [-3.78654265e-03]\\n\",\n      \" [-4.21314378e-01]\\n\",\n      \" [-4.34513755e-01]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LinearRegression\\n\",\n    \"lin_reg = LinearRegression()\\n\",\n    \"lin_reg.fit(housing.data, housing.target.reshape(-1, 1))\\n\",\n    \"\\n\",\n    \"print(np.r_[lin_reg.intercept_.reshape(-1, 1), lin_reg.coef_.T])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using Batch Gradient Descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Gradient Descent requires scaling the feature vectors first. We could do this using TF, but let's just use Scikit-Learn for now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"scaler = StandardScaler()\\n\",\n    \"scaled_housing_data = scaler.fit_transform(housing.data)\\n\",\n    \"scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.00000000e+00  6.60969987e-17  5.50808322e-18  6.60969987e-17\\n\",\n      \" -1.06030602e-16 -1.10161664e-17  3.44255201e-18 -1.07958431e-15\\n\",\n      \" -8.52651283e-15]\\n\",\n      \"[ 0.38915536  0.36424355  0.5116157  ... -0.06612179 -0.06360587\\n\",\n      \"  0.01359031]\\n\",\n      \"0.11111111111111005\\n\",\n      \"(20640, 9)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(scaled_housing_data_plus_bias.mean(axis=0))\\n\",\n    \"print(scaled_housing_data_plus_bias.mean(axis=1))\\n\",\n    \"print(scaled_housing_data_plus_bias.mean())\\n\",\n    \"print(scaled_housing_data_plus_bias.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Manually computing the gradients\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0 MSE = 9.161542\\n\",\n      \"Epoch 100 MSE = 0.7145004\\n\",\n      \"Epoch 200 MSE = 0.56670487\\n\",\n      \"Epoch 300 MSE = 0.5555718\\n\",\n      \"Epoch 400 MSE = 0.5488112\\n\",\n      \"Epoch 500 MSE = 0.5436363\\n\",\n      \"Epoch 600 MSE = 0.5396291\\n\",\n      \"Epoch 700 MSE = 0.5365092\\n\",\n      \"Epoch 800 MSE = 0.53406775\\n\",\n      \"Epoch 900 MSE = 0.5321473\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\\\"X\\\")\\n\",\n    \"y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n    \"error = y_pred - y\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n    \"gradients = 2/m * tf.matmul(tf.transpose(X), error)\\n\",\n    \"training_op = tf.assign(theta, theta - learning_rate * gradients)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        if epoch % 100 == 0:\\n\",\n    \"            print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n    \"        sess.run(training_op)\\n\",\n    \"    \\n\",\n    \"    best_theta = theta.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 2.0685523 ],\\n\",\n       \"       [ 0.8874027 ],\\n\",\n       \"       [ 0.14401656],\\n\",\n       \"       [-0.34770882],\\n\",\n       \"       [ 0.36178368],\\n\",\n       \"       [ 0.00393811],\\n\",\n       \"       [-0.04269556],\\n\",\n       \"       [-0.6614529 ],\\n\",\n       \"       [-0.6375279 ]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using autodiff\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Same as above except for the `gradients = ...` line:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\\\"X\\\")\\n\",\n    \"y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n    \"error = y_pred - y\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"gradients = tf.gradients(mse, [theta])[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0 MSE = 9.161542\\n\",\n      \"Epoch 100 MSE = 0.71450037\\n\",\n      \"Epoch 200 MSE = 0.56670487\\n\",\n      \"Epoch 300 MSE = 0.5555718\\n\",\n      \"Epoch 400 MSE = 0.54881126\\n\",\n      \"Epoch 500 MSE = 0.5436363\\n\",\n      \"Epoch 600 MSE = 0.53962916\\n\",\n      \"Epoch 700 MSE = 0.5365092\\n\",\n      \"Epoch 800 MSE = 0.53406775\\n\",\n      \"Epoch 900 MSE = 0.5321473\\n\",\n      \"Best theta:\\n\",\n      \"[[ 2.0685523 ]\\n\",\n      \" [ 0.8874027 ]\\n\",\n      \" [ 0.14401656]\\n\",\n      \" [-0.3477088 ]\\n\",\n      \" [ 0.36178365]\\n\",\n      \" [ 0.00393811]\\n\",\n      \" [-0.04269556]\\n\",\n      \" [-0.66145283]\\n\",\n      \" [-0.6375278 ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"training_op = tf.assign(theta, theta - learning_rate * gradients)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        if epoch % 100 == 0:\\n\",\n    \"            print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n    \"        sess.run(training_op)\\n\",\n    \"    \\n\",\n    \"    best_theta = theta.eval()\\n\",\n    \"\\n\",\n    \"print(\\\"Best theta:\\\")\\n\",\n    \"print(best_theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"How could you find the partial derivatives of the following function with regards to `a` and `b`?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_func(a, b):\\n\",\n    \"    z = 0\\n\",\n    \"    for i in range(100):\\n\",\n    \"        z = a * np.cos(z + i) + z * np.sin(b - i)\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.21253923284754914\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_func(0.2, 0.3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"a = tf.Variable(0.2, name=\\\"a\\\")\\n\",\n    \"b = tf.Variable(0.3, name=\\\"b\\\")\\n\",\n    \"z = tf.constant(0.0, name=\\\"z0\\\")\\n\",\n    \"for i in range(100):\\n\",\n    \"    z = a * tf.cos(z + i) + z * tf.sin(b - i)\\n\",\n    \"\\n\",\n    \"grads = tf.gradients(z, [a, b])\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compute the function at $a=0.2$ and $b=0.3$, and the partial derivatives at that point with regards to $a$ and with regards to $b$:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-0.21253741\\n\",\n      \"[-1.1388495, 0.19671397]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    print(z.eval())\\n\",\n    \"    print(sess.run(grads))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using a `GradientDescentOptimizer`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\\\"X\\\")\\n\",\n    \"y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n    \"error = y_pred - y\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n    \"training_op = optimizer.minimize(mse)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0 MSE = 9.161542\\n\",\n      \"Epoch 100 MSE = 0.7145004\\n\",\n      \"Epoch 200 MSE = 0.56670487\\n\",\n      \"Epoch 300 MSE = 0.5555718\\n\",\n      \"Epoch 400 MSE = 0.54881126\\n\",\n      \"Epoch 500 MSE = 0.5436363\\n\",\n      \"Epoch 600 MSE = 0.53962916\\n\",\n      \"Epoch 700 MSE = 0.5365092\\n\",\n      \"Epoch 800 MSE = 0.53406775\\n\",\n      \"Epoch 900 MSE = 0.5321473\\n\",\n      \"Best theta:\\n\",\n      \"[[ 2.0685523 ]\\n\",\n      \" [ 0.8874027 ]\\n\",\n      \" [ 0.14401656]\\n\",\n      \" [-0.3477088 ]\\n\",\n      \" [ 0.36178365]\\n\",\n      \" [ 0.00393811]\\n\",\n      \" [-0.04269556]\\n\",\n      \" [-0.66145283]\\n\",\n      \" [-0.6375278 ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        if epoch % 100 == 0:\\n\",\n    \"            print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n    \"        sess.run(training_op)\\n\",\n    \"    \\n\",\n    \"    best_theta = theta.eval()\\n\",\n    \"\\n\",\n    \"print(\\\"Best theta:\\\")\\n\",\n    \"print(best_theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using a momentum optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\\\"X\\\")\\n\",\n    \"y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n    \"error = y_pred - y\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,\\n\",\n    \"                                       momentum=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"training_op = optimizer.minimize(mse)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best theta:\\n\",\n      \"[[ 2.068558  ]\\n\",\n      \" [ 0.82962847]\\n\",\n      \" [ 0.11875335]\\n\",\n      \" [-0.26554456]\\n\",\n      \" [ 0.3057109 ]\\n\",\n      \" [-0.00450249]\\n\",\n      \" [-0.03932662]\\n\",\n      \" [-0.8998645 ]\\n\",\n      \" [-0.8705207 ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        sess.run(training_op)\\n\",\n    \"    \\n\",\n    \"    best_theta = theta.eval()\\n\",\n    \"\\n\",\n    \"print(\\\"Best theta:\\\")\\n\",\n    \"print(best_theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Feeding data to the training algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Placeholder nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[6. 7. 8.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"A = tf.placeholder(tf.float32, shape=(None, 3))\\n\",\n    \"B = A + 5\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    B_val_1 = B.eval(feed_dict={A: [[1, 2, 3]]})\\n\",\n    \"    B_val_2 = B.eval(feed_dict={A: [[4, 5, 6], [7, 8, 9]]})\\n\",\n    \"\\n\",\n    \"print(B_val_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 9. 10. 11.]\\n\",\n      \" [12. 13. 14.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(B_val_2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Mini-batch Gradient Descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n    \"error = y_pred - y\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n    \"training_op = optimizer.minimize(mse)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 100\\n\",\n    \"n_batches = int(np.ceil(m / batch_size))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def fetch_batch(epoch, batch_index, batch_size):\\n\",\n    \"    np.random.seed(epoch * n_batches + batch_index)  # not shown in the book\\n\",\n    \"    indices = np.random.randint(m, size=batch_size)  # not shown\\n\",\n    \"    X_batch = scaled_housing_data_plus_bias[indices] # not shown\\n\",\n    \"    y_batch = housing.target.reshape(-1, 1)[indices] # not shown\\n\",\n    \"    return X_batch, y_batch\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for batch_index in range(n_batches):\\n\",\n    \"            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"\\n\",\n    \"    best_theta = theta.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 2.0703337 ],\\n\",\n       \"       [ 0.8637145 ],\\n\",\n       \"       [ 0.12255152],\\n\",\n       \"       [-0.31211877],\\n\",\n       \"       [ 0.38510376],\\n\",\n       \"       [ 0.00434168],\\n\",\n       \"       [-0.0123295 ],\\n\",\n       \"       [-0.83376896],\\n\",\n       \"       [-0.8030471 ]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Saving and restoring a model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0 MSE = 9.161542\\n\",\n      \"Epoch 100 MSE = 0.7145004\\n\",\n      \"Epoch 200 MSE = 0.56670487\\n\",\n      \"Epoch 300 MSE = 0.5555718\\n\",\n      \"Epoch 400 MSE = 0.54881126\\n\",\n      \"Epoch 500 MSE = 0.5436363\\n\",\n      \"Epoch 600 MSE = 0.53962916\\n\",\n      \"Epoch 700 MSE = 0.5365092\\n\",\n      \"Epoch 800 MSE = 0.53406775\\n\",\n      \"Epoch 900 MSE = 0.5321473\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000                                                                       # not shown in the book\\n\",\n    \"learning_rate = 0.01                                                                  # not shown\\n\",\n    \"\\n\",\n    \"X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\\\"X\\\")            # not shown\\n\",\n    \"y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\\\"y\\\")            # not shown\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")                                      # not shown\\n\",\n    \"error = y_pred - y                                                                    # not shown\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")                                    # not shown\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)            # not shown\\n\",\n    \"training_op = optimizer.minimize(mse)                                                 # not shown\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        if epoch % 100 == 0:\\n\",\n    \"            print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())                                # not shown\\n\",\n    \"            save_path = saver.save(sess, \\\"/tmp/my_model.ckpt\\\")\\n\",\n    \"        sess.run(training_op)\\n\",\n    \"    \\n\",\n    \"    best_theta = theta.eval()\\n\",\n    \"    save_path = saver.save(sess, \\\"/tmp/my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 2.0685523 ],\\n\",\n       \"       [ 0.8874027 ],\\n\",\n       \"       [ 0.14401656],\\n\",\n       \"       [-0.3477088 ],\\n\",\n       \"       [ 0.36178365],\\n\",\n       \"       [ 0.00393811],\\n\",\n       \"       [-0.04269556],\\n\",\n       \"       [-0.66145283],\\n\",\n       \"       [-0.6375278 ]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from /tmp/my_model_final.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"/tmp/my_model_final.ckpt\\\")\\n\",\n    \"    best_theta_restored = theta.eval() # not shown in the book\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(best_theta, best_theta_restored)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want to have a saver that loads and restores `theta` with a different name, such as `\\\"weights\\\"`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"saver = tf.train.Saver({\\\"weights\\\": theta})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default the saver also saves the graph structure itself in a second file with the extension `.meta`. You can use the function `tf.train.import_meta_graph()` to restore the graph structure. This function loads the graph into the default graph and returns a `Saver` that can then be used to restore the graph state (i.e., the variable values):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from /tmp/my_model_final.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"# notice that we start with an empty graph.\\n\",\n    \"\\n\",\n    \"saver = tf.train.import_meta_graph(\\\"/tmp/my_model_final.ckpt.meta\\\")  # this loads the graph structure\\n\",\n    \"theta = tf.get_default_graph().get_tensor_by_name(\\\"theta:0\\\") # not shown in the book\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"/tmp/my_model_final.ckpt\\\")  # this restores the graph's state\\n\",\n    \"    best_theta_restored = theta.eval() # not shown in the book\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.allclose(best_theta, best_theta_restored)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This means that you can import a pretrained model without having to have the corresponding Python code to build the graph. This is very handy when you keep tweaking and saving your model: you can load a previously saved model without having to search for the version of the code that built it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualizing the graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"TensorBoard is a great tool to visualize TensorFlow graphs, training curves, and much more. Our TensorFlow code will write various files in a log directory, and the TensorBoard server will regularly read these files and produce nice interactive visualizations. It can plot graphs, learning curves (i.e., how the loss evaluated on the training set or test set evolves as a function of the epoch number), profiling data to identify performance bottlenecks, and more. In short, it helps keep track of everything. Here's the overall picture:\\n\",\n    \"\\n\",\n    \"`TensorFlow writes logs to ===> log directory ===> TensorBoard reads data and displays visualizations`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we want to visualize different graphs, or learning curves for different training runs, we don't want the log files to get all mixed up. So we will need one log subdirectory per graph, or per run. Let's use a root log directory that we will call `tf_logs`, and a sub-directory that we will call `run-` followed by the current timestamp (you can use any other name you prefer in your own code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"now = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n    \"root_logdir = \\\"tf_logs\\\"\\n\",\n    \"logdir = \\\"{}/run-{}/\\\".format(root_logdir, now)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-20210325095200/'\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logdir\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In fact, let's create a function that will generate such a subdirectory path every time we need one:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_log_subdir(run_id=None):\\n\",\n    \"    if run_id is None:\\n\",\n    \"        run_id = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n    \"    return \\\"{}/run-{}/\\\".format(root_logdir, run_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's save the default graph to our log subdirectory using `tf.summary.FileWriter()`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file_writer = tf.summary.FileWriter(logdir, graph=tf.get_default_graph())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now the root log directory contains one subdirectory:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['run-20210325095200']\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"os.listdir(root_logdir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And this subdirectory contains one log file (called a \\\"TF events\\\" file) for the graph:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['events.out.tfevents.1616665937.kiwimac']\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"os.listdir(logdir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"However, the actual graph data may still be in the OS's file cache, so we need to `flush()` or `close()` the `FileWriter` to be sure that it's well written to disk:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file_writer.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Okay, now let's start TensorBoard! It runs as a web server in a separate process, so we first need to start it. One way to do that is to run the `tensorboard` command in a terminal window. Another is to use the `%tensorboard` Jupyter extension, which takes care of starting TensorBoard, and it allows us to view TensorBoard's user interface directly within Jupyter. Let's load this extension now:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The tensorboard extension is already loaded. To reload it, use:\\n\",\n      \"  %reload_ext tensorboard\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext tensorboard\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, let's use the `%tensorboard` extension to start the TensorBoard server. We need to point it to the root log directory:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-9bb87ff5937c48c\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-9bb87ff5937c48c\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Great! We can now visualize graphs. :)\\n\",\n    \"\\n\",\n    \"In fact, let's make this easy by creating a `save_graph()` function that will automatically create a new log subdir and save the given graph (by default `tf.get_default_graph()`) to this directory:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def save_graph(graph=None, run_id=None):\\n\",\n    \"    if graph is None:\\n\",\n    \"        graph = tf.get_default_graph()\\n\",\n    \"    logdir = make_log_subdir(run_id)\\n\",\n    \"    file_writer = tf.summary.FileWriter(logdir, graph=graph)\\n\",\n    \"    file_writer.close()\\n\",\n    \"    return logdir\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's see if it works:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-20210325095244/'\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's look at TensorBoard again. Note that this will reuse the existing TensorBoard server since we're reusing the same root log directory:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:00:45 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-6698e989b3fca11b\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-6698e989b3fca11b\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that you can switch between runs by picking the log subdirectory you want from the \\\"Run\\\" dropdown list (at the top left).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualizing Learning Curves\\n\",\n    \"\\n\",\n    \"Now let's see how to visualize learning curves:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n    \"error = y_pred - y\\n\",\n    \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n    \"training_op = optimizer.minimize(mse)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"logdir = make_log_subdir()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mse_summary = tf.summary.scalar('MSE', mse)\\n\",\n    \"file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 10\\n\",\n    \"batch_size = 100\\n\",\n    \"n_batches = int(np.ceil(m / batch_size))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.Session() as sess:                                                        # not shown in the book\\n\",\n    \"    sess.run(init)                                                                # not shown\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):                                                 # not shown\\n\",\n    \"        for batch_index in range(n_batches):\\n\",\n    \"            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\\n\",\n    \"            if batch_index % 10 == 0:\\n\",\n    \"                summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"                step = epoch * n_batches + batch_index\\n\",\n    \"                file_writer.add_summary(summary_str, step)\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"\\n\",\n    \"    best_theta = theta.eval()                                                     # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file_writer.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 2.07033372],\\n\",\n       \"       [ 0.86371452],\\n\",\n       \"       [ 0.12255151],\\n\",\n       \"       [-0.31211874],\\n\",\n       \"       [ 0.38510373],\\n\",\n       \"       [ 0.00434168],\\n\",\n       \"       [-0.01232954],\\n\",\n       \"       [-0.83376896],\\n\",\n       \"       [-0.80304712]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 75,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's look at TensorBoard. Try going to the SCALARS tab:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:02:08 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-6549bb5697dded37\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-6549bb5697dded37\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Name scopes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\") as scope:\\n\",\n    \"    error = y_pred - y\\n\",\n    \"    mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n    \"training_op = optimizer.minimize(mse)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"mse_summary = tf.summary.scalar('MSE', mse)\\n\",\n    \"\\n\",\n    \"logdir = make_log_subdir()\\n\",\n    \"file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best theta:\\n\",\n      \"[[ 2.07033372]\\n\",\n      \" [ 0.86371452]\\n\",\n      \" [ 0.12255151]\\n\",\n      \" [-0.31211874]\\n\",\n      \" [ 0.38510373]\\n\",\n      \" [ 0.00434168]\\n\",\n      \" [-0.01232954]\\n\",\n      \" [-0.83376896]\\n\",\n      \" [-0.80304712]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 10\\n\",\n    \"batch_size = 100\\n\",\n    \"n_batches = int(np.ceil(m / batch_size))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for batch_index in range(n_batches):\\n\",\n    \"            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\\n\",\n    \"            if batch_index % 10 == 0:\\n\",\n    \"                summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"                step = epoch * n_batches + batch_index\\n\",\n    \"                file_writer.add_summary(summary_str, step)\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"\\n\",\n    \"    best_theta = theta.eval()\\n\",\n    \"\\n\",\n    \"file_writer.flush()\\n\",\n    \"file_writer.close()\\n\",\n    \"print(\\\"Best theta:\\\")\\n\",\n    \"print(best_theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss/sub\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(error.op.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss/mse\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(mse.op.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a\\n\",\n      \"a_1\\n\",\n      \"param/a\\n\",\n      \"param_1/a\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"a1 = tf.Variable(0, name=\\\"a\\\")      # name == \\\"a\\\"\\n\",\n    \"a2 = tf.Variable(0, name=\\\"a\\\")      # name == \\\"a_1\\\"\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"param\\\"):       # name == \\\"param\\\"\\n\",\n    \"    a3 = tf.Variable(0, name=\\\"a\\\")  # name == \\\"param/a\\\"\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"param\\\"):       # name == \\\"param_1\\\"\\n\",\n    \"    a4 = tf.Variable(0, name=\\\"a\\\")  # name == \\\"param_1/a\\\"\\n\",\n    \"\\n\",\n    \"for node in (a1, a2, a3, a4):\\n\",\n    \"    print(node.op.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Modularity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An ugly flat code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_features = 3\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"\\n\",\n    \"w1 = tf.Variable(tf.random_normal((n_features, 1)), name=\\\"weights1\\\")\\n\",\n    \"w2 = tf.Variable(tf.random_normal((n_features, 1)), name=\\\"weights2\\\")\\n\",\n    \"b1 = tf.Variable(0.0, name=\\\"bias1\\\")\\n\",\n    \"b2 = tf.Variable(0.0, name=\\\"bias2\\\")\\n\",\n    \"\\n\",\n    \"z1 = tf.add(tf.matmul(X, w1), b1, name=\\\"z1\\\")\\n\",\n    \"z2 = tf.add(tf.matmul(X, w2), b2, name=\\\"z2\\\")\\n\",\n    \"\\n\",\n    \"relu1 = tf.maximum(z1, 0., name=\\\"relu1\\\")\\n\",\n    \"relu2 = tf.maximum(z1, 0., name=\\\"relu2\\\")  # Oops, cut&paste error! Did you spot it?\\n\",\n    \"\\n\",\n    \"output = tf.add(relu1, relu2, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Much better, using a function to build the ReLUs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X):\\n\",\n    \"    w_shape = (int(X.get_shape()[1]), 1)\\n\",\n    \"    w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")\\n\",\n    \"    b = tf.Variable(0.0, name=\\\"bias\\\")\\n\",\n    \"    z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")\\n\",\n    \"    return tf.maximum(z, 0., name=\\\"relu\\\")\\n\",\n    \"\\n\",\n    \"n_features = 3\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"relus = [relu(X) for i in range(5)]\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-relu1/'\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph(run_id=\\\"relu1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:04:52 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-88e2356596cb12df\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-88e2356596cb12df\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Even better using name scopes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X):\\n\",\n    \"    with tf.name_scope(\\\"relu\\\"):\\n\",\n    \"        w_shape = (int(X.get_shape()[1]), 1)                          # not shown in the book\\n\",\n    \"        w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")    # not shown\\n\",\n    \"        b = tf.Variable(0.0, name=\\\"bias\\\")                             # not shown\\n\",\n    \"        z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")                      # not shown\\n\",\n    \"        return tf.maximum(z, 0., name=\\\"max\\\")                          # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_features = 3\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"relus = [relu(X) for i in range(5)]\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-relu2/'\"\n      ]\n     },\n     \"execution_count\": 90,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph(run_id=\\\"relu2\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:05:43 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-57e36dbbc0430494\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-57e36dbbc0430494\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sharing Variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sharing a `threshold` variable the classic way, by defining it outside of the `relu()` function then passing it as a parameter:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X, threshold):\\n\",\n    \"    with tf.name_scope(\\\"relu\\\"):\\n\",\n    \"        w_shape = (int(X.get_shape()[1]), 1)                        # not shown in the book\\n\",\n    \"        w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")  # not shown\\n\",\n    \"        b = tf.Variable(0.0, name=\\\"bias\\\")                           # not shown\\n\",\n    \"        z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")                    # not shown\\n\",\n    \"        return tf.maximum(z, threshold, name=\\\"max\\\")\\n\",\n    \"\\n\",\n    \"threshold = tf.Variable(0.0, name=\\\"threshold\\\")\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"relus = [relu(X, threshold) for i in range(5)]\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X):\\n\",\n    \"    with tf.name_scope(\\\"relu\\\"):\\n\",\n    \"        if not hasattr(relu, \\\"threshold\\\"):\\n\",\n    \"            relu.threshold = tf.Variable(0.0, name=\\\"threshold\\\")\\n\",\n    \"        w_shape = int(X.get_shape()[1]), 1                          # not shown in the book\\n\",\n    \"        w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")  # not shown\\n\",\n    \"        b = tf.Variable(0.0, name=\\\"bias\\\")                           # not shown\\n\",\n    \"        z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")                    # not shown\\n\",\n    \"        return tf.maximum(z, relu.threshold, name=\\\"max\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"relus = [relu(X) for i in range(5)]\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"with tf.variable_scope(\\\"relu\\\"):\\n\",\n    \"    threshold = tf.get_variable(\\\"threshold\\\", shape=(),\\n\",\n    \"                                initializer=tf.constant_initializer(0.0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.variable_scope(\\\"relu\\\", reuse=True):\\n\",\n    \"    threshold = tf.get_variable(\\\"threshold\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.variable_scope(\\\"relu\\\") as scope:\\n\",\n    \"    scope.reuse_variables()\\n\",\n    \"    threshold = tf.get_variable(\\\"threshold\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X):\\n\",\n    \"    with tf.variable_scope(\\\"relu\\\", reuse=True):\\n\",\n    \"        threshold = tf.get_variable(\\\"threshold\\\")\\n\",\n    \"        w_shape = int(X.get_shape()[1]), 1                          # not shown\\n\",\n    \"        w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")  # not shown\\n\",\n    \"        b = tf.Variable(0.0, name=\\\"bias\\\")                           # not shown\\n\",\n    \"        z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")                    # not shown\\n\",\n    \"        return tf.maximum(z, threshold, name=\\\"max\\\")\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"with tf.variable_scope(\\\"relu\\\"):\\n\",\n    \"    threshold = tf.get_variable(\\\"threshold\\\", shape=(),\\n\",\n    \"                                initializer=tf.constant_initializer(0.0))\\n\",\n    \"relus = [relu(X) for relu_index in range(5)]\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-relu6/'\"\n      ]\n     },\n     \"execution_count\": 99,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph(run_id=\\\"relu6\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:06:21 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-f55ce8541b1e56b0\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-f55ce8541b1e56b0\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X):\\n\",\n    \"    with tf.variable_scope(\\\"relu\\\"):\\n\",\n    \"        threshold = tf.get_variable(\\\"threshold\\\", shape=(), initializer=tf.constant_initializer(0.0))\\n\",\n    \"        w_shape = (int(X.get_shape()[1]), 1)\\n\",\n    \"        w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")\\n\",\n    \"        b = tf.Variable(0.0, name=\\\"bias\\\")\\n\",\n    \"        z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")\\n\",\n    \"        return tf.maximum(z, threshold, name=\\\"max\\\")\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"with tf.variable_scope(\\\"\\\", default_name=\\\"\\\") as scope:\\n\",\n    \"    first_relu = relu(X)     # create the shared variable\\n\",\n    \"    scope.reuse_variables()  # then reuse it\\n\",\n    \"    relus = [first_relu] + [relu(X) for i in range(4)]\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-relu8/'\"\n      ]\n     },\n     \"execution_count\": 102,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph(run_id=\\\"relu8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:06:45 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-41cda150228bada\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-41cda150228bada\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def relu(X):\\n\",\n    \"    threshold = tf.get_variable(\\\"threshold\\\", shape=(),\\n\",\n    \"                                initializer=tf.constant_initializer(0.0))\\n\",\n    \"    w_shape = (int(X.get_shape()[1]), 1)                        # not shown in the book\\n\",\n    \"    w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")  # not shown\\n\",\n    \"    b = tf.Variable(0.0, name=\\\"bias\\\")                           # not shown\\n\",\n    \"    z = tf.add(tf.matmul(X, w), b, name=\\\"z\\\")                    # not shown\\n\",\n    \"    return tf.maximum(z, threshold, name=\\\"max\\\")\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n    \"relus = []\\n\",\n    \"for relu_index in range(5):\\n\",\n    \"    with tf.variable_scope(\\\"relu\\\", reuse=(relu_index >= 1)) as scope:\\n\",\n    \"        relus.append(relu(X))\\n\",\n    \"output = tf.add_n(relus, name=\\\"output\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'tf_logs/run-relu9/'\"\n      ]\n     },\n     \"execution_count\": 105,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph(run_id=\\\"relu9\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6006 (pid 43590), started 0:07:06 ago. (Use '!kill 43590' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-5f6a27b4563df923\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-5f6a27b4563df923\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6006;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Extra material\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x0: my_scope/x\\n\",\n      \"x1: my_scope/x_1\\n\",\n      \"x2: my_scope/x_2\\n\",\n      \"x3: my_scope/x\\n\",\n      \"x4: my_scope_1/x\\n\",\n      \"x5: my_scope/x\\n\",\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"with tf.variable_scope(\\\"my_scope\\\"):\\n\",\n    \"    x0 = tf.get_variable(\\\"x\\\", shape=(), initializer=tf.constant_initializer(0.))\\n\",\n    \"    x1 = tf.Variable(0., name=\\\"x\\\")\\n\",\n    \"    x2 = tf.Variable(0., name=\\\"x\\\")\\n\",\n    \"\\n\",\n    \"with tf.variable_scope(\\\"my_scope\\\", reuse=True):\\n\",\n    \"    x3 = tf.get_variable(\\\"x\\\")\\n\",\n    \"    x4 = tf.Variable(0., name=\\\"x\\\")\\n\",\n    \"\\n\",\n    \"with tf.variable_scope(\\\"\\\", default_name=\\\"\\\", reuse=True):\\n\",\n    \"    x5 = tf.get_variable(\\\"my_scope/x\\\")\\n\",\n    \"\\n\",\n    \"print(\\\"x0:\\\", x0.op.name)\\n\",\n    \"print(\\\"x1:\\\", x1.op.name)\\n\",\n    \"print(\\\"x2:\\\", x2.op.name)\\n\",\n    \"print(\\\"x3:\\\", x3.op.name)\\n\",\n    \"print(\\\"x4:\\\", x4.op.name)\\n\",\n    \"print(\\\"x5:\\\", x5.op.name)\\n\",\n    \"print(x0 is x3 and x3 is x5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first `variable_scope()` block first creates the shared variable `x0`, named `my_scope/x`. For all operations other than shared variables (including non-shared variables), the variable scope acts like a regular name scope, which is why the two variables `x1` and `x2` have a name with a prefix `my_scope/`. Note however that TensorFlow makes their names unique by adding an index: `my_scope/x_1` and `my_scope/x_2`.\\n\",\n    \"\\n\",\n    \"The second `variable_scope()` block reuses the shared variables in scope `my_scope`, which is why `x0 is x3`. Once again, for all operations other than shared variables it acts as a named scope, and since it's a separate block from the first one, the name of the scope is made unique by TensorFlow (`my_scope_1`) and thus the variable `x4` is named `my_scope_1/x`.\\n\",\n    \"\\n\",\n    \"The third block shows another way to get a handle on the shared variable `my_scope/x` by creating a `variable_scope()` at the root scope (whose name is an empty string), then calling `get_variable()` with the full name of the shared variable (i.e. `\\\"my_scope/x\\\"`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Strings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[b'Do' b'you' b'want' b'some' b'caf\\\\xc3\\\\xa9?']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"text = np.array(\\\"Do you want some café?\\\".split())\\n\",\n    \"text_tensor = tf.constant(text)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    print(text_tensor.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Autodiff\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the autodiff content was moved to the [extra_autodiff.ipynb](extra_autodiff.ipynb) notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 11.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 12. Logistic Regression with Mini-Batch Gradient Descent using TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's create the moons dataset using Scikit-Learn's `make_moons()` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import make_moons\\n\",\n    \"\\n\",\n    \"m = 1000\\n\",\n    \"X_moons, y_moons = make_moons(m, noise=0.1, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a peek at the dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYcAAAEFCAYAAAAIZiutAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f96d104cef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(X_moons[y_moons == 1, 0], X_moons[y_moons == 1, 1], 'go', label=\\\"Positive\\\")\\n\",\n    \"plt.plot(X_moons[y_moons == 0, 0], X_moons[y_moons == 0, 1], 'r^', label=\\\"Negative\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We must not forget to add an extra bias feature ($x_0 = 1$) to every instance. For this, we just need to add a column full of 1s on the left of the input matrix $\\\\mathbf{X}$:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_moons_with_bias = np.c_[np.ones((m, 1)), X_moons]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's check:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.        , -0.05146968,  0.44419863],\\n\",\n       \"       [ 1.        ,  1.03201691, -0.41974116],\\n\",\n       \"       [ 1.        ,  0.86789186, -0.25482711],\\n\",\n       \"       [ 1.        ,  0.288851  , -0.44866862],\\n\",\n       \"       [ 1.        , -0.83343911,  0.53505665]])\"\n      ]\n     },\n     \"execution_count\": 112,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_moons_with_bias[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looks good. Now let's reshape `y_train` to make it a column vector (i.e. a 2D array with a single column):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_moons_column_vector = y_moons.reshape(-1, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's split the data into a training set and a test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_ratio = 0.2\\n\",\n    \"test_size = int(m * test_ratio)\\n\",\n    \"X_train = X_moons_with_bias[:-test_size]\\n\",\n    \"X_test = X_moons_with_bias[-test_size:]\\n\",\n    \"y_train = y_moons_column_vector[:-test_size]\\n\",\n    \"y_test = y_moons_column_vector[-test_size:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ok, now let's create a small function to generate training batches. In this implementation we will just pick random instances from the training set for each batch. This means that a single batch may contain the same instance multiple times, and also a single epoch may not cover all the training instances (in fact it will generally cover only about two thirds of the instances). However, in practice this is not an issue and it simplifies the code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def random_batch(X_train, y_train, batch_size):\\n\",\n    \"    rnd_indices = np.random.randint(0, len(X_train), batch_size)\\n\",\n    \"    X_batch = X_train[rnd_indices]\\n\",\n    \"    y_batch = y_train[rnd_indices]\\n\",\n    \"    return X_batch, y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at a small batch:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.        ,  1.93189866,  0.13158788],\\n\",\n       \"       [ 1.        ,  1.07172763,  0.13482039],\\n\",\n       \"       [ 1.        , -1.01148674, -0.04686381],\\n\",\n       \"       [ 1.        ,  0.02201868,  0.19079139],\\n\",\n       \"       [ 1.        , -0.98941204,  0.02473116]])\"\n      ]\n     },\n     \"execution_count\": 116,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_batch, y_batch = random_batch(X_train, y_train, 5)\\n\",\n    \"X_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1],\\n\",\n       \"       [0],\\n\",\n       \"       [0],\\n\",\n       \"       [1],\\n\",\n       \"       [0]])\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Great! Now that the data is ready to be fed to the model, we need to build that model. Let's start with a simple implementation, then we will add all the bells and whistles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First let's reset the default graph.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The _moons_ dataset has two input features, since each instance is a point on a plane (i.e., 2-Dimensional):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's build the Logistic Regression model. As we saw in chapter 4, this model first computes a weighted sum of the inputs (just like the Linear Regression model), and then it applies the sigmoid function to the result, which gives us the estimated probability for the positive class:\\n\",\n    \"\\n\",\n    \"$\\\\hat{p} = h_\\\\boldsymbol{\\\\theta}(\\\\mathbf{x}) = \\\\sigma(\\\\boldsymbol{\\\\theta}^T \\\\mathbf{x})$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Recall that $\\\\boldsymbol{\\\\theta}$ is the parameter vector, containing the bias term $\\\\theta_0$ and the weights $\\\\theta_1, \\\\theta_2, \\\\dots, \\\\theta_n$. The input vector $\\\\mathbf{x}$ contains a constant term $x_0 = 1$, as well as all the input features $x_1, x_2, \\\\dots, x_n$.\\n\",\n    \"\\n\",\n    \"Since we want to be able to make predictions for multiple instances at a time, we will use an input matrix $\\\\mathbf{X}$ rather than a single input vector. The $i^{th}$ row will contain the transpose of the $i^{th}$ input vector $(\\\\mathbf{x}^{(i)})^T$. It is then possible to estimate the probability that each instance belongs to the positive class using the following equation:\\n\",\n    \"\\n\",\n    \"$ \\\\hat{\\\\mathbf{p}} = \\\\sigma(\\\\mathbf{X} \\\\boldsymbol{\\\\theta})$\\n\",\n    \"\\n\",\n    \"That's all we need to build the model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs + 1), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\\n\",\n    \"theta = tf.Variable(tf.random_uniform([n_inputs + 1, 1], -1.0, 1.0, seed=42), name=\\\"theta\\\")\\n\",\n    \"logits = tf.matmul(X, theta, name=\\\"logits\\\")\\n\",\n    \"y_proba = 1 / (1 + tf.exp(-logits))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In fact, TensorFlow has a nice function `tf.sigmoid()` that we can use to simplify the last line of the previous code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_proba = tf.sigmoid(logits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we saw in chapter 4, the log loss is a good cost function to use for Logistic Regression:\\n\",\n    \"\\n\",\n    \"$J(\\\\boldsymbol{\\\\theta}) = -\\\\dfrac{1}{m} \\\\sum\\\\limits_{i=1}^{m}{\\\\left[ y^{(i)} \\\\log\\\\left(\\\\hat{p}^{(i)}\\\\right) + (1 - y^{(i)}) \\\\log\\\\left(1 - \\\\hat{p}^{(i)}\\\\right)\\\\right]}$\\n\",\n    \"\\n\",\n    \"One option is to implement it ourselves:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"epsilon = 1e-7  # to avoid an overflow when computing the log\\n\",\n    \"loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"But we might as well use TensorFlow's `tf.losses.log_loss()` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 123,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"loss = tf.losses.log_loss(y, y_proba)  # uses epsilon = 1e-7 by default\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The rest is pretty standard: let's create the optimizer and tell it to minimize the cost function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n    \"training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"All we need now (in this minimal version) is the variable initializer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And we are ready to train the model and use it for predictions!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There's really nothing special about this code, it's virtually the same as the one we used earlier for Linear Regression:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0 \\tLoss: 0.792602\\n\",\n      \"Epoch: 100 \\tLoss: 0.343463\\n\",\n      \"Epoch: 200 \\tLoss: 0.30754\\n\",\n      \"Epoch: 300 \\tLoss: 0.292889\\n\",\n      \"Epoch: 400 \\tLoss: 0.285336\\n\",\n      \"Epoch: 500 \\tLoss: 0.280478\\n\",\n      \"Epoch: 600 \\tLoss: 0.278083\\n\",\n      \"Epoch: 700 \\tLoss: 0.276154\\n\",\n      \"Epoch: 800 \\tLoss: 0.27552\\n\",\n      \"Epoch: 900 \\tLoss: 0.274912\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 50\\n\",\n    \"n_batches = int(np.ceil(m / batch_size))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for batch_index in range(n_batches):\\n\",\n    \"            X_batch, y_batch = random_batch(X_train, y_train, batch_size)\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val = loss.eval({X: X_test, y: y_test})\\n\",\n    \"        if epoch % 100 == 0:\\n\",\n    \"            print(\\\"Epoch:\\\", epoch, \\\"\\\\tLoss:\\\", loss_val)\\n\",\n    \"\\n\",\n    \"    y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: we don't use the epoch number when generating batches, so we could just have a single `for` loop rather than 2 nested `for` loops, but it's convenient to think of training time in terms of number of epochs (i.e., roughly the number of times the algorithm went through the training set).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For each instance in the test set, `y_proba_val` contains the estimated probability that it belongs to the positive class, according to the model. For example, here are the first 5 estimated probabilities:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.54895616],\\n\",\n       \"       [ 0.70724374],\\n\",\n       \"       [ 0.51900256],\\n\",\n       \"       [ 0.9911136 ],\\n\",\n       \"       [ 0.50859052]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 127,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_proba_val[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To classify each instance, we can go for maximum likelihood: classify as positive any instance whose estimated probability is greater or equal to 0.5:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ True],\\n\",\n       \"       [ True],\\n\",\n       \"       [ True],\\n\",\n       \"       [ True],\\n\",\n       \"       [ True]], dtype=bool)\"\n      ]\n     },\n     \"execution_count\": 128,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = (y_proba_val >= 0.5)\\n\",\n    \"y_pred[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Depending on the use case, you may want to choose a different threshold than 0.5: make it higher if you want high precision (but lower recall), and make it lower if you want high recall (but lower precision). See chapter 3 for more details.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compute the model's precision and recall:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.86274509803921573\"\n      ]\n     },\n     \"execution_count\": 129,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import precision_score, recall_score\\n\",\n    \"\\n\",\n    \"precision_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.88888888888888884\"\n      ]\n     },\n     \"execution_count\": 130,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"recall_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot these predictions to see what they look like:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYcAAAEFCAYAAAAIZiutAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nykEQBEHwQpSDIAiC4MX/B4BXNRO7H//+AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f96d0e11f98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_pred_idx = y_pred.reshape(-1) # a 1D array rather than a column vector\\n\",\n    \"plt.plot(X_test[y_pred_idx, 1], X_test[y_pred_idx, 2], 'go', label=\\\"Positive\\\")\\n\",\n    \"plt.plot(X_test[~y_pred_idx, 1], X_test[~y_pred_idx, 2], 'r^', label=\\\"Negative\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well, that looks pretty bad, doesn't it? But let's not forget that the Logistic Regression model has a linear decision boundary, so this is actually close to the best we can do with this model (unless we add more features, as we will show in a second).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's start over, but this time we will add all the bells and whistles, as listed in the exercise:\\n\",\n    \"* Define the graph within a `logistic_regression()` function that can be reused easily.\\n\",\n    \"* Save checkpoints using a `Saver` at regular intervals during training, and save the final model at the end of training.\\n\",\n    \"* Restore the last checkpoint upon startup if training was interrupted.\\n\",\n    \"* Define the graph using nice scopes so the graph looks good in TensorBoard.\\n\",\n    \"* Add summaries to visualize the learning curves in TensorBoard.\\n\",\n    \"* Try tweaking some hyperparameters such as the learning rate or the mini-batch size and look at the shape of the learning curve.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Before we start, we will add 4 more features to the inputs: ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ and ${x_2}^3$. This was not part of the exercise, but it will demonstrate how adding features can improve the model. We will do this manually, but you could also add them using `sklearn.preprocessing.PolynomialFeatures`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 132,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train_enhanced = np.c_[X_train,\\n\",\n    \"                         np.square(X_train[:, 1]),\\n\",\n    \"                         np.square(X_train[:, 2]),\\n\",\n    \"                         X_train[:, 1] ** 3,\\n\",\n    \"                         X_train[:, 2] ** 3]\\n\",\n    \"X_test_enhanced = np.c_[X_test,\\n\",\n    \"                        np.square(X_test[:, 1]),\\n\",\n    \"                        np.square(X_test[:, 2]),\\n\",\n    \"                        X_test[:, 1] ** 3,\\n\",\n    \"                        X_test[:, 2] ** 3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is what the \\\"enhanced\\\" training set looks like:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 133,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[  1.00000000e+00,  -5.14696757e-02,   4.44198631e-01,\\n\",\n       \"          2.64912752e-03,   1.97312424e-01,  -1.36349734e-04,\\n\",\n       \"          8.76459084e-02],\\n\",\n       \"       [  1.00000000e+00,   1.03201691e+00,  -4.19741157e-01,\\n\",\n       \"          1.06505890e+00,   1.76182639e-01,   1.09915879e+00,\\n\",\n       \"         -7.39511049e-02],\\n\",\n       \"       [  1.00000000e+00,   8.67891864e-01,  -2.54827114e-01,\\n\",\n       \"          7.53236288e-01,   6.49368582e-02,   6.53727646e-01,\\n\",\n       \"         -1.65476722e-02],\\n\",\n       \"       [  1.00000000e+00,   2.88850997e-01,  -4.48668621e-01,\\n\",\n       \"          8.34348982e-02,   2.01303531e-01,   2.41002535e-02,\\n\",\n       \"         -9.03185778e-02],\\n\",\n       \"       [  1.00000000e+00,  -8.33439108e-01,   5.35056649e-01,\\n\",\n       \"          6.94620746e-01,   2.86285618e-01,  -5.78924095e-01,\\n\",\n       \"          1.53179024e-01]])\"\n      ]\n     },\n     \"execution_count\": 133,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_train_enhanced[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ok, next let's reset the default graph:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 134,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's define the `logistic_regression()` function to create the graph. We will leave out the definition of the inputs `X` and the targets `y`. We could include them here, but leaving them out will make it easier to use this function in a wide range of use cases (e.g. perhaps we will want to add some preprocessing steps for the inputs before we feed them to the Logistic Regression model).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\\n\",\n    \"    n_inputs_including_bias = int(X.get_shape()[1])\\n\",\n    \"    with tf.name_scope(\\\"logistic_regression\\\"):\\n\",\n    \"        with tf.name_scope(\\\"model\\\"):\\n\",\n    \"            if initializer is None:\\n\",\n    \"                initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\\n\",\n    \"            theta = tf.Variable(initializer, name=\\\"theta\\\")\\n\",\n    \"            logits = tf.matmul(X, theta, name=\\\"logits\\\")\\n\",\n    \"            y_proba = tf.sigmoid(logits)\\n\",\n    \"        with tf.name_scope(\\\"train\\\"):\\n\",\n    \"            loss = tf.losses.log_loss(y, y_proba, scope=\\\"loss\\\")\\n\",\n    \"            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n    \"            training_op = optimizer.minimize(loss)\\n\",\n    \"            loss_summary = tf.summary.scalar('log_loss', loss)\\n\",\n    \"        with tf.name_scope(\\\"init\\\"):\\n\",\n    \"            init = tf.global_variables_initializer()\\n\",\n    \"        with tf.name_scope(\\\"save\\\"):\\n\",\n    \"            saver = tf.train.Saver()\\n\",\n    \"    return y_proba, loss, training_op, loss_summary, init, saver\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a little function to get the name of the log directory to save the summaries for Tensorboard:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 136,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"def log_dir(prefix=\\\"\\\"):\\n\",\n    \"    now = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n    \"    root_logdir = \\\"tf_logs\\\"\\n\",\n    \"    if prefix:\\n\",\n    \"        prefix += \\\"-\\\"\\n\",\n    \"    name = prefix + \\\"run-\\\" + now\\n\",\n    \"    return \\\"{}/{}/\\\".format(root_logdir, name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, let's create the graph, using the `logistic_regression()` function. We will also create the `FileWriter` to save the summaries to the log directory for Tensorboard:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 137,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = 2 + 4\\n\",\n    \"logdir = log_dir(\\\"logreg\\\")\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs + 1), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\\n\",\n    \"\\n\",\n    \"file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"At last we can train the model! We will start by checking whether a previous training session was interrupted, and if so we will load the checkpoint and continue training from the epoch number we saved. In this example we just save the epoch number to a separate file, but in chapter 11 we will see how to store the training step directly as part of the model, using a non-trainable variable called `global_step` that we pass to the optimizer's `minimize()` method.\\n\",\n    \"\\n\",\n    \"You can try interrupting training to verify that it does indeed restore the last checkpoint when you start it again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 138,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0 \\tLoss: 0.629985\\n\",\n      \"Epoch: 500 \\tLoss: 0.161224\\n\",\n      \"Epoch: 1000 \\tLoss: 0.119032\\n\",\n      \"Epoch: 1500 \\tLoss: 0.0973292\\n\",\n      \"Epoch: 2000 \\tLoss: 0.0836979\\n\",\n      \"Epoch: 2500 \\tLoss: 0.0743758\\n\",\n      \"Epoch: 3000 \\tLoss: 0.0675021\\n\",\n      \"Epoch: 3500 \\tLoss: 0.0622069\\n\",\n      \"Epoch: 4000 \\tLoss: 0.0580268\\n\",\n      \"Epoch: 4500 \\tLoss: 0.054563\\n\",\n      \"Epoch: 5000 \\tLoss: 0.0517083\\n\",\n      \"Epoch: 5500 \\tLoss: 0.0492377\\n\",\n      \"Epoch: 6000 \\tLoss: 0.0471673\\n\",\n      \"Epoch: 6500 \\tLoss: 0.0453766\\n\",\n      \"Epoch: 7000 \\tLoss: 0.0438187\\n\",\n      \"Epoch: 7500 \\tLoss: 0.0423742\\n\",\n      \"Epoch: 8000 \\tLoss: 0.0410892\\n\",\n      \"Epoch: 8500 \\tLoss: 0.0399709\\n\",\n      \"Epoch: 9000 \\tLoss: 0.0389202\\n\",\n      \"Epoch: 9500 \\tLoss: 0.0380107\\n\",\n      \"Epoch: 10000 \\tLoss: 0.0371557\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 10001\\n\",\n    \"batch_size = 50\\n\",\n    \"n_batches = int(np.ceil(m / batch_size))\\n\",\n    \"\\n\",\n    \"checkpoint_path = \\\"/tmp/my_logreg_model.ckpt\\\"\\n\",\n    \"checkpoint_epoch_path = checkpoint_path + \\\".epoch\\\"\\n\",\n    \"final_model_path = \\\"./my_logreg_model\\\"\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    if os.path.isfile(checkpoint_epoch_path):\\n\",\n    \"        # if the checkpoint file exists, restore the model and load the epoch number\\n\",\n    \"        with open(checkpoint_epoch_path, \\\"rb\\\") as f:\\n\",\n    \"            start_epoch = int(f.read())\\n\",\n    \"        print(\\\"Training was interrupted. Continuing at epoch\\\", start_epoch)\\n\",\n    \"        saver.restore(sess, checkpoint_path)\\n\",\n    \"    else:\\n\",\n    \"        start_epoch = 0\\n\",\n    \"        sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(start_epoch, n_epochs):\\n\",\n    \"        for batch_index in range(n_batches):\\n\",\n    \"            X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\\n\",\n    \"        file_writer.add_summary(summary_str, epoch)\\n\",\n    \"        if epoch % 500 == 0:\\n\",\n    \"            print(\\\"Epoch:\\\", epoch, \\\"\\\\tLoss:\\\", loss_val)\\n\",\n    \"            saver.save(sess, checkpoint_path)\\n\",\n    \"            with open(checkpoint_epoch_path, \\\"wb\\\") as f:\\n\",\n    \"                f.write(b\\\"%d\\\" % (epoch + 1))\\n\",\n    \"\\n\",\n    \"    saver.save(sess, final_model_path)\\n\",\n    \"    y_proba_val = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})\\n\",\n    \"    os.remove(checkpoint_epoch_path)\\n\",\n    \"\\n\",\n    \"file_writer.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once again, we can make predictions by just classifying as positive all the instances whose estimated probability is greater or equal to 0.5:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 139,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_pred = (y_proba_val >= 0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.97979797979797978\"\n      ]\n     },\n     \"execution_count\": 140,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"precision_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 141,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.97979797979797978\"\n      ]\n     },\n     \"execution_count\": 141,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"recall_score(y_test, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 142,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYcAAAEFCAYAAAAIZiutAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f96d29e95f8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_pred_idx = y_pred.reshape(-1) # a 1D array rather than a column vector\\n\",\n    \"plt.plot(X_test[y_pred_idx, 1], X_test[y_pred_idx, 2], 'go', label=\\\"Positive\\\")\\n\",\n    \"plt.plot(X_test[~y_pred_idx, 1], X_test[~y_pred_idx, 2], 'r^', label=\\\"Negative\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that's much, much better! Apparently the new features really helped a lot.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's open tensorboard, find the latest run and look at the learning curve:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 143,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now you can play around with the hyperparameters (e.g. the `batch_size` or the `learning_rate`) and run training again and again, comparing the learning curves. You can even automate this process by implementing grid search or randomized search. Below is a simple implementation of a randomized search on both the batch size and the learning rate. For the sake of simplicity, the checkpoint mechanism was removed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 144,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration 0\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606195328/\\n\",\n      \"  batch size: 19\\n\",\n      \"  learning_rate: 0.00443037524522\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.979797979798\\n\",\n      \"  recall: 0.979797979798\\n\",\n      \"Iteration 1\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606195605/\\n\",\n      \"  batch size: 80\\n\",\n      \"  learning_rate: 0.00178264971514\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.969696969697\\n\",\n      \"  recall: 0.969696969697\\n\",\n      \"Iteration 2\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606195646/\\n\",\n      \"  batch size: 73\\n\",\n      \"  learning_rate: 0.00203228544324\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.969696969697\\n\",\n      \"  recall: 0.969696969697\\n\",\n      \"Iteration 3\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606195730/\\n\",\n      \"  batch size: 6\\n\",\n      \"  learning_rate: 0.00449152382514\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.980198019802\\n\",\n      \"  recall: 1.0\\n\",\n      \"Iteration 4\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606200523/\\n\",\n      \"  batch size: 24\\n\",\n      \"  learning_rate: 0.0796323472178\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.980198019802\\n\",\n      \"  recall: 1.0\\n\",\n      \"Iteration 5\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606200726/\\n\",\n      \"  batch size: 75\\n\",\n      \"  learning_rate: 0.000463425058329\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.912621359223\\n\",\n      \"  recall: 0.949494949495\\n\",\n      \"Iteration 6\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606200810/\\n\",\n      \"  batch size: 86\\n\",\n      \"  learning_rate: 0.0477068184194\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.98\\n\",\n      \"  recall: 0.989898989899\\n\",\n      \"Iteration 7\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606200851/\\n\",\n      \"  batch size: 87\\n\",\n      \"  learning_rate: 0.000169404470952\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.888888888889\\n\",\n      \"  recall: 0.808080808081\\n\",\n      \"Iteration 8\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606200932/\\n\",\n      \"  batch size: 61\\n\",\n      \"  learning_rate: 0.0417146119941\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.980198019802\\n\",\n      \"  recall: 1.0\\n\",\n      \"Iteration 9\\n\",\n      \"  logdir: tf_logs/logreg-run-20170606201026/\\n\",\n      \"  batch size: 92\\n\",\n      \"  learning_rate: 0.000107429229684\\n\",\n      \"  training: .....................\\n\",\n      \"  precision: 0.882352941176\\n\",\n      \"  recall: 0.757575757576\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import reciprocal\\n\",\n    \"\\n\",\n    \"n_search_iterations = 10\\n\",\n    \"\\n\",\n    \"for search_iteration in range(n_search_iterations):\\n\",\n    \"    batch_size = np.random.randint(1, 100)\\n\",\n    \"    learning_rate = reciprocal(0.0001, 0.1).rvs(random_state=search_iteration)\\n\",\n    \"\\n\",\n    \"    n_inputs = 2 + 4\\n\",\n    \"    logdir = log_dir(\\\"logreg\\\")\\n\",\n    \"    \\n\",\n    \"    print(\\\"Iteration\\\", search_iteration)\\n\",\n    \"    print(\\\"  logdir:\\\", logdir)\\n\",\n    \"    print(\\\"  batch size:\\\", batch_size)\\n\",\n    \"    print(\\\"  learning_rate:\\\", learning_rate)\\n\",\n    \"    print(\\\"  training: \\\", end=\\\"\\\")\\n\",\n    \"\\n\",\n    \"    reset_graph()\\n\",\n    \"\\n\",\n    \"    X = tf.placeholder(tf.float32, shape=(None, n_inputs + 1), name=\\\"X\\\")\\n\",\n    \"    y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"    y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(\\n\",\n    \"        X, y, learning_rate=learning_rate)\\n\",\n    \"\\n\",\n    \"    file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\\n\",\n    \"\\n\",\n    \"    n_epochs = 10001\\n\",\n    \"    n_batches = int(np.ceil(m / batch_size))\\n\",\n    \"\\n\",\n    \"    final_model_path = \\\"./my_logreg_model_%d\\\" % search_iteration\\n\",\n    \"\\n\",\n    \"    with tf.Session() as sess:\\n\",\n    \"        sess.run(init)\\n\",\n    \"\\n\",\n    \"        for epoch in range(n_epochs):\\n\",\n    \"            for batch_index in range(n_batches):\\n\",\n    \"                X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\\n\",\n    \"                sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"            loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\\n\",\n    \"            file_writer.add_summary(summary_str, epoch)\\n\",\n    \"            if epoch % 500 == 0:\\n\",\n    \"                print(\\\".\\\", end=\\\"\\\")\\n\",\n    \"\\n\",\n    \"        saver.save(sess, final_model_path)\\n\",\n    \"\\n\",\n    \"        print()\\n\",\n    \"        y_proba_val = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})\\n\",\n    \"        y_pred = (y_proba_val >= 0.5)\\n\",\n    \"        \\n\",\n    \"        print(\\\"  precision:\\\", precision_score(y_test, y_pred))\\n\",\n    \"        print(\\\"  recall:\\\", recall_score(y_test, y_pred))\\n\",\n    \"\\n\",\n    \"file_writer.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `reciprocal()` function from SciPy's `stats` module returns a random distribution that is commonly used when you have no idea of the optimal scale of a hyperparameter. See the exercise solutions for chapter 2 for more details. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {\n   \"height\": \"603px\",\n   \"width\": \"616px\"\n  },\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": true\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "10_introduction_to_artificial_neural_networks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 10 – Introduction to Artificial Neural Networks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 10._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/10_introduction_to_artificial_neural_networks.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions. In particular, the 1st edition is based on TensorFlow 1, while the 2nd edition uses TensorFlow 2, which is much simpler to use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    # %tensorflow_version only exists in Colab.\\n\",\n    \"    %tensorflow_version 1.x\\n\",\n    \"except Exception:\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"def reset_graph(seed=42):\\n\",\n    \"    tf.reset_default_graph()\\n\",\n    \"    tf.set_random_seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['axes.labelsize'] = 14\\n\",\n    \"plt.rcParams['xtick.labelsize'] = 12\\n\",\n    \"plt.rcParams['ytick.labelsize'] = 12\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"ann\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Perceptrons\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note**: we set `max_iter` and `tol` explicitly to avoid warnings about the fact that their default value will change in future versions of Scikit-Learn.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.datasets import load_iris\\n\",\n    \"from sklearn.linear_model import Perceptron\\n\",\n    \"\\n\",\n    \"iris = load_iris()\\n\",\n    \"X = iris.data[:, (2, 3)]  # petal length, petal width\\n\",\n    \"y = (iris.target == 0).astype(np.int)\\n\",\n    \"\\n\",\n    \"per_clf = Perceptron(max_iter=100, tol=-np.infty, random_state=42)\\n\",\n    \"per_clf.fit(X, y)\\n\",\n    \"\\n\",\n    \"y_pred = per_clf.predict([[2, 0.5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1])\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure perceptron_iris_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAsgAAAEYCAYAAABBfQDEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd4FdXWx/HvSiHUSA0qEJAmikgL5YoKFhQUFEVRpFgvgmB5rVhQKSYBEhSVIipWBEFBxAIovUVEUbmgYkFQ6R1CDdnvHyccQ0g3OZOE3+d55vGcNXtm1gkQVyZ79jLnHCIiIiIi4hPkdQIiIiIiIgWJCmQRERERkVRUIIuIiIiIpKICWUREREQkFRXIIiIiIiKpqEAWEREREUlFBbKIiIiISCoBK5DNLMzMXjez9Wa2z8y+M7P2mYz/PzPbbGZ7zWy8mYWl2lfDzOaZ2QEz+8nMLg/MpxARERGRoi6Qd5BDgD+B1sBpwFPAZDOrkXagmV0J9AcuA6oDNYGBqYZMBFYCFYAngQ/MrFI+5i4iIiIipwjzspOemf0ADHTOfZgm/h7wh3PuiZT3lwETnHOnm1ldYBVQ0Tm3L2X/opT9YwP7CURERESkqAnx6sJmVhmoC6xOZ3d9YHqq998Dlc2sQsq+348Xx6n218/gOr2AXgClShVvevbZVfIg+8Jv69bd/PXXjhNi5cpVo0yZCI8yEhEREcnchg0Z74uMzM4xf+DcdsvqOp4UyGYWCkwA3nLO/ZTOkNLAnlTvj78uk86+4/vTrXydc+OAcQBNm9Z2CQnx/yLzomX+/FXccstwtm/fC8CuXX9y9tmXcsstYyhWrITH2YmIiIicqHfvjPc98UR2jonK1nUCvoqFmQUB7wBHgH4ZDNsPhKd6f/z1vnT2Hd+/D8mRNm0akJAQT5MmtfyxhIS3iIu7iJ07M/kRTURERKQIC2iBbGYGvA5UBjo7545mMHQ10DDV+4bAFufcjpR9Nc2sTJr96U3VkCxERlZi3rxoevS4xB/bsOEboqOb8vPP8zzMTERERORE4WlvkWYRz2pfRgL6kJ6ZjQUaAZc75/ZnMq4d8CZwKbARmAosd871T9mfACzGtxJGe+ANoI5zbltm19cUi4w55xg79nMeeuh1kpKOARAUFEznznFceun9+H62ERERESm8eve2b5xzWc6zCOQ6yNWBu/EVyJvNbH/K1s3MIlNeRwI452YCw4B5wAZgPfBMqtPdjG8SyS4gFrghq+JYMmdm9OlzFbNnDyIi4jQAkpOPMWXK/zF+fHeOHDngcYYiIiIigeHpMm+BpjvI2fP33zu46aahLF++1h+rVq0Rd989lYoVz/IwMxEREZHcK3B3kKXwqFKlAnPmPMcdd7T1x/788ztiYqJYs+YLDzMTERERyX8qkCVdYWGhjB3bl9Gj+xAa6lsNMDFxJy+/3I5Zs4ZxKv3mQURERE4tmmIhWUpI+ImbbhrKpk27/LGmTbvQs+d4wsJKeZiZiIiISOYefRT27j3+LgrnVmS58oDuIEuWWrasR0JCPBdccI4/9s03kxk6tCVbt/7qYWYiIiIimfunOM4+FciSLWecUZ7ZswfRu3d7f2zjxv8RG9uM//3vcw8zExEREclbKpAl24oVC+XFF+9m3Lh+FCvmm5d84MBuRo26ms8+e07zkkVERKRIUIEsOXbbbZczb140VatWAHxNRj7++CleeaUzhw6p47eIiIgUbiqQJVeaNatLQkI8F19c3x/77rtpxMa2YPPmnz3MTEREROTfUYEsuRYRUZbPPx/Ivfd28Mc2b/6R2Njm/PDDDA8zExEREfEJD8/5MVrmTfLEhAnz6dNnNIcOHfHHrr76Ga6++mmCgvRzmIiIiHhPnfQkoLp1a8OCBTFUr17JH/v004GMGXMtBw/u8TAzERERkZxRgSx5pnHjWixbFs+ll57vj61a9Qmxsc3ZuHGNh5mJiIiIZJ8KZMlTFSuG88knz/Dgg538sS1b1jJ0aAtWrpzqYWYiIiIi2RPidQJS9ISEBBMbexuNG9eiV6+XOHjwCIcP7+eVVzrTrt0TXHPNIIKCgr1OU0RERDzUu3fG+8aOTT/epw+k9/icGYwZkzd5ge4gSz666aaLWLRoGDVrVvbHZs6MZtSoDiQm7vIwMxERESmMMlpbIq/XnFCBLPnq/PNrsHRpHFdc0dgfW716JjExUfz99yoPMxMRERFJnwpkyXfly5dh+vSnePTRzv7Y9u2/M3RoS1asmOxhZiIiIiInU4EsAREcHMyQIT2YNOlRSpUqDsCRIwd47bWb+PDDRzl2LMnjDEVERER8Alogm1k/M1thZofN7M1Mxo01s/2ptsNmti/V/vlmdijVfvU2LiSuv/4CFi8eRu3aZ/pjX3wxnJdeas/+/Ts8zExERETEJ9B3kDcCQ4DxmQ1yzvV2zpU+vgETgSlphvVLNebsfMpX8kH9+pEsXTqMq676p5HNTz99SUxMFBs2rPQwMxERESnIzHIWz/V1vGg1bWZDgKrOuduyMbYUsBno4JxbkBKbD7zrnHstJ9dVq+mCJTk5mcGD3+e55973x0JDi9O9+2u0aNHNw8xERESkKCpKraY7A9uAhWniMWa23cyWmFmbjA42s14p0zpWbN++Nz/zlBwKCgrimWe68sEHj1OmTAkAjh49xBtvdGfy5P/j2LGjHmcoIiIip6LCUCDfCrztTrzV/RhQE6gCjANmmFmt9A52zo1zzkU556IqVgzP/2wlx665pgVLlgzn7LOr+mNz577AyJFXsHfvVg8zExERkVNRgS6QzSwSaAO8nTrunPvKObfPOXfYOfcWsAS4yoMUJY/Uq1eVJUuGcc01LfyxtWvnExMTxfr1KzzMTERERE41Bb3VdA9giXPu9yzGOSCPp2dLoIWHl2Ty5MeIjf2AgQMn4pxj164/GTHiAm66aRwXXHCb1ymKiIgUOo8+CnvTmWUaHg7DhgU+n0A78fM3bZqdYwK9zFuImRUHgoFgMytuZpkV6T2BN9Oco6yZXXn8WDPrBlwMzMy3xCVggoKCeOKJLnz00ZOcdlpJAA4fPsrbb9/OxIn9SEo64nGGIiIihUt6xXFm8aImN58z0FMsngIOAv2B7imvnzKzyJT1jCOPDzSz/wBVOXl5t1B8S8VtA7YD9wKdnHNrA5C/BEj79lEsXRrHuef6/0qwYMEoXnjhMvbs2exhZiIiIlLUBbRAds4965yzNNuzzrkNKesZb0g1dplzrpRzbl+ac2xzzjVzzpVxzpV1zrV0zn0RyM8hgVGnzpksXjyU66+/wB/79dfFREc35fffEzzMTERERIqyAv2Qnkjp0iWYOPERoqN7EhTk++u6Z89GRoxozaJFr3qcnYiIiBRFKpClwDMzHn74embMGEC5cqUBSEo6woQJvZgw4W6OHj3scYYiIiJSlKhAlkKjbdvGLFsWR4MGNfyxRYvGMWJEG3bv3uhdYiIiIgVYeAZtIDKKFzW5+ZyetJr2ilpNFw2JiYfo3XsU77+/yB8LD69Mr14fULv2hR5mJiIiIgVZUWo1LXKCUqWK8/bbDzJ8+B0EB/v+Cu/du4URIy5h/vzRnEo/9ImIiEjeU4EshZKZcf/91/DZZ89yvIV4cnISkyb15Z137uTo0UMeZygiIiKFlQpkKdQuueR8li2Lo3Hjmv7Y0qVvEBd3ETt3/ulhZiIiIlJYFfRW0yJZql49gvnzY+jbdyzvvjsPgPXrVxAT05S77prM2We38TZBERERDwWi1XRRa2etO8hSJJQoEcbrr9/H88/fRUhIMAD79m3jxRcvY86ckZqXLCIip6xAtJouau2sVSBLkWFm9O3bgVmzBhERcRoAx44lM2XKA7z5Zk+OHDngcYYiIiJSGKhAliLnoovqk5AQT7Nmdfyxr756l+HDL2T79j+8S0xEREQKBRXIUiRVrVqROXOe4/bbL/fH/vxzJTExUfz445ceZiYiIiIFnQpkKbKKFy/G2LF9GTWqD6GhvudRExN38OKLVzJ7dpzmJYuIiEi6VCBLkWZm/Pe/V/Lll0M4/fRyADiXzNSpj/D66105fDjR4wxFRETyVyBaTRe1dtZqNS2njE2bdnLzzcNYtuwnf6xKlQb07j2NSpVqeZiZiIiIBIJaTYukccYZ5fnii8HcfXc7f+zvv1cRExPF6tUzPcxMREREChIVyHJKKVYslJde6s0rr/SlWDHfvOQDB3bz8stX8fnn0ZqXLCIiIiqQ5dR0++1tmTcvmipVKgDgnGP69CcZN+4GDh3a53F2IiIi4qWAzkE2s37AbUADYKJz7rYMxt0GvA4cTBXu4Jybn7K/BvAG0ALYAPRzzmW5dpfmIEtaW7bspmvXYSxevMYfO+OMc+ndexqVK9f1MDMRESlMCnKr5d69M943duzJsdx8lkB9/j59IL3S1QzGjMlOblE4t8Kyuk6g7yBvBIYA47MxdplzrnSqbX6qfROBlUAF4EngAzOrlOfZSpFXuXJZZs0aRL9+HfyxTZvWEB/fmB9++MTDzEREpDApSq2Wc/NZAvX5M7qvm9n93tzkENAC2Tk31Tn3EbAjt+cws7pAE+AZ59xB59yHwCqgcx6lKaeY0NAQRoy4i9dfv5/ixYsBsHfvAUaP7sinnw4iOTnZ4wxFREQkkAryHOTGZrbdzNaa2QAzC0mJ1wd+d86lnij6fUr8JGbWy8xWmNmK7dsL4Y9xEjA9elzC/PnRREb+88uIGTOeYezY6zh4cI+HmYmIiEggFdQCeSFwHhCB785wV+CRlH2lgbTVyh6gTHoncs6Nc85FOeeiKlYspKtVS8A0aVKbZcviuOSSBv7YDz98TGxsczZt+tHDzERERCRQCmSB7Jz73Tm3zjmX7JxbBQwCbkjZvR9IW+mGA1p6QPJEpUqn8emnz/J//3etP7Zly1piY5uzcuU0DzMTERGRQCiQBXI6HHD8icPVQE0zS33HuGFKXCRPhIQEM3To7bz99oOUKOGbl3z48H5eeeV6Pv54AMnJxzzOUERECpKi1Go5N58lUJ/fMlh/IqN4bnMI9DJvIUAI8AxQFfgvkOScS0ozrj3wrXNui5nVAz4ApjjnBqbsTwAWA08B7fEt+VbHObcts+trmTfJje+/X0eXLrGsW7fFH6tfvz133DGBUqXKeZiZiIiI5ERBbTX9FL61jfsD3VNeP2VmkWa238wiU8ZdBvxgZonAZ8BUIDrVeW4GooBdQCxwQ1bFsUhuNWx4FsuWxdG2bSN/bPXqz4mNbcbff//Pw8xEREQkPwT0DrLXdAdZ/o1jx47x9NPvMXz4h/5YWFgpevZ8g6ZNb/QwMxEREcmOgnoHWaTQCg4O5rnnevDee49QqlRxAA4fTuTVV7swdepjmpcsIiJSRIRkPUREUrvhhlbUq1eVLl1i+fXXTQDMnj2MP/9cyZ13TqR06QoeZygiUjQV5HbOgZKbVss5lZuv879vAZ296wSK7iCL5MJ551Vn6dLhtG/f1B/78ccviImJ4s8/v/MwMxGRoqsotXPOrdy0Ws6p3Hyd87IFdEH481SBLJJLZcuWZtq0J3niiS7+2I4dfzBs2AUsX/6eh5mJiIjIv6ECWeRfCAoK4tlnb2HKlP6UKVMCgKNHDzJ+fDemTHmQY8eSsjiDiIiIFDQqkEXywLXXtmTx4mHUrVvFH5sz53lefPEK9u3TCoQiIiKFiQpkkTxyzjnVWLp0OB07NvfHfv55HtHRTVm//hsPMxMREZGcUIEskofCw0syZUp/nnmmK5bS93LXrj8ZPrwVy5a95XF2IiKFW1Fq55xbuWm1nFO5+TrnZQvogvDnqUYhIvnks89WcOutI9iz54A/1qZNP268cQTBwaEeZiYiInJqUqMQEY9ddVUUS5fGcc451fyx+fNf5vnnL2Pv3i0eZiYiIiKZUYEsko/q1DmTxYuHcd11//HHfv11EdHRTVm37isPMxMREZGMqEAWyWdlypRg0qRHGTKkh39e8u7dfxMffzFLlrzucXYiIiKSVrZbTZtZSaAREEGawto5NzWP8xIpUsyMRx/tTKNGNenRI55du/aTlHSEd965iz/++JouXUYSGhrmdZoiIiJCNgtkM7scmAhUSGe3A4LzMimRouqKKxqzbFkcN94Yy6pVfwCwaNEr/P33D/Tq9QFly57pbYIiIsCjj6bf7jc8HIYNC3w+/0bv3hnvGzs2/XifPum3SDaDMWO8PSY3fzY5PaYo/fnnVnanWIwEPgWqOueC0mwqjkVyoGbN01m4MJYuXS7yx37/fRnR0U359dclHmYmIuKTXnGUWbyoyWiBr8wW/grUMbn5s8npMaf6nz9kv0CuAQx2zm3Mx1xEThmlShXnnXceZNiw2wkK8v0z3Lt3MyNHtmHBgjGcSssvioiIFDTZLZCXAGfnZyIipxoz44EHruWzz56hQoUyABw9msTEiffwzjt3cfToIY8zFBEROTVlWCCbWZPjGzAWiDOzu8ysRep9KftFJJcuvbQhCQnxNGpU0x9bunQ8cXEXs3Pnnx5mJiIicmrK7A7yCuDrlP9+ANQDxgHLUmIrUo3JFjPrZ2YrzOywmb2ZybhbzewbM9trZn+Z2TAzC0m1f76ZHTKz/Snbz9nNQaQgql49ggULYujWrY0/tn7918TENGXt2gXeJSYiInIKyqxAPguomfLfzLaaGZ0gHRuBIcD4LMaVBB4AKgItgMuAh9OM6eecK52yafqHFHolSoQxfvz9PP/8XQQH+/5p7tu3jRdeuIy5c1/UvGQRCZjw8JzFi5qUJeuzHQ/kMbn5s8npMaf6nz+AZed/umZ2MbDUOZeUJh4CXOCcW5iji5oNwbcixm3ZHP8gcIlzrmPK+/nAu86513Jy3aZNa7uEhPicHCLiiYUL/0fXrsPZtm2PP9aiRQ+6dXuFYsVKeJiZiIhI4dW7t33jnIvKalx2H9KbB5RPJ35ayr78djGwOk0sxsy2m9kSM2uT0YFm1itlWseK7dtPofVJpFC7+OLzSEiIIyqqjj/21VfvMHx4K3bsWO9hZiIiIkVfdgtkw9cQJK0KQGLepZPOhc3uAKKAuFThx/BN7aiCb170DDOrld7xzrlxzrko51xUxYqn0O8GpNCrVq0Sc+c+x623XuaP/fnnSqKjm/LTT3M9zExERKRoy7RANrOPzexjfMXxu8ffp2yfAl8AS/MrOTPrBMQA7Z1z24/HnXNfOef2OecOO+fewrcM3VX5lYeIV4oXL8a4cf146aW7CQ31PaeamLiDkSPb8sUX8ZqXLCIikg+yajW9I+W/BuwCDqbadwRYDLyaD3lhZu1Szn21c25VFsNdSo4iRY6Zcffd7WnQoAY33zyMzZt34VwyH374MOvXr6BHj9cICyvldZoiIvkuEG2WA5lbQb5OThXUvHIr0wLZOXc7gJn9AcQ55/7VdIqUh/pCgGAg2MyKA0npPPx3KTABuM45tzzNvrL4VrZYACQBN+Gbo3z/v8lNpKC74IJzSEiI5+abh5KQ4FvZcMWKSWzatIbevadRqVJOFpQRESl8AtFmObeK2nVyqqDmlVvZmoPsnBv4b4vjFE/huwvdH+ie8vopM4tMWc84MmXcAHwPAH6Waq3jz1P2heJbKm4bsB24F+jknFubB/mJFGhnnlmeL74Ywn//e6U/9vffPxATE8Xq1bM8zExERKToyPAOspmtI/0H807inMvWrSvn3LPAsxnsLp1q3CWZnGMb0Cw71xMpisLCQhk1qg9Nm9bmvvte4ciRJA4c2MXLL7fn2mujufLKx7DMFtEUERGRTGV2B/llYFTK9ha+FSt+A95N2X5Lib2ZvymKSHruuKMtc+dGU6VKBQCcc3z00eO8+moXDh3a53F2IiIihVeGd5Cdc/6OGiltoYc656JTjzGzx4H6+ZadiGSqefO6JCTE07XrMBYvXgPAt99+kDIv+SMqV66TxRlEREQkreyug3w9MDmd+BTgmrxLR0RyqnLlssyaNYi+fa/2xzZtWkNsbDNWrfrUw8xERPJWINos51ZRu05OFdS8ciu7raY3AQPStnY2s7uAIc650/MpvzylVtNS1L399lz69h3D4cNHAd8ScR06DKR9+ycJCsruz8MiIiJFU163mn4eGGVmY83stpRtLPBSyj4RKQB69ryUBQtiqFatIuCblzxjxtO88sr1HDxYSNfaERERCbDsLvM2DOgBNABGpGwNgFudc0PzLz0RyakmTWqTkBBP69bn+WPffz+d2NjmbN78k4eZiYiIFA7Z/p2rc26yc66Vc658ytbKOZfevGQR8VilSqfx+ecDeeCBfx4R2LLlZ2Jjm/Pddx95mJmIiEjBl1WraREppEJCghk27A4aN65F796jOHjwCIcO7WPs2Ou46qoBdOjwrOYlyymhqLXALSoKcttokQz/72hme82sYsrrfSnv090Cl66I5FTXrq1ZsCCWGjUi/LHPPhvM6NEdOXBgt4eZiQRGUWuBW1QU5LbRIpndQb4X2Jfqdba66olIwdOoUU2WLYujR494vvzyewD+97/PiIlpRp8+H3HmmVrOXERE5LjMGoW8ler1mwHJRkTyTYUK4cyY8TQDBkwgLm4qANu2/Up8fBS33PIOTZve4HGGIiIiBUO2JiCa2RNm9h8z05xlkUIsODiY6OieTJjwMKVKFQcgMfEQr756I9OmPU5y8jGPMxQREfFedp/QaQ/MA3aZ2eyUgvkCFcwihdONN17IokVDqVXrnx4/s2bF8vLLV5GYuNPDzERERLyX3XWQLwLKAdcBX+ErmOfgK5hn5V96IpJfzjuvOkuXxtGuXRN/bM2a2cTERPHXX997mJlI3ipqLXCLioLcNlokW62mTzjArDJwKXA10AVIcs6VzIfc8pxaTYuc7NixYwwaNImYmCn+WGhoCXr2fJ1mzbp6mJmIiEjeytNW02bWxcxGm9mPwO/Af4FfgLb47iyLSCEVHBzMwIHdmDy5P6VL++YlHz16kNdfv4UPPniIY8eSPM5QREQksLI7B3kS0BkYD1Ryzl3qnBvonFvgnDucf+mJSKB06tSSJUuGU7duFX/syy9H8OKLV7Jv3zYPMxMREQms7BbIvYDZ+NZD3mhmM8zsITNrYmaWf+mJSCCdc041liwZRocOzf2xn3+eS0xMFBs2fOthZiIiIoGTmznItYA2+KZXXAfsd85VyOax/YDbgAbAROfcbZmM/T/gMaAk8AHQ5/jdajOrAbwBtAA2AP2cc19mdX3NQRbJnuTkZKKjpzBo0ER/LDS0ON26jaNlyx4eZiZSdPTpA+n9L9gMxowpfNcpqG2g1dJaUsvTOcgAZhZkZi2AG/A9nNcBMGBtDvLaCAzBN1Ujs2tdCfQHLgOqAzWBgamGTARWAhWAJ4EPzKxSDvIQkUwEBQXx1FM3MW3ak4SH+57BPXr0EG++2ZP337+fY8eOepyhSOGX0f2pHN63KjDXKahtoNXSWnIjuw/pfQ7sAhYBnYBv8c1JLuec+092L+acm+qc+wjYkcXQW4HXnXOrnXO7gMH47jxjZnWBJsAzzrmDzrkPgVUp+YhIHrr66mYsXTqcevWq+mPz5r3ICy9czt69WzzMTEREJP9k9w7yd/juGpdzzv3HOfe4c26Wcy4xn/KqD6ReiPV7oLKZVUjZ97tzbl+a/fXTO5GZ9TKzFWa2Yvt2/egnklN161ZhyZLhdOrU0h/75ZeFREc3Zd265R5mJiIikj+y2ygkvwvitEoDe1K9P/66TDr7ju8vk96JnHPjnHNRzrmoihW1krhIbpQpU4L333+MwYO7c/y53N27/yY+/iKWLHnd4+xERETyVrbnIAfYfiB1NXv89b509h3fvw8RyTdmxmOP3cDHHw+gbNlSACQlHeGdd+7ivff6kJR0xOMMRURE8kZBLZBXAw1TvW8IbHHO7UjZV9PMyqTZvzqA+Ymcsq68sgnLlsVx3nnV/bGFC8cyYsQl7NmzycPMRAqXjBZJzevFUwN1nYLaBlotrSU3crzM27+6mFkIEAI8A1TF15EvyTmXlGZcO+BNfC2tNwJTgeXOuf4p+xOAxcBTQHt8S77Vcc5l2s1Ay7yJ5J3ExEP06vUyU6Ys9sdOO+0MevX6gFq1LvAwMxERkfTl+TJveeQp4CC+Jdy6p7x+yswizWy/mUUCOOdmAsOAefjWOV6Pr6g+7mYgCt/KGrHADVkVxyKSt0qVKs677z5EbOxtBAX5vpXs2bOJESPasHDhKwTyh28REZG8FNA7yF7THWSR/DFnzvd07x7Hjh3/PArQqtWd3Hzzy4SGFvcwMxERkX/86zvIZrbPzPZmZ8vb1EWksLnssoYsWxZHw4Zn+WNLlrxOfHxrdu36y8PMREREci4kk339ApaFiBR6NWpUZsGCWPr0Gc3EiQsA+OOP5URHN6VXrynUqXOxxxmKiIhkT4YFsnPurUAmIiKFX8mSYbz55gNERdXm0Uff4NixZPbt28rzz1/GjTc+T5s2ff3rKIuIiBRUBXWZNxEppMyMe+/tyMyZA6lU6TQAkpOTeP/9e3nrrds4cuSgxxmKiIhkLlsFspkVM7OBZrbWzA6Z2bHUW34nKSKFT+vWDUhIiKNp09r+WELC28TFXciOHes9zExERCRz2b2DPBi4FYgHkoFHgFHADuCe/ElNRAq7atUqMW9eNLfeepk/tmHDt8TERPHzz/M8zExERCRj2S2QuwC9nXOvAMeA6c65+/CtTdw2v5ITkcKvePFijBvXjxdf7EVISDAA+/dvZ+TItnz55QitlywiIgVOdgvkysCalNf7gbIpr2cCV+R1UiJStJgZvXtfxRdfDKZyZd+3j+TkY3zwwUOMH9+NI0cOeJyhiIjIP7JbIG8Azkx5/StwZcrr/+DrhicikqVWrc4lISGeFi3O9se+/noiw4ZdwPbt6zzMTERE5B/ZLZCnAccnEY4EBprZOuBN4LV8yEtEiqgqVSrw5ZdDuOuuf3759Ndf3xMTE8WaNbM9zExERMQnWwWyc+5x59xzKa8/AC4EXgKud849mY/5iUgRFBYWyujR9zBmzD0UK+Zbjj0xcScvvdSeWbOGal6yiIh4KrvLvF1sZv6mIs65r5yywB9UAAAgAElEQVRzI4CZZqb2WCKSK3feeQVz5jzHmWeWB8C5ZKZN68+rr97EoUP7Pc5OREROVdmdYjEPKJ9O/LSUfSIiudKixdkkJMTTqtU5/ti3305h2LCWbN36q4eZiYjIqSq7BbIB6f3OswKQmHfpiMip6PTTyzFr1iD69LnKH9u4cTWxsc1YteozDzMTEZFTUaYFspl9bGYf4yuO3z3+PmX7FPgCWBqIREWkaCtWLJSRI3vx2mv3EhYWCsCBA7sZPboDn302hOTkZI8zFBGRU0VWd5B3pGwG7Er1fgfwFzAW6J6fCYrIqaVnz8uYPz+GatUqAuCc4+OPB/DKK505eHCvx9mJiMipwLLztLiZPQPEOecK9XSKpk1ru4SEeK/TEJFs2Lp1N7fcMpyFC1f7Y6efXo/evT/i9NPPzuRIERGR9PXubd8456KyGpfdZd4GOucSzSzKzG4ys1IAZlYq9eoWIiJ5JSKiLJ9/PpD77uvoj23e/BOxsc34/vuPPcxMRESKuuwu81bZzBKA5cB7+FpPA4wAsn1L1szKm9k0M0s0s/VmdksG4z43s/2ptiNmtirV/j/M7GCq/eouIFIEhYaGEBd3J2+++X+UKFEMgEOH9jFmzLXMmPGM5iWLiEi+yO4qFs8DW/CtWnEgVXwKcEW6R6RvFHAEX4HdDRhjZvXTDnLOtXfOlT6+4XsQcEqaYR1TjclJDiJSyNxyS2vmz4+levVK/tinnw5izJhrOXBgt4eZiYhIUZTdAvky4Enn3K408d+AyOycIGVaRmdggHNuv3NuMfAx0COL42oAFwFvZzNXESmCGjeuybJl8Vx2WUN/bNWqT4iNbc7GjWs8zExERIqa7BbIJfDd+U2rEnAom+eoCyQ559amin0PnHQHOY2ewCLn3B9p4hPMbJuZzTazhukcB4CZ9TKzFWa2Yvt2PQEvUphVrBjOjBlP8+CDnfyxrVt/YejQFnz77YceZiYiIkVJdgvkhcBtqd47MwsGHgPmZPMcpYG0FeoeoEwWx/UE3kwT6wbUAKrj6+Q3y8zKpnewc26ccy7KORdVsWJ4NlMVkYIqJCSY2NjbePfdhylZMgyAw4f3M27cDXz00RMkJx/zOEMRESnsslsgPwr818y+AMLwPZi3BmgFPJ7Nc+wH0lao4cC+jA4wswuB04EPUsedc0uccwedcwecczHAbnzTMETkFNGly4UsWjSUmjUr+2MzZ8bw8stXk5i408PMRESksMvuMm9rgPOBZcBsoDi+h+YaO+d+y+a11gIhZlYnVawhsDqD8QC3AlOdc/uzShFfMxMROYU0aFCDZcviufLKJv7YmjWziIlpxl9//eBhZiIiUphl9w4yzrlNzrmnnXMdnHNXOeeecs5tysHxicBUYFDK+smtgGuBd9Ibb2YlgC6kmV5hZpFm1srMiplZcTN7BKgILMluLiJSdJQrV5qPPnqSxx67wR/bvv13hg37D19/PcnDzEREpLDKtEA2s5JmNsrM/jazrWb2nplV/BfXuwffA39bgYlAH+fcajO7yMzS3iXuhG/qxLw08TLAGHytr/8G2gHtnXM7/kVeIlKIBQcHM3hwd95//zFKly4OwJEjB3j99a58+OEjHDuW5HGGIiJSmGTaatrMhuMraifgW62iKzDfOXdjYNLLW2o1LVL0rVnzJzfcEMOvv270x+rVu4y77ppE6dL/5ud7EREp7PKq1fT1wJ3OuV7OufuAq4FOKStYiIgUOOeeW41ly4Zz9dXN/LGffppDdHQUGzas9DAzEREpLLIqkKsBi46/cc4tB5KAM/MzKRGRf+O000rx4YePM2DAzf7Yzp3rGT78AhIS0n3sQURExC+rAjmYkxuEJAEh+ZOOiEjeCAoKYsCAm/nwwycIDy8JwNGjh3jzzZ5MnvwAx44d9ThDEREpqLIqkA1418w+Pr7hW+Lt1TQxEZECqWPH5ixZMpx69ar6Y3PnjuSFF9qyd+9WDzMTEZGCKqsC+S1gI7Aj1fYu8GeamIhIgXX22VVYsmQ4117b0h/75ZcFxMQ05Y8/vvYwMxERKYgyXcWiqNEqFiKntuTkZIYN+5BnnnmP49/7QkLCuOWWMVxwwe0eZyciIvktr1axEBEpMoKCgujf/0amT3+KsmVLAZCUdJi3376DiRP7kpSU9pELERE5FalAFpF0bd26gBUr/suSJdexYsV/2bp1gdcp5Zl27ZqydGkc9etH+mMLFozm+ecvZc+ezR5mJiIiBYEKZBE5ydatC/jtt9EcPrwNcBw+vI3ffhtdpIrk2rXPYNGiodxwQyt/7LfflhAd3YTff1/mYWYiIuI1FcgicpING94lOfnwCbHk5MNs2PCuRxnlj9KlSzBhwsPExNxKUJDv2+GePZuIj2/NokXjPM5ORES8ogJZRE5y+PD2HMULMzPjoYeu45NPnqZ8+TIAHDt2lAkT7ubdd3tx9OjhLM4gIiJFjQpkETlJWFjFHMWLgssvb8SyZXE0bHiWP7Z48auMGNGaXbv+9jAzEREJNBXIInKSyMjuBAWFnRALCgojMrK7RxkFxllnVWbBgli6dm3tj61b9xUxMU355ZdFHmYmIiKBpAJZRE4SEdGaWrXuISysEmCEhVWiVq17iIhoneWxhV3JkmG8+eYDxMXdQXCw71vk3r1beP75S5k/fxSn0trxIiKnqhCvExCRgikiovUpURCnx8y4775raNjwLLp2Hc727XtJTk5i0qR+rF+/gltuGUNoaHGv0xQRkXyiO8giIhlo3boBCQnxNGlSyx9btuxN4uIuYufODR5mJiIi+UkFsohIJiIjKzFvXjQ9elzij61fv4Lo6Kb8/PM8DzMTEZH8ogJZRCQLJUqE8dpr9zFyZC9CQoIB2L9/OyNHtmXOnBc0L1lEpIgJaIFsZuXNbJqZJZrZejO7JYNxz5rZUTPbn2qrmWp/IzP7xswOpPy3UeA+hYikpyi3pgbfvOQ+fa5i9uxBVK5cFoDk5GNMmfJ/jB/fnSNHDnicoYiI5JVA30EeBRwBKgPdgDFmVj+Dse8750qn2n4HMLNiwHTgXaAc8BYwPSUuIh44FVpTH3fhhfVJSIinefO6/tjXX7/H8OGt2L59nYeZiYhIXglYgWxmpYDOwADn3H7n3GLgY6BHDk/VBt/qGy845w47514EDLg0L/MVkew7VVpTH1elSgXmzHmOO+9s64/9+ed3xMREsWbNFx5mJiIieSGQd5DrAknOubWpYt8DGd1B7mhmO81stZn1SRWvD/zgTpz090NG5zGzXma2wsxWbN++99/kLyIZOJVaUx8XFhbKmDF9GT26D6GhvhUzExN38tJL7Zg1a5jmJYuIFGKBLJBLA2kr1D1AmXTGTgbOASoB/wWeNrOuqc6zJ5vnwTk3zjkX5ZyLqlgxPLe5i0gmTsXW1MfdddeVzJkzhDPOKAeAc8lMm/YYr712M4cPJ3qcnYiI5EYgC+T9QNoKNRzYl3agc26Nc26jc+6Yc24pMBK4IafnEZHAOFVbUx/XsmU9EhLiueCCc/yxb76ZzNChLdm69VcPMxMRkdwIZCe9tUCImdVxzv2SEmsIrM7GsQ7fPGNSxj9kZpZqmsX5+B4AFBEPHO+4t2HDuxw+vJ2wsIpERnY/pTrxnXFGeWbPHsTDD49n7NjPAdi48X/Exjbjjjve47zz2nucoUjBYJZMRMR2KlfeTXDwMa/TkSLk2LFgtmwpy9atFXHu390DtkDOkzOzSfiK3buARsBnwAXOudVpxl0LLAR2A82AacATzrm3Ular+AUYAYzFNwXjEaCOc+5IZtdv2rS2S0iIz9sPJSKSxltvzaFfv7EcPnwU8C0Rd801Q2jX7nHMLIujRYq2WrU2cMYZRvnylQkODtW/CckTzjmOHTvKzp1b2LTJ8dtvkemO693bvnHORWV1vkAv83YPUALYCkwE+jjnVpvZRWa2P9W4m4Ff8U2beBsY6px7CyClCO4E9MRXQN8BdMqqOBYRCZRbb72MefOiqVq1AuD7xj19+pO88kpnDh3SbDA5tYWHJ1KpUhVCQoqpOJY8Y2aEhBSjUqUqhIf/++c/AlogO+d2Ouc6OedKOecinXPvpcQXOedKpxrX1TlXIWX943opS7mlPs9K51xT51wJ51wT59zKQH4OEZGsREXVISEhnosv/meBne++m0ZsbAs2b/7Zw8xEvGemRr6SP/Lq75b+hoqI5JOIiLJ8/vlA7r23gz+2efOPxMY254cfZniYmYiIZCaQD+mJSBpbty4IyINtq1Y9zd69P/jfh4efT4MGg/I0t0B9lkBdJ6+EhoYQH38XTZrUpk+f0Rw6dIRDh/YyevQ1dOjwLFddNYCgIN2rEBEpSPRdWcQjgWrPnLY4Bti79wdWrXo6z3IL1GcpzC2tu3Vrw4IFMVSvXskf++STZxk7thMHD6Zd2l1EJPs6dWpD//79vE6jSFGBLOKRQLVnTlscZxXPTW6B+iyFvaV148a1WLYsnksvPd8f++GHGcTGNmfjxjUeZiYiWbn33tuIiDDi4wefEF+yZD4REcaOHdnvHJrdgvbee2+jW7cOWY57442pPPVUTLavn9aBAwd47rknaN68NtWqFadevYpcfXUrpk6dmO1zbNjwBxERxnffrch1HgWJCmQRjxTk9sw5zS1Qn6Ugf82yq2LFcD755BkefLCTP7Zly1qGDm3BypVTPcxMpHCoXx8iIk7e6tfP+th/q3jx4owaNZzt27fl/8Wy4cgR3wJe5cqVp3TpdBsKZ8sjj/Tmo4/eZ8iQF1iy5CemTPmCG27ozq5dO/Mq1UJHBbKIRwpye+ac5haoz1KQv2Y5ERISTGzsbbzzzkOULOnrQHj48H5eeaUzH330JMnJap4gkpFtGdSmGcXzUqtWl1CtWg1GjBic6bhlyxbSrl0LqlUrzrnnVmbAgP/zF7P33nsbS5cuYPz4UUREGBERxoYNf2Tr+sfvKL/44lAaNqxKo0ZVgZPvSH/yyVRatz6fyMgS1K1bnmuvbc3WrVsyPO+sWR9z//2Pc8UVHYiMrEGDBo25/fY+3HlnX/8Y5xwvvTSMZs1qERlZgtatGzBlyj+/vYuKOguAK65oRkSE0alTGwCSk5OJjx9Mo0bVqFo1jNatG/D559NPuH5c3CCaNKlO1aph1K9/On379vTvmzt3Jh07XkSdOuWoW7c8Xbpcydq1P2br6/VvqEAW8Uig2jOHh5+fo3hucgvUZylqLa1vuukiFi4cSs2alf2xmTOjGTWqA4mJuzzMTETSExQUxIABsbz11ljWrfst3TGbNv1N167tOe+8xsyZs5IXXnidqVMnMmTI4wA899xIoqL+Q9eut7Nq1SZWrdpElSrVsp3D0qULWLPmByZNmskHH8w5af+WLZu5++6buemmW1m8+EemT1/IjTf2yPScERGnM3fuTPbuzfh5iJiYp3jvvdcZOnQUixat4b77HueRR+7miy8+BWDWrOUATJo0k1WrNvHGG77fiI0bN5JRo4YzYMBQFixYRfv213H77dezatV3AMyY8SGjR8cxdOhoEhJ+YcKET2jSpLn/uomJifTq9QCzZi1n2rT5hIefRvfuHf0/cOQXrWIh4pFAtWdu0GBQjlexyGlugfosRbGl9fnn12Dp0jh69hzB7Nm+Jd1Xr55JbGwzeveeRpUqDTzOUERSu/zyq2jevBUxMU8ybtykk/a/8cZoKlc+k2HDRhMUFETduucwYEAsDz98N/37DyY8/DSKFStGiRIlqVz59Bxfv3jx4owcOZ6wsLB092/ZspGjR4/SseMNVKtWHYBzzjkv03PGx4+jT59u1KtXkXPOaUCzZhfQrt21tGnTFvAVqWPHjmDy5Nm0bHkRANWrn8XKlcsZP34UbdteTYUKvgeQy5evcMLnGj06jnvueZjOnW8BoH//QSQkLGT06DjGjHmXv/5aT+XKZ9CmzRWEhoZStWokjRr90+iuY8fOJ+Q6cuQb1KoVzrffLqdlywtz8qXLERXIIh6KiGgdkOIuqyXd0pPT3AL1WQJ1nUAqX74M06c/xTPPvMewYR8CsG3bbwwd2pKePd8gKqqLxxmKSGoDBgzlqqv+Q9++j5y0b+3aH2natOUJyzc2b34hR44cYd26X6lfP+Pf3mVHvXrnZVgcA9Sv35CLL76ciy8+jzZtruDiiy+nY8cbqFixEn/9tYELLzzXP/aBB57ggQee4D//uZivv/6db75JYPnyJSxaNJcuXa6gR49exMe/wtq1azh06BA339wO+Kf7YVLSUapVq5FhLvv27WXz5o00b97qhHiLFhfy5ZefAXDNNTfy6qsjiYo6i0suuZJLL23HlVde4/+M69b9xtChA/jmm6/YsWMbycnJJCcn8/ffG3Lx1cs+TbEQESkAgoODGTKkB5MmPUqpUsUBOHLkAK+9dhMffvgox44leZyhiBzXpElzOnTozKBBj+bouLxorV2yZKlM9wcHBzNlymwmT57Nueeez3vvvU7LlnX43/++5/TTz2Tu3O/826239vYfFxoaSsuWF3Hfff2ZMmU2/fsP5p13xrFhwx8kJycD8M47M044fuHC1UyePDtXn+P416JKlWosXfozcXGvUKZMOM888xBt2zYlMdHXLrp79w5s376NuLhXmDnzK+bOXUlISAhHj+bvFAsVyCIiBcj111/A4sXDqF37TH/siy+G89JL7dm/f4eHmYkUDJUq5SyeX554IpqEhEXMnTvzhHjduufwzTcJ/qISYPnyxRQrVowaNWoBEBpajGPH8u9hXDOjWbP/8MgjzzB79tecfvqZTJ/+PiEhIdSsWdu/lStXPsNz1K3ru9OcmLifs88+l7CwMP76a/0Jx9esWds/jaNYsWIAJ3yuMmXCOf30M1m+fMkJ5/7qq8X+84Nv2kjbtlczePDzzJr1NT/9tJrly5ewc+cOfvnlJx544Alat76cunXPYf/+fSQl5f8NA02xEBEpYOrXj2Tp0mHcdtsLfPaZb03Rn376kpiYKHr3nka1ao08zlDEO6tXe52BT82atenRoxevvjryhPjtt9/DuHEv8Oij99Cr1/2sX/87gwf35447+lGyZEkAIiNrsHLlcjZs+INSpUpTrlz5POuouWJFAgsXfskll1xJpUqVWbVqJX///ecJBWlanTq14brrutKoURTlylVg7do1REc/QZ069ahb9xyCg4O5556HefbZh3HO0bLlxSQm7uebbxIICgqiZ89eVKwYQYkSJZg3bxbVqtWgePHihIefRt++jzB06NPUrFmHhg2bMmXKuyQkLOLLL78FYNKkN0lKSqJJkxaUKlWa6dPfJzQ0lJo161C2bDkqVKjIu+++yplnVmPz5r8ZOPARQkLyv3xVgSzioV9/HcuWLbOBZCCIypWvoHbt3pkeE4i20blR2FpAF3Rly5Zm6tQnGDz4fZ577n0Aduz4g2HDLqB791dp0aKbxxmKyEMPPc377791QuyMM6owceLnDBz4CJde2ojw8LJ07nwLTz4Z7R9zzz0P06/frVx00bkcPHiQFSvWERlZI09yCg8/jeXLl/Daay+xd+9uzjyzGg8+OIAbb8x4tZ9LLrmSKVPeISbmSRIT9xMRcTqtW7floYeeJjg4GID+/QdTqVJlRo+O49FH+1CmTDj16zeiXz/fNJOQkBCee+5F4uMHERc3kJYtL+Kjj+bz3//ex/79+xg06FG2bdtC7dpnM378h5x3XsOUfMvy0ktDefbZh0lKOkrduufyxhtTqV7dt2zcuHHv8+ST99G69XmcdVZtnn02njvu6Jz+B8lD5pzL94sUFE2b1nYJCfFepyECHC+OZ54Ur1y5XYZFcnptoyHzIvl4e+bUHeiCgsKoVeuePCtgA3GNU9mMGcu57bbn2bfvoD926aUP0LnzMIKDQz3MTCTnGjf+kbPOOsfrNKQIW7fuR1auTP/vWO/e9o1zLirdnaloDrKIR3x3jrMfh8C0jc6Nwt4CuqDr2LE5S5YM5+yzq/pjc+e+wMiRV7B371YPMxMRKZpUIIt4JjmH8dwJRHvmotACuqCrV68qS5YM45prWvhja9fOJyYmivXrV3iYmYhI0aMCWcQzGf3zy9t/loFoz1xUWkAXdOHhJZk8+TEGDuzmXyJp164/GT78QpYufdPb5EREihAVyCIeqVz5ihzFITBto3OjqLWALsiCgoJ4/PEb+eijJzntNN8T8UlJh3n77duZOLEfSUn5uzaoiMipIKAFspmVN7NpZpZoZuvN7JYMxj1iZv8zs31mts7MHkmz/w8zO2hm+1O23K1SLeKh2rV7U7lyO/75ZxiU6QN64OuIl7YYzk7b6Fq17iEsrBJghIVVyvOH5wJxDTlR+/ZRLFsWz7nnRvpjCxaM4oUXLmPPns0eZiYiUvgFdBULM5uIrxq4E2gEfApc4JxbnWbco8CXwA9ALWA28JhzblLK/j+Au5xzX+bk+lrFQkSKmv37D3LXXS8xdepSf+y0087k7rs/pGbNlh5mJpI+rWIh+a1QrWJhZqWAzsAA59x+59xi4GOgR9qxzrlhzrlvnXNJzrmfgelAq7TjREROdaVLl2DixEeIju7pbzSwZ89GRoxozaJFr3qcnYhI4RTIKRZ1gSTn3NpUse+B+pkdZL4nUS4C0vbOmWBm28xstpk1zOT4Xma2wsxWbN++N7e5i4gUWGbGww9fz4wZAyhXrjQASUlHmDChFxMm3M3Ro4ezOIOIiKQWyAK5NJC2Qt0DlMniuGfx5flGqlg3oAZQHZgHzDKzsukd7Jwb55yLcs5FVawYnou0RUQKh7ZtG7NsWRznn1/DH1u0aBwjRrRh9+6N3iUmIlLIBLLV9H4gbYUaDuzL6AAz6wf0BC5yzvlvgTjnlqQaFmNmt+K7yzwj79KVoiJQLZBz0zb6m2/u5dChP/3vixevRtOmL2V6zJIlnYFjqSLBtGr1YRbHdAFSr25QjFatJmd6zFdf3UFS0k7/+5CQ8rRoMT7D8YH6OquldeZq1jydhQuHcvfdL/P++4sAWLcugejoJvTq9QG1a1/ocYYiRVenTm2oV+88YmNf9joV+ZcCeQd5LRBiZnVSxRpy8tQJAMzsDqA/cJlz7q8szu0Ay5MspUg53gL58OFtgOPw4W389ttotm5dkKfX+adt9PEmH8ls2TKTX38dm+ExaYtjgEOH/uSbb+7N8JiTi2OAYynxjI5JWxwDHEmJpy9tcQyQlLSTr766I93xgfo6B+o6hV3JkmG8/faDDB9+B8HBvm/ze/duYcSIS5g/fzSBfDhbpKi4997b6NatQ6Zj3nhjKk89FZPraxw4cIDnnnuC5s1rU61acerVq8jVV7di6tSJ2T7Hhg1/EBFhfPedGgj9GwErkJ1zicBUYJCZlTKzVsC1wDtpx5pZNyAaaOuc+z3Nvkgza2VmxcyseMoScBWBJWnPIxKoFsi5aRudtjjOKu6TtjjOKg4nF8dZxTmpOM4qHqivs1paZ5+Zcf/91/D55wM5Pr0sOTmJSZP68s47d3L06CGPMxTJvd27J7B2bQ1Wrw5i7doa7N49wdN8jhzxfT8tV648pUtnNXM0Y4880puPPnqfIUNeYMmSn5gy5QtuuKE7u3al/71X8k+gG4XcA5QAtgITgT7OudVmdpGZ7U81bghQAfg61VrHx2/FlQHGALuAv4F2QHvn3I6AfQopNALXAjkwbaMLqkB9ndXSOufatGlAQkI8jRvX9MeWLn2DuLiL2Lkzsx/GRAqm3bsnsHFjL44eXQ84jh5dz8aNvQJaJB+/m/zii0Np2LAqjRpVBXxTLPr37+cf98knU2nd+nwiI0tQt255rr22NVu3bsnwvLNmfcz99z/OFVd0IDKyBg0aNOb22/tw5519/WOcc7z00jCaNatFZGQJWrduwJQp/9wkiIo6C4ArrmhGRITRqVMbAJKTk4mPH0yjRtWoWjWM1q0b8Pnn00+4flzcIJo0qU7VqmHUr386ffv29O+bO3cmHTteRJ065ahbtzxdulzJ2rU/5v6LWMAFtEB2zu10znVyzpVyzkU6595LiS9yzpVONe4s51yoc650qq13yr7VzrnzU85RwTl3mXNOv0eQdAWuBXJg2kYXVIH6Oqulde5ERlZi/vwYune/xB9bv34FMTFNWbtW01OkcNm69UmcO3BCzLkDbN36ZEDzWLp0AWvW/MCkSTP54IM5J+3fsmUzd999MzfddCuLF//I9OkLufHGk1a2PUFExOnMnTuTvXv3ZDgmJuYp3nvvdYYOHcWiRWu4777HeeSRu/nii08BmDVrOQCTJs1k1apNvPHGVADGjRvJqFHDGTBgKAsWrKJ9++u4/fbrWbXqOwBmzPiQ0aPjGDp0NAkJvzBhwic0adLcf93ExER69XqAWbOWM23afMLDT6N7947+u+dFTSAf0hMJuMjI7vz22+gTfi2fHy2QK1e+ImUO8snxjBQvXi3d6RTFi1fL5ErBpD+dIjiTY4qR/nSKYhkeERJSPt3pFCEh5dMdH6ivc6CuUxSVKBHG66/fR1RUbR5+eDxJScfYt28bL7xwGZ07x3PppffhW1VTpGA7enRDjuL5pXjx4owcOZ6wsLB092/ZspGjR4/SseMNVKtWHYBzzjkv03PGx4+jT59u1KtXkXPOaUCzZhfQrt21tGnTFvAVqWPHjmDy5Nm0bHkRANWrn8XKlcv5//buPbyq4l7j+PclCQGBiAiJgHIHL3AKCig+FtBzRK0WL8ejUkHrpSoiVY54t1RAEfEEtVpRUZEq9VKeomCtSC0q1YqA0mrxgqVq5CaoVUiAEMycP9YibGISIMBekLyf51kPYdbM5Leyd3Z+e/asmUmT7qdfv1PYf/9mADRpsj95eQeU9T1hQj5DhlzDmWdGmxjfcMNo5s6dw4QJ+TzwwBSWLv2MvLzmHHvsCWRlZXHgga3o1m3LfnKA/qwAABF0SURBVBr9+299r8uvfvUY7dvn8M478+jVq+bd/Fs7hres1krXFsjV2Ta6e/f7vpcMb2sVi2i1ivLJcNWrWESrVZRPhqtexeKooyZ9LxmuahWLdP2cvaX1zpHEkCGn8NJLo8nN3ReA0tLvmDp1GJMnn8/Gjeu20YNZ8rKyWu1Q+e5yyCFdKk2OATp37kqfPsfTp08XLrzwTB577AG+/HI1AEuXFtCmTcOy4557bgfg6KP7MH/+v5g2bTannXY2S5Ys5uyzT2D48MsAWLz4fTZs2MCAASdt1X7y5Af49NMllcaydu0aVq5czpFHbr3n2lFH/ZDFi98H4NRTz6K4eAM9erRl2LCLmTFjKsXFWwYjPvlkCYMHn0vPnu1p1y6Hzp3zKC0tZdmy9L4xSRePIFuNl5vbNy0JVIcOg7e5rFt521rSrSLbWtKt4jZVL+lWkaqWdKtIun7O6fo+NVnv3p2ZO3c855wzjvnzPwbgrbemsHz5Ii67bBpNm7ZJNkCzKuTmjmH58ku3mmYh7UNu7pi0xrHPPg2qPJ+RkcHUqbNYsGAur746iyeffJQxY27kuede45BDOjN79t/K6u6335YBiaysLHr16k2vXr258sobuOuu27jjjhFcddWNlJZG97U88cTztGy59RuCrKysal3H5k+OWrY8iL/+9SP+8pc/M2fOy9xyy3Dy80fx4otv0aBBAwYN+jHNmx9Ifv5DNG/ekszMTH74w8MoKamZUyw8gmxmVgsdeGBTZs++nQsvPL6s7PPPFzJ2bA8++ODlBCMzq1rjxgNp0WIiWVmtAZGV1ZoWLSbSuPHApEP7Hkn07Hk01157C7NmzeeAA1owffozZGZm0q5dh7IjNUEur1OnwwAoKirk4IMPIzs7m6VLP9uqfbt2HcqmcdStG31i+N13W6bjNWqUwwEHtGDevK0X/HrrrdfL+odo2ki/fqdw661389JL8/nww0XMm/cGX3/9FR9//CHDht1E377H06nToRQWrmXTpk277Ge1p/EIsplZLZWdncWDD15Bjx4dGTbsYUpKNlFU9BX33nsiZ5wxjn79hntesu2RGjceuEcmxKkWLJjLnDkvc9xxJ9KsWR7vvbeQZcs+3yohLe/004/ljDN+QrduPdhvv/1ZvPh9br/9Jjp2PIROnQ4lIyODIUOuYeTIawgh0KtXH4qKCnn77bnUqVOH88+/lKZNc6lfvz6vvPISBx3Uhnr16pGTsy9XXHEt48b9knbtOtK1a3emTp3C3Ll/4eWX3wHg6acns2nTJo444igaNGjI9OnPkJWVRbt2HWnceD/2378pU6Y8TIsWB7Fy5TJGjbqWzMyam0bW3CszM7NtksQll5xIly6tOeeccaxc+W9CKGXatGspKFjAeec9SnZ21R8lm9n35eTsy7x5b/DII/exZs03tGhxEFdfPYKzzqr8puLjjjuRqVOfYOzYmykqKiQ39wD69u3H8OG/JCMjuv/khhtupVmzPCZMyOe66y6nUaMcOnfuxtCh1wGQmZnJmDH3Mn78aPLzR9GrV2+ee+5VLrnkSgoL1zJ69HWsXv0FHToczKRJv6dLl65xvI25775xjBx5DZs2ldCp02E89tg0WreOlo2bOPEZbr75Svr27ULbth0YOXI8F11U+SZVezvVph2VunfvEObOHZ90GGZme6QVK75mwIA7efPND8vKWrb8DwYPfpZmzdonGJnVJIcf/gFt2x6adBhWg33yyQcsXFjxc2zwYL0dQuhR4ckUHkE2q8CqVa9RUDCF4uIvyc5uSqtWg/aYG8Oiba1nEW1CUoe8vBO2eXNgddpY7dO8eRP+9KdbGT78UR56KFq2cNmy9xg7tgcXX/wUnTuflHCEZmbp4Zv0zMpZteo1liyZQHHxaiBQXLyaJUsmsGpV8hsqRInuTLbs0FfKF1/M5J//fHCXtrHaq27dLO67bzAPPXQFdetGYyjr1n3Dr399MjNnjqU2fepoZrWXE2SzcgoKpmy1EQVAaWkxBQVTKmmRPtEo8PaXV7eN2YUX9uOVV26nZcv9gWh72+eeu4mJE89iw4a1CUdnZrZ7OUE2K6e4+MsdKk+v0h0sr24bM+jZsxNz546nd+/OZWULF/6eceN68cUXixOMzMxs93KCbFZOdnbTHSpPr8p+Zav6Va5OG7NIXl5jZs4cxdChPy4rW7HifcaO7cm77/4hwchsb+apOra77Krnlv9CmpXTqtUg6tTZevvQOnWyadWq8qV50iUv74QdKq9uG7NUWVmZ3HXXz5g06Srq1Ys2IdiwYQ0TJvTnhRdGl+3uZbY9SkqyKClZn3QYVkOVlKynpKR6uwqmcoJsVk5ubl/atx9CdnYzQGRnN6N9+yF7xCoWHToMJi/vJLb86tYhL++kKlekqE4bs4oMGnQcr702llatmpWVPf/8LTz44BmsX/9tgpHZ3qSgIJcVK5axceM6jyTbLhNCYOPGdaxYsYyCgtyd7s/rIJuZ2Q5ZvfpbBg3K55VX3isry8s7mMGDn6V5c69va9uWk7OGVq1WkZVVknQoVoOUlGRRUJDLmjU5ldbZ3nWQnSCbmdkO27TpO26++XHuvnt6WVl2dkMuuOBxDj/8jAQjMzOr3PYmyJ5iYWZmOywzM4Nx4y7k8cevpn79aF5ycXEhDz3038yYMYLS0u8SjtDMrPqcIJuZWbUNGNCHOXPG0bZtXlnZH/94G/ff35+ion8nGJmZWfWlNUGW1ETSs5KKJH0m6dxK6knSOElfxcc4SUo5303S25LWxf92S99VmJlZqq5d2/Lmm/n067flpXjRohe5446eLFv2jwQjMzOrnnSPIN8PbATygIHAA5I6V1DvUuB0oCvwA6A/cBmApLrAdGAKsB/wG2B6XG5mZglo0qQRM2aM4NprzywrW716CXfe2Yu3356aYGRmZjsubQmypAbAmcCIEEJhCOF1YAZwXgXVfwqMDyEsDSEsA8YDF8TnjgUygXtCCMUhhHsBAf+5my/BzMyqkJGRwZgx5/HUU9fRoEE9AIqLi3j44bOZNu16z0s2s71GZhq/VydgUwghdX/SvwMVLS7bOT6XWq9zyrl3w9bLb7wbl88s35GkS4lGpAGK69Y93Z/31V5NgT1hv2hLjp8DCZk1605mzboz6TDAz4Hazo+/Hbw9ldKZIDcE1pQr+xZoVEndb8vVaxjPQy5/rqp+CCFMBCYCSFqwPUt7WM3kx9/8HDA/B2o3P/4macH21EvnHORCoPzKzTnA2u2omwMUxqPGO9KPmZmZmdkOSWeCvBjIlNQxpawrsKiCuovicxXVWwT8IHVVC6Ib+Srqx8zMzMxsh6QtQQ4hFAHTgNGSGkg6BjgNeKKC6o8DV0tqKakFMByYHJ97FfgOuFJStqShcfns7Qhj4k5cgu39/PibnwPm50Dt5sfftus5kNatpiU1ASYB/YCvgBtCCE9K6g28GEJoGNcTMA74Wdz0EeD6zTfmSTo8LjsM+AC4OISwMG0XYmZmZmY1VloTZDMzMzOzPZ23mjYzMzMzS+EE2czMzMwsRa1IkCU1kfSspCJJn0k6N+mYLH0kDZW0QFKxpMlJx2PpFd/M+2j8u79W0t8k/SjpuCy9JE2RtELSGkmLJf1s262sppHUUdIGSVOSjsXSS9Kr8WNfGB8fVVW/ViTIwP3ARiAPGAg8IKlz1U2sBlkO3EZ0g6jVPpnA50S7du4L/AL4naQ2CcZk6TcWaBNCyAFOBW6T1D3hmCz97gfmJx2EJWZoCKFhfFS5o16NT5AlNQDOBEaEEApDCK8DM4Dzko3M0iWEMC2E8BzRyilWy4QQikIII0MIn4YQSkMIfwA+AZwc1SIhhEUhhOLN/42P9gmGZGkmaQDwDfDnpGOxPV+NT5CBTsCmEMLilLK/Ax5BNquFJOURvS54c6FaRtIESeuAD4EVwB8TDsnSRFIOMBq4OulYLFFjJX0p6Q1Jx1ZVsTYkyA2BNeXKvgUaJRCLmSVIUhbwW+A3IYQPk47H0iuEMITotb830cZVxVW3sBrkVuDREMLSpAOxxFwPtANaEm0W8rykSj9Fqg0JciGQU64sB1ibQCxmlhBJdYh27twIDN1GdauhQgjfxVPtDgQuTzoe2/0kdQOOB+5OOhZLTgjhrRDC2hBCcQjhN8AbwMmV1c9MX2iJWQxkSuoYQvg4LuuKP141qzXi3TkfJbpR9+QQQknCIVnyMvEc5NriWKANUBC9FNAQyJB0WAjhiATjsmQFQJWdrPEjyCGEIqKP0kZLaiDpGOA0opEkqwUkZUqqB2QQvSjWk1Qb3hzaFg8AhwL9Qwjrkw7G0ktSrqQBkhpKypB0IvATfLNWbTGR6M1Qt/h4EHgBODHJoCx9JDWWdOLmv/+SBgJ9gJmVtanxCXJsCFAfWAU8BVweQvAIcu3xC2A9cAMwKP76F4lGZGkjqTVwGdEfxpUpa2AOTDg0S59ANJ1iKfBvIB8YFkKYkWhUlhYhhHUhhJWbD6KplxtCCKuTjs3SJotoudfVwJfAz4HTyy3gsBWFENIUm5mZmZnZnq+2jCCbmZmZmW0XJ8hmZmZmZimcIJuZmZmZpXCCbGZmZmaWwgmymZmZmVkKJ8hmZmZmZimcIJuZ7eUkXSCpcBt1PpV0TbpiqoqkNpKCpB5Jx2JmVhEnyGZmu4CkyXHSFySVSPqXpHxJDXawjz/szjjTrSZek5nVfN5u18xs13kZOI9o16bewCNAA6Jd3MzMbC/hEWQzs12nON7O9vMQwpPAb4HTN5+UdJikFyStlbRK0lOSDojPjQR+CpySMhJ9bHzuDkkfSVofT5W4U1K9nQlU0r6SJsZxrJX0WuqUh83TNiT9l6R/SCqS9IqktuX6uVHSF3HdxyXdIunTbV1TrLWkP0laJ+l9Sf125prMzHYVJ8hmZrvPeqLRZCQ1B+YA/wCOBI4HGgLTJdUB8oHfEY1CN4+Pv8b9FAEXAYcCQ4ABwM3VDUqSgBeAlsCPgcPj2GbHcW6WDdwYf++jgcbAgyn9DABuiWM5AvgAuDqlfVXXBDAGuBfoCswHnpbUsLrXZWa2q3iKhZnZbiDpSOBc4M9x0eXA30MI16fUOR/4GugRQpgnaT3xKHRqXyGEW1P++6mk24FrgBHVDO84oBvQLISwPi4bIak/0RSRO+OyTOCKEMJHcbz5wCRJCiEE4Cpgcgjhkbj+WEnHAZ3iuAsruqYoPwfg7hDC83HZTcD5cVyvV/O6zMx2CSfIZma7zknxahKZRCPH04Gfx+e6A30qWW2iPTCvsk4l/Q8wDOhANOqcER/V1R3YB1idkqwC1Itj2ax4c3IcWw7UBfYjSuwPAR4u1/dbxAnydni3XN8AudvZ1sxst3GCbGa268wBLgVKgOUhhJKUc3WIpjVUtNTaF5V1KKkX8DQwCvhf4BvgVKLpC9VVJ/6evSs4tybl603lzoWU9rtC2c8nhBDiZN1T/8wscU6Qzcx2nXUhhH9Wcu4d4Gzgs3KJc6qNfH9k+BhgWeo0C0mtdzLOd4A8oDSE8K+d6OdDoCcwKaXsyHJ1KromM7M9mt+pm5mlx/3AvsAzko6S1E7S8fFKEo3iOp8CXSQdLKmppCxgMdBS0sC4zeXAT3YylpeBN4huEPyRpLaSjpY0SlJFo8qV+RVwgaSLJHWUdB1wFFtGmiu7JjOzPZoTZDOzNAghLCcaDS4FZgKLiJLm4viAaD7vB8ACYDVwTHwT2/8B9xDN2e0H/HInYwnAycDs+Ht+RLTaxMFsmQu8Pf08DdwK3AEsBLoQrXKxIaXa965pZ2I3M0sHRa+TZmZmO0/Ss0BmCKF/0rGYmVWX5yCbmVm1SNqHaPm6mUQ39J0JnBb/a2a21/IIspmZVYuk+sDzRBuN1Ac+BsbFuwiame21nCCbmZmZmaXwTXpmZmZmZimcIJuZmZmZpXCCbGZmZmaWwgmymZmZmVkKJ8hmZmZmZin+Hxztdxu0fLjJAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\\n\",\n    \"b = -per_clf.intercept_ / per_clf.coef_[0][1]\\n\",\n    \"\\n\",\n    \"axes = [0, 5, 0, 2]\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(\\n\",\n    \"        np.linspace(axes[0], axes[1], 500).reshape(-1, 1),\\n\",\n    \"        np.linspace(axes[2], axes[3], 200).reshape(-1, 1),\\n\",\n    \"    )\\n\",\n    \"X_new = np.c_[x0.ravel(), x1.ravel()]\\n\",\n    \"y_predict = per_clf.predict(X_new)\\n\",\n    \"zz = y_predict.reshape(x0.shape)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 4))\\n\",\n    \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"bs\\\", label=\\\"Not Iris-Setosa\\\")\\n\",\n    \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n    \"\\n\",\n    \"plt.plot([axes[0], axes[1]], [a * axes[0] + b, a * axes[1] + b], \\\"k-\\\", linewidth=3)\\n\",\n    \"from matplotlib.colors import ListedColormap\\n\",\n    \"custom_cmap = ListedColormap(['#9898ff', '#fafab0'])\\n\",\n    \"\\n\",\n    \"plt.contourf(x0, x1, zz, cmap=custom_cmap)\\n\",\n    \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n    \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n    \"plt.legend(loc=\\\"lower right\\\", fontsize=14)\\n\",\n    \"plt.axis(axes)\\n\",\n    \"\\n\",\n    \"save_fig(\\\"perceptron_iris_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Activation functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def sigmoid(z):\\n\",\n    \"    return 1 / (1 + np.exp(-z))\\n\",\n    \"\\n\",\n    \"def relu(z):\\n\",\n    \"    return np.maximum(0, z)\\n\",\n    \"\\n\",\n    \"def derivative(f, z, eps=0.000001):\\n\",\n    \"    return (f(z + eps) - f(z - eps))/(2 * eps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure activation_functions_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 792x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"z = np.linspace(-5, 5, 200)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(11,4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.plot(z, np.sign(z), \\\"r-\\\", linewidth=1, label=\\\"Step\\\")\\n\",\n    \"plt.plot(z, sigmoid(z), \\\"g--\\\", linewidth=2, label=\\\"Sigmoid\\\")\\n\",\n    \"plt.plot(z, np.tanh(z), \\\"b-\\\", linewidth=2, label=\\\"Tanh\\\")\\n\",\n    \"plt.plot(z, relu(z), \\\"m-.\\\", linewidth=2, label=\\\"ReLU\\\")\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.legend(loc=\\\"center right\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"Activation functions\\\", fontsize=14)\\n\",\n    \"plt.axis([-5, 5, -1.2, 1.2])\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.plot(z, derivative(np.sign, z), \\\"r-\\\", linewidth=1, label=\\\"Step\\\")\\n\",\n    \"plt.plot(0, 0, \\\"ro\\\", markersize=5)\\n\",\n    \"plt.plot(0, 0, \\\"rx\\\", markersize=10)\\n\",\n    \"plt.plot(z, derivative(sigmoid, z), \\\"g--\\\", linewidth=2, label=\\\"Sigmoid\\\")\\n\",\n    \"plt.plot(z, derivative(np.tanh, z), \\\"b-\\\", linewidth=2, label=\\\"Tanh\\\")\\n\",\n    \"plt.plot(z, derivative(relu, z), \\\"m-.\\\", linewidth=2, label=\\\"ReLU\\\")\\n\",\n    \"plt.grid(True)\\n\",\n    \"#plt.legend(loc=\\\"center right\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"Derivatives\\\", fontsize=14)\\n\",\n    \"plt.axis([-5, 5, -0.2, 1.2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"activation_functions_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def heaviside(z):\\n\",\n    \"    return (z >= 0).astype(z.dtype)\\n\",\n    \"\\n\",\n    \"def mlp_xor(x1, x2, activation=heaviside):\\n\",\n    \"    return activation(-activation(x1 + x2 - 1.5) + activation(x1 + x2 - 0.5) - 0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x1s = np.linspace(-0.2, 1.2, 100)\\n\",\n    \"x2s = np.linspace(-0.2, 1.2, 100)\\n\",\n    \"x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n    \"\\n\",\n    \"z1 = mlp_xor(x1, x2, activation=heaviside)\\n\",\n    \"z2 = mlp_xor(x1, x2, activation=sigmoid)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10,4))\\n\",\n    \"\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.contourf(x1, x2, z1)\\n\",\n    \"plt.plot([0, 1], [0, 1], \\\"gs\\\", markersize=20)\\n\",\n    \"plt.plot([0, 1], [1, 0], \\\"y^\\\", markersize=20)\\n\",\n    \"plt.title(\\\"Activation function: heaviside\\\", fontsize=14)\\n\",\n    \"plt.grid(True)\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.contourf(x1, x2, z2)\\n\",\n    \"plt.plot([0, 1], [0, 1], \\\"gs\\\", markersize=20)\\n\",\n    \"plt.plot([0, 1], [1, 0], \\\"y^\\\", markersize=20)\\n\",\n    \"plt.title(\\\"Activation function: sigmoid\\\", fontsize=14)\\n\",\n    \"plt.grid(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# FNN for MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using the Estimator API (formerly `tf.contrib.learn`)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: `tf.examples.tutorials.mnist` is deprecated. We will use `tf.keras.datasets.mnist` instead. Moreover, the `tf.contrib.learn` API was promoted to `tf.estimators` and `tf.feature_columns`, and it has changed considerably. In particular, there is no `infer_real_valued_columns_from_input()` function or `SKCompat` class.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\\n\",\n    \"X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\\n\",\n    \"X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\\n\",\n    \"y_train = y_train.astype(np.int32)\\n\",\n    \"y_test = y_test.astype(np.int32)\\n\",\n    \"X_valid, X_train = X_train[:5000], X_train[5000:]\\n\",\n    \"y_valid, y_train = y_train[:5000], y_train[5000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Using default config.\\n\",\n      \"WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpuflzeb_h\\n\",\n      \"INFO:tensorflow:Using config: {'_evaluation_master': '', '_session_config': None, '_model_dir': '/tmp/tmpuflzeb_h', '_task_type': 'worker', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f4fcb4e15c0>, '_save_summary_steps': 100, '_is_chief': True, '_save_checkpoints_steps': None, '_log_step_count_steps': 100, '_master': '', '_service': None, '_keep_checkpoint_every_n_hours': 10000, '_task_id': 0, '_tf_random_seed': None, '_num_ps_replicas': 0, '_global_id_in_cluster': 0, '_train_distribute': None, '_num_worker_replicas': 1, '_save_checkpoints_secs': 600, '_keep_checkpoint_max': 5}\\n\",\n      \"INFO:tensorflow:Calling model_fn.\\n\",\n      \"INFO:tensorflow:Done calling model_fn.\\n\",\n      \"INFO:tensorflow:Create CheckpointSaverHook.\\n\",\n      \"INFO:tensorflow:Graph was finalized.\\n\",\n      \"INFO:tensorflow:Running local_init_op.\\n\",\n      \"INFO:tensorflow:Done running local_init_op.\\n\",\n      \"INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpuflzeb_h/model.ckpt.\\n\",\n      \"INFO:tensorflow:loss = 122.883514, step = 0\\n\",\n      \"INFO:tensorflow:global_step/sec: 480.267\\n\",\n      \"INFO:tensorflow:loss = 9.599711, step = 100 (0.209 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 599.191\\n\",\n      \"INFO:tensorflow:loss = 19.580772, step = 200 (0.167 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 640.184\\n\",\n      \"INFO:tensorflow:loss = 2.1866307, step = 300 (0.157 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 716.395\\n\",\n      \"INFO:tensorflow:loss = 11.493204, step = 400 (0.138 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 713.653\\n\",\n      \"INFO:tensorflow:loss = 4.0078278, step = 500 (0.140 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 722.021\\n\",\n      \"INFO:tensorflow:loss = 10.612131, step = 600 (0.139 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 669.446\\n\",\n      \"INFO:tensorflow:loss = 6.692636, step = 700 (0.149 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 720.49\\n\",\n      \"INFO:tensorflow:loss = 4.2058306, step = 800 (0.139 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 766.548\\n\",\n      \"INFO:tensorflow:loss = 9.13055, step = 900 (0.130 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 773.506\\n\",\n      \"INFO:tensorflow:loss = 4.1445055, step = 1000 (0.129 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 755.713\\n\",\n      \"INFO:tensorflow:loss = 8.442559, step = 1100 (0.132 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 762.721\\n\",\n      \"INFO:tensorflow:loss = 1.4401194, step = 1200 (0.131 sec)\\n\",\n      \"<<821 more lines>>\\n\",\n      \"INFO:tensorflow:loss = 0.021663003, step = 42300 (0.127 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 763.347\\n\",\n      \"INFO:tensorflow:loss = 0.011599571, step = 42400 (0.131 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 762.321\\n\",\n      \"INFO:tensorflow:loss = 0.0044469903, step = 42500 (0.131 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 768.549\\n\",\n      \"INFO:tensorflow:loss = 0.0019147585, step = 42600 (0.130 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 771.429\\n\",\n      \"INFO:tensorflow:loss = 0.0054854164, step = 42700 (0.130 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 793.871\\n\",\n      \"INFO:tensorflow:loss = 0.0017117725, step = 42800 (0.126 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 770.1\\n\",\n      \"INFO:tensorflow:loss = 0.012048513, step = 42900 (0.130 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 744.636\\n\",\n      \"INFO:tensorflow:loss = 0.06634566, step = 43000 (0.134 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 696.882\\n\",\n      \"INFO:tensorflow:loss = 0.0003919307, step = 43100 (0.144 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 705.516\\n\",\n      \"INFO:tensorflow:loss = 0.06582007, step = 43200 (0.141 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 699.244\\n\",\n      \"INFO:tensorflow:loss = 0.0038124803, step = 43300 (0.143 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 792.079\\n\",\n      \"INFO:tensorflow:loss = 0.003364585, step = 43400 (0.126 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 753.586\\n\",\n      \"INFO:tensorflow:loss = 0.00725976, step = 43500 (0.133 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 720.951\\n\",\n      \"INFO:tensorflow:loss = 0.024148291, step = 43600 (0.139 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 770.384\\n\",\n      \"INFO:tensorflow:loss = 0.013779048, step = 43700 (0.130 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 799.363\\n\",\n      \"INFO:tensorflow:loss = 0.014951154, step = 43800 (0.125 sec)\\n\",\n      \"INFO:tensorflow:global_step/sec: 791.774\\n\",\n      \"INFO:tensorflow:loss = 0.0015594304, step = 43900 (0.126 sec)\\n\",\n      \"INFO:tensorflow:Saving checkpoints for 44000 into /tmp/tmpuflzeb_h/model.ckpt.\\n\",\n      \"INFO:tensorflow:Loss for final step: 0.0012097486.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x7f4f62b23be0>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"feature_cols = [tf.feature_column.numeric_column(\\\"X\\\", shape=[28 * 28])]\\n\",\n    \"dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=10,\\n\",\n    \"                                     feature_columns=feature_cols)\\n\",\n    \"\\n\",\n    \"input_fn = tf.estimator.inputs.numpy_input_fn(\\n\",\n    \"    x={\\\"X\\\": X_train}, y=y_train, num_epochs=40, batch_size=50, shuffle=True)\\n\",\n    \"dnn_clf.train(input_fn=input_fn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Calling model_fn.\\n\",\n      \"INFO:tensorflow:Done calling model_fn.\\n\",\n      \"INFO:tensorflow:Starting evaluation at 2018-05-18-19:12:49\\n\",\n      \"INFO:tensorflow:Graph was finalized.\\n\",\n      \"INFO:tensorflow:Restoring parameters from /tmp/tmpuflzeb_h/model.ckpt-44000\\n\",\n      \"INFO:tensorflow:Running local_init_op.\\n\",\n      \"INFO:tensorflow:Done running local_init_op.\\n\",\n      \"INFO:tensorflow:Finished evaluation at 2018-05-18-19:12:50\\n\",\n      \"INFO:tensorflow:Saving dict for global step 44000: accuracy = 0.9798, average_loss = 0.10096103, global_step = 44000, loss = 12.779877\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_input_fn = tf.estimator.inputs.numpy_input_fn(\\n\",\n    \"    x={\\\"X\\\": X_test}, y=y_test, shuffle=False)\\n\",\n    \"eval_results = dnn_clf.evaluate(input_fn=test_input_fn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'accuracy': 0.9798,\\n\",\n       \" 'average_loss': 0.10096103,\\n\",\n       \" 'global_step': 44000,\\n\",\n       \" 'loss': 12.779877}\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"eval_results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Calling model_fn.\\n\",\n      \"INFO:tensorflow:Done calling model_fn.\\n\",\n      \"INFO:tensorflow:Graph was finalized.\\n\",\n      \"INFO:tensorflow:Restoring parameters from /tmp/tmpuflzeb_h/model.ckpt-44000\\n\",\n      \"INFO:tensorflow:Running local_init_op.\\n\",\n      \"INFO:tensorflow:Done running local_init_op.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'class_ids': array([7]),\\n\",\n       \" 'classes': array([b'7'], dtype=object),\\n\",\n       \" 'logits': array([ -3.809414 ,  -4.1564407,  -0.426081 ,   3.2636993, -11.065331 ,\\n\",\n       \"         -8.790985 , -10.436305 ,  19.935707 ,  -6.9282775,   2.2807484],\\n\",\n       \"       dtype=float32),\\n\",\n       \" 'probabilities': array([4.8710768e-11, 3.4428106e-11, 1.4354495e-09, 5.7469666e-08,\\n\",\n       \"        3.4389070e-14, 3.3431518e-13, 6.4506329e-14, 1.0000000e+00,\\n\",\n       \"        2.1533745e-12, 2.1505466e-08], dtype=float32)}\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred_iter = dnn_clf.predict(input_fn=test_input_fn)\\n\",\n    \"y_pred = list(y_pred_iter)\\n\",\n    \"y_pred[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Using plain TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"n_inputs = 28*28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 100\\n\",\n    \"n_outputs = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def neuron_layer(X, n_neurons, name, activation=None):\\n\",\n    \"    with tf.name_scope(name):\\n\",\n    \"        n_inputs = int(X.get_shape()[1])\\n\",\n    \"        stddev = 2 / np.sqrt(n_inputs)\\n\",\n    \"        init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)\\n\",\n    \"        W = tf.Variable(init, name=\\\"kernel\\\")\\n\",\n    \"        b = tf.Variable(tf.zeros([n_neurons]), name=\\\"bias\\\")\\n\",\n    \"        Z = tf.matmul(X, W) + b\\n\",\n    \"        if activation is not None:\\n\",\n    \"            return activation(Z)\\n\",\n    \"        else:\\n\",\n    \"            return Z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = neuron_layer(X, n_hidden1, name=\\\"hidden1\\\",\\n\",\n    \"                           activation=tf.nn.relu)\\n\",\n    \"    hidden2 = neuron_layer(hidden1, n_hidden2, name=\\\"hidden2\\\",\\n\",\n    \"                           activation=tf.nn.relu)\\n\",\n    \"    logits = neuron_layer(hidden2, n_outputs, name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\\n\",\n    \"                                                              logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 40\\n\",\n    \"batch_size = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def shuffle_batch(X, y, batch_size):\\n\",\n    \"    rnd_idx = np.random.permutation(len(X))\\n\",\n    \"    n_batches = len(X) // batch_size\\n\",\n    \"    for batch_idx in np.array_split(rnd_idx, n_batches):\\n\",\n    \"        X_batch, y_batch = X[batch_idx], y[batch_idx]\\n\",\n    \"        yield X_batch, y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Batch accuracy: 0.9 Val accuracy: 0.9146\\n\",\n      \"1 Batch accuracy: 0.92 Val accuracy: 0.936\\n\",\n      \"2 Batch accuracy: 0.96 Val accuracy: 0.945\\n\",\n      \"3 Batch accuracy: 0.92 Val accuracy: 0.9512\\n\",\n      \"4 Batch accuracy: 0.98 Val accuracy: 0.9558\\n\",\n      \"5 Batch accuracy: 0.96 Val accuracy: 0.9566\\n\",\n      \"6 Batch accuracy: 1.0 Val accuracy: 0.9612\\n\",\n      \"7 Batch accuracy: 0.94 Val accuracy: 0.963\\n\",\n      \"8 Batch accuracy: 0.98 Val accuracy: 0.9652\\n\",\n      \"9 Batch accuracy: 0.96 Val accuracy: 0.966\\n\",\n      \"10 Batch accuracy: 0.92 Val accuracy: 0.9688\\n\",\n      \"11 Batch accuracy: 0.98 Val accuracy: 0.969\\n\",\n      \"12 Batch accuracy: 0.98 Val accuracy: 0.967\\n\",\n      \"13 Batch accuracy: 0.98 Val accuracy: 0.9706\\n\",\n      \"14 Batch accuracy: 1.0 Val accuracy: 0.9714\\n\",\n      \"15 Batch accuracy: 0.94 Val accuracy: 0.9732\\n\",\n      \"16 Batch accuracy: 1.0 Val accuracy: 0.9736\\n\",\n      \"17 Batch accuracy: 1.0 Val accuracy: 0.9742\\n\",\n      \"18 Batch accuracy: 1.0 Val accuracy: 0.9746\\n\",\n      \"19 Batch accuracy: 0.98 Val accuracy: 0.9748\\n\",\n      \"20 Batch accuracy: 1.0 Val accuracy: 0.9752\\n\",\n      \"21 Batch accuracy: 1.0 Val accuracy: 0.9752\\n\",\n      \"22 Batch accuracy: 0.98 Val accuracy: 0.9764\\n\",\n      \"23 Batch accuracy: 0.98 Val accuracy: 0.9752\\n\",\n      \"24 Batch accuracy: 0.98 Val accuracy: 0.9772\\n\",\n      \"25 Batch accuracy: 1.0 Val accuracy: 0.977\\n\",\n      \"26 Batch accuracy: 0.98 Val accuracy: 0.9778\\n\",\n      \"27 Batch accuracy: 1.0 Val accuracy: 0.9774\\n\",\n      \"28 Batch accuracy: 0.96 Val accuracy: 0.9754\\n\",\n      \"29 Batch accuracy: 0.98 Val accuracy: 0.9776\\n\",\n      \"30 Batch accuracy: 1.0 Val accuracy: 0.9756\\n\",\n      \"31 Batch accuracy: 0.98 Val accuracy: 0.9772\\n\",\n      \"32 Batch accuracy: 0.98 Val accuracy: 0.9772\\n\",\n      \"33 Batch accuracy: 0.98 Val accuracy: 0.979\\n\",\n      \"34 Batch accuracy: 1.0 Val accuracy: 0.9784\\n\",\n      \"35 Batch accuracy: 1.0 Val accuracy: 0.9778\\n\",\n      \"36 Batch accuracy: 0.98 Val accuracy: 0.978\\n\",\n      \"37 Batch accuracy: 1.0 Val accuracy: 0.9776\\n\",\n      \"38 Batch accuracy: 1.0 Val accuracy: 0.9792\\n\",\n      \"39 Batch accuracy: 1.0 Val accuracy: 0.9776\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Batch accuracy:\\\", acc_batch, \\\"Val accuracy:\\\", acc_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"./my_model_final.ckpt\\\") # or better, use save_path\\n\",\n    \"    X_new_scaled = X_test[:20]\\n\",\n    \"    Z = logits.eval(feed_dict={X: X_new_scaled})\\n\",\n    \"    y_pred = np.argmax(Z, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted classes: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\\n\",\n      \"Actual classes:    [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Predicted classes:\\\", y_pred)\\n\",\n    \"print(\\\"Actual classes:   \\\", y_test[:20])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"root_logdir = os.path.join(os.curdir, \\\"tf_logs\\\")\\n\",\n    \"\\n\",\n    \"def make_log_subdir(run_id=None):\\n\",\n    \"    if run_id is None:\\n\",\n    \"        run_id = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n    \"    return \\\"{}/run-{}/\\\".format(root_logdir, run_id)\\n\",\n    \"\\n\",\n    \"def save_graph(graph=None, run_id=None):\\n\",\n    \"    if graph is None:\\n\",\n    \"        graph = tf.get_default_graph()\\n\",\n    \"    logdir = make_log_subdir(run_id)\\n\",\n    \"    file_writer = tf.summary.FileWriter(logdir, graph=graph)\\n\",\n    \"    file_writer.close()\\n\",\n    \"    return logdir\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'./tf_logs/run-20210325195134/'\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext tensorboard\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-466133ca0f0df87e\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-466133ca0f0df87e\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6007;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using `dense()` instead of `neuron_layer()`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: previous releases of the book used `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function, except for a few minor differences:\\n\",\n    \"* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\\n\",\n    \"* the default `activation` is now `None` rather than `tf.nn.relu`.\\n\",\n    \"* a few more differences are presented in chapter 11.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = 28*28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 100\\n\",\n    \"n_outputs = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\") \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, name=\\\"hidden1\\\",\\n\",\n    \"                              activation=tf.nn.relu)\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, name=\\\"hidden2\\\",\\n\",\n    \"                              activation=tf.nn.relu)\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"    y_proba = tf.nn.softmax(logits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Batch accuracy: 0.9 Validation accuracy: 0.9024\\n\",\n      \"1 Batch accuracy: 0.92 Validation accuracy: 0.9254\\n\",\n      \"2 Batch accuracy: 0.94 Validation accuracy: 0.9372\\n\",\n      \"3 Batch accuracy: 0.9 Validation accuracy: 0.9416\\n\",\n      \"4 Batch accuracy: 0.94 Validation accuracy: 0.9472\\n\",\n      \"5 Batch accuracy: 0.94 Validation accuracy: 0.9512\\n\",\n      \"6 Batch accuracy: 1.0 Validation accuracy: 0.9548\\n\",\n      \"7 Batch accuracy: 0.94 Validation accuracy: 0.961\\n\",\n      \"8 Batch accuracy: 0.96 Validation accuracy: 0.962\\n\",\n      \"9 Batch accuracy: 0.94 Validation accuracy: 0.9648\\n\",\n      \"10 Batch accuracy: 0.92 Validation accuracy: 0.9656\\n\",\n      \"11 Batch accuracy: 0.98 Validation accuracy: 0.9668\\n\",\n      \"12 Batch accuracy: 0.98 Validation accuracy: 0.9684\\n\",\n      \"13 Batch accuracy: 0.98 Validation accuracy: 0.9702\\n\",\n      \"14 Batch accuracy: 1.0 Validation accuracy: 0.9696\\n\",\n      \"15 Batch accuracy: 0.94 Validation accuracy: 0.9718\\n\",\n      \"16 Batch accuracy: 0.98 Validation accuracy: 0.9728\\n\",\n      \"17 Batch accuracy: 1.0 Validation accuracy: 0.973\\n\",\n      \"18 Batch accuracy: 0.98 Validation accuracy: 0.9748\\n\",\n      \"19 Batch accuracy: 0.98 Validation accuracy: 0.9756\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"n_batches = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Batch accuracy:\\\", acc_batch, \\\"Validation accuracy:\\\", acc_valid)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'./tf_logs/run-20210325195336/'\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 8.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 9.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Train a deep MLP on the MNIST dataset and see if you can get over 98% precision. Just like in the last exercise of chapter 9, try adding all the bells and whistles (i.e., save checkpoints, restore the last checkpoint in case of an interruption, add summaries, plot learning curves using TensorBoard, and so on)._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First let's create the deep net. It's exactly the same as earlier, with just one addition: we add a `tf.summary.scalar()` to track the loss and the accuracy during training, so we can view nice learning curves using TensorBoard.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = 28*28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 100\\n\",\n    \"n_outputs = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\") \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, name=\\\"hidden1\\\",\\n\",\n    \"                              activation=tf.nn.relu)\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, name=\\\"hidden2\\\",\\n\",\n    \"                              activation=tf.nn.relu)\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"    loss_summary = tf.summary.scalar('log_loss', loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"    accuracy_summary = tf.summary.scalar('accuracy', accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need to define the directory to write the TensorBoard logs to:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"def log_dir(prefix=\\\"\\\"):\\n\",\n    \"    now = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n    \"    root_logdir = \\\"tf_logs\\\"\\n\",\n    \"    if prefix:\\n\",\n    \"        prefix += \\\"-\\\"\\n\",\n    \"    name = prefix + \\\"run-\\\" + now\\n\",\n    \"    return \\\"{}/{}/\\\".format(root_logdir, name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"logdir = log_dir(\\\"mnist_dnn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can create the `FileWriter` that we will use to write the TensorBoard logs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Hey! Why don't we implement early stopping? For this, we are going to need to use the validation set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"m, n = X_train.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0 \\tValidation accuracy: 92.180% \\tLoss: 0.30208\\n\",\n      \"Epoch: 5 \\tValidation accuracy: 95.980% \\tLoss: 0.15037\\n\",\n      \"Epoch: 10 \\tValidation accuracy: 97.100% \\tLoss: 0.11160\\n\",\n      \"Epoch: 15 \\tValidation accuracy: 97.700% \\tLoss: 0.09562\\n\",\n      \"Epoch: 20 \\tValidation accuracy: 97.840% \\tLoss: 0.08309\\n\",\n      \"Epoch: 25 \\tValidation accuracy: 98.040% \\tLoss: 0.07706\\n\",\n      \"Epoch: 30 \\tValidation accuracy: 98.140% \\tLoss: 0.07287\\n\",\n      \"Epoch: 35 \\tValidation accuracy: 98.280% \\tLoss: 0.07133\\n\",\n      \"Epoch: 40 \\tValidation accuracy: 98.220% \\tLoss: 0.06968\\n\",\n      \"Epoch: 45 \\tValidation accuracy: 98.220% \\tLoss: 0.06993\\n\",\n      \"Epoch: 50 \\tValidation accuracy: 98.160% \\tLoss: 0.07093\\n\",\n      \"Epoch: 55 \\tValidation accuracy: 98.280% \\tLoss: 0.06994\\n\",\n      \"Epoch: 60 \\tValidation accuracy: 98.200% \\tLoss: 0.06894\\n\",\n      \"Epoch: 65 \\tValidation accuracy: 98.260% \\tLoss: 0.06906\\n\",\n      \"Epoch: 70 \\tValidation accuracy: 98.220% \\tLoss: 0.07057\\n\",\n      \"Epoch: 75 \\tValidation accuracy: 98.280% \\tLoss: 0.06963\\n\",\n      \"Epoch: 80 \\tValidation accuracy: 98.320% \\tLoss: 0.07264\\n\",\n      \"Epoch: 85 \\tValidation accuracy: 98.200% \\tLoss: 0.07403\\n\",\n      \"Epoch: 90 \\tValidation accuracy: 98.300% \\tLoss: 0.07332\\n\",\n      \"Epoch: 95 \\tValidation accuracy: 98.180% \\tLoss: 0.07535\\n\",\n      \"Epoch: 100 \\tValidation accuracy: 98.260% \\tLoss: 0.07542\\n\",\n      \"Early stopping\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 10001\\n\",\n    \"batch_size = 50\\n\",\n    \"n_batches = int(np.ceil(m / batch_size))\\n\",\n    \"\\n\",\n    \"checkpoint_path = \\\"/tmp/my_deep_mnist_model.ckpt\\\"\\n\",\n    \"checkpoint_epoch_path = checkpoint_path + \\\".epoch\\\"\\n\",\n    \"final_model_path = \\\"./my_deep_mnist_model\\\"\\n\",\n    \"\\n\",\n    \"best_loss = np.infty\\n\",\n    \"epochs_without_progress = 0\\n\",\n    \"max_epochs_without_progress = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    if os.path.isfile(checkpoint_epoch_path):\\n\",\n    \"        # if the checkpoint file exists, restore the model and load the epoch number\\n\",\n    \"        with open(checkpoint_epoch_path, \\\"rb\\\") as f:\\n\",\n    \"            start_epoch = int(f.read())\\n\",\n    \"        print(\\\"Training was interrupted. Continuing at epoch\\\", start_epoch)\\n\",\n    \"        saver.restore(sess, checkpoint_path)\\n\",\n    \"    else:\\n\",\n    \"        start_epoch = 0\\n\",\n    \"        sess.run(init)\\n\",\n    \"\\n\",\n    \"    for epoch in range(start_epoch, n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val, loss_val, accuracy_summary_str, loss_summary_str = sess.run([accuracy, loss, accuracy_summary, loss_summary], feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        file_writer.add_summary(accuracy_summary_str, epoch)\\n\",\n    \"        file_writer.add_summary(loss_summary_str, epoch)\\n\",\n    \"        if epoch % 5 == 0:\\n\",\n    \"            print(\\\"Epoch:\\\", epoch,\\n\",\n    \"                  \\\"\\\\tValidation accuracy: {:.3f}%\\\".format(accuracy_val * 100),\\n\",\n    \"                  \\\"\\\\tLoss: {:.5f}\\\".format(loss_val))\\n\",\n    \"            saver.save(sess, checkpoint_path)\\n\",\n    \"            with open(checkpoint_epoch_path, \\\"wb\\\") as f:\\n\",\n    \"                f.write(b\\\"%d\\\" % (epoch + 1))\\n\",\n    \"            if loss_val < best_loss:\\n\",\n    \"                saver.save(sess, final_model_path)\\n\",\n    \"                best_loss = loss_val\\n\",\n    \"            else:\\n\",\n    \"                epochs_without_progress += 5\\n\",\n    \"                if epochs_without_progress > max_epochs_without_progress:\\n\",\n    \"                    print(\\\"Early stopping\\\")\\n\",\n    \"                    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"os.remove(checkpoint_epoch_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_deep_mnist_model\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, final_model_path)\\n\",\n    \"    accuracy_val = accuracy.eval(feed_dict={X: X_test, y: y_test})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9796\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_val\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {\n   \"height\": \"264px\",\n   \"width\": \"369px\"\n  },\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "11_deep_learning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 11 – Deep Learning**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 11._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/11_deep_learning.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions. In particular, the 1st edition is based on TensorFlow 1, while the 2nd edition uses TensorFlow 2, which is much simpler to use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    # %tensorflow_version only exists in Colab.\\n\",\n    \"    %tensorflow_version 1.x\\n\",\n    \"except Exception:\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"def reset_graph(seed=42):\\n\",\n    \"    tf.reset_default_graph()\\n\",\n    \"    tf.set_random_seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['axes.labelsize'] = 14\\n\",\n    \"plt.rcParams['xtick.labelsize'] = 12\\n\",\n    \"plt.rcParams['ytick.labelsize'] = 12\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"deep\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Vanishing/Exploding Gradients Problem\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def logit(z):\\n\",\n    \"    return 1 / (1 + np.exp(-z))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure sigmoid_saturation_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xl4Tdf6wPHvisyDOVJEDTXGPLXILTFXUUNoqbHaKlo/LUqvoVeq1dYY91YHnaKCKkUNNZaosYSG0opWY4gIgpDIJMn6/bGPNMMJCSc5Gd7P8+wn2Xuvs9d7tiPvWXuvvZbSWiOEEEIUNDbWDkAIIYQwRxKUEEKIAkkSlBBCiAJJEpQQQogCSRKUEEKIAkkSlBBCiAJJEpR4IEqpIKXUR9aOA3IWi1LqhFJqRj6FlL7eAKXUxnyox0cppZVS5fOhrpFKqfNKqVRrnNNMsQxXSsVaMwaRd5Q8ByUyU0q5A37A00BFIBo4AXygtd5uKlMWuKO1jrFaoCY5iUUpdQJYrbWekUcx+AC7AHetdVS67aUw/p9FW7Cus8BHWuu56bbZA2WByzoP/1MrpcoAV4DxwGogRmudLwlCKaWB/lrr1em2OQFuWusr+RGDyF+21g5AFEjfA87Ai8BfQAWgHVDubgGt9XXrhJZVQYolM631zXyqJwmIzIeqqmL83diotb6UD/Xdk9Y6Hoi3dhwij2itZZElbQFKAxrodJ9yQRjf4u+uewDrMf5YnANewGh1zUhXRgOjgR+AOOA00B7wBLYCt4EQoFmmuvoCvwGJwAVgKqbWfzaxVDDVcTeWEZljMfN+HjO9JtIUx1GgR6Yy9sAs0zETgb+B/wOqmd5b+iXA9JoAjD/mACOBy0CJTMddDqzPSRym95qhLtN2H9N6+Vyct7PANOAz4BYQDrx5j3M03Mz7rAbMAE6YKRubbn2G6d9gAHAGiAHWpY/XVG5YupgvA0vSxZq+3rPm6jFtewXji1WS6efLmfZr07/FKtM5/hsYbO3/e7JkXeQelMgs1rQ8o5RyzMXrlmB8u+4A9AIGm9YzmwZ8CzQGgk2/fwl8DDQFIjD+qAOglGqO8YdkDdAQeAv4N/DaPWIJAGoCnYDewFCMP6T34gpsBjqbYvseWKOUqpvpPQ7FuLxVD6OFGY3xx9/XVKY+xmXRcWbqWAWUMtVx9/25YpyvwBzG0RcjkbxjqqeiuTeTi/P2BkZCaAZ8CMxWSrU2d0xgJfCU6ffHTXVfyKasOdWA54A+QBeMf+/30sX8Ckay/BpohHGJ+YRpd0vTz5dN9d5dz0Ap1Qf4CPAHGgALgY+VUj0zFX0b44tAY9P7+kop9Wgu3ovID9bOkLIUvAXjj+11IAE4AMwFnshUJghTqwWog/GttFW6/VWAFLK2oN5Pt97AtG18um0+pGsJAMuAnZnqngGEZxNLbdPrvdPtr5o5lhyeh4PANNPvtUzHfSqbshniTrc9AFMLyrS+Bliabn0wcBNwzEkcpvWzwMR71Z/D83YWWJGpzJ/p6zITSwtTPdUyHTcnLagEoFS6bVOBv9Kth2Pc58yubg30u089+4CvzPwb7L3H59AWo0UvragCtkgLSmShtf4eqAT0xPg23wY4qJSaks1L6gKpGC2iu8e4gNEayux4ut8vm37+ZmZbBdPPehh/dNLbC1RWSpU0c/x6plgOpYvlXDaxpFFKuSilZiulfldK3TD1DGsB3P1W3dR03F33Ok4OBAK9lVLOpvVBwPda64QcxpFTOT1vxzOVieCfc29p53TGe3JpdSmlKgCVgZ8eso7s3rdXpm1p71trnQxcJe/et3hAkqCEWVrrBK31dq31O1rrNhiX4WaYeos9jDvpq7nHtpx8Nu/VWy23PdnmAv2B6RgdQppgJLmHfb+ZbQKSgV6mP8qd+OfyXn7Fkf7c3DGzL7d/F1IBlWmbnZlylqjrQWX+PFgzFpFD8g8icup3jEsh5u5LncL4LDW/u0Ep5YnRCntYfwDembb9C+NSlblu5XdjeTxdLI/mIJZ/Ad9orb/XWh/HuNz0WLr9Iabjts/m9UmmnyXuVYnWOhHj3tAgjPsxkRiXKHMax9267lkPuT9vD+Mq4KGUSp+kmuTmANroJn4R6HiPYnd48Pf9e27iEQWDJCiRgVKqnFJqp1JqsFKqkVKqulKqPzAJ+ElrfSvza7TWoRi98D5VSrVSSjXBuNEdR+5bMpnNA9oppWYopWorpQYBE4DZ5gqbYtkCfKaUam2KJYD7d0U+DfRRSjVTSjXEaNWkJWOt9WngO+ALpZSv6bw8qZQaYipyDuO9dldKuZs6P2QnEOgKjMK4B5Sa0zhMzgJPKqUq3+PB3Fydt4cUhPEM1hSl1GNKqReBfg9wnPeA15VSb5hibqKUmpBu/1mgo1LqEdPzWObMAYYopV5VStVSSo3F+DKQF+9b5DFJUCKzWIyb8uOA3cBJjK7VyzG+8WdnOMa3/SCM7ubLMB7oTHiYYLTWRzEueflieljYtNxr5IjhQBiwE9hgiv3sfaoab4p3D8Z9t4Om39MbajrWfzFaagEYvfLQWl8E/oPxR/byfeLbg9Fa8CLj5b2cxvE2RieUMxitlywe8Lw9EK31HxiPD4zEuLfTGeMzk9vjfAK8itFT7wTGF4366YpMwGjBXgB+zeYY64CxGL0Tf8f4HI/RWm/IbTzC+mQkCZEnTN/sI4CBpk4XQgiRKzKShLAIpVQHwA2jR14FjJZEFMa3YCGEyDWLXeJTSr2mlApWSiUqpQLuUW6YUuqIUuqWUirc1KVWEmXhZwe8i5GgNmDcf2qrtb5t1aiEEIWWxS7xKaX6YnQ37Qo4aa2HZ1NuNMb15V8Ad4z7Fau01h9YJBAhhBBFgsVaLlrrNQBKqRYYY6tlV+6TdKsXlVLLyL7rrhBCiGKqIFxaa4vRU8wspdRIjN5BODk5Na9SpUp+xZUjqamp2NhIZ8j7kfOUMxcuXEBrzaOPyrBw95Pfn6nIhEicSzhT0s7cACYFV0H8v3f69OkorbX7/cpZNUEppUZgDOPyUnZltNaLgcUALVq00MHBwdkVtYqgoCB8fHysHUaBJ+cpZ3x8fIiOjiYkJMTaoRR4+fmZmrx9MrP3z2aCzwTebvd2vtRpKQXx/55S6lxOylktQSmlegPvY0zrEHW/8kIIYQ3zD8xn9v7ZjGkxhultp1s7nGLFKglKKfUU8DnQXWv92/3KCyGENSw7vowJ2ybQz6sf/+32XzKO5iTymsUSlKmruC3GWFklTHMJJZtGCk5frgPGKAN9tNaHsh5JCCEKhrDoMNpXa09gn0BK2NxvGEBhaZa8czYNY7yztzDmuIkHpimlHlVKxaabDGw6xvAwP5q2xyqlNlswDiGEeCgpqSkATGs7ja2Dt+Jg62DliIoniyUorfUMrbXKtMzQWp/XWrtqrc+byrXXWtuatt1dulkqDiGEeBihUaE0+KQBhy4aF3jsSpibOUTkh4LQzVwIIQqEiJgIugZ2Je5OHGUcsxswXeQXSVBCCAFEJ0TzVOBTXIu/RtCwIGqVq2XtkIo9SVBCiGIvITmBXt/24lTUKTY9v4nmlZrf/0UizxWsx4uFEMJKyjuX55s+39D5sc7WDkWYSAtKCFFsaa2JuxOHi70Lq/uvluecChhpQQkhii2/3X60/rI10QnRkpwKIElQQohi6dPgT/Hb7UeLSi0o5VDK2uEIMyRBCSGKne9//54xm8bQo3YPFvdcLK2nAkoSlBCiWNlzbg/Pr3meVp6tWNlvJbY2ciu+oJIEJYQoVqqXqc4zdZ5h4/MbcbZztnY44h7kq4MQoli4HHuZ8s7l8Szpyar+q6wdjsgBaUEJIYq8K7ev8K+v/8UrG1+xdigiFyRBCSGKtJjEGLov787FWxcZ0XSEtcMRuSCX+IQQRVZSShK+3/ny66VfWfvcWtpUaWPtkEQuSIISQhRZozeOZvvf2/nqma/oWaentcMRuSQJSghRZL3U7CUaeTTihaYvWDsU8QAkQQkhipzfLv9GQ4+GtK7SmtZVWls7HPGApJOEEKJIWRKyhEafNmLtH2utHYp4SJKghBBFxqbTm3hx/Yt0qtGJ7rW7Wzsc8ZAkQQkhioSD4Qfpv6o/TR5pwppn12Bfwt7aIYmHJAlKCFHoXY+/To/lPahcsjI/DvoRNwc3a4ckLEA6SQghCr2yTmVZ0HUB3o96U8GlgrXDERYiLSghRKF1Pf46v4T/AsCQxkOoUaaGlSMSlmTRBKWUek0pFayUSlRKBdyn7BtKqUil1C2l1FdKKQdLxiKEKNoSUhLouaInTy17iuiEaGuHI/KApVtQEcC7wFf3KqSU6gq8BXQEqgI1AD8LxyKEKKKSU5OZ+cdMDlw4wOc9P6e0Y2lrhyTygNJaW/6gSr0LeGqth2ezfzlwVms9xbTeEVimtX7kXsd1c3PTzZs3z7Dt2WefZcyYMcTFxfH0009nec3w4cMZPnw4UVFR9OvXL8v+0aNH89xzz3HhwgWGDBmSZf+ECRPo2bMnoaGhvPJK1pGQe/bsyYQJEwgJCeH111/Psn/WrFm0adOG/fv3M2XKlCz7/f39adKkCTt27ODdd9/Nsv+zzz6jTp06bNiwgXnz5mXZv3TpUqpUqcLKlSv55JNPsuxfvXo15cuXJyAggICAgCz7f/zxR5ydnfn444/57rvvsuwPCgoCYO7cuWzcuDHDPicnJzZv3gzAzJkz+emnnzLsL1euHN9//z0AgwYN4uLFixn2e3p6EhgYCMDrr79OSEhIhv21a9dm8eLFAIwcOZLTp09n2N+kSRP8/f0BGDx4MOHh4Rn2t27dmvfffx8AX19frl27lmF/x44dmT59OgDdunUjPj4+w/4ePXowceJEAHx8fMgsLz57ISEhJCcn06JFi/t+9qZNm0anTp2K3WdPozlT/wwXK1zk46c/JmpL1D0/e//+9785cOBAhv3F6bPXqVMnSpfOmMAf9u/ew372du/efURr3SLLjkys1UmiPvBDuvVjgIdSqpzWOsO/pFJqJDASwM7OjujojE3506dPExQUREJCQpZ9AKdOnSIoKIibN2+a3X/y5EmCgoK4cuWK2f2//fYbbm5unD9/3uz++Ph4goKC+Ouvv8zuP3r0KElJSZw4ccLs/uDgYKKjozl27JjZ/b/88guXLl3it99+M7v/wIEDnDlzhpMnT5rdv2/fPkqVKsWpU6fM7v/5559xdHTk9OnTZvff/SNx5syZLPvvvneAsLCwLPtTU1PT9iclJWXZb2dnl7Y/PDw8y/6IiIi0/REREVn2h4eHp+2/fPlylv3nz59P23/16lVu3bqVYX9YWFja/uvXr5OYmJhh/5kzZ9L2mzs3efHZS05ORmtNdHT0fT97x44dw9bWtth99m543uBihYsMqDiAerfr8U3YN/f87Jk7f8Xps5eSkpKlzIP83dPahtRUF1JSnNm69QJ//nmEv/++xPnz9UlNdUBrR1JTHUhNdeTDD6FMmbNcvOjAyZMjSU11JDXVEa0dSE11AJ7MUqc51mpBnQFe1VpvMa3bAUlAda312eyO26JFCx0cHGzxeB9GUFCQ2W84IiM5Tznj4+NDdHR0lm/04h8pqSms+n0VHlc9aN++vbXDKfCCgoJo186H27fh2jW4ft1Y0v9+4wbExBjLrVv//J5+PS7OklGpAt2CigVKplu/+3uMFWIRQhQCG09vpLFHY6qUqsKABgPSWhjFVXw8XL4MkZH//Ey/REUZSSgysg2xsXDnzsPX6eb2z+LqCs7O4OT0z5J5PbttPXrkrD5rJaiTQGPg7oXnxsDlzJf3hBACYMffO+i7si++Xr6s8F1h7XDyXFIShIfDuXNw/ryx3P39wgW4dAlu3szp0YwRNZycoGxZKFfO+Hl3KVcOSpeGkiX/ST7mfndxAZuH6FZ3+vRpzp8/T6dOnXL8GosmKKWUremYJYASSilHIFlrnZyp6DdAgFJqGUbPv2lAgCVjEUIUDUcijtBnZR/qlq/LJ92zdsYorKKj4c8/jeX0aePnmTNGEoqMhPvdfbGzAw8PeOSRf5b06+XLG8knNHQ/3bu3wckpf96XOcuXL+eFF16gWbNm1ktQGInmP+nWBwN+SqmvgN8BL631ea31FqXUbGAX4AR8n+l1QgjBX9f/4unlT1POqRxbBm8pdN3JtTYuv/32m7GcOAGhoUYyuno1+9eVKAGVK8Ojj0LVqsbPu0uVKsa+MmVAqfvHcO1aktWSU0JCAmPGjGHlypUkJSWRmpqaq9dbNEFprWcAM7LZ7Zqp7HxgviXrF0IULW9uf5OU1BS2Dt5KJbdK1g7nnpKTjQR05AgcP/5PUoqKMl/eyQlq1TKW2rWNnzVrQrVqULEi2Bbygej+/vtvnn76ac6fP5/WjT63nfIK+SkQQhRlAb0COHfzHHXK17F2KBloDWFhcOjQP8vRo0bHhcxKloQGDaBhQ2OpV89ISJUqPdw9nYJszZo1DBs2jLi4uAytJqu2oIQQ4mElJifywd4PmOQ9iVKOpWjk2MjaIZGcbCSg3bvh55/h4EHzLaPHHoOWLaFRo38S0qOP5uxSXFFw584d3njjDb766qssDx+DtKCEEIVYSmoKg9cOZvXvq2lRqYXVJh1MTTUS0o4dRlLauxdiYzOWcXeHxx//Z2nZ0uiUUFydP3+eHj168Ndff5lNTiAJSghRSGmtGbdlHKt/X83cznPzPTlduQLbtsGWLcbPzJ0YatWCdu2MxdvbuFdUXFpG97Np0yYGDhxIXFwcKSkp2ZaTS3xCiEJp1p5ZLDq8iImtJzKhzYR8qfOPP2DNGli3DjIPUlO1KnTpAh06QNu2xj0jkdWUKVPw9/fPttWUnrSghBCFzrW4a/j/4s+QRkP4sPOHeVaP1kYvuzVrYO1aOHXqn32Ojkbr6KmnjKVOHWkh5UR4eDhaa0qUKHHP1hNIghJCFELlnMtx6KVDeJb0xEZZvmvbn3/C0qUQGGj0vrurbFl45hno2xc6djSG5RG588033zB16lSmTZvGxo0bSUxMzDYRSYISQhQae87tYdfZXUxvO53qZapb9NhRUbBypZGYfvnln+0VKxoJqW9f49JdYX/eqCCoU6cO3333HY0aNeLEiRPZlpMEJYQoFH67/BvPfPsMHi4evN7qdUo6lLz/i+5DawgKgk8/NS7h3R0g1dUVfH1hyBDw8TFGahCWtXPnTsLSN08x5oy7c+cOycnGaHfSSUIIUeCdiz7HU8uewtnOma2Dtz50coqOhiVLjMR0976SjY1xL2nIEOjdWy7f5bVJkyZx+/btDNsqVKiAj48PK1eu5M6dO9KCEkIUbFFxUXQN7MrtpNvseWEPVUtXfeBjnToF8+bBsmX/jOJQsSK8/LKxeHpaKGhxT7t37yY0NDTDNldXV2bPns2zzz7LzJkz8fPzy1FPv/QkQQkh8tXB8INcjLnIj8//SEOPhg90jH37YNq0Buzb98+2Tp1g9Gjo2dMY6VvknzfffDNL66ls2bL069cPgCpVqvDFF1/k+riSoIQQ+apH7R6cHXeWcs65G3YhNRU2bIDZs2H/foDyODjA8OHwxhtGt3CR//bs2cPJkyczbHNxceGDDz7A5iEHGyyiQxUKIQqSVJ3KyA0jWfPHGoBcJSet4YcfoEkT417S/v3GVBNDhpzl3DnjvpMkJ+uZNGkScZnmgy9TpgzPPvvsQx9bEpQQIs+9teMtPj/6Ob9f/T3Hr9HaGHbo8ceNxPTbb8Y9JX9/Y1K/ESPO4uGRh0GL+9q/fz/Hjx/PsM3V1ZX333+fEhboKimX+IQQeWre/nnM2T+HV1u+ytQnp+boNbt3w7RpxiCtYMwUO3Wq0fHB0TEPgxW5Yq71VLJkSQYMGGCR40uCEkLkmcDjgUzcPpH+Xv1Z+NRC1H3GDvrrL5g40bikB8bo4JMnw6uvSjfxguaXX37h119/zbDN1dWVWbNmYWuhp58lQQkh8syxyGO0r9aepX2WUsIm+0s+N2/Cu+/CwoXGw7UuLjBpErz+ujHhnyh4Jk+enKX15OLiwqBBgyxWhyQoIYTFpepUbJQNszvPJiklCQdbB7PlUlLgq6+My3lXrhjbhg+HWbOM55lEwRQcHMyhQ4cybHN1deW9996zWOsJpJOEEMLCQqNCafpZU367/BtKqWyT06+/whNPwMiRRnLy9obDh+HrryU5FXSTJ08mISEhwzYnJyeGDh1q0XokQQkhLObirYt0CezCpZhLONk5mS0TF2dcvmvZ0pj6okoV+PZb2LMHWrTI54BFrv36668cOHAgw7BFLi4uzJw5EzsLPyEtl/iEEBYRnRDNU8ue4nr8dYKGBVGzbM0sZbZtg1GjjCkvbGyMe0wzZxqDuYrC4a233jLbenrhhRcsXpckKCHEQ4u/E88zK54hNCqUHwf9SPNKzTPsv34dxo0z5mMCaNQIvvjCaEWJwuP48ePs2bMnS+vJz88Pe3t7i9dn0Ut8SqmySqm1SqnbSqlzSqnnsynnoJT6VCl1WSl1XSm1QSlV2ZKxCCHyT3JqMo62jizts5RONTpl2LdtGzRoYCQnR0f44ANjenVJToXPW2+9RWJiYoZtDg4OvPjii3lSn6VbUIuAJMADaAJsUkod01qfzFRuHNAaaATcBBYD/wP6WjgeIUQe0lqTmJKIm4MbWwdvzfCcU1yc8QzTRx8Z697eEBAANbNe+ROFwPXr19myZUuW1tOMGTNwcDDfEeZhWawFpZRyAXyB6VrrWK31XmA9MMRM8erAVq31Za11ArASqG+pWIQQ+cNvtx8+AT7EJMZkSE5HjkDz5kZysrU1uo3v3i3JqTArW7Ys27dvp2nTpri4uABgb2/Pyy+/nGd1WrIFVRtI1lqfTrftGNDOTNkvgYVKqUpANDAI2GzuoEqpkcBIAA8PD4KCgiwY8sOLjY0tcDEVRHKeciY6OpqUlJRCca5+iPgB/z/96fZIN4L3B6OUIjUVVqx4lK+/rkZKig1Vq95mypQ/qF07lj17LFu/fKZyxpLnqUSJEsyfP5+QkBA+//xzunXrxsGDBy1ybLO01hZZgCeByEzbXgaCzJQtBXwLaCAZ+BUoe786mjdvrguaXbt2WTuEQkHOU860a9dON27c2Nph3Neqk6u0mqF0j+U99J2UO1prra9e1fqpp7Q2hnnV+v/+T+u4uLyLQT5TOVMQzxMQrHOQVyzZSSIWyDwoSUkgxkzZRYADUA5wAdaQTQtKCFGw7D67m0FrBtG6SmtW9luJrY0tBw5A06bG6OPlysGPPxrDFjmZfxRKiByxZII6DdgqpWql29YYyNxBAowOFAFa6+ta60SMDhKPK6XKWzAeIUQeqFyyMl0e68KGgRtwsnXG3x/atoXwcGjd2hghols3a0cpigKLJSit9W2MltA7SikXpZQ30AtYaqb4YWCoUqqUUsoOGANEaK2jLBWPEMKyouKi0FpTs2xNNgzcQImksvTvb8xmm5xs/AwKMkaGEMISLD3U0RjACbgCrABGa61PKqWeVErFpis3EUgA/gSuAk8DfSwcixDCQq7cvkLrL1szfut4AP78E1q1gu+/N0YbX70a5s+HPHhWUxRjFn0OSmt9HehtZvsewDXd+jWMnntCiAIuJjGGp5c9zcVbF3m2/rP89BP07w83bhgP4K5dK93HCyIfHx8aNGhAv379rB3KA5PBYoUQ2UpKSaLvd30JiQzhu36rOPJDa7p2NZJTz56wf3/RSk5Xr15lzJgxVKtWDQcHBzw8POjYsSPbt2/P0euDgoJQShEVlX93KwICAnA1M5jhmjVreP/99/MtjrwgY/EJIbL18oaX2fH3Dj5/egkb5ndn8WJj+7//bUwwaFPEvuL6+voSFxfHl19+Sc2aNbly5Qq7d+/m2rVr+R5LUlLSQ41vV7ZsWQtGYx1F7OMlhLCkwQ0HM7PVIpZNGsrixeDgAMuWGSNDFLXkFB0dzZ49e/jggw/o2LEjVatWpWXLlkycOJEBAwYAEBgYSMuWLXFzc6NChQr079+fixcvAnD27Fnat28PgLu7O0ophg8fDhiX21577bUM9Q0fPpwePXqkrfv4+DB69GgmTpyIu7s73t7eAMyfP59GjRrh4uJC5cqVeemll4iOjgaMFtsLL7zA7du3UUqhlGLGjBlm66xWrRrvvvsur7zyCiVLlsTT05M5c+ZkiOn06dO0a9cOR0dH6tSpw48//oirqysBAQGWOcm5VMQ+YkIISwiNCgWgtm1nlr8xhqAgYxLBPXvgebNDQBd+rq6uuLq6sn79+izTSdyVlJSEn58fx44dY+PGjURFRTFw4EAAqlSpwvfffw/AyZMnuXTpEgsXLsxVDIGBgWit2bNnD9988w0ANjY2+Pv7c/LkSZYvX86hQ4cYO3YsAG3atMHf3x9nZ2cuXbrEpUuXmDhxYrbHX7BgAQ0bNuTo0aNMnjyZSZMmceDAAQBSU1Pp06cPtra2HDx4kICAAPz8/LIMDpuf5BKfECKDgJAAXlz/Iv9t8jPvvuJNZKTRGWLzZvD0tHZ0ecfW1paAgABefvllFi9eTNOmTfH29qZ///488cQTAIwYMSKtfI0aNfjkk0+oV68e4eHheHp6pl1Wq1ChAuXL5/6xzurVqzNv3rwM215//fW036tVq8bs2bPp1asXS5Yswd7enlKlSqGU4pFHHrnv8bt06ZLWqho7diz//e9/+emnn2jdujXbt28nNDSUbdu2UbmyMbnEggUL0lpy1iAtKCFEmk2nN/HS+pdocvtN3hrUhshI8PExWk5FOTnd5evrS0REBBs2bKBbt27s37+fVq1aMWvWLACOHj1Kr169qFq1Km5ubrQwTQF8/vx5i9TfvHnzLNt27txJ586d8fT0xM3Njb59+5KUlERkZGSuj9+oUaMM65UqVeLKlSsAnDp1ikqVKqUlJ4CWLVtiY8VruZKghBAAHLhwgP6r+lPl7FSOz3+f2FjFgAHG8EWlS1s7uvzj6OhI586defvtt9m/fz8vvvgiM2bM4ObNm3Tt2hVnZ2eWLl3K4cOH2bJlC2Bc+rsXGxubDNNUANy5cydLubujhN917tw5unfvTr169Vi1ahVHjhxrAd4+AAAgAElEQVThq6++ylGd5mSekt0Y4Dc118fJL5KghBBcuX2FHit64PzLO5z92o/kZMWbbxodIvJoqp9Cw8vLi+TkZEJCQoiKimLWrFm0bduWunXrprU+7rrb6y4lJSXDdnd3dy5dupRh27Fjx+5bd3BwMElJSSxYsIDWrVtTu3ZtIiIistSZub4HUbduXSIiIjIcPzg42KoJTBKUEAJ35wq0OLmdaxsmopQx0Ovs2UWvp969XLt2jQ4dOhAYGMjx48cJCwtj1apVzJ49m44dO+Ll5YWDgwMfffQRf//9N5s2bWL69OkZjlG1alWUUmzatImrV68SG2sMoNOhQwc2b97M+vXrCQ0NZfz48Vy4cOG+MdWqVYvU1FT8/f0JCwtjxYoV+Pv7ZyhTrVo1EhIS2L59O1FRUcTFxT3Q++/cuTN16tRh2LBhHDt2jIMHDzJ+/HhsbW0zzPWVn4rRx08Ikdn1+OuEXDrOmDGw7Ztm2Noarab/+z9rR5b/XF1dadWqFQsXLqRdu3bUr1+fKVOm8Pzzz7Ny5Urc3d1ZsmQJ69atw8vLCz8/P+bPn5/hGJUrV8bPz4+pU6fi4eGR1iFhxIgRaYu3tzdubm706XP/0d0aNWrEwoULmT9/Pl5eXnzxxRfMnTs3Q5k2bdowatQoBg4ciLu7O7Nnz36g929jY8PatWtJTEzk8ccfZ9iwYUydOhWlFI6Ojg90zIeWkzk5Csoi80EVXnKeciY/54O6nXRbt/rsX9qh2XcatHZw0HrDhnyp2iLkM5UzD3OeQkJCNKCDg4MtF5DO+XxQ0s1ciGIoOTWZfisGcXDBeDjVB1dXWL8eTM+ZimJq7dq1uLi4UKtWLc6ePcv48eNp3LgxzZo1s0o8kqCEKGa01ryw6jU2+42Gv7tQpozxjJPpUR9RjMXExDB58mQuXLhAmTJl8PHxYcGCBVa7ByUJSohi5rMDgQROHgDnfPDwgG3bINPjMaKYGjp0KEOHDrV2GGkkQQlRjMTGwvK3BsE5GypV0uzapahd29pRCWGe9OITophY/etWOnVJYs8eGypXht27JTmJgk1aUEIUAz8c28mzvV3R5+3x9IRdu4rWPE6iaJIEJUQRtzv0V/o+44w+34rKnqkEBdnw2GPWjkqI+5NLfEIUYSHn/qJT1zuknm9FJc9kft4tyUkUHpKghCii4uOhY7dYks89TsXKd9iz25YaNawdlRA5JwlKiCIoMRH69oXrfzShvMcdfg6yk+QkCh1JUEIUMbHxiTTvEsqWLVC+POzeaScdIkShJAlKiCIk6U4KXp0Pc/LnOriWvMP27eDlZe2ohHgwFk1QSqmySqm1SqnbSqlzSqnn71G2mVLqZ6VUrFLqslJqnCVjEaK4SUnRNOnxCxf2/QsH5yR2bLOjSRNrRyXEg7N0N/NFQBLgATQBNimljmmtT6YvpJQqD2wB3gBWA/ZAMZhQWoi8oTV49w/mj21tsHVIYttmexlbTxR6FmtBKaVcAF9gutY6Vmu9F1gPDDFTfDywVWu9TGudqLWO0Vr/YalYhChOtIax42P5ZW1LbGzvsHG9LW3bWjsqIR6eJVtQtYFkrfXpdNuOAe3MlG0F/KaU2g/UBH4BXtVan89cUCk1EhgJ4OHhQVBQkAVDfnixsbEFLqaCSM5TzkRHR5OSkpKrcxUY+ChfflmDEiVS+c9/TuJgH01xONXymcqZwnyeLJmgXIFbmbbdBNzMlPUEmgGdgd+A2cAKwDtzQa31YmAxQIsWLbSPj4/lIraAoKAgClpMBZGcp5wpXbo00dHROT5Xkz74ky+/rIFSsGyZDc89V3xuOslnKmcK83myZIKKBUpm2lYSiDFTNh5Yq7U+DKCU8gOilFKltNY3LRiTEEXWgq/OMWeK8XDT/IWJPPecg5UjEsKyLNmL7zRgq5SqlW5bY+CkmbLHAZ1uXZspI4TIxooNkYwf+QjoEoz/dzSvj5XkJIoeiyUorfVtYA3wjlLKRSnlDfQClpop/jXQRynVRCllB0wH9krrSYj727n/BoOfdYUUBwaMuMbc90pbOyQh8oSlH9QdAzgBVzDuKY3WWp9USj2plIq9W0hrvROYAmwyla0JZPvMlBDC8Ndf0LenE6kJrnToeZVln5fDSrNxC5HnLPoclNb6OtDbzPY9GJ0o0m/7BPjEkvULUZRdugRdusDN64607ZDI5tXu2MhYMKIIk4+3EIXA9RupNGwTTlgYtGwJG9c5YG9v7aiEyFuSoIQo4OLjoXHbs1w760n5R6PYtAnczD28IUQRIwlKiAIsORladv2T8BM1cCl3g8O7y+Hubu2ohMgfkqCEKKC0ho79/+LknlrYucSyf1dJqlWTHhGi+JAEJUQBNWUK/LyuJjb2CWzfbE+jhiWsHZIQ+UoSlBAF0Lx5mg8+AFtbzerV0O5J6REhih9JUEIUMJfjujBxonEp7+uvFX16Olo5IiGsQxKUEAVI5M3mRP41C4Ap711l8GArBySEFUmCEqKA2LLrFqEnZoK25YWxkbw3RbrrieJNEpQQBUDwrwn07KEg2Rm3Siv5cuEj1g5JCKuTBCWElZ07B890tyc5zg3XSjuoXn62jK8nBBYei08IkTtXrmg6d4FLl2xo106TmjqbW7dSrB2WEAWCJCghrCQmBpq2jSDidGUaNkrlhx9s6NUrKUu59evXExISQsOGDalfvz6PPfYYJUrIM1Gi6JMEJYQVJCbC450uEBFaBTePK2zd4k6pUubLnjlzBj8/P1xdXUlJSSEpKQlPT08aNmzI448/ToMGDahfvz7Vq1eXxCWKFElQQuSzlBTweSacU4eq4FDqBof3lKVixexvOo0ePZp3332X69evp20LCwsjLCyMH3/8EWdn5wyJq1GjRjz++OMMGDCAGjVq5MdbEiJPSCcJIfKR1tB7yEUObvOkhFMMQTscqVPr3t8THR0deffdd3FxccmyLzk5mVu3bnH79m3u3LlDWFgYP/zwA9OmTePAgQN59TaEyBeSoITIR9OmwcYVlbGxS+SH9ZpWLZxy9LqXXnoJV1fX+xcE7O3t6dq1K88/L5NUi8JNEpQQ+eS92XHMmgUlSsC67x3o3qlkjl9rZ2fHhx9+aLYVlVnJkiVZtmwZSvqqi0JOEpQQ+eCjz28xbbIzAF99BT175v4YgwcPpmzZsvcs4+DgQK1atR4kRCEKHElQQuSx79bEMXaUkZzG/ecsQ4c+2HFKlCjB3Llz79mKSkxM5MiRI9SpU4e9e/c+WEVCFBCSoITIQz/tusPAASUg1ZbnRv+F/4xqD3W8fv36UbFixXuWSUpKIioqii5dujB9+nRSUuTBX1E4SYISIo+EhEC3HndIveOAj28oKxbVfOhj2tjYsGDBgiytKEfHrFNyxMfHM3/+fJ544gnCw8Mfum4h8ptFE5RSqqxSaq1S6rZS6pxS6p7diJRS9kqpP5RS8r9HFCl//gldu8KdOGcadwhlx8o6Fhtfr3v37lSvXj1t3dnZmVdffRVXV1dsbDL+l46LiyMkJAQvLy/WrVtnmQCEyCeWbkEtApIAD2AQ8IlSqv49yr8JXLVwDEJYVUQEdOh0hytXoHNn+OXHOlhygAelFP7+/jg7O+Pk5MTAgQOZO3cuJ06coGHDhjg7O2con5KSQkxMDIMGDeKll14iPj7ecsEIkYcslqCUUi6ALzBdax2rtd4LrAeGZFO+OjAYeN9SMQhhbVeuQAvvaMLP21Gv8S3WrAEHB8vX07FjR+rXr0/FihX53//+B0DVqlUJDg5m7NixODllfb4qLi6O5cuX06BBA37//XfLByWEhSmttWUOpFRTYJ/W2jndtolAO611lk61SqmNwJfADSBQa+2ZzXFHAiMBPDw8mn/77bcWiddSYmNjc/wAZXFWHM7TrVu2jBpXi0tnPXCq+BdLF4VTrkzujvH666+TkpKSlnTu5e7QR+a6noeEhPD2228THx9PcnJyhn1KKezt7RkzZgw9e/YstM9LFYfPlCUUxPPUvn37I1rrFvctqLW2yAI8CURm2vYyEGSmbB9gs+l3HyA8J3U0b95cFzS7du2ydgiFQlE/Tzdval2v8S0NWjt6nNVnzsc+0HHatWunGzdubJGYrl69qjt06KCdnZ01kGVxdnbW3bt31zdu3LBIffmtqH+mLKUgnicgWOfgb74l70HFApkfjS8JxKTfYLoUOBv4PwvWLYTV3L4NXbol8scxN2zLXeDgz67UqHL/ER/yWvny5dmxYwfvvfdetpf8duzYQe3atdm/f78VIhTi3iyZoE4Dtkqp9I+xNwZOZipXC6gG7FFKRQJrgIpKqUilVDULxiNEnktIgN694Zf9DpRyj+GnHdC4djlrh5VGKcXrr7/OgQMHqFKlSpbu6ImJiVy9epVOnToxY8YMeWZKFCgWS1Ba69sYyeYdpZSLUsob6AUszVT0BFAFaGJaXgIum36/YKl4hMhrSUnQu28SO3ZAhQrwyx432japYu2wzGrcuDF//PEHvr6+WXr5gfHM1Jw5c2jTpg0XL160QoRCZGXpbuZjACfgCrACGK21PqmUelIpFQugtU7WWkfeXYDrQKppXb6+iUIhORkGPp/M1s32KOcbbNycQJ061o7q3lxcXAgMDOSLL77I9pmpo0eP4uXlxfr1660UpRD/sGiC0lpf11r31lq7aK0f1VovN23fo7U2241Eax2ks+nBJ0RBlJICw19IZc33tuBwkw8CjtKyWdaRHAqqgQMHcvz4cerXr5+lNXV3fqmBAwfyyiuvkJCQYKUohZChjooUHx8fXnvtNWuHUaSlpMALL2iWBdqAXSwTF+1iUv+O1g4r16pXr86RI0cYM2ZMth0oli5dSsOGDTl16pQVIhRCEhRXr15lzJgxVKtWDQcHBzw8POjYsSPbt2/P0etDQkJQShEVFZXHkf4jICDA7HMNa9as4f335bnnvJKSAsOGwdKlCuxiGT73O+a82NvaYT0wOzs75syZw4YNGyhTpgx2dnYZ9sfHx3PmzBmaN2/OF198cfcRESHyTbFPUL6+vhw6dIgvv/yS06dPs3HjRrp168a1a9fyPZakpKSHen3ZsmVxc3OzUDQiveRkGDoUli0DV1fNm5/8xFdjX7B2WBbRsWNHQkND8fb2znLJT2tNXFwc48aNo1evXty8edNKUYpiKScPSxWUxdIP6t64cUMDevv27dmWWbp0qW7RooV2dXXV7u7uul+/fjo8PFxrrXVYWFiWhx+HDRumtTYeuHz11VczHGvYsGG6e/fuaevt2rXTo0aN0hMmTNDly5fXLVq00FprPW/ePN2wYUPt7OysK1WqpF988cW0hyl37dqVpc7//Oc/ZuusWrWqnjlzph45cqR2c3PTlStX1rNnz84QU2hoqG7btq12cHDQtWvX1ps2bdIuLi7666+/fqBzmp2C+LBgTt25o/XAgVqD1q6uqXrv3ryry5IP6uZWamqqnjt3rnZycjL7YK+Dg4P28PDQBw4csEp8mRXmz1R+KojnCSs8qFvouLq64urqyvr167O9GZyUlISfnx/Hjh1j48aNREVFMXDgQACqVKmCn58fACdPnuTSpUssXLgwVzEEBgaitWbPnj188803gDGlgr+/PydPnmT58uUcOnSIsWPHAtCmTZu0gUIvXbrEpUuXmDhxYrbHX7BgAQ0bNuTo0aNMnjyZSZMmceDAAQBSU1Pp06cPtra2HDx4kICAAPz8/EhMTMzVeyjKkpNhyBBYsQKwj6Hj9Ll4e1s7qryhlGLChAns27ePypUrm31m6vLly3To0IGZM2eSmppqpUhFsZGTLFZQlrwY6mj16tW6TJky2sHBQbdq1UpPmDBBHzx4MNvyf/zxhwb0hQsXtNZaL1iwQAP66tWrGcrltAXVsGHD+8a4efNmbW9vr1NSUrTWWn/99dfaxcUlSzlzLagBAwZkKFOzZk09c+ZMrbXWW7Zs0SVKlEhrEWqt9b59+zQgLSitdUKC1n36GC0nHG7qxyYO0dHx0XlapzVbUOnFxMToAQMG3HOYpFatWumIiAirxVgYP1PWUBDPE9KCyhlfX18iIiLYsGED3bp1Y//+/bRq1YpZs2YBcPToUXr16kXVqlVxc3OjRQtjfMPz589bpP7mzZtn2bZz5046d+6Mp6cnbm5u9O3bl6SkJCIjI3N9/EaNGmVYr1SpEleuXAHg1KlTVKpUicqVK6ftb9myZZbnY4qj27fhmWdg7VpQjjd5ZPQwfn77A0o5lrJ2aPnC1dWVFStWsHjxYlxcXMw+MxUcHEzdunXZtGmTlaIURZ38JcKYjbRz5868/fbb7N+/nxdffJEZM2Zw8+ZNunbtirOzM0uXLuXw4cNs2bIFuH+HBhsbmyy9nu7cuZOlXOaZUc+dO0f37t2pV68eq1at4siRI3z11Vc5qtOczD2zlFJyaeY+oqONyQa3bQM7txuUGtWb3dM+pJJbJWuHlu8GDRrE8ePHqVevXrbPTPXv359XX31VLg0Li5MEZYaXlxfJycmEhIQQFRXFrFmzaNu2LXXr1k1rfdxla2sLkGUMM3d3dy5dupRh27Fjx+5bd3BwMElJSSxYsIDWrVtTu3ZtIiIiMpSxt7e3yJhpdevWJSIiIsPxg4ODi3UCu3oVOnSAffvA0xO270rkp0nzqF2utrVDs5oaNWrw66+/MnLkSLPPTMXHx7N48WK2bdtmhehEUVasE9S1a9fo0KEDgYGBHD9+nLCwMFatWsXs2bPp2LEjXl5eODg48NFHH/H333+zadMmpk+fnuEYHh4eKKXYtGkTV69eJTY2FoAOHTqwefNm1q9fT2hoKOPHj+fChfsPNVirVi1SU1Px9/cnLCyMFStW4O/vn6FMtWrVSEhIYPv27URFRREXF/dA779z587UqVOHYcOGcezYMQ4ePMj48eOxtbUttHMEPYzwcGjbFn79Fcp7RrP75xTaNX+EZhWbWTs0q7Ozs2PBggWsW7eO0qVLZ2iZ29nZ0aZNG7p3727FCEVRVKwTlKurK61atWLhwoW0a9eO+vXrM2XKFJ5//nlWrlyJu7s7S5YsYd26dXh5eeHn58f8+fMzHMPd3R0/Pz+mTp2Kh4dH2kgOI0aMSFu8vb1xc3OjT58+942pUaNGLFy4kPnz5+Pl5cUXX3zB3LlzM5Rp06YNo0aNYuDAgbi7uzN79uwHev82NjasXbuWxMREHn/8cYYNG8bUqVNRSmXpwVXUhYbCk0/CqVNQ6tFzRD1Xlwtqr7XDKnC6dOlCaGgorVq1Srvk5+LiwqpVq+TepbC8nPSkKCiLTFiY90JCQjSgg4ODLXrcgnye9u7VumxZo7eeR52/NZPK6Pn751slloLSi+9+UlJS9Icffqjt7Oz0tm3brBJDQf5MFSQF8TyRw158ttZOkMK61q5di4uLC7Vq1eLs2bOMHz+exo0b06xZ8bistXYtPP+8Ma9T7danOd2+KZN8XuON1m9YO7QCzcbGhkmTJjFu3DgcHBysHY4ooqRNXszFxMTw2muv4eXlxaBBg6hXrx5bt24tFvegPvoIfH2N5DTohVjOP9WcYS3780GnD6wdWqEhyUnkJWlBFXNDhw5l6NCh1g4jX6Wmwr//DXdv3b37LkyZ4sqkK/uoV75esUjOQhQGkqBEsRIXBy+8AN99B7a28OYHoTzSfi9KvUgjj0b3P4AQIt/IJT5RbISHGz31vvsO3Nzgv0v/5uM7TzDvwDwSkmViPmupVq1alp6qQoC0oEQxcfAg9OkDkZHw2GPwSeBFhu37F672rmwZvAVH2+LVrT6/DR8+nKioKDZu3Jhl3+HDh7OMqCIEFIMWVGRkJN26dWPZsmUyFEsxtXQp+PgYyal9e9i0M4rXgjsQnxzP1sFbebTUo9YOsVhzd3fPMoySNTzsfGzC8op8gvr444/ZsWMHo0aNwt3dnTfeeCPLEESiaEpJgcmTjYkGExNhzBjYuhUOXN/IhZsX2DhwI/Ur1Ld2mMVe5kt8SikWL15M//79cXFxoUaNGgQGBmZ4zcWLF3nnnXcoU6YMZcqUoXv37vz5559p+8+cOUOvXr145JFHcHFxoVmzZllab9WqVWPGjBmMGDGC0qVLM2jQoLx9oyLXinSCSk5OZtGiRSQnJxMbG0tMTAyLFi1iyZIl1g5N5LHLl6FLF6OnXokS8PHHsGgR2NnB8CbDCX0tFO9Hi+jETkXAO++8Q69evTh27BjPPfccI0aMSJtBIC4ujvbt22Nvb8/u3bs5cOAAFStWpFOnTmnDfsXGxtKtWze2b9/OsWPH8PX1pW/fvpw6dSpDPfPnz6du3boEBwenzWAgCo4inaA2bdqUZQRxGxsbBg8ebKWIRH74+Wdo2hR27gQPD9ixA14ZlcprP77G/gv7AahSqoqVoxT3MmTIEAYPHkzNmjWZOXMmtra2/PzzzwB8++23aK2ZPHkyjRo1om7dunz22WfExsamtZIaN27MqFGjaNiwITVr1mTq1Kk0a9aM1atXZ6inXbt2TJo0iZo1a1KrVq18f5/i3op0gpo9ezYxMTEZtj355JN4enpaKSKRl1JT4YMPjPtMly79M/Crjw9M2j6JRYcX8fO5n60dpsiB9POY2dra4u7unjaTwJEjRwgLC+Ppp59OmxW7VKlS3LhxgzNnzgBw+/ZtJk2ahJeXF2XKlMHV1ZXg4OAs87jdnd9NFEwW7cWnlCoLfAl0AaKAf2utl5sp9yYwDKhqKvex1nqOJWP5+++/OXr0aIZtbm5u95weXRRe16/DsGFw9zbDW2/BzJnGs05z989l3oF5vNbyNSZ7T7ZuoCJH7jWPWWpqKk2aNOGNN97giSeeyFCubNmyAEycOJEtW7Ywd+5catWqhbOzM0OHDs3SEUJ6DxZslu5mvghIAjyAJsAmpdQxrfXJTOUUMBQ4DjwGbFNKXdBaf2upQP73v/9lmTPJ2dmZzp07W6oKUUDs3Gkkp/BwKFMGvvkGevQw9n1z7Bve3P4mz9Z/Fv+n/GWUiCKgWbNmrFixglKlSlGzZk2zZfbu3cvQoUPx9fUFICEhgTNnzlC7dvGd16swsliCUkq5AL5AA611LLBXKbUeGAK8lb6s1jr9/BChSqkfAG/AIgkqMTGRL7/8MsP9JycnJ8aNGydTAhQhCQkwZQosWGCsP/EEfPstVKtmrGut2fzXZjpU78A3vb+hhE0Jq8Uq4NatW4SEhGTYVrp06VwfZ9CgQcydO5epU6fi5ubGo48+yoULF/jhhx8YNWoUtWrVonbt2qxdu5ZevXphZ2eHn58fCQnyMHZhY8kWVG0gWWt9Ot22Y0C7e71IGV9pnwQ+y2b/SGAkGJMDBgUF3TeQHTt2kJycnGFbcnIy9erVy9HrcyM2NtbixyyKLH2e/vrLhffe8+LsWRdsbDRDh55l8ODznD2rOXvWSE5KKV4q+xJJpZM4sPeAxerOS9HR0aSkpBS5z1RkZCR79uyhadOmGba3bds2rXWT/j2fPHmS8uXLp61nLvP+++/z8ccf07t3b27fvk25cuVo0qQJv//+OxcvXqR///7MmTMHb29vXF1d6devH15eXkRGRqYdw1y9RVGh/huVkzk5crJgJJnITNteBoLu8zo/jETmcL86cjofVJMmTTSQtiildK9evXI6VUmuFMS5VgoiS52n5GStP/xQazs7Y/6mWrW0/uWXjGX+uPqHbvd1O33h5gWL1JmfCst8UAWB/N/LmYJ4nrDCfFCxQMlM20oCMWbKAqCUeg3jXtSTWmuLDPNw4sQJQkNDM2xzdnaWzhFFwPHj8PLLcOiQsT56NMyZA+nvc1+8dZGugV1JSE4gMVlGDhGiMLPkDZnTgK1SKv3DBI2BzB0kAFBKjcC4N9VRax1uqSD8/f2z9NQpX7483t7yUGZhFR9vTI/RvLmRnCpXNnrrffxxxuR0I/4GTy17ihvxN9gyaAuPlX3MekELIR6axRKU1vo2sAZ4RynlopTyBnoBSzOXVUoNAmYBnbXWf1sqhtu3b7N8+fIMvfecnZ2ZMGGC9N4qpH76CRo2NJ5vSkmBV1+F33+H7t0zlou/E88z3z7D6WunWTdgHU0rNjV/QCFEoWHpLm1jACfgCrACGK21PqmUelIpFZuu3LtAOeCwUirWtHz6sJUvX748Sy+91NTUYjchX1Fw6ZLRdbxTJzhzBurXh337jFlwS2a+kAzEJMUQmxTL0j5L6VC9Q/4HLISwOIs+B6W1vg70NrN9D+Cabr26Jes1HZM5c+Zw+/bttG02Njb4+vpSqlQpS1cn8khCgtFtfNYsiI0FBweYPh3efBPs7bOW11qTqlOp4FKBwy8fxtZGZpARoqgoMg8FBQcHExERkWGbo6Mj48ePt1JEIje0hu+/By8v49mm2Fjo1QtOnICpU80nJ4D/BP0H3+98SUpJkuQkRBFTZBLUvHnziI+Pz7CtatWqNGvWzEoRiZwKDjbGz+vXD8LCoEEDY4DXdesgm4ECAFh0aBEzf55Jeefy2NnYZV9QCFEoFYkEdePGDX744Ye0sboAXF1dpWt5AXf8uDHLbcuWsHs3lCtn9Mz79Vfo2PHer111chVjN4/lmTrP8GmPT6UTjBBFUJG4JhIQEJClc4TWmgEDBlgpInEvp07BjBmwcqWx7uQEY8caA7yWKXP/1+8M28ngtYPxftSbb32/lUt7QhRRhf5/ttaa+fPnp01UBsbw/EOGDCkQ00iLf/z+O3z4IQQGGlNj2NsbD9u+9RY88kjOj+Ni50Jrz9asfW4tTnZOeRewEMKqCn2C2r17N9HR0Rm22dnZMW7cOCtFJNLTGvbuhSlTGnDANByera0xIsTUqVAlF/MGxiTG4ObgxhOeT7Br2C65rCdEEVfo70HNmzeP2NjYDNu8vLyoW7eulSISYDxUu2YNtGljTBx44EB5HB1hzBgIDYVPP81dcroce5mmn8yIIesAAA6sSURBVDVl9j5jIHxJTkIUfYUqQcXHx7Nt27a0zhCXL19mx44dGcq4uroyadIka4QngCtXjMt4tWqBry8cPAhly8LQoWc5fx4WLYIaNXJ3zFuJt+i2rBsRMRG0rdo2bwIXQhQ4heoS37Vr1+jWrRsVKlRg3LhxREVFZSljY2ND795ZnhUWeejuZbxPPoHVq+HuNFzVqsH48TBiBBw+fBZ392q5PnZiciJ9V/bl+OXjrB+4nlaerSwauxCi4CpUCcrW1hYbGxsiIyN55513SEpKyjDunr29PSNHjsQ+u6c6hUVdvAjLl8OSJXDSNCSwUsZstqNHQ9euUOIh5gjUWjP8h+H8FPYTS3ov4elaT1smcCFEoVDoEpSDgwPJyclZHsoF477EkCFDrBBZ8REba9xbWrrUGMjVmNILPDzgpZeMzg9Vq1qmLqUUTz32FC0qtmBoYxlPUYjiptAlqBL3+EpeokQJnnjiCfr168f48eOzzN4pHkxsLGzZYgxFtH493O3Rb29vtJYGDzZGF7dkwzX8VjieJT0Z1mSY5Q4qhChUClUnCVtb23v23oqLiyMhIYHly5fTrFkzPv/883yMrmi5ft24dNerF7i7Q//+8O23RnLy9jZ64f1/e/cfXFV95nH8/eSGREJ+CGIR5IdIYV2pJUgKSyklitXQahU7Si21ZbsV1wIdpkut1nXGarvd6XRKO9aRUtktgsViS3fBiFVrg9KOsrCbqKwIZRHFEeVXIAmBEPLsH+deSWKSe0MunHNzP6+Z7+Sek++9eXLm5Dz53vO9z/fdd4OkNXNmepPTsv9exugHR/PynpfT96IiknEybgTV+p5TZ8455xwmTZrELbfcchai6h1aWqCmJhgpPf10sLRF60M9eTLceGPQujsLrzvWvrGWuU/O5aqLr9KaTiJZLuMSVPvVctsrKCjgpptu4pFHHiE3N6N+vbNuz56gBt4zz8Af/gDvvXfqe7FYsBbTzJlwww0wZMiZj+fPb/2ZWb+dxYTBE/jdzb8jL6bJLiLZLKOu4LFYjBOJOcwdKCgo4O677+aee+7RBzk7sHt3kJASbefOtt8fOhQqKoI2fTqce+5ZjK12N9etuo5hxcOo/FIlhXmFyZ8kIr1aRiUoM6OgoKDNooQJBQUFLF26lNmzZ4cQWfQcPgxbtsCmTafaO++07VNUBJ/6FFx5JcyYEazFFFZeH1YyjAUTFzCndA7n9zs/nCBEJFIyKkEBlJSUfChBFRYWsm7dOsrLy8MJKmQHD8KrrwZt8+YgGW3bdmoKeEJJCUydCtOmQXk5lJYGdfHCdODoAeqb6hlx7gi+d8X3wg1GRCIl4xJU//79P1g5Nzc3l/79+1NVVcWll14acmRn3tGjQR27RDJKtHYLCQPBrLrS0mCtpYkTgzZmDOREaN5mQ1MD1666lvcb3uf1ea/rnpOItJFxCWrgwIEA5OfnM2LECKqqqhg8eHDIUaXP8ePBqrLbt8OOHUFLPN6zp+PnFBTA2LFw2WVw+eVBMvr4xyE//+zG3h0nTp5g1m9nsemdTTxx0xNKTiLyIRmXoC644AJycnKYNGkSlZWVFBZmzs109+DtuLfeatt27z71eO/eD781l5CbC6NGBYmodRs5smclhc42d+e2dbdRuaOSJZ9bwo1/e2PYIYlIBGVcgpoyZQqFhYUsWbIkMtPIGxth//4gubRu773Xdvvdd09VYehMTk5QKmjMmKCNHh20MWOC/RH5lXvkwU0PsrxmOfdNu4/by24POxwRiaiMu9wtWLAg7a/Z0gINDXDkCNTVnWq1tcGI58CB4Gv7xwcPwr59U0ny0aw2ioqCRDN8eNBaPx4+PPi8UW9IQl2ZUzqHHMth3ifmhR2KiERYWi+FZjYAWAZcDewH7nb3X3fQz4B/Bb4e3/UIcJd7Z29uBZqb4c03gxFLoh09mtp2Q0OQdFonocTjdusddlOMvDwYODBYtjzRBg1qu53YV1LSk5+V2Z7f9TyTLpxEcX4x8yfODzscEYm4dP+v/hDQBAwCSoFKM6tx963t+s0FbgDGAQ48C+wClnT14jU1wf2WM6Ffv2B0U1wcfC0qCpLJeecFC+4lviZaYvu1116gouLToX1+KFNsPriZ7774XeZ9Yh6LKxaHHY6IZABLMmhJ/YXM+gGHgI+5+/b4vhXAO+5+V7u+fwF+5e5L49v/ANzm7l2uRpeTM97z8taTk9NELHacnJxT7dR2U3z72AePE9u5uQ3EYo3EYkeJxRrIzU08bsSs5bR+79raWs49myUXMlBdUR3V46rpe6wvpf9TSu7JXv4eZg9UV1fT3NxMWVlZ2KFEnv72UhPF47Rhw4Yt7p70JE/nlWIM0JxITnE1wLQO+o6Nf691v7EdvaiZzSUYcdGnTx8uuaSix4G2tASti6pJKTt58iS1tbU9f6Fe6ni/4/z1sr8Sa4ox4sUR1B/v0fupvV5zczPurnMqBfrbS00mH6d0JqhC4Ei7fYeBok76Hm7Xr9DMrP19qPgoaylAWVmZb968OX0Rp0FVVVXWVrBIxt2ZvGwy/Q/15ydjf8KXf/TlsEOKvPLycmpra6murg47lMjT315qonicUq2Vms4EVQ8Ut9tXDNSl0LcYqE82SUIyi5mxYuYKjhw/Qt32jk4DEZHOpbPwzXYg18xGt9o3Dmg/QYL4vnEp9JMMdKz5GL/c8kvcndHnjWbCkAlhhyQiGShtCcrdG4A1wP1m1s/MpgDXAys66P4o8C0zu9DMhgD/BPwqXbFIeE62nGT2mtnMfXIuL+15KexwRCSDpbt06DeAvsD7wCrgDnffamZTzaz13fFfAOuAV4HXgMr4Pslg7s78p+az5vU1LL5mMZOHTQ47JBHJYGmd7+vuBwk+39R+/4sEEyMS2w7cGW/SSzzwwgMs2bKE70z5Dgv/bmHY4YhIhovQ4guSyXYe3Mn3X/g+Xx33VX44/YdhhyMivYA+MSlpMWrAKDZ+bSPjLxif8hRSEZGuaAQlPbLhzQ2s3roagIkXTqRPrE/IEYlIb6ERlJy2mr01fP7xzzOseBgzL5mp5CQiaaURlJyWXYd2UfFYBUV5RTw1+yklJxFJO42gpNv2NezjmpXXcKz5GBv/fiPDS4aHHZKI9EJKUNJtv9n6G94+8jbP3focYz/SYY1fEZEeU4KSbps/cT4zPjqDUQNGhR2KiPRiugclKWnxFhY+vZDqvUGVbSUnETnTlKAkKXdn0TOL+NnLP+PZnc+GHY6IZAklKEnqx3/5MYtfWsyCiQtY9MlFYYcjIllCCUq6tLx6OXc+dyc3j72Zn1b8VFUiROSsUYKSTrk7T/zvE0wfOZ1Hb3iUHNPpIiJnj2bxSafMjDWz1tB0son83PywwxGRLKN/ieVDtu3fxozHZrCvYR95sTwK8wqTP0lEJM00gpI29hzZw9UrrqbpZBN1TXWc3+/8sEMSkSylBCUfONR4iIqVFdQeq2XDnA1c3P/isEMSkSymBCUANJ5o5LpV17Hj4A7Wz17P+MHjww5JRLKc7kEJAAcaD7D/6H5WzlzJlSOvDDscERGNoLKdu+M4Q4uH8sodr5AXyws7JBERQCOorHfvn+5lzn/MobmlWclJRCJFCSqL/XzTz/nBiz8gP5ZPzGJhhyMi0oYSVJZavXU131z/Ta7/m+t5+NqHVcJIRCInLQnKzAaY2e/NrMHMdpvZl7ro+20ze83M6sxsl5l9Ox0xSOqe3/U8t/7+VqYMn8KqL6wiN0e3IkUketJ1ZXoIaAIGAaVApZnVuPvWDvoa8BXgFWAU8IyZve3uj6cpFknCMMqGlLH2i2vp26dv2OGIiHSoxwnKzPoBXwA+5u71wEYzWwvcCtzVvr+7/6jV5htm9p/AFEAJ6gxrPNFI3z59uWLkFWy8aKPe1hORSEvHCGoM0Ozu21vtqwGmJXuiBVfIqcAvuugzF5gb36w3szd6EOuZMBDYH3YQGUDHKXUDzUzHKjmdU6mJ4nEakUqndCSoQuBIu32HgaIUnnsfwX2wf++sg7svBZaebnBnmpltdveysOOIOh2n1OlYpUbHKTWZfJySTpIwsyoz807aRqAeKG73tGKgLsnrzie4F/U5dz9+ur+AiIj0TklHUO5e3tX34/egcs1stLvviO8eB3Q0QSLxnK8R3J/6tLvvST1cERHJFj2eZu7uDcAa4H4z62dmU4DrgRUd9Tez2cC/AJ9x9//r6c+PgMi+/RgxOk6p07FKjY5TajL2OJm79/xFzAYA/wZ8BjgA3OXuv45/byqw3t0L49u7gKFA67f1Vrr7P/Y4EBER6TXSkqBERETSTaWOREQkkpSgREQkkpSg0szMRpvZMTNbGXYsUWNm+Wa2LF6vsc7Mqs1sRthxRUV3alpmK51D3ZfJ1yQlqPR7CPivsIOIqFzgbYIqIyXAPwOrzeyiEGOKktY1LWcDD5vZ2HBDihydQ92XsdckJag0MrMvArXAH8OOJYrcvcHd73P3N929xd2fBHYBE8KOLWytalre6+717r4RSNS0lDidQ92T6dckJag0MbNi4H7gW2HHkinMbBBBLcdOP9SdRTqraakRVBd0DnWuN1yTlKDS5wFgmSpjpMbM+gCPAcvdfVvY8URAT2paZiWdQ0ll/DVJCSoFyeoRmlkpcBWwOOxYw5RC3cZEvxyCSiNNwPzQAo6W06ppma10DnWtt1yTtJRqClKoR7gQuAh4K77GUiEQM7NL3f3yMx5gRCQ7TvDBEivLCCYCfNbdT5zpuDLEdrpZ0zJb6RxKSTm94JqkShJpYGYFtP3vdxHByXGHu+8LJaiIMrMlBKsuXxVf4FLizOxxwIGvExyjp4BPdrIyddbSOZRcb7kmaQSVBu5+FDia2DazeuBYJp0IZ4OZjQBuJ6jDuLfVir63u/tjoQUWHd8gqGn5PkFNyzuUnNrSOZSa3nJN0ghKREQiSZMkREQkkpSgREQkkpSgREQkkpSgREQkkpSgREQkkpSgREQkkpSgREQkkpSgREQkkv4fpnIt6Q3iZsAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"z = np.linspace(-5, 5, 200)\\n\",\n    \"\\n\",\n    \"plt.plot([-5, 5], [0, 0], 'k-')\\n\",\n    \"plt.plot([-5, 5], [1, 1], 'k--')\\n\",\n    \"plt.plot([0, 0], [-0.2, 1.2], 'k-')\\n\",\n    \"plt.plot([-5, 5], [-3/4, 7/4], 'g--')\\n\",\n    \"plt.plot(z, logit(z), \\\"b-\\\", linewidth=2)\\n\",\n    \"props = dict(facecolor='black', shrink=0.1)\\n\",\n    \"plt.annotate('Saturating', xytext=(3.5, 0.7), xy=(5, 1), arrowprops=props, fontsize=14, ha=\\\"center\\\")\\n\",\n    \"plt.annotate('Saturating', xytext=(-3.5, 0.3), xy=(-5, 0), arrowprops=props, fontsize=14, ha=\\\"center\\\")\\n\",\n    \"plt.annotate('Linear', xytext=(2, 0.2), xy=(0, 0.5), arrowprops=props, fontsize=14, ha=\\\"center\\\")\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.title(\\\"Sigmoid activation function\\\", fontsize=14)\\n\",\n    \"plt.axis([-5, 5, -0.2, 1.2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"sigmoid_saturation_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Xavier and He Initialization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the book uses `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function. The main differences relevant to this chapter are:\\n\",\n    \"* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\\n\",\n    \"* the default `activation` is now `None` rather than `tf.nn.relu`.\\n\",\n    \"* it does not support `tensorflow.contrib.framework.arg_scope()` (introduced later in chapter 11).\\n\",\n    \"* it does not support regularizer params (introduced later in chapter 11).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING:tensorflow:From <ipython-input-6-da109dac52d3>:3: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\\n\",\n      \"Instructions for updating:\\n\",\n      \"Use keras.layers.Dense instead.\\n\",\n      \"WARNING:tensorflow:From /Users/ageron/miniconda3/envs/tf1/lib/python3.7/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\\n\",\n      \"Instructions for updating:\\n\",\n      \"Please use `layer.__call__` method instead.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"he_init = tf.variance_scaling_initializer()\\n\",\n    \"hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu,\\n\",\n    \"                          kernel_initializer=he_init, name=\\\"hidden1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Nonsaturating Activation Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Leaky ReLU\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def leaky_relu(z, alpha=0.01):\\n\",\n    \"    return np.maximum(alpha*z, z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure leaky_relu_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(z, leaky_relu(z, 0.05), \\\"b-\\\", linewidth=2)\\n\",\n    \"plt.plot([-5, 5], [0, 0], 'k-')\\n\",\n    \"plt.plot([0, 0], [-0.5, 4.2], 'k-')\\n\",\n    \"plt.grid(True)\\n\",\n    \"props = dict(facecolor='black', shrink=0.1)\\n\",\n    \"plt.annotate('Leak', xytext=(-3.5, 0.5), xy=(-5, -0.2), arrowprops=props, fontsize=14, ha=\\\"center\\\")\\n\",\n    \"plt.title(\\\"Leaky ReLU activation function\\\", fontsize=14)\\n\",\n    \"plt.axis([-5, 5, -0.5, 4.2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"leaky_relu_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementing Leaky ReLU in TensorFlow:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def leaky_relu(z, name=None):\\n\",\n    \"    return tf.maximum(0.01 * z, z, name=name)\\n\",\n    \"\\n\",\n    \"hidden1 = tf.layers.dense(X, n_hidden1, activation=leaky_relu, name=\\\"hidden1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train a neural network on MNIST using the Leaky ReLU. First let's create the graph:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 100\\n\",\n    \"n_outputs = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=leaky_relu, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=leaky_relu, name=\\\"hidden2\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING:tensorflow:From /Users/ageron/miniconda3/envs/tf1/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\\n\",\n      \"Instructions for updating:\\n\",\n      \"Use tf.where in 2.0, which has the same broadcast rule as np.where\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's load the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: `tf.examples.tutorials.mnist` is deprecated. We will use `tf.keras.datasets.mnist` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\\n\",\n    \"X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\\n\",\n    \"X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\\n\",\n    \"y_train = y_train.astype(np.int32)\\n\",\n    \"y_test = y_test.astype(np.int32)\\n\",\n    \"X_valid, X_train = X_train[:5000], X_train[5000:]\\n\",\n    \"y_valid, y_train = y_train[:5000], y_train[5000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def shuffle_batch(X, y, batch_size):\\n\",\n    \"    rnd_idx = np.random.permutation(len(X))\\n\",\n    \"    n_batches = len(X) // batch_size\\n\",\n    \"    for batch_idx in np.array_split(rnd_idx, n_batches):\\n\",\n    \"        X_batch, y_batch = X[batch_idx], y[batch_idx]\\n\",\n    \"        yield X_batch, y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Batch accuracy: 0.86 Validation accuracy: 0.9044\\n\",\n      \"5 Batch accuracy: 0.94 Validation accuracy: 0.9496\\n\",\n      \"10 Batch accuracy: 0.92 Validation accuracy: 0.9654\\n\",\n      \"15 Batch accuracy: 0.94 Validation accuracy: 0.971\\n\",\n      \"20 Batch accuracy: 1.0 Validation accuracy: 0.9764\\n\",\n      \"25 Batch accuracy: 1.0 Validation accuracy: 0.9778\\n\",\n      \"30 Batch accuracy: 0.98 Validation accuracy: 0.978\\n\",\n      \"35 Batch accuracy: 1.0 Validation accuracy: 0.9788\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 40\\n\",\n    \"batch_size = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        if epoch % 5 == 0:\\n\",\n    \"            acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"            acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"            print(epoch, \\\"Batch accuracy:\\\", acc_batch, \\\"Validation accuracy:\\\", acc_valid)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ELU\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def elu(z, alpha=1):\\n\",\n    \"    return np.where(z < 0, alpha * (np.exp(z) - 1), z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure elu_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(z, elu(z), \\\"b-\\\", linewidth=2)\\n\",\n    \"plt.plot([-5, 5], [0, 0], 'k-')\\n\",\n    \"plt.plot([-5, 5], [-1, -1], 'k--')\\n\",\n    \"plt.plot([0, 0], [-2.2, 3.2], 'k-')\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.title(r\\\"ELU activation function ($\\\\alpha=1$)\\\", fontsize=14)\\n\",\n    \"plt.axis([-5, 5, -2.2, 3.2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"elu_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementing ELU in TensorFlow is trivial, just specify the activation function when building each layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.elu, name=\\\"hidden1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### SELU\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This activation function was proposed in this [great paper](https://arxiv.org/pdf/1706.02515.pdf) by Günter Klambauer, Thomas Unterthiner and Andreas Mayr, published in June 2017. During training, a neural network composed exclusively of a stack of dense layers using the SELU activation function and LeCun initialization will self-normalize: the output of each layer will tend to preserve the same mean and variance during training, which solves the vanishing/exploding gradients problem. As a result, this activation function outperforms the other activation functions very significantly for such neural nets, so you should really try it out. Unfortunately, the self-normalizing property of the SELU activation function is easily broken: you cannot use ℓ<sub>1</sub> or ℓ<sub>2</sub> regularization, regular dropout, max-norm, skip connections or other non-sequential topologies (so recurrent neural networks won't self-normalize). However, in practice it works quite well with sequential CNNs. If you break self-normalization, SELU will not necessarily outperform other activation functions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.special import erfc\\n\",\n    \"\\n\",\n    \"# alpha and scale to self normalize with mean 0 and standard deviation 1\\n\",\n    \"# (see equation 14 in the paper):\\n\",\n    \"alpha_0_1 = -np.sqrt(2 / np.pi) / (erfc(1/np.sqrt(2)) * np.exp(1/2) - 1)\\n\",\n    \"scale_0_1 = (1 - erfc(1 / np.sqrt(2)) * np.sqrt(np.e)) * np.sqrt(2 * np.pi) * (2 * erfc(np.sqrt(2))*np.e**2 + np.pi*erfc(1/np.sqrt(2))**2*np.e - 2*(2+np.pi)*erfc(1/np.sqrt(2))*np.sqrt(np.e)+np.pi+2)**(-1/2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def selu(z, scale=scale_0_1, alpha=alpha_0_1):\\n\",\n    \"    return scale * elu(z, alpha)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure selu_plot\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(z, selu(z), \\\"b-\\\", linewidth=2)\\n\",\n    \"plt.plot([-5, 5], [0, 0], 'k-')\\n\",\n    \"plt.plot([-5, 5], [-1.758, -1.758], 'k--')\\n\",\n    \"plt.plot([0, 0], [-2.2, 3.2], 'k-')\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.title(r\\\"SELU activation function\\\", fontsize=14)\\n\",\n    \"plt.axis([-5, 5, -2.2, 3.2])\\n\",\n    \"\\n\",\n    \"save_fig(\\\"selu_plot\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default, the SELU hyperparameters (`scale` and `alpha`) are tuned in such a way that the mean output of each neuron remains close to 0, and the standard deviation remains close to 1 (assuming the inputs are standardized with mean 0 and standard deviation 1 too). Using this activation function, even a 1,000 layer deep neural network preserves roughly mean 0 and standard deviation 1 across all layers, avoiding the exploding/vanishing gradients problem:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Layer 0: mean -0.00, std deviation 1.00\\n\",\n      \"Layer 100: mean 0.02, std deviation 0.96\\n\",\n      \"Layer 200: mean 0.01, std deviation 0.90\\n\",\n      \"Layer 300: mean -0.02, std deviation 0.92\\n\",\n      \"Layer 400: mean 0.05, std deviation 0.89\\n\",\n      \"Layer 500: mean 0.01, std deviation 0.93\\n\",\n      \"Layer 600: mean 0.02, std deviation 0.92\\n\",\n      \"Layer 700: mean -0.02, std deviation 0.90\\n\",\n      \"Layer 800: mean 0.05, std deviation 0.83\\n\",\n      \"Layer 900: mean 0.02, std deviation 1.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"Z = np.random.normal(size=(500, 100)) # standardized inputs\\n\",\n    \"for layer in range(1000):\\n\",\n    \"    W = np.random.normal(size=(100, 100), scale=np.sqrt(1 / 100)) # LeCun initialization\\n\",\n    \"    Z = selu(np.dot(Z, W))\\n\",\n    \"    means = np.mean(Z, axis=0).mean()\\n\",\n    \"    stds = np.std(Z, axis=0).mean()\\n\",\n    \"    if layer % 100 == 0:\\n\",\n    \"        print(\\\"Layer {}: mean {:.2f}, std deviation {:.2f}\\\".format(layer, means, stds))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `tf.nn.selu()` function was added in TensorFlow 1.4. For earlier versions, you can use the following implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def selu(z, scale=alpha_0_1, alpha=scale_0_1):\\n\",\n    \"    return scale * tf.where(z >= 0.0, z, alpha * tf.nn.elu(z))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"However, the SELU activation function cannot be used along with regular Dropout (this would cancel the SELU activation function's self-normalizing property). Fortunately, there is a Dropout variant called Alpha Dropout proposed in the same paper. It is available in `tf.contrib.nn.alpha_dropout()` since TF 1.4 (or check out [this implementation](https://github.com/bioinf-jku/SNNs/blob/master/selu.py) by the Institute of Bioinformatics, Johannes Kepler University Linz).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a neural net for MNIST using the SELU activation function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 100\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=selu, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=selu, name=\\\"hidden2\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\\n\",\n    \"n_epochs = 40\\n\",\n    \"batch_size = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's train it. Do not forget to scale the inputs to mean 0 and standard deviation 1:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Batch accuracy: 0.88 Validation accuracy: 0.923\\n\",\n      \"5 Batch accuracy: 0.98 Validation accuracy: 0.9578\\n\",\n      \"10 Batch accuracy: 1.0 Validation accuracy: 0.9664\\n\",\n      \"15 Batch accuracy: 0.96 Validation accuracy: 0.9682\\n\",\n      \"20 Batch accuracy: 1.0 Validation accuracy: 0.9694\\n\",\n      \"25 Batch accuracy: 1.0 Validation accuracy: 0.9688\\n\",\n      \"30 Batch accuracy: 1.0 Validation accuracy: 0.9694\\n\",\n      \"35 Batch accuracy: 1.0 Validation accuracy: 0.97\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"means = X_train.mean(axis=0, keepdims=True)\\n\",\n    \"stds = X_train.std(axis=0, keepdims=True) + 1e-10\\n\",\n    \"X_val_scaled = (X_valid - means) / stds\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            X_batch_scaled = (X_batch - means) / stds\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch_scaled, y: y_batch})\\n\",\n    \"        if epoch % 5 == 0:\\n\",\n    \"            acc_batch = accuracy.eval(feed_dict={X: X_batch_scaled, y: y_batch})\\n\",\n    \"            acc_valid = accuracy.eval(feed_dict={X: X_val_scaled, y: y_valid})\\n\",\n    \"            print(epoch, \\\"Batch accuracy:\\\", acc_batch, \\\"Validation accuracy:\\\", acc_valid)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final_selu.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Batch Normalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the book uses `tensorflow.contrib.layers.batch_norm()` rather than `tf.layers.batch_normalization()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.batch_normalization()`, because anything in the contrib module may change or be deleted without notice. Instead of using the `batch_norm()` function as a regularizer parameter to the `fully_connected()` function, we now use `batch_normalization()` and we explicitly create a distinct layer. The parameters are a bit different, in particular:\\n\",\n    \"* `decay` is renamed to `momentum`,\\n\",\n    \"* `is_training` is renamed to `training`,\\n\",\n    \"* `updates_collections` is removed: the update operations needed by batch normalization are added to the `UPDATE_OPS` collection and you need to explicity run these operations during training (see the execution phase below),\\n\",\n    \"* we don't need to specify `scale=True`, as that is the default.\\n\",\n    \"\\n\",\n    \"Also note that in order to run batch norm just _before_ each hidden layer's activation function, we apply the ELU activation function manually, right after the batch norm layer.\\n\",\n    \"\\n\",\n    \"Note: since the `tf.layers.dense()` function is incompatible with `tf.contrib.layers.arg_scope()` (which is used in the book), we now use python's `functools.partial()` function instead. It makes it easy to create a `my_dense_layer()` function that just calls `tf.layers.dense()` with the desired parameters automatically set (unless they are overridden when calling `my_dense_layer()`). As you can see, the code remains very similar.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING:tensorflow:From <ipython-input-32-2f36d39a8af9>:15: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version.\\n\",\n      \"Instructions for updating:\\n\",\n      \"Use keras.layers.BatchNormalization instead.  In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.batch_normalization` documentation).\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 100\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"\\n\",\n    \"training = tf.placeholder_with_default(False, shape=(), name='training')\\n\",\n    \"\\n\",\n    \"hidden1 = tf.layers.dense(X, n_hidden1, name=\\\"hidden1\\\")\\n\",\n    \"bn1 = tf.layers.batch_normalization(hidden1, training=training, momentum=0.9)\\n\",\n    \"bn1_act = tf.nn.elu(bn1)\\n\",\n    \"\\n\",\n    \"hidden2 = tf.layers.dense(bn1_act, n_hidden2, name=\\\"hidden2\\\")\\n\",\n    \"bn2 = tf.layers.batch_normalization(hidden2, training=training, momentum=0.9)\\n\",\n    \"bn2_act = tf.nn.elu(bn2)\\n\",\n    \"\\n\",\n    \"logits_before_bn = tf.layers.dense(bn2_act, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"logits = tf.layers.batch_normalization(logits_before_bn, training=training,\\n\",\n    \"                                       momentum=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"training = tf.placeholder_with_default(False, shape=(), name='training')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To avoid repeating the same parameters over and over again, we can use Python's `partial()` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from functools import partial\\n\",\n    \"\\n\",\n    \"my_batch_norm_layer = partial(tf.layers.batch_normalization,\\n\",\n    \"                              training=training, momentum=0.9)\\n\",\n    \"\\n\",\n    \"hidden1 = tf.layers.dense(X, n_hidden1, name=\\\"hidden1\\\")\\n\",\n    \"bn1 = my_batch_norm_layer(hidden1)\\n\",\n    \"bn1_act = tf.nn.elu(bn1)\\n\",\n    \"hidden2 = tf.layers.dense(bn1_act, n_hidden2, name=\\\"hidden2\\\")\\n\",\n    \"bn2 = my_batch_norm_layer(hidden2)\\n\",\n    \"bn2_act = tf.nn.elu(bn2)\\n\",\n    \"logits_before_bn = tf.layers.dense(bn2_act, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"logits = my_batch_norm_layer(logits_before_bn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's build a neural net for MNIST, using the ELU activation function and Batch Normalization at each layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"batch_norm_momentum = 0.9\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"training = tf.placeholder_with_default(False, shape=(), name='training')\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    he_init = tf.variance_scaling_initializer()\\n\",\n    \"\\n\",\n    \"    my_batch_norm_layer = partial(\\n\",\n    \"            tf.layers.batch_normalization,\\n\",\n    \"            training=training,\\n\",\n    \"            momentum=batch_norm_momentum)\\n\",\n    \"\\n\",\n    \"    my_dense_layer = partial(\\n\",\n    \"            tf.layers.dense,\\n\",\n    \"            kernel_initializer=he_init)\\n\",\n    \"\\n\",\n    \"    hidden1 = my_dense_layer(X, n_hidden1, name=\\\"hidden1\\\")\\n\",\n    \"    bn1 = tf.nn.elu(my_batch_norm_layer(hidden1))\\n\",\n    \"    hidden2 = my_dense_layer(bn1, n_hidden2, name=\\\"hidden2\\\")\\n\",\n    \"    bn2 = tf.nn.elu(my_batch_norm_layer(hidden2))\\n\",\n    \"    logits_before_bn = my_dense_layer(bn2, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"    logits = my_batch_norm_layer(logits_before_bn)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"    \\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: since we are using `tf.layers.batch_normalization()` rather than `tf.contrib.layers.batch_norm()` (as in the book), we need to explicitly run the extra update operations needed by batch normalization (`sess.run([training_op, extra_update_ops],...`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 200\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.8952\\n\",\n      \"1 Validation accuracy: 0.9202\\n\",\n      \"2 Validation accuracy: 0.9318\\n\",\n      \"3 Validation accuracy: 0.9422\\n\",\n      \"4 Validation accuracy: 0.9468\\n\",\n      \"5 Validation accuracy: 0.954\\n\",\n      \"6 Validation accuracy: 0.9568\\n\",\n      \"7 Validation accuracy: 0.96\\n\",\n      \"8 Validation accuracy: 0.962\\n\",\n      \"9 Validation accuracy: 0.9638\\n\",\n      \"10 Validation accuracy: 0.9662\\n\",\n      \"11 Validation accuracy: 0.9682\\n\",\n      \"12 Validation accuracy: 0.9672\\n\",\n      \"13 Validation accuracy: 0.9696\\n\",\n      \"14 Validation accuracy: 0.9706\\n\",\n      \"15 Validation accuracy: 0.9704\\n\",\n      \"16 Validation accuracy: 0.9718\\n\",\n      \"17 Validation accuracy: 0.9726\\n\",\n      \"18 Validation accuracy: 0.9738\\n\",\n      \"19 Validation accuracy: 0.9742\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run([training_op, extra_update_ops],\\n\",\n    \"                     feed_dict={training: True, X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"What!? That's not a great accuracy for MNIST. Of course, if you train for longer it will get much better accuracy, but with such a shallow network, Batch Norm and ELU are unlikely to have very positive impact: they shine mostly for much deeper nets.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that you could also make the training operation depend on the update operations:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\\n\",\n    \"    with tf.control_dependencies(extra_update_ops):\\n\",\n    \"        training_op = optimizer.minimize(loss)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"This way, you would just have to evaluate the `training_op` during training, TensorFlow would automatically run the update operations as well:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"sess.run(training_op, feed_dict={training: True, X: X_batch, y: y_batch})\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One more thing: notice that the list of trainable variables is shorter than the list of all global variables. This is because the moving averages are non-trainable variables. If you want to reuse a pretrained neural network (see below), you must not forget these non-trainable variables.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['hidden1/kernel:0',\\n\",\n       \" 'hidden1/bias:0',\\n\",\n       \" 'batch_normalization/gamma:0',\\n\",\n       \" 'batch_normalization/beta:0',\\n\",\n       \" 'hidden2/kernel:0',\\n\",\n       \" 'hidden2/bias:0',\\n\",\n       \" 'batch_normalization_1/gamma:0',\\n\",\n       \" 'batch_normalization_1/beta:0',\\n\",\n       \" 'outputs/kernel:0',\\n\",\n       \" 'outputs/bias:0',\\n\",\n       \" 'batch_normalization_2/gamma:0',\\n\",\n       \" 'batch_normalization_2/beta:0']\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[v.name for v in tf.trainable_variables()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['hidden1/kernel:0',\\n\",\n       \" 'hidden1/bias:0',\\n\",\n       \" 'batch_normalization/gamma:0',\\n\",\n       \" 'batch_normalization/beta:0',\\n\",\n       \" 'batch_normalization/moving_mean:0',\\n\",\n       \" 'batch_normalization/moving_variance:0',\\n\",\n       \" 'hidden2/kernel:0',\\n\",\n       \" 'hidden2/bias:0',\\n\",\n       \" 'batch_normalization_1/gamma:0',\\n\",\n       \" 'batch_normalization_1/beta:0',\\n\",\n       \" 'batch_normalization_1/moving_mean:0',\\n\",\n       \" 'batch_normalization_1/moving_variance:0',\\n\",\n       \" 'outputs/kernel:0',\\n\",\n       \" 'outputs/bias:0',\\n\",\n       \" 'batch_normalization_2/gamma:0',\\n\",\n       \" 'batch_normalization_2/beta:0',\\n\",\n       \" 'batch_normalization_2/moving_mean:0',\\n\",\n       \" 'batch_normalization_2/moving_variance:0']\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[v.name for v in tf.global_variables()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Gradient Clipping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a simple neural net for MNIST and add gradient clipping. The first part is the same as earlier (except we added a few more layers to demonstrate reusing pretrained models, see below):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 50\\n\",\n    \"n_hidden3 = 50\\n\",\n    \"n_hidden4 = 50\\n\",\n    \"n_hidden5 = 50\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name=\\\"hidden2\\\")\\n\",\n    \"    hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, name=\\\"hidden3\\\")\\n\",\n    \"    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name=\\\"hidden4\\\")\\n\",\n    \"    hidden5 = tf.layers.dense(hidden4, n_hidden5, activation=tf.nn.relu, name=\\\"hidden5\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden5, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we apply gradient clipping. For this, we need to get the gradients, use the `clip_by_value()` function to clip them, then apply them:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"threshold = 1.0\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"grads_and_vars = optimizer.compute_gradients(loss)\\n\",\n    \"capped_gvs = [(tf.clip_by_value(grad, -threshold, threshold), var)\\n\",\n    \"              for grad, var in grads_and_vars]\\n\",\n    \"training_op = optimizer.apply_gradients(capped_gvs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The rest is the same as usual:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 200\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.288\\n\",\n      \"1 Validation accuracy: 0.7936\\n\",\n      \"2 Validation accuracy: 0.8798\\n\",\n      \"3 Validation accuracy: 0.906\\n\",\n      \"4 Validation accuracy: 0.9164\\n\",\n      \"5 Validation accuracy: 0.9218\\n\",\n      \"6 Validation accuracy: 0.9296\\n\",\n      \"7 Validation accuracy: 0.9358\\n\",\n      \"8 Validation accuracy: 0.9382\\n\",\n      \"9 Validation accuracy: 0.9414\\n\",\n      \"10 Validation accuracy: 0.9456\\n\",\n      \"11 Validation accuracy: 0.9474\\n\",\n      \"12 Validation accuracy: 0.9478\\n\",\n      \"13 Validation accuracy: 0.9534\\n\",\n      \"14 Validation accuracy: 0.9568\\n\",\n      \"15 Validation accuracy: 0.9566\\n\",\n      \"16 Validation accuracy: 0.9574\\n\",\n      \"17 Validation accuracy: 0.959\\n\",\n      \"18 Validation accuracy: 0.9622\\n\",\n      \"19 Validation accuracy: 0.9612\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reusing Pretrained Layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reusing a TensorFlow Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First you need to load the graph's structure. The `import_meta_graph()` function does just that, loading the graph's operations into the default graph, and returning a `Saver` that you can then use to restore the model's state. Note that by default, a `Saver` saves the structure of the graph into a `.meta` file, so that's the file you should load:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"saver = tf.train.import_meta_graph(\\\"./my_model_final.ckpt.meta\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next you need to get a handle on all the operations you will need for training. If you don't know the graph's structure, you can list all the operations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X\\n\",\n      \"y\\n\",\n      \"hidden1/kernel/Initializer/random_uniform/shape\\n\",\n      \"hidden1/kernel/Initializer/random_uniform/min\\n\",\n      \"hidden1/kernel/Initializer/random_uniform/max\\n\",\n      \"hidden1/kernel/Initializer/random_uniform/RandomUniform\\n\",\n      \"hidden1/kernel/Initializer/random_uniform/sub\\n\",\n      \"hidden1/kernel/Initializer/random_uniform/mul\\n\",\n      \"hidden1/kernel/Initializer/random_uniform\\n\",\n      \"hidden1/kernel\\n\",\n      \"hidden1/kernel/Assign\\n\",\n      \"hidden1/kernel/read\\n\",\n      \"hidden1/bias/Initializer/zeros\\n\",\n      \"hidden1/bias\\n\",\n      \"hidden1/bias/Assign\\n\",\n      \"hidden1/bias/read\\n\",\n      \"dnn/hidden1/MatMul\\n\",\n      \"dnn/hidden1/BiasAdd\\n\",\n      \"dnn/hidden1/Relu\\n\",\n      \"hidden2/kernel/Initializer/random_uniform/shape\\n\",\n      \"hidden2/kernel/Initializer/random_uniform/min\\n\",\n      \"hidden2/kernel/Initializer/random_uniform/max\\n\",\n      \"hidden2/kernel/Initializer/random_uniform/RandomUniform\\n\",\n      \"hidden2/kernel/Initializer/random_uniform/sub\\n\",\n      \"hidden2/kernel/Initializer/random_uniform/mul\\n\",\n      \"hidden2/kernel/Initializer/random_uniform\\n\",\n      \"hidden2/kernel\\n\",\n      \"hidden2/kernel/Assign\\n\",\n      \"hidden2/kernel/read\\n\",\n      \"hidden2/bias/Initializer/zeros\\n\",\n      \"hidden2/bias\\n\",\n      \"hidden2/bias/Assign\\n\",\n      \"hidden2/bias/read\\n\",\n      \"dnn/hidden2/MatMul\\n\",\n      \"dnn/hidden2/BiasAdd\\n\",\n      \"<<210 more lines>>\\n\",\n      \"GradientDescent/update_hidden4/bias/ApplyGradientDescent\\n\",\n      \"GradientDescent/update_hidden5/kernel/ApplyGradientDescent\\n\",\n      \"GradientDescent/update_hidden5/bias/ApplyGradientDescent\\n\",\n      \"GradientDescent/update_outputs/kernel/ApplyGradientDescent\\n\",\n      \"GradientDescent/update_outputs/bias/ApplyGradientDescent\\n\",\n      \"GradientDescent\\n\",\n      \"eval/in_top_k/InTopKV2/k\\n\",\n      \"eval/in_top_k/InTopKV2\\n\",\n      \"eval/Cast\\n\",\n      \"eval/Const\\n\",\n      \"eval/accuracy\\n\",\n      \"init\\n\",\n      \"save/filename/input\\n\",\n      \"save/filename\\n\",\n      \"save/Const\\n\",\n      \"save/SaveV2/tensor_names\\n\",\n      \"save/SaveV2/shape_and_slices\\n\",\n      \"save/SaveV2\\n\",\n      \"save/control_dependency\\n\",\n      \"save/RestoreV2/tensor_names\\n\",\n      \"save/RestoreV2/shape_and_slices\\n\",\n      \"save/RestoreV2\\n\",\n      \"save/Assign\\n\",\n      \"save/Assign_1\\n\",\n      \"save/Assign_2\\n\",\n      \"save/Assign_3\\n\",\n      \"save/Assign_4\\n\",\n      \"save/Assign_5\\n\",\n      \"save/Assign_6\\n\",\n      \"save/Assign_7\\n\",\n      \"save/Assign_8\\n\",\n      \"save/Assign_9\\n\",\n      \"save/Assign_10\\n\",\n      \"save/Assign_11\\n\",\n      \"save/restore_all\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for op in tf.get_default_graph().get_operations():\\n\",\n    \"    print(op.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Oops, that's a lot of operations! It's much easier to use TensorBoard to visualize the graph:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"root_logdir = os.path.join(os.curdir, \\\"tf_logs\\\")\\n\",\n    \"\\n\",\n    \"def make_log_subdir(run_id=None):\\n\",\n    \"    if run_id is None:\\n\",\n    \"        run_id = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n    \"    return \\\"{}/run-{}/\\\".format(root_logdir, run_id)\\n\",\n    \"\\n\",\n    \"def save_graph(graph=None, run_id=None):\\n\",\n    \"    if graph is None:\\n\",\n    \"        graph = tf.get_default_graph()\\n\",\n    \"    logdir = make_log_subdir(run_id)\\n\",\n    \"    file_writer = tf.summary.FileWriter(logdir, graph=graph)\\n\",\n    \"    file_writer.close()\\n\",\n    \"    return logdir\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'./tf_logs/run-20210325200138/'\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"save_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext tensorboard\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Reusing TensorBoard on port 6007 (pid 46883), started 0:09:56 ago. (Use '!kill 46883' to kill it.)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-c62f5e9faf227de6\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-c62f5e9faf227de6\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          url.port = 6007;\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"  \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%tensorboard --logdir {root_logdir}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once you know which operations you need, you can get a handle on them using the graph's `get_operation_by_name()` or `get_tensor_by_name()` methods:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X = tf.get_default_graph().get_tensor_by_name(\\\"X:0\\\")\\n\",\n    \"y = tf.get_default_graph().get_tensor_by_name(\\\"y:0\\\")\\n\",\n    \"\\n\",\n    \"accuracy = tf.get_default_graph().get_tensor_by_name(\\\"eval/accuracy:0\\\")\\n\",\n    \"\\n\",\n    \"training_op = tf.get_default_graph().get_operation_by_name(\\\"GradientDescent\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you are the author of the original model, you could make things easier for people who will reuse your model by giving operations very clear names and documenting them. Another approach is to create a collection containing all the important operations that people will want to get a handle on:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for op in (X, y, accuracy, training_op):\\n\",\n    \"    tf.add_to_collection(\\\"my_important_ops\\\", op)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This way people who reuse your model will be able to simply write:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X, y, accuracy, training_op = tf.get_collection(\\\"my_important_ops\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now you can start a session, restore the model's state and continue training on your data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"    # continue training the model...\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Actually, let's test this for real!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.9636\\n\",\n      \"1 Validation accuracy: 0.9632\\n\",\n      \"2 Validation accuracy: 0.9658\\n\",\n      \"3 Validation accuracy: 0.9652\\n\",\n      \"4 Validation accuracy: 0.9646\\n\",\n      \"5 Validation accuracy: 0.965\\n\",\n      \"6 Validation accuracy: 0.969\\n\",\n      \"7 Validation accuracy: 0.9682\\n\",\n      \"8 Validation accuracy: 0.9682\\n\",\n      \"9 Validation accuracy: 0.9684\\n\",\n      \"10 Validation accuracy: 0.9704\\n\",\n      \"11 Validation accuracy: 0.971\\n\",\n      \"12 Validation accuracy: 0.9668\\n\",\n      \"13 Validation accuracy: 0.97\\n\",\n      \"14 Validation accuracy: 0.9712\\n\",\n      \"15 Validation accuracy: 0.9726\\n\",\n      \"16 Validation accuracy: 0.9718\\n\",\n      \"17 Validation accuracy: 0.971\\n\",\n      \"18 Validation accuracy: 0.9712\\n\",\n      \"19 Validation accuracy: 0.9712\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_new_model_final.ckpt\\\")    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, if you have access to the Python code that built the original graph, you can use it instead of `import_meta_graph()`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 50\\n\",\n    \"n_hidden3 = 50\\n\",\n    \"n_hidden4 = 50\\n\",\n    \"n_hidden5 = 50\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name=\\\"hidden2\\\")\\n\",\n    \"    hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, name=\\\"hidden3\\\")\\n\",\n    \"    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name=\\\"hidden4\\\")\\n\",\n    \"    hidden5 = tf.layers.dense(hidden4, n_hidden5, activation=tf.nn.relu, name=\\\"hidden5\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden5, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"threshold = 1.0\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"grads_and_vars = optimizer.compute_gradients(loss)\\n\",\n    \"capped_gvs = [(tf.clip_by_value(grad, -threshold, threshold), var)\\n\",\n    \"              for grad, var in grads_and_vars]\\n\",\n    \"training_op = optimizer.apply_gradients(capped_gvs)\\n\",\n    \"\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And continue training:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.9642\\n\",\n      \"1 Validation accuracy: 0.963\\n\",\n      \"2 Validation accuracy: 0.9656\\n\",\n      \"3 Validation accuracy: 0.9652\\n\",\n      \"4 Validation accuracy: 0.9642\\n\",\n      \"5 Validation accuracy: 0.965\\n\",\n      \"6 Validation accuracy: 0.9686\\n\",\n      \"7 Validation accuracy: 0.9686\\n\",\n      \"8 Validation accuracy: 0.9684\\n\",\n      \"9 Validation accuracy: 0.9684\\n\",\n      \"10 Validation accuracy: 0.9702\\n\",\n      \"11 Validation accuracy: 0.9716\\n\",\n      \"12 Validation accuracy: 0.9676\\n\",\n      \"13 Validation accuracy: 0.97\\n\",\n      \"14 Validation accuracy: 0.9706\\n\",\n      \"15 Validation accuracy: 0.9724\\n\",\n      \"16 Validation accuracy: 0.972\\n\",\n      \"17 Validation accuracy: 0.9712\\n\",\n      \"18 Validation accuracy: 0.9712\\n\",\n      \"19 Validation accuracy: 0.9708\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_new_model_final.ckpt\\\")    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In general you will want to reuse only the lower layers. If you are using `import_meta_graph()` it will load the whole graph, but you can simply ignore the parts you do not need. In this example, we add a new 4th hidden layer on top of the pretrained 3rd layer (ignoring the old 4th hidden layer). We also build a new output layer, the loss for this new output, and a new optimizer to minimize it. We also need another saver to save the whole graph (containing both the entire old graph plus the new operations), and an initialization operation to initialize all the new variables:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_hidden4 = 20  # new layer\\n\",\n    \"n_outputs = 10  # new layer\\n\",\n    \"\\n\",\n    \"saver = tf.train.import_meta_graph(\\\"./my_model_final.ckpt.meta\\\")\\n\",\n    \"\\n\",\n    \"X = tf.get_default_graph().get_tensor_by_name(\\\"X:0\\\")\\n\",\n    \"y = tf.get_default_graph().get_tensor_by_name(\\\"y:0\\\")\\n\",\n    \"\\n\",\n    \"hidden3 = tf.get_default_graph().get_tensor_by_name(\\\"dnn/hidden3/Relu:0\\\")\\n\",\n    \"\\n\",\n    \"new_hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name=\\\"new_hidden4\\\")\\n\",\n    \"new_logits = tf.layers.dense(new_hidden4, n_outputs, name=\\\"new_outputs\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"new_loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=new_logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"new_eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(new_logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"new_train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"new_saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And we can train this new model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.9126\\n\",\n      \"1 Validation accuracy: 0.9374\\n\",\n      \"2 Validation accuracy: 0.946\\n\",\n      \"3 Validation accuracy: 0.9498\\n\",\n      \"4 Validation accuracy: 0.953\\n\",\n      \"5 Validation accuracy: 0.9528\\n\",\n      \"6 Validation accuracy: 0.9564\\n\",\n      \"7 Validation accuracy: 0.96\\n\",\n      \"8 Validation accuracy: 0.9616\\n\",\n      \"9 Validation accuracy: 0.9612\\n\",\n      \"10 Validation accuracy: 0.9634\\n\",\n      \"11 Validation accuracy: 0.9626\\n\",\n      \"12 Validation accuracy: 0.9648\\n\",\n      \"13 Validation accuracy: 0.9656\\n\",\n      \"14 Validation accuracy: 0.9664\\n\",\n      \"15 Validation accuracy: 0.967\\n\",\n      \"16 Validation accuracy: 0.968\\n\",\n      \"17 Validation accuracy: 0.9678\\n\",\n      \"18 Validation accuracy: 0.9684\\n\",\n      \"19 Validation accuracy: 0.9678\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = new_saver.save(sess, \\\"./my_new_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you have access to the Python code that built the original graph, you can just reuse the parts you need and drop the rest:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300 # reused\\n\",\n    \"n_hidden2 = 50  # reused\\n\",\n    \"n_hidden3 = 50  # reused\\n\",\n    \"n_hidden4 = 20  # new!\\n\",\n    \"n_outputs = 10  # new!\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")       # reused\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name=\\\"hidden2\\\") # reused\\n\",\n    \"    hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, name=\\\"hidden3\\\") # reused\\n\",\n    \"    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name=\\\"hidden4\\\") # new!\\n\",\n    \"    logits = tf.layers.dense(hidden4, n_outputs, name=\\\"outputs\\\")                         # new!\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"However, you must create one `Saver` to restore the pretrained model (giving it the list of variables to restore, or else it will complain that the graphs don't match), and another `Saver` to save the new model, once it is trained:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.9024\\n\",\n      \"1 Validation accuracy: 0.9332\\n\",\n      \"2 Validation accuracy: 0.943\\n\",\n      \"3 Validation accuracy: 0.947\\n\",\n      \"4 Validation accuracy: 0.9516\\n\",\n      \"5 Validation accuracy: 0.9532\\n\",\n      \"6 Validation accuracy: 0.9558\\n\",\n      \"7 Validation accuracy: 0.9592\\n\",\n      \"8 Validation accuracy: 0.9586\\n\",\n      \"9 Validation accuracy: 0.9608\\n\",\n      \"10 Validation accuracy: 0.9626\\n\",\n      \"11 Validation accuracy: 0.962\\n\",\n      \"12 Validation accuracy: 0.964\\n\",\n      \"13 Validation accuracy: 0.9662\\n\",\n      \"14 Validation accuracy: 0.966\\n\",\n      \"15 Validation accuracy: 0.9662\\n\",\n      \"16 Validation accuracy: 0.9672\\n\",\n      \"17 Validation accuracy: 0.9674\\n\",\n      \"18 Validation accuracy: 0.9682\\n\",\n      \"19 Validation accuracy: 0.9678\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\\n\",\n    \"                               scope=\\\"hidden[123]\\\") # regular expression\\n\",\n    \"restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):                                            # not shown in the book\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size): # not shown\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})        # not shown\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})     # not shown\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)                   # not shown\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_new_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reusing Models from Other Frameworks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this example, for each variable we want to reuse, we find its initializer's assignment operation, and we get its second input, which corresponds to the initialization value. When we run the initializer, we replace the initialization values with the ones we want, using a `feed_dict`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 2\\n\",\n    \"n_hidden1 = 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 61.  83. 105.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"original_w = [[1., 2., 3.], [4., 5., 6.]] # Load the weights from the other framework\\n\",\n    \"original_b = [7., 8., 9.]                 # Load the biases from the other framework\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"# [...] Build the rest of the model\\n\",\n    \"\\n\",\n    \"# Get a handle on the assignment nodes for the hidden1 variables\\n\",\n    \"graph = tf.get_default_graph()\\n\",\n    \"assign_kernel = graph.get_operation_by_name(\\\"hidden1/kernel/Assign\\\")\\n\",\n    \"assign_bias = graph.get_operation_by_name(\\\"hidden1/bias/Assign\\\")\\n\",\n    \"init_kernel = assign_kernel.inputs[1]\\n\",\n    \"init_bias = assign_bias.inputs[1]\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init, feed_dict={init_kernel: original_w, init_bias: original_b})\\n\",\n    \"    # [...] Train the model on your new task\\n\",\n    \"    print(hidden1.eval(feed_dict={X: [[10.0, 11.0]]}))  # not shown in the book\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the weights variable created by the `tf.layers.dense()` function is called `\\\"kernel\\\"` (instead of `\\\"weights\\\"` when using the `tf.contrib.layers.fully_connected()`, as in the book), and the biases variable is called `bias` instead of `biases`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Another approach (initially used in the book) would be to create dedicated assignment nodes and dedicated placeholders. This is more verbose and less efficient, but you may find this more explicit:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 61.  83. 105.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 2\\n\",\n    \"n_hidden1 = 3\\n\",\n    \"\\n\",\n    \"original_w = [[1., 2., 3.], [4., 5., 6.]] # Load the weights from the other framework\\n\",\n    \"original_b = [7., 8., 9.]                 # Load the biases from the other framework\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"# [...] Build the rest of the model\\n\",\n    \"\\n\",\n    \"# Get a handle on the variables of layer hidden1\\n\",\n    \"with tf.variable_scope(\\\"\\\", default_name=\\\"\\\", reuse=True):  # root scope\\n\",\n    \"    hidden1_weights = tf.get_variable(\\\"hidden1/kernel\\\")\\n\",\n    \"    hidden1_biases = tf.get_variable(\\\"hidden1/bias\\\")\\n\",\n    \"\\n\",\n    \"# Create dedicated placeholders and assignment nodes\\n\",\n    \"original_weights = tf.placeholder(tf.float32, shape=(n_inputs, n_hidden1))\\n\",\n    \"original_biases = tf.placeholder(tf.float32, shape=n_hidden1)\\n\",\n    \"assign_hidden1_weights = tf.assign(hidden1_weights, original_weights)\\n\",\n    \"assign_hidden1_biases = tf.assign(hidden1_biases, original_biases)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    sess.run(assign_hidden1_weights, feed_dict={original_weights: original_w})\\n\",\n    \"    sess.run(assign_hidden1_biases, feed_dict={original_biases: original_b})\\n\",\n    \"    # [...] Train the model on your new task\\n\",\n    \"    print(hidden1.eval(feed_dict={X: [[10.0, 11.0]]}))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that we could also get a handle on the variables using `get_collection()` and specifying the `scope`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<tf.Variable 'hidden1/kernel:0' shape=(2, 3) dtype=float32_ref>,\\n\",\n       \" <tf.Variable 'hidden1/bias:0' shape=(3,) dtype=float32_ref>]\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\\\"hidden1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Or we could use the graph's `get_tensor_by_name()` method:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'hidden1/kernel:0' shape=(2, 3) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.get_default_graph().get_tensor_by_name(\\\"hidden1/kernel:0\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'hidden1/bias:0' shape=(3,) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 70,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.get_default_graph().get_tensor_by_name(\\\"hidden1/bias:0\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Freezing the Lower Layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300 # reused\\n\",\n    \"n_hidden2 = 50  # reused\\n\",\n    \"n_hidden3 = 50  # reused\\n\",\n    \"n_hidden4 = 20  # new!\\n\",\n    \"n_outputs = 10  # new!\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")       # reused\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name=\\\"hidden2\\\") # reused\\n\",\n    \"    hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, name=\\\"hidden3\\\") # reused\\n\",\n    \"    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name=\\\"hidden4\\\") # new!\\n\",\n    \"    logits = tf.layers.dense(hidden4, n_outputs, name=\\\"outputs\\\")                         # new!\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"train\\\"):                                         # not shown in the book\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)     # not shown\\n\",\n    \"    train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,\\n\",\n    \"                                   scope=\\\"hidden[34]|outputs\\\")\\n\",\n    \"    training_op = optimizer.minimize(loss, var_list=train_vars)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.8964\\n\",\n      \"1 Validation accuracy: 0.9298\\n\",\n      \"2 Validation accuracy: 0.94\\n\",\n      \"3 Validation accuracy: 0.9442\\n\",\n      \"4 Validation accuracy: 0.948\\n\",\n      \"5 Validation accuracy: 0.951\\n\",\n      \"6 Validation accuracy: 0.9508\\n\",\n      \"7 Validation accuracy: 0.9538\\n\",\n      \"8 Validation accuracy: 0.9554\\n\",\n      \"9 Validation accuracy: 0.957\\n\",\n      \"10 Validation accuracy: 0.9562\\n\",\n      \"11 Validation accuracy: 0.9566\\n\",\n      \"12 Validation accuracy: 0.9572\\n\",\n      \"13 Validation accuracy: 0.9578\\n\",\n      \"14 Validation accuracy: 0.959\\n\",\n      \"15 Validation accuracy: 0.9576\\n\",\n      \"16 Validation accuracy: 0.9574\\n\",\n      \"17 Validation accuracy: 0.9602\\n\",\n      \"18 Validation accuracy: 0.9592\\n\",\n      \"19 Validation accuracy: 0.9602\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\\n\",\n    \"                               scope=\\\"hidden[123]\\\") # regular expression\\n\",\n    \"restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_new_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300 # reused\\n\",\n    \"n_hidden2 = 50  # reused\\n\",\n    \"n_hidden3 = 50  # reused\\n\",\n    \"n_hidden4 = 20  # new!\\n\",\n    \"n_outputs = 10  # new!\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden1\\\") # reused frozen\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden2\\\") # reused frozen\\n\",\n    \"    hidden2_stop = tf.stop_gradient(hidden2)\\n\",\n    \"    hidden3 = tf.layers.dense(hidden2_stop, n_hidden3, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden3\\\") # reused, not frozen\\n\",\n    \"    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden4\\\") # new!\\n\",\n    \"    logits = tf.layers.dense(hidden4, n_outputs, name=\\\"outputs\\\") # new!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The training code is exactly the same as earlier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.902\\n\",\n      \"1 Validation accuracy: 0.9302\\n\",\n      \"2 Validation accuracy: 0.9438\\n\",\n      \"3 Validation accuracy: 0.9478\\n\",\n      \"4 Validation accuracy: 0.9514\\n\",\n      \"5 Validation accuracy: 0.9522\\n\",\n      \"6 Validation accuracy: 0.9524\\n\",\n      \"7 Validation accuracy: 0.9556\\n\",\n      \"8 Validation accuracy: 0.9556\\n\",\n      \"9 Validation accuracy: 0.9558\\n\",\n      \"10 Validation accuracy: 0.957\\n\",\n      \"11 Validation accuracy: 0.9552\\n\",\n      \"12 Validation accuracy: 0.9572\\n\",\n      \"13 Validation accuracy: 0.9582\\n\",\n      \"14 Validation accuracy: 0.9582\\n\",\n      \"15 Validation accuracy: 0.957\\n\",\n      \"16 Validation accuracy: 0.9566\\n\",\n      \"17 Validation accuracy: 0.9578\\n\",\n      \"18 Validation accuracy: 0.9594\\n\",\n      \"19 Validation accuracy: 0.958\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\\n\",\n    \"                               scope=\\\"hidden[123]\\\") # regular expression\\n\",\n    \"restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_new_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Caching the Frozen Layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300 # reused\\n\",\n    \"n_hidden2 = 50  # reused\\n\",\n    \"n_hidden3 = 50  # reused\\n\",\n    \"n_hidden4 = 20  # new!\\n\",\n    \"n_outputs = 10  # new!\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden1\\\") # reused frozen\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden2\\\") # reused frozen & cached\\n\",\n    \"    hidden2_stop = tf.stop_gradient(hidden2)\\n\",\n    \"    hidden3 = tf.layers.dense(hidden2_stop, n_hidden3, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden3\\\") # reused, not frozen\\n\",\n    \"    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden4\\\") # new!\\n\",\n    \"    logits = tf.layers.dense(hidden4, n_outputs, name=\\\"outputs\\\") # new!\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reuse_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\\n\",\n    \"                               scope=\\\"hidden[123]\\\") # regular expression\\n\",\n    \"restore_saver = tf.train.Saver(reuse_vars) # to restore layers 1-3\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\\n\",\n      \"0 Validation accuracy: 0.902\\n\",\n      \"1 Validation accuracy: 0.9302\\n\",\n      \"2 Validation accuracy: 0.9438\\n\",\n      \"3 Validation accuracy: 0.9478\\n\",\n      \"4 Validation accuracy: 0.9514\\n\",\n      \"5 Validation accuracy: 0.9522\\n\",\n      \"6 Validation accuracy: 0.9524\\n\",\n      \"7 Validation accuracy: 0.9556\\n\",\n      \"8 Validation accuracy: 0.9556\\n\",\n      \"9 Validation accuracy: 0.9558\\n\",\n      \"10 Validation accuracy: 0.957\\n\",\n      \"11 Validation accuracy: 0.9552\\n\",\n      \"12 Validation accuracy: 0.9572\\n\",\n      \"13 Validation accuracy: 0.9582\\n\",\n      \"14 Validation accuracy: 0.9582\\n\",\n      \"15 Validation accuracy: 0.957\\n\",\n      \"16 Validation accuracy: 0.9566\\n\",\n      \"17 Validation accuracy: 0.9578\\n\",\n      \"18 Validation accuracy: 0.9594\\n\",\n      \"19 Validation accuracy: 0.958\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"n_batches = len(X_train) // batch_size\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_model_final.ckpt\\\")\\n\",\n    \"    \\n\",\n    \"    h2_cache = sess.run(hidden2, feed_dict={X: X_train})\\n\",\n    \"    h2_cache_valid = sess.run(hidden2, feed_dict={X: X_valid}) # not shown in the book\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        shuffled_idx = np.random.permutation(len(X_train))\\n\",\n    \"        hidden2_batches = np.array_split(h2_cache[shuffled_idx], n_batches)\\n\",\n    \"        y_batches = np.array_split(y_train[shuffled_idx], n_batches)\\n\",\n    \"        for hidden2_batch, y_batch in zip(hidden2_batches, y_batches):\\n\",\n    \"            sess.run(training_op, feed_dict={hidden2:hidden2_batch, y:y_batch})\\n\",\n    \"\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={hidden2: h2_cache_valid, # not shown\\n\",\n    \"                                                y: y_valid})             # not shown\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)               # not shown\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_new_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Faster Optimizers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Momentum optimization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,\\n\",\n    \"                                       momentum=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Nesterov Accelerated Gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,\\n\",\n    \"                                       momentum=0.9, use_nesterov=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## AdaGrad\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## RMSProp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate,\\n\",\n    \"                                      momentum=0.9, decay=0.9, epsilon=1e-10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adam Optimization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Learning Rate Scheduling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 50\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name=\\\"hidden2\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"train\\\"):       # not shown in the book\\n\",\n    \"    initial_learning_rate = 0.1\\n\",\n    \"    decay_steps = 10000\\n\",\n    \"    decay_rate = 1/10\\n\",\n    \"    global_step = tf.Variable(0, trainable=False, name=\\\"global_step\\\")\\n\",\n    \"    learning_rate = tf.train.exponential_decay(initial_learning_rate, global_step,\\n\",\n    \"                                               decay_steps, decay_rate)\\n\",\n    \"    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)\\n\",\n    \"    training_op = optimizer.minimize(loss, global_step=global_step)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.959\\n\",\n      \"1 Validation accuracy: 0.9688\\n\",\n      \"2 Validation accuracy: 0.9726\\n\",\n      \"3 Validation accuracy: 0.9804\\n\",\n      \"4 Validation accuracy: 0.982\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 5\\n\",\n    \"batch_size = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Avoiding Overfitting Through Regularization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## $\\\\ell_1$ and $\\\\ell_2$ regularization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's implement $\\\\ell_1$ regularization manually. First, we create the model, as usual (with just one hidden layer this time, for simplicity):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden1, n_outputs, name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we get a handle on the layer weights, and we compute the total loss, which is equal to the sum of the usual cross entropy loss and the $\\\\ell_1$ loss (i.e., the absolute values of the weights):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1 = tf.get_default_graph().get_tensor_by_name(\\\"hidden1/kernel:0\\\")\\n\",\n    \"W2 = tf.get_default_graph().get_tensor_by_name(\\\"outputs/kernel:0\\\")\\n\",\n    \"\\n\",\n    \"scale = 0.001 # l1 regularization hyperparameter\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\\n\",\n    \"                                                              logits=logits)\\n\",\n    \"    base_loss = tf.reduce_mean(xentropy, name=\\\"avg_xentropy\\\")\\n\",\n    \"    reg_losses = tf.reduce_sum(tf.abs(W1)) + tf.reduce_sum(tf.abs(W2))\\n\",\n    \"    loss = tf.add(base_loss, scale * reg_losses, name=\\\"loss\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The rest is just as usual:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.831\\n\",\n      \"1 Validation accuracy: 0.871\\n\",\n      \"2 Validation accuracy: 0.8838\\n\",\n      \"3 Validation accuracy: 0.8934\\n\",\n      \"4 Validation accuracy: 0.8966\\n\",\n      \"5 Validation accuracy: 0.8988\\n\",\n      \"6 Validation accuracy: 0.9016\\n\",\n      \"7 Validation accuracy: 0.9044\\n\",\n      \"8 Validation accuracy: 0.9058\\n\",\n      \"9 Validation accuracy: 0.906\\n\",\n      \"10 Validation accuracy: 0.9068\\n\",\n      \"11 Validation accuracy: 0.9054\\n\",\n      \"12 Validation accuracy: 0.907\\n\",\n      \"13 Validation accuracy: 0.9084\\n\",\n      \"14 Validation accuracy: 0.9088\\n\",\n      \"15 Validation accuracy: 0.9064\\n\",\n      \"16 Validation accuracy: 0.9066\\n\",\n      \"17 Validation accuracy: 0.9066\\n\",\n      \"18 Validation accuracy: 0.9066\\n\",\n      \"19 Validation accuracy: 0.9052\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 200\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, we can pass a regularization function to the `tf.layers.dense()` function, which will use it to create operations that will compute the regularization loss, and it adds these operations to the collection of regularization losses. The beginning is the same as above:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 50\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we will use Python's `partial()` function to avoid repeating the same arguments over and over again. Note that we set the `kernel_regularizer` argument:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"scale = 0.001\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"my_dense_layer = partial(\\n\",\n    \"    tf.layers.dense, activation=tf.nn.relu,\\n\",\n    \"    kernel_regularizer=tf.contrib.layers.l1_regularizer(scale))\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = my_dense_layer(X, n_hidden1, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = my_dense_layer(hidden1, n_hidden2, name=\\\"hidden2\\\")\\n\",\n    \"    logits = my_dense_layer(hidden2, n_outputs, activation=None,\\n\",\n    \"                            name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next we must add the regularization losses to the base loss:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):                                     # not shown in the book\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(  # not shown\\n\",\n    \"        labels=y, logits=logits)                                # not shown\\n\",\n    \"    base_loss = tf.reduce_mean(xentropy, name=\\\"avg_xentropy\\\")   # not shown\\n\",\n    \"    reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\\n\",\n    \"    loss = tf.add_n([base_loss] + reg_losses, name=\\\"loss\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And the rest is the same as usual:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.8274\\n\",\n      \"1 Validation accuracy: 0.8766\\n\",\n      \"2 Validation accuracy: 0.8952\\n\",\n      \"3 Validation accuracy: 0.9016\\n\",\n      \"4 Validation accuracy: 0.908\\n\",\n      \"5 Validation accuracy: 0.9096\\n\",\n      \"6 Validation accuracy: 0.9126\\n\",\n      \"7 Validation accuracy: 0.9154\\n\",\n      \"8 Validation accuracy: 0.9178\\n\",\n      \"9 Validation accuracy: 0.919\\n\",\n      \"10 Validation accuracy: 0.92\\n\",\n      \"11 Validation accuracy: 0.9224\\n\",\n      \"12 Validation accuracy: 0.9212\\n\",\n      \"13 Validation accuracy: 0.9228\\n\",\n      \"14 Validation accuracy: 0.9224\\n\",\n      \"15 Validation accuracy: 0.9216\\n\",\n      \"16 Validation accuracy: 0.9218\\n\",\n      \"17 Validation accuracy: 0.9228\\n\",\n      \"18 Validation accuracy: 0.9216\\n\",\n      \"19 Validation accuracy: 0.9214\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 200\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dropout\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: the book uses `tf.contrib.layers.dropout()` rather than `tf.layers.dropout()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dropout()`, because anything in the contrib module may change or be deleted without notice. The `tf.layers.dropout()` function is almost identical to the `tf.contrib.layers.dropout()` function, except for a few minor differences. Most importantly:\\n\",\n    \"* you must specify the dropout rate (`rate`) rather than the keep probability (`keep_prob`), where `rate` is simply equal to `1 - keep_prob`,\\n\",\n    \"* the `is_training` parameter is renamed to `training`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"training = tf.placeholder_with_default(False, shape=(), name='training')\\n\",\n    \"\\n\",\n    \"dropout_rate = 0.5  # == 1 - keep_prob\\n\",\n    \"X_drop = tf.layers.dropout(X, dropout_rate, training=training)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X_drop, n_hidden1, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden1\\\")\\n\",\n    \"    hidden1_drop = tf.layers.dropout(hidden1, dropout_rate, training=training)\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1_drop, n_hidden2, activation=tf.nn.relu,\\n\",\n    \"                              name=\\\"hidden2\\\")\\n\",\n    \"    hidden2_drop = tf.layers.dropout(hidden2, dropout_rate, training=training)\\n\",\n    \"    logits = tf.layers.dense(hidden2_drop, n_outputs, name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)\\n\",\n    \"    training_op = optimizer.minimize(loss)    \\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"    \\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.9264\\n\",\n      \"1 Validation accuracy: 0.9446\\n\",\n      \"2 Validation accuracy: 0.9488\\n\",\n      \"3 Validation accuracy: 0.9556\\n\",\n      \"4 Validation accuracy: 0.9612\\n\",\n      \"5 Validation accuracy: 0.9598\\n\",\n      \"6 Validation accuracy: 0.9616\\n\",\n      \"7 Validation accuracy: 0.9674\\n\",\n      \"8 Validation accuracy: 0.967\\n\",\n      \"9 Validation accuracy: 0.9706\\n\",\n      \"10 Validation accuracy: 0.9674\\n\",\n      \"11 Validation accuracy: 0.9678\\n\",\n      \"12 Validation accuracy: 0.9698\\n\",\n      \"13 Validation accuracy: 0.97\\n\",\n      \"14 Validation accuracy: 0.971\\n\",\n      \"15 Validation accuracy: 0.9702\\n\",\n      \"16 Validation accuracy: 0.9718\\n\",\n      \"17 Validation accuracy: 0.9716\\n\",\n      \"18 Validation accuracy: 0.9734\\n\",\n      \"19 Validation accuracy: 0.972\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch, training: True})\\n\",\n    \"        accuracy_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", accuracy_val)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Max norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's go back to a plain and simple neural net for MNIST with just 2 hidden layers:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 50\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"momentum = 0.9\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name=\\\"hidden2\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum)\\n\",\n    \"    training_op = optimizer.minimize(loss)    \\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, let's get a handle on the first hidden layer's weight and create an operation that will compute the clipped weights using the `clip_by_norm()` function. Then we create an assignment operation to assign the clipped weights to the weights variable:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"threshold = 1.0\\n\",\n    \"weights = tf.get_default_graph().get_tensor_by_name(\\\"hidden1/kernel:0\\\")\\n\",\n    \"clipped_weights = tf.clip_by_norm(weights, clip_norm=threshold, axes=1)\\n\",\n    \"clip_weights = tf.assign(weights, clipped_weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can do this as well for the second hidden layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"weights2 = tf.get_default_graph().get_tensor_by_name(\\\"hidden2/kernel:0\\\")\\n\",\n    \"clipped_weights2 = tf.clip_by_norm(weights2, clip_norm=threshold, axes=1)\\n\",\n    \"clip_weights2 = tf.assign(weights2, clipped_weights2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's add an initializer and a saver:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now we can train the model. It's pretty much as usual, except that right after running the `training_op`, we run the `clip_weights` and `clip_weights2` operations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.9568\\n\",\n      \"1 Validation accuracy: 0.9696\\n\",\n      \"2 Validation accuracy: 0.972\\n\",\n      \"3 Validation accuracy: 0.9768\\n\",\n      \"4 Validation accuracy: 0.9784\\n\",\n      \"5 Validation accuracy: 0.9786\\n\",\n      \"6 Validation accuracy: 0.9816\\n\",\n      \"7 Validation accuracy: 0.9808\\n\",\n      \"8 Validation accuracy: 0.981\\n\",\n      \"9 Validation accuracy: 0.983\\n\",\n      \"10 Validation accuracy: 0.9822\\n\",\n      \"11 Validation accuracy: 0.9854\\n\",\n      \"12 Validation accuracy: 0.9822\\n\",\n      \"13 Validation accuracy: 0.9842\\n\",\n      \"14 Validation accuracy: 0.984\\n\",\n      \"15 Validation accuracy: 0.9852\\n\",\n      \"16 Validation accuracy: 0.984\\n\",\n      \"17 Validation accuracy: 0.9844\\n\",\n      \"18 Validation accuracy: 0.9844\\n\",\n      \"19 Validation accuracy: 0.9844\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:                                              # not shown in the book\\n\",\n    \"    init.run()                                                          # not shown\\n\",\n    \"    for epoch in range(n_epochs):                                       # not shown\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size): # not shown\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"            clip_weights.eval()\\n\",\n    \"            clip_weights2.eval()                                        # not shown\\n\",\n    \"        acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})   # not shown\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", acc_valid)                 # not shown\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")               # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The implementation above is straightforward and it works fine, but it is a bit messy. A better approach is to define a `max_norm_regularizer()` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def max_norm_regularizer(threshold, axes=1, name=\\\"max_norm\\\",\\n\",\n    \"                         collection=\\\"max_norm\\\"):\\n\",\n    \"    def max_norm(weights):\\n\",\n    \"        clipped = tf.clip_by_norm(weights, clip_norm=threshold, axes=axes)\\n\",\n    \"        clip_weights = tf.assign(weights, clipped, name=name)\\n\",\n    \"        tf.add_to_collection(collection, clip_weights)\\n\",\n    \"        return None # there is no regularization loss term\\n\",\n    \"    return max_norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then you can call this function to get a max norm regularizer (with the threshold you want). When you create a hidden layer, you can pass this regularizer to the `kernel_regularizer` argument:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28\\n\",\n    \"n_hidden1 = 300\\n\",\n    \"n_hidden2 = 50\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"momentum = 0.9\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"max_norm_reg = max_norm_regularizer(threshold=1.0)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n    \"    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu,\\n\",\n    \"                              kernel_regularizer=max_norm_reg, name=\\\"hidden1\\\")\\n\",\n    \"    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu,\\n\",\n    \"                              kernel_regularizer=max_norm_reg, name=\\\"hidden2\\\")\\n\",\n    \"    logits = tf.layers.dense(hidden2, n_outputs, name=\\\"outputs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.name_scope(\\\"loss\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"    loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum)\\n\",\n    \"    training_op = optimizer.minimize(loss)    \\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Training is as usual, except you must run the weights clipping operations after each training operation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_epochs = 20\\n\",\n    \"batch_size = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Validation accuracy: 0.9556\\n\",\n      \"1 Validation accuracy: 0.9698\\n\",\n      \"2 Validation accuracy: 0.9726\\n\",\n      \"3 Validation accuracy: 0.9744\\n\",\n      \"4 Validation accuracy: 0.9762\\n\",\n      \"5 Validation accuracy: 0.9772\\n\",\n      \"6 Validation accuracy: 0.979\\n\",\n      \"7 Validation accuracy: 0.9816\\n\",\n      \"8 Validation accuracy: 0.9814\\n\",\n      \"9 Validation accuracy: 0.9812\\n\",\n      \"10 Validation accuracy: 0.9818\\n\",\n      \"11 Validation accuracy: 0.9816\\n\",\n      \"12 Validation accuracy: 0.9802\\n\",\n      \"13 Validation accuracy: 0.9822\\n\",\n      \"14 Validation accuracy: 0.982\\n\",\n      \"15 Validation accuracy: 0.9812\\n\",\n      \"16 Validation accuracy: 0.9824\\n\",\n      \"17 Validation accuracy: 0.9836\\n\",\n      \"18 Validation accuracy: 0.9824\\n\",\n      \"19 Validation accuracy: 0.9826\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clip_all_weights = tf.get_collection(\\\"max_norm\\\")\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"            sess.run(clip_all_weights)\\n\",\n    \"        acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid}) # not shown\\n\",\n    \"        print(epoch, \\\"Validation accuracy:\\\", acc_valid)               # not shown\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")             # not shown\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 7.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8. Deep Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: Build a DNN with five hidden layers of 100 neurons each, He initialization, and the ELU activation function._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will need similar DNNs in the next exercises, so let's create a function to build this DNN:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"he_init = tf.variance_scaling_initializer()\\n\",\n    \"\\n\",\n    \"def dnn(inputs, n_hidden_layers=5, n_neurons=100, name=None,\\n\",\n    \"        activation=tf.nn.elu, initializer=he_init):\\n\",\n    \"    with tf.variable_scope(name, \\\"dnn\\\"):\\n\",\n    \"        for layer in range(n_hidden_layers):\\n\",\n    \"            inputs = tf.layers.dense(inputs, n_neurons, activation=activation,\\n\",\n    \"                                     kernel_initializer=initializer,\\n\",\n    \"                                     name=\\\"hidden%d\\\" % (layer + 1))\\n\",\n    \"        return inputs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = 28 * 28 # MNIST\\n\",\n    \"n_outputs = 5\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"dnn_outputs = dnn(X)\\n\",\n    \"\\n\",\n    \"logits = tf.layers.dense(dnn_outputs, n_outputs, kernel_initializer=he_init, name=\\\"logits\\\")\\n\",\n    \"Y_proba = tf.nn.softmax(logits, name=\\\"Y_proba\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: Using Adam optimization and early stopping, try training it on MNIST but only on digits 0 to 4, as we will use transfer learning for digits 5 to 9 in the next exercise. You will need a softmax output layer with five neurons, and as always make sure to save checkpoints at regular intervals and save the final model so you can reuse it later._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's complete the graph with the cost function, the training op, and all the other usual components:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate)\\n\",\n    \"training_op = optimizer.minimize(loss, name=\\\"training_op\\\")\\n\",\n    \"\\n\",\n    \"correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's create the training set, validation and test set (we need the validation set to implement early stopping):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train1 = X_train[y_train < 5]\\n\",\n    \"y_train1 = y_train[y_train < 5]\\n\",\n    \"X_valid1 = X_valid[y_valid < 5]\\n\",\n    \"y_valid1 = y_valid[y_valid < 5]\\n\",\n    \"X_test1 = X_test[y_test < 5]\\n\",\n    \"y_test1 = y_test[y_test < 5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.116407\\tBest loss: 0.116407\\tAccuracy: 97.58%\\n\",\n      \"1\\tValidation loss: 0.180534\\tBest loss: 0.116407\\tAccuracy: 97.11%\\n\",\n      \"2\\tValidation loss: 0.227535\\tBest loss: 0.116407\\tAccuracy: 93.86%\\n\",\n      \"3\\tValidation loss: 0.107346\\tBest loss: 0.107346\\tAccuracy: 97.54%\\n\",\n      \"4\\tValidation loss: 0.302668\\tBest loss: 0.107346\\tAccuracy: 95.35%\\n\",\n      \"5\\tValidation loss: 1.631054\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"6\\tValidation loss: 1.635262\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"7\\tValidation loss: 1.671200\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"8\\tValidation loss: 1.695277\\tBest loss: 0.107346\\tAccuracy: 19.27%\\n\",\n      \"9\\tValidation loss: 1.744607\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"10\\tValidation loss: 1.629857\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"11\\tValidation loss: 1.810803\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"12\\tValidation loss: 1.675703\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"13\\tValidation loss: 1.633233\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"14\\tValidation loss: 1.652905\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"15\\tValidation loss: 1.635937\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"16\\tValidation loss: 1.718919\\tBest loss: 0.107346\\tAccuracy: 19.08%\\n\",\n      \"17\\tValidation loss: 1.682458\\tBest loss: 0.107346\\tAccuracy: 19.27%\\n\",\n      \"18\\tValidation loss: 1.675366\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"19\\tValidation loss: 1.645800\\tBest loss: 0.107346\\tAccuracy: 19.08%\\n\",\n      \"20\\tValidation loss: 1.722334\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"21\\tValidation loss: 1.656418\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"22\\tValidation loss: 1.643529\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"23\\tValidation loss: 1.644233\\tBest loss: 0.107346\\tAccuracy: 19.27%\\n\",\n      \"Early stopping!\\n\",\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_0_to_4.ckpt\\n\",\n      \"Final test accuracy: 97.26%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"checks_without_progress = 0\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train1))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train1) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train1[rnd_indices], y_train1[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val, acc_val = sess.run([loss, accuracy], feed_dict={X: X_valid1, y: y_valid1})\\n\",\n    \"        if loss_val < best_loss:\\n\",\n    \"            save_path = saver.save(sess, \\\"./my_mnist_model_0_to_4.ckpt\\\")\\n\",\n    \"            best_loss = loss_val\\n\",\n    \"            checks_without_progress = 0\\n\",\n    \"        else:\\n\",\n    \"            checks_without_progress += 1\\n\",\n    \"            if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                print(\\\"Early stopping!\\\")\\n\",\n    \"                break\\n\",\n    \"        print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"            epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"./my_mnist_model_0_to_4.ckpt\\\")\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test1, y: y_test1})\\n\",\n    \"    print(\\\"Final test accuracy: {:.2f}%\\\".format(acc_test * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This test accuracy is not too bad, but let's see if we can do better by tuning the hyperparameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.3.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: Tune the hyperparameters using cross-validation and see what precision you can achieve._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a `DNNClassifier` class, compatible with Scikit-Learn's `RandomizedSearchCV` class, to perform hyperparameter tuning. Here are the key points of this implementation:\\n\",\n    \"* the `__init__()` method (constructor) does nothing more than create instance variables for each of the hyperparameters.\\n\",\n    \"* the `fit()` method creates the graph, starts a session and trains the model:\\n\",\n    \"  * it calls the `_build_graph()` method to build the graph (much lile the graph we defined earlier). Once this method is done creating the graph, it saves all the important operations as instance variables for easy access by other methods.\\n\",\n    \"  * the `_dnn()` method builds the hidden layers, just like the `dnn()` function above, but also with support for batch normalization and dropout (for the next exercises).\\n\",\n    \"  * if the `fit()` method is given a validation set (`X_valid` and `y_valid`), then it implements early stopping. This implementation does not save the best model to disk, but rather to memory: it uses the `_get_model_params()` method to get all the graph's variables and their values, and the `_restore_model_params()` method to restore the variable values (of the best model found). This trick helps speed up training.\\n\",\n    \"  * After the `fit()` method has finished training the model, it keeps the session open so that predictions can be made quickly, without having to save a model to disk and restore it for every prediction. You can close the session by calling the `close_session()` method.\\n\",\n    \"* the `predict_proba()` method uses the trained model to predict the class probabilities.\\n\",\n    \"* the `predict()` method calls `predict_proba()` and returns the class with the highest probability, for each instance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.base import BaseEstimator, ClassifierMixin\\n\",\n    \"from sklearn.exceptions import NotFittedError\\n\",\n    \"\\n\",\n    \"class DNNClassifier(BaseEstimator, ClassifierMixin):\\n\",\n    \"    def __init__(self, n_hidden_layers=5, n_neurons=100, optimizer_class=tf.train.AdamOptimizer,\\n\",\n    \"                 learning_rate=0.01, batch_size=20, activation=tf.nn.elu, initializer=he_init,\\n\",\n    \"                 batch_norm_momentum=None, dropout_rate=None, random_state=None):\\n\",\n    \"        \\\"\\\"\\\"Initialize the DNNClassifier by simply storing all the hyperparameters.\\\"\\\"\\\"\\n\",\n    \"        self.n_hidden_layers = n_hidden_layers\\n\",\n    \"        self.n_neurons = n_neurons\\n\",\n    \"        self.optimizer_class = optimizer_class\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.batch_size = batch_size\\n\",\n    \"        self.activation = activation\\n\",\n    \"        self.initializer = initializer\\n\",\n    \"        self.batch_norm_momentum = batch_norm_momentum\\n\",\n    \"        self.dropout_rate = dropout_rate\\n\",\n    \"        self.random_state = random_state\\n\",\n    \"        self._session = None\\n\",\n    \"\\n\",\n    \"    def _dnn(self, inputs):\\n\",\n    \"        \\\"\\\"\\\"Build the hidden layers, with support for batch normalization and dropout.\\\"\\\"\\\"\\n\",\n    \"        for layer in range(self.n_hidden_layers):\\n\",\n    \"            if self.dropout_rate:\\n\",\n    \"                inputs = tf.layers.dropout(inputs, self.dropout_rate, training=self._training)\\n\",\n    \"            inputs = tf.layers.dense(inputs, self.n_neurons,\\n\",\n    \"                                     kernel_initializer=self.initializer,\\n\",\n    \"                                     name=\\\"hidden%d\\\" % (layer + 1))\\n\",\n    \"            if self.batch_norm_momentum:\\n\",\n    \"                inputs = tf.layers.batch_normalization(inputs, momentum=self.batch_norm_momentum,\\n\",\n    \"                                                       training=self._training)\\n\",\n    \"            inputs = self.activation(inputs, name=\\\"hidden%d_out\\\" % (layer + 1))\\n\",\n    \"        return inputs\\n\",\n    \"\\n\",\n    \"    def _build_graph(self, n_inputs, n_outputs):\\n\",\n    \"        \\\"\\\"\\\"Build the same model as earlier\\\"\\\"\\\"\\n\",\n    \"        if self.random_state is not None:\\n\",\n    \"            tf.set_random_seed(self.random_state)\\n\",\n    \"            np.random.seed(self.random_state)\\n\",\n    \"\\n\",\n    \"        X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"        y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"        if self.batch_norm_momentum or self.dropout_rate:\\n\",\n    \"            self._training = tf.placeholder_with_default(False, shape=(), name='training')\\n\",\n    \"        else:\\n\",\n    \"            self._training = None\\n\",\n    \"\\n\",\n    \"        dnn_outputs = self._dnn(X)\\n\",\n    \"\\n\",\n    \"        logits = tf.layers.dense(dnn_outputs, n_outputs, kernel_initializer=he_init, name=\\\"logits\\\")\\n\",\n    \"        Y_proba = tf.nn.softmax(logits, name=\\\"Y_proba\\\")\\n\",\n    \"\\n\",\n    \"        xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\\n\",\n    \"                                                                  logits=logits)\\n\",\n    \"        loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"        optimizer = self.optimizer_class(learning_rate=self.learning_rate)\\n\",\n    \"        training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"        correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"        accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"        init = tf.global_variables_initializer()\\n\",\n    \"        saver = tf.train.Saver()\\n\",\n    \"\\n\",\n    \"        # Make the important operations available easily through instance variables\\n\",\n    \"        self._X, self._y = X, y\\n\",\n    \"        self._Y_proba, self._loss = Y_proba, loss\\n\",\n    \"        self._training_op, self._accuracy = training_op, accuracy\\n\",\n    \"        self._init, self._saver = init, saver\\n\",\n    \"\\n\",\n    \"    def close_session(self):\\n\",\n    \"        if self._session:\\n\",\n    \"            self._session.close()\\n\",\n    \"\\n\",\n    \"    def _get_model_params(self):\\n\",\n    \"        \\\"\\\"\\\"Get all variable values (used for early stopping, faster than saving to disk)\\\"\\\"\\\"\\n\",\n    \"        with self._graph.as_default():\\n\",\n    \"            gvars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\\n\",\n    \"        return {gvar.op.name: value for gvar, value in zip(gvars, self._session.run(gvars))}\\n\",\n    \"\\n\",\n    \"    def _restore_model_params(self, model_params):\\n\",\n    \"        \\\"\\\"\\\"Set all variables to the given values (for early stopping, faster than loading from disk)\\\"\\\"\\\"\\n\",\n    \"        gvar_names = list(model_params.keys())\\n\",\n    \"        assign_ops = {gvar_name: self._graph.get_operation_by_name(gvar_name + \\\"/Assign\\\")\\n\",\n    \"                      for gvar_name in gvar_names}\\n\",\n    \"        init_values = {gvar_name: assign_op.inputs[1] for gvar_name, assign_op in assign_ops.items()}\\n\",\n    \"        feed_dict = {init_values[gvar_name]: model_params[gvar_name] for gvar_name in gvar_names}\\n\",\n    \"        self._session.run(assign_ops, feed_dict=feed_dict)\\n\",\n    \"\\n\",\n    \"    def fit(self, X, y, n_epochs=100, X_valid=None, y_valid=None):\\n\",\n    \"        \\\"\\\"\\\"Fit the model to the training set. If X_valid and y_valid are provided, use early stopping.\\\"\\\"\\\"\\n\",\n    \"        self.close_session()\\n\",\n    \"\\n\",\n    \"        # infer n_inputs and n_outputs from the training set.\\n\",\n    \"        n_inputs = X.shape[1]\\n\",\n    \"        self.classes_ = np.unique(y)\\n\",\n    \"        n_outputs = len(self.classes_)\\n\",\n    \"        \\n\",\n    \"        # Translate the labels vector to a vector of sorted class indices, containing\\n\",\n    \"        # integers from 0 to n_outputs - 1.\\n\",\n    \"        # For example, if y is equal to [8, 8, 9, 5, 7, 6, 6, 6], then the sorted class\\n\",\n    \"        # labels (self.classes_) will be equal to [5, 6, 7, 8, 9], and the labels vector\\n\",\n    \"        # will be translated to [3, 3, 4, 0, 2, 1, 1, 1]\\n\",\n    \"        self.class_to_index_ = {label: index\\n\",\n    \"                                for index, label in enumerate(self.classes_)}\\n\",\n    \"        y = np.array([self.class_to_index_[label]\\n\",\n    \"                      for label in y], dtype=np.int32)\\n\",\n    \"        \\n\",\n    \"        self._graph = tf.Graph()\\n\",\n    \"        with self._graph.as_default():\\n\",\n    \"            self._build_graph(n_inputs, n_outputs)\\n\",\n    \"            # extra ops for batch normalization\\n\",\n    \"            extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\\n\",\n    \"\\n\",\n    \"        # needed in case of early stopping\\n\",\n    \"        max_checks_without_progress = 20\\n\",\n    \"        checks_without_progress = 0\\n\",\n    \"        best_loss = np.infty\\n\",\n    \"        best_params = None\\n\",\n    \"        \\n\",\n    \"        # Now train the model!\\n\",\n    \"        self._session = tf.Session(graph=self._graph)\\n\",\n    \"        with self._session.as_default() as sess:\\n\",\n    \"            self._init.run()\\n\",\n    \"            for epoch in range(n_epochs):\\n\",\n    \"                rnd_idx = np.random.permutation(len(X))\\n\",\n    \"                for rnd_indices in np.array_split(rnd_idx, len(X) // self.batch_size):\\n\",\n    \"                    X_batch, y_batch = X[rnd_indices], y[rnd_indices]\\n\",\n    \"                    feed_dict = {self._X: X_batch, self._y: y_batch}\\n\",\n    \"                    if self._training is not None:\\n\",\n    \"                        feed_dict[self._training] = True\\n\",\n    \"                    sess.run(self._training_op, feed_dict=feed_dict)\\n\",\n    \"                    if extra_update_ops:\\n\",\n    \"                        sess.run(extra_update_ops, feed_dict=feed_dict)\\n\",\n    \"                if X_valid is not None and y_valid is not None:\\n\",\n    \"                    loss_val, acc_val = sess.run([self._loss, self._accuracy],\\n\",\n    \"                                                 feed_dict={self._X: X_valid,\\n\",\n    \"                                                            self._y: y_valid})\\n\",\n    \"                    if loss_val < best_loss:\\n\",\n    \"                        best_params = self._get_model_params()\\n\",\n    \"                        best_loss = loss_val\\n\",\n    \"                        checks_without_progress = 0\\n\",\n    \"                    else:\\n\",\n    \"                        checks_without_progress += 1\\n\",\n    \"                    print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"                        epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"                    if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                        print(\\\"Early stopping!\\\")\\n\",\n    \"                        break\\n\",\n    \"                else:\\n\",\n    \"                    loss_train, acc_train = sess.run([self._loss, self._accuracy],\\n\",\n    \"                                                     feed_dict={self._X: X_batch,\\n\",\n    \"                                                                self._y: y_batch})\\n\",\n    \"                    print(\\\"{}\\\\tLast training batch loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"                        epoch, loss_train, acc_train * 100))\\n\",\n    \"            # If we used early stopping then rollback to the best model found\\n\",\n    \"            if best_params:\\n\",\n    \"                self._restore_model_params(best_params)\\n\",\n    \"            return self\\n\",\n    \"\\n\",\n    \"    def predict_proba(self, X):\\n\",\n    \"        if not self._session:\\n\",\n    \"            raise NotFittedError(\\\"This %s instance is not fitted yet\\\" % self.__class__.__name__)\\n\",\n    \"        with self._session.as_default() as sess:\\n\",\n    \"            return self._Y_proba.eval(feed_dict={self._X: X})\\n\",\n    \"\\n\",\n    \"    def predict(self, X):\\n\",\n    \"        class_indices = np.argmax(self.predict_proba(X), axis=1)\\n\",\n    \"        return np.array([[self.classes_[class_index]]\\n\",\n    \"                         for class_index in class_indices], np.int32)\\n\",\n    \"\\n\",\n    \"    def save(self, path):\\n\",\n    \"        self._saver.save(self._session, path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's see if we get the exact same accuracy as earlier using this class (without dropout or batch norm):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.116407\\tBest loss: 0.116407\\tAccuracy: 97.58%\\n\",\n      \"1\\tValidation loss: 0.180534\\tBest loss: 0.116407\\tAccuracy: 97.11%\\n\",\n      \"2\\tValidation loss: 0.227535\\tBest loss: 0.116407\\tAccuracy: 93.86%\\n\",\n      \"3\\tValidation loss: 0.107346\\tBest loss: 0.107346\\tAccuracy: 97.54%\\n\",\n      \"4\\tValidation loss: 0.302668\\tBest loss: 0.107346\\tAccuracy: 95.35%\\n\",\n      \"5\\tValidation loss: 1.631054\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"6\\tValidation loss: 1.635262\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"7\\tValidation loss: 1.671200\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"8\\tValidation loss: 1.695277\\tBest loss: 0.107346\\tAccuracy: 19.27%\\n\",\n      \"9\\tValidation loss: 1.744607\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"10\\tValidation loss: 1.629857\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"11\\tValidation loss: 1.810803\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"12\\tValidation loss: 1.675703\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"13\\tValidation loss: 1.633233\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"14\\tValidation loss: 1.652905\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"15\\tValidation loss: 1.635937\\tBest loss: 0.107346\\tAccuracy: 20.91%\\n\",\n      \"16\\tValidation loss: 1.718919\\tBest loss: 0.107346\\tAccuracy: 19.08%\\n\",\n      \"17\\tValidation loss: 1.682458\\tBest loss: 0.107346\\tAccuracy: 19.27%\\n\",\n      \"18\\tValidation loss: 1.675366\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"19\\tValidation loss: 1.645800\\tBest loss: 0.107346\\tAccuracy: 19.08%\\n\",\n      \"20\\tValidation loss: 1.722334\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"21\\tValidation loss: 1.656418\\tBest loss: 0.107346\\tAccuracy: 22.01%\\n\",\n      \"22\\tValidation loss: 1.643529\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"23\\tValidation loss: 1.644233\\tBest loss: 0.107346\\tAccuracy: 19.27%\\n\",\n      \"24\\tValidation loss: 1.690035\\tBest loss: 0.107346\\tAccuracy: 18.73%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DNNClassifier(activation=<function elu at 0x1243639d8>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42)\"\n      ]\n     },\n     \"execution_count\": 122,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn_clf = DNNClassifier(random_state=42)\\n\",\n    \"dnn_clf.fit(X_train1, y_train1, n_epochs=1000, X_valid=X_valid1, y_valid=y_valid1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model is trained, let's see if it gets the same accuracy as earlier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 123,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9725627553998832\"\n      ]\n     },\n     \"execution_count\": 123,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"\\n\",\n    \"y_pred = dnn_clf.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Yep! Working fine. Now we can use Scikit-Learn's `RandomizedSearchCV` class to search for better hyperparameters (this may take over an hour, depending on your system):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 50 candidates, totalling 150 fits\\n\",\n      \"[CV] n_neurons=10, learning_rate=0.05, batch_size=100, activation=<function elu at 0x1243639d8> \\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.143224\\tBest loss: 0.143224\\tAccuracy: 95.82%\\n\",\n      \"1\\tValidation loss: 0.143304\\tBest loss: 0.143224\\tAccuracy: 96.60%\\n\",\n      \"2\\tValidation loss: 0.106488\\tBest loss: 0.106488\\tAccuracy: 96.95%\\n\",\n      \"3\\tValidation loss: 0.307107\\tBest loss: 0.106488\\tAccuracy: 92.34%\\n\",\n      \"4\\tValidation loss: 0.157948\\tBest loss: 0.106488\\tAccuracy: 95.50%\\n\",\n      \"5\\tValidation loss: 0.131002\\tBest loss: 0.106488\\tAccuracy: 96.40%\\n\",\n      \"6\\tValidation loss: 0.931847\\tBest loss: 0.106488\\tAccuracy: 58.29%\\n\",\n      \"7\\tValidation loss: 0.872748\\tBest loss: 0.106488\\tAccuracy: 57.97%\\n\",\n      \"8\\tValidation loss: 0.699336\\tBest loss: 0.106488\\tAccuracy: 58.29%\\n\",\n      \"9\\tValidation loss: 0.853343\\tBest loss: 0.106488\\tAccuracy: 57.27%\\n\",\n      \"10\\tValidation loss: 0.738493\\tBest loss: 0.106488\\tAccuracy: 59.19%\\n\",\n      \"11\\tValidation loss: 0.670431\\tBest loss: 0.106488\\tAccuracy: 59.23%\\n\",\n      \"12\\tValidation loss: 0.717334\\tBest loss: 0.106488\\tAccuracy: 59.11%\\n\",\n      \"13\\tValidation loss: 0.718714\\tBest loss: 0.106488\\tAccuracy: 56.57%\\n\",\n      \"14\\tValidation loss: 0.679313\\tBest loss: 0.106488\\tAccuracy: 59.07%\\n\",\n      \"15\\tValidation loss: 0.732966\\tBest loss: 0.106488\\tAccuracy: 58.41%\\n\",\n      \"16\\tValidation loss: 0.666333\\tBest loss: 0.106488\\tAccuracy: 60.48%\\n\",\n      \"17\\tValidation loss: 0.677045\\tBest loss: 0.106488\\tAccuracy: 61.18%\\n\",\n      \"18\\tValidation loss: 0.666103\\tBest loss: 0.106488\\tAccuracy: 59.97%\\n\",\n      \"19\\tValidation loss: 0.710005\\tBest loss: 0.106488\\tAccuracy: 63.21%\\n\",\n      \"20\\tValidation loss: 1.037921\\tBest loss: 0.106488\\tAccuracy: 64.03%\\n\",\n      \"21\\tValidation loss: 1.626959\\tBest loss: 0.106488\\tAccuracy: 19.27%\\n\",\n      \"22\\tValidation loss: 1.615710\\tBest loss: 0.106488\\tAccuracy: 18.73%\\n\",\n      \"23\\tValidation loss: 1.609028\\tBest loss: 0.106488\\tAccuracy: 20.91%\\n\",\n      \"Early stopping!\\n\",\n      \"[CV]  n_neurons=10, learning_rate=0.05, batch_size=100, activation=<function elu at 0x1243639d8>, total=   4.7s\\n\",\n      \"[CV] n_neurons=10, learning_rate=0.05, batch_size=100, activation=<function elu at 0x1243639d8> \\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.8s remaining:    0.0s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.137274\\tBest loss: 0.137274\\tAccuracy: 96.33%\\n\",\n      \"1\\tValidation loss: 0.145733\\tBest loss: 0.137274\\tAccuracy: 95.97%\\n\",\n      \"2\\tValidation loss: 0.171077\\tBest loss: 0.137274\\tAccuracy: 95.90%\\n\",\n      \"3\\tValidation loss: 0.139310\\tBest loss: 0.137274\\tAccuracy: 96.79%\\n\",\n      \"<<5140 more lines>>\\n\",\n      \"51\\tValidation loss: 0.400818\\tBest loss: 0.265362\\tAccuracy: 97.11%\\n\",\n      \"52\\tValidation loss: 0.509595\\tBest loss: 0.265362\\tAccuracy: 96.99%\\n\",\n      \"Early stopping!\\n\",\n      \"[CV]  n_neurons=90, learning_rate=0.1, batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x13eb49f28>, total=  11.3s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 46.9min finished\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.069587\\tBest loss: 0.069587\\tAccuracy: 98.12%\\n\",\n      \"1\\tValidation loss: 0.045462\\tBest loss: 0.045462\\tAccuracy: 98.48%\\n\",\n      \"2\\tValidation loss: 0.046439\\tBest loss: 0.045462\\tAccuracy: 98.40%\\n\",\n      \"3\\tValidation loss: 0.037278\\tBest loss: 0.037278\\tAccuracy: 98.59%\\n\",\n      \"4\\tValidation loss: 0.039989\\tBest loss: 0.037278\\tAccuracy: 98.51%\\n\",\n      \"5\\tValidation loss: 0.039621\\tBest loss: 0.037278\\tAccuracy: 98.79%\\n\",\n      \"6\\tValidation loss: 0.035959\\tBest loss: 0.035959\\tAccuracy: 99.06%\\n\",\n      \"7\\tValidation loss: 0.033321\\tBest loss: 0.033321\\tAccuracy: 99.06%\\n\",\n      \"8\\tValidation loss: 0.044559\\tBest loss: 0.033321\\tAccuracy: 98.87%\\n\",\n      \"9\\tValidation loss: 0.035999\\tBest loss: 0.033321\\tAccuracy: 99.10%\\n\",\n      \"10\\tValidation loss: 0.042629\\tBest loss: 0.033321\\tAccuracy: 98.98%\\n\",\n      \"11\\tValidation loss: 0.059839\\tBest loss: 0.033321\\tAccuracy: 98.71%\\n\",\n      \"12\\tValidation loss: 0.044683\\tBest loss: 0.033321\\tAccuracy: 98.87%\\n\",\n      \"13\\tValidation loss: 0.051294\\tBest loss: 0.033321\\tAccuracy: 98.75%\\n\",\n      \"14\\tValidation loss: 0.050140\\tBest loss: 0.033321\\tAccuracy: 98.98%\\n\",\n      \"15\\tValidation loss: 0.051109\\tBest loss: 0.033321\\tAccuracy: 98.79%\\n\",\n      \"16\\tValidation loss: 0.072444\\tBest loss: 0.033321\\tAccuracy: 97.97%\\n\",\n      \"17\\tValidation loss: 0.063308\\tBest loss: 0.033321\\tAccuracy: 98.71%\\n\",\n      \"18\\tValidation loss: 0.051853\\tBest loss: 0.033321\\tAccuracy: 98.87%\\n\",\n      \"19\\tValidation loss: 0.058982\\tBest loss: 0.033321\\tAccuracy: 98.91%\\n\",\n      \"20\\tValidation loss: 0.046894\\tBest loss: 0.033321\\tAccuracy: 99.06%\\n\",\n      \"21\\tValidation loss: 0.039036\\tBest loss: 0.033321\\tAccuracy: 99.02%\\n\",\n      \"22\\tValidation loss: 0.057221\\tBest loss: 0.033321\\tAccuracy: 98.32%\\n\",\n      \"23\\tValidation loss: 0.054618\\tBest loss: 0.033321\\tAccuracy: 98.75%\\n\",\n      \"24\\tValidation loss: 0.039252\\tBest loss: 0.033321\\tAccuracy: 99.14%\\n\",\n      \"25\\tValidation loss: 0.111809\\tBest loss: 0.033321\\tAccuracy: 98.05%\\n\",\n      \"26\\tValidation loss: 0.060662\\tBest loss: 0.033321\\tAccuracy: 98.98%\\n\",\n      \"27\\tValidation loss: 0.073774\\tBest loss: 0.033321\\tAccuracy: 99.02%\\n\",\n      \"28\\tValidation loss: 0.048667\\tBest loss: 0.033321\\tAccuracy: 99.18%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\\n\",\n       \"          estimator=DNNClassifier(activation=<function elu at 0x1243639d8>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42),\\n\",\n       \"          fit_params=None, iid='warn', n_iter=50, n_jobs=None,\\n\",\n       \"          param_distributions={'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160], 'batch_size': [10, 50, 100, 500], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x124366d08>, <function elu at 0x1243639d8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x133807c80>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x13eb49f28>]},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=42, refit=True,\\n\",\n       \"          return_train_score='warn', scoring=None, verbose=2)\"\n      ]\n     },\n     \"execution_count\": 124,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"\\n\",\n    \"def leaky_relu(alpha=0.01):\\n\",\n    \"    def parametrized_leaky_relu(z, name=None):\\n\",\n    \"        return tf.maximum(alpha * z, z, name=name)\\n\",\n    \"    return parametrized_leaky_relu\\n\",\n    \"\\n\",\n    \"param_distribs = {\\n\",\n    \"    \\\"n_neurons\\\": [10, 30, 50, 70, 90, 100, 120, 140, 160],\\n\",\n    \"    \\\"batch_size\\\": [10, 50, 100, 500],\\n\",\n    \"    \\\"learning_rate\\\": [0.01, 0.02, 0.05, 0.1],\\n\",\n    \"    \\\"activation\\\": [tf.nn.relu, tf.nn.elu, leaky_relu(alpha=0.01), leaky_relu(alpha=0.1)],\\n\",\n    \"    # you could also try exploring different numbers of hidden layers, different optimizers, etc.\\n\",\n    \"    #\\\"n_hidden_layers\\\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\\n\",\n    \"    #\\\"optimizer_class\\\": [tf.train.AdamOptimizer, partial(tf.train.MomentumOptimizer, momentum=0.95)],\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"rnd_search = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\\n\",\n    \"                                cv=3, random_state=42, verbose=2)\\n\",\n    \"rnd_search.fit(X_train1, y_train1, X_valid=X_valid1, y_valid=y_valid1, n_epochs=1000)\\n\",\n    \"\\n\",\n    \"# If you have Scikit-Learn 0.18 or earlier, you should upgrade, or use the fit_params argument:\\n\",\n    \"# fit_params = dict(X_valid=X_valid1, y_valid=y_valid1, n_epochs=1000)\\n\",\n    \"# rnd_search = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\\n\",\n    \"#                                 fit_params=fit_params, random_state=42, verbose=2)\\n\",\n    \"# rnd_search.fit(X_train1, y_train1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'n_neurons': 90,\\n\",\n       \" 'learning_rate': 0.01,\\n\",\n       \" 'batch_size': 500,\\n\",\n       \" 'activation': <function __main__.leaky_relu.<locals>.parametrized_leaky_relu(z, name=None)>}\"\n      ]\n     },\n     \"execution_count\": 125,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9891029383148473\"\n      ]\n     },\n     \"execution_count\": 126,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Wonderful! Tuning the hyperparameters got us up to 98.91% accuracy! It may not sound like a great improvement to go from 97.26% to 98.91% accuracy, but consider the error rate: it went from roughly 2.6% to 1.1%. That's almost 60% reduction of the number of errors this model will produce!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It's a good idea to save this model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"rnd_search.best_estimator_.save(\\\"./my_best_mnist_model_0_to_4\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.4.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: Now try adding Batch Normalization and compare the learning curves: is it converging faster than before? Does it produce a better model?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train the best model found, once again, to see how fast it converges (alternatively, you could tweak the code above to make it write summaries for TensorBoard, so you can visualize the learning curve):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.083541\\tBest loss: 0.083541\\tAccuracy: 97.54%\\n\",\n      \"1\\tValidation loss: 0.052198\\tBest loss: 0.052198\\tAccuracy: 98.40%\\n\",\n      \"2\\tValidation loss: 0.044553\\tBest loss: 0.044553\\tAccuracy: 98.71%\\n\",\n      \"3\\tValidation loss: 0.051113\\tBest loss: 0.044553\\tAccuracy: 98.48%\\n\",\n      \"4\\tValidation loss: 0.046304\\tBest loss: 0.044553\\tAccuracy: 98.75%\\n\",\n      \"5\\tValidation loss: 0.037796\\tBest loss: 0.037796\\tAccuracy: 98.91%\\n\",\n      \"6\\tValidation loss: 0.048525\\tBest loss: 0.037796\\tAccuracy: 98.67%\\n\",\n      \"7\\tValidation loss: 0.039877\\tBest loss: 0.037796\\tAccuracy: 98.75%\\n\",\n      \"8\\tValidation loss: 0.038729\\tBest loss: 0.037796\\tAccuracy: 98.98%\\n\",\n      \"9\\tValidation loss: 0.064167\\tBest loss: 0.037796\\tAccuracy: 98.24%\\n\",\n      \"10\\tValidation loss: 0.057274\\tBest loss: 0.037796\\tAccuracy: 98.79%\\n\",\n      \"11\\tValidation loss: 0.064388\\tBest loss: 0.037796\\tAccuracy: 98.55%\\n\",\n      \"12\\tValidation loss: 0.056382\\tBest loss: 0.037796\\tAccuracy: 98.63%\\n\",\n      \"13\\tValidation loss: 0.049408\\tBest loss: 0.037796\\tAccuracy: 98.91%\\n\",\n      \"14\\tValidation loss: 0.038494\\tBest loss: 0.037796\\tAccuracy: 99.10%\\n\",\n      \"15\\tValidation loss: 0.064619\\tBest loss: 0.037796\\tAccuracy: 98.67%\\n\",\n      \"16\\tValidation loss: 0.055027\\tBest loss: 0.037796\\tAccuracy: 98.91%\\n\",\n      \"17\\tValidation loss: 0.054773\\tBest loss: 0.037796\\tAccuracy: 98.91%\\n\",\n      \"18\\tValidation loss: 0.076131\\tBest loss: 0.037796\\tAccuracy: 98.71%\\n\",\n      \"19\\tValidation loss: 0.063031\\tBest loss: 0.037796\\tAccuracy: 98.59%\\n\",\n      \"20\\tValidation loss: 0.120501\\tBest loss: 0.037796\\tAccuracy: 98.55%\\n\",\n      \"21\\tValidation loss: 3.922006\\tBest loss: 0.037796\\tAccuracy: 94.14%\\n\",\n      \"22\\tValidation loss: 0.395737\\tBest loss: 0.037796\\tAccuracy: 96.83%\\n\",\n      \"23\\tValidation loss: 0.237014\\tBest loss: 0.037796\\tAccuracy: 96.56%\\n\",\n      \"24\\tValidation loss: 0.159249\\tBest loss: 0.037796\\tAccuracy: 97.07%\\n\",\n      \"25\\tValidation loss: 0.228444\\tBest loss: 0.037796\\tAccuracy: 95.74%\\n\",\n      \"26\\tValidation loss: 0.134490\\tBest loss: 0.037796\\tAccuracy: 96.99%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x13e25ea60>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=500, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=140,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42)\"\n      ]\n     },\n     \"execution_count\": 128,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn_clf = DNNClassifier(activation=leaky_relu(alpha=0.1), batch_size=500, learning_rate=0.01,\\n\",\n    \"                        n_neurons=140, random_state=42)\\n\",\n    \"dnn_clf.fit(X_train1, y_train1, n_epochs=1000, X_valid=X_valid1, y_valid=y_valid1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best loss is reached at epoch 5.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's check that we do indeed get 98.9% accuracy on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9898812998637867\"\n      ]\n     },\n     \"execution_count\": 129,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = dnn_clf.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Good, now let's use the exact same model, but this time with batch normalization:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.046685\\tBest loss: 0.046685\\tAccuracy: 98.63%\\n\",\n      \"1\\tValidation loss: 0.040820\\tBest loss: 0.040820\\tAccuracy: 98.79%\\n\",\n      \"2\\tValidation loss: 0.046557\\tBest loss: 0.040820\\tAccuracy: 98.67%\\n\",\n      \"3\\tValidation loss: 0.032236\\tBest loss: 0.032236\\tAccuracy: 98.94%\\n\",\n      \"4\\tValidation loss: 0.056148\\tBest loss: 0.032236\\tAccuracy: 98.44%\\n\",\n      \"5\\tValidation loss: 0.035988\\tBest loss: 0.032236\\tAccuracy: 98.98%\\n\",\n      \"6\\tValidation loss: 0.037958\\tBest loss: 0.032236\\tAccuracy: 98.94%\\n\",\n      \"7\\tValidation loss: 0.034588\\tBest loss: 0.032236\\tAccuracy: 99.02%\\n\",\n      \"8\\tValidation loss: 0.031261\\tBest loss: 0.031261\\tAccuracy: 99.34%\\n\",\n      \"9\\tValidation loss: 0.050791\\tBest loss: 0.031261\\tAccuracy: 98.79%\\n\",\n      \"10\\tValidation loss: 0.035324\\tBest loss: 0.031261\\tAccuracy: 99.02%\\n\",\n      \"11\\tValidation loss: 0.039875\\tBest loss: 0.031261\\tAccuracy: 98.98%\\n\",\n      \"12\\tValidation loss: 0.048575\\tBest loss: 0.031261\\tAccuracy: 98.94%\\n\",\n      \"13\\tValidation loss: 0.028059\\tBest loss: 0.028059\\tAccuracy: 99.18%\\n\",\n      \"14\\tValidation loss: 0.044112\\tBest loss: 0.028059\\tAccuracy: 99.14%\\n\",\n      \"15\\tValidation loss: 0.039050\\tBest loss: 0.028059\\tAccuracy: 99.22%\\n\",\n      \"16\\tValidation loss: 0.033278\\tBest loss: 0.028059\\tAccuracy: 99.14%\\n\",\n      \"17\\tValidation loss: 0.031734\\tBest loss: 0.028059\\tAccuracy: 99.18%\\n\",\n      \"18\\tValidation loss: 0.034500\\tBest loss: 0.028059\\tAccuracy: 99.14%\\n\",\n      \"19\\tValidation loss: 0.032757\\tBest loss: 0.028059\\tAccuracy: 99.26%\\n\",\n      \"20\\tValidation loss: 0.023842\\tBest loss: 0.023842\\tAccuracy: 99.53%\\n\",\n      \"21\\tValidation loss: 0.026727\\tBest loss: 0.023842\\tAccuracy: 99.41%\\n\",\n      \"22\\tValidation loss: 0.027016\\tBest loss: 0.023842\\tAccuracy: 99.41%\\n\",\n      \"23\\tValidation loss: 0.033038\\tBest loss: 0.023842\\tAccuracy: 99.34%\\n\",\n      \"24\\tValidation loss: 0.035490\\tBest loss: 0.023842\\tAccuracy: 99.18%\\n\",\n      \"25\\tValidation loss: 0.060346\\tBest loss: 0.023842\\tAccuracy: 98.75%\\n\",\n      \"26\\tValidation loss: 0.051341\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"27\\tValidation loss: 0.033108\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"28\\tValidation loss: 0.042162\\tBest loss: 0.023842\\tAccuracy: 99.18%\\n\",\n      \"29\\tValidation loss: 0.036313\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"30\\tValidation loss: 0.033812\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"31\\tValidation loss: 0.038173\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"32\\tValidation loss: 0.029853\\tBest loss: 0.023842\\tAccuracy: 99.37%\\n\",\n      \"33\\tValidation loss: 0.026557\\tBest loss: 0.023842\\tAccuracy: 99.37%\\n\",\n      \"34\\tValidation loss: 0.035003\\tBest loss: 0.023842\\tAccuracy: 99.37%\\n\",\n      \"35\\tValidation loss: 0.027140\\tBest loss: 0.023842\\tAccuracy: 99.34%\\n\",\n      \"36\\tValidation loss: 0.038988\\tBest loss: 0.023842\\tAccuracy: 99.34%\\n\",\n      \"37\\tValidation loss: 0.048149\\tBest loss: 0.023842\\tAccuracy: 98.98%\\n\",\n      \"38\\tValidation loss: 0.049070\\tBest loss: 0.023842\\tAccuracy: 99.02%\\n\",\n      \"39\\tValidation loss: 0.041233\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"40\\tValidation loss: 0.038571\\tBest loss: 0.023842\\tAccuracy: 99.26%\\n\",\n      \"41\\tValidation loss: 0.036886\\tBest loss: 0.023842\\tAccuracy: 99.34%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x14030a378>,\\n\",\n       \"       batch_norm_momentum=0.95, batch_size=500, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=90,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42)\"\n      ]\n     },\n     \"execution_count\": 130,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn_clf_bn = DNNClassifier(activation=leaky_relu(alpha=0.1), batch_size=500, learning_rate=0.01,\\n\",\n    \"                           n_neurons=90, random_state=42,\\n\",\n    \"                           batch_norm_momentum=0.95)\\n\",\n    \"dnn_clf_bn.fit(X_train1, y_train1, n_epochs=1000, X_valid=X_valid1, y_valid=y_valid1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best params are reached during epoch 20, that's actually a slower convergence than earlier. Let's check the accuracy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9941622883829538\"\n      ]\n     },\n     \"execution_count\": 131,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = dnn_clf_bn.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Great, batch normalization improved accuracy! Let's see if we can find a good set of hyperparameters that will work even better with batch normalization:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 132,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 50 candidates, totalling 150 fits\\n\",\n      \"[CV] n_neurons=70, learning_rate=0.01, batch_size=50, batch_norm_momentum=0.99, activation=<function relu at 0x124366d08> \\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.098522\\tBest loss: 0.098522\\tAccuracy: 97.81%\\n\",\n      \"1\\tValidation loss: 0.080233\\tBest loss: 0.080233\\tAccuracy: 98.08%\\n\",\n      \"2\\tValidation loss: 0.068767\\tBest loss: 0.068767\\tAccuracy: 98.01%\\n\",\n      \"3\\tValidation loss: 0.057095\\tBest loss: 0.057095\\tAccuracy: 98.28%\\n\",\n      \"4\\tValidation loss: 0.067008\\tBest loss: 0.057095\\tAccuracy: 98.12%\\n\",\n      \"5\\tValidation loss: 0.058910\\tBest loss: 0.057095\\tAccuracy: 98.55%\\n\",\n      \"6\\tValidation loss: 0.038421\\tBest loss: 0.038421\\tAccuracy: 98.91%\\n\",\n      \"7\\tValidation loss: 0.071075\\tBest loss: 0.038421\\tAccuracy: 98.36%\\n\",\n      \"8\\tValidation loss: 0.063073\\tBest loss: 0.038421\\tAccuracy: 98.28%\\n\",\n      \"9\\tValidation loss: 0.057488\\tBest loss: 0.038421\\tAccuracy: 98.75%\\n\",\n      \"10\\tValidation loss: 0.049557\\tBest loss: 0.038421\\tAccuracy: 98.75%\\n\",\n      \"11\\tValidation loss: 0.039810\\tBest loss: 0.038421\\tAccuracy: 99.06%\\n\",\n      \"12\\tValidation loss: 0.061837\\tBest loss: 0.038421\\tAccuracy: 98.55%\\n\",\n      \"13\\tValidation loss: 0.062008\\tBest loss: 0.038421\\tAccuracy: 98.51%\\n\",\n      \"14\\tValidation loss: 0.075937\\tBest loss: 0.038421\\tAccuracy: 98.44%\\n\",\n      \"15\\tValidation loss: 0.053910\\tBest loss: 0.038421\\tAccuracy: 98.71%\\n\",\n      \"16\\tValidation loss: 0.051419\\tBest loss: 0.038421\\tAccuracy: 98.94%\\n\",\n      \"17\\tValidation loss: 0.049013\\tBest loss: 0.038421\\tAccuracy: 98.98%\\n\",\n      \"18\\tValidation loss: 0.048979\\tBest loss: 0.038421\\tAccuracy: 99.10%\\n\",\n      \"19\\tValidation loss: 0.058969\\tBest loss: 0.038421\\tAccuracy: 98.59%\\n\",\n      \"20\\tValidation loss: 0.060048\\tBest loss: 0.038421\\tAccuracy: 98.79%\\n\",\n      \"21\\tValidation loss: 0.088256\\tBest loss: 0.038421\\tAccuracy: 98.32%\\n\",\n      \"22\\tValidation loss: 0.055535\\tBest loss: 0.038421\\tAccuracy: 98.59%\\n\",\n      \"23\\tValidation loss: 0.054632\\tBest loss: 0.038421\\tAccuracy: 98.94%\\n\",\n      \"24\\tValidation loss: 0.092021\\tBest loss: 0.038421\\tAccuracy: 98.20%\\n\",\n      \"25\\tValidation loss: 0.042263\\tBest loss: 0.038421\\tAccuracy: 99.02%\\n\",\n      \"26\\tValidation loss: 0.041139\\tBest loss: 0.038421\\tAccuracy: 99.30%\\n\",\n      \"27\\tValidation loss: 0.054255\\tBest loss: 0.038421\\tAccuracy: 99.06%\\n\",\n      \"Early stopping!\\n\",\n      \"[CV]  n_neurons=70, learning_rate=0.01, batch_size=50, batch_norm_momentum=0.99, activation=<function relu at 0x124366d08>, total=  39.0s\\n\",\n      \"[CV] n_neurons=70, learning_rate=0.01, batch_size=50, batch_norm_momentum=0.99, activation=<function relu at 0x124366d08> \\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   39.1s remaining:    0.0s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<<7081 more lines>>\\n\",\n      \"36\\tValidation loss: 0.032222\\tBest loss: 0.021774\\tAccuracy: 99.14%\\n\",\n      \"37\\tValidation loss: 0.025638\\tBest loss: 0.021774\\tAccuracy: 99.34%\\n\",\n      \"38\\tValidation loss: 0.031702\\tBest loss: 0.021774\\tAccuracy: 99.02%\\n\",\n      \"39\\tValidation loss: 0.027012\\tBest loss: 0.021774\\tAccuracy: 99.30%\\n\",\n      \"40\\tValidation loss: 0.027163\\tBest loss: 0.021774\\tAccuracy: 99.30%\\n\",\n      \"41\\tValidation loss: 0.029205\\tBest loss: 0.021774\\tAccuracy: 99.26%\\n\",\n      \"42\\tValidation loss: 0.024973\\tBest loss: 0.021774\\tAccuracy: 99.41%\\n\",\n      \"43\\tValidation loss: 0.036898\\tBest loss: 0.021774\\tAccuracy: 98.94%\\n\",\n      \"44\\tValidation loss: 0.040366\\tBest loss: 0.021774\\tAccuracy: 99.14%\\n\",\n      \"45\\tValidation loss: 0.033711\\tBest loss: 0.021774\\tAccuracy: 99.02%\\n\",\n      \"46\\tValidation loss: 0.046615\\tBest loss: 0.021774\\tAccuracy: 98.79%\\n\",\n      \"47\\tValidation loss: 0.032732\\tBest loss: 0.021774\\tAccuracy: 99.26%\\n\",\n      \"48\\tValidation loss: 0.020177\\tBest loss: 0.020177\\tAccuracy: 99.45%\\n\",\n      \"49\\tValidation loss: 0.031700\\tBest loss: 0.020177\\tAccuracy: 99.37%\\n\",\n      \"50\\tValidation loss: 0.035962\\tBest loss: 0.020177\\tAccuracy: 99.14%\\n\",\n      \"51\\tValidation loss: 0.031128\\tBest loss: 0.020177\\tAccuracy: 99.18%\\n\",\n      \"52\\tValidation loss: 0.038107\\tBest loss: 0.020177\\tAccuracy: 99.14%\\n\",\n      \"53\\tValidation loss: 0.036671\\tBest loss: 0.020177\\tAccuracy: 99.18%\\n\",\n      \"54\\tValidation loss: 0.029867\\tBest loss: 0.020177\\tAccuracy: 99.30%\\n\",\n      \"55\\tValidation loss: 0.039179\\tBest loss: 0.020177\\tAccuracy: 99.10%\\n\",\n      \"56\\tValidation loss: 0.028410\\tBest loss: 0.020177\\tAccuracy: 99.10%\\n\",\n      \"57\\tValidation loss: 0.037625\\tBest loss: 0.020177\\tAccuracy: 99.06%\\n\",\n      \"58\\tValidation loss: 0.035516\\tBest loss: 0.020177\\tAccuracy: 99.22%\\n\",\n      \"59\\tValidation loss: 0.030096\\tBest loss: 0.020177\\tAccuracy: 99.37%\\n\",\n      \"60\\tValidation loss: 0.032056\\tBest loss: 0.020177\\tAccuracy: 99.22%\\n\",\n      \"61\\tValidation loss: 0.026143\\tBest loss: 0.020177\\tAccuracy: 99.37%\\n\",\n      \"62\\tValidation loss: 0.022387\\tBest loss: 0.020177\\tAccuracy: 99.45%\\n\",\n      \"63\\tValidation loss: 0.026331\\tBest loss: 0.020177\\tAccuracy: 99.41%\\n\",\n      \"64\\tValidation loss: 0.034930\\tBest loss: 0.020177\\tAccuracy: 99.10%\\n\",\n      \"65\\tValidation loss: 0.029928\\tBest loss: 0.020177\\tAccuracy: 99.30%\\n\",\n      \"66\\tValidation loss: 0.028943\\tBest loss: 0.020177\\tAccuracy: 99.30%\\n\",\n      \"67\\tValidation loss: 0.034912\\tBest loss: 0.020177\\tAccuracy: 99.18%\\n\",\n      \"68\\tValidation loss: 0.037118\\tBest loss: 0.020177\\tAccuracy: 99.18%\\n\",\n      \"69\\tValidation loss: 0.034165\\tBest loss: 0.020177\\tAccuracy: 99.37%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\\n\",\n       \"          estimator=DNNClassifier(activation=<function elu at 0x1243639d8>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42),\\n\",\n       \"          fit_params={'X_valid': array([[0., 0., ..., 0., 0.],\\n\",\n       \"       [0., 0., ..., 0., 0.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 0., ..., 0., 0.],\\n\",\n       \"       [0., 0., ..., 0., 0.]], dtype=float32), 'y_valid': array([0, 4, ..., 1, 2], dtype=int32), 'n_epochs': 1000},\\n\",\n       \"          iid='warn', n_iter=50, n_jobs=None,\\n\",\n       \"          param_distributions={'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160], 'batch_size': [10, 50, 100, 500], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x124366d08>, <function elu at 0x1243639d8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x1500bd2f0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x1500bd378>], 'batch_norm_momentum': [0.9, 0.95, 0.98, 0.99, 0.999]},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=42, refit=True,\\n\",\n       \"          return_train_score='warn', scoring=None, verbose=2)\"\n      ]\n     },\n     \"execution_count\": 132,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"\\n\",\n    \"param_distribs = {\\n\",\n    \"    \\\"n_neurons\\\": [10, 30, 50, 70, 90, 100, 120, 140, 160],\\n\",\n    \"    \\\"batch_size\\\": [10, 50, 100, 500],\\n\",\n    \"    \\\"learning_rate\\\": [0.01, 0.02, 0.05, 0.1],\\n\",\n    \"    \\\"activation\\\": [tf.nn.relu, tf.nn.elu, leaky_relu(alpha=0.01), leaky_relu(alpha=0.1)],\\n\",\n    \"    # you could also try exploring different numbers of hidden layers, different optimizers, etc.\\n\",\n    \"    #\\\"n_hidden_layers\\\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\\n\",\n    \"    #\\\"optimizer_class\\\": [tf.train.AdamOptimizer, partial(tf.train.MomentumOptimizer, momentum=0.95)],\\n\",\n    \"    \\\"batch_norm_momentum\\\": [0.9, 0.95, 0.98, 0.99, 0.999],\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"rnd_search_bn = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50, cv=3,\\n\",\n    \"                                   random_state=42, verbose=2)\\n\",\n    \"rnd_search_bn.fit(X_train1, y_train1, X_valid=X_valid1, y_valid=y_valid1, n_epochs=1000)\\n\",\n    \"\\n\",\n    \"# If you have Scikit-Learn 0.18 or earlier, you should upgrade, or use the fit_params argument:\\n\",\n    \"# fit_params = dict(X_valid=X_valid1, y_valid=y_valid1, n_epochs=1000)\\n\",\n    \"# rnd_search_bn = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\\n\",\n    \"#                                    fit_params=fit_params, random_state=42, verbose=2)\\n\",\n    \"# rnd_search_bn.fit(X_train1, y_train1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 133,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'n_neurons': 160,\\n\",\n       \" 'learning_rate': 0.01,\\n\",\n       \" 'batch_size': 10,\\n\",\n       \" 'batch_norm_momentum': 0.98,\\n\",\n       \" 'activation': <function tensorflow.python.ops.gen_nn_ops.relu(features, name=None)>}\"\n      ]\n     },\n     \"execution_count\": 133,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search_bn.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 134,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9949406499318934\"\n      ]\n     },\n     \"execution_count\": 134,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search_bn.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Slightly better than earlier: 99.49% vs 99.42%. Let's see if dropout can do better.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.5.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: is the model overfitting the training set? Try adding dropout to every layer and try again. Does it help?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's go back to the model we trained earlier and see how it performs on the training set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9950781082816178\"\n      ]\n     },\n     \"execution_count\": 135,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = dnn_clf.predict(X_train1)\\n\",\n    \"accuracy_score(y_train1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model performs significantly better on the training set than on the test set (99.51% vs 99.00%), which means it is overfitting the training set. A bit of regularization may help. Let's try adding dropout with a 50% dropout rate:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 136,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.131152\\tBest loss: 0.131152\\tAccuracy: 96.91%\\n\",\n      \"1\\tValidation loss: 0.105306\\tBest loss: 0.105306\\tAccuracy: 97.46%\\n\",\n      \"2\\tValidation loss: 0.091219\\tBest loss: 0.091219\\tAccuracy: 97.73%\\n\",\n      \"3\\tValidation loss: 0.089638\\tBest loss: 0.089638\\tAccuracy: 97.85%\\n\",\n      \"4\\tValidation loss: 0.091288\\tBest loss: 0.089638\\tAccuracy: 97.69%\\n\",\n      \"5\\tValidation loss: 0.081112\\tBest loss: 0.081112\\tAccuracy: 98.05%\\n\",\n      \"6\\tValidation loss: 0.075575\\tBest loss: 0.075575\\tAccuracy: 98.24%\\n\",\n      \"7\\tValidation loss: 0.084841\\tBest loss: 0.075575\\tAccuracy: 97.77%\\n\",\n      \"8\\tValidation loss: 0.075269\\tBest loss: 0.075269\\tAccuracy: 97.65%\\n\",\n      \"9\\tValidation loss: 0.076625\\tBest loss: 0.075269\\tAccuracy: 98.12%\\n\",\n      \"10\\tValidation loss: 0.072509\\tBest loss: 0.072509\\tAccuracy: 97.97%\\n\",\n      \"11\\tValidation loss: 0.071006\\tBest loss: 0.071006\\tAccuracy: 98.44%\\n\",\n      \"12\\tValidation loss: 0.073272\\tBest loss: 0.071006\\tAccuracy: 98.08%\\n\",\n      \"13\\tValidation loss: 0.076293\\tBest loss: 0.071006\\tAccuracy: 98.16%\\n\",\n      \"14\\tValidation loss: 0.074955\\tBest loss: 0.071006\\tAccuracy: 98.05%\\n\",\n      \"15\\tValidation loss: 0.066207\\tBest loss: 0.066207\\tAccuracy: 98.20%\\n\",\n      \"16\\tValidation loss: 0.067388\\tBest loss: 0.066207\\tAccuracy: 98.08%\\n\",\n      \"17\\tValidation loss: 0.061916\\tBest loss: 0.061916\\tAccuracy: 98.40%\\n\",\n      \"18\\tValidation loss: 0.064908\\tBest loss: 0.061916\\tAccuracy: 98.40%\\n\",\n      \"19\\tValidation loss: 0.064921\\tBest loss: 0.061916\\tAccuracy: 98.40%\\n\",\n      \"20\\tValidation loss: 0.069939\\tBest loss: 0.061916\\tAccuracy: 98.40%\\n\",\n      \"21\\tValidation loss: 0.069870\\tBest loss: 0.061916\\tAccuracy: 98.32%\\n\",\n      \"22\\tValidation loss: 0.062807\\tBest loss: 0.061916\\tAccuracy: 98.24%\\n\",\n      \"23\\tValidation loss: 0.065312\\tBest loss: 0.061916\\tAccuracy: 98.44%\\n\",\n      \"24\\tValidation loss: 0.067044\\tBest loss: 0.061916\\tAccuracy: 98.44%\\n\",\n      \"25\\tValidation loss: 0.072251\\tBest loss: 0.061916\\tAccuracy: 98.16%\\n\",\n      \"26\\tValidation loss: 0.064444\\tBest loss: 0.061916\\tAccuracy: 98.20%\\n\",\n      \"27\\tValidation loss: 0.069022\\tBest loss: 0.061916\\tAccuracy: 98.44%\\n\",\n      \"28\\tValidation loss: 0.069079\\tBest loss: 0.061916\\tAccuracy: 98.28%\\n\",\n      \"29\\tValidation loss: 0.148266\\tBest loss: 0.061916\\tAccuracy: 96.52%\\n\",\n      \"30\\tValidation loss: 0.119943\\tBest loss: 0.061916\\tAccuracy: 96.72%\\n\",\n      \"31\\tValidation loss: 0.167303\\tBest loss: 0.061916\\tAccuracy: 96.68%\\n\",\n      \"32\\tValidation loss: 0.131897\\tBest loss: 0.061916\\tAccuracy: 96.52%\\n\",\n      \"33\\tValidation loss: 0.146681\\tBest loss: 0.061916\\tAccuracy: 95.43%\\n\",\n      \"34\\tValidation loss: 0.125731\\tBest loss: 0.061916\\tAccuracy: 96.64%\\n\",\n      \"35\\tValidation loss: 0.099879\\tBest loss: 0.061916\\tAccuracy: 97.89%\\n\",\n      \"36\\tValidation loss: 0.096915\\tBest loss: 0.061916\\tAccuracy: 97.73%\\n\",\n      \"37\\tValidation loss: 0.096422\\tBest loss: 0.061916\\tAccuracy: 97.85%\\n\",\n      \"38\\tValidation loss: 0.108040\\tBest loss: 0.061916\\tAccuracy: 97.54%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x14e8501e0>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=500, dropout_rate=0.5,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=90,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42)\"\n      ]\n     },\n     \"execution_count\": 136,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn_clf_dropout = DNNClassifier(activation=leaky_relu(alpha=0.1), batch_size=500, learning_rate=0.01,\\n\",\n    \"                                n_neurons=90, random_state=42,\\n\",\n    \"                                dropout_rate=0.5)\\n\",\n    \"dnn_clf_dropout.fit(X_train1, y_train1, n_epochs=1000, X_valid=X_valid1, y_valid=y_valid1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best params are reached during epoch 17. Dropout somewhat slowed down convergence.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's check the accuracy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 137,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9861840825063242\"\n      ]\n     },\n     \"execution_count\": 137,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = dnn_clf_dropout.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are out of luck, dropout does not seem to help. Let's try tuning the hyperparameters, perhaps we can squeeze a bit more performance out of this model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 138,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 50 candidates, totalling 150 fits\\n\",\n      \"[CV] n_neurons=70, learning_rate=0.01, dropout_rate=0.5, batch_size=100, activation=<function relu at 0x124366d08> \\n\",\n      \"0\\tValidation loss: 0.218595\\tBest loss: 0.218595\\tAccuracy: 93.63%\\n\",\n      \"1\\tValidation loss: 0.210470\\tBest loss: 0.210470\\tAccuracy: 94.61%\\n\",\n      \"2\\tValidation loss: 0.224635\\tBest loss: 0.210470\\tAccuracy: 95.50%\\n\",\n      \"3\\tValidation loss: 0.200494\\tBest loss: 0.200494\\tAccuracy: 94.84%\\n\",\n      \"4\\tValidation loss: 0.184056\\tBest loss: 0.184056\\tAccuracy: 95.58%\\n\",\n      \"5\\tValidation loss: 0.187698\\tBest loss: 0.184056\\tAccuracy: 96.33%\\n\",\n      \"6\\tValidation loss: 0.151692\\tBest loss: 0.151692\\tAccuracy: 96.17%\\n\",\n      \"7\\tValidation loss: 0.176633\\tBest loss: 0.151692\\tAccuracy: 96.21%\\n\",\n      \"8\\tValidation loss: 0.187090\\tBest loss: 0.151692\\tAccuracy: 96.01%\\n\",\n      \"9\\tValidation loss: 0.204406\\tBest loss: 0.151692\\tAccuracy: 96.40%\\n\",\n      \"10\\tValidation loss: 0.193938\\tBest loss: 0.151692\\tAccuracy: 95.74%\\n\",\n      \"11\\tValidation loss: 0.190056\\tBest loss: 0.151692\\tAccuracy: 96.21%\\n\",\n      \"12\\tValidation loss: 0.183601\\tBest loss: 0.151692\\tAccuracy: 96.05%\\n\",\n      \"13\\tValidation loss: 0.179737\\tBest loss: 0.151692\\tAccuracy: 96.25%\\n\",\n      \"14\\tValidation loss: 0.289718\\tBest loss: 0.151692\\tAccuracy: 96.29%\\n\",\n      \"15\\tValidation loss: 0.188605\\tBest loss: 0.151692\\tAccuracy: 95.86%\\n\",\n      \"16\\tValidation loss: 0.195911\\tBest loss: 0.151692\\tAccuracy: 96.01%\\n\",\n      \"17\\tValidation loss: 0.158151\\tBest loss: 0.151692\\tAccuracy: 96.25%\\n\",\n      \"18\\tValidation loss: 0.168049\\tBest loss: 0.151692\\tAccuracy: 96.25%\\n\",\n      \"19\\tValidation loss: 0.170637\\tBest loss: 0.151692\\tAccuracy: 96.40%\\n\",\n      \"20\\tValidation loss: 0.192890\\tBest loss: 0.151692\\tAccuracy: 96.21%\\n\",\n      \"21\\tValidation loss: 0.178800\\tBest loss: 0.151692\\tAccuracy: 95.97%\\n\",\n      \"22\\tValidation loss: 0.185295\\tBest loss: 0.151692\\tAccuracy: 96.44%\\n\",\n      \"23\\tValidation loss: 0.150369\\tBest loss: 0.150369\\tAccuracy: 96.91%\\n\",\n      \"24\\tValidation loss: 0.161164\\tBest loss: 0.150369\\tAccuracy: 96.52%\\n\",\n      \"25\\tValidation loss: 0.180860\\tBest loss: 0.150369\\tAccuracy: 96.13%\\n\",\n      \"26\\tValidation loss: 0.182730\\tBest loss: 0.150369\\tAccuracy: 96.52%\\n\",\n      \"27\\tValidation loss: 0.184583\\tBest loss: 0.150369\\tAccuracy: 96.09%\\n\",\n      \"28\\tValidation loss: 0.183952\\tBest loss: 0.150369\\tAccuracy: 95.39%\\n\",\n      \"29\\tValidation loss: 0.211111\\tBest loss: 0.150369\\tAccuracy: 95.54%\\n\",\n      \"30\\tValidation loss: 0.225760\\tBest loss: 0.150369\\tAccuracy: 95.97%\\n\",\n      \"31\\tValidation loss: 0.170313\\tBest loss: 0.150369\\tAccuracy: 96.91%\\n\",\n      \"<<5625 more lines>>\\n\",\n      \"8\\tValidation loss: 0.086624\\tBest loss: 0.065724\\tAccuracy: 98.01%\\n\",\n      \"9\\tValidation loss: 0.069571\\tBest loss: 0.065724\\tAccuracy: 98.44%\\n\",\n      \"10\\tValidation loss: 0.094720\\tBest loss: 0.065724\\tAccuracy: 98.20%\\n\",\n      \"11\\tValidation loss: 0.070504\\tBest loss: 0.065724\\tAccuracy: 98.51%\\n\",\n      \"12\\tValidation loss: 0.090169\\tBest loss: 0.065724\\tAccuracy: 98.24%\\n\",\n      \"13\\tValidation loss: 0.080667\\tBest loss: 0.065724\\tAccuracy: 98.20%\\n\",\n      \"14\\tValidation loss: 0.120917\\tBest loss: 0.065724\\tAccuracy: 96.60%\\n\",\n      \"15\\tValidation loss: 0.105030\\tBest loss: 0.065724\\tAccuracy: 97.62%\\n\",\n      \"16\\tValidation loss: 0.138571\\tBest loss: 0.065724\\tAccuracy: 97.85%\\n\",\n      \"17\\tValidation loss: 0.078942\\tBest loss: 0.065724\\tAccuracy: 97.97%\\n\",\n      \"18\\tValidation loss: 0.081645\\tBest loss: 0.065724\\tAccuracy: 97.89%\\n\",\n      \"19\\tValidation loss: 0.054128\\tBest loss: 0.054128\\tAccuracy: 98.44%\\n\",\n      \"20\\tValidation loss: 0.051510\\tBest loss: 0.051510\\tAccuracy: 98.44%\\n\",\n      \"21\\tValidation loss: 0.071159\\tBest loss: 0.051510\\tAccuracy: 98.67%\\n\",\n      \"22\\tValidation loss: 0.084647\\tBest loss: 0.051510\\tAccuracy: 98.28%\\n\",\n      \"23\\tValidation loss: 0.081601\\tBest loss: 0.051510\\tAccuracy: 98.36%\\n\",\n      \"24\\tValidation loss: 0.152964\\tBest loss: 0.051510\\tAccuracy: 97.93%\\n\",\n      \"25\\tValidation loss: 0.173249\\tBest loss: 0.051510\\tAccuracy: 97.03%\\n\",\n      \"26\\tValidation loss: 0.128901\\tBest loss: 0.051510\\tAccuracy: 96.13%\\n\",\n      \"27\\tValidation loss: 0.110458\\tBest loss: 0.051510\\tAccuracy: 97.93%\\n\",\n      \"28\\tValidation loss: 0.108197\\tBest loss: 0.051510\\tAccuracy: 97.30%\\n\",\n      \"29\\tValidation loss: 0.104204\\tBest loss: 0.051510\\tAccuracy: 97.85%\\n\",\n      \"30\\tValidation loss: 0.126637\\tBest loss: 0.051510\\tAccuracy: 98.32%\\n\",\n      \"31\\tValidation loss: 0.142045\\tBest loss: 0.051510\\tAccuracy: 97.62%\\n\",\n      \"32\\tValidation loss: 0.103701\\tBest loss: 0.051510\\tAccuracy: 97.69%\\n\",\n      \"33\\tValidation loss: 0.120295\\tBest loss: 0.051510\\tAccuracy: 97.42%\\n\",\n      \"34\\tValidation loss: 0.151388\\tBest loss: 0.051510\\tAccuracy: 97.85%\\n\",\n      \"35\\tValidation loss: 0.096931\\tBest loss: 0.051510\\tAccuracy: 97.58%\\n\",\n      \"36\\tValidation loss: 0.153569\\tBest loss: 0.051510\\tAccuracy: 97.11%\\n\",\n      \"37\\tValidation loss: 0.120552\\tBest loss: 0.051510\\tAccuracy: 98.05%\\n\",\n      \"38\\tValidation loss: 0.076677\\tBest loss: 0.051510\\tAccuracy: 98.55%\\n\",\n      \"39\\tValidation loss: 0.071904\\tBest loss: 0.051510\\tAccuracy: 98.55%\\n\",\n      \"40\\tValidation loss: 0.072618\\tBest loss: 0.051510\\tAccuracy: 98.12%\\n\",\n      \"41\\tValidation loss: 0.086680\\tBest loss: 0.051510\\tAccuracy: 98.08%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\\n\",\n       \"          estimator=DNNClassifier(activation=<function elu at 0x1243639d8>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42),\\n\",\n       \"          fit_params={'X_valid': array([[0., 0., ..., 0., 0.],\\n\",\n       \"       [0., 0., ..., 0., 0.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 0., ..., 0., 0.],\\n\",\n       \"       [0., 0., ..., 0., 0.]], dtype=float32), 'y_valid': array([0, 4, ..., 1, 2], dtype=int32), 'n_epochs': 1000},\\n\",\n       \"          iid='warn', n_iter=50, n_jobs=None,\\n\",\n       \"          param_distributions={'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160], 'batch_size': [10, 50, 100, 500], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x124366d08>, <function elu at 0x1243639d8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x14e850620>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x14e850d08>], 'dropout_rate': [0.2, 0.3, 0.4, 0.5, 0.6]},\\n\",\n       \"          pre_dispatch='2*n_jobs', random_state=42, refit=True,\\n\",\n       \"          return_train_score='warn', scoring=None, verbose=2)\"\n      ]\n     },\n     \"execution_count\": 138,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"\\n\",\n    \"param_distribs = {\\n\",\n    \"    \\\"n_neurons\\\": [10, 30, 50, 70, 90, 100, 120, 140, 160],\\n\",\n    \"    \\\"batch_size\\\": [10, 50, 100, 500],\\n\",\n    \"    \\\"learning_rate\\\": [0.01, 0.02, 0.05, 0.1],\\n\",\n    \"    \\\"activation\\\": [tf.nn.relu, tf.nn.elu, leaky_relu(alpha=0.01), leaky_relu(alpha=0.1)],\\n\",\n    \"    # you could also try exploring different numbers of hidden layers, different optimizers, etc.\\n\",\n    \"    #\\\"n_hidden_layers\\\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\\n\",\n    \"    #\\\"optimizer_class\\\": [tf.train.AdamOptimizer, partial(tf.train.MomentumOptimizer, momentum=0.95)],\\n\",\n    \"    \\\"dropout_rate\\\": [0.2, 0.3, 0.4, 0.5, 0.6],\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"rnd_search_dropout = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\\n\",\n    \"                                        cv=3, random_state=42, verbose=2)\\n\",\n    \"rnd_search_dropout.fit(X_train1, y_train1, X_valid=X_valid1, y_valid=y_valid1, n_epochs=1000)\\n\",\n    \"\\n\",\n    \"# If you have Scikit-Learn 0.18 or earlier, you should upgrade, or use the fit_params argument:\\n\",\n    \"# fit_params = dict(X_valid=X_valid1, y_valid=y_valid1, n_epochs=1000)\\n\",\n    \"# rnd_search_dropout = RandomizedSearchCV(DNNClassifier(random_state=42), param_distribs, n_iter=50,\\n\",\n    \"#                                         fit_params=fit_params, random_state=42, verbose=2)\\n\",\n    \"# rnd_search_dropout.fit(X_train1, y_train1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 139,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'n_neurons': 160,\\n\",\n       \" 'learning_rate': 0.01,\\n\",\n       \" 'dropout_rate': 0.2,\\n\",\n       \" 'batch_size': 100,\\n\",\n       \" 'activation': <function tensorflow.python.ops.gen_nn_ops.relu(features, name=None)>}\"\n      ]\n     },\n     \"execution_count\": 139,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_search_dropout.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9889083479276124\"\n      ]\n     },\n     \"execution_count\": 140,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = rnd_search_dropout.predict(X_test1)\\n\",\n    \"accuracy_score(y_test1, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Oh well, dropout did not improve the model. Better luck next time! :)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"But that's okay, we have ourselves a nice DNN that achieves 99.49% accuracy on the test set using Batch Normalization, or 98.91% without BN. Let's see if some of this expertise on digits 0 to 4 can be transferred to the task of classifying digits 5 to 9. For the sake of simplicity we will reuse the DNN without BN.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## 9. Transfer learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: create a new DNN that reuses all the pretrained hidden layers of the previous model, freezes them, and replaces the softmax output layer with a new one._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's load the best model's graph and get a handle on all the important operations we will need. Note that instead of creating a new softmax output layer, we will just reuse the existing one (since it has the same number of outputs as the existing one). We will reinitialize its parameters before training. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 141,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"restore_saver = tf.train.import_meta_graph(\\\"./my_best_mnist_model_0_to_4.meta\\\")\\n\",\n    \"\\n\",\n    \"X = tf.get_default_graph().get_tensor_by_name(\\\"X:0\\\")\\n\",\n    \"y = tf.get_default_graph().get_tensor_by_name(\\\"y:0\\\")\\n\",\n    \"loss = tf.get_default_graph().get_tensor_by_name(\\\"loss:0\\\")\\n\",\n    \"Y_proba = tf.get_default_graph().get_tensor_by_name(\\\"Y_proba:0\\\")\\n\",\n    \"logits = Y_proba.op.inputs[0]\\n\",\n    \"accuracy = tf.get_default_graph().get_tensor_by_name(\\\"accuracy:0\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To freeze the lower layers, we will exclude their variables from the optimizer's list of trainable variables, keeping only the output layer's trainable variables:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 142,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"output_layer_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\\\"logits\\\")\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate, name=\\\"Adam2\\\")\\n\",\n    \"training_op = optimizer.minimize(loss, var_list=output_layer_vars)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 143,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"five_frozen_saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: train this new DNN on digits 5 to 9, using only 100 images per digit, and time how long it takes. Despite this small number of examples, can you achieve high precision?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create the training, validation and test sets. We need to subtract 5 from the labels because TensorFlow expects integers from 0 to `n_classes-1`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 144,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train2_full = X_train[y_train >= 5]\\n\",\n    \"y_train2_full = y_train[y_train >= 5] - 5\\n\",\n    \"X_valid2_full = X_valid[y_valid >= 5]\\n\",\n    \"y_valid2_full = y_valid[y_valid >= 5] - 5\\n\",\n    \"X_test2 = X_test[y_test >= 5]\\n\",\n    \"y_test2 = y_test[y_test >= 5] - 5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Also, for the purpose of this exercise, we want to keep only 100 instances per class in the training set (and let's keep only 30 instances per class in the validation set). Let's create a small function to do that:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 145,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def sample_n_instances_per_class(X, y, n=100):\\n\",\n    \"    Xs, ys = [], []\\n\",\n    \"    for label in np.unique(y):\\n\",\n    \"        idx = (y == label)\\n\",\n    \"        Xc = X[idx][:n]\\n\",\n    \"        yc = y[idx][:n]\\n\",\n    \"        Xs.append(Xc)\\n\",\n    \"        ys.append(yc)\\n\",\n    \"    return np.concatenate(Xs), np.concatenate(ys)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 146,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train2, y_train2 = sample_n_instances_per_class(X_train2_full, y_train2_full, n=100)\\n\",\n    \"X_valid2, y_valid2 = sample_n_instances_per_class(X_valid2_full, y_valid2_full, n=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's train the model. This is the same training code as earlier, using early stopping, except for the initialization: we first initialize all the variables, then we restore the best model trained earlier (on digits 0 to 4), and finally we reinitialize the output layer variables.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 147,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\\n\",\n      \"0\\tValidation loss: 1.361167\\tBest loss: 1.361167\\tAccuracy: 43.33%\\n\",\n      \"1\\tValidation loss: 1.154602\\tBest loss: 1.154602\\tAccuracy: 57.33%\\n\",\n      \"2\\tValidation loss: 1.054218\\tBest loss: 1.054218\\tAccuracy: 53.33%\\n\",\n      \"3\\tValidation loss: 0.981128\\tBest loss: 0.981128\\tAccuracy: 62.67%\\n\",\n      \"4\\tValidation loss: 0.995353\\tBest loss: 0.981128\\tAccuracy: 59.33%\\n\",\n      \"5\\tValidation loss: 0.967000\\tBest loss: 0.967000\\tAccuracy: 65.33%\\n\",\n      \"6\\tValidation loss: 0.955700\\tBest loss: 0.955700\\tAccuracy: 61.33%\\n\",\n      \"7\\tValidation loss: 1.015331\\tBest loss: 0.955700\\tAccuracy: 58.67%\\n\",\n      \"8\\tValidation loss: 0.978280\\tBest loss: 0.955700\\tAccuracy: 62.00%\\n\",\n      \"9\\tValidation loss: 0.923389\\tBest loss: 0.923389\\tAccuracy: 69.33%\\n\",\n      \"10\\tValidation loss: 0.996236\\tBest loss: 0.923389\\tAccuracy: 63.33%\\n\",\n      \"11\\tValidation loss: 0.976757\\tBest loss: 0.923389\\tAccuracy: 62.67%\\n\",\n      \"12\\tValidation loss: 0.969096\\tBest loss: 0.923389\\tAccuracy: 63.33%\\n\",\n      \"13\\tValidation loss: 1.023069\\tBest loss: 0.923389\\tAccuracy: 63.33%\\n\",\n      \"14\\tValidation loss: 1.104664\\tBest loss: 0.923389\\tAccuracy: 55.33%\\n\",\n      \"15\\tValidation loss: 0.950175\\tBest loss: 0.923389\\tAccuracy: 65.33%\\n\",\n      \"16\\tValidation loss: 1.002944\\tBest loss: 0.923389\\tAccuracy: 63.33%\\n\",\n      \"17\\tValidation loss: 0.895543\\tBest loss: 0.895543\\tAccuracy: 70.67%\\n\",\n      \"18\\tValidation loss: 0.961151\\tBest loss: 0.895543\\tAccuracy: 66.67%\\n\",\n      \"19\\tValidation loss: 0.896372\\tBest loss: 0.895543\\tAccuracy: 67.33%\\n\",\n      \"20\\tValidation loss: 0.911938\\tBest loss: 0.895543\\tAccuracy: 69.33%\\n\",\n      \"21\\tValidation loss: 0.929007\\tBest loss: 0.895543\\tAccuracy: 68.00%\\n\",\n      \"22\\tValidation loss: 0.939231\\tBest loss: 0.895543\\tAccuracy: 65.33%\\n\",\n      \"23\\tValidation loss: 0.919057\\tBest loss: 0.895543\\tAccuracy: 68.67%\\n\",\n      \"24\\tValidation loss: 0.994529\\tBest loss: 0.895543\\tAccuracy: 65.33%\\n\",\n      \"25\\tValidation loss: 0.901279\\tBest loss: 0.895543\\tAccuracy: 68.67%\\n\",\n      \"26\\tValidation loss: 0.916238\\tBest loss: 0.895543\\tAccuracy: 68.67%\\n\",\n      \"27\\tValidation loss: 1.007434\\tBest loss: 0.895543\\tAccuracy: 65.33%\\n\",\n      \"28\\tValidation loss: 0.924729\\tBest loss: 0.895543\\tAccuracy: 70.00%\\n\",\n      \"29\\tValidation loss: 0.974399\\tBest loss: 0.895543\\tAccuracy: 66.00%\\n\",\n      \"30\\tValidation loss: 0.899418\\tBest loss: 0.895543\\tAccuracy: 68.00%\\n\",\n      \"31\\tValidation loss: 0.940563\\tBest loss: 0.895543\\tAccuracy: 66.00%\\n\",\n      \"32\\tValidation loss: 0.920235\\tBest loss: 0.895543\\tAccuracy: 68.00%\\n\",\n      \"33\\tValidation loss: 0.929848\\tBest loss: 0.895543\\tAccuracy: 68.67%\\n\",\n      \"34\\tValidation loss: 0.930288\\tBest loss: 0.895543\\tAccuracy: 66.67%\\n\",\n      \"35\\tValidation loss: 0.943884\\tBest loss: 0.895543\\tAccuracy: 64.67%\\n\",\n      \"36\\tValidation loss: 0.939372\\tBest loss: 0.895543\\tAccuracy: 68.00%\\n\",\n      \"37\\tValidation loss: 0.894239\\tBest loss: 0.894239\\tAccuracy: 67.33%\\n\",\n      \"38\\tValidation loss: 0.888806\\tBest loss: 0.888806\\tAccuracy: 69.33%\\n\",\n      \"39\\tValidation loss: 0.933829\\tBest loss: 0.888806\\tAccuracy: 66.00%\\n\",\n      \"40\\tValidation loss: 0.911836\\tBest loss: 0.888806\\tAccuracy: 72.67%\\n\",\n      \"41\\tValidation loss: 0.896729\\tBest loss: 0.888806\\tAccuracy: 70.00%\\n\",\n      \"42\\tValidation loss: 0.929394\\tBest loss: 0.888806\\tAccuracy: 68.00%\\n\",\n      \"43\\tValidation loss: 0.919418\\tBest loss: 0.888806\\tAccuracy: 69.33%\\n\",\n      \"44\\tValidation loss: 0.907830\\tBest loss: 0.888806\\tAccuracy: 65.33%\\n\",\n      \"45\\tValidation loss: 1.004304\\tBest loss: 0.888806\\tAccuracy: 71.33%\\n\",\n      \"46\\tValidation loss: 0.871899\\tBest loss: 0.871899\\tAccuracy: 74.00%\\n\",\n      \"47\\tValidation loss: 0.904889\\tBest loss: 0.871899\\tAccuracy: 67.33%\\n\",\n      \"48\\tValidation loss: 0.914138\\tBest loss: 0.871899\\tAccuracy: 66.00%\\n\",\n      \"49\\tValidation loss: 0.930001\\tBest loss: 0.871899\\tAccuracy: 69.33%\\n\",\n      \"50\\tValidation loss: 0.962153\\tBest loss: 0.871899\\tAccuracy: 68.67%\\n\",\n      \"51\\tValidation loss: 0.925021\\tBest loss: 0.871899\\tAccuracy: 65.33%\\n\",\n      \"52\\tValidation loss: 0.974412\\tBest loss: 0.871899\\tAccuracy: 67.33%\\n\",\n      \"53\\tValidation loss: 0.897499\\tBest loss: 0.871899\\tAccuracy: 68.67%\\n\",\n      \"54\\tValidation loss: 0.933581\\tBest loss: 0.871899\\tAccuracy: 60.67%\\n\",\n      \"55\\tValidation loss: 0.988574\\tBest loss: 0.871899\\tAccuracy: 68.67%\\n\",\n      \"56\\tValidation loss: 0.927290\\tBest loss: 0.871899\\tAccuracy: 66.67%\\n\",\n      \"57\\tValidation loss: 1.018713\\tBest loss: 0.871899\\tAccuracy: 64.00%\\n\",\n      \"58\\tValidation loss: 0.964709\\tBest loss: 0.871899\\tAccuracy: 66.00%\\n\",\n      \"59\\tValidation loss: 1.004696\\tBest loss: 0.871899\\tAccuracy: 59.33%\\n\",\n      \"60\\tValidation loss: 1.008746\\tBest loss: 0.871899\\tAccuracy: 58.67%\\n\",\n      \"61\\tValidation loss: 0.948558\\tBest loss: 0.871899\\tAccuracy: 68.00%\\n\",\n      \"62\\tValidation loss: 0.966037\\tBest loss: 0.871899\\tAccuracy: 64.00%\\n\",\n      \"63\\tValidation loss: 0.922541\\tBest loss: 0.871899\\tAccuracy: 68.00%\\n\",\n      \"64\\tValidation loss: 0.892541\\tBest loss: 0.871899\\tAccuracy: 72.00%\\n\",\n      \"65\\tValidation loss: 0.890340\\tBest loss: 0.871899\\tAccuracy: 70.67%\\n\",\n      \"66\\tValidation loss: 0.957904\\tBest loss: 0.871899\\tAccuracy: 66.00%\\n\",\n      \"Early stopping!\\n\",\n      \"Total training time: 1.9s\\n\",\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_five_frozen\\n\",\n      \"Final test accuracy: 64.02%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"checks_without_progress = 0\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_best_mnist_model_0_to_4\\\")\\n\",\n    \"    t0 = time.time()\\n\",\n    \"        \\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train2[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val, acc_val = sess.run([loss, accuracy], feed_dict={X: X_valid2, y: y_valid2})\\n\",\n    \"        if loss_val < best_loss:\\n\",\n    \"            save_path = five_frozen_saver.save(sess, \\\"./my_mnist_model_5_to_9_five_frozen\\\")\\n\",\n    \"            best_loss = loss_val\\n\",\n    \"            checks_without_progress = 0\\n\",\n    \"        else:\\n\",\n    \"            checks_without_progress += 1\\n\",\n    \"            if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                print(\\\"Early stopping!\\\")\\n\",\n    \"                break\\n\",\n    \"        print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"            epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    print(\\\"Total training time: {:.1f}s\\\".format(t1 - t0))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    five_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_five_frozen\\\")\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test2, y: y_test2})\\n\",\n    \"    print(\\\"Final test accuracy: {:.2f}%\\\".format(acc_test * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well that's not a great accuracy, is it? Of course with such a tiny training set, and with only one layer to tweak, we should not expect miracles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.3.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: try caching the frozen layers, and train the model again: how much faster is it now?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by getting a handle on the output of the last frozen layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 148,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"hidden5_out = tf.get_default_graph().get_tensor_by_name(\\\"hidden5_out:0\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's train the model using roughly the same code as earlier. The difference is that we compute the output of the top frozen layer at the beginning (both for the training set and the validation set), and we cache it. This makes training roughly 1.5 to 3 times faster in this example (this may vary greatly, depending on your system): \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 149,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\\n\",\n      \"0\\tValidation loss: 1.416103\\tBest loss: 1.416103\\tAccuracy: 44.00%\\n\",\n      \"1\\tValidation loss: 1.099216\\tBest loss: 1.099216\\tAccuracy: 53.33%\\n\",\n      \"2\\tValidation loss: 1.024954\\tBest loss: 1.024954\\tAccuracy: 59.33%\\n\",\n      \"3\\tValidation loss: 0.969193\\tBest loss: 0.969193\\tAccuracy: 60.00%\\n\",\n      \"4\\tValidation loss: 0.973461\\tBest loss: 0.969193\\tAccuracy: 64.67%\\n\",\n      \"5\\tValidation loss: 0.949333\\tBest loss: 0.949333\\tAccuracy: 64.67%\\n\",\n      \"6\\tValidation loss: 0.922953\\tBest loss: 0.922953\\tAccuracy: 66.67%\\n\",\n      \"7\\tValidation loss: 0.957186\\tBest loss: 0.922953\\tAccuracy: 62.67%\\n\",\n      \"8\\tValidation loss: 0.950264\\tBest loss: 0.922953\\tAccuracy: 68.00%\\n\",\n      \"9\\tValidation loss: 1.053465\\tBest loss: 0.922953\\tAccuracy: 59.33%\\n\",\n      \"10\\tValidation loss: 1.069949\\tBest loss: 0.922953\\tAccuracy: 54.00%\\n\",\n      \"11\\tValidation loss: 0.965197\\tBest loss: 0.922953\\tAccuracy: 62.67%\\n\",\n      \"12\\tValidation loss: 0.949233\\tBest loss: 0.922953\\tAccuracy: 63.33%\\n\",\n      \"13\\tValidation loss: 0.926229\\tBest loss: 0.922953\\tAccuracy: 63.33%\\n\",\n      \"14\\tValidation loss: 0.922854\\tBest loss: 0.922854\\tAccuracy: 67.33%\\n\",\n      \"15\\tValidation loss: 0.965205\\tBest loss: 0.922854\\tAccuracy: 66.67%\\n\",\n      \"16\\tValidation loss: 1.050026\\tBest loss: 0.922854\\tAccuracy: 59.33%\\n\",\n      \"17\\tValidation loss: 0.946699\\tBest loss: 0.922854\\tAccuracy: 64.67%\\n\",\n      \"18\\tValidation loss: 0.973966\\tBest loss: 0.922854\\tAccuracy: 64.00%\\n\",\n      \"19\\tValidation loss: 0.902573\\tBest loss: 0.902573\\tAccuracy: 66.67%\\n\",\n      \"20\\tValidation loss: 0.933625\\tBest loss: 0.902573\\tAccuracy: 65.33%\\n\",\n      \"21\\tValidation loss: 0.938296\\tBest loss: 0.902573\\tAccuracy: 64.00%\\n\",\n      \"22\\tValidation loss: 0.938790\\tBest loss: 0.902573\\tAccuracy: 66.67%\\n\",\n      \"23\\tValidation loss: 0.936572\\tBest loss: 0.902573\\tAccuracy: 68.00%\\n\",\n      \"24\\tValidation loss: 1.039109\\tBest loss: 0.902573\\tAccuracy: 65.33%\\n\",\n      \"25\\tValidation loss: 1.146837\\tBest loss: 0.902573\\tAccuracy: 59.33%\\n\",\n      \"26\\tValidation loss: 0.958702\\tBest loss: 0.902573\\tAccuracy: 68.67%\\n\",\n      \"27\\tValidation loss: 0.915434\\tBest loss: 0.902573\\tAccuracy: 70.67%\\n\",\n      \"28\\tValidation loss: 0.915402\\tBest loss: 0.902573\\tAccuracy: 66.00%\\n\",\n      \"29\\tValidation loss: 0.920591\\tBest loss: 0.902573\\tAccuracy: 70.67%\\n\",\n      \"30\\tValidation loss: 1.029216\\tBest loss: 0.902573\\tAccuracy: 64.67%\\n\",\n      \"31\\tValidation loss: 1.039922\\tBest loss: 0.902573\\tAccuracy: 55.33%\\n\",\n      \"32\\tValidation loss: 0.925041\\tBest loss: 0.902573\\tAccuracy: 64.00%\\n\",\n      \"33\\tValidation loss: 0.944033\\tBest loss: 0.902573\\tAccuracy: 67.33%\\n\",\n      \"34\\tValidation loss: 0.941914\\tBest loss: 0.902573\\tAccuracy: 66.67%\\n\",\n      \"35\\tValidation loss: 0.866297\\tBest loss: 0.866297\\tAccuracy: 69.33%\\n\",\n      \"36\\tValidation loss: 0.900787\\tBest loss: 0.866297\\tAccuracy: 70.67%\\n\",\n      \"37\\tValidation loss: 0.889670\\tBest loss: 0.866297\\tAccuracy: 66.67%\\n\",\n      \"38\\tValidation loss: 0.968139\\tBest loss: 0.866297\\tAccuracy: 62.00%\\n\",\n      \"39\\tValidation loss: 0.929764\\tBest loss: 0.866297\\tAccuracy: 66.00%\\n\",\n      \"40\\tValidation loss: 0.889130\\tBest loss: 0.866297\\tAccuracy: 68.00%\\n\",\n      \"41\\tValidation loss: 0.940024\\tBest loss: 0.866297\\tAccuracy: 70.00%\\n\",\n      \"42\\tValidation loss: 0.896472\\tBest loss: 0.866297\\tAccuracy: 69.33%\\n\",\n      \"43\\tValidation loss: 0.893887\\tBest loss: 0.866297\\tAccuracy: 67.33%\\n\",\n      \"44\\tValidation loss: 0.925727\\tBest loss: 0.866297\\tAccuracy: 68.67%\\n\",\n      \"45\\tValidation loss: 0.945748\\tBest loss: 0.866297\\tAccuracy: 66.00%\\n\",\n      \"46\\tValidation loss: 0.897087\\tBest loss: 0.866297\\tAccuracy: 70.00%\\n\",\n      \"47\\tValidation loss: 0.923855\\tBest loss: 0.866297\\tAccuracy: 68.67%\\n\",\n      \"48\\tValidation loss: 0.944244\\tBest loss: 0.866297\\tAccuracy: 66.67%\\n\",\n      \"49\\tValidation loss: 0.975582\\tBest loss: 0.866297\\tAccuracy: 66.67%\\n\",\n      \"50\\tValidation loss: 0.889869\\tBest loss: 0.866297\\tAccuracy: 68.67%\\n\",\n      \"51\\tValidation loss: 0.895552\\tBest loss: 0.866297\\tAccuracy: 69.33%\\n\",\n      \"52\\tValidation loss: 0.943707\\tBest loss: 0.866297\\tAccuracy: 66.00%\\n\",\n      \"53\\tValidation loss: 0.902883\\tBest loss: 0.866297\\tAccuracy: 70.67%\\n\",\n      \"54\\tValidation loss: 0.958292\\tBest loss: 0.866297\\tAccuracy: 68.67%\\n\",\n      \"55\\tValidation loss: 0.917368\\tBest loss: 0.866297\\tAccuracy: 67.33%\\n\",\n      \"Early stopping!\\n\",\n      \"Total training time: 1.1s\\n\",\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_five_frozen\\n\",\n      \"Final test accuracy: 61.16%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"checks_without_progress = 0\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_best_mnist_model_0_to_4\\\")\\n\",\n    \"    t0 = time.time()\\n\",\n    \"    \\n\",\n    \"    hidden5_train = hidden5_out.eval(feed_dict={X: X_train2, y: y_train2})\\n\",\n    \"    hidden5_valid = hidden5_out.eval(feed_dict={X: X_valid2, y: y_valid2})\\n\",\n    \"        \\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            h5_batch, y_batch = hidden5_train[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={hidden5_out: h5_batch, y: y_batch})\\n\",\n    \"        loss_val, acc_val = sess.run([loss, accuracy], feed_dict={hidden5_out: hidden5_valid, y: y_valid2})\\n\",\n    \"        if loss_val < best_loss:\\n\",\n    \"            save_path = five_frozen_saver.save(sess, \\\"./my_mnist_model_5_to_9_five_frozen\\\")\\n\",\n    \"            best_loss = loss_val\\n\",\n    \"            checks_without_progress = 0\\n\",\n    \"        else:\\n\",\n    \"            checks_without_progress += 1\\n\",\n    \"            if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                print(\\\"Early stopping!\\\")\\n\",\n    \"                break\\n\",\n    \"        print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"            epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"\\n\",\n    \"    t1 = time.time()\\n\",\n    \"    print(\\\"Total training time: {:.1f}s\\\".format(t1 - t0))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    five_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_five_frozen\\\")\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test2, y: y_test2})\\n\",\n    \"    print(\\\"Final test accuracy: {:.2f}%\\\".format(acc_test * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.4.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: try again reusing just four hidden layers instead of five. Can you achieve a higher precision?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's load the best model again, but this time we will create a new softmax output layer on top of the 4th hidden layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 150,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_outputs = 5\\n\",\n    \"\\n\",\n    \"restore_saver = tf.train.import_meta_graph(\\\"./my_best_mnist_model_0_to_4.meta\\\")\\n\",\n    \"\\n\",\n    \"X = tf.get_default_graph().get_tensor_by_name(\\\"X:0\\\")\\n\",\n    \"y = tf.get_default_graph().get_tensor_by_name(\\\"y:0\\\")\\n\",\n    \"\\n\",\n    \"hidden4_out = tf.get_default_graph().get_tensor_by_name(\\\"hidden4_out:0\\\")\\n\",\n    \"logits = tf.layers.dense(hidden4_out, n_outputs, kernel_initializer=he_init, name=\\\"new_logits\\\")\\n\",\n    \"Y_proba = tf.nn.softmax(logits)\\n\",\n    \"xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"loss = tf.reduce_mean(xentropy)\\n\",\n    \"correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\\\"accuracy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now let's create the training operation. We want to freeze all the layers except for the new output layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 151,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"output_layer_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\\\"new_logits\\\")\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate, name=\\\"Adam2\\\")\\n\",\n    \"training_op = optimizer.minimize(loss, var_list=output_layer_vars)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"four_frozen_saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And once again we train the model with the same code as earlier. Note: we could of course write a function once and use it multiple times, rather than copying almost the same training code over and over again, but as we keep tweaking the code slightly, the function would need multiple arguments and `if` statements, and it would have to be at the beginning of the notebook, where it would not make much sense to readers. In short it would be very confusing, so we're better off with copy & paste.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 152,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\\n\",\n      \"0\\tValidation loss: 1.073254\\tBest loss: 1.073254\\tAccuracy: 51.33%\\n\",\n      \"1\\tValidation loss: 1.039487\\tBest loss: 1.039487\\tAccuracy: 64.00%\\n\",\n      \"2\\tValidation loss: 0.991418\\tBest loss: 0.991418\\tAccuracy: 59.33%\\n\",\n      \"3\\tValidation loss: 0.902691\\tBest loss: 0.902691\\tAccuracy: 64.67%\\n\",\n      \"4\\tValidation loss: 0.919874\\tBest loss: 0.902691\\tAccuracy: 63.33%\\n\",\n      \"5\\tValidation loss: 0.879734\\tBest loss: 0.879734\\tAccuracy: 72.00%\\n\",\n      \"6\\tValidation loss: 0.877940\\tBest loss: 0.877940\\tAccuracy: 70.67%\\n\",\n      \"7\\tValidation loss: 0.899513\\tBest loss: 0.877940\\tAccuracy: 71.33%\\n\",\n      \"8\\tValidation loss: 0.879717\\tBest loss: 0.877940\\tAccuracy: 67.33%\\n\",\n      \"9\\tValidation loss: 0.826527\\tBest loss: 0.826527\\tAccuracy: 75.33%\\n\",\n      \"10\\tValidation loss: 0.890165\\tBest loss: 0.826527\\tAccuracy: 67.33%\\n\",\n      \"11\\tValidation loss: 0.876235\\tBest loss: 0.826527\\tAccuracy: 68.67%\\n\",\n      \"12\\tValidation loss: 0.877598\\tBest loss: 0.826527\\tAccuracy: 71.33%\\n\",\n      \"13\\tValidation loss: 0.898070\\tBest loss: 0.826527\\tAccuracy: 74.67%\\n\",\n      \"14\\tValidation loss: 0.923526\\tBest loss: 0.826527\\tAccuracy: 68.00%\\n\",\n      \"15\\tValidation loss: 0.859624\\tBest loss: 0.826527\\tAccuracy: 70.00%\\n\",\n      \"16\\tValidation loss: 0.896264\\tBest loss: 0.826527\\tAccuracy: 67.33%\\n\",\n      \"17\\tValidation loss: 0.800813\\tBest loss: 0.800813\\tAccuracy: 73.33%\\n\",\n      \"18\\tValidation loss: 0.811318\\tBest loss: 0.800813\\tAccuracy: 74.00%\\n\",\n      \"19\\tValidation loss: 0.809687\\tBest loss: 0.800813\\tAccuracy: 75.33%\\n\",\n      \"20\\tValidation loss: 0.807125\\tBest loss: 0.800813\\tAccuracy: 72.67%\\n\",\n      \"21\\tValidation loss: 0.819150\\tBest loss: 0.800813\\tAccuracy: 71.33%\\n\",\n      \"22\\tValidation loss: 0.849812\\tBest loss: 0.800813\\tAccuracy: 76.67%\\n\",\n      \"23\\tValidation loss: 0.801709\\tBest loss: 0.800813\\tAccuracy: 74.67%\\n\",\n      \"24\\tValidation loss: 0.832877\\tBest loss: 0.800813\\tAccuracy: 74.00%\\n\",\n      \"25\\tValidation loss: 0.792853\\tBest loss: 0.792853\\tAccuracy: 72.67%\\n\",\n      \"26\\tValidation loss: 0.842031\\tBest loss: 0.792853\\tAccuracy: 76.00%\\n\",\n      \"27\\tValidation loss: 0.872236\\tBest loss: 0.792853\\tAccuracy: 71.33%\\n\",\n      \"28\\tValidation loss: 0.782557\\tBest loss: 0.782557\\tAccuracy: 78.00%\\n\",\n      \"29\\tValidation loss: 0.802515\\tBest loss: 0.782557\\tAccuracy: 73.33%\\n\",\n      \"30\\tValidation loss: 0.812652\\tBest loss: 0.782557\\tAccuracy: 72.67%\\n\",\n      \"31\\tValidation loss: 0.825467\\tBest loss: 0.782557\\tAccuracy: 76.00%\\n\",\n      \"32\\tValidation loss: 0.791320\\tBest loss: 0.782557\\tAccuracy: 76.67%\\n\",\n      \"33\\tValidation loss: 0.785207\\tBest loss: 0.782557\\tAccuracy: 77.33%\\n\",\n      \"34\\tValidation loss: 0.815450\\tBest loss: 0.782557\\tAccuracy: 76.67%\\n\",\n      \"35\\tValidation loss: 0.865081\\tBest loss: 0.782557\\tAccuracy: 71.33%\\n\",\n      \"36\\tValidation loss: 0.852323\\tBest loss: 0.782557\\tAccuracy: 74.67%\\n\",\n      \"37\\tValidation loss: 0.836967\\tBest loss: 0.782557\\tAccuracy: 72.00%\\n\",\n      \"38\\tValidation loss: 0.807404\\tBest loss: 0.782557\\tAccuracy: 77.33%\\n\",\n      \"39\\tValidation loss: 0.821566\\tBest loss: 0.782557\\tAccuracy: 75.33%\\n\",\n      \"40\\tValidation loss: 0.817326\\tBest loss: 0.782557\\tAccuracy: 76.00%\\n\",\n      \"41\\tValidation loss: 0.807987\\tBest loss: 0.782557\\tAccuracy: 70.67%\\n\",\n      \"42\\tValidation loss: 0.838029\\tBest loss: 0.782557\\tAccuracy: 74.00%\\n\",\n      \"43\\tValidation loss: 0.820425\\tBest loss: 0.782557\\tAccuracy: 76.00%\\n\",\n      \"44\\tValidation loss: 0.785871\\tBest loss: 0.782557\\tAccuracy: 76.00%\\n\",\n      \"45\\tValidation loss: 0.844337\\tBest loss: 0.782557\\tAccuracy: 78.67%\\n\",\n      \"46\\tValidation loss: 0.764127\\tBest loss: 0.764127\\tAccuracy: 78.67%\\n\",\n      \"47\\tValidation loss: 0.789726\\tBest loss: 0.764127\\tAccuracy: 77.33%\\n\",\n      \"48\\tValidation loss: 0.839190\\tBest loss: 0.764127\\tAccuracy: 72.67%\\n\",\n      \"49\\tValidation loss: 0.849353\\tBest loss: 0.764127\\tAccuracy: 75.33%\\n\",\n      \"50\\tValidation loss: 0.869818\\tBest loss: 0.764127\\tAccuracy: 74.00%\\n\",\n      \"51\\tValidation loss: 0.805526\\tBest loss: 0.764127\\tAccuracy: 76.67%\\n\",\n      \"52\\tValidation loss: 0.850749\\tBest loss: 0.764127\\tAccuracy: 72.67%\\n\",\n      \"53\\tValidation loss: 0.838693\\tBest loss: 0.764127\\tAccuracy: 71.33%\\n\",\n      \"54\\tValidation loss: 0.791396\\tBest loss: 0.764127\\tAccuracy: 75.33%\\n\",\n      \"55\\tValidation loss: 0.846888\\tBest loss: 0.764127\\tAccuracy: 76.00%\\n\",\n      \"56\\tValidation loss: 0.826717\\tBest loss: 0.764127\\tAccuracy: 74.67%\\n\",\n      \"57\\tValidation loss: 0.878286\\tBest loss: 0.764127\\tAccuracy: 70.67%\\n\",\n      \"58\\tValidation loss: 0.878869\\tBest loss: 0.764127\\tAccuracy: 72.67%\\n\",\n      \"59\\tValidation loss: 0.822241\\tBest loss: 0.764127\\tAccuracy: 72.67%\\n\",\n      \"60\\tValidation loss: 0.864925\\tBest loss: 0.764127\\tAccuracy: 73.33%\\n\",\n      \"61\\tValidation loss: 0.804545\\tBest loss: 0.764127\\tAccuracy: 73.33%\\n\",\n      \"62\\tValidation loss: 0.891784\\tBest loss: 0.764127\\tAccuracy: 72.67%\\n\",\n      \"63\\tValidation loss: 0.810186\\tBest loss: 0.764127\\tAccuracy: 74.00%\\n\",\n      \"64\\tValidation loss: 0.810786\\tBest loss: 0.764127\\tAccuracy: 74.67%\\n\",\n      \"65\\tValidation loss: 0.818044\\tBest loss: 0.764127\\tAccuracy: 74.00%\\n\",\n      \"66\\tValidation loss: 0.853420\\tBest loss: 0.764127\\tAccuracy: 74.67%\\n\",\n      \"Early stopping!\\n\",\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_four_frozen\\n\",\n      \"Final test accuracy: 69.10%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"checks_without_progress = 0\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_best_mnist_model_0_to_4\\\")\\n\",\n    \"        \\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train2[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val, acc_val = sess.run([loss, accuracy], feed_dict={X: X_valid2, y: y_valid2})\\n\",\n    \"        if loss_val < best_loss:\\n\",\n    \"            save_path = four_frozen_saver.save(sess, \\\"./my_mnist_model_5_to_9_four_frozen\\\")\\n\",\n    \"            best_loss = loss_val\\n\",\n    \"            checks_without_progress = 0\\n\",\n    \"        else:\\n\",\n    \"            checks_without_progress += 1\\n\",\n    \"            if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                print(\\\"Early stopping!\\\")\\n\",\n    \"                break\\n\",\n    \"        print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"            epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    four_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_four_frozen\\\")\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test2, y: y_test2})\\n\",\n    \"    print(\\\"Final test accuracy: {:.2f}%\\\".format(acc_test * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Still not fantastic, but much better.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.5.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_Exercise: now unfreeze the top two hidden layers and continue training: can you get the model to perform even better?_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 153,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"unfrozen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\\\"hidden[34]|new_logits\\\")\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate, name=\\\"Adam3\\\")\\n\",\n    \"training_op = optimizer.minimize(loss, var_list=unfrozen_vars)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"two_frozen_saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 154,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_four_frozen\\n\",\n      \"0\\tValidation loss: 1.054859\\tBest loss: 1.054859\\tAccuracy: 74.00%\\n\",\n      \"1\\tValidation loss: 0.812410\\tBest loss: 0.812410\\tAccuracy: 78.00%\\n\",\n      \"2\\tValidation loss: 0.750377\\tBest loss: 0.750377\\tAccuracy: 80.67%\\n\",\n      \"3\\tValidation loss: 0.570973\\tBest loss: 0.570973\\tAccuracy: 84.67%\\n\",\n      \"4\\tValidation loss: 0.805442\\tBest loss: 0.570973\\tAccuracy: 79.33%\\n\",\n      \"5\\tValidation loss: 0.920925\\tBest loss: 0.570973\\tAccuracy: 80.00%\\n\",\n      \"6\\tValidation loss: 0.817471\\tBest loss: 0.570973\\tAccuracy: 81.33%\\n\",\n      \"7\\tValidation loss: 0.777876\\tBest loss: 0.570973\\tAccuracy: 84.00%\\n\",\n      \"8\\tValidation loss: 1.030498\\tBest loss: 0.570973\\tAccuracy: 74.67%\\n\",\n      \"9\\tValidation loss: 1.074356\\tBest loss: 0.570973\\tAccuracy: 81.33%\\n\",\n      \"10\\tValidation loss: 0.912521\\tBest loss: 0.570973\\tAccuracy: 83.33%\\n\",\n      \"11\\tValidation loss: 1.356695\\tBest loss: 0.570973\\tAccuracy: 79.33%\\n\",\n      \"12\\tValidation loss: 0.918798\\tBest loss: 0.570973\\tAccuracy: 82.00%\\n\",\n      \"13\\tValidation loss: 0.971029\\tBest loss: 0.570973\\tAccuracy: 82.67%\\n\",\n      \"14\\tValidation loss: 0.860108\\tBest loss: 0.570973\\tAccuracy: 83.33%\\n\",\n      \"15\\tValidation loss: 1.074813\\tBest loss: 0.570973\\tAccuracy: 82.00%\\n\",\n      \"16\\tValidation loss: 0.867760\\tBest loss: 0.570973\\tAccuracy: 84.00%\\n\",\n      \"17\\tValidation loss: 0.858290\\tBest loss: 0.570973\\tAccuracy: 85.33%\\n\",\n      \"18\\tValidation loss: 0.996560\\tBest loss: 0.570973\\tAccuracy: 85.33%\\n\",\n      \"19\\tValidation loss: 1.304507\\tBest loss: 0.570973\\tAccuracy: 83.33%\\n\",\n      \"20\\tValidation loss: 1.134808\\tBest loss: 0.570973\\tAccuracy: 80.67%\\n\",\n      \"21\\tValidation loss: 1.189581\\tBest loss: 0.570973\\tAccuracy: 82.00%\\n\",\n      \"22\\tValidation loss: 1.131344\\tBest loss: 0.570973\\tAccuracy: 81.33%\\n\",\n      \"23\\tValidation loss: 1.240507\\tBest loss: 0.570973\\tAccuracy: 82.67%\\n\",\n      \"Early stopping!\\n\",\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_two_frozen\\n\",\n      \"Final test accuracy: 78.09%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"checks_without_progress = 0\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    four_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_four_frozen\\\")\\n\",\n    \"        \\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train2[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val, acc_val = sess.run([loss, accuracy], feed_dict={X: X_valid2, y: y_valid2})\\n\",\n    \"        if loss_val < best_loss:\\n\",\n    \"            save_path = two_frozen_saver.save(sess, \\\"./my_mnist_model_5_to_9_two_frozen\\\")\\n\",\n    \"            best_loss = loss_val\\n\",\n    \"            checks_without_progress = 0\\n\",\n    \"        else:\\n\",\n    \"            checks_without_progress += 1\\n\",\n    \"            if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                print(\\\"Early stopping!\\\")\\n\",\n    \"                break\\n\",\n    \"        print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"            epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    two_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_two_frozen\\\")\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test2, y: y_test2})\\n\",\n    \"    print(\\\"Final test accuracy: {:.2f}%\\\".format(acc_test * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's check what accuracy we can get by unfreezing all layers:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 155,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate, name=\\\"Adam4\\\")\\n\",\n    \"training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"no_frozen_saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 156,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_two_frozen\\n\",\n      \"0\\tValidation loss: 0.863416\\tBest loss: 0.863416\\tAccuracy: 86.00%\\n\",\n      \"1\\tValidation loss: 0.695079\\tBest loss: 0.695079\\tAccuracy: 90.00%\\n\",\n      \"2\\tValidation loss: 0.402921\\tBest loss: 0.402921\\tAccuracy: 92.00%\\n\",\n      \"3\\tValidation loss: 0.606936\\tBest loss: 0.402921\\tAccuracy: 92.00%\\n\",\n      \"4\\tValidation loss: 0.354645\\tBest loss: 0.354645\\tAccuracy: 90.67%\\n\",\n      \"5\\tValidation loss: 0.376935\\tBest loss: 0.354645\\tAccuracy: 90.67%\\n\",\n      \"6\\tValidation loss: 0.593208\\tBest loss: 0.354645\\tAccuracy: 90.00%\\n\",\n      \"7\\tValidation loss: 0.388302\\tBest loss: 0.354645\\tAccuracy: 92.67%\\n\",\n      \"8\\tValidation loss: 0.503276\\tBest loss: 0.354645\\tAccuracy: 91.33%\\n\",\n      \"9\\tValidation loss: 1.440716\\tBest loss: 0.354645\\tAccuracy: 80.00%\\n\",\n      \"10\\tValidation loss: 0.464323\\tBest loss: 0.354645\\tAccuracy: 92.00%\\n\",\n      \"11\\tValidation loss: 0.410302\\tBest loss: 0.354645\\tAccuracy: 93.33%\\n\",\n      \"12\\tValidation loss: 1.131754\\tBest loss: 0.354645\\tAccuracy: 88.00%\\n\",\n      \"13\\tValidation loss: 0.511544\\tBest loss: 0.354645\\tAccuracy: 92.00%\\n\",\n      \"14\\tValidation loss: 0.402083\\tBest loss: 0.354645\\tAccuracy: 94.00%\\n\",\n      \"15\\tValidation loss: 1.149943\\tBest loss: 0.354645\\tAccuracy: 92.00%\\n\",\n      \"16\\tValidation loss: 0.405171\\tBest loss: 0.354645\\tAccuracy: 94.00%\\n\",\n      \"17\\tValidation loss: 0.304346\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"18\\tValidation loss: 0.386952\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"19\\tValidation loss: 0.387063\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"20\\tValidation loss: 0.384417\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"21\\tValidation loss: 0.381116\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"22\\tValidation loss: 0.379346\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"23\\tValidation loss: 0.378128\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"24\\tValidation loss: 0.376642\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"25\\tValidation loss: 0.375432\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"26\\tValidation loss: 0.374804\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"27\\tValidation loss: 0.373952\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"28\\tValidation loss: 0.373471\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"29\\tValidation loss: 0.373027\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"30\\tValidation loss: 0.373124\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"31\\tValidation loss: 0.373098\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"32\\tValidation loss: 0.373206\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"33\\tValidation loss: 0.372812\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"34\\tValidation loss: 0.373109\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"35\\tValidation loss: 0.372616\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"36\\tValidation loss: 0.372491\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"37\\tValidation loss: 0.372270\\tBest loss: 0.304346\\tAccuracy: 94.67%\\n\",\n      \"Early stopping!\\n\",\n      \"INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_no_frozen\\n\",\n      \"Final test accuracy: 91.34%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"checks_without_progress = 0\\n\",\n    \"best_loss = np.infty\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    two_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_two_frozen\\\")\\n\",\n    \"        \\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train2[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        loss_val, acc_val = sess.run([loss, accuracy], feed_dict={X: X_valid2, y: y_valid2})\\n\",\n    \"        if loss_val < best_loss:\\n\",\n    \"            save_path = no_frozen_saver.save(sess, \\\"./my_mnist_model_5_to_9_no_frozen\\\")\\n\",\n    \"            best_loss = loss_val\\n\",\n    \"            checks_without_progress = 0\\n\",\n    \"        else:\\n\",\n    \"            checks_without_progress += 1\\n\",\n    \"            if checks_without_progress > max_checks_without_progress:\\n\",\n    \"                print(\\\"Early stopping!\\\")\\n\",\n    \"                break\\n\",\n    \"        print(\\\"{}\\\\tValidation loss: {:.6f}\\\\tBest loss: {:.6f}\\\\tAccuracy: {:.2f}%\\\".format(\\n\",\n    \"            epoch, loss_val, best_loss, acc_val * 100))\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    no_frozen_saver.restore(sess, \\\"./my_mnist_model_5_to_9_no_frozen\\\")\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test2, y: y_test2})\\n\",\n    \"    print(\\\"Final test accuracy: {:.2f}%\\\".format(acc_test * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare that to a DNN trained from scratch:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 157,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\tValidation loss: 0.674618\\tBest loss: 0.674618\\tAccuracy: 80.67%\\n\",\n      \"1\\tValidation loss: 0.584845\\tBest loss: 0.584845\\tAccuracy: 88.67%\\n\",\n      \"2\\tValidation loss: 0.647296\\tBest loss: 0.584845\\tAccuracy: 84.00%\\n\",\n      \"3\\tValidation loss: 0.530389\\tBest loss: 0.530389\\tAccuracy: 87.33%\\n\",\n      \"4\\tValidation loss: 0.683215\\tBest loss: 0.530389\\tAccuracy: 90.67%\\n\",\n      \"5\\tValidation loss: 0.538040\\tBest loss: 0.530389\\tAccuracy: 89.33%\\n\",\n      \"6\\tValidation loss: 0.670196\\tBest loss: 0.530389\\tAccuracy: 90.67%\\n\",\n      \"7\\tValidation loss: 0.836470\\tBest loss: 0.530389\\tAccuracy: 85.33%\\n\",\n      \"8\\tValidation loss: 0.837684\\tBest loss: 0.530389\\tAccuracy: 92.67%\\n\",\n      \"9\\tValidation loss: 0.588950\\tBest loss: 0.530389\\tAccuracy: 88.00%\\n\",\n      \"10\\tValidation loss: 0.643213\\tBest loss: 0.530389\\tAccuracy: 90.67%\\n\",\n      \"11\\tValidation loss: 1.010521\\tBest loss: 0.530389\\tAccuracy: 88.00%\\n\",\n      \"12\\tValidation loss: 0.931423\\tBest loss: 0.530389\\tAccuracy: 90.00%\\n\",\n      \"13\\tValidation loss: 1.563524\\tBest loss: 0.530389\\tAccuracy: 88.67%\\n\",\n      \"14\\tValidation loss: 2.340119\\tBest loss: 0.530389\\tAccuracy: 89.33%\\n\",\n      \"15\\tValidation loss: 1.402095\\tBest loss: 0.530389\\tAccuracy: 88.00%\\n\",\n      \"16\\tValidation loss: 1.269974\\tBest loss: 0.530389\\tAccuracy: 86.00%\\n\",\n      \"17\\tValidation loss: 1.036325\\tBest loss: 0.530389\\tAccuracy: 89.33%\\n\",\n      \"18\\tValidation loss: 1.578565\\tBest loss: 0.530389\\tAccuracy: 88.67%\\n\",\n      \"19\\tValidation loss: 0.993890\\tBest loss: 0.530389\\tAccuracy: 93.33%\\n\",\n      \"20\\tValidation loss: 0.958130\\tBest loss: 0.530389\\tAccuracy: 87.33%\\n\",\n      \"21\\tValidation loss: 1.505322\\tBest loss: 0.530389\\tAccuracy: 88.67%\\n\",\n      \"22\\tValidation loss: 1.378772\\tBest loss: 0.530389\\tAccuracy: 89.33%\\n\",\n      \"23\\tValidation loss: 0.999445\\tBest loss: 0.530389\\tAccuracy: 88.00%\\n\",\n      \"24\\tValidation loss: 2.366345\\tBest loss: 0.530389\\tAccuracy: 90.00%\\n\",\n      \"Early stopping!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DNNClassifier(activation=<function elu at 0x1243639d8>,\\n\",\n       \"       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\\n\",\n       \"       initializer=<tensorflow.python.ops.init_ops.VarianceScaling object at 0x117bf5828>,\\n\",\n       \"       learning_rate=0.01, n_hidden_layers=4, n_neurons=100,\\n\",\n       \"       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\\n\",\n       \"       random_state=42)\"\n      ]\n     },\n     \"execution_count\": 157,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn_clf_5_to_9 = DNNClassifier(n_hidden_layers=4, random_state=42)\\n\",\n    \"dnn_clf_5_to_9.fit(X_train2, y_train2, n_epochs=1000, X_valid=X_valid2, y_valid=y_valid2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 158,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.8481793869574161\"\n      ]\n     },\n     \"execution_count\": 158,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_pred = dnn_clf_5_to_9.predict(X_test2)\\n\",\n    \"accuracy_score(y_test2, y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Transfer learning allowed us to go from 84.8% accuracy to 91.3%. Not too bad!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10. Pretraining on an auxiliary task\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this exercise you will build a DNN that compares two MNIST digit images and predicts whether they represent the same digit or not. Then you will reuse the lower layers of this network to train an MNIST classifier using very little training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 10.1.\\n\",\n    \"Exercise: _Start by building two DNNs (let's call them DNN A and B), both similar to the one you built earlier but without the output layer: each DNN should have five hidden layers of 100 neurons each, He initialization, and ELU activation. Next, add one more hidden layer with 10 units on top of both DNNs. You should use TensorFlow's `concat()` function with `axis=1` to concatenate the outputs of both DNNs along the horizontal axis, then feed the result to the hidden layer. Finally, add an output layer with a single neuron using the logistic activation function._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**! There was an error in the book for this exercise: there was no instruction to add a top hidden layer. Without it, the neural network generally fails to start learning. If you have the latest version of the book, this error has been fixed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You could have two input placeholders, `X1` and `X2`, one for the images that should be fed to the first DNN, and the other for the images that should be fed to the second DNN. It would work fine. However, another option is to have a single input placeholder to hold both sets of images (each row will hold a pair of images), and use `tf.unstack()` to split this tensor into two separate tensors, like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 159,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_inputs = 28 * 28 # MNIST\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, 2, n_inputs), name=\\\"X\\\")\\n\",\n    \"X1, X2 = tf.unstack(X, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We also need the labels placeholder. Each label will be 0 if the images represent different digits, or 1 if they represent the same digit:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 160,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y = tf.placeholder(tf.int32, shape=[None, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's feed these inputs through two separate DNNs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 161,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dnn1 = dnn(X1, name=\\\"DNN_A\\\")\\n\",\n    \"dnn2 = dnn(X2, name=\\\"DNN_B\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And let's concatenate their outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 162,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dnn_outputs = tf.concat([dnn1, dnn2], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each DNN outputs 100 activations (per instance), so the shape is `[None, 100]`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 163,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([Dimension(None), Dimension(100)])\"\n      ]\n     },\n     \"execution_count\": 163,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn1.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 164,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([Dimension(None), Dimension(100)])\"\n      ]\n     },\n     \"execution_count\": 164,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn2.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And of course the concatenated outputs have a shape of `[None, 200]`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 165,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([Dimension(None), Dimension(200)])\"\n      ]\n     },\n     \"execution_count\": 165,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dnn_outputs.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now lets add an extra hidden layer with just 10 neurons, and the output layer, with a single neuron:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 166,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"hidden = tf.layers.dense(dnn_outputs, units=10, activation=tf.nn.elu, kernel_initializer=he_init)\\n\",\n    \"logits = tf.layers.dense(hidden, units=1, kernel_initializer=he_init)\\n\",\n    \"y_proba = tf.nn.sigmoid(logits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The whole network predicts `1` if `y_proba >= 0.5` (i.e. the network predicts that the images represent the same digit), or `0` otherwise. We compute instead `logits >= 0`, which is equivalent but faster to compute: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 167,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_pred = tf.cast(tf.greater_equal(logits, 0), tf.int32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's add the cost function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 168,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_as_float = tf.cast(y, tf.float32)\\n\",\n    \"xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_as_float, logits=logits)\\n\",\n    \"loss = tf.reduce_mean(xentropy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And we can now create the training operation using an optimizer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 169,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"momentum = 0.95\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.MomentumOptimizer(learning_rate, momentum, use_nesterov=True)\\n\",\n    \"training_op = optimizer.minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will want to measure our classifier's accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 170,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_pred_correct = tf.equal(y_pred, y)\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(y_pred_correct, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And the usual `init` and `saver`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 171,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 10.2.\\n\",\n    \"_Exercise: split the MNIST training set in two sets: split #1 should containing 55,000 images, and split #2 should contain contain 5,000 images. Create a function that generates a training batch where each instance is a pair of MNIST images picked from split #1. Half of the training instances should be pairs of images that belong to the same class, while the other half should be images from different classes. For each pair, the training label should be 0 if the images are from the same class, or 1 if they are from different classes._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The MNIST dataset returned by TensorFlow's `input_data()` function is already split into 3 parts: a training set (55,000 instances), a validation set (5,000 instances) and a test set (10,000 instances). Let's use the first set to generate the training set composed image pairs, and we will use the second set for the second phase of the exercise (to train a regular MNIST classifier). We will use the third set as the test set for both phases.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 172,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train1 = X_train\\n\",\n    \"y_train1 = y_train\\n\",\n    \"\\n\",\n    \"X_train2 = X_valid\\n\",\n    \"y_train2 = y_valid\\n\",\n    \"\\n\",\n    \"X_test = X_test\\n\",\n    \"y_test = y_test\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's write a function that generates pairs of images: 50% representing the same digit, and 50% representing different digits. There are many ways to implement this. In this implementation, we first decide how many \\\"same\\\" pairs (i.e. pairs of images representing the same digit) we will generate, and how many \\\"different\\\" pairs (i.e. pairs of images representing different digits). We could just use `batch_size // 2` but we want to handle the case where it is odd (granted, that might be overkill!). Then we generate random pairs and we pick the right number of \\\"same\\\" pairs, then we generate the right number of \\\"different\\\" pairs. Finally we shuffle the batch and return it:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 173,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_batch(images, labels, batch_size):\\n\",\n    \"    size1 = batch_size // 2\\n\",\n    \"    size2 = batch_size - size1\\n\",\n    \"    if size1 != size2 and np.random.rand() > 0.5:\\n\",\n    \"        size1, size2 = size2, size1\\n\",\n    \"    X = []\\n\",\n    \"    y = []\\n\",\n    \"    while len(X) < size1:\\n\",\n    \"        rnd_idx1, rnd_idx2 = np.random.randint(0, len(images), 2)\\n\",\n    \"        if rnd_idx1 != rnd_idx2 and labels[rnd_idx1] == labels[rnd_idx2]:\\n\",\n    \"            X.append(np.array([images[rnd_idx1], images[rnd_idx2]]))\\n\",\n    \"            y.append([1])\\n\",\n    \"    while len(X) < batch_size:\\n\",\n    \"        rnd_idx1, rnd_idx2 = np.random.randint(0, len(images), 2)\\n\",\n    \"        if labels[rnd_idx1] != labels[rnd_idx2]:\\n\",\n    \"            X.append(np.array([images[rnd_idx1], images[rnd_idx2]]))\\n\",\n    \"            y.append([0])\\n\",\n    \"    rnd_indices = np.random.permutation(batch_size)\\n\",\n    \"    return np.array(X)[rnd_indices], np.array(y)[rnd_indices]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's test it to generate a small batch of 5 image pairs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 174,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 5\\n\",\n    \"X_batch, y_batch = generate_batch(X_train1, y_train1, batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each row in `X_batch` contains a pair of images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 175,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((5, 2, 784), dtype('float32'))\"\n      ]\n     },\n     \"execution_count\": 175,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_batch.shape, X_batch.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at these pairs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 176,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 216x1080 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(3, 3 * batch_size))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"plt.imshow(X_batch[:,0].reshape(28 * batch_size, 28), cmap=\\\"binary\\\", interpolation=\\\"nearest\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.subplot(122)\\n\",\n    \"plt.imshow(X_batch[:,1].reshape(28 * batch_size, 28), cmap=\\\"binary\\\", interpolation=\\\"nearest\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And let's look at the labels (0 means \\\"different\\\", 1 means \\\"same\\\"):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 177,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1],\\n\",\n       \"       [0],\\n\",\n       \"       [0],\\n\",\n       \"       [1],\\n\",\n       \"       [0]])\"\n      ]\n     },\n     \"execution_count\": 177,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Perfect!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 10.3.\\n\",\n    \"_Exercise: train the DNN on this training set. For each image pair, you can simultaneously feed the first image to DNN A and the second image to DNN B. The whole network will gradually learn to tell whether two images belong to the same class or not._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's generate a test set composed of many pairs of images pulled from the MNIST test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 178,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_test1, y_test1 = generate_batch(X_test, y_test, batch_size=len(X_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now, let's train the model. There's really nothing special about this step, except for the fact that we need a fairly large `batch_size`, otherwise the model fails to learn anything and ends up with an accuracy of 50%:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 179,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Train loss: 0.69103277\\n\",\n      \"0 Test accuracy: 0.542\\n\",\n      \"1 Train loss: 0.6035354\\n\",\n      \"2 Train loss: 0.54946035\\n\",\n      \"3 Train loss: 0.47047246\\n\",\n      \"4 Train loss: 0.4060757\\n\",\n      \"5 Train loss: 0.38308156\\n\",\n      \"5 Test accuracy: 0.824\\n\",\n      \"6 Train loss: 0.39047274\\n\",\n      \"7 Train loss: 0.3390794\\n\",\n      \"8 Train loss: 0.3210671\\n\",\n      \"9 Train loss: 0.31792685\\n\",\n      \"10 Train loss: 0.24494292\\n\",\n      \"10 Test accuracy: 0.8881\\n\",\n      \"11 Train loss: 0.2929235\\n\",\n      \"12 Train loss: 0.23225449\\n\",\n      \"13 Train loss: 0.23180929\\n\",\n      \"14 Train loss: 0.19877923\\n\",\n      \"15 Train loss: 0.20065464\\n\",\n      \"15 Test accuracy: 0.9203\\n\",\n      \"16 Train loss: 0.19700499\\n\",\n      \"17 Train loss: 0.18893136\\n\",\n      \"18 Train loss: 0.19965452\\n\",\n      \"19 Train loss: 0.24071647\\n\",\n      \"20 Train loss: 0.18882024\\n\",\n      \"20 Test accuracy: 0.9367\\n\",\n      \"21 Train loss: 0.12419197\\n\",\n      \"22 Train loss: 0.14013417\\n\",\n      \"23 Train loss: 0.120789476\\n\",\n      \"24 Train loss: 0.15721135\\n\",\n      \"25 Train loss: 0.11507861\\n\",\n      \"25 Test accuracy: 0.948\\n\",\n      \"26 Train loss: 0.13891116\\n\",\n      \"27 Train loss: 0.1526081\\n\",\n      \"28 Train loss: 0.123436704\\n\",\n      \"<<50 more lines>>\\n\",\n      \"70 Test accuracy: 0.9743\\n\",\n      \"71 Train loss: 0.019732744\\n\",\n      \"72 Train loss: 0.039464083\\n\",\n      \"73 Train loss: 0.04187814\\n\",\n      \"74 Train loss: 0.05303406\\n\",\n      \"75 Train loss: 0.052625064\\n\",\n      \"75 Test accuracy: 0.9756\\n\",\n      \"76 Train loss: 0.038283084\\n\",\n      \"77 Train loss: 0.026332883\\n\",\n      \"78 Train loss: 0.07060841\\n\",\n      \"79 Train loss: 0.03239444\\n\",\n      \"80 Train loss: 0.03136283\\n\",\n      \"80 Test accuracy: 0.9731\\n\",\n      \"81 Train loss: 0.04390848\\n\",\n      \"82 Train loss: 0.015268046\\n\",\n      \"83 Train loss: 0.04875638\\n\",\n      \"84 Train loss: 0.029360933\\n\",\n      \"85 Train loss: 0.0418443\\n\",\n      \"85 Test accuracy: 0.9759\\n\",\n      \"86 Train loss: 0.018274888\\n\",\n      \"87 Train loss: 0.038872603\\n\",\n      \"88 Train loss: 0.02969683\\n\",\n      \"89 Train loss: 0.020990817\\n\",\n      \"90 Train loss: 0.045234833\\n\",\n      \"90 Test accuracy: 0.9769\\n\",\n      \"91 Train loss: 0.039237432\\n\",\n      \"92 Train loss: 0.031329047\\n\",\n      \"93 Train loss: 0.033414133\\n\",\n      \"94 Train loss: 0.025883088\\n\",\n      \"95 Train loss: 0.019567214\\n\",\n      \"95 Test accuracy: 0.9765\\n\",\n      \"96 Train loss: 0.020650322\\n\",\n      \"97 Train loss: 0.0339851\\n\",\n      \"98 Train loss: 0.047079965\\n\",\n      \"99 Train loss: 0.03125228\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 100\\n\",\n    \"batch_size = 500\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for iteration in range(len(X_train1) // batch_size):\\n\",\n    \"            X_batch, y_batch = generate_batch(X_train1, y_train1, batch_size)\\n\",\n    \"            loss_val, _ = sess.run([loss, training_op], feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        print(epoch, \\\"Train loss:\\\", loss_val)\\n\",\n    \"        if epoch % 5 == 0:\\n\",\n    \"            acc_test = accuracy.eval(feed_dict={X: X_test1, y: y_test1})\\n\",\n    \"            print(epoch, \\\"Test accuracy:\\\", acc_test)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_digit_comparison_model.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"All right, we reach 97.6% accuracy on this digit comparison task. That's not too bad, this model knows a thing or two about comparing handwritten digits!\\n\",\n    \"\\n\",\n    \"Let's see if some of that knowledge can be useful for the regular MNIST classification task.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 10.4.\\n\",\n    \"_Exercise: now create a new DNN by reusing and freezing the hidden layers of DNN A and adding a softmax output layer on top with 10 neurons. Train this network on split #2 and see if you can achieve high performance despite having only 500 images per class._\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create the model, it is pretty straightforward. There are many ways to freeze the lower layers, as explained in the book. In this example, we chose to use the `tf.stop_gradient()` function. Note that we need one `Saver` to restore the pretrained DNN A, and another `Saver` to save the final model: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 180,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"dnn_outputs = dnn(X, name=\\\"DNN_A\\\")\\n\",\n    \"frozen_outputs = tf.stop_gradient(dnn_outputs)\\n\",\n    \"\\n\",\n    \"logits = tf.layers.dense(frozen_outputs, n_outputs, kernel_initializer=he_init)\\n\",\n    \"Y_proba = tf.nn.softmax(logits)\\n\",\n    \"\\n\",\n    \"xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.MomentumOptimizer(learning_rate, momentum, use_nesterov=True)\\n\",\n    \"training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"dnn_A_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\\\"DNN_A\\\")\\n\",\n    \"restore_saver = tf.train.Saver(var_list={var.op.name: var for var in dnn_A_vars})\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now on to training! We first initialize all variables (including the variables in the new output layer), then we restore the pretrained DNN A. Next, we just train the model on the small MNIST dataset (containing just 5,000 images):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 181,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_digit_comparison_model.ckpt\\n\",\n      \"0 Test accuracy: 0.9455\\n\",\n      \"10 Test accuracy: 0.9634\\n\",\n      \"20 Test accuracy: 0.9659\\n\",\n      \"30 Test accuracy: 0.9656\\n\",\n      \"40 Test accuracy: 0.9655\\n\",\n      \"50 Test accuracy: 0.9656\\n\",\n      \"60 Test accuracy: 0.9655\\n\",\n      \"70 Test accuracy: 0.9656\\n\",\n      \"80 Test accuracy: 0.9654\\n\",\n      \"90 Test accuracy: 0.9654\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 100\\n\",\n    \"batch_size = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    restore_saver.restore(sess, \\\"./my_digit_comparison_model.ckpt\\\")\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train2[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        if epoch % 10 == 0:\\n\",\n    \"            acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\\n\",\n    \"            print(epoch, \\\"Test accuracy:\\\", acc_test)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_mnist_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Well, 96.5% accuracy, that's not the best MNIST model we have trained so far, but recall that we are only using a small training set (just 500 images per digit). Let's compare this result with the same DNN trained from scratch, without using transfer learning:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 182,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"n_inputs = 28 * 28  # MNIST\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n    \"y = tf.placeholder(tf.int32, shape=(None), name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"dnn_outputs = dnn(X, name=\\\"DNN_A\\\")\\n\",\n    \"\\n\",\n    \"logits = tf.layers.dense(dnn_outputs, n_outputs, kernel_initializer=he_init)\\n\",\n    \"Y_proba = tf.nn.softmax(logits)\\n\",\n    \"\\n\",\n    \"xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n    \"loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"optimizer = tf.train.MomentumOptimizer(learning_rate, momentum, use_nesterov=True)\\n\",\n    \"training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"dnn_A_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\\\"DNN_A\\\")\\n\",\n    \"restore_saver = tf.train.Saver(var_list={var.op.name: var for var in dnn_A_vars})\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 183,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Test accuracy: 0.8694\\n\",\n      \"10 Test accuracy: 0.9276\\n\",\n      \"20 Test accuracy: 0.9299\\n\",\n      \"30 Test accuracy: 0.935\\n\",\n      \"40 Test accuracy: 0.942\\n\",\n      \"50 Test accuracy: 0.9435\\n\",\n      \"60 Test accuracy: 0.9442\\n\",\n      \"70 Test accuracy: 0.9447\\n\",\n      \"80 Test accuracy: 0.9448\\n\",\n      \"90 Test accuracy: 0.945\\n\",\n      \"100 Test accuracy: 0.945\\n\",\n      \"110 Test accuracy: 0.9458\\n\",\n      \"120 Test accuracy: 0.9456\\n\",\n      \"130 Test accuracy: 0.9458\\n\",\n      \"140 Test accuracy: 0.9458\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 150\\n\",\n    \"batch_size = 50\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        rnd_idx = np.random.permutation(len(X_train2))\\n\",\n    \"        for rnd_indices in np.array_split(rnd_idx, len(X_train2) // batch_size):\\n\",\n    \"            X_batch, y_batch = X_train2[rnd_indices], y_train2[rnd_indices]\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        if epoch % 10 == 0:\\n\",\n    \"            acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\\n\",\n    \"            print(epoch, \\\"Test accuracy:\\\", acc_test)\\n\",\n    \"\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_mnist_model_final.ckpt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Only 94.6% accuracy... So transfer learning helped us reduce the error rate from 5.4% to 3.5% (that's over 35% error reduction). Moreover, the model using transfer learning reached over 96% accuracy in less than 10 epochs.\\n\",\n    \"\\n\",\n    \"Bottom line: transfer learning does not always work, but when it does it can make a big difference. So try it out!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {\n   \"height\": \"360px\",\n   \"width\": \"416px\"\n  },\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "12_distributed_tensorflow.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 12 – Distributed TensorFlow**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 12._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/12_distributed_tensorflow.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions. In particular, the 1st edition is based on TensorFlow 1, while the 2nd edition uses TensorFlow 2, which is much simpler to use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    # %tensorflow_version only exists in Colab.\\n\",\n    \"    %tensorflow_version 1.x\\n\",\n    \"except Exception:\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"def reset_graph(seed=42):\\n\",\n    \"    tf.reset_default_graph()\\n\",\n    \"    tf.set_random_seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['axes.labelsize'] = 14\\n\",\n    \"plt.rcParams['xtick.labelsize'] = 12\\n\",\n    \"plt.rcParams['ytick.labelsize'] = 12\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"distributed\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Local server\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"c = tf.constant(\\\"Hello distributed TensorFlow!\\\")\\n\",\n    \"server = tf.train.Server.create_local_server()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b'Hello distributed TensorFlow!'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session(server.target) as sess:\\n\",\n    \"    print(sess.run(c))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Cluster\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cluster_spec = tf.train.ClusterSpec({\\n\",\n    \"    \\\"ps\\\": [\\n\",\n    \"        \\\"127.0.0.1:2221\\\",  # /job:ps/task:0\\n\",\n    \"        \\\"127.0.0.1:2222\\\",  # /job:ps/task:1\\n\",\n    \"    ],\\n\",\n    \"    \\\"worker\\\": [\\n\",\n    \"        \\\"127.0.0.1:2223\\\",  # /job:worker/task:0\\n\",\n    \"        \\\"127.0.0.1:2224\\\",  # /job:worker/task:1\\n\",\n    \"        \\\"127.0.0.1:2225\\\",  # /job:worker/task:2\\n\",\n    \"    ]})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"task_ps0 = tf.train.Server(cluster_spec, job_name=\\\"ps\\\", task_index=0)\\n\",\n    \"task_ps1 = tf.train.Server(cluster_spec, job_name=\\\"ps\\\", task_index=1)\\n\",\n    \"task_worker0 = tf.train.Server(cluster_spec, job_name=\\\"worker\\\", task_index=0)\\n\",\n    \"task_worker1 = tf.train.Server(cluster_spec, job_name=\\\"worker\\\", task_index=1)\\n\",\n    \"task_worker2 = tf.train.Server(cluster_spec, job_name=\\\"worker\\\", task_index=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Pinning operations across devices and servers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"with tf.device(\\\"/job:ps\\\"):\\n\",\n    \"    a = tf.Variable(1.0, name=\\\"a\\\")\\n\",\n    \"\\n\",\n    \"with tf.device(\\\"/job:worker\\\"):\\n\",\n    \"    b = a + 2\\n\",\n    \"\\n\",\n    \"with tf.device(\\\"/job:worker/task:1\\\"):\\n\",\n    \"    c = a + b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session(\\\"grpc://127.0.0.1:2221\\\") as sess:\\n\",\n    \"    sess.run(a.initializer)\\n\",\n    \"    print(c.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"with tf.device(tf.train.replica_device_setter(\\n\",\n    \"        ps_tasks=2,\\n\",\n    \"        ps_device=\\\"/job:ps\\\",\\n\",\n    \"        worker_device=\\\"/job:worker\\\")):\\n\",\n    \"    v1 = tf.Variable(1.0, name=\\\"v1\\\")  # pinned to /job:ps/task:0 (defaults to /cpu:0)\\n\",\n    \"    v2 = tf.Variable(2.0, name=\\\"v2\\\")  # pinned to /job:ps/task:1 (defaults to /cpu:0)\\n\",\n    \"    v3 = tf.Variable(3.0, name=\\\"v3\\\")  # pinned to /job:ps/task:0 (defaults to /cpu:0)\\n\",\n    \"    s = v1 + v2            # pinned to /job:worker (defaults to task:0/cpu:0)\\n\",\n    \"    with tf.device(\\\"/task:1\\\"):\\n\",\n    \"        p1 = 2 * s         # pinned to /job:worker/task:1 (defaults to /cpu:0)\\n\",\n    \"        with tf.device(\\\"/cpu:0\\\"):\\n\",\n    \"            p2 = 3 * s     # pinned to /job:worker/task:1/cpu:0\\n\",\n    \"\\n\",\n    \"config = tf.ConfigProto()\\n\",\n    \"config.log_device_placement = True\\n\",\n    \"\\n\",\n    \"with tf.Session(\\\"grpc://127.0.0.1:2221\\\", config=config) as sess:\\n\",\n    \"    v1.initializer.run()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Readers – the old way\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1.0, 6, 44]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"default1 = tf.constant([5.])\\n\",\n    \"default2 = tf.constant([6])\\n\",\n    \"default3 = tf.constant([7])\\n\",\n    \"dec = tf.decode_csv(tf.constant(\\\"1.,,44\\\"),\\n\",\n    \"                    record_defaults=[default1, default2, default3])\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    print(sess.run(dec))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"No more files to read\\n\",\n      \"[array([[ 4.,  5.],\\n\",\n      \"       [ 1., -1.]], dtype=float32), array([1, 0], dtype=int32)]\\n\",\n      \"[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\\n\",\n      \"No more training instances\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"test_csv = open(\\\"my_test.csv\\\", \\\"w\\\")\\n\",\n    \"test_csv.write(\\\"x1, x2 , target\\\\n\\\")\\n\",\n    \"test_csv.write(\\\"1.,, 0\\\\n\\\")\\n\",\n    \"test_csv.write(\\\"4., 5. , 1\\\\n\\\")\\n\",\n    \"test_csv.write(\\\"7., 8. , 0\\\\n\\\")\\n\",\n    \"test_csv.close()\\n\",\n    \"\\n\",\n    \"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\\n\",\n    \"filename = tf.placeholder(tf.string)\\n\",\n    \"enqueue_filename = filename_queue.enqueue([filename])\\n\",\n    \"close_filename_queue = filename_queue.close()\\n\",\n    \"\\n\",\n    \"reader = tf.TextLineReader(skip_header_lines=1)\\n\",\n    \"key, value = reader.read(filename_queue)\\n\",\n    \"\\n\",\n    \"x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\\n\",\n    \"features = tf.stack([x1, x2])\\n\",\n    \"\\n\",\n    \"instance_queue = tf.RandomShuffleQueue(\\n\",\n    \"    capacity=10, min_after_dequeue=2,\\n\",\n    \"    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\\n\",\n    \"    name=\\\"instance_q\\\", shared_name=\\\"shared_instance_q\\\")\\n\",\n    \"enqueue_instance = instance_queue.enqueue([features, target])\\n\",\n    \"close_instance_queue = instance_queue.close()\\n\",\n    \"\\n\",\n    \"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(enqueue_filename, feed_dict={filename: \\\"my_test.csv\\\"})\\n\",\n    \"    sess.run(close_filename_queue)\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            sess.run(enqueue_instance)\\n\",\n    \"    except tf.errors.OutOfRangeError as ex:\\n\",\n    \"        print(\\\"No more files to read\\\")\\n\",\n    \"    sess.run(close_instance_queue)\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            print(sess.run([minibatch_instances, minibatch_targets]))\\n\",\n    \"    except tf.errors.OutOfRangeError as ex:\\n\",\n    \"        print(\\\"No more training instances\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#coord = tf.train.Coordinator()\\n\",\n    \"#threads = tf.train.start_queue_runners(coord=coord)\\n\",\n    \"#filename_queue = tf.train.string_input_producer([\\\"test.csv\\\"])\\n\",\n    \"#coord.request_stop()\\n\",\n    \"#coord.join(threads)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Queue runners and coordinators\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[ 7.,  8.],\\n\",\n      \"       [ 1., -1.]], dtype=float32), array([0, 0], dtype=int32)]\\n\",\n      \"[array([[4., 5.]], dtype=float32), array([1], dtype=int32)]\\n\",\n      \"No more training instances\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\\n\",\n    \"filename = tf.placeholder(tf.string)\\n\",\n    \"enqueue_filename = filename_queue.enqueue([filename])\\n\",\n    \"close_filename_queue = filename_queue.close()\\n\",\n    \"\\n\",\n    \"reader = tf.TextLineReader(skip_header_lines=1)\\n\",\n    \"key, value = reader.read(filename_queue)\\n\",\n    \"\\n\",\n    \"x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\\n\",\n    \"features = tf.stack([x1, x2])\\n\",\n    \"\\n\",\n    \"instance_queue = tf.RandomShuffleQueue(\\n\",\n    \"    capacity=10, min_after_dequeue=2,\\n\",\n    \"    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\\n\",\n    \"    name=\\\"instance_q\\\", shared_name=\\\"shared_instance_q\\\")\\n\",\n    \"enqueue_instance = instance_queue.enqueue([features, target])\\n\",\n    \"close_instance_queue = instance_queue.close()\\n\",\n    \"\\n\",\n    \"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\\n\",\n    \"\\n\",\n    \"n_threads = 5\\n\",\n    \"queue_runner = tf.train.QueueRunner(instance_queue, [enqueue_instance] * n_threads)\\n\",\n    \"coord = tf.train.Coordinator()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(enqueue_filename, feed_dict={filename: \\\"my_test.csv\\\"})\\n\",\n    \"    sess.run(close_filename_queue)\\n\",\n    \"    enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            print(sess.run([minibatch_instances, minibatch_targets]))\\n\",\n    \"    except tf.errors.OutOfRangeError as ex:\\n\",\n    \"        print(\\\"No more training instances\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[ 4.,  5.],\\n\",\n      \"       [ 1., -1.]], dtype=float32), array([1, 0], dtype=int32)]\\n\",\n      \"[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\\n\",\n      \"No more training instances\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"def read_and_push_instance(filename_queue, instance_queue):\\n\",\n    \"    reader = tf.TextLineReader(skip_header_lines=1)\\n\",\n    \"    key, value = reader.read(filename_queue)\\n\",\n    \"    x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\\n\",\n    \"    features = tf.stack([x1, x2])\\n\",\n    \"    enqueue_instance = instance_queue.enqueue([features, target])\\n\",\n    \"    return enqueue_instance\\n\",\n    \"\\n\",\n    \"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\\n\",\n    \"filename = tf.placeholder(tf.string)\\n\",\n    \"enqueue_filename = filename_queue.enqueue([filename])\\n\",\n    \"close_filename_queue = filename_queue.close()\\n\",\n    \"\\n\",\n    \"instance_queue = tf.RandomShuffleQueue(\\n\",\n    \"    capacity=10, min_after_dequeue=2,\\n\",\n    \"    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\\n\",\n    \"    name=\\\"instance_q\\\", shared_name=\\\"shared_instance_q\\\")\\n\",\n    \"\\n\",\n    \"minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\\n\",\n    \"\\n\",\n    \"read_and_enqueue_ops = [read_and_push_instance(filename_queue, instance_queue) for i in range(5)]\\n\",\n    \"queue_runner = tf.train.QueueRunner(instance_queue, read_and_enqueue_ops)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(enqueue_filename, feed_dict={filename: \\\"my_test.csv\\\"})\\n\",\n    \"    sess.run(close_filename_queue)\\n\",\n    \"    coord = tf.train.Coordinator()\\n\",\n    \"    enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            print(sess.run([minibatch_instances, minibatch_targets]))\\n\",\n    \"    except tf.errors.OutOfRangeError as ex:\\n\",\n    \"        print(\\\"No more training instances\\\")\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setting a timeout\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2.0\\n\",\n      \"6.0\\n\",\n      \"3.0\\n\",\n      \"4.0\\n\",\n      \"Timed out while dequeuing\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])\\n\",\n    \"v = tf.placeholder(tf.float32)\\n\",\n    \"enqueue = q.enqueue([v])\\n\",\n    \"dequeue = q.dequeue()\\n\",\n    \"output = dequeue + 1\\n\",\n    \"\\n\",\n    \"config = tf.ConfigProto()\\n\",\n    \"config.operation_timeout_in_ms = 1000\\n\",\n    \"\\n\",\n    \"with tf.Session(config=config) as sess:\\n\",\n    \"    sess.run(enqueue, feed_dict={v: 1.0})\\n\",\n    \"    sess.run(enqueue, feed_dict={v: 2.0})\\n\",\n    \"    sess.run(enqueue, feed_dict={v: 3.0})\\n\",\n    \"    print(sess.run(output))\\n\",\n    \"    print(sess.run(output, feed_dict={dequeue: 5}))\\n\",\n    \"    print(sess.run(output))\\n\",\n    \"    print(sess.run(output))\\n\",\n    \"    try:\\n\",\n    \"        print(sess.run(output))\\n\",\n    \"    except tf.errors.DeadlineExceededError as ex:\\n\",\n    \"        print(\\\"Timed out while dequeuing\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Data API\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Data API, introduced in TensorFlow 1.4, makes reading data efficiently much easier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.reset_default_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start with a simple dataset composed of three times the integers 0 to 9, in batches of 7:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = tf.data.Dataset.from_tensor_slices(np.arange(10))\\n\",\n    \"dataset = dataset.repeat(3).batch(7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first line creates a dataset containing the integers 0 through 9. The second line creates a new dataset based on the first one, repeating its elements three times and creating batches of 7 elements. As you can see, we start with a source dataset, then we chain calls to various methods to apply transformations to the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we create a one-shot-iterator to go through this dataset just once, and we call its `get_next()` method to get a tensor that represents the next element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"iterator = dataset.make_one_shot_iterator()\\n\",\n    \"next_element = iterator.get_next()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's repeatedly evaluate `next_element` to go through the dataset. When there are not more elements, we get an `OutOfRangeError`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3 4 5 6]\\n\",\n      \"[7 8 9 0 1 2 3]\\n\",\n      \"[4 5 6 7 8 9 0]\\n\",\n      \"[1 2 3 4 5 6 7]\\n\",\n      \"[8 9]\\n\",\n      \"Done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            print(next_element.eval())\\n\",\n    \"    except tf.errors.OutOfRangeError:\\n\",\n    \"        print(\\\"Done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Great! It worked fine.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that, as always, a tensor is only evaluated once each time we run the graph (`sess.run()`): so even if we evaluate multiple tensors that all depend on `next_element`, it is only evaluated once. This is true as well if we ask for `next_element` to be evaluated twice in just one run:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([0, 1, 2, 3, 4, 5, 6]), array([0, 1, 2, 3, 4, 5, 6])]\\n\",\n      \"[array([7, 8, 9, 0, 1, 2, 3]), array([7, 8, 9, 0, 1, 2, 3])]\\n\",\n      \"[array([4, 5, 6, 7, 8, 9, 0]), array([4, 5, 6, 7, 8, 9, 0])]\\n\",\n      \"[array([1, 2, 3, 4, 5, 6, 7]), array([1, 2, 3, 4, 5, 6, 7])]\\n\",\n      \"[array([8, 9]), array([8, 9])]\\n\",\n      \"Done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            print(sess.run([next_element, next_element]))\\n\",\n    \"    except tf.errors.OutOfRangeError:\\n\",\n    \"        print(\\\"Done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `interleave()` method is powerful but a bit tricky to grasp at first. The easiest way to understand it is to look at an example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.reset_default_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = tf.data.Dataset.from_tensor_slices(np.arange(10))\\n\",\n    \"dataset = dataset.repeat(3).batch(7)\\n\",\n    \"dataset = dataset.interleave(\\n\",\n    \"    lambda v: tf.data.Dataset.from_tensor_slices(v),\\n\",\n    \"    cycle_length=3,\\n\",\n    \"    block_length=2)\\n\",\n    \"iterator = dataset.make_one_shot_iterator()\\n\",\n    \"next_element = iterator.get_next()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0,1,7,8,4,5,2,3,9,0,6,7,4,5,1,2,8,9,6,3,0,1,2,8,9,3,4,5,6,7,Done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            print(next_element.eval(), end=\\\",\\\")\\n\",\n    \"    except tf.errors.OutOfRangeError:\\n\",\n    \"        print(\\\"Done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because `cycle_length=3`, the new dataset starts by pulling 3 elements from the previous dataset: that's `[0,1,2,3,4,5,6]`, `[7,8,9,0,1,2,3]` and `[4,5,6,7,8,9,0]`. Then it calls the lambda function we gave it to create one dataset for each of the elements. Since we use `Dataset.from_tensor_slices()`, each dataset is going to return its elements one by one. Next, it pulls two items (since `block_length=2`) from each of these three datasets, and it iterates until all three datasets are out of items: 0,1 (from 1st), 7,8 (from 2nd), 4,5 (from 3rd), 2,3 (from 1st), 9,0 (from 2nd), and so on until 8,9 (from 3rd), 6 (from 1st), 3 (from 2nd), 0 (from 3rd). Next it tries to pull the next 3 elements from the original dataset, but there are just two left: `[1,2,3,4,5,6,7]` and `[8,9]`. Again, it creates datasets from these elements, and it pulls two items from each until both datasets are out of items: 1,2 (from 1st), 8,9 (from 2nd), 3,4 (from 1st), 5,6 (from 1st), 7 (from 1st). Notice that there's no interleaving at the end since the arrays do not have the same length.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Readers – the new way\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Instead of using a source dataset based on `from_tensor_slices()` or `from_tensor()`, we can use a reader dataset. It handles most of the complexity for us (e.g., threads):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.reset_default_graph()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"filenames = [\\\"my_test.csv\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = tf.data.TextLineDataset(filenames)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We still need to tell it how to decode each line:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def decode_csv_line(line):\\n\",\n    \"    x1, x2, y = tf.decode_csv(\\n\",\n    \"        line, record_defaults=[[-1.], [-1.], [-1.]])\\n\",\n    \"    X = tf.stack([x1, x2])\\n\",\n    \"    return X, y\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we can apply this decoding function to each element in the dataset using `map()`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = dataset.skip(1).map(decode_csv_line)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, let's create a one-shot iterator:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"it = dataset.make_one_shot_iterator()\\n\",\n    \"X, y = it.get_next()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1. -1.] 0.0\\n\",\n      \"[4. 5.] 1.0\\n\",\n      \"[7. 8.] 0.0\\n\",\n      \"Done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    try:\\n\",\n    \"        while True:\\n\",\n    \"            X_val, y_val = sess.run([X, y])\\n\",\n    \"            print(X_val, y_val)\\n\",\n    \"    except tf.errors.OutOfRangeError as ex:\\n\",\n    \"        print(\\\"Done\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Coming soon**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {},\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "13_convolutional_neural_networks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Chapter 13 – Convolutional Neural Networks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_This notebook contains all the sample code and solutions to the exercises in chapter 13._\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/13_convolutional_neural_networks.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions. In particular, the 1st edition is based on TensorFlow 1, while the 2nd edition uses TensorFlow 2, which is much simpler to use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# To support both python 2 and python 3\\n\",\n    \"from __future__ import division, print_function, unicode_literals\\n\",\n    \"from io import open\\n\",\n    \"\\n\",\n    \"# Common imports\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    # %tensorflow_version only exists in Colab.\\n\",\n    \"    %tensorflow_version 1.x\\n\",\n    \"except Exception:\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"# to make this notebook's output stable across runs\\n\",\n    \"def reset_graph(seed=42):\\n\",\n    \"    tf.reset_default_graph()\\n\",\n    \"    tf.set_random_seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"\\n\",\n    \"# To plot pretty figures\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['axes.labelsize'] = 14\\n\",\n    \"plt.rcParams['xtick.labelsize'] = 12\\n\",\n    \"plt.rcParams['ytick.labelsize'] = 12\\n\",\n    \"\\n\",\n    \"# Where to save the figures\\n\",\n    \"PROJECT_ROOT_DIR = \\\".\\\"\\n\",\n    \"CHAPTER_ID = \\\"cnn\\\"\\n\",\n    \"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", CHAPTER_ID)\\n\",\n    \"os.makedirs(IMAGES_PATH, exist_ok=True)\\n\",\n    \"\\n\",\n    \"def save_fig(fig_id, tight_layout=True, fig_extension=\\\"png\\\", resolution=300):\\n\",\n    \"    path = os.path.join(IMAGES_PATH, fig_id + \\\".\\\" + fig_extension)\\n\",\n    \"    print(\\\"Saving figure\\\", fig_id)\\n\",\n    \"    if tight_layout:\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"    plt.savefig(path, format=fig_extension, dpi=resolution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A couple utility functions to plot grayscale and RGB images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_image(image):\\n\",\n    \"    plt.imshow(image, cmap=\\\"gray\\\", interpolation=\\\"nearest\\\")\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"\\n\",\n    \"def plot_color_image(image):\\n\",\n    \"    plt.imshow(image.astype(np.uint8),interpolation=\\\"nearest\\\")\\n\",\n    \"    plt.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And of course we will need TensorFlow:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Convolutional layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import load_sample_image\\n\",\n    \"china = load_sample_image(\\\"china.jpg\\\")\\n\",\n    \"flower = load_sample_image(\\\"flower.jpg\\\")\\n\",\n    \"image = china[150:220, 130:250]\\n\",\n    \"height, width, channels = image.shape\\n\",\n    \"image_grayscale = image.mean(axis=2).astype(np.float32)\\n\",\n    \"images = image_grayscale.reshape(1, height, width, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fmap = np.zeros(shape=(7, 7, 1, 2), dtype=np.float32)\\n\",\n    \"fmap[:, 3, 0, 0] = 1\\n\",\n    \"fmap[3, :, 0, 1] = 1\\n\",\n    \"plot_image(fmap[:, :, 0, 0])\\n\",\n    \"plt.show()\\n\",\n    \"plot_image(fmap[:, :, 0, 1])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, height, width, 1))\\n\",\n    \"feature_maps = tf.constant(fmap)\\n\",\n    \"convolution = tf.nn.conv2d(X, feature_maps, strides=[1,1,1,1], padding=\\\"SAME\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    output = convolution.eval(feed_dict={X: images})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure china_original\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_image(images[0, :, :, 0])\\n\",\n    \"save_fig(\\\"china_original\\\", tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure china_vertical\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_image(output[0, :, :, 0])\\n\",\n    \"save_fig(\\\"china_vertical\\\", tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure china_horizontal\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_image(output[0, :, :, 1])\\n\",\n    \"save_fig(\\\"china_horizontal\\\", tight_layout=False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Simple example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvWmIZdl2JvadO89xh7gxZQw5RVZlZg2vyq8k0WgA6QnTspANNtiSsVseeMJGxr8aG4zAtKFb9g//aFA3frgNraYR2KAWtOw/MkitZyTqPVVVZg1ZlUNkZMxx487zfI9/RK2VO07uvc8+996sivcUC5LIe4a999nDt9f69tprW7Zt40qu5Equ5Ep+8sX3bRfgSq7kSq7kSuYjV4B+JVdyJVfyUyJXgH4lV3IlV/JTIleAfiVXciVX8lMiV4B+JVdyJVfyUyJXgH4lV3IlV/JTIleAfiVXciVX8lMirx3QLcvKWpb1ryzLaluWtWdZ1m+97jyv5Equ5Er+NkrgG8jjDwAMACwD+A6A/9uyrIe2bX/xDeR9JVdyJVfyt0as17lT1LKsOIAqgLds237y9bV/AeDItu3//rVlfCVXciVX8rdQXreGfgfAiMD8a3kI4Jd0LyUSCXtzc5N/06RjWdbMBZpHGpchj1nldZfxMtTBvMtA6dm2/dq+73XW27fRJvPOU4UFTsX0MvS/eYhlWej1enjx4gVarZbrR71uQE8AaDiu1QEknQ9alvV9AN8HgEwmgz/8wz903jf+rWpMXSPL3p9lAPt8F5cn3MpkWdYrnVInpuVR1Yvs/WnSNE3b5F2T/L20g7MNTMrhbAdZP1P1B5NrXn+rAMytznT9WSa6upq3yMrjlSkwrSfV89OIW56miqeuTcV/dO3Zs2f4lV/5FaMyvm5AbwFIOa6lADSdD9q2/QMAPwCAzc1NZeuaDBrZgJtmsLld18lkMgFwPlAmk8mFAaMCb9l1Z2chkX2jeG1aUL3MMmsZxXaguhIHlxuoyOr2ddalWB43zdSkbKqJSAfm1I9NnzcRKrOzPKagrptUxWfEdFXjxfmsW7ll41E2NnXlkdWfDMx16ankdQP6EwABy7K2bdt++vW1dwEYL4jqOqqqIXTm17SA50WcDeb3+y+kqwJesaPLAGcWIJF1lmmpAzcNVXZP1Sll369rV6+ABLwEcp/Pp9SKVGVyy1/Vlm71Oo3C4cyXnpF9g/MZ5zXxugy0deL1eVOhdpxGsRLbgMaPmzjbTddHxbHpRXR9QqaNq54zldcK6LZtty3L+mMA/8CyrP8S514u/y6AvzNtmjoA8GIOyzq2qUYjE9LEgYvauZjOvADBBHBM09LRC7o8nXnpJgqvg8ArHypq3iqwoWdkaTvrQNVebv1KpsG5KRDTTKiyyV3Xn+fl+GCiGXsRU1pSN35UzzqviX+decnaX/a+F1EpkaaW1LTyTbgt/tcA/g8AZwDKAP4rE5dFldalA3EdEOg6g8n7biICiazsMo1b9oyq7LK0nKKrM6d14KQavA4UVSeUgYjbhOJFI1HVnxPIVWatTFRtodJ8Ta651Y/zutu7zv4glk8FQDpgkL2nKoPq2VkXIlVtZmJFyUQ1xmYZa9OISuOW/dWNl2msgtcO6LZtVwD8e7Ok4TaAdA0+rfnitXOqwNQkHTeg1wGO85rueTEvWaeZxiqQDRpVOUw0RhMgcRMvYO4mJhr9vMUNkHR1bWJVuN0znSBeh6ZuUibTsa7r0/Mou0ndipOUrHyqyZr+77zmJt+Ehj6TeK14N03cNF2vdItMTN93Mwm9pqtKx00Tmcaclg1y1cDymv68qAKxDFRG5zXxuuq37LqX75kXAKomGZXGpyqHjhpzUw7m8S2mlMu0Mk16ptq6qq9YlvWKpaFSTOi6CNizKjSXGtC9aIWm99waeVbN3JmHlwZRgaFYLlONXyY6esPNSnDLxwkkqu9wExn4TzvQvZquOm3X1HJyXp8VVEyuy+6ZlNekbLr2nBWAnd5fXt6TlckpKpA0eUclqnqlBXdVXuL4kgH4NFgnk0sN6CSzdpxpAMpNxFnYVLMxER23Ny2YywayquN61aCnmSBNrIZZwdy0fG7l0r0/L01Vlq6XcswjH2d+pu+/jnJR2qJ4taScY1JllZiKsx1E8Kb/y/qyCV01T0v0UgO6yaLONFqHLC1nnqbpqAB4MpkYgeu0Zr9MvGhlKg3aFOhNNPBpO6quDG7t49T4bNt+xevITabRkFXPTMu3vw4g1/W11803u8ks/d7rxDSNciSCtgzAxXK4jSXdupWIazJF0U0uNaAD6pnZ1AT22hlnNe+9pmNionlJ08QsdwNF1XUTsJcNAhNO3yRdt4Ho5C4pX9Wim2pyVeXjRbubh/Y6L+tE155OKkD2/KxWpyjz2pFqunA8rVIhA26VFi57V7yv+us2/nSWtEouNaB7AXOVTKsh6WTe26Sp4eYBAl5MO5OBKj7jZcJUDahpQfF1L6hOO4HOmq+qLJTWtFbBvBQTZ7omzzll2vHihXaRrX84n1VpvF7q2MuYUeXjZt2KHLtt2xiPx67lI7m0gK7SVlWzlswcV82o02gcbp3SbRBN0wF0eTg1SZUmrKoXVX5u6TnLItNodZ1fvOdMfxoQ0vkuu4mzfG7eSiam/TyUBudEpkpf159laaisDpOyz/Jdqs1eYn3r+opOvFiZsrEg/t+JG6YKzCy44hz7syoEPxGA7vwtq2w3msBpOnmVaVfkZxW37zJ5VnVPtnAk4++AV7fPi51PB/ayMrhxi6oyyyZNcQeoW7u6DdBptTa3Mk4rsnbwSnuZ5uMm87Be3MaPG51h2r9lwOictGUKy7QY4VRqVHmrFDedguq1ni8toOsaVEZPuGmhutlzHgNw1jRMJiTdO6r6kNWjrKPIftNzh4eHeP78OZ49e4ZKpYJUKoVr167h+vXrWF1dxcLCAgKBi11J1qlNANMrMMm0PNW7bpqsiUmso4HEyVBltcjEzcIxSUN2X9dndHnMSlOaBPsyAXcvFoVJOzqv6Z7X5WOqkJC4xb5RjVG67iV2zqUF9Fk6rwlYXTZRzcbO75rGrPP6/bZ97hli2zY++eQTfP755/j888/RbDYRCASwsLCAdruNZrOJSqWC1dVVLC0tIZ1Oey7LrM853/ECpCQy60Q1wdHzqvxU4G9SblWaXsTtXZ1JP4sFC5hx5brNRCZ92/TbZNYNPaPSxr1Yw7rrsvvTUClOq8JELi2gmwCc6to8OvU8ZVrzHdDzqSbpzcLJ1Wo1NBoN1Ot1VCoV+Hw+BAIBDAYDtFotnJ2dYTKZIBgMIhKJIBwOIxKJuJZnXhOs03wWAXcaLVelFcrSUb0/by7Yq+isNre85kkXmYhbOUzoJZU4qVYngKus2VlBWFWOb0ouLaBTpcs4RNVgmXZm14nKFc5EvOQp4/N0+coGnw4sTerGWQYCab/ff6EeiEefTCYYDocYDAYYjUbKdHXXZtUKqdwyS0ZXp9PSCl7L+U0B5LwsH1NQV2mN4lrTLHXlRSFT9SHVdefkr0p3Gnkdk6KX9C4toAN68FY9L4qJtiK+5zV903KZTAYmabh1OLdB4LWjhUIh1rojkQiazSb8fj9GoxFs20YgEEA4HEYoFEIwGHyFR5eVwVleHc3hJrIDK8R8ZtG2VaKbSHWm+euYNLykq5pcp60LlZPAvP3MScTym+4rcF6fZfzoyiaKM0TzNBSpl/yccqkBncRk0JuCqtd7bmFxqXwmIvsONy3WNG0TUPRK3xCgx2IxJBIJNJtNdLtdWJaFcDiMaDSKQCCAYDCIUCiEQCCgbR83y8Mr8Ml2hYr5iX9l5XGK6YBXLa7K0ppmwjJ51pQCMrlmcs8pJsA9y0TmdZzrrFVV28xTZBq/7L7X+ieL2FQuNaA7B6VskKq0+G+Ku5qmw85qksk6r0m6XqwdAl8C7kgkgtFoBL/fj8lkgsFggG63y9q6SJF5yUf3Xd+2zKLBimm8jufnUVfT0k4mAGOSnooi0YlKc1ctgM5DxH4wjcZtQvV5nXxVcmkBXQd+uoaS3TM1xXXiZZZ008DnLbNMYrryjcdjZDIZJBIJJBIJvk6Lo9FoFKlUCqlUCrFYbCYwp/KbvmPaHqo0v+kFwHmI1wVlp5aqmqBmobq8pmVCi5iO+Wm+x0s6unKaXHcqnqo1HeezzrRkRyeqZGbCy7KssGVZ/8yyrD3LspqWZT2wLOvvfn3vumVZtmVZLeHf73lJnypCVSGmJr54zU1oA434z4u4lc1NnN9qmhf9k5Vf7BSyd5xCVFM6nUY0GkU8Hkc+n0cgEIDP50Ov10MgEEAsFkM8HkcikdBSILryi2UylclkYuyf61afsvp4HWAv68fTpCFL0ylexoZpmcS+JNa9qv5k/U33XaIW7KwrL23kfN+t3qdd25JhkiqMLtWb3+9nJwPxuvh/Z/5e+uI8NPQAgAMAvwRgH8CvAfg/Lct6W3gmbdv2q24QhuLVNJtlMIoVasKfT5O/riOavG/ClZtqLrqF3slkgqOjI4RCIaytrWF7exsbGxtYW1tDNBqFZVkYj8fw+/3816R8srznsXgklt3tvlfu3rRczgFuolXLNOlZyiBLx6kteknTeV6rU0GQ1aUI5rJ8xXvOdGT1o/pGnczal3Tvq75R/K2rA1nbuKVnIjMDum3bbQD/o3DpTy3L2gXwbwH4aNb0neJWybOKs9O6iayTunU0k0ErgoFqcMoGlCo95/+dZZFpfqVSCeVyGScnJygUCtjc3GQXRdu2EQwGEY1GAYA3Ik1LNzm/21Rk5r+sDCagKv5V3Xe2yzTld+bhdbLQKQTT1LmbqBQbHdB6aUOx7lX159bnTb7FZKJwtrPqfdUkZgL0biIbi6Yydw7dsqxlAHcAiAdB71mWZQP4MwB/37btkuS97wP4PgBkMhm69kr6r5v71IH4NJ3Gi7hpUdNqbiaToDPv8XiMdruNRqOBVquFwWCAQqGAyWQCv9+PXC6HbDYLy7JYM9eVf1bu0ot4bQMdt+n2jky7nKWfzALIqry9KABuYnIYt6xsJjJPK1gmXihA3f+d15xALt6TWaG6POn3tG0012hTlmUFAfxLAP/ctu2vAJQAfABgC+cae/Lr+6+Ibds/sG37u7Ztf1dcgLuSK7mSK7kSM5mbhm5Zlg/AvwAwAPC7AGDbdgvA33z9SMGyrN8FcGJZVtK27aYmLSWnOI05Py8xMcF0lIOKu9R9i8oENJ3FdZSN7v1SqYSnT5/iwYMHePjwIUajEQKBAAKBAG8kCoVCuH79Ot566y3cv38fS0tLCIfDr830f12io1dkz7i1l+mzsnfc6Div3L/4nMyUnydNCcz/rACn6OiuadKSva/StlWUimwcOReNvZZ12m+aC6Bb57n/MwDLAH7Ntu2h4lH6cqNWdwI78OrRbiYDwbmoo5Jpt/mrTF0TU94Lz6bjTlXlkU06Yh06y2XbNrrdLp49e4Yvv/wSn376Kdrt9oXV+/F4jMFgwDz7xx9/jGaziY2NDVy/fh0rKytG5Ze1mRuX/TrFlAaZp5Kh6zsm9aUqo+q6CIaz1K0I3LqAW17Ea7ncxpzqWRk9ZvrXJC9dGaehUr4NDv2fArgL4Hu2bXfpomVZPwugBuApgAyAfwzgL2zbrk+b0TQHGsiA3NkJvYSoNBU3LdoLQMuuq9Jzdl5Z59TlTVv9E4kEFhYWOE5LMpnk+C6dTgfLy8vsxjgejzEcDhEMBpXAM432OouYWEris24aq+lipzN/1bO6PN04Wy/5OJ+ZhqOVad4yPt20TXULn7Jn56H4iPed/8TrsvLJ+q7J4qWXevaqSMpkZkC3LGsLwO8A6AM4FT76dwBMAPxDAEsAGjhfFP1NtzRlq+RetBUTcZqJztV700FGz8tEB5yq97zQMqYeJV7qigZlIpFAOp1GPp9Ht9tFKBRigI/FYohEIkilUkin0wiHw1hZWUE6nUY8HjfK38uA1pVVl4YXC8vLO6rnp9XQvT7jrE+dZqvqQ177i07h0Xm+mFofujK5KSBuotO6vfZLpwU5T1Gl+Y1q6LZt7wHQ9co/miZdt873TZrionjRuE3NZhNtY5qZ3iQ/UUukPMbjMWzbRq/XQ6/XQ7vdht/vx3A4RLfb5Y1Ftn0eNz2bzcLv9yMYDPLA9zKJTNO+bmBumo4XukQHjibWk1fw9/o9OgvIDbhMLBRgPhz5tFaa29qGLB8ddTJNOVRtberBMo14SePSbv3/tmVeDaTTTkWwnlW7cy6s6fKT3SOZTCY4OTlBvV5Ho9FAv99HIpHA0tISstksFhcXEY/HUa/X0ev1MBgMUK1WcXp6yuEAIpGINCKeDtS81oGb/7nq+9w0PZ2WqBITa8O5aO6cRJ1tZwqwpmXUlVcHek5Rbfs37Yu69E0nX7d7Jpq4qtymYvqsauJ0K8s0eQGXFNCnncG9iKpTTtvAYhpOUS2OeunAOqDSpeemGYoLpQBQLBbx9OlTfPrpp/joo49Qq9UwGAywsLCAbreLZrOJ3d1d7O7uotvtIhAI4N69e/D5fFhcXES1WgVwHjLAuXNURxU4wU0FZtPsEzC9LqsnNzPYtA3pe7zQIrprJvdkMg+qQHaMnGyhUVc+VZ+ddQJT5a1qL1MKRUYDm+btvOZVy/cilxLQ5ynOuBN0DfDG7zlFp13KOqmpNqpbEJVpHOLzJiCjK2u5XEa5XMbx8THK5TJ8Ph8ikQhz6alUCuFwGMFgEMFgkBdCDw8PUa/XcXBwgPv37yObzeKdd96RbhBzltFZFre6NzmT0lk3Xtt03grFrOsFXhZbTdOZJg0vhzybKi26Z6YBd1kfko0RFZC7ac9e621Wy8BkTIhy6QFdN7OafKgKzEXT0QQkdA0vM51l75t0ChMXMKfbofjX68q7ZZ27IdbrdZydneHg4AD1eh39fh/xeBydTge5XA6ZTIbPE63X6xwLvdVqoVQqMa/earVw7949jEYjvPfee8jlcpyP13qaVtyASyyHk/qQPec2QTrTdKbvvKYqi/O6TntVtbdKcfBCw4liulahuq4aJ7pruuuqPL0sVura1K0+dLSezMJU5eWmAE4rlxbQvdARTpFp5XTd7f8mDa3qmF5ENtDcwNkEYEysDtH8n0wmqNfrKBaLOD4+RqFQQL/fRzqdxmg0Qj6fRzabRTAYRCaTQTgcRjqdxrVr1zAej3F6eopisYhGo4FcLod+v4+vvvoKg8EAAPD2229jeXlZu27gpe7cJl2TiV+Wt0ndysS0n6rSdwNzr2Agk2nrGnhV2TF93w34nH3cRKtW1YsXi1d33W1Comd0WrxJu83D6lPJpQV0t5lNB6imPuXflunqTMP0nkrbdmoEbu/Sc5PJBGdnZygWi9jd3eWFzZWVFbRaLQQCAZydnSEcDiMcDiObzeLo6AiDwQC5XA6NRgOBQADZbBaNRgOlUgnZbBaTyQTPnj1Dr9fDaDTCO++8g5WVFdi2zcfUyUxOL5bLLDKNKT/t8ypNzc3kNtHsTPLV5SVLy62ex+Ox8jnZpC0rp+y+ajzLQFLGZ+usETcxUa5kz+uuq7RuseyqSexvjYbuVTNxinOFflZuk9Iw0Qpl12edqXVWg2qRioB8MpmgVCrh+PgYz58/R6VSQbvdRj6fR6vVQrvdRqvVQiqVwsLCAp8p2m63MR6PEQqFAADr6+vodruYTCaoVquo1+tYW1uDz+dDvV7Hzs4ObNvGYDBgrZ4WS+dRB9PIPNrdtB1VE5ezfUw0zGmtCF2ZnGKqDMloShO6QnzWBCxVoK/Tpt0mS1l+znfc1npMyuplIp3mukwuLaDLZNbOPM/IcNOmoeNUTU0yN9NQBxCTyQT9fh+VSgXHx8c4PDxEu91GKBRCPp+Hz+dDKBRCr9cDcO6tkkgkkM1mUavVEAwGMRgMOBwAebYsLy8jkUgwD0/b/1utFlqtFprNJu7evYtbt24hEomwpm5q7Zisc3yT4tQIdYCsa1eVZaV6x4vGaCLTaotEa8riuLgBk668qnrQaeSqNNwmS1UZ3SZrai9nHZmG29ZNFrPKpQV0lYY5q8gGj6xzmJhvJjOwqfYmlkvkuN3EBNxJJpMJms0myuUy9vf3USwW0Wq1kEgk+NShcDiMvb09jMdj/h0OhzEej1GpVHhj0XA4RDweR6vVQqfTQTAY5PNGx+MxyuUye8NUKhV0Oh1Uq1VUq1Xcv38f+XxeWm4dgDjFaXHpBribOW0KJLJ7To1b9qwbMMv6DJXbVPM17XcmY8stT3GCpf+bTrq6/u2lP89Do9W9Nw3tAujXD9zSdT4r+62TSwvowHxnLq/+yzJT10RMAFy3OKQrj2lnl3Um2jB0cnKCg4MDNBoNTCYThMNhhEIhxONxxGIxplToqKxgMIhkMolut8unFI1GI4TDYUQiEVSrVfaAsW0bGxsbaLVaKBQKODw85HudTge2bTOv/u677yKbzQLABQrGlEeUgYgb1aUyg037mQkIq0SnqesGv/NdU2vArexeeWaZ0JiaV3Au2cQ4jUJnuo6k++v8vxeZZqLymp5KLjWgqwaiTEw8W0imGcDT0CBe03KbDHSr604RF6+KxSKq1Sp2d3dRKpUwGAwQCoUQi8WwsLCAhYUFBAIB9Ho99Pt9AEAqlYJlWcjn8wiFQiiVXp5JMhwOEQ6H0W63YVkWQqEQyuUyYrEYotEo+v0+YrEYBoMBisUiEokEkskk+v0+jo+PcXJygtPTU9y9exc3b95EKpWa6uxWkmlN/XmCnE5LnodiMg/uXJR5puUUt7Kaar6yxU/Zc/TbhC/X5anqDyZ1pbOm5m1J6OTSArqsck1AXfWuSTpufOc3MRE4zXidZibT5MbjMSzLQrN5Hm6+Vqvh8PAQe3t7GA6H6PV6WFpa4g1DkUgE4/EYo9GIj5Hz+XyIxWKYTCZIpVI4OztDJBJhLdvv92MwGKDX6yGdTjMHn0qlUK1Wsba2hnw+j4ODA1QqFfR6PXS7XayvryMYDGI0GuGzzz7j8AJ37tzB6uoqx1yXiZNe8XI8oGm7yfqPqu94GYxuFIcbaOksu1lByJmXF3Hy56YTpOm6gcnYEetElpbb73lNurK2VfUj3TXZvZ8aysVUvGwIkl1zdgyn6aezFFQr6yogNuHkVXno+FzabNTr9VCr1bC/vw8AePHiBfr9PgaDAYLBIBKJBFMmsVgM4/GYw99OJpMLfHg2m8VgMEC/3+ddoZPJBJFIBMPhkAN29Xo95HI5VKtVPp6uUqlgZWUFqVQKBwcH6Pf72NvbQz6fRyQSQTAYxP7+Pv976623cPv2baTTaU/aqKxddBOqCgxlPLsOJNwAZFrxaomZPqeyIKbV/FVui7K619XVLPWmm7xMrTJnWiZiMtHSbxWtapKHbtyr5NICuhftwakpTDPYTDRzN63dVPNXaX1u5SbtWbxPQN7v91Eul1EsFvHixQve2NNqtRCNRpHNZpFOp3k7PkVUtG0b/X4fw+GQNe1YLIZwOIzl5WUcHx9jaWkJh4eHGAwGCIfD6Pf7rKnTTtJmswm/349YLIZKpYJgMIiNjQ0cHh5ibW0N7XabNyGFw2HkcjlEo1EMBgN8+umnqFQqKBaLuHv3LtbW1pjDF0XmUSHr9CorR6xTGTiL7SDrf7qJQ9VeXi1NnWZmQmOo0tRZel5Edo7ANCEBqCzT0FxObdiLlj0Nb22q7au+Z5pvnNZ6urSALtMkvm0xGYxeJg96x3lN9bxICRC1MhwOUS6XcXJygp2dHYxGIwZmAAgEAgy0sVgM/X6fB4Tf70e73Uaz2YRt2+h0OlhcXEQkEkEul8OLFy/QbrdRrVbR6XQQCoUwHA7ZD508YQhoM5kM6vU6gsEgYrEYarUaFhcXkUgkcHh4iGw2i16vx6EFMpkMUqkUAoEATk9PcXJygr29Pbz33nu4efMmFhcXXwFssZ78fr/U1HUOLJVvtfNdGbDPwyQ3mejdnnVLQwcAXsDJTVTnCJimY1KnboqT7No07eNWb04xsZRV96eRab5pXkfQ/QWAnwMw+vrSkW3bb3x977cA/CMAizg/4OI/t227Mo98ZeJ2Kvm8QNkpXrQnMQ+npik+L9PsabGz2+2iVquhUCjg+fPnGI1GGI1GCIVCuHnzJr+XTqcRCAQwmUyYJ59MJhgMBvy30Whw3kS37O/vo16vc15+vx8+nw+dTocpFwCcRjwe50iLkUgEjUYD8XgcwWAQnU4HN27cQL1ex8nJCSKRCFqtForFIur1OnK5HFZWVtDr9bCzs4PHjx/j7bffxvvvv4/BYIB0Oo1Op4N0Oo21tTUuJ9WRm9ucCgx0prLs/2Jabn3EbaJQpaG6NyvfrUvPa5pi/ZKGbpqeqRbtpoGbWjle8nGKiQUm/n9aIHejgb4tyuV3bdv+38ULlmXdB/C/Afh3AHwM4AcA/gmA/8gkQdMGVHm4kHitaK+gbjqzi79FELIsiwcJuf85zWRasGy1WrwpaH9/H/1+H93u+al/oVAI0WgUk8mETw4SgVws72QyQaPRQLVaRaFQQDgcxsLCAnq9HobDIer1OiKRCI6Pj+Hz+ZgeIS3dtm0uK+Xl8/mQTCbRaDSYJ6/Vasjlcuh2u0gkEtje3kahUOCJoNfr4ezsDJVKBdlsFrlcDoFAALu7uzg4OMB4PEY8HsfS0hIWFxcZyPP5/Cv17ubRJDONvQKbbI3Fec+ZrmgJOOkgXXm93HOWUXxeRSF5TdcpIt2iAju3MaybIN0AVJeuTkwnZbHe3CZE1TXTMsyj3YHXT7n8xwD+tW3bfwkAlmX9HoAvLctK2rbd1L3o5SNkWrmO23OjOWYxr73kQcBImrbf70cgEMDGxgYikQjz1KPRiGOlPH36FMViEe12G8D5OZ+Li4vI5XK8EUhlpZAngs/nQ6vVQr1eR6lUYl/yYDCIQCCAwWCAZDKJo6Mj9Pt9joVOWjp5o4xGIz5DtN/vI5VKodls8gEXrVaLA3ZZ1vmxdu12G6urq8jlcjg9PUWtVsN4PEav10O1WuUdqslkEslkEuPxGNVqFR9//DFu3ryJVqsFAHjnnXdw9+5dXlegson1L06MssGp05DFdlL99mIIjKgZAAAgAElEQVTyq/qDm3Zm2hdlE4SOQnJeV+VlesC6M21ZiA0d3WIKkCZtpnvWWR63snihRFVt6xVPnJPVt6Wh/yPLsn4fwGMA/4Nt238B4D6Av6IHbNvesSxrAOAOgI90iek+yqSCZGah2/vi4Hd7RvWeWHY3s5kWCU9OTtBsNnHt2jWOg2JZFkdB3NnZwcnJCfr9PvPToVAIrVYLwWAQqVQK8XictXOxLDQQLctiAC4Wi3j27Blr19/5zneY3z45OcF4PEatVmNA7nQ6XKdEudi2jVAohMlkgl6vh2AwyAuj5C0Tj8c5RAC5NEajUa6vmzdv4vj4GKenpxxuoNlsIh6Po9lsolKpYDQaodPpcLheWigNh8NoNBpIJpN89B154RDAy7RlVVuaaIg6Me2fqr5lMtmY5O31vps1KgNy2TWdhj4tsLmlb2pNuYmufkyUPvEbZWnq+p9J2bzQOfMC9P8OwCMAA5zTKf/asqzvAEgAqDuerQNIOhOwLOv7AL4PgHcQkphqKyqN3IvPssqklgG2UPYL1wiQxcagf7SY6fP5UK1WUSwWLwDh0dERjo6OUKlUcHp6imaziWg0inw+j6WlJQDguOQLCwuwLIs9VoiWEQcc+XUHAgHm0nO5HFqtFhqNBtrtNorFIrrdLobDIWKxGI6OjjAajbC4uIhms4lYLAYAfHIRASfVVTQaxWg04kUyUasPBAIIBoMol8vIZDJoNpt8cEa73cbW1hZyuRzK5TIGgwFOT0+Z7gHOFz5DoRCCwSD29vbYv/7p06e4ceMGFhcXuV5Iq08mk4hGo568L9woFFWfmdVUnoZ3nYZu0Gnw04jXkNNexER7n2bCM3nHJG9dHl5ENbHPQonNBdBt2/5Q+PnPLcv6TQC/BqAFIOV4PAXgFbrFtu0f4Jxjx9bWlg147xBeYkmI6ZvOlDIRNXoRuCeTCWuURAGNRiO+N5lMcPfuXeTzeZyenqJUKuHs7AydToe32YtuholEAqFQCLlcDpZlIRgMcvnFGCp0j8pLC1bknUI7RguFAtMgyWQStVoNo9EIpVIJsViMFy4jkQj8fj9rw91uF5ZlcZ5+v/9CJxyNRshkMmi327xbtNvt8sEYpN1PJhP2iY/H4wiHwwDAoXuPj4/R7XbRaDTYukgkErxQ2+/3cXR0hEgkwpbJ3bt3sb29jdu3byMej/NkJ1Jw9Fc2aLwCpRfKxfncNGCnmnRM8psFvEUxCZs7CyDJZJb3vdInuslDRt+J103EhDKaBp9IXheHbgOwAHwB4F26aFnWTQBhAE9eU74A5P7KJnwhiY5HFXlZ5zukgRcKBQYyv9/PwEXUQa/X4/gph4eH7LHS6/WYkgiFQrAsC4FAgEF9cXERAJhrJ46drsk8P6j85Lmys7ODzz77jHeHptNppjbu37/PHbZeryMUCrFGnEwmGZBHoxF7uQQCAaZeqDz0XK/XYw17OBwim81iNBox0PZ6Pa5rMbhXLBbD2toayuUyFhcXeZI5Oztjjp14fMqfIkCSxZDP57keHjx4wHWxtbWFTCbzioapsr5k/WBawHKzCETRTS6ye25Wpe6aF1GFzZ2VsprlHVUa02rbTvFCn6goJrF/qSYNXf8yrZeZAd2yrDSAnwXwb3DutvgfAvhFAP8tgCCAv7Ys6xdw7uXyDwD8se2yIPp1up7KYaqVq37rNCexsdwWWkkj7HQ6qNfrGI1GKBaLsG0bo9GI3Q7D4TBTIOQHTu6Aq6urCIVCSCQSiEaj/H0U2pZ2dZL2T4NUtAIoJotlWeh2uyiVSnj+/DmePHmCfr+PdruNXC6H8XiMRqOB0WiEH/7wh4jH41hfX+dvSSQS6PV6HKul1+td8CpqNBo8+ViWxZbJYDDg3aTkTtnr9RAOh2Hb5/HRaafpYDC48A00YebzedRqNdy8eRPFYhH3799HoVAAcD7BFItFDl1AESO//PJLNJtN7Ozs4M6dO9je3kY4HMYnn3yCwWCAUqmEe/fuYXNzU0pDuIG1m4Y1i6bm5X0xb9l3OMulu+8lT5l140xfptGKMg/QNpF5WSXT5uukcQE5hjjLKa57eRVr1o+2LCsP4P8B8CaAMYCvAPyebdt/9vX93wLw+wByAP5fAP+Z7eKHvrW1Zf/pn/4ppc/X3dwTxee88qNOLU1sAJXpI6vwTqeDJ0+eYHd3F4eHh8w/BwIBjMdjdDodtFotVKtVdsVbXl5GLpdjjVmkTkgDp8lgOBxyp6CFQBEMSVuljUWNRgM7Ozt4+vQpa+btdpufH4/HvGYRDodx584ddhWkXaHJZJKpEqJwCHjJM0ZcH6Dv7Xa7/Ey/3+ey0aKlSAuJ/sz0XTQ5NptNZLNZHB8fI51OAwBOT08BgBdSA4EA0uk0UqkUWwq0qEuUTbfbRSgUwt27d/HOO+/gO9/5Dk9GYnvq+oms/b2As9sgNU3LTfGgvyrQnqZspkHQphGvVJRK+5V9u/jONJOvl7Ko7ukUSDfK5unTp/jVX/1V7O/vuxZ4ZkB/HaICdJ2Y+CDreHCZmGgw4j2iEegUIDpMuV6vYzwesya5urqKcDjM747HY6ZL6FooFOK44+JhEKQF27aNaDTKIEngOx6PcXBwgOPjYwDAkydPcHBwgMPDQ1jWeYgACptLHjPkEw689AhaWVlBOp1GNBplYC2VSuyumEyer2vT4mcikYBlWex/Thw/1aHf7+ewvGIkSFpPoIMzSJOnSZm+rVKpYDweo1gscn23223W+tvtNgKBABKJBILBIHPoFKZgNBphZWUF77//Pt59912888477DYqWjumUR/nBfKzUBWm/VJ2z6tWLktbBlbTgqRXQJ93/rOITnmcR1s+e/YM3/ve94wA/dJu/fcqbn7nXiYuncamep5OAup0Ojg4OMCnn36KWq2GyWSCW7duIZlM8olA5MudSCT4eDfyMiGagrxFCBwHgwHz3gSIjx8/RqfTwWQy4YOZJ5MJDg8P2V/b6Y1CgEoWAE0cg8EArVYLlmUhHA6jWq2iUqmw1h8MBhngiS4iQI5EIjyZlMtl+P1+dLtdBnVx8iKaBQCDLWnkpOmTRk2x08W2Je+XSCTCC8UALrhzrq6uYnFxEY1GA7VaDZVKhePHkL89Hbk3HA6RyWRg2+cunDRBUn+hhW2vWqkKZLxozDpRaegqjVB8z2SdQBTZ0Y1u6TjL5ZaHM22d6L5xlvUBr/Uiviez6L2kMS+59IBu0kAmpuAslWmi2RNAHh0d4eDgAIVCAQcHBxgOh9jd3cX6+jru3buH27dvYzweIxwOo9PpoFKpMEjRd5ydnWE8HiMajeLZs2cMkuTmOJlMUKlU0Gg0GJxpoZT+T6BJkw3FeAHA1gLx9jSB2Pb5ARSNRgMnJyewrPNFWTr8ggJ9AeCJh2gg4NyqGI3Ooz8QCNq2za6Lfr+fPVJo/SAYDMLn810Im0vaeTKZRLvdxmg0Qr/f5x2pwMugYzQREfDTgRqLi4vI5/N48803MRgM8Pz5cxSLRfzJn/wJYrEYcrkclpaWkMlkcOfOHWSzWQ4cBpyD2NLS0gWPIVm767wivGhpXhUO1cKo7h3nMyaarbixSLZZyPm+E+xNxp7XsWnq2DDrJKmq12kmRSqT13fc3nPKpQZ00wbxct6k2MFUi0ZeOqDY6MFgEG+//TaCwSDG4zG63S4eP36MQCCASqWCzz77DMlkkjXLbDaLcDiMcrkM4Bwk6TAKAl3S6EUtkbTGRCLB3z8ajTjULYGts04sy+Jt/OQ3Tpq2ZVnodDqsfdPEQO+1Wi12X6T8hsMhp0P8OuVBAC6COfHpoqujZVmsZVMdilo5LfD2ej0OGQCcTx5EC5Ff/d7eHgDg5OQEfr8fN2/eRCwWQzwexzvvvMOhBk5PT3F0dMQ01GeffcaTGlkTt27dwu3bt3Hz5k0sLCwo+8WsFAOJG8cqu26atls+uudFIHeOs2mBWHx33lrrtBq6Chd0z6vyV2GJW//xSgnL5NICuuwjdICt6yBO4NZ16mkGilMrWVlZQT6fRyqVQq/XYx6Y3ACr1Sp8Ph+ePXvG3yVqqKQlAi8XQweDAQfgEmOzEICK3K8zWJKorRDoUh60SYjcAMmTRqwn2mE6GAxYsyY3SUqPJiCaCIjqIUuAykl+7FRfBPKUFvHazu+wrHOf+VTqfFsDpf/06dML9E6hUOBwv36/H4VCgdsjEokgk8kgEolgZWWFF6hpMxOFLUilUrxDVqSBVIvrzmsmYK0bvOJ92eK97B1nftMCvkxUjgikcDjTVilKXugUE3Gm5yUvk3tObxUT0X2/KDrLzqSMKrm0gC4Tk9jLuhna1PR1A27dvadPn2IwGOD4+BjtdhvpdBqlUonBirbF07eQCyL9A176ZBNdMh6PL+z4JA2XvDmoTOKJQ86OLlIf4tmglC/lRYuz5ONNC7Z07Fw4HGZfcvIzJ4AXJw26RmWnclH5xRAFtGgpbv6hMtIkQ/dpbYAmnXg8znkHg0FeIBXdGgGgXC4jlUohmUwilUpxOGGaJGzbZgvHtm0+zandbl+gelR9yslfm/Q/FbDrrEWTRTcvwOAVNEzGnypNnbIl046n0bJ1axQ6wDfRlHX5mt6fFZ/c5CcK0HXitSOpZJpKpc7SarXQbDbx6NEjfPbZZ7xhh7RbAjkCVgJz0mKBl1qp2PCixwi9Q0KUB/CStxY1W0qLtHDS+CmAlgjg9JeAlECeJgNKmzYSidQMgb74DJWXtHr6HpGzH41G/NsZpoAmENqQNRwOGaBpFyrFZu/1ehiPx7y7lTyOEokEl6fRaCAajSIej3Mgs3A4zDHdaQIaDAZ48OABKpUKDg8P8f7772NjY0NrkrtpyTKwMdHuVdfEfLw87zZBeBFZEDyTdEysYVNgN11w1bWH7L4qTTewd5ugTWXadvmpAHQ3msXtnoxHVA0UnQaQSCSwuLiIW7duoVKpoNvtIhwOs+81aarBYJB3PBJPTeBHG4PETUMEhmJEQQJpGlQiOAIXNSkRjG37PAxAp9PhyYLSJi2aFlX9fj9SqRQDHR1yQYALnFM2lAb5jpNWTZubqAzib9pwRH7s9E1ELVGZG40GT5bid4meN61Wi+vP5/Oh1+txeOCzszMAQDQaRSaTwerqKh/qEYlEOMSBuFGKwi+QOyZ5weg0X5XmLNI1bsAtyqw8ro6KUJXPpGzOXdiyTTKmFoKOa/YyMZiUW1SSVG1hCta6+pWV0YvVQWnLLGwT+akAdBJVw+g0IudvZ+WbcGAEugSudIYnHetGLnjEMRMoUpRAAllKU6ROnOUnUBaPaJNtw3YCC+VL5SOAX1xc5FOKiAeniYUWWImGiEQiDJT9fv+VE4NIEw6Hwxe8aGhzFYVAIAsgEAigVqvxJEcgX6/X0W63OY5LIpFAJBJhF0+SXq+HVqvFQcBoUkokEjzBid9UKBRQLBZhWRZyuRxyudyFDVTD4RBvvPEGPvjgA3zwwQdYX1/ndHR9xkQjNtW0VBqeitpxy8sNTLxMMiReaRfZuJONMzdgNVW63N6Vaee69934eN2zzjI61yNkE5lq4jCRnzhAF4GTRNRanSIDZZ0GYMJRyiQQCODRo0f4+OOP8aMf/Qhffvklv0/aOZWZ8iCAJ4CmmCfEXYugTtoQgZ8oRKXQs2KYWeKVI5EIa8Ki/3g+n0coFMLi4iK7/xG4kh94u92+ECuGAHs0GiEajXLcFgpL0G63Lyxy0gQmujTSBJdIJNiHnQ6vpomOFkuHwyGHL6BvoHKSNi9SQ1QXlnW++EsThUhFtdttjsVO9MvP//zP46233sK7776LlZUVppCcogMWGfA6n5Hd16XvFFkfVQGBF+3YFEBEmsULvaO6pwNm3SQlmxRU36DLz7ScOgXPVHSTFq0r0XOy/7vJpQV0XeeSgTmJ24ebNoqX2Z/M/Xv37uHu3bv43ve+h7/5m7/BX/3VX+HDDz/kXZjixEMAQ9yt+F3iTkrqpFQe4n1JW6Vj3ui9Xq/3CrVB2jfFDvf5zk8WCoVCvOOTTjtqNptMxdDzFO+cFnXz+TwfaCFSQ6PRCK1WiycCAttIJMKgbts2B9Gq1WoIBALodDo8efl8Pp5YaKMQHXRBB2ED57767XYb3W73QiAwypMmRgJ+yh94SQ3F43FMJhNsbGzgZ37mZ/Dd734X77//Pj8no0qo/abRdlWaoUn64rMqLVKVhowGkokXbVDnKiyCqyp92f9NKRSTunWK22Q1DSWmaiPVxGLSP0zLq5KfSEAXxYsPOokJ6E+TBmmJ+Xwev/Ebv4Ff//VfR7lcxl/+5V/iz//8z/Hw4UM+LELky2nzzuLiInu+UPq0hZ1oG+AlSMdiMXY/pIVT0Y2QyunznQcLI40ZAHPllUrlQtwUy7J4stja2mKvkUKhwMfT0UIk7TIl4IzFYjxZENCTpWHbNrsBplIpBu9QKIRyuczeN3RQRrlcZo47l8vxpHTz5k0A56Bcq9VQKpWwv7/P1Av52BMo028Som7G4zFWV1fxcz/3c/jggw/w5ptvXpg83UBGx/966VM6UBCfkQ12N+DXpan6Jlk5nX7oosg8z7yAkSmIi2mbXleBvGpiFetO176y+hT/LypUYqwgZxmcedJvZ9v9xHPok8kE3W6XuVfg1YbWbfM3EZ22bTJbu+VDYJ1Op/Hd736XqYcPP/yQNV0aDNls9kJgKeBcEycNkjxUiJIR46vQQQ6xWIy1YjFeymAw4FgyxIvXajU8evSID5sAwNSLSIcQENICaTabhd/vx+HhIU5OTvhd4tPJ80X0Z6c06XckEmErw+c7P8mINOhoNIpUKoWFhQUkEgmeRPb397GxscGWCXDu5bK+vo5Op4N3330XnU4HjUYDh4eHKBaLKJVK6Pf7XCd0WHY0GsX169fx3e9+F++88w7u3LnDz4jAqeNV3TRBU5Hloet3IrViCvyydLxcA/THz7kpUl5pCmekQR24mnDfqt+6unGzhmT9gRSlwWCAYrHI9bK2tsYWOJ0I1mg02OGA4jo5056W2rmUgG5ZFi9+6YBc5jKlqwgT3k42qN34UPFd8Rn6vbq6il/+5V/mwxcODw/x4MED9Pt9NBoNAC95bwJikV8nrw3Lsvg54pWPj4/RaDR4gZC0A/GotmAwyO57VG8UyIryajQafLAFuVSKnHcgEGCevFqtAgDTK36/n0FaDHIluiOK30cxb8jThMoZjUZxenqKyWTCgbds20Ymk0GhUMAbb7zB4Qtu377NNArV02QywcrKCrrdLiaTCU5PT1GtVlEqlRAOh/HWW2/h/v37uHfvHrLZLGKxmHRzjNhX3EBX109m1eZl7zu1SFmZ3IBuXqKrN1lZZNdlwOqmtbuNSzeQVj1jIiqtfDAYcBhoaptyuYyTkxNWWmjBn2Ik3b59m5UhcWPhLOW7tIBOGp/zuvOa87dOY9DNvKqG0l1zu08dzufzIZ/PM7B+8cUX2N7extnZGQqFAlqtFmq1GgeYKpfLqNVqvEGG+GvSOC3L4g011BFoURUAR1QEXroVirQDLYhSrJLJZIJ6vc5cfzqdxuLiIjKZDIbDIU5OTjgOOQAO+UseMqFQiDvpZHJ+QEU6ncZgMECtVmOqJ5VKwbKsC8frFYtF9jmnsL4EyEQtEQ+/t7fHtNODBw+QTCaxvr6OTCaD69ev86RCHitra2tot9tMr9y6deuCxuQERJXm62xPlbhxxM50VddFSsX5nPN5labufE7X31Xmv5vI6BZSSAB1iACdVeG87xTVWoHb+J0GIGXWuqzeyIp88uQJ9vf34fP5sLW1hVgshlqthh//+MdMKS4vL/MpYRSSWkWfiXmYlv9SAjqgX3QA5q95eElPZQ7K0qL75E2RTqexsrKCSqWCjz/+GM1mk10JybWO4nYTN01AR5YLeZwsLy8jmUxyvJJ0Os2HaYhl8fl8HCOGOHPg5aq66E8sfhO5+4nb+KlM3W73QjwXomloEbXVajHHTnQRnXYUCAQQi8V4oiHufmlpCdlslrUbOobOsiwus2h5pFIpPkDkyZMn6PV6fEDH4uIi4vE4ut0u006lUgnZbFa689MERHTmvxfR9R8TDX4akWnzurxNxbmGZbKWZQpSJuXxqqFPk5dssu10OhyTv91uY2dnh5UIcinu9XpswVLkz263y2cOnJ2dcXC+5eVlZLNZJW1mKvM4sajluBQF8E9s2/5vLMu6DmAXQFu4/z/btv0/6dKk+Nwit2QqMr5P1sncFohkzzvviW5GqrTFzksWhrjdPBAI4JNPPkGhUGCOGzg/KJuOnqvVaqhWq3w2ZyaTwfLyMuLxOAfUGgwGqFar7P9Oni8EqgSm5JstLraIO1Vt22ZOOx6Ps4cLRR2kA6qJ66YdofSXOjO5IAIvj4ujtQC/38++7USVUDnEODVUJvKSIcsNOOcmaZKgGOl0aPbOzg4+/fRTdDod5PN5vPfee1hcXORAW+KhILo2N+kDMpFRBioFxY128Som6em45WnylWniptSn7rqJpj3NpGRqZTnFts89sUhBODg4AADs7e3h6OiIj0pcW1tDNptlb6t2u41KpcKb7SqVCufTarXwxhtvcCC4lZUV434mk5kB3bbtBP3fsqwEgFMA/5fjsbRt2yMYSrfbxV//9V9jZWUFuVwO+XxezM/oY1WawrQDSKZVmJRDV17yzCAumvzPSaulazJaiTqyz3d+og/x10S7EPABLzVtKjMBrhhGgDR1kacnflt0q6QJg8A4Ho+zFUFltSyLQ+mKLow0WVC9OLVGKpsTaEWAJ+qIrqVSqQuePoeHh7Dtc9fITCaD8XiMhw8f8k7TN998k09oEttIFDcO1ykqkDGhAlTpO32Sde+blku8rjL1pwF3maeZqcYrPif281mAWdWeJhOO7l2qL1qfOTk5AQB89tlnqNVqvK+D6FXLslAqlbC7u4tWq4Xl5WUsLy/Dts/P7LXtc484coogpUjmNWTa9vOmXP59AGcAfjhLIjRw6UR4040Mouh81XUiM6tVoKzjv9w0ieFwiLt376LVaqFUKqFSqaBUKvHCIT1PQEgATPFXxGPfxEiJwMvAVcDFw5fFgyYIPMUNP0SpUJjZZrPJlIXIwRNIU+z0aDR6IZ14PI5sNot+v49+v8/+9mJ0RjHOuHiNykRulhSoKxaLwbZt5vmBl+sGRFeRVw15tVC5s9ksD7RcLmfcRm5ioh2aaIMygPNCXZiUT0dhmvD6XjRG59gzAXcV1aADYxUX73xW9rwsT7cyiu9ns1k+tH1xcZHP6aWInrdu3eIDVDqdDqrVKnvvkZXa6XTY02Vzc5P7+CwW07wB/e8B+EP71ZGxZ1mWDeDPAPx927ZLukTInc2yzjefrK6uXvhQ04ZQ+c/qTEQZmDvT0L2nG6TiOz6fD+VyGd1ulxteXFCS8eBiLBfadk/cPHB+cDLFWyEhLxoy/YjeAMBhCaiTkd94vV5Hp9PhuOLkbUL10Ol0sL+/z/eJgxdjvIi7eWniEa/RBEXuloFAAM1mkw/MpokkFAqhXq9zHdK3UX40ARAdJMaGEaNYUp8R21Rl0nsxeb3wndOAh0pL91JOmaXghVr0KjINU1Ym1TOy8e02npz4IKajm3hlFK3u+8fjMU5OTvDgwQN88cUXAMA7mO/du4fl5WXEYjHeeLe4uIhr167xYSzU38PhMJaWlvishIcPHyKXyyGRSDAO0NqYF5kboFuWtQXglwD8F8LlEoAPADzA+SHRfwDgXwL4tyXvfx/A9wGwGZ/JZJgXnaajuW06UnGoppqWaXlkndWyLD5VZ2VlBcfHxxxIip4jykME28FggHK5zO57dPgDLVBSXVGnIJpErAvSDug+aVHRaPTCu6JPO1ExlA9FiSTOnIJiyTRM5wCif5QOadniIR1kmYTDYdawgZf0D01QrVaLwZ1iztPRfbRgS9y9GC9HLJebpu0GJl76gXMCkPU9Wb9006LdyuWl3Kr83b7TbbyJ903ypzK41ZtMI5cBuUxUypkzfdFirtfrqFarPF7p5LB4PI5MJsP9r91uYzAYsItut9vlMZLP5/kYyocPH3Io562tLY5FNBwOsby8jOPj42+FcvlPAPx/tm3v0gXbtlsA/ubrnwXLsn4XwIllWUnbtpviy7Zt/wDADwAgm83a9XqdeSkvs5QqzguJamcbINeCdKLTPNw0N+o0oneASJPUajU0m00GJOLu6DQjMV2RdxM5aPpeZ/okPp8PiUQCsVgMCwsLFzbXEKCSmyA9T+kTneH8XtFjhiYKuibWL1FHdI2O5fP7/Uin0zxhxONxpkrC4fCFDRy2bbPVIVJQFElStBx0A0KmtcuoN7HdTMTkWa80j6mYgLEK+FTjwHRcqMafF+rTpOzOMrtZAtPWtfjddDYteaUA54CeTCZ5wfT09JTpF3LxrdfrePr0KSqVChYXF3H9+nWsr68jFovhxo0b6Ha7HB2UDltZXV3ltZ9vA9D/UwC/7/IMlUrbssPhEGdnZ8jn81JfdHEWdoobd65buJnGzNZdozKKpp+zYei3eLKPyBOLE5AY60WlQcjA3ql5EBhalsWLn4PBAMFgkDlvihlOfuC07V9cFBU1deBVisu2X3L/TqHOT/kSB06g3O12EY/HeaGY0ul2uwDAvunlchkLCwusrVuWxQBPIQjEc0Jlda/qB25A76bh6/qE81mZmGrf4rep8najDHVldhOdBSNToMTymKyN6dIXr6m0bfrrVu86y4x+27aNaDR6wVmDYhNFIhFYloXDw0McHx/D5/NhZWUFb731Fu7du4fNzU0+wJ0C5i0vL2MwGLCWPxqNsLKygtXVVSwvLyOdTuP09NR4MpwLoFuW9XcAXIPDu8WyrJ8FUAPwFEAGwD8G8Be2bde1hQoEsLa2hoWFBSSTyQsr/m5agthwbluWnWmp3BCd6cryV82g1AmcfC4tEJL2Laglb5QAACAASURBVGqUThB0AqJT8yUhXl2MrCieGUrhcwHwlnz6S2Ulvpz4dfpOonOcQcWIGxdj01DZaDOFs37E4Fd0MhGlAQC5XI7jvVB88lKpxF5PANhfn4Jrra2tMY1Tq9V4gxMdcrGwsMCTkJi/G62h6g9u16cBRi/5OMvqtMBU/dGtfDq6RTa56dIika1hue34Vn2DbDJzo8xk7eyVjiEh6o4W7QFwSI7hcMjeLH6/HwsLC6jVaqjX69jf38fW1hZu3LgB27axuLiI8XiMZrOJRCKBXC7HO6ifPHmCwWCA1dVV5PN57OzsXHBK0Mm8NPS/B+CPnTQKgJsA/iGAJQANnC+K/qZbYqFQCDdu3MD6+vqFo80A984zy0DSvavqvCZm3mQyQa/XQ7vdxocffgjgvBPfvXv3gjshPQuAD2umnaLAy85EmrEYN8WyrFf8yekdqkPi3ckCIDonnU5z1EXyPScPFXqWwg/QhicqH+VPk4LoOkmTEy1eEmjT82I9BYNBPoc1mUzyzlG/34/BYIDDw0M8fvyYN3MQL769vc1xbzY2NlCr1RAOh3ldgSY4Giz1eh3JZJLLTgAhBiej9N00bVV7i39lfdcL8OpoGxOQcoKZl3xNy+E2BpziFuTLDWzFsqjqdB5Ui/iuz3ce4G53dxe7u7t48eIF6vVzvXR1dRX1eh3D4RDHx8d48uQJ+6EHAgFUq1X2CLtx4wY2NzdRKBQwHA7RbDZRqVRwcnKCYrGIs7MzrK2tIZlMwrZt7O3t4fnz5xeoT53MBdBt2/4dxfU/AvBH88jjSq7kSq7kSvRyKbf+00IWHUxMMg8zVrVrVLWIpDL/nBqRyJWTiIcpf/LJJ/joo4/w0Ucf4caNG/jggw948eTx48fY29vjELFEmZBGS94kxHnToiS5G4o7PymqodO9kbRxSuvWrVtYWVnB4uLiBZ9zOl+Tyi9y8/Qu0SmUtniyEeVNdAztWhWtCPoGsTzxeJy/m1w5aYGTtO1Go3HhgItcLse+u6PRCOVyGfl8HtevX+egXVSux48fM220sbGBhYUFfPjhhxx+4M6dO0gkEhdi44jt79ZnnKLTimexOE21bTcqyaS8Xq3RaZ8F1M4Mqu8QaSAdpWLyXaqy01+KdbS3t4cnT57g7OyMrWQA7GNeLBb56EKiVW7duoVcLsdhoYvFIhqNBoLBIMrlMprNJmq1GuLxOLa3t/Huu+/C7/ej2+2iWq1id3cX+/v77AzgJpcS0MnEVvlhzgrsup2XJLoOQ/cJWMXThcRwsQQqCwsLWFpa4oBXH374IX784x/zlnyiFSiCIlEj4s5NOkOTyuTz+S6EoRW/Rfw+cZcmlSkUCnGYz2QyiaWlJSQSCTSbTbx48YLBVOTIiXsW6RWRIqKykasgTWRU32IoXpq46D2Kx0JpE+DTZEUHXog0DdVzsVjkAZdIJBAMBjlEr9/vRzQaxerqKpaWljAcDnFwcIAXL14gl8txnPdKpYKjoyO8/fbbuHXr1oWJx9kfSGR0jEm/9NJ3VcqEyGer0tat+XilT9zKNg9FSxZ0z7nO5awP1YLnPMWyzp0ICoUC9vf3cXx8zJFA0+k059/v97GyssLga1kW7wGhfkTxhFKpFGKxGKrVKm86SqfTOD4+Zgq00+nw2HCOcZ1cSkCfTCZotVo4PT1FPB5/ZWFNBbLiM4B88KhW3VXP0zvifZGvFgEOALvMURCsXq+HZDKJSqWCbDaLjY0N5t4IbIkPJy6YOrLo+SIecEFAKh7GTJOIE/iAl1o6fTsB6PXr15mTTyQSKJfLSCaT7AVDHiX0HsWZETf0EHh3u12eAKg+6P9ixEdaEAsGg1haWuJAY+KgJA6etHKqv1qtxs+Qh4zP5+PgSBQSOBaLwe/3Y3NzExsbGyiVSkin06hUKtjZ2cHp6SkvnFKQs62tLfT7/Qs7bmkiEtcmaMKh/ucGgDJFQLbA6Hye2ktmKcosSDEtN65eVm7ZuNEtfnr9bll6bqJyanDT3mXgr3pO9VdcW6Fx3Ov1kEqlWMmh2Pw0Lumw8Wg0yhr2559/DsuyUCgUEAqFkE6ncXJygnA4jOPjY46LFIvF+Azder2OTCaDXC7Hh744j51UyaUEdFohzmQyF+Kie2kcmVAjuXm/OMWp6RCQHx0d4ejoCOVyGaFQiD0zwuEwzs7OUKvVUKlU8Mknn2AymaBQKHCZSVMWFy8pQiF5vjhji1M5RD9v4CUA2rbNgETfKL5H4EqLm+SGOBgMUKlUEAwGkcvlLmyhp234oVAI7XYb0WiUA3ZRPVD5KXQtafupVAr1eh2RSARnZ2eseQcCAfbhJdpFnHB6vR6nSxs1kskktra22HqZTCY4ODjAcDhEqVS6QPN0Oh34/X6USiUcHBzg2rVrWFtb47jUFAed6m57exvRaBTRaBSPHj3iIGjhcBi2bfOOPvLsEfuiDlDFazLRAa8M+J2A45anCohlWq8oqjRNJxLnN6kmNS/i9IqRXTeZ9Jw4optcxevLy8uYTCZ49uwZKpUKqtUqK2apVArZbJY98trtNsrlMu8OJWeGfD6PTqfD94+OjpBKpbC0tISDgwN2PLh79y62trawvb2NcrnMC64mcikBfTQa4fj4GMC5lre5uXlh+zYg1zim0didYuLqSOBDp//QNngCF1HrJlc60lapgSn2A828Tu5b1Pwpxgpdd5ZJjGRIgC4+RyYh0R6BQAC5XI43Q5BfeTAYRKfTQSaTYQ8TOrOz3+8jHo/Dtu0LnjfhcBitVov9xcPhMJrNJjKZDLrdLrLZLAaDAZaXl9l7JhaLMfUC4JVDnC3LukD30Hd2u13O17Is3L59G5PJBGtra+xJUKvVeNLZ3NzErVu3sLy8jFu3bqHVavHO41arhePjYxSLRXzxxRfY29vD48ePOXYQRb4j3/xyuYxUKoVAIICtrS3l0WJU3yZctReeWQWgOtpBBlROkNMFxHJOKs40TYFap/2r8naKbFyqNgjKyirLRwf8lOdk8jI6aiKRQCgUwurqKj7//HNmDjKZDEajEc7OzrC8vIxKpYLl5WXe0Z1IJHDv3j0UCgX0+32cnp4yLUN0IcUrikaj2NvbQzgcZkrGOZ51cikBPRQKsesOmfpifBJgOh59Vq7N2anIFen09BR7e3t8nTYaAC85bYrrTUGyaPGTykSnnQBg7l10ARQnEueBFQBYqyatn+iR0WiEVCrF/tu0G61cLqNcLvNBG+l0GgsLC4hGo+h0Ohx3wrIsnpCIOiGeOhKJYDgcsqsjcfORSIQXbClOOn1LKpVCr9fjg50pDfq2wWDA8WPEbfx02DNp1r1eD7FYDN1ulwNxhUIh5HI59Ho9bG1todfr8drE0dERVlZW2EKhAbq9vY1+v4+zszPs7u6iWCyi3+/jk08+QTqdZvfM0WjEWhO51Yr9SaWJe6VlVCBvwm3r6BvTZ3XPOS0Er5q5m/Vs8pwoJiCvykdmnYhloLUbCvNMm9rIAv/FX/xF3sVJCo9lWcyJHx0dIZFI8I7varWK0WiE9fV1bG9v4/j4GMPhkPdTBINBrK6uYjweI5PJcEyi69evY29v7wKtq5NLCeikddJqMQAsLy/z6TqyBldp6bMAOJVFFKIFaPamyITioiaVX3yXFgLpXZFKIu2UuGma+QlIxI1GpLmK0Q1Fv3Lacry2tgYA2NjYQDKZxOnpKXZ2dnB2doZ2u80HQBSLRe6sR0dHyGQyzEN3u12mQwikCVxpA5JIQYmUD01YrVYLyWSSNfLBYMBhBujUJfHg6nQ6jV6vx+YppddqtRCLxXgQx2IxnJ2dIRwOY29vD4uLiygUCvD5fHyU3XA4xOnpKdbW1nDnzh0kk0kkEgmePGzb5smEduX5fD4OEnZycoKzszOeYG/fvo1gMIh0Ou0K5ioA1mnS1B90ac3an1ViYi3INHbxuixNJ9ibln/ab/ayA1WVL31jrVbDs2fP8Mknn6BUKmEwGCAajSKZTOKNN97gaIuksFCQvHQ6jVarhZ2dHbbIad2IqEpaLxsOh/D5zk84onOFaff02toa1tfX2Xo2kUsJ6J1OBw8ePEA6ncb29jYHpQqHw68sUDr//7qFKAriZ58/f840SyAQQCaTgWVZHMucFv2IFiEKhtzpCBjpsAZy1xTP9KTNPHTIMZ3LSQGoMpkMQqEQNjc3sbq6img0yhPGcDjE4eEhSqUSL7gA4FjOPt95ZEsyLaPR6IVFSTF2CpUdOB84jUYD8Xicj8orFApIJBKoVquIRqNotVqspVD4ANu2OYwAafa0EYhi2ASDQRwdHXHbWtb5LldxcZLi/NTrdSwsLODs7IxPSwqFQojFYmi320zVNBoNDk9MC8PiOgAtdnY6Hdi2jUgkgpWVFa4b2u5NXKm4s1XGbTvFye86r5Oo+G8dbaDKT8W/y941oXp05VO9Y2JZyN6bdkyLypbzumnZaP2Kdh8DwPPnz9ndkBYrSUMnYM9ms4hEIhxviSxaWtjs9Xp48eIFvvrqK7Yo0+k0lpaWLqQjHlATCARYmTWRSwnoNMDIVCZzJhKJsCufTkOaJ8DLzLpgMIhqtYparcbaKi0wit4ntPhI3iLiKT2Wdb79n2KUk6ZOgEmUC4WEpbKQm2Q6ncba2hqWlpaQz+c5j1arhSdPnvDiIW07Jg2bJg/i7Cld+o6dnR3WJOhEombz5QZgAkQ6VIP+Hh0dIRAIXDiWi3zlbdvmsKFivJhiscgnKYmLweFwmCdI0lZo8JAMh0M++aXZbLJVQ39PTk7Q7XaRTCaRyWRg2+dukOTzS5MiDX5qC8qTjtOj9LrdLj799FM0m00cHx/jvffeQzablbq8qsBbdl91T3VflqaTPnByw2JabouruvK7pScrt2pBUmeJ6J7RXQfkC6YygBfvqyalUCiEa9euYTweM71KFGSlUkEqlQIAHB0dYTAYYGNjg50Kkskkbt68iZ2dHT6SsdfrYXl5mb1jEokE6vU67ty5wxo/KRTkdiuLg6STSwno5Fa3vr6Oa9euYXNzkxcexMVRVed3M+1mBfzJZILt7W0ezK1WC19++SUDErn9kdeHyI2TW6G4uOf0d6ZFQsuyGHio89FCKpmEu7u7sCyLD1smbxnR+yQYDLK5aFkWa/sEZOSSRW6B5GUkbvcXw+TSZEsheCkKYjAYZMppNBohGo0yWNPCKa0liKv/NAmS1iKCN3nGiPEygHMrjugoCvtLeVD8FgKkk5OTC7HayTWT2pKCgEWjUQ7BS2BOroyxWAzr6+scIS8WiykBQUYL6rRB2W8n6Lvxzzo+W6X5yqxdem9W6sdNM9dNZipLRzeJyMqjc3Bwu39ycoKDgwM8evQItVoNS0tLzBR88cUXfCwiSSgUwqNHj/Dmm28ik8ng3XffZffDL7/8kte0Tk5O0Ol0MB6PsbS0hF6vh88//xy1Wg3Xr1/HrVu3cOPGDUwmE/aS29/fN7Z0LiWgW5aFtbU13L59G+vr61hYWEC3271w6AKZxBThTHzXTWZdHCVtz7Jehr/tdrtoNpvsR04LepSXbDOOMzKieGiFSlshikC0Ugj06LroqmhZFmvJRNOEw2Fcu3aNFwYpTnOv1+P/0w5NAAya1KHFA6xF7Zo8YsjaIDqFzlMkzVv0mGk0GnyIBcWPofQJdMWTlmjnMNFDNOGRJbOwsMDnNo5GI3Q6HcRiMXQ6HRweHiIajaJUKl0ITkbcPS0qUx40Ca6urmJtbQ2bm5vcB8gCk2noJv3Hq/Y5ixVqCq5uk4GXNMXnVLTRNKCvuudlTJs8R9oyHVZRKBSwubnJLrWlUomt0c3NTYzHY6ysrOD58+fw+/14+PAhe8FVKhXYts0eX9R3Go0GK29ffvklDg8PcXZ2hu3tbVy7dg22baNYLKJYLP5kuy0SKJEvNHA+uGq1GgdYSqfTsCwL165dY80LMG/YWRZsLMvCyckJHj58iE8//RQPHz4EgAsa6HA4ZE2Pyk8AQOAui/rnBGY6Pk48mk48rIHyIsuAJhmaaKLRKG9pp7xt28b+/j7S6TSbiAT8RDUQ4JFmQYs3tD5AoEcgTXnRdzkDglmWxa6TxD9T+cmisW2bdwYHg0E+BFvceER50QJ5LBZDJBJh/3L6nc/nce3aNTQaDa4fAl+yLsRIl7S7lNIdDofY2NjgU2hu3rx5wbNI3KDmZg2agLFK+xT7hUl/devHJhq/V5lGQfI6CbqJ1wnO7XlaD1tZWUGv18PCwgKCwSAWFxf5ZDDaKToYDLC+vo56vY52u41Go4FCocDjQNxNTVQiAJ4wiH6l/RqFQoGVqVQqxetHJnIpAZ08PUgjrNVqODo6wldffYXDw0OODkhmj6j1yjhC1QKOytyVPSuKz+fDrVu3cO/ePfzCL/wCfvSjH+GHP/whPvroIwwGA2SzWaY3SFOlVXCiBEi7FeOlAGD6gcpDYOfcYk8xVwhUxMMiAFyY5EjjFQ9oJmCnzkQ0Cy3I5nI5dhkljYIoDzp4grRhcccrUSOUTywW4zqjzkzP0oYlkcaJRCLs5z4ajS7w9yK1QCETxF2rCwsLDNJUX5FIBIlEghdLycOI3EfFiZfqoNPpYHt7GxsbG9jc3OSwAWL9Ov383TRZNwBRWZkyKlF133lPJ7JJRnXNVEGSld/tnnid8tNx6br1B9P7Jm1FY+z69esAgEqlgnA4zJvT1tfXL4yxzc1NXh+KxWJ49OgR9vb28OGHH2I4HGJhYYH7DLkFkzVL37y7u8tKBY2VSqXCY8VELiWgk+uZz+fj8JGk6eXzeQ5/StSAuMlDtmhKIjaYjsMTn5WJeERbPp/HrVu3UK1WsbW1hdPTU/ZeOTg4YGqIFjQBsGseaavilnhx8iGgJMCihRp6VqRsKEgVgAvufUTh0ERBBzqLPH+v12NtJJPJsAVB+VO5qGzkq93r9dijJpPJAHg5kYiulqJ2bFkvt7QTdUbfRout5XIZwLnZu7q6ypt9qH3pL5WTgosRT37jxg22ICitwWDAZSPfeKJ8KFYG7XYVffpF7wKxT7nxsyRUXidYeKETnP1SdV93z3lNlaaMx9YBsht3LctL946Yv0hnySwY2fh2mwRkdaQrK51MRGOhXC7j9PSU16AA4I033sDm5iZbw6VSCY1Gg2MGiaE56EBzOqCF+uxwOEQsFuO9Gtvb23yM3cOHD3+yOfTBYICPPvrowtl6zWaTBxl5KRweHqLdbiObzfI27YODA17sW19fZ7NIFFXl6LhEErGjUwd4++238Z3vfIc15mfPnuGrr77Cl19+ia+++gq5XI43zFDUwsXFRd45SguEwDkg0uq5uLgmHkRB2jX5iJMlQ5y9GNCM6AiKeEiUDf0jX9h0Oo1IJIJYLMbaKlEaZEUQbURpECdP2rrYVq1WC41GA+12m71JxAMzRqMRBytbWVlBOp1mimd1dRXASzqHXEMLhcKFc1aXlpawsrKC9fV1RCIRdDodRCIRPHnyhCd9ABwXgyYemtRIIWg0GrDt85Nostks+/KLu3hFMFABikycsYN0gKISExpFp3U6y6srg5s2LbOATbVz3TWnZm2Sps76EctpWt/ONiardzKZsMstnaxFYw44pyMfP36M5eVlDIdD1Ot1NBoN+Hw+ZDIZFAoFtNttxjDLsnhd5+TkBNFo9IKrYqlUwhdffMH9tFqt/mQDOgDeFejsQGdnZxiNRhyh7ODgALFYDOl0GrFYDMlkkretE5CIWrsobqat8xnZe8DL0LL0LoEeAI6ISAt2ZJYRKFMIAOowxKW1Wi20Wi00m80Lh0D3+310u12Ew2He5LC6unrBpZMWWwDg8PAQlUqFg4WtrKwgmUzyiSnD4ZA3MxA1sbOzg263i4ODA1603NraQiaTwbVr13gtgzZQ9Ho9jpY4HA5xcnKCWq3G6wk+n483YIk7R1utFnfiRCLBB2qQK6QYZY7OmBUnPuAcrMfjMVsoBNSVSoWjRmazWSQSCbx48QLVahWHh4fY29vDzZs3+WSspaUlZDIZ3jRUq9WwsrKCRCKBu3fv8iRrom2KYrplWyYyAHajC5zvqu65Wag6cbMu3EDdRMtXva9bf3Ded6NlVOKMDzMejxGNRi8oOIRFwEu/8cPDQz6oRTyknfzN33rrLdi2zRz5/v4+W90UVoD6fCKRwP3793mPxaNHj7RlJjECdOv8cOffBvA2gD+ybfu3hXu/AuAPAGwC+BDAb9u2vff1vTCAfwrgPwDQAfC/2Lb9v5rkSVRKtVplnpQW44iDqlarvJhFHhb/P3tvFhtZtqXn/YcMTjGRweA8JDOzKrOGW1Utta7UrQcZ0IvlF0uC7QcbEOCWYPWTID/5xW6or9CCAbUBAxbaltGCYEC20ZANtx8MQzJaUMttwBJuXZWqblVW5cDM5MxgMOYIkskh4vgh8ltcsesEyeu+EFJAb4AgGcMZ9tl7Df/611pUPKNGAk2msaZDaykcw7T/z2Ih8YB4nSI8CNfj42MVi0UVi0XNzc0ZfHF5eWmWZb1eV6fTsRox0qC1B95MHXECyFdXVwanSFK73bbEpziOtb+/b5mak5OTtlDHxsasoNjx8bFBLfDXv/nmG0nSgwcPjCHDe9R0RqijaKhM5616hHqn01G5XNbe3p5+7/d+T3Ec6wc/+IHy+bzW19d1eXmpzc1NpVIpHRwcaGxszJKWJFnS0IsXL8yKwr2t1+uq1+t6+fKlVlZWNDo6qqOjIx0fHxv7ZXV1VWdnZ5b0NDU1pY8++kjZbHbAY6KL0s8qyBl3gWaGCf0QMw89hJtGkpC7zcoL13wIUSYJ6psE7F0Edahckq71rnN91+/d5tFIfSMNeIX0fPI91tfXtby8rIcPH0rqG02bm5va3983CHJlZUWpVMqyoZFF4+Pjmpqa0tOnT7W0tGSeAEW96C06MjKicrmsubk5C5zeZdzVQj+Q9Lck/TlJU27C5iT9rqT/RNL/Iek3JP1DSb/89iM/kvRI0oakJUm/H0XRt3Ec/+ObTobWkqTV1VVjeTQaDXW7XSswH0WRZmZmTBu22221Wi1ls1m9//77evTokZVoZQxz6W7C5ZI+z+9wUZ6fn+vly5f6/PPP9dOf/tQgmUePHqnZbKpcLuvzzz/X7u6uKpWKqtWqJiYmtLa2Zok4h4eH2tvbM4oTuHkUXTNF4ji29PRisWgNnYvFoglwSTZX0nUj6mq1ahF26rfUajULvKAYSHKAmnh+fq5Xr16pVCopm81ahhufRQmT1euLiknXSUnwuqll0+l0dHFxoa2tLY2Pj2tvb8+UuM/olGQWPdALPF6ENHU1JJmyPDg4sP6jvtY8SiudTpvHcu/ePf30pz81765QKNj6C704/3vYhrsrLHOXcVsxqvCcP6tASxKwN53HfyfJMg7P4yGaYecP4cxwJBUTu004D7u2pM8BFTYaDX377beq1+tWzXN+fl5ra2s6PDzU3NycrcFCoaB79+6pWq3q9evXllEax7EZNRsbG6pWq8pkMia0T09PVS6XzSilYB5G2dHRkVVa/LkGReM4/t23N/xDSWvurX9P0pM4jv/Xt+//SFIliqIP4zh+qn6v0V+J47guqR5F0d9T39K/UaADPWBpU3UMtghBMlgNJKIAtaRSKe3s7KjRaGhra0v37t3T/fv3LdIc0s1uwhSTgit8J2mMj4/r/fffV7vd1snJiY6PjzU7O6uNjQ2lUin9i3/xLzQ9Pa29vT1J/WbHExMTZhUjrKMoGggWAkERLEbQ+5oyp6enxvlGqBE85BhYs54dRPIT8z43N6c47jeKTqVSAwk71Ffhu2Do2WzWytbGcTzQxAKsn1IFYc/RKOrTGwlCwjH3MQSoh546yPWSwEXKP/g/90puAAwknj/3xfNHcHMOv9GTgnb+OoZ5dElC8jahxfAC3Gc73oUumbSmQ2vfX194beE13qSwQiF92z7x7w8T0Lddf9K1DvvcTUFafz6IA61WS59//rm++uorlUolW6fz8/M6OjrSxsbGQK5IFEVWugKv9eTkxCp7Tk5OqlKpGIaez+c1MTGhlZUV/cIv/IIeP36sVCqlo6MjPXnyRKVSySjIxLh+3hb6sPEDSV/xTxzHJ1EUvZT0gyiKjiQt+/ff/v0Xkw4URdGvSvpVqW+FlUol01xQ4XwNjpWVFWUyGV1dXZnbH0V9LvXFxYXVGKlWq9rd3dXu7q7ef/99PXz4MLET/U0bdZhlMQyyAQv2qeWSLLLtuw9dXl6aBwG9EGHkBaGnGZJpirXrqY/89mwQPgtk4xcjwzNsuGaOQfMNqFQoGgZURi9wfd0a34XIt8tj+Jo3PvDLNXmGjKd08hrKn4zYk5OTgSJqzBnXzRqCyulLKsRxPKAQQtghaR0gJPz7/r27WLhJY5jlnlSnJElIDTvfXa4hSTElHXuY15JkCCUJ6psgm6TXk7Jwpe/PwU3nSLpmBgk/lK2Fg762tqalpSX94i/+oiYnJ7W1taWtrS1JskJwY2NjVi/ok08+0dLSkhYXF61QF+uPqorEdrDAj4+P1el0jOvOtZfL5VvhMsYfVqBnJR0HrzUl5d6+x//he98bcRz/tqTflqTp6el4dXVVpVJJcdynvM3NzVlQYmZmxvjBkrS5uamdnR11u10dHx8rm81atT8EWLPZVL1eV61W09LS0vfKUYaCm5FkXd2mLb0FxXf9QvRdb7w774s98b5vcuHpih5GQfAjtPw1EJH32aQILa7FH5vADElSHBNqFTCQV0IwcHALoSNK/QxaaqXjUaAcCKJy/CQsGSEbZtVyL8wlymBxcdGgOaomeggP6IhWeT7w7iE05pD3h5Wc4FqGQTA3wQ5JQidpffnnxRgm7G8Lwv6sNU2GlTFIEvSMYUrMv36bgkiaj7vMe9Ixk+Jm/jlyXewBmp3gNdKqsdPpqF6vq1gsWqxO6pekhvGCgQWeTuxvampKQ1kLVQAAIABJREFU+XzerO3nz58b8rC8vGxU4ziOrYMYQn96elr//J//8+/NZ9L4wwr0jqR88FpeUvvte/z/JnjvxgGLZX5+3mpq+MYCNGDY3d2VJKuJPT4+rtnZWaXTaVUqFSsLe3JyYg2Qs9msdaNhk2AFDltww0a4cMOFF25+LyhYZGFHIYZfhFiTXpD6v73S8JloDK+sUCJJUECoOLyF7j0Cn8nK+3wWYc0Cx9Ngw4Bx+wqSwDh4J1jsfo59CVLWSDab1fz8vKVnZzIZtdttw/45D1x76q/TqKLZbFqXKa6R2jIoEB8gvcnq9XPqn3f447/j7897R37wPZ84F76PQg+/E14jv4cplGFssPB6b/ocn/lZXr/NYh+mpEJK6DAL3XtbScYa3zs/P9fOzo5evHihZ8+e6fDwUNVq1by+Bw8eWAJbu922HgjHx8d6+PCh4rhfc+jo6EhTU1PG9PIlNDDEjo6O9NFHH6lQKCiTyQyUEqCgH7WFQCfuMv6wAv2J+jg5E5SR9J76uHo9iqJDSb8g6ffefuQX3n7nxnF5eamdnR2Nj49rcXHR+NJk+9HEoNvt2qYm2MXm++ijj9RoNCzgls/nNT8/b4wXLE1pcBN5SIDFi0DFmvW0RC+QgT64BgQFlD1c+btYFRzbHw/B4QfXw/uhMPZZm5wDq5uFzjG4NjYs3oN/nWOBfSPshwl4skI5PxF8Sgz4ufBWuFdSCPswuH1xcWHxk8vLS3U6HWOk+JIEKA/onhyH50rsAg/EW+7kCfj5Dq+RefWWnr/O8LklCS2v7P3zugm+CAVYKKiT1tdN0EwYJ0j6XigY7woFhMe9SRmEhpGfu3DOh3kqtykJ6RqmOT8/V7PZ1NnZmQ4ODlSr1ay5BWtnfHxcL1++VDqdtnpG5EvQh4Bzbmxs6PLyUicnJ9rf3zf4ZnV1Vb/0S7+kdDqtUqmkSqWi4+NjZTIZ/bE/9sc0MTGhw8NDvXnzRi9evDDZRZP0u4y70hZTbz87Kmk0iqJJSVeS/ndJ/1UURf++pP9T0t+Q9NO4HxCVpH8g6deiKPqJpEVJf1XSX77tfKOjo2Z5wZFGgGApgm9VKhVrVzc3N2dJKhTKQhAUCgVrQFEul5VOpy2b0B+TkgPeQgzT3j1MgcAi4IeyOD09tcCkdA2NkJwTWussTF/7gcAIEIbPKOWzoZKAWeKVlQ+qsGEIsnrLmGsERiFNnhZZWCrMgcfaPawhXXddQvhznlQqZQlJwCT8sGh9ZivHYE74DLCJj1FkMhnl83krV+AbovBDkTdJFvSFbcPwCo3MPr8W/PMKRyiovOALYS7/fhLEcJOl7McwBREK3VA5DDtW0vnD6/R/32Z5JykhrxSSjnMbHJXk9dx0DSHG7r0y2GfNZlOlUkm1Ws1yRbLZrAqFgiYmJpROp9Vut/Xll18OXD85E+Pj41pbW9P9+/fV6XS0ubmpdrttBunIyIjq9bpWV1cVRf1A6tzcnLLZrO7du6eZmRk9ePBAkvTJJ59oYmLCciZ+3rVcfk3Sr7v//5KkvxnH8Y/eCvPfkvQ/qc9D/w/d535dfR76tqQzSX87voWyyECYQfHBGqR1GtgotULGx8et1jXMh5GRETUaDUlSvV5XNps1l933xcQS9Vapb+HmLVBoeVhvYMUIVX58TXQvcPls6AnwO7RSk1xFRuhqI/i8kPPDf5b7I7sUaxkKIoEasGYsVX99XBtJURT3kq5phQhXMksRomTOcl2+XDBWrhd8PrgqXQeY4btTxIssVc5Ndi7Hz2Qy5qVVKhWLs3i2EDEKvAZfo94HWpM8pnCuveDhGd5WOS/8fNLz8yOEQJIsWr+e+E74mr9HjumPm+RFhII5FNjDvBh/bUn3N0zghz/h/SUdj33tn1+pVLKscthjWOgQK6hEWq/XjT21urqqDz/8UGNjY9Yk5vz8XKenp3r8+LHiONbz5891fHyszc1NXV1dGR2R0tA0byGnZnt724wbGsxgkGSzWc3MzPx8G1zEcfwj9TnlSe/9E0kfDnnvXNJfeftz5xFFkXK5nBW4kvqZgmRMgn2en5+ba91utxXHsVGEwE5Jg8/lcvZ/JpPR+fm5sV281csmDjcq+HQcxxbI43X/HX7zuodDQiiB17lnfnthybF8KV5/zZ5Vwm+fuerhGh80RdB5TNwfe3R0dCBTk8BnaJ2SsYqFggDHuuYcsACAkZhPBKb3iphD5hsFS3BVumbeEMBlvmi0Sy12Cokxj4uLi3Ye+qJSkRGl45Wzh1/882KeQiHm2UVJVu0w5TxshOvCH+c2C9l/LjxnGGT2fyedxxslrD3PRAqvOen1JG+EtZCEEfN6ktcyzLvwx2T4a2m1Wtrd3dX29raq1aqKxaLy+bwWFxfV6/W0vr5ur7Putre3LWfk8PDQZAoeaSaTUaFQsPyOUqmkZrOpOO6XzJVktZ2iKNLnn38uSVa0a2VlRa9fvx6o8UTy3fj4uFUGvct4J1P/R0dHlc/njTkBJAA9Ees4l8up1Wrp6upKMzMzun//vkErkPKhAfV6/cbAhUJB8/PzAy4w1ioLx1P8eN1fmxeSfvF4QeBHHF/3ECQBKDxu+HlP2UNA+gXurVdGEleZ6w2tzVARMPx5vbXvi22F5wT66HQ6BmN1u11LqQdioW3c5eWlKYFut6uTkxP7HsXIvALk+kPLM5PJaGFhQTMzM5Y6TX9XXzoCjwqhzTNA+aGg4ebTGzKOrxtrhMwlvApe89eVZCknDX/M26ADP4YJ8rseI4QrwjWbZLmHiVWSEg0fvu+PnwThhIbLMAHtr3fYsZL+95/1yqtWq6lcLhtLZXd3VxMTE/r93/99LS0taW5uTouLiyaga7WaZXcC/R4dHdm6lGSeP9dAMlAURZZkJ8nqNxWLRat9tLW1paOjI7169UrLy8s2nzSXyeVyP5MR8E4KdEkGZ2BRzs7OamxsTIuLi1ZPgVogb9680czMjPb39/XkyRPrOI+b02w2tbS0pHQ6rfX19e8FG3u9fnnKTqdjixQL31upSXTEcJEmDW+5ezw2tNpDfncIPXjrmKAiw5e59XXkgZC8MObco6OjVkM8iq5rlyPksKrZeEksnJGREasSx/m4VuAMn90JLEPLPBSFV4qSrG4NzxlMnQAluPfU1JQlXzQaDc3MzGhiYsKEsiTrC+otaFxwCogdHBwYfATmTzZr2NA7iqIBSMY/U28NooS8APXxk7sKfL+OwtfCc/F3uBb92iWZimdNbCZphHBM+N5NSmSY4rlJ+CZBNOE9JXkcw67Rf7fT6ajZbOr4+FhbW1tqNpsDUGO5XLbkue+++24AlpGum9eTR8J+kWTyg3VHadxcLmeZyNlsVvl8Xp1OxzoZ0T1raWlJH3/8sUG6z58/t/IU9Ci9y3gnBXoURQNYNcFENiQNmAmwjYz0y66CQbXbbXvNW8QEUX2wUuo/8Gq1qkajoXK5rJOTEyP3r66uGuaaJLxJYmGMjIzo5OTE6s0gsLBCqdmSdCww8RCP924pgtqza6Rr6zzE0H2RIIQ2Vms+nzeLmGzLXq9nBcHodIRVy/0Ad1BorNlsWnCYMqDUSj84OLDnOTc3Z41JJFnA+/LyUtVqdQByoZ/p6emp5ufndXh4qMnJSauP7is/TkxMWN3zbDarYrGoxcVFq8CJsHr8+LE14uVZwSLwTbrpGSv1FSXn5Dkw98wNQXGvfCmXAB4qaSAWwfH4fZOQ8+s0tK6HCc2QVeOtVOJTvMeaC88VnjO8Dj+P4bUwPyGtcBgE5OcgyXNIgob8ucIx7DXWLn1mWQN0FoqiftMciA2tVkvn5+dWqXNyclLLy8tWXE7qywBqK83Pz5sxwHt4BtJ1o54f//jHevnypaanp/XZZ5/pww8/1Pvvv6/9/X0rNdBsNq39JvDwbeOdFOhjY2NW1a9er1sfzUKhYAFK+KEwMbCuw0JWrVbLkluy2ay5WGw4SSZU6vW69cTc3d21Fnek2w/D5fx74cY4OjrST37yE3399deWrOBpSAgHFpsX2igebyF4+MWzSvgO8IFnzXgrcmJiYkA5eIsYgcPngVKka0sOaxrhR/IEaf+ZTMYYMt1uV4uLiwPeCLVjiI9QuvfTTz9Vo9FQtVo1hY0CSKVSeu+99yTJOL3ANvQxpWIljaGl/sanaBt1MygyRnEyz/HPZDLmbaAofHVMBgLRCyYMDv96kuC5TfgkCU4vKMN15uGRUEj+LDBO0uDcofUe4tpJngbKzTdWYb2G9+jXW+ix3TZnITzk594rEGjD6XRaKysrZj2z9yltgYyhbHM+n1er1RqIyVAa11f+5EeSGQmjo6Mmj+bm5qxY18bGhv70n/7Tymazevq0TwgEKqQzVhT1g/bb29v23l3GOynQr676heSpWsYmrlQqyuVyGhkZUbFYVLfbtY4eZAuCV2EpEvzsdrs6ODhQvV5XFF2n00vX6b6Ugc3lclpaWrLsLihOITMlXJhYjHS8Dyl9nk9NNcYk6wah7av8sSC73a4JeioOYtkDi3gLTBqsie6zVH0K/NLSkj766CNTTl6Y8znK2lLGNpPJWDA0jmPNzMxY84vJyUlbiGEwM4oiu4exsTE1m0275k6no8XFRdtsOzs7GhkZ0cHBgYrFojELLi8vtbq6KklaWVmxa/PYucfeZ2dnzf2lCBoURjZdp9OxABQVM2dmZgZa5Enf7ybvFW4oBL0n5QVuyCJJisN4Jgrf8Yol9OT8OmKEMNAwIZ9kVYfXHXoUScf05/PNQrj+JOHrE+FCxXeTkrqLVyP1i/bVajW9evVKr1+/tgqlwLg0ollYWLDMagyW7e1tZTIZKxh3ddVvgrK+vm6eP7WYyEYnEZJ9ROOLubk5iyVtb28rm83q8PBQ6XRa5XLZ0AF465Q18WWjbxvvpECXZPCAJAuezczM6OzsTLVaTc1m0/BZcFMsKRICECw+HZ7Nc3V1ZZRGz4ag6h6NXU9PT00Y5HI5E6rAJmwAhDBUI2/pAQdMTU1pbm5OlUploB45WLFnYlSrVc3NzZlbNzMzY7RM2BgkVGUyGYsbdDodjYyM2CYCpqCucrFYVLVaVTabNRgoiq4LCOFBnJ2daXl5Wa9evVK329XKyopKpZJBYczHxMSEOp2Ojo6OLLqfSqUsm5eCWbikfpNTzCiXy5l1LV17A9PT01ZNk45I8/Pz9hmYMb7vKUrOC1vfno8gp8eQWRcoHZQZUBTXyvF41iGf3HsxXrB7BeAFr/9MGPBNglaS8O4kWCZkmNxkxYcCFq/Jnz98H8Mh3K8+oO7ZTF7hAf0x78Ms6/DebvI2botjNRoNvXr1yoqx3b9/X6urq2q32yqVSioUCsrlclpbWxuAGR8+fKgHDx7o22+/1djYmI6Pj+2+8Aila4+t3W5bYTugSD5PcJXnc3l5qW+++cY48BhmxIUIxJN/MixmEI53UqBPTk7qww8/tPR92otJMndnbGzMmg6MjIwY/nt6emqV/3CXEaoE1eI4NoUgyQQhwgC87PT01Cw/LE4ocLiRWGBYoWQskmkm9T0LLOzR0VFTFmGggwVNhNtj/XC9vaW+v7+vXq+narU6gJF6IYfFCYbn65z0ej3jg/d6PS0sLFiwkevb2NiQ1Bd8jx49UrfbVaPRsMSeRqNhpT99j1AUJN+VZPxe6bqBCcqaICwB7omJCYuFeMqmx9mxpngWQBJeoTEnKACuwVd1RLiQ0Ts2NmaZgMViUbOzs9bmDsWJwJeug97h8F6bV/5cKwLNBzv960lCOckaDwW4D8QmMZ9C9pZXLOH5PWzhB16uh6xQdOHnvLWNgIPKyvX58hLDxl2Fvfd4gM5WVlb09OlTVatVfffdd6pUKtrY2LDGKtPT0yqVSlYwa2pqSul0Wmtra3r48KG+/vpr7e7u6ssvv7T+BVASwc2J6VFYEMVVqVQsoRESQa1W09jYmCW5XVxcaHV11Y7ZarX06tWrgXu6y3gnBfrV1ZU2NzclXdN33rx5o2azaRs5l8vZpjo9PbXSAESS79+/L6lfD/zi4kK1Wk35fN60L00ZON/k5KSmp6c1PT2txcVFxXFsyQSnp6dmqUE7Ojs7M48ApgUC0gdA0LqNRsOEDAvCW/feOvJMCDal37x+w4buOFDKMPcYC4TP48qdnp6q2+1auysEIHgixyBY1Ov1y9ISXyDgixXiE6uwSGAUYHVQItdDRMBDSdYhz4oBEyqp3G0qlRrw3N68eWMUV3BTArUP3jbtmJ+fNwF/cnKiZrOpWq2mer2udrv9vQQjD+mEGy58rv4ZEphjjoDn/D1I12UoQoF1kzXvoR7OlbSWKHWA1Y/HgcfGXPd6PRNwvvE5a4/sYZ+gRoZ2LpczZekH36UdG/Pn58iv3yTl5t8L/w7Xgs8f6Xa76nQ6mpqa0nfffad8Pq9arabZ2VmtrKxYzKbRaOj169fWKatararT6dh6i6LIhLFvIEMbRPYAexL2DKSJbDZrkOXk5KTBKrCsCoWC/vyf//NaWlpSrVbT3//7f193Ge+kQB8ZGdHS0pIODw9tQ71588b4zDMzMwMCjA0BTlUoFHR1dWXBjF6vZxAAwmNyctL6jdLUVdIAPIASOTk5seDe4uKiaW9qf5A0wOLe2tqyFGJJBiWE7BrpetOG+CsLMtzoSb9ZyAjPEJP1yiC08MJEIwaWNhsP6zbko2OdetgofJb+PhCEvtgYn8FSJzmIrF82i7/XsbExq3LX6XRUq9VsI6Es4zjWo0eP9IMf/MA2K5TOk5MT1Wo1HR0dWfAJocTchLAJ682n8PukpiTBg7BCcEnXDUR8Yo4X5CF+7t+DTsvzQcn4ktAIhiSIhKp+lDR48+bNgKeAp4QAjON+A5JQwXqrnLXN/xhIeLuh94ASx7igAF9opIRWeBKcElrjIU4fx/28Ahrf9Ho9bW1tGZQaRZFqtZo2NzfNI11fXze4D4+fmiytVku9Xs/WkiSTA56yC6xLuRG8EJ4fXiUeY7vdNgMQKFPqW+oQPu4y3kmBLsmCYr1ezyqW4dZAM2MR+8VFv0sgEJ96TpU03H4miQJgbHYCG8A08/Pzmp+ft4mGlnR2dmYbJJXqt41rNpvmYvnItxeeLOok64v3Q8Ed/p30v3QttL1b7QVKr9czK4wgD9mcnpEAVsvnqIcD5MIiRaAjpPAwEO7Q9BAcExMTyuVyVndlZmbGkoPm5+dtI4Frwjwh6O0VQxgg5PztdlvVatWUDLximveiIAgSY3F5GEDSAKTiN6uvcum9ISAH/8wR+p5RFVZwTEoSQ2ADh3l4gvv0awR2loc+mDOuAard6emp1eRhXfhSEDx3oCevQELLF++HdQB+zHz438xxCMMw1+l02mjGHkZLUnihsvPMI+6V94iBTExMGHV1ZGREMzMzOj8/1+zsrNGM9/b29M0335jRuLKyIumapcIaqtVqA/uZ5vXEhTDmmE86hBFUhVFHEJTnDBqwubmpw8NDy8H4N5rl0u129fz5c4vywmsmIaLX61l3IibQp6XDYMCtnpmZMbz99PTU6iWsrfWbL2WzWR0fH9sGLRQKmpubG8BXgWOAJsCpCXiQqnt+fq6FhQUtLy8PWLNsBtw+mmAgCH0QD0UWBpNC6xdoxLus/O1T/7EGwTtZePz4TkE+cEmZhFwuZw2Ucb99FTrPnEG4c31Qt2CU4J7TLhABc3l5qVqtphcvXqhWqxlezVxBafP1X7AApWvhizCDEzw7O2ub0V8jSv7y8tKonEnzGwpILyz8vHvPyGcee5YLgskrBQSjVxIIRa8o+Am59ewXqkmyLjmuvy+u10NoSXCI9+oQ1sNgQO/B+Hnjd2iweCXiMXvWKZUMmavwmXhPgR8P2XnjxVu1pNizB1CkxJQIqJOQODp6XVufOM7IyIjee+89i1sBuUBE4PpQvqenp2YYkaQmyfYC85pOpy0uSN0XSQO9k8N5GDbeSYEu9ZktfmODyWEppNNpw2xJCrm4uFCz2VQ6nVYqlbIWUAQpr66ulE6njRlC0I4yAxSwJzpNQSgyCcfHx7W0tGSJRgiGYrGodDqt09NTvXr1Sl999ZW5/Sw0hIC3yLw1x+Jmc4Nn4p55V84LL1/yVRqsXc7wPHQ+QxB0cXFRy8vLxgqCakiyz8HBgdUSBwdEwHJfKC6fneotShQGSoXrQxkwBygi7jWdTiuOY8PpvXDw9xfGF9jM3Ie3rsOAIALUc/d5NlwnAsLXvfEZxOF1cT4fMPdWqS9JwPPwz9RfL58HCsEzCq3lJLgItz30xiABhALTW7ycGziE5+eFMEE9cOnQkubYYXY0f3sPBE+M3/4ZeUXiISrWtY+rAPXwTPmObwm5s7OjOI7NQkc29Ho9HR8fW7Ig10UOzHvvvWeQoPeieWbUGzo/P1ehUFChULD94TFyZBPPBEVCPM4/E/o/lMtl3WW8kwL97OxML1++1MTEhKanp5VOpzU7O2ubqNVqqd1uWzAhk8nYoidDEHoatL9er6epqSnTpkAHklStVi2AMzs7a13rZ2dnrdfn8fGxUZKwvBBKcdynMdEhHMuTBeuTh8JFisWE5SIN1lPxbqvHavntBbX/vN9MfuNy71HUb7Dt4Q3/HlZeaGGhhHD9JX1PgXj3m3tFWXEOWvRxzdwPGyvEa8PMWO+Ree8EAeFxVX89oQJFAGCFwVmmHv/KyooePHigDz74wM7t74vzh4LMW8ChJcw1AH2hQPzgGRDboHWhVyLeAke4hdZ0GKMI8VjmyRsaXllgNGBBJ0EhwDM+WMpPUkzF7wu8DuJSZHozz3iCPhHPZ+pK15U9Gd4D4RlxnNHRUT148MAs9Xq9bgy3er2uarU64IUhzEdHR7W9va1ms9+AjSYq0nXOi9TnvMO4y+Vy5mV2Oh0rs3FxcaH9/X2rze89KDKigV78+rzLeCcFureOzs/PjVqEcMaV4cEhfAqFgrLZrKIosqpnWOeFQkGlUsmCm57a5wvvwEah883IyIjVOPYBNW+x8ZDJOON4oZUgaUAQSt8v8MUiYkOiuT0t0Qvy0OIJH7yHfPy1sKm81YmAxer3CSFYQt7L8JvXC1Z/Td4SDoVaEibrhaMvJOYxZeYRYeCFHO633yTEDbD08TQookTyE0JzfX3dej12Oh0dHBwYFx7ucRzH2tjY+F5gM8TfvaD3sR6anmMc+GxK1gA1tP08e8we4ebPESpg/z+BUBSAhy0YodcRPm/O6dcuzwmPgPXiMWz/G+vaY+UoZ/oBc89ATHwvhH7C/ePXuYcgiV1MTk5aZ6BOp2NlLoiL0YNhdnbW4BCYbnEcWyyI9cd9YUSmUikLpNbrdYvh8N2pqSktLy9rdXXVcj5o7E5WehzHVmojLBNx23gnBTqTMD8/r3w+b1QehEy327UiN2Rcdrtdy7Yi+WR2dtaSJEj5zefzRp8DA4uiyFgu4MLj4+Pa3d01rHt2dlarq6tWi8QLoCTrKhQsLAL+lgbT8r0gCIVDGHhjEXsLiU0aJnxIg2n7nsbmj4Pg9667Z0CghAjqhC4+m4e5IN7BwBLBwveYuFdYKEL/Ha4Bup90Xc6XwmJQDaVrpYm1VC6XjSGVyWS0uLiopaUlLS8vD/DgT05OrJ4PigKIiGunpjulm9fX1wfgIK45VKReUTKPXLcXqAg4rxxDb4U59785JkpZ0gBzhO/64LcPrIaKIGkN8Zx9jIQfv4491BjHsbG8MCC4B+aA19Pp9EBNnDiOzVpnLn1gnOOH18nw9x5Fkebm5izP4c2bN1b9kwzOQqGgKOr3+1xYWDCoBjYU7J1UKmVxN57Z0tKSPvjgA0tAevbsmVqtlgl5jD2S9kZG+qUmIFxAi6XPg4//Af3eZdy1Y9Ffk/Qrkj6V9DtxHP/K29d/WdJvSPoTkrqS/pmkvx7H8eHb938k6b+Q5Pl6n8Vx/Eo3jPHxcd2/f98q4TWbTdvYuCIIY9wpcF0KOs3NzalWq2l6etrcTCxfghBeiExOTqpcLiuKIqPnUTedheRdWqkv/M7OznR0dKTR0VHt7+8PWC+h4OR6387NAM/YW2BAEx43912HvBAMlQHH5nwog9CqYiB8sLJ8ynISiwSPiPN5q3p0dNQErcdIPRcZixba5+np6UAaPslcKAyvdLzQ8YKAc+XzeaXTaeVyOWWzWS0uLmp2dlaFQsHgCyyhKOp3U280GorjWLlcTsViUY8fP7ZNSD9HamH/wR/8gbnovV5PjUZD7XZbn3zyiWGoPDOPiydBR1yDh238uvE/4Rz47/kBBsvwa5DrABOXZAF6v2a8gOYYfm35teONixA2SrL6vTcTKhniYD4HQZJZrF7BeQXqIa9QuHthDrw4NTWln/zkJ9ra2tLW1pYWFhZULBattLUk3b9/X5VKZYAyXSgUrF8oljMGRBzH+lf/6l9pc3PToBwENNRMSVZeGgx9fn5+wJOp1WpGlQRufvz4sUZGRvT8+XPdZdzVQj+Q9Lck/TlJvuxXQdJvS/q/1G9J91uS/gdJ/477zD+M4/gv3fE8kvoC9ptvvtHU1JRNJswKCnVdXl5qfn7eFvH09LS63a5Z7NQ+r9VqA+ntBOhoKizJznN6eqqdnR11u13t7e1pbW3NsHRquvisxUajoa2tLR0eHhpDhGp/BFoIhng6XNIILS4Pi/hN7BWRdL1pEfxhIoeP6nuWEMqDY/kSsb1ezzBRv+B8AMd/3wdSeQ8lyH1wX0AfBL94NtPT0wZp4Mb3ej17noVCYcDdZsCCAR6bmprSzMyMdTIisEoNDdbGzMyMNjY29N5771nGKt5fp9MZyE2QZAH6o6Mjy2FgLe3t7anb7VpeAwrBwxqhIPPPzz/38D0PxUmDZZLD0s6hwE86JusAZphfP7zvz3vTCKE1L9z9Og4hEr9+vdXuvVMPS/prD78fQkPha15ZQknudDpGqMBzokwFtZ9WV1ct0a5UKllCwu0iAAAgAElEQVQPYk9AYM7wupApnBNhPjo6arKHe2Yf0S7Ts6O4Lur7+3PcNu7aseh3307UDyWtudf/UTDhvyXp/77TmW8Y0IKgYR0fHxu+NDc3Z0HSbDZrqdoscDZStVqVJMOtED7QgLDwJFmNB4Q3i75QKKhYLA4EAX1SDAKs3W7r6dOnGh8fVz6ft5owJChxT3gTb+dK0nWbNH6HrjXDQx5J7/G+d405r+eMc9/UTymXyyaQCEx53jfXSmyAkrfUgel0Onrw4IGxcfw9AEdhmeFZAF+EMJIkozaSiZvP5y0XwG9mvBNfR4PvsZEoH7CwsKB0Oq1PP/3UICNgh5OTE5XLZVWrVcVxbE057t27p9PTUx0dHVl/WrxESgecnZ1pf3/fSiZgPHz33XdKpVJWOgDDIWkgeD3LhbnzdEaeHWvPfz/8O8k65hi85oVnKPxDQch3wuPyOc+i8t5JGM8Jr897caHi99/11xFa4eF7oXLx93x5ealcLqd79+6p2WxqcXHRDD8ClMViUeVyWYeHh/ruu++MnogMwIqHFi0NMr3q9br1bEBmkOy4uLho9dhpEI03infLPut2++U5KE2dpOCSxs8bQ/+3JD0JXvt3oyiqSTqU9FtxHP/d2w4yMjKihYUFK3PLwwbvov2cT+VFI46O9isZplL9cpi5XG5gEyRZRL4S4kcffWSBqZA5AObYarXUarXMbZ6entbo6KhR+4jK0w8VrHd2dtZcSxYfljxcaH8t4aIOLSIEDEIbrNzXMfGNJ7y1R+IEAec3b95YMwc4s1hPnAsBDwyB8sDihibK3JFHAC5OPAJrhLlhYzC/XoihbF+9ejUwHx4CIHgJTILCu7i4MEubImWFQkFnZ2f2Gkp7fn7emglsbW0N8PTn5+d1cXFhNd7r9bqlhnP/FGMj2w+cVpIF6ocJpiiKBoKtvOaplKHgvYn14OEb72H5gRAddk1JmK0XxECAeD9huYYkuDF83zOAQggljC3wndCYSbpnf07vhbDHKBGyt7enKIqslMfk5KRevXqlvb09yyQlD4b9kUqlLDvZN7jAMwbOnZ2d1QcffKBWq2UwYqPRUD6fN2iJ3gh4mbCWkB8k1o2NjenZs2dDn7cfPzeBHkXRZ5L+hqS/4F7+X9SHZI4k/ZKk/y2KokYcx7+T8P1flfSrUv8h0GWI4lAUgqJeNlmbdMHhoRHMwDJnoicnJ82KwNphU+D2e/wQvJ42VOfn51pdXVWlUlG9Xler1TLaIw8aihHJGF4ZnZ+fq1Kp2PkRkqGgdfORaKUkbQIUAALZB2D5LlYgeHk6nbb5uLy8tJRmLDdgLXBt3yTaR/hTqZSOjo5MgDO3QD1xHA8Iqyi65u97No+fhyjqB2F5fijoQqEw0COWuj1QLo+OjnR0dKRyuayXL1+q1+vZs5udndXjx4/18ccf69GjR5qYmLCUaxoUdLtdg1GYa7wAkl2iKLKs4QcPHiiKIjWbTW1tbVn2XxRF+uN//I9rcnLSuiUhPD033BsJHm/3AjG0ikNIwn8uVBp+Lfm1EP4krbdwhDi+LzUQUiWHXX+4bpOuM+kzw+4p6T6ScPTwWOvr61paWtKf/bN/1uRIHMfWWKJSqSidTqvT6dieX1tbMy+NpEeMJTKcHz58qFQqpZOTE+VyOX399dcGvy4tLRmi4PnocRwP8NC73X4xrzjuJyyurKxYpdG7jJ+LQI+i6H1J/0jSfxrH8f/jJu9b97H/N4qi/0bSfyDpewI9juPfVl/4a2pqKob69+LFC2vYSgADS6zRaBjnG7eJTRNF1915wEh9UAbGh3Rd7/vtddiGaTabOjw81N7enhqNhr7++msTxmxWX12QCpCk9GK9+IJHnM9bT2FyRBIjxiftMPgbYeu5sd4j8S42VKyTkxNrNoHCYbGdnZ2ZAEMQRVFkfTg5N9dJFJ95I9sUa5na4tls1pLDuEasJpLCgMsoigREdnZ2pqdPn5o1D2OF45Hp2uv1zFUFtpFkHZDOz8+1u7trAXQ8j5BGidfkPRzWHYqajQkVbWlpSUdHR1Y8TpLNIxanNz484yhhPww8YxTj6OioWY0cAyvUC7bwOMOEeviah2FCYZ8kHL2x4D/L30kCNhyhEguVQ9L9DLtf7xl4w2lkZMRYcJKM7YSCZU/j5R8cHAzw36GzLi8v6/HjxxZQl/oVTA8PD3V0dKRPP/1UH3zwgRqNhjY3N/XFF18ol8up3W7r/v37iqI+A+74+NhKYgDLtlotWyu+tDRspbuMP7RAj6JoQ9I/kfQbcRz/j7d8PJZ065Uxublczmp9xHFsPHCKvmNteqqcL5SDNectQRYZuJc0SI9DOOOO4ypVKhULwHI8KGxYdtI1nu0FcJIVEVqk/t6TXFf+BmuLosjolQgjn5nIBgtpkKS6FwoFK1hF7RrKiBaLxYEATrfbtcQJXNZer2c8WurnSBqoT+H5unB1CVQSJJqdnTUX9PLyUvl83ho+c18IXhY71wFNr9FoqNFoWJOBWq1mhbt6vX5G7OXlpZrN5kCf006no0wmY7U0PPZLQMozjSQZ1EAMIooiC2p5Rf/69WvD8x89emRlJvxz9sp5mEUaWsVAB/79pCChXzPheW/7LFAX14cnzLXcdPybLGuvLPw4OTnRycmJHY+Kh/6zSefxP+H+4rn5/09OTlSpVFSpVMzAu3//vq6urrS3t6cnT57o1atXVnE0iiJlMhktLS3pF3/xF5XL5bS8vKx79+7pxYsXVhH24cOHevbsmc7OzvTq1St98803JpP+1J/6Uzo+PlYmk9Hh4aFKpZKtOeoKYdix10ZHR81I293dNbrsXcZdaYupt58dlTQaRdGk+qyWRUn/VH1s/L9P+N5fkPQHkhqS/qSkvy7pP7/D+SzYSHeRhYUF5fN5i1LjlkxPTw80TyAwh1XOg0ZQey6zHwhr72IT5CAQgiDHcvPQAxs9SQhIg4WbvALyVEBfN4N5CC0nSQPC2ZfDxQL3wiJUGt1u1wLLQEV0Rmo0GuZZUACN+2ST4clQSGl9fd0WHN3PsTiwpol3+IAluOKzZ89UqVQslgCbAEv/6urKOqTPz8/bwl5cXNTFxYVBR1iGxWLRlD9lTRuNhprNpjqdjnZ3d83KorkIpWHBrFEe3AeelhcyWPAIVAwJmC5U7ZudnbVyzKG1nGR1DftMyDoJFb636IcJ2aTPh7AFn8HD8MNb3ncZ4b2E5+Q9DxX6a/W0SL8vwuGZYOH5fCE+YDZ63VKhEyYdvPTT01PV63Wtr68rjmPdu3fP5MrLly/1/PlzNZtNoxIeHBxYB6RcLqerqyu9fv1a+Xxe+Xxejx8/ViqV0ldffaVGo6FOp6N79+4pm82q2WwqjmPr9OWN0OnpaZsXlMdtI0rCp773oT6f/NeDl/+m+hb3jySd+DfiOM6+/d7vSPq3JU1I2pP038Vx/HduO9/4+Hj8wQcfqFgsKp/P68MPP9TDhw8tKFUul9Vut80NJZAHrQ6LBeuPe/Q4L/RCSVYHm4SmVCqlnZ0d09hPnz61ACguOpaypzDxQ9AE15zFxuICYuBYBER4z3OAQ2YCxws5vR7T9kwTFAHYNxt1amrKLMf5+Xm7Fwpl8cP/CERquRCEpZEIEMbV1ZVF+IvFogVe4zg2Xn8ulzMhSSBxdHRU7XbbLGMwTOptcN0seOijHq6CHlqtVg0y456w5qG/sgHpyL6ysqJ79+4NrEPPqMEIwJpqt9sDypj5okH1wsLCQCNinqf/fRuskOSlhco9HElW87BzDLPW/fvh+rrJAk+65qRruOla/PlCRcDweyI8XxhgZb/DGnny5Im++uorHR8fm+ch9SGVjY0Nyyx/9uyZMa6KxaI++ugjMwI6nY4+//xzyzjFcABe5O98Pm9Nydvttra2ttRqtYwIEMexeb5Aj37PSLJ98dOf/lTn5+e3atI7CfR/3WNqair+M3/mz9jmnZmZMRij2+1a6VXKucKkQBjncjmzFEM+t3QNsfgFBvPi6upKR0dH1nmk2WwanEBrOWAHHr6nlIFJgw9HUWRCGxyWCm4hFdLzuH1wEevEB3aBjGDGIBR9/XAG10/w8MGDB1paWtLS0pLS6bQVQKtUKspkMjo5OTF+7MTEhJrNprW5o5HI5OSkwV1wej00EMexNcWG3gh2jzVEghi4MN4AyorA05s3byxjmI4uHJe5g7/OPINvp9Npw+JppwdFE0hnY2ND9+7d03vvvWfYNNYhGxSFgNLAG/NMlNnZWWPMZDKZ78Eq0qAQGjaGvT8MHw+/c9vx76JYfMIR++em79wEvQwboSAeZoH7YPCwcyTNNYP11Gw29eMf/1hffvml9vf3rVRzHMc6ODgwNtIPf/hDS/6h1/DoaL/T2OnpqXZ3d3VycmJZrIeHhwOwFwFOIOPJyUk9evTI9vDV1ZWePHmiVqul7e3tARgVuJPiXe12W/V6XU+fPtXV1dWtk/pOpv5L+p6gQ0CTtt/r9VSpVMxSx2KDjpfL5SyIB4wSQhae5ZJKpbSxsaFer6d8Pq+9vT0dHBxYVxGEBLxkhDZURJgc0nWSDkoEIQNzBsGFgAeC8QE4cFz+j6J+QA2KGMIUgTM1NWWKyi/s09NTg1QILM/MzGhpacmadaAosFguLi6MxUBkn16klBSlIfTMzIwqlYpmZ2fVaDSUzWbV6XQsQ5e68bi2BJ6gRVKQiaQslAPQS6vVMu775OTkQHr/+fm59Y3l+hESPskMYZBOp9VqtVStVi0gBYMG+AmoDWUJDMa6AesEekNxFItFTU9Pa25ubkDxMjyD5Kb3pOHY9k0CnBHCJ+Exb4I/hr2fdIybznHT8ALcj2HCmPeSGDjhtXmjLemc4NMeCwcuK5fLurq60sbGhpEwFhcXVSwWdXl5qS+++EJff/21rQeMQOk6zgTEA414aWlJa2trevz4sZaXl826//bbb41VBYzHGiTGRaYpBsjPNfX/X/cYGRmxCor8j7XKQ2y1WgNFgcgORDBI10XvKb7kU52xaCWZVQjRv1qtamdnxyiS8EWxvil6Dy7vk31wzcF64cgjbKXBans8UDB2rGsWDKwUgozSdX9RuO8+mAfkA7yDcnnvvfeUzWYNXioUCtb1h0BzOp22JAfSjymWRFzBM2BI38da9dxhsHysYKzx8fFxNRoNTU9Pq9lsmgIYHx+3Pq/cNwqG2AkCWZJ5Eghi4hs8S9YKFLKTkxNzfzOZjF69eqW5uTnbgPQMRVn78grQNcHMuV/ajS0uLmpxcVFra2sDvG8vXLzA8qURMBTC9R+ynIbBMozb3k8Svl7ZJVntoaXvcfrw3P54w5RNkkK4CfP3f4fxKH8c6ftKks/xfbJESRSamZlRJpPRBx98oFqtps8++0zZbNZgWknGtNrb29PLly/VaDSszAeZ0Zwb9AA5xeuVSkWnp6f6l//yX2p/f99iOjxjst2jqM/thzXlSRo+Ce+2cbeajH80/mj80fij8UfjnR/vpIUeRZHRFiWZFQcWOjo6ahFkLDHqWpBGj/bGciUACQQTx9fJGXwOSwyIxGdvYsVRPIdax54JAQyANQ9TA6vZp/X6Kn5S31r3cIKHTgiw4mXAfffZnZIMYspkMoY1p9NpPX78WG/evFGtVjOrOZ/PmwtIkNazS7i3drutdDqter1u9z47O2sVKE9OTqxaHDGN2dlZdbtdq68Drk8wmaAQDQEIfp6dnWl6etoYJFRIPDs7M6sFKIggNTDM2NiYWdd4KMwXiVPAPrOzs1pfXzcrCIueYxKT8XAYPUixpKRrzHxpackakN8FdmB9SbJ591CEZ2ElWaZ+hJa8HzdZdUmwjg/eJmHS3voOoaBhjBZ/bWFZaObRf89b3cNw85vgmZAp42MAMEvwgim4tbi4aNcyOTmpnZ0d7e/vW80X1inQZq/X70aUFLNgfUDSKJVKmpyctFK6QLN8jqqdHi6i4iI1X25i+ITjnRTokPhx6dkAtI+jUhkQQLfbtU3tWSgef0Kw+/KcDBYBgp9iT8fHxxaFlmQBwqmpKR0fHyubzZrbDaYq9ZNYoC+CcfviTygfWBqwRTxkgZKCKcNCBE66vLxUoVAYKBOAC0cHc0kGIb148UJv3rzR0tKSYfDMNeeGCYP76JUM1wT1kLZYnU7HakdTGwUMMJ/Pq9lsWmLF1NSUzs7OrKwxZY7B09ms3DNwD0lQlUpF09PTkvoCNpW6brvGd8jUi6LI6uc3Gg2D1DKZjJ4/f248dBTlzs6O1tbW9N5772lpaUmSrGYNDAkC3xsbG9b0oFwua2dnR/l8Xqurq8rlckMxZgacd+b/tjEsg5NNz9/+N+f3UIQ/jr82hLcvJpZ03mH4POdNEsa9Xs/6BdTrdcsS9q3WkoK4Ictm2OBz4b0BvRGgZM/Pzs6aoIQuS4ehmZkZ7ezsaG9vT6VSScfHxxabWltbs2TGg4MDS4KTruN9rNlcLqe5uTnlcjkzJsjNoDsaawlqMIYoBmsURbYPwzIIN413UqB3u121Wi3V6/WBrMOxsTELhNIObmVlxSqg+eQPLMJMJmNJLUxMGAgCEz44OND+/r6ePHmiUqlkVjAW5tTUlAnZXC430AAWTwGLUpKV3OW7XKPfaHgB/t5JVCJoAlaPksJK5rOUiF1ZWdHy8rJh0ZKsLgWJOpKszRYWK0rOp+pzXViepB/DBMBSZhFz7ZzXB6OJXXC+iYkJnZ2daWZmRo1GQ4VCwZg00MRQCNScJosOC+f8/HyAIuaxcxSTr9zY6XR0eXmp4+PjgdiH1PeACLxKMrZPpVKxY0xNTalYLGpubs6Cn2zIOI4tGWoYNi3djX/uRxJ+HI5QkPnvIjTC44VYeBh0DHFpL8DDa/frhmD01dXVwDxgFMEgGibAw//99Q4LDnNNxLrwzn3yGveI4Od5YSTh+ZfLZdVqNXU6HT19+lSNRkOpVEozMzNaXl6W1PfKOp2OUWOlvrzx6x7vf3Z2VjMzM7q4uDAaJLGwy8tLSzDiGMgHPIiRkRHbZ/9GC/SRkREVCgVjXMB9lmTdZGZmZixIhhWPlkMBzMzMWL/PkZHr9PZGozHQAUm6nrCpqSmtra0Zw0LqC5ORkRGrk44AYTEDVXgKJRsJyAZLku8STORzHjahet/777+vbDZrQo8FMT8/r3K5rGw2a7W+aWzrYSBJWl5e1s7OjtLptLa3t81SoQ65JINusGbYDBwL1gr1mmu1mrLZrFqtlrLZrCWAtVotZTKZAeEM3TCVSlm9+NevXyuVSplnQPlSOONY+tJ1rXq6wFAPJ5vNqlQqDWxY3/wCGG17e1upVMqepS+OhpKfnp42hfz8+XOrGZPP57W+vm6slVqtZpbZwsKCarWaGo2GWaEff/zxAN0vXFv/f2mGfl/weazQYZBLEpPGf8+Pmyh/fHcY64WmIFjBpKonWdc3Ka3bAqM3BWhpRAPUQVEtz/4C7gDS8l3JSDyDrvztt98aywT2CaVFqPy5vLyser0uSfriiy+MV47sWVpaMgNsb2/Pymr42j6sHQwuSoegEIB5QhryTeOdFOjS9QJlolKplFZWVozqJ8k0q69atry8rPn5eeOXVqtVbW9vD2h8z2mX+gtmfHxcU1NT+vTTT/XDH/7Q6n589913ev78ub7++mur6Dc+Pm44uTSYfg132rNVgDdwo9D6YPlg9A8ePND8/LwWFxeVy+VM4N27d0/tdtuaMLRaLT148MAswGKxqIuLC+OJI/glWf/VFy9eWIU4+LFYD0AkCLdOpzMQyWdjYFVXq1XzTEh8wJvJZDL2+Wq1ai2/4LXn83m1Wi1NTU0ZfLW3tzdAc9zZ2TErHgojzSZgEMBO4fnD2fXxCuAwLGygrlwuZ7CY573TFIOkI0/3pFzq4uKixsfHTXExsMyS2CIhc8TjvEkjyaqXBi3mYe/xmocUvcD2EEWSkIBZkSQ8w/NGUWSZ3B6mScKxeS8Jd/fwSniOkLcfwj4wprjnKIoGaKkIbyjPeGe8/vLlS5VKJW1ubqrdbmt2dlaffPKJ8vm8ut2uDg4OBsqEvHz5UkdHR4axS9csOQwpyk9fXl6qVCppd3fXeh+zR/A0kQGTk5N6+PChMctqtZplndKn4S7jnUwsWllZiX/zN39T+/v7ZpH5gIR0vXCgjRUKBcsOPD4+HmivBc5MnRCgADYkQhWanA/e+AV6dnamZ8+e6cmTJ3r58qXq9fr3AloeH/M4KcdEOeGmPXz4UMViUffv31cul1O9XrekFOqB46axEDxeOTExYbAAxanOzs60tbUlSdrc3NSXX35p72M5SH2FSK1mgqTMhceogX7wkhDkkgYycfkuNVlOT0/NsgePxz3G45FkG4/PEzBGqSPQO52OKdF6vW5CnPeYExQq+CTHY+NMTEyoWCza5+fn562TD9cBn3xsbEzfffedyuWyzs7O9NFHH+n9999XsVi02hwImE8++WRo2dlQkN81YOn/H2Yl8/u2vYwVyx7CGpS+X++ccZNVnTR4Zn7feM8i/PlZhr9XDBDpOsmMNUXDZwL66XRa09PTFnTnenZ3d/XjH/9Yn3/+uY6Pj7W6uqrV1VV9/PHH+sEPfmAQTqVSUalUUrvd1uvXr1WpVHRwcDCw76EOA0kCL1LvCKNBGiy0h7FBtvvIyIjReJeXl62XwZdffqnLy8t/MxOLzs/PtbW1pZGREeMKY1F7biabnqADn/EcTnBVYA8SkEZHRy0TFezbp837TQT2Pjo6qg8//NAs+hcvXhin1OOd4NyeY+vxZayLkZERlUoljY6Oanp62nC68fFxra2tGeSxsrKiOI5NAIO/kRYPF/vFixd6+fKltre3tbe3J0km8Lrdft34crlsMBEL7vj4WG/evNHKyooJPrwJYC8wePjp1WpVY2Nj2t/fNysaT2N8fFzlctkgMbJRPSzTbrctE296etrqaJRKJRP+zBmbg4UvXVesJDALm4b3cJV9OVxJVvKB9Hzw3ZOTE21tbWl8fFy1Wk2jo6PmZsOdf/PmjQkK0voZXjiFFmpSgHLY8MdMwoq9gGS9eavWrzF+e3gizBUIcfrbYBGO7zNHQ4UVCuphcE4Sjj7M0vf3Qd0cgvPEe6KoX8p4d3dXBwcH1gQHJY0Cw2hot9taWlrSvXv3LH6ysLCg5eVlFQoFVatVffXVV/riiy/0/PlzXV5e6v79+1Y2o1arSboOuksy46vVaimOY6uNxPx4goO/7/Pzc2tgPzExoXq9rufPn3+PwHHbeCcFOvAKhXTAqL1m98LXu4/eSo6iyCxjAqRRdJ2w491UzxZJwj+l63T7UDB7jQsOLWlAAaEwEC4sQDY6td4JdHI/PsWfe+U4aH02qa89gndAdUiwfWpYcA3AVdPT0wbTgN1hpQLjpFIpNZtNcwu5Jl9Ui+DU5OSkKTueByn8xBC4N77LtfOsPQMEdgACmHumpZ1fFyg4zguOShyDYlwvXrywYNXCwoIWFxd1dXWlarVqQWwyjxnhOgsDjF6A3zRus06T8PYQdw+Pw1oK8Wv+xgtNUho3XU8o+FlzDPbfsOBpklDmdT+34Xf9nPrPkoRGQ28UOFnjvV7Pirdtb28bRIaRRWcrqa/gHz16ZGu7XC7r66+/VqlU0snJiQlmjru7u2teqA+EIgNYE7DlQg+IufNzMTMzo7m5OZNzXNvZ2dlAw5a7jHdSoDPApSYnr3s4SrKmBKFlTLTdWwQIaX5Cbcm4DdeEFoj7nk6njXpFEAhuLRZE2OgijmMrQeA9Ca7fCwceIu6br9nN8BFxvos3Am6PUoS7T9VEAsgsRII2cLixun0sgPrPUn9zEjTiM0A/nBMaKYpibGzMgpxANVBKsbLoZITCAp6BkokyIkaCgvNMKFhJVHgk8EpAPJfLqdls2mt4cZlMRp1Ox9qCMf/+nlG4bEoMA6/Mk/Dn8FjDLOJhwu+mNeotVwRKKpUy44XjJnkN/O/3jBfM4TVJ18rUr92brvEmqx9lkaQI/Pf9fLE/CIIeHx+r1Wppa2vL1g70XQQ5PHAyRCVZoxpw7UePHimbzRpf/c2bN3rx4oWazaZarZaRAVCMDOo9sffZG16Is7emp6ct4D4yMmLXTuVHz2gbHR21NU8hsNvGOyvQveV9cXGhly9fmkCnWYFfeCzkMAAVWgE+qOJHKCg5BudgA29tbenZs2eWCkxtcZJLpH45TTBXL6ixhrHSYU/gMSBcEZAEb/kfjiqCD4Xg75XP4CVApeN/Oo9D25qcnBxInIDi5wU7sQZwaYSfL3SFwmWj+eeAcPa/SY7yRb18+VpKCVAAiRLJKDrcbboUUfGOAB0xg9XVVd27d09ra2vq9XoG2UCH3N3d1b179yyYCvfcN7hmPr0g9PflvaQQAkkS7qFV7Neit4YlDcBF3iDxx4yi67RxrDs8F28YhNcxbAyDQm4TvLzuGTV+r/lrD+8jnJfwfJKMp+2rZ2KsQHc9PDw0j/T169cGVXqD8PXr1yoWiyoUCspms3r27JnFFzB6SCQrl8tm0V9cXFiT8LGxMavOuba2ZklEtJzz3hKeOnEogp6zs7NaXV21vrl7e3tW0RQyQqlUsvLRdxnvrEBn4FbjkviH7DeXXwyhwPZwxU0WEK/74/jfIyMjev/9941yuLu7q6urKy0uLmp9fV3FYlE7OzvGLFldXdXExIQymYy2tras+h+CMZVKGeecLFIPh2BZgu8h3LEogBqwCIghYLlK15F/ICU2OHEDat+gDChYlc1mTVDhmXjPhgpxxCUajYYxfzKZjCU90bHJc7Q5D5g+TXOvrq7M8yFZCQsaRQbkwr36jkQe6yej9uDgQIeHhzo+PtbY2JjW1taUy+XsGmAjwDn2lFOYU0kYMHPhBTjzPmzzha+HAt6vVxR8u902Jehr1oSfZ5/4blCh5Z00kizxUJAmvYalHK4znm+o2BgYXv41vwaYNRYAACAASURBVK/DACp/430j8FqtlmUul0ol1Wo11Wo1W7MYJwREZ2Zm1Ov1DHPf3983eiz5J9vb25JkWcqlUkkvX75UrVYbiIFRq5zEuFQqZR4A6w4j0BuH0B9J5ltYWJB0HdT1njlldn2LxbuMd1agewHsrRYvZMObDC10fvMzzL297TUGAgYohW5KH374oQUu9/f37Zqvrq704YcfKp1OW/9Kqgn69P1arWZd50NFk06nlcvlBkpxhviwdA214GqDcfr06pGREWM5YF1ms1lNTU1pdnZ2AM9GQGBZYMEjiH0luPPzc6Nqkbp/enqqN2/e6PDw0DYMEEtS8AwBkc/njRVQLBZVLBaNcdNqtfTy5UtJ/YSp3d3dgWqNCD6Oy8YaGxtTuVy2PIHp6Wnlcjmjm7XbbROWeBd4JSgqn1eQZIEjhEP8etj69OstCVvmXnymctIxQut3mNHj13boGYSWs/8/9AD9eUMPVuorWtLVeaY+LwKjxZ/DC/Pw+lGa9EDAW81kMoafY/zAVGq328pmszo7O1O1WjUIkmbuFKabmpoyzwYaYbVatfVH8trl5eVAIbdOp6ODgwNTABgGYSyvUCiYQIcCDINFkpVz5n4ePXqk5eVl8wQ2NzcVRdFA96jbxjsp0HmABM+oEYKlhtCSfrbSo+F7oTXO58LB+6lUv9b6/Py8WQVwu1l4fN4HihBuBBY7nY7q9bqazeZAQ44kiwYsjvRgknC4dxYZOGGlUhm471QqpWKxqIWFBc3NzdlCRgCDc5fLZcuk3N/ft3ZuPqlhZGTEkm3gzONBEMhMpVJW64V2cGSjcm/cr58v/mY+K5WKNfDN5/O6uroyNpMkm0sUOwHWKLpOmebY3Cf4vQ9ceUvYXxt170ulkl69eqXnz59brITv4k5zL0mCMVxHtxkRSYKTn7taaXcxXIa9nnSOJC84vE6s0TiObZ4QdHiYvgqhp/El3bd03Qqv3W5b6zgfZO90Otre3tbR0ZF2d3eNdkztFJ774uKilbleWFgwQU0LSQgB0Byr1aolxvV6PQuMYsBgFHG9vjqiT/bCu+L5oUDYLzBiqIA6NTWl9fV1IxXEcazd3d1Er2zYuGsLur8m6VckfSrpd+I4/pW3r9+X9FqDHYv+dhzHv/H2/QlJf1f9xtCnkn4zjuP/+rbzgRFz4z59fGxszJJiwmj723Pa38OwTP8eI0kxJLmd8ErPzs4ME+f6vPDwcAPHubq6Mjqhr8+edB7e6/V6hhNDw6IMLgX3K5WKBSy5D38/zGGn0xngwlJGN5vNGvedAE6j0bBrwDodGen3ZXzy5Im+/fZb5XI5CyQtLCwol8upWq1qb29Pm5ubOjw8NMXmN6K3woBSeL5+3r2ARqmxWXiP4yNM8ChwXz3DyDObSDbhh4xiqW9xeevv9PTU6sL7zk14Sj7QHVqefr0Ney/pf76TtCb88YbBOB66SDqP/30T9OgVCfON0kXZp9NpE5AIOwLjJIdJ/UA2niQp+v4ZElRkrUCTBVvmmbfbbdVqNcsUphVit9svV4t3RUP5arWqRqOhKIr06tUrvXnzxvoCeEoqDJher6fnz5/r8PBQr1+/NkEdRddMIWSTdF2WIlT2fn+TNUoSVBzH5lEDfbZaLb148WKACUec4OdtoR9I+luS/pykqYT3Z+I4vkp4/UeSHknakLQk6fejKPo2juN/fNsJESoIK6iM6+vrhjMPc92TLImb3Ez/Ot/zFpx/z2thr7WBKEhkuLi40Ndff62XL1+awCqVShbdprKjr7Dorz+8XqkflT89PVWtVtPOzo7iOB542H5zessRfDGXy5nwpvZ5FPUz654+fWotsjwDKHR/PY+52WzqJz/5iTUBQWienZ2ZIMX65ln5Ily+aFq4Cbh3b/l6lo+/Po7BORAgvkQD2C4QFDEAOO6lUskEOgW2Tk5O9Pr1a5XLZbVaLc3Pz2tyctL6ScJuILsUz3GYFR5auTcNv06HbeYkOIXhleMwyCRc87wefiecc4TtycmJBZElWSD78vLSuNY+GYx6PsQqfH9aL8SZVxggNOHO5XJGLV1fX9fa2pp91ie65XI5pdNpzc/P2zpvNptWdG1sbEzb29t6+vSpTk5OtLe3p1arpfHxcR0dHVnDGuYe6nQURQNQo4czEfie3IB8IKZDLSo8bZQlnixrHdkAFOM92NvGnQR6HMe/+/ah/1DS2i0f9+M/lvQrcRzXJdWjKPp76lv6Nwr0OO4T8oFd6AMpyUrAgrcmWSC3WeN8bthg8vL5vFVnu7q6Uq1WM9cc4dZqtfT8+XPt7e3p7OzMMsoymYweP36spaUldbtdbW5uWvnVUFjfxRX3nwtjCyH9MmmjSn1LAqspk8lYnZiLiwt7j+OF5/CvY7myAWHNsFG9lQiMETJfsG5hK7H4veUOdu9ZO6FlCU3Rl0/mWj2HP/TAYEeMjIxoaWnJUtglWangXC6nQqGgqakpqwmzv7+vVKrfnmx9fV2STEhRRyhJgPrn7dcZrCEvwBEmzGEYzOe3F8o3wSHh+ZOMGYTLMCOJ53h+fm5UP3pu8gwogUxdlJA6GcexCWe8PqA5DyOOjo6qXq+bFdvtdi1Lend3154xUGa5XLbvNptN7ezs6OrqyiooLi8vq9vt6vPPP9f4+Lh1LCIzmyJxUHcPDg7Mw8ADISgKjOQT2XiWvI7Axlv0e4k4HOt9ZGTEmHJzc3O6d++e1cchtkaVx7uMnxeGvh1FUSzp9yT9Z3EcV6IoKkhalvSV+9xXkv5i0gGiKPpVSb8q9TdItVpVr9dTtVo17UYdEh7C4uLiAB0p4Zj2e5hr6v8OrSKgDoJr3gqTrqvyFQoFPX78WA8ePNCzZ8/09ddfa29vTzs7O5bMQNAToYngS7rG29zhJEGBwPIWlnRtVbGpEK7eO/B8dC9IgEP89YTeAEIAi5xzcRyCS9SBZ7H7eeaafUKYp75xToS8dJ385I/D357lgMXkOxtlMhllMhmNjY1pY2PDsnGhw4HhkxUrybJ4iSOA32N84D6HVnOoXENLnaCrn2OaaCcNP+/DhLtfB0mwDK+H8AvDKxCPCTOnWKoIdGrw+GSxOI7VarXM2+GafInYbDY70GAbK5n9TMCSwnFk/uI9zc/Pq9lsanFxUe12W/v7+5qdndX8/Lzx0LPZrCWVffbZZ5L6lFdiO5VKRel02lgmYO/QFOM41uHhod0Da5Rr4jqZYzKIUQgcI4RjyL2gNpQvMcyc4On6cg23jT+sQK9I+pOSvpRUlPTfSvqf1Ydmsm8/03Sfb0rKJR0ojuPflvTbkjQ7Oxv7ZJ2rqyt7MDxw7954kv+QYyf+PexzTDh/I9QQRCzykZF+7Qis8y+++EL1el0HBwf2oPxnvVb2qb/SIA3uJgXkIZlQ+EmDWXvDvhvOg4/Qe+uWgKlPPQ4Dih7u8Jx67kmSCTqsUX886q6EisNfRzhP/je4OMdg8DfWu3f7fVwG1g7Ft3imH3/8sXlUQH/5fF6PHz/W3NycWY4UHMM681mv/tmEFnb4N9fL2vDZgSHsclcvNLTaQ8vcv8frCBPcfklW/wiee7lctvVCVVPiK61Wy6AS4h7U8GcPF4tFzczMqNVqDQS6p6enrSx0tVq197g+X2Zjenpa5XLZyjOcnJxoZWVFh4eHlkEaRZFVX8zlciqXy7beWq2WQWm9Xk8rKyuWQNRsNk2Y8n0gYOYRz1G6jjHhzTGPtVptoLoqcgBBjqeXz+e1uLhotaZKpZIODg70/PlzKxp412zRP5RAj+O4I+knb/89ivrB08MoinKSqE2bl/TG/d2+7bjdbtdYIKenp9ZoGFcd1gTlcD37QLqZWxu6kv63t36ka20sXVu6vI8gw4KAf00TDqy8w8NDSbJIvz825w3x8/C6k9zpYRBNkit+k5WPsPX84FBZ8PmQyeGvI4qusWufXs5zYsC99/ftj5UEF/mAqPcgGPCvPdNAuu5qlE6nre4Nm8lj9+l0WhsbG+Y9bW5u6vz8XKurq1paWtJnn32mKIoscIaXgwdzdnamVCqldDr9PRzdz1uStU6xsHDNJsFxtylmb5WH3wkVv/9caEEjzFj/1WrVgnPEcFCg2WxW1WpV+Xxeu7u7AzWVgK1oZMHeDSEZSkIcHR3p5OTESlt7OuHFxYWVYia/IIr6tVt89jF9QTudjprN5gBlGKydGNLHH39skN/R0ZHRcjFMsLBHRkYGevpKGrCaCdrX63Xjo2MwYNj5Ms+Uup6bm7P6SaOjo9rb2zNq89XVlSm30Ki5afy8aYusrpE4jutRFB1K+gX1oRi9/fvJbQe5vLxUs9k0Aj+RYdLRfVr5MFhC+r5QHPa5Ye7nMAXghR1BtWazqUqlor29PQtwgBWOjY3ZosZKDwOuQA5gbngnnr0R/sRxbPPgBV1IB+Nc4f2EgU+/6UO2CNeOV8R9hAqG7+BqeosTYeiP6amHw6zOYcoLj21sbMyCcD6rlvco0QD8xQbhHikJQIkCr9QpB0zmLyUG2NBx3KejLS4uan5+3gqPUaffC/gQypCuy6eGay5pHYYKO1Qcw+aOzyNoaZ9GHX0+h+JkTqDCQu8DmsO6JIZ0cXGhnZ0djY6OWiby5eWlarWadSmS+qwTavmAnQO/gE1jrZ+dnSmOY8vaBIbF8gayYO6z2ayxrLa2tjQzM2N1yfP5vDXFQRmPj49b8bh6va7j42NTQhsbGzo9PdXu7q7dg68llRSYj+PYylTwHk1QEOqUw8b7nZqaGqj332q1NDExofX1daM1fvvttwPr5bZxV9pi6u1nRyWNRlE0KelK0p+Q1JD0QlJB0t+R9M/iOAZm+QeSfi2Kop9IWpT0VyX95Tucb6CMLRoLjBYNK10HkXxNZr8hk4RB0vnC/5OwUJ9IAw5dqVR0fHxsFp8vziXJKEkEvjy8kKQw/Hm9RTzM8vaQzjBrMDy2h1a8QPXwQDh/QCpJliGDY/ggJLxvoA7PcmFzRVFknYb4P7xnlJ3H0z28wb1wDu8SSxpQjgiE0dHrQl1Uuru4uNDi4qJWVlY0Pz+vubk5xXG/5vzx8bFZjtTdmJmZGSj94A0NrNVwnfk5DOfZrwfuNYSwkjw0v07C5xNCaf4ZUboYgYogOT4+tkQejCjgTQwJ2gJWKhUdHR2pXC5rdXXVkoey2azq9bqKxaLdB0louVxOa2tr1gIOiKZSqdg6oEonvWkXFha0tLSk3d1dC5T6hLtGo6H9/X1JUqlUUirVr0CK0CSFPpfLmfDM5/O6f/++Hj58aHzy169fq1qtGsGBtcI6Zv7xuv0zQnlRFA+ZRUkG5AiB0aWlJYOy+C4UTRg+jUbjexDtsHFXC/3XJP26+/8vSfqbkp5J+i8lLUhqqW+J/0fuc7+uPg99W9KZ+hz1WymLkix6ToYXD4/6J5K0s7Oj8fFx41IvLS0NCIPQdWUMs9SHvYfgbDQaajQa2t7eVrlctnrJLHDqf8RxbC55GNzz6fnesvYCOrwWb8GG1reHGJLuI1RMXqh44R4OhKykAfwOoeCToYbNGb89C8VfNx5Or9cbYNkkPQesb3+v3DtBzvDzXknxXVxssmOLxaKlhtMrknK8lUpF9XrdSq2SsSrJmpQvLS1penraOjhtbm4aJj89PW3MGRQY98/vm9ZiKOSHWeN++PURPhuUMg1QgBRoWAKmm06nrc4PvTDBhKm+OTY2plKpZFbpgwcP9Mu//MtWOgIq3urq6kC5BhLjvPIHs65Wq5JkntLJyYkWFxftOuk0JvWpjrSXrFQqlvyDF9Dtdq3GD3AohiACEu+6XC5bYbZer2eeNsaIJKuEytz7aot4hawtSbYG2C+ZTMYSE31eBN6cL6KHHJiYmND777+vVCqlb775Zugz9+OutMUfqc8pTxq/c8P3ziX9lbc/dx4jI/3sqU6nY3VGpOsqixSTymQytqF81/Uk4Z2EK4abadj3pO8XTfLvhdYPAthbnP5BcY+hJR4K2CS3O7Tah21ub7mGUElo6YX34Zkx4H9cN5imvz6u21MMsUIkmdXHxqR+DbinJMNowyAi9yzJkp84J54bcRagLc59eXlpdTmAsnyK/9nZmTY3N63119TUlBYWFrSysmLMKvpHMh98dmVlRSsrKyoUCrq8vNT+/r729/e1u7trzQrwSvzz4NqTvED/TMKRpATCNcDzOzs7MwGXSqUsaAsrBasTBUpyjaTv7bnDw0NjmkGvI1hIkTz45sx5tVq1OFez2bT583GHo6Mjw45ZE6yPubk5q+EPDDQ1NaW5uTnzeoDJ6vW61d9h3RE8xQCT+p4yQVbaEZKK70sV8Do0Rr+HgV34nC93wG/vefvsWbqdeY/IQ8Y8Ryx35h/mVZhAOWy8k6n/CKCpqSkrTgO0AAWOzUsKOwR+vp8krG/CoUJXNxT+WIi+CJa3tr1QD63jEGsO4Q/OH7rfSRZWCCcgQD0mH1p+oYUevpdEF4SGCP7HHPAMkq4RSIlaGCG8QjIJwtd3iMKaAUPleuM4tnozwFyS7BpgFSAUENY8R6lvzdGRCZhhdnZWy8vLVtcFuuL5+bmOj49NSIC505Zufn5eKysrmp6e1tTUlEqlkg4PD41dAT4briVv3fEsw3UXzmeo2EPjJHyOIZTmSzgjMKGNkoFJ4SnWj8/I7na7Nr+5XM7iAVi7mUzGKH6FQkH1+v/H3pvFOJpmZ3ovyQgGI0gG9z3WzKyqrmq11K1RoVvtVqulaS1QNyC3NXc2bNmwZQwwN/aVFwzUgg3D9oUvjPGMLcMwBsZg4PF24RnYgGEL0J0lGVB1d1VWVVZGRMbCncEt9iDj9wXzOTxkpapKRkOuAvQDhcyKZJA/v//7znnPe95zTt8K15hZe3p6agaP/kXhcNicN5+JAqZcLtsA5uvraxu9GIvFtLOzo2g0aioa1DhQJSBuWmsgJ04kEkqlUmo0Gjo5OVEQzLTlmUxG+/v7Ojw8tN5A6+vrqlQqevPNNy2x+uLFC6NbeZ7LtCpAge/DfuRs8hrWHgC6tra2MDnN6/Ph2D9Nxeevz6VBD4fnlZS9Xm+hNB30k8lklEwmFzYJh+ZV/PGnXX9R+Mth4nCAIqAk4G0x8DijZQUH3wsDiqFfNuz+d7wTgsZZ/rflRJWnGaS5qsQ7H39PfxEaZONhpDc2NizyeJUzkmS8Jj2jQW+E7aPRyOSBHDxeh7FYX19fkM7RwwYDg3Eg1OdgcWh8Ys+j/b29PZN/8XvZbHahNSmo3TuHcDhslMqjR4+UzWaVzWbVbrd1cHCgRqNhU6B4tkQbdJ9kz7IH/LW8hsv/vvy65cs7Bq+8IPk4Go3U7XYNQfM6qAsSciQZC4WCcekXFxc2O/Xh4cFGu/E87u/vbbI9XHu/3zcaDFoSRMxcg0QioXw+r0gkYpRIv9+3Qj6iSQwfmnH6mlAIFo/H7TNJbN7d3emNN96wz+M5PXnyRKurq3r77betkyF9xjGsUDidTkexWMwGRTOSkTNErsyfMQ+IfD6NXAVrDhhjzOHd3Z1yuZweHmYtJpBI0oyOvf5TTYr+VV8YO38gQIwsOhrg8XhsciWPIj6Jjli+PMftaRtvUO/u7tTtdm3M28XFhaFHaW6seegYT1+56LloPL3nwJf/fNV9epWLT9L4y4dor3IUy1l6/xrWgvtioyMN9JsLKWkoFLJK0clkYnJT72h5nkQ4XvKFcfahqkejt7e3ymazCwacKsHpdGoT3qkCzGaz1s6YGZKDwUCtVssGXOzv79s9IpMlPMfJZLNZxWIxbW9vK51Oq1Ao6Pb2Vh988IHRK+wbDjrfAU6UCIfntKw2Wq6AfdUe/ItQfBDMKy+94gYDQtIXI03ikPegGhMdN2XwJPAATJlMxhRFDH6gJD2fzyubzUqaGbZ2u21JvVqtZgib75ZMJq0En8K80Whk7ZbhrkulkuLxuO7v71Uul62nOdXhnU7H2s7mcjlVq1XrFQPtdnt7q7OzM21sbFjbDb/XBoOBRYrValU/+7M/q/v7e6slaTQaptLhuSFu8Ggax8/e5Wc+GieRDmoPgkCdTudjIoJut2vgjST0Z6VbpM+xQccDEqpLMi8HJ3VxcaF0Oq1sNmvcJobAHy5/WJDTeQ8LPXB9fW1jyjAOGE1vsKX5mLfV1VUrNAHFTKfThaHFy1SL9HGpIBtm+UDzWn7mJYjL/8Z7+1J5PpMkoS+VB/167k9aVITc3NwsaGn5fc//85lEA7QS8NGCN0IehYJmPD3DPTC9CA4zHo+bYuLx48emE/cJayYRTadTHRwcqFgsajweW+9qdN/SnJ9Ee80a0WyJvVUul7WxsWHJ8JOTE3Po7A3kdxcXFxb6w09z8P0aLye3uV4lbWS6fCgUspwDjsTrm/n+FxcXSiaTluAkasFRYvChumhElkqljHMnEm21Wjo/PzcdeCwWUz6fX+CGGWaeTqdtPufZ2Zk++ugjnZycLFQGb25u2gDzq6srk5sySQsHcX5+ro2NDZ2cnFjxD+9BjoSe5eRSmPUaicz6iNObRZKePHmiSqViTpa2IiQsWReabuHUvBP2z9BHi+wb6gk8YILmK5VKFqUhBCAiHI1GFj3Cs3s76NtpfNr1uTTo4XBYu7u7VliAp9rc3DTjjrcH+bRaLYVCsyISr5yANkgkErbpz8/PF7rAEYbz/7FYzFq2ovSgj4tvKfvwMOvL0Gq17D44kGTIiSjQoMLTwh97VQwdHDGofiN5JLfsINiYfxGv7Y06701VGpvYI7dlSoUCLh8ZsNG5f9Aa98L3CIXmM0eJHFgfUG+lUlG1WlW5XDYnCiVTr9eNLkgmk0YVUIhxeHioUqmkfr+vQqFgKoe7uzs7tPS/TiaTRqmMRqOPjcKLxWKWvHry5ImePHmi1157TY1GQ8+ePdPJyYnG47GhL9aB/Xl9fW3NoGhdMZ1OLYojenwVKvcGi3/DkdXrdbtPIlaeBwUs5+fnur+/t+9EPqLX65mqBN13t9tVr9cz1cp0OrWzlMlkrI3s/f29vvSlLy3khZBm3t/fq16vq9ls2vff3t62ni7kUXwNxsbGhnZ2dgzRs2bI9pLJpMbjsQ4ODiz68dEmFCffL5lMKpfL2WdGIhH7XoeHhzZ4fW9vz5xTvV7X5uamBoOBUqmUVfqiomMIezqdtmZ4JG79xbmT5q2DubzjJmLijBDJLtNjgCuiGz9k/rMac+lzatAxvNFodMFIXFxcaDQa2axANgwJMg4wKBGExyECbZOQ8UORpfm0eNp2gsBpHtTtdu31sVjMepFzYECphNyE87w33CCvyefzxiODmEqlki4vLxcOEZua7+N5ep+dl+YbzVdj+k3xKmfgL6KF5SQb388reXwyDUcVi8VUq9VMM4yRDYVmGfxer6der2eNzHq9nvGf7733nlKplCWJIpGIGo2GMpmMSqWSVQlKM0kbo++Q2w0GA02nU7XbbXNC9OlIpVL2vUkO8n2DYKZNRpO8ubmpTCajy8tL/eQnP1Gj0dDx8bEeHh4sCvHqHDTqqVTKCmvW19f17rvvajKZtUx+7bXXtL+/b9yqR+I8N/8M4J4ZuXZ9fW35Bf797u5OGxsbdvjH47FRFtVq1frS393dqdlsqtlsLqivMKyS7P2gakDV0+nUivt8JFIoFPT48WPrVHl6eqrT01Nls1n7XplMRpJsHibgqFwuW+99aJdEImGfHw6H9Vu/9VtW2PPixQuLztkDqVTKpM3STFYINUYUQUHY06dPFYlErIyfvx8cHGh7e1uRyKyvDFWa0FT08nn27NlCIZmnJdmnnjv3URj25Pb21mbVQmX66Jxqc+/0X5X8/rTrc2nQMTokmghxSLpdXl4uTI/HsCKn8s1teD/fDMsn2qT5MGr+jnSoXq9bf2UaEEnzze+z91z+wXtn4kM2Hnyn01lodiRpYZIQ3z+TyVgLYZQU0nwCu88bEC6CZHECfm2hXpCdEV53Oh2LFlgrCqNSqZTy+bwqlYpKpZIlCm9ubgxF393d2b0StXz44YdG2ZBQC4L5RKBQKKR+v28l4plMxjrhoXW+vLxc6IUhyRQQqFegkYbDocbjsfV639/fV61WM8keewPni5750aNHlmiXpBcvXpghJznFM0SXTlFLOp3Wo0ePVK/XdXx8rIODA+vOuLu7a5JIkozwr4TtXipKMowocDweW+m6jySo9qQMn+e1tramyWQ2i/L6+lqrq6s6Pz9f6KSJogTDjmKEqPHy8lLZbNYS7sfHx1bYg8b88PDQSuup5GQUIV08keihlKFB27vvvmvSz0ePHhnt8OMf/9iiitPTUzvXw+FQxWLRmvGVSiV1u13FYjFzUisrszFwGxsb1loaBRX8/MrKigqFghnvN99806gx6Ku1tTUdHx+bgx+NRiqVSqrX6wstLDxfzr7wdBg/wwmRaOdelwGFr6z2tJjP7X0m2/mXsf5/VVcymQy+853vLEgFmV6DkUPLTFKHMN4bf/hU34iJw8zmleae1PfvBrleXl7aA4IXHgwGGo1GCzrTZZrDJ0MlvRJRL3tgr8DwPHgsFrP79s4L6sSH44PBQJubmxZNMPfTo3pJ1hCoWCyqUChY2Le+vm5UF9wpxjCbzarZbKpYLCocDqvVaqlUKunZs2d2aEC2KA5AUzRRQoJGvgJUBD223PMGjTlDPWq1miTZBCZmiXrqgyG7cK3QNxz0+/t71Wo1VatVG3OXSqW0s7Ojdruter2uo6MjdTod20/L6iE4Y7rroQqq1+sajUbWayQcDmt7e9uoJKSQKH6IBkCz4XDYkNzBwcECTeX3Ftwzk5gkWbUh+R1G6wFSoFv6/b6F9pyfaDS6UJwFj8uYRWnmyHAkRJIkuofDobW45cxQ0MPvj0YjA2RexQGqZtJQs9k0cMF3HA6HtgegltgvzBjY3t42OgoqZzqdWo6N4jbmy3JGiA4AWhcXF+aQb1hl5AAAIABJREFUqEMg3+LPrY9uObfeRvlzz7mNRCJKp9NaX19XIpGw5zCdTi13w17z4/peRimfqvL4XBr0eDwefOtb3zKOWpL160gkEjYejIWSZB5Y0gKyWdZnFwoFMwJcJJ3ImofDYUsW+cEHHEYSMp7W8PSFN5zeCPiyf1/owr+xYZhFGAqF7HBEo1Ftb28rk8lYY38GV/heyVTqsW7ZbFY3NzcmkYpGo5awJbSEKigUCjo5ObF2pP1+X6lUSh999JHu7++1vb2tTqejzc1NS6Ctra1Z6wP6Y6ytrZmh9oYDp+nnf3KIWF/QCusIsqRCcXNzU9IMFRG+er1vJBIx5wBPTkUn/DrRBg4hnU5rOBwabXB6errwDHGsyDdJzqZSKQMJvnDq/PxcrVbLil5A6D7fwbMiqY7R29/fN/XW2dmZceCouXCQRDo8Z+ggJlPd3d0pHo+rUChobW1NrVZLvV5Pz58/t4pJxrtBQWHYPZ2GgeTcra2tmYSUXMXNzc3CkOT19XVzrjs7O6pWq5Lm4ILiHiKmcDisRqOhy8tLZTIZ1Wo16+BIzgtHV6lUTJaIAyNSk2RVoCcnJwqHwzaUmWeXSCQsuodGhSrEMTDKUZLRuJ1Ox/bbssCBPcI0JqIvn9D3qjPAmFfHEIFyj1TjRiKz/jbPnz/X3d3dF9OgJxKJ4Dd/8zfNyFG5hgEHRTIfkI0dCoUMKXLgKXPGwKOGQQsqzRaZajj4d7TT0+lUlUpF29vbttHw1j7ZSD+LbrdrLT+lecdGn0gFvWDMSJZibBjp9vDwsDBqj/J4Rmpxnx7p49h88ytJhuJJyKyvr9usUd6bQoZMJmNGCEPJGkJp+G54QRAolUqpWCyaxBHZlW/K9Sr5FXUEXEhTMXq++x/ITJJNy0E7vbW1pWKxaAY6Ho8rnU5LkiF4IhCkktfX17q6ujLjOxgMrLAJpIQUMwgCe090w9wb2nokhPQOYb3b7bY6nY5evHhhzswP+87lcva9VldXDeVixEhCQu1hBKGMQLGDwcDeH+N0e3tr+5IzQDdQ+rfAKWPAoeTQXl9dXVmzK28QWUcSwLRM4Nk9evRI29vb5qzW1tb0/Plz1et1ywmQSI1Gozo5OVGz2TQjur29bePtYrGYgRdazZK4fPHihZ2zYrFoem72KU51c3PTorcgCExA0Wg0VCqV1Gw29fAwm68LZVOr1UzrfnZ2psFgYPt5OS8FZeOlk8hqfaEa9gqp7cbGhtkLcgRIRvnZ6enpFxuhf/Ob39RgMLANRy8EeqGT8SacBDlxMDw/eHV1ZdweDxYjKs1CwUajYeEVvCbeFuMkyfTJ0BW5XM7uzXvci4sLa1w0GAx0fX1tsyg5HPCVHint7u5qb2/PEomnp6eml2Vy02AwULPZNMcC4uciLGcduLzhTKfTyufzZvTorIcTRaJIogYelIvEWSg00/P6AhpvDLxckuHUOApQ/N3dnbWgxThRHeqROdWd0oxy4Vl6rTvI0lNF7XZbR0dHOjw81PHxsQaDgRkfmkTl83nlcjmLYqDx7u7uDDHR1W84HGo6nZpx8MVdOFecBc8BI7K+vq6TkxPF43E1Gg3F4/GFni/VatVkev57kRvpdDpW9g5iJ6pkLcbjsRkcEvzkOrzC5vr6WoPBwBwR3UyXaz9WV1dt/0MTVCoVQ9ooVBKJhJW153I5+xMbg4ySoh0iEKSiUKNw1TRMw3Gdn5+bqgVZJtXk3iHwc/TuKGc4a1CrRCr0syGi9/JoDC/7CedORMC5AmFjr5D2Yuw5pwAB7BiRrWcSIpGI1dQQpXyhDfrm5mbw27/921ZliFGgApF2nmxcuCaQN3wZRpPDRfUb3dvYON4ADIdD69TGASSU9OEpiBikgkdH+kTDMAwTHhd1DPpd+khAM1DIgAHkYC0jb3ThvV5P9XpdH3300YJ+1ZcmL0sdQ6HZgIBCoaB0Om19R9jMbLDlhB2Ij42JAfayTxLT0F7o8pHEET0RUa2trWl/f19PnjwxSol1JDEMYuX9JNl4rn6/r3a7rUajoU6no36/v9Bvhn3AAeLQ8kwZqgCFEgTzlsQYSXoFQcXBgYdCITPaHFrWC0DhDQTKqna7rXQ6rXa7rZ2dHSsA4rlSvBUEgYX6RFBUoQJkNjc3bfg3aw8tQ5sMIgloBVrC+igLioRzRpUnlBBJa1QznDPutdVqmWKrVCqpXC5bZO3115ynbrdrjglaBYSMrBeHxPeiQpyCNs5FOp3W+fm5iRz4HjxrSQZK4LhZB3T6RBrHx8fWDsEnMqFJfOGgr/UAyPEzqBlPtXgaD3BGfxxsDXkPL3cdj8f64IMPvtgG/Xvf+571k6Zvs+ebR6ORVlZWTLpEAoZ/Rzboe4WgM/YGWpJl56EPvCSJMIlD4Y2F9PGWAXjoZWkhVW6ULNO/gk3mJYAoECQthOIgTh89oCnmMGCwfRbdl8Ejm6OAA9kfKNBz26Bzxm3BrRKG+8EHJFlDoZAZwWw2a1EAfXeo5uPAUthDT/lms6nj42Odnp6q0WhYFz7WnefCMyfU5WfwnKArn4TG8PoqPppMbW5uWkUlh2ltbU3ZbNYqDCUtUHG+oyYXa+ila3DAk8nEOF64d/ZJPp9n7ysUCpmyiggRitBHq6PRSGdnZ2bc0+m0GXkSrnT9Q9KJAUTt4/M+UI5BENh+hW4DeFQqFUPygJKNjQ1zDPF43Oi8lZUVVSoVO7cAK96LSKnb7Vo056OTtbU1O0uFQsGqLQE2k8nEdN6cUYDaxsaGcrmcotGorYNXo7D/QMCdTkcnJycLlApr4vNd/kyzdl6GivHHQHNm2C8+Ov/Sl76kh4dZz6p2u63xeGzKJByPT5B/Fg79cylblGRcNwvXarV0dXVlBpo/MR6EvxgzCoNIUuGxyUb7BwJCIcyC2uFB+gTqMtL1nhiUBifK7/b7fQsd+V3fKxnERSjPYQTx8v5kyPls5ISPHj3ScDhUu922VqLLyABHBeoHBVM1iQEkMSzNEqocGNQ0HNhCoaDd3V0bJwYP6qMdOtkR6p6dnanT6ajVapkGud/vm8wSxO4rWSWZMyC05eKgsH7T6dSMAHQBiIq1lhbH9HnVD9WUFF0VCgVLNKLzluaAgeiEfeGbmVH4M53OhiagMOGz0cb7KUf+Yh040EQnrBH0Fvp8ohnWhU6CRIfo1dfW1kwWSY8UXgOi9kMqVldXLWdQrVYNTKBCgc7hT9aCq9Vq2fkhN1Mulw2IwflfX1+r2+3qa1/7muUgoEKlGUoFeXsn02w2jSaqVCo2GJr7WV9ftypg1GLstWKxaLr0TqejlZUVdTodK5aSZIl/ciLsOZ6tJJMcAsbI3+FAWRNaELNXcMbQMeSPAICAJ+94P+36XBp0Fmc4HBp6RsNMwREyKx4SySEMCoUQcK8YbZJYcKOSFrw+7w+3hSFFHoZ++9GjR6rVatadDxS4vb1tofjJyYlNWIGTHo/HZtyvrq4MDZOgglOm8g8kjREG1Q2HQ6MjotGocrmcKpWKHU5vqKfTqa0hmmQMeqlUMtkcSDWfz5uqAaoCpwItReHW2dmZDQRA8vfixQvT73OYORQ4YnIO2WzW0CbhfBAExkv7hkZ8H2nuWHHOGB9pXujh25+SlMLYcCjh6+GQKWZLJBILewVjiuP2Y+OguKAt/HfwzhRdMxxyv983KR/fj+dGP5xWq2XUIvuQXAy0IO1mARQ+4qKoh+9MT3HoRxKhgAQijGazaaBmZWXF0Hg4HNaXv/xl7e3t6fLyUsfHx1YhCydNBEHVqZdDMiuUs0vETNUquY1OpyNJC4YYVYtPJhaLRRWLRaOHAHa1Ws34+/Pzc1t7RATNZlNnZ2em2y8Wi/asyQF5EEa0x/15gODXDRvEfbJPAX7sGW+kAXeICvz+ReXio8BPuj7rxKK/I+l3JX1F0j8OguB3X/78X5T0X7mXhiWtS/qFIAj+n1Ao9ENJ/76kW/eanw2C4ODTPpNwCokU4RGUBQ+VTc6mYGNCA/iEC5tHksm0JH1Mp+p5Yp9w5HWE+s+ePTOZEVWLeG7QKuE6VATvgWEGeZFgurq6WniocJO+OtZTKCBI/gMlUCCTSCRUrVb11a9+1aoHMUgYG1AAYXi329V7772nXq+nTqejTqdjyho4ZEJLnzjzGx2JGwZVmnczBG2x5tw3v+uRiU80Q5X4y1f7gurZ/H5AAREDeZhCoaBkMmlJtVqtZo29OExw7tAJVGT6cm6ffIYG8DkHECXPh3VCrdRutxeSokSZhPe0jgBlUixHIRD89P39vRXZpFIpQ+GoV6isvLy8NP381taWJBmfjnKKJGE+n1epVNLe3p4Vi93c3Ojp06fa3d1VJBIxmWE8HlelUllIih8dHWk4HKrf79szAmhdXV0pGo2qXC6rXC7bWXj69KlFeOyJzc1N20sIHHyiXZolXClCu7m50dnZmSaTeX8a2i+j4Yf/p7x/OBwuqM5w+OjsiWD9JCIEBT5SOz8/t5/xJ3Ql4E2aAxOiQc7McDi0Hv68lhqGz3J9Jg49FAr9C5IeJP2GpHUM+ite97uS/q6kJ0EQBC8N+pMgCP6lz3Q3L69kMhn84Ac/sPDFHyiPyiSZoWPBSRBxIODWI5HIAldOkydploiCu6MvBggGXgwjSNhIMhGUfHt7awlNCid8Bttrz0nGoujgtX5iCgiMYhykmqBcX7gEH/jw8KDz83O1221rSsR6+IES29vbKpVKZvSbzab1qgFFYHDZ5J5m8CjJc7CSFiaa41xBlx69YJiXOfpXhZc4HqIcaa7vJ6Lh3rgvXstB4DUgQxqq8Syz2ay2trbsPu7uZjMv/We4fW4HnuQwxgdnTnLZa+XZK/QaoS0tckpphmABEw8PD9Z8LAgCU2GxjzDYRB9EFeQkcBKsr6cfiV59OwHWiffEiDLUgsZjGxsbpuvnnhuNhprNptbX1/XVr37VWvZ2Oh0zbiRcNzc39eTJE+XzeV1fX+vk5EQHBwc2gBqDRgdVWu7C8SMFzWQyGg6H5qxwYhQ5EbWgYAGIQYfSxIz9Cf13c3Oj8Xisra0tJRIJAz3Pnj2zfRcKhcygs9/YI9ynH07hJ3bhJHgvWjBgJzgXRMmJREJ/8id/osvLy59uUjQUCv2HkrY+waD/kWYzRf/g5f//UP8fDPrGxkbwjW98wxKHmUzGhPZkuOHZQF0UdaDpJIzE8+KRpXnxgfs8kyNSyNHv9xeQJAv88PBgWWkMMcaaSlIONw7CG0IMEnyZT7LCC8K/Lnfqk+YGkMZaGHQQ5fn5uTqdjtrttiRZ90iMP4lAP5Hd0xsYQD6X3/Gtb7mWw3QfzcB5S4szMb1SAKPLs+LnkkwnDrWDDAzDh3MCuUuy9/P91RlSQCju0XO5XFatVlOtVtPW1pZFBICHyWRiTg45o0fdyxEE39+je5JqRCInJyc6PDy01qm7u7tmQLlwxuwnkqQ+We7DdUlGTUFtIctj2j0Oghmpl5eXRokhC2VfUcDDvu/1eqZV57ORBhaLRXW7XZ2fn+vk5MSqMCORiElMiQRwuKwPg0foB0Puh0Qo9KdH2uQ4YrGYMpmMGWxyNDjgcrlsa9Ltdq1tBcq5drttz5MzTuUpA6M9M0DrBZ/0JMeDwg0g8PDwYPSQF1n4/7yKDEPOsybyIwKLRqM6Ojr66atcPsmgh0KhXUkHmhnww5c/+6Gkf0vSVFJD0t8LguAffNrnrK+vB7/4i79oBSUkmiTZrEMOsl9YOCseFKX/PnvPe21ubprx8RVZFIlw4CQZEicSAD1PJrO2nyAxIgfeF0OOEgXEUygUDAWTfJJknC+H1vep8Q8XROg3pHcqFxcXZhRoL4pTwyGQ4CT34A21tNi0nw3qLx9GYlQwXj45xxWPx00KxvciryHNK+1wBHx+NBq1ZmWonaS5UgFH7vXay9JVjC9SuVgsplwup93dXT1+/Fjtdtu4X74L0ZI/YPyJMYX+8ny+j2TYv9K8eK1er9ua4Kzq9bq9jr4o8XjcDAeG1+uU4XI90mY/UHPB2lLtSZUwRpKkOvuJWgnOiaSFXue0kED+mM/nLf+EYz4/P9fz58+tpgGDxf7muWWzWVMYZbNZq3yGNoHeY09BTZDE7Pf7dh9E6ShnKBBD7cKagO7j8bhFyTc3N9rZ2TGFSaPRsI6qqFSIhgAh2BLWl//3HDl/LuvLlwUZGHL/WvYUzzYajer4+PinXyn6KQb970r6m0EQfMf97C1JA0ktSV+X9D9J+reDIPjYHNJQKPR7kn5PkqLR6N/4hV/4BWt9ixCfQhAUCBgk0DC9IXigoEZKvNGAgzSZdUjjK3gxFhPjgfHFeJCAgV5gw7CJQOLw9iC54GW1Ib0j2BAY1VAoZBWQ/X7fdMQcbn4HiofNCh98dzdrg9tut423BCHwnTCWHBDfQx6VD69b1rJTACTN5ZDL8kjPf0M3EPqjQPFFUFzINyk9JxGJk1vmEKGYPL2DYyJC4nfItfBcAAU89263q0gkYvUDxWLRnJWXSZKv8I2uMPQ4TdYJmoXPPTs708HBgXG7yWTS+HLK8KU5vYQhA6xA1RGlYsCkeYjvX+OpGJ8cxpkT+aKoYK+xv4IgsNwTtRWsFRWnJJA5N/RkAaUTfVIpenx8rF6vZ03Y2Ffkbh4/fqxoNKpGo2EOiFYBFxcXev78uUVh0+msC6Q0c1jD4dCkoDw3yv5B8VBcJOZvbm5s/CAiDD8bdXm/QZtwXvjT7zWfyOcC2CzTkBhwn68jN+DBJLZqMpn8lRr0Z5L+oyAI/ttP+P1/R9LbQRD8zid9zsbGRvD222+bEYPLZnpOqVSyBUYpAjJLJBLmSVlcNjGyvG63a0hVkh0u3wALbyrNw1rQGmoBuGCoGLL7aOdHo5EZ9mVtOrym7w8xmUwsCqD4g0NIMpXvhQMgUTwejw2d4Ail+WbySBOnQpsBDIIPMQn/JJlRIqnp6QEcmiSbJYmWmhwChhzukvf15fdenkXSFcTjC3MwYhwYH3EsV5aS7wCdt1ot41lHo5HG47H18lhdXbXufygkeI4YL54lBtfvOw4of3IooQKIDhh03Ol0rGc4ORJJVuxE3UOn0zG5LuoYEBxok/0AP04NAQYalQo/J1IkT8SeRX6KwgMHXy6Xjc70Dat2d3d1fX2tXC6n8Xisdrutq6srVatVPXnyROVyWcPh0EAAhjIej9skISTG0ICcZ95vNBppNBqZxNOX16dSKa2urlrbBhwD81J9EhxVGYCOyJJ1IiF+d3dns0cRAPjLd1Zdtp2c72X6xEtkl403kS72QVqcZkYC/iVN/FejQw+FQv+cpKqk//FTXhpI+tSbenh4MA+9ujqbIUhCkkQniBlkjkel9wWyKRoZgZwLhYKKxeKCGoKkFyoAqks5nNIcXWJUarXaAg9MQorMOUbx7u7OSqulmYG9ubkxdEv0weYMh8M2xuv29tYOLK12QdalUsk+A6RHmA0SkBYRA/fORuLAgngwqtJc+uflmkjFpLmTA/0SHeG8UHb4XiTQERgjn3BlY4M0JVnUhBKFhJwkG45RKBRMa9xsNnVycqLnz5+bzjmbzaparVr/bqShz549swS0z3sQHXCguAeMNvcLlcMavjwHBjxWVlZMiUKHwevrax0cHJjktlarWa7kxYsX9rxIrgMutra2FugpcgFURm9ubury8lK5XM5K8YMgsD/5HvDhRBgMdYhEIjo/P1ej0VgojCuXy6pUKsrlckokEur1eqbmevPNNw0cjcdjRaNRffnLX1Y0Ohvm8uGHH+qP//iPtbW1pe3tbUmydTg5ObE2uXDdu7u7tp5HR0dWdFQoFBaUPKFQyFQ5jUbDzg3DS8gRPX782H5+cnJiZ9jTqlB15KPQ7nNePF3C73PmKLrjuQMWoWI56/7c8W9eautpTYAOZ4rn4CteP+36rCqXFc2M/+9L2pL0b0iaBEEwefnvfygpFgTBv7z0e78t6Y81o13elvS/SPr3giD4h5/0eaurq8Fbb71lWXEOFP02SORRrutF/BiJWCxmr4G/hWf0FIQ0pxoeHh4sIQJ/i+ECRWIQMVI8cDw8BRGgT1C/Tyqiq11ZWbEwDy6S5IqfFQk6Q34F0mUjcjFwF+pDms/k7HQ6pqP1HLwkk2IRYYDyQIB+U/oeKSCil89sgQsk0eplfPw/a0Ci2a81rXaz2ax9P4zc8fGxJXtbrZZx4ZVKxQwPiKbT6ajRaCxMto9EZo2wcIwffPCBVlZWVK/XrTLy9ddfNyeBIWGNpLkxBZ0jvyPJRSSI82o0GlYUB1XCazAorIU0L3IjsqBjI/kDIjsKhTxdhlaa93l5BiXNO3hyVjAshULBGlhBQdEzh5wC34NBFzizk5MT48VLpZJNnWKv0zeHiwHI0J9EhgAp6EWiMqIKqnW73a46nY6p0HjN5eWlJWkxpr4dAs7YR8QoT3AC9y8b1mEHfA9+nKJPuNOKQJI1/YK2QR3FswmHwwv0G8/F5yr4DF9UCEOAA3t4ePjpUC4vk5u/v/TjPwiC4IehUCgmqSnpd4Ig+D+Xfu8fS/p1SWuSTiX9/SAI/vNP+7x4PB788i//soX5fHH6tYBq4JRBmUj76PeBh2OD+b7L8ISSzDghKUNlgjGiDJyQ7/T01KRcVEpi2EjGMnknHA5bUtQjYUkmMYOOYB4iSBs+jwQOmwlk5Y0t3eva7bY5G0kW2UiyRFO3212gl+iih+Mk8evR/LKKBVTBM4KnJXz0qBDjCHfqDQMOh6EG7XbbikzS6bTplOFqMVgU5QyHQ7VaLUOL2WxWQRDYdCBaJScSCTO+aNIp+YYbnkwmlnjb3983Q0ISnv0Si8Xsu8N9ewQPHdfv923PkrDDIfX7feXzeWudgJ7el7dDxVD1CNWF1JDP9NwsRp0Sdg84fCEXzgf6kFYa/D6fRzO1UqlkESm5DipnPRXC7Fb2yMnJycJ3Q+GSy+UMCUNvlctlM8Kc4eFwqHQ6bc6z2WwaX87e9FW7zEP1a0FUDHBYWVmxQsV8Pq+trS0bVv306VM7dxhZ3+PcA0FvPzznjsPCePf7/YVnwV7yCVRyWd4e+4jxZdT2xezlkkgkgl//9V+XNAtb4D3hqTE+JBExJNLsYME5gkJ5GBwCKBkeDIf95ubGwrMgCOxzUNJgaElqkmSF0wR1jEYjS+jApUvzXhbwazgJjyowOhwOz+PSKW55rBp0kKdy4C0xODg1JszAkeIIcHRw0fDGoBg4Zw6O7/NCaTlcqG+7S2/vVqtlCgbCXHq9gAZx0kjqBoOBcd8UhRG+S9KXv/xltVotPX78WC9evFC1WtVgMFA+n9dgMFAymTStMQPFGWFIkzbW5+zsbEFCms/ndX5+rmq1aiX2tEXAQGDAmb85Go0sjwHCovEZnSW9Dp8GXz6JNh6PF/qHs4b0/2EvQftQy0APfBpeQdskEgnr1+MTwHSN9BWY3DPnJZlMmkyXe8G5pdNpDQYDhUIhixwikYjtyclkYvUNfLednR3t7e2p3W4b6mfyUK1WU6FQUDabNcoR/n1lZWWhDQMtMtbW1mwIy/39vfVTR7tNRMBsA4QLJKI5i0ha19bWLHFKzQsX68PZ9fSkp2YBM8vyRk/DcIY4P+vr6xbhXV1dWbTBmg8GAzUajZ9+UvSv6komk8H3v/99OzDn5+cajUZmjEF/IHUMbSgUsjFVhUJBw+FQBwcHOjw81M3NjSFRSoKp0JJmh2c4HFqRBAbNV+phvOBbUdLwfjxoUC/cJHM0/cNc7tWCSgLOlj8xAEQLVPqhfPFcPZsTnbCkhaIrOGqQOMUb9/f3lhj2Va5ea42xJiqAo4V77XQ6htiQsDHSrVwuK5PJ2MYlEsJYn52dKRwO21iwSCSivb09c8g46OVELbJAnAeHNB6P2zgyxr6xboyO63Q6xr0vt5fgQNFvu1arWRuGWGzWstYPdMChAzJGo5Fps0F4fmgBsjwKyDCokizyQ4XDerH3KIQhMQmqg0rhGZNgLxQKRk0dHR1ZTgaKBbQrzYxNtVrV7u6ustmsUqmUTk5OTA3GfuLzATDINUOhkLWt3d/f193dnfUXkmaggpoSUH48HreWwORIiMLr9bolWovFoiUwG42G6vW6OTCix8lkolqtZrNsW62WDg4OzMmzh6gOBZhhyImWSIZCr4HUSfSD0qEs+WwqgAGBnDOvOQeYEglDZUL14IS9XcZZfWERejKZDH7jN37DjJNXQGDsuAgLSdwQnknzBl8MEG61Wnr+/LkVbCBLzOfzpjag0pLm+BgUtOhMmI9EItYHHPQQj8d1fn6uSCRiMzU9bwca5MCCOHjAoVDIeNFoNGodJjGgIBXvZEgee12sNC89RjeMIySioABFkvHdcO9QMbyW/0CaHGKSuXRtBInc3NwYImekGM8sl8tZ2F+tVjUcDrW1taXT01PlcjnV63XVajVdXl6qUqkYjULoDULN5XJqNBrK5/Pqdrs2wT2Xy1mUA5cJskSCRxVjEATWwhbtMaPzQJ+sJQVIJAq73a5Ff1Am9XrdeFhoum63a8k4KA2e483NjTqdzoIOnWQqssOdnR1JMg0/dQV+GLW0qDUnoTgej206E0gVlLq6umol+0QwOA+iLM4b6hAMMxQExp39SZHQysqsJxIgh2cmybpakuiGAyfhmE6nFYlEdHZ2ZtLJcrmsXq9n6hgKvpA0EjEy5GRtbTahqV6vG7/Of6urq/aMaW2AQ2e/kivi8nUBGHqiZvYHzpwzhvHHcEP9cv49OAHdo33nrAKwKIb6Qhv07373uybbQ3LkZYYYIDaxNOMWeWg8ZAYQe2OHQgZESdJhOp1aJR3elgoxeOlut2tcsm8IhsY8nU5bYu/h4cGa+BN6kkjBkFOSz4HjIYJkOOAPL4uLksmkZfJBCb4oCsoJhI7mFQQIVcJhQw5I+IlBJwqS5sOo4fqRixJS93qey5iJAAAgAElEQVQ9K1Pe2NiwEXl7e3s6PT3VkydPdHx8rEePHqnb7Wpra0vdbtd6gnOwc7mcut2uNjY21Ol0VC6XTelwfn6+QJGk02k1m02jRqAANjY27L07nY7S6bShcxzz6emprRtJQWk+0mxlZcUij+FwqNFoZH3bHz16pK2tLUu6Y1w43CTPQGZra2vWXIueIaiy/JQnhmaPx2PL0UiytTk7O5M0V2RBT5FL4fnR2TOdTi8oK7x+nqQ4SW2oIYzqZDKxnAUFeKxBv9+33BBoNJ1Oq1qtmsSWDohQUyRGSWKGw2HLdVBturKyYkNLiEyZhYDRb7fbC3NLfYSMEggHS80KAASpMgnMSCRiveZB19K8BbO0KEv0+SHWz0uDvfQQ8EWk6usFMOzknrw8l+jZR/s4ipf26Ytp0OPxePD1r3/dOEJQNx5bkoV5IBS4TThTwjfCMhAHpc1wmpLMCdB7go2ZTCZN5426gxaYqGcw9ujd2awkfUDSZO/ZmGxyvLk074wIQveFJRgHKAEQMgeLwyHNEYA0V/BMJpMFx0eoSnMwCqV8SwIfHsLT+g24sbGher2unZ0dHR8f68mTJ+p2uyqXyxqNRianI7Hnpx6BOHEiOC6fhOVzCedxLDx/3gdqjnXj+V1fX9vzjkZnw0BisZh6vZ5FE4lEwgZgX1xcmINYXV1VvV43WV4ymVSlUtGTJ0/02muvWRR2cnJiERLGm2gGPbUfBAy9huaZ3AayRZwDEQl7CgPC2lAlieyQPi0YblC+pw6YS+pRJPuBdb+/v1ehUNDq6qpef/11pVIpqz2ApoKioGEYyW4KzzKZjJ0lKpbZiz7nlUwmra8Q34f+7fyHsgSKi1YM1FGQTJxOpyqXy2ZE19fX9cEHH2gymejHP/6xrQdrSgRDXog9zbpglAE1GHkvq8V2esPsqRn2JBw7v+eVaRh2aa584T8vZ3wJAL+YBj2RSAS/+qu/ask1aYFHMmqEai8KbzAAw+HQNnosFrOhBGx0nAOIP5/PW9Y8CAK1Wi2Fw2EdHR2ZFtwnPQeDgSGol/dr74Wj8T2UOTBwxxcXF4bYCTOl+eQkPpPDhFMCocPjY3DZeP6zMd4kSzkoRATJZNLWjo3k+VAcBokb1g9VAnzocDhUJpMxyRsHwOcdrq+vlUql1Ov1lMvlrMNgt9tVsVjUYDCwniC5XE6dTkfJZFL9ft/QealU0vn5ua37eDy2STWZTMbek9AcB4tx56A2m02LQiKRiEqlkvHI0BwkGEnMwYkXi0VVKhWrJCV6bLfb+rmf+zk9ffrUUC4KClB/q9VaGG6BkcZwYTDQ/IOmaSqFoYKmow2v75mDMyH6wzng4OH6mdgViUSsaC+TySifz9t7oRzZ2toyR0E+i4ZhACToKsr3MWi0DfBtI4hs0IGDyImASLKSFPdVrpFIxCKBSqVi7zuZTCwpnslk9MYbb2g8HqvZbFrSFcAXi8XUbrc1nU51dHRk380bcmgzaFCMspc8+0pvHAK2SpoX3IH8PUih6h1axVf6YoOg6XC0L2nbL+aAi+l0amH8w8ODcd2Ekag9yHSTBGMzrq6u2lgr+ChKnTmEPktNQy7Q28rKio6OjiRpYVpNt9u1hR+NRgtT4+lN4akTkkRBEBgni8KAEBljyZ/o3W9ubqzoBUfl0RqyLlQqkUjEerp49AWy4zsTLtOSdGNjw5KLKBXQBnPYUVh4DTyTVzKZjC4vL631Z7lcttB4NBpZT2zukWEP/FyaD9fg7zhIDBnOifuSZGG3r0GAU/bdAklwgb5LpZKy2ayazaZisZgNbqY9MCFurVYzhzcajRbmZZ6dnWk8HpsxjcVieuedd0zF4jnycHhWKIYyA7kk5fe+/kCS9Qli+ALrSoQB/UhTL1pL1Ot144JBjOw1nCwUE07u/v7eBAXsDZwyiJYIA5UQskDktKenp/a66XRqwApNeLvdtu/G5y1HBrlcztA5To/9FonMKpv9TIBGo6Gf/OQnqtfrqlarSiaTKhQKNmOWe2c/YdQpdPOqGJyFR+k4U0+l+NdQZeqjRArq+DnvxXn1ERbVxqj2YCLu7u6sgyUOmBqKzwq8P1vX9L++/vr66+uvr7++PvfX55JyobBImg/5RZLmpUU0KoI+QdbH75AYRII2HA7tfcjgSzNEjyyS0M7rsZnZyaBnUBJ9lkFyKD7ouTGZzDotMhyZNr9IuUBO/p7hX70igr7QcLR8riSjUFKplPVlBp1JMpRNYoYoBvoGNA9FwUUCB5ThVUbIFyme6Pf71vqgVqtZEVW/31cqldLDw6xtK0hzOZzle5DQDYLgYzMgCXl9ZSr3Dl8tzbl1PgOaiATa+fm5dR1E40tyLxqNmrzSJ4lZz1wuZ2vfbDZNaeL7exAxEu0gkWOfEPqDvH0iT5IhaqKaIAhUKpWsGApKhWHaPE/UGe1229QfcPYkPf2zJ+IASXteHNQfjUa1tbVlRU7sh/Pzc/sM9iwUBJEokQtJPmmu1+b8QgGm02k9efLEGu2xvsga2Y+j0Uinp6cKgsDkna+99prllMinTCYT7e3tGb3yzjvv6OjoSNFo1HIAaM+RhnK/rBmoGE6cKJfvhiiDi1wF54zvzHv4RCnnkeQzBXa+3z3fmff+rJTL59Kgr6+vB9/5zncsCUcSD+6t0+nYOCmqATOZjC1Wo9Ew1QqTwgnXeA2GWZr31qbaEM4KvTca7ZWVFZvAEo1GdXJyYk2g0BmjYScJBm/GrE9JVt6L8UajikwQY4Tx42c4BrLeUCw8eJKwvmUByh82FUmWi4sLK2zxmxW6B5kWDgJVBUaOBCSHKZvNGnUEfeU5YCiKwWCgbDZrf/b7fXO2pVJJnU5HpVJJrVZLhULB+n2cnp6qWCxapWUul9NwOFSlUtHZ2ZmKxaLOzs6sRDyTyejs7Ey5XE5nZ2f2mZVKRaPRyD5TmtM3GDr06nS8hAJhIhW/x1AFDqfX9o/HYwvBoQhJxK+srNj0ehLHUEmSrJgnn89bwVuj0dD5+bk1vyIhilS2UCgYp4xKCsXW8fGxTk5OTM0jzXutAxjYfxg3jC+fx5hC6g9IdkLRUF0JDZrL5Sw56tVkFAr6JDdOlHMIWLm9vVWxWFStVrO9jzYdIIeiLZfLaWNjwxp5kXcBXPB8OL+dTsfeA6dBzovzgcPn7HGRDN7c3LTnFQ6HrW88F3QLTsCfZWgYzh0VoV77jm1Gnx4EwReTQ19ZWTFds/dSeE6MLLpj0BAFFtVq1aou8exsPPqPe1kePB2SLhJintv1o+TgHff39+1hUs3KQ6T3BsNe0VyjEcZ4shlXV1ftAJBYomeHJEMFGAi8OgklEK6Xc3Kdnp4a8oMHhEcFXfrEKjLGXq9na4UhAjWR9OGZkOQClTebTdPsUwwTj8dN8UOidDQaaX193Qo7SBRK87mLGCJ65EgypEwVKfwoSajpdGoHrlgsWo6CeZZ+WMNkMtHrr79uCTnyLtFo1NoQFItF20e+90itVluQcWIYie5wnJPJxNaGNgi1Ws2MHPe6urqqSqViBzwIApNdbm1tKZfL2We3Wi01Gg1TyRAZlMtlU4ahzkJZxTPGuFBEJkmvvfaa7TkMX6VSsYgUvXw8Hrd2uuSy4MZZf2mWc/L5nI2NDSs4QznEhCaiJpLtNzc3VhX8skrSnl+9Xlen01GtVrMz3ev1dHV1pVqtZmj9z/7sz0y1wx4l3xAKhayPOu2DiagREHiD65v1wYdTcIUBxpjzmuU+MBhzaV4fwhkCLBFJLRcDftbrc4nQfaUoxpH7HAwG5ml9yTha7IeHB/V6PV1fX1uSB2dAoQ6SJ4zZ5uamJpNZS9V+v28SvXw+L0mGriRZVhwZFdQPVaeFQsE2OyW7DBwADdA/wxfsFAqFhaIGKhw5eMgIvcMAxW9sbKhWq1kBhO9nw5xVEsMbGxuG5JlViiJCmk8hwqghTwTxY7gJs0lcTiYT5XI5XVxcqFqtqtlsmmIFNAwa9UlLZJF+8/d6PT08PKjZbCqVSunq6soQdrlcliTT2lOHQH1Aq9UyeSeVhRSHPTw86MWLFxZ2M4bv8vLSDBIFKMw+jUZnAzaIUDqdjrrdroIgUDqd1t7enj1b6hkYDg41sba2ZuX8KysrljT3zpq9WCgUDDDk83nd38/azZ6enloxDb1hgiDQs2fP7NnQEwXDWa1WNRqN9N577+nw8ND2l2+kBhpeW1uzAjucAUleiqg8xQXgkWT7hVYRyCUBEFB5aLJRnVE3UCgUDGAROfCdCoWCKpWK1RFQuco+BFyA7mlqR+I5Fotpd3fX6g1ub291cnJizxunSTEZDpD74dyl02mT3SaTSTu/7EVoNV/Q5/+DtvOacy5fKcrve2mjpM+M0D+XBj0ejwe/9Eu/tNAqlYd1c3Njza/gcmmdiaFH7tRut+31oHfCK1Qvkqw8nc3sufFOp2Mqj0KhsNCPHIUFZfFIDtGME4bibCRZtzhpRodAB0mysBnkDMc3nU6tv4Uka3cAJcT6lMtl40CZ9o7xyWaz1mwKuRoVaBxcz51i2OFZkUuSZ4DnR9EB0gb5s2FBRbHYbJpNPB5Xq9UyiiSdTuv09FSlUkntdltbW1tmuIfDoQqFghXaHBwc2DNrNBqqVCq6vb1VJpPR1dWV0um0jo+PFY/HdXh4qM3NTdNeoypBRYPqZGVlxYABkR5cPq2QoY9wSPf396ZGQToIhdDr9RQOh3VycqJ+v29KHIp9+B0UO/QIJ/LAQSWTSe3v75tEFw2375kCrx8KhQzVg+hvbm50dHSkFy9e6OTkxKS6XKhDABTkQHCurA8GkudORAq1h8IDehGAhcLr6upKa2trduY4hxh1BtZsb28bnUZ9Caoaevtw7pDiUpUNWKHgikIzolUquQFyDw8PajQaBmJwkBRw4XCleUtkED3n29exSPMZt55m8dpzaV4Rynry/kRMTJ6C0jw+PrbXvDT6X0yDnkgkgu9///sW1hP+Y9CYzE7YQwKzUqlYuHl7Oxva/Pz5c/V6PfX7fdtMNzc3SiQSH5tPCdK+v7+3CrObmxuTKfEA2czJZNISr5IWphMxZIGZjiBtPocDQ5iFpDKRSJh8C3rG5wB8CTjJUhAoXe28DI5RehwqJt2Ql0AixoGk7S+biEQSa+AnC6Gb5bNwAuQ6KNiBC0UrToK52+2qVqup2+2aQUfXXSwWbXgClZ8YAUlWxCLNGx6R/PNDur3Mk9f5QiY6GkJ9XV5e2r2SIM9msyqXy0a7XVxc6MMPP7ScAdXBoVDIKilPT0+tqhODh1EhemCPwZ1KMtCBAWWNac+cSqW0u7trhVoU6FSrVTN477//vt5//32dnZ2ZAZLmlbBcyE83NzeVy+Ws+AhHHo/HlcvlDBy12231ej2TY3ojBrLkzLLnABKSTIbIsA+GthCxglB5nU92cxahvcghAGDo1R4Oz+a0SjKkPplMVCwWrZYkFoup1WoZKCQaIfdG216fV+LZQK3w3VlH1tYXB3GxRr5AzBcPYdz5PLpwSrKo4QtdWLS+vh58+9vftsNCRzi84v39vXK5nPVxQekhyUr2PYdF8hSEiQFks2B8SYgSQmJ0/fs2Gg2bikTTf1BSJBKxkn3QGEjfl0xPJhMz0ui5iSzgkmnsxHck/MPwguJ9MgkuGdWJJIsicEI4ASgb+F04crhfKBE069QCEFpTwi1pgbOH0/el7zgzkkno6yWZgwMZQymcnp5aO2A2fjKZNIO+sbFh6JmCEZKU7BO02hggkqJ7e3uWAG+327q9vVUul1MqlTJ+H+OytbWl119/XTs7O6b7brVa+vGPf2wcKoqhdrutZrNpCA4OmwML5cB603MGBMh78X3gXEOhkNF2/AwK5+d//udVqVT07rvvWkRJoRpDP4bDofVhoV7BV1HzrCnHD4fD1m8eA42j9UYL5ZU0b40LFcNzJZHMd+N5bWxs6Pj42CIOXyXskS5TjciHTSYTiwI4A9SH8LkUSOHgKFwjyXt1daWtrS2l02kFQaCjoyO1Wi3j0THWPiqRFmcWe2XTdDq1PeNpKC5v/KV5PtD/GzaH9eReiay+0CqXWCwWvP3226YGkGQ9Hfr9/kKi0meoeUC+WCIej1tXOy/yJ6MtyRJhXjpHV7wgCCzZhrSIsAg6whuSarWqRCKhWq1mKBJjy/3DK1JqD0qj8IBWnnDV0E281pd7e6nVYDAwNQkGmIPLpuTnHDK4ZTa6P3S0sqUsG8fBIQepY7RIaHIQQF8cWipc7+/vGXqrXq9nXQ1JlpEgzGaztu48c6grkt4+uQn/CUe5s7NjRVwYYoYi+/sGEfoBGRjCZDKpUqmkR48eWUJ6NBrp9vbW+pJDtaBsQTGDgXh4eFCr1bJQm+Q1TZ6kuQHY2NiwgiL6qzAEhetnfuZnlMvlrNfN8+fPrUUxzbjgc+n0SVQ6nU7N4CcSCSUSCSvCg6Lc2Niws8F6kVTGaNO1Edkje5P/UJDQglqSNU7DsbF/6UlPMR7/HgqFTHHFWMPJZGKUJQAAOTDSX/7NU0MUeuXz+QVJ8c3NjeV+ptOpTk9P7T680ILnQyWpf2Y+wsI444hxkFxeQswaw69nMhkrMqKgyxck/VQol1AotCbp70v6rqSspOeS/t0gCP63l//+NyX9F5J2JP3fkn43CIIX7nf/gaS/JelK0n8aBMF/9mk3tb6+Hnz961/XeDxeCOvhcWOxmGl6OdT0ayGxFQSBHQh47FarZYaeEFySHRgSUjc3N0qlUqrX64pEIpbYhK9/7bXXLGRCFy/NlSK+vBqUDmUDr44Gd3d312RSlJpzj0i4fKe3IJj3s0HlQjKKknc/MAENMe164UdRIKyvry/MXfRdFiklJ6KgtSmcOAcMVIuxQv42nU4XIiFfXQefSuUelYpw+MjUptOpOp2O9b4hEkun09rY2DBkdXR0ZAeYddre3rYKxIuLC33wwQe2f8iFUInsG2VJMt55c3NTb7zxhr72ta+ZVBYKj7XCidB+mVwKyUePzK+uroxKYJ2huPz3ouYAdOylrMVi0ZqQHR0dWWOzTCajR48eGcdPuwFfqUvDLaI1wASaeCI2ksMonEDHnBmSghgoRwtYBJlKpVQulw3N0qGQc0VzMWmeOyBJy57gT1+vQDKdZwz9eX9/r2azaffK3sM4h0IhG91IlOmBAdE0aJtnsqwy8bSLpIWz6SmVV3Hq2Ahe4+sqeK2nBvm8l//9VGSLK5JOJP2ypGNJvyXpn4RCoa9IupD0P0v61yX9r5L+A0n/vaRvvPzdH0p6TdKupLKkPwqFQu8FQfC/f9IHoqwg0cHDYAFpEhSLxYxP92XvPungp4GDvgjlWDDUAX6KzbNnz4zCYINidH70ox8Z/winDJVBskqSaZQpymDjscG4Fw40SNrrv2OxmA0yluZOg9yC59FBxB4FsLF9MhOFDzw3UQR8MUbKz2AcDAYKh8OmCUY9Qi4D9AryRAKKeoh7wwHRsuDu7s4SVDi5XC630NIgk8lYLxcOPNLBTCZjSLHf7xt64/v7iwMH4mKtOUzeCCDPpOjsnXfesf46RGVEcNFoVGdnZ7q6ulpo9gQ6Y0/6cJ48A8VuoGYaY5F0ZbgGoGUwGOjg4ECbm5vKZrNiVCMFPf1+X5IWmk+R9Idq63a7Ojg4MJ6a/YvmPpVKGWcNJSPJ7jmXy1l/cSR+OHkUUdFoVDc3N3r+/Lk9CxAn64Nj5Vmcnp7aHucZYsiJZpHccsYBTShPqtWqjaX0um6cBwoqz4dz9pE2egPOWQWt8/y8k0GNx/fg90HegB/2BK9nHTw9TOQDVTyZTBYc3addn2rQgyC41Mwwc/3TUCh0KOlvSMpJejcIgv/h5Zf/oaRuKBT6UhAE70v6VzRD7H1J/VAo9F9L+l1Jn2jQCc9ZcLhjDA0XSZFQKGRhNZWgvvcDnCUGh8QgGw09KIMqHh4ezLh6jTDjtZjggidvNBrWUB/JFg/Ht79EQglVRLa/3+/r+vraELBHy16qSPUmyA5+kYiFsJW1kWSZf3hd+puAoECw6KTRFkuyvANUFkVeVPlxqKBSvOSSQ448kCiAaU4knqA6eO5QGNAmrJ1XAvBaHD/IimfHc4XGcnvZEtFeH+wPZiQSsedDuA8NgsFmhF8ikbDe2hTe5HI5M+q0Xsa48OwwQkEQKJVKmVPmHlBlsS9J9KLE2draMs6V/YvSKhKJqN1uG8K/vLxUvV7X2dmZJevX1tYsEphOpzZx6v7+Xufn57aHQM0kjXFix8fH9uzY05wXnpvnz7e2tiTJksC0XcY4l0olrazMRtBB/3igw/eiopb9TP0HKqdIJGIUEwbQ0x/w7l4lg/NYX1+3M+plmJKs2yT7cRm5e1niq/hy6lc4U1yslad1vKPwoOyzUuN/aQ49FAqVJL2Q9FVJf1tSNAiCv+3+/SeazR/9vySdSyoHQdB6+W9/S9LvB0HwlU/6jM3NzeD73/++giCwaUWeS2PxaIpFQyAWczgcfmwoLwk2uhdCN0gylEUrgdFopF6vp+FwqLW1NRtswGtBoDQBA+lSAUdoDIrd2NgwGggnBPKFquDwoukOh8OmyCFy4H6hBdhcIJZer2cokw2NIsDLNDGafB7adGZL4iByuZzRU2xuKAuSpTgeb3xJiEEZUeWLQZFkoTyHjAlGFHbEYjEVCoUFZQLJVUkWkqKNZl4n+QCiG3hWnjv0lR/gEI1Gtbm5aWoVjC3fOxwO2xQceuSfn5+r2WwaZw4yBlVRHFepVGxUGoaKgR+ocnAGkmzfQAlmMhlDcfV63dRNsVjMHCVODKNGAh6OHs6eNYvH4wutjKmnYC/Ry58zhNIrFAqZZpyI7+LiwjTdzB2A7wdMoBKBRvT6btYQipSoHNUSZyESiZiEkiKdfD5vVa6j0cgMr6dMvLbbUy9UORPxjsdjO29eionR5ff50ytZAJ7ekPvP9H+y96V5BALA8Cie//jszypb/EtVioZCoVVJ/0jSPwyC4P1QKJSQ1Fl62VBSUlLC/f/yv73qvX9P0u9Js8TQo0ePDPnA8VF+75NL/X5frVbLDAVcLwf68vLSHjIPEp6WRBvOAP662+3aZ4RCIZM+8hkgbxJmtCMIh8PWjB9qAyNG2Tb9aFKplOUE2EhsdhJVUBkUyDBb0qMBkrih0KwrImXmbChJC61heT9Cy/v7e5sCw+GhXQBaez8wgIOG1AwFD0aM+ycqWl1dNbUL+QxkoSAvZHo+2z+dTtXtdlWpVIzCogeINB8QgpPc2trS9va2xuOx2u22Op2OoR8iG2kedRQKhYVDyLpwkEhKQW1guNmHGO5Op2O8PklQnzdYWVkxqiYajarT6ZjEEHqDZDz34fvPNBoN+/1qtarHjx9bwhwDxb0wECadThtw6PV6NtELBwo9BNAgIjw7OzMKjCQ30SCKH6IJuGaqN6+vr3V4eGj0ESovSeY8SIJzBrw8EOAzmUxsFij5Iows+4Jo9ejoyHIx0Cae0mAvsbYkF/ludDL0Z4W9zX5ZNsz83SN19gwgySdUX2X8/eUjHWleuOc/6y8Duj+zQQ+FQmFJ/52kO0l/5+WPLyRtLr10U9L45b/x/zdL//axKwiCP5T0h5KUSqWCer1u6IAF4sFDQTSbTRuzdXNzo3Q6bRImWm6CcCjSIcEWBIHxljiKUqmkaDSqSqVi7UgZmOFL7Nno7XbblAN49Ol0ahI4ONJms2ml/Xt7e8aJXV9fq16vG10ElxkKhSxHgKRsOBxaewJkjGwiSrPZ9Bh3SZYoxllIc2QAOgVFolBAzTCZTBZUChwkZJHJZFJ7e3tWSYtBGQ6HxhviQHEKICryFuQ94HYxvjgY1C/LG517Xc4XsB5IDFkjqKxcLrcgteP1RB0kIVln9hHVv6PRSK1Wy5KwXoPMPUPjrKys2GxR1o48BYnHIAgMCXNPfoAxEdN4PNb+/r45KLT2DHWA76ZKGFoBFEu+iegA58g4RZ+3KZVKhq6hyKgBoJITypBpS0QbKyuzfkeAIyglac6Ne/06eSX+Ph6PrbjHo3RUNFwMZaGvjiRD1Ox1ck0e5TJ0Gikrht+jZF/i73n8V2nJl2zkx3T+/vf4HZwVjoZ7Zz9zefrws16fyaCHZp/y30gqSfqtIAhY2Xc148l5XVzSY8149X4oFGpI+jlJ/8fLl/zcy9/5xOvh4UGdTmchaYBhYsHIdtOACBrh6upKuVzOhuMyvBjufH9/X+HwfPC0NOctSfrwvtFo1GgIEHu5XDbZIvpuEpatVsv05UydB/Fyj8fHx0Y/ILUsl8u6vb1d6FWCQQWJEmYiH7y7uzNDwdBhcgy0AJDmkkxCYTa9JEt6sqYk3vhcDDL0CO8TCoUWetcgdcSI++IREla8F6G1NHekTIRiyhNIB002KIx8iCTT6NLtD/lcIpFQpVKxZDJyxYeHB2suhSTVT3OCmpPmvTloAndwcGBJN74TCHd3d9dK5j/88EMbSE7C1+cOQG3QFCQscSCSLCJNpVJqNBom/aMaOBaLqVarqVKpSJpVzD59+lT1et3GxK2vr2s0GlmCGxoSaS38da1Ws0iBtUJVheY9kUgYSCLSCofDJmdFiUP7DMQJOENfUekVPZ1Ox8ACESOJXNRFaOaJntmb0mwoDfeERBLAFQqFFqo9fTQLVYRx5d9A5Rh4ohIvsMARsT891+0NPJ/l0bk39N6BIhEluud8LkcGP1UOPRQK/ZeacebfDYLgwv28IOkjSf+apH8m6Q8k/XIQBN94+e//saRflPTPa+YM/kjSv/ppKpd4PB58+9vftkUlVCeJw2FEjlgoFLS9vW3IT5IhZh42iAUNOgdTmk8ToTMbhQ/IGck0PzzMJrSgiV1bWzOn4N9Lmsm6oEIYiAsFhMoBw4gCJ5VKKZfLWeETmmvUOfweyVqMLEhkNBqZ9BGjKclQP/fIpmTj+MIeX8Llc9wAABVPSURBVBGLM0EtEQqFLGEIckJaiLHxVaY4Phzi+fm58e4cWD4TJQxGqd1uL1RCQrV51QPUCM6CdYIO4hlMJhMzuihIoITQKKfTaeXzeRWLRRUKBa2tren09FTvv/++xuOxIdVer2fPMp1Om/Lk+vraEqQUDIHmyKVIst45mUxGxWJRh4eHurubD2DG2OdyOUP9SAQfP35svU18Yr3b7erFixc6PDy0qATpI+9BHqlQKJhx9KicPjW0Z/ANykDlw+HQnqlvEUHVNWorzgYOk/3kWweA4hnkTR4LQ+hbSWNUiXoo8iPPgVNmj/N+GHRP/3BuOKvck6dQ/OURsjeuqKL4f14H2vfcuefMuU8fCXrHsOwEPJ/+WTj0z6JD35V0JOlW0sT9078ZBME/CoVC35X09zSTJqJDP3r5u16Hfi3pPwk+ow79G9/4hvVYAA0T+kMv+MQmsjl492w2a7piUDQH7erqSufn5wtor1wumzTKJ4jo4+Kb/oBefIhJiEgBxurqqrrdrh06fofIAekUjgU0AhrA+eAUfEXfdDo1RIoBg9qAG4eeoFETBlGShcOgXigkDgRolfdA18ufnu+nCAnFzt3dnbWn9dWCvuKWcBilyGAwUL/fNw4VnbXvZeMLuaTZIYKSazab1rsjk8mYoR2NRtbvnEQgnDDqA5LhNIkCHFxdXemdd94xXhmqBu0yRqFcLtv9DodDvXjxwiYU4WikeU/rl+dC2WxW6+vrFp3wOqKgs7Mzy7PQ0I21LJfLqtVqikajOjw8NCULyeRut2vJYHJJkixZj5zv9vbWZpdCF/mZA9CGOOhSqaRKpaJweNZA7Uc/+pHtW9Y0HA4bj4+jB1zwHXz+hmfo2+FCgxGV0zMGY0wEwPkliUqVNuvd7XaNDuNceToF4/mqhCbfGWMKaPIUn/9zWW/u+XQ+h3/3Mki+F3t6OUnK319GFV/MStFEIhH8yq/8iqFFNhveNB6PWydCUBzTvEFJ9/f3yufzJmX0XRGDIDAJoiRTkWCg2CioT0CkkozeoEcLGxquEDQMcmcDUqSzvr5uypybm1njsHa7bQUf5AlAg2xijBmOA++N3C0ej1tzfAYbSzKjD0rgO4EOQHDovjG0HCyMEO+DgsYXe+HIoFBI1IK6OVQY8nK5bPkAP+zA69fX19etmAjHQ5KL+2FAMEUqIDuMFiG8NxA+usJpUwCzvb1tzoCOiVdXV1ZElE6nVSqVVCwWrW0vLWXL5bKazab1A+n1esbVIxFF/kl0ViwWLeKkmdpoNNIbb7yhvZcDGugcurIy68mOkSP/Q48VKDXPw0JHEBUiC+UZo90nl4KzQ77KmkO9cDZoKUCSHY27fx/oKfYcF3kjcmI0vIPy7HQ6C/yy37f8HSMYCoW0v79vUe3KyoqePXtmtoJcmUfS2DsMrTfErJs3qJIsJ4JhfRXl4qNHnzTlnjHU/tl4I8/PeC3v43n9L6xBj8fjwa/92q+Z5IlkD8gXw0wCBDoFJAM3TIm2bzvKHFB+T5prtqE5MExs6vv7e7VaLdOL00UO/h46hoNDsROhKMYMORdoFs03xkaaT6zhZ2h/z8/PjWKilShFTxhFWraiS5ZkRgyUgWIAHtFXfIJWCQ2JbJhsBLqBQiGhRUIRagIEf3c3GwJMFSFhJpTC9fW1tWUYDAaKRqPWh4O8BsgNaZ93xCAdkJ/n3jGcXumCoggpJbwxpf1vvfWWisWipJmhfP78udrttjnHx48fS5K2t7cNAUoyienz58/1/Plz49DRsHNQSVZKMiNN0pPX8Nxp3buzsyNJZlhB83w2hpZEKYMneD+4WSIv6EUiKYzq8mSw6+try9Hk83nt7e1Zn/I//dM/1c3Njb70pS8pHo9bXxyKuog2AFzsexRk/DvPGQMMUsU5+3oIHFI4HLZiMi/7g3KTZBQhEaVfj1ddXo3iwc8nvd7TLMuVpDxjDDXOZ/lzQOmszSepYn5qlMv/H9fa2lrw1ltvGa0hzQX6hIF+o4CWY7GYcrmcdXdDJw7ynEwmdsCodpRkEreHhwcruPBVchh6jBRGGEqC0B3DB1riwdIlj8/2PDVhLpsVjtyrQnyilEo8uuOh9kEvTrLORxncKweMtQEVsY5QLxgYElsYGVBtJBIxA+z11J1Ox2oAIpHIx2ghujby/jhCNjGFOySu+E7wpclk0nIk6+vryufzC4eeARO+2Ap1CbwtSJK6BFr07uzsaH9/X5PJxBROOEccZ7VateHZ29vbxh9/8MEHRp91Op0FGauXcnLW6JPuJXhcOFeMFOqr9fV1lctlM2b5fF6Xl5caj8cmpRyNRgvUpD8btKpNp9MqFotGI+KcKWACgbMfNzc3TYNPy4jj42NdXV0Z1ZROp42GGgwGxmuHw+EFug0D56u5qR5FkODVJf53iCjZa5xpHCdO8Pb2VmdnZx9D01ygbc6OR/sgYZ6BR9gYX29svUH39+urWv19eLTtuXOP4nlf/3r+7Qtr0BOJRPCDH/zAuGzf0B5kzZ9BEBiSA/VKMkfAoYDb6vf7NjkGpMDfUTsQ1qIyAMmgr2bkHA+CxCkhK32NfQEFGxjngLaZBBQoFooG7h/1APw4ci3CyfX19QVFgU9gSrL2oRh4RuWxPiA+kLv/LlBbaLdJgmL4aSSE4oCEbBDMS5hBZSiHQITw7kQvoEHaJDOMAIdSKpXMUXOx8cm1QMNxf76qGB4ZJMhnYgBpS7uysqJer6dut6tms2ltl6vVqmnzNzY29Pz5c9VqNTNaUDLT6dQ6OGazWQ2Hw4XCI1RQaPf39/eVSCSscI0aiul0qkajoXfeeccGryAXhJIjMQ2tBA3DmfBRmDdqrBdqJGSH0WjUVGB0UmR4CvQNEQgOCQoHqms6ndq6SbJENGcRMEMSn33qqTFv1AFAABNPvcCvE6WhyffI3CNeb0CJWDH4GHhyAMtOyCPyZc6d3wfw8FrYAkCLHwri+XlPx3Af0pzLd6//4hr0b37zm8Y1BsFMM44hf3iYldtvbm6aXO/y8lKdTscSlSwKgxdQkNAGFQQkySbWgMrRC49GI+PgQAU4FnhyNLo0OiL7jz4WeqFQKJiEi54kbEyMP6X4ZO9xRKg/aES1srKibDZrqA8liaSFTSrJ5J90VaRASJJ9FvQKKgb4fighbwzgSEExODboDEmWVCPcx2HxbDCMJBklWcMrZGugeUlWrk7yUpIhfi6vK+bv7G1oJaoyc7mc4vG46e2bzaZJ6agJ2NzctNoDnBw9zzOZjCVycbYkM0GbpVJpYRjzZDLR48ePdXx8bNJVwMHe3p729/ft+RE9cG/1et2kg77/CYZAmqM8zky327Ve/Rg+ojccHXuCmobpdNYWoVgsmiwSWqvb7Rr1R3REuwHqMXi2FJudnZ2p0+lYFSz7NRQKmQoJWtQnEXmO/mcYUc/HIyLAoJNb4vJqEWluRD0/D9XB/3u07NU10JCeb19WxeA4yeuBzGlL4ffk8r3ymcvvuUTDfDEN+ubmZvC9733PEpUoWDCIhULBQi0OVhAE1s8cAyLJeGcMHZwq/VwkGVVD50GQM9Nu4Ml52KlUykJFeFiUIXQPRNmSSqUMicKDgmRIrNGigMSXpyVIPlJVifEgL8B0dJpJsanYtMyGZNq9L1iiLFyaV2GSPMZZoSOnm50kk3bSk57WpawnFBF5BSKLWCxmhoD+Lz4qGgwGhu5IRpMDGQwGFhlJsjml1A1QfBUKhXR2dmbOmn+jaheHxEHFqFarVdNX49AYgYfShWiDiIQIBI7aq4lolRyLxVSpVKz1wmQya2nL3oa22dvbs+9VLpctGTqdTvXs2TMzwtA4RHSSLHIEpPhKXrjkRCKhbDa7kEdotVqKRCIGZkC14XDYclWM2bu5udHp6an6/b6q1aqt13g81o9+9CNFo1FrG+2T5yRYuYhaU6mU9f/nPqW5AcOQe17bc84YWY+8Me7SnNfm3HljynsTLXImoZ/IhUHZQQ96tO8pE5LrHn0DmshXLM9o4Dssc+Wvkk7ynl9og/47v/M7tsEIkSeTWam1PywcLJQghULB/j8IArVaLTPS0BkI+kulkiSZA4AXhn8FKayurmpnZ8fK/UEWk8nEJIDIDyeTySsbYxEVENri7X0HweVkjK+OZS34DJAcSA96xvfQlmZtYElS0pQJeSehKRsLnh3unsiEHEHwUh0EcvHDtzkEhKyEi0QwOCj68dAzh/FtNCYjEsFIUlGKsffhO3QLyJ1Olw8PD8blRiIR69NCYpAo6ujoSJPJxIY5wK8/PDyY80FW+N5772kymdhoO7TrpVJJzWZTt7e3Ojw8NIe4t7dn1Fan0zFgUalUrIWAbyjn+9RTNFer1awaut1uW6ESVaR+ZBz/IWHFMHjq0SenUQERKdE5lM8Ih8MqFAp66623bKTd4eGhWq2WCRB8joMius3NTXOiOzs7ajabVk/x0Ucf2XlDMUNOAkUTEaFXmng5oDfQ/jx5lO4BlEf5HgGzF5LJpN58803rI3N0dKTRaKQPP/xwIdHuk6oAGiJSBAO+noNognvHHknzBKh/TxA/zs/TNl94hI5skYQFMyuRZUGJRKNRQ7ggDOR9JE0ZSMDAAG80vZFEZ7qzs2Pl1EgU7+/vF4qa8NggZVA54StKAvjSTCZjRU4MRkABA8qm7L1QKCx0UUThQVtVSWb0kIYVi0U7RFAYoGQvHWTkF937ut2u8Zg4ieWEHJJEDCM9QqAacBYkkH0TL9aVi7oBDDzyOZwJfW9ubm4sYTmZTGwiFMoXaXYAKGbh3qF+4CsxYByqu7s7FYtF6/Pd7XZ1eHhoctPd3V0bX9hsNu25ZLNZW3+cOiPx6FA4Go1sotJwODREj6SP5/Ho0SMbQPGTn/xET58+tTWXpL29PXMYGxsbOjg40Pn5uaFpjBQOl89BNeMFACsrK4aUaa9LkRNyWO6L+ghovlAoZGqwUChkM0cZNM7zbzaburm5UaVSMUXMaDTSV77yFcViMTUaDf35n/+5JNl3jcfj1s4BcHZxcfExg8a1jM6X/x06juQkIx+lRXULe4S/S7PW2cViUdFoVFtbWzbg4uLiwv4OFSUtSjC9gcbBsHbLNNIy/cMe9u+1/P8ADEnke76YBj0ejwff+ta3bPNy0agfQw9Sjkaj1n6WhcDoezkYk+Hv7++Ni5RkEj+kkPzMh6CoF+BVecCg0c3NTU2nU0OfcOo8aD4L74yBh2+TZEUXIBCqEYkS+HxolOvra+O94Z05tGw8NhIFNlBYkqx/yPX1tZWcEw0Q4oM8cAZk76Ea+H++B+E6qAbU7w/c3d2dtZaFWoEuIgqj8yLPhaQoBxFExjOAfpFk+m1QOc714uJC1WrV1g6kS8QGsr2/v9fp6amF5EySGY/H1uwqEpkNah4Oh3a/OFMcYLVaNQMJpVQul83ow6/y7CRZ+9darWbGlspW1obqYkbf4Wj9mYCu4ftRZEevHZA1UQ5Vtpwz7+Tz+bzS6bR2dnb02muvmVDh5OREp6enNr4unU6rVqtJmhmrdDqtRqNhSPbp06c6Pj5Wo9FYyH8gBuD3iKh5FlAfnjohOYrRC4J5m2KPgJdpGUAGFOrW1tb/297ZhNZRhWH4eWMkQpJiY2IVFw0t1koWWai40Irgv4gLg2JTl62gZNWNKyGgmIp7FaG11VrrRiuC21b8oWIW/lBIAkUqYloI2DT39g/luDhn5HS494arIXPO8D0wkDnn3vC+8879ZuacuXP/vSIqrkSKz2mcU/zlpOLMvSDe74vPS3EiEU/QxjqKOlMcAFoNwxR3zBRn+NkWdEkrwHzVOtaIYWCpahFrhHlJk7p4qYsPWHsvm51zI6u9qKvH564j8865u6sWsRZImjUv6WFe0qMuPqA6L52/EmUYhmFkgxV0wzCMmpBqQX+vagFriHlJE/OSHnXxARV5SXJS1DAMw+ieVM/QDcMwjC6xgm4YhlETkirokoYkfSapKemMpMmqNXWDpBOSLktqhGU+6psMnpqSjkka6vS/1hNJU5JmJV2RdLDU95CkOUkXJR2X/wWroq9P0gFJFySdlbR33cWXaOdF0qgkF2XTkPRq1J+Ul6Bnf9hnViT9KOmJqD+bXDp5yS2XoOmwpMWgaUHS7qiv2lziRzhWvQAfA58AA8D9wDIwVrWuLvSfAHa3aB8DVoAHgrcjwNGq9Ub6nsH/7us7wMGofThk8CxwA/AWcDLqnwG+BjYCdwJngccT9TIKOKC3zfuS8gL0A9NBdw/wVNiHRnPLZRUvWeUSNI0BfeHv7UHTXSnkUtlGaRP6VWBb1PYhsK9qbV14aFfQ3wCOROtbg9fBqjWXdL5eKoIvAt+VMroEbA/rfwCPRv2vkciBqoWX1QpHsl4iTT8DEznn0sJL1rkAdwCLwHMp5JLSkMs24C/n3ELU9hP+aJgTM5KWJH0r6cHQNob3AoBz7jTh4FWBvm4o624Cp4ExSRuBW+N+8sjrjKTfJb0vaRggBy+SNuH3l1NknkvJS0FWuUh6W9JFYA5f0L8kgVxSKugDwIVS2zIwWIGW/8orwBbgNvx9qF9I2or3tlx6bQ7eOukeiNbLfSmyBNwDbMZfHg8CH4W+pL1Iuh6v9ZBzbo6Mc2nhJctcnHMvBx07gE+BKySQS0rPcmkAG0ptG/BjbVngnPs+Wj0kaSfwJPl666S7Ea1fLvUlh3OuAcyG1XOSpoBFSYMk7EVSD37o8SowFZqzzKWVl1xzAXDO/Q18I+kF4CUSyCWlM/QFoFfS7VHbONdeluWGA4T3MF40StoC9OE9p0xZdz9+/P+Uc+5P/KXmePT6nPIqvlHXk6oXSQL2A5uACedc8WvS2eXSwUuZ5HNpQS9h+1N1LlVPKpQmGI7i73TpB+4jo7tcgBuBx/Cz273ALqCJHyscww8n7QjeDpPWxE5v0D2DP4MqPIyEDCZC25tcO2u/D/gKP2u/PeywVd+B0M7LvfgJrB7gJvzdVMcT9/IucBIYKLXnmEs7L1nlAtwMPI8fQrkufOabwNMp5FJZwG021hBwLGyg34DJqjV1oX0E+AF/CXU+7LyPRP2TwVMT+BwYqlpzpG0af2YUL9Oh72H8xM8l/F08o9H7+oAD+IPVOWBvql6AncCvYfsvAh8At6TqBT+m7PCX541o2ZVbLp28ZJjLSCjK54OmX4A9UX+ludizXAzDMGpCSmPohmEYxv/ACrphGEZNsIJuGIZRE6ygG4Zh1AQr6IZhGDXBCrphGEZNsIJuGIZRE6ygG4Zh1AQr6IZhGDXhH+aYt43S5HgRAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.datasets import load_sample_images\\n\",\n    \"\\n\",\n    \"# Load sample images\\n\",\n    \"china = load_sample_image(\\\"china.jpg\\\")\\n\",\n    \"flower = load_sample_image(\\\"flower.jpg\\\")\\n\",\n    \"dataset = np.array([china, flower], dtype=np.float32)\\n\",\n    \"batch_size, height, width, channels = dataset.shape\\n\",\n    \"\\n\",\n    \"# Create 2 filters\\n\",\n    \"filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\\n\",\n    \"filters[:, 3, :, 0] = 1  # vertical line\\n\",\n    \"filters[3, :, :, 1] = 1  # horizontal line\\n\",\n    \"\\n\",\n    \"# Create a graph with input X plus a convolutional layer applying the 2 filters\\n\",\n    \"X = tf.placeholder(tf.float32, shape=(None, height, width, channels))\\n\",\n    \"convolution = tf.nn.conv2d(X, filters, strides=[1,2,2,1], padding=\\\"SAME\\\")\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    output = sess.run(convolution, feed_dict={X: dataset})\\n\",\n    \"\\n\",\n    \"plt.imshow(output[0, :, :, 1], cmap=\\\"gray\\\") # plot 1st image's 2nd feature map\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for image_index in (0, 1):\\n\",\n    \"    for feature_map_index in (0, 1):\\n\",\n    \"        plot_image(output[image_index, :, :, feature_map_index])\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Using `tf.layers.conv2d()`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(shape=(None, height, width, channels), dtype=tf.float32)\\n\",\n    \"conv = tf.layers.conv2d(X, filters=2, kernel_size=7, strides=[2,2],\\n\",\n    \"                        padding=\\\"SAME\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    output = sess.run(conv, feed_dict={X: dataset})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(output[0, :, :, 1], cmap=\\\"gray\\\") # plot 1st image's 2nd feature map\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## VALID vs SAME padding\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"VALID:\\n\",\n      \" [[[[184.]\\n\",\n      \"   [389.]]]]\\n\",\n      \"SAME:\\n\",\n      \" [[[[143.]\\n\",\n      \"   [348.]\\n\",\n      \"   [204.]]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"filter_primes = np.array([2., 3., 5., 7., 11., 13.], dtype=np.float32)\\n\",\n    \"x = tf.constant(np.arange(1, 13+1, dtype=np.float32).reshape([1, 1, 13, 1]))\\n\",\n    \"filters = tf.constant(filter_primes.reshape(1, 6, 1, 1))\\n\",\n    \"\\n\",\n    \"valid_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='VALID')\\n\",\n    \"same_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='SAME')\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    print(\\\"VALID:\\\\n\\\", valid_conv.eval())\\n\",\n    \"    print(\\\"SAME:\\\\n\\\", same_conv.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"VALID:\\n\",\n      \"184.0\\n\",\n      \"389.0\\n\",\n      \"SAME:\\n\",\n      \"143.0\\n\",\n      \"348.0\\n\",\n      \"204.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"VALID:\\\")\\n\",\n    \"print(np.array([1,2,3,4,5,6]).T.dot(filter_primes))\\n\",\n    \"print(np.array([6,7,8,9,10,11]).T.dot(filter_primes))\\n\",\n    \"print(\\\"SAME:\\\")\\n\",\n    \"print(np.array([0,1,2,3,4,5]).T.dot(filter_primes))\\n\",\n    \"print(np.array([5,6,7,8,9,10]).T.dot(filter_primes))\\n\",\n    \"print(np.array([10,11,12,13,0,0]).T.dot(filter_primes))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Pooling layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size, height, width, channels = dataset.shape\\n\",\n    \"\\n\",\n    \"filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\\n\",\n    \"filters[:, 3, :, 0] = 1  # vertical line\\n\",\n    \"filters[3, :, :, 1] = 1  # horizontal line\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X = tf.placeholder(tf.float32, shape=(None, height, width, channels))\\n\",\n    \"max_pool = tf.nn.max_pool(X, ksize=[1,2,2,1], strides=[1,2,2,1],padding=\\\"VALID\\\")\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    output = sess.run(max_pool, feed_dict={X: dataset})\\n\",\n    \"\\n\",\n    \"plt.imshow(output[0].astype(np.uint8))  # plot the output for the 1st image\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure china_original\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Saving figure china_max_pool\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_color_image(dataset[0])\\n\",\n    \"save_fig(\\\"china_original\\\")\\n\",\n    \"plt.show()\\n\",\n    \"    \\n\",\n    \"plot_color_image(output[0])\\n\",\n    \"save_fig(\\\"china_max_pool\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: instead of using the `fully_connected()`, `conv2d()` and `dropout()` functions from the `tensorflow.contrib.layers` module (as in the book), we now use the `dense()`, `conv2d()` and `dropout()` functions (respectively) from the `tf.layers` module, which did not exist when this chapter was written. This is preferable because anything in contrib may change or be deleted without notice, while `tf.layers` is part of the official API. As you will see, the code is mostly the same.\\n\",\n    \"\\n\",\n    \"For all these functions:\\n\",\n    \"* the `scope` parameter was renamed to `name`, and the `_fn` suffix was removed in all the parameters that had it (for example the `activation_fn` parameter was renamed to `activation`).\\n\",\n    \"\\n\",\n    \"The other main differences in `tf.layers.dense()` are:\\n\",\n    \"* the `weights` parameter was renamed to `kernel` (and the weights variable is now named `\\\"kernel\\\"` rather than `\\\"weights\\\"`),\\n\",\n    \"* the default activation is `None` instead of `tf.nn.relu`\\n\",\n    \"\\n\",\n    \"The other main differences in `tf.layers.conv2d()` are:\\n\",\n    \"* the `num_outputs` parameter was renamed to `filters`,\\n\",\n    \"* the `stride` parameter was renamed to `strides`,\\n\",\n    \"* the default `activation` is now `None` instead of `tf.nn.relu`.\\n\",\n    \"\\n\",\n    \"The other main differences in `tf.layers.dropout()` are:\\n\",\n    \"* it takes the dropout rate (`rate`) rather than the keep probability (`keep_prob`). Of course, `rate == 1 - keep_prob`,\\n\",\n    \"* the `is_training` parameters was renamed to `training`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"height = 28\\n\",\n    \"width = 28\\n\",\n    \"channels = 1\\n\",\n    \"n_inputs = height * width\\n\",\n    \"\\n\",\n    \"conv1_fmaps = 32\\n\",\n    \"conv1_ksize = 3\\n\",\n    \"conv1_stride = 1\\n\",\n    \"conv1_pad = \\\"SAME\\\"\\n\",\n    \"\\n\",\n    \"conv2_fmaps = 64\\n\",\n    \"conv2_ksize = 3\\n\",\n    \"conv2_stride = 2\\n\",\n    \"conv2_pad = \\\"SAME\\\"\\n\",\n    \"\\n\",\n    \"pool3_fmaps = conv2_fmaps\\n\",\n    \"\\n\",\n    \"n_fc1 = 64\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"inputs\\\"):\\n\",\n    \"    X = tf.placeholder(tf.float32, shape=[None, n_inputs], name=\\\"X\\\")\\n\",\n    \"    X_reshaped = tf.reshape(X, shape=[-1, height, width, channels])\\n\",\n    \"    y = tf.placeholder(tf.int32, shape=[None], name=\\\"y\\\")\\n\",\n    \"\\n\",\n    \"conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps, kernel_size=conv1_ksize,\\n\",\n    \"                         strides=conv1_stride, padding=conv1_pad,\\n\",\n    \"                         activation=tf.nn.relu, name=\\\"conv1\\\")\\n\",\n    \"conv2 = tf.layers.conv2d(conv1, filters=conv2_fmaps, kernel_size=conv2_ksize,\\n\",\n    \"                         strides=conv2_stride, padding=conv2_pad,\\n\",\n    \"                         activation=tf.nn.relu, name=\\\"conv2\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"pool3\\\"):\\n\",\n    \"    pool3 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\\\"VALID\\\")\\n\",\n    \"    pool3_flat = tf.reshape(pool3, shape=[-1, pool3_fmaps * 7 * 7])\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"fc1\\\"):\\n\",\n    \"    fc1 = tf.layers.dense(pool3_flat, n_fc1, activation=tf.nn.relu, name=\\\"fc1\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"output\\\"):\\n\",\n    \"    logits = tf.layers.dense(fc1, n_outputs, name=\\\"output\\\")\\n\",\n    \"    Y_proba = tf.nn.softmax(logits, name=\\\"Y_proba\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)\\n\",\n    \"    loss = tf.reduce_mean(xentropy)\\n\",\n    \"    optimizer = tf.train.AdamOptimizer()\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"init_and_save\\\"):\\n\",\n    \"    init = tf.global_variables_initializer()\\n\",\n    \"    saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: `tf.examples.tutorials.mnist` is deprecated. We will use `tf.keras.datasets.mnist` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\\n\",\n    \"X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\\n\",\n    \"X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\\n\",\n    \"y_train = y_train.astype(np.int32)\\n\",\n    \"y_test = y_test.astype(np.int32)\\n\",\n    \"X_valid, X_train = X_train[:5000], X_train[5000:]\\n\",\n    \"y_valid, y_train = y_train[:5000], y_train[5000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def shuffle_batch(X, y, batch_size):\\n\",\n    \"    rnd_idx = np.random.permutation(len(X))\\n\",\n    \"    n_batches = len(X) // batch_size\\n\",\n    \"    for batch_idx in np.array_split(rnd_idx, n_batches):\\n\",\n    \"        X_batch, y_batch = X[batch_idx], y[batch_idx]\\n\",\n    \"        yield X_batch, y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 Last batch accuracy: 0.99 Test accuracy: 0.9775\\n\",\n      \"1 Last batch accuracy: 0.98 Test accuracy: 0.9841\\n\",\n      \"2 Last batch accuracy: 0.98 Test accuracy: 0.979\\n\",\n      \"3 Last batch accuracy: 0.99 Test accuracy: 0.9886\\n\",\n      \"4 Last batch accuracy: 0.99 Test accuracy: 0.9883\\n\",\n      \"5 Last batch accuracy: 1.0 Test accuracy: 0.9892\\n\",\n      \"6 Last batch accuracy: 0.99 Test accuracy: 0.9891\\n\",\n      \"7 Last batch accuracy: 1.0 Test accuracy: 0.9899\\n\",\n      \"8 Last batch accuracy: 1.0 Test accuracy: 0.9871\\n\",\n      \"9 Last batch accuracy: 1.0 Test accuracy: 0.989\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 10\\n\",\n    \"batch_size = 100\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\\n\",\n    \"        print(epoch, \\\"Last batch accuracy:\\\", acc_batch, \\\"Test accuracy:\\\", acc_test)\\n\",\n    \"\\n\",\n    \"        save_path = saver.save(sess, \\\"./my_mnist_model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Exercise solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. to 6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"See appendix A.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7. High Accuracy CNN for MNIST\\n\",\n    \"Exercise: Build your own CNN and try to achieve the highest possible accuracy on MNIST.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following CNN is similar to the one defined above, except using stride 1 for the second convolutional layer (rather than 2), with 25% dropout after the second convolutional layer, 50% dropout after the fully connected layer, and trained using early stopping. It achieves around 99.2% accuracy on MNIST. This is not state of the art, but it is not bad. Can you do better?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"height = 28\\n\",\n    \"width = 28\\n\",\n    \"channels = 1\\n\",\n    \"n_inputs = height * width\\n\",\n    \"\\n\",\n    \"conv1_fmaps = 32\\n\",\n    \"conv1_ksize = 3\\n\",\n    \"conv1_stride = 1\\n\",\n    \"conv1_pad = \\\"SAME\\\"\\n\",\n    \"\\n\",\n    \"conv2_fmaps = 64\\n\",\n    \"conv2_ksize = 3\\n\",\n    \"conv2_stride = 1\\n\",\n    \"conv2_pad = \\\"SAME\\\"\\n\",\n    \"conv2_dropout_rate = 0.25\\n\",\n    \"\\n\",\n    \"pool3_fmaps = conv2_fmaps\\n\",\n    \"\\n\",\n    \"n_fc1 = 128\\n\",\n    \"fc1_dropout_rate = 0.5\\n\",\n    \"\\n\",\n    \"n_outputs = 10\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"inputs\\\"):\\n\",\n    \"    X = tf.placeholder(tf.float32, shape=[None, n_inputs], name=\\\"X\\\")\\n\",\n    \"    X_reshaped = tf.reshape(X, shape=[-1, height, width, channels])\\n\",\n    \"    y = tf.placeholder(tf.int32, shape=[None], name=\\\"y\\\")\\n\",\n    \"    training = tf.placeholder_with_default(False, shape=[], name='training')\\n\",\n    \"\\n\",\n    \"conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps, kernel_size=conv1_ksize,\\n\",\n    \"                         strides=conv1_stride, padding=conv1_pad,\\n\",\n    \"                         activation=tf.nn.relu, name=\\\"conv1\\\")\\n\",\n    \"conv2 = tf.layers.conv2d(conv1, filters=conv2_fmaps, kernel_size=conv2_ksize,\\n\",\n    \"                         strides=conv2_stride, padding=conv2_pad,\\n\",\n    \"                         activation=tf.nn.relu, name=\\\"conv2\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"pool3\\\"):\\n\",\n    \"    pool3 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\\\"VALID\\\")\\n\",\n    \"    pool3_flat = tf.reshape(pool3, shape=[-1, pool3_fmaps * 14 * 14])\\n\",\n    \"    pool3_flat_drop = tf.layers.dropout(pool3_flat, conv2_dropout_rate, training=training)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"fc1\\\"):\\n\",\n    \"    fc1 = tf.layers.dense(pool3_flat_drop, n_fc1, activation=tf.nn.relu, name=\\\"fc1\\\")\\n\",\n    \"    fc1_drop = tf.layers.dropout(fc1, fc1_dropout_rate, training=training)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"output\\\"):\\n\",\n    \"    logits = tf.layers.dense(fc1_drop, n_outputs, name=\\\"output\\\")\\n\",\n    \"    Y_proba = tf.nn.softmax(logits, name=\\\"Y_proba\\\")\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)\\n\",\n    \"    loss = tf.reduce_mean(xentropy)\\n\",\n    \"    optimizer = tf.train.AdamOptimizer()\\n\",\n    \"    training_op = optimizer.minimize(loss)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"init_and_save\\\"):\\n\",\n    \"    init = tf.global_variables_initializer()\\n\",\n    \"    saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `get_model_params()` function gets the model's state (i.e., the value of all the variables), and the `restore_model_params()` restores a previous state. This is used to speed up early stopping: instead of storing the best model found so far to disk, we just save it to memory. At the end of training, we roll back to the best model found.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_model_params():\\n\",\n    \"    gvars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\\n\",\n    \"    return {gvar.op.name: value for gvar, value in zip(gvars, tf.get_default_session().run(gvars))}\\n\",\n    \"\\n\",\n    \"def restore_model_params(model_params):\\n\",\n    \"    gvar_names = list(model_params.keys())\\n\",\n    \"    assign_ops = {gvar_name: tf.get_default_graph().get_operation_by_name(gvar_name + \\\"/Assign\\\")\\n\",\n    \"                  for gvar_name in gvar_names}\\n\",\n    \"    init_values = {gvar_name: assign_op.inputs[1] for gvar_name, assign_op in assign_ops.items()}\\n\",\n    \"    feed_dict = {init_values[gvar_name]: model_params[gvar_name] for gvar_name in gvar_names}\\n\",\n    \"    tf.get_default_session().run(assign_ops, feed_dict=feed_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's train the model! This implementation of Early Stopping works like this:\\n\",\n    \"* every 100 training iterations, it evaluates the model on the validation set,\\n\",\n    \"* if the model performs better than the best model found so far, then it saves the model to RAM,\\n\",\n    \"* if there is no progress for 100 evaluations in a row, then training is interrupted,\\n\",\n    \"* after training, the code restores the best model found.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0, last batch accuracy: 98.0000%, valid. accuracy: 98.6000%, valid. best loss: 0.048916\\n\",\n      \"Epoch 1, last batch accuracy: 96.0000%, valid. accuracy: 98.1200%, valid. best loss: 0.047337\\n\",\n      \"Epoch 2, last batch accuracy: 100.0000%, valid. accuracy: 98.6000%, valid. best loss: 0.037833\\n\",\n      \"Epoch 3, last batch accuracy: 100.0000%, valid. accuracy: 98.9000%, valid. best loss: 0.037833\\n\",\n      \"Epoch 4, last batch accuracy: 100.0000%, valid. accuracy: 98.7600%, valid. best loss: 0.037833\\n\",\n      \"Epoch 5, last batch accuracy: 100.0000%, valid. accuracy: 98.9800%, valid. best loss: 0.037833\\n\",\n      \"Epoch 6, last batch accuracy: 100.0000%, valid. accuracy: 98.8400%, valid. best loss: 0.037833\\n\",\n      \"Epoch 7, last batch accuracy: 100.0000%, valid. accuracy: 98.8200%, valid. best loss: 0.037833\\n\",\n      \"Epoch 8, last batch accuracy: 100.0000%, valid. accuracy: 98.8600%, valid. best loss: 0.037833\\n\",\n      \"Epoch 9, last batch accuracy: 100.0000%, valid. accuracy: 98.8400%, valid. best loss: 0.037833\\n\",\n      \"Epoch 10, last batch accuracy: 100.0000%, valid. accuracy: 98.8400%, valid. best loss: 0.037833\\n\",\n      \"Epoch 11, last batch accuracy: 100.0000%, valid. accuracy: 98.9600%, valid. best loss: 0.037833\\n\",\n      \"Epoch 12, last batch accuracy: 100.0000%, valid. accuracy: 99.1800%, valid. best loss: 0.037833\\n\",\n      \"Early stopping!\\n\",\n      \"Final accuracy on test set: 0.9896\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 1000\\n\",\n    \"batch_size = 50\\n\",\n    \"iteration = 0\\n\",\n    \"\\n\",\n    \"best_loss_val = np.infty\\n\",\n    \"check_interval = 500\\n\",\n    \"checks_since_last_progress = 0\\n\",\n    \"max_checks_without_progress = 20\\n\",\n    \"best_model_params = None \\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\\n\",\n    \"            iteration += 1\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch, training: True})\\n\",\n    \"            if iteration % check_interval == 0:\\n\",\n    \"                loss_val = loss.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"                if loss_val < best_loss_val:\\n\",\n    \"                    best_loss_val = loss_val\\n\",\n    \"                    checks_since_last_progress = 0\\n\",\n    \"                    best_model_params = get_model_params()\\n\",\n    \"                else:\\n\",\n    \"                    checks_since_last_progress += 1\\n\",\n    \"        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        acc_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\\n\",\n    \"        print(\\\"Epoch {}, last batch accuracy: {:.4f}%, valid. accuracy: {:.4f}%, valid. best loss: {:.6f}\\\".format(\\n\",\n    \"                  epoch, acc_batch * 100, acc_val * 100, best_loss_val))\\n\",\n    \"        if checks_since_last_progress > max_checks_without_progress:\\n\",\n    \"            print(\\\"Early stopping!\\\")\\n\",\n    \"            break\\n\",\n    \"\\n\",\n    \"    if best_model_params:\\n\",\n    \"        restore_model_params(best_model_params)\\n\",\n    \"    acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\\n\",\n    \"    print(\\\"Final accuracy on test set:\\\", acc_test)\\n\",\n    \"    save_path = saver.save(sess, \\\"./my_mnist_model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## 8.  Classifying large images using Inception v3.\\n\",\n    \"\\n\",\n    \"### 8.1.\\n\",\n    \"Exercise: Download some images of various animals. Load them in Python, for example using the `matplotlib.image.mpimg.imread()` function or the `scipy.misc.imread()` function. Resize and/or crop them to 299 × 299 pixels, and ensure that they have just three channels (RGB), with no transparency channel. The images that the Inception model was trained on were preprocessed so that their values range from -1.0 to 1.0, so you must ensure that your images do too.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"width = 299\\n\",\n    \"height = 299\\n\",\n    \"channels = 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"images_path = os.path.join(PROJECT_ROOT_DIR, \\\"images\\\", \\\"cnn\\\")\\n\",\n    \"os.makedirs(images_path, exist_ok=True)\\n\",\n    \"DOWNLOAD_ROOT = \\\"https://raw.githubusercontent.com/ageron/handson-ml2/master/\\\"\\n\",\n    \"filename = \\\"test_image.png\\\"\\n\",\n    \"print(\\\"Downloading\\\", filename)\\n\",\n    \"url = DOWNLOAD_ROOT + \\\"images/cnn/\\\" + filename\\n\",\n    \"urllib.request.urlretrieve(url, os.path.join(images_path, filename))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.image as mpimg\\n\",\n    \"test_image = mpimg.imread(os.path.join(\\\"images\\\",\\\"cnn\\\",\\\"test_image.png\\\"))[:, :, :channels]\\n\",\n    \"plt.imshow(test_image)\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ensure that the values are in the range [-1, 1] (as expected by the pretrained Inception model), instead of [0, 1]:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"test_image = 2 * test_image - 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8.2.\\n\",\n    \"Exercise: Download the latest pretrained Inception v3 model: the checkpoint is available at https://github.com/tensorflow/models/tree/master/research/slim. The list of class names is available at https://goo.gl/brXRtZ, but you must insert a \\\"background\\\" class at the beginning.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"TF_MODELS_URL = \\\"http://download.tensorflow.org/models\\\"\\n\",\n    \"INCEPTION_V3_URL = TF_MODELS_URL + \\\"/inception_v3_2016_08_28.tar.gz\\\"\\n\",\n    \"INCEPTION_PATH = os.path.join(\\\"datasets\\\", \\\"inception\\\")\\n\",\n    \"INCEPTION_V3_CHECKPOINT_PATH = os.path.join(INCEPTION_PATH, \\\"inception_v3.ckpt\\\")\\n\",\n    \"\\n\",\n    \"def download_progress(count, block_size, total_size):\\n\",\n    \"    percent = count * block_size * 100 // total_size\\n\",\n    \"    sys.stdout.write(\\\"\\\\rDownloading: {}%\\\".format(percent))\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"def fetch_pretrained_inception_v3(url=INCEPTION_V3_URL, path=INCEPTION_PATH):\\n\",\n    \"    if os.path.exists(INCEPTION_V3_CHECKPOINT_PATH):\\n\",\n    \"        return\\n\",\n    \"    os.makedirs(path, exist_ok=True)\\n\",\n    \"    tgz_path = os.path.join(path, \\\"inception_v3.tgz\\\")\\n\",\n    \"    urllib.request.urlretrieve(url, tgz_path, reporthook=download_progress)\\n\",\n    \"    inception_tgz = tarfile.open(tgz_path)\\n\",\n    \"    inception_tgz.extractall(path=path)\\n\",\n    \"    inception_tgz.close()\\n\",\n    \"    os.remove(tgz_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading: 100%\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fetch_pretrained_inception_v3()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"images_path = os.path.join(PROJECT_ROOT_DIR, \\\"datasets\\\", \\\"inception\\\")\\n\",\n    \"os.makedirs(images_path, exist_ok=True)\\n\",\n    \"DOWNLOAD_ROOT = \\\"https://raw.githubusercontent.com/ageron/handson-ml2/master/\\\"\\n\",\n    \"filename = \\\"imagenet_class_names.txt\\\"\\n\",\n    \"print(\\\"Downloading\\\", filename)\\n\",\n    \"url = DOWNLOAD_ROOT + \\\"datasets/inception/\\\" + filename\\n\",\n    \"urllib.request.urlretrieve(url, os.path.join(images_path, filename))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"\\n\",\n    \"CLASS_NAME_REGEX = re.compile(r\\\"^n\\\\d+\\\\s+(.*)\\\\s*$\\\", re.M | re.U)\\n\",\n    \"\\n\",\n    \"def load_class_names():\\n\",\n    \"    path = os.path.join(\\\"datasets\\\", \\\"inception\\\", \\\"imagenet_class_names.txt\\\")\\n\",\n    \"    with open(path, encoding=\\\"utf-8\\\") as f:\\n\",\n    \"        content = f.read()\\n\",\n    \"        return CLASS_NAME_REGEX.findall(content)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class_names = [\\\"background\\\"] + load_class_names()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['background',\\n\",\n       \" 'tench, Tinca tinca',\\n\",\n       \" 'goldfish, Carassius auratus',\\n\",\n       \" 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',\\n\",\n       \" 'tiger shark, Galeocerdo cuvieri']\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"class_names[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8.3.\\n\",\n    \"Exercise: Create the Inception v3 model by calling the `inception_v3()` function, as shown below. This must be done within an argument scope created by the `inception_v3_arg_scope()` function. Also, you must set `is_training=False` and `num_classes=1001` [...]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.contrib.slim.nets import inception\\n\",\n    \"import tensorflow.contrib.slim as slim\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name=\\\"X\\\")\\n\",\n    \"with slim.arg_scope(inception.inception_v3_arg_scope()):\\n\",\n    \"    logits, end_points = inception.inception_v3(\\n\",\n    \"        X, num_classes=1001, is_training=False)\\n\",\n    \"predictions = end_points[\\\"Predictions\\\"]\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.4.\\n\",\n    \"Exercise: Open a session and use the `Saver` to restore the pretrained model checkpoint you downloaded earlier.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from datasets/inception/inception_v3.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, INCEPTION_V3_CHECKPOINT_PATH)\\n\",\n    \"    # ...\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.5.\\n\",\n    \"Run the model to classify the images you prepared. Display the top five predictions for each image, along with the estimated probability (the list of class names is available at https://goo.gl/brXRtZ). How accurate is the model?\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from datasets/inception/inception_v3.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_test = test_image.reshape(-1, height, width, channels)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, INCEPTION_V3_CHECKPOINT_PATH)\\n\",\n    \"    predictions_val = predictions.eval(feed_dict={X: X_test})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"276\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"most_likely_class_index = np.argmax(predictions_val[0])\\n\",\n    \"most_likely_class_index\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus'\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"class_names[most_likely_class_index]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus: 93.13%\\n\",\n      \"hyena, hyaena: 2.57%\\n\",\n      \"European fire salamander, Salamandra salamandra: 0.06%\\n\",\n      \"bearskin, busby, shako: 0.05%\\n\",\n      \"swimming trunks, bathing trunks: 0.05%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"top_5 = np.argpartition(predictions_val[0], -5)[-5:]\\n\",\n    \"top_5 = reversed(top_5[np.argsort(predictions_val[0][top_5])])\\n\",\n    \"for i in top_5:\\n\",\n    \"    print(\\\"{0}: {1:.2f}%\\\".format(class_names[i], 100 * predictions_val[0][i]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model is quite accurate on this particular image: if makes the right prediction with high confidence.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 9. Transfer learning for large image classification.\\n\",\n    \"\\n\",\n    \"### 9.1.\\n\",\n    \"Exercise: Create a training set containing at least 100 images per class. For example, you could classify your own pictures based on the location (beach, mountain, city, etc.), or alternatively you can just use an existing dataset, such as the [flowers dataset](https://goo.gl/EgJVXZ) or MIT's [places dataset](http://places.csail.mit.edu/) (requires registration, and it is huge).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's tackle the flowers dataset. First, we need to download it:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"FLOWERS_URL = \\\"http://download.tensorflow.org/example_images/flower_photos.tgz\\\"\\n\",\n    \"FLOWERS_PATH = os.path.join(\\\"datasets\\\", \\\"flowers\\\")\\n\",\n    \"\\n\",\n    \"def fetch_flowers(url=FLOWERS_URL, path=FLOWERS_PATH):\\n\",\n    \"    if os.path.exists(FLOWERS_PATH):\\n\",\n    \"        return\\n\",\n    \"    os.makedirs(path, exist_ok=True)\\n\",\n    \"    tgz_path = os.path.join(path, \\\"flower_photos.tgz\\\")\\n\",\n    \"    urllib.request.urlretrieve(url, tgz_path, reporthook=download_progress)\\n\",\n    \"    flowers_tgz = tarfile.open(tgz_path)\\n\",\n    \"    flowers_tgz.extractall(path=path)\\n\",\n    \"    flowers_tgz.close()\\n\",\n    \"    os.remove(tgz_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fetch_flowers()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each subdirectory of the `flower_photos` directory contains all the pictures of a given class. Let's get the list of classes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"flowers_root_path = os.path.join(FLOWERS_PATH, \\\"flower_photos\\\")\\n\",\n    \"flower_classes = sorted([dirname for dirname in os.listdir(flowers_root_path)\\n\",\n    \"                  if os.path.isdir(os.path.join(flowers_root_path, dirname))])\\n\",\n    \"flower_classes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's get the list of all the image file paths for each class:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"image_paths = defaultdict(list)\\n\",\n    \"\\n\",\n    \"for flower_class in flower_classes:\\n\",\n    \"    image_dir = os.path.join(flowers_root_path, flower_class)\\n\",\n    \"    for filepath in os.listdir(image_dir):\\n\",\n    \"        if filepath.endswith(\\\".jpg\\\"):\\n\",\n    \"            image_paths[flower_class].append(os.path.join(image_dir, filepath))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's sort the image paths just to make this notebook behave consistently across multiple runs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for paths in image_paths.values():\\n\",\n    \"    paths.sort()    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a peek at the first few images from each class:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class: daisy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class: dandelion\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class: roses\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class: sunflowers\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAloAAADwCAYAAADYQ8pLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXv0bdlV1/mZc629z+91b92qJJWQkAeB8JJOaOLwgYo4RO04WmNH2wcKqNAq2MCwM5BWFJW2G+kWocEH2rY2D58g2KBN28YGFZ/YTcRBK4EgiSGpSj1uVd17f79z9l5rzv5jrr3PPr/7u7eqMAVF2N8xzvj9zt777Pfe67u+8zvnEndnxYoVK1asWLFixYce+tO9AytWrFixYsWKFR+uWInWihUrVqxYsWLFC4SVaK1YsWLFihUrVrxAWInWihUrVqxYsWLFC4SVaK1YsWLFihUrVrxAWInWihUrVqxYsWLFC4SVaK1YsWLFihUrVrxAWInWihUrVqz4WQER+RYR+YCIPCMi7xKRz2vTf4GI/H0ReVJEHhORbxWRj1j8TkTkq0Tkifb5KhGR57C9h0Xkr4nI+0XkaRH5JyLy8++x7F8SEReRj1lMe0hEvkNE7ojIe0TkMz8U52HFTy1WorVixYoVK3624CuB17n7deDXAn9cRN4MPAj8BeB1wGuBW8BfXvzudwG/DngT8Ebg1wC/+zls7wz4fuDNwEPANwJ/V0TOlguJyC8GPvqK3/8ZYABeDvxW4M+JyM95Lge64sWDlWitWLFixYqfFXD3H3L33fS1fT7a3b/b3b/V3Z9x93PgTwO/aPHTzwG+2t3f5+4/AXw18NsBROQ3ici/F5Hr7ftbROQREXmZu/+Yu/8pd/+Au1d3/wtAD3zctGIRycDXA1+43FcROQV+PfCH3f22u38f8J3AZ32oz8uKFxYr0VqxYsWKFT9rICJ/VkTOgX8HfAD4P65Y7NOAH1p8/znAv158/9dtGu7+N4B/CnydiLwE+F+Bz3P3x67Y9icTROtHF5N/H/CP3P0HLy3+sUBx93ddtd0VP3OQf7p3YMWKFStWrPipgrt/gYh8IfALgU8Hdsv5IvJG4MuBty4mnwFPL74/DZyJiHgMGPx7gR8Evhf4Lnf/O5e32xSvbwb+mLs/3aa9mghBvvmKXT0Dnrk07Wng2nM60BUvGqyK1ooVK1as+FmFFsb7PuAjgc+fpjcj+ncDX+zu/3jxk9vA9cX368DtRrJw96eAbwU+iQgrHkBEjoHvAv65u3/lYtbXAl8xEa9LuLzNabu3ntNBrnjRYCVaK1asWLHiZysyzYQuIq8F3gH8d+7+zZeW+yHCCD/hTSxCiy0k+DuBvwZ83fKHIrIB/jbwPu420P9y4H9qnq5H2rR/1rIL3wVkEXnDvba74mcGVqK1YsWKFSs+7NFKLfxmETkTkSQivwr4LcA/EJFXAf838Kfd/Ruu+Pk3Af+NiLxKRF4JvB3439p6j4BvAf4g8DuAV4nIF7R5HfBtwAXwOe5ul9b7sQR5+uT2gcho/A53vwN8O/AVInIqIr+ICGdeJoErXuSQpnyuWLFixYoVH7YQkZcRpOdNhMjwHuDr3P1/EZE/AvxR4M7yN+5+1n4rwFcBn9dm/UXgS93dReRrgI9397e0Zd8EfA/w84FXEr6tC2BJst5yKTQ57aMDb3D3H23fHwL+EvArgCeA/9bd/+p/3JlY8VONlWitWLFixYoVK1a8QFhDhytWrFixYsWKFS8QVqK1YsWKFStWrFjxAmElWiueM0Tke0VkKyK32+eHF/M+s43FdUdE/nbzFkzzflLjdYnIJ4rIvxKRm+3zDhH5xMX83yciP9bGLXu/iHxNq7I8zX+diHyPiJyLyL8Tkc/4UJ2LFStWrFix4rlgJVorni/+a3c/a5+PA2hjb/15YmiIlwPnwJ9d/OYnO17X+4HfQIwR9lJi+Im/vpj/ncCntHHLPokwuX7RYv5fA34AeAnwZcC3NUPsihUrVqxY8VOClWit+FDgtxLVkP+Ru98G/jDwNhG59mzjdYnInxORvzWtSES+SkT+Qau4/JS7/3grCihABeaR7d393a1QIG2+TfNF5GOBTwH+iLtfuPvfAv5N25cVK1asWLHipwTrEDwrni++UkT+BPDDwJe5+/cSY2/902kBd3+3iAxEjRjj6vG6fmn7/+3AO0XktwPvBj4X+GRfpMOKyFPEcBRKDI3BYt5nAt9ADEvxeFsfbZ9+zN2XVZTXccJWvOD4h//yA56yAkZ1AxRcEAXV6Nu6O+1fzIxeEpqgGNRa2ZqDJPAKQFQXiBGQAdwWlQJcWWaPiySmSgLTdFFHROjEeegsc9xlVKFU55E7MI4jpZTYTtsWgLgerGfepCz2yR33WL8i87JxjDrvv2hMVwQRIeu0fI11qTBWp7phZrg7p12HC5RiFDMMqCW2tdz+dNwuIG6IOl1KpMVyvTpJIE3ygsZ0VUUEcnIS7dhNSFnmY5vO57Tddii4O7Y/XbjHb8wMfK9juMQ1UuKcOGOcA9GDY9mvxxGdruelebY/59O2pv9HzYxD3c8j+qi1JMyd0SqGU9t+lsVvzQUzqO64C2KxbbP9scfx+pXXfrnvy+XdPc617e+h6fzZ4aFdwuG9NN+D7f6e1o/4fF72v9zvh3rc0pogq9Cr0He5Xev4kdU6r7uYx/Ngcb62FUqN51hEMIFSCmZGQsg5LtTnvvWN9zyalWiteD74UuD/I8KAvxn4rlYR+fI4YLAfk6tyn/G63P1cRD6LGPbiFvCF7v6+5cLufqMpY59D1L5ZzvurwF9t1ZM/G3i0zbrXPr3q+RzwihXPF5seTAyrcxuAEC/4g0ZVWsugCcVJKdElsAQUw90Y6yHBmt7kzqIBFp8JEYA3oiIiaJoaS8dRBqtcFEWT0qmgarziZGRXlTvjhovR2Q0W+6OKT5uQe7eIHgvMy00N4OXGt9aKqkbjb04xSCmRPNahLoDjzvy5GAuoNDIBoormQwIY5NBIYnQiZBFyl+iSIth81lLSIHca18Xa3CxB9FQhtV128UaSWyOt6eA8iC2JbZBjAKva1jVdjyAyuLX1KCJO8j3B2i97SGiXpBwOCZaZURsRGaruiUh1qjtWg0K4K8WNWuM3oxkusa4gtHFXuTMTdvG4XxFv+7wg3peI+PKcLAnW5Xshzmk7rik+wV2rOlzv4hwjwv5buy7zChoB1HvvRwJUHMGpxLmqAr1OHYJgYtXArHJeghdWFMdRzQviZvE7EZI4x5tEzvenUivRWvGc4e7/YvH1G0XktwC/mvuPyWX3mTevV0R+DHgY+Jv32PYdEfkG4DER+QR3/+Cl+T8iIj9EeMPe9iz7tGLFC4aUBDEDnKSJyaFxoDrpvjEQEbAKTdkRdXIKclQntco9GshJOZobvz3Z4jL5uARxMBN2g+E+cpSFvlNO+47sQWBMCruhQFOdnHrlMR4ey165UOkOtn+vOo1tgMBo8DwUulorktJMPqyRgsbk5kY5ztlerQBHROlSYiNOSkKXlT4n3HVWUjwpSaPBheC5KpAJ9VGn9VpsayKrgqLTefZL6sriGA9Vxf31OSSe3vZX0NSI90K5Wf7+YL2+/14cnCCfoUKF0jftr7ngIhiOGYzFMAQnVCs3x7CmVGncN5fIybTvd0+DdHCsCzK9OB+HxyWzcqUOzjR/0XG44j456Ews/p+O8/L5mtRIBypXPwN3bWM67qTTKcAsbhkTadc7zh446lAxqEbfJzabDVllJtr3wkq0VvzHYOqbHIwDJiKvBzbEWF3RaRR5g7v/SFvk8jhhv7ct/37g9wPLQVeXUOCEUKU+eMX8edyytv7Xi8i1RfjwTcBaVXnFC4qJBAiJnEIJiTCOgQii2hrSSjTEGq9xF9yj9U84qKKNaEUoyg+Uo8UW25/YbkLbupsy4fHgiDgGXIwjY1VKhmvSU1KsolPlWOFcgtSZ+7Idvc/x7snE/QjDrDBcmhdNWByjLqe7z0rSdA6n1lBFyE0dElG0EayjpKSkdElIjT9ok1IKRlJF2zqsyVficQ1i9yLMNZOFRmpF9orSErH/+2lZ4xLJHJZq5EBpodW9giIiwY+5m6gtv8d2p1CwUNutUEwbUV2EEWsoddVj69WcsYJIEKsp4jyRHbd9aHfapi7C1JdJ86QeTiT54D60u5XMJVIjexPJWmJ5nyynLf9OiFti37G46tylplbO61qsU6SFvqfjVQUUcxhrjXMny99GSDk6Hk7fd2w2G1JKmBm3b99eidaKDw1E5AYxpMQ/BArwm4BPA74Y6IiBUH8J8P8CXwF8+0RwRGQar+vziPG83gp8apv3scAfBz6dyFb8lyLy3e7+ThH5FYTv6geB07bcTeDftt9+HvCd7v5BibIPfwD4ewDu/i4ReSfwR0TkDwFvAd7IaoZf8VOEnFL4cbwiVCbTzaTIzJYTEZCEeUU0GgCRBG5kUarsG+J9KO9wWwftjMhMUOZwh4ZfCQyTDjNnLMZ2N6J09NkQcY6Sc3qU2ZlQ6kQYDrEkQsvQ19TQX15uaijjXPj8Pxw2jst9dhMi4LM4R9NyCH0WkiiaIDVFKyehUyUlISVBxRB3pHlrcAuS1UKHgiOSWuOviBgstjl5b5Zs8ypCME2fiNRE2ESiAY9zUBsxlH1YtxGP5XU6JCrWyPekogURHz3UF/O9v2rvyVJqrRRbEC4XpC07KZ+Tl84mZXW6Lot9EUlXEq1Zy1qEwe9HsOCQZB2e0XvjKt/XwXSJ+2Sevt+xOBbf++cmgrWMfIZCrDgayl+18GUtn0ucThM5xfVSIG0yIsput+PifEfF6bqj+x7LSrRWPFd0BNH5eMJ39e+AX+fN5C4ivwf4K0QphXcQg6tO+AJivK4PEuN1fb67/5BEzatvAb7K3f91W88fBL5ZRH4ucAP4euAjibHC/iXwn7n7tq33FwH/vYicAY8B30pkPE74zcTArzeB9wK/wd0f+1CdkBUrroSF2hINZVOWVMmTA5g6h44A8IomwRfBFFdBTNDk0EJDIoKKNMP3coPOwkZDJ4omb2pPhClFlXGs9OoR+nAFEoMJqRijQZeVLve8/Hqh1gJmPH7hRG9/IhiKIeQ0Ih6NWc4JE6hmnA9Lk/7So6NUIGkY12YvkOgc9TwIPWnMx1OoVRJhvk2XSAKGk3QiemkmOcGT4vgmFSJ2pZJJ0MKvyKQ81hZaFJS0J7BimB+qacvQ5aRgiAh5sR3Ziyxt/qQypnmZSQmp3kJgfqjemEV4bCKmo/l8/ovF9XYzqtXwVYlSMMydUmEojXg1UlS9NE9b82KxUOv2O9rupL1qitW4r2Z1NLY7hQXbD9o8wcTu6gBMqtEU9p6OcVLL9NKyB/CrVdJJyTIhIp/uuAparIWk96HDOZyYQtlUd7Q6XQJVB+0ZxxFHqKqUEvd+V7b03Ybjow2p6xirsS0jxSrb8xEYY59zRs0opVze+8PzcK8Y+ooVK1aseP74Vz/4iF9WegAQO2hQL/fYl2/i0kKGpXprYPdGepfDujypKWQT+cqeyB1h+tb9Wq2FIGv1MFK3UE+Eu8IX1qXEUR8mcndjWzNjCf9UraEQJO1ABhKJ1JhFdShuDLV5hlqDnFKeG8uhWvNHOWCIaztHTV3BMW90s+22qpIaKVKEoyxz+EqbQjSFG4O8GuoRWuxmBUNwMWxKLGjKzpwR6ZPidvl63K2oTH+nTLwJs2qXrvamXVZ7lgRCOMwatTlEGYb3yYtVJz+WeTNtxz7U5mczM6opQ2v0J6JVSkFJdx1DnJdLhvulcrQ4hMuK0pWeKrl72cvHfC+V6qpluUfG6wRbbM9wsi3IeruWqVFCSaA4nUDWRGoS03Sux1IZx5HR4r44PdrM52A3FoZaKC2syIKAT8Z6d+e3/5pPuqdQtypaK1asWPEhxN7bs28ovPmrlgRr+f9lj8e+QbrbpKwwl2uYUtc72Xt+VMKoq6oz0bLassxEcC/RiKYIRy1LQVSPcFVKGTBOUmZIhTJC1dpsYjVULZVZncEqUmOdJo5rEJekLZRVKr3QSlo0BUlAJRQZsyjfIK0xTlND2UI2Mxls3iY0HxIsWlZfi70KxuS7mkKzk7p4+TwnPZShZtKxdPkslDA4JFRXGbaX61n+f3nZIHQWSlJjKlGAYQoZGtWCyHoL/43FZnIBS5I1hRrj/Pry3vG7CdLyeO7lkbpqv69afvIvXbXsZdxr/pXq1bNg3j9nfsYmJBaBbx/b/ZBmpbC6YSUyYXFDk3Ccc2QQptzuW2M3lNYJSBiXVLhnOdYJK9FasWLFig8hggTF32gI4rOsKbX8ez/Mvqa6J17m+5BMhA2VcAPFNlWDmKjuiYIIFJuWZxGn8Yh3HTRYQp1DRCObFKUpzHJTxCpujShhmEZeXhVHa0VymMtVE2aVsY4IhS73qDYyBOHDEqE2ZSXJYbaYiLQSFJAkSGPX5hmQZArn+V4pk/BwJfQuhUpncuAI4fGK8OrUuC+VJVm22XcTqkt+oGl/D61yV6tYszLZ/q++TwjYe7GmcGFcN3dr5RoiS3MKA5o4ZlO4kVaSQhpVm7avd/us5uM8vEcOfHN+d62yJa4iX5eP936/uZ8BPn5/188PjkF9v8xlCq1zBbHW+SBqaKWW6FAbObUaNcdSSvSpJ+dQYG/e3s7JA95qnWm7TgdlJxZh2PthJVorVqxY8SFEapmGUyFJuDtUOM2Hqxuk6bdThErmGlyQF/3q4BIWiovQWkthtEpqDYKKoJLZJABBuq4Z7puCcikMppEKh5nRJSOhsf0kqHbkdEQtoQgUq9AywJaq3eSvqdWRfoPIUdTNyjIfn5lRqsxepYkuBpkI0tHnUAaDSEkzvh9mBcZRBaZCqkn0QLnAIeeluX4iTrK4DmUxX2dCeLCBaTv3YAETAV4mEYhIa/YPydZ8Hpw5POgulJZVOJGr/TxvpR1afbOmBE6ZcrOaJe2StNjaZQXmgDReOozlMZtfJmFXh/+W1+BA5Tpc8+yfWpK3+xFTdDpXh5OXKYHTbzpkGdGLDgdBrhNCv+lI0/kax1CQVej7ULBqCxHe3m6pBpDjGWuZulPyQnKffWHLY1yJ1ooVK1b8NOG5qlfL3n00xtPLfBkylGiBF+GeyRlvXqNBcsdNyYTFJQtzPaKpUOdUWkDbNlKXDsI2aap/JIJIonolSkTEdFUFKS2kJ4wYYhGqi8zGKPLoMNfFCr/WJJ45pTWx2UN9ox1pivzMVvoqSjSohpFZJKrriwhWQ5k7CMNiWDPXT2FURGbyMNXCit/YghRE4sLhNbuigT/A1WGuqMrPfB3nv1f4l/YhP8GaOlOdqIBfAbGmvtBCvpMfKTIH3CPcOpd3WN5HV9RUuyoMeL9Q3VXL3U/FejbP1VJNe64ht6uW88X0mWBfComqh4cvS5DEOEcVr+Hhk6xRLDdHXbGhGMNYGZs0lhbPmLT6WUqUZqktBP18juNFSbRe86aPc1cL6RlrtUYqKplSStyQteBUqg/hM5CK5niA0pSJQEsjTnpwo7jEy8gHqDvwMgKKlUNpNTwOTXZNQneU6Tqh73tSbmnEOZZLKc29OTPDzSg1sovcjFqEOhrVPDIULOropC7PbFmS0vd9PIApkpuzRGXf4oZYSOSToXXKTElJQJ3jox6V1t8thdE8ep7DONeMwcJMO+xGhm3FxklCdbosUUMmh4zvi6wPIRElnKMAoI+FOkT53LnXLc2zgZObd8DdkdSk15wgtQrNeUrNlji+UqhFoBrjbqRWn3ulAEzejMln4SEOa06kI2FzlNANdJuMaY2wiQpe9wZkowIZrGVk1bYemzwysi9uuGiguGRwjZ56XAOq7fcxwaZr5t+kVCrSel/JoRDZMSJOkkjBVs2xjkIbGiPetqWUmNcaAO0SuU+4Ol1aeCdcoWqkaU/nRuM5UHF8FFw9ng1xNg5HpyO/5U0P8iVvu8MXfccF73jnNWQoqCk/57Uv5fve8wj5SXDfsh0zx97x6JC5nnbkE6IHmCt9TvRZMZW2faFWZ7gzMuzi3ioXcZ+mdg1UJUJLCXIXz1ciQh6PvvPJ5/bW+hmAqxqgy43VVY3OIdlyXLUV5bx8ajSS6wj5o0apcxBHqyBqJBNqmgpyNqIhU4X0w4Y41LOm7BhzmM2EucyCE++E6f0W5nNBPQoViO/VvNjfpijVilmNsIyAe40yAhLD3NAyDNseNdoT76k8VVBPzMcQhGv/XO8N9lHWYcoinOJKe9P7nmTBskSF4j6Vd4C5Yv+i1MO9rvG9Ql9XkY7LHqTp+1S1fyrlUKesQdpQOo24TmdnNsE78dsWZpyOLTxph+8Iu0fh2fuF+y6HG69a5ieL+dzrsz8nd3nBlAOj/jR9+Typxr2YNdTNwcucaJFSYrPpUFV2wwW7obAr1pTCFEFXm4ajStF58Zgv99ivZ8OLkmjtypbUK2Zh+NNkrWEtVHHMxkg/pnXwJKLuQTxo0l+c+OKgo80PWREjWay71kiJNSd+ZJNvgIMrqapIIwddl0k5pGdohA1hsH26r7tThrFVm221cGqdY+ziMNToEdKGmEiidHmfGdJNLymrDGJIEWqZiM00dEfcdNGQhSlVNFJvqyfGUtq5SPOLdDKbqsRxFSkwkTVKmFhTQqS2N2v7EDVLXDxqrtRGGtpr0UXoNLaTW5ghWZ7PCdpq22yiOnOn+8am1oVJWPZpx9M+qwbhqgJKjtdOhZQT2gldp0gmUuEX43DVeBPNBekQxSvk1iM2F5izd2QOoYhEJWN3QyWF1yMFf1ETcI3zQ8GmkEcb6qSK0Wn4Q0bXyEqpLT1fHXFphSuV3AFjRU0oKm3oDCOJRqOkkMjR+8qOZvCWhUULIdTRcIvrJhpEXFPCrBmNe9+/gJPi0rFNZ/y8TwBLld/4Kco73qmQO/odXNg5Nx7oGLbOrpzSyY6bu4F3/dmOj//C27wyP0TtjNwnUg6zcepaEcjauPgmGrYB6NwwSy3DLMiVJyP1guZ4ZpOnmax/uOCq3nv2ViHb7w4nRSeijTEn8ayYt+eAeNFP7zTVDpNddHhcENMgRDKpUAOQ8aZ4iQpZbR7jT9zmDkUSwUyaZ6kRqkWmYsJb5fe6D+NIdKXciWs+LSx7RWcmiq3DOqlg4to6UBGGcYe0GN7kLvVFp0KiE6Li/t5UzxzXineI4+q4FyRLK7552IAv1R534Ui23KovYZMrrjfZekcisTn/fo67n8+uO2aXII8XyLBDumNGD8Ij3R3Qnq4OdC6YZEYvDALSdSgFqwM2vpyT+l6eeOC13Lh4nEcLPNQ/Tb31MmoaotDoVFB0riFWSeUIS09R5BizU9Qq4plzfRyzE/puS719g6PTLeOuTrHkRhbjepmP9yYHi6Kf82W8j5JlZlEw1BfD/szByXoYFvRlePegMdlfNt9vaw63LrYV05f761GAVqY23ef/B4/xCzVBl2IsTyE6mv1YSKlDW0fgYtgx1sKuJOIuj+PJ0z6m7kDhnYrY1pYi4Xic63sofZfxoiRalYJY0NbUVCpvDVYtBZ/SMABTR6xGJePZwNlSmycRx+Kl5s0LUL1Etd0aPTSpSi0FMUGaxCgphxIQ7TPSyJWq4rVCCrIWRMFxmV5eylgL41BJGtWKi1ZAUTzGQFPHUsiR4vGzqCNjVCtQoVgQydSMjV5qDFEhzSthzVthcRMoSqmOENOG0tSqZvbzavsXukxqE6QuboEo+xOEUlKYSV2gVsMtVB+vUbnZqrfKuk2dSdLSaZ3UZTqRMMi2ZyqlIGB9l2Yla+odA/RtSAMzpxZr0/3gIRcROs0tmyheppIE6YAc/2tTpISpUF00U1MK71RduTiNsE4P/dKnMb0cIKWMNAVOtCLi1AQ+KTSqlAKi0nrVFUlKpYDkUNO8VQL39mBKEG9xKHUkkakts0gayRKIY+tpSlCdiY1PKoYQ4RqtIWcT29IUv89dimuaFC/xcrYqWCpQldd1icQxn/DqASsbNuk2ck24cXydv/LWW/yyr+x4+HjDU3cG3v/113nf7ZG3f9YR3/L3tDXainYJ6RW1GoRSBUsydxLMM+g4V5/WJGRN0Cl0Ht8tSC92qQH4GYyrXrrSQlhLV8pyjLu5Q0FTLTwCcOZR20laGMlxrA7oRHKJLDtFMbG5KdtVSO19YSYkTXh7X+UU5SOn/Uxpz3Kj5MOi8Zv/3XcCl4b2uw90qrl0N3wy0cyDYKdGxOb2anHupqjCnjgEcV2oNovlp+d72velf2uav9/H6LhPHi7DyN0IvsV2p/QZuvEYdr8SnnKOupfjr3yEelTY6TnmG06LI6kAXdSvUqhs2NVnOEkfSWc/TldugG+is6wf4CLtGL7nIZ565Zfx0IOfyW27hnSFOkzKVryTvXmTBnkQO3kcxlOynyM+MnrC9Bpp9xLEHyHxStLxD1Pqy7B0ivruvsd/dxhRr5w/TbsqVBjv0WjT9mFWwRekban2XVZwD++Jy3fJHsvn42Af3GdCP1neNSeOmviSJLJwk8d4nYJQup6xOmUYsBqlRtw5eB6X+2kti3XqXCy9YskX5ELuViqvPJb7zv1pQpKQURFrZEexMmJlpI4FKwOVSvGClUoVwUolVfBS200bikYZoj5GHQt1LIzbStkZPhheHMaKjyUa+lJboxzqkLYGeQqdRTZOiQJxpYTUa0aRce6NjGOlliBR21Z7ZjCP/afiQOfKJqUIs2k0PopQi1HGGgZTdzp3qlS8lEsjphsFoyoxGrtII0SZ3VAZysi4GxgGYzcWduPAboRxMMpuZNwVxqHg7eUVNWqcvMl0R4m+o6l3HSmlCLPFKz0gy8yVFutO0fCqCtIpXS+kjdD1iU2fOd4kuk1ms9mw2Wzou45Nnxs5SpQdWLt2oXilqMgrGlke5oiPra5ODkLXpzZobiOYU5jXBK2J3ivJg8SJRQFIahvLqmU8YU6yUOnEjKA1U2/fSZIRHfFkkCLmn7IjG0OPhH4DaePoRoJ0qCGSsNH2VYvF8GzEwLdNUSCUVXeP/S0tRNjUDu1iANyUndwnpNMgXhtFckLIMURKiQZWjhLChi0FIWrAbCTxqz/hiDe+aeTilpEWRoASAAAgAElEQVTTiEumli0vuQEu55ypce2h2zzUCU/d7vgz/8X72VT4grecc3ZywZe89YT33j4nl8LnvjlRucN1gdOzkYzw5o/aoV1G9YR+I6RN4iinpl6O9PmIvu/JJ3B8fIycOpvjzHHX0Wnc/5bgRfoq+klh2chN/0+fqfYT3B0SOfidVcRqdGoWDZY2xZlLhCjWF/f+NNjw/Ned0bQNotx+o2mxTZ8/mqZO6jRN7vq0Hbzys1zXwXqVg9/uw1xRnFRTU3wX/y//TvukuggF3ifsddX8g+XaPoTS1iFaUUZUM10Wep6hP3Hy0Q1O6gVHtz8bO38fqmccc42anaKJ6gmxRMrP4KIx+LDsUIes1yHBkJ5kwyvpn/lGbt66Sbn1J9medhzJgLk2qVxaJ3c/KPWRbcmjkseebvcQfbmG2hnl6BFEKzmB9gN6/kn0egPj1l0Eazrey9OvIlD3uocvf99HH567DH2va7G8R0KtfPbPdC+E56rslS8vZIyswqYT+i7eQ0lgWyrbUhhKZTBHNKNpc+WxXrXfz3Z+ng0vyrdbhI5a1oDDaCUsCWKQDJJgVloPQLCh4MUpo8Wo5EOlDpWyK9jg2BZsC74TGAWqUmsoUaXS/E7xoqp1Imq272HYnrWWMYhbqTAWp1TBrcMtUU0YxsJQK+NQYr2jMY61FRmMmi0mjtvCDFlrqBq1to3FjWOiTaGBLKmRnojjJxf6Rth6Im3VLAyxtVjbF2McnHGQIFdjZdgVxnHEasajhYuXf9eRNNNJR9Y017apU82WUvdZVM3TpAeGy3hJpC5CYsd9x1GX2Ww6+j6zOe45Puo52nT0OdM1bxpAqa1yTHvBaErQCXRp/mhO9G3f+pTpuxyhAdX2Im5BFo8H1sUI/hxhw/CJxH5baQOqjuFjsWLtnREGWjFHWrp0qQNeFTXBPKpa04pDigNd24fqSLGFetYaxzTdz5OHID5mitVMLUqt1sZjk4hwplhnykLqQskKFayNYVcL1QbMK7dz4c555e3/ec/tMnCEYMMO95E7d5yv+aJzvu3zjnjPX0kcnQ5kv8VQe06PBB17dueFd/yuHf/+scTn/5Jb0emwgc99s/O+J3d88VsGfBBOr49w23nbL63kl5xwbJnv/3Mj3/w7Ony7o+/3qu15cnKCXjf45gKScnQEnBjHXT/fx3iEq7yGl+zDBXqPz1WE5eBemV7cLUwHIHgUwTKfaZJeIm1R0POQxE2oxNh4wzAwFqNUb+UCPHxQHhrYAenS/Qexu/7eK9wkIvNzMX1i2JKYniQ60UnCPpCTzEObXF52+l9xxG2eFvXz7/O51HAvG/B5DMrLDaR37SBKI7KFlH+E05opmyOeeUDZbL+ZB8f/kmsX/5ik51h/mzHDqBtMd+jwetSVo3SE2wWVY3ZeuFMv2PoxWn6ci5tfzemDL2OzexTXUy5Kj5ap3pdOV7vZWRyVO2jZUBgZpGerwtDd5NajD/PgtVucpJHdtpI2N6nbR8n20ruuy7P6iO5BmGfivLgP5u+XSfdz2M5z8TNdSeivXllkE2oMu5QUrI70Kmy6xPGmo+97RIRxrFzsBsYaAoa50F6ycI9n5tn2L0Ln9a7zdD+8KEOHJhFm8MRiPKeCt1izzXKd4qUc+CEmz9/kRTKfivJNvxnihLXSsELUoakWIZh5ZHBvBEwKfQt91Rrmw4ziOYhNUsFLrLPMFZQraswG5JQEkYwgWI2w5WRmj96VY7XMUrYo5FHw7O0BbGOl0dSenHBSC8mFeJpb4UBvyloYq2sofrUizSsRIaqKZydLRtXJScnHXVRSbkZRK3G8qpkyhGKHh7F19jU5gCG5i3Om8QLtc5ADq96M74ku51C+RMOjZE2Fa8eXUsJ1b6B3gW6+bu2lmxKSEjRzt2gLq6rjMg1D0gr5OaBpHzptBNGb30y8GUlFaJGt+WUwqQBmkeU0Whu6QY1da5SyZKg1GsUIdhO6n8z3MCK4Gl4r6hEvNSkwj18WpG56RHXhW3OvOELFyUzVoWOfKbHpWp2LW4V3fOkr+ZRPvMl/9QuMt/9d5e/8/QvOOOXX/4IWWpYddp75gT95yve9T3jrl25REljPaRo4v9nx237Jk/zaj3mIUzG6eosHNyd8zWfuePeTxvUH4KHU8/7kfPable/6R8K7v3FEHg1CfvZQwXZHbMttqhX0whhV6TtB7ZjSVXLeQFWkD4JVS7tXq8MY99uHCybFZVk9fN+ALBZc3G+XX/baSEGXIjTiEec4GMpEfaEMET65Gto7vhg3cCoGiiiq3gp02mwUjkKeizT9yX+68N9M79fp3bivPTURl/b9UghweVxxPq6+zrPYdY82Lxq4KJvg7uil7D6ZHD5XhKcOYfMxeWssqieozZ6iBbeE5B+m6844v3mTQXf0Rx/B0e7dpM1nYbd+P+nal1CT4fk2XlJ0ztIFWMat4nrMzp7CPXMsr+HJRz6dut3RdRvOzl7BzQ9+E+nB382wfT+WE27SSjk4Y6vYv00Vo2OrzzDmJ4ATxM74uNOXoHwl59tb5O4rcOvDh5Vv4fXw2O8aneDSOb0qxLecf1XIMUJpbdpEMOZw8L2v3732YcLzMZeXMkQmYBL6rBz1G/r2/jSc8+2wb4/dIqzprSwGxlTKQ57DPtxFxFoI3psIMt2T98OLkmjFDWBz2I45fh2F2epQoYJbqBSKYksPEtHcRqxVwhDt7UF0OchCVGt1VlTwpgi5R8OPh79Ip0yWnEFjnjSDnEgYF60aVmw2q4snaok0ZhdQiZedWYQtp/Rlr8KuGrnzeYgFVKgyIJLIreF2CZ9FDLURg4pS6zz2mVuct1qMUiy8Q5NfIcYNaI11U45ymjMz+r7Da4FWrK02da+MBfcUtXA8elriwlRYz8QiFVzCE9W3VGw0qj1rN5n888IQK4gHsY3QmoWR3MP3kWh+sPYgm1l4lzTTaaZKeKY0S/MFpdZBmdJuW8/Qg2hG8oEzlLpPW0dbrkM8LLMXQPZeCSRRMVKNStrJlZQ0xtRSGJkKC6YoTDkZ6qd7UB2XSkjdCtO1RUKwnL6bBxlEEE8tYbAZiCX8Oh5tZBxTMbxUMMFQsvW8vL9NIoE5f+JXXvA5P1d529fe4ks+4waMt+CWo0cKpeMXv/Sc7/0KuHUnc3z9Np0f8Wi5xef+3A1stzz6dMfAMWbOL3/thldcr4x+zAcu7uCeuNEL/8/bz3nXD+xI3YaTsy2vupZ438UFJ6cd9hSgFRjYbTv+uy/6NL7s6/8euT+l6zoQoZYRWnKFlcjI/XDC0ltyELZZkJB4T+0z3y6Tsr49M3N4xhNGZKSZEUqqW9NAGjFCQh2ptqi7FT3+rEqxihKdT7GmaImTvGU+t4SKyXAureMSHYC9+fnA7nQ5vKRy5bzleTnwXuo+YnD53MWy0zqCTM5emYOGXebfuuy9lpdDs/vvNh9PPINDG/LomB0g2zPS6TspeUOXK73vKHVgPM7Q3wL7WtKdkT79PurmGibvZbQzRMBK4qLeDiI6Fq7X11F2/4L8wXdx6yWQ+gvqRnh493t44r3G0x/9+fSPva8RAKW6UGrcG+PFR3H9ge/hpL6Mp8sxun09H3n2z+luvoIHnuywXcfTD/0q6vXPYLdNjLnS+75JXx7/3IlfEKblvXgVUbi3R2vZQdi/OzF51vUfEra7l728z1fCnbOTYzZdZtMl8JHdnTvc2UVbaC4MEhGhSgJJqDsiLXAu4TELr1l3130SbfrCt3jp3Fzer+eiiL0oQ4ddjdCY14SPRhnK/ILxnWNja3TGEn6XanixOZPQPVKExVva8LJMgWicZ2Me1HSSqydjd0pC7lOL8SZMFU8SPs9iDF6otYWAvPUap78GWGIcKwwtbDjAMER8eLst7HZjlFwYDa8jTqEUw1sm4txZHG1utKs3E3+TLHdeGa0yjKGQFauzUsbk72CfNBApzBMDDyk45/aCTZku5QiVFaWMzrDdBdkqpZn2WzgTi5dzU6qkDwNF6sO87Ukj24mps6Mt8uERIpxUvOaTSBpeqK5P9JtMv8l0XaLrMn3KZMmIRMpfUYtyGBoZbNoKEDbBHZsag3bYMWBtXPeEHCgIUyVlbd0zN4Oa4tNKWGjzx2gza9dxIrHWws6tl2+C1G4fIvLwknQSyQyKg48kNbQKFEGqoSVIVoRiNAZFrdGQVReGIVF3gpogdQAXTrpT7HiD5IoXQbrCybHCeIFzCx0zrz3q+Td/uMfOOigdZeygjNhuC575+Fcb733qaTKOycD12nO869hYokjByo7jPpPU2A0dj985p4zKcXKsDJT+nAdPH6BcwPXNyAPHyu9865t4+uImponhRDk+OuKXvfml/O//7N9ydnadrKkR33D7TfcjQJ+VTu+fSv8zClYQr2SFLknz+jmiJXxAjChNZSY6I7QeN0yEQVqpjyjrMnUYcoIuQ3KL8IkkknSRdAFoI1NZnC4ZGzU6qSSpnKbEcVIyMUizqy8yDG1+RwJzOCsej0kBqIgYmur+PWVTaQbZD4PC1YrWXR4fZpfEfRr8GJh7OT2IWeWytycy0G6RUteq0T9A3Z6R6oY09mwMSs44W4SOXG9R8wn5g9/K+ZN/nt3R45xenNPZY1T/GMruKfR4RDZHqN+mDo5dHHOsF1y/9RX48E0cdT2ye4ju6FGGcsK2v+BETjgdjhARHr8G5eJJbp0+ze07Z5yWC061cP30GL3zJzm6DWVUhtxxUYW+OE+e3OKkH3jjq/4Qr3zmV9A980qeSa/gNS/7u7xWP4PdeJ3bpxvkunFj+23cHp/GNmd025N4E7Z37FR2yKvBNuFHyvV0zKjnuDubS0b4y9dreV0OPVpxb0yDeU/ruCvEqFGiwjxsPkm1Fc9tCRnVIiIR8fCIVKQQJVQMkbER//BsGxXXkYeP4cZx4ihH+3R7ZzxdEhdJ2aXEmMP3JhiJgvrIvkAt0cF3RcgHxzaJAdLEAEzCP0s6+MzHK3cTy3vhRUm0UnaoA14LxZVaRqxW6lhaDapI71fVyPgjlCh1R91DFUExTUSvcXlCone29OFMvUnVTNd1bDYbsnZo15O0I3tCx1b/CoURvJbZYG+2b3wnH1At4c+qxRmHym4wxq1hW8N2RhmCQI5DGLVp9WJqC5UWcSQphSBZYdYX3FJkF5rPYcpS4rwUa14vhNLIwXTc5h4EJSupy5H9N9X/IvqL1YzqMb6T1cS4dergVFdEO7RTUp9mY2tk5IV02wG5kYqsYeSeakZNhu/p3IzjSBmjzli1SHhA96bZrk90XYIkLSwoVGqra2akbJCip61p6gntjb+Vqaq2NYntcPDPyRsXHiyP4oAe91KtjjVSXz0M+lYr1FYOpHnqvHi8KEajDHFvjkONWmlTp2Aau8zj/1qFsYWMSylR+6YlWbgJY6ux5tWg1EbGwL0wdj196qF03OiE23eE/+vLHybLEc/c6dHSI/5SCkoqlYtbzjW9jZ87tlVsm5GiuO/IFV7z8AYpgnpGTbFieI3eoFrCB2ejPbtaON0c0eV4VtJ4wkMPXueZx8/Jm4GNnpC6p/jFv/BTkNuJo1QpzzzNx750y1/+4g3v/dGfiExKj7poboWalZQzue/oTnqOjnq6/kX5KvpJIaVmWl+Yyml1zdJsCjcUayTG9mbwSw2dS9p3INznukmXPV4HCo7uG5RI7GiNx2xMb94uZE6+mNZ1uZr99Pcq78yzeml4fuGg5TYvT7tMwu46T9P52b2MWkfwHdvhCbby4+z0A1z4OYNB3RW0gBSnpGsc12M4fQ+bp/4oYh/NxVkl6Q0oG3S+dndvczw+huFvUi7ehfRHbEtHJ4VUhZ1X4JzRj5ACXn6csssoG3LX6uuJsenOKRfvpB6fM8odrncvZTcMvMSFT37Vn2BTvonNSyq7/BBvPPmbvGL3hdy+vWXTa9RzzIDfAR+ZislOhGE6X1M2qxxtOd/ueOKJC8azHu0z+c7VnZurSPJeqLj39bnXdBGZ60z6Yl2ugnpFvYb/UFqZBc3UlEFyI0UjWQrXNokHNz256xmLsR1GzrcDw1DwxT1ylZp573vsasP9PoHD7zn/7un3xosydDj0BbndRX2R2+fh3QG8eNRcalVfsQhdTTVIZj9BtXZkrcTALJtaU8Wb/C3hCZCmjGhK4eshai0x3SgtbGYCtQjiQmmqRVVBNCr5jsNILT6rSJ6YM9mYXgReMTHUcytUF2HGEH/SnFWjEsbR2VJghgmUMdKRIY5Z0TYMRtTsinBcGxTWCkUq2kovGwSh6UKNmhRQr0aVpgbWII1eEtQxCqQCZFANw6m3gjwpRxq6dIKkhKbI8kA8QnwSvfWs0XOIDnCJc1WN4jVKI7RSEpPJNyGMOFiYFvdZJu3mT3Gtio5Rzwsi+3P2rDCHaeIcReFXb4NjLaI4hFwfxHX/MtdG3MFV8CRBfEUQ25ersLIMU+xD1ylp82NNfj+P0goTe2uotTaPQKLUGkREovzEFDIe8haxazzgW3Z2m+94+8DX/eMjvuNJ55M+6TZHDxR+5P2F17/G2I632NaOTp3kCbaGW+J8O3J9c4LbBUoPVTg7qeAdXgfEnGRBtp2CWSFbZOTcceHWrS3bAkLhgaw8+sg5R/2G0RNdNX7bZ/w6vuZbv52tnvHWT97yxz77AbpnnoTyND/xVM+D10FSIqWMlxJVLlLCW2Zv7gX5MFK0jvt08NJfIl4/Ma2602zYrdRMEF1DKY3w1EayJulnbrxaTQRvfkNpZT2iKONUzd3BoEilkw7R0n6bDupLXYXDsNzdDdV+3MDnhqUCcC9P0FWQSw0o7MNg03qXn9zBWAvqZ7iOZLlBsZ6LesH5Axu6J44RPcE3H+SivJpr9us52X07T6UbpB/suPaGf8LNaw9x/ZlPg5MW2nfHLAEa7xuBIQ88OP4THvnAW+he9T3U7YPU9Ayb2vGIwKZ7gvPyCVy79cOMz/wZvCSyCPlox86NrQlnZx8kPfpl6Ed8A3LnjCf7H+dG/wZ+3usehttgr3wZ6bH/lI84O+P8vXcYH95wR42j4x3HG6HYltvb9yC2o+OYsiBDIjJbb+JdqVyrmZe94RxuPspTd17HxY0NUu7c99zf71o9l/DjvCxhW2mNIUjU0RNpoTuLeoARQY0yJxtNHB0pfddF+HqMzt4Td7YhDLR2PPKIElPB5qvDxvv/l3+XIfsllvfY5WO+fF7uRUCXeFESrVcn5b0bh5tj1J2qeZH1JlEvZkp7bn6WSG1u8rfmaLQbaw7fT5lP9lQDKiT6JfMOljqfyNYohlW0kkp8KxJmUpOojBwmxjA9L+szySytWvM2sehtBElCEpIcyZnU6ZyZo2nfSwgiFWnw0mozVQ/SWL3VtJIgeyoRQnCpiBraSYRJLaTcnMPftM9gdMYaCQVl8L0SV0dwj3o97R2jSdBWbsOlEUJiPUoUIRWpINClDWItnq0tpCo2G3PDB8UscUMoeEqTbN3pNB5MU4sHTaO4aihbNYZZQMLQLhGW80WYxSVejKTwgkxjZIlYqGU1TJJiQQ6NOmu87haESlKb5u0lECpbVIKdFIbIghXJQNw3Qa+D1CnA5HObZf0mVVsocCKtgKVLNLoaCtOZnlFPdnhWPvJa4vWvPuZPvW3ga9+qjLeUL3rLwAee2VKzkc5P0DpyvgPfbbl565iTroRfcGt0x4LvWrhVKljBWnie0UgpU4dCRZBipNTx2M0LigZRlSR0RRlzxoDeFD91yo2P5OiJ7+KH/+IJ+WYfmb39NX75//BBHj4+o7QQl5WRTdeFkmjRAUgt3JSen/DxokaaG5M9lRERinmrk8XcuEDwoUQkZUSN9SD7ZlNBmP07ZX7Bt8ZEW4cQaIk/Tlk0OLFtB4lqsiLQi2HE8Dk6JwAtQyH7bV3VkLj7AUW73OBexkHY79L/z4VsPR+UeoFZgTRSzx8AO0Z14Ho2bqbbaN5xYRm1ylF6H3L7e9jmM842tzi9dp3hx34hNz7xB3ls85/wkmmltXWWfN+Z74ojeoNT+TFuPvnXOTv5LG7XTG9G1YyzQf0CHb6fobybzdEx57sdzggktkXIIpzaj/BBGzg5cTYXb+D1H/M/wu4jeOr0CTaPFK591MCj39nz8CvuYLtKHq+Tjy6iaK2D1R3TGJGarA3VEyHpKbnLzMi7Mz5w/DQf/f638Xj/1ezOhHS+w/q7CdXyulylHN4Lz3Y9r1rXzhLiRsI5lpag0aIUm5SQDKhg5jx9USijU6YOrMYYnJjMVd+v2g5wRVbgIYG8fFxL8n5V+Pvycf2MDB1+9x8wHpDH2R0bHR2CYaUw+AY1p1yMfPlnXmc3GH1vjAzhf9Ce2mL3MJGsyOCaMZk1VXBayrxNPaaIzaoFkZrqdNAM7GWojLuCbQfqxch4MTCe7/CxYKW0m9pnr8X+Yu3DbF2X0L4jbzJ6pOhGSEeZ7igqz/ebjrzROV7szYEE4TF2iLDTYBRzhrEyFKM0IlOoVIlQROqULim5F/KJ0h9nuqOOPneM2akaPrUyVLZ3KsPFQNkO2DBC2Ze5EIsU69lT06WoJyXxcGt2up74XzNGpvp+CKNJGfTZLGlAaeHbCIGWShija1TfVQyTgqeCq0E2chckx3LBqBQ3PEEVm3tImvex9kyQQyHM+XQKrXTCNOSRe42SIeqotSzCRl6rWisz0QxX6rjWuVEy2jFaJTwL7aHTvWyfangJpzAvs1KqODV+0+5HkURHVOkXzYh2lFTRLlPHI/7DI5lOhe5I6fKG/oktX/SpRzztBsOWlAfGrWA11v/4+Tm7i/AajrcLo3WRiLEbw5M2KuVcqAp+7vi5YmNCxsxGe37iiS0QQwd1I2hJ8VwMCStw/YEL3vnER/Gp2/+T//mzM/nJI5DbDLe3/O1/NfDYEy/BO6ercd9X6UgD9CROEEwzm5RRTeiHkaJ1FaSpuwfhlLkxAyR8UzqVPVCjSz5XONFWENjdI8Q/2RRKhL6nQqNT/UCzCJfHJ8LS28HZjbAbnbEYQ4nkin0pm7sbn/3g1ocermn+81W1nssyl8M/V233qvMrIlBSU7yPcHkUP/p61L+J6+fXuXb+cAysrcY4voZT/ilycZNt6uiPHMvnvOyl8MRP/Epe7pDoEDog/HOyqIbe+TW2dcP1I7i4+ecxvaCyYcDI9Az1AXK5iWz/PoNtycfOsB2pFTChulI8c6KPccGG//D4f+ATHv43XOu+lMfvvBfnBn3/QXzrsN3RbR7AuUZ/9DSbo+go1yFU96SbEBGanUKaai+N8CvO4Bs+9vRB7vzoD3B+5yYXG2drV5fWmEvXLM7r8wkZXgXVUFx18ZtpG1mjTNHpUce1kw3XTjZcPz1CkzOOhTvnO54537GtwiBR6scvhwnt8N59tlDmfuKhp2zymV1VvmHpQTso6zB9vw9elIqW+pa/8Xsf5pf+0acY+2PSWCEnOow7VD77N17nN336HT71o0749C+/w7XuGmMaMKts+h4Jm29kGmpkoTFl55hHZ7ONBycI00jzs38HZrM8rVxAreP8QgLC/5TCdJdapXjVlpFHRdtLNcz1Gpl1qfmimgISSPMywUHCS5U1aj9JU06KGbRidp61yaROamoZCJ10uDSVZ1Je+tTCkDCNgSeiPGDKUCpTpDNqeDWZvIUpvMTwGq6E8tNe9EMZW1ZmZL8h4XcTAbeCqtNJaiSnPVxZWhHPCN8lVUqtraI8UWZDdSbJ0glW4oWizb9FjpCHAlUcSRJZRrW9fD0y2aYkiAhHhlkmEarkNHZlocQ+J4FGImsbQzAauzZUiGjjzYKLziMAOELXSoS4a/O6xHKTwuc0bxhRCmTSK1Qk6m5pvMCzxPFnnJINlUznhqaMUMlD5hZbnMx7/m3Hq19r7HRgc9yx2SU+/zMGHn0sc+Os44G+8tStkcETuQgftC2v4YRRd5ycZy6ksOl7dDSGnTFsY/imZ3YjKjsuKHhyhhHqzkEU+f+5e/Ng29LzrO/3fsNaaw9nunMPt1vq1tCSLEvGWPKELWxJtjHYcWyHoTBD4UoE2IEkYBJIgQmFq6ASKAIh5ZjBpjA4DGXigMcIO/EQOybClmSp1bS6Wz3c+dxz9tnTWuub8sf3rX32ubpXalwxNFpdu8+5++xh7b2+4X2f93mfJ2QvUBWztErbdRgLyZ3nseoZbNfQC1RhRZCasE48NoUUOhplUVXAJaGOHX1Tc3Qy500P7xPvdHRKU5HlOT5XjrNyMnmBH/TV8kwd+IuDi+AWT7RsjpXeVP4KEq8IsaBPKQu+SLEoI8a8+cQ08Hfzhosu5fpUhE8VOuaO2yARqyMYhZVBnT0jZ9ubVT6tDVaw+Vwb7iWfuQR4P6RgOF4NErAdZN2vlLUdFIoIdrQgpBFOVpj+cVbznrn708jkO9mZ/TTefj6u6vFygg2/RN9DM+qJsUJiSz99iIP1dcLND8DDRaohlhJVYtNxjRnjwwxVTRjxHGu7T1xHWomYZOn6fWz/qyT3L0li8Vpo1wmCoJIl1RHnhUov6efC1HSk8bdR+6dIDcR+RTe6wrg/Zu9RxzKuWK8PuHzpHJEl4gxdDyIWrRp8SBgVEOwZvpICbFVxcXLCmy5+Bx9qzjFiTSU13d6K0H36dXgQivmga/2ga7J9KJGN26Iq4IE2uTlDC1RKGBlIoggpa1Qu+pB5r2WPUjoWMn2hyQyIk2LT9f2g93/QmHvQZ/xMZdNfDwr7mkS0olTU5xx/4XcHjI+gfS6biSBG84GvFVoUl69q/sEfb5j7FXiHUuDDaTnq0zKhbPyXy2aJTYapdemYCREXMqnc+yzs6VpHt27xfcixTRw0f1TpMDw7IHPHosJUCmXzzTaWUW1pRlm802o2ZHQp4pcphvITXO9ZuY7WezrnNuKnyYfNLZfsdLYK0jqXAAgbqQprNLYyGy7WqYh0fl+fInLPBY8AACAASURBVCFFfCz+VMVrb5DFGCZp8oHoUyb794EY84Lfu5TJ2yHlf0ehd0KICh8VLqSN+OtQMttch5RRv2GzgXJttd50sqhC3DcmexkaWwxjJXOIkiSSZGHSjYr0EODEYXM65cRISlg1lEsLQbQ8byi9bDI7yaURryBJ5haErSaHEEvYbkBXmbyv9YBC5oaAdPYEsuZLOlWjFpFcjqsyWqZtLulWxmLrCmVGJEksYs3tlaeaK+x4xXf949soGaHXiRgUrbSMXKIJFc9cmxOiRiVNEzVBJRbLwPWjnjpUrOcRv9CsTyLdccKvcsndqMBqneiWDrdM+GWWTZEo+C4SW43zgIPWQUgV0sJ6saJzCaGjGY3AaPRxQonluNUEFQha4/qYv0w7YeJa/v53fwuv3FyTrEZ0h4uJ/nMnzmLlXZ67IdDHiPO5O7gLgS6EMq99Dn4iuWOwjL2hi89qQ20rai00RrFbafYaxV6j2K8VE2s3osDWKJREtEpYySV3lU6RqBghKU1UGifC0gVWPrH2kTaASwnnU+ZNBiF4IQZFDArvMi81eMm/9xCd0LssfOpjph6ElLuch67k7U1q0427hSjcW455EDrSNTv4bor20Fd3qThB/A5GLaGboABte7RRBBwh7KDcgiBTqrHn3M430rQV/W3Frn0/o8WEOPtmdqtbtPavsu+EWvJ+YKqG1t2iAg7O/a/cenlF3Rzg2w4VFMEZhArnPao+Zna8g2sDk3OwNOcIfk50U0zoWITIlfhXmc1f5tzOk/gjC9LRhYqge4zXJOcx04q3XRzzxa/7/YxwHLk5xEPiuqNqV8yvr8FC6mBvIty81rNKkS6MCceQRKPWicmOIsQOb9YEb/J6GFt6afjyp76Pff96+md/nAVTHt99hicO38ZBWDOShhM7IzElmgVdFcHOsOnilmisnEG8sixCSYhLUK+UoFXA6DwOs2XU8DhwJQE1wEgJ+7XiysRyYWzZrXI34uEqcGPec2PecWvRsvbglSZpnUENyMn0fdC2QTrotGIwJLzFmo3h6acNA5sOw3uCqu3X3BY03V67t5+7kS76DMdrEtEyQWH9mm/6wil/6UfmdGGESomYHO//zZqDxiJec672fPWXjfmmnw78yNMB4zRVNHjVYpTNBrwUt/lQOgUGfpHOteHc0BcyZl+sXqJkJfRBJVyLJhBKdqpLEJfLQNqajb7SoAKOVogiBwrF3y97zymCL0T9mDf8UKSfkg+EraAtE9Nz8JeDpJARHPJg8TGRdCoq7kN7deYihYG7FMvfhyAs5gAtRU+eiblc4VXCGFM+u2RkilyWkFAmDZlsJDGSTNbhikUzq5gHbgJckUDSKk+ulCUUQcjYhUOJwSdfOGzZJHTIUjLvDURHTEhkWZhBskOKV+TpoM5AyGkQFwuqFGI+Z9FZbBQRXAxoOVVfVuSurFwGJLfuSmR7vXchI6C50zWjnVqEqCKiDElltFVLLjuHlFFSidmloOiGbKQooExYqzcIZ4oZDjSSfTJtDJjacNxHbvx1IRrPS/PAt3+v8KMf3yHehruN45IGGy0hWQ7qQNsZrh0uGekKG0NuAkgVd9aO3VThK0eDYqUjdcicOodGq8Ry3rNrBBcFYzXdzOOWiXUFBOiqyMjUrFzPARVMRyzVkikV8ZxBLQyydKxGhrufTMyWmiruMlvPMTJFusQ73ij8vt/7fv7Qd/9zLu46Fm6csWabSO5zJ9KSgnwMx7AxPOgTZsCqUATK+ByMAoauYGDj1pBSwnWcPq68yPD6RmdD+Wy+m9e7VAQ5VSJ3Pw7PiZ6kMtoQI5iU8GQOGFuvP2xGSUpCsrUBZUX1wrN8kAcin45ODT/vR0ge/q5eucOlNx+yWr2ZhdvBr26i6wVL52mmLSF5fO9JASZ6RE+WZ0kBQvCAxphdakkcz05omh2U/DQnh9/IYw8nFo3GdUtG4zExemzVoE1ktV6ibeT2Kzc42NujWztENG27ZDRVzGYdi9mSMO0Zj0a0x7CrJ/T9AtwIq9a4+FHEV6jmkOW1tmz3GQDQhcIgKjFu/ipenob0OlhUONUjqaaTNcuFx7WO8USzXq+JqUcljXdLUgAxFYgj9iNUUozcLnoH1v0Jy3CBt5y3PPfCn6fWhmTmXLlywO5DP8knf+kaa/M8UU+4UE9o3QzLlHUy7KYxHZ9E2L/vdby3GWE4Qsra/fnaDdWJfFShp66KZI/K2m4pJZbrNieyqUgUFXHqe9ylziCXn+14EN70b8Mf+//776/JQEtMpPY1dRX4y7+z4o/9s4DyFV/5my/yF7/lGBMC08bR1IbVyvGn/6Dmn/9JkDrhVY+OuVSni3L4Sgs6JBrr2d+radYdh87jU4NPEaMVweWLPFzgzQKnclejpLQhU6eUCpIEujI5qCoBQl78MjpjTEY6kmQy8UBcT4MqdsxejdnWMeKk+OSVTqLoMkk9osBACmURUwlU5hBk3Kl0IZJIIZVqXob3fWlR1lqwSuN9RKua6FOO9SVm8mESos8TKKM8A0+tlA9Clh6IxhYtqIBVQrSZIG8SJKNwLlAbS0/MXnZJZSshkdxVkkpgMXBO8pyEgQAsYDSkFHIpVko2HgJRR2IsRTlJWTtNYt5MohAkC7iKLplNTCQXEJMD3/wl5YlqUw68cufpkBEVpeiYMzSVYr5mxUMzi+MKyeSgTPn8PZkilqolQdRZ1TvojVQHWmWoXGdyvtY6m50KGx5bbmhQjKzQ6wriHKhAd0RjeXSv46f+jCXFNR/8xV2++EnL8dIyMgmNQ2pht64Q45gtA/toRkoxD4bx0vP08ZLPvzLhxt2OydgSmpapaG7O1jy+u88rt1aMzyfiynEyVhzd9qy8p10JRiVMTKhxIJ1UqEqYTzyVEy5c0HAz0S0inXOElaPTY37h+RP+yLtq3vvmBRPx/Jqc5yN+wnf9uZ9nZwIujRnbQBIDUWjDZ19A/0M7UkEyN4jpEJcMmxTZ4mtozBmes/1Thi6BpAranJEErTNKbCV3VPlSMk+AVoogmcYwUFL7GDCS0JJL3qLyyqEpDSEF6fVEJORu7bwWDMnK6WdCMu9xU0qMZc0SRWm1JlGoK5LLwiJypgP+s5Vvhp+T8+f5+I+/iycvPc/+3ldgL/4ML8kNdsKjuO4Y5YRG7SO6ZhnmeU6F3PUck8PGCuECkbuczGtMbdjXe9T90/g7e+jzkbheoE2W3Fm3OWiczRyXL4+4c3NN3wghOCTmHtHJjmJ2u2b/XOT2jYpLTxns+kU6PyaIIybFef1R3PI5VNCY8ZLlsqdqcll5CLSyxEeiWX4/Mr5INUq4cMT6ZJxR+XpNCg1KLZE0ZrGY00xAR82qXxC9JdlzKBVJKVuUiTgSJyRpeOTCmide97e58XQkqp6Tu4an3rni6OiXMYBtniekx1GzqzTTa4S1Q0aPwmpObTV93L5GW9eOs52gm6BZNYUVmMe4ShFTAvKxVVSV3YzzEAKLtmXlzr7uMB4EzgRbr6aMp+4Jxk4bS3595b7heQ8K7l5N6Xtzbr+ud/8NPiKKWlX0Eb7qCxrOV4awUPyVP7XHE9MGo2FkDI22+Ji4ctDwW9/imdQVY5WoGps762wmmF+yikljmbeen/0fzvNlT0V6PQYiVguxlM6ytEJexLTNJUAxoKptYTwyL0tnw2RjTBb+zAI5BUaEFPyZQdjHiA9Zg6n3AecSwRXiqsuCpdFF+r7Hd1lraoD+iR5xRTeriL+l4EE8IbqNJIMftJACxfQ6ozBKqWKBk02iex8I0UPR1DFKZ0J7o6gbizIGbW0uPdYZkUspopJCfJE1cAnvIXYhNwok8D7jQiGEXNZLgZQCMfaQXNGmyuWzQddqQ7YlAAFRoXQNKaLOnCBCpEpC7BymlFmSz0FRNpwORRw0W2GkcGrjI1Zv5CjyJD+rq5X1vXJASWmMUOhSSi5aa84TveD7VDSzIqE0P0gRN9UyBKm5g0YkYSuT9WpMJn1XKExtUVZD6f6kUqQGqBSxFjA5AE+qoZIVbq0xeLSpSSrinfDe33zI6EBT+cDJ0rHqEs7DSEE6sXTHwroL6CDspY5DLygXuHsEi5nj1s2WfmnoFrBc9Eymius35pwcCeu5sGyFsIb5zNO1ufkktMLdW5F9F5mlFpM8l85PCbPE7Hakm3UQLfM2UFUdf+ytY77k9YcsfA2TxD96MfI3/851/GgNYgsybFBiSFphjOVz5digMeksclMZgy1m8tvUhlNdt1PeyZlbGaPbN6MFa1QWwVVZGNVqTWWy0brVGqs1Rmdyfa1hZDWjylBXmsYYKgPabJfsijBkkUoJJFwRGQ6FagB5IwshllvW3QvFazaE3LkcfMrecluf6X7lwfttVNvfS5AFO5f/BB95oeb6iz/H0UsXubT6p9iTf8047KOMprd36MzLoLrynuRWcRWIqiHGS3RdS1Ir5qsj/PI8e2qPax/PqK01hhBcRvURrK3YP6gxNrG3u8fhrdnmfCpjIUX2dncwsaKeBmaHDbL6GNpEjB2hm5Zz9QcJrcfqlsrWxJjQ2mzQO0kpVxYkoo5ewvAmXEho1+FdR7tsaRc9Vk1ompr1EpyDnd0KI4JrXeZ+2iew1qLEYUWzVp71asK5acM73/B3OfzUf8G+v0DoNbdv7rNa3qE/nnD+XI2SmsQ5iDcIcUyix/McWizJP7oZtw8KUoYGj6E8pyULQ+sU0UTGRphYzbRSTOoqV0+8Z907Fr1n6SGI3tzSxvMxlbLj2ffaPpcH/b75933Ki59p7N0vgHs1wdmrDeBek4hWUp5UOcadhRj5E1/j+VhjeLy/xUvdksZqaiNZxDQp1i7wHb99yrf9jYgd2VPycsoIR7AK41v+y99/wNUdxz/+tZqq6gi2JhKoUiIYKSWuosVVrpSGYsIqpW06Z4VaZ7XyJGy6z2IhokcfNjY/3ufAwvuM0oDKG3VXhC9jRk1ybTkVzlci6lRsd9Sm9Jnb/1NGU1RGuFIqgqacBhhiKMFRNsnWQeEpQViUjX9k/qBF2bko81JK3blBThELxGucIpZgT6KQVLYBMlrnYMgnKpO/8yGwiRGiFlQSgivt56TShh6y76IqGlQqUUnpeN+q7VMaDFxKoDIKFcnlPS8ZLSIlgh88GIFUSMc6XxOG7k0/qLDnazbA+Pl6Z2QgxoRSuUycrXHyd0aE6PJrhcIPQ4G4LLuRycSD5ppCSeaDWS2l2xCUMRnN0BmpkMLpSiXL894hCWoTiKZiYs7zTz4Y+eZvnFHFKaGXIrURiC2MD6C/behWCU/EROGcEY6WkeMlyE6kCor9BIeryEeXR3z+uQP+9fNHVALjHUVY1fjWI8lwfJLQdWKWAmGkWJ0k0hicCJdsxY1bay7taOxoxLkx+HlPXEVqNCsrVCeJYCtMWjFrR0g1Yk9H/uFznp/4iTmSEtMAYtOm8zMWtCz++hLO1+RhB4svfbro5yxdSmkwkWVLTsdf7/3ZzaLcH71s1qLcmVo2uBLEIIIqDS6D13OMkarOtcfBX1Aqi0ox0yLiaRKYO8xy0nL2GhTtQS24sK0XlzdTEdmgxBm5y2Vw5/rybCnrGKhQ6AgSPm1zFMn+r5vPfc+mGNeO3h8zvtyxfw7G65bFje9m1HjabobS72Ak307Sv4eTHcBplMuSJc6uqdRDNNW76fWPsaN3MLphrj7JcayxVc/6mcT6fMXlR/a5s7jFZDplcbKkrjSLuWcyrpGDKe2ypRo7rK3o5xO67hajBlpnENPDtR+gOfhanLtLMiOmqx/mcBmRYHF9FsIOocN7GI0MfduhESyK+sIu1eWP8MonRlSuJ4Ydlv0Je80uR0fXib0j+I5z56u8F/SK1QmY0S7r6bei7ZSeWzSMOdC7vPNdP4l/4du586EZ2l7i2vom5w4OuPKeOxxfu4jhkNZEOvcuevsiSj9J1HOU2eHz3YhX5BX6JqBdc+aaDGNmOwQSElI6tZvU5cTdmKydGHNjWEqJu2uFj8VDNr8oyTa5Y5vTccQWijaIsN57yFYn/macDOjxMI630KZtZOze5onPhJTd2xBw7/FqmkFOP8tr8NCqpjIadI9W8J63e/77r4n4tWcqE3SMaJ21MyBP4Lc9bplOOtRIUY81thHqsaEaKcaV5Y1vVfzl3yW8+FKPGq1JZoxYMEkjlaBqhdQJPQqYcUSPEsZmhEIkl5+UOb1I8QwiI5ACKpXgIioImTTqfbbh6TuPayP9ssOvfUZEeofvPKEPhM5ni4fBKzEWaYqCtInO4o64ooAeEs5l3FVEGFpNExnSGjJThc46UZgiSJkDwYF4O5huQl44s1p8aRAw+aZLFi5GlWxcsjaVnC7OufwGRlH4SsMAPEWXUipdjeRyZExZ0b7zGe1qe0/vAi7IRg4h+hzcQkJiJMZTk2gjlIlZypNlOCcpkysmVLmFQoZLIUtwDJpDie3zzN6ESXKTQBiu5fD5tFCWCRK5O2bIUFMqaKaYooKcyy4pJVA6PzaUzW2LSIkuemQJdFS0kM2XnaPtj/hTP3mE7S8Qpc02SMea2GpMEpJV7O8bag39yrFuE0erjsu7FrOEo+set9ZMQiD2ULUVn3xpxj6K2YmBaLBrYXmsON9EbhzO0SIsZh6csF5CNw+YTnju+RVTaZi1gckoEXyHOhmhk2YZApWvOG4dowbmRzVWHA9ZwwdPDH/xB8ew7nGxYuWazC1J2wuUKkbHnzvHhiTLFnE2j3wGhfZt9en7kXOHLsAQBnuxcMZ9IvMWU+a8GFVMotkovg9/M0pQ5C99W05omJdaCbo8TsvpfBgCvMgp6pZSCYq3dLe2ycDDOsCgcp8UMUnxTz2L3m13cd+LIAyff7xzjsfGv4md0ODjLisUZrRkJjOa+T6ToxeR+X9N1301k/bcxkFCKYWXliCCMRewVlgvLtL1a2w4R1xptBoTNCzuwuHNRRbSJdFURQNQanxY0YyE1dJjdU6471zz4PZwix1GOxXee0bqFZKKaBxa9vCru/gIyduMPpVrabUpAakvGmagLuwSTircItD6LCUUvCDRMJpAtwZrDdYK7dqzWgiuB1s9ghpdLULMGTnfq+YsXvmzdLMZpq5Zp1sc7F1hdzdzBaeTRNtGfPd2Qt1Q9VeYywtUUiN6xa78SWJYYeUK98oXDL/nawUZdChuHkYxbhqaqsKYzBteec+iC8xbTx/zyjk8V0io6CGduiPk5CIBRf7ms5TlHoRCDWP8s3GyPlMZ8kHPvxc5e7XHa3J1q3SPVSFnZUp46NIE7S03Xu7pug5RWeCw91CJYETTTHreeTUxGmkqtaYZGXQj7IyFuzHw899zHtX3/KUfXrG3t4+tFKPRCLUTkbEwqoWq6ZjaGjMxNKln2YJThkaZ3N0mUsRny4a+gfR97r4rwU8ul5G1tbpAaD2hyxpVrnP4rs/BlYsbknUKuUQ4lBuGBUxLyN2LWxtR8onBVFUkISpgVEKpHIAaU2EISMrNABmBE4wxuSNo6PZTkvlNvnCZfAIfcC4QKZyjYqWTJCNEeSXPwZgyQrQgJiM3SrMJyk6lJIasIetlCKfBiSfh0mlXVuizPZHrY+5wKnY1WYE9I07b2UkgnTGR9QM6OPxdC6HY2+jSSB8GdCClorcDqFy61CaXg7UYtJhcTjYDipSwxftStEJJyvyYzWSLGCHbYuiIGMGFQJLsx7WxTCpoW76cHvGR5F0OKkmMVM/1deDWsgYXmY6E7/grhyiXz1WPAmqVEJ9tc9Ce3X3F3sgiIWWR0VXPeFTRzTy3jpYERjzURPoVnDjDwyPo5ktu3WlZHjmObnc8ud/Q2Cm4QMWY+bKlWxiWM8W52vDijcg0KXzUHFwAv7ToVeLmKgKaGzcXjMcNVRO4c33FTmO5e6Xn69+p+fPfGqn9PmahCF1PcIH1OuGdUNvIGx7eYdmfohqfK8d9SxoPKF8M5cQzJHNASS6v5gcLKbIpWQ9rRKYvqE3QZuwg6aKwxcdUIdwbGGVto8GNgo1Y8hCsnQn4trgu22Toe8950JI7vU8VXtLZIGqwshooDkPQNdiJOedwznHMp3DNm3Dq83Ae9nfniKvYOXmCpRwzMx1zvcTHX6E5/CNkPqqmMrZ0KYPWO+hKYSdzoizAzRnLimSW9HKeSna4fWNFVcF6uUQplb1pOwjR0bslwYPW0K1bnn1mxrMfafjUJ+fcvVkz2lti7K9x9+QTaMlelBLmIDVJOVyfirwLWGsREULIPE/vPWnPszicsTpasjiu0OqQqjas1x0Hew1a1dSNRhtBSU23EIIHq69gmn2UjSg9praWq294Do4/zkrt47t9umPLwdiwXiRCv8vR0RGT8ZR2/hZieJSRUoymV1kcdUzVm7jx9N/lwH4xfTf7DGN6+FcOxo0xVJXZrI2x6Dt2QdElhRObm6/L80yRsrGE0+SDszpuDwpiXi0n6rM970Hv8aD7Xm0p8UHHazLQMlZhqyxyaAxMraXvPEEUy+ARLJ0Lm+yqMoKWine/RRFjTydTWiV89Xse542P7vJz/2MNfgknU374mSZPxKrnbW/f59u+4XWc1wpjE29/W8V3vi/whodX/NBfmbL83yb87i9Y4GzeBAI58IgpdyUSY1as96GU6dJmUfEx4kLIN+fwvS/8K7/xxsvIVNiQpkMIm/dQVoFNqCb7UimrcjmybO7bPoXD4mZMhbV5gdHWou2g31WkIETQSRVifpFr6P3p4lZ0S4aWcCjIQwxoBUrFTeChjKAs2Ap0ldA1GfUbfNbKwB4y71Pk55R7kj3HMqqIy7yOzoWNb+TgP7jheJQmASm66zlrz8icSqDLLZdTE8pHDLK5b/jeRA1isGwQLWsM2uqNEr4yiapSKBUxRqEtiImYSlCmlFhL16AUuQ5tCkJlTo1HB9X9Qd8IlRBdUA0FkRyUZzAtcWclzP+GYfY/tfzM98AH3ic8K2P+zN9TSBeQXvBWkfqIXlti0kStGO3WHIwMKijO1ZY7syWhqVjNFHeOFiy7micOFJdGmk8cJ3YuVtTNGP3whPqSJl0Wjrpj4rkGSXPuJosXh8Q5N19JjCaRrl1xsGu58ymFuQ3P3DqmcZbr/8axe9BgLjiWh/AVv2PKlbfVvG6/Ztc4/vPvS/zAdz3GtfYOYXVC3yrm62N+yxec522Xa375wy8TYvvvaHX5jT8GtCqX6DJKKjLQA862nufARqHlVAYhE9Y1Rqkspqsjore4Jii0tRhToVQOwpQkrFHUlWJUCXWlqOyg0acxtsJqhTXCyArjSmXOllHZvqfcBtcIYzWVNtSS0VOLQqls8B7KrhlDFjz1LuB6j3eBGIQUCyle6SyPQuaW9i5LwfggOA+9S8SUOaOrLpLYo0tHxKRRckBIAXeyw53dJ1DVd2HWidW6ofVznL2Na6f4fkVcAXd36OL3cnDna5m7c7hRw+5iD60c62YfiQalVmjT4LXHTBpG4z0Sh2g5ZKwadpsLaCwxLNFph+BbgrPYumE0qrnzsqE7NFy9AKPJTfoEL3xiyTO/cJ4LlwNXrv1Bpky57jrmPmBcR/KWepXAgChN162pbI9bGqzeJ3hHs4DZtQYbe9xJT49mt4mMdhV3Dnt29y3R9HTegO6YH/fsG8Xdy19GzXm0WXNFX+Opt34cc+s/Y+mgFuHkeE7vHJ3cZTzR2OUJVX3A/I5HVV/BWr3IcT0n9isuXXmIR9vvoe7fztHBXVRqNu4hUNbalANwfIchMDKakTU0GmxKnKzzbdFB66U0hRV/XTnlTSXJjhuB7Gc43ERrZIuScy/Kuyk1k2kbg5xISNnRI6hEULlcnUoTBirTYobPso0Yxxg3Cc7Z+ZupOBT5CjY7xakQ7HAbpC2Gfe9Bx2uSowURY05tdEiRtZfSOg+rpcPuGEIIWZTU5E3rCy6OULGn0h1X33qRd5x7jDe/83k+by/hveVnfv6EprGs04hJY/nSr3iSpxrP3/vRF/n+Pz3l6x5b0mH5jlGDtA7WJ/zDXxkjXY8EEJ87cgKDsF+GWKJRpBDOZHaGHJAlH0vZqiBR8ZRUnyQHLFGKtIDWebO3iqoqBs5aZ8X0CMn7LM6WJHcNKdnS49Kb11ZKUSlFVHmAxxSyrAS5DOBjIJXyAzFmc+OYNggSgK40YSAmqkIsJ2UrIZXnhqnsaZtyYvN9MDwu5VJlKJ9XCm9DqVxaRYQoCp+yKKBSeYGOMQssBhnkIsjkSCkdUEqBRJwA6IwgpkKcL+KCCogq5TFTJpLeGPLm9x7m1zAJB9scirDpUNJLKUGImZemtia70RidbYqUIs+mlLLI4T3zbijTUEqOWY6jyGZECH02LO9XHbQjuvEJl3Xk29+7ywe+uqOaV/gFUAvGKmgScRaQVhDRROUxVri0b7h1GHjowHLrEOZes7q1ZG9fcdcJV87XTE3HGx5rWLrIlZuJUYIL5y3vff9jXLt7xJVHztHFNS8uHW9+3XmuHXUcNJpfvdbx2x9tuHtL+NgsMr0Er/zKMe9+7y67uyPu3F7y6Fs0Xnco2yBrzfVf7fnonxPe+l/9HJf2dkihYuk6/vh/+jp+8J/9G1ZrnRsW+s8dktYZHtIWQr1NCP+09vjSTbidqORxz30fn4Zy/5BlJ1VeQ+XGmmEclzKg0hTu4yCly2kX4Kdl6uX9pCiNS/E0LQlDOUOK+urm6SJCTA4SxGytcPYzkjZr4fD4EAKNafD9nOPZxxhVb6LHUY2Ocb3GqZrx4QnmyXcRjj/OyY2359eRBb69gJgWbQIxGWZzGKWP0KyFav3lvHzhgxAtF33NFM9axxzQ6swls1KhDaiuIfWJZz+85qEnDcFXrNwJtWlILjHvl6W7u2F+7JBQ41uoTUTZHu9OePbDUDcf4uTFd/PI+X/BneuBfvUQMr7O7G7urDU6Mhk1RL9GYqBfLRHglWfuMLsdSUER0OxM9+i7Q4BStQAAIABJREFUjroO4BXLVct0PGLddkx2arTdYy6KffkAs/WLvO3tL6KO/ybzF/4h6yONhJprN4945NFzVCOyD2wMvCSK0dERLy3eRf/6r2XHj3l45xX21TexvvVRXnxhTHf+faSTCTa9hEqjctXScNFRAs2oyuMqJjqfxYxTyg4Em/HJaTO5AKdX/Z6Rdg8PaoNqbV6FMg9Of1dpi+9Y+M33zhHZFnlI5X/b5Pdyd/R5jqh7z0E9mL9177m+muM1GWhJyuWsGE6NlVddpA+eSWXwXrNuHZO6IcYOKxVKdVw9Jxx1Pe9671P8gbd+OW+/+vd568MTok7ou/AH/mmkGSfCfMVk6vhP3gF3PvzL/MJ/O+bNT3SktcHu3qaKV+iC4vkXcwChqprouiJFkL/0mCMVlGRFZl0i6DMwOnlDVkmK+XEiqkRSCikSB0oXDSYNYjRK29wJZHOpL/N4EjEqkmGT0Zq6yUGWpphslwxA65x9KIUuyEokoVUhihdoPalsPtt3FOJ37qwbFnbxBcELuTNm0N0VyeU022Q+llJDtJ+DPR/K5yWhY/6bMYM+lZRWdrDJIjErpofe58VZcua/yfBLe7ApwZhWKm8gEsm9qWx4KpC1WFLcMpb2ebMYfAYxOiNhkpEnSSXjUiAmYcmoV0RlG6DCuUtBwGbNr9MJmHkIxmR+TFJ54mf19MzKFwO0px1Xm5KNzrIUMihOe4geQoxcrho+8Ldb/tofnWCjxR2vaNuG6lxEe0tsA/3cYyqLiAIXoVeooQFhCbtYUjRcmDhsSMjFPW7eXnLp8V2CinzRE5d48bkFT16xvFw8CLt5RY3iLVfPE7ziR37iiC/5wgM+9cIJvfeMx2PqR0Zcu9lzd9Uxvlhzcs3wVV+5x/x6z9F8zoWnKly7xOqG5cst12+2JL2P+I7d0T4rIl/5pY7f8kVT/vrf+iSVNnR2TR12CGH172Bl+fd3bCO5D1q478dT+myvd/qc7BCRgx3ZcKjy66ZiAj1k7gPRvWxKSZ15XUpCm99/QBRS0YejBFv3L4uqYVPMJElKSoEUnbwhxtuQmGOk65fEeAcJIyLH1GNhuRIqO4XUo8/1+NmStH8F/dwRTM/jOkeIaywNPi4I8YjJuCLQ084uUPc/x7T+BRTvobGRuQ1U6+IqURpmjM2yKjo1CAu6RaSd1dQ7YAwQdaZ/CGiTCC5lr71V/j59OybpFUp52mUiOKH2v4bd+15WTpPMDbRAiJ4UNJUR6kpD1EjMdm7aaFZHkeCkcMJy6bLt15iUk2GlFK7P1l+pr9DxFvbCNzDr9ml2ZqjF93Fy+8fY8QqnIzdvdOzuTZjuak4WK+p6zHw1Qy12efa5GRff8s280oxR3RXM7DfxwrMrWgzWjpnI1yAq4MeTTCfZCkI2iJLvGWSIQiQn4oOUgrBBrjZj4zPEIw8iod/vcf825PPP9l73vtbwc3N/ymP8vnMw3fPzVRyvyUCLYJHo8SWP0Evo21LvjwpJIWc7Eqm0QitPUBVawx/+w1/POx4zfNPFH0KPJmjbEWPFP/jf13zBu9/Iex465Le9oeVNr9do80vEJ1TeVtcZuNf+PCwd9Qh+6Bc8tp7i1h0oS1IdWhfZguhRakKMjspUaBWx2tLFnpgaKhtwA/lZK0IMWWJGsv5MGngRJsOPYkq7uxEwmkZMzhRVIiZF1ehM5NYZVtWVzhu7KBIJ0Qmt9OlCVymMFrJ3X0bXBggVcptt8Jlgn3wkSEaOpFgL9TGhO5c9AilBhiSSBLSyuawXI1IsFBDB98VUOyl6HxlXqWj55NQ3c5MURENM3Wn3SkHONqa6Q1avNCK5zVwpvXF/TzGT0lOMJFQmnYYsrppJt9vq9gKSF6vofQ7cyecbS6AYJWCSypCzAqOEOmbZDx/CZpZEycgfZBQspcyHoXwuheBUwiQh6GyEGnUuD8UYCzqQyBGzIwRBfGS16vN59Z5v/a2G/+5boJs57gTFRBkwK+7cNvguMh0pptOK1HekWZ31jCohzh3SZzX21VpY92tEGpqDNVZXPPmmi3zoI4dM9Zi5W3JJKp59+pgLV0dcf2FNYy+xePEus8Uuqb3NI49aXm8tzeNjnn5uxYmDZCOvLDw6NvS3PF/yxA4nL0bMFY0dC6wV3XLE0fOBZYosdIPpDzn2kd/05Zd55xtnpL7iL/+tO1zUI1wTsX6Eoy8WGp8bxzbBe3sxjvF0XN6Pq/WZ/na/9/i0zaFshKFsErJRJrpXWuHe1z/9ffs18/kMn6WQ3TVFr+v+ZOIhAMvBVl7r7n3fexE+rTSzkznBOZQ1xFVgVF9k3TpGtmcdE2P9GG65ZlF9HRz/GOOdffrQ0nYGYyZUTZs7Hkdg7V1WccTF/n348P/i6keoTCZtKxTifeFu5QYgHxUJj0JxfCNxZZyoVJ3ld0gYa9EV0CaMKObLnumBsPSR2FqkEkR6fCvYTrO++wMkCcQA+HMgx1mipnjE9r0iekdMARUt7dITuoiqpTTgJJyLWG1oQ6Cua1btjLoxrOY9VYI172NyKXDlyiuE2/8UVktc3IG0IDnJlZv2BGuzl+38pOKCcbyys8OhvJXX+4c4/9D/wtP/5Dp9M2Vee/bWe1x6/C20wSJ2gVYjzkYTgxVRwYOE0qxlMn66pYWXx8LmaRuh6DPjjm1Q9f6B1L8tJ+tBj783gDt9j2G+bUdPiYGM/2rm66s5XpOBlsQWnzSgsTEQQ84sKsn+eqq0OTvnqLVFo1DGsf/kJb4uPMNXnr8FxhDFI6LQC/g/r3v+0je8yPkmce5ChbIBP8sSATLzMLPUVxKyzmarldP84M8KBIeOCqccdmoJrafqNKmpSDEQg2Jtcku3ImEDxJErIqenC6LYjDRpLfgU0VlxNNeIC3FTtClCdrnenDuxsqWLDwFtK6LLHYMqZWkFJOYuo0L8HIKv4T9UnrxKCqJF5pudcsJLWbCgK0LJonQOPDctvcPnGH6PYJRF4iDEmrV0VFJFkVnR94GqyBvUdc0wrU7LhwOqJ1mBfTORi4hiOb/BiFQkCzTGmPAJYjSnOj0hf0cZnhskLOS0+2kI4gpvSiRn5yllzlYkZgRQilm2Ar+NjjFIYLDpzMqlxHKekgOzXGpU4Iu+kTrlAEhpGBClSlat6ELMXl6dp1aG3/mkoNOM8e45mjDj2ksVSlmMdShlWK0UiyOH0ZpKOSpfoU96jLWI5JL1dA+imTJ3PY+d20EaTewsl881zK4ZfvjXlrz70RYVJtAZzk0abs6X7D5qmdolbvci6+stn7y+YGfVEduWb/gd+/zUB5dUV0Zc3XPIC5Y40tjRAlNZ7COK9ScifeW4c+JoxwZDz0pPuH1b8/4nj3hsOuXGnZb9FIiVMFYjvG7RjWUx/9wJtE6J30OgVdCbEv7kezb1trJYp/su2mcX9K0NSHJJZ2t0lj1iGzUrAdamBDMUdIbJr0qUdfZ9RaXN3MsGGNsN9Vn/JRYHhO1zlK1TFHT5/fT1T3ksw2ZGoR7UTKdX+fCH/yVvf8v72R3v08UTRBlwAr3QTmp2TqB97AdYfehLUPULjEZNVk7vJlTmAm34FHsJ3LiGKrE6VKhLX49tfxdiMoqfQj5JLQIqYipwoScmqDQs7vZ0FxWmGRFiS4oJsQalA14SldUQoKoSXXR4Z+jXFl31JCIhJpbXr2EM+N6g1YIgEa00TaUIvsd3kJzOfLUO3KoixpYYI3UNIUlBIIV2HRmNsgWSSMJ3CdyI0e6X8ejrPk7l/gVxvSSswddLQlthTKSpE23Xsb83ZdFFoq9ommP06EuZXHw/V+s/zr/6J9+HxxC0Ziz7VJNPMqt+C6hrmIWgm7Ol38BALVGbsZ2tnnJHt9qIV5UfwzC7BwI6E+Rs0cTvTRyG++4NwO4NgO4XQH2mAOjeMv5w3I8w/yAE+jP97X7HazLQ8t4QnKeuq3yRAkU3q+RpURAVmYwbfN8RtaXqLNP+Gl95OddsIwbVZYji5U/Ad3+jIpie6WSCrYS0jiw6aJRhOe/o1olLly3BOagqZjcSt/p9kBapIruxpnNQmZ6TkwOuPjHnA9804T1vdJwf1fz4jy/4b/65gnFP3U/wclZ0TZuBW5RotCWlhK1NThFjJlAra3JgkUD5bOFy2u0DQqKqzEZrSyhQvOTHKlGIktKG25NJhpokIdf/t7wHh+6lsLXIZnkCGKZGlj84HcwpZfPrGErZIcaNbVAUkCg4HxDROZtWkPBoXRFCtyHubspoOhMsjVEbT8IN50MPBMYcSGkAoTQRCEPXZQzZWiilBDF3vJAyly6VoFCboo+VMnKYiJzWQiktgOVaxYFkmUskgxCpqLzQ6FKaHTSCJBWCaMqK3H4IJk0uDxgtRSF7wBdKI0BSWVutDxByzLkOkT/y9xM/876LmEUPssejn9exOkwcvmgwUUgS0T5bNC19ZOVAB6Ge99TGUk8b6p2ebhnou8jzn/I89vqK29dP2FdjfvXlW3zpk7u4mLi7mHP3kzXee15/cY9PvHzC2968x8lLM47v9DxxteE54BE/4tZLPe//4ot86KN3+LkPB77oqQY/v8vuU4/B5Jj+mZ67IfHy8z3m3AT6Fa7OyN2b9y0X5pHg5uxeXfO+r/ntjHc8n3j6BVZ2h5svzvjiz3/8N2o5+fd+bHfobZcQtzkl2518bObc/Rf//MvZ94gxbjatUFBkRZkPqIIuDcT8AVka0KytjUyGv2etujNdkOn0/M9uaMNNUFJtnp9fdGiCCWe+gwH1M8YQk1DVY772fb+Hn/7Zv8Nbn3oP49Hrwc4IGuruAjZ8jLv7Vzh/fEj6mmfR/9fvZfLID3LwMMRO0S07VCss6sT51Rq6irjXo26dEPT/TLPb0OGRmJuG0NB2K/YPGq7dXqDCLjppjD7izqf22X+4R1Uufz/egIoggbrS7O2ZHGo4T21rIsckPyGGwKoWpq3F12skWNoTTRsdO7sV45FmfrIgrBXRa7wzHN1do+x5TOpYLVY8/pYRq1WLUpZ+lbhzq6cZOXb3R7TrFSqM0K97E1e/8MMsn/uj2JMlS7nAPByi7uxxtFjjtcPWLdYYTo4jh7dWvP7xFb/80wc88R/dYvVThg+FKSdVRuMO/A5x3XLHWh7eS6RPLZFHJ6R54fiVy69TyiT0ZLMUUALElRKbJ8Wh27yMSbUZWagzKvNb6UG6T4JVqjKn0kFn54Eu5exNkrKFkA7HvcFXjH5rzqTNe4icNi4hw+bDpnv9wUjY6fFqUK3XZKD19IcbLj4EOwc9Ux2QpBlpwQfwITGqioK5d0xMzXzZU+3U7O2PWKeWqp3gwxKmgXq1x061Zp79cmimAd8q+l4To2bdetyqYpE61F3D/p6iVsJf+z96msbgQ+YytRjCaM1f+859ft9XLFGyiwtrWDZYFnzRGyuCcti2orcteHvPBUooo9GmELN15mVZXWUBUJ1hbQRC2ehTMX0eTDGHnyJZagAoLdG5dLhRrDc5oBAyxcqFhIRMiM8eYGmjoJzSKW8qhS3+hcr8KZEiTyBDW6/KgYMToi5t5ltCiill6GxAcYaFVW0yl3gmm5CiX6WlSC9sNoTiJ5lS/rfWlEIkQYCYtVdCiMQAISYoWj2a4TyGTExK2VVhN9o9pT1+4FPJICx62oo+EPG1PkX2tCiSLgFclKw1FmWzMCgpgqUKovjMhRtEUrN42CnvzRXR2pDv98HzzDLxZd/e84t/foS6NCelmmrsufqGmnbekpZgnc18ERHaGDA9tL1wfKuj0i3nH9/j4pWe6dKAM7z0zBFGaZ47POZd77jK8zfvMruz4FaX+MD7r/Lhf3ODue946xsbPvGxBeOdijc9Fbhzq+Wt+5aZsnziwzP2vmqfJ67uMzp3TPucZu+NV5DxnPYV4YVnA88vHEwMk5M5+49PcbSMDw0n7TGmqdjdCcwufymXq1us1yt+61c/xEO7l/m8N7yZDz39f/+GrCX/Pg4/lJeH+S+Dw0E6ba7gdN3foFxbi/iQjGyTx9kya07xLKlcoaEIl+oSQaWtoCqJQ7Jx6FapsJzHRlxrO0O/l8gORVAlJ1/qFIkThgRKFeR7MHkfurYANKZe4dsrnIxehsWUx90B7e3fQXfuP6ayX8XhrOapx7+Il579RS6//gXq6bvQPhLsXaK6xCSucPVVDo5fYfbm70S5f4awRqop1kG9u2IaBFclYnTEFqa7PW7ZE9sJdkS2GBJDt3LE5MHA7iW4+dyaGkclNa5fkHqDGE1ls7io0TlgjcpDHZgfavb3p9y6e4wxNes+EFLPpKtYMye1OcqIMeQ96+IaiQe0S9irp6yd4tbJkrUoqv4OtR2RWHPuyh4vP3/CuEocH3Y0VhFFI7FDd6Dw7F++Ai/+ddR6xbID354wwnJj6ehPWp546jGUzFi5GZoRO6M9bt2a48ZHvPL/tCwWGh0X7KiaTsNJOmYtC3Y4hz/qMJdAnTSZTzyMN8ljKCWfE9E4jBhVxlMJRreQWbU1rtN9ApQ8bk8fs+15KUptnAiGOTKAEFHShmqwPYe2+blqK0BK91Qmtt8/biHL2+Ndna2nn3le2rp/0Ab7bMdrUt7hX/2ru/z8L3V84pMTQoE4FKclpLoSrM5cLaUMTtnsteUdlZ+QFi2uV9Qi+HXPLFYEW7E7VWiniH2X7SVcwHlPivmi9m0OKkDzKy+uSQJWR6woGuv4R3/W8AfeDSI7uOixvUaU486i5kc/usSmmFXtV6Zo3eTPo1QREVQKURqjbUawikRDtiM5DZxijLiUcKX0OKBQoXC+tLYbXHbDy6Bo3SjJpTDZFgo8C4+e2t6czaojaSP2uZ1xb2fLw3sOHIzhcYNOz4AA6YEAKzqryctpe+326wzvPWS82/dvPl85hyEQPF38hczbE1LSGWn7NInxXM6TAQGLuUx6itKlzf0pnt5HyKXH7e9H5DSbzwha/r6ixA0ZfvuaDJ/DFfHV7dfaVkAeFhCrNCbWPH0t0s96OBoTnccYS9Jrml3LaHdE8D24BN7ThMwB9K2j7wMntxWL40jfVnifOJwdM1Wa2QqEipOjE5ZHgdZodEj8/Eeucen8lIujCT//sWPa5NmZBK4vDD2aMG243c44qGG5mLFzwfPIwQEvmzVSL6E1nLzS8ewrnpg68IHptEFCS+o0bd/DWGHxiJlwPe5zThmaeg/lI+d3K376V36Gl47vvpql4T+IY5hjD7rBgB6dbTc/HYunj713LA2/D3p12/pbQ2KQH5M+7TkZZSqB0EYolVJCzLdYLLxSzJWD4fMMx+n5pK33GFCr041xe6wPt7snI4y/xvnVOXZGPfPdQz727L9gfe1P4A//ELb/fkaPfQEPveO3sVoIafZD1KqiVvuoqDFhSjLHOHmESfOFWKvAV3TrBbY5xNZjxtMRewc7HJzfZzSqccFRVYZ1u0KKbtXwvXoHrv//uHvzoGuyu77vc5be7vbs776MRjPSaBcSWkpyWGVCSEgwFFsBgRCgnKRwGZfLcuLgKlxQtuNAIMbGpmyDjQnGFdvlhS3YIEWIYdEy2kYzo1nf/dnv2tvZ8sfp+2zzzkihgjzkVPX73tu3+3Tf+5w+57d8f98vpElC3o9qDiFEkunZuOnmrfgb1LVFeIVtBfi4TjjjI4wgcMSFaDoH1nuOaHO8B60T6rrGGmiqltmiPHL4tMowpmFtLSNJS5xv0DrDIRgMM3qFQpLQlhqFwLrP0rbP08wSyllBWwvG24FqNkfIjGJQ0doJBeewfsZoVfLMc573fNl1Dm5XqMTROE3jPN6MED7ibHurX4XIdqNWpNR40R5tgUi6Gvz9qBDOjo37p+S+0DTb2f5Ovl/id89+drL//zfXEcEdbXh79HrJLbd8Tk5uy+cockOe2F6mvSIjWo8fOp7dn5F8esLP/eCAQgkGaUppLDiJdBJhHPiUpmrpG4fsZ3irCY1lUUtWLiW4w4adnSZarsay0u/jmprap8wPPI1tCeOU3BiqROMbywWdgYXfeyEjwVFbzU//RcU3vjXQeE+QDu9aEqcwbWDaCG49u+Bnf1Ng20gq6b2NlWR0USCZHEelpMBLgRagRIJpLUJ1pKEhPsCRvTzK+ahIV3xUmYh3BBlwUeniSJRZIUhVJ80TuqrB4PDO442jsdFKcc7graBtfORx8jH9eOwgR9yUEHEBDyFELS0pjyobvfBHLPJCBrwQ6K4iUCXdEFQOLTTSATJy6QgvjowVFyIQ34eA8UtZn4AQ0VsK1uODx3bcVCqEo4dMEUO71kdiVU8EuiM73coQ5UTphHelUkjpjhYIlIzAehli2fpRmnFpTtqYcg0OhMRahxOQeoXXIHzExSEjge0RUD8E2qDohQVODKgJCGzkR5JdetSK6Ek7aIMDdBQCD0uDNmW1V5O9SlDuGvRYo1YgpBlCB0gbkgsJuIRwyzDbj1QaVelIVErvimbtYcnerQXlxDPZVTgMadpHzDQmHNBIzwPDPtva8Py9KduLmmduO9ZGIz63e8gH7qR8/3uH/MZTNXf2KtI6I1zK2HrecnBFMrzs+NNfvga7ksd+q+JWaLnnZ6wO+lzrQ44kp4edzzFzwWLuePC1PX5ve8b7yg+SDCU//LmUB1/9Jn7lY4+ztdE7EVX5k9+OjIzjHUevvYhCX6dSb12K/kXndynFZTu96Jy8oiScMNwR4j6LTnjJhUgIdcKxWt4XLzrv9H2cABHL4xNO93FMUQGBIgVrcvpW4lFM9YT+yrcSpv+Sg+3f58Lrfxc1+Tq0ehXJubdRlVsE84cM9ZtIwiZNmONkCn6CUCt42UYN2LZF98FLhVQW7wLeWXr9lGruSGTCeNzSDyo6azgI4FqBDZBrQdKzVNOURHik15TTltH57m8gAqbqNBGrBuk0tm1xbSBBx6pkFyXA7NIxFl2k3UVG9DQpqOZznIFWBmbzlpAk8Q8pE+q24dJaQZZVZInCtIL5vGJrVOBbQztPyOQK9WLCuXyb8X7JfKyRTlPOLIe7jqKnqYNFD3aRYcS92wfI0GNuDrn28BZN5REV1A5IClrXEkyC8pYkQHbu67FyjqoLqjSQhkDAdow9soM+aKQ4TdawHLuhmz3vBxJ/KUzVEeE0RN6rk2O4G4iig3gcR4Jf7PS/FF7qfviu+7Wz+1/u/cm+YoTtvl2eaq9IQ+tbv2zII1drrBpibEvqLFqmpMHTVI7SEUWGbaBxlp5OKPctw02FmTl07mHmaGfQGkUiJP01QWgr2pAyO3DMahjkGtNq5lic97QLg2SLTz52iBiu8pfeN+WbX5eQrjlmC4O0mpmAzb7GtI6diWD/pudr/npgWguUj5plSIUwEbwdKwIlbsmYrqLcSCJVFwkCZ4+lfLxbSsy4qIEnu/MQkcemY0+OrYseKRGrELt9MdWnaFqHaQzGuBMSNBGnIILDtZ0HvUyL6QiUFcKDOgrqApHMUHWkidho2B0J0qpYwi1DZ8gcpQU7rUAEIbgutdAx6NsoFyRFrOTzQiBc5MryMtCGgPbR+FFZxHz5JYheRqZ3LZfhZh+robrKqCACBAgqkCxxb1ocGUc+hCP+luWjG2Qg68D4XqoOvhWiV780DruKQro0oRARfG47z1f4QOE845DTdzVC1ASXdZ4gcUGKuSOkVB2Q1HchbIfS0CRzktZixyv0VsY0VjPbafC1xNaOQaLJhgn0PPmFjKRYcPOzFWXl2Li0SrIquf1Eia080zLj0mrO7f09mnHggeueD3/MsrIy4sPP7FMVmkc21wgCNtcN+9WMB6+u0PiUf/3ZA77m+gZPVgve9ciIzxxOGQtJ/ULNnY9MuLiS8MnnS3pFymioeP3WKoMBrFxKSNc8yUrDuQc1WQIqH2LEgm9/w4D5XOA1vHEvYyoWXL6mKBqHEvkXZW75YrRlZe/JyflkHMAT6WFO2pbyJFaEOH7vt2AdHS/V6fcq4gyPF7QljmXpactTC8RRtLozChBd9fOZY6Q8LfZ9bAQeS7HEU5ZRrZNG2HHVIkBSJoSRYk/kmGbMsNlCv/l/J3zWMdj4V5jJRUTzIAsL/cF3shr+Mrv129nhUXzvkwj7MGvZ12DlPawsSROD9wnrG30ElsYbvGnI0gIhwLQtUkrKWcnVqxcpFxVF2qdpKmzrkT6jbTyVguEa7D9jSGVAuARrM3CaclyhUoE0ffbvVfhGID1oAab0KDJ82+LbAEHRBE/wMorcK4kQgabxjA8c5cSTpZqiv0Jj98EHkgRqFgSRc/W1GbefmWDLAbuTGecvaO7dqBhm4OsUpfaZzqH3OMymPYJqKauSZrpGqqdkqeeRt1xgurdDkZW0M4lOG0w9YtKO+dSHDKt5n2mbspCH9HtDvBtjTcnqxtfjL30doWpItMJLjzB9EA0ieLzsBKOPERnHY+J+4+NMgYWXSxgIJ6340wbSKaMooHzEeR0f9+LnYOmoxDn1LN/c8TnL+3lxHyfSmy/xrB3fU9eXW+IPjx3/z9dekYbWmx522FmfIl0wbxPm3lNYB7HoEDMzGEA4hQ2ChWjxBAZpn/m+IzsvcAuPNYpF2UAG670U5yW3byxwbcJwC9JJTh2mlE2C70maTLJ3Y0ITMj7y3y0ASZl6QilwPkEES1EEsKvMDqZs33R89Y8d4toCZTVGWRITsUYOH9NoouOTChIRJDLEBd/5pbQFGGvw9iRANqYdQ/CgVKexF797hB5FYWnnIjFmsAG0xBoXaxRDwDlD601kbW5st9hHzqngiVtHUCplZO+NodFYDYkMKKERy8rIDsgf7zlO2l7EcmdE1C88Ns1iEYI64v1SR6lJ3+HDjPf4LuUXXAdQ7zwa6WKq2KvYpzU+0koEBTpGqqwgFlBKUImMkTNOyP3IGJHzzqEUKKVjlEt2WlgdQF2ojicoBIKQeBk6rqzj/H9AokIkefXBRSCWivJGxkdjUoSAEYE3rcCuylWyAAAgAElEQVTv3ZhR5QNSOaJyDdZ16ZqumggBiugpu9QTmiaK8gZJv4U//dVbmFUDzYBsFLV3rAxIlTNdeMqbJd7AGpqV1REPvD5Wit544oCkv8Ki9qz2JG3dMq4MN283XNoY8IsfvsEDKyO2RUW43OcNfdi6LpBOkx0a+lUfWzVk2vM2sYXeLHj1xPPPnt5jZdKgNtd4+FJK1Q+8/isucfmw5XOfOODBVw3wxrFQDaEvOX+ph1xtwCfY/ZKd7ZpCF4QVGBRRq+09D6yws/ZV2Pld/IoA+1KUhn/y2pERc2KxESG+P8UpJOWR1M3ZtkyN+HB/fMmLNQLPHCf8qchWTM0vF5WTC444giScvX78P5xZvI7PC+F+0bGTaX2IJma81zL1pPu/QzZ6F1JfIDSS1cGI/dXvYNZ8kHNin3l1DpHVmMU/ZTX5AJcvPM7txfs4OFxjc/Akd+/9JqNzX8vAW3QSaJoUqQW2XaDFCCUibjS4gLWeRKQY0+IN1GVDUUQDCu+QIvILOgNeB/IetLMlPifga4lAI11AhpS6qujpjBAcWa4QbayW9gacCQSvaLxHCYUMIYqn65iC3d+toIWkSGgCOBJU8AQXqE1gtKVBabZvBJypKXoZ9cziaoXzCh0UdQur6yPGOzU6N4y3LWWp6A08KnM8/OaUfKWhvOWp2lV2tw+49qDm3s0Fdw8cKyOYzQI+OaSfrFKXJYnM8Br0+rtoTEZiK5qsJLHnEGEOQRCEQvqI+ZNEGMiynY4kLSOY8fc7adCLoI9wVieIH+iCnSec3uP3cKzNGV4mcvSi9OLLpCpfZEydhKyc3H3/S8Xzl7CdlznubHtFGlrpsKC81+KLlMxZWpvGyE0WyIRiVkYQo60NBIlJJJkW3H1+Rm0U50cFTWgoA9QC1jfAtY6DaYxwyaHn8krKpz9Zkg0LZrJChISLlWOSwfncMw6OQml8qzEa3MLTCljtp+xvH3Bv1/FdP1PTLAZ4UaMESJPS6AWJ1SC6yBTL6qHo7UWukYg3CmGpjRjwNnSRps5algLRcTWFjgYheAdeEpzEBotxNvI5CYFrGoJMcS4aP957bPDY1hBsxA6ooCMrfFgKV8eUg1MO0VX5BRlLuZVSJKk6ylGnSYyOhwA2yFhtKAUE36UeOpyS85GWIiy11+gYomMkbKntKJzHed8JgwdUF3FDdNUqQkTAu9QxO6E0QYNKQvytPVgl0UhsnPEhiK7SMeLBYvVVNP+i5rSK1UIBvIwpN0QkRQ1B4bzvCHItSmisIRp2y+yI76wvFRnjnXeRyysEnPekOuGvfIfndz8T+Du/1TBvBOGEBJH3oIKKFZsyGtzeOAQJh7MWR8p/8kb42T9nSFoHmYJ9SVoIshzKg5rUZ6ReUDvNzoHjuecm3D4Yc/HSFue31rDWsXdguHe3ZPXKOs/dKnFB8/R8zpXNVfprGdcuax4MDU4p1s73oWrpbY2o54FpU3BNJOzuTLk5nvK5F2re9+B5Lp5L+PRzY4oLNc8+m3DxWYfpHfBrnyr53t6Q/XJGSAomY8PeUwucN5w7rym2NFtbgrn33HjSYvuWZhzoqZu8tvwJ7PoF5naT2zz0xZ9o/pia8KHT4TyxM8AyQShPTOmOcKoi6+jwl/L6u3bW0FoucC/GZb3EPZ5anI5B8qfXIXFqsVz2H889CZb3pxbbY4fnxJcHlFphb/zrbNhHOTf6DmT+WnbnDdnaN7D3ud+myn+aVFqsEGT9nFl1l8Xd72aj/7+xPngnt/dy1PBR7h3+FBfEV7OxHmllauvRFEgncCFg2jaun7G2hTzpsXvvAGMbtEwITnY0NAYlAzak1POG9a0Bd2ZtxOnIwGzcMFrJcG1DW1ucCehcY7whTSTWaXxtY4S+jen/xgbyRNEaA96RiAy8Yjax5BJknjMZ11gLOpUQLLaFC5dz7j23YD4GrQ29lVUOb08AiXcFXpZoDf0VyXw64N6tA7SQbAwvYvJbPPyGDVR/zr27+2Ryhc88dsADD/UY7ynGkxlDejRVSS0s0sMgVZSLFq81aX4dvfFlNN4ghWARYCjmCD3BuYJARggSiUaEqE5yvxbOjNtT2MKjsXTmnKUjcTbVCEvU8akxFNuLBadfKjV4dv/ZyNXZZ+T+TsXpdrLPL9TYekWC4UNtGW4IqruOLEmRPlAtoqJ5BhSJohwH5nPPbBE4XBiq0jOZSZrSM582TJ3HldBLFdU4YzGVlDcTCiF47fWcm5+tiTwlBu8l2nlqNKLReKehTpiW0LYtC2tZ+BIlc/zMcmO75b//OcdTNxR//b/exCIRWmGLkqwsqGuPqQ12rJEU6JBEbFVXXeZtlKIwna5faD1huc/5zhhyeG/jPuvwptMUM6bDJkXgeQRiEnFX1tGYlrKpWTSWtmyxnUizsGBNi3M+ijebQHAWOukbKSVBOqRyqFSQpKBVIEsFaXKCrFAEkA4hOw0/6XHegHIoRJzIQqdv1gltB6J4bONM1Fj00MZSwShXEwKtj8zoMYUao3Siw0mlMqb2lIzVmkGFqB2mPEIrVJqQZAmyUGitkJlGaEmqY6pWLCWCQrzWUlty+Yx5Ah7bqaBE18n7+P0lnoyaIHtI75EiofYtrWtJfRF/cxcwjeT2fstb3pDxA9+8xt54CDYa0aLD3LkQYmmyA9tEUtj5QvJ//tU1mkeH2I8kfOBnC7SoscJjLLiswZWG2W5LNVXMy5qFE4jMsXVOcfkBwXvescrq0HF7Z4yZG+Ym4cqrzlNNKy6uSy6vaTbzlqtbPS5c8IzWPK95/UUuXFtBMosVt6MeZtCyda3PtqzINgqKUcZ/9p5rPHtYMdh0zFpHWWcMR5ZHH92mHRd8zTtX+MiNCb/7uGGYVfRcyqsuD3nXN63RHzTIueDus4HJYY3LJbs3AqJKaWyLtzm9dsz67AX+7k/8yn+0+eb/67acpF2ImpsnjRWIUenlhO+PxvxpMPzJfu7XBKd14ODlQMAvBuqevLYggaAJXuGswDsZS/XDMU/d2Xs6fU3ZpQwlZw2z482j3A46+RJuP/33ef4TX82s/E5cmpHpXa5sfi83D/p4Y+khqUtLKVLW03/D4uBtlO3/yOXXPswLNyz9esLe4W9hHQgdMZ6ai+AdtrUEB0mSgRe0jaVfDJhPGpoG2iYWP0VcZRRb1TKhqWF1PSd4sCFWA88PWnAC01gW84a2OYZuyCSqdixly6wPWAPWRCC8Nx5jYqTZu/iZ8CmJKJjPaoz1ke7GeZTMuXS9z9NPjmPhHlCVFvyIunIEk2KtQbGCF3Pu3T0g0wVpomibe6ytx4rL/f2UyS4892QgLWByIDjY9dQNuLqkLtew2qBEgan2SaREioZCv5Fs9DZSXZOpPsYPKbIJMpkilUEK31W1ylMqAi8aZS46nnQGiBQCJSVaKZbC0bKjJTp6fZ+x+/mchCUQPQTHKS3RM+0LAcefBfCfLCx5Ka3Fs/1/Ie0VGdGSPtAKA3nKnZsl61s9GmsRyQBKS5YEjGmo2hjxoZbcFQZtYpn/1sUc1UYh1sN5Q5la8kriJjUPv3uVZr/h1lOC1XOBgzJAoQltG0P1KqEk4LSHzJH6FFN7tOyxuTbjiacT/uovCB59RvClb9J871fO+ZF/bplXOe/40oTv/jrFu64E3np+wL39Ae/9C3e5awakKLwjijIL4kNmTBxyoovmHP8CQFzslYqGQDRIBMHaLoUlYvhbRvwUIXp3LTFNiTexEiaEji2+cxqcw9uAUglCOIRSnVafRyeiE5XtiDUVmIj27gwGF9nO/XLijbQPMoaIMD6gCMfGUggkQkfwvvedIEeXMvQSF0z8tqHLc3uI4WIPKuK9otBzjLZ55fFddV8IgSBDR7+4rHyMv2+EUYku2uRxHUheBXHkQbkQI2NHi0SIsjvexUiXFAGVxr9EFTRS1zjnMaYlTTNEcFR+giDFLmqk9OS1Q7QFOrVYV6GTlJaY6g3O4YXE+AorMnI3J6SSYhT48kfmuJlATSVtW6F8jor1tlitIZGoniVTHlEn1AtYzD3loqGZWnZuStYuCzau9mhVj0v9GU/e3KOfJJR7Dj2U9DZWGIw8h41kOOozNzOyfoZLNsl7hqTQnBueQxpF2JqTmIxGOsb1hCtXCz78aMX5KxfY251zabjBuQdH/IvfeJK3v3ODg1nF+7/3HPR6VJkjv9rgnCQfCVTVMFgdcnev4fCeJcs9RrQU2SrXN1o+8PQU24z44W9+Rfp8f6TWSfyhxQnPW5xJTSxTLAKcOH76j/b5Y94piHPiyUiClseYLAiRZLQz4JYRs2OAvIgRtRcJ3y6jDTY6HcKj5NkFRJ489KjfcAoQfdLDT3HeILtSf4JGSIGUAecbtq5+G7m9weTGT/HsJ36JZPCv2bp+ld21X+ChC/vwqXPUowVpYgn7K8x9QZYdQPuTlJ/7G7z9+k+yeenH+I1f+8foywItM1rTMilvMxpuIGQksm5tDdKBVhhb0esrttYvcuP5W6ytDqmqhqyfIxMV0/tNTr0wCNVSH0KRDWhbwXynZGfi2T10bKiCvXmLWIHi0gp7d++RVZJp45E+papaagn9Xo1wULV9xs2CEGIhjbroqRHcut1y7jJUZUCnkKYtj/7aCyQJBBJCnXLv7pwBKYnUtOoQrQqEd9z6jKenU5qyJs0kxUiSqhFPfaRme7uiKEDKEucTppOauvEEEmZIsuEhqb9Oyws0zYCs8MyyLV79zh9mQQPesmd2SJJAazdpFjfQ2YjK1rjgI0Y3b1H1GmM/RyaaPpJZsoHUFUU5PTJGzhrc/qTTsIwIwSmNwaNoU3eukEtC3W4khiVuUHT9iaN04llMVhdf6z6LHJMnr38SphP7Pk5NBhF1cb1YOkXdaYEOT3x8TXHmuX6p9oqc3YSGRBX0BoZaJeiRptgoKG3LpDU0xjEapEhnoXUcTBoq49idwe7CMp9XNHvw3F7JvJJUNwKHYxg+1IO65LEPNSglqapAZTx+YUl8QrWQHE4bJvOG1gcsCbOFwXvP6prj9nOKH/llz394OuPr32v4e99hMGOYNRn/x4/3+cU/n/MVDztetTHgiduCQzOm9AlZOPYgnY3JA6RDJksjSYPiRdZzCNHYCiYcRYei9l5HdyAkHhElaLq0ZEw/dl5np+O3xCtp3ZWEJzJGp5REdIZVkBHcvtykFBGb4AXSBoRx0dBoYxTOOXF0T84KXBNwrcW0nTHmgBBwOIyLW3Au3jcOIaMhHI3OznNQ4hhAomIFp9YSpUAkgkSD6rwgxfL3Ch1BazSWvApHAGAbkV946/G1wTQGb30n1+PxzkRhZ2dxNn7m25jWxUbv1BFQVvD9X55xcODReUFaV8xrQxPA1A6CQuJ598MpZtoAgbdfajEhyhy5rhQ81A5Ttvi2oSw9f/NbzpO3Ab8KKm+ZhxqjJEYHRF/itUEbiQ4C5RNMI7GlhAoS41kVmtWVnAcfKhj1FRfXBzzxyTtIqbl+fQ2dKt797i2E0Wz2S7I84cFHUpyvsVKSrw0I0nP5wnnOr51Dq4QqVOg0YaduGKynZMMhdXDshZK+ahEbgrsv7PM7H36aSytDbj855tu//io/+0/mHDw3pUgTfNtg9yXBZtx4VvC7v7nHzacVfh7Qteba9VWq5IBf/b2Guk4J+Yyqaf6jzDV/HE12eEURjl+f5As6m957OYwWnI4MAS+KZJ3tKwiOqFo8MYLrgj+mbji1yPj4LMpYSfyFeukv5dkfV5ZFGpoQlnNBQkZGUIrR+T9LbVJW0y3cZM7jH3uCh+xXkIZ/z6efLFHbgtqnyKFHygOkS9CDwPr6CDl/P3s3f5CvfM9/ybzUVO2cLOuTD2AyvYMPFVXVENpAO/UIGxDKsXGxz/hgj35PUC88K4Mtmrqimjb41pCnGW2jGK4oApq2lVjjmc/hcI+oTuI9TQvTCcz2ato5aJ2SJwmt9TggLxRagVRDBD0mh9C6iJHL04zpeIFtgaBwJs5Tbe1pakiSFaoKzFwgmlgkZa2PEUbrmRzMqReeqmpJtaQ/HCBFweOfnnLrZouSCXUFSg1pjKRcuG7NsKQ0tEHT1LfohRH50FPaks1z30Sm30pdOhqxQPYvsbnWI+cDuHwVL6+QpldI8wFJqjHzPsEbLssVzo8q9j/+t3nt6s+ztfj5U2Pgfq/vFzU6O3Zebpzdb//nj34t+46Rr5Pb2SjY/Qqfj8jul///ESNar0hDq65aZOHJ1nIuXpOUBESuCYkgXUkYK0kYKWQhMFJSGw1BcuNeiVU59Rw+e3vBrElwZYNa9Yx9xfkrgXtPJMwmFp0HDg5ialAbMKVlOnNUbSBIhXWCxaxFq4TRSHHrWfj+f9TwwWc9P/o98P6vTQm55Fdu1PzBz0heU1SUlSd3CU/vTJgnik8/J9k5EFhvwC+NI7AIBAle0pGWhigQncjOsOjI2ST4Lm0YWo9rTTS7RTdIOwMu0OHBuj+8EqCSpItWKYRSEXibaJI8RRcpWktQS6vfo7U+svRdF7kqg2URLKVzVM5hao+1RGHVxnY8MR5rAt5KnI2Gl3VR5sa3YCwYG6uXorcdcV9S2Ziak0RjL1XR8EwVZNEYTBKNkjJ6PTKgtMRHGFgUEnKcMDyj5+KCOE7HtI7Q2piWtQFnfLdZ8BA6EKsznTSHcQTro/aj89FoNY6dRvL+r63Z+V8F7700o0pS0pAiFxzxYxVZjzc+GEA4mEq++5sKVpLIkh28AC+6+5CItsH3e/znD3ue/3cKbwI0nv6KoFhPyNcNZBaZSEIu8XkgzQIrKxn9oae3IshGGtsHoxsqDLV3vHBjynvfsMF8r6IfHK++1OfXP3wP2QuYKiXLa27tj1n4FJVL7t7ZJu0rbk/u8YnPPcVkWjKQCc/v1RSDnMO2ZJIuOHceHri+jppXNBPP+mtWefh1Ix55cEie9dided731oT2MKfBU9/Iuf14xa6fsf3ZQEbGVpbxlrf0mQvDBx+dsHNXUwwEMtS0RvGNf+X/P2B4GV5i64yvkwbY8j28DL7lJRan06m5E6nHEzxYwUcHRKCO+J28O+brO2lwQYeLOepL3Lf/s/d66jUOKfTxtUWMY1sbEC7Fmns0+TkG574T4Sbk6jJtqtj+aI7c/zrW3vSjPH+7ZWtWUGeHSL+CtoF6v+HwsGFzIMnmP82s+kFkiPxsrimp64aVYUrdOrQQ6NAnCSM0A0JQNKblYLumn69QzxcsJmNylSKsRwZJmlbsbFckuodKFdYFatPirMabWLlsXBtThC6hHhtUgMmiZjEzBCQWSeIT6jlUpWM2m5EKCFaQ6kgncbATI1wCjaBT+fAwGq4wnRnGh4bpuEQYhTEWQYJAU5UObwTeRLxTmuaMD6fc255iTY4zkT8RkdAYqKtoFHoncA6k6GFai86gMQHTlhTZiN6rfpwplmFRkOshl8xzDO0/QZQXUO46bVPT1g3C5KQyY+X845xf/zcMw7dzzn8PD1z5m+in/wd44eVT/8t5/OSm7uMwnBz7p8D0J4y0swbTyfF7to+j8848i4quCv3E8SfbSeD9SXvsC6FyuF97RaYO56ZP5gJKB2SRkbSCxljK2tLLc1aGGfO2Rq6k3LhXIQrJ7V3L43XLeOeQQMGiablUSJoiY002rKwM0LXiY3+wz8ogZzKDg6mh39O0iWQyrVC9lKInaZ0lWXgG6z0uDBM+UVn+7scv8MY3P82PfktKyGuUkGytFrwns8yrQJV4VGtpbMLmQAOCv/yrNb0ViRcZQnSEq3gUAYHrdA0VPhPooKKgqIgVh0L4LoQpurSgjNgn0YVIEbGKCYHsFKq1PlFFB0iX4AMo61BS44k4MSkl6ID0UVtRJLCs4o4GnsRY21VJLskTI8Deuw7M7gXCCZwKBOmWJ0eOIC9jiFV5hAmRt8o7EsXR99AixXvbBbAk3lusi6NYioBKJEoHtI7pQFQsiVfdN29tl470dMoiEfkqWgg+VhtaO0eGdUzwOFOTonBKYLwlVQKrAkkQLB9SK2JoWPqATzUeiRIS1ZTgEmat5Ce/Dd7/6wt+9aMZ3lkUGoKnVZ7LK/H+fep47zVLbXKM9GRFj/nCIaXFi5pRlvL1bxmz8RYJc02QDQwLfv6XAt/13hHi/A5CJIQsQYQa4TQkAbFo6CPwqaRWllQLJl7Ty1NE35ELwTOfm/HIw+fYnjjGe7s0IdAXHtmT6GGPge9zWBvqPU3qax541SW2F4KR3uTuwQF3yobz5zTaBYRJyEWPnds1fZey39b0tMYeeu7OGwQlD7x6RJ5OePyOwM8PeQeC4UPrPPhuydMfdVx7Tc4L5YInDis++fsO3RP0VhOsMuhe4NFPKZ6arfJVX/uFFEn/yWj3q3qKKcH741HgGNx+cv9ysVmmA09mP856/6dwYCdwNC9VeSWW6fUQ2eS7T7p/u6h7B2y//5c8po04ed2ARyqNc3RKCw6tU9omULkFwkGZTzn/uh/h5v/9Ezht2BitceAOqZ+Ha488ye3yjTz17JNcfR00YgKqz0iu0agZpfesDXpMwy/hGx+xT6lAKZiNNXmvR1U6tu8taGaWrK/orxYIrXC1oJ4INjdWuHljwvkLq/TSmtYkeD3j7m3YWO9jQyS0ds5ASAghcumZrkoRobCzFilSXCZIvGFaGqTQ6FbRWMV01nJwaDl/QTIbey5dT5mODfsHhkRHYlQhNMY0DIoMQcrh3oS6IuK2tO4oZCRN47DWHUmk2VawKD2Hs0BtYOtij9pYmrpB5xnz+RxjHUmikTpFeI9Vip4AoySymLGSbnFjvMvVrQMmN/YZ6JaifDXJ1k9wc+fLqdJ3k/UEm71nGOpPUfAHiPox5s9/lLE4pADsvQGTxWu4O3mC86/7sZcdc2fH39l236rX+wDbzxr6L4dj/EKb7Ib5Mg1/9lov177Q416Rhla2liJsHb0i6UiLgvF+TaYzgvZgKiYHNWk6ZDiqWYQEl3uGowQ7GLBdGdyuYLQCl4YenRQ8shX47f9rjg0509Lx/EHNeiYIIuPe1FCMNAMlCCYgvaRY7dPLa+60nttTzw+96Q6JCBgV6AvNykAxm7cRMC8Eqfe0vQHXRoHB0PGHnxH8/jO9WL3nHEEr9DJipSOP0tKb9IIjVvJIoxC9wGVFmyCGPkWsEY+TshRHIq5BRsNMqASpT2A0JAinOoWzzljzEUQopCIytzuk7kQHZcfm3v0dIquxJNh4fX/CsPHeobsqvuBFlLYIdLI9cZL2Pkr7qNANSBn7kTLm26VWEcslYsrTdzwoUgikckeyPSGETkYhVqkY7/DEyFbwPkanXDjyxuOPEhAyR4WAaEtKleG9o61aUpVinUclikY4hIrGHZ23E2fTQKolVkV1+oUNuMxBk/K3vxN+5XdniCKNKZLGgUv5U69LSVSJtTUPnEvYO6hZXfF89SM1/+CvDdDv3OPC+ojv+yrJX/q+HGaADCRtD+8a/pu3Oar5HEyK7vUQyuOUjsaclvheAANt6XEHisWe4+lnGyrRcv6qglZy9UqPRx+7yYObaygx5KC+w3pvlSKRHG7XhHMZuasY14K14ZAn9rYp5wahS9ZGBYeTMbnqc1jtkcgRz9ydcnVlyGJ/xqDQXD+fM1l3PPahMV/75st88GM3eXu9irogeM+qZP1PrdEWljDQ/OFvzdG5QQwV/VVBr5DUiQBt+ecfsfzqkwmvurbGpWstVzcGX7wJ5o+52SPVhhM7BcfcbeGl6RSWr5eG09FrTmKulsed1RzkxX2fWATUkZ5iBJ/E49QRWbEQywjU8fsIdTjd7/K82NexURe7DrRtrAZ33jIr77KxdhkhE6xOUG6ToimZiW2uvLuhfjZDJR4hBpjZnL17/5AHN1YR2YgPfXSfL31ripObjO0L9AUYA4cMKOYFbW+Bn65BBVXa8vzvNWQrLeXC4o1AW0XY8Zh6Rpb1oBFsP3/IlUsrSLvC859aMFrrUZY1Tiokiu07AZUKbNuSSU2SCpxvqVtJkSh0kdP4Bav9glvzins+IBcw7A+YHc4pMRyUKZ96foVL53JeuHuLaxcy7h0EmoWjqTLydYlpKrzpkeSw8ClPPbVLoiRFpvHCUVpJIcA7TWPKqLARiJFJ59geVwSlUGlgOj8gkQolclyimS7m8e+tPdJbkhxSUVM7WC8GTA7nPH14yPv+zOM8/cIzrK1/CSurz5K6H+WJf/8JCvkvWTz9DcxSmBcj0qyHTiUyMyS9Gjd5IzfUs4yKkv7qHTauPsak5xDhpVPhLxUJfbnjl/jEsxHVs07I0fHh9Ng91Z+Ma8fZpjrw5JGKC9HtDoLouJ/AZR1VEf8RbLtXpKGFcAQNbd2S5BnONCRJghCW2SSgtWa02mN/POfKtTVu39zjjvH4IOiljv0aZiS8Y1UxzBWmdvzyv6to5yleVdzeDZxbg2mTslsuWFnNI0g9WHpZSpIoRllLvS3Zm1ZcTlNCv6F1mqsXoZelbE8bpjMPRVywvUjp6TltE7izvck3/eiUolC4YHFaITvyJtFVCkpEVIYPEc9gvT8aqAhxRIq2TA8iZAS3WxvJPbvJN4ZT1VEYNPiIrI0DzR2nGUXHY3U0iUeMWBAd+F24DoQYCwqOZW3AO09wJvJj2dDpXHWKoSEQOiLTAMggCB25aFxY4vW1kGi6iVx4hBQ4sYRWRUIsGUA4ccQSfopvJQi8s7AE2ztQQWK7tN/S+OyOjg9em1KrFkHKUCyoTUoiBFLFSiXpJEGFDigZIiZNRvNOdr+JRNFTC1zbi3FIL9HjPs/+TM6lH9DkRU2qC9rQcGUrjbxndWBaSr7k4X3etqn5kb/g4I7mza91fMs7FD/0XS2JLHCqQdUJnho5GvAlf6PmHa9WvH615t0PjHnNpR6zWw22aQh1IHER8xJajWkaXAicX19ByJrrD62xtzthfx8evDDk6b1DZGhuVMAAACAASURBVKm5lufY1IOzOJWTpZq7lUD1ckaF4s7twA4pbzu/jtB7iFqx0zSMD1I2Zcm51R7KCTZ7PbBTembIBz+5z1seSbkznvPut51jWteM65Lx3nnWtSDtWX7/tw/ZNwMWaspiO6HeNpgEXC65pQRv2pL8vbdCqioUGU69vITFn7R2tuLprCF01iB6qTLzZXM+8sydXmROaIaKkwuSfdH1hRAviprFdtpQWhpbLxU9OL5PeXTN46hXPM5ai5KRqHc+n9DvraHEIKas8ufYaDMKscndvGQ09ZjzQ4bbc9pshG2n7M3GDC/D27nOwdMLrj74AtkIposRqzpgxAyfplR2hGwFSh6CgrWRZ3+hCF4x6I+YHhxS6D5lVSJrgZCeUT/lzs0aU1nyIUwO5+gsUJUKk7bM5i39vgIcdWOBLIrSlwonokypUwGCIe9lvOMtb6W9V/LZj32aQa6wwlHN4AMf3eXhy5o3P1wwK0uaFhSdMeTro2KINE24fXuGaSHLMkxto4qG0iQiElOrJGNeliRZTlk1kISYEvSK3rAAJlgjSJIRrTUolSKEQ6eOvBBcuDKkuhcIeoabz8i85Evf9iNMwjpbG69jNP87vPDb/xOHN6dkK9CIjP4D17DSodQOzs+xLkOYFN9kqLVP85C4RjOc0Ut/mf30zXie/7x4qvuN86Uc0vKzl8N3nfz/6Dk6+fnniSzdb0yfjdiejGp9If18oe0VaWi1dYtKAzLNkCIhYGlMixCB4VBRN5qDnSkb54bMD+e89jXnWDQHTEygrC37M8OuM/zTx1LEfJ9pLdlMJI0puT2WvO5i4PY0oZAN59ZS+pmll6SsFn162iBEw62bkqoN2FHKqqixQZHpjMZZdnYa2lYSMtAqI00MeZKANnz8qZzv/geGtUHKIoAUnZYSaWQl7yY7g0d3YHdrl7Iz3bQnJZKA6CJIHiIdr4uT2tLaFz504tOB4C3BCqSGEGQX1PGRPqKz9kWnrxiNM7DBIrXsgLIBmUiUSugnOWlSYLymalt821KXU3zVHvFvOTxBqmNNR60QvjP+ZGe4SImWmkRrhOzIW1UkmY2tS2OqyHt1VFklwHYkprFqPBI3+k6jUDgFLsoJxV7CERnk8iGIZeugVMIP/Vea8TTw47+4YHNrBedbUqFZShdJHwhSIjWRMkNLdKbxypOqHlubLSMCxnv2tKERNWrc5+e+Z8H7/9UA0wrKhWGwGgghx4uWKjT8+p9bRT/s8XUf3074b79sgz/7rRbMoOMUM+ANwmnqiWG13+Ob33rIV35JwkI0THYqhg8lJFmG7vjDmr3A4XZJYlOk91HYfFawu7tNNck4v5kwuaNYH9WEbJ0XnruH8hrX/Z1fuDclHfR46tl9ZH8dmVs++dg+uzcPuLoyYB6mrDAkUxX7vofam9O4hFllcdOWC1uWXmEZZuvUTcm/+J1dBsCrX7MGX5Hwt/7aCzw0EmRrPS6vVSTJgJAIbJFyYBw7c8MGnk2paBuFziBPAmn/iza9fFHaS03EL7UYHTEG+ePUheJ4vojG1DE+S6qTBtCyb3nEjr0Uc186apEd734L133uM8gTfb54gQoh0CpIbEUinqa0OxDWKdQ6SvTIREEIFbVK2br6IM89/ju89fqbsNkaWbtCKSEkM9btFgeXvp3+/i9RXQWmc3ySk05H7N7e4fL6C+xOFPUzQx54WNNTM1rtsXaAENBzAZck7NwT9HuB85cF1fMwnwsmdYvzMJktUFJhTIp2hsW0Jc9TjHZINSQRJa31qEywcwBFP6U10cHatY7quZyhtiR1ixoFWpswUpqFN7zl3RfR6x9nt30ni21PMYQPfSInXTN843vXeOH5KWaqaS6nJDbiprJigk5BJDDs9Zku9ljcy6mHKeuhonGGNoP5vGHFKQgNoteCFIzHNVne53DhSZShpwyZVdig8YUlvbhAjQsWbUuWaYSV2BoO79XotOWhK+d48iM75Jfex+rD38JeFdj7t9f53GKMZUF/M4M8gjOMnTNzJZcLjZMjvF/FqSkbuqapL7Lo3SDPr1KvvgHTbIPR6CIDZ/HeIJxFiQQpUoRQ2LAHYQVEQZAGRIVzUVrtdEXii42g+6XJl+P3FOBAnDbE7nfu2c+Pxv+J6yo4ZWsFESExy4iyF/JFBt/nS5e+Ig2tJEuI1TCKIAKz0tDLU6RwNK0l0ZaNUU55MCMr+vhFiQyG12zmiPWUwe6cy9WQnWrOdJahM8fH9lqurqb0RhmfOCh51Ypka0WwlgXO9QuU97TzlslUYoOmDS0iF2jjaZOUpBGgLIsDhdOQ9AO5TijzwNAHFrljdlvx5/9xy2ZvjaBrelZEbFOQSBfwiSQYF9nHjccSsUnOxPBkZOEVCG+jfp/vJmAfAer4JV3DcRksgqiNKGX83EVAq1oSpboYvfJadRnJLvojQCUq/s4CRKIZDlZY6W/S762yMrxEmqbs793jYLLDOMmYmB2EjbiqY2GOE6FbIQgqph4ToaOkh4ogTSm62KuXqBBTiEtnWoljni4AfEDLjgaiI1AVEBeAjlpCBIUI8qgMfpmnMcGRWUWaNzif423gB/7TlLU1x//yP/f5hh/Y5w9uara3BZdGCXJYUZUJmXRkQkWDMBEgK/IkwauWBzYdhhTtLUUJ6AGJVrzvTT3qfzYnS/v8xW99B9Xk42TDNGK7ak96ZYAZ1yRpRZUUfNdXWhgr5MATaBBtCsJjEke1SPkPPzWFWuOsZqC2uHNnxvnVlIODinIRv2eN4KErK5i9hrs3LZPDgFuUrG5mbF5J2Lu94O4tS5V5HtyqGK4BtmJ6CGpNcX29x1P1Aec2hjSLGbPDnCvDFOMtO+0M2abgS3KdYmYL+r0R66mmrhuunMspcsnaoE9OQjHQjDLBn/nGTf7wsQW/+W/3mTvLYZLRzksqH5jbktop6AVGA8fqquL8Rh9ReKQMaNHD6QVZ80dEmb7C2ymv/ASR54u89zOs8ct04dlJ/BgPdfq9EKev9VI4mD8KpuV+5yi3QpELmh1Duv99DFc3WGQb1NUaTfgcWm3hmktcuPIueg9u8fTuL3Dt6g8zoSJjhKrG4G+zevEnGcw+yr3xHYYpDFrN5NoOG7fh2ZurrK6PmU9n3Hq2YPNyICHDuZpGWhL6FMOaYaU4vAvJaJPNjQO2Ngv2Dww79xLytKC1LY0do52imUM/eIr+KtPxmCKRBJtE2a3gsHUs8NEKxrfgsedK/otvG+Jvl9hpCUmfaciZ3yn4zMcrLj7c8JlPfAjlYTC4QG+4zWoI6J4kue5g1OBv9OiNxggLa72EQWqodlJ8f4+ETe4Vcy4sDHve0LsK4YkV0pUEp/YQMqetW4wH62QsijIO5S3ZQCJUTZoWFKsbNPOWw/0x1kX0Q5CRP9GHlosX4ZmnKurNNTbPv4nnP/X3mdz9Ndp6D51BkiRRfUPHMg3rKi7lIxZuhyQUNJvPkDvJvltn8//h7s2DLVvP8r7fN6xxj2ef+fR8+96+upKuZiSEQKDC2MJObGxcCSaGJLisSsrBuBIg5TIxSjkkVLDLZRNDmcE4KewoJjhgEwoFAgQhgSVd6Up3nnruMw97WntN35A/1j6nT/ftvpLKGKv0Ve06vfdee621e3/D873v8z7PYAcffwsq/DEOKodUkjBQcFI5LHEqoFQFjglOWlJzHq+meA7mgQGNkDHCVXfjofdzG0/A/n2A60uAmge11wOse4HS/X39QcDp9Pj5SseS+KMgk/1Rt/G/vODxkqqqsfOKDSEUxkGdS6ZljfAVSa9FNi7ZPbT0e4rDoaPwFfiATzx/yEEGC4OYKHRcu11xbqHD0zcP2duHM0uaN69KmAlakWO1HdFKJZUomlQajaN7EEIYKLTyhKHCRxbVUwShIxCSQElcHJHvWv78z1V4p3BEKN/oMzkaN3claLRIhGwESW2TDjPG4GtANNY4x9mv41SY93NleHs3R33aaNbPo2Q61A9IK6i7x5/sgBs9lEAIrJ5zxJRAyYBHL76FKGzTihdYWbpEK+1SVQXXbrzA9u41btx4DVfmOOfB2nuroNSxRINEItBCNvw0fVxZMo+mzb3QHBakRwcCoRvzUk5VOTnXKLY72UhNGCoK5xr/K+YVUdbNgWRTwo5VaAlSFmSmjwoypLP82ScEP/n9MYQxOhiCDPFnPPvXFX/mh0tevCW4uBSSuRqpAtrCkkcJbeXQkebDj5f8yLdXBLVmPBWECSSpYzz1xKnnrf9DwnhSsPPTDmQITjbq9mqKClLKUY0zinxmiUNIFoIm+ieb9CpaY3yJNm2MKNBec2PziKlJOB/EFL7CeU9Vlpx76yKf/MUj2jLGupIw8kRG021p+qsx+1sl49ueYpSxvL7Cxz+zxdmNNhcuGBgEvHiQYdIQ0Y5465vW+a3ff4Yz7UVuzByBLOmpmF4UUuQTIhVhMoEaWVbaFnFgON/v8dzmLk88tsEXXtzlKBMcHdWcPxtw6DMC2+bCYkSUqsYCyhsm1jILJDqCKA0QsSNqCUIhsGlBHCZIZXjvdw+/JtDWa9f2/Zci/L7RDvh0mXnjP3o3kvSwc56OTN1Tsehfv5jc/9k3WlweFhkIRQjaMq1HhPI85vDTVMO/wsrgiN7qo9QeqnqH4bU7TI8803zK4opi9dH34ZPvZVh/M7lbI/YFU7/IYOevITd/DrfomY4kas3TGSXs75VI5ZhNFdNhyBPvabiZ06OQtD9lNjGkUQxOcOP5gsgOmBYHLK5pZOywNqDMJddv5mS7kOq0icjXFYOuxFQFUocUxjMuHcYJauOI45hZ6vHjHDKonCAI+9x4/ogLaz3yfMRiJyCJa0yVcPXQMrhUcecZuLPZI2mPWFlrEVmH64UMd0asngUtNnj6c3usXBLcuhpz5vKYnSN48fY63/ufbHHjhT6DlkBMj1g8A61Wi/GsBC9QgSYrclopLC/GRKHAOcMws4xmDYVFaEuSqqbISTZzvQ48QxVzLpkx2xpQyEPiCGTcJj4D+azEGEcUxCSRwtgC52oSsYDvH7KRnkWqA8qZ5exbv4Pb7p8wyfaJZIjIZ3gVUbdiXLmEEqCFR4oaV09xzjTc3laFFA5hI0DgfI2tQ5Ss7qkX9OJ0n3twX3xQf3xQO32MOzV+7hkP9/G8vtR5js/1IKD1ofdfeeig/qqUdxBO4I1sjIOFxpuQ4UFGXdSUpqYsS3IHJrcc5ZIwtozyGuUFi8sxR9s53/KuFc6tx6wETQpSO8+QnNEU3nyhxe7Y8Jnrgqe2S6x2ODzDqaEsY6oiaITK/N2S7FBKwrnwXqQ8wlt0qEjaltHWlO/6ZzO0SkhUB+FrkCVSWQS+idhoQRIEiECipTqJ3oh5ffexuKgxFlebRjX9+OH9vNqQU0BKnLwmPdi6UUWuyxpbm7nGVRMF8dLPTaHnBtVSIEIJstGgUnjSVohWCXVpWByssrS4SqfTYTBYYXnpLIsL55BSz0GQvWfiPyEKHpsIzhVInXCYU1Ykzh8Ll959zTuQXiIc9wBJrY9tQeb6Y/PvcjworLV4GgNqdBM186Gj0DOGZUiiJgRGUlaCn/2UJ+y0iHyGMBprBHpbsBIbPvN3FdlvC/7yt2XsTiRlpiHVtJ2FsImyrS4ofC2Z5QJTWfKxwxSSRDepz7XuHh0BTc7T4WjUlEUQQe2pMo91BlN4yqyJbLhc4G2Nswpqi5Ax7BcwbozJy3EbVcNMWJadYjHs0Wkv8Olf26e77PG9Cp0YFvuK5X6HnVsZNve0csmZRyMuXumS+4Kzay18XdPzKdWmZUEp+qHDZiX/+8df4mCccFRbVtOCbGo4s6DIJmOEsfRbKaEoObPQeDC+802LlGnFmVaLW3cmTIeGjaWA3lrMwdRy8yjg9nBGaR1ZkVFUnqKWKJuQIhi0JeuLAedXQwYtTX+5JPURr97O+Ni/rP+dzil/nO24DP3+EnMt7z6U4J6HEDTadVIgVVNhKNWDS9wf9DjdvtzN84NSMl9uynNU1hR5Qjs4w8S8SrT4HtbOPMP23hbXnn2KfPs6Pp+x/OQ5lt9+lnBxhRvbEbc/8SnGT30/g+LbORv8FNVwDRkesL/6Mxx0HmN4sMFyv4vagmFUY72iKj1JLAgDwf6mQflmg1tkDmFDysJT1TnLG5LZdEaiFxjtCPrtFlqXlHXOY48tIwOYljPyuqKqKmZZjVQthHRNCt+4xj+3hMmwJt7PyY5AtRKimae+OebcwjL7owlpClkFt/di9maNNte1z0Nnpc1jl8ZcvgK2zNmqcqqjERtdCHOoD0a86WJNkku+/l1TLg6W2R/2ufToFsWNPp/6PFTZEWJRYgRM8gJjobaG0hYgYLAAxawgzwR5lnBw6AgUCGkR87WgrkxTHU7D/e3nDrsfU7Q8ZTslWIpwrSllWWKtJVAhYRiR5zllWSGFIGkXhDJks9yiMI5LZype8f8H7vAaXVVRlx0mg1Xc4godMWYj+UWWon9KO/gFlPgX1OHLmGSG68RU4xWK8YCyiigxmKCijO+O+dNk91M9koZv2whPHz8/PRbeqN2zsYF51f/dv4KHj6mHne+eTcxXEFn7qkwdSimZjguSWGMqi3WOSKWEQlL4nHYnoRU5nr9REQUG5zSxFkxUTlK1WNqI6QU1T6zGXL9VIa0hV4aVoMWFtYpUey5fajG8nTN2MDzS6CAn7SS0gppYQxQERLKx/AkDSRhZvKmJ45RAWKJYEkk4OrQ8tRXzX3yD5Iu3xnx2N8UfSKZTRxR4olgT6QACga0NGkFtG+VAqQXWOrRuNFiYcymOC3+EB3eMtsXdtHHD1ztG5w0Xydei0dOSjXIuUqBFY10jlUcpiZLHgoYCS5O6E74xhbbW4p1CKEe326PfH6CExnlPu9Mn2GvNqwoN3oNCYq2bV0/OE4hzL0A/J95L4SlxOKfRJ0rXGm88Svl5+bLAOdVUN8qGPC98k840NCAukBItQgQWb5pqRz/nPqq5ryPCE6IwRYv9X5H8xh+E/Mf/c8Y0l5wbBHzwr23z//54jzDOCZ3F1RrpQ9AFfjvmv/kP4W/+5w50xV/9nzy/+rmAR7sxtTAIV5BNLXXpsKUnjTXTiaXbjjCV43f/q2Xe/RMLUF7Dqi5KGaz2KKvxVeNlqHQFtUQrh50JlKwoK0kkSupEYl5p3A66T/YY3x7RigKmDvzGEzz/zOfwroZKotIeiSxptwXjO5aXPjtjoaMwrpHssF1DdrXERyn7e0Nubjkef0sb/d6cxb5ikGte+YJH2JqL7RZHdY31lqv7JVQhX7y+x2Pr6xSHM4a7E5gqprqgV8UM9wuefnFCJ5XEOmH9XMALz+W8608M2NnULNWOlV7FlfM9VDihUiGHRc7Y1FgdUEvNrdGE0R3FoXP8009lfOE5h0bwM98f/fFNMP/O29103pcbxRKAuiciLU6MqL+szz8ovciXBl1fUfrj1M6/l2bUwYhhHjAIz+OMYS/cZe3sTW5vfjfh7PfQhwvMSuguep58t+P2ay2yHY2zhs1PjIjX/hZnH1eMdj/Cfhf0W1+i80WBLSFqw8RCbyCZjSLqwhKlGTu3AoybMlhQlNMIqWpmU4PyEWFYsnKuZHcrp5gK7ryaE3UCWnHAdDTmzNkeN66NGI8r2oHgYGgJI0cndVQlmJLGU7V05KWh1V4jyGqy6gCXKypvYZajXEheFlROMskCFpYjwnSGyPrcujmk11VMDxUblwTdbIXN69tIWdOPNhCtTSZ7Kb31GS8/D+2lPR69DJe7Addu1IS5oL0MWy+1ab1lQl5adBhiMWggTpo5rzCO/WFFZWqiToBQFmMd8/0pCgiVRgmBNxZbKIZO4leOiDUUDgIJ5cwQhiFBEODqGmtqkiSg1U7IqRjEbTrLjm5fs1X/LH465WjtPHHxAhcG/xoz/OdM9p/CHyZcv1URxpakC3EPzq510EkfpVqw9AFm0/NUk3dSVGcpa4E3HQjKh4P7E/rUfREnee+YetDn73/thLP4ADL+w/r6H2W276sSaE0nOYGKmQ0NwllqA2kYUGQWgSZUnqvXRpxZXWPn4Ij2giDLFflYsnkwYS0OcYM2mzf22DiracUdXrk5ZqlvuXZD41ZyxtuCzDm0VJhIMPGK811BP4lQwjUk3aQJwaq501M0UIRpjVKCqtBsjyty43hyvUR5w9dfkvxAlNPpRCSxZvvI8sKu5OUdeHkbvnANptOYxaBmWlcUvkQLjZEJ0mWNSKaYV/PI49D/PExpLVLJJoXo/EmeWvrGY6qpUm2qEU9SelogtUcqiVRN9Mofozg354UJgXeQZVOOppv0WitEYYLwoLRqKj9lTBiG2LrGWoswohEMBSym6cTGYUM1J+M6vBdI2YDAylmM1GgEnmpuhM1cR6yJ6um5GKn3jTmrmJPgQzWXu/CGhq4mmu8pmtSh1LJJQzqHdRGVOaDY6/ONFxzbP90j6e7zHT9m+NU/iLlxJ+TsiiNJGuK7FzWi1oAlcBq2wKYhP/1XZvzsf+f4zh88YCtb5c5egXiiIHQpRoK3EiEMk8zQ78XUouADjx7CTKEWGq6d9IBxlDMHPiSUIc4b6lKjKnBRhJuWVB0Iyoib45rL71ymzCfkRYCxHqFhfP0qJTHr3QpXCUaTgnzHU615vK8QFyWt2BL4iDiEcEUzfK2iHcNb/uwil1szDqZHHLkFlrxsKr9GNVoFJNJhKwm+YClIQdd0goSothTWI0vDahwiSs35JdieWN58ZpXWwoTyKOHz45wrT7YQRyW///IBa0HA8mqLG/U+Z+KQpKtZWIWW1GS1ZFYWtPsKHyVMpjkXL/bY3HfsjBXvXE/++CaYP6b2lex8j0HWybEnpPj7d/jH7XS1YLPTvx9sPey6DyMIv9F3uL8CLJ4u4AW0kxE+mxHqIVoKsoULpPn/B/UvUvJ3SA6O2NuqCMMBi8uaTbvHXm5ZaOfImeb2M3+L5Yu/wIb7Hgr/5zHbC+SrM5gIolSjgpqkE2CMQ9Gope/dhjqz9HotbD0lmxqUc9AKidoV/WXJTIVMDh34ABULbD0jCtqkaYIpK5QKmAwrrt3MefMjMdZ56tIThBKsRBGwNcyIfYgZQdmKqFWBHVcshIrcBBhRknQUh0djWl1BXhXILEQtWIZbFXc2A/zoNrQWmVBwdFDRHbS4M8kIV0NaiykrG4psqNm/uUPSrfnQN4Rs7Xa4sC6wNmKaFXQ1KA1hCEGg2ds2xFGKsZ6qrhDOUuSQJAG2qhtrsQCEV5jSNpZt6QwbehZtSCAUM1djkLTjiNpUzPIxUsHScruZi51hqRUwWMzpzJ5gO/8HZGtrPNL6AaL6KWb5q+TXEvYP9xlNW4TdhAsf7tJKAjrtBB0GkJWUeU1VTikPfr7xLR7UiOJPIoc/hIpWKd19m4QvZ2NxHwh6ECg6vWF5UGr8KwVRp8eyeMBnv2R07auRo3XwC5e8c45iUtPyLYrKUIgSVwtyW2NqjUgUcVpT1YIgSdnfz7Ah6MCTZwJqQbCoObhZYGLLC3cqNg8r8rrmTRd6fP7lChV4BokiO6hwDt66LDjTCQkjSRgJuh1Pt6foL6QEoaXyltoadBiRaof1CivBUGGURwceMfcmDHVAJxHESZMCsMIREzPN4e/935p//OsQxhOoW6AMVW5OVMaPWxPtmT+Zp9awrrGFmfs8CXfMaWomZS8b2xoR6gZkadn4FqJAuKaaUTSin3PBcoQTWNsIqPYH63zz+/4iK8sXkAhGw4yiKnnptWf59Cd/BV+XeNuAHKUa3tUxqUSEd6salbirVA+yERr1TVpEhw2ADUON0rIRJVWcHH9isjvfz4tAgDWUdq4vdkIItpjaN0KvrjHjPhpWmP9zmZEdIYuASghmcsqZlYT3fGTM5/7REqZQBNKAdg3hAwnO43yjoiKVYiYL0lrzM79f8ImnVvmpv3RIdlsiQomWHhkJrHQooYl6kqK0RGlO0m4hIxpOXO6ZHlY4F9IbCO5cz4lETH9gkWmCsBUEjh/++SH50YC//6MGN3Zs3xLk2tKKFTMpudJR+DDk95+rWbQWVVm6ixHV2z9I+v/8Ji6P0L6mt9YlqCz1I4Y8HTIZtpn5EmGh3ZYUM8laHz7xsYJ2J+LAzShVCNKSzaDbTgks7AyndMOIhShkoTK4keItF2N2C48d1+zsT8kXA3ZezojORCx1Fb/21ITv/9YWu3nApXOa/lsULjb4iSfogwsVL9+YULkeuMY029SCA2NZCxRvPyORH9z9muBoXbu6Px8Qp8FOs/mA16cggMYh4RSoedhO+7idPlfz9+61nHP38jhPga7T1zgdOTtuD6rauv9czQ0/qFQeqiBH54vN1lSF6OJvY8f/C+5wRHxmlfHLY0Y2ayq2XURYV1TK07KrSLHDhcf/HDsHWxzsfppB0CLoCo5GBWmaIoVnfKvAFZphmQOLJK0WyDukkcROPeM8onQZ5/opV6/PEFXA2mAB29kjv+nZGQfszTy+MCzWHW4dZUwmjvW0yyFjOrrDQV4yo2Ij6LGfjRh0Ug6zHIqYenGRsLpNtAL1YUJl4LWsxZsHFVuTMSu6R68z4uY2LK60Scsph16zWxgudiNMUXIkUxZCh4wd21nI2V5IXVoObow4s9HlsBiznCb4fs7k4DxnejcZrwtW2wM+9akDLi+Ct9BLO0jlqMIMEpARCBvRTkpaScPPnVWOdKGFVyVKKYqiJIyg1Urw3lFVNWlLESQWJxxpsICtU4rModSEd3/TAnk+ZW8nY3Q051u5lMrOMMB7vuk8/bMB9XTKi1+siLQgakWEy3sMgg28CyhUTUcHjIofYW/7e5gyhSBD+QOsGLyuD52MCTgxZZ+PoKY/3ifJcP+aCc1actI/Ae7T0Dp+rwluiNf18Qf17ZPx5dVd8OabOwP45vdffuj89dUJtP7xOZ9nMD2saKmYujCEbhUe4AAAIABJREFUHYkl4nAyQoYhTmtaCxZsI1swGht0q8X1V7bp9hKKsMWtm1sQaoo65c7BkEE34vk9w+qC4uaW4fHLMS+9NKGbNOmvlQTevhGx0pcs9kKiUBAECmMM+5mh8gLVkujUELdCRFA1+lA6RFIxMw0ISANFP5REUlBYx6gvCL1gZi3SKkIMw6nku//HlHFdIeoA42qcsY0+lveYuTzC8Xxra3tCkrU4lBMn5rXSgzO+4VxpeaKkrgKJDECp4xjscbk3jSUM4FQjFipRGFcSRS3e9uS3stJ/hCRJ8BYm2ZRnXvgcr73wSXxZI7ym8Y6aew0qGu4XwJxn0uQT707aEonSDU9Ma4kKJEkcIudAK9B3o2Qn9iC4ua3E3BPMuHsGgPUeZwy1acLPokpRStBKpnzh78aMnaXdbmMOPaKfE1ctpvWYbtBGhvXJ4uR9Q0p3NKKOTjQmviauwMf8zZ8f8xN/rstwZBF10PxG0uNkww3pxSl14mkZDV2P6DiEUtRHjqr2zCaG5fU2m1dnaK3pdB25LVloJ4jEoP7yDPOxRShKZqXmYLemDC3nVgOCsWcbxfVS0S8KFsOAySRj4dwCh7cPWB628ZGlE2nUQFJ3PDNbMBzV1MLjkbTSFFcaNhYlw0Kx9a8KRKSYKhjNDCJqfrs0ktjaMBrnLEYprVgy3K/4usfbHO5YeknIp6/u06okn92ekSQRa2c7zMyQldaAS2dDzi9r/GKJ1B60goUStGKWF1gfUpqacQ2jqcBrwbQoGBvB1y12WP3Tt79mgJYQ4iT38bCo0Gnw0wgSfyU77uOF4a78w4mNjn/9wvEgHtfx66cXrjcCWvdwaKR43WtCCHzZwnZvgmuhZx5frRPkTxPpH6De+yTxlTMcvWI4vJUh+lNClxCYCB9MQAuoDP1Oiidmb3/E0kKMiizDYYG3AjPSzKY10reYZBkyhDgSLAxaXH91Sqpj1KRgbJZ4ZCnncC9kR43woWPRpozzms2hZXwA/ZlgZ2Rh0GJyK6O7coal5Ttsb0LYCdC6pt9u89rTNWefLCn34VXgkTLghq/xNfTjhNZyzs4X+8jzlpiaF2/1WG6N2MkKds0S3/a4xW4pKjHCaMmTlxOu3xxSHCVsjnLWN1IWlg3f9Geu8M/+ybNcXo944aWEt7x/yHhH8vKB4/ZYs56u4uM7fGA1wAUGlzQyG91ugqgcgQqo7JQzGwm2rqhriwgUQRrgtWM6rVgYhOhAkucN36vXa+rHVaCJ4xjnoMwrXO3YOCspS0ORW4TUKKXI6wwZKGQQsnymR9zxXH9tiHchH3jvCrPlEi8XCQ53KLeHJO3H0evXKeMnuPbUx5noGxjVRoUWXZwHtt+gz8uTef54nnZzzvMb8QlhzhnmdKTr9XzEeY9/XX9/8HGnNjUe5D3naySO3v/1DyfDq49+9KMPvdl/X234qX/4UVM7vBHkmUfSYpbNGOaOsnDUmeDwoCSJEyaHlpdenlD4EOqaWSHZLQxFUGKnAd0oZHElpiwsaT/lsTOSyhiG44q1pT64miIPKAt4ZddTFpAVFZM9x2hkqGzA/qjCaUXcBp16klaEDC2lk9S+4Vxl1tNRkpVE0wkhjRXEUGqBdh5rHUqAKyUlHu01V9Yzfv25gMDGeG+aToXHiOaHbH5jzzwpCPP3jyfH439755qeJeYm1XOxUCkFSje75WOfM+8k1jZiqc7TACLn8c6CC6lry8rqBnG8iPOeuqioqhlb29cZH+00KQ0vG34X8sQY+li3ys1348448E0o2Ht/vK1oPiMlSiu0Eig9r5A5JgQLMddBnQeRXfPt7bHY6Vy2wlrb3J8xOBMwPoLxUU1JybQM+c73B3QCSWEy0lBSFzlRmBBIS6TmGNBJbO2QKLwTKJvg64YI6oREVQrrNH/yvYLpTMxFUhtQKJXCe4fSIc6DKx2JkxS+RrUlrrZUE09WSIrMIKkosoaCWRWO0kExkrRFyEfeI+ichXxWcbhVN2Av8CysS/6jH93n1//A0jm7zpuWLTbPG+NmVVDagI3VmPRsgFrw+FZJ0PUkvYiFJUlLJ6SFhwqWzzumWcDsFYHZtghVkSQpq7GnFSl0ZWhJgawsnUBzeGgp90raLcFAJVSZ4Ln9I1Y7EaMgYrorsLHmzY9qfvNzlje3c3b2KsqRZ+VKC9OvkR2PLz1eeMJUEMaSuOVY7Gu8sOwe1ngn0T5iZ1rzyDt++L//9zLh/BG30TD/KICQDzd+vv8155pxfty/xbGg3EMfx+Tge0HW/Kyvu6djjspxO00+PubAfKn0xz2ff8gxpZrRrltoC1PtSSNHEA6Y8RGc7lLc+HXOXEkJFyJ2XguJ1iv6yZTKKqRo0es6qsxTZg6kASNxtiAKQAmNVBGGGipLMfWUM7AzTVEaltcVee45cI5x5dg8yllcgYO65id+7gLf+Z9WVNs5iRWY0rFjJG5pEbdlOdPvcPtwB1m1mWQxk1lOd+M8/+BX9njn5YSPv1bRTnvUucOE8MpuzNkNT1Fo6iICO+OxXshT2xlnz1i6F9o8GSmunB1y7UaAG1SsP9rjUGR85kbOsAS9lrIYSWa5w/uQF16ZsL1VYipLZi2vbTnCWZ8cTbddcHBbcJCtcbl/SNTSiNBhBbTilLqYIVxFbylssgnO40WAEJrKNhzTXifCYSjKmiCCNA0JwkYLMgja4FOGhwdoWdHpCKq8oK4sZeWonaGua5TsMZzlRGmC8RlB7HjbO9/C+bcvspfdpNqdUNw+oKw8C1cuQr8gH32IZ1/6F9TRkCCKiWSKrAyyzvHqLsn9eA04fhz3NClo9hVi/u8H9Fch5kLXx1VW873+w4DW/WPx9Mbj9Lkf9DeRFi09WnoC1VRZKuHZOLP00Pnrq5KjFRSC7NAxGRd0em2u7e/iTMRRdUin1WU/r3nzYsLN7SmxiXnswiKVc2SuprfWotzLOBxW+L7Cji2zw4y3XG7TiQN+7akjPvTEIrd2HbdeHXKz0pzrZuRFwHLPUJqYazuWtQuKVigYTmZ0+xFhopAheOco8hppDIHWyEBjrWUxCOhIEFZSeyiqisoIKisxQhB4i+tq1geaV64X/PWfhNcmXSIqKjfGy6DJpdtjwCJP1OEb25pGJLTpHBKvBfo4jebBSo/0HmctCnAa1FzSwViaCNYc77i6qfxrdt3N/7k1Hk2Fk5JZVjBhG6UUzivKMkfgGv6VVjhjG5scCULNSYbubljXWovw8sRap6madEg5rySc/87WOQLRAEpz/MapDu+bfChCSJR3WCRiDrCEg9g3pE7lC/7wxzd453t3YJZQiwo1lY12mA9QElp0AIsvNc6AdxqpHN5BZTwoqMlRUkLV8MeclI0A30zRihXZzCHCJtyMmptbWxC1xwtFZi0K3Xg/jsDUinw8Q8mY0cgiKoUJXKNxgwJpmezA2ntalEXG5EiiTcgkzrm8pvitz8HvXYu4pDTximXoJG87E6DPh9z6ZI4Kc8S6wAuJCTxqJ8K3pvijNkU1Y+/AE8cxC33P8MBSzQoOXigJRylBJfFZRaFLBu0uZlwQxyGToiQJQgbK8fi71yjzEkmITyvedn6FOpMcXt3halbjpiXtZcWTazXb05DHLwS8+etbZNemtB5PMekUvSAReOzUo1KBEgpfGda7ljgN+MIrULoZlQz4Wml2DoTEfZ4d4tRO/CRtJ5r31X0T/pdu9y8cx3+Pwdq9VVxNKvJ0kbng2OPQHUfFOH2OpjnvT+m+n4pc+dOg7O69J3SZuAp8RFvWZG5CLSJ0muH1d9Np/yDF7LOM9v4GC2c/yWqyzPXtED+e0o0yirYhE5AGkvXAsblXEKouZV2hIgdRRtSCWrR44oJiuO25+eqIfD8k366Y6hRzrSYK23QGBYcvR8TRgB/7qze487EEUcXcGhfsWYksoJ1O2Y7g9szwudfggx+y3DjyFIC8eZO/9N4Wt/envP1izNXbinc9Irh5UDMba7YyQ0DE5Jbkc+US4zuWx3ttjq5b7I0xbTlgNM6YJRmdbc3YwWQS8f6FkH01xGqPSXOiFsxyTzJVDAIQusdAFzy5rLixc8T5fsLLWcTbH/doewPbDhmVFYtdwfpSj/FoRruj6HRDqjKndAJTN3tiHYJOodeLqeuKbivCCUNZ1tja4WyP2tfM3GiunN8wkquZw4kupcmBGKlinKyJF4Z8y3vW8c6QTTX720NuXX2KTrtPJ2nD4jL63ARhb3Ht2Z+k9O9AqZCQrYZHN/e+FcEC0+g1WvXi3R79BtEk8KCO9SPFCYg6PkSIeTpdctfSDn/XBPq+TPzJy8dFWqcHUXPxpsjsuP8fgzDvG7u5E5DmEfgH7W3uaV+VQEu5GlVqKhtyc/+QpbUWowPHo2fXmFZHzLznauk4l8QIWXIwdRRFQdBtsbs95vxyDzWb8OKWoaoKvvXJDT7/+R2W2jHCBeyODVI25MlvvBDyws2a9qBma0exvFSzEgu0lUwzS3ctJAkE0tWEFqJUEbQEXic4UaCFIptAIRWBknhbMTmY2y0knjCFMBUQKmpjGM08G8ue7/4Q/Ogve+raQ2QRlcQZO9/ZNtwrZ+fpOQ9WW44n0Kaq8G5VhpMCZY7DpI1anZiT3N18crfVXNiTRm1eerDivrC/d3gPRTnjsNo7SQdWdU1VFUipsDQE9JN0gmwmdimPTWQbk2spxNyX9thWyJ/8RRwbVQuMAyUboKZlYwnknDu5thACj8N5jzQeIwTKeox1lCrEOEdURLzrh67if7vH9s6M/iBBdWeoOmpETXOPNYBtKnCCSKISC8ajnKaqHQI/V8v3SCsa42/XfD8vPDpodNACoRCBaCYqE2JqA04QSEnuHHHtiXLIy4DZqKQuQ1qDiPFuhigNpAKnBcoLglgzMzXp7gSkpM7BJUcs0UOl8OH/cgtKwdYfXiKYbVOHHhNFHD0/o1QGUQf4IsaXJfKcx7ctopZIb9jbcnQWoJUU1DNFpyPQfUM4WOYwP+TcpRUmI8NLNyDyOcp6Bh1BngseO9tmfOjpdzybmwWmCyrU/PNfuco7rwz4g+sz3vNowuU3tXnu2SnLrR5PPGrYP/LceOWQlYtd6oOcwLTgfIFTIBfmG4Wq4WtJGdGLClQ9w/mUwHztyDs0rTFPP92Onz+wKvABqb03rnw6fbw/AU337uLvP9+pTz8gevWgaz4o7Xn62PvbTA1JkTg8mdP060WsNIxUQZS2cNxmq75CsP7rJPu/RD79G1xaU4y7Adu7Je2tFksLzebyYLpAr1ugtWAyFhQzRSxD2lHFYTmhMCnEmuEIXG7pmoR4o8Y+Isj2S/aGMJ6OoKh4rH6cp7de4p1PXGBS3KZ2oLsD/s2NPTYGZ+i1PI+d32Tn+ZzLqxc5LHN+7znHk28fsH3U4zdubpGrgjhZ5OmbB1yOcw73lrAL+6RBn4vJNh880+I37mhGVDweVdw52CG9nNAfzUgXDLGdcnU443fqGWIEpYc3LQYEwhIqT7oWc/bxdbK9W9w5rLHpEu0zNdkdS60kZSBxJFx4PGdvDLfG8Pkvjri03uZcD+piAk5SWjeXB2l+JyUDjC0II01R5ASBptfuYK0nG+V4SlRsiWLIZk3UPgwTjHSsnQ+RSlAZg3OWy29ZYO/2FFOGKCW4fDlCEAMpbiOC2Rgzm1Hn30vV/xBlXeGq1xDxI1i7T13N8FwnEDGx6SFObRju703Hffm4Oh3nacgQjuMI2DH4b8ZWMwaata3J8sjj9OEDxteDxsHx5uR0leLrIl1C3c0zCd88/xKbo69KoGWqiJE9YG/kWXxskaWewYU1QTQipkM3qllNYHezpBd28FXF6MBRHhUMWjE3bx0hEk0oLHsTz2/9zi6EhvO9lJ2jjJ3DQy6f6/HUSyP6bY8oBN6FpNIymglUFqI6NReXZZPmsZKw1wjAeSEpagGFozIRSs64eKFHryU4PCrZHwa0+xa0o5ZQisaLL7Cgo5Q/fO6IH/pHXaY1yHqM0SFREVILNy+umxtJn0R3TBPVcXpOdj/9gx/TlxxeC4RrbFqEAoFrfAnvlgc2u1rXABl/3DmOS2XnEg9IQTYcE6QhRkkkjqquMfWsOb+SjUiqZ+4m3/BBnLVNqhKBFY2ey2meiKDhcUkl0VrNfQ4k3lqs8IBDBcE93lU6aMJGXjagy0cKnZcUOKTSFJmhqjxpK+ex1jJ/8Qen/G//bYc6r9nfDei1FGHoiEKBF5bSZaRphNAKZxzSePyskZJwXuJ1c/XaOZyTSCwi0CitsJVHScts1mgg1bXHzoGxdR7lJbX3+JkgXY4ZH1bkmcN7Ragt1RQiPJRgvEDVHoln8YkAKTw2g7V1wXCSMlhyTExAIBTVb66yc7NgaTFE2xmy55g9K/CRxsw89mxNcBtsplC5A7nAwdYe62txI57aqfB5Rli3uH4U0B9WXL5yjs1bu4QKVtOKs4sDcjVBWMVGr4tOQeWGpz+zz5lHNsgmNaO65olHBkyd49vft8r/9YdbuCzmbRdDtqZjCtflrd8YsXnN4kvJzrTkbCiZPu1IL4eIJYsip25pAquxPsfVIe+6uIgyjoPp1xrQen17EIC5++bpiNbdyNSXsF07Ptm8GKaJpN2Nlj0YTJ2+h+OKxQfe01fYhBCkhSJPFQ5FVAzJZAFijU6lyarXsJwjdIpwoUXd+z7cM/+a27d+lZWNSzx+JeXmi1e5ea1Cx4LeQkM1qNyMpBuCENQTCMo2g/aM6zdnVIVEJW2KquRoktN6CvZXIQknxDWkIXR1ztXnbzAI1vnDTw8ZKUXSdwSHYy7EHQrucLADT745YrjpEL0twlrwofZZnr1+i91qiQ+d6XLxjOEnf/eAD3/gPK+8dIdeuM+jbfjU5yq+4X3wsc9XfPByytFsxG/f7POtj+as9VOuHkTcHs54xg64NEh4ZnOKsyXf96cjfufpmnd1PHGvJl0QnHvM8sVbCVGguPBBw+1fMjxxJaNVhrjc8PKm4X0hhKqNyVOqwtHvJ0TBhLoAIRPqKiNKBEprnLdYS8PZ1RDqCFNVZMMZ+EbguTJ2/lmIIrAWyjLHBYq6trRjaHcDQDI7tIQqJ4y6YEP2binwHh1NkOVttIUyfx/74w9QFR9HLdb43mXC/Brt6Ao69XjzGKaqMOY2pXpAocXdV4AGOPm7y9TrKhNPorc0gYnmjXv75cOu8UaVug8EWTT0FnAnVj3iOKjwRmPjq5EM//QPdP30qItZNZxZT3n5YMaVRxvx0tAJJrOcqggwTrC7PeP5q1NaskVpPGlL8/Y3p2wOR1w/gryccWmjzUvbFdJ53na2y2evj+jFnheHFj01LG4kvPjSjEhr7gzhwrrlvWsRjy4p2osdwiRHe0WRO1CSsKVoLQiSrqPX8/TbiuGR5OadAptYlAyx0hOEnjiAIFD87tMZf/sXIw6HiiCU1EISeQ/GU1mQWHBN1OTYQ1xK2aThBDTIpJGYF0Ii9TwN4Zof3ipQ3iGVQwaq8f9zDuYK9NjG99CZJnp0QhRUonHGcQ6NR2pNu7/EUncNAol0itoU7B/cJq8Koiii1ekQJy2stUwnI+pZgXf2RFTUOT+vMgRoBFrd3FdRKkUYabxszMEDKRp1eAxCScS8klEJgdbq5JyVNVjjyE0JtUPplJ/+voI/9QHL0ncafvw/8/zI/1rz0e/p8uF31pzvh4xKwegwIw4josSRJrIBLzOHLxzSR5isQPQCCl/i5qPTIQhlo54vtEdHDldIgsCztwk4iTcCUzY/VektUQWFgH7lqRY0vXbMrc0crRUXHkt45TNDWpHGSotNIHWCUFpaZ1JCDZnKqQvHQjtGvyVmef06e790lv0ypzNQRG0FG5bsJcXm2KHnIq6X39NG7FTN7BgHTDYtUkO6AqUVHB1mrK+3GDvHr/y9inefb1HtTxBdzWIcEquY5VWY1Io4dtx6OWPtbMgzN3MW+wkHQ8fNzSE61dzeznjywgKhDvjCi/t8z19YZ+fFGYuPCMo6ZXBWEaw5vKxxtWVrKBngqIBRbhmsKjqrDtdWTUn6GD7+bwrixYiNSPC279r+t1vpv0raa9cOT02qd6Na9++i76k69K8/5o2Az+sWDHn6PXvXsYG7C4hzr7+H5r2H39eDrtfMS/cuQCfnpcTWCVYpvDYweRYCD8l5VHER/BbGlThbEEeWInic9et/n2df/DssdKZc/LqY668Zqt0eyu6jBYStCOtK4kBz+xmDqlKiXkERejb3PK12wP5tQ74V0J95dgaa3/5Cwdf5hFkSEW0c0RvBoQNfgNYQ+YRhCVfznDhQPLGxyhde3ae3HPG7n68YtxXvWnRcPyr4yDed41PjW+TPLnFreZ8/sTDg5viQYGGZT3wi469/7xL/8F+N+FPvljz/Ssb0qOIvfHiFj/3OLkUeQSvg6y86rvRqLIu88uo2S7Hk1T1H1A04f94w2g0Z3yl54k0Jr10rODvQbB7VvP18wlPPOh57l6XXN4zsAFVM2dmuyDM4d7HHI49PKTNLEgQUlaGzpJoIFBBEGh04Wv1mHbJl3WhKi4C6cDhn6fQlSSvFWItXTZGQsbbJ1smYtK1odxRC142gaa5QYZMgp+6hQouTJaEICJSg18/otuHlvQuMdnYQRwViKWLKO2gNPkhr4f2o1goiukDwgOrau33tbqpdiIaTDPcaUR8/b4qnTnro3ZShl/dEqI7PfzeSfO/1T/q0ez05/vg95U1jC3f8XDQFW+9495seOmC/KoHWc//1sp8IwUiUnL+4RGvQKH+rylLYmoORIQgiqskMZ0NevDVjZ1hyRkZcn2astUNmqknV6W7IzTtTOr2Qt17q8MUX9nj04hKffWUbWyle27XEieLxjQRXOl7aK+mrmHdcCnjHYs3GeoQMFCJ26EigIlCxxAmHiiQ2F0wOSgorIHYkkWLtTEraGRLZs3z897b53p/tU+WGoA+YkNJkKKEapF4batuQ6bw/FbFC4XwNtkHPTsyRvRQoJSEQjVG0EKDmKtJibrkw16cyxuEsjUp8ZagLhzdNheJxsOk4qiW0QuMQUhO0O7SjBKkiEI66LpnMJgjvOH/xMgv9ZbQIQEqG4yG7m1eZVSW+rpFza5yGZyaQYr7/EB5kA+SiOEALcEoQKYXQTdWVnlclSiWQQqGEwMxZ/NZaTD1jRkLkKkbUfNO5ml/+YYlptbH7U3QqYSGh2irYnXm0sXS6Kdkwh0rjrCRpSdK4ItQBkyOLmELc1WTeNUr2dm4a6g2R1E34XTYCtdor9jYtygHCQhUyyysq4QldEyFUU4PthUStmsk+hK2QIM4ZbnrCIEXGDuksC72UydGMZEWyshJRt2YEdQS9lBvP7BAxQDpHq2NQrYD4SoHZjrl5K0PJsAmpu4JHFgKqfkhoLbN9T9xNmZVTdNTINsTKQCvg6S8qqt+pEKbgTW89w3PPbJMMAvJSsdpPWFo3TDYlUd9z8+Wc7pLgKKvZdYYrKy1+6pe3+Q/et4iLNJ9/dod3XEr5tu+6CO0chorp9oSoE6BWFEIDqsR7xd5BRTlWRJFlUnqq0uOqmKEZIqKIWV1yYRDzfLnCd3zk+a8JoPXKa7v+YaDljXbUD3pNePc60NUsNKcXAvnQzx8/F2LOnTw5l3sgsPpy1wMjJoSih3f/P3fvHWRZdt/3fU648eXO0xN3Z2c2YBdhl1gAJAEwAKQkUiySJm26SJWkomzRdsmkyoFVpmSDVpmWTJuqEiWRkiVZlsoMoCkbklCkQBEgARBxgc1hdsJO6unc/eKNJ/iP2z3TMzvLoEDCPFVdPe/d99698/qcc3/hG0Js5Qlb+xiXYqocHYVI38dXnkT9KJH4OKJeY30LXL0MvotUXQI9IA7PYpxFtB+h1X4b+1/5YeLZLbReYKfOOHa8z2S8w2SvQjiFyyw6gDqDq9cFSdIiTjxVXbC+IamtYLJZ0esvsHW5ZJZaKpuxWkcMTcKoGHLiWMInX8vZKRUrfc3bHmpR5imfeXaXd50MeGY85mwAw/BBvv7sdf7R5yRhXPFti8cwc+t84nNLdJbHfMOxgmc2E9quJllOsTtjTp6HF144xkJ3g9PHu7z00gg0ZFLw9vOacr+P9jXL8YQvXjzJ9314i49+ytPpOr4uKOme6/DKbsS7VnKe+Z0ZDzwmCFLP1QuKTqfH9pog0yO+ek1z7HTBmQC+8RwEA8m049BG0U2TZj8NDZ2uZTnpszGqmE2mmByiRBOkirANqlXe/ptKKYjTCCmbIKKuiiMOJgIZSLr9EOFyKtvgv4osoSoai7V8GqFaE+LUg0uQYsRwBq32MjocIbI+06xkcPa/YfH4f8ZOMSYQd5KEQ1mf23P2Hq24w2NKSqxzmIOixG0Clnhzdez250l50Is8CLwO19JBoKUOBLalb67nkNnoaFiPEm5r2/kjbMaj6+XdT///LND6zR9e9mfOhQQtT+Uce8I2vnkhCBkxqTRdVWBxDHcdbmqZTOHq/pS4brG1m6FDycpih1dubtBf7JD0JbdulTxyusMrN7d4+sEBn3hunw+em+O5GxlFFVLUGeNMIW3Jcj/idFLz0ErKQk/QaWvipGkvOQHWSIz1yMAQtRq5AqEVInBMs4i/92tT/snvaKKwiwonCAKECtFa462lFvJ2AHHYKjz0CjzaAvAHk+h2OxFANnpZQhukEoShRGmHFwftvANNnro0mMpRFhZTeKqiRhiBtQeT7+DzpD5gYCiBUoqo1UEp1WSqsvHTaoCUHd7x9nfTaS8ADU5rmk+59PqLbGxex9cHfUp3mEG72yxCpABtCQJJFEUEUuMlOCxh0CwpJ5rMQ2mNlA0o09oaZwxlbTE11NKTBjV/989qPvStimJWEmeGKpaELiKbzVBOIVXA5rjGlRatAqQQmLyRptACwgDKwrG0ANQJe9MMpRS18xjfxLfSeeJAo71FtiW1U/vhAAAgAElEQVTagRgLRgXEAdTTgtFQUiuBsA7tA0RRQ6KxiaecCaJUEqiK/Y2AIHIQQVtY2kkbZz1yXtJ7YExQBXA8YPRqRXE1pFQZYRjSGQjCEx5Te1xg2b0lsRnQquiqhO6SRe1FyFbArJhSuObaWkkASuJdQCnGRM8lrL8xZUTItVs1oqgxzrMQh3xlY8KHn15ke9+wuGx4bUvTiyrGayUnH+zy2S/sMj/XZmwL8onkm97VZn7es/o+EL0AKyz6ZgxCYiKLEA7VlkCNVx4igZ9ALmt+/XckT52vUd5RWk1ZwCevBvyTlyVf/uVbfywCrUtXtt9iU/29g6x7h7onGHpLwPARcLqQb87ED9fjncdvrpzd7+bxVtfolcGbxm5MS0tZGWoBIloirkdE1bPI8p9hs7+NEiA1rF+NiMK6wcd68AaqqUb3DIx6aGUoFgxBGtFWHj2NGWfbrCwOmO5qLr2yTTcN8aJCO9gZR+zvGUIRo6VkPJkwtRAWMNtPmO3mrBdgdMjDc5aLa45Z4BnYVa7Vt1g6JnFVzN56xsKJkC/esJzqCl5YN3zgdMRQSL70YsB7zlfUeZ/5Yxt84kX40BOC567GPHW2w4XtGY+d6vLa6xPOPZry0ktTzp2wvLoRU+mCc8dayPGU9f2ETrtgY19ikpKe6/LS5pCnj4FJBVdf9LzzsUdpBa/SymJ2h5pHH6nIpxE7wwknTkdM9lvMLVRsT6akUZsJGbXsciaoUIGhnSbk8YjFNKXdzWj3IJQB4xs1t3ZjEDXtriBqaaJUoBKLiKsG8qEUQspG1NrbBq/rIIxB6wAh1IHLSI0SDmREkXl03Jhw1wUsHYtwAgrjSeMBu+ubhFGb2mhy5en7Eaz8GcTCz5OISbPHkr95LgMCebvocO/clB6MP9RaPMAgc3fCcS+p5KhEySFxCxrzbWgCrSa4u9v70IumJn1voHX0PIevf/Kpc2+5oL8mMVoLyxrvM7azFsmxPu24pMgEO8MKT0XULdgawemTqwynG5S5oKJiLo5o9ywtJdnOYnxdszSX4HXFzcseF0k+/qUd4r7k5esFq4OQW7sz3vtoi3/+7D4PryTsbMOVXcn8Upuz/YxHVlrEHQtWUhmLLQ7UtYUjry1OBlS7jlrWaCGQoibt1zz+oKPzQoe6rvBlitE1oQ4wZY1RAcqZpq3nQHrfVHKEPPAvPMAsqbAx5RQHkp8Hk0MGBwKgoUBq0VB0I40SHpRHSIs3HiktAkNVlDTgwQYFLw4EPrk9oRr2BFLitUSJRlVeCEXT+mtsblZWVjh+7CRJ3Dso13rSOGW9M4fcunEn0j/sZUt5GwQvhEAEEqlVo/MlPUpKlFTNl9A0u++6FTWTXFAZqA2UtSQKcsq6xd/5jXU+9HQfFRSgejAZQ6BI4w6lKSlmBf12i6nJsd6T5xVJqLAVWAOFgUTH5FNDNs7pLQVMMk8sJFNTopymlB5b1KRa44eWOA3pBJ4qU4i6Jmm32NmYgdJYIXBVYycjs5rCSYQTYAzr13K8sHgpCLwk6kTcfH3IsUcDnNcEdg7Ty7n4i0PiIKHyE9K0RZJGoEp0rAg6IZefySDwSOVQPqYfh+Q3LDbK0RPHtCgZDJIGAGolIvAghqi1BHFWk7+c0GlXPPm2hOuvKU6dafHCpYzHl9vceCNHe83lrZqn3tnl6q0Ra7uKL1yfEUp4sOXpKodaEMzFjsXzASKJELZsKomLHr8POgCkwFY1oh0gghJqSdm2pFXI937Y8diP5QRxDxn18FFE0l4kDLb/0PaXP4xxvyrRvfiPo8fUPSD0e0Hyv5uAaYOxP9zwJY3G3ZvPc79rOmyr/EGHtDHOjxCmj+3meBnQcoL58AvsF3+L/Z1/ht6TJLFi5C1ZPkeSWqpS4kqLPNBDSnsxk1lNGGZY63GbllFpmbiAdjzl2MkFfAnz84rknV1e/NIMk/XQwQyhS5bnJFVWMxxWqDRmPlN4P2OoCkoFPQdhMsdmucGgHdPW88zEjMVCUZWWL7zkOfsITIo+37Lq+MVnh/zAUy0+diXlHYvbnDk9z1OnN3hxUnDhZov3PNgnMiUfeKzio1/Y4Xue7vHsK+u87bzg+Rcyzp/ucKO0vO+JgPWLI4YbJfsm5PQjCr8lePyEpKMWWU4g3+7xdQ/CK2+M+O4PPcBzl0espDGddohLS0Y7MXmes7AMW9cdwtdsllM+87rk2x+fIkrwyZDWI7Bfg0grjrUUg24BPsabgFpbLt2sEbam09O02gp0jaGRE0r0gci01DjnKPIKVNNejYMApe7MG2s8Ve2QdoXajlBRzmwI/X7EsQdi8mxEVmji9klGo5wwSqnqrGFpyyWm6hQrqz9DFawjS4+0Ae4e4Vt3O4hxOPMWLWxv71hTHXR58KDu0s26+15y22kBaGQimvWkD6pg8vbaaMS8D2teR+9nt9fPkQ/+/eIavyYDrTAA70NEpDBkDNrzXF6/ytmTq2xtbWHKmKl1XFlbY3ejZn9qaCuNlSBEQDeVdGTC1JbMxoLVRQlzCS7MSXptin2D6gEzwbHlPuvbOUvdAbuTjNzWdMKY0tRcWKtRZsx8K6aoS6xrsELGWaTSoBUqKZCqJgkivJoSxW2cqnnPccWP/ImMn/0ViYgcCIc1pnmfyPEubNrD/tB8NoAjYD4pZaOuLB3KHmyWRxiJXhyUQhEIr1CikVZoYvJGSFSKpuqkAgHGNnRz67AHrL/D4UVj/cOB7o+UTTZxxHoTISBJY6IooRUnCOmp7YEBtGgCv/qwn84RBtMhK0R6DqrQB9WzRoxUiCbQ8wf/59tBmQfvDM42X4u3npiM0ji0zPnSxQGub9CZop7NqHROqGKcrdHaMEMhyopWFDLKcrRSFLMDSQ4FdVkzyUs6nQ6djmO05SCSWNkwRo2FAKi9p7KO0EmMc1jv0VYihMLag4VYN3Ia3hh8U7vEGkkaByQxzEY5URdqC6lSxHGMECUq1sQ9SenKRturcCjtMK6FFiVTauZDRb1ryCOHTC115hARKCkoRiUuFphZSO0NrV4fVxeNQrQS+MpQiYggErjdjF7iWRtKTnc1Z94bsrmfE8YhF9ZyHj3RZjkVvDotubw/Il1YRVxbYxAG1FNLGHv2JhEPLGnmjjXVZq8PWlhegW7a0sgGa+eNRxmPUwJJQGwdPvQ4Ap54uOby9RwkpL6PzfeQaevf657yhzmazv2RzPjg+fu19Py9j+8TlN2bnb85YDoatDUmvEcxWvBmtfjfbdwPe3Xv8M6SsEgtt9kzO3TUOcLJlOHFP8FoZphWHgJLZTrIKiA1YMUeWgjQB+b0vmRSlcylMHRQiB7xZMRAGRCGrICvfDanlQpaLU+nLXjskeNcem2TaaGRU0F7EKC7NWlb87nLlsXSokoYzIeY6TxZdYubexvML5/k0vYaW9UGhbKUe+AjzSxU9ASsjyyf3xwzN0h4PdOcX5Fc2Uo4d2Kdf/ilkA8/VNDvatr9NX77OpzYTXnP43Pc3Bgzv3yaFy5d48RKn8sbFcuqyyduTqijgEdPdXl3N+CN14asXXR0TwkqPSHKT7Dd2eRj/0pQdjWvjG+ybGsu5oqHTjra3QcYXbnA6okFomSHb3z/SW5cmfL8lys+8FQIFFzcSbl6NWau2uT0+Q7znQnRYhdb7TPoeXZ3La+9XBGJDt1BQRQ7kGUjYh1qdCBw1mGNx7kS6xuvxDg68D40lrouaQRLFBwQmXywQUJEWQScPdfF+5qdnSl11mJx5TjrezsUZdaQHpIBKnB04i1OP5xzQ2yRmhBFU4U66gbSNFqOJhv3zLnbbcA7bga3JUpkY1N3dA7ftdzuWj+gheZQwPvuk9i718CRKzo8/yFu+A9CHvnaDLTaNKwvWeB24ZW9N6h0gk5mBNqwSUkrCJhf6XFjY4v5tiAvVbPB25KdiaAlZpyc79GZN7S6Mc9f3WNkJFppwrDi1QuOU92QT1zbJJ8pWj2Y7/dYWjXMZYaLF2d84BGF8TGjoiKKIrSwJEkLEUCYuCaT1wIZxCgHSsc44QhEQKkk39nzfPPjU37qF1p88XqMKwpcEBMYjZWmkSxwAuc9Pmg8AJ0TTcDTmOQACuVcw0xD4NB4Wzd9bdeIhhhn0UY3MgSi8UMUeUAucsI6bcr7SoLvgCrBOwKpEQdVFkdNiMZJQRAqwjhASt0YQPuGjSgjT6hD2klMp91GCNGAJz24uqDwHrzDY4itotAa6RpApdQCd4gn8xYpPIHSHFJ2D+UohLpzU7HO4hD4CrxpgLzGK7wFqx1RKBlfT0g7FTrOieUC04lEBDm+cAxaCXt7BXkNqY8pqBBhQJ2V1E4RRBHCNeKAO8OSQatFPrG4QCICQeg9hVEob8kLEMpRjTR0DOG0Rg4CbOZpu6gxf3YWYyBUkmJmybOSaBBCCNKnRLWltJqWr7j4zDYLJ1IMinZX4lcUL/5vks7iXAOwVzm1U7QDRWEq+nMxbzxfEygII8Wgp+iGguG2xOwWmFgSek0xLRj0DcJ3gAJfJ+zdGrH8UJ8vfnST9334JELskbzfEVyzROsh//rXt3j/u7uk2qPakJYxXRRlOaQjHe99rM8nX/D0E8+ppZSw72g92MLpCk8JB6ryKIGoa7w0IAJUR8NeDnMtSu8Q6ZSw7FLJir/x5+f53p+c0dGQhyOkLFGkf3Qbzr/ncWdDvlfe4T5g8jft3XeEFe8Otg5Bwve+yd3FYDwcR8HA92K+3go3dm+Ad3REOmFa1figJtn9B+B+np2tmrArSdKIPintUCNjS27GFKbEVfP0F0viRDUQCiS1yZjYHsfMiN3LBW4KlYbxtKlmpz0wtWdnV7F5I2K8dZM4EeggJjMFdhSiXBOkv+34jLWrEq1Trj2XM794i69uw8jEPPxwjjrvKN/o8PgDmiubCVcvZzz9eM2L1wd863sMc6nl1Twim5S0ju/z0FzKV19f5v2P7WPDiOsbJRcnC3zXUyWvbRXsrc8IB21O9Rv7m0uXc+KBIQsSumnB3p7iypUhnUfnybtd3NIe+3WKk0NkdIMsj1k57tkYJjwWlkR5zHe+P+afP7PN1x/fQJ+CSxdGLC8KPr93i72dmrQt6TlPJSXvOp7xwfeO+ep6xFxPsfvKCa6UaxwLYKFdo4QiNikPnFIUoUXrJsCyFmzmqSpLEHsECqk1sdYILNmkwpiKIIC4JQ+kdgTGQFU5dNB4LobKcuP6LkEESafDNJNcuv46dQ2DbherxtjUYcUxzhz/NPvpOvF+gVUBcWuRWb5GKFrcJYMiBd419nL6TVHQ4bxs9N2a1qHj9pPySJB2zzv1kSUiDyV6vbito3VHf+4Am+Xqg9Xkb7cOG1eVhsR1dPx+4Fdfkxit5378pJ8lNb05yYiaadxnMtlmZXWRV281eBG3o/nYM+u853yXOVEQuIDJHjzU1tzYqVmsPBemFacXOog5KErHb720R9QRzPVDjnVTrm2PGXTavPTylN6qx5cBWW7odwVohc4F/8H7QpK2oNPSIBxeCmQkSSSo2FFJiRdNz9hYj0FijKFyFisC6jwHF/GpZ1v8+P81ZD5pIypDLQ7EOw9K90L6OxmuVHdtjPLQRNnbppKFQ+uDqlNII5cgBUqLIyrwGi1LsllIyQRlEkyd42tHJAS1sygR4YXHC42QBSrQaK2JoqSpVN222RBI5Th75iyPnHs7g/4yeIH1lq3NNT79zJfZfuPF5pprQ6Y8Go93hwGUQGhxgGMTxKEijtQd2x7hUVo3OY73zUIzDuE9pm6+T2stppGyR0hLGCm2J47Rr0omI09U1UTaY2yPaVZS1TndNKIooM4sdSUw3qGkBgs2sxS+Yq7bJ5tOSQmxdU47SSm9pVCSsHbkzpEbi5ABkShJ05ROIRjmJamL2dzJmUxKaq2JopBqWCO8IJprIaMRv/lZx9sfDjDC0Upq5lPF+FaX5ScNC8dq9MMtPv1XhyysOvppylZREbQVS0shrUVJuuB4/YJjrpVgxYzeckLnmGHzMxYZQhV7EgEzb+jHLdrHIiozxpZN1medIBABkw1F58Gc6LjH3/TY1Rj3acjWFRsbE2hp6rbjVOS4cV3htGI0nBFFAZdvlXzr+1LCjqJ/NoLUN8F9T0DkQdWYGvSOBBPi52qMcQRhSL6TE80rZCKwZYUqJAwE7/9r+9S1RKcpqBRkxGf/4ZU/FhitK1e2Gv7Hm5iD7q7g5ejx+/kNNu94q+qS4W5MypsB80c/783VrN/dS/H3HG5GEc5jvIZnPsRS/AzBckXalQiVIoXGqxwnKnAJzkfEylHbEa4GX4fgA5SsCMoUwwj6IVvDOQIqUgmzPXjjlYJs6ClnFmE9diaocofEkOmQMJQEosKMHC0XMZu0Ge/uMk1gOO4QyZy9yjKZhlws24hyl2vTBc4uTpEi4PgxeGmrJJaSJ8/2+cSL+7xvJeX5iabPNoP5iCKDwnVZapXIjuXTX5xxYvkks/AG8zFs7a8QRBsMWitMqhmlrTDDmEffNsf2/k0mMzh+XJBda3N8NcCYKS0juFRaHoxispFjUpbslzHr1YjveEcHmyU8+c6YY6spn/ilW0StEZHu4MQEX4R8fqNiob3MQEx48WrGg8uaIJD05mApCfB1jm45+suCzrwnok1lSxx3rNN04BtAvGogItZajDcEAYRhgAw0XuYNhtwLvFCEkcSUhlYrRmmLU9AZDCiM5frVfTqdDsrHB+X7LaT6dh44+d+yv/IkupgitKKlKkbllEguULv8NmvQHiDv3WFX57Y20d3D+4MKrecA3tLcR610bxn0BIeJiT+8rzb746Fw9702U/4AA4YUIBv4jPDcJpXcfT3Ne55691uD4b8mK1qVL0lCQVFITBBRVUPGpaHYHBMlKVFdkiVT/vT7B1SjjL6OODcImUSS0caE0hlulRphFWVds3fDMn/K4WXCyVXB+nbJcDxjOqnZ250Sz1lSn/DGfo2mZM3GtKykn2RsXtH05xRxX9DpBUTtxrhadCxhy2NFjfeesoyYTiuMa8qRoYoRpWEWRwC8/91T/p+zMT/4P+YoYcGqO+rsUja4GppkVAiFkXewTU4I8AFCOsDhnaA2dYO3qsHIA+zTbSahaMRUradG0LZQuiHeJoQYVNjGljVC0ODCKA+0qmRTMaodSIXRh0q8jlBKyrxid3cX71RzjaZifX2Tuh5hvMOaAkNI2MSCt4H9TniUvIPbctIhZFOBPGivH2QRcDTLV07fLi/7g0DTYQmlQDjL6XbCX/07NT/yfWOWwx6+0gg5ot9vs7mpmU5qnD2wJTIOUWp8YBHKE6YKXbSZ7k6I0giUxxcx44nFFxVpJ6FMJCJzdH3AsKqZ2oBZUdJbCak2QipZE8mowZO4CQSCTQybmxOePCMw9RxfuXGVlRPzvP084ObYsJqFhSknzneY3hzxlf9pSNAtmY3blFKQW0kPCCJHlkluvGqYn3MUWcYD5yI4WXL9n1bIUOATRawD7HJFIvrsySEm95R5RZIkjDdqTpzuMp4VzL/TILQn25CIOUOyZdnbn9J7eJlb2460KwmFp4o8CwuSzjFYuxRDaDgXRARRQHs1wbdqROzRXjRYGw1IiZIO31XMXqlIB44g0ViToTsh5a4gXMmQaRuXWtxE8QPfHPHc1QE3xxNs5fH/Bjihr9VxNEk6CkA/bMPL+4J8D55reubc8TA8vJG86Sx3PRK3sZ13Ht+FVzls5f8eifVh2/7w386PkXKOzFUYVaJ1RFTPSF1ElEnqYIh6+28wnv080vyvhGIdLUtcIHA2QqkIRIVkQr4bU2QBdWXw1oP3lDMFegRW4a8qlJhRGM9+XlDllsFCl8G8Ym8zZ3ezonAxRlioDNpVXFiHQIWsJDE392uSzi5adlFijNMT1qdw/pTm+asGtT3E9uHRFc2JpGYWBFy8NeHBuQ7DUcWV7REdUTLrSubyhGmRMNeGnfUuTu6QdFvcuFDwtrcNyMsK7fvs7XsW5w21T5iWu/gqoR1rkpOKa9fWqErPYD4m240ppOD1jR18nYCfIVyP16MKwYxExjxx3hC/7MkKxWQ45GMfL5mb65C6iH5H88gTPfb2Al798h6n5mAxmTDbyzk+H3FqqcuW34YoQQUzuscgbAmSOEQXJcOybKywYo8KHSpofMjKkQUcQnlkIInSAKEc1tWYosZYiRABYawQ2mJ8SdAFIVoY45lfMYz2NdevTWh3NKrO0WQ4VZOmf5b26b/EcOkMarTXYHCtZWpB0qF25UGC0CTd8vZacIcL6b5z1DlHENwpVByuD1fbxtnj4DWHiXzDMlQNjEXc0/KzhwmHP/K7AcF7wDvXCGl7f/uxvwsvKe5iS77V+Jr0Otz7/M98ZDituLZv2Jpm7IxgodcikBkXdw1fvjljqROQTyoeSHvsDB23Ngp+69KYVzdyZBTQDyqeWF3k6v42S8sJA93nlfUtPnuhJFExVWmI5kOCIOLc8YRrtyzTaUbYa6ELjxcZ7+oFtNohUSXIcsv2Vs7WtmFry7K/65nuecqxhDIgSg29bsRcX9FvWaz0THFoD5WV+MDRCir+w2/S/NPfKDC1x1uHsxZnGw0qYwz29k+FqSucMXhrEN7gXaNC7jB47xDW4eq6+ZwavHG42uGtR9Sa3FZ0RMDqSsiz/0tEGio+edmgsxAXVpS1BV0iZRvpTKMd4hoDaIHA2ApnDdY6vK1RUiG9xFjLZDZmMh5xa/MGxf6YtXHJieMzxpME5w2GQ3DjUaxW46smhWyES6VECHngG3jgX+gaP8OGrdgIgkoB1lucs0gJSjXawLVwfOqy4X/+j5e4tTOi14e6VGAc3W5CUdZooRqrH920HROhCMwBPiDISJMWvpbMhjPmFxOiWFPWlmpqIRO02hF1XRFLRVWURLViPCoY+ZJ+u4UVhqLK2Zm26a0EPHcxYTcbs7gy4NjZFl/3SI9PPLPNq284fvNze3zDB/t86ssl/+cvr/PrX6p44FgEtWT+eMrUzKitYHU5xFSO3M1YnouIpiErjxv8guP1nzWc/nBI65GEceoxOiOOuxR+SCsKqPOKpYWU0bggDBzSKZLlConi9csl/bkAaUOEjEhnGqqa0dgTDgpOHmvRXgooZ5r2sYBqVhLqhP6KZzwMmH8QfFojAoEPDb4CETTEBkETLFVZTehSbFA2OIgIwsSwvRbz6ecnnBtoZM9wZRteua75wHveRbrQYycb80Mf/tGf/KPdef7djP392UfgrVsKd7LmI8c9R7SwDnFXdxTf/23Gbdzj7wNT0jCv7ow6MITFCpEckciKqA4RRUjd8syUZuhT4m5Kanqws4ZSL6CFxpoKrQTToSMfh+zd8jhRI1WAcYq6chSFpTKefOaZTR2ziaHIK8qyoi4kpg4ZT2ZksxzhNYEIGe3OqGqDjhIcMYIW+5szOmHMaGsKQ0UY9XhhPaPjNI+cOc4rL1R87mZFMr/M1FtEotBVG50Ytvf69Lswm0mG45ozD3dYvyFIOxIfana2Pe35MeiQ6TQk6TqECqgNZFlGu2dYX5+ADzA1tAcR45FiOs3o9vp05jyjfUUa5Eg1I1CaKDS0kpi0ZYgjSUtKhnXKpemIze0W3dqzeNJxYyuhNStAlhjrWdvQqOkep594hL6b8MqVgsFZiLTluYsRK90CUxqWVwe0uo40stRTwdpGio4KhIAgVAgEtbGUpSWJ1UHM3uzVToCzAkuzj0aRwssaY2qiSNJp95kMc5KuZeF0hxdf3mY6mtJvxyQ+xmgwQUZRek6+67epYoPer7GH6greN4UA5+5aB/eSNYA3BTD3vvZeQknDuL/zow40GYUQt/HN91sTd1WWD6/haKvx8DNuY5DFETyxvJ1MHT+x+Jb719dkRSvbK4k7Aec7nmvjkN7CMpdHu6QdwWSjZnUQ0mlLNndhx1eUqsZVJUnkWFjsICvFmdUuV9bHhGXMsy/vsrqqeOJEh95SxUq/x85wDykk+7sFV3SAszlLx1pMZwVB23O+HXFyMaGrPWkCUUvR6gQ47ahxuKpRjR8az+6worjpMNQ45Wi1IxY6muMtyZqt6CcCGQtGrQjGIb/yE4bv/EhKN5xifEXLt5hJB94j/SHfVDZ4JhoNMQ5ajSiJ9BptKqbKEZYKr2s8JR6FCD2iCvHMUEqym9e88f+2gIz/+ocU//gTJWum5j//7g7f/Y49Pv7VR/i1r+xy44ah0g0g3xqHcBO0azNzu/QIKDRs7wtCoymcIXSGtz9c8ne/9zpfyDK+4wnP1Qs9Hv1LY5SUyEohIkVNQFsU5EI3FSoroHIIb5FJgNQS5cD7O2B9g0HSWO0I2fTjVQD2wO7HH9ojGEc3kHS+f43xv+yxc7VkbkFRTwXMSnodKApFNXVI6ZGdmnqYkHgNtqJULVxQI2KYizvsDgtMLmm3NIvHNdsbNdtXMuZWIyocC+mA/XJIS6asPiS5dtkQduHiBUsn9LRXe3S665w5d4rf/O0tll8c8nVvj/mOJxOK3YoP/thZvvTFCUkI/90PLvPTv7rHA+/uc+XFEVd2h0TeEPkuxgp04JifH1BuZiw+JUD2uPJ/ZETfILi2VmOF58HzAhW1+PxXxpxb6TLeq2gFNfvDCl8GiASS45admzWbWyFLywn765ZWoIhaBRxvUz9Xots1qyfnUG2DJ6E9mOJ6LUKr0b2C/rLmK1dKRBTilALlqJUh7Eawl8AgwwuB9JbOccnV52pWVgR6SaMSA0HA0imoxvATL57kqy9fY9HNs1FvoYM5lvtTlp969I9ot/l3P94qwLqfRMMdwLw9qFrJI8HVm2187n3fW53/d8Nh3e+mdtexI3cZZROs2gE0pjqFlwVh2mKh+iWm9U8S7V6gfDZi6ktINEvLZ6hzy8bGFriaKDIsLgV0liU6UVSVIXUCfIQ3ntEoY+eGIgwlMhbYylDmkrqIMVWE1mvvRHYAACAASURBVJ4klhRZSTVzDTA+0QynOV5rxGTKSgwyMcQh7NeWhf4evdJTesO1yYS5hzXj65Btzegc6zKqdyEz9MIQqdtUXqI7Bav9Pm9cGxHFLVyQsLZ+C6Ei6twy3K5p9x2DpEctZ3jXxqqKW5uOd73rAYb7E/b3JoRxQNSqsCbm5tYWD51dptWDuqxod1tM9ko6nYhApjg7od3qY/JNpHFcfrXDw2c0Dz8R8pmvjrmxN+HbvrHHjVs14UChkw7ZG2MuP/sa2QOSJx5MqDZzXluL+VNPZXzqykneuHKT975rzOYli+mCXDKsLsDYNaw8ZxWutngvkc5j4wDvLRaPszVOQhTpBi9MTVF5pA6ZW+qAnPDUNyzxzMsLvPr511m5fgsVhuhwkSIbQstSBt/Dw0/8PIX07BabBKGEsFGPbxh8TVvuUMLImSNz9Z4Y5/ea4/cmK0cfv4lAcp/3Hh13kcMOWoZHsZGHQd+9YqmN7+Lv7U/6NVnRyn7rZz6SaNjez9Df8i1Mnn+JxSe/nnh0k7V9S68Vc2u75sqmI8sKVrxis1B0vEaImkc7ARhJ2I5ZnoPzj/XpLUBR1uy5mNdeH3Fi0Ob6rYxrec3xfsqkFuzeysDBqU7MSuw5fzxh5XhIZ0HQ6QnigSTqQBA7VDsgnVO05xydecXyQNPvKbptTaQleSVYH025sZby1z9a8pf/fp+PfjzkheshD56QPDwY8fk3eoQ+xsUCITVKHmiZKHnA5lMopRFaI8KwmdRBhA4i6iQgQCMjgfMVOk6IbUwlBZqkAezpkL/x4wlPP1zhph4/cHzoVMRUSX7q+z0njoWcXtznIz83BR2iqQ4i7wJpBN//oQn/4M9pfu43FJGtsc4yIsfuV/zFHyj4r77rAkmr4vxpAULy134549U3GvZiHnSpy6a6qBAElgZfdrDQbruwe38bkGitwzpPVdd4Zxt2iVJ3tIQA4X0DrHcOIQWBk3QSyb/4pOOH/5RnlllaSWOX5IoQZzL6811Gw4zEB4hAYrynHoGSNXYUNlgEShZ7KZ2eIIhhZ8PiMsnKyQQlHWuXciZ7ghMnAqyGigi5O2VutYUoDQ883eNLv73Obl3wPR8+jswE737vHLKacvbJZbJxhihylh5sIet9hvshP/TdK/z3//t13vFEj3aacmvNs7rgSDsBC3Mpw2nOyfd2WPtKycV/VRCfyOiJAbQkx88oXrmUsbGniQNL7gwtr1hZ6TCaWSY5pO2InVsV2SRGhyNMnsC0Yu6dGi6DiGF7w5BTsbDURSQxXmUETiNajmoHvJakxyMuvDLhzJN9pKohkU0eYDzTmyOiTgq6MRK3eMpJxdZUsrETsHS68ao00lGWBXnvWzh/4jSDlZQPPv6tFG6EJsDLkPe97fv/2Fa07pc1330judPmu//zf7Bxv5vUW7Ea3zT8nePaBFidUYoAog0iHyP0d7H58k+zdUmzvR0yVF2IO8wvWbwYU7uCY8dWSNopzsDO7pi6hkhLMApXKcrMUOeucSZYCtBKUuaO8b7FGI+1FcbmLC/26XU7eCMospq6iqmtJxYt9nZLIqPopHPc2qvwNqAdCvZ2Fvncq1NWO4rF5Yp4yfLVXc2j5LzzXMaSmuP4wHFq1bCVVUyyIb35lK39PaTuEraGrN0c0xlonnjnMq8/X7NyokNvILlyYQ9Nj1a/JA0fZLAy4pnP7TKdlSSJQ+sWt67vEaeOsycfZTrdYetWjW8HXLgyIu7H7E4htwUXrhQY7RhPBSvdKR8420H7fZ65POVkO2Z50GIqDR98coUgHXFjd4cKx3f9Bcn2F2DiQkYy4aGHPL/+pYJHT894ZC7hV1+qWV4J6E4dc2KJm9E+A92Qj2xdY61DC9E4dIgSKS06cOhAH7CpFbYWxK0Og2M1vcWa3OREqeZzn9khu7zH8kpIEViUF7hqRByHdE/+fR56+19h36/hZUYaBUBMXXqUPpyDh2vhULfq/tXWJih76zl9bxJxLw7xTQzfo597z+/7Bkn3WR/3BnXOudtrRQjBid+lovU1CYb/6P/9Dv+Qfhu/bj/H19sP8JvDL/DSWsaftxlhb45yuEumNAaPLwRl5elIRz10zKxjPo1YIGKaVRQS1vZKXpvMeODkAFHNGBm4cStn9XiLrc2aJG5T2xHLPcXNnRpyz4fO9XjktOTcQyHdhRACg2/RsKu8wCsak0tb46xg5iTTrKYooDICrxrLm45o0abg6f+hxURo4kKxz5BBFJHNFCIU1EYQuKYdeNQSwDmHkgEohQxCtGpanVJKQmVY6Qoub07wTnJsUfCOUxM+/mwbUxdEOiAva3Z+RSN7JUwNZV9SrFm0bZP2NpFhGz9SvOvHKjbW+8hgSp45KhfxI//JlJ/6kxFRKHj/j065tB4gcUzpcOEXApYHMwhDsFOECqGISL+jJAkqKmf56z+Y8vYznj/zMxOmsxbegT8A6wvp0boRLlVKonRTSnYWnDfUtkIpQSuKQDaBlrV32pHGmKbNKjTSTfBVSJwY/os/2eI//ZBE6ylR3EJUgmpW4KqYKIQst5QzoPYI4yl3PGFLIE3IbFoglafdlyRzKUpZpBcQO3zucFKhzqQ8//EdHn1fSn7FULUULa/YGRfocsAk2WL+xAlmr+3ggpK030e0oHh9SHRqgXboUNbz7KtT2toj/JTuSofFMyv81N++hjczvv2xhMcf7dDt1EzClGd/YZukFdA+lTIRBafOB+yNDOlcgJM5pVF4HP24y5zOmZiS/VxD4PBlBKU7qIZWSAnHH0/ptDzZc6CXDcEZ32AZbxq0CZFhCBV4Ydi7WOOVZOHxhH/9y5t86w8sInWN6xiks7hKIfYE2S6kDzpE0FRhfan44icLPl1mXLgq+Ysf7vB1j8Kl9YzfCL8PNzOIoOBc/2GujbeY1EMCb/kvv+dX/1iA4S9f3nzTpvoHoYI3r/+325fvd+P6/QZaR08d+oACj5EzYt8i8V9hfPU7GenThLKgFQTo4AB7aYvG2BiDrWb0+zFYj7Oa6aREW02Vm9ssYmwDxBbKoLVGa0lVFWRTTz4LqApFEhU4I8hHjtkYhmNBXXtiF7A/cVB7nI+Y1Zq6dKztz3h4JeX62BClFZcv9qil4dmR5Xvev8SnX98gk12ejkdcytqcWHLsDS2tfkXa73Pp9Zwo0VhXMR31GKzskY/bOLmDcy1aXY+OMkYTmO63CKMZ0q6Qdgs6fcvm2oSFpZg6F4wnOUnYodUr8FWXfCawbodQ9hj0JPk0IAhKIifYHdZEXU1QRqh6l/h0m/31EStxH2MKimnESmB47wdavPx8Sa8n2N+bcu0Nw/FzMbfWeqye3aOaDHhjd4t3HJ8jbe8h5+BYIHHaEYbQ6zQMTecszhsEugF+qxqhPZ05iFuaIFD4qCQK24wnNUUJRW5wXhKpGgLIS5gLQMWncL3vZeUDf5N65wXassWO26IlT+LqKaoFrorvmtP3tgvvpyHnuE/wdWR+Hw2Wjlaf7g2mGnb73QD2w3G3iO+bSShHr/Xe4Kyp/srbAd7T73n0LRfV12RF6x9/6Sc+8vzsOtWe4LPjlxnbkq2XoeqkLD10ljhtc/XyJr6qyGaevguYVTkITYLl5GCANxU744Jd7fimpxZZ7QZcvDRGxIqkG6CCgDc2Cs6c6jKpJqx0AnbGgo0txyDxPLAomVvQnDrfwfVKfEtDKJCBwFuPHRtMKSmdZFrZBmNgBIWHylW0lWA16iHViJ2h5lNXJePpIqXyJKQgSmoidKVQyoNqwOEIhZQKoTTQGIOiFVpFRFFCGMWEUcJcB/6jb6j59Kt9nNF8+qdzfuibNV/6asGeSwnDDu99osP3f2gXr0RjOBw50rAibFUI28b6AqkTnjoV80uf6fB9f1rxsz8m+Vt/uebDj4UEwuCl4LXXPS9c6/HhD8AXf07SGhhMVKMyEDoAZ/gLP1+zfstz/iHNv/grCU+eM7QC+PIVwa0dC8LgUEjZ6JtIKdChRisFtslim/Ksw/vGAiHQqnFul4IoDUi6CTrQ4Dx1WRGJGqFCal0S1zG/fbHgz30opisE09wRtwQqCJG6QrmIfFoy6EcUM4vSUeNKX0M1NKRxAKVjtK25/ErG9Utw4eUpH/u1PT72mSmnTsSUm4YHjwcMdw2t+R6hSvjy72xy7uke08qg28f44i++zGPf9gCjSwWBgvHlnPV2m7WXd0kSyRefvcU3ftsxZltTzj/epzQCvOPazSkX1guGteQf/ct9LrxYwKanUjHDyYR2z7O8mjDczVlabTOdGdJ2QD7zUAacmGssbmaVwspGo62eNkyizEyRIuLx962iqhxhBf5MDj0DtWP3akFMBy01tc7Qg6aaZWpJpSo6CzGbmxndMCRePGCGaok0zd+G0KJD8FIiLJA4rq55znQ9n1tzfG7X8Vs359lIHqYVG5RcRDrBZLqPai0S2BLhNe9+5Hv/2FW03qqydDhu3wyQRzL4O62VN6X1v49xv+T5rSQc7jfuOiIlXgyRBCjbwtlNbPkVksVLdP4/7t48WLLrvu/7nOWuvXe//b3ZZ7ASGwlwNyUuIilGliiGkiMpKkUuJZKtVMpJpWRbscpwYkdWHFtJXIpkyZVEsRZqCWVJ1MpFBCkSIAkCIEBgZjD79tZ+vS93O+fkj/sGHIADUko5JYqnqut19+vb3TPv3Ht+5/v7LhVJq1UhrhZEPsRCsdd1pElO5Acl6lzkZPOMMAqY9BTDfsZ0WqYmTKeO/b2cbG4xuUG6CsJpwOB5hjiG+cwwGTmSCdhCYZE4YZmMLcL4FJ7BKodnPZzK2HF1wpmm4k3Zdw0+/SXN8lrKqN+kagcs1RMWrGShtcTJIzuc23RUahIUWFGlN+xSr1cpzIz2quby5TGrGw2qzZDReM7S8jIvnh5SCdt0Vi1RuIjzt7BWMBl5hFGT3M5L4rlfxbqQWTJmXDiiZkwuYG5yBpMJtXaNSzf2qVYCcAmt5ox+L2AwVtyzkbNztcDpJgvNEdXCICoWP0hxLqbX7SE9SVy3bF3RGH+IsjVs3oPFOuvLI1p1hwo9ViqSKC6LJ2MMhUmxtgBh0cLD9xWVqk+t4VFrCKQyQIFJBDvXM+YjgzM+rhAEnqLrxTRkgN4v8NZ+huXX/UuK6GH8cJdUVJgXBUvhYfb1FYI4Ro/nGKVKDqJwB4KPr4ijXjlHXyqaXmVu3w4BuylAuV2hBaV48Gvxsr7Kd+5W+uRtVMIvPb7Fw27j0NJfL47W9as+K6se+9kEM2+yt1tgdMITWznrD0Xkn/tz7l1vQ5qxOUvo2RRjPaoC7o/reMWcF8Y5994ZcWEIT53uca5nWT1VoaMFT52b46KC1bakuz9FeTlPXnbcfTjk3oagJQ3ViiaslguVEhblDARlFqENC1yuGVxOSJwkzXwmytLy5lSimI4fMPILSDLOTqA7z6iKKlIUeF4FIWcY28TzHE4WiAN33Ft1D8JJHAIrHUorpPQRUqG1j/ZDZkrxfe/o8+Pfs8uZMymrixFJZPn5/9Lnb/zjDbSe8J1vyNHKYLXCyZILhfSxc4l0BmVCnBjxwB11fuMf7vG2txUUQalGsZMcaUIKl3P0SJP/7dGYD77FgMowGryhV/pcKYvqC/7dn/j80++1/MCbLM5PGM1dGb2R5+SiJNdrYUuDOglOOAymzG/kQEabG6wrUVvnHHNboJRHrH06y4LIWyAxMy6LCpubY9arjlMrmnYVqn5OlAf86L/Y4rf++7tpFAOSXQdeTtCSOJ3REAH5LCf0oTACfE1YGIpGxmTo4ayH1yiIEsu4llILfP7mO45w/Jjg8ieuMSsUO9WQrJcyn45Z2ajyhvcucOWSx8aJOd7OHpNvuwORTTALkq2tHicfCAiGEdPmgFwZ3vqtp7j0mT3O9wp6N6YcfkARm4h51/ItDy6ykVve+Bqfz34x43/65Iz3Pxhx92pMEEv2ti0bp2rkswl3HW9y5uIMIyLWWwlX+wKXWwqhiauOnSsZgVD4dUE9bFCvGWb9bYJOgDAzfBdQZNC/lpJFbWxmwHfoSGI9Q5HliFjjcoWxKUsnFc88l/Ctd1QRcoiwIagCRIDXcOAr8nGZfuCmCUvHBFtnCh5+6Aj1owtgY0wQYrIqNpjgCkE3GXN3c41NFxBX/b/CK85/2JELhSotHsnyKX6kyZ1EZPZlC8JNsm6Z0mC+0o6gRHqce3Vk63Y7+pvPv9J363bk4VLqXr7XTcHj7dAuZ3OgchBcP8Ooe5Brf8pEKjz7EfYHP8FCNWI07pNnbWy+h0eVyf6MRBV4eKRpjPIS+mOD5ymQBlsUKAFhBM75jMeWUX+IyEvHeE+WPCE/ism8jCLMMMogCo00GqkierszKiaiZyJajR69rQqra1O2zmac3IBK7njt0QEby4YLQ4MMFM22Ync3Zay3ee4FsGaJ1EvIZZdjG44L56oYMQfZIAo01XiRre4+ywtNanXDtSsZqgKLxzyunrMcPqIpxpqwIrhwdkBUq7C2Jtjfy2m22wzdZcY9j9XFBrPhNvW4w6ETDbo7Kb3enCiuMpgLTqwbpv2QymLOnSctN3rL7PpXuXB6j9X7PN75gUM89ueXiF2H0+fnnLivxv5WlR/60VX+6DdeZDyF7t6Qz01jPnBozMLdy7hxn70nU8SRgHpsqdQ0cWOO70k8dWB63IsZ9Yf0Ngu0liDK3NsgqmBFggoEnqeQlFQrk+QsMOfQiRVWX7/BePY/wPCnaPmaYLgOagkV3oWRJxD+uzCiw6x6nNwoTPZlfCnwdZM0Az+YY1IfIT0K2SPyaszmOalI8YI6Kpu+bM7fOj9fiW59VU7iLehWubZ+dQh6+foDWwfMwWeIA3ukl3t73XpeHAhmb36rr8vPgm/QQuvE4hLd8RijFdaMKZRCR5Iiy/nDxz7H2upxjh86RvfznwBTJRtNOHwootazfGkwYumOJe45kbA3TClSQaPl8eZlnzObQ77QndHoGJ6/DJMx3PWaKhcv5pw4HJJNUwJfsdQKCJRiPnP4NQdeiJFJiTQ5UEikDZkPDHHTp1m1tH1JZOqMJ4YrKmFnxzCZW4xy5KnhwkCB76EdSB2T29IWwimNMQVKOqxVt+QZFiVhUYLCR2mJ0hKUxPM8BJrFjRRUwb2PhJg5hC7HPyJ57aldntle5k1Hd8mtw3MWlINM4lJHlgqUEEgFyovwooy3vSnDGYUuFIgCnYrSBd1L+IF3FzRWBzgVAgKVOlxukbElF5Kf+hXFL/+E4u2HMsaZoJg5JI6qiLi4Nybwmtz0bin5Z+VuXVKg1cFCY8BpSs4WFqUkoVdHBQLZjlhdqfO+O1O+5cQ+a+GY1EJ3EmBnBZaodEJXBi9dYLi/x8jmKKWIUs3gXEkur60LtAjxyLCpY5rOKaIAIX10mjOdKiZDS2U9Jl4UrK8t8Lk/vcKh5TY2Ljh1IqRoCKZ+HWsq7G2P8IspJ44vsrVdx/Y2OdSEmljk8ueu847vXOHX/qDL33pPl5HSrK/VSegz1xXWKyFBM+LX/+A6nj/igZPLtA7l/Oq/7/HehYB+rlls5VQeiFhxOaNeyup6lWw2wwmfzetzJAZPWQYTD5gRN0OUKNi+krPQqEI0wvMihF9QMCOMFzB6jjQxNpvQu6IIGjW2ticstBUi0IhAYqRBhRKRlHMd57G2oPm5GyPeOmyiF2Mcc4TzgTnZwBEclkgleOFjE+55YwObzPDjCkUhyEcetrqANjOEAptacqmpaZ/d4XVCHZPMZn81F5v/H0aRTpCBQBBRrfjM52CMwNOvwkU5GH9RZeDXG7drw9yWi/ISLezVvLpe/XtWxzk2/kGC6INcubKG9hvsbc5xLiQOIpSSOGagMjKbk/QkDR0wnScIrZFKkjhD4STaeeVnKIvBkFvLzFhEYUh25+XCJkA6ic0cJgO/yBBRzLmLY+571xJnHt9nqSrIbEZvb4Xh/X1W/UWe1iOK7SaH9R77Y8nV6x5h1aO24HHPmzJ6mz0I5gz2Kjz9uSFSlcVwmliePrvNQkVz8liTnSTn9FnBfXcnrIslzj0/Jg4jRpNNslmFSSq4595FjL/F1edrHLlzxnQ2YT6VLK/GXD6/w/JShetXRmxv7uMH4PmS5ZUaDTvnucvLCLvDf3RXm488m3Bi5TLvWFfcOLXKhz81Ivi184gOfPbqLusb8ORnYOnImP/xJ7c4uhHRXlhDBpvcf2FGVy7QPJ/x4rMpnUWPySBmtNtHezlhDHHs40mPfJ4SRfvctIUS0uJFILXDyhlOmlIRLi3WGYoDcVIyVbx4psfFC/tIleP5AiFypHoRJc7h+19Ae4a48g/xtcC3Er/yXVQO/3MGSYc8nyG9ERkaJ64QBSvMkxwrcyIvxc76BMLn9i5atyG632be3u7xVxHZy6PL39ub597LNxu3bkLMS+fSq3yxrzG+IQutzXSbyPcxfUnhfHyRIWREWPGZZD3S6R57zsO7mtP6rkf4VPgE7U9L7KjPiTtWEbrgCxfGVJqa8SzhrtUmv/qpLSSS9XWP7sxwuBozbwsm/YJWVXF5c8JioLmxmeBJy0N3N/DjjOm+IGil6JoulRNCYgWoiuXF65ZiawzaoxMrZHPCeCroC1AWHjsX8NzVKue3FiicRjtL7oOfiwNi+E2/Dw+EKf+4BxEAznoIyvwZIR1gS78irVGextmsLNB8TZ4mKO1hrY+0jp/7oT7v/JkF/PoUVXg4mSM8jUsExQwmc4tygkrdlMWjJyGvI1QOUYGdlBFD1kwxeYVqw4FnoCijO8Vcll0NrSl2C958ssEji1NGuUU7i3GSZhCi8pTdUVDuoPzSeV4Ih5AOicZikEqAKL2FChyh8vBCnyD0MJGhKWr8/o9eoT84j6iEJGPJ+TFIofBEgfIlkdIIwHoWP1IUaYYSkOeWQjhEUJDOIHnGIryChSMxomYIWgHenmE2EwQVD6sy8rFh+0bC/pmU7imHVpIbXxpz8v41dq4M4Kqiuh7hzwdkztJ5YIFzn9qjeiIgOrTB3ukRNA3v/vE7+H9+7jLvfFeLL33KED+Uks4LPvx7locf2OXG1PHww8d472KVE80FnvjiPs9/2YBMef3rjxGYEZ3lVf7083v8zixjRUs6qwnFWOCUIJ0mVFoBYarRMQQNgS0Uwx1DrdrAyAkqr6ADh6csQoWARe1pci9h82pB2Aw4f22fih+iPAmRwQpd5u5FGpE6RODQdo6owhsXqjz9hU0eeVcT4R0Y5kqPZ69OeaQR4ZoF//WnWvyv/oS93NCua6pGcXbbw6iEN90bMiXAD30K4/CEZHvW40i9Cdn8Va8Hf91GNajijE/OHjb3kaJGqARWfP1/41+00LqdcvHW52/HU7ldoXW7z3ul7cQrP8c5R9BYIBldQsdrzIKfZnjpXyDm14gXali9hxBNilwwG4b4XkKl4VMMLYUpFcbWlX6AzkJWTEs6gRNIJXHSvYRq16qVMus0LzCZIU8NRerYN4ZQpxxtaCZXR+QTuFpIJonmvjc3ePj1Mb/wS5fYSeH62OcD33GCMxdn1JanIHIy4Tj9yTZelODoEFah3sxoLhkunxMcudOxMIm4dF3yhTOSwx3Bd3zrCo89NmLxjpQomhPGOZFe5fL1Te6+5wT9wRXG4yZ3Ppjw/BcFy4c1zWYdKTWHj9ap1RT1uqXZ8bF5zIsvDNEiIKkZ7m/vE8Uhv3s+5z0nNE93fT7+hTYf/BubvGGxhjgakqtF9KTLpF9w570R++MRK6sLbG13WVoLmHVjVh9S7D7R5YuL0DjsUc3qjKN9FuMKSoGxKS4Hpx2hr0mLjDAUpUGpsoRVh/QMhQOhwPctTpbu6MKV4eC12CfPU4y1KK3IC4FzoH2f3CnmmcI6R74/x1iJUh7GfJilrY+h649A9d1UOu/CJDVk6xC9QUKj0mbeS/GiKZ21mO7uCF9VX6EcPFAA3qLE/Xqbg9vxquB2LctX+M7darV16/sIXqbK/Yty3L8hOVp/8uzPPTqbpIReQDWK8CKoLsxwWMYzcCLiyd0h+/fcxbDzAmGs8V67zIuHLJeqq8gvvcjR9RWu9Gf4MuXG1QnGCO59bQffBSzqiMvdMfNMsTlMiKoBoQUdwN1LglpguedIi7imaTQcypOYLEf6GuEcUpYSd2kcySzBKsvWaM5vPNni8ErKdJpjpMfOzPLY6TXsgZqvUIrABhRK4YQo3eA5gD5vVWC8zOXZopTG9wOk0ni+R+BrlA75L97Zxco5yvOwqUDmOW4usMrw8NqMThhTXZyBFThrkHONKxxJLlC48n0kuEJQZNOS3O8cKvdxGIpRhGcMRSVFGL9s81kQs1KNJrAUQ0PTz3FeQmg9hFVEviYWlvNdwW9/MUJHAYGSSE/geRKtBFpLlFbl/4EsneMb7Rr1dpV6O6baCGiEMVEn4rl+m9euGUSeMEsEInAgPTxhEYFCSfAUeMKQY8lTg5MewskyvFpJtCwVnDjB8PqM4ZmC6eUCb0ESNjXFNMEVEYUzrD5Yx2SC4ycr1GRGazVi8/IQX2uqHQ9NgFqOYGIxskK9XWP4woTx9pA73rbOx3/7HCeXA5KZR/tQxGef7rEaORqLDVaPK/pbMW/6lg3+6I/OcN+9R/noExMuf2mfQycFd5yq0WbOckfx+Nkx3/MDhxjvFXzqzIj7OhGNlZAonnJ8rcFKW1FrQ2slpb5SY/OFEZ6NkVnOQjNk/Q5LY9FQq8dUOx5W5Dhh6O0Y0jxm0C8QwmehJWkcmLaKg2iBbGrJZzCXOdV2QOagu5WhRIMghkrTlfxSm+MyxQf+ryHfsdbi5z5h+dDjA37svRVO3h3xhrUBp5YGPLH/IF++uMVdzWNIHFYbfC8oA6ido8gsj9z1/m8KjlZ3f/NRJxxeuMhkNqMSe8gDVKA8rd1LP79yu73f1f8XhOt23LCv4paI0s9OZQgpSgAAIABJREFUCFHy7F79zW77HabFeSrNOlk+Yn3j3Vj9FhLxGWKbYHNNpT6g3ayg/Cn5vM0sHZNq0L4uqQLGoq3ALxQBAs8plAWZgcolohDIQpCmDlu40h/QOjzp42ufRhiTZSHT+ZzKEUsyDNGMqWGZFjGnv3SFP9tp8tBChlueEPT79OScnW1BmsN0DvWmR72jETolmedsbU3wwwhBi53dHbrTglZ8go0jV8CPOHupR2chJnQCX8a0Oo69rYQ7HyzIzB7ZTFJfmLB92WdpLWAw28UVLUbDPt2dGa36Kjs7E5K5Zj6bIojwgoQodVycJSTW8kjT5/H9KacqTe57jePC5Qn3vSbn9DMNOnKb0Ehq9SX6gwGNtsT3M5YXYTyM6A+nXN0MOHEsJeiASy1+VXAorKKqI+J6QRRYPK9A6wzt5VQbmlo9Iq4q/NAS1CCoSFToqNZ9wqqHH2vCSOJXFFHFh2COHzriKvihQCiD9h2FMDhlMK4A5VCexvMV9UaVO07GZIGh3zvD4OrHGF77BF6jhVZtKt4qs/mcPLIYFhn0DbUovm2Q+q02EK+c86+c5y87rgzRQQj50g0EDlGmoxzEVJe9mLKwdJTWQvbA6PQlcs+r1FaHvwZH6xuz0Pr4v34UD1yUE8cVQt8QexFOjlk9LFhcyVnsFIhwzFLcwJchWTZDpxXObO8xO/kgx/Z3OPaBH6d7T53RXXXqDx1FVR+g36uwd/0c9brPxZEjUIbId2yPPWIFal5wbKlJkM04ejQmWALlF0ityGwBBpIZmKKgsaDp7onS+r+iuWc14Rc+WuGe4wpCw7EFnz9/QWLTKlYFlOacHkJkeFJhZPlHlgisEyhZ9oytcygnMS4vSeMqQAqFVh5+EKFVgCXjR779BrIoI2mEEYhCIZwlTaAd51SiOX7FgQRpJaawFDOHSUsRhikszmmUFCgnSIxAWg9pMlwRMpikpLag1pBl6DMOUXjYxCF9A5kkG0nGc4OHR9keLFheFOgAfvspxbMXPQLtgVcWVzcDq1FlcqNxEukpWksNFpfb1DoVGu0KUSNEBYIicOSdDg/EfTzRpwK0m5qObwmURDuLEIpx5hhOPeYpFFaTZyDcQQr7TW8y4bBWYI1DaEHuLKPrgk6suXJuzmTq2O3NscLHEx6L3pxgeYHpSNPyBK4dU1uVdE9PiFbqbD29S7a3z14yZ/3QEn6zyqgr0Cbnzz65xzu/Z52nPjXnvW9UZEnEE08MkEurnGjNefbikPSaod8tePyZfV77bcuMN1MSqXjso12Of+sidk/zrz7UpZ+knFyCt95TRwSGtaUGIizJsa6TMutWee73RvihTyAdupazeryK0AXGF8g4LzcGvqJwYIxkPk2wtiDNJFHFUGtZRDLG6RCbO+Yjj9QqjLY0Wzn9kWS/WyADw5evD7nzyBJaOggNk7HmDzYb/PunAqDF1p7gE89OeP0Rh5bgYVkOLrFXPMinLp/hzOUedyxGjPI5TqWQalabC9x97N3fFIXWXr/6qBCS6XSTxcZx0rkkK0ZIKW6LML3MDPEW1KjcdHFLtPtXbo6vXlhuR9q9vZXESwfwkm+Wc3BTUX9w/3YLys33yW2FmlxiPN4HLanKBvWF/wxvEjLMP0qeWCq6hi1m+LWEZqdBp1qhMAlRxSeo+OQYCuEoMFhhSy8nKXASrBCl2XEhwQmEteAshZUUDnZGBV6WEueWG9cstTCjtr7BfDPnrrszPnvZcf+9G/hmjDetYlc0O5vgeS3WT8JkNmF3b05h58ymCxg5od4x6EBTb2r8UKBtxnBYY1IYdDagHUZorbi6M+PIyjLj+ZDJsEMYxYi8jrQhk1lKs1YnrlgG+wrfDxhPhyx3akzGc4JqQq1epT8YU0wXEUKhm4a4CGl4Cd20wmDfcOKY5cqLBUMUV16A1zwy5jNn4UJqOFIdU+1YjPHZvpayuuYxnE5Y3YhZjeqc70E+XmKYFTQ7MYNexmwgKbKASiyIKopqXVDrSKoLDq+So6OcsA6qIsAHHWm8GNAWfIvwQfoCq3KkjNA6QCoPBASRhxcolFUEviQMPfwooFAGWc3x6wV7V0bs9H2wPqEniIMBk8lnmFz5TXZe/CcMrvwb1prvJ/A9pO6iRIaxHi/fjJQ3cUvyws15/8rNxSvbi/YmB/qgULM3kTEHzt5Euw7OIVdm8750vPjK3Bfiq81Pb47Dh5b/ehVaH3nqpx91qlRjeb4gCCVawnK1RU3UqUYx9UCxulRBFilzMaNGjV2VcagjENUhxR1Vnh88iRIzfDUiDDJmlRfYD3Y584WMs1tzTq17bPcN/anlWCOnOy4vOvvdBOVHNLyC5cWAIqYsFEzpaK4s9EaSSzsGFxryVEDoEDLkvlMj/uXvtnndiRGFVhxrWT5/QYFUB2GUEqW9g6tlKQ8tiXoOZx3WFkhZur/fzEZTSqA9H6k1nuehtEBJyY+863p5nDGIQpLMBGlWXoSVFkRaIgMJziIKIJdkU0GeOrAKjCDJDSZ3eFJSpBLpFApIi5TpVFBkhnoYlrCtFthBuQ9AK0TmMe2nLC4G+BKkMnRWPaQnSOaOX/t4wuVuiFQOXwuskqAc+ma0j3SElYjOYoOV1QUW1zo0W1UqVR8VwMwmOFPgI3i8v8a3LV5j5jtcYhhNHVaWqFzsCyqeo1WxdCJJp6pohpY4EnjSIa2FzFEUMJ8VGCswRiCUJVI+w82MlaM1OosetWXFZDfn1JKPWXNoodGVGZP9gnYjRlWbvPDYNUxLcORUh8HcEfYlQz+nN9B89MNbvOe9LVZPbLB3dZvRzoCPfynj3d99mJ/915f5wQ9G/OHHu7zvXRtkynDqDXUGl2ac7e9xvZdx4SJEnuIPv5jyvrc1eN2pOjuXukyCJr/02A79vQJrHDsXR1w9rXjmkzOS6xle6Ag1tO8LWbrTQ0RTEIrJQLJzPWewLzCZILcJOM2NG44i8vCrMb36ErXJhK1E0s5AViSDXoEfFxSZpb7ocWUnZdiXWHKqQcyP/dIO/8m3C4SR5NOM3/1yQCYtK37AxevXGc5jfusTCYWn8GWBby2y+Rq0SLCuwuXB46i962wsvxbpGwbTHo/c9cFvikJrs3f6UVm0qGsfk/TxdUxRGIS2X1VcvZKk/lX3X601ccvF/2uNr9naeAUK8PX4Yrc+ruiCvttna/Y51vQD9OwU2ZQstt+Lsu9nbp+i3ztPvQXaU+wP54yHM5LcHqRNOLTy8YMYTxVIJZDClekUhjLlwjik1bii3BgWzpFkkqyAUKYUssowTYnWTjHYs6SDfYa1jMV2xpnnakjRY2+S4GY5n9+CI40GrdUeF543XDxXsLaxwMbhDqNkC+VJokqLC6cTur0hvV1DuORRDUec2qhwbVujmx6zgcd//J2LfP6LVxgOfZaO7bN9NWT7RpfWYo4fRMxmXQZdSXM5wckpteAwo/EujUaHPE/Y3hzzyJs26O7ucPxuS+/alMbRnBcvrHP/fUNCrXnsySnXfMd/89aI66OMZ8+3ef8dilN3VGkEmovnMuJqSGclg9xw/jS84713cCG5xrKd8+lnc852Q77rPVM2b0AxS6hUBeuHNHHVIPziIArNP6CxCJBlNwQJ5gDhQVAqT3ElV0uCcxnaK9dmR4ajAGEwsQIPdCBQQhLmjnag6fiCpNlG7k5p+hXSvGAmHAkJRuY09HGYTHjm3CdYPfowQe0euiNF4Nnb7zK4PSfrL8Ix/CpF4i2k9pefFu5lv3rZu74qovXXrNB6a/q/PLq5U6G6uIYRkk6wTGET/MCnP5sgg4zCgacVic1RWjOTPqu6TuiHNAnIi5iarLCWBPSf6eK+bJj88RbT/Rb5MOE1d0XkqaEaWKZTyzRXHF2tsKRTmosxNrVs7RtECgvVCC+wuMBhcsnuwNAbZowmBSZRCAwaCSJF+JJ33mv5735rlXfePSTuOF44W2WSB+AkUjnsAYqlEGWlLVwZwmocElvmD7oyfgYESmm08lBSo70AKRXWOn702/pYUqRRkCv6vYzMlr5TSrlSZWgF5Aph3IGbvaPIQFqByWypsigcWSKQGJRypEYwmWjSucMjQFqBDiwUDtW1FFWJSgT5OKNSDxiPU5xQVNsSGVnGo4LJVPGZs4IrfYlBY5EIZVGqJLv7gU+z06K50KS92GJhsUWr2aAaBWjlyEzCNM3wzBwjxiim/N54gXc0pqBy4jhGW4MAisKCEwdWEQIpFXnh8DKNygWBCfCcT0ACiSadCGxWoLMaQzMn8xS9YUJrMSZer7JxdxXjOXYuQVRTVPoeSWLpjqcEds6xpUXOvniDiu6QTRyf+8wWh5ZabJ0dcHg15pnnM556/EVyr87jXxjxvvfU2b1U54d++DDdYcDTTw745PM5NrU88IYFfv8PuvzkT97BQw+eYJr1uf94SH9vyIwxN3YGFFOf/XTASsvyseclf3o2Y63l0IXm6GJIZcnhwhDa4DLDcGzYH8A0cWQiw2oP6wnmheP6pYSdaxFJmDOKDtMIrtGcj9nLBb9w9ij3qxH1Fgz7hqiqsVlMdcFw+nzOPC1Q2tEj59/8riayJ3nzvSPOduHT50sO4M++2+edJ+b8H5/NMFLy2S+l/P5nc8bpOm/7lrvYWDmM4vNEU3jt0eN0bMKKr2lkY47f833fFIXWfjd9NAz6+FzhM2ffR6u5QEO9hUyMX1ZcfU3+1MHzr+pX+pcstG7XlvxaXqhfz5ZiZKvUZM6R+PX0giG+t0o+GrATf45Z4qiZ7+LQ8hKj7gUEdRqdFq1DHZZWlinSgulghpkVjG4kpEPNvG9J+oJsoCjGPmbqwdSjcCmC0kjTOcVgnDKZGm4MYWVJ0z9X5clnb1CNFGMnObLk+MRTVSoNRzEO+OzenGN3rlOZlx5f88Rw7FSdxaUWe/0d+l2LxScO6yTZlPai4DX3nyKMLQu1kMmowgvnd1lpCyoyZDDYo7l4BPSQ9aM1hgODUDkbh3ykzJlOoRrVWDsc8PyXZqyudxhPtjl5/DBnT19nYdknrFTY2tpj1qsxn6fIyGPzTM6b3+j4xEcNrWaTI4clD7di/uff3+fdD0vmecbepM3efJdK4dPoSHrdOZ7WbJ5TvP/7j/DRP34R1w15vp9z7N4mr12fc+aphEMLcPdDmvaSxYq0RGsIsKZClqVYU3KsCluAB06WXRBnQfseB103nATP0wR+iTJaaxGUwiwpfWrOQ+YZgpzCpVQOaTYebLD6QI1Dhy29oebq/gA/jgi9gJhlJANS20V2BO/83tMM8sOkSZeqzLCvEj/1EvL0Nebnq1mrfBXq6w7SF2596UGh9RX0+BV13quMr1VofUOS4asi5y3DFJ1cYLeboPVZovoSshgym1myxCD8KrPpHv25ohVbrB7Rjhfpz2d8qNrlddsRkdU8s5exM01xVnLHWpUsuU7Y0bx4MSW3loVWTCWYs1jVeOOcekuyXq8SqSkTa7jYTek+llOpahZakswZpMqZKU2gHEampJEiceDbELTABDl/9703+Hv/dpl//LcmvP2+TX7908sYv06RGrTWSFF6ZoFFO0HhDEJanC0wRiKsKy0lsEinkYrSh8QUCKtICoFNAkxljMxLLlIt1ljryF3pv6MDVcKiKTihkVIghQEDWVYghU9h8gM/EEWeOwojmJkczyg6oUFKwayfE8Y+Noso7Ax/7GHzgsIohuOMRidExDkyEpgEpiPFKM2RWmNzgRE5UpoDArzEDz3qrQa1WoXaQo16o0YlrhBEGqwhK3KMMdRFzlDFRIwo1JxglvLoU3BU1Pnn75wz066EqqUGK3EqQyhHuu/I+hZvwUNSpq3nWYFNNUI74rrFZAGJmqEzj2IiOHXfBlt711nO6gxyy+DJCUcfbjLq9RnXY2ZGsve8IZ4mJFozuhwy96akiUNFLT7/mevUlztsfXmLqBPwwL2HuHxjxj/4B/fwK//3NWryLL/4G4af+JGjfNsbHM9cHPH4ix4f/ntP8aF/eic/8xOXGLQsp5oRnx71WFjo4KaSUSF4Yn/ARj1iLgyVWsrdDZ9lE6E8wx6GihBEtYxMGIayQOERqYB5XhAUHsZZ9nZhMjI0ZEQRZOwOCh5cvIZLYqYq5xfP3o2jw6/v9Plvj+TouiXJLVrMQWoGew7tgTWKYWrJ84CPnfaYfewkL+4+h4kjtkZ9lN/hoQ2PE50Ze1seE+kzy3KW1xY5vLLG6StPULOKpebdNGybVgX8wGeQ5H+1F53/gENfP03x4P1M9z/H4njC8M/+NsHb34/RM6ppC9OY0p23afs9xiYkln1SL6AqWsjRHlZI5qqBZojJl1BBF2EVyoYor6AgBfvyUN1XG1+xh3CA91KLBCj5eFCuqC875ivtRstXjB5fWnCEIPemyJnHYP4E6ebvMZucYcYXWKpM6Y/m6M4SXmOdQw/cINAtejdWSK/1uHDlCunMIAGtQlQUYrLhweJeCmJKxKvk7s33HcomBL6HE5ZCSmbOkrqAWTFlv5jylocjtnam1IKY7o7Pg/dJhleHJGtN3r4bEckbXJ9WOHpiBdSIKxemBHHK0bsabG6DchaT7PDw65p86HcEKj/N1rDC8nqN7b0xr39di73dmD17g+Gwwsk7Y1581icfKfo3BA88vMKXT19kZXmNoDpkd9swGw04eW9M3rVM9i1P729x5wOL3LiW4wcCqZrUNzKiMCYdZCwfavDYZ4c8+PpVLl24QTMJuVFY/tH3tfnfP9rhbz9wmV89fYPGoM2xpQwaDTpLO5hBQeUoXNyZs3IU+qMpC7ttltyUy1dC7jzapNPYZ38HotCjWtHgCVI3hyAl8sE6i3ECLcqUW3nAaCqkpbAGSal2l0JijMDlKYFfIS8kaI3nQ5aPmYoCXa1ibI4OC+6+r4P2DcV0jkoVD7+lRr3S4vwLV9GBIVEJURYwDxO8hfdz/coUR59KRZGkFnGbduDBDL2t/9atqsJXorS3azUePDj4+fLNjnuZBcTLEd5bY7Fuq+a9zfiGRLSe+Z1/9agIJLmWiLBGYmO6szGpVeAk01Qz8SGb9CkCjfYkQeSRaMNoMOG1ukn/akricqwXUKlDoy44dy1F6Qor1ZjDKz43riWstTSL1YhmkXGk5RFoTZrlNBYEC2EEfoEXSiweozzHaYeSIHWBcRZPexRKEWlDjsSKDCmg0QiZjAt+8Q8reF7GzkCVFy6TIynJ5sbkYA+kpxQU+ZTvfiTkxHLG8xdTikySZRlSWwIV4IelhN7TAmslP/C2PpGfI5iB1UhHKRtWFt+p0ptHC2SuEDOHdaVcPys80qGiyBKs0RhnUcZgjIcxBk9KWhWFyBUmV8wKx3ymmM0y0lRh5xaEYzyFIJZEdYcMJUJY5kNLbyzIcsOfPhNwcSCweoovPYTQGN9SrfssLDZprrRptus06tXSakI4CpuSJHNym4IQZH5Obi370xSl5vgZvOkuwak7JA3tIYqivPgrcMoghI9WoAPJ8EbOdCjIRoJ8kpNMLMVEkAwc6cSRzywyKS00Gss+0kpELjnzyQFLd7fpj4ZEVpJpQa2mQRQQ1NneG7G0VuPci3vMQ8nF/YL6WpUgS/D/89dy7ovbjCdj7lpS7PQlFy7N+Rtv7nDfA21++df32FiOOHVike//7jqzzYzv/mebfOiXH+bjH7nOJy4X3LvUIZU5b3pdld/84+u87R0VBjsWz0l+8B1tPvLkmIdOKe68s0a7nVOrhdTrEFdDhKeZJpaO5+PbgLPXMlwqmecH/D9RsDk0PHinYponGKF5otvk2nAV6eVEUZ0l9oi1z14vxa/n5Nbn2nWBsDnWK/jj5yVPXsr5+99SZ6exymZ3mwp1pnPD21ebrNT6+HbKh58zOGtZqSh+4qf+U7Z7Z5hdukIwGvNQ29COqyhbpRkOOdI5jbf+Y98UiNbpz93zqLt6gyD4CMerV6msBuyc/jX06j/B1zfIXEbEKvbadxCnbyANV1BGgN1iKiuMPE1tMMN1IsJ8E+c80B5OzClyi3DqLxHM8/IF5iYRv+xKlpRf8YpWzCv9h+CW4uvgNTobYSqWKL4Hb/o3abenrN6TU10e01r18apThL/D7tlFzj89ob+zzdROqdQ8ag2PSt3HjyRCQKYEwtelvYjvgydxWuI8x6woi6tpbhhPHHIu0XPB+lKFUS9FuibDiSFq+HhRSG1hgsxStgrBU58PEMsh+RwWQ83aHRO61zwG6YzljTouyej2C0LlkTnLc2dmvOYun+u7NTaWAnZ6O3jyHgY7l1EyQcbrtMKCZ798kcaCzzTLsMJy/caI6oJimswZ9QSdhSqVRpvdLfBrXbSqE7dDdq8H1FsJ6XxKxIymF6CSnJmZI4uUifGo+zNGpk1D5KysTDm3J4kLD78ypTc2FGlGu5mT+wV3PRDhS4M0IYGaMrlhydKQdt3R2zbsVXJyPaW52EbKhLipkfEc4RWEniJWEcj8oBt3U5hVqgutLTf7ogBnDOQWW1hcXiBsRFGkCJXghwnOJvjCR4uMil/yW4vEsndjzvaljN0rgktXUibXdghExvKhGpnn8ItHqN3x8yxt/DQyfic22kNIKKzEEKLF7TcS1r5cSfjKeXu7+6+GGjtuKg5v0t/dV559FYuIl59PXynCjhxe+euFaF3ZKWhH4IWKZJZSrUn29iBa8NjrTxnMJVUjGedVqhTIMOTG7pSVRpWLO2M61hE3PWp1eO7cgPWlOme2LLV2wVKkeO5Cj1PrMbWaosjnxDalEvt4oaAZC3wtScYKuWhYCDwSFDJI0Wjy1DFVHqHMmVtHw9NoZZgUBk/6SCC3GmEtP/ztBZ8/M+eLpxu4Ikd6E4QHSVGgxYE8HlX+wURBmsKdx0a89VTK331/mVF3uG4Z5oLPXkj51LOG03uKyUTgR4LT1xQPL0j0vI0rZrhMME8hSxVBLPFV6V1FIciHBToOyUyCyx2edAy6Ci80oBWJK3A6J6wKPE9gEks6hSxx5M6RKwfaoq1FK0WBoCgMlZpXFiDCkk8F87nApBatNf/2+yx/57dTHr/eYOwsGMFC0KYaz4jqMdVqSKNaIfR8CgTSGnKbkzswpuQRFPOC3/3ePjbOQU/Lk3w0R001NiuQUoNVGOMoxhGj3oTAKVQeIg64amURptCiRLf8QCOMw7eS4XSGNSHnn9nB0xHrRwTH3lJj2tvn6B0VCuPRvZyxv5uyeofmhcs51y/NmaYpr1luUAklr1+vsUOX8yPD2m+e4cqNnGMPxnw5k5ycSB7c0Dx+aQffNjl1XJDducQzT+/zxDMDljfanP+VY/zszzzNQlvxjuWAP/nSDu95uMrF3Rl3bdSZXvC561DAv3t8lzOXp9TaAZWoSt4fsKdirJ6iVczMTHHaxwsM19IZ9abHsSMh5y7N8XSpns0nBZ3DHSaJJdKS6zPJx2/cy2p1jvZjPrC8y92+z/lewpW+x73rNZgWJN4QpSMio/j+ezz6VwVvP77PyNU5K8vv4FOwmRjudxHvutOjrfv05pYf/jvv48nzZ2n1n+SDzT6y4ojrA4rKLrrTAXZw9pvHRysUTYbdX+XYsYdxUpCqhE5jH6UMY1MhVC289DOk6afpzv8ZzcrPI6s5Jj+CUj1a3oxm5xTF/v/JoDiMF50gKQogI/RipPPI3V8eAXy1BecvsiN/JX8rDgVifpSe7lIPajTyMXtXLLXVKlEQUK9a8mxAfOeY9SMhg75h83qFvZ0J1mT4GpQqXT2cOnDlVqW3n3W2NFTGoRQU1uKUQIYCpj7JfE6UW7wQhsmAak0yHFiyqUN0PLJtOHbM8MLGAn//vwr4qX/U421v6fPYUymVYJE3v85w9UqXrXmFkys+w1SwvZMR1epUw4iFIymXvtxF1WucPL7F/giyosF6e8K5nYLXPdwi0ic5d/4y9z5QY2/Hsbvt0MGcStUxHnQZFg5bkahsEaIhceGod3yssIgoYDyX5EXAcDRhtXGYYTLj4WMp26M6b9wY8Py+I92PWW0UzGc9nrj+/3L33sG6b2d932eVX337rmeX0+45t1dVroSEGiMFCSdY4KGHYMdgJuNJCDhjnHEsktgZPA7jFuwEJzGYamQQJiCKQIWrcnWlq9v7OfeUvc/Z9d1v/bXV8sd7hEFIQDyZROb5Z7ffvHtm7/Wu9azn+Tzfb8obNxyXdtbohTnj8YQvfFSytdxhWI8gOYXuFdx8yfPwXXPqpWWe/fwRWR82O4f0YoGWmhxJnIjFQBI1UbyQOtCRQmiBYMHGieBu6auFxdCEvMXQKRDKYY0Cm1HMAo2Zk8QVUQLVrEDSRaicWXkMQpLEijNr60x7PfauaqrndyhnCXe89ecZzU9w7pDI5cQip6knKJET6ZSS+R+rKt1ajV8BXfzjrOGXq2j9Wdf8lz7/Jw2v/Gkt/K/KROt4VLOxHOFiONwp6dSaYSWxRxUug+qooLAxmz2FixMmdU2/3eHS4ZzVU6uIqeWkrhh7SSJa3CgKkI48pOxVDUrkVN7T1IFWN+b8uqYbSZQI6LZESMdyx+FNhFUR6yuewkaYyNOOFIKakHRohTk/8/EZH3/C8q1va3HXXRXLnYSmUsyFZTr3/NA3e37wH45pxwprFhA5ejFlsxhVVSwa4JaEkr/5v/Z4+IGID3yH5Z7tgtU8g1bFvfcO+f5v6OCaG8zGbV68kXLlJOINDaAmBBcjHSgroDag5BdxRrQLmFJiapCppplZIh0oZg4aj44EEYHSNfRDRCuKMMZSTFh4XAWPivwtKH+hizKb13SWI4RaKI2a0lHPHHUlUAEiIUhW4Ce+R/KvPzvhvQ9kvDwp+Dsf8dx5ekC7q6hiSSwEqYqwQtBUlhAWGibONUgZ8M0JhTWk44rZkSZSAWMEjQmYIFA+oJxFCU2kGjpLKcE34Bq0lShhiIRAe42pFFURqOYNUkiaoFjbXiKv1zjtAAAgAElEQVQfSOpSoCaHVEWH5VMtVntd6isN1XKLc2c1r9S7lKMe3fGUlZ7gtFV8+tUTVAzPHcObNzRr25K3f+MZ/G/f4M57NVevBb7w/D7COF64DMf5Ht91ccCN33mZN737HmZhwI/8k+c52ZtxEjwPbXe5uJlx5rYWH/q9Y64fNtxxLuUffeiQb3x9l/ffowkbXZ56NGE5S7nwoCGJE8qixahumAsATyk6VKbiaGzJMsFgCUYHCdga2eog53Nk7DEKzqaGH733s8zTNeZG0fETKue52Iu40FXUs5q4lfLm1Q6fO6mJdcab1zV3fmdFPzWc8TntdptyOsES2J1IOCPYWBH8xbdLntrdpn/uFI986lf4xddKJlbRbgts4tDdAicnKBcQX51b0b9X6MyQFZrJ8BkaIiplEVayPHsUO9igqDN6zX9HKpc4kh+Co/+UIB5gJdIMa0PR9DkQr2H29FP07/ld4ItJEreYElgI2X2l+PJsy797nS89kP6oLtEf/vjF6sGXtnDc+AJH+lV6nZTj8I/orvwAW6Hi1Z0OKpqS5oY0v8XxRBO6m4H1sw0nxwJTRTirqArYfbXCuYXtlveAt3DL8lUE6GaCWmsKCUYGbAm1AJkHRjvw8Nee59qlgLdXqE9K9i71uf/BEcP9dfYOXuWn/7mgs+55+gn4+q89zSsvHfDI5wMygg4x+7OG+Yni3G0pB+MJTz4xocphe3UL2znm5s0ZB3uSuG1ZXqqJ45hgu8yrA0YHM7K2od3qMZSSg+sNZ28bMCwjdsdHvHF9k+snhngOV2xKlOyTqWVwBWfX1wnNnNU0Zu5uMsDwyAuS+84UfPAxx9suLNFf6fCzH73Kd70p5dKe4vyq45FnbnIxiTh79zIn42PmM4mMcn7vN/d408Pw+rvhyVdyNgdHfMs7ByzJCp2UtDNBElmkWLC+QntU6lEyLNpwatFmVpqF9IEMiFjixa1Kj1yIlcpIkGQdUj1i7VzF6QdXoRrQjCJuXk+pzTHGDxFCcOf2vfQGGWSey489zs1fE9j2/Sw/8Aucu+f97B9fZW1lnfl0hp01GF/g5TJKxsz8JVJ56g/W3pfTjfuzJEtf7tmvxHf9kd8Rwq15tVuXE/iDKtcfrnb9aRD+F+OrsnX4i5/81Q9EHcfJgSPXCeN6xngMB6PAuUGEaseMhlPqJMYWjhsnhknt2J82HB/NSNKIdivnaFjRyi3TERzOE07mNUkdqKzBO8WFjmCQadZ7mu3NlHZbo6UijyNMFJNmARkMlZEs9yP6bUVjFwdP5GtoR5w7o3jXBcX//HMzfvxnPL/4Mc+lk4KtjUAvU0gX8xufPMFbj7V2YfNUFzhncKYmGEuwFhE8VVWRyYZrV2pePKp554Mxy0sLNkE0Ec4oCJC1GlZPS+7ZOEKnCd6AtAqMhUahgyCREmcCs9JTz8AZhZl7tBaYecC4QFMGxmMI3mNNYDQTWBtIhUIETT3xmEpibCA4kD4QIVAePIF8CbxU1FWFMxHT0eKmI6xHKEU/EjjpuH87YWIL9uaag6LDf/v6a1yiR+H76GjxNw9B0pg5TVNTNSXWz/Gu4oPfMoPSoytHSBU6hSjTyMjTjjRx4sgyT9ZaiGsGFfBB4J2isQ22lBSFpC78wrjae3RQCzPawtNMA8f7c06fXmE4MbjCkyy3MDhcNWNkEq589oRBt4WZaP7Fbx/zddt9vnAY8faHBDdFi40VxcPvWuP1D5+iUQnXLk2ZOUtkE87d3+Gxlw2tgeblnZrd2DArAo9+bocvfOqAb3h4mUc/XfG+92xwcwife2HGnbdHfOyTY545dnz25YZ/8l+f4/LBkOeuZzS1Y2Qj4mXLT/9swXu+bZW4O6GVZyx1I/pLEaeWBFtLgqUo4Czs3WzIGkeiG+I4WSSeSrA2iFjfjhj0MhI3ZpDNiGKB7NfEuSdYgbIxNJ4emt2yomsluYwZtNsgGqYi47N7U5pqhrWeXpbxrs2aONK0EsvSm9/L05ef4m9tH6KkQwZDEkUwkAgtkCZegLW1Q2z/jT8XrcO9F3/sA1JLsrhg7gMZK5S+IAzvRW1/HThPPv2bFIXE4QiTho2LKc88/gaceR39C/cx+vh/Rbf9MJ1z30vjNUiJFhpnHc7fykK+YvwxsnfxWfBfRr/rS276wB/0FvnKh5lJ99BqCdnUrJx6HS9dfyM3h7/GxYszsixgmkUlpJr3mc4CrW7OfDq/JVEjSDJIMshz6HVyBr2Yfjei10rptRd7ca+tMZXHerCNRUiJtYrJ1HL/m1pcebFi5+qIaqJJU8Pm6SX6SzOEavHpnSGbnYR5JqmOUmK5zace38Vowz23tWnFLa6P4OLpiKDANoFu7xRRLFheiwke5mVJ3r6DN77O8MB9HR59coY2c0aTObfdDvc8uES3tcqvf+gqSysNF+8eEOmC8XjExqkBk/KIxkzp9zq0Q2BtxTA7tuTtnMOTmmuHJ5ROoW2LwmTcvRk4nrZ56+2KV2dTHnsq4kf+E82Hnpzwn18s+acfTTh1ukcm56ilkuObLWJZ0MljXvNaA4crDOuCOx9a5bBRRHbEtMhY3WzIu5C1HFI6AgbjLEiH1rc0pILHC7+YJowEUaJBeYQEpRdCpTqS6EhTmxlRGtHrLVPOa06G40XiemrOysYSp7a2WN1qk6YzXnz2mI986Cqj3R5139DtXmR4+VM8/fs/SDV7nOv7n0CUEYPWAKsyLIqkBVrnCPfvkv4/SVvuD7Nbf/x7X7na9JWA+a8Uf5C0fYWf/0mtQ/H/tIT2/0W8/QcfDJHqknUT3pI7Vm48xbKzfHw3sHqqR515rncy2pdGVDdmrKxGDI9n1K7D/ecV116tcO1A5CVLAzgcSS7drFjOInywZMqTGM/Dd/XQomFrvUW+CYNI0zQOV9Z4lVDHFmpBZR2+kmxva7bWYvb2AyfWEMWSSbDM6obSaqqJ55d/v+LffMpjCxbTgDisEIvJxDxGYEisohYL42QhFmBfJB0mjbCu5O//wAp//ZskpDXORigVcE4gC4lwEpTDxqC1XRxSNiJUHjUNhGlgWi78qmJhaIxkNjNgBYlXdLuacg4nkwJnFVf3KpAJCMu0DAyWoSdiVGTBSTCCEAt8JtDC0s80QgjUQNJoQ9paGL06F6hqQ0SEMxbZVix1BYaGeamZVYJfvdLiLReGpFryU3v3kfpNfBLoJANmrsJXDY01jOZDCjPj771mxLKcMq6G5FkPlWoSNUaUSzQUOCsJXoAB7xxWaDoxtPPFNEljPKYG1ywqCsoueDXpAlUZKF0g7hqWLvaZvlrgyoje+YTJq3PybY05aPBRwt6kob4055rQvOdd5/jEv73M05cqNm/L8DJmbXnC3HaZFHOc9BzddKzf1aUdSWbDhrObCb/1xIS7l1L2pxMy02Fnz3DXbW1+5dmS++KGe+5q8boH2lyeBH724zf51gfW+cnfvM7aVpeqqnhlX/L939bhp/7NlLVccDIyVJWl1dbc3fF849vaPPDGBJ2nqLShKOClZyqO9i3tXoyIPDpWgEUnESr2eDxBLiyRtPREGnpZylJvwfot/PYEVnrS64IbRzXS6VsedYpOopi0e3z/k5r68Ji68qzFff7l108hSdmtI35x8jYuf+GD/I8PSaT1JNoh4wi/5plEijB1RFpTliWb7z/+s+96X8Xx0oficDjRLPcjWr5ieNAQ8h7r26vsdf4GZzt/hXKm2b8aY6eGbidh1FTY4j7uuPf9mOTXmDxzBd96F/kDv0TtdsBBIjtIZTCh+lNu0H+4ovVHK19fvhXzZ4vF0MwiIp0zHld0+1Om5UU60fOkrGKv/Ev2pv8nxC+x3IPIL/wM07SFk+aWUORCzkbiaSqFvXWRC0ZQTaEpPHVhMAamswgzl0yOa5oSbN3ieDjn699/Gz/5P11mcxtmJx18e4rqt0nHJS9Nc0RjqelSzvbpnG7ziSct5waa7nqDEDlCxqiOZ3+3xFHRSbvkSzVPXUo5M5gyLC1KdajKORuDNiEyFKGh2l+iv9xwfNSQ5TFx0iPrFkynAUdgKTFU1Fy6GrO1apg2lq0Viaxj6qKHSI6Zl5ZOp8PweEqkE+IoUJUNvTwiiIRO7JmWJbIQDLYUnEhOLc35nctrvKM1Z2vbc3W/JNUdOumUeUj59r9yOz/3j5+j23b87hXBgelxz2jEyp0pz50E4qrm/nvbvOuNDa20QSmQKsIkFqUkUazQGuI0IOXCvUPLBWrhb7XqggSpFEkc41TJYF2xeVeCzhMQisPrc45uwGg3Zbzvqecz4jjC2kCtPDp2iCTCCE1t57T9GpEviDozKt/jtq/5MIXfpKwa8rQmNH90fX6Rj/pyFaSFZ+gfXeNfLjn7461E/yVff/F59QdM1pcmetZ/eRj+7V/32q/4hvqqrNf3kzEzv4ebdXim/RDV6W/A1i9Aa4g/HDO7NObOC11253NclBKUxwBpYhlVKWow446lPp9+6pi9ocCZwGYvRrqKmJj1TNNrwfa6QOicODK04ohWy7DRy4hlyouXR0RkmK4nqTw2CuyfGC7vTznd75GnjhkeVVlSBwGFb8G3/0c53/2ehtqlXDuYs9VNec1Sj8qf8GMf1vyrD1fIUz1aUYOzAWsMAZibQDoNuFiwveGoM0/iNUI7sBHKNfhWhMBCqJAhJYQWpqqJKwezgKsU1gmEt9RFSeNTsiyQJynzylJbTwMkuSA3MaZUJLLEx55h4xBSYF3g5tTTSsB7SxpHiKImtjHkknoOQVpa/QRY6NvUtcE0nihKEKKhnUV4ArOxZ1RrnAQfV7z3QoGVEfsmZXzimelDdK0xuqG2NaUzOGcoyjlCOH7osZhvO93nWy+UXJpa2l5S2WVCNCF2CiEsudJEWqCSGKU9NgRE0EijSJUhSzSIGJzHm4WRdeEVja6oa8OTn5bMH7nOO959Cld7Tl4Yolcj9l5siNspw52aM9sRj6cJft/yiV/Z4Y7zy7T6Q379sTnveYviUy/B93xXxlPPSF7/1m0ObxQ88+QNtFBEA0N3sIqfjGg9IBDPdXjqaM6aVPzjjx5xZ9aif1/M5b2EZ68NuXIy4zvfs8GP/+IB99+dkaY1Z7o9ehsN156boZ3kgfszllLNJz9TUtkpraWE518yXL7hyNQUJWOSJCaJJVmqsZVHIzHBLoQHvUd4UJEmiRa+klIrkFB4w3gskd7Q66Y4WzGbKsrg2MxjOAIRR7jGIXLI4kBHSeZNQZCCYVXQzCESEz7t38Lndx7lRy/mSG9Jo4ogc1RwzK4LTBZo9yCPPUlQ/39uOf+vRuygrUqU05R1Q11BNXVMDl9h8/QHEL2P0OppZN3Q6efMKgizJZJkSC6uc3X3ZYZ1w8X2/ZjM0D8QDDtboIaMD/4u6Ukfe+6vEpIMbwZI+TJtN8L48xR6TtsoZJTjmFM3BUr38MzQLgEMKvY0FnxYKGXntkMlhljZR8ZzVK3R0RRbx6g4QmuNsw3OOfABJVPqUtPpGKytaMtDCD1m4Qbijv+MfPR12PFvMpt/iNQ8TTnKqEyB1ppI+T84BLVcDN9o71nokSpCHWEqizMS4TzVfkzUNZQBol7C8FrFpOhRGUfaBTfv0hQTel3BjUNPNo7ZbksOuiX+uuXFBi7OHHff5slbEeVsRl2lWD9hfDOi0w/ksgMtw3A/4c7bS648l3L6bIx1CtGNmIyPme9LVtaWWbrzhJtXl0G3SFOD6MzoZAJFw+GRZGhTRDzhjtsTRscxUUgxIeCNQCZ7xHnC4UiSZxMy1UEnlqZ0nDmzwquv7DHoBZoqIw4pUjZcOizpzlsUQXJb6tFn5jz17BKnztSEIjCZwWBQ8xs/+yKdpUB7nvCWrTYnBw3pg13ObNTsfarmju0esp5x7Ypncyujuwzd5cDFNcVkWjGZOlwB+IQkc8TaM0sCeb6MUi2CTEDGRElMohrS1ozO8hTtZgxfCuxc1lx5VuF9g5djpPb4WFFLUHlGFEq8dMgAojGkIkGmIxrnGLsl1jd/lEZuY82UYHqotEVNhSJBRyNcvYy8pTtplCDIGlfFtLJDxPwU1iXU6ZQ4m1POHHm8inW7RKJPMDGRbBHUAbUfouUZQryPr7dI44LZNCfqTKlmPdJsgrUei0bpEu8c0vbRWuLCBCH7LLsRV8OApXAT6JEo2P1T6lVflRWtt/6X3ZCFlCaV3BUrLsxLor0ZRyamm0Y8f21O3LM8dH6JR5+ZcPFswvHc4kzM5lLNfA6v7tdsruY4q3nqhQkX1iSdSHJmNWWjBUo4Tp/O0R1QsWKpA2unE6J4AXbP9wTX9i0msgQkBqBaGC4XCCaHNb22QsaBpJVhfMO8CkwriyPDOYNQ0MkFy7Hgpx+P+OVPO7orSwQ9JlYZALPxHGcWPPm0njO9FHPb3YYX/vkqInE4SqgihNdI1+ZS0eH84CY2eOLgCS5gRzAfSTIhUM4xm8F0CipRpFlDp9XGGEOQiiRWWFPjlACr2DuouXTDsjv0pMpinSQoCXPLSq5Z1YLgNFHfkWqB9pKo42mtSYyUBO2xBiKpSFJDoiPmBcyaBptKQmrQQmMcNBGgJZ+82eIjR2fJRKBqSrSSBOGRwuG9A2EJXqC8IrQU1cTyfXcMec/aDB1pjOoQ+RJiCNovYHcbCEWMNICV+MYjioCpFmC/bwSFmaPTFkFYbuxaTGXpn29z5h7P85933P3AEsfHJdUR9Lc6XHlph1P3nuYT//oGXkx427vPc+nRI54+UIS2YWMJXv+aU+ip4kDMODqZE0vJlXLK7o2S/+Zvv5nf+dkjMjnhUllQTxpy2eOFG4Y0kVx76YTZLGYlrXjLAyt8+NkSVVVU3ZzrexNWeglzF4g7mmQqeX5U8b1vj3jxRsPHXgg8sOXp9mLe1I3oZCleTJCRJIujBQqoNSiLiiNEFNCpgGgh2aEiidIB4oUzgJYe8KC45fMlEUrhg0FFmrqsaRUJy1OP9gk6EfQHAdNO+aFXznJj50VClVOUDT/39gFZOuN/KO7nyjPP8hP3zslaYLwgGEeWRJRljUgUriUgEhRFzam/dPjnoqJ16YNJOBo1dLpthJtxcBVmc0GcBoKAJO2wemaKm2fIVkNpHMUM2p2YM3c1HOy0GB0HTm1HTNsPoaIfh/A0g/qf4ZvPoTb/PlZ/L1FZUNtfohGKmfx2eqlENztMlSI0OVmWIGVBNdNkeUnt1hHqlqm4TdBRIIQSHQwhMjSujXOeLIoIYbF/FVGDL5bRSiGFRmuLrRQ1B4v1FRI0DiEqhO3hZ6/Qyp+jpV/ATi9zePJT6AhkSDCjelFAcECIkUHR1AFhHM6GRbJlI5qyoZg7mgbufyhnUsDOriXVd/BrP3eAs1Pe+V0lv/JTK1TlERfODFAmQKM5Nke4Zbhno8enLmWUWcn1l8acX9tEdhuee97ytvdMeempZXxcE61W7FyxrEQJz+yW/IV33M/u+EUm1SYX7rzCI/9XxsW7Oki3zMn4iOF4hKPh/DnFC9cc73rDOr/wawfEInDfXS10njKb1hhb0NSCTs8znyQ8cFfEY5+vWNkQrHX6jEvDyb7jtjMNlYXhAWyfSdjfKYmFI1KaC6fXefbmmK89L/jsMxkPbNzkI4/Bd37TgOuX5py7LTDaC5zclNx2b2D3qiE/tY442edzQ7h9A657xV2uz9r6EF8HtItIk4UTxPl74M73rpJ1JZ3+YtrzYGe0kKwhYj3RVPWCh22cx3lP0CDlhOlIcbwvuXE5wTlHoCLvNAC4sBh08Grxr1YKhNME6dE6RsgIYxyNLWjFd7K9/sPkpzY5HDvaaYJXr+CbgJQxzlaEcExwHsMRSkPavAmf7CCCRPiGycmUdPkQxRvwBKSImRWGJPPMy4MFVxdinDcIbZAiQmhFpDZpps+jdQvBGkTPY/wJVKeRekCSSoIz1P45svBWvMsxXMOnm5Q2R4ZHEU1KV95DGV3mLe/+ka+4f31VJlr/+49sha1Qs5w4JmVNEClPPj1hdTPjc1ehKAqmNuLMZuDqYcPZQZcnX5nwhte02bnRAA29TNOOI+ajhaXKxlJgpS1ZW0pY7gQ0kuWBxucQdyT9GPpnIqQKWCHQRrB3pWRUaZpgMCJgUeAdkYBJGdg7EJhIEoWCnuigU4duWYJXDGmYloJXxjGPvNomSTdp5x6RdbGUeOZUpqGaNXgrsXUgTxuuPj7k5k7Jx//BBm+83eGEQzWwIy/wq/sXubb7DGtUpCsDHBnvWx1ygT3qWcBVDk1MPfdMhpaZ95y70KeclbRaAucXVjxlYcnyhKI0eBs4nsCNacP1g4aiCWgtOdqb8cb71nCHY2ICeSsmOMiUor+hSXuB2i4UhNNU08pBScl0bBmWFtuPkS1z680QCNIxkw7jE37ihYvY2jKxDb2W4sgc0FIZXtZoGYg0aBmRhBinEqbTOYaYomy4fmz5pe8w3L/loZZ4HQhGEo4C1W4NSJwXCBnjrUFYSXALg2k3AREkxlXMrKcq2jhdsHquTa4LJBGd830mezUhV/jaUYzm6KU+T38ONs0hanWdT3zmEt2VDuNpxV/79rN88pU9Dk9izm5Jth66m0/83mPc+/A2Ozc1j3zkOufPex57xjGxjru7js9dE9x1m+fkuqUYa7rBsLq+zJlTFa/cSPiN50a87e6I3YOa1zzYY9xkPLQtuOtuwRu/7wb/xbtjWt0Ojz435fKR5y3blm96fYw2knYvResFzIoSKL2YFJKRQMSBoANSgowkqMV0aFDgpAVucRgGvBRY71BKUcWW04Vi6aSDK2usk4hM0OoJTJzw3x/ezktXPkceDbi8P+UnXtvnyf4FPj90vGn+JO/b0mSpZ1I6liJwkWQ687RShR44DkrPzMPFb9v/c5FoXf5gJ0xmc6pakQhLOYkYjzztruP4ECoDmxuKLMnxyZSy0dQu5vT5wNJyyckxTMY9+ttjxofv5Pxrf5nja+vctQ17x12a/heIqr/M7qu/jZTw2vuf4un8FFEjSEZdpqsvIqpVhJ+Tyy1s45BhRul3aKVtZLWNdUOEOsTWFhMK4mgZ5yvKeU0aWYrGITNN10VM5k+hlSWECUoaSvMygRzfrGMpcZVHladp9Z5gsPzT2CJeKKHohkwNqGrJrDwm0i2ctQQb8NaBC1SFRwuJ9x5rF1OIaSzp9/t0Oz32b17j2pWctDdFRHD1qRaTyZzbX5PzOx+MWN8aYxpBUcRcH+WsXjxB7sIjL2+T3n7CVmjzGzf2+aH3tvitJxrOL3V4/pKguzFGLFmuPgHnbsvZPYl48KLkkSdPWGolnF1t8dwVC9GEe+9Z4eZOyfKpguFhYDBYYT4bUNsrXHrZ8Je+8yJ7Ow1PPXFAmjs6fUcaL9PuRFy7ZFndKrnykuPsKYvSGbvjMbnR3HNflw//dsl9d2tm8ymJbpMk84UuoOlyZWfC+97U4uC64zNXNDY+4S9/7Tl+93dusLJiabU8stjg9nunfO6TMy7c0ceKEdZEtFsZ6WrN85+VLK9WSK/pthRLXQ2uwbiGKIbBekTe9rQHnjhXjIeBpoqQPsGpGr8wXlsImMpAkIayzIhiR9qydJct8laV0lhQUmOcQMcJaIv1DUJ6pNUELNZzS4ML4jRivbWBS3YZTicsp7cR7BHWTEgziQ8ZpirxJiWODARNlCYLK7logqkydFTiyg51PCUpYpToIpI5k6okjQdMihOiRNKPPMZApCVN7TF1Rt4SlKogS1rMJ4as22AmCRFtZD6mLDzOeZIEghng5RgbPN3QJnIRE3OCDyDzBI3lzX/N/oeVaD32I6vBZZ7bNhKGI8es9Lz44piVVc2nPw0uN4wI6CxiZ79BtzR1YbjrXIKvAzcPDOsrmk4KcePRLcVqFtEbeFZWcnRmWZExKgukmaA9SFB5TdbXBLGALhsliY4Nz70ClTI4IQnSARp8RSlb3Lg65t/uKbalIksseSyIooR2J6GsK06m8FyZM5YrnD61zaCbY73H+DmzUGOmE4ZTx3j/ClYspgSVVygSNsKED/5QDxXahHrOP33qTvaV5EE55Wee3aUbdYmXMr5pzfIOvUuiY6oQ0CNB3ZQkeUJBIElzgnJ0okA1D7jgCfWC4QjaUxzFDM0E1Yp59mq5gNyjjFYmGQ/H3LbaAzzTecnssGE5abN+TtJtaYqmRqUx7a7DWEk9FpTWMe00hHzBcqUCTAhU0qFywYd3FJ/fe5CqOSASU0yk+e4tyYWVQ/7O8wlLqkDkgiXdY1RDK1ZMx5qTak5VRJR+xrD0fOvtir/1Vo1pApEAnIIasFBOLfXcExqNx2GdpLEW5T3WSkBiG4+dGeRGCzux3HFfhg0xu1f3aYoeG6djjBvy6tOK+WzC6ZV1PvPoISeJYRS1GWSad76ly2PPH9LbSqgnFQ/ducIzewUrmxtMG81P//rj/MD7t/ilj424t2953f1r/O7vn3DxbIfHXxlzdAVetxnRTAVPvnDCW956htXlhI8+cZ3Hnq+4eH+fzc6MJ246errNbzxS8rqHLN//vg3+4c/v0UsC3/wNA/7uz5zwfQ93+ZptQZQrIrHgqzwOny6qVUIHiEBEasHHRJIgHCK+5T/JQhxXCIFSESKASyoyEbFVtnBHltAs2sm1CeRa02QBG2f8C17Lpy99hr6IuXJjwl+8+3Uc9ROe3nmR/2W1oZPX1G4hwNtvpVgERTVlKe9QJw3HJ4aZjLjj+2/+uUi0nvmJKCgZM689pvFkWcbJ4RgVpRRVxGQ6RQFLnT5eNxShIAjN0pJFm5ws7XJzdMTKNkRYSguylRAax31fs8H1J6acjOZsP+zYn93Hox813LV6k4aY42lCJ6QkWUGSWbw8JJgN5nWgtOdIO2Nso/FulVkhaHUH1LoLRjDo7CLqm8ThJeLkIl5GGNaQcoiSOaaMsMag4lepTcDWGwxWUs4/+J7oc0oAACAASURBVAjVyGJHGc08ZV4OaRzEseLshUA7z5BugIxH1BNBMXXMThzF1OPLhKYpUWrROnbWoljso1EUYcqY6zcPmU9ANgOO9ic0xuGqFmXUsHNF0M4V7dzgas31ScO735HyU78f8dbbPR95csr62Q0mhzDlhI2sYWgEtU0wqaXjVxHtGdXI09+ec/kJzf2vX2I43+N4b4mlUwU7l2KWVkq63S7TkWHrbMLejRN8mqGGPQ45QMmMLDYM+jGH+xOGx5577u8zHQt8mNO4PluDips7E7rbSzRHY/q9FV49nHD3BcVzX2i494Ee1189ZG0ZYrFGlFQ8/2xF3BZoH7G2kdIcRHTVPpunEpRt027PiOjiwpCDPYmTjlUVeHneZz2zPPZKyje/bR+5CisrKXFcoSUkWmBMIIsXerUuSAgRSIGIalQUiDMQCFwA4xZGy0pBUyikCsjIQwQEBUKjpUOoReVLSvABGguEiBAS4tgT6QxnNUIkJJ0+djJEtQrKIiPJhsTqDnSyydB9in6xRfANru6QtK/hEWQdGBc52DtBP08UaqqpImtnjOqKdrqCLRSxrinn5YLFi8e4KCOJHPP5nEQP8G6MtYaiaSH0DZayi5imwjRz4qzAmIQ4m5FFawsJC2tI5R0IFZjoXWK9QeRrBBVNOSJKLvKOv/rJ/7AYLdmzi4mnluV0GnNjt2B1OWVpJWF7a4Ixga6TDNYEK1IyLASiL4lmgTyWrJ+NaaWLykzsNWku6KaKdCDpDRRRBqkQZEJAEnCiot1N8aFBRpoQLDpYRDsiiSqSWFEZydx4iBUiiVCTKe1BxJ2Hko8d1xgfY62lsQVR4mi1l+nZiHHqGaxntLoprbyPwzKuS5QxlF5xuHsZlQhCAwiNdQalBa8Mc5SDqntMxAYvN5quO+CJJGaj1eGwCJxbbnMpWuWz1QZL5LzTPs0TvsNfaO0wrwPtjRg7L8hlzGwUEFHAzQVBOEKk0DaikRUrqwmlcZxdaiESR5JpjncL7rkwoC5Z2NV0+9CUvHTDsLSsMZHHFoJ2LlG1YH+3xEeSugVoiENAa09deUziSPKIV4YpL81P48UhSo5wTVgkrV3J2kbKX/cVP/mK47s31nnf/SPCcswP/h8185BjiobvumD4joc72M4EVSpCo5CqIlQRQkJoOYK3ZH1FhgQ8lApqxfFNR1UvbmW2sXgFuttivleTrUkuvQyyHtLKWszywOjYUk0T1k7FmKrFjcOYh96wxn5Z8OQLBd3Y8NjTllR3CFXFW19/nquTmkQa9sdjVpYD3/LO87ztNWf4Z7/wOC+R87/9gx1++P3LfP7lGduDPjfjYy7bDn/73Tk/32rYefUGL76YMKnhh7/nLJsDyfWqw89/+jo/8G7F2fUMkHzskTFbS4EDJF94vOIdD2l2DwVP+ZrVFc9at0stHWlf0MsEQgqiREFY+GYiBciAkxAHixAKKSVCKOI4ZuznlLPAydDiI0G9JklPWzaWJLqKsJ83VJUmcpZGW9IlyZLOCc4DkpeqkswF2vMRK2ci5o1He0GcxFjjqSx4r6nqQDUUaBPR+yq88P37xsGeJY9hbi3GaWbNeNE+icGWDZWBJAYXT6maFlaBKCImdcD5gnae4bGkscTpFvJwzmhmuOuCRk4mlEdjctOhW8955LemnF26yGS+SpT3yZe/QFbHSL04HGZ1G+E3mTcrBLFOMz4m7U4QSQcpUkzcoaxux4dLdNRF8tYS9XwP26wzs8es6UdR2qOjKbncog4FfrzBSj+jvf0cneUx+0/1sE2FxiJaDcuDTfqDnE6vxrsCVxvqZpe6UJhKUdeB0hgaL5BRQGqHMQ5joN1SxDLCmJrRvGB4IBAhIZJdKnmMJ0fHFcFkTI/nnN5YR6f7zGfQ2zhDvr7Pb31+zpliwCPPzRhOYDXcpNuRvO72ZT7y6RFn1tbobewjQ5fnr+6S1y3y9YyOTzl9f83lV/dZTpcYLBcUI01naYb3A4qiZjKxpPua3lpKUczJNiD2geMTj7E5dT0luEUCVM4FSV6yc8Ww1D7hOBgq2SU78MxpIcWUjX7g2iue07fBzpWawaCNrR1eTDguWpw+NeHUoMPJoWYjPuaTo0C30yWJ2tTuBjev5Tz0pn2e+Txsnmlz85pDvKbDh//VMedOL9O5fZ8yUaw0HewwEOUNIpJ4rUm1xpkSqTyxXrC3CAVKI5Sntm6xbyARMtzSJUxoxcUCDpcxzoP1DudqvIvwhoVps1wwd1pkCBJkkhLsFKU13guCVExHBWqyj6w8aaqgXKbkVbQ/RLNEEe2gQhsvNCGSBLtGcAXGNETS4G0LyJAhYlZeoo5fS9XUdPIu82oPl5XUpaEdR0Q+R5FTlzvIbIagxpkM1HmORw4VTxFaYKoLC8FsGkQdoZsIUZ/GRc9QihdxzQqDUAC76KCw9gB8jZbJn7gnfFXKOww/9WMfKJTg3FYLmopBlmIjCFIQA0WZsLSiSBNJmig2lwKdWLHWjdhYFqx3Ypa6Ed2uot/RJC1JnkjaPU2WCdLEoZPAciemtRyRdhRBmVu3/AXzIwg4FSGmMJlbdEsuJBGMQcsYoyPKcjGd9epYUrmFaXKsIjxQu4K6nBEPVthauo0sa9HOYup6RkAjhebqSwdMmoKW6mNVTeM8SqjFGHbP85lXEy5uPsyvH5zFHN9kFgS3b5xlNJfUueaOtTZKSdqRIx9kXMpex6RxvJq/hU+m57lDHNERimrmkdphbUw9r4milMY0FKWnmXmKxuBnGpMF0kwy3bdUzlF7ze7VKSGKOBk3uMYx2IwQ3pN5aDx02oqbOwYrwCRAK5CnMXFY8FE+U+SZYGxyPrY3YO5njNRNqkbhrKGoAm87m3Auq1jpwdt7KQ+9aYYLEuEC732wx7ecmvMdFxLueMgRjEPWIEuB84G93Rl5ntBgaOYKV0nKuSVUktm0QZARlMXGihBAaoGIQKiFTEUSZUxmgbidEKUSoy1nL7YZDQ1mPmW2Z7l+2HBh3fPUNUNUlxwFyTCUvPvhcxxNR6yv9PnM87vsFjWryYT03EO8dGWH7Y0OP/7zVzh9e8VKFvPwecWj16aEYBlN5uyPFQ9uBn7l8YaehKO55j9+uMfgjsDFsznPjRuu7bf52nOaH//lEW9/TcITl0pMLpnWgmeeAyEtKY5BS9HtQnslohKGpqnJY0kjBLXwNInDxmHh1xkt+D0ZKVyiqINj5hoaEZiYmlgnrKWKswPNdivm6nDKSDS8OgrMSkP3/pSTF2qYeRwVV9pbXBkeEaKUcmYIJKQJdMyYt+YW3wQSocAGnA/YyuNrga8NFIIGS7KlyB784T8X8g4v/+7f/cDJ0LK+tcX6mTs5muyjM8HxoWE+61KUgRB6EBdUZhniiKTwyKimmENZaLK2Y3k1MHSBMI7oyJSVs5Znny3wbhW9pdi5EdHKzmF4il6S0IvG9GVNnlb0Yo3w+2iT0oo1nZagE3+CxB6xvbxP6p+iI2+SuRt0eJqOeJyBPGFJ30BN58haE8aa5ZU+XuzTmAZv+zib0V9J2Lr3MkU1YngAs7lFdwxL2y3O3LdCviyJcmh84HhaUNqIOnRoxoqmANMIvPN45wnC4OoUKWIUEbOxZzq1FNNAVYAgxtYxQVQIadjbUUSpYV5WTI8CZ9Y28K6gxpKdH2Argz5O2Xqo5ni4wj13NXz8qQ53Lw34zLP73H/bGa7dnOBVyaOf9dx7d4drh1M6Vcal4ykdk3H2jhZXhxOOdwR3/N/cvVmspOl53/d7l2+v9dTZT+/dM9MzPeRwuAyHpCSKomRtTmRJkB0oBgIksI3AAeIECJKLICbsBE5uEt/EARI7SuJISQQrsmMJJCWRDCWK4qIhh8NZeqb37tNnP7V/+7vkoibKVXxlwCLrroC6KKC+er/ne57/8/vd3OX4ZMzuJc3RyZLN9T4iGnPnriFYjDjKC3aSHr2tioVZkPohQs0YDGLCIOb8dEG/L3k6Neylz9DrzFnYhktbKeW4S6Ur1roJ+WLVBJicWCIdsrnVZ75cstntcTCdUiRwdj/D9OC5vYKTgyWDAWytd3l4x3P5RsbDe5Krg5LXDz0f3uxy++mEn7nRQYoaYSpMWxMFPbTOsM7S+pxoMMBpj9XtCo2TeFwgMcoh8AilV6jH973TTljk+wpO6w1CW3TgiRJwLqA1HqV6eNvB+S5hlBGEIULEeFfhrcM5gbMggxhNQ9P2KJocbVN6YYZrx7i8Alug6gxlWqQtUO0AZybY0oOU1M1ThN1EBRXn8wCNImRKL1ng2kOkL1dttTbGuhqcpCo8zgkEfXTsyIIGnxu083SDNZr6COkDvCgw0yuY+ilZtEC363hzvvo+YoCuJ9QmozaWyr6AtxE3Pv7v/mBJpY/++L/6nAtgb5TgmhZhBDaGqlmFmvOlI+22RGFAoB0Ci3ARWQzDfkCoFVEMQSpRwqFiSSgFUawIIuj3V63PrLsKCztlkaFayZ7l+w4CPEiFWMLT0wYZKmS0EiK3pSIOLXXtME1JVcHTSoBUSO+QUtDgyJTAuHplKO/2CGVC3ZYgLXWrOD+pCaRjUc1AGIRcwUulEoSB52jR8qBKuTHaZFIsWdYBJ82SplKsbYZsRV1KBUkQEdiL5M2D1cjSWg5Pj/lE8pQAizcaJTxVI6mdRTlFk3vq1uEbhTAG1e9AEmKtYz5tiHuWooRBL8Frh7OSKJQEUcj5ScF6HKKilYh6uRT42CIiSZwqtPA47yi1JFtvicOAr5073pz2mBuHKBVFk+OKBNcI/q1rjjCtaCqPV5KRDiiVIm6hrcd4mVAsHapxhFioAxZFi0pjqioEa3GtR0cNUnk8EulX3i5fO9qFpR57FvOGZrm60ZsKqqahnFbIfkSnZ+k0DWoRcbA/xU40SVcRDTOafM7tu/AjH4v4J1+Z8KGrEa++usk//v37/ORPDLmwHhEHkrPS842HLeu9nEl+g4V+xPED+KM/zQmko5OGpOGIP/zGgk4n4m9+usdvfi0ncQ3//O2an//oHv/02yXbUYd//795yMMnNRc3AzYGkg88V/M//M6SX/rxNUIc5xPDr/zkiLIUzCuHpyZwmrh29HVA2g2xUiKVQwYaYx2NhaLxlA1Mly2L3DKbe4oSygrKUmBMRFVWXO5KnA/51rgkSgzILn3bUAw14SmYRw2IDq3QPB5d5ulijHMt53lDqlOUFtyQFR9Wq0fcgFWhK5A4U9NUEIiYvKjpX4np3jCISz8cHK3Tt/7u51B9luUJozVFnE0YdBO++2bIg0cledtwPrfsH2kODyOm84KkZ+kNLONDEMYwWXgWpcc+cbS5ZXN3yPHRnGUOs7LBBwEbu1OuXttneyPg+VfHiGTB4X4HU52ykDHSdenHJSd5zubgEJvFpLJDVTdEUYAsJwyCAUE8IbYewxrj+g1iKRHr5wSpxbhTqnxG7T9Epebsbijq6CHzcU61XOfCFcdzz0asbTYk2RW8e8DxvYgHb58yOR8jmprFWYmdOwrb4Aw0rUF4gQ4U0jmct3hpcdITEaPdSselI4UtA4qxZ2uz4aOfEpwVEV/4wiqM3rgU4yYrUHEJv3e74IVnY0ZXC/7g8wFOlXzxzZp/8xdq3n5twYsvwKOHC/audjg+Lbi4J1Bxh9PjgriX0OlJZtWEurFc2NlgUeV4b7CuQtQRw06X9bWEQSdlPK7wYUuWhCyqFXC1G3Zoq4amcSDD1Yg4jMjLFmFhczfh5LQmigEU08mYQZpCGKKrgkJKejR0ezHLpuZCP+PR5Ix37w65vm3Juj1eGUy5sRdwcuaQYYJoFwif0po5e0NHVcR85bWGzRdaTu9mrO+GSFcitMIq8KqipcQH4LXEWoNpPdZInNWre5+yCCUIhOb9FZkVMUQLdBQgA1CRRscKHUIUKpQSNKolSj1JtybrGKoqYlkH5HWAC3LW0zm0FYgODQX9tR5j8wplb0gnDZD9Ka2Y44srlN0JurzCpOmwkBvouMSZGaXu0tM7aP1dZstrtMrh6ymtqIlHHSJ6zMdbzMNHjB/voOVlpm1OUd1E509wHctMX0U0OZX9IJODN6iTABqHrYac50PC7QkUY3RSENoJ98YZaXyCLtc5C1uW7RrL6SFhfMaEa5jJgspWfODH/tYPVqH18Dt/73NbGxGRaEiTDm0F2ZZiOq3IgoTKGNJejEokaSywDUgt6XU9g64mTDxxF+KOQgpHnEkSJYkiIGi4cCki7Wp85P5M1iyURyi1Yp5JsarcHQRG896TlrqsaWxIlGqcrymtoACaVtKJ4b3TFi8UYaAxzuMDVnkgL1hWMw6ePGC8PEVoTRptMZ7GhME5s/qQrc4mRVmgQkukNFoIhLOA4ng+5ht33qExloenT7h75wlPzh7ziRsvEvU12oVUaAJREsoOkQpQPgfdUvk+t3TJmYs5aWLk0mDaiJkViCxE7txk//JHuRu1nE4mDAtPZltEqglFSOSgtIamglwY0iylapdsbfQJfUkSBjSFxAiL6Cn6sSJVK6aWHMZcumJJrOQf3/Z86dE2M7ukqHNsFfPf/pLmb/2cIjpuub5m6HQ0pnZQCeZFzcaapPUtgQqQdYiPQpAlptH4sMW00FYVWZZQ5AZfS6qppJx7TBtQLCy+UDinyJcSVzt8K1Be4s1KBxSagHQjZu+K4qt/cMbGRpfRZYNWXdYvO1pCXvuTmpOZpSxyXrtTcGVvwO+8MeXGZkopPNoIfv3L++yupWzuRVzfXuN//J2nOA7Y7q2zKKe0TcuDA8GV7Yw79xd8/JUeR2dTvv/IcWOoMa7g088O+a1vHfFzH+jz+pvH/OrPb/KNuzO2Lht0MOCLXzkjaeHBOOfGzpCbVwNsVaCjiBsXNP3cc3Ft5dgME4HWjl6kEQuPyx3YVQZQe4FrHUJopIyYzeZ0OilSepSUKCV4YafHd/ZzFt7R7SnCIKJWFfJY88IbAe5E0ywqjqeWuihYji7x0C4xlWG+rBmIDmjFzwYl27YEG1CXBlNDtVhljloTszxfsnWrR/ayxUUgt384Olp//L//nc8dH5Q8/8EN4lTTH65GLZduBFzcG9LrG5LEc3akaNo5WaaZnjU8fCAo2wGNS7lxo6A+SRBCopXF4ylmnZXkNzKcnTUsTjwnT6AqFGdzx3e+nSPMMwRasaznuPiYSDcUi8s0fk6gElwumFZLVOapqgqnLbaImKolKnmILEJkWhBWL3BUzjh52PDCcyHOrLE5isHcJ+47usE2w71jXGA4edpfbQ1yyvx8F+9i+oMOUgkmx54wCjCuQjuFdP79QkqiECgEOnJ0vCSpAwpZQGIR1sNC0b8a8YGP97j87Ih7Dxt+49dzSpNwdGi4lKWAp+w02Ah6gy6xHPP0TgfVtbx9Br/8YssXv5Rx5YVbfOO24oWXPQ/fm7C3lbCfA62gP1yJkC9e2OHx/jlZGlNVhn4/4ehwQpoMaO2Stl39ju++e45XjvXRBk0tycsJ03FLnBpsA+sb6yRpymQyYTBcI8sy+p2I8SQnzSBfCKq8pdNRTMcLlnlBJ4rxoWK92+Xw8ITaWNpFwe2jgO3RkltRjz89WPLysz2WxuLqFmcMS9MjqGr6XcWDo5avHrZsxIqXbna4Fs65fqFCx5o40EShRGJXZ2ftMMZjrcFZi3fvc9KUWPl3ncQai3EW5x3eebxbgU2lXsURhFRIJZBaIZQiDCOyuIMSIY0piTsKrbu0dcaziWaafIxCfpC02yX0jkV1m063JRRHbA875OcFoZoQyxHSd0ldSy3eJep22YwjZvllEnnGPLrM5GxGJ9YkekoWx1T+nNJ8iMq8S5zuYuwOIniEtjcQ/X02+pZiVrNI97jgWkoRIu13MeYKWtYsloolKS6xdJIzDssNRsOYbN7jtLvJfvlRkjRgJ44xWZ9ikXLhGUGZf4Iovse8kXz40//BD1ahdfT9/+Jzextdypkly0A5g0okplT40JLnjv66QAUeaRSuApVJOrEi7GrCoCHqaHQkUDJCxSucQ6A1QezpDwPQKw2DQ6ykqsojhMR7h5cOqVYBP2p48MBikVSNYLGAMgFloMWjrCNQCQ9zxcJ5cAojHcorkA6UoLIW5y2mqpmNj3m6/5ijkzdY5HPq1lI3p3gdoEWAwGGFo8Stvg8OaT2Tcoq1Bi8ESmreevSY6cIQRRm7W5vge9SmYP/8mPHS4aIBJ03G7xZX+a1pxB+V23xBbPP46qs0lz/CsruDP31K/NaX2Twes+FXTzC1UTgr6XQt3qeESYsXsDFUFKYmjmLqpSUdKnpRy2Iq6Kchg6QmygKSkaG3ExNHgt+/1/Kf/cmQJ3UPJ6f828/O+OU9yV9+vmF7VOMWlpduOWanBpmAsWAxhEoTC4WKBGaqsE2DUh5TGcanYAtBtx+jpSKMJPmioakNVkhkAFq39AcR3Z4nCWoIJGEvIgwlPm7oDAM6w4So60liwf/2Px1y85V1ru1m/LMvHPKRv7jB3W9V7OyGvHG+JEFy0kAgY/pdzfq25g8fLJhPCj756mXOi5aqhbBb8+CwRKU5a+t7fP61Q5oakrTDZ19a4/e+ekKaae4/nbGpQ3pByNFU8MZ+TbbwxLHhbGw5j2P6VcWHPtrhvacVJ08MP/Jhy9pah195ZcB3H87547cWmMLz5p2S9qRlY9gyHEVYLwm8pJOGSN0QDCS9TUHcj1HGEMxZjV4rQeQgUxrnDbayBCiqqeXM5mSXMmpf0T1I2HzguXI3JpxqiqXndNpwvBTcP1syqST5xec4USVFvqA2LVZENPWMH+mEDGzL/mHNk2PH+dSwrAWu9czyGlfB1isRNq5om4jgwn/4Q1Fofel//jufy3PHsy9ECAHnkwVp3MGXAUF0zqDTYXd3jTt3S9pWEcSeq1fWuX23RXQyFs2Cj9zKGD+pIGjxPqKqKkyrqEoHwtIbSGSb0pQS41v2jzUv3vo0KmqY+1P29IhJvkAEQ1TUoZicU1UlmUpY+gQfjmjqHnmp6XVCCt8Q21tUVUl3uIurN0gGOzw+OifVMZPZMU/3D2mWG9x9FOOKlv19h6mf5YOfEAxHQwKVk60H9NcV/XXL+kaXIHBYsUDKBKtrvLT40OECi1MOpyGdK5bWslCG0MSERpGMIoY3M559JmA6rvnuN854+3sNTkCQGoa9XdbUjGQgee12TNhp8a4irvs8zgtu9Ld5fNaQS0XWbVmcH/DKTcf3317yzNULnE5yrux0ODiforQjTgJmszlKC5QMmU7nFDkM12K297o0VYz3KdPFhK29CNNkWFewsZVQFYLnX9xkOXfUZUXTNrRNg1QBTd1gnSVfzAl1RF0v0TImCkOaekmvM0AFmjQKOTwb008iwjBlMl3yoS3HeLpOYGCZLfBoqrklVgOEMaxvZpycaW693DA5TTBJzc3tTd47W1BvjljfMoyblk0V0R21JImlk0En1WSdgDgUSO9WAxwH3q5ykhKN8AqEwCPw7v8FcwJ2lXO174uXDQ6HwOAJw1WBaq0lSUNql6+29hLF4nyfoJ9wlN9GdWKsy9gLWowMcfMGpEWWVzmfnxKvxQibk9ebJLqi39tj0uwTBBlB1Ken36VZdkjcCB3PsLGnLhJUNGY5N5hwi6LTYz14BbPIyYKYJvhx7p+8Qz9ew3e/jy/WkWFFFmtav07TvUXYeYfdbos0GWvtCLFleVKe081D1tKArFdiQ8k0l0RpjHOCej5k79o+dXWLW5/4qz9YhZar/svPjdYdfhBwYCuOkoYoD+l0JJtJyPmk5ur1DLwjn7aY1tNZj0A2DIerAivtaIIIhGgQgQOtMI3n6uVstY4aAAFIsargpRR4+b5/0L1fJHmByBX3D1vaylH4dqXQqSyFkOhGUCmBtRUpikcTSZM6dG0wGiKtSZRGIQmkovYNtRUYVSJDTeNXLVorHbEMcDga71bdtECtqPGA8x7tFMoLkBYCCIOARZnz3sO77J8+5e7jd3jt9W9xNJ7x6OSAp9MZbXABEW+zqxwflO/wl+wjfvTxa2y+/SfoP73HYHGGqxWjMEFEUCxrnG/QsaRcQlU7ysIQpRHdfoTNQVGTJRLTQDVtGWQBow1BNIwJLincdgxZgA4U17cTPjIwHOQ1v3IRrkWaNVGyeSNAKYWSJbVQjLYixmNJKA0qEGz2ViNjORCopadtBIu2JNFdmhrWhimIluWiIUk0dW1pKvDeIJUijjRB3OJigV3zzGRNdMGQ1QJbSWyhaOqGuB/wxd8+59andvnmH05osoiPvDrirW8v2Ewj3rx9yl/85C5vv3VKnkek2vO7r415YbfP7bM5v/SjQy4+1+ONN0756sOCe3dKWqeZLLt8836Jb1tGnQjvHGVTsLkR8Oi45EIYcvckQkYVNC3P9RXHoeLZjQ5b3YxXdwR/9LTFCYiN5c2HOZ969QphZPiHX8iJvaetLDvDPi9dc1xYg81hQpNbOhr6Q0E6WHWwBCG2Npi2xWmBHUiCviQIPBjPQEeMlOQCimeThOeU5MqpZvuxon+kyaZAq6gbS9u07OctX3+05EuPC7525Hj9pKVz8TpmWFHOC+rKIWVCB8eN1qLnC773aMHJwlK0hqptmNeSolCErUJaQ6efknQb2P3h6Gh95X/9259b20h56aMbzMcG6VIWU8H4ScbBowqtU1S05HuvW7qblvkSPvQxhUgq7t+f09Se2WmJ0BmNaWhay8aoR9xbrmjkag0RLokjCWK10XnxymewosDo92jKkiQ+YG67qHKPREyYLDKEDJDe00QboG4iRUSiNpC9t9nOepStoFGWjA8zMwHt8oRxGxIQ0e0MWd/UhOk2LnyWbqpZlB6k5uDROfnMECYRqleRDWIcp0wnCzrpOkKX2PoCzOeIyhNYTWBDlNHEZKjdLt1hj243oHt1ycWXAy49l7I20vzJPz3l6Tua88fgKkUWxdxcDAAAIABJREFUZfSimFgvyBc1Lm0ZzyIu7dY8eOcKna05FwLH47MxNy8E/F/v5LzysV0+9kLJ00PL9c0+bx6eEHdC3rvXcm2vQ1G3tI1hmRekcYJHkGUZShvqxlBXjvlywWAto2kN/f4WrZkhhWY+K2htgTWa6WzK5sYGcRxTlQ3u/QWPNIlJoxjrCrAxqJzBIEP6hLrJca0h1BGDgcIaSVs7ojDEO8EirtjoC57fE9x7rBnqnMgsiDcM52eCTFQcz1LO8xk72ZCz2TlJ2Gd5fE500GM7kayHBW3kCRT0MkkSB0SBRGpHGEQEWqOkRLASesMKaIxaNSGE/P8MJlIJVuEMwIO1KwSCt1BWBVJ7dKAwVqFUTNxpCDtjgrUQP/EEdo5rDZ2u5uToKY2oCMUUG3TIywqdbZAOrzHPv0fd/xiBPWFxNsX0PknW3ONOsyCZbXIavUSprnLqA5b6Y0S+z0y9hGlqZBGQ+COSYMFJ9U2yK5vktmQR/RSX4jeZdz9APs3RxJQc0DJg5lM2UseWGjGpDhHNNTxPiMUpctnF+Tv00zG+ekyobxAPH9JUj9hda5mfT+ntnHL11v9/9OHPJd6h+PZFf/vuOaezBBU7hDdYLM/2OlwKNQ/3LXt7iicPW05ODHXjSDdWc+KLmwEVwPsBvtAriraiRaIaz9YoorsBBBai97H8+fsdrNiDXlXwQgvAUe0b/vR+Q6U0Jvf8vW+EPDuUXO4XPLPuqZ0BEWGc5f9412KiAM1K9VM1DmM9jZRor2l8g1QgSZA4jM0JgwCIMW2Fw9MGEiUhQWClRGCwAgIlwAmcVNimBanRCPpZjFQtQ2/Z6afcGkqe1TGDMGc0t6jWoXyImFkWuebNR3OqWFEbSxk4esqyrkMUCi8lYQIm8BjjqWrLxec6zJclRWkIdcBwFFHNPfliyagTcKUboVtHsbQ4I3CBIbmoSS8oxChEqmAVAJIxyBnndZfe+oxAOlASOxOY2FIcKrS2aK1R0hK6gGYoCLylnksW04ZOP2A6a4mVYjhcEY2TJGY2sWSJhlZigopIanCO8GpCO58SrG2zeFqhzz1J6iiWBe9+SzCf1YRKo8OGbHuLs+KYC1d36DSOo9OGbhTzzrtTlonk5GGLKme8NenwY89FtLGiUZ7XH+V84GrEVMLdw5Z744Kf+Zjmi98KqYOazWHIyZOK3a0BB08KBv3VmOD0pKLrJXUm+LkLPTLR4+9/8QnPXFwjiWZ88sUuJzZgV3j+uz8846c/GfLtNy2ffKbHyck5NhhwbzKFHKISfvXVPqOuQmwYAqOQduWT629Klq1DhB7hJUa0CKVphcAJt9puK0OajQH/y2TC30w/zffiLj/zzd8hrzKMWXKaG5xV5Nbz+kHBd08sx23ASQ15XfMjn/0Um5/pcPjgLmUFbaG5EMfkb9xmu/QUbUMWSEZpQCAcWqyeojeyEJTj0o2UH/2FIXzm3g8F3uE//Zme73UDnrk+ZXO4ztH+kk5P4KVleu6YzBvSPrz+vSGLtsW6LjtDw0/8tGB8Oubxw5i7JzFGW/RkyWgg+OQrDYsx9AfQHSoWbQ/nlvRHERUNSxOxObzK7LzitbfGvPLMVbqXcjZ23+b2awkXN7eZRM/jp3fwqk/VXMO7ryOXgjDIuf/Qc2knIUimLGTE9d0EH/Tx9RNEtMXnf/Mek8Mh0aZnY71i7eJlnnvueZbnKa99+zfoDjZ4/uOXOH7nZzkt/gFa5lzZfoaw8ya+1SR9w+YQklQQBAHCSurKspy3dDshUb+DcTHv/N4p5amntJYSz3gOcaSZzwzerXAtZeXx+TZ6fMSkzFCXImgKWIb4Zsm+c/zYB3v8xtccdrzkp/+NEV/4vYYXhzF3o4LxScXLW/CV+4rrOxKrEuI4Zjwd0+lllE1J29YohghdogNLmScoGbGozhiOAuqiQxDVTGcLRoMdOn3PwZMlg37IfL5Aq5imadBavs+RaqhKz9pmzPlJQ5p22N0d8PZbj7m6m1CYjAvrjrsPSwadjFC0hJGnqRyn4yVP5vCvX11jp9dw7hq6bYhkyf4E9q5BfpLhdMUl1eFRM2N0EaZT2BlBJDWNcSQxbGzGRFGAkJbWFAi9QjGo9wspoVZTGKSnZbU8hFotIwopUXp1T4LVe/Sq2yUltMIRB70VWd0u+cDLu1QVHDydEPmUrQtjjh6DrNfIq4Jk3TF+nLG2cU6+1HTiIYfHY3qDG/Q7lrOTezzNPSoY0BNTLm5KxmWKED2S7JCwTVDuAvfH7xHJaxjXsLE+4NS+Raf77+Em32GtfwdvIwqzZOFnNOd/gb3hbXauSO7fvU84yNjux3z9CwuufaBB2IT9wxIuXCZc1BxMjlkLttgannG07KK7HyQ+/QYy28Prih4Jy+ge5dGQX/nPxz9YeIc3bp/jkj79XsGyWUmBg1hhtMPVnt0rmihtiZMQoQw6jQnSljgLWdtQVHUDkUAHq5LGy5jKQjP3PHlUcms7wflVt8hLgQwCXGFXkEfNasPifatXUXqE1GgjMarkQi/jjXaHd84ivnKS84m9R1yKC8yiYSdLOTCSwra0BCAdUkuca2hkg3SCiABBSyMtUiUI78nbEqUkSihC7QiQKK8wYuWbiqRgmHRYDxwf3gv50LpjM5LIuUHNW9q5obSajW1H2hUEssAsJYWxSEKq1lAvBQ9nSx7ammujlI2NiChdbZVMHhSklcK0LabVNI0jS1aB+zYvaUvJoB9zeGZoTgsGvYDIBNx56Lj1HyncFDqPLOEywFYBSiqq0yXvPqkh8owGFtuuCtLKzxgNLT7Q+DZAtAVKpRhhSJRCOY+IJRSC6m6FSxLmLbQebAsgWc4Ngx6EQUpZ5mid0jYNSaqIhV4FrvcsUGIZIo4nBIkk6kbc+5phfNzikpA0CpmaimoSU09mXL+1zk484M7TQzq9HjOzYP+04OS05vooZSlSNvstmzrhnaJmLUl5+UrKq88N+QdfPuMzL404/cqC190FLq8dcXdseePNhltXQup2ymg9xLseh9M5SRAhY0sxMby2X7C9ofnrP7nOYtkyzLZYtgVffX3MSGl+5kVF1YTc2vB84/tTXriZcjSvuKQVa1dSnokrOlmLzhqyRYTqKlxXoGgxuSfQGt86SmfxXhF2PHG8cphZA4U2VMUpf90GnNff5KWjltlTz4NwRpM7Hs0sT4uGe/OahwuN1hLf1nSlYLSZoAOI4g5OtgRCYyPBrG65fQxfP22IopBEtXR1DULSVY61KGEzL9nsxdh7LYt/MObnPvOv7sz5l/mq24q2jhC2Q74owHkGg4TzswbT5HQiQawzet0J8/0BjZ0zExnf+ZbnU5++SGOPOV56nhxP6KUR58sA5zt4t+B435DnlmBrgg4DpsslRHDhQo/9e28zyjb58Kf/NfzpVyjrh5zN4Td/p+SXXyyYP/MN9rTh7tv36G7tc/Fii4qWPLgNb7xWc/HnG8Kgi7RrSHMZ2X+NycMB5vIxOy8kpNtDSj3lQx+8gK9OOT/JaWSf4aWXCe1NmvE6InjM9RtXiIOCUQ+eebFLvVxHq4CzIkeEFU63hIFnoEPWZcpyPGdyb8zZfbU6N0c1Se7pVFCmIdYowlggheL81CFshFOnpMMRM5fz9H7BTt8TbszJlxGD0w5Hd8/51I7iny9Cfu0LU/7axwJ+47UFr/YkvX7Cl44SPnZFMVYTzCLnLJ8QhCnj8ylOwHCtT9MskSLh9GjOaCMgSRTLwpKFu2i1ZD6zbK1fBDXj4V2z2lQzPZIkoandigPW1kSxppNldLKQ5XLM7u4FDg+POTqq2NzsE+qWs2lBqRvmM4OyLbE2nM1AZ12ujna4llb0ri548B1B74ZH64KtQZ9KNjy6KxHkGA9/PMtpFym3XJeLg2NkGxMFknitxbqWybwgSaDTlSRZgKFeXbTO47AoDTqUKLXiYiEFXogVrkF6rHBIBO79waFG/9mIUSvxvhtwxea7d/cpoe6QqD6T5SHlG9Ad9ThtI54ej7m6GDKpQ3rxDpGasJgExGnM5u4Dou4mp3di4qhHtPEJzh/8Pi++uEVwOObAHxDVQwozZ7RVMnQBLv0RFrP/m9Yuyapdehu/zpe+mrN1GTZvOoaxZe3YoS9/j3634vT7E778lREf//CUK5/NKGWHo3GO4nm29hRuOMG4nOubHdrTi7w3mZLs3GQw2EYtE9avH3P37gZRv2T/8Hn6evIvPBP+XBZaRxOJmDpkD4QziDRg6D031hP80qAHAleELMoSpCBIKsIo5MpuQBC1BGmwAqmZlVDYS1A4pBIMNiLu3l5w7YWYVd8oh1AgyoBiYpE6JYksuBrvJGW7unCc8FiteXmr4mBSIckxgeE78xe4M36ICnPGQtLGAalrKWkJhSDyntR6AiuRgQYcodIUHlK9csZtDENSZYlFTKY8WeS4st7yckfxzDCgHzu0qqlrvepELVoWh4bT05rFGJTThHFFtqYIzUqaXC4d1dygPRSLluO8ZmYUF/ZiNi+FpENJstYln5ZUl2MWY8P8pCUxNVtxl2VREScC5ySDXkBtBUJ7lI6pq5rWGaSzCFmikpbikoBI4sMV6HL/qyEiXiJtxHkpUbJBuGa1vOAFygmcr1Z8FNeiA48SoJXC1oAW1E3AvDHIYIWMyHOH8AIpNcVyNUJ1VoFpkT4kWA/AOXAtMo6oxyWagv1JwUYv5TTPqZxEdDU6qgnSkPVCYXqCp0ctD75/zNe+esRLN0ecLg4IJiG/+KvP89Xfvce8EGxtDfnsXkJ5uuD42DI1ExAh//VvH9BbC/jae1NEv8f0m8d0Ake3BzdUhLABB2cFvSggpKWtYH3dQx3wC8/3+P1vzInqCp/UZGHGG2djFm3JX7m1wSOTExWSSsy5exhw45LjYOzoS0USe7pViR5FHMwtL6QZ2ZrHeOhnirJuQUokHm8FDs28aqgXEKYBZAInGqI4IMDQdj2RqTgbecKfklRPPV/4YsFX3zQQBrgY6jYgbQxOBujaE6iatlwQ8wxBEGBFhBkbvIDKK2qvKSvLuRM455F4Gh+yFpRc6US8GrZkqmZnMPpXe+j8S3xduNzj+MkY3/YoTUFbecanFQf7HldtkXYED945YjgCKRsmeUHZ5rz5tsaLGBU6lifnbMotmrihsRO+/fUB1y9nhPEUb0OcaBAKtEzJegH33z5nZ6PDoDtmMvsnnAa/SjbdZ10+4kdfPOasc8yj1/u0UcbDdwpe+guOQXaR8fJ1nn21T39Yk3bXsEmf8riL7Qte+4NzntxOef4Xf5FnP/Qybz76LZ7c/iR0Dnjy9UPeeDjmpZ+O2L7ys5Tmtzk4qVjb1Aw2Sq7dyFnrQ36eU5QL5ucp6UYP6VNsbpiVnnLZcvx0iphEq/9ybBFlRYikyTRVIhgtG1oHdQW2tfjU0etKrLP8sy+csz6C7WFM19Xcz3vEszmm6jPdCbl7FPLChSWzxwH//ecDPv6BHt88GrM79PzY3owzFdGeB6igZJimqLBLEEYrjE/laZqA1sz50Ievc7A/4+RoynDQR+mC86MWHVgms32skVy4vMXZicI5RxCEmNZQVRVaSuIwwjQNayNN0/Q4He8ThpDEm8ymZ/SkJ4rW6HdjkrglyySpLrjoY85Eg6gPeesgoxaSZz8guPdWRTvsYaxgeVpy5wQ++uENdrpTju8JzheOss148RaMTyukSCiqlk4nJAo9dd0ymzla4+murUoA+X5jwQuLcw7rLVEs8EqukjRihS9yUiAVq3MWhVL6z6TKgQJjSpwHJTOahaX0S5xfkootmuwUo3O21nJ2O1d4fOYYbvwU98b3uJpd48uv/UMubGxz9/4Rz/1ojNspSc0zvPv0iOsf/nF071uM351RqiEzZjh9gScH+1wdJTTBlzg+ecql4Q2OzhJs0efjn7nN7u4rzOUTzJ0pUdrnOK/orfX5+oMJBwdL/jQRZNcEydY2B+Il9HCbjUtvIA7exVYJd98ZcXH7KUenl5gUj3luLeDW5pSDJzcJOjuIzPGdR3/Mv/OX/8Xn15/LjNZ73/y7n0u8oKwFUWv44KjHeuyIQoHKApxomBx7Ts4ctvGojkFGiou7GisdUrNiUTlB6y3OrqZXi9JTC0EkFItZSn+jAS1BKISVBCrkt79wgI5i1jsCETjOnzZUTlN6h6kcWVhiaJgLWIvXiYqnLNWEEk3kHco2dCT0UHS0JggEaSxRgUDqld6naR1tY8hrGPY9f+OZmF+8LPmFa56fv674yUueD617NhPFE9fh3XbEe9UaJ1NBeZJTnjfkY7ClwhYwXzaoIGK4nqKlxTQesbCoqIMWnvOxwaLRm4rOnmC0FdLppLgAtPLEacxgCGGm0akiEBJqS6IDnGsRgWZeLsFJdvcEy8LyxrsVax3Fs5/u0haOJBIo41HWI+qQ9euWSCiMkRi38keOAkWvowkzgRQe6UKIPXKp0EISRAJHgJaaerFaO5YKwkCyNgpZLBzOWEQriKSkqQ2N1SRa4o1isT8lHSrsmsQtSgIfgvcEQQaUSBXRNoYkFQgv0QQsZ5LTU81zNyVJqHn1xy/y+a9M+Ct/6SZy0PDO6/u8e2/Bbj/EVGC95fBswVsPpnz8hTUelBVaaIS1PDpdkm16ytxxNoXJqeLimmO28KRrDb2ow8Wu4/4TibaWtnFUhaMG9mL4zMcvsrPt+PY9z8ee6XFnarl+qeZ44bhzIrm51+HmjZQ7TxZcXo/Z7Fl6iSMi5tpFSbgJxnvSzJENBE3TMlpPkQIEljj2dHqSbl+S9SVhKFGRQ+iV19BYixUC6VqEdsRJyM2XQv7qL4V89rOaqon49us1560k0YaFd4RWk5uCWz/2Mk+P7hAGXZaLgm4Y8+DdU4qlpXUOJQQWQSsCpGso3Mp9tpV4/uO//SI3f3oNsf03figyWl/9R//J57LUs7FREfiU+cwxWVoen0QUpmJRtrReUpQZZekpc0+VS9aGIU5ZzmYtw60R08UJbQFCK84nOYOhIs5aosxiVEKoI3Sw6qRcfEbQvyDIdmIiX3H3aYckVewlORevTHnt8TaT6Cex13+cK4MHXLl+gTyf8uU/mLC7V/PeHwTUWUq8HSDdELVZcPvLj3nzbcHzrzxDZ3uBk1PG+1eRVy6TL+dU4pRQxHTWlzx69x1mt2e8/BMzPvDCgCwSTPdnlCcp+UQTBynNbEZxnDM7nDM/WdLOS1INMgQVNOAd1rBCs7gUYSKsq1BAHEi88YSRoHYtug25/UiSGkdgPOG6py0CdlXDQZpzfBTT1yVv3fdc/2BGpyo5HnuuX06YzEuaICTWOZytIzo1Pg6pkwWzB4q2N4fC0e/FLBYlaZJQlgvSWOHanFAozPvXdBZn4BzT8zlpnKCimMdPzvAY4kiRBBrpDdOFIOyWnJ4VDHop1VSzvhbRVkviaES7yAk7lnzuWTZLokwzay2y8SxEnx3mvHB9yL37c168AkUp6GQaGQrWB4J+Z8Ht27Aer0T0VTPm6TimbGFn0BBnGY2pcNIRxgqtFVIA2qADCRGgHQQeqVbbh8Z7pPBg3g/IO4mQDbW1BCFYkxCrnEiGqKDBFpLOMMYHlsHuiOGeZnvnJuHaIWsXI4K2y9FEofU6mdSMl4aHB49Yf+7TFG3GZrBNVX+Ho2mHV28e8a0/yYiSCjttif0Ztj7mK/+n42uHFdeevUgdjFi3BQ+OF2zHC0rVpbe1zZtPA/ZuXmW0eZuQGc4UnI4Vu9c9D98wCOnY2DF85OMRL71gWdY9RD7kuDlh1rlOkKZExzWF8CxHA+KeIcuu0Q/H5CLlI/qMX/v1iqODd7l6MaYVMdvZHlde+ms/WGH4R6///c89c0XxiQ8FXOmnPD1q2NmOCQOHyQyy1jx8XOFbTV07wiymO3SsjWLwDiFACAUO6tbjnGaRC+al4SC37PUDDg4W4FKygVt9VliEaXjzruT2eyUvfnSEzlvuHTQU+NWI0VrmPiC2JUaMmMwPwZdIC4lWiFggwpCF9pShZ6kNS2Go8eTCMq1bpiWczhqmpcdoyaSO+Pz+hO8sPePS8dp5wZeOLb/9MOHX7u/gRIddW7JV7hMXc/yyoioURe2paoP3Amctmxci0kwSBwGxkiyLFrvwzM5LHs4NbSLYejZkcxRTtAbbkWAjJnXF/8Pdmz5bml3lnb+99zueebjzvZl58+Y8VNZcJVVpLkpCEgKDEUg0YNqiCbttHDa02kF3Yyswjmigowm3wwNN2LJQY4JGQkICjCRKSKVSVUk1V1blPN+bdz7zOe+89+4Pp/oj/kQEMvsPeM+JE+9ZsfZ61vP8cCyRVig0rlB4asobtAhwfHqTqaPQr3ls7iTMt0q0woJyK6TRBse3U4RGYDDSoDKPfKwR1YJ2tYwdamZLDspK9kYJjZaLDA0gEK6APlP+HgYdCZQ1DHY0yhNIV9NoVjAmJ5oYPAHKShxhSTNBYQxlAQaF1wpwZwtkUiBKPiIxWC3wSgqrLVHkke3GOI7H/k5Cv5sTtn0WlxPA5W6uePE72/zIR9f4/B9tUS0bhGjxyD0BT7+5z2zbZTCRfPPlMR94+ypff7bPe8/Mc6k/olVySB2BinOsJ1luO4RhRqxyBj2HuZqg6sL29lRSMY5D08tZrLTZ6Qz56Q+f4NNfu8wXX9K8696Q1ZMh2tc892LCww+1uf5qgfYiLp6PGNky4w4camWcWK6xMi+IioyS8Km1wGv4mMKjNethSWjOBDRmJOOhwbGGMHBRnqTQBeOhJBrkTMYJWWrQGRTWIS8g0wXJWBLFkCs4uir4iY/A3/2Ax5X+MoOuYZKlzMzNc+zBNfaH1xESxsMCTypuX+kSjzVTUNpbRVtojJToXKOVSxFWeO/Diu985xIn3vXP/0Y0Wtvf+g+fGo806zs5L76SsT9RXN8zdJMmURGy0RkRVtts7XdozLYZTIYUWqKEx2RiKVIH3zMIkWBFQKcfkxroD3LabY9qycUtJbheyuE1j9W1GvMrgsXjczRmXBaOzeH0t4l23+TSq1sEszFh+SQl86cUt0c88qE6cedJRLVE0vS5s3GAA4cHvPOkZJK/j119l1n7GubgT3L8vgGnF6+w4HyX539nl/f/wDpPPb2AKJ9FVOdBrPC+RzY5dnTIuXdLDtbb7G7ssL81IU0VWWzxXQtmQpQY4lQD4IcBQTkg0wU6UySJpihgba3G0nKDsJIh1YBiGCKKMukkRwlDOg7Y2zAMNg1njwcElZw7u5YaJbpeAtpidkJOH0oZuSGP39ci2nXoNiPqgUe1njKJcq7fyak6c7RbO7i1Cp2djL3NjLmZHGyDsj/h7t2EVqvKeDzE2pxCa+r1NpubPYQjGY8TfN/F8zyiKEZKRZ5FLMw1sYVB55pSGFCt1GjNR6xfCSiVDOWSh+ellMOccbdEGPYxssK8bxjkCTN+lXnf8sj9yzz7Uod3nkoJ6iGdjSHLZZe5+Qaj3KG3O2JhVnL9puTM8Rb74zGVsqJExtq8w+E5n6WGoRlq/DrMtALqJfBdgR86CM+iSi5S/f/zLE2l5iMDgZaaoORihcFxwPMkUmo8ZSgJhUsF5Ji8qLE/GOPV4aGPrTJ7KOf4PSlLRyvU57uUKjOsXw9441LAvNxgxno0SwYZ9unt7nPylKEkvoO78U0W565jbMG7PvIQvu9zyhtw8MgQZ8nh8sVDLDQP0ZmP+MGHV1hu3+ZAtYvWEwLhcndnkaMH59i720X4I2bamnC0j+lmMKpRro7I8oCH3l9HVBJa6l0899U3efmbIYNel8Ru0lRj5vPXia/c4fC5mK88lxHF97D37Mv8zv+9z1Jlm4ePd5DuMQZHf5xjp+qseRMO3zfh+uU+977zLzfzfE9Kh08+oiicDDsJ2OtOUDgEypAFAk+7DDoZY+1QRBGRmeZmHV5yMVn6lmtQkFuLEQVpIShySGLNYJJwd6h48IBHqeJx6/qIxbUyRibT2ATlUsghNlD8x9+7w5lTLda3BdVyQj1wGamQUjqmT0A7X+dSxyVDEjkKJ82wRjDMUgIrUY5ABgXCd3CAOIU8U8SxQeLj+Rm+AOnF+DJgZ2T44khTFD5YzSOHEz7zfVuo3KAnOf3IIROKVEnCUKNGlrTkMB4VIKYonHIo0VlCmrmMe5aKleyMDKmSzKx61ELFfpqhpaGufCY6YaHlMx7maKkh0KRGEMU5snAxY0GlnhOq6RRwdzOjVLZs70QMJpIkjrn5aZeP/ZMUY8pY46BsivYMrlKYcYFwY+YOK15/1dKoQCKm0xydK3JSRGrxRQkjNGRiCsfyJDoR+MF0LJ1OxmBdRAFB4JLGGuFIbCGRTozMJNrNkMKQCYMyAbKXY4RikhhKYcwkk5Cm9IaCvBehyg5zB6F/M4VQceVyQb+XM+fUOX9+n6EcceGCzzsfK/Hyt0fcfzLATAIeuafM9WsDdrZuMdeucvXmPqaX0xeCYmLxg4B4NyNYSHBil/4EzixLtK4Q7Y8JKBPIlJPG0mx6GF8zXw35xus3eP/jLbr7CX4V/vMX1/n+d9T40Duq3In6HD0NL77i8sBDkkk8oWQq1EWFwEtozJSp6zLCd0nzIU4kcaoFGolTsVSWQMdw5B6PjWsFwoKwBWVPQtOQxA5Z5pBrQ5pDEeUIXLRVuI4hdy0KjcwVRWb4XKvF1rDL9/3IIb702Wts7N5GGIErqmhdQogUx3EAgeM4xHmBlBLXSBAFkfCohIbhIGP23YfZPvkz/Mbv/SYf+WuuO39VZ3Nzg9y6FKpN2HYRboOS1+W1Nzo4SlIJXa7vDNAxpHd2CPyAXCf4oWEUTcgy2N12iSNoLbhoDV6pSmombO8aXJ1RjhrsxH06GxGt+ZwDx12qWwPGkwHXbiny/ZAv/16Kp8vsDyf4p89y9Y1rTG5eYNY9SP30NdYvT3jzyj73rGj2ukNu5TGsIU65AAAgAElEQVTZ7KcJvA/Qno9p2wfYS31U/gxBZYfHPuBTm1vHKxSzjRs89siLHC3lXH3jLu0DZWbny6xf6JEVhtSAHwj8asioM0RoMFLiupKiMCRZgk3AFNNp1tJMmVq1RG+/z93bQyYDKHKwJkHrDL+kEMJhYz3BGIesYfn1L084d6qFx5hhmLKyqzFrc/SKAcZv4w532bw2YWnewxd1riQBuzsd2qHkxINVnrvQZysuUd4bMBq5OBLyxCeNNX7d48ixMsNBjOdUyVJLYSYk6ZjFAyWSLKRcLhPHMcPhiHK5jON4oA2Dbpd6q0Vvv8NkMiFLUhAJzWaFUs1h+5ai2RJcvpBw7GgVE/kYmzEeRURRg2SSoL0Gb752g1rFZ+tGSqlU5+DhFBPl9JI+acdQbTpcuZMyP+/whS9MOHxU8fyVmHc+VEL5MZN0xDgCRQXMmKKUU686lEs+fi1Ee5b94RAs+FLgOR5RmqJ8gVua1oOg/BYXV1t0VhCWXcoNST8e0Ci1WVpLWTq0BPgw6UKlQn/YoHMjh7tNXn4xg/o85H1cH779jYhRVuaRd09YqTdpypA3Lkm++fmY2pkatfo+j9cmbL085M+/GHDiCRgvz1I7eYK9zpj0bs4tP2P7zn/H4sI+r998gQMzMfefvYucbaIGQypFTstp0NstuP66z9ZgHzOBUqNLOkmQfglV+VOuXqjz2pZmdgA/+ImPgK5x8bXfJd/psTZ/kPe8v0HJl9xZSHlXNeW+NYf9OxWcQ12iu/+ZhQcl56/mHAjWuPjp5+CX/vKa8D3ZaGkZ4iJIspzhGJyaAqVxXAcTGa53E0TqkOMSGUNJ2GmImnApJjmTrABXYZFkiSYrUiZYLC4z2iJ9iV+2LDo+V7cKji/55IWF0DB0JLF0ybXglYsRkdBEfcNaSzBb1WhK5GpEyZVsXMwZaot1XaQoKIppJIQ1OdIT+GWJ9DOEq9BFCtoB64NIMYGPPzOVwgK3hDKGXBU06jkfP6v42HEH12oSo3GbHgtNw3gs6HYU48SiLThYKr4iF1AUYDOBykOKJKdULxPvjJkMc0ahxJEOW0PLzFIVx3eIpWQEDAaaJE4JlEsytigriTXkaUqaWEZZGd/X02lZGbZ7liEZB5tlllqCp57bxrYWkfsp0iqscFDCoHWBH/qYDJSW3PuI5urLCUXgoVMXFRYI4yKtIXEKVDIFajsZkHs4MiXXGheHBIvKUxzXm6YYW4FOBULn2NwlCaZsN600Yd8i6xasSzSBShOkEYSF4tL1CSc+tEClVaCHOaoacON3NznwRIPjH3D40q/t0HcLrj69y/f/8DJvXMx4/s0e4UKZ1WWP//R7d3j2ouT4qTanT7R45cUdhl1BaA0fPrfA559fxysyuj6U04CqgZFJMEOP9e0uZ4/UwAhcAi4PEqrdhFYQsXq4ySCO2Lzr0mzBy2/GVCuW2FjiKGXjUoV+3EOUwE89Xr2Z877DY/aHIffWAihJFhqaUWQ5eajJ5StjNAptwRMBxAWyFWC6fVYOSy7dTEk2S8we9Cm5OapkKVmLNoo414zGhjwq8IXLmAK/AunEUAvGfGvuJ+hvfZVTH9dob5P7f97jPYc+Af6EIKyBzRDSYgWUg5BxMZg6mKzGao3rgplYHn7nLD/3Kz+LbVieeOcvcTYI/rrLzl/ZEa5luO+xEeXcut2lMd+nN86g5OG4HonVjCcxc80WaZQgTIAVOf3hhPmFFlbAjVtdHn7gBN999TLlkscgSQgcn+u3YmYqAYePxm9pbpa8yLl+KScoRTiOYrmpudaJaa/4uGbCaA/CpXdxspqzGbxGeOweDrY/D3GLmwvvoBx8jdKhf8rX3vxTnli4y4NHvo5UoIp/hBdAuQLGhBw+W6GbDXjbfd/m0Qciep1bvHwN1h4p4zku27c6aNFi4XCJ8oKGYMz4Vk6RV0kHJYzZQ8op1BwsQgi8wGW27TAZJdy+OSEeuAgbEvgW4xpGkwwRaBxXkUYOuU2pNF2qgccPP6qY7HU5vLLC9c4G1UaF9VsDtJuyu5ky064TdzU2y7h+pUc4V6Gbp9zagdlhwMHliEbTZbcbsLxUo1a3XLrU44FzC1y7fZN00iSJx4SlBFRMo1ql2xlRrzYwIqcoNJ5y8Gs1tLbk6YRy4GGMYuP2PktLDZSQpElCvdpgb2eCETFzCx7NygKuHZBnIzxKJJMe1cNlin6flaMKLx9xz5E5Fhr7DDpw6tCIa1ckWnpkexWqlX2y1DAzV0EiyO2Q+x6e5+LWDrPtCqaIaLdCXD9guJdSDqaRPNGkIEkLPJ1Qmy3TbNYZ9XrEmUZVXQRQFBbPA6umO7dCCIQB6SqckoeQGeM+LIRtJjdrvH75Nl/5fMFjH/8wd159mavfuI0Zlnji53a5ub9NfKvK2x4O2dtyuL5b0B1Y6m/A2lKPrb0Kd7sRYwdWjyvOroFI+5x/fpcXtuep7xSc8AfIW59hffZ9OA/+EPOjv+C7vT55dZ0kyzmyqrjbPYZBsLXpsDBf0L9zkVe/DTdvhWyLHC9XlLdzfD9l9WzB7PJ9HH5ogXj3dd52JON4bRPtb3PvIcXg5gJqySW5uskbty/w3u9f4OChiGuvaCbVgnZrjwPlwwj3Q4xsnzf2zzP//v96esP3pHTI3q98yiYunf2cYWypBVBrKYQ0bG8adnoWUhgXObEWVEqWAysOJtNkmctkbLCuwkgLhUOqIdeaODdEERxYqUxZhlLy0uURZ06ESDOBic9jPzvhqy/nfP2r8AP/6Cf5V//HHV67EzB0anxjo8vafJlnns/5f7+l6Oy6TCaWaHvCsKOJdgsmuwVJ15D2DelYkI9BjwRJX2FGHpNBDKaMygS20OhOiLExvT3LRx8O+O0PKO6bU0gs45FFOwZjDHkE0TAnnUz3p6SQCAdc1xL6Lo6yVMqKNCsYjTIGnRiRSNaHCeGcT63lIQJB4aaMsoJaySO24HkOIjfsjw2jKMdKBdoijUXm0ArB9WE4iki1T5YrZkqWnWHG3v6Yfhow16zSaqaIXLzFiZzuWVA4FAiUMRSRZO6YoNcZMT/jogsXJQ2mEIBCxQphHEwqKTJNkU3lXCUF1gpsIsi0wRQWnVmklhgxfbaSlsJKaqFCVQWTDYs1KVmc4zRA5i7OrGVpzSO6kxAc8Lj93A6eZ5l/l0e8XQAJz34hZWHO4fhhj1u3YoyTcna1zOVNjRinqFqJo4ca1PyMLPG4fWePDVcSZIrt3T6DkaHqBVSs5Og8vH495kCzxKBfsDSrONN0yQcxjaaHzFPa8zMMukPm5xxeOt9H+Ql//FLKwZWMpfkaMwdLvHJRsLQ8YZhZjsz7PH9B8sT9DnHPZX7FkBaCIwcVkVCszCnwDHOLHtFIIzxDpi31psJmBcJx0a5iZkYySDTXbyZs7EB3N2OwJ+l3YDS2xMYhMz5S5sRasVxSfPbrCXfl91OrxGyPdwitgxEB8zMug/QKTqWGEClbt4YM4oSK63P75oh+3yKUxbMusRbMLlb4l//up3jix57k/MZFfv6D/4aGB/3M8Ev/y98M6fD5L/zqpwaJx/ogp1AhkzwmrPkoz0WjQUiso4gnCUG5RpzktKpVBr2IPFd0eiOOnK7Tmnfp9zKwikwbyuU6yWRCNQC3cMnigPHAEkUW6SjSxKBzgYPl2AMO9UXFsZNN6mHEncYJgmKbXq44dewL3H01Z8GPWTrwMNf2co4d3KKsyqjQ5cj8FqICha5QT1yIcsplj7tbYzqbmredg/HdPtbUmD+0QFrs0tvQFFqy9ohCuCOyPCKLcjzpo5RlOOqhM4HVBmunqwae5+Mql/5eQjQxFPnUWSgFICxCWqQyOC4UGQx6OXk+XQW4si+4ck3Sl5JvXunw4PFZXNWnEjgEVuPZlLHyqbgKaxzO7yYErkO1kMxVLa35kGGecetagpIBIrfYvMf6uo9j9rC2gZF9ymFApTzDoJ/juh5BYBEqwXECHMehyAryLMd1p2HXRmsEgoX5GfZ2O/ieSxbHdHcM7cUEX7aBjDjap7ddozk3JHBCar7LhctjDh4os78h6exFJCNF3k2Yac3Q2RiQWYNfb1AL9pkQsLSSIyKDqGoWym22x7scXBYsNXJMonFlgc4TFpcMee6hzfT3nHILBcqB2bkWruugdUGc5Pihg+MojDbT/WWpsWiU66IcFysNDhmhaNLdvMulZ7tUwjElz6fwcs498CNk5RKXtl5heWaZvcEMNwaKum9pHTxBXtqjPmNZWqhRZAHr5lHk3IhStcXxtScxvXXefHNM/UDGTC3h0OEU4hmi1HDy/kdYrlcIBpcphz12br7K7lbCylKFoBGig30C3eLEMRhsTXjxm7CfePQ9Taqa7G5NKFUty2sBO9EpRN3lzIOPsnpyh8u7i9wYlomyQzz/7YxT913h2ldD/uKFhEMzY0bVB8hKx3hwdY/xhuDkoTHohBEP0Fg8yz33fYuVI395/fqenGhJUyKaZPTHGuUqfA8MlmwiuL0zgdQj1hopJU4gSAqLlJLMpqSZS5QLgszi+hprp6gcYxU5OcqRrA+GLJY9/EzR39FknkORhGxdlwTNkEk84tNf+2k+/8Vv8LafuYfxpefZHOxwdHGB33+pRdhY5Pg9Mb3OmDgS6FGJyahgPMhIJhk2B1NYoiQHLI4QEE6Tde3E53/85Pv53J//F9RoiYnTox1LvvzJBothQVHkDHNBHGeAh51Y4r4gLaAopkq6DCTSWsgUnp06GpO+RtUdTBxjtQIrSYspc2u+5eIrxV6UITxBKRCMdMbeKGam7DHWmiTJMVYwjgukBgpJreHgpA63NrsoTzJzQDNz3EeYgvNPRTgIDh+QOC+OcI9rjAkwST7FGlkDIkHhokcZiXDQu5rTi3WSgUdQHULhIK2LUAaRCsgNVkv0SIO2WCWmRdm4jEcxNlQIAyY1FBisL3ExJEaQJ5pqT1I4BrcpUMKl4nrQy9G+wO4WDGJonvHZe7VH+3CLsKaZXCmotOqMxhkr90bUQ0mnk6JlyMc+uMRnvniXqKO5EU64dVeweH+D1dU5/vAbl1k9NseZhse3X8wwRcHaAc2wq3j7Wc3YtvgXn5jjj596FZl7mCjja1cHnGo5XN82tCtlxLiDN+OihxJRhhOH6xw7Y/jynw0ZLqT88XNDfuiRMk+/ajl92MOTDtXGmNFehSBMaJV8Kk3LnVspR+8PoGZRZUMWaVZOOOyua9ASHIVICkQViFJk6HD0TMDtdEhL54hckZmCophKfGFZopyMbiR57oWUv/P/JFRD+KefnUOSUhktkkWXqVSqRFkPX1X5/Fe+wWy1RNiAg94Ck5HCC8GvS+I9zTt+9H5+7KOPUD28xu985vf585/+NNpmBK7HOM0IMvXXV3D+is/OQDPOq0z0BO16VMuWJMmwAqwQxElMqVKhVA0YTfpoCiZJgvAsuY1AOXz3xSHVMgR+jDQuDhl7u7uUvcoUYG8j8qJABuKtrKYC5YAShtsRNDcNRw96jJ0O9RDWb7psyCVaczO88Idf44UbPg+v5nzwI3/II49WKA88jhy6hlPAxmt1MJp4NEbOwnhf0Bs0ePzDD+Itfpn1zgB/aZGGW5BPbuCXVnCPbxAqiLSgFM6S9zN0VLB/c8LORoKvmhjRIwg8MNNaaLQmyzWur6emF6WQ1kVrjTb6re8gSUch0TiDIqdZr7C5MaY+UJycHxMqqKdgSpK9jRB/ZIgOLoAZMGdCRv4OFQnvXnLIF3LOvybwWjX2tyM6Ww7HTqRkOOSjAQtLZe42FGvLVdYHhmpjiQtvrhOUEmaaTUpln+3tDo4S1OpiKoHqDFcoSp5imEzADdBaM+j3WZxtk6UpJc9lfk0yGdbRTs5kBL5scv9jOS99W5KamMfWDLY8zyJjjh637PdaFAhmFmuEvTGDchtXaDzdZW6+ieiV6e5u0O8V9C6scertN2iEISXtUC/HtJvT3O1SuU7Uz8CLpyYxT5FppmDmPU00uoVXgmq7yVx9icGgRzoeT1FJQUGp5ZLqnNzkOI5DZg3D3KMcaEorPg+e8CgvW/aef5JEevzJn1/G6Hke/4G/x8B7jtnGY5TEhOujgNLhJ3i09FV6136bp68lnH3//TTCD7Jx936Cg+ugr9AKO+ThE2zka7QOXOS1C5t841t9fv4XYl75g8/w/NWQ4nbBR/9VjcXSJzl+35scPXmH/+sXLmLQrK6NCFTCxp7HoXMut58ZYm2TSbxL1fGZm0spuYrL3/wWR0/WOb36DP2izJe+9BRblxr4os/P/WKZb9/+RU5/POJvP/ZZ/uSNNh86qxh+6yv8k2fLRJOMv/3uMvd94GXCeodDrkfv/Cw8+ZfXhO/JRktnCft7lsy6KGGotz2sSdjZFYzS6YJeKCTJW4Gj+AWYAul6jIcFO6OEuTCYFvGRpj8pkKkkSUAJh92h4WgdBjXN0YWAD/7zIf/w+5b4F7/TR8zV+MxvfYLnvvNtbuzcxe70sOUDfPCJDzLoao6M+ri+QkrJTndIur2D2S+zs7NLx/XZIcPkhtAR5FqR5obCQlg4oAPe+YNn+I3/8PucvPcAc01Ju+bym09KSnbMzV0HH0GWQK4leZHjGYUxxZQ3JQuwEotEaEGt7FJMMjbvZmx3M5xWBakNQckjHuUUrqHqO7TLMNLT6VCUFOxMckSUkuaSuZpPFEfEhSbPNM5bjsOScNHjBOuEzPg13v4PSkx6OeOBw0Jb8eC7m9ASdO4kPP27I/w3Z1k9nSALB3IwViP1lNLu+A5hCr2BR9CcEHgWO6hDbYLAwYps+sfPFFoIbCzRWoOxSNfDZAJSlxSDKqZTMCnAihzhS8bZdKdoohLkuk94yJB3LCJIyEKJPzQ4VUPjoEN0M2F23icPLCZTlGcLblwYMLtUJRp5GJ0xlprA0/zBH62zN4w5dyRk7dQq3reHbCQJL33rOjOVNoEvubOfcnTRwXUmqHKNnizINj0ee3udX/vca9x3ssF3tju898QsL311l2rJ43Q9YWB8rsSStWpIcqDgAA0+980+J5dd1k4rjrUd3nFvlT/8eo/7T1W4sR7RTwvOLXmExFSkz+GFkDwR5DNjhnc85o7k6NjFqxTkGcwdCdi9GUEWUAQWd6KR9QoMNE7F4cl3Gv7u5xS+CkBoqu4C8+Ysn/n33+Hiy7u4MqNakygB/+6L/zPX+heJI8Nyc4a7zj41kROrCtu3Yw7MBpSbOa7j4K/sUjIRj5/1GV+b4dzjpxiMLvPG1j7/+hf+LarjAAVSQGEzBA6pl/+11py/yuPX5nntpU2qKw2MHBOnIKSH8Mok8QDlCQqdMBl7yNylHTbY39plbWWJznATrzKFtwvX4pVqjPsJQrnUax42zhiNMsK2wvWmDmvXU+BbSjWYXwx4+wHJ1o5EFIpJ10P7bW7EE3TY4dUXXuXvPFLHCxPOnn4Id+EQq+ILOOUCt6jgJBn9nQHCb7Deg4Me2OC9PPpEQqJeRMQHOXrfDk7lFtH2DFs3Aw7ObeH4LmFlhngY0C/6CCfCqQuqiwa/WaY/GGELiDoZJVXDGo1gQuBWMXmG8MCrCka9CK0tKpcM9i2h9sn0hDCAUrXE098ck6V1PIYsr7nc2rbc6RQUFyJO1yzycEo9n7DfixmEZW5ehnNnqvj1hF5HUMkydjqSO5OCv/+RZa5sjbmzE7HaluztjLkbL1Fd77IxSagNPWpOheMriguXOtwKQw5LmFmYp9vP6HX6LC00sOUxu9sZ9TBkf5ByaNXQ61sGPU2pXKcgJSDh5qjMgwdjkkqTp893WCwr5mZaeKrgpRt93vtBwde/kfOulYxamBC4s2xdyumScOxkhU63T2pnuLEdU2o30ZlgNLGI5RssLkz3+lRjzFgYAgmh72JkSnkmIUzLOK6lN4hQroNBkKkCX015sPtbPapRzvyhBSbVCt1BH5iwtS9Yak53NMeFwnU0UEeqhCJOoSSZbZU5e/9lnrr9D9gIX2KRhKXSBV54ZpP7P7DJm1d8bly6yT1Lb6NW3eL5q9CLF+i+0cFbe4Xdyy6zpeuMzTNcGHosP/Amm2++yKj9IDc756B2nit7AQ8+mTJ/pEO3BxsX12gFT3F69TZqZ8KbjsOYFtcvjKEFj54rs+lMOP0wpLd6bHtQXUqpH/LZGVoumFX8eJfjQ82zT92lOpFs1fu87zSIrYzqgwu05ZdoLbTppx9mJvkv/P6FJv1iwLzrUHL66J7DgcZtlptN7or/eo7W96R0OLj2658axx5BWZEMezQakiRXvHbNIGT6FujSIXcEUsPqcZgtu8gEzm8VbGxY5huCUFn2Io/OIMGLehhVJvIcBntjzqxKhIiIhjlfuxVwY9vFuPfw937xUaQeURFNXj1/C0dWIE+olSr4YcbS8QNMkh3W93c5unKEH/7oj/Mn559iLa0zGQyoOlO322ItZDTMMarMytIsQT3gk7/6j5noFxn2I1ZPVahXS7zneI8jVU0nKYhSwzi1ZMZidYFS0/TuzILJDQaFTKY3ldaMi/IsL+8J9vYLpC5YWa4x0/Teyjhx8B1Fs+SQFZDIgn6ucdwSFUcgjODWbsyoHzEYQjxOaAU1pJH0RmY6Ps4kUhpCt2D+fokfDmk1fIQ/QFgfIo3jO5w9G1I/ZtCDAiVcbGGxWoKZfo7RAke7pEmCLgJMZnALF+EreAtaGm/bKQLJaKKxRguFFQZtQeSWKM5Q2iHLcjyhMEhyKxl3NVkgKKPwjaC+6jHe0bjSA1di+gZZdpmoFJFIwoZDVkmQsYDIsrtt8KTAyISdKzl9HFwRMkkSThzwWVuDxbmAV1/u8TM/vsy12yO2hj5HTylGkeWZ17ocu6fK1Ssp1zZyfvUTZ/mx37zF6rLmzMkK65sJ33g944H7LecWS3itnJsDj8BRzM649IcR877P5Z2Id7y9xq27hlbDkpqAz32zQ3W2xPXrCedOuByasxyqOMxXJMszHmmcsLBSoNT0Xai4AW4jR+QSU9KozFBuepg0QxUOCDB5jixJtEkQeYnH5zSXbrQZpD1cCf1ig/e8p8WPfPSDVGccrmzc5X/99Z+kZzJuDq7gGMtut8NSbUAXQW8rIy6g0ja0Kg5VRwMWI0PmPIdDB6tcubjJb/1PV/nuV67gZS6myDFTMMCUwmCnbND/7Z/9s78R0uFnf+OTnzpy7CibvS5aZ1TKNbK8ILUWgUWKKbpEoSjSBJNFrMzX2N/toC0oTzCONUIYSq4BK5mkKakWpJklzQ3vetQl1zlZagAIwgZZrOl1M3a3yiwdSahXHWYPWjZvV7nUi9l441nKtYM8+J4fo9zs8ui5G/gGGgs7+I5BGRdPurz5esbOngEheeXNkCc/4TNUL9FeVswvN8iEwk2P07m7QVBOCVsu1i4RRT5lfQffBri5B7Fm+6pLMnDwlGCh1CAeWbQWCCcDz6BVhnQleSGZxJY888jjGnHkEoRQFCl+oHBUjVvXY4RrmVtOOXSwROblNMqGY/Owtpzzws2CmqjQ2cpo1+bRcsDcokG6kvVbKWMTcGwt5PH3uCgTsbHj0G5bXr/uInPN4Zk2d/ZzHj0HMlQcbHhoZ8xfvFjlvjWHlUWDiZq8trtD1cbMLDQQssSlizHloCDOahxYCbh53cd1XBwV4rldoqzMy1c9/vsnNF+5VGXUj/nY+9r8xfUEk+aESI4swxvPpjjaYWWhTmkSshvv4xmHet2Sxw5eVSNihasMhQgR0pBEKSeXH2G2dhcrUzzfoVSRuC4IYbFW4zqWMPCRjsb1LHkx3XdW0kG6BcpxkALyIp3Cvz1JtVrCOmP8UolBx+BjmPEVXb8gNAkid8hSn+ayZG7FEpb3ufXCSdKozursFte++zW644SaMwvlO5Qax9lPuiT6EEuHMzxzG++A4tTMUVqtpzm7ZlDhiI1JjUff8Qt45gFeGibIQ0epL5V579mjqN5FPJVy/9vg2vYBFo4uc2o15PVv3+bZFyv4eUpdWZZm53COpvSYpV01rBw3HLrX5aGTLSrZkNEk5OTx09z3vnNc/vOX+KOve3z4pxd47EzIzOGQTfsoR0oFo+wQF6MfYvnoDdaa32T/2YBWN+ah79Ooww7VkmQxXOYv/tMOb/uhGu3VX/pvK94hvvIrn2rVXK7diYnTKo1Zy63rkmExleKIHbIgpygyhCxx7kQZ6bokw5St7QndzDK0E9aX/wccp2Cj8RivNB6n37wH5VQYnfpxvp4c5/X4Xm4sv4/77z3G4+94mKWDi9S8Mf2u5lsvfof13T7D7pg8y+mMc9ptnyKPqNRKnDp2mIUln++++DJZXrA75/LxT/wsO7f22Lu1wcyRJf7NM/+Wzewl/NmQ5XtmuXzpz9jNyzCJqc2FeG7C87ddXu1M8IoSFTcjEFM2oudKjDGYQjKOIJ4IOpHPbVnnhfwYT4/u5XXzELK6yHx0m2phaLYVtVaIFTl5YXGVJYtyuoOEApfM5MRFwY3tlOv7EVkMxuR0B5bBRCCKAmMyRsOMPDaYPGPOr+HYgtoJj8CRSLdA5GVsnlFoB88KdFkjug6OJ7G5QWgJOBhtscYiC4GJDI5ySccFmRGYSYHUCutMQzXzPYtEYrQhj8QU2JmaadOWW4pMYo1kMijQUYFT9hiPckZpju8qhBRUpEKVNFnXQZYcilTgVSF1c8q+wkNgMxc5sQz7DlkC6WjKj1QVj829FJuP2ZsYHj5SZmuz4OoNOHhYceXCiM99Y8jDD9RI45jnnu0TtFu87azk6a/vcfxsm9kZ+NNnbvLJn1rhjZsjvvr8Ls58zNsPOPzB0xkPPdTg8usRr95OePSUx9xKldcujri4F/PeBxrEfbiwP6RdnSOQKTZTHD6g2d23zDSrzDsFbiyotxWzDUN7voTruDRrDvgGT4bIOMU2HdzYUpQtUhcgHUgMAoXEwcegCC4AACAASURBVFiLchWWDDe0pF/e5vIzCc++WpCrkELB5vazvOMDP8QD71ql3GhxY+s89y4dB13GGA8tVun3b+N4sHzAox36eNLDLzmUKooZeQBXu6TpLN11h531XaKRRikfY3KscQGJkmLqIEXxy7/8y38jGq3v/OH/+amr17dxShXyPEVIhzhJiU2BYrpzqAtwhEKalHo1wCQpSrjUGiHDScxoYglLLsomGGtJtaEQHsY6KCennAqiscQahywrkEHCgdUyR0/OcPyeef74D7bIhy1kENEbdtgb/C3iVHD24CMcWUlph+uE0U327jxA+9AVVOFhRg5xXPDKmw5p4nLqxBxv3Ohz4oE6p04dIai4mLRDb5Bxd2OHXk8yO3uQ2myG58QQ9RkUNba2BXe3hxiTYsZzDDs5mJiNYc7MoTKN5QmluqVIPGTkkPZdlAjQiUbajHbbMDcrWVyqMhoaxmOX3Z0EgWBuycUvaYZxjoosNoZC+wzTAmtdEsdlZj5mtjbCiRWakGubE7z2DEIOUWmV9Utd5suCP3k5oulqfEewXJ5leXHExdtDRCrY6Fa5tWVYW0xJR5K4POLy1QrNukNDWtKKZjRKGCYjHlhRREWdxvyYnTuKcji9qGozwAqohyEnljJevlhlSXQ4s2p46caYfq/Kh+4VDLKcm3cMM60qn/jhglfOS7xalzSBgwsOrgJlA2IxQU4M28OCncLy8AefpHFggd7mOpXGBKfSxlMpYWhRjkVJi3IEQQCOB4aUajMkK3KUmjIMM1uAeavxciRWZiALpDL4/lFsdpfyskOsXZKuRpUMdeFjjCEs1Zg9BL6r8YSDP3qDSBuOzwvKtsPqPW1mK/s4rsu5hRC1+ACOO4tDmcX6HKebCjV4Bq+6QW12wtHyAe45p9C184j6MknvJh0UZ86tcFTe4Ld+7Q5XLsxQCjW0Poxq3QfDXXxni/XrAyrGZXU1pXVySJ/302GO1SCkcaTJ6cOLCLdDEiaUWg8Rr58n57tsbcJ2doqZR36UYhJSKe4hbL2B1gFvvvxZxnuXWDu8TFk2mVSuUFtWNOaqeDbm1NI5Xv9Ozqt5A1H9Kc498P7/tliHf+t/f6f90dNvY3L5Je6pP0PVL7isPsL8mXPceeq3+cILfT52rqBwJU7Q5kcf73PV+QjP7MzTMyFVLyHp90AKCsfDGBiM95lptdGFQkcFSZYizIRGewmDoRHWSSYG6Y556bU3uLXT5cJLl8nDMstLC4R1wdGVNiZTVHGpHKrR2Ysospw4Mww29jh59gCxKTi5epD+eIKXap49fwWTRyy2V/mzp59ltXGEgdmlXQvQ2YRx39IfFRTakqcOjlVTyQaLtpJxbNgbp9RmWhw+fYQjayvcU59woNShme6hh13y7elt+YGzDRabDlkO3f0YpS0ycen1EzrSMvFdhli2xkMKJXG8EjJL0Fh8F0QmscBwpJktKSqFYckNCLXgwPe3WJnPyD2N0ODIHAqLlS7kCaJwsTUJ4wxrFCLKwfEpTI4jfNKBxkYW40wbMITFx8GbgVxlxHc1KlO4SjHpGeKsYJznuL7CtdObvJEWx/G49UZC+4Qi6mb4cz6+0YShYm6tRP/agObhEsYWWMdQhIKqK7DGJzM5uRCMewWtmmYSuyiboULL/p7iW0+NSfMShw4Jjh2y/M5XEx5e8RjbDKkahF7O9iChUvXpDFwOLVjOXzWcOuOSaIVIPUr1nDOzVb74x1cYmzqXtoa87Z6AP315yD/+iWU6uy7X9uA//sk2f/8HAnb2NU+9MeYj76tw5WrCQyc8nnvOJRYDqoGln8DCQoXlUGON4ftWFY25YGqd9y3Sh3bDRzuaJI5YOlLDTS3FnMBJBKgcrIBUYhKD9UFZpripUE23jBsh7/7QDmK2wvWbCY8+ucYPfvw9RFmOLgqSyZhyPSQex0RFn4q/xOvd55kJx1So0rMxDpqqU6MkZrh0p4udlHn91WsoGfLeD5wBVaGztcuXfvd1tq/3Uc50nOU6JbApea4ptP0bwTr8h4+Ftvv/cfemz7pdd53fZ62152d+njOfc4dzZ0lX0pVkWZItyQPGxjQm0CFAg6mmG0KqQjqVVKVD0l3VEdUvuuhiajp0ugqDCUmgGYrBNpOx8WxJWNZ0dXWv7nzm+TzTfva81sqLx5V35FWqAt7/wa7atfZvfX/f7/cTZ+ynLlY5SNdSGE3pOQhjsSVgHaQpCWXBuZPzHK4f0G106I8PyQzgtrFiQiOEsnLIdQ7Wgazk7JLD+TMlrgv1BrhegFUOjgt5lRHnAXdvx5yeE5y9Iqg1mrw1/lFeeuuQi42bhFXJI8+OWNLrNHpw9w1wywDykIO0z59+WVKXlisPWqSQvP22IGxowlAwHCoe/sAyrdMJq6frNJoJo33B4Zal5mqqkZ7W7FBSVCVZ6CGEwJGGmilxazXczgSvBknscfJSQdHpEvhjqoll66uzpEeH+KZEGRhmdTrtiIPdIVv3cqwNyfIUT0fsqwRroRpLsrHhnfsepSlYWpjjeFdj1RGB8gjxCf0xFTAZQdZrUJNjbmz6PHyhwBaWw72AvXKGPsfUlaa9lHPntQ7vfy7i8ze3aKsundqA4/XLPPKdb7J2FXbSJk+1G6ypjMOhYFGMcWuKGzseC70Gk2yDE2GN6wcThNthPnLpdC1+3/L5wYAfOlPjixtDnlyBU42TDIYb5IHAn/hYlVIpqGufIlcMRwnN+RVkdshB3+dffOK/xS4t8ZW/2mLn07/NuYf3EH6G0CGoGCnBcSSOUlhKlOPiuNDqOfR6He7e3UaXkGbqW6xCiR96+KGlKlPq9YjW0gz9tXUWVpYRs6c589SP8/Jv/xQqqON6Y5R1cVRJs+vj1kJwFNlwQkbO2usRewcn2dg9hU1eZOWBWcIbtxEL8PB7PNSBz1fXLA9cbrDYHPDl38/5q9dA6xrPv9vj8cePSGtLvHn8FFuHmsdbr/HyzQ3OdxRLXcUnvnKaIlri0Qce41z7T5gVM5SN+/RkxZ3iJ3l68Q+I1Vl0/iXuHbyPx0/u8dXPnuArr13l7EqB7jzDxXc/w7X0DXr+cywUb9I/uENNDtndCbnwyCqn3U+TpylfG/wQTS/kzvYt/CjkzKkrDI/HLAW/g+clzM50WewaVt937289v/5OKlq//p/+lxeu7n6J3srHOZp/ihvmAnumYu2gYM+f5ezj72az9SjF4mOMu49y1XyYd/Ym7A22CIzEuAK3sgzMMZ5tMjzeYrGzSKhajLKUQDjYEGpBA+F7BEEDt+YSSIert24wGU3ozsyzvLrAc09fphFU01SK9pldbJBVkrIq0CbHd6at5sunZsk8w0ytwcFgjzzLyXPJytIsZxdn+Ecf/3EassMX/+KL1BbaONplFDv0jww6kRhrUK6krDRlrhjlgsFIk5fQnulw7pHTnLu0yoXlgCeDv2Eh3oZ0gtIOrXrOQten3ZBEkU9a5njSn+JsUo2LoikkwzRmXBq0daEUWAw2LXBFgC41cTIFYQut6CmBLSSXFgPSoeXN/REXLlR4QYltFthDH2ENVmXIskbpJ8hjg3GmKUYrFSadEiOzeFqeWiUwKXJUAcMyw68EnhAoT+IojyLWWKMxmSLNSvJKUWpLNikoKonjuOQ24fggpzkfkBxW+B2FyKDueDTrHm7o4yYVWlYUrqCOQ9w3FMYB15LuV3h1QS49wrFDve7gLAlmz9d4+GN1nnxOojKDaQWEWwXGUazOWrxqwmYfAi/irVuH9OabfPZv9jl71uO3/viA86seV87X+IPPDbhzd5dtHTDRJc9caXEwgpsHGSrTNELF2zd2OL2sSfOCzf2Sp55os9wLuflmztJZn/FQU29r3v1wi50Ny+OXAkaDCQthjZPzAV6twu05uL7BDyRpVtCoecytBDhSUIQWN9aYEEzJdFWlDMJIlPEpbDVd12Sa0UBzHMPy+z/C5fd+lOf+i3fTXYoRXg1bDlByHt+RZPkxcXFMt3UWY1xOtJdI0yOKMON0fYnh/jJbOwU33jniYG+MKS3Dw5BXPn+XRx+ZRwclFy8s8fz7H+G93/kQUUNyeDRiNMq5eGWen/iZi3zHUz/1baFo/eF/+DcvjNOSYWpQ0qWy1ZQZZ0EhkVagK03oueiyoEwTRDVd72hbkWRmWs9hMjzPxRhJmhcoCZ4wLPYcOi2JkBYtwFDhRBYVVDhehd8MOHkhR6QdHnpsifmTBa/dXsGZ7bJw6iHcmks+qAhHKf2DFFE5jI4L4tiilcv6Vgi6Rl4U7PUX6BcjcqAydcap5J17h7z+l5Zvfm5EvBPy4JM1uksunRMh9GJkM6bWNrQXQ4KZnDMXFSdPC6LHNPPnGzRXUrqnJe3FLjfvF7jXfUbXQ3a/aeiFOSvzDZK+ZOOW5PSlee7f2+be3QrPhSSvcFxF35R06uCWMDmyaAnNhQaf+XqGrTTnlh3mwgysQyY8JipDxw6nT66yMU5Yu5+z0NG4VZNR7FKbmyWWh3gI5Fjw0JkuO7dTNg6OiZ0ZHpgbM98xDI/2eeO+5cLiMqeDQ+4mCqFyHl2V7O1q9gc+T64mjEd9Ti91eXU948c+cIakOODMasbnvzGm0RFcbvj80XXLU60ZPM8yyQ/Ixk3euZVxbkFymDSxE49SVdRrDUbjkrMXPbZHMVvrKecfXaG5vMqtOzcYXH+ToAXt1pQG4fkWqaYVGhaD54P0PJTrUeqEZrtGVsQYC5jppddxpt9nEASURYnWhjjpc/KBZY7vb+GXBxwdvcbKox/k/t03qQoHaR2ioEJXTeKJxGlDXddIbcwb3/CZmIBdOaA89Oi2BwRNxcnLIfWgQuqUzaTB5fMD9m62+cyfJ9wM6mztjwlkysWTp5g9KbDqBifnKubto1y+vMfFOcNsGNA64ZIkDqpVEa+do7XUAKtoNG8Rzc5wonoFq9cJGwXzywN0vEeWxbz5xjpjd5HGisBffZAGPT7Y2+fGDhwdvIjyO/zV1ZS5S2cRuoaTudy626d2/jzOyuMcHgw51exRi36Hs919Fv2Yx987xHGG+DN/e+rw7+Sg9cu/+PMvqLpAOu8w374CjsZzA2wlqEU+YdDAFXp6M7cl2eQQx/VwnQZezaEoU0xlcEWP2VYX5XYYTEbkRYJrS6xfogqL12zha0vgGvKkoEo0t9av4wUhk/GQt+9usb17QJwqWh2fZr3FZNKnUJqqMFAohFBIGVOgCTE0/CYnO2dp9gyNZp33v/f7+MS/+7/4pf/5Exzcuku91sJ3fUpjSSuBrQRKKgg8FA5S+BjhYIwCCyr0OHPpNGfPn6Dd9nlmcZcH5TYyMoQ1Sbvl0Gh6dBqWxV6AJwq08ehPIM1TSgRWSJKhJq1ctLGkxpDrChdB3XEp04p+XNCfaKrSoRa4hLqiZnyiNCPxAsb7OY98tEImNbABKp8gpEeZG5QjMBOBqgcUQ4M0FiUCyrREpw5CCMaZxlUeKoZKSSIVkNgMfSTxGnWqSUJ17CAQiNIwzKZrR5NOS1R9FWCcCS+9CO1AUW85THKLLyy2qmgFguFeStASxFpjPU0oQ6TxSNKCesslHZUI6eH6ml7H4J1SqLaLcEoOX5UUxxWyEBgXbNHgXR/U5KlFKdjZ8Ih8yVxkWZqLuHtnhFaGlQWfU+c9br494vW7h7znkSZx3yOe5Jy5KLhxd8TWdsZcR2C8AmyEo+DW/ZzLjzQ4HBo2NxM6bU3QAKEnvHXb8MBph801jROUEKeQgx9KZiLJ4ryLW7fUPIlyFcrTSKWodS24DqgC6QQIbbDWYI1FOT5Wal7ZTPjtbzjc3bPc3xBsH0u+cJTw2jDixs4bfPZv/hK/OWBo3mFkdjiOXyIpdxhVG1SqYDe+h1X3qcwux8ea3/2lI37nf73JyRNn0Y1DbCkpbUXN7XD1tdtM+pqXv7DJy39xl8//6XV292+SN/oEC4rnPnSSD3ysx/zFjIoBH336b29W/vv0/NYvvPBCYyZiNJkmhYNoqkgJ6yCxOIA2Glc5oCswlvlul/Gwz2hsUK7A8RsUZUrNV5S5psSgXA9TauZmAwI/Qzqg/GmdllQKhMRxXWRY4gWSbjNh9qQlSTI+9w2Fal7h3LklZvg8ncYr9KKAkchw0ohxUdBPNFZ5rK/nTHKPnX7Grf6IsVYcD12EG1DZBJu1oTdhGFcEfkIx6bO7lrO3lpBFDxMFFwnCOfxOkzRaRIVnuLtV8PaLp4hvdRB7DeIbsPtazKIvqRpjkjKlu6QobMrLL+XEueCxDwa88fIuydihWW+AqKiwFJWAlos91ORjwaSM8OoOXVXw3HsES52A3d0hsY6ohMYUCT23S9UrSIbHmDJjdhlECVVcpxMa4vE+lx52cSYB47Hlm2/1ufgeh6ff7eHmMXtbTT77quY7n5kh3a7z5t4xW+ks24nkB04nfObPJoy6Tb7jsRpfv90nqEe8fX3EhSjkq7f2GR1INtcKfvr5Fl95c8wPfFdEIxzSsGN6rSYLgcfiQsj5xxwGhyFudYyhYC8GP4ophWWyn/Dlq4Yr76rh+BnnHvpOvv7VW5RH19DBiJmuRVcG1wOlQEqJlALPVwhnOnQVhcbagigKSdOCZt3BcSxRLWQ0zlBKfKspXqCE5pFnHqXQQ8Z7GlUl9FYFJ1YESvkMRzGT3EEojywfMjlIIa/jRSHNXkhfLaGCx5jt5oTOArUziuRAYcsSGgV3XtQ4umB3N+P+nmScRiw6HgvLOeHqEG90ntHuAZ36FifnHPbvZhxueBwdJHQXNd1Zl498zwHBRHHh8S9SFw6Bf8ScalPqDTzZoxVNqJExNCWnVoY8vgpPPT3i7a1zuLOneN/cZ6nW/w/uNlMCMUM5XOHqZka0usxhP+StexMG5Ty5jZHDmDPyT+nVt3l29g6TdYFTVFzdhgvvqSOcv2cerX/97//tC5N1S9CRfOW1T/PEg++jETRQAWzu7eOHegp6NlNTqVDTFVWe5dSDDoHr02idZDgZMKly0mwfpESrFOsoXNMlK1+j45xCNSOOtr/O/PwKd+69w8r8RR44HfE7f/ESrXqbLBtx/twZvLBg/+CAZCJptcLptD+7wHx3hnNLjxJql1rHod6WeB6cXn6cIoEffvd/j7UxkVCIRGK0xoiMygnASLSBXAmUUpRSUCER0kH5Hsr36C0v0p1p0Gz6PLpU8n2LL6NNhVQS39XUA4iwzEQ+ygp0YZDCI0k1nhcgpMN4mOAIjzTOoLIkWcHICsqiQhjB/iDDMw51x6PhSmYELKC4NBcSOA6FLrDC5+IzAYKMqjBYNyDv51MjNtN11PHehEbHJY0dskmBrzyqpKLKwPMVWVqiAsVkW1NTFXroEs24uKpAGoXfhcn+GJ25SLdJmRUMjyWuMjSigHSo2RxkSO0SBpaKFFF4hI6P0i7NOZdRoQlCix/4iMphuD8h6ARgc/w0pHlqgh8ocCKEa5msDUn7Pr3zAqUVNshZ27H0GgG2SGh2LI1WjdtvlawsBQhH0m1nGOEw22xw77U+jz7R4tVrY6yIwLEMy4yFeou8iJFZRSVdAuVRlA65M0Tnis2kouVVjAuXwbgkPXTYijOSscNDZyLSdIKdWJYjhRe61JsVTekRS8ODZ10cR2FrlsCRIN1pga0UOH6JcDysTNCFiyoF0pX/T3XDcs/ni7t9vj6U7DgOnz+Ewq3jNQ5x3JgTpzx21wWznRplnhOWTURd4hqYjeZwSxjcnucX/tWbvPSHe4ySFGnh3U89hKoZJsNdCi2gVDimzt5aH+OabwUjDNv3C/aupXgB3Nvb4PhAU1OALvme5//5t8Wg9Yf/8edf0LLgaFjRbHbJijFe5OHJqQqs5BTSW5YaV0qatRprtwecXZ3DCyyDUUlWGoLQ0olCsrxA+D6VUAxHJd2u4tRSiOc7GFFO8VXuFJmENFivIvQjythjZ2vC5jpc3aso9n0eWhlz/4t/RWOxwmkE9MeWbMdSCE1SKoTrc3SYkhmX3VEOURsj61PcVVriVhYxKcnLEN9K5toRcysZzTnwOhUPtvsM37nL1a+tsb1d8Mk/ucKrV5/n9bevcLR1k2c+dJVkcsw4yVi6KOkbRVkaTp/p4blN3np1xMNPeJy/1OTuOxOKsUKqCm0LhAzJjcE6hlah2UyBpsv8gsNiJ+Hgts/+ek7SLxASdvZdKpuztOQyPpqwVjl4mWA+lKB7eJFAqTGJzHG9JtV+jdF6n0uP5zQ7PlHSYm/k8423LE+esjz7oYRkL+buWPHhxzxmaznvWkj4hc8VPPbEAo/NZHz1pTFipPGCkplGm87ZjEbu4jRzlmZO8KWbBR95epHf/PQuFyOX8ESLw7tDYp2T6TF7r5ZktQmemKcsY+oLq+zcTZGex2y9xq7KKO8rZtr7vPbWK+zcGTInYoaDlEvzDfAkQpVIKXAcheM4GFOCa9BGozVkuWZxcY7RcIzWhqgmOHHqBEkyJk6KqQJuNMrAm2+u8/B3PkjrdMXtV/skt/coVcbJMzOceleP1mzIyXNQ8zPYlSR6xLAf0jyxz+3t76IsJ7SaGXdbD/KUt4MY3KYRaBrOKn/5pZiFx+DMcoQ9zjCTlFOzOQ9dDphdXObVG5K1e226bYfj+A5rb0DuTNg5bnJ61aHKznG6e4czq9dpFz6i6iOwLDsbvLFWQ0UjamVAvg9B3WU31tRrDo2i4s7WKT72HZ8nefU6v/yrER4HPHD2iIRZYgVPnziDMC5m+SyZN2YubTC7/DpB+A/48EN/zPY7lo21nOt3GmxdzdF5nZNXfubvl0fr5HMdK/Iap/wh7WfOMNvzaYWLLM0scTRao9deRYsDrPUpdMZS5zSV1TieS1rEtPyIg+yQeXcWx99ld+CRln26zWWKJKUZ3eRguIN2MyI/YGwKqqGiqg2wI4d4WOfpxx/g5ho8sDjPK69vc/JUg62Nkl5vlpYfcXvvKnPzPQonxvNmiFQdSZOLj1zAzwVPX/qn1KWL41q8UuAFltCz+A2FdhuotqCSBqscKinQjgRtUJ6PRUIpkSiiZoOZXg2/7vGpH9/F5kP6fRAmw1cOoXBQ6TTJZA0IqdC5JjaSSaw4Hia4ZUCYC3YHY2aCOjtpytaoIHfFFH1TKQZZiW8NjlS0hYdbCK5cDDhKSkwEa4cJcRnyoz9dYrTEKxzKxFIW0yFRSgerK472DZ2uJRtLdGJo1hTpEIrcIBzFJMlh4mKMIfAsg7hkNnDpPNRAj2OEU0MlmrVNS78/RPQa7L0y5iP/7ASvfTPnta/ssbrisDOUXD4dMEpiOpGi2fbRssSvg9+pUWyNiCuYOx1QpSmOqNO8CDZ2ML0YlYZsX0tZeraNjhNUZLj+doVBQ1UjPhhgSoe6KMknhniiqLUdZuem7fSf+syQxa7AeA5393M8VdE8OcveJsycHrC+6TMYlQhpOLXk8srrKU8+0mV/DNfeGlOEBY+ecxkMXdquZu1Is9z2qQ4zYqN54nwLRE7db1BWE5o1B+OV6Ezw/nc3kHUIQo0jNa2OhxaGIPQp4xK3rrBCYG3FJC/xTIhSJSiD4wOh5Kuf84ldS+pLbo4LNvAohOVCDVpezr31klOzTXpqyK3JCl94K8XsjlHdMedXevjJs/ziJ34XxhYhHH7wR76D+sUjNu5vY5A0VIf774x542trKDttijcGXKWohMaxivNPNHBOGbo1l6yUfO2T+98WHq0feMC3Sims0SAtlTVEtRrDpMAybchvtxo4WpOnGVVlEBhU1GA40WSFJs9y5udqmHKCtmCEJHJhdbFOJ0yYXzZ4NR/pW4K6wAvKaWLRDcnNmJf+FM6vtpg/WdE5I3nr+Ed5e+OID19+lau/dsxj/+Qs5+c3seMJ69fGjMY+k6wkLl1efyPHC112d0ryWkRmPESVE+icpXaLg90+sgtRVefMCckjT2R4jYqwZbCyTk802F7f4cYOfPx/kDT9Nuv3Bsh+A683BBzGQ8HCYojNMgZHBaOBQrktGp2U+FBTxZJ8kpEYj6o0lLmlKqHKBHk+xUgFskaelkzigqwA7TikbpP0+JjQwp3bUDhNbOhx6/4h3/tIjf3BhCCao7tsODocE8c5OFNlS1cpjju96Dq1lMDt8MqNjOVVi59KDr2EyV6Hd50ZcX+rRb2X0+nNce/eGr7vMokr8thybezRdCrm5j3e1bH82TstznZSQiE5d1rTCuDm7RHtBYVbs9TpMjxW1JolG/eOCdwGruuyO0mYb2eYhotRmo23Db5QPHx5GmSJQmh3PRqNGpWtKKqcICoIIkUYOWRlTrPpk+YVUmiUCBCqoDIglaE743N0GOI3BwQenDwX8PUvZCzMt8iyGFyLSAIaCwXHScUjDz/MW1+8ysU5n5HOWXgyQKUuncsthtKj5e1hkw62UhzcH/G1NxR/9AcNHljc5D0fC9h9GeIipLnwLN2lBsuNl3j76ClUFTPpv8LJXpu8nGHraI9G9xwz9XuUheFEfZN2L+Z3/qhi/1U4GNV44TcmDPfmqLXGKJGy+zqM1qAzG5GXPm9enyAktNoexsTMzMxRsE+9qzDUqbyEzrzkr39f8eJbCXe15MkFw4/8V7DR/zn2jt7kePsqJxYNcu4ZvKri7htruL0h//DMN7n5coNv3hrTW3B4e9My68G/+Wz198uj9XOf/LkXTAGTVHDt5Ywzj+YsLD9Pf7hBzZ+jNEPGQ4HjaB5cfZLD+D4Ii7UCTMVROqLjSQaDz/Kla2/y1KX34KsZ8irBD1zKYoCO9hinIf14wqhQeDrECJfAVxAJBtkQK+6wna1BY40ocMjFJjSu0+0sEYtbNKJHafQ8ZnorfO2lv+L3fuVr/NI//0/81ic+Q00ZjIZxJamqkkIz5WUVGqwhEuCWJTVH4gpBLXJxPYERBTjllKBuKjzXRfmWj15Oec/iAKUTwqCiXgvwhEEBIpQguS/7fwAAIABJREFUJcKV05uIVTilZDg0eLU6kyxjUlYIITBVQUNLlJ76G9rGw7cKmUoenG2z0vGZr2sWZgPCZeiXOWHNkhQCkzqsPNgkdCVEYCcGV4fEo4rRRCOtQqmEeBCANEgrmQyh13PJ+hW5KNnYyPAch3hXMHO6xky3RmnqKCeBzMeMClLl8Je/fcAzP7zC1S/tceVjCxzvaY42Drn0oWXeud/nzPtPMduuYasxN28WzJ+pSEcB4WzF8c2Y9mIHLywQ2TSp5fuGo3s5tZ7i+JplNEhYeroFTszedsbGRsWp1RoCTZoWWBvhphmzS23uv245+6BHbbEiQPL1F4ecng2pzypUYlg50UArl7w/wfUz4oOI3BTsrOesnAqId3O8ruT1q5qFkyXnz7RohwVHY0FDCgZFRt0zrG2XrHQkDSW5dFbQbZREkSCwAq/rEjmaVuAxyWO6PR8hDM2ZkCCokJ4LfooKI/bvD6lFEVaXKCFQgcfOfoIrnSnnMBMcZ0ccbGW4h5Y2OSdNzKNlhkpjmgnMeZbf+LMhv/S/ZXzxM7scXe9zsAHLD0QcJjm9mT6PXHw/V2/ewFSWD7z/YYw3ZjIpsMIgKsn21ojBQYZBoTEoAa4LnnUojWF3M+eRU3OM3QmybviJ7/6fvi0UrU/83L96QUgXT8lvxevBEZqap2gEisCBmifRVYEpS4S1WCXpj0o8t4kxgmbdEriC0joUhSV0DDMtS7ehqXmWrDJgK8q0oioq/MBBOi5RLWS+dQotjgmjLkHDp956gGxyhYXZddRgldbiFg8/t8OJ3hFHk4KjXUVlHVAuFo/t9ZzAcVH4ZFWBrQqankOkBEoKtHCpNRVSCzyV0J2vwLNkBezuWGw2IvBCvuuHZgldy91rQ0wWIgOJxcfzJCdXp+fb/t2C4yEEkcv5i8tsr+8SxxohKxynRpqUFLmekiaMQJcaXzn4EvJCESeGUkukN01EJ2lKFCpmlwJu3YfGjMPhlubEgqLMEzqzAcdHmvE4QVZNHFHgKUUYCrKJoNnwqUoXzwM/Ddk9zlm92KJRlDjlDKYYEzglnVqF0g5K9anSGrpoIp0x3brLhaUc5QT4VIwtXGrlHE0mNGqGs2dheBgz0zrJYDxBOS7H+2AZMBontHvQaLkIf0R/UmNhIeDrL5akeYQoPd73UYWqfLwAlNJgNWGtwvMNtZpFWAhDnzzPCGoOcVqiPIsS4CgfLQq80GINFKXF2oDuQkZ31qHegNOrM7xz44h2vY0kI0ewf1xSD0Pi4ZjH33Oa/W9YLn23gx7DS1+KGQ9L9MAQ5136x4bNI3BDzeENTSNwWV3s04tK/FlYOj/CZ5Ni8jc0Oj1ubkacXvkOtg/eQHGBzYNtVh4a4zU36KiItDB0nQWCcJOgK7GVZXfb57Fn6zjhIbbUZLtNmpQkR4L1O4LNDcOgyNjbdxiMAw4HOScXIpRKKDOLo3IqGTDKM557X5PRUZ1Ll1I++myIdEreeHNE/811+u+s8ezpku3qEboLKasth9vv3OWDj4+48YZhe9Di9INP8vB7l/E6KzzxgX/692t1+PO/+a9fkMagC4krFFu3PGpz91ldfh7PO+I4PeCHn/0Rtsc3Scs+TrBC5Fd0HIXjuDjqZfLxTfqmxsZGys3t1zh17mGyRNOu9Vm79xYqlEySlJrnolRJ5mQoJUlGFkrJIB+jlSD0BA0/QAlB2FQILTg82ubWWwW/92vf4P/8t1/jU7/+Va5/YcDwMMGvQehKSmMoDaANlRVUVlBYqKwgdBVuVhIUEHVqqJqPG/gYabBljrIGS4VyJI5KcVXBTz6pWF4ZoiMXx1WgDRaLkBKrxfTnZgUoEJ6DPSyZZA6ZSYkzh2wC+BadSVKdM9uocbrnstMvONF1mX9kjke/r8/8k5q5JxQzVzRRz7JwRbBY9zj/cMGFhwJqMkWOzdQLEIbYNIXCoUo1k1JiM0UQatKhxPMNprBkR5ANBI6b0Gs3iIcFM27IYHvIz/32Md//j3s4xwWH1wyiWeP1uxM++t8t8uofHnL5Q4t88le3cesS98Qsv/7J63z8+8/xe7/2FqcemtLoL/+Dy7zx57dZPdfj4O6Eiw832NkY4/sBtYHCnVXozNCYb7L/Tow7q5k5KxFLDtc/PWEwkczP1UjiCcZoJkc1DscTznRCdEvSe7rElBELJ0J04jPqW2otF98TtHoOnpDUXYmwgmFS4WBZ7rnUAku8Z5BKE/qCmRDcSpEPMpJxwUJbkIwLQi+kGsHIGPBcVmoOF0/7RJGHWxfUawppDHgCNxAIG7FwytCdEQRNhXABMVUzEZZa02ft9oA0c3Fcl9KM8TwXL/RZu6fJtUAGHv+76mAWGhzUF8l6K2ypBq++UfHCfzjidz+tiTcrmtYiXEthBXFecrRtOH+xzcHomIU5Q7d1iXu3N7l8aQFvccywX2IwiErhiSZbGwN0UeIgkGJabZCXGikFUkgcx9Ba1hgd8BPf+z9+Wwxaf/yb/+6FJE8I/ABrDFVZURQGX2rQGoElTwvK0iKVixAO+6OKIPApyhxjcgLP0GqFlMalyjUn5iQLs9CoaXxXIJRFWIHUEpu7FGYa0KnKCU7lk8sRVqbI2oisaNNY/EPOyBvkiSL1rjC/9DheCq5zhq276xgDSVrgqhp7exlZLsF6CK8gdASNSGFNSZxmVMogMmg3JEuLBq9mkDIiiBzuX1X8yWdKPv5fn2Px1H1e/WZF6HvUjYdsjwhrhtkFyeGOYvueIR5q2vNzrJxosb15l3LyLTSXhTQ1ZKnGahACMICGKAihEBzHGdYBN/RRnsLokhnRwYiE0qm4veuweqHCiTPmHcN6vsDu1oClRgfinNmljONDQ5G6hLUMbTQWyPMJWeLiuEO6Cz5rtxIaXsr5hyXZJKUT1YnHKUFgONipqNdzbCmJfEOz7WIHCs83xDsuTj2lZw2rJ+cZj4Z4tZK1jamVcnyQ4JuKIArx6ym6BF022TsQVKqg6fpYPSK4ZOhPCk4v1pDFGMepaNZdovpUbLDWoPW0d833ArTJiWoB1gpc16JCizABwikQKNzA4DguZSFZOSs4Oiq48q5lijLDqw3odWvcvTGmGUWkMid065giIRnnnHmkS+tSjXc+t8X+2xan7mNtymBQZ3Rngf2NIZ/91AJFERCYdfyGZm6pw1IzJuoaAscnsiGBm2L9I07MZpw8/zrjw0U2D24x2de860ofvTvgrWsdtuMu/a2Qg5v3cJqWhcsezVbKUvsHqXXfRKUR8V5K/77Hq7sVN25VjIYlF1cd0knJaGgYxJpG5NJtzlAkknGSQ7dkYaWLchSf3zjBT39fHSkW2Tz4bl77xhe4cXtMY6bHhbNHuDP/mLu3fpVFdYP0cJnZ9pg0E+wlY+7vHLC1PuLS0y0euPKTf78QPJZvtRkGGkyFiCxhEbKb7NJwMn7s2Z/ipet/wWwz4NX1u/jBl1iqKb58L6ce+Fyam0G1fPbe3Kc77+Njefvmp3ls8QJre3cQ9YLDMdTbENgejUIilcP+8ZBiCONxys61NlvrluNhRlKOEDKjKDKk6+CHHq7UVKMJ7aaHLgqKLJ9y/owgsxq+BU/V2qDUNEJrhCQvDeN+ThC5hHVFXhYI4VGZilJbhHSQ0hI67pRH5YIfSG4YwdOJgwSqYIJDA6srrLYgNUKK6Y3PKZAjh+MNDUVOMBdSa5Y0WhH5eIgMa2TH08j1dn/MzqDC7zgUL+7x4D9xsBsCqUrQPrKcYI9DqiWB40TIUY5VIVoZHKOhtEjPn/a0GI/D/ZQsjIh3JzRbitGhIvIFVZFBLonvOpx83KHWaLH+zYT55xb40XrCi3+yx+UnGnzq5Zh/dFbx/Edm+JV/tsXHflCx/uUh//KnZ7ina3zt6zv87L+8wo/82Ov88s9f5OrndvneH1/g+p9eQ8mI9vmAsNlkuFHQ8R3KdcvoZEVrYsliS3Z4hDPfpHfSwHzJtf84oVwSNJo+tbZltBtSJBVnzlgeOekgBxF0c4habOYj7tws0a2cer1JvSHJipJGXZGJklFscHzNmUWH8UCxPOvSciQ3koSF2YA4E9jAUpWSqJ1iZIStJPUWjN0Mp5DMuopBVTDTCfAiix95pKIilBXlvmFmxiMrAScmYJGgXkClsc500MYAKkPYGqfPN7l7N+fFv5nQ64X05krKfoasK2phwXgkWM0LRL/i9K0jzN6ItDDcu2c468JaZEi1YawFkS8QWBSK8WHFN78w4uHnGmwOd3jv86c47j/EynyHHb2PIqfEQShJGLhURYGrBFiBlBZXGiQOpdEoqSnzAJeUNK3+fz51/r97lk+0uXt3QGFLHMfD80IkBqgozfSnOO3D8knykiwr8IMAREUYaSyW0FckSUIx0bhYZjs1GuGEMAKjDYHvICuIXB/pQSImmBLGByDSGK8DWoe0OxUzCzHH+12uvThgP3R58srb/O6fX+Efvvs9NJPXqQWCVFRMLFhTEDUiJoeQFgVWCEKlSOOCvNQUBpAax2q6LcVsN0Q5Y5RwcAQUYsQvfvJ5Zs6+xfodENJQb1v8zOfgGGbPBVz7ZkxRWDxPUJ9p0Vso2Ng4xLEhZZlhdItRPMSva2Q8rYGrCosw4DkuaTqhSBz8CKxj0CahKgWe8hmkfU6sLLC7c8D73yUZHEpGg5ILTzT5/F8MODPfpJR7dJcgzxq0emNsmSGrJotLlp3NlHYPMBXHfWg4AbPhEZ25iBe/MubMuRaDrQEFPqIoabYU7VabqjwiVF3GxxmylnF6HlpOjisFsikYHO4y040IXZd2U5IVfRqNgHrkYpwBa9dCFs/4TMoB3ozLretdHrh4TC30GFcSM4xw6y41JQl8jeOVU26hgCjyweRIoCxLHNeABuW4eEFELvpIOeUWYqfA6VrdQ1tNa6ai0JBkMe25eUp9nzaC1UsRe3dSGjMtrDHYEpIc/vqzt/jIx8+y2+9xuHFE0Aw5cWYZ627Rmzlga63DXGOFdH+BZ5+/xuZRTK0bwyiA44ztzZzOLBjt0C4c/OYmW28Z5roJ2vapz55FxMew/RR7xwWmUXG4+xaLVnFytsHcBcvTHyqQ2X0oHETmI+2Eu7dqbPYrhB9gqoqjJMepQUSBLAJu3E4xzpB6CzxX4KoWGzcfYvP4AnIm5Bu3t/ncl2+yK97giWe/hzfLL/OZ2xNGL0Ysn/l97nxpwCiAmSu7rMUx4ZLkkjvP0UDS6w2gvPn/eib8nfRoLX+wZoWVUAlIJaV2+NGf/AD319bwOx7/2eMf5hN/9rNcefAMhY45OdfkG2/3aTRDlmZ9nBLGJsF1Qtqez6m5Dpu7MYGyjCrF8cGIYQKT4xq7W/c5GFqajRKEw2yrjac7HO4pjBH0sxGpsQhbUBYWU1ryPEenFhtX2GKIyRVFZbBCYoTBMi0brUoLyG+9lcEKEBbqUhK4ligSiG6EP6MgiKY79tDB9xS1povrClw/oNFu8MTJgP/mmQMadoTVhkm/JAin6tYUK1hhjYuwDvk9w+H9hNJzCCLFyDEcZAZX+lRFSTEyNF3D9k5BZRVxBEku8UY5H/vZWdo6RkWG1I0I748oghCvDKGeAxpiiWkJdKwRxqJKg0hCxvsl8X5F7CqKScn8bI397RFzMwE69ujfSbi/WTI6MPzgv1hkYsFPPA4mE2rHMXq2R759TNjqMfByXvvKmA9+qMvgXsE33z7gQ989x0tvGTY2jmhblye+e4blEwv8ygtf5UP/+TlWl3zWb+zR6jnk9zJaJyNmFlz66xVZFpOteqxeVPSPYfQNGM1ovHpC0HA5teJAS6HHcP+a5uxHApK1nOtvg/ESVOAgJoJKWLzDKV/uzNkaB/dTtvc1xtX0xxrPcYnLjOVuRDwpeONawUxbgSuxhSYKNEI1sI5Bipy9fUMSaI73JbdH07XQQ3OKdz84g7+QUWQlYRgiK8MkKahgCmk2imeer0/xJUpOVS1rQTroPMFWLtIRXH9HsLebMZpUGGU48mqUrTYnd0ecGBr2RjllZci1oEwK+geSrx+kfPYI0tglsyWR61BRYSxYI8kLw+PvW2L2/JDFXo3T3fcy757jDfsp1t8pGSUxkWyRHMErf32HqhQgNJ6SuGrK7Swrha4K/sufeYj1wS5F6vG539j+tvBo/cyHHavxGEwUw0mO40UcHQ6pNLiuS56XRDUHa6Zx/FarjSLFoqeMUCkYJxopHZ44JQkiQRCUWAxR3SVoKFStwhVgKk1sLKLZwCkKQqOZyIiPvg/Goxy/FnF/vc/lh+e5vpPiOjlf+vqHCZKCU5fGrOWKJ9yX8ENDnoTsbqXcX9f0j32yMidNG8T9MUK54PkUZczZeZg/BbUIIi/C7SWgPFzX5bu+v86NV/YoSugtrJAeb1JvNaidyti9JsnTHOdbCmzgdok6xxTHHlYIDvdz8gxOn25zsDcmPg4pdQxaTs+uHEaHKUJIdCQw1dS3ZSozBc8bQbtmiAeQpx3W+30c3aNIjwgkKMdn7SDn9lqE1044MRuhSgdVTOjMBazfz1k8AXkSImVFVQp29xJWL/gc7eUEfp1RFtOc7eDbPq7yOd5tMX8iZThJqLTFsS6iZinHTcrqkLposz0ZUItc9KROs1EyP98gNTts7sP5S7O8+eIBp5a6KMdyb7PPg4+d4atfXcftCB6aW2Z+/j5JDI6FlbP/N3dvFmtbdp3nfbNZ/V67O2ef/tz+Vt3qi1Uki50oiSqRkkiRsiXFQuTEBhIFggOkMxIhQRLRiGMjgQA/JRIi2VYsW5BNyJZEx5SohqJYrCKLZBVZfdW9Vbc7557+7Hb1a86Zh1MW8kI9BAFCeb7vl425xhrrH//4/gjjCrQPXqhRyjtbdFFnd8cPAwQVtoHZHAbLCfFKAeLfokYszgo6PcvyeoB1Ff1VS3fQpbQzIm+bqhojvRzp+rz0hVOqPEB1IqbNhJ4eUhUeBKdc+GCPb/xDTR3vIwcB5y89xPnRGxzMChZlj/XjKdZ2SHqWW/dKDm8YvLiDSxc4JwiMI9oIEapkaZgy2nRYoxBBBrLl9B70goRbuxkVHufPdZgcjOkOFf1lw+n1HvO9Kbfe8PjyKxEPrC6YOcVLuw2HjIgDi1fX+K3HaOkUO/d47D1dzl074RM/dQ2SXfZfH/Li8wu+436al55/lSCRRMt9Nrs+RTVg9/Ae565uk+3tMl+A22wI37rBp35kjQudr2AcnN8e0DjoPXH6XevX96Si5YUOaS1GO3w/YBD4EB2ytdLS6b+XP775DcIwQi8SjmSNn2gevjikN6ippjUzLOdXY6QZ4Nuce3u30eb93JwdcufkVSI5oM4SPNHDD2JIjnF1Qls3tEGBp6F0McLrnL0gW4s0Po4Szxc4p6ibGufVWBROaHxjqKv2DKBnDK0RyD//ey0gz/wxQlG5BuEkopXIMsecxpBmtKImGnRRnkBJn06iiJIEFTheOlFkJiJyR7AICVOF9NyZZFwKaCVWAictp/sFdRRy4aJAKEGcOYyMyVXDYiZIliXN3GflgsfJkeRoNsc0FtcVfO6zcz76GcGD7w/x5gW2P8Q7nGFin9Prc/pLHbyexeY1s4OWwWCArQzZrMU6g44hODTU2nLzxpj1UcTum5a19ZrhD66QvlPTuDnfeKZirbHEcYV1Bfpyh2xnSrS9RLMM9Z/WGN9x75WMF67v8shTW/zRt2u2ljo89PB5wkUDteOXf/1tVpYiHr2ieevVMTduljxSJXjnUgbdlsXtkvFpi7k/4uIVzY0vTJkmknigiLsNvX6HgIbTA8nxK5YLj4dcfLLmK79XouwEQYoxAbossR1J5zQk6Tucc2exFIMOR4s9sjql2zVI2xIOfALPYq2kH0uSIMDrlKhWE/oxLY5gUCOalHKR0YkD2qLAZg6rBO+MFY9nc/qJRgqFTkuaucZYTTKwWFfifJ9bNyouXBDYmjMejgJMjTUeXiyglDzwWMXRSUvUUbjWxxcVa7tzTGEobI0vFEFs6U8VhdLEPcGjKuJWUfNC2SBbQdW2+EohhKURIKXHt5+7x4+du8jhacmw+yKPX3uE116DRTEh8GJsY/E87+ye05L4EGpBUxtKC6Zx/Hs/dx/79RkS4mv/Zu//t3rz//UJMWdbcq0jjX2COOD4BAZLQ4xp0L5AKwE4BAJnyzOMh/GZT8/MyvMShqMO3bhA6ho/kCAiiqrET88a1bBnMLXD1Kt0og/S2K8SpUfkdUmdObKThvk4JBSKo4OMaNEQ90Zo/QbXvavsnT5F5AlM/DzOtyzGC3QA919d4t6OJS9qbt6Zs7HuUTbgPIvWms3lFhEG6LhCRzlOeyD7LK10uP3Nd5ARrKx7ZHu7dFY7jLM5Ry92cWpBlPjEfozEEkUV8+MI2xpOTxyLQnHlkZDjvQnTU7B1TtiJEE6RzyuKrCEIfKTUZFGOnSkCHWOspawy0oFiceJTFQEHJ2PiTshicUJnCXTd42RRcuG+iMFmiakT3r6esb0dw0Kxu5OzfsHhqRTpzbl3C9ZSj42NgGldMUwGaDMldx63T8c81IO6rPD9I771nGPrIUiSLu1khr/o0wRzwiRk/3jC/RsB00mNvzqmkT4700OaBlSxzPTujKXlKxwubhAScPV+uPP2Ozz6qEb1JGF9i2nex+/MMNMQU+f0lhOqJsc5gwW0L/BDAQ6cc0SBxr4bqzOdZCQjjZUWIfpIdYRAYJyj2xd0+zFablDLm/gRSD0n1B2s1ZSiZOdt2DrXZyEOCNMIshrnTaCyHDw75+pPXqU88umYipl4m0TlLIVgxYQ7d0DpkvGbDeVkhYOTQzy/Qog+Qk/Z2ApYy1sWDfS3Q268WrOxWZA2huJogNeMyVyBmQXEqWZ/d8z4cECZWxxT6gVkJwl372RUccONaZ/cWsZFgwlnOK1pZEFZWdYV2ECwfzAlHQa8/vwBe/caZH6bfHkdvRcwLTXTgzvwxtuIp5+kY2KCeJOt9SUGaxf45tfvcm57zu5RjAlvsncjoKklnjfDWUPvL6gJ35MerS9+6X//rCBm9bFtfvbpj/Hwh24jyAkIcfF9PP/1Z/nAe57gzsE79DYkx7dLNrYTbNagy5Q01ty7k9GaTbz0+9nc+jhJvMzO+CaTfIIqFG+92tC2jihcUOYSqSGIArRnsbbDSSMxqqUGPK1wpjnzRglxhldwEqkFfugRdEKEJxFCUhXmXQaWQwhwwiGse9dkAOBAgCckOIOxjsZZ8KC/0iUKzwKrvchDOo0QFuE1/OcfFDwQ7GItuI5CixaswNUCjENKj/bYcXyvRsaS5U0fPVQINNPdmuOypagEWWNoMsu0NOSt4LguaKygthIVCKxyzGaCV/8s59FPS6qsQXkd6lmD7irKiWR2YDHTDr01SVvA7I6FoEFqjckNyoS0Ux+hYHevZGszYO9uwcCEZJNj2tOI7kCxow3nzjl+73drrjzl8fqzjsEln2f+2T389R7F7SlaV4ggppiFvP3iDuuPdrn3asOrb57iRiN2ro+ZTRbUjaH2Qjyl6V3ukD9/yuq6x4vXC0ZbCSpqefkLC2waEfmO5ZFmMFDoXJONYfnBhP5mwZtvtLzzWkPbVGz3YiaNo7/usAimJx5rPR+hBNsbEcXMMJ4saHSI8EDKFpV69LoSUzuc8pjNHZFvCNKANBBYafFiiGIf7VsWM0cSKOaFZUHNl+4pnjxniJVidM7hCYn1PBpT43kS3RFYznLuFplhpbeEFnMqI7DSQzkH0uHs2RqqbCSHpw1GapwySM8xUzXJ1KGkR6wMAytRhERtTfP4JarzIRv3r/H2QYZXteStwjiLRuCU/XOvTLYQXLgaE3ckB9ltjmeGWaagbXDOp84aDu5mpCEI6xhnCuvO8is/8RP34a3MycuKF/5owmwPfvF//O7Av79M5+u/+3c+21lS3NixeBG0rSVRMf1eRTWvCP2GpYHGFC2DgYBWUFcpOlxgmxQlNA9uSa6ulBA7pLP4KwrhS2Rp8LUkTGryNkWmliTN2Ck+yJPnp6xs51xLCr79FY+7Nw3XXww4fsfnXFwyCz0ar8cfvlQynXmsDSyD1nI5fIveoGTvQDGbv5e19RtsDi26tAyTNZZX53QTycrAI+2WeKnCbxXesoPQkYQW4V/i8iMdivKEYWfI8c4crQTTaU3daoyCSDUsrXs4FmfKj4XTaclkLqlMi/ItrjUc3HNoPyaIPcJQYRaW7LhCK6gig0sE5BacxYoa4xxtHaBVzXQO87ogSiVV3pCGAYnqUuUFu8cVSresrkRImbM6XOP2jTG5kkzGhujd0GsDHNV9Bt0AHSxQLuDu4YJWp1x+pEN5bEiEoXQeMlgi3spxJbSAnRlWL7T4i5i6NcxbQ2NDNkY1hycpnU7AKIGvvqaounOO9hL6FxrevpUzWutRVZY0lHSSltQzJO+mNwQqwIkCK6E3OHtv+D6gLM5GBIGHCmqUVKhQULiKZBCQLqVUVYqMFiBatGeQootQis5Sheq1eH2Ln3RBxaDBeQ6nQVvN5sM9PverM5ZVy2an5UTVyFAjfEvRGpqZoTPwuPDY/dh0k0w+TrzyKCpcIxlusrCab718ynQ3o7KwyC2TecvRLGBep4wXC7QHixPJ/qJlZmpmFYzzEjPVTI7f3aweak7nLZceLLl3ssJIZ1Rlwfye5O6+ILCCuTIcjS2tSKkQFIXEEYI2rCWK/QNBvxsTkNFWJZO8RQD3XfKwViP2xnzr9SOW3vN+Hlrb4ObOW3SHEY3ZYVF0GFfHJP0hD2wcwSKiV9zklXcs9TgiSmvWHvnsXy4zfHLwP3z2R67UrHYvoUYntM0RhWvYOU5RGdw9OGSw4jEKFf225vJ9KzT7DceHEwq9yWjjr6LSh6llH1MrFsffoTPskZ8eUts5OzdCFjPB0qhP5OdM5hVSS5RweBrSTo9jA60PUjnmBIOoAAAgAElEQVRCT581WPKsaVISVBAgAkk0iCBW6FDi+QJci3Bnpkph5RmqQcL/U1MUArSA0JfEg4jBRspwrUfUS+j1ltBeiKlKMmcQtubims9/9Pg+0tNIZREGmlKinEEaCY2HUQYvkHSWFPFII/saWbccvtXwnRcm5OWZCpMVNXHSAWUoy4bQaholKJuSmYGWjGamufyZR/gnf/smjz+9hJ9lBB7MDwSq41BIyrahPHaUE0G02hL5CdM7BXhnzJK6zqgXEl9JxuOK2AsQGz69zRQ7KelcDDgfWe69lHHxAY/P/dN7PPLpSxy/PeXm7ZJ+0CBXA053YCEEH3jviNeznPmtmquPrpDNTrmyPeCZL+3z439tgwR44KmUr750yq/8n6c8/ellnnvpgPvv7/DmnYZ8cUK3G3Fu6Fhdjek9KLj7tZrTccvFz3R48bkTXv12jbEBkfHYGhp2K0ecQuUU7dxyXBm2ehLpDMLm+M7nsKixUqKCGq00zodhz0fimMwb6kIQ+JyNfyIJwuIngjhVSAVNIVCJo64MrdK8edTy+jzkzankxx5VVDhkcGZ497SmzWsa3xF6Pk62fP6ZMU9d6yG0wjU1VoJE4azFCjDOoH3HdObTihohNFIrPOvoGYWIIU5iXn/fE+w/9gT3rryX2fp9tGs9fDXn5isnNEZgjKV0igALWmAljE8rLl66QHdNc3P3gGxhKScZTgls7Yi8Lkc39nBGMs0lWktqIXjg2ir3fZ/m9HTMy18WHFyvcM7xi7/43QvVX6bz0hf/58862TKbSnzfsJg1JKFmkeX4OqVpKtrWIJykKCy2MawtGfo9SxJL0i4kcUmcgNYd4sQSJj5KFYQBTOeW+sISS5McldfIXLOsvoPqH3MhCNnZq/mdz7e8dgdu7jeczKCf1qxdBvyGm3tP43XWMUJyeDTj0vkjvrlb8+ruZVpvysefblFBxe5dh0sXeB3QvqOxNWECcay5c9hSlD3Wz3UYDTUPP7iNKW7ilGPvaIJONK30AYu0hk7QsrWecrLf4BpJ2vXY31swzRyKgNrUbG332Ltb0u17aM8wPqnwlaTIKrRSSE9hlAME2nooD9rWpywalld9DvcbjLMkiYezljSJifyA6XzCeGq4/4EluqlPkRVkU8dsvCAOYX3J59yG4uV8i+uHUwbdEY/fD+2i5e47NcfCcHfmM4xypvOcrjLMig1cxxG5U/pDELZPeZBT+gmv36g4t+zjyZywB6tXau4cwZ2bfU6PLfFgzrUHHVc3u+y+4PGNt0744Y+u43lHRL5hed0SBGfPpfZqohiq8uy5DkJHmDjiKMQYkBLCxNE0Jb6n0YHCipa0G1FUBUEQcLB3TOCHhJGgbQTCK2hNw9LyCun2HKtaHB5C9GhtB6RDeAbtPKLhjEHX42tfbNi56bj/iRVC5nhAP/bIjlumx6fsvrNLKKfoukY1x/TDjGd/5yYvfWWX1I6oZU5veYMWQWVbjGuYnGbkkyHzeUnd1gw2LNLzOb6neOtlSzlXhB3D+jlNngvWLvlsnu9DMMVMEk5nJbaSvP5GiAwSBBlVY8hFzcIvccriCYkWhjUvYTEvSNOSOIB0FOANDKNuSlnD0rk32NjsMJ1DOPAY9i9xujjC1CPWlrZZjCvavMbTY67c9zY3v36N6u5dxpOKz/9ByBdfMvzsz333rNbvydGhc32+Jc/hjwKUvctJC41bwEJyUh2RJiF7d6ece09FcyzIDkvGJxN07xLbFz7B7cmYRdbQtpbWVmwmHs+8/G9I5QWKRYOzMZ1Eo5RCSQ+tNWCRSmHalsacAUFbedZkecZhfYWpwSiHaAUyEEjPRwcC4UAGEqvOWCVFWDI7NdSFxbRwFkRz5uESzgICIR1CCYSSaF8zWBqd5UUJi3M1zsQ4f8LYOT79gEE5yb8dQc5L8GqHF0jqSqKERSSAASoDTiGkxU3g5KAgTru0ssQ3hlZIkI7xrKZsLZ7z2B0v3v2ZwGsUB5S0X7hO//KI//W/P+J/+bU+9nVNfyVjshPThjXac7hSYmTNyYFH6s8Zbfc4eWdOow26L5GZQ1tN0tGM90t6/S5aN8w7MW/8zgGPPtjlG6/mFAP41Geu8K3fuwGpYm3DIBzsvFbjYoEtSv7smSNO85DQFLzy5dto3fL5z98m7RfklaQsZ+w/E2FuWX76gzXdZZ+j24Y3vQW+Cun6I4bdinAzRG4FzL5VMbkrefw/7nH7yyecvOkTrEdkxzkPPDTk+TczVi/4NKYl7ihu3syIhwHGGJZ6IaawZI1BeRID9KKAeW6wgY+UoLSjag1aKbQALQTKPwO7+qFDeBbgLIjbM/ieIqhbkAFSNtxuHHU7wugMzRmDrGlajFOESoFoUcrjvtUez99ueeSqh2gE4t0NLSHP0AIIQafr0TQ1+JJGtGijOUmhXyo8oWj7muOVh2mRGGPwzJzD2RHjumEjgKw8uzfOnSUZGGnREnQQcbgzY/tqF2MMdZPjRIizNWEQ4NeSK5eXcJ7i1t4hJ7sGHcLlRwecnh5i24h7N8YoD8Sfexn/8h+dtKRexH3rBb1+j+PxFB1YxjPwvDnWncUidRJHHIc0jSE0HgiHH0MjHeNTRzfsovw5M+HTIUc0Q0q/JNrMyW5k/Pr4Azxw5VGuPPg4F9OfJzj1+NorE269Bq9NAtyKxAwKTrOGF95QDJYNV35AEqUhg6XzBF1HNlnwL17ewV/6KMtLK3zo8j8mic/xRl7Tjt5mmHZpKs10XCFkiwolBJalCw1BPebwOlz5+AaBex477yBiwyBImUxr+kOPpFfimSWO7ja8MJ3RtRcI+7eYLzR79zroYEGYGnQrOTleMFr1mE8tnudz7aGUt78zBqWRqUfVlCRBSJVX+PEZ2y6IK7YvD3nr1VPSXoD1DNWiIfSApsCQs3JOce6hgPL0hKI4Q4xcvDhkd++UNJFM85I/uB5y99ZtfugyXNoo+OUvF3xyq8OFxxJen2XsvS64sOyzvRKwEki+/NUZ41sLPvkjA77+zpj+LGBaO9bimo6D2kW8uZ9xWmqWb9YclTAMDlld6bJzsE50vMDzpyw/HvPR81CVe0gFo/UI5WmsmGPanKTj4VzD5nmJFBG3b0zoDwfMF2OkloRBD+GPCRJomhDh50SBx2KR4/ngBTWrG5pe3+fVb8949MkhRpRMi5yyPkWLh3Ciwaka4+YgDJ5MsG0XKw1aP8KVj9zl6uN9/u7fusNv/gOB9XvMioyy1kRRxKXLHusrBeV0TpxmyMDgex0uvucEEQre+daQF4+OmN7eR3sS5VpCAWtJzM35lKRyLFowoWW4ATK0iGXLH/5pTc8bcfm84urDB9iOIm8nlBPH6VGDqTV3djuMLi24s1vhG0ff62BNSNvkOAOJsqQhLCYNohWkqSRMHfOqQi9WuD0/xDWaq7mjNzrk3/8Y/KN/doM3jy2vX3+L+x8rePHGBd77kZAL6gO889bLvP36La4++VuspI4Hlu7nb5iXWOyHf2FN+J5UtP7Ov/6tzyZRn1I6vvHlCZu9S2i9T14+xXde/Rqf+OBH+YknQsrDgsW8ZOXip1m+9P0Y1+Hm2DJd5DQ1FMWCIi+oeQXyOxw21+mYDcaVJOkEoCSRKsmzd78wxdnIJQpjTmqD8CxJ4NFJQjzPp61aNJIwComjkDA6Y1910pAwUQQdGIwSRCDQzuFKh2gsnjgbI0p3Zo3XUhBqRzIMGa6vsXX5Ir21TfwoIVtk1E1L6RYII7m0POcXnoQ6bFHA0b7FFoI41szGBs8T+AOHaw22UCjrIayg2q+4c8PQFALhN6yseCx1AmbzmnleEYYa21gapVDKMupCJxWICA7vOoqJYHkl5sLllH/8d+/y/veG4DTdzYpmCraUWCGQvkbPHVlWkOcKGcNwmNCe1hyNW7wwpG4calkTdh23vzjBa0s6H10iHaQMhzH3Xjgk6qesPNHn+PYcfXkNnOLXv3bK41cGvHRX89ytY5qpZeuqIu0NGKeKt97O+eQPbnLrlTnjtyuu3pfwh2/l9OjS2VKsjIbEQc2Vax7DzRBb+tz94xmTXcOkqHn04yn/8jcOqUVEVo2JS8hawb2JJR0Z0r4GX7HzZgldj5U4IBENg46mbTTjskEFmtAz9JKAKJaEAWid4/khe3sG6SDwJGEsUQloX6Hjs5iV1ggKYwm0Is+hlpabVUFRK4KoQTZDrpwvcEhq67DW4IcKz/NxwlKWgtpMeW3P54GhI4rPIpZqa89UJQAkUnjculMg0CinUFJhg7NYK3HxGt9c+RCl9WmRWFcgnWWWl7QHx1yaTTjOHKVxCGkxRuBJTaQc3Y7gvU9dJkwF0FIbS9HWKCTCah68eo6/+td/ivf/0Ht48PHLfPH3XuXKpVU2Hy2YzQTf/tIJ+bhFIOj6lv/mv/t3Q9F686u/9NnxUU4SaEzbgLNEsaSpDOsrKYE2RL6jG2tc2zLoxjQGhHAEcYMXGJrSEWpFmDaoNMT3fbJiDpGPlZaLQU3ca7kdOEYyYzW0uNkdLq6PyLKc197qUDUO4QIio9kIKi6c7zBpJdH2D7O6fh9vvPEnHO+e8L4nf5jhYJPFwQ0ub1qK6AlmRx+mGH8L4ef44TL3jk6ZVy0yTDFBxFCvktiAKMp439ND9ndm7J/UjK2laVsSTxAaqGYtB4cFUbfhqP0waXqPNHW8/vKcwbKPpsFxFhVjaemkUJWOleUtTk/G0ApkGNCKFtTZGFU5iXMK4xouXl3j7RtHOKvxgpa6dggroDlTgLvDDjKASVbgSQ88wHeowJFlLWsbfYIyYWOjSxE1XFvXTJoO610PqSbs7PfQmeY9j0a8+KJHaAumhSHsOpY3WhaHmtd3V7hytaTqlgRHFjFIuDOZsL3ZIe30aGTBN97xSeMBfmfCpJxzabWmoyCUDpP7bG8HxGlDGAhqCnzPw/MkUknixFA1Bt9r2Nhc5eBwwmglpm4q2qYm6fgsckPa0xhnaW1DJ42p24bGNDS1RfsV6xtLvPbSKUGnYWUlZr6w9FZLpJVoEaNRmPoY27Yo46F1n9xcx1PL+FFN2gl4/ksLZL/E60jytkZoSZpY0kjSipKsaMhzQ2kKvv+TI8rS8syf7nNQpxTaosKEBp+qMgxiMLJDUZ6pt1cv67NcJN8S92A6g/FpQV0JlgZ9hiOfwQqU44ssyppikdMdlFy6onn2zxqiHkip8IVhGJ95xXqhoxc6pgtBksLGRkzSNaSjs2W1Ujc4K2Gc4PVneC6lv1nxL/9UUNUhk+mE93/wKU52CmRzi75/yCODMZ18n54sOTg64M2bcDiNuP8D3z2C53tS0cqyGcd5yPGtE/YXJTe/+CY/8qn3sj3w+Mm/9Z8xP/kCn/v911h7aER88TLHMxDZHYpCEJqKXhxQFSXHzlE6Q30ywoX36IsEs3ZEuLuG52uWV3tUpyVGHyBagZMO2pC2qZDSw4s8Ak+jtSLQAUncQRQVwgto25q2aLDKIv0apSKk8nDGUtkCk/s0U4NoLaZxaCFojUNojVQtnq9wSjJYHzFY2cIGgqLKwTvjwTTGkBUL/o+nY4hAlbA7rfEcONty717LxorCS85id8h9aCwmbpDzkL1dS2sEfgKjUYhSgp39hqWej19llDZgbEomdc3xvkXXPj/+1wMe+3AXPZjjFhKRVVSThp/51DqTuxnZSUh+2yPZ9hjc3zJ9tcIuYmqV44Ud3NyQjyXX78yIYs3lhwRHtxp28oLRKGKjmzL4AcE3vjLj0t2S8XzCzq5m/bGEP3phn4ePR5Rhjy/863eoIsXt0vGrzx1y86jhyqpHI0vu7Y94fjLnSlKy2TFM9ib86fWcH7zs4Xc7PHWx5uBwwTO/u+Dc+xK2tlP23jKcv9jy0jOH6FHExQ3NSzcNr/yrPbQXspgYFrMYN1QkPZ/BqsGPNFlRM50Ywl6AURWe72M9xc6swkWKtaWIo3sVQdhStBXDJUeYxlibYp1EfadERxInzJmaJcGLJCL2sLalcpbWa/El+JEkcQpPBxjAtQFfncEP4Yhbi/MEWilaoajbEuccKEmsfWw24Tf+JOY/+HiHqKPwUFRli/KhocYvfIIeVPOYRVvhhwbjx4z7p7yy8iGkKZFNhd8ICqGAAGsk6WiDpd4dLg8t/cCxVwimWlO1jrqRfPqnPsiHfvx9zGcTdm99kzTzmU/mhKGPbc/GRguxy+3xK/y9n38OJeHDnxyxM97n5KTh8KZDhj4dXXOx/++OomWspGzB91uSKKVxc5SsGfQgCGdIdTbyEbJB+5IgrphmEIQ1OgE8kBG0IgcLys8Y5+fpbt2lbRaIrMt+qNgqp3hxn9X1x3AhdDZPGZdHCKFZiwNOygZRBihj6awEnE7m6GSDvXIHb9KQTe+w1Hk/XT2kUAUm1pz4f4Vps0G87GEnOYumx+rwAVwccfPuLeJ8QNUqtkYJPT2gvHONndvPMJloZDikQ8FiNoEY2ggO1SpH6jxfeU2wc73hp360ZHJQs7SaMJsv6Hk+fhhTNBMuXuzy1qsztjb7zGZHLKbg+YrGnEFwg0DSZi1BGDKfS85fjdjbH4PRKG1pSoerBaEfUTYF0hPUpqa1Fb3UZ3x8Bu/0Q0XVlCyPQOkGYxY8e9Dn4/2So0UAekZfVyxFXdYuHfPql3wmhzNWH4CRTsnKObJOWVpdYtfsM/EW7LwNr9Y+6yYgiFuWPTg+mTNzOSdVj488FhMFO9QnS2yvl1gFUVwTWIlWJdr3UVoCjsTvgNAoXzGdnNAbQNOAaQV5MSbLWvKypNf3OTmoKQtL1BG05Jh3Fe2qqgjDgLyoWN/uMpuWLPITltfgcBfaokPYPyZNLHW5oJgu0FqjREJTni1R1dUhMpDgj2mqHo98UFI1NdVxTX8Y4VtLREmsDB0PAp3gxQ1+UmMF3PhmwfhuwcZI8vZtjdVQ4PB0TBgHVM0xTRbQtpAX0NQtSRQShB6VV3B4t8ULllmYYw5PLBdMxuwkRCQJXnaZbnTA8sYOoyQl6pywKHxc5fB0weZA0rYh80yQZwVSClbXW/zQYpxABx6hX7HI4Nk/DvjIQzXztyDsVAQdRTRa5cLKMSkj4sUea55mu/t1ltIDrmykPPOVbY7GpwR+Q9SZs5jO/8Ka8D3ZaEWdLtbVbK1vsrli6I8kqdjiqQfOceedL/L12wve++GfxooWVTsKcQdtffpdgXUS20aEyyGXakXir7IoluF4hYNwj+f/bI7ueiRJAI0BwBc+i7ZChYLWFEgliUiYNVOiyLCcRCgtiH0PY5bPqOdGUrUNZZZhmpbKjLHWoLXACxV+VxAsS2QssK3EFI6idFgcfuhhlcfSxnkMHrPF/IwPUx0zz/ao6gzaAq0NYe2zOBFkhcetyYzX7ipWBx6ffCLAqQqMwOYeVjUEXQ0Lze7dkqYGL4ZO6tFK2JuXyMDn2lXBrByizjkerVboPyZR3QqmIWY+Q9gZ9sSHqoDaw5MeWdUgkxDXGGwaMNtrOH69wdealcc95gce1XGDswLpO9J3m7/jnQLhEh59aJWjYyBNmL24ICkU/+rrJR97ckTjZ8ilAT/wYzFf/cYrvHBgWNtMOcwqntwWxKuS8FaAkhndUchbxYz1js9MKB66b5nb+4esLll2Mkf5xzt85tMr/OpvFJy7kvLc7x/yn/5Xm9QP5vz2r93kPd+XUkSKz391zOqyIow8FvsGryfYuqLoLpdnGzyRRPmOnRsGkStUR+BFEfNFw2mgCGzFtg6o2or1VfBVn1Y0+IEC3yLaM45N2BE0rcBDoJQEaUFZfF/RGIkxNbYVtEqgPUPXV/jdBrlXEcchOs/42vGDfGzlTZxwNF6ALXOk9jCtJVI+i2DGxbTD23nDr/zeLr/ws9vI2CBcy7SJ0YRk8YI00eTzMUEQUc1LfKAWEen4OkW0TiM8nCixSMqmYDY/Ibc5QXfAE8sndLcVz+7WfHm3YWUUsrQ15KmPPUjeLtC+wg/OQmyTnqLJDa2bMs4Fb9084Zd+4ds4B5/4kWvsV/uYesHLX8rRviBRNfcveWx3v/cwM/9vz+l0DsqjlA2hL1ABICBKUlo3RyiN1CHohk60hpMZhgle5yxEvlEttQ6pRU4qwJaOdw4l59Mt+v5t4iRnz1/CUz9KIt8hUz4nE8Fy8fN0Bq+TrP4268tj5H4DIgfg/kdWePSpY+42U97YjVFhSOhfYLC0zM2db7OytcX5K/fhjx7ECw2dYI1t7+9x794LTLlEb+s8d549oTkQCBnxQmkYBIrkqOX7DgzaNyyPphTNMnF/gzu7NTu3fa5PL3LzeEGyvESqX6TJF3S9bWpzhPPA+TXSr3no2oimNiwPlqhKQ1VVWNfSComVDi1BSYdTZ25XHbSUdcTJUUWgQwIPFnWLkoqqyLEO0mFMWZc0pUO0luFyQlXXVG1DoKGtIfAM2SDlJ/wFN8ewsak5yTK6NUznC/ZfUywv1fipZm3Upzk4JvZjVHfA7bt3WA1gexkC22F1Lunphonz6fRhUFZ4/ZRvHZ7ykSs++/srnESHXFrxOK4bgl5IcVISd0B5AZ7sYsUxRbkg6sI8g5WNhLrOiCOfpjEo0TAYwfi0JdBd4rimNQ29uMu8mhGEGt/3KIoC4yyj1Q5HkxlJDNQpQs4pFoqqLDETCyT4XYlYzMmmLYFawZkpQo0p5zXmaJ3hRkte5HRWJIuyJlCCuuIsCcS0aOtR5w1+UtA0llgqFqcDhg/mjJ7o8M63F8RyAWHCJK8wpoKmQSWaQNU0JRgrkCKgyhu81LK2GfGBD4V89WsZdekYZzkbmwmLOmNavh/VGdOJ77B+Ee59u2X1ApzuGKZFTQF4gY8zgqpsKfKG81d8NtcU2s9prOPOnXU2r3qsdU7ZvlxzuKh5dDXB9eZMgI204kd/aEy7VzNSV9h68LcZhRKVz+kNBSr5T0g2f4kuhpOTC6ynt/7CmvA92WhdXE2ZTEtar2ZhazrZZT7ygffw8kv/lFePLJ31TXanu8QqIen4aBeThBHCaLSnydWU8ekxd8cTrIX3bF6jHA2J7BKb29c5qDLAoaTA9wMCJck9jW0NUnrkZUNLQeo6DMKIq+eHrPVC5rOCF27f4/vuu8pJJZmWkM8dJyeHNJmjNWeRGpVsUCnEWxrbSIRxVHNQc2iNIowjPJ1Qao2vLXN7ijmZsyj3qeqzwmgFCG35b/+w5PxyycFcMTlVfOgi/Nx7E6xYoEzMdJqRxB5+rGDWcu+2Jc8dfgeijqRoLZOZoW00jz0ooT77glZ3FdF9BeZmiFsRKFvRLgQKia0sWA+pPZSG2HMQOaKeh1KO8duS1rc0M8fB12vaI4eX+ni+oTOMaGhJRj7LhcKEPnUp+NKfHFGdHnJtI+FWL+LphxS/+eu3yYTCrZYEi13edD5XHosYeSlhnTEtp0wKn4ef9kiqLrs3ZqihT57VCL/ijdszPvxQj+ZGxlMPdjBzwT//xoSnPrbB//Zb1/mVX9zmv/ifvsMnvi/gwY8t8+xLM9KRY2sr5PZ1x4VzjvsflfSWQuLlltKGTOeWt15vaQrDyopPJR3IFikBzzH0QYQevrAsJxKLh5M1QeTAGawBlGTn7hwde9S5xZcSpR3Kd0jPIbREK/kuzdpQGIcOwG/go4HinwQCIRzzcsGLY833L/uklCALdD/ldLoApZmXJaL00EOPJl+QJgl//3MFT25N+fgP9InagpOpIfYiVryWO63Dr3Nk1wcjqI2mf/QHnG7+DfqupZKALUnwiaIOrjQcrq8TPfU0Jpry0SjkY50BcZLgpZqiKqGpEEDgxQzDkDRomBc5TmjmE8Hv/84uiQuwsWP0SMPRfs7RYYrNCwax5kracLXnWOn8O4HQAsDFKcuRoVaKWhX0mi5WZWRRzkh40DY0qcRTFaJd0IQRHV+Q6CUWSxa/nLNY6/Av/q+H+ZsfeoE0gQsPrPLMrfNUxUN85NybDNsJ86hk68p/yP7Bq1x54Mdp64yVNOKp932BxW7FX/mpIfPilEUu+cx/rXnpDxRp/DDy7pC7dw5pjGH35FV++OmfIQ4k2Aprxzz/3Itsr22zsbLF1C1Y61win36Lz/zk32Q6t/gDiyoazp+/htRDnr33M6AqmvGExWnNs1/7PJc2Fd12k+HDmzSHz3Ff/1F+/LEvcjAH5XZpa0l3KHnkSszRDrzw3BFxGmFtzGI+Iwk0UeiolSFSMBARx9Yg25retRGjOubNN9+kOwyoi4qidGgFRdGSdDx0IMmb6RmvKzpjDdauwaga1/oY19DrSrppy/DIcpC3DFcVtc5YTmCQhNx7puLcluXStQ679zKkOsZ0QYoW1B3aImJ/v+C+JfCNwM1nPP6Ih/Ici7IEJJGX8UMjxfHxPjKA7YsBZVnxwFqEE5Z8GZQPRs7pL9dkGfRThVSWbiRpTYbzFUI7/OBdDmMAy6OAt16boYXP2rmaMpsRqQgdiLNsNQt+lDKeznj6U5t884V96myOKuDCA4K92y23b8Gzv5mze2/KxWs+T37oMrk8YNFksNik597BJbeosw5tGTLbgb//j9b523/tDv2B5PSkQzA6JasMfRXQUqOMZjpvsf4xtw8ifucfDinbBQ/1PPbygk7tiDstS5uSxU2fuc0ZpjEPXakJehrZaSlVzf6OZnx6BLWmYwUXBwm+kQQelOOvcO3x1xkNR+z8EeSV4pOf8nj5K5pCFHgLyFRNPa2JupqBlvjLGaaNCIYO1zX82Z94/Pbn7rAULNHakvd+BOrljG64xEaZ8mv/5Wuc7BUshsfUxQHhrsfpvKSYwr3rpzxx8VeYZy3xEL7dLvNH3+jx4Z/77jXhe9Kj9c+/9MufjZIETytiUmKvwzNf/3WmboW50iQ6IYl8PClBRfgyAHvmNTqZHHI4n2I8Segp1ocDjDG8c1zzyt497uwf4BpLHAZEQYCWkpunQZ8AACAASURBVKKesMhqfM+R5wVB6nH/pXMkfY/vf+wCQ6/G9yXPvLHLcKi5vNJF2IJOqKnKjP1pRlUtKJsFraiwqkaHDi+VqL5EpxoZC1Qo+b+5e88gS7P7vO93whtvvn07d0/oCTszO7uLzbvYBbALgOASAoo5mCJo0aJsinTpg0p2ySrbgktlOUgoVVm0TYl2FSUwSkwiAYJIuyQWm+PMzk6e6Qmd081vfs/xhzuyP5mfWMVwqvrbre6uW2+d95zn/zy/R1dcdKWG32wiQo30IoTsgzwAmfAf84myMGgpSRDsmzo12aETwj/7fEIWupR9GEQJrUaIlJANDIOdgmgssM6kvHqMoZ9OIr33n6gipGA/kXhKIiuW119KWHnWodwwJOsKL1JIIKvm+FWQ/j0bfwRFZGBskZkmmEmRpUdpJyOxXMAwFmghufnmmLurBfP3t3jxt7apP9bhT14c8oXPatrOHBdvJnz95U2WWyHvboz51GMzfPPDLnEl4aFjTbIyY7NfMBxFzE51KFXMs2eWePf1Af5sxqCImZuu0etDU0vevprx4e0Rm13Nxnafj5xo8sq5De5cTwiI2R9pFpanUFWPOEtZu11weNbwwifnaNRTLlxKuLpacONSRLFbYf9OjqMsvispjQ91g6oIfK1pVhVSuTw05VCrALUqpczRvqC0TFhWViI9ydWLJQUGYRVSTuj+TqiQrkA6gBJYXEZ9Q5aXSK0YDktC3/LdHYWvFVlpme5U+fadbT5+1KVTDSnSBI3BkxLPkUSypOoYLvcFm9YjaSyw7j/Nly8f4VhlzPHFHjs7JZUa/DdvLFKc/D6K6hJR5lNUpxiJecrqLAUGq8CaktCroxyfKOmxPLuADAtqUx3yakAhBmjfQ0hFlqUEgaEowWRQCwTDeEwU5ySZQZUV9jZ7pEnK8586wVj3SCOHqqixslJnJutypA6dhqRZUXziC//tXwuP1luv/aMv+lJQUiKDgjROcQMfmi5lP0NKS+anuE4b109wWwXjfka1WSd2Ihq6xr6Y4/ylPWbUfdw3GyGrMWUSsDZYYssYOv4Rlk49ggiqtGeOMxrtMt1ucOfmkFf/5HX8GcvUsZzWkqZzpEEWNbh8u8NO/yzf/eAKC4cmwNhaeIiVoyuMhn08T3Ln7i2MgLmZRdburBHUPJJoBHqfja1d7jv1JNdv9rh2bZXVtQ2url7mvlNLBK6lSHMCt4quVMjFFF71KFs711l053lw8QZe5Q57WUnXt5w9MsPCbMLgoMLmVh/P1YRhg9FohMSi1eT7s66gLhS9MkOhmb7vBF5u2dnpEscxUohJEhyJMBMymXIVUoOlREo5ac0QgoKcMhc4jqASOLiqQBqX7Z2EwkJQc3CdksAFjSSJYOVEjTgb4mgfPxQU1lCpugQeTHqvfKzNcZXH/JKlM+Xdu1RJHEfiOuAHAlEKXE/j+ApjCqZaEu2AFAahoFZXaGdywXJdB4tBKolUYCdVtkg1+ZPWWPzAx3UF3W4GEoKKACRJmeB6EkdPgjzatSwtVZlenOJgfwSZRqkSpcCR03QP9jl0eIpub8SF83s8+Mj9KKeH1Hts7U8hc0MZl7RaNfo7OQsnO7z58ia7ezlONaNRh9BzsaVLWHfIqGCCmCJp0Kok3LreZfuuZKwM9VpJp+ES+gLQbPRipNuh0THUmzmPPJNx/LSmVWtx63LEG68ZtKpgMstDjzs88okB2ltiOL5JqF/gzpvvcW6zwnDacNRxubI6pjW1zGwzx6tpgkqBM21wXI/6IYeDvgeOwfEUYXhAvFWjp3pUXcPp4x7jcoE9sU/Y0Xh5D1F6ZKMJ7mf1cko+lmRpwWBDs7FnUHKR4b7HZ54/yVMPdGgt/fhfLbzDK+/9+hczr6DONL3BiFvr76OLFt+5e56D3oBbm3t4ns/0VBMtJc1ghiSPGCdjXO3TcgKq1RZtb57MBryzus6Risf+7Zvc7kb4jk+r4qNChcaQjPtsRwd87vnPsHzYo3M4p6zu8vDhFTwM1vexRnN1fZMnTxwlpURbg/Byrt6NGI13iNI+iYoIPBCOpVQWrUEokC4YpTFCoYMAVAXfc6nUFa2mpN6clEoLNFJoBAKlPOqNJjMziyx05pmaqvMzT0oWG4qoO6YoJbWGwmLY282wiaYhNYPMIkOXRExKVq2FI0s+QUWwvpWy17NML2hGOyWiInjt6yVnHle4UwLZglIKvMLBFALKAkqBVBM1Rnl6oshYB8+XBMIlHYF0SmohYEp02yWQkrLuYrMBSc/j4WcUkQ0ZRUM+eKnL488ssHpzG+EF3NgfUG+6yKrGdXLOHK2jAsHpByvMLAmefPwo711dh0ZBvdFm1JWEtZg512eYGxxPcnzG5fQJh5cujai7Fl14zB7zOXn/HK+/f0DYVmhb0KyUPHKmxvnLgt/4yiZvXUhpt0NOHXUwY4koiwmUMzSYhqBaVQQNyYxradZd7jtaIcgjvIqAwEBQIK2LFQmkDnkOju+AI7l2w+C4kGDRhaVScSb+G9cl8CxJnlEoyHqSKLYID3Z7GY2Gy7duZtT8CpaSldkVNkYHfHS+huMkeAIaNRdfWVSmODTtk1jB2q5lIz+Cqk5PnrNizM3RDG/sfy9/3JvmRlzlvuXjaM+idJVR2KSsLZNVWmgjcVyHsswQdpKU9ExAP9vCygLPn4ymTZzSDjtYWZKnAxx/Mu4zJkEUU6TjuySmII0LotSipSUZOMTZgAc/1mKvP6SWBzz0zEmmzywyNRMy58c0XWjqgid+4r//a3HQeu+t/+GLngJwKWxBAXiyQuEMwfEJwjqhC3HokJkSN8/Y7uW4vkQGGRVCmHuOURzTnnmUpU4NN73LVtTi1sYqnc4K/TikOtXGKNgbplTViOtXrvLd1y/xzrkeT3/6aUbFDKm5HyNXWL16lpfernF5fUAiSnY2+5w9/TCHDx/m0sXLDLp73F69wX5vHyMEN27eIs8KXL8NhSBKtul1x2S55NU3XyEuEzqzs2jlcPHDD4mHPVxlaNcDsnzA0swx5jpNevk2S52H+dint9m6eItpWVJvHSY8YimKIVubEdFQUa+3UQji8QDHEaAsQkl8IdBSUPiatJcxf/oEO5dX6faHE1X4nhAq7cSuoFwH5Uw8RkaWCCmRSk3ur8JA4eG4OWEg8ZVg1IO93uTCWKtJtDL4robcEoYurY7DOErQjsQPHYo8pxIKtHZx/AybeyRJTsVXrJxQKAV5GeH4CilKpCzxfUGRTgIwjiMwtqTdAqXs5H+SUKv7CGHQjkQ7amJDcUEqiZXlJMnsqAlaSDgYO7EXZGlJHFl8T1OtVSjKhErdJ0lSfNfHCkO17lBbimlV7mNjbR0lPLQ2aG0YxRlRZNAqpD6Vc/PKGJXPUq0YWosOuxuQjucZj3cQ0lCvW/xqhW/9YUpQz5mfcanVJu+HMouQqsbMYkAxktz6zphmCK7y2e17JOMUrKS7Lxj2fYSXMDMV8eQjLo3A0uk4eJWU0PcY7Oa8/qpEi5TjJzXNKYVXS6k2U0hCLu/OcTCqEi7+HMcPV7lx531CF4Q/IPQUs/eXjIcGHc5Sb/U59kAFv9Hnw7cUtbBOf8dh89YYh5D7TmjCmZJEKbRosTwdoJIuoz1Yu2oZ7Etu37AUtk6SWt6+nuPNz0Brhp2RT1Ep6EUVDp/4ib9aZnjpObScOrt37uLqKk5W8M7BFl6lTpmWCBPzyvlz3N7Z5z/52Gc46K8iCICQvVHG3siQj28zN3WHzz/2E9zfkuhQcO7CW/fSW4bI5IRGIJ0KpRU8/cjjDJ279OVVBuOCjdWUhz/pYQsPRxiMcfieR4/x8OxTXLzzIhej9zjW/igPHfH4zt2cqLSEqjppQSlLpMgpMRSlxVgHg8TzXBxCSu2iNbja4ikIPB9rHUpX40qLrCi069NoNGg1ajQrNbTX5uTUHzGIRujcx/ULShyu7cNwNOa40qwnBl1VBFVDJjKU8ZidKaj6lrX1hNV+wVJL4pmcN/sZhxoBloKxVQTGQA4qtxS6oKw6qFIiM0HRNxTjEqfio2WOKCwm0UTDMVPzIQddlzzNGGeWSj8leDBk62KfYeoxvb/DlT+qsDCTMnOizspzhu1RxMVtQSIzTp6q8p31hI+frOLNV3jzzTVkZYmZszVWb27TN6uQdXj+wSm+c/UKzzzm886NAq0V4ZSkZTPu3jQsC8GPfeYEv/QH13h6ukokCn77znVeeHqKb1zaZ+Z0g42e5OVXI6ZDwY/+QJ3q2OfazS7XroSopiBsZviOT6fmoHTBYDdDiZCtwDDVNNzd6jJbC8AV4CuMLZFuQTwqGI9dklHCgi8wqUD5KUiDIxyMlni+wpUZhRhTcR2y1GEQlxgJ2rdoNUErWGtRAtJsSG40u6MujlvjH76u+NrfUOzmGVlfQcPDekNsWsMVOSdmct7ZmBTMViREZUa7OUtQj5nvu1j/GIUnsVFK32b40kIJJQJl7b3WAoF0NMootOtSr9bI4gTP81AuSFeQmRgba1qtDqN0iJUOnuOzo94kHZa0wirbToxQBXlRoPIuTz2wxCDdx6aCfHOL8f4s7nINdV+L3qFHUWmGP+7/RW87f24rKBoUoo9RHm6U4vsOZSRASKyNQcaMoipBEOJXKsT9HmE9JTc5dc8lGwyoVI7z4KmUpl3kg0rEs9GQU/Ub7B5/gkc/9gRlWeKmlvEwprXs8LXf/BqjKCUJBOHcHNXZJ9m6nbC7lxMEgtVrCe/duMjdzZt89jM/SJYOGMUjbr/3KltrXQ4vzjEYdgmqHvubW8x2FuhMzyKdkjILyLMKpszRjs/+cBXyidLVbHTo7XXZ29rk0KEF0mHJqIx4/U9f4sThIyTakMo79Hu3cDKws3McXtrCyVOcfY+31pc4PB1TlpY47uO6FqEMpQDHdakWhq7NcUqXXMH2hzfYH40wFgJnEj5ytKIwZhIycEFIQSlKJoqTQFgzYRcaUKGdQJ+LHEtItxchHPACjeNMsDISKIyhM6vp9XpUKpAWOVpNvIiIHGNzggp4wRhfw1QHPD8iHjsUJQQqwxrQEhxtMdZibYESFs8DVwtyU6D0RAg3xkwOVtJiRYnUYuIFVgp1r8lN3WOIITR5mYEd01mosraaEsXgpyVhWGM8GOOHHnE8ptqssL8b0zoa4de6HL7PZeNGQlFApZWj90IsEf1Bil8JKE2f6x9aNm95HDouOPa4y/Z6lY21KsUwYu3CLisPeWgMgZQ0qg6uN6JS02jj4LcUg9QSzOyxsOxQqJLUzTnuJFxfg5iUqIR66PLAkUm9FHGG18i4fM1Bb8HUVMjLrwxxlaGqDe2qIC8iXL3IhVcPoF3n228FlO79HIt7VKoHDIcLaHcdg09l1uGRFxrcubiBW4lp14HRiE/+wCHOv7TN7uYed275dI6WdLwxsg5Bo8py/YCZuiUsFEFQYX3NsvphRNipcJAP2dsoyUXBVMPng7c2yJOAIyeOsJ80SezRP3NP+Et50FJuSb4nkcJnc+t97vQqpKXEtzG58EBDqDTdzU3+51/7V3zsoU8jTEpapCg3x4iEcTQi1D5VUubmpthLYHZmmvjmDXChWW2hKMAqOkvzPHDfGV6//GVu3C7JU81oKHn/5l0eXllmXDgom+A4Lud23iGtdjhR+SzReI9hnHD3YEDhKTJcQqPJlMHFkJsUm1jSBByh8JU3uWVIgTWT9KAVPoVSONZSrYcopZBCEwQV6tWQalgjdAJclXFtdBjXzpFVQob+PJvZCertMW79LpsHr1LP+jSnFXmeU3U9hMzwPJ/17ZJba5YyUwRL8M6WQmiFIkEGOe+/4/HwacgGAqeeocsAT1nSoWQwKtBWIaxGiYJBTzM6yJAYZC4QhcEroFZUqHuQz6QkvZx8q+DRLyzSPTdk6ljJTD3g0m+POH68Rc3bYWOmzWu7MSLKOHVYYJanGUcDFo5aDs0N0f0RnY7L0dlpBm14d2eVfunzwU6P50+H/OkHY2aKGusjj57X59IQ2hcSDjdd5pcUo7JBFvd48/UBz97f5rvfPeD0mQafeM7j7df6vPOOoaEzzpyqc/RIQbNRZSc19EcJmWMosPjTgmE2xhiI1gq2jea8TiZAP2nR0qGIhix1AtrtjJmpEOkr3n51D6lCjJFARmJLlHaZngqxYYQ2Jb2hZdQvKI2Dc+/G63guSDHh6JiMUkhqYY0sGdEI4e+86PKlT5ZEuqBaQEOHHIxyjHI43FQkGzltDHk04MThE1SrdQ6yA6zOMBrytIYMBE3lEOdD0iIHJFYUiFLjCIcyi8h0Bqak4deQXoPCArLA2oJcxBQ2YRwJGpWQJM/YTy8jYks/TkjyfPK8CEvguNy/VEMcU2wcpNRKj5UTLTbUDotljSwakZiMzEK/Xf0L3nX+/JZIBwzdCo2GRzUzJO0KcXlArYR+Yx7d/M9INv9HfDPC+E3u3hpStQrjFaztHaHRLHj5xcu89K1v8uPf7zAT9lmd+2e44kWOptfZemeBD66/DbbgB3/oRzn/8lWCYJnGtGTu+CKnOius3Riyvh7RnLEElRlWxy/hODkPnloiUJscOnaGG6tbWAJWTrYZ93s4XoDnhxzrLINRmFxx7PQJmn6NP/jKi2h3cqH6+7/wT2lXJa+88jKvv/46x+87zXCY8sb7t7HFJYKpkI8/8730Njd44bnPMlf/BrdfBuFo1KjNt175BLf1Ma4djPmfHv0PrB2kjIcDpCxw3MmYTGuHojT0qwq7ltNYCHn4uec5/+I7GAN+qKEsUVqggDwv8WoTcHNpCxATjA73DjHWGEzi4PkZnpz4utbWE4wEv6pxdHHPp6YwqcFxDXE2pFYP6Y8i2jMBcTRJ8oZ1h9LklLnmyAnFeJCyfKwkjkFJQ7MhEK6k0IbAcXEdgfYMRkhQEkdPSKPWgBe6iKwgiVLCaoBQKVYUVIKAzKYTtppS9zp0JwodOkHkgCNQ7ohSQ5Rp0p0htYqDFxh8X6CFxWQpe9uS6e2Q1myXzuJRFk8rRvvbbKwaajslu3tQqQiiocYPmtSbEOe7XLuk+PB8iS136HUdNteW2Rn3Of1+hR/5kZL1mw4LU/1JuKNqcLWDW+1y0NV0Fk7AyS7G9Fg4c4i17ZucPBD4vmXQBb8+ovAUNsip1kpGB4LA19y5DO9d2aNdDXjioxmtRobnxBSJ4uaddaaXjvC1F1/g2toVavWCSmiJNpbxBiOe/vHf4rWv/d/srP8azp88xjj6KkuP/3fMn/0m733D473feJdgpcqpYyXV1xS6DoGFxIUD98cwwSKN2RGHm7f45i//PqvXLLtdwd2dEUMajPISGcLP/uxZFhYWGPUEw8E2/evXOXXm9p+5J/ylHB1+9+1vfvHq6msoUaXA49rWHjbL0Z5BFiXGFdQdaLVrLMwcYmMnYX806dwKKy5FqTBa083h8t3rfOTwHNmoi9UuZQrzp+qM7Bv02QPdZaE2zcvnvsb1m4reQYa1Ew5QXGY8dvo0Jo9xXActfLICpCy5u/MeH6xdIU16uLbBxkGJzTzSQiFzTZ5BmgrKVFPGClO6ONLB1xNqeF7kRHmGcMBicVyJH7oEoU8Q+Hi+i1QWa2OSfEgiMvadB8j8o4zcwxgr+V73a5wMr7KSX6e7HjEdQIwhSi2FUfSSkjt3MrZ3c3IjSYzhYiF59eqI43WHSstlb5ixvR2zNN3AyEmaJBpCNBQkmSVoWIwWHPRzxnuC8bAENEK5SKckKENEnmMSA7YkmHcIOh614w5rX73D8tEK+5HPwWspq919/ujFbYbjgKePwNWdHpuVkJan6W0f8NwLy2SZz6XrCQ89tsB9cy1yBTOhx9rmmLNHa5SZ4WAPCiemH+WITJMZxZXdIXEuWZkNmNMZm70D4tQnlJrNccpHnlpi3Eu5eaPLoXaVn/x0h5VDPuO+ZZwWDAtLSYwXuHhCUZeSiiMYSwflCqpVjUajTI4jBAqXNDXko4ypaQPjCp1Zh91hxHDgEJfm3kFaoiw0G5apWYV2JdIakszlzrohj0vQmkE3pZAK6xhe27zHu/IELd+lqErGGchSMihmOd3oYazF+A5JktKuSQpheb+3jC19Fg8do1brUNoxXuaR6xtoGyBEgOdLKCdBkDgfY/MCIQuQCkd5tKsVkjLBCoujBb4bYsWkIUHriffD9SpYE+MIl05lievbLxLIkoNBlTiKKa1mnKQo63Ci7rKpIg56OU9NVShna7haYshxnQCR5Xiuplar8smH/9O/FqPD737nl744TpdQ8jZT9Xn+5HqH2VZBUyd0pz7F2Ps0g7VXeP6zc2Tuxxl1DyOiNdyaYXX8KJt9yZU7OeN8yFKtxYuXX+L+hRO8cSnl/PU2nrfOF37o5xmWe/zm7/8mNgl48JEznD57iqlwAfQWQkiEtoyGMXvdu9y4fokbtzY5efwIH3/28wyzLt3xDYSwPPDAQ+zt7TLdmSaOU1ZWTrI4t8Ty/CG0WyX0PPb3r3Pxw0skqWF6rs0bb79Evz8gy2CqtcShQ4fY2lrF2mlE4eJlikdOLHK3L7m6bai0f5Cpx/4hb75nsWWXg811Hqj0ccwVyrKPUqAcAVIgpQJjsWXJaFhweL5FMNXioDdkZ7eLSDKkr1BKIgUYA3lhQMoJCFjdqzxTAgsTEUgJBOUEsSI1Ukj6gxLtuAjHoLXFVxJpJWVhcD1FKSyeVyXNUsJaSYkgLwxhTWONg1QZjiuJhoZKlUkQxgg8XyMcg7AWKSc/o5GZVLgpyHJDu6kmHrJ7s08pBcqxeL5AaQvKmcggQuBoBZKJKiYtngtFLtDKQekSUwrS1BCGPuNxRmeqSlkm+BVNXuRIAioVSRi0EWGfeBwSNjSd5YBsq8Pm1g5uANiCJEsxIkOYKXI1ZrpWpRY61OsOv/Pr26xvhKx9uEV7OqfdqlNrRGTCUHgWKg5lCQpDaneJx8eohDFLC/tsDg1eBXQAaQFULf6UpCod2m1BmbbZ2m6i3Qr1tmJ6qYeamiOVQ8IWOI7g8m3L2ubHcDs1LtxaJ81S0myElwzZ4zT1zg4mrlCYw/zSlz9gbrnL7fxBiug4IySD+AnanQeodsasbR2gG4tovchuHrItqgRVlzL9Xar2LVbPaQ4GhlEqWU8FueuQ24LSwPDtTV751cu8/+9ucevb23z0xD6zbLD4xP8/B/AvpaK1uXEemx9iOLzLXjlN0o1AaJqNkNOnAkqlECqjLAqKbERQ0+TGIyktB+MKnudhjYvMLasHQ770jbf4+Wc/wnQIjz60xFhMc3v3Gs2wQOWCu/3X2R9K4iT7f/vibK3kRz/xCcbJGOP5mNzDIjDWIsqE915TfPddjdAJ1oknBHmHCflWycnLpLDkGSTxhNeC0jg2IUdhBFgKonhAxatj76kAyslAZqRkxANFkeWkouD+Q6eouwoZuBiTkpgKG/okc/EFLnywR6URkClLf2gRAeztx2QDgQ2h7lkGImZ1pPjWhy6PtA1dm3Eo3sN/7L9gMF3ldyKHH9j8XzFZm5GUODrBKpfBRoFJPUTmIV2L40GRpDglVEuPcT9hfycjz33yAmpXR5RJwclPthkfnuHXfi/hp3/K8MfbOY3pFt/7N33+xb++zZ2uy519wZGK4CDpMnN8nl/5lZv8vX/wLL/8777KIw971E8tY1WFK1fHHKiCrDegPVuwO7bMByHnNzKibMCJpRCPCr6xpENLbWWGxaLH5b2cv/mDDX7p9wb86jd2OTKT8z0n6wyGJb/5nX3aDZeFWknVDdBlgpATbEKpDdK3dFoBy7pgZ6S5vp0jZEwqNPGwxK/kdNcinnsuJE408/NAreDyOYssU1AO0kgKCoLAo9l2sGWOAIoCjNYT46tRjIxEFAYVCqQSiLIkycdUxTTdYkzSdyn9PWzeYaSn+devX+dvP6tw0wLHNVjjkpYjRDDNfL2K72XExbsM8psYkZIkI5ziLo7ROOVD+M4CmSuAMcpaMpshTUGZJdQq8+SixCkE1BQ2k7hKk4sULT2MyJCZQGmXNB2TVbtUXOhsx8TeElt5wqmVZXZe3iARDoNWm2h/ixYe023B7453CaXAj+s8fOoQ7fn7yIsxxpi/4F3nz291y+ME6gjN+mW296us7j/BUvUVvNmStnqO1eLr3Bp+FL9nYf8+5p96kP1vvkFSjrm9E/Le+Yvgjbj/8dN85cIbVGLFL375d2nOF9xcX2V9p8N9y09x9rGH+JNzL/HuuRd56IkVDrqSliy5OzK4fp/tnTs0wiW2dm5y6tjT9NNzxKnLOBuys3fA9NRpKloxGI/Y2d2nWavjuj6VSgNhFFo6vPjKb3By+TE2N3rkScDpRx/jj//4j9nb75IXKXPTHQaD25T0CFzFqQenubO+z+Z4g3Kqxrn3X+Pk4gNMtS0mfY9hfoEP10pOtUuefXyf4XoPDEhHUViDujcOLPICLQQfmZlnNe8yK1vcPX+FJIV6xWNgUkLtAgJDiVICqTRW3nuO7MRvOinCmBxmHA0yrWJshFAKL4Qid/GcFCUnlhJbgDEWqQUVP2Q0iKnX66RlH8d3cHKw0mCNj1tJSMc5rU5AniWUpUVri+drjLIUWKwxaC2xFpSrsaKkzEB5EiMFCCa9uRKyLKciFMKRlEWBcCSUBqUU1lpsUUwUOgtBMFGAC5uxsDjFlf4eWZajHQ/HDyiLhCLNkQrSJONgy2fhWExCgvRy0sxFqU1mZ6fobkIeaZozBbmBOAMvOMArfWIzIop8/DDh6Y832d3ZpxZMPHRupYtQPr6viXTC+Z3PUXUPaLoOef4hU4tTrO7eIdk0pIMWei4ltw3S4QySPTp+xuZ6j6MPTzHw9nnn3BxWVREk1NQKx70G1dl5TLlDzW1SCza4tbWP6M/iqZRHVjpofxEzdYiDC79Ku/jb7EY38WdPoh+5zdXdKlsHr9FWYwbZpwAAIABJREFUZ/nyv/8Gjz37ALWTP8LWbpsru1+hv1rFVO4wu/QAD3hLnFRfY0qtowcNjO5TAHHuoryShCEUoMo2l7YPODkl6LRzmvUqGzYhGi7x+J+xJ/ylVLR+/fd/8YvWekgbce7GHtakSCyFKpChph+lDEcTjEFW5ChyjElB5EhACIUrJHmckGc5vZEgYsjDhzrc7Qv2yn10uotUPr2oz9quZXszoR5WEGSYIueZxx9iYaaOdis41BGmZJBHXN7+CvNTD9OLIq5eu4WnHDBiwnzJc/I0pchysiwnTQuyvKAocsgT0jLFCkmJpTQGiyKoayp1S1DRCJWCKEmzBFOk4FikI1hqz9FqdQhEl/292wyGGRqP9SjkjY1ZXr51m4+0hwylpq4Fd8cxGstK02XOF0S4vLbv8pUDSIVhWVU48/yPMZ5+CDH/FFPVFW58+BtcksuslgM+Nuewu1WCMTjawdgcaSVZkWFiST4sqMqAIs/Z3i2JSkksUlKTMeqVZELTXNSIqOTi3pB+r8b3fM6SD1ICR3D7Vs6plRaDImXqpAd+QLebcP8ji9y4eYOjS5KFI3V29nNu3Nzm6HHDi6+W/PATGXf6NXZ3uqRaM+jnlCbBT32i3ZwIyaePVxj1Cw6drHLu6gG//O2Yn/x8i2sfTOoytHXJhmOOL/l0Wop6wzDXtjQP+UzNOnSmXGZCl2JckAwhyg17Uc5+tyTPLULmlJlibzXhCz/T4sqOpOhLlpddkhuGtThHCUlhLRKBlJJ6oDi05IK12FIQRXB30zAaaUqZMIwU1hQIx+dWWrB6MIGWKiUIdIX2fJMoHnJiaYmd4W3uXM64bWp8bNkhvUdxdzPNhWSR1pRgWJxnbLcYRzH9vsKUHmmaMIgkb904x9bOiIEVfPPVV7Bas9CsczDaooLi/e1NvLhABYKiBFf7GGmxUqMUCBNiNBiR44gUR9eIu3exrUfwp0OmZ6fJXEmtXmewtUfh5Wz3EhY7dXpzOb4yLC/OcmRxBaUN/WiT3aRHPxvwfY/+7F8LRWtH6i96tSqj8RMcevB5Vo46CNvk3SsBVi9Qa6VcuX6Ti2sJw2Kek/ct8iu/9zXu7Mzy9OPP8DM/9/Pc3X6LK++9y+HpBZaWD3H6zAq93SGkHp5b59zlt3n1lXdohYvUGw3mpju8+db7EHq0mlXCqqYsLZWqz9mzT3D52odEw5z9gwGXr93l3LkPeO/d9/nw4k2KpM+nPvUp8sLywNkH6HV32e9ustvb4L4jx/nS//lLnL98l4NBxOn7T5FkEccPzfKpZz9JxS9YmFlid2+T27e7xPEOfjnP/uYad9f3ePrhjzAYvc7awTU2+teYijv8vb+xzRPHXySsbNEdgbUtkiyiGtSJBhHJ0GVm0fDwU9N8eHub06dcrn2wQ5H66MBgnBApUpSRyFJS2klyr/TKSem5klgpJ20cTBRkxaSZQwuJsC6D/uQyhJvjBiWeo9DWIjAEVQftuZiyxPFz0AnaU2At2jUErsY6MUpoTGkJXE2eFhPF15koU1aWk3o1LXE0ZLHGigzlWESpqVYsiBKlHBy3nASmpMEqhesLlFNS2ILSlBiRYwHXDSYHsKrAKjtRmqVFuxl+RROnBUo69IcDIMBYjRdaXMdlMMhotepoTyLsCJuXeGIG2dxCFBX664a9myVeqFGOi+M4RFlCXoLvKxT3lKfI0ghLKgpwXGjn1LVkkGWs3b3G/OwLnHniB3n7UsL67hb1zvOM3MM4sz/AZ54dcOXGOtd7P8WZ08fZz+v8+1dmeOnNKn/0UpObozHV+QVe+PxP0p6Zwm0fouI/QekKzt3epZc+yY27XcajMT/8459nXAr2+3vsbl5j+ewDGOHw0ONPkscR7373NT7y0Y9y9sRpelHB6p1tLl+9RcIu/XREUFtgpztgfW+MlTl7oz1EMIsNHWQ4YG/NMowN3/P9S8QHmr/7CxXqQhPv9fipn6nx4ON1dvdjPv+FgF6UIKsjHnrqH//VUrSyGOr1gG6scZoGlYdoZQmrLnluUTgoAZ720MJBSYXv6AmfyHFxlUKWFif0qIQujhas9j1e27IUjqSRjNh2CpIEsjKlNxKYomB5ZoHrty7hVaa4u7/PJx54hqKUjIohSk4TjS/S317gX3z1P7C9E6Okh1AZjrUYK5FSAgJTGgpTYu4ZjBEWK11sotnKetRUFeVLag2HoiixZYXSxAijyAY5juMw3znMdHuKIhswMqvs9u5wtzhgY2+AUwS0q4cI3TquY5ntWExeYVTASCUcDhsE2vDWQcmvXszYyS2hcujgMFQO9UYPf+v3OOIX/Fe/8+tIYfhXP71A986A81uWSx+MWTnlMRhBNBIkpSGsTHoc8zKn2giQ5MRjQykLhBaoXCByQX3RY3GlTj9L+Z0/iPj+53ze3Ut446vQVZb9K32eOtPkS3+4wZMfP0m0u4Pxc7Z7MG+7nGxLPhjUuXJuC9UJOTWj+d3vjJBOzpd+L0eNS04crzDsCVCaxPfpYil8QTOrcX1jwPd/epF//vt3aAWGf/LTU/yj/2OLz70wzc0PBqycCGkvVJGeh3tK0mieZO/bF9DnoagDjmJIxkgUFFLSzbMJp0poRn1BWJQE1uNv/a02768OcK2mtpQz3DasXo0Qsx6lkUhhyMblpG9rxqfIUgocJBnDTLOxNSBPFE7dJ95KcVxF6Y65vBvgS0lsJdIR7I02ECPwrWah2eLVc1dpTi9zO97hf3lzmp97LOL2qODMbE4+OMeV7YxA1NFKMshKTFmSpB5CJmztD9nvlih1i/2tGIqC21dvcen8h+hmQZaPUWWAX3PY2u7yj//Of44rPQZRgRdMwLuoHk7hULgeJpd4OkW5hxHBgDpNBsOEuhOhOzX8x0/y5geXKRMHOS1IhMHWDHvDXTb2t5BiEkEfxwKKv0aK1tVVGken+Td/8G+4/8ICDzy7zKVrr1Kbf5JxNqCja0hp2Bk6zM4LspFDGcyS9uGgn/HhhXOkyQhDiee6nDxxgrNnz3LkyGF+6zd/m153j0rFQ7sed9Zu8/ijDzHq94lGPbJ0og5ubGxx/cpVPvvZz1EPQ+LhgKef/CTrW6t8/dsvMTV1BNe2waZsb3W5dPEan/jYMxRFxvLyIr1ul1q1wbXrF5mZmUGpPnubOzhKcf/ps1R8gSN8qrU6U+0FNvc2WViYIxsrjh1rcfTYEu9du0HLy1g48Ty7wQ0uvLLOL/zoJgvheSgWudIb0ahbdrdHVGuSZKxQwNH7DYfua7C+OuKJj7fYXU/Ic0nQKBmODZ5rsBZsWWImw0GsEBOkg73XMGDBGoGwFiEVEguFQUhJWhYUxoIocF2NUhJh7KRt4T8uacFOlC3hTJQxK0AyKWUXZlIdJSVYNZlUApPPq/9PSZMWpAbPF8Q5aK0x7qSkXbkCqUA5EmMtStzrGnV8sjzGDyRFYbGlM/ndqpx05Ao5Ca4oBaacUPMDh0bLYbAnEA5keQpCk0aKWtOiypLeIGKmHSLEGM91EVrjugHtxQFX306p1EKGe+MJNHS2gvTq5KZPlEq0EAh3jFcNCMZTJJVNTCQYxBa3LOgca/F4kBOnXS5fus67Fz/gky88S2X6LFPThmTU5+LqZ7ixfghTyUlNm3dv3kW0ZxgMdnnwmUdZWumQJ2OydEyr3uL2zRvc2b/LtStrrG0MOXLygA8urmJtzjPf81Hu3lknGgx55JFHmFmZ4YN33qMWNkiSibJ3cDDg5LGzNGszwMu02x1u3VljaWGewf6Q66s36ZdDjp9doeVOg8i5dX2P6oka1SlBfZRT7xzw6R8YUA7hyUdq+JlmfjGCAh583FDkMYFaxA03/8w94S/lQWv9zoDG2SmKwqXdmSJNLMaMcV3NcqdOxQ1xHA+tPJTwMdaitYcRZsJLKSBLi0mCS4LjeJyaazHMDLt775PpTfp5Ri1YZnzgMVufpogjNocBC/MNpOOQx/u8duEdjhw5A6mkl1xgc6vGbndEicVzFWWeYUqFNRapDZQlQkxYVBPDtJjI1kIQlREPLFfJ8oCNwYjZysT16bou2tNgJFWnRb3ewnMlpYpp1heIsy6D0RbdscDEFu2GCMcQFbdQqkkv7/KxjmIUpzS1ZKrqspYW/NZVy1pasLxYwYnHDPs521lIIGLqUnG46nCQQreA6SBAxzlxIShjidIlL980zC1JOp2ISlohjUriYYywkFiLEgGxW+C1NHUkNs1JFZii5MJrewxGJT/yuWl6MuOHPtPmn/yDm/zdH27w7TsH3LhZ8NwT8/zhwTo/dbxCp+5z/urEAPn7Xx0wfcZhGCnSvZzvvD3myHJIYlK2o4y6VZy7kHB4tkmURlQKn9nQELkhN/d3eXZliv/6f9/in/6XU7xz1fJvf3uTf/n3V/jai3tc6hfMr3i88v4mWgpq7wuc5gHajzjRbqDcSd+c77vgSkqRkR149O5aWjXDTNVBKZcXfrjGret9TBLSS0fMqQb7Fxw27iQEM5aiFCg9OTtIJEbklIVDpgtECb1xjhEBSifs7iYIbSiVZjOZZpRrcttHC0iSBCFdKspleW6FYXaXuNDUgxRGHgPl8eXbT3Ei/RYvpgFRX5GWHmU4psxLPFngepY4G9H2n2U7f5PlTkGpSmqx4dNnvo+vn3+Vx07nlKViXBr2NhX7/S6z9Q7/8p//X/zb/+0XeXf9AwZJROhOkRUlqRL45ZAM+MabAx440UIWBXtktN2EkfToJbv86QeXiAYFunQQTgZC4JkKOaDdhCyehCrmqwEd+WeXsv5VWo5pcuHinzI/93H6u/u889o+gXsGozJmZ5vUnCaVIGD7oEtSjCFrsLO3zlxjiV53h964T5mHtKeXMHnGoHvA66+/ynA45rnnP8Y3vv5tkizHtQohFPt7fXwhePLxp/AqdbTWVPwKDz308KRxYjSm4rlE0Q0eeehBev2MV9/+DrVaDWsDbO7TqDax1pKmMdgS3wuZ/n+4e7NgS6/zPO9Zwz/v6ex9xj6n5250owE0BpIACU7gJJEEFdIaKpQpR7KkyHbZFcsVS1VxUhVWnNgXTqVSGlKxlUhxaTZjkhJFKpQoQgAIgph7RHejp9Pd53Sfec/7H9daudidOFfMjapEeV3vU/+uOrW//1vf977PO7fA3c013ve+9/HCc98hWNKUecHS0grpZIAfJozHOZ4a8dnPfpbNjYybt95GyT2OHvkcDz56j+vn36ZzYINWcojH5jdpJ6+xPVLc7q8z20i4fWNMbqCpIzZ7XU4c6xA3drl+uWAy8lg+2uLcq0OsCRAmJYoDKkZorahKh8P9v7VWSLBC4JxAODFtftxUXK+EorqfBFIZc/+zDs+TCGmgvN9kSUCBExYnKtBy2ggJsMaihQTstCm7j2YQzgLTPFukAwXy/gpziuoRhJFgYkBogQ6nmjLly+l3VwJrLVJNOV0wbRJn2nVmZpqsXt+jLEuMzQlCgTFTXS/KgACDQXuKqC4Z7k1djc5aytKQTqDZBhkYRsOSWQZQRlRZSU32yU2DhWVH3ByR7o2o6QRjcnZujWnur6ESMFiMKdHaEEQO3eqiNZS9AkXAWJdcfWOIXz/B7vA69Md87CMf5dThBxnsdbl87jaj8Tbr3gHupROW9w+4t9lFJg9zSr/Nvnc9zDe/+RyPPfQTZCLk9uotQl9BptjsbfL8K5dpz8/RmpScfuJREBWXLl1kPBow02jhjOXaO1dZWFqmqAxnz57nYx/7BP/XN/+MN187R5lb9h88Qlrk3Lp9g/E45eqN67RnOwQ9w95al4c+/jDf/uYbnFo8yGzvHGlpmNsPtgp4+GHF3VsGbQ0HD1X4Yh/jbId3Px1y94aklrSJE/V9a8IPZKNlU8fW7dsEB2Lec+AYe90N9i8do9NICKRGigjjBAgPKeWUU6MjMpNjKkdVloyzCNIdMjtiaHJ2iiGbO68QBBW3bh/k7t0enr8Htgb+kFh7GAc3+wsIIYjkPLc2uixceZPD+wLCOKKXpohBgRmDqgS4Ams9nMxxVk+nV9oSCMhlSVAmOG0oTMg/+PFFPnWiRr/UeEaxZzvcHXoUuoanquktSSfUvTZxGHLp9vP8yfd+g6ThaNcDrBVYWxJJAVLiGUGfETjF1d2SLDbMiIgv7VoubmcshzXm6x6VtFSTEKsMC3oqpsQTaFewmVl+4sE2Y0KcnDApJWlvzDgM+eRHPJRwjLo1Sl1AFhLMKaQDxiW6EoSloj+YTgYxHip0xJGhNeuR1BvsTXIeekBz7ZUhqS5YHyV89NnDbFwd86VrJZ9KIC7hue/1Ob2s2LqTcuhYnWtXhiwfaXHDdonrdRLrc8+MGWeS3Fa0mw16Y0OvF+OSHOt51JsFhwQ0fM1Ty0OuflfTnxg+/clZXj+zyXs+0eLq744JEqgrweJsgPQ0RVoRFDWGZUFYBy9S9IqC3tiR54Kk5WjOKkQCqSqp4pI//FqfIFIEtqARNnjuW2MGaUmoBAdlDKIgz6a3axMIbOFRCYMUPnuDiks3dkjiBlmkmXRHxCph4hcM9LuoNa7RH0xATfV9RQn1aJakVnLp1i7KWDZ6GWGYkIQRV7PvcHYYoyaSyO/jeR7kc1RVB2SHwNMgNKa4RVwTBEIipY9K9ri0+XUOzkvurKb4saIyjrzMyLo1UjdkXFh2R9s81HoEpwXXxrdJe10i6Rjniq+/ssYXnj7BuNykkj5+mZGhEM7SDmZ4+uF38e3nX2Vlvk0ZDcA6TDVBlz5S+hwJFQddhpdZ/MHOX3PV+as7A9tDTQ7jJq/Q9SM21zbpLM7yzNGHaIYtsjTF5Ck7Wz0GvR2yyYTZZh0pJjjXY7bxIPXF9/LHf/pveeI9H0EIQZrnPPvss2zv7HH50lXube9hjUA5wcbmDr/8j/8xV2/cpNaaoV5PaNQOU5UlvV6PfQuLGGNo1+a5euUNjh9ro+UH8cM6Z86+xmg0Yn5+nqIoaLfbrN68ydzsAltbW0RRRJhOp+w6giDwcVJgETgkQnm8fuY8b154nXp9maOnQrY2BM3mEtkgZNd+h+0LPR4cnOcXf+YWwxsd9sohfhBy9eyY0FM0EsvepuHIYY9JusvomkTohH1HSn771++ysE/SnLEMuiFBLcOTgJMYYbHSYZ0A6XASlNTTKde0p0IipixDIXDOYYTD4BDefc6VN13xG2MR+v6kSKup1ksKpHZIf+oSx1mccJj7jZaVdjphmj4IqUAIe3+DcT/P0jlQliBUiPGUq6gVVJXDGgFYDA4nLNoLcMZQWjN1n2vF0pE5Vm9t4O7TKrxAYyoLWGA6PXMWUI4gEkR1C2ismCItKgN5UYGEyk3wwgRfzDDamzAeThBBh1ojpjUXMOkXOGmoxQF55hgPRsRa4ofyvpNTI1WObVYEOdiGpSFy0rZlwR1gU3+IBx+s0WwlLM3s5+r5q3iJ4O6duzhnGLrLmHCHdLDIwWQFm64zqxqsXbnHbKNN2Z9wd22NShgaBw7xzW88T2NBsnh4npWD+3jo5Gk6HZ80TYkixaDZZGV+ia3NHepzDa5dv0nsbXJ3Y4t7/R4b21vs33eQ5aMH2N7dYWevS7PZZHNzk/F4iB8k+Krg+P79XLzwAtfXbnB4+RBHH3mSxvGzXL82oSosd1c1Sd1jd3dCZ1lTlQPKsmAwAK1bFLxDRP371oQfyEZr7oTg7Nk1/uuf/WnKiYSDywAo7aG1RiuJA6T0sEZObxc6xKtinJVTjEG8R2E6vP6ty5x6rECXBW9caGBIkWZA4QyaOYRvcBV02jF31m/ji3gqhqx5RJ4h6dQZq5BxavByy25WUaYFZZUjhEWKHGSMkAXGOhYbMU+dXub0bJffev4eG4M673vK5/MfXOFQo4X2Bb2RYiJr7OQxhY3YyRVpWSCEBAP97Ao9ewWdGMpKM8wquP9jVFojgLGz+KZCSs21MuTebkgeGTzPp1OziLqlhqbhfFRUcWOr4MZWRS+d8JFWRGahWW9wtF2nGErKQvLAu4ec+EANBg6RK7CWsLKMNgRCpLCnKDKBcB55lWFSRzaqyCYwGhlGmWRzN+Mjn53lzNe3+Og/epi1swNePrfJB5d9tBnz3fMpW+sZj3sef7BaMRdVVM4n6Vj2bklG/ohTD7TZmZQ83vZZLxNeu7pLex4WogpnakSFxfcL5ps+k9yxdtOhEkPcj2icUszvb5CNx0yUYO+qx2s7Jbuv3uXkis/tGylF6VE6zUJcEc1qvFCA55GJgqKsyFOPMPAIGzl5UZGmFllMkRiBcdi0oJCW2W6N7q0JNSCu3885LFKEkxgkaIUVGYX16Y4gkSnnqgfpPv5e7KU/IR01iJVPSsVO1mCc9In8GtZsURk7Fc1bSzrJySvBYFTS8FpsOJ+sn/HAYz0mxqPWNJjqCIPNFveqMYHH1H1V7WJkRV1KJv55kroiIJ5OApSlLArKLEUHHgUWtMYLodbWDLcrorDG8298hZnFh2mwwLF9y8zvm+fi9kW+9t3zvH/lMH2bE4r5adixmcfGY67fvMvu7i7vXLpBqz5DrQZUAk845vw6ByWcDg2JzZnkBauTnOHk+98I/yadq+tr2FTw3nc9ztbA0H7qQ4wnA4arQ7yVFrd7V7h6Z41/8V/9z2QMaSw48iohmZtlaAtOLNU5dOAAi+3/lLnZY0wmE/74G3/Kf/ff/0v27TvAOzfuEMYeSgbTt7tVvHHuDEmzxTurNzi6b5lh39Ht9nFWcfv2Jo3OIt978zxvXzrHIw8/yvLyQRpJjQ//vS/wnZdeRsiCqig5d+YiQeCBrdi8t4kOfCbjPocPLnH3zl0qk/Grv/4r7O0O6HRWMPQJ/Q7ppIspN3n3Ez9LT5/n6o2rrK29yk++6xkefeR/wxveYuu1Fuc2B8RVQBCM6CzFzC1o3EizI/aY7DZZOW7JGFKbiXj9FcfsPvB8SyUsul5iKqiFLbJqjBMWoQRKSPDEVJ/l3HSLoBxaawQWbEVlCqIoYjwxIAVeoKY6Kl0irARhEVIj5LTOIi06kCjP4UUCaRwUEiUkzk4bFykFQjukkHi+mIrWvWp6kfVBS4XAonAkTfDGgHRoz4BVFKVDhxVKCXxPY6nQSpNnJVHss74+oLN8gc4+w91VjR8kVC4j8HwqUyIECK1xJQjl8LTj0fdHvP5iBnIaoCyFYq+nyFN49MACfrSCrTYJW4psHGHKTeJwluOPxmxuD/FCgQhzqq4h8Vpkro+p6qT5BM+NKESL2kBh210azRWoZon9v82ZvX+FSO5S859muOFz8c1v0KydoN3u0Nn/MJ5y9G6s0l46gh8U3Lj7Bg8deIZde53GfJP++oT9+/ez2Onwf/zeb7K9OSJo1dnr73Ly1EFOPXSUJKxRSzT1pIEpUzoHOshScPzYIi9feJU4aTDXmUerCOdLOvNz7Gxsk5UjPvC+J7hxe51+v8vxwwdoJJqk3mTl6I8QugL1Rotl33D84OPU1KMMRv+C00/dY+/eLqIEncUsNxboDTcpRimBFyH2oOj3aLIfsRd835rwA9lo5cMm/9nPvheshxUlUiick1PQnLM4EyKVxZQV1kiUEFOXR5lTlZbC5XzpS19hVECewe1hg7Au8SWQhUzSHnF9lqLI8PEYlxXpvR5GxgSejzGGohIoPCajjNFoQr3W5uDR9/OAepN7a7sYphgAUxq0G1GVoAOfsOHz7RfO8ewvfZSfjyL+92/e4hc/9QDLDR+/JVGFRDV84qJknh2cLdE02HI+RQ4bwzt0R3exypLUNFIYlCdxmcKJ6bhaWElNOITwGZuCyk3Y8DTtyqeOYKHZwQmPuxslL94cc+V6Tn8rnd5ClaBQmnN7ghEVM3XJh06vo4zGpiUyFaB98mCMTkO8tmK5Dts3wCQKGTrKcYGnA4x1hC5A6Zyk5bHi5by3OU+3gtM/s8xrb+9Q9jJOzki+d73P0WaHB2tNZlOPl9e2MBPNzXdGRDOwaGZJxZBZ3+etV/fYfxJu7Uhuj/vMNiRm6LPQdKzfKsi9gFjHCOeY82fZltt0iBi5kkleccQL2DvWojp7j5rq8mBiOfZ3lnEDS1XkxCam2R5jw4RuUTDuV6QF+JFAa0G9UeGFBWE9pu1Pcwitmt6G0z6ESx5f/40RJ/YPmQsFSS1gJlQof3pLLawDDLiKuAxY3XU8pnO+5D2Or5pcPfc8P9xUXN0uUDqiMgNe6VYcTRz1JCGKQ8b5iNI4Aj+iMgX3djOGo4pWqw2TlGQ25s//8irtZshjpz/EOJVU4Zi48MjzDN85pNWUJfjtnE6zSVX4TKr+9EbsHNrzkEIR5IJxWZLZEqUUWhQ0ag4V+/z+l2/z1LsqCi34+tmAMy+/zsz+g3ziyUfYHu4wWOvTkGukF7f5oWOCf71zgKKbUogMVIzA49Sc5Mm2z2Frubc1ZmvLUs0qijpYqSidZqz9v+6y81d26nVD2RDkMmI5nlDFPTqe5u6NO3zk2Z/gta+c5+d/4ZcpzR46qnPx+io7ky7DDYE1Bb4+y6MnnsQrZ5FSstvrcu3aDfwg5s76PZqtGYybTCNWhCLPM/YGXeaWFyi3Mu7du8dMu4lAEcUJea44ePgI+w+f4hM//BnCMKbIxxxcWeDW6jVW9i8wGg0QDjqdOXq9HdJ0grEl2mhm2x1CP+LSuQu8+OLzdObaZLmh1Wqxsdtj0uvj6ekk5Stf/g0effBj3LMXefvGJv/sJ98mu7PKznqD9eGYKCjJo0NcveZ48HCfIw9tU0lIhoLUDNhY15x8/DBvne9RlJbZejIVhRcBvicQwmc0GqEEUyeeEAg9DTQ3wuGsw1oDUk0ja6zAWIutwPM8nKsAO9V0KQA3XS2q6eSL+7orodR0dXg/Ek0IgRDTCZax99eMwk63GPwHfZeUUwOM+v/otIQAdf8505Xf9DPOTZM7lJYoPc0OVUpSlg51ZJLZAAAgAElEQVRdlnhRzCQbsXJgnt2NCQKNZfobNXYqjRGAcQLN9Dkzs96UPk+Fw2CdIdA1/LrirVe3CRYE+/a1kMmYml+wfitDpDleNKQ2A8M0xZYeXtxg0KuozUiiaB92PED4GX45Rzq6yiB/mrnQcWatYvPuy/TKd/H5Dz3FVm+HSdXiwIOPsNg4BapHEmuyQHLtjW+x/MSPIKlxu9xiPL7DbLKPbuY41HmAwIUMJ12uXn+HRRNw/OgRjjaWOXHiQcJIEoUCayV5ZhkNBvguIIkaCBGwubHH8ZMHCcOY4WSNJx97D3v9HkcOHsKWcHB5iTCJuXjxIt3eHo16RBT7uDzF+Gu89Paf0u0K/uy565x64A+p81627o4J/R0CI0knE3wdEuQBg70cnIdLBqhJnZ3+HrXF8fetCT+QrsPf/+b/+sX3PHqE/njMIMvxhZ6iHMoKWxiszUlHQ8p0gCmHpOMh4/EQZQ1OWv7Nb/4OpZziFQzTMW5NBZRVCkDgRxhXgjUY6zBFjq8kUghmWi0EECcBvh8TBw20P8NkNODtC99Be4rDiw+wu76Oh6Bfjgn9GCGh1vQYZim1TpPf+/YtTi03ef+jDU4enqFTryHylDQvkFVFVvXxxADnJmhnqUc1bu5c4MroLSZlH+kMRSkI/QBPahwOxTT4SuopU8U4hac1s1GDfTKm4ydImmzvzfH2Oty+47PZrSiKMX44jXIQwufRQ4bAq9HqKD7xeBe56WETM9UTYZFGI0c+KhOIfMq2iZsVSTsgqASiMIS+wvcLaq2KxX1NsnTC2qpj9a7l/I0txHCGVjBmMfCZBIYPP7rIK6/scuBogyOPRURFyMJsjYNHHFHzEH//D27wU4/Psrdp8BcCTixH3FyH5Y6gv2tIJxYrYyZVQSv22TeroZ/S704IbMi+BHbXp/mH+x9VnP3LezQX6zRbMS/crLhyvkeeTajFAcWwh049hCzxrYfvJFHgqPmORGt8qQm1IPQ8ooYBUSEFCKe4ayJeeD6ntdzmwcSi/QotPDKrGGUFYWdq2zbO4auAzXsjSCV3Dz7CuBSENcn1vZA7eYPZlffi969invgML7x6hWE6pt1ss7O7h7EG4abOMWsLomSGvDBI2+FesUpZ7VBYRV44bt65xs7mLlFtgVAGOG3RwicMm8T+BD+6w2CUk4QWo8BNr+MUhcEaS5lZekWBCH20KSmNIVAJTubMLpTslX18r0c7zjl0JOJ40yMzt5n3K9piH6fOnuekb3jiaIO/7MX00xEiB/ISP3D83EHHI16Jryy7KVxYH1PmmkZNgzDcGErO5XU+95l/8h+F6/Ctcy9+8ejho2AlTkek/QIvinj5ldd44onTNPw67aTJ2BT4keCrX/5d+kVBlRd4UtHd3uXm7Wtcv3GDm2tr3L27zXBS4tAkSTzFBSiJ5/mApF5vsm9+iflOh3rgs7i8QpYXKE8SJyHaF+ztdRHC4lxFHPpEUUCc1HjzzFkajXmE9KnV29PcTd9ipAFlicMO25vbvPrKq9y8fYd7W12e+fAPYcoCJyry1BLFMXluqMjoDTVRXXF0/2FaseLiqub8nRV66ghR8HFY+RHa8Yg0nPCdC4KHG4+xPbhFMQ4JRYu5Y7Dbq9i8k5J4Ka4qES4GXyI8gRlkNFWCtYbcGpTv8JRFYjBy6oqFCmSFkiG2UrhKIHAoV6NIxwjtyJ3FSQg0CBUynmiisKAYS6IwJhNjvLqH9CUIsIXP3D7oDUpkOUdlmE7DpI8VOa4MMNYigwjl53jKx1QG5RmEAudbunsSLzQo6TDSTJ2DEqIgwGHvN3IW3w+oKkkYp/iBx9KpNrIs6G2N8T1JWtTQ8RgnFFWpSfyEcT9l38GSzkmfd17rEasZnC1BTOOzsiKnXku49lqP9XcyfDdHszlHYykg6GyiFlusv94nXvxRBmYPL9+lS8GxwNEVBwgf+6fI9TfoH/l7lOU1Ll1JKXiGi2c22OlfYfnIEjNLC6A6BHGThYV5qqJABYLClTzcPsmF1VVMlfPAoSX6/TGjScWZqxd49czzNJsxnYVFrt3a5J0rm0RhnceePM2BlSUCXyKdo0hLlNIkuo72HJ3FNqmruLF2g42tDWZmaqyurnHkyEmioI50gkB75FnKyr5liiylKnKGwz3SFFrtFTqL88TBHK/9yevcyWOu3Yl48sGCfXNnmQsEblRSThy1sgZjx053Qu4O4MiJy4LxXs4wLqkqePiZv2EcLSsFazspWmVEwmOclUS+wtoSKz2GIkVWIHWTKA4JQ4kb9mg06vyLX/lf0HFEYRy+c4SepFFXDEY9lAxRiun+WkokgiJPac3MUBlwQpIWFSrwMYUjqsd4XsRk0Gdr4wJeori7vUms7/Hj//n7+LX/4S949uMtPv6efbSDhP/x6xcYjASjYc6BTsK/e2mD/+LHj+OXglGWossMIy1W+FTjgpHLMVoQWJ/za29wqb9BMdZorUFUNBJBZSYUVqKlZlIaLIIZaiQxuEpgM4nLWnSzGrv9EU6HGJnhyAgaGTN6SNLwyVJL2lN0x4puYTnlKT6x0oM0wjQmqNQH1H29gcHFZmqzCRVWOJyetmF+BMFKjaJrMNs+w5uCq5tdRN0xtyCZ1QEzowaRGdAxNb519jY/8sML/Ng/ucev/pc+v/77N6nHCdKMUa0GS76md+kOX/xAzPNnt2ntT2i6lGvrHkqnrN6BUwdrbPQKfDdGipK29RneKbi7m7OEIg5KTrQ6zJx0HGoLWiWoOODvfuYIv/yvL/KFv3WA1XdG1PcX7FzNiXyfzKsoRhHaTaj5MaFWBJFGSENcM4ggoC9SJjsKP9ZENUF3J+WtYJ7BEUUWGNq7HgsrHofrJTuZ5vZIQF+TzqTU8JHdknajRuAFvE1EhCQfegR+n4XGftYqw+jAaaJyC200SwuHSJI6fgCma7GVISs8ZoTP5uZdQtFhV93BDwu0lcSBQ/s+opJUcsBg73V0TdOIG8hA4mqv4+caZxVCQWWmL2hLhjHl9McmDKHn41sfV1iM1HRaPk46hJE04oRGzSADSc33iP2QSVFS8z1C7ych2GaQ1OnNBlib4FESKJ9MVJRU6CqgjsR4gsoInFRsem02u1uoeJZ98x5xMGB7q/3XWHH+as/i4gLD0ZBWe4beXpdaPcIJQHh869vPsbi4iPY8RAzPv/gC71y/gfYCiqxAaEdWOd6+do2yLGnO1JhMMg4fP8n1azfZ3tmjUU/Q4XQCWI9isixj//791Ot1/Jkm27tdarUYay15kZJl0xgl6xxFURDHMVEUcebMGYIgYHGpjacDut0+WVZQq4X4nsL3G1w4e57XXn2DazdXieOY2U6LPBuzsrJCt9tFCs0711YJwxApJPn2XVrqfeRDQ1ZqsvExvvPSN/jsJ3+SQVJj96Wv8MkDF/lAZ8h7fyRib30fQz7KbOtlJqvbnDxS49v/TtGsFdSrGi5RWJNiKagy8BPBIB+gBFihMUZgXICzkiBOSfMM7U11VlVhcfb+5MpBWuQUOIRiilUQbko7dTmevO/s0+C8Cimhqgq09nGuQnsS4woePt3k9Re3SWr1qWi+MkQhTLolynMYxsSeh9JuysQTAt9XWNx01SgsVvw/+q3pyrKoKrz7wddC/Icw7HwM2Tgk7zuWTvbJjMfmWk4cbZMP4ymdIhqR5z6zB+scenwBvD6oHhljKlERBFDmFXHcYjjqMbMgKYsBzz93geIvYHFxnlpzH/l8weGnZvnjPy05fuRvsb3zh9y+N4f0d3n08QeZ1A5SLf99CutoLP0su299ncHt11g6tcDJzsPsP3CEuk6ozzmKvIAcGi1LOU7wx4vYefjW888hhOXKlUucfug0l99+m5dfe5OV5QX+8rmXuXLpDmfeukCt2aDbu4uwhtXVm3hiOrF85JFHp67MUGEnFpAUaY7v+7RmElpJk2ipTqsxw77FZdbWVomTOo2kwbmzFwiCAK01hw8fpsgtSRyxde4CW6lhpAr+9hMRm909fv/fNHnov91m7e6YZqzZ2a6o2xEmD7Em4fChDTa2BDduJ4y7MaPNLVYOnvi+NeEHcqL1b7/0G18sbUhWGoRfIpyhclBZSaAjlA3o5SPGqkSV7v6IvMnvfe0bjNw03dx3mlI6lIMqr/D9kEoIGmFEljvKMqXVTHDCo6wsUkqkUGilwUka9Vkmo4ws7zMpN1BIqirEVZbH3vUAa2fu8lM/UePzHzvKk0dmOLES0UwEL7w9JJGC1FmanscfvXSbBx+IaSiLzQy2dBSFYWSG7O5O+N4tw7c39rixu0lmS3w5vUEpPUVFSEKU9fGkpuVFNERIPvbYuKe4fDFnfU2z21f0shIjHaUbYFwXq1LQ6fSlrByiFAwNCBlwrw+/9LSk1y0h1WTbJWkaUOWWtFdgRhqZKca7MN6UFFsZcitieMsxum0Z3MopNiX9SUVZl8RzilArhqXCAPVZj710zMYArl92yLrjwIEGX/vGDr/0dw5x8cyAz//0HB0VEfo5b73T46gOefCgYLtv6TlDfSgwleOp0zPo3FJNEsIsxxESBBkzOD64UmMprqFyx+c/3ubDxxTCOO524VbP0r/T451teOm1Lg+f8nnzzT0OHwxohpL5ZsRcLJltaeaWFY0lj2hGIj2DLD2KyiGUw7iSMI4RsqIxm7C73Wfd1tm3tMh3GmPeHfs8sBJRWwk4OOfRiRxBpYilxK9b4thyb/EQZVlHhprNcZdur8LzarSaHoEYc2zpXbz6vbe4uLHJUr3BaNzHVALrJFp7ZEWG9AXRTEJfdvFdjA0UOhAIXeARMNOIOHVak8yA55XgT/BtQOkkORZtHKHvkZclprIIBL7nUwsCxl2DF0HoW0IvRKqSOFIEniadTJA2ohGGyMjgMsVsp8b5S5J6UxPVDrIVjnj11oCDquRqNcckHVCVjiJPCfyQZw5q4iJDBJrr4wm/81bOag9ujy01bZiUgheuDPkHP/Pf/Ecx0Xr59T//YpQkXLt+lTjwub26yrHjD3Do0EmEUly8dJGvfO3LvH35Em9fukwQJvfF1lNDAtonM46k2aKwFVll6Q1GpFnGwtwC7VaHdDzCVY48y9FKc/H8edrtNmVekZcjBoMelSlwDuI4JkkSarUac3Pz+J7P+fPniaKIo0ePYIoMKRzj0ZB+r8tMq8W1d65ijaMxV+PFl14nz0Hg8Zlnn+XE0RWyMiUIfHxfs7uzS2kqyryisoKoXuMDH/kwjVYLVfM4fvIYL774InIkmT39DH/07QnNhkGFH2EzT5kpSs7ePUrzvSd4/atXWZwbIq0j8aFSktGkQJPgC0FVKAY9H6cqlPbxGwWlVxDUHFm/xI+mUGmcxlaSopygtaHWCOj2c3QUYWSFsYKaV2EmUJWOOHRYF0JkcXGOUiD9COUppCpptQP2H5K02opBL8eaKWBaSkNeQFVKPN/iRQo/tARxQmkdfmhBGDzt0e+B8O19pANI7aGUneJxtMDzNEpLnDPTUG3jWLtVcvqTxymrx6mvOJaPlWA9hIooMkde5Xzwx0+wcDRiZzRCRxWv/PHj1Ds3SahTDduY2FLaEUHgkU0UpoxJojpR4JFnY7q9OmuDT5OWP8pG3uSF1y6APs7qvds88bF/yFf//SVee+FbbGZ99jcTzpy7zsadLqOdlM88+ykOLR+kpWcIOh7DPfDllG02zgM6tWWE2+R3/88/4NDJwxw4tJ+lhX2MRhP6o4w8sxgjiJMWRQ4Gx0d/6IN85kc/SS2I2bewyPLSEjOtGfIiZ5LlvHPjMvPziwz6I/K8ZL7TZrajOb7vCC889yJn3zzD25cusL52m2G3z3dfepmXv/cGm1vbWFsSB4qluTZvn32DP/6LF+juFRTt/dSXW3zhyYSfffYmo7GPG3qsXTdMcstCe5ZLFwa8/kbJ+k3L8eUDzDS3qIdjDj5wkPknNlg6/s/+Zk200nSXophltCPZ3dVIL8MXfZpNQbMREFpHqT2O6g5Bo8GgHHFpdZX1URdTVfgolKdpBCEjUyCspqgsgfLopyNwkIQJ/WE2TVTXIWVZEoWK0A/I85K97j2UMkzGEwpTESsPwgJPRZw5u85TTxzn7z7jU/NDMlViioxTsy2y8irS96CELNAstGucO1uyfzhhpA1JzUPXHGlRMM59rg136XcLwrYmEhrtTS2/xhhirakrTSJ8yrHixobg0q0Bm7tdXOlRFIYorOhYRwOJlZbCDBCBRbopFRghKIVmL4BaoPnUIcE//0IFfs69bYcaOIa7imDWoMVUl5anOdZIdOQzGed4VYz1cvJEQmCRDkRZEk4EpSwpA02QWKKqYHjLMRg6VrctP/3zLUw9oXdzm+VHJW+94vHq7T1+/h+u8Nt/cIeqFlDt5Pzcjx3mt17oMb9neOZAnT9fHdAX4CnDy69P8Pcqbm/todoxD7YNj7RjrmwaaEvu3nP81OcWKHYm/MGrW4hai099uMlvvbjO1q4ka4e4TPHSS0Nm98PaWo7wFfMJnDweMHdA4gU+w26BKSSNmgfzJUUUkI18SqcIwgw8ReEsT84bPlHr8pKdQWcx/3xg+EbDEVgJjZJmM6RpptrBQW7Zm4wQ8UHIh9jcY9YPae9vY2SBj0YETUS2zi/+4n/C//Rrf0JSaxEONumJkqqq0EohVEC7VadPn8CADceoSmOdoJQhMixYOSLJM0FlDTNBncDL6OcVfmFJRclMLaIwOVpYvPu6TaUF4+2CoDmlVlun8FUF+GgtqaqpE8rzKnqDgo5rIFuOV852+e53dmiGdVTYol17hBvdDd7Qjqg+Xc9nRQlCUVWW2MJW4dBlxdZQc+veiFB5rA9KKuPTDATdUfXXVm/+qk+RG8Z5j6Ko2NraIQwTAh0yKXssH1hmfeMOm9vbfPhjH+XSlcv0BgO62ZjZdofRaIInFYnvYbKCzBaEYUhRlmit2d7bYTweE0chWZaBcOR5ji1KXn/9TT79w5+k2ZgaeoQQlPeRBs45tFLkec54OCFNUx5++GF6vT3qySxlmVNLWmjtc2ftBnkxojWzn63eHrOzHUzVAzt9VqtRY7Fa4O69eyilOP7AEYyTnDt3gaTh0WhGmHyMzUfMJgnbm13e//gTzLQzbm6f470ff5LlhfewW6zzxta7iXDcvnqLn/pHG7x1TjNOY4QXsJv18BjR7CikctT9GbZubNGszdL3U+LKJ+1avBiUruGLEUoBbsp8s7bC9yXKl1TWgmEK4s0hkHYa3+PACA3SIJ3EqimywVrQcrp2CmMFSLTW5GmGHygG/ZQkqQMTitwi8DAYcAGlm+DyCs8PQGVICViJAIQocYJpZiHgAOMclTEoBVpN6z84rGD6jPwAFIdR0Q7W7bHySJtyVGcy8KYrXLtLXloaySLGrrM1Pk253aTTeR38u4S5plABaVURevfRE6ZAGEeY5PQ3b3Lp7nn2FkbsjXY49eARynFOvdNh0k05ePQhlJFkZofzZ+7QW99iY22Xrb1dvvKl3+Tpp5/m4JGnuHZrjcQ7SLdco+Y36DRWuLu1za31y9iq4OFjj+J7HpEf0Nvrs7vbY99yB8/zuHXrDkcPP8yR4w+wsdnn2pe/yXsee5TjRw5TWUdeVvSHQ3Z3d+nMzjAeD0niBmU+4s6dO8zPa7IsJQg8OrMz5ExNBgcOrvCtP/szRrlkMLrF7TurfPrTz3Dl7UsIB5/9/Ge5dW+XU83vsTy7RC3pc3sjI8vADqAqYsZpRdoxjMYQhQ26kwHv3FqjLh2hDui8e8TG+vfXaP1ANlpSSrSFqipxsqBQCikSBgNI6iFEmhPJAjIKuNPrcfnyPYbpLh4SF3hYBOk4ZVKC9AOsMZQK2o0W+d4ezbkOk94AHU7H6UpDFNfI04L+YBflaZwr7qe8Gygtk9LgIsXCbMLEd7x+eZ2f223wCx95L42ZESviHLlIEaWjdBpDQZjHCK/izuaY9coipKGTWOJ5yD2PK+WYsZzQPhCQ+AJPBojSEEvJ4cYC8/WQQEFaCG4x5rLtoYKcZl0zGFVIJynzEpc5gihB2hQjJVleUFbTf60zlqJwYCrazufnnplwZ8+wVAuZ75S4ECYFtGcc3W6FH3pEsyWIiCrNCWoB6bBEhB41zzEZl1gczvPQWqJ3PMytkqsbIw4fm+HQSZhkJY89HJLuCJ5+SPIv/6jiWDChlxfsm2vz1Vd3efzpZb7679eY6Izf+/MN1tZT+nXQTrJzY4K3HPN4J2S+oVie8/jyOKOcZDRnPE7PtXjzu3c4eaTBRz+3j6vXJ4zv9Ok8MIPbg1/77U0+97E53llzHPMgOl1y+Zphtuaxu5vyxEmf2WaCinJ2binKIqdzVDCzbEFHpKlgyQvpUzA0jiLypqvlPCXUJYVMeFpd4ZKXMA7m2O4lzHW2kZ6HclMytcQwkwg6SchzLiMJPCQJu6MxMirwRAzW0IrrfO3bL1HJfXSW9jMZZ4ShRxiGTMYZaVnheZIw8DAE6EBRZpZ226MWanTskFoiTU6FY4JFK4VNA+rCxwpLzUlsCi4dI0qLqRxxI0TFBV4Yo1sOY3MqBDYXpMMCGXjEYUAYGKqqpD4Tk/aHRLUWr7+5TawD3I0r5PU6vYVjfOiRE7x18RVW4lksGs9XVEWOtZahKEgnFcNc8NzqGCU9MmHxhOCNexmxlGTG/f9Uhb85Z3Z+gVE6Ik1H7GxscvTQEXZ2dkAU3Lx1jSwrCMMathKcOvkQl69cYZBPmGTplKsnJL2tLvVabRoubAqEdMRJSDoR5HmOUpIgDJlMJqR5Ri2MGI0nXLtxk0cff4DpK1zgeRKlFGEYkhdT592lG5c5ceIEo9EIrTXd3S5SCjqdNmG4yK3VKxw6uJ84CjgQ7mO2M8O1d26wuLCPWi2epgwMBtSShItvX+bYsWMMh2O2txe4fncXXYfCWqzx6G9e4+S+wywvhszaN/mnv/MGr6WC9Yc/zdGjD3Bn89tcWL3Njz32JN/56gWM1XQnfWLfZ2mxTVwbkvuWQS/i2rU9gsAi/S3C8EmyrfPMtGrMLM6wtbOFryQG0NqnrACbEicRKMiyqavbViXSOgKpGOaW3DWIGvOU7iaBEFgJ0gNbTJtTi5j+nXEYY0nieGrvv4+PUB54OiCXU2Cq0tNVZWHHOKfQDkI9dWvDfbCpmEJOpRUIKamEQdgpMkgqUPq+2F356GBEd+MNWvuv47q/gI3/FX1zi1byBfzYJy3/gqrsIGyPineI44i0OaRfLVFmn6Hp/zm1YpOMAM+LSdMJUkz5engFynkkgaZRDygrH1WGRJ4lFzuMKsfqzTWWTh1i9dZ5uvdS1nfHrJ1bJew0ETbj9Qu30EmHLI5p1GYIHMwtHqFh6qyv/gqVWmYy2eHYsQco8pzI87FFiSsrnjj9CKNijJKS2U6L1ZtdhqMUlGTQM2xvD2g2BoSRJgkDdnZ2qdVqLMwuMJmM2NvdwVpHmqacP3+H9z/5DFs7u4yLisWVZU4+dIpBf0RZODw/ZDgcYpzg5Zff4N2PnML3Y2YOLHJ48SCPPbBJfvm7jNc8VLOFq3qYAnzlY5Ugn+QcPdJmcCalLCKu7wrkaJY7q/f43CGP0br8vjXhB3J1+Ntf/tUvzs7Os9nvU+Up1XhEVVgqU9GeaTIeZGxlltfOXubm3RHdcU6ebtCfjLBMXSFL7dZ0LMlUkxVHCbONGVxpGKUTvMDHCagFyf3uV2GznLSqIAjJswmy0kR+wfLKfj7y1FFWbxe8+8QSN+/1ODS3jw+cWORPLlzlpx/KGXe32dwxfPmNHbSbEoQzSvwgYDctiIxHtLFHT8SUh06w23mSPbtHrZ6RiBq1NGG+pzllAx4JahydSWh5mtRmpE6yN9KYaszjh5s8dcznwGxE7C1zauU4KwvLaC0Bw7DIGI0cRQZlKchLQVFCmsckwvBDTyfUSsGNHcP6dsXanuH/5u5Nfy27zjO/31p73mc+5547TzXcqiJrIItDkRJFW7M12pZstbvbdmR3Owkc20HcQWygGx3oQ3fSA5AgHcAx2oatlmVblmzZGtwSNZriJLLIYpHFmqtuDXe+98xnz8Pa+XCUfLMDBAEi6/0PNrDxrnet93men5dlNChxb33iTMyUTjYq8PyCOExIiwIR6JCBk0tc38QYSzp7CTvjmNkjJocWGkwtS6Ses3+Qc+1myvVnu3xnqsERTVGvh/zMT53mX/+Hm5hVh91bY1656/OPnl7hXafqXNwP+PG1KTb3FR97d4uXL/X4L3/2KOe/t8vh1Rqa0nisbTPqxTw151KaqbD6oSN87UtXcF3Bsbe3Of83B1zaytCnU/a2YX0Y8/xrHivLGs9+b8xjD2pMzwny0GZzy2fpsIHTNjBbKQYm+xsh/liyeMRiZkYhpMUojDHRccpglQ2cskVWpBSaxkIj54Tlc1lNcSY8IDcFuCCMCcewMAETLnqLSGnTG21QrtZwjCp5mhFlEYqETlJi90pBYrvoZhmt6BF7iiAJsRwHleXUKiXs+oDZBUVjGuxaQqmW41oGYaBA6mxt+ty8kfP6xQ5vXg24eivjzl2Hu3sVukEF11rgXWdW+OBDczw8X8XJE7aHY7qdDEMDWwfbLFMYGkYxYaSVSwa6kSB0SZakZJ7Dyvws87MugW4w6AVYboPydI3NrfvYhktWKPwgIM8CpDKoOS2+enmL87s5L9wCYWog5cQxJgS5mDi+/vtf+Zc/EqvDm3df/9T0TJuja4eYnprCMi2GoyFhOmI4HHPtyk0WZpbwgwgh4OwjZ3jm68/QrDcIfZ9SqUypXCGIIyhyUAVCQp7nE6fZD2ZS27Yneh9NIhDUalWkZnDs6BE0qWOZE0C9EIJer0eW51QqVfIsp91uo9Qka0llGQsLs5TLJYLA4/7GfXZ392lPzRKNR1Rrde7cvcNjjz9Ku93EMCT9wRjfD3+b5k0AACAASURBVBiO+rRnpqGAY8ePYwU6//Ajv8LCTIt60+B/+/TvcfHuK9zY6XF/f5rrGzdp1BUvv/5NRlsJthWQ2pKff9cebz/ZJz48TWM5YLmRENzxCNyMQT/H2xOUdJfA1kmck1j2L9Nqf4UTJ2rEWcrA8yAvKISJyiVpkqNpGYYpyJUgjhQKDWQ2gaqj0Y8UpfojOI1DZNk9bAW5yDFdnSzTQBPYtkGeh5Qcm/a0SRInDDo6lpOSJjnlqiAYCwqloZspQk+pVqZYWjPwwhDLdIgCRZHrjHoa0pqI4DUNhNQQ2iSiQoqJg1CKAt2c5GtF5Diyxp0rcORkjUB8D5EpSq5FFMUkiT8ZBIsYW3cwZBXiDs++fIZx0kUVBf7+Cnn5Kq7uk+chrlNF10skiSSLIfWXKVWhevgJ5hdP0r2/Ts2p4Hsuo9EW9zavcfn6c5w++y6yuMKuf4ON+z1a7TpR4LM4d4zdgx5xOuJ0w4AE7l65yZXv/Cm/9FMv4u1Mk2aLFG6d9vQMhq6xs7VFEsdololSBVkWc/LkA3ztr7+J50UkcUaS5ex3ekgpcZ0StuNQqVao12sMuj3eeusiR44eYmNjA98P6HR7DAchz7/0KuMww3JKVKt1Lr1xlTTKiTKF7bpo+uRV96knnuTVV14n1EN6+ymV+X/IwuF95lfuEMeKfKRjSYVKdTa2fVbnXcLxmLwwuLKlOH85ZDsaY0+3OP/CATevG/z0f/O3968fykHrd77w7z9lGS5O7qAQSGHi2A4oyf5BwKDrEYYJ0TiDLMIbdqhUC/w4Jvcmg4GKU3IFqcoxTYt6rcbm3bukukBoGhXbQWY5g9DDlQZJlpGkCTONGsQhlikI45xHzj7GmSWbemmGsf9dXri2Sclq0ffGvHa7y5/9Ro1hd4s8kHi7OX916WASjkeBkSvSOKZUqXA3SFm3dU5/+KOYc8fBDOlvv4V3Kyd+ZYh2yyffi6n4Ei2SqETQHUUEhYRCcXiuzlMPLvD4Uo212RkW6ouU20u0G4v4eYiXjNke9xiOPDRhk6U6KtPJUwORG2iFYG02ZSeoMgxAS0JsZ9LAdc2kG4SsrpRxDEF/OyL0JGIcUZc2rqZTkGCkkqjIGCgdbVpRq5u0KjaNBY1CJNxd9xl1YXeUULEKKoslDp7f4OmfcHjxecHc0YTDy03M/ZT504Ll+SoLCxr/8bN7rE0JxqnGZtJnJha89+lVvvvSLnqg8+03x/zqOx2eWK0hqi5BJeKhp5c5/9nXOHqiwfev9rnyWohfqlObMxjdDbgyhr2BYv4kjDYLPvAeh+9+P2JteYpWLcapw7gv2bod0Z6zMNsSUTVx64rODtzZDNFLGo2aSRBA6CuKSBGnKYYBtisphCLKU4xin6XFGiLW0cfphF5Lhkg0yOA6R0HZKBNsrUQSpqRpAgW0yy1e/9YtolKLqm3RKLVRRY+9g10s2yH2QpI05NgDM1TaAclQ587tnPMvRbz83ZiXnhty+dKIK2+N2L2f0ztIyAKNPJloPDSpIXRFgUQ3DOabGou1nPlmzIk5jScOlzk9Veb+VsLubkzUDbCkjl4WCGPyzSVXI0sVcWywOjXP6lKdZrNEreFiOxpBPMbUqtRrDeJQEaY+YRigEg1NKIZ+wXfu+3SHBkpMQhZNfcKBk7oETUdqGr/5y3+7xuHvU928e/lTg26X/n4HW6+zML/KoSPLKMtlY/sW3f2U11+7yqkza5QqClOf4ZMff5qFZpWZhkN//4B9b58g1LAMG8MogWFhVAp0o4ZQMUpNgPPheEzNdZAU9Ppdas06jZLGlctX2d7a5Nr1K9y/f4/ZuRWScZcXn32W5aUl8jRlPBwipaBU0TnoHJCkGcPRmExFCKkxMzvLZ/74M5gi5mPv/wQaPtOtGc6/eZG8MEjSjHq9gWUIVpdmOLTUxl7I+dyX/w9eevO7nL/8EqYpiMIUTYO98TZJJtjb99B1l7gIWV45xc8+0OLJx9/A02tsvHFAsBdSSx9kT44IApM0T8hlAmaVrPav8KunSI2I9z55idAQXDufoIsQtDKCnPEomACeLUkUpaRJRqE0SthERYQhwBQmYup92Gd/HRl1iNMXKYU6mZlguhU0XZInBeWyhR+GLC23MZwhG3d0LMdl7QGXMM0wNehsJlB1cLMM4RaAQncKnnr/AnubHUhhnH8co93GOrhNzzBoaVVy3SOxNESa45SrRJkiiHOaFQOR5dgOSDPBsgwuv7zFcD+mNpuju1Ok+h3yWFI1H4DkPsPhVWxVY78bcu3eGQzPptk4TG25iXPkA5izv8YoNXHi8xRqjBIRQlNIlTBKeySeya2rI6rz07z4/StoVp8Mya37uywtn+BvvvMMw3CTaBSyunSSnd1t8mTEr3z8ozx64hRSVfjcM99mZe0wxx44zMW797nefzdR7STu7BLVeonAK7j01lvUp6qEsY9lVEhUilICCkmW73D0aJvFpWlsw6LbH3HvYIPLNy7TPzjg+We+x9a9XcZRxMNLLVSes1JbRIkSQX/Ei5euIQ0T27EZR3DhjSsoBYYuEVqFJEpQShCmKefOnaYQCevDHc6d+jAffOgbyME69650GW9JsnHOzoHitYuCpx52EKOMvQHciWz2QwutolGfs6m1B/z2b8zytieHtE//7a7DH8pB67N/9jufyoucmdVlBnv3CdI+aRjjGi4qjiiKAkOYJGrMoH9AtNeh3DToeAFSSUqOzWgwRuoSq+RQK0z2eh2ccgldSKarDfaGPYQU2IZFoReUTBchTIZBQJCkaNLkl37mx5h2dXKjTKXaZrjXIRJzKBIsBTNzZQ5rgvRgj6ibsbWR8b27PmgKUWjopkEap2gZlOs63XHE69fe5G1nTrP52qvc+spVOq8PGB7kJJkkTxXuEIqhYnyQ0hulFIVgZdZhZUan2SxTrVYQpkEmGozUFKPxiN3RPtfu3eR+r4NeWJMmDBRCQxM6QmpEqaTQoFWDQ21JvTlDxVogHbho0ufoDJQrBcNBiKUJXEuiO4JhIUhVgmG6BHpMq+nQbAn0LMOpGtilnJ37BSQ5eZHhK6g5Fp1hRrcz5OzRGVotOPzwNH/4h9usPehw6Q2PEyslvvjMAQ3d4vQRi4O+z3vefZzBVpeHP/YYWzfHXL6yzcpymfc/YWNqkus9yeOPOeQbYy6/tEe2NMfObod3nT3En7/c57WNkMFoxMoRh6U1i0qjhOaNac853F6PeHhWYpoJDhWm2zq6ZVCq5cSpYutGiq7H1CoNlpZSZpqSgx70hwFHVx0ypZBljUKZeL0cbz8lHRSIHA5i2N2PObHkTlYPEwsruaUjZcC17Cih6lIVUwjdJM4DMpFg2haeZtDZ2qbj+WhFTsnIkHJiZY78EF0rUUhBoab42n++x+2rMdvbGXlUINMcS7MxDYXCRGgmurRJk4lrSUqByi2USjCFYKoiOTwNR9pQL8UYRUAUwF4QcNBJiX3Jek+nSHP2NhP29nN8mTH2BINIsLebc3b1MM1WFdctYxkTR2WcxnijjFqzQRTGhGlIHBcoYjRhoTSX/qiLWUiUpqFrE/TJBHMiKESKkIrf/OSPhhh+f7D+qcOHlym7LmvHjjAYDxn7Q26s36Tq1NGMEWmiEEVMyZ2m3tKZa1a5ceMmV69fxXGrTC/ME3iKds1meWEB3dBIs4jpqSXyLCP5ATJG13WSJMFxSwih0e+PcCzJoD9kv9shTVNOnz6Nygu+9czXCcOIEw88iKZJbNtBahqjkQdo2JZLlqUc7O/jOi5pqnj5tbdo1socO7ZCf3CLrY19pJ7RqLaJQw/dEKweXmFj5z7fe/F77O3vEATBxIWoCVrNFgCOY+OULCzLQdc00jQClbMwt8yhIz0WKx3qGmxfDynVS4TZDv1cYXgJBqCXzxGXf5GhE2MJh6xocWrl6xxseni7EqElZJqOoQnyPAc0lMoQRYGmmSgl6WUBDbsETsrufkbrqX9G0U+oalX84YtYeQIaKKrkGRRygiOyLFhYdNB16HcSnJJOo51TqVqESjK4l6C7BUJllGot4mzMo++YRjdCdu7mxKHgxuURD77jfdy58BxLc9PEdNBVhdCWuBQUIkLKycChqZxKySBMdIq8iR8Osd2U8TCms2Fy9HiMbbnomsuwv4/MQaYlTGCv4/DWnSUKFbFyfJVKs4yQdcr6Perlx9na30XQIQtSSnYDRQKmw/2tCuX6Kb758l+T5Ao/GmJqNUy9QpIoTp95mKnmDP1eQLlUxvO6eN6AX/nFX+To2hpv3bzO3OIiJ46f4PLlK1iGxdTsPLVmG123EKLANEp0ewdkWYLUBLmSxKmHa5UQucHGvW1cu8zp0w/hOja1Rh3DMsmThIcePM2F77/GaOyzv9+duGINC5VnFHnMOBiSazqrK8tUKmX2e31Cz/9BlIlNfzigUi5PdMsSDE2yt71DVcaUtBJj7yhbexallo9mdhF9iaaZdA4ErUZIERXc30+510nIyimRLGhML7LfzTn/UoftnQY//vHf/nsmhq9XKAYdkCHulIvIM0bDAcHIwzJsoiSknOqoxMD2CiJhUGgG0jKwBFRLFqOeQBSQRiGRyCmXXQxNA6mx5w2wpEGmFJoscE2X7t4erZkGTz3yAKvzcxRpQmd/F9Ot0+vt8Uef/12izGV67gEkgneeexDbDLkRzWJNrWJ941voYcqSo7ORmQiZkOYJQtcYhxFW4kyaYpjxb//w91i776NijUAqXKXjJTGubhCmBf0oIzwIiGyDQ1ZEs1KjYpkYmiBOEg48wVsHY27e67Ez6PD9m1cYdAJ0QyfMcwy0CfBaFWRJRpwoVKxxL0ixNZu1ekbTAFFNsUoavcEMX7gR8zeXBow0lz1VEJY83nb6CVasAU3/Lp84ldMWLmkWcTDOKTkOfs/HcEpkTsDWdozMXIbjiCzOmVk1cDompdWCr35xxNknCn7u10/wkJlxeL5g3LdZbJfxByFzT5ZYvwAbdzbZGFZ54Y/f5LEnpwkijWv3Ik4fr/Cn3+/wyXfO8eye5CfPznMlvscf/fU9yqLgpRubtGYzPvy+eURvzOc/P+LRs5I31wPe94TDzuaYU0cc/EKBMKjqQ7b3qrSqglILUhvqZQ3NEtzfHHPpouLwSUG7LekObV6+0WOpUaVpKLZEhDUFIpVoYiIaX1UZxDFfeEMRzml80s1hLNBcSWGX8VKNqdICozgkigbkosDQXTRlons2Hzra5fefrWFWTdaOL3D9xg2yLMJySwyHQ6JIx4/72I5OPlZkWQwS9NwkJ0PXTAwpKIAiC9GlgMKiUCY6BXmskxop4SjCzB3KSmGqFEMUCBGT5DAKEoZegTd22TsoKFkOjVpEvKPo55JYT7HSnL7vY4QOSkqUdLEqDtNGg539LuPxCKtkUYwKUpVCIciyjMyWSK1CYuaU88nAH6bFROeiFFI3/u912I9CSaFYX79Fs1Vn/d5teoM+0oBmuYzKBcG4w5kzixRZhcX5JQq1T1I0yClACqIo4nBzlfvaiEF3lxOHVxCFSZJqjPtjPvYzH+cbz3wXbzwkCSMCzydNoFAaeaZ449IVHnv0HG0pmZltMzU1zR99+j/RqNU5deoUpdLkv6rWTcrlMv3emFLJ/EF6ukWvN2B19TC97ohHnzyHK8dcu3WBONpjdxuq9Snu969z4uSDHFk7wqf/7DPMLM7gq4iqcJiZmZlchk2NZqOJpmk0m3UG432EZtAyG2RJiOd5VColStEuvh9S2HWm2hYidwi8GFUotPIsifVhMuc9ZPYYmY8wjFlU1AOVsHsvI8vKZEWOFIJMTyeuPQpQxYQmojkUeY4tFX7kQ2ihqhnBSNJaXKD76ncochPNCEmlQsoMDTk5lLWEZnMKaYxJEhvb0XBcSEVMs2mxPpghT3yMLEOzJL6nMGrgzuQkQw8hJJZpkcb3yPycxsoJCLfI7YJEGBgqR9MlSmQYZoFQDr7vUSpLdCPhysWUlZMZCANHlsjHBZe/pVg84eDOjWjMpHhbVZKhSV66QRQ/xSsXL/LUo08gXIOD/ojF+Vk++4d/jF7L+JV/9K9pagGdjT/h1o0/xx+F6BWLx598F9u9edYeOEajegjPG+EWBugmV65f4YHjDxJFHkKZqCIijhscWZ2jPTvHN771HS5ffZMPfORjXL1+m0MrR+n3u5w8dZbByMMPIxzbpD8Ys7CwgO+P2N3bpD1VQSsEQRCwPL/A6ROP0WxVWJ5bYL49w3PnLzBLjXbJxVAghEEQZfgHG7ygUqS0WXxsBr3IuXLXY3VlDqUmmKPAG3B07RAl02YwGJDEEalpoFRBqVylczDk0uvXMewpNl/9Kv/gf0kZjX36e2AFM0TBBHtU5AVBVJCGMByDH4BWdnGdnDQfMbdch0Sjm6V/Z0/4oRy0hgc7VOuziDSlyDK8IMCwNcpTFmWrghZPo7ycMPHZ8g5QKmZ7L6LVAOwKRTR5LciLhAeW1ri5d8BsyWCjM8SuV7ANG+KMimNDFvP0I6eoV12yZEhhSga9EaIqKQwT2wz49gtfJQtd4jTgZ3/iOIWvUSQC2y3IsxGtbz3H5uaIGAvdspFmjq1M0iBB13WyPMHrBbTnKnhIitCnExjEvkIJDV9GzFdMDFfHCxVDPyLJFJBy4vFjuGUDNIMojOkECa9uwPM3huzvjrl1dxfPi8gQkE6I8npRoDQPs7CRqiBXikgJ0izn0s0hDyw0mK2m1JMRpjAxdcjihMNzBttRQhAXLEyt8FRzxIebezTsnEGgCPSYzKoQB7tofQerqtHr5BjKoDYHezs5xx8wuXUno7epOHlsmmf+eodqS/LgJ0p8+l/e4FoE3cLgxv6Y9x3P+cZGiYNnu7z77Qt85VtbnHmswpH5Cv/pc+u8+9wM1wYRn/tOn/1MxxOSN7++y2+fH/G53zrEP/45yb/6nXX+8ZM2N96CJU3wxZspCyddDkYpP3ZY49lvBfz0e22SPKesSw4vFIx8m1LVx2pYFErg5iZ6PUMJG0eLaB02yIOMN9YjqhVBu2XRGcHdrs/ZtRrP3c1plh02/IJhbLJYX2Zsa1zLLhB0IvzuNGvRFu35Fg+sSRx7miwbEsQZwnSx1JhhGlC2CyJTodfPsXikyzDzuXb7DoZuITQblWTkuYZpaGhKx9B0lBkjYlChQBmCHIVKfpANJHRyJZETmxOCDKSGkRfEQchwBP2RwyhJmZKQZxaFyMmzhE5QMMxdVB6BZhL6GieW27SrBi/du4ubZGSZTW+cYZcNksIgFwWSnCRJwCjIVIbUrckhi0WuTVxqQuUYeoKSEs1WBH5GHGfIXCO2dKraZCD7UalOp0MQeGiaRpxmHD58mE6/Q6NcZ29/h0NLj/GVr32aX/+v/g39/hYbmx4PHrMp4AfCdZfRcMjS/AIvPfcGRw91qFXqDJTgxuUrvDFbQqmMMIowdJO5+RYiV5TLZSzLwvcPGA6HLC0tQQ5vvv4mU60qtlVjdnaeIPQoVVyq1SqdTgeJxLYsLBsuXHiV0A8IPJ/VlSWWrENcvvQX2OajHOwFNGd0phvnOH62Sqff4ZXXX6Y13aDbO6DeqFIkk8TyIAgwEompT3i0pmlSKrtEQUGlUkEXgjvr62RJwn4y4olqm7uXe8weX2b9zh3SvZCKBK/1C3TULN3RHUQvYmnKxNSGyNQmLjTSIkGvRKThHJI9srRGrnIoCjQ5MVYhMhAZzZJJ0DVJ8oKZo/8dp1YX+b3zf8bp9BIq7+AngsIG2xghVZkk02jPWEw1KwgRonJJtW4h0MkVCFMnFu/Adg6wi4JxPkZzQlaPlUgDn0JIhCawLItmO2T3wkvUV5bZu34NXYewImiEAUqT6JZA12xyqQgT6PcyGg3F/GwZ3RwisRCpjdJ3GY5g8xt9rHqJ0lzCE4/XwZ0nkB7r52Nurm/xG7/6Lva9MZ/93B+x0K5y6f4NHn3gbeyMLvLC+n3OHv0gaw+fYTR4mZ43wqwtcu/KPZRSuI7kyOEHmanVabZn2fuD+9zfuMnasUM0+jZemPGRj3yYIhf8/mc+i9AkTz79FHt7O8zMzDD2fY6eOM61mzeoVOvYts3BwR7zc4uYls7t2zeplBukeYouNUzH5MLrL/DUE28nS2J0TdLr9ilUzFyrgYHJwtwyYy9ASUm17FDYFd586yamqTj3wBFK5TpJOCJJC9xylZPH13j00cd54403EbqG607y5goB/sDnxOFFGs0au1pM0g+Q2WHU4AJlDbqjMXkkCKMMlE4mbQplYFoZ5YqDKizm52p0hgfgwdiP/x8vij+Uq8N/99l/96nWbBtXlPCiEGRGqznNTHOOudYCNaeFVhgEo5Cd7S4DBY6hWDo6x3h8QH9/jHRgcXaeqzducfTIGvfu36ZSLlEkGbayWZme5id/4p0sz7XR9BxEQZDkSKWjZEzZNkmjLi9cfAld01hcyIkTl+n5I1y4dJnSlIvjuGRxhnXqHMlSg4PXbvNmocjikFyCKhTSMMjTjExlVF2TLIcMjSSD9CAijAu8TIKp0HNQcYGXZvRGEU9/cIETZ+tUmw6hiuhHFvf7Ji9eDrh8dcDm1j6d0YAs1yErEIVAIpBCIZRAJQnk+URPm2akSU5eCKI44cGlnLJhUS7Z7Ozs8ejphLcvKZ5eTfmnDxt8YLZLNRmSKp1uB+ZXY1JNcv2eR7PqYmQpcSaQUmMvjjBlTq5SOvdtJHDikM3XvndAs2Xzof9injf+pw3OvbdO4iek5YLTZyxevRrwW7+6yCvXenRzycK0zVQ14a++3+e//flDfPpr25w+rjNbaxB6Ps88N+LRMzVGQYbulvnDv7jNz3+kzBdeGFKuVbl8yyMep6ytCOJQsRdFPHrcxXYtpuoT63QqJIsrJt44QekKd84hkzEyzTBEgVnRQBYImWLZNmkBd+4kSENimjo3NzLOLid8eafF2uqTeMIlqc3w6vZ5wizn9WsJ37o25ueOxvRHEb3T/4LB6DKDdIRtO6SkmLqDHeuUrRbaoMsgjknMDKFaVMsOSZwwGuyRZII0ydG1KodWauh2hj9OSRMFUkMVOSIvJhBd8QP7uBRomkTTDQzLROoakKNbEl1GrM1mnF51sW2BbloMopTOuGB9JyZNJc16hamagVAxb39kkaVZi59++3HeWN/CsCHwJRW3TpZ6jPyAA7/PaDzECwc4wiWVZQLvAM+PJgHDSYZrumRpiioKpMpJgoIo1BFagamBlgsMQ/Hrv/gvfiRWh5euvfgpxy0DktdefQV/NEKmgq8/85d0dnWCOOPjH/9F/vTP/g2n1t7G2tIDKDNge3uHJIkJvITZpQVajQXOPv4oTtlma+MeM+0mK/NHaLkQRAnx2Me1bAa9PpWyw+ryAprMOPPASdpTVfIsoerW6Xd66EbIg6ffhmGZtFpV7t69w4XXXueF51/GLTl8+St/znf+5hsMhiN8PyFXMddvXSQcJTxy5mlarWPkmuLUo49yff8C4yxgb9Ch2qgx6veYn25TRCn1ZgPHcTh06BCtVhNvOKLkulAodMPm0OIRQi9gcWGesw8/TJJkHG15mMkdjp5+kq88d46u/iGG8t10mm8n012yUOFo0GhlBEmNYdShEyuK7efQtsMJVqxZwp75EiL4HkmST5x1moIiR5BRyIzeTopx4peovO23WJx9gj/58q/x2P3nWF38EFpsksgNbAeEnIjXnXKZc0/NkqoeKmljWZLhaESpVMOqjtkaPcD98c+Rjp+jrPexlwRPv3+F+qyFKBbRdUUwiujtjYk6giy/y9aOx3Q9JWYGJ9mnNWvilCH0DQzDIUnG5AjixCUbCGpTY2KRT9A/ckQUGyQOlDDRkhQinWh3h//9dzf45//rEldvHxA7Js8/9y0urb+Fn+hEwwDDFAyjEa9fuM7WnU0yLeJKd4+bd2YQ1WnuHNxjFIxZW3qYNCjQMgslbD7/ha/R6Xjs7HR44+I1jh97mINuH4HB8aOnGIQpe70hq4fWaFYtkIresMfO3i5jf8TVy5e4fvUtbl6+whf+/LO89MLz7O8dUK3U0Qxwpcudu9cZ+5t0+vcJAh8hdKIgQdN1Xn7+Fb793ef4/Je/zPT8HKsrS9TbOu2lFba397l+4x43b+3wyBPvYOP2RaRuc3TtQU6vHUZFHq7tsLW7i8gltm2QqQQlMo6trTIz3UY7kJiyIJ3+MC/fXSTXz2CkNbR4RJIkRHGN7niMN8jQTItxOGQocnIZkqmEME6RaAyHY/7BJ/+eJcPLRNDt7dEQOqk+xJYujl2m7NSpuk3QJbmnEOhkaYEuNayGQxZEWI5Bmo5xpKQ9N8OuN6K7vY7mTDOOfRpmmSceWubBEycZej0ymaCTEiUFSZFTd8poJZNb65fY3b1BUU6xpCKQDo1WwvlXv4mr16nqBb4vyKsVtqIxu3/wFUR1lTTaR2kTjRe6JM9zlII8h7DIUEpiSg3V1NE2I/IE0kwx9gUjbZJ/ZRQFwhTohobAoBCCMAIvlRwMFGkBRTZCI8ZEkqY5qhATzYuWoAudXGiIXEEBuZKkCoSY5NH0RjlBZJOR0O3nRGmNZ56JeOTJgPmawZubEYm0aZoBSV4wPV8ho8LdDQ/XsbBME6PQGI0GdIOQuuGQ2BrhVkC7KqlMaVy8MCRNXE6dUGxudjjznlmylqJartEa9thaj5ie0jn/is9oX7FQFlxLxtTbJcKR4K27Ax4/V8EuWQxGAdNrs2x2NuhHik/8dIWvP3vAP/1YlVC12ByOmTV7TC3VWZnVqKmEfZFgCZ1k5DDQPOpOTr1RIixg2CtoL1j0D3T298bU2hZJICgbOrmfMyzAdXT2BjmGKJhZLDHYDVGFoNYouLhu8LY5yXYqyBt11u/fo2JD4Jng+bh6xGjlg1RmjnHX72JYU5TzlHE8RlcFsS4wzQFGEVGyQ8LCi8iv8gAAIABJREFUwUkURVVNbrpjA9NwGQcDTGPy0lEq28zqNaKwIE0GpJGa3LbiYgKXLX7AUcsLpGEg9Ymr7/9ypUktpepoTNUsTF1DSkVeqIkmpSiwbJ1SplMuWziOgWtImnWdRkljtubwU+87w2e+dAGCA0bBMtUyRKHH/thDZQk5ORVVQZTdH/zziqIoyPMCyzCxTYM0yBBCkZGDlmFpNkmUk5khmD+Urej/VSlS/CCnXq3xtice5ZWXX6ZVbnD8xCNUyvP46Zg7d7aYnp7m6tXX+KkPfoxBOkQy0RfZ7kRmcOzYMdZvXWB1aZbbN6/hJTmXL17mfe8+Sxpscf2tN7DsMtOzCwwHXbY2c6q1EscOHeKvv/bnnD//OodWTvDRj/4U333+GlEc4rgmSRJx4cIFDN3mypXrFFLDdFze8/YnKZWqaKKMH3bwfIf3vu+jfOUrf0K/mzK/UsLYUizOzjL2PaQq2NvZQQjB1s4emhCYJWcSi6AUpmnSarWQUlKuuGimS687ol5tUSuXGY0GrK6uYmXzbBys8MLWIUSjjMLDEDpp7iCyFOVEVIwqdXmMfWOdxGxTlmN2x7OUzC6xfIowfg/m/JA0T0HkCJGCAikNBAKlIppLH6Nfe5jZ8jHOv/67HLcSikRw595/xDUrmGYFEpdCxRi6y+yCAKFh2jnDYYIiI0tMyjVFUVQZB4+jtJzcPE2WbVEt19AtnXG0T8VpkJNj2jpRlIMjsHNwCo3tcZ1SK0L3Ic51VpZthoMeFBZC6tiuRhInCMdkEKdMT08x6ngUpJOXmdTH9yOmphr0M4kxVJw82+D8sISQFnUzp5wLBklCqdwmH+1TNqtk0sRLdimMnLE/g+5UWD4uGY53qJVLtI8uUXZnWD7UYHd7zF9+6Yt89Cd/ljv3blEq2/jeiFq1ySPnnkKkKc+/+ALlZp2n3vkOTh8/yaVXnyeNJ4HIhT6hmdQqFbYGQwqVcu6xh9CkSRjD9as3OHlmDb1aIQljSm6dcnmOkZ/hhwmu67B+6T7ff+UCRqlMeWqKucVpRp0ufjbm4QWdUqNB4TTYHPbZ3dvEMkwajQb7uwc4rRoOBfvbGzSrdSqLLqPRiDv3A5qNWcajiCLOME9XyTcPqDND3xyyPriMKPnMlRJModD6GYVukAcFep5i6tC2LPJEUBQlgqHA90IMo/x39oQfyu4myAjDEENlTJVaFNKibLdo1eZplquQCoKBR5pBFOdoJdClouyU0V2Tm7rHanuZsNPn6bXjKNvALLUokio1M0crDzkYrGNYOlGQYRkGOSG6bnD+9efY2FlnqrKGH1o8srBCrTrgwaUygzjgL1/fZZD2+ZNnv8o/+cDPsPDCF/G2oF922cm6jCiQWORBglIKTTOw9Am4Wkslhi4JBWh+giknOSxmVpANYTuNkTWDhq1RLQm8jk9np4dySgzCjNs9jzs7OsPuLq4zEfFZwmB/HBPGKY7rYpo6hUpJo4Q0mQTv5alOXEikpiOKhFGYc303RmWSeVdnb3tEZ1/yhc8IdsYp1imbf/bRgDWrhj6VcOPKkDgywRoTJzaagPFIkEnJ9EyV++t7nKq3mD/iEIwShl2Fic0HflIjSywq2wJxzmb0xpilUxr+axnv//kF/vlv3Was+pxe0dj3PU40NL7//YRf+4UWf/DNLhv7EZ943OU/PDPkF54SHD/kMFQewWCVH3uiz+9+fps89/mff7nF/Vs6zQWP9as+i2tTzEQKt6oRSg9zVCEzEtI0p96qMA66ZN0yzeWMwRDGXQPhBPQCg1xlLLbKXLkRURgpuaWTRhmlBuiOYLSryLyM2NtiYyoiCCIWp020CgQljYX3TfGl/9xHLZ3m6t4ATQ/IrA5pPgGK+0aMUeTEcYP3uFfoSp39eAlTr9Kc1hHCIejtk6oYwzDIY/CSHo3KCRrTNq7r0Gq4bG8O8b2MfscnS0EIHSgQho40NQxXRzcngPQ8BlNmHJ5zmG46WIaBaRYocqQAioxmxcC2DJo1SaNpcXy1ztKMQ6tSxqw5nLOnsT72dn7/iy/y1o2LPHjkOCobcrDRY3PPRwiN+tE6zYUUw3AQIgGRo2sWoigwNA1NSlzDIskFelbQaksenNG4eDnhQw8u/P/ddv4/K8/vszC/yp31W3z1y3+BhuDI4hHGI4lV3scwa7SmbQz5Ni68/HVu3zxBec7CdV1s26bTCZidncW0LWqOxrFDC9xYXeKgO6LvxVx86zprq/Oce+QsN+/cp9/d5/ChhzBMKLkme1ubHOzvUCm5dDodLl68yNzsNGEY0m43WL83wen8k1/+r7n05m9y6vQZSmWTer2K72XMzqwijWmi5ICvfPuvuLe3TqVUZej1qLgtNGmgSXANi1LdJnBcDMtGSolt61QqFRzHIUkiVlZWGI/HqCIjCBKm23NkaUQaZziOw3A45J4jWDUfwm1OEcYddGljaNtM22cYRxuIwAbbIK0PqappammObkqG5kdI5JN4zjsYmDuwc4GaipFahi4ERQJCTbS4vg/qkadpRDpF9Cp3B10W726g5gRNHUh1NBlR6AfEhcLUc2rNCkmSU21W8XuQZwJNuhh2iMhm6XaO8trNVxA3Be9/WIBwyXINxzVJ1RjTUEgJaQq+lTHlw14SsymnOMxNKpUq41FEq+2imz2KIkaTNroBfjzGKAsKLebutSnml7rohUXgaZh2hlO26IxGpNUyaZYh9DHVUplR0UXDwqKMIwv6vsJ1fIZRhK0LhDIYxi63d4b82KOrSHKma4tIYZElJuasz82tK9y6sU+YDHnplWd59NGzqCLCdit4Xo/RUJH7PrVahYVDi+QKkiTh5RdfZnZ+gfbCHELXuHTpEqams7y8QjTq89SPP87Xv/YtvGHEaBSweX+DE+88hu+HPHbqHCNPMjvrMhqPKETK3TubVGtNGvNzVNpN5lp1FttNAqFx9/YtImVQMupkRkyWBBiahjcOKAyNsczRyBG5IvR9Tp4+xM2bY5aW5zj72JM8/+2X2Fi/x0yjyfLRw1jZDT68epWmc5nNuwMKG3QF0oxo1FoMhwPyNMU1YaYu8AvF5kGXilbBKsW4JfV39oQfytXhl77zJ5/y4oTGTJNqcxbHcii5DSpuDcswkErQH3bZ3uqwtbGD4Qoeevwkt+9d5tFjp1g5dJo4KxNkNoPEJoxsDs/otFqKONuh4lqULZ04ibDtErnQyFVCkSnQJU8+/GO88M3v4CU675qv4Uif+WWXs4sOH3jHKp846vJ0q4l7bQ/fzyiKMhgFt50m94MudgKW40zyUgyBpgnICmJy8jhD6jrW/QxSUCh0MUkEMDUNXRcIrQBdEQ0ipKxQWDG7ns+Lb2yRiBHNqsa5tQZnD9eolTVubw5AsyiVSziOPUE5SEEY5sSRIFZAZjFRd5oIw8TQDQyjII81gn5E4oEXx9gljaLt8N2uyfqB4oSraCwYbB+4REFEo2SjJwV2M2cYa2iDmJWpGjEKy9UotBSnsJk5nBGHMd5dnfkPFIiuJPECMl9y5OE68f2A/r2UUydMvvF6wsmTFVZadQIz5I+/fcBy2SRJE+7sDvnJp+f59oUeD58uYVgGf/TVe9TMCTbjoZMuV+9mvPWmx9G2xrCfUjdgaqqEVY8h09iPfIpAUC4pRBFQm20SRDnZyMCpC/R6SppLoqxArxQMQ8H0VMFbsk489xFMWSUe36UITZK+wnY10qZBtwDXtjkYhBRji34wJi803v54mf/h37/IO56YJh0P8LWMeDQi1HIMrcTD42eZ2bvEjW7MvTEozUY4kgoCswQ7e2O8ICeJcoIwJC8cHntoleaUi1MuUS2XqVRsNF2S5AI/DCewcaFhOBZmyURzDYQGIk9IQoWQBfMtjdU5m3ZFx3QgyzP8IKIziNkfJkhhMtWq0K7bzDZdpqoS1zGwtYIiU2i6hp/lbO4O6R74LEy1KFSAJW0Mx0QAmqqQ4DEYehQiI0tyapUKfhIhioLpahVPjVlZNPkfPzbNh55s8cl31njbKY3y8q//SKwOd0d3/k/u3ivatiwv7/vNufJeO++Tz7nnnJtT3bp1K4fuqo7VdMIYGmiSAUkWw4BsyRoew7YcypaHEZKFhZERAqlBIHCD1I2gm44U0F25qivcqls35xP32TmsvNacfjj1Kp48hmnm+3xba65v/ef3/b5nJsMBty5cYlopce3yBuOtCScfPY7KDVq1OrYh8SslBv0pFy68wcmTJ9nubpOMEuJgyvue+BTtK6/w6AP3MRpssLMX8PIrF7CsMr3hbZZXVjBtwcH1Ra5evMyHPvRJmq0qvfE2lQiiyMSttFg9doTVg4v0727SWJmnWi/z7Nef5fHHPshXv/JN/vSbz/HDn/khFmYX8f0qtm3RHWwyGPUIghTykFqpwWxzgXplEcexqFRdypUySilA4HslSo6HZ7tUa1Vs2yMMY/JcY5gOGgOvVMWRYh99oyHLC2zbxrKgoiTlioNr7V+FU0iUUUWa+5DQTCdYHkymU2ZbKwRhhlMpI0t1UmeOMO4h4oIoKvDUSziZwDMKMlUhy0b0Mo239jOkkc3M7HEGus/kxovMT25jVODIqiC3LbJ4inBr5Img7MTMLDepVDV5aJAEgsl4hFf2wJDcaK/xz77UJbjR50q8yfccGVGeF8zMlPfxQhg4lse0N+b6hRTbFMgERk7GVn+Nexa79OKYppNz+qEqoz4EQYTJHHlWYFvRPvR4Fn7lN+vM110OzkUMwym1qE5S3fdXqjAnLafM9Gp8/XLG/MI6BYKpzojTCFPEFKnAEJooyfHcCo7QeJbB4cNrGNJgGoRUa2WieMpkEHP98h3aG7sIwwQKhNznt02DKf1hD0uKfeTMiaOE4ynrS6u889YlvvgnX+LGndsIC7I8ot6oc/z4ceZmF7FKDicOn2EcZJQbLaySx0svvUG5XAEpOXHqNJZpcOXqZS5ducRkGlKZm2ft8Dof/NBTrB9YZNDbJc4jHDTjcEgU9LHdhONHlzCdMkm3z4WbN+kEYxZ8j898/JNMRmOSPCORBa1WnZLr4LsOrzz/OkKYbKUZafsOP/2JMZZ/gW4wYW7BIb5lMxrkNGsFF74TUFkv6O66SDtjPI1ZWXCYq6U03IiThzwOLVmc+sh/992Fd/jHv/dLzxxfWyEOE1ZWV/GwcbwKwrCRKicQOZPhlI3be9y8vcn7v/d+DF1hnHhME5OtnU3aoxEHF+Z47EyT2WZKLlOGQYRhwqVX3mHu+AqDXoRpQYbAM3K0VhRxzvMvfZv7HjrDxq02a7NlmqWMum/jN0tkYch0HBLs5YyGU6KxIItidhotXh7u04mRct8grDVoyGKwbY84yIkLSMIYMzEoxhma/amTEhqkJMsLxlFOlGnSsaZIE6RUBJEiCDMOzPocXCpxZr3B6oxPsySJ0oIb3YRK2aNecfEcm2mRk4QFWZFRaPYPMKNKLjMqlGgHgryALNMYwkCojNAvY3oGW7MG7UmHB1cltWFCEQgqJcW84+OUoVFSSNtmfmrQSXIKK2X+qMf4pqKyYJPlGnLN+IbLoadtxBR2r0dYVY9KQyDqBe6ChRwI3roRc+Swx4MzFl9/dY+Kkiw0NSdP+cRKM5kqPCPjkTMGf/TimINLJdaXXS5eGeG4PidLIZfvpty3btAqVzjQMJhqycqqplqyCHOBYdikJcUbexbpSDNX15TKmqmZE08kUd8mzBIybZFEkjyNMV2HeTtCTNq8rK8xkDXalsVly+bNEdzZTPB6Cc4wxh1nZKOU4USx0Q+5cjXg4KLi26/1SdSItdkKFRHzQ40NHq+/zcGmyXzFZdTLCIcJqhihpcmkcMjyMpOwoNvuoIqC6TTBKVW45+gSfs3FkhJpmuQYhEmxbxBVAmEonJKJ43lYnkAa+3HzJE7RiSZPNGEa43oOblWj8pg0yZCmSaokhXYwDEmiDSqeoFk2aJZNHDNHMEUVY4o05uBsneF0wOZeTG+Ucu/xA5RqNrMzTaoNi5mZBcbBiEEQIynQqaTkOljSxEax0jJILfjw0SoP31tlqQmVkqbsKOT8Xw+h9Y/+yTPPXHv+TbbjKdVSlYcefpQf+KkfhyTHs1wMaZELCEVGc36e1uIi/c02KofOpMNwmLF54Tt8+KkfJax0+fwXvozSDrVynYsXXuGxM49Ssgo808D3XE4dP4E0oNffoFAhcwdWef38y7Tbd4inPcq2xdMf/h60YZMnOc1GneWlRY4eOcgnPvU0m90bxPmIcdClN9kj1SEYBYYNWmgqtRq2a2F7NtKUFOToIsU0JNVKGdOU1GtVGvUaRayQCiq+T9UvI1RByXGQWuO4LlKYKAWOY++jHkxJyffQGsIoodWYpcgVljBxTIc4CZiZnSEIpkhD0O92mF+cwTJd8hwkBnme49o2eZ5Sj1/EzQtEEXE1TGmu/je8/3t+nW4GM7MNnn/739DrbHPm5Dl2Nr5Nq2IgfY/GkSnhbo1UDjG0gVkUrByvEAYN/Lkeu3c8pBXhl22Eu8Xf/cV1SkaP6XRAJ4cfezhi5qyFhY0wJNKOMPKCzWs2ne0JurDIpKSZC16fGMwLn6X5gGSkOXF/ncUVg3hq0d7rog0JhqKsPZqGzb87n/PaXokvfN2mVV1mdm2LRHt4wiQOAmZnLbbbE559t8XKoRU2trZAC6ZBgBRi/wNjmJQrdeIkJVc5lWqJxaWF95pDUoIgoNlosbO9s09fX5inNdvAti0KVTANpiil2Nra4qknn8QwDPI8Y3Z2gSTV/MIv/h9ESQ5Ssr29yzsXLvLDn/kMk+EECRw8cpgbN27QaDUwHINavcbB9QNk0YBuZ4erV6/xymtXGA5yhsOC4SjlUx98nEMrK+xsbuGaNjvtbYJpwCAN+bGf+DHuu/cspoLv/+T34iuLN+7cJE4gjTK++fxz3Gxvc/DwUaJpTNlz8J0SK4srvH3+Iu3dAUWhMHI4MLPCZ79/k4oOWKjO0umNsJcEpZbBtKf49PdV+NafJOBatIcmyVjSbwvSwME1bLauTelv5Tz5U//zd5fQevmdN5+x7BqmXWOwF5MUFXzbo+qAaYIRGXQHXXZvb/LIR95HqxWh84SdXohtwtn1Azz9xFGU6KC1punUafouhm0irBKV+XneuDhifsYlV5IZI2OU2FTdCoVXol5p8c75t4mnU37yxx7kfcdXqTVqVPwapoDxYMhgNyfYEsgg51q1xlf2tgimObmQCA2GIciTAlm4hIOcySAhiXOUAonEMmzCdkyBfM/TIBDSQGlNoQRBKjAcmyXPRKaaQS8i1xYz8xbz8w4z9QoNv0Ka5Vzbirm4leI4Gm0J8qIgDCeoaYFONVoITGliWBklu0yaFyjTJYxcajXJXFnRmpMYkQmpRjoV7j18ltfe7ROeegrpKKpqiqVT0njIsGdwq2MwNAoOn5Q0Zkt0bsTM3mPQ3wTTFNx5V+I/mFCvW3T3AgxbkiUR/kEHYQhe+tUcuzZkY2Sw6EuSXDBjSBbnTO5uRgwizZGWot2He45J3no34mjFZS+PKYcK01asNmJqZYfTiz6u5XDhTo/TB2x29kIefXiOSZBRbeZkSjGKYn71W3DmsKS9AScf00wGDuWZmFgpDG2jkxgJGGWX/jhEpwaOHWH1bF77vT7TdyLc6wPquyFeYNBSmgUD5lD4OmUxzzmdSU7YijVhkfgm/aBD726XN28MkP5R7j08wMgdMqno9BR5XJBMUsyoQ0e5jHPIEpPJpE8YZqAEvSDk2NoCoQqZRimjaUR/HNPtxfSGMZkusFwT05E4LggbsFLyNKZIMwy9X0+eC8G17pT2OMI1QQqJZ1m4totXcmjUbEyZ0Szb1FwomykmEbIIcIoMkcXUyxYPnTnMyzc2CboRleoMSwcatFp1GrMlDMtip9cnCzWq0EyjmGa5jpSSSTDFL7nEecojB1zWWgWWyBBZRhpluKt/96+F0PqdL37umVm7wj0feJwjK+vce/oM3fEIlRX74GWtUVpz4d13IMs5dvgYf/j7v4vveURo8lQzazn8F//V3+G3/uBfsrc7QWCDNhgOdwiGGYuLsygN3f6Q4WTK4uoKYTxB6RRL+9y4cZWSa3H/2XPcvb3BO1fvsLHRxnV8PvGxT1CuVLBtC03B6uohfK+KKva7TuvVFjqXBEFKyXGx35uAVytVyn4ZoSWObVOt1tEKGo0WfqlMUWgs08F1Hdz39tmOheM4WJaFlBau6yMwsCwH0zSRhiTPClzXA22QpQVrK2vkeUGS5Phln8FwSKs1g1LglXw67fZ+n2GhEVJiWQZSClzbQHbPk2eKURZinfyHjO06t26/xoMrH+B3n/1HTDM4tHoAhI1jhIw2t3DsFI1LMQnJ0Fi6hlIhKweqmLaFNqYMdwWeaRNECSsnHX7533iUrT692GXtwDEeWBuyfo+iSAyUBi0CRGbz1osjskShDIUTF4xZ4I3NmI3tJY6vBpSdiANHPIrEo9aCvZ0crWYo9JjISGj48ObFZZyqpChaXLoW8JmHEoKwRFL08CuCeqXBu9crXIk8HMsjijPiJHkPr5Lh+xUcd3/KqLTGdWyG4yGra8skYUSlUqFSrpFlGbvtHUzLZq+zR1FkWJZFlqVIaRBFER/+0Ee4c/v2fjew52E5Jfr9Ea+88jqW42JYFkmakqcpp0+dJEtT6rUa43DC3m4baRloCVmW8eB99/Pqiy/S6fa4fuM2q6sniKKcoigYjIdMOncRAtYPHeLlV1/m5t1bIOD06TM0ylVu37qJY9mcOnaCOIqQ9QrLiwvMNRqEOqHaqDJTb/Dkw49Q8cusLC0Txzkbm7vs7nbRwiR39tjqjrnx1mmy4DiD8SXKTc3SisfORoWFuqa7GeMsagY3JXESM0pNhlHBOJHsDlLGqcHuWPEDP/cfF1p/JT1a3fGQmu9REQmzqw1WWiUG4zEbd0OG0xinbENm8+RnPouf73D04CqvXTvPqeMrLM8e57Vbb1CkNe5ZOMzVzhabwS2Imvgl+PKf3uHUuQZnVirEMsOTJiPDoz8d8+3zbUp2iSDX+IVFaLi0qj6p5/PmzRFfeGPE8TmTk14VVQyZ6ClvSPjWpI8hJblUFNMcSwsKW2C7DsNeSJ4plHrvClHtF0YrV5LJgqwAoygwTZNCZ/ul0gjIM6aeoJAW4VTTGcckbka9JTmwNoMDDMYJNzpTnnu7QxKmtGNBI9EIoSkSieE4IGJEkpLkEbY02djao1StUrHmUL5LP5QYix61ksRo7pI99AiLfpO5hsU47tISS+xYDbaKgkkwILddgiTgfzv3AlTLjHcCpn1oHFcEOwqqkvabEa2HDFI0va0cDE08sVi8x4NUMpxMWb9X0N1tcO+JjKPrLpcuRMzO2awerPDKZkaQJSyV6qwvRCTdnOW6Q8Pz2L07ImwoDs24KEcyzGJmVzJ27iTUbJdyucyJgwadfkyuUxpVk8FY49g+leqUX/hzzf/1vR5/+HnNp348o7vr4pRyDGkQZiYyN8h7GtuwMaoG0TBi2TN4+mmXaxfGNGwfISJSu0ApE41Eioy6bZM7mkKZxEWIn5k8WXRwajW+NLBI8pB//+I7/MHLdX7i/oJSe4goK6Tlg0roRzY95RDJBFfuF6kJA7QoqJc9dvZG9Df7RLkmnKb0uwmTUYoQglJZ7FcwKQ0iQQtBVoQIIbGlSU6B4+yX71q25PJWymJFcKBRxTCh5Bh4ZUiLglbFwhIpvqHIAsU0STCVQhcKbBuciCSAn//kCf6X3znP29eusrr+fjxPIkyJkj6GEhRGsW8ktiR5nlPyNJlWtIeacllTkynZRBPFgqmCSZBy5v/XU+f/u3Xfo4/ywMJBLo2297l/oymGKbFKZaIowBSSZr1KzfZYrs9x8eU3ccsmd7ZuYdfnqdQqPHr2CfbGV9jaaHN47QS3NrYwXYNU5Bw5c4q4UAjD4M0L19nZ7bB+9DhSWiSxZsar0yjXyIuMzu4e00nMy6+9TL1U58+efYFf/9V/zUMPP8Bnf/wH0aIgG43xfZ/66hpKqf1EYzlhUSmyPCJNU7Isw8RCKolUBpZtEgX7hdfBJMZ1XWqVJpNgjGEY+1F6pSmVfNI0xTQNZKGxDBvhmDiOs4+/SCcIWxCHKZZp45gW169fZ23tIFG4R5YKSk6dKFRoZTIJAyzLYTAesDS/wmg0wbZt8iQlzSLCcQtzrkVoPUo4KKjNd7lzfYObtS0yc8r8zDlct0UubWozT1PsXCcZhITTGk17F0OC0D5JNtzv/MsVOgVLx8RhQnPNYOv6AebnBHGgseotjGDI+Y0W5/R1wMKQJaThMdqD7l5IybXIixwpJGMzIc/n2cmmvHr7JJ85+wpFbGNZTbA3WFp1uHg+wyk7+FUYRiHhJGY6znD9mGTe4xd+x+ETH2nx4OkJ407OJO2y3T/NPSv3cKN9g6LQqFyj0RiGhRCCIIj2xa5SSFOSBzlvv32Bh8+epVZtEMcx29vbNBoNkiRBa021UmGv3aVSqRAFIb7vc/PGDUzTpF6vIeV+j+ZkMkYIje/7pFlMtVonCCWXLl3i/Y8/RqNRY7fXx7QMyuUye/0uruvywre+jdYOhw+eIggVu3ttkjRjHEyo1soIS/D2xXd58TvfIVUFM/NzHDpykFOHj9Pe3UanOTPz86SqoB9NKYoMQ+c4huL+++9jeWWJDzzyGOPNHa68e5GdTg9luFQrTQoEpmmC8CgqBi/cfYeTT5q870M5+V7B7rWQuDPBfhCOPDHPwlsBup+zuGdwZQpJVpAmMIo0lbqH9L8LOVp5kdKehJw+PIdhpYyiCdpULKz6HJAVfLuCY0KadqlXqxxdWefMkTM898aL3OpucP/6fbx9411s38ayy3iGIEgMvvLnG5w5Ps+Cr1CGQ13abE2mXLs5pF6ZozVXRyVDqiKiN1KUC5Ot3i5BuOXiAAAgAElEQVQH5ucx+0OuXNxj47rFn+JxZLbBTv42OyrHSkMSYVMXNn07I09zDCURhsbEJCcHCgxrnz2ENsmKffNcmoOpTHI0thIkaYYQAiFs8lHOle6Ehl0imcRYvkfYyZCp5vYoJe5P+fxz21y/FhMUOcNxyk/8/U/ywotfInZNFlsLnDn9MP/n//05fuEf/DydImaiEl569RWOzcyx1bGoWHeYqUhU8wzi5IcwwzFr3iw7UZ/F2TkyZ0CSRBiyRLlcQZopZ50BuDajOwGMBY0HfZhM6KQh3CihzxZoXSbYjlhc1ghVZuY+gTYLaOV0vqFxvIItUg57Do0nKmRf6vLA31oirNY4cGWb7laB4ynqZk6raeMHKU7dpRlM8TVY9Zw8hLmWQcW2efgTFlvfSPnGxTH/yX0mg62Yo497jIaKg4dL1CYRy+cDZKPKP3g25pc/bfHVL0Y8+HgdpxoRTU0sJcAs0LMaV0uyXoRhlpgUGU694NwDdcZqjuzeczTikMlLz5PNmHhRBq6BYWbkeYLnuIQioxpIJknGJ6sTtqjx+1c0DTngnz0v+Mn7j3EwvQVOijZLXPCOonSFIgupV1oodReEJkWQjkLubPW5urFJPIrQ7wk8w5GUaxLblxiWgU41qoC8KEBnGK5HHufktoVjCUw7pxAxKMFitUHFFlTKLp7vkqkMrfU+BymPiMOAyVixuTUiDUxsUVBpeTjVCLdawirN8Tc/eZxf/4NbfPvl1/no00+RJSGICOGWMbIhBoIgK8gRCMPFsg0KHWIjMQpNPNWgCjrTnHa3+GsjtHzfpxtN8H0f36+STiM8s0ys9on9jmMh0dx/71nOv/om8TjEq/hMgogkSgmKKf1c8cu/9s+JQ8E72xcZxxlLq0tUm7O8e+MyjVKN4WSKW24hnQlFodnZaLM4N0tZOCgl0UrSbQ8YTyJct0ScpiwsL7O3tUOnN+J3/+3nOXPvCU6eXicM9z+uaEm5XKZSKmGaJoa5CKj3/FiglCLPFUEwwXVdiqIgiiKCIGBv3MGvlTCECUqABstwydEYwkIYGRRgGxZlr4Lr2gyG+9wt1ysxnY7JREEcx2xsbLAwv0iv10Oa1r5okBLLzIjiEMczGU0HLC8f4MqVK8zOtgj6Y8Ta44yykCSqkRaXiXWVTDYp/DGCEnkYYuomQR6wsvQ+oju/jk4sUrmLFiaCnMLcRcc2RTEhT5pYVQPbLiiMnNpynf/9v4wAA4yZ/Xd+OqQzPQm6hzZiDGkiiwp3r7XR+X43pZObJNWccCcjzUs0yjt8+2aLp5ZnGAxT5hdissShNZ+jCNDKJO2Mqc02oOrjFAPSSCMcySVVYe9rmms3PE6t9jluQGcwSzjbJ00zwjjFdUskSUKlUt4HZ2tQeYEWEEURhjTZ3emy8PQSe3t7ZFm2X2SfxEyDKZZrITAwTZvd3T2OHDnC7Ozs/l7DoCgKarUavlcijgI81yQvYprNOqWSy92NKZZjMpqOkYZAvdfTOZ1OadTqpGlGZ7uHYXnce+5+FlYO8GfffpGK65IVEx546AQLtTrPvfgS12/f4LHHHuOec2c4fOQQcS+hUa2h8wwpJVt722z1dvcFYhqTJVM8r0WjXOO3f+t32Lt9iyzVvPXuFUq1We657wFsxwPAS06gVI/U2eXAqTl8/0mcuUswuo19XDLuDVl6OuZrn0s5c6/NzfMxaaVgGsBgWDDfLBPrCfXmX34m/JW8OvyVP/y1Z+rS5Pp2F21LqvUCYeUoCpIiJohDRumIfjihPR7y7s51Lt65TalSomm63Jj0MVFIXcIqmzz30g63t7q06jbHDjZwDJtcKL5zcY9BX1OkITYB2699mQdPeCyWc/r9CUFSoNKC6Sji/zm/x3BQoKRFZKdshrfYi0aQJeSFgytMJnGMoQWWK0CYTEaKNJEIqTAEKAykWWCYFqWSJB7kFJEGUVBoSBOFYTuEuQK7QEWa7amkJzJKpstsRXLP4VlKcxFRlnNta8RGbx25doyKVeJn/t6P8OrFt/n+D/4In/rojyBKDYa6z8/+7Z9EmDa1ms9Lr79ArVJmc2+TR86uc+ahp1ClNXLfoS4XmFuo8Plv/zbfefcSV3c2uHD7Jhu7m7THIwKVMskc/tYjb9O7HuMr8E8IiiBFT32Kq5Lt9Slzziy3bvUpNWHm2BqO6IKrEdWMi78RYi97dEZglQWtlqK+aHDnjYy1j7a4tjHgyrsxrUaJWsNms5+zVDfITUG5Jtnai6i4BgdqAlk2qVV94iylVpLc/4kSr38lxqiaLK5ILr+WcuB+C5OcSkPy4GGfP7qoKPkFX7oS8taGQ86QuOsxe0AifQNh56QRpEmOMiVxkZKNbURiEfVH1D/944TTDkkh2FhuES0s05ldoBlMsaRCuYKiGGFLwBRYhGhpU67lvP9MgxeuthGx4OL2kNKBIzTyATg5/UTR7QhMx6Hm+fSGexRZSjIt0IaLa1ps3e6jYkGeKHRcYDgFpVqB64JhKMgVWaYp8gKhTLJYo1KBrSW2JVBCkRaaWcfk5LLFkWWfetXDsgwMqTEB2ylQRUY4CLl0LeYLX+nzjb8I2dpRFIlibt5hbs7HMSR+dY7t8ZCNjRiKguX5FYTw2G13GY6HpEVKHhfUXAvDs4jiCNd08JyYE77ClprNzZxLN2IuXkv42Gf/enC0OlsXn5GexYxfI3UsdJqzMNNiOJkyN9NAq5Qv/NEX+Y3f/k1Gccw0SZlbPsTt3W1KpmYySvnKGy/Q3y6zGwSEWUoS5XT6e0zHmsPr81y5fJckywmjFMswufz6i6y35jm1epjhoMNOMATDYHFmAQ+Dxx56kBfffQOnZJOlBSpP6XR22dnZ4JEnPoY0yph2lVKpTrU2hzAclLaIshFb7TtMwhHjYEiUBiRZSpEmpGlMkkSgFbZl4ro2ZdPBNSwWZxZYW1xlfWmdI6tHWV1YZX6+xeLcMvMzi9QqNWaaszRqDWrlJtVyFdtxMS0DDIlb8rh6/TrHjhyi2+8wnoxJsxhpamwbciMmCmOuXLnKuQfuJ04TJsGIGavK9XEEc1W8yQRZKISy8Kx5psNr/PAHfwxVTCnVMi689C9xiusUsx9jafEZptEXSPMFLHI80yWXDs5MQr9TxsGgm/X5uZ9tcatQpIZJqWbiDFzmlk/Q3X2NNF7mzAMB0VjS3YUXv7FDpWyB1uQICuFwY6OGMBdxrTWqzjZfeWuFn/vpHqPpDpISZnGG/rBNFCZU6jb9zoR2dpTrtzYoe/MUGuakwDRTtoMlvnprm2s31nmlP2apvswg6FOkOdLYnxoWxT4jMk8SVJFTKI0UEsdy6ff6rK0sorUmyzLiOMawTJRSjKcTpLL5+Mc/yXA4wHVdDEPSaDTw/BJCSmzLotvZY/vuXX76p/4znn/hOcaTHq5j8sEPP8W1a5eJooBKvUzFq4JWLC4uYpoWFa/K5/7559jsXUfa4Pku7XYX05D8jb/xE5RckyxXHDl4iNMnT3Lxwtu4hqCzvcns4SNcfPcdJpMxzUaDKAjxbJcozPAqHm6rylxjlbJdJ1eKVGp+9Ic+y3anTz+MmUYZcVZguy7J7B2sfIY8lGx0lzAWT3L+8gMsr73I2klFqSp54fdNzj4QceWWzcFzVeIixC3ZVCqCpcWC9UXF6rzg1Ee+y64Oo3DIrlLoDO5sTLm7kZCkYyzbQMiccsnn4PIss9US9YpLoiOiUcTlO5s8dPYBatfGjF2PqMj41rObGLaBabo8fm4dVMatbsCFNzdozcyhDciyiKTikwnN5Tu7rLUa1HyXrSLl5Vf7lFcTjq+sMj8Dh1shp50Rq+MZVLbIoBfxyvaQryUBhWOTJhoUKFVgmjaZyNDsc0UMQ6C1ud/BJSy0NEhFgVQGhpBgwChMcAyDItBEpo2X7idLbMdk1q+xtCo4OVfiX1xa5MtvDomHQ5ozHrpZpr2xw6NrRxgz4OrWJaplTb21wmDYJc4LomiCzAtOrx1gqebSWJzDASZGwrn1R/lf/9XfZDisYpsOwtSgFKahSdKMONxho3eXb//tRZJbMF9pwGwKQ8m0D+HNKdmKQ8OucPNWh/n1GuPeAFvtUJQsjBnNq78kqKw4xGFIWoFyWbJ0vIIYWSydm8E51+Lyb26hTEiihES5GJ5G2ApfWtimwnEttJfSqlcwnZTJIKbZBEu4XL0IP/MLTX7lF9vcf2+FaN7kwvMFP/hTFuNEMU01Hz885VtbFXxfkumYb1+3+HJvgv+8zdqM5Mi85H3HNGbNwtSaJPfRcz5GtUFt7T+n07mG55RIbIUfTKiIMhPR5sbJBziZ7vCdfo2jp5aozFfxbZeWanH9T/8ppShkGEz4Oz9whF/+/S0mQvKNV+5Qe2CGpaTHqpxy15mBQuBYBogM266C3MNxbTKlyTPe65QUGKbGdAQCgxyBzgVpCFlWYGoDXSiEMt4LO0jyuNifdomCcSToDAu6kaKZG8gUbMdFy4wiidEJRJHgnWsxL51XdIYG28OC1SUo2wLP1Gh3n8326ANnuXjtW1y7epOTh07glsCWNlJY6DQjywryNMe0LQC0Cal22JtKJqMRV+4Y3N1O6Q7+8nj0d9VyLMxi34fSdGr0mLLd7bI+U+H85fNcvXuXbjTl4afex0y9zo3r15lt7rOz2lFGfzhlzp9jmuwik4KkyMkNSZ4qFDl37nYRlkZJhRICy2viV+tcvHWX40cPMZ6OIYoZTweM+ju0mnP8/n/4Y6qihplaCDlhZXWFRx8+x93tmziOw3DYp1z2yfKQ77z5KpXqfp9lMB6weqiFYVnEkQJtoHSIMgpMz8GQNlWzimVYGKbLNBni+j7tfp87vR2effZZfN/nwQcfpO6tYbkT4miM55XI4oxapY7pGfh+hYXZWVr1eaIoQmvNE/c/ThZPOby8Tp7tT7r6g86+eTvpcGvQp1Wr89w3/oSnnnqKO6lkYpRww12aU8FIlQkmQ+oNi6sb/54PPPIRtpWkXDe5tr3BSnKeqS4RdZ5nVLsXMzUQXhtnIul5FoOLHT64dI5w7w4TMeILXzhG62iT3iimZguMzATLZ6/9OquzLd58W/PE+wVCVDh//lVcz0LKFIVNkUgCERKqFcZZiOem+OYKh1cD3rlqcvaYzbiTMY2GzC6EhEmJmCGGmqNlmZw8eoid21PSWsY0s0jThCK7y1L9KANgoZQxmW5S8qrofES5ZNPv92k2m0wmIXGoaM3XmARjdOEQRAnHjhyiH/UxhYlhGFiexd5WGyklZ4/fw/raIV544VlWDyzjeDamaZNnCsPYtwSoIqGzlfGb/+qPuHVjlyefepRv/cUL3Lm5RRJJzt1/D4iUOMlQZUV1tsygs8e4Pea5515l4MFD959jZnmFw8eOs7m5ww9+/w+ytbFNEUKzNYttGZQcF9d1iVPF0vIBXvjqn9CanyPKTbrDEY5tk0Yp9bJPoXMMIWm0bDqdDUxPcPLkaf7gd/8d23e2EJ5LrAvMMCJQIbPOIWRlBEqzvXmBN55d4eR6zMPnFK98cUK7D2uPGQy3zqH8LSrHDZZ3JWGsuVnLKdkm0odDh/5yKfVXcqL1ua/92jNSu5QsC8vQSFFQqTQplz1cu4rSglGYcmNnyKXNHnc3x4x1zsyMzzAe8tjDD/Gt595ic1shyibhcMIT71+lP4x4+1KH7m6O49qkugBSgiBnzY2ZBkMGvZB63aNesrnb7pOlDrVC8/hD8MHTivXZBFlIukHMdKSJhgmdTsh4T6G9GpkKiNICKU1MQ6DzHENKlACBxpDv+aYCk87tiEKBKjRaanIpsaRFmuakud4ftwqLQkpqrs/1x57ksycj/tMvH2L79l2Go4BmtUY/3cNIW3z0g2eRuUmzAmW7SSZyZGETZhEqmzJt9zh95DjebJPZZgPbrVNEUx468xi/9cV/wlJ1ndlqg+a8QUlbFPl+rQ/CxDI09xSKTx5MEK6FriaMb8ds7Sqcbsr8+yqMwgm9jsJr2cRBQsmzmalK5KzNN/9pl5lVj3gyoSdMtJNRadrMNC0mVyMyx6DdnfCdb3Vo+RYGFp0oozAK5moeFd9GmCa32gGlsmC26oGjUXlBpSVJMsnKHESZYtZ2eP5ixkpTkKiC3LKYn5esNEy+7301/vEf7eI1SjglC43E8y1m6mV6k4RruznPbcE7Vx1OHDqAOPsQzJ/Cqx5A59tk4r3otrTJipxR2mepfoh/+7Vnef7KkH7vLq9dusq9J49TRFNStU20ex2TAq+ICZOUM+fWuXypjfYEr+/GLLQW2eyNGcRlTNukWvboDUZE4ZQoKhgPpiwtLdNpD8kmKQKBZVg4joGyFLkqSFOIxjlGJBC5gcpA5QY6M8hTRVHsX/mkk5hJINgZJpi2QDrh/nOqU+IspkhigknExnbO86+HvHNFgJI0S5qn7qty5KhJtekRaMGdgaY78VFasNVuE0xiapUWvWnG3nAPU2h0ofBdD79aJo8iTM9m5cAyF0c5166HXL445PJuTnuq+Xt//z/+R/jdtHZ6t58Zj8cUSpG+l/TsdDu8/s6bbG3vMj+/wMr8Inac8eknP8xqrcneoEu7u8dkEmIaLtNJgGkZ2JaFQmBYJqZp41gOfrnM6VOn2NjaouyXkZaBY0pKhqZZLfHpxz/O195+jdX1dTq3tlBBwYH7H2Bva5tpHACSe0+f4b6zZwjiEMcssTA3jwRUntNstTBNk83NDSwpSdL9PShJrVxFFDm1RpMkStEIpNAkcUiYTgmnil6vT6NapeJ7FMWAy1fPc+v2O6wfOkCSRVRqTcqVFkKYBOmYwXhMrnKmQchOe5t2d5d2d5ut9ha9XpvRZMQkHBNnEaWyS6nsMdc4zPFTxzh26ASry8eYn1vFLCsKB4RbZRxlpHabaT7F8n3euXyN9s5tLKNCMdpFj+aJJt8A00QnIU7zEAxvIkseMvRJ3Bid5YyHO/jmAl/4izr1w5+kOXeShmdgqSonT87y4P2nWFhoUjBiOHQ5eChleTHmja/v4VccJmmB1haOyNgKBUunP84Hnv5eHn38Qf7kq98kSSZcfavF/Y+klN2MZKIwSxXyAAo9ob/r8BdvRDQXFyn7NTqTPaQyKJV8HNsmiEJKJZ8wjLEsC8+rgtBovQ8MjqIEw9qHTE+DCbZtYpoO0/GIg4dWma3VkAiiScx4MOKDT36E9dWDRGHMu5cu0GjWEVKiNfvpTtdDo/enZbnmxefeIohS+qMu95w5wcLCMsPhiN6gy+bmBrdu3mYwGLC0uEQQRlx45zJf/9qfc/3Gbex6hQfOnabkekgpWVla2Wcd1quEQcjm1iad9i6XLl1icXGRdy5c5LkXnkPpHNd16fcHVCoVbNve77QsudieQxCF2IbFsDvgrVffwnfKnH/7PMdOHcewLcaDCXFagBDEWY5tGYz6IUHU4/rVDXq9TeypSahGuD6M0o8y2DiHsp7l4MkSV1+fMqg5lMgpLIfG0RJqkHPw0f/xuyt1+Lnf+h+eUX6DocpQqUYLSZKNCKKMJMtIkpBEpQht4dslHB0xDUx2d1NubnT44z8/z/HD9/DWtQuk05hD99S4cyFnJxyj8LArJo7lIHXKNDW5Z+0++oMb+FLT6U6Jg4TVlWU29yYYOVS0gS0EM4slyiWHVEf0ugmXb045vzHg6m7CMBKMhiGtSpO8JMimEaNhirRdCiNG5gZaFxiG4NDxKsF2zLSrUFpjWyZhXPB9Tz/FY598iLn3r5OvwxMfu4/q8iJXXr3FyZ//FLe23ubt9DA63GSkc6ruAkE2pgg0yghxBlNWDtRA+2S5ppf1qVs+QdCh7DRxKzWUFIAilZKqlHj1eW5v3WQY7GI2HOaWljg6u0jDqRNsDCimMYRw3+Gj/NLPDjFEhcmgINwe8S3vYzjtCxx5okE2SLgzUJQsm1EYUiu5ZCJgbq3Ea/9iSn1ZECUFmS8p5gwWyxLbk9RnXd7+0y2WD5d58dUQQxpomYO2ubYVUG0YND1NpaEYBzmdGEo+uJak7AjkgoenDZSbUsQKqzA594jHsy/1ObxcIstgNACrbjMuQkRR4r//KZf/9vcS6i0XhMRwcsYjgagKbL+GiUMsAr7+VpdPf/R+tKVR5AQaPKtOPt2kcDx8XHoq5Dc+/1WqpkVoKCxh47iC/t4G6ytr7HbeYhZBVQfEGnRhM+gPWTm0zsUbQ3SacquT0BbzmBTkApbnFxlMBsRRxiSMKdll7JLFOElpLTRx6jba1hRmAdoijwXZWFMEQKFRyiDPNaY2IJP7QMEiJ09z0sRAZQlhaNLuhriuiSMtlEqI4gIywXiquXg95sU3Rmx3JBrFkSWD952tML/i4HiSDJv+1GDYM1mcn+f6xi67O3vMtWoUmaQ3bIOGaaLxbJtaxafhl1g5tMbGZMLV65u0A006UuShwjEMfv6//p/+Wgitd6+//ky/36feqLLXa/PNP/sm33nzO9y6u8mNm7cQSnLqyDEa5TK3bt2g3+9Sbza5dusGqtAEQYyQJlqC0JJC75+BAEoJpDBYnJ+nUvbp9nskcYyB5uzp47QaNXwMLmzcoOTYuAKKNObjP/QZ9na3CcKQPIOjh47gOjZXrr5LEiQszS8ipWSv3cGwTYLplJ3tTVaXV1lbW0EoidQGnluiWiozHU9xHA+lFah8P1WsEs7ffI0XX/8LYjHi6sYFdkc3MMsFTk3z9ltvkWeKyTjFtis0WrO0u1sUaG7dukWUhORFSpROsT2DnBSlCsI4YDgZMZwMidOIIJ4waWeEeYcsiZHSIYpDvvTsv+ZA+STf99HPYmvB+uy9mGGNowerZMm7zNTXmAY73N1+g0fu+VG2d/6YWQ+iICWxNcnoLkYFrDwjVhrPzsmDx2gdXuPi4H5KszNMh/DUI4/zkSef5NjJJp5rsLlzgzRXuPYBfPMQx4/f4frzQ7DcfUaeSDGyDDn/OGsPfw+jUYgi4uEnPsR/+MLnCfNZ4vj/5e49nyxL7/u+z8nh5tQ5TfdMT56d2Z1N2IjFAkuAIAiCBAgSpCzapsqiIbok2hLlklVrS2WX9UJVpknapmTKgkkQIkAuIMTdxS42YPPMTs49PdPd0+H2zeHck5/HLxrFd9YrV1nA+Qfui1P3Od96fr/v5wNPPZbHa9bpRz4FJ4vjhty8obDem+Hy7etMT4yz2biHpjiAAoCmG4CCqhkYpoVI9/yK7W4PTTdIhSCVEl1ViZMYy7Lp94fkszni0KeUKyBTKBaKLC7sp17f5datFaSUZPIOhWIO09r7jThOQdkra6VxguNkeO/dSwyGI+I0xhv1eeihh0jThEajTpwIVFVnZ6tF6A3QTJMkVbh65Sa65bL/0CJjlTyhH+DaDq6TodVsEYQhcRwiRYIQAsuyaHba3Lq9QqVaY3JukmajgWXaVKtVhBAoto6dyZAiiJMYRzO4dXWFxnaDUX+Er8S4eZesYRKFMb2fqMGk1SLj6lh6iTjx8X2V8fFFJvP3QWVIs97l/Su/iFr4AbPlHrPLLpt1n9jM0OoFzJeK5CYltarF2MF/9NMVtL76wv/yvGprWKnAzdm0gr1QFSYSkgShxGi6jmMY+NEQH51IxMRpiCpBNw2K+QzeUFCtVEl8m0SLUdIUlRQdA0ULiNUiz53I85UX/i/yVZelwhi7gy6tZkC2pNHvBQSJ5OHlGqV8zPSEg2OlRKOIdj3i7kZIt5HQ9RMSxUVXTRr1IYoK2UqGQScmSlM0Vd1zbukmqReTpIK4q+J3JSIR/N6/+BLPfG4/b9y9yNVunWZ7E0M3aPZ6lMoOxj6T6cl9ZLWIfmeHICwBkihOUVSHStHBKc7hqTEnDs3THQ7QNB0j3SMMl7IZRrDXhrEE4WjIA4ce5tbdGxTLE/ybF/45sT4CfMKkBQaEkWS4EbGz1ifU4Q9+S8OMNbw7Q/7krT7fUn6ZZ8IfUspmMTUJOZ1omNLwAkxTRVEUsnnBpe+lRDoYpiQtWyiGwM7pGJaCpqrUFlTOvOxz8os1XnupiV3VsXWLV7Y88lZK0dapjZu4psbIU2jHMY6rEgQqY2MapqMyDBIyRYVQkRSsCE1TWTyU4Y1LPQ7Pqlj5lPqWwvRMDk3E9Dop//xTWf7lmRhHV4m1DEJNyTs5HNdAmAmaaiCVkI998gGiWJDICClihCoJUsGsW+X5r3yNrfUOrmaiKTqOpqBYktSQPHviNDkzZDT06OVsHrdjWnGMTBMMTWCGHrOTY9zcGhGnEeVMmVBtE+pNpgoLbNW3QaZ0ux5xKhivVImkpFQosLQ4z8z0JKah4IcR8Sgg9gVSCKQqKeRsslmbNEnQdAVNUYmiBCETdF2SLVnMHrQpTRmomk01n6GSz6BrJr6f4IUxnXbCrTsj2p2UoycW+JVn55kf8xgbczAyDl6kMAg0dtoCv+ehqgY7vS6+55PJlxjEI5IAkijENEzKFZeWhNubTdQwolTKE0QJZr5IxYCyo/Cbf+e//ZkIWu3e9vP5QoE/+MN/yb2tdUaRTzafJVEs+r0RzWaT9898yKMfe4LvvPUaHRly/b2LTM/PE8YJumETRSmW4yDQUTUNwzTRdQNVURFS0u20Wdw3x/KBJZaX9+N5HhOT46iqxma4w/HFAxQyDrWJMqkSoW41kRmFhbkZZChYmpnm4gdvkXc02r1dzp3/gHyxQHl8gss3byCUlK3dLYKBz5uvv0Ov28fUNF787nd56Ts/ZOv2dS6cvcA7b73P2q1d1ld2ePXlt2nsdnj2yU9RLszg2hNMV09w3/Iz7Jt8iIOLJzA1hWNHDqLIlP/yy19GNzIcPniY8fEJyqUSlqFg2zpSpNiOganvoSFMS8OyDYRMieMYT9kmTDz+9Kt/xFsXvsmVjR9RqkjudF7jR+9/i3fOvMKjp38JL9qm3rqBac2Qj3tMj51AyR3g2spl9u3/IlH/EqwF2mgAACAASURBVEFGkC38F+j6KySRgZXGqKnFhfZH2ZGH6ZqPUy3WqLhZbq+/wuTCNPmcyebaJibTjFcz/NInv8hkrUZ7JHn5RRPDqePYu2SMAOHpeInD0mO/T081sVOfUTDEdjIsTc5wc+U2N29t8Sf/u8fP/co4o7t9hFrhxOkCL7wWs9OrEqqCna01DiwfYXurTiaT2QO/xhJV04jShHyhgKNbbO1sYmecPS6kkBiahaomjLwRQiosLkyzb3GWXC7L+FhpL0gpkjANUC0dJ+eQLWbRrD3ckGGaBGGEYzuMvABN23M+thpdXn7ldaSmoBoaaRJSKOSZmKhy34ljnD9/DalYKIrNYLBDIeswOzeNmbFo9pssL8+RdzJ7JQdNxzRMWu0mYTiiP+ixvbnJKPQZhT75Qp5qrUatVmNzt07GdlEkZLNZUrF3qz+Wz6LGCWsrt+h2u6AKssUMuXKeK9eu8Pv/5L/h9NFDHDiwn/cuXQU/xI99HKdMGIbkCwaz85NMzk8RGHXGl36Jp0/9z9xmhbzjM1m8QaH0y2zfOsegrVKqaLy7dj9Z/ToHDxUpzPzXP11B62uv/Kvn+xs7REmfopthe6uPFCmaFpLPV1F0nTRW8EYBqqajCYGqCDRD23NF2S6ua9FuNfBDHw0QirLH4FJMIgvU1ojnnpjmj/7kq7hFk9SSHJ1cYmt7Bz8VKIaBlsIwTXn6dIWlWShlJZYm8EYh9bpPoy0IhQ6aghACoagkaYqp27gTLo3dFkqqoSQCgUomb5NKiWHYHHngIGMP7sc4oHDuzG1u7jaIhIutxmTsLP1eQK0wRahnmawucf36OmEi8EYWcSqxTHuPQ5N4WJkKBydMnj66n9ZGgzA2GSsYqLpOMVPAEymmKhgkTazIpjI+xXZ9DWlYfO/Ff4tVsMhmUvJmiiZ9opZPZ73HzqrH1u6Ajbd8/oc/NvjqX3g88nttbjYcFu02x6oDjp0u4Psjor7ECxNi6aBoJnY+ZOV9jTRWqE4YjGxJIasTORJDlai6wmQR7LLJ2pWUpU9luPyBT9ZN0cIBgTuJrYWUMhqlqoEgod+X+EZElEh6o4R9+3SkJkCT6IaO4moIYZNNVZyswo3rMQMJlaJD1om4fXPI5JxBNQ9bfcHxjOD1zRBbz6IpkkiXkEAswc4qWI7LqePLoDhs7+ziBRoHKiXUjMMff+d7iDhFSgXb0Aj1mLyTRWoSPYp56tgxDAJaXo9eY8SzMwM0HDphQBAZuJZKRu2iZgrcbSWk4ZDa7BLz40sU3TGagzW63RFxBLrqMlbL4RQDsqWYMPFQDUiVlDTda94IKTEdqNZMJpctMjUNU9qYYm+0I7SI8rjOwokM08sGlXkFaWqsNfoImcUxdTIZl31T40gEsYhIYoXx2TF+6ecPcmwxYDIfUZosotg26DZeoLHbCthtJtxY3cSwTXr9IWPjU7T6bZQU4jDGUjVKuTxbvTZ36yO6XY8kiemNQkax4MCDB3ng44/w1AOf+5kIWrc3rz+/u1tHyJhKtUI+l0PVDT68dIUkjSlm86hCcnhuiZxq4aQqx06cZmrfHE4ui67bBEFE6AegaHtjmzgmTZK/8UiWCnkWZmfRdQVD1+gNPEQi6A36XPjwJmdubXDu4k062wNKTpXUzBMQs3L7LrMz86hSkM/a9PtNDDNhcd88QRRj2ll2Gg1c1+by5QuISOKNAkrlAmHkcefOLQqZIjL2uHN3g+2dNjdurLNyc436bgtvN+bCe9dob/fpt4ZcP38Zv9PHa/dIhcXi4jSKIvjmt75Jp9ujMj5Ot93kxs1rFPJZRJogREquUMD3A9IkJUkjoiggikKGwwGjkYeZd5GpytETB7ly8wKTc+PkzCJpmsWyQg4fnOTy+yvk3ICd7bOk4Q69tk1nvcvkRJ6PnDqFXVri3Q8GpOkJlNwcRv8qliYRcYI0AzYyn2d6vkDjbofJmSnqvTa24WKjUczPMQq7jFenWZxbptm8htcTdL2A1e0brK8VqFRtTK1FGudIKg+QGTuJNwoYtnaIU4HneRw7cJSXf/gVMuYkMuvwl1/p88UvBNQ7O8xN7eOf/fEKfc8kVSWuqbO6tsXc3AytVpt8oYCuG5ju3regWC7wsSef5urNa6RJwsgfoQH9dg9dUygWq8zOzFDf3WJne4OpqQkOHVpG/ITrFiYxqqaQyWboD3okiUBVFaSUaFLF9wOybgZVgzSR1KpjFPIFtjY39iC0tiSXz+I6NtVyjcuXr9IfjJCKYDBo8Ruf/wwyCSnkcxw4tB8FSSGfpdVqUyiUsCyLMAxpNOq0Wk0y2SydXgdN0xAISqUKw34f07bY3NqiWq1SLBSxbAPXtsmoJteuXiH0A2zTpNNugwLZrMvm7bv8xpd+lTfefJNBEBCHMTubG6hybA8Evljm5MkD7F+e5M69i9hmka2dDb7zo3/C8pHjKIMBGaVIKD7O5QsfMj0tOLfiYY8/y2T5IteuzHH/k7/z0xW0/uzf/8HziWshRwJsEy/RiPUEVc0QJB1UkaKqElXXieOY9CetF5kKdEVDKgrbW3VOnzxFZ9hDM+Qe00czmBjPcXqhxuL+Scq5Ki+9+haWpRPEKY8eOUm/3SBIIlrDPfP3YJRy33KFSiFGN2LSNCKKDAaJQpwYhEnMZNFCERqjQOAlGrqjEjo+hmHT7/hoqURRNQZ+SOjFnLr/JNc2d/C8JlpooDsqaSwp5YqkaGTzBWbnFjCKRezE5NbqGuFghFvOYKoOhuXjJxIZ9jBzRWJ/yIXzN3ng5BLnmn/NyuAib195j/cuv8rN5rucv36W8YkHObG4wGqriaVJvMgDTbK+eZa8pTBbcCkoYKc6SRNGjYjmRoQZGmTGbH7nHw+5uJ1g53XqOxF//GWN/lCyf2rvVml7O2G8VqIXDMkU4O6lCD2K6aoxTkXDzoCQMTgGmqKiAlPjGtJT2N1UmZx3WFnzSBMN94nPEd16H81WKGVUnKzGKBEkA8CQJBpIqTM9vacassoafpxiGAmBSJgfV9i6E3D4eIFr9SEZJNLQyGVcLmz71PIZsnlJraLx7fdjfF0nxsXSU1JTY75Qo6cG5HSNd9+7xpNPHuXr3/se1zZu8Jcvvc0H711DSgUtNcjZOsJKmZucQI1DpKGzUCyxPDXBzd0N7mw2uX/SomoJMm6EJnSuNkJ2wwQUmMxLbu6oDIOQYKeNauUoFfO0OjuIUUqQJgR+zP7ZGbI5C9XU2NjcpdutY2Uj8gVJriSpTdnUpkxm5m20oqDrC7RURXcV8pM6hSmVqQMWmarEsEHT5U9CmsnaTp9ekBBJMF243e1zvZnS03T2H83z6L6UsjNganYaPQ+angVNYxiH9IcBr753j3pniG2YRPiksYrtOHi+v6cTURUmx2eIdZXRKEWmEPigKAqmo9DoDbm7ucVvf+53fyaC1nvn3nj+/PlzNFt17m1s4A1HbG9u8+gTH8G2LLyhh24a1LcbKHKPIXXy0cf4+rdf4O69DXr9IRO1CdqdFrphomkqpAJkClIgFYmu6BTzOUrFArWxGl/7d99grDaGNxjS6I2oTO/DyZZodvusbGzjaybD0WgP2JzLEQY+J04c4dCRQ9y9eYZBvw9S583X3+G+Uw+gawqN+jZSUXDcHJKUZnOTdquOqujk8nnMbJYYgzBVMS0T27WYP7RAQMjK9iofXDlLvdPg7Q/eoThW4ZGPPEIcBwih8OGFS9y8vYq0UyDGcnUcV6fV2SVOU5qtJnc3NvD8Ac3OLo1OnVa3SZSGRGnI9k6Hgis5f/lddFNDpODVWyRJARKDoKNQzLaQ8S7JKEVNVNrpNmos9wCu11/k/mP/CbGusrbu00o+pBruYjiboFqMknmUiYcI+tBpr2C6NokI0VWHQSvk0IHD6HpAEgl2W5u89e4P6HYjvHCESGGEgj98gkTzMZzb6Au/xW6rx2R1AtMQpJpLkgzRFYdrV1cRuom0JVoennhkjiMHTT54e8RLFwqoUse0HDQpsHMFjhw5RK/XJY73dqacjAOqBODk8ROYjkUqEgJ/hKEb3H/sPiamp3Ezea5fv0Gx5PKFL3yO5f1L7O40KBXL5HMFHMuh0+og4hTXcjAsmygIMQ1rb884Tchlc/hxhOu4GJqJm3FYX79LMPKoTZUIRj69dg/TtLl06QqT05MIIQhSwX/6K7/IwswUxWKeKIyRUqfdaVIoFtE1g1y2wGjY48dvvsFYrcIoDHBdF1XX9lheoxH+yMe1XLr9LkeOH8NxXWzToJIrcO7CNYJRyLEjJ1i9scInPvYJotGIOIhIewNO3HeUlc0NgjTl5P5DXL50di/A1UzmD1fZ3tzBUDVOnTxFtz1gYkahXDlJyVWYyc/z5psdDjz8NN969UOS1KOvHGXlxj0MPeGV9yx+/Tf/380W/1EGra9+94+eV9KQgReipCO0UCKlRqlko+g6laKDqasMRh4yEXtEWkXF1NWfMKj2CMXj5RrNQZOcnuG+EzmWJiZ5+eWv8OMPV3n7/fe5fPU2A1WArpNxcpw+chyCAUHgs9ka4mYyhF5KyVHw2iO2dyQ7rZStlsJuJ2QwUBAqTLgaWV3D9yIGiUJtXxZclZEf0B8JJJIgSvnUL/w6meUDrN+to5Aw8mJUzcfN11g8cAT0DHnHQWoWXpJihxabuw10PcTIaaBohL0eXndAMNxGUXIkiSTrJvzdz3+ca3fWGY3ukIqU/kBjvCawc1kqNZ31nfd459Lr9LWLDDtjjFWmeOf1b5PJOBzM6xRUmyxZ1Mgmigooeo7c1CSaoyD8EZmChmHpuHqGuX0Wz03EDE1BDpvGVo+phSw6Pr1Q4fa1IUagM3Qd8o7Ovn0ZTBcGQbr3juyU8ZyNHAXcW0vwggSRDdm+F+FJQbT/GE79JpqhUcwYmLbGKAZDlURCQcnv7RxIqZAfkwgJmlQxDR3D0jEVg/Fyjhs3PRoNi1YaUMzGDEObR5cTzm6EiEShVhD88vEcV5jm9vU75AoVDCFpi4SqWmDkxFiOw3tnP0BxgKHAztgYuQxmqqBYMZZuM0oT0nhPSUIa8Bsf/ziJlXBrdZX7FucIV2Mu+jZX2xle2ol58U7Kpa7N1a7kg7ZGNevS74aMdPC6LRb2L3KvsYGCzaAfo2kWs7MTOKUcloDd5ohyrkQwGhEme5BbxYzJ5HV8I0WJYxJPxYtSFDdCz40ojulY7l7BQpUKEgm6hSoVNCMgSGC93eNyu03di/D0gHIeHp+wqJY0xspF1IKBrZXRHBtUiKOAKE7Y7sVsroeYho6S9Rm2YgqVIt3BgDQWSJniFsfwVItAgKHraI5BIlKkqiFEQhAI/t4X//7PRNBqD7ef//DDs2xtrmPoJrWxGj/33KeYnppgZmYa1TJYWd/g+CMPsdnrcLdRZ3erwSAa4WRcBr0hkR8yNTlBrzvYYyDJPd6UoigoCKJAcHB5iVwuwwvffIF8scq9jU0cxyE7W6O30QRNwZjIM3F0ic52nX7XZxTENJp1jh4+TKGY5fjJ+3jl23/GWG2CZqtPsTSGk9nb31m7s0qYCubm92EYECUevt9nsjZFGCdsNzo0ukOiWDAc9RFKTLWSYXH/AkeOHOX4sVOkoYFGll/93N/GKAzQNZNsrsSTTz/La2+9weH79zE2VsYfDRh4AzY21+kN+3R6XXbbTVIZI4iRqiCVMWESEiYhs5OLvPn6V8gXDDRDRxEq1YzKY4cTHjrW4emHWnzk6D22L53HHcRUtCYt6xCGkiE2IVZMdu+8zaMPfQYcUFSLg5bBIL5Kt19g7d5hKqVluoMG1blp5EjS3uyTK6eE2Byen6Cxu46quqxtv8/GdotIpGxt3mLn3j2sUpWtHnxw/ibVXMzEI7+DY4bEXZ9QeASpjhADgpHk8Y8+yv6jC9Qmahhljfe+m2NM7VJvqLy3OknO0YiFII1DVNOi2dhiefkgnjciSWLCOMIwdRYXF5ifnqHVajIzO8OnPvkpjh85xu3rK9xcvc299W2OHD3Mz33yWZAxYeCTc8qksWA09DF1i1pljEq5jKZojMII4yetvySNKOSLKFLBsC1M3cSxXRq726yu3qbb63H0vsM89/FPIBLJmTPnKBZLbG5vYjsmEgsn6pMGIxRFYXxqlnq9S5z6uG4GRdHQVJ3d+jYqYNsGzU4XP9xDiGSzOVq7TVw3gyYVrKxDvlSkUChQLZc58+N3uLu5C4qGP/B59IGH6bU65JwssxNTnDy4DIbC5bVVsqUSth9Rnahw+uRTCCMgO65x/PBxJsdqKGrA9NRBcjmHkBxPHH+GM6+foceAwx95jNfefYfd8ChTmQz10OLMhYRMLc+XvvDbP11B68+++wfPJ0lMHCukQnDq2EFkcI9+r0MmP8ad1pCMq3DywBQnDkxRyseU8hrVYm7vtsuQGCoszVVYrCUcXaqyuRsxMZbnr/7sPaQiUYWOH4ToqgEYCGlwbG6chCEkKlvNIYHvg3Q4ta+A6yW8e9bje694BKqC61oYmsQRCpWqhWEldNohXmzgjrtsBz6jQbjXOFEt5o8couUFjFodZOAx6noomk62WGZydo7IS+h0txj4klKhiKHoNDtdwriHahiIAWh6yCDoEgQ+qmFSrFZQVIWt7SH3HZ7nwu01YvMeSWAwM51DN3TSYcLsZJbtzYT+nRxbVwRba7e4dv08tlGiXJggn69iW/YehV6oRHYGqzxBeayIU8qiujpDb4gmBb4e8d9/pkbs+PhdjQOzLuUqpIrNq5d6eL0UKzbpYWIZKXYRJouSrJYyjBQkChVdMDZl4pRMfvzjEUoqUFSbG6LA99MF5nfXMPUQRxOYBqiWRpqGoGgMwhQra6BZ0O5ElCs6lmVgOgpRGqNLBS+MKTkxd7Z9luYdmp5gvREwN2MQhnBoWufGmkqzlZKpjkj1Y3zsyUd5ZGY/D5wYJ2/rdDo7eL6CYgpyVpXucAPFsVG1DGbqo2QsYpmgWTo5O0Np3EYZCo4tu5xYfISO36fsxPzoz6/RCA3ujGJevnOPe80UTVUYL+XQdYGhSYqlEjk3j9fzCHxBz2tTzBaIY0EQBiSeYPnwHLZtUsgWqY1nMFyP1qBLvxcTJRKJQhqDoezRmlVDYjkKlimwLA1TV9FViVA0JBJFKNi6im4JbEPFtFUsRyFvqmQcg3zGZtatsJhXyeccVMvGsXJolkOUpMR7ojk0VUVaNtdvtLA1C800CGMP3cwg/BQ/kegIMk4VH0kcxIAk8H0s1UCmoKsqhga/84WfDQXPhytvP48OM9PTPPb44yztW6S1u0sxX0KNBaePHuGph05TLeeQIqTba1LMFomjgPuOH0PRYOB5FMolsoU8QpEYhsvI9/GDED8MqNVKHD92jMHQ4+U33sAyMxQyBdqtPhqShD2/naHo7N69h2nqqLpBIhOEqrGydo93zpzjwtUrZApZsPKY2QLtfp+t7Q1URbC9fY9TBx/AcODHL/2A2flpRq0BZMa4t9Ok3epgIXj0weOkwmNsssKg12B7Y4Od1TXifpvPfvIpPvmJx8nnDAzdwfMCDF2j29hha2OFQ8eWkUGEbeoUSi7FQhEhYHFhjqnJKsWcy/65GQr2BBPlwyzMz3Ly0LOsXj9LotU4ccBHb3pMyXWm5YBW64uceqxLqbxO63aei9d2EaU8f/2dGT771CqBd53V9RKH7v9lrl74NjeuvEa/3+Pg/CKX61f5/hsx33mxwMjyOPLgQ7gZF13ZUxXNLE2zemeTZnuH/YuL9PtdUhEyHEWcu3SW1dub+FFCoWKSM46xevcG3WHE+VsO925dp1W/TrGQYWH6BFs7NwhCSA2PnFuh1xowWZlgtrLAg08+Qls7ycBd5vHHj7PT2EAQoVsZkCaJiNja3Gb5wAFM08SyFIhDDi7M8eRDz1ApFnEti3ajTpKEHDh8gMc/8ignThxicqoCUqCkP1l3iQNSkWJYOn7k0+m18f0RQ2+IpSuoKHjDIflsCcdxQVHQLIek3We4eo/t1i5WKcv1W1f53b/7Ze7evcvsvhlOHztCKBIU28KKVUb9Dtd3dpg7cpR2e8C3vvENvvRrn+XY4gHWVtcIgoBsLUsYh9RqY+zutPFGQ2zbplyp4tou3d0Ot2/cpDo3Q7HgMpaf5M2XXqXX3MDrdSlky6ys3+HdK+cwihlazR2qrsFStUA7ifng8lWOHjpKycqQegOW5xb4xg+/ztTsGLadBZGiICiVqtTKNrlslWI2y1hO597WPURsMlWdZthYRZdDzl65SavZYxj7dPpd/t5v/95PF0crjiW2aTFQIoZhRBjt0ot8kjDEtm2mrBTXcMGFRG1TqxTw4wRLNZmoZRGqhkHE8dkib6zVuXh5nYcWq2BooBqASio1UqGjY6EKlUhksDMZNLK0eyNyDtQHElvE5GouJwpFqsUujzwSo5VzGFaKofn0t2P6QYBMBHbOQmun9HodjJKG7hgULAXFGzIxMcF6fZNmu4mSmoyCAF1RqeRmsUOT3WEDEQsmZsfQDZdw4GGYTcxEUimW6DtbDHp9Ul9lamycyItp7N7D0ODXPvMZXnhnjcPzNyCepG9vslSpcv2qwr3rGh/+YIibKeMHPZLEYGa2wHh5lvmFaXIlG921aId9YjUiUJq0mgP214ropo5RzCCVhNHQp93YwR7oCCtDt9PigUUD1/L5xjt9LLfDtNT46qrNLxw30boRaArzboFQDGi1EuYmTcqlFKnatHckwg7JWiboKkkQcFXdh56t4Kz/kGTcIKcrCKEg1L2mS5CIPYK+CqYGVt5kt6UyrodYgKJJJFDMWwx9j5lFl6vnPIa9IZVKluu3JYmi8Fwp4lOnCnz90pCtM3myY29yI/woUSyg3+Wx+59B0TOcFCNeO3+HUw9kaLZqJKpNpxVg5nIkMqFc0ukNIg4ezRHrfaxKnlJWsN24Qy0nuXitTmXS4rLaI4k8yraD6bhM1/ZTydfwR00sJ0O+MIZhZVj5N3+K7Wjs7LYplicYhm08b4SpZbDtvepy1rDYDe6wsr1GkChopkaMQMYCKRS0SKJlDVQVLDXFsjU0TUMqkjQBDYnUFYRIEKmGrhqopgr6XqtNM9hbSFYtTHOMUFHwRICrqURCEsWAohOnPoa+h6KYr0XMz9e4+EGLyYN5kAHeYI8ubYkQGUr6wx5dVQWhECcRtmkhkxSpKui2g6HJ//8OnP+PH78/pLPb5PCh5T0+T30Ly7FJwohOp8X01ASN1hY/evMNVtbu4Mcxh2ePsL21wbUrV3GyNr/5pV/j3sYWr//4x/ijEUmYUMxnmZ2epFDIkbEMFsbG6GRdnvjks9w9f4vQ88jmMwy6AzTDRqQhvXYHw9i7NYyjvdGjadukisA0TcJQcvoTv4hlWfT7XfLtDnHgUd/e4onHHuXk0ZO89PoPefjBB/CTmCgRePVtet0tDENleWkZXVNYnJvl2s2rmLaDriu4bpY4Eays3kE1XA4dnKMftJmeqnHz6hWq5SJ//8u/y7/9xl/gBz2EDHHzNobpMj05Ra6Yo99tMzc1g5rmuLV1HaFe56D+AGdXXkUot5iKe3zi4S5/+MYEbjlE5ir0zRb/+g8P8Kmfn6B95xpx+iSRnnDyuUf41tt/ioZJJMs4TpXxyWOs375FNbnLj364xVOf/TTffLHN1P458sUu3XaParmIbZs0gxG9ToNBr00ml6VYKKEIH4DeqI2mq6gqIFV6gz7lokMqVHqDAEu3Wbvdp9MwSJNzBHFIbXyR9d1zyLCE5w8BlXZrwPzsAp7nkctl8FMfXTf4uU89R+BHnDt/iXsbdRTVIZ/Nsb52j/3LB3j//DssTM8gUVlZW6VaLaNZOplCnjD0GY58mq1dgiBA1TRU0wRdIfD8PQ0NkJDghSNGwYiJ2hhSEXT7XdbX1ymVykxNTxOnCZZhY6s6u32Pw8sHOfuD62Qnqpw+dT+Jl9DxBrgtDSWKmRovc299g9r8FAeOL7J66yYXzp6h2e0gRcLrr73F/n0TDIZNBn6IVpfMTI7j9X2qxQJxMiJKQE0tcm4Z19nkscdOcfj4CcIw5MqlNVZvb7C0+CCt7TqDtQFjGZN//C/+GX/+f/97yqUq/XYHr+qw2wtodbt0+z6monFnY52vffsFRClDplJkZmaKai6PDEM0VUWxJKu31nGtHMpUmVJ1mjvb1xjEXTKZHHfW65hujp7vUxibwDCV/+CZ8B9l0CoUMoSBh9QTKtUi9d42WiSROJhpwN1rA8IDgvi2g2MqhHGI1FSiqEEhq1E0Yra6KpvbAsuOeeRQiUTPE/YliYxBsUkVFVU1UBUDVTMgVWj3PPZVDUaxQX5bp+cJUpGQtXWsrMV9948TmgaJFVPv9Rj0NYo1k96OSXfYJBYmZs4nHS/S6/n4gaDXG2CnNmytE/o6hYzLxHiJdhBg2jka3hU2eu9iGxr5TIlr167uQVnzJVRNEsQjvMZdjNRC1aE64zIIrjGIYbF2ioX7H+fcpX/HkSUfRgpLM5IL7y7w599v4YceSZRCWkIOu1iuQ3Usx/6DMyzNlRgbL5KxbNAk/UBwZ/s8W81V1u91yOZLjJcnsHSTifExZD/Ea2/RGCX8g399gW//VxOs7rS41QgZz2RoZu/jfzzf4h+e8Fnb2aU87jAuTV7pBHxmzGArgX22Qb21Z4/f2Anp1iNM2yIg4lgxx/qGje5vYZVMhKahqClEColIwNTpdQI0Q9v7I2gJjgNdL8T1TGzLomhIAjVEiyLGxgvs3h3hqSqP/fx+vv7iXeaLGrom+XbvWb7S1rk/eY2iPcBqjmDaQHeq3FhLaXz3f+JvL5c4Yz7Mc489gXTWMG946DXB3JiCriv0ZYxINWoTOjtrPTJ5ycSYxkj4JEaFh5Z+Dt/XGDa+z3RikC89ijDzuJZLqTiGH7SxMnPsgbANRBxxYGmZth6T9gAAIABJREFU2zcvI1Wb3UaDjJtD1wLiUYxpuDi2ztZwnUu3Vuh2JDZ73DVNSdFMUFVBgI4/ChFC4LgmMtjbx4I9+Kyj6ii6QapoSCEgiTEtFc0CCSiqIJFgUsbOHKKRjJCjNVTVx0XDUbOYlo6iGCgIDMMiZwr2LeiceSWEgY6bNQjCEVIrkEQxWqoShxGGk0ca6U/ajT66oaHZNiZ7t1o/K8/5M+fwfQ9T02k3WyiKwszMDCs3blIul+n1+2iqztNPf5RnzE9w8fIlxrNlln/h00xOTxCnCS++/ApvvPFj3GyGubk5ivkSt2/fxh8NkCJm+uAivu8xPT5GXtMJI59CNsdw5BMGCbYKUiaoiiTj2sRJgCIkqqrsAUGFhm1a7Oy2OXP2Co8//hEefvQJXvrBd9AMk3q9ycLCEqkIaO/ucuSh01zfuI2qqoQjnzQc8slPfxZDdfjaX3yDUjmDoYCKQioVFGWP/fXWO2+zsbHLcBDy8KPHePWHP6DTbHDx3Hk+//nPU8uPMbQcdEul2dklny1Qq87gjwJq1WnSwCVRFOb3TXDl1hXOvHOb+WmJOrCZd95EaVdY3xqjVCnijB2m6iyxsf19Ll75OOffu8X8vpPYuas8cOJBSrdypELHtnKo8Q5PPPXrvJV/idb1M2RLOSbKJ/iD//Uj/P4/+u9488c32Wl3qZZLfPSpJ7FNnZU7q6yvr3P8vlMIAcPhkMFgQLvd/ol8eYCZscjlMgRRxI1btxGkRIogl0mo1zP0h3V6vQF/6zefwGiugExotnaw1BxBEDEzM8fK6g12G5u0R3XGrRksowwi5KknniZMPL75V9+nvl1nvDrG6sptnnj0Ca5cOo+OQq6QZWNni36/y9TMNLab+RuBdxAEdLtt0gREqpAkCVGyp95SDZ0wDGi3W0S+T7fbJYx85hYW2L9/mfpunWK2jO+F3Dl/kaKT4d3NOoeWD1JdmOGBU/cz7AwYhQHEAfdu3mbygRPsmxxHujkyBYtHTvwyb77/LvuPH8YPAz744Czzs88gRcLO5j2mZ6fIOVkuvHeRSqlGzxuihTG1SgmZxuSzOS5fuMLVSzd47tOf5cevv83E5AzTU7PcvXUFveDyhc/9Im+/+CYrH1zgrqlRHHN568r7JIbLw489xbe/8z0eOvUgk/sXefSTHyWMIqSukiQRwchHRDGKIjl38zwmWU7fN8uf/8VfIYTN9OIcmmNw+fwFjj3wMBeu30Gz8oSodDqt/+CZ8B/l6PBbL/+r51EU0lSSd7IMvBA/HmA7LjcudHES8G6GXLt0i9aNVQJNJYhTHNNGxilCcZkcs1mcKZGImHonQbUkGzfWuFNvI1HRdYtMLkch56JZBlnT4vD+A1TdCEX4NBs9dpsRqrSYmIKZMYVGYYnjMyEYZcJ4iFBMFDFA+AGV+UMMBhE7q21q8zW6ocqB+QfZf+ohSkuH6fWGYG0gtS7t4QbJSBAM+8R+hK5lSUVKEAlUM0bVNLRYQ08FjqGjxjp2ViEWCVHcxUqy1KbKTJe3yZUv4BgqJxfmaN7T+evXIm7f7BN0A+JQg0RFypgjx2dZWhrnuaeXODCfw3ZjBE2ipE4v6HFr9wKN7k0Gg4hWQyBTMOwMGTeProAfjvAbI9JuDy/IYDz2BMHCI3SKh2jlDrLarHPzWofnPwf1JGZMt/lbL+g8vFzg4UmBU0iJFIikycZ1IFTQDYhUDanqnKjC/7k+y/16iylngOKqqEJFlxphGqBZDp3eCMsysUyQhoKRqoSpoN8XqCKmVAEzVJifsoli2Gp5iJHCvB2wfuIL9FoD7GSIv3UbY/ww/coD7BQewp+5j4+m1/nS5Iv8ynKD+yqSmVKWa2ev0t68Ql8/wsT2FXJJhDMIMDyfrU3odk3URCWJRqiKQ7PuEQQOqzs3+OHF73Nk6UHmjzyC3x7i1ubI5SZxXQMpAkSioGoRUvykwm5aSC1Dd3eLURyTyhGWmkWmAlOzuf/UcVLFZ3XtPOFwSDWr49oSx5RkXYWso2PqCmhgKuDqBoqakJoKYZogFQWZKoSpxBuFpLECqSQNDVQJUqokiYJIVJQEivoE2cwi7V4TRIO1zTapzOKoLoaRousCIRJSaSLFiGbbQ08i7ruvwr3uiH5vhGEXUQTIROAYLnq2xNDv4UcRpq5jawamriF0Fc00+M8+/Xd+JkaHF6998PwzH3uGGzeuoaoqpXKZIIjo/g2pe4CmacRpgjccEvoB//lv/haDfp/+sM/W5hYrK7eYGK9x4MABjh45zNHDB/nYM08zPztHIZfDtU0SRVLO5TCGEUY+R73XRlUUbNNh0O+hKqAqCqqmEkcxtmUTxRG6aWAYJkkqUBTJL3z8GRzb4s3XXmV+fg7DdBiNUi5evM7y/mluX1+hvbNLo9tE03QszeYf/oMvs7W5wysvv44iNeIoolQsEgQBcRTT7Q9QVEhlstecBPbNTfKXf/k1bMvEROPcmXN8+uc/S7O3y9WrV5idneb0g6cxNY1qtUQYeXxw6S/Y2DrPoCPQkwrJjspUbp71rYiTizuoygzff6dIecKkKxxa2w0ce4Hzq6/y3vk219euce0inL3wPZaPHMVyNGQUoSsx75y7wNr2babGJun222zc2ESEHjMzNVbu1lF0j2IhQ3Nnm2efeZYrl2/ghynZXIXlfftIYg8pJWESsNvawhuEqIaCZQlgglHoE6UhlmUjlIhsUSDjCvVtwRNPnsRRx7hz9wPcfIZ+Z8DpU6cJgoBme5vd9iaT01N4Q59qcZJOu8Ng2EdTIfQTxipVvOGQjJvBGw144NgJlhf3kyJJREqxXOLu2hrecEjgj0hFSiazV4TI5bJ7Y0Oxt5oQxCFRFNEb9JFCYmg6hmGQz+eo1mr4/gghJAiFKxevsTw9R7vRplguka2USZB0ez2UWMFXY8wk4frZszQjn8172xiOS7HgcufmCo1Wk1ESoRs6ZTPL4YNL9AcjojABCccPH+fyhSvcvHqL+f0H6fbaSGJazR2GvYAkVrh98xpS0bh+/S6arnHy1AG27q5gjVVJMfnBS28RECCyCr6hstnz+cxnPs3q6jq/+sUvUSgWGSU+a9v3GLV6xFFCKgUyTVF1HcNx2N7ucHB5AU0V/NN/+n8wta9IvugyMTHDYGcTVIOp2Xl2GnVyuSxTE+P8ys9/8adrR+uF7/9vz4dGyGjUo1yt0u93iX2Fcq7Gve0BmuYgYjD9lFazT7wzYvZYlaUDVbRI5dChKjdunCNMs0xWF8jmTfKuwze//m1SwyXjZpmolhir/j/cvemTrNd93/c55zz70/t0z3r3/V7gYrmACBDgTlAkIUqkFkeWRUtWokh2EssVJXFSSuSgSnJJTqoSOXIiKYpKThztMk1SIimQBEGQAIj1XuDu68zc2Wd6pren+9mf5+TFsPTSeeMqLecv6KruPvU9v+XzsWjWXaaqVaoVk9lWhQNtE6UStncHrG2FSKEZY+POXsCiw+s3fP7Ny3fYzI+xE3bYK4/gHX0Pi4nAmZ+j+ugpvrWiscxpzEaDaBwS7u0ggbgXsH6vh1FWyK2CUmaEGw7NWUi1xhUOnjJBSkqRUQqLQuRg54RJjE5KHj7X4eypgmbNwbJTfG+KI6nDl761weamZG05oEwFBZCVJa2pBk8+dYiPffQhOgsGmhTDsunFAwzhsxeXrHW3QGygy5IiF4z7MAkzlJK4hkCUitEwoHu/z+445Ed+9kPMzU4hyogyT+kNYsZDwS9/dMKluzH1SsavvXGWRtXj6t2cj50asTGSXL+TMlif8AfpMzzmL1FKhTDAlYKaJ7hb+wEWet+k6hQII8ZUBrHKKTNFUEQkaYqJDa5ClDnKNHDzAqEM3pA1mtsBDz5okWcVorRkMImQmWBiVGiOlnnjrs2NeIapfIhpjplce4GF4XX+0dl1drdi6lOKvWGJzsE04ObqhMkwZNM7wHR4j9x096W6hWZ5R9EfVZBpyAcPZyxIzbVhSBBpyiCCWHJx+R1kIbhw6jzbQYnISxKhsUrFsAxQQuKYFuMs5u7aCr2dLap+lZ3uNmmqycsUU9WYjEa0Dx1EqRBTS+brbVq1Br5boVWp0qnVaboVqoaDbxtUDI+GL2jadUyZYhcau1QomWPkkMYgDFCZZFCElBODfpQQhFCEmlGQYRUGDhbBaJulzS1urmTcW0+oNzV1L8YhwypDClEiCk2tzHjvYw3OH63z6tUthkGJpW3CZEyeFJRZgaz45Cqn5rpQCmzHREgLJQp8ZfIT3/fTfyuC1r31m8/1+n3mFmYYh2OuXr1KqzVFu9VASYlh2pSl3p8RNUwOHz5IEsVMognD0QDLNDh25DBHDh+i026Czul2N9jb2SFNQra3Nqk027QaU3Q3tqjWqmjHYP7QAVbXV4njmLnZDkJqGs0GRbmvyxHaxrAtSlGSZwlFkVKpuEy1PTa2N9nY2uTGjds89PAjHDi4QLXi8tjjD/G1L/wFrrLJDIEhFP/dP/kvuXHxIl/58y+RZimlAeM4JKdEAZZtYTiKVEccPDxPUaQszM+xtnyLtY0uvZ2AufYBsijj7OmTBJMxU80m87PzNOstJIIsC7l5/SpH5z7N2cM/QL06xfGTJ/H9BfJiBqvj89KllNcv+djNPo53gflqjcX1a0jTYmtLMHusIElDsryku2nxne+8xe5ySjGKWN27w6sXtxjeGXJto4fjzkFxjbX7i7imz4999tOUssKZM+cYDoa0mlVWN9eJswxlGjx09jQ72+tMJhPCeMzG1iqTIMc2JdPTdbpbiu6gj5TgeRWcmkdemhRiAkJw5+ZVjh+vUzUOcn/tLp/51Kfpbu8wGQ/pTXo0p6cwdIUkSuhurpEmIe978oOcPPYQv/un/y9aCKbabT79g5/mxPHDLMzN0qpVmZ+dZTSeMBoHmJa1r8sxDDJRIIAkjhkMBmRlimWbeJ5Lo9nAskxq1TpHDx7j+LETNGpT2JaDlmCaFhXXY6rZ5utffoFvvfQdrt25x+vvXqa1MEN3r4vveiyuLiOLkpuLS/iNKYZpyoUPPM3x2XlmGlMsbW1Qb9aYrTU51JpifX2Fufl5xkHEKAgpsoipeo0oHLO2skpu20hp8u6lq7hOlVPHTzE/v0Acpqx1N9DSoCxDnvyes3ziA++jPnG4d/Ump86e5db6OhQSM4aO5VNpd3j2+z9BSQpqH30x1eow154hz0uqtTqO4aILQXevz8kjZwgnEbt7Az766e+jRDDoblG3JJu7fW4vbSJsm4888wFaDY+Z9hTvf8/3/s0KWt98+/nnJvmALCqwPU081ozH8f4WVpLiUyHPIspScOChGRozLivXl9hd2SDL11i+exWVxqxs3OT9Z8/ik/CFr32dOFUorWi3ajTbglotplIt0LJAqTHNWpO5epU0GzJJc5ZWA3QpSPOUoydnUdrj/sZdPNdgdXUFrUuUUxANNwmTjDDJ2d4ZYhkmrusyiQKyJGQSBFBGbCwt4XouWTah3amzdiVGRgnjnsvCkRZhOgGhQUiklAgjB62oWB4PHveZPZlRyS2GtmZKCBg2+NxXunS70N1JuL8+QWf7r8csExw46PFT//FTnD0xRUGAiQSZs7i5xVdf+Q57WYCQmzj+CMPIyXMo85JSC8rCZjiIyL5re08HA4J4zCd+4Ck6C1VyRkDBSq9kdX0PHef8xNN7vLaa8atf9Tn56GnqXpNwssuF9oSlFY1FxEtTn2C24+DtXqNhmaR2QcMwafvwpeWEmUpCXSTEhULKEpVZ5GZBP9SUmYMjBbafYPk+hd6/2OM0o6zVOXm4ws2LIZ0ZRZHk3N/NKSybiiz5Z6/NYVcNHnT2mKr3iTZyhv0KrU7GI4cU84cmhJEiyjRSGEgpuLKYUwrBmbMP0t9YQucJhRAMYsHiALRhsjEe0wtsnjiZ8l9caCNzh+tb2X7lIJNsDvusbtzjkw8/weJwhNSSMSleVpAXBUmR8fqVy9y+fpet7h6u4aOLIUrYBMEEu1oln0SEmWZ5cw9NFb/SoV7tUHPbNPwOFWcG12vjVtu03AUa9SZ17zDzM01q9hS+fZCKO0XDrHCg4XBySvHATIFvOVjjgqDMcQuJnUokAUrY3N7J8csE4cDalsHN20PGo5DjHYd2XVJzcxAmrZrkN97p86WXU77/qSorwwoXlxM2+gPyJKbq1khKjSqhNT2PLDWFLpCmhWda7M9sexRlwU996m9H0Hr17W8+5/oumxurbG5ucvjwYTy3iuuYZFmG7bhU6012u3vMzk7T29tjt7tDa6rFJNzfygqCAMqS0XjIaDTEdSwM02B+bp5Rf8iZ84+QDMa4vsdAZljKpOb47A77RJMQyzKJo5jRJGB2fp71jU0EJnE8QRrgWA6GIdFlysmHTjNJE5qtGY6fPk27XsWxJUk0olqtcfvyTYLBCOnZhJMxe5tdXvv2ixw/fpy8zIl0gV/zEVLgmBZhGGK7DsLUFEXCoN/j8ce+h6C/x95gQhgkJJOYU6dOock4evQRHnnoCZRyUdJjMIggN2g1pmm1ZihYQUuBZR4lEPfoTrZ5dOEcl+7dZBBsYbkFx88/w72lb2DZx0jTAr8W89CJp6k3IMlLRmlKZvWJRIRjmXSaLpWpBmdOn2SSpoThkIMHD9KbXCNOc0zq1DvHWF1Zo1mvc/bMMe6v3kdZktmFGQ5Oz+O5CsMwGYx67PW76MIkSWJMQ9NsnGcSRQyDgGA4IctNKn4DaWSYTsTi9R0OzS1w7EQby/NwTI9wMmEwGJCKgkJoFDGOadKoHOKTz/wgZS74oz/+fbrJkHE4ZmZmhiPHjhKFwX7LNs+QWjBJEoShEEJh2xZxNMGueBRFgWWYKEPQaDZRpgm6oOJXyIsC1/UQ2iBLMm5cu8mhgwcZDHoMgxHDwZDRIODk4ZO8+dY7aMMA02BzZwvP97lw4VGCcUARJyxtrDMOI6RhcubRhxlv7nLr3auMRYbnudQtm/lmi0KV9Ad9Nje3KcuSLIu5cOFhdnd3WV1Z5tkf+hHu3LpPf3fCoDfBNHIGox1sq8YgGlCUEIVDPvXsh/h3v/f7/Npvf4WN/hKWKwiCklGY4DQ9vI7Dnav3CKIRhw7PI0RJZ2qaMi8QSmL5LrZpo7TAVAadVptSF6jSRQvJIBlx4th5dtc2qFkJUekSZpLa1BSt6Rb1WpWyLHnykY/8zQpa33j3S891+2uYUiGNksEwYDyJmJ87xebuHq6jqB50UfY2w40x2/d6VKerNDo+Zc3EcQ3yMMaUBneXVnjr0jWiMIfCoTPt4dQi3FqEtiaERUBaDsnzMdJUHJ3tUJQDgiDk2p0NlOVy4uhRjh0/y8bydc4fOkW3v8swjpmZn6IYBkRC4ZkW37x0izJrkWcFQina1TphNCIuNVmQEu4GJFmGZdhs3AhwTYtJXmCkCbV5j1EcYNs2hsgps4KK6TE/Y1GbCnB9m2ppEFU17ys6bAQuX7y4jZuWJLZDq1Xl0GyTMNZk2YSPfPQk73//aXyvAMr9lVlVomlzfXWR+bmS2ZZBrSoxRQFKogsJaITWUCZE6QhBwXg8xHI1586d4eCxQ5SyoMBkeyC4s17SsCocqCr+xy8NePeeiWEkTLc67OUhadnjyHgTbWvemft71EpFiWJVVjistslLONNxWAkmqNmniHWTRn4PqSwsNFpLEhRlqrlXmvj2fkVO6QRHauLSROgCOWlz0E44cNjmK+/GmDJjHGl8XRAYDS5twNPVZdp2SJYr4tDi6rKi6eY8eqqB0IogLchyjacsMh3yyq2APDbYu38XpSAVJnlecrsrGGY2kdCURcnOXsz2uMLxzoDeQPJKt8XDJ0+yG++RjUakhcvq4C4fP/sYN3sbKGGSsB+0BsM+y9fukCT7w8pSGyzMVYmSmGCU0azUyeMCZ6rGZDKkOwjY2hsSpxnKsxiHPbRUWJaDKQXSUkjDR/p9IrWF5VvYao6K28SrtVk4dJqjnR7H6zWOtTVdLFRfsjiZ496wwNFVbuyZFJlgdW/Ey++uIkqTI0dnONzx6LRTzk15iHLIooAn/skqq/cHrO3m1FsRf/xqRG41EPmIMMkoZYGpTVRZUOnMkQuNEg6GqRnnGY1Kk1gIlAn/4BM/9bciaC3du/pcmsRsdzc4deoUeZIyGU1QtoFpmGRZiiFB64I0T1nbWN8PIw8/xsbqOmlWogHHshiPQzrNNoZSuBWXQRjywgsvs3l7leMnT1GfahKHIxoNj83VdZavL2EYHhsrWyhhYiqDp596D+Ooy+ZOl7woMO3KX85RTU9P8/jpxzg6e5CzR4/RcF2mZ45w+foad++PWHz7EocOzXB3sc9UyyVJB2yv7aHKDM+cIg4iusNVbHOKLIKIjIbbYrIXIBCMyxgm0HGmeffmazRnWoSUDEuNXa1yeq7KG1cvc+r0MTzfJ4oTpJIkeY5fbRLFPdJsH5qZZfsA6LmZOUbCYOvuRcJgHc8+yaHZk9y6v81sp0ZnZp40s6l6GYfnDrLQnKVh1bjw4MM0HJ8ojYkzgzOnT1OKErcaossK7c5R/LpPMN4gDiccnnmUM+dOMXNwAU8JuluL9HdHPP7Yx+n482xtvcPmxjKWY9Ab7LKyskrTdXAqFtXGOb7+ta8iKfHdKpRjsjDBUA651jhulSvvXKJiKh5+6AEGgz1WdlZZG2wxM3OQqlWhEC43b93kZ3/iZ3j5tZf58ot/xuLWMlGcIoUkL0tajQa+ZxOlITkF2+GER0+fwIgmGFnMqL9HtdPB0AJDKoqipCw0URjtt5a1gZIGhnKw7ArxeMKr3/46K/cvc+PmJZ79zPcTDEc4wmRnc4tzjz/E1XduUAoTu9IkGIzYvL9Op9nhwMnjBMGEIhjzd3/gM1S0YrCywde/+jUub72N780xTAIW715hZWcNFYJVa7AXxAzHXf7eMx/m0ktv02xViDGZaXV4/kt/QfvoISZlwWhjlczIefKJx5idmuHOreuMBiOOnjpBZkue+dgnOXLsMGEU8cRT7+Pa5WuINKWqLIJxRM31efiRx8gKjWt7mEJRbVWQGvIwxnN8cgFhFuNKgaMkru9x6PgJ+ncWefuFb1P1Z1gN+rTnWswutCmLnDCYkMYJ73/Px/9mBa0//dYfPBeEYx458yB7exF7w5giF0xNzZGrCa0Fg1G0QY06G4sRuDbTzTq0fbysJJ7sY/uLVFHmBUGSkQUKVYNaraQ+VYAREmUxRSkpyxQDB9MwOT1zCIqIOMm4ubRHnGn8is/e7jKHpw6yFu1Sr8xRr5o4yqAfBviuQW87YXco0CqkKEtmpg5wa3kZ2zGZDEL6W1tkewFFKcgmIAuBoMBEUNom0TikPutTlPul6fOnKkzNgbAGHGod4MBMFXYVP1fWefL8HH/0nW2CxCArxH5rYRhRmhEPnZ3jgTOHOXp8hkarQChBfzTGVDZ7gcNbS5epNkYoy0SXEWVp7gMRZQkiR0rwXZdmQzDbrqBERqs2w/kzj+PVDKJyzF6wxnY/ZGknpVVpUCQ7YCnGo01GeYGXJZRGTi/ZgdjitnOKxcZhHK0phURrl+VhxgPZInFccLii+OdvGniVCp/93me5cvHz+HiERY42C8ZjjVKS27VphmHOvKexLJNUWpTFGN+rsbFj8/q6ya9flFzZrvP8fYsj1ohRnOF1prl3a5Wayqn5BkKXbOzCm9cLBmnGf/SkxDAEcQogqLgOK92S3/rznLk5AaokEPuE/42R5sqOhUKSmgKpNdI0GexFfHXF5epGRDKQDAvFuXPfw9TMATZX3yUcS9aje5yYOsQoKTG0QijBrTu36Q8DrNIgzXMqnkej4RCWMVFQoO2SfJzTaHfoT8Y4honneRyebnGo7UCZ0mo0iZOUUhboNMV2B/QmN+lFEW5pgOiQkKFESq+MmfEEbdXnll3lf/43m+yIGtEkRSQpw9zCECV2LgmSEmciGJUh3d2cSTHkp588Q+alfG4955d/a4xhDFG5iWPEvL6sWNvo0d8OMS1JmUXI3KRQYAlBtd4mLzTCBKlcBsNdunGAyHNcy+EnP/EP/lYErV/7zeeeO3PuNJVqi+FojDQMlGXgey6GUnS7XS5eusTBI4fZ3N5iMpkwGY1pNZoMgwCUwSSc0OsPME2DcBzhuh5zBw/y+c9/kWtXbrG+vssrr77G7//+H/JDn/4h/uRP/oQXXnyZIMrpB0Mc1yNNEoSAXq/Ps88+yzMf+wCPPHyenZ0eSpiAotcLuHH7Dtfv3MZvVDE9h3/5m7/O4toyvWCPbFjyygsvcmC6w8x0BSkF1foMXmzQ8FukYYjfcrBMSavu4CqDzb1VGgdq2HVNzTAwDZfGzBx1v401SfjYk09x4dwZ3rn0OgdOH0JLh1Z7irwo0LrENi0spTCkIk8VFa+OIW3iMMWQFroQCC1YvnuZtIgpC5dGtcFMtcLKTo/hZEwwGaKMmNWVNebmj1FgcWPxOp3ZGY4ePc7x48dJ4gTTMEkjh0MHO9y8cYmTRx6l5h6gu7uJp2Jc26DbjZiabrOzu8z6+ipnTz2AoV1aU5o0tpAGbGyvMhpNkGYdv+Yw6kmkLAnjkHFWIhxFqY19jh0leWnt+yxNySjYYjsZoi1JVRlMdrtE6YS//8kf5/Sx0/zXv/gcL37nNdZ2dwmzHK1tbLfKZBxz5cpN3nrzXW7duc/NW0uEYUl/NObe+jrzR46yud0jS0smk5g8K5mME0DhuVWiMCOMQjSS8TgmjXOuXrzCw+cf5tEHH0Yom3oMaZDw2qVrPP6e9/NHv/ob3N/dwvErREmGUsa+VH5zh288/wIb99c4MHeAZq3Gu+++zTgK8Gsejx44TWdunt7GKpHl0akcpzPj84n3XqC72WWv12UwGPFP/9vn+Gf//Dn8Woukv4+m2NreoH9/jUa1SSDg/KlTjIOIc2fP8/Dkft/ZAAAgAElEQVT5h7l08TLf/MbLvPTKm6xtbJEkBTfu3qdar2OaJqNRwD/8+Z/jyJHDXLr4NhubG3i+g6ZkMglRysCwHFqdNqZhoNOCWIFZrzFd7/DzP/mPqJ48zMyTjyAPdKgrk2q9iuPY+L5PFqcIKXjf43/DWodffPP556a8Gktrq6xuL5EOYmKhUabJuVOzrO6u4xkuu7dTgn6G50m0LbEtEy0E8TghjQvSuCBPJfGkoNqs0WzY+A3ACsmyHLRElAKJRSZiGu4Bzh88Q1r2yNKE28u7KGxqNYej89N4rSlazhSD8SaNSpMknrAZxMxONXnl3bsIYRNORhyaP8Hq2n1c12IcTJhqVFi+chtDKLI8p9SALFFCUiqBYUvSouTjz1ocmIOZaQe3LqlKqLsHGa0oXvjfr/OQbfHgE7N8a6/Ci+tjSrH/Z0aXmBryUjJIJhRGRpbHDCYleeby1Vdf5927i+wMl2jVEnYHmuW1EXshVD2Tiq2xTKgYNq6hcH2BX1HYHrSnFK1WTCm3SfL7pKP7hElMd+Qw117AJmcv2EYmMcMkhUlKrsFxXYZZn0KHaGtCGfaZjId0dyPWVldZmGpzo7vKhxYMDhyu8K83q9xcWqdSsahVp9DBJjH73+feMOCLiw102SHsVTnf6eMIKEuNyib82WaHq0GV1dzEkAWGyJnyXV4b2Lw9sHjpRo9eN6fsa6ZaBQjF69dirtyKWV0v+JlP1rFaOWVuIPMSKTN+/+Ux//qPChIt6DQVFVUyjgzujRxyKVG+DQoMSyINiWFbyBLSWFFkKVmS49oWXmuKhWPn0aVmdekuo3GPY60qk0JQYrC5uc32Xh+BxDRMEBbtpo3IcsZ9jcZBC4nt+Xi2g1sVvOfMQU7MuVzdus3q1i6O56FsgzwrMD2DregGk3FKSUnLPgq5TbvWBFXSNDN+94V1PvdGwF98cw9HGaAlWRKjAdORkJXERY7K9v2ROgPp5ERK8oevdfn8u33uLJVEk4iKUSczCgoUOs4gLxGqZGGuRS5tMDLkJEUqjT81jbAUrjQoigkhMRXLwTAUkyjiH376Z/9WBK0//Xe//dzudpe52YNUXB9DKRx3PyCHUcRed/cvN74m4QRlGCgE7VabXr8PCtI0Ba0xbRPLsDBsm6n2FN96+RW2N3ep1ps4jkutUufenbvMzM3h+DXcSo1qvbY/FO96REnCsN/n+rVrXLr0Lu9cukKWFMRpghASITVRHDOeTLh96yaOa/PRj32YnZ1toklAmORcOHsK1xE4zj4WpFGbZjAa0ppu8ejjD9Mf9rCVRRqEUErcuk+QB1QrLlXDx3LrOLUGBbBQq3Fgqk0eR8zNz2L6dWYPHqFSrZJkKVKIfbehZWNZNmE0wnZMXNdAiJI4DpiemaLMNcvL1xBC41emydOc7/3wR7h67wppnvPIIxdoeg2ajRZ5kdMb7DIKY0oN48kYXWoajQaet6/yyfKQKBjzqWd/mBvXbiFFwnBnkWZzCstqsrG9jVcTLC/epeZNMTczj9A5UTxgHCi2dtcpy5SyMBEypukfQOoM17cokYRlSJ7llFmM5wgMy0dKjWGXdFoKE41tmRiew8kDJ/ixZ3+Y196+yue+9Cd0w4JCCdyqQRyP0VoSR8k+9iMvKYuSOMkIwpCt7T2uXr9GpktWN7cwDAvX9rAcRZKkrK+vsbGxiWEY1Ot1NBLH8UiSBF1KXv3Wd2hUayipGPa3cR3wW1VKse9M/fZXvozbamFYNnFWoAwDISQoE8dx/3LsJUsyjh85QDAcYBsKp2wyyFfZ2enT7MzT8A2Ozrfpb9xnrztkb9BnaXmFKC5Z21zBsT1u37iLLHMePf8gWZ6zsrON47r0uptEWU4wiXnhxZdY39hCGTaVRosszTEMG6dS3X+s7O3hWA5vvPkaEsnf+dEfwfFcBtEIw1Z4pkNeFKRZTjAaoZSi4jrocYgcTBj1BywNuzxy+iwzXg3KgvlOm2a9QcXzydMcz/OwbZvHH/zA3yyO1vrGIrNelWC8xmScoy0HtRuST0Xcvt+jSZPtWzHpSCAJyTKNqwvyNCLNYBImKAxyDbZpMHe4Sa1m4NUztJ2RJCV5LsgzTVoUGEIQFZI745uMH7yAaXi4TgKU7Pb2+MVf+K+4fuUiluNQIqlb+9uMo0lOGI4ZDBqU2kKJkkMLR+hu9jFNgyRJaLVbvPvit5GFIEcjlEQJY99MrkqUKQFJHhe0Z+d4541Vbl4O6fYSlONjLl6nnWmOLNisb4T84aWcV5MAV1YoVIRyNFR8NAUKBVlJrztmOAipN6osFVsoQ+NIRSoki5slkhy0gyxiHDPHsQz8io1VSrRWSEtSkKFKgyjOKKUiTIYUiUbEAt9r0W7NoMuM3nCHrZ1NqjOHcJRFqkLyQpNEEU5FYpglFBEi9oiSDJMxjz50li+8+BcEQZX/5YMe7/mF+9ztCywbBuvf4F/9Dz/D61cv4TZ8bgQ1VsNjHJqOCSsGO8MJv/6mw3/+6BBLCly/yc3LNp6a4HrQn0SkWUBcCh6vGMybAa/cN7iyWDIuQFrg2JrL1wt0Kcgzyf2uxGublGWK8iV7Y8HzFwu0znn9LVjdMXjfBYMjBwRT2AwnHomMkRhIQBc5hQZKjWWDkQuSNOHe4gqqbtNseTRnDjF94BjfeuHz7A5f4n3v+QiDKAZD4QkDLSQpmtlmkyxNKEpI0hRLFEitmG24nH3wID4mVrNCmAseOtRi2BwRlzlxOWY03OFEq0E2HINhkxcZtqhitVpIIbi1vsXGdsrq/V2kEAhl7QMsJwmGVAi1vyVWaoUoNQUCZdgU7gShHPRE41ddBJo4VAhMUh0ic02BJskLDMvGtA3urARkIkPlFtQazKYpBi6GjliYU7x+K8O2TQqtKPOUsvyrvXP+Q55Wq0Wz1qTpVTFNk2AyIitToigiiiLGUUir1SJOEizLYqfb5cKDj3BvaYl6vU4QhaRpjmu7JElIo9ZEl4I4Trn49mWm6tMEkwlSK/IkpV6p0usHTMIY0/XRacJUZwadF6RZBo6FbZi4fp0gCMjzAnSx7+U0JK7VQIgCXWZcfucKj114lIfPPkBvpsfK3h6PnjrLxuJlDAXB/YDTR4/Sqfp89CMfxDJMXnzjFc4/+jCvvvoqvrbYfy9IGrUKDx29wM2lDUaTEL9p4ZsVdnd3GEchfquNLRpMT0+jtQYhkOa+Vi1JEqSUtKc6aK0Z9IdIJVDKJk1ziiLDtm32eilR2GO33OXqtRscnj/M1k6XIorZWL3PXrfLM9/7CbY3d6m3mriWjeM4VPwKQRCQJgkayaDX58zJB1ldXuLK1Yu8970Ps9ZbZHn5JhceP8bXvvQarQ4kacby8l2+55H3MxmH5HmOlCZ7e3uEYYgqNJZpYStNxQbD8ljb2mZhvskj5y5gS9jtrrG+E7Ox0cf1PO7cucm5B8/jux47ewM+9IOf5M+++Dwv33mDe8s7GHUDbQK6hm+3GI720KVAfrcVGCUxSoJlWShpUxiSazfuodA0GzW+5+FHaXUqACRptM9PrHok6QTb8smyjE57mp2tPY4cOc6f/Nsv0GpUePqJJxhLRRjEHJg+gsht+qXgE48/SqksXvjWq9hOizTN0NrZV2yNxqi9Abu7uzxy9ge5deUas9Mdvv3tO7z32TN0FnxOHzjFTneDZz7wISa9DS5e/mMsU1FIGE7GCCXRRYmyTLL+kNHGFmfPn+XW+ipty+H4qRMsLq2xMD9Da7rD9nYXjSTOYhr1FmEYk8kxjudAXiMJY6Qo+MKf/hl37tzhyQ+/lyNnjhAnEUYhKbSmUW/tb8zGEWUquH75Esv3Flnp7/H0Zz5FvVIl6Q1pePs+xtFohGXuz1xKKbFt+997J/y1hNfUZxZR0mCvN6LVauFbFtLdF1mahUs4MDClgTJzsjIjT3OKpCCahITBmLzQZFqgLJP2tI/XKHEaGaUZMY5GpLkmyUqiTJOmBlEqKDOJLCvYsokofHRW4rsWYQl3N+5jeVUoNXESsBGPSW2LxfUhpw7P8Mpb98nChOnpNklSoC29P8xoKu5dvk6epAhdonWOchS5yDEchekZSFshLKjPOPzLX7nHWy9DFGVUKg52LaPqmBw7ZHK84nG24dApx3SiHe4urTGZRDQrTWqeT8V2KZIQgYnjtKk3ToA0iPUAw1JkhkmaFZRpBmKfSH5s3membmAXMBok7HZjdrdTVu6OWb4RcvvOgO21iHE3xEk1eZ6TFJpguEM8GnD3/k2u37vMIBxgGAYH6hVmWj6GlFjCxpQuJk2KqEExbuKOG9B1ef7zbzNZMugtp+BplmMLX9XwjSpvXt1koV3lQ5/9JYJmE5Rgp3uH3TDg7maP3WxCbjQZlzX6qeKldWhWFb7rEhUOWS5xlyLe0y/4wEzI6jbcuJ1hZ4IkUrz8Rs7zL8aEkQRpITH441f3SOKCHJP+GN69FxP0U6RykKViZ1nz+a/GvHixJMwsml7KkbbNsabPiRmLswc8zh7wOT7vcepAiwfPdDh6uIZOE1aubzEclOQkyAI+8r0/TBL6fPXb36RJSJUUYe4/CtwMgsmQXAj0d3VS2Da5kMiKwf3dDUZqQJSUkPWRpma2UaFREXgi5dCcw831awjDJ8tDHGlSbdUwDIFEUjeb3Lx2B0WCkY3JZApSQqlB7rN0KPfBe0IIhCkRMqcqqthKYDUkpcygLICcXEJpmISpJgoLLGUgyYnjHMeUGIWi6dZxKChEyqi/RjHpcmlxhHQT8tjEUPvg1CzN/2ovnf+Ax6+0+NrXv8mVd9/l3p1btKemaFRrTKKQ4XDIeDym3mpiOft8oyAI2O332NnZASDLMhzHIU1zqtUqUhi0Wm0+92+/SKVaZxInuLb9XWp2h1GYsLndJUlS1lZWCQYB29vbDMcBcwcWOHHyNOMoZHFpheFowngU7EuHx2P2ersURUYYhkipaNam2FxZx0RyYGaOZz75NFgGa3vb9IKIje6QufkDtKttbl+7wW/91m8RaoPtNORu0OeRBy9wsD1HsL6LWVisbO5Rb04xN91hp7fJ1mCA12pz4YkneeX1N2hPzZGm6X6wUmAYEhRIKTEMgzyTeG4DJV0kLvXqNJZRR34XEGkrF6lMGu06X3npRcrU59Txc3R3VvjWN/+cQ/OznDp2ljQyOH/uPA88cJ5jR47T6XRwbJuiKFhfW0aUgpdfepn/63d+g7yI2ekO0IZDFA+4e/sNjh05xur6Ls3WAmsb6+SZxLRjkhiabUm7U8M0PMoyxzAMDMtkamoKW0EeDjm+ME3dVXQaHhVfMDddp1Ex6TQ8tnYH9LOMd67e5LM/8GP8xm/+Di+88w6LWzv47Tq73SFlDptrq9xfvEOexETjgMloSL+3gy5z8jxlMgkYTyYMegMsaSGEYmtzlzuLS+hSYJkODzxwlunpNpXKPuphb69POIkIwxDf93njrYvkWhKEORevXuXuxj2ibMSgv8MLX/sK7qzPwoF9QGdnukWZxywszJElMUJJbN8nLkv644jtXkBn9iDXrt/hXvcyhlnhmU+8l8HebcaDHm+9+RL3t7u0pmfZ2tqiVtsHQSshieOEYZGwkgfUOi0+8OAjfOT9T+J1fBzTYhKM2N3dh7B6FX+/6u+YTKIQ03GxHYuHHjpHHO//rrOk5PDhw6xtbHB/eRVPK46255hbmMdxbOJxwMxUCylga2eTb199k2HT4MRTjyDjlH4RUUw5WL5NmiU4to2hrH3bg5RkWfbvvRP+Wla0FIIsFZguKJHjmJC5VUxT4+sptBiSewV0Naa2gHK/9BlLhKEwCkEmoDbl4VUF1XqJlhlRVJCGDkkm0ZmgLEyK3EAJAyVzhKMYZxHKiDBNE98yaVYt+rt7IHLmq22U8ri4dI+7Sxt0WlUu3VijNAUHZw+SxaAziS4TBPsbOMHKFllRAAa22GcXVRyP0twn0OZotAVlZlDzBQKFMjwyO2Gm3mDW6nGibfHRkx1On/KQUw67N0uujRJ0kRDFI3IdEudDtJGhzRJp1BmXd5HJGNM2SAqNoxO0lDiWxCwkuSrY2YhYXUoJIkkcJkjpUKIQpovUkkKOcWSKbwocldFpmCg7p5gEJMYt1ru7mKZJ1TWp2CWHW7PYFkzGEUKDoasY2iWPJoQpGNpEGoK2W2UyVfK//V2Y+6fLtFyoTzcYF7scrdTZ7u7wc//qC/ydD7lE0f56tk5KKpkikCWGKPjNu1X+m9MBr/ePkBYWeT5A6hTz7pDGGiyPMu7d19zoGaDFvqFeFZS5RgkToXIUOUoKfufLBZ95umCmknGnX/KLfzBkadFC6IRSaXIJKnS4fCXhg0fqPPXQHJWmIElHREWGISVlsU9Oz7KcPJF4VY/jBzyyXDLuDQnKBuVMiV8ozr//o1x+8es8/9pFPvDIw/iixrWNLn7NZrY5w6i/BUWBAhwhmGQpvpLYWtL2DzGOu6AN0mxCpHMMWcW1eiwPVnBMiyyJKUqF8ixyBJ4yWd7b4RtX30AWkrGGmiUxSxOkxrAlwlQUusARFikZGRJUQbvqEegAy64iwpLCLUkTiRTgSIM0CnFNwHYpRIGWGkcpRGjh13OyQlOzXUplE2iL0vGxihLLMBkZKdEkRymBZZh/tZfOf8Dz4lvXWdreAxtee/s7/Pb/89v8Jz/7nyEsqDXqpGmKZVkEQUCpNQcPHmQ4CCiKgnE4wa9U6PUDKl4VrUvq9Tpf/OKf89VvfoNavYGhFJ5h0A8mTMiwfQ9dFPiGiW9YjEcRpdI43r4Hr1r18es1pg/6ROOI3a0uOteYwiEvJN1Rd//z9MYMJ2NWPrdGHI7xXY+PfPwsFXUMu9Xm8p0derHNv/jd3yO3PSw5oUwSXLvB3NwsF86d5rXXX+aXnvvv+U/Vj/LCW69zdTRmFHQ5c+QgH3jvZ9m+32Pm8DxGy+R9P/pxXnjna7T8FjOzHaan25R5geXYWK6DZTl87k9/nThOybKcB86d55Of/D6uXLnEH33+izx9/iQDoajWWzz6/vMMIp9GbrG8fov+cI/nfvlX0IXF//E7v0lSwPhKRLNZJ8sylFL4FZdHH32Uj334WTY27vLEY+d48NxjZLlke7fLq288T2/rHZbvf4ejJ36Mn/zsP+bWnXdYljfY3t7k4GEH5DqLSysEo4g0LRGGQawFg8JkqjlLVcADZ89xqNIg3RsQVxRvv/ZtHn/PxznUbvJj3/cpJgP42EPPsnBghl/5n/5XDp46SXO2xuz8Y2SJpFZU6A36zNYOoaYkzbrJ9laXoijI3ALbs4miiLLMyaWBg6bmV0jTlH5ccGNxkeV7q8zNz/ChDz+F5wnyfMJ4HOJX6iAKLNtkNJwQJAlOpUYpBPcTk+9vneD+7fuUVpPP/ORP8/PzDe4v3+J9CwdZOHqYA/MLXLtynW8NvkMepftzdSU41Sa/9+WvEYdjPNfmmR/8IFkiqU6qPPbUB5lpzNAfKf7gy/8nFaNKuzNDd2+V7s4a1VqFrZU93vPU4xzzWuzcWOR3v/M7/Pg//nFONeo8/+pFvv+DH+Ivvv4NXK05efQYb1x8F9u3wbAosoLBcIelJYOZ2SlkanB/Z4ed8ZhGq8Ptm3c5hskrzz/Pub//LE89/iTjvT2uvPUm79y4zBPvfy8feN9HOLFwiPE45NrqCk5ukFkmq4yp+g55nmMqSa/XI8ni/9874a9l0MrFGNdzcFHkwgRzRBimGK6JdPe9aAkCaQi0LBFAnuaI1KTcb3iAaWGaJq5jILQkCXOySJNFmjiBMjHJMdGFhWUKHD+nanjY1gyONOipAY6teOrppykLTcWvEqcJ795cZGRY9Ic9RkGMlg7tRoVCSKJxTF6ANBRlkrCzvUGuNZZQKN9ECIHplhRlgSgh1wqURiGQqkSbBrZQ5GWKYxoclCbHDnmcOFTh3BNNStfnq3dXeWGzi2EbSDS5jtAGuEohhIEQMRQRZVZiKIVINUa53ypVeUZkCMZaowVkKeTfTeJaC4oyQWtNnkyAfStLYAviVFK1DNgtKWWGpwQt32ah1kTLAsd0mO208cwSqyzIdb4fdiOD1NwPkVlSUDFzpLbRysbH4Ve/MmG+2cT0YJAOafg12k6FX/i/v8rP/cjH2Nh6iUIXmAKqrRl29mKs1MZ0DHzD4nfvnSGKxsTRGNMyyIWB7NjsrkWoUUqoDNI0BAwMISkKTSkM0rxAF6AyTVwWlCPJL/3emGYl4917mpUlE0G6X/ESGiFyWgdcPvqpDzA1X2MYraNci34Y8/+1d2cxlqfnfd+//307+6nl1Nq19b7MTHfPTA85MxyS4mpTpqxYEhQYyAIFuYhpOwF86dCIjTgBIgU2YktBLASy5cSkaFImJZLSjIYzQ87SPXvP9Fbdte919nP+++aL0yESBOGVWhaD93NXN4VzClX/89T7PO/vKVo2ChKSBBqgKiaZA3WjimxKhCFkuU6Ugpxo5An0Bi7j81do7d/mxes3uHzmPF9ZukipUkKWFP70jRap6xFnAblSIlc1KsUCdkEnCF1srUJKSBB6aIpGJmVstu9hKgpBGhJnObKsQipjGTVCz2eY9GjvB8iyjJVlpLkKZJDJ5JKMBOjIxHmKlMtoOpiqiZvFaIpFGiZIikLmjRLkkXOkLMAwNKJQQs1zpExFJyNDIjdTHNNCkiSSVMKQoVQo0xl6JFlMNIhQVXUUakmOnv7/ZwXPwUGXldkZTMeg40EkjXPjBzf41K+8QBKHqHLO4dEOEhrxMGJ6aobdaBOzVCKIMpAzdE2jVLbRcgsvjLi/vUnBqEGgIEspfT8mjBKKRYskCollg8Nhl2qljhT4GKoGcYoXuWRJSppnxK0UVVUZn2oQ+cHoNEDXUdUCuSwhmzKjvI0M2dLxfJdbt/qY1k1KpSJnz1W4/voNNNXBUjMO9nrUKhXiYMjuTpsgVXhn0OVb336RMydOsNXtQ+hydnaJ6ZVTrG3tMlGv4scDtj46JkskJqqTBG6TMHSIwoxKuUJOhOcf82++8W3u72yTGSqGAq23fsx/8mu/wbdu/jEHRxaDKGOgRLhxm3t39vn04gXSLMWuTvAH7/yEBb3O96+/TTP0KSkWcydmMQyDKAiJ45iV5dNIWc79zXsEvssT585z/e13qFXHkDWd5annib0OB/vvULRjMreNIasY5hiu14W0hmWYLC9e4MHWfQxNJQ0j9FhFUwd0Wz6qKtGYqmLlJtvN+9z68C51tYh3eIQ+CHlnd4+joMe//uMf0KhN88SVL/LGuz8iTHySHHzPo1go0+630QydYBjTHI4iNEanKDJ4oxPoPIMk8ZGVHG/YRlUU1KKNk4CuK+wdt1jbajE9NcF4vYhuqCCN2l6h70GUIAUxulNAslSuXViCmkZ4oLN0YgZdbaMYDpmss7ezS6NepdU84vTp03z4wW22do9wHAeJHM/to8pQLBZpN1uoukNxwuTe0RadZovnP1nh+PAB/+vf/21efv0lvvXdf0NRL5INhxQlh+OkjdPtsL63Q216nPZWm4Lk8J3vv8zy9CJ7WztUKkUmz5yg2WxRmy7S62VYlkEQDLD0IsNeNHo+qTGWF6LaFi23za//4uf56oXLXP6FawTdDqUo5aPNHeaUMls4HA9cKo7BvYNDPM9jpjFFnqd0+j2KpQL+cNTy73p9isUiSmqMug8/w1/KQuv//K+bP/tV/wX5L371P/Qr+H+7BPw3/6FfhPBIfe0R/d79z7/xaL6v8P90cm6KyfkK7350k4NOBxOFO8e7zN/ZYaYxQb81xCya+IlPqVYGK0P1VFRZotfroWmjwflut4uUuUzOTDAcDEDKSeIMVZPIknSUnZSmoxPbLEPKckLfxXEc/CDAMAx8z0VVVdI8Q1JGbclarTYqxADP85AkhTgIQZHRdZ0sTkAB07TZ2z3g9LklLLuAIsvUamPEXkQqZzjFAsPhENvSODw8pFwsUa/Xuf7O27x34y1WfuFJVFWlUqmwsbZO96jJ/PQUrjvA9Tz8KObChYuUiiZhGCFJCr1Bl2LJ5JVXX+Xu6j3K45N4SUCapkiqjG6ofO9736M8eYr7m+u0vBat7ibRMOOT5XmCgYdW0SjVy3zvRy/x7oN1FENjrjFDr9cjy0a3GqvVKq7rkoQRtmPhD/rsHR7gOA4Dz8U0cxJiKuUGw36Do+YmlmNzYm6G1157g1/54l8jio5QNZn19XXyPKdQKNCPu0RJQs220LUiupkShDppP0VybC4unEVNU/pDn6XL5/nGN79JpTJGu32MKkvsvraBaoMk56iShlMoYJomExMT9AZ9HNsmi0cD2HkuEYYhqqqOViMFEVkWIUnZaEA9l8nyUZyDG8VIqsRHt2/SaY9x8fxpqmWb3WaTLIjIo5SfvPYmpbEanX6LmeoUZ06dpFZRsaxj5ufnGQ5Guw9Pn7rI6updkjRja2uHnZ332Ds4xDIdkjglShNAQlY1sozR/GmQ4fsBSRixtbXF762v8Td++T9ir7nJ5aev8L2XfsCpsUl6xy2CYEC5ZqFbJigynh/yi7/0Vf74T35I5Lms7h0heRH92KccJzQaDVTDQVUcvMGQ7c2E0I+IY5k4Hu1HjiyVVM7IA5/FySn2dw9YvHSBDzd2WI8jvvDcC+hNn6aWsp33R4n/YUi5XB51y/KUYrFIGIQYhkWUxBRKRdIs48y503ie9zOfCX8pbx0KgiD8vPrhj//d1+dsm9ffuE6l0qCvSThRzMbaAeVihfn5eRRdY2p+6uHFh4ihG4zm1HJwnCJ+4BEEPk6pTJKGvPv2O6QRyJKKqigkaYosyzDKN0aSFMhzkjjBcUbhl5IkkZGP5u3k0RoeWZZHu+2QMAwDWZZptg7RdBXLNMjSBEmWUSSFwA+QUMgkKFcreK6Pphs0j1o4RZvhYAhIkOe4wx7LKydoD33MbDRrlZ/RIz0AAB+jSURBVNk6Tzz+OKSQKxLzs3OUSw5BELB/dIgkQW18AvKMOI7xQx/HMVjbeMAHH75PkmTMn1hgbGwcWYbucYvpiRmuXHmKL3/ui6wdfIxesNDtIl7LZcEsPTy9dXG1lA8//BgMC61QxG/20RyTPM8pForUajWSJMU2R+uKLMNAlhWGrosXBMRJTE5CBgRBgO/vo+sWulZg2O9z/sxFPPeYodtB0SwOm7s0j7r4YUChZGPqY2QotDpHDL0hc40ZdgZtpmSb1e117GqJhcsXWX1wH98PsGydodtF1cEpFUiyhPBhoGYaR+iaQRRGZFlGFI2KUkka5YulafrwNCtB01TIRzNuD6ctkSVAlZFkiSAc4ntD4iBAU3SqUw22VteoaSWODttstI+o1B1WFqY5MTeHbmTcX93EMBzsgsXaxjpVp87Y+AS7Ozv0+h5xMrq1Nxy6eA8vMYxPThBGEUmSkkQJURgxNzfDoN+j1W5xdHjMe+/c5M23XuNff+ObtLoDFmZXMHSNXr+JbsLFx6+imyYf37vHpSefJJdlmq02Hx0c8f67N8GxcCoOJ2ZmGZtuMDc7i+cOybMUf+CiKSq9/gBZliiUS6xvbPHclfP8p7/0ywR+hKo72KrK2u0HyLqJkiQszc3z4XvvUpuaxXWHqKpK0bGR5dE/IVEUMT4+QbVapVQqUCoV2dra5PDwgBeuffnnK95BEATh59Vbb/y7rx8O2kSpwtbuPr3DQx577jLzy/N4iUur3ebg8IjNB9ssTy1S0ops7G8x6A8xdJMoihgMB+i6Qd8b0GsdEnsDtnf2QFYAiSxPsCxzFHEiK6RpgqEbJHFMFAZk5IRBgGWa5BKYxqi98X8N7UZJzMAdjvbilYuUy2VM0yKKItyBhyJryJICivLwpt0Wrufz4P59KuUCSRQhyzLdThfLsfjUp5+jP+gQxglf/eW/RmN+mqn5WVZOn0SWYHNrk631dcYmx5FkCcPQCcMQ4pTtrU1MQ0eRcu4/uMvd+3dxA58Ti4vUilWKls3ZU2f41V/9Fd74yZt87jOfY2P/Lk8+/ji2bnN80CRTct4/2mC7d4yrZ0zOTXG6MIVhl9hpHVMuV5hsTLK4uEitWiUIAkAiiWJsQ6VSqtIdDEAZFVt9t0+m+DjWFIVCg72j63jDLmOlGkvz8yAbFByIE5du32d9exVFMgjTmHKlSJY5hFHG1t4Wiqlya3ebRq3B1s4Bi/UZHv/MFZ5ZPMW582e58cFHyHKOJEOcJphWCc+LRxlhWUoUxrhDD8cqkCUZsqyMRj5yMHSdOEpGJ5qMCiwYLY1O05wsG0UwGLFKnkoYjo2kGLQGQzb3DllZmGG5NgldD88NWe3t8olrl5kbr6DKEmnmkacKs3PLBFFEFId8/PFHdLotzp09Q7N9jGHqTM5McvrkAo2pGqouUak5NJtHPP7YBXRTZf3BEbVahaPjI7Z3d9na3kMzSuwdu7QGLl7ioeDRbjU5deo8h8dD/uZ/9huoRol/+D/+Nk8/fwVMnceuXGW8cYJ3Xv+ASq1CtWSQBX1KUzMcbK2zubHOzPQUWxvb6LpBnCTIskKp5vC5zz7FP/47f5cqBlptjInyOKamsnz1Eq47YKvf5I31W2RFm/mZRWrVGoWiQ56lNBoNSqUKsqyRZSm9Xo8oCtnb22N6eppSqcTls58UhZYgCMJfhP/pd/7R1+1cRlV1Pv3ss3zpr/9Vgr195s+eYG9/m+WlRZxCganxBo5hoeUy3WhAlmZ4rg9AvT5OmiUYlkke+xxtb5FIEj3PR2KU6q4oClmekjNqHSqKTJ5nyDKjbDdVIYwCJHLSLB3lHAFZlpFLIMkysqKgagr9/pA4SSgViyiySpLEZFkKEqMTtDhG13TCIGRhfpajg31kScIwLI6Pm8RxSGN6HN3QKRYcGlOTHBweMHRdXv3Jq7jukMZEg1K5hGOZZGlKtTRaOt7rHrG3s0X76JgnLl+mXC5x9epVxibHIUoYq4xxYmEez/dQcoVzZ85z1DtAUwzkXObSxUvUJsa4tbtBtTzGwuI8O7s7yIcuG4f79POUhfkZZmZmcRwHd+gyGAyoVKoEvk+5VMALQ7qDwcOfTUa328VxbAIvwTYd9g9uI0s5uqLjWDZzJ06TpX1cr4ukmLhhD9+NCaIQ01KpFObxvYQ4CxifrLO6vk5RLzF1YpHgsMWnvnQNue/xe9/+Awq1KoPBgH6/D7lMGKYYhkkYueRZhq4ZxFFM4Ac4poUXjoav83x0iqUoCrIsk8QZORmGbv30tCtHIstT5GT0+yIbNigKmqISxyEXZxucnJnm6ctPsHJyGR+JSxfO0e10OT7e5/SZBVptF1W1MA2D/qCPXbRYW19nrF4jCiP8KECWcmqVItPTDaqVIrZj02l3uHDxIkmakGUardYxZ8+eo9XsUB+bwDJNXDfHsDScgsb89BjucIAfJjx57ZOsPtjgt37rn5PkActnV4iSkEqpwvL8Er/zv/w+pbpOvWJQty2GYUYQ+JiGSaVUZntrf/TzyVKyLGPizDRHrUO8Vpe5yRkO9o+Qoojj40PmG1MQRTilAlfPXMROZT64v0ahUEDTFYqOQ78/pN3ujuasNXV00lUsMTY2Tp7LeF7AU5d+znK0BEEQfl6dXjpNOw0Ybu3T7h2h3nTZbu0T3omZqtfJ44gkSjkK+njegKJl0+8N2d7cYW52liwdtYMsy8IsONy7/zFFx2ShUKIfbuC5EbZRICUnk0CSMlBk4iwlI0eTlVH7SFOJknj0YSyN5rl0XWc4HCLlMrZto2kanjdE0VQkSaI/HKKqOsVikTRNCSOf4XCIrusEnodtWviBS8k2ieOMge+j6jphnFCp1cnaR6imTCQlzM/P8r0ffp9yuYSUQ57n2KZBkiQ4lo0uKxy3h6hyTBoFHB01Odo9ptvvsb27Q0pM0SpSrZTI4pR2r83Y+CT9/pDIH3B764DJyUnu3buPbmr8yhe+TNpJSLOQ4XBIx1U4ef4sV08tUndGw/Z7e3tEQYhhGBwdHdFptrAtjTyX0HWT3qCLro9yDmuVMXq9AZKWUa8tkUaHuH6T6ZmrNJtNNHm0lzJJEjzPw7IswsMD0nh0MpimKb4f0u32GOwdcz+3cP2EmqEzW6nzm7/7WzSW5imWKkyOV3nzjXcxLWuUzSRlGJpKkowC5iRFJkkSUvJRgZ3lo+wxRoPwsqSiaRAno/eW5xJJkqEoo7YxSkqUZeRRhK4o1CoOuq0yYRq8/daPka9cpdUZ8plrz3DUbWMX6kh5QC5LSJKCLKkkSUKlXKbvDqlUq+wdHOH2h0g56LpJt92mJsuUSyUKBbAsh8gPIc2JSUjijLv31qjWxsnThNU7HzNwc+yiweLyKQzdRpY7OCWTrb1VvvXNP6XXCZA02N3dpVgy6VUmyKOMK0+f43Cwj66rRG6f5eUneLC/joxCu9UFRqd7siyPWqtpwsqZFXa9Pt9943WaR4eoCiwtzOO/47K6doeZC2cxWgFnCw325jOSJKHb9THHx5AkCcdxKBbK5HmAadi4ro+hKfR6A4rF8s98JvylzNESBEH4eeXnOZOFGiXTpGZb3HpwC9MNCToDmruH+H2PUsFG0mSC3KMfdRkOh2xvb6M9DEFUVRVDt9Bkhb2dXchTNEVmYXmRS5cvoarqTz9oARRF+enXeT7KvIvjGMsYzSWpqkqajv67N00TRVGIooggCNA0A5BH81yKTBB4+JEPUoaua1RKBUxdxXiYO9VptZHylGvXnmJsbAxVVanV6+zu7DM91cAuFvCD0QB7LuW0ux3SPGNxcZHCw+HuNIqplMpkUUy1bNNtHjI5PsGDO6vcvXWXTquNaZroiopjWgx6fbJcol6vE4cROjKa4+BlKaVymUatRvPeGmUMvOMehmailh2mTsyhaDJREHB8fIzv+ziOQ6FQoNvtUqlURm03RUZSRsVnnmUUCgVkSaderxAnPo41hh/GlCujU45arfIwwV7HMAw0TaPbHeUJmqZJHEbU63Us3SAIApQw4Sfvv8uPfvASXpbx6g9f5CDss3LpPJqaMTc7xWc++zyh76KrMOi10FSVLMt+esNwlLk2fLiaKCaJYqSc0eB3FKEqCmSjmbw0TUmSUTadpmn4ko9sq5RKo8J1rFyE0OVEo0ESuNx4+3UKRYPde2sYskltrEGhXCIIArKfpgmPss28ICZHIUlydN2GXCXwQmzTxu0PGfSGqLJGySniuQHkMhONcTrdPuubO/S6g4fZUz71momqQOuoQ7vpUyzVgJRiVaPTdqnVJrDtAmtrawy6HbrtJlHm8+xnX+DKtaucPXcGKU04v7jChQuXqNfGGQwGP43wAMjynGfOPcZEeQy1VGBj0OGue8hNf4e3tu7x7dXrdIIhf/b6K7x2/yMeDJs/fc+O4xBFEZqmoarq6AJFFDPo9yHL8d2ASqlC4P3siAfROhQEQfhz9I++/re/LqcJ07UxziytsDg1z/LMCVKrSMdz2W8fcO/2x1w6c26U4ScZrJydoTa+jCZBrSYjaw6TE5P80Xf/Jc32EaX5FQ5u3+Yf/4Ov88y5x/ntf/G7lC2H8WqVPIdyuYBKhplmPOi0UItFZMNGNnQGgx4FU6LZ6aCbBmgaum0hazKSImFbozUktuMQhilm0UG3LHJVIopCPN8jIyfwPAxTxyoUSVQF1/WolorYRZ0w9djYXmP5/BKT5SoL4zOUKzW++cffY2VxgReevsbM4gle+sa/5Yf/6hvMagWsVObYyKgWNJrdAYpuIuvyw1w3Fc10uHjhHBu72yR5RsEp0Ot2+P6f/IAfv/0u9+7c4fXXXqPT6fL2u++zcPIUjfI4X/ryF9m4t8pGu8newSGxl3B43KJYLDLVGMM2TLa3tqnWyzRmJ/AGA/I0w7QssjSj3xuQJQlSScFICsTNXUhM6uNLzE2fY2pWYnFmhXarheeGxJHPcbtHFCfECTiORhgqrG0ccHv1Hmv31zFL42R+yrmLM/yD//bv8b//3j9FN1OGbZWnHr8CQch4vcSVZ57k5R+/jq5ZaKigKmRShqEqVAtF4iwhzXIUVUHTdWRFod/rATmyLBElEYQxhqqhGyZxlozaybmGYxVYnplkYazAcqPE+ZUpXnnxRQpzK/S8nI/v3eTZp5+hEw/xvCa243Dz/S0mJsexbBNNL4Oksru7QaVUwvc8FE3GMDSSNCUmwbAckixn6A6pV8vomkHzuMOVi2d59plnaExMYhg6u/t75IrE+ccWqdXq9PtDSoUczcg4d+4xPvPpX+QPv/1HZHIKioTrBdTHa2zsrFNQirz1+uucWVogGg4omAWunDhHOctwNAOnXOLB+gZ5nhLGAacunMLzXFbv3KPX7qLpEk8/eRVHs/HdCHKTyoRCoe9h1xowO4NtK+iqTJokBEFIFITkWY4iS+Qp5LlMqTT62xu6fVqtQz517edsqbQgCMLPq//hn/x3X6+NT5AqMh/eucMHd28R5DlPnr5EOxjSG3RZmJwCQ+W1H/8YeRjiJwMunHyCG+++RbViMNeY59jt8dZLP2Rh6Rzb3V1+/TNfZeHKOb75u/+C+ztNTE1HBZrNAwpqTkVXOTs/zZnHznO4v0PkDojcLrOTNcYKJotTp8jjDM8PIYNuq4up6uh5gEJO4LrohkYU+ORRhK3qXFiYpj45gayphFmMJOcYZBimQbVcottpMzk9gV2wmZqa4HMvfBZV0kiSjFzT2NzZ5LHTZ7iwfIqPXnuLv/K5T3Phuat0oiG9ozbl0hhJ3OOg06UbhtTsIkXTYOnUSexKGbfXo91uMzc/T7vZQgLyLGfu5DwLS0s8/YlrTIxP8MwnnuHlF3/E9oND1ncfUBgz+c4fvYSsFQliH9fvcGrlLJ1Oh+ZhB1AoFW2SLIRcQpIlMklmMBzQ7fWp1KrkeooW69y68RZqpoMMkm5glhT8bgBSxtAbAHB79S66brC7d0ShYDFWWyDKVNbX1/E9lzAeFcN/5UufYrZR4fr115icn2fgy6zeWSPLcw6bTcYmxvn441t4wwF+CKqqkyUxhmbghwHdQQ/DKOEHo9BbcokkjZAVebR5JM8JgoAwG21uSPMcVVIoWBU0UsbGyxy32rQ7XU7NTNFuHxBKKlu9Dktjk+glm+eevEo+aNP1PBTFoFIto6oaUQS5LON5PRzbHt0qDCIs0+T48JgwiZHl0V3HOA7w/JBOe8j9+1vcfv9jTp8+Ta1e5eTpFa4+dYW1zU10W+eJc5eoKgbHx1s8+/yzfOKp57j97i1eefMdUkDRNDRFoVopEAURdz++y0GzT5JlrCwvcvnSJYJWn0iK+dwLn2a6Ps6P3nyDjf1tnv7kNc6eXuHtd65Tr9eZmBjDdV267TaKrKBaGl4QouUechCzeO4xdod90ijB8wOCMEJTFXRDR9NVJFmiWq6T5zn9fo8kiUnTmC99+QtMVhbEjJYgCMJfhCeeeYaVUyd59723GQQDoiimlkd89N4HzC/NcPf2TULb4XBrh09dvcZ4ocwbt36C3+ihqjJvvfUmF08+zvW798ndgLnqLJHikyU5X/va17D7QyxdJo19ckOlXqrgux5y4NNSZP77/+pv0friF3lwf52337nOV7/yFfLAI3VUNrY2CaWcg4MDGhMTnF0+SZAo7OzvMfAjmp0uXt8jDSOWTizwVz/9SW7sbPNnb72FbmrIeUZZkXiwu0+aFpFliUuXLiEbEidmZwjdCMt08H2fomPz2eeeoyRrfONf/j6TksMfbq2x8uRFnnvuWV77wctUzRK+6VOv18naAw6Pj3j6qSsUS2VSReHG9Te4cOECaZzg+z4rS0vUqmP0wjZDL6Df79M6arK7uc3J+QXGiw1UU6LdaxElEaohUy4XCUOfJIoJ/Ihut8/szInRzJhh0Unc0QoqJcMnRSkYaI6DlRfY3vyYQXJIUZ6g3z9mbGqW+7ciGvVdNFVGNwyQJCzLotPpMFYfR1M1er0Bnc4QPxytiBl4MYVM4dqTT3Dz/RtImoFl1xmECd1Oh/HxOlMz0wx7fT79/DPcuXOH9e0WQ7eNbVrAaKehbpkUCwYFRx/N0IURjlMgyzL63R6KolAfGycmxYtCsiygZBfIUpDymN7OLpZexpIk3nztBteunmWt41EcqzDmjLHW3GHleIrU69M82GVp+TEOjw+wSxU0zRplUqUJqQyGadIZdihIMsWig2zqKIqCospoukkuxyS9mAwVu1TnJ2+8ycXHz5JkIVmW8sInn6cfdYjShL7fw9Y1Mi/m337jW3z4/l1yWSKPH64DkxTyXEJRNErVIm6ic+fefTrH+xhf+CwcDrl9sI43CJmsTDA9Pc2dw23qjTHefPNN4iym1W2RSRmNRoPAcwmikOpYleHAR1V1qvUC5WqNbHuLfhg8HHzXKBRKaNqoNZ/nOcfNNkgZaZoSBB6PP36JG+9c5+KJF/4/nwmi0BIEQfhz9NQTT3Pzow9QEol6oYxa0TnaP2Jj5xYXB2f55JWnuL+/ybNnLmHIOpKloyky/V6HPE8pFG0aE3Xu373F51/4PBYGS4UqyydXsN/S+fwXv8T2738XFIk8G83hRChoksHq1iG/85v/DEnVuHDhHE+dvcy7r17nwqmT1CuTzD52giRJWPjSCYadHrHrUxi3OaxWCaOEiekZNh+s809+8zeJtzf59WeewvZ9jCRirGijmwbdw300WSKOAiYnx5mZmSKMg1GOU66NwjtLRba3tzk1t8B7P3mD8WKFWm2ciiGjbHd46b1v8Pxf/yV+8PpbzJ+t4rsBJcViNx7w0cYDzukqtmJSrVYpl8scHR6TJcnD4f02aZKSRxmb99e4+/FtnnrsKpdOnqHkFNDsIv/Ht/6AickKed6nUj5FGlV5+eWXiaKIw8MWtepdPvcLz1KtlShaDnGcUCoUIAiINZ3YHZBEEY5pECkZ6pjDYP+AXneIrRXZ2l1lamKK4XCIZhqo0mjc2fd9bBtMXSaKImRZJUlzdF3nicuXSOOYV155iamZBfS8hBIeMT5RY3VtlYWT8+zsbrG4NEe5YpEbH7D+YANV0YniAN3UKDglxss6U1Mz7O8d0O4MGPQjklwhS1VyWcINfKq1EuWSSZ6YVFWdKHbw/AFPnzyLHpnMTDToRQdcOr3M4Y0bOKpOHOa4qs93vv+HLDWmKJdKeIMhO1vbzM2rlBoT9LsdDGM0myYhM/Q9rOEQp1LC833ieDS/JUk5tmGSRl3SKMa3VY63Nuh7PS6dP8f87DSZGzHoDRhqMtPnT/LGtz+iUZ4gizWu3/wmqlVGlhUUFLIsI45SLNsgwieXMorlErqu8PHtO5QDk3du3UWODMadEvuDJpefehI3jLBLZaaWZ0YJ+FGEG/k4jk2e5wTDARW7wNJcnWmjgh9ElEtVonAU9Kuq6sP5RwkebgDRdYU4CfG8IYuLJ9jb38H3/Z/5TBCtQ0EQhD9H7+7c/PrW4T5nzp3EqRTouUNQFdpuxNbxPm+//x7jpSqPzS0zPdZgqj7B3eYqD3a6mKpC63ibnd09erv7PHblWcJsyM7qLgUi3jve50+/+RpWtUCaZiRpTpTlhHFMlmQUi0Xs6RnM8To37txhv9ejMjWNn0gYeoFuu4elmHz3O9/j+y/9Gf/qO9/hgw9v0Trq8eIPXsRtDdjb2WT+xDTdYYej4y5pGlA0ZZ567AL9TpMYmXKpgCrlnD1zCrvoUK4UybOUguXg+i7N5iFp4NPf3GWyWGVibAIrTGiFEU0gqpS4u73BFx+/QqhkHO41UTSLBKjXx3jl5VcZ9j3mlufY29uj3+2RJAm5JOEFAaEbgqKwsbHFZH2cr/3Gf0nc7KHEMpqmMb+0gOIYXL36NO1Wh821dSrjdcrVApmSsru7x2uv3mBz85AyCoNmm/sffUhvf4fO1hbu4T71Ssr05AL7+4domkQaSrzyoxtUx1Wcoo3nh+i6iaqobG6vk0vgBwGlkk6WF2h1PIIkJE4Tzp9Z4j/+ta/w6ot/yvraXf7GV/5zLs08xUrV4cbtD4nynDt3VsnCnAfr22zu7KHrJidPrLB7cIxqm+RZyrhj8eTKHBVDo7W3x1ixQKHo4JgG9WoZ00j4xJlTnJ2bomLozE000HWT1Nvjq5/5LGenl/D9PmHmUynVubOzBYaKt9ti46BLvW4SqBmaXsX1Bty/u0W5WKB33EFHZ3C8x2y1zrhTYXN1DUlS8XyXNElI8gjdMLCNArpmUrJstje2iPwBbupRcMqEQcYHN29z6/Yqhm0zUR3DLjik5FSL47z0Jy+x0Jhipl5grzfamStJoMgysRTTmG5QmihyenERQ0ooWBr7e4fcvtfh4ukLbO3ssb6zwy/+2lc5ubLA5FiN+bkpLMtAliQq1QozjSlW5hdYnp1ncWWRseIEugxSKuNrDoGqYMkJlmFi6hrkClKmQCZBJhElITMzU9TrVTa31jENmzSBa4+/IFqHgiAIfxGeWDnD3u1Vuls7HB8ckgcxxCmLS0s8ONgijny2V9c4Pn2BcqVG+84arV4X2Z4h81yKToG7a/epKDoHrS4RQ9RSGdnzsRp1SsUBA99FU1VM1SQMhliajBRFTNWm+Ju//Hl6vQ7DlQZZFPP0E1f43/7pPyPtnSYJfD7yPR5sb7LZPKC+MM+DoxZtN8JWTe5v7/GJ569Qm6qjN2qcPHGWd66/ws7+Fssnl2nt71OfWRwljMsS3W6Xk8UzSDKEoU/RKZIkEaHvcbi9yxeeeIab773P1NQUviVTyGxawQCz7NDe3CA2MoqRSUaOVSux+f57ZF5IvzvgvrTD/Jk5smxUQGZJSqFQwPOaZJKMYxdQNYOZmTmSNMdxHGqFWfb6W7Q6O5w6c5FeP+De6gNME3q9HqWag2Wb6JYOUszG+i6qHLO8skgixdTLRZLAR1cVphsXOO4coEldcFXUrM/O7jqt/jPU6zntdpukmiFLOfV6nZu3bhKECa6Xo8ijfXi27eAHLlcuP4ZlKDSbTTJkqsUxjEgdxUfkMu7QxTAdyDUcp0C3dcTmxjaGekwmy4RRjJTFXDx3moph0m53UdOcWqmIkeZ4UYzjOOSpyfLkOP1OHylKmFqY4d7mPtNTVRxFxQsDlHGHZrvFcNdjJ+qSaTHDdpeuL/N0bZn3d3v0egG5lHLu3AXW11a5/dEtbK3M2oObWBfPszy7iJnLdAZ9ipUiwdClMFkCRntzddUgTxNUchrjdWS3j+ulaJpBtTbOcbtJO3QpDk3sRCcIfDJZJdd1LFPH3z+kYDscDg5Haf4lhzgOyciRVYkTC7P4/Sbto2M8z6NYX6BertBqHoGc0u80MSIDu2wj5RmOblKwbOoT4xiqhpHLRH5Arz9Ek0yiJMPSTBTLxNAkdDcabVZIc8h4uNJIgixlenYKRZXZ3t6lVC4TRDFplv7MZ4L0f78iLAiCIAiCIPz5ETlagiAIgiAIj4gotARBEARBEB4RUWgJgiAIgiA8IqLQEgRBEARBeEREoSUIgiAIgvCIiEJLEARBEAThERGFliAIgiAIwiMiCi1BEARBEIRHRBRagiAIgiAIj4gotARBEARBEB4RUWgJgiAIgiA8IqLQEgRBEARBeEREoSUIgiAIgvCIiEJLEARBEAThERGFliAIgiAIwiMiCi1BEARBEIRHRBRagiAIgiAIj4gotARBEARBEB4RUWgJgiAIgiA8IqLQEgRBEARBeEREoSUIgiAIgvCIiEJLEARBEAThERGFliAIgiAIwiMiCi1BEARBEIRHRBRagiAIgiAIj4gotARBEARBEB6Rfw8eBjmaJ+9QYwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class: tulips\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.image as mpimg\\n\",\n    \"\\n\",\n    \"n_examples_per_class = 2\\n\",\n    \"\\n\",\n    \"for flower_class in flower_classes:\\n\",\n    \"    print(\\\"Class:\\\", flower_class)\\n\",\n    \"    plt.figure(figsize=(10,5))\\n\",\n    \"    for index, example_image_path in enumerate(image_paths[flower_class][:n_examples_per_class]):\\n\",\n    \"        example_image = mpimg.imread(example_image_path)[:, :, :channels]\\n\",\n    \"        plt.subplot(100 + n_examples_per_class * 10 + index + 1)\\n\",\n    \"        plt.title(\\\"{}x{}\\\".format(example_image.shape[1], example_image.shape[0]))\\n\",\n    \"        plt.imshow(example_image)\\n\",\n    \"        plt.axis(\\\"off\\\")\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice how the image dimensions vary, and how difficult the task is in some cases (e.g., the 2nd tulip image).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.2.\\n\",\n    \"Exercise: Write a preprocessing step that will resize and crop the image to 299 × 299, with some randomness for data augmentation.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's implement this using NumPy and SciPy:\\n\",\n    \"\\n\",\n    \"* using basic NumPy slicing for image cropping,\\n\",\n    \"* NumPy's `fliplr()` function to flip the image horizontally (with 50% probability),\\n\",\n    \"* and SciPy's `imresize()` function for zooming.\\n\",\n    \"  * Note that `imresize()` is based on the Python Image Library (PIL).\\n\",\n    \"\\n\",\n    \"For more image manipulation functions, such as rotations, check out [SciPy's documentation](https://docs.scipy.org/doc/scipy-0.19.0/reference/ndimage.html) or [this nice page](http://www.scipy-lectures.org/advanced/image_processing/).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from skimage.transform import resize\\n\",\n    \"\\n\",\n    \"def prepare_image(image, target_width = 299, target_height = 299, max_zoom = 0.2):\\n\",\n    \"    \\\"\\\"\\\"Zooms and crops the image randomly for data augmentation.\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    # First, let's find the largest bounding box with the target size ratio that fits within the image\\n\",\n    \"    height = image.shape[0]\\n\",\n    \"    width = image.shape[1]\\n\",\n    \"    image_ratio = width / height\\n\",\n    \"    target_image_ratio = target_width / target_height\\n\",\n    \"    crop_vertically = image_ratio < target_image_ratio\\n\",\n    \"    crop_width = width if crop_vertically else int(height * target_image_ratio)\\n\",\n    \"    crop_height = int(width / target_image_ratio) if crop_vertically else height\\n\",\n    \"        \\n\",\n    \"    # Now let's shrink this bounding box by a random factor (dividing the dimensions by a random number\\n\",\n    \"    # between 1.0 and 1.0 + `max_zoom`.\\n\",\n    \"    resize_factor = np.random.rand() * max_zoom + 1.0\\n\",\n    \"    crop_width = int(crop_width / resize_factor)\\n\",\n    \"    crop_height = int(crop_height / resize_factor)\\n\",\n    \"    \\n\",\n    \"    # Next, we can select a random location on the image for this bounding box.\\n\",\n    \"    x0 = np.random.randint(0, width - crop_width)\\n\",\n    \"    y0 = np.random.randint(0, height - crop_height)\\n\",\n    \"    x1 = x0 + crop_width\\n\",\n    \"    y1 = y0 + crop_height\\n\",\n    \"    \\n\",\n    \"    # Let's crop the image using the random bounding box we built.\\n\",\n    \"    image = image[y0:y1, x0:x1]\\n\",\n    \"\\n\",\n    \"    # Let's also flip the image horizontally with 50% probability:\\n\",\n    \"    if np.random.rand() < 0.5:\\n\",\n    \"        image = np.fliplr(image)\\n\",\n    \"\\n\",\n    \"    # Now, let's resize the image to the target dimensions.\\n\",\n    \"    # The resize function of scikit-image will automatically transform the image to floats ranging from 0.0 to 1.0\\n\",\n    \"    image = resize(image, (target_width, target_height))\\n\",\n    \"    \\n\",\n    \"    # Finally, let's ensure that the colors are represented as 32-bit floats:\\n\",\n    \"    return image.astype(np.float32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: at test time, the preprocessing step should be as light as possible, just the bare minimum necessary to be able to feed the image to the neural network. You may want to tweak the above function to add a `training` parameter: if `False`, preprocessing should be limited to the bare minimum (i.e., no flipping the image, and just the minimum cropping required, preserving the center of the image).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's check out the result on this image:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(6, 8))\\n\",\n    \"plt.imshow(example_image)\\n\",\n    \"plt.title(\\\"{}x{}\\\".format(example_image.shape[1], example_image.shape[0]))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There we go:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"prepared_image = prepare_image(example_image)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.imshow(prepared_image)\\n\",\n    \"plt.title(\\\"{}x{}\\\".format(prepared_image.shape[1], prepared_image.shape[0]))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's look at a few other random images generated from the same original image:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1008x576 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"rows, cols = 2, 3\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(14, 8))\\n\",\n    \"for row in range(rows):\\n\",\n    \"    for col in range(cols):\\n\",\n    \"        prepared_image = prepare_image(example_image)\\n\",\n    \"        plt.subplot(rows, cols, row * cols + col + 1)\\n\",\n    \"        plt.title(\\\"{}x{}\\\".format(prepared_image.shape[1], prepared_image.shape[0]))\\n\",\n    \"        plt.imshow(prepared_image)\\n\",\n    \"        plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looks good!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, it's also possible to implement this image preprocessing step directly with TensorFlow, using the functions in the `tf.image` module (see [the API](https://www.tensorflow.org/api_docs/python/) for the full list). As you can see, this function looks very much like the one above, except it does not actually perform the image transformation, but rather creates a set of TensorFlow operations that *will* perform the transformation when you run the graph.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def prepare_image_with_tensorflow(image, target_width = 299, target_height = 299, max_zoom = 0.2):\\n\",\n    \"    \\\"\\\"\\\"Zooms and crops the image randomly for data augmentation.\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    # First, let's find the largest bounding box with the target size ratio that fits within the image\\n\",\n    \"    image_shape = tf.cast(tf.shape(image), tf.float32)\\n\",\n    \"    height = image_shape[0]\\n\",\n    \"    width = image_shape[1]\\n\",\n    \"    image_ratio = width / height\\n\",\n    \"    target_image_ratio = target_width / target_height\\n\",\n    \"    crop_vertically = image_ratio < target_image_ratio\\n\",\n    \"    crop_width = tf.cond(crop_vertically,\\n\",\n    \"                         lambda: width,\\n\",\n    \"                         lambda: height * target_image_ratio)\\n\",\n    \"    crop_height = tf.cond(crop_vertically,\\n\",\n    \"                          lambda: width / target_image_ratio,\\n\",\n    \"                          lambda: height)\\n\",\n    \"\\n\",\n    \"    # Now let's shrink this bounding box by a random factor (dividing the dimensions by a random number\\n\",\n    \"    # between 1.0 and 1.0 + `max_zoom`.\\n\",\n    \"    resize_factor = tf.random_uniform(shape=[], minval=1.0, maxval=1.0 + max_zoom)\\n\",\n    \"    crop_width = tf.cast(crop_width / resize_factor, tf.int32)\\n\",\n    \"    crop_height = tf.cast(crop_height / resize_factor, tf.int32)\\n\",\n    \"    box_size = tf.stack([crop_height, crop_width, 3])   # 3 = number of channels\\n\",\n    \"\\n\",\n    \"    # Let's crop the image using a random bounding box of the size we computed\\n\",\n    \"    image = tf.random_crop(image, box_size)\\n\",\n    \"\\n\",\n    \"    # Let's also flip the image horizontally with 50% probability:\\n\",\n    \"    image = tf.image.random_flip_left_right(image)\\n\",\n    \"\\n\",\n    \"    # The resize_bilinear function requires a 4D tensor (a batch of images)\\n\",\n    \"    # so we need to expand the number of dimensions first:\\n\",\n    \"    image_batch = tf.expand_dims(image, 0)\\n\",\n    \"\\n\",\n    \"    # Finally, let's resize the image to the target dimensions. Note that this function\\n\",\n    \"    # returns a float32 tensor.\\n\",\n    \"    image_batch = tf.image.resize_bilinear(image_batch, [target_height, target_width])\\n\",\n    \"    image = image_batch[0] / 255  # back to a single image, and scale the colors from 0.0 to 1.0\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's test this function!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"input_image = tf.placeholder(tf.uint8, shape=[None, None, 3])\\n\",\n    \"prepared_image_op = prepare_image_with_tensorflow(input_image)\\n\",\n    \"\\n\",\n    \"with tf.Session():\\n\",\n    \"    prepared_image = prepared_image_op.eval(feed_dict={input_image: example_image})\\n\",\n    \"    \\n\",\n    \"plt.figure(figsize=(6, 6))\\n\",\n    \"plt.imshow(prepared_image)\\n\",\n    \"plt.title(\\\"{}x{}\\\".format(prepared_image.shape[1], prepared_image.shape[0]))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looks perfect!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.3.\\n\",\n    \"Exercise: Using the pretrained Inception v3 model from the previous exercise, freeze all layers up to the bottleneck layer (i.e., the last layer before the output layer), and replace the output layer with the appropriate number of outputs for your new classification task (e.g., the flowers dataset has five mutually exclusive classes so the output layer must have five neurons and use the softmax activation function).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by fetching the inception v3 graph again. This time, let's use a `training` placeholder that we will use to tell TensorFlow whether we are training the network or not (this is needed by operations such as dropout and batch normalization).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.contrib.slim.nets import inception\\n\",\n    \"import tensorflow.contrib.slim as slim\\n\",\n    \"\\n\",\n    \"reset_graph()\\n\",\n    \"\\n\",\n    \"X = tf.placeholder(tf.float32, shape=[None, height, width, channels], name=\\\"X\\\")\\n\",\n    \"training = tf.placeholder_with_default(False, shape=[])\\n\",\n    \"with slim.arg_scope(inception.inception_v3_arg_scope()):\\n\",\n    \"    logits, end_points = inception.inception_v3(X, num_classes=1001, is_training=training)\\n\",\n    \"\\n\",\n    \"inception_saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need to find the point in the graph where we should attach the new output layer.  It should be the layer right before the current output layer. One way to do this is to explore the output layer's inputs:  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'InceptionV3/Logits/Conv2d_1c_1x1/BiasAdd:0' shape=(?, 1, 1, 1001) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits.op.inputs[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Nope, that's part of the output layer (adding the biases). Let's continue walking backwards in the graph:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'InceptionV3/Logits/Conv2d_1c_1x1/Conv2D:0' shape=(?, 1, 1, 1001) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits.op.inputs[0].op.inputs[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's also part of the output layer, it's the final layer in the inception layer (if you are not sure you can visualize the graph using TensorBoard). Once again, let's continue walking backwards in the graph:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'InceptionV3/Logits/Dropout_1b/cond/Merge:0' shape=(?, 1, 1, 2048) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logits.op.inputs[0].op.inputs[0].op.inputs[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Aha! There we are, this is the output of the dropout layer. This is the very last layer before the output layer in the Inception v3 network, so that's the layer we need to build upon. Note that there was actually a simpler way to find this layer: the `inception_v3()` function returns a dict of end points: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'Conv2d_1a_3x3': <tf.Tensor 'InceptionV3/InceptionV3/Conv2d_1a_3x3/Relu:0' shape=(?, 149, 149, 32) dtype=float32>,\\n\",\n       \" 'Conv2d_2a_3x3': <tf.Tensor 'InceptionV3/InceptionV3/Conv2d_2a_3x3/Relu:0' shape=(?, 147, 147, 32) dtype=float32>,\\n\",\n       \" 'Conv2d_2b_3x3': <tf.Tensor 'InceptionV3/InceptionV3/Conv2d_2b_3x3/Relu:0' shape=(?, 147, 147, 64) dtype=float32>,\\n\",\n       \" 'MaxPool_3a_3x3': <tf.Tensor 'InceptionV3/InceptionV3/MaxPool_3a_3x3/MaxPool:0' shape=(?, 73, 73, 64) dtype=float32>,\\n\",\n       \" 'Conv2d_3b_1x1': <tf.Tensor 'InceptionV3/InceptionV3/Conv2d_3b_1x1/Relu:0' shape=(?, 73, 73, 80) dtype=float32>,\\n\",\n       \" 'Conv2d_4a_3x3': <tf.Tensor 'InceptionV3/InceptionV3/Conv2d_4a_3x3/Relu:0' shape=(?, 71, 71, 192) dtype=float32>,\\n\",\n       \" 'MaxPool_5a_3x3': <tf.Tensor 'InceptionV3/InceptionV3/MaxPool_5a_3x3/MaxPool:0' shape=(?, 35, 35, 192) dtype=float32>,\\n\",\n       \" 'Mixed_5b': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_5b/concat:0' shape=(?, 35, 35, 256) dtype=float32>,\\n\",\n       \" 'Mixed_5c': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_5c/concat:0' shape=(?, 35, 35, 288) dtype=float32>,\\n\",\n       \" 'Mixed_5d': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_5d/concat:0' shape=(?, 35, 35, 288) dtype=float32>,\\n\",\n       \" 'Mixed_6a': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_6a/concat:0' shape=(?, 17, 17, 768) dtype=float32>,\\n\",\n       \" 'Mixed_6b': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_6b/concat:0' shape=(?, 17, 17, 768) dtype=float32>,\\n\",\n       \" 'Mixed_6c': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_6c/concat:0' shape=(?, 17, 17, 768) dtype=float32>,\\n\",\n       \" 'Mixed_6d': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_6d/concat:0' shape=(?, 17, 17, 768) dtype=float32>,\\n\",\n       \" 'Mixed_6e': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_6e/concat:0' shape=(?, 17, 17, 768) dtype=float32>,\\n\",\n       \" 'Mixed_7a': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_7a/concat:0' shape=(?, 8, 8, 1280) dtype=float32>,\\n\",\n       \" 'Mixed_7b': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_7b/concat:0' shape=(?, 8, 8, 2048) dtype=float32>,\\n\",\n       \" 'Mixed_7c': <tf.Tensor 'InceptionV3/InceptionV3/Mixed_7c/concat:0' shape=(?, 8, 8, 2048) dtype=float32>,\\n\",\n       \" 'AuxLogits': <tf.Tensor 'InceptionV3/AuxLogits/SpatialSqueeze:0' shape=(?, 1001) dtype=float32>,\\n\",\n       \" 'PreLogits': <tf.Tensor 'InceptionV3/Logits/Dropout_1b/cond/Merge:0' shape=(?, 1, 1, 2048) dtype=float32>,\\n\",\n       \" 'Logits': <tf.Tensor 'InceptionV3/Logits/SpatialSqueeze:0' shape=(?, 1001) dtype=float32>,\\n\",\n       \" 'Predictions': <tf.Tensor 'InceptionV3/Predictions/Reshape_1:0' shape=(?, 1001) dtype=float32>}\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"end_points\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, the `\\\"PreLogits\\\"` end point is precisely what we need:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'InceptionV3/Logits/Dropout_1b/cond/Merge:0' shape=(?, 1, 1, 2048) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"end_points[\\\"PreLogits\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can drop the 2nd and 3rd dimensions using the `tf.squeeze()` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prelogits = tf.squeeze(end_points[\\\"PreLogits\\\"], axis=[1, 2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then we can add the final fully connected layer on top of this layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_outputs = len(flower_classes)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"new_output_layer\\\"):\\n\",\n    \"    flower_logits = tf.layers.dense(prelogits, n_outputs, name=\\\"flower_logits\\\")\\n\",\n    \"    Y_proba = tf.nn.softmax(flower_logits, name=\\\"Y_proba\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, we need to add the usual bits and pieces:\\n\",\n    \"\\n\",\n    \"* the placeholder for the targets (`y`),\\n\",\n    \"* the loss function, which is the cross-entropy, as usual for a classification task,\\n\",\n    \"* an optimizer, that we use to create a training operation that will minimize the cost function,\\n\",\n    \"* a couple operations to measure the model's accuracy,\\n\",\n    \"* and finally an initializer and a saver.\\n\",\n    \"\\n\",\n    \"There is one important detail, however: since we want to train only the output layer (all other layers must be frozen), we must pass the list of variables to train to the optimizer's `minimize()` method:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y = tf.placeholder(tf.int32, shape=[None])\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"train\\\"):\\n\",\n    \"    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=flower_logits, labels=y)\\n\",\n    \"    loss = tf.reduce_mean(xentropy)\\n\",\n    \"    optimizer = tf.train.AdamOptimizer()\\n\",\n    \"    flower_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\\\"flower_logits\\\")\\n\",\n    \"    training_op = optimizer.minimize(loss, var_list=flower_vars)\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"eval\\\"):\\n\",\n    \"    correct = tf.nn.in_top_k(flower_logits, y, 1)\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n    \"\\n\",\n    \"with tf.name_scope(\\\"init_and_save\\\"):\\n\",\n    \"    init = tf.global_variables_initializer()\\n\",\n    \"    saver = tf.train.Saver() \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['flower_logits/kernel:0', 'flower_logits/bias:0']\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[v.name for v in flower_vars]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that we created the `inception_saver` before adding the new output layer: we will use this saver to restore the pretrained model state, so we don't want it to try to restore new variables (it would just fail saying it does not know the new variables). The second `saver` will be used to save the final flower model, including both the pretrained variables and the new ones.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.4.\\n\",\n    \"Exercise: Split your dataset into a training set and a test set. Train the model on the training set and evaluate it on the test set.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, we will want to represent the classes as ints rather than strings:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"flower_class_ids = {flower_class: index for index, flower_class in enumerate(flower_classes)}\\n\",\n    \"flower_class_ids\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It will be easier to shuffle the dataset set if we represent it as a list of filepath/class pairs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"flower_paths_and_classes = []\\n\",\n    \"for flower_class, paths in image_paths.items():\\n\",\n    \"    for path in paths:\\n\",\n    \"        flower_paths_and_classes.append((path, flower_class_ids[flower_class]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, lets shuffle the dataset and split it into the training set and the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"test_ratio = 0.2\\n\",\n    \"train_size = int(len(flower_paths_and_classes) * (1 - test_ratio))\\n\",\n    \"\\n\",\n    \"np.random.shuffle(flower_paths_and_classes)\\n\",\n    \"\\n\",\n    \"flower_paths_and_classes_train = flower_paths_and_classes[:train_size]\\n\",\n    \"flower_paths_and_classes_test = flower_paths_and_classes[train_size:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the first 3 instances in the training set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('datasets/flowers/flower_photos/daisy/19834392829_7d697871f6.jpg', 0),\\n\",\n       \" ('datasets/flowers/flower_photos/sunflowers/5957007921_62333981d2_n.jpg', 3),\\n\",\n       \" ('datasets/flowers/flower_photos/tulips/7166635566_ee240b5408_n.jpg', 4)]\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"flower_paths_and_classes_train[:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we will also need a function to preprocess a set of images. This function will be useful to preprocess the test set, and also to create batches during training. For simplicity, we will use the NumPy/SciPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from random import sample\\n\",\n    \"\\n\",\n    \"def prepare_batch(flower_paths_and_classes, batch_size):\\n\",\n    \"    batch_paths_and_classes = sample(flower_paths_and_classes, batch_size)\\n\",\n    \"    images = [mpimg.imread(path)[:, :, :channels] for path, labels in batch_paths_and_classes]\\n\",\n    \"    prepared_images = [prepare_image(image) for image in images]\\n\",\n    \"    X_batch = 2 * np.stack(prepared_images) - 1 # Inception expects colors ranging from -1 to 1\\n\",\n    \"    y_batch = np.array([labels for path, labels in batch_paths_and_classes], dtype=np.int32)\\n\",\n    \"    return X_batch, y_batch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_batch, y_batch = prepare_batch(flower_paths_and_classes_train, batch_size=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 299, 299, 3)\"\n      ]\n     },\n     \"execution_count\": 72,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_batch.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('float32')\"\n      ]\n     },\n     \"execution_count\": 73,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_batch.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4,)\"\n      ]\n     },\n     \"execution_count\": 74,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_batch.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int32')\"\n      ]\n     },\n     \"execution_count\": 75,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y_batch.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looking good. Now let's use this function to prepare the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_test, y_test = prepare_batch(flower_paths_and_classes_test, batch_size=len(flower_paths_and_classes_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(734, 299, 299, 3)\"\n      ]\n     },\n     \"execution_count\": 77,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_test.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We could prepare the training set in much the same way, but it would only generate one variant for each image. Instead, it's preferable to generate the training batches on the fly during training, so that we can really benefit from data augmentation, with many variants of each image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now, we are ready to train the network (or more precisely, the output layer we just added, since all the other layers are frozen). Be aware that this may take a (very) long time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_test, y_test = prepare_batch(flower_paths_and_classes_test, batch_size=len(flower_paths_and_classes_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(734, 299, 299, 3)\"\n      ]\n     },\n     \"execution_count\": 79,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_test.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We could prepare the training set in much the same way, but it would only generate one variant for each image. Instead, it's preferable to generate the training batches on the fly during training, so that we can really benefit from data augmentation, with many variants of each image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And now, we are ready to train the network (or more precisely, the output layer we just added, since all the other layers are frozen). Be aware that this may take a (very) long time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from datasets/inception/inception_v3.ckpt\\n\",\n      \"Epoch 0.........................................................................  Last batch accuracy: 0.55\\n\",\n      \"Epoch 1.........................................................................  Last batch accuracy: 0.7\\n\",\n      \"Epoch 2.........................................................................  Last batch accuracy: 0.575\\n\",\n      \"Epoch 3.........................................................................  Last batch accuracy: 0.75\\n\",\n      \"Epoch 4.........................................................................  Last batch accuracy: 0.7\\n\",\n      \"Epoch 5.........................................................................  Last batch accuracy: 0.575\\n\",\n      \"Epoch 6.........................................................................  Last batch accuracy: 0.675\\n\",\n      \"Epoch 7.........................................................................  Last batch accuracy: 0.65\\n\",\n      \"Epoch 8.........................................................................  Last batch accuracy: 0.675\\n\",\n      \"Epoch 9.........................................................................  Last batch accuracy: 0.7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_epochs = 10\\n\",\n    \"batch_size = 40\\n\",\n    \"n_iterations_per_epoch = len(flower_paths_and_classes_train) // batch_size\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    init.run()\\n\",\n    \"    inception_saver.restore(sess, INCEPTION_V3_CHECKPOINT_PATH)\\n\",\n    \"\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        print(\\\"Epoch\\\", epoch, end=\\\"\\\")\\n\",\n    \"        for iteration in range(n_iterations_per_epoch):\\n\",\n    \"            print(\\\".\\\", end=\\\"\\\")\\n\",\n    \"            X_batch, y_batch = prepare_batch(flower_paths_and_classes_train, batch_size)\\n\",\n    \"            sess.run(training_op, feed_dict={X: X_batch, y: y_batch, training: True})\\n\",\n    \"\\n\",\n    \"        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n    \"        print(\\\"  Last batch accuracy:\\\", acc_batch)\\n\",\n    \"\\n\",\n    \"        save_path = saver.save(sess, \\\"./my_flowers_model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"INFO:tensorflow:Restoring parameters from ./my_flowers_model\\n\",\n      \"Computing final accuracy on the test set (this will take a while)...\\n\",\n      \"Test accuracy: 0.67843395\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_test_batches = 10\\n\",\n    \"X_test_batches = np.array_split(X_test, n_test_batches)\\n\",\n    \"y_test_batches = np.array_split(y_test, n_test_batches)\\n\",\n    \"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    saver.restore(sess, \\\"./my_flowers_model\\\")\\n\",\n    \"\\n\",\n    \"    print(\\\"Computing final accuracy on the test set (this will take a while)...\\\")\\n\",\n    \"    acc_test = np.mean([\\n\",\n    \"        accuracy.eval(feed_dict={X: X_test_batch, y: y_test_batch})\\n\",\n    \"        for X_test_batch, y_test_batch in zip(X_test_batches, y_test_batches)])\\n\",\n    \"    print(\\\"Test accuracy:\\\", acc_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Okay, 67.84% accuracy is not great (in fact, it's really bad), but this is only after 10 epochs, and freezing all layers except for the output layer. If you have a GPU, you can try again and let training run for much longer (e.g., using early stopping to decide when to stop). You can also improve the image preprocessing function to make more tweaks to the image (e.g., changing the brightness and hue, rotate the image slightly). You can reach above 95% accuracy on this task. If you want to dig deeper, this [great blog post](https://kwotsin.github.io/tech/2017/02/11/transfer-learning.html) goes into more details and reaches 96% accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.\\n\",\n    \"Exercise: Go through TensorFlow's [DeepDream tutorial](https://goo.gl/4b2s6g). It is a fun way to familiarize yourself with various ways of visualizing the patterns learned by a CNN, and to generate art using Deep Learning.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"Simply download the notebook and follow its instructions. For extra fun, you can produce a series of images, by repeatedly zooming in and running the DeepDream algorithm: using a tool such as [ffmpeg](https://ffmpeg.org/) you can then create a video from these images. For example, here is a [DeepDream video](https://www.youtube.com/watch?v=l6i_fDg30p0) I made... as you will see, it quickly turns into a nightmare. ;-) You can find hundreds of [similar videos](https://www.youtube.com/results?search_query=+deepdream) (often much more artistic) on the web.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"nav_menu\": {},\n  \"toc\": {\n   \"navigate_menu\": true,\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"threshold\": 6,\n   \"toc_cell\": false,\n   \"toc_section_display\": \"block\",\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n\n"
  },
  {
    "path": "book_equations.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Equations**\\n\",\n    \"\\n\",\n    \"*This notebook lists all the equations in the book. If you decide to print them on a T-Shirt, I definitely want a copy! ;-)*\\n\",\n    \"\\n\",\n    \"**Warning**: GitHub's notebook viewer does not render equations properly, but Jupyter or Colab work well.\\n\",\n    \"\\n\",\n    \"<table align=\\\"left\\\">\\n\",\n    \"  <td>\\n\",\n    \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/book_equations.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n    \"  </td>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Warning**: these are the equations for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition equations and code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 1\\n\",\n    \"**Equation 1-1: A simple linear model**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\text{life_satisfaction} = \\\\theta_0 + \\\\theta_1 \\\\times \\\\text{GDP_per_capita}\\n\",\n    \"$\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 2\\n\",\n    \"**Equation 2-1: Root Mean Square Error (RMSE)**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\text{RMSE}(\\\\mathbf{X}, h) = \\\\sqrt{\\\\frac{1}{m}\\\\sum\\\\limits_{i=1}^{m}\\\\left(h(\\\\mathbf{x}^{(i)}) - y^{(i)}\\\\right)^2}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Notations (page 38):**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"  \\\\mathbf{x}^{(1)} = \\\\begin{pmatrix}\\n\",\n    \"  -118.29 \\\\\\\\\\n\",\n    \"  33.91 \\\\\\\\\\n\",\n    \"  1,416 \\\\\\\\\\n\",\n    \"  38,372\\n\",\n    \"  \\\\end{pmatrix}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"  y^{(1)}=156,400\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"  \\\\mathbf{X} = \\\\begin{pmatrix}\\n\",\n    \"  (\\\\mathbf{x}^{(1)})^T \\\\\\\\\\n\",\n    \"  (\\\\mathbf{x}^{(2)})^T\\\\\\\\\\n\",\n    \"  \\\\vdots \\\\\\\\\\n\",\n    \"  (\\\\mathbf{x}^{(1999)})^T \\\\\\\\\\n\",\n    \"  (\\\\mathbf{x}^{(2000)})^T\\n\",\n    \"  \\\\end{pmatrix} = \\\\begin{pmatrix}\\n\",\n    \"  -118.29 & 33.91 & 1,416 & 38,372 \\\\\\\\\\n\",\n    \"  \\\\vdots & \\\\vdots & \\\\vdots & \\\\vdots \\\\\\\\\\n\",\n    \"  \\\\end{pmatrix}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 2-2: Mean Absolute Error**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\text{MAE}(\\\\mathbf{X}, h) = \\\\frac{1}{m}\\\\sum\\\\limits_{i=1}^{m}\\\\left| h(\\\\mathbf{x}^{(i)}) - y^{(i)} \\\\right|\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**$\\\\ell_k$ norms (page 39):**\\n\",\n    \"\\n\",\n    \"$ \\\\left\\\\| \\\\mathbf{v} \\\\right\\\\| _k = (\\\\left| v_0 \\\\right|^k + \\\\left| v_1 \\\\right|^k + \\\\dots + \\\\left| v_n \\\\right|^k)^{\\\\frac{1}{k}} $\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 3\\n\",\n    \"**Equation 3-1: Precision**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\text{precision} = \\\\cfrac{TP}{TP + FP}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 3-2: Recall**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\text{recall} = \\\\cfrac{TP}{TP + FN}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 3-3: $F_1$ score**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"F_1 = \\\\cfrac{2}{\\\\cfrac{1}{\\\\text{precision}} + \\\\cfrac{1}{\\\\text{recall}}} = 2 \\\\times \\\\cfrac{\\\\text{precision}\\\\, \\\\times \\\\, \\\\text{recall}}{\\\\text{precision}\\\\, + \\\\, \\\\text{recall}} = \\\\cfrac{TP}{TP + \\\\cfrac{FN + FP}{2}}\\n\",\n    \"$\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 4\\n\",\n    \"**Equation 4-1: Linear Regression model prediction**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{y} = \\\\theta_0 + \\\\theta_1 x_1 + \\\\theta_2 x_2 + \\\\dots + \\\\theta_n x_n\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-2: Linear Regression model prediction (vectorized form)**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{y} = h_{\\\\boldsymbol{\\\\theta}}(\\\\mathbf{x}) = \\\\boldsymbol{\\\\theta} \\\\cdot \\\\mathbf{x}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-3: MSE cost function for a Linear Regression model**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\text{MSE}(\\\\mathbf{X}, h_{\\\\boldsymbol{\\\\theta}}) = \\\\dfrac{1}{m} \\\\sum\\\\limits_{i=1}^{m}{(\\\\boldsymbol{\\\\theta}^T \\\\mathbf{x}^{(i)} - y^{(i)})^2}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-4: Normal Equation**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{\\\\boldsymbol{\\\\theta}} = (\\\\mathbf{X}^T \\\\mathbf{X})^{-1} \\\\mathbf{X}^T \\\\mathbf{y}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"** Partial derivatives notation (page 114):**\\n\",\n    \"\\n\",\n    \"$\\\\frac{\\\\partial}{\\\\partial \\\\theta_j} \\\\text{MSE}(\\\\boldsymbol{\\\\theta})$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-5: Partial derivatives of the cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\dfrac{\\\\partial}{\\\\partial \\\\theta_j} \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) = \\\\dfrac{2}{m}\\\\sum\\\\limits_{i=1}^{m}(\\\\boldsymbol{\\\\theta}^T \\\\mathbf{x}^{(i)} - y^{(i)})\\\\, x_j^{(i)}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-6: Gradient vector of the cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\nabla_{\\\\boldsymbol{\\\\theta}}\\\\, \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) =\\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \" \\\\frac{\\\\partial}{\\\\partial \\\\theta_0} \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) \\\\\\\\\\n\",\n    \" \\\\frac{\\\\partial}{\\\\partial \\\\theta_1} \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) \\\\\\\\\\n\",\n    \" \\\\vdots \\\\\\\\\\n\",\n    \" \\\\frac{\\\\partial}{\\\\partial \\\\theta_n} \\\\text{MSE}(\\\\boldsymbol{\\\\theta})\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \" = \\\\dfrac{2}{m} \\\\mathbf{X}^T (\\\\mathbf{X} \\\\boldsymbol{\\\\theta} - \\\\mathbf{y})\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-7: Gradient Descent step**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\boldsymbol{\\\\theta}^{(\\\\text{next step})} = \\\\boldsymbol{\\\\theta} - \\\\eta \\\\nabla_{\\\\boldsymbol{\\\\theta}}\\\\, \\\\text{MSE}(\\\\boldsymbol{\\\\theta})\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ O(\\\\frac{1}{\\\\text{iterations}}) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{y} = 0.56 x_1^2 + 0.93 x_1 + 1.78 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ y = 0.5 x_1^2 + 1.0 x_1 + 2.0 + \\\\text{Gaussian noise} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\dfrac{(n+d)!}{d!\\\\,n!} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\alpha \\\\sum_{i=1}^{n}{{\\\\theta_i}^2}$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-8: Ridge Regression cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(\\\\boldsymbol{\\\\theta}) = \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) + \\\\alpha \\\\dfrac{1}{2}\\\\sum\\\\limits_{i=1}^{n}{\\\\theta_i}^2\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-9: Ridge Regression closed-form solution**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{\\\\boldsymbol{\\\\theta}} = (\\\\mathbf{X}^T \\\\mathbf{X} + \\\\alpha \\\\mathbf{A})^{-1} \\\\mathbf{X}^T \\\\mathbf{y}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-10: Lasso Regression cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(\\\\boldsymbol{\\\\theta}) = \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) + \\\\alpha \\\\sum\\\\limits_{i=1}^{n}\\\\left| \\\\theta_i \\\\right|\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-11: Lasso Regression subgradient vector**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"g(\\\\boldsymbol{\\\\theta}, J) = \\\\nabla_{\\\\boldsymbol{\\\\theta}}\\\\, \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) + \\\\alpha\\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \"  \\\\operatorname{sign}(\\\\theta_1) \\\\\\\\\\n\",\n    \"  \\\\operatorname{sign}(\\\\theta_2) \\\\\\\\\\n\",\n    \"  \\\\vdots \\\\\\\\\\n\",\n    \"  \\\\operatorname{sign}(\\\\theta_n) \\\\\\\\\\n\",\n    \"\\\\end{pmatrix} \\\\quad \\\\text{where } \\\\operatorname{sign}(\\\\theta_i) =\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"-1 & \\\\text{if } \\\\theta_i < 0 \\\\\\\\\\n\",\n    \"0 & \\\\text{if } \\\\theta_i = 0 \\\\\\\\\\n\",\n    \"+1 & \\\\text{if } \\\\theta_i > 0\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-12: Elastic Net cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(\\\\boldsymbol{\\\\theta}) = \\\\text{MSE}(\\\\boldsymbol{\\\\theta}) + r \\\\alpha \\\\sum\\\\limits_{i=1}^{n}\\\\left| \\\\theta_i \\\\right| + \\\\dfrac{1 - r}{2} \\\\alpha \\\\sum\\\\limits_{i=1}^{n}{{\\\\theta_i}^2}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-13: Logistic Regression model estimated probability (vectorized form)**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{p} = h_{\\\\boldsymbol{\\\\theta}}(\\\\mathbf{x}) = \\\\sigma(\\\\boldsymbol{\\\\theta}^T \\\\mathbf{x})\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-14: Logistic function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\sigma(t) = \\\\dfrac{1}{1 + \\\\exp(-t)}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-15: Logistic Regression model prediction**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{y} =\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"  0 & \\\\text{if } \\\\hat{p} < 0.5, \\\\\\\\\\n\",\n    \"  1 & \\\\text{if } \\\\hat{p} \\\\geq 0.5.\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-16: Cost function of a single training instance**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"c(\\\\boldsymbol{\\\\theta}) =\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"  -\\\\log(\\\\hat{p}) & \\\\text{if } y = 1, \\\\\\\\\\n\",\n    \"  -\\\\log(1 - \\\\hat{p}) & \\\\text{if } y = 0.\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-17: Logistic Regression cost function (log loss)**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(\\\\boldsymbol{\\\\theta}) = -\\\\dfrac{1}{m} \\\\sum\\\\limits_{i=1}^{m}{\\\\left[ y^{(i)} log\\\\left(\\\\hat{p}^{(i)}\\\\right) + (1 - y^{(i)}) log\\\\left(1 - \\\\hat{p}^{(i)}\\\\right)\\\\right]}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-18: Logistic cost function partial derivatives**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\dfrac{\\\\partial}{\\\\partial \\\\theta_j} \\\\text{J}(\\\\boldsymbol{\\\\theta}) = \\\\dfrac{1}{m}\\\\sum\\\\limits_{i=1}^{m}\\\\left(\\\\mathbf{\\\\sigma(\\\\boldsymbol{\\\\theta}}^T \\\\mathbf{x}^{(i)}) - y^{(i)}\\\\right)\\\\, x_j^{(i)}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-19: Softmax score for class k**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"s_k(\\\\mathbf{x}) = ({\\\\boldsymbol{\\\\theta}^{(k)}})^T \\\\mathbf{x}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-20: Softmax function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{p}_k = \\\\sigma\\\\left(\\\\mathbf{s}(\\\\mathbf{x})\\\\right)_k = \\\\dfrac{\\\\exp\\\\left(s_k(\\\\mathbf{x})\\\\right)}{\\\\sum\\\\limits_{j=1}^{K}{\\\\exp\\\\left(s_j(\\\\mathbf{x})\\\\right)}}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-21: Softmax Regression classifier prediction**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{y} = \\\\underset{k}{\\\\operatorname{argmax}} \\\\, \\\\sigma\\\\left(\\\\mathbf{s}(\\\\mathbf{x})\\\\right)_k = \\\\underset{k}{\\\\operatorname{argmax}} \\\\, s_k(\\\\mathbf{x}) = \\\\underset{k}{\\\\operatorname{argmax}} \\\\, \\\\left( ({\\\\boldsymbol{\\\\theta}^{(k)}})^T \\\\mathbf{x} \\\\right)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-22: Cross entropy cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(\\\\boldsymbol{\\\\Theta}) = - \\\\dfrac{1}{m}\\\\sum\\\\limits_{i=1}^{m}\\\\sum\\\\limits_{k=1}^{K}{y_k^{(i)}\\\\log\\\\left(\\\\hat{p}_k^{(i)}\\\\right)}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Cross entropy between two discrete probability distributions $p$ and $q$ (page 141):**\\n\",\n    \"$ H(p, q) = -\\\\sum\\\\limits_{x}p(x) \\\\log q(x) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 4-23: Cross entropy gradient vector for class _k_**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\nabla_{\\\\boldsymbol{\\\\theta}^{(k)}} \\\\, J(\\\\boldsymbol{\\\\Theta}) = \\\\dfrac{1}{m} \\\\sum\\\\limits_{i=1}^{m}{ \\\\left ( \\\\hat{p}^{(i)}_k - y_k^{(i)} \\\\right ) \\\\mathbf{x}^{(i)}}\\n\",\n    \"$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 5\\n\",\n    \"**Equation 5-1: Gaussian RBF**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"{\\\\displaystyle \\\\phi_{\\\\gamma}(\\\\mathbf{x}, \\\\boldsymbol{\\\\ell})} = {\\\\displaystyle \\\\exp({\\\\displaystyle -\\\\gamma \\\\left\\\\| \\\\mathbf{x} - \\\\boldsymbol{\\\\ell} \\\\right\\\\|^2})}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-2: Linear SVM classifier prediction**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{y} = \\\\begin{cases}\\n\",\n    \" 0 & \\\\text{if } \\\\mathbf{w}^T \\\\mathbf{x} + b < 0, \\\\\\\\\\n\",\n    \" 1 & \\\\text{if } \\\\mathbf{w}^T \\\\mathbf{x} + b \\\\geq 0\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-3: Hard margin linear SVM classifier objective**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"&\\\\underset{\\\\mathbf{w}, b}{\\\\operatorname{minimize}}\\\\quad{\\\\frac{1}{2}\\\\mathbf{w}^T \\\\mathbf{w}} \\\\\\\\\\n\",\n    \"&\\\\text{subject to} \\\\quad t^{(i)}(\\\\mathbf{w}^T \\\\mathbf{x}^{(i)} + b) \\\\ge 1 \\\\quad \\\\text{for } i = 1, 2, \\\\dots, m\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-4: Soft margin linear SVM classifier objective**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"&\\\\underset{\\\\mathbf{w}, b, \\\\mathbf{\\\\zeta}}{\\\\operatorname{minimize}}\\\\quad{\\\\dfrac{1}{2}\\\\mathbf{w}^T \\\\mathbf{w} + C \\\\sum\\\\limits_{i=1}^m{\\\\zeta^{(i)}}}\\\\\\\\\\n\",\n    \"&\\\\text{subject to} \\\\quad t^{(i)}(\\\\mathbf{w}^T \\\\mathbf{x}^{(i)} + b) \\\\ge 1 - \\\\zeta^{(i)} \\\\quad \\\\text{and} \\\\quad \\\\zeta^{(i)} \\\\ge 0 \\\\quad \\\\text{for } i = 1, 2, \\\\dots, m\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-5: Quadratic Programming problem**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\underset{\\\\mathbf{p}}{\\\\text{Minimize}} \\\\quad & \\\\dfrac{1}{2} \\\\mathbf{p}^T \\\\mathbf{H} \\\\mathbf{p} \\\\quad + \\\\quad \\\\mathbf{f}^T \\\\mathbf{p}  \\\\\\\\\\n\",\n    \"\\\\text{subject to} \\\\quad & \\\\mathbf{A} \\\\mathbf{p} \\\\le \\\\mathbf{b} \\\\\\\\\\n\",\n    \"\\\\text{where } &\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"  \\\\mathbf{p} & \\\\text{ is an }n_p\\\\text{-dimensional vector (} n_p = \\\\text{number of parameters),}\\\\\\\\\\n\",\n    \"  \\\\mathbf{H} & \\\\text{ is an }n_p \\\\times n_p \\\\text{ matrix,}\\\\\\\\\\n\",\n    \"  \\\\mathbf{f} & \\\\text{ is an }n_p\\\\text{-dimensional vector,}\\\\\\\\\\n\",\n    \"  \\\\mathbf{A} & \\\\text{ is an } n_c \\\\times n_p \\\\text{ matrix (}n_c = \\\\text{number of constraints),}\\\\\\\\\\n\",\n    \"  \\\\mathbf{b} & \\\\text{ is an }n_c\\\\text{-dimensional vector.}\\n\",\n    \"\\\\end{cases}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-6: Dual form of the linear SVM objective**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\underset{\\\\mathbf{\\\\alpha}}{\\\\operatorname{minimize}}\\n\",\n    \"\\\\dfrac{1}{2}\\\\sum\\\\limits_{i=1}^{m}{\\n\",\n    \"  \\\\sum\\\\limits_{j=1}^{m}{\\n\",\n    \"  \\\\alpha^{(i)} \\\\alpha^{(j)} t^{(i)} t^{(j)} {\\\\mathbf{x}^{(i)}}^T \\\\mathbf{x}^{(j)}\\n\",\n    \"  }\\n\",\n    \"} \\\\quad - \\\\quad \\\\sum\\\\limits_{i=1}^{m}{\\\\alpha^{(i)}}\\\\\\\\\\n\",\n    \"\\\\text{subject to}\\\\quad \\\\alpha^{(i)} \\\\ge 0 \\\\quad \\\\text{for }i = 1, 2, \\\\dots, m\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-7: From the dual solution to the primal solution**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"&\\\\hat{\\\\mathbf{w}} = \\\\sum_{i=1}^{m}{\\\\hat{\\\\alpha}}^{(i)}t^{(i)}\\\\mathbf{x}^{(i)}\\\\\\\\\\n\",\n    \"&\\\\hat{b} = \\\\dfrac{1}{n_s}\\\\sum\\\\limits_{\\\\scriptstyle i=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(i)} > 0}}^{m}{\\\\left(t^{(i)} - ({\\\\hat{\\\\mathbf{w}}}^T \\\\mathbf{x}^{(i)})\\\\right)}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-8: Second-degree polynomial mapping**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\phi\\\\left(\\\\mathbf{x}\\\\right) = \\\\phi\\\\left( \\\\begin{pmatrix}\\n\",\n    \"  x_1 \\\\\\\\\\n\",\n    \"  x_2\\n\",\n    \"\\\\end{pmatrix} \\\\right) = \\\\begin{pmatrix}\\n\",\n    \"  {x_1}^2 \\\\\\\\\\n\",\n    \"  \\\\sqrt{2} \\\\, x_1 x_2 \\\\\\\\\\n\",\n    \"  {x_2}^2\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-9: Kernel trick for a 2^nd^-degree polynomial mapping**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\phi(\\\\mathbf{a})^T \\\\phi(\\\\mathbf{b}) & \\\\quad = \\\\begin{pmatrix}\\n\",\n    \"  {a_1}^2 \\\\\\\\\\n\",\n    \"  \\\\sqrt{2} \\\\, a_1 a_2 \\\\\\\\\\n\",\n    \"  {a_2}^2\\n\",\n    \"  \\\\end{pmatrix}^T \\\\begin{pmatrix}\\n\",\n    \"  {b_1}^2 \\\\\\\\\\n\",\n    \"  \\\\sqrt{2} \\\\, b_1 b_2 \\\\\\\\\\n\",\n    \"  {b_2}^2\\n\",\n    \"\\\\end{pmatrix} = {a_1}^2 {b_1}^2 + 2 a_1 b_1 a_2 b_2 + {a_2}^2 {b_2}^2 \\\\\\\\\\n\",\n    \" & \\\\quad = \\\\left( a_1 b_1 + a_2 b_2 \\\\right)^2 = \\\\left( \\\\begin{pmatrix}\\n\",\n    \"  a_1 \\\\\\\\\\n\",\n    \"  a_2\\n\",\n    \"\\\\end{pmatrix}^T \\\\begin{pmatrix}\\n\",\n    \"    b_1 \\\\\\\\\\n\",\n    \"    b_2\\n\",\n    \"  \\\\end{pmatrix} \\\\right)^2 = (\\\\mathbf{a}^T \\\\mathbf{b})^2\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text about the kernel trick (page 162):**\\n\",\n    \"[...], then you can replace this dot product of transformed vectors simply by $ ({\\\\mathbf{x}^{(i)}}^T  \\\\mathbf{x}^{(j)})^2 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-10: Common kernels**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\text{Linear:} & \\\\quad K(\\\\mathbf{a}, \\\\mathbf{b}) = \\\\mathbf{a}^T \\\\mathbf{b} \\\\\\\\\\n\",\n    \"\\\\text{Polynomial:} & \\\\quad K(\\\\mathbf{a}, \\\\mathbf{b}) = \\\\left(\\\\gamma \\\\mathbf{a}^T \\\\mathbf{b} + r \\\\right)^d \\\\\\\\\\n\",\n    \"\\\\text{Gaussian RBF:} & \\\\quad K(\\\\mathbf{a}, \\\\mathbf{b}) = \\\\exp({\\\\displaystyle -\\\\gamma \\\\left\\\\| \\\\mathbf{a} - \\\\mathbf{b} \\\\right\\\\|^2}) \\\\\\\\\\n\",\n    \"\\\\text{Sigmoid:} & \\\\quad K(\\\\mathbf{a}, \\\\mathbf{b}) = \\\\tanh\\\\left(\\\\gamma \\\\mathbf{a}^T \\\\mathbf{b} + r\\\\right)\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Equation 5-11: Making predictions with a kernelized SVM**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"h_{\\\\hat{\\\\mathbf{w}}, \\\\hat{b}}\\\\left(\\\\phi(\\\\mathbf{x}^{(n)})\\\\right) & = \\\\,\\\\hat{\\\\mathbf{w}}^T \\\\phi(\\\\mathbf{x}^{(n)}) + \\\\hat{b} = \\\\left(\\\\sum_{i=1}^{m}{\\\\hat{\\\\alpha}}^{(i)}t^{(i)}\\\\phi(\\\\mathbf{x}^{(i)})\\\\right)^T \\\\phi(\\\\mathbf{x}^{(n)}) + \\\\hat{b}\\\\\\\\\\n\",\n    \" & = \\\\, \\\\sum_{i=1}^{m}{\\\\hat{\\\\alpha}}^{(i)}t^{(i)}\\\\left(\\\\phi(\\\\mathbf{x}^{(i)})^T \\\\phi(\\\\mathbf{x}^{(n)})\\\\right)  + \\\\hat{b}\\\\\\\\\\n\",\n    \" & = \\\\sum\\\\limits_{\\\\scriptstyle i=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(i)} > 0}}^{m}{\\\\hat{\\\\alpha}}^{(i)}t^{(i)} K(\\\\mathbf{x}^{(i)}, \\\\mathbf{x}^{(n)}) + \\\\hat{b}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-12: Computing the bias term using the kernel trick**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\hat{b} & = \\\\dfrac{1}{n_s}\\\\sum\\\\limits_{\\\\scriptstyle i=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(i)} > 0}}^{m}{\\\\left(t^{(i)} - {\\\\hat{\\\\mathbf{w}}}^T \\\\phi(\\\\mathbf{x}^{(i)})\\\\right)} = \\\\dfrac{1}{n_s}\\\\sum\\\\limits_{\\\\scriptstyle i=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(i)} > 0}}^{m}{\\\\left(t^{(i)} - {\\n\",\n    \" \\\\left(\\\\sum_{j=1}^{m}{\\\\hat{\\\\alpha}}^{(j)}t^{(j)}\\\\phi(\\\\mathbf{x}^{(j)})\\\\right)\\n\",\n    \" }^T \\\\phi(\\\\mathbf{x}^{(i)})\\\\right)}\\\\\\\\\\n\",\n    \" & = \\\\dfrac{1}{n_s}\\\\sum\\\\limits_{\\\\scriptstyle i=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(i)} > 0}}^{m}{\\\\left(t^{(i)} -\\n\",\n    \"\\\\sum\\\\limits_{\\\\scriptstyle j=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(j)} > 0}}^{m}{\\n\",\n    \"  {\\\\hat{\\\\alpha}}^{(j)} t^{(j)} K(\\\\mathbf{x}^{(i)},\\\\mathbf{x}^{(j)})\\n\",\n    \"}\\n\",\n    \"\\\\right)}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 5-13: Linear SVM classifier cost function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(\\\\mathbf{w}, b) = \\\\dfrac{1}{2} \\\\mathbf{w}^T \\\\mathbf{w} \\\\quad + \\\\quad C {\\\\displaystyle \\\\sum\\\\limits_{i=1}^{m}max\\\\left(0, t^{(i)} - (\\\\mathbf{w}^T \\\\mathbf{x}^{(i)} + b) \\\\right)}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 6\\n\",\n    \"**Equation 6-1: Gini impurity**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"G_i = 1 - \\\\sum\\\\limits_{k=1}^{n}{{p_{i,k}}^2}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 6-2: CART cost function for classification**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"&J(k, t_k) = \\\\dfrac{m_{\\\\text{left}}}{m}G_\\\\text{left} + \\\\dfrac{m_{\\\\text{right}}}{m}G_{\\\\text{right}}\\\\\\\\\\n\",\n    \"&\\\\text{where }\\\\begin{cases}\\n\",\n    \"G_\\\\text{left/right} \\\\text{ measures the impurity of the left/right subset,}\\\\\\\\\\n\",\n    \"m_\\\\text{left/right} \\\\text{ is the number of instances in the left/right subset.}\\n\",\n    \"\\\\end{cases}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Entropy computation example (page 173):**\\n\",\n    \"\\n\",\n    \"$ -\\\\frac{49}{54}\\\\log_2(\\\\frac{49}{54}) - \\\\frac{5}{54}\\\\log_2(\\\\frac{5}{54}) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 6-3: Entropy**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"H_i = -\\\\sum\\\\limits_{k=1 \\\\atop p_{i,k} \\\\ne 0}^{n}{{p_{i,k}}\\\\log_2(p_{i,k})}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 6-4: CART cost function for regression**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"J(k, t_k) = \\\\dfrac{m_{\\\\text{left}}}{m}\\\\text{MSE}_\\\\text{left} + \\\\dfrac{m_{\\\\text{right}}}{m}\\\\text{MSE}_{\\\\text{right}} \\\\quad\\n\",\n    \"\\\\text{where }\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"\\\\text{MSE}_{\\\\text{node}} = \\\\sum\\\\limits_{\\\\scriptstyle i \\\\in \\\\text{node}}(\\\\hat{y}_{\\\\text{node}} - y^{(i)})^2\\\\\\\\\\n\",\n    \"\\\\hat{y}_\\\\text{node} = \\\\dfrac{1}{m_{\\\\text{node}}}\\\\sum\\\\limits_{\\\\scriptstyle i \\\\in \\\\text{node}}y^{(i)}\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 7\\n\",\n    \"\\n\",\n    \"**Equation 7-1: Weighted error rate of the $j^\\\\text{th}$ predictor**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"r_j = \\\\dfrac{\\\\displaystyle \\\\sum\\\\limits_{\\\\textstyle {i=1 \\\\atop \\\\hat{y}_j^{(i)} \\\\ne y^{(i)}}}^{m}{w^{(i)}}}{\\\\displaystyle \\\\sum\\\\limits_{i=1}^{m}{w^{(i)}}} \\\\quad\\n\",\n    \"\\\\text{where }\\\\hat{y}_j^{(i)}\\\\text{ is the }j^{\\\\text{th}}\\\\text{ predictor's prediction for the }i^{\\\\text{th}}\\\\text{ instance.}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Equation 7-2: Predictor weight**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\alpha_j = \\\\eta \\\\log{\\\\dfrac{1 - r_j}{r_j}}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 7-3: Weight update rule**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"& \\\\text{ for } i = 1, 2, \\\\dots, m \\\\\\\\\\n\",\n    \"& w^{(i)} \\\\leftarrow\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"w^{(i)} & \\\\text{if }\\\\hat{y_j}^{(i)} = y^{(i)}\\\\\\\\\\n\",\n    \"w^{(i)} \\\\exp(\\\\alpha_j) & \\\\text{if }\\\\hat{y_j}^{(i)} \\\\ne y^{(i)}\\n\",\n    \"\\\\end{cases}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 194:**\\n\",\n    \"\\n\",\n    \"Then all the instance weights are normalized (i.e., divided by $ \\\\sum_{i=1}^{m}{w^{(i)}} $).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 7-4: AdaBoost predictions**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{y}(\\\\mathbf{x}) = \\\\underset{k}{\\\\operatorname{argmax}}{\\\\sum\\\\limits_{\\\\scriptstyle j=1 \\\\atop \\\\scriptstyle \\\\hat{y}_j(\\\\mathbf{x}) = k}^{N}{\\\\alpha_j}} \\\\quad \\\\text{where }N\\\\text{ is the number of predictors.}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 8\\n\",\n    \"\\n\",\n    \"**Equation 8-1: Principal components matrix**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{V}^T =\\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \"  \\\\mid & \\\\mid & & \\\\mid \\\\\\\\\\n\",\n    \"  \\\\mathbf{c_1} & \\\\mathbf{c_2} & \\\\cdots & \\\\mathbf{c_n} \\\\\\\\\\n\",\n    \"  \\\\mid & \\\\mid & & \\\\mid\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 8-2: Projecting the training set down to _d_ dimensions**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{X}_{d\\\\text{-proj}} = \\\\mathbf{X} \\\\mathbf{W}_d\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 8-3: PCA inverse transformation, back to the original number of dimensions**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{X}_{\\\\text{recovered}} = \\\\mathbf{X}_{d\\\\text{-proj}} {\\\\mathbf{W}_d}^T\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\sum_{j=1}^{m}{w_{i,j}\\\\mathbf{x}^{(j)}} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 8-4: LLE step 1: linearly modeling local relationships**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"& \\\\hat{\\\\mathbf{W}} = \\\\underset{\\\\mathbf{W}}{\\\\operatorname{argmin}}{\\\\displaystyle \\\\sum\\\\limits_{i=1}^{m}} \\\\left\\\\|\\\\mathbf{x}^{(i)} - \\\\sum\\\\limits_{j=1}^{m}{w_{i,j}}\\\\mathbf{x}^{(j)}\\\\right\\\\|^2\\\\\\\\\\n\",\n    \"& \\\\text{subject to }\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"  w_{i,j}=0 & \\\\text{if }\\\\mathbf{x}^{(j)} \\\\text{ is not one of the }k\\\\text{ c.n. of }\\\\mathbf{x}^{(i)}\\\\\\\\\\n\",\n    \"  \\\\sum\\\\limits_{j=1}^{m}w_{i,j} = 1 & \\\\text{for }i=1, 2, \\\\dots, m\\n\",\n    \"\\\\end{cases}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 223:**\\n\",\n    \"\\n\",\n    \"[...] then we want the squared distance between $\\\\mathbf{z}^{(i)}$ and $ \\\\sum_{j=1}^{m}{\\\\hat{w}_{i,j}\\\\mathbf{z}^{(j)}} $ to be as small as possible.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 8-5: LLE step 2: reducing dimensionality while preserving relationships**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{\\\\mathbf{Z}} = \\\\underset{\\\\mathbf{Z}}{\\\\operatorname{argmin}}{\\\\displaystyle \\\\sum\\\\limits_{i=1}^{m}} \\\\left\\\\|\\\\mathbf{z}^{(i)} - \\\\sum\\\\limits_{j=1}^{m}{\\\\hat{w}_{i,j}}\\\\mathbf{z}^{(j)}\\\\right\\\\|^2\\n\",\n    \"$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 9\\n\",\n    \"\\n\",\n    \"**Equation 9-1: Rectified linear unit**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"h_{\\\\mathbf{w}, b}(\\\\mathbf{X}) = \\\\max(\\\\mathbf{X} \\\\mathbf{w} + b, 0)\\n\",\n    \"$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 10\\n\",\n    \"\\n\",\n    \"**Equation 10-1: Common step functions used in Perceptrons**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\operatorname{heaviside}(z) =\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"0 & \\\\text{if }z < 0\\\\\\\\\\n\",\n    \"1 & \\\\text{if }z \\\\ge 0\\n\",\n    \"\\\\end{cases} & \\\\quad\\\\quad\\n\",\n    \"\\\\operatorname{sgn}(z) =\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"-1 & \\\\text{if }z < 0\\\\\\\\\\n\",\n    \"0 & \\\\text{if }z = 0\\\\\\\\\\n\",\n    \"+1 & \\\\text{if }z > 0\\n\",\n    \"\\\\end{cases}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 10-2: Perceptron learning rule (weight update)**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"{w_{i,j}}^{(\\\\text{next step})} = w_{i,j} + \\\\eta (y_j - \\\\hat{y}_j) x_i\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**In the text page 266:**\\n\",\n    \"\\n\",\n    \"It will be initialized randomly, using a truncated normal (Gaussian) distribution with a standard deviation of $ 2 / \\\\sqrt{\\\\text{n}_\\\\text{inputs}} $.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 11\\n\",\n    \"**Equation 11-1: Xavier initialization (when using the logistic activation function)**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"& \\\\text{Normal distribution with mean 0 and standard deviation }\\n\",\n    \"\\\\sigma = \\\\sqrt{\\\\dfrac{2}{n_\\\\text{inputs} + n_\\\\text{outputs}}}\\\\\\\\\\n\",\n    \"& \\\\text{Or a uniform distribution between -r and +r, with }\\n\",\n    \"r = \\\\sqrt{\\\\dfrac{6}{n_\\\\text{inputs} + n_\\\\text{outputs}}}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 278:**\\n\",\n    \"\\n\",\n    \"When the number of input connections is roughly equal to the number of output\\n\",\n    \"connections, you get simpler equations (e.g., $ \\\\sigma = 1 / \\\\sqrt{n_\\\\text{inputs}} $ or $ r = \\\\sqrt{3} / \\\\sqrt{n_\\\\text{inputs}} $).\\n\",\n    \"\\n\",\n    \"**Table 11-1: Initialization parameters for each type of activation function**\\n\",\n    \"\\n\",\n    \"* Logistic uniform: $ r = \\\\sqrt{\\\\dfrac{6}{n_\\\\text{inputs} + n_\\\\text{outputs}}} $\\n\",\n    \"* Logistic normal: $ \\\\sigma = \\\\sqrt{\\\\dfrac{2}{n_\\\\text{inputs} + n_\\\\text{outputs}}} $\\n\",\n    \"* Hyperbolic tangent uniform: $ r = 4 \\\\sqrt{\\\\dfrac{6}{n_\\\\text{inputs} + n_\\\\text{outputs}}} $\\n\",\n    \"* Hyperbolic tangent normal: $ \\\\sigma = 4 \\\\sqrt{\\\\dfrac{2}{n_\\\\text{inputs} + n_\\\\text{outputs}}} $\\n\",\n    \"* ReLU (and its variants) uniform: $ r = \\\\sqrt{2} \\\\sqrt{\\\\dfrac{6}{n_\\\\text{inputs} + n_\\\\text{outputs}}} $\\n\",\n    \"* ReLU (and its variants) normal: $ \\\\sigma = \\\\sqrt{2} \\\\sqrt{\\\\dfrac{2}{n_\\\\text{inputs} + n_\\\\text{outputs}}} $\\n\",\n    \"\\n\",\n    \"**Equation 11-2: ELU activation function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\operatorname{ELU}_\\\\alpha(z) =\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"\\\\alpha(\\\\exp(z) - 1) & \\\\text{if } z < 0\\\\\\\\\\n\",\n    \"z & if z \\\\ge 0\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 11-3: Batch Normalization algorithm**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"1.\\\\quad & \\\\mathbf{\\\\mu}_B = \\\\dfrac{1}{m_B}\\\\sum\\\\limits_{i=1}^{m_B}{\\\\mathbf{x}^{(i)}}\\\\\\\\\\n\",\n    \"2.\\\\quad & {\\\\mathbf{\\\\sigma}_B}^2 = \\\\dfrac{1}{m_B}\\\\sum\\\\limits_{i=1}^{m_B}{(\\\\mathbf{x}^{(i)} - \\\\mathbf{\\\\mu}_B)^2}\\\\\\\\\\n\",\n    \"3.\\\\quad & \\\\hat{\\\\mathbf{x}}^{(i)} = \\\\dfrac{\\\\mathbf{x}^{(i)} - \\\\mathbf{\\\\mu}_B}{\\\\sqrt{{\\\\mathbf{\\\\sigma}_B}^2 + \\\\epsilon}}\\\\\\\\\\n\",\n    \"4.\\\\quad & \\\\mathbf{z}^{(i)} = \\\\gamma \\\\hat{\\\\mathbf{x}}^{(i)} + \\\\beta\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 285:**\\n\",\n    \"\\n\",\n    \"[...] given a new value $v$, the running average $v$ is updated through the equation:\\n\",\n    \"\\n\",\n    \"$ \\\\hat{v} \\\\gets \\\\hat{v} \\\\times \\\\text{momentum} + v \\\\times (1 - \\\\text{momentum}) $\\n\",\n    \"\\n\",\n    \"**Equation 11-4: Momentum algorithm**\\n\",\n    \"\\n\",\n    \"1. $\\\\mathbf{m} \\\\gets \\\\beta \\\\mathbf{m} - \\\\eta \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta})$\\n\",\n    \"2. $\\\\boldsymbol{\\\\theta} \\\\gets \\\\boldsymbol{\\\\theta} + \\\\mathbf{m}$\\n\",\n    \"\\n\",\n    \"**In the text page 296:**\\n\",\n    \"\\n\",\n    \"You can easily verify that if the gradient remains constant, the terminal velocity (i.e., the maximum size of the weight updates) is equal to that gradient multiplied by the learning rate η multiplied by $ \\\\frac{1}{1 - \\\\beta} $.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 11-5: Nesterov Accelerated Gradient algorithm**\\n\",\n    \"\\n\",\n    \"1. $\\\\mathbf{m} \\\\gets \\\\beta \\\\mathbf{m} - \\\\eta \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta} + \\\\beta \\\\mathbf{m})$\\n\",\n    \"2. $\\\\boldsymbol{\\\\theta} \\\\gets \\\\boldsymbol{\\\\theta} + \\\\mathbf{m}$\\n\",\n    \"\\n\",\n    \"**Equation 11-6: AdaGrad algorithm**\\n\",\n    \"\\n\",\n    \"1. $\\\\mathbf{s} \\\\gets \\\\mathbf{s} + \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta}) \\\\otimes \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta})$\\n\",\n    \"2. $\\\\boldsymbol{\\\\theta} \\\\gets \\\\boldsymbol{\\\\theta} - \\\\eta \\\\, \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta}) \\\\oslash {\\\\sqrt{\\\\mathbf{s} + \\\\epsilon}}$\\n\",\n    \"\\n\",\n    \"**In the text page 298-299:**\\n\",\n    \"\\n\",\n    \"This vectorized form is equivalent to computing $s_i \\\\gets s_i + \\\\left( \\\\dfrac{\\\\partial J(\\\\boldsymbol{\\\\theta})}{\\\\partial \\\\theta_i} \\\\right)^2$ for each element $s_i$ of the vector $\\\\mathbf{s}$.\\n\",\n    \"\\n\",\n    \"**In the text page 299:**\\n\",\n    \"\\n\",\n    \"This vectorized form is equivalent to computing $ \\\\theta_i \\\\gets \\\\theta_i - \\\\eta \\\\, \\\\dfrac{\\\\partial J(\\\\boldsymbol{\\\\theta})}{\\\\partial \\\\theta_i} \\\\dfrac{1}{\\\\sqrt{s_i + \\\\epsilon}} $ for all parameters $\\\\theta_i$ (simultaneously).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 11-7: RMSProp algorithm**\\n\",\n    \"\\n\",\n    \"1. $\\\\mathbf{s} \\\\gets \\\\beta \\\\mathbf{s} + (1 - \\\\beta ) \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta}) \\\\otimes \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta})$\\n\",\n    \"2. $\\\\boldsymbol{\\\\theta} \\\\gets \\\\boldsymbol{\\\\theta} - \\\\eta \\\\, \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta}) \\\\oslash {\\\\sqrt{\\\\mathbf{s} + \\\\epsilon}}$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 11-8: Adam algorithm**\\n\",\n    \"\\n\",\n    \"1. $\\\\mathbf{m} \\\\gets \\\\beta_1 \\\\mathbf{m} - (1 - \\\\beta_1) \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta})$\\n\",\n    \"2. $\\\\mathbf{s} \\\\gets \\\\beta_2 \\\\mathbf{s} + (1 - \\\\beta_2) \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta}) \\\\otimes \\\\nabla_\\\\boldsymbol{\\\\theta}J(\\\\boldsymbol{\\\\theta})$\\n\",\n    \"3. $\\\\hat{\\\\mathbf{m}} \\\\gets \\\\left(\\\\dfrac{\\\\mathbf{m}}{1 - {\\\\beta_1}^T}\\\\right)$\\n\",\n    \"4. $\\\\hat{\\\\mathbf{s}} \\\\gets \\\\left(\\\\dfrac{\\\\mathbf{s}}{1 - {\\\\beta_2}^T}\\\\right)$\\n\",\n    \"5. $\\\\boldsymbol{\\\\theta} \\\\gets \\\\boldsymbol{\\\\theta} + \\\\eta \\\\, \\\\hat{\\\\mathbf{m}} \\\\oslash {\\\\sqrt{\\\\hat{\\\\mathbf{s}} + \\\\epsilon}}$\\n\",\n    \"\\n\",\n    \"**In the text page 309:**\\n\",\n    \"\\n\",\n    \"We typically implement this constraint by computing $\\\\left\\\\| \\\\mathbf{w} \\\\right\\\\|_2$ after each training step\\n\",\n    \"and clipping $\\\\mathbf{w}$ if needed $ \\\\left( \\\\mathbf{w} \\\\gets \\\\mathbf{w} \\\\dfrac{r}{\\\\left\\\\| \\\\mathbf{w} \\\\right\\\\|_2} \\\\right) $.\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 13\\n\",\n    \"\\n\",\n    \"**Equation 13-1: Computing the output of a neuron in a convolutional layer**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"z_{i,j,k} = b_k + \\\\sum\\\\limits_{u = 0}^{f_h - 1} \\\\, \\\\, \\\\sum\\\\limits_{v = 0}^{f_w - 1} \\\\, \\\\, \\\\sum\\\\limits_{k' = 0}^{f_{n'} - 1} \\\\, \\\\, x_{i', j', k'} \\\\times w_{u, v, k', k}\\n\",\n    \"\\\\quad \\\\text{with }\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"i' = i \\\\times s_h + u \\\\\\\\\\n\",\n    \"j' = j \\\\times s_w + v\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Equation 13-2: Local response normalization**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"b_i = a_i  \\\\left(k + \\\\alpha \\\\sum\\\\limits_{j=j_\\\\text{low}}^{j_\\\\text{high}}{{a_j}^2} \\\\right)^{-\\\\beta} \\\\quad \\\\text{with }\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"  j_\\\\text{high} = \\\\min\\\\left(i + \\\\dfrac{r}{2}, f_n-1\\\\right) \\\\\\\\\\n\",\n    \"  j_\\\\text{low} = \\\\max\\\\left(0, i - \\\\dfrac{r}{2}\\\\right)\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 14\\n\",\n    \"\\n\",\n    \"**Equation 14-1: Output of a recurrent layer for a single instance**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{y}_{(t)} = \\\\phi\\\\left({\\\\mathbf{W}_x}^T{\\\\mathbf{x}_{(t)}} + {{\\\\mathbf{W}_y}^T\\\\mathbf{y}_{(t-1)}} + \\\\mathbf{b} \\\\right)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 14-2: Outputs of a layer of recurrent neurons for all instances in a mini-batch**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\mathbf{Y}_{(t)} & = \\\\phi\\\\left(\\\\mathbf{X}_{(t)} \\\\mathbf{W}_{x} + \\\\mathbf{Y}_{(t-1)} \\\\mathbf{W}_{y} + \\\\mathbf{b} \\\\right) \\\\\\\\\\n\",\n    \"& = \\\\phi\\\\left(\\n\",\n    \"\\\\left[\\\\mathbf{X}_{(t)} \\\\quad \\\\mathbf{Y}_{(t-1)} \\\\right]\\n\",\n    \"  \\\\mathbf{W} + \\\\mathbf{b} \\\\right) \\\\text{ with } \\\\mathbf{W}=\\n\",\n    \"\\\\left[ \\\\begin{matrix}\\n\",\n    \"  \\\\mathbf{W}_x\\\\\\\\\\n\",\n    \"  \\\\mathbf{W}_y\\n\",\n    \"\\\\end{matrix} \\\\right]\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 391:**\\n\",\n    \"\\n\",\n    \"Just like in regular backpropagation, there is a first forward pass through the unrolled network (represented by the dashed arrows); then the output sequence is evaluated using a cost function $ C(\\\\mathbf{Y}_{(t_\\\\text{min})}, \\\\mathbf{Y}_{(t_\\\\text{min}+1)}, \\\\dots, \\\\mathbf{Y}_{(t_\\\\text{max})}) $ (where $t_\\\\text{min}$ and $t_\\\\text{max}$ are the first and last output time steps, not counting the ignored outputs)[...]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 14-3: LSTM computations**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\mathbf{i}_{(t)}&=\\\\sigma({\\\\mathbf{W}_{xi}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{hi}}^T \\\\mathbf{h}_{(t-1)} + \\\\mathbf{b}_i)\\\\\\\\\\n\",\n    \"\\\\mathbf{f}_{(t)}&=\\\\sigma({\\\\mathbf{W}_{xf}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{hf}}^T \\\\mathbf{h}_{(t-1)} + \\\\mathbf{b}_f)\\\\\\\\\\n\",\n    \"\\\\mathbf{o}_{(t)}&=\\\\sigma({\\\\mathbf{W}_{xo}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{ho}}^T \\\\mathbf{h}_{(t-1)} + \\\\mathbf{b}_o)\\\\\\\\\\n\",\n    \"\\\\mathbf{g}_{(t)}&=\\\\operatorname{tanh}({\\\\mathbf{W}_{xg}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{hg}}^T \\\\mathbf{h}_{(t-1)} + \\\\mathbf{b}_g)\\\\\\\\\\n\",\n    \"\\\\mathbf{c}_{(t)}&=\\\\mathbf{f}_{(t)} \\\\otimes \\\\mathbf{c}_{(t-1)} \\\\, + \\\\, \\\\mathbf{i}_{(t)} \\\\otimes \\\\mathbf{g}_{(t)}\\\\\\\\\\n\",\n    \"\\\\mathbf{y}_{(t)}&=\\\\mathbf{h}_{(t)} = \\\\mathbf{o}_{(t)} \\\\otimes \\\\operatorname{tanh}(\\\\mathbf{c}_{(t)})\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 14-4: GRU computations**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\mathbf{z}_{(t)}&=\\\\sigma({\\\\mathbf{W}_{xz}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{hz}}^T \\\\mathbf{h}_{(t-1)}) \\\\\\\\\\n\",\n    \"\\\\mathbf{r}_{(t)}&=\\\\sigma({\\\\mathbf{W}_{xr}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{hr}}^T \\\\mathbf{h}_{(t-1)}) \\\\\\\\\\n\",\n    \"\\\\mathbf{g}_{(t)}&=\\\\operatorname{tanh}\\\\left({\\\\mathbf{W}_{xg}}^T \\\\mathbf{x}_{(t)} + {\\\\mathbf{W}_{hg}}^T (\\\\mathbf{r}_{(t)} \\\\otimes \\\\mathbf{h}_{(t-1)})\\\\right) \\\\\\\\\\n\",\n    \"\\\\mathbf{h}_{(t)}&=(1-\\\\mathbf{z}_{(t)}) \\\\otimes \\\\mathbf{h}_{(t-1)} + \\\\mathbf{z}_{(t)} \\\\otimes \\\\mathbf{g}_{(t)}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 15\\n\",\n    \"\\n\",\n    \"**Equation 15-1: Kullback–Leibler divergence**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"D_{\\\\mathrm{KL}}(P\\\\|Q) = \\\\sum\\\\limits_{i} P(i) \\\\log \\\\dfrac{P(i)}{Q(i)}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation: KL divergence between the target sparsity _p_ and the actual sparsity _q_**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"D_{\\\\mathrm{KL}}(p\\\\|q) = p \\\\, \\\\log \\\\dfrac{p}{q} + (1-p) \\\\log \\\\dfrac{1-p}{1-q}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 433:**\\n\",\n    \"\\n\",\n    \"One common variant is to train the encoder to output $\\\\gamma = \\\\log\\\\left(\\\\sigma^2\\\\right)$ rather than $\\\\sigma$.\\n\",\n    \"Wherever we need $\\\\sigma$ we can just compute $ \\\\sigma = \\\\exp\\\\left(\\\\dfrac{\\\\gamma}{2}\\\\right) $.\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Chapter 16\\n\",\n    \"\\n\",\n    \"**Equation 16-1: Bellman Optimality Equation**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"V^*(s) = \\\\underset{a}{\\\\max}\\\\sum\\\\limits_{s'}{T(s, a, s') [R(s, a, s') + \\\\gamma . V^*(s')]} \\\\quad \\\\text{for all }s\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Equation 16-2: Value Iteration algorithm**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"  V_{k+1}(s) \\\\gets \\\\underset{a}{\\\\max}\\\\sum\\\\limits_{s'}{T(s, a, s') [R(s, a, s') + \\\\gamma . V_k(s')]} \\\\quad \\\\text{for all }s\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 16-3: Q-Value Iteration algorithm**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"  Q_{k+1}(s, a) \\\\gets \\\\sum\\\\limits_{s'}{T(s, a, s') [R(s, a, s') + \\\\gamma . \\\\underset{a'}{\\\\max}\\\\,{Q_k(s',a')}]} \\\\quad \\\\text{for all } (s,a)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text page 458:**\\n\",\n    \"\\n\",\n    \"Once you have the optimal Q-Values, defining the optimal policy, noted $\\\\pi^{*}(s)$, is trivial: when the agent is in state $s$, it should choose the action with the highest Q-Value for that state: $ \\\\pi^{*}(s) = \\\\underset{a}{\\\\operatorname{argmax}} \\\\, Q^*(s, a) $.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 16-4: TD Learning algorithm**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"V_{k+1}(s) \\\\gets (1-\\\\alpha)V_k(s) + \\\\alpha\\\\left(r + \\\\gamma . V_k(s')\\\\right)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 16-5: Q-Learning algorithm**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"Q_{k+1}(s, a) \\\\gets (1-\\\\alpha)Q_k(s,a) + \\\\alpha\\\\left(r + \\\\gamma . \\\\underset{a'}{\\\\max} \\\\, Q_k(s', a')\\\\right)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation 16-6: Q-Learning using an exploration function**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"Q(s, a) \\\\gets (1-\\\\alpha)Q(s,a) + \\\\alpha\\\\left(r + \\\\gamma \\\\, \\\\underset{a'}{\\\\max}f(Q(s', a'), N(s', a'))\\\\right)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Equation 16-7: Target Q-Value**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"y(s,a)=r+\\\\gamma\\\\,\\\\max_{a'}\\\\,Q_\\\\boldsymbol\\\\theta(s',a')\\n\",\n    \"$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Appendix A\\n\",\n    \"\\n\",\n    \"Equations that appear in the text:\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{H} =\\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \"\\\\mathbf{H'} & 0 & \\\\cdots\\\\\\\\\\n\",\n    \"0 & 0 & \\\\\\\\\\n\",\n    \"\\\\vdots & & \\\\ddots\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\mathbf{A} =\\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \"\\\\mathbf{A'} & \\\\mathbf{I}_m \\\\\\\\\\n\",\n    \"\\\\mathbf{0} & -\\\\mathbf{I}_m\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ 1 - \\\\frac{1}{5}^2 - \\\\frac{4}{5}^2 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ 1 - \\\\frac{1}{2}^2 - \\\\frac{1}{2}^2  $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{2}{5} \\\\times $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{3}{5} \\\\times 0 $\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Appendix C\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Equations that appear in the text:\\n\",\n    \"\\n\",\n    \"$ (\\\\hat{x}, \\\\hat{y}) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{\\\\alpha} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ (\\\\hat{x}, \\\\hat{y}, \\\\hat{\\\\alpha}) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{cases}\\n\",\n    \"\\\\frac{\\\\partial}{\\\\partial x}g(x, y, \\\\alpha) = 2x - 3\\\\alpha\\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial}{\\\\partial y}g(x, y, \\\\alpha) = 2 - 2\\\\alpha\\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial}{\\\\partial \\\\alpha}g(x, y, \\\\alpha) = -3x - 2y - 1\\\\\\\\\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ 2\\\\hat{x} - 3\\\\hat{\\\\alpha} = 2 - 2\\\\hat{\\\\alpha} = -3\\\\hat{x} - 2\\\\hat{y} - 1 = 0 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{x} = \\\\frac{3}{2} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{y} = -\\\\frac{11}{4} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{\\\\alpha} = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation C-1: Generalized Lagrangian for the hard margin problem**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\mathcal{L}(\\\\mathbf{w}, b, \\\\mathbf{\\\\alpha}) = \\\\frac{1}{2}\\\\mathbf{w}^T \\\\mathbf{w} - \\\\sum\\\\limits_{i=1}^{m}{\\\\alpha^{(i)} \\\\left(t^{(i)}(\\\\mathbf{w}^T \\\\mathbf{x}^{(i)} + b) - 1\\\\right)} \\\\\\\\\\n\",\n    \"\\\\text{with}\\\\quad \\\\alpha^{(i)} \\\\ge 0 \\\\quad \\\\text{for }i = 1, 2, \\\\dots, m\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**More equations in the text:**\\n\",\n    \"\\n\",\n    \"$ (\\\\hat{\\\\mathbf{w}}, \\\\hat{b}, \\\\hat{\\\\mathbf{\\\\alpha}}) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ t^{(i)}(\\\\hat{\\\\mathbf{w}}^T \\\\mathbf{x}^{(i)} + \\\\hat{b}) \\\\ge 1 \\\\quad \\\\text{for } i = 1, 2, \\\\dots, m $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ {\\\\hat{\\\\alpha}}^{(i)} \\\\ge 0 \\\\quad \\\\text{for } i = 1, 2, \\\\dots, m $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ {\\\\hat{\\\\alpha}}^{(i)} = 0 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ t^{(i)}((\\\\hat{\\\\mathbf{w}})^T \\\\mathbf{x}^{(i)} + \\\\hat{b}) = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ {\\\\hat{\\\\alpha}}^{(i)} = 0 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation C-2: Partial derivatives of the generalized Lagrangian**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\nabla_{\\\\mathbf{w}}\\\\mathcal{L}(\\\\mathbf{w}, b, \\\\mathbf{\\\\alpha}) = \\\\mathbf{w} - \\\\sum\\\\limits_{i=1}^{m}\\\\alpha^{(i)}t^{(i)}\\\\mathbf{x}^{(i)}\\\\\\\\\\n\",\n    \"\\\\dfrac{\\\\partial}{\\\\partial b}\\\\mathcal{L}(\\\\mathbf{w}, b, \\\\mathbf{\\\\alpha}) = -\\\\sum\\\\limits_{i=1}^{m}\\\\alpha^{(i)}t^{(i)}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation C-3: Properties of the stationary points**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\hat{\\\\mathbf{w}} = \\\\sum_{i=1}^{m}{\\\\hat{\\\\alpha}}^{(i)}t^{(i)}\\\\mathbf{x}^{(i)}\\\\\\\\\\n\",\n    \"\\\\sum_{i=1}^{m}{\\\\hat{\\\\alpha}}^{(i)}t^{(i)} = 0\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation C-4: Dual form of the SVM problem**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\mathcal{L}(\\\\hat{\\\\mathbf{w}}, \\\\hat{b}, \\\\mathbf{\\\\alpha}) = \\\\dfrac{1}{2}\\\\sum\\\\limits_{i=1}^{m}{\\n\",\n    \"  \\\\sum\\\\limits_{j=1}^{m}{\\n\",\n    \"  \\\\alpha^{(i)} \\\\alpha^{(j)} t^{(i)} t^{(j)} {\\\\mathbf{x}^{(i)}}^T \\\\mathbf{x}^{(j)}\\n\",\n    \"  }\\n\",\n    \"} \\\\quad - \\\\quad \\\\sum\\\\limits_{i=1}^{m}{\\\\alpha^{(i)}}\\\\\\\\\\n\",\n    \"\\\\text{with}\\\\quad \\\\alpha^{(i)} \\\\ge 0 \\\\quad \\\\text{for }i = 1, 2, \\\\dots, m\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**Some more equations in the text:**\\n\",\n    \"\\n\",\n    \"$ \\\\hat{\\\\mathbf{\\\\alpha}} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ {\\\\hat{\\\\alpha}}^{(i)} \\\\ge 0 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{\\\\mathbf{\\\\alpha}} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{\\\\mathbf{w}} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{b} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\hat{b} = t^{(k)} - {\\\\hat{\\\\mathbf{w}}}^T \\\\mathbf{x}^{(k)} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation C-5: Bias term estimation using the dual form**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\hat{b} = \\\\dfrac{1}{n_s}\\\\sum\\\\limits_{\\\\scriptstyle i=1 \\\\atop {\\\\scriptstyle {\\\\hat{\\\\alpha}}^{(i)} > 0}}^{m}{\\\\left[t^{(i)} - {\\\\hat{\\\\mathbf{w}}}^T \\\\mathbf{x}^{(i)}\\\\right]}\\n\",\n    \"$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Appendix D\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Equation D-1: Partial derivatives of $f(x,y)$**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"\\\\dfrac{\\\\partial f}{\\\\partial x} & = \\\\dfrac{\\\\partial(x^2y)}{\\\\partial x} + \\\\dfrac{\\\\partial y}{\\\\partial x} + \\\\dfrac{\\\\partial 2}{\\\\partial x} = y \\\\dfrac{\\\\partial(x^2)}{\\\\partial x} + 0 + 0 = 2xy \\\\\\\\\\n\",\n    \"\\\\dfrac{\\\\partial f}{\\\\partial y} & = \\\\dfrac{\\\\partial(x^2y)}{\\\\partial y} + \\\\dfrac{\\\\partial y}{\\\\partial y} + \\\\dfrac{\\\\partial 2}{\\\\partial y} = x^2 + 1 + 0 = x^2 + 1 \\\\\\\\\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text:**\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial g}{\\\\partial x} = 0 + (0 \\\\times x + y \\\\times 1) = y $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial x}{\\\\partial x} = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial y}{\\\\partial x} = 0 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial (u \\\\times v)}{\\\\partial x} = \\\\frac{\\\\partial v}{\\\\partial x} \\\\times u + \\\\frac{\\\\partial u}{\\\\partial x} \\\\times u  $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial g}{\\\\partial x} = 0 + (0 \\\\times x + y \\\\times 1)  $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial g}{\\\\partial x} = y $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation D-2: Derivative of a function _h_(_x_) at point _x_~0~**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"h'(x) & = \\\\underset{\\\\textstyle x \\\\to x_0}{\\\\lim}\\\\dfrac{h(x) - h(x_0)}{x - x_0}\\\\\\\\\\n\",\n    \"      & = \\\\underset{\\\\textstyle \\\\epsilon \\\\to 0}{\\\\lim}\\\\dfrac{h(x_0 + \\\\epsilon) - h(x_0)}{\\\\epsilon}\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation D-3: A few operations with dual numbers**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\begin{split}\\n\",\n    \"&\\\\lambda(a + b\\\\epsilon) = \\\\lambda a + \\\\lambda b \\\\epsilon\\\\\\\\\\n\",\n    \"&(a + b\\\\epsilon) + (c + d\\\\epsilon) = (a + c) + (b + d)\\\\epsilon \\\\\\\\\\n\",\n    \"&(a + b\\\\epsilon) \\\\times (c + d\\\\epsilon) = ac + (ad + bc)\\\\epsilon + (bd)\\\\epsilon^2 = ac + (ad + bc)\\\\epsilon\\\\\\\\\\n\",\n    \"\\\\end{split}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text:**\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial x}(3, 4) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial y}(3, 4) $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation D-4: Chain rule**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"\\\\dfrac{\\\\partial f}{\\\\partial x} = \\\\dfrac{\\\\partial f}{\\\\partial n_i} \\\\times \\\\dfrac{\\\\partial n_i}{\\\\partial x}\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text:**\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial n_7} = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial n_5} = \\\\frac{\\\\partial f}{\\\\partial n_7} \\\\times \\\\frac{\\\\partial n_7}{\\\\partial n_5} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial n_7} = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial n_7}{\\\\partial n_5} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial n_7}{\\\\partial n_5} = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial n_5} = 1 \\\\times 1 = 1 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial n_4} = \\\\frac{\\\\partial f}{\\\\partial n_5} \\\\times \\\\frac{\\\\partial n_5}{\\\\partial n_4} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial n_5}{\\\\partial n_4} = n_2 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial n_4} = 1 \\\\times n_2 = 4 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial x} = 24 $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\frac{\\\\partial f}{\\\\partial y} = 10 $\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Appendix E\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Equation E-1: Probability that the i^th^ neuron will output 1**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"p\\\\left(s_i^{(\\\\text{next step})} = 1\\\\right) \\\\, = \\\\, \\\\sigma\\\\left(\\\\frac{\\\\textstyle \\\\sum\\\\limits_{j = 1}^N{w_{i,j}s_j + b_i}}{\\\\textstyle T}\\\\right)\\n\",\n    \"$\\n\",\n    \"\\n\",\n    \"**In the text:**\\n\",\n    \"\\n\",\n    \"$ \\\\dot{\\\\mathbf{x}} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\dot{\\\\mathbf{h}} $\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Equation E-2: Contrastive divergence weight update**\\n\",\n    \"\\n\",\n    \"$\\n\",\n    \"w_{i,j}^{(\\\\text{next step})} = w_{i,j} + \\\\eta(\\\\mathbf{x}\\\\mathbf{h}^T - \\\\dot{\\\\mathbf{x}} \\\\dot {\\\\mathbf{h}}^T)\\n\",\n    \"$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Glossary\\n\",\n    \"\\n\",\n    \"In the text:\\n\",\n    \"\\n\",\n    \"$\\\\ell _1$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$\\\\ell _2$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$\\\\ell _k$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$ \\\\chi^2 $\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just in case your eyes hurt after all these equations, let's finish with the single most beautiful equation in the world. No, it's not $E = mc²$, it's obviously Euler's identity:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"$e^{i\\\\pi}+1=0$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "datasets/housing/README.md",
    "content": "# California Housing\n\n## Source\nThis dataset is a modified version of the California Housing dataset available from [Luís Torgo's page](http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html) (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.\n\nThis dataset appeared in a 1997 paper titled *Sparse Spatial Autoregressions* by Pace, R. Kelley and Ronald Barry, published in the *Statistics and Probability Letters* journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).\n\n## Tweaks\nThe dataset in this directory is almost identical to the original, with two differences:\n\n* 207 values were randomly removed from the `total_bedrooms` column, so we can discuss what to do with missing data.\n* An additional categorical attribute called `ocean_proximity` was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data.\n\nNote that the block groups are called \"districts\" in the Jupyter notebooks, simply because in some contexts the name \"block group\" was confusing.\n\n## Data description\n\n    >>> housing.info()\n    <class 'pandas.core.frame.DataFrame'>\n    RangeIndex: 20640 entries, 0 to 20639\n    Data columns (total 10 columns):\n    longitude             20640 non-null float64\n    latitude              20640 non-null float64\n    housing_median_age    20640 non-null float64\n    total_rooms           20640 non-null float64\n    total_bedrooms        20433 non-null float64\n    population            20640 non-null float64\n    households            20640 non-null float64\n    median_income         20640 non-null float64\n    median_house_value    20640 non-null float64\n    ocean_proximity       20640 non-null object\n    dtypes: float64(9), object(1)\n    memory usage: 1.6+ MB\n    \n    >>> housing[\"ocean_proximity\"].value_counts()\n    <1H OCEAN     9136\n    INLAND        6551\n    NEAR OCEAN    2658\n    NEAR BAY      2290\n    ISLAND           5\n    Name: ocean_proximity, dtype: int64\n    \n    >>> housing.describe()\n              longitude      latitude  housing_median_age   total_rooms  \\\n    count  16513.000000  16513.000000        16513.000000  16513.000000   \n    mean    -119.575972     35.639693           28.652335   2622.347605   \n    std        2.002048      2.138279           12.576306   2138.559393   \n    min     -124.350000     32.540000            1.000000      6.000000   \n    25%     -121.800000     33.940000           18.000000   1442.000000   \n    50%     -118.510000     34.260000           29.000000   2119.000000   \n    75%     -118.010000     37.720000           37.000000   3141.000000   \n    max     -114.310000     41.950000           52.000000  39320.000000   \n\n           total_bedrooms    population    households  median_income  \n    count    16355.000000  16513.000000  16513.000000   16513.000000  \n    mean       534.885112   1419.525465    496.975050       3.875651  \n    std        412.716467   1115.715084    375.737945       1.905088  \n    min          2.000000      3.000000      2.000000       0.499900  \n    25%        295.000000    784.000000    278.000000       2.566800  \n    50%        433.000000   1164.000000    408.000000       3.541400  \n    75%        644.000000   1718.000000    602.000000       4.745000  \n    max       6210.000000  35682.000000   5358.000000      15.000100\n \n"
  },
  {
    "path": "datasets/housing/housing.csv",
    "content": "longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value,ocean_proximity\n-122.23,37.88,41.0,880.0,129.0,322.0,126.0,8.3252,452600.0,NEAR BAY\n-122.22,37.86,21.0,7099.0,1106.0,2401.0,1138.0,8.3014,358500.0,NEAR BAY\n-122.24,37.85,52.0,1467.0,190.0,496.0,177.0,7.2574,352100.0,NEAR BAY\n-122.25,37.85,52.0,1274.0,235.0,558.0,219.0,5.6431,341300.0,NEAR BAY\n-122.25,37.85,52.0,1627.0,280.0,565.0,259.0,3.8462,342200.0,NEAR BAY\n-122.25,37.85,52.0,919.0,213.0,413.0,193.0,4.0368,269700.0,NEAR BAY\n-122.25,37.84,52.0,2535.0,489.0,1094.0,514.0,3.6591,299200.0,NEAR BAY\n-122.25,37.84,52.0,3104.0,687.0,1157.0,647.0,3.12,241400.0,NEAR BAY\n-122.26,37.84,42.0,2555.0,665.0,1206.0,595.0,2.0804,226700.0,NEAR BAY\n-122.25,37.84,52.0,3549.0,707.0,1551.0,714.0,3.6912,261100.0,NEAR BAY\n-122.26,37.85,52.0,2202.0,434.0,910.0,402.0,3.2031,281500.0,NEAR BAY\n-122.26,37.85,52.0,3503.0,752.0,1504.0,734.0,3.2705,241800.0,NEAR BAY\n-122.26,37.85,52.0,2491.0,474.0,1098.0,468.0,3.075,213500.0,NEAR BAY\n-122.26,37.84,52.0,696.0,191.0,345.0,174.0,2.6736,191300.0,NEAR BAY\n-122.26,37.85,52.0,2643.0,626.0,1212.0,620.0,1.9167,159200.0,NEAR BAY\n-122.26,37.85,50.0,1120.0,283.0,697.0,264.0,2.125,140000.0,NEAR BAY\n-122.27,37.85,52.0,1966.0,347.0,793.0,331.0,2.775,152500.0,NEAR BAY\n-122.27,37.85,52.0,1228.0,293.0,648.0,303.0,2.1202,155500.0,NEAR BAY\n-122.26,37.84,50.0,2239.0,455.0,990.0,419.0,1.9911,158700.0,NEAR BAY\n-122.27,37.84,52.0,1503.0,298.0,690.0,275.0,2.6033,162900.0,NEAR BAY\n-122.27,37.85,40.0,751.0,184.0,409.0,166.0,1.3578,147500.0,NEAR BAY\n-122.27,37.85,42.0,1639.0,367.0,929.0,366.0,1.7135,159800.0,NEAR BAY\n-122.27,37.84,52.0,2436.0,541.0,1015.0,478.0,1.725,113900.0,NEAR BAY\n-122.27,37.84,52.0,1688.0,337.0,853.0,325.0,2.1806,99700.0,NEAR BAY\n-122.27,37.84,52.0,2224.0,437.0,1006.0,422.0,2.6,132600.0,NEAR BAY\n-122.28,37.85,41.0,535.0,123.0,317.0,119.0,2.4038,107500.0,NEAR BAY\n-122.28,37.85,49.0,1130.0,244.0,607.0,239.0,2.4597,93800.0,NEAR BAY\n-122.28,37.85,52.0,1898.0,421.0,1102.0,397.0,1.808,105500.0,NEAR BAY\n-122.28,37.84,50.0,2082.0,492.0,1131.0,473.0,1.6424,108900.0,NEAR BAY\n-122.28,37.84,52.0,729.0,160.0,395.0,155.0,1.6875,132000.0,NEAR BAY\n-122.28,37.84,49.0,1916.0,447.0,863.0,378.0,1.9274,122300.0,NEAR BAY\n-122.28,37.84,52.0,2153.0,481.0,1168.0,441.0,1.9615,115200.0,NEAR BAY\n-122.27,37.84,48.0,1922.0,409.0,1026.0,335.0,1.7969,110400.0,NEAR BAY\n-122.27,37.83,49.0,1655.0,366.0,754.0,329.0,1.375,104900.0,NEAR BAY\n-122.27,37.83,51.0,2665.0,574.0,1258.0,536.0,2.7303,109700.0,NEAR BAY\n-122.27,37.83,49.0,1215.0,282.0,570.0,264.0,1.4861,97200.0,NEAR BAY\n-122.27,37.83,48.0,1798.0,432.0,987.0,374.0,1.0972,104500.0,NEAR BAY\n-122.28,37.83,52.0,1511.0,390.0,901.0,403.0,1.4103,103900.0,NEAR BAY\n-122.26,37.83,52.0,1470.0,330.0,689.0,309.0,3.48,191400.0,NEAR BAY\n-122.26,37.83,52.0,2432.0,715.0,1377.0,696.0,2.5898,176000.0,NEAR BAY\n-122.26,37.83,52.0,1665.0,419.0,946.0,395.0,2.0978,155400.0,NEAR BAY\n-122.26,37.83,51.0,936.0,311.0,517.0,249.0,1.2852,150000.0,NEAR BAY\n-122.26,37.84,49.0,713.0,202.0,462.0,189.0,1.025,118800.0,NEAR BAY\n-122.26,37.84,52.0,950.0,202.0,467.0,198.0,3.9643,188800.0,NEAR BAY\n-122.26,37.83,52.0,1443.0,311.0,660.0,292.0,3.0125,184400.0,NEAR BAY\n-122.26,37.83,52.0,1656.0,420.0,718.0,382.0,2.6768,182300.0,NEAR BAY\n-122.26,37.83,50.0,1125.0,322.0,616.0,304.0,2.026,142500.0,NEAR BAY\n-122.27,37.82,43.0,1007.0,312.0,558.0,253.0,1.7348,137500.0,NEAR BAY\n-122.26,37.82,40.0,624.0,195.0,423.0,160.0,0.9506,187500.0,NEAR BAY\n-122.27,37.82,40.0,946.0,375.0,700.0,352.0,1.775,112500.0,NEAR BAY\n-122.27,37.82,21.0,896.0,453.0,735.0,438.0,0.9218,171900.0,NEAR BAY\n-122.27,37.82,43.0,1868.0,456.0,1061.0,407.0,1.5045,93800.0,NEAR BAY\n-122.27,37.82,41.0,3221.0,853.0,1959.0,720.0,1.1108,97500.0,NEAR BAY\n-122.27,37.82,52.0,1630.0,456.0,1162.0,400.0,1.2475,104200.0,NEAR BAY\n-122.28,37.82,52.0,1170.0,235.0,701.0,233.0,1.6098,87500.0,NEAR BAY\n-122.28,37.82,52.0,945.0,243.0,576.0,220.0,1.4113,83100.0,NEAR BAY\n-122.28,37.82,52.0,1238.0,288.0,622.0,259.0,1.5057,87500.0,NEAR BAY\n-122.28,37.82,52.0,1489.0,335.0,728.0,244.0,0.8172,85300.0,NEAR BAY\n-122.28,37.82,52.0,1387.0,341.0,1074.0,304.0,1.2171,80300.0,NEAR BAY\n-122.29,37.82,2.0,158.0,43.0,94.0,57.0,2.5625,60000.0,NEAR BAY\n-122.29,37.83,52.0,1121.0,211.0,554.0,187.0,3.3929,75700.0,NEAR BAY\n-122.29,37.82,49.0,135.0,29.0,86.0,23.0,6.1183,75000.0,NEAR BAY\n-122.29,37.81,50.0,760.0,190.0,377.0,122.0,0.9011,86100.0,NEAR BAY\n-122.3,37.81,52.0,1224.0,237.0,521.0,159.0,1.191,76100.0,NEAR BAY\n-122.3,37.81,48.0,828.0,182.0,392.0,133.0,2.5938,73500.0,NEAR BAY\n-122.3,37.81,52.0,1010.0,209.0,604.0,187.0,1.1667,78400.0,NEAR BAY\n-122.3,37.81,48.0,1455.0,354.0,788.0,332.0,0.8056,84400.0,NEAR BAY\n-122.29,37.8,52.0,1027.0,244.0,492.0,147.0,2.6094,81300.0,NEAR BAY\n-122.3,37.81,52.0,572.0,109.0,274.0,82.0,1.8516,85000.0,NEAR BAY\n-122.29,37.81,46.0,2801.0,644.0,1823.0,611.0,0.9802,129200.0,NEAR BAY\n-122.29,37.81,26.0,768.0,152.0,392.0,127.0,1.7719,82500.0,NEAR BAY\n-122.29,37.81,46.0,935.0,297.0,582.0,277.0,0.7286,95200.0,NEAR BAY\n-122.29,37.81,49.0,844.0,204.0,560.0,152.0,1.75,75000.0,NEAR BAY\n-122.29,37.81,46.0,12.0,4.0,18.0,7.0,0.4999,67500.0,NEAR BAY\n-122.29,37.81,20.0,835.0,161.0,290.0,133.0,2.483,137500.0,NEAR BAY\n-122.28,37.81,17.0,1237.0,462.0,762.0,439.0,0.9241,177500.0,NEAR BAY\n-122.28,37.81,36.0,2914.0,562.0,1236.0,509.0,2.4464,102100.0,NEAR BAY\n-122.28,37.81,19.0,1207.0,243.0,721.0,207.0,1.1111,108300.0,NEAR BAY\n-122.29,37.81,23.0,1745.0,374.0,1054.0,325.0,0.8026,112500.0,NEAR BAY\n-122.28,37.8,38.0,684.0,176.0,344.0,155.0,2.0114,131300.0,NEAR BAY\n-122.28,37.81,17.0,924.0,289.0,609.0,289.0,1.5,162500.0,NEAR BAY\n-122.27,37.81,52.0,210.0,56.0,183.0,56.0,1.1667,112500.0,NEAR BAY\n-122.28,37.81,52.0,340.0,97.0,200.0,87.0,1.5208,112500.0,NEAR BAY\n-122.28,37.81,52.0,386.0,164.0,346.0,155.0,0.8075,137500.0,NEAR BAY\n-122.28,37.81,35.0,948.0,184.0,467.0,169.0,1.8088,118800.0,NEAR BAY\n-122.28,37.81,52.0,773.0,143.0,377.0,115.0,2.4083,98200.0,NEAR BAY\n-122.27,37.81,40.0,880.0,451.0,582.0,380.0,0.977,118800.0,NEAR BAY\n-122.27,37.81,10.0,875.0,348.0,546.0,330.0,0.76,162500.0,NEAR BAY\n-122.27,37.8,10.0,105.0,42.0,125.0,39.0,0.9722,137500.0,NEAR BAY\n-122.27,37.8,52.0,249.0,78.0,396.0,85.0,1.2434,500001.0,NEAR BAY\n-122.27,37.8,16.0,994.0,392.0,800.0,362.0,2.0938,162500.0,NEAR BAY\n-122.28,37.8,52.0,215.0,87.0,904.0,88.0,0.8668,137500.0,NEAR BAY\n-122.28,37.8,52.0,96.0,31.0,191.0,34.0,0.75,162500.0,NEAR BAY\n-122.27,37.79,27.0,1055.0,347.0,718.0,302.0,2.6354,187500.0,NEAR BAY\n-122.27,37.8,39.0,1715.0,623.0,1327.0,467.0,1.8477,179200.0,NEAR BAY\n-122.26,37.8,36.0,5329.0,2477.0,3469.0,2323.0,2.0096,130000.0,NEAR BAY\n-122.26,37.82,31.0,4596.0,1331.0,2048.0,1180.0,2.8345,183800.0,NEAR BAY\n-122.26,37.81,29.0,335.0,107.0,202.0,91.0,2.0062,125000.0,NEAR BAY\n-122.26,37.82,22.0,3682.0,1270.0,2024.0,1250.0,1.2185,170000.0,NEAR BAY\n-122.26,37.82,37.0,3633.0,1085.0,1838.0,980.0,2.6104,193100.0,NEAR BAY\n-122.25,37.81,29.0,4656.0,1414.0,2304.0,1250.0,2.4912,257800.0,NEAR BAY\n-122.25,37.81,28.0,5806.0,1603.0,2563.0,1497.0,3.2177,273400.0,NEAR BAY\n-122.25,37.81,39.0,854.0,242.0,389.0,228.0,3.125,237500.0,NEAR BAY\n-122.25,37.81,52.0,2155.0,701.0,895.0,613.0,2.5795,350000.0,NEAR BAY\n-122.26,37.81,34.0,5871.0,1914.0,2689.0,1789.0,2.8406,335700.0,NEAR BAY\n-122.24,37.82,52.0,1509.0,225.0,674.0,244.0,4.9306,313400.0,NEAR BAY\n-122.24,37.81,52.0,2026.0,482.0,709.0,456.0,3.2727,268500.0,NEAR BAY\n-122.25,37.81,52.0,1758.0,460.0,686.0,422.0,3.1691,259400.0,NEAR BAY\n-122.24,37.82,52.0,3481.0,751.0,1444.0,718.0,3.9,275700.0,NEAR BAY\n-122.25,37.82,28.0,3337.0,855.0,1520.0,802.0,3.9063,225000.0,NEAR BAY\n-122.25,37.82,52.0,1424.0,289.0,550.0,253.0,5.0917,262500.0,NEAR BAY\n-122.25,37.82,32.0,3809.0,1098.0,1806.0,1022.0,2.6429,218500.0,NEAR BAY\n-122.25,37.82,26.0,3959.0,1196.0,1749.0,1217.0,3.0233,255000.0,NEAR BAY\n-122.25,37.83,52.0,2376.0,559.0,939.0,519.0,3.1484,224100.0,NEAR BAY\n-122.25,37.83,35.0,1613.0,428.0,675.0,422.0,3.4722,243100.0,NEAR BAY\n-122.25,37.83,52.0,1279.0,287.0,534.0,291.0,3.1429,231600.0,NEAR BAY\n-122.25,37.83,28.0,5022.0,1750.0,2558.0,1661.0,2.4234,218500.0,NEAR BAY\n-122.25,37.83,52.0,4190.0,1105.0,1786.0,1037.0,3.0897,234100.0,NEAR BAY\n-122.23,37.84,50.0,2515.0,399.0,970.0,373.0,5.8596,327600.0,NEAR BAY\n-122.23,37.84,47.0,3175.0,454.0,1098.0,485.0,5.2868,347600.0,NEAR BAY\n-122.24,37.83,41.0,2576.0,406.0,794.0,376.0,5.956,366100.0,NEAR BAY\n-122.24,37.85,37.0,334.0,54.0,98.0,47.0,4.9643,335000.0,NEAR BAY\n-122.23,37.85,52.0,2800.0,411.0,1061.0,403.0,6.3434,373600.0,NEAR BAY\n-122.24,37.84,52.0,3529.0,574.0,1177.0,555.0,5.1773,389500.0,NEAR BAY\n-122.24,37.85,52.0,2612.0,365.0,901.0,367.0,7.2354,391100.0,NEAR BAY\n-122.22,37.85,28.0,5287.0,1048.0,2031.0,956.0,5.457,337300.0,NEAR BAY\n-122.22,37.84,50.0,2935.0,473.0,1031.0,479.0,7.5,295200.0,NEAR BAY\n-122.21,37.84,44.0,3424.0,597.0,1358.0,597.0,6.0194,292300.0,NEAR BAY\n-122.21,37.83,40.0,4991.0,674.0,1616.0,654.0,7.5544,411500.0,NEAR BAY\n-122.2,37.84,30.0,2211.0,346.0,844.0,343.0,6.0666,311500.0,NEAR BAY\n-122.21,37.84,34.0,3038.0,490.0,1140.0,496.0,7.0548,325900.0,NEAR BAY\n-122.19,37.84,18.0,1617.0,210.0,533.0,194.0,11.6017,392600.0,NEAR BAY\n-122.2,37.84,35.0,2865.0,460.0,1072.0,443.0,7.4882,319300.0,NEAR BAY\n-122.21,37.83,34.0,5065.0,788.0,1627.0,766.0,6.8976,333300.0,NEAR BAY\n-122.19,37.83,28.0,1326.0,184.0,463.0,190.0,8.2049,335200.0,NEAR BAY\n-122.2,37.83,26.0,1589.0,223.0,542.0,211.0,8.401,351200.0,NEAR BAY\n-122.19,37.83,29.0,1791.0,271.0,661.0,269.0,6.8538,368900.0,NEAR BAY\n-122.19,37.82,32.0,1835.0,264.0,635.0,263.0,8.317,365900.0,NEAR BAY\n-122.2,37.82,37.0,1229.0,181.0,420.0,176.0,7.0175,366700.0,NEAR BAY\n-122.2,37.82,39.0,3770.0,534.0,1265.0,500.0,6.3302,362800.0,NEAR BAY\n-122.18,37.81,30.0,292.0,38.0,126.0,52.0,6.3624,483300.0,NEAR BAY\n-122.21,37.82,52.0,2375.0,333.0,813.0,350.0,7.0549,331400.0,NEAR BAY\n-122.2,37.81,45.0,2964.0,436.0,1067.0,426.0,6.7851,323500.0,NEAR BAY\n-122.21,37.8,50.0,2833.0,605.0,1260.0,552.0,2.8929,216700.0,NEAR BAY\n-122.21,37.8,38.0,2254.0,535.0,951.0,487.0,3.0812,233100.0,NEAR BAY\n-122.21,37.81,52.0,1389.0,212.0,510.0,224.0,5.2402,296400.0,NEAR BAY\n-122.22,37.81,52.0,1971.0,335.0,765.0,308.0,6.5217,273700.0,NEAR BAY\n-122.22,37.8,52.0,2183.0,465.0,1129.0,460.0,3.2632,227700.0,NEAR BAY\n-122.22,37.8,52.0,2286.0,464.0,1073.0,441.0,3.0298,199600.0,NEAR BAY\n-122.22,37.8,52.0,2721.0,541.0,1185.0,515.0,4.5428,239800.0,NEAR BAY\n-122.22,37.81,52.0,2024.0,339.0,756.0,340.0,4.072,270100.0,NEAR BAY\n-122.22,37.81,52.0,2944.0,536.0,1034.0,521.0,5.3509,302100.0,NEAR BAY\n-122.23,37.8,52.0,2033.0,486.0,787.0,459.0,3.1603,269500.0,NEAR BAY\n-122.23,37.81,52.0,1433.0,229.0,612.0,213.0,4.7708,314700.0,NEAR BAY\n-122.22,37.81,52.0,2927.0,402.0,1021.0,380.0,8.1564,390100.0,NEAR BAY\n-122.23,37.81,52.0,2315.0,292.0,861.0,258.0,8.8793,410300.0,NEAR BAY\n-122.24,37.81,52.0,2485.0,313.0,953.0,327.0,6.8591,352400.0,NEAR BAY\n-122.24,37.81,52.0,1490.0,238.0,634.0,256.0,6.0302,287300.0,NEAR BAY\n-122.23,37.81,52.0,2814.0,365.0,878.0,352.0,7.508,348700.0,NEAR BAY\n-122.24,37.81,52.0,2093.0,550.0,918.0,483.0,2.7477,243800.0,NEAR BAY\n-122.24,37.8,52.0,888.0,168.0,360.0,175.0,2.1944,211500.0,NEAR BAY\n-122.25,37.8,52.0,2087.0,510.0,1197.0,488.0,3.0149,218400.0,NEAR BAY\n-122.24,37.81,52.0,2513.0,502.0,1048.0,518.0,3.675,269900.0,NEAR BAY\n-122.25,37.81,46.0,3232.0,835.0,1373.0,747.0,3.225,218800.0,NEAR BAY\n-122.25,37.8,42.0,4120.0,1065.0,1715.0,1015.0,2.9345,225000.0,NEAR BAY\n-122.25,37.8,43.0,2364.0,792.0,1359.0,722.0,2.1429,250000.0,NEAR BAY\n-122.25,37.8,41.0,1471.0,469.0,1062.0,413.0,1.6121,171400.0,NEAR BAY\n-122.25,37.8,29.0,2468.0,864.0,1335.0,773.0,1.3929,193800.0,NEAR BAY\n-122.24,37.79,27.0,1632.0,492.0,1171.0,429.0,2.3173,125000.0,NEAR BAY\n-122.25,37.79,45.0,1786.0,526.0,1475.0,460.0,1.7772,97500.0,NEAR BAY\n-122.25,37.79,50.0,629.0,188.0,742.0,196.0,2.6458,125000.0,NEAR BAY\n-122.25,37.79,52.0,1339.0,391.0,1086.0,363.0,2.181,138800.0,NEAR BAY\n-122.25,37.8,36.0,1678.0,606.0,1645.0,543.0,2.2303,116700.0,NEAR BAY\n-122.25,37.8,43.0,2344.0,647.0,1710.0,644.0,1.6504,151800.0,NEAR BAY\n-122.24,37.8,52.0,996.0,228.0,731.0,228.0,2.2697,127000.0,NEAR BAY\n-122.24,37.8,52.0,1591.0,373.0,1118.0,347.0,2.1563,128600.0,NEAR BAY\n-122.24,37.8,52.0,1586.0,398.0,1006.0,335.0,2.1348,140600.0,NEAR BAY\n-122.24,37.8,47.0,2046.0,588.0,1213.0,554.0,2.6292,182700.0,NEAR BAY\n-122.23,37.8,52.0,1192.0,289.0,772.0,257.0,2.3833,146900.0,NEAR BAY\n-122.24,37.8,52.0,1803.0,420.0,1321.0,401.0,2.957,122800.0,NEAR BAY\n-122.24,37.8,49.0,2838.0,749.0,1487.0,677.0,2.5238,169300.0,NEAR BAY\n-122.23,37.8,52.0,783.0,184.0,488.0,186.0,1.9375,126600.0,NEAR BAY\n-122.23,37.8,51.0,1590.0,414.0,949.0,392.0,1.9028,127900.0,NEAR BAY\n-122.23,37.8,50.0,1746.0,480.0,1149.0,415.0,2.25,123500.0,NEAR BAY\n-122.23,37.8,52.0,1252.0,299.0,844.0,280.0,2.3929,111900.0,NEAR BAY\n-122.23,37.79,43.0,5963.0,1344.0,4367.0,1231.0,2.1917,112800.0,NEAR BAY\n-122.23,37.79,52.0,1783.0,395.0,1659.0,412.0,2.9357,107900.0,NEAR BAY\n-122.23,37.79,30.0,999.0,264.0,1011.0,263.0,1.8854,137500.0,NEAR BAY\n-122.24,37.79,39.0,1469.0,431.0,1464.0,389.0,2.1638,105500.0,NEAR BAY\n-122.24,37.79,47.0,1372.0,395.0,1237.0,303.0,2.125,95500.0,NEAR BAY\n-122.24,37.79,52.0,674.0,180.0,647.0,168.0,3.375,116100.0,NEAR BAY\n-122.24,37.79,43.0,1626.0,376.0,1284.0,357.0,2.2542,112200.0,NEAR BAY\n-122.25,37.79,51.0,175.0,43.0,228.0,55.0,2.1,75000.0,NEAR BAY\n-122.25,37.79,39.0,461.0,129.0,381.0,123.0,1.6,112500.0,NEAR BAY\n-122.25,37.79,52.0,902.0,237.0,846.0,227.0,3.625,125000.0,NEAR BAY\n-122.26,37.8,20.0,2373.0,779.0,1659.0,676.0,1.6929,115000.0,NEAR BAY\n-122.22,37.77,52.0,391.0,128.0,520.0,138.0,1.6471,95000.0,NEAR BAY\n-122.22,37.77,52.0,1137.0,301.0,866.0,259.0,2.59,96400.0,NEAR BAY\n-122.23,37.77,52.0,769.0,206.0,612.0,183.0,2.57,72000.0,NEAR BAY\n-122.23,37.78,52.0,472.0,146.0,415.0,126.0,2.6429,71300.0,NEAR BAY\n-122.23,37.78,52.0,862.0,215.0,994.0,213.0,3.0257,80800.0,NEAR BAY\n-122.22,37.78,50.0,1920.0,530.0,1525.0,477.0,1.4886,128800.0,NEAR BAY\n-122.23,37.78,43.0,1420.0,472.0,1506.0,438.0,1.9338,112500.0,NEAR BAY\n-122.23,37.78,52.0,986.0,258.0,1008.0,255.0,1.4844,119400.0,NEAR BAY\n-122.23,37.78,44.0,2340.0,825.0,2813.0,751.0,1.6009,118100.0,NEAR BAY\n-122.23,37.79,48.0,1696.0,396.0,1481.0,343.0,2.0375,122500.0,NEAR BAY\n-122.23,37.79,49.0,1175.0,217.0,859.0,219.0,2.293,106300.0,NEAR BAY\n-122.22,37.79,37.0,2343.0,574.0,1608.0,523.0,2.1494,132500.0,NEAR BAY\n-122.23,37.79,30.0,610.0,145.0,425.0,140.0,1.6198,122700.0,NEAR BAY\n-122.23,37.79,40.0,930.0,199.0,564.0,184.0,1.3281,113300.0,NEAR BAY\n-122.22,37.79,44.0,1487.0,314.0,961.0,272.0,3.5156,109500.0,NEAR BAY\n-122.22,37.79,52.0,3424.0,690.0,2273.0,685.0,3.9048,164700.0,NEAR BAY\n-122.21,37.79,52.0,762.0,190.0,600.0,195.0,3.0893,125000.0,NEAR BAY\n-122.22,37.79,46.0,2366.0,575.0,1647.0,527.0,2.6042,124700.0,NEAR BAY\n-122.22,37.79,49.0,1826.0,450.0,1201.0,424.0,2.5,136700.0,NEAR BAY\n-122.22,37.79,38.0,3049.0,711.0,2167.0,659.0,2.7969,141700.0,NEAR BAY\n-122.2,37.79,29.0,1640.0,376.0,939.0,340.0,2.8321,150000.0,NEAR BAY\n-122.21,37.79,47.0,1543.0,307.0,859.0,292.0,2.9583,138800.0,NEAR BAY\n-122.21,37.79,34.0,2364.0,557.0,1517.0,516.0,2.8365,139200.0,NEAR BAY\n-122.21,37.79,35.0,1745.0,409.0,1143.0,386.0,2.875,143800.0,NEAR BAY\n-122.21,37.8,39.0,2003.0,500.0,1109.0,464.0,3.0682,156500.0,NEAR BAY\n-122.21,37.8,39.0,2018.0,447.0,1221.0,446.0,3.0757,151000.0,NEAR BAY\n-122.2,37.8,43.0,3045.0,499.0,1115.0,455.0,4.9559,273000.0,NEAR BAY\n-122.2,37.8,52.0,1547.0,293.0,706.0,268.0,4.7721,217100.0,NEAR BAY\n-122.21,37.8,52.0,3519.0,711.0,1883.0,706.0,3.4861,187100.0,NEAR BAY\n-122.2,37.8,41.0,2070.0,354.0,804.0,340.0,5.1184,239600.0,NEAR BAY\n-122.21,37.8,48.0,1321.0,263.0,506.0,252.0,4.0977,229700.0,NEAR BAY\n-122.19,37.8,48.0,1694.0,259.0,610.0,238.0,4.744,257300.0,NEAR BAY\n-122.19,37.8,46.0,1938.0,341.0,768.0,332.0,4.2727,246900.0,NEAR BAY\n-122.19,37.79,50.0,968.0,195.0,462.0,184.0,2.9844,179900.0,NEAR BAY\n-122.2,37.79,40.0,1060.0,256.0,667.0,235.0,4.1739,169600.0,NEAR BAY\n-122.2,37.8,46.0,2041.0,405.0,1059.0,399.0,3.8487,203300.0,NEAR BAY\n-122.19,37.8,52.0,1813.0,271.0,637.0,277.0,4.0114,263400.0,NEAR BAY\n-122.19,37.79,45.0,2718.0,451.0,1106.0,454.0,4.6563,231800.0,NEAR BAY\n-122.19,37.79,28.0,3144.0,761.0,1737.0,669.0,2.9297,140500.0,NEAR BAY\n-122.2,37.79,35.0,1802.0,459.0,1009.0,390.0,2.3036,126000.0,NEAR BAY\n-122.2,37.79,49.0,882.0,195.0,737.0,210.0,2.6667,122000.0,NEAR BAY\n-122.2,37.79,44.0,1621.0,452.0,1354.0,491.0,2.619,134700.0,NEAR BAY\n-122.21,37.79,45.0,2115.0,533.0,1530.0,474.0,2.4167,139400.0,NEAR BAY\n-122.2,37.79,45.0,2021.0,528.0,1410.0,480.0,2.7788,115400.0,NEAR BAY\n-122.21,37.78,46.0,2239.0,508.0,1390.0,569.0,2.7352,137300.0,NEAR BAY\n-122.21,37.78,52.0,1477.0,300.0,1065.0,269.0,1.8472,137000.0,NEAR BAY\n-122.21,37.78,52.0,1056.0,224.0,792.0,245.0,2.6583,142600.0,NEAR BAY\n-122.21,37.78,49.0,898.0,244.0,779.0,245.0,3.0536,137500.0,NEAR BAY\n-122.22,37.78,44.0,2968.0,710.0,2269.0,610.0,2.3906,111700.0,NEAR BAY\n-122.21,37.78,43.0,1702.0,460.0,1227.0,407.0,1.7188,126800.0,NEAR BAY\n-122.21,37.78,47.0,881.0,248.0,753.0,241.0,2.625,111300.0,NEAR BAY\n-122.22,37.77,40.0,494.0,114.0,547.0,135.0,2.8015,114800.0,NEAR BAY\n-122.22,37.78,50.0,1776.0,473.0,1807.0,440.0,1.7276,102300.0,NEAR BAY\n-122.22,37.78,44.0,1678.0,514.0,1700.0,495.0,2.0801,131900.0,NEAR BAY\n-122.22,37.78,51.0,1637.0,463.0,1543.0,393.0,2.489,119100.0,NEAR BAY\n-122.21,37.76,52.0,1420.0,314.0,1085.0,300.0,1.7546,80600.0,NEAR BAY\n-122.21,37.77,52.0,591.0,173.0,353.0,137.0,4.0904,80600.0,NEAR BAY\n-122.21,37.77,52.0,745.0,153.0,473.0,149.0,2.6765,88800.0,NEAR BAY\n-122.2,37.77,49.0,2272.0,498.0,1621.0,483.0,2.4338,102400.0,NEAR BAY\n-122.21,37.77,46.0,1234.0,375.0,1183.0,354.0,2.3309,98700.0,NEAR BAY\n-122.21,37.77,43.0,1017.0,328.0,836.0,277.0,2.2604,100000.0,NEAR BAY\n-122.19,37.77,42.0,932.0,254.0,900.0,263.0,1.8039,92300.0,NEAR BAY\n-122.2,37.77,39.0,2689.0,597.0,1888.0,537.0,2.2562,94800.0,NEAR BAY\n-122.2,37.77,41.0,1547.0,415.0,1024.0,341.0,2.0562,102000.0,NEAR BAY\n-122.2,37.78,52.0,2300.0,443.0,1225.0,423.0,3.5398,158400.0,NEAR BAY\n-122.2,37.78,39.0,1752.0,399.0,1071.0,376.0,3.1167,121600.0,NEAR BAY\n-122.2,37.78,50.0,1867.0,403.0,1128.0,378.0,2.5401,129100.0,NEAR BAY\n-122.2,37.77,43.0,2430.0,502.0,1537.0,484.0,2.898,121400.0,NEAR BAY\n-122.21,37.78,44.0,1729.0,414.0,1240.0,393.0,2.3125,102800.0,NEAR BAY\n-122.19,37.78,52.0,1026.0,180.0,469.0,168.0,2.875,160000.0,NEAR BAY\n-122.19,37.77,52.0,2170.0,428.0,1086.0,425.0,3.3715,143900.0,NEAR BAY\n-122.19,37.77,52.0,2329.0,445.0,1144.0,417.0,3.5114,151200.0,NEAR BAY\n-122.19,37.78,52.0,2492.0,415.0,1109.0,375.0,4.3125,164400.0,NEAR BAY\n-122.19,37.78,52.0,2198.0,397.0,984.0,369.0,3.22,156500.0,NEAR BAY\n-122.18,37.78,33.0,142.0,31.0,575.0,47.0,3.875,225000.0,NEAR BAY\n-122.19,37.78,49.0,1183.0,205.0,496.0,209.0,5.2328,174200.0,NEAR BAY\n-122.19,37.78,52.0,1070.0,193.0,555.0,190.0,3.7262,166900.0,NEAR BAY\n-122.2,37.78,45.0,1766.0,332.0,869.0,327.0,4.5893,163500.0,NEAR BAY\n-122.18,37.79,39.0,617.0,95.0,236.0,106.0,5.2578,253000.0,NEAR BAY\n-122.18,37.79,41.0,1411.0,233.0,626.0,214.0,7.0875,240700.0,NEAR BAY\n-122.18,37.79,46.0,2109.0,387.0,922.0,329.0,3.9712,208100.0,NEAR BAY\n-122.19,37.79,47.0,1229.0,243.0,582.0,256.0,2.9514,198100.0,NEAR BAY\n-122.19,37.79,50.0,954.0,217.0,546.0,201.0,2.6667,172800.0,NEAR BAY\n-122.18,37.81,37.0,1643.0,262.0,620.0,266.0,5.4446,336700.0,NEAR BAY\n-122.18,37.8,34.0,1355.0,195.0,442.0,195.0,6.2838,318200.0,NEAR BAY\n-122.18,37.8,23.0,2317.0,336.0,955.0,328.0,6.7527,285800.0,NEAR BAY\n-122.13,37.77,24.0,2459.0,317.0,916.0,324.0,7.0712,293000.0,NEAR BAY\n-122.16,37.79,22.0,12842.0,2048.0,4985.0,1967.0,5.9849,371000.0,NEAR BAY\n-122.17,37.78,42.0,1524.0,260.0,651.0,267.0,3.6875,157300.0,NEAR BAY\n-122.17,37.77,30.0,3326.0,746.0,1704.0,703.0,2.875,135300.0,NEAR BAY\n-122.18,37.78,43.0,1985.0,440.0,1085.0,407.0,3.4205,136700.0,NEAR BAY\n-122.18,37.78,50.0,1642.0,322.0,713.0,284.0,3.2984,160700.0,NEAR BAY\n-122.17,37.78,49.0,893.0,177.0,468.0,181.0,3.875,140600.0,NEAR BAY\n-122.17,37.78,52.0,653.0,128.0,296.0,121.0,4.175,144000.0,NEAR BAY\n-122.16,37.77,47.0,1256.0,,570.0,218.0,4.375,161900.0,NEAR BAY\n-122.16,37.77,48.0,977.0,194.0,446.0,180.0,4.7708,156300.0,NEAR BAY\n-122.16,37.77,45.0,2324.0,397.0,968.0,384.0,3.5739,176000.0,NEAR BAY\n-122.16,37.77,39.0,1583.0,349.0,857.0,316.0,3.0958,145800.0,NEAR BAY\n-122.17,37.77,39.0,1612.0,342.0,912.0,322.0,3.3958,141900.0,NEAR BAY\n-122.17,37.77,31.0,2424.0,533.0,1360.0,452.0,1.871,90700.0,NEAR BAY\n-122.17,37.76,41.0,1594.0,367.0,1074.0,355.0,1.9356,90600.0,NEAR BAY\n-122.17,37.76,47.0,2118.0,413.0,965.0,382.0,2.1842,107900.0,NEAR BAY\n-122.18,37.76,37.0,1575.0,358.0,933.0,320.0,2.2917,107000.0,NEAR BAY\n-122.17,37.76,38.0,1764.0,397.0,987.0,354.0,2.4333,98200.0,NEAR BAY\n-122.18,37.76,50.0,1187.0,261.0,907.0,246.0,1.9479,89500.0,NEAR BAY\n-122.18,37.76,52.0,754.0,175.0,447.0,165.0,3.9063,93800.0,NEAR BAY\n-122.18,37.76,49.0,2308.0,452.0,1299.0,451.0,1.8407,96700.0,NEAR BAY\n-122.18,37.77,27.0,909.0,236.0,396.0,157.0,2.0786,97500.0,NEAR BAY\n-122.18,37.77,42.0,1180.0,257.0,877.0,268.0,2.8125,97300.0,NEAR BAY\n-122.18,37.76,43.0,2018.0,408.0,1111.0,367.0,1.8913,91200.0,NEAR BAY\n-122.19,37.76,49.0,1368.0,282.0,790.0,269.0,1.7056,91400.0,NEAR BAY\n-122.18,37.77,52.0,2744.0,547.0,1479.0,554.0,2.2768,96200.0,NEAR BAY\n-122.18,37.77,51.0,2107.0,471.0,1173.0,438.0,3.2552,120100.0,NEAR BAY\n-122.18,37.77,52.0,1748.0,362.0,1029.0,366.0,2.0556,100000.0,NEAR BAY\n-122.19,37.76,52.0,2024.0,391.0,1030.0,350.0,2.4659,94700.0,NEAR BAY\n-122.2,37.76,47.0,1116.0,259.0,826.0,279.0,1.75,85700.0,NEAR BAY\n-122.19,37.77,41.0,2036.0,510.0,1412.0,454.0,2.0469,89300.0,NEAR BAY\n-122.19,37.77,45.0,1852.0,393.0,1132.0,349.0,2.7159,101400.0,NEAR BAY\n-122.19,37.76,41.0,921.0,207.0,522.0,159.0,1.2083,72500.0,NEAR BAY\n-122.19,37.76,45.0,995.0,238.0,630.0,237.0,1.925,74100.0,NEAR BAY\n-122.2,37.75,36.0,606.0,132.0,531.0,133.0,1.5809,70000.0,NEAR BAY\n-122.2,37.76,37.0,2680.0,736.0,1925.0,667.0,1.4097,84600.0,NEAR BAY\n-122.19,37.76,38.0,1493.0,370.0,1144.0,351.0,0.7683,81800.0,NEAR BAY\n-122.19,37.75,19.0,2207.0,565.0,1481.0,520.0,1.3194,81400.0,NEAR BAY\n-122.19,37.75,28.0,856.0,189.0,435.0,162.0,0.8012,81800.0,NEAR BAY\n-122.19,37.76,26.0,1293.0,297.0,984.0,303.0,1.9479,85800.0,NEAR BAY\n-122.19,37.74,36.0,847.0,212.0,567.0,159.0,1.1765,87100.0,NEAR BAY\n-122.18,37.74,42.0,541.0,154.0,380.0,123.0,2.3456,83500.0,NEAR BAY\n-122.19,37.73,44.0,1066.0,253.0,825.0,244.0,2.1538,79700.0,NEAR BAY\n-122.19,37.74,43.0,707.0,147.0,417.0,155.0,2.5139,83400.0,NEAR BAY\n-122.19,37.73,45.0,1528.0,291.0,801.0,287.0,1.2625,84700.0,NEAR BAY\n-122.18,37.73,42.0,909.0,215.0,646.0,198.0,2.9063,80000.0,NEAR BAY\n-122.18,37.73,43.0,1391.0,293.0,855.0,285.0,2.5192,76400.0,NEAR BAY\n-122.18,37.73,44.0,548.0,119.0,435.0,136.0,2.1111,79700.0,NEAR BAY\n-122.18,37.73,42.0,4074.0,874.0,2736.0,780.0,2.455,82400.0,NEAR BAY\n-122.17,37.74,41.0,1613.0,445.0,1481.0,414.0,2.4028,97700.0,NEAR BAY\n-122.17,37.74,47.0,463.0,134.0,327.0,137.0,2.15,97200.0,NEAR BAY\n-122.17,37.74,43.0,818.0,193.0,494.0,179.0,2.4776,101600.0,NEAR BAY\n-122.17,37.73,43.0,1473.0,371.0,1231.0,341.0,2.1587,86500.0,NEAR BAY\n-122.18,37.74,35.0,504.0,126.0,323.0,109.0,1.8438,90500.0,NEAR BAY\n-122.17,37.74,46.0,1026.0,226.0,749.0,225.0,3.0298,107600.0,NEAR BAY\n-122.17,37.74,46.0,769.0,183.0,693.0,178.0,2.25,84200.0,NEAR BAY\n-122.18,37.74,46.0,2103.0,391.0,1339.0,354.0,2.2467,88900.0,NEAR BAY\n-122.18,37.75,45.0,330.0,76.0,282.0,80.0,4.0469,80700.0,NEAR BAY\n-122.18,37.75,46.0,941.0,218.0,621.0,195.0,1.325,87100.0,NEAR BAY\n-122.17,37.75,38.0,992.0,,732.0,259.0,1.6196,85100.0,NEAR BAY\n-122.18,37.75,45.0,990.0,261.0,901.0,260.0,2.1731,82000.0,NEAR BAY\n-122.19,37.75,36.0,1126.0,263.0,482.0,150.0,1.9167,82800.0,NEAR BAY\n-122.18,37.75,43.0,1036.0,233.0,652.0,213.0,2.069,84600.0,NEAR BAY\n-122.18,37.75,36.0,1047.0,214.0,651.0,166.0,1.712,82100.0,NEAR BAY\n-122.17,37.76,33.0,1280.0,307.0,999.0,286.0,2.5625,89300.0,NEAR BAY\n-122.17,37.75,43.0,1587.0,320.0,907.0,306.0,1.9821,98300.0,NEAR BAY\n-122.17,37.75,41.0,1257.0,271.0,828.0,230.0,2.5043,92300.0,NEAR BAY\n-122.17,37.75,44.0,1218.0,248.0,763.0,254.0,2.3281,88800.0,NEAR BAY\n-122.17,37.75,48.0,1751.0,390.0,935.0,349.0,1.4375,90000.0,NEAR BAY\n-122.16,37.76,45.0,2299.0,514.0,1437.0,484.0,2.5122,95500.0,NEAR BAY\n-122.16,37.75,38.0,2457.0,624.0,1516.0,482.0,1.5625,91700.0,NEAR BAY\n-122.17,37.75,47.0,998.0,211.0,597.0,185.0,3.1587,100400.0,NEAR BAY\n-122.17,37.76,40.0,1685.0,343.0,949.0,342.0,1.8426,94800.0,NEAR BAY\n-122.16,37.76,46.0,1827.0,307.0,881.0,302.0,4.6696,164300.0,NEAR BAY\n-122.16,37.76,36.0,2781.0,574.0,1438.0,519.0,2.4598,155500.0,NEAR BAY\n-122.15,37.76,39.0,1823.0,286.0,763.0,270.0,6.0749,196900.0,NEAR BAY\n-122.14,37.77,27.0,2229.0,365.0,1297.0,355.0,4.8304,279100.0,NEAR BAY\n-122.14,37.76,34.0,1513.0,231.0,545.0,211.0,5.5701,252800.0,NEAR BAY\n-122.13,37.76,26.0,3266.0,491.0,1222.0,533.0,5.37,275400.0,NEAR BAY\n-122.12,37.75,33.0,1809.0,261.0,808.0,219.0,6.86,250000.0,NEAR BAY\n-122.12,37.75,28.0,794.0,111.0,329.0,109.0,7.6923,329800.0,NEAR BAY\n-122.14,37.75,36.0,690.0,105.0,299.0,109.0,4.0313,195500.0,NEAR BAY\n-122.14,37.75,33.0,1334.0,200.0,579.0,202.0,6.8323,255900.0,NEAR BAY\n-122.13,37.75,30.0,414.0,54.0,137.0,50.0,4.975,311100.0,NEAR BAY\n-122.13,37.75,36.0,768.0,93.0,229.0,93.0,5.3602,330000.0,NEAR BAY\n-122.13,37.74,41.0,4400.0,666.0,1476.0,648.0,5.0,248900.0,NEAR BAY\n-122.16,37.75,46.0,954.0,161.0,429.0,154.0,2.925,142900.0,NEAR BAY\n-122.15,37.75,40.0,1445.0,256.0,849.0,255.0,3.8913,126300.0,NEAR BAY\n-122.15,37.75,44.0,1938.0,399.0,946.0,331.0,3.225,135800.0,NEAR BAY\n-122.15,37.74,41.0,856.0,178.0,571.0,191.0,3.1458,130600.0,NEAR BAY\n-122.16,37.75,24.0,1790.0,454.0,1137.0,386.0,2.537,107900.0,NEAR BAY\n-122.16,37.75,44.0,617.0,131.0,378.0,135.0,2.5568,111100.0,NEAR BAY\n-122.15,37.74,43.0,1383.0,275.0,853.0,272.0,3.5083,122000.0,NEAR BAY\n-122.15,37.74,49.0,1325.0,277.0,764.0,282.0,3.3125,118000.0,NEAR BAY\n-122.16,37.75,40.0,1227.0,294.0,928.0,261.0,1.8235,95200.0,NEAR BAY\n-122.16,37.75,35.0,667.0,140.0,406.0,133.0,3.8047,94300.0,NEAR BAY\n-122.17,37.74,34.0,1223.0,281.0,824.0,280.0,2.2917,92500.0,NEAR BAY\n-122.17,37.75,37.0,1379.0,287.0,835.0,259.0,2.4962,91800.0,NEAR BAY\n-122.16,37.74,43.0,1534.0,300.0,826.0,295.0,4.0417,109400.0,NEAR BAY\n-122.16,37.74,46.0,1029.0,181.0,567.0,211.0,3.4844,129500.0,NEAR BAY\n-122.16,37.74,52.0,771.0,147.0,355.0,144.0,4.1458,143400.0,NEAR BAY\n-122.16,37.74,47.0,824.0,223.0,533.0,166.0,2.625,98200.0,NEAR BAY\n-122.16,37.74,44.0,1097.0,239.0,609.0,215.0,2.0227,103100.0,NEAR BAY\n-122.29,37.9,49.0,1283.0,238.0,576.0,236.0,3.3333,276800.0,NEAR BAY\n-122.29,37.9,52.0,1604.0,263.0,594.0,286.0,5.338,270900.0,NEAR BAY\n-122.29,37.89,52.0,2248.0,422.0,870.0,377.0,3.4732,246200.0,NEAR BAY\n-122.3,37.9,38.0,2263.0,522.0,1027.0,509.0,3.5125,224200.0,NEAR BAY\n-122.29,37.89,52.0,1571.0,349.0,693.0,326.0,3.1375,229100.0,NEAR BAY\n-122.3,37.89,46.0,1520.0,402.0,815.0,375.0,2.8036,211600.0,NEAR BAY\n-122.3,37.9,15.0,5083.0,1212.0,2420.0,1146.0,4.5824,256100.0,NEAR BAY\n-122.3,37.89,36.0,1077.0,293.0,518.0,276.0,3.0208,206300.0,NEAR BAY\n-122.3,37.89,52.0,1248.0,283.0,620.0,275.0,4.0875,221300.0,NEAR BAY\n-122.33,37.89,42.0,1342.0,291.0,551.0,266.0,4.5268,207400.0,NEAR BAY\n-122.34,37.88,37.0,3061.0,930.0,2556.0,924.0,1.7375,350000.0,NEAR BAY\n-122.29,37.89,52.0,3171.0,698.0,1498.0,696.0,3.1795,218200.0,NEAR BAY\n-122.29,37.88,50.0,1211.0,261.0,523.0,227.0,3.8672,216700.0,NEAR BAY\n-122.29,37.89,52.0,979.0,175.0,374.0,153.0,5.1675,270600.0,NEAR BAY\n-122.29,37.89,52.0,2269.0,380.0,1004.0,371.0,5.1696,261400.0,NEAR BAY\n-122.28,37.88,52.0,1844.0,332.0,769.0,334.0,4.2614,261300.0,NEAR BAY\n-122.29,37.89,52.0,2178.0,421.0,940.0,423.0,5.0551,232200.0,NEAR BAY\n-122.27,37.9,42.0,1650.0,274.0,645.0,256.0,5.6228,375400.0,NEAR BAY\n-122.26,37.9,52.0,1927.0,279.0,705.0,288.0,7.8864,357300.0,NEAR BAY\n-122.27,37.9,52.0,2079.0,273.0,684.0,275.0,7.9556,374400.0,NEAR BAY\n-122.27,37.9,52.0,1803.0,240.0,572.0,236.0,6.174,358800.0,NEAR BAY\n-122.27,37.9,52.0,2041.0,270.0,671.0,253.0,6.9414,417500.0,NEAR BAY\n-122.27,37.89,52.0,3046.0,373.0,975.0,365.0,8.8342,430500.0,NEAR BAY\n-122.28,37.9,52.0,2318.0,328.0,779.0,312.0,7.1754,362900.0,NEAR BAY\n-122.28,37.9,52.0,2003.0,250.0,658.0,244.0,10.0825,397000.0,NEAR BAY\n-122.28,37.9,52.0,2261.0,328.0,819.0,335.0,4.9083,346800.0,NEAR BAY\n-122.28,37.89,52.0,1225.0,169.0,412.0,168.0,5.7912,327100.0,NEAR BAY\n-122.28,37.89,52.0,2315.0,408.0,835.0,369.0,4.5893,290100.0,NEAR BAY\n-122.28,37.89,52.0,2070.0,329.0,722.0,306.0,5.4171,292000.0,NEAR BAY\n-122.28,37.89,52.0,2616.0,473.0,1085.0,487.0,4.125,270900.0,NEAR BAY\n-122.27,37.89,52.0,2640.0,366.0,973.0,355.0,7.266,371100.0,NEAR BAY\n-122.27,37.89,52.0,1978.0,293.0,723.0,272.0,5.3989,335600.0,NEAR BAY\n-122.26,37.9,37.0,2220.0,335.0,903.0,362.0,7.8336,371300.0,NEAR BAY\n-122.25,37.89,41.0,1125.0,195.0,356.0,181.0,6.1593,344000.0,NEAR BAY\n-122.26,37.89,52.0,3078.0,494.0,1005.0,462.0,6.381,342200.0,NEAR BAY\n-122.26,37.89,52.0,3706.0,531.0,1205.0,504.0,6.6828,370900.0,NEAR BAY\n-122.25,37.89,37.0,3000.0,457.0,987.0,450.0,7.5385,350000.0,NEAR BAY\n-122.25,37.89,42.0,2863.0,460.0,1031.0,448.0,6.7138,368600.0,NEAR BAY\n-122.26,37.88,52.0,2551.0,417.0,894.0,404.0,6.2425,391800.0,NEAR BAY\n-122.26,37.88,52.0,2255.0,410.0,823.0,377.0,5.7979,415300.0,NEAR BAY\n-122.27,37.88,52.0,3360.0,648.0,1232.0,621.0,4.2813,284900.0,NEAR BAY\n-122.27,37.88,52.0,1693.0,391.0,669.0,367.0,3.5417,287500.0,NEAR BAY\n-122.27,37.88,44.0,2252.0,592.0,989.0,550.0,3.0132,272900.0,NEAR BAY\n-122.28,37.88,52.0,2495.0,491.0,1058.0,464.0,4.1429,259600.0,NEAR BAY\n-122.28,37.88,52.0,1193.0,200.0,506.0,207.0,4.1912,254500.0,NEAR BAY\n-122.28,37.88,52.0,1172.0,215.0,489.0,218.0,3.9167,235600.0,NEAR BAY\n-122.29,37.88,52.0,2159.0,424.0,824.0,388.0,3.8897,218400.0,NEAR BAY\n-122.29,37.88,48.0,2365.0,490.0,1034.0,475.0,3.1065,229200.0,NEAR BAY\n-122.29,37.88,46.0,1895.0,442.0,920.0,425.0,2.9926,192100.0,NEAR BAY\n-122.29,37.88,52.0,1650.0,395.0,841.0,380.0,3.556,179300.0,NEAR BAY\n-122.3,37.88,45.0,453.0,146.0,749.0,137.0,1.475,187500.0,NEAR BAY\n-122.3,37.88,52.0,409.0,97.0,208.0,98.0,1.6971,138800.0,NEAR BAY\n-122.3,37.87,10.0,503.0,118.0,228.0,100.0,2.1705,150000.0,NEAR BAY\n-122.3,37.86,50.0,499.0,127.0,287.0,128.0,2.75,140600.0,NEAR BAY\n-122.29,37.85,52.0,477.0,119.0,218.0,106.0,2.5682,120000.0,NEAR BAY\n-122.3,37.88,46.0,1647.0,376.0,854.0,355.0,2.9,144800.0,NEAR BAY\n-122.3,37.87,52.0,3123.0,749.0,1695.0,684.0,2.2208,144800.0,NEAR BAY\n-122.28,37.88,52.0,957.0,188.0,403.0,172.0,3.2344,245500.0,NEAR BAY\n-122.28,37.87,52.0,589.0,132.0,288.0,131.0,3.5156,200000.0,NEAR BAY\n-122.28,37.87,49.0,2026.0,548.0,963.0,521.0,1.9805,173700.0,NEAR BAY\n-122.29,37.87,50.0,1829.0,536.0,1129.0,516.0,2.6684,185600.0,NEAR BAY\n-122.29,37.87,52.0,895.0,198.0,386.0,204.0,3.875,182600.0,NEAR BAY\n-122.28,37.88,52.0,1909.0,416.0,811.0,406.0,3.006,227900.0,NEAR BAY\n-122.28,37.87,46.0,3022.0,696.0,1293.0,675.0,2.543,220700.0,NEAR BAY\n-122.28,37.87,46.0,1777.0,446.0,805.0,431.0,2.8676,212000.0,NEAR BAY\n-122.27,37.88,52.0,2803.0,930.0,1372.0,876.0,2.1907,271400.0,NEAR BAY\n-122.27,37.87,30.0,1465.0,439.0,862.0,425.0,1.7778,268800.0,NEAR BAY\n-122.27,37.88,37.0,2619.0,682.0,1152.0,616.0,2.52,277800.0,NEAR BAY\n-122.26,37.88,52.0,1149.0,255.0,483.0,249.0,4.2788,332500.0,NEAR BAY\n-122.26,37.88,52.0,2363.0,604.0,1558.0,573.0,2.944,338900.0,NEAR BAY\n-122.26,37.88,52.0,2604.0,837.0,1798.0,769.0,1.725,287500.0,NEAR BAY\n-122.25,37.87,41.0,685.0,141.0,266.0,123.0,5.2289,384600.0,NEAR BAY\n-122.25,37.87,42.0,1756.0,465.0,2184.0,422.0,2.5562,371400.0,NEAR BAY\n-122.25,37.87,52.0,1204.0,460.0,2016.0,477.0,0.949,350000.0,NEAR BAY\n-122.25,37.87,52.0,609.0,236.0,1349.0,250.0,1.1696,500001.0,NEAR BAY\n-122.26,37.87,52.0,1087.0,371.0,3337.0,350.0,1.4012,175000.0,NEAR BAY\n-122.26,37.87,52.0,2773.0,998.0,1721.0,949.0,1.1859,241700.0,NEAR BAY\n-122.27,37.87,35.0,3218.0,1108.0,1675.0,1000.0,1.7464,216700.0,NEAR BAY\n-122.27,37.87,49.0,1350.0,368.0,707.0,350.0,2.8846,211300.0,NEAR BAY\n-122.27,37.87,52.0,3084.0,698.0,1424.0,694.0,2.7372,210200.0,NEAR BAY\n-122.28,37.86,52.0,938.0,195.0,393.0,189.0,3.8594,196400.0,NEAR BAY\n-122.28,37.87,52.0,1813.0,353.0,828.0,339.0,3.5625,191700.0,NEAR BAY\n-122.28,37.87,52.0,1233.0,300.0,571.0,292.0,2.2788,182300.0,NEAR BAY\n-122.29,37.87,44.0,2539.0,755.0,1382.0,713.0,2.537,175000.0,NEAR BAY\n-122.29,37.87,46.0,1267.0,324.0,792.0,321.0,2.525,165900.0,NEAR BAY\n-122.29,37.86,52.0,1665.0,404.0,815.0,372.0,1.9946,156900.0,NEAR BAY\n-122.28,37.86,52.0,1659.0,367.0,788.0,346.0,2.8214,164300.0,NEAR BAY\n-122.29,37.87,52.0,2225.0,460.0,1145.0,430.0,2.6165,150000.0,NEAR BAY\n-122.29,37.86,50.0,2485.0,607.0,1354.0,563.0,1.9483,150500.0,NEAR BAY\n-122.28,37.86,52.0,2031.0,450.0,958.0,445.0,1.9327,169900.0,NEAR BAY\n-122.28,37.86,49.0,2932.0,668.0,1361.0,608.0,1.9798,147400.0,NEAR BAY\n-122.28,37.85,52.0,2246.0,472.0,1005.0,449.0,2.4167,152700.0,NEAR BAY\n-122.27,37.86,49.0,2052.0,435.0,924.0,414.0,2.5417,182700.0,NEAR BAY\n-122.28,37.86,52.0,1999.0,417.0,780.0,358.0,3.3906,179300.0,NEAR BAY\n-122.28,37.86,52.0,3007.0,691.0,1582.0,636.0,2.5652,157700.0,NEAR BAY\n-122.28,37.86,41.0,2214.0,550.0,1213.0,568.0,2.2845,153100.0,NEAR BAY\n-122.27,37.86,52.0,1088.0,305.0,486.0,267.0,2.6071,250000.0,NEAR BAY\n-122.27,37.86,52.0,834.0,186.0,494.0,175.0,3.15,206300.0,NEAR BAY\n-122.27,37.86,52.0,1769.0,372.0,849.0,365.0,2.6914,218800.0,NEAR BAY\n-122.27,37.86,52.0,2307.0,583.0,1127.0,548.0,1.8447,198200.0,NEAR BAY\n-122.26,37.86,35.0,5161.0,1744.0,3276.0,1742.0,1.6307,253600.0,NEAR BAY\n-122.26,37.86,52.0,3497.0,832.0,1493.0,794.0,2.9044,257400.0,NEAR BAY\n-122.26,37.86,52.0,3774.0,744.0,1461.0,679.0,2.9405,289500.0,NEAR BAY\n-122.26,37.86,52.0,2888.0,604.0,1253.0,538.0,3.3893,241700.0,NEAR BAY\n-122.25,37.86,48.0,2153.0,517.0,1656.0,459.0,3.0417,489600.0,NEAR BAY\n-122.25,37.86,52.0,1389.0,191.0,514.0,202.0,7.0897,446200.0,NEAR BAY\n-122.25,37.86,52.0,1709.0,318.0,719.0,295.0,5.0463,456300.0,NEAR BAY\n-122.25,37.86,52.0,1587.0,444.0,878.0,449.0,1.7652,336800.0,NEAR BAY\n-122.24,37.86,52.0,1668.0,225.0,517.0,214.0,7.8521,500001.0,NEAR BAY\n-122.24,37.85,52.0,3726.0,474.0,1366.0,496.0,9.3959,500001.0,NEAR BAY\n-122.25,37.86,52.0,4048.0,663.0,1316.0,590.0,5.3794,376900.0,NEAR BAY\n-122.26,37.85,52.0,3618.0,768.0,1508.0,755.0,3.2619,309600.0,NEAR BAY\n-122.27,37.85,52.0,4076.0,920.0,1800.0,815.0,2.7054,182300.0,NEAR BAY\n-122.27,37.85,47.0,2077.0,400.0,719.0,326.0,2.2431,172700.0,NEAR BAY\n-122.27,37.85,50.0,1279.0,300.0,675.0,255.0,1.9028,150800.0,NEAR BAY\n-122.27,37.85,52.0,1974.0,426.0,875.0,363.0,1.5817,153600.0,NEAR BAY\n-122.27,37.85,52.0,335.0,83.0,152.0,77.0,2.2841,106300.0,NEAR BAY\n-122.27,37.85,47.0,1375.0,307.0,843.0,319.0,1.3785,142300.0,NEAR BAY\n-122.28,37.85,52.0,610.0,145.0,281.0,132.0,2.9018,119400.0,NEAR BAY\n-122.28,37.85,44.0,1025.0,198.0,506.0,204.0,1.73,147900.0,NEAR BAY\n-122.28,37.85,48.0,2063.0,484.0,1054.0,466.0,2.2625,132900.0,NEAR BAY\n-122.29,37.84,35.0,1872.0,419.0,1017.0,414.0,2.2106,132500.0,NEAR BAY\n-122.28,37.83,52.0,3108.0,813.0,1623.0,765.0,2.6997,126900.0,NEAR BAY\n-122.3,37.84,14.0,7355.0,2408.0,3100.0,2051.0,4.0018,143800.0,NEAR BAY\n-122.23,37.83,52.0,2990.0,379.0,947.0,361.0,7.8772,500001.0,NEAR BAY\n-122.22,37.82,39.0,2492.0,310.0,808.0,315.0,11.8603,500001.0,NEAR BAY\n-122.22,37.82,42.0,2991.0,335.0,1018.0,335.0,13.499,500001.0,NEAR BAY\n-122.23,37.82,52.0,3242.0,366.0,1001.0,352.0,12.2138,500001.0,NEAR BAY\n-122.23,37.82,52.0,3051.0,381.0,1005.0,369.0,8.1872,466100.0,NEAR BAY\n-122.23,37.82,52.0,3494.0,396.0,1192.0,383.0,12.3804,500001.0,NEAR BAY\n-122.24,37.83,52.0,1757.0,246.0,585.0,227.0,5.8948,457800.0,NEAR BAY\n-122.24,37.83,52.0,2449.0,312.0,916.0,316.0,8.1194,471600.0,NEAR BAY\n-122.23,37.82,52.0,1611.0,203.0,556.0,179.0,8.7477,500001.0,NEAR BAY\n-122.24,37.82,52.0,3665.0,517.0,1470.0,520.0,6.155,398600.0,NEAR BAY\n-122.25,37.82,52.0,2474.0,403.0,1104.0,398.0,5.883,340700.0,NEAR BAY\n-122.23,37.76,52.0,3037.0,516.0,1242.0,518.0,5.2128,289900.0,NEAR BAY\n-122.23,37.76,52.0,2269.0,323.0,805.0,321.0,4.7188,335300.0,NEAR BAY\n-122.23,37.76,52.0,1316.0,177.0,378.0,162.0,5.2915,333000.0,NEAR BAY\n-122.24,37.77,52.0,1153.0,235.0,481.0,223.0,2.6411,241000.0,NEAR BAY\n-122.23,37.77,52.0,772.0,179.0,409.0,160.0,3.3214,189600.0,NEAR BAY\n-122.24,37.77,52.0,1711.0,386.0,885.0,373.0,3.6417,206300.0,NEAR BAY\n-122.25,37.77,52.0,2650.0,566.0,1468.0,567.0,3.0161,215700.0,NEAR BAY\n-122.25,37.77,52.0,1038.0,220.0,482.0,215.0,3.1771,210200.0,NEAR BAY\n-122.25,37.77,52.0,1527.0,320.0,825.0,264.0,3.4531,208800.0,NEAR BAY\n-122.25,37.77,52.0,859.0,157.0,429.0,158.0,4.3098,197900.0,NEAR BAY\n-122.26,37.78,52.0,970.0,217.0,528.0,208.0,3.3438,201300.0,NEAR BAY\n-122.26,37.78,52.0,1045.0,239.0,496.0,216.0,2.9213,190800.0,NEAR BAY\n-122.27,37.78,52.0,1408.0,280.0,718.0,265.0,2.6806,207900.0,NEAR BAY\n-122.27,37.78,52.0,1222.0,264.0,630.0,265.0,3.7708,215300.0,NEAR BAY\n-122.27,37.78,45.0,1169.0,263.0,723.0,286.0,3.9444,212900.0,NEAR BAY\n-122.27,37.78,13.0,2020.0,535.0,959.0,486.0,5.2601,292700.0,NEAR BAY\n-122.28,37.79,30.0,4145.0,869.0,3668.0,855.0,2.5444,275000.0,NEAR BAY\n-122.3,37.77,42.0,2038.0,368.0,2037.0,355.0,2.6447,200000.0,NEAR BAY\n-122.28,37.78,29.0,5154.0,,3741.0,1273.0,2.5762,173400.0,NEAR BAY\n-122.29,37.78,42.0,1241.0,309.0,821.0,300.0,1.9427,102200.0,NEAR BAY\n-122.28,37.77,52.0,1468.0,363.0,870.0,347.0,2.9688,220800.0,NEAR BAY\n-122.28,37.77,27.0,3997.0,1073.0,1901.0,966.0,3.75,242800.0,NEAR BAY\n-122.29,37.76,18.0,2873.0,763.0,1243.0,663.0,5.1702,265400.0,NEAR BAY\n-122.28,37.78,50.0,1487.0,306.0,730.0,327.0,2.5139,219000.0,NEAR BAY\n-122.26,37.77,52.0,1670.0,350.0,793.0,299.0,2.9732,282100.0,NEAR BAY\n-122.27,37.77,52.0,2252.0,388.0,1033.0,434.0,5.5337,372000.0,NEAR BAY\n-122.27,37.77,52.0,1731.0,377.0,872.0,363.0,4.1667,225800.0,NEAR BAY\n-122.27,37.77,52.0,1710.0,481.0,849.0,457.0,2.7115,220800.0,NEAR BAY\n-122.27,37.77,52.0,2388.0,559.0,1121.0,518.0,3.3269,234500.0,NEAR BAY\n-122.25,37.77,52.0,2156.0,458.0,872.0,445.0,3.2685,254200.0,NEAR BAY\n-122.26,37.77,52.0,1565.0,315.0,637.0,297.0,4.7778,351800.0,NEAR BAY\n-122.26,37.77,52.0,1704.0,371.0,663.0,340.0,4.226,275000.0,NEAR BAY\n-122.26,37.77,52.0,1210.0,168.0,411.0,172.0,3.3571,405400.0,NEAR BAY\n-122.26,37.77,52.0,2097.0,444.0,915.0,413.0,2.9899,228100.0,NEAR BAY\n-122.26,37.77,52.0,1848.0,479.0,921.0,477.0,2.875,234000.0,NEAR BAY\n-122.24,37.77,43.0,955.0,284.0,585.0,266.0,2.3882,162500.0,NEAR BAY\n-122.25,37.77,43.0,4329.0,1110.0,2086.0,1053.0,2.975,243400.0,NEAR BAY\n-122.23,37.76,52.0,3011.0,542.0,1303.0,535.0,5.1039,273800.0,NEAR BAY\n-122.23,37.76,52.0,1049.0,185.0,374.0,176.0,4.1458,248500.0,NEAR BAY\n-122.24,37.76,52.0,2504.0,516.0,979.0,472.0,3.4762,244000.0,NEAR BAY\n-122.24,37.76,52.0,1846.0,471.0,827.0,446.0,2.6833,240900.0,NEAR BAY\n-122.23,37.76,52.0,1705.0,246.0,658.0,253.0,5.75,306300.0,NEAR BAY\n-122.23,37.75,50.0,1542.0,289.0,654.0,268.0,3.9632,240000.0,NEAR BAY\n-122.24,37.75,45.0,891.0,,384.0,146.0,4.9489,247100.0,NEAR BAY\n-122.24,37.75,27.0,4051.0,753.0,1499.0,797.0,4.8711,286600.0,NEAR BAY\n-122.24,37.76,52.0,2646.0,581.0,1128.0,522.0,3.0718,266700.0,NEAR BAY\n-122.24,37.76,49.0,2428.0,525.0,1110.0,492.0,3.6719,229800.0,NEAR BAY\n-122.24,37.76,52.0,2567.0,436.0,1119.0,415.0,4.6094,229300.0,NEAR BAY\n-122.24,37.73,21.0,7031.0,1249.0,2930.0,1235.0,4.5213,228400.0,NEAR BAY\n-122.25,37.74,25.0,1914.0,365.0,897.0,390.0,4.4562,206200.0,NEAR BAY\n-122.24,37.72,5.0,18634.0,2885.0,7427.0,2718.0,7.611,350700.0,NEAR BAY\n-122.27,37.73,31.0,5785.0,1379.0,2973.0,1312.0,3.2689,231000.0,NEAR BAY\n-122.25,37.76,52.0,2876.0,648.0,1340.0,632.0,3.567,252900.0,NEAR BAY\n-122.27,37.74,28.0,6909.0,1554.0,2974.0,1484.0,3.6875,353900.0,NEAR BAY\n-122.27,37.77,23.0,5679.0,1270.0,2690.0,1151.0,4.7695,291700.0,NEAR BAY\n-122.28,37.75,20.0,1156.0,365.0,583.0,326.0,3.1972,100000.0,NEAR BAY\n-122.06,37.77,12.0,14316.0,2045.0,5781.0,2007.0,7.2634,341600.0,NEAR BAY\n-122.06,37.73,5.0,3596.0,467.0,1738.0,512.0,7.0568,412500.0,NEAR BAY\n-122.06,37.71,36.0,3541.0,570.0,1478.0,529.0,4.635,248600.0,NEAR BAY\n-122.07,37.71,40.0,1808.0,302.0,746.0,270.0,5.3015,254900.0,NEAR BAY\n-122.07,37.71,36.0,2879.0,480.0,1235.0,455.0,4.9801,241500.0,NEAR BAY\n-122.07,37.72,26.0,3204.0,477.0,1411.0,484.0,5.4834,295200.0,NEAR BAY\n-122.08,37.72,31.0,3866.0,531.0,1368.0,521.0,6.187,340400.0,NEAR BAY\n-122.08,37.72,32.0,2476.0,368.0,1048.0,367.0,5.6194,274700.0,NEAR BAY\n-122.08,37.71,35.0,2211.0,350.0,1004.0,365.0,5.4639,238600.0,NEAR BAY\n-122.09,37.71,35.0,2663.0,387.0,1086.0,367.0,5.1498,266400.0,NEAR BAY\n-122.1,37.72,30.0,2599.0,366.0,922.0,350.0,5.8382,330200.0,NEAR BAY\n-122.11,37.71,36.0,4569.0,824.0,1950.0,819.0,4.65,206800.0,NEAR BAY\n-122.1,37.7,25.0,2973.0,622.0,1413.0,595.0,4.3819,209200.0,NEAR BAY\n-122.1,37.69,30.0,3115.0,625.0,1444.0,568.0,3.7222,195800.0,NEAR BAY\n-122.09,37.71,31.0,1843.0,282.0,749.0,269.0,5.2855,253500.0,NEAR BAY\n-122.09,37.7,31.0,2053.0,336.0,867.0,329.0,4.3375,241800.0,NEAR BAY\n-122.1,37.71,27.0,6740.0,1073.0,2723.0,1035.0,5.2131,252500.0,NEAR BAY\n-122.09,37.7,30.0,1751.0,269.0,731.0,263.0,6.005,263900.0,NEAR BAY\n-122.08,37.71,38.0,1663.0,295.0,781.0,301.0,5.0519,227000.0,NEAR BAY\n-122.08,37.71,38.0,3716.0,657.0,1784.0,652.0,4.8237,220900.0,NEAR BAY\n-122.08,37.7,32.0,2718.0,447.0,1156.0,410.0,5.2497,259300.0,NEAR BAY\n-122.07,37.7,39.0,1420.0,272.0,645.0,277.0,4.125,232500.0,NEAR BAY\n-122.06,37.7,37.0,1893.0,310.0,821.0,315.0,4.6005,231600.0,NEAR BAY\n-122.06,37.7,33.0,3906.0,790.0,1912.0,770.0,3.5187,209400.0,NEAR BAY\n-122.07,37.7,32.0,3400.0,736.0,1487.0,694.0,3.0,223200.0,NEAR BAY\n-122.08,37.7,25.0,3402.0,758.0,1645.0,710.0,3.4934,209900.0,NEAR BAY\n-122.09,37.7,33.0,4413.0,1107.0,2239.0,1051.0,2.9861,208200.0,NEAR BAY\n-122.07,37.69,29.0,2304.0,618.0,1021.0,552.0,2.5362,203800.0,NEAR BAY\n-122.08,37.69,36.0,2350.0,499.0,1105.0,467.0,3.3021,195700.0,NEAR BAY\n-122.07,37.69,31.0,5914.0,1309.0,2999.0,1295.0,3.0964,190500.0,NEAR BAY\n-122.08,37.69,43.0,1575.0,324.0,740.0,284.0,2.8512,181000.0,NEAR BAY\n-122.08,37.69,42.0,1414.0,274.0,629.0,244.0,3.3478,184900.0,NEAR BAY\n-122.08,37.68,37.0,848.0,202.0,314.0,205.0,2.3958,190800.0,NEAR BAY\n-122.08,37.68,15.0,3051.0,685.0,1479.0,668.0,3.5295,242200.0,NEAR BAY\n-122.09,37.69,20.0,4296.0,817.0,1732.0,800.0,4.8036,188300.0,NEAR BAY\n-122.15,37.74,52.0,2898.0,557.0,1338.0,550.0,3.851,183500.0,NEAR BAY\n-122.14,37.74,52.0,1071.0,201.0,440.0,192.0,4.0662,204200.0,NEAR BAY\n-122.14,37.73,51.0,2619.0,403.0,922.0,393.0,4.6042,251900.0,NEAR BAY\n-122.15,37.74,49.0,1494.0,316.0,611.0,288.0,2.2,187500.0,NEAR BAY\n-122.16,37.73,52.0,2260.0,416.0,994.0,412.0,4.1164,198200.0,NEAR BAY\n-122.15,37.74,52.0,1394.0,223.0,545.0,230.0,3.95,219000.0,NEAR BAY\n-122.15,37.73,52.0,1028.0,129.0,317.0,143.0,4.9135,275000.0,NEAR BAY\n-122.15,37.73,45.0,3758.0,819.0,1573.0,736.0,2.8355,245400.0,NEAR BAY\n-122.17,37.73,46.0,2163.0,470.0,925.0,435.0,3.25,177500.0,NEAR BAY\n-122.16,37.73,49.0,1699.0,408.0,768.0,385.0,2.8301,171600.0,NEAR BAY\n-122.17,37.73,52.0,1555.0,289.0,620.0,292.0,3.7159,183300.0,NEAR BAY\n-122.16,37.73,52.0,1114.0,206.0,425.0,207.0,2.5625,175000.0,NEAR BAY\n-122.18,37.72,45.0,1498.0,313.0,1003.0,305.0,3.8047,156700.0,NEAR BAY\n-122.18,37.71,45.0,726.0,147.0,519.0,135.0,3.375,157500.0,NEAR BAY\n-122.17,37.71,38.0,890.0,200.0,481.0,198.0,3.244,179800.0,NEAR BAY\n-122.18,37.7,36.0,2639.0,533.0,1209.0,519.0,4.0268,205500.0,NEAR BAY\n-122.18,37.7,35.0,2562.0,554.0,1398.0,525.0,3.3906,178900.0,NEAR BAY\n-122.19,37.71,36.0,361.0,69.0,158.0,58.0,5.5461,262500.0,NEAR BAY\n-122.17,37.72,42.0,3008.0,659.0,1817.0,664.0,3.371,165000.0,NEAR BAY\n-122.17,37.72,46.0,1369.0,284.0,766.0,289.0,3.5313,159700.0,NEAR BAY\n-122.17,37.72,5.0,1692.0,398.0,814.0,328.0,3.663,158300.0,NEAR BAY\n-122.16,37.72,38.0,1007.0,245.0,618.0,239.0,2.875,144800.0,NEAR BAY\n-122.17,37.72,43.0,3783.0,814.0,2139.0,789.0,4.0202,166300.0,NEAR BAY\n-122.16,37.71,37.0,1507.0,242.0,632.0,253.0,4.5553,191000.0,NEAR BAY\n-122.16,37.71,36.0,666.0,132.0,366.0,134.0,3.4643,175000.0,NEAR BAY\n-122.16,37.72,10.0,2229.0,601.0,877.0,485.0,3.3431,137500.0,NEAR BAY\n-122.15,37.73,28.0,2215.0,587.0,830.0,573.0,2.1898,141700.0,NEAR BAY\n-122.15,37.72,31.0,1616.0,372.0,739.0,379.0,2.9097,210900.0,NEAR BAY\n-122.15,37.72,47.0,1190.0,251.0,540.0,266.0,3.375,198300.0,NEAR BAY\n-122.15,37.72,29.0,4169.0,1047.0,2024.0,962.0,2.8125,157400.0,NEAR BAY\n-122.14,37.73,52.0,2024.0,320.0,823.0,334.0,5.0,264700.0,NEAR BAY\n-122.14,37.73,38.0,1723.0,394.0,711.0,353.0,3.0673,218400.0,NEAR BAY\n-122.14,37.73,43.0,2264.0,390.0,931.0,368.0,3.8125,235100.0,NEAR BAY\n-122.13,37.73,33.0,1996.0,268.0,686.0,270.0,6.9096,341800.0,NEAR BAY\n-122.13,37.72,26.0,2862.0,394.0,1030.0,397.0,7.912,367300.0,NEAR BAY\n-122.13,37.72,35.0,2183.0,383.0,976.0,392.0,3.8393,243500.0,NEAR BAY\n-122.12,37.71,35.0,1037.0,207.0,552.0,210.0,4.0,167900.0,NEAR BAY\n-122.13,37.72,25.0,1134.0,153.0,340.0,171.0,6.5095,371200.0,NEAR BAY\n-122.14,37.72,39.0,786.0,132.0,288.0,132.0,3.5156,218900.0,NEAR BAY\n-122.14,37.72,45.0,1397.0,253.0,555.0,248.0,2.983,202700.0,NEAR BAY\n-122.13,37.72,45.0,2315.0,451.0,1006.0,444.0,3.524,186200.0,NEAR BAY\n-122.13,37.71,44.0,1613.0,339.0,776.0,346.0,3.1103,188900.0,NEAR BAY\n-122.13,37.71,44.0,1421.0,298.0,609.0,270.0,3.5781,180000.0,NEAR BAY\n-122.15,37.71,18.0,5778.0,1526.0,2441.0,1352.0,3.1682,202700.0,NEAR BAY\n-122.14,37.71,27.0,3094.0,866.0,1364.0,789.0,2.6101,181700.0,NEAR BAY\n-122.14,37.71,18.0,3905.0,1007.0,2197.0,1044.0,3.6932,166800.0,NEAR BAY\n-122.13,37.7,43.0,3046.0,557.0,1333.0,544.0,3.4583,183700.0,NEAR BAY\n-122.13,37.7,19.0,3516.0,710.0,1810.0,703.0,3.9032,218000.0,NEAR BAY\n-122.15,37.71,36.0,998.0,178.0,531.0,183.0,4.0208,191500.0,NEAR BAY\n-122.15,37.7,36.0,1464.0,244.0,672.0,261.0,3.5547,194700.0,NEAR BAY\n-122.14,37.7,17.0,1463.0,292.0,695.0,330.0,4.5859,187200.0,NEAR BAY\n-122.13,37.7,21.0,4124.0,1054.0,2162.0,998.0,2.6321,223100.0,NEAR BAY\n-122.13,37.69,17.0,2380.0,769.0,1216.0,643.0,3.395,271300.0,NEAR BAY\n-122.14,37.7,36.0,1266.0,228.0,606.0,239.0,3.9702,194100.0,NEAR BAY\n-122.16,37.7,36.0,2239.0,391.0,1203.0,379.0,5.0043,190400.0,NEAR BAY\n-122.14,37.69,38.0,1571.0,317.0,874.0,301.0,4.4659,189100.0,NEAR BAY\n-122.15,37.69,38.0,1246.0,221.0,637.0,222.0,3.6625,184600.0,NEAR BAY\n-122.15,37.69,36.0,1501.0,287.0,703.0,276.0,3.8864,197300.0,NEAR BAY\n-122.15,37.7,36.0,1468.0,252.0,733.0,229.0,3.4583,192600.0,NEAR BAY\n-122.16,37.69,36.0,1118.0,219.0,625.0,228.0,3.7813,192200.0,NEAR BAY\n-122.16,37.7,36.0,1097.0,208.0,568.0,225.0,2.9917,194600.0,NEAR BAY\n-122.16,37.7,36.0,1719.0,303.0,836.0,311.0,4.4375,193500.0,NEAR BAY\n-122.17,37.7,24.0,1755.0,365.0,952.0,362.0,4.0,202600.0,NEAR BAY\n-122.16,37.68,16.0,1687.0,348.0,568.0,352.0,2.3869,83300.0,NEAR BAY\n-122.17,37.69,24.0,2262.0,391.0,1125.0,366.0,4.7609,212600.0,NEAR BAY\n-122.18,37.68,5.0,2087.0,407.0,840.0,401.0,5.4858,187800.0,NEAR BAY\n-122.16,37.69,36.0,1480.0,278.0,796.0,283.0,4.3971,205700.0,NEAR BAY\n-122.15,37.69,36.0,1545.0,273.0,863.0,267.0,4.0109,192900.0,NEAR BAY\n-122.15,37.68,35.0,2632.0,447.0,1349.0,486.0,4.3864,205200.0,NEAR BAY\n-122.15,37.68,30.0,2261.0,443.0,929.0,383.0,4.2841,213400.0,NEAR BAY\n-122.15,37.69,39.0,1670.0,308.0,957.0,335.0,5.1312,183600.0,NEAR BAY\n-122.14,37.69,37.0,2141.0,535.0,1093.0,555.0,2.9958,178400.0,NEAR BAY\n-122.14,37.68,27.0,3337.0,613.0,1489.0,607.0,3.6364,219200.0,NEAR BAY\n-122.14,37.68,31.0,3184.0,716.0,1561.0,628.0,2.7955,183100.0,NEAR BAY\n-122.1,37.69,44.0,2341.0,500.0,1256.0,485.0,2.9507,157100.0,NEAR BAY\n-122.12,37.69,30.0,1197.0,269.0,695.0,279.0,3.4375,157800.0,NEAR BAY\n-122.13,37.69,34.0,1131.0,278.0,560.0,237.0,2.875,161700.0,NEAR BAY\n-122.12,37.71,38.0,1164.0,284.0,632.0,289.0,3.0345,152100.0,NEAR BAY\n-122.12,37.7,41.0,3495.0,787.0,1849.0,750.0,2.679,144900.0,NEAR BAY\n-122.12,37.7,17.0,2488.0,617.0,1287.0,538.0,2.9922,179900.0,NEAR BAY\n-122.12,37.69,35.0,2681.0,508.0,1580.0,536.0,4.1042,179100.0,NEAR BAY\n-122.12,37.7,19.0,2495.0,635.0,1571.0,579.0,2.5833,159900.0,NEAR BAY\n-122.11,37.7,23.0,1689.0,461.0,828.0,443.0,2.1552,161400.0,NEAR BAY\n-122.11,37.7,29.0,1298.0,306.0,835.0,338.0,2.3274,170400.0,NEAR BAY\n-122.11,37.7,19.0,2693.0,789.0,1765.0,724.0,2.4206,137500.0,NEAR BAY\n-122.1,37.69,41.0,746.0,,387.0,161.0,3.9063,178400.0,NEAR BAY\n-122.11,37.69,37.0,2444.0,651.0,1562.0,618.0,2.6464,155200.0,NEAR BAY\n-122.11,37.69,42.0,1472.0,310.0,768.0,309.0,3.4643,160900.0,NEAR BAY\n-122.12,37.69,10.0,2227.0,560.0,1140.0,472.0,2.3973,167300.0,NEAR BAY\n-122.03,37.69,20.0,200.0,25.0,83.0,31.0,6.5,340000.0,NEAR BAY\n-121.97,37.64,32.0,1283.0,194.0,485.0,171.0,6.0574,431000.0,<1H OCEAN\n-122.02,37.63,6.0,2445.0,590.0,1189.0,573.0,3.8958,301100.0,NEAR BAY\n-122.04,37.63,21.0,1307.0,236.0,586.0,249.0,4.7813,241900.0,NEAR BAY\n-122.05,37.63,5.0,3785.0,936.0,2240.0,792.0,3.2829,162500.0,NEAR BAY\n-122.04,37.66,10.0,2031.0,357.0,867.0,352.0,5.3169,299200.0,NEAR BAY\n-122.04,37.65,10.0,8299.0,1326.0,3827.0,1288.0,6.2579,315500.0,NEAR BAY\n-122.05,37.68,23.0,7518.0,1279.0,3827.0,1294.0,5.1701,216800.0,NEAR BAY\n-122.07,37.68,36.0,1815.0,426.0,1280.0,431.0,3.25,218100.0,NEAR BAY\n-122.06,37.68,30.0,5367.0,1207.0,2667.0,1047.0,3.1796,170300.0,NEAR BAY\n-122.08,37.68,26.0,1167.0,370.0,253.0,137.0,2.4196,275000.0,NEAR BAY\n-122.08,37.68,26.0,2607.0,682.0,1401.0,607.0,2.6563,184100.0,NEAR BAY\n-122.08,37.67,29.0,493.0,168.0,233.0,152.0,0.9637,160000.0,NEAR BAY\n-122.09,37.67,48.0,1252.0,305.0,673.0,308.0,2.3357,175000.0,NEAR BAY\n-122.09,37.68,41.0,1382.0,353.0,704.0,314.0,3.5114,197500.0,NEAR BAY\n-122.09,37.69,43.0,500.0,110.0,273.0,120.0,3.3125,150000.0,NEAR BAY\n-122.09,37.68,29.0,2333.0,538.0,1120.0,540.0,2.4042,205600.0,NEAR BAY\n-122.09,37.68,41.0,1834.0,463.0,1105.0,467.0,2.8322,170300.0,NEAR BAY\n-122.09,37.68,43.0,1415.0,348.0,569.0,293.0,2.5156,190900.0,NEAR BAY\n-122.1,37.68,37.0,1352.0,342.0,691.0,324.0,3.4032,196900.0,NEAR BAY\n-122.1,37.68,37.0,2116.0,503.0,1109.0,448.0,2.535,174000.0,NEAR BAY\n-122.11,37.68,37.0,1976.0,481.0,1197.0,465.0,2.5772,170200.0,NEAR BAY\n-122.1,37.68,38.0,1779.0,413.0,1061.0,400.0,3.0962,180900.0,NEAR BAY\n-122.1,37.67,34.0,3659.0,897.0,2479.0,903.0,2.9564,150500.0,NEAR BAY\n-122.1,37.68,31.0,1892.0,428.0,1162.0,389.0,3.125,167100.0,NEAR BAY\n-122.12,37.68,35.0,1958.0,484.0,1146.0,448.0,2.95,148900.0,NEAR BAY\n-122.12,37.68,40.0,1553.0,253.0,724.0,267.0,4.38,196400.0,NEAR BAY\n-122.12,37.68,37.0,2412.0,394.0,975.0,375.0,4.0417,191100.0,NEAR BAY\n-122.11,37.67,36.0,2110.0,389.0,952.0,370.0,3.8,187500.0,NEAR BAY\n-122.12,37.68,45.0,2179.0,401.0,1159.0,399.0,3.4839,180600.0,NEAR BAY\n-122.13,37.68,45.0,2457.0,445.0,1129.0,422.0,4.0588,182800.0,NEAR BAY\n-122.13,37.68,44.0,2147.0,399.0,1175.0,401.0,4.1974,179300.0,NEAR BAY\n-122.13,37.68,43.0,1676.0,340.0,924.0,328.0,3.6,179400.0,NEAR BAY\n-122.14,37.68,35.0,2976.0,518.0,1424.0,538.0,4.267,210300.0,NEAR BAY\n-122.14,37.67,34.0,3036.0,533.0,1366.0,500.0,4.2386,192300.0,NEAR BAY\n-122.15,37.67,35.0,2472.0,398.0,1171.0,390.0,5.5797,198100.0,NEAR BAY\n-122.14,37.67,36.0,1487.0,249.0,641.0,243.0,4.0682,196200.0,NEAR BAY\n-122.13,37.67,38.0,2012.0,347.0,880.0,332.0,3.1734,181600.0,NEAR BAY\n-122.14,37.67,37.0,3342.0,,1635.0,557.0,4.7933,186900.0,NEAR BAY\n-122.14,37.67,37.0,3156.0,534.0,1495.0,543.0,4.8125,188300.0,NEAR BAY\n-122.13,37.67,40.0,1748.0,318.0,914.0,317.0,3.8676,184000.0,NEAR BAY\n-122.12,37.67,33.0,3429.0,681.0,1798.0,694.0,3.9395,184700.0,NEAR BAY\n-122.13,37.67,42.0,3592.0,703.0,1625.0,665.0,3.2434,179900.0,NEAR BAY\n-122.11,37.67,38.0,1035.0,247.0,599.0,224.0,3.0917,167200.0,NEAR BAY\n-122.11,37.67,32.0,3028.0,811.0,2037.0,703.0,3.0645,165400.0,NEAR BAY\n-122.09,37.67,33.0,2431.0,655.0,1854.0,603.0,2.7019,154000.0,NEAR BAY\n-122.09,37.67,39.0,2069.0,500.0,1408.0,478.0,3.1115,153500.0,NEAR BAY\n-122.09,37.66,40.0,1340.0,313.0,766.0,271.0,3.4722,135400.0,NEAR BAY\n-122.09,37.66,39.0,1160.0,259.0,725.0,274.0,2.2222,158300.0,NEAR BAY\n-122.05,37.68,32.0,2015.0,318.0,1019.0,340.0,6.1104,240700.0,NEAR BAY\n-122.06,37.67,22.0,3882.0,816.0,1830.0,743.0,4.2733,180700.0,NEAR BAY\n-122.07,37.67,27.0,3239.0,671.0,1469.0,616.0,3.2465,230600.0,NEAR BAY\n-122.07,37.67,28.0,2932.0,739.0,1198.0,624.0,3.2417,210800.0,NEAR BAY\n-122.07,37.67,38.0,2104.0,409.0,1039.0,394.0,3.875,165300.0,NEAR BAY\n-122.04,37.67,29.0,1694.0,251.0,690.0,242.0,6.0501,254200.0,NEAR BAY\n-122.04,37.67,18.0,3000.0,419.0,1155.0,415.0,6.8233,332600.0,NEAR BAY\n-122.04,37.66,23.0,2419.0,348.0,1066.0,384.0,6.3501,350000.0,NEAR BAY\n-122.07,37.66,28.0,2280.0,610.0,1255.0,587.0,2.6719,161200.0,NEAR BAY\n-122.07,37.66,21.0,5031.0,1168.0,2461.0,1042.0,3.875,179300.0,NEAR BAY\n-122.08,37.66,33.0,1547.0,372.0,1063.0,356.0,2.5625,154300.0,NEAR BAY\n-122.07,37.65,31.0,3300.0,790.0,2181.0,740.0,3.016,161800.0,NEAR BAY\n-122.08,37.65,17.0,5018.0,1439.0,3069.0,1299.0,2.7694,161900.0,NEAR BAY\n-122.08,37.65,35.0,1813.0,393.0,1093.0,374.0,3.6818,165400.0,NEAR BAY\n-122.08,37.66,37.0,1997.0,436.0,1349.0,437.0,2.1382,166600.0,NEAR BAY\n-122.1,37.66,37.0,901.0,191.0,599.0,206.0,3.7303,149700.0,NEAR BAY\n-122.1,37.66,35.0,686.0,142.0,480.0,149.0,3.875,162100.0,NEAR BAY\n-122.1,37.66,33.0,1954.0,464.0,1293.0,448.0,3.0489,152600.0,NEAR BAY\n-122.09,37.65,27.0,2630.0,722.0,1414.0,634.0,2.8203,195200.0,NEAR BAY\n-122.09,37.65,35.0,1130.0,192.0,543.0,184.0,4.3897,190600.0,NEAR BAY\n-122.09,37.65,35.0,1184.0,200.0,572.0,194.0,4.7143,193800.0,NEAR BAY\n-122.1,37.66,34.0,656.0,115.0,342.0,112.0,4.6875,200600.0,NEAR BAY\n-122.11,37.66,29.0,2544.0,643.0,2332.0,603.0,3.2091,150000.0,NEAR BAY\n-122.1,37.66,36.0,1305.0,225.0,768.0,234.0,4.275,185300.0,NEAR BAY\n-122.11,37.66,36.0,1755.0,316.0,913.0,299.0,4.1302,172700.0,NEAR BAY\n-122.11,37.66,35.0,2843.0,652.0,1726.0,643.0,3.09,174100.0,NEAR BAY\n-122.1,37.65,25.0,2538.0,494.0,1185.0,501.0,4.5417,194400.0,NEAR BAY\n-122.1,37.64,28.0,1784.0,311.0,735.0,278.0,4.6635,206700.0,NEAR BAY\n-122.1,37.65,31.0,1797.0,327.0,796.0,319.0,4.4427,204500.0,NEAR BAY\n-122.1,37.65,36.0,410.0,76.0,252.0,82.0,4.5303,175000.0,NEAR BAY\n-122.12,37.65,26.0,162.0,27.0,86.0,25.0,2.375,137500.0,NEAR BAY\n-122.1,37.63,18.0,9963.0,2031.0,5613.0,1946.0,3.8171,187200.0,NEAR BAY\n-122.1,37.61,35.0,2361.0,458.0,1727.0,467.0,4.5281,173600.0,NEAR BAY\n-122.13,37.66,19.0,862.0,167.0,407.0,183.0,4.3456,163000.0,NEAR BAY\n-122.11,37.65,18.0,4335.0,808.0,2041.0,734.0,3.4861,331600.0,NEAR BAY\n-122.11,37.64,31.0,1487.0,280.0,854.0,301.0,5.2312,197600.0,NEAR BAY\n-122.11,37.64,8.0,3592.0,849.0,1907.0,746.0,3.6708,197900.0,NEAR BAY\n-122.12,37.64,40.0,432.0,102.0,264.0,77.0,3.8875,228100.0,NEAR BAY\n-122.09,37.63,34.0,1457.0,242.0,735.0,249.0,3.9167,189500.0,NEAR BAY\n-122.09,37.64,32.0,1578.0,284.0,836.0,292.0,3.9063,184200.0,NEAR BAY\n-122.1,37.63,29.0,2172.0,435.0,1377.0,408.0,3.7895,180900.0,NEAR BAY\n-122.08,37.64,36.0,1340.0,245.0,789.0,248.0,3.8,172000.0,NEAR BAY\n-122.08,37.64,36.0,1116.0,199.0,662.0,226.0,5.7309,177900.0,NEAR BAY\n-122.09,37.64,36.0,1885.0,307.0,853.0,271.0,4.1141,173100.0,NEAR BAY\n-122.09,37.64,36.0,1180.0,212.0,664.0,200.0,5.2838,172600.0,NEAR BAY\n-122.08,37.64,30.0,5267.0,1253.0,4065.0,1113.0,3.3479,182100.0,NEAR BAY\n-122.08,37.64,23.0,1897.0,440.0,1109.0,418.0,3.142,179500.0,NEAR BAY\n-122.08,37.63,31.0,767.0,171.0,548.0,185.0,3.7614,176000.0,NEAR BAY\n-122.08,37.63,33.0,691.0,127.0,431.0,149.0,4.25,192600.0,NEAR BAY\n-122.08,37.64,36.0,786.0,133.0,463.0,160.0,3.9338,182700.0,NEAR BAY\n-122.07,37.64,22.0,5861.0,1516.0,5436.0,1463.0,2.5158,134900.0,NEAR BAY\n-122.07,37.63,27.0,2784.0,723.0,2028.0,693.0,2.4808,157600.0,NEAR BAY\n-122.07,37.64,25.0,4524.0,860.0,2426.0,862.0,4.7083,190900.0,NEAR BAY\n-122.06,37.64,37.0,1468.0,304.0,1038.0,282.0,4.1652,158200.0,NEAR BAY\n-122.06,37.64,20.0,1655.0,450.0,857.0,430.0,3.5541,350000.0,NEAR BAY\n-122.06,37.65,33.0,1227.0,286.0,848.0,291.0,3.8036,158200.0,NEAR BAY\n-122.06,37.64,33.0,1160.0,252.0,729.0,220.0,3.8259,146100.0,NEAR BAY\n-122.04,37.63,33.0,952.0,172.0,369.0,159.0,3.2331,226700.0,NEAR BAY\n-122.03,37.62,35.0,2072.0,352.0,1001.0,350.0,4.7109,198700.0,NEAR BAY\n-122.03,37.62,32.0,2964.0,547.0,1472.0,527.0,4.2468,221200.0,NEAR BAY\n-122.04,37.62,35.0,1032.0,173.0,453.0,176.0,6.396,208500.0,NEAR BAY\n-122.04,37.62,35.0,657.0,118.0,328.0,134.0,3.8125,204200.0,NEAR BAY\n-122.04,37.62,32.0,1540.0,324.0,793.0,302.0,3.2857,193200.0,NEAR BAY\n-122.03,37.62,35.0,1298.0,236.0,632.0,204.0,3.8929,209500.0,NEAR BAY\n-122.03,37.61,36.0,1409.0,271.0,1002.0,281.0,3.7262,164900.0,NEAR BAY\n-122.03,37.61,37.0,1383.0,259.0,808.0,241.0,4.0125,161400.0,NEAR BAY\n-122.04,37.61,36.0,1151.0,216.0,727.0,215.0,4.1719,187000.0,NEAR BAY\n-122.04,37.62,35.0,899.0,179.0,455.0,185.0,4.2857,190400.0,NEAR BAY\n-122.08,37.63,37.0,1793.0,364.0,1534.0,346.0,3.6458,156600.0,NEAR BAY\n-122.08,37.62,17.0,2485.0,518.0,1139.0,550.0,2.6875,157300.0,NEAR BAY\n-122.07,37.63,35.0,1931.0,376.0,1175.0,337.0,3.7292,168100.0,NEAR BAY\n-122.07,37.63,24.0,2329.0,465.0,1401.0,453.0,4.5913,177600.0,NEAR BAY\n-122.06,37.63,12.0,6711.0,1374.0,3388.0,1289.0,3.8625,208900.0,NEAR BAY\n-122.06,37.63,23.0,1939.0,356.0,841.0,364.0,3.3611,169200.0,NEAR BAY\n-122.05,37.61,16.0,1642.0,346.0,705.0,351.0,2.8971,163900.0,NEAR BAY\n-122.08,37.63,34.0,1619.0,293.0,1148.0,310.0,4.0326,164700.0,NEAR BAY\n-122.08,37.63,35.0,517.0,108.0,391.0,107.0,4.0682,156900.0,NEAR BAY\n-122.09,37.63,36.0,1570.0,274.0,992.0,249.0,5.3644,168800.0,NEAR BAY\n-122.09,37.63,35.0,1213.0,221.0,790.0,243.0,4.7019,174100.0,NEAR BAY\n-122.08,37.62,27.0,1826.0,309.0,1016.0,313.0,5.64,206500.0,NEAR BAY\n-122.08,37.61,26.0,2261.0,443.0,1039.0,395.0,3.7931,203900.0,NEAR BAY\n-121.99,37.61,9.0,3666.0,711.0,2341.0,703.0,4.6458,217000.0,<1H OCEAN\n-122.02,37.6,36.0,1633.0,345.0,1382.0,338.0,3.694,159600.0,NEAR BAY\n-122.02,37.6,31.0,2155.0,522.0,1858.0,437.0,2.652,159800.0,NEAR BAY\n-122.02,37.6,32.0,1295.0,280.0,1156.0,300.0,3.5,154300.0,NEAR BAY\n-122.02,37.6,32.0,1295.0,295.0,1097.0,328.0,3.2386,149600.0,NEAR BAY\n-122.03,37.61,33.0,1518.0,302.0,831.0,268.0,5.097,188600.0,NEAR BAY\n-122.04,37.6,17.0,3314.0,638.0,1873.0,602.0,4.3875,238500.0,NEAR BAY\n-122.06,37.6,22.0,3009.0,497.0,1640.0,514.0,4.625,235300.0,NEAR BAY\n-122.08,37.61,6.0,2605.0,474.0,1568.0,433.0,5.0406,261400.0,NEAR BAY\n-122.06,37.6,17.0,5159.0,832.0,3174.0,817.0,5.8704,234400.0,NEAR BAY\n-122.06,37.6,18.0,1726.0,276.0,1186.0,310.0,5.3226,231700.0,NEAR BAY\n-122.08,37.59,16.0,1816.0,365.0,1367.0,355.0,4.235,156300.0,NEAR BAY\n-122.07,37.59,13.0,2578.0,551.0,1680.0,528.0,4.825,222000.0,NEAR BAY\n-122.08,37.58,15.0,2576.0,418.0,1657.0,410.0,5.5218,254400.0,NEAR BAY\n-122.07,37.58,16.0,1644.0,251.0,1033.0,267.0,6.5116,244300.0,NEAR BAY\n-122.07,37.58,16.0,1606.0,240.0,1117.0,268.0,6.0661,247000.0,NEAR BAY\n-122.08,37.58,16.0,3349.0,544.0,2003.0,488.0,6.0074,236500.0,NEAR BAY\n-122.07,37.59,15.0,3475.0,686.0,2568.0,653.0,4.6211,151400.0,NEAR BAY\n-122.07,37.58,16.0,1893.0,338.0,1461.0,344.0,5.225,213700.0,NEAR BAY\n-122.04,37.59,14.0,1727.0,302.0,1116.0,273.0,5.3428,243600.0,NEAR BAY\n-122.05,37.59,15.0,6243.0,1273.0,3163.0,1274.0,3.7462,212500.0,NEAR BAY\n-122.03,37.6,24.0,2077.0,383.0,1488.0,389.0,4.5721,214700.0,NEAR BAY\n-122.02,37.59,18.0,1165.0,333.0,855.0,319.0,3.6923,213200.0,NEAR BAY\n-122.03,37.59,16.0,4371.0,889.0,2530.0,817.0,4.6786,256000.0,NEAR BAY\n-122.01,37.59,2.0,838.0,295.0,240.0,149.0,2.875,237500.0,NEAR BAY\n-122.02,37.58,15.0,3052.0,760.0,2097.0,728.0,3.3617,178100.0,NEAR BAY\n-122.01,37.58,17.0,4313.0,717.0,2629.0,721.0,5.7579,231800.0,NEAR BAY\n-122.09,37.6,36.0,385.0,94.0,295.0,92.0,2.9706,147900.0,NEAR BAY\n-122.08,37.6,10.0,3046.0,678.0,2056.0,628.0,3.9022,191700.0,NEAR BAY\n-121.97,37.57,21.0,4342.0,783.0,2172.0,789.0,4.6146,247600.0,<1H OCEAN\n-121.96,37.58,15.0,3575.0,597.0,1777.0,559.0,5.7192,283500.0,<1H OCEAN\n-121.98,37.58,20.0,4126.0,1031.0,2079.0,975.0,3.6832,216900.0,<1H OCEAN\n-121.99,37.58,31.0,2878.0,478.0,1276.0,485.0,6.2073,282500.0,<1H OCEAN\n-122.0,37.58,6.0,4405.0,717.0,2071.0,688.0,5.8151,295600.0,<1H OCEAN\n-122.01,37.57,14.0,16199.0,2993.0,8117.0,2847.0,5.8322,281800.0,NEAR BAY\n-122.04,37.58,14.0,14917.0,2708.0,8012.0,2606.0,5.6277,269800.0,NEAR BAY\n-122.04,37.57,12.0,5719.0,1064.0,3436.0,1057.0,5.2879,231200.0,NEAR BAY\n-122.07,37.57,8.0,8647.0,1407.0,5019.0,1379.0,6.5615,318300.0,NEAR BAY\n-122.06,37.58,15.0,8112.0,1376.0,4576.0,1348.0,5.6758,253400.0,NEAR BAY\n-122.05,37.57,7.0,10648.0,1818.0,6075.0,1797.0,6.1047,278200.0,NEAR BAY\n-121.93,37.49,5.0,1150.0,311.0,648.0,245.0,3.5714,300000.0,<1H OCEAN\n-122.07,37.52,3.0,14014.0,2861.0,7205.0,2753.0,6.0824,273500.0,NEAR BAY\n-122.02,37.56,23.0,4332.0,857.0,2461.0,829.0,4.3594,223400.0,NEAR BAY\n-122.02,37.56,35.0,1716.0,312.0,914.0,316.0,5.5737,214500.0,NEAR BAY\n-122.03,37.56,31.0,4981.0,964.0,2841.0,924.0,4.8962,220200.0,NEAR BAY\n-122.03,37.56,24.0,8444.0,1492.0,4446.0,1491.0,4.6978,240300.0,NEAR BAY\n-122.01,37.56,6.0,3028.0,778.0,1531.0,736.0,4.4259,158000.0,NEAR BAY\n-122.01,37.55,26.0,2068.0,532.0,1434.0,495.0,3.3008,224200.0,NEAR BAY\n-122.02,37.55,33.0,1325.0,274.0,909.0,267.0,4.5687,177200.0,NEAR BAY\n-122.01,37.56,24.0,2563.0,485.0,1174.0,501.0,3.8179,216100.0,NEAR BAY\n-121.99,37.56,18.0,5505.0,1005.0,2641.0,971.0,5.0,269700.0,<1H OCEAN\n-121.99,37.56,20.0,6462.0,1294.0,3288.0,1235.0,4.3393,231200.0,<1H OCEAN\n-121.96,37.55,4.0,3746.0,993.0,1606.0,838.0,4.1387,162500.0,<1H OCEAN\n-121.97,37.56,13.0,8918.0,1823.0,4518.0,1772.0,4.8052,254000.0,<1H OCEAN\n-121.97,37.54,31.0,1949.0,344.0,986.0,322.0,4.6349,196200.0,<1H OCEAN\n-121.97,37.55,17.0,4924.0,1247.0,3080.0,1182.0,3.168,189400.0,<1H OCEAN\n-121.98,37.54,17.0,5133.0,1375.0,3386.0,1339.0,3.1326,220800.0,<1H OCEAN\n-121.99,37.55,16.0,6647.0,2098.0,4649.0,1903.0,2.9074,213800.0,<1H OCEAN\n-121.94,37.56,15.0,5674.0,748.0,2412.0,714.0,8.3996,442900.0,<1H OCEAN\n-121.95,37.55,21.0,10687.0,1540.0,4552.0,1520.0,6.6478,333400.0,<1H OCEAN\n-121.93,37.54,25.0,1354.0,192.0,596.0,220.0,6.629,352400.0,<1H OCEAN\n-121.94,37.54,27.0,3715.0,526.0,1631.0,538.0,6.2179,305300.0,<1H OCEAN\n-121.94,37.53,33.0,2095.0,342.0,941.0,304.0,5.761,259600.0,<1H OCEAN\n-121.95,37.54,29.0,3517.0,645.0,1724.0,585.0,4.6641,248900.0,<1H OCEAN\n-121.94,37.54,31.0,2537.0,382.0,1067.0,410.0,6.7599,356000.0,<1H OCEAN\n-121.96,37.54,14.0,5106.0,1207.0,2738.0,1108.0,3.9909,236000.0,<1H OCEAN\n-121.96,37.53,23.0,2215.0,475.0,1278.0,492.0,4.2955,218800.0,<1H OCEAN\n-121.96,37.53,28.0,2949.0,529.0,1538.0,545.0,4.9615,228000.0,<1H OCEAN\n-121.96,37.53,18.0,2375.0,652.0,1252.0,586.0,2.6198,235900.0,<1H OCEAN\n-121.97,37.54,28.0,2312.0,496.0,1344.0,467.0,4.7135,203200.0,<1H OCEAN\n-121.97,37.53,35.0,2277.0,420.0,1353.0,413.0,4.75,197000.0,<1H OCEAN\n-121.97,37.53,26.0,2506.0,387.0,1273.0,406.0,5.4299,236400.0,<1H OCEAN\n-121.98,37.53,28.0,2829.0,566.0,1610.0,540.0,4.6,223200.0,<1H OCEAN\n-121.98,37.53,26.0,3179.0,703.0,2142.0,639.0,4.1947,222700.0,<1H OCEAN\n-121.99,37.54,26.0,2332.0,371.0,1285.0,404.0,5.388,225000.0,<1H OCEAN\n-121.99,37.54,18.0,3584.0,715.0,1673.0,661.0,3.9444,240100.0,<1H OCEAN\n-121.99,37.54,28.0,3046.0,507.0,1772.0,516.0,5.3283,227900.0,<1H OCEAN\n-121.99,37.55,28.0,2414.0,415.0,1106.0,453.0,4.8403,268600.0,<1H OCEAN\n-122.0,37.54,29.0,4133.0,744.0,2023.0,749.0,5.1616,275100.0,<1H OCEAN\n-122.01,37.55,34.0,2791.0,495.0,1276.0,468.0,4.9167,256300.0,NEAR BAY\n-122.0,37.55,27.0,6103.0,1249.0,3026.0,1134.0,4.1591,332400.0,<1H OCEAN\n-122.01,37.54,32.0,2572.0,406.0,1128.0,395.0,5.0,287600.0,NEAR BAY\n-122.0,37.54,26.0,1910.0,371.0,852.0,357.0,5.8325,298900.0,<1H OCEAN\n-122.01,37.53,27.0,1890.0,303.0,889.0,314.0,5.7057,287600.0,NEAR BAY\n-121.99,37.53,25.0,5405.0,939.0,2831.0,923.0,5.0423,222200.0,<1H OCEAN\n-121.97,37.52,26.0,3761.0,623.0,1776.0,613.0,4.5317,232600.0,<1H OCEAN\n-121.97,37.51,25.0,3333.0,511.0,1671.0,504.0,5.4359,258300.0,<1H OCEAN\n-121.97,37.52,23.0,4925.0,948.0,2530.0,894.0,5.0824,230900.0,<1H OCEAN\n-121.95,37.52,33.0,3994.0,764.0,2721.0,763.0,5.2308,196900.0,<1H OCEAN\n-121.96,37.51,22.0,5811.0,1125.0,3215.0,1086.0,4.4107,223500.0,<1H OCEAN\n-121.96,37.52,26.0,4211.0,741.0,2352.0,734.0,5.2396,223900.0,<1H OCEAN\n-121.93,37.53,27.0,5532.0,973.0,2855.0,960.0,4.7478,243500.0,<1H OCEAN\n-121.92,37.53,7.0,28258.0,3864.0,12203.0,3701.0,8.4045,451100.0,<1H OCEAN\n-121.89,37.49,9.0,4909.0,577.0,1981.0,591.0,9.7194,500001.0,<1H OCEAN\n-121.92,37.49,10.0,7441.0,1588.0,3571.0,1466.0,5.1643,193100.0,<1H OCEAN\n-121.92,37.48,23.0,4314.0,676.0,1972.0,623.0,5.3813,264400.0,<1H OCEAN\n-121.92,37.47,26.0,2016.0,322.0,1105.0,357.0,6.0878,246900.0,<1H OCEAN\n-121.91,37.47,13.0,5377.0,744.0,2759.0,760.0,6.868,337300.0,<1H OCEAN\n-122.03,37.55,22.0,9167.0,1373.0,4319.0,1404.0,6.992,284800.0,NEAR BAY\n-122.03,37.55,26.0,3087.0,532.0,1597.0,483.0,4.9118,217300.0,NEAR BAY\n-122.04,37.55,23.0,3170.0,532.0,1446.0,515.0,4.4357,291700.0,NEAR BAY\n-122.04,37.54,26.0,2145.0,369.0,1285.0,377.0,4.9464,223800.0,NEAR BAY\n-122.05,37.54,25.0,4209.0,731.0,2568.0,703.0,5.2882,223100.0,NEAR BAY\n-122.05,37.55,23.0,4247.0,835.0,2357.0,823.0,5.1321,211300.0,NEAR BAY\n-122.04,37.53,25.0,4458.0,922.0,2998.0,890.0,3.9667,218500.0,NEAR BAY\n-122.04,37.5,17.0,407.0,97.0,307.0,100.0,3.1696,156300.0,NEAR BAY\n-122.06,37.54,20.0,6483.0,1068.0,3526.0,1060.0,5.0838,248200.0,NEAR BAY\n-122.03,37.54,35.0,1867.0,343.0,1213.0,338.0,4.8214,186000.0,NEAR BAY\n-122.03,37.54,6.0,2918.0,672.0,1911.0,639.0,4.1406,178200.0,NEAR BAY\n-122.04,37.53,34.0,2316.0,478.0,1524.0,467.0,3.7364,190400.0,NEAR BAY\n-122.02,37.54,31.0,1240.0,264.0,719.0,236.0,3.535,210300.0,NEAR BAY\n-122.03,37.54,16.0,4458.0,856.0,3038.0,870.0,5.0739,208000.0,NEAR BAY\n-122.03,37.53,18.0,1746.0,437.0,1268.0,404.0,3.256,183300.0,NEAR BAY\n-122.01,37.53,19.0,4572.0,712.0,2346.0,709.0,6.0667,245700.0,NEAR BAY\n-122.02,37.53,21.0,4280.0,673.0,2216.0,681.0,5.7072,242200.0,NEAR BAY\n-122.0,37.51,7.0,6352.0,1390.0,3223.0,1316.0,4.9867,181700.0,<1H OCEAN\n-121.92,37.72,4.0,7477.0,1576.0,2937.0,1506.0,5.1437,299400.0,<1H OCEAN\n-121.92,37.72,22.0,4638.0,716.0,2302.0,687.0,5.347,219500.0,<1H OCEAN\n-121.91,37.71,25.0,4377.0,668.0,2038.0,671.0,5.7233,231800.0,<1H OCEAN\n-121.93,37.72,26.0,3816.0,637.0,1935.0,642.0,4.4697,221300.0,<1H OCEAN\n-121.93,37.72,26.0,2806.0,459.0,1453.0,444.0,4.9107,213800.0,<1H OCEAN\n-121.93,37.71,26.0,4822.0,845.0,2288.0,805.0,4.2281,206000.0,<1H OCEAN\n-121.94,37.71,15.0,6473.0,1027.0,2484.0,970.0,5.0143,271100.0,<1H OCEAN\n-121.96,37.71,6.0,8072.0,1050.0,3386.0,1062.0,7.2494,336500.0,<1H OCEAN\n-121.92,37.64,46.0,1280.0,209.0,512.0,208.0,5.1406,315600.0,INLAND\n-121.93,37.66,24.0,3166.0,424.0,1081.0,400.0,8.3337,500001.0,<1H OCEAN\n-121.92,37.69,13.0,3742.0,555.0,1590.0,559.0,7.316,285400.0,<1H OCEAN\n-121.9,37.66,18.0,7397.0,1137.0,3126.0,1115.0,6.4994,323000.0,INLAND\n-121.92,37.68,23.0,1655.0,223.0,706.0,219.0,7.2211,291900.0,<1H OCEAN\n-121.93,37.7,3.0,2456.0,582.0,793.0,456.0,4.4087,225600.0,<1H OCEAN\n-121.91,37.69,23.0,2179.0,308.0,926.0,299.0,5.9345,259600.0,<1H OCEAN\n-121.91,37.68,20.0,1804.0,254.0,831.0,260.0,6.177,262900.0,<1H OCEAN\n-121.91,37.68,18.0,3631.0,547.0,1700.0,520.0,5.817,257300.0,<1H OCEAN\n-121.91,37.69,18.0,2876.0,423.0,1395.0,427.0,6.3132,259200.0,<1H OCEAN\n-121.89,37.68,12.0,7490.0,1207.0,3329.0,1136.0,6.3373,339700.0,<1H OCEAN\n-121.88,37.68,23.0,2234.0,270.0,854.0,286.0,7.333,337200.0,INLAND\n-121.89,37.68,22.0,1898.0,239.0,734.0,245.0,6.2918,334100.0,<1H OCEAN\n-121.88,37.67,16.0,4070.0,624.0,1543.0,577.0,6.5214,311500.0,INLAND\n-121.88,37.67,25.0,2244.0,301.0,937.0,324.0,6.4524,296900.0,INLAND\n-121.89,37.67,20.0,2948.0,471.0,1181.0,474.0,6.0604,247900.0,INLAND\n-121.89,37.67,19.0,2034.0,288.0,852.0,295.0,6.5285,300400.0,INLAND\n-121.9,37.67,15.0,2130.0,273.0,876.0,285.0,7.2639,332400.0,<1H OCEAN\n-121.9,37.67,7.0,9540.0,1294.0,3926.0,1229.0,7.4353,389800.0,<1H OCEAN\n-121.88,37.66,29.0,2702.0,680.0,1360.0,642.0,3.1127,233000.0,INLAND\n-121.87,37.66,39.0,522.0,116.0,161.0,102.0,2.4896,238500.0,INLAND\n-121.87,37.66,52.0,775.0,134.0,315.0,123.0,5.0677,233300.0,INLAND\n-121.89,37.66,3.0,1565.0,464.0,769.0,461.0,2.1187,231300.0,INLAND\n-121.88,37.64,20.0,1309.0,184.0,514.0,172.0,10.9506,475800.0,INLAND\n-121.87,37.57,13.0,5519.0,833.0,2444.0,825.0,7.0691,393200.0,<1H OCEAN\n-121.87,37.67,10.0,4337.0,800.0,1813.0,743.0,5.5,247200.0,INLAND\n-121.87,37.67,28.0,1812.0,294.0,853.0,278.0,4.9879,229400.0,INLAND\n-121.85,37.68,4.0,4719.0,741.0,1895.0,742.0,6.8132,282500.0,INLAND\n-121.85,37.66,14.0,4236.0,701.0,1833.0,663.0,5.6399,300600.0,INLAND\n-121.86,37.66,22.0,3634.0,664.0,1699.0,640.0,4.1597,293200.0,INLAND\n-121.87,37.66,27.0,1569.0,242.0,583.0,214.0,5.7519,278500.0,INLAND\n-121.84,37.66,13.0,13182.0,2074.0,4847.0,1950.0,5.6417,352900.0,INLAND\n-121.85,37.72,43.0,228.0,40.0,83.0,42.0,10.3203,400000.0,INLAND\n-121.82,37.73,47.0,127.0,23.0,51.0,21.0,4.3472,375000.0,INLAND\n-121.86,37.7,13.0,9621.0,1344.0,4389.0,1391.0,6.6827,313700.0,INLAND\n-121.89,37.69,4.0,6159.0,1510.0,2649.0,1241.0,3.62,139300.0,<1H OCEAN\n-121.77,37.65,16.0,4290.0,554.0,1952.0,576.0,7.3588,327500.0,INLAND\n-121.62,37.61,26.0,1786.0,306.0,771.0,279.0,5.7239,430600.0,INLAND\n-121.61,37.77,32.0,404.0,74.0,144.0,58.0,4.2083,125000.0,INLAND\n-121.72,37.7,17.0,1671.0,352.0,729.0,252.0,6.1023,450000.0,INLAND\n-121.73,37.71,12.0,5608.0,1049.0,2595.0,1067.0,3.9864,200200.0,INLAND\n-121.75,37.71,11.0,12070.0,2220.0,5826.0,2125.0,4.8624,192400.0,INLAND\n-121.77,37.74,25.0,494.0,81.0,254.0,85.0,9.1531,418800.0,INLAND\n-121.8,37.7,22.0,5533.0,943.0,2474.0,910.0,4.7361,216800.0,INLAND\n-121.8,37.69,17.0,3956.0,639.0,2222.0,662.0,5.4324,215500.0,INLAND\n-121.82,37.69,12.0,1906.0,351.0,802.0,319.0,4.9375,227700.0,INLAND\n-121.76,37.69,29.0,3433.0,711.0,1919.0,709.0,3.3841,184400.0,INLAND\n-121.77,37.68,36.0,1687.0,372.0,950.0,372.0,3.5532,158400.0,INLAND\n-121.78,37.69,34.0,2358.0,498.0,1157.0,461.0,3.3618,174600.0,INLAND\n-121.78,37.69,35.0,2853.0,588.0,1761.0,572.0,4.3533,168400.0,INLAND\n-121.79,37.69,25.0,6296.0,1082.0,3200.0,1047.0,4.5357,188400.0,INLAND\n-121.76,37.7,9.0,3980.0,736.0,1705.0,679.0,5.7068,256700.0,INLAND\n-121.75,37.69,26.0,2647.0,536.0,1422.0,522.0,3.7212,183800.0,INLAND\n-121.75,37.68,35.0,1755.0,299.0,702.0,263.0,5.2443,183400.0,INLAND\n-121.76,37.68,35.0,1864.0,357.0,1189.0,349.0,4.2361,177500.0,INLAND\n-121.76,37.68,32.0,1078.0,207.0,555.0,197.0,3.1856,186900.0,INLAND\n-121.73,37.68,17.0,20354.0,3493.0,8768.0,3293.0,5.4496,238900.0,INLAND\n-121.78,37.68,17.0,3112.0,872.0,1392.0,680.0,3.0222,172500.0,INLAND\n-121.77,37.68,44.0,495.0,112.0,277.0,109.0,2.6667,179200.0,INLAND\n-121.76,37.68,52.0,2157.0,418.0,929.0,419.0,3.7301,204400.0,INLAND\n-121.77,37.68,41.0,1501.0,299.0,629.0,288.0,4.6806,209400.0,INLAND\n-121.77,37.67,20.0,8068.0,1217.0,3489.0,1259.0,5.7907,264200.0,INLAND\n-121.76,37.67,6.0,3023.0,518.0,1225.0,468.0,6.3705,350000.0,INLAND\n-121.78,37.67,26.0,2211.0,344.0,1024.0,321.0,5.2649,199800.0,INLAND\n-121.79,37.67,26.0,2163.0,339.0,947.0,346.0,6.0797,211000.0,INLAND\n-121.78,37.67,28.0,1773.0,278.0,804.0,269.0,4.8571,201100.0,INLAND\n-121.78,37.66,25.0,1947.0,418.0,900.0,354.0,3.8523,193000.0,INLAND\n-121.79,37.66,22.0,14701.0,2210.0,6693.0,2232.0,5.98,245000.0,INLAND\n-119.78,38.69,17.0,1364.0,282.0,338.0,152.0,2.45,117600.0,INLAND\n-119.93,38.72,15.0,2061.0,465.0,573.0,196.0,2.2417,97900.0,INLAND\n-120.0,38.52,16.0,3045.0,543.0,202.0,102.0,3.15,140600.0,INLAND\n-120.56,38.48,14.0,3545.0,702.0,946.0,411.0,3.4609,120900.0,INLAND\n-120.59,38.45,13.0,2944.0,558.0,1091.0,466.0,3.0,119000.0,INLAND\n-120.55,38.46,16.0,1443.0,249.0,435.0,181.0,3.2031,129200.0,INLAND\n-120.55,38.45,17.0,2277.0,474.0,767.0,356.0,2.5208,99100.0,INLAND\n-120.55,38.43,18.0,1564.0,357.0,618.0,277.0,2.3549,108900.0,INLAND\n-120.25,38.55,15.0,4403.0,891.0,1103.0,433.0,3.0125,111700.0,INLAND\n-120.79,38.54,34.0,1133.0,254.0,495.0,187.0,2.05,68900.0,INLAND\n-120.8,38.51,23.0,1001.0,195.0,369.0,157.0,3.125,96400.0,INLAND\n-120.65,38.5,10.0,1783.0,337.0,638.0,262.0,2.65,116700.0,INLAND\n-120.76,38.47,17.0,1521.0,309.0,607.0,240.0,3.5,123800.0,INLAND\n-120.88,38.45,25.0,1374.0,297.0,657.0,288.0,2.5476,97900.0,INLAND\n-120.79,38.43,40.0,1391.0,246.0,546.0,214.0,3.9107,129800.0,INLAND\n-120.69,38.44,13.0,1473.0,265.0,597.0,228.0,4.2917,121300.0,INLAND\n-120.93,38.5,15.0,1248.0,234.0,529.0,216.0,3.3393,107200.0,INLAND\n-120.97,38.42,16.0,1748.0,322.0,4930.0,287.0,4.3029,121900.0,INLAND\n-120.87,38.37,28.0,3998.0,765.0,1614.0,698.0,2.8125,113400.0,INLAND\n-120.98,38.34,27.0,3471.0,653.0,1793.0,600.0,3.5508,99100.0,INLAND\n-120.88,38.32,18.0,2791.0,492.0,1187.0,438.0,3.2589,103000.0,INLAND\n-120.93,38.26,13.0,2084.0,449.0,834.0,305.0,3.2937,114200.0,INLAND\n-120.72,38.42,17.0,5654.0,1085.0,2237.0,953.0,3.0465,144100.0,INLAND\n-120.77,38.38,15.0,4221.0,816.0,1737.0,743.0,2.3125,128600.0,INLAND\n-120.72,38.38,9.0,1787.0,347.0,806.0,306.0,2.525,157200.0,INLAND\n-120.66,38.4,18.0,2144.0,420.0,985.0,381.0,3.175,118500.0,INLAND\n-120.65,38.42,23.0,1538.0,305.0,730.0,267.0,2.6078,116700.0,INLAND\n-120.62,38.39,15.0,3750.0,691.0,1444.0,603.0,2.7399,134800.0,INLAND\n-120.69,38.36,19.0,3267.0,614.0,1252.0,566.0,2.7236,109900.0,INLAND\n-120.71,38.34,16.0,1257.0,231.0,559.0,213.0,4.4531,144300.0,INLAND\n-120.8,38.31,37.0,1341.0,256.0,533.0,242.0,3.2135,123600.0,INLAND\n-121.83,39.76,12.0,9831.0,1921.0,4644.0,1775.0,3.1142,112600.0,INLAND\n-121.82,39.76,23.0,6010.0,1116.0,2710.0,1149.0,3.0068,107300.0,INLAND\n-121.86,39.78,12.0,7653.0,1578.0,3628.0,1494.0,3.0905,117800.0,INLAND\n-121.85,39.77,17.0,5273.0,1177.0,2446.0,1199.0,1.9362,89900.0,INLAND\n-121.84,39.76,14.0,2351.0,620.0,1215.0,548.0,2.3155,102300.0,INLAND\n-121.86,39.76,19.0,7254.0,1785.0,4030.0,1667.0,2.0094,87300.0,INLAND\n-121.89,39.76,15.0,10265.0,1860.0,4591.0,1906.0,3.07,142600.0,INLAND\n-121.88,39.74,12.0,14631.0,3298.0,7517.0,3262.0,1.6785,153100.0,INLAND\n-121.87,39.75,22.0,1707.0,296.0,822.0,297.0,3.6625,126600.0,INLAND\n-121.86,39.75,18.0,1651.0,309.0,856.0,293.0,3.5046,118300.0,INLAND\n-121.87,39.74,7.0,1737.0,290.0,747.0,265.0,3.9,147000.0,INLAND\n-121.85,39.75,39.0,568.0,127.0,267.0,129.0,1.8095,78100.0,INLAND\n-121.85,39.74,39.0,1139.0,265.0,623.0,264.0,2.2833,85800.0,INLAND\n-121.85,39.74,41.0,2901.0,689.0,1426.0,632.0,1.5633,84500.0,INLAND\n-121.85,39.73,52.0,444.0,80.0,1107.0,98.0,3.4191,137500.0,INLAND\n-121.85,39.73,17.0,3425.0,827.0,2469.0,758.0,0.9393,88900.0,INLAND\n-121.86,39.74,13.0,3494.0,843.0,1571.0,784.0,1.1019,120200.0,INLAND\n-121.84,39.75,29.0,4362.0,1053.0,2053.0,1000.0,1.7284,74500.0,INLAND\n-121.84,39.74,43.0,2976.0,599.0,1181.0,560.0,2.2621,85100.0,INLAND\n-121.83,39.74,34.0,3263.0,604.0,1290.0,594.0,2.575,130300.0,INLAND\n-121.8,39.75,28.0,2551.0,378.0,1011.0,374.0,4.3309,125200.0,INLAND\n-121.82,39.75,29.0,7744.0,1375.0,3316.0,1365.0,3.0253,111400.0,INLAND\n-121.82,39.75,37.0,2236.0,372.0,974.0,379.0,3.2016,97000.0,INLAND\n-121.8,39.75,11.0,7212.0,1355.0,3264.0,1264.0,3.1125,122600.0,INLAND\n-121.79,39.73,8.0,5690.0,1189.0,2887.0,1077.0,3.0625,116300.0,INLAND\n-121.78,39.71,8.0,140.0,28.0,84.0,29.0,2.125,179200.0,INLAND\n-121.82,39.73,44.0,2923.0,659.0,1371.0,626.0,2.2925,85800.0,INLAND\n-121.84,39.73,52.0,677.0,152.0,379.0,154.0,1.6797,94800.0,INLAND\n-121.84,39.73,52.0,502.0,100.0,311.0,100.0,1.5481,200000.0,INLAND\n-121.84,39.73,52.0,857.0,232.0,520.0,198.0,0.987,112500.0,INLAND\n-121.84,39.72,52.0,1457.0,389.0,802.0,342.0,0.9566,69000.0,INLAND\n-121.84,39.73,52.0,957.0,263.0,513.0,223.0,1.3672,55000.0,INLAND\n-121.83,39.73,52.0,1741.0,401.0,753.0,377.0,2.0064,77900.0,INLAND\n-121.85,39.72,18.0,7272.0,1559.0,5022.0,1524.0,1.6911,98800.0,INLAND\n-121.83,39.72,52.0,1890.0,420.0,974.0,383.0,1.6827,78700.0,INLAND\n-121.81,39.7,21.0,5051.0,1054.0,2948.0,980.0,1.5863,81300.0,INLAND\n-121.82,39.73,33.0,2242.0,517.0,1160.0,449.0,1.7426,60300.0,INLAND\n-121.82,39.72,42.0,2978.0,694.0,1879.0,679.0,1.5064,66300.0,INLAND\n-121.81,39.71,18.0,1222.0,250.0,708.0,281.0,2.0288,116700.0,INLAND\n-121.87,39.82,11.0,5103.0,825.0,2456.0,810.0,4.5032,159700.0,INLAND\n-121.89,39.71,26.0,2741.0,451.0,1217.0,437.0,3.7007,139200.0,INLAND\n-121.97,39.79,16.0,1453.0,299.0,904.0,286.0,3.5735,89600.0,INLAND\n-121.84,39.68,38.0,549.0,105.0,275.0,94.0,3.5375,153100.0,INLAND\n-121.8,39.64,25.0,2202.0,422.0,1109.0,403.0,2.8306,87500.0,INLAND\n-121.77,39.66,20.0,3759.0,,1705.0,600.0,4.712,158600.0,INLAND\n-121.74,39.59,24.0,1535.0,279.0,726.0,272.0,2.3833,95100.0,INLAND\n-121.9,39.59,20.0,1465.0,278.0,745.0,250.0,3.0625,93800.0,INLAND\n-121.75,39.88,16.0,2867.0,559.0,1203.0,449.0,2.7143,95300.0,INLAND\n-121.68,39.82,15.0,3996.0,748.0,1786.0,728.0,3.5189,141300.0,INLAND\n-121.54,40.06,17.0,858.0,262.0,47.0,27.0,2.4028,67500.0,INLAND\n-121.51,39.97,22.0,1468.0,285.0,611.0,235.0,2.3036,73000.0,INLAND\n-121.59,39.86,14.0,1527.0,269.0,665.0,261.0,2.8657,119600.0,INLAND\n-121.58,39.83,16.0,4591.0,904.0,1904.0,812.0,2.2419,93200.0,INLAND\n-121.6,39.8,10.0,1742.0,307.0,721.0,312.0,2.4537,117900.0,INLAND\n-121.62,39.79,11.0,3835.0,727.0,1456.0,658.0,2.5374,97200.0,INLAND\n-121.59,39.82,12.0,1958.0,369.0,875.0,354.0,2.3507,97600.0,INLAND\n-121.6,39.83,12.0,3744.0,699.0,1532.0,660.0,2.3079,95300.0,INLAND\n-121.63,39.82,11.0,3974.0,727.0,1610.0,634.0,2.6147,107700.0,INLAND\n-121.6,39.79,18.0,2672.0,533.0,1151.0,532.0,2.567,102900.0,INLAND\n-121.59,39.79,20.0,743.0,171.0,395.0,168.0,1.625,88300.0,INLAND\n-121.59,39.78,16.0,2754.0,570.0,1063.0,543.0,1.4048,86500.0,INLAND\n-121.59,39.78,18.0,945.0,205.0,385.0,207.0,2.1838,58000.0,INLAND\n-121.6,39.77,26.0,1503.0,343.0,699.0,296.0,1.875,84000.0,INLAND\n-121.61,39.77,25.0,1612.0,313.0,837.0,303.0,2.963,89500.0,INLAND\n-121.63,39.78,28.0,1677.0,327.0,770.0,309.0,2.6823,93400.0,INLAND\n-121.57,39.8,23.0,790.0,137.0,365.0,152.0,2.1912,115200.0,INLAND\n-121.57,39.78,18.0,2221.0,459.0,952.0,440.0,2.0458,105700.0,INLAND\n-121.59,39.77,24.0,1535.0,276.0,664.0,273.0,2.3068,97300.0,INLAND\n-121.58,39.79,19.0,2636.0,523.0,1184.0,465.0,2.7863,108600.0,INLAND\n-121.57,39.76,20.0,1384.0,257.0,557.0,232.0,2.0882,104900.0,INLAND\n-121.58,39.76,19.0,2487.0,485.0,1110.0,453.0,3.1061,110200.0,INLAND\n-121.58,39.76,18.0,1676.0,332.0,733.0,318.0,1.7875,103800.0,INLAND\n-121.59,39.75,20.0,908.0,206.0,481.0,211.0,2.2,80800.0,INLAND\n-121.6,39.77,23.0,2263.0,497.0,1138.0,455.0,2.3403,87300.0,INLAND\n-121.6,39.76,22.0,2447.0,556.0,1157.0,556.0,1.8245,85500.0,INLAND\n-121.61,39.76,31.0,2431.0,512.0,1026.0,427.0,2.5428,85000.0,INLAND\n-121.62,39.77,23.0,1759.0,366.0,788.0,359.0,1.8125,93500.0,INLAND\n-121.65,39.76,31.0,1599.0,318.0,794.0,303.0,3.0,96700.0,INLAND\n-121.63,39.76,22.0,2598.0,482.0,1151.0,490.0,2.8182,109700.0,INLAND\n-121.62,39.76,14.0,2063.0,559.0,934.0,529.0,1.7788,85800.0,INLAND\n-121.62,39.75,20.0,1173.0,261.0,523.0,258.0,1.0625,92800.0,INLAND\n-121.63,39.75,37.0,1296.0,296.0,569.0,257.0,1.8616,70500.0,INLAND\n-121.64,39.74,20.0,1808.0,334.0,763.0,335.0,2.3711,121800.0,INLAND\n-121.71,39.71,17.0,2748.0,556.0,1174.0,514.0,3.066,102600.0,INLAND\n-121.66,39.66,17.0,3502.0,655.0,1763.0,613.0,2.9625,101200.0,INLAND\n-121.56,39.69,8.0,2836.0,522.0,1163.0,512.0,3.13,168300.0,INLAND\n-121.57,39.74,17.0,1619.0,292.0,705.0,285.0,2.4623,126100.0,INLAND\n-121.59,39.74,17.0,1646.0,330.0,750.0,344.0,2.3798,83800.0,INLAND\n-121.6,39.75,19.0,2888.0,591.0,984.0,499.0,1.9766,92600.0,INLAND\n-121.6,39.68,15.0,1677.0,345.0,844.0,330.0,2.3958,111200.0,INLAND\n-121.5,39.83,15.0,1896.0,408.0,893.0,334.0,1.6948,87500.0,INLAND\n-121.41,39.72,17.0,1583.0,331.0,730.0,306.0,2.3895,87500.0,INLAND\n-121.39,39.61,22.0,2828.0,610.0,986.0,391.0,2.8871,94700.0,INLAND\n-121.24,39.65,35.0,632.0,148.0,221.0,102.0,2.3684,62500.0,INLAND\n-121.19,39.55,17.0,1483.0,284.0,481.0,211.0,1.4896,83300.0,INLAND\n-121.36,39.52,15.0,2490.0,527.0,1229.0,497.0,2.3917,85700.0,INLAND\n-121.56,39.52,9.0,818.0,197.0,358.0,197.0,1.7708,79500.0,INLAND\n-121.56,39.52,26.0,1957.0,429.0,945.0,397.0,1.7308,53600.0,INLAND\n-121.56,39.53,12.0,1733.0,421.0,1861.0,415.0,1.5771,65200.0,INLAND\n-121.54,39.6,15.0,886.0,204.0,576.0,205.0,2.1467,84100.0,INLAND\n-121.46,39.54,14.0,5549.0,1000.0,1822.0,919.0,2.9562,142300.0,INLAND\n-121.49,39.52,25.0,848.0,153.0,436.0,155.0,3.9028,93800.0,INLAND\n-121.44,39.5,26.0,1652.0,325.0,790.0,292.0,3.0446,90800.0,INLAND\n-121.47,39.49,17.0,1554.0,242.0,553.0,230.0,3.2174,91800.0,INLAND\n-121.47,39.51,19.0,3720.0,636.0,1304.0,607.0,2.6921,97500.0,INLAND\n-121.53,39.53,35.0,1806.0,293.0,683.0,295.0,4.5156,91200.0,INLAND\n-121.53,39.52,30.0,1030.0,161.0,448.0,159.0,2.4821,73800.0,INLAND\n-121.53,39.52,24.0,1028.0,185.0,471.0,186.0,2.9688,86400.0,INLAND\n-121.54,39.51,33.0,3585.0,757.0,1887.0,765.0,2.502,62100.0,INLAND\n-121.52,39.51,30.0,3085.0,610.0,1688.0,575.0,2.334,72200.0,INLAND\n-121.55,39.51,39.0,1551.0,353.0,684.0,310.0,2.0357,57600.0,INLAND\n-121.55,39.51,48.0,827.0,198.0,396.0,161.0,0.8024,58300.0,INLAND\n-121.55,39.51,50.0,1050.0,288.0,485.0,260.0,1.1607,51700.0,INLAND\n-121.56,39.51,47.0,1064.0,245.0,603.0,190.0,1.3654,57900.0,INLAND\n-121.56,39.51,46.0,1885.0,385.0,871.0,347.0,1.6352,53100.0,INLAND\n-121.57,39.5,31.0,2023.0,469.0,1073.0,436.0,1.5714,56100.0,INLAND\n-121.58,39.52,25.0,2409.0,490.0,1384.0,479.0,1.9956,58000.0,INLAND\n-121.58,39.51,24.0,1865.0,372.0,1087.0,385.0,1.6389,56700.0,INLAND\n-121.58,39.5,29.0,1947.0,383.0,925.0,337.0,2.1658,57600.0,INLAND\n-121.62,39.5,18.0,2105.0,416.0,974.0,385.0,1.6346,63300.0,INLAND\n-121.61,39.52,24.0,1610.0,324.0,909.0,323.0,1.8661,59800.0,INLAND\n-121.65,39.53,23.0,1387.0,325.0,640.0,289.0,1.4833,65200.0,INLAND\n-121.57,39.48,15.0,202.0,54.0,145.0,40.0,0.8252,42500.0,INLAND\n-121.55,39.5,26.0,3215.0,827.0,2041.0,737.0,1.0585,45100.0,INLAND\n-121.54,39.5,38.0,1438.0,310.0,779.0,275.0,1.3289,39400.0,INLAND\n-121.53,39.49,19.0,1537.0,329.0,617.0,274.0,1.5313,50300.0,INLAND\n-121.54,39.48,29.0,2896.0,596.0,1809.0,617.0,1.8047,53800.0,INLAND\n-121.54,39.47,14.0,1724.0,315.0,939.0,302.0,2.4952,53900.0,INLAND\n-121.55,39.48,41.0,461.0,107.0,284.0,90.0,2.2045,41800.0,INLAND\n-121.49,39.49,20.0,2505.0,468.0,1174.0,429.0,2.9965,88900.0,INLAND\n-121.52,39.5,33.0,1462.0,241.0,569.0,231.0,3.2833,82600.0,INLAND\n-121.52,39.49,30.0,1217.0,238.0,677.0,233.0,2.6563,63600.0,INLAND\n-121.52,39.48,21.0,2628.0,494.0,1364.0,468.0,2.0455,59400.0,INLAND\n-121.55,39.45,18.0,2278.0,523.0,1185.0,475.0,1.3611,60600.0,INLAND\n-121.53,39.44,26.0,1340.0,255.0,662.0,239.0,2.6071,57100.0,INLAND\n-121.55,39.44,31.0,1434.0,283.0,811.0,289.0,1.7727,49000.0,INLAND\n-121.52,39.43,15.0,2119.0,389.0,1079.0,374.0,2.3566,80400.0,INLAND\n-121.46,39.4,17.0,3659.0,735.0,1970.0,667.0,2.425,96200.0,INLAND\n-121.39,39.39,52.0,189.0,34.0,121.0,37.0,3.0208,60000.0,INLAND\n-121.54,39.33,27.0,720.0,150.0,359.0,138.0,2.5313,61300.0,INLAND\n-121.59,39.39,22.0,2515.0,482.0,1284.0,462.0,2.1776,73800.0,INLAND\n-121.67,39.37,27.0,2599.0,502.0,1241.0,502.0,1.9943,86300.0,INLAND\n-121.65,39.35,24.0,1003.0,251.0,1098.0,227.0,1.7552,86400.0,INLAND\n-121.67,39.34,22.0,1217.0,224.0,537.0,187.0,2.6607,84600.0,INLAND\n-121.65,39.32,40.0,812.0,154.0,374.0,142.0,2.7891,73500.0,INLAND\n-121.69,39.36,29.0,2220.0,471.0,1170.0,428.0,2.3224,56200.0,INLAND\n-121.7,39.37,32.0,1852.0,373.0,911.0,365.0,1.7885,57000.0,INLAND\n-121.7,39.36,46.0,1210.0,243.0,523.0,242.0,1.91,63900.0,INLAND\n-121.7,39.36,37.0,2330.0,495.0,1505.0,470.0,2.0474,56000.0,INLAND\n-121.69,39.36,34.0,842.0,186.0,635.0,165.0,1.8355,63000.0,INLAND\n-121.74,39.38,27.0,2596.0,435.0,1100.0,409.0,2.3243,85500.0,INLAND\n-121.8,39.33,30.0,1019.0,192.0,501.0,185.0,2.5259,81300.0,INLAND\n-121.71,39.42,21.0,1432.0,284.0,862.0,275.0,2.2813,57600.0,INLAND\n-121.71,39.41,22.0,1814.0,342.0,941.0,323.0,2.1728,59400.0,INLAND\n-121.75,39.4,29.0,1687.0,327.0,864.0,334.0,2.4943,81900.0,INLAND\n-121.79,39.48,39.0,1105.0,180.0,408.0,166.0,3.3929,82100.0,INLAND\n-120.46,38.15,16.0,4221.0,781.0,1516.0,697.0,2.3816,116000.0,INLAND\n-120.55,38.12,10.0,1566.0,325.0,785.0,291.0,2.5,116100.0,INLAND\n-120.56,38.09,34.0,2745.0,559.0,1150.0,491.0,2.3654,94900.0,INLAND\n-120.55,38.07,27.0,1199.0,224.0,463.0,199.0,2.9063,92200.0,INLAND\n-120.54,38.07,37.0,736.0,148.0,339.0,140.0,2.2875,79900.0,INLAND\n-120.67,37.97,9.0,7450.0,1475.0,2233.0,930.0,2.6528,133000.0,INLAND\n-120.46,38.09,16.0,3758.0,715.0,1777.0,615.0,3.0,122600.0,INLAND\n-120.79,38.24,19.0,1003.0,235.0,538.0,190.0,2.9821,90400.0,INLAND\n-120.9,38.2,16.0,3120.0,641.0,1319.0,526.0,2.0472,93200.0,INLAND\n-120.88,38.16,8.0,2029.0,387.0,1000.0,364.0,4.0109,125900.0,INLAND\n-120.91,38.11,9.0,3585.0,680.0,1800.0,598.0,3.636,133100.0,INLAND\n-120.76,38.12,7.0,7188.0,1288.0,3175.0,1115.0,3.8488,130600.0,INLAND\n-120.65,38.28,21.0,3095.0,681.0,1341.0,546.0,2.1382,104000.0,INLAND\n-120.72,38.24,32.0,2685.0,543.0,1061.0,492.0,2.5473,101600.0,INLAND\n-120.67,38.19,17.0,2967.0,611.0,1387.0,564.0,2.0417,92600.0,INLAND\n-120.57,38.2,13.0,4110.0,847.0,1796.0,706.0,2.6417,122300.0,INLAND\n-120.43,38.25,13.0,763.0,161.0,311.0,125.0,2.4583,112500.0,INLAND\n-120.56,38.39,20.0,1326.0,307.0,563.0,237.0,2.6667,86600.0,INLAND\n-120.54,38.41,21.0,1435.0,294.0,668.0,267.0,2.5667,77400.0,INLAND\n-120.42,38.42,18.0,2912.0,663.0,999.0,411.0,2.7344,91900.0,INLAND\n-120.41,38.33,17.0,1463.0,338.0,529.0,226.0,3.024,100900.0,INLAND\n-120.55,38.31,18.0,1411.0,312.0,592.0,230.0,1.625,94700.0,INLAND\n-120.57,38.35,17.0,1504.0,358.0,661.0,250.0,2.2604,84800.0,INLAND\n-120.36,38.21,10.0,4300.0,845.0,1480.0,609.0,2.8208,139900.0,INLAND\n-120.34,38.23,10.0,3757.0,722.0,546.0,223.0,3.75,121400.0,INLAND\n-120.33,38.26,13.0,2962.0,546.0,252.0,103.0,4.4063,155800.0,INLAND\n-120.34,38.25,17.0,5497.0,1056.0,997.0,408.0,2.9821,111500.0,INLAND\n-120.37,38.23,13.0,4401.0,829.0,924.0,383.0,2.6942,123500.0,INLAND\n-120.37,38.25,13.0,4495.0,856.0,1149.0,459.0,2.5352,113700.0,INLAND\n-120.27,38.29,10.0,3486.0,695.0,298.0,124.0,3.3542,103800.0,INLAND\n-120.27,38.31,13.0,3297.0,662.0,267.0,97.0,3.075,108300.0,INLAND\n-120.19,38.42,11.0,1568.0,369.0,82.0,33.0,3.125,77500.0,INLAND\n-121.91,39.03,48.0,1096.0,218.0,657.0,199.0,2.7841,65800.0,INLAND\n-122.0,38.99,39.0,1548.0,323.0,815.0,286.0,2.9489,67500.0,INLAND\n-122.13,39.0,23.0,3832.0,774.0,2435.0,747.0,2.2754,59200.0,INLAND\n-121.99,39.15,17.0,6440.0,1204.0,3266.0,1142.0,2.7137,72000.0,INLAND\n-122.04,39.22,27.0,1446.0,295.0,670.0,281.0,3.2625,92800.0,INLAND\n-122.08,39.25,52.0,224.0,38.0,120.0,45.0,3.017,112500.0,INLAND\n-122.33,39.1,10.0,266.0,62.0,154.0,49.0,2.25,75000.0,INLAND\n-122.2,39.15,33.0,1064.0,174.0,434.0,147.0,3.125,108000.0,INLAND\n-122.09,39.13,28.0,4169.0,895.0,2587.0,810.0,2.331,65500.0,INLAND\n-122.05,39.34,44.0,1064.0,230.0,494.0,175.0,2.875,61500.0,INLAND\n-122.17,39.31,35.0,2791.0,552.0,1395.0,476.0,2.5625,62700.0,INLAND\n-122.51,39.3,19.0,1629.0,386.0,551.0,214.0,1.7463,68800.0,INLAND\n-122.01,39.21,50.0,1592.0,372.0,781.0,307.0,2.2679,69100.0,INLAND\n-122.01,39.21,52.0,1989.0,392.0,985.0,396.0,2.5556,75800.0,INLAND\n-122.01,39.21,39.0,1214.0,250.0,660.0,249.0,2.4559,75000.0,INLAND\n-121.96,39.3,39.0,701.0,130.0,271.0,89.0,2.1845,112500.0,INLAND\n-121.65,38.03,28.0,3144.0,694.0,1095.0,482.0,3.4402,192400.0,INLAND\n-121.63,38.03,17.0,2549.0,596.0,1169.0,500.0,3.6694,209400.0,INLAND\n-121.63,38.04,25.0,2019.0,411.0,888.0,326.0,3.2619,183800.0,INLAND\n-121.73,38.0,3.0,9217.0,1522.0,3578.0,1272.0,5.0016,189100.0,INLAND\n-121.72,38.0,7.0,7957.0,1314.0,4460.0,1293.0,4.9618,156500.0,INLAND\n-121.72,37.98,5.0,7105.0,1143.0,3523.0,1088.0,5.0468,168800.0,INLAND\n-121.68,37.98,19.0,3388.0,599.0,1707.0,575.0,3.6411,162800.0,INLAND\n-121.7,37.98,9.0,3079.0,519.0,1562.0,512.0,5.1041,172900.0,INLAND\n-121.71,37.99,27.0,3861.0,718.0,2085.0,707.0,3.3558,129700.0,INLAND\n-121.67,37.99,22.0,1046.0,195.0,527.0,164.0,4.375,213500.0,INLAND\n-121.68,37.93,44.0,1014.0,225.0,704.0,238.0,1.6554,119400.0,INLAND\n-121.7,37.94,36.0,1710.0,320.0,861.0,300.0,2.8828,131100.0,INLAND\n-121.69,37.95,15.0,1850.0,441.0,1348.0,403.0,3.8125,125400.0,INLAND\n-121.7,37.96,33.0,2396.0,452.0,1391.0,465.0,3.2813,151400.0,INLAND\n-121.66,37.93,19.0,2055.0,358.0,1064.0,350.0,4.7426,263100.0,INLAND\n-121.7,37.91,17.0,1962.0,291.0,825.0,267.0,4.8958,187100.0,INLAND\n-121.7,37.93,19.0,2005.0,405.0,972.0,403.0,2.2216,156700.0,INLAND\n-121.74,37.95,5.0,4980.0,774.0,2399.0,763.0,5.7104,186300.0,INLAND\n-121.7,37.93,10.0,3258.0,612.0,1779.0,558.0,4.6587,152500.0,INLAND\n-121.6,37.91,13.0,2479.0,394.0,1075.0,350.0,5.1017,241400.0,INLAND\n-121.6,37.9,5.0,14684.0,2252.0,4276.0,1722.0,6.9051,340900.0,INLAND\n-121.61,37.86,30.0,1428.0,287.0,989.0,287.0,3.691,154400.0,INLAND\n-121.64,37.85,22.0,1999.0,415.0,967.0,320.0,4.4583,253900.0,INLAND\n-121.8,38.01,46.0,2273.0,495.0,1088.0,447.0,2.2532,109400.0,INLAND\n-121.81,38.01,47.0,1942.0,430.0,1074.0,393.0,2.2361,105100.0,INLAND\n-121.81,38.01,52.0,1124.0,245.0,528.0,226.0,2.2639,128500.0,INLAND\n-121.82,38.02,46.0,176.0,43.0,101.0,40.0,2.2361,93800.0,INLAND\n-121.82,38.01,42.0,1017.0,253.0,798.0,266.0,2.1719,99100.0,INLAND\n-121.82,38.01,50.0,1120.0,281.0,625.0,239.0,1.5988,96400.0,INLAND\n-121.82,38.01,25.0,3018.0,606.0,1614.0,568.0,3.4722,127000.0,INLAND\n-121.84,38.02,46.0,66.0,22.0,37.0,21.0,0.536,87500.0,INLAND\n-121.78,38.01,19.0,2688.0,469.0,1216.0,422.0,4.4491,133900.0,INLAND\n-121.79,38.01,17.0,4032.0,814.0,1749.0,618.0,3.1728,146800.0,INLAND\n-121.79,38.0,34.0,3090.0,593.0,1588.0,566.0,3.6118,124700.0,INLAND\n-121.8,38.01,44.0,3184.0,581.0,1399.0,548.0,2.7234,110200.0,INLAND\n-121.8,38.01,37.0,3058.0,567.0,1351.0,523.0,3.5179,130800.0,INLAND\n-121.78,38.0,8.0,2371.0,375.0,1094.0,396.0,5.3245,174500.0,INLAND\n-121.77,38.01,13.0,2983.0,534.0,1417.0,510.0,3.9861,168100.0,INLAND\n-121.81,37.99,18.0,2807.0,445.0,1315.0,437.0,4.8194,170400.0,INLAND\n-121.81,37.99,22.0,2331.0,359.0,1086.0,340.0,5.1435,150800.0,INLAND\n-121.82,37.98,13.0,3995.0,605.0,1969.0,607.0,5.0164,165200.0,INLAND\n-121.82,38.01,47.0,1265.0,254.0,587.0,247.0,2.6364,93500.0,INLAND\n-121.82,38.0,29.0,2070.0,452.0,985.0,420.0,2.8466,113400.0,INLAND\n-121.81,38.0,37.0,2724.0,579.0,1400.0,540.0,2.905,97300.0,INLAND\n-121.82,38.0,30.0,3268.0,567.0,1714.0,565.0,4.4583,131000.0,INLAND\n-121.85,38.0,26.0,3364.0,570.0,1806.0,566.0,4.2647,133400.0,INLAND\n-121.85,38.0,24.0,2269.0,584.0,1239.0,542.0,2.0411,100000.0,INLAND\n-121.83,38.0,15.0,6365.0,1646.0,3838.0,1458.0,2.5495,103600.0,INLAND\n-121.83,38.0,25.0,1710.0,288.0,799.0,259.0,4.8359,145300.0,INLAND\n-121.83,37.99,23.0,1150.0,174.0,572.0,174.0,4.9167,152400.0,INLAND\n-121.83,37.99,23.0,1970.0,296.0,935.0,279.0,4.4853,145900.0,INLAND\n-121.83,37.99,18.0,2741.0,449.0,1507.0,460.0,4.7566,142500.0,INLAND\n-121.83,38.0,8.0,2572.0,738.0,1384.0,684.0,1.7161,75800.0,INLAND\n-121.84,37.99,13.0,4545.0,952.0,2188.0,901.0,3.3625,126100.0,INLAND\n-121.84,37.99,15.0,2380.0,385.0,1292.0,388.0,4.6029,142600.0,INLAND\n-121.83,37.99,16.0,2919.0,462.0,1456.0,453.0,5.6779,164700.0,INLAND\n-121.79,38.0,36.0,1141.0,234.0,562.0,213.0,2.5893,108500.0,INLAND\n-121.79,37.99,10.0,4156.0,609.0,1878.0,586.0,5.6506,178600.0,INLAND\n-121.79,37.99,18.0,3646.0,534.0,1651.0,535.0,5.7321,164700.0,INLAND\n-121.8,38.0,34.0,2738.0,475.0,1316.0,459.0,3.5368,122500.0,INLAND\n-121.8,37.99,16.0,3077.0,465.0,1575.0,446.0,5.5,179500.0,INLAND\n-121.77,37.99,4.0,5623.0,780.0,2429.0,716.0,5.4409,205100.0,INLAND\n-121.88,38.03,10.0,2769.0,619.0,1045.0,469.0,4.1111,158600.0,INLAND\n-121.9,38.04,36.0,1489.0,331.0,838.0,259.0,1.2024,90200.0,INLAND\n-121.86,38.04,52.0,242.0,59.0,188.0,54.0,1.3958,67500.0,INLAND\n-121.87,38.02,52.0,2264.0,439.0,1403.0,476.0,2.7083,99400.0,INLAND\n-121.88,38.03,52.0,1225.0,250.0,725.0,231.0,2.0,101400.0,INLAND\n-121.9,38.03,51.0,2982.0,689.0,1831.0,608.0,2.0034,87700.0,INLAND\n-121.88,38.02,46.0,2112.0,466.0,1249.0,382.0,2.5737,87000.0,INLAND\n-121.89,38.02,36.0,2707.0,550.0,1827.0,545.0,3.3371,94600.0,INLAND\n-121.9,38.02,5.0,1560.0,369.0,1037.0,372.0,3.6154,181800.0,INLAND\n-121.87,38.02,31.0,3644.0,746.0,2229.0,678.0,3.1389,117800.0,INLAND\n-121.88,38.0,16.0,2605.0,440.0,1352.0,408.0,4.1947,140300.0,INLAND\n-121.88,38.01,9.0,5329.0,1284.0,2827.0,1202.0,2.7374,150000.0,INLAND\n-121.89,38.01,30.0,4114.0,743.0,1994.0,722.0,4.2227,134400.0,INLAND\n-121.88,38.0,22.0,721.0,117.0,367.0,129.0,5.3098,151900.0,INLAND\n-121.86,38.0,4.0,4075.0,927.0,2239.0,849.0,3.5857,165200.0,INLAND\n-121.86,38.0,16.0,3216.0,464.0,1504.0,453.0,5.25,161700.0,INLAND\n-121.89,37.99,4.0,2171.0,597.0,928.0,461.0,4.1016,170500.0,INLAND\n-121.88,37.99,16.0,3787.0,515.0,1606.0,507.0,5.5676,174200.0,INLAND\n-121.87,38.0,17.0,2713.0,442.0,1475.0,415.0,4.8542,144100.0,INLAND\n-121.87,37.99,15.0,2203.0,312.0,1051.0,311.0,4.9783,163900.0,INLAND\n-121.89,38.01,28.0,3639.0,751.0,2362.0,641.0,3.0042,103900.0,INLAND\n-121.9,38.02,12.0,1497.0,360.0,943.0,341.0,2.1417,122200.0,INLAND\n-121.9,38.01,16.0,2604.0,454.0,1696.0,481.0,4.6628,136000.0,INLAND\n-121.92,38.02,8.0,2750.0,479.0,1526.0,484.0,5.102,156500.0,INLAND\n-121.89,38.01,32.0,1000.0,188.0,663.0,212.0,4.0972,99200.0,INLAND\n-121.9,38.01,34.0,3779.0,766.0,2356.0,722.0,3.5129,110600.0,INLAND\n-121.9,38.0,14.0,1930.0,363.0,990.0,322.0,4.1094,162200.0,INLAND\n-121.9,38.0,14.0,2677.0,368.0,1288.0,375.0,6.0497,177500.0,INLAND\n-121.92,38.01,7.0,1632.0,248.0,879.0,262.0,6.1237,166000.0,INLAND\n-121.93,38.01,9.0,2294.0,389.0,1142.0,365.0,5.3363,160800.0,INLAND\n-121.93,38.02,13.0,1524.0,286.0,940.0,308.0,5.1337,154800.0,INLAND\n-121.95,38.03,5.0,5526.0,,3207.0,1012.0,4.0767,143100.0,INLAND\n-121.94,38.03,27.0,1654.0,478.0,1141.0,420.0,1.4871,87100.0,INLAND\n-121.96,38.02,35.0,2691.0,542.0,1409.0,505.0,3.016,95300.0,INLAND\n-121.95,38.02,9.0,3360.0,833.0,2041.0,810.0,2.1013,100700.0,INLAND\n-121.94,38.02,29.0,5765.0,1170.0,3266.0,1131.0,2.7907,113900.0,INLAND\n-121.92,38.03,16.0,2176.0,464.0,1410.0,434.0,3.5436,100200.0,INLAND\n-121.92,38.02,16.0,1840.0,355.0,1288.0,338.0,4.2067,125000.0,INLAND\n-121.91,38.02,15.0,2966.0,558.0,1687.0,527.0,3.4817,129800.0,INLAND\n-121.97,38.04,38.0,2505.0,554.0,1595.0,498.0,2.5833,83500.0,INLAND\n-121.97,38.03,17.0,3685.0,685.0,1939.0,649.0,3.7043,139800.0,INLAND\n-121.98,38.05,31.0,2810.0,518.0,1640.0,503.0,3.3661,98500.0,INLAND\n-122.02,38.02,44.0,1465.0,247.0,817.0,237.0,4.8693,156900.0,NEAR BAY\n-122.0,38.03,4.0,2341.0,408.0,1235.0,431.0,6.0424,165900.0,INLAND\n-122.14,38.02,44.0,1625.0,432.0,825.0,385.0,2.0523,133900.0,NEAR BAY\n-122.14,38.03,42.0,118.0,34.0,54.0,30.0,2.5795,225000.0,NEAR BAY\n-122.13,38.02,52.0,2378.0,508.0,940.0,451.0,2.9583,166000.0,NEAR BAY\n-122.13,38.01,48.0,2123.0,494.0,859.0,474.0,1.8523,144800.0,NEAR BAY\n-122.14,38.01,50.0,1760.0,341.0,741.0,316.0,4.5,178300.0,NEAR BAY\n-122.13,38.01,51.0,1262.0,309.0,608.0,298.0,2.1974,139300.0,NEAR BAY\n-122.13,38.0,33.0,2821.0,652.0,1206.0,640.0,2.5481,150800.0,NEAR BAY\n-122.16,38.02,40.0,1800.0,290.0,761.0,277.0,5.1265,196100.0,NEAR BAY\n-122.11,38.01,39.0,1313.0,306.0,575.0,231.0,3.1711,116100.0,NEAR BAY\n-122.11,38.01,41.0,1345.0,272.0,718.0,283.0,3.3831,129400.0,NEAR BAY\n-122.12,38.0,20.0,6992.0,1404.0,3221.0,1334.0,4.2042,166400.0,NEAR BAY\n-122.12,38.01,50.0,1300.0,263.0,691.0,239.0,3.9519,126500.0,NEAR BAY\n-122.12,38.01,50.0,1738.0,355.0,837.0,363.0,3.609,135700.0,NEAR BAY\n-122.12,38.01,42.0,2225.0,367.0,864.0,381.0,4.1189,172400.0,NEAR BAY\n-122.09,38.02,37.0,1742.0,339.0,1128.0,345.0,3.8824,113700.0,NEAR BAY\n-122.1,38.02,28.0,4308.0,824.0,2086.0,776.0,3.6523,159700.0,NEAR BAY\n-122.07,38.0,37.0,978.0,202.0,462.0,184.0,3.625,156300.0,NEAR BAY\n-122.09,38.0,6.0,10191.0,1882.0,4377.0,1789.0,5.2015,204200.0,NEAR BAY\n-122.11,38.0,9.0,3424.0,583.0,1460.0,543.0,5.76,212600.0,NEAR BAY\n-122.08,37.99,19.0,4657.0,739.0,1914.0,732.0,5.0509,199900.0,NEAR BAY\n-122.09,37.99,19.0,3073.0,506.0,1773.0,493.0,5.4496,205400.0,NEAR BAY\n-122.11,37.99,16.0,3913.0,710.0,1782.0,676.0,5.1297,206700.0,NEAR BAY\n-122.11,37.99,10.0,2864.0,514.0,1300.0,507.0,4.3875,287700.0,NEAR BAY\n-122.08,37.97,9.0,2643.0,439.0,1105.0,467.0,6.6579,245200.0,NEAR BAY\n-122.09,37.97,5.0,5303.0,779.0,2017.0,727.0,6.9961,294100.0,NEAR BAY\n-122.11,37.98,11.0,4371.0,679.0,1790.0,660.0,6.135,297300.0,NEAR BAY\n-122.09,37.98,14.0,5381.0,871.0,2296.0,872.0,5.6875,211000.0,NEAR BAY\n-122.12,37.99,33.0,1660.0,277.0,741.0,261.0,4.675,225400.0,NEAR BAY\n-122.1,37.97,18.0,4326.0,655.0,1753.0,646.0,5.6931,269600.0,NEAR BAY\n-122.1,37.96,20.0,3796.0,650.0,1679.0,611.0,4.3571,228200.0,NEAR BAY\n-122.1,37.96,25.0,1374.0,206.0,569.0,235.0,6.3699,235500.0,NEAR BAY\n-122.07,37.99,28.0,3310.0,574.0,1811.0,597.0,4.5401,166900.0,NEAR BAY\n-122.07,37.98,12.0,6915.0,1639.0,2940.0,1468.0,4.0154,186100.0,NEAR BAY\n-122.07,37.97,20.0,1705.0,353.0,856.0,341.0,3.7262,211800.0,NEAR BAY\n-122.07,37.96,34.0,1692.0,290.0,836.0,289.0,5.0172,197100.0,NEAR BAY\n-122.06,37.96,37.0,1784.0,313.0,788.0,304.0,4.2917,189600.0,NEAR BAY\n-122.08,37.96,21.0,9135.0,1534.0,3748.0,1502.0,6.0859,266000.0,NEAR BAY\n-122.06,37.95,36.0,2213.0,386.0,950.0,370.0,4.7386,186400.0,NEAR BAY\n-122.07,37.95,39.0,2199.0,388.0,1025.0,385.0,4.5893,190000.0,NEAR BAY\n-122.08,37.95,24.0,3173.0,548.0,1351.0,536.0,5.0672,243000.0,NEAR BAY\n-122.08,37.95,33.0,1043.0,157.0,425.0,148.0,4.8702,235600.0,NEAR BAY\n-122.07,37.96,37.0,1217.0,199.0,552.0,194.0,5.0445,196200.0,NEAR BAY\n-122.06,37.96,10.0,7136.0,1691.0,2959.0,1507.0,3.9816,182000.0,NEAR BAY\n-122.05,37.95,34.0,1408.0,277.0,738.0,269.0,4.175,169400.0,NEAR BAY\n-122.05,37.94,22.0,4162.0,1194.0,1804.0,1185.0,2.5459,179300.0,NEAR BAY\n-122.06,37.94,19.0,4005.0,972.0,1896.0,893.0,2.5268,235700.0,NEAR BAY\n-122.07,37.94,36.0,2639.0,488.0,1111.0,476.0,3.5057,205100.0,NEAR BAY\n-122.07,37.94,30.0,1260.0,276.0,707.0,221.0,2.892,220800.0,NEAR BAY\n-122.08,37.93,35.0,4043.0,689.0,1832.0,662.0,5.0761,233200.0,NEAR BAY\n-122.08,37.94,44.0,2185.0,357.0,943.0,366.0,4.725,232100.0,NEAR BAY\n-122.07,37.94,43.0,1454.0,234.0,683.0,258.0,4.475,265700.0,NEAR BAY\n-122.09,37.95,32.0,1339.0,209.0,601.0,209.0,6.0265,247900.0,NEAR BAY\n-122.09,37.94,29.0,6895.0,1022.0,2634.0,1022.0,6.1922,273200.0,NEAR BAY\n-122.04,38.0,16.0,3077.0,733.0,1447.0,709.0,3.2484,91100.0,NEAR BAY\n-122.04,37.99,32.0,1504.0,279.0,749.0,267.0,3.2,134500.0,NEAR BAY\n-122.04,37.99,36.0,2765.0,495.0,1478.0,441.0,4.125,136200.0,NEAR BAY\n-122.05,37.97,16.0,60.0,10.0,65.0,19.0,6.1359,250000.0,NEAR BAY\n-122.06,37.99,16.0,2445.0,469.0,721.0,474.0,2.8043,87500.0,NEAR BAY\n-122.06,37.99,17.0,1319.0,316.0,384.0,269.0,1.8229,137500.0,NEAR BAY\n-122.05,38.0,36.0,2476.0,472.0,1213.0,393.0,3.7333,136400.0,NEAR BAY\n-122.05,38.0,16.0,1085.0,217.0,356.0,232.0,2.3462,75000.0,NEAR BAY\n-122.03,37.98,16.0,1209.0,477.0,627.0,482.0,1.3894,156300.0,NEAR BAY\n-122.04,37.97,10.0,974.0,316.0,631.0,286.0,2.3152,140600.0,NEAR BAY\n-122.03,38.0,25.0,3577.0,581.0,1753.0,593.0,5.7295,178300.0,NEAR BAY\n-122.03,38.01,27.0,3228.0,562.0,1666.0,588.0,4.5707,175900.0,NEAR BAY\n-122.04,37.99,38.0,2675.0,541.0,1378.0,480.0,3.8897,139900.0,NEAR BAY\n-122.03,37.98,45.0,2842.0,567.0,1261.0,535.0,3.6042,138200.0,NEAR BAY\n-122.02,38.0,28.0,2965.0,533.0,1591.0,472.0,4.6375,178200.0,NEAR BAY\n-122.03,37.99,35.0,3103.0,537.0,1614.0,566.0,4.9022,169300.0,NEAR BAY\n-122.02,37.99,37.0,2247.0,416.0,1237.0,397.0,4.45,161900.0,NEAR BAY\n-122.03,37.99,37.0,1755.0,327.0,882.0,350.0,4.59,166600.0,NEAR BAY\n-122.03,37.98,44.0,1254.0,252.0,498.0,217.0,3.4531,148900.0,NEAR BAY\n-122.01,37.98,25.0,1476.0,336.0,777.0,297.0,3.5179,165500.0,NEAR BAY\n-122.01,37.98,29.0,2001.0,373.0,956.0,370.0,4.317,194000.0,NEAR BAY\n-122.0,37.98,32.0,1013.0,169.0,436.0,173.0,5.1118,226900.0,INLAND\n-122.0,37.97,27.0,2491.0,428.0,1171.0,431.0,5.1021,202800.0,INLAND\n-122.01,37.97,32.0,3012.0,527.0,1288.0,512.0,3.6449,211500.0,NEAR BAY\n-122.02,37.98,40.0,1797.0,401.0,756.0,369.0,2.8456,165500.0,NEAR BAY\n-122.02,37.98,37.0,1474.0,343.0,782.0,331.0,3.4187,161700.0,NEAR BAY\n-122.0,37.99,28.0,4035.0,641.0,1881.0,659.0,5.4607,192300.0,INLAND\n-122.0,37.98,31.0,2030.0,337.0,867.0,341.0,5.0915,193200.0,INLAND\n-121.99,37.98,23.0,2293.0,411.0,969.0,399.0,4.4536,184000.0,INLAND\n-121.99,37.97,28.0,2839.0,428.0,1372.0,443.0,6.2135,217200.0,INLAND\n-122.0,37.98,35.0,1192.0,201.0,535.0,172.0,4.9219,182000.0,INLAND\n-122.0,37.98,36.0,404.0,77.0,237.0,88.0,4.525,161300.0,INLAND\n-122.01,37.98,34.0,1256.0,267.0,638.0,252.0,4.0507,161000.0,NEAR BAY\n-122.01,37.99,28.0,1900.0,401.0,918.0,351.0,3.7841,144900.0,NEAR BAY\n-121.95,37.96,18.0,2739.0,393.0,1072.0,374.0,6.1436,259500.0,INLAND\n-121.97,37.97,27.0,1691.0,289.0,807.0,296.0,6.1168,210500.0,INLAND\n-121.97,37.97,24.0,1330.0,183.0,656.0,205.0,5.0092,244100.0,INLAND\n-121.97,37.96,28.0,1433.0,290.0,877.0,313.0,4.7891,184800.0,INLAND\n-121.96,37.96,28.0,1838.0,273.0,899.0,270.0,5.2145,229200.0,INLAND\n-121.96,37.95,7.0,3418.0,740.0,1583.0,676.0,3.6133,196100.0,INLAND\n-121.98,37.96,22.0,2987.0,,1420.0,540.0,3.65,204100.0,INLAND\n-121.97,37.97,26.0,1977.0,264.0,817.0,273.0,5.7512,240200.0,INLAND\n-121.98,37.97,26.0,2714.0,390.0,1232.0,409.0,5.9617,231100.0,INLAND\n-121.98,37.97,26.0,2738.0,428.0,1316.0,430.0,5.2442,213200.0,INLAND\n-121.99,37.97,22.0,2823.0,509.0,1271.0,474.0,5.1333,207200.0,INLAND\n-121.99,37.97,30.0,3320.0,589.0,1470.0,543.0,4.6071,184100.0,INLAND\n-122.0,37.96,32.0,3364.0,666.0,1980.0,678.0,3.7,179000.0,INLAND\n-122.0,37.96,28.0,4071.0,713.0,2033.0,647.0,4.5833,190700.0,INLAND\n-121.96,37.95,13.0,3216.0,765.0,1627.0,715.0,3.0859,167800.0,INLAND\n-121.97,37.95,8.0,4253.0,709.0,1883.0,662.0,5.431,246700.0,INLAND\n-121.98,37.96,12.0,5048.0,1122.0,2209.0,1014.0,3.1573,126700.0,INLAND\n-121.99,37.96,16.0,3324.0,479.0,1470.0,461.0,7.6166,260400.0,INLAND\n-121.99,37.96,17.0,2756.0,423.0,1228.0,426.0,5.5872,200600.0,INLAND\n-121.98,37.95,14.0,6290.0,854.0,2724.0,820.0,6.7371,267400.0,INLAND\n-121.98,37.95,16.0,2984.0,406.0,1317.0,397.0,6.7821,265900.0,INLAND\n-122.01,37.97,34.0,3259.0,498.0,1250.0,478.0,5.3794,206200.0,NEAR BAY\n-122.02,37.97,36.0,2342.0,436.0,1191.0,416.0,4.0,171000.0,NEAR BAY\n-122.03,37.97,45.0,1613.0,338.0,865.0,336.0,3.25,151100.0,NEAR BAY\n-122.04,37.97,26.0,2470.0,626.0,1174.0,573.0,2.9861,160900.0,NEAR BAY\n-122.03,37.97,20.0,3968.0,931.0,2629.0,903.0,2.9915,166700.0,NEAR BAY\n-122.04,37.96,16.0,2913.0,723.0,1705.0,693.0,2.9097,106300.0,NEAR BAY\n-122.04,37.97,21.0,6445.0,1839.0,3621.0,1735.0,2.5841,112500.0,NEAR BAY\n-122.04,37.97,39.0,1323.0,245.0,705.0,261.0,3.1968,151000.0,NEAR BAY\n-122.04,37.96,20.0,1143.0,346.0,578.0,298.0,2.2411,151800.0,NEAR BAY\n-122.05,37.96,35.0,2190.0,384.0,1154.0,401.0,3.8456,159800.0,NEAR BAY\n-122.04,37.96,28.0,1207.0,252.0,724.0,252.0,3.6964,165700.0,NEAR BAY\n-122.04,37.96,27.0,2587.0,729.0,1500.0,623.0,1.837,175000.0,NEAR BAY\n-122.05,37.95,27.0,3513.0,791.0,1875.0,694.0,3.1838,182000.0,NEAR BAY\n-122.05,37.95,20.0,563.0,107.0,246.0,123.0,5.4482,190800.0,NEAR BAY\n-122.01,37.95,8.0,3866.0,539.0,1555.0,513.0,6.0901,298200.0,NEAR BAY\n-122.02,37.96,25.0,2615.0,368.0,935.0,366.0,6.6727,305100.0,NEAR BAY\n-122.03,37.96,20.0,2636.0,691.0,1142.0,627.0,2.1083,162500.0,NEAR BAY\n-122.03,37.95,14.0,3287.0,793.0,1601.0,716.0,3.1719,220500.0,NEAR BAY\n-122.02,37.95,25.0,1205.0,260.0,608.0,272.0,2.4519,208300.0,NEAR BAY\n-122.03,37.95,32.0,1955.0,313.0,804.0,317.0,4.9485,202300.0,NEAR BAY\n-122.02,37.95,22.0,3526.0,510.0,1660.0,508.0,5.6642,237000.0,NEAR BAY\n-122.02,37.94,23.0,3516.0,661.0,1465.0,623.0,4.2569,213100.0,NEAR BAY\n-122.01,37.94,23.0,3741.0,,1339.0,499.0,6.7061,322300.0,NEAR BAY\n-122.02,37.94,19.0,3192.0,612.0,1317.0,594.0,4.125,267100.0,NEAR BAY\n-122.01,37.94,26.0,1619.0,224.0,706.0,220.0,6.0704,268000.0,NEAR BAY\n-122.01,37.94,18.0,2077.0,298.0,937.0,292.0,6.3809,273600.0,NEAR BAY\n-122.0,37.95,9.0,2214.0,256.0,848.0,239.0,6.8145,339200.0,INLAND\n-122.01,37.93,25.0,2652.0,335.0,1062.0,334.0,7.5898,330200.0,NEAR BAY\n-122.04,37.95,33.0,1653.0,334.0,814.0,328.0,3.1406,163100.0,NEAR BAY\n-122.04,37.94,24.0,5732.0,873.0,2444.0,888.0,5.6292,231400.0,NEAR BAY\n-122.05,37.94,22.0,2105.0,354.0,993.0,365.0,4.6602,227800.0,NEAR BAY\n-122.04,37.95,29.0,866.0,138.0,341.0,133.0,4.7188,197100.0,NEAR BAY\n-122.05,37.95,22.0,5175.0,1213.0,2804.0,1091.0,2.85,144600.0,NEAR BAY\n-122.03,37.94,21.0,5541.0,776.0,2214.0,737.0,5.5777,279300.0,NEAR BAY\n-122.03,37.93,21.0,4712.0,624.0,1773.0,615.0,6.0918,344800.0,NEAR BAY\n-122.05,37.93,5.0,4274.0,1153.0,1503.0,881.0,4.0473,266500.0,NEAR BAY\n-122.05,37.93,15.0,7803.0,1603.0,2957.0,1546.0,4.45,184900.0,NEAR BAY\n-122.05,37.92,14.0,12713.0,2558.0,4741.0,2412.0,4.7094,234700.0,NEAR BAY\n-122.02,37.92,26.0,5077.0,640.0,1872.0,636.0,7.4713,351200.0,NEAR BAY\n-122.03,37.92,23.0,3318.0,408.0,1124.0,393.0,6.5847,358800.0,NEAR BAY\n-122.01,37.91,21.0,10093.0,1269.0,3645.0,1219.0,7.6877,367700.0,NEAR BAY\n-122.03,37.91,29.0,5438.0,871.0,2310.0,890.0,5.0362,275300.0,NEAR BAY\n-122.06,37.91,15.0,5393.0,1422.0,2133.0,1288.0,4.1612,232800.0,NEAR BAY\n-122.06,37.9,25.0,5869.0,1685.0,2669.0,1554.0,2.6998,216100.0,NEAR BAY\n-122.06,37.89,21.0,4985.0,1590.0,2575.0,1458.0,3.1002,114300.0,NEAR BAY\n-122.07,37.93,45.0,1544.0,244.0,614.0,238.0,5.0255,226000.0,NEAR BAY\n-122.07,37.93,25.0,7201.0,1521.0,3264.0,1433.0,3.7433,252100.0,NEAR BAY\n-122.07,37.92,26.0,3872.0,739.0,1629.0,684.0,4.4312,225000.0,NEAR BAY\n-122.08,37.92,28.0,2377.0,469.0,1068.0,435.0,4.4561,250000.0,NEAR BAY\n-122.08,37.92,26.0,1733.0,265.0,796.0,274.0,6.195,264900.0,NEAR BAY\n-122.07,37.91,28.0,1731.0,295.0,810.0,295.0,5.0391,259800.0,NEAR BAY\n-122.07,37.91,33.0,1550.0,277.0,638.0,254.0,3.6833,292500.0,NEAR BAY\n-122.08,37.9,32.0,1075.0,170.0,486.0,173.0,5.0499,306800.0,NEAR BAY\n-122.09,37.91,18.0,9576.0,1455.0,3486.0,1380.0,7.0895,306900.0,NEAR BAY\n-122.07,37.89,28.0,3410.0,746.0,1428.0,670.0,4.3864,266800.0,NEAR BAY\n-122.08,37.89,39.0,3018.0,501.0,1223.0,489.0,6.2924,283900.0,NEAR BAY\n-122.08,37.9,29.0,4133.0,770.0,1691.0,744.0,5.1097,288000.0,NEAR BAY\n-122.07,37.89,38.0,2139.0,343.0,809.0,340.0,5.5636,268800.0,NEAR BAY\n-122.07,37.89,38.0,757.0,124.0,319.0,123.0,5.6558,263300.0,NEAR BAY\n-122.06,37.88,34.0,4781.0,703.0,1879.0,714.0,6.5378,340900.0,NEAR BAY\n-122.05,37.87,30.0,2296.0,329.0,847.0,322.0,6.7192,397500.0,NEAR BAY\n-122.05,37.9,24.0,4125.0,1020.0,1699.0,873.0,2.9526,271000.0,NEAR BAY\n-122.05,37.9,32.0,2676.0,484.0,986.0,473.0,4.6528,335700.0,NEAR BAY\n-122.05,37.89,37.0,1677.0,269.0,689.0,283.0,4.2625,310600.0,NEAR BAY\n-122.04,37.89,33.0,2423.0,322.0,998.0,346.0,7.5349,349100.0,NEAR BAY\n-122.05,37.9,32.0,4498.0,862.0,1818.0,851.0,4.8088,321200.0,NEAR BAY\n-122.04,37.9,20.0,5467.0,1044.0,2310.0,963.0,5.6986,275800.0,NEAR BAY\n-122.02,37.89,29.0,6349.0,858.0,2450.0,778.0,7.5,356200.0,NEAR BAY\n-122.04,37.88,32.0,3250.0,550.0,1230.0,557.0,4.6424,312700.0,NEAR BAY\n-122.03,37.86,29.0,3025.0,477.0,1035.0,452.0,6.112,390600.0,NEAR BAY\n-122.04,37.85,27.0,6039.0,780.0,2181.0,761.0,9.5862,469400.0,NEAR BAY\n-121.94,37.73,22.0,1980.0,291.0,861.0,290.0,6.2726,258200.0,<1H OCEAN\n-121.94,37.73,22.0,6719.0,1068.0,2843.0,994.0,6.1265,260300.0,<1H OCEAN\n-121.93,37.73,23.0,2564.0,347.0,1043.0,351.0,6.2048,275000.0,<1H OCEAN\n-121.93,37.73,8.0,831.0,231.0,404.0,224.0,3.375,350000.0,<1H OCEAN\n-121.95,37.74,19.0,1127.0,170.0,518.0,167.0,6.3325,250000.0,<1H OCEAN\n-121.95,37.74,19.0,5721.0,837.0,2653.0,813.0,6.2631,266000.0,<1H OCEAN\n-121.94,37.75,17.0,2559.0,370.0,1238.0,377.0,6.2781,269800.0,<1H OCEAN\n-121.92,37.73,24.0,1407.0,327.0,501.0,295.0,2.4821,157200.0,<1H OCEAN\n-121.93,37.74,16.0,3326.0,419.0,1272.0,402.0,6.8806,343500.0,<1H OCEAN\n-121.94,37.75,16.0,5121.0,735.0,2464.0,761.0,6.6204,296100.0,<1H OCEAN\n-121.97,37.79,17.0,5688.0,824.0,2111.0,773.0,6.6131,312500.0,<1H OCEAN\n-121.98,37.8,17.0,3354.0,422.0,1457.0,425.0,7.6473,345800.0,<1H OCEAN\n-121.98,37.8,16.0,2498.0,330.0,1027.0,343.0,8.155,343700.0,<1H OCEAN\n-121.98,37.81,18.0,2903.0,387.0,1127.0,372.0,5.5921,359100.0,<1H OCEAN\n-121.96,37.81,12.0,6488.0,778.0,2404.0,765.0,8.3188,403400.0,<1H OCEAN\n-121.97,37.8,17.0,3279.0,418.0,1222.0,381.0,7.9168,356000.0,<1H OCEAN\n-121.94,37.8,8.0,11336.0,1657.0,4089.0,1555.0,7.8287,369200.0,<1H OCEAN\n-121.97,37.79,16.0,3873.0,484.0,1451.0,501.0,6.7857,341300.0,<1H OCEAN\n-121.95,37.78,4.0,14652.0,2826.0,5613.0,2579.0,6.3942,356700.0,<1H OCEAN\n-121.96,37.76,8.0,3865.0,463.0,1548.0,432.0,9.7037,425100.0,<1H OCEAN\n-121.94,37.76,4.0,6875.0,1439.0,2889.0,1307.0,4.6932,356100.0,<1H OCEAN\n-121.93,37.76,5.0,2255.0,269.0,876.0,258.0,10.3345,461400.0,<1H OCEAN\n-121.92,37.74,8.0,452.0,51.0,140.0,43.0,12.5915,432400.0,<1H OCEAN\n-121.93,37.78,2.0,227.0,35.0,114.0,49.0,3.1591,434700.0,<1H OCEAN\n-121.96,37.74,2.0,200.0,20.0,25.0,9.0,15.0001,350000.0,<1H OCEAN\n-121.97,37.76,8.0,3743.0,581.0,1633.0,567.0,6.7027,381900.0,<1H OCEAN\n-121.97,37.77,13.0,7241.0,1007.0,3221.0,947.0,7.2216,324600.0,<1H OCEAN\n-121.98,37.74,8.0,2865.0,389.0,1376.0,417.0,7.9393,399300.0,<1H OCEAN\n-122.02,37.84,34.0,1879.0,265.0,729.0,263.0,7.7072,443800.0,NEAR BAY\n-122.01,37.83,30.0,3917.0,549.0,1330.0,544.0,6.5617,386600.0,NEAR BAY\n-122.0,37.82,20.0,2206.0,458.0,926.0,432.0,4.6042,256400.0,<1H OCEAN\n-121.99,37.82,22.0,1248.0,271.0,579.0,269.0,3.375,200000.0,<1H OCEAN\n-122.03,37.83,24.0,5948.0,738.0,1997.0,710.0,9.8708,500001.0,NEAR BAY\n-121.99,37.77,14.0,8213.0,1364.0,3283.0,1286.0,5.1755,294800.0,<1H OCEAN\n-121.99,37.81,17.0,465.0,83.0,146.0,75.0,4.9018,188500.0,<1H OCEAN\n-122.02,37.8,11.0,6200.0,907.0,2286.0,896.0,7.6518,359300.0,NEAR BAY\n-122.03,37.87,21.0,3521.0,447.0,1396.0,467.0,8.2673,358700.0,NEAR BAY\n-122.02,37.88,16.0,3031.0,438.0,1087.0,421.0,7.3732,287300.0,NEAR BAY\n-122.02,37.87,14.0,3056.0,369.0,1209.0,377.0,8.4352,441400.0,NEAR BAY\n-122.03,37.86,25.0,3004.0,393.0,1145.0,376.0,7.2655,494000.0,NEAR BAY\n-122.0,37.86,18.0,8953.0,1074.0,3011.0,993.0,10.7372,500001.0,<1H OCEAN\n-121.97,37.87,4.0,1029.0,126.0,416.0,122.0,13.4883,500001.0,INLAND\n-121.96,37.84,29.0,7479.0,977.0,2744.0,943.0,7.5139,398200.0,INLAND\n-122.0,37.84,16.0,7681.0,946.0,2777.0,908.0,9.5271,500001.0,<1H OCEAN\n-121.96,37.85,10.0,3209.0,379.0,1199.0,392.0,12.2478,500001.0,INLAND\n-121.99,37.83,16.0,2939.0,380.0,1177.0,396.0,8.0839,372000.0,<1H OCEAN\n-121.98,37.82,18.0,9117.0,1248.0,3280.0,1167.0,8.003,351300.0,<1H OCEAN\n-121.95,37.81,5.0,7178.0,898.0,2823.0,907.0,9.0776,450400.0,<1H OCEAN\n-122.1,37.93,20.0,10212.0,1424.0,4083.0,1374.0,8.039,382200.0,NEAR BAY\n-122.12,37.94,22.0,4949.0,626.0,1850.0,590.0,10.4549,500001.0,NEAR BAY\n-122.1,37.9,38.0,827.0,144.0,368.0,136.0,6.5095,294400.0,NEAR BAY\n-122.14,37.9,32.0,5738.0,746.0,2099.0,732.0,10.3224,500001.0,NEAR BAY\n-122.12,37.91,34.0,5683.0,755.0,1962.0,723.0,8.3678,455300.0,NEAR BAY\n-122.09,37.89,35.0,880.0,139.0,352.0,132.0,6.8686,406500.0,NEAR BAY\n-122.1,37.89,21.0,3282.0,653.0,1398.0,601.0,5.2079,310300.0,NEAR BAY\n-122.11,37.89,32.0,2372.0,516.0,1067.0,492.0,4.3235,279500.0,NEAR BAY\n-122.11,37.88,37.0,4005.0,614.0,1602.0,606.0,6.4666,348200.0,NEAR BAY\n-122.12,37.88,35.0,2785.0,362.0,1001.0,363.0,8.0448,433300.0,NEAR BAY\n-122.12,37.89,30.0,3227.0,733.0,1260.0,684.0,4.125,257100.0,NEAR BAY\n-122.13,37.89,27.0,744.0,214.0,295.0,169.0,2.7411,350000.0,NEAR BAY\n-122.13,37.87,18.0,1820.0,220.0,728.0,229.0,10.3713,426100.0,NEAR BAY\n-122.14,37.88,34.0,6986.0,1096.0,2865.0,1124.0,6.2275,394400.0,NEAR BAY\n-122.07,37.88,11.0,1077.0,318.0,590.0,264.0,3.5536,387200.0,NEAR BAY\n-122.08,37.88,24.0,2059.0,462.0,410.0,294.0,2.3971,99400.0,NEAR BAY\n-122.08,37.88,26.0,2947.0,,825.0,626.0,2.933,85000.0,NEAR BAY\n-122.08,37.87,24.0,6130.0,1359.0,1750.0,1286.0,2.9167,102700.0,NEAR BAY\n-122.06,37.85,17.0,7475.0,1556.0,2092.0,1449.0,3.6437,186500.0,NEAR BAY\n-122.07,37.86,23.0,1025.0,205.0,263.0,191.0,3.12,155000.0,NEAR BAY\n-122.06,37.86,16.0,5187.0,1014.0,1512.0,986.0,4.4551,252400.0,NEAR BAY\n-122.07,37.86,17.0,1102.0,224.0,317.0,208.0,3.5893,206300.0,NEAR BAY\n-122.08,37.87,26.0,2405.0,564.0,680.0,531.0,2.4896,73400.0,NEAR BAY\n-122.1,37.88,35.0,3701.0,528.0,1511.0,517.0,7.2315,367100.0,NEAR BAY\n-122.09,37.86,27.0,5484.0,760.0,2212.0,770.0,7.6202,402600.0,NEAR BAY\n-122.11,37.87,33.0,3398.0,500.0,1351.0,457.0,6.5814,314200.0,NEAR BAY\n-122.12,37.85,18.0,5252.0,686.0,1870.0,657.0,8.0074,454100.0,NEAR BAY\n-122.08,37.84,17.0,1320.0,159.0,1722.0,141.0,11.7064,500001.0,NEAR BAY\n-122.12,37.81,26.0,4048.0,513.0,1486.0,498.0,7.6717,416500.0,NEAR BAY\n-122.12,37.82,26.0,2269.0,317.0,918.0,313.0,6.6657,364500.0,NEAR BAY\n-122.11,37.83,19.0,5130.0,741.0,1887.0,712.0,7.203,369900.0,NEAR BAY\n-122.08,37.82,4.0,2045.0,237.0,830.0,252.0,11.3421,500001.0,NEAR BAY\n-122.14,37.86,20.0,6201.0,1182.0,2415.0,1141.0,4.5744,314000.0,NEAR BAY\n-122.14,37.85,27.0,9147.0,1276.0,3371.0,1269.0,7.3267,389900.0,NEAR BAY\n-122.14,37.84,24.0,2131.0,343.0,874.0,373.0,5.6349,355600.0,NEAR BAY\n-122.16,37.83,16.0,4596.0,705.0,1480.0,650.0,7.52,370200.0,NEAR BAY\n-122.16,37.86,36.0,3359.0,493.0,1298.0,483.0,8.1586,404300.0,NEAR BAY\n-122.18,37.88,36.0,542.0,119.0,231.0,121.0,4.9,354200.0,NEAR BAY\n-122.18,37.86,33.0,4449.0,636.0,1684.0,617.0,8.9571,399700.0,NEAR BAY\n-122.16,37.89,32.0,1779.0,241.0,721.0,258.0,8.7589,434500.0,NEAR BAY\n-122.17,37.88,32.0,3633.0,508.0,1393.0,506.0,7.6917,401800.0,NEAR BAY\n-122.17,37.88,33.0,3626.0,502.0,1348.0,480.0,7.6107,423200.0,NEAR BAY\n-122.17,37.87,38.0,1261.0,177.0,472.0,183.0,6.917,438000.0,NEAR BAY\n-122.22,37.88,20.0,95.0,13.0,31.0,15.0,2.4444,475000.0,NEAR BAY\n-122.2,37.89,37.0,3881.0,560.0,1315.0,517.0,7.3195,367500.0,NEAR BAY\n-122.2,37.88,36.0,1065.0,160.0,398.0,155.0,7.7736,378100.0,NEAR BAY\n-122.18,37.91,31.0,7200.0,876.0,2428.0,843.0,10.9405,500001.0,NEAR BAY\n-122.2,37.9,36.0,2107.0,287.0,740.0,280.0,10.3416,500001.0,NEAR BAY\n-122.18,37.9,36.0,4760.0,610.0,1511.0,572.0,9.0064,500001.0,NEAR BAY\n-122.18,37.89,18.0,4845.0,735.0,1634.0,734.0,8.1489,499000.0,NEAR BAY\n-121.84,37.98,8.0,7505.0,1089.0,3325.0,1016.0,5.2699,204200.0,INLAND\n-121.81,37.97,8.0,1584.0,236.0,615.0,202.0,6.4753,166800.0,INLAND\n-121.78,37.97,4.0,17032.0,2546.0,7653.0,2359.0,5.5601,213700.0,INLAND\n-121.83,37.95,17.0,1133.0,244.0,716.0,235.0,2.875,162500.0,INLAND\n-121.94,37.83,11.0,2836.0,373.0,959.0,335.0,10.5815,500001.0,INLAND\n-121.89,37.82,4.0,11444.0,1355.0,3898.0,1257.0,13.2949,500001.0,INLAND\n-121.91,37.81,7.0,3477.0,416.0,1216.0,395.0,13.1499,500001.0,INLAND\n-121.82,37.81,12.0,4711.0,659.0,2089.0,621.0,8.3209,485400.0,INLAND\n-121.96,37.99,2.0,3129.0,707.0,1606.0,698.0,2.9591,210100.0,INLAND\n-121.96,37.94,26.0,3084.0,505.0,1557.0,501.0,5.1582,194700.0,INLAND\n-121.96,37.95,25.0,4026.0,791.0,1850.0,709.0,4.1483,181200.0,INLAND\n-121.95,37.94,21.0,3153.0,411.0,1318.0,431.0,6.8642,285400.0,INLAND\n-121.97,37.93,4.0,3241.0,464.0,1552.0,494.0,6.6134,307000.0,INLAND\n-122.01,37.92,18.0,2808.0,337.0,1038.0,337.0,8.3956,353600.0,NEAR BAY\n-122.01,37.92,16.0,2638.0,345.0,1055.0,334.0,8.1163,365800.0,NEAR BAY\n-121.99,37.92,14.0,1780.0,224.0,764.0,226.0,9.0243,427700.0,INLAND\n-121.93,37.89,13.0,2085.0,292.0,852.0,264.0,7.3445,366700.0,INLAND\n-121.92,37.96,14.0,5332.0,884.0,2093.0,839.0,5.2798,237400.0,INLAND\n-121.94,37.95,18.0,2541.0,355.0,986.0,346.0,7.1978,288000.0,INLAND\n-121.91,37.93,13.0,1610.0,198.0,703.0,217.0,8.7059,329400.0,INLAND\n-121.94,37.94,26.0,1299.0,174.0,533.0,180.0,6.2296,291700.0,INLAND\n-121.94,37.93,16.0,3421.0,427.0,1341.0,428.0,7.5695,320400.0,INLAND\n-121.93,37.93,16.0,2876.0,391.0,1089.0,377.0,6.299,286900.0,INLAND\n-121.93,37.93,16.0,2169.0,262.0,877.0,245.0,6.6049,312600.0,INLAND\n-121.95,37.94,27.0,1469.0,216.0,578.0,219.0,5.9346,253600.0,INLAND\n-122.25,38.02,16.0,1803.0,267.0,946.0,266.0,5.7001,205100.0,NEAR BAY\n-122.25,38.03,15.0,3338.0,532.0,1834.0,520.0,5.6293,197600.0,NEAR BAY\n-122.21,38.02,15.0,2150.0,327.0,1094.0,324.0,6.0224,198500.0,NEAR BAY\n-122.2,37.96,9.0,6306.0,962.0,2581.0,911.0,6.7741,310700.0,NEAR BAY\n-122.23,38.06,52.0,1350.0,266.0,490.0,257.0,3.125,171100.0,NEAR BAY\n-122.23,38.05,52.0,1736.0,358.0,638.0,297.0,2.5517,147100.0,NEAR BAY\n-122.21,38.06,52.0,2735.0,559.0,1076.0,487.0,3.6154,155700.0,NEAR BAY\n-122.2,38.04,31.0,3029.0,500.0,1236.0,487.0,5.6022,197000.0,NEAR BAY\n-122.26,38.03,25.0,2239.0,361.0,928.0,353.0,4.4474,203700.0,NEAR BAY\n-122.27,38.04,47.0,1685.0,405.0,835.0,372.0,2.3103,134500.0,NEAR BAY\n-122.26,38.03,41.0,1631.0,282.0,752.0,288.0,3.9345,150200.0,NEAR BAY\n-122.26,38.04,41.0,2512.0,539.0,1179.0,480.0,2.694,123000.0,NEAR BAY\n-122.25,38.05,30.0,1255.0,297.0,779.0,307.0,1.6767,147700.0,NEAR BAY\n-122.32,38.06,4.0,7999.0,1611.0,3596.0,1396.0,5.0969,174200.0,NEAR BAY\n-122.28,38.0,26.0,2335.0,413.0,980.0,417.0,3.4471,178900.0,NEAR BAY\n-122.29,38.0,16.0,4986.0,1081.0,2805.0,1016.0,4.025,173200.0,NEAR BAY\n-122.36,38.03,32.0,2159.0,393.0,981.0,369.0,4.3173,175400.0,NEAR BAY\n-122.3,38.0,34.0,1712.0,317.0,956.0,341.0,4.4394,162000.0,NEAR BAY\n-122.31,38.0,26.0,3735.0,641.0,1708.0,633.0,4.621,191100.0,NEAR BAY\n-122.31,38.01,18.0,4123.0,874.0,1895.0,772.0,3.2759,195000.0,NEAR BAY\n-122.28,37.99,28.0,3801.0,622.0,1654.0,571.0,4.375,193300.0,NEAR BAY\n-122.26,37.98,28.0,2038.0,329.0,947.0,349.0,5.1178,198000.0,NEAR BAY\n-122.27,37.99,16.0,4921.0,737.0,2312.0,725.0,5.8899,243200.0,NEAR BAY\n-122.27,37.98,23.0,3455.0,479.0,1375.0,474.0,6.0289,218600.0,NEAR BAY\n-122.26,38.02,5.0,3846.0,786.0,2053.0,716.0,5.0473,184800.0,NEAR BAY\n-122.24,38.01,11.0,3751.0,565.0,1949.0,555.0,5.7862,269400.0,NEAR BAY\n-122.24,38.01,16.0,2084.0,315.0,1154.0,307.0,6.0102,235600.0,NEAR BAY\n-122.25,38.0,16.0,2978.0,411.0,1531.0,400.0,6.5006,237700.0,NEAR BAY\n-122.26,38.0,14.0,2338.0,391.0,1003.0,398.0,4.2269,170500.0,NEAR BAY\n-122.26,38.0,6.0,678.0,104.0,318.0,91.0,5.2375,246400.0,NEAR BAY\n-122.27,38.0,15.0,1216.0,166.0,572.0,178.0,5.8418,240300.0,NEAR BAY\n-122.27,38.0,12.0,1592.0,242.0,969.0,233.0,6.1576,248700.0,NEAR BAY\n-122.26,38.0,5.0,6265.0,908.0,3326.0,872.0,6.2073,272900.0,NEAR BAY\n-122.29,37.98,27.0,2133.0,347.0,850.0,350.0,5.1046,209800.0,NEAR BAY\n-122.28,37.96,35.0,1579.0,243.0,734.0,264.0,5.5,201000.0,NEAR BAY\n-122.27,37.97,10.0,15259.0,2275.0,7266.0,2338.0,6.0666,272400.0,NEAR BAY\n-122.3,37.97,30.0,4030.0,772.0,1777.0,718.0,3.6393,184000.0,NEAR BAY\n-122.3,37.97,34.0,2854.0,528.0,1211.0,452.0,3.5353,164700.0,NEAR BAY\n-122.29,37.97,20.0,3426.0,632.0,1512.0,580.0,4.4911,227400.0,NEAR BAY\n-122.3,37.97,35.0,1811.0,377.0,911.0,340.0,3.375,149700.0,NEAR BAY\n-122.29,37.94,20.0,7578.0,1426.0,3637.0,1362.0,4.4387,190000.0,NEAR BAY\n-122.32,37.95,37.0,1887.0,353.0,895.0,359.0,4.45,196600.0,NEAR BAY\n-122.31,37.94,38.0,1794.0,349.0,810.0,335.0,3.8343,191400.0,NEAR BAY\n-122.31,37.94,38.0,2172.0,403.0,945.0,384.0,4.3958,194200.0,NEAR BAY\n-122.32,37.95,36.0,1425.0,245.0,573.0,239.0,4.35,185000.0,NEAR BAY\n-122.31,37.99,25.0,6508.0,1137.0,3259.0,1081.0,4.2348,157800.0,NEAR BAY\n-122.3,37.98,25.0,3807.0,806.0,1821.0,792.0,3.6518,164300.0,NEAR BAY\n-122.32,37.97,29.0,2347.0,464.0,1135.0,490.0,3.9722,161000.0,NEAR BAY\n-122.32,38.01,26.0,3054.0,492.0,1495.0,496.0,4.6944,171100.0,NEAR BAY\n-122.33,38.0,35.0,3779.0,711.0,2493.0,679.0,2.9781,109000.0,NEAR BAY\n-122.31,38.0,29.0,3108.0,534.0,1687.0,516.0,4.3333,170800.0,NEAR BAY\n-122.32,37.99,24.0,4865.0,968.0,2315.0,893.0,4.2852,173500.0,NEAR BAY\n-122.32,38.0,32.0,2275.0,397.0,1233.0,418.0,4.0437,162800.0,NEAR BAY\n-122.34,37.99,42.0,1531.0,326.0,1271.0,377.0,2.6167,85100.0,NEAR BAY\n-122.33,37.98,3.0,2850.0,544.0,1024.0,515.0,6.0115,175000.0,NEAR BAY\n-122.33,37.99,4.0,3999.0,1079.0,1591.0,887.0,3.911,112500.0,NEAR BAY\n-122.39,38.0,33.0,44.0,6.0,23.0,11.0,4.125,212500.0,NEAR BAY\n-122.36,37.96,39.0,246.0,57.0,316.0,52.0,0.716,104200.0,NEAR BAY\n-122.36,37.96,31.0,1157.0,276.0,956.0,232.0,1.5347,80400.0,NEAR BAY\n-122.36,37.95,40.0,408.0,102.0,302.0,81.0,1.8333,69800.0,NEAR BAY\n-122.37,37.95,35.0,215.0,45.0,100.0,34.0,1.6023,81300.0,NEAR BAY\n-122.37,37.95,32.0,1298.0,363.0,716.0,268.0,0.9797,76400.0,NEAR BAY\n-122.37,37.96,37.0,1572.0,402.0,1046.0,350.0,0.7403,68600.0,NEAR BAY\n-122.41,37.98,36.0,60.0,15.0,42.0,25.0,1.4583,67500.0,NEAR BAY\n-122.34,37.98,33.0,2014.0,410.0,1354.0,427.0,3.9773,131300.0,NEAR BAY\n-122.35,37.98,34.0,3756.0,726.0,2237.0,686.0,3.7562,132900.0,NEAR BAY\n-122.34,37.97,19.0,392.0,109.0,287.0,81.0,6.0426,110000.0,NEAR BAY\n-122.35,37.96,32.0,1991.0,504.0,1139.0,423.0,2.0353,113600.0,NEAR BAY\n-122.35,37.97,43.0,2178.0,482.0,1545.0,471.0,2.5863,112200.0,NEAR BAY\n-122.35,37.97,31.0,2892.0,685.0,2104.0,641.0,3.2188,113800.0,NEAR BAY\n-122.32,37.97,33.0,1595.0,292.0,991.0,300.0,4.6937,134100.0,NEAR BAY\n-122.32,37.97,33.0,1156.0,190.0,643.0,209.0,4.5,156600.0,NEAR BAY\n-122.33,37.97,19.0,5151.0,1335.0,2548.0,1165.0,3.3125,158800.0,NEAR BAY\n-122.33,37.98,32.0,1967.0,348.0,1144.0,364.0,4.4135,150100.0,NEAR BAY\n-122.33,37.97,45.0,1982.0,376.0,1179.0,398.0,3.5463,130800.0,NEAR BAY\n-122.34,37.97,19.0,2237.0,580.0,1438.0,551.0,2.3382,120700.0,NEAR BAY\n-122.33,37.96,46.0,1222.0,236.0,819.0,251.0,3.9118,129400.0,NEAR BAY\n-122.35,37.96,36.0,2191.0,531.0,1563.0,524.0,2.5164,114200.0,NEAR BAY\n-122.35,37.96,29.0,1899.0,524.0,1357.0,443.0,1.875,97200.0,NEAR BAY\n-122.35,37.96,35.0,1326.0,346.0,1023.0,295.0,2.0724,97700.0,NEAR BAY\n-122.35,37.95,31.0,2449.0,595.0,1801.0,548.0,2.6328,110300.0,NEAR BAY\n-122.35,37.96,34.0,1428.0,335.0,1272.0,319.0,2.5461,93900.0,NEAR BAY\n-122.36,37.96,30.0,950.0,317.0,1073.0,280.0,1.8664,107800.0,NEAR BAY\n-122.32,37.96,25.0,1728.0,403.0,934.0,412.0,3.375,133700.0,NEAR BAY\n-122.32,37.96,34.0,2070.0,357.0,784.0,294.0,4.0417,182800.0,NEAR BAY\n-122.32,37.95,35.0,1612.0,354.0,887.0,331.0,2.5769,146100.0,NEAR BAY\n-122.33,37.95,22.0,2099.0,569.0,1135.0,509.0,2.1915,120800.0,NEAR BAY\n-122.34,37.96,15.0,6487.0,1717.0,3408.0,1560.0,2.1991,133300.0,NEAR BAY\n-122.34,37.96,33.0,1817.0,441.0,1220.0,389.0,2.5382,103600.0,NEAR BAY\n-122.32,37.94,38.0,2751.0,522.0,1390.0,489.0,3.7277,165100.0,NEAR BAY\n-122.32,37.94,46.0,1901.0,295.0,833.0,352.0,5.5196,210800.0,NEAR BAY\n-122.32,37.93,40.0,1141.0,213.0,434.0,196.0,3.9464,186900.0,NEAR BAY\n-122.33,37.95,42.0,1627.0,336.0,848.0,316.0,3.7708,144600.0,NEAR BAY\n-122.33,37.94,45.0,1226.0,279.0,590.0,260.0,2.8833,140400.0,NEAR BAY\n-122.33,37.94,47.0,1882.0,361.0,797.0,342.0,3.5848,140800.0,NEAR BAY\n-122.33,37.94,43.0,1876.0,389.0,807.0,377.0,3.1571,141600.0,NEAR BAY\n-122.33,37.94,42.0,1695.0,345.0,719.0,334.0,3.9417,139100.0,NEAR BAY\n-122.33,37.94,44.0,1769.0,332.0,828.0,309.0,4.0526,150800.0,NEAR BAY\n-122.34,37.95,45.0,1128.0,240.0,702.0,270.0,3.6719,134100.0,NEAR BAY\n-122.33,37.95,45.0,1585.0,329.0,981.0,373.0,3.0313,135800.0,NEAR BAY\n-122.33,37.95,46.0,1543.0,339.0,777.0,322.0,4.0927,142600.0,NEAR BAY\n-122.34,37.95,44.0,1675.0,317.0,806.0,311.0,3.0694,135300.0,NEAR BAY\n-122.34,37.95,44.0,1788.0,368.0,933.0,329.0,2.875,133400.0,NEAR BAY\n-122.34,37.95,39.0,1986.0,427.0,1041.0,385.0,3.2333,135100.0,NEAR BAY\n-122.34,37.95,38.0,1340.0,298.0,766.0,241.0,3.2833,111700.0,NEAR BAY\n-122.35,37.95,45.0,2142.0,431.0,1318.0,431.0,3.0737,111600.0,NEAR BAY\n-122.35,37.95,42.0,1485.0,290.0,971.0,303.0,3.6094,114600.0,NEAR BAY\n-122.36,37.95,38.0,1042.0,289.0,773.0,248.0,2.7714,104700.0,NEAR BAY\n-122.34,37.94,47.0,2313.0,433.0,947.0,430.0,3.942,143300.0,NEAR BAY\n-122.34,37.94,44.0,1917.0,444.0,936.0,435.0,2.7391,140300.0,NEAR BAY\n-122.34,37.94,31.0,1611.0,455.0,786.0,411.0,1.681,145500.0,NEAR BAY\n-122.34,37.94,42.0,2206.0,451.0,989.0,444.0,3.125,143900.0,NEAR BAY\n-122.35,37.94,45.0,2112.0,493.0,1406.0,452.0,2.3456,105200.0,NEAR BAY\n-122.35,37.94,34.0,1880.0,459.0,1358.0,422.0,1.6571,105200.0,NEAR BAY\n-122.35,37.94,47.0,1275.0,275.0,844.0,273.0,2.8967,95600.0,NEAR BAY\n-122.36,37.95,38.0,1066.0,248.0,729.0,286.0,1.5139,81700.0,NEAR BAY\n-122.36,37.94,45.0,907.0,188.0,479.0,161.0,3.0862,79000.0,NEAR BAY\n-122.36,37.94,27.0,844.0,249.0,583.0,265.0,0.9687,105800.0,NEAR BAY\n-122.36,37.94,26.0,1540.0,343.0,1007.0,338.0,1.3365,72900.0,NEAR BAY\n-122.36,37.94,41.0,2591.0,585.0,1638.0,462.0,1.822,79700.0,NEAR BAY\n-122.37,37.94,49.0,969.0,229.0,599.0,195.0,1.3167,71600.0,NEAR BAY\n-122.35,37.93,19.0,1334.0,366.0,1048.0,316.0,1.7865,88000.0,NEAR BAY\n-122.35,37.93,28.0,1995.0,488.0,1182.0,439.0,2.3352,84300.0,NEAR BAY\n-122.36,37.94,43.0,369.0,107.0,371.0,111.0,1.6,79400.0,NEAR BAY\n-122.36,37.93,17.0,1258.0,254.0,885.0,229.0,3.05,121600.0,NEAR BAY\n-122.37,37.94,40.0,1064.0,266.0,912.0,239.0,1.0521,69100.0,NEAR BAY\n-122.37,37.93,45.0,3150.0,756.0,1798.0,749.0,1.75,37900.0,NEAR BAY\n-122.38,37.91,18.0,3507.0,711.0,1224.0,676.0,5.0524,269800.0,NEAR BAY\n-122.42,37.93,47.0,3453.0,779.0,1353.0,728.0,4.016,274500.0,NEAR BAY\n-122.41,37.94,52.0,154.0,33.0,89.0,38.0,3.2875,275000.0,NEAR BAY\n-122.34,37.93,30.0,2515.0,481.0,1327.0,428.0,2.1287,95000.0,NEAR BAY\n-122.35,37.93,39.0,2002.0,416.0,1166.0,395.0,1.7257,91500.0,NEAR BAY\n-122.36,37.93,44.0,1891.0,449.0,1047.0,432.0,1.7727,86100.0,NEAR BAY\n-122.36,37.93,42.0,1796.0,389.0,1107.0,372.0,1.9375,87000.0,NEAR BAY\n-122.37,37.93,37.0,709.0,190.0,644.0,174.0,0.8641,84200.0,NEAR BAY\n-122.33,37.91,36.0,1954.0,513.0,1437.0,440.0,1.125,93800.0,NEAR BAY\n-122.35,37.92,36.0,921.0,200.0,585.0,236.0,1.9224,94000.0,NEAR BAY\n-122.36,37.92,52.0,215.0,41.0,126.0,43.0,1.3929,104200.0,NEAR BAY\n-122.35,37.91,4.0,2851.0,798.0,1285.0,712.0,4.2895,186800.0,NEAR BAY\n-122.33,37.93,34.0,2326.0,471.0,1356.0,441.0,2.3475,90300.0,NEAR BAY\n-122.33,37.93,27.0,2158.0,424.0,1220.0,442.0,3.0156,111500.0,NEAR BAY\n-122.34,37.93,32.0,2389.0,652.0,1672.0,584.0,1.4423,88300.0,NEAR BAY\n-122.34,37.93,45.0,2225.0,486.0,1304.0,459.0,2.64,112100.0,NEAR BAY\n-122.35,37.93,41.0,268.0,75.0,198.0,82.0,3.2222,156300.0,NEAR BAY\n-122.32,37.92,29.0,2304.0,399.0,1377.0,454.0,5.0187,140600.0,NEAR BAY\n-122.33,37.92,26.0,3887.0,779.0,2512.0,740.0,2.2301,122400.0,NEAR BAY\n-122.32,37.92,28.0,4649.0,977.0,2606.0,953.0,3.2674,129100.0,NEAR BAY\n-122.32,37.91,34.0,2669.0,647.0,1341.0,555.0,2.6399,119600.0,NEAR BAY\n-122.31,37.91,39.0,2955.0,696.0,1417.0,682.0,2.7628,167800.0,NEAR BAY\n-122.31,37.91,43.0,2549.0,511.0,1060.0,528.0,3.6417,178400.0,NEAR BAY\n-122.31,37.91,45.0,3924.0,834.0,1992.0,773.0,4.1146,177800.0,NEAR BAY\n-122.31,37.93,39.0,2505.0,371.0,872.0,345.0,5.3433,286500.0,NEAR BAY\n-122.31,37.93,36.0,2403.0,408.0,917.0,404.0,5.0399,253400.0,NEAR BAY\n-122.32,37.93,40.0,3056.0,489.0,1103.0,481.0,5.1067,247300.0,NEAR BAY\n-122.32,37.94,47.0,1911.0,283.0,697.0,275.0,6.2712,267700.0,NEAR BAY\n-122.3,37.92,32.0,3943.0,605.0,1524.0,614.0,6.0677,321600.0,NEAR BAY\n-122.29,37.92,35.0,583.0,88.0,235.0,84.0,5.943,288200.0,NEAR BAY\n-122.29,37.92,32.0,1736.0,234.0,602.0,231.0,6.516,401000.0,NEAR BAY\n-122.3,37.93,34.0,2254.0,357.0,715.0,306.0,4.5,304000.0,NEAR BAY\n-122.31,37.93,36.0,1526.0,256.0,696.0,263.0,3.5089,261900.0,NEAR BAY\n-122.32,37.93,33.0,296.0,73.0,216.0,63.0,2.675,22500.0,NEAR BAY\n-122.31,37.92,30.0,1014.0,236.0,537.0,204.0,2.8456,183300.0,NEAR BAY\n-122.31,37.92,38.0,1250.0,236.0,631.0,279.0,3.724,220100.0,NEAR BAY\n-122.31,37.92,12.0,1895.0,600.0,983.0,519.0,2.5,195800.0,NEAR BAY\n-122.32,37.92,22.0,1119.0,220.0,565.0,199.0,3.3594,186900.0,NEAR BAY\n-122.31,37.92,43.0,2116.0,407.0,900.0,361.0,4.1587,212200.0,NEAR BAY\n-122.3,37.92,33.0,1615.0,271.0,710.0,285.0,4.0804,239000.0,NEAR BAY\n-122.31,37.91,31.0,1432.0,348.0,681.0,348.0,2.7243,218100.0,NEAR BAY\n-122.3,37.91,39.0,2686.0,569.0,1159.0,559.0,2.9441,200400.0,NEAR BAY\n-122.3,37.91,40.0,2866.0,617.0,1305.0,589.0,3.6321,209100.0,NEAR BAY\n-122.3,37.9,37.0,2125.0,489.0,912.0,462.0,2.9219,217200.0,NEAR BAY\n-122.3,37.9,41.0,2053.0,435.0,873.0,415.0,3.4091,223000.0,NEAR BAY\n-122.3,37.9,35.0,1102.0,308.0,688.0,303.0,2.3946,141700.0,NEAR BAY\n-122.3,37.9,30.0,1772.0,471.0,880.0,437.0,2.2672,162500.0,NEAR BAY\n-122.29,37.92,36.0,1450.0,235.0,568.0,234.0,6.0,311400.0,NEAR BAY\n-122.29,37.91,38.0,2591.0,424.0,905.0,378.0,5.1691,263200.0,NEAR BAY\n-122.29,37.91,46.0,2085.0,346.0,748.0,354.0,4.0536,262000.0,NEAR BAY\n-122.29,37.91,40.0,2085.0,329.0,796.0,339.0,5.5357,273700.0,NEAR BAY\n-122.29,37.9,52.0,2657.0,500.0,1131.0,489.0,4.4286,234900.0,NEAR BAY\n-122.28,37.91,38.0,2501.0,348.0,805.0,329.0,6.5576,358500.0,NEAR BAY\n-122.28,37.9,49.0,3191.0,516.0,1148.0,507.0,6.3538,333700.0,NEAR BAY\n-122.28,37.9,52.0,1369.0,249.0,490.0,248.0,4.1212,287500.0,NEAR BAY\n-122.28,37.91,41.0,3009.0,482.0,1053.0,490.0,5.828,324400.0,NEAR BAY\n-122.27,37.91,47.0,1930.0,315.0,692.0,296.0,6.3669,315500.0,NEAR BAY\n-122.28,37.91,48.0,2083.0,298.0,685.0,286.0,7.3089,331200.0,NEAR BAY\n-124.17,41.8,16.0,2739.0,480.0,1259.0,436.0,3.7557,109400.0,NEAR OCEAN\n-124.3,41.8,19.0,2672.0,552.0,1298.0,478.0,1.9797,85800.0,NEAR OCEAN\n-124.23,41.75,11.0,3159.0,616.0,1343.0,479.0,2.4805,73200.0,NEAR OCEAN\n-124.21,41.77,17.0,3461.0,722.0,1947.0,647.0,2.5795,68400.0,NEAR OCEAN\n-124.19,41.78,15.0,3140.0,714.0,1645.0,640.0,1.6654,74600.0,NEAR OCEAN\n-124.22,41.73,28.0,3003.0,699.0,1530.0,653.0,1.7038,78300.0,NEAR OCEAN\n-124.21,41.75,20.0,3810.0,787.0,1993.0,721.0,2.0074,66900.0,NEAR OCEAN\n-124.17,41.76,20.0,2673.0,538.0,1282.0,514.0,2.4605,105900.0,NEAR OCEAN\n-124.16,41.74,15.0,2715.0,569.0,1532.0,530.0,2.1829,69500.0,NEAR OCEAN\n-124.14,41.95,21.0,2696.0,578.0,1208.0,494.0,2.275,122400.0,NEAR OCEAN\n-124.16,41.92,19.0,1668.0,324.0,841.0,283.0,2.1336,75000.0,NEAR OCEAN\n-124.3,41.84,17.0,2677.0,531.0,1244.0,456.0,3.0313,103600.0,NEAR OCEAN\n-124.15,41.81,17.0,3276.0,628.0,3546.0,585.0,2.2868,103100.0,NEAR OCEAN\n-123.91,41.68,22.0,1880.0,360.0,743.0,314.0,2.9688,152700.0,<1H OCEAN\n-123.83,41.88,18.0,1504.0,357.0,660.0,258.0,3.13,116700.0,<1H OCEAN\n-123.92,41.54,22.0,2920.0,636.0,1382.0,499.0,2.0202,71100.0,NEAR OCEAN\n-119.94,38.96,20.0,1451.0,386.0,467.0,255.0,1.5536,212500.0,INLAND\n-119.95,38.95,8.0,430.0,107.0,36.0,18.0,2.625,187500.0,INLAND\n-119.95,38.95,22.0,1058.0,352.0,851.0,269.0,2.02,87500.0,INLAND\n-119.95,38.95,21.0,2046.0,580.0,952.0,353.0,1.7245,92200.0,INLAND\n-119.94,38.95,25.0,1789.0,536.0,1134.0,396.0,2.32,91300.0,INLAND\n-119.95,38.94,24.0,2180.0,517.0,755.0,223.0,2.5875,173400.0,INLAND\n-119.93,38.94,27.0,1709.0,408.0,97.0,44.0,2.4917,200000.0,INLAND\n-119.97,38.94,26.0,1485.0,334.0,406.0,180.0,1.9667,84600.0,INLAND\n-119.97,38.93,24.0,856.0,185.0,388.0,108.0,3.1806,107200.0,INLAND\n-119.98,38.96,25.0,2443.0,444.0,868.0,342.0,3.5417,114800.0,INLAND\n-119.96,38.94,19.0,1429.0,292.0,585.0,188.0,2.2589,131600.0,INLAND\n-119.96,38.94,27.0,1492.0,393.0,717.0,254.0,1.8906,104200.0,INLAND\n-119.96,38.93,22.0,2731.0,632.0,1215.0,483.0,2.83,110500.0,INLAND\n-119.94,38.92,24.0,1258.0,216.0,235.0,96.0,4.6,136800.0,INLAND\n-119.98,38.94,25.0,1339.0,328.0,503.0,219.0,1.9018,109700.0,INLAND\n-119.98,38.94,23.0,1564.0,298.0,339.0,147.0,4.0417,99300.0,INLAND\n-119.99,38.94,24.0,1216.0,289.0,421.0,185.0,3.1625,103600.0,INLAND\n-119.99,38.94,22.0,3119.0,640.0,786.0,351.0,3.0806,118500.0,INLAND\n-119.99,38.93,23.0,1882.0,414.0,673.0,277.0,2.9091,141900.0,INLAND\n-119.98,38.93,28.0,1194.0,272.0,494.0,203.0,2.3281,85800.0,INLAND\n-119.98,38.93,25.0,1262.0,293.0,534.0,226.0,2.6607,90400.0,INLAND\n-119.98,38.92,27.0,2682.0,606.0,1010.0,399.0,3.15,86900.0,INLAND\n-119.98,38.92,28.0,1408.0,312.0,522.0,221.0,2.0708,89600.0,INLAND\n-120.0,38.93,17.0,8005.0,1382.0,999.0,383.0,3.9722,313400.0,INLAND\n-120.01,38.93,22.0,3080.0,610.0,1045.0,425.0,2.996,126100.0,INLAND\n-120.0,38.92,17.0,1106.0,207.0,466.0,180.0,3.3295,126600.0,INLAND\n-120.0,38.92,26.0,529.0,116.0,191.0,83.0,3.5,103600.0,INLAND\n-120.01,38.92,23.0,964.0,246.0,485.0,198.0,1.7188,96100.0,INLAND\n-120.01,38.92,25.0,1758.0,357.0,689.0,278.0,2.675,104200.0,INLAND\n-120.02,38.91,22.0,2138.0,493.0,829.0,330.0,2.2056,107200.0,INLAND\n-120.02,38.92,24.0,1194.0,246.0,414.0,151.0,3.2396,101900.0,INLAND\n-120.01,38.91,27.0,968.0,191.0,283.0,143.0,2.0938,94400.0,INLAND\n-120.01,38.91,17.0,2732.0,609.0,1005.0,499.0,1.9851,86700.0,INLAND\n-120.01,38.89,24.0,1669.0,422.0,589.0,281.0,3.0089,100800.0,INLAND\n-120.0,38.9,21.0,1653.0,419.0,737.0,308.0,1.9727,114100.0,INLAND\n-119.99,38.88,17.0,2807.0,529.0,675.0,251.0,2.7457,107800.0,INLAND\n-119.98,38.9,16.0,3109.0,572.0,885.0,334.0,3.5,134700.0,INLAND\n-119.92,38.91,15.0,3831.0,625.0,984.0,328.0,5.0718,162500.0,INLAND\n-120.0,38.87,12.0,1437.0,268.0,395.0,144.0,4.225,127600.0,INLAND\n-119.96,38.84,17.0,2722.0,512.0,828.0,289.0,3.5714,109700.0,INLAND\n-120.02,38.76,15.0,3142.0,618.0,725.0,285.0,4.3333,121400.0,INLAND\n-120.04,38.86,16.0,2708.0,481.0,712.0,261.0,3.7891,117700.0,INLAND\n-120.03,38.89,15.0,3042.0,588.0,918.0,336.0,3.8333,118800.0,INLAND\n-120.02,38.86,19.0,2429.0,459.0,883.0,300.0,3.017,97600.0,INLAND\n-120.13,39.06,22.0,2465.0,539.0,381.0,146.0,2.875,87500.0,INLAND\n-120.16,39.04,18.0,2040.0,402.0,350.0,129.0,4.0313,126000.0,INLAND\n-120.16,39.01,16.0,1463.0,264.0,54.0,26.0,4.975,206300.0,INLAND\n-120.06,39.01,19.0,2967.0,528.0,112.0,48.0,4.0714,437500.0,INLAND\n-120.1,38.91,33.0,1561.0,282.0,30.0,11.0,1.875,500001.0,INLAND\n-120.97,38.91,7.0,4341.0,716.0,1978.0,682.0,4.8311,172200.0,INLAND\n-121.04,38.81,11.0,3522.0,623.0,1456.0,544.0,3.93,163400.0,INLAND\n-120.72,38.94,10.0,1604.0,352.0,540.0,190.0,3.7625,113200.0,INLAND\n-120.88,38.91,15.0,3876.0,778.0,1960.0,691.0,2.902,127300.0,INLAND\n-120.92,38.86,11.0,1720.0,345.0,850.0,326.0,3.2027,128600.0,INLAND\n-120.87,38.83,12.0,2180.0,423.0,1070.0,377.0,2.8562,128200.0,INLAND\n-120.84,38.81,11.0,1280.0,286.0,609.0,248.0,3.1635,132600.0,INLAND\n-120.81,38.89,17.0,1438.0,324.0,675.0,268.0,2.9444,119300.0,INLAND\n-120.79,38.83,15.0,1374.0,291.0,709.0,239.0,1.7222,118500.0,INLAND\n-120.71,38.85,8.0,1877.0,479.0,884.0,323.0,3.4688,120100.0,INLAND\n-120.5,38.87,10.0,81.0,41.0,55.0,16.0,4.9583,87500.0,INLAND\n-120.3,38.9,11.0,1961.0,435.0,113.0,53.0,0.9227,95500.0,INLAND\n-121.09,38.68,15.0,5218.0,711.0,1949.0,659.0,4.7083,213300.0,INLAND\n-121.08,38.67,10.0,2499.0,331.0,1040.0,333.0,6.844,239600.0,INLAND\n-121.07,38.66,22.0,1831.0,274.0,813.0,269.0,4.6394,173400.0,INLAND\n-121.06,38.7,9.0,13255.0,1739.0,5001.0,1627.0,6.314,228900.0,INLAND\n-121.0,38.58,12.0,3425.0,549.0,1357.0,451.0,5.3344,217500.0,INLAND\n-121.01,38.73,7.0,6322.0,1046.0,2957.0,1024.0,4.7276,197500.0,INLAND\n-120.99,38.69,5.0,5743.0,1074.0,2651.0,962.0,4.1163,172500.0,INLAND\n-121.02,38.66,4.0,7392.0,1155.0,3096.0,1065.0,4.5246,198900.0,INLAND\n-120.99,38.67,8.0,4913.0,744.0,2005.0,723.0,5.4413,187900.0,INLAND\n-120.98,38.67,13.0,3432.0,516.0,1286.0,470.0,5.584,186600.0,INLAND\n-120.98,38.66,9.0,2073.0,404.0,916.0,373.0,3.225,163300.0,INLAND\n-120.98,38.68,5.0,4810.0,909.0,2242.0,900.0,3.2964,176900.0,INLAND\n-120.95,38.69,10.0,3421.0,563.0,1689.0,545.0,5.2032,217100.0,INLAND\n-120.96,38.66,11.0,2339.0,436.0,1062.0,380.0,3.9036,180800.0,INLAND\n-120.97,38.65,9.0,3707.0,602.0,1601.0,555.0,4.0714,300600.0,INLAND\n-120.93,38.65,12.0,2213.0,384.0,1097.0,351.0,4.5568,170100.0,INLAND\n-120.91,38.62,12.0,4545.0,748.0,2033.0,718.0,4.1843,207600.0,INLAND\n-120.95,38.79,12.0,3247.0,579.0,1459.0,517.0,4.3981,202800.0,INLAND\n-120.93,38.77,9.0,2229.0,355.0,788.0,341.0,5.5111,196300.0,INLAND\n-120.91,38.73,11.0,5460.0,859.0,2645.0,838.0,4.835,230600.0,INLAND\n-120.87,38.71,13.0,2692.0,470.0,1302.0,420.0,4.0,167400.0,INLAND\n-120.84,38.77,11.0,1013.0,188.0,410.0,158.0,4.825,184600.0,INLAND\n-120.86,38.75,15.0,1533.0,300.0,674.0,287.0,2.5625,146100.0,INLAND\n-120.83,38.74,17.0,3685.0,775.0,1714.0,734.0,2.2269,128300.0,INLAND\n-120.84,38.73,17.0,2616.0,492.0,1158.0,457.0,2.8807,142600.0,INLAND\n-120.81,38.73,38.0,2005.0,385.0,882.0,353.0,2.5104,120500.0,INLAND\n-120.78,38.74,28.0,4236.0,877.0,2008.0,881.0,2.1603,111300.0,INLAND\n-120.81,38.74,29.0,2259.0,482.0,1099.0,463.0,2.3314,121600.0,INLAND\n-120.76,38.76,21.0,3509.0,606.0,1576.0,564.0,2.6392,148500.0,INLAND\n-120.76,38.73,17.0,512.0,129.0,314.0,140.0,1.5625,108300.0,INLAND\n-120.78,38.73,31.0,3117.0,616.0,1606.0,588.0,2.9844,127900.0,INLAND\n-120.81,38.73,42.0,1276.0,260.0,799.0,259.0,2.7273,128600.0,INLAND\n-120.78,38.72,19.0,4414.0,767.0,1865.0,699.0,3.6406,150900.0,INLAND\n-120.67,38.76,35.0,2104.0,403.0,1060.0,400.0,2.1682,138100.0,INLAND\n-120.7,38.75,19.0,2325.0,430.0,967.0,376.0,2.9,158700.0,INLAND\n-120.71,38.73,17.0,2146.0,396.0,862.0,351.0,2.9219,141300.0,INLAND\n-120.58,38.77,15.0,2155.0,394.0,857.0,356.0,4.03,141200.0,INLAND\n-120.58,38.77,21.0,1661.0,406.0,789.0,319.0,2.3583,108700.0,INLAND\n-120.6,38.76,22.0,1236.0,273.0,615.0,248.0,3.0217,106900.0,INLAND\n-120.59,38.76,21.0,1728.0,417.0,731.0,334.0,1.7266,94700.0,INLAND\n-120.63,38.75,17.0,3145.0,621.0,1432.0,559.0,2.7201,117500.0,INLAND\n-120.7,38.69,13.0,4492.0,821.0,2093.0,734.0,4.0709,151700.0,INLAND\n-120.63,38.73,11.0,4577.0,836.0,1944.0,700.0,4.0675,140200.0,INLAND\n-120.62,38.71,10.0,6305.0,1150.0,2597.0,921.0,4.0197,132200.0,INLAND\n-120.63,38.68,14.0,1821.0,316.0,769.0,266.0,3.0789,131700.0,INLAND\n-120.54,38.75,9.0,3006.0,540.0,1102.0,418.0,3.9812,136600.0,INLAND\n-120.76,38.6,14.0,2925.0,625.0,1226.0,437.0,2.5865,133800.0,INLAND\n-120.66,38.61,19.0,2715.0,596.0,1301.0,473.0,2.5042,126400.0,INLAND\n-120.72,38.57,8.0,892.0,185.0,427.0,164.0,2.6833,118800.0,INLAND\n-120.59,38.53,15.0,432.0,87.0,208.0,73.0,3.6125,100000.0,INLAND\n-120.44,38.61,9.0,2598.0,548.0,796.0,297.0,3.5192,98000.0,INLAND\n-120.32,38.71,13.0,1115.0,255.0,86.0,32.0,3.5667,115600.0,INLAND\n-120.08,38.8,34.0,1988.0,511.0,36.0,15.0,4.625,162500.0,INLAND\n-120.88,38.58,8.0,3417.0,604.0,1703.0,623.0,4.0827,170700.0,INLAND\n-120.84,38.63,12.0,1313.0,231.0,731.0,232.0,5.7373,208300.0,INLAND\n-120.81,38.67,14.0,8396.0,1578.0,3952.0,1474.0,3.0565,118800.0,INLAND\n-120.76,38.65,17.0,2319.0,430.0,1126.0,372.0,3.5511,155900.0,INLAND\n-120.85,38.69,18.0,5928.0,1097.0,2697.0,1096.0,3.4872,141400.0,INLAND\n-120.79,38.7,13.0,5036.0,1034.0,2243.0,923.0,2.3319,138500.0,INLAND\n-119.81,36.73,51.0,956.0,196.0,662.0,180.0,2.101,56700.0,INLAND\n-119.81,36.73,47.0,1314.0,416.0,1155.0,326.0,1.372,49600.0,INLAND\n-119.81,36.74,36.0,607.0,155.0,483.0,146.0,1.5625,47500.0,INLAND\n-119.79,36.73,52.0,112.0,28.0,193.0,40.0,1.975,47500.0,INLAND\n-119.8,36.73,45.0,925.0,231.0,797.0,228.0,1.7011,44800.0,INLAND\n-119.8,36.72,43.0,1286.0,360.0,972.0,345.0,0.9513,50400.0,INLAND\n-119.81,36.72,46.0,1414.0,268.0,902.0,243.0,1.5833,56700.0,INLAND\n-119.81,36.73,50.0,772.0,194.0,606.0,167.0,2.2206,59200.0,INLAND\n-119.77,36.73,44.0,1960.0,393.0,1286.0,381.0,2.1518,53000.0,INLAND\n-119.77,36.72,43.0,1763.0,389.0,1623.0,390.0,1.4427,47700.0,INLAND\n-119.78,36.73,52.0,1377.0,319.0,1280.0,259.0,1.2344,43300.0,INLAND\n-119.77,36.73,45.0,1081.0,241.0,821.0,230.0,1.7829,52600.0,INLAND\n-119.77,36.75,39.0,1287.0,332.0,1386.0,306.0,1.5227,46900.0,INLAND\n-119.77,36.74,20.0,1855.0,519.0,1091.0,443.0,1.5547,93900.0,INLAND\n-119.78,36.74,15.0,1461.0,415.0,924.0,356.0,2.5045,90300.0,INLAND\n-119.78,36.75,35.0,2114.0,506.0,2050.0,474.0,1.2375,50000.0,INLAND\n-119.78,36.75,31.0,1404.0,379.0,1515.0,387.0,1.2813,56400.0,INLAND\n-119.79,36.74,35.0,853.0,296.0,1228.0,289.0,1.0513,39600.0,INLAND\n-119.79,36.74,52.0,173.0,87.0,401.0,84.0,2.1094,75000.0,INLAND\n-119.8,36.74,25.0,1717.0,542.0,1343.0,471.0,0.799,51800.0,INLAND\n-119.8,36.75,41.0,1659.0,466.0,1391.0,447.0,1.3527,61200.0,INLAND\n-119.79,36.75,33.0,3161.0,934.0,3530.0,846.0,1.123,46700.0,INLAND\n-119.82,36.74,52.0,610.0,128.0,406.0,122.0,1.8967,43800.0,INLAND\n-119.82,36.72,25.0,2581.0,528.0,1642.0,509.0,1.6435,52600.0,INLAND\n-119.82,36.72,17.0,1276.0,242.0,927.0,238.0,2.6176,54100.0,INLAND\n-119.83,36.73,21.0,1702.0,358.0,1347.0,316.0,2.4137,62100.0,INLAND\n-119.83,36.72,28.0,60.0,10.0,46.0,13.0,4.35,67500.0,INLAND\n-119.83,36.71,43.0,355.0,81.0,233.0,75.0,2.4167,73900.0,INLAND\n-119.79,36.72,19.0,1719.0,391.0,1369.0,368.0,1.25,53000.0,INLAND\n-119.8,36.71,29.0,1541.0,291.0,1007.0,313.0,2.0043,53500.0,INLAND\n-119.81,36.71,25.0,1026.0,221.0,789.0,183.0,1.5625,52800.0,INLAND\n-119.8,36.72,19.0,1334.0,336.0,1171.0,319.0,1.0481,48500.0,INLAND\n-119.8,36.72,15.0,1908.0,417.0,1383.0,375.0,1.0472,57800.0,INLAND\n-119.79,36.7,23.0,1731.0,363.0,1210.0,341.0,1.3922,49500.0,INLAND\n-119.8,36.7,28.0,1592.0,304.0,962.0,282.0,1.3304,51300.0,INLAND\n-119.81,36.7,52.0,314.0,57.0,178.0,66.0,1.2404,52500.0,INLAND\n-119.78,36.72,22.0,354.0,121.0,530.0,115.0,2.1458,34400.0,INLAND\n-119.79,36.72,41.0,1562.0,322.0,927.0,277.0,1.3047,44100.0,INLAND\n-119.78,36.71,35.0,1987.0,394.0,1233.0,383.0,1.3587,45300.0,INLAND\n-119.74,36.71,17.0,5872.0,1250.0,5034.0,1224.0,2.1905,61800.0,INLAND\n-119.76,36.71,29.0,1745.0,441.0,1530.0,391.0,1.5611,44400.0,INLAND\n-119.76,36.72,24.0,1240.0,265.0,1035.0,232.0,2.875,60600.0,INLAND\n-119.75,36.71,38.0,1481.0,,1543.0,372.0,1.4577,49800.0,INLAND\n-119.74,36.73,34.0,1254.0,272.0,1056.0,279.0,2.3269,50800.0,INLAND\n-119.74,36.72,25.0,3972.0,842.0,2863.0,729.0,2.1304,58500.0,INLAND\n-119.75,36.72,22.0,3247.0,859.0,4179.0,881.0,1.3343,60800.0,INLAND\n-119.76,36.73,46.0,1347.0,282.0,854.0,267.0,1.8723,52600.0,INLAND\n-119.76,36.73,39.0,1553.0,363.0,1449.0,341.0,1.4419,45500.0,INLAND\n-119.75,36.73,39.0,2290.0,539.0,1685.0,536.0,1.6325,52100.0,INLAND\n-119.74,36.73,42.0,1236.0,272.0,946.0,261.0,2.0536,50000.0,INLAND\n-119.69,36.75,6.0,1926.0,303.0,965.0,316.0,4.7463,93100.0,INLAND\n-119.67,36.74,19.0,2788.0,614.0,1365.0,525.0,2.7813,120300.0,INLAND\n-119.69,36.74,17.0,2438.0,598.0,1563.0,538.0,1.5449,62500.0,INLAND\n-119.69,36.75,13.0,2343.0,409.0,1347.0,405.0,4.0027,93100.0,INLAND\n-119.67,36.73,27.0,2845.0,417.0,1219.0,460.0,4.9196,117900.0,INLAND\n-119.69,36.73,30.0,2437.0,349.0,1005.0,380.0,7.2211,171700.0,INLAND\n-119.69,36.74,23.0,2097.0,385.0,911.0,405.0,3.5128,121600.0,INLAND\n-119.69,36.71,25.0,556.0,79.0,249.0,71.0,4.4583,108300.0,INLAND\n-119.67,36.72,31.0,843.0,140.0,453.0,149.0,2.6875,153800.0,INLAND\n-119.73,36.73,7.0,2461.0,647.0,1587.0,551.0,1.4007,225000.0,INLAND\n-119.72,36.73,9.0,1914.0,491.0,1116.0,424.0,1.4646,65900.0,INLAND\n-119.72,36.72,15.0,1713.0,246.0,766.0,232.0,6.8162,127200.0,INLAND\n-119.73,36.72,26.0,2645.0,1005.0,1660.0,991.0,0.6991,89500.0,INLAND\n-119.73,36.73,9.0,1621.0,428.0,678.0,394.0,2.2437,54200.0,INLAND\n-119.71,36.73,19.0,3972.0,585.0,1586.0,560.0,5.2608,151400.0,INLAND\n-119.72,36.71,7.0,2456.0,463.0,1350.0,424.0,3.0179,91600.0,INLAND\n-119.73,36.72,15.0,2246.0,456.0,1190.0,403.0,2.0294,70400.0,INLAND\n-119.73,36.68,32.0,755.0,205.0,681.0,207.0,1.7986,49300.0,INLAND\n-119.69,36.69,36.0,1432.0,269.0,836.0,237.0,2.1563,88300.0,INLAND\n-119.76,36.68,29.0,1243.0,312.0,836.0,277.0,1.8355,74200.0,INLAND\n-119.67,36.65,20.0,2512.0,449.0,1464.0,450.0,3.9211,92300.0,INLAND\n-119.63,36.64,33.0,1036.0,181.0,620.0,174.0,3.4107,110400.0,INLAND\n-119.65,36.62,6.0,1931.0,422.0,1344.0,414.0,1.6607,58000.0,INLAND\n-119.68,36.63,39.0,1237.0,256.0,638.0,239.0,3.0139,65300.0,INLAND\n-119.74,36.65,19.0,2546.0,463.0,1257.0,418.0,2.9013,89500.0,INLAND\n-119.68,36.62,31.0,834.0,229.0,616.0,211.0,1.6602,61200.0,INLAND\n-119.73,36.62,35.0,2080.0,365.0,1026.0,333.0,3.5781,92800.0,INLAND\n-119.73,36.59,31.0,1551.0,296.0,1058.0,287.0,3.3438,92600.0,INLAND\n-119.8,36.68,31.0,2214.0,432.0,1326.0,416.0,2.1691,66700.0,INLAND\n-119.78,36.65,27.0,1226.0,240.0,706.0,211.0,2.77,68400.0,INLAND\n-119.82,36.64,30.0,1694.0,312.0,1008.0,321.0,2.2466,96000.0,INLAND\n-119.8,36.65,34.0,2263.0,423.0,1184.0,407.0,1.7692,74200.0,INLAND\n-119.89,36.73,43.0,524.0,93.0,302.0,93.0,2.6146,81300.0,INLAND\n-119.87,36.72,30.0,1584.0,316.0,984.0,300.0,2.0658,67900.0,INLAND\n-119.89,36.7,32.0,1485.0,269.0,867.0,271.0,2.5809,78300.0,INLAND\n-119.85,36.74,35.0,1191.0,190.0,537.0,182.0,3.5375,96700.0,INLAND\n-119.84,36.77,6.0,1853.0,473.0,1397.0,417.0,1.4817,72000.0,INLAND\n-119.83,36.76,15.0,3291.0,772.0,1738.0,634.0,1.976,67300.0,INLAND\n-119.82,36.75,41.0,1022.0,209.0,741.0,213.0,2.0781,48800.0,INLAND\n-119.84,36.75,34.0,1186.0,300.0,774.0,271.0,1.575,57100.0,INLAND\n-119.83,36.75,33.0,662.0,183.0,607.0,181.0,1.3929,55600.0,INLAND\n-119.81,36.76,48.0,2059.0,388.0,834.0,405.0,2.9306,67900.0,INLAND\n-119.81,36.76,51.0,2419.0,486.0,1284.0,426.0,2.2029,54200.0,INLAND\n-119.81,36.75,52.0,1827.0,356.0,855.0,353.0,1.7636,55100.0,INLAND\n-119.82,36.76,41.0,1973.0,399.0,1107.0,375.0,1.8971,66900.0,INLAND\n-119.82,36.76,46.0,2194.0,563.0,924.0,542.0,1.4028,68500.0,INLAND\n-119.8,36.76,52.0,1853.0,437.0,764.0,390.0,1.6429,69200.0,INLAND\n-119.8,36.75,52.0,1788.0,449.0,1156.0,418.0,1.7298,58400.0,INLAND\n-119.81,36.76,52.0,1792.0,352.0,1049.0,357.0,2.4375,57100.0,INLAND\n-119.8,36.76,52.0,2224.0,418.0,832.0,406.0,2.3952,78400.0,INLAND\n-119.79,36.76,52.0,1185.0,260.0,635.0,239.0,1.175,56100.0,INLAND\n-119.79,36.75,52.0,377.0,97.0,530.0,96.0,1.0,45000.0,INLAND\n-119.8,36.75,46.0,2625.0,593.0,1368.0,551.0,1.5273,59000.0,INLAND\n-119.79,36.76,52.0,2408.0,498.0,1361.0,465.0,2.1055,61300.0,INLAND\n-119.78,36.76,47.0,1425.0,323.0,949.0,325.0,1.7344,51300.0,INLAND\n-119.78,36.75,49.0,1175.0,307.0,982.0,278.0,1.2937,52000.0,INLAND\n-119.78,36.75,43.0,2070.0,512.0,1925.0,444.0,1.4635,46600.0,INLAND\n-119.78,36.76,50.0,1343.0,322.0,1063.0,342.0,1.75,49800.0,INLAND\n-119.76,36.76,23.0,3800.0,1003.0,3786.0,917.0,1.4766,50600.0,INLAND\n-119.76,36.75,35.0,1607.0,383.0,1407.0,382.0,2.19,53400.0,INLAND\n-119.76,36.75,41.0,1576.0,417.0,1567.0,366.0,1.2545,45500.0,INLAND\n-119.77,36.76,40.0,2009.0,519.0,2219.0,505.0,1.2101,49100.0,INLAND\n-119.77,36.76,43.0,1623.0,294.0,781.0,272.0,1.869,56000.0,INLAND\n-119.77,36.76,43.0,1945.0,413.0,1492.0,422.0,1.5174,54600.0,INLAND\n-119.76,36.75,35.0,2347.0,526.0,1676.0,481.0,1.6548,49400.0,INLAND\n-119.76,36.74,52.0,2137.0,448.0,1194.0,444.0,1.3029,69100.0,INLAND\n-119.77,36.74,50.0,1325.0,280.0,811.0,281.0,1.8667,62800.0,INLAND\n-119.77,36.74,51.0,1454.0,235.0,729.0,252.0,3.3125,70100.0,INLAND\n-119.77,36.75,44.0,1818.0,412.0,1680.0,418.0,1.7083,48300.0,INLAND\n-119.76,36.75,39.0,2233.0,563.0,2031.0,491.0,1.8641,50800.0,INLAND\n-119.74,36.75,47.0,2236.0,418.0,1042.0,397.0,2.9545,59600.0,INLAND\n-119.74,36.74,39.0,4893.0,1210.0,4749.0,1067.0,1.2065,55600.0,INLAND\n-119.75,36.74,39.0,1740.0,351.0,1098.0,347.0,1.8958,51300.0,INLAND\n-119.75,36.75,50.0,1515.0,294.0,852.0,297.0,1.9955,54200.0,INLAND\n-119.75,36.75,49.0,2331.0,460.0,1290.0,477.0,2.5111,55400.0,INLAND\n-119.74,36.76,42.0,2093.0,470.0,1621.0,438.0,1.7994,58700.0,INLAND\n-119.74,36.76,36.0,912.0,216.0,842.0,219.0,1.4766,52800.0,INLAND\n-119.75,36.76,29.0,2077.0,524.0,1887.0,489.0,1.4107,59800.0,INLAND\n-119.75,36.76,32.0,2072.0,497.0,2002.0,470.0,1.3278,44500.0,INLAND\n-119.72,36.76,23.0,6403.0,,3573.0,1260.0,2.3006,69000.0,INLAND\n-119.72,36.75,11.0,4832.0,993.0,2190.0,888.0,2.6611,74700.0,INLAND\n-119.73,36.76,30.0,1548.0,282.0,886.0,311.0,3.1,71300.0,INLAND\n-119.72,36.75,27.0,1691.0,282.0,869.0,337.0,3.9514,86900.0,INLAND\n-119.73,36.74,14.0,6202.0,1551.0,5561.0,1435.0,1.6073,64700.0,INLAND\n-119.73,36.75,39.0,1745.0,321.0,901.0,303.0,3.1719,67900.0,INLAND\n-119.7,36.75,11.0,3626.0,779.0,1819.0,731.0,2.4956,87500.0,INLAND\n-119.71,36.74,18.0,8099.0,1670.0,4476.0,1514.0,2.4728,88300.0,INLAND\n-119.71,36.76,28.0,2675.0,527.0,1392.0,521.0,2.3108,72000.0,INLAND\n-119.7,36.8,34.0,1768.0,303.0,888.0,314.0,3.8088,87700.0,INLAND\n-119.71,36.79,34.0,1891.0,323.0,966.0,355.0,3.6681,82000.0,INLAND\n-119.71,36.77,11.0,5112.0,1384.0,2487.0,1243.0,2.1461,75900.0,INLAND\n-119.73,36.8,15.0,2376.0,538.0,1197.0,510.0,3.1417,74600.0,INLAND\n-119.72,36.8,16.0,2396.0,526.0,1338.0,518.0,2.1653,78800.0,INLAND\n-119.72,36.8,15.0,3045.0,689.0,1340.0,588.0,3.1953,85700.0,INLAND\n-119.71,36.8,17.0,2056.0,366.0,1259.0,367.0,3.9338,84700.0,INLAND\n-119.71,36.8,17.0,1415.0,267.0,861.0,293.0,3.25,81400.0,INLAND\n-119.71,36.81,9.0,1122.0,290.0,662.0,284.0,2.0536,55000.0,INLAND\n-119.7,36.8,31.0,1746.0,321.0,1186.0,360.0,2.6932,66400.0,INLAND\n-119.71,36.8,25.0,875.0,156.0,646.0,166.0,3.0,72800.0,INLAND\n-119.72,36.8,23.0,2128.0,442.0,1047.0,450.0,2.625,71500.0,INLAND\n-119.73,36.8,24.0,1316.0,249.0,781.0,260.0,3.7578,69200.0,INLAND\n-119.72,36.81,15.0,2175.0,564.0,1194.0,482.0,2.6767,87500.0,INLAND\n-119.73,36.77,24.0,4410.0,939.0,2362.0,862.0,2.9406,73000.0,INLAND\n-119.74,36.77,30.0,2427.0,482.0,1375.0,518.0,2.5737,76900.0,INLAND\n-119.75,36.77,32.0,1962.0,399.0,1005.0,392.0,2.6726,70400.0,INLAND\n-119.75,36.78,35.0,1129.0,220.0,474.0,242.0,2.4405,74300.0,INLAND\n-119.75,36.78,33.0,1145.0,197.0,508.0,198.0,2.3333,81300.0,INLAND\n-119.74,36.78,27.0,4049.0,947.0,2254.0,882.0,2.2467,70700.0,INLAND\n-119.76,36.77,36.0,2507.0,466.0,1227.0,474.0,2.785,72300.0,INLAND\n-119.76,36.77,38.0,3804.0,814.0,2142.0,816.0,2.1439,60200.0,INLAND\n-119.77,36.77,38.0,3065.0,658.0,1441.0,625.0,2.0564,64700.0,INLAND\n-119.77,36.78,36.0,3616.0,779.0,1994.0,786.0,2.5434,67300.0,INLAND\n-119.77,36.78,40.0,1411.0,284.0,609.0,296.0,1.9375,67700.0,INLAND\n-119.77,36.77,29.0,2554.0,705.0,2669.0,655.0,1.2176,61900.0,INLAND\n-119.78,36.77,45.0,1315.0,256.0,666.0,240.0,2.3562,58100.0,INLAND\n-119.78,36.78,37.0,2185.0,455.0,1143.0,438.0,1.9784,70700.0,INLAND\n-119.79,36.77,30.0,1610.0,410.0,1000.0,397.0,2.0357,60200.0,INLAND\n-119.8,36.77,52.0,2964.0,512.0,1114.0,486.0,3.8105,87600.0,INLAND\n-119.8,36.78,50.0,1818.0,374.0,737.0,338.0,2.2614,73000.0,INLAND\n-119.79,36.78,41.0,2227.0,462.0,1129.0,415.0,2.319,59100.0,INLAND\n-119.79,36.77,43.0,2323.0,502.0,1144.0,471.0,2.3967,58700.0,INLAND\n-119.81,36.78,52.0,2281.0,371.0,839.0,367.0,3.5972,89900.0,INLAND\n-119.81,36.77,49.0,1749.0,314.0,705.0,300.0,3.15,72200.0,INLAND\n-119.81,36.77,48.0,1805.0,329.0,741.0,331.0,2.5804,78900.0,INLAND\n-119.81,36.77,43.0,2341.0,395.0,890.0,375.0,3.4265,85000.0,INLAND\n-119.81,36.78,37.0,1965.0,364.0,796.0,335.0,3.625,83400.0,INLAND\n-119.82,36.78,36.0,1370.0,289.0,812.0,282.0,2.6127,69600.0,INLAND\n-119.82,36.77,41.0,1441.0,274.0,646.0,296.0,3.0568,71300.0,INLAND\n-119.82,36.77,36.0,2252.0,468.0,1117.0,442.0,2.9081,65600.0,INLAND\n-119.83,36.77,32.0,2867.0,615.0,1705.0,570.0,2.4286,68100.0,INLAND\n-119.83,36.77,23.0,2168.0,503.0,1190.0,425.0,2.625,71600.0,INLAND\n-119.83,36.78,30.0,3162.0,640.0,1660.0,639.0,2.8359,80300.0,INLAND\n-119.87,36.79,8.0,2875.0,548.0,1718.0,551.0,3.6522,80200.0,INLAND\n-119.87,36.79,7.0,1932.0,419.0,1014.0,389.0,3.0938,76700.0,INLAND\n-119.89,36.79,5.0,3821.0,705.0,2179.0,694.0,3.7821,80400.0,INLAND\n-119.87,36.78,4.0,6102.0,1114.0,3406.0,1115.0,3.4213,84500.0,INLAND\n-119.85,36.78,8.0,3096.0,684.0,1454.0,545.0,2.7857,79700.0,INLAND\n-119.86,36.78,7.0,2232.0,490.0,1274.0,499.0,2.9853,74700.0,INLAND\n-119.85,36.77,9.0,1142.0,314.0,620.0,283.0,2.0446,81300.0,INLAND\n-119.85,36.77,27.0,1510.0,344.0,847.0,295.0,2.9315,83200.0,INLAND\n-119.85,36.76,10.0,2067.0,450.0,845.0,354.0,1.8214,80100.0,INLAND\n-119.85,36.75,24.0,1143.0,245.0,608.0,240.0,2.8194,81100.0,INLAND\n-119.87,36.76,34.0,1649.0,323.0,919.0,316.0,2.875,74500.0,INLAND\n-119.9,36.79,22.0,1970.0,332.0,1066.0,319.0,3.3125,106100.0,INLAND\n-119.89,36.76,17.0,1987.0,335.0,1152.0,313.0,4.1719,126400.0,INLAND\n-119.92,36.77,18.0,1422.0,243.0,702.0,230.0,3.6204,119800.0,INLAND\n-120.08,36.79,38.0,1446.0,285.0,928.0,255.0,2.9808,89600.0,INLAND\n-120.21,36.77,20.0,1745.0,348.0,1093.0,302.0,2.3194,90600.0,INLAND\n-120.1,36.66,19.0,2020.0,416.0,1341.0,360.0,1.7,69000.0,INLAND\n-119.98,36.74,26.0,1453.0,251.0,896.0,260.0,3.4861,112500.0,INLAND\n-120.0,36.7,33.0,1902.0,370.0,1168.0,358.0,2.6852,70800.0,INLAND\n-120.04,36.74,14.0,3182.0,730.0,2298.0,721.0,1.6168,71800.0,INLAND\n-120.05,36.72,24.0,1961.0,422.0,1559.0,374.0,1.8299,57800.0,INLAND\n-120.08,36.72,22.0,1339.0,251.0,820.0,276.0,3.6,83200.0,INLAND\n-120.06,36.72,32.0,981.0,237.0,736.0,249.0,1.8,60400.0,INLAND\n-120.07,36.74,19.0,2627.0,502.0,1295.0,441.0,3.087,88200.0,INLAND\n-119.95,36.8,30.0,1233.0,214.0,620.0,199.0,3.4297,112500.0,INLAND\n-119.99,36.8,45.0,1270.0,242.0,598.0,214.0,3.2813,105400.0,INLAND\n-120.02,36.8,25.0,1270.0,255.0,1050.0,245.0,2.1618,55300.0,INLAND\n-120.04,36.79,48.0,1341.0,239.0,671.0,208.0,2.7917,82800.0,INLAND\n-119.91,36.83,29.0,2205.0,366.0,1072.0,345.0,3.8056,165400.0,INLAND\n-119.88,36.81,30.0,2288.0,474.0,1435.0,425.0,1.3221,61200.0,INLAND\n-119.88,36.85,8.0,2580.0,372.0,1111.0,393.0,7.5,256200.0,INLAND\n-119.87,36.83,4.0,4833.0,784.0,2088.0,789.0,5.1781,122500.0,INLAND\n-119.85,36.83,15.0,2563.0,335.0,1080.0,356.0,6.7181,160300.0,INLAND\n-119.85,36.83,11.0,2497.0,427.0,1101.0,405.0,4.8036,141600.0,INLAND\n-119.86,36.82,12.0,1488.0,253.0,675.0,223.0,4.7622,89300.0,INLAND\n-119.85,36.82,9.0,3995.0,778.0,1691.0,712.0,3.3239,91300.0,INLAND\n-119.85,36.82,15.0,1387.0,236.0,638.0,195.0,5.5842,88900.0,INLAND\n-119.85,36.82,16.0,1852.0,274.0,887.0,286.0,5.5405,119300.0,INLAND\n-119.88,36.83,2.0,4055.0,735.0,1730.0,654.0,4.2132,96500.0,INLAND\n-119.86,36.81,4.0,4530.0,1070.0,1804.0,837.0,3.3942,72100.0,INLAND\n-119.87,36.81,6.0,1891.0,341.0,969.0,330.0,4.6726,107800.0,INLAND\n-119.85,36.81,15.0,1743.0,310.0,1011.0,325.0,3.755,68000.0,INLAND\n-119.85,36.8,14.0,1876.0,324.0,1031.0,311.0,3.6563,88800.0,INLAND\n-119.85,36.8,14.0,4177.0,914.0,2300.0,867.0,2.9565,73000.0,INLAND\n-119.86,36.8,18.0,2536.0,516.0,1196.0,466.0,2.5595,67900.0,INLAND\n-119.84,36.85,8.0,3791.0,487.0,1424.0,475.0,10.5144,345900.0,INLAND\n-119.85,36.84,12.0,2272.0,304.0,840.0,305.0,8.9669,213900.0,INLAND\n-119.84,36.84,12.0,2396.0,290.0,863.0,258.0,8.7716,229200.0,INLAND\n-119.84,36.83,17.0,2273.0,298.0,700.0,263.0,6.8645,195900.0,INLAND\n-119.83,36.83,14.0,2351.0,341.0,1128.0,363.0,6.9903,141200.0,INLAND\n-119.82,36.83,14.0,2982.0,412.0,1408.0,423.0,5.3241,123000.0,INLAND\n-119.84,36.83,17.0,3012.0,408.0,987.0,362.0,7.4201,229700.0,INLAND\n-119.82,36.83,16.0,2868.0,376.0,1016.0,379.0,6.1175,144700.0,INLAND\n-119.84,36.82,17.0,2807.0,376.0,996.0,353.0,5.5357,167700.0,INLAND\n-119.83,36.82,14.0,1087.0,165.0,365.0,176.0,7.2909,155600.0,INLAND\n-119.82,36.82,28.0,2268.0,336.0,752.0,330.0,5.2809,151500.0,INLAND\n-119.82,36.81,25.0,3305.0,551.0,1149.0,500.0,5.0698,150900.0,INLAND\n-119.84,36.81,18.0,2789.0,378.0,937.0,364.0,7.7062,188300.0,INLAND\n-119.78,36.86,10.0,2902.0,363.0,1200.0,363.0,8.3608,187300.0,INLAND\n-119.77,36.86,7.0,4139.0,544.0,1843.0,562.0,8.2737,193500.0,INLAND\n-119.77,36.85,8.0,1519.0,234.0,711.0,248.0,5.9897,123600.0,INLAND\n-119.77,36.84,15.0,1924.0,262.0,848.0,277.0,5.3886,125300.0,INLAND\n-119.77,36.84,15.0,2058.0,412.0,891.0,378.0,3.2569,124400.0,INLAND\n-119.78,36.84,7.0,4907.0,1075.0,2014.0,909.0,3.2147,111900.0,INLAND\n-119.78,36.85,12.0,782.0,166.0,292.0,164.0,2.8274,79500.0,INLAND\n-119.78,36.86,8.0,3468.0,675.0,1604.0,626.0,4.2071,128300.0,INLAND\n-119.79,36.85,11.0,2596.0,619.0,1765.0,539.0,1.9511,54000.0,INLAND\n-119.79,36.84,22.0,1529.0,375.0,1543.0,395.0,1.7926,51700.0,INLAND\n-119.8,36.86,7.0,6434.0,1201.0,2733.0,1045.0,3.7656,145000.0,INLAND\n-119.81,36.85,17.0,2340.0,370.0,1174.0,396.0,4.2304,94400.0,INLAND\n-119.82,36.84,7.0,2289.0,342.0,1077.0,354.0,5.4868,158800.0,INLAND\n-119.82,36.84,9.0,2340.0,544.0,860.0,520.0,3.3229,119300.0,INLAND\n-119.81,36.83,19.0,6789.0,1200.0,2325.0,1109.0,4.049,126000.0,INLAND\n-119.81,36.83,10.0,5780.0,922.0,2712.0,883.0,5.6445,135500.0,INLAND\n-119.78,36.83,11.0,2754.0,663.0,1328.0,604.0,2.3667,69300.0,INLAND\n-119.79,36.82,25.0,2330.0,462.0,1215.0,467.0,3.2143,93000.0,INLAND\n-119.8,36.83,17.0,1560.0,261.0,709.0,258.0,4.3315,95800.0,INLAND\n-119.79,36.83,15.0,3356.0,694.0,1232.0,627.0,2.2215,72200.0,INLAND\n-119.79,36.82,23.0,4358.0,819.0,1852.0,802.0,3.4167,105200.0,INLAND\n-119.78,36.82,22.0,4241.0,1147.0,1929.0,971.0,1.7708,53500.0,INLAND\n-119.8,36.82,24.0,5377.0,1005.0,2010.0,982.0,3.4542,121200.0,INLAND\n-119.81,36.81,33.0,3972.0,594.0,1324.0,561.0,5.4513,143300.0,INLAND\n-119.8,36.8,43.0,1951.0,288.0,725.0,308.0,6.3359,169300.0,INLAND\n-119.81,36.8,38.0,2252.0,325.0,777.0,314.0,6.1575,160100.0,INLAND\n-119.83,36.8,24.0,3756.0,681.0,1586.0,739.0,3.8571,90100.0,INLAND\n-119.82,36.8,33.0,1670.0,256.0,528.0,250.0,2.9471,99500.0,INLAND\n-119.81,36.8,29.0,2806.0,552.0,1242.0,540.0,3.5958,88800.0,INLAND\n-119.83,36.8,16.0,6101.0,1200.0,3407.0,1134.0,3.125,80800.0,INLAND\n-119.84,36.8,16.0,2849.0,506.0,1508.0,478.0,3.4074,72700.0,INLAND\n-119.84,36.8,19.0,3244.0,776.0,1463.0,710.0,2.0469,66900.0,INLAND\n-119.83,36.79,35.0,1872.0,363.0,1054.0,369.0,3.3272,65600.0,INLAND\n-119.83,36.78,35.0,1789.0,357.0,933.0,357.0,2.5223,66200.0,INLAND\n-119.84,36.78,24.0,3242.0,795.0,2764.0,773.0,1.3385,58800.0,INLAND\n-119.84,36.79,21.0,3235.0,648.0,1820.0,614.0,3.3447,71400.0,INLAND\n-119.83,36.79,24.0,3505.0,819.0,2098.0,774.0,1.9575,67000.0,INLAND\n-119.81,36.78,35.0,1012.0,245.0,633.0,240.0,2.0324,55500.0,INLAND\n-119.81,36.78,36.0,1650.0,313.0,660.0,298.0,3.0,79700.0,INLAND\n-119.82,36.78,36.0,1582.0,313.0,761.0,318.0,2.6055,69200.0,INLAND\n-119.82,36.79,35.0,1474.0,291.0,709.0,294.0,2.6522,65900.0,INLAND\n-119.82,36.79,18.0,5822.0,1439.0,3415.0,1224.0,1.6854,64700.0,INLAND\n-119.81,36.79,35.0,2314.0,443.0,954.0,457.0,2.9506,73800.0,INLAND\n-119.79,36.79,33.0,3433.0,785.0,1806.0,783.0,1.9386,67500.0,INLAND\n-119.79,36.78,38.0,1912.0,456.0,1131.0,408.0,2.03,58800.0,INLAND\n-119.8,36.78,43.0,2382.0,431.0,874.0,380.0,3.5542,96500.0,INLAND\n-119.81,36.79,39.0,2471.0,460.0,1118.0,431.0,2.4167,71900.0,INLAND\n-119.8,36.79,45.0,1337.0,187.0,471.0,187.0,5.187,153800.0,INLAND\n-119.79,36.81,35.0,1877.0,328.0,1155.0,353.0,3.069,69600.0,INLAND\n-119.78,36.8,34.0,2200.0,493.0,1243.0,431.0,1.8514,66500.0,INLAND\n-119.79,36.8,27.0,2462.0,484.0,852.0,449.0,3.32,124700.0,INLAND\n-119.79,36.81,33.0,1461.0,261.0,494.0,254.0,4.25,132200.0,INLAND\n-119.77,36.79,34.0,2679.0,460.0,1141.0,470.0,3.2642,89600.0,INLAND\n-119.78,36.78,31.0,2164.0,456.0,959.0,463.0,2.3293,73400.0,INLAND\n-119.79,36.79,26.0,1700.0,423.0,909.0,386.0,2.256,64500.0,INLAND\n-119.79,36.79,19.0,1524.0,448.0,960.0,386.0,1.5122,47500.0,INLAND\n-119.78,36.79,33.0,2260.0,440.0,966.0,413.0,2.9301,68300.0,INLAND\n-119.76,36.79,26.0,3654.0,837.0,1976.0,830.0,2.1544,72800.0,INLAND\n-119.76,36.78,30.0,6117.0,1330.0,2768.0,1224.0,2.1383,78800.0,INLAND\n-119.77,36.79,27.0,2258.0,427.0,1076.0,423.0,2.9937,81100.0,INLAND\n-119.76,36.79,32.0,2463.0,468.0,1261.0,486.0,3.3281,75100.0,INLAND\n-119.74,36.79,28.0,2857.0,619.0,1614.0,592.0,2.1573,71400.0,INLAND\n-119.75,36.78,28.0,3257.0,752.0,1981.0,712.0,2.293,71700.0,INLAND\n-119.76,36.8,29.0,3494.0,662.0,1781.0,616.0,2.5893,70900.0,INLAND\n-119.77,36.8,32.0,3461.0,665.0,1507.0,649.0,2.9244,84600.0,INLAND\n-119.78,36.8,34.0,3426.0,623.0,1938.0,647.0,2.8994,66000.0,INLAND\n-119.76,36.8,20.0,6257.0,1346.0,2795.0,1267.0,2.2094,83700.0,INLAND\n-119.77,36.8,24.0,3748.0,770.0,1827.0,719.0,2.7222,83100.0,INLAND\n-119.74,36.8,18.0,10862.0,2401.0,5466.0,2209.0,2.4678,74300.0,INLAND\n-119.75,36.8,25.0,2718.0,504.0,1257.0,465.0,2.3333,90600.0,INLAND\n-119.75,36.8,30.0,3308.0,662.0,1894.0,648.0,2.197,74500.0,INLAND\n-119.76,36.81,19.0,4643.0,1429.0,4638.0,1335.0,1.2716,69400.0,INLAND\n-119.77,36.81,25.0,1565.0,271.0,661.0,275.0,3.4279,84700.0,INLAND\n-119.76,36.82,17.0,6932.0,1486.0,3056.0,1453.0,2.3375,99300.0,INLAND\n-119.77,36.81,28.0,1713.0,302.0,663.0,282.0,3.567,85500.0,INLAND\n-119.78,36.82,25.0,5016.0,,2133.0,928.0,3.625,89500.0,INLAND\n-119.78,36.83,18.0,4164.0,741.0,1817.0,681.0,4.2153,95200.0,INLAND\n-119.76,36.83,22.0,2803.0,438.0,1234.0,457.0,4.5179,99600.0,INLAND\n-119.76,36.83,20.0,3214.0,446.0,1360.0,463.0,5.2595,110900.0,INLAND\n-119.77,36.83,19.0,3237.0,507.0,1378.0,510.0,4.7804,101100.0,INLAND\n-119.77,36.83,16.0,2360.0,355.0,1034.0,359.0,5.0635,108500.0,INLAND\n-119.76,36.83,17.0,3690.0,628.0,1888.0,601.0,4.0196,84200.0,INLAND\n-119.75,36.83,15.0,2793.0,436.0,1411.0,441.0,4.9292,109400.0,INLAND\n-119.74,36.83,14.0,4675.0,829.0,2235.0,787.0,4.1098,108200.0,INLAND\n-119.77,36.91,3.0,7520.0,1143.0,2878.0,1077.0,5.3272,174200.0,INLAND\n-119.75,36.87,3.0,13802.0,2244.0,5226.0,1972.0,5.0941,143700.0,INLAND\n-119.74,36.85,3.0,10425.0,2121.0,4432.0,1778.0,3.9032,140800.0,INLAND\n-119.7,36.94,15.0,1449.0,277.0,649.0,265.0,2.4861,86300.0,INLAND\n-119.71,36.88,17.0,2236.0,315.0,992.0,312.0,6.9405,165200.0,INLAND\n-119.69,36.86,20.0,1676.0,263.0,786.0,240.0,4.0,164600.0,INLAND\n-119.69,36.85,20.0,2655.0,432.0,1081.0,379.0,4.5398,143100.0,INLAND\n-119.67,36.89,15.0,2373.0,364.0,1280.0,386.0,5.308,167500.0,INLAND\n-119.7,36.83,23.0,3532.0,756.0,1885.0,758.0,2.5904,71400.0,INLAND\n-119.7,36.82,25.0,2379.0,540.0,1482.0,484.0,2.3173,68200.0,INLAND\n-119.7,36.81,32.0,2623.0,528.0,1570.0,492.0,2.7159,68000.0,INLAND\n-119.71,36.83,5.0,1087.0,338.0,623.0,362.0,1.8061,113400.0,INLAND\n-119.71,36.83,15.0,2727.0,500.0,1228.0,436.0,3.5078,109000.0,INLAND\n-119.73,36.83,8.0,3602.0,,1959.0,580.0,5.3478,138800.0,INLAND\n-119.73,36.83,14.0,3348.0,491.0,1584.0,493.0,5.0828,111400.0,INLAND\n-119.71,36.82,12.0,2144.0,568.0,1320.0,566.0,2.3381,112500.0,INLAND\n-119.71,36.81,19.0,2282.0,550.0,1034.0,500.0,1.6618,69700.0,INLAND\n-119.71,36.81,19.0,1648.0,368.0,557.0,354.0,1.7969,72800.0,INLAND\n-119.72,36.81,28.0,1651.0,305.0,780.0,309.0,2.9453,72200.0,INLAND\n-119.73,36.81,19.0,1699.0,356.0,994.0,368.0,2.7778,79700.0,INLAND\n-119.72,36.82,16.0,2627.0,613.0,1054.0,623.0,1.9483,112500.0,INLAND\n-119.72,36.82,15.0,946.0,239.0,550.0,246.0,2.2639,52500.0,INLAND\n-119.69,36.83,7.0,2075.0,353.0,1040.0,362.0,3.9943,100200.0,INLAND\n-119.69,36.83,8.0,943.0,189.0,475.0,155.0,4.9327,89500.0,INLAND\n-119.69,36.83,32.0,1098.0,,726.0,224.0,1.4913,54600.0,INLAND\n-119.69,36.83,28.0,1868.0,350.0,898.0,329.0,3.1814,78900.0,INLAND\n-119.67,36.83,3.0,2029.0,336.0,1003.0,340.0,4.4356,111300.0,INLAND\n-119.67,36.83,4.0,2145.0,334.0,1024.0,308.0,5.0864,113700.0,INLAND\n-119.68,36.83,11.0,2455.0,344.0,1110.0,339.0,6.1133,120000.0,INLAND\n-119.67,36.82,2.0,2579.0,376.0,1133.0,342.0,4.5577,123300.0,INLAND\n-119.67,36.81,4.0,1262.0,216.0,622.0,199.0,4.9432,114400.0,INLAND\n-119.68,36.81,13.0,2589.0,413.0,1356.0,435.0,5.0253,106200.0,INLAND\n-119.69,36.82,17.0,1897.0,433.0,1207.0,384.0,1.8021,55900.0,INLAND\n-119.69,36.82,15.0,3303.0,512.0,1687.0,505.0,4.81,93600.0,INLAND\n-119.68,36.81,16.0,2668.0,454.0,1536.0,457.0,3.9792,88900.0,INLAND\n-119.69,36.81,13.0,1524.0,366.0,994.0,370.0,2.5446,93800.0,INLAND\n-119.69,36.81,15.0,2892.0,496.0,1634.0,501.0,4.4934,88000.0,INLAND\n-119.68,36.8,7.0,2855.0,518.0,1748.0,498.0,4.2066,88400.0,INLAND\n-119.69,36.8,31.0,2576.0,458.0,1306.0,418.0,3.2813,68700.0,INLAND\n-119.67,36.8,9.0,3712.0,508.0,1632.0,474.0,6.011,163100.0,INLAND\n-119.69,36.79,13.0,1736.0,313.0,993.0,314.0,3.7697,83600.0,INLAND\n-119.68,36.79,16.0,1551.0,,1010.0,292.0,3.5417,71300.0,INLAND\n-119.69,36.79,15.0,2524.0,451.0,1207.0,424.0,2.7404,76300.0,INLAND\n-119.69,36.77,22.0,2456.0,496.0,1720.0,417.0,2.6875,60600.0,INLAND\n-119.69,36.79,5.0,2613.0,476.0,1490.0,481.0,4.0993,83000.0,INLAND\n-119.68,36.77,21.0,1260.0,182.0,583.0,205.0,6.0132,150800.0,INLAND\n-119.64,36.85,15.0,2397.0,353.0,1258.0,347.0,4.9904,157300.0,INLAND\n-119.64,36.82,14.0,4872.0,656.0,2085.0,617.0,5.6739,173800.0,INLAND\n-119.63,36.79,19.0,1317.0,189.0,517.0,187.0,4.526,148700.0,INLAND\n-119.63,36.76,22.0,4126.0,614.0,1795.0,613.0,4.925,154700.0,INLAND\n-119.63,36.7,42.0,1338.0,215.0,617.0,222.0,3.0833,133300.0,INLAND\n-119.53,36.78,20.0,2822.0,479.0,1372.0,455.0,4.5625,136900.0,INLAND\n-119.58,36.83,13.0,6135.0,863.0,2473.0,774.0,5.4895,156700.0,INLAND\n-119.58,36.77,19.0,3225.0,548.0,1760.0,542.0,4.0227,126500.0,INLAND\n-119.59,36.72,18.0,1284.0,193.0,621.0,190.0,4.5375,130600.0,INLAND\n-119.5,36.74,20.0,1089.0,208.0,531.0,212.0,4.5938,106900.0,INLAND\n-119.57,36.72,11.0,2510.0,460.0,1248.0,445.0,3.6161,99500.0,INLAND\n-119.56,36.71,29.0,1963.0,392.0,1208.0,398.0,2.5741,73000.0,INLAND\n-119.56,36.71,37.0,1609.0,374.0,1173.0,344.0,2.181,59900.0,INLAND\n-119.57,36.71,10.0,1657.0,359.0,958.0,380.0,2.6458,84800.0,INLAND\n-119.57,36.7,34.0,1759.0,354.0,899.0,337.0,2.6823,72900.0,INLAND\n-119.56,36.7,40.0,1195.0,326.0,1135.0,315.0,2.1182,58900.0,INLAND\n-119.55,36.7,31.0,1671.0,372.0,1371.0,347.0,2.3687,63900.0,INLAND\n-119.57,36.7,30.0,2370.0,412.0,1248.0,410.0,3.1442,72300.0,INLAND\n-119.57,36.7,7.0,1761.0,309.0,974.0,308.0,3.7261,83900.0,INLAND\n-119.58,36.69,42.0,1032.0,215.0,812.0,225.0,1.9766,58100.0,INLAND\n-119.55,36.69,21.0,1551.0,423.0,1519.0,406.0,1.7132,55900.0,INLAND\n-119.54,36.7,20.0,1815.0,375.0,1665.0,357.0,2.2448,58900.0,INLAND\n-119.55,36.71,32.0,1963.0,508.0,2052.0,518.0,1.9076,55800.0,INLAND\n-119.55,36.72,6.0,1186.0,234.0,1135.0,218.0,2.1515,63900.0,INLAND\n-119.52,36.71,21.0,1834.0,321.0,1120.0,314.0,2.59,69300.0,INLAND\n-119.47,36.69,19.0,3351.0,589.0,1578.0,542.0,3.2917,160100.0,INLAND\n-119.41,36.68,18.0,1802.0,332.0,945.0,292.0,3.4044,115300.0,INLAND\n-119.43,36.63,25.0,1784.0,312.0,904.0,303.0,3.625,107600.0,INLAND\n-119.39,36.64,38.0,949.0,190.0,578.0,187.0,2.3618,80000.0,INLAND\n-119.4,36.59,37.0,1486.0,296.0,977.0,290.0,3.5074,93800.0,INLAND\n-119.49,37.1,24.0,2532.0,555.0,1564.0,507.0,2.3359,92400.0,INLAND\n-119.61,36.94,14.0,863.0,151.0,315.0,135.0,4.2679,151800.0,INLAND\n-119.57,37.02,16.0,4199.0,794.0,2140.0,722.0,3.332,111800.0,INLAND\n-119.48,37.0,16.0,2904.0,551.0,1467.0,509.0,3.1736,111800.0,INLAND\n-119.46,36.91,12.0,2980.0,495.0,1184.0,429.0,3.9141,123900.0,INLAND\n-119.33,36.89,15.0,1879.0,411.0,755.0,294.0,2.0,83300.0,INLAND\n-119.21,37.25,44.0,3042.0,697.0,335.0,115.0,4.1838,85600.0,INLAND\n-118.94,37.13,12.0,2255.0,472.0,1006.0,334.0,4.1563,94000.0,INLAND\n-119.4,37.09,22.0,2211.0,477.0,773.0,288.0,3.3269,102700.0,INLAND\n-119.34,37.12,23.0,1881.0,380.0,64.0,37.0,3.875,125000.0,INLAND\n-119.28,37.11,34.0,1901.0,394.0,171.0,73.0,3.0729,144600.0,INLAND\n-119.32,37.06,15.0,3111.0,651.0,276.0,107.0,5.1314,179200.0,INLAND\n-118.91,36.79,19.0,1616.0,324.0,187.0,80.0,3.7857,78600.0,INLAND\n-119.24,36.8,17.0,2052.0,405.0,975.0,340.0,2.6902,94400.0,INLAND\n-119.25,36.71,13.0,2813.0,579.0,1385.0,512.0,2.2202,100000.0,INLAND\n-119.12,36.69,13.0,3963.0,812.0,1905.0,671.0,2.2278,90500.0,INLAND\n-119.34,36.62,26.0,1922.0,339.0,1148.0,332.0,2.6058,92200.0,INLAND\n-119.31,36.63,26.0,1874.0,416.0,1834.0,432.0,1.6486,55200.0,INLAND\n-119.31,36.62,33.0,1485.0,374.0,1544.0,329.0,1.7292,52000.0,INLAND\n-119.32,36.62,15.0,1070.0,256.0,1070.0,243.0,1.5642,51500.0,INLAND\n-119.31,36.62,25.0,831.0,230.0,947.0,244.0,1.4481,51700.0,INLAND\n-119.43,36.61,19.0,1484.0,296.0,1296.0,298.0,2.4219,65800.0,INLAND\n-119.44,36.6,5.0,2353.0,608.0,2505.0,573.0,2.2863,69200.0,INLAND\n-119.44,36.6,34.0,864.0,184.0,579.0,171.0,2.0417,72500.0,INLAND\n-119.45,36.6,42.0,510.0,88.0,247.0,99.0,2.5,73000.0,INLAND\n-119.46,36.61,13.0,1348.0,258.0,719.0,246.0,3.625,108300.0,INLAND\n-119.45,36.61,24.0,1302.0,,693.0,243.0,3.7917,90500.0,INLAND\n-119.44,36.61,17.0,1531.0,280.0,775.0,246.0,3.9073,91600.0,INLAND\n-119.43,36.59,15.0,1371.0,306.0,1266.0,309.0,1.767,63300.0,INLAND\n-119.44,36.59,32.0,1153.0,236.0,761.0,241.0,2.825,67600.0,INLAND\n-119.45,36.6,36.0,2294.0,489.0,1430.0,454.0,1.8975,60900.0,INLAND\n-119.44,36.59,28.0,1343.0,330.0,1331.0,305.0,1.516,56700.0,INLAND\n-119.46,36.6,18.0,1404.0,226.0,754.0,229.0,3.9844,118100.0,INLAND\n-119.45,36.59,41.0,1749.0,342.0,1171.0,314.0,1.6875,66100.0,INLAND\n-119.44,36.58,37.0,1054.0,,879.0,257.0,2.5234,63500.0,INLAND\n-119.45,36.58,18.0,1425.0,280.0,753.0,266.0,3.7813,87300.0,INLAND\n-119.45,36.59,28.0,1274.0,215.0,572.0,202.0,3.825,84200.0,INLAND\n-119.55,36.6,18.0,2379.0,448.0,1638.0,436.0,2.309,57100.0,INLAND\n-119.54,36.61,20.0,1490.0,318.0,1474.0,326.0,1.4937,54700.0,INLAND\n-119.55,36.61,14.0,3004.0,793.0,3535.0,735.0,1.586,56900.0,INLAND\n-119.5,36.62,34.0,1440.0,267.0,1018.0,265.0,2.2206,63400.0,INLAND\n-119.49,36.58,21.0,2106.0,410.0,867.0,380.0,1.9913,95300.0,INLAND\n-119.52,36.61,33.0,1225.0,275.0,1065.0,248.0,1.8958,55100.0,INLAND\n-119.53,36.61,33.0,587.0,170.0,730.0,162.0,1.5625,55800.0,INLAND\n-119.6,36.66,27.0,1388.0,296.0,1056.0,284.0,1.6094,55200.0,INLAND\n-119.59,36.64,27.0,823.0,171.0,798.0,200.0,3.0521,113800.0,INLAND\n-119.53,36.65,43.0,1676.0,320.0,1056.0,276.0,2.5562,93200.0,INLAND\n-119.63,36.6,33.0,1589.0,294.0,1102.0,307.0,1.9676,62400.0,INLAND\n-119.62,36.58,13.0,1788.0,405.0,1652.0,411.0,2.6858,62400.0,INLAND\n-119.59,36.57,19.0,1733.0,303.0,911.0,281.0,3.5987,131700.0,INLAND\n-119.6,36.58,28.0,1452.0,300.0,919.0,308.0,2.8287,73100.0,INLAND\n-119.61,36.58,29.0,1312.0,280.0,788.0,271.0,2.6974,73000.0,INLAND\n-119.62,36.59,17.0,2287.0,390.0,1330.0,393.0,4.0197,88000.0,INLAND\n-119.61,36.59,10.0,2842.0,620.0,1443.0,576.0,2.2727,92700.0,INLAND\n-119.6,36.57,42.0,2311.0,439.0,1347.0,436.0,2.5556,69700.0,INLAND\n-119.6,36.57,33.0,1923.0,403.0,1205.0,389.0,1.8333,68300.0,INLAND\n-119.61,36.57,42.0,2242.0,521.0,1359.0,483.0,1.5833,65100.0,INLAND\n-119.63,36.58,22.0,1794.0,435.0,1127.0,359.0,1.2647,55300.0,INLAND\n-119.6,36.56,36.0,738.0,168.0,737.0,186.0,1.4415,54400.0,INLAND\n-119.64,36.56,34.0,576.0,117.0,363.0,97.0,2.0658,92500.0,INLAND\n-119.61,36.56,34.0,1911.0,497.0,1886.0,481.0,1.625,53000.0,INLAND\n-119.62,36.56,30.0,1722.0,372.0,1467.0,403.0,1.8878,51600.0,INLAND\n-119.53,36.55,34.0,2065.0,343.0,1041.0,313.0,3.2917,111500.0,INLAND\n-119.54,36.52,16.0,2703.0,415.0,1106.0,372.0,4.2045,120900.0,INLAND\n-119.55,36.51,46.0,1889.0,390.0,971.0,403.0,2.2132,76600.0,INLAND\n-119.56,36.51,37.0,1018.0,213.0,663.0,204.0,1.6635,67000.0,INLAND\n-119.56,36.51,9.0,3860.0,809.0,2157.0,770.0,2.5033,70100.0,INLAND\n-119.55,36.52,31.0,1986.0,417.0,1042.0,422.0,3.0294,70200.0,INLAND\n-119.56,36.53,19.0,2746.0,495.0,1670.0,518.0,3.2019,95700.0,INLAND\n-119.73,36.56,32.0,1513.0,272.0,1038.0,272.0,3.0469,82700.0,INLAND\n-119.67,36.57,32.0,1604.0,292.0,868.0,276.0,2.1908,110000.0,INLAND\n-119.65,36.51,30.0,1671.0,319.0,966.0,282.0,3.1333,100000.0,INLAND\n-119.59,36.52,35.0,990.0,192.0,674.0,178.0,3.3214,101600.0,INLAND\n-119.73,36.52,20.0,1741.0,331.0,1466.0,289.0,2.5921,94200.0,INLAND\n-119.77,36.44,26.0,1727.0,289.0,802.0,259.0,3.2083,75000.0,INLAND\n-119.69,36.46,29.0,1702.0,301.0,914.0,280.0,2.8125,79200.0,INLAND\n-119.69,36.43,29.0,1799.0,356.0,1278.0,387.0,1.7813,57900.0,INLAND\n-119.87,36.54,34.0,1370.0,287.0,818.0,269.0,2.4044,72500.0,INLAND\n-119.79,36.55,32.0,1393.0,276.0,999.0,245.0,2.0216,76800.0,INLAND\n-119.81,36.51,31.0,1241.0,254.0,767.0,226.0,2.7321,83600.0,INLAND\n-119.84,36.54,19.0,1310.0,241.0,702.0,217.0,2.4375,78200.0,INLAND\n-119.83,36.54,31.0,1732.0,332.0,979.0,294.0,2.5208,60000.0,INLAND\n-119.89,36.64,34.0,1422.0,237.0,716.0,222.0,2.975,90000.0,INLAND\n-119.97,36.57,17.0,1497.0,308.0,1425.0,247.0,2.0313,69400.0,INLAND\n-119.9,36.58,20.0,1935.0,363.0,1319.0,359.0,2.4814,74600.0,INLAND\n-119.81,36.6,24.0,2246.0,462.0,1291.0,394.0,2.4006,76400.0,INLAND\n-119.86,36.45,19.0,2439.0,462.0,1416.0,469.0,2.4474,75600.0,INLAND\n-119.86,36.43,34.0,1175.0,251.0,683.0,261.0,1.7176,58400.0,INLAND\n-119.85,36.43,23.0,1824.0,354.0,1146.0,362.0,2.8913,60900.0,INLAND\n-119.97,36.44,18.0,1128.0,237.0,772.0,220.0,2.1771,39200.0,INLAND\n-120.08,36.34,18.0,1524.0,414.0,2030.0,356.0,2.1153,112500.0,INLAND\n-120.1,36.16,17.0,598.0,160.0,715.0,146.0,2.3295,55000.0,INLAND\n-120.1,36.21,12.0,1462.0,356.0,1708.0,367.0,1.5086,64700.0,INLAND\n-120.09,36.19,12.0,1923.0,559.0,2809.0,535.0,1.4191,55100.0,INLAND\n-120.27,36.29,11.0,1337.0,412.0,1376.0,318.0,2.4398,87500.0,INLAND\n-120.43,36.18,29.0,579.0,116.0,218.0,99.0,2.1458,104200.0,INLAND\n-120.37,36.16,36.0,613.0,124.0,310.0,124.0,3.0658,65000.0,INLAND\n-120.36,36.14,18.0,1206.0,274.0,622.0,217.0,1.8264,62000.0,INLAND\n-120.38,36.15,17.0,2279.0,448.0,1200.0,420.0,2.7461,70000.0,INLAND\n-120.37,36.13,10.0,2522.0,533.0,1335.0,493.0,3.2639,86400.0,INLAND\n-120.37,36.15,34.0,2084.0,339.0,868.0,347.0,4.381,86300.0,INLAND\n-120.35,36.16,18.0,1519.0,296.0,846.0,272.0,2.7792,85300.0,INLAND\n-120.35,36.14,9.0,2671.0,647.0,1484.0,541.0,1.7075,60400.0,INLAND\n-120.36,36.13,29.0,1938.0,434.0,1306.0,415.0,3.0134,55500.0,INLAND\n-120.31,36.65,24.0,943.0,209.0,514.0,156.0,2.25,76600.0,INLAND\n-120.25,36.65,31.0,1177.0,221.0,744.0,223.0,2.4937,66000.0,INLAND\n-120.18,36.59,25.0,948.0,198.0,613.0,171.0,2.3026,90600.0,INLAND\n-120.19,36.61,29.0,1479.0,338.0,1408.0,322.0,2.293,57200.0,INLAND\n-120.19,36.6,25.0,875.0,214.0,931.0,214.0,1.5536,58300.0,INLAND\n-120.22,36.49,14.0,1508.0,347.0,1679.0,345.0,2.4786,56000.0,INLAND\n-120.41,36.77,24.0,1335.0,312.0,1180.0,267.0,1.947,68900.0,INLAND\n-120.67,36.72,18.0,819.0,198.0,996.0,198.0,2.5,112500.0,INLAND\n-120.51,36.55,20.0,1193.0,263.0,1274.0,241.0,1.9417,38800.0,INLAND\n-120.39,36.78,11.0,1947.0,488.0,2104.0,486.0,1.7184,55200.0,INLAND\n-120.38,36.75,25.0,1689.0,495.0,1745.0,457.0,1.9056,60000.0,INLAND\n-120.38,36.76,25.0,991.0,272.0,941.0,262.0,1.8125,58000.0,INLAND\n-120.38,36.76,12.0,932.0,244.0,1043.0,243.0,1.4038,54300.0,INLAND\n-120.46,36.87,20.0,1287.0,310.0,954.0,269.0,1.3386,63000.0,INLAND\n-120.45,36.86,34.0,673.0,173.0,539.0,182.0,2.3523,66000.0,INLAND\n-120.44,36.84,29.0,1563.0,293.0,883.0,288.0,2.8182,90500.0,INLAND\n-120.45,36.85,20.0,1519.0,376.0,1681.0,370.0,2.1759,58100.0,INLAND\n-120.51,36.86,21.0,1779.0,399.0,1446.0,371.0,2.4414,71900.0,INLAND\n-120.55,36.97,42.0,1766.0,344.0,1084.0,323.0,2.3295,74400.0,INLAND\n-120.69,36.84,18.0,902.0,195.0,771.0,174.0,2.2083,55000.0,INLAND\n-119.55,36.51,46.0,55.0,11.0,26.0,5.0,4.125,67500.0,INLAND\n-119.54,36.51,36.0,49.0,7.0,28.0,2.0,4.625,162500.0,INLAND\n-122.2,39.75,18.0,2603.0,576.0,1616.0,588.0,2.0192,63700.0,INLAND\n-122.18,39.75,30.0,4157.0,834.0,1885.0,774.0,1.6948,67500.0,INLAND\n-122.16,39.74,20.0,707.0,126.0,337.0,125.0,3.0469,85000.0,INLAND\n-122.19,39.74,39.0,4179.0,814.0,2111.0,809.0,2.3507,65600.0,INLAND\n-122.25,39.79,16.0,2127.0,412.0,1104.0,369.0,3.0469,72200.0,INLAND\n-122.16,39.78,32.0,1288.0,221.0,562.0,203.0,2.325,69600.0,INLAND\n-122.13,39.74,20.0,1401.0,280.0,668.0,250.0,2.2569,94300.0,INLAND\n-122.18,39.7,23.0,1658.0,307.0,836.0,297.0,3.35,85400.0,INLAND\n-122.23,39.75,16.0,2026.0,396.0,1031.0,382.0,1.9375,73100.0,INLAND\n-122.74,39.71,16.0,255.0,73.0,85.0,38.0,1.6607,14999.0,INLAND\n-122.38,39.68,21.0,1155.0,210.0,510.0,175.0,2.3851,67500.0,INLAND\n-122.14,39.65,33.0,419.0,77.0,190.0,67.0,3.6429,87500.0,INLAND\n-122.18,39.55,28.0,1471.0,259.0,673.0,246.0,3.25,81600.0,INLAND\n-122.53,39.5,25.0,1231.0,240.0,658.0,211.0,2.4861,71900.0,INLAND\n-122.19,39.53,34.0,2679.0,533.0,1287.0,505.0,2.165,58700.0,INLAND\n-122.2,39.53,22.0,3265.0,658.0,1647.0,594.0,2.3566,71000.0,INLAND\n-122.23,39.53,8.0,1268.0,336.0,1237.0,326.0,1.3708,125000.0,INLAND\n-122.22,39.51,17.0,1201.0,268.0,555.0,277.0,2.1,66900.0,INLAND\n-122.2,39.52,39.0,2551.0,482.0,1181.0,437.0,2.0625,63400.0,INLAND\n-122.2,39.51,37.0,2358.0,413.0,1060.0,424.0,2.8333,69700.0,INLAND\n-122.19,39.5,23.0,462.0,97.0,261.0,90.0,2.1705,53000.0,INLAND\n-122.04,39.72,23.0,2502.0,481.0,1443.0,455.0,2.5625,70000.0,INLAND\n-122.05,39.6,34.0,2051.0,342.0,958.0,322.0,2.8466,95300.0,INLAND\n-122.1,39.47,43.0,1320.0,215.0,512.0,197.0,2.4917,77100.0,INLAND\n-121.94,39.45,39.0,844.0,161.0,535.0,165.0,1.832,70500.0,INLAND\n-122.01,39.74,20.0,2332.0,518.0,1856.0,495.0,2.1746,58700.0,INLAND\n-124.17,40.8,52.0,661.0,316.0,392.0,244.0,0.957,60000.0,NEAR OCEAN\n-124.16,40.8,52.0,2416.0,618.0,1150.0,571.0,1.7308,80500.0,NEAR OCEAN\n-124.16,40.79,52.0,1264.0,277.0,591.0,284.0,1.7778,76900.0,NEAR OCEAN\n-124.17,40.8,52.0,1606.0,419.0,891.0,367.0,1.585,75500.0,NEAR OCEAN\n-124.17,40.8,52.0,1557.0,344.0,758.0,319.0,1.8529,62500.0,NEAR OCEAN\n-124.16,40.79,52.0,2148.0,421.0,975.0,430.0,2.2566,92700.0,NEAR OCEAN\n-124.16,40.78,50.0,2285.0,403.0,837.0,353.0,2.5417,85400.0,NEAR OCEAN\n-124.17,40.78,39.0,1606.0,330.0,731.0,327.0,1.6369,68300.0,NEAR OCEAN\n-124.19,40.78,37.0,1371.0,319.0,640.0,260.0,1.8242,70000.0,NEAR OCEAN\n-124.18,40.79,39.0,1836.0,352.0,883.0,337.0,1.745,70500.0,NEAR OCEAN\n-124.18,40.79,40.0,1398.0,311.0,788.0,279.0,1.4668,64600.0,NEAR OCEAN\n-124.17,40.79,43.0,2285.0,479.0,1169.0,482.0,1.9688,70500.0,NEAR OCEAN\n-124.16,40.78,43.0,2241.0,446.0,932.0,395.0,2.9038,82000.0,NEAR OCEAN\n-124.16,40.77,35.0,2141.0,438.0,1053.0,434.0,2.8529,85600.0,NEAR OCEAN\n-124.17,40.77,30.0,1895.0,366.0,990.0,359.0,2.2227,81300.0,NEAR OCEAN\n-124.18,40.78,37.0,1453.0,293.0,867.0,310.0,2.5536,70200.0,NEAR OCEAN\n-124.18,40.78,33.0,1076.0,222.0,656.0,236.0,2.5096,72200.0,NEAR OCEAN\n-124.18,40.78,34.0,1592.0,364.0,950.0,317.0,2.1607,67000.0,NEAR OCEAN\n-124.17,40.75,13.0,2171.0,339.0,951.0,353.0,4.8516,116100.0,NEAR OCEAN\n-124.17,40.76,26.0,1776.0,361.0,992.0,380.0,2.8056,82800.0,NEAR OCEAN\n-124.19,40.77,30.0,2975.0,634.0,1367.0,583.0,2.442,69000.0,NEAR OCEAN\n-124.15,40.81,50.0,340.0,74.0,235.0,83.0,1.75,67500.0,NEAR OCEAN\n-124.14,40.8,32.0,1373.0,312.0,872.0,306.0,2.5,72600.0,NEAR OCEAN\n-124.15,40.8,47.0,1486.0,335.0,765.0,329.0,1.755,74100.0,NEAR OCEAN\n-124.16,40.8,52.0,2167.0,480.0,908.0,451.0,1.6111,74700.0,NEAR OCEAN\n-124.16,40.8,52.0,1703.0,500.0,952.0,435.0,1.1386,74100.0,NEAR OCEAN\n-124.14,40.79,38.0,1552.0,290.0,873.0,291.0,2.4896,81000.0,NEAR OCEAN\n-124.15,40.78,41.0,2127.0,358.0,911.0,349.0,3.1711,104200.0,NEAR OCEAN\n-124.16,40.78,46.0,1975.0,346.0,791.0,349.0,3.8,81800.0,NEAR OCEAN\n-124.16,40.79,46.0,3042.0,597.0,1206.0,541.0,2.1135,90600.0,NEAR OCEAN\n-124.15,40.79,37.0,2692.0,488.0,1263.0,486.0,3.0216,86400.0,NEAR OCEAN\n-124.14,40.78,35.0,2426.0,423.0,982.0,432.0,3.4219,92800.0,NEAR OCEAN\n-124.14,40.77,27.0,3046.0,605.0,1407.0,571.0,2.9143,99600.0,NEAR OCEAN\n-124.15,40.76,24.0,2858.0,511.0,1388.0,512.0,3.375,100600.0,NEAR OCEAN\n-124.15,40.78,36.0,2112.0,374.0,829.0,368.0,3.3984,90000.0,NEAR OCEAN\n-124.11,40.81,23.0,959.0,212.0,425.0,175.0,2.5536,96100.0,NEAR OCEAN\n-124.13,40.78,34.0,2142.0,420.0,1056.0,382.0,2.1101,86900.0,NEAR OCEAN\n-124.13,40.8,31.0,2152.0,462.0,1259.0,420.0,2.2478,81100.0,NEAR OCEAN\n-124.13,40.79,29.0,2474.0,453.0,1130.0,427.0,2.8833,83000.0,NEAR OCEAN\n-124.13,40.79,32.0,2017.0,359.0,855.0,346.0,3.5833,92800.0,NEAR OCEAN\n-124.06,40.86,34.0,4183.0,,1891.0,669.0,3.2216,98100.0,NEAR OCEAN\n-124.05,40.85,31.0,2414.0,428.0,1005.0,401.0,3.5156,143000.0,NEAR OCEAN\n-124.02,40.8,22.0,2588.0,435.0,1198.0,442.0,3.9792,133900.0,<1H OCEAN\n-124.08,40.86,18.0,1287.0,484.0,805.0,502.0,1.1157,150000.0,NEAR OCEAN\n-124.09,40.86,25.0,1322.0,387.0,794.0,379.0,1.1742,75000.0,NEAR OCEAN\n-124.09,40.87,44.0,692.0,206.0,398.0,211.0,1.1576,87500.0,NEAR OCEAN\n-124.07,40.87,47.0,1765.0,326.0,796.0,333.0,2.2138,99200.0,NEAR OCEAN\n-124.08,40.87,29.0,1710.0,469.0,990.0,425.0,1.1479,101100.0,NEAR OCEAN\n-124.09,40.88,50.0,921.0,187.0,420.0,187.0,2.2188,105800.0,NEAR OCEAN\n-124.07,40.87,31.0,334.0,134.0,780.0,130.0,0.7684,153100.0,NEAR OCEAN\n-124.15,40.88,33.0,2235.0,506.0,1165.0,441.0,1.725,57500.0,NEAR OCEAN\n-124.1,40.88,35.0,2987.0,578.0,1581.0,585.0,2.0657,81100.0,NEAR OCEAN\n-124.09,40.88,31.0,1982.0,495.0,1052.0,467.0,1.5326,74100.0,NEAR OCEAN\n-124.09,40.88,26.0,2683.0,555.0,1353.0,526.0,2.4321,82100.0,NEAR OCEAN\n-124.1,40.9,18.0,4032.0,798.0,1948.0,775.0,2.7321,92600.0,NEAR OCEAN\n-124.23,40.81,52.0,1112.0,209.0,544.0,172.0,3.3462,50800.0,NEAR OCEAN\n-124.06,40.88,12.0,2087.0,424.0,1603.0,438.0,2.5667,139500.0,NEAR OCEAN\n-123.74,40.66,25.0,2395.0,431.0,983.0,375.0,3.0469,136000.0,<1H OCEAN\n-124.08,40.91,13.0,2522.0,719.0,1381.0,628.0,1.6667,78800.0,NEAR OCEAN\n-123.76,41.03,24.0,2386.0,565.0,1058.0,414.0,2.0644,79800.0,<1H OCEAN\n-123.85,41.32,31.0,938.0,238.0,425.0,157.0,1.0486,36700.0,<1H OCEAN\n-123.72,41.09,19.0,1970.0,431.0,1166.0,363.0,1.8208,50000.0,<1H OCEAN\n-123.63,41.11,19.0,1797.0,384.0,1033.0,327.0,1.4911,59200.0,<1H OCEAN\n-123.66,41.3,22.0,1580.0,372.0,686.0,264.0,1.8065,62700.0,<1H OCEAN\n-123.52,41.01,17.0,1564.0,345.0,517.0,222.0,2.1542,83800.0,INLAND\n-124.08,41.36,29.0,1029.0,239.0,509.0,196.0,2.0156,62800.0,NEAR OCEAN\n-124.06,41.13,22.0,3263.0,799.0,1384.0,578.0,2.4708,119400.0,NEAR OCEAN\n-124.14,41.06,32.0,1020.0,215.0,421.0,198.0,3.0208,143400.0,NEAR OCEAN\n-124.1,41.04,26.0,1633.0,380.0,890.0,370.0,1.9741,97900.0,NEAR OCEAN\n-124.01,40.97,21.0,1513.0,319.0,943.0,301.0,3.538,102700.0,<1H OCEAN\n-124.0,40.92,29.0,1429.0,,672.0,266.0,2.9485,98800.0,<1H OCEAN\n-124.01,40.89,28.0,1470.0,336.0,811.0,314.0,2.4559,75600.0,<1H OCEAN\n-123.98,40.88,41.0,1719.0,372.0,844.0,336.0,2.6923,84200.0,<1H OCEAN\n-123.88,40.93,28.0,1272.0,259.0,519.0,220.0,3.2891,106300.0,<1H OCEAN\n-124.16,41.02,23.0,1672.0,385.0,1060.0,390.0,2.1726,75500.0,NEAR OCEAN\n-124.08,40.99,18.0,3297.0,662.0,1554.0,578.0,2.6847,111300.0,NEAR OCEAN\n-124.16,40.95,20.0,1075.0,214.0,529.0,196.0,3.1406,96000.0,NEAR OCEAN\n-124.11,40.95,19.0,1734.0,365.0,866.0,342.0,2.96,81700.0,NEAR OCEAN\n-124.09,40.95,18.0,2250.0,484.0,1248.0,472.0,2.5893,99600.0,NEAR OCEAN\n-124.11,40.93,25.0,2392.0,474.0,1298.0,461.0,3.5076,73600.0,NEAR OCEAN\n-124.11,40.93,17.0,1661.0,329.0,948.0,357.0,2.7639,90200.0,NEAR OCEAN\n-124.08,40.94,18.0,1550.0,345.0,941.0,335.0,2.3147,70100.0,NEAR OCEAN\n-124.09,40.92,12.0,2497.0,491.0,1153.0,462.0,2.8182,126900.0,NEAR OCEAN\n-124.05,40.94,14.0,1452.0,217.0,516.0,181.0,5.0329,165600.0,NEAR OCEAN\n-124.1,40.95,17.0,1485.0,345.0,823.0,316.0,1.8993,78400.0,NEAR OCEAN\n-124.07,40.81,23.0,2103.0,411.0,1019.0,387.0,2.9911,119700.0,NEAR OCEAN\n-124.02,40.72,28.0,3513.0,634.0,1658.0,598.0,3.8095,119900.0,<1H OCEAN\n-124.1,40.73,33.0,644.0,129.0,334.0,121.0,3.9659,111800.0,NEAR OCEAN\n-124.17,40.74,17.0,2026.0,338.0,873.0,313.0,4.0357,128900.0,NEAR OCEAN\n-124.14,40.72,18.0,2581.0,499.0,1375.0,503.0,2.8446,100500.0,NEAR OCEAN\n-124.14,40.67,23.0,580.0,117.0,320.0,109.0,4.2054,130600.0,NEAR OCEAN\n-124.19,40.73,21.0,5694.0,1056.0,2907.0,972.0,3.5363,90100.0,NEAR OCEAN\n-124.21,40.75,32.0,1218.0,331.0,620.0,268.0,1.6528,58100.0,NEAR OCEAN\n-124.27,40.69,36.0,2349.0,528.0,1194.0,465.0,2.5179,79000.0,NEAR OCEAN\n-124.18,40.62,35.0,952.0,178.0,480.0,179.0,3.0536,107000.0,NEAR OCEAN\n-124.17,40.62,32.0,1595.0,309.0,706.0,277.0,2.8958,86400.0,NEAR OCEAN\n-124.13,40.62,43.0,2131.0,399.0,910.0,389.0,2.5804,92100.0,NEAR OCEAN\n-124.16,40.6,39.0,1322.0,283.0,642.0,292.0,2.4519,85100.0,NEAR OCEAN\n-124.15,40.59,39.0,1186.0,238.0,539.0,212.0,2.0938,79600.0,NEAR OCEAN\n-124.14,40.6,27.0,1148.0,206.0,521.0,219.0,4.025,128100.0,NEAR OCEAN\n-124.14,40.59,22.0,1665.0,405.0,826.0,382.0,1.5625,66800.0,NEAR OCEAN\n-124.14,40.59,17.0,2985.0,610.0,1544.0,584.0,2.178,76800.0,NEAR OCEAN\n-124.05,40.59,32.0,1878.0,340.0,937.0,353.0,3.4408,95200.0,<1H OCEAN\n-124.09,40.55,24.0,2978.0,553.0,1370.0,480.0,2.7644,97300.0,<1H OCEAN\n-123.96,40.57,31.0,1854.0,365.0,883.0,310.0,2.3167,92600.0,<1H OCEAN\n-123.73,40.48,25.0,2015.0,524.0,746.0,251.0,1.7153,77100.0,INLAND\n-124.14,40.58,25.0,1899.0,357.0,891.0,355.0,2.6987,92500.0,<1H OCEAN\n-124.14,40.57,29.0,2864.0,600.0,1314.0,562.0,2.1354,75100.0,<1H OCEAN\n-124.11,40.57,33.0,1348.0,234.0,573.0,236.0,2.4896,74100.0,<1H OCEAN\n-124.13,40.55,38.0,544.0,,240.0,91.0,3.25,94800.0,<1H OCEAN\n-124.1,40.5,30.0,1927.0,393.0,996.0,374.0,2.2357,72300.0,<1H OCEAN\n-124.1,40.5,42.0,2380.0,553.0,1300.0,504.0,1.7574,57500.0,<1H OCEAN\n-124.09,40.44,38.0,2220.0,426.0,1041.0,401.0,2.3947,70500.0,<1H OCEAN\n-124.1,40.47,52.0,1196.0,236.0,965.0,265.0,3.5345,55000.0,<1H OCEAN\n-124.03,40.45,34.0,1006.0,213.0,443.0,158.0,2.6094,71300.0,<1H OCEAN\n-124.26,40.58,52.0,2217.0,394.0,907.0,369.0,2.3571,111400.0,NEAR OCEAN\n-124.23,40.54,52.0,2694.0,453.0,1152.0,435.0,3.0806,106700.0,NEAR OCEAN\n-124.35,40.54,52.0,1820.0,300.0,806.0,270.0,3.0147,94600.0,NEAR OCEAN\n-124.25,40.28,32.0,1430.0,419.0,434.0,187.0,1.9417,76100.0,NEAR OCEAN\n-124.08,40.06,17.0,1319.0,267.0,393.0,163.0,2.625,135600.0,NEAR OCEAN\n-124.0,40.22,16.0,2088.0,535.0,816.0,326.0,1.319,70700.0,<1H OCEAN\n-123.84,40.28,28.0,2809.0,605.0,1093.0,438.0,2.0962,74000.0,<1H OCEAN\n-123.68,40.24,31.0,1852.0,452.0,917.0,359.0,1.725,54300.0,<1H OCEAN\n-123.82,40.16,19.0,2283.0,634.0,1184.0,453.0,1.2227,76800.0,<1H OCEAN\n-123.82,40.12,33.0,2985.0,591.0,1221.0,486.0,2.087,82400.0,<1H OCEAN\n-123.75,40.11,35.0,2052.0,477.0,900.0,402.0,1.9625,101500.0,<1H OCEAN\n-123.78,40.05,17.0,2019.0,496.0,899.0,347.0,2.1864,101900.0,<1H OCEAN\n-115.52,33.12,38.0,1327.0,262.0,784.0,231.0,1.8793,60800.0,INLAND\n-115.52,33.13,18.0,1109.0,283.0,1006.0,253.0,2.163,53400.0,INLAND\n-115.51,33.12,21.0,1024.0,218.0,890.0,232.0,2.101,46700.0,INLAND\n-115.46,33.19,33.0,1234.0,373.0,777.0,298.0,1.0,40000.0,INLAND\n-115.51,33.24,32.0,1995.0,523.0,1069.0,410.0,1.6552,43300.0,INLAND\n-115.6,33.2,37.0,709.0,187.0,390.0,142.0,2.4511,72500.0,INLAND\n-115.73,33.09,27.0,452.0,103.0,258.0,61.0,2.9,87500.0,INLAND\n-115.62,33.04,17.0,1009.0,231.0,745.0,217.0,2.0463,61200.0,INLAND\n-115.62,33.04,20.0,1121.0,244.0,766.0,230.0,2.2969,62000.0,INLAND\n-115.6,33.04,31.0,314.0,61.0,152.0,56.0,3.3472,91700.0,INLAND\n-115.41,32.99,29.0,1141.0,220.0,684.0,194.0,3.4038,107800.0,INLAND\n-115.59,32.96,17.0,841.0,146.0,473.0,154.0,3.1979,113500.0,INLAND\n-115.52,32.98,32.0,1615.0,382.0,1307.0,345.0,1.4583,58600.0,INLAND\n-115.52,32.98,21.0,1302.0,327.0,1244.0,316.0,2.2054,66400.0,INLAND\n-115.53,32.99,25.0,2578.0,634.0,2082.0,565.0,1.7159,62200.0,INLAND\n-115.51,32.99,20.0,1402.0,287.0,1104.0,317.0,1.9088,63700.0,INLAND\n-115.54,32.99,23.0,1459.0,373.0,1148.0,388.0,1.5372,69400.0,INLAND\n-115.54,32.98,27.0,1513.0,395.0,1121.0,381.0,1.9464,60600.0,INLAND\n-115.54,32.99,17.0,1697.0,268.0,911.0,254.0,4.3523,96000.0,INLAND\n-115.55,32.98,24.0,2565.0,530.0,1447.0,473.0,3.2593,80800.0,INLAND\n-115.54,32.97,41.0,2429.0,454.0,1188.0,430.0,3.0091,70800.0,INLAND\n-115.53,32.97,35.0,1583.0,340.0,933.0,318.0,2.4063,70700.0,INLAND\n-115.55,32.98,33.0,2266.0,365.0,952.0,360.0,5.4349,143000.0,INLAND\n-115.56,32.96,21.0,2164.0,480.0,1164.0,421.0,3.8177,107200.0,INLAND\n-115.53,32.97,34.0,2231.0,545.0,1568.0,510.0,1.5217,60300.0,INLAND\n-115.52,32.97,24.0,1617.0,366.0,1416.0,401.0,1.975,66400.0,INLAND\n-115.52,32.97,10.0,1879.0,387.0,1376.0,337.0,1.9911,67500.0,INLAND\n-115.32,32.82,34.0,591.0,139.0,327.0,89.0,3.6528,100000.0,INLAND\n-115.39,32.76,16.0,1136.0,196.0,481.0,185.0,6.2558,146300.0,INLAND\n-115.4,32.86,19.0,1087.0,171.0,649.0,173.0,3.3182,113800.0,INLAND\n-115.37,32.81,23.0,1458.0,294.0,866.0,275.0,2.3594,74300.0,INLAND\n-115.37,32.82,14.0,1276.0,270.0,867.0,261.0,1.9375,80900.0,INLAND\n-115.37,32.82,30.0,1602.0,322.0,1130.0,335.0,3.5735,71100.0,INLAND\n-115.38,32.82,38.0,1892.0,394.0,1175.0,374.0,1.9939,65800.0,INLAND\n-115.38,32.81,35.0,1263.0,262.0,950.0,241.0,1.8958,67500.0,INLAND\n-115.37,32.81,32.0,741.0,191.0,623.0,169.0,1.7604,68600.0,INLAND\n-115.57,32.85,33.0,1365.0,269.0,825.0,250.0,3.2396,62300.0,INLAND\n-115.57,32.85,17.0,1039.0,256.0,728.0,246.0,1.7411,63500.0,INLAND\n-115.57,32.84,29.0,1207.0,301.0,804.0,288.0,1.9531,61100.0,INLAND\n-115.57,32.83,31.0,1494.0,289.0,959.0,284.0,3.5282,67500.0,INLAND\n-115.59,32.85,20.0,1608.0,274.0,862.0,248.0,4.875,90800.0,INLAND\n-115.6,32.87,3.0,1629.0,317.0,1005.0,312.0,4.1293,83200.0,INLAND\n-115.49,32.87,19.0,541.0,104.0,457.0,106.0,3.3583,102800.0,INLAND\n-115.64,32.8,23.0,1228.0,235.0,569.0,235.0,3.1667,125000.0,INLAND\n-115.69,32.79,18.0,1564.0,340.0,1161.0,343.0,2.1792,55200.0,INLAND\n-115.73,32.8,44.0,472.0,81.0,206.0,57.0,2.2083,93800.0,INLAND\n-115.72,32.75,16.0,348.0,99.0,123.0,54.0,2.0938,87500.0,INLAND\n-115.64,32.75,19.0,377.0,69.0,198.0,55.0,1.625,112500.0,INLAND\n-115.58,32.81,5.0,805.0,143.0,458.0,143.0,4.475,96300.0,INLAND\n-115.55,32.82,34.0,1540.0,316.0,1013.0,274.0,2.5664,67500.0,INLAND\n-115.58,32.81,10.0,1088.0,203.0,533.0,201.0,3.6597,87500.0,INLAND\n-115.57,32.8,17.0,1492.0,426.0,1595.0,428.0,1.7188,69900.0,INLAND\n-115.57,32.8,16.0,1307.0,359.0,961.0,327.0,1.1853,72600.0,INLAND\n-115.57,32.8,16.0,2276.0,594.0,1184.0,513.0,1.875,93800.0,INLAND\n-115.48,32.8,21.0,1260.0,246.0,805.0,239.0,2.6172,88500.0,INLAND\n-115.52,32.77,18.0,1715.0,337.0,1166.0,333.0,2.2417,79200.0,INLAND\n-115.53,32.73,14.0,1527.0,325.0,1453.0,332.0,1.735,61200.0,INLAND\n-115.52,32.73,17.0,1190.0,275.0,1113.0,258.0,2.3571,63100.0,INLAND\n-115.5,32.75,13.0,330.0,72.0,822.0,64.0,3.4107,142500.0,INLAND\n-115.55,32.8,23.0,666.0,142.0,580.0,160.0,2.1136,61000.0,INLAND\n-115.55,32.79,22.0,565.0,162.0,692.0,141.0,1.2083,53600.0,INLAND\n-115.54,32.79,30.0,752.0,194.0,733.0,186.0,1.6607,56100.0,INLAND\n-115.54,32.79,23.0,1712.0,403.0,1370.0,377.0,1.275,60400.0,INLAND\n-115.55,32.79,23.0,1004.0,221.0,697.0,201.0,1.6351,59600.0,INLAND\n-115.57,32.8,33.0,1192.0,213.0,1066.0,211.0,4.5714,68600.0,INLAND\n-115.56,32.8,25.0,1311.0,375.0,1193.0,351.0,2.1979,63900.0,INLAND\n-115.56,32.8,28.0,1672.0,416.0,1335.0,397.0,1.5987,59400.0,INLAND\n-115.56,32.8,15.0,1171.0,328.0,1024.0,298.0,1.3882,69400.0,INLAND\n-115.56,32.79,18.0,1178.0,438.0,1377.0,429.0,1.3373,58300.0,INLAND\n-115.56,32.78,34.0,2856.0,555.0,1627.0,522.0,3.2083,76200.0,INLAND\n-115.56,32.79,20.0,2372.0,835.0,2283.0,767.0,1.1707,62500.0,INLAND\n-115.57,32.79,50.0,1291.0,277.0,864.0,274.0,1.6667,68100.0,INLAND\n-115.56,32.78,46.0,2511.0,490.0,1583.0,469.0,3.0603,70800.0,INLAND\n-115.56,32.78,35.0,1185.0,202.0,615.0,191.0,4.6154,86200.0,INLAND\n-115.57,32.78,25.0,2007.0,301.0,1135.0,332.0,5.128,99600.0,INLAND\n-115.56,32.78,29.0,1568.0,283.0,848.0,245.0,3.1597,76200.0,INLAND\n-115.55,32.78,5.0,2652.0,606.0,1767.0,536.0,2.8025,84300.0,INLAND\n-115.58,32.78,5.0,2494.0,414.0,1416.0,421.0,5.7843,110100.0,INLAND\n-115.59,32.79,8.0,2183.0,307.0,1000.0,287.0,6.3814,159900.0,INLAND\n-115.58,32.79,14.0,1687.0,507.0,762.0,451.0,1.6635,64400.0,INLAND\n-115.57,32.79,34.0,1152.0,208.0,621.0,208.0,3.6042,73600.0,INLAND\n-115.57,32.78,29.0,2321.0,367.0,1173.0,360.0,4.0375,86400.0,INLAND\n-115.57,32.78,20.0,1534.0,235.0,871.0,222.0,6.2715,97200.0,INLAND\n-115.57,32.78,15.0,1413.0,279.0,803.0,277.0,4.3021,87500.0,INLAND\n-115.56,32.76,15.0,1278.0,217.0,653.0,185.0,4.4821,140300.0,INLAND\n-115.59,32.69,30.0,935.0,177.0,649.0,148.0,2.5769,94400.0,INLAND\n-115.49,32.69,17.0,1960.0,389.0,1691.0,356.0,1.899,64000.0,INLAND\n-115.4,32.7,19.0,583.0,113.0,531.0,134.0,1.6838,95800.0,INLAND\n-115.49,32.67,25.0,2322.0,573.0,2185.0,602.0,1.375,70100.0,INLAND\n-115.49,32.67,24.0,1266.0,275.0,1083.0,298.0,1.4828,73100.0,INLAND\n-115.48,32.68,15.0,3414.0,666.0,2097.0,622.0,2.3319,91200.0,INLAND\n-115.5,32.68,18.0,3631.0,913.0,3565.0,924.0,1.5931,88400.0,INLAND\n-115.5,32.67,35.0,2159.0,492.0,1694.0,475.0,2.1776,75500.0,INLAND\n-115.49,32.67,29.0,1523.0,440.0,1302.0,393.0,1.1311,84700.0,INLAND\n-115.51,32.68,11.0,2872.0,610.0,2644.0,581.0,2.625,72700.0,INLAND\n-115.52,32.67,6.0,2804.0,581.0,2807.0,594.0,2.0625,67700.0,INLAND\n-116.05,33.33,17.0,290.0,94.0,135.0,57.0,1.7292,81300.0,INLAND\n-116.0,33.19,16.0,245.0,57.0,81.0,33.0,1.2639,51300.0,INLAND\n-115.88,32.93,15.0,208.0,49.0,51.0,20.0,4.0208,32500.0,INLAND\n-116.0,32.74,26.0,1134.0,280.0,329.0,158.0,1.4338,43900.0,INLAND\n-115.9,32.69,18.0,414.0,86.0,98.0,54.0,1.5417,57500.0,INLAND\n-116.01,33.41,20.0,1996.0,515.0,659.0,295.0,2.8684,62800.0,INLAND\n-115.99,33.4,15.0,1945.0,536.0,515.0,273.0,2.0109,54300.0,INLAND\n-115.94,33.38,5.0,186.0,43.0,41.0,21.0,2.7,58800.0,INLAND\n-115.98,33.32,8.0,240.0,46.0,63.0,24.0,1.4688,53800.0,INLAND\n-115.91,33.36,15.0,459.0,95.0,160.0,73.0,0.922,67500.0,INLAND\n-115.9,33.34,19.0,1210.0,248.0,329.0,155.0,1.7857,62800.0,INLAND\n-115.96,33.3,27.0,322.0,81.0,112.0,57.0,1.125,54400.0,INLAND\n-115.95,33.28,12.0,99.0,25.0,37.0,17.0,1.8958,53800.0,INLAND\n-115.8,33.26,2.0,96.0,18.0,30.0,16.0,5.3374,47500.0,INLAND\n-114.73,33.43,24.0,796.0,243.0,227.0,139.0,0.8964,59200.0,INLAND\n-114.98,33.07,18.0,1183.0,363.0,374.0,127.0,3.1607,57500.0,INLAND\n-115.73,33.36,19.0,749.0,238.0,476.0,169.0,1.7727,50000.0,INLAND\n-115.73,33.35,23.0,1586.0,448.0,338.0,182.0,1.2132,30000.0,INLAND\n-114.65,32.79,21.0,44.0,33.0,64.0,27.0,0.8571,25000.0,INLAND\n-114.55,32.8,19.0,2570.0,820.0,1431.0,608.0,1.275,56100.0,INLAND\n-114.63,32.76,15.0,1448.0,378.0,949.0,300.0,0.8585,45000.0,INLAND\n-114.66,32.74,17.0,1388.0,386.0,775.0,320.0,1.2049,44000.0,INLAND\n-118.18,37.35,16.0,3806.0,794.0,1501.0,714.0,2.1212,108300.0,INLAND\n-118.43,37.4,19.0,2460.0,405.0,1225.0,425.0,4.1576,141500.0,INLAND\n-118.6,37.39,19.0,2682.0,518.0,1134.0,399.0,3.2132,166000.0,INLAND\n-118.45,37.25,20.0,1468.0,283.0,721.0,270.0,3.0817,118800.0,INLAND\n-118.45,37.37,26.0,3135.0,524.0,1385.0,523.0,4.337,139700.0,INLAND\n-118.42,37.35,21.0,3302.0,557.0,1413.0,520.0,4.375,180400.0,INLAND\n-118.42,37.36,18.0,2281.0,520.0,1425.0,465.0,1.7388,54400.0,INLAND\n-118.4,37.36,34.0,2465.0,619.0,1172.0,575.0,1.9722,116100.0,INLAND\n-118.39,37.37,25.0,3295.0,824.0,1477.0,770.0,1.8325,105800.0,INLAND\n-118.39,37.36,38.0,1813.0,410.0,902.0,396.0,2.3261,98400.0,INLAND\n-118.3,37.17,22.0,3480.0,673.0,1541.0,636.0,2.75,94500.0,INLAND\n-117.9,36.95,19.0,99.0,26.0,51.0,22.0,1.7292,137500.0,INLAND\n-118.31,36.94,35.0,2563.0,530.0,861.0,371.0,2.325,80600.0,INLAND\n-118.05,36.64,34.0,2090.0,478.0,896.0,426.0,2.0357,74200.0,INLAND\n-118.18,36.63,23.0,2311.0,487.0,1019.0,384.0,2.2574,104700.0,INLAND\n-117.69,36.13,25.0,1709.0,439.0,632.0,292.0,1.7868,45500.0,INLAND\n-117.02,36.4,19.0,619.0,239.0,490.0,164.0,2.1,14999.0,INLAND\n-116.22,36.0,14.0,1372.0,386.0,436.0,213.0,1.1471,32900.0,INLAND\n-119.02,35.42,42.0,2271.0,458.0,1124.0,447.0,2.7583,64900.0,INLAND\n-119.03,35.42,45.0,1628.0,352.0,754.0,334.0,2.5703,62400.0,INLAND\n-119.03,35.42,38.0,2952.0,598.0,1491.0,568.0,2.6094,67900.0,INLAND\n-119.03,35.45,14.0,3520.0,604.0,1748.0,582.0,4.3162,87100.0,INLAND\n-119.05,35.44,5.0,2133.0,487.0,1182.0,438.0,3.0268,77000.0,INLAND\n-119.02,35.44,29.0,3415.0,631.0,1527.0,597.0,4.0125,84400.0,INLAND\n-119.02,35.43,39.0,2033.0,370.0,956.0,379.0,3.1736,70700.0,INLAND\n-119.02,35.42,40.0,1089.0,226.0,520.0,218.0,2.2727,67200.0,INLAND\n-119.02,35.42,36.0,2044.0,447.0,1021.0,374.0,1.8472,57400.0,INLAND\n-119.02,35.42,40.0,1912.0,439.0,1015.0,413.0,1.4598,52600.0,INLAND\n-119.03,35.42,42.0,1705.0,418.0,905.0,393.0,1.6286,54600.0,INLAND\n-119.03,35.41,41.0,1808.0,435.0,1005.0,373.0,1.7857,54300.0,INLAND\n-119.04,35.42,47.0,1691.0,402.0,913.0,358.0,1.8403,54700.0,INLAND\n-119.05,35.42,41.0,1992.0,421.0,1006.0,419.0,2.8393,57000.0,INLAND\n-119.05,35.42,35.0,2353.0,483.0,1368.0,455.0,2.325,63200.0,INLAND\n-119.02,35.41,21.0,2534.0,554.0,1297.0,517.0,2.0575,67000.0,INLAND\n-119.02,35.41,41.0,2221.0,516.0,1106.0,473.0,1.97,51900.0,INLAND\n-119.03,35.41,37.0,1761.0,443.0,911.0,365.0,2.0331,53200.0,INLAND\n-119.04,35.41,25.0,1577.0,310.0,844.0,309.0,3.0625,69400.0,INLAND\n-119.02,35.41,31.0,2348.0,701.0,1413.0,611.0,1.3222,51400.0,INLAND\n-119.03,35.4,35.0,2608.0,620.0,1566.0,583.0,2.1818,63500.0,INLAND\n-119.04,35.41,20.0,3268.0,833.0,1622.0,758.0,1.3587,67500.0,INLAND\n-119.08,35.42,10.0,4159.0,608.0,2089.0,591.0,5.5261,132000.0,INLAND\n-119.07,35.42,19.0,3889.0,832.0,1872.0,731.0,2.6812,107600.0,INLAND\n-119.09,35.41,12.0,3449.0,522.0,1754.0,551.0,5.6235,130600.0,INLAND\n-119.11,35.42,52.0,154.0,,37.0,16.0,10.0263,200000.0,INLAND\n-119.09,35.43,28.0,254.0,35.0,118.0,37.0,4.8571,237500.0,INLAND\n-119.05,35.4,18.0,1894.0,319.0,846.0,317.0,3.8611,126400.0,INLAND\n-119.08,35.39,10.0,6435.0,1040.0,3242.0,1030.0,5.575,132200.0,INLAND\n-119.01,35.4,11.0,8739.0,2190.0,4781.0,1919.0,1.7109,44600.0,INLAND\n-119.01,35.39,29.0,1820.0,459.0,1134.0,419.0,1.8289,59400.0,INLAND\n-119.01,35.38,36.0,790.0,224.0,426.0,208.0,1.4427,50600.0,INLAND\n-119.02,35.39,30.0,227.0,75.0,169.0,101.0,1.3527,60000.0,INLAND\n-118.99,35.4,43.0,2225.0,392.0,890.0,374.0,4.0208,90400.0,INLAND\n-118.99,35.4,48.0,1908.0,331.0,789.0,321.0,3.5714,84600.0,INLAND\n-119.0,35.4,44.0,2250.0,378.0,928.0,379.0,4.3906,93900.0,INLAND\n-119.0,35.39,42.0,2839.0,516.0,1203.0,487.0,3.7708,79400.0,INLAND\n-119.0,35.39,51.0,1373.0,284.0,648.0,300.0,2.8295,72100.0,INLAND\n-118.97,35.41,36.0,1896.0,315.0,937.0,303.0,3.996,85500.0,INLAND\n-118.97,35.4,34.0,1859.0,323.0,854.0,309.0,3.1906,76200.0,INLAND\n-118.98,35.4,34.0,813.0,171.0,440.0,170.0,2.8393,69800.0,INLAND\n-118.98,35.4,36.0,1443.0,273.0,680.0,259.0,2.9821,73100.0,INLAND\n-118.98,35.4,36.0,1864.0,331.0,1052.0,325.0,3.4205,76600.0,INLAND\n-118.98,35.41,36.0,1482.0,266.0,640.0,274.0,3.875,94500.0,INLAND\n-118.96,35.4,28.0,4667.0,875.0,2404.0,841.0,3.2325,89000.0,INLAND\n-118.96,35.41,29.0,3548.0,729.0,1542.0,659.0,2.947,87100.0,INLAND\n-118.95,35.4,23.0,4483.0,894.0,2136.0,883.0,3.6875,101700.0,INLAND\n-118.96,35.4,27.0,2473.0,400.0,1271.0,427.0,3.5524,89100.0,INLAND\n-118.96,35.39,23.0,5624.0,1148.0,2842.0,1042.0,3.1297,79000.0,INLAND\n-118.96,35.38,34.0,2047.0,347.0,888.0,352.0,3.6734,92900.0,INLAND\n-118.95,35.38,35.0,2220.0,388.0,906.0,373.0,3.5938,95200.0,INLAND\n-118.96,35.38,41.0,2417.0,435.0,973.0,406.0,3.0568,85600.0,INLAND\n-118.94,35.38,26.0,4800.0,831.0,2365.0,743.0,3.7533,87000.0,INLAND\n-118.95,35.38,30.0,2594.0,478.0,1419.0,480.0,3.725,83100.0,INLAND\n-118.92,35.38,33.0,3122.0,579.0,1733.0,545.0,3.8307,70600.0,INLAND\n-118.95,35.41,21.0,3999.0,727.0,1889.0,688.0,3.875,99500.0,INLAND\n-118.94,35.4,14.0,5548.0,941.0,2815.0,935.0,4.2214,104600.0,INLAND\n-118.94,35.39,27.0,3074.0,452.0,1223.0,452.0,5.4592,139100.0,INLAND\n-118.94,35.39,13.0,3137.0,417.0,1318.0,397.0,7.7751,194100.0,INLAND\n-118.9,35.41,6.0,4656.0,971.0,2320.0,935.0,3.0938,100800.0,INLAND\n-118.94,35.41,10.0,3216.0,526.0,1539.0,483.0,6.3639,143000.0,INLAND\n-118.91,35.4,10.0,3587.0,774.0,1398.0,763.0,2.569,113000.0,INLAND\n-118.87,35.37,14.0,2458.0,433.0,1352.0,411.0,3.5441,87000.0,INLAND\n-118.91,35.37,32.0,4121.0,755.0,2590.0,721.0,3.3462,67600.0,INLAND\n-118.88,35.34,20.0,1351.0,255.0,762.0,253.0,2.1111,105300.0,INLAND\n-118.92,35.37,17.0,3589.0,701.0,1746.0,640.0,2.4919,75700.0,INLAND\n-118.93,35.37,34.0,2412.0,446.0,1558.0,421.0,2.6903,62800.0,INLAND\n-118.94,35.37,33.0,3372.0,741.0,2352.0,704.0,2.0643,57600.0,INLAND\n-118.94,35.37,23.0,1106.0,252.0,790.0,230.0,1.8523,59700.0,INLAND\n-118.95,35.37,34.0,1672.0,359.0,1059.0,349.0,2.1588,61300.0,INLAND\n-118.94,35.37,37.0,1667.0,362.0,971.0,335.0,2.875,57400.0,INLAND\n-118.95,35.37,37.0,1475.0,327.0,946.0,295.0,1.6728,55400.0,INLAND\n-118.96,35.37,41.0,1463.0,339.0,1066.0,318.0,1.7467,52400.0,INLAND\n-118.96,35.37,40.0,1603.0,374.0,1026.0,337.0,1.365,54300.0,INLAND\n-118.97,35.39,38.0,2121.0,433.0,1547.0,441.0,2.774,59500.0,INLAND\n-118.97,35.38,32.0,1361.0,363.0,1483.0,297.0,1.625,46800.0,INLAND\n-118.98,35.38,39.0,1497.0,383.0,1182.0,355.0,1.0648,50000.0,INLAND\n-118.97,35.38,42.0,1185.0,358.0,1038.0,299.0,0.9951,48000.0,INLAND\n-118.97,35.37,41.0,2396.0,602.0,1781.0,543.0,1.8819,58000.0,INLAND\n-118.97,35.38,35.0,1673.0,426.0,1041.0,413.0,1.375,57500.0,INLAND\n-118.99,35.38,26.0,1317.0,374.0,1025.0,304.0,1.4024,51000.0,INLAND\n-118.99,35.38,30.0,1390.0,361.0,1116.0,298.0,1.3451,57500.0,INLAND\n-118.98,35.39,22.0,1812.0,457.0,1592.0,420.0,1.4146,49100.0,INLAND\n-118.98,35.38,24.0,1807.0,465.0,1460.0,410.0,1.4786,54800.0,INLAND\n-118.98,35.38,34.0,1020.0,247.0,795.0,228.0,1.625,50800.0,INLAND\n-118.98,35.38,28.0,1171.0,299.0,1193.0,273.0,0.8639,49400.0,INLAND\n-118.99,35.39,39.0,2228.0,542.0,1516.0,435.0,1.6009,48800.0,INLAND\n-118.99,35.39,36.0,1438.0,348.0,1054.0,341.0,1.8319,55400.0,INLAND\n-118.99,35.39,52.0,2805.0,573.0,1325.0,522.0,2.5083,70100.0,INLAND\n-118.98,35.39,29.0,607.0,177.0,476.0,143.0,1.1875,50700.0,INLAND\n-118.98,35.39,32.0,2620.0,682.0,2375.0,684.0,1.2618,46900.0,INLAND\n-119.0,35.37,41.0,303.0,78.0,216.0,80.0,2.2212,55500.0,INLAND\n-118.99,35.37,36.0,832.0,198.0,814.0,174.0,1.4773,57400.0,INLAND\n-118.99,35.37,38.0,918.0,220.0,743.0,222.0,1.7292,58100.0,INLAND\n-118.98,35.37,35.0,825.0,179.0,670.0,181.0,1.1638,57900.0,INLAND\n-118.97,35.37,52.0,425.0,119.0,380.0,97.0,1.4125,42500.0,INLAND\n-119.01,35.38,44.0,434.0,110.0,274.0,86.0,1.1944,57500.0,INLAND\n-119.01,35.38,52.0,114.0,26.0,158.0,26.0,1.075,67500.0,INLAND\n-119.01,35.37,35.0,120.0,35.0,477.0,41.0,1.9125,47500.0,INLAND\n-119.01,35.37,44.0,593.0,136.0,364.0,121.0,1.4779,66000.0,INLAND\n-119.02,35.38,52.0,90.0,35.0,36.0,31.0,0.8054,60000.0,INLAND\n-119.02,35.38,48.0,346.0,92.0,129.0,63.0,1.1875,63800.0,INLAND\n-119.02,35.39,52.0,191.0,52.0,106.0,49.0,2.0455,72500.0,INLAND\n-119.01,35.37,45.0,629.0,143.0,568.0,139.0,1.7321,84400.0,INLAND\n-119.03,35.38,38.0,2122.0,394.0,843.0,410.0,3.0,91800.0,INLAND\n-119.03,35.38,52.0,1695.0,290.0,540.0,260.0,2.7312,147100.0,INLAND\n-119.03,35.37,52.0,1503.0,367.0,554.0,277.0,1.6786,126600.0,INLAND\n-119.04,35.37,44.0,1618.0,310.0,667.0,300.0,2.875,82700.0,INLAND\n-119.03,35.39,28.0,4513.0,764.0,1593.0,763.0,2.9821,118700.0,INLAND\n-119.04,35.36,36.0,1942.0,414.0,1051.0,411.0,1.8362,66900.0,INLAND\n-119.05,35.36,30.0,4635.0,800.0,2307.0,754.0,3.6548,84700.0,INLAND\n-119.05,35.36,16.0,4507.0,1049.0,2261.0,959.0,3.3261,118400.0,INLAND\n-119.07,35.36,19.0,5254.0,894.0,2155.0,831.0,4.6705,110700.0,INLAND\n-119.08,35.36,12.0,6442.0,1116.0,2966.0,1092.0,4.5791,123400.0,INLAND\n-119.06,35.36,9.0,1228.0,234.0,409.0,212.0,4.3482,95200.0,INLAND\n-119.03,35.37,42.0,2508.0,483.0,1035.0,442.0,2.6513,72300.0,INLAND\n-119.03,35.36,41.0,1944.0,363.0,977.0,388.0,3.9097,81300.0,INLAND\n-119.04,35.36,40.0,1533.0,312.0,771.0,306.0,3.0435,69500.0,INLAND\n-119.04,35.37,46.0,1637.0,338.0,714.0,297.0,2.1818,75300.0,INLAND\n-119.02,35.37,44.0,2687.0,620.0,1521.0,549.0,1.7213,61600.0,INLAND\n-119.02,35.36,48.0,1833.0,396.0,947.0,363.0,2.2827,70000.0,INLAND\n-119.02,35.36,47.0,1631.0,340.0,847.0,315.0,2.5062,73700.0,INLAND\n-119.03,35.36,41.0,2551.0,594.0,1342.0,595.0,1.9671,76800.0,INLAND\n-119.01,35.37,38.0,1702.0,380.0,1191.0,366.0,1.8801,57800.0,INLAND\n-119.01,35.36,38.0,1838.0,388.0,1203.0,373.0,1.6797,60700.0,INLAND\n-119.01,35.36,36.0,2658.0,626.0,1490.0,529.0,1.2157,57000.0,INLAND\n-119.01,35.36,24.0,1941.0,484.0,1277.0,435.0,1.056,51600.0,INLAND\n-119.01,35.37,33.0,821.0,181.0,579.0,172.0,1.2469,46700.0,INLAND\n-119.0,35.36,40.0,850.0,227.0,764.0,186.0,0.9407,43600.0,INLAND\n-119.0,35.36,39.0,896.0,217.0,805.0,197.0,1.25,42500.0,INLAND\n-119.0,35.35,35.0,1164.0,277.0,992.0,284.0,1.4015,48700.0,INLAND\n-119.0,35.36,35.0,1021.0,280.0,1258.0,239.0,1.7375,48600.0,INLAND\n-118.98,35.37,36.0,1562.0,398.0,1223.0,329.0,0.9675,47100.0,INLAND\n-118.98,35.36,29.0,1244.0,266.0,933.0,227.0,1.6981,49400.0,INLAND\n-118.98,35.36,15.0,1482.0,338.0,1059.0,279.0,1.2617,42700.0,INLAND\n-118.99,35.36,18.0,1524.0,354.0,1210.0,344.0,1.1136,47800.0,INLAND\n-118.99,35.36,31.0,1498.0,359.0,1168.0,340.0,1.2232,49300.0,INLAND\n-118.94,35.36,19.0,2714.0,512.0,1823.0,500.0,3.1281,76200.0,INLAND\n-118.95,35.36,30.0,2294.0,508.0,1753.0,482.0,2.1078,54700.0,INLAND\n-118.96,35.37,32.0,1025.0,259.0,874.0,236.0,1.9612,53400.0,INLAND\n-118.96,35.36,35.0,2285.0,497.0,1738.0,480.0,2.4848,54000.0,INLAND\n-118.97,35.37,34.0,1379.0,333.0,1156.0,315.0,1.7197,48900.0,INLAND\n-118.97,35.36,31.0,1418.0,306.0,1219.0,312.0,1.5743,46700.0,INLAND\n-118.95,35.32,29.0,3480.0,608.0,2007.0,541.0,3.2738,78700.0,INLAND\n-118.98,35.35,21.0,496.0,131.0,511.0,124.0,1.7614,33200.0,INLAND\n-118.99,35.35,27.0,1615.0,355.0,1380.0,332.0,1.6632,49800.0,INLAND\n-118.99,35.33,36.0,1590.0,367.0,1311.0,390.0,1.6786,52900.0,INLAND\n-118.99,35.32,26.0,875.0,199.0,567.0,204.0,0.9288,36600.0,INLAND\n-119.0,35.35,31.0,2931.0,716.0,1969.0,588.0,2.2155,62100.0,INLAND\n-118.99,35.35,32.0,1293.0,317.0,1109.0,286.0,1.1786,45600.0,INLAND\n-119.01,35.35,34.0,1354.0,325.0,922.0,304.0,2.1875,58000.0,INLAND\n-119.01,35.34,44.0,1730.0,343.0,782.0,278.0,3.0208,63700.0,INLAND\n-119.02,35.35,42.0,1239.0,251.0,776.0,272.0,1.983,63300.0,INLAND\n-119.01,35.35,39.0,598.0,149.0,366.0,132.0,1.9125,57900.0,INLAND\n-119.02,35.35,38.0,1472.0,305.0,670.0,282.0,2.2407,76000.0,INLAND\n-119.02,35.34,38.0,1463.0,294.0,692.0,295.0,2.3125,65800.0,INLAND\n-119.03,35.34,36.0,3474.0,645.0,1679.0,616.0,2.7256,71900.0,INLAND\n-119.03,35.35,34.0,1441.0,294.0,695.0,275.0,2.6875,73700.0,INLAND\n-119.04,35.35,31.0,1607.0,336.0,817.0,307.0,2.5644,73000.0,INLAND\n-119.04,35.35,27.0,4590.0,897.0,2212.0,894.0,3.1753,85000.0,INLAND\n-119.06,35.35,20.0,9351.0,2139.0,4584.0,1953.0,2.575,69900.0,INLAND\n-119.05,35.34,14.0,3580.0,984.0,1933.0,912.0,2.6637,175000.0,INLAND\n-119.05,35.33,18.0,12707.0,2685.0,7009.0,2552.0,2.9438,87200.0,INLAND\n-119.05,35.34,17.0,2387.0,437.0,1204.0,423.0,4.0529,106200.0,INLAND\n-119.07,35.35,24.0,4119.0,865.0,1294.0,879.0,2.4123,86200.0,INLAND\n-119.07,35.34,16.0,4201.0,786.0,1667.0,724.0,4.8839,134100.0,INLAND\n-119.12,35.33,4.0,8574.0,1489.0,4250.0,1444.0,5.1036,103400.0,INLAND\n-119.1,35.35,5.0,4597.0,1071.0,1916.0,870.0,4.0327,131000.0,INLAND\n-119.08,35.34,18.0,4070.0,512.0,1580.0,540.0,10.5941,245800.0,INLAND\n-119.08,35.35,20.0,892.0,129.0,331.0,135.0,7.1837,176300.0,INLAND\n-119.09,35.35,14.0,2113.0,256.0,842.0,265.0,8.5325,224100.0,INLAND\n-119.08,35.34,16.0,1535.0,238.0,768.0,236.0,5.4449,118500.0,INLAND\n-119.08,35.34,15.0,1474.0,235.0,768.0,238.0,4.1528,130100.0,INLAND\n-119.1,35.33,4.0,6640.0,898.0,3121.0,902.0,6.759,170300.0,INLAND\n-119.08,35.32,8.0,11609.0,2141.0,5696.0,2100.0,5.0012,106300.0,INLAND\n-119.09,35.33,9.0,7085.0,1148.0,3084.0,1052.0,4.997,142900.0,INLAND\n-119.06,35.32,15.0,3944.0,746.0,2355.0,757.0,3.569,70700.0,INLAND\n-119.07,35.33,13.0,9027.0,1901.0,4870.0,1797.0,3.406,100700.0,INLAND\n-119.06,35.33,14.0,5264.0,1064.0,3278.0,1049.0,3.8117,82800.0,INLAND\n-119.02,35.34,34.0,2861.0,510.0,1375.0,486.0,3.4286,71400.0,INLAND\n-119.02,35.33,26.0,3691.0,826.0,2072.0,827.0,2.1553,84700.0,INLAND\n-119.03,35.33,21.0,3057.0,698.0,1627.0,680.0,2.7083,84700.0,INLAND\n-119.03,35.34,34.0,2221.0,436.0,1131.0,408.0,3.0486,68500.0,INLAND\n-119.01,35.34,36.0,973.0,219.0,613.0,187.0,1.5625,46700.0,INLAND\n-119.01,35.33,42.0,1120.0,255.0,677.0,213.0,1.5429,39400.0,INLAND\n-119.0,35.33,35.0,991.0,221.0,620.0,207.0,1.9417,53800.0,INLAND\n-119.01,35.33,32.0,3068.0,628.0,1897.0,607.0,2.4234,63700.0,INLAND\n-119.02,35.33,35.0,2053.0,412.0,1193.0,387.0,2.75,65800.0,INLAND\n-119.02,35.34,35.0,1650.0,390.0,1145.0,343.0,1.5357,56500.0,INLAND\n-118.99,35.32,35.0,1576.0,405.0,870.0,282.0,1.6575,59500.0,INLAND\n-118.99,35.3,33.0,2248.0,434.0,1461.0,405.0,2.9402,56200.0,INLAND\n-119.0,35.31,37.0,1337.0,275.0,767.0,273.0,1.6522,53300.0,INLAND\n-119.09,35.3,3.0,2821.0,519.0,1353.0,495.0,3.6852,109800.0,INLAND\n-119.04,35.32,20.0,37.0,11.0,34.0,8.0,1.2,50000.0,INLAND\n-119.05,35.32,11.0,7035.0,1455.0,3525.0,1387.0,3.4827,93600.0,INLAND\n-119.04,35.31,11.0,2161.0,371.0,1267.0,388.0,4.1957,92700.0,INLAND\n-119.03,35.32,12.0,2721.0,549.0,1294.0,523.0,2.5575,100200.0,INLAND\n-119.02,35.32,14.0,2927.0,588.0,1821.0,561.0,3.3529,82600.0,INLAND\n-119.05,35.3,9.0,10822.0,1994.0,6241.0,1906.0,4.0631,88200.0,INLAND\n-119.03,35.3,10.0,829.0,146.0,447.0,173.0,4.1484,102900.0,INLAND\n-119.02,35.3,10.0,7397.0,1369.0,4611.0,1310.0,3.6369,81600.0,INLAND\n-119.01,35.32,23.0,4870.0,965.0,2717.0,928.0,2.596,70000.0,INLAND\n-119.01,35.31,19.0,7092.0,1517.0,4101.0,1436.0,2.1006,74800.0,INLAND\n-119.01,35.3,7.0,8596.0,1597.0,4893.0,1520.0,3.9054,80900.0,INLAND\n-119.07,35.27,25.0,3081.0,635.0,1830.0,591.0,2.5804,97900.0,INLAND\n-119.13,35.22,5.0,6268.0,1003.0,3269.0,980.0,5.1457,118200.0,INLAND\n-119.01,35.24,6.0,80.0,16.0,66.0,21.0,3.125,65000.0,INLAND\n-119.01,35.28,10.0,7011.0,1453.0,4163.0,1307.0,2.7659,77500.0,INLAND\n-118.99,35.24,40.0,282.0,59.0,213.0,71.0,2.35,91700.0,INLAND\n-118.99,35.27,32.0,444.0,102.0,242.0,87.0,1.1528,150000.0,INLAND\n-118.93,34.82,24.0,806.0,168.0,323.0,136.0,3.5,113900.0,INLAND\n-118.95,34.83,18.0,3278.0,762.0,1338.0,550.0,2.9891,116500.0,INLAND\n-118.95,34.81,30.0,2817.0,604.0,1089.0,412.0,3.1364,123500.0,INLAND\n-119.15,34.83,6.0,8733.0,1600.0,2006.0,736.0,4.5724,168400.0,INLAND\n-119.16,34.95,14.0,4054.0,787.0,1581.0,579.0,3.0882,148200.0,INLAND\n-118.93,34.82,8.0,508.0,111.0,229.0,84.0,4.0332,128300.0,INLAND\n-119.4,35.06,21.0,2213.0,458.0,1250.0,440.0,2.9187,52100.0,INLAND\n-119.45,35.07,45.0,973.0,183.0,500.0,177.0,2.6389,30000.0,INLAND\n-119.42,35.19,26.0,890.0,172.0,483.0,170.0,4.15,68200.0,INLAND\n-119.5,35.27,23.0,3827.0,696.0,1993.0,617.0,3.0742,57900.0,INLAND\n-119.48,35.17,36.0,116.0,20.0,39.0,18.0,3.125,37500.0,INLAND\n-119.45,35.16,34.0,3437.0,696.0,1783.0,608.0,2.3912,52900.0,INLAND\n-119.46,35.17,40.0,4164.0,812.0,1998.0,773.0,2.8323,50800.0,INLAND\n-119.46,35.14,30.0,2943.0,,1565.0,584.0,2.5313,45800.0,INLAND\n-119.47,35.14,19.0,4190.0,690.0,1973.0,702.0,3.9929,88300.0,INLAND\n-119.45,35.15,33.0,5050.0,964.0,2293.0,919.0,3.1592,75400.0,INLAND\n-119.45,35.13,34.0,1440.0,309.0,808.0,294.0,2.3013,26600.0,INLAND\n-119.46,35.13,46.0,2745.0,543.0,1423.0,482.0,2.1955,26900.0,INLAND\n-119.47,35.13,44.0,4599.0,877.0,2140.0,831.0,2.9952,63800.0,INLAND\n-119.47,35.4,32.0,2167.0,421.0,1301.0,394.0,1.9718,69800.0,INLAND\n-119.42,35.4,24.0,2585.0,480.0,1442.0,424.0,2.8452,104700.0,INLAND\n-119.12,35.41,12.0,5589.0,941.0,3018.0,917.0,4.4561,96900.0,INLAND\n-119.11,35.39,22.0,984.0,176.0,451.0,170.0,3.25,88900.0,INLAND\n-119.12,35.39,13.0,1264.0,202.0,552.0,187.0,4.5903,94300.0,INLAND\n-119.19,35.41,12.0,2835.0,471.0,1399.0,413.0,4.4125,149000.0,INLAND\n-119.2,35.37,6.0,7383.0,1095.0,3415.0,1059.0,5.3119,157300.0,INLAND\n-119.12,35.38,18.0,1521.0,269.0,706.0,279.0,4.4196,121000.0,INLAND\n-119.11,35.38,37.0,2044.0,394.0,894.0,359.0,2.9453,82800.0,INLAND\n-119.12,35.37,13.0,4527.0,713.0,2170.0,671.0,4.8266,146200.0,INLAND\n-119.18,35.5,36.0,1253.0,259.0,932.0,249.0,2.1635,110400.0,INLAND\n-119.28,35.52,36.0,786.0,194.0,573.0,134.0,2.2321,37500.0,INLAND\n-119.27,35.49,39.0,2649.0,572.0,1815.0,547.0,2.3533,65400.0,INLAND\n-119.26,35.5,38.0,2536.0,409.0,1133.0,430.0,4.2375,78600.0,INLAND\n-119.27,35.5,34.0,1367.0,329.0,796.0,319.0,2.8269,61100.0,INLAND\n-119.27,35.51,28.0,1089.0,179.0,544.0,190.0,3.2279,95800.0,INLAND\n-119.28,35.5,34.0,1923.0,379.0,1101.0,351.0,2.4044,65800.0,INLAND\n-119.28,35.5,28.0,3107.0,782.0,3260.0,738.0,1.6944,58600.0,INLAND\n-119.27,35.5,21.0,2171.0,483.0,1315.0,450.0,1.7105,52100.0,INLAND\n-119.38,35.49,34.0,2304.0,437.0,1506.0,403.0,2.2071,64600.0,INLAND\n-119.34,35.6,16.0,1584.0,309.0,1011.0,268.0,2.4961,58800.0,INLAND\n-119.36,35.55,29.0,510.0,84.0,236.0,73.0,2.7,125000.0,INLAND\n-119.35,35.58,13.0,1657.0,362.0,1186.0,376.0,1.1903,63200.0,INLAND\n-119.34,35.59,33.0,3240.0,654.0,1809.0,616.0,2.3934,71900.0,INLAND\n-119.34,35.6,33.0,2143.0,488.0,1732.0,509.0,1.9362,59000.0,INLAND\n-119.33,35.6,32.0,2703.0,683.0,2682.0,675.0,1.4619,60500.0,INLAND\n-119.35,35.59,14.0,2719.0,502.0,1497.0,457.0,3.176,71800.0,INLAND\n-119.33,35.59,20.0,3085.0,691.0,2645.0,676.0,1.7868,54100.0,INLAND\n-119.69,35.62,18.0,820.0,239.0,1345.0,207.0,2.1186,47500.0,INLAND\n-119.77,35.65,21.0,2403.0,483.0,1647.0,415.0,2.6066,80000.0,INLAND\n-119.14,35.76,30.0,735.0,137.0,421.0,113.0,2.5625,156300.0,INLAND\n-119.29,35.76,15.0,3938.0,789.0,3500.0,768.0,2.1295,59800.0,INLAND\n-119.19,35.64,29.0,1476.0,220.0,902.0,205.0,2.6726,83300.0,INLAND\n-119.24,35.68,21.0,1885.0,398.0,1539.0,388.0,2.5208,58500.0,INLAND\n-119.22,35.68,16.0,2874.0,677.0,3078.0,651.0,1.8843,55200.0,INLAND\n-119.24,35.67,32.0,3216.0,750.0,2639.0,709.0,2.0025,54700.0,INLAND\n-119.25,35.78,27.0,1513.0,342.0,1346.0,323.0,2.7411,59800.0,INLAND\n-119.25,35.77,35.0,1618.0,378.0,1449.0,398.0,1.6786,56500.0,INLAND\n-119.25,35.76,36.0,2332.0,656.0,2175.0,610.0,1.6045,57300.0,INLAND\n-119.25,35.75,36.0,1598.0,443.0,1658.0,417.0,1.517,52100.0,INLAND\n-119.25,35.79,8.0,3271.0,797.0,2700.0,688.0,1.7418,62200.0,INLAND\n-119.25,35.78,35.0,1927.0,386.0,1371.0,414.0,2.2981,69900.0,INLAND\n-119.23,35.78,8.0,1612.0,343.0,1230.0,330.0,2.1806,67200.0,INLAND\n-119.23,35.79,31.0,2862.0,606.0,2467.0,600.0,2.3125,62100.0,INLAND\n-119.24,35.77,28.0,1737.0,521.0,1764.0,514.0,1.7813,67800.0,INLAND\n-119.23,35.77,36.0,3225.0,635.0,2034.0,593.0,2.4044,72500.0,INLAND\n-119.23,35.74,16.0,2275.0,659.0,1914.0,614.0,2.033,68400.0,INLAND\n-119.23,35.77,26.0,2636.0,468.0,1416.0,485.0,4.1917,84000.0,INLAND\n-118.92,35.47,6.0,1755.0,280.0,664.0,254.0,6.2885,216400.0,INLAND\n-119.01,35.44,20.0,3458.0,651.0,1465.0,621.0,2.5806,82500.0,INLAND\n-118.93,35.44,13.0,1439.0,237.0,557.0,227.0,6.1563,204200.0,INLAND\n-118.31,35.74,18.0,2327.0,642.0,799.0,335.0,1.8419,92300.0,INLAND\n-118.06,35.68,15.0,1962.0,403.0,730.0,321.0,2.25,67500.0,INLAND\n-118.44,35.75,23.0,3166.0,700.0,1097.0,493.0,2.6288,96000.0,INLAND\n-118.47,35.72,18.0,4754.0,1075.0,1366.0,690.0,2.0694,81200.0,INLAND\n-118.5,35.7,18.0,3303.0,814.0,986.0,522.0,1.5957,101400.0,INLAND\n-118.59,35.72,28.0,1491.0,408.0,98.0,48.0,1.4205,90000.0,INLAND\n-118.87,35.65,33.0,1504.0,325.0,584.0,223.0,3.4792,94600.0,INLAND\n-118.23,35.48,17.0,2354.0,514.0,775.0,380.0,1.8369,59400.0,INLAND\n-118.33,35.64,15.0,2966.0,669.0,1007.0,465.0,1.5667,72500.0,INLAND\n-118.41,35.63,15.0,5907.0,1257.0,2310.0,1001.0,2.3125,96900.0,INLAND\n-118.45,35.62,18.0,2304.0,527.0,782.0,390.0,1.4141,75800.0,INLAND\n-118.47,35.64,17.0,2248.0,535.0,927.0,427.0,1.3023,68500.0,INLAND\n-118.48,35.61,17.0,4002.0,930.0,1614.0,731.0,1.6236,67300.0,INLAND\n-118.45,35.58,16.0,5396.0,1182.0,1802.0,807.0,1.8819,69700.0,INLAND\n-118.61,35.47,13.0,2267.0,601.0,756.0,276.0,2.5474,78400.0,INLAND\n-117.73,35.73,35.0,2916.0,594.0,1870.0,432.0,3.625,55000.0,INLAND\n-117.66,35.63,33.0,2579.0,564.0,1155.0,431.0,2.0441,42100.0,INLAND\n-117.67,35.65,18.0,2737.0,589.0,1128.0,533.0,2.8,72000.0,INLAND\n-117.68,35.65,15.0,2701.0,576.0,1245.0,513.0,3.3269,81900.0,INLAND\n-117.69,35.65,5.0,1131.0,276.0,520.0,232.0,4.0167,87500.0,INLAND\n-117.7,35.64,8.0,2683.0,416.0,1154.0,399.0,5.8625,109400.0,INLAND\n-117.68,35.64,15.0,3253.0,573.0,1408.0,586.0,5.2043,95700.0,INLAND\n-117.67,35.64,6.0,2115.0,342.0,927.0,337.0,6.1935,115700.0,INLAND\n-117.67,35.63,32.0,3661.0,787.0,1613.0,706.0,3.0687,63500.0,INLAND\n-117.68,35.63,18.0,4334.0,641.0,1777.0,661.0,6.0653,105300.0,INLAND\n-117.69,35.63,5.0,3151.0,482.0,1335.0,428.0,5.5773,109000.0,INLAND\n-117.7,35.62,18.0,2657.0,496.0,1426.0,483.0,3.5931,71900.0,INLAND\n-117.68,35.61,9.0,4241.0,832.0,1929.0,742.0,3.5988,84500.0,INLAND\n-117.68,35.6,12.0,1678.0,241.0,765.0,281.0,6.0532,102800.0,INLAND\n-117.7,35.6,16.0,2678.0,483.0,1473.0,487.0,3.858,70200.0,INLAND\n-117.68,35.62,30.0,2994.0,741.0,1481.0,581.0,2.1458,52400.0,INLAND\n-117.64,35.61,10.0,2656.0,506.0,1349.0,501.0,4.25,83200.0,INLAND\n-117.66,35.62,11.0,5897.0,1138.0,2728.0,1072.0,4.15,85700.0,INLAND\n-117.66,35.61,5.0,5735.0,932.0,2623.0,862.0,4.8494,87200.0,INLAND\n-117.66,35.6,14.0,1740.0,391.0,850.0,317.0,2.5812,91700.0,INLAND\n-117.76,35.63,12.0,2014.0,372.0,1027.0,356.0,3.9261,101300.0,INLAND\n-117.68,35.55,9.0,3811.0,605.0,1518.0,568.0,5.5551,142500.0,INLAND\n-117.81,35.65,19.0,1124.0,290.0,598.0,261.0,1.8984,54300.0,INLAND\n-117.87,35.73,13.0,2566.0,449.0,1181.0,414.0,4.1518,91800.0,INLAND\n-117.84,35.54,11.0,1751.0,316.0,765.0,296.0,5.0762,98000.0,INLAND\n-117.74,35.65,15.0,2357.0,484.0,1110.0,442.0,3.1755,81700.0,INLAND\n-117.84,35.35,28.0,1913.0,486.0,858.0,371.0,1.9962,50800.0,INLAND\n-118.0,35.05,21.0,1739.0,425.0,945.0,362.0,3.4015,86500.0,INLAND\n-117.82,35.03,30.0,2555.0,510.0,1347.0,467.0,3.3693,71800.0,INLAND\n-117.76,35.22,4.0,18.0,3.0,8.0,6.0,1.625,275000.0,INLAND\n-117.79,35.21,4.0,2.0,2.0,6.0,2.0,2.375,137500.0,INLAND\n-118.01,35.12,15.0,1926.0,361.0,917.0,316.0,3.3889,68500.0,INLAND\n-117.98,35.13,5.0,4849.0,920.0,2504.0,847.0,3.5391,81900.0,INLAND\n-117.95,35.13,4.0,2630.0,502.0,1150.0,422.0,4.25,104400.0,INLAND\n-117.95,35.08,1.0,83.0,15.0,32.0,15.0,4.875,141700.0,INLAND\n-117.98,35.1,4.0,923.0,166.0,352.0,135.0,4.5724,84500.0,INLAND\n-117.99,35.16,15.0,2180.0,416.0,960.0,370.0,2.875,87800.0,INLAND\n-118.27,34.92,20.0,873.0,175.0,422.0,159.0,2.9583,91700.0,INLAND\n-118.34,34.86,11.0,7353.0,1482.0,3571.0,1308.0,2.8097,130000.0,INLAND\n-117.68,35.03,28.0,2969.0,648.0,1644.0,570.0,3.4338,54900.0,INLAND\n-117.68,34.99,33.0,1589.0,307.0,853.0,272.0,4.2292,64400.0,INLAND\n-117.65,35.0,36.0,1184.0,316.0,672.0,241.0,1.9107,39800.0,INLAND\n-118.15,34.86,10.0,4597.0,1009.0,2227.0,821.0,2.6149,83500.0,INLAND\n-118.17,34.87,9.0,1507.0,293.0,761.0,278.0,3.0184,87900.0,INLAND\n-118.19,34.87,2.0,2103.0,389.0,923.0,338.0,5.0553,111100.0,INLAND\n-118.17,34.86,21.0,2370.0,540.0,1488.0,554.0,2.7361,83300.0,INLAND\n-118.15,35.06,15.0,1069.0,296.0,569.0,263.0,2.0441,73300.0,INLAND\n-118.16,35.05,44.0,1297.0,307.0,776.0,278.0,2.5875,68900.0,INLAND\n-118.19,35.05,14.0,2992.0,573.0,1631.0,526.0,3.7452,83200.0,INLAND\n-118.15,35.04,29.0,1671.0,368.0,821.0,337.0,2.16,56800.0,INLAND\n-118.51,35.16,7.0,4371.0,727.0,1932.0,654.0,4.625,136800.0,INLAND\n-118.66,35.2,7.0,9664.0,1692.0,3617.0,1370.0,4.0581,162900.0,INLAND\n-118.34,35.27,10.0,2939.0,605.0,1167.0,446.0,2.3917,79000.0,INLAND\n-118.48,35.14,4.0,8417.0,1657.0,4631.0,1468.0,3.6949,115800.0,INLAND\n-118.61,35.08,6.0,3660.0,646.0,1243.0,482.0,3.4911,137200.0,INLAND\n-118.61,34.99,11.0,4031.0,766.0,1539.0,564.0,3.8917,120800.0,INLAND\n-118.46,35.13,19.0,3109.0,640.0,1457.0,620.0,2.6417,94900.0,INLAND\n-118.46,35.12,16.0,4084.0,812.0,2033.0,668.0,3.2405,85500.0,INLAND\n-118.43,35.12,8.0,1968.0,376.0,930.0,360.0,3.2632,99800.0,INLAND\n-118.44,35.13,34.0,1170.0,290.0,602.0,266.0,1.7917,80000.0,INLAND\n-118.44,35.13,21.0,1899.0,447.0,1133.0,391.0,1.8636,67900.0,INLAND\n-118.91,35.3,28.0,1793.0,358.0,1233.0,351.0,2.7845,82200.0,INLAND\n-118.95,35.26,24.0,1341.0,214.0,667.0,184.0,4.0,94500.0,INLAND\n-118.83,35.27,33.0,1190.0,217.0,717.0,196.0,2.6302,81300.0,INLAND\n-118.92,35.13,29.0,1297.0,262.0,909.0,253.0,1.9236,106300.0,INLAND\n-118.85,35.23,26.0,1639.0,352.0,1222.0,395.0,1.7656,68000.0,INLAND\n-118.82,35.23,31.0,2358.0,580.0,2302.0,574.0,1.9688,53900.0,INLAND\n-118.83,35.2,17.0,1959.0,484.0,1763.0,453.0,2.1357,53500.0,INLAND\n-118.82,35.2,34.0,2185.0,469.0,1910.0,455.0,2.1136,57300.0,INLAND\n-118.85,35.2,17.0,2783.0,678.0,2566.0,641.0,1.9907,51200.0,INLAND\n-118.91,35.27,29.0,1401.0,317.0,1344.0,306.0,2.0921,61400.0,INLAND\n-118.9,35.26,31.0,6145.0,1492.0,5666.0,1457.0,1.9066,54600.0,INLAND\n-118.92,35.26,20.0,3815.0,924.0,3450.0,920.0,2.0174,63700.0,INLAND\n-118.91,35.24,29.0,2888.0,753.0,2949.0,699.0,1.7716,45500.0,INLAND\n-119.69,36.41,38.0,1016.0,202.0,540.0,187.0,2.2885,75000.0,INLAND\n-119.57,36.44,30.0,1860.0,337.0,1123.0,347.0,3.4926,94200.0,INLAND\n-119.55,36.37,26.0,1912.0,339.0,1002.0,311.0,3.0375,126300.0,INLAND\n-119.69,36.38,25.0,1688.0,302.0,879.0,277.0,3.3214,103100.0,INLAND\n-119.78,36.37,41.0,831.0,149.0,443.0,146.0,3.1406,100000.0,INLAND\n-119.83,36.37,25.0,1549.0,269.0,819.0,272.0,2.7159,101400.0,INLAND\n-119.87,36.34,26.0,1414.0,265.0,779.0,249.0,2.9167,83900.0,INLAND\n-119.93,36.32,25.0,8363.0,1636.0,7679.0,1580.0,2.0285,106300.0,INLAND\n-119.79,36.32,19.0,3252.0,614.0,1971.0,607.0,3.0667,75800.0,INLAND\n-119.77,36.32,14.0,3400.0,618.0,1867.0,612.0,3.9926,92500.0,INLAND\n-119.78,36.31,14.0,1287.0,291.0,737.0,269.0,3.1667,126400.0,INLAND\n-119.77,36.31,14.0,3677.0,863.0,2191.0,785.0,2.6218,69100.0,INLAND\n-119.79,36.31,25.0,4984.0,1029.0,2414.0,961.0,2.2937,72300.0,INLAND\n-119.77,36.3,24.0,2202.0,471.0,1052.0,439.0,2.1038,62000.0,INLAND\n-119.78,36.3,30.0,1846.0,391.0,1255.0,352.0,2.1681,66600.0,INLAND\n-119.79,36.3,16.0,1717.0,277.0,903.0,289.0,4.3438,93100.0,INLAND\n-119.79,36.29,6.0,1265.0,227.0,764.0,246.0,4.2917,104200.0,INLAND\n-119.82,36.32,18.0,942.0,193.0,424.0,174.0,2.0673,87500.0,INLAND\n-119.78,36.33,16.0,1006.0,212.0,515.0,200.0,3.2386,112500.0,INLAND\n-119.78,36.27,29.0,1871.0,315.0,1066.0,309.0,4.5714,100800.0,INLAND\n-119.8,36.29,7.0,479.0,84.0,327.0,103.0,5.1728,107500.0,INLAND\n-119.81,36.28,24.0,544.0,112.0,442.0,106.0,3.1071,56100.0,INLAND\n-119.72,36.34,33.0,1287.0,214.0,580.0,210.0,3.2019,112500.0,INLAND\n-119.72,36.32,40.0,1185.0,221.0,676.0,256.0,2.2721,52600.0,INLAND\n-119.7,36.32,40.0,1178.0,244.0,586.0,187.0,2.6477,43500.0,INLAND\n-119.73,36.31,20.0,2440.0,433.0,1579.0,400.0,2.8281,60200.0,INLAND\n-119.7,36.3,10.0,956.0,201.0,693.0,220.0,2.2895,62000.0,INLAND\n-119.67,36.35,10.0,1090.0,164.0,470.0,158.0,4.9432,118800.0,INLAND\n-119.65,36.37,4.0,3725.0,783.0,1478.0,600.0,3.5486,148000.0,INLAND\n-119.65,36.35,21.0,1745.0,266.0,837.0,292.0,4.3911,107900.0,INLAND\n-119.65,36.35,38.0,3148.0,568.0,1378.0,537.0,2.8788,85500.0,INLAND\n-119.66,36.35,15.0,1724.0,374.0,947.0,391.0,3.1094,91900.0,INLAND\n-119.64,36.36,13.0,2360.0,340.0,1055.0,312.0,5.2134,97400.0,INLAND\n-119.64,36.35,23.0,3182.0,563.0,1525.0,585.0,3.8108,90400.0,INLAND\n-119.63,36.35,4.0,1684.0,343.0,920.0,324.0,4.2396,90600.0,INLAND\n-119.62,36.35,10.0,3674.0,734.0,1864.0,718.0,2.6145,80300.0,INLAND\n-119.64,36.35,30.0,1765.0,310.0,746.0,298.0,2.8125,70200.0,INLAND\n-119.63,36.34,26.0,1463.0,261.0,699.0,219.0,3.5536,71400.0,INLAND\n-119.63,36.33,14.0,2928.0,600.0,1633.0,559.0,1.8385,67500.0,INLAND\n-119.61,36.33,32.0,1492.0,284.0,926.0,264.0,3.0139,61500.0,INLAND\n-119.63,36.32,36.0,1518.0,287.0,749.0,255.0,2.2333,61000.0,INLAND\n-119.66,36.34,32.0,1338.0,276.0,859.0,286.0,2.6397,59700.0,INLAND\n-119.65,36.34,46.0,1730.0,337.0,752.0,323.0,1.8529,67200.0,INLAND\n-119.65,36.34,47.0,1869.0,357.0,832.0,315.0,3.0846,76100.0,INLAND\n-119.64,36.34,32.0,2958.0,670.0,1504.0,627.0,1.8606,56700.0,INLAND\n-119.64,36.33,41.0,3095.0,766.0,1852.0,721.0,1.4524,51700.0,INLAND\n-119.65,36.33,52.0,1257.0,257.0,624.0,243.0,2.3523,59100.0,INLAND\n-119.65,36.33,47.0,1059.0,268.0,693.0,241.0,1.3882,53800.0,INLAND\n-119.68,36.32,26.0,592.0,121.0,268.0,116.0,1.7596,120800.0,INLAND\n-119.67,36.33,19.0,1241.0,244.0,850.0,237.0,2.9375,81700.0,INLAND\n-119.66,36.33,16.0,2048.0,373.0,1052.0,388.0,4.0909,92800.0,INLAND\n-119.66,36.33,10.0,1623.0,409.0,988.0,395.0,1.4194,58100.0,INLAND\n-119.66,36.32,24.0,2652.0,568.0,1532.0,445.0,2.3256,56800.0,INLAND\n-119.68,36.32,28.0,1325.0,276.0,873.0,240.0,2.5833,54400.0,INLAND\n-119.68,36.31,12.0,2739.0,535.0,1859.0,498.0,2.9936,60600.0,INLAND\n-119.66,36.3,18.0,1147.0,202.0,717.0,212.0,3.3681,70500.0,INLAND\n-119.65,36.32,11.0,1294.0,314.0,713.0,290.0,1.5433,50400.0,INLAND\n-119.64,36.32,32.0,2205.0,523.0,1772.0,479.0,1.3569,43100.0,INLAND\n-119.65,36.3,28.0,941.0,175.0,588.0,180.0,2.3466,53400.0,INLAND\n-119.64,36.31,27.0,1513.0,314.0,1071.0,284.0,1.5909,50100.0,INLAND\n-119.61,36.31,25.0,1847.0,371.0,1460.0,353.0,1.8839,46300.0,INLAND\n-119.57,36.27,20.0,2673.0,452.0,1394.0,449.0,2.625,97400.0,INLAND\n-119.69,36.25,35.0,2011.0,349.0,970.0,300.0,2.395,94100.0,INLAND\n-119.63,36.18,23.0,207.0,45.0,171.0,50.0,2.4286,100000.0,INLAND\n-119.59,36.11,32.0,752.0,159.0,524.0,155.0,2.25,50000.0,INLAND\n-119.57,36.09,6.0,2015.0,413.0,992.0,319.0,2.3889,53200.0,INLAND\n-119.58,36.1,21.0,1382.0,327.0,1469.0,355.0,1.3967,46500.0,INLAND\n-119.56,36.1,29.0,424.0,78.0,284.0,73.0,1.5313,43800.0,INLAND\n-119.56,36.1,25.0,1093.0,262.0,893.0,252.0,2.13,50800.0,INLAND\n-119.56,36.09,35.0,1648.0,285.0,792.0,265.0,3.2847,64700.0,INLAND\n-119.56,36.09,14.0,1267.0,290.0,1077.0,279.0,1.85,52300.0,INLAND\n-119.56,36.08,37.0,766.0,189.0,639.0,190.0,1.6607,42100.0,INLAND\n-119.58,36.11,21.0,2004.0,385.0,1397.0,398.0,2.2169,61600.0,INLAND\n-119.57,36.1,16.0,1461.0,400.0,1201.0,384.0,1.5727,54800.0,INLAND\n-119.57,36.1,36.0,1729.0,317.0,737.0,278.0,3.5313,68800.0,INLAND\n-119.57,36.1,37.0,1676.0,316.0,707.0,274.0,2.0595,60700.0,INLAND\n-119.74,36.15,21.0,1548.0,308.0,1137.0,306.0,2.4688,61300.0,INLAND\n-119.9,36.2,43.0,187.0,38.0,106.0,40.0,1.875,137500.0,INLAND\n-119.82,36.19,33.0,1293.0,272.0,694.0,229.0,2.0221,52200.0,INLAND\n-119.99,36.09,23.0,333.0,92.0,198.0,55.0,0.4999,100000.0,INLAND\n-119.96,35.99,25.0,1047.0,270.0,1505.0,286.0,2.0976,47700.0,INLAND\n-119.8,36.02,20.0,156.0,39.0,171.0,37.0,3.05,225000.0,INLAND\n-120.14,36.04,27.0,2533.0,518.0,1371.0,461.0,2.9708,60900.0,INLAND\n-120.12,36.01,18.0,1165.0,334.0,1119.0,308.0,2.2167,48500.0,INLAND\n-120.14,36.0,33.0,1726.0,420.0,1371.0,388.0,2.0335,43900.0,INLAND\n-120.12,35.99,7.0,2049.0,482.0,1387.0,422.0,2.25,56200.0,INLAND\n-120.13,35.87,26.0,48.0,8.0,13.0,8.0,2.375,71300.0,INLAND\n-120.0,35.91,16.0,259.0,53.0,131.0,38.0,3.125,62500.0,INLAND\n-122.89,39.42,16.0,411.0,114.0,26.0,19.0,0.4999,73500.0,INLAND\n-122.9,39.23,39.0,1295.0,240.0,534.0,179.0,3.9519,98900.0,INLAND\n-122.91,39.18,43.0,89.0,18.0,86.0,27.0,2.0208,72500.0,INLAND\n-122.91,39.17,44.0,202.0,42.0,142.0,39.0,4.35,68300.0,INLAND\n-122.9,39.17,45.0,1314.0,277.0,649.0,232.0,2.575,73600.0,INLAND\n-122.88,39.14,20.0,1125.0,231.0,521.0,196.0,2.2188,106300.0,INLAND\n-123.07,39.12,24.0,1098.0,193.0,353.0,145.0,3.8333,92600.0,<1H OCEAN\n-122.95,39.13,17.0,380.0,69.0,225.0,72.0,3.25,137500.0,INLAND\n-122.89,39.11,10.0,1588.0,333.0,585.0,254.0,2.2551,71100.0,INLAND\n-122.9,39.09,15.0,2483.0,544.0,835.0,380.0,1.9141,143200.0,INLAND\n-122.92,39.08,24.0,341.0,64.0,146.0,57.0,4.0,166300.0,INLAND\n-122.94,39.1,18.0,681.0,120.0,272.0,105.0,2.8906,140600.0,INLAND\n-122.99,39.02,14.0,1582.0,301.0,851.0,273.0,3.45,164100.0,<1H OCEAN\n-122.91,39.07,21.0,2202.0,484.0,1000.0,381.0,2.4423,102300.0,INLAND\n-122.91,39.06,21.0,1236.0,238.0,601.0,261.0,1.939,100300.0,INLAND\n-122.92,39.05,16.0,1548.0,295.0,605.0,250.0,3.5652,119000.0,INLAND\n-122.91,39.05,20.0,1128.0,229.0,621.0,210.0,3.2216,93500.0,INLAND\n-122.92,39.05,38.0,3131.0,624.0,1591.0,568.0,2.5457,80700.0,INLAND\n-122.91,39.05,27.0,789.0,208.0,295.0,108.0,3.7667,95000.0,INLAND\n-122.91,39.03,14.0,2374.0,557.0,723.0,427.0,1.3532,95800.0,INLAND\n-122.7,39.14,13.0,532.0,111.0,214.0,62.0,3.3929,108300.0,INLAND\n-122.87,39.13,15.0,1927.0,427.0,810.0,321.0,1.6369,86500.0,INLAND\n-122.86,39.08,24.0,3127.0,674.0,1015.0,448.0,2.0417,78800.0,INLAND\n-122.83,39.09,26.0,2191.0,495.0,679.0,371.0,1.4679,94700.0,INLAND\n-122.79,39.09,20.0,1798.0,395.0,685.0,331.0,1.625,66800.0,INLAND\n-122.8,39.08,17.0,1880.0,467.0,798.0,342.0,1.4676,65000.0,INLAND\n-122.79,39.08,23.0,952.0,200.0,321.0,128.0,1.5208,89000.0,INLAND\n-122.78,39.05,15.0,1601.0,323.0,661.0,269.0,2.6181,108900.0,INLAND\n-122.53,39.09,11.0,1264.0,271.0,370.0,177.0,1.3,69700.0,INLAND\n-122.69,39.04,9.0,254.0,50.0,66.0,29.0,2.7639,112500.0,INLAND\n-122.73,39.04,23.0,1618.0,395.0,425.0,244.0,1.9833,111500.0,INLAND\n-122.69,39.02,27.0,2199.0,527.0,744.0,316.0,2.1094,72400.0,INLAND\n-122.66,39.03,27.0,1446.0,329.0,594.0,255.0,1.165,53300.0,INLAND\n-122.66,39.02,16.0,3715.0,810.0,943.0,510.0,1.7446,109400.0,INLAND\n-122.7,39.0,18.0,793.0,148.0,186.0,59.0,2.3125,162500.0,INLAND\n-122.7,38.99,18.0,1177.0,224.0,181.0,105.0,2.3558,134700.0,INLAND\n-122.65,38.99,16.0,4279.0,951.0,1596.0,666.0,1.8571,75900.0,INLAND\n-122.52,38.99,16.0,975.0,219.0,337.0,155.0,1.6607,77800.0,INLAND\n-122.68,38.98,27.0,2300.0,508.0,526.0,254.0,2.1838,109700.0,INLAND\n-122.65,38.97,32.0,1856.0,472.0,703.0,292.0,1.1912,60000.0,INLAND\n-122.63,38.96,17.0,1708.0,459.0,633.0,312.0,1.75,64000.0,INLAND\n-122.62,38.95,19.0,2230.0,538.0,832.0,359.0,1.6865,58800.0,INLAND\n-122.62,38.94,14.0,1731.0,400.0,638.0,282.0,2.3179,57500.0,INLAND\n-122.61,38.93,14.0,231.0,36.0,108.0,31.0,4.3897,71300.0,INLAND\n-122.6,38.93,16.0,1657.0,390.0,572.0,301.0,1.4767,62000.0,INLAND\n-122.62,38.96,16.0,1914.0,446.0,828.0,332.0,2.0577,69000.0,INLAND\n-122.63,38.96,20.0,2507.0,577.0,1072.0,457.0,2.3083,60200.0,INLAND\n-122.65,38.96,27.0,2143.0,580.0,898.0,367.0,1.6769,63200.0,INLAND\n-122.64,38.96,29.0,883.0,187.0,326.0,136.0,1.7273,58200.0,INLAND\n-122.64,38.95,28.0,1503.0,370.0,522.0,268.0,1.2029,68900.0,INLAND\n-122.63,38.95,11.0,686.0,127.0,246.0,86.0,1.7083,77300.0,INLAND\n-122.63,38.94,25.0,661.0,144.0,192.0,93.0,1.7566,49000.0,INLAND\n-122.63,38.94,18.0,3844.0,969.0,1832.0,845.0,1.125,81800.0,INLAND\n-122.62,38.94,13.0,524.0,129.0,215.0,90.0,1.5455,55000.0,INLAND\n-122.7,38.97,17.0,2554.0,540.0,723.0,319.0,3.2375,114200.0,INLAND\n-122.75,39.01,17.0,4162.0,967.0,889.0,414.0,3.4187,200500.0,INLAND\n-122.79,39.02,23.0,642.0,203.0,265.0,84.0,1.8833,96900.0,INLAND\n-122.78,38.97,11.0,5175.0,971.0,2144.0,792.0,3.0466,97300.0,INLAND\n-122.69,38.94,9.0,1245.0,234.0,517.0,187.0,3.125,93400.0,INLAND\n-122.71,38.91,20.0,41.0,18.0,94.0,10.0,1.375,55000.0,INLAND\n-122.86,39.05,20.0,1592.0,327.0,647.0,253.0,2.5326,136800.0,INLAND\n-122.85,39.0,20.0,1580.0,318.0,753.0,252.0,1.8704,88500.0,INLAND\n-122.83,38.99,15.0,289.0,49.0,191.0,54.0,1.6833,113900.0,INLAND\n-122.83,38.98,17.0,1383.0,347.0,719.0,296.0,1.6164,77800.0,INLAND\n-122.84,38.98,21.0,939.0,176.0,556.0,178.0,1.7196,75000.0,INLAND\n-122.92,38.97,20.0,2067.0,384.0,904.0,333.0,2.9934,134200.0,<1H OCEAN\n-122.89,38.93,20.0,1214.0,247.0,504.0,223.0,2.7188,105700.0,<1H OCEAN\n-122.83,38.96,15.0,1318.0,296.0,567.0,276.0,1.8692,93800.0,INLAND\n-122.77,38.92,26.0,712.0,140.0,293.0,100.0,4.0119,119400.0,INLAND\n-122.83,38.89,11.0,640.0,134.0,268.0,90.0,3.4514,100000.0,<1H OCEAN\n-122.72,38.88,29.0,2781.0,,890.0,310.0,1.9906,96600.0,INLAND\n-122.74,38.83,12.0,4515.0,909.0,1554.0,528.0,3.3531,90800.0,<1H OCEAN\n-122.48,38.9,10.0,304.0,63.0,161.0,61.0,2.1964,112500.0,INLAND\n-122.59,38.92,15.0,1410.0,329.0,599.0,273.0,2.1953,75000.0,INLAND\n-122.6,38.9,23.0,292.0,56.0,92.0,41.0,2.9583,91700.0,INLAND\n-122.62,38.92,13.0,520.0,115.0,249.0,109.0,1.8417,84700.0,INLAND\n-122.65,38.92,30.0,70.0,38.0,20.0,13.0,4.125,112500.0,INLAND\n-122.64,38.87,16.0,1177.0,240.0,519.0,199.0,1.5739,73500.0,INLAND\n-122.5,38.82,12.0,2394.0,443.0,877.0,341.0,2.5625,109200.0,INLAND\n-122.51,38.76,9.0,2589.0,482.0,1050.0,374.0,4.0435,132600.0,INLAND\n-122.55,38.81,7.0,3639.0,637.0,1027.0,421.0,3.8831,132100.0,INLAND\n-122.59,38.78,15.0,764.0,145.0,366.0,143.0,3.375,103100.0,INLAND\n-122.66,38.81,22.0,852.0,176.0,461.0,142.0,3.4375,83300.0,INLAND\n-122.68,38.76,29.0,994.0,226.0,302.0,117.0,2.3125,67900.0,INLAND\n-122.62,38.73,21.0,1425.0,323.0,727.0,287.0,2.1474,85300.0,INLAND\n-122.64,38.71,20.0,531.0,126.0,231.0,96.0,2.625,89600.0,INLAND\n-122.52,38.7,26.0,102.0,17.0,43.0,13.0,0.536,87500.0,INLAND\n-121.11,41.07,26.0,1707.0,308.0,761.0,250.0,2.7188,48100.0,INLAND\n-120.96,41.12,29.0,779.0,136.0,364.0,123.0,2.5,59200.0,INLAND\n-121.07,40.85,17.0,976.0,202.0,511.0,175.0,3.6641,80800.0,INLAND\n-120.38,40.98,27.0,777.0,185.0,318.0,115.0,1.6833,40000.0,INLAND\n-120.35,40.63,33.0,240.0,49.0,63.0,22.0,3.625,200000.0,INLAND\n-120.77,40.65,11.0,2635.0,667.0,280.0,132.0,1.7214,118300.0,INLAND\n-121.02,40.51,17.0,890.0,167.0,406.0,154.0,3.3036,78300.0,INLAND\n-120.96,40.28,19.0,683.0,139.0,302.0,111.0,2.5,64100.0,INLAND\n-121.03,40.35,52.0,5486.0,1044.0,1977.0,754.0,2.1833,49500.0,INLAND\n-120.67,40.5,15.0,5343.0,,2503.0,902.0,3.5962,85900.0,INLAND\n-120.57,40.43,15.0,2045.0,461.0,1121.0,402.0,2.6902,71500.0,INLAND\n-120.66,40.42,35.0,1450.0,325.0,717.0,297.0,2.5074,66400.0,INLAND\n-120.65,40.42,39.0,3240.0,652.0,1467.0,621.0,2.1875,64300.0,INLAND\n-120.66,40.41,52.0,2081.0,478.0,1051.0,419.0,2.2992,70200.0,INLAND\n-120.64,40.41,50.0,1741.0,424.0,987.0,383.0,1.5066,59300.0,INLAND\n-120.71,40.36,19.0,4462.0,828.0,2229.0,777.0,3.5536,105700.0,INLAND\n-120.58,40.37,16.0,3412.0,667.0,1873.0,590.0,2.2661,61800.0,INLAND\n-120.42,40.38,26.0,1652.0,313.0,762.0,280.0,2.4757,85600.0,INLAND\n-120.36,40.45,19.0,689.0,143.0,355.0,127.0,1.7333,70000.0,INLAND\n-120.51,40.41,36.0,36.0,8.0,4198.0,7.0,5.5179,67500.0,INLAND\n-120.54,40.29,17.0,3391.0,623.0,1529.0,571.0,3.4028,91000.0,INLAND\n-120.49,40.31,16.0,1821.0,360.0,969.0,359.0,3.4643,85100.0,INLAND\n-120.37,40.17,21.0,789.0,141.0,406.0,146.0,2.1198,73500.0,INLAND\n-120.2,40.26,26.0,2399.0,518.0,1037.0,443.0,2.6765,47600.0,INLAND\n-120.09,39.92,19.0,2335.0,518.0,1028.0,383.0,1.7267,60700.0,INLAND\n-118.27,34.27,27.0,5205.0,859.0,2363.0,888.0,6.1946,276100.0,<1H OCEAN\n-118.28,34.26,32.0,1079.0,207.0,486.0,167.0,4.9833,213000.0,<1H OCEAN\n-118.29,34.26,33.0,3177.0,713.0,1845.0,612.0,4.008,191100.0,<1H OCEAN\n-118.3,34.26,37.0,2824.0,633.0,1619.0,573.0,3.5568,184500.0,<1H OCEAN\n-118.3,34.26,42.0,2186.0,423.0,1145.0,439.0,4.81,191900.0,<1H OCEAN\n-118.28,34.25,35.0,2045.0,450.0,1166.0,407.0,3.5214,197600.0,<1H OCEAN\n-118.28,34.25,29.0,2559.0,,1886.0,769.0,2.6036,162100.0,<1H OCEAN\n-118.29,34.25,19.0,1988.0,594.0,1399.0,527.0,2.4727,175000.0,<1H OCEAN\n-118.29,34.25,8.0,5568.0,1514.0,3565.0,1374.0,3.0795,161500.0,<1H OCEAN\n-118.27,34.25,37.0,2489.0,454.0,1215.0,431.0,5.0234,257600.0,<1H OCEAN\n-118.27,34.25,35.0,2091.0,360.0,879.0,326.0,4.4485,261900.0,<1H OCEAN\n-118.27,34.24,30.0,2180.0,369.0,1050.0,390.0,6.3688,277600.0,<1H OCEAN\n-118.27,34.25,35.0,779.0,143.0,371.0,150.0,4.6635,230100.0,<1H OCEAN\n-118.27,34.25,39.0,699.0,150.0,358.0,143.0,4.4375,195800.0,<1H OCEAN\n-118.28,34.24,32.0,2542.0,526.0,1278.0,493.0,4.45,263600.0,<1H OCEAN\n-118.28,34.24,29.0,3390.0,580.0,1543.0,576.0,5.6184,316900.0,<1H OCEAN\n-118.3,34.25,36.0,1300.0,304.0,688.0,261.0,3.1523,176700.0,<1H OCEAN\n-118.33,34.24,31.0,6434.0,1188.0,3540.0,1131.0,4.2639,293300.0,<1H OCEAN\n-118.35,34.22,19.0,9259.0,1653.0,3963.0,1595.0,5.997,228700.0,<1H OCEAN\n-118.35,34.22,30.0,1260.0,222.0,638.0,229.0,4.1302,258300.0,<1H OCEAN\n-118.35,34.21,39.0,1470.0,312.0,1047.0,284.0,3.275,181400.0,<1H OCEAN\n-118.35,34.21,42.0,1073.0,220.0,804.0,226.0,3.75,172600.0,<1H OCEAN\n-118.31,34.28,34.0,3258.0,610.0,1810.0,633.0,5.1145,219900.0,<1H OCEAN\n-118.31,34.27,35.0,1446.0,274.0,759.0,291.0,6.0808,215600.0,<1H OCEAN\n-118.3,34.26,43.0,1510.0,310.0,809.0,277.0,3.599,176500.0,<1H OCEAN\n-118.3,34.26,40.0,1065.0,214.0,605.0,183.0,4.1964,185900.0,<1H OCEAN\n-118.31,34.26,38.0,2264.0,460.0,1124.0,388.0,4.2685,189600.0,<1H OCEAN\n-118.31,34.26,41.0,1297.0,327.0,733.0,315.0,3.0583,160300.0,<1H OCEAN\n-118.31,34.26,34.0,1797.0,363.0,948.0,363.0,4.1339,187300.0,<1H OCEAN\n-118.32,34.26,32.0,3690.0,791.0,1804.0,715.0,4.4875,222700.0,<1H OCEAN\n-118.35,34.28,30.0,3214.0,513.0,1700.0,533.0,4.6944,248200.0,<1H OCEAN\n-118.33,34.27,29.0,3034.0,732.0,1776.0,702.0,3.1349,230200.0,<1H OCEAN\n-118.35,34.27,32.0,604.0,108.0,314.0,113.0,6.2037,205400.0,<1H OCEAN\n-118.34,34.26,37.0,1776.0,301.0,702.0,265.0,5.2661,314900.0,<1H OCEAN\n-118.35,34.25,34.0,2795.0,460.0,1267.0,443.0,6.1464,354400.0,<1H OCEAN\n-118.36,34.26,34.0,3677.0,573.0,1598.0,568.0,6.838,378000.0,<1H OCEAN\n-118.3,34.26,28.0,1643.0,489.0,1142.0,458.0,3.1607,200600.0,<1H OCEAN\n-118.3,34.25,44.0,1442.0,285.0,859.0,292.0,4.5833,197300.0,<1H OCEAN\n-118.31,34.26,37.0,1444.0,246.0,624.0,239.0,5.76,239400.0,<1H OCEAN\n-118.31,34.26,36.0,1882.0,453.0,1005.0,409.0,3.8,217100.0,<1H OCEAN\n-118.32,34.26,24.0,5106.0,1010.0,2310.0,957.0,4.4375,191500.0,<1H OCEAN\n-118.39,34.28,24.0,4694.0,820.0,3566.0,777.0,4.4818,166200.0,<1H OCEAN\n-118.38,34.28,22.0,4428.0,825.0,3152.0,836.0,4.7932,166300.0,<1H OCEAN\n-118.4,34.28,16.0,6573.0,1480.0,6161.0,1473.0,3.3304,154900.0,<1H OCEAN\n-118.38,34.27,8.0,3248.0,847.0,2608.0,731.0,2.8214,158300.0,<1H OCEAN\n-118.41,34.29,35.0,1008.0,204.0,1162.0,215.0,3.35,147600.0,<1H OCEAN\n-118.41,34.29,32.0,1591.0,320.0,1818.0,306.0,4.2969,145800.0,<1H OCEAN\n-118.42,34.28,29.0,1271.0,272.0,1338.0,266.0,4.125,150000.0,<1H OCEAN\n-118.41,34.28,32.0,2574.0,531.0,2609.0,472.0,3.7566,146700.0,<1H OCEAN\n-118.4,34.28,22.0,3517.0,810.0,3134.0,847.0,2.6652,164800.0,<1H OCEAN\n-118.42,34.28,35.0,822.0,200.0,1197.0,203.0,3.2865,133300.0,<1H OCEAN\n-118.42,34.28,34.0,1999.0,427.0,2391.0,439.0,2.8,144300.0,<1H OCEAN\n-118.42,34.27,33.0,1209.0,341.0,1097.0,283.0,1.6295,134300.0,<1H OCEAN\n-118.42,34.27,33.0,937.0,216.0,1216.0,212.0,3.3214,131300.0,<1H OCEAN\n-118.42,34.27,35.0,674.0,153.0,808.0,173.0,2.6667,147800.0,<1H OCEAN\n-118.43,34.28,30.0,1384.0,308.0,2054.0,301.0,3.0132,142600.0,<1H OCEAN\n-118.43,34.27,31.0,1130.0,276.0,1533.0,269.0,4.2353,156800.0,<1H OCEAN\n-118.43,34.27,36.0,1002.0,250.0,1312.0,249.0,3.024,148000.0,<1H OCEAN\n-118.42,34.27,37.0,1024.0,246.0,1371.0,239.0,3.225,147500.0,<1H OCEAN\n-118.43,34.26,30.0,1246.0,373.0,1990.0,369.0,3.5104,140900.0,<1H OCEAN\n-118.43,34.26,37.0,1269.0,348.0,1835.0,335.0,3.2583,147200.0,<1H OCEAN\n-118.44,34.27,35.0,777.0,187.0,1022.0,186.0,3.4,139600.0,<1H OCEAN\n-118.43,34.26,43.0,729.0,172.0,935.0,174.0,2.9519,140900.0,<1H OCEAN\n-118.42,34.25,37.0,1545.0,341.0,1909.0,352.0,3.6791,148100.0,<1H OCEAN\n-118.43,34.25,35.0,1447.0,335.0,1630.0,306.0,2.9205,143100.0,<1H OCEAN\n-118.42,34.26,26.0,1788.0,521.0,2582.0,484.0,2.1062,136400.0,<1H OCEAN\n-118.41,34.26,38.0,870.0,205.0,1065.0,222.0,2.5313,136100.0,<1H OCEAN\n-118.42,34.26,36.0,973.0,221.0,1086.0,218.0,3.4519,143300.0,<1H OCEAN\n-118.42,34.26,37.0,1789.0,424.0,2279.0,411.0,3.9,138600.0,<1H OCEAN\n-118.41,34.27,38.0,858.0,203.0,1250.0,204.0,2.9219,137900.0,<1H OCEAN\n-118.42,34.27,35.0,2700.0,702.0,3444.0,679.0,1.4867,124000.0,<1H OCEAN\n-118.4,34.26,13.0,4379.0,872.0,2560.0,853.0,4.2538,154300.0,<1H OCEAN\n-118.4,34.25,13.0,1872.0,497.0,1927.0,432.0,2.2019,134200.0,<1H OCEAN\n-118.41,34.25,19.0,280.0,84.0,483.0,87.0,1.95,137500.0,<1H OCEAN\n-118.41,34.25,33.0,827.0,192.0,981.0,184.0,2.6429,143100.0,<1H OCEAN\n-118.41,34.25,36.0,1146.0,259.0,1173.0,272.0,3.6016,153800.0,<1H OCEAN\n-118.41,34.25,18.0,3447.0,857.0,3663.0,817.0,3.2284,157100.0,<1H OCEAN\n-118.42,34.24,17.0,2049.0,548.0,2243.0,541.0,2.525,163700.0,<1H OCEAN\n-118.42,34.25,36.0,1430.0,331.0,1502.0,312.0,3.6292,145200.0,<1H OCEAN\n-118.43,34.32,34.0,2657.0,515.0,1948.0,532.0,4.233,157400.0,<1H OCEAN\n-118.43,34.33,18.0,5891.0,920.0,2882.0,911.0,5.901,235600.0,<1H OCEAN\n-118.44,34.32,14.0,6235.0,1286.0,3568.0,1190.0,4.1724,211600.0,<1H OCEAN\n-118.42,34.31,19.0,6755.0,1443.0,4205.0,1395.0,3.9583,163200.0,<1H OCEAN\n-118.42,34.3,29.0,3334.0,712.0,2919.0,718.0,3.6548,180300.0,<1H OCEAN\n-118.41,34.32,18.0,6572.0,1105.0,3473.0,1067.0,5.2987,203400.0,<1H OCEAN\n-118.41,34.3,28.0,3187.0,569.0,2205.0,559.0,5.1668,187400.0,<1H OCEAN\n-118.42,34.32,30.0,3027.0,604.0,1970.0,590.0,4.3409,156000.0,<1H OCEAN\n-118.44,34.31,14.0,4151.0,941.0,3163.0,915.0,4.0301,154300.0,<1H OCEAN\n-118.44,34.31,22.0,3182.0,822.0,2661.0,746.0,2.7472,160100.0,<1H OCEAN\n-118.44,34.3,38.0,1595.0,314.0,1181.0,327.0,3.4,155500.0,<1H OCEAN\n-118.45,34.31,9.0,1739.0,358.0,820.0,323.0,4.0556,182500.0,<1H OCEAN\n-118.45,34.32,23.0,3481.0,641.0,1952.0,682.0,4.26,189400.0,<1H OCEAN\n-118.46,34.32,10.0,5777.0,1112.0,2917.0,1056.0,4.1514,194100.0,<1H OCEAN\n-118.45,34.31,28.0,1532.0,287.0,977.0,275.0,4.4773,173100.0,<1H OCEAN\n-118.46,34.3,32.0,2424.0,476.0,2291.0,419.0,4.0337,158500.0,<1H OCEAN\n-118.46,34.31,24.0,2920.0,601.0,1460.0,598.0,4.2708,218200.0,<1H OCEAN\n-118.47,34.32,13.0,2664.0,518.0,1468.0,521.0,4.8988,325200.0,<1H OCEAN\n-118.48,34.33,9.0,2384.0,395.0,1697.0,402.0,6.0891,270100.0,<1H OCEAN\n-118.48,34.31,31.0,1091.0,256.0,892.0,238.0,3.0,172400.0,<1H OCEAN\n-118.47,34.3,16.0,2495.0,551.0,2314.0,567.0,3.6736,192200.0,<1H OCEAN\n-118.46,34.29,24.0,3668.0,890.0,3151.0,810.0,3.0526,183300.0,<1H OCEAN\n-118.47,34.29,18.0,4256.0,987.0,3401.0,955.0,4.2935,190000.0,<1H OCEAN\n-118.49,34.29,26.0,4516.0,611.0,1714.0,581.0,9.2873,431800.0,<1H OCEAN\n-118.49,34.28,27.0,2535.0,389.0,1071.0,386.0,6.8695,319400.0,<1H OCEAN\n-118.49,34.28,31.0,3508.0,585.0,1957.0,588.0,6.6458,285500.0,<1H OCEAN\n-118.49,34.31,25.0,1024.0,145.0,357.0,147.0,7.0598,356300.0,<1H OCEAN\n-118.52,34.32,18.0,7498.0,976.0,3189.0,955.0,8.1248,374000.0,<1H OCEAN\n-118.51,34.3,24.0,6145.0,868.0,2710.0,875.0,7.5078,344000.0,<1H OCEAN\n-118.51,34.28,29.0,4239.0,653.0,1890.0,631.0,6.3911,301700.0,<1H OCEAN\n-118.51,34.29,29.0,1287.0,194.0,525.0,187.0,6.4171,319300.0,<1H OCEAN\n-118.52,34.29,28.0,2272.0,320.0,868.0,312.0,7.7464,474600.0,<1H OCEAN\n-118.52,34.3,17.0,4542.0,621.0,2144.0,597.0,8.8467,450700.0,<1H OCEAN\n-118.45,34.3,27.0,2676.0,,2661.0,623.0,4.3047,152100.0,<1H OCEAN\n-118.45,34.3,35.0,4085.0,919.0,3988.0,906.0,3.4812,160200.0,<1H OCEAN\n-118.54,34.28,18.0,5481.0,780.0,2477.0,764.0,6.7248,377200.0,<1H OCEAN\n-118.55,34.28,16.0,8879.0,,3468.0,1200.0,8.1125,428600.0,<1H OCEAN\n-118.54,34.28,10.0,7665.0,999.0,3517.0,998.0,10.8805,500001.0,<1H OCEAN\n-118.54,34.3,22.0,4423.0,622.0,1995.0,582.0,8.2159,376200.0,<1H OCEAN\n-118.57,34.29,4.0,6995.0,1151.0,2907.0,1089.0,7.0808,341200.0,<1H OCEAN\n-118.45,34.28,38.0,1527.0,332.0,1303.0,340.0,3.5714,152000.0,<1H OCEAN\n-118.46,34.28,23.0,1663.0,302.0,1242.0,283.0,5.5931,217600.0,<1H OCEAN\n-118.47,34.27,33.0,1549.0,264.0,881.0,289.0,5.1408,222900.0,<1H OCEAN\n-118.48,34.28,35.0,2132.0,368.0,1128.0,341.0,5.3107,227100.0,<1H OCEAN\n-118.48,34.28,35.0,1511.0,274.0,873.0,254.0,5.5608,226700.0,<1H OCEAN\n-118.47,34.27,17.0,1444.0,282.0,523.0,270.0,2.7353,192400.0,<1H OCEAN\n-118.47,34.27,35.0,1150.0,185.0,741.0,178.0,5.741,220600.0,<1H OCEAN\n-118.48,34.27,34.0,1231.0,222.0,702.0,222.0,4.9323,223700.0,<1H OCEAN\n-118.48,34.27,33.0,2649.0,449.0,1303.0,437.0,4.9955,216800.0,<1H OCEAN\n-118.45,34.27,33.0,1194.0,229.0,839.0,230.0,3.705,185800.0,<1H OCEAN\n-118.45,34.27,35.0,1579.0,300.0,1012.0,265.0,5.1296,195900.0,<1H OCEAN\n-118.46,34.27,30.0,1576.0,282.0,1004.0,284.0,4.8015,179700.0,<1H OCEAN\n-118.46,34.27,28.0,1865.0,463.0,1182.0,440.0,2.6193,172300.0,<1H OCEAN\n-118.44,34.27,36.0,1111.0,275.0,1333.0,266.0,3.5347,158100.0,<1H OCEAN\n-118.44,34.27,29.0,1701.0,419.0,1616.0,371.0,3.3603,142400.0,<1H OCEAN\n-118.45,34.26,35.0,1724.0,311.0,992.0,315.0,4.8359,195600.0,<1H OCEAN\n-118.45,34.25,36.0,1453.0,270.0,808.0,275.0,4.3839,204600.0,<1H OCEAN\n-118.45,34.26,35.0,1637.0,300.0,894.0,302.0,4.175,209600.0,<1H OCEAN\n-118.46,34.26,33.0,1358.0,247.0,738.0,235.0,5.0947,210300.0,<1H OCEAN\n-118.46,34.26,36.0,1394.0,254.0,761.0,262.0,4.9485,217100.0,<1H OCEAN\n-118.46,34.25,33.0,2202.0,433.0,1135.0,407.0,4.2143,224200.0,<1H OCEAN\n-118.46,34.25,32.0,2217.0,422.0,1064.0,427.0,3.6989,208600.0,<1H OCEAN\n-118.47,34.26,34.0,1300.0,289.0,650.0,291.0,3.8875,199200.0,<1H OCEAN\n-118.47,34.26,35.0,1898.0,344.0,1123.0,347.0,5.5792,218400.0,<1H OCEAN\n-118.48,34.26,36.0,1770.0,296.0,938.0,304.0,5.749,238000.0,<1H OCEAN\n-118.49,34.26,27.0,2722.0,468.0,1164.0,419.0,4.6591,239900.0,<1H OCEAN\n-118.47,34.25,34.0,1732.0,399.0,1120.0,401.0,4.1492,195700.0,<1H OCEAN\n-118.48,34.25,36.0,1951.0,395.0,1040.0,375.0,5.1619,195300.0,<1H OCEAN\n-118.48,34.25,35.0,1442.0,276.0,795.0,268.0,4.9688,216900.0,<1H OCEAN\n-118.48,34.25,35.0,1865.0,335.0,1074.0,337.0,5.1068,223300.0,<1H OCEAN\n-118.49,34.25,33.0,2088.0,383.0,960.0,362.0,4.3333,232900.0,<1H OCEAN\n-118.49,34.27,34.0,4877.0,815.0,2521.0,781.0,5.5714,225900.0,<1H OCEAN\n-118.49,34.27,33.0,3047.0,527.0,1578.0,507.0,4.58,236200.0,<1H OCEAN\n-118.5,34.27,35.0,2235.0,390.0,1148.0,416.0,4.869,221600.0,<1H OCEAN\n-118.51,34.28,34.0,3580.0,565.0,1694.0,524.0,5.4065,243800.0,<1H OCEAN\n-118.52,34.28,33.0,1975.0,271.0,801.0,287.0,7.8193,379600.0,<1H OCEAN\n-118.51,34.27,34.0,3787.0,771.0,1966.0,738.0,4.055,222500.0,<1H OCEAN\n-118.51,34.27,36.0,2276.0,429.0,1001.0,419.0,4.1042,252100.0,<1H OCEAN\n-118.52,34.27,36.0,3204.0,538.0,1499.0,499.0,5.5649,271200.0,<1H OCEAN\n-118.53,34.27,32.0,1931.0,298.0,948.0,314.0,5.3847,329200.0,<1H OCEAN\n-118.53,34.26,18.0,3674.0,,1590.0,550.0,8.176,308400.0,<1H OCEAN\n-118.54,34.26,22.0,5303.0,838.0,2372.0,807.0,5.6912,311800.0,<1H OCEAN\n-118.54,34.27,28.0,2309.0,300.0,931.0,302.0,6.7415,348200.0,<1H OCEAN\n-118.53,34.27,33.0,1927.0,305.0,896.0,293.0,5.634,320500.0,<1H OCEAN\n-118.54,34.26,23.0,4960.0,592.0,1929.0,586.0,10.9052,500001.0,<1H OCEAN\n-118.55,34.26,21.0,4018.0,536.0,1508.0,529.0,8.203,445400.0,<1H OCEAN\n-118.55,34.27,25.0,4919.0,661.0,2183.0,625.0,8.1356,352800.0,<1H OCEAN\n-118.5,34.26,33.0,2831.0,510.0,1340.0,504.0,4.8316,237300.0,<1H OCEAN\n-118.52,34.26,21.0,8850.0,2139.0,4717.0,1979.0,3.7816,254200.0,<1H OCEAN\n-118.51,34.26,29.0,2472.0,354.0,1109.0,397.0,5.5433,332500.0,<1H OCEAN\n-118.52,34.25,11.0,7849.0,1664.0,3561.0,1500.0,4.6625,290900.0,<1H OCEAN\n-118.49,34.26,25.0,8389.0,1872.0,4483.0,1747.0,3.5497,261300.0,<1H OCEAN\n-118.49,34.25,28.0,4054.0,712.0,2164.0,746.0,5.0,258000.0,<1H OCEAN\n-118.57,34.27,20.0,7384.0,845.0,2795.0,872.0,9.6047,500001.0,<1H OCEAN\n-118.59,34.26,20.0,8146.0,1131.0,3562.0,1054.0,7.167,357100.0,<1H OCEAN\n-118.61,34.24,17.0,5406.0,895.0,2337.0,882.0,6.0137,375900.0,<1H OCEAN\n-118.63,34.24,9.0,4759.0,924.0,1884.0,915.0,4.8333,277200.0,<1H OCEAN\n-118.62,34.26,15.0,10860.0,1653.0,4178.0,1581.0,6.3249,262100.0,<1H OCEAN\n-118.6,34.26,18.0,6154.0,1070.0,3010.0,1034.0,5.6392,271500.0,<1H OCEAN\n-118.61,34.25,16.0,8295.0,1506.0,3903.0,1451.0,5.5111,276600.0,<1H OCEAN\n-118.63,34.22,18.0,1376.0,225.0,670.0,205.0,6.5146,277600.0,<1H OCEAN\n-118.64,34.22,25.0,2762.0,410.0,1166.0,439.0,6.8643,333700.0,<1H OCEAN\n-118.66,34.23,18.0,897.0,142.0,263.0,110.0,6.1288,350000.0,<1H OCEAN\n-118.61,34.23,26.0,3727.0,572.0,1724.0,530.0,6.1419,327300.0,<1H OCEAN\n-118.61,34.22,24.0,5256.0,758.0,2474.0,780.0,7.3252,333700.0,<1H OCEAN\n-118.6,34.23,19.0,8866.0,2355.0,5005.0,2194.0,3.2564,230300.0,<1H OCEAN\n-118.59,34.23,17.0,6592.0,1525.0,4459.0,1463.0,3.0347,254500.0,<1H OCEAN\n-118.56,34.25,31.0,1962.0,243.0,697.0,242.0,8.565,500001.0,<1H OCEAN\n-118.56,34.24,23.0,2980.0,362.0,1208.0,378.0,8.1714,500001.0,<1H OCEAN\n-118.57,34.25,34.0,5098.0,778.0,2239.0,778.0,5.6149,273100.0,<1H OCEAN\n-118.58,34.24,26.0,3239.0,647.0,1529.0,590.0,3.2426,236900.0,<1H OCEAN\n-118.59,34.25,15.0,9716.0,2387.0,4992.0,2225.0,3.6231,193300.0,<1H OCEAN\n-118.57,34.25,20.0,4679.0,609.0,1945.0,609.0,8.7471,419900.0,<1H OCEAN\n-118.58,34.25,23.0,4883.0,769.0,2119.0,725.0,5.521,280800.0,<1H OCEAN\n-118.56,34.23,36.0,3215.0,529.0,1710.0,539.0,5.5126,248400.0,<1H OCEAN\n-118.56,34.23,36.0,2406.0,432.0,1242.0,454.0,4.6944,221800.0,<1H OCEAN\n-118.57,34.23,22.0,3275.0,648.0,1746.0,585.0,4.9676,221900.0,<1H OCEAN\n-118.58,34.23,29.0,3907.0,773.0,2037.0,727.0,4.1023,230200.0,<1H OCEAN\n-118.59,34.23,14.0,4407.0,1209.0,2676.0,1128.0,3.4091,168800.0,<1H OCEAN\n-118.58,34.23,35.0,1917.0,314.0,1019.0,340.0,4.8929,234900.0,<1H OCEAN\n-118.58,34.22,35.0,1969.0,339.0,950.0,340.0,4.875,230400.0,<1H OCEAN\n-118.59,34.22,17.0,6015.0,1464.0,3056.0,1347.0,4.0077,229000.0,<1H OCEAN\n-118.51,34.25,24.0,4338.0,558.0,1514.0,549.0,8.8612,500001.0,<1H OCEAN\n-118.51,34.24,31.0,5297.0,664.0,1986.0,657.0,8.6454,483500.0,<1H OCEAN\n-118.51,34.23,36.0,3324.0,448.0,1190.0,423.0,7.2772,477200.0,<1H OCEAN\n-118.52,34.24,6.0,3218.0,949.0,2295.0,876.0,3.0926,418500.0,<1H OCEAN\n-118.52,34.23,35.0,1471.0,210.0,735.0,219.0,8.3841,472200.0,<1H OCEAN\n-118.53,34.25,20.0,6331.0,1537.0,2957.0,1509.0,3.3892,323100.0,<1H OCEAN\n-118.54,34.24,24.0,4631.0,1164.0,2360.0,1083.0,3.0977,264000.0,<1H OCEAN\n-118.53,34.24,24.0,2718.0,719.0,3018.0,644.0,2.9076,275300.0,<1H OCEAN\n-118.54,34.23,35.0,3422.0,601.0,1690.0,574.0,4.375,232900.0,<1H OCEAN\n-118.53,34.23,27.0,2131.0,543.0,1065.0,528.0,3.2404,230400.0,<1H OCEAN\n-118.54,34.25,26.0,2639.0,378.0,1191.0,401.0,6.2788,322200.0,<1H OCEAN\n-118.55,34.24,21.0,5751.0,1082.0,2230.0,1016.0,4.3458,407500.0,<1H OCEAN\n-118.55,34.23,25.0,4409.0,1018.0,4579.0,1010.0,2.8727,245100.0,<1H OCEAN\n-118.51,34.23,27.0,4580.0,918.0,2252.0,850.0,4.7926,454400.0,<1H OCEAN\n-118.52,34.22,21.0,4617.0,1101.0,2891.0,1031.0,3.2289,318100.0,<1H OCEAN\n-118.53,34.23,32.0,4039.0,984.0,2675.0,941.0,3.0321,240000.0,<1H OCEAN\n-118.54,34.22,34.0,2193.0,513.0,1299.0,497.0,3.6187,211600.0,<1H OCEAN\n-118.54,34.23,29.0,1753.0,342.0,1318.0,333.0,4.125,241400.0,<1H OCEAN\n-118.45,34.25,34.0,2094.0,380.0,1207.0,380.0,5.2801,212300.0,<1H OCEAN\n-118.46,34.24,11.0,5363.0,1160.0,2783.0,1034.0,3.8583,170700.0,<1H OCEAN\n-118.47,34.25,21.0,2692.0,477.0,1330.0,456.0,4.5417,238900.0,<1H OCEAN\n-118.47,34.24,19.0,2405.0,661.0,1855.0,621.0,2.3111,255400.0,<1H OCEAN\n-118.48,34.24,32.0,2621.0,412.0,1285.0,414.0,6.6537,267600.0,<1H OCEAN\n-118.49,34.25,30.0,2871.0,470.0,1335.0,458.0,5.0232,253900.0,<1H OCEAN\n-118.49,34.24,35.0,2707.0,446.0,1224.0,445.0,5.2939,244200.0,<1H OCEAN\n-118.49,34.24,34.0,1971.0,316.0,917.0,307.0,6.0965,262300.0,<1H OCEAN\n-118.5,34.25,32.0,2333.0,389.0,969.0,331.0,4.8164,241100.0,<1H OCEAN\n-118.5,34.25,32.0,2411.0,380.0,1040.0,344.0,6.155,257300.0,<1H OCEAN\n-118.5,34.24,34.0,2634.0,412.0,1114.0,423.0,5.9401,315300.0,<1H OCEAN\n-118.5,34.23,26.0,3082.0,573.0,1590.0,586.0,4.5167,319000.0,<1H OCEAN\n-118.49,34.23,32.0,4373.0,683.0,2040.0,693.0,5.2668,242300.0,<1H OCEAN\n-118.49,34.22,30.0,1756.0,314.0,899.0,288.0,5.0325,238200.0,<1H OCEAN\n-118.47,34.23,22.0,8350.0,2717.0,9135.0,2452.0,2.5008,160000.0,<1H OCEAN\n-118.48,34.23,35.0,1963.0,310.0,919.0,297.0,4.7583,258600.0,<1H OCEAN\n-118.48,34.23,30.0,1762.0,263.0,761.0,292.0,6.5268,273100.0,<1H OCEAN\n-118.48,34.23,29.0,3354.0,707.0,1752.0,650.0,4.5484,239900.0,<1H OCEAN\n-118.46,34.23,20.0,4609.0,1499.0,5349.0,1377.0,2.7121,169400.0,<1H OCEAN\n-118.46,34.23,16.0,6338.0,1768.0,4718.0,1632.0,3.0187,154600.0,<1H OCEAN\n-118.43,34.25,38.0,921.0,239.0,1023.0,241.0,3.4514,151900.0,<1H OCEAN\n-118.42,34.24,36.0,1181.0,220.0,775.0,218.0,4.7228,183800.0,<1H OCEAN\n-118.43,34.24,36.0,1488.0,313.0,1221.0,296.0,4.0208,171400.0,<1H OCEAN\n-118.42,34.24,35.0,1507.0,281.0,1025.0,286.0,4.5833,177200.0,<1H OCEAN\n-118.41,34.23,35.0,1026.0,195.0,753.0,185.0,4.5909,179200.0,<1H OCEAN\n-118.41,34.24,38.0,490.0,101.0,402.0,100.0,3.125,175900.0,<1H OCEAN\n-118.44,34.26,34.0,1102.0,212.0,949.0,212.0,4.0792,165100.0,<1H OCEAN\n-118.44,34.26,28.0,1077.0,288.0,1377.0,293.0,3.9167,153900.0,<1H OCEAN\n-118.43,34.25,32.0,2433.0,553.0,2318.0,532.0,3.6384,159300.0,<1H OCEAN\n-118.44,34.26,34.0,325.0,60.0,433.0,83.0,5.5124,174300.0,<1H OCEAN\n-118.44,34.25,35.0,1583.0,324.0,1481.0,351.0,3.7,176000.0,<1H OCEAN\n-118.45,34.25,21.0,2143.0,565.0,1803.0,497.0,3.9833,162500.0,<1H OCEAN\n-118.45,34.24,11.0,9053.0,2193.0,7096.0,2038.0,3.5082,136500.0,<1H OCEAN\n-118.45,34.23,15.0,5738.0,1767.0,4620.0,1581.0,2.3584,157600.0,<1H OCEAN\n-118.45,34.24,7.0,3299.0,794.0,2343.0,647.0,3.0865,205900.0,<1H OCEAN\n-118.44,34.25,33.0,1121.0,231.0,1038.0,236.0,4.8958,173700.0,<1H OCEAN\n-118.43,34.24,35.0,1416.0,261.0,995.0,272.0,3.7143,178700.0,<1H OCEAN\n-118.43,34.24,35.0,1488.0,293.0,1112.0,288.0,4.4688,182500.0,<1H OCEAN\n-118.44,34.24,36.0,1660.0,301.0,1225.0,307.0,4.095,184000.0,<1H OCEAN\n-118.44,34.24,35.0,2344.0,435.0,1531.0,399.0,3.725,178200.0,<1H OCEAN\n-118.42,34.23,33.0,2478.0,457.0,1567.0,446.0,5.6629,186700.0,<1H OCEAN\n-118.42,34.22,29.0,1807.0,323.0,1234.0,310.0,5.3767,233000.0,<1H OCEAN\n-118.43,34.23,35.0,2049.0,390.0,1286.0,385.0,4.4432,181500.0,<1H OCEAN\n-118.43,34.24,36.0,1379.0,265.0,896.0,246.0,4.6827,183800.0,<1H OCEAN\n-118.42,34.23,34.0,1550.0,279.0,1011.0,288.0,4.5375,189000.0,<1H OCEAN\n-118.43,34.23,35.0,1225.0,228.0,720.0,231.0,3.4013,176500.0,<1H OCEAN\n-118.43,34.23,37.0,1737.0,369.0,1061.0,356.0,3.9615,173700.0,<1H OCEAN\n-118.43,34.24,37.0,1279.0,241.0,987.0,233.0,4.0057,172700.0,<1H OCEAN\n-118.44,34.23,36.0,1730.0,387.0,1099.0,353.0,4.0368,183100.0,<1H OCEAN\n-118.43,34.22,34.0,1588.0,360.0,1080.0,340.0,3.66,184600.0,<1H OCEAN\n-118.44,34.22,41.0,1030.0,214.0,664.0,223.0,3.8083,183800.0,<1H OCEAN\n-118.44,34.22,39.0,1529.0,344.0,913.0,314.0,3.325,178200.0,<1H OCEAN\n-118.44,34.23,43.0,2257.0,429.0,1418.0,442.0,4.5278,181800.0,<1H OCEAN\n-118.46,34.22,35.0,2288.0,617.0,2222.0,566.0,2.6299,170700.0,<1H OCEAN\n-118.46,34.22,39.0,1500.0,333.0,998.0,309.0,3.9625,168200.0,<1H OCEAN\n-118.46,34.22,31.0,2057.0,601.0,2397.0,579.0,2.871,184400.0,<1H OCEAN\n-118.45,34.22,24.0,3442.0,1168.0,4625.0,1097.0,2.0699,183000.0,<1H OCEAN\n-118.45,34.23,25.0,4393.0,1369.0,3781.0,1267.0,2.5833,183700.0,<1H OCEAN\n-118.45,34.22,8.0,2609.0,786.0,1803.0,695.0,2.7714,185700.0,<1H OCEAN\n-118.46,34.23,19.0,9902.0,2814.0,7307.0,2660.0,2.585,145400.0,<1H OCEAN\n-118.44,34.22,36.0,1191.0,266.0,718.0,248.0,3.4612,178800.0,<1H OCEAN\n-118.44,34.21,37.0,1665.0,335.0,1011.0,343.0,4.8703,185100.0,<1H OCEAN\n-118.44,34.21,41.0,1440.0,325.0,1014.0,322.0,2.875,168600.0,<1H OCEAN\n-118.44,34.22,41.0,1582.0,399.0,1159.0,378.0,2.825,168600.0,<1H OCEAN\n-118.43,34.22,34.0,2300.0,429.0,1447.0,455.0,4.2656,233700.0,<1H OCEAN\n-118.43,34.21,17.0,3667.0,1209.0,2636.0,1054.0,2.425,175500.0,<1H OCEAN\n-118.43,34.22,36.0,1372.0,295.0,774.0,306.0,3.6618,187300.0,<1H OCEAN\n-118.4,34.22,43.0,1220.0,222.0,729.0,230.0,3.6442,186300.0,<1H OCEAN\n-118.4,34.21,30.0,2453.0,544.0,1753.0,506.0,2.9803,191500.0,<1H OCEAN\n-118.41,34.21,35.0,2215.0,459.0,1594.0,446.0,4.0167,193200.0,<1H OCEAN\n-118.4,34.21,45.0,972.0,181.0,554.0,187.0,4.8194,181300.0,<1H OCEAN\n-118.4,34.22,36.0,2557.0,540.0,1556.0,491.0,3.6591,183800.0,<1H OCEAN\n-118.4,34.23,37.0,1404.0,266.0,889.0,274.0,4.0049,190000.0,<1H OCEAN\n-118.38,34.25,38.0,983.0,185.0,513.0,170.0,4.8816,231500.0,<1H OCEAN\n-118.37,34.24,40.0,1283.0,246.0,594.0,236.0,4.1121,229200.0,<1H OCEAN\n-118.37,34.23,32.0,1444.0,317.0,1177.0,311.0,3.6,164600.0,<1H OCEAN\n-118.37,34.22,17.0,1787.0,463.0,1671.0,448.0,3.5521,151500.0,<1H OCEAN\n-118.38,34.24,38.0,125.0,42.0,63.0,29.0,1.3594,158300.0,<1H OCEAN\n-118.4,34.24,35.0,2552.0,545.0,1850.0,503.0,4.775,179500.0,<1H OCEAN\n-118.39,34.23,18.0,3405.0,831.0,3001.0,795.0,3.0083,181900.0,<1H OCEAN\n-118.39,34.23,43.0,1193.0,299.0,1184.0,320.0,2.1518,161600.0,<1H OCEAN\n-118.4,34.23,36.0,1643.0,349.0,1414.0,337.0,4.1181,172700.0,<1H OCEAN\n-118.41,34.21,35.0,2830.0,518.0,1577.0,524.0,5.35,210500.0,<1H OCEAN\n-118.41,34.21,35.0,1789.0,292.0,897.0,267.0,5.592,239900.0,<1H OCEAN\n-118.39,34.22,40.0,712.0,149.0,533.0,155.0,3.695,165200.0,<1H OCEAN\n-118.39,34.22,35.0,1790.0,334.0,1277.0,345.0,5.0818,186800.0,<1H OCEAN\n-118.39,34.21,32.0,1869.0,441.0,1516.0,432.0,3.6845,178500.0,<1H OCEAN\n-118.39,34.21,14.0,2807.0,868.0,2729.0,803.0,2.6667,172400.0,<1H OCEAN\n-118.38,34.22,20.0,1176.0,344.0,864.0,318.0,2.375,177700.0,<1H OCEAN\n-118.38,34.21,35.0,1468.0,303.0,1295.0,300.0,3.7708,170600.0,<1H OCEAN\n-118.38,34.21,33.0,1981.0,484.0,1665.0,466.0,3.0833,179100.0,<1H OCEAN\n-118.42,34.22,34.0,3004.0,589.0,1938.0,568.0,4.1857,198600.0,<1H OCEAN\n-118.42,34.21,29.0,2893.0,543.0,1636.0,540.0,5.1586,237400.0,<1H OCEAN\n-118.42,34.2,34.0,161.0,48.0,66.0,33.0,1.0,187500.0,<1H OCEAN\n-118.42,34.23,34.0,1531.0,278.0,1064.0,274.0,5.6687,207300.0,<1H OCEAN\n-118.37,34.21,36.0,1392.0,326.0,1181.0,303.0,3.1563,176400.0,<1H OCEAN\n-118.37,34.21,36.0,2080.0,455.0,1939.0,484.0,4.2875,176600.0,<1H OCEAN\n-118.37,34.21,34.0,2272.0,558.0,2164.0,484.0,3.7143,175700.0,<1H OCEAN\n-118.37,34.22,11.0,2127.0,581.0,1989.0,530.0,2.9028,174100.0,<1H OCEAN\n-118.38,34.22,32.0,362.0,100.0,348.0,102.0,2.2679,150000.0,<1H OCEAN\n-118.36,34.23,15.0,2485.0,742.0,1994.0,670.0,2.8333,183200.0,<1H OCEAN\n-118.36,34.22,37.0,1512.0,348.0,1545.0,351.0,3.7663,160300.0,<1H OCEAN\n-118.35,34.22,41.0,1560.0,374.0,1668.0,389.0,3.025,154300.0,<1H OCEAN\n-118.36,34.21,41.0,337.0,65.0,198.0,50.0,1.8929,152900.0,<1H OCEAN\n-118.38,34.21,42.0,715.0,145.0,730.0,158.0,3.8,169500.0,<1H OCEAN\n-118.38,34.2,23.0,4138.0,1171.0,3911.0,1068.0,3.0125,181700.0,<1H OCEAN\n-118.39,34.2,17.0,2594.0,1028.0,3950.0,973.0,2.0348,177200.0,<1H OCEAN\n-118.37,34.21,33.0,2034.0,470.0,1990.0,423.0,3.7455,159600.0,<1H OCEAN\n-118.37,34.2,34.0,2199.0,609.0,2488.0,597.0,2.9861,171800.0,<1H OCEAN\n-118.38,34.21,38.0,1363.0,395.0,1798.0,405.0,2.3182,171200.0,<1H OCEAN\n-118.37,34.2,33.0,1438.0,309.0,1378.0,306.0,2.8917,170400.0,<1H OCEAN\n-118.36,34.19,11.0,2921.0,685.0,1512.0,664.0,4.1445,176400.0,<1H OCEAN\n-118.36,34.18,34.0,1471.0,423.0,995.0,386.0,2.9583,188700.0,<1H OCEAN\n-118.36,34.18,36.0,2233.0,605.0,1934.0,599.0,2.8784,194900.0,<1H OCEAN\n-118.37,34.18,33.0,1829.0,512.0,1345.0,500.0,3.1629,198900.0,<1H OCEAN\n-118.37,34.19,19.0,2890.0,821.0,2203.0,705.0,2.6696,185100.0,<1H OCEAN\n-118.39,34.19,41.0,2000.0,485.0,1439.0,461.0,3.0491,192000.0,<1H OCEAN\n-118.39,34.19,23.0,1875.0,710.0,2555.0,657.0,2.0968,162500.0,<1H OCEAN\n-118.39,34.2,19.0,2012.0,732.0,3483.0,731.0,2.2234,181300.0,<1H OCEAN\n-118.37,34.19,41.0,2924.0,867.0,2751.0,836.0,2.1,171600.0,<1H OCEAN\n-118.38,34.19,37.0,1434.0,394.0,1667.0,404.0,2.4375,176300.0,<1H OCEAN\n-118.38,34.2,32.0,993.0,285.0,1044.0,248.0,2.4306,187500.0,<1H OCEAN\n-118.4,34.2,13.0,4859.0,1293.0,3351.0,1200.0,3.6875,211900.0,<1H OCEAN\n-118.4,34.19,37.0,934.0,231.0,587.0,230.0,3.625,181300.0,<1H OCEAN\n-118.4,34.2,30.0,2392.0,655.0,1987.0,609.0,2.8424,226400.0,<1H OCEAN\n-118.4,34.19,35.0,2180.0,599.0,1483.0,574.0,3.0395,191300.0,<1H OCEAN\n-118.41,34.19,39.0,1169.0,242.0,612.0,247.0,4.1429,200000.0,<1H OCEAN\n-118.41,34.2,32.0,2734.0,654.0,2209.0,610.0,3.5164,217200.0,<1H OCEAN\n-118.42,34.2,24.0,3148.0,908.0,2850.0,839.0,1.9549,221500.0,<1H OCEAN\n-118.41,34.19,42.0,779.0,145.0,450.0,148.0,3.9792,193800.0,<1H OCEAN\n-118.42,34.19,34.0,2622.0,572.0,1997.0,573.0,3.338,222500.0,<1H OCEAN\n-118.42,34.2,27.0,3201.0,970.0,3403.0,948.0,2.2377,231700.0,<1H OCEAN\n-118.43,34.2,28.0,3386.0,,2240.0,737.0,3.0221,290100.0,<1H OCEAN\n-118.42,34.19,33.0,3353.0,790.0,2318.0,775.0,2.2589,269700.0,<1H OCEAN\n-118.42,34.19,33.0,3285.0,830.0,2281.0,786.0,2.6165,230800.0,<1H OCEAN\n-118.42,34.18,27.0,3760.0,880.0,2022.0,812.0,3.1551,225600.0,<1H OCEAN\n-118.42,34.18,31.0,2887.0,646.0,1626.0,637.0,3.6745,335500.0,<1H OCEAN\n-118.42,34.18,30.0,1323.0,353.0,856.0,333.0,3.3594,202200.0,<1H OCEAN\n-118.43,34.18,33.0,2717.0,662.0,1546.0,597.0,3.9099,267500.0,<1H OCEAN\n-118.43,34.18,31.0,2417.0,510.0,1102.0,507.0,3.8906,282200.0,<1H OCEAN\n-118.42,34.18,40.0,1013.0,150.0,449.0,166.0,5.7143,382400.0,<1H OCEAN\n-118.41,34.19,45.0,1106.0,225.0,595.0,228.0,3.6625,190700.0,<1H OCEAN\n-118.41,34.18,43.0,1840.0,356.0,966.0,323.0,4.7171,237900.0,<1H OCEAN\n-118.41,34.18,30.0,2008.0,513.0,1052.0,496.0,3.0119,262200.0,<1H OCEAN\n-118.41,34.19,37.0,1993.0,425.0,939.0,400.0,2.8021,224600.0,<1H OCEAN\n-118.4,34.19,30.0,521.0,126.0,306.0,129.0,4.1125,216700.0,<1H OCEAN\n-118.4,34.19,35.0,1631.0,356.0,862.0,368.0,3.6007,261800.0,<1H OCEAN\n-118.4,34.18,32.0,3724.0,899.0,1912.0,791.0,3.5711,312700.0,<1H OCEAN\n-118.4,34.17,27.0,3588.0,911.0,1891.0,871.0,3.4013,286000.0,<1H OCEAN\n-118.39,34.19,25.0,3794.0,989.0,2454.0,876.0,2.9982,204200.0,<1H OCEAN\n-118.39,34.18,42.0,1957.0,389.0,985.0,414.0,2.9327,240200.0,<1H OCEAN\n-118.39,34.17,26.0,3345.0,818.0,1599.0,773.0,3.3516,241500.0,<1H OCEAN\n-118.39,34.18,44.0,477.0,91.0,220.0,112.0,3.3906,223800.0,<1H OCEAN\n-118.39,34.19,36.0,904.0,191.0,627.0,191.0,2.4167,192900.0,<1H OCEAN\n-118.4,34.16,45.0,1176.0,250.0,471.0,228.0,2.3333,364700.0,<1H OCEAN\n-118.4,34.16,35.0,1354.0,284.0,501.0,262.0,3.8056,384700.0,<1H OCEAN\n-118.4,34.16,34.0,2638.0,580.0,1150.0,551.0,4.2989,364700.0,<1H OCEAN\n-118.41,34.16,32.0,3060.0,505.0,1159.0,510.0,6.3703,465800.0,<1H OCEAN\n-118.41,34.17,35.0,2027.0,428.0,879.0,402.0,4.692,330900.0,<1H OCEAN\n-118.38,34.19,42.0,1308.0,289.0,950.0,302.0,2.7379,181500.0,<1H OCEAN\n-118.38,34.18,40.0,2079.0,568.0,1396.0,526.0,3.0061,190800.0,<1H OCEAN\n-118.38,34.18,27.0,4834.0,1527.0,3847.0,1432.0,2.1449,165300.0,<1H OCEAN\n-118.38,34.18,24.0,1983.0,651.0,2251.0,574.0,2.4792,200000.0,<1H OCEAN\n-118.38,34.17,33.0,1588.0,454.0,739.0,392.0,2.8208,238500.0,<1H OCEAN\n-118.39,34.17,40.0,1696.0,372.0,835.0,385.0,3.6563,222400.0,<1H OCEAN\n-118.37,34.18,36.0,1608.0,373.0,1217.0,374.0,2.9728,190200.0,<1H OCEAN\n-118.37,34.18,42.0,1140.0,300.0,643.0,252.0,3.3958,178400.0,<1H OCEAN\n-118.38,34.18,44.0,901.0,179.0,473.0,179.0,3.3125,186400.0,<1H OCEAN\n-118.38,34.19,30.0,977.0,264.0,736.0,258.0,1.9866,177400.0,<1H OCEAN\n-118.37,34.18,35.0,2949.0,794.0,2106.0,746.0,2.9228,177300.0,<1H OCEAN\n-118.38,34.18,32.0,3553.0,1060.0,3129.0,1010.0,2.5603,174200.0,<1H OCEAN\n-118.36,34.18,31.0,1109.0,354.0,1119.0,334.0,2.3056,200000.0,<1H OCEAN\n-118.36,34.17,31.0,1939.0,505.0,1584.0,466.0,2.5234,199500.0,<1H OCEAN\n-118.37,34.17,42.0,1713.0,416.0,1349.0,427.0,3.2596,191800.0,<1H OCEAN\n-118.41,34.18,35.0,2785.0,663.0,1631.0,614.0,3.9038,276100.0,<1H OCEAN\n-118.41,34.17,27.0,3277.0,648.0,1382.0,615.0,3.875,366100.0,<1H OCEAN\n-118.41,34.18,35.0,1975.0,384.0,882.0,406.0,4.375,291700.0,<1H OCEAN\n-118.43,34.17,32.0,3202.0,696.0,1573.0,621.0,3.4449,292900.0,<1H OCEAN\n-118.43,34.17,35.0,2922.0,507.0,1130.0,485.0,5.451,341800.0,<1H OCEAN\n-118.43,34.17,37.0,1982.0,331.0,794.0,340.0,5.9275,336900.0,<1H OCEAN\n-118.43,34.16,34.0,2459.0,489.0,1139.0,463.0,4.0347,353600.0,<1H OCEAN\n-118.43,34.16,41.0,2050.0,478.0,850.0,490.0,3.4208,343400.0,<1H OCEAN\n-118.43,34.16,40.0,1134.0,184.0,452.0,187.0,4.569,333900.0,<1H OCEAN\n-118.42,34.15,27.0,2795.0,602.0,1073.0,535.0,5.1496,365000.0,<1H OCEAN\n-118.42,34.17,31.0,2235.0,363.0,914.0,370.0,6.1359,359700.0,<1H OCEAN\n-118.42,34.16,28.0,4664.0,1040.0,1963.0,961.0,3.9028,367900.0,<1H OCEAN\n-118.41,34.16,14.0,577.0,150.0,372.0,130.0,4.1875,275000.0,<1H OCEAN\n-118.42,34.16,25.0,2769.0,566.0,1201.0,545.0,3.6641,386100.0,<1H OCEAN\n-118.4,34.17,24.0,4443.0,1283.0,2421.0,1180.0,2.2652,269200.0,<1H OCEAN\n-118.4,34.17,24.0,6347.0,,2945.0,1492.0,3.3545,221500.0,<1H OCEAN\n-118.39,34.17,28.0,2790.0,748.0,1351.0,697.0,3.2052,283600.0,<1H OCEAN\n-118.39,34.17,26.0,6429.0,1611.0,2806.0,1491.0,3.1929,265200.0,<1H OCEAN\n-118.39,34.16,46.0,1582.0,279.0,603.0,283.0,5.1169,414300.0,<1H OCEAN\n-118.39,34.16,37.0,1388.0,286.0,547.0,258.0,5.1584,444700.0,<1H OCEAN\n-118.38,34.17,30.0,2016.0,622.0,1359.0,557.0,2.7708,192300.0,<1H OCEAN\n-118.38,34.16,46.0,2609.0,593.0,1055.0,585.0,3.3177,309400.0,<1H OCEAN\n-118.38,34.16,31.0,2197.0,501.0,944.0,474.0,3.7312,319400.0,<1H OCEAN\n-118.36,34.17,44.0,2295.0,560.0,1543.0,528.0,2.3851,194100.0,<1H OCEAN\n-118.37,34.17,15.0,3327.0,1011.0,2683.0,857.0,2.3784,185400.0,<1H OCEAN\n-118.37,34.17,42.0,600.0,171.0,377.0,181.0,2.4107,184400.0,<1H OCEAN\n-118.37,34.17,10.0,1431.0,473.0,1438.0,429.0,2.2756,221400.0,<1H OCEAN\n-118.37,34.17,6.0,854.0,350.0,542.0,321.0,0.8198,325000.0,<1H OCEAN\n-118.37,34.16,6.0,6526.0,2007.0,3298.0,1790.0,2.7231,250000.0,<1H OCEAN\n-118.37,34.16,10.0,2606.0,748.0,1373.0,680.0,3.6128,225000.0,<1H OCEAN\n-118.37,34.16,25.0,2450.0,618.0,1054.0,578.0,3.6375,262500.0,<1H OCEAN\n-118.37,34.16,40.0,1973.0,382.0,774.0,352.0,4.4122,282300.0,<1H OCEAN\n-118.36,34.16,43.0,2850.0,709.0,1510.0,670.0,2.4835,274300.0,<1H OCEAN\n-118.36,34.16,32.0,2455.0,556.0,989.0,493.0,4.0764,325000.0,<1H OCEAN\n-118.37,34.16,17.0,4150.0,1148.0,1808.0,1041.0,3.5051,232400.0,<1H OCEAN\n-118.37,34.16,11.0,2901.0,871.0,1659.0,789.0,3.1106,209400.0,<1H OCEAN\n-118.36,34.16,45.0,1755.0,335.0,822.0,342.0,5.1423,322900.0,<1H OCEAN\n-118.36,34.16,42.0,2304.0,442.0,862.0,429.0,4.3542,417900.0,<1H OCEAN\n-118.35,34.15,35.0,2245.0,393.0,783.0,402.0,4.1544,500001.0,<1H OCEAN\n-118.43,34.2,20.0,4090.0,1271.0,2824.0,1053.0,2.773,140500.0,<1H OCEAN\n-118.43,34.21,26.0,2867.0,671.0,1955.0,640.0,4.125,226500.0,<1H OCEAN\n-118.44,34.2,28.0,1732.0,435.0,1198.0,417.0,2.9219,241300.0,<1H OCEAN\n-118.44,34.21,20.0,5756.0,1477.0,4031.0,1369.0,3.2448,221200.0,<1H OCEAN\n-118.45,34.21,30.0,2331.0,733.0,2172.0,707.0,2.1888,195600.0,<1H OCEAN\n-118.45,34.2,19.0,3666.0,1150.0,2657.0,1090.0,2.9688,202100.0,<1H OCEAN\n-118.46,34.2,13.0,2926.0,816.0,1867.0,802.0,3.5255,202700.0,<1H OCEAN\n-118.46,34.21,7.0,2081.0,657.0,1456.0,535.0,3.5,186900.0,<1H OCEAN\n-118.47,34.21,34.0,2512.0,603.0,1805.0,584.0,2.9735,220000.0,<1H OCEAN\n-118.47,34.2,20.0,3939.0,1143.0,2475.0,1002.0,2.9025,229100.0,<1H OCEAN\n-118.48,34.21,25.0,2879.0,723.0,2077.0,649.0,3.3864,197400.0,<1H OCEAN\n-118.48,34.2,26.0,2027.0,559.0,1545.0,513.0,2.8974,189900.0,<1H OCEAN\n-118.48,34.22,22.0,3430.0,1214.0,3618.0,1092.0,2.1974,93800.0,<1H OCEAN\n-118.49,34.21,25.0,1131.0,449.0,746.0,420.0,1.3565,225000.0,<1H OCEAN\n-118.49,34.19,23.0,2087.0,571.0,1809.0,553.0,3.1667,202000.0,<1H OCEAN\n-118.49,34.2,35.0,1109.0,206.0,515.0,202.0,5.2118,215800.0,<1H OCEAN\n-118.5,34.2,34.0,1617.0,344.0,938.0,305.0,3.915,217700.0,<1H OCEAN\n-118.48,34.2,12.0,3831.0,1083.0,2258.0,967.0,2.4375,255400.0,<1H OCEAN\n-118.48,34.2,23.0,2850.0,864.0,2249.0,777.0,2.6957,191700.0,<1H OCEAN\n-118.48,34.19,20.0,5699.0,1594.0,3809.0,1381.0,2.8646,221100.0,<1H OCEAN\n-118.48,34.19,36.0,2058.0,423.0,1132.0,423.0,3.8833,210400.0,<1H OCEAN\n-118.47,34.2,25.0,4590.0,1477.0,2723.0,1195.0,2.7118,281700.0,<1H OCEAN\n-118.47,34.19,33.0,3879.0,943.0,2113.0,843.0,3.892,292900.0,<1H OCEAN\n-118.45,34.2,27.0,1572.0,450.0,1039.0,455.0,2.5562,190500.0,<1H OCEAN\n-118.45,34.2,18.0,2729.0,800.0,2099.0,742.0,2.5842,230800.0,<1H OCEAN\n-118.46,34.2,22.0,4855.0,1350.0,2519.0,1258.0,3.0893,205600.0,<1H OCEAN\n-118.45,34.19,37.0,1073.0,254.0,739.0,253.0,2.4667,192200.0,<1H OCEAN\n-118.46,34.19,20.0,5992.0,1820.0,4826.0,1632.0,2.7237,233500.0,<1H OCEAN\n-118.46,34.19,35.0,1491.0,295.0,779.0,309.0,6.1142,256300.0,<1H OCEAN\n-118.43,34.2,29.0,3051.0,694.0,1942.0,679.0,3.1118,238100.0,<1H OCEAN\n-118.44,34.2,36.0,2698.0,623.0,1544.0,554.0,2.7375,234900.0,<1H OCEAN\n-118.44,34.2,35.0,1717.0,478.0,1628.0,495.0,2.5197,225600.0,<1H OCEAN\n-118.44,34.2,17.0,2934.0,950.0,2517.0,889.0,2.936,232500.0,<1H OCEAN\n-118.43,34.19,27.0,3440.0,739.0,1827.0,712.0,4.125,245500.0,<1H OCEAN\n-118.44,34.19,37.0,1516.0,344.0,983.0,347.0,5.0,243600.0,<1H OCEAN\n-118.44,34.19,19.0,3487.0,959.0,2278.0,835.0,2.6709,215500.0,<1H OCEAN\n-118.44,34.19,29.0,1599.0,459.0,1143.0,438.0,2.4583,199100.0,<1H OCEAN\n-118.43,34.18,22.0,2052.0,568.0,1254.0,572.0,2.6364,271100.0,<1H OCEAN\n-118.44,34.18,36.0,2077.0,496.0,1206.0,528.0,2.2326,221000.0,<1H OCEAN\n-118.44,34.19,11.0,2891.0,951.0,2166.0,768.0,2.891,178100.0,<1H OCEAN\n-118.44,34.18,17.0,1546.0,592.0,2423.0,556.0,2.1977,154200.0,<1H OCEAN\n-118.45,34.19,11.0,2479.0,900.0,2466.0,855.0,2.2264,181300.0,<1H OCEAN\n-118.45,34.18,22.0,2516.0,826.0,3350.0,713.0,2.0192,158300.0,<1H OCEAN\n-118.46,34.18,27.0,2582.0,719.0,2038.0,718.0,3.0877,174200.0,<1H OCEAN\n-118.47,34.19,41.0,1104.0,196.0,495.0,196.0,5.0929,225000.0,<1H OCEAN\n-118.45,34.18,39.0,1810.0,388.0,839.0,380.0,3.7171,228800.0,<1H OCEAN\n-118.45,34.18,34.0,1843.0,442.0,861.0,417.0,3.6875,246400.0,<1H OCEAN\n-118.46,34.18,33.0,1791.0,386.0,844.0,397.0,4.5081,251400.0,<1H OCEAN\n-118.46,34.18,35.0,1819.0,465.0,1336.0,419.0,3.4583,253200.0,<1H OCEAN\n-118.44,34.18,35.0,972.0,270.0,550.0,256.0,2.2461,215000.0,<1H OCEAN\n-118.44,34.17,25.0,4966.0,1134.0,1941.0,958.0,3.8081,286700.0,<1H OCEAN\n-118.45,34.18,33.0,2077.0,486.0,1021.0,450.0,3.6019,225000.0,<1H OCEAN\n-118.43,34.18,25.0,3830.0,1105.0,2328.0,1017.0,2.6238,210000.0,<1H OCEAN\n-118.44,34.18,33.0,2127.0,414.0,1056.0,391.0,4.375,286100.0,<1H OCEAN\n-118.43,34.17,33.0,1679.0,404.0,933.0,412.0,2.6979,266000.0,<1H OCEAN\n-118.43,34.17,34.0,2180.0,424.0,906.0,429.0,4.4464,353100.0,<1H OCEAN\n-118.43,34.17,42.0,777.0,102.0,284.0,113.0,11.2093,500001.0,<1H OCEAN\n-118.44,34.17,29.0,2685.0,642.0,1085.0,599.0,3.2763,279400.0,<1H OCEAN\n-118.43,34.16,34.0,2622.0,467.0,1233.0,476.0,4.0474,379700.0,<1H OCEAN\n-118.44,34.16,35.0,3080.0,642.0,1362.0,623.0,4.1218,328500.0,<1H OCEAN\n-118.44,34.16,33.0,1616.0,322.0,580.0,311.0,4.0391,337500.0,<1H OCEAN\n-118.45,34.16,22.0,4982.0,1358.0,2237.0,1220.0,3.7105,272600.0,<1H OCEAN\n-118.45,34.17,21.0,2152.0,527.0,996.0,470.0,3.2386,277300.0,<1H OCEAN\n-118.45,34.17,33.0,3100.0,687.0,1388.0,658.0,4.3333,261300.0,<1H OCEAN\n-118.46,34.17,24.0,2814.0,675.0,1463.0,620.0,4.1875,309300.0,<1H OCEAN\n-118.46,34.17,22.0,6707.0,1737.0,2620.0,1610.0,3.1478,273700.0,<1H OCEAN\n-118.45,34.16,33.0,2544.0,500.0,1035.0,492.0,4.475,314800.0,<1H OCEAN\n-118.46,34.16,28.0,2795.0,622.0,1173.0,545.0,4.4423,280400.0,<1H OCEAN\n-118.46,34.16,26.0,2548.0,647.0,1098.0,540.0,4.3839,299100.0,<1H OCEAN\n-118.54,34.21,22.0,6064.0,1826.0,4876.0,1697.0,2.875,227100.0,<1H OCEAN\n-118.54,34.21,32.0,2593.0,566.0,1596.0,547.0,3.9886,199200.0,<1H OCEAN\n-118.54,34.22,35.0,1664.0,300.0,1000.0,309.0,4.6731,224100.0,<1H OCEAN\n-118.54,34.2,37.0,1600.0,349.0,1012.0,366.0,4.1597,201600.0,<1H OCEAN\n-118.54,34.19,33.0,2205.0,453.0,1242.0,419.0,4.1319,203700.0,<1H OCEAN\n-118.51,34.22,36.0,2794.0,523.0,1334.0,472.0,4.3462,222100.0,<1H OCEAN\n-118.5,34.21,36.0,1254.0,229.0,629.0,245.0,4.9643,236100.0,<1H OCEAN\n-118.5,34.21,36.0,1656.0,310.0,817.0,308.0,5.5675,215900.0,<1H OCEAN\n-118.51,34.22,36.0,1952.0,387.0,1156.0,392.0,4.185,209200.0,<1H OCEAN\n-118.51,34.21,36.0,2396.0,421.0,1064.0,398.0,4.7,223600.0,<1H OCEAN\n-118.51,34.22,36.0,1493.0,285.0,766.0,272.0,4.8646,213200.0,<1H OCEAN\n-118.52,34.22,35.0,1275.0,222.0,959.0,226.0,5.0282,195400.0,<1H OCEAN\n-118.52,34.22,35.0,1620.0,272.0,1052.0,248.0,5.5209,203300.0,<1H OCEAN\n-118.52,34.21,36.0,2394.0,424.0,1490.0,427.0,4.3261,206700.0,<1H OCEAN\n-118.52,34.21,34.0,1663.0,299.0,762.0,282.0,5.1265,211000.0,<1H OCEAN\n-118.53,34.22,29.0,4101.0,849.0,2630.0,867.0,4.6607,199800.0,<1H OCEAN\n-118.53,34.21,18.0,3124.0,796.0,1855.0,725.0,2.9389,213200.0,<1H OCEAN\n-118.55,34.21,35.0,2592.0,490.0,1427.0,434.0,5.0623,246400.0,<1H OCEAN\n-118.56,34.22,35.0,1843.0,329.0,1041.0,317.0,4.4271,205100.0,<1H OCEAN\n-118.56,34.21,36.0,1286.0,242.0,788.0,248.0,3.5333,196800.0,<1H OCEAN\n-118.56,34.22,34.0,1599.0,294.0,819.0,306.0,4.3194,197000.0,<1H OCEAN\n-118.55,34.21,33.0,3910.0,838.0,2097.0,810.0,3.8247,208700.0,<1H OCEAN\n-118.56,34.21,13.0,8327.0,1849.0,4126.0,1773.0,3.7313,189800.0,<1H OCEAN\n-118.52,34.2,35.0,2891.0,594.0,1757.0,581.0,4.3571,199800.0,<1H OCEAN\n-118.53,34.2,33.0,3270.0,818.0,2118.0,763.0,3.225,205300.0,<1H OCEAN\n-118.5,34.21,35.0,1668.0,332.0,807.0,311.0,4.5125,200300.0,<1H OCEAN\n-118.51,34.2,35.0,1614.0,308.0,850.0,330.0,4.1806,209000.0,<1H OCEAN\n-118.51,34.2,37.0,2066.0,434.0,1031.0,414.0,4.0924,188400.0,<1H OCEAN\n-118.52,34.21,36.0,1328.0,287.0,823.0,273.0,4.5648,193700.0,<1H OCEAN\n-118.5,34.2,42.0,1558.0,322.0,884.0,334.0,2.2304,203800.0,<1H OCEAN\n-118.51,34.2,33.0,2327.0,479.0,1166.0,472.0,4.2344,262500.0,<1H OCEAN\n-118.51,34.2,34.0,2871.0,581.0,1350.0,535.0,3.7049,227500.0,<1H OCEAN\n-118.51,34.19,35.0,2537.0,418.0,1161.0,421.0,5.3028,229200.0,<1H OCEAN\n-118.51,34.19,38.0,2182.0,409.0,1141.0,379.0,4.2865,221100.0,<1H OCEAN\n-118.5,34.2,18.0,4249.0,933.0,2047.0,909.0,4.1304,229100.0,<1H OCEAN\n-118.5,34.19,35.0,2720.0,490.0,1158.0,445.0,5.0796,228300.0,<1H OCEAN\n-118.5,34.19,26.0,2156.0,509.0,1142.0,470.0,4.0,224700.0,<1H OCEAN\n-118.52,34.2,19.0,4315.0,1304.0,2490.0,1222.0,2.6437,195000.0,<1H OCEAN\n-118.52,34.2,37.0,1795.0,346.0,1082.0,354.0,4.9102,207200.0,<1H OCEAN\n-118.53,34.2,26.0,2221.0,662.0,1998.0,603.0,2.8701,191100.0,<1H OCEAN\n-118.55,34.2,21.0,2549.0,651.0,1624.0,628.0,3.6905,179800.0,<1H OCEAN\n-118.55,34.19,18.0,5862.0,,3161.0,1280.0,3.1106,170600.0,<1H OCEAN\n-118.55,34.2,31.0,1963.0,420.0,1494.0,415.0,3.5313,211800.0,<1H OCEAN\n-118.52,34.19,42.0,881.0,170.0,464.0,163.0,2.9511,203900.0,<1H OCEAN\n-118.52,34.19,37.0,1560.0,275.0,763.0,284.0,3.8516,206900.0,<1H OCEAN\n-118.52,34.19,37.0,1892.0,347.0,1039.0,343.0,4.8295,212100.0,<1H OCEAN\n-118.53,34.19,32.0,2618.0,692.0,1961.0,633.0,2.625,192300.0,<1H OCEAN\n-118.52,34.18,34.0,2307.0,388.0,1168.0,427.0,4.2143,245400.0,<1H OCEAN\n-118.53,34.18,26.0,4175.0,885.0,2118.0,778.0,4.2083,240300.0,<1H OCEAN\n-118.56,34.2,36.0,1544.0,308.0,891.0,286.0,4.175,190900.0,<1H OCEAN\n-118.56,34.2,35.0,2273.0,,1431.0,403.0,4.0789,196700.0,<1H OCEAN\n-118.56,34.19,35.0,782.0,144.0,425.0,140.0,5.4548,201400.0,<1H OCEAN\n-118.56,34.19,34.0,1237.0,242.0,671.0,221.0,3.9615,183600.0,<1H OCEAN\n-118.56,34.19,34.0,2579.0,561.0,1237.0,517.0,4.433,235100.0,<1H OCEAN\n-118.56,34.18,36.0,1366.0,224.0,719.0,270.0,4.8264,251000.0,<1H OCEAN\n-118.54,34.19,22.0,3380.0,790.0,2199.0,737.0,2.5739,239200.0,<1H OCEAN\n-118.54,34.18,25.0,1938.0,457.0,1280.0,425.0,3.9632,240300.0,<1H OCEAN\n-118.55,34.19,36.0,978.0,170.0,475.0,192.0,4.675,222500.0,<1H OCEAN\n-118.55,34.19,31.0,1856.0,370.0,990.0,360.0,4.3654,223800.0,<1H OCEAN\n-118.58,34.21,24.0,2642.0,696.0,1649.0,633.0,3.0187,217700.0,<1H OCEAN\n-118.59,34.21,26.0,2335.0,669.0,1986.0,645.0,2.9974,178800.0,<1H OCEAN\n-118.59,34.2,18.0,847.0,185.0,733.0,178.0,5.2149,201900.0,<1H OCEAN\n-118.58,34.2,37.0,1389.0,252.0,826.0,249.0,5.015,220900.0,<1H OCEAN\n-118.57,34.22,27.0,2795.0,606.0,1702.0,586.0,3.7798,258400.0,<1H OCEAN\n-118.57,34.21,23.0,4891.0,793.0,2447.0,765.0,5.8798,270500.0,<1H OCEAN\n-118.57,34.22,17.0,3262.0,753.0,1879.0,708.0,4.1359,255200.0,<1H OCEAN\n-118.58,34.21,13.0,6227.0,1317.0,3739.0,1226.0,4.0313,299300.0,<1H OCEAN\n-118.58,34.22,35.0,2560.0,441.0,1428.0,468.0,5.6345,228200.0,<1H OCEAN\n-118.58,34.21,27.0,2209.0,353.0,1034.0,344.0,4.7125,250900.0,<1H OCEAN\n-118.59,34.21,34.0,1943.0,320.0,895.0,305.0,5.0462,227700.0,<1H OCEAN\n-118.59,34.21,34.0,2389.0,521.0,1560.0,514.0,4.8333,225400.0,<1H OCEAN\n-118.6,34.21,21.0,9512.0,2560.0,7282.0,2387.0,2.8039,227500.0,<1H OCEAN\n-118.61,34.21,34.0,3494.0,557.0,1861.0,576.0,5.6407,251500.0,<1H OCEAN\n-118.62,34.22,33.0,1636.0,275.0,866.0,289.0,5.6356,241300.0,<1H OCEAN\n-118.62,34.21,26.0,3234.0,517.0,1597.0,513.0,6.1074,258600.0,<1H OCEAN\n-118.62,34.22,34.0,2633.0,471.0,1313.0,428.0,4.0909,232900.0,<1H OCEAN\n-118.61,34.21,41.0,1058.0,228.0,778.0,245.0,3.3534,180500.0,<1H OCEAN\n-118.61,34.2,16.0,1718.0,467.0,896.0,475.0,3.6296,160900.0,<1H OCEAN\n-118.61,34.21,33.0,2609.0,431.0,1208.0,406.0,5.4527,227100.0,<1H OCEAN\n-118.62,34.2,23.0,3098.0,542.0,1486.0,492.0,5.7613,235800.0,<1H OCEAN\n-118.63,34.21,31.0,3952.0,647.0,1762.0,588.0,5.5709,244800.0,<1H OCEAN\n-118.64,34.22,16.0,4312.0,574.0,1902.0,574.0,8.4438,390000.0,<1H OCEAN\n-118.65,34.21,5.0,5429.0,665.0,2315.0,687.0,9.6465,500001.0,<1H OCEAN\n-118.65,34.2,23.0,7480.0,1084.0,3037.0,1058.0,6.9223,338400.0,<1H OCEAN\n-118.62,34.2,32.0,3233.0,553.0,1678.0,545.0,5.0025,234900.0,<1H OCEAN\n-118.63,34.2,19.0,7411.0,1045.0,2814.0,950.0,6.7785,336100.0,<1H OCEAN\n-118.59,34.21,17.0,2737.0,868.0,2924.0,785.0,2.5797,183500.0,<1H OCEAN\n-118.59,34.2,21.0,1789.0,,2300.0,677.0,2.754,179800.0,<1H OCEAN\n-118.6,34.2,10.0,2869.0,941.0,2162.0,829.0,3.2297,150000.0,<1H OCEAN\n-118.6,34.21,19.0,2581.0,857.0,2004.0,784.0,2.6159,182300.0,<1H OCEAN\n-118.56,34.2,35.0,1770.0,362.0,1083.0,355.0,5.0483,221000.0,<1H OCEAN\n-118.57,34.21,36.0,878.0,167.0,499.0,179.0,4.1181,190400.0,<1H OCEAN\n-118.57,34.2,18.0,7157.0,1869.0,4642.0,1699.0,3.1818,208000.0,<1H OCEAN\n-118.58,34.2,21.0,2979.0,744.0,1824.0,692.0,3.5,223700.0,<1H OCEAN\n-118.56,34.19,36.0,2600.0,441.0,1246.0,426.0,4.1111,215600.0,<1H OCEAN\n-118.57,34.2,36.0,2559.0,469.0,1358.0,445.0,4.5568,201500.0,<1H OCEAN\n-118.57,34.2,33.0,1759.0,311.0,943.0,315.0,5.223,209200.0,<1H OCEAN\n-118.58,34.2,35.0,1558.0,267.0,793.0,249.0,5.1463,220200.0,<1H OCEAN\n-118.58,34.2,35.0,1323.0,228.0,756.0,216.0,4.233,221300.0,<1H OCEAN\n-118.56,34.19,34.0,2185.0,372.0,986.0,347.0,4.8125,266700.0,<1H OCEAN\n-118.58,34.19,35.0,2329.0,399.0,966.0,336.0,3.8839,224900.0,<1H OCEAN\n-118.58,34.19,27.0,4286.0,1071.0,2863.0,1033.0,3.3125,222800.0,<1H OCEAN\n-118.58,34.19,15.0,3061.0,1079.0,2173.0,1078.0,2.85,187500.0,<1H OCEAN\n-118.58,34.18,28.0,908.0,142.0,368.0,143.0,5.6159,340500.0,<1H OCEAN\n-118.62,34.2,29.0,2421.0,402.0,1120.0,388.0,5.0309,244800.0,<1H OCEAN\n-118.62,34.18,25.0,3124.0,468.0,1241.0,439.0,6.4044,333100.0,<1H OCEAN\n-118.62,34.19,35.0,1934.0,307.0,905.0,315.0,5.5101,267400.0,<1H OCEAN\n-118.61,34.2,29.0,1673.0,284.0,794.0,270.0,5.5191,245800.0,<1H OCEAN\n-118.61,34.19,28.0,3824.0,749.0,1790.0,701.0,4.1154,246400.0,<1H OCEAN\n-118.61,34.19,34.0,703.0,127.0,369.0,127.0,4.3125,210100.0,<1H OCEAN\n-118.6,34.19,16.0,14912.0,4183.0,5105.0,3302.0,2.8312,213900.0,<1H OCEAN\n-118.63,34.19,32.0,3568.0,591.0,1741.0,563.0,5.1529,259600.0,<1H OCEAN\n-118.63,34.18,32.0,1646.0,242.0,697.0,233.0,6.6689,433000.0,<1H OCEAN\n-118.64,34.19,30.0,2399.0,373.0,1062.0,377.0,6.0094,245600.0,<1H OCEAN\n-118.64,34.19,33.0,3017.0,494.0,1423.0,470.0,5.6163,248400.0,<1H OCEAN\n-118.64,34.18,33.0,3808.0,623.0,1784.0,615.0,5.1641,263400.0,<1H OCEAN\n-118.64,34.19,28.0,3274.0,571.0,1424.0,521.0,4.4167,247300.0,<1H OCEAN\n-118.65,34.18,27.0,1793.0,339.0,1016.0,326.0,4.925,240300.0,<1H OCEAN\n-118.65,34.19,27.0,2772.0,511.0,1346.0,497.0,5.2016,243000.0,<1H OCEAN\n-118.66,34.19,23.0,7544.0,1031.0,3221.0,1043.0,7.642,374900.0,<1H OCEAN\n-118.63,34.17,33.0,4769.0,787.0,2019.0,743.0,5.5798,338200.0,<1H OCEAN\n-118.64,34.17,26.0,6767.0,903.0,2574.0,883.0,7.7846,409000.0,<1H OCEAN\n-118.57,34.18,36.0,2981.0,441.0,1243.0,413.0,6.5304,439800.0,<1H OCEAN\n-118.57,34.17,35.0,2072.0,318.0,908.0,342.0,6.0928,327300.0,<1H OCEAN\n-118.58,34.17,29.0,3393.0,574.0,1471.0,587.0,6.2064,334900.0,<1H OCEAN\n-118.59,34.18,7.0,11853.0,2691.0,4404.0,2447.0,4.2009,271300.0,<1H OCEAN\n-118.61,34.17,19.0,5944.0,1345.0,2372.0,1250.0,3.8819,328900.0,<1H OCEAN\n-118.62,34.17,32.0,1491.0,355.0,756.0,296.0,3.0404,262800.0,<1H OCEAN\n-118.62,34.17,34.0,3268.0,538.0,1463.0,519.0,6.8482,308300.0,<1H OCEAN\n-118.63,34.18,33.0,5252.0,760.0,2041.0,730.0,6.7977,389700.0,<1H OCEAN\n-118.65,34.18,26.0,4607.0,656.0,1769.0,643.0,7.4918,367600.0,<1H OCEAN\n-118.66,34.18,25.0,6612.0,857.0,2519.0,843.0,8.3912,419000.0,<1H OCEAN\n-118.61,34.17,31.0,1689.0,362.0,705.0,360.0,4.0,278500.0,<1H OCEAN\n-118.61,34.15,32.0,4491.0,815.0,1696.0,749.0,4.9102,319100.0,<1H OCEAN\n-118.61,34.16,29.0,4364.0,647.0,1550.0,624.0,6.8107,367400.0,<1H OCEAN\n-118.63,34.16,33.0,2896.0,455.0,1116.0,411.0,6.0192,347700.0,<1H OCEAN\n-118.63,34.16,30.0,3346.0,487.0,1296.0,495.0,7.457,392700.0,<1H OCEAN\n-118.62,34.15,26.0,5661.0,791.0,2493.0,780.0,7.9814,409900.0,<1H OCEAN\n-118.57,34.17,31.0,1950.0,383.0,870.0,357.0,3.1875,500001.0,<1H OCEAN\n-118.58,34.17,18.0,6476.0,1082.0,2413.0,958.0,5.6327,500001.0,<1H OCEAN\n-118.59,34.17,36.0,1887.0,359.0,761.0,329.0,5.4847,296000.0,<1H OCEAN\n-118.6,34.16,37.0,3441.0,584.0,1283.0,544.0,4.1656,313100.0,<1H OCEAN\n-118.6,34.16,32.0,3999.0,667.0,1628.0,631.0,6.0794,338500.0,<1H OCEAN\n-118.57,34.15,22.0,5791.0,706.0,2059.0,673.0,10.9201,500001.0,<1H OCEAN\n-118.58,34.15,21.0,3856.0,547.0,1422.0,535.0,8.4196,450700.0,<1H OCEAN\n-118.59,34.15,29.0,2023.0,330.0,747.0,304.0,6.7694,369700.0,<1H OCEAN\n-118.6,34.15,28.0,4570.0,744.0,1693.0,695.0,6.14,361900.0,<1H OCEAN\n-118.59,34.14,19.0,1303.0,155.0,450.0,145.0,10.5511,483100.0,<1H OCEAN\n-118.49,34.18,31.0,3073.0,674.0,1486.0,684.0,4.8984,311700.0,<1H OCEAN\n-118.51,34.18,37.0,1893.0,365.0,911.0,324.0,4.8036,295300.0,<1H OCEAN\n-118.51,34.17,31.0,3252.0,834.0,1411.0,760.0,3.1885,219000.0,<1H OCEAN\n-118.52,34.18,43.0,1700.0,380.0,930.0,349.0,3.675,213100.0,<1H OCEAN\n-118.52,34.18,46.0,2082.0,438.0,1047.0,393.0,3.6534,216000.0,<1H OCEAN\n-118.52,34.18,42.0,1611.0,410.0,879.0,386.0,3.1923,221800.0,<1H OCEAN\n-118.53,34.17,18.0,6430.0,1412.0,2897.0,1348.0,3.855,243800.0,<1H OCEAN\n-118.54,34.17,11.0,1080.0,174.0,386.0,160.0,6.1274,315900.0,<1H OCEAN\n-118.55,34.18,32.0,3011.0,529.0,1287.0,525.0,5.0605,311000.0,<1H OCEAN\n-118.56,34.18,39.0,1819.0,291.0,770.0,278.0,5.4088,457300.0,<1H OCEAN\n-118.54,34.18,17.0,7214.0,1994.0,4100.0,1823.0,3.0943,174500.0,<1H OCEAN\n-118.53,34.18,16.0,7194.0,1976.0,3687.0,1894.0,3.1887,189300.0,<1H OCEAN\n-118.54,34.17,34.0,2458.0,433.0,1034.0,373.0,5.6738,443600.0,<1H OCEAN\n-118.54,34.17,25.0,3352.0,891.0,1815.0,860.0,2.8528,425000.0,<1H OCEAN\n-118.55,34.17,36.0,2127.0,297.0,761.0,274.0,7.8392,500001.0,<1H OCEAN\n-118.56,34.17,35.0,2987.0,391.0,1244.0,387.0,7.1322,500001.0,<1H OCEAN\n-118.52,34.17,20.0,17377.0,4457.0,7450.0,4204.0,3.2154,259600.0,<1H OCEAN\n-118.52,34.16,39.0,2693.0,478.0,1219.0,435.0,5.17,335400.0,<1H OCEAN\n-118.53,34.16,32.0,3554.0,762.0,1623.0,750.0,3.6141,290600.0,<1H OCEAN\n-118.51,34.16,23.0,11154.0,1995.0,4076.0,1809.0,5.4609,500001.0,<1H OCEAN\n-118.48,34.16,32.0,2108.0,309.0,769.0,274.0,8.7172,500001.0,<1H OCEAN\n-118.49,34.16,37.0,3333.0,488.0,1171.0,485.0,6.4958,500001.0,<1H OCEAN\n-118.5,34.16,34.0,3547.0,523.0,1187.0,500.0,7.139,424000.0,<1H OCEAN\n-118.5,34.15,33.0,3104.0,387.0,1111.0,376.0,13.4196,500001.0,<1H OCEAN\n-118.5,34.17,37.0,880.0,,369.0,155.0,4.1429,303600.0,<1H OCEAN\n-118.49,34.15,33.0,2829.0,360.0,1010.0,363.0,10.3587,500001.0,<1H OCEAN\n-118.49,34.14,28.0,3539.0,441.0,1190.0,421.0,10.6796,500001.0,<1H OCEAN\n-118.49,34.13,24.0,4394.0,,1443.0,528.0,11.2979,500001.0,<1H OCEAN\n-118.51,34.14,28.0,6748.0,904.0,2431.0,876.0,12.8879,500001.0,<1H OCEAN\n-118.53,34.14,28.0,6920.0,906.0,2515.0,860.0,9.2189,500001.0,<1H OCEAN\n-118.54,34.15,26.0,10111.0,1295.0,3599.0,1257.0,10.2292,500001.0,<1H OCEAN\n-118.56,34.14,23.0,9657.0,1189.0,3585.0,1162.0,10.4399,500001.0,<1H OCEAN\n-118.43,34.15,28.0,6270.0,1706.0,2549.0,1497.0,3.2241,295800.0,<1H OCEAN\n-118.43,34.15,42.0,1293.0,214.0,459.0,217.0,7.672,467600.0,<1H OCEAN\n-118.43,34.15,26.0,2900.0,667.0,1090.0,590.0,3.7125,447400.0,<1H OCEAN\n-118.43,34.15,31.0,1856.0,425.0,795.0,426.0,2.8448,360600.0,<1H OCEAN\n-118.44,34.15,29.0,5474.0,1457.0,2352.0,1326.0,3.415,382500.0,<1H OCEAN\n-118.44,34.15,44.0,1778.0,251.0,641.0,251.0,10.0549,500001.0,<1H OCEAN\n-118.44,34.15,15.0,4420.0,1076.0,1669.0,1016.0,4.6375,359100.0,<1H OCEAN\n-118.44,34.15,37.0,1335.0,286.0,539.0,279.0,3.2813,301700.0,<1H OCEAN\n-118.44,34.15,38.0,1387.0,337.0,713.0,343.0,2.9352,304500.0,<1H OCEAN\n-118.45,34.16,22.0,7828.0,2038.0,3303.0,1922.0,3.6171,318300.0,<1H OCEAN\n-118.45,34.15,10.0,1091.0,260.0,517.0,266.0,4.1727,332600.0,<1H OCEAN\n-118.45,34.15,20.0,3876.0,799.0,1334.0,753.0,4.5656,478400.0,<1H OCEAN\n-118.46,34.16,38.0,1495.0,300.0,598.0,280.0,3.4698,265400.0,<1H OCEAN\n-118.46,34.16,16.0,4590.0,1200.0,2195.0,1139.0,3.8273,334900.0,<1H OCEAN\n-118.47,34.15,7.0,6306.0,1473.0,2381.0,1299.0,4.642,457300.0,<1H OCEAN\n-118.47,34.16,30.0,3823.0,740.0,1449.0,612.0,4.6,392500.0,<1H OCEAN\n-118.47,34.15,43.0,804.0,117.0,267.0,110.0,8.2269,500001.0,<1H OCEAN\n-118.48,34.15,31.0,2536.0,429.0,990.0,424.0,5.4591,495500.0,<1H OCEAN\n-118.48,34.16,30.0,3507.0,536.0,1427.0,525.0,6.7082,500001.0,<1H OCEAN\n-118.48,34.14,31.0,9320.0,1143.0,2980.0,1109.0,10.3599,500001.0,<1H OCEAN\n-118.46,34.14,34.0,5264.0,771.0,1738.0,753.0,8.8115,500001.0,<1H OCEAN\n-118.47,34.14,36.0,2873.0,420.0,850.0,379.0,8.153,500001.0,<1H OCEAN\n-118.47,34.14,34.0,3646.0,610.0,1390.0,607.0,7.629,500001.0,<1H OCEAN\n-118.43,34.14,44.0,1693.0,239.0,498.0,216.0,10.9237,500001.0,<1H OCEAN\n-118.43,34.13,37.0,4400.0,695.0,1521.0,666.0,8.2954,500001.0,<1H OCEAN\n-118.45,34.14,33.0,1741.0,274.0,588.0,267.0,7.9625,490800.0,<1H OCEAN\n-118.35,34.15,52.0,1680.0,238.0,493.0,211.0,9.042,500001.0,<1H OCEAN\n-118.36,34.15,41.0,3545.0,698.0,1221.0,651.0,4.3,500001.0,<1H OCEAN\n-118.36,34.15,34.0,3659.0,921.0,1338.0,835.0,3.6202,366100.0,<1H OCEAN\n-118.37,34.15,23.0,4604.0,1319.0,2391.0,1227.0,3.1373,263100.0,<1H OCEAN\n-118.37,34.15,29.0,2630.0,617.0,1071.0,573.0,3.3669,376100.0,<1H OCEAN\n-118.38,34.16,42.0,2358.0,546.0,1065.0,523.0,3.1289,320600.0,<1H OCEAN\n-118.38,34.15,36.0,2933.0,619.0,1115.0,579.0,4.3036,365900.0,<1H OCEAN\n-118.39,34.15,29.0,3110.0,650.0,1212.0,642.0,4.2031,394400.0,<1H OCEAN\n-118.39,34.15,29.0,917.0,181.0,379.0,183.0,3.4612,425000.0,<1H OCEAN\n-118.39,34.16,20.0,4084.0,1062.0,1637.0,987.0,3.2388,256300.0,<1H OCEAN\n-118.4,34.15,31.0,3881.0,909.0,1535.0,846.0,3.0398,369100.0,<1H OCEAN\n-118.41,34.15,33.0,4032.0,868.0,1695.0,869.0,4.3468,425900.0,<1H OCEAN\n-118.42,34.15,48.0,680.0,131.0,268.0,126.0,4.615,371400.0,<1H OCEAN\n-118.42,34.15,18.0,1880.0,420.0,681.0,333.0,4.3214,372300.0,<1H OCEAN\n-118.42,34.15,31.0,1861.0,430.0,736.0,360.0,5.2853,355900.0,<1H OCEAN\n-118.42,34.16,17.0,277.0,70.0,119.0,59.0,4.0208,341700.0,<1H OCEAN\n-118.42,34.16,46.0,54.0,9.0,20.0,6.0,0.536,375000.0,<1H OCEAN\n-118.4,34.15,41.0,2394.0,500.0,837.0,417.0,4.3889,380400.0,<1H OCEAN\n-118.4,34.15,44.0,2515.0,510.0,967.0,484.0,5.0754,374500.0,<1H OCEAN\n-118.41,34.15,24.0,3891.0,866.0,1568.0,830.0,4.1656,364700.0,<1H OCEAN\n-118.41,34.15,46.0,1628.0,259.0,500.0,258.0,6.083,424000.0,<1H OCEAN\n-118.36,34.14,30.0,1376.0,317.0,629.0,320.0,3.6823,295200.0,<1H OCEAN\n-118.37,34.14,8.0,4382.0,1560.0,2138.0,1411.0,3.5714,197900.0,<1H OCEAN\n-118.37,34.14,21.0,4670.0,1161.0,1914.0,1094.0,3.7986,367700.0,<1H OCEAN\n-118.38,34.14,42.0,1253.0,225.0,492.0,224.0,7.7112,386700.0,<1H OCEAN\n-118.39,34.14,19.0,5076.0,1034.0,2021.0,960.0,5.5683,309200.0,<1H OCEAN\n-118.39,34.15,36.0,2696.0,713.0,905.0,659.0,3.1146,373500.0,<1H OCEAN\n-118.35,34.13,39.0,1610.0,278.0,511.0,278.0,4.3333,385900.0,<1H OCEAN\n-118.34,34.12,41.0,3257.0,679.0,1237.0,638.0,4.2415,409600.0,<1H OCEAN\n-118.36,34.13,36.0,6871.0,1180.0,2216.0,1130.0,8.0499,495600.0,<1H OCEAN\n-118.37,34.14,23.0,1883.0,512.0,774.0,478.0,3.5096,396400.0,<1H OCEAN\n-118.37,34.13,28.0,4287.0,627.0,1498.0,615.0,8.5677,500001.0,<1H OCEAN\n-118.39,34.14,34.0,4624.0,781.0,1572.0,719.0,6.5533,500001.0,<1H OCEAN\n-118.38,34.14,40.0,1965.0,354.0,666.0,357.0,6.0876,483800.0,<1H OCEAN\n-118.4,34.13,32.0,8262.0,1156.0,2712.0,1125.0,10.5575,500001.0,<1H OCEAN\n-118.4,34.14,52.0,1695.0,281.0,595.0,264.0,6.0678,399300.0,<1H OCEAN\n-118.4,34.14,45.0,417.0,89.0,187.0,88.0,5.1377,360700.0,<1H OCEAN\n-118.41,34.14,33.0,778.0,143.0,258.0,130.0,5.738,497600.0,<1H OCEAN\n-118.42,34.14,27.0,3990.0,892.0,1417.0,800.0,4.0439,500001.0,<1H OCEAN\n-118.42,34.13,38.0,3830.0,518.0,1292.0,516.0,12.7823,500001.0,<1H OCEAN\n-118.22,34.14,52.0,2298.0,406.0,1203.0,387.0,5.5291,274600.0,<1H OCEAN\n-118.22,34.14,52.0,1388.0,271.0,735.0,239.0,3.7404,247700.0,<1H OCEAN\n-118.2,34.14,52.0,3800.0,646.0,1842.0,620.0,5.5524,293900.0,<1H OCEAN\n-118.19,34.14,46.0,2387.0,488.0,1181.0,456.0,3.6058,257900.0,<1H OCEAN\n-118.19,34.14,38.0,1826.0,300.0,793.0,297.0,5.2962,291500.0,<1H OCEAN\n-118.23,34.14,39.0,277.0,89.0,182.0,91.0,2.3958,175000.0,<1H OCEAN\n-118.22,34.14,50.0,3657.0,708.0,1725.0,644.0,5.5456,258100.0,<1H OCEAN\n-118.22,34.13,35.0,2983.0,526.0,1614.0,543.0,5.7794,272400.0,<1H OCEAN\n-118.2,34.14,52.0,2090.0,466.0,1219.0,390.0,4.0909,204200.0,<1H OCEAN\n-118.21,34.14,44.0,1681.0,407.0,1105.0,387.0,3.2222,186500.0,<1H OCEAN\n-118.21,34.14,25.0,1908.0,628.0,1412.0,588.0,2.2267,189800.0,<1H OCEAN\n-118.2,34.14,51.0,1941.0,378.0,1012.0,371.0,3.9375,217000.0,<1H OCEAN\n-118.19,34.14,47.0,2525.0,523.0,1514.0,498.0,4.3359,209200.0,<1H OCEAN\n-118.19,34.13,52.0,2012.0,458.0,1314.0,434.0,3.925,180400.0,<1H OCEAN\n-118.2,34.13,52.0,2035.0,459.0,2589.0,438.0,3.5349,193600.0,<1H OCEAN\n-118.21,34.13,52.0,2465.0,611.0,1433.0,570.0,3.25,214200.0,<1H OCEAN\n-118.22,34.13,47.0,1585.0,420.0,949.0,366.0,2.7098,173800.0,<1H OCEAN\n-118.22,34.13,52.0,791.0,174.0,501.0,162.0,3.3542,178100.0,<1H OCEAN\n-118.18,34.13,52.0,2228.0,475.0,1311.0,452.0,3.5341,182100.0,<1H OCEAN\n-118.18,34.12,52.0,1081.0,311.0,904.0,283.0,1.9219,165100.0,<1H OCEAN\n-118.18,34.12,45.0,2397.0,488.0,1569.0,471.0,4.21,167900.0,<1H OCEAN\n-118.17,34.12,35.0,2568.0,672.0,1696.0,605.0,2.9154,169200.0,<1H OCEAN\n-118.18,34.12,29.0,2640.0,737.0,1795.0,655.0,2.369,173400.0,<1H OCEAN\n-118.17,34.12,30.0,3376.0,720.0,1990.0,725.0,3.7813,232000.0,<1H OCEAN\n-118.19,34.13,50.0,1309.0,302.0,883.0,293.0,3.1287,198000.0,<1H OCEAN\n-118.19,34.12,52.0,679.0,132.0,483.0,163.0,4.2344,162500.0,<1H OCEAN\n-118.19,34.12,41.0,2591.0,682.0,2366.0,583.0,2.3071,146400.0,<1H OCEAN\n-118.19,34.12,44.0,1219.0,324.0,1036.0,282.0,4.0417,170600.0,<1H OCEAN\n-118.19,34.12,36.0,2833.0,720.0,2148.0,709.0,2.7012,172100.0,<1H OCEAN\n-118.2,34.13,30.0,3369.0,824.0,2032.0,795.0,4.0052,196400.0,<1H OCEAN\n-118.2,34.13,50.0,2929.0,588.0,1931.0,574.0,3.3438,173600.0,<1H OCEAN\n-118.2,34.12,52.0,1580.0,426.0,1462.0,406.0,3.3326,167600.0,<1H OCEAN\n-118.2,34.12,41.0,1908.0,503.0,1557.0,453.0,2.9194,162000.0,<1H OCEAN\n-118.21,34.12,52.0,1590.0,360.0,1127.0,321.0,3.4625,173900.0,<1H OCEAN\n-118.21,34.12,41.0,1904.0,514.0,1666.0,498.0,3.6845,175800.0,<1H OCEAN\n-118.21,34.12,52.0,1301.0,389.0,1189.0,361.0,2.5139,190000.0,<1H OCEAN\n-118.2,34.11,52.0,678.0,173.0,791.0,186.0,4.0625,171300.0,<1H OCEAN\n-118.2,34.11,46.0,3659.0,1068.0,4153.0,993.0,2.5211,162900.0,<1H OCEAN\n-118.2,34.11,52.0,1901.0,525.0,1856.0,480.0,3.0,156400.0,<1H OCEAN\n-118.2,34.12,44.0,1565.0,398.0,1500.0,407.0,2.8125,155600.0,<1H OCEAN\n-118.19,34.12,46.0,3387.0,820.0,2833.0,813.0,2.987,176900.0,<1H OCEAN\n-118.19,34.11,40.0,1266.0,348.0,1032.0,315.0,2.1667,150000.0,<1H OCEAN\n-118.19,34.12,35.0,2524.0,749.0,2487.0,679.0,2.4932,167700.0,<1H OCEAN\n-118.18,34.11,33.0,1523.0,391.0,753.0,298.0,2.6591,183800.0,<1H OCEAN\n-118.18,34.11,44.0,1346.0,398.0,1204.0,344.0,2.3984,152200.0,<1H OCEAN\n-118.19,34.11,38.0,1158.0,309.0,1051.0,322.0,2.286,169300.0,<1H OCEAN\n-118.19,34.11,26.0,1638.0,457.0,1155.0,437.0,3.4227,143800.0,<1H OCEAN\n-118.19,34.1,42.0,1577.0,379.0,1317.0,378.0,3.2121,153900.0,<1H OCEAN\n-118.2,34.11,36.0,1441.0,534.0,1809.0,500.0,2.1793,185700.0,<1H OCEAN\n-118.2,34.11,37.0,2040.0,611.0,1698.0,545.0,1.9355,166300.0,<1H OCEAN\n-118.2,34.1,30.0,3643.0,1197.0,4336.0,1163.0,2.07,154500.0,<1H OCEAN\n-118.21,34.11,32.0,2759.0,499.0,1661.0,533.0,4.3812,228200.0,<1H OCEAN\n-118.21,34.1,40.0,1684.0,316.0,795.0,330.0,5.2723,218300.0,<1H OCEAN\n-118.21,34.1,47.0,5077.0,1271.0,3348.0,1106.0,3.0377,186800.0,<1H OCEAN\n-118.22,34.1,35.0,4003.0,788.0,2785.0,764.0,4.1213,252100.0,<1H OCEAN\n-118.23,34.1,38.0,1051.0,249.0,799.0,229.0,2.712,143800.0,<1H OCEAN\n-118.23,34.1,46.0,2483.0,587.0,2121.0,553.0,2.2788,152900.0,<1H OCEAN\n-118.21,34.1,36.0,2000.0,533.0,1234.0,535.0,3.7437,241700.0,<1H OCEAN\n-118.21,34.09,34.0,1660.0,412.0,1678.0,382.0,2.7708,148200.0,<1H OCEAN\n-118.22,34.1,33.0,1903.0,386.0,1187.0,340.0,4.0469,196600.0,<1H OCEAN\n-118.23,34.1,41.0,1353.0,379.0,1536.0,416.0,2.1687,157000.0,<1H OCEAN\n-118.22,34.09,42.0,1706.0,488.0,1941.0,447.0,2.5213,149700.0,<1H OCEAN\n-118.22,34.09,36.0,1427.0,415.0,1835.0,410.0,2.48,138900.0,<1H OCEAN\n-118.22,34.09,45.0,1072.0,275.0,996.0,243.0,2.8194,165000.0,<1H OCEAN\n-118.23,34.09,41.0,438.0,201.0,690.0,161.0,2.0476,181300.0,<1H OCEAN\n-118.23,34.13,47.0,1162.0,235.0,781.0,268.0,4.6528,244400.0,<1H OCEAN\n-118.23,34.13,48.0,737.0,166.0,462.0,131.0,3.5893,212500.0,<1H OCEAN\n-118.22,34.13,40.0,2749.0,580.0,1375.0,511.0,4.825,205800.0,<1H OCEAN\n-118.23,34.13,34.0,609.0,149.0,407.0,145.0,4.5766,185800.0,<1H OCEAN\n-118.23,34.13,48.0,1308.0,,835.0,294.0,4.2891,214800.0,<1H OCEAN\n-118.23,34.13,37.0,1799.0,426.0,1088.0,417.0,2.975,244500.0,<1H OCEAN\n-118.22,34.12,28.0,3306.0,1025.0,2670.0,942.0,3.0919,185400.0,<1H OCEAN\n-118.21,34.12,35.0,1937.0,439.0,1523.0,412.0,3.5638,170500.0,<1H OCEAN\n-118.22,34.11,36.0,2870.0,529.0,1371.0,565.0,5.2083,220900.0,<1H OCEAN\n-118.22,34.12,37.0,1298.0,242.0,750.0,255.0,5.2049,240800.0,<1H OCEAN\n-118.23,34.12,28.0,1546.0,465.0,974.0,408.0,2.2843,183800.0,<1H OCEAN\n-118.24,34.12,41.0,1213.0,301.0,801.0,300.0,3.1806,204200.0,<1H OCEAN\n-118.23,34.12,32.0,2094.0,491.0,1413.0,479.0,4.5089,221100.0,<1H OCEAN\n-118.23,34.11,31.0,1021.0,191.0,495.0,191.0,5.5051,223500.0,<1H OCEAN\n-118.23,34.11,35.0,4148.0,971.0,3220.0,892.0,3.3389,187100.0,<1H OCEAN\n-118.25,34.12,11.0,1281.0,418.0,1584.0,330.0,2.8889,153100.0,<1H OCEAN\n-118.24,34.12,29.0,2904.0,892.0,3320.0,765.0,2.6111,168800.0,<1H OCEAN\n-118.24,34.12,34.0,80.0,26.0,125.0,35.0,0.8907,154200.0,<1H OCEAN\n-118.24,34.11,50.0,2141.0,451.0,1777.0,426.0,2.7679,178800.0,<1H OCEAN\n-118.24,34.11,39.0,1148.0,348.0,1161.0,333.0,2.2167,176700.0,<1H OCEAN\n-118.23,34.11,33.0,2612.0,646.0,2496.0,606.0,3.133,156000.0,<1H OCEAN\n-118.26,34.12,45.0,2839.0,698.0,1768.0,653.0,3.1306,214000.0,<1H OCEAN\n-118.26,34.11,47.0,2183.0,510.0,1445.0,503.0,3.6667,210900.0,<1H OCEAN\n-118.25,34.11,43.0,2230.0,583.0,1667.0,543.0,2.8667,217800.0,<1H OCEAN\n-118.25,34.11,43.0,2222.0,635.0,1817.0,606.0,2.7466,208900.0,<1H OCEAN\n-118.25,34.11,39.0,1415.0,369.0,1467.0,351.0,3.015,156300.0,<1H OCEAN\n-118.24,34.1,42.0,1525.0,456.0,1688.0,432.0,3.1691,141300.0,<1H OCEAN\n-118.25,34.11,52.0,125.0,42.0,99.0,40.0,3.4375,170000.0,<1H OCEAN\n-118.25,34.1,42.0,598.0,147.0,312.0,144.0,2.625,164300.0,<1H OCEAN\n-118.25,34.1,42.0,4198.0,956.0,1935.0,878.0,3.7184,277300.0,<1H OCEAN\n-118.26,34.11,52.0,1740.0,402.0,749.0,335.0,3.5673,270700.0,<1H OCEAN\n-118.27,34.14,29.0,3768.0,1211.0,2320.0,1030.0,2.7685,204500.0,<1H OCEAN\n-118.27,34.13,47.0,1375.0,359.0,1512.0,418.0,2.1071,208900.0,<1H OCEAN\n-118.27,34.13,46.0,1613.0,396.0,966.0,416.0,2.9392,230300.0,<1H OCEAN\n-118.27,34.11,36.0,1832.0,539.0,934.0,486.0,3.0521,276600.0,<1H OCEAN\n-118.27,34.11,39.0,3825.0,916.0,1378.0,746.0,4.4094,352600.0,<1H OCEAN\n-118.28,34.11,38.0,4453.0,1156.0,1830.0,1099.0,3.6181,495600.0,<1H OCEAN\n-118.28,34.11,45.0,1607.0,331.0,633.0,332.0,3.1445,438300.0,<1H OCEAN\n-118.28,34.12,50.0,2384.0,312.0,836.0,337.0,12.8763,500001.0,<1H OCEAN\n-118.26,34.12,52.0,1942.0,476.0,1375.0,477.0,2.7348,209100.0,<1H OCEAN\n-118.26,34.12,52.0,2290.0,520.0,1278.0,485.0,3.8393,238200.0,<1H OCEAN\n-118.27,34.12,50.0,2037.0,440.0,1089.0,417.0,3.575,230600.0,<1H OCEAN\n-118.29,34.11,30.0,2774.0,570.0,1076.0,580.0,5.296,500001.0,<1H OCEAN\n-118.29,34.11,40.0,2681.0,737.0,1144.0,669.0,3.0461,264300.0,<1H OCEAN\n-118.29,34.1,39.0,2196.0,582.0,1165.0,538.0,2.9417,254200.0,<1H OCEAN\n-118.29,34.1,43.0,1711.0,443.0,1190.0,429.0,3.5172,265500.0,<1H OCEAN\n-118.29,34.11,24.0,3696.0,1125.0,1685.0,1031.0,2.3789,266700.0,<1H OCEAN\n-118.29,34.11,49.0,2850.0,379.0,1113.0,380.0,12.9591,500001.0,<1H OCEAN\n-118.29,34.11,48.0,1448.0,295.0,681.0,287.0,3.2333,436400.0,<1H OCEAN\n-118.3,34.11,52.0,3136.0,675.0,1213.0,606.0,3.5806,391900.0,<1H OCEAN\n-118.3,34.1,37.0,5305.0,1980.0,3895.0,1874.0,2.0672,283300.0,<1H OCEAN\n-118.3,34.11,25.0,1590.0,218.0,568.0,206.0,8.4389,500001.0,<1H OCEAN\n-118.3,34.11,52.0,1954.0,245.0,645.0,237.0,6.9391,500001.0,<1H OCEAN\n-118.31,34.11,52.0,1875.0,303.0,735.0,293.0,5.8659,433300.0,<1H OCEAN\n-118.31,34.12,39.0,3895.0,561.0,1271.0,536.0,8.0073,500001.0,<1H OCEAN\n-118.31,34.13,40.0,2822.0,443.0,907.0,414.0,7.2692,498700.0,<1H OCEAN\n-118.32,34.12,52.0,3410.0,800.0,1218.0,783.0,4.15,393500.0,<1H OCEAN\n-118.32,34.11,42.0,2462.0,543.0,857.0,482.0,4.0833,434400.0,<1H OCEAN\n-118.32,34.11,33.0,5135.0,1450.0,2404.0,1292.0,3.2462,435700.0,<1H OCEAN\n-118.32,34.11,48.0,4472.0,1579.0,2796.0,1397.0,2.3974,410700.0,<1H OCEAN\n-118.33,34.11,38.0,3495.0,1100.0,1939.0,994.0,2.2148,438300.0,<1H OCEAN\n-118.33,34.11,48.0,1601.0,464.0,784.0,461.0,3.0642,342900.0,<1H OCEAN\n-118.33,34.11,37.0,2330.0,434.0,846.0,457.0,8.2335,430200.0,<1H OCEAN\n-118.32,34.13,34.0,1856.0,273.0,540.0,264.0,4.0833,500001.0,<1H OCEAN\n-118.33,34.12,23.0,1894.0,416.0,769.0,392.0,6.0352,500001.0,<1H OCEAN\n-118.34,34.13,45.0,2375.0,417.0,751.0,410.0,6.6739,500001.0,<1H OCEAN\n-118.32,34.14,23.0,4574.0,1423.0,1624.0,995.0,4.0965,500001.0,<1H OCEAN\n-118.34,34.11,40.0,5485.0,1242.0,2034.0,1133.0,3.6974,500001.0,<1H OCEAN\n-118.35,34.1,26.0,3977.0,1050.0,1720.0,935.0,3.358,364500.0,<1H OCEAN\n-118.36,34.1,52.0,1295.0,281.0,578.0,273.0,2.976,405100.0,<1H OCEAN\n-118.36,34.1,52.0,1096.0,247.0,423.0,230.0,3.0179,500001.0,<1H OCEAN\n-118.35,34.1,18.0,7432.0,2793.0,3596.0,2270.0,2.8036,225000.0,<1H OCEAN\n-118.35,34.1,16.0,2930.0,1038.0,1648.0,980.0,2.6458,372200.0,<1H OCEAN\n-118.35,34.1,20.0,2745.0,782.0,1161.0,739.0,3.9044,436400.0,<1H OCEAN\n-118.35,34.1,18.0,4109.0,1301.0,2103.0,1116.0,2.325,250000.0,<1H OCEAN\n-118.35,34.1,24.0,5477.0,1803.0,2863.0,1755.0,1.845,237500.0,<1H OCEAN\n-118.34,34.11,51.0,937.0,348.0,527.0,333.0,4.3571,468800.0,<1H OCEAN\n-118.34,34.1,29.0,3193.0,1452.0,2039.0,1265.0,1.8209,500001.0,<1H OCEAN\n-118.34,34.1,24.0,1996.0,791.0,1215.0,672.0,1.5429,325000.0,<1H OCEAN\n-118.34,34.1,28.0,2223.0,752.0,1271.0,684.0,2.5434,232100.0,<1H OCEAN\n-118.33,34.1,30.0,5293.0,2446.0,4123.0,2127.0,1.51,187500.0,<1H OCEAN\n-118.33,34.1,43.0,2732.0,1646.0,3049.0,1429.0,1.3157,333300.0,<1H OCEAN\n-118.31,34.1,40.0,4984.0,2158.0,4828.0,2028.0,1.6903,350000.0,<1H OCEAN\n-118.32,34.1,28.0,1759.0,716.0,1463.0,620.0,1.7306,450000.0,<1H OCEAN\n-118.3,34.1,35.0,7517.0,2961.0,5899.0,2769.0,1.9354,340000.0,<1H OCEAN\n-118.3,34.1,25.0,3926.0,1715.0,4865.0,1612.0,1.6112,262500.0,<1H OCEAN\n-118.31,34.1,36.0,2288.0,1033.0,3030.0,890.0,1.5328,250000.0,<1H OCEAN\n-118.31,34.1,33.0,766.0,347.0,918.0,305.0,1.705,350000.0,<1H OCEAN\n-118.33,34.1,48.0,1116.0,524.0,1610.0,483.0,1.625,237500.0,<1H OCEAN\n-118.33,34.1,45.0,1913.0,696.0,1552.0,611.0,2.0888,237500.0,<1H OCEAN\n-118.32,34.1,43.0,1615.0,734.0,1460.0,644.0,1.4005,193800.0,<1H OCEAN\n-118.32,34.09,34.0,2473.0,1171.0,2655.0,1083.0,1.6331,162500.0,<1H OCEAN\n-118.33,34.09,30.0,1679.0,682.0,1445.0,579.0,2.1403,200000.0,<1H OCEAN\n-118.33,34.1,29.0,732.0,288.0,691.0,278.0,2.1866,250000.0,<1H OCEAN\n-118.31,34.1,34.0,399.0,141.0,482.0,134.0,1.625,67500.0,<1H OCEAN\n-118.31,34.09,34.0,2065.0,839.0,2626.0,775.0,1.8214,211100.0,<1H OCEAN\n-118.31,34.09,28.0,720.0,267.0,891.0,265.0,1.8977,100000.0,<1H OCEAN\n-118.31,34.09,37.0,773.0,,835.0,312.0,1.8576,193800.0,<1H OCEAN\n-118.32,34.09,32.0,563.0,191.0,626.0,185.0,2.0341,250000.0,<1H OCEAN\n-118.32,34.09,27.0,210.0,98.0,332.0,112.0,2.5556,175000.0,<1H OCEAN\n-118.32,34.1,36.0,1655.0,690.0,1957.0,633.0,1.7325,221900.0,<1H OCEAN\n-118.32,34.09,34.0,1478.0,675.0,1976.0,653.0,2.0557,225000.0,<1H OCEAN\n-118.32,34.1,31.0,622.0,229.0,597.0,227.0,1.5284,200000.0,<1H OCEAN\n-118.32,34.1,30.0,2193.0,965.0,2197.0,836.0,1.8277,137500.0,<1H OCEAN\n-118.32,34.1,52.0,786.0,270.0,756.0,273.0,2.2311,206300.0,<1H OCEAN\n-118.3,34.1,29.0,3403.0,1367.0,3432.0,1174.0,1.7083,166700.0,<1H OCEAN\n-118.3,34.09,40.0,3058.0,1215.0,3953.0,1223.0,1.8156,218800.0,<1H OCEAN\n-118.31,34.09,36.0,787.0,420.0,1506.0,360.0,1.2412,216700.0,<1H OCEAN\n-118.3,34.1,38.0,2067.0,914.0,2717.0,853.0,1.7641,250000.0,<1H OCEAN\n-118.3,34.1,36.0,2284.0,899.0,1964.0,839.0,1.9297,203300.0,<1H OCEAN\n-118.29,34.09,34.0,2716.0,1114.0,2991.0,1021.0,1.7514,187500.0,<1H OCEAN\n-118.3,34.09,36.0,2332.0,993.0,3155.0,927.0,2.2612,230400.0,<1H OCEAN\n-118.29,34.1,39.0,631.0,298.0,744.0,274.0,2.7054,162500.0,<1H OCEAN\n-118.29,34.09,29.0,2240.0,792.0,2254.0,739.0,2.3317,172500.0,<1H OCEAN\n-118.28,34.09,38.0,790.0,275.0,664.0,194.0,3.0357,175000.0,<1H OCEAN\n-118.29,34.09,43.0,1583.0,658.0,1941.0,614.0,1.9835,225000.0,<1H OCEAN\n-118.29,34.09,28.0,1562.0,648.0,1974.0,597.0,1.9766,112500.0,<1H OCEAN\n-118.29,34.09,35.0,2198.0,998.0,3441.0,912.0,2.0467,158300.0,<1H OCEAN\n-118.29,34.09,35.0,2624.0,1116.0,3548.0,1008.0,2.0132,198400.0,<1H OCEAN\n-118.29,34.09,39.0,336.0,173.0,586.0,151.0,1.8056,262500.0,<1H OCEAN\n-118.29,34.09,52.0,1272.0,322.0,984.0,353.0,1.9063,261600.0,<1H OCEAN\n-118.3,34.09,29.0,3245.0,1190.0,3906.0,1102.0,2.1927,253300.0,<1H OCEAN\n-118.3,34.09,25.0,2345.0,852.0,2860.0,862.0,1.4497,205600.0,<1H OCEAN\n-118.3,34.09,32.0,2202.0,674.0,2178.0,635.0,2.0307,226700.0,<1H OCEAN\n-118.31,34.09,36.0,2517.0,842.0,2446.0,689.0,2.1524,187500.0,<1H OCEAN\n-118.31,34.09,30.0,3165.0,1263.0,3678.0,1141.0,2.0,240600.0,<1H OCEAN\n-118.31,34.09,42.0,1951.0,846.0,2500.0,813.0,1.5195,218200.0,<1H OCEAN\n-118.32,34.09,44.0,2666.0,,2297.0,726.0,1.676,208800.0,<1H OCEAN\n-118.32,34.09,30.0,1871.0,766.0,2595.0,819.0,2.0044,212500.0,<1H OCEAN\n-118.32,34.09,28.0,2173.0,819.0,2548.0,763.0,1.879,218800.0,<1H OCEAN\n-118.33,34.09,36.0,654.0,186.0,416.0,138.0,3.6953,200000.0,<1H OCEAN\n-118.33,34.09,36.0,561.0,180.0,340.0,127.0,1.4375,165000.0,<1H OCEAN\n-118.33,34.09,40.0,2004.0,687.0,1514.0,542.0,1.9911,220000.0,<1H OCEAN\n-118.34,34.09,5.0,2665.0,954.0,1733.0,766.0,2.3568,204700.0,<1H OCEAN\n-118.34,34.09,14.0,3032.0,999.0,1691.0,841.0,2.2,210000.0,<1H OCEAN\n-118.34,34.08,50.0,3457.0,854.0,1584.0,841.0,3.1078,346700.0,<1H OCEAN\n-118.34,34.09,52.0,1731.0,502.0,849.0,466.0,3.2946,321600.0,<1H OCEAN\n-118.34,34.08,52.0,1430.0,186.0,547.0,178.0,10.3661,500001.0,<1H OCEAN\n-118.35,34.09,47.0,1800.0,546.0,921.0,478.0,2.8021,280600.0,<1H OCEAN\n-118.35,34.08,52.0,1003.0,200.0,514.0,204.0,3.8472,395700.0,<1H OCEAN\n-118.35,34.08,52.0,2088.0,388.0,908.0,375.0,3.8141,342000.0,<1H OCEAN\n-118.35,34.09,42.0,2210.0,643.0,1228.0,605.0,2.5982,315800.0,<1H OCEAN\n-118.35,34.08,52.0,1710.0,350.0,727.0,355.0,4.5833,333900.0,<1H OCEAN\n-118.36,34.08,52.0,1965.0,480.0,794.0,451.0,3.2824,304800.0,<1H OCEAN\n-118.32,34.08,52.0,1164.0,257.0,575.0,251.0,3.125,380400.0,<1H OCEAN\n-118.33,34.08,52.0,1777.0,454.0,671.0,439.0,3.5083,500001.0,<1H OCEAN\n-118.33,34.08,50.0,2989.0,832.0,1345.0,775.0,3.2426,442900.0,<1H OCEAN\n-118.31,34.08,30.0,1390.0,457.0,1460.0,423.0,2.2422,254500.0,<1H OCEAN\n-118.31,34.08,31.0,2275.0,823.0,2189.0,720.0,1.7542,287500.0,<1H OCEAN\n-118.32,34.08,46.0,2038.0,534.0,1250.0,525.0,2.4196,358100.0,<1H OCEAN\n-118.31,34.08,23.0,1443.0,521.0,1264.0,450.0,2.7543,220000.0,<1H OCEAN\n-118.32,34.08,52.0,1137.0,304.0,754.0,297.0,3.37,330300.0,<1H OCEAN\n-118.32,34.08,52.0,2370.0,473.0,1053.0,434.0,4.1429,380300.0,<1H OCEAN\n-118.3,34.08,34.0,2501.0,1047.0,3326.0,970.0,1.8771,247500.0,<1H OCEAN\n-118.3,34.08,36.0,1276.0,503.0,1502.0,450.0,2.1766,205600.0,<1H OCEAN\n-118.31,34.08,27.0,1514.0,510.0,1603.0,518.0,2.8971,251100.0,<1H OCEAN\n-118.31,34.08,26.0,1609.0,534.0,1868.0,497.0,2.7038,227100.0,<1H OCEAN\n-118.29,34.08,34.0,479.0,182.0,557.0,170.0,1.525,210000.0,<1H OCEAN\n-118.29,34.08,23.0,1864.0,937.0,2795.0,858.0,1.8495,212500.0,<1H OCEAN\n-118.3,34.08,34.0,1562.0,651.0,1774.0,559.0,1.5685,225000.0,<1H OCEAN\n-118.3,34.08,32.0,2880.0,1063.0,3646.0,1028.0,2.3846,258300.0,<1H OCEAN\n-118.29,34.08,25.0,2459.0,823.0,2635.0,763.0,2.4,173900.0,<1H OCEAN\n-118.29,34.08,38.0,380.0,130.0,445.0,140.0,1.9286,137500.0,<1H OCEAN\n-118.37,34.12,34.0,2821.0,399.0,843.0,391.0,11.615,500001.0,<1H OCEAN\n-118.36,34.12,26.0,3902.0,610.0,1468.0,632.0,8.5136,500001.0,<1H OCEAN\n-118.35,34.11,33.0,7478.0,1678.0,2701.0,1500.0,4.1717,500001.0,<1H OCEAN\n-118.36,34.11,35.0,3946.0,695.0,1361.0,620.0,6.5195,500001.0,<1H OCEAN\n-118.38,34.11,38.0,2601.0,523.0,870.0,474.0,7.1134,416700.0,<1H OCEAN\n-118.37,34.11,42.0,5518.0,979.0,1863.0,957.0,8.5842,500001.0,<1H OCEAN\n-118.36,34.1,36.0,2963.0,838.0,1129.0,745.0,2.5588,500001.0,<1H OCEAN\n-118.37,34.1,37.0,407.0,67.0,100.0,47.0,15.0001,500001.0,<1H OCEAN\n-118.38,34.1,39.0,3798.0,586.0,975.0,525.0,9.3092,500001.0,<1H OCEAN\n-118.39,34.1,30.0,6310.0,957.0,1776.0,880.0,8.944,500001.0,<1H OCEAN\n-118.39,34.09,41.0,730.0,126.0,230.0,125.0,4.3214,500001.0,<1H OCEAN\n-118.36,34.09,38.0,2158.0,582.0,1061.0,577.0,2.9643,355300.0,<1H OCEAN\n-118.36,34.08,40.0,3110.0,764.0,1557.0,763.0,1.9937,367100.0,<1H OCEAN\n-118.37,34.08,52.0,2946.0,695.0,1258.0,650.0,3.9783,374100.0,<1H OCEAN\n-118.37,34.08,22.0,3008.0,938.0,1224.0,816.0,3.2149,300000.0,<1H OCEAN\n-118.37,34.09,31.0,2697.0,706.0,1059.0,689.0,2.8942,500001.0,<1H OCEAN\n-118.36,34.08,45.0,2195.0,483.0,1265.0,455.0,3.3864,397900.0,<1H OCEAN\n-118.37,34.08,52.0,1466.0,254.0,600.0,253.0,5.7524,393600.0,<1H OCEAN\n-118.37,34.08,52.0,1234.0,223.0,543.0,213.0,6.0338,423700.0,<1H OCEAN\n-118.26,34.1,48.0,2566.0,571.0,1421.0,563.0,3.6579,318600.0,<1H OCEAN\n-118.26,34.1,52.0,1310.0,263.0,689.0,208.0,4.0721,350000.0,<1H OCEAN\n-118.27,34.1,51.0,3149.0,519.0,1082.0,510.0,6.4459,421600.0,<1H OCEAN\n-118.27,34.1,41.0,3729.0,740.0,1364.0,707.0,5.7778,412700.0,<1H OCEAN\n-118.27,34.11,41.0,4138.0,1130.0,1859.0,1030.0,2.978,306800.0,<1H OCEAN\n-118.28,34.11,46.0,1156.0,203.0,514.0,213.0,4.2019,352100.0,<1H OCEAN\n-118.28,34.11,52.0,2036.0,348.0,775.0,332.0,5.4122,397500.0,<1H OCEAN\n-118.28,34.11,52.0,1803.0,437.0,787.0,388.0,4.5781,360500.0,<1H OCEAN\n-118.28,34.1,44.0,2728.0,585.0,1227.0,567.0,4.0602,324000.0,<1H OCEAN\n-118.27,34.1,50.0,2113.0,398.0,793.0,418.0,4.7132,304600.0,<1H OCEAN\n-118.28,34.1,49.0,1767.0,467.0,1066.0,438.0,3.0958,210900.0,<1H OCEAN\n-118.28,34.1,48.0,805.0,246.0,633.0,235.0,2.3421,200000.0,<1H OCEAN\n-118.28,34.1,49.0,2843.0,880.0,2004.0,796.0,2.7875,217300.0,<1H OCEAN\n-118.27,34.09,48.0,1527.0,295.0,589.0,286.0,4.1667,344800.0,<1H OCEAN\n-118.27,34.09,52.0,3225.0,763.0,1559.0,710.0,3.9674,268800.0,<1H OCEAN\n-118.28,34.09,52.0,2273.0,663.0,1480.0,597.0,2.3333,196500.0,<1H OCEAN\n-118.27,34.09,52.0,2327.0,555.0,1048.0,491.0,3.7847,252300.0,<1H OCEAN\n-118.26,34.09,51.0,1532.0,366.0,669.0,333.0,3.6434,278800.0,<1H OCEAN\n-118.26,34.09,36.0,3503.0,833.0,2652.0,788.0,3.8448,241400.0,<1H OCEAN\n-118.27,34.09,52.0,1079.0,222.0,439.0,201.0,4.625,230800.0,<1H OCEAN\n-118.27,34.09,44.0,3646.0,912.0,1783.0,861.0,2.9709,225000.0,<1H OCEAN\n-118.26,34.08,52.0,984.0,276.0,994.0,260.0,2.3816,166700.0,<1H OCEAN\n-118.26,34.08,41.0,1396.0,360.0,1069.0,363.0,2.4375,203300.0,<1H OCEAN\n-118.26,34.08,46.0,945.0,250.0,910.0,252.0,3.5039,187500.0,<1H OCEAN\n-118.27,34.08,35.0,1147.0,365.0,1016.0,296.0,2.3913,198800.0,<1H OCEAN\n-118.26,34.07,36.0,2306.0,813.0,2823.0,765.0,2.0214,170500.0,<1H OCEAN\n-118.26,34.07,28.0,579.0,184.0,673.0,202.0,2.625,187500.0,<1H OCEAN\n-118.27,34.07,27.0,1190.0,,1795.0,422.0,1.7016,160000.0,<1H OCEAN\n-118.27,34.08,38.0,2265.0,801.0,2899.0,792.0,2.5521,157500.0,<1H OCEAN\n-118.27,34.08,46.0,3007.0,854.0,2587.0,814.0,2.7179,184300.0,<1H OCEAN\n-118.28,34.08,42.0,997.0,374.0,982.0,372.0,2.9423,200000.0,<1H OCEAN\n-118.28,34.08,52.0,2465.0,773.0,2328.0,746.0,2.6178,203100.0,<1H OCEAN\n-118.27,34.08,43.0,962.0,253.0,658.0,244.0,3.2386,185000.0,<1H OCEAN\n-118.27,34.07,38.0,1270.0,556.0,1692.0,450.0,1.87,170800.0,<1H OCEAN\n-118.28,34.08,42.0,1618.0,522.0,1454.0,440.0,3.1607,182000.0,<1H OCEAN\n-118.28,34.09,52.0,1739.0,464.0,938.0,482.0,2.4429,228800.0,<1H OCEAN\n-118.28,34.08,39.0,3162.0,896.0,1934.0,818.0,2.875,213500.0,<1H OCEAN\n-118.28,34.08,40.0,1630.0,543.0,1568.0,510.0,2.7366,169100.0,<1H OCEAN\n-118.28,34.09,49.0,3828.0,1197.0,2862.0,1009.0,2.4677,219200.0,<1H OCEAN\n-118.23,34.07,35.0,1335.0,440.0,1586.0,445.0,1.9722,156300.0,<1H OCEAN\n-118.23,34.07,40.0,506.0,119.0,397.0,114.0,3.1944,143800.0,<1H OCEAN\n-118.24,34.08,52.0,109.0,20.0,86.0,24.0,4.9844,187500.0,<1H OCEAN\n-118.24,34.08,52.0,137.0,26.0,65.0,24.0,4.025,137500.0,<1H OCEAN\n-118.23,34.09,47.0,859.0,239.0,913.0,234.0,2.6442,136100.0,<1H OCEAN\n-118.23,34.09,49.0,1638.0,456.0,1500.0,430.0,2.6923,150000.0,<1H OCEAN\n-118.23,34.09,45.0,1747.0,484.0,1680.0,441.0,2.6051,155500.0,<1H OCEAN\n-118.25,34.09,52.0,2050.0,429.0,957.0,418.0,3.5603,210000.0,<1H OCEAN\n-118.25,34.09,52.0,104.0,20.0,32.0,17.0,3.75,241700.0,<1H OCEAN\n-118.25,34.08,44.0,1425.0,438.0,1121.0,374.0,2.1108,200000.0,<1H OCEAN\n-118.25,34.08,47.0,2133.0,689.0,2104.0,662.0,2.6136,169200.0,<1H OCEAN\n-118.25,34.09,43.0,1903.0,635.0,1919.0,613.0,2.6336,174300.0,<1H OCEAN\n-118.25,34.09,52.0,1866.0,470.0,1211.0,417.0,2.935,189400.0,<1H OCEAN\n-118.26,34.08,50.0,1791.0,660.0,2183.0,675.0,1.7945,166700.0,<1H OCEAN\n-118.26,34.08,45.0,2174.0,627.0,1992.0,557.0,2.5428,167800.0,<1H OCEAN\n-118.25,34.09,52.0,3142.0,765.0,1728.0,682.0,3.1864,189800.0,<1H OCEAN\n-118.26,34.08,45.0,2225.0,827.0,2699.0,792.0,1.9209,179200.0,<1H OCEAN\n-118.26,34.07,46.0,2341.0,703.0,2371.0,648.0,2.4038,181700.0,<1H OCEAN\n-118.26,34.07,52.0,830.0,200.0,701.0,189.0,2.7625,232100.0,<1H OCEAN\n-118.25,34.07,47.0,2059.0,618.0,2033.0,544.0,1.9028,217900.0,<1H OCEAN\n-118.25,34.07,43.0,764.0,322.0,1275.0,306.0,2.0,175000.0,<1H OCEAN\n-118.25,34.06,20.0,41.0,17.0,87.0,25.0,1.5491,225000.0,<1H OCEAN\n-118.24,34.07,27.0,223.0,80.0,249.0,82.0,1.6136,137500.0,<1H OCEAN\n-118.25,34.07,18.0,4297.0,1420.0,4332.0,1286.0,2.2545,192500.0,<1H OCEAN\n-118.25,34.07,16.0,719.0,225.0,801.0,218.0,2.3942,133300.0,<1H OCEAN\n-118.22,34.09,40.0,1081.0,282.0,970.0,263.0,1.875,150000.0,<1H OCEAN\n-118.22,34.08,31.0,394.0,117.0,573.0,131.0,1.8173,154200.0,<1H OCEAN\n-118.22,34.08,34.0,1709.0,562.0,2105.0,503.0,1.9704,152100.0,<1H OCEAN\n-118.19,34.08,38.0,1241.0,298.0,1055.0,263.0,2.3409,115500.0,<1H OCEAN\n-118.2,34.08,49.0,1320.0,309.0,1405.0,328.0,2.4375,114000.0,<1H OCEAN\n-118.2,34.07,21.0,1353.0,380.0,1688.0,367.0,1.9937,139600.0,<1H OCEAN\n-118.2,34.07,34.0,1765.0,551.0,2203.0,500.0,2.2708,159600.0,<1H OCEAN\n-118.21,34.08,39.0,986.0,361.0,1347.0,299.0,2.2907,133900.0,<1H OCEAN\n-118.21,34.08,26.0,2574.0,807.0,3163.0,802.0,1.9495,173200.0,<1H OCEAN\n-118.2,34.09,39.0,1594.0,430.0,1668.0,378.0,2.5343,138200.0,<1H OCEAN\n-118.2,34.08,41.0,1807.0,429.0,1699.0,424.0,2.2222,126000.0,<1H OCEAN\n-118.19,34.1,39.0,2054.0,423.0,1205.0,403.0,4.239,213000.0,<1H OCEAN\n-118.21,34.08,52.0,3672.0,808.0,3062.0,764.0,2.6806,153000.0,<1H OCEAN\n-118.21,34.09,39.0,1287.0,353.0,1171.0,345.0,1.6118,138500.0,<1H OCEAN\n-118.21,34.09,39.0,1561.0,445.0,1780.0,391.0,2.4632,144200.0,<1H OCEAN\n-118.21,34.09,37.0,1822.0,498.0,1961.0,506.0,1.9881,159200.0,<1H OCEAN\n-118.22,34.07,35.0,1504.0,477.0,2059.0,498.0,2.0133,145800.0,<1H OCEAN\n-118.22,34.07,36.0,839.0,250.0,1079.0,245.0,1.7463,158300.0,<1H OCEAN\n-118.21,34.07,31.0,1077.0,300.0,1198.0,274.0,2.1333,160200.0,<1H OCEAN\n-118.21,34.07,52.0,1770.0,,1848.0,439.0,2.4135,167200.0,<1H OCEAN\n-118.21,34.07,47.0,1346.0,383.0,1452.0,371.0,1.7292,191700.0,<1H OCEAN\n-118.21,34.07,31.0,1453.0,404.0,1486.0,389.0,2.3859,153100.0,<1H OCEAN\n-118.21,34.07,42.0,902.0,318.0,1312.0,323.0,1.9375,168800.0,<1H OCEAN\n-118.21,34.06,29.0,1478.0,413.0,1580.0,394.0,1.8781,147500.0,<1H OCEAN\n-118.16,34.09,36.0,3334.0,920.0,2881.0,800.0,2.1696,170800.0,<1H OCEAN\n-118.17,34.1,48.0,2514.0,595.0,2484.0,601.0,3.1146,142500.0,<1H OCEAN\n-118.17,34.1,37.0,299.0,89.0,318.0,92.0,1.3125,145800.0,<1H OCEAN\n-118.17,34.09,45.0,1327.0,271.0,1069.0,284.0,3.3977,153800.0,<1H OCEAN\n-118.18,34.09,40.0,2744.0,708.0,2747.0,674.0,2.6226,148800.0,<1H OCEAN\n-118.18,34.09,44.0,1688.0,426.0,1605.0,384.0,3.3785,184900.0,<1H OCEAN\n-118.19,34.09,41.0,2090.0,530.0,2043.0,537.0,1.9706,144200.0,<1H OCEAN\n-118.18,34.1,10.0,1907.0,398.0,921.0,369.0,4.875,200400.0,<1H OCEAN\n-118.18,34.1,10.0,1940.0,445.0,763.0,412.0,4.975,166700.0,<1H OCEAN\n-118.18,34.1,7.0,2529.0,689.0,1215.0,577.0,4.7853,153100.0,<1H OCEAN\n-118.18,34.1,8.0,1116.0,267.0,435.0,235.0,4.9231,230900.0,<1H OCEAN\n-118.18,34.08,35.0,2226.0,602.0,2230.0,549.0,2.9167,129300.0,<1H OCEAN\n-118.19,34.08,35.0,1554.0,381.0,1487.0,374.0,1.9038,139500.0,<1H OCEAN\n-118.19,34.07,38.0,2965.0,665.0,2128.0,650.0,3.0241,166300.0,<1H OCEAN\n-118.19,34.07,42.0,1555.0,337.0,1152.0,348.0,3.375,169600.0,<1H OCEAN\n-118.19,34.06,44.0,1734.0,364.0,1133.0,351.0,2.5132,163100.0,<1H OCEAN\n-118.16,34.09,33.0,1515.0,415.0,1345.0,346.0,2.375,175000.0,<1H OCEAN\n-118.16,34.09,50.0,1568.0,302.0,1093.0,333.0,3.1442,162100.0,<1H OCEAN\n-118.17,34.09,36.0,3066.0,797.0,3097.0,780.0,2.5523,156500.0,<1H OCEAN\n-118.17,34.09,33.0,2907.0,797.0,3212.0,793.0,2.2348,146600.0,<1H OCEAN\n-118.18,34.08,31.0,1318.0,311.0,1164.0,289.0,2.9939,135500.0,<1H OCEAN\n-118.18,34.08,33.0,1369.0,408.0,1475.0,377.0,2.3281,151900.0,<1H OCEAN\n-118.16,34.08,43.0,1523.0,378.0,1338.0,352.0,3.2031,144600.0,<1H OCEAN\n-118.17,34.08,39.0,787.0,181.0,731.0,179.0,3.2279,158500.0,<1H OCEAN\n-118.17,34.08,33.0,851.0,231.0,936.0,228.0,3.375,147500.0,<1H OCEAN\n-118.18,34.07,28.0,2616.0,630.0,2218.0,621.0,2.6842,156000.0,<1H OCEAN\n-118.17,34.07,19.0,2150.0,544.0,1510.0,467.0,3.4952,150000.0,<1H OCEAN\n-118.16,34.07,41.0,247.0,55.0,925.0,50.0,3.5769,135700.0,<1H OCEAN\n-118.17,34.07,37.0,1155.0,225.0,814.0,241.0,3.875,148500.0,<1H OCEAN\n-118.18,34.06,27.0,2025.0,565.0,2189.0,577.0,2.6083,148600.0,<1H OCEAN\n-118.19,34.06,32.0,555.0,159.0,748.0,163.0,1.9762,137500.0,<1H OCEAN\n-118.19,34.06,47.0,2324.0,658.0,3020.0,594.0,1.1868,93800.0,<1H OCEAN\n-118.2,34.06,46.0,453.0,119.0,533.0,132.0,2.2961,112500.0,<1H OCEAN\n-118.2,34.06,40.0,1181.0,335.0,1441.0,337.0,2.1136,111800.0,<1H OCEAN\n-118.2,34.05,8.0,762.0,204.0,728.0,174.0,2.4886,137500.0,<1H OCEAN\n-118.19,34.05,46.0,1051.0,302.0,1435.0,305.0,1.6667,133600.0,<1H OCEAN\n-118.2,34.05,36.0,2672.0,675.0,2883.0,674.0,2.0885,142800.0,<1H OCEAN\n-118.19,34.05,37.0,349.0,79.0,276.0,64.0,3.2125,125000.0,<1H OCEAN\n-118.2,34.06,46.0,321.0,101.0,401.0,86.0,2.1029,109400.0,<1H OCEAN\n-118.21,34.06,30.0,511.0,153.0,1152.0,149.0,2.3611,156800.0,<1H OCEAN\n-118.22,34.06,52.0,48.0,6.0,41.0,10.0,10.2264,112500.0,<1H OCEAN\n-118.22,34.05,36.0,1243.0,470.0,1668.0,444.0,1.0714,137500.0,<1H OCEAN\n-118.21,34.06,52.0,470.0,115.0,434.0,123.0,2.095,109100.0,<1H OCEAN\n-118.22,34.06,34.0,1083.0,364.0,1132.0,338.0,2.2344,153100.0,<1H OCEAN\n-118.22,34.05,34.0,1113.0,,928.0,290.0,3.1654,155000.0,<1H OCEAN\n-118.22,34.05,41.0,1422.0,478.0,1640.0,434.0,1.6122,157100.0,<1H OCEAN\n-118.21,34.05,26.0,745.0,258.0,694.0,236.0,1.3846,129200.0,<1H OCEAN\n-118.21,34.05,28.0,1079.0,306.0,1358.0,285.0,2.52,131900.0,<1H OCEAN\n-118.21,34.05,28.0,950.0,357.0,1485.0,345.0,1.9271,136400.0,<1H OCEAN\n-118.21,34.05,45.0,2146.0,607.0,2868.0,625.0,2.121,144000.0,<1H OCEAN\n-118.2,34.05,43.0,1165.0,317.0,1279.0,303.0,1.9615,141700.0,<1H OCEAN\n-118.2,34.05,40.0,1146.0,323.0,1354.0,321.0,1.9205,121900.0,<1H OCEAN\n-118.2,34.05,40.0,1082.0,318.0,1085.0,273.0,1.7054,117200.0,<1H OCEAN\n-118.2,34.05,42.0,1703.0,586.0,2490.0,581.0,2.02,147200.0,<1H OCEAN\n-118.2,34.05,41.0,1268.0,398.0,1887.0,407.0,2.625,150000.0,<1H OCEAN\n-118.19,34.05,43.0,977.0,266.0,1084.0,259.0,2.7708,127900.0,<1H OCEAN\n-118.19,34.05,41.0,1098.0,264.0,1178.0,245.0,2.1058,124300.0,<1H OCEAN\n-118.2,34.05,50.0,1407.0,401.0,1526.0,385.0,2.29,121800.0,<1H OCEAN\n-118.19,34.04,45.0,963.0,234.0,1194.0,239.0,2.1806,134900.0,<1H OCEAN\n-118.19,34.04,40.0,1095.0,305.0,1322.0,281.0,1.9688,150000.0,<1H OCEAN\n-118.19,34.03,52.0,1053.0,246.0,1036.0,249.0,2.1071,136700.0,<1H OCEAN\n-118.19,34.03,50.0,1183.0,246.0,851.0,231.0,3.2639,142600.0,<1H OCEAN\n-118.2,34.04,52.0,1249.0,307.0,1223.0,297.0,2.07,136300.0,<1H OCEAN\n-118.2,34.04,47.0,1894.0,408.0,1629.0,379.0,3.7619,127600.0,<1H OCEAN\n-118.2,34.03,52.0,1754.0,452.0,1849.0,445.0,2.3716,122800.0,<1H OCEAN\n-118.2,34.04,44.0,1399.0,386.0,1419.0,373.0,1.8224,143800.0,<1H OCEAN\n-118.2,34.04,36.0,1625.0,490.0,2003.0,478.0,2.181,147200.0,<1H OCEAN\n-118.2,34.04,44.0,1582.0,544.0,1998.0,515.0,1.6888,125000.0,<1H OCEAN\n-118.2,34.04,18.0,796.0,227.0,547.0,218.0,1.0333,135400.0,<1H OCEAN\n-118.21,34.04,36.0,1825.0,479.0,2097.0,480.0,2.1862,135300.0,<1H OCEAN\n-118.21,34.04,37.0,845.0,249.0,881.0,252.0,2.2454,165000.0,<1H OCEAN\n-118.21,34.04,47.0,1325.0,393.0,1557.0,352.0,2.8,148400.0,<1H OCEAN\n-118.21,34.05,28.0,1841.0,809.0,3199.0,727.0,1.6319,151600.0,<1H OCEAN\n-118.21,34.04,52.0,846.0,271.0,1153.0,281.0,2.1923,155000.0,<1H OCEAN\n-118.22,34.05,43.0,1153.0,411.0,1667.0,409.0,1.9402,139300.0,<1H OCEAN\n-118.21,34.05,47.0,722.0,235.0,930.0,226.0,2.5455,114300.0,<1H OCEAN\n-118.22,34.04,43.0,798.0,308.0,1417.0,325.0,1.4189,141700.0,<1H OCEAN\n-118.22,34.05,44.0,1105.0,346.0,1598.0,372.0,1.2,115600.0,<1H OCEAN\n-118.22,34.04,43.0,2343.0,803.0,2468.0,707.0,1.5163,115000.0,<1H OCEAN\n-118.21,34.04,43.0,1502.0,477.0,1844.0,477.0,1.9405,152500.0,<1H OCEAN\n-118.21,34.04,47.0,1306.0,391.0,1499.0,346.0,2.2788,139600.0,<1H OCEAN\n-118.22,34.03,45.0,554.0,214.0,888.0,218.0,1.8125,139600.0,<1H OCEAN\n-118.21,34.03,45.0,1860.0,472.0,1893.0,456.0,2.6573,141800.0,<1H OCEAN\n-118.21,34.03,44.0,1550.0,407.0,1718.0,403.0,2.5268,141100.0,<1H OCEAN\n-118.21,34.03,52.0,497.0,132.0,547.0,121.0,2.2083,146300.0,<1H OCEAN\n-118.21,34.03,47.0,876.0,228.0,872.0,231.0,2.2656,145000.0,<1H OCEAN\n-118.2,34.03,41.0,1292.0,334.0,1150.0,322.0,1.925,135200.0,<1H OCEAN\n-118.2,34.02,49.0,1098.0,317.0,1411.0,301.0,2.75,146000.0,<1H OCEAN\n-118.2,34.03,52.0,774.0,209.0,813.0,203.0,2.3472,135200.0,<1H OCEAN\n-118.2,34.03,37.0,1583.0,392.0,1776.0,377.0,2.7266,140800.0,<1H OCEAN\n-118.2,34.03,52.0,583.0,157.0,730.0,174.0,1.4115,140600.0,<1H OCEAN\n-118.21,34.02,45.0,792.0,203.0,872.0,188.0,2.6875,129700.0,<1H OCEAN\n-118.19,34.02,44.0,2702.0,770.0,3260.0,721.0,2.3575,144800.0,<1H OCEAN\n-118.2,34.02,48.0,2230.0,593.0,2419.0,598.0,2.3944,130700.0,<1H OCEAN\n-118.2,34.02,26.0,36.0,9.0,35.0,9.0,1.625,175000.0,<1H OCEAN\n-118.2,34.02,42.0,498.0,120.0,548.0,119.0,3.7543,126600.0,<1H OCEAN\n-118.21,34.02,52.0,22.0,7.0,55.0,7.0,7.5752,67500.0,<1H OCEAN\n-118.21,34.02,43.0,1811.0,513.0,2123.0,487.0,1.3615,133300.0,<1H OCEAN\n-118.23,34.05,52.0,346.0,270.0,346.0,251.0,2.5313,225000.0,<1H OCEAN\n-118.24,34.05,13.0,1703.0,697.0,1823.0,669.0,0.8288,181300.0,<1H OCEAN\n-118.24,34.04,52.0,116.0,107.0,171.0,92.0,1.0769,112500.0,<1H OCEAN\n-118.24,34.06,33.0,390.0,199.0,435.0,193.0,1.1979,350000.0,<1H OCEAN\n-118.24,34.06,8.0,1204.0,552.0,1074.0,517.0,1.0227,87500.0,<1H OCEAN\n-118.24,34.06,19.0,2870.0,1021.0,3325.0,978.0,1.7395,162500.0,<1H OCEAN\n-118.25,34.05,52.0,2806.0,1944.0,2232.0,1605.0,0.6775,350000.0,<1H OCEAN\n-118.25,34.05,8.0,3105.0,1256.0,1086.0,997.0,0.8131,275000.0,<1H OCEAN\n-118.25,34.06,12.0,4011.0,1438.0,1673.0,1088.0,5.3081,287500.0,<1H OCEAN\n-118.26,34.05,52.0,58.0,52.0,41.0,27.0,4.0972,500001.0,<1H OCEAN\n-118.26,34.04,6.0,1529.0,566.0,1051.0,473.0,2.462,162500.0,<1H OCEAN\n-118.25,34.06,52.0,174.0,66.0,249.0,57.0,1.7763,312500.0,<1H OCEAN\n-118.26,34.06,15.0,326.0,123.0,490.0,105.0,1.4886,175000.0,<1H OCEAN\n-118.26,34.06,40.0,637.0,273.0,1150.0,263.0,1.8625,131300.0,<1H OCEAN\n-118.26,34.07,52.0,1802.0,613.0,2382.0,587.0,1.8438,185900.0,<1H OCEAN\n-118.26,34.07,40.0,680.0,273.0,995.0,249.0,2.2607,165600.0,<1H OCEAN\n-118.26,34.07,30.0,929.0,238.0,763.0,214.0,2.5227,187500.0,<1H OCEAN\n-118.26,34.06,38.0,715.0,282.0,1174.0,300.0,2.345,225000.0,<1H OCEAN\n-118.26,34.06,42.0,2541.0,1282.0,3974.0,1189.0,1.5854,87500.0,<1H OCEAN\n-118.27,34.07,42.0,1175.0,428.0,1593.0,407.0,2.3438,213300.0,<1H OCEAN\n-118.27,34.06,30.0,1771.0,788.0,2188.0,764.0,1.5885,154200.0,<1H OCEAN\n-118.27,34.07,34.0,786.0,341.0,1239.0,320.0,2.3438,152100.0,<1H OCEAN\n-118.27,34.07,32.0,1657.0,579.0,2071.0,598.0,2.1135,152500.0,<1H OCEAN\n-118.27,34.06,33.0,1416.0,686.0,2013.0,614.0,1.9818,208300.0,<1H OCEAN\n-118.27,34.07,33.0,1177.0,468.0,1533.0,430.0,2.3981,183300.0,<1H OCEAN\n-118.28,34.07,41.0,1072.0,331.0,1111.0,314.0,1.9233,207100.0,<1H OCEAN\n-118.28,34.07,24.0,3247.0,1281.0,2642.0,1182.0,2.4632,216700.0,<1H OCEAN\n-118.28,34.07,14.0,1924.0,926.0,2226.0,792.0,2.2552,265900.0,<1H OCEAN\n-118.28,34.07,32.0,1777.0,536.0,1684.0,489.0,2.3636,190000.0,<1H OCEAN\n-118.28,34.07,25.0,7522.0,3179.0,7221.0,2902.0,2.0173,177500.0,<1H OCEAN\n-118.28,34.06,14.0,1787.0,853.0,2251.0,763.0,1.1642,400000.0,<1H OCEAN\n-118.28,34.06,17.0,2518.0,1196.0,3051.0,1000.0,1.7199,175000.0,<1H OCEAN\n-118.27,34.06,17.0,2124.0,1168.0,3915.0,1137.0,1.1346,137500.0,<1H OCEAN\n-118.27,34.06,25.0,1714.0,1176.0,3723.0,1036.0,1.3241,112500.0,<1H OCEAN\n-118.27,34.06,45.0,564.0,353.0,1172.0,319.0,1.494,187500.0,<1H OCEAN\n-118.27,34.06,26.0,513.0,338.0,1204.0,321.0,1.4904,275000.0,<1H OCEAN\n-118.26,34.06,33.0,1950.0,1047.0,3707.0,1012.0,1.7238,110000.0,<1H OCEAN\n-118.27,34.05,37.0,350.0,245.0,1122.0,248.0,2.7634,137500.0,<1H OCEAN\n-118.27,34.05,52.0,1292.0,864.0,2081.0,724.0,0.9563,275000.0,<1H OCEAN\n-118.28,34.06,52.0,1261.0,616.0,2309.0,581.0,1.6184,225000.0,<1H OCEAN\n-118.28,34.06,52.0,936.0,454.0,990.0,354.0,1.1122,187500.0,<1H OCEAN\n-118.28,34.06,42.0,2472.0,,3795.0,1179.0,1.2254,162500.0,<1H OCEAN\n-118.27,34.05,25.0,1316.0,836.0,2796.0,784.0,1.7866,325000.0,<1H OCEAN\n-118.27,34.05,26.0,1164.0,674.0,1685.0,541.0,1.5727,225000.0,<1H OCEAN\n-118.28,34.05,28.0,1306.0,637.0,2079.0,598.0,1.4615,275000.0,<1H OCEAN\n-118.28,34.05,31.0,1525.0,730.0,2510.0,652.0,1.6355,162500.0,<1H OCEAN\n-118.28,34.05,41.0,1788.0,774.0,2931.0,702.0,1.4413,158900.0,<1H OCEAN\n-118.28,34.05,41.0,1075.0,597.0,2260.0,614.0,1.3,162500.0,<1H OCEAN\n-118.28,34.05,44.0,968.0,384.0,1805.0,375.0,1.4801,212500.0,<1H OCEAN\n-118.27,34.05,47.0,661.0,359.0,1406.0,307.0,1.3169,112500.0,<1H OCEAN\n-118.27,34.04,13.0,1784.0,,2158.0,682.0,1.7038,118100.0,<1H OCEAN\n-118.27,34.05,24.0,323.0,214.0,751.0,189.0,1.8304,225000.0,<1H OCEAN\n-118.27,34.05,12.0,535.0,328.0,1194.0,365.0,1.2012,275000.0,<1H OCEAN\n-118.32,34.07,52.0,2156.0,306.0,861.0,311.0,8.8062,500001.0,<1H OCEAN\n-118.33,34.06,52.0,1841.0,240.0,693.0,218.0,15.0001,500001.0,<1H OCEAN\n-118.33,34.07,52.0,2248.0,255.0,813.0,265.0,15.0001,500001.0,<1H OCEAN\n-118.33,34.07,52.0,1482.0,171.0,531.0,161.0,15.0001,500001.0,<1H OCEAN\n-118.34,34.06,52.0,2089.0,309.0,883.0,281.0,7.4574,500001.0,<1H OCEAN\n-118.34,34.06,52.0,1311.0,310.0,707.0,290.0,3.4812,432800.0,<1H OCEAN\n-118.29,34.08,49.0,649.0,315.0,987.0,329.0,1.6806,316700.0,<1H OCEAN\n-118.29,34.07,19.0,3013.0,1118.0,2465.0,1008.0,2.5386,290600.0,<1H OCEAN\n-118.29,34.06,9.0,1554.0,815.0,1704.0,761.0,2.0185,141700.0,<1H OCEAN\n-118.29,34.07,22.0,492.0,269.0,634.0,261.0,1.6406,300000.0,<1H OCEAN\n-118.29,34.07,26.0,2302.0,1124.0,2660.0,1004.0,2.3567,253100.0,<1H OCEAN\n-118.29,34.08,43.0,3056.0,1345.0,3920.0,1304.0,1.925,300000.0,<1H OCEAN\n-118.3,34.07,31.0,1489.0,664.0,1793.0,556.0,2.4348,230600.0,<1H OCEAN\n-118.3,34.07,46.0,5677.0,2610.0,7443.0,2406.0,1.8238,237500.0,<1H OCEAN\n-118.3,34.07,26.0,2107.0,757.0,2660.0,740.0,2.3375,282300.0,<1H OCEAN\n-118.3,34.07,36.0,2657.0,738.0,2274.0,723.0,3.425,281700.0,<1H OCEAN\n-118.31,34.07,28.0,2362.0,949.0,2759.0,894.0,2.2364,305600.0,<1H OCEAN\n-118.31,34.08,49.0,2549.0,630.0,1539.0,594.0,2.6218,350900.0,<1H OCEAN\n-118.31,34.07,40.0,2657.0,794.0,2149.0,749.0,2.2653,445000.0,<1H OCEAN\n-118.32,34.07,52.0,2980.0,366.0,967.0,359.0,11.2185,500001.0,<1H OCEAN\n-118.31,34.06,36.0,369.0,147.0,145.0,136.0,0.8804,450000.0,<1H OCEAN\n-118.32,34.06,52.0,983.0,246.0,578.0,204.0,5.7393,500001.0,<1H OCEAN\n-118.32,34.07,25.0,2740.0,707.0,1420.0,664.0,3.5909,404500.0,<1H OCEAN\n-118.32,34.06,52.0,955.0,100.0,457.0,120.0,15.0001,500001.0,<1H OCEAN\n-118.31,34.07,26.0,5062.0,2055.0,4533.0,1822.0,2.3105,166700.0,<1H OCEAN\n-118.31,34.06,30.0,3110.0,1269.0,2535.0,1218.0,1.6987,412500.0,<1H OCEAN\n-118.3,34.07,18.0,3759.0,,3296.0,1462.0,2.2708,175000.0,<1H OCEAN\n-118.31,34.07,20.0,3264.0,1248.0,2919.0,1191.0,2.3674,500001.0,<1H OCEAN\n-118.3,34.06,20.0,1782.0,896.0,1749.0,823.0,2.2094,75000.0,<1H OCEAN\n-118.31,34.06,34.0,2470.0,1197.0,2326.0,1055.0,1.9038,325000.0,<1H OCEAN\n-118.29,34.07,24.0,4021.0,1707.0,3727.0,1529.0,1.7365,112500.0,<1H OCEAN\n-118.3,34.07,28.0,5221.0,2530.0,5840.0,2374.0,1.8829,300000.0,<1H OCEAN\n-118.3,34.06,43.0,1366.0,740.0,942.0,672.0,1.6953,150000.0,<1H OCEAN\n-118.29,34.06,42.0,3894.0,2293.0,6846.0,2156.0,1.5553,70000.0,<1H OCEAN\n-118.29,34.06,27.0,2456.0,1111.0,4137.0,1104.0,1.5954,187500.0,<1H OCEAN\n-118.29,34.06,46.0,1759.0,1012.0,2716.0,877.0,2.1637,350000.0,<1H OCEAN\n-118.3,34.06,47.0,1390.0,872.0,2860.0,827.0,1.468,137500.0,<1H OCEAN\n-118.29,34.06,23.0,2040.0,778.0,2235.0,697.0,1.9309,233300.0,<1H OCEAN\n-118.3,34.06,33.0,2437.0,1283.0,3906.0,1084.0,2.0332,270000.0,<1H OCEAN\n-118.3,34.06,21.0,3960.0,1490.0,3468.0,1335.0,1.8214,475000.0,<1H OCEAN\n-118.3,34.06,23.0,2512.0,1203.0,3720.0,1118.0,1.7896,322200.0,<1H OCEAN\n-118.31,34.06,24.0,1336.0,453.0,1268.0,426.0,2.8202,500001.0,<1H OCEAN\n-118.31,34.06,47.0,3038.0,1533.0,4225.0,1472.0,1.6725,187500.0,<1H OCEAN\n-118.31,34.06,34.0,1848.0,667.0,1351.0,589.0,2.0547,410000.0,<1H OCEAN\n-118.31,34.06,52.0,2124.0,756.0,1920.0,756.0,2.1435,328900.0,<1H OCEAN\n-118.31,34.06,31.0,2827.0,1084.0,3107.0,993.0,2.0278,360000.0,<1H OCEAN\n-118.31,34.06,14.0,1559.0,646.0,1639.0,567.0,1.9949,380000.0,<1H OCEAN\n-118.32,34.05,42.0,3292.0,713.0,2224.0,674.0,3.5517,291500.0,<1H OCEAN\n-118.32,34.06,43.0,2808.0,584.0,1654.0,569.0,4.125,436800.0,<1H OCEAN\n-118.32,34.06,36.0,3239.0,722.0,1383.0,612.0,4.5918,337000.0,<1H OCEAN\n-118.33,34.06,52.0,1368.0,231.0,737.0,248.0,8.3617,433800.0,<1H OCEAN\n-118.33,34.05,48.0,2405.0,527.0,1868.0,502.0,3.375,257800.0,<1H OCEAN\n-118.33,34.05,44.0,1574.0,390.0,1323.0,404.0,2.5284,226300.0,<1H OCEAN\n-118.33,34.05,45.0,1707.0,519.0,1446.0,466.0,2.1736,171300.0,<1H OCEAN\n-118.32,34.05,50.0,1389.0,364.0,976.0,302.0,1.5882,327300.0,<1H OCEAN\n-118.32,34.05,42.0,3343.0,1183.0,3480.0,1146.0,1.9923,250000.0,<1H OCEAN\n-118.31,34.05,35.0,1692.0,423.0,1578.0,406.0,2.5313,305800.0,<1H OCEAN\n-118.31,34.05,40.0,1667.0,365.0,1161.0,384.0,3.1406,417600.0,<1H OCEAN\n-118.3,34.05,44.0,1612.0,650.0,2028.0,593.0,1.9152,115600.0,<1H OCEAN\n-118.3,34.05,42.0,1476.0,610.0,1605.0,545.0,1.721,214300.0,<1H OCEAN\n-118.31,34.05,42.0,443.0,223.0,582.0,223.0,2.2937,350000.0,<1H OCEAN\n-118.3,34.05,46.0,1386.0,457.0,1845.0,485.0,2.1414,157700.0,<1H OCEAN\n-118.3,34.05,31.0,1744.0,720.0,2034.0,633.0,2.2684,193800.0,<1H OCEAN\n-118.29,34.05,30.0,1417.0,589.0,1615.0,540.0,1.3867,193800.0,<1H OCEAN\n-118.29,34.05,34.0,1102.0,,1325.0,439.0,1.5972,168800.0,<1H OCEAN\n-118.3,34.05,36.0,1723.0,569.0,1664.0,501.0,1.9323,161100.0,<1H OCEAN\n-118.3,34.05,34.0,1453.0,588.0,1987.0,589.0,2.096,187500.0,<1H OCEAN\n-118.29,34.05,18.0,3585.0,1661.0,5229.0,1534.0,1.847,250000.0,<1H OCEAN\n-118.29,34.05,11.0,677.0,370.0,1143.0,341.0,2.3864,350000.0,<1H OCEAN\n-118.29,34.05,31.0,2818.0,1252.0,4126.0,1200.0,2.053,229200.0,<1H OCEAN\n-118.35,34.08,52.0,1801.0,313.0,714.0,293.0,4.6838,479000.0,<1H OCEAN\n-118.35,34.07,52.0,2497.0,406.0,1030.0,412.0,4.89,500001.0,<1H OCEAN\n-118.35,34.07,52.0,2315.0,356.0,894.0,345.0,4.1328,500001.0,<1H OCEAN\n-118.34,34.07,52.0,2066.0,319.0,981.0,297.0,5.8632,450000.0,<1H OCEAN\n-118.34,34.08,52.0,1421.0,163.0,495.0,167.0,10.586,500001.0,<1H OCEAN\n-118.34,34.08,52.0,1721.0,195.0,688.0,196.0,15.0001,500001.0,<1H OCEAN\n-118.34,34.07,52.0,1621.0,284.0,588.0,272.0,6.2223,500001.0,<1H OCEAN\n-118.34,34.07,52.0,3421.0,598.0,1203.0,564.0,4.1618,500001.0,<1H OCEAN\n-118.34,34.08,52.0,2756.0,542.0,971.0,510.0,5.5871,500001.0,<1H OCEAN\n-118.35,34.08,52.0,2877.0,721.0,1186.0,704.0,3.2645,175000.0,<1H OCEAN\n-118.36,34.08,52.0,2373.0,601.0,1135.0,576.0,3.1765,225000.0,<1H OCEAN\n-118.36,34.08,52.0,1902.0,488.0,848.0,478.0,2.9621,175000.0,<1H OCEAN\n-118.35,34.07,48.0,890.0,255.0,434.0,232.0,3.6111,450000.0,<1H OCEAN\n-118.35,34.07,45.0,7803.0,2154.0,3359.0,2041.0,3.3594,287500.0,<1H OCEAN\n-118.35,34.07,46.0,1651.0,410.0,512.0,397.0,4.0179,350000.0,<1H OCEAN\n-118.35,34.07,45.0,3312.0,880.0,1157.0,809.0,3.5719,500001.0,<1H OCEAN\n-118.36,34.07,40.0,1821.0,447.0,777.0,441.0,2.3375,355200.0,<1H OCEAN\n-118.36,34.07,48.0,1740.0,360.0,748.0,357.0,4.7019,411100.0,<1H OCEAN\n-118.37,34.07,50.0,2519.0,,1117.0,516.0,4.3667,405600.0,<1H OCEAN\n-118.36,34.07,52.0,2046.0,451.0,944.0,435.0,3.4265,456900.0,<1H OCEAN\n-118.37,34.07,52.0,2203.0,437.0,899.0,384.0,4.25,486900.0,<1H OCEAN\n-118.37,34.07,52.0,2195.0,435.0,884.0,432.0,5.24,486400.0,<1H OCEAN\n-118.37,34.07,44.0,2703.0,663.0,1045.0,619.0,3.201,287500.0,<1H OCEAN\n-118.37,34.07,39.0,2309.0,526.0,870.0,546.0,3.1677,453400.0,<1H OCEAN\n-118.37,34.07,52.0,1084.0,247.0,468.0,255.0,3.4286,474300.0,<1H OCEAN\n-118.38,34.08,25.0,4625.0,1307.0,1739.0,1191.0,3.3989,485000.0,<1H OCEAN\n-118.38,34.07,16.0,4814.0,1381.0,1897.0,1209.0,3.3725,375000.0,<1H OCEAN\n-118.38,34.07,21.0,3653.0,956.0,1510.0,890.0,3.5573,500001.0,<1H OCEAN\n-118.34,34.07,52.0,3175.0,1057.0,1594.0,997.0,3.1766,225000.0,<1H OCEAN\n-118.35,34.06,52.0,3446.0,1360.0,1768.0,1245.0,2.4722,500001.0,<1H OCEAN\n-118.34,34.06,52.0,1314.0,170.0,629.0,214.0,7.1669,500001.0,<1H OCEAN\n-118.34,34.05,52.0,2530.0,458.0,1122.0,449.0,3.9167,321600.0,<1H OCEAN\n-118.34,34.06,52.0,2069.0,417.0,826.0,377.0,3.5481,396000.0,<1H OCEAN\n-118.34,34.06,52.0,1482.0,336.0,768.0,300.0,3.7167,327300.0,<1H OCEAN\n-118.35,34.06,52.0,2837.0,602.0,1164.0,551.0,3.2411,250000.0,<1H OCEAN\n-118.35,34.06,48.0,1354.0,279.0,716.0,309.0,3.7167,385000.0,<1H OCEAN\n-118.35,34.06,52.0,1368.0,322.0,617.0,303.0,5.3819,440900.0,<1H OCEAN\n-118.35,34.05,33.0,2880.0,836.0,1416.0,736.0,2.6781,328800.0,<1H OCEAN\n-118.35,34.06,48.0,3551.0,826.0,1601.0,827.0,3.2279,400000.0,<1H OCEAN\n-118.36,34.06,39.0,2810.0,670.0,1109.0,624.0,3.25,355000.0,<1H OCEAN\n-118.36,34.06,52.0,2130.0,455.0,921.0,395.0,2.9605,500001.0,<1H OCEAN\n-118.37,34.06,52.0,843.0,160.0,333.0,151.0,4.5192,446000.0,<1H OCEAN\n-118.37,34.06,52.0,1608.0,289.0,630.0,252.0,5.5596,500001.0,<1H OCEAN\n-118.38,34.06,28.0,2522.0,616.0,991.0,574.0,3.1475,362500.0,<1H OCEAN\n-118.38,34.06,29.0,3946.0,1008.0,1676.0,876.0,2.7824,450000.0,<1H OCEAN\n-118.38,34.06,31.0,4345.0,1158.0,1987.0,1070.0,2.8233,310000.0,<1H OCEAN\n-118.38,34.06,25.0,2558.0,661.0,1183.0,636.0,3.5556,500000.0,<1H OCEAN\n-118.37,34.05,35.0,2457.0,552.0,1159.0,523.0,3.0862,345300.0,<1H OCEAN\n-118.37,34.04,52.0,1197.0,231.0,671.0,219.0,3.825,278500.0,<1H OCEAN\n-118.37,34.05,48.0,1266.0,234.0,539.0,222.0,4.005,275000.0,<1H OCEAN\n-118.37,34.05,41.0,2369.0,544.0,1252.0,522.0,2.9883,296100.0,<1H OCEAN\n-118.37,34.06,52.0,2239.0,423.0,832.0,411.0,5.0858,470000.0,<1H OCEAN\n-118.37,34.05,52.0,2346.0,437.0,1121.0,400.0,4.0583,444300.0,<1H OCEAN\n-118.37,34.05,52.0,1563.0,306.0,776.0,308.0,3.625,440900.0,<1H OCEAN\n-118.37,34.06,52.0,2402.0,506.0,878.0,464.0,4.0217,500001.0,<1H OCEAN\n-118.36,34.05,48.0,1825.0,404.0,728.0,363.0,3.3824,322600.0,<1H OCEAN\n-118.36,34.05,47.0,1424.0,300.0,632.0,278.0,4.0625,295200.0,<1H OCEAN\n-118.36,34.05,48.0,1835.0,380.0,956.0,370.0,3.2813,243600.0,<1H OCEAN\n-118.36,34.04,49.0,995.0,184.0,462.0,194.0,2.7917,242000.0,<1H OCEAN\n-118.36,34.05,45.0,1879.0,395.0,946.0,409.0,3.3333,254700.0,<1H OCEAN\n-118.36,34.05,50.0,3518.0,812.0,1724.0,758.0,3.0833,338100.0,<1H OCEAN\n-118.38,34.05,52.0,2053.0,480.0,900.0,417.0,3.0707,417900.0,<1H OCEAN\n-118.39,34.05,47.0,1621.0,314.0,724.0,311.0,5.7509,474100.0,<1H OCEAN\n-118.38,34.05,52.0,1004.0,231.0,590.0,226.0,4.2404,351000.0,<1H OCEAN\n-118.38,34.05,52.0,1241.0,210.0,526.0,214.0,4.4191,334100.0,<1H OCEAN\n-118.38,34.05,49.0,702.0,,458.0,187.0,4.8958,333600.0,<1H OCEAN\n-118.38,34.05,40.0,2352.0,598.0,1133.0,563.0,3.238,287500.0,<1H OCEAN\n-118.38,34.05,35.0,3517.0,879.0,1632.0,784.0,3.0956,500001.0,<1H OCEAN\n-118.35,34.05,47.0,2815.0,679.0,1533.0,594.0,2.5806,234100.0,<1H OCEAN\n-118.35,34.05,44.0,1856.0,493.0,1374.0,469.0,2.0984,158000.0,<1H OCEAN\n-118.36,34.05,42.0,1372.0,,674.0,271.0,2.8793,202100.0,<1H OCEAN\n-118.36,34.05,45.0,2283.0,,1093.0,475.0,2.5658,252000.0,<1H OCEAN\n-118.35,34.05,46.0,2149.0,451.0,905.0,443.0,2.8841,290800.0,<1H OCEAN\n-118.34,34.05,50.0,2009.0,419.0,1130.0,402.0,3.1944,213500.0,<1H OCEAN\n-118.34,34.05,39.0,975.0,292.0,723.0,285.0,2.2725,140600.0,<1H OCEAN\n-118.35,34.05,52.0,1971.0,414.0,1065.0,409.0,3.6435,195800.0,<1H OCEAN\n-118.34,34.05,52.0,2194.0,504.0,997.0,438.0,2.6654,259400.0,<1H OCEAN\n-118.32,34.04,39.0,2965.0,812.0,2638.0,794.0,2.532,172700.0,<1H OCEAN\n-118.32,34.04,39.0,1294.0,330.0,1140.0,313.0,2.2554,165000.0,<1H OCEAN\n-118.32,34.04,48.0,1184.0,328.0,953.0,311.0,2.3526,156300.0,<1H OCEAN\n-118.33,34.04,31.0,1090.0,251.0,955.0,239.0,2.913,192500.0,<1H OCEAN\n-118.33,34.05,46.0,3015.0,795.0,2300.0,725.0,2.0706,268500.0,<1H OCEAN\n-118.33,34.04,52.0,2545.0,401.0,1004.0,372.0,3.6373,420000.0,<1H OCEAN\n-118.34,34.05,41.0,2099.0,472.0,1369.0,465.0,2.7409,167100.0,<1H OCEAN\n-118.34,34.04,35.0,2345.0,607.0,2042.0,565.0,2.5955,139700.0,<1H OCEAN\n-118.34,34.04,40.0,2064.0,662.0,2140.0,617.0,2.2254,127100.0,<1H OCEAN\n-118.34,34.04,37.0,1466.0,529.0,1835.0,500.0,1.7014,123200.0,<1H OCEAN\n-118.35,34.04,36.0,1956.0,601.0,1672.0,546.0,1.8685,150700.0,<1H OCEAN\n-118.35,34.04,49.0,1104.0,266.0,668.0,297.0,3.0856,151600.0,<1H OCEAN\n-118.35,34.04,45.0,1579.0,357.0,713.0,335.0,2.1711,179200.0,<1H OCEAN\n-118.36,34.04,48.0,1769.0,429.0,993.0,405.0,2.3214,139400.0,<1H OCEAN\n-118.36,34.04,45.0,1767.0,417.0,1052.0,379.0,3.5161,157000.0,<1H OCEAN\n-118.35,34.04,38.0,1626.0,375.0,1019.0,372.0,2.3687,146800.0,<1H OCEAN\n-118.36,34.03,43.0,1690.0,379.0,1017.0,359.0,2.1078,133500.0,<1H OCEAN\n-118.37,34.03,37.0,1236.0,,966.0,292.0,3.0694,122200.0,<1H OCEAN\n-118.36,34.04,34.0,3239.0,806.0,2331.0,765.0,2.0538,125800.0,<1H OCEAN\n-118.35,34.04,41.0,1617.0,423.0,1110.0,375.0,2.4635,169400.0,<1H OCEAN\n-118.35,34.03,44.0,865.0,208.0,537.0,183.0,1.9,110900.0,<1H OCEAN\n-118.35,34.04,45.0,1839.0,459.0,1312.0,460.0,2.5625,138000.0,<1H OCEAN\n-118.34,34.04,42.0,1681.0,360.0,987.0,337.0,2.6,171400.0,<1H OCEAN\n-118.34,34.03,48.0,1426.0,331.0,784.0,356.0,1.6581,118800.0,<1H OCEAN\n-118.34,34.04,42.0,2010.0,494.0,1203.0,427.0,1.9408,134600.0,<1H OCEAN\n-118.35,34.03,35.0,1438.0,333.0,794.0,306.0,1.975,138100.0,<1H OCEAN\n-118.33,34.04,33.0,1806.0,444.0,1161.0,393.0,2.5927,161500.0,<1H OCEAN\n-118.33,34.03,33.0,2314.0,624.0,1714.0,582.0,1.7377,183900.0,<1H OCEAN\n-118.33,34.04,48.0,2437.0,443.0,1400.0,426.0,2.628,251100.0,<1H OCEAN\n-118.32,34.04,47.0,1989.0,532.0,1430.0,519.0,1.8333,151100.0,<1H OCEAN\n-118.32,34.04,42.0,1766.0,404.0,1117.0,367.0,2.0259,168800.0,<1H OCEAN\n-118.32,34.04,44.0,1008.0,223.0,544.0,223.0,2.8654,176800.0,<1H OCEAN\n-118.32,34.03,35.0,3189.0,935.0,2221.0,801.0,2.1046,114000.0,<1H OCEAN\n-118.33,34.03,39.0,2840.0,826.0,1911.0,688.0,1.9018,137500.0,<1H OCEAN\n-118.32,34.03,47.0,1082.0,198.0,455.0,193.0,3.0132,223200.0,<1H OCEAN\n-118.32,34.03,50.0,1845.0,349.0,1109.0,335.0,2.8971,127800.0,<1H OCEAN\n-118.32,34.03,47.0,1734.0,453.0,1272.0,438.0,3.1731,121500.0,<1H OCEAN\n-118.32,34.02,52.0,2511.0,587.0,1660.0,546.0,2.6098,127100.0,<1H OCEAN\n-118.32,34.02,51.0,2010.0,460.0,1355.0,433.0,2.0304,133400.0,<1H OCEAN\n-118.32,34.02,47.0,1648.0,346.0,1120.0,338.0,2.0042,114200.0,<1H OCEAN\n-118.33,34.02,45.0,1667.0,399.0,928.0,375.0,1.8783,118200.0,<1H OCEAN\n-118.33,34.02,46.0,1528.0,391.0,933.0,366.0,2.1979,125700.0,<1H OCEAN\n-118.33,34.03,46.0,2312.0,625.0,1552.0,603.0,1.6429,125000.0,<1H OCEAN\n-118.34,34.02,48.0,1614.0,320.0,684.0,318.0,4.2218,181000.0,<1H OCEAN\n-118.34,34.02,44.0,2067.0,385.0,1046.0,441.0,3.5357,156900.0,<1H OCEAN\n-118.35,34.03,49.0,2334.0,530.0,1334.0,447.0,1.89,124000.0,<1H OCEAN\n-118.35,34.03,43.0,2122.0,524.0,1510.0,436.0,2.2273,123300.0,<1H OCEAN\n-118.35,34.02,52.0,427.0,92.0,233.0,116.0,3.25,134700.0,<1H OCEAN\n-118.35,34.03,42.0,2043.0,512.0,1634.0,501.0,1.9928,125400.0,<1H OCEAN\n-118.36,34.03,36.0,1083.0,342.0,1023.0,295.0,2.1324,143800.0,<1H OCEAN\n-118.36,34.03,40.0,2323.0,661.0,1847.0,614.0,1.8316,113500.0,<1H OCEAN\n-118.36,34.03,38.0,1400.0,376.0,1139.0,315.0,2.2368,120000.0,<1H OCEAN\n-118.36,34.03,35.0,1819.0,499.0,1666.0,482.0,1.6452,125900.0,<1H OCEAN\n-118.36,34.03,38.0,2365.0,638.0,2259.0,607.0,2.0879,120700.0,<1H OCEAN\n-118.37,34.03,43.0,1350.0,287.0,811.0,307.0,3.3636,140900.0,<1H OCEAN\n-118.34,34.03,46.0,2437.0,502.0,1151.0,477.0,2.4444,134100.0,<1H OCEAN\n-118.34,34.02,49.0,1609.0,371.0,896.0,389.0,2.5156,136600.0,<1H OCEAN\n-118.34,34.02,50.0,1172.0,261.0,685.0,260.0,3.1442,130300.0,<1H OCEAN\n-118.34,34.03,49.0,1295.0,276.0,765.0,265.0,3.4306,130200.0,<1H OCEAN\n-118.34,34.03,47.0,1927.0,561.0,1349.0,508.0,1.3444,125000.0,<1H OCEAN\n-118.36,34.02,43.0,1356.0,333.0,796.0,329.0,1.7159,189700.0,<1H OCEAN\n-118.37,34.03,41.0,1425.0,285.0,838.0,296.0,3.9732,188400.0,<1H OCEAN\n-118.37,34.02,44.0,1944.0,458.0,981.0,377.0,2.6154,193200.0,<1H OCEAN\n-118.28,34.05,35.0,1627.0,838.0,3013.0,791.0,1.5565,152500.0,<1H OCEAN\n-118.28,34.04,39.0,1155.0,433.0,1857.0,424.0,2.1696,153400.0,<1H OCEAN\n-118.29,34.04,41.0,659.0,291.0,1224.0,290.0,2.0817,132500.0,<1H OCEAN\n-118.29,34.05,40.0,907.0,349.0,1426.0,323.0,1.8571,143800.0,<1H OCEAN\n-118.29,34.04,48.0,1353.0,488.0,1945.0,487.0,2.4359,123700.0,<1H OCEAN\n-118.3,34.04,50.0,1757.0,522.0,2080.0,488.0,1.7225,180000.0,<1H OCEAN\n-118.3,34.05,51.0,1005.0,314.0,1227.0,306.0,2.4297,162500.0,<1H OCEAN\n-118.3,34.05,39.0,993.0,506.0,1765.0,464.0,1.2786,121900.0,<1H OCEAN\n-118.31,34.05,38.0,1864.0,515.0,1768.0,439.0,1.9336,190600.0,<1H OCEAN\n-118.31,34.05,35.0,2007.0,571.0,1513.0,554.0,2.1162,229200.0,<1H OCEAN\n-118.31,34.05,26.0,1809.0,640.0,2543.0,640.0,2.3536,500000.0,<1H OCEAN\n-118.31,34.04,33.0,2691.0,726.0,2390.0,681.0,2.4048,208300.0,<1H OCEAN\n-118.31,34.04,29.0,2038.0,578.0,2070.0,570.0,2.0658,214600.0,<1H OCEAN\n-118.31,34.03,29.0,2438.0,867.0,2114.0,753.0,0.8351,150000.0,<1H OCEAN\n-118.32,34.03,31.0,2206.0,501.0,1194.0,435.0,1.9531,227800.0,<1H OCEAN\n-118.31,34.04,52.0,1277.0,285.0,954.0,334.0,2.5833,234600.0,<1H OCEAN\n-118.3,34.04,24.0,2092.0,585.0,1757.0,538.0,1.7109,175000.0,<1H OCEAN\n-118.3,34.04,37.0,1470.0,399.0,1579.0,390.0,2.006,150000.0,<1H OCEAN\n-118.31,34.04,37.0,2338.0,686.0,2376.0,630.0,1.767,170300.0,<1H OCEAN\n-118.29,34.04,44.0,1941.0,579.0,2049.0,535.0,2.0405,143000.0,<1H OCEAN\n-118.3,34.03,41.0,1653.0,426.0,1868.0,393.0,1.78,162500.0,<1H OCEAN\n-118.3,34.04,35.0,1090.0,345.0,1605.0,330.0,2.1518,152800.0,<1H OCEAN\n-118.29,34.04,31.0,700.0,299.0,1272.0,298.0,2.1542,128100.0,<1H OCEAN\n-118.29,34.04,32.0,432.0,182.0,702.0,186.0,2.1471,125000.0,<1H OCEAN\n-118.29,34.03,42.0,1680.0,557.0,2099.0,526.0,1.9167,136400.0,<1H OCEAN\n-118.28,34.03,45.0,943.0,289.0,953.0,238.0,2.0673,151600.0,<1H OCEAN\n-118.29,34.03,42.0,907.0,378.0,822.0,288.0,1.2875,179200.0,<1H OCEAN\n-118.29,34.03,29.0,3544.0,1384.0,3323.0,1213.0,1.0219,258300.0,<1H OCEAN\n-118.29,34.03,38.0,1501.0,437.0,1777.0,441.0,2.0848,135200.0,<1H OCEAN\n-118.29,34.03,22.0,3313.0,1235.0,2381.0,1063.0,0.7473,168800.0,<1H OCEAN\n-118.29,34.03,27.0,1084.0,287.0,1085.0,279.0,2.135,119600.0,<1H OCEAN\n-118.31,34.03,46.0,2173.0,510.0,1343.0,476.0,2.025,135500.0,<1H OCEAN\n-118.31,34.03,52.0,1902.0,406.0,1233.0,385.0,2.8295,132200.0,<1H OCEAN\n-118.31,34.02,52.0,1173.0,284.0,814.0,295.0,2.45,111400.0,<1H OCEAN\n-118.31,34.02,44.0,1555.0,324.0,931.0,265.0,1.4712,105800.0,<1H OCEAN\n-118.31,34.02,45.0,1423.0,278.0,822.0,276.0,2.4519,98100.0,<1H OCEAN\n-118.31,34.02,52.0,1832.0,441.0,1186.0,420.0,1.2434,98400.0,<1H OCEAN\n-118.3,34.03,47.0,2241.0,559.0,1775.0,504.0,2.1571,147900.0,<1H OCEAN\n-118.3,34.03,40.0,1695.0,374.0,1138.0,357.0,2.7125,150000.0,<1H OCEAN\n-118.31,34.03,47.0,1315.0,,785.0,245.0,1.23,138400.0,<1H OCEAN\n-118.3,34.03,37.0,2781.0,766.0,2586.0,729.0,1.8564,187500.0,<1H OCEAN\n-118.31,34.03,34.0,2041.0,517.0,1479.0,495.0,2.1173,156600.0,<1H OCEAN\n-118.31,34.02,23.0,1703.0,397.0,1333.0,361.0,1.3187,127100.0,<1H OCEAN\n-118.31,34.02,46.0,1976.0,469.0,1409.0,431.0,2.2981,112100.0,<1H OCEAN\n-118.3,34.02,49.0,2120.0,483.0,1522.0,416.0,1.85,116800.0,<1H OCEAN\n-118.29,34.02,21.0,1641.0,491.0,1526.0,453.0,2.087,141300.0,<1H OCEAN\n-118.3,34.02,34.0,3184.0,772.0,2474.0,705.0,1.631,137500.0,<1H OCEAN\n-118.3,34.02,31.0,1933.0,478.0,1522.0,423.0,1.5781,119300.0,<1H OCEAN\n-118.28,34.02,29.0,515.0,229.0,2690.0,217.0,0.4999,500001.0,<1H OCEAN\n-118.27,34.03,50.0,395.0,232.0,948.0,243.0,1.7546,175000.0,<1H OCEAN\n-118.27,34.03,51.0,1280.0,422.0,1560.0,381.0,1.7115,125000.0,<1H OCEAN\n-118.28,34.04,19.0,460.0,241.0,890.0,229.0,1.6089,162500.0,<1H OCEAN\n-118.28,34.04,24.0,1283.0,545.0,1932.0,516.0,1.2969,160200.0,<1H OCEAN\n-118.28,34.04,25.0,1582.0,780.0,2390.0,719.0,1.4167,200000.0,<1H OCEAN\n-118.28,34.04,20.0,1193.0,454.0,1880.0,453.0,2.1806,180000.0,<1H OCEAN\n-118.28,34.05,44.0,1273.0,474.0,1754.0,460.0,1.6037,275000.0,<1H OCEAN\n-118.28,34.03,41.0,1933.0,791.0,3121.0,719.0,1.8539,147500.0,<1H OCEAN\n-118.28,34.03,40.0,2118.0,796.0,2195.0,658.0,1.7976,164600.0,<1H OCEAN\n-118.28,34.04,48.0,1521.0,513.0,1772.0,458.0,2.2232,162500.0,<1H OCEAN\n-118.27,34.02,39.0,2004.0,633.0,3050.0,621.0,1.875,127300.0,<1H OCEAN\n-118.28,34.02,52.0,281.0,103.0,470.0,96.0,1.9375,38800.0,<1H OCEAN\n-118.27,34.02,40.0,561.0,284.0,662.0,205.0,0.9234,187500.0,<1H OCEAN\n-118.28,34.03,26.0,2107.0,809.0,2821.0,572.0,0.844,350000.0,<1H OCEAN\n-118.28,34.03,25.0,1407.0,550.0,1193.0,472.0,1.2989,225000.0,<1H OCEAN\n-118.25,34.02,50.0,180.0,89.0,356.0,76.0,2.1944,158300.0,<1H OCEAN\n-118.24,34.03,52.0,142.0,47.0,137.0,45.0,1.8333,312500.0,<1H OCEAN\n-118.26,34.03,49.0,299.0,90.0,287.0,68.0,1.2096,100000.0,<1H OCEAN\n-118.25,34.03,52.0,1274.0,418.0,1655.0,368.0,2.1905,124000.0,<1H OCEAN\n-118.25,34.02,32.0,1375.0,448.0,1698.0,432.0,1.6302,130700.0,<1H OCEAN\n-118.26,34.02,38.0,980.0,285.0,1308.0,310.0,1.5652,123100.0,<1H OCEAN\n-118.26,34.02,38.0,1091.0,349.0,1786.0,340.0,2.131,136500.0,<1H OCEAN\n-118.26,34.02,40.0,1259.0,362.0,1499.0,327.0,1.8382,126400.0,<1H OCEAN\n-118.26,34.02,37.0,1551.0,501.0,2173.0,474.0,2.1667,117700.0,<1H OCEAN\n-118.26,34.02,48.0,1465.0,440.0,1859.0,400.0,1.3134,96200.0,<1H OCEAN\n-118.26,34.02,46.0,1249.0,357.0,1607.0,331.0,2.0703,114800.0,<1H OCEAN\n-118.26,34.02,41.0,848.0,323.0,1428.0,313.0,1.5603,109600.0,<1H OCEAN\n-118.26,34.02,39.0,698.0,232.0,1046.0,228.0,2.2356,119500.0,<1H OCEAN\n-118.25,34.02,35.0,1368.0,486.0,2239.0,461.0,1.913,114300.0,<1H OCEAN\n-118.25,34.02,33.0,1676.0,525.0,2564.0,515.0,2.1957,100800.0,<1H OCEAN\n-118.25,34.01,28.0,481.0,136.0,596.0,128.0,1.2396,90300.0,<1H OCEAN\n-118.25,34.02,32.0,1311.0,410.0,1792.0,396.0,2.3304,119900.0,<1H OCEAN\n-118.24,34.02,48.0,542.0,150.0,571.0,114.0,1.8485,90600.0,<1H OCEAN\n-118.24,34.01,48.0,396.0,99.0,485.0,110.0,2.375,107500.0,<1H OCEAN\n-118.24,34.01,43.0,1456.0,444.0,2098.0,433.0,1.8929,99200.0,<1H OCEAN\n-118.25,34.01,31.0,1301.0,403.0,1952.0,377.0,2.1466,100800.0,<1H OCEAN\n-118.24,34.01,30.0,405.0,86.0,376.0,68.0,1.7813,127500.0,<1H OCEAN\n-118.25,34.01,45.0,782.0,270.0,1030.0,235.0,1.0898,93400.0,<1H OCEAN\n-118.25,34.01,36.0,879.0,262.0,1034.0,236.0,1.2857,99300.0,<1H OCEAN\n-118.25,34.01,43.0,1429.0,386.0,1412.0,354.0,1.3287,107200.0,<1H OCEAN\n-118.25,34.01,43.0,1575.0,475.0,1980.0,469.0,1.7425,100500.0,<1H OCEAN\n-118.25,34.01,30.0,962.0,291.0,1280.0,263.0,1.4464,110200.0,<1H OCEAN\n-118.26,34.01,38.0,697.0,208.0,749.0,206.0,1.4653,118800.0,<1H OCEAN\n-118.26,34.01,47.0,1269.0,323.0,1628.0,325.0,1.5089,115800.0,<1H OCEAN\n-118.26,34.01,37.0,2451.0,668.0,2824.0,598.0,1.9074,99600.0,<1H OCEAN\n-118.27,34.01,42.0,990.0,289.0,1167.0,281.0,1.4524,126800.0,<1H OCEAN\n-118.26,34.01,46.0,879.0,253.0,1010.0,221.0,2.1776,118300.0,<1H OCEAN\n-118.27,34.02,21.0,1314.0,375.0,1505.0,366.0,2.319,97200.0,<1H OCEAN\n-118.27,34.01,35.0,1193.0,355.0,1784.0,341.0,1.8652,116100.0,<1H OCEAN\n-118.27,34.01,35.0,1672.0,556.0,2106.0,519.0,1.2206,129200.0,<1H OCEAN\n-118.27,34.01,47.0,921.0,264.0,881.0,221.0,1.4327,114100.0,<1H OCEAN\n-118.27,34.01,43.0,1235.0,385.0,1745.0,372.0,2.0817,113300.0,<1H OCEAN\n-118.27,34.0,46.0,1748.0,428.0,1707.0,409.0,2.148,103800.0,<1H OCEAN\n-118.27,34.0,48.0,1869.0,461.0,1834.0,441.0,1.7052,107400.0,<1H OCEAN\n-118.27,34.0,47.0,780.0,237.0,888.0,215.0,1.75,95800.0,<1H OCEAN\n-118.26,34.01,43.0,2179.0,682.0,2624.0,609.0,1.8641,108200.0,<1H OCEAN\n-118.26,34.0,41.0,1733.0,492.0,1776.0,453.0,1.6221,104200.0,<1H OCEAN\n-118.25,34.0,36.0,1033.0,267.0,1112.0,229.0,1.7237,105800.0,<1H OCEAN\n-118.25,34.0,36.0,1176.0,309.0,1267.0,292.0,1.6382,105000.0,<1H OCEAN\n-118.25,34.0,32.0,1218.0,342.0,1292.0,304.0,1.5781,102900.0,<1H OCEAN\n-118.25,34.0,41.0,1768.0,475.0,1721.0,474.0,1.303,90400.0,<1H OCEAN\n-118.25,34.0,34.0,1905.0,552.0,2194.0,521.0,1.4792,95800.0,<1H OCEAN\n-118.24,34.0,23.0,588.0,157.0,716.0,173.0,1.2056,87500.0,<1H OCEAN\n-118.24,34.0,43.0,863.0,206.0,788.0,187.0,0.9463,95000.0,<1H OCEAN\n-118.25,34.0,29.0,1419.0,363.0,1696.0,317.0,2.2813,101300.0,<1H OCEAN\n-118.24,34.0,38.0,1715.0,414.0,1714.0,389.0,1.7132,108200.0,<1H OCEAN\n-118.24,33.99,44.0,448.0,116.0,504.0,96.0,1.875,98600.0,<1H OCEAN\n-118.24,33.99,41.0,1425.0,372.0,1803.0,353.0,1.6731,88200.0,<1H OCEAN\n-118.25,33.99,42.0,2261.0,574.0,2496.0,527.0,1.5556,98500.0,<1H OCEAN\n-118.25,33.99,39.0,1274.0,319.0,1362.0,324.0,2.1793,107900.0,<1H OCEAN\n-118.26,34.0,27.0,1611.0,479.0,1457.0,458.0,0.8941,91900.0,<1H OCEAN\n-118.26,34.0,37.0,2615.0,697.0,2484.0,630.0,1.9208,103400.0,<1H OCEAN\n-118.27,34.0,40.0,2099.0,599.0,2311.0,529.0,1.852,101500.0,<1H OCEAN\n-118.27,34.0,43.0,1638.0,434.0,1213.0,390.0,1.3403,110800.0,<1H OCEAN\n-118.27,34.0,43.0,1258.0,381.0,1276.0,358.0,1.8917,106900.0,<1H OCEAN\n-118.26,33.99,47.0,1865.0,465.0,1916.0,438.0,1.8242,95000.0,<1H OCEAN\n-118.26,33.99,36.0,2016.0,505.0,1807.0,464.0,1.6901,103500.0,<1H OCEAN\n-118.27,33.99,30.0,504.0,140.0,529.0,123.0,1.9531,100000.0,<1H OCEAN\n-118.27,33.99,35.0,932.0,294.0,1153.0,282.0,1.4886,100000.0,<1H OCEAN\n-118.27,33.99,38.0,1407.0,447.0,1783.0,402.0,1.8086,97100.0,<1H OCEAN\n-118.28,34.01,46.0,441.0,167.0,621.0,144.0,1.8824,162500.0,<1H OCEAN\n-118.28,34.02,46.0,1098.0,426.0,1510.0,374.0,2.1382,156300.0,<1H OCEAN\n-118.29,34.01,40.0,885.0,312.0,799.0,221.0,1.1667,143800.0,<1H OCEAN\n-118.3,34.01,48.0,4217.0,1095.0,3298.0,949.0,1.9152,122300.0,<1H OCEAN\n-118.3,34.02,27.0,2190.0,626.0,1768.0,528.0,1.2446,103800.0,<1H OCEAN\n-118.29,34.02,26.0,2001.0,582.0,2044.0,557.0,1.1563,118800.0,<1H OCEAN\n-118.3,34.02,42.0,2386.0,670.0,2327.0,661.0,1.6699,108000.0,<1H OCEAN\n-118.3,34.01,35.0,1147.0,290.0,818.0,281.0,1.7961,111700.0,<1H OCEAN\n-118.31,34.01,50.0,1463.0,354.0,912.0,293.0,1.7386,109400.0,<1H OCEAN\n-118.31,34.02,41.0,1046.0,216.0,727.0,201.0,1.6667,116900.0,<1H OCEAN\n-118.31,34.02,43.0,2255.0,533.0,1568.0,470.0,1.6955,115200.0,<1H OCEAN\n-118.31,34.01,39.0,2073.0,566.0,1246.0,547.0,2.0417,117100.0,<1H OCEAN\n-118.31,34.02,46.0,2217.0,489.0,1227.0,448.0,1.6851,108800.0,<1H OCEAN\n-118.31,34.01,48.0,2544.0,532.0,1357.0,498.0,2.5263,121000.0,<1H OCEAN\n-118.31,34.01,52.0,2547.0,475.0,1417.0,444.0,1.8214,123200.0,<1H OCEAN\n-118.31,34.01,52.0,1793.0,350.0,1303.0,366.0,3.0759,123700.0,<1H OCEAN\n-118.29,34.01,42.0,814.0,223.0,511.0,188.0,2.3942,123200.0,<1H OCEAN\n-118.29,34.01,50.0,2238.0,673.0,2247.0,583.0,1.6505,125000.0,<1H OCEAN\n-118.3,34.0,52.0,1743.0,421.0,1206.0,384.0,1.6875,116000.0,<1H OCEAN\n-118.3,34.01,52.0,1908.0,428.0,1271.0,394.0,2.5885,136200.0,<1H OCEAN\n-118.3,34.01,52.0,1444.0,343.0,1154.0,334.0,2.0625,134400.0,<1H OCEAN\n-118.28,34.01,34.0,2305.0,775.0,2450.0,740.0,1.7143,132000.0,<1H OCEAN\n-118.28,34.0,46.0,1650.0,463.0,1992.0,458.0,2.3403,114100.0,<1H OCEAN\n-118.29,34.0,6.0,1487.0,468.0,1509.0,403.0,1.4639,112500.0,<1H OCEAN\n-118.29,34.01,39.0,751.0,207.0,1010.0,231.0,1.6036,137500.0,<1H OCEAN\n-118.29,34.01,30.0,1385.0,518.0,1730.0,472.0,1.0539,142500.0,<1H OCEAN\n-118.28,34.01,50.0,2601.0,794.0,3080.0,770.0,1.8656,122900.0,<1H OCEAN\n-118.28,34.0,43.0,713.0,245.0,880.0,237.0,1.2065,103600.0,<1H OCEAN\n-118.28,34.01,52.0,795.0,308.0,1118.0,275.0,1.2175,131300.0,<1H OCEAN\n-118.28,34.01,48.0,483.0,190.0,775.0,188.0,2.3309,126600.0,<1H OCEAN\n-118.28,34.0,38.0,3335.0,921.0,3612.0,887.0,2.125,118800.0,<1H OCEAN\n-118.28,34.0,42.0,1534.0,417.0,1295.0,380.0,2.0938,119200.0,<1H OCEAN\n-118.28,34.0,42.0,855.0,284.0,890.0,247.0,1.2778,112500.0,<1H OCEAN\n-118.28,34.0,44.0,2636.0,725.0,2182.0,651.0,1.432,124000.0,<1H OCEAN\n-118.28,34.0,48.0,1514.0,376.0,1353.0,344.0,2.1607,96100.0,<1H OCEAN\n-118.29,34.0,44.0,1753.0,387.0,1165.0,380.0,2.1354,105800.0,<1H OCEAN\n-118.29,34.0,41.0,1807.0,493.0,1731.0,471.0,1.2347,111700.0,<1H OCEAN\n-118.29,34.0,52.0,1319.0,295.0,898.0,271.0,2.7727,128600.0,<1H OCEAN\n-118.3,34.0,40.0,1131.0,281.0,859.0,230.0,1.1806,134600.0,<1H OCEAN\n-118.3,34.0,52.0,1420.0,355.0,1080.0,353.0,1.5179,116100.0,<1H OCEAN\n-118.29,34.0,52.0,2579.0,494.0,1558.0,458.0,2.0809,109600.0,<1H OCEAN\n-118.3,34.0,52.0,1686.0,377.0,982.0,356.0,2.0958,116400.0,<1H OCEAN\n-118.3,34.0,52.0,1296.0,246.0,853.0,238.0,3.05,111600.0,<1H OCEAN\n-118.31,34.0,52.0,1542.0,309.0,939.0,276.0,1.6892,129100.0,<1H OCEAN\n-118.3,34.0,52.0,1718.0,354.0,1026.0,312.0,2.0,128000.0,<1H OCEAN\n-118.31,34.0,52.0,1630.0,379.0,1413.0,405.0,1.933,120000.0,<1H OCEAN\n-118.31,34.0,52.0,2709.0,642.0,1751.0,613.0,2.1116,122500.0,<1H OCEAN\n-118.31,34.0,47.0,1551.0,362.0,1329.0,322.0,1.9792,116400.0,<1H OCEAN\n-118.31,33.99,48.0,2235.0,433.0,1363.0,433.0,1.6559,101400.0,<1H OCEAN\n-118.31,33.99,47.0,1525.0,359.0,982.0,333.0,2.0915,126600.0,<1H OCEAN\n-118.31,33.99,45.0,1489.0,339.0,791.0,316.0,2.2339,104800.0,<1H OCEAN\n-118.31,33.99,44.0,1703.0,358.0,789.0,249.0,1.7083,100000.0,<1H OCEAN\n-118.29,33.99,46.0,2608.0,636.0,1766.0,596.0,1.5846,114800.0,<1H OCEAN\n-118.3,33.99,47.0,2637.0,588.0,1903.0,521.0,1.8317,96500.0,<1H OCEAN\n-118.3,33.99,47.0,2212.0,533.0,1903.0,554.0,1.9853,101100.0,<1H OCEAN\n-118.28,33.99,46.0,2577.0,703.0,2446.0,687.0,1.275,98300.0,<1H OCEAN\n-118.29,33.99,46.0,2198.0,530.0,2067.0,497.0,2.0542,103400.0,<1H OCEAN\n-118.28,33.99,52.0,1283.0,342.0,1363.0,329.0,2.5848,101900.0,<1H OCEAN\n-118.28,33.99,49.0,2174.0,481.0,1861.0,484.0,1.7159,95000.0,<1H OCEAN\n-118.28,33.99,46.0,1211.0,321.0,1153.0,282.0,1.7849,99300.0,<1H OCEAN\n-118.32,34.01,52.0,3104.0,645.0,1498.0,581.0,2.6667,128000.0,<1H OCEAN\n-118.32,34.02,50.0,1655.0,256.0,672.0,260.0,4.2554,194300.0,<1H OCEAN\n-118.32,34.02,48.0,1949.0,308.0,823.0,340.0,3.3906,189700.0,<1H OCEAN\n-118.32,34.01,50.0,1842.0,377.0,817.0,341.0,3.1548,157700.0,<1H OCEAN\n-118.32,34.01,47.0,1745.0,371.0,1079.0,368.0,2.4022,123400.0,<1H OCEAN\n-118.33,34.02,46.0,2223.0,361.0,968.0,373.0,4.26,182600.0,<1H OCEAN\n-118.33,34.01,47.0,1320.0,259.0,653.0,291.0,3.7727,193000.0,<1H OCEAN\n-118.33,34.02,42.0,2043.0,378.0,869.0,416.0,3.5,181100.0,<1H OCEAN\n-118.32,34.01,44.0,4032.0,913.0,1622.0,848.0,2.4934,165800.0,<1H OCEAN\n-118.33,34.01,43.0,2227.0,564.0,956.0,472.0,2.0217,187500.0,<1H OCEAN\n-118.33,34.01,44.0,1762.0,463.0,786.0,445.0,1.9231,188500.0,<1H OCEAN\n-118.33,34.01,44.0,2182.0,492.0,878.0,493.0,1.9631,181300.0,<1H OCEAN\n-118.32,34.0,50.0,3250.0,693.0,2300.0,675.0,1.95,112600.0,<1H OCEAN\n-118.32,34.0,50.0,2189.0,460.0,1097.0,469.0,2.4583,120900.0,<1H OCEAN\n-118.33,34.0,47.0,1671.0,388.0,895.0,317.0,2.2054,121500.0,<1H OCEAN\n-118.33,34.0,47.0,825.0,187.0,416.0,173.0,2.3333,133300.0,<1H OCEAN\n-118.33,34.0,24.0,873.0,320.0,529.0,308.0,0.9304,151600.0,<1H OCEAN\n-118.32,33.99,43.0,2028.0,479.0,1074.0,394.0,2.5909,98700.0,<1H OCEAN\n-118.32,33.99,49.0,1407.0,269.0,889.0,283.0,1.9779,114200.0,<1H OCEAN\n-118.33,33.99,44.0,1918.0,387.0,1041.0,364.0,2.8542,126500.0,<1H OCEAN\n-118.33,33.99,46.0,1582.0,315.0,777.0,286.0,3.2083,149600.0,<1H OCEAN\n-118.34,33.99,48.0,2009.0,335.0,919.0,297.0,4.8125,170500.0,<1H OCEAN\n-118.32,33.99,48.0,1260.0,284.0,791.0,280.0,2.1875,115200.0,<1H OCEAN\n-118.32,33.99,43.0,1257.0,232.0,735.0,232.0,3.7167,108900.0,<1H OCEAN\n-118.33,33.99,43.0,2224.0,550.0,1598.0,545.0,2.8274,122500.0,<1H OCEAN\n-118.34,33.99,46.0,1217.0,322.0,662.0,305.0,3.1731,140300.0,<1H OCEAN\n-118.32,33.98,47.0,949.0,210.0,574.0,217.0,2.175,114700.0,<1H OCEAN\n-118.32,33.98,40.0,1298.0,277.0,791.0,255.0,3.2344,104300.0,<1H OCEAN\n-118.32,33.98,47.0,1528.0,331.0,864.0,308.0,1.9732,101000.0,<1H OCEAN\n-118.32,33.98,46.0,1611.0,339.0,921.0,314.0,3.0833,103300.0,<1H OCEAN\n-118.33,33.98,28.0,3889.0,1199.0,3121.0,1046.0,1.8806,113900.0,<1H OCEAN\n-118.33,33.98,32.0,1784.0,489.0,1472.0,476.0,1.6477,108900.0,<1H OCEAN\n-118.33,33.98,38.0,3063.0,796.0,2153.0,721.0,1.8472,149100.0,<1H OCEAN\n-118.34,33.99,48.0,2225.0,433.0,1170.0,401.0,2.9643,140400.0,<1H OCEAN\n-118.34,33.98,46.0,2126.0,409.0,1292.0,414.0,2.9315,149000.0,<1H OCEAN\n-118.35,33.99,47.0,2183.0,380.0,927.0,371.0,4.9531,180100.0,<1H OCEAN\n-118.35,33.98,47.0,2512.0,461.0,1082.0,426.0,3.8235,207600.0,<1H OCEAN\n-118.34,33.99,34.0,397.0,132.0,250.0,121.0,1.675,166700.0,<1H OCEAN\n-118.32,33.98,44.0,1448.0,314.0,861.0,310.0,2.2396,108600.0,<1H OCEAN\n-118.32,33.97,52.0,1778.0,320.0,795.0,279.0,3.5114,138800.0,<1H OCEAN\n-118.32,33.98,49.0,1412.0,333.0,901.0,328.0,1.7067,118600.0,<1H OCEAN\n-118.33,33.98,30.0,3112.0,931.0,2739.0,841.0,1.6531,118500.0,<1H OCEAN\n-118.33,33.97,44.0,2526.0,579.0,1423.0,573.0,2.5363,158800.0,<1H OCEAN\n-118.33,33.97,47.0,1830.0,369.0,922.0,377.0,4.1635,156400.0,<1H OCEAN\n-118.36,34.02,46.0,3745.0,798.0,1502.0,808.0,3.8643,195800.0,<1H OCEAN\n-118.36,34.01,33.0,3140.0,466.0,1214.0,464.0,6.5044,350400.0,<1H OCEAN\n-118.37,34.02,33.0,2263.0,430.0,900.0,382.0,4.4028,246800.0,<1H OCEAN\n-118.38,34.02,31.0,1893.0,450.0,819.0,426.0,4.3077,140600.0,<1H OCEAN\n-118.33,34.02,11.0,1249.0,313.0,625.0,336.0,0.8702,170500.0,<1H OCEAN\n-118.34,34.01,35.0,1359.0,359.0,655.0,341.0,2.5568,312500.0,<1H OCEAN\n-118.34,34.01,37.0,4291.0,1102.0,1941.0,953.0,1.7945,106300.0,<1H OCEAN\n-118.34,34.01,38.0,2318.0,735.0,1407.0,702.0,1.6187,266700.0,<1H OCEAN\n-118.34,34.02,44.0,1527.0,246.0,608.0,245.0,4.0357,187800.0,<1H OCEAN\n-118.35,34.02,34.0,3978.0,1073.0,2725.0,1035.0,1.7622,167900.0,<1H OCEAN\n-118.35,34.02,34.0,5218.0,1576.0,3538.0,1371.0,1.5143,118800.0,<1H OCEAN\n-118.35,34.02,27.0,3358.0,1069.0,2415.0,956.0,1.4589,87500.0,<1H OCEAN\n-118.35,34.01,33.0,3246.0,601.0,1585.0,603.0,3.6629,353200.0,<1H OCEAN\n-118.35,34.01,35.0,3776.0,,1583.0,749.0,3.5486,332100.0,<1H OCEAN\n-118.35,34.0,28.0,3085.0,621.0,1162.0,558.0,3.25,301000.0,<1H OCEAN\n-118.28,33.99,35.0,1138.0,304.0,1128.0,311.0,1.8818,100000.0,<1H OCEAN\n-118.28,33.99,37.0,1971.0,513.0,1673.0,464.0,1.4625,103000.0,<1H OCEAN\n-118.29,33.99,39.0,979.0,235.0,857.0,236.0,2.5547,108900.0,<1H OCEAN\n-118.29,33.98,41.0,1582.0,416.0,1422.0,370.0,1.0516,108300.0,<1H OCEAN\n-118.29,33.99,43.0,1902.0,398.0,1153.0,363.0,1.9375,112900.0,<1H OCEAN\n-118.3,33.99,44.0,1458.0,326.0,1159.0,283.0,1.1645,98200.0,<1H OCEAN\n-118.3,33.99,43.0,1534.0,384.0,1231.0,329.0,2.5437,94500.0,<1H OCEAN\n-118.3,33.98,44.0,1597.0,388.0,902.0,321.0,1.9556,93300.0,<1H OCEAN\n-118.3,33.99,45.0,1701.0,452.0,1484.0,427.0,1.84,91400.0,<1H OCEAN\n-118.31,33.98,44.0,222.0,54.0,234.0,77.0,5.1136,111700.0,<1H OCEAN\n-118.31,33.99,49.0,857.0,196.0,694.0,228.0,2.895,108000.0,<1H OCEAN\n-118.31,33.98,52.0,1837.0,426.0,1062.0,343.0,2.0,96500.0,<1H OCEAN\n-118.31,33.98,52.0,1607.0,331.0,900.0,295.0,3.5982,96600.0,<1H OCEAN\n-118.31,33.98,50.0,1985.0,454.0,1090.0,410.0,1.825,106600.0,<1H OCEAN\n-118.31,33.98,52.0,1975.0,379.0,1043.0,371.0,2.3977,112200.0,<1H OCEAN\n-118.32,33.98,49.0,1993.0,446.0,1052.0,394.0,2.2138,119800.0,<1H OCEAN\n-118.29,33.98,44.0,2261.0,555.0,1348.0,455.0,1.9125,97200.0,<1H OCEAN\n-118.29,33.98,48.0,1124.0,231.0,783.0,223.0,3.4444,93100.0,<1H OCEAN\n-118.3,33.98,52.0,1371.0,315.0,986.0,277.0,2.9432,94000.0,<1H OCEAN\n-118.3,33.98,48.0,2010.0,445.0,1208.0,404.0,1.6611,95800.0,<1H OCEAN\n-118.3,33.98,48.0,1998.0,410.0,1176.0,382.0,3.0455,102400.0,<1H OCEAN\n-118.3,33.98,44.0,1242.0,270.0,811.0,274.0,2.9375,95200.0,<1H OCEAN\n-118.29,33.98,42.0,2833.0,768.0,2542.0,725.0,1.3479,100000.0,<1H OCEAN\n-118.29,33.98,46.0,1118.0,300.0,786.0,254.0,1.4042,110000.0,<1H OCEAN\n-118.28,33.98,39.0,1306.0,345.0,1332.0,331.0,1.9564,92200.0,<1H OCEAN\n-118.28,33.98,47.0,865.0,193.0,782.0,217.0,2.2411,93000.0,<1H OCEAN\n-118.28,33.98,45.0,1720.0,416.0,1382.0,365.0,0.9337,92300.0,<1H OCEAN\n-118.28,33.98,19.0,883.0,313.0,726.0,277.0,0.9809,121400.0,<1H OCEAN\n-118.29,33.98,30.0,1162.0,318.0,1207.0,289.0,1.223,100000.0,<1H OCEAN\n-118.28,33.97,31.0,1068.0,271.0,1091.0,281.0,1.689,102600.0,<1H OCEAN\n-118.28,33.97,31.0,2017.0,566.0,2063.0,521.0,1.9219,107000.0,<1H OCEAN\n-118.28,33.97,35.0,2305.0,634.0,1978.0,568.0,1.375,100000.0,<1H OCEAN\n-118.29,33.97,43.0,2660.0,672.0,2133.0,588.0,1.7734,107300.0,<1H OCEAN\n-118.3,33.97,46.0,1425.0,317.0,1140.0,304.0,3.375,98500.0,<1H OCEAN\n-118.3,33.97,44.0,1521.0,289.0,1074.0,285.0,2.0673,99800.0,<1H OCEAN\n-118.3,33.97,42.0,944.0,200.0,567.0,190.0,2.6311,124100.0,<1H OCEAN\n-118.3,33.97,50.0,2270.0,451.0,1000.0,412.0,2.1221,119400.0,<1H OCEAN\n-118.31,33.97,52.0,1595.0,325.0,823.0,302.0,3.2188,124200.0,<1H OCEAN\n-118.31,33.97,47.0,2066.0,422.0,1156.0,380.0,2.7917,125800.0,<1H OCEAN\n-118.31,33.97,52.0,1629.0,277.0,819.0,288.0,3.725,142600.0,<1H OCEAN\n-118.31,33.97,48.0,1541.0,314.0,819.0,312.0,3.0917,136100.0,<1H OCEAN\n-118.31,33.94,41.0,1353.0,286.0,751.0,250.0,2.7401,131700.0,<1H OCEAN\n-118.3,33.95,50.0,1843.0,326.0,892.0,314.0,3.1346,120000.0,<1H OCEAN\n-118.31,33.95,43.0,1823.0,358.0,1065.0,342.0,3.2708,131000.0,<1H OCEAN\n-118.31,33.95,44.0,1558.0,333.0,1095.0,316.0,4.0043,133500.0,<1H OCEAN\n-118.31,33.95,44.0,2490.0,430.0,1305.0,411.0,4.8295,149600.0,<1H OCEAN\n-118.31,33.94,43.0,2104.0,393.0,1132.0,394.0,3.0682,142000.0,<1H OCEAN\n-118.31,33.96,43.0,2118.0,569.0,1266.0,500.0,1.747,121000.0,<1H OCEAN\n-118.31,33.96,43.0,2149.0,493.0,1316.0,462.0,1.528,131800.0,<1H OCEAN\n-118.31,33.96,46.0,1686.0,303.0,870.0,320.0,3.4643,136300.0,<1H OCEAN\n-118.31,33.96,48.0,2015.0,356.0,1020.0,338.0,4.0625,138700.0,<1H OCEAN\n-118.29,33.97,48.0,3139.0,587.0,1319.0,506.0,3.5208,134200.0,<1H OCEAN\n-118.29,33.96,36.0,1717.0,417.0,902.0,368.0,1.4868,113200.0,<1H OCEAN\n-118.3,33.96,43.0,2009.0,442.0,1095.0,439.0,2.8299,109500.0,<1H OCEAN\n-118.3,33.96,39.0,2802.0,618.0,1524.0,529.0,2.6518,136300.0,<1H OCEAN\n-118.28,33.96,41.0,1175.0,340.0,1241.0,352.0,1.2273,98400.0,<1H OCEAN\n-118.28,33.96,34.0,2074.0,562.0,1913.0,514.0,1.6156,102100.0,<1H OCEAN\n-118.28,33.97,34.0,2771.0,802.0,2782.0,715.0,1.6652,99000.0,<1H OCEAN\n-118.29,33.96,31.0,4022.0,1208.0,3707.0,1007.0,1.3096,116300.0,<1H OCEAN\n-118.3,33.96,47.0,2112.0,417.0,1161.0,368.0,3.9722,117400.0,<1H OCEAN\n-118.31,33.96,52.0,2523.0,460.0,1167.0,413.0,3.0625,127400.0,<1H OCEAN\n-118.31,33.96,47.0,1586.0,322.0,1077.0,339.0,4.4861,140400.0,<1H OCEAN\n-118.31,33.96,47.0,2005.0,392.0,1134.0,415.0,3.7143,140300.0,<1H OCEAN\n-118.26,33.99,30.0,1702.0,443.0,1966.0,442.0,1.5521,97500.0,<1H OCEAN\n-118.27,33.98,30.0,1966.0,584.0,2028.0,535.0,1.625,101500.0,<1H OCEAN\n-118.28,33.99,38.0,1454.0,323.0,1098.0,297.0,1.5109,104000.0,<1H OCEAN\n-118.27,33.99,41.0,656.0,162.0,730.0,170.0,1.8047,101800.0,<1H OCEAN\n-118.28,33.98,43.0,1240.0,312.0,1100.0,311.0,1.575,97500.0,<1H OCEAN\n-118.27,33.98,45.0,1696.0,424.0,1502.0,429.0,1.3042,99200.0,<1H OCEAN\n-118.27,33.98,39.0,2062.0,588.0,1933.0,570.0,1.3801,97000.0,<1H OCEAN\n-118.27,33.98,44.0,1722.0,457.0,2177.0,401.0,2.125,92500.0,<1H OCEAN\n-118.26,33.98,43.0,762.0,206.0,854.0,188.0,1.2315,98200.0,<1H OCEAN\n-118.26,33.97,46.0,1295.0,351.0,1120.0,323.0,1.7121,98200.0,<1H OCEAN\n-118.26,33.97,44.0,1246.0,308.0,1031.0,295.0,1.9556,96300.0,<1H OCEAN\n-118.26,33.97,46.0,1521.0,352.0,1100.0,334.0,1.55,100600.0,<1H OCEAN\n-118.26,33.97,46.0,1086.0,249.0,880.0,250.0,1.5962,95700.0,<1H OCEAN\n-118.27,33.97,45.0,1546.0,371.0,1186.0,366.0,1.64,96800.0,<1H OCEAN\n-118.28,33.97,40.0,2180.0,642.0,2464.0,631.0,1.5521,90100.0,<1H OCEAN\n-118.27,33.97,39.0,2569.0,688.0,2601.0,630.0,2.0754,101400.0,<1H OCEAN\n-118.28,33.97,38.0,1819.0,497.0,2110.0,499.0,1.6027,97300.0,<1H OCEAN\n-118.27,33.96,38.0,977.0,295.0,1073.0,292.0,1.0208,86400.0,<1H OCEAN\n-118.27,33.96,42.0,796.0,203.0,697.0,177.0,2.037,92600.0,<1H OCEAN\n-118.28,33.96,37.0,1812.0,500.0,1640.0,447.0,1.9348,99100.0,<1H OCEAN\n-118.26,33.97,47.0,1504.0,374.0,1168.0,358.0,1.4625,94200.0,<1H OCEAN\n-118.26,33.96,40.0,1475.0,347.0,1222.0,298.0,1.5303,95300.0,<1H OCEAN\n-118.26,33.96,39.0,1255.0,323.0,902.0,327.0,1.5812,94000.0,<1H OCEAN\n-118.26,33.97,52.0,1331.0,346.0,1144.0,362.0,1.5326,90600.0,<1H OCEAN\n-118.27,33.97,34.0,1462.0,394.0,1310.0,351.0,1.1557,90100.0,<1H OCEAN\n-118.27,33.96,38.0,1126.0,270.0,999.0,265.0,0.5495,91700.0,<1H OCEAN\n-118.27,33.96,34.0,1040.0,276.0,1083.0,255.0,1.6467,90900.0,<1H OCEAN\n-118.27,33.95,39.0,1529.0,358.0,1154.0,357.0,1.2091,97900.0,<1H OCEAN\n-118.26,33.95,38.0,1387.0,346.0,1240.0,355.0,1.6898,95100.0,<1H OCEAN\n-118.26,33.95,44.0,1513.0,369.0,1088.0,344.0,1.2969,94400.0,<1H OCEAN\n-118.26,33.96,39.0,1542.0,375.0,1256.0,361.0,1.7167,100000.0,<1H OCEAN\n-118.26,33.96,37.0,1625.0,383.0,1243.0,350.0,1.3971,89800.0,<1H OCEAN\n-118.28,33.96,42.0,1604.0,399.0,1581.0,387.0,1.7656,96700.0,<1H OCEAN\n-118.28,33.95,40.0,2044.0,538.0,2150.0,524.0,2.1437,94800.0,<1H OCEAN\n-118.28,33.96,42.0,1206.0,304.0,1167.0,250.0,1.615,101300.0,<1H OCEAN\n-118.28,33.96,39.0,882.0,221.0,697.0,189.0,1.8472,99100.0,<1H OCEAN\n-118.28,33.96,23.0,1983.0,611.0,2048.0,600.0,1.5313,91400.0,<1H OCEAN\n-118.29,33.95,35.0,1401.0,362.0,1357.0,327.0,2.0917,99300.0,<1H OCEAN\n-118.29,33.95,32.0,721.0,205.0,842.0,208.0,1.6029,89700.0,<1H OCEAN\n-118.29,33.96,39.0,1340.0,409.0,1463.0,367.0,1.5294,111400.0,<1H OCEAN\n-118.29,33.95,40.0,2808.0,695.0,2357.0,627.0,1.9655,102300.0,<1H OCEAN\n-118.29,33.95,39.0,1701.0,428.0,1468.0,411.0,1.9702,93200.0,<1H OCEAN\n-118.28,33.94,43.0,1201.0,292.0,840.0,252.0,2.7917,105600.0,<1H OCEAN\n-118.29,33.94,34.0,1089.0,278.0,995.0,315.0,2.3352,107700.0,<1H OCEAN\n-118.27,33.95,35.0,2073.0,494.0,1753.0,490.0,1.5,93600.0,<1H OCEAN\n-118.27,33.95,43.0,1156.0,291.0,1074.0,299.0,1.8814,94900.0,<1H OCEAN\n-118.27,33.94,30.0,1041.0,275.0,877.0,270.0,1.5268,91600.0,<1H OCEAN\n-118.28,33.94,38.0,637.0,204.0,679.0,162.0,1.5714,89700.0,<1H OCEAN\n-118.28,33.95,41.0,835.0,208.0,707.0,192.0,1.4103,86200.0,<1H OCEAN\n-118.27,33.95,40.0,935.0,226.0,818.0,236.0,1.8798,101300.0,<1H OCEAN\n-118.27,33.95,34.0,987.0,248.0,902.0,221.0,2.3365,98000.0,<1H OCEAN\n-118.27,33.95,34.0,1261.0,315.0,1027.0,303.0,2.2946,88800.0,<1H OCEAN\n-118.27,33.95,29.0,1579.0,351.0,1056.0,322.0,2.3056,98500.0,<1H OCEAN\n-118.25,33.95,35.0,1405.0,326.0,1086.0,273.0,1.0375,89800.0,<1H OCEAN\n-118.26,33.94,45.0,1080.0,218.0,850.0,237.0,2.25,93400.0,<1H OCEAN\n-118.26,33.95,44.0,1771.0,378.0,1296.0,399.0,1.6389,96700.0,<1H OCEAN\n-118.26,33.95,44.0,1481.0,329.0,999.0,315.0,1.5147,94600.0,<1H OCEAN\n-118.26,33.94,44.0,1103.0,265.0,760.0,247.0,1.6887,99600.0,<1H OCEAN\n-118.25,33.94,37.0,1002.0,270.0,1092.0,273.0,1.6333,94500.0,<1H OCEAN\n-118.26,33.94,41.0,1510.0,410.0,1408.0,389.0,1.65,94200.0,<1H OCEAN\n-118.27,33.94,38.0,1314.0,318.0,1080.0,285.0,1.5872,89800.0,<1H OCEAN\n-118.26,33.94,44.0,795.0,181.0,716.0,167.0,2.0,90300.0,<1H OCEAN\n-118.26,33.93,35.0,1562.0,403.0,1587.0,406.0,1.4917,93200.0,<1H OCEAN\n-118.27,33.93,41.0,570.0,135.0,466.0,121.0,2.6458,91300.0,<1H OCEAN\n-118.27,33.94,34.0,721.0,165.0,661.0,171.0,2.0789,92400.0,<1H OCEAN\n-118.27,33.94,37.0,973.0,221.0,842.0,178.0,1.6645,94900.0,<1H OCEAN\n-118.27,33.93,36.0,1467.0,369.0,1247.0,347.0,1.8191,92700.0,<1H OCEAN\n-118.26,33.93,36.0,1102.0,247.0,702.0,225.0,1.5256,95400.0,<1H OCEAN\n-118.26,33.93,42.0,1433.0,295.0,775.0,293.0,1.1326,104800.0,<1H OCEAN\n-118.25,33.93,42.0,657.0,147.0,526.0,132.0,2.5,110200.0,<1H OCEAN\n-118.26,33.92,40.0,1076.0,244.0,705.0,255.0,1.7986,98900.0,<1H OCEAN\n-118.27,33.92,36.0,1465.0,346.0,1147.0,324.0,1.7262,88800.0,<1H OCEAN\n-118.27,33.94,43.0,1309.0,344.0,1182.0,340.0,1.6625,88700.0,<1H OCEAN\n-118.27,33.94,30.0,1764.0,397.0,1406.0,362.0,1.449,93100.0,<1H OCEAN\n-118.27,33.94,39.0,2078.0,561.0,1901.0,504.0,1.1468,96900.0,<1H OCEAN\n-118.28,33.93,21.0,847.0,278.0,1283.0,277.0,1.4329,94100.0,<1H OCEAN\n-118.28,33.94,32.0,1381.0,375.0,1268.0,354.0,1.1051,94200.0,<1H OCEAN\n-118.28,33.94,9.0,456.0,130.0,438.0,114.0,0.8952,81300.0,<1H OCEAN\n-118.28,33.94,44.0,1631.0,338.0,1197.0,355.0,3.0788,100000.0,<1H OCEAN\n-118.28,33.93,42.0,1898.0,460.0,1503.0,429.0,2.5179,97400.0,<1H OCEAN\n-118.29,33.93,43.0,2021.0,379.0,1051.0,352.0,3.3836,129900.0,<1H OCEAN\n-118.29,33.94,47.0,1782.0,338.0,1003.0,329.0,2.5398,105700.0,<1H OCEAN\n-118.28,33.93,41.0,936.0,257.0,913.0,226.0,2.0313,122600.0,<1H OCEAN\n-118.29,33.92,40.0,1935.0,461.0,1616.0,433.0,2.875,120200.0,<1H OCEAN\n-118.29,33.93,41.0,896.0,198.0,605.0,168.0,2.2778,128100.0,<1H OCEAN\n-118.27,33.93,38.0,2073.0,500.0,1657.0,470.0,1.2098,88400.0,<1H OCEAN\n-118.28,33.92,39.0,1274.0,282.0,975.0,277.0,1.5114,90400.0,<1H OCEAN\n-118.28,33.93,43.0,269.0,74.0,295.0,79.0,2.2969,90600.0,<1H OCEAN\n-118.28,33.93,45.0,529.0,112.0,448.0,120.0,3.5833,90600.0,<1H OCEAN\n-118.28,33.93,52.0,117.0,33.0,74.0,45.0,0.4999,90600.0,<1H OCEAN\n-118.25,33.94,44.0,1463.0,312.0,940.0,312.0,2.3333,99800.0,<1H OCEAN\n-118.25,33.94,43.0,1113.0,378.0,1305.0,334.0,1.1434,91300.0,<1H OCEAN\n-118.25,33.94,43.0,793.0,,736.0,231.0,0.8527,90400.0,<1H OCEAN\n-118.23,33.95,37.0,2667.0,671.0,2865.0,683.0,0.6831,87500.0,<1H OCEAN\n-118.24,33.95,40.0,1193.0,280.0,1210.0,286.0,1.35,89500.0,<1H OCEAN\n-118.24,33.95,37.0,649.0,147.0,653.0,147.0,1.4792,97500.0,<1H OCEAN\n-118.24,33.95,36.0,2316.0,543.0,1938.0,507.0,1.25,97400.0,<1H OCEAN\n-118.24,33.95,21.0,1260.0,342.0,1167.0,310.0,0.9708,107600.0,<1H OCEAN\n-118.25,33.95,28.0,2136.0,,1799.0,476.0,1.5427,95700.0,<1H OCEAN\n-118.24,33.95,37.0,441.0,125.0,390.0,98.0,1.6513,90200.0,<1H OCEAN\n-118.25,33.95,25.0,764.0,200.0,801.0,220.0,1.1384,100000.0,<1H OCEAN\n-118.25,33.93,40.0,975.0,270.0,1068.0,270.0,0.9889,87500.0,<1H OCEAN\n-118.25,33.93,36.0,2452.0,734.0,2664.0,667.0,0.9298,100000.0,<1H OCEAN\n-118.25,33.93,42.0,819.0,233.0,899.0,228.0,1.1346,85400.0,<1H OCEAN\n-118.24,33.94,37.0,869.0,241.0,1040.0,233.0,2.0,84200.0,<1H OCEAN\n-118.24,33.94,34.0,796.0,180.0,673.0,144.0,2.0769,88300.0,<1H OCEAN\n-118.24,33.93,37.0,1027.0,258.0,824.0,248.0,1.5132,86300.0,<1H OCEAN\n-118.24,33.93,32.0,779.0,201.0,861.0,219.0,1.0625,89800.0,<1H OCEAN\n-118.24,33.94,42.0,380.0,106.0,411.0,100.0,0.9705,90000.0,<1H OCEAN\n-118.24,33.94,39.0,1215.0,273.0,1211.0,265.0,1.7212,95500.0,<1H OCEAN\n-118.23,33.94,39.0,1141.0,258.0,1313.0,234.0,2.0187,90100.0,<1H OCEAN\n-118.23,33.94,35.0,1090.0,267.0,1339.0,263.0,2.1607,97600.0,<1H OCEAN\n-118.23,33.94,36.0,1110.0,,1417.0,302.0,2.3333,92100.0,<1H OCEAN\n-118.24,33.94,30.0,940.0,211.0,1071.0,204.0,1.2679,92000.0,<1H OCEAN\n-118.23,33.93,36.0,501.0,123.0,487.0,118.0,1.3,87000.0,<1H OCEAN\n-118.23,33.93,39.0,2065.0,532.0,2015.0,535.0,0.8478,104900.0,<1H OCEAN\n-118.24,33.93,32.0,1063.0,282.0,992.0,253.0,0.8984,88700.0,<1H OCEAN\n-118.39,34.12,29.0,6447.0,1012.0,2184.0,960.0,8.2816,500001.0,<1H OCEAN\n-118.4,34.11,32.0,5578.0,753.0,1567.0,697.0,15.0001,500001.0,<1H OCEAN\n-118.42,34.12,27.0,2089.0,303.0,654.0,270.0,12.3767,500001.0,<1H OCEAN\n-118.43,34.11,27.0,10806.0,1440.0,3511.0,1352.0,12.7296,500001.0,<1H OCEAN\n-118.43,34.09,27.0,1613.0,200.0,497.0,197.0,7.9835,500001.0,<1H OCEAN\n-118.45,34.12,20.0,10722.0,1617.0,3731.0,1511.0,9.7449,500001.0,<1H OCEAN\n-118.44,34.09,36.0,3129.0,392.0,862.0,334.0,15.0001,500001.0,<1H OCEAN\n-118.43,34.08,46.0,778.0,90.0,238.0,93.0,15.0001,500001.0,<1H OCEAN\n-118.45,34.08,52.0,1500.0,176.0,384.0,145.0,7.1576,500001.0,<1H OCEAN\n-118.45,34.1,31.0,6675.0,842.0,2092.0,796.0,11.8442,500001.0,<1H OCEAN\n-118.46,34.08,35.0,3247.0,525.0,1065.0,484.0,7.8426,500001.0,<1H OCEAN\n-118.47,34.1,32.0,8041.0,1141.0,2768.0,1106.0,11.1978,500001.0,<1H OCEAN\n-118.49,34.11,27.0,6603.0,879.0,2336.0,868.0,13.2935,500001.0,<1H OCEAN\n-118.48,34.07,29.0,4767.0,777.0,1500.0,638.0,10.7937,500001.0,<1H OCEAN\n-118.48,34.07,40.0,3351.0,484.0,1564.0,523.0,8.5153,500001.0,<1H OCEAN\n-118.48,34.07,37.0,4042.0,549.0,1318.0,542.0,12.8665,500001.0,<1H OCEAN\n-118.49,34.06,42.0,2861.0,360.0,829.0,310.0,15.0001,500001.0,<1H OCEAN\n-118.49,34.07,36.0,2929.0,366.0,1054.0,352.0,13.5728,500001.0,<1H OCEAN\n-118.51,34.11,29.0,9013.0,1117.0,2919.0,1061.0,13.947,500001.0,<1H OCEAN\n-118.5,34.05,39.0,1487.0,163.0,414.0,160.0,15.0,500001.0,<1H OCEAN\n-118.53,34.09,37.0,5477.0,833.0,1925.0,757.0,8.1888,500001.0,<1H OCEAN\n-118.52,34.05,45.0,1814.0,325.0,709.0,311.0,4.825,500001.0,<1H OCEAN\n-118.52,34.04,47.0,1985.0,315.0,819.0,340.0,6.5147,500001.0,<1H OCEAN\n-118.57,34.09,14.0,7970.0,1142.0,2926.0,1096.0,11.2866,500001.0,<1H OCEAN\n-118.54,34.05,33.0,6778.0,1092.0,2540.0,1052.0,8.565,500001.0,<1H OCEAN\n-118.55,34.04,41.0,1482.0,239.0,617.0,242.0,8.8619,500001.0,<1H OCEAN\n-118.56,34.03,34.0,2095.0,343.0,662.0,299.0,8.2934,500001.0,<1H OCEAN\n-118.56,34.06,24.0,2332.0,349.0,761.0,325.0,7.3031,500001.0,<1H OCEAN\n-118.54,34.06,21.0,3755.0,525.0,1493.0,526.0,11.4233,500001.0,<1H OCEAN\n-118.55,34.03,35.0,9075.0,1858.0,3646.0,1724.0,6.0307,500001.0,<1H OCEAN\n-118.52,34.04,42.0,993.0,130.0,368.0,134.0,10.8082,500001.0,<1H OCEAN\n-118.52,34.04,43.0,2167.0,254.0,761.0,256.0,13.6842,500001.0,<1H OCEAN\n-118.53,34.04,45.0,1711.0,264.0,735.0,261.0,9.1078,500001.0,<1H OCEAN\n-118.53,34.03,40.0,4350.0,763.0,1551.0,665.0,7.0318,500001.0,<1H OCEAN\n-118.5,34.05,36.0,4152.0,542.0,1461.0,550.0,15.0001,500001.0,<1H OCEAN\n-118.55,33.99,39.0,2603.0,456.0,928.0,410.0,7.9096,500001.0,NEAR OCEAN\n-118.51,34.04,38.0,4715.0,691.0,1660.0,637.0,10.1882,500001.0,<1H OCEAN\n-118.47,34.06,45.0,1271.0,190.0,419.0,171.0,7.6447,500001.0,<1H OCEAN\n-118.47,34.06,45.0,3030.0,433.0,916.0,399.0,9.4664,500001.0,<1H OCEAN\n-118.48,34.05,48.0,3623.0,528.0,1282.0,516.0,9.5221,500001.0,<1H OCEAN\n-118.49,34.05,42.0,1918.0,216.0,632.0,224.0,15.0001,500001.0,<1H OCEAN\n-118.47,34.06,26.0,6577.0,1789.0,2937.0,1652.0,4.801,500001.0,<1H OCEAN\n-118.46,34.06,20.0,5448.0,1532.0,2202.0,1442.0,4.2554,500001.0,<1H OCEAN\n-118.47,34.05,25.0,2689.0,719.0,1229.0,663.0,3.5909,500001.0,<1H OCEAN\n-118.46,34.05,25.0,4077.0,1151.0,1719.0,1017.0,3.7721,337500.0,<1H OCEAN\n-118.46,34.05,21.0,3639.0,1002.0,1489.0,983.0,4.6197,387500.0,<1H OCEAN\n-118.47,34.05,22.0,5215.0,1193.0,2048.0,1121.0,4.7009,500001.0,<1H OCEAN\n-118.47,34.05,27.0,4401.0,1033.0,1725.0,962.0,4.175,500001.0,<1H OCEAN\n-118.48,34.05,36.0,2143.0,434.0,751.0,396.0,6.7496,500001.0,<1H OCEAN\n-118.49,34.05,45.0,1346.0,214.0,415.0,209.0,7.0285,500001.0,<1H OCEAN\n-118.42,34.08,46.0,1399.0,148.0,410.0,152.0,15.0001,500001.0,<1H OCEAN\n-118.43,34.07,34.0,3203.0,483.0,949.0,439.0,10.3467,500001.0,<1H OCEAN\n-118.43,34.07,38.0,3251.0,656.0,1251.0,593.0,7.7382,500001.0,<1H OCEAN\n-118.44,34.07,26.0,3535.0,748.0,1322.0,666.0,7.1674,500001.0,<1H OCEAN\n-118.44,34.07,35.0,1973.0,332.0,1257.0,296.0,8.9565,500001.0,<1H OCEAN\n-118.44,34.06,28.0,3910.0,959.0,1763.0,867.0,5.5,500001.0,<1H OCEAN\n-118.44,34.07,21.0,730.0,263.0,965.0,224.0,2.0511,350000.0,<1H OCEAN\n-118.44,34.06,14.0,520.0,292.0,282.0,213.0,2.2857,500001.0,<1H OCEAN\n-118.45,34.07,13.0,4284.0,1452.0,3806.0,1252.0,1.3125,350000.0,<1H OCEAN\n-118.45,34.07,19.0,4845.0,1609.0,3751.0,1539.0,1.583,350000.0,<1H OCEAN\n-118.45,34.06,20.0,3367.0,1264.0,2667.0,1131.0,2.2444,500000.0,<1H OCEAN\n-118.46,34.06,46.0,1302.0,215.0,482.0,226.0,7.0674,500001.0,<1H OCEAN\n-118.46,34.07,43.0,2511.0,456.0,808.0,407.0,6.7703,500001.0,<1H OCEAN\n-118.46,34.07,49.0,2418.0,301.0,850.0,318.0,14.2867,500001.0,<1H OCEAN\n-118.46,34.07,42.0,2564.0,460.0,913.0,414.0,9.2225,500001.0,<1H OCEAN\n-118.44,34.06,9.0,5102.0,1695.0,2609.0,1450.0,3.2545,500001.0,<1H OCEAN\n-118.44,34.05,18.0,4780.0,1192.0,1886.0,1036.0,4.4674,500001.0,<1H OCEAN\n-118.44,34.05,32.0,1880.0,435.0,798.0,417.0,4.7109,500000.0,<1H OCEAN\n-118.44,34.05,15.0,5368.0,1312.0,2269.0,1232.0,5.7097,316700.0,<1H OCEAN\n-118.43,34.06,11.0,3184.0,641.0,911.0,463.0,7.2675,500001.0,<1H OCEAN\n-118.43,34.06,41.0,1463.0,267.0,601.0,267.0,5.3777,500001.0,<1H OCEAN\n-118.43,34.05,22.0,4251.0,1073.0,1581.0,881.0,5.2555,500001.0,<1H OCEAN\n-118.44,34.06,13.0,4833.0,1119.0,1649.0,807.0,6.2389,500001.0,<1H OCEAN\n-118.42,34.06,40.0,2933.0,565.0,1077.0,536.0,6.1527,500001.0,<1H OCEAN\n-118.43,34.06,31.0,1317.0,284.0,523.0,274.0,7.4219,500001.0,<1H OCEAN\n-118.43,34.06,38.0,2982.0,664.0,1122.0,572.0,4.1908,500001.0,<1H OCEAN\n-118.43,34.06,20.0,4600.0,1018.0,1675.0,932.0,5.1999,500001.0,<1H OCEAN\n-118.42,34.06,44.0,533.0,90.0,291.0,97.0,10.8045,500001.0,<1H OCEAN\n-118.42,34.06,52.0,1881.0,334.0,640.0,321.0,6.871,500001.0,<1H OCEAN\n-118.42,34.05,38.0,4888.0,1126.0,1698.0,937.0,4.8304,500001.0,<1H OCEAN\n-118.43,34.05,52.0,1693.0,290.0,727.0,305.0,6.7115,500001.0,<1H OCEAN\n-118.43,34.05,24.0,3832.0,949.0,1613.0,893.0,3.9673,477300.0,<1H OCEAN\n-118.44,34.05,20.0,5943.0,1538.0,2492.0,1429.0,4.1141,305000.0,<1H OCEAN\n-118.44,34.05,22.0,3970.0,871.0,1588.0,791.0,4.8618,500001.0,<1H OCEAN\n-118.43,34.04,52.0,2425.0,435.0,962.0,412.0,5.8587,494700.0,<1H OCEAN\n-118.45,34.05,28.0,801.0,399.0,936.0,406.0,2.1875,181300.0,<1H OCEAN\n-118.45,34.04,22.0,3319.0,1045.0,1848.0,940.0,3.6673,283300.0,<1H OCEAN\n-118.45,34.04,23.0,3771.0,1321.0,2031.0,1241.0,2.7679,277500.0,<1H OCEAN\n-118.46,34.05,25.0,6902.0,2138.0,3136.0,1844.0,2.6509,410000.0,<1H OCEAN\n-118.45,34.05,23.0,4099.0,1287.0,2103.0,1217.0,3.7549,275000.0,<1H OCEAN\n-118.46,34.04,19.0,3522.0,1036.0,1820.0,977.0,3.2663,337500.0,<1H OCEAN\n-118.47,34.04,21.0,5041.0,1491.0,2719.0,1420.0,3.5335,268800.0,<1H OCEAN\n-118.45,34.04,19.0,3330.0,1010.0,1837.0,915.0,3.0173,393800.0,<1H OCEAN\n-118.46,34.04,25.0,2142.0,718.0,1390.0,699.0,3.0069,325000.0,<1H OCEAN\n-118.46,34.04,25.0,2768.0,850.0,1558.0,784.0,3.6976,360000.0,<1H OCEAN\n-118.46,34.04,17.0,2729.0,897.0,1404.0,758.0,3.1235,420800.0,<1H OCEAN\n-118.46,34.04,31.0,2621.0,707.0,1632.0,673.0,3.287,348100.0,<1H OCEAN\n-118.45,34.04,21.0,2819.0,648.0,1435.0,593.0,3.9489,360200.0,<1H OCEAN\n-118.45,34.03,45.0,727.0,168.0,520.0,175.0,2.6528,300000.0,<1H OCEAN\n-118.45,34.03,39.0,1657.0,402.0,931.0,363.0,3.7813,336300.0,<1H OCEAN\n-118.44,34.04,49.0,32.0,7.0,14.0,7.0,2.1875,225000.0,<1H OCEAN\n-118.44,34.04,16.0,18.0,6.0,3.0,4.0,0.536,350000.0,<1H OCEAN\n-118.44,34.04,31.0,2670.0,662.0,1535.0,631.0,3.0714,347800.0,<1H OCEAN\n-118.43,34.04,52.0,1782.0,308.0,735.0,307.0,5.2954,485100.0,<1H OCEAN\n-118.42,34.04,46.0,1508.0,276.0,639.0,273.0,4.925,409800.0,<1H OCEAN\n-118.43,34.04,42.0,2725.0,569.0,1115.0,516.0,4.5833,427500.0,<1H OCEAN\n-118.41,34.05,16.0,9728.0,2211.0,3026.0,1899.0,5.8758,500001.0,<1H OCEAN\n-118.42,34.05,33.0,2921.0,652.0,1124.0,608.0,5.0151,500001.0,<1H OCEAN\n-118.42,34.05,52.0,2533.0,402.0,981.0,386.0,7.8164,500001.0,<1H OCEAN\n-118.4,34.05,26.0,4473.0,923.0,1518.0,805.0,5.0762,500001.0,<1H OCEAN\n-118.4,34.04,40.0,2079.0,268.0,720.0,282.0,9.272,500001.0,<1H OCEAN\n-118.4,34.04,42.0,1298.0,174.0,420.0,190.0,12.8483,500001.0,<1H OCEAN\n-118.4,34.03,36.0,1831.0,296.0,871.0,269.0,8.1484,500001.0,<1H OCEAN\n-118.41,34.03,33.0,1730.0,386.0,994.0,363.0,3.7277,500001.0,<1H OCEAN\n-118.41,34.04,49.0,601.0,95.0,228.0,106.0,8.0239,500001.0,<1H OCEAN\n-118.39,34.05,35.0,2458.0,576.0,1047.0,554.0,4.0283,412500.0,<1H OCEAN\n-118.39,34.05,42.0,3105.0,559.0,1253.0,531.0,5.222,500001.0,<1H OCEAN\n-118.4,34.05,34.0,2113.0,459.0,859.0,432.0,3.6953,500001.0,<1H OCEAN\n-118.39,34.05,25.0,2814.0,701.0,1139.0,658.0,4.0153,460000.0,<1H OCEAN\n-118.41,34.04,52.0,1907.0,261.0,681.0,249.0,10.9805,500001.0,<1H OCEAN\n-118.41,34.04,52.0,2113.0,332.0,800.0,327.0,11.1768,500001.0,<1H OCEAN\n-118.41,34.03,36.0,3053.0,635.0,1234.0,577.0,5.1637,500001.0,<1H OCEAN\n-118.42,34.04,51.0,1975.0,348.0,771.0,357.0,6.626,500001.0,<1H OCEAN\n-118.42,34.04,52.0,1358.0,272.0,574.0,267.0,5.6454,500001.0,<1H OCEAN\n-118.39,34.04,45.0,2089.0,312.0,834.0,305.0,7.3028,500001.0,<1H OCEAN\n-118.39,34.04,44.0,1873.0,286.0,635.0,283.0,5.5951,461300.0,<1H OCEAN\n-118.4,34.04,43.0,3863.0,537.0,1398.0,511.0,8.5938,500001.0,<1H OCEAN\n-118.4,34.05,43.0,1028.0,145.0,394.0,149.0,10.4519,500001.0,<1H OCEAN\n-118.39,34.05,42.0,1467.0,203.0,577.0,204.0,6.6368,500001.0,<1H OCEAN\n-118.38,34.04,35.0,2237.0,592.0,1794.0,543.0,2.2961,207700.0,<1H OCEAN\n-118.38,34.04,31.0,2846.0,727.0,2120.0,672.0,2.7226,254200.0,<1H OCEAN\n-118.38,34.04,45.0,767.0,130.0,254.0,118.0,6.2895,340400.0,<1H OCEAN\n-118.38,34.04,36.0,3005.0,771.0,2054.0,758.0,2.0437,309100.0,<1H OCEAN\n-118.38,34.04,39.0,2614.0,569.0,1665.0,553.0,3.4063,271600.0,<1H OCEAN\n-118.39,34.04,41.0,101.0,23.0,85.0,30.0,4.125,237500.0,<1H OCEAN\n-118.39,34.04,52.0,1492.0,277.0,666.0,289.0,4.7386,340400.0,<1H OCEAN\n-118.39,34.04,49.0,1230.0,279.0,669.0,269.0,3.9038,308300.0,<1H OCEAN\n-118.39,34.03,28.0,1722.0,536.0,1161.0,481.0,3.2228,232500.0,<1H OCEAN\n-118.39,34.03,25.0,3442.0,1050.0,1890.0,914.0,3.0574,319400.0,<1H OCEAN\n-118.4,34.03,24.0,1101.0,318.0,491.0,287.0,3.2222,319400.0,<1H OCEAN\n-118.4,34.03,43.0,1006.0,201.0,520.0,199.0,6.5669,372800.0,<1H OCEAN\n-118.41,34.03,26.0,4376.0,1394.0,2435.0,1250.0,2.8418,327300.0,<1H OCEAN\n-118.4,34.02,19.0,7297.0,2331.0,3870.0,2144.0,3.116,300000.0,<1H OCEAN\n-118.4,34.02,27.0,515.0,201.0,397.0,228.0,2.4135,184400.0,<1H OCEAN\n-118.41,34.03,24.0,3711.0,1192.0,1764.0,1147.0,3.1642,366700.0,<1H OCEAN\n-118.41,34.02,24.0,2610.0,756.0,1322.0,692.0,3.5022,281300.0,<1H OCEAN\n-118.41,34.02,16.0,5825.0,1866.0,3390.0,1752.0,3.0965,320000.0,<1H OCEAN\n-118.39,34.03,19.0,1450.0,509.0,746.0,437.0,3.1415,55000.0,<1H OCEAN\n-118.4,34.03,13.0,6152.0,1978.0,3397.0,1845.0,3.4058,275000.0,<1H OCEAN\n-118.38,34.03,36.0,2101.0,569.0,1756.0,527.0,2.9344,222100.0,<1H OCEAN\n-118.39,34.03,39.0,1366.0,375.0,1237.0,370.0,3.7206,230900.0,<1H OCEAN\n-118.37,34.04,43.0,888.0,170.0,514.0,161.0,3.1827,202800.0,<1H OCEAN\n-118.37,34.04,42.0,1809.0,424.0,1094.0,382.0,2.767,143000.0,<1H OCEAN\n-118.38,34.03,43.0,912.0,255.0,705.0,246.0,2.6402,185700.0,<1H OCEAN\n-118.37,34.04,25.0,542.0,161.0,442.0,131.0,2.25,333300.0,<1H OCEAN\n-118.37,34.04,43.0,1465.0,278.0,727.0,290.0,4.0781,289400.0,<1H OCEAN\n-118.42,34.03,45.0,1262.0,223.0,637.0,221.0,5.0866,427300.0,<1H OCEAN\n-118.43,34.03,45.0,1740.0,311.0,788.0,306.0,5.2099,373600.0,<1H OCEAN\n-118.43,34.03,39.0,1733.0,429.0,855.0,387.0,3.2308,340800.0,<1H OCEAN\n-118.43,34.03,26.0,1706.0,516.0,894.0,435.0,3.1875,372700.0,<1H OCEAN\n-118.42,34.03,44.0,904.0,176.0,358.0,158.0,3.3542,344200.0,<1H OCEAN\n-118.44,34.03,25.0,2059.0,659.0,1349.0,588.0,3.2396,352400.0,<1H OCEAN\n-118.43,34.03,36.0,1552.0,388.0,867.0,352.0,3.6467,346700.0,<1H OCEAN\n-118.44,34.03,37.0,1193.0,205.0,488.0,224.0,3.625,357600.0,<1H OCEAN\n-118.44,34.03,37.0,975.0,189.0,489.0,202.0,4.2434,331000.0,<1H OCEAN\n-118.44,34.03,30.0,1039.0,303.0,606.0,274.0,3.125,343800.0,<1H OCEAN\n-118.45,34.03,41.0,1240.0,320.0,711.0,304.0,3.3482,318100.0,<1H OCEAN\n-118.45,34.03,41.0,2083.0,528.0,993.0,481.0,4.0231,353900.0,<1H OCEAN\n-118.44,34.03,41.0,1164.0,265.0,561.0,251.0,4.2411,350900.0,<1H OCEAN\n-118.44,34.02,37.0,1592.0,308.0,783.0,321.0,6.2583,386000.0,<1H OCEAN\n-118.44,34.01,43.0,1408.0,246.0,651.0,240.0,4.5795,400000.0,<1H OCEAN\n-118.44,34.01,41.0,1309.0,221.0,534.0,228.0,5.1708,418800.0,<1H OCEAN\n-118.45,34.01,37.0,1328.0,250.0,626.0,228.0,5.8666,440100.0,<1H OCEAN\n-118.45,34.01,40.0,1361.0,240.0,559.0,229.0,6.3516,354300.0,<1H OCEAN\n-118.45,34.01,36.0,2424.0,418.0,1123.0,417.0,6.4755,405800.0,<1H OCEAN\n-118.44,34.02,32.0,2242.0,490.0,921.0,461.0,4.0429,500001.0,<1H OCEAN\n-118.43,34.01,43.0,1487.0,242.0,675.0,247.0,5.3403,489800.0,<1H OCEAN\n-118.44,34.02,39.0,3278.0,632.0,1321.0,617.0,6.2917,465700.0,<1H OCEAN\n-118.43,34.02,38.0,2172.0,437.0,830.0,368.0,3.9091,500001.0,<1H OCEAN\n-118.43,34.02,42.0,1528.0,244.0,634.0,242.0,8.1631,500001.0,<1H OCEAN\n-118.43,34.02,41.0,2403.0,516.0,1001.0,514.0,4.3906,500001.0,<1H OCEAN\n-118.42,34.03,44.0,629.0,131.0,326.0,156.0,4.5278,374300.0,<1H OCEAN\n-118.41,34.03,20.0,4374.0,1311.0,2165.0,1185.0,3.6019,463600.0,<1H OCEAN\n-118.42,34.02,21.0,3244.0,815.0,1423.0,781.0,3.6488,340800.0,<1H OCEAN\n-118.42,34.02,34.0,2243.0,444.0,973.0,413.0,4.9676,414100.0,<1H OCEAN\n-118.42,34.02,28.0,3167.0,737.0,1248.0,665.0,3.1941,394700.0,<1H OCEAN\n-118.42,34.02,34.0,2995.0,942.0,2626.0,947.0,2.2402,450000.0,<1H OCEAN\n-118.41,34.02,34.0,1430.0,357.0,805.0,362.0,3.3462,307000.0,<1H OCEAN\n-118.42,34.02,22.0,3292.0,1134.0,1655.0,898.0,3.1746,348800.0,<1H OCEAN\n-118.42,34.02,26.0,2664.0,842.0,1745.0,789.0,3.4269,301900.0,<1H OCEAN\n-118.41,34.02,35.0,1728.0,442.0,1161.0,420.0,3.725,310000.0,<1H OCEAN\n-118.41,34.02,19.0,4702.0,1472.0,2636.0,1334.0,3.3955,225000.0,<1H OCEAN\n-118.41,34.02,27.0,2224.0,618.0,1594.0,625.0,3.0833,315500.0,<1H OCEAN\n-118.42,34.01,33.0,2731.0,535.0,1280.0,510.0,4.7083,420100.0,<1H OCEAN\n-118.42,34.01,32.0,1300.0,356.0,703.0,311.0,3.5667,394000.0,<1H OCEAN\n-118.43,34.01,27.0,3133.0,1021.0,2242.0,1002.0,2.697,412500.0,<1H OCEAN\n-118.43,34.01,41.0,1527.0,279.0,746.0,285.0,6.4232,446600.0,<1H OCEAN\n-118.43,34.01,31.0,2526.0,528.0,1046.0,504.0,4.7009,500001.0,<1H OCEAN\n-118.44,34.01,42.0,2061.0,396.0,907.0,393.0,6.0804,420000.0,<1H OCEAN\n-118.44,34.0,44.0,1462.0,338.0,821.0,341.0,2.599,362200.0,<1H OCEAN\n-118.44,34.0,44.0,1798.0,353.0,835.0,314.0,4.75,355800.0,<1H OCEAN\n-118.45,34.0,39.0,1909.0,359.0,867.0,345.0,4.7,334700.0,<1H OCEAN\n-118.44,34.0,40.0,1287.0,346.0,806.0,311.0,3.875,321300.0,<1H OCEAN\n-118.43,34.0,30.0,2148.0,597.0,1341.0,559.0,3.3995,324000.0,<1H OCEAN\n-118.44,34.0,22.0,5822.0,1707.0,3335.0,1585.0,3.1579,243100.0,<1H OCEAN\n-118.44,34.0,41.0,1562.0,377.0,874.0,368.0,4.1083,324300.0,<1H OCEAN\n-118.42,34.01,42.0,1594.0,369.0,952.0,362.0,3.099,335400.0,<1H OCEAN\n-118.42,34.01,29.0,1996.0,489.0,960.0,449.0,3.6611,344200.0,<1H OCEAN\n-118.42,34.0,31.0,1930.0,456.0,1002.0,410.0,3.9798,458600.0,<1H OCEAN\n-118.43,34.0,28.0,6128.0,1963.0,3586.0,1815.0,2.7058,310900.0,<1H OCEAN\n-118.46,34.01,43.0,513.0,98.0,266.0,103.0,5.6428,343100.0,<1H OCEAN\n-118.46,34.0,39.0,4098.0,1100.0,2054.0,1053.0,2.918,345600.0,<1H OCEAN\n-118.46,34.0,37.0,388.0,83.0,248.0,84.0,5.1664,326700.0,<1H OCEAN\n-118.46,34.0,36.0,1392.0,260.0,679.0,247.0,4.7344,346900.0,<1H OCEAN\n-118.46,34.01,39.0,711.0,148.0,347.0,153.0,4.2813,297200.0,<1H OCEAN\n-118.47,34.0,38.0,1235.0,390.0,891.0,376.0,2.7143,287500.0,<1H OCEAN\n-118.47,34.0,41.0,2331.0,636.0,1839.0,537.0,2.288,263500.0,<1H OCEAN\n-118.46,34.0,39.0,614.0,174.0,538.0,159.0,2.3542,235700.0,<1H OCEAN\n-118.47,33.99,34.0,1875.0,501.0,1491.0,526.0,2.8417,321400.0,<1H OCEAN\n-118.47,34.0,37.0,2586.0,765.0,1801.0,737.0,2.6042,305800.0,<1H OCEAN\n-118.47,33.99,31.0,1312.0,376.0,1178.0,330.0,2.0714,300000.0,<1H OCEAN\n-118.47,33.99,41.0,1146.0,310.0,833.0,270.0,2.5938,285000.0,<1H OCEAN\n-118.47,33.99,33.0,854.0,235.0,645.0,198.0,2.1471,239300.0,<1H OCEAN\n-118.51,33.98,40.0,1901.0,679.0,865.0,587.0,2.3417,425000.0,<1H OCEAN\n-118.48,33.99,46.0,2219.0,686.0,1107.0,590.0,2.5523,387500.0,<1H OCEAN\n-118.47,33.99,50.0,1568.0,501.0,764.0,478.0,3.015,414300.0,<1H OCEAN\n-118.47,33.99,52.0,1523.0,447.0,636.0,408.0,3.0682,412500.0,<1H OCEAN\n-118.47,33.99,52.0,2167.0,622.0,1095.0,570.0,2.8514,358700.0,<1H OCEAN\n-118.47,33.99,37.0,2155.0,721.0,1082.0,637.0,3.4071,267500.0,<1H OCEAN\n-118.47,33.99,24.0,1438.0,454.0,665.0,416.0,2.975,500001.0,<1H OCEAN\n-118.5,33.97,52.0,709.0,329.0,388.0,313.0,2.2643,350000.0,<1H OCEAN\n-118.45,33.99,52.0,1010.0,244.0,573.0,242.0,4.1861,363200.0,<1H OCEAN\n-118.46,33.99,52.0,1158.0,253.0,528.0,253.0,3.5234,334700.0,<1H OCEAN\n-118.46,33.99,44.0,1122.0,287.0,531.0,256.0,4.0598,335900.0,<1H OCEAN\n-118.46,34.0,44.0,941.0,230.0,493.0,206.0,3.6458,325800.0,<1H OCEAN\n-118.46,34.0,52.0,888.0,206.0,376.0,194.0,3.875,372000.0,<1H OCEAN\n-118.45,34.0,46.0,1777.0,362.0,896.0,334.0,4.45,348300.0,<1H OCEAN\n-118.45,34.0,43.0,1606.0,408.0,862.0,354.0,3.962,345800.0,<1H OCEAN\n-118.45,34.0,48.0,1923.0,408.0,1142.0,433.0,4.575,326700.0,<1H OCEAN\n-118.45,33.99,33.0,3125.0,785.0,1720.0,713.0,2.9722,325000.0,<1H OCEAN\n-118.45,33.99,52.0,1829.0,472.0,779.0,424.0,3.1607,339000.0,<1H OCEAN\n-118.46,33.99,41.0,885.0,285.0,562.0,268.0,3.1992,303800.0,<1H OCEAN\n-118.46,33.99,37.0,1828.0,460.0,1075.0,453.0,4.337,360600.0,<1H OCEAN\n-118.46,33.98,32.0,2388.0,591.0,1009.0,556.0,5.2121,466700.0,<1H OCEAN\n-118.46,33.99,35.0,1214.0,300.0,478.0,265.0,4.0156,500001.0,<1H OCEAN\n-118.46,33.98,27.0,2217.0,520.0,806.0,458.0,3.8935,500001.0,<1H OCEAN\n-118.5,33.97,29.0,2737.0,808.0,1157.0,696.0,5.128,500001.0,<1H OCEAN\n-118.45,33.99,45.0,1132.0,269.0,654.0,264.0,4.5673,343100.0,<1H OCEAN\n-118.45,33.99,26.0,1919.0,405.0,953.0,371.0,6.0672,420800.0,<1H OCEAN\n-118.46,33.98,19.0,2520.0,726.0,964.0,663.0,3.8068,500001.0,<1H OCEAN\n-118.46,33.97,19.0,1658.0,427.0,648.0,378.0,3.8698,500001.0,<1H OCEAN\n-118.46,33.97,18.0,9430.0,2473.0,3408.0,2003.0,6.1726,500001.0,<1H OCEAN\n-118.46,33.97,19.0,2461.0,521.0,777.0,447.0,10.0,500001.0,<1H OCEAN\n-118.48,33.96,16.0,895.0,181.0,237.0,149.0,12.0088,500001.0,<1H OCEAN\n-118.4,34.0,10.0,1526.0,339.0,705.0,268.0,5.8083,321800.0,<1H OCEAN\n-118.41,34.0,35.0,684.0,161.0,381.0,159.0,2.8393,272000.0,<1H OCEAN\n-118.41,34.0,30.0,3550.0,934.0,3738.0,880.0,3.191,271200.0,<1H OCEAN\n-118.41,34.0,18.0,1307.0,441.0,884.0,456.0,2.9338,276300.0,<1H OCEAN\n-118.42,34.0,14.0,3771.0,1180.0,2355.0,978.0,3.1603,287500.0,<1H OCEAN\n-118.42,33.99,35.0,1701.0,482.0,1428.0,494.0,3.725,284600.0,<1H OCEAN\n-118.43,33.99,35.0,2243.0,495.0,1135.0,465.0,4.3281,324000.0,<1H OCEAN\n-118.43,33.98,19.0,8324.0,1590.0,2927.0,1538.0,7.5426,351700.0,<1H OCEAN\n-118.43,33.99,45.0,1899.0,461.0,1260.0,415.0,2.6667,320000.0,<1H OCEAN\n-118.43,33.99,43.0,2483.0,548.0,1212.0,493.0,4.0189,302900.0,<1H OCEAN\n-118.42,33.99,23.0,5548.0,1245.0,2847.0,1229.0,4.4228,366900.0,<1H OCEAN\n-118.42,33.98,3.0,475.0,155.0,236.0,153.0,3.6667,450000.0,<1H OCEAN\n-118.43,33.99,42.0,2558.0,558.0,1328.0,541.0,4.875,301300.0,<1H OCEAN\n-118.44,33.99,43.0,1432.0,308.0,782.0,303.0,4.3333,303900.0,<1H OCEAN\n-118.41,33.99,39.0,3014.0,822.0,3212.0,777.0,1.1985,215000.0,<1H OCEAN\n-118.42,33.99,38.0,740.0,171.0,599.0,194.0,4.0893,248900.0,<1H OCEAN\n-118.42,33.99,35.0,1724.0,419.0,1079.0,394.0,4.5521,263800.0,<1H OCEAN\n-118.41,34.0,35.0,1062.0,305.0,1026.0,307.0,2.7153,265500.0,<1H OCEAN\n-118.41,33.99,36.0,1089.0,234.0,632.0,215.0,3.6538,276100.0,<1H OCEAN\n-118.4,33.99,39.0,1613.0,380.0,1113.0,356.0,2.825,276700.0,<1H OCEAN\n-118.41,33.98,33.0,3331.0,777.0,1695.0,735.0,3.9727,307200.0,<1H OCEAN\n-118.39,33.97,38.0,993.0,175.0,374.0,180.0,6.2673,357200.0,<1H OCEAN\n-118.38,33.97,43.0,2715.0,458.0,1151.0,434.0,7.4897,362600.0,<1H OCEAN\n-118.39,33.97,44.0,1097.0,186.0,513.0,185.0,6.235,361400.0,<1H OCEAN\n-118.39,33.97,43.0,2700.0,510.0,1146.0,506.0,5.1333,345900.0,<1H OCEAN\n-118.39,33.97,46.0,2198.0,352.0,839.0,335.0,6.5778,350800.0,<1H OCEAN\n-118.39,33.96,45.0,1361.0,252.0,581.0,263.0,5.8143,340700.0,<1H OCEAN\n-118.39,33.96,45.0,1436.0,374.0,662.0,292.0,3.625,329400.0,<1H OCEAN\n-118.36,33.98,40.0,1113.0,234.0,584.0,231.0,3.0927,316000.0,<1H OCEAN\n-118.37,33.98,39.0,303.0,69.0,131.0,73.0,4.3438,331800.0,<1H OCEAN\n-118.37,33.97,32.0,6699.0,1781.0,2725.0,1544.0,3.3889,285700.0,<1H OCEAN\n-118.41,33.97,37.0,1629.0,275.0,668.0,266.0,6.1333,387200.0,<1H OCEAN\n-118.41,33.97,44.0,2298.0,388.0,849.0,360.0,5.5941,363500.0,<1H OCEAN\n-118.4,33.96,44.0,1877.0,314.0,877.0,320.0,6.8197,363600.0,<1H OCEAN\n-118.41,33.96,44.0,1802.0,306.0,753.0,282.0,6.0286,356000.0,<1H OCEAN\n-118.41,33.97,43.0,1464.0,224.0,581.0,232.0,6.2022,365900.0,<1H OCEAN\n-118.41,33.97,44.0,2789.0,503.0,3732.0,474.0,4.6176,352300.0,<1H OCEAN\n-118.42,33.97,44.0,1462.0,240.0,562.0,237.0,4.9375,365200.0,<1H OCEAN\n-118.42,33.96,44.0,1282.0,216.0,494.0,209.0,5.618,365900.0,<1H OCEAN\n-118.43,33.96,38.0,1104.0,216.0,415.0,163.0,6.1985,422000.0,<1H OCEAN\n-118.43,33.96,13.0,1860.0,345.0,800.0,355.0,5.8864,500001.0,<1H OCEAN\n-118.44,33.96,33.0,2799.0,491.0,978.0,447.0,5.6435,500001.0,<1H OCEAN\n-118.43,33.96,20.0,1901.0,270.0,704.0,254.0,8.7819,500001.0,<1H OCEAN\n-118.43,33.97,16.0,70.0,7.0,17.0,4.0,7.7197,500001.0,<1H OCEAN\n-118.42,33.96,24.0,4393.0,966.0,1257.0,579.0,5.0714,356100.0,<1H OCEAN\n-118.43,33.96,16.0,14891.0,3984.0,6270.0,3595.0,5.1064,283200.0,<1H OCEAN\n-118.4,33.97,35.0,913.0,161.0,451.0,172.0,5.6169,411200.0,<1H OCEAN\n-118.4,33.98,39.0,714.0,118.0,314.0,117.0,5.9856,432100.0,<1H OCEAN\n-118.4,33.98,36.0,2526.0,452.0,996.0,441.0,5.611,456600.0,<1H OCEAN\n-118.4,33.97,38.0,1089.0,174.0,502.0,180.0,7.5953,434800.0,<1H OCEAN\n-118.4,33.97,37.0,1364.0,248.0,494.0,242.0,4.6364,456300.0,<1H OCEAN\n-118.4,33.97,44.0,2825.0,453.0,1221.0,461.0,5.9544,377200.0,<1H OCEAN\n-118.4,33.96,43.0,2416.0,454.0,1028.0,409.0,5.6732,330700.0,<1H OCEAN\n-118.37,33.97,41.0,1833.0,355.0,847.0,348.0,5.726,287800.0,<1H OCEAN\n-118.38,33.97,42.0,1859.0,337.0,813.0,326.0,6.019,294500.0,<1H OCEAN\n-118.38,33.96,44.0,2395.0,458.0,1287.0,450.0,4.6923,299000.0,<1H OCEAN\n-118.38,33.95,35.0,3259.0,967.0,2003.0,920.0,3.2708,400000.0,<1H OCEAN\n-118.38,33.95,29.0,1821.0,588.0,1397.0,523.0,2.5833,187500.0,<1H OCEAN\n-118.37,33.95,5.0,6955.0,2062.0,3591.0,1566.0,3.111,247600.0,<1H OCEAN\n-118.4,33.96,44.0,1138.0,228.0,497.0,228.0,4.1852,303300.0,<1H OCEAN\n-118.41,33.96,15.0,412.0,128.0,310.0,137.0,3.9792,266700.0,<1H OCEAN\n-118.41,33.96,32.0,1044.0,219.0,567.0,222.0,4.1471,284400.0,<1H OCEAN\n-118.41,33.94,45.0,2038.0,394.0,1086.0,387.0,4.7375,289600.0,<1H OCEAN\n-118.45,33.96,24.0,3097.0,791.0,1075.0,639.0,5.723,500001.0,<1H OCEAN\n-118.45,33.96,36.0,2723.0,547.0,1090.0,519.0,6.3267,500001.0,<1H OCEAN\n-118.44,33.95,37.0,2076.0,332.0,771.0,327.0,6.2551,500001.0,<1H OCEAN\n-118.28,33.92,37.0,1761.0,409.0,1309.0,357.0,2.1875,175900.0,<1H OCEAN\n-118.28,33.91,41.0,620.0,133.0,642.0,162.0,2.6546,159600.0,<1H OCEAN\n-118.29,33.9,42.0,1273.0,309.0,1203.0,348.0,4.4636,162000.0,<1H OCEAN\n-118.29,33.91,41.0,2475.0,532.0,1416.0,470.0,3.8372,156400.0,<1H OCEAN\n-118.29,33.91,31.0,2025.0,618.0,2231.0,593.0,2.4741,151200.0,<1H OCEAN\n-118.29,33.92,23.0,2503.0,532.0,1735.0,505.0,2.7368,162800.0,<1H OCEAN\n-118.28,33.88,19.0,2758.0,675.0,2047.0,591.0,2.6618,179700.0,<1H OCEAN\n-118.29,33.88,32.0,2307.0,493.0,1754.0,528.0,4.317,232800.0,<1H OCEAN\n-118.29,33.88,27.0,2082.0,612.0,2009.0,548.0,2.9063,184100.0,<1H OCEAN\n-118.29,33.89,35.0,2810.0,614.0,1578.0,601.0,3.59,200600.0,<1H OCEAN\n-118.29,33.87,32.0,1700.0,340.0,864.0,317.0,4.381,238700.0,<1H OCEAN\n-118.3,33.86,35.0,2016.0,365.0,1083.0,369.0,5.1727,230200.0,<1H OCEAN\n-118.3,33.86,35.0,1511.0,274.0,853.0,308.0,4.9792,251300.0,<1H OCEAN\n-118.29,33.85,10.0,1391.0,420.0,1378.0,377.0,1.9049,222200.0,<1H OCEAN\n-118.31,33.84,5.0,3559.0,869.0,2965.0,794.0,2.6491,216700.0,<1H OCEAN\n-118.3,33.84,36.0,1428.0,268.0,825.0,250.0,4.7222,239600.0,<1H OCEAN\n-118.3,33.84,37.0,1241.0,226.0,621.0,255.0,4.9196,232400.0,<1H OCEAN\n-118.3,33.85,38.0,123.0,36.0,142.0,40.0,2.3942,200000.0,<1H OCEAN\n-118.3,33.83,31.0,2693.0,661.0,1598.0,618.0,3.1851,240200.0,<1H OCEAN\n-118.3,33.83,33.0,2716.0,660.0,1807.0,661.0,3.5473,226300.0,<1H OCEAN\n-118.31,33.83,27.0,2880.0,802.0,2083.0,727.0,2.9619,223400.0,<1H OCEAN\n-118.3,33.82,35.0,1499.0,340.0,1141.0,326.0,2.6136,213600.0,<1H OCEAN\n-118.3,33.82,26.0,2080.0,560.0,2096.0,506.0,2.8106,184400.0,<1H OCEAN\n-118.3,33.82,25.0,2659.0,765.0,2629.0,726.0,2.6368,175900.0,<1H OCEAN\n-118.3,33.81,17.0,5544.0,1068.0,3008.0,1038.0,5.322,282700.0,<1H OCEAN\n-118.3,33.8,27.0,2790.0,513.0,1498.0,519.0,5.3106,268300.0,<1H OCEAN\n-118.31,33.8,30.0,3096.0,757.0,2048.0,704.0,3.125,233300.0,<1H OCEAN\n-118.3,33.79,35.0,2793.0,686.0,2255.0,682.0,3.0057,235300.0,<1H OCEAN\n-118.3,33.79,9.0,2782.0,884.0,1790.0,748.0,2.9154,203300.0,<1H OCEAN\n-118.3,33.79,13.0,3569.0,924.0,2159.0,880.0,3.163,224200.0,<1H OCEAN\n-118.3,33.78,35.0,2572.0,504.0,1172.0,519.0,4.6207,304100.0,<1H OCEAN\n-118.26,33.8,41.0,2004.0,481.0,1658.0,456.0,3.1779,171100.0,<1H OCEAN\n-118.25,33.8,36.0,1697.0,394.0,1274.0,396.0,3.35,163100.0,<1H OCEAN\n-118.26,33.79,30.0,1291.0,230.0,835.0,215.0,5.5,181500.0,<1H OCEAN\n-118.25,33.79,32.0,1205.0,340.0,1799.0,370.0,2.375,128000.0,NEAR OCEAN\n-118.24,33.8,28.0,636.0,169.0,788.0,143.0,3.6161,131300.0,NEAR OCEAN\n-118.23,33.8,26.0,239.0,135.0,165.0,112.0,1.3333,187500.0,NEAR OCEAN\n-118.27,33.8,38.0,1446.0,327.0,980.0,319.0,3.35,177700.0,<1H OCEAN\n-118.27,33.79,36.0,2218.0,561.0,1789.0,527.0,3.1613,182300.0,<1H OCEAN\n-118.27,33.79,39.0,1513.0,365.0,1227.0,354.0,3.3929,184600.0,<1H OCEAN\n-118.28,33.8,38.0,1471.0,329.0,1207.0,335.0,4.0,165500.0,<1H OCEAN\n-118.28,33.79,28.0,1895.0,420.0,1422.0,389.0,4.3816,191300.0,<1H OCEAN\n-118.28,33.79,36.0,1989.0,458.0,1668.0,455.0,3.3009,168000.0,<1H OCEAN\n-118.28,33.78,20.0,2233.0,591.0,1915.0,558.0,3.2011,169100.0,<1H OCEAN\n-118.29,33.79,10.0,3708.0,1016.0,2855.0,948.0,2.0,165400.0,<1H OCEAN\n-118.29,33.79,16.0,1867.0,571.0,951.0,498.0,3.3427,154200.0,<1H OCEAN\n-118.3,33.79,21.0,1068.0,283.0,1180.0,274.0,2.5,157500.0,<1H OCEAN\n-118.3,33.8,8.0,1115.0,412.0,1472.0,396.0,3.1392,146200.0,<1H OCEAN\n-118.27,33.79,31.0,1535.0,369.0,1291.0,340.0,2.9375,174000.0,<1H OCEAN\n-118.26,33.78,27.0,1672.0,491.0,1723.0,462.0,2.0458,174500.0,NEAR OCEAN\n-118.27,33.79,26.0,2109.0,651.0,2120.0,605.0,2.1447,158700.0,<1H OCEAN\n-118.27,33.79,39.0,1417.0,359.0,1450.0,367.0,2.8462,172000.0,<1H OCEAN\n-118.25,33.79,34.0,1349.0,371.0,1716.0,380.0,2.7143,138100.0,NEAR OCEAN\n-118.25,33.79,38.0,1730.0,460.0,1724.0,424.0,2.7308,150400.0,NEAR OCEAN\n-118.25,33.79,39.0,981.0,286.0,1183.0,298.0,1.9208,139800.0,NEAR OCEAN\n-118.26,33.78,21.0,2188.0,706.0,2265.0,652.0,1.9923,164700.0,NEAR OCEAN\n-118.26,33.79,42.0,1162.0,264.0,1044.0,241.0,3.5488,205600.0,<1H OCEAN\n-118.23,33.78,20.0,59.0,24.0,69.0,23.0,2.5588,350000.0,NEAR OCEAN\n-118.26,33.78,35.0,1239.0,473.0,1524.0,387.0,2.0956,154700.0,NEAR OCEAN\n-118.25,33.78,32.0,296.0,139.0,511.0,133.0,1.4444,182100.0,NEAR OCEAN\n-118.24,33.78,24.0,574.0,173.0,784.0,162.0,2.25,152300.0,NEAR OCEAN\n-118.26,33.78,36.0,2191.0,739.0,2931.0,692.0,2.1311,163100.0,NEAR OCEAN\n-118.26,33.77,36.0,886.0,253.0,809.0,219.0,2.4545,164200.0,NEAR OCEAN\n-118.27,33.77,26.0,2272.0,694.0,2567.0,595.0,1.9964,150600.0,NEAR OCEAN\n-118.27,33.78,21.0,3354.0,1174.0,4426.0,1110.0,2.5262,167300.0,<1H OCEAN\n-118.28,33.78,37.0,1212.0,304.0,1076.0,293.0,3.2115,160100.0,<1H OCEAN\n-118.27,33.77,39.0,1731.0,485.0,2115.0,478.0,1.5369,141300.0,NEAR OCEAN\n-118.27,33.76,46.0,22.0,11.0,32.0,7.0,3.125,112500.0,NEAR OCEAN\n-118.3,33.77,18.0,3821.0,795.0,2831.0,769.0,2.9571,346200.0,<1H OCEAN\n-118.3,33.76,18.0,9659.0,1716.0,4336.0,1674.0,5.7764,290500.0,<1H OCEAN\n-118.28,33.77,47.0,307.0,69.0,374.0,65.0,2.9063,146900.0,<1H OCEAN\n-118.28,33.75,41.0,1305.0,381.0,1384.0,369.0,2.45,186800.0,NEAR OCEAN\n-118.28,33.75,18.0,393.0,189.0,429.0,188.0,1.8393,187500.0,NEAR OCEAN\n-118.28,33.74,40.0,1751.0,512.0,1939.0,503.0,1.5394,200000.0,NEAR OCEAN\n-118.28,33.75,21.0,2442.0,719.0,1916.0,646.0,1.2276,208300.0,NEAR OCEAN\n-118.28,33.74,16.0,855.0,271.0,486.0,250.0,0.7591,350000.0,NEAR OCEAN\n-118.3,33.75,19.0,2356.0,440.0,1291.0,418.0,4.2308,416100.0,<1H OCEAN\n-118.3,33.76,6.0,6097.0,1270.0,2678.0,1226.0,5.1269,285200.0,<1H OCEAN\n-118.3,33.75,42.0,967.0,175.0,481.0,163.0,5.6611,265600.0,<1H OCEAN\n-118.3,33.75,48.0,1958.0,386.0,1098.0,380.0,4.625,273400.0,<1H OCEAN\n-118.3,33.74,47.0,2223.0,410.0,1163.0,413.0,4.4671,270800.0,NEAR OCEAN\n-118.31,33.74,36.0,2464.0,472.0,1111.0,457.0,4.5074,350000.0,NEAR OCEAN\n-118.31,33.74,22.0,5042.0,974.0,2260.0,935.0,4.3472,351200.0,NEAR OCEAN\n-118.29,33.75,27.0,1650.0,443.0,1359.0,386.0,2.5795,192400.0,NEAR OCEAN\n-118.3,33.75,23.0,1957.0,517.0,1454.0,526.0,3.5056,203100.0,<1H OCEAN\n-118.29,33.75,37.0,1319.0,292.0,766.0,285.0,2.7031,218900.0,NEAR OCEAN\n-118.29,33.74,29.0,1503.0,411.0,1368.0,390.0,2.1473,195300.0,NEAR OCEAN\n-118.29,33.74,52.0,1438.0,472.0,1018.0,399.0,2.2188,306700.0,NEAR OCEAN\n-118.29,33.74,41.0,1382.0,361.0,905.0,344.0,2.75,238300.0,NEAR OCEAN\n-118.29,33.74,30.0,2074.0,533.0,1311.0,531.0,2.0329,225800.0,NEAR OCEAN\n-118.29,33.73,43.0,1854.0,519.0,1151.0,486.0,2.5759,225000.0,NEAR OCEAN\n-118.29,33.73,21.0,2492.0,711.0,1699.0,672.0,2.1382,242300.0,NEAR OCEAN\n-118.29,33.73,30.0,2835.0,711.0,1669.0,652.0,3.3616,268500.0,NEAR OCEAN\n-118.3,33.73,42.0,1731.0,,866.0,403.0,2.7451,255400.0,NEAR OCEAN\n-118.3,33.74,23.0,3075.0,860.0,1584.0,806.0,2.9386,260500.0,NEAR OCEAN\n-118.31,33.73,36.0,1725.0,295.0,799.0,306.0,5.0874,368500.0,NEAR OCEAN\n-118.31,33.73,52.0,1665.0,280.0,656.0,282.0,5.249,351900.0,NEAR OCEAN\n-118.3,33.74,20.0,2625.0,673.0,1184.0,606.0,3.9167,285200.0,NEAR OCEAN\n-118.3,33.73,47.0,2852.0,603.0,1130.0,560.0,4.194,293900.0,NEAR OCEAN\n-118.31,33.73,52.0,2025.0,361.0,957.0,363.0,4.2059,350000.0,NEAR OCEAN\n-118.28,33.74,44.0,1853.0,556.0,2090.0,539.0,1.8584,248100.0,NEAR OCEAN\n-118.28,33.73,39.0,2602.0,802.0,2178.0,737.0,2.0469,234500.0,NEAR OCEAN\n-118.28,33.73,52.0,2085.0,588.0,1767.0,516.0,2.1935,243200.0,NEAR OCEAN\n-118.28,33.73,45.0,2137.0,559.0,1550.0,529.0,1.9167,227200.0,NEAR OCEAN\n-118.29,33.73,30.0,3161.0,,1865.0,771.0,2.7139,231700.0,NEAR OCEAN\n-118.29,33.72,25.0,2469.0,584.0,1253.0,535.0,3.1932,257500.0,NEAR OCEAN\n-118.3,33.72,28.0,2647.0,658.0,1459.0,595.0,3.4474,253400.0,NEAR OCEAN\n-118.3,33.72,28.0,2510.0,583.0,1388.0,554.0,3.3397,267800.0,NEAR OCEAN\n-118.3,33.73,40.0,2582.0,606.0,1304.0,588.0,3.5694,276400.0,NEAR OCEAN\n-118.31,33.73,52.0,1642.0,287.0,692.0,288.0,4.1812,321500.0,NEAR OCEAN\n-118.31,33.72,26.0,2711.0,508.0,1372.0,459.0,4.1451,326700.0,NEAR OCEAN\n-118.33,33.69,41.0,2168.0,357.0,1171.0,374.0,4.7216,311900.0,NEAR OCEAN\n-118.31,33.73,33.0,2265.0,366.0,986.0,388.0,5.4533,409800.0,NEAR OCEAN\n-118.32,33.73,25.0,1099.0,168.0,407.0,159.0,7.6886,500001.0,NEAR OCEAN\n-118.33,33.72,25.0,6191.0,1081.0,2297.0,1023.0,6.4246,446700.0,NEAR OCEAN\n-118.29,33.71,36.0,3135.0,746.0,1815.0,697.0,3.7596,300000.0,NEAR OCEAN\n-118.31,33.67,42.0,1297.0,246.0,611.0,242.0,5.3074,401900.0,NEAR OCEAN\n-118.3,33.72,35.0,2790.0,,1167.0,441.0,6.2028,361500.0,NEAR OCEAN\n-118.29,33.71,40.0,1644.0,471.0,780.0,416.0,3.1071,464300.0,NEAR OCEAN\n-118.29,33.71,40.0,1933.0,475.0,902.0,412.0,4.25,332800.0,NEAR OCEAN\n-118.29,33.71,31.0,2809.0,666.0,1316.0,633.0,4.5606,303800.0,NEAR OCEAN\n-118.29,33.72,39.0,2651.0,590.0,1103.0,508.0,3.274,254300.0,NEAR OCEAN\n-118.29,33.72,21.0,1568.0,452.0,801.0,422.0,3.5109,225000.0,NEAR OCEAN\n-118.28,33.68,8.0,2842.0,522.0,1624.0,510.0,3.7282,287500.0,NEAR OCEAN\n-118.23,34.24,31.0,3857.0,607.0,1695.0,572.0,7.642,396400.0,<1H OCEAN\n-118.23,34.23,34.0,2377.0,362.0,1055.0,362.0,6.0,367100.0,<1H OCEAN\n-118.24,34.24,31.0,3019.0,469.0,1349.0,462.0,7.1463,394100.0,<1H OCEAN\n-118.24,34.24,31.0,3812.0,595.0,1645.0,591.0,7.585,380100.0,<1H OCEAN\n-118.23,34.22,36.0,2288.0,439.0,1079.0,434.0,4.5486,361000.0,<1H OCEAN\n-118.24,34.23,41.0,1912.0,308.0,896.0,314.0,5.3473,352700.0,<1H OCEAN\n-118.24,34.23,43.0,1061.0,208.0,514.0,208.0,6.01,254200.0,<1H OCEAN\n-118.24,34.23,42.0,1541.0,280.0,753.0,264.0,5.1028,292100.0,<1H OCEAN\n-118.25,34.23,35.0,2839.0,592.0,1413.0,538.0,4.1667,271200.0,<1H OCEAN\n-118.25,34.25,34.0,3150.0,518.0,1392.0,480.0,4.9355,336900.0,<1H OCEAN\n-118.25,34.23,34.0,2421.0,475.0,1232.0,454.0,4.6852,296200.0,<1H OCEAN\n-118.26,34.24,35.0,1535.0,283.0,816.0,287.0,6.1873,312100.0,<1H OCEAN\n-118.26,34.24,35.0,1666.0,280.0,788.0,273.0,6.6277,344400.0,<1H OCEAN\n-118.26,34.24,35.0,2485.0,418.0,1226.0,406.0,5.7083,329500.0,<1H OCEAN\n-118.26,34.24,42.0,890.0,179.0,555.0,200.0,4.4821,271900.0,<1H OCEAN\n-118.25,34.23,41.0,1979.0,496.0,1157.0,459.0,4.4083,217700.0,<1H OCEAN\n-118.25,34.22,30.0,2062.0,396.0,1089.0,375.0,5.5362,301200.0,<1H OCEAN\n-118.25,34.23,37.0,1954.0,368.0,967.0,370.0,5.0862,261300.0,<1H OCEAN\n-118.26,34.23,43.0,1428.0,325.0,836.0,302.0,4.5759,209200.0,<1H OCEAN\n-118.26,34.23,38.0,1107.0,194.0,518.0,195.0,7.5582,263700.0,<1H OCEAN\n-118.26,34.23,33.0,1805.0,303.0,838.0,301.0,5.4306,326600.0,<1H OCEAN\n-118.22,34.21,29.0,2174.0,418.0,1030.0,395.0,3.5707,341700.0,<1H OCEAN\n-118.23,34.21,36.0,2988.0,719.0,1357.0,657.0,3.5174,268000.0,<1H OCEAN\n-118.23,34.21,29.0,2584.0,608.0,1217.0,568.0,3.3287,273400.0,<1H OCEAN\n-118.23,34.21,32.0,1464.0,406.0,693.0,380.0,2.5463,200000.0,<1H OCEAN\n-118.23,34.21,38.0,1399.0,390.0,859.0,386.0,3.4148,234800.0,<1H OCEAN\n-118.24,34.22,41.0,2476.0,506.0,1271.0,485.0,3.4531,263900.0,<1H OCEAN\n-118.24,34.22,34.0,1722.0,406.0,926.0,371.0,4.1523,252000.0,<1H OCEAN\n-118.25,34.22,34.0,2510.0,535.0,1516.0,542.0,3.8068,267000.0,<1H OCEAN\n-118.23,34.21,50.0,309.0,47.0,121.0,45.0,6.213,285000.0,<1H OCEAN\n-118.23,34.2,51.0,1477.0,280.0,750.0,295.0,5.3925,317900.0,<1H OCEAN\n-118.23,34.2,48.0,1473.0,294.0,807.0,296.0,3.399,306300.0,<1H OCEAN\n-118.24,34.2,41.0,2067.0,452.0,1282.0,455.0,5.5756,309900.0,<1H OCEAN\n-118.24,34.21,32.0,3817.0,886.0,1888.0,829.0,3.5777,245600.0,<1H OCEAN\n-118.24,34.22,36.0,2507.0,517.0,1232.0,470.0,5.529,241300.0,<1H OCEAN\n-118.27,34.22,34.0,8206.0,1186.0,3141.0,1150.0,7.2812,462200.0,<1H OCEAN\n-118.23,34.18,47.0,1853.0,345.0,757.0,310.0,3.6875,422000.0,<1H OCEAN\n-118.23,34.18,43.0,1708.0,280.0,768.0,276.0,6.207,457400.0,<1H OCEAN\n-118.23,34.18,45.0,2332.0,,943.0,339.0,8.1132,446600.0,<1H OCEAN\n-118.26,34.18,32.0,14556.0,2077.0,5459.0,2017.0,8.1657,500001.0,<1H OCEAN\n-118.22,34.19,36.0,959.0,204.0,446.0,210.0,3.215,331300.0,<1H OCEAN\n-118.22,34.19,31.0,4704.0,920.0,1895.0,886.0,4.9297,400000.0,<1H OCEAN\n-118.22,34.19,36.0,2443.0,492.0,1115.0,493.0,3.9777,409800.0,<1H OCEAN\n-118.21,34.18,14.0,2672.0,335.0,1113.0,318.0,12.1579,500001.0,<1H OCEAN\n-118.23,34.17,37.0,4524.0,1005.0,2099.0,937.0,3.5781,366700.0,<1H OCEAN\n-118.21,34.17,24.0,8590.0,1231.0,3401.0,1178.0,8.1325,472700.0,<1H OCEAN\n-118.21,34.16,25.0,434.0,74.0,199.0,75.0,5.9199,420500.0,<1H OCEAN\n-118.19,34.16,49.0,1788.0,267.0,735.0,266.0,6.6009,375700.0,<1H OCEAN\n-118.2,34.16,31.0,5550.0,881.0,2465.0,862.0,6.8317,446100.0,<1H OCEAN\n-118.23,34.16,31.0,3105.0,582.0,1359.0,547.0,5.1718,429100.0,<1H OCEAN\n-118.23,34.15,26.0,1649.0,522.0,1332.0,483.0,3.1004,257100.0,<1H OCEAN\n-118.23,34.15,40.0,2124.0,370.0,998.0,372.0,5.3369,370400.0,<1H OCEAN\n-118.24,34.16,40.0,2549.0,591.0,1156.0,546.0,3.3333,374300.0,<1H OCEAN\n-118.24,34.16,52.0,2187.0,284.0,733.0,274.0,9.5823,406200.0,<1H OCEAN\n-118.24,34.16,52.0,1904.0,297.0,797.0,286.0,6.6603,380400.0,<1H OCEAN\n-118.25,34.16,52.0,2477.0,385.0,993.0,371.0,4.9135,368100.0,<1H OCEAN\n-118.25,34.17,52.0,1532.0,292.0,631.0,275.0,5.1242,372900.0,<1H OCEAN\n-118.25,34.16,24.0,5131.0,1436.0,2690.0,1371.0,2.5668,280000.0,<1H OCEAN\n-118.26,34.17,20.0,5949.0,1417.0,2593.0,1337.0,3.8576,318600.0,<1H OCEAN\n-118.26,34.16,17.0,2943.0,769.0,1312.0,656.0,3.1484,187500.0,<1H OCEAN\n-118.26,34.16,19.0,2919.0,857.0,1866.0,811.0,3.1733,206300.0,<1H OCEAN\n-118.26,34.16,20.0,3407.0,885.0,1883.0,870.0,3.7321,351100.0,<1H OCEAN\n-118.27,34.17,48.0,1560.0,280.0,825.0,269.0,5.5118,354700.0,<1H OCEAN\n-118.27,34.16,15.0,5036.0,1299.0,3164.0,1175.0,2.9148,238700.0,<1H OCEAN\n-118.27,34.16,45.0,1865.0,360.0,973.0,349.0,3.6587,321200.0,<1H OCEAN\n-118.28,34.17,52.0,2332.0,433.0,1135.0,440.0,5.5658,331200.0,<1H OCEAN\n-118.27,34.17,52.0,2010.0,,908.0,326.0,6.9135,374000.0,<1H OCEAN\n-118.27,34.17,52.0,2287.0,295.0,829.0,296.0,7.8383,500001.0,<1H OCEAN\n-118.27,34.18,52.0,3034.0,406.0,1158.0,399.0,6.2976,498400.0,<1H OCEAN\n-118.28,34.18,47.0,2243.0,339.0,911.0,319.0,7.4046,446800.0,<1H OCEAN\n-118.28,34.18,52.0,2602.0,418.0,1137.0,419.0,5.3185,358000.0,<1H OCEAN\n-118.29,34.18,52.0,1602.0,265.0,667.0,251.0,5.049,323500.0,<1H OCEAN\n-118.28,34.18,50.0,2195.0,336.0,878.0,309.0,6.884,365600.0,<1H OCEAN\n-118.28,34.17,22.0,2664.0,651.0,1553.0,629.0,3.6354,256300.0,<1H OCEAN\n-118.29,34.17,17.0,3852.0,1066.0,2986.0,993.0,2.3482,255400.0,<1H OCEAN\n-118.29,34.17,52.0,1732.0,305.0,875.0,311.0,4.325,292600.0,<1H OCEAN\n-118.29,34.18,10.0,4292.0,1075.0,2719.0,987.0,3.6974,286600.0,<1H OCEAN\n-118.29,34.16,31.0,1262.0,338.0,1019.0,332.0,3.7083,241900.0,<1H OCEAN\n-118.29,34.16,42.0,413.0,107.0,349.0,107.0,4.3438,189800.0,<1H OCEAN\n-118.29,34.17,12.0,2238.0,682.0,1882.0,611.0,2.9,208300.0,<1H OCEAN\n-118.3,34.17,17.0,4041.0,1169.0,3309.0,1117.0,2.6016,222400.0,<1H OCEAN\n-118.3,34.17,30.0,48.0,14.0,74.0,16.0,5.0056,162500.0,<1H OCEAN\n-118.29,34.16,35.0,1257.0,318.0,764.0,319.0,3.2083,238000.0,<1H OCEAN\n-118.3,34.16,40.0,1875.0,460.0,869.0,438.0,3.2321,243600.0,<1H OCEAN\n-118.3,34.16,35.0,3213.0,874.0,2401.0,819.0,2.8342,256800.0,<1H OCEAN\n-118.27,34.16,47.0,1453.0,356.0,787.0,345.0,3.0114,255500.0,<1H OCEAN\n-118.27,34.15,22.0,2265.0,637.0,1684.0,561.0,2.6729,217100.0,<1H OCEAN\n-118.27,34.15,25.0,3018.0,806.0,2205.0,742.0,3.0199,220200.0,<1H OCEAN\n-118.27,34.15,14.0,1744.0,536.0,1494.0,531.0,3.2171,230800.0,<1H OCEAN\n-118.27,34.16,52.0,830.0,183.0,479.0,179.0,3.1397,253700.0,<1H OCEAN\n-118.27,34.16,48.0,1301.0,253.0,637.0,260.0,4.3438,252700.0,<1H OCEAN\n-118.28,34.16,49.0,1393.0,290.0,605.0,282.0,2.9491,257400.0,<1H OCEAN\n-118.26,34.16,18.0,1775.0,525.0,950.0,522.0,3.5417,177100.0,<1H OCEAN\n-118.26,34.15,14.0,2981.0,894.0,1941.0,863.0,3.0,178600.0,<1H OCEAN\n-118.26,34.15,6.0,3340.0,945.0,2315.0,846.0,2.8884,252300.0,<1H OCEAN\n-118.26,34.15,18.0,2481.0,756.0,1763.0,675.0,2.8088,247500.0,<1H OCEAN\n-118.24,34.16,52.0,850.0,162.0,493.0,160.0,6.9408,298800.0,<1H OCEAN\n-118.24,34.15,20.0,2734.0,658.0,1562.0,607.0,3.3906,284100.0,<1H OCEAN\n-118.24,34.15,45.0,1235.0,271.0,499.0,263.0,3.1435,282600.0,<1H OCEAN\n-118.25,34.15,15.0,3712.0,1005.0,1888.0,890.0,3.6875,209600.0,<1H OCEAN\n-118.25,34.16,14.0,3700.0,945.0,1681.0,905.0,3.9054,200000.0,<1H OCEAN\n-118.24,34.15,17.0,5282.0,1605.0,4116.0,1574.0,3.052,209800.0,<1H OCEAN\n-118.24,34.15,19.0,4852.0,1465.0,3171.0,1332.0,2.5924,192900.0,<1H OCEAN\n-118.25,34.15,31.0,1238.0,338.0,605.0,331.0,2.8478,228100.0,<1H OCEAN\n-118.25,34.15,32.0,1377.0,444.0,768.0,422.0,2.2621,187500.0,<1H OCEAN\n-118.25,34.15,20.0,3960.0,1027.0,1729.0,978.0,3.0441,193800.0,<1H OCEAN\n-118.23,34.14,33.0,2865.0,864.0,2061.0,790.0,2.6268,201300.0,<1H OCEAN\n-118.24,34.14,20.0,3196.0,994.0,2929.0,983.0,3.0206,219500.0,<1H OCEAN\n-118.24,34.14,27.0,2909.0,1021.0,2614.0,935.0,2.1444,229000.0,<1H OCEAN\n-118.23,34.15,19.0,2294.0,716.0,1686.0,680.0,3.0288,258300.0,<1H OCEAN\n-118.23,34.14,25.0,2864.0,844.0,1745.0,803.0,2.9167,224300.0,<1H OCEAN\n-118.24,34.14,28.0,1843.0,554.0,1402.0,512.0,2.462,254000.0,<1H OCEAN\n-118.24,34.14,36.0,1813.0,560.0,1501.0,544.0,1.9125,238000.0,<1H OCEAN\n-118.24,34.13,37.0,1644.0,395.0,959.0,383.0,3.3636,257700.0,<1H OCEAN\n-118.24,34.13,45.0,2170.0,401.0,1043.0,394.0,5.6921,269000.0,<1H OCEAN\n-118.24,34.15,7.0,2063.0,670.0,1892.0,643.0,1.7301,202300.0,<1H OCEAN\n-118.24,34.14,9.0,4877.0,1488.0,4486.0,1458.0,2.4421,222100.0,<1H OCEAN\n-118.25,34.14,30.0,1615.0,570.0,1245.0,544.0,1.8929,196900.0,<1H OCEAN\n-118.25,34.14,37.0,584.0,260.0,552.0,235.0,1.8235,275000.0,<1H OCEAN\n-118.25,34.15,13.0,1107.0,479.0,616.0,443.0,0.8185,187500.0,<1H OCEAN\n-118.26,34.14,51.0,902.0,320.0,650.0,334.0,1.5417,268800.0,<1H OCEAN\n-118.26,34.14,29.0,3431.0,1222.0,4094.0,1205.0,2.2614,248100.0,<1H OCEAN\n-118.27,34.14,10.0,1060.0,332.0,1025.0,288.0,3.0074,175000.0,<1H OCEAN\n-118.26,34.14,6.0,1727.0,506.0,1200.0,439.0,4.1083,210700.0,<1H OCEAN\n-118.27,34.15,7.0,2837.0,776.0,2287.0,736.0,3.008,229000.0,<1H OCEAN\n-118.26,34.13,25.0,3208.0,1111.0,2843.0,1005.0,2.6673,218100.0,<1H OCEAN\n-118.26,34.13,37.0,1383.0,470.0,1185.0,451.0,2.5,207100.0,<1H OCEAN\n-118.26,34.13,37.0,196.0,74.0,194.0,68.0,1.2188,218800.0,<1H OCEAN\n-118.26,34.14,23.0,1336.0,396.0,1255.0,359.0,2.5388,205000.0,<1H OCEAN\n-118.25,34.14,13.0,3487.0,1131.0,3749.0,1072.0,2.1602,221900.0,<1H OCEAN\n-118.25,34.14,25.0,5980.0,1856.0,5217.0,1772.0,2.506,184500.0,<1H OCEAN\n-118.25,34.13,22.0,2340.0,773.0,2226.0,754.0,2.5417,217500.0,<1H OCEAN\n-118.24,34.13,45.0,1971.0,439.0,1245.0,430.0,4.0272,260500.0,<1H OCEAN\n-118.25,34.13,52.0,322.0,88.0,229.0,89.0,2.125,243800.0,<1H OCEAN\n-118.25,34.13,36.0,2946.0,1025.0,2542.0,912.0,2.2244,255900.0,<1H OCEAN\n-118.25,34.12,21.0,739.0,265.0,861.0,246.0,2.4856,181300.0,<1H OCEAN\n-118.31,34.22,27.0,7714.0,1132.0,3199.0,1100.0,7.1262,446200.0,<1H OCEAN\n-118.33,34.21,31.0,3190.0,489.0,1362.0,480.0,6.981,402900.0,<1H OCEAN\n-118.34,34.21,36.0,1834.0,316.0,864.0,309.0,4.7885,302200.0,<1H OCEAN\n-118.29,34.18,36.0,3120.0,620.0,1441.0,612.0,3.9041,320400.0,<1H OCEAN\n-118.3,34.19,14.0,3615.0,913.0,1924.0,852.0,3.5083,280900.0,<1H OCEAN\n-118.3,34.19,52.0,1704.0,277.0,746.0,262.0,4.7986,326100.0,<1H OCEAN\n-118.3,34.19,51.0,1502.0,243.0,586.0,231.0,4.375,332400.0,<1H OCEAN\n-118.31,34.19,27.0,4713.0,1169.0,2372.0,1077.0,3.7015,287900.0,<1H OCEAN\n-118.3,34.19,52.0,2962.0,468.0,1364.0,466.0,4.9042,343500.0,<1H OCEAN\n-118.31,34.2,36.0,1692.0,263.0,778.0,278.0,5.0865,349600.0,<1H OCEAN\n-118.32,34.2,36.0,2110.0,346.0,984.0,342.0,6.9909,345300.0,<1H OCEAN\n-118.32,34.2,36.0,759.0,136.0,372.0,135.0,4.9886,328900.0,<1H OCEAN\n-118.32,34.2,36.0,1978.0,337.0,834.0,311.0,3.9866,294400.0,<1H OCEAN\n-118.33,34.2,43.0,1325.0,254.0,613.0,248.0,3.6071,289000.0,<1H OCEAN\n-118.33,34.2,43.0,2322.0,418.0,1106.0,433.0,4.3631,284600.0,<1H OCEAN\n-118.34,34.2,41.0,2860.0,682.0,1516.0,621.0,3.0431,262900.0,<1H OCEAN\n-118.34,34.19,41.0,1524.0,393.0,1176.0,375.0,2.875,192400.0,<1H OCEAN\n-118.36,34.2,14.0,1878.0,614.0,1874.0,559.0,2.5267,231800.0,<1H OCEAN\n-118.32,34.2,29.0,2209.0,444.0,952.0,403.0,4.375,341200.0,<1H OCEAN\n-118.32,34.19,37.0,1335.0,249.0,485.0,240.0,4.1731,352100.0,<1H OCEAN\n-118.32,34.19,37.0,1519.0,331.0,613.0,315.0,3.0179,272500.0,<1H OCEAN\n-118.31,34.19,42.0,724.0,149.0,420.0,150.0,3.0625,361700.0,<1H OCEAN\n-118.32,34.19,37.0,589.0,119.0,375.0,122.0,3.3897,222700.0,<1H OCEAN\n-118.33,34.2,23.0,7179.0,1985.0,4757.0,1924.0,3.1051,206500.0,<1H OCEAN\n-118.3,34.18,13.0,7174.0,1997.0,4293.0,1872.0,3.0973,251900.0,<1H OCEAN\n-118.3,34.17,37.0,350.0,115.0,342.0,111.0,3.0687,200000.0,<1H OCEAN\n-118.3,34.18,5.0,5492.0,1549.0,2997.0,1405.0,3.3205,172100.0,<1H OCEAN\n-118.31,34.18,11.0,3112.0,890.0,1700.0,851.0,3.1587,181300.0,<1H OCEAN\n-118.31,34.19,13.0,3801.0,1116.0,1986.0,1078.0,2.0875,222700.0,<1H OCEAN\n-118.32,34.18,49.0,192.0,41.0,83.0,38.0,3.0179,118800.0,<1H OCEAN\n-118.32,34.18,44.0,1594.0,389.0,832.0,340.0,3.4,212100.0,<1H OCEAN\n-118.32,34.17,39.0,1995.0,564.0,1202.0,544.0,3.5875,250000.0,<1H OCEAN\n-118.32,34.17,47.0,2589.0,465.0,1284.0,485.0,5.1008,247100.0,<1H OCEAN\n-118.33,34.17,48.0,2584.0,483.0,1118.0,459.0,4.2396,245100.0,<1H OCEAN\n-118.33,34.19,46.0,2115.0,463.0,1133.0,439.0,3.7344,222000.0,<1H OCEAN\n-118.33,34.18,45.0,2570.0,517.0,1256.0,510.0,4.6898,226000.0,<1H OCEAN\n-118.33,34.19,45.0,1505.0,347.0,799.0,319.0,3.138,217000.0,<1H OCEAN\n-118.34,34.19,48.0,814.0,165.0,490.0,176.0,3.1406,223100.0,<1H OCEAN\n-118.33,34.18,45.0,1552.0,315.0,785.0,316.0,3.7411,235500.0,<1H OCEAN\n-118.33,34.18,49.0,1969.0,377.0,977.0,367.0,3.8462,231300.0,<1H OCEAN\n-118.33,34.18,48.0,2122.0,385.0,926.0,362.0,5.6975,231400.0,<1H OCEAN\n-118.34,34.19,43.0,1029.0,252.0,613.0,255.0,2.6827,219900.0,<1H OCEAN\n-118.34,34.19,47.0,1721.0,343.0,834.0,334.0,4.1923,231200.0,<1H OCEAN\n-118.34,34.18,45.0,3046.0,633.0,1448.0,599.0,3.24,226900.0,<1H OCEAN\n-118.34,34.18,46.0,1393.0,301.0,714.0,295.0,2.8125,229900.0,<1H OCEAN\n-118.35,34.19,45.0,903.0,190.0,557.0,204.0,4.0313,209100.0,<1H OCEAN\n-118.36,34.19,46.0,1676.0,322.0,846.0,295.0,5.1814,209500.0,<1H OCEAN\n-118.35,34.18,46.0,2711.0,491.0,1277.0,490.0,4.282,224700.0,<1H OCEAN\n-118.35,34.18,46.0,1840.0,379.0,866.0,360.0,3.3056,230400.0,<1H OCEAN\n-118.35,34.17,44.0,2572.0,613.0,1280.0,570.0,3.5583,232000.0,<1H OCEAN\n-118.35,34.17,47.0,858.0,170.0,365.0,171.0,2.0385,225000.0,<1H OCEAN\n-118.35,34.17,42.0,1604.0,326.0,814.0,329.0,4.4408,216000.0,<1H OCEAN\n-118.36,34.17,46.0,1268.0,240.0,661.0,239.0,4.0742,229100.0,<1H OCEAN\n-118.34,34.18,45.0,1328.0,290.0,720.0,289.0,3.875,226900.0,<1H OCEAN\n-118.34,34.18,45.0,3566.0,701.0,1601.0,653.0,3.8668,232000.0,<1H OCEAN\n-118.34,34.17,49.0,3033.0,580.0,1284.0,561.0,4.1161,232500.0,<1H OCEAN\n-118.35,34.16,42.0,2267.0,478.0,1083.0,458.0,3.2015,250000.0,<1H OCEAN\n-118.35,34.16,45.0,1390.0,281.0,538.0,270.0,4.2212,293800.0,<1H OCEAN\n-118.35,34.16,49.0,1305.0,228.0,584.0,255.0,5.636,267900.0,<1H OCEAN\n-118.33,34.17,44.0,1934.0,375.0,750.0,365.0,2.473,251800.0,<1H OCEAN\n-118.33,34.16,37.0,2381.0,575.0,1235.0,499.0,3.7941,247800.0,<1H OCEAN\n-118.34,34.17,46.0,1718.0,344.0,756.0,343.0,3.2125,247000.0,<1H OCEAN\n-118.34,34.17,52.0,1133.0,212.0,545.0,222.0,4.875,249500.0,<1H OCEAN\n-118.34,34.16,44.0,1717.0,391.0,848.0,353.0,3.6111,254500.0,<1H OCEAN\n-118.34,34.16,46.0,1396.0,294.0,608.0,246.0,3.692,244500.0,<1H OCEAN\n-118.33,34.16,44.0,2705.0,649.0,1676.0,654.0,3.4286,247900.0,<1H OCEAN\n-118.33,34.15,39.0,493.0,168.0,259.0,138.0,2.3667,17500.0,<1H OCEAN\n-118.33,34.15,44.0,1321.0,303.0,471.0,301.0,4.2679,331800.0,<1H OCEAN\n-118.34,34.15,40.0,3068.0,756.0,1190.0,695.0,3.5637,497400.0,<1H OCEAN\n-118.34,34.15,16.0,1586.0,377.0,625.0,344.0,4.0893,450000.0,<1H OCEAN\n-118.34,34.16,25.0,6082.0,1763.0,2616.0,1644.0,3.6486,246900.0,<1H OCEAN\n-118.32,34.17,45.0,3448.0,690.0,1562.0,643.0,4.0648,258800.0,<1H OCEAN\n-118.31,34.16,38.0,2347.0,665.0,1317.0,547.0,3.2112,349300.0,<1H OCEAN\n-118.32,34.16,49.0,1074.0,170.0,403.0,208.0,6.2547,366700.0,<1H OCEAN\n-118.32,34.17,40.0,1868.0,356.0,799.0,403.0,2.9306,279300.0,<1H OCEAN\n-118.33,34.16,23.0,1359.0,428.0,770.0,380.0,3.4016,234600.0,<1H OCEAN\n-118.32,34.16,46.0,2345.0,453.0,1031.0,427.0,4.3173,278300.0,<1H OCEAN\n-118.3,34.17,16.0,1353.0,398.0,1211.0,357.0,3.1551,205000.0,<1H OCEAN\n-118.31,34.16,37.0,2144.0,446.0,860.0,435.0,3.9464,315000.0,<1H OCEAN\n-118.31,34.17,12.0,3188.0,931.0,2118.0,850.0,3.1823,218300.0,<1H OCEAN\n-118.31,34.17,24.0,2910.0,917.0,2522.0,873.0,2.4074,219400.0,<1H OCEAN\n-118.43,34.3,33.0,2443.0,498.0,1601.0,484.0,4.0223,146000.0,<1H OCEAN\n-118.43,34.3,37.0,1394.0,313.0,1111.0,327.0,3.6023,161800.0,<1H OCEAN\n-118.43,34.29,39.0,1769.0,410.0,1499.0,390.0,3.1212,153500.0,<1H OCEAN\n-118.42,34.29,34.0,1489.0,326.0,1389.0,313.0,3.4821,160300.0,<1H OCEAN\n-118.43,34.3,28.0,271.0,61.0,246.0,62.0,1.7062,164600.0,<1H OCEAN\n-118.44,34.29,35.0,2606.0,447.0,1555.0,404.0,4.6864,193800.0,<1H OCEAN\n-118.43,34.29,38.0,1704.0,347.0,1384.0,374.0,2.865,155500.0,<1H OCEAN\n-118.43,34.29,38.0,1237.0,298.0,1073.0,293.0,3.6726,154600.0,<1H OCEAN\n-118.43,34.29,50.0,1181.0,265.0,1196.0,269.0,3.2095,167000.0,<1H OCEAN\n-118.43,34.28,27.0,862.0,280.0,1243.0,267.0,2.3724,154200.0,<1H OCEAN\n-118.44,34.29,32.0,1260.0,382.0,1434.0,342.0,2.0286,122900.0,<1H OCEAN\n-118.44,34.29,30.0,1632.0,401.0,1357.0,401.0,3.1588,160100.0,<1H OCEAN\n-118.45,34.29,30.0,762.0,228.0,840.0,226.0,2.3375,154200.0,<1H OCEAN\n-118.44,34.28,32.0,527.0,146.0,582.0,143.0,1.7708,138800.0,<1H OCEAN\n-118.44,34.28,46.0,11.0,11.0,24.0,13.0,2.875,162500.0,<1H OCEAN\n-118.44,34.28,37.0,944.0,244.0,1107.0,235.0,1.9688,144100.0,<1H OCEAN\n-118.44,34.28,47.0,843.0,194.0,800.0,180.0,3.3687,151700.0,<1H OCEAN\n-118.44,34.28,38.0,1156.0,305.0,1359.0,289.0,2.5147,137100.0,<1H OCEAN\n-118.45,34.28,36.0,2602.0,638.0,2780.0,620.0,2.7155,149800.0,<1H OCEAN\n-117.71,34.15,17.0,17715.0,2370.0,7665.0,2312.0,7.9068,349100.0,INLAND\n-117.73,34.12,26.0,1279.0,163.0,412.0,157.0,6.1731,293800.0,INLAND\n-117.78,34.13,18.0,7798.0,1161.0,3710.0,1227.0,5.8819,260500.0,INLAND\n-117.76,34.13,8.0,16759.0,2274.0,7249.0,2156.0,7.4837,358700.0,INLAND\n-117.8,34.15,14.0,7876.0,1253.0,3699.0,1162.0,5.5423,248700.0,INLAND\n-117.8,34.11,25.0,5039.0,821.0,2654.0,802.0,4.7969,211700.0,INLAND\n-117.8,34.1,17.0,5153.0,1164.0,2949.0,1083.0,3.5603,174600.0,INLAND\n-117.8,34.1,13.0,2996.0,495.0,1187.0,464.0,6.2456,161700.0,INLAND\n-117.79,34.12,16.0,2426.0,426.0,1319.0,446.0,4.8125,224500.0,INLAND\n-117.79,34.11,18.0,3814.0,721.0,1881.0,692.0,4.4722,215600.0,INLAND\n-117.83,34.14,26.0,8254.0,1153.0,3460.0,1131.0,6.5253,349900.0,INLAND\n-117.83,34.15,20.0,2421.0,306.0,1023.0,298.0,8.0683,451500.0,INLAND\n-117.82,34.13,27.0,3770.0,573.0,1606.0,562.0,6.1321,309700.0,INLAND\n-117.84,34.13,26.0,3773.0,694.0,2103.0,688.0,4.6937,198000.0,INLAND\n-117.87,34.15,24.0,5745.0,735.0,2061.0,679.0,8.2827,451400.0,INLAND\n-117.93,34.15,14.0,9610.0,2005.0,4723.0,1907.0,4.0393,156800.0,INLAND\n-117.9,34.15,21.0,2056.0,461.0,1332.0,429.0,3.3942,212800.0,INLAND\n-117.9,34.14,29.0,2240.0,457.0,1187.0,407.0,3.8365,184200.0,<1H OCEAN\n-117.9,34.14,35.0,2259.0,505.0,1561.0,509.0,3.3043,155500.0,<1H OCEAN\n-117.91,34.14,42.0,2225.0,485.0,1544.0,464.0,2.2442,166700.0,INLAND\n-117.89,34.14,15.0,4644.0,967.0,2855.0,867.0,3.3654,222100.0,<1H OCEAN\n-117.88,34.14,23.0,2308.0,322.0,1001.0,317.0,7.5112,355500.0,INLAND\n-117.88,34.14,32.0,1764.0,365.0,924.0,329.0,3.875,186700.0,INLAND\n-117.88,34.13,33.0,3713.0,718.0,2106.0,720.0,4.0023,185500.0,<1H OCEAN\n-117.85,34.14,35.0,2899.0,429.0,1251.0,429.0,6.1049,297200.0,INLAND\n-117.85,34.14,35.0,1582.0,248.0,654.0,221.0,4.9091,275000.0,INLAND\n-117.86,34.14,33.0,2344.0,363.0,1098.0,359.0,6.2089,283400.0,INLAND\n-117.87,34.15,37.0,2655.0,415.0,1056.0,401.0,5.4224,269500.0,INLAND\n-117.86,34.14,36.0,3097.0,667.0,1484.0,634.0,3.1905,235300.0,INLAND\n-117.87,34.14,30.0,2495.0,586.0,1139.0,559.0,2.9375,209200.0,INLAND\n-117.85,34.13,31.0,1959.0,318.0,1021.0,303.0,4.3145,233000.0,INLAND\n-117.85,34.12,30.0,4367.0,1033.0,2524.0,954.0,3.0448,192100.0,INLAND\n-117.86,34.13,33.0,2383.0,428.0,1269.0,421.0,4.636,245500.0,INLAND\n-117.86,34.13,40.0,1304.0,280.0,607.0,256.0,2.588,209500.0,INLAND\n-117.86,34.13,29.0,630.0,145.0,378.0,148.0,3.4107,170800.0,INLAND\n-117.87,34.13,32.0,1741.0,373.0,872.0,333.0,3.4219,194500.0,<1H OCEAN\n-117.87,34.13,29.0,1677.0,413.0,873.0,400.0,3.12,194300.0,<1H OCEAN\n-117.82,34.12,26.0,3118.0,528.0,1546.0,545.0,5.27,209400.0,INLAND\n-117.84,34.12,25.0,3465.0,566.0,1722.0,536.0,4.8304,228900.0,INLAND\n-117.83,34.11,29.0,2671.0,437.0,1484.0,445.0,4.9844,203000.0,INLAND\n-117.84,34.11,17.0,3499.0,621.0,1911.0,621.0,4.8894,191700.0,INLAND\n-117.84,34.12,34.0,2026.0,345.0,1142.0,332.0,4.392,187600.0,INLAND\n-117.85,34.11,25.0,9255.0,1659.0,4944.0,1627.0,4.5708,223000.0,INLAND\n-117.81,34.08,13.0,18448.0,2474.0,7775.0,2397.0,7.7876,348900.0,INLAND\n-117.81,34.12,23.0,7063.0,1176.0,3100.0,1112.0,4.8229,192600.0,INLAND\n-117.81,34.11,21.0,3481.0,808.0,1866.0,746.0,3.6201,150400.0,INLAND\n-117.81,34.1,19.0,1935.0,399.0,1126.0,389.0,3.8929,144600.0,INLAND\n-117.83,34.1,18.0,11026.0,1978.0,5407.0,1923.0,4.075,231100.0,INLAND\n-117.76,34.1,28.0,4086.0,871.0,1973.0,853.0,2.621,202200.0,INLAND\n-117.78,34.09,32.0,2643.0,516.0,1862.0,478.0,3.7177,177200.0,INLAND\n-117.79,34.1,26.0,1664.0,344.0,1024.0,339.0,3.5192,190500.0,INLAND\n-117.76,34.12,16.0,9020.0,1509.0,3575.0,1486.0,4.2415,275700.0,INLAND\n-117.77,34.12,15.0,4260.0,770.0,2007.0,695.0,4.4609,230000.0,INLAND\n-117.76,34.11,22.0,4935.0,954.0,2874.0,938.0,3.9825,180500.0,INLAND\n-117.77,34.1,50.0,2388.0,494.0,1241.0,459.0,2.8818,167200.0,INLAND\n-117.77,34.11,28.0,1998.0,414.0,1124.0,389.0,3.75,180900.0,INLAND\n-117.78,34.11,23.0,7079.0,1381.0,3205.0,1327.0,3.0735,212300.0,INLAND\n-117.74,34.11,28.0,3494.0,566.0,1391.0,522.0,5.3637,214700.0,INLAND\n-117.75,34.12,25.0,5411.0,998.0,2243.0,1019.0,4.3148,240700.0,INLAND\n-117.74,34.1,26.0,2723.0,604.0,1847.0,498.0,2.6779,136000.0,INLAND\n-117.74,34.1,29.0,2742.0,488.0,2477.0,532.0,3.5072,121900.0,INLAND\n-117.75,34.1,21.0,8069.0,2174.0,4369.0,2036.0,3.2756,156800.0,INLAND\n-117.71,34.12,20.0,11250.0,1893.0,4952.0,1859.0,5.6785,239500.0,INLAND\n-117.73,34.12,26.0,6459.0,894.0,2487.0,885.0,6.2089,261800.0,INLAND\n-117.71,34.1,41.0,555.0,130.0,1492.0,123.0,2.2813,125000.0,INLAND\n-117.71,34.1,52.0,567.0,152.0,2688.0,126.0,1.875,212500.0,INLAND\n-117.72,34.1,52.0,2867.0,496.0,978.0,513.0,3.1477,291200.0,INLAND\n-117.72,34.1,32.0,3241.0,895.0,1592.0,810.0,2.4952,181800.0,INLAND\n-117.72,34.1,46.0,2477.0,458.0,1034.0,455.0,5.5,289700.0,INLAND\n-117.73,34.1,37.0,3457.0,,1344.0,530.0,5.8891,226000.0,INLAND\n-117.71,34.09,36.0,2637.0,476.0,1385.0,483.0,4.1739,158700.0,INLAND\n-117.71,34.08,26.0,2744.0,494.0,1411.0,465.0,4.2639,154200.0,INLAND\n-117.72,34.09,36.0,1473.0,328.0,785.0,299.0,3.2566,151800.0,INLAND\n-117.72,34.09,33.0,4979.0,934.0,2575.0,874.0,3.7958,152500.0,INLAND\n-117.73,34.09,30.0,2345.0,496.0,1897.0,454.0,2.4375,112100.0,INLAND\n-117.73,34.09,36.0,1543.0,297.0,1355.0,303.0,3.5313,117800.0,INLAND\n-117.73,34.08,33.0,1350.0,265.0,1251.0,257.0,2.9063,115200.0,INLAND\n-117.74,34.09,30.0,3199.0,591.0,2192.0,563.0,3.4871,136400.0,INLAND\n-117.75,34.08,33.0,2824.0,523.0,1797.0,493.0,3.6359,135100.0,INLAND\n-117.75,34.08,33.0,1067.0,194.0,600.0,201.0,4.0368,139100.0,INLAND\n-117.75,34.09,36.0,3094.0,556.0,1672.0,545.0,4.2143,146900.0,INLAND\n-117.76,34.08,37.0,2263.0,502.0,1677.0,522.0,2.9388,139200.0,INLAND\n-117.77,34.08,27.0,5929.0,932.0,2817.0,828.0,6.0434,214800.0,INLAND\n-117.77,34.07,36.0,2922.0,652.0,2392.0,629.0,2.8661,124000.0,INLAND\n-117.77,34.07,29.0,2976.0,662.0,2452.0,633.0,3.0638,113600.0,INLAND\n-117.75,34.07,52.0,1548.0,348.0,1131.0,343.0,2.63,127300.0,INLAND\n-117.76,34.07,51.0,1538.0,394.0,1173.0,388.0,2.3156,109800.0,INLAND\n-117.76,34.07,48.0,1157.0,247.0,677.0,218.0,2.8594,127200.0,INLAND\n-117.76,34.06,33.0,1831.0,486.0,1625.0,472.0,1.9937,103600.0,INLAND\n-117.76,34.06,47.0,508.0,108.0,384.0,86.0,1.9583,92600.0,INLAND\n-117.77,34.06,27.0,2178.0,629.0,2379.0,591.0,1.9766,108000.0,INLAND\n-117.78,34.07,18.0,3610.0,772.0,2899.0,765.0,3.9784,113500.0,INLAND\n-117.78,34.06,25.0,1712.0,491.0,1807.0,478.0,2.2258,114800.0,INLAND\n-117.79,34.07,33.0,1694.0,333.0,1689.0,301.0,3.7583,116300.0,INLAND\n-117.79,34.07,34.0,975.0,192.0,870.0,183.0,3.7933,116100.0,INLAND\n-117.78,34.06,33.0,1056.0,272.0,964.0,300.0,2.4464,128700.0,INLAND\n-117.78,34.05,39.0,2933.0,590.0,1886.0,550.0,3.9224,131300.0,INLAND\n-117.8,34.05,5.0,4536.0,1178.0,2485.0,909.0,4.1118,125900.0,<1H OCEAN\n-117.8,34.06,34.0,1081.0,205.0,1325.0,252.0,3.6298,108500.0,INLAND\n-117.82,34.05,21.0,4031.0,923.0,2558.0,834.0,3.1641,117300.0,<1H OCEAN\n-117.76,34.06,30.0,1700.0,504.0,1719.0,459.0,2.227,91900.0,INLAND\n-117.76,34.05,36.0,2910.0,819.0,3055.0,782.0,1.9029,98000.0,INLAND\n-117.75,34.05,35.0,1293.0,339.0,1494.0,312.0,1.6645,93300.0,INLAND\n-117.76,34.05,36.0,3839.0,1004.0,4711.0,942.0,2.3859,116200.0,INLAND\n-117.74,34.08,35.0,1613.0,298.0,911.0,293.0,3.4398,134300.0,INLAND\n-117.74,34.07,42.0,2504.0,553.0,1550.0,509.0,3.0294,135700.0,INLAND\n-117.74,34.06,48.0,2438.0,599.0,1508.0,548.0,2.8983,129200.0,INLAND\n-117.74,34.07,52.0,1868.0,316.0,947.0,328.0,4.2415,140100.0,INLAND\n-117.75,34.07,52.0,1279.0,213.0,444.0,204.0,5.2269,161000.0,INLAND\n-117.75,34.07,52.0,2550.0,586.0,1246.0,576.0,1.6006,146200.0,INLAND\n-117.72,34.08,34.0,2742.0,491.0,1761.0,496.0,3.2481,128800.0,INLAND\n-117.72,34.07,33.0,4100.0,740.0,2580.0,730.0,3.7321,134200.0,INLAND\n-117.73,34.08,28.0,5173.0,1069.0,3502.0,954.0,3.8438,130800.0,INLAND\n-117.73,34.07,34.0,4038.0,725.0,2716.0,759.0,4.1339,135000.0,INLAND\n-117.72,34.06,32.0,2209.0,654.0,1718.0,569.0,1.9643,113200.0,INLAND\n-117.73,34.06,34.0,344.0,108.0,315.0,119.0,3.1786,117800.0,INLAND\n-117.73,34.06,51.0,498.0,115.0,368.0,112.0,1.4063,98800.0,INLAND\n-117.73,34.07,33.0,1921.0,489.0,1430.0,467.0,2.3406,122600.0,INLAND\n-117.73,34.07,33.0,1025.0,261.0,854.0,269.0,2.2596,119400.0,INLAND\n-117.73,34.05,36.0,975.0,243.0,809.0,233.0,2.8929,118100.0,INLAND\n-117.73,34.05,28.0,2758.0,771.0,2877.0,694.0,2.0734,113300.0,INLAND\n-117.74,34.05,30.0,1185.0,317.0,1466.0,302.0,2.625,94300.0,INLAND\n-117.74,34.05,29.0,2452.0,700.0,3029.0,665.0,2.1354,110700.0,INLAND\n-117.74,34.05,27.0,852.0,237.0,1024.0,221.0,2.1141,110900.0,INLAND\n-117.75,34.05,46.0,1480.0,358.0,1511.0,348.0,1.9718,110600.0,INLAND\n-117.73,34.04,26.0,3827.0,814.0,3367.0,810.0,3.15,129700.0,INLAND\n-117.73,34.03,42.0,1967.0,378.0,1459.0,348.0,3.0375,118100.0,INLAND\n-117.74,34.02,33.0,2318.0,464.0,1904.0,451.0,3.7454,116400.0,INLAND\n-117.74,34.04,27.0,2215.0,440.0,1987.0,449.0,3.0429,129600.0,INLAND\n-117.74,34.03,27.0,3623.0,809.0,3712.0,754.0,3.4609,123300.0,INLAND\n-117.75,34.04,22.0,2948.0,636.0,2600.0,602.0,3.125,113600.0,INLAND\n-117.76,34.04,34.0,1914.0,,1564.0,328.0,2.8347,115800.0,INLAND\n-117.76,34.04,36.0,2242.0,448.0,2052.0,447.0,3.4464,113000.0,INLAND\n-117.85,34.0,26.0,2712.0,402.0,1389.0,377.0,5.6513,227900.0,<1H OCEAN\n-117.86,33.99,10.0,17820.0,2812.0,8686.0,2666.0,6.3875,310700.0,<1H OCEAN\n-117.8,34.03,25.0,4240.0,643.0,1885.0,637.0,6.2384,247600.0,<1H OCEAN\n-117.78,34.03,8.0,32054.0,5290.0,15507.0,5050.0,6.0191,253900.0,<1H OCEAN\n-117.83,34.01,16.0,9446.0,1650.0,4911.0,1534.0,5.0111,212900.0,<1H OCEAN\n-117.8,34.02,23.0,3351.0,591.0,1535.0,522.0,5.0869,230600.0,<1H OCEAN\n-117.81,34.01,12.0,9197.0,1642.0,4332.0,1554.0,4.9589,282100.0,<1H OCEAN\n-117.79,34.02,5.0,18690.0,2862.0,9427.0,2777.0,6.4266,315600.0,<1H OCEAN\n-117.84,34.0,26.0,797.0,117.0,383.0,114.0,6.8758,253800.0,<1H OCEAN\n-117.83,33.99,14.0,17527.0,2751.0,8380.0,2676.0,6.2734,267000.0,<1H OCEAN\n-117.84,33.98,26.0,3638.0,557.0,1993.0,593.0,6.1076,221200.0,<1H OCEAN\n-117.83,33.97,11.0,21533.0,3078.0,9671.0,2890.0,7.0329,368300.0,<1H OCEAN\n-117.87,34.04,7.0,27700.0,4179.0,15037.0,4072.0,6.6288,339700.0,<1H OCEAN\n-117.86,34.02,19.0,6300.0,937.0,3671.0,943.0,5.9716,262100.0,<1H OCEAN\n-117.86,34.01,16.0,4632.0,,3038.0,727.0,5.1762,264400.0,<1H OCEAN\n-117.87,34.02,16.0,3552.0,575.0,2120.0,573.0,6.4333,271500.0,<1H OCEAN\n-117.84,34.04,4.0,9959.0,1544.0,4904.0,1429.0,6.9754,402500.0,<1H OCEAN\n-117.85,34.06,24.0,3128.0,497.0,1406.0,472.0,7.5286,462700.0,<1H OCEAN\n-117.85,34.08,23.0,1160.0,166.0,467.0,178.0,8.105,386200.0,<1H OCEAN\n-117.85,34.07,32.0,761.0,101.0,295.0,95.0,11.1077,500001.0,<1H OCEAN\n-117.86,34.08,31.0,2524.0,349.0,1003.0,343.0,7.5196,380900.0,<1H OCEAN\n-117.87,34.08,33.0,4518.0,716.0,2037.0,764.0,5.6015,267200.0,<1H OCEAN\n-117.87,34.07,21.0,4723.0,882.0,2210.0,768.0,3.8167,258700.0,<1H OCEAN\n-117.85,34.1,22.0,5179.0,944.0,2315.0,884.0,4.51,189900.0,<1H OCEAN\n-117.86,34.09,29.0,3855.0,585.0,2205.0,609.0,5.5496,218200.0,<1H OCEAN\n-117.85,34.09,16.0,4556.0,639.0,2066.0,651.0,6.4667,263900.0,<1H OCEAN\n-117.86,34.09,26.0,3408.0,542.0,1664.0,543.0,6.1498,239100.0,<1H OCEAN\n-117.87,34.1,25.0,2208.0,477.0,1084.0,424.0,3.775,191700.0,<1H OCEAN\n-117.87,34.09,31.0,1484.0,327.0,927.0,317.0,3.6484,189600.0,<1H OCEAN\n-117.88,34.1,32.0,3357.0,621.0,1696.0,604.0,4.2685,216600.0,<1H OCEAN\n-117.89,34.09,36.0,1811.0,320.0,1005.0,332.0,5.5629,188300.0,<1H OCEAN\n-117.87,34.09,36.0,1267.0,191.0,640.0,200.0,5.2405,220000.0,<1H OCEAN\n-117.87,34.08,33.0,3630.0,800.0,2257.0,796.0,3.2469,206900.0,<1H OCEAN\n-117.88,34.09,29.0,3416.0,790.0,2223.0,728.0,3.5109,186000.0,<1H OCEAN\n-117.86,34.1,23.0,2535.0,490.0,1327.0,466.0,3.5977,180600.0,<1H OCEAN\n-117.86,34.1,29.0,1185.0,197.0,588.0,196.0,5.0832,196900.0,<1H OCEAN\n-117.87,34.1,15.0,6409.0,1363.0,3359.0,1267.0,3.875,173300.0,<1H OCEAN\n-117.84,34.1,17.0,7836.0,1624.0,4419.0,1526.0,3.8465,180700.0,<1H OCEAN\n-117.85,34.11,27.0,1748.0,403.0,985.0,416.0,3.1133,180600.0,INLAND\n-117.87,34.12,33.0,2059.0,361.0,1073.0,339.0,4.2454,183800.0,<1H OCEAN\n-117.88,34.12,33.0,1485.0,274.0,1006.0,258.0,5.1708,158500.0,<1H OCEAN\n-117.88,34.12,34.0,912.0,165.0,522.0,150.0,4.0417,178000.0,<1H OCEAN\n-117.87,34.12,34.0,1004.0,220.0,772.0,217.0,3.8571,174500.0,<1H OCEAN\n-117.87,34.11,23.0,4066.0,819.0,2105.0,737.0,4.6556,199600.0,<1H OCEAN\n-117.87,34.11,34.0,1324.0,211.0,799.0,228.0,4.5234,192200.0,<1H OCEAN\n-117.88,34.11,30.0,3082.0,602.0,2008.0,619.0,4.1411,182700.0,<1H OCEAN\n-117.89,34.12,35.0,1470.0,241.0,885.0,246.0,4.9239,168800.0,<1H OCEAN\n-117.88,34.12,35.0,1574.0,276.0,1088.0,289.0,4.0938,165300.0,<1H OCEAN\n-117.89,34.11,36.0,806.0,147.0,446.0,153.0,4.5221,151300.0,<1H OCEAN\n-117.88,34.11,18.0,2923.0,670.0,1751.0,656.0,3.2383,157000.0,<1H OCEAN\n-117.89,34.12,35.0,1447.0,272.0,1224.0,268.0,3.9934,141900.0,<1H OCEAN\n-117.89,34.12,35.0,1566.0,321.0,1396.0,317.0,4.05,141300.0,<1H OCEAN\n-117.89,34.11,27.0,2434.0,535.0,1623.0,498.0,3.6875,140200.0,<1H OCEAN\n-117.9,34.11,37.0,1286.0,255.0,1047.0,249.0,4.2019,140100.0,<1H OCEAN\n-117.88,34.13,25.0,2559.0,654.0,1674.0,623.0,2.8547,155600.0,<1H OCEAN\n-117.88,34.12,36.0,2029.0,351.0,1327.0,364.0,4.1836,164300.0,<1H OCEAN\n-117.89,34.13,34.0,2159.0,386.0,1443.0,385.0,4.1995,147400.0,<1H OCEAN\n-117.9,34.13,25.0,3076.0,856.0,2868.0,752.0,2.6619,117600.0,<1H OCEAN\n-117.9,34.13,32.0,1640.0,391.0,1312.0,358.0,2.6292,136100.0,<1H OCEAN\n-117.9,34.13,5.0,1126.0,316.0,819.0,311.0,1.5,139800.0,<1H OCEAN\n-117.9,34.13,37.0,1801.0,422.0,1564.0,425.0,3.1597,133000.0,<1H OCEAN\n-117.9,34.12,33.0,1788.0,456.0,1787.0,361.0,2.6629,124100.0,<1H OCEAN\n-117.9,34.12,33.0,1555.0,361.0,1571.0,386.0,4.0529,138200.0,<1H OCEAN\n-117.9,34.12,35.0,957.0,194.0,804.0,221.0,3.3322,151400.0,<1H OCEAN\n-117.91,34.13,34.0,1540.0,328.0,1037.0,317.0,2.2132,138500.0,<1H OCEAN\n-117.92,34.13,42.0,1762.0,398.0,1526.0,365.0,2.8643,132600.0,INLAND\n-117.91,34.12,41.0,2673.0,578.0,2259.0,592.0,3.7846,145500.0,<1H OCEAN\n-117.91,34.12,33.0,1391.0,309.0,1038.0,298.0,4.1944,149500.0,<1H OCEAN\n-117.92,34.12,32.0,2552.0,576.0,2161.0,548.0,2.9459,144400.0,<1H OCEAN\n-117.93,34.12,36.0,294.0,67.0,266.0,80.0,3.5385,134400.0,INLAND\n-117.9,34.11,23.0,4776.0,1316.0,4797.0,1187.0,2.1667,142600.0,<1H OCEAN\n-117.91,34.11,20.0,3158.0,684.0,2396.0,713.0,3.525,153000.0,<1H OCEAN\n-117.92,34.11,24.0,2838.0,695.0,2151.0,645.0,3.2202,126200.0,<1H OCEAN\n-117.94,34.1,31.0,1239.0,254.0,929.0,244.0,3.3625,153400.0,<1H OCEAN\n-117.97,34.11,18.0,123.0,28.0,121.0,26.0,3.0417,137500.0,INLAND\n-117.99,34.08,11.0,2399.0,527.0,2307.0,531.0,3.5625,141000.0,INLAND\n-117.99,34.07,31.0,1507.0,369.0,1548.0,347.0,3.4327,147200.0,INLAND\n-117.98,34.07,15.0,3543.0,888.0,3131.0,823.0,3.0184,139400.0,INLAND\n-117.98,34.07,28.0,441.0,106.0,504.0,108.0,2.9107,152500.0,INLAND\n-117.99,34.06,32.0,2491.0,616.0,2660.0,595.0,2.564,145800.0,INLAND\n-118.0,34.07,34.0,1696.0,456.0,1609.0,426.0,2.25,138500.0,INLAND\n-117.99,34.07,35.0,1681.0,360.0,1648.0,373.0,2.4911,145900.0,INLAND\n-117.99,34.08,35.0,1032.0,207.0,954.0,191.0,2.8906,134800.0,INLAND\n-117.97,34.08,8.0,2027.0,480.0,1781.0,447.0,3.0806,142400.0,INLAND\n-117.97,34.08,30.0,2227.0,474.0,1961.0,481.0,3.3261,164100.0,INLAND\n-117.96,34.07,32.0,2910.0,709.0,2583.0,670.0,3.7736,158400.0,<1H OCEAN\n-117.97,34.07,20.0,2063.0,496.0,1573.0,468.0,3.2,157100.0,<1H OCEAN\n-117.97,34.07,22.0,1438.0,364.0,1325.0,335.0,2.7802,162500.0,<1H OCEAN\n-117.98,34.08,17.0,3640.0,830.0,3537.0,807.0,3.4784,152200.0,INLAND\n-117.98,34.1,22.0,5661.0,1209.0,5389.0,1178.0,3.7727,159700.0,INLAND\n-117.98,34.09,31.0,3073.0,617.0,2640.0,594.0,3.5,161300.0,INLAND\n-117.97,34.09,27.0,3569.0,761.0,3339.0,762.0,4.1304,160500.0,INLAND\n-117.95,34.11,29.0,1986.0,448.0,2013.0,432.0,3.1034,140800.0,INLAND\n-117.96,34.1,35.0,4036.0,904.0,3878.0,846.0,3.2957,141600.0,INLAND\n-117.97,34.1,33.0,1558.0,316.0,1600.0,338.0,2.9712,143900.0,INLAND\n-117.97,34.1,26.0,1399.0,277.0,1285.0,276.0,4.0,160100.0,INLAND\n-117.96,34.09,30.0,2686.0,613.0,2477.0,573.0,3.4427,160800.0,INLAND\n-117.96,34.09,6.0,1954.0,534.0,1584.0,496.0,3.1621,131000.0,INLAND\n-117.96,34.1,30.0,2775.0,657.0,2847.0,642.0,3.2266,141800.0,INLAND\n-117.97,34.09,31.0,2779.0,639.0,2259.0,670.0,3.4032,143400.0,INLAND\n-117.95,34.09,21.0,2215.0,484.0,1792.0,419.0,2.8375,166500.0,<1H OCEAN\n-117.95,34.08,34.0,2278.0,476.0,1728.0,448.0,3.125,154100.0,<1H OCEAN\n-117.95,34.09,18.0,1179.0,324.0,1296.0,331.0,2.851,140600.0,<1H OCEAN\n-117.96,34.08,39.0,1076.0,338.0,1242.0,332.0,2.2679,151800.0,<1H OCEAN\n-117.96,34.08,33.0,4151.0,850.0,3563.0,848.0,3.1912,159900.0,<1H OCEAN\n-117.96,34.08,28.0,2831.0,552.0,2330.0,557.0,3.9741,173100.0,<1H OCEAN\n-117.94,34.09,21.0,2707.0,675.0,1742.0,626.0,2.1062,176700.0,<1H OCEAN\n-117.94,34.08,32.0,2704.0,514.0,1669.0,497.0,4.4653,195400.0,<1H OCEAN\n-117.94,34.08,34.0,1970.0,476.0,1269.0,406.0,4.064,201200.0,<1H OCEAN\n-117.94,34.08,35.0,2393.0,417.0,1336.0,418.0,4.87,187700.0,<1H OCEAN\n-117.95,34.08,37.0,1137.0,203.0,672.0,226.0,3.2969,189000.0,<1H OCEAN\n-117.95,34.07,37.0,1987.0,399.0,1279.0,378.0,4.1172,176500.0,<1H OCEAN\n-117.93,34.09,34.0,2192.0,431.0,1376.0,428.0,3.9861,163900.0,<1H OCEAN\n-117.93,34.09,35.0,782.0,153.0,499.0,163.0,4.2062,161300.0,<1H OCEAN\n-117.93,34.09,35.0,1891.0,353.0,1093.0,382.0,4.0167,165500.0,<1H OCEAN\n-117.93,34.09,37.0,1185.0,225.0,769.0,235.0,4.4625,154200.0,<1H OCEAN\n-117.92,34.08,36.0,1285.0,228.0,679.0,231.0,3.8705,191900.0,<1H OCEAN\n-117.92,34.08,35.0,2108.0,408.0,1257.0,414.0,4.1312,185200.0,<1H OCEAN\n-117.93,34.07,34.0,1409.0,305.0,819.0,273.0,3.3977,188800.0,<1H OCEAN\n-117.93,34.08,36.0,1371.0,246.0,806.0,241.0,4.5078,187100.0,<1H OCEAN\n-117.93,34.08,35.0,689.0,128.0,379.0,128.0,3.9583,206000.0,<1H OCEAN\n-117.93,34.08,36.0,1597.0,285.0,901.0,272.0,4.3947,197000.0,<1H OCEAN\n-117.93,34.08,36.0,1788.0,317.0,1139.0,320.0,4.125,185800.0,<1H OCEAN\n-117.91,34.08,33.0,2325.0,452.0,1170.0,445.0,3.6625,217100.0,<1H OCEAN\n-117.91,34.08,35.0,1443.0,266.0,861.0,262.0,3.5795,186900.0,<1H OCEAN\n-117.92,34.08,36.0,1479.0,251.0,741.0,245.0,4.2986,189600.0,<1H OCEAN\n-117.92,34.08,35.0,1860.0,323.0,1011.0,305.0,3.5536,207000.0,<1H OCEAN\n-117.92,34.08,35.0,1897.0,311.0,965.0,323.0,5.7039,199400.0,<1H OCEAN\n-117.91,34.1,28.0,3694.0,722.0,1999.0,718.0,3.2813,181100.0,<1H OCEAN\n-117.91,34.09,20.0,4327.0,1037.0,2296.0,963.0,3.0441,185400.0,<1H OCEAN\n-117.92,34.09,35.0,1810.0,318.0,1164.0,332.0,5.0123,165700.0,<1H OCEAN\n-117.92,34.1,33.0,1921.0,397.0,1492.0,393.0,4.375,150500.0,<1H OCEAN\n-117.9,34.1,31.0,3007.0,653.0,1766.0,616.0,3.7083,166000.0,<1H OCEAN\n-117.91,34.1,35.0,2746.0,478.0,1779.0,501.0,4.25,166700.0,<1H OCEAN\n-117.92,34.1,35.0,2994.0,603.0,1933.0,561.0,4.0052,160700.0,<1H OCEAN\n-117.89,34.1,27.0,3341.0,728.0,1762.0,679.0,2.9437,180400.0,<1H OCEAN\n-117.89,34.1,35.0,3185.0,544.0,1858.0,564.0,3.8304,175900.0,<1H OCEAN\n-117.89,34.1,34.0,2048.0,411.0,1456.0,416.0,3.125,168600.0,<1H OCEAN\n-117.9,34.1,35.0,2739.0,475.0,1481.0,483.0,4.5655,176600.0,<1H OCEAN\n-117.9,34.09,34.0,1562.0,272.0,825.0,266.0,4.125,220800.0,<1H OCEAN\n-117.9,34.08,32.0,2068.0,356.0,976.0,370.0,5.212,201200.0,<1H OCEAN\n-117.89,34.09,37.0,1055.0,280.0,538.0,206.0,2.4167,181300.0,<1H OCEAN\n-117.89,34.09,35.0,1205.0,330.0,583.0,319.0,2.3971,188900.0,<1H OCEAN\n-117.89,34.09,37.0,1813.0,394.0,1100.0,375.0,3.4453,176700.0,<1H OCEAN\n-117.9,34.09,39.0,1726.0,333.0,892.0,335.0,4.3409,191800.0,<1H OCEAN\n-117.88,34.08,30.0,6132.0,1538.0,3147.0,1449.0,2.7763,187800.0,<1H OCEAN\n-117.89,34.08,35.0,1711.0,335.0,825.0,356.0,3.5,215600.0,<1H OCEAN\n-117.89,34.08,25.0,2115.0,489.0,1107.0,477.0,3.1949,207400.0,<1H OCEAN\n-117.89,34.07,32.0,2374.0,450.0,1580.0,427.0,3.8837,200300.0,<1H OCEAN\n-117.9,34.08,32.0,5482.0,1251.0,3426.0,1117.0,3.2943,204400.0,<1H OCEAN\n-117.87,34.06,25.0,3652.0,470.0,1525.0,484.0,10.1248,428500.0,<1H OCEAN\n-117.88,34.06,23.0,6689.0,1124.0,3081.0,1047.0,5.9254,491200.0,<1H OCEAN\n-117.9,34.06,35.0,1313.0,194.0,599.0,209.0,7.5,287200.0,<1H OCEAN\n-117.9,34.06,33.0,1701.0,290.0,831.0,275.0,5.4469,274700.0,<1H OCEAN\n-117.9,34.06,33.0,1330.0,209.0,578.0,192.0,5.6406,266200.0,<1H OCEAN\n-117.89,34.07,35.0,1439.0,261.0,804.0,271.0,3.9808,188600.0,<1H OCEAN\n-117.9,34.07,35.0,1646.0,294.0,1056.0,280.0,3.055,172000.0,<1H OCEAN\n-117.89,34.07,35.0,834.0,137.0,392.0,123.0,4.5179,218800.0,<1H OCEAN\n-117.9,34.06,34.0,2956.0,469.0,1488.0,464.0,5.3664,268300.0,<1H OCEAN\n-117.9,34.07,36.0,1009.0,164.0,466.0,149.0,5.8519,249400.0,<1H OCEAN\n-117.91,34.07,36.0,1390.0,270.0,887.0,266.0,5.0897,189000.0,<1H OCEAN\n-117.91,34.07,33.0,2938.0,561.0,1519.0,549.0,4.5594,204200.0,<1H OCEAN\n-117.92,34.06,34.0,2819.0,609.0,1718.0,558.0,3.5547,197600.0,<1H OCEAN\n-117.92,34.07,36.0,1057.0,207.0,658.0,207.0,4.7708,191700.0,<1H OCEAN\n-117.92,34.07,29.0,1699.0,399.0,1052.0,411.0,3.2122,195500.0,<1H OCEAN\n-117.91,34.06,29.0,3250.0,521.0,1382.0,513.0,5.112,218300.0,<1H OCEAN\n-117.91,34.05,35.0,3189.0,,1727.0,500.0,5.0758,211100.0,<1H OCEAN\n-117.92,34.06,35.0,2894.0,467.0,1420.0,479.0,5.184,224900.0,<1H OCEAN\n-117.93,34.06,37.0,1505.0,262.0,798.0,259.0,5.4635,202100.0,<1H OCEAN\n-117.93,34.06,35.0,1022.0,183.0,628.0,187.0,3.9375,187500.0,<1H OCEAN\n-117.93,34.05,36.0,1340.0,221.0,848.0,244.0,4.1731,205100.0,<1H OCEAN\n-117.93,34.05,32.0,3055.0,623.0,1902.0,565.0,4.2926,190700.0,<1H OCEAN\n-117.92,34.07,38.0,175.0,22.0,129.0,20.0,9.7066,182500.0,<1H OCEAN\n-117.93,34.06,28.0,3342.0,688.0,2210.0,647.0,3.4596,202800.0,<1H OCEAN\n-117.95,34.05,34.0,1428.0,227.0,890.0,249.0,5.8722,204800.0,<1H OCEAN\n-117.94,34.06,32.0,3418.0,662.0,2003.0,622.0,4.0333,210200.0,<1H OCEAN\n-117.94,34.06,34.0,1921.0,422.0,1230.0,447.0,3.6648,193900.0,<1H OCEAN\n-117.93,34.07,36.0,1207.0,209.0,683.0,213.0,5.3559,207300.0,<1H OCEAN\n-117.94,34.07,25.0,1814.0,404.0,1187.0,363.0,3.3523,170800.0,<1H OCEAN\n-117.95,34.07,37.0,1375.0,260.0,951.0,272.0,3.2083,195200.0,<1H OCEAN\n-117.95,34.06,32.0,2252.0,415.0,1370.0,411.0,4.6312,184800.0,<1H OCEAN\n-117.96,34.07,35.0,2819.0,529.0,1508.0,485.0,4.6118,191700.0,<1H OCEAN\n-117.96,34.06,34.0,2226.0,381.0,1464.0,365.0,4.4102,183200.0,<1H OCEAN\n-117.96,34.06,31.0,2017.0,462.0,1462.0,457.0,2.067,167300.0,<1H OCEAN\n-117.97,34.05,33.0,1452.0,268.0,1274.0,278.0,3.6563,162700.0,<1H OCEAN\n-117.97,34.06,34.0,3580.0,684.0,2786.0,636.0,4.0469,166800.0,<1H OCEAN\n-117.98,34.06,33.0,1353.0,228.0,1079.0,237.0,4.5417,160300.0,<1H OCEAN\n-117.97,34.06,31.0,2516.0,,2194.0,497.0,3.2413,155500.0,<1H OCEAN\n-117.98,34.06,36.0,2391.0,407.0,1967.0,398.0,4.0274,160700.0,<1H OCEAN\n-117.98,34.05,35.0,2342.0,426.0,2176.0,416.0,3.7454,156900.0,<1H OCEAN\n-117.99,34.05,35.0,1792.0,317.0,1441.0,306.0,3.7917,151100.0,<1H OCEAN\n-117.97,34.05,33.0,2028.0,422.0,1727.0,371.0,2.8438,157600.0,<1H OCEAN\n-117.97,34.05,36.0,931.0,160.0,746.0,201.0,3.1667,158000.0,<1H OCEAN\n-117.97,34.05,36.0,1299.0,206.0,763.0,216.0,3.5179,161400.0,<1H OCEAN\n-117.97,34.05,34.0,2050.0,495.0,1832.0,465.0,2.8333,155700.0,<1H OCEAN\n-117.98,34.05,33.0,1560.0,315.0,1467.0,313.0,4.1429,159800.0,<1H OCEAN\n-117.98,34.04,34.0,2547.0,537.0,2108.0,498.0,3.4722,154600.0,<1H OCEAN\n-117.97,34.04,32.0,1507.0,295.0,1326.0,324.0,4.119,163300.0,<1H OCEAN\n-117.97,34.04,28.0,1686.0,417.0,1355.0,388.0,2.5192,157300.0,<1H OCEAN\n-117.96,34.03,35.0,2093.0,,1755.0,403.0,3.4115,150400.0,<1H OCEAN\n-117.96,34.03,35.0,1623.0,331.0,1462.0,312.0,3.9803,152600.0,<1H OCEAN\n-117.95,34.03,33.0,1453.0,326.0,1609.0,319.0,3.7578,155800.0,<1H OCEAN\n-117.95,34.03,35.0,804.0,159.0,727.0,179.0,2.7361,145700.0,<1H OCEAN\n-117.96,34.05,35.0,1254.0,263.0,1092.0,268.0,4.6364,163100.0,<1H OCEAN\n-117.96,34.05,36.0,1475.0,270.0,1149.0,284.0,3.0904,158600.0,<1H OCEAN\n-117.95,34.05,35.0,1309.0,276.0,1113.0,253.0,4.375,156500.0,<1H OCEAN\n-117.95,34.04,36.0,1044.0,200.0,982.0,205.0,4.7679,153900.0,<1H OCEAN\n-117.96,34.04,35.0,1141.0,212.0,924.0,212.0,3.1591,148300.0,<1H OCEAN\n-117.96,34.04,33.0,1458.0,268.0,1115.0,257.0,4.7955,158100.0,<1H OCEAN\n-117.96,34.04,34.0,1381.0,265.0,1020.0,268.0,4.025,146900.0,<1H OCEAN\n-117.96,34.05,32.0,1993.0,388.0,1385.0,380.0,3.7258,181900.0,<1H OCEAN\n-117.94,34.05,34.0,1729.0,324.0,1341.0,324.0,3.7708,163500.0,<1H OCEAN\n-117.94,34.04,36.0,1431.0,354.0,1367.0,334.0,3.5592,160200.0,<1H OCEAN\n-117.94,34.04,34.0,1403.0,274.0,977.0,257.0,3.8409,163000.0,<1H OCEAN\n-117.94,34.05,34.0,1519.0,304.0,1262.0,300.0,3.3409,161200.0,<1H OCEAN\n-117.95,34.05,31.0,2349.0,539.0,2028.0,521.0,3.494,154500.0,<1H OCEAN\n-117.94,34.04,33.0,1493.0,331.0,1571.0,354.0,3.8864,158900.0,<1H OCEAN\n-117.95,34.04,27.0,2610.0,846.0,2296.0,750.0,2.274,150800.0,<1H OCEAN\n-117.94,34.03,35.0,1499.0,289.0,1112.0,268.0,3.83,149000.0,<1H OCEAN\n-117.94,34.03,35.0,1375.0,249.0,1015.0,239.0,4.0521,151800.0,<1H OCEAN\n-117.95,34.03,33.0,1782.0,394.0,1517.0,376.0,3.3389,157900.0,<1H OCEAN\n-117.93,34.01,23.0,3188.0,836.0,3883.0,840.0,2.1863,157600.0,<1H OCEAN\n-117.93,34.01,33.0,1733.0,361.0,1757.0,375.0,4.2266,153800.0,<1H OCEAN\n-117.94,34.02,27.0,5026.0,955.0,3899.0,930.0,3.871,162900.0,<1H OCEAN\n-117.95,34.02,19.0,1129.0,258.0,900.0,228.0,3.875,135600.0,<1H OCEAN\n-117.95,34.02,22.0,1919.0,411.0,1203.0,363.0,4.2578,144100.0,<1H OCEAN\n-117.92,34.03,35.0,1341.0,233.0,898.0,216.0,4.1118,157300.0,<1H OCEAN\n-117.92,34.03,32.0,1819.0,375.0,1728.0,375.0,3.975,162400.0,<1H OCEAN\n-117.92,34.03,35.0,1469.0,306.0,1285.0,308.0,3.9219,159500.0,<1H OCEAN\n-117.93,34.03,35.0,2160.0,399.0,1694.0,403.0,3.8581,163100.0,<1H OCEAN\n-117.93,34.03,30.0,2246.0,446.0,1837.0,431.0,4.7917,164500.0,<1H OCEAN\n-117.93,34.04,23.0,6361.0,1168.0,4580.0,1109.0,4.9342,181000.0,<1H OCEAN\n-117.93,34.04,30.0,1336.0,239.0,905.0,253.0,4.8854,178100.0,<1H OCEAN\n-117.9,34.04,15.0,11989.0,2185.0,6652.0,2081.0,4.5554,278300.0,<1H OCEAN\n-117.9,34.05,33.0,629.0,76.0,253.0,75.0,7.6286,330400.0,<1H OCEAN\n-117.91,34.04,15.0,8749.0,1761.0,5278.0,1691.0,4.6324,168800.0,<1H OCEAN\n-117.92,34.05,36.0,2241.0,419.0,1743.0,448.0,4.6587,161900.0,<1H OCEAN\n-117.88,34.01,16.0,6756.0,1332.0,4429.0,1299.0,4.7589,178200.0,<1H OCEAN\n-117.88,34.0,21.0,5459.0,1086.0,3394.0,1087.0,3.6308,192100.0,<1H OCEAN\n-117.89,34.01,23.0,4535.0,955.0,3881.0,930.0,3.6275,154100.0,<1H OCEAN\n-117.9,34.01,27.0,1702.0,381.0,2091.0,352.0,3.7917,131100.0,<1H OCEAN\n-117.9,34.01,27.0,2383.0,472.0,2079.0,494.0,3.9702,141400.0,<1H OCEAN\n-117.91,34.02,17.0,5973.0,1384.0,4349.0,1229.0,3.2799,199300.0,<1H OCEAN\n-117.9,34.02,15.0,14058.0,2486.0,8997.0,2497.0,5.0704,226200.0,<1H OCEAN\n-117.91,34.02,22.0,6269.0,,5587.0,1251.0,3.8201,136200.0,<1H OCEAN\n-117.92,34.01,35.0,3055.0,634.0,3738.0,615.0,3.375,127200.0,<1H OCEAN\n-117.98,34.04,29.0,1468.0,310.0,1390.0,276.0,3.75,190600.0,<1H OCEAN\n-117.96,34.02,33.0,349.0,124.0,460.0,83.0,2.375,133300.0,<1H OCEAN\n-117.98,34.03,21.0,797.0,162.0,484.0,166.0,2.625,191100.0,<1H OCEAN\n-117.9,33.99,18.0,8076.0,1789.0,5325.0,1707.0,3.443,171900.0,<1H OCEAN\n-117.92,34.0,36.0,116.0,30.0,193.0,35.0,3.8125,136300.0,<1H OCEAN\n-117.88,34.0,32.0,265.0,51.0,170.0,50.0,3.9375,187500.0,<1H OCEAN\n-117.89,33.99,22.0,3272.0,618.0,1784.0,591.0,4.0324,211300.0,<1H OCEAN\n-117.87,33.99,21.0,2837.0,515.0,2031.0,555.0,4.9271,209700.0,<1H OCEAN\n-118.01,34.05,37.0,682.0,172.0,813.0,173.0,3.8125,138000.0,<1H OCEAN\n-117.99,34.04,30.0,4468.0,959.0,4027.0,938.0,3.185,168300.0,<1H OCEAN\n-118.02,34.04,27.0,5640.0,1001.0,3538.0,978.0,5.065,215400.0,<1H OCEAN\n-118.0,34.03,25.0,6909.0,1154.0,3912.0,1121.0,5.257,226100.0,<1H OCEAN\n-117.98,34.02,32.0,2945.0,651.0,2044.0,652.0,3.1979,183900.0,<1H OCEAN\n-117.98,34.02,33.0,3512.0,632.0,1971.0,598.0,4.4653,193200.0,<1H OCEAN\n-117.98,34.01,27.0,2643.0,418.0,1344.0,381.0,5.7057,262100.0,<1H OCEAN\n-117.99,34.0,26.0,2988.0,397.0,1371.0,415.0,6.6988,382500.0,<1H OCEAN\n-118.02,34.02,21.0,5992.0,986.0,2647.0,969.0,5.2405,302400.0,<1H OCEAN\n-117.97,34.01,33.0,3530.0,700.0,2959.0,679.0,3.7459,152900.0,<1H OCEAN\n-117.97,34.01,33.0,2006.0,381.0,1410.0,346.0,3.7083,165500.0,<1H OCEAN\n-117.97,34.0,28.0,1983.0,375.0,1407.0,367.0,3.8319,179000.0,<1H OCEAN\n-117.97,33.99,22.0,5284.0,982.0,2613.0,932.0,4.7332,289900.0,<1H OCEAN\n-117.98,34.0,22.0,3632.0,538.0,1968.0,566.0,6.019,324900.0,<1H OCEAN\n-117.98,33.99,26.0,3343.0,455.0,1429.0,457.0,7.0778,334300.0,<1H OCEAN\n-117.98,33.98,27.0,2275.0,346.0,1039.0,333.0,6.2217,333500.0,<1H OCEAN\n-117.99,33.98,18.0,8399.0,1144.0,3727.0,1107.0,6.9695,360400.0,<1H OCEAN\n-117.95,34.0,34.0,2376.0,468.0,1858.0,449.0,4.1328,176300.0,<1H OCEAN\n-117.96,34.0,33.0,5462.0,952.0,3527.0,914.0,4.4038,186700.0,<1H OCEAN\n-117.96,34.0,34.0,2777.0,540.0,1954.0,522.0,4.5163,183800.0,<1H OCEAN\n-117.92,33.98,10.0,16414.0,2919.0,8907.0,2714.0,6.1552,362500.0,<1H OCEAN\n-117.95,33.98,15.0,16042.0,2602.0,7732.0,2552.0,5.6716,330400.0,<1H OCEAN\n-117.94,33.99,18.0,6100.0,1018.0,3112.0,982.0,4.9932,284000.0,<1H OCEAN\n-117.95,33.99,15.0,3978.0,692.0,2418.0,665.0,5.0142,269900.0,<1H OCEAN\n-117.95,33.99,24.0,1219.0,177.0,610.0,185.0,6.7978,325000.0,<1H OCEAN\n-117.95,33.99,25.0,1075.0,138.0,451.0,132.0,6.8492,332200.0,<1H OCEAN\n-117.96,33.99,25.0,1348.0,210.0,660.0,200.0,5.2852,297600.0,<1H OCEAN\n-117.96,33.99,25.0,2799.0,388.0,1348.0,389.0,6.2517,311100.0,<1H OCEAN\n-117.97,33.99,23.0,3335.0,570.0,1560.0,555.0,5.7268,300300.0,<1H OCEAN\n-117.96,33.98,25.0,1259.0,184.0,599.0,170.0,5.7407,302200.0,<1H OCEAN\n-117.88,33.96,16.0,19059.0,3079.0,10988.0,3061.0,5.5469,265200.0,<1H OCEAN\n-117.89,33.98,5.0,3088.0,711.0,1415.0,641.0,2.5125,184500.0,<1H OCEAN\n-117.9,33.98,20.0,9893.0,2283.0,7228.0,2159.0,3.253,186700.0,<1H OCEAN\n-117.9,33.97,23.0,7353.0,1255.0,4014.0,1124.0,5.4155,213200.0,<1H OCEAN\n-117.75,34.06,52.0,1171.0,318.0,1126.0,276.0,1.9762,105800.0,INLAND\n-117.75,34.06,52.0,62.0,9.0,44.0,16.0,0.4999,112500.0,INLAND\n-117.74,34.06,4.0,1391.0,506.0,727.0,369.0,1.4722,137500.0,INLAND\n-117.75,34.05,27.0,437.0,108.0,469.0,97.0,1.7206,107500.0,INLAND\n-117.75,34.05,37.0,378.0,92.0,503.0,103.0,2.1908,94600.0,INLAND\n-117.75,34.06,44.0,477.0,135.0,502.0,117.0,2.0156,112500.0,INLAND\n-117.75,34.06,52.0,24.0,6.0,46.0,7.0,1.625,67500.0,INLAND\n-117.94,34.15,33.0,859.0,144.0,421.0,138.0,4.4821,220100.0,INLAND\n-117.95,34.16,17.0,7116.0,1089.0,3538.0,1083.0,6.2654,273800.0,INLAND\n-117.94,34.14,33.0,1620.0,283.0,868.0,275.0,5.411,219400.0,INLAND\n-117.95,34.14,13.0,3859.0,710.0,2283.0,759.0,4.5594,184500.0,INLAND\n-117.95,34.14,33.0,1943.0,440.0,1526.0,353.0,3.038,137500.0,INLAND\n-117.96,34.14,27.0,2221.0,542.0,1328.0,523.0,2.5275,151700.0,INLAND\n-117.96,34.14,33.0,1994.0,405.0,993.0,403.0,3.766,163900.0,INLAND\n-117.96,34.14,9.0,907.0,207.0,619.0,194.0,3.9464,179600.0,INLAND\n-117.97,34.14,15.0,3595.0,964.0,1839.0,877.0,2.6014,150300.0,INLAND\n-117.97,34.14,33.0,1328.0,348.0,903.0,329.0,3.1094,136000.0,INLAND\n-117.97,34.13,42.0,683.0,127.0,541.0,138.0,3.4375,151700.0,INLAND\n-117.98,34.14,24.0,1596.0,388.0,1329.0,352.0,3.0417,148000.0,INLAND\n-117.97,34.13,33.0,2038.0,473.0,1546.0,469.0,3.4777,144500.0,INLAND\n-117.98,34.13,37.0,1447.0,309.0,1279.0,290.0,4.0083,142900.0,INLAND\n-117.98,34.13,29.0,2110.0,460.0,1890.0,448.0,3.6806,130500.0,INLAND\n-117.97,34.15,33.0,2474.0,472.0,1268.0,437.0,6.4576,500001.0,INLAND\n-117.97,34.17,35.0,5005.0,848.0,2112.0,813.0,4.9968,295000.0,INLAND\n-118.0,34.16,42.0,1020.0,156.0,398.0,157.0,6.101,311800.0,INLAND\n-118.01,34.16,47.0,1745.0,270.0,753.0,275.0,5.532,318500.0,INLAND\n-117.99,34.18,38.0,2981.0,432.0,1063.0,437.0,6.5254,365000.0,INLAND\n-117.99,34.16,40.0,3838.0,696.0,1851.0,674.0,4.2407,262000.0,INLAND\n-118.0,34.16,52.0,1354.0,227.0,531.0,206.0,4.8059,270600.0,INLAND\n-118.0,34.15,48.0,3436.0,673.0,1540.0,648.0,4.275,256800.0,INLAND\n-118.01,34.15,52.0,2234.0,472.0,986.0,439.0,3.9125,265500.0,INLAND\n-118.02,34.15,44.0,2419.0,437.0,1045.0,432.0,3.875,280800.0,INLAND\n-118.02,34.17,32.0,3868.0,548.0,1558.0,528.0,9.4667,500001.0,INLAND\n-118.03,34.16,36.0,1640.0,239.0,693.0,253.0,6.6888,500001.0,INLAND\n-118.03,34.15,43.0,1694.0,283.0,674.0,267.0,4.1797,486800.0,INLAND\n-118.02,34.15,44.0,2267.0,426.0,980.0,372.0,3.6,307400.0,INLAND\n-118.06,34.17,38.0,2726.0,398.0,1059.0,380.0,7.2419,410400.0,INLAND\n-118.05,34.17,45.0,2535.0,455.0,1036.0,437.0,5.0482,388900.0,INLAND\n-118.04,34.17,52.0,1885.0,401.0,764.0,373.0,4.0385,265700.0,INLAND\n-118.04,34.18,37.0,3134.0,532.0,1220.0,508.0,5.2865,455400.0,INLAND\n-118.04,34.16,38.0,1594.0,249.0,633.0,247.0,5.9582,350700.0,INLAND\n-118.05,34.16,36.0,3908.0,732.0,1688.0,725.0,4.5625,376800.0,INLAND\n-118.05,34.16,41.0,3320.0,713.0,1236.0,659.0,3.5694,278600.0,INLAND\n-118.06,34.16,46.0,1467.0,298.0,816.0,267.0,3.6705,286500.0,INLAND\n-118.06,34.16,34.0,2297.0,419.0,909.0,412.0,4.8214,362500.0,INLAND\n-118.07,34.16,35.0,3585.0,671.0,1401.0,623.0,4.125,330000.0,INLAND\n-118.03,34.16,36.0,1401.0,218.0,667.0,225.0,7.1615,484700.0,INLAND\n-118.03,34.16,39.0,2731.0,366.0,1034.0,338.0,9.8098,500001.0,INLAND\n-118.05,34.15,32.0,5131.0,665.0,1877.0,622.0,8.2004,500001.0,INLAND\n-118.06,34.15,37.0,1980.0,226.0,697.0,226.0,15.0001,500001.0,INLAND\n-118.05,34.14,39.0,2125.0,295.0,862.0,303.0,8.9728,500001.0,INLAND\n-118.06,34.13,40.0,4307.0,918.0,1769.0,845.0,3.6341,391500.0,INLAND\n-118.06,34.14,42.0,2461.0,379.0,1179.0,360.0,7.0315,437300.0,INLAND\n-118.06,34.14,40.0,2662.0,379.0,1151.0,387.0,8.4889,500001.0,INLAND\n-118.03,34.14,44.0,1446.0,250.0,721.0,243.0,4.7308,352200.0,INLAND\n-118.04,34.13,22.0,3359.0,643.0,1227.0,588.0,4.645,276200.0,INLAND\n-118.04,34.15,34.0,1523.0,311.0,676.0,295.0,3.3621,377200.0,INLAND\n-118.04,34.13,35.0,249.0,31.0,268.0,29.0,15.0001,500001.0,INLAND\n-118.05,34.13,23.0,3264.0,729.0,1475.0,668.0,3.735,218300.0,INLAND\n-118.06,34.13,28.0,12139.0,2873.0,5359.0,2731.0,3.292,227300.0,INLAND\n-118.02,34.14,34.0,1077.0,257.0,478.0,199.0,2.6316,252800.0,INLAND\n-118.02,34.13,32.0,3308.0,718.0,1803.0,667.0,3.9464,273600.0,INLAND\n-118.03,34.14,31.0,4353.0,1117.0,2338.0,1037.0,3.0727,242600.0,INLAND\n-118.01,34.13,36.0,1332.0,217.0,648.0,203.0,4.7159,365900.0,INLAND\n-118.02,34.13,33.0,2874.0,458.0,1239.0,431.0,5.2329,430900.0,INLAND\n-118.02,34.13,34.0,1966.0,319.0,980.0,297.0,7.7307,429000.0,INLAND\n-118.03,34.13,33.0,2352.0,373.0,995.0,359.0,4.9583,445700.0,INLAND\n-118.02,34.12,37.0,2250.0,360.0,989.0,329.0,6.1536,366000.0,INLAND\n-118.02,34.11,35.0,2454.0,458.0,1110.0,435.0,3.8029,414800.0,INLAND\n-118.02,34.12,36.0,1471.0,246.0,751.0,230.0,5.4555,395100.0,INLAND\n-118.02,34.12,38.0,1778.0,288.0,870.0,281.0,6.5784,408500.0,INLAND\n-118.03,34.11,34.0,2837.0,460.0,1344.0,458.0,6.5722,437400.0,INLAND\n-118.01,34.14,20.0,3350.0,831.0,1816.0,744.0,2.8352,161700.0,INLAND\n-118.01,34.14,23.0,3425.0,754.0,2202.0,729.0,3.52,187300.0,INLAND\n-118.02,34.14,31.0,6854.0,1578.0,4131.0,1524.0,3.5878,222800.0,INLAND\n-117.99,34.15,44.0,2492.0,611.0,1951.0,596.0,3.1304,185600.0,INLAND\n-118.0,34.15,43.0,2583.0,633.0,1544.0,617.0,2.5417,178300.0,INLAND\n-118.01,34.15,32.0,6597.0,1579.0,3689.0,1459.0,3.2377,184100.0,INLAND\n-117.98,34.14,27.0,6341.0,1289.0,2899.0,1192.0,3.6336,235200.0,INLAND\n-117.99,34.14,30.0,2346.0,,1988.0,474.0,2.5625,153000.0,INLAND\n-118.0,34.13,24.0,2584.0,520.0,1869.0,503.0,3.2841,167000.0,INLAND\n-118.0,34.14,31.0,1298.0,431.0,1131.0,425.0,1.0548,178100.0,INLAND\n-118.0,34.14,39.0,1302.0,303.0,800.0,291.0,3.2723,166900.0,INLAND\n-118.0,34.13,35.0,1005.0,224.0,742.0,221.0,3.5481,158100.0,INLAND\n-117.99,34.13,37.0,1568.0,371.0,1618.0,350.0,2.9605,129400.0,INLAND\n-117.99,34.12,37.0,1527.0,331.0,1504.0,324.0,3.2857,130100.0,INLAND\n-117.99,34.12,35.0,1040.0,231.0,1040.0,242.0,2.5395,139200.0,INLAND\n-118.0,34.12,37.0,1340.0,325.0,928.0,333.0,3.9219,175000.0,INLAND\n-118.0,34.12,42.0,870.0,170.0,546.0,164.0,4.625,173800.0,INLAND\n-118.01,34.13,38.0,3374.0,671.0,1906.0,640.0,4.0729,212300.0,INLAND\n-118.01,34.12,43.0,1185.0,207.0,657.0,198.0,4.5491,214800.0,INLAND\n-118.01,34.12,32.0,1937.0,332.0,922.0,340.0,3.94,278400.0,INLAND\n-118.01,34.11,32.0,1978.0,536.0,826.0,470.0,2.5114,212200.0,INLAND\n-118.01,34.11,43.0,1858.0,345.0,1054.0,329.0,3.5625,211600.0,INLAND\n-118.01,34.11,36.0,1722.0,320.0,794.0,322.0,4.2132,212200.0,INLAND\n-118.02,34.1,36.0,1928.0,361.0,1008.0,368.0,4.733,233700.0,INLAND\n-118.02,34.1,36.0,452.0,80.0,248.0,83.0,1.9688,226000.0,INLAND\n-118.02,34.11,39.0,1504.0,280.0,718.0,261.0,4.625,219000.0,INLAND\n-118.03,34.1,38.0,2301.0,416.0,1079.0,398.0,4.4236,233600.0,INLAND\n-118.03,34.1,30.0,2773.0,634.0,1376.0,540.0,2.7857,201700.0,INLAND\n-118.03,34.1,31.0,2647.0,539.0,1473.0,520.0,3.94,223900.0,INLAND\n-118.03,34.1,32.0,2668.0,609.0,1512.0,541.0,2.9422,233100.0,INLAND\n-118.03,34.11,38.0,2076.0,361.0,988.0,332.0,5.9175,416900.0,INLAND\n-118.04,34.11,44.0,773.0,120.0,405.0,121.0,5.0,447100.0,INLAND\n-118.04,34.11,37.0,1275.0,177.0,598.0,174.0,7.1885,500001.0,INLAND\n-118.05,34.11,42.0,3677.0,627.0,1779.0,622.0,5.1509,426500.0,INLAND\n-118.04,34.13,39.0,2485.0,382.0,1072.0,342.0,6.0878,430200.0,INLAND\n-118.04,34.12,39.0,2522.0,380.0,1113.0,357.0,5.2249,445200.0,INLAND\n-118.04,34.12,44.0,2007.0,288.0,921.0,307.0,6.5989,500001.0,INLAND\n-118.04,34.12,30.0,2170.0,318.0,984.0,309.0,5.6916,500001.0,INLAND\n-118.05,34.12,20.0,5218.0,959.0,2302.0,850.0,3.55,476700.0,INLAND\n-118.06,34.12,25.0,3891.0,848.0,1848.0,759.0,3.6639,248100.0,INLAND\n-118.06,34.12,34.0,2941.0,558.0,1660.0,576.0,4.5667,271500.0,INLAND\n-118.06,34.12,35.0,2053.0,369.0,1044.0,375.0,4.1875,275400.0,INLAND\n-118.05,34.11,48.0,1410.0,304.0,677.0,274.0,3.2596,272400.0,INLAND\n-118.06,34.11,36.0,2178.0,485.0,914.0,412.0,2.7656,239500.0,INLAND\n-118.06,34.11,39.0,2603.0,547.0,1196.0,487.0,3.0854,248700.0,INLAND\n-118.04,34.1,39.0,2302.0,412.0,1590.0,406.0,4.8017,273800.0,INLAND\n-118.05,34.1,36.0,1606.0,318.0,889.0,294.0,4.7931,272600.0,INLAND\n-118.06,34.1,38.0,3229.0,636.0,1599.0,609.0,3.8646,257100.0,<1H OCEAN\n-118.06,34.1,42.0,1576.0,313.0,697.0,282.0,4.3523,283600.0,<1H OCEAN\n-118.06,34.1,43.0,1833.0,355.0,786.0,334.0,3.5761,256700.0,<1H OCEAN\n-118.06,34.1,38.0,2438.0,442.0,1308.0,461.0,3.6995,260100.0,<1H OCEAN\n-118.04,34.1,38.0,2317.0,451.0,1155.0,426.0,4.1488,235300.0,INLAND\n-118.04,34.09,34.0,2597.0,461.0,1542.0,470.0,4.6211,248900.0,INLAND\n-118.05,34.1,30.0,2143.0,427.0,1107.0,416.0,4.2321,252200.0,INLAND\n-118.05,34.1,42.0,2065.0,404.0,1313.0,402.0,4.0179,274300.0,INLAND\n-118.06,34.09,38.0,2036.0,388.0,1096.0,371.0,4.0625,262500.0,<1H OCEAN\n-118.06,34.1,38.0,1960.0,330.0,874.0,308.0,4.8594,265900.0,<1H OCEAN\n-118.06,34.09,36.0,1239.0,238.0,717.0,237.0,3.244,258100.0,<1H OCEAN\n-118.06,34.09,40.0,1975.0,389.0,1116.0,378.0,4.2898,251600.0,<1H OCEAN\n-118.07,34.09,40.0,1745.0,370.0,1293.0,357.0,2.5474,198100.0,<1H OCEAN\n-118.07,34.09,35.0,1224.0,267.0,887.0,276.0,4.0987,202400.0,<1H OCEAN\n-118.08,34.09,33.0,2557.0,578.0,1715.0,530.0,2.9196,208800.0,<1H OCEAN\n-118.08,34.09,34.0,1823.0,457.0,1485.0,401.0,3.7222,207200.0,<1H OCEAN\n-118.08,34.09,32.0,3214.0,718.0,2316.0,751.0,3.7066,206800.0,<1H OCEAN\n-118.04,34.09,34.0,2001.0,388.0,1461.0,397.0,3.8304,183000.0,INLAND\n-118.04,34.08,35.0,1148.0,258.0,975.0,253.0,4.037,173300.0,<1H OCEAN\n-118.04,34.09,32.0,1339.0,334.0,817.0,349.0,2.8333,186000.0,INLAND\n-118.05,34.09,23.0,602.0,135.0,409.0,123.0,3.5268,146400.0,<1H OCEAN\n-118.02,34.09,32.0,1747.0,399.0,1199.0,402.0,3.4286,191800.0,INLAND\n-118.02,34.08,29.0,2741.0,667.0,2449.0,677.0,3.6944,175200.0,INLAND\n-118.02,34.09,24.0,2080.0,514.0,1976.0,478.0,2.6917,170000.0,INLAND\n-118.03,34.09,29.0,1219.0,338.0,1152.0,323.0,2.8029,180900.0,INLAND\n-118.03,34.08,32.0,1780.0,484.0,1732.0,454.0,2.4464,169600.0,INLAND\n-118.0,34.1,34.0,2249.0,460.0,1544.0,441.0,3.4005,176300.0,INLAND\n-118.01,34.09,29.0,3402.0,747.0,2331.0,690.0,3.6094,179200.0,INLAND\n-118.01,34.1,27.0,2424.0,542.0,1713.0,557.0,3.8085,181400.0,INLAND\n-118.01,34.1,35.0,2120.0,412.0,1375.0,405.0,3.4609,166300.0,INLAND\n-118.0,34.08,29.0,2003.0,401.0,1520.0,364.0,3.994,195300.0,INLAND\n-118.01,34.08,30.0,2281.0,522.0,1969.0,500.0,3.6531,166300.0,INLAND\n-118.02,34.08,28.0,2769.0,631.0,2452.0,581.0,2.6071,175900.0,INLAND\n-118.01,34.08,35.0,1852.0,358.0,1414.0,347.0,4.275,173600.0,INLAND\n-118.01,34.09,32.0,1613.0,361.0,1283.0,404.0,3.1944,181700.0,INLAND\n-118.0,34.08,23.0,1627.0,318.0,1279.0,289.0,4.6467,185100.0,INLAND\n-118.02,34.08,31.0,2402.0,632.0,2830.0,603.0,2.3333,164200.0,INLAND\n-118.03,34.08,37.0,775.0,179.0,726.0,183.0,3.25,159200.0,INLAND\n-118.03,34.08,42.0,1597.0,373.0,1311.0,352.0,2.9688,162800.0,INLAND\n-118.04,34.07,52.0,177.0,59.0,269.0,75.0,2.3611,131300.0,<1H OCEAN\n-118.05,34.08,34.0,572.0,154.0,752.0,182.0,2.0433,138800.0,<1H OCEAN\n-118.05,34.08,25.0,4909.0,1422.0,4983.0,1293.0,2.7254,143500.0,<1H OCEAN\n-118.05,34.08,30.0,1572.0,427.0,1857.0,428.0,2.4914,159200.0,<1H OCEAN\n-118.06,34.08,37.0,778.0,205.0,850.0,198.0,2.5119,180500.0,<1H OCEAN\n-118.06,34.08,34.0,1197.0,260.0,942.0,245.0,3.4202,189100.0,<1H OCEAN\n-118.06,34.08,42.0,1988.0,402.0,1239.0,402.0,3.2569,201500.0,<1H OCEAN\n-118.07,34.08,38.0,2462.0,553.0,1843.0,538.0,3.2312,211900.0,<1H OCEAN\n-118.07,34.07,19.0,1554.0,393.0,1427.0,370.0,3.125,207100.0,<1H OCEAN\n-118.07,34.08,37.0,1210.0,252.0,733.0,243.0,2.806,214600.0,<1H OCEAN\n-118.05,34.07,32.0,4492.0,1075.0,4119.0,1035.0,3.2373,183100.0,<1H OCEAN\n-118.06,34.07,30.0,2308.0,674.0,3034.0,691.0,2.3929,184400.0,<1H OCEAN\n-118.03,34.07,37.0,1091.0,269.0,905.0,242.0,3.1042,152000.0,<1H OCEAN\n-118.03,34.06,36.0,1018.0,305.0,1307.0,292.0,2.1453,162100.0,<1H OCEAN\n-118.04,34.07,39.0,1382.0,315.0,1090.0,308.0,3.8125,174000.0,<1H OCEAN\n-118.04,34.07,39.0,2451.0,649.0,2536.0,648.0,2.3098,173100.0,<1H OCEAN\n-118.01,34.07,22.0,6311.0,1572.0,6666.0,1456.0,2.9334,182600.0,INLAND\n-118.01,34.06,26.0,557.0,153.0,455.0,196.0,2.7721,155400.0,INLAND\n-118.01,34.07,24.0,5684.0,1485.0,6626.0,1481.0,2.2559,166800.0,INLAND\n-118.02,34.07,21.0,3245.0,959.0,3528.0,887.0,2.3236,156300.0,INLAND\n-118.03,34.06,31.0,1513.0,389.0,1396.0,364.0,2.4706,170600.0,<1H OCEAN\n-118.03,34.06,24.0,2343.0,834.0,3537.0,824.0,2.1094,135200.0,<1H OCEAN\n-118.03,34.06,27.0,2510.0,783.0,3481.0,726.0,2.4875,157800.0,<1H OCEAN\n-118.04,34.06,31.0,957.0,295.0,1300.0,287.0,2.1383,153400.0,<1H OCEAN\n-118.04,34.06,30.0,2019.0,551.0,2481.0,484.0,3.1875,154200.0,<1H OCEAN\n-118.04,34.06,39.0,1258.0,245.0,988.0,228.0,3.2132,176100.0,<1H OCEAN\n-118.05,34.06,32.0,2286.0,654.0,2991.0,655.0,2.1781,174500.0,<1H OCEAN\n-118.05,34.06,45.0,531.0,164.0,722.0,166.0,2.1406,162500.0,<1H OCEAN\n-118.05,34.06,25.0,1022.0,291.0,1570.0,297.0,3.023,142000.0,<1H OCEAN\n-118.06,34.06,28.0,2127.0,625.0,3160.0,620.0,2.5763,173900.0,<1H OCEAN\n-118.06,34.06,28.0,1778.0,605.0,2184.0,574.0,1.9189,165900.0,<1H OCEAN\n-118.07,34.07,31.0,1370.0,284.0,1062.0,277.0,3.5156,199300.0,<1H OCEAN\n-118.08,34.07,32.0,4089.0,975.0,3775.0,955.0,3.29,205500.0,<1H OCEAN\n-118.07,34.06,34.0,2873.0,718.0,2758.0,699.0,2.5985,168600.0,<1H OCEAN\n-118.04,34.05,32.0,1252.0,273.0,1337.0,263.0,2.6579,156800.0,<1H OCEAN\n-118.05,34.05,36.0,1084.0,202.0,920.0,199.0,3.7279,162200.0,<1H OCEAN\n-118.06,34.04,28.0,1516.0,363.0,1011.0,344.0,2.6288,160300.0,<1H OCEAN\n-118.03,34.06,24.0,2469.0,731.0,3818.0,712.0,2.5445,151400.0,<1H OCEAN\n-118.04,34.05,34.0,1058.0,230.0,1043.0,229.0,3.0536,137500.0,<1H OCEAN\n-118.04,34.04,35.0,1734.0,363.0,1527.0,344.0,3.0,160600.0,<1H OCEAN\n-118.04,34.04,32.0,1619.0,323.0,1492.0,342.0,3.5,165100.0,<1H OCEAN\n-118.05,34.04,33.0,1348.0,,1098.0,257.0,4.2917,161200.0,<1H OCEAN\n-118.06,34.03,36.0,21.0,7.0,21.0,9.0,2.375,175000.0,<1H OCEAN\n-118.02,34.06,26.0,2929.0,970.0,3792.0,817.0,2.2577,173800.0,<1H OCEAN\n-118.02,34.05,34.0,1610.0,513.0,2050.0,508.0,2.5562,156300.0,<1H OCEAN\n-118.03,34.05,36.0,1359.0,317.0,1557.0,370.0,2.7955,157500.0,<1H OCEAN\n-118.03,34.05,36.0,1345.0,331.0,1511.0,309.0,3.5129,142300.0,<1H OCEAN\n-118.01,34.05,31.0,1135.0,355.0,1717.0,368.0,2.1602,161700.0,<1H OCEAN\n-118.02,34.05,28.0,991.0,255.0,1145.0,265.0,2.3611,167000.0,<1H OCEAN\n-118.02,34.05,33.0,2464.0,627.0,2932.0,568.0,3.0625,165800.0,<1H OCEAN\n-118.02,34.04,28.0,6175.0,1449.0,5041.0,1408.0,2.8821,158100.0,<1H OCEAN\n-118.09,34.18,34.0,3113.0,409.0,1139.0,418.0,10.2289,500001.0,INLAND\n-118.07,34.17,34.0,4062.0,597.0,1525.0,566.0,7.8588,454800.0,INLAND\n-118.07,34.17,36.0,2415.0,394.0,1215.0,413.0,5.5418,326100.0,INLAND\n-118.07,34.17,35.0,2142.0,373.0,986.0,374.0,5.7051,326000.0,INLAND\n-118.1,34.18,39.0,2321.0,336.0,880.0,339.0,7.7108,450000.0,INLAND\n-118.11,34.19,50.0,1430.0,186.0,620.0,201.0,9.532,483300.0,INLAND\n-118.12,34.19,52.0,2405.0,299.0,970.0,319.0,8.7835,444100.0,INLAND\n-118.11,34.2,36.0,4915.0,725.0,1897.0,700.0,6.827,359400.0,INLAND\n-118.13,34.2,46.0,2676.0,427.0,1022.0,395.0,6.4288,295500.0,INLAND\n-118.13,34.19,48.0,2539.0,425.0,930.0,364.0,4.7269,303900.0,INLAND\n-118.13,34.21,36.0,1449.0,235.0,621.0,210.0,6.1824,274100.0,INLAND\n-118.13,34.2,45.0,1213.0,206.0,529.0,231.0,5.6629,234000.0,INLAND\n-118.13,34.2,46.0,1271.0,236.0,573.0,210.0,4.9312,240200.0,INLAND\n-118.13,34.19,43.0,1621.0,365.0,1015.0,329.0,2.92,242200.0,INLAND\n-118.14,34.19,45.0,3595.0,619.0,1686.0,607.0,4.73,201000.0,INLAND\n-118.14,34.2,39.0,2569.0,426.0,1282.0,432.0,5.0953,207400.0,INLAND\n-118.15,34.2,37.0,1997.0,361.0,1037.0,363.0,3.7932,210300.0,INLAND\n-118.15,34.2,52.0,1786.0,306.0,1018.0,322.0,4.1518,182100.0,INLAND\n-118.15,34.2,46.0,1505.0,261.0,857.0,269.0,4.5,184200.0,INLAND\n-118.15,34.21,34.0,2765.0,515.0,1422.0,438.0,5.4727,238900.0,INLAND\n-118.15,34.19,48.0,1854.0,360.0,1126.0,382.0,3.2216,161600.0,<1H OCEAN\n-118.15,34.19,47.0,1717.0,314.0,868.0,295.0,3.6094,160700.0,<1H OCEAN\n-118.16,34.19,40.0,1840.0,358.0,1218.0,347.0,4.25,177900.0,<1H OCEAN\n-118.16,34.2,43.0,1810.0,343.0,988.0,307.0,3.8203,176000.0,<1H OCEAN\n-118.18,34.22,40.0,1983.0,298.0,853.0,271.0,5.9845,241700.0,<1H OCEAN\n-118.19,34.22,32.0,10626.0,1504.0,4353.0,1482.0,9.8413,500001.0,<1H OCEAN\n-118.21,34.22,37.0,2260.0,322.0,941.0,303.0,8.3695,500001.0,<1H OCEAN\n-118.18,34.2,44.0,1473.0,250.0,668.0,239.0,8.72,415900.0,<1H OCEAN\n-118.19,34.2,41.0,2031.0,294.0,859.0,302.0,7.419,483700.0,<1H OCEAN\n-118.19,34.21,41.0,1602.0,228.0,680.0,225.0,6.553,500001.0,<1H OCEAN\n-118.2,34.21,40.0,1477.0,228.0,609.0,224.0,7.8375,500001.0,<1H OCEAN\n-118.2,34.21,42.0,1493.0,237.0,665.0,224.0,6.7571,443900.0,<1H OCEAN\n-118.21,34.21,41.0,1676.0,263.0,757.0,255.0,4.7734,450800.0,<1H OCEAN\n-118.22,34.23,34.0,3296.0,483.0,1268.0,478.0,8.4802,500001.0,<1H OCEAN\n-118.21,34.22,39.0,3008.0,386.0,1240.0,370.0,9.2463,500001.0,<1H OCEAN\n-118.22,34.22,39.0,2686.0,417.0,1094.0,402.0,7.0059,500001.0,<1H OCEAN\n-118.23,34.22,37.0,1376.0,237.0,618.0,226.0,5.9771,431800.0,<1H OCEAN\n-118.21,34.2,35.0,3646.0,552.0,1409.0,534.0,6.3794,500001.0,<1H OCEAN\n-118.2,34.2,44.0,2890.0,438.0,1219.0,429.0,6.987,500001.0,<1H OCEAN\n-118.18,34.19,48.0,1371.0,,528.0,155.0,15.0001,500001.0,<1H OCEAN\n-118.19,34.19,34.0,2061.0,260.0,825.0,254.0,15.0001,500001.0,<1H OCEAN\n-118.2,34.19,38.0,2176.0,266.0,798.0,243.0,15.0001,500001.0,<1H OCEAN\n-118.18,34.17,43.0,4269.0,591.0,1467.0,582.0,9.0702,500001.0,<1H OCEAN\n-118.18,34.16,34.0,5012.0,746.0,1699.0,715.0,9.4987,500001.0,<1H OCEAN\n-118.15,34.18,46.0,2230.0,488.0,1985.0,456.0,2.2328,142100.0,<1H OCEAN\n-118.15,34.17,46.0,2553.0,558.0,1740.0,492.0,2.0278,127500.0,<1H OCEAN\n-118.16,34.18,44.0,1870.0,389.0,1345.0,391.0,1.8932,136100.0,<1H OCEAN\n-118.16,34.18,48.0,568.0,145.0,559.0,135.0,2.4135,135700.0,<1H OCEAN\n-118.16,34.17,52.0,1193.0,228.0,703.0,221.0,3.1741,163800.0,<1H OCEAN\n-118.17,34.18,44.0,1401.0,246.0,607.0,271.0,2.8472,218800.0,<1H OCEAN\n-118.15,34.18,42.0,1521.0,320.0,1118.0,311.0,3.3125,154900.0,<1H OCEAN\n-118.16,34.19,42.0,2076.0,462.0,1641.0,436.0,2.2326,149200.0,<1H OCEAN\n-118.16,34.19,44.0,2195.0,449.0,1377.0,417.0,3.5887,153500.0,<1H OCEAN\n-118.17,34.18,38.0,1280.0,231.0,828.0,237.0,4.375,166700.0,<1H OCEAN\n-118.17,34.19,45.0,1790.0,316.0,1035.0,312.0,4.0985,173100.0,<1H OCEAN\n-118.13,34.19,42.0,2203.0,412.0,1012.0,377.0,4.0714,234000.0,INLAND\n-118.14,34.18,47.0,3457.0,622.0,1700.0,579.0,3.5164,226500.0,<1H OCEAN\n-118.14,34.19,49.0,1678.0,277.0,737.0,287.0,3.7222,237000.0,INLAND\n-118.15,34.19,38.0,1750.0,411.0,1398.0,409.0,2.3967,163100.0,<1H OCEAN\n-118.1,34.18,47.0,2168.0,352.0,902.0,361.0,5.894,300900.0,INLAND\n-118.11,34.18,52.0,3571.0,510.0,1434.0,490.0,5.9009,376000.0,INLAND\n-118.12,34.18,44.0,2357.0,342.0,891.0,337.0,6.3467,352700.0,INLAND\n-118.13,34.18,52.0,3094.0,519.0,1309.0,488.0,6.4223,310900.0,INLAND\n-118.1,34.17,46.0,1774.0,315.0,753.0,330.0,4.7241,279600.0,INLAND\n-118.1,34.17,48.0,1111.0,229.0,421.0,202.0,3.2813,268100.0,INLAND\n-118.11,34.17,50.0,3374.0,598.0,1484.0,569.0,4.99,261600.0,INLAND\n-118.11,34.17,46.0,2837.0,592.0,1453.0,549.0,3.1115,234600.0,INLAND\n-118.12,34.18,47.0,2344.0,513.0,1537.0,481.0,3.4777,230600.0,INLAND\n-118.13,34.18,52.0,1464.0,211.0,603.0,226.0,5.8309,309100.0,INLAND\n-118.12,34.17,37.0,2705.0,676.0,1551.0,608.0,2.2692,225000.0,INLAND\n-118.12,34.17,52.0,1835.0,330.0,777.0,317.0,3.7159,315400.0,INLAND\n-118.13,34.17,52.0,1784.0,368.0,966.0,345.0,3.6094,261500.0,<1H OCEAN\n-118.14,34.18,52.0,1700.0,317.0,996.0,329.0,3.9688,175000.0,<1H OCEAN\n-118.14,34.17,52.0,2667.0,486.0,1681.0,504.0,4.0524,173100.0,<1H OCEAN\n-118.14,34.17,42.0,2757.0,713.0,2112.0,653.0,2.7148,166800.0,<1H OCEAN\n-118.14,34.18,50.0,1493.0,326.0,1000.0,323.0,3.3068,154400.0,<1H OCEAN\n-118.14,34.17,40.0,1054.0,251.0,1056.0,276.0,2.3,146700.0,<1H OCEAN\n-118.15,34.18,45.0,2612.0,664.0,3117.0,584.0,2.3029,148800.0,<1H OCEAN\n-118.15,34.16,18.0,1711.0,383.0,1474.0,415.0,1.2891,181300.0,<1H OCEAN\n-118.15,34.16,38.0,2471.0,529.0,1661.0,441.0,2.2765,146600.0,<1H OCEAN\n-118.16,34.16,44.0,1284.0,278.0,925.0,261.0,1.7321,178400.0,<1H OCEAN\n-118.16,34.17,46.0,1508.0,261.0,674.0,255.0,3.5909,155400.0,<1H OCEAN\n-118.16,34.16,43.0,1276.0,226.0,545.0,209.0,4.1518,230700.0,<1H OCEAN\n-118.16,34.16,52.0,1576.0,239.0,696.0,249.0,6.07,261800.0,<1H OCEAN\n-118.16,34.15,19.0,1721.0,290.0,571.0,278.0,6.6031,500001.0,<1H OCEAN\n-118.16,34.15,17.0,821.0,163.0,229.0,164.0,7.3715,263000.0,<1H OCEAN\n-118.14,34.16,39.0,2776.0,840.0,2546.0,773.0,2.575,153500.0,<1H OCEAN\n-118.14,34.15,52.0,407.0,160.0,227.0,148.0,1.5156,187500.0,<1H OCEAN\n-118.15,34.15,52.0,275.0,123.0,273.0,111.0,1.1667,500001.0,<1H OCEAN\n-118.15,34.16,52.0,1925.0,597.0,2258.0,594.0,1.6921,162500.0,<1H OCEAN\n-118.15,34.15,49.0,806.0,199.0,698.0,172.0,2.3654,137500.0,<1H OCEAN\n-118.14,34.17,34.0,2384.0,604.0,2073.0,540.0,2.3062,158000.0,<1H OCEAN\n-118.14,34.16,30.0,2598.0,757.0,2869.0,769.0,2.1377,142300.0,<1H OCEAN\n-118.15,34.16,20.0,2410.0,632.0,2135.0,578.0,1.6887,148600.0,<1H OCEAN\n-118.15,34.17,36.0,930.0,280.0,1024.0,300.0,1.0846,146400.0,<1H OCEAN\n-118.13,34.17,49.0,1962.0,435.0,1329.0,457.0,3.2898,200000.0,<1H OCEAN\n-118.14,34.16,36.0,2973.0,807.0,2846.0,784.0,2.6217,156300.0,<1H OCEAN\n-118.14,34.17,52.0,2687.0,600.0,1716.0,544.0,2.7201,205700.0,<1H OCEAN\n-118.13,34.16,36.0,2162.0,658.0,1337.0,590.0,2.2095,176700.0,<1H OCEAN\n-118.13,34.15,24.0,1125.0,341.0,579.0,321.0,2.8125,141700.0,<1H OCEAN\n-118.14,34.15,41.0,1256.0,407.0,855.0,383.0,1.9923,500001.0,<1H OCEAN\n-118.14,34.15,17.0,1896.0,674.0,971.0,652.0,0.8438,175000.0,<1H OCEAN\n-118.14,34.16,38.0,1843.0,565.0,1449.0,524.0,2.2174,215400.0,<1H OCEAN\n-118.12,34.16,30.0,1762.0,416.0,940.0,398.0,2.8631,188600.0,<1H OCEAN\n-118.13,34.16,33.0,2682.0,716.0,2050.0,692.0,2.4817,169500.0,<1H OCEAN\n-118.13,34.15,18.0,1665.0,477.0,1095.0,390.0,2.6038,155600.0,<1H OCEAN\n-118.12,34.15,19.0,557.0,216.0,673.0,212.0,2.1763,168800.0,<1H OCEAN\n-118.12,34.15,22.0,1671.0,480.0,1005.0,443.0,3.0119,171400.0,<1H OCEAN\n-118.13,34.15,9.0,2099.0,625.0,1252.0,554.0,3.1875,173100.0,<1H OCEAN\n-118.13,34.15,28.0,1119.0,334.0,739.0,323.0,2.5903,125000.0,<1H OCEAN\n-118.12,34.17,52.0,2948.0,542.0,1363.0,495.0,4.7098,287900.0,INLAND\n-118.13,34.16,52.0,1872.0,357.0,984.0,364.0,4.0,250400.0,<1H OCEAN\n-118.13,34.16,52.0,1596.0,314.0,1024.0,292.0,3.6719,227900.0,<1H OCEAN\n-118.13,34.16,52.0,1787.0,427.0,1107.0,410.0,2.5664,215000.0,<1H OCEAN\n-118.09,34.17,36.0,2875.0,552.0,1131.0,458.0,4.3083,269300.0,INLAND\n-118.1,34.16,44.0,2795.0,496.0,1235.0,469.0,4.2386,283700.0,INLAND\n-118.1,34.17,44.0,4505.0,894.0,2296.0,899.0,3.4811,300500.0,INLAND\n-118.12,34.17,52.0,2166.0,483.0,1308.0,467.0,3.0417,222600.0,INLAND\n-118.11,34.16,52.0,3158.0,459.0,1229.0,444.0,5.4223,325600.0,INLAND\n-118.11,34.16,52.0,2489.0,437.0,1101.0,438.0,4.2065,320300.0,INLAND\n-118.11,34.16,52.0,1353.0,274.0,852.0,306.0,3.4583,239900.0,INLAND\n-118.11,34.15,40.0,1950.0,509.0,1038.0,438.0,2.6172,196100.0,<1H OCEAN\n-118.11,34.15,26.0,2193.0,558.0,1186.0,559.0,3.6474,184100.0,<1H OCEAN\n-118.12,34.15,35.0,1760.0,447.0,984.0,384.0,3.4167,198200.0,<1H OCEAN\n-118.12,34.16,52.0,2218.0,437.0,1211.0,422.0,5.0237,241900.0,<1H OCEAN\n-118.1,34.16,46.0,2863.0,513.0,1412.0,498.0,5.3272,330200.0,INLAND\n-118.1,34.15,32.0,978.0,227.0,543.0,211.0,3.0096,199000.0,INLAND\n-118.1,34.15,14.0,1442.0,369.0,782.0,343.0,2.7431,177500.0,INLAND\n-118.08,34.16,42.0,3490.0,665.0,1713.0,620.0,4.5461,242400.0,INLAND\n-118.09,34.15,46.0,271.0,74.0,150.0,55.0,2.2321,237500.0,INLAND\n-118.09,34.15,52.0,670.0,141.0,268.0,110.0,3.5568,205000.0,INLAND\n-118.09,34.15,49.0,1467.0,259.0,688.0,260.0,4.3452,260100.0,INLAND\n-118.09,34.16,45.0,2199.0,358.0,942.0,353.0,5.0393,321100.0,INLAND\n-118.07,34.16,39.0,1804.0,265.0,730.0,276.0,6.4761,397500.0,INLAND\n-118.07,34.16,35.0,2459.0,438.0,970.0,437.0,4.2143,369400.0,INLAND\n-118.07,34.15,45.0,1095.0,237.0,672.0,234.0,3.4087,209200.0,INLAND\n-118.07,34.14,42.0,3200.0,685.0,1668.0,628.0,3.375,260400.0,INLAND\n-118.08,34.15,28.0,238.0,58.0,142.0,31.0,0.4999,500001.0,INLAND\n-118.08,34.14,45.0,2923.0,604.0,1903.0,560.0,3.1729,218700.0,INLAND\n-118.08,34.14,52.0,1282.0,189.0,431.0,187.0,6.1159,470800.0,INLAND\n-118.08,34.13,46.0,1238.0,147.0,377.0,145.0,8.4546,500001.0,INLAND\n-118.08,34.13,35.0,1897.0,279.0,733.0,291.0,7.4185,500001.0,INLAND\n-118.08,34.14,42.0,2690.0,589.0,1149.0,535.0,3.88,281100.0,INLAND\n-118.09,34.14,40.0,3092.0,549.0,1457.0,536.0,5.3377,373800.0,INLAND\n-118.09,34.15,45.0,1345.0,356.0,749.0,327.0,2.8007,210900.0,INLAND\n-118.1,34.14,45.0,3066.0,659.0,1287.0,625.0,3.5804,324400.0,<1H OCEAN\n-118.11,34.14,52.0,2401.0,332.0,810.0,308.0,6.0948,358700.0,<1H OCEAN\n-118.1,34.14,26.0,6262.0,1645.0,3001.0,1505.0,3.6572,213200.0,<1H OCEAN\n-118.11,34.14,44.0,3298.0,615.0,1417.0,643.0,4.1324,434800.0,<1H OCEAN\n-118.12,34.14,52.0,2337.0,352.0,981.0,328.0,5.8692,490400.0,<1H OCEAN\n-118.12,34.14,25.0,3420.0,977.0,1718.0,947.0,3.1033,217900.0,<1H OCEAN\n-118.13,34.13,39.0,2099.0,397.0,1500.0,380.0,4.8304,493200.0,<1H OCEAN\n-118.13,34.14,23.0,5465.0,1494.0,2511.0,1359.0,3.4531,210900.0,<1H OCEAN\n-118.13,34.14,29.0,3559.0,1034.0,1658.0,965.0,3.2643,163900.0,<1H OCEAN\n-118.14,34.14,24.0,10239.0,2823.0,4210.0,2565.0,3.6997,225000.0,<1H OCEAN\n-118.14,34.14,17.0,3404.0,1011.0,1694.0,949.0,2.9511,282300.0,<1H OCEAN\n-118.15,34.14,27.0,1499.0,426.0,755.0,414.0,3.875,258300.0,<1H OCEAN\n-118.15,34.14,45.0,543.0,191.0,454.0,181.0,2.3,55000.0,<1H OCEAN\n-118.15,34.14,52.0,403.0,117.0,361.0,105.0,1.625,187500.0,<1H OCEAN\n-118.16,34.14,27.0,1551.0,254.0,816.0,233.0,8.2435,500001.0,<1H OCEAN\n-118.16,34.14,41.0,3039.0,482.0,973.0,446.0,7.4817,500001.0,<1H OCEAN\n-118.17,34.14,45.0,2257.0,285.0,759.0,305.0,11.7894,500001.0,<1H OCEAN\n-118.18,34.13,44.0,2734.0,415.0,1057.0,424.0,7.9213,477800.0,<1H OCEAN\n-118.18,34.13,39.0,2902.0,460.0,1007.0,420.0,6.1953,363000.0,<1H OCEAN\n-118.18,34.14,38.0,3039.0,487.0,1131.0,465.0,7.7116,360900.0,<1H OCEAN\n-118.15,34.13,30.0,2763.0,520.0,1143.0,465.0,4.7298,500001.0,<1H OCEAN\n-118.15,34.13,50.0,2443.0,494.0,947.0,451.0,4.7344,314700.0,<1H OCEAN\n-118.16,34.13,36.0,4003.0,647.0,1337.0,631.0,7.723,500001.0,<1H OCEAN\n-118.13,34.13,52.0,2826.0,381.0,924.0,365.0,7.9976,500001.0,<1H OCEAN\n-118.13,34.12,46.0,3156.0,430.0,1109.0,423.0,10.7397,500001.0,<1H OCEAN\n-118.14,34.13,16.0,3569.0,821.0,1505.0,783.0,4.9167,251100.0,<1H OCEAN\n-118.14,34.13,49.0,4438.0,803.0,1650.0,741.0,5.1072,479700.0,<1H OCEAN\n-118.15,34.13,34.0,824.0,224.0,430.0,213.0,3.6389,215000.0,<1H OCEAN\n-118.12,34.13,52.0,2935.0,341.0,975.0,327.0,11.706,500001.0,<1H OCEAN\n-118.11,34.12,48.0,2476.0,313.0,900.0,303.0,10.6767,500001.0,<1H OCEAN\n-118.11,34.12,52.0,2954.0,371.0,1152.0,347.0,11.5609,500001.0,<1H OCEAN\n-118.12,34.12,52.0,2907.0,317.0,956.0,279.0,15.0001,500001.0,<1H OCEAN\n-118.12,34.11,52.0,2787.0,353.0,1057.0,364.0,10.2317,500001.0,<1H OCEAN\n-118.13,34.11,52.0,2568.0,345.0,1102.0,345.0,9.1373,500001.0,<1H OCEAN\n-118.14,34.11,52.0,2742.0,422.0,1153.0,414.0,8.1124,500001.0,<1H OCEAN\n-118.1,34.13,44.0,1917.0,265.0,754.0,257.0,12.4237,500001.0,<1H OCEAN\n-118.1,34.13,44.0,1745.0,237.0,693.0,248.0,9.7912,500001.0,<1H OCEAN\n-118.09,34.12,45.0,2966.0,415.0,1231.0,409.0,7.8347,500001.0,<1H OCEAN\n-118.1,34.12,49.0,3783.0,579.0,1601.0,539.0,6.3013,500001.0,<1H OCEAN\n-118.1,34.12,50.0,1835.0,231.0,636.0,211.0,11.6471,500001.0,<1H OCEAN\n-118.1,34.13,47.0,2234.0,276.0,749.0,260.0,15.0001,500001.0,<1H OCEAN\n-118.08,34.13,39.0,788.0,128.0,413.0,139.0,5.9546,396700.0,INLAND\n-118.08,34.12,41.0,1598.0,280.0,807.0,282.0,5.5067,325000.0,<1H OCEAN\n-118.09,34.12,38.0,2638.0,432.0,1284.0,433.0,5.4536,342700.0,<1H OCEAN\n-118.09,34.12,38.0,1713.0,285.0,779.0,286.0,5.6152,359900.0,<1H OCEAN\n-118.07,34.13,27.0,3787.0,913.0,1992.0,853.0,3.301,251200.0,INLAND\n-118.08,34.13,28.0,4465.0,985.0,2273.0,949.0,3.5671,228500.0,INLAND\n-118.07,34.12,30.0,2201.0,559.0,1194.0,531.0,4.1136,279900.0,INLAND\n-118.07,34.12,43.0,1554.0,287.0,802.0,277.0,4.2312,272600.0,INLAND\n-118.08,34.12,34.0,2921.0,641.0,1541.0,562.0,3.6827,264100.0,<1H OCEAN\n-118.08,34.12,27.0,1685.0,341.0,757.0,317.0,4.2434,270500.0,<1H OCEAN\n-118.07,34.11,41.0,2869.0,563.0,1627.0,533.0,5.0736,270700.0,<1H OCEAN\n-118.07,34.11,47.0,832.0,194.0,419.0,156.0,3.1576,225000.0,<1H OCEAN\n-118.08,34.11,42.0,2628.0,525.0,1494.0,523.0,3.9464,257200.0,<1H OCEAN\n-118.08,34.11,42.0,1969.0,353.0,927.0,354.0,5.5924,285300.0,<1H OCEAN\n-118.08,34.1,32.0,2830.0,645.0,1500.0,527.0,3.0819,214600.0,<1H OCEAN\n-118.09,34.11,36.0,2966.0,527.0,1231.0,482.0,4.6442,316800.0,<1H OCEAN\n-118.09,34.11,45.0,1883.0,275.0,764.0,289.0,6.5078,414800.0,<1H OCEAN\n-118.1,34.1,52.0,1788.0,313.0,792.0,294.0,3.75,280000.0,<1H OCEAN\n-118.1,34.11,49.0,3367.0,523.0,1317.0,495.0,6.706,351400.0,<1H OCEAN\n-118.11,34.11,50.0,2131.0,294.0,753.0,284.0,6.7099,352200.0,<1H OCEAN\n-118.11,34.1,49.0,2812.0,478.0,1329.0,490.0,5.2503,292900.0,<1H OCEAN\n-118.12,34.11,48.0,2124.0,319.0,785.0,319.0,5.2131,359600.0,<1H OCEAN\n-118.12,34.1,49.0,2057.0,430.0,1103.0,414.0,4.0556,282600.0,<1H OCEAN\n-118.12,34.1,34.0,2918.0,555.0,1435.0,568.0,4.2344,306300.0,<1H OCEAN\n-118.13,34.1,19.0,2742.0,756.0,1396.0,703.0,2.5663,197500.0,<1H OCEAN\n-118.13,34.09,21.0,3862.0,1186.0,2773.0,1102.0,2.7816,188200.0,<1H OCEAN\n-118.13,34.1,26.0,3050.0,825.0,2153.0,772.0,3.1103,214100.0,<1H OCEAN\n-118.14,34.09,20.0,3447.0,1007.0,2622.0,934.0,2.918,208700.0,<1H OCEAN\n-118.13,34.11,45.0,1780.0,289.0,755.0,328.0,4.825,351100.0,<1H OCEAN\n-118.13,34.1,24.0,4670.0,1185.0,2478.0,1107.0,3.1975,252400.0,<1H OCEAN\n-118.14,34.1,52.0,4061.0,861.0,2290.0,790.0,2.8919,258400.0,<1H OCEAN\n-118.14,34.11,52.0,3367.0,545.0,1427.0,535.0,5.2292,444500.0,<1H OCEAN\n-118.15,34.1,52.0,4325.0,823.0,1927.0,795.0,3.9485,419100.0,<1H OCEAN\n-118.15,34.11,52.0,2375.0,369.0,930.0,351.0,7.4111,469100.0,<1H OCEAN\n-118.15,34.11,52.0,1746.0,330.0,704.0,306.0,3.7895,364800.0,<1H OCEAN\n-118.15,34.11,52.0,1000.0,192.0,363.0,158.0,4.2981,352800.0,<1H OCEAN\n-118.15,34.12,36.0,6119.0,1513.0,2719.0,1402.0,3.8427,319700.0,<1H OCEAN\n-118.15,34.12,43.0,1810.0,427.0,742.0,429.0,3.8529,350000.0,<1H OCEAN\n-118.15,34.12,52.0,1518.0,344.0,725.0,296.0,3.4018,204500.0,<1H OCEAN\n-118.15,34.12,49.0,1789.0,288.0,848.0,311.0,6.0199,500000.0,<1H OCEAN\n-118.16,34.12,38.0,2231.0,489.0,940.0,484.0,5.4165,435100.0,<1H OCEAN\n-118.17,34.12,37.0,2246.0,481.0,995.0,459.0,4.2944,354700.0,<1H OCEAN\n-118.15,34.11,39.0,2618.0,582.0,1314.0,532.0,3.5875,309300.0,<1H OCEAN\n-118.15,34.1,39.0,3856.0,867.0,1847.0,830.0,3.4559,364900.0,<1H OCEAN\n-118.16,34.11,31.0,5715.0,1154.0,2639.0,1079.0,4.1661,364400.0,<1H OCEAN\n-118.16,34.11,48.0,1091.0,236.0,632.0,234.0,3.7235,263600.0,<1H OCEAN\n-118.17,34.11,39.0,1758.0,436.0,892.0,447.0,3.6406,278900.0,<1H OCEAN\n-118.17,34.11,26.0,4971.0,996.0,2370.0,932.0,4.9676,381400.0,<1H OCEAN\n-118.17,34.1,25.0,4444.0,647.0,1922.0,652.0,8.058,477300.0,<1H OCEAN\n-118.14,34.1,27.0,4073.0,1013.0,2411.0,933.0,3.108,231000.0,<1H OCEAN\n-118.15,34.09,27.0,1935.0,460.0,1456.0,382.0,2.8062,192800.0,<1H OCEAN\n-118.15,34.09,52.0,2203.0,430.0,1238.0,403.0,4.4306,225800.0,<1H OCEAN\n-118.16,34.09,52.0,1722.0,448.0,1122.0,425.0,3.1204,224000.0,<1H OCEAN\n-118.15,34.1,36.0,3514.0,818.0,2277.0,828.0,3.1211,229300.0,<1H OCEAN\n-118.15,34.08,44.0,1053.0,251.0,941.0,256.0,3.125,205600.0,<1H OCEAN\n-118.15,34.08,48.0,3697.0,816.0,2446.0,787.0,3.3988,199200.0,<1H OCEAN\n-118.14,34.09,28.0,4164.0,1127.0,2934.0,1014.0,2.7483,218800.0,<1H OCEAN\n-118.14,34.09,38.0,1745.0,457.0,1547.0,460.0,2.85,219000.0,<1H OCEAN\n-118.14,34.08,24.0,3988.0,1098.0,2909.0,1034.0,2.7036,170000.0,<1H OCEAN\n-118.14,34.08,30.0,1433.0,397.0,1110.0,346.0,2.3464,191700.0,<1H OCEAN\n-118.14,34.08,24.0,2999.0,786.0,2937.0,796.0,2.9405,217800.0,<1H OCEAN\n-118.14,34.07,52.0,695.0,145.0,523.0,170.0,3.665,220400.0,<1H OCEAN\n-118.11,34.1,44.0,2012.0,435.0,1454.0,456.0,3.3229,226600.0,<1H OCEAN\n-118.12,34.09,25.0,4870.0,1371.0,3518.0,1296.0,3.2307,188400.0,<1H OCEAN\n-118.13,34.09,42.0,700.0,212.0,662.0,210.0,3.0078,191700.0,<1H OCEAN\n-118.13,34.09,42.0,2562.0,781.0,1936.0,687.0,2.2214,219000.0,<1H OCEAN\n-118.13,34.08,40.0,1931.0,449.0,1367.0,446.0,2.575,228400.0,<1H OCEAN\n-118.09,34.1,27.0,6010.0,1532.0,3620.0,1445.0,2.7436,201700.0,<1H OCEAN\n-118.1,34.1,41.0,1379.0,315.0,1249.0,309.0,2.6553,183100.0,<1H OCEAN\n-118.09,34.09,36.0,1068.0,246.0,949.0,250.0,2.3462,188500.0,<1H OCEAN\n-118.1,34.09,44.0,2352.0,484.0,1517.0,463.0,4.2833,258000.0,<1H OCEAN\n-118.1,34.1,29.0,1937.0,448.0,1352.0,433.0,3.81,234600.0,<1H OCEAN\n-118.11,34.1,20.0,3090.0,802.0,2109.0,738.0,3.3801,192500.0,<1H OCEAN\n-118.1,34.1,34.0,2578.0,645.0,1628.0,617.0,2.34,210900.0,<1H OCEAN\n-118.07,34.1,34.0,2253.0,522.0,1262.0,511.0,3.4375,259800.0,<1H OCEAN\n-118.07,34.09,33.0,2178.0,445.0,1153.0,400.0,3.6083,212000.0,<1H OCEAN\n-118.07,34.1,28.0,676.0,177.0,543.0,185.0,3.2361,187500.0,<1H OCEAN\n-118.07,34.1,32.0,4275.0,,2812.0,1012.0,3.3512,214100.0,<1H OCEAN\n-118.08,34.1,36.0,2679.0,548.0,1605.0,533.0,3.5313,213200.0,<1H OCEAN\n-118.09,34.1,40.0,1904.0,393.0,1183.0,364.0,3.6696,210400.0,<1H OCEAN\n-118.08,34.08,38.0,1889.0,407.0,1330.0,396.0,3.9219,205200.0,<1H OCEAN\n-118.08,34.08,43.0,1716.0,402.0,1343.0,386.0,2.9688,211400.0,<1H OCEAN\n-118.09,34.09,40.0,855.0,208.0,745.0,222.0,3.0125,224000.0,<1H OCEAN\n-118.09,34.08,34.0,1513.0,384.0,986.0,336.0,2.6901,235600.0,<1H OCEAN\n-118.09,34.08,33.0,1430.0,344.0,1165.0,328.0,3.0357,206000.0,<1H OCEAN\n-118.09,34.08,42.0,1003.0,236.0,769.0,231.0,3.1607,218300.0,<1H OCEAN\n-118.1,34.09,42.0,1460.0,289.0,829.0,273.0,4.875,227300.0,<1H OCEAN\n-118.1,34.08,37.0,2894.0,659.0,1977.0,636.0,2.543,208100.0,<1H OCEAN\n-118.1,34.08,24.0,4510.0,1296.0,3985.0,1240.0,2.6884,204600.0,<1H OCEAN\n-118.1,34.09,46.0,2822.0,525.0,1434.0,520.0,3.8906,238300.0,<1H OCEAN\n-118.11,34.08,42.0,3172.0,644.0,1829.0,642.0,3.3966,243200.0,<1H OCEAN\n-118.11,34.08,30.0,2350.0,472.0,945.0,467.0,3.3421,201000.0,<1H OCEAN\n-118.11,34.08,45.0,1106.0,226.0,779.0,205.0,4.5446,244800.0,<1H OCEAN\n-118.11,34.07,46.0,1130.0,229.0,698.0,209.0,5.2719,244400.0,<1H OCEAN\n-118.12,34.09,25.0,3603.0,1003.0,2719.0,913.0,2.6981,208000.0,<1H OCEAN\n-118.12,34.08,52.0,1437.0,290.0,980.0,282.0,5.3032,245700.0,<1H OCEAN\n-118.12,34.08,49.0,1782.0,374.0,1010.0,367.0,3.1583,268200.0,<1H OCEAN\n-118.13,34.08,34.0,3848.0,991.0,2814.0,972.0,2.6716,227100.0,<1H OCEAN\n-118.12,34.08,35.0,2248.0,,1762.0,622.0,3.0,253900.0,<1H OCEAN\n-118.13,34.08,35.0,2517.0,662.0,1883.0,607.0,2.5787,223000.0,<1H OCEAN\n-118.12,34.08,36.0,2433.0,585.0,1565.0,563.0,3.2344,234900.0,<1H OCEAN\n-118.13,34.07,32.0,1880.0,428.0,1404.0,424.0,3.085,220500.0,<1H OCEAN\n-118.12,34.07,45.0,1770.0,423.0,1410.0,389.0,3.0592,212500.0,<1H OCEAN\n-118.12,34.07,43.0,1050.0,252.0,820.0,244.0,2.025,215600.0,<1H OCEAN\n-118.12,34.06,23.0,1190.0,347.0,965.0,327.0,2.2261,211800.0,<1H OCEAN\n-118.13,34.06,17.0,1714.0,572.0,1590.0,568.0,1.1875,183900.0,<1H OCEAN\n-118.13,34.07,20.0,2130.0,654.0,1870.0,578.0,2.3664,192200.0,<1H OCEAN\n-118.12,34.06,17.0,5137.0,1614.0,4945.0,1535.0,2.4599,181600.0,<1H OCEAN\n-118.11,34.07,19.0,3215.0,907.0,3072.0,870.0,2.3393,202300.0,<1H OCEAN\n-118.11,34.06,18.0,2609.0,721.0,2221.0,703.0,2.3224,192300.0,<1H OCEAN\n-118.14,34.07,42.0,1036.0,199.0,656.0,215.0,4.1902,235000.0,<1H OCEAN\n-118.14,34.06,39.0,2390.0,444.0,1246.0,422.0,3.7857,245700.0,<1H OCEAN\n-118.14,34.06,37.0,1339.0,258.0,706.0,238.0,4.7569,253800.0,<1H OCEAN\n-118.15,34.07,52.0,1983.0,344.0,887.0,331.0,3.2875,234400.0,<1H OCEAN\n-118.15,34.07,44.0,1626.0,383.0,1063.0,334.0,2.4348,220700.0,<1H OCEAN\n-118.16,34.07,47.0,2994.0,543.0,1651.0,561.0,3.8644,241500.0,<1H OCEAN\n-118.16,34.07,42.0,3836.0,777.0,2118.0,754.0,3.6364,254600.0,<1H OCEAN\n-118.15,34.07,40.0,1072.0,204.0,940.0,200.0,3.125,242700.0,<1H OCEAN\n-118.15,34.06,28.0,3855.0,922.0,2517.0,874.0,3.505,204300.0,<1H OCEAN\n-118.16,34.06,25.0,4284.0,741.0,2163.0,701.0,6.1509,315100.0,<1H OCEAN\n-118.16,34.06,27.0,1675.0,274.0,785.0,275.0,5.828,301100.0,<1H OCEAN\n-118.14,34.05,25.0,5478.0,1136.0,3062.0,1096.0,3.4118,341100.0,<1H OCEAN\n-118.15,34.05,31.0,3362.0,799.0,1939.0,754.0,3.5089,305800.0,<1H OCEAN\n-118.15,34.05,33.0,3287.0,649.0,1783.0,653.0,3.8472,293300.0,<1H OCEAN\n-118.13,34.06,30.0,1692.0,398.0,1130.0,365.0,2.8672,198500.0,<1H OCEAN\n-118.14,34.06,27.0,5257.0,1082.0,3496.0,1036.0,3.3906,237200.0,<1H OCEAN\n-118.13,34.05,35.0,3229.0,616.0,1879.0,595.0,3.9531,268400.0,<1H OCEAN\n-118.14,34.05,39.0,1880.0,367.0,954.0,349.0,3.875,236400.0,<1H OCEAN\n-118.12,34.06,35.0,1729.0,438.0,1308.0,412.0,2.5321,197200.0,<1H OCEAN\n-118.12,34.06,25.0,1137.0,293.0,800.0,281.0,2.4286,233300.0,<1H OCEAN\n-118.12,34.06,25.0,1526.0,388.0,1304.0,378.0,3.1892,214700.0,<1H OCEAN\n-118.11,34.06,32.0,1273.0,344.0,1148.0,368.0,2.1061,214700.0,<1H OCEAN\n-118.11,34.06,16.0,2416.0,565.0,1750.0,514.0,2.8229,163700.0,<1H OCEAN\n-118.11,34.06,14.0,2628.0,668.0,2208.0,574.0,2.9764,160300.0,<1H OCEAN\n-118.09,34.07,45.0,593.0,133.0,481.0,128.0,2.5938,199300.0,<1H OCEAN\n-118.09,34.07,38.0,1036.0,226.0,1058.0,235.0,3.2578,184200.0,<1H OCEAN\n-118.1,34.08,21.0,1349.0,352.0,1188.0,330.0,2.5,182100.0,<1H OCEAN\n-118.1,34.07,29.0,1179.0,313.0,1255.0,308.0,2.5964,176800.0,<1H OCEAN\n-118.11,34.07,39.0,1270.0,299.0,1073.0,278.0,3.3088,186600.0,<1H OCEAN\n-118.1,34.07,36.0,1240.0,349.0,1383.0,338.0,2.4931,170300.0,<1H OCEAN\n-118.1,34.07,36.0,3661.0,956.0,3816.0,931.0,2.5104,185000.0,<1H OCEAN\n-118.09,34.07,26.0,794.0,182.0,709.0,170.0,3.175,170800.0,<1H OCEAN\n-118.1,34.07,33.0,3437.0,1081.0,3817.0,1042.0,2.25,203700.0,<1H OCEAN\n-118.09,34.07,45.0,726.0,146.0,568.0,160.0,3.0347,183200.0,<1H OCEAN\n-118.09,34.07,31.0,1054.0,252.0,1032.0,258.0,2.3424,188500.0,<1H OCEAN\n-118.09,34.06,30.0,1980.0,552.0,2264.0,511.0,2.6094,179400.0,<1H OCEAN\n-118.09,34.06,38.0,3230.0,840.0,3485.0,827.0,2.629,171600.0,<1H OCEAN\n-118.08,34.04,20.0,5841.0,1146.0,3273.0,1131.0,4.7222,185100.0,<1H OCEAN\n-118.09,34.06,31.0,1146.0,289.0,1163.0,258.0,2.2083,185600.0,<1H OCEAN\n-118.1,34.06,31.0,2852.0,740.0,3100.0,725.0,2.9524,178800.0,<1H OCEAN\n-118.1,34.06,36.0,1463.0,369.0,1492.0,366.0,3.25,179200.0,<1H OCEAN\n-118.11,34.06,30.0,1547.0,436.0,1700.0,410.0,2.5488,187500.0,<1H OCEAN\n-118.09,34.05,22.0,1764.0,357.0,1379.0,363.0,3.5357,199000.0,<1H OCEAN\n-118.1,34.05,31.0,3559.0,734.0,2975.0,715.0,3.756,183300.0,<1H OCEAN\n-118.1,34.05,26.0,1495.0,328.0,1296.0,304.0,2.913,152300.0,<1H OCEAN\n-118.09,34.04,18.0,5580.0,1369.0,3842.0,1276.0,3.6512,168500.0,<1H OCEAN\n-118.09,34.04,24.0,1543.0,257.0,824.0,271.0,6.4385,272600.0,<1H OCEAN\n-118.11,34.05,23.0,3436.0,565.0,1729.0,529.0,5.9941,266700.0,<1H OCEAN\n-118.11,34.04,28.0,3913.0,696.0,2264.0,697.0,5.2446,258000.0,<1H OCEAN\n-118.12,34.05,32.0,3775.0,786.0,2416.0,792.0,3.6625,247600.0,<1H OCEAN\n-118.12,34.04,35.0,1038.0,209.0,598.0,190.0,5.9214,254900.0,<1H OCEAN\n-118.13,34.04,36.0,1938.0,364.0,1118.0,374.0,3.5833,227300.0,<1H OCEAN\n-118.13,34.04,40.0,1444.0,312.0,881.0,303.0,3.1083,220500.0,<1H OCEAN\n-118.14,34.03,38.0,1447.0,293.0,1042.0,284.0,4.1375,211500.0,<1H OCEAN\n-118.15,34.04,33.0,818.0,195.0,664.0,198.0,2.1944,203300.0,<1H OCEAN\n-118.14,34.04,43.0,1949.0,464.0,1216.0,457.0,3.3214,209300.0,<1H OCEAN\n-118.14,34.04,40.0,1966.0,391.0,1120.0,362.0,3.7109,198800.0,<1H OCEAN\n-118.14,34.04,37.0,1129.0,212.0,509.0,202.0,2.6146,243200.0,<1H OCEAN\n-118.11,34.03,36.0,1493.0,316.0,989.0,293.0,3.5272,213700.0,<1H OCEAN\n-118.13,34.04,42.0,2205.0,451.0,1392.0,423.0,4.3646,211400.0,<1H OCEAN\n-118.12,34.04,34.0,2103.0,427.0,1355.0,434.0,4.5795,235300.0,<1H OCEAN\n-118.12,34.04,35.0,1064.0,203.0,608.0,201.0,4.0938,246900.0,<1H OCEAN\n-117.93,33.95,31.0,3600.0,468.0,1382.0,435.0,7.4597,500001.0,<1H OCEAN\n-117.95,33.95,29.0,4943.0,674.0,1913.0,641.0,6.8189,379300.0,<1H OCEAN\n-117.95,33.97,33.0,1113.0,145.0,424.0,137.0,8.3474,500001.0,<1H OCEAN\n-117.97,33.96,30.0,4873.0,667.0,1995.0,638.0,7.2472,441900.0,<1H OCEAN\n-117.98,33.95,35.0,4055.0,652.0,1758.0,639.0,6.1898,282400.0,<1H OCEAN\n-117.98,33.94,36.0,4297.0,717.0,2038.0,700.0,5.2851,258800.0,<1H OCEAN\n-117.99,33.95,30.0,2217.0,284.0,851.0,291.0,10.4835,498600.0,<1H OCEAN\n-118.0,33.95,35.0,1431.0,210.0,505.0,213.0,6.8109,401000.0,<1H OCEAN\n-118.01,33.95,35.0,1755.0,322.0,774.0,290.0,5.0861,296700.0,<1H OCEAN\n-118.0,33.96,37.0,2414.0,323.0,878.0,305.0,9.1541,453800.0,<1H OCEAN\n-117.99,33.97,18.0,4078.0,484.0,1490.0,482.0,10.8034,500001.0,<1H OCEAN\n-118.05,34.02,31.0,40.0,8.0,25.0,7.0,2.125,375000.0,<1H OCEAN\n-118.03,34.01,10.0,6531.0,1036.0,2975.0,1018.0,6.2319,403700.0,<1H OCEAN\n-118.06,34.02,25.0,3548.0,639.0,2653.0,664.0,5.2557,188800.0,<1H OCEAN\n-118.06,34.01,34.0,1962.0,396.0,1488.0,332.0,3.9091,155100.0,<1H OCEAN\n-118.07,34.01,38.0,2245.0,444.0,1540.0,419.0,3.7986,171000.0,<1H OCEAN\n-118.07,34.01,36.0,1391.0,283.0,1025.0,275.0,3.2375,176800.0,<1H OCEAN\n-118.08,34.01,32.0,1973.0,401.0,1322.0,386.0,3.4861,158100.0,<1H OCEAN\n-118.07,34.0,42.0,1392.0,351.0,1471.0,348.0,2.63,143800.0,<1H OCEAN\n-118.08,34.0,35.0,1188.0,342.0,1373.0,332.0,2.9107,150900.0,<1H OCEAN\n-118.08,34.01,34.0,1914.0,549.0,2122.0,529.0,2.5969,150200.0,<1H OCEAN\n-118.08,34.02,14.0,3789.0,810.0,2551.0,793.0,2.9321,144200.0,<1H OCEAN\n-118.08,34.01,33.0,1091.0,233.0,890.0,226.0,2.7679,176400.0,<1H OCEAN\n-118.08,34.01,36.0,1248.0,322.0,1282.0,326.0,3.2031,147600.0,<1H OCEAN\n-118.09,34.01,36.0,1465.0,363.0,1538.0,342.0,3.5469,150600.0,<1H OCEAN\n-118.09,34.01,42.0,897.0,229.0,1094.0,238.0,2.0729,114100.0,<1H OCEAN\n-118.09,34.01,31.0,1108.0,238.0,1151.0,229.0,4.3333,149500.0,<1H OCEAN\n-118.09,34.0,36.0,1722.0,353.0,1174.0,335.0,3.045,160600.0,<1H OCEAN\n-118.09,34.0,35.0,1580.0,331.0,1290.0,338.0,4.1458,162500.0,<1H OCEAN\n-118.09,33.99,34.0,1369.0,270.0,1005.0,272.0,3.692,172600.0,<1H OCEAN\n-118.1,33.99,36.0,1529.0,290.0,1271.0,287.0,3.6875,175200.0,<1H OCEAN\n-118.1,33.99,35.0,1326.0,272.0,933.0,267.0,3.4306,162500.0,<1H OCEAN\n-118.1,33.99,31.0,965.0,217.0,599.0,206.0,2.7202,190300.0,<1H OCEAN\n-118.08,33.98,36.0,1492.0,282.0,1041.0,270.0,4.0677,165800.0,<1H OCEAN\n-118.08,33.98,39.0,1042.0,221.0,863.0,228.0,3.6033,157800.0,<1H OCEAN\n-118.09,33.98,37.0,1270.0,272.0,1092.0,274.0,3.5,160700.0,<1H OCEAN\n-118.09,33.99,35.0,2787.0,639.0,1923.0,614.0,3.5757,177900.0,<1H OCEAN\n-118.08,33.99,38.0,1683.0,328.0,1369.0,339.0,3.6196,170700.0,<1H OCEAN\n-118.08,33.99,37.0,1419.0,310.0,1125.0,296.0,2.5,162000.0,<1H OCEAN\n-118.08,33.99,36.0,2024.0,590.0,2028.0,573.0,2.8152,163900.0,<1H OCEAN\n-118.08,34.0,32.0,1165.0,358.0,997.0,361.0,0.9817,166300.0,<1H OCEAN\n-118.07,33.99,35.0,1625.0,302.0,1134.0,288.0,4.5595,164900.0,<1H OCEAN\n-118.07,33.99,41.0,1204.0,252.0,1002.0,248.0,3.0577,163300.0,<1H OCEAN\n-118.07,33.99,39.0,552.0,151.0,807.0,168.0,3.25,153300.0,<1H OCEAN\n-118.06,33.98,40.0,1723.0,370.0,1221.0,370.0,3.3562,169200.0,<1H OCEAN\n-118.06,33.98,40.0,1410.0,255.0,932.0,273.0,4.2206,178000.0,<1H OCEAN\n-118.06,33.98,42.0,1342.0,243.0,615.0,208.0,5.4381,186900.0,<1H OCEAN\n-118.06,33.98,50.0,1146.0,238.0,579.0,213.0,2.9583,172600.0,<1H OCEAN\n-118.06,33.98,38.0,1862.0,319.0,975.0,305.0,4.7266,177600.0,<1H OCEAN\n-118.06,34.0,34.0,5002.0,917.0,2597.0,893.0,3.9243,219800.0,<1H OCEAN\n-118.07,34.0,37.0,2976.0,636.0,2117.0,598.0,4.1058,167300.0,<1H OCEAN\n-118.05,33.99,42.0,2480.0,401.0,1085.0,438.0,5.193,263400.0,<1H OCEAN\n-118.04,33.99,36.0,3531.0,754.0,1613.0,697.0,3.2359,198600.0,<1H OCEAN\n-118.05,33.99,38.0,1619.0,,886.0,357.0,3.7328,182400.0,<1H OCEAN\n-118.06,33.99,38.0,862.0,178.0,484.0,176.0,4.375,186200.0,<1H OCEAN\n-118.06,33.99,47.0,1588.0,309.0,827.0,292.0,3.7833,166100.0,<1H OCEAN\n-118.06,33.99,46.0,1203.0,219.0,637.0,211.0,3.3611,174400.0,<1H OCEAN\n-118.06,33.99,45.0,1471.0,255.0,670.0,250.0,4.5478,188000.0,<1H OCEAN\n-118.04,33.98,50.0,1951.0,458.0,1362.0,454.0,3.0,163200.0,<1H OCEAN\n-118.05,33.98,41.0,1406.0,428.0,1174.0,390.0,2.0147,137500.0,<1H OCEAN\n-118.05,33.98,41.0,1694.0,413.0,1222.0,387.0,2.8311,155300.0,<1H OCEAN\n-118.04,34.0,30.0,5308.0,854.0,2114.0,838.0,5.1985,279200.0,<1H OCEAN\n-118.03,33.99,52.0,2792.0,461.0,1177.0,439.0,3.4312,243800.0,<1H OCEAN\n-118.03,33.98,46.0,1974.0,465.0,880.0,441.0,2.7578,236800.0,<1H OCEAN\n-118.04,33.98,43.0,2446.0,764.0,1699.0,692.0,2.625,163300.0,<1H OCEAN\n-118.04,33.98,28.0,1617.0,507.0,1158.0,486.0,1.9688,165600.0,<1H OCEAN\n-118.04,33.98,25.0,3040.0,831.0,1580.0,735.0,2.3182,182100.0,<1H OCEAN\n-118.04,33.99,47.0,2530.0,565.0,1262.0,509.0,3.6475,197100.0,<1H OCEAN\n-118.02,33.98,23.0,1995.0,306.0,707.0,293.0,8.6677,332700.0,<1H OCEAN\n-118.03,33.97,32.0,2468.0,552.0,1190.0,479.0,3.8275,238500.0,<1H OCEAN\n-118.02,33.97,34.0,1903.0,293.0,887.0,306.0,6.148,313800.0,<1H OCEAN\n-118.01,33.97,36.0,1451.0,224.0,608.0,246.0,6.0648,290800.0,<1H OCEAN\n-118.0,33.97,30.0,6540.0,991.0,3124.0,953.0,6.0663,372600.0,<1H OCEAN\n-118.01,33.96,36.0,1805.0,288.0,882.0,308.0,5.3054,273500.0,<1H OCEAN\n-118.02,33.96,36.0,2002.0,361.0,913.0,311.0,4.5446,244700.0,<1H OCEAN\n-118.02,33.96,36.0,2071.0,398.0,988.0,404.0,4.6226,219700.0,<1H OCEAN\n-118.03,33.97,36.0,1601.0,290.0,715.0,284.0,4.8152,232400.0,<1H OCEAN\n-118.03,33.97,39.0,1996.0,389.0,1029.0,387.0,4.65,224300.0,<1H OCEAN\n-118.03,33.97,39.0,2126.0,434.0,1103.0,433.0,3.2852,196200.0,<1H OCEAN\n-118.03,33.97,36.0,2149.0,527.0,1359.0,481.0,2.824,167900.0,<1H OCEAN\n-118.04,33.97,29.0,2376.0,700.0,1968.0,680.0,2.6082,162500.0,<1H OCEAN\n-118.04,33.97,25.0,2945.0,914.0,2313.0,832.0,2.5686,177500.0,<1H OCEAN\n-118.03,33.97,22.0,2185.0,623.0,1644.0,606.0,2.593,192000.0,<1H OCEAN\n-118.02,33.95,38.0,2139.0,426.0,1138.0,412.0,4.2917,168900.0,<1H OCEAN\n-118.02,33.95,36.0,1681.0,329.0,964.0,311.0,4.108,181200.0,<1H OCEAN\n-118.03,33.96,37.0,1745.0,365.0,1022.0,368.0,4.0536,171400.0,<1H OCEAN\n-118.03,33.96,37.0,1180.0,256.0,614.0,242.0,3.117,164600.0,<1H OCEAN\n-118.03,33.95,34.0,1882.0,428.0,1034.0,375.0,3.6509,173200.0,<1H OCEAN\n-118.03,33.95,37.0,1772.0,321.0,934.0,326.0,4.1471,177800.0,<1H OCEAN\n-118.04,33.95,36.0,2722.0,515.0,1390.0,486.0,3.8214,178500.0,<1H OCEAN\n-118.04,33.96,37.0,1948.0,395.0,1163.0,379.0,3.225,154000.0,<1H OCEAN\n-118.04,33.96,42.0,1430.0,338.0,1269.0,321.0,3.3214,148800.0,<1H OCEAN\n-118.05,33.96,37.0,2622.0,652.0,2778.0,644.0,2.9714,160300.0,<1H OCEAN\n-118.04,33.97,34.0,1759.0,431.0,1282.0,391.0,3.0491,158200.0,<1H OCEAN\n-118.05,33.98,34.0,2142.0,390.0,1305.0,406.0,4.0379,172800.0,<1H OCEAN\n-118.05,33.97,36.0,2854.0,688.0,2816.0,673.0,3.6,154000.0,<1H OCEAN\n-118.06,33.97,37.0,1645.0,308.0,1077.0,320.0,4.3203,159200.0,<1H OCEAN\n-118.07,33.98,32.0,3304.0,714.0,2032.0,690.0,3.2093,167800.0,<1H OCEAN\n-118.07,33.98,41.0,1478.0,273.0,916.0,281.0,3.9688,169800.0,<1H OCEAN\n-118.06,33.97,39.0,1639.0,300.0,988.0,309.0,3.9612,175800.0,<1H OCEAN\n-118.07,33.97,36.0,1887.0,370.0,1006.0,329.0,3.1554,170700.0,<1H OCEAN\n-118.08,33.97,38.0,1026.0,190.0,789.0,193.0,4.2,163200.0,<1H OCEAN\n-118.07,33.97,32.0,3400.0,826.0,3017.0,793.0,2.4607,155600.0,<1H OCEAN\n-118.07,33.96,30.0,928.0,230.0,913.0,214.0,2.6991,147100.0,<1H OCEAN\n-118.07,33.97,36.0,1265.0,273.0,1052.0,253.0,4.8929,156200.0,<1H OCEAN\n-118.08,33.97,36.0,1620.0,298.0,1258.0,309.0,3.9773,166700.0,<1H OCEAN\n-118.08,33.97,35.0,825.0,155.0,590.0,144.0,4.6333,161200.0,<1H OCEAN\n-118.08,33.97,36.0,1678.0,323.0,1380.0,352.0,3.5481,163300.0,<1H OCEAN\n-118.09,33.98,39.0,936.0,194.0,691.0,211.0,3.6875,169500.0,<1H OCEAN\n-118.09,33.97,39.0,1473.0,297.0,1108.0,294.0,4.1389,166000.0,<1H OCEAN\n-118.09,33.97,35.0,2664.0,541.0,2033.0,491.0,3.7326,164300.0,<1H OCEAN\n-118.1,33.97,35.0,2426.0,529.0,2010.0,514.0,2.9922,163500.0,<1H OCEAN\n-118.1,33.98,34.0,1357.0,310.0,1042.0,287.0,3.4083,156700.0,<1H OCEAN\n-118.09,33.98,37.0,1226.0,255.0,1068.0,271.0,3.1607,172200.0,<1H OCEAN\n-118.1,33.98,33.0,1927.0,482.0,1623.0,479.0,3.5268,152000.0,<1H OCEAN\n-118.11,33.97,33.0,2125.0,500.0,1672.0,476.0,3.6397,166600.0,<1H OCEAN\n-118.11,33.98,36.0,446.0,108.0,410.0,117.0,3.3942,147200.0,<1H OCEAN\n-118.09,33.96,36.0,3271.0,603.0,2593.0,616.0,3.3621,169700.0,<1H OCEAN\n-118.09,33.96,36.0,1116.0,229.0,719.0,233.0,3.425,163200.0,<1H OCEAN\n-118.09,33.95,36.0,1991.0,396.0,1306.0,403.0,4.5,166600.0,<1H OCEAN\n-118.1,33.96,38.0,1657.0,335.0,1195.0,309.0,4.1711,160100.0,<1H OCEAN\n-118.1,33.96,36.0,1184.0,240.0,946.0,232.0,4.0357,162500.0,<1H OCEAN\n-118.1,33.97,20.0,1878.0,548.0,1461.0,516.0,2.9821,142500.0,<1H OCEAN\n-118.1,33.96,36.0,2013.0,435.0,1476.0,475.0,3.9549,192100.0,<1H OCEAN\n-118.1,33.96,40.0,1743.0,328.0,981.0,291.0,3.6667,173100.0,<1H OCEAN\n-118.08,33.95,32.0,1962.0,387.0,1274.0,398.0,4.8304,160600.0,<1H OCEAN\n-118.08,33.95,37.0,1743.0,348.0,1328.0,354.0,3.0944,162800.0,<1H OCEAN\n-118.08,33.96,35.0,2104.0,399.0,1659.0,387.0,4.0096,165000.0,<1H OCEAN\n-118.08,33.96,34.0,1431.0,310.0,1162.0,288.0,4.369,165400.0,<1H OCEAN\n-118.09,33.96,20.0,1911.0,472.0,1407.0,465.0,2.7647,163000.0,<1H OCEAN\n-118.09,33.95,32.0,1083.0,206.0,737.0,218.0,3.5583,170800.0,<1H OCEAN\n-118.09,33.94,36.0,2762.0,472.0,1576.0,493.0,4.0846,183400.0,<1H OCEAN\n-118.09,33.93,36.0,1585.0,323.0,1205.0,343.0,4.5306,183400.0,<1H OCEAN\n-118.09,33.94,33.0,1976.0,404.0,1379.0,395.0,3.8542,175400.0,<1H OCEAN\n-118.08,33.94,21.0,3933.0,949.0,2219.0,820.0,2.4926,171400.0,<1H OCEAN\n-118.04,33.95,35.0,1945.0,357.0,1227.0,359.0,5.2162,171900.0,<1H OCEAN\n-118.04,33.94,31.0,3808.0,670.0,2430.0,660.0,4.625,173900.0,<1H OCEAN\n-118.04,33.95,36.0,1976.0,368.0,1236.0,355.0,4.615,174000.0,<1H OCEAN\n-118.05,33.95,33.0,1954.0,390.0,1600.0,376.0,3.6125,170800.0,<1H OCEAN\n-118.05,33.94,34.0,495.0,120.0,527.0,130.0,1.9453,149000.0,<1H OCEAN\n-118.04,33.93,36.0,1045.0,239.0,1165.0,230.0,3.1979,161800.0,<1H OCEAN\n-118.05,33.93,31.0,894.0,203.0,883.0,190.0,3.6771,141500.0,<1H OCEAN\n-118.05,33.93,35.0,2107.0,480.0,2241.0,443.0,3.1513,150000.0,<1H OCEAN\n-118.05,33.92,33.0,1999.0,470.0,2170.0,466.0,3.2371,154700.0,<1H OCEAN\n-118.03,33.93,22.0,3382.0,800.0,2688.0,784.0,3.875,164700.0,<1H OCEAN\n-118.02,33.92,35.0,2075.0,424.0,1312.0,396.0,3.7969,164800.0,<1H OCEAN\n-118.03,33.92,30.0,1414.0,332.0,1307.0,315.0,3.0,158300.0,<1H OCEAN\n-118.03,33.92,35.0,2108.0,405.0,1243.0,394.0,3.6731,167000.0,<1H OCEAN\n-118.04,33.93,35.0,1805.0,387.0,1505.0,366.0,4.1667,151900.0,<1H OCEAN\n-118.04,33.92,34.0,1995.0,417.0,1573.0,407.0,3.4907,153500.0,<1H OCEAN\n-118.04,33.93,36.0,1726.0,332.0,1293.0,310.0,4.3849,144100.0,<1H OCEAN\n-118.04,33.92,35.0,2469.0,522.0,2151.0,537.0,3.4219,156200.0,<1H OCEAN\n-118.02,33.93,35.0,2400.0,398.0,1218.0,408.0,4.1312,193800.0,<1H OCEAN\n-118.03,33.93,35.0,2470.0,416.0,1386.0,411.0,5.2736,179500.0,<1H OCEAN\n-118.03,33.94,37.0,1699.0,302.0,889.0,271.0,4.3542,179800.0,<1H OCEAN\n-118.03,33.94,30.0,2572.0,521.0,1564.0,501.0,3.4861,177200.0,<1H OCEAN\n-118.04,33.94,37.0,1328.0,273.0,1115.0,275.0,4.2051,164400.0,<1H OCEAN\n-118.03,33.94,34.0,1748.0,386.0,917.0,378.0,3.4792,169000.0,<1H OCEAN\n-118.01,33.95,36.0,1579.0,290.0,816.0,276.0,4.4318,181100.0,<1H OCEAN\n-118.02,33.95,36.0,1705.0,299.0,871.0,296.0,4.6184,179800.0,<1H OCEAN\n-118.02,33.95,36.0,1632.0,295.0,797.0,283.0,4.2292,179500.0,<1H OCEAN\n-118.01,33.95,37.0,1165.0,210.0,627.0,221.0,4.6923,181000.0,<1H OCEAN\n-118.02,33.94,33.0,2382.0,404.0,1339.0,389.0,5.3016,192200.0,<1H OCEAN\n-118.02,33.94,23.0,4815.0,1081.0,3232.0,1016.0,3.488,191800.0,<1H OCEAN\n-118.02,33.95,35.0,2085.0,400.0,1112.0,391.0,3.4886,173900.0,<1H OCEAN\n-117.98,33.94,32.0,2562.0,491.0,1222.0,446.0,4.0985,226200.0,<1H OCEAN\n-117.98,33.93,27.0,3142.0,509.0,1520.0,503.0,6.2924,232500.0,<1H OCEAN\n-117.99,33.93,33.0,2299.0,431.0,1049.0,447.0,3.6458,208100.0,<1H OCEAN\n-117.99,33.94,34.0,1519.0,301.0,758.0,304.0,4.3125,214000.0,<1H OCEAN\n-117.99,33.94,30.0,2395.0,565.0,1214.0,521.0,3.7045,212300.0,<1H OCEAN\n-118.0,33.94,35.0,2603.0,482.0,1305.0,507.0,3.9543,214400.0,<1H OCEAN\n-118.0,33.94,36.0,2911.0,534.0,1395.0,486.0,5.1738,203700.0,<1H OCEAN\n-118.01,33.94,36.0,1921.0,329.0,969.0,327.0,4.9191,188700.0,<1H OCEAN\n-118.01,33.94,35.0,1323.0,235.0,807.0,247.0,4.2708,174800.0,<1H OCEAN\n-118.01,33.93,31.0,3395.0,742.0,1886.0,737.0,4.4118,174400.0,<1H OCEAN\n-118.02,33.93,33.0,4711.0,988.0,2984.0,931.0,3.6028,184700.0,<1H OCEAN\n-117.99,33.93,36.0,1287.0,233.0,779.0,229.0,4.8523,175800.0,<1H OCEAN\n-118.0,33.93,35.0,1288.0,240.0,758.0,250.0,4.9205,173900.0,<1H OCEAN\n-118.01,33.93,34.0,2424.0,468.0,1293.0,444.0,3.275,189900.0,<1H OCEAN\n-118.0,33.93,35.0,802.0,153.0,445.0,150.0,5.0077,185000.0,<1H OCEAN\n-118.0,33.94,37.0,903.0,158.0,444.0,158.0,3.75,174400.0,<1H OCEAN\n-118.01,33.92,34.0,4039.0,694.0,2269.0,663.0,5.2305,205100.0,<1H OCEAN\n-118.01,33.92,35.0,1606.0,289.0,829.0,273.0,5.273,187600.0,<1H OCEAN\n-118.02,33.92,34.0,1478.0,251.0,956.0,277.0,5.5238,185300.0,<1H OCEAN\n-118.0,33.92,26.0,2830.0,399.0,1204.0,404.0,6.1273,289600.0,<1H OCEAN\n-118.0,33.91,19.0,5166.0,770.0,2374.0,753.0,5.979,285200.0,<1H OCEAN\n-117.99,33.93,27.0,3708.0,718.0,1921.0,721.0,4.375,210400.0,<1H OCEAN\n-118.0,33.93,24.0,4534.0,967.0,2547.0,895.0,3.9575,215400.0,<1H OCEAN\n-117.98,33.92,27.0,3700.0,,1793.0,552.0,5.3668,219800.0,<1H OCEAN\n-117.99,33.92,27.0,5805.0,1152.0,3106.0,1144.0,4.061,222700.0,<1H OCEAN\n-117.98,33.91,16.0,10621.0,1782.0,3836.0,1480.0,5.0923,257200.0,<1H OCEAN\n-117.99,33.9,33.0,2161.0,383.0,1235.0,383.0,5.6454,202800.0,<1H OCEAN\n-117.99,33.9,30.0,1677.0,372.0,1021.0,332.0,3.5859,199700.0,<1H OCEAN\n-118.0,33.89,35.0,1065.0,176.0,574.0,171.0,5.0384,200800.0,<1H OCEAN\n-118.0,33.89,34.0,1932.0,315.0,1053.0,316.0,5.1377,213300.0,<1H OCEAN\n-118.0,33.9,35.0,1758.0,309.0,972.0,338.0,4.3831,209800.0,<1H OCEAN\n-118.0,33.9,35.0,1942.0,332.0,1127.0,325.0,4.5144,206300.0,<1H OCEAN\n-118.01,33.9,26.0,2968.0,674.0,1655.0,628.0,4.6094,201000.0,<1H OCEAN\n-118.01,33.89,34.0,1653.0,292.0,1003.0,310.0,4.6,203400.0,<1H OCEAN\n-118.01,33.89,33.0,2046.0,327.0,1018.0,320.0,4.2292,212800.0,<1H OCEAN\n-118.0,33.89,35.0,1011.0,183.0,578.0,171.0,3.9861,188700.0,<1H OCEAN\n-118.02,33.9,36.0,2417.0,421.0,1276.0,426.0,5.5601,205200.0,<1H OCEAN\n-118.02,33.9,34.0,2678.0,511.0,1540.0,497.0,4.4954,202900.0,<1H OCEAN\n-118.01,33.89,36.0,1589.0,265.0,804.0,272.0,4.6354,202900.0,<1H OCEAN\n-118.02,33.89,36.0,1375.0,,670.0,221.0,5.0839,198200.0,<1H OCEAN\n-118.02,33.92,34.0,2169.0,418.0,1169.0,406.0,3.2222,218700.0,<1H OCEAN\n-118.01,33.91,32.0,2722.0,571.0,2541.0,462.0,4.2305,221400.0,<1H OCEAN\n-118.01,33.9,36.0,1382.0,257.0,685.0,255.0,5.125,211700.0,<1H OCEAN\n-118.02,33.91,35.0,2182.0,390.0,1248.0,399.0,5.4236,216700.0,<1H OCEAN\n-118.02,33.9,34.0,1636.0,358.0,977.0,357.0,3.5938,209900.0,<1H OCEAN\n-118.03,33.9,35.0,1434.0,279.0,744.0,252.0,3.7308,202400.0,<1H OCEAN\n-118.02,33.91,35.0,1337.0,234.0,692.0,235.0,5.1155,213700.0,<1H OCEAN\n-118.02,33.91,34.0,2518.0,429.0,1309.0,421.0,4.7861,210700.0,<1H OCEAN\n-118.03,33.91,32.0,4040.0,832.0,2526.0,798.0,3.2143,160100.0,<1H OCEAN\n-118.03,33.91,35.0,2323.0,406.0,1741.0,398.0,4.2437,164100.0,<1H OCEAN\n-118.03,33.9,36.0,1143.0,193.0,826.0,188.0,5.3184,171100.0,<1H OCEAN\n-118.04,33.9,36.0,15.0,5.0,15.0,6.0,0.4999,162500.0,<1H OCEAN\n-118.09,34.03,27.0,3797.0,597.0,2043.0,614.0,5.5,276800.0,<1H OCEAN\n-118.09,34.02,28.0,1984.0,313.0,1099.0,343.0,4.5526,250200.0,<1H OCEAN\n-118.09,34.02,33.0,4853.0,1105.0,2855.0,1006.0,3.2622,208600.0,<1H OCEAN\n-118.1,34.02,33.0,1143.0,172.0,508.0,174.0,4.9107,279900.0,<1H OCEAN\n-118.11,34.02,17.0,9559.0,1911.0,5279.0,1844.0,5.1515,318900.0,<1H OCEAN\n-118.12,34.02,25.0,2655.0,558.0,1466.0,525.0,3.0529,265800.0,<1H OCEAN\n-118.12,34.03,20.0,2595.0,428.0,1751.0,479.0,5.6112,308000.0,<1H OCEAN\n-118.1,34.01,29.0,2077.0,564.0,2087.0,543.0,2.66,189200.0,<1H OCEAN\n-118.1,34.01,23.0,1724.0,576.0,1336.0,542.0,1.3365,183300.0,<1H OCEAN\n-118.1,34.01,42.0,1436.0,298.0,1005.0,298.0,3.4297,195800.0,<1H OCEAN\n-118.1,34.02,37.0,1022.0,232.0,653.0,238.0,3.0625,189400.0,<1H OCEAN\n-118.11,34.02,40.0,1727.0,309.0,932.0,313.0,3.95,210200.0,<1H OCEAN\n-118.11,34.01,43.0,1539.0,386.0,1122.0,377.0,2.4605,196000.0,<1H OCEAN\n-118.12,34.02,36.0,1595.0,383.0,1105.0,359.0,2.4286,205600.0,<1H OCEAN\n-118.12,34.02,32.0,1789.0,528.0,1429.0,517.0,1.8906,224500.0,<1H OCEAN\n-118.13,34.03,31.0,4267.0,1070.0,3176.0,1071.0,3.0212,208200.0,<1H OCEAN\n-118.13,34.02,38.0,1243.0,310.0,788.0,286.0,2.5852,185100.0,<1H OCEAN\n-118.13,34.02,40.0,2988.0,690.0,2144.0,667.0,2.3359,189300.0,<1H OCEAN\n-118.13,34.02,41.0,734.0,190.0,565.0,191.0,2.2813,192000.0,<1H OCEAN\n-118.13,34.02,43.0,396.0,91.0,261.0,73.0,2.9044,172900.0,<1H OCEAN\n-118.14,34.02,45.0,1307.0,283.0,967.0,254.0,2.75,178300.0,<1H OCEAN\n-118.14,34.03,44.0,2003.0,390.0,1291.0,392.0,4.0625,201100.0,<1H OCEAN\n-118.13,34.03,42.0,2203.0,467.0,1470.0,488.0,2.8385,192200.0,<1H OCEAN\n-118.14,34.03,45.0,1569.0,359.0,1203.0,359.0,2.4612,180500.0,<1H OCEAN\n-118.15,34.02,43.0,2172.0,605.0,2386.0,597.0,2.8239,150600.0,<1H OCEAN\n-118.15,34.03,43.0,2006.0,472.0,1687.0,463.0,1.7991,158800.0,<1H OCEAN\n-118.16,34.03,41.0,1377.0,293.0,1142.0,272.0,3.1724,141600.0,<1H OCEAN\n-118.15,34.03,44.0,603.0,207.0,588.0,218.0,2.0536,186400.0,<1H OCEAN\n-118.15,34.03,42.0,1481.0,411.0,1206.0,394.0,2.6806,189300.0,<1H OCEAN\n-118.15,34.04,39.0,1099.0,263.0,787.0,269.0,3.7794,194600.0,<1H OCEAN\n-118.15,34.04,44.0,647.0,142.0,457.0,143.0,3.6875,162500.0,<1H OCEAN\n-118.16,34.04,45.0,332.0,70.0,302.0,60.0,3.1895,156300.0,<1H OCEAN\n-118.16,34.04,22.0,2991.0,791.0,2486.0,754.0,1.5078,181900.0,<1H OCEAN\n-118.16,34.04,38.0,1076.0,286.0,1535.0,323.0,2.7026,145000.0,<1H OCEAN\n-118.16,34.04,11.0,852.0,215.0,806.0,202.0,1.3971,134400.0,<1H OCEAN\n-118.17,34.03,31.0,1014.0,252.0,1064.0,247.0,2.4167,125500.0,<1H OCEAN\n-118.17,34.04,38.0,385.0,102.0,402.0,95.0,1.625,129700.0,<1H OCEAN\n-118.17,34.04,46.0,705.0,167.0,655.0,149.0,3.5938,141100.0,<1H OCEAN\n-118.17,34.06,36.0,871.0,201.0,2862.0,181.0,2.1845,123800.0,<1H OCEAN\n-118.17,34.05,35.0,1256.0,294.0,2990.0,302.0,3.1528,121800.0,<1H OCEAN\n-118.18,34.05,41.0,389.0,102.0,455.0,107.0,2.7031,109200.0,<1H OCEAN\n-118.17,34.06,44.0,1856.0,461.0,1853.0,452.0,2.5033,131900.0,<1H OCEAN\n-118.17,34.06,43.0,464.0,,416.0,120.0,2.475,142600.0,<1H OCEAN\n-118.18,34.06,33.0,278.0,71.0,266.0,56.0,0.8941,98200.0,<1H OCEAN\n-118.19,34.06,37.0,1715.0,456.0,2052.0,440.0,2.3125,116100.0,<1H OCEAN\n-118.18,34.06,45.0,934.0,228.0,893.0,192.0,2.53,140300.0,<1H OCEAN\n-118.18,34.05,52.0,1070.0,231.0,925.0,220.0,1.825,133000.0,<1H OCEAN\n-118.19,34.05,42.0,1291.0,345.0,1535.0,332.0,1.9083,119200.0,<1H OCEAN\n-118.19,34.05,35.0,1296.0,307.0,1423.0,276.0,2.7432,135200.0,<1H OCEAN\n-118.19,34.05,47.0,1273.0,264.0,1193.0,260.0,2.4375,122900.0,<1H OCEAN\n-118.18,34.05,38.0,3272.0,731.0,3299.0,726.0,2.8295,126500.0,<1H OCEAN\n-118.18,34.05,41.0,762.0,147.0,817.0,176.0,3.75,123100.0,<1H OCEAN\n-118.18,34.05,41.0,616.0,196.0,814.0,180.0,3.3333,115100.0,<1H OCEAN\n-118.18,34.04,36.0,1807.0,630.0,2118.0,669.0,1.55,129000.0,<1H OCEAN\n-118.18,34.04,44.0,1079.0,275.0,1249.0,249.0,3.0417,141700.0,<1H OCEAN\n-118.19,34.04,34.0,1011.0,274.0,1164.0,262.0,2.8542,146900.0,<1H OCEAN\n-118.19,34.04,39.0,1074.0,323.0,1613.0,308.0,2.3015,131700.0,<1H OCEAN\n-118.19,34.05,29.0,855.0,199.0,785.0,169.0,2.6964,122200.0,<1H OCEAN\n-118.17,34.04,44.0,691.0,155.0,613.0,142.0,1.9667,133900.0,<1H OCEAN\n-118.17,34.04,39.0,563.0,138.0,682.0,137.0,2.75,150000.0,<1H OCEAN\n-118.18,34.03,39.0,609.0,145.0,690.0,134.0,2.9167,145800.0,<1H OCEAN\n-118.17,34.04,43.0,908.0,232.0,1005.0,224.0,1.75,134000.0,<1H OCEAN\n-118.17,34.04,45.0,911.0,238.0,1005.0,229.0,2.8167,114000.0,<1H OCEAN\n-118.17,34.05,39.0,962.0,229.0,999.0,221.0,3.375,126000.0,<1H OCEAN\n-118.17,34.05,45.0,733.0,178.0,715.0,165.0,2.5962,124100.0,<1H OCEAN\n-118.18,34.04,42.0,1670.0,,1997.0,452.0,2.788,150500.0,<1H OCEAN\n-118.18,34.03,26.0,859.0,255.0,835.0,232.0,1.1929,143800.0,<1H OCEAN\n-118.19,34.03,27.0,1346.0,340.0,1177.0,295.0,1.7995,153400.0,<1H OCEAN\n-118.19,34.04,40.0,1279.0,316.0,1438.0,329.0,2.1774,157600.0,<1H OCEAN\n-118.19,34.04,43.0,1682.0,422.0,1706.0,409.0,2.1029,153300.0,<1H OCEAN\n-118.19,34.03,31.0,525.0,136.0,627.0,145.0,2.6964,125000.0,<1H OCEAN\n-118.18,34.03,37.0,2115.0,580.0,2842.0,572.0,2.239,121300.0,<1H OCEAN\n-118.19,34.03,42.0,2250.0,629.0,2588.0,609.0,1.9719,134200.0,<1H OCEAN\n-118.18,34.03,40.0,2631.0,698.0,2920.0,677.0,2.0764,145600.0,<1H OCEAN\n-118.19,34.02,45.0,1535.0,432.0,1820.0,419.0,1.7801,142800.0,<1H OCEAN\n-118.18,34.02,36.0,1138.0,296.0,1484.0,320.0,2.2813,150700.0,<1H OCEAN\n-118.18,34.02,37.0,2631.0,734.0,3228.0,701.0,2.15,132200.0,<1H OCEAN\n-118.19,34.02,40.0,474.0,124.0,546.0,121.0,2.3438,137500.0,<1H OCEAN\n-118.18,34.02,35.0,661.0,142.0,720.0,143.0,2.8977,142500.0,<1H OCEAN\n-118.19,34.02,34.0,1478.0,369.0,1735.0,348.0,1.8875,136700.0,<1H OCEAN\n-118.18,34.02,33.0,832.0,226.0,987.0,220.0,3.0972,125000.0,<1H OCEAN\n-118.18,34.02,43.0,887.0,219.0,965.0,217.0,2.625,133900.0,<1H OCEAN\n-118.18,34.01,42.0,1845.0,497.0,2191.0,492.0,2.3462,127300.0,<1H OCEAN\n-118.17,34.03,41.0,2099.0,530.0,2325.0,528.0,2.1979,140800.0,<1H OCEAN\n-118.17,34.03,43.0,1636.0,506.0,1855.0,502.0,2.2902,152400.0,<1H OCEAN\n-118.17,34.03,42.0,882.0,292.0,1248.0,281.0,2.761,120000.0,<1H OCEAN\n-118.18,34.03,44.0,1629.0,420.0,1893.0,387.0,2.2991,137500.0,<1H OCEAN\n-118.17,34.02,41.0,676.0,216.0,851.0,199.0,2.3077,140600.0,<1H OCEAN\n-118.17,34.02,34.0,760.0,219.0,968.0,202.0,1.7813,145000.0,<1H OCEAN\n-118.17,34.02,39.0,759.0,215.0,883.0,226.0,2.125,143800.0,<1H OCEAN\n-118.17,34.02,33.0,346.0,103.0,488.0,107.0,1.8681,112500.0,<1H OCEAN\n-118.16,34.03,45.0,894.0,231.0,925.0,222.0,2.6042,145000.0,<1H OCEAN\n-118.16,34.03,40.0,2201.0,636.0,2682.0,595.0,2.359,143400.0,<1H OCEAN\n-118.16,34.02,35.0,1734.0,493.0,2053.0,508.0,2.1442,149200.0,<1H OCEAN\n-118.16,34.02,41.0,1256.0,391.0,1511.0,381.0,1.7981,166000.0,<1H OCEAN\n-118.16,34.02,47.0,1055.0,298.0,1303.0,302.0,2.6964,138800.0,<1H OCEAN\n-118.17,34.02,42.0,946.0,272.0,1191.0,261.0,2.45,132000.0,<1H OCEAN\n-118.16,34.02,34.0,1474.0,511.0,1962.0,501.0,1.8715,139600.0,<1H OCEAN\n-118.15,34.02,42.0,2729.0,725.0,3004.0,722.0,2.3438,154300.0,<1H OCEAN\n-118.15,34.02,37.0,2344.0,631.0,2195.0,610.0,2.7022,151900.0,<1H OCEAN\n-118.16,34.02,42.0,814.0,216.0,773.0,208.0,2.5313,156900.0,<1H OCEAN\n-118.16,34.01,37.0,690.0,261.0,952.0,255.0,1.6354,158900.0,<1H OCEAN\n-118.16,34.01,40.0,1552.0,,1919.0,427.0,2.2596,137500.0,<1H OCEAN\n-118.16,34.02,44.0,1218.0,374.0,1175.0,342.0,1.9688,173900.0,<1H OCEAN\n-118.14,34.02,44.0,1715.0,460.0,1740.0,423.0,2.7019,153300.0,<1H OCEAN\n-118.14,34.02,40.0,1912.0,502.0,2077.0,500.0,2.6,180600.0,<1H OCEAN\n-118.13,34.02,36.0,984.0,275.0,1024.0,284.0,2.125,153500.0,<1H OCEAN\n-118.14,34.01,42.0,1007.0,277.0,1060.0,268.0,3.0179,153700.0,<1H OCEAN\n-118.14,34.02,42.0,1384.0,458.0,1825.0,455.0,1.4178,145500.0,<1H OCEAN\n-118.14,34.01,46.0,1746.0,447.0,1296.0,392.0,2.3929,156800.0,<1H OCEAN\n-118.14,34.01,42.0,1973.0,510.0,1841.0,502.0,2.5326,156500.0,<1H OCEAN\n-118.13,34.01,45.0,1179.0,268.0,736.0,252.0,2.7083,161800.0,<1H OCEAN\n-118.13,34.01,40.0,2412.0,629.0,2119.0,600.0,2.075,151100.0,<1H OCEAN\n-118.13,34.01,43.0,782.0,207.0,827.0,223.0,3.1538,154300.0,<1H OCEAN\n-118.11,34.01,41.0,815.0,252.0,775.0,231.0,2.2847,190000.0,<1H OCEAN\n-118.12,34.0,31.0,3281.0,768.0,2385.0,733.0,2.7308,173800.0,<1H OCEAN\n-118.12,34.01,40.0,1417.0,338.0,1068.0,331.0,2.4259,164600.0,<1H OCEAN\n-118.12,34.01,33.0,1956.0,478.0,1472.0,464.0,1.9867,166300.0,<1H OCEAN\n-118.1,34.0,32.0,2122.0,591.0,1929.0,539.0,2.7311,169300.0,<1H OCEAN\n-118.11,34.0,24.0,2403.0,590.0,2103.0,547.0,2.7292,193800.0,<1H OCEAN\n-118.11,34.0,38.0,2573.0,484.0,1568.0,459.0,3.0208,193700.0,<1H OCEAN\n-118.11,34.0,33.0,2886.0,726.0,2650.0,728.0,2.625,178700.0,<1H OCEAN\n-118.11,34.01,22.0,1141.0,332.0,1189.0,321.0,2.2042,162500.0,<1H OCEAN\n-118.12,33.99,27.0,2316.0,559.0,2012.0,544.0,2.8155,176800.0,<1H OCEAN\n-118.12,33.99,26.0,2296.0,534.0,1777.0,507.0,2.5395,191000.0,<1H OCEAN\n-118.12,33.98,44.0,932.0,179.0,717.0,180.0,3.6875,178100.0,<1H OCEAN\n-118.12,33.99,24.0,1705.0,479.0,2037.0,459.0,2.4219,137500.0,<1H OCEAN\n-118.14,34.01,36.0,702.0,210.0,834.0,216.0,2.25,162500.0,<1H OCEAN\n-118.15,33.98,17.0,3361.0,925.0,3264.0,914.0,2.2813,145600.0,<1H OCEAN\n-118.15,34.0,32.0,3218.0,739.0,2368.0,730.0,3.1406,175300.0,<1H OCEAN\n-118.16,34.0,37.0,1341.0,336.0,1233.0,306.0,3.6583,150500.0,<1H OCEAN\n-118.17,34.01,30.0,1228.0,358.0,1603.0,323.0,3.0225,130800.0,<1H OCEAN\n-118.16,34.01,36.0,931.0,246.0,732.0,235.0,1.7679,142800.0,<1H OCEAN\n-118.17,34.01,36.0,1657.0,425.0,1689.0,418.0,2.7799,149300.0,<1H OCEAN\n-118.18,34.01,39.0,322.0,82.0,319.0,90.0,2.6364,148800.0,<1H OCEAN\n-118.21,33.99,39.0,47.0,16.0,51.0,23.0,3.2188,112500.0,<1H OCEAN\n-118.23,34.0,35.0,167.0,60.0,267.0,55.0,1.5227,350000.0,<1H OCEAN\n-118.23,33.99,37.0,378.0,176.0,714.0,156.0,2.1912,112500.0,<1H OCEAN\n-118.22,33.99,24.0,1402.0,482.0,1976.0,466.0,2.6964,163200.0,<1H OCEAN\n-118.22,33.99,6.0,1499.0,437.0,1754.0,447.0,4.3164,143200.0,<1H OCEAN\n-118.22,33.99,4.0,1849.0,577.0,1529.0,418.0,2.7708,186300.0,<1H OCEAN\n-118.22,33.98,34.0,2283.0,809.0,3032.0,832.0,2.4387,175000.0,<1H OCEAN\n-118.22,33.98,27.0,1095.0,340.0,1300.0,318.0,2.6548,123200.0,<1H OCEAN\n-118.22,33.98,15.0,1011.0,274.0,899.0,219.0,2.7045,190600.0,<1H OCEAN\n-118.23,33.98,25.0,986.0,310.0,1439.0,251.0,2.39,183300.0,<1H OCEAN\n-118.23,33.99,5.0,706.0,203.0,839.0,199.0,4.5208,165000.0,<1H OCEAN\n-118.23,33.98,30.0,2562.0,959.0,3909.0,955.0,1.9929,150600.0,<1H OCEAN\n-118.24,33.99,28.0,312.0,89.0,498.0,87.0,2.4107,96400.0,<1H OCEAN\n-118.24,33.98,30.0,861.0,250.0,1062.0,231.0,1.75,115400.0,<1H OCEAN\n-118.24,33.99,33.0,885.0,294.0,1270.0,282.0,2.1615,118800.0,<1H OCEAN\n-118.25,33.99,41.0,2215.0,544.0,2054.0,480.0,1.5272,100300.0,<1H OCEAN\n-118.25,33.99,41.0,1486.0,509.0,2312.0,541.0,1.3963,92900.0,<1H OCEAN\n-118.25,33.98,47.0,617.0,162.0,754.0,144.0,2.2969,116700.0,<1H OCEAN\n-118.25,33.98,44.0,1087.0,335.0,1441.0,310.0,1.6667,112500.0,<1H OCEAN\n-118.25,33.98,40.0,1867.0,633.0,2223.0,609.0,1.7207,105100.0,<1H OCEAN\n-118.25,33.98,37.0,1045.0,361.0,1666.0,337.0,1.7929,97200.0,<1H OCEAN\n-118.25,33.98,39.0,1553.0,461.0,2271.0,437.0,1.7378,121900.0,<1H OCEAN\n-118.25,33.98,37.0,1503.0,392.0,1886.0,401.0,2.5637,125000.0,<1H OCEAN\n-118.24,33.98,37.0,1196.0,364.0,1622.0,327.0,2.125,108900.0,<1H OCEAN\n-118.24,33.98,45.0,972.0,249.0,1288.0,261.0,2.2054,125000.0,<1H OCEAN\n-118.24,33.98,45.0,173.0,42.0,230.0,57.0,3.0724,110700.0,<1H OCEAN\n-118.22,33.98,30.0,1971.0,645.0,2650.0,605.0,2.0357,169900.0,<1H OCEAN\n-118.22,33.98,42.0,626.0,143.0,625.0,156.0,3.125,166300.0,<1H OCEAN\n-118.22,33.98,32.0,2643.0,737.0,2784.0,711.0,2.5352,184400.0,<1H OCEAN\n-118.22,33.98,36.0,1514.0,453.0,1496.0,448.0,2.1044,148200.0,<1H OCEAN\n-118.22,33.98,18.0,1781.0,765.0,1913.0,702.0,1.2059,255000.0,<1H OCEAN\n-118.22,33.98,34.0,2225.0,753.0,2980.0,736.0,1.6685,128800.0,<1H OCEAN\n-118.23,33.98,35.0,1366.0,496.0,2160.0,497.0,2.2059,150000.0,<1H OCEAN\n-118.2,33.98,38.0,867.0,243.0,950.0,235.0,1.8929,163100.0,<1H OCEAN\n-118.21,33.97,35.0,1863.0,537.0,2274.0,510.0,2.1005,171300.0,<1H OCEAN\n-118.21,33.98,39.0,1315.0,306.0,1257.0,298.0,3.2788,169000.0,<1H OCEAN\n-118.21,33.98,37.0,788.0,215.0,883.0,221.0,2.6818,164600.0,<1H OCEAN\n-118.21,33.98,35.0,1705.0,562.0,2212.0,539.0,2.325,161500.0,<1H OCEAN\n-118.2,33.99,35.0,1705.0,523.0,2252.0,508.0,2.3421,154200.0,<1H OCEAN\n-118.2,33.99,33.0,1134.0,375.0,1615.0,354.0,2.1468,141700.0,<1H OCEAN\n-118.19,33.99,38.0,1212.0,272.0,1129.0,263.0,2.6673,142300.0,<1H OCEAN\n-118.19,33.99,40.0,1547.0,434.0,1930.0,427.0,3.3869,157300.0,<1H OCEAN\n-118.19,33.98,40.0,973.0,272.0,1257.0,258.0,2.8214,158000.0,<1H OCEAN\n-118.19,33.99,42.0,1429.0,436.0,1537.0,389.0,3.0114,157500.0,<1H OCEAN\n-118.19,33.99,37.0,2073.0,614.0,2544.0,598.0,2.9054,156300.0,<1H OCEAN\n-118.2,33.99,35.0,1608.0,465.0,2140.0,488.0,3.1979,154700.0,<1H OCEAN\n-118.2,33.99,30.0,1474.0,459.0,1844.0,464.0,2.551,160000.0,<1H OCEAN\n-118.19,33.99,35.0,1172.0,436.0,1741.0,408.0,2.4596,154700.0,<1H OCEAN\n-118.19,33.98,34.0,1022.0,286.0,1058.0,275.0,2.6042,156700.0,<1H OCEAN\n-118.19,33.99,36.0,1273.0,379.0,1398.0,353.0,2.4516,147800.0,<1H OCEAN\n-118.2,33.99,31.0,1186.0,387.0,2087.0,409.0,1.9132,154600.0,<1H OCEAN\n-118.2,33.98,43.0,1091.0,320.0,1418.0,316.0,2.1522,159400.0,<1H OCEAN\n-118.19,33.98,33.0,151.0,83.0,380.0,83.0,1.4224,189600.0,<1H OCEAN\n-118.19,33.97,30.0,1790.0,556.0,1827.0,520.0,1.7562,181300.0,<1H OCEAN\n-118.19,33.97,34.0,2700.0,763.0,2815.0,767.0,2.4196,178400.0,<1H OCEAN\n-118.19,33.98,36.0,4179.0,,4582.0,1196.0,2.0087,172100.0,<1H OCEAN\n-118.2,33.98,32.0,1403.0,399.0,1506.0,375.0,2.0,172700.0,<1H OCEAN\n-118.2,33.98,30.0,2369.0,753.0,3259.0,770.0,2.1964,158500.0,<1H OCEAN\n-118.2,33.97,30.0,1911.0,562.0,2055.0,534.0,2.3917,154600.0,<1H OCEAN\n-118.18,33.99,36.0,988.0,337.0,1508.0,351.0,2.4375,154800.0,<1H OCEAN\n-118.18,33.99,38.0,1010.0,315.0,1157.0,301.0,1.6341,161800.0,<1H OCEAN\n-118.17,33.98,27.0,1871.0,556.0,2542.0,581.0,2.8427,164400.0,<1H OCEAN\n-118.18,33.98,24.0,1880.0,642.0,2646.0,605.0,2.1836,162000.0,<1H OCEAN\n-118.18,33.98,30.0,1735.0,573.0,2237.0,545.0,2.3444,156100.0,<1H OCEAN\n-118.18,33.99,35.0,1230.0,407.0,1512.0,364.0,2.152,170800.0,<1H OCEAN\n-118.17,33.98,41.0,428.0,111.0,585.0,139.0,3.1786,132100.0,<1H OCEAN\n-118.17,33.97,31.0,3388.0,1059.0,3558.0,957.0,2.4049,159000.0,<1H OCEAN\n-118.17,33.97,33.0,2410.0,641.0,2106.0,593.0,2.2422,168200.0,<1H OCEAN\n-118.17,33.98,36.0,627.0,177.0,834.0,175.0,2.9844,163600.0,<1H OCEAN\n-118.17,33.98,41.0,756.0,,873.0,212.0,2.7321,156000.0,<1H OCEAN\n-118.18,33.98,36.0,903.0,266.0,1068.0,251.0,3.0398,165400.0,<1H OCEAN\n-118.18,33.97,30.0,2887.0,866.0,2806.0,830.0,2.2122,169400.0,<1H OCEAN\n-118.18,33.97,34.0,3214.0,899.0,3086.0,808.0,2.0057,189400.0,<1H OCEAN\n-118.18,33.98,40.0,1698.0,431.0,1280.0,405.0,2.625,206300.0,<1H OCEAN\n-118.18,33.98,38.0,1477.0,374.0,1514.0,408.0,2.5703,178600.0,<1H OCEAN\n-118.15,33.98,37.0,1184.0,290.0,1320.0,276.0,2.3,165600.0,<1H OCEAN\n-118.15,33.97,32.0,1174.0,373.0,1758.0,361.0,2.4263,158100.0,<1H OCEAN\n-118.15,33.97,33.0,1903.0,469.0,1882.0,435.0,2.4071,170500.0,<1H OCEAN\n-118.16,33.97,23.0,1516.0,457.0,1977.0,435.0,2.3068,157800.0,<1H OCEAN\n-118.16,33.97,30.0,2419.0,715.0,3208.0,719.0,2.1743,176000.0,<1H OCEAN\n-118.14,33.97,29.0,1846.0,530.0,2576.0,528.0,2.63,156000.0,<1H OCEAN\n-118.14,33.97,31.0,1161.0,267.0,1175.0,282.0,3.0114,177000.0,<1H OCEAN\n-118.14,33.96,38.0,590.0,139.0,620.0,132.0,2.1731,143800.0,<1H OCEAN\n-118.15,33.97,32.0,927.0,250.0,970.0,248.0,2.1591,181500.0,<1H OCEAN\n-118.14,33.97,31.0,2064.0,612.0,2461.0,573.0,2.0524,160800.0,<1H OCEAN\n-118.14,33.97,36.0,1407.0,385.0,1763.0,350.0,2.6364,150000.0,<1H OCEAN\n-118.16,33.98,33.0,1196.0,313.0,1448.0,320.0,2.9375,162500.0,<1H OCEAN\n-118.16,33.97,39.0,1444.0,447.0,1890.0,416.0,2.1181,176600.0,<1H OCEAN\n-118.16,33.97,32.0,1347.0,434.0,1756.0,438.0,1.9464,190600.0,<1H OCEAN\n-118.16,33.97,31.0,1363.0,428.0,1897.0,364.0,2.3929,191100.0,<1H OCEAN\n-118.16,33.97,13.0,221.0,63.0,286.0,64.0,1.9063,175000.0,<1H OCEAN\n-118.17,33.98,31.0,1236.0,329.0,1486.0,337.0,3.0938,155400.0,<1H OCEAN\n-118.15,33.96,33.0,1201.0,340.0,1482.0,334.0,2.4821,150000.0,<1H OCEAN\n-118.15,33.96,33.0,1471.0,451.0,2272.0,482.0,2.5385,160900.0,<1H OCEAN\n-118.17,33.96,25.0,2249.0,681.0,2621.0,628.0,2.3,164200.0,<1H OCEAN\n-118.16,33.96,24.0,1635.0,507.0,2480.0,481.0,2.4432,187500.0,<1H OCEAN\n-118.17,33.96,25.0,3297.0,1066.0,5027.0,1041.0,2.2817,164200.0,<1H OCEAN\n-118.17,33.96,29.0,2913.0,787.0,3803.0,740.0,2.5556,146500.0,<1H OCEAN\n-118.18,33.96,20.0,427.0,118.0,402.0,105.0,1.4167,137500.0,<1H OCEAN\n-118.19,33.96,28.0,3507.0,969.0,3740.0,970.0,2.0162,142000.0,<1H OCEAN\n-118.18,33.97,26.0,6895.0,1877.0,8551.0,1808.0,2.3175,154500.0,<1H OCEAN\n-118.19,33.97,27.0,2911.0,972.0,3559.0,945.0,1.9485,146300.0,<1H OCEAN\n-118.2,33.97,28.0,2474.0,702.0,2830.0,694.0,2.754,166200.0,<1H OCEAN\n-118.2,33.97,43.0,825.0,212.0,820.0,184.0,1.8897,174300.0,<1H OCEAN\n-118.2,33.96,44.0,3114.0,779.0,2959.0,776.0,3.1875,171700.0,<1H OCEAN\n-118.21,33.96,38.0,2090.0,519.0,1871.0,504.0,2.4688,169000.0,<1H OCEAN\n-118.21,33.97,49.0,1409.0,313.0,1268.0,317.0,3.9408,170600.0,<1H OCEAN\n-118.21,33.97,43.0,1751.0,400.0,1558.0,379.0,3.0313,166100.0,<1H OCEAN\n-118.21,33.97,52.0,4220.0,908.0,3731.0,892.0,3.1901,167600.0,<1H OCEAN\n-118.22,33.97,43.0,381.0,67.0,259.0,60.0,3.0313,166100.0,<1H OCEAN\n-118.22,33.97,47.0,1147.0,297.0,1097.0,307.0,2.6384,162900.0,<1H OCEAN\n-118.23,33.97,44.0,2748.0,715.0,2962.0,703.0,2.6951,169300.0,<1H OCEAN\n-118.23,33.96,44.0,3186.0,876.0,3913.0,842.0,3.0143,148200.0,<1H OCEAN\n-118.22,33.97,47.0,1688.0,386.0,1663.0,381.0,4.0609,171300.0,<1H OCEAN\n-118.22,33.97,47.0,1058.0,295.0,1097.0,274.0,2.881,183300.0,<1H OCEAN\n-118.23,33.97,47.0,932.0,295.0,1226.0,264.0,1.6065,111400.0,<1H OCEAN\n-118.24,33.97,38.0,1657.0,467.0,2033.0,443.0,2.1429,118500.0,<1H OCEAN\n-118.24,33.97,43.0,1357.0,349.0,1657.0,331.0,2.0819,111800.0,<1H OCEAN\n-118.24,33.97,41.0,1182.0,346.0,1644.0,346.0,2.1473,115100.0,<1H OCEAN\n-118.24,33.97,37.0,1053.0,263.0,1354.0,292.0,2.5833,112500.0,<1H OCEAN\n-118.25,33.97,32.0,879.0,257.0,1057.0,230.0,1.6776,114800.0,<1H OCEAN\n-118.25,33.97,36.0,1026.0,294.0,1316.0,268.0,1.7708,102600.0,<1H OCEAN\n-118.25,33.97,39.0,1346.0,380.0,1520.0,356.0,1.1635,108700.0,<1H OCEAN\n-118.25,33.97,43.0,1735.0,535.0,2288.0,524.0,1.9119,98800.0,<1H OCEAN\n-118.24,33.97,37.0,1212.0,314.0,1403.0,279.0,2.5536,117200.0,<1H OCEAN\n-118.24,33.96,30.0,859.0,221.0,912.0,191.0,1.9041,105100.0,<1H OCEAN\n-118.25,33.97,37.0,794.0,210.0,814.0,213.0,2.2917,112000.0,<1H OCEAN\n-118.25,33.96,43.0,1876.0,454.0,1571.0,458.0,2.0323,112500.0,<1H OCEAN\n-118.25,33.97,38.0,1231.0,346.0,1217.0,354.0,1.8661,106600.0,<1H OCEAN\n-118.24,33.96,34.0,1724.0,432.0,1876.0,416.0,2.1078,100600.0,<1H OCEAN\n-118.25,33.96,48.0,1052.0,234.0,793.0,216.0,1.6585,92900.0,<1H OCEAN\n-118.25,33.96,42.0,1326.0,295.0,918.0,258.0,2.3864,98800.0,<1H OCEAN\n-118.25,33.96,43.0,2015.0,419.0,1543.0,399.0,1.8672,98100.0,<1H OCEAN\n-118.25,33.95,48.0,1766.0,424.0,1655.0,420.0,0.9751,95500.0,<1H OCEAN\n-118.25,33.95,41.0,1576.0,339.0,1252.0,302.0,1.9798,98100.0,<1H OCEAN\n-118.23,33.96,39.0,405.0,163.0,686.0,164.0,1.695,94800.0,<1H OCEAN\n-118.23,33.96,36.0,1062.0,270.0,1136.0,273.0,1.6597,109100.0,<1H OCEAN\n-118.24,33.96,44.0,1338.0,366.0,1765.0,388.0,1.7778,109900.0,<1H OCEAN\n-118.24,33.96,39.0,643.0,186.0,821.0,191.0,2.5729,97300.0,<1H OCEAN\n-118.24,33.96,34.0,946.0,254.0,1101.0,239.0,1.7396,105900.0,<1H OCEAN\n-118.24,33.96,37.0,1602.0,388.0,1553.0,342.0,2.0655,93400.0,<1H OCEAN\n-118.23,33.95,42.0,705.0,173.0,739.0,140.0,0.9166,99000.0,<1H OCEAN\n-118.23,33.95,27.0,504.0,142.0,789.0,167.0,0.9518,91400.0,<1H OCEAN\n-118.21,33.96,39.0,2050.0,529.0,1959.0,485.0,2.1389,168900.0,<1H OCEAN\n-118.21,33.96,39.0,2265.0,628.0,2323.0,599.0,2.1522,155300.0,<1H OCEAN\n-118.22,33.96,32.0,2232.0,603.0,2361.0,608.0,2.5966,170900.0,<1H OCEAN\n-118.22,33.96,36.0,1542.0,458.0,1711.0,468.0,1.9028,164200.0,<1H OCEAN\n-118.22,33.96,35.0,1437.0,474.0,2113.0,484.0,2.6179,158800.0,<1H OCEAN\n-118.22,33.96,42.0,1380.0,331.0,1290.0,288.0,2.8,161800.0,<1H OCEAN\n-118.21,33.95,38.0,1889.0,565.0,2087.0,559.0,1.7778,154000.0,<1H OCEAN\n-118.22,33.95,36.0,1679.0,483.0,2249.0,487.0,2.8167,160400.0,<1H OCEAN\n-118.22,33.95,42.0,3896.0,981.0,4496.0,993.0,3.153,150900.0,<1H OCEAN\n-118.23,33.96,42.0,1977.0,570.0,2406.0,557.0,2.5913,151600.0,<1H OCEAN\n-118.23,33.95,43.0,1683.0,520.0,2190.0,494.0,2.2391,152800.0,<1H OCEAN\n-118.22,33.94,42.0,1046.0,287.0,1218.0,289.0,2.6538,143400.0,<1H OCEAN\n-118.22,33.94,42.0,1115.0,297.0,1412.0,325.0,3.0903,153500.0,<1H OCEAN\n-118.22,33.94,38.0,788.0,224.0,1155.0,208.0,3.3542,153800.0,<1H OCEAN\n-118.22,33.94,41.0,928.0,249.0,1108.0,236.0,3.4323,144600.0,<1H OCEAN\n-118.2,33.96,44.0,2144.0,477.0,1760.0,452.0,2.3221,161600.0,<1H OCEAN\n-118.2,33.96,43.0,1233.0,306.0,1190.0,282.0,2.8371,161300.0,<1H OCEAN\n-118.2,33.96,41.0,1512.0,400.0,1690.0,367.0,3.055,167000.0,<1H OCEAN\n-118.2,33.96,37.0,2127.0,533.0,2021.0,480.0,2.9773,164600.0,<1H OCEAN\n-118.21,33.96,43.0,1686.0,446.0,1590.0,474.0,2.3241,159300.0,<1H OCEAN\n-118.21,33.96,48.0,284.0,104.0,422.0,119.0,1.2826,145500.0,<1H OCEAN\n-118.21,33.95,43.0,1500.0,419.0,1726.0,440.0,1.8641,165100.0,<1H OCEAN\n-118.2,33.95,35.0,1924.0,520.0,2101.0,541.0,2.4267,151500.0,<1H OCEAN\n-118.2,33.95,41.0,679.0,184.0,788.0,185.0,2.1406,165300.0,<1H OCEAN\n-118.21,33.95,32.0,1116.0,328.0,1265.0,302.0,2.295,155200.0,<1H OCEAN\n-118.21,33.95,35.0,2134.0,650.0,2248.0,587.0,2.2988,153400.0,<1H OCEAN\n-118.21,33.95,35.0,2129.0,614.0,2376.0,618.0,2.0372,160800.0,<1H OCEAN\n-118.2,33.94,42.0,618.0,163.0,680.0,179.0,3.3472,154200.0,<1H OCEAN\n-118.2,33.94,45.0,1818.0,408.0,1705.0,373.0,4.0441,157500.0,<1H OCEAN\n-118.21,33.94,40.0,2227.0,594.0,2244.0,580.0,2.4459,143800.0,<1H OCEAN\n-118.21,33.94,41.0,1807.0,442.0,1628.0,443.0,2.84,156100.0,<1H OCEAN\n-118.19,33.95,44.0,1436.0,271.0,850.0,269.0,3.2768,179100.0,<1H OCEAN\n-118.19,33.94,45.0,1871.0,371.0,1315.0,382.0,3.3661,160800.0,<1H OCEAN\n-118.19,33.94,45.0,1403.0,315.0,1111.0,311.0,3.3846,168100.0,<1H OCEAN\n-118.19,33.95,41.0,1368.0,309.0,1244.0,312.0,3.0833,164800.0,<1H OCEAN\n-118.19,33.95,42.0,1651.0,463.0,1559.0,436.0,2.3882,148100.0,<1H OCEAN\n-118.2,33.94,44.0,1413.0,298.0,1200.0,307.0,3.5125,169300.0,<1H OCEAN\n-118.2,33.94,45.0,1570.0,328.0,1321.0,300.0,3.7361,171800.0,<1H OCEAN\n-118.2,33.94,43.0,1934.0,511.0,1895.0,493.0,2.5029,159700.0,<1H OCEAN\n-118.19,33.96,40.0,979.0,296.0,934.0,292.0,2.6354,151800.0,<1H OCEAN\n-118.19,33.95,42.0,2309.0,685.0,2609.0,673.0,2.7206,162100.0,<1H OCEAN\n-118.16,33.94,32.0,2210.0,456.0,1270.0,484.0,4.7708,178600.0,<1H OCEAN\n-118.16,33.93,35.0,757.0,151.0,474.0,132.0,3.7361,179800.0,<1H OCEAN\n-118.18,33.94,44.0,1337.0,245.0,968.0,240.0,3.4688,183600.0,<1H OCEAN\n-118.18,33.95,39.0,2121.0,579.0,1991.0,528.0,2.9094,152200.0,<1H OCEAN\n-118.18,33.95,42.0,2608.0,610.0,2062.0,616.0,3.5341,167500.0,<1H OCEAN\n-118.18,33.94,43.0,2724.0,612.0,2340.0,570.0,2.7,165000.0,<1H OCEAN\n-118.17,33.94,17.0,1145.0,209.0,499.0,202.0,4.6389,165500.0,<1H OCEAN\n-118.17,33.95,23.0,1991.0,584.0,1380.0,535.0,1.9107,181900.0,<1H OCEAN\n-118.17,33.92,36.0,2447.0,503.0,1532.0,498.0,4.3667,171800.0,<1H OCEAN\n-118.17,33.92,43.0,2099.0,398.0,1276.0,387.0,3.1528,166800.0,<1H OCEAN\n-118.16,33.92,44.0,1368.0,277.0,899.0,271.0,3.5938,161300.0,<1H OCEAN\n-118.16,33.91,41.0,1806.0,408.0,1146.0,374.0,2.9643,162200.0,<1H OCEAN\n-118.17,33.91,39.0,1157.0,273.0,877.0,305.0,3.1087,171000.0,<1H OCEAN\n-118.18,33.92,32.0,2035.0,519.0,2282.0,480.0,3.2734,136400.0,<1H OCEAN\n-118.18,33.92,29.0,749.0,185.0,708.0,196.0,2.4583,136900.0,<1H OCEAN\n-118.19,33.92,36.0,1356.0,314.0,1469.0,300.0,2.0785,139800.0,<1H OCEAN\n-118.18,33.93,31.0,1516.0,400.0,1820.0,398.0,2.1641,122900.0,<1H OCEAN\n-118.18,33.93,35.0,952.0,271.0,949.0,261.0,2.4297,147200.0,<1H OCEAN\n-118.19,33.93,40.0,1334.0,276.0,1226.0,278.0,3.4712,144300.0,<1H OCEAN\n-118.19,33.93,42.0,1829.0,391.0,1614.0,377.0,3.1912,146400.0,<1H OCEAN\n-118.2,33.93,40.0,1929.0,417.0,1780.0,419.0,3.4402,149400.0,<1H OCEAN\n-118.19,33.93,44.0,1613.0,345.0,1227.0,342.0,3.1667,145700.0,<1H OCEAN\n-118.19,33.92,43.0,2339.0,487.0,1732.0,449.0,3.0987,139400.0,<1H OCEAN\n-118.2,33.92,42.0,1411.0,314.0,1432.0,322.0,3.0871,138800.0,<1H OCEAN\n-118.2,33.93,38.0,1626.0,307.0,1280.0,295.0,3.5313,146500.0,<1H OCEAN\n-118.21,33.94,34.0,892.0,318.0,1443.0,341.0,2.1903,162500.0,<1H OCEAN\n-118.21,33.94,34.0,710.0,205.0,1134.0,233.0,2.7734,141100.0,<1H OCEAN\n-118.21,33.93,30.0,2831.0,862.0,3649.0,883.0,1.9668,152100.0,<1H OCEAN\n-118.2,33.93,36.0,2210.0,634.0,2341.0,553.0,2.1715,131100.0,<1H OCEAN\n-118.21,33.93,33.0,2739.0,801.0,3423.0,741.0,2.2847,132700.0,<1H OCEAN\n-118.2,33.93,41.0,857.0,201.0,934.0,227.0,2.6339,145700.0,<1H OCEAN\n-118.2,33.93,36.0,1191.0,345.0,1193.0,295.0,2.5185,138800.0,<1H OCEAN\n-118.21,33.93,36.0,1337.0,382.0,1769.0,393.0,2.6953,121000.0,<1H OCEAN\n-118.21,33.93,39.0,354.0,73.0,184.0,58.0,2.7679,108900.0,<1H OCEAN\n-118.22,33.93,39.0,1921.0,483.0,2286.0,470.0,3.0167,130000.0,<1H OCEAN\n-118.22,33.94,40.0,930.0,258.0,1203.0,244.0,2.5938,115400.0,<1H OCEAN\n-118.23,33.93,23.0,545.0,131.0,610.0,126.0,1.4861,95100.0,<1H OCEAN\n-118.23,33.93,30.0,1147.0,260.0,1219.0,210.0,2.0658,93200.0,<1H OCEAN\n-118.22,33.93,30.0,443.0,170.0,903.0,189.0,2.1964,125000.0,<1H OCEAN\n-118.21,33.93,41.0,619.0,138.0,636.0,145.0,2.5083,118100.0,<1H OCEAN\n-118.21,33.92,37.0,1705.0,403.0,1839.0,410.0,2.5833,132700.0,<1H OCEAN\n-118.21,33.92,35.0,1669.0,445.0,1870.0,412.0,3.0417,117300.0,<1H OCEAN\n-118.21,33.92,28.0,2949.0,1003.0,4551.0,930.0,1.9026,131900.0,<1H OCEAN\n-118.22,33.92,43.0,1195.0,256.0,1251.0,262.0,3.4539,125000.0,<1H OCEAN\n-118.23,33.93,37.0,239.0,49.0,308.0,52.0,1.4028,105400.0,<1H OCEAN\n-118.23,33.93,35.0,1149.0,277.0,909.0,214.0,1.7411,96700.0,<1H OCEAN\n-118.23,33.92,32.0,2698.0,640.0,1953.0,613.0,1.2222,107200.0,<1H OCEAN\n-118.24,33.93,19.0,325.0,74.0,354.0,87.0,2.75,90600.0,<1H OCEAN\n-118.24,33.92,42.0,328.0,100.0,605.0,87.0,2.4464,97400.0,<1H OCEAN\n-118.24,33.92,44.0,1079.0,210.0,601.0,182.0,2.2411,106400.0,<1H OCEAN\n-118.25,33.93,38.0,180.0,43.0,246.0,56.0,2.85,90000.0,<1H OCEAN\n-118.25,33.93,27.0,581.0,135.0,647.0,131.0,3.2917,83100.0,<1H OCEAN\n-118.25,33.93,42.0,763.0,191.0,754.0,174.0,2.0486,101800.0,<1H OCEAN\n-118.25,33.92,46.0,723.0,154.0,411.0,165.0,2.0893,96500.0,<1H OCEAN\n-118.25,33.92,44.0,1737.0,363.0,1184.0,343.0,2.5363,95900.0,<1H OCEAN\n-118.25,33.92,44.0,1137.0,235.0,747.0,225.0,2.0,92600.0,<1H OCEAN\n-118.26,33.92,42.0,3320.0,682.0,2105.0,632.0,1.9809,104600.0,<1H OCEAN\n-118.26,33.91,44.0,892.0,139.0,440.0,159.0,2.8859,120800.0,<1H OCEAN\n-118.27,33.92,34.0,1178.0,260.0,1166.0,244.0,1.9185,93300.0,<1H OCEAN\n-118.27,33.92,35.0,1818.0,374.0,1444.0,372.0,2.745,106800.0,<1H OCEAN\n-118.28,33.92,39.0,1472.0,302.0,1036.0,318.0,3.0,110000.0,<1H OCEAN\n-118.28,33.92,37.0,742.0,151.0,729.0,144.0,3.055,105400.0,<1H OCEAN\n-118.27,33.91,42.0,1786.0,358.0,1318.0,373.0,2.625,101100.0,<1H OCEAN\n-118.27,33.91,37.0,3018.0,547.0,1720.0,512.0,2.7269,124100.0,<1H OCEAN\n-118.27,33.91,32.0,2238.0,471.0,1292.0,467.0,1.1705,110600.0,<1H OCEAN\n-118.27,33.89,32.0,1969.0,397.0,1349.0,370.0,4.4659,138100.0,<1H OCEAN\n-118.27,33.87,21.0,6108.0,1130.0,3244.0,1113.0,4.2768,181400.0,<1H OCEAN\n-118.25,33.9,42.0,1386.0,320.0,1163.0,319.0,2.4271,89500.0,<1H OCEAN\n-118.26,33.9,38.0,1566.0,318.0,981.0,318.0,4.0234,111900.0,<1H OCEAN\n-118.26,33.9,22.0,894.0,232.0,754.0,222.0,2.0096,110700.0,<1H OCEAN\n-118.25,33.92,36.0,949.0,164.0,502.0,163.0,4.1042,124400.0,<1H OCEAN\n-118.25,33.91,36.0,1950.0,365.0,1125.0,374.0,3.1111,119300.0,<1H OCEAN\n-118.25,33.9,36.0,1135.0,231.0,614.0,227.0,2.5521,113100.0,<1H OCEAN\n-118.25,33.91,35.0,1479.0,272.0,963.0,292.0,3.4917,109500.0,<1H OCEAN\n-118.26,33.91,39.0,935.0,210.0,711.0,193.0,2.4375,101900.0,<1H OCEAN\n-118.26,33.91,33.0,954.0,241.0,655.0,218.0,2.5882,92800.0,<1H OCEAN\n-118.26,33.91,39.0,967.0,256.0,903.0,256.0,1.9038,93100.0,<1H OCEAN\n-118.24,33.91,40.0,972.0,240.0,761.0,225.0,1.4688,88200.0,<1H OCEAN\n-118.24,33.91,38.0,745.0,152.0,721.0,160.0,1.875,102900.0,<1H OCEAN\n-118.24,33.91,36.0,1446.0,316.0,1286.0,314.0,2.7083,103600.0,<1H OCEAN\n-118.24,33.91,37.0,1607.0,377.0,1526.0,375.0,1.7158,94300.0,<1H OCEAN\n-118.24,33.92,40.0,1772.0,369.0,1122.0,324.0,3.2768,96100.0,<1H OCEAN\n-118.23,33.92,32.0,1735.0,430.0,1699.0,386.0,1.1793,103800.0,<1H OCEAN\n-118.23,33.91,34.0,661.0,146.0,742.0,143.0,2.1734,88200.0,<1H OCEAN\n-118.23,33.91,33.0,677.0,182.0,984.0,174.0,2.5893,88900.0,<1H OCEAN\n-118.23,33.91,34.0,789.0,200.0,1041.0,191.0,3.119,90300.0,<1H OCEAN\n-118.23,33.91,27.0,1694.0,393.0,1890.0,373.0,3.0341,89100.0,<1H OCEAN\n-118.22,33.92,32.0,1263.0,333.0,1789.0,346.0,1.9957,89300.0,<1H OCEAN\n-118.22,33.91,31.0,571.0,153.0,841.0,158.0,2.6154,89200.0,<1H OCEAN\n-118.23,33.91,34.0,1060.0,276.0,1215.0,250.0,2.0804,84700.0,<1H OCEAN\n-118.23,33.92,24.0,1555.0,406.0,1665.0,361.0,1.6437,98800.0,<1H OCEAN\n-118.22,33.92,23.0,926.0,409.0,1856.0,408.0,2.1366,100000.0,<1H OCEAN\n-118.21,33.91,24.0,1545.0,391.0,1807.0,388.0,2.6429,105300.0,<1H OCEAN\n-118.21,33.91,26.0,2422.0,632.0,2601.0,583.0,1.7824,110200.0,<1H OCEAN\n-118.22,33.9,38.0,796.0,159.0,679.0,167.0,3.6607,110400.0,<1H OCEAN\n-118.22,33.91,28.0,1847.0,500.0,2263.0,473.0,1.5161,103200.0,<1H OCEAN\n-118.2,33.9,26.0,1000.0,275.0,1178.0,263.0,2.12,105000.0,<1H OCEAN\n-118.21,33.9,35.0,2420.0,579.0,2010.0,540.0,2.0817,104600.0,<1H OCEAN\n-118.21,33.9,41.0,941.0,233.0,973.0,253.0,1.9583,102300.0,<1H OCEAN\n-118.22,33.9,35.0,1649.0,424.0,1786.0,388.0,1.4091,105600.0,<1H OCEAN\n-118.22,33.9,30.0,1007.0,260.0,1112.0,238.0,1.7262,115600.0,<1H OCEAN\n-118.2,33.92,36.0,414.0,104.0,477.0,130.0,3.6719,130400.0,<1H OCEAN\n-118.2,33.92,45.0,1283.0,,1025.0,248.0,3.2798,141200.0,<1H OCEAN\n-118.2,33.92,39.0,1050.0,217.0,895.0,207.0,3.1538,155600.0,<1H OCEAN\n-118.21,33.91,37.0,1073.0,265.0,1197.0,250.0,2.7109,133000.0,<1H OCEAN\n-118.21,33.92,41.0,1722.0,363.0,1432.0,326.0,3.2976,151200.0,<1H OCEAN\n-118.21,33.92,36.0,602.0,150.0,645.0,145.0,3.1964,115400.0,<1H OCEAN\n-118.18,33.91,41.0,1260.0,299.0,1535.0,322.0,3.0134,128100.0,<1H OCEAN\n-118.18,33.91,36.0,1138.0,238.0,878.0,224.0,2.0625,134400.0,<1H OCEAN\n-118.19,33.91,43.0,1531.0,357.0,1509.0,376.0,2.6354,128100.0,<1H OCEAN\n-118.19,33.91,33.0,915.0,225.0,826.0,212.0,2.7708,117400.0,<1H OCEAN\n-118.19,33.92,35.0,915.0,241.0,1153.0,252.0,3.305,115800.0,<1H OCEAN\n-118.19,33.91,35.0,2695.0,748.0,2935.0,706.0,2.0134,132400.0,<1H OCEAN\n-118.2,33.91,43.0,1381.0,278.0,1494.0,298.0,3.5878,118400.0,<1H OCEAN\n-118.2,33.9,33.0,1435.0,322.0,1298.0,299.0,2.7813,105100.0,<1H OCEAN\n-118.2,33.91,36.0,2283.0,499.0,1836.0,462.0,2.8793,118100.0,<1H OCEAN\n-118.19,33.9,36.0,1073.0,271.0,1385.0,288.0,2.3214,104800.0,<1H OCEAN\n-118.19,33.9,32.0,2762.0,652.0,2677.0,632.0,2.5719,105600.0,<1H OCEAN\n-118.19,33.9,36.0,2326.0,543.0,2073.0,494.0,1.9952,112900.0,<1H OCEAN\n-118.19,33.89,31.0,886.0,224.0,1154.0,247.0,2.1071,99500.0,<1H OCEAN\n-118.19,33.89,32.0,1696.0,438.0,1639.0,376.0,2.0357,107300.0,<1H OCEAN\n-118.2,33.89,37.0,2394.0,568.0,2499.0,551.0,2.5321,105100.0,<1H OCEAN\n-118.2,33.9,34.0,1552.0,444.0,2093.0,413.0,2.2125,103200.0,<1H OCEAN\n-118.19,33.89,38.0,4018.0,986.0,3702.0,927.0,2.9293,113600.0,<1H OCEAN\n-118.2,33.89,40.0,2538.0,564.0,2170.0,541.0,2.7212,107900.0,<1H OCEAN\n-118.21,33.89,42.0,1254.0,225.0,929.0,235.0,4.3646,116200.0,<1H OCEAN\n-118.21,33.89,39.0,1565.0,364.0,1389.0,360.0,2.7443,113900.0,<1H OCEAN\n-118.21,33.89,42.0,1739.0,370.0,1104.0,297.0,2.2125,120700.0,<1H OCEAN\n-118.21,33.9,43.0,1810.0,357.0,1335.0,358.0,3.1189,118800.0,<1H OCEAN\n-118.21,33.89,45.0,1211.0,234.0,1128.0,261.0,3.4792,110700.0,<1H OCEAN\n-118.21,33.88,38.0,929.0,166.0,686.0,183.0,3.4485,119400.0,<1H OCEAN\n-118.21,33.88,29.0,1976.0,444.0,1254.0,371.0,2.1782,126800.0,<1H OCEAN\n-118.22,33.89,26.0,266.0,75.0,252.0,59.0,2.1211,138100.0,<1H OCEAN\n-118.22,33.89,41.0,990.0,228.0,776.0,207.0,2.125,120200.0,<1H OCEAN\n-118.22,33.89,37.0,797.0,190.0,485.0,166.0,2.7434,95200.0,<1H OCEAN\n-118.22,33.89,36.0,873.0,240.0,1086.0,217.0,2.25,126600.0,<1H OCEAN\n-118.23,33.89,16.0,5003.0,1180.0,4145.0,1159.0,2.1389,133400.0,<1H OCEAN\n-118.24,33.89,34.0,1479.0,332.0,1166.0,322.0,2.6165,100900.0,<1H OCEAN\n-118.22,33.91,27.0,500.0,159.0,732.0,162.0,2.7426,103100.0,<1H OCEAN\n-118.22,33.9,22.0,312.0,107.0,583.0,119.0,1.9423,98400.0,<1H OCEAN\n-118.22,33.9,40.0,1802.0,496.0,2096.0,468.0,2.3542,97900.0,<1H OCEAN\n-118.23,33.9,31.0,2143.0,522.0,2276.0,519.0,1.8095,100800.0,<1H OCEAN\n-118.23,33.9,34.0,2462.0,553.0,2334.0,502.0,1.641,96800.0,<1H OCEAN\n-118.23,33.9,28.0,1108.0,284.0,1498.0,289.0,2.4706,88800.0,<1H OCEAN\n-118.23,33.9,45.0,1285.0,238.0,840.0,211.0,3.4107,112500.0,<1H OCEAN\n-118.24,33.9,38.0,2055.0,442.0,1518.0,425.0,2.3382,103000.0,<1H OCEAN\n-118.24,33.9,35.0,1079.0,247.0,1055.0,243.0,2.375,93600.0,<1H OCEAN\n-118.24,33.9,39.0,642.0,129.0,475.0,123.0,1.2083,92600.0,<1H OCEAN\n-118.25,33.9,37.0,2119.0,442.0,1372.0,406.0,1.9605,106200.0,<1H OCEAN\n-118.24,33.9,40.0,1308.0,272.0,901.0,257.0,2.8269,98000.0,<1H OCEAN\n-118.25,33.9,38.0,1201.0,223.0,733.0,206.0,3.3804,105800.0,<1H OCEAN\n-118.25,33.89,35.0,1582.0,391.0,1957.0,404.0,2.4537,91500.0,<1H OCEAN\n-118.24,33.89,32.0,1132.0,266.0,1211.0,279.0,2.1838,98300.0,<1H OCEAN\n-118.25,33.89,41.0,1476.0,286.0,1086.0,278.0,2.4632,111700.0,<1H OCEAN\n-118.25,33.89,36.0,406.0,71.0,268.0,77.0,3.9,115800.0,<1H OCEAN\n-118.25,33.89,34.0,1367.0,288.0,1183.0,286.0,2.6812,104100.0,<1H OCEAN\n-118.26,33.89,36.0,2230.0,417.0,1395.0,381.0,2.8493,109600.0,<1H OCEAN\n-118.26,33.89,36.0,923.0,165.0,603.0,191.0,3.5687,120700.0,<1H OCEAN\n-118.24,33.88,37.0,1843.0,366.0,1207.0,351.0,2.4821,111000.0,<1H OCEAN\n-118.25,33.88,37.0,1027.0,217.0,1042.0,254.0,2.2121,98600.0,<1H OCEAN\n-118.25,33.89,37.0,1042.0,213.0,699.0,196.0,2.9643,103200.0,<1H OCEAN\n-118.25,33.89,36.0,1527.0,309.0,1154.0,279.0,3.3095,105500.0,<1H OCEAN\n-118.26,33.88,39.0,1756.0,320.0,1055.0,322.0,3.2375,105200.0,<1H OCEAN\n-118.26,33.88,40.0,519.0,102.0,330.0,95.0,3.0972,108500.0,<1H OCEAN\n-118.26,33.88,36.0,1212.0,222.0,775.0,224.0,5.5591,136500.0,<1H OCEAN\n-118.22,33.88,37.0,1149.0,280.0,1016.0,250.0,2.125,101900.0,<1H OCEAN\n-118.22,33.88,35.0,998.0,313.0,1335.0,311.0,1.6574,102500.0,<1H OCEAN\n-118.23,33.89,35.0,1255.0,344.0,1782.0,343.0,2.1949,95100.0,<1H OCEAN\n-118.23,33.88,35.0,842.0,201.0,763.0,189.0,2.6719,109800.0,<1H OCEAN\n-118.23,33.88,41.0,1941.0,367.0,1204.0,323.0,3.0417,113700.0,<1H OCEAN\n-118.23,33.89,36.0,2598.0,514.0,1872.0,514.0,3.1667,117700.0,<1H OCEAN\n-118.22,33.86,16.0,8732.0,1489.0,3944.0,1493.0,5.1948,203500.0,<1H OCEAN\n-118.24,33.85,25.0,9594.0,1489.0,5237.0,1496.0,5.9684,193300.0,<1H OCEAN\n-118.23,33.84,25.0,1106.0,207.0,888.0,216.0,5.3307,207000.0,<1H OCEAN\n-118.24,33.83,22.0,7368.0,1367.0,4721.0,1342.0,4.8438,213100.0,<1H OCEAN\n-118.25,33.84,19.0,1731.0,420.0,1032.0,364.0,3.8125,208100.0,<1H OCEAN\n-118.25,33.87,18.0,6812.0,1263.0,3704.0,1216.0,4.25,169200.0,<1H OCEAN\n-118.25,33.86,26.0,3022.0,476.0,1852.0,452.0,6.0531,186400.0,<1H OCEAN\n-118.26,33.85,25.0,2324.0,326.0,1087.0,328.0,5.293,207000.0,<1H OCEAN\n-118.26,33.85,24.0,9071.0,1335.0,4558.0,1327.0,5.542,197500.0,<1H OCEAN\n-118.27,33.86,33.0,1685.0,333.0,1484.0,318.0,4.3527,167000.0,<1H OCEAN\n-118.27,33.86,29.0,2587.0,489.0,2115.0,475.0,3.7466,168600.0,<1H OCEAN\n-118.27,33.86,26.0,1097.0,167.0,701.0,188.0,6.5799,196600.0,<1H OCEAN\n-118.28,33.85,27.0,489.0,98.0,403.0,97.0,5.144,180800.0,<1H OCEAN\n-118.28,33.84,27.0,2326.0,533.0,1697.0,546.0,3.8633,187900.0,<1H OCEAN\n-118.28,33.83,18.0,5923.0,1409.0,3887.0,1322.0,3.4712,194400.0,<1H OCEAN\n-118.29,33.84,11.0,2274.0,617.0,1897.0,622.0,3.5094,162900.0,<1H OCEAN\n-118.29,33.84,33.0,896.0,208.0,843.0,200.0,3.5,183000.0,<1H OCEAN\n-118.29,33.84,34.0,2617.0,558.0,1396.0,515.0,5.061,218000.0,<1H OCEAN\n-118.29,33.84,23.0,3626.0,799.0,2321.0,731.0,4.7393,237900.0,<1H OCEAN\n-118.29,33.83,24.0,4092.0,893.0,2819.0,893.0,4.1378,216500.0,<1H OCEAN\n-118.28,33.82,26.0,4586.0,1042.0,3680.0,1027.0,4.174,205100.0,<1H OCEAN\n-118.29,33.82,21.0,4383.0,901.0,2689.0,913.0,3.4375,218800.0,<1H OCEAN\n-118.29,33.81,19.0,7023.0,1538.0,3993.0,1412.0,5.0532,218200.0,<1H OCEAN\n-118.29,33.8,21.0,9944.0,1623.0,4185.0,1582.0,4.526,329400.0,<1H OCEAN\n-118.28,33.82,30.0,3615.0,760.0,2813.0,752.0,5.3849,217700.0,<1H OCEAN\n-118.28,33.81,29.0,2755.0,508.0,2046.0,488.0,5.2034,212400.0,<1H OCEAN\n-118.27,33.82,36.0,1593.0,334.0,1427.0,320.0,4.4015,166900.0,<1H OCEAN\n-118.27,33.82,33.0,1596.0,337.0,1650.0,329.0,4.3687,173500.0,<1H OCEAN\n-118.27,33.82,37.0,943.0,218.0,803.0,216.0,5.2287,191100.0,<1H OCEAN\n-118.27,33.81,10.0,1881.0,571.0,1769.0,553.0,3.9286,114000.0,<1H OCEAN\n-118.27,33.81,42.0,865.0,208.0,811.0,218.0,3.8621,165300.0,<1H OCEAN\n-118.27,33.81,38.0,1607.0,337.0,1130.0,334.0,4.4821,190700.0,<1H OCEAN\n-118.27,33.82,39.0,1357.0,249.0,763.0,229.0,4.25,200300.0,<1H OCEAN\n-118.27,33.8,28.0,4698.0,902.0,3287.0,881.0,4.8508,215900.0,<1H OCEAN\n-118.27,33.84,24.0,6303.0,1277.0,3728.0,1252.0,3.9227,208600.0,<1H OCEAN\n-118.28,33.83,28.0,880.0,168.0,717.0,142.0,4.5469,175700.0,<1H OCEAN\n-118.27,33.83,28.0,2152.0,415.0,1623.0,429.0,4.35,200500.0,<1H OCEAN\n-118.27,33.83,34.0,1124.0,245.0,717.0,242.0,3.1667,186900.0,<1H OCEAN\n-118.26,33.83,24.0,3059.0,,2064.0,629.0,3.5518,184600.0,<1H OCEAN\n-118.27,33.82,28.0,1642.0,434.0,1575.0,420.0,4.1292,201900.0,<1H OCEAN\n-118.26,33.82,28.0,5091.0,1074.0,4753.0,1033.0,3.6477,117400.0,<1H OCEAN\n-118.26,33.83,28.0,4112.0,861.0,3211.0,841.0,4.4539,192200.0,<1H OCEAN\n-118.21,33.84,28.0,822.0,205.0,627.0,192.0,3.4583,166300.0,<1H OCEAN\n-118.22,33.84,35.0,1131.0,273.0,1007.0,269.0,4.0219,168300.0,<1H OCEAN\n-118.22,33.84,38.0,1928.0,429.0,1358.0,399.0,4.0687,160300.0,<1H OCEAN\n-118.22,33.83,42.0,1370.0,299.0,1018.0,328.0,4.4474,160200.0,<1H OCEAN\n-118.22,33.83,44.0,1792.0,404.0,1115.0,358.0,3.9091,174400.0,<1H OCEAN\n-118.22,33.83,43.0,1426.0,272.0,871.0,276.0,3.7083,175200.0,<1H OCEAN\n-118.21,33.83,38.0,793.0,193.0,601.0,187.0,2.8837,176100.0,NEAR OCEAN\n-118.07,33.93,5.0,906.0,187.0,1453.0,158.0,4.125,171900.0,<1H OCEAN\n-118.08,33.93,39.0,859.0,164.0,673.0,172.0,3.7143,158200.0,<1H OCEAN\n-118.08,33.93,39.0,1478.0,324.0,1127.0,320.0,3.525,158000.0,<1H OCEAN\n-118.08,33.92,38.0,1335.0,,1011.0,269.0,3.6908,157500.0,<1H OCEAN\n-118.08,33.92,39.0,1631.0,322.0,1034.0,328.0,4.5382,165700.0,<1H OCEAN\n-118.07,33.92,36.0,1560.0,320.0,1348.0,314.0,3.622,174000.0,<1H OCEAN\n-118.08,33.92,34.0,2118.0,437.0,1414.0,442.0,3.7238,166800.0,<1H OCEAN\n-118.08,33.93,33.0,2263.0,511.0,1626.0,457.0,3.5556,172800.0,<1H OCEAN\n-118.08,33.93,34.0,1558.0,375.0,1179.0,337.0,3.2188,165100.0,<1H OCEAN\n-118.09,33.92,36.0,2381.0,419.0,1669.0,444.0,4.6976,171100.0,<1H OCEAN\n-118.09,33.92,36.0,847.0,185.0,713.0,194.0,4.8542,167400.0,<1H OCEAN\n-118.09,33.92,36.0,1226.0,211.0,711.0,219.0,4.5699,170800.0,<1H OCEAN\n-118.09,33.92,33.0,879.0,181.0,547.0,169.0,5.3146,168600.0,<1H OCEAN\n-118.09,33.92,31.0,1983.0,419.0,1157.0,390.0,3.5455,168300.0,<1H OCEAN\n-118.09,33.93,37.0,1950.0,356.0,1183.0,338.0,4.1449,175300.0,<1H OCEAN\n-118.1,33.92,35.0,2017.0,383.0,1388.0,386.0,4.0774,171600.0,<1H OCEAN\n-118.1,33.93,36.0,1124.0,217.0,707.0,234.0,4.375,174500.0,<1H OCEAN\n-118.1,33.93,33.0,1474.0,325.0,1205.0,335.0,3.1397,166800.0,<1H OCEAN\n-118.11,33.92,32.0,1016.0,190.0,729.0,177.0,4.3,151300.0,<1H OCEAN\n-118.11,33.92,34.0,1414.0,263.0,983.0,264.0,4.1767,156600.0,<1H OCEAN\n-118.1,33.94,33.0,639.0,129.0,460.0,118.0,3.1607,189000.0,<1H OCEAN\n-118.1,33.93,35.0,1622.0,302.0,845.0,284.0,4.5769,186100.0,<1H OCEAN\n-118.1,33.95,34.0,3635.0,781.0,2171.0,720.0,3.7308,196900.0,<1H OCEAN\n-118.1,33.95,27.0,1666.0,365.0,995.0,354.0,4.5694,204300.0,<1H OCEAN\n-118.11,33.95,36.0,2049.0,334.0,1105.0,363.0,4.8036,261300.0,<1H OCEAN\n-118.1,33.94,34.0,1947.0,284.0,841.0,277.0,6.1814,453600.0,<1H OCEAN\n-118.11,33.95,34.0,2319.0,334.0,941.0,356.0,6.4319,452300.0,<1H OCEAN\n-118.11,33.95,34.0,1723.0,279.0,617.0,252.0,6.7501,400000.0,<1H OCEAN\n-118.12,33.97,33.0,3099.0,839.0,2025.0,750.0,3.183,191100.0,<1H OCEAN\n-118.12,33.96,38.0,1301.0,264.0,877.0,275.0,4.625,191300.0,<1H OCEAN\n-118.11,33.96,29.0,2784.0,582.0,1278.0,550.0,4.3882,261600.0,<1H OCEAN\n-118.12,33.95,35.0,1604.0,280.0,802.0,280.0,5.752,291000.0,<1H OCEAN\n-118.12,33.96,34.0,2863.0,451.0,1243.0,466.0,6.0723,297200.0,<1H OCEAN\n-118.12,33.96,38.0,2105.0,348.0,956.0,350.0,4.4125,246000.0,<1H OCEAN\n-118.12,33.96,36.0,1426.0,235.0,698.0,240.0,4.8523,267300.0,<1H OCEAN\n-118.12,33.97,35.0,708.0,145.0,471.0,153.0,3.2,197400.0,<1H OCEAN\n-118.13,33.97,36.0,1759.0,295.0,837.0,267.0,4.6992,251900.0,<1H OCEAN\n-118.13,33.96,36.0,1933.0,341.0,958.0,335.0,4.4732,266000.0,<1H OCEAN\n-118.13,33.96,38.0,1040.0,202.0,557.0,228.0,4.0,254700.0,<1H OCEAN\n-118.14,33.96,34.0,2744.0,541.0,1333.0,503.0,4.0536,277200.0,<1H OCEAN\n-118.13,33.96,35.0,1500.0,250.0,706.0,250.0,4.5625,253500.0,<1H OCEAN\n-118.13,33.97,34.0,1736.0,297.0,823.0,292.0,5.4042,241600.0,<1H OCEAN\n-118.13,33.95,37.0,1709.0,333.0,778.0,344.0,3.9036,326600.0,<1H OCEAN\n-118.14,33.95,44.0,1812.0,338.0,822.0,314.0,6.7744,294100.0,<1H OCEAN\n-118.14,33.95,42.0,1413.0,228.0,630.0,219.0,6.8564,300000.0,<1H OCEAN\n-118.14,33.95,37.0,1462.0,243.0,600.0,236.0,5.2015,302000.0,<1H OCEAN\n-118.14,33.95,36.0,1942.0,355.0,891.0,348.0,3.6635,282100.0,<1H OCEAN\n-118.15,33.96,33.0,2418.0,485.0,1397.0,477.0,3.1083,285500.0,<1H OCEAN\n-118.15,33.95,31.0,1053.0,230.0,686.0,211.0,4.0,263200.0,<1H OCEAN\n-118.12,33.95,36.0,2752.0,459.0,1211.0,452.0,5.0526,269800.0,<1H OCEAN\n-118.12,33.94,35.0,1813.0,313.0,825.0,316.0,5.2485,323800.0,<1H OCEAN\n-118.12,33.94,33.0,2394.0,576.0,1166.0,548.0,2.7317,264700.0,<1H OCEAN\n-118.13,33.94,34.0,522.0,138.0,373.0,139.0,3.5481,265000.0,<1H OCEAN\n-118.13,33.95,24.0,6662.0,1852.0,3792.0,1735.0,3.1104,230000.0,<1H OCEAN\n-118.11,33.93,35.0,2670.0,493.0,1196.0,488.0,3.8427,283500.0,<1H OCEAN\n-118.11,33.94,37.0,1434.0,262.0,786.0,256.0,4.4375,244900.0,<1H OCEAN\n-118.11,33.94,32.0,2098.0,378.0,1036.0,385.0,5.0258,255400.0,<1H OCEAN\n-118.12,33.94,31.0,2210.0,519.0,1047.0,472.0,3.3292,271300.0,<1H OCEAN\n-118.12,33.94,33.0,2206.0,393.0,973.0,364.0,4.675,283000.0,<1H OCEAN\n-118.11,33.94,36.0,1949.0,319.0,909.0,325.0,5.1587,296600.0,<1H OCEAN\n-118.11,33.93,17.0,1205.0,347.0,736.0,342.0,3.2011,162500.0,<1H OCEAN\n-118.12,33.92,27.0,6336.0,1628.0,4673.0,1505.0,2.5893,183700.0,<1H OCEAN\n-118.12,33.93,27.0,580.0,143.0,466.0,133.0,3.0909,187500.0,<1H OCEAN\n-118.13,33.92,28.0,3069.0,864.0,1932.0,835.0,2.4925,177200.0,<1H OCEAN\n-118.13,33.93,38.0,2040.0,458.0,1775.0,445.0,3.5227,202400.0,<1H OCEAN\n-118.13,33.92,36.0,984.0,183.0,615.0,206.0,4.1786,201500.0,<1H OCEAN\n-118.14,33.92,31.0,3731.0,853.0,2313.0,801.0,3.2237,218200.0,<1H OCEAN\n-118.14,33.93,31.0,3205.0,727.0,1647.0,664.0,3.3681,223900.0,<1H OCEAN\n-118.14,33.93,32.0,2065.0,438.0,1075.0,442.0,4.4279,226400.0,<1H OCEAN\n-118.15,33.93,30.0,3096.0,628.0,1676.0,587.0,4.6583,207300.0,<1H OCEAN\n-118.13,33.93,34.0,2122.0,517.0,1578.0,488.0,3.1496,191900.0,<1H OCEAN\n-118.13,33.93,19.0,1793.0,447.0,1222.0,452.0,2.6862,195800.0,<1H OCEAN\n-118.14,33.94,31.0,2841.0,774.0,1612.0,708.0,2.9205,196600.0,<1H OCEAN\n-118.14,33.94,35.0,2987.0,601.0,1561.0,606.0,4.0039,226500.0,<1H OCEAN\n-118.15,33.94,36.0,1948.0,341.0,992.0,363.0,4.2594,242400.0,<1H OCEAN\n-118.15,33.94,37.0,1594.0,321.0,1003.0,323.0,3.3289,199700.0,<1H OCEAN\n-118.15,33.95,35.0,2753.0,702.0,1592.0,614.0,2.7875,209000.0,<1H OCEAN\n-118.16,33.94,25.0,3341.0,789.0,1685.0,751.0,3.6936,238300.0,<1H OCEAN\n-118.15,33.93,25.0,1948.0,433.0,1128.0,429.0,3.7614,255900.0,<1H OCEAN\n-118.16,33.94,25.0,5675.0,1224.0,3317.0,1119.0,3.9352,232900.0,<1H OCEAN\n-118.15,33.93,42.0,1839.0,346.0,1034.0,339.0,4.9808,212300.0,<1H OCEAN\n-118.15,33.93,34.0,1745.0,404.0,1084.0,410.0,3.3411,220500.0,<1H OCEAN\n-118.16,33.92,36.0,2062.0,351.0,1134.0,358.0,4.4881,218900.0,<1H OCEAN\n-118.14,33.92,35.0,2378.0,559.0,1799.0,546.0,3.9327,190500.0,<1H OCEAN\n-118.15,33.92,36.0,1890.0,400.0,1232.0,386.0,4.375,184200.0,<1H OCEAN\n-118.15,33.92,40.0,1335.0,281.0,804.0,282.0,4.3194,198400.0,<1H OCEAN\n-118.15,33.92,28.0,1038.0,252.0,912.0,245.0,2.5875,161200.0,<1H OCEAN\n-118.15,33.92,30.0,915.0,234.0,646.0,211.0,2.5208,182800.0,<1H OCEAN\n-118.11,33.91,19.0,3056.0,759.0,1561.0,740.0,3.1369,196900.0,<1H OCEAN\n-118.12,33.91,34.0,682.0,132.0,491.0,144.0,4.6389,173800.0,<1H OCEAN\n-118.12,33.91,35.0,620.0,122.0,381.0,124.0,3.7917,183900.0,<1H OCEAN\n-118.12,33.91,35.0,1518.0,279.0,857.0,251.0,3.6917,197500.0,<1H OCEAN\n-118.13,33.91,35.0,561.0,104.0,261.0,105.0,4.9375,183800.0,<1H OCEAN\n-118.13,33.91,34.0,916.0,162.0,552.0,164.0,4.9107,222000.0,<1H OCEAN\n-118.14,33.91,36.0,1096.0,204.0,569.0,201.0,4.475,182300.0,<1H OCEAN\n-118.14,33.91,34.0,2011.0,472.0,1087.0,423.0,3.0465,187800.0,<1H OCEAN\n-118.1,33.91,40.0,513.0,100.0,399.0,99.0,4.875,167600.0,<1H OCEAN\n-118.1,33.91,36.0,1080.0,201.0,719.0,201.0,4.2679,175800.0,<1H OCEAN\n-118.1,33.91,36.0,726.0,,490.0,130.0,3.6389,167600.0,<1H OCEAN\n-118.1,33.9,35.0,1151.0,248.0,809.0,246.0,4.7813,160000.0,<1H OCEAN\n-118.11,33.91,29.0,889.0,166.0,597.0,163.0,4.9609,186700.0,<1H OCEAN\n-118.11,33.91,22.0,1981.0,472.0,1231.0,457.0,4.0878,153700.0,<1H OCEAN\n-118.1,33.91,29.0,505.0,113.0,411.0,113.0,2.6397,164400.0,<1H OCEAN\n-118.09,33.91,14.0,2369.0,604.0,1546.0,464.0,3.7969,159400.0,<1H OCEAN\n-118.09,33.91,34.0,1582.0,343.0,1356.0,324.0,3.4211,141100.0,<1H OCEAN\n-118.09,33.91,36.0,1442.0,271.0,990.0,268.0,4.0517,162200.0,<1H OCEAN\n-118.09,33.92,35.0,1994.0,419.0,1491.0,428.0,3.7383,166200.0,<1H OCEAN\n-118.1,33.91,35.0,1592.0,335.0,1238.0,320.0,4.9732,165000.0,<1H OCEAN\n-118.08,33.91,36.0,1551.0,297.0,1100.0,322.0,5.1187,168100.0,<1H OCEAN\n-118.08,33.91,30.0,1366.0,460.0,920.0,410.0,0.9946,159900.0,<1H OCEAN\n-118.09,33.9,36.0,1215.0,279.0,862.0,285.0,3.7604,158700.0,<1H OCEAN\n-118.09,33.9,37.0,1112.0,222.0,771.0,224.0,4.2132,164600.0,<1H OCEAN\n-118.1,33.91,35.0,1653.0,325.0,1072.0,301.0,3.2708,159700.0,<1H OCEAN\n-118.08,33.91,30.0,3259.0,942.0,2744.0,895.0,2.8608,165600.0,<1H OCEAN\n-118.07,33.91,29.0,2387.0,570.0,1978.0,548.0,3.1957,159200.0,<1H OCEAN\n-118.08,33.91,18.0,1573.0,396.0,1200.0,365.0,2.895,146900.0,<1H OCEAN\n-118.06,33.91,24.0,4880.0,1044.0,4516.0,1050.0,4.1387,157700.0,<1H OCEAN\n-118.06,33.91,21.0,2863.0,701.0,1489.0,621.0,3.2031,180700.0,<1H OCEAN\n-118.06,33.91,36.0,1360.0,271.0,909.0,275.0,4.6731,173300.0,<1H OCEAN\n-118.07,33.91,35.0,2228.0,463.0,1558.0,427.0,4.023,157700.0,<1H OCEAN\n-118.05,33.9,41.0,550.0,129.0,642.0,125.0,1.875,119900.0,<1H OCEAN\n-118.05,33.9,36.0,1047.0,227.0,975.0,239.0,3.1897,155000.0,<1H OCEAN\n-118.06,33.9,37.0,1161.0,254.0,882.0,236.0,4.4167,158000.0,<1H OCEAN\n-118.06,33.89,26.0,2483.0,412.0,1538.0,449.0,5.1104,220500.0,<1H OCEAN\n-118.07,33.89,29.0,1138.0,217.0,964.0,222.0,4.537,185300.0,<1H OCEAN\n-118.07,33.89,17.0,2223.0,544.0,2008.0,512.0,3.0777,160800.0,<1H OCEAN\n-118.07,33.89,35.0,1145.0,274.0,1651.0,265.0,3.125,120300.0,<1H OCEAN\n-118.08,33.89,28.0,1035.0,275.0,1545.0,269.0,3.0357,123400.0,<1H OCEAN\n-118.08,33.89,35.0,1071.0,290.0,1412.0,274.0,3.1917,114900.0,<1H OCEAN\n-118.08,33.89,37.0,1152.0,259.0,981.0,225.0,3.2857,153600.0,<1H OCEAN\n-118.07,33.9,39.0,2502.0,546.0,1849.0,518.0,3.8846,164100.0,<1H OCEAN\n-118.07,33.9,42.0,1007.0,224.0,776.0,228.0,3.8672,162700.0,<1H OCEAN\n-118.07,33.9,45.0,1776.0,353.0,1180.0,337.0,4.6406,169200.0,<1H OCEAN\n-118.08,33.9,44.0,1167.0,237.0,733.0,237.0,4.2083,168300.0,<1H OCEAN\n-118.08,33.9,42.0,1768.0,372.0,1155.0,368.0,3.558,161100.0,<1H OCEAN\n-118.09,33.9,37.0,1147.0,258.0,742.0,242.0,4.0461,153500.0,<1H OCEAN\n-118.09,33.9,33.0,3326.0,720.0,2533.0,689.0,3.1441,176300.0,<1H OCEAN\n-118.1,33.9,43.0,1237.0,243.0,776.0,246.0,4.325,166000.0,<1H OCEAN\n-118.1,33.9,40.0,1880.0,377.0,1229.0,378.0,4.4167,174600.0,<1H OCEAN\n-118.08,33.89,41.0,834.0,166.0,603.0,179.0,3.7321,167500.0,<1H OCEAN\n-118.09,33.89,42.0,1150.0,215.0,708.0,204.0,3.6875,171500.0,<1H OCEAN\n-118.09,33.89,42.0,991.0,,717.0,219.0,4.0926,164400.0,<1H OCEAN\n-118.09,33.89,27.0,3399.0,882.0,2465.0,811.0,3.099,166600.0,<1H OCEAN\n-118.1,33.89,34.0,2242.0,436.0,1483.0,443.0,4.4934,185600.0,<1H OCEAN\n-118.1,33.9,37.0,1061.0,202.0,768.0,206.0,4.75,161900.0,<1H OCEAN\n-118.1,33.9,37.0,796.0,175.0,740.0,183.0,3.6,156400.0,<1H OCEAN\n-118.1,33.89,36.0,769.0,142.0,498.0,137.0,4.7159,182100.0,<1H OCEAN\n-118.1,33.89,35.0,994.0,203.0,602.0,185.0,3.5865,178000.0,<1H OCEAN\n-118.11,33.9,35.0,2604.0,495.0,1465.0,470.0,4.4896,184600.0,<1H OCEAN\n-118.11,33.9,26.0,4173.0,893.0,2471.0,863.0,3.5052,196000.0,<1H OCEAN\n-118.11,33.89,35.0,1139.0,197.0,772.0,233.0,4.375,204700.0,<1H OCEAN\n-118.11,33.88,35.0,1623.0,304.0,868.0,272.0,3.5893,276000.0,<1H OCEAN\n-118.11,33.89,34.0,2508.0,594.0,1549.0,545.0,3.2069,236500.0,<1H OCEAN\n-118.11,33.9,35.0,1323.0,269.0,1084.0,240.0,5.0753,178000.0,<1H OCEAN\n-118.11,33.9,36.0,1347.0,278.0,748.0,278.0,5.1423,183100.0,<1H OCEAN\n-118.11,33.91,36.0,1088.0,231.0,617.0,211.0,3.8824,193100.0,<1H OCEAN\n-118.12,33.91,36.0,1432.0,265.0,749.0,261.0,3.5772,207400.0,<1H OCEAN\n-118.12,33.91,36.0,2053.0,386.0,1023.0,394.0,3.0,216600.0,<1H OCEAN\n-118.13,33.9,38.0,1475.0,269.0,827.0,265.0,4.7663,191600.0,<1H OCEAN\n-118.13,33.91,36.0,1967.0,316.0,910.0,306.0,4.4948,190600.0,<1H OCEAN\n-118.14,33.91,37.0,932.0,171.0,578.0,175.0,4.375,177600.0,<1H OCEAN\n-118.14,33.9,26.0,2145.0,471.0,1150.0,429.0,3.5972,225800.0,<1H OCEAN\n-118.14,33.91,34.0,1766.0,410.0,974.0,404.0,3.0703,180800.0,<1H OCEAN\n-118.14,33.91,32.0,1981.0,472.0,1371.0,431.0,3.1204,204200.0,<1H OCEAN\n-118.15,33.91,35.0,1590.0,350.0,1299.0,335.0,4.0313,163200.0,<1H OCEAN\n-118.15,33.91,38.0,901.0,205.0,760.0,208.0,2.9643,147400.0,<1H OCEAN\n-118.15,33.9,20.0,2850.0,737.0,1855.0,662.0,2.809,144600.0,<1H OCEAN\n-118.16,33.9,28.0,2410.0,616.0,2399.0,594.0,2.7339,156700.0,<1H OCEAN\n-118.16,33.91,28.0,2922.0,739.0,3013.0,673.0,2.9531,127100.0,<1H OCEAN\n-118.16,33.91,35.0,1403.0,338.0,1415.0,367.0,3.0967,144000.0,<1H OCEAN\n-118.15,33.91,35.0,574.0,116.0,610.0,147.0,3.3182,133300.0,<1H OCEAN\n-118.15,33.91,25.0,2053.0,578.0,1721.0,507.0,2.3456,146100.0,<1H OCEAN\n-118.16,33.91,6.0,3445.0,847.0,2467.0,712.0,3.1507,144000.0,<1H OCEAN\n-118.17,33.9,12.0,3653.0,993.0,3215.0,854.0,2.8681,114200.0,<1H OCEAN\n-118.17,33.91,42.0,856.0,167.0,748.0,195.0,3.8,145800.0,<1H OCEAN\n-118.17,33.91,37.0,1499.0,288.0,1237.0,344.0,3.9333,162300.0,<1H OCEAN\n-118.18,33.9,25.0,1709.0,442.0,1177.0,410.0,2.4333,155000.0,<1H OCEAN\n-118.18,33.9,32.0,1452.0,365.0,1888.0,366.0,3.5461,146400.0,<1H OCEAN\n-118.18,33.9,32.0,778.0,227.0,933.0,209.0,2.7292,143800.0,<1H OCEAN\n-118.18,33.9,31.0,2536.0,603.0,2625.0,576.0,3.0909,150900.0,<1H OCEAN\n-118.18,33.9,34.0,1248.0,320.0,1960.0,338.0,3.1012,140400.0,<1H OCEAN\n-118.16,33.89,38.0,483.0,113.0,389.0,108.0,2.1859,143800.0,<1H OCEAN\n-118.16,33.89,6.0,1655.0,536.0,1201.0,487.0,1.7344,145800.0,<1H OCEAN\n-118.17,33.89,52.0,63.0,12.0,47.0,8.0,7.2423,350000.0,<1H OCEAN\n-118.17,33.89,11.0,3605.0,880.0,3637.0,873.0,2.6328,160700.0,<1H OCEAN\n-118.18,33.89,25.0,5896.0,1464.0,4149.0,1362.0,2.6742,131900.0,<1H OCEAN\n-118.15,33.89,30.0,4426.0,995.0,4196.0,921.0,3.274,148300.0,<1H OCEAN\n-118.15,33.88,24.0,4232.0,1092.0,2688.0,1035.0,2.52,146000.0,<1H OCEAN\n-118.15,33.89,33.0,2848.0,641.0,2319.0,647.0,3.407,190800.0,<1H OCEAN\n-118.16,33.89,46.0,940.0,219.0,599.0,214.0,3.2813,190900.0,<1H OCEAN\n-118.16,33.88,30.0,1694.0,398.0,1181.0,383.0,2.9779,169500.0,<1H OCEAN\n-118.13,33.9,36.0,1814.0,350.0,886.0,347.0,3.4868,208400.0,<1H OCEAN\n-118.13,33.89,33.0,3668.0,867.0,2368.0,845.0,2.8906,204900.0,<1H OCEAN\n-118.14,33.89,33.0,2867.0,786.0,1774.0,705.0,2.9292,183400.0,<1H OCEAN\n-118.13,33.89,36.0,599.0,125.0,361.0,139.0,5.0395,225800.0,<1H OCEAN\n-118.13,33.9,36.0,1477.0,305.0,788.0,291.0,3.625,195800.0,<1H OCEAN\n-118.14,33.9,39.0,1379.0,282.0,883.0,291.0,3.3375,180100.0,<1H OCEAN\n-118.13,33.9,35.0,1458.0,261.0,686.0,236.0,3.9038,202700.0,<1H OCEAN\n-118.12,33.9,35.0,3478.0,730.0,1885.0,673.0,2.9375,206500.0,<1H OCEAN\n-118.12,33.89,22.0,6876.0,1960.0,5162.0,1879.0,2.9293,170800.0,<1H OCEAN\n-118.12,33.89,29.0,2666.0,848.0,2030.0,781.0,2.5432,180900.0,<1H OCEAN\n-118.12,33.9,38.0,1222.0,282.0,756.0,256.0,4.125,173900.0,<1H OCEAN\n-118.12,33.88,25.0,1768.0,559.0,983.0,488.0,2.6184,243800.0,<1H OCEAN\n-118.12,33.88,40.0,2344.0,571.0,1305.0,544.0,3.1923,191900.0,<1H OCEAN\n-118.13,33.88,32.0,3088.0,1024.0,1981.0,956.0,2.2027,192700.0,<1H OCEAN\n-118.13,33.89,29.0,2823.0,737.0,1723.0,678.0,2.7121,165500.0,<1H OCEAN\n-118.14,33.89,37.0,1159.0,238.0,740.0,243.0,4.9107,179600.0,<1H OCEAN\n-118.14,33.89,39.0,1744.0,339.0,1048.0,330.0,4.5735,195500.0,<1H OCEAN\n-118.14,33.88,30.0,2596.0,580.0,1662.0,539.0,4.0507,179500.0,<1H OCEAN\n-118.14,33.89,33.0,1250.0,276.0,866.0,268.0,4.1708,175000.0,<1H OCEAN\n-118.14,33.88,41.0,1531.0,343.0,1119.0,341.0,4.3646,161400.0,<1H OCEAN\n-118.14,33.88,24.0,3305.0,982.0,2085.0,881.0,2.6641,168200.0,<1H OCEAN\n-118.15,33.87,29.0,2690.0,659.0,1747.0,617.0,3.3713,198200.0,<1H OCEAN\n-118.11,33.88,19.0,3203.0,708.0,1761.0,667.0,4.0911,239700.0,<1H OCEAN\n-118.11,33.87,33.0,1379.0,254.0,795.0,297.0,4.6713,231800.0,<1H OCEAN\n-118.11,33.87,15.0,3254.0,598.0,1772.0,618.0,5.0417,240800.0,<1H OCEAN\n-118.12,33.87,21.0,3764.0,1081.0,1919.0,977.0,2.5057,156300.0,<1H OCEAN\n-118.12,33.88,36.0,1083.0,218.0,557.0,210.0,3.0795,218400.0,<1H OCEAN\n-118.13,33.88,32.0,1788.0,459.0,1131.0,461.0,3.5278,166100.0,<1H OCEAN\n-118.13,33.87,20.0,3638.0,868.0,2326.0,822.0,3.3304,194600.0,<1H OCEAN\n-118.14,33.87,21.0,6618.0,1773.0,4396.0,1649.0,3.0989,171400.0,<1H OCEAN\n-118.04,33.88,17.0,4807.0,838.0,3059.0,853.0,5.7619,297300.0,<1H OCEAN\n-118.04,33.87,17.0,2358.0,396.0,1387.0,364.0,6.299,285800.0,<1H OCEAN\n-118.06,33.88,17.0,7187.0,1073.0,3844.0,1068.0,6.5901,337400.0,<1H OCEAN\n-118.05,33.87,18.0,4928.0,773.0,2952.0,754.0,5.8855,313800.0,<1H OCEAN\n-118.07,33.88,16.0,4934.0,825.0,2668.0,810.0,5.748,284200.0,<1H OCEAN\n-118.07,33.88,17.0,2407.0,539.0,1422.0,524.0,4.2619,139700.0,<1H OCEAN\n-118.07,33.88,18.0,2436.0,375.0,1303.0,386.0,6.1968,344700.0,<1H OCEAN\n-118.07,33.87,18.0,3405.0,556.0,1945.0,509.0,5.7652,299100.0,<1H OCEAN\n-118.08,33.86,17.0,2259.0,383.0,1378.0,386.0,5.8733,287000.0,<1H OCEAN\n-118.08,33.85,19.0,4261.0,678.0,2621.0,661.0,6.2427,288700.0,<1H OCEAN\n-118.07,33.86,17.0,3666.0,562.0,2104.0,579.0,5.6818,338900.0,<1H OCEAN\n-118.07,33.85,16.0,3771.0,606.0,2196.0,564.0,7.0113,319700.0,<1H OCEAN\n-118.06,33.86,16.0,5603.0,938.0,3045.0,893.0,5.0778,293700.0,<1H OCEAN\n-118.05,33.86,16.0,2851.0,626.0,1985.0,603.0,5.4089,265600.0,<1H OCEAN\n-118.03,33.87,16.0,2306.0,393.0,1368.0,387.0,5.93,277600.0,<1H OCEAN\n-118.04,33.87,18.0,4626.0,822.0,2794.0,763.0,5.6917,275100.0,<1H OCEAN\n-118.04,33.86,21.0,2870.0,437.0,1671.0,470.0,7.2628,322700.0,<1H OCEAN\n-118.05,33.86,16.0,2676.0,391.0,1377.0,395.0,6.5513,350400.0,<1H OCEAN\n-118.06,33.85,16.0,4851.0,726.0,2527.0,704.0,6.0142,437400.0,<1H OCEAN\n-118.1,33.88,18.0,8046.0,1221.0,4276.0,1228.0,6.5515,319600.0,<1H OCEAN\n-118.1,33.86,21.0,3052.0,624.0,1588.0,568.0,4.3397,268100.0,<1H OCEAN\n-118.09,33.85,19.0,8120.0,1371.0,5026.0,1345.0,6.3093,286500.0,<1H OCEAN\n-118.1,33.85,19.0,993.0,174.0,572.0,175.0,5.7039,277600.0,<1H OCEAN\n-118.08,33.88,27.0,3065.0,736.0,1840.0,719.0,3.6417,208100.0,<1H OCEAN\n-118.09,33.88,27.0,3119.0,635.0,1887.0,567.0,3.8654,195300.0,<1H OCEAN\n-118.07,33.89,32.0,1819.0,386.0,1679.0,360.0,3.5562,146000.0,<1H OCEAN\n-118.08,33.89,33.0,2131.0,435.0,2045.0,426.0,4.0,145700.0,<1H OCEAN\n-118.08,33.88,27.0,923.0,186.0,1014.0,204.0,3.825,159500.0,<1H OCEAN\n-118.08,33.88,30.0,1901.0,519.0,2685.0,496.0,3.2639,120100.0,<1H OCEAN\n-118.08,33.88,26.0,1507.0,270.0,931.0,275.0,5.1645,244900.0,<1H OCEAN\n-118.08,33.87,23.0,2536.0,552.0,2012.0,556.0,4.1406,200800.0,<1H OCEAN\n-118.07,33.87,28.0,2399.0,436.0,1613.0,429.0,3.6339,220100.0,<1H OCEAN\n-118.09,33.87,31.0,3498.0,728.0,2098.0,697.0,3.9837,246000.0,<1H OCEAN\n-118.07,33.86,31.0,2943.0,518.0,1703.0,472.0,3.7091,225900.0,<1H OCEAN\n-118.07,33.86,28.0,1789.0,352.0,1347.0,330.0,3.425,189700.0,<1H OCEAN\n-118.08,33.86,26.0,778.0,173.0,539.0,186.0,3.2679,236500.0,<1H OCEAN\n-118.08,33.86,29.0,3260.0,783.0,1969.0,737.0,3.5268,215500.0,<1H OCEAN\n-118.08,33.86,29.0,1018.0,235.0,684.0,248.0,3.3333,198800.0,<1H OCEAN\n-118.08,33.84,31.0,2377.0,600.0,2042.0,593.0,3.625,170400.0,<1H OCEAN\n-118.08,33.84,31.0,2906.0,578.0,1806.0,553.0,4.8448,194600.0,<1H OCEAN\n-118.09,33.84,23.0,4412.0,910.0,2380.0,825.0,4.54,213100.0,<1H OCEAN\n-118.09,33.84,27.0,1594.0,295.0,1061.0,320.0,4.7917,217700.0,<1H OCEAN\n-118.08,33.85,22.0,1055.0,204.0,682.0,216.0,6.0,191300.0,<1H OCEAN\n-118.08,33.84,28.0,4216.0,948.0,2997.0,896.0,3.7961,162700.0,<1H OCEAN\n-118.08,33.84,25.0,3696.0,953.0,2827.0,860.0,3.3438,153300.0,<1H OCEAN\n-118.06,33.84,26.0,6960.0,1454.0,4367.0,1437.0,4.7953,210900.0,<1H OCEAN\n-118.06,33.84,20.0,5643.0,1231.0,3841.0,1195.0,4.0542,168400.0,<1H OCEAN\n-118.07,33.82,27.0,3481.0,576.0,1660.0,560.0,5.7965,228200.0,<1H OCEAN\n-118.08,33.82,26.0,4259.0,588.0,1644.0,581.0,6.2519,345700.0,<1H OCEAN\n-118.08,33.83,30.0,2188.0,556.0,2727.0,525.0,2.7759,136800.0,<1H OCEAN\n-118.08,33.82,30.0,2636.0,652.0,3412.0,649.0,2.8095,118300.0,<1H OCEAN\n-118.07,33.83,17.0,4822.0,1168.0,3868.0,1117.0,2.5978,142900.0,<1H OCEAN\n-118.11,33.86,33.0,2389.0,410.0,1229.0,393.0,5.3889,234900.0,<1H OCEAN\n-118.11,33.86,35.0,1255.0,252.0,685.0,279.0,4.2,226900.0,<1H OCEAN\n-118.12,33.86,44.0,2663.0,511.0,1277.0,462.0,4.3194,199500.0,<1H OCEAN\n-118.12,33.87,43.0,1633.0,355.0,837.0,350.0,3.0405,188000.0,<1H OCEAN\n-118.13,33.87,45.0,1606.0,300.0,735.0,295.0,4.6765,198400.0,<1H OCEAN\n-118.13,33.86,45.0,1320.0,256.0,645.0,256.0,4.4,209500.0,<1H OCEAN\n-118.13,33.86,45.0,1866.0,343.0,919.0,344.0,3.5833,200200.0,<1H OCEAN\n-118.14,33.87,44.0,1661.0,315.0,985.0,319.0,4.3942,219500.0,<1H OCEAN\n-118.14,33.86,43.0,2104.0,382.0,1071.0,396.0,5.0,208900.0,<1H OCEAN\n-118.14,33.86,44.0,1436.0,257.0,745.0,233.0,4.625,213400.0,<1H OCEAN\n-118.14,33.86,44.0,1276.0,234.0,538.0,213.0,4.8667,218300.0,<1H OCEAN\n-118.14,33.87,44.0,1607.0,271.0,799.0,283.0,5.084,214100.0,<1H OCEAN\n-118.15,33.86,36.0,1578.0,312.0,827.0,311.0,4.8942,194100.0,<1H OCEAN\n-118.15,33.86,34.0,2403.0,413.0,1385.0,386.0,4.4934,213800.0,<1H OCEAN\n-118.16,33.88,33.0,2180.0,522.0,1634.0,467.0,3.0114,167000.0,<1H OCEAN\n-118.15,33.87,33.0,2373.0,552.0,1673.0,571.0,3.0685,181800.0,<1H OCEAN\n-118.16,33.87,32.0,1854.0,471.0,1363.0,478.0,2.6406,156700.0,<1H OCEAN\n-118.16,33.88,18.0,2287.0,662.0,1804.0,537.0,1.9903,170300.0,<1H OCEAN\n-118.17,33.88,29.0,815.0,206.0,590.0,183.0,3.0052,166700.0,<1H OCEAN\n-118.17,33.88,42.0,1645.0,371.0,1161.0,351.0,3.0893,162700.0,<1H OCEAN\n-118.18,33.88,47.0,882.0,185.0,536.0,174.0,4.625,163000.0,<1H OCEAN\n-118.18,33.88,44.0,1308.0,267.0,783.0,237.0,4.7361,167700.0,<1H OCEAN\n-118.18,33.88,42.0,2326.0,503.0,1832.0,501.0,3.1713,161000.0,<1H OCEAN\n-118.19,33.87,27.0,4701.0,1359.0,2571.0,1216.0,2.5417,184100.0,<1H OCEAN\n-118.19,33.87,35.0,1769.0,436.0,1166.0,386.0,2.875,178300.0,<1H OCEAN\n-118.19,33.86,42.0,1999.0,431.0,1060.0,399.0,3.7031,167100.0,<1H OCEAN\n-118.19,33.86,46.0,1824.0,438.0,1200.0,451.0,3.4375,156700.0,<1H OCEAN\n-118.19,33.86,38.0,2009.0,524.0,1449.0,451.0,2.7045,155400.0,<1H OCEAN\n-118.19,33.86,35.0,1133.0,296.0,774.0,271.0,2.2381,137500.0,<1H OCEAN\n-118.19,33.86,36.0,2013.0,546.0,1659.0,522.0,3.1215,153600.0,<1H OCEAN\n-118.2,33.86,27.0,2732.0,867.0,1690.0,794.0,2.6465,160200.0,<1H OCEAN\n-118.19,33.87,42.0,1213.0,269.0,628.0,229.0,3.6987,152100.0,<1H OCEAN\n-118.2,33.88,40.0,1699.0,346.0,1188.0,329.0,4.2083,147300.0,<1H OCEAN\n-118.2,33.88,40.0,2945.0,725.0,2858.0,690.0,3.2368,136900.0,<1H OCEAN\n-118.21,33.88,31.0,1332.0,417.0,1405.0,363.0,2.0125,143000.0,<1H OCEAN\n-118.21,33.88,32.0,1507.0,379.0,1082.0,350.0,3.225,138200.0,<1H OCEAN\n-118.2,33.87,26.0,703.0,202.0,757.0,212.0,2.525,155500.0,<1H OCEAN\n-118.2,33.87,42.0,1482.0,310.0,1052.0,317.0,3.9469,158200.0,<1H OCEAN\n-118.2,33.87,36.0,1554.0,273.0,974.0,264.0,4.2135,161400.0,<1H OCEAN\n-118.17,33.87,40.0,2462.0,587.0,1821.0,536.0,3.5646,162600.0,<1H OCEAN\n-118.17,33.86,44.0,1701.0,396.0,1091.0,384.0,3.025,162300.0,<1H OCEAN\n-118.17,33.87,49.0,1937.0,445.0,1339.0,440.0,3.0319,162800.0,<1H OCEAN\n-118.17,33.87,48.0,2258.0,509.0,1395.0,492.0,3.765,164800.0,<1H OCEAN\n-118.17,33.87,45.0,2110.0,494.0,1404.0,454.0,2.9803,165900.0,<1H OCEAN\n-118.18,33.87,44.0,2137.0,461.0,1126.0,439.0,3.4408,172900.0,<1H OCEAN\n-118.18,33.87,44.0,1832.0,401.0,1056.0,405.0,4.0658,175100.0,<1H OCEAN\n-118.18,33.87,38.0,2664.0,626.0,1627.0,604.0,3.7527,161900.0,<1H OCEAN\n-118.16,33.86,26.0,6607.0,1663.0,4066.0,1558.0,2.5068,156300.0,<1H OCEAN\n-118.17,33.86,10.0,1664.0,508.0,1369.0,493.0,2.9886,175000.0,<1H OCEAN\n-118.17,33.85,39.0,2247.0,526.0,1670.0,525.0,3.07,173000.0,<1H OCEAN\n-118.18,33.85,38.0,3596.0,862.0,2416.0,832.0,3.6897,169800.0,<1H OCEAN\n-118.18,33.86,43.0,2752.0,645.0,1674.0,614.0,3.6719,161300.0,<1H OCEAN\n-118.18,33.86,39.0,2925.0,732.0,1702.0,642.0,2.375,160800.0,<1H OCEAN\n-118.17,33.86,40.0,1301.0,342.0,954.0,336.0,2.3804,158000.0,<1H OCEAN\n-118.14,33.86,37.0,1404.0,257.0,652.0,258.0,4.2062,195400.0,<1H OCEAN\n-118.15,33.85,30.0,4071.0,1067.0,2144.0,970.0,2.7268,218100.0,<1H OCEAN\n-118.15,33.85,36.0,1435.0,249.0,606.0,234.0,4.1439,212600.0,<1H OCEAN\n-118.15,33.85,36.0,1491.0,259.0,699.0,266.0,4.0781,217300.0,<1H OCEAN\n-118.15,33.86,32.0,2630.0,559.0,1069.0,491.0,2.4659,209000.0,<1H OCEAN\n-118.16,33.85,36.0,2668.0,473.0,1315.0,478.0,4.0714,215600.0,<1H OCEAN\n-118.16,33.85,36.0,1979.0,339.0,952.0,339.0,4.0815,216200.0,<1H OCEAN\n-118.13,33.86,37.0,2259.0,425.0,1183.0,413.0,5.1805,201600.0,<1H OCEAN\n-118.13,33.85,36.0,2110.0,416.0,1128.0,403.0,4.6019,208400.0,<1H OCEAN\n-118.13,33.85,36.0,1885.0,391.0,1049.0,405.0,3.55,212800.0,<1H OCEAN\n-118.14,33.86,36.0,1774.0,348.0,934.0,333.0,4.8571,203300.0,<1H OCEAN\n-118.14,33.86,36.0,1703.0,325.0,845.0,308.0,5.0106,210800.0,<1H OCEAN\n-118.1,33.85,28.0,2825.0,470.0,1352.0,469.0,5.2639,242000.0,<1H OCEAN\n-118.1,33.85,36.0,1473.0,253.0,713.0,257.0,5.9493,228000.0,<1H OCEAN\n-118.1,33.85,36.0,956.0,159.0,416.0,157.0,4.6429,223700.0,<1H OCEAN\n-118.11,33.85,36.0,887.0,163.0,482.0,157.0,4.125,219500.0,<1H OCEAN\n-118.11,33.85,36.0,2418.0,389.0,1138.0,387.0,4.8393,216300.0,<1H OCEAN\n-118.11,33.86,36.0,2750.0,487.0,1386.0,458.0,4.9904,221700.0,<1H OCEAN\n-118.12,33.86,34.0,2116.0,427.0,972.0,396.0,4.8516,213600.0,<1H OCEAN\n-118.12,33.85,37.0,2584.0,453.0,1333.0,481.0,4.3661,219900.0,<1H OCEAN\n-118.12,33.85,37.0,2386.0,409.0,1101.0,399.0,4.6908,218200.0,<1H OCEAN\n-118.1,33.84,35.0,1790.0,269.0,924.0,263.0,5.296,226200.0,<1H OCEAN\n-118.1,33.84,36.0,1915.0,316.0,850.0,319.0,4.7222,225800.0,<1H OCEAN\n-118.1,33.83,37.0,2059.0,349.0,825.0,334.0,4.0603,225200.0,<1H OCEAN\n-118.11,33.83,36.0,1726.0,287.0,820.0,288.0,5.5767,218100.0,<1H OCEAN\n-118.1,33.84,36.0,1557.0,270.0,697.0,251.0,4.5417,219600.0,<1H OCEAN\n-118.1,33.84,36.0,2572.0,421.0,1318.0,434.0,5.1836,219700.0,<1H OCEAN\n-118.1,33.84,36.0,690.0,109.0,316.0,104.0,3.7813,209100.0,<1H OCEAN\n-118.11,33.84,37.0,1588.0,272.0,692.0,245.0,4.8594,220300.0,<1H OCEAN\n-118.11,33.84,36.0,1756.0,297.0,798.0,287.0,5.5581,218300.0,<1H OCEAN\n-118.12,33.84,37.0,2143.0,382.0,1047.0,377.0,4.4423,216000.0,<1H OCEAN\n-118.12,33.84,37.0,2706.0,462.0,1331.0,476.0,5.0719,220000.0,<1H OCEAN\n-118.11,33.84,36.0,1074.0,188.0,496.0,196.0,4.625,217400.0,<1H OCEAN\n-118.11,33.84,36.0,1523.0,263.0,717.0,278.0,4.875,218900.0,<1H OCEAN\n-118.11,33.83,36.0,1784.0,303.0,964.0,299.0,4.2703,220900.0,<1H OCEAN\n-118.12,33.83,44.0,1712.0,314.0,691.0,293.0,4.3594,221300.0,<1H OCEAN\n-118.12,33.84,37.0,1242.0,221.0,565.0,213.0,4.1094,215800.0,<1H OCEAN\n-118.11,33.84,36.0,1463.0,257.0,722.0,260.0,4.8438,226300.0,<1H OCEAN\n-118.13,33.84,35.0,3008.0,674.0,1584.0,671.0,3.5465,213200.0,<1H OCEAN\n-118.13,33.84,46.0,2439.0,429.0,944.0,374.0,4.2841,312400.0,<1H OCEAN\n-118.13,33.83,44.0,1710.0,333.0,786.0,344.0,4.2917,314700.0,<1H OCEAN\n-118.14,33.84,43.0,2107.0,439.0,876.0,429.0,3.2024,339400.0,<1H OCEAN\n-118.13,33.84,48.0,1895.0,294.0,881.0,293.0,6.3364,307400.0,<1H OCEAN\n-118.14,33.84,45.0,1908.0,361.0,890.0,342.0,4.575,336000.0,<1H OCEAN\n-118.14,33.84,44.0,3043.0,619.0,1316.0,607.0,4.4286,254900.0,<1H OCEAN\n-118.14,33.84,36.0,3002.0,484.0,1322.0,471.0,4.933,228900.0,<1H OCEAN\n-118.15,33.84,29.0,2448.0,354.0,894.0,349.0,7.6526,481300.0,<1H OCEAN\n-118.15,33.84,36.0,2987.0,491.0,1360.0,497.0,4.8013,224100.0,<1H OCEAN\n-118.15,33.84,37.0,1508.0,252.0,635.0,241.0,3.75,221300.0,<1H OCEAN\n-118.16,33.84,36.0,2444.0,432.0,1199.0,424.0,4.1538,218800.0,<1H OCEAN\n-118.16,33.84,36.0,1348.0,234.0,643.0,221.0,3.6447,211000.0,<1H OCEAN\n-118.16,33.84,36.0,2831.0,573.0,1462.0,569.0,3.8646,214600.0,<1H OCEAN\n-118.16,33.84,36.0,2220.0,367.0,1002.0,351.0,5.0719,219500.0,<1H OCEAN\n-118.17,33.85,37.0,3714.0,708.0,1956.0,694.0,4.2218,200500.0,<1H OCEAN\n-118.17,33.84,45.0,1533.0,331.0,791.0,335.0,3.4605,186600.0,<1H OCEAN\n-118.17,33.84,29.0,4716.0,1372.0,2515.0,1272.0,2.726,208700.0,<1H OCEAN\n-118.17,33.84,45.0,1853.0,328.0,945.0,320.0,5.0787,219200.0,<1H OCEAN\n-118.17,33.83,45.0,2019.0,363.0,880.0,339.0,4.1023,217300.0,NEAR OCEAN\n-118.18,33.85,40.0,2597.0,582.0,1285.0,559.0,3.975,213800.0,<1H OCEAN\n-118.18,33.84,35.0,1415.0,294.0,591.0,291.0,2.9798,315600.0,NEAR OCEAN\n-118.19,33.84,44.0,2731.0,577.0,1396.0,555.0,4.1771,219100.0,NEAR OCEAN\n-118.19,33.84,24.0,1228.0,320.0,537.0,273.0,2.25,192000.0,NEAR OCEAN\n-118.18,33.85,30.0,2548.0,717.0,2086.0,700.0,0.7007,134400.0,<1H OCEAN\n-118.18,33.85,44.0,1890.0,465.0,1378.0,430.0,3.8819,143200.0,<1H OCEAN\n-118.19,33.85,30.0,3533.0,1061.0,2678.0,1033.0,2.2417,151900.0,NEAR OCEAN\n-118.19,33.85,49.0,1073.0,260.0,725.0,272.0,3.8026,149700.0,NEAR OCEAN\n-118.19,33.85,45.0,1167.0,302.0,773.0,287.0,3.2798,150300.0,NEAR OCEAN\n-118.2,33.85,33.0,2557.0,731.0,2286.0,700.0,2.3041,149100.0,<1H OCEAN\n-118.2,33.85,46.0,1854.0,462.0,1360.0,429.0,2.4844,158200.0,<1H OCEAN\n-118.2,33.84,35.0,3405.0,779.0,1953.0,671.0,2.7813,159200.0,NEAR OCEAN\n-118.2,33.83,35.0,3737.0,613.0,1305.0,583.0,7.2096,490300.0,NEAR OCEAN\n-118.19,33.83,43.0,2641.0,411.0,1011.0,444.0,6.4468,444200.0,NEAR OCEAN\n-118.19,33.83,42.0,1773.0,360.0,815.0,299.0,4.9,406300.0,NEAR OCEAN\n-118.17,33.83,45.0,1808.0,315.0,800.0,302.0,4.8693,277700.0,NEAR OCEAN\n-118.17,33.83,46.0,1362.0,214.0,531.0,222.0,4.3125,290500.0,NEAR OCEAN\n-118.18,33.83,44.0,1497.0,277.0,542.0,274.0,5.0052,321800.0,NEAR OCEAN\n-118.18,33.83,45.0,1535.0,274.0,591.0,276.0,4.2411,371700.0,NEAR OCEAN\n-118.18,33.83,39.0,3622.0,745.0,1330.0,648.0,3.3125,425500.0,NEAR OCEAN\n-118.18,33.84,43.0,2561.0,544.0,1063.0,537.0,3.835,418600.0,NEAR OCEAN\n-118.17,33.82,50.0,3587.0,693.0,1513.0,651.0,5.5106,252200.0,NEAR OCEAN\n-118.17,33.82,52.0,2539.0,497.0,1152.0,488.0,4.1354,268200.0,NEAR OCEAN\n-118.18,33.83,52.0,2569.0,484.0,1030.0,451.0,4.1301,268400.0,NEAR OCEAN\n-118.18,33.82,52.0,2618.0,472.0,943.0,440.0,3.7895,254000.0,NEAR OCEAN\n-118.18,33.82,43.0,284.0,65.0,167.0,68.0,4.25,207500.0,NEAR OCEAN\n-118.19,33.83,30.0,2246.0,552.0,1032.0,548.0,3.5871,347100.0,NEAR OCEAN\n-118.19,33.82,19.0,2953.0,895.0,1914.0,855.0,3.5521,290000.0,NEAR OCEAN\n-118.2,33.82,21.0,2251.0,452.0,913.0,420.0,4.6042,272200.0,NEAR OCEAN\n-118.2,33.82,43.0,1758.0,347.0,954.0,312.0,5.2606,198900.0,NEAR OCEAN\n-118.19,33.82,11.0,872.0,203.0,422.0,221.0,4.6364,156300.0,NEAR OCEAN\n-118.19,33.81,21.0,1835.0,427.0,1038.0,384.0,4.4559,198500.0,NEAR OCEAN\n-118.2,33.81,43.0,3013.0,574.0,1525.0,529.0,4.95,194000.0,NEAR OCEAN\n-118.2,33.82,34.0,2807.0,768.0,2217.0,744.0,2.4286,204800.0,NEAR OCEAN\n-118.19,33.81,23.0,954.0,390.0,804.0,373.0,2.5833,181300.0,NEAR OCEAN\n-118.2,33.81,45.0,944.0,178.0,533.0,193.0,3.4808,206900.0,NEAR OCEAN\n-118.2,33.81,46.0,1388.0,254.0,742.0,241.0,4.6458,212100.0,NEAR OCEAN\n-118.2,33.81,47.0,2347.0,437.0,1219.0,420.0,5.3096,209900.0,NEAR OCEAN\n-118.21,33.82,34.0,1719.0,398.0,1444.0,372.0,2.8438,139300.0,NEAR OCEAN\n-118.21,33.81,43.0,905.0,199.0,764.0,204.0,3.3214,162200.0,NEAR OCEAN\n-118.21,33.81,40.0,1815.0,428.0,1807.0,413.0,3.0882,160700.0,NEAR OCEAN\n-118.21,33.82,33.0,1278.0,311.0,1157.0,320.0,3.5054,146800.0,NEAR OCEAN\n-118.22,33.82,30.0,1680.0,469.0,1779.0,429.0,3.6086,146300.0,NEAR OCEAN\n-118.21,33.82,45.0,455.0,92.0,394.0,89.0,4.9562,165700.0,NEAR OCEAN\n-118.21,33.82,43.0,1005.0,199.0,723.0,191.0,4.3424,162500.0,NEAR OCEAN\n-118.22,33.82,17.0,5357.0,1332.0,3030.0,1266.0,1.9311,138100.0,NEAR OCEAN\n-118.21,33.81,45.0,1693.0,337.0,1255.0,333.0,3.6923,159700.0,NEAR OCEAN\n-118.21,33.81,45.0,1816.0,398.0,1524.0,388.0,3.8586,157900.0,NEAR OCEAN\n-118.22,33.81,38.0,1486.0,359.0,1345.0,326.0,3.3988,147800.0,NEAR OCEAN\n-118.22,33.81,41.0,726.0,166.0,602.0,183.0,3.7885,156900.0,NEAR OCEAN\n-118.21,33.8,45.0,1160.0,274.0,1095.0,269.0,2.7308,139000.0,NEAR OCEAN\n-118.21,33.8,41.0,1251.0,279.0,1053.0,278.0,3.2778,150800.0,NEAR OCEAN\n-118.22,33.8,36.0,1285.0,347.0,1291.0,337.0,3.7708,157100.0,NEAR OCEAN\n-118.22,33.8,33.0,1984.0,477.0,1764.0,440.0,3.875,165100.0,NEAR OCEAN\n-118.22,33.79,28.0,3008.0,629.0,2537.0,596.0,2.3,137500.0,NEAR OCEAN\n-118.21,33.79,39.0,1598.0,458.0,1691.0,399.0,2.3605,141800.0,NEAR OCEAN\n-118.21,33.79,32.0,2020.0,613.0,2557.0,562.0,2.1397,145300.0,NEAR OCEAN\n-118.21,33.8,44.0,1387.0,280.0,984.0,302.0,4.25,143100.0,NEAR OCEAN\n-118.19,33.8,38.0,2010.0,595.0,1535.0,525.0,1.9848,160400.0,NEAR OCEAN\n-118.19,33.79,29.0,3497.0,1096.0,2994.0,919.0,1.8109,137500.0,NEAR OCEAN\n-118.2,33.79,47.0,767.0,195.0,569.0,195.0,2.9514,185200.0,NEAR OCEAN\n-118.2,33.79,48.0,2105.0,592.0,1807.0,539.0,2.7183,190400.0,NEAR OCEAN\n-118.2,33.79,47.0,2549.0,626.0,1388.0,606.0,3.0135,192700.0,NEAR OCEAN\n-118.2,33.8,52.0,1786.0,445.0,1090.0,430.0,2.8988,194900.0,NEAR OCEAN\n-118.19,33.8,41.0,2125.0,591.0,1604.0,555.0,2.9943,190600.0,NEAR OCEAN\n-118.2,33.8,42.0,4577.0,1146.0,2749.0,1094.0,2.5012,197500.0,NEAR OCEAN\n-118.2,33.8,45.0,2456.0,495.0,1300.0,450.0,3.9792,210200.0,NEAR OCEAN\n-118.2,33.8,52.0,1009.0,216.0,614.0,231.0,4.0074,200800.0,NEAR OCEAN\n-118.18,33.8,42.0,2301.0,621.0,2114.0,561.0,2.0579,132700.0,NEAR OCEAN\n-118.19,33.8,36.0,2326.0,729.0,2635.0,657.0,2.1985,141800.0,NEAR OCEAN\n-118.18,33.79,42.0,1571.0,435.0,1631.0,417.0,1.6384,128000.0,NEAR OCEAN\n-118.19,33.79,41.0,2114.0,612.0,2357.0,529.0,1.7938,142600.0,NEAR OCEAN\n-118.19,33.79,43.0,1823.0,600.0,2339.0,560.0,1.6792,130600.0,NEAR OCEAN\n-118.18,33.8,30.0,2734.0,758.0,2951.0,691.0,1.7689,117600.0,NEAR OCEAN\n-118.17,33.79,36.0,948.0,303.0,1042.0,301.0,1.55,100000.0,NEAR OCEAN\n-118.18,33.82,43.0,2210.0,469.0,1042.0,418.0,3.5,216700.0,NEAR OCEAN\n-118.18,33.81,27.0,471.0,132.0,315.0,96.0,1.75,154200.0,NEAR OCEAN\n-118.16,33.8,9.0,3564.0,835.0,1530.0,807.0,5.1806,175000.0,NEAR OCEAN\n-118.16,33.79,25.0,5463.0,1265.0,3010.0,1179.0,3.233,199100.0,NEAR OCEAN\n-118.17,33.8,26.0,1589.0,380.0,883.0,366.0,3.5313,187500.0,NEAR OCEAN\n-118.18,33.8,15.0,2407.0,589.0,1591.0,506.0,3.0513,148100.0,NEAR OCEAN\n-118.12,33.83,45.0,1579.0,278.0,687.0,285.0,5.0424,225900.0,<1H OCEAN\n-118.12,33.83,45.0,1734.0,331.0,797.0,293.0,4.8917,222800.0,<1H OCEAN\n-118.12,33.82,43.0,1544.0,286.0,701.0,298.0,4.1375,226000.0,<1H OCEAN\n-118.12,33.82,42.0,1493.0,277.0,671.0,267.0,3.2794,224500.0,<1H OCEAN\n-118.13,33.82,44.0,1619.0,280.0,815.0,284.0,5.5449,232200.0,<1H OCEAN\n-118.13,33.82,44.0,1785.0,307.0,779.0,291.0,4.3056,228600.0,<1H OCEAN\n-118.13,33.83,45.0,3087.0,574.0,1474.0,567.0,5.5196,227600.0,<1H OCEAN\n-118.1,33.83,36.0,1408.0,250.0,702.0,251.0,4.875,222500.0,<1H OCEAN\n-118.11,33.83,36.0,1462.0,233.0,664.0,220.0,5.1171,225300.0,<1H OCEAN\n-118.11,33.82,36.0,1999.0,390.0,887.0,379.0,3.8162,221900.0,<1H OCEAN\n-118.11,33.82,36.0,1742.0,340.0,857.0,341.0,4.6875,218200.0,<1H OCEAN\n-118.11,33.83,37.0,1249.0,202.0,517.0,189.0,4.4196,223100.0,<1H OCEAN\n-118.11,33.83,36.0,1820.0,313.0,899.0,295.0,4.918,225200.0,<1H OCEAN\n-118.09,33.83,36.0,2734.0,448.0,1308.0,441.0,5.9265,227300.0,<1H OCEAN\n-118.1,33.82,36.0,2422.0,420.0,1193.0,421.0,4.8462,225700.0,<1H OCEAN\n-118.1,33.82,36.0,1930.0,354.0,915.0,328.0,5.2713,244400.0,<1H OCEAN\n-118.1,33.83,36.0,2000.0,343.0,956.0,352.0,5.3735,234400.0,<1H OCEAN\n-118.08,33.81,20.0,6295.0,937.0,2292.0,874.0,7.6084,402500.0,<1H OCEAN\n-118.09,33.82,36.0,2219.0,393.0,1042.0,396.0,5.2299,239800.0,<1H OCEAN\n-118.09,33.81,36.0,1878.0,323.0,846.0,325.0,7.1937,254400.0,<1H OCEAN\n-118.1,33.81,36.0,1111.0,184.0,444.0,177.0,3.7031,245300.0,<1H OCEAN\n-118.1,33.81,36.0,856.0,146.0,451.0,164.0,5.1993,246000.0,<1H OCEAN\n-118.11,33.81,36.0,1252.0,209.0,558.0,214.0,3.9722,235600.0,<1H OCEAN\n-118.11,33.82,37.0,1987.0,347.0,1095.0,357.0,4.3203,232800.0,<1H OCEAN\n-118.1,33.82,36.0,1946.0,346.0,871.0,336.0,5.2155,254800.0,<1H OCEAN\n-118.11,33.82,37.0,1756.0,345.0,836.0,335.0,4.375,218200.0,<1H OCEAN\n-118.12,33.81,36.0,2565.0,458.0,1155.0,443.0,4.6087,224600.0,<1H OCEAN\n-118.12,33.81,37.0,1798.0,331.0,860.0,340.0,4.2143,228500.0,<1H OCEAN\n-118.13,33.81,37.0,1013.0,199.0,493.0,183.0,4.7845,231400.0,<1H OCEAN\n-118.13,33.81,37.0,1228.0,237.0,572.0,242.0,4.325,223900.0,<1H OCEAN\n-118.13,33.82,37.0,1530.0,290.0,711.0,283.0,5.1795,225400.0,<1H OCEAN\n-118.13,33.82,36.0,665.0,114.0,273.0,112.0,3.7321,223700.0,<1H OCEAN\n-118.13,33.81,34.0,1903.0,343.0,928.0,349.0,5.395,241900.0,<1H OCEAN\n-118.13,33.81,36.0,1749.0,322.0,855.0,319.0,4.6473,227100.0,<1H OCEAN\n-118.13,33.8,36.0,1026.0,182.0,505.0,176.0,4.3438,233600.0,<1H OCEAN\n-118.13,33.8,36.0,1496.0,271.0,743.0,265.0,4.4312,226000.0,<1H OCEAN\n-118.13,33.8,41.0,1509.0,325.0,821.0,314.0,4.0893,223000.0,<1H OCEAN\n-118.15,33.8,44.0,1886.0,399.0,1167.0,372.0,3.1042,219800.0,NEAR OCEAN\n-118.12,33.81,36.0,1774.0,299.0,784.0,298.0,5.0447,249200.0,<1H OCEAN\n-118.12,33.81,36.0,1665.0,291.0,721.0,294.0,4.6875,250700.0,<1H OCEAN\n-118.12,33.8,35.0,1835.0,435.0,774.0,418.0,2.7092,256300.0,<1H OCEAN\n-118.12,33.8,36.0,1257.0,205.0,530.0,211.0,5.3701,251400.0,<1H OCEAN\n-118.11,33.8,36.0,1837.0,319.0,810.0,305.0,4.3897,235000.0,<1H OCEAN\n-118.11,33.79,36.0,1993.0,354.0,884.0,337.0,5.587,244900.0,<1H OCEAN\n-118.11,33.79,36.0,2223.0,370.0,1039.0,370.0,5.7942,257000.0,<1H OCEAN\n-118.09,33.81,36.0,1371.0,250.0,666.0,257.0,5.0795,243300.0,<1H OCEAN\n-118.09,33.8,36.0,1724.0,322.0,838.0,328.0,4.4831,253900.0,<1H OCEAN\n-118.11,33.8,36.0,1680.0,291.0,744.0,280.0,4.66,244800.0,<1H OCEAN\n-118.11,33.8,35.0,1034.0,180.0,444.0,177.0,5.4602,231600.0,<1H OCEAN\n-118.11,33.81,37.0,1694.0,280.0,776.0,271.0,6.2187,257900.0,<1H OCEAN\n-118.1,33.81,36.0,1962.0,325.0,786.0,315.0,5.62,239600.0,<1H OCEAN\n-118.1,33.8,37.0,1814.0,329.0,850.0,328.0,5.0574,240800.0,<1H OCEAN\n-118.09,33.79,36.0,4210.0,657.0,1911.0,631.0,5.8491,247300.0,<1H OCEAN\n-118.1,33.78,35.0,4466.0,740.0,2134.0,743.0,5.7389,251800.0,<1H OCEAN\n-118.1,33.79,35.0,2370.0,379.0,996.0,380.0,5.7368,287200.0,<1H OCEAN\n-118.1,33.79,36.0,3359.0,596.0,1522.0,565.0,5.1805,249400.0,<1H OCEAN\n-118.11,33.78,16.0,3985.0,567.0,1327.0,564.0,7.9767,500001.0,<1H OCEAN\n-118.13,33.79,29.0,2937.0,524.0,1132.0,528.0,4.6133,500001.0,NEAR OCEAN\n-118.13,33.78,31.0,3039.0,739.0,1199.0,697.0,3.7232,500001.0,NEAR OCEAN\n-118.13,33.79,36.0,1245.0,211.0,508.0,221.0,5.3441,480600.0,NEAR OCEAN\n-118.12,33.79,43.0,1471.0,301.0,767.0,311.0,4.3317,232400.0,<1H OCEAN\n-118.12,33.79,41.0,1762.0,314.0,738.0,300.0,4.1687,240700.0,<1H OCEAN\n-118.13,33.79,44.0,2153.0,375.0,947.0,364.0,5.0072,236200.0,NEAR OCEAN\n-118.13,33.79,45.0,2317.0,448.0,1057.0,428.0,4.375,234800.0,NEAR OCEAN\n-118.14,33.79,45.0,1519.0,263.0,681.0,267.0,4.6452,212500.0,NEAR OCEAN\n-118.13,33.79,20.0,6678.0,1797.0,3625.0,1599.0,3.7716,242900.0,NEAR OCEAN\n-118.14,33.8,43.0,2506.0,531.0,1230.0,543.0,3.4211,203900.0,NEAR OCEAN\n-118.15,33.79,5.0,3700.0,993.0,1657.0,848.0,3.7826,196300.0,NEAR OCEAN\n-118.14,33.79,23.0,2573.0,688.0,1478.0,604.0,3.4833,209400.0,NEAR OCEAN\n-118.14,33.79,44.0,2388.0,619.0,1461.0,592.0,3.1711,215400.0,NEAR OCEAN\n-118.14,33.78,44.0,2101.0,496.0,1038.0,500.0,3.108,217900.0,NEAR OCEAN\n-118.15,33.79,25.0,4013.0,1097.0,2297.0,969.0,3.0453,185900.0,NEAR OCEAN\n-118.15,33.78,17.0,1584.0,435.0,904.0,406.0,2.0875,181300.0,NEAR OCEAN\n-118.16,33.78,14.0,1709.0,558.0,1939.0,520.0,1.9808,139100.0,NEAR OCEAN\n-118.16,33.78,33.0,2048.0,585.0,2074.0,597.0,2.0156,152700.0,NEAR OCEAN\n-118.16,33.79,25.0,3742.0,1180.0,3916.0,1063.0,2.4,153700.0,NEAR OCEAN\n-118.16,33.79,26.0,3061.0,844.0,2135.0,769.0,2.875,164000.0,NEAR OCEAN\n-118.17,33.79,32.0,2171.0,672.0,3002.0,648.0,2.375,139700.0,NEAR OCEAN\n-118.17,33.79,30.0,1349.0,519.0,2646.0,552.0,1.9318,115900.0,NEAR OCEAN\n-118.18,33.78,36.0,1697.0,550.0,1379.0,434.0,1.2746,129700.0,NEAR OCEAN\n-118.18,33.79,27.0,1580.0,510.0,1896.0,448.0,2.0186,130000.0,NEAR OCEAN\n-118.17,33.79,28.0,1219.0,408.0,1816.0,348.0,1.7589,118300.0,NEAR OCEAN\n-118.18,33.79,20.0,1255.0,360.0,1201.0,318.0,1.2206,162500.0,NEAR OCEAN\n-118.19,33.78,29.0,1170.0,369.0,1398.0,373.0,2.2543,156300.0,NEAR OCEAN\n-118.19,33.79,37.0,1834.0,551.0,1967.0,476.0,2.137,126600.0,NEAR OCEAN\n-118.19,33.79,30.0,3107.0,994.0,3543.0,850.0,1.9387,141700.0,NEAR OCEAN\n-118.19,33.78,24.0,225.0,72.0,439.0,71.0,2.8533,137500.0,NEAR OCEAN\n-118.2,33.79,25.0,2851.0,968.0,3744.0,906.0,2.0675,116700.0,NEAR OCEAN\n-118.21,33.79,33.0,32.0,18.0,96.0,36.0,4.5938,112500.0,NEAR OCEAN\n-118.21,33.79,44.0,121.0,29.0,153.0,30.0,2.1964,150000.0,NEAR OCEAN\n-118.22,33.79,48.0,143.0,41.0,222.0,50.0,1.7,104200.0,NEAR OCEAN\n-118.23,33.76,21.0,49.0,14.0,29.0,16.0,5.0,87500.0,NEAR OCEAN\n-118.19,33.78,21.0,2741.0,1029.0,2924.0,969.0,1.3274,218800.0,NEAR OCEAN\n-118.19,33.78,35.0,1511.0,593.0,914.0,539.0,0.9318,187500.0,NEAR OCEAN\n-118.2,33.78,46.0,1889.0,651.0,1545.0,587.0,1.7064,175000.0,NEAR OCEAN\n-118.2,33.78,48.0,1766.0,497.0,1908.0,466.0,1.9872,168800.0,NEAR OCEAN\n-118.2,33.78,52.0,2662.0,893.0,3018.0,763.0,2.3305,162500.0,NEAR OCEAN\n-118.19,33.77,35.0,1574.0,603.0,820.0,514.0,1.2321,137500.0,NEAR OCEAN\n-118.2,33.77,40.0,2034.0,899.0,1257.0,797.0,1.2864,131300.0,NEAR OCEAN\n-118.2,33.77,24.0,2404.0,819.0,1566.0,753.0,1.5076,145800.0,NEAR OCEAN\n-118.2,33.77,22.0,1118.0,437.0,1190.0,399.0,1.9797,143800.0,NEAR OCEAN\n-118.2,33.77,52.0,1375.0,457.0,1089.0,317.0,2.2344,200000.0,NEAR OCEAN\n-118.2,33.77,41.0,1158.0,396.0,1209.0,336.0,2.7813,129200.0,NEAR OCEAN\n-118.2,33.77,42.0,517.0,233.0,995.0,212.0,2.225,106300.0,NEAR OCEAN\n-118.18,33.77,45.0,1434.0,627.0,735.0,518.0,1.5,162500.0,NEAR OCEAN\n-118.19,33.76,25.0,1442.0,392.0,632.0,385.0,4.6629,162500.0,NEAR OCEAN\n-118.19,33.77,52.0,1562.0,616.0,692.0,512.0,1.4048,200000.0,NEAR OCEAN\n-118.18,33.77,39.0,1645.0,547.0,1339.0,499.0,1.5536,155000.0,NEAR OCEAN\n-118.18,33.77,36.0,1833.0,688.0,1128.0,620.0,1.1483,112500.0,NEAR OCEAN\n-118.19,33.77,21.0,2103.0,727.0,1064.0,603.0,1.6178,137500.0,NEAR OCEAN\n-118.19,33.77,31.0,1711.0,691.0,1092.0,568.0,1.0714,150000.0,NEAR OCEAN\n-118.18,33.78,20.0,1852.0,556.0,1712.0,556.0,1.4565,152500.0,NEAR OCEAN\n-118.18,33.78,17.0,1419.0,436.0,1300.0,360.0,2.0769,100000.0,NEAR OCEAN\n-118.18,33.78,52.0,1180.0,381.0,1046.0,332.0,1.5603,162500.0,NEAR OCEAN\n-118.19,33.78,31.0,1648.0,484.0,898.0,457.0,1.5844,162500.0,NEAR OCEAN\n-118.19,33.78,8.0,992.0,393.0,694.0,331.0,2.5544,162500.0,NEAR OCEAN\n-118.19,33.78,29.0,1013.0,392.0,1083.0,316.0,1.8438,162500.0,NEAR OCEAN\n-118.19,33.78,42.0,1021.0,300.0,533.0,187.0,1.8036,175000.0,NEAR OCEAN\n-118.17,33.78,29.0,2920.0,962.0,3580.0,772.0,1.7393,140200.0,NEAR OCEAN\n-118.17,33.78,23.0,3768.0,1261.0,3940.0,1098.0,1.9647,186200.0,NEAR OCEAN\n-118.18,33.78,26.0,3042.0,1253.0,4812.0,1141.0,1.7701,146200.0,NEAR OCEAN\n-118.17,33.78,44.0,2364.0,746.0,3184.0,672.0,1.918,147500.0,NEAR OCEAN\n-118.17,33.77,36.0,2933.0,881.0,2077.0,838.0,2.2538,181300.0,NEAR OCEAN\n-118.17,33.77,45.0,2508.0,797.0,1340.0,720.0,2.6786,191100.0,NEAR OCEAN\n-118.17,33.77,38.0,2239.0,721.0,984.0,684.0,2.346,165600.0,NEAR OCEAN\n-118.17,33.77,39.0,2953.0,878.0,1379.0,785.0,2.1378,180400.0,NEAR OCEAN\n-118.17,33.77,12.0,4409.0,1401.0,3068.0,1262.0,2.2808,154700.0,NEAR OCEAN\n-118.18,33.77,29.0,1776.0,606.0,1391.0,488.0,1.1295,137500.0,NEAR OCEAN\n-118.18,33.77,30.0,1418.0,439.0,720.0,417.0,2.6371,159400.0,NEAR OCEAN\n-118.17,33.77,37.0,1127.0,327.0,492.0,331.0,2.675,241700.0,NEAR OCEAN\n-118.18,33.74,30.0,5915.0,1750.0,2136.0,1503.0,4.0968,310000.0,NEAR OCEAN\n-118.17,33.77,45.0,2151.0,643.0,1047.0,579.0,3.1149,218800.0,NEAR OCEAN\n-118.17,33.77,45.0,2143.0,697.0,1004.0,594.0,3.0153,220000.0,NEAR OCEAN\n-118.18,33.77,37.0,2653.0,754.0,1087.0,698.0,2.3523,325000.0,NEAR OCEAN\n-118.18,33.77,41.0,2048.0,601.0,852.0,533.0,2.5726,193800.0,NEAR OCEAN\n-118.18,33.77,49.0,2297.0,759.0,1105.0,629.0,1.8388,175000.0,NEAR OCEAN\n-118.16,33.77,49.0,3382.0,787.0,1314.0,756.0,3.8125,382100.0,NEAR OCEAN\n-118.17,33.74,36.0,2006.0,453.0,807.0,426.0,3.7838,500001.0,NEAR OCEAN\n-118.16,33.77,30.0,4439.0,1105.0,1749.0,1011.0,3.8984,306300.0,NEAR OCEAN\n-118.15,33.77,39.0,2428.0,634.0,1312.0,612.0,2.7212,266300.0,NEAR OCEAN\n-118.16,33.77,30.0,2800.0,757.0,1292.0,742.0,2.7614,272200.0,NEAR OCEAN\n-118.16,33.77,38.0,3235.0,769.0,1284.0,752.0,2.9384,304100.0,NEAR OCEAN\n-118.16,33.77,29.0,3078.0,786.0,1460.0,736.0,2.875,232500.0,NEAR OCEAN\n-118.17,33.77,25.0,4405.0,1262.0,2178.0,1090.0,3.0503,225000.0,NEAR OCEAN\n-118.16,33.78,15.0,4798.0,1374.0,3087.0,1212.0,2.127,163300.0,NEAR OCEAN\n-118.16,33.78,39.0,4075.0,1085.0,2470.0,1025.0,2.3317,222500.0,NEAR OCEAN\n-118.16,33.78,52.0,3248.0,853.0,1819.0,815.0,3.1739,222900.0,NEAR OCEAN\n-118.16,33.78,29.0,3684.0,1301.0,3891.0,1143.0,1.6955,179700.0,NEAR OCEAN\n-118.15,33.78,13.0,3056.0,861.0,1600.0,824.0,3.3003,207800.0,NEAR OCEAN\n-118.15,33.78,12.0,4436.0,1133.0,2176.0,1002.0,3.5812,198600.0,NEAR OCEAN\n-118.15,33.78,35.0,2768.0,752.0,1277.0,651.0,3.6193,250000.0,NEAR OCEAN\n-118.14,33.78,42.0,1898.0,488.0,940.0,483.0,3.4107,233300.0,NEAR OCEAN\n-118.13,33.78,45.0,1016.0,172.0,361.0,163.0,7.5,434500.0,NEAR OCEAN\n-118.14,33.77,49.0,2792.0,690.0,1301.0,648.0,3.2917,307400.0,NEAR OCEAN\n-118.14,33.77,51.0,2812.0,621.0,1171.0,566.0,3.875,342900.0,NEAR OCEAN\n-118.15,33.77,52.0,2204.0,498.0,899.0,445.0,4.1765,393900.0,NEAR OCEAN\n-118.15,33.77,41.0,3448.0,896.0,1621.0,838.0,4.5,339800.0,NEAR OCEAN\n-118.15,33.77,27.0,3043.0,787.0,1398.0,747.0,3.5528,271100.0,NEAR OCEAN\n-118.14,33.76,37.0,3242.0,698.0,1080.0,629.0,3.901,432500.0,NEAR OCEAN\n-118.16,33.72,29.0,2743.0,708.0,1059.0,651.0,3.625,500000.0,NEAR OCEAN\n-118.15,33.76,36.0,2916.0,785.0,1183.0,749.0,3.5985,500001.0,NEAR OCEAN\n-118.15,33.77,36.0,4366.0,1211.0,1912.0,1172.0,3.5292,361800.0,NEAR OCEAN\n-118.13,33.76,52.0,2216.0,526.0,940.0,530.0,4.5469,381000.0,NEAR OCEAN\n-118.13,33.76,44.0,1543.0,463.0,652.0,406.0,4.25,439300.0,NEAR OCEAN\n-118.14,33.75,39.0,1995.0,634.0,867.0,567.0,4.0795,400000.0,NEAR OCEAN\n-118.14,33.76,52.0,2677.0,642.0,1144.0,624.0,4.3889,378000.0,NEAR OCEAN\n-118.14,33.76,50.0,2960.0,761.0,1179.0,718.0,3.5214,398100.0,NEAR OCEAN\n-118.14,33.76,44.0,1633.0,536.0,741.0,513.0,3.385,408300.0,NEAR OCEAN\n-118.14,33.77,52.0,2208.0,409.0,791.0,408.0,5.8408,500000.0,NEAR OCEAN\n-118.14,33.76,50.0,914.0,167.0,322.0,165.0,4.7361,418800.0,NEAR OCEAN\n-118.13,33.76,46.0,2834.0,673.0,1175.0,670.0,4.7875,363800.0,NEAR OCEAN\n-118.13,33.76,44.0,2532.0,621.0,961.0,550.0,3.9352,406900.0,NEAR OCEAN\n-118.12,33.76,43.0,3070.0,668.0,1240.0,646.0,3.7813,461500.0,NEAR OCEAN\n-118.12,33.75,47.0,3330.0,569.0,1220.0,557.0,7.3672,500001.0,NEAR OCEAN\n-118.12,33.76,45.0,3035.0,516.0,1127.0,527.0,7.0796,500001.0,NEAR OCEAN\n-118.12,33.75,41.0,2072.0,491.0,742.0,414.0,3.9934,500001.0,NEAR OCEAN\n-118.14,33.71,36.0,2484.0,525.0,792.0,446.0,5.1815,500001.0,NEAR OCEAN\n-118.1,33.76,15.0,4690.0,1002.0,1879.0,974.0,5.6051,267400.0,NEAR OCEAN\n-118.11,33.77,15.0,9103.0,1847.0,3333.0,1712.0,5.1508,367300.0,NEAR OCEAN\n-118.12,33.77,20.0,4534.0,954.0,1941.0,892.0,6.0362,463500.0,NEAR OCEAN\n-118.12,33.77,10.0,7264.0,1137.0,2528.0,1057.0,10.2233,500001.0,NEAR OCEAN\n-118.13,33.77,52.0,3697.0,691.0,1436.0,671.0,4.6852,395200.0,NEAR OCEAN\n-118.13,33.77,37.0,4365.0,926.0,1661.0,868.0,5.3046,360700.0,NEAR OCEAN\n-118.32,33.35,27.0,1675.0,521.0,744.0,331.0,2.1579,450000.0,ISLAND\n-118.33,33.34,52.0,2359.0,591.0,1100.0,431.0,2.8333,414700.0,ISLAND\n-118.32,33.33,52.0,2127.0,512.0,733.0,288.0,3.3906,300000.0,ISLAND\n-118.32,33.34,52.0,996.0,264.0,341.0,160.0,2.7361,450000.0,ISLAND\n-118.48,33.43,29.0,716.0,214.0,422.0,173.0,2.6042,287500.0,ISLAND\n-118.29,33.96,32.0,3508.0,917.0,2794.0,839.0,1.554,100000.0,<1H OCEAN\n-118.3,33.95,36.0,1786.0,418.0,1367.0,393.0,1.5078,99600.0,<1H OCEAN\n-118.29,33.95,31.0,2839.0,792.0,2390.0,729.0,2.0,109800.0,<1H OCEAN\n-118.3,33.95,41.0,2057.0,550.0,1805.0,506.0,1.2455,100800.0,<1H OCEAN\n-118.3,33.95,27.0,1774.0,444.0,1622.0,402.0,2.2031,96900.0,<1H OCEAN\n-118.3,33.95,35.0,1182.0,305.0,977.0,283.0,1.5898,94000.0,<1H OCEAN\n-118.29,33.94,38.0,2407.0,630.0,1774.0,562.0,1.5615,108600.0,<1H OCEAN\n-118.3,33.94,36.0,3504.0,862.0,2521.0,836.0,2.5679,114900.0,<1H OCEAN\n-118.3,33.94,36.0,2041.0,531.0,1390.0,464.0,2.0114,99300.0,<1H OCEAN\n-118.29,33.94,32.0,2701.0,708.0,1880.0,590.0,1.6716,123800.0,<1H OCEAN\n-118.29,33.93,32.0,1815.0,488.0,1715.0,475.0,1.7244,111200.0,<1H OCEAN\n-118.3,33.93,35.0,1300.0,356.0,1216.0,326.0,1.2,99200.0,<1H OCEAN\n-118.3,33.93,36.0,2196.0,633.0,2017.0,583.0,1.3962,124300.0,<1H OCEAN\n-118.3,33.93,40.0,2434.0,477.0,1646.0,453.0,3.2024,128000.0,<1H OCEAN\n-118.31,33.93,37.0,1282.0,244.0,852.0,249.0,4.2917,127900.0,<1H OCEAN\n-118.31,33.94,44.0,1854.0,367.0,976.0,335.0,3.6583,126700.0,<1H OCEAN\n-118.31,33.94,40.0,1917.0,438.0,1021.0,383.0,2.2448,175000.0,<1H OCEAN\n-118.31,33.94,40.0,1323.0,243.0,684.0,229.0,3.2206,145800.0,<1H OCEAN\n-118.31,33.94,40.0,1550.0,,798.0,270.0,3.775,153800.0,<1H OCEAN\n-118.31,33.93,42.0,1173.0,201.0,602.0,186.0,5.5787,142000.0,<1H OCEAN\n-118.31,33.93,43.0,1834.0,292.0,997.0,295.0,4.9464,150300.0,<1H OCEAN\n-118.32,33.94,37.0,2740.0,504.0,1468.0,479.0,4.5368,168800.0,<1H OCEAN\n-118.32,33.93,37.0,2379.0,462.0,1327.0,445.0,4.25,172100.0,<1H OCEAN\n-118.33,33.93,37.0,1831.0,356.0,925.0,338.0,4.4091,148400.0,<1H OCEAN\n-118.33,33.93,38.0,694.0,112.0,412.0,119.0,6.0718,156000.0,<1H OCEAN\n-118.34,33.93,35.0,1213.0,284.0,742.0,253.0,4.0625,159900.0,<1H OCEAN\n-118.32,33.94,37.0,1487.0,296.0,863.0,291.0,3.1563,186200.0,<1H OCEAN\n-118.32,33.94,38.0,1067.0,170.0,499.0,169.0,4.6389,183800.0,<1H OCEAN\n-118.32,33.94,36.0,1722.0,280.0,830.0,261.0,4.0536,189000.0,<1H OCEAN\n-118.32,33.94,36.0,1153.0,224.0,639.0,226.0,4.0,192000.0,<1H OCEAN\n-118.33,33.94,31.0,3757.0,1102.0,3288.0,964.0,1.9309,137500.0,<1H OCEAN\n-118.32,33.96,47.0,1453.0,247.0,721.0,276.0,5.5176,191000.0,<1H OCEAN\n-118.32,33.95,43.0,3819.0,708.0,1505.0,712.0,3.1719,183500.0,<1H OCEAN\n-118.33,33.96,24.0,6513.0,1290.0,2636.0,1271.0,4.2099,189800.0,<1H OCEAN\n-118.32,33.95,44.0,2023.0,325.0,992.0,326.0,4.6667,175600.0,<1H OCEAN\n-118.32,33.95,44.0,2131.0,360.0,1040.0,330.0,5.0912,169800.0,<1H OCEAN\n-118.34,33.95,33.0,1923.0,459.0,1412.0,361.0,5.4359,194100.0,<1H OCEAN\n-118.32,33.97,52.0,1590.0,302.0,844.0,295.0,2.7139,164900.0,<1H OCEAN\n-118.32,33.96,47.0,1885.0,361.0,954.0,357.0,3.8512,171300.0,<1H OCEAN\n-118.32,33.96,47.0,1297.0,292.0,704.0,264.0,3.3214,166500.0,<1H OCEAN\n-118.32,33.97,46.0,1504.0,270.0,814.0,306.0,4.3919,157100.0,<1H OCEAN\n-118.34,33.97,45.0,2230.0,364.0,949.0,344.0,5.5,188200.0,<1H OCEAN\n-118.33,33.96,42.0,1686.0,361.0,737.0,319.0,2.3,189200.0,<1H OCEAN\n-118.33,33.96,42.0,2084.0,517.0,1062.0,451.0,2.0057,198200.0,<1H OCEAN\n-118.35,33.97,33.0,1495.0,474.0,1272.0,447.0,2.0694,143500.0,<1H OCEAN\n-118.35,33.97,25.0,1864.0,616.0,1710.0,575.0,2.2303,159400.0,<1H OCEAN\n-118.35,33.97,26.0,1725.0,431.0,1130.0,404.0,3.2708,128100.0,<1H OCEAN\n-118.35,33.97,26.0,3832.0,1074.0,2340.0,904.0,2.6734,143400.0,<1H OCEAN\n-118.35,33.98,33.0,1884.0,477.0,1518.0,449.0,3.1194,152800.0,<1H OCEAN\n-118.35,33.98,42.0,3081.0,680.0,1785.0,609.0,3.745,170800.0,<1H OCEAN\n-118.34,33.98,47.0,2649.0,684.0,2374.0,607.0,2.3882,137700.0,<1H OCEAN\n-118.34,33.98,40.0,2108.0,526.0,1922.0,544.0,3.163,137800.0,<1H OCEAN\n-118.34,33.98,45.0,1298.0,294.0,1064.0,268.0,3.7067,136600.0,<1H OCEAN\n-118.35,33.97,30.0,1548.0,330.0,757.0,349.0,3.8056,323800.0,<1H OCEAN\n-118.35,33.96,21.0,2714.0,881.0,1549.0,853.0,1.2094,157500.0,<1H OCEAN\n-118.35,33.96,26.0,2773.0,681.0,1560.0,631.0,3.1354,164300.0,<1H OCEAN\n-118.35,33.95,28.0,4770.0,1328.0,3201.0,1196.0,2.681,147700.0,<1H OCEAN\n-118.34,33.95,25.0,3762.0,1281.0,4015.0,1178.0,2.1587,143800.0,<1H OCEAN\n-118.35,33.95,30.0,2661.0,765.0,2324.0,724.0,3.0519,137500.0,<1H OCEAN\n-118.35,33.95,42.0,1779.0,431.0,1507.0,380.0,2.8892,159800.0,<1H OCEAN\n-118.35,33.95,45.0,1076.0,213.0,781.0,238.0,3.95,164000.0,<1H OCEAN\n-118.36,33.95,42.0,1116.0,303.0,1082.0,299.0,3.7237,170800.0,<1H OCEAN\n-118.36,33.96,21.0,1802.0,556.0,1286.0,557.0,2.7284,146900.0,<1H OCEAN\n-118.36,33.96,25.0,1849.0,518.0,1498.0,451.0,2.8378,170000.0,<1H OCEAN\n-118.36,33.96,26.0,3543.0,,2742.0,951.0,2.5504,151300.0,<1H OCEAN\n-118.36,33.95,26.0,3231.0,1089.0,3193.0,1020.0,2.6535,177200.0,<1H OCEAN\n-118.36,33.98,45.0,1559.0,305.0,891.0,341.0,4.4038,259400.0,<1H OCEAN\n-118.36,33.98,46.0,1425.0,283.0,782.0,273.0,5.057,246300.0,<1H OCEAN\n-118.36,33.98,39.0,813.0,185.0,344.0,154.0,3.5833,218800.0,<1H OCEAN\n-118.37,33.97,26.0,6672.0,1729.0,3333.0,1557.0,2.9646,179800.0,<1H OCEAN\n-118.36,33.97,18.0,1284.0,283.0,990.0,289.0,4.0179,195800.0,<1H OCEAN\n-118.37,33.97,21.0,3616.0,1060.0,2515.0,945.0,2.7464,153100.0,<1H OCEAN\n-118.36,33.98,29.0,2861.0,816.0,1715.0,775.0,2.7712,160900.0,<1H OCEAN\n-118.36,33.97,19.0,4651.0,1281.0,2917.0,1121.0,2.6823,142500.0,<1H OCEAN\n-118.36,33.96,17.0,3431.0,934.0,2365.0,810.0,3.0393,129200.0,<1H OCEAN\n-118.36,33.96,37.0,2146.0,573.0,2009.0,592.0,3.6583,177300.0,<1H OCEAN\n-118.37,33.96,26.0,138.0,23.0,100.0,20.0,4.875,175000.0,<1H OCEAN\n-118.37,33.95,35.0,924.0,349.0,1376.0,358.0,2.2297,262500.0,<1H OCEAN\n-118.36,33.95,42.0,1139.0,302.0,1283.0,306.0,4.1635,163900.0,<1H OCEAN\n-118.36,33.95,42.0,2532.0,627.0,2038.0,591.0,2.875,177500.0,<1H OCEAN\n-118.37,33.95,32.0,1067.0,286.0,1053.0,277.0,2.8438,181700.0,<1H OCEAN\n-118.37,33.95,52.0,836.0,175.0,747.0,166.0,4.125,174000.0,<1H OCEAN\n-118.36,33.94,39.0,1390.0,410.0,1666.0,371.0,3.3056,156800.0,<1H OCEAN\n-118.36,33.94,38.0,2169.0,688.0,3036.0,639.0,2.3125,148500.0,<1H OCEAN\n-118.37,33.94,29.0,2265.0,813.0,3425.0,781.0,2.3675,149400.0,<1H OCEAN\n-118.36,33.93,30.0,1132.0,347.0,1433.0,341.0,2.68,170000.0,<1H OCEAN\n-118.36,33.93,40.0,1625.0,500.0,2036.0,476.0,2.6298,156500.0,<1H OCEAN\n-118.36,33.94,33.0,939.0,284.0,1309.0,250.0,3.4063,152300.0,<1H OCEAN\n-118.34,33.93,36.0,1528.0,486.0,1824.0,470.0,2.2679,153900.0,<1H OCEAN\n-118.35,33.93,35.0,1050.0,252.0,918.0,236.0,1.7344,146900.0,<1H OCEAN\n-118.35,33.93,34.0,617.0,189.0,810.0,180.0,1.9766,162500.0,<1H OCEAN\n-118.35,33.93,33.0,2040.0,576.0,2649.0,561.0,2.3375,170600.0,<1H OCEAN\n-118.35,33.94,35.0,1451.0,435.0,1888.0,420.0,2.8462,149100.0,<1H OCEAN\n-118.35,33.94,38.0,1794.0,508.0,2188.0,454.0,2.6654,142200.0,<1H OCEAN\n-118.35,33.94,36.0,2225.0,601.0,2755.0,610.0,2.5547,150400.0,<1H OCEAN\n-118.35,33.94,42.0,1028.0,278.0,1369.0,261.0,3.3125,144600.0,<1H OCEAN\n-118.34,33.94,37.0,3107.0,903.0,3456.0,734.0,2.182,147500.0,<1H OCEAN\n-118.34,33.94,36.0,2796.0,1041.0,4033.0,944.0,2.4886,160700.0,<1H OCEAN\n-118.33,33.93,37.0,4916.0,1134.0,3533.0,1035.0,3.2862,152300.0,<1H OCEAN\n-118.34,33.93,33.0,4294.0,1224.0,4512.0,1189.0,2.8304,143700.0,<1H OCEAN\n-118.34,33.93,32.0,1254.0,399.0,1281.0,386.0,2.2976,155700.0,<1H OCEAN\n-118.34,33.93,37.0,1638.0,407.0,1341.0,369.0,3.0677,167700.0,<1H OCEAN\n-118.35,33.93,26.0,3156.0,857.0,2394.0,787.0,3.01,191900.0,<1H OCEAN\n-118.35,33.93,31.0,2746.0,697.0,1973.0,598.0,3.5139,192800.0,<1H OCEAN\n-118.35,33.93,25.0,2260.0,692.0,1603.0,673.0,2.11,223300.0,<1H OCEAN\n-118.36,33.93,27.0,4445.0,1231.0,3340.0,1113.0,3.1656,204500.0,<1H OCEAN\n-118.34,33.92,29.0,1475.0,349.0,965.0,370.0,3.3558,199600.0,<1H OCEAN\n-118.35,33.92,24.0,2728.0,845.0,2023.0,773.0,2.75,239700.0,<1H OCEAN\n-118.35,33.92,29.0,736.0,232.0,584.0,231.0,3.6167,200000.0,<1H OCEAN\n-118.36,33.92,26.0,3695.0,1144.0,2308.0,1009.0,2.6667,229300.0,<1H OCEAN\n-118.36,33.92,46.0,1231.0,231.0,793.0,256.0,4.1023,226800.0,<1H OCEAN\n-118.36,33.92,19.0,2807.0,883.0,1546.0,815.0,2.6375,233800.0,<1H OCEAN\n-118.36,33.93,19.0,3103.0,918.0,2033.0,738.0,2.6961,212500.0,<1H OCEAN\n-118.37,33.93,10.0,199.0,41.0,61.0,56.0,2.8958,245800.0,<1H OCEAN\n-118.37,33.92,44.0,938.0,181.0,502.0,171.0,4.4722,218300.0,<1H OCEAN\n-118.37,33.92,36.0,1075.0,197.0,509.0,197.0,4.9688,238900.0,<1H OCEAN\n-118.37,33.92,40.0,928.0,187.0,521.0,185.0,5.5255,242700.0,<1H OCEAN\n-118.37,33.92,39.0,1073.0,206.0,556.0,204.0,4.8611,245600.0,<1H OCEAN\n-118.37,33.93,46.0,1130.0,201.0,503.0,196.0,4.4861,246300.0,<1H OCEAN\n-118.37,33.93,46.0,442.0,88.0,255.0,94.0,4.4474,246900.0,<1H OCEAN\n-118.36,33.93,44.0,520.0,116.0,392.0,106.0,3.0132,202500.0,<1H OCEAN\n-118.36,33.91,41.0,2048.0,439.0,1191.0,429.0,3.8,222500.0,<1H OCEAN\n-118.36,33.91,42.0,1949.0,422.0,1184.0,423.0,4.3333,225600.0,<1H OCEAN\n-118.36,33.9,42.0,1935.0,388.0,1136.0,379.0,4.74,230000.0,<1H OCEAN\n-118.36,33.9,40.0,1271.0,276.0,725.0,234.0,5.0452,231900.0,<1H OCEAN\n-118.37,33.91,41.0,1869.0,427.0,1334.0,435.0,3.9355,227800.0,<1H OCEAN\n-118.37,33.91,35.0,1742.0,283.0,812.0,282.0,5.6704,303700.0,<1H OCEAN\n-118.37,33.9,35.0,1651.0,269.0,707.0,252.0,5.6482,294800.0,<1H OCEAN\n-118.37,33.9,32.0,332.0,103.0,177.0,102.0,3.3409,256300.0,<1H OCEAN\n-118.38,33.91,36.0,2904.0,515.0,1463.0,534.0,5.8374,289600.0,<1H OCEAN\n-118.35,33.91,19.0,1949.0,559.0,1282.0,498.0,2.7813,231300.0,<1H OCEAN\n-118.35,33.91,31.0,2583.0,663.0,1675.0,612.0,3.5234,265000.0,<1H OCEAN\n-118.35,33.91,28.0,2108.0,534.0,1485.0,536.0,4.0775,241400.0,<1H OCEAN\n-118.35,33.91,26.0,2159.0,523.0,1331.0,520.0,3.87,264500.0,<1H OCEAN\n-118.35,33.91,25.0,1884.0,554.0,1337.0,549.0,2.8512,272800.0,<1H OCEAN\n-118.35,33.9,25.0,3309.0,902.0,2299.0,837.0,3.0417,237000.0,<1H OCEAN\n-118.35,33.91,32.0,1660.0,366.0,928.0,398.0,4.3187,269700.0,<1H OCEAN\n-118.35,33.91,29.0,2461.0,535.0,1236.0,482.0,4.8409,244000.0,<1H OCEAN\n-118.35,33.91,34.0,2055.0,448.0,1134.0,408.0,3.825,235400.0,<1H OCEAN\n-118.36,33.91,36.0,2064.0,474.0,1366.0,421.0,4.1,243100.0,<1H OCEAN\n-118.35,33.9,32.0,1056.0,225.0,565.0,231.0,3.9485,230000.0,<1H OCEAN\n-118.36,33.9,39.0,1166.0,222.0,640.0,206.0,3.5313,230400.0,<1H OCEAN\n-118.33,33.92,23.0,969.0,288.0,670.0,251.0,3.267,185400.0,<1H OCEAN\n-118.33,33.91,39.0,1224.0,312.0,1106.0,333.0,3.3491,181800.0,<1H OCEAN\n-118.34,33.91,17.0,3724.0,1023.0,2536.0,971.0,3.2649,202100.0,<1H OCEAN\n-118.33,33.91,35.0,1092.0,302.0,962.0,297.0,3.5903,183300.0,<1H OCEAN\n-118.34,33.91,8.0,3937.0,1404.0,2691.0,1142.0,2.4741,185700.0,<1H OCEAN\n-118.34,33.92,6.0,1047.0,271.0,740.0,248.0,3.425,193800.0,<1H OCEAN\n-118.34,33.91,12.0,9975.0,3638.0,7429.0,3405.0,2.6689,192300.0,<1H OCEAN\n-118.33,33.91,8.0,10731.0,3335.0,7211.0,3028.0,2.455,192700.0,<1H OCEAN\n-118.31,33.91,31.0,1415.0,339.0,874.0,289.0,3.8173,177900.0,<1H OCEAN\n-118.31,33.91,36.0,961.0,173.0,625.0,179.0,4.2596,181100.0,<1H OCEAN\n-118.32,33.91,33.0,1729.0,396.0,1073.0,344.0,4.2083,180500.0,<1H OCEAN\n-118.32,33.91,35.0,940.0,197.0,640.0,215.0,4.2,181300.0,<1H OCEAN\n-118.32,33.91,35.0,881.0,159.0,605.0,170.0,3.6654,184500.0,<1H OCEAN\n-118.32,33.91,34.0,3041.0,677.0,1920.0,640.0,4.5304,181300.0,<1H OCEAN\n-118.32,33.91,34.0,1068.0,198.0,757.0,231.0,5.7528,180500.0,<1H OCEAN\n-118.32,33.9,35.0,3189.0,680.0,1882.0,651.0,3.6625,188000.0,<1H OCEAN\n-118.31,33.92,35.0,1307.0,246.0,672.0,219.0,4.8456,146400.0,<1H OCEAN\n-118.31,33.93,35.0,1580.0,266.0,926.0,282.0,5.0653,158000.0,<1H OCEAN\n-118.32,33.93,34.0,1536.0,273.0,804.0,287.0,4.9615,157800.0,<1H OCEAN\n-118.32,33.92,35.0,1281.0,219.0,710.0,184.0,4.8304,152800.0,<1H OCEAN\n-118.29,33.93,31.0,3894.0,1017.0,3590.0,962.0,2.0437,137200.0,<1H OCEAN\n-118.29,33.92,34.0,1799.0,362.0,1293.0,355.0,3.692,145200.0,<1H OCEAN\n-118.29,33.92,34.0,1374.0,240.0,906.0,248.0,5.3292,155500.0,<1H OCEAN\n-118.3,33.92,34.0,2053.0,382.0,1258.0,380.0,3.0139,154700.0,<1H OCEAN\n-118.3,33.93,29.0,2228.0,396.0,1140.0,352.0,3.7969,169400.0,<1H OCEAN\n-118.3,33.91,30.0,1842.0,476.0,1491.0,420.0,3.0147,155100.0,<1H OCEAN\n-118.3,33.91,34.0,1617.0,493.0,1530.0,500.0,2.6182,172600.0,<1H OCEAN\n-118.31,33.9,28.0,1576.0,400.0,891.0,378.0,2.6312,171300.0,<1H OCEAN\n-118.29,33.91,28.0,1501.0,446.0,1028.0,418.0,2.3043,177500.0,<1H OCEAN\n-118.29,33.9,27.0,1013.0,394.0,1067.0,400.0,1.7714,159400.0,<1H OCEAN\n-118.3,33.9,29.0,2617.0,668.0,1868.0,647.0,3.6,208800.0,<1H OCEAN\n-118.3,33.9,27.0,3267.0,762.0,2099.0,647.0,3.4,224100.0,<1H OCEAN\n-118.3,33.9,13.0,2455.0,661.0,1975.0,618.0,2.9559,173600.0,<1H OCEAN\n-118.3,33.9,19.0,2421.0,689.0,1726.0,660.0,3.287,181400.0,<1H OCEAN\n-118.3,33.89,30.0,2756.0,858.0,1806.0,787.0,3.0329,207800.0,<1H OCEAN\n-118.3,33.89,37.0,2132.0,565.0,1369.0,565.0,3.285,218100.0,<1H OCEAN\n-118.31,33.9,38.0,1400.0,399.0,1131.0,405.0,3.5417,198400.0,<1H OCEAN\n-118.29,33.89,33.0,2138.0,567.0,1072.0,528.0,2.7428,208900.0,<1H OCEAN\n-118.29,33.89,32.0,2355.0,583.0,1605.0,571.0,4.2171,218200.0,<1H OCEAN\n-118.29,33.88,36.0,1751.0,438.0,1175.0,419.0,3.0739,218600.0,<1H OCEAN\n-118.29,33.88,21.0,4946.0,1231.0,3186.0,1167.0,3.3281,237000.0,<1H OCEAN\n-118.3,33.87,27.0,3144.0,722.0,1510.0,680.0,3.1597,214700.0,<1H OCEAN\n-118.3,33.87,31.0,1398.0,261.0,823.0,263.0,5.0641,234900.0,<1H OCEAN\n-118.3,33.88,26.0,1221.0,312.0,807.0,330.0,4.0536,253600.0,<1H OCEAN\n-118.3,33.88,29.0,850.0,229.0,563.0,204.0,3.7375,247700.0,<1H OCEAN\n-118.3,33.88,30.0,1348.0,333.0,885.0,322.0,3.2574,195300.0,<1H OCEAN\n-118.3,33.88,35.0,3227.0,749.0,1881.0,696.0,2.8445,242100.0,<1H OCEAN\n-118.31,33.88,32.0,2421.0,671.0,1491.0,587.0,3.5644,242300.0,<1H OCEAN\n-118.31,33.88,35.0,1939.0,412.0,1036.0,400.0,3.5556,238000.0,<1H OCEAN\n-118.31,33.88,21.0,1490.0,430.0,686.0,400.0,2.3812,237500.0,<1H OCEAN\n-118.31,33.9,30.0,2407.0,581.0,1724.0,531.0,3.4792,194700.0,<1H OCEAN\n-118.31,33.89,37.0,2278.0,508.0,1257.0,498.0,3.7639,220600.0,<1H OCEAN\n-118.31,33.89,35.0,2144.0,423.0,1192.0,417.0,4.1458,231500.0,<1H OCEAN\n-118.32,33.9,36.0,1520.0,300.0,831.0,291.0,4.0473,212100.0,<1H OCEAN\n-118.32,33.9,36.0,1741.0,412.0,1245.0,423.0,4.1344,210300.0,<1H OCEAN\n-118.32,33.9,37.0,1664.0,401.0,1316.0,409.0,3.0526,216400.0,<1H OCEAN\n-118.32,33.89,45.0,1928.0,453.0,1323.0,458.0,4.2813,210100.0,<1H OCEAN\n-118.32,33.89,44.0,1300.0,252.0,695.0,249.0,5.1669,220600.0,<1H OCEAN\n-118.32,33.89,34.0,2675.0,560.0,1270.0,492.0,4.5053,242000.0,<1H OCEAN\n-118.33,33.9,21.0,6603.0,1984.0,5546.0,1745.0,2.6091,163900.0,<1H OCEAN\n-118.34,33.9,23.0,2395.0,498.0,1309.0,493.0,4.9779,224600.0,<1H OCEAN\n-118.34,33.9,37.0,542.0,105.0,355.0,118.0,5.5133,227300.0,<1H OCEAN\n-118.34,33.9,36.0,1158.0,219.0,628.0,253.0,4.7426,242700.0,<1H OCEAN\n-118.34,33.9,36.0,1342.0,259.0,706.0,261.0,4.1776,236600.0,<1H OCEAN\n-118.33,33.89,39.0,1880.0,361.0,982.0,357.0,4.1953,226900.0,<1H OCEAN\n-118.33,33.89,42.0,1816.0,338.0,897.0,306.0,5.1874,230800.0,<1H OCEAN\n-118.34,33.89,36.0,2392.0,444.0,1346.0,445.0,6.0088,245900.0,<1H OCEAN\n-118.34,33.89,36.0,2274.0,411.0,1232.0,423.0,5.373,244500.0,<1H OCEAN\n-118.35,33.9,13.0,2887.0,853.0,2197.0,800.0,2.8777,207900.0,<1H OCEAN\n-118.35,33.9,31.0,1547.0,,956.0,287.0,3.4698,225000.0,<1H OCEAN\n-118.35,33.9,31.0,981.0,222.0,734.0,239.0,4.875,232400.0,<1H OCEAN\n-118.35,33.89,29.0,2940.0,708.0,2175.0,684.0,3.6486,229000.0,<1H OCEAN\n-118.35,33.89,34.0,1740.0,387.0,1249.0,375.0,4.1552,233900.0,<1H OCEAN\n-118.36,33.9,41.0,1355.0,349.0,655.0,329.0,2.9551,205000.0,<1H OCEAN\n-118.35,33.9,22.0,1127.0,287.0,697.0,241.0,3.3971,220300.0,<1H OCEAN\n-118.35,33.89,30.0,1143.0,299.0,776.0,273.0,4.2829,240000.0,<1H OCEAN\n-118.36,33.89,37.0,1719.0,426.0,1266.0,424.0,3.375,228000.0,<1H OCEAN\n-118.36,33.89,34.0,760.0,174.0,723.0,198.0,5.3169,227600.0,<1H OCEAN\n-118.36,33.9,18.0,3380.0,922.0,2276.0,854.0,4.0727,214000.0,<1H OCEAN\n-118.36,33.89,27.0,2837.0,684.0,2141.0,648.0,3.1325,215000.0,<1H OCEAN\n-118.36,33.88,33.0,2408.0,534.0,1644.0,523.0,4.2454,236800.0,<1H OCEAN\n-118.36,33.88,28.0,1313.0,319.0,827.0,308.0,2.65,260800.0,<1H OCEAN\n-118.36,33.88,25.0,2845.0,710.0,1611.0,628.0,3.2049,267400.0,<1H OCEAN\n-118.36,33.88,22.0,1388.0,336.0,930.0,287.0,2.7981,275000.0,<1H OCEAN\n-118.36,33.88,26.0,1375.0,286.0,829.0,278.0,3.9844,230700.0,<1H OCEAN\n-118.35,33.89,25.0,1769.0,440.0,1371.0,414.0,3.0833,232700.0,<1H OCEAN\n-118.35,33.88,36.0,1583.0,411.0,1097.0,350.0,4.0737,238200.0,<1H OCEAN\n-118.34,33.88,42.0,725.0,183.0,493.0,172.0,3.2589,233300.0,<1H OCEAN\n-118.35,33.88,44.0,822.0,180.0,480.0,177.0,4.4,225800.0,<1H OCEAN\n-118.35,33.88,36.0,1567.0,362.0,1054.0,386.0,3.2594,233900.0,<1H OCEAN\n-118.35,33.88,25.0,1459.0,362.0,1150.0,354.0,3.35,237500.0,<1H OCEAN\n-118.3,33.74,40.0,1896.0,416.0,950.0,383.0,2.425,255000.0,NEAR OCEAN\n-118.3,33.74,28.0,1065.0,215.0,887.0,217.0,3.9375,270500.0,NEAR OCEAN\n-118.39,33.92,41.0,80.0,20.0,61.0,23.0,5.25,247200.0,<1H OCEAN\n-118.4,33.92,25.0,1453.0,271.0,695.0,283.0,5.9499,345800.0,<1H OCEAN\n-118.4,33.92,32.0,2828.0,629.0,1313.0,534.0,4.5987,363800.0,<1H OCEAN\n-118.4,33.93,35.0,2217.0,447.0,1000.0,450.0,4.7319,376100.0,<1H OCEAN\n-118.41,33.93,38.0,3328.0,625.0,1455.0,619.0,5.0596,363900.0,<1H OCEAN\n-118.41,33.92,32.0,2590.0,607.0,1132.0,555.0,4.2333,358000.0,<1H OCEAN\n-118.41,33.92,22.0,2340.0,584.0,1141.0,554.0,4.5729,337500.0,<1H OCEAN\n-118.41,33.93,22.0,2514.0,605.0,1225.0,568.0,4.1818,339700.0,<1H OCEAN\n-118.41,33.92,38.0,1437.0,272.0,590.0,250.0,5.2338,358000.0,<1H OCEAN\n-118.41,33.92,29.0,1436.0,401.0,674.0,343.0,3.6389,275000.0,<1H OCEAN\n-118.42,33.92,25.0,3521.0,852.0,1524.0,764.0,3.8086,361300.0,<1H OCEAN\n-118.42,33.92,41.0,1621.0,279.0,756.0,277.0,5.0594,346000.0,<1H OCEAN\n-118.42,33.93,39.0,2988.0,605.0,1466.0,610.0,4.9286,341400.0,<1H OCEAN\n-118.42,33.93,28.0,4603.0,993.0,2191.0,943.0,4.5743,382200.0,<1H OCEAN\n-118.42,33.9,29.0,1929.0,523.0,686.0,455.0,5.5347,500001.0,<1H OCEAN\n-118.43,33.9,27.0,1536.0,377.0,553.0,326.0,5.4088,500001.0,<1H OCEAN\n-118.4,33.9,34.0,2674.0,435.0,1087.0,431.0,7.3151,492200.0,<1H OCEAN\n-118.4,33.9,37.0,2458.0,400.0,920.0,375.0,7.8924,500001.0,<1H OCEAN\n-118.41,33.9,39.0,2311.0,404.0,1044.0,380.0,8.468,472100.0,<1H OCEAN\n-118.41,33.9,39.0,2040.0,336.0,926.0,351.0,7.5552,500001.0,<1H OCEAN\n-118.44,33.88,35.0,2020.0,451.0,724.0,399.0,6.6494,500001.0,NEAR OCEAN\n-118.42,33.9,37.0,1576.0,345.0,662.0,340.0,5.308,500001.0,<1H OCEAN\n-118.42,33.9,43.0,1394.0,321.0,552.0,296.0,5.9596,500001.0,<1H OCEAN\n-118.41,33.89,31.0,1428.0,320.0,677.0,331.0,7.2316,500001.0,<1H OCEAN\n-118.41,33.89,35.0,1194.0,292.0,507.0,295.0,9.0812,500001.0,<1H OCEAN\n-118.41,33.89,38.0,4166.0,828.0,1600.0,770.0,6.3861,500001.0,<1H OCEAN\n-118.41,33.89,34.0,2959.0,639.0,1143.0,593.0,6.348,500001.0,<1H OCEAN\n-118.41,33.89,31.0,702.0,161.0,236.0,144.0,5.0497,500001.0,<1H OCEAN\n-118.4,33.9,38.0,2868.0,466.0,1098.0,438.0,7.9059,477100.0,<1H OCEAN\n-118.4,33.89,36.0,2334.0,430.0,1033.0,407.0,6.6321,481500.0,<1H OCEAN\n-118.4,33.89,31.0,2926.0,492.0,1149.0,476.0,7.9611,500001.0,<1H OCEAN\n-118.4,33.89,36.0,2127.0,314.0,807.0,306.0,8.1596,500001.0,<1H OCEAN\n-118.39,33.9,7.0,4314.0,725.0,1699.0,718.0,8.2037,500001.0,<1H OCEAN\n-118.38,33.89,35.0,1778.0,330.0,732.0,312.0,6.5745,379300.0,<1H OCEAN\n-118.39,33.89,30.0,2532.0,464.0,1056.0,419.0,6.3434,460400.0,<1H OCEAN\n-118.39,33.89,38.0,1851.0,332.0,750.0,314.0,7.3356,422700.0,<1H OCEAN\n-118.39,33.89,40.0,826.0,143.0,389.0,147.0,7.1845,438100.0,<1H OCEAN\n-118.36,33.89,40.0,756.0,122.0,371.0,130.0,5.0299,329200.0,<1H OCEAN\n-118.36,33.88,44.0,1362.0,237.0,709.0,247.0,4.4271,336200.0,<1H OCEAN\n-118.37,33.88,44.0,1325.0,245.0,669.0,253.0,4.4211,324000.0,<1H OCEAN\n-118.37,33.88,21.0,966.0,172.0,417.0,158.0,5.5335,342600.0,<1H OCEAN\n-118.38,33.88,33.0,1313.0,244.0,561.0,217.0,5.2999,359400.0,<1H OCEAN\n-118.37,33.89,21.0,2696.0,548.0,1142.0,473.0,5.6091,356800.0,<1H OCEAN\n-118.37,33.88,20.0,2439.0,474.0,1219.0,497.0,5.9619,335900.0,<1H OCEAN\n-118.36,33.88,31.0,2518.0,543.0,1107.0,508.0,4.7404,295800.0,<1H OCEAN\n-118.36,33.87,19.0,2512.0,575.0,1275.0,544.0,4.9375,293000.0,<1H OCEAN\n-118.37,33.87,19.0,757.0,148.0,361.0,141.0,6.02,304200.0,<1H OCEAN\n-118.37,33.88,27.0,1688.0,331.0,811.0,327.0,4.5357,334200.0,<1H OCEAN\n-118.38,33.87,27.0,2287.0,491.0,1101.0,466.0,4.675,316900.0,<1H OCEAN\n-118.38,33.88,27.0,3039.0,606.0,1421.0,564.0,5.5771,345500.0,<1H OCEAN\n-118.37,33.88,26.0,2620.0,530.0,1282.0,525.0,4.4828,340700.0,<1H OCEAN\n-118.36,33.87,17.0,1082.0,291.0,598.0,281.0,3.9868,281900.0,<1H OCEAN\n-118.36,33.87,22.0,2114.0,541.0,1300.0,538.0,3.4208,290000.0,<1H OCEAN\n-118.37,33.87,18.0,2516.0,485.0,1128.0,433.0,5.0114,338600.0,<1H OCEAN\n-118.37,33.87,13.0,2907.0,726.0,1573.0,694.0,3.5048,294000.0,<1H OCEAN\n-118.36,33.86,35.0,2126.0,434.0,1044.0,433.0,5.5456,297400.0,<1H OCEAN\n-118.36,33.86,37.0,1768.0,314.0,802.0,290.0,5.0784,295900.0,<1H OCEAN\n-118.36,33.86,34.0,1865.0,345.0,963.0,302.0,5.543,305900.0,<1H OCEAN\n-118.37,33.86,28.0,2685.0,581.0,1243.0,529.0,4.119,324000.0,<1H OCEAN\n-118.37,33.87,23.0,1829.0,331.0,891.0,356.0,6.5755,359900.0,<1H OCEAN\n-118.38,33.87,21.0,4151.0,1018.0,2054.0,925.0,4.9821,292900.0,<1H OCEAN\n-118.38,33.87,17.0,2791.0,579.0,1467.0,583.0,5.7415,321900.0,<1H OCEAN\n-118.38,33.87,23.0,2387.0,418.0,1008.0,415.0,5.8518,337900.0,<1H OCEAN\n-118.39,33.87,19.0,3303.0,584.0,1329.0,569.0,7.521,340400.0,<1H OCEAN\n-118.38,33.86,12.0,4235.0,735.0,1798.0,683.0,6.4242,365500.0,<1H OCEAN\n-118.38,33.86,29.0,2787.0,475.0,1182.0,444.0,6.7613,352700.0,<1H OCEAN\n-118.38,33.86,24.0,3124.0,560.0,1312.0,542.0,6.3021,333800.0,<1H OCEAN\n-118.38,33.86,15.0,1778.0,311.0,908.0,330.0,7.674,329300.0,<1H OCEAN\n-118.38,33.87,33.0,1993.0,371.0,918.0,361.0,6.9021,337600.0,<1H OCEAN\n-118.38,33.88,39.0,1489.0,282.0,743.0,270.0,4.8611,456100.0,<1H OCEAN\n-118.38,33.88,34.0,1830.0,315.0,822.0,307.0,5.0602,453700.0,<1H OCEAN\n-118.39,33.88,33.0,2496.0,387.0,1098.0,404.0,7.6685,474300.0,<1H OCEAN\n-118.39,33.88,34.0,1973.0,367.0,843.0,345.0,6.077,472700.0,<1H OCEAN\n-118.39,33.88,33.0,2543.0,439.0,1098.0,416.0,5.9683,495500.0,<1H OCEAN\n-118.39,33.88,31.0,1448.0,244.0,607.0,259.0,8.1513,500001.0,<1H OCEAN\n-118.39,33.88,35.0,1267.0,216.0,521.0,191.0,6.0441,470000.0,<1H OCEAN\n-118.38,33.88,36.0,2501.0,443.0,1031.0,422.0,4.75,442100.0,<1H OCEAN\n-118.4,33.88,36.0,3022.0,482.0,1278.0,494.0,7.2651,500001.0,<1H OCEAN\n-118.4,33.88,36.0,1543.0,214.0,474.0,187.0,9.3399,500001.0,<1H OCEAN\n-118.4,33.88,35.0,1753.0,296.0,615.0,275.0,7.5,500001.0,<1H OCEAN\n-118.41,33.88,34.0,540.0,107.0,213.0,104.0,6.3403,500001.0,<1H OCEAN\n-118.4,33.88,35.0,1060.0,191.0,444.0,196.0,8.0015,500001.0,<1H OCEAN\n-118.41,33.88,43.0,2492.0,449.0,1033.0,437.0,7.9614,500001.0,<1H OCEAN\n-118.41,33.88,40.0,925.0,254.0,371.0,227.0,5.2533,500001.0,<1H OCEAN\n-118.41,33.88,34.0,1471.0,308.0,498.0,264.0,7.0842,500001.0,<1H OCEAN\n-118.43,33.87,41.0,847.0,173.0,344.0,170.0,6.822,500001.0,NEAR OCEAN\n-118.4,33.87,26.0,6712.0,1441.0,2803.0,1394.0,5.2276,434500.0,<1H OCEAN\n-118.39,33.87,34.0,2395.0,469.0,1087.0,438.0,5.9683,394600.0,<1H OCEAN\n-118.43,33.86,34.0,358.0,87.0,162.0,84.0,7.1264,500001.0,NEAR OCEAN\n-118.4,33.88,42.0,1516.0,341.0,634.0,327.0,6.2356,472700.0,<1H OCEAN\n-118.4,33.87,40.0,1679.0,372.0,719.0,385.0,6.435,479500.0,<1H OCEAN\n-118.4,33.87,38.0,2398.0,431.0,911.0,392.0,5.2319,500001.0,<1H OCEAN\n-118.4,33.87,45.0,2093.0,497.0,842.0,472.0,6.3231,500001.0,<1H OCEAN\n-118.4,33.87,34.0,3145.0,786.0,1352.0,727.0,5.0976,469800.0,<1H OCEAN\n-118.4,33.87,45.0,2181.0,505.0,965.0,471.0,5.3816,500001.0,<1H OCEAN\n-118.39,33.86,34.0,2361.0,442.0,915.0,437.0,5.687,392400.0,<1H OCEAN\n-118.39,33.86,28.0,3619.0,764.0,1735.0,789.0,6.1404,368400.0,<1H OCEAN\n-118.39,33.86,24.0,2386.0,582.0,1152.0,568.0,4.8971,400700.0,<1H OCEAN\n-118.4,33.86,18.0,5152.0,1365.0,2286.0,1243.0,5.1677,380800.0,<1H OCEAN\n-118.4,33.85,29.0,2085.0,533.0,919.0,489.0,5.6017,430000.0,<1H OCEAN\n-118.4,33.86,41.0,2237.0,597.0,938.0,523.0,4.7105,500001.0,<1H OCEAN\n-118.42,33.85,43.0,1584.0,477.0,799.0,433.0,5.0322,435000.0,NEAR OCEAN\n-118.38,33.85,28.0,4430.0,928.0,2131.0,885.0,4.9384,378100.0,<1H OCEAN\n-118.38,33.85,31.0,3533.0,729.0,1647.0,679.0,5.5843,384600.0,<1H OCEAN\n-118.39,33.85,24.0,4373.0,871.0,1830.0,824.0,5.7128,366200.0,<1H OCEAN\n-118.41,33.85,16.0,6123.0,1989.0,2853.0,1789.0,4.425,336400.0,NEAR OCEAN\n-118.39,33.85,17.0,1610.0,379.0,670.0,341.0,4.3594,349000.0,<1H OCEAN\n-118.38,33.84,25.0,5775.0,1149.0,2637.0,1117.0,5.4968,379800.0,<1H OCEAN\n-118.38,33.83,20.0,2270.0,498.0,1070.0,521.0,4.4615,384800.0,<1H OCEAN\n-118.38,33.83,40.0,3070.0,570.0,1264.0,506.0,5.1626,432700.0,<1H OCEAN\n-118.38,33.84,26.0,2869.0,567.0,1157.0,538.0,6.0382,355300.0,<1H OCEAN\n-118.39,33.84,16.0,2472.0,572.0,965.0,529.0,5.137,348600.0,<1H OCEAN\n-118.43,33.83,19.0,6206.0,1611.0,2455.0,1472.0,5.145,420200.0,NEAR OCEAN\n-118.39,33.83,32.0,2075.0,539.0,954.0,519.0,5.637,500001.0,NEAR OCEAN\n-118.39,33.82,30.0,3433.0,918.0,1526.0,828.0,4.5817,500001.0,NEAR OCEAN\n-118.43,33.82,34.0,2112.0,614.0,946.0,574.0,4.6048,500001.0,NEAR OCEAN\n-118.37,33.82,32.0,2815.0,607.0,1338.0,609.0,4.5687,381200.0,<1H OCEAN\n-118.38,33.82,35.0,3053.0,623.0,1311.0,589.0,5.1589,439200.0,NEAR OCEAN\n-118.38,33.82,38.0,1318.0,237.0,547.0,225.0,6.0308,416700.0,NEAR OCEAN\n-118.38,33.83,35.0,2152.0,454.0,902.0,414.0,4.5179,427200.0,<1H OCEAN\n-118.31,33.88,33.0,2147.0,505.0,1371.0,498.0,2.4219,260700.0,<1H OCEAN\n-118.32,33.87,28.0,3763.0,762.0,1967.0,724.0,5.3244,271900.0,<1H OCEAN\n-118.32,33.88,35.0,1818.0,339.0,828.0,319.0,4.3036,282100.0,<1H OCEAN\n-118.32,33.88,34.0,1803.0,341.0,947.0,333.0,5.5538,280300.0,<1H OCEAN\n-118.32,33.88,37.0,1402.0,254.0,722.0,251.0,6.4781,269000.0,<1H OCEAN\n-118.33,33.88,30.0,1856.0,444.0,899.0,435.0,3.1505,270000.0,<1H OCEAN\n-118.33,33.88,36.0,1271.0,346.0,811.0,345.0,3.2417,283300.0,<1H OCEAN\n-118.33,33.87,44.0,724.0,133.0,373.0,133.0,3.9167,265600.0,<1H OCEAN\n-118.34,33.87,28.0,4605.0,1188.0,2558.0,1093.0,3.6988,266600.0,<1H OCEAN\n-118.34,33.88,31.0,3122.0,727.0,1885.0,715.0,3.8657,298400.0,<1H OCEAN\n-118.31,33.87,27.0,3348.0,695.0,1545.0,625.0,4.7543,296300.0,<1H OCEAN\n-118.31,33.86,29.0,2243.0,361.0,1051.0,352.0,6.6632,325200.0,<1H OCEAN\n-118.32,33.86,32.0,3485.0,678.0,1715.0,649.0,4.6563,291700.0,<1H OCEAN\n-118.32,33.87,35.0,2380.0,404.0,1212.0,422.0,5.6254,283800.0,<1H OCEAN\n-118.34,33.87,34.0,1069.0,217.0,601.0,212.0,4.6406,255900.0,<1H OCEAN\n-118.33,33.87,36.0,2219.0,406.0,1219.0,403.0,4.2614,267100.0,<1H OCEAN\n-118.33,33.87,35.0,743.0,128.0,385.0,137.0,6.4891,278100.0,<1H OCEAN\n-118.32,33.86,34.0,495.0,90.0,269.0,93.0,6.4391,252300.0,<1H OCEAN\n-118.33,33.86,38.0,914.0,176.0,519.0,174.0,6.0335,255400.0,<1H OCEAN\n-118.33,33.86,36.0,854.0,160.0,473.0,150.0,6.3992,259600.0,<1H OCEAN\n-118.33,33.86,38.0,1059.0,218.0,561.0,205.0,4.0,248600.0,<1H OCEAN\n-118.34,33.86,36.0,2223.0,360.0,1162.0,376.0,5.259,279400.0,<1H OCEAN\n-118.34,33.86,35.0,1936.0,343.0,1008.0,346.0,5.4791,285900.0,<1H OCEAN\n-118.34,33.87,28.0,2948.0,566.0,1445.0,524.0,5.3743,286500.0,<1H OCEAN\n-118.35,33.87,37.0,1420.0,286.0,886.0,290.0,4.5833,261300.0,<1H OCEAN\n-118.35,33.87,34.0,2823.0,500.0,1429.0,483.0,5.5,279600.0,<1H OCEAN\n-118.35,33.87,28.0,2319.0,579.0,1369.0,564.0,3.6169,257000.0,<1H OCEAN\n-118.35,33.86,28.0,2075.0,463.0,1216.0,446.0,3.9732,281500.0,<1H OCEAN\n-118.35,33.86,24.0,2139.0,481.0,971.0,418.0,4.3859,271300.0,<1H OCEAN\n-118.34,33.84,36.0,1561.0,252.0,740.0,253.0,6.2778,309700.0,<1H OCEAN\n-118.33,33.84,36.0,1364.0,251.0,668.0,245.0,5.3131,314100.0,<1H OCEAN\n-118.34,33.84,36.0,1407.0,231.0,676.0,231.0,5.269,331900.0,<1H OCEAN\n-118.34,33.83,34.0,1761.0,329.0,965.0,329.0,5.399,358500.0,<1H OCEAN\n-118.34,33.83,35.0,1818.0,353.0,853.0,321.0,5.8972,350900.0,<1H OCEAN\n-118.36,33.86,37.0,1249.0,218.0,583.0,214.0,5.7422,330700.0,<1H OCEAN\n-118.35,33.85,34.0,1770.0,291.0,916.0,289.0,5.0,354200.0,<1H OCEAN\n-118.35,33.85,35.0,1248.0,206.0,551.0,185.0,5.6426,348200.0,<1H OCEAN\n-118.36,33.85,36.0,1390.0,230.0,683.0,219.0,4.8906,334400.0,<1H OCEAN\n-118.36,33.86,36.0,681.0,122.0,360.0,128.0,5.2799,332600.0,<1H OCEAN\n-118.37,33.85,34.0,2415.0,404.0,1278.0,414.0,6.1599,341200.0,<1H OCEAN\n-118.36,33.85,34.0,1086.0,197.0,509.0,158.0,6.1133,349300.0,<1H OCEAN\n-118.37,33.85,25.0,5622.0,998.0,2537.0,1009.0,5.785,395300.0,<1H OCEAN\n-118.35,33.84,22.0,13133.0,3680.0,7180.0,3522.0,3.5414,354700.0,<1H OCEAN\n-118.36,33.84,22.0,11016.0,3170.0,6664.0,2838.0,3.703,361300.0,<1H OCEAN\n-118.37,33.85,29.0,3662.0,586.0,1626.0,611.0,6.3974,410000.0,<1H OCEAN\n-118.37,33.84,27.0,3245.0,605.0,1572.0,556.0,5.3773,379000.0,<1H OCEAN\n-118.35,33.83,36.0,1102.0,193.0,522.0,172.0,6.1187,342000.0,<1H OCEAN\n-118.36,33.83,36.0,1660.0,300.0,943.0,300.0,5.1984,353600.0,<1H OCEAN\n-118.36,33.84,35.0,1577.0,279.0,743.0,274.0,5.7654,343000.0,<1H OCEAN\n-118.37,33.84,35.0,1792.0,322.0,978.0,326.0,4.9583,342800.0,<1H OCEAN\n-118.36,33.83,35.0,1378.0,247.0,645.0,217.0,5.9143,343400.0,<1H OCEAN\n-118.36,33.83,35.0,2828.0,487.0,1439.0,490.0,5.6013,350200.0,<1H OCEAN\n-118.37,33.83,35.0,1207.0,207.0,601.0,213.0,4.7308,353400.0,<1H OCEAN\n-118.37,33.84,32.0,1751.0,328.0,819.0,323.0,6.7105,339000.0,<1H OCEAN\n-118.33,33.83,5.0,13038.0,2679.0,5272.0,2523.0,5.5023,286400.0,<1H OCEAN\n-118.32,33.85,42.0,3146.0,770.0,1859.0,740.0,3.5073,234800.0,<1H OCEAN\n-118.31,33.84,52.0,1819.0,464.0,1068.0,424.0,3.625,270700.0,<1H OCEAN\n-118.32,33.83,51.0,2399.0,516.0,1160.0,514.0,3.8456,318900.0,<1H OCEAN\n-118.32,33.84,42.0,1486.0,420.0,897.0,377.0,1.6228,376100.0,<1H OCEAN\n-118.31,33.83,50.0,696.0,311.0,382.0,234.0,2.775,225000.0,<1H OCEAN\n-118.31,33.83,45.0,2929.0,755.0,1635.0,652.0,2.9375,273000.0,<1H OCEAN\n-118.31,33.82,35.0,3462.0,814.0,1902.0,700.0,3.402,279900.0,<1H OCEAN\n-118.32,33.83,19.0,3792.0,790.0,2105.0,834.0,5.2363,310000.0,<1H OCEAN\n-118.31,33.82,39.0,2198.0,425.0,1160.0,436.0,4.1406,323700.0,<1H OCEAN\n-118.31,33.82,26.0,2345.0,408.0,1195.0,377.0,5.4925,361700.0,<1H OCEAN\n-118.31,33.81,23.0,3942.0,748.0,1679.0,711.0,4.1169,362600.0,<1H OCEAN\n-118.31,33.81,30.0,1773.0,356.0,905.0,352.0,4.3056,336000.0,<1H OCEAN\n-118.32,33.82,25.0,2587.0,512.0,1219.0,509.0,4.4271,382100.0,<1H OCEAN\n-118.32,33.82,22.0,2508.0,402.0,1254.0,395.0,7.0935,379500.0,<1H OCEAN\n-118.32,33.81,28.0,2142.0,445.0,1140.0,422.0,4.8438,346200.0,<1H OCEAN\n-118.32,33.81,27.0,2113.0,380.0,1109.0,360.0,4.7062,357000.0,<1H OCEAN\n-118.33,33.82,26.0,5591.0,934.0,2824.0,939.0,6.5861,417800.0,<1H OCEAN\n-118.34,33.8,25.0,4177.0,832.0,2123.0,789.0,5.0814,446800.0,<1H OCEAN\n-118.35,33.82,28.0,7591.0,1710.0,3420.0,1635.0,4.0708,328900.0,<1H OCEAN\n-118.36,33.82,36.0,1784.0,311.0,901.0,293.0,6.2247,339000.0,<1H OCEAN\n-118.37,33.82,39.0,2794.0,444.0,1319.0,441.0,5.878,387800.0,<1H OCEAN\n-118.37,33.82,36.0,2463.0,447.0,1125.0,424.0,6.0176,352700.0,<1H OCEAN\n-118.37,33.82,36.0,2416.0,394.0,1115.0,386.0,6.256,366900.0,<1H OCEAN\n-118.36,33.82,36.0,1083.0,187.0,522.0,187.0,5.7765,339500.0,<1H OCEAN\n-118.36,33.82,26.0,5166.0,1313.0,2738.0,1239.0,3.3565,360800.0,<1H OCEAN\n-118.36,33.82,28.0,67.0,15.0,49.0,11.0,6.1359,330000.0,<1H OCEAN\n-118.36,33.81,25.0,9042.0,2022.0,4458.0,1944.0,4.5592,378800.0,<1H OCEAN\n-118.36,33.81,26.0,1575.0,300.0,881.0,309.0,5.1778,359900.0,<1H OCEAN\n-118.37,33.81,36.0,2031.0,339.0,817.0,337.0,5.1271,458300.0,NEAR OCEAN\n-118.38,33.81,33.0,2349.0,407.0,954.0,373.0,6.4956,483600.0,NEAR OCEAN\n-118.38,33.81,20.0,1975.0,306.0,703.0,292.0,8.5491,410300.0,NEAR OCEAN\n-118.44,33.81,33.0,3994.0,990.0,1647.0,931.0,5.0106,500001.0,NEAR OCEAN\n-118.38,33.82,34.0,1822.0,364.0,750.0,366.0,5.9907,500001.0,NEAR OCEAN\n-118.36,33.81,34.0,2211.0,502.0,1113.0,488.0,4.7026,356800.0,<1H OCEAN\n-118.37,33.81,33.0,5057.0,790.0,2021.0,748.0,6.8553,482200.0,NEAR OCEAN\n-118.37,33.81,36.0,1283.0,209.0,563.0,209.0,6.9296,500001.0,NEAR OCEAN\n-118.38,33.81,41.0,1889.0,301.0,802.0,278.0,6.015,488500.0,NEAR OCEAN\n-118.38,33.81,39.0,2400.0,373.0,877.0,372.0,5.7361,500001.0,NEAR OCEAN\n-118.39,33.81,35.0,1008.0,165.0,391.0,167.0,3.7778,487500.0,NEAR OCEAN\n-118.38,33.81,36.0,1018.0,148.0,329.0,169.0,10.5045,500001.0,NEAR OCEAN\n-118.33,33.79,29.0,4389.0,873.0,2069.0,901.0,4.1071,365600.0,<1H OCEAN\n-118.34,33.79,36.0,716.0,123.0,388.0,124.0,5.0254,350000.0,<1H OCEAN\n-118.34,33.8,34.0,1730.0,427.0,1008.0,393.0,3.9408,327700.0,<1H OCEAN\n-118.34,33.8,33.0,2194.0,469.0,987.0,397.0,5.0951,318900.0,<1H OCEAN\n-118.35,33.8,32.0,1434.0,296.0,672.0,285.0,4.875,311700.0,<1H OCEAN\n-118.35,33.8,19.0,6224.0,1105.0,3152.0,1076.0,5.9541,500001.0,<1H OCEAN\n-118.31,33.8,29.0,2795.0,572.0,1469.0,557.0,3.7167,308900.0,<1H OCEAN\n-118.32,33.8,29.0,3254.0,717.0,1593.0,680.0,4.0536,285800.0,<1H OCEAN\n-118.31,33.79,35.0,2290.0,563.0,1374.0,530.0,3.2472,254700.0,<1H OCEAN\n-118.31,33.8,31.0,4464.0,991.0,2420.0,947.0,4.0425,277900.0,<1H OCEAN\n-118.32,33.8,39.0,1415.0,298.0,729.0,278.0,3.1648,244800.0,<1H OCEAN\n-118.32,33.79,35.0,2924.0,658.0,1675.0,602.0,3.8287,279900.0,<1H OCEAN\n-118.32,33.79,32.0,2381.0,467.0,1264.0,488.0,4.1477,315100.0,<1H OCEAN\n-118.32,33.8,29.0,4317.0,1037.0,2102.0,959.0,3.1275,286400.0,<1H OCEAN\n-118.31,33.79,38.0,1601.0,352.0,711.0,304.0,3.3958,250000.0,<1H OCEAN\n-118.31,33.78,30.0,4573.0,819.0,2411.0,819.0,3.5804,383800.0,<1H OCEAN\n-118.32,33.79,21.0,6638.0,1634.0,3240.0,1568.0,3.6797,271100.0,<1H OCEAN\n-118.34,33.78,25.0,11016.0,1626.0,4168.0,1584.0,8.1782,500001.0,NEAR OCEAN\n-118.36,33.79,34.0,5166.0,704.0,2071.0,668.0,8.3609,500001.0,NEAR OCEAN\n-118.37,33.79,36.0,1596.0,234.0,654.0,223.0,8.2064,500001.0,NEAR OCEAN\n-118.36,33.8,38.0,2553.0,400.0,1042.0,393.0,6.9742,500001.0,NEAR OCEAN\n-118.36,33.8,34.0,2629.0,369.0,966.0,375.0,10.1241,500001.0,NEAR OCEAN\n-118.38,33.8,36.0,4421.0,702.0,1433.0,624.0,8.0838,500001.0,NEAR OCEAN\n-118.45,33.8,31.0,4803.0,575.0,1490.0,577.0,11.9993,500001.0,NEAR OCEAN\n-118.39,33.79,30.0,4402.0,563.0,1582.0,551.0,10.898,500001.0,NEAR OCEAN\n-118.4,33.78,24.0,4787.0,562.0,1653.0,548.0,12.9758,500001.0,NEAR OCEAN\n-118.44,33.79,27.0,2141.0,260.0,635.0,240.0,11.6648,500001.0,NEAR OCEAN\n-118.41,33.77,22.0,7554.0,991.0,2808.0,946.0,10.06,500001.0,NEAR OCEAN\n-118.46,33.77,28.0,3065.0,406.0,1101.0,391.0,10.5536,500001.0,NEAR OCEAN\n-118.42,33.78,36.0,2093.0,303.0,802.0,300.0,8.0957,500001.0,NEAR OCEAN\n-118.37,33.77,26.0,6339.0,876.0,2540.0,880.0,10.1447,500001.0,NEAR OCEAN\n-118.38,33.77,21.0,11353.0,1537.0,4649.0,1504.0,9.821,500001.0,NEAR OCEAN\n-118.38,33.77,17.0,10950.0,2207.0,4713.0,2043.0,6.3064,418300.0,NEAR OCEAN\n-118.38,33.79,32.0,10445.0,1620.0,4474.0,1576.0,7.7042,500001.0,NEAR OCEAN\n-118.4,33.78,26.0,5005.0,776.0,2357.0,790.0,8.5421,500001.0,NEAR OCEAN\n-118.42,33.75,22.0,17591.0,2604.0,6897.0,2492.0,8.2831,500001.0,NEAR OCEAN\n-118.34,33.76,34.0,5586.0,674.0,1871.0,636.0,15.0001,500001.0,NEAR OCEAN\n-118.35,33.74,25.0,8272.0,1132.0,3392.0,1132.0,10.0973,500001.0,NEAR OCEAN\n-118.38,33.75,23.0,8277.0,1290.0,3176.0,1159.0,7.6986,500001.0,NEAR OCEAN\n-118.39,33.71,18.0,1193.0,233.0,475.0,228.0,7.5594,500001.0,NEAR OCEAN\n-118.41,33.75,4.0,311.0,51.0,128.0,46.0,9.8091,500001.0,NEAR OCEAN\n-118.31,33.77,20.0,5776.0,956.0,2757.0,936.0,6.6447,416800.0,<1H OCEAN\n-118.31,33.76,26.0,4486.0,709.0,1873.0,719.0,6.5704,414700.0,<1H OCEAN\n-118.31,33.75,36.0,2715.0,474.0,1303.0,457.0,4.6042,357300.0,NEAR OCEAN\n-118.31,33.75,34.0,2338.0,393.0,1031.0,373.0,6.287,396400.0,NEAR OCEAN\n-118.33,33.77,33.0,4244.0,595.0,1534.0,557.0,9.8214,500001.0,NEAR OCEAN\n-118.32,33.75,33.0,2996.0,398.0,1048.0,387.0,9.267,500001.0,NEAR OCEAN\n-118.32,33.75,37.0,1080.0,135.0,366.0,142.0,11.6677,500001.0,NEAR OCEAN\n-118.32,33.74,24.0,6097.0,794.0,2248.0,806.0,10.1357,500001.0,NEAR OCEAN\n-118.32,33.77,37.0,627.0,95.0,259.0,106.0,6.887,500001.0,<1H OCEAN\n-118.34,34.09,37.0,1442.0,501.0,998.0,503.0,2.4432,200000.0,<1H OCEAN\n-118.35,34.09,35.0,1989.0,634.0,1108.0,593.0,1.6081,288900.0,<1H OCEAN\n-118.35,34.09,35.0,2705.0,785.0,1526.0,793.0,3.0349,266700.0,<1H OCEAN\n-118.35,34.09,35.0,2234.0,689.0,1334.0,662.0,2.5444,236100.0,<1H OCEAN\n-118.36,34.09,34.0,2832.0,883.0,1594.0,843.0,1.7558,312500.0,<1H OCEAN\n-118.36,34.09,36.0,1616.0,465.0,773.0,429.0,2.6,313600.0,<1H OCEAN\n-118.36,34.09,33.0,3463.0,1170.0,1845.0,1134.0,2.0205,243800.0,<1H OCEAN\n-118.36,34.09,30.0,2353.0,728.0,1365.0,718.0,2.0702,283300.0,<1H OCEAN\n-118.36,34.1,37.0,7097.0,2010.0,2913.0,1939.0,2.875,300000.0,<1H OCEAN\n-118.36,34.09,36.0,1390.0,458.0,874.0,468.0,2.5812,200000.0,<1H OCEAN\n-118.36,34.09,28.0,1111.0,300.0,526.0,294.0,2.6136,383300.0,<1H OCEAN\n-118.37,34.09,38.0,1349.0,344.0,547.0,309.0,3.2159,383300.0,<1H OCEAN\n-118.37,34.09,38.0,4408.0,1295.0,1690.0,1229.0,3.0156,300000.0,<1H OCEAN\n-118.37,34.09,33.0,3180.0,865.0,1347.0,841.0,4.0651,500001.0,<1H OCEAN\n-118.37,34.09,31.0,6348.0,1827.0,2559.0,1755.0,3.2818,225000.0,<1H OCEAN\n-118.37,34.09,22.0,4247.0,1253.0,1766.0,1170.0,3.1517,341700.0,<1H OCEAN\n-118.37,34.09,24.0,630.0,172.0,257.0,147.0,5.5224,400000.0,<1H OCEAN\n-118.37,34.08,28.0,4376.0,1202.0,1847.0,1128.0,2.6713,312500.0,<1H OCEAN\n-118.38,34.08,52.0,1479.0,360.0,652.0,346.0,2.945,431400.0,<1H OCEAN\n-118.38,34.08,30.0,4524.0,1312.0,1910.0,1243.0,2.8889,335300.0,<1H OCEAN\n-118.39,34.08,52.0,1244.0,304.0,444.0,282.0,3.5114,430800.0,<1H OCEAN\n-118.38,34.08,48.0,1226.0,288.0,370.0,264.0,3.9375,450000.0,<1H OCEAN\n-118.38,34.09,24.0,8264.0,2437.0,3148.0,2274.0,3.5659,281300.0,<1H OCEAN\n-118.38,34.09,28.0,4001.0,1352.0,1799.0,1220.0,2.5784,272900.0,<1H OCEAN\n-118.39,34.09,27.0,4312.0,1214.0,1634.0,1097.0,3.6207,362500.0,<1H OCEAN\n-118.39,34.09,28.0,2347.0,608.0,785.0,548.0,4.4167,425000.0,<1H OCEAN\n-118.39,34.08,28.0,833.0,230.0,349.0,210.0,3.067,375000.0,<1H OCEAN\n-118.4,34.1,27.0,3979.0,510.0,1351.0,520.0,15.0001,500001.0,<1H OCEAN\n-118.4,34.09,45.0,2686.0,283.0,857.0,259.0,15.0001,500001.0,<1H OCEAN\n-118.39,34.08,52.0,3759.0,464.0,1407.0,422.0,15.0001,500001.0,<1H OCEAN\n-118.4,34.08,52.0,3815.0,439.0,1266.0,413.0,15.0001,500001.0,<1H OCEAN\n-118.41,34.09,37.0,2716.0,302.0,809.0,291.0,15.0001,500001.0,<1H OCEAN\n-118.42,34.09,40.0,3552.0,392.0,1024.0,370.0,15.0001,500001.0,<1H OCEAN\n-118.42,34.08,48.0,2413.0,261.0,770.0,248.0,15.0001,500001.0,<1H OCEAN\n-118.41,34.07,52.0,3562.0,394.0,1163.0,361.0,15.0001,500001.0,<1H OCEAN\n-118.41,34.07,52.0,1202.0,142.0,408.0,138.0,15.0001,500001.0,<1H OCEAN\n-118.38,34.07,48.0,2799.0,596.0,1235.0,561.0,4.4896,500001.0,<1H OCEAN\n-118.39,34.07,33.0,5301.0,1281.0,2243.0,1159.0,4.2386,500001.0,<1H OCEAN\n-118.39,34.07,45.0,3143.0,553.0,1153.0,564.0,5.7762,500001.0,<1H OCEAN\n-118.39,34.08,27.0,6605.0,1710.0,2665.0,1520.0,3.8088,500001.0,<1H OCEAN\n-118.4,34.07,22.0,2170.0,593.0,850.0,520.0,2.9107,500001.0,<1H OCEAN\n-118.37,34.06,36.0,1661.0,395.0,690.0,365.0,3.3438,500001.0,<1H OCEAN\n-118.38,34.06,50.0,1509.0,291.0,690.0,259.0,6.2344,500001.0,<1H OCEAN\n-118.39,34.06,52.0,1213.0,194.0,503.0,194.0,8.0095,500001.0,<1H OCEAN\n-118.38,34.06,52.0,1311.0,217.0,578.0,205.0,7.6771,500001.0,<1H OCEAN\n-118.39,34.06,43.0,1879.0,397.0,873.0,382.0,3.8158,500001.0,<1H OCEAN\n-118.39,34.06,37.0,2975.0,705.0,1291.0,654.0,5.3316,500001.0,<1H OCEAN\n-118.39,34.06,39.0,3299.0,831.0,1649.0,759.0,3.3295,500001.0,<1H OCEAN\n-118.4,34.06,37.0,3781.0,873.0,1725.0,838.0,4.1455,500001.0,<1H OCEAN\n-118.4,34.06,47.0,3652.0,967.0,1438.0,887.0,3.6964,500001.0,<1H OCEAN\n-118.4,34.06,52.0,1871.0,326.0,646.0,284.0,8.2961,500001.0,<1H OCEAN\n-118.4,34.06,52.0,2501.0,362.0,748.0,349.0,6.6343,500001.0,<1H OCEAN\n-118.41,34.06,43.0,2665.0,556.0,1015.0,506.0,4.1411,500001.0,<1H OCEAN\n-118.41,34.06,43.0,4994.0,1057.0,1830.0,969.0,5.5321,500001.0,<1H OCEAN\n-118.41,34.07,47.0,2979.0,626.0,1076.0,571.0,3.9904,500001.0,<1H OCEAN\n-118.45,34.06,52.0,204.0,34.0,1154.0,28.0,9.337,500001.0,<1H OCEAN\n-118.49,34.05,52.0,2416.0,291.0,810.0,270.0,13.8556,500001.0,<1H OCEAN\n-118.49,34.04,50.0,2597.0,340.0,964.0,339.0,13.3036,500001.0,<1H OCEAN\n-118.49,34.04,48.0,2381.0,345.0,859.0,306.0,8.0257,500001.0,<1H OCEAN\n-118.5,34.04,52.0,3000.0,374.0,1143.0,375.0,15.0001,500001.0,<1H OCEAN\n-118.5,34.04,52.0,2233.0,317.0,769.0,277.0,8.3839,500001.0,<1H OCEAN\n-118.49,34.04,31.0,4066.0,951.0,1532.0,868.0,4.8125,500001.0,<1H OCEAN\n-118.49,34.03,30.0,4061.0,927.0,1487.0,865.0,4.1827,435100.0,<1H OCEAN\n-118.51,34.04,40.0,1382.0,167.0,483.0,178.0,11.7045,500001.0,<1H OCEAN\n-118.5,34.03,52.0,1506.0,208.0,547.0,186.0,7.8705,500001.0,<1H OCEAN\n-118.5,34.03,52.0,1711.0,245.0,671.0,242.0,7.7572,500001.0,<1H OCEAN\n-118.5,34.03,44.0,2146.0,394.0,851.0,355.0,6.48,500001.0,<1H OCEAN\n-118.51,34.03,37.0,4072.0,905.0,1468.0,923.0,3.8571,500001.0,<1H OCEAN\n-118.52,34.02,24.0,7418.0,1755.0,2713.0,1577.0,5.0867,500001.0,<1H OCEAN\n-118.49,34.03,31.0,4949.0,1293.0,1985.0,1244.0,4.252,436700.0,<1H OCEAN\n-118.5,34.03,32.0,6365.0,1784.0,2767.0,1698.0,3.6451,383300.0,<1H OCEAN\n-118.5,34.02,28.0,5109.0,1482.0,2313.0,1451.0,3.3266,483300.0,<1H OCEAN\n-118.5,34.02,24.0,2924.0,1013.0,1492.0,943.0,2.775,291700.0,<1H OCEAN\n-118.5,34.02,35.0,2914.0,934.0,1334.0,870.0,2.9934,350000.0,<1H OCEAN\n-118.52,34.01,25.0,2757.0,738.0,1014.0,633.0,3.1433,500001.0,<1H OCEAN\n-118.49,34.03,32.0,3851.0,900.0,1456.0,836.0,4.5208,442100.0,<1H OCEAN\n-118.49,34.03,31.0,3155.0,808.0,1208.0,745.0,3.6769,450000.0,<1H OCEAN\n-118.49,34.02,27.0,4725.0,1185.0,1945.0,1177.0,4.1365,470800.0,<1H OCEAN\n-118.48,34.03,32.0,1793.0,476.0,1143.0,448.0,2.8981,353600.0,<1H OCEAN\n-118.49,34.02,28.0,2545.0,752.0,1548.0,679.0,2.9125,475000.0,<1H OCEAN\n-118.49,34.02,29.0,2709.0,799.0,1238.0,793.0,3.1563,330000.0,<1H OCEAN\n-118.48,34.04,47.0,1956.0,277.0,724.0,277.0,8.9616,500001.0,<1H OCEAN\n-118.48,34.04,49.0,3780.0,741.0,1435.0,690.0,4.3158,500001.0,<1H OCEAN\n-118.48,34.04,40.0,1395.0,285.0,610.0,262.0,3.9659,500001.0,<1H OCEAN\n-118.48,34.04,36.0,2539.0,535.0,979.0,500.0,3.6667,500001.0,<1H OCEAN\n-118.47,34.04,32.0,2909.0,748.0,1310.0,706.0,4.516,350000.0,<1H OCEAN\n-118.47,34.03,32.0,3024.0,784.0,1323.0,740.0,3.3889,347900.0,<1H OCEAN\n-118.48,34.03,39.0,1530.0,401.0,1074.0,375.0,3.5076,381800.0,<1H OCEAN\n-118.47,34.03,29.0,3287.0,882.0,1523.0,823.0,3.7381,290600.0,<1H OCEAN\n-118.47,34.03,31.0,2642.0,681.0,1303.0,625.0,3.5987,340500.0,<1H OCEAN\n-118.48,34.03,19.0,902.0,284.0,414.0,272.0,1.3333,310000.0,<1H OCEAN\n-118.48,34.02,22.0,1249.0,483.0,1106.0,481.0,2.5261,375000.0,<1H OCEAN\n-118.48,34.02,29.0,1585.0,542.0,1019.0,487.0,2.7072,375000.0,<1H OCEAN\n-118.49,34.02,30.0,2075.0,687.0,1026.0,592.0,3.1635,366700.0,<1H OCEAN\n-118.46,34.03,27.0,1965.0,631.0,1042.0,596.0,2.75,327300.0,<1H OCEAN\n-118.46,34.03,39.0,1244.0,283.0,886.0,284.0,3.125,325000.0,<1H OCEAN\n-118.46,34.03,52.0,523.0,,317.0,130.0,2.2794,337500.0,<1H OCEAN\n-118.46,34.02,29.0,2329.0,833.0,1953.0,800.0,2.6639,233300.0,<1H OCEAN\n-118.47,34.02,38.0,2163.0,651.0,1759.0,584.0,2.3382,297500.0,<1H OCEAN\n-118.48,34.02,11.0,72.0,16.0,150.0,20.0,2.625,250000.0,<1H OCEAN\n-118.47,34.02,35.0,3057.0,774.0,2223.0,732.0,2.0745,332500.0,<1H OCEAN\n-118.48,34.02,30.0,2027.0,609.0,1425.0,562.0,2.2917,330800.0,<1H OCEAN\n-118.48,34.02,25.0,1583.0,460.0,983.0,422.0,2.7019,293800.0,<1H OCEAN\n-118.49,34.02,28.0,1394.0,582.0,716.0,543.0,1.5132,450000.0,<1H OCEAN\n-118.49,34.01,28.0,651.0,252.0,333.0,174.0,1.9722,500001.0,<1H OCEAN\n-118.51,34.0,52.0,1241.0,502.0,679.0,459.0,2.3098,500001.0,<1H OCEAN\n-118.48,34.01,31.0,2851.0,804.0,1410.0,782.0,4.0893,381500.0,<1H OCEAN\n-118.48,34.0,41.0,2584.0,743.0,1058.0,668.0,3.2061,370000.0,<1H OCEAN\n-118.48,34.01,30.0,3078.0,954.0,1561.0,901.0,3.4852,425000.0,<1H OCEAN\n-118.49,34.0,32.0,3407.0,1071.0,1463.0,986.0,3.0369,500001.0,<1H OCEAN\n-118.48,34.01,31.0,1829.0,458.0,719.0,392.0,4.4,353800.0,<1H OCEAN\n-118.47,34.0,28.0,1259.0,302.0,668.0,280.0,4.2813,384400.0,<1H OCEAN\n-118.47,34.0,42.0,1271.0,301.0,574.0,312.0,3.1304,340500.0,<1H OCEAN\n-118.48,34.0,25.0,4149.0,1067.0,1749.0,1000.0,3.9722,450000.0,<1H OCEAN\n-118.48,34.0,29.0,1727.0,479.0,741.0,431.0,3.6121,500000.0,<1H OCEAN\n-118.48,34.0,52.0,1359.0,395.0,521.0,368.0,2.6736,500001.0,<1H OCEAN\n-118.5,33.99,22.0,3484.0,975.0,1268.0,952.0,3.2609,500001.0,<1H OCEAN\n-118.47,34.02,41.0,2136.0,549.0,986.0,557.0,2.7254,444400.0,<1H OCEAN\n-118.47,34.01,44.0,2175.0,475.0,1019.0,448.0,4.793,470800.0,<1H OCEAN\n-118.47,34.01,41.0,2704.0,557.0,1047.0,478.0,4.4211,462900.0,<1H OCEAN\n-118.48,34.01,40.0,2198.0,611.0,1023.0,567.0,3.755,398300.0,<1H OCEAN\n-118.47,34.01,41.0,752.0,201.0,482.0,207.0,2.5417,418200.0,<1H OCEAN\n-118.46,34.01,48.0,1640.0,322.0,664.0,301.0,4.0,500001.0,<1H OCEAN\n-118.46,34.01,46.0,1379.0,239.0,688.0,269.0,6.8901,500001.0,<1H OCEAN\n-118.47,34.01,44.0,2017.0,343.0,958.0,382.0,6.1014,480100.0,<1H OCEAN\n-118.47,34.01,43.0,1160.0,304.0,393.0,250.0,2.9167,461100.0,<1H OCEAN\n-118.47,34.01,27.0,1782.0,471.0,837.0,422.0,3.7727,413000.0,<1H OCEAN\n-118.45,34.02,41.0,2956.0,700.0,1212.0,645.0,3.4583,421900.0,<1H OCEAN\n-118.45,34.02,45.0,1230.0,201.0,565.0,219.0,6.3521,493400.0,<1H OCEAN\n-118.46,34.02,45.0,3803.0,970.0,1690.0,871.0,3.0476,456200.0,<1H OCEAN\n-118.46,34.02,46.0,2571.0,502.0,1225.0,501.0,6.0436,473000.0,<1H OCEAN\n-118.46,34.02,39.0,3599.0,776.0,1569.0,763.0,5.2571,405400.0,<1H OCEAN\n-118.37,34.03,39.0,213.0,44.0,138.0,52.0,2.125,196400.0,<1H OCEAN\n-118.38,34.03,44.0,1913.0,441.0,1295.0,432.0,3.9537,266400.0,<1H OCEAN\n-118.38,34.02,45.0,2098.0,486.0,1343.0,481.0,3.9615,268600.0,<1H OCEAN\n-118.39,34.02,38.0,2447.0,636.0,1312.0,574.0,3.5909,279400.0,<1H OCEAN\n-118.39,34.02,45.0,1577.0,421.0,1042.0,375.0,3.4375,314500.0,<1H OCEAN\n-118.38,34.01,18.0,9528.0,2075.0,3922.0,1920.0,4.7612,304100.0,<1H OCEAN\n-118.39,34.01,25.0,1101.0,285.0,543.0,294.0,2.3571,340600.0,<1H OCEAN\n-118.39,34.02,38.0,2521.0,647.0,1091.0,597.0,4.1296,322900.0,<1H OCEAN\n-118.39,34.01,35.0,4424.0,918.0,2101.0,888.0,3.9688,355100.0,<1H OCEAN\n-118.4,34.01,44.0,1494.0,262.0,618.0,266.0,5.4035,356300.0,<1H OCEAN\n-118.4,34.02,40.0,593.0,137.0,371.0,132.0,4.6932,332800.0,<1H OCEAN\n-118.39,34.0,40.0,1565.0,269.0,826.0,268.0,5.2035,485700.0,<1H OCEAN\n-118.39,33.99,32.0,2612.0,418.0,1030.0,402.0,6.603,369200.0,<1H OCEAN\n-118.39,33.99,43.0,612.0,135.0,402.0,142.0,5.1322,314900.0,<1H OCEAN\n-118.39,34.0,35.0,1465.0,386.0,1104.0,345.0,4.056,339100.0,<1H OCEAN\n-118.4,34.0,34.0,1816.0,335.0,872.0,339.0,4.85,329400.0,<1H OCEAN\n-118.4,34.0,37.0,1534.0,258.0,751.0,259.0,5.444,336000.0,<1H OCEAN\n-118.4,33.99,36.0,1280.0,240.0,704.0,217.0,5.9632,328100.0,<1H OCEAN\n-118.4,33.99,36.0,1225.0,213.0,591.0,227.0,5.4663,326700.0,<1H OCEAN\n-118.4,34.0,44.0,2122.0,385.0,1012.0,367.0,4.6687,344300.0,<1H OCEAN\n-118.4,34.01,48.0,1427.0,253.0,693.0,268.0,5.7405,351600.0,<1H OCEAN\n-118.41,34.01,44.0,2010.0,394.0,961.0,365.0,4.5982,333500.0,<1H OCEAN\n-118.41,34.0,37.0,1426.0,259.0,689.0,261.0,5.5284,331000.0,<1H OCEAN\n-118.41,34.01,26.0,2503.0,449.0,1218.0,426.0,5.3683,500001.0,<1H OCEAN\n-118.41,34.01,43.0,2000.0,529.0,1290.0,514.0,4.7031,302500.0,<1H OCEAN\n-118.41,34.01,33.0,3306.0,974.0,2475.0,924.0,2.8797,285300.0,<1H OCEAN\n-118.42,34.01,42.0,1700.0,438.0,997.0,436.0,2.9213,305000.0,<1H OCEAN\n-118.41,34.0,38.0,324.0,70.0,268.0,73.0,2.55,271400.0,<1H OCEAN\n-118.42,34.0,45.0,1807.0,355.0,883.0,371.0,5.0357,329800.0,<1H OCEAN\n-118.41,34.0,46.0,105.0,20.0,69.0,19.0,3.9643,275000.0,<1H OCEAN\n-118.42,34.0,33.0,1139.0,299.0,734.0,257.0,3.2708,325000.0,<1H OCEAN\n-118.43,34.0,37.0,1340.0,358.0,1008.0,340.0,3.7614,314300.0,<1H OCEAN\n-118.43,33.99,45.0,2092.0,451.0,1190.0,429.0,3.8021,323000.0,<1H OCEAN\n-118.44,33.99,44.0,305.0,72.0,156.0,70.0,5.9641,275000.0,<1H OCEAN\n-118.44,33.98,21.0,18132.0,5419.0,7431.0,4930.0,5.3359,500001.0,<1H OCEAN\n-118.38,33.99,21.0,11308.0,3039.0,5127.0,2839.0,4.6277,228300.0,<1H OCEAN\n-118.37,33.99,32.0,4018.0,564.0,1400.0,568.0,8.6718,439100.0,<1H OCEAN\n-118.36,33.99,35.0,3702.0,648.0,1449.0,614.0,5.3194,313700.0,<1H OCEAN\n-118.37,33.99,36.0,3228.0,543.0,1305.0,520.0,5.1695,397000.0,<1H OCEAN\n-118.38,33.98,25.0,7105.0,1012.0,2519.0,1004.0,6.8112,500001.0,<1H OCEAN\n-118.35,34.0,30.0,1879.0,226.0,740.0,266.0,6.431,492500.0,<1H OCEAN\n-118.35,34.0,46.0,3402.0,503.0,1389.0,504.0,5.3462,270400.0,<1H OCEAN\n-118.35,33.99,48.0,2741.0,439.0,1115.0,459.0,5.0514,269100.0,<1H OCEAN\n-118.36,33.99,43.0,2657.0,548.0,1145.0,524.0,4.1375,287100.0,<1H OCEAN\n-118.36,33.99,45.0,2005.0,368.0,909.0,364.0,4.6406,268900.0,<1H OCEAN\n-118.35,33.99,45.0,1764.0,401.0,679.0,334.0,3.2021,222100.0,<1H OCEAN\n-118.34,34.0,44.0,3183.0,513.0,1183.0,473.0,5.0407,314900.0,<1H OCEAN\n-118.34,34.0,49.0,2863.0,411.0,1108.0,406.0,5.8993,313300.0,<1H OCEAN\n-118.33,34.0,52.0,1114.0,169.0,486.0,176.0,4.2917,247600.0,<1H OCEAN\n-118.34,34.0,49.0,2465.0,372.0,1018.0,359.0,4.0,296800.0,<1H OCEAN\n-118.34,33.99,47.0,1107.0,199.0,437.0,178.0,3.7344,179400.0,<1H OCEAN\n-118.34,33.99,48.0,1172.0,205.0,497.0,190.0,3.825,183000.0,<1H OCEAN\n-118.35,34.0,40.0,2894.0,395.0,1063.0,409.0,6.939,372000.0,<1H OCEAN\n-118.6,34.13,20.0,14291.0,1934.0,5452.0,1875.0,9.1232,472000.0,<1H OCEAN\n-118.58,34.12,42.0,718.0,140.0,324.0,131.0,6.4018,500001.0,<1H OCEAN\n-118.59,34.11,35.0,2396.0,472.0,1054.0,457.0,6.4504,407000.0,<1H OCEAN\n-118.6,34.09,43.0,2228.0,438.0,960.0,395.0,7.6091,438500.0,<1H OCEAN\n-118.6,34.08,40.0,866.0,181.0,399.0,176.0,6.91,380000.0,<1H OCEAN\n-118.6,34.07,16.0,319.0,59.0,149.0,64.0,4.625,433300.0,<1H OCEAN\n-118.69,34.08,23.0,204.0,40.0,117.0,41.0,9.7646,500001.0,NEAR OCEAN\n-118.66,34.1,12.0,2560.0,365.0,907.0,366.0,10.076,500001.0,NEAR OCEAN\n-118.63,34.11,35.0,3795.0,690.0,1521.0,653.0,5.8735,448100.0,<1H OCEAN\n-118.67,34.16,17.0,16544.0,2206.0,6214.0,2118.0,9.1228,500001.0,<1H OCEAN\n-118.68,34.08,18.0,102.0,17.0,55.0,21.0,3.9934,500001.0,NEAR OCEAN\n-118.68,34.13,9.0,11251.0,1594.0,3029.0,1227.0,6.7273,500001.0,<1H OCEAN\n-118.76,34.13,10.0,4355.0,716.0,2030.0,674.0,6.5571,500001.0,NEAR OCEAN\n-118.75,34.1,34.0,2255.0,402.0,857.0,317.0,4.5333,377300.0,NEAR OCEAN\n-118.72,34.14,7.0,23866.0,4407.0,9873.0,4012.0,5.4032,318500.0,NEAR OCEAN\n-118.78,34.16,9.0,30405.0,4093.0,12873.0,3931.0,8.0137,399200.0,NEAR OCEAN\n-118.8,34.15,9.0,1143.0,179.0,647.0,180.0,6.8474,356700.0,NEAR OCEAN\n-118.75,34.17,16.0,2950.0,387.0,1228.0,379.0,5.3749,346100.0,NEAR OCEAN\n-118.84,34.11,12.0,7508.0,1058.0,2484.0,965.0,5.8788,500001.0,NEAR OCEAN\n-118.82,34.14,22.0,11668.0,1730.0,4054.0,1671.0,6.9935,385500.0,NEAR OCEAN\n-118.79,34.14,7.0,3003.0,504.0,1143.0,466.0,5.8548,500001.0,NEAR OCEAN\n-118.74,34.05,19.0,3487.0,686.0,2782.0,584.0,7.9184,500001.0,NEAR OCEAN\n-118.86,34.07,16.0,1409.0,244.0,970.0,172.0,8.0144,500001.0,NEAR OCEAN\n-118.88,34.02,19.0,15990.0,2611.0,5175.0,2173.0,7.7848,500001.0,NEAR OCEAN\n-118.85,34.04,21.0,3837.0,578.0,1509.0,509.0,8.4476,500001.0,NEAR OCEAN\n-118.78,34.05,28.0,1343.0,215.0,487.0,199.0,6.83,500001.0,NEAR OCEAN\n-118.58,34.06,25.0,4440.0,693.0,1560.0,636.0,8.8666,500001.0,<1H OCEAN\n-118.6,34.02,36.0,2043.0,467.0,606.0,326.0,8.4331,500001.0,NEAR OCEAN\n-118.62,34.06,25.0,3546.0,584.0,1530.0,601.0,7.4001,500001.0,NEAR OCEAN\n-118.66,34.02,23.0,8798.0,1465.0,2750.0,1208.0,8.7364,500001.0,NEAR OCEAN\n-117.76,34.71,15.0,2981.0,625.0,1694.0,540.0,2.9541,106700.0,INLAND\n-117.84,34.63,5.0,6739.0,1251.0,4614.0,1266.0,4.002,115100.0,INLAND\n-117.78,34.58,6.0,10263.0,1864.0,6163.0,1781.0,3.8803,120000.0,INLAND\n-117.96,34.71,32.0,3511.0,646.0,1733.0,510.0,3.46,123900.0,INLAND\n-118.09,34.74,34.0,1218.0,285.0,797.0,248.0,2.4348,104800.0,INLAND\n-118.06,34.71,14.0,2606.0,514.0,1228.0,512.0,2.5764,150000.0,INLAND\n-118.09,34.68,4.0,23386.0,4171.0,10493.0,3671.0,4.0211,144000.0,INLAND\n-118.11,34.68,6.0,7430.0,1184.0,3489.0,1115.0,5.3267,140100.0,INLAND\n-118.12,34.69,17.0,2479.0,390.0,1219.0,363.0,4.6417,125700.0,INLAND\n-118.12,34.68,12.0,5319.0,875.0,2439.0,779.0,4.6629,131500.0,INLAND\n-118.13,34.69,34.0,2156.0,397.0,1269.0,388.0,2.75,96800.0,INLAND\n-118.13,34.68,28.0,718.0,124.0,347.0,121.0,4.025,102600.0,INLAND\n-118.1,34.65,33.0,873.0,177.0,425.0,142.0,2.67,187500.0,INLAND\n-118.09,34.71,5.0,5807.0,1182.0,2602.0,1007.0,2.4012,159400.0,INLAND\n-118.1,34.7,5.0,10356.0,1647.0,4562.0,1427.0,4.9806,141100.0,INLAND\n-118.09,34.7,6.0,4558.0,804.0,1543.0,563.0,2.8548,138500.0,INLAND\n-118.1,34.71,16.0,3914.0,819.0,1524.0,795.0,2.415,137500.0,INLAND\n-118.12,34.71,26.0,4230.0,823.0,2789.0,793.0,2.5179,104000.0,INLAND\n-118.12,34.71,46.0,40.0,10.0,14.0,7.0,1.125,225000.0,INLAND\n-118.12,34.7,7.0,4915.0,885.0,2833.0,874.0,4.3229,130000.0,INLAND\n-118.13,34.7,34.0,1943.0,500.0,1078.0,446.0,1.1296,93800.0,INLAND\n-118.13,34.69,32.0,3670.0,765.0,1986.0,673.0,3.682,108800.0,INLAND\n-118.12,34.69,27.0,3019.0,501.0,1580.0,523.0,3.7804,113500.0,INLAND\n-118.14,34.68,31.0,2666.0,662.0,1337.0,602.0,2.4432,101100.0,INLAND\n-118.14,34.69,35.0,2118.0,374.0,1108.0,360.0,3.4327,100300.0,INLAND\n-118.14,34.68,33.0,2815.0,485.0,1447.0,489.0,4.2679,119600.0,INLAND\n-118.14,34.68,25.0,1703.0,342.0,775.0,309.0,4.5455,126500.0,INLAND\n-118.16,34.68,17.0,2994.0,832.0,1571.0,695.0,2.5902,85400.0,INLAND\n-118.15,34.69,32.0,1300.0,234.0,712.0,249.0,3.25,107500.0,INLAND\n-118.16,34.68,9.0,4303.0,900.0,2240.0,861.0,3.7807,110900.0,INLAND\n-118.15,34.67,5.0,12317.0,2953.0,6291.0,2654.0,3.5732,146900.0,INLAND\n-118.14,34.65,20.0,1257.0,201.0,551.0,186.0,4.6591,247200.0,INLAND\n-118.14,34.72,15.0,2181.0,361.0,1057.0,300.0,4.625,118100.0,INLAND\n-118.16,34.71,27.0,6007.0,998.0,2680.0,882.0,4.1719,117200.0,INLAND\n-118.15,34.71,36.0,1338.0,250.0,709.0,250.0,3.5625,101400.0,INLAND\n-118.14,34.71,33.0,2347.0,461.0,1482.0,374.0,2.8194,93000.0,INLAND\n-118.14,34.71,32.0,1164.0,248.0,588.0,270.0,1.1917,86900.0,INLAND\n-118.15,34.71,35.0,1503.0,309.0,842.0,300.0,2.5278,97700.0,INLAND\n-118.14,34.7,36.0,1205.0,317.0,678.0,290.0,2.0182,98400.0,INLAND\n-118.14,34.7,12.0,1984.0,614.0,1071.0,574.0,1.2532,102100.0,INLAND\n-118.15,34.7,36.0,2696.0,454.0,1192.0,452.0,3.9615,116300.0,INLAND\n-118.16,34.7,33.0,2918.0,494.0,1365.0,478.0,4.8787,127700.0,INLAND\n-118.16,34.69,35.0,3114.0,583.0,1974.0,545.0,3.9028,126800.0,INLAND\n-118.14,34.69,34.0,1439.0,327.0,708.0,298.0,3.2699,100000.0,INLAND\n-118.14,34.69,48.0,1379.0,327.0,696.0,304.0,2.1167,94900.0,INLAND\n-118.28,34.76,19.0,3430.0,601.0,1817.0,571.0,4.7875,163600.0,INLAND\n-118.19,34.77,16.0,2035.0,370.0,704.0,330.0,2.1979,146400.0,INLAND\n-118.2,34.69,5.0,9076.0,1503.0,7694.0,1278.0,4.875,163400.0,INLAND\n-118.17,34.69,12.0,4881.0,803.0,2188.0,724.0,4.1667,171900.0,INLAND\n-118.17,34.68,13.0,5341.0,773.0,2288.0,724.0,6.6772,185600.0,INLAND\n-118.19,34.67,8.0,11275.0,1822.0,5731.0,1692.0,5.0285,167900.0,INLAND\n-118.17,34.67,5.0,8352.0,1555.0,3723.0,1389.0,4.5659,140300.0,INLAND\n-118.19,34.65,33.0,1781.0,326.0,913.0,314.0,3.9963,126800.0,INLAND\n-118.17,34.66,9.0,1561.0,253.0,731.0,233.0,5.7049,173200.0,INLAND\n-118.22,34.67,28.0,2357.0,408.0,1162.0,384.0,4.3636,179700.0,INLAND\n-118.23,34.66,25.0,2627.0,387.0,1059.0,338.0,3.6382,138200.0,INLAND\n-118.22,34.66,17.0,3810.0,662.0,1867.0,586.0,4.9,152400.0,INLAND\n-118.21,34.65,17.0,4001.0,814.0,2313.0,756.0,3.0441,140100.0,INLAND\n-118.23,34.65,17.0,1827.0,348.0,766.0,335.0,3.5673,136300.0,INLAND\n-118.27,34.68,19.0,552.0,129.0,314.0,106.0,3.2125,185400.0,INLAND\n-118.29,34.65,18.0,6893.0,1372.0,2837.0,1221.0,3.3173,218400.0,INLAND\n-118.32,34.62,31.0,1398.0,273.0,884.0,299.0,4.8409,264900.0,INLAND\n-118.38,34.58,18.0,1859.0,375.0,913.0,372.0,4.3456,148900.0,INLAND\n-118.61,34.73,25.0,3080.0,587.0,1558.0,510.0,5.0839,156700.0,INLAND\n-118.4,34.7,10.0,4122.0,814.0,2164.0,710.0,4.2941,151600.0,INLAND\n-117.92,34.63,34.0,81.0,26.0,53.0,14.0,1.4091,137500.0,INLAND\n-117.92,34.59,7.0,681.0,125.0,485.0,104.0,2.7396,125600.0,INLAND\n-117.93,34.57,5.0,5613.0,1060.0,3569.0,999.0,3.1946,132700.0,INLAND\n-117.9,34.53,8.0,3484.0,647.0,2169.0,619.0,3.9766,135800.0,INLAND\n-117.96,34.53,10.0,2907.0,559.0,1681.0,531.0,3.8594,141000.0,INLAND\n-117.98,34.53,13.0,2815.0,535.0,1492.0,491.0,4.0945,135700.0,INLAND\n-118.09,34.63,31.0,1537.0,416.0,1239.0,397.0,1.9722,99200.0,INLAND\n-118.02,34.62,38.0,248.0,55.0,261.0,53.0,2.1413,96900.0,INLAND\n-118.18,34.63,19.0,3562.0,606.0,1677.0,578.0,4.1573,228100.0,INLAND\n-118.17,34.61,7.0,2465.0,336.0,978.0,332.0,7.1381,292200.0,INLAND\n-118.16,34.6,2.0,11008.0,1549.0,4098.0,1367.0,6.4865,204400.0,INLAND\n-118.12,34.6,33.0,2189.0,497.0,1459.0,443.0,2.3958,94500.0,INLAND\n-118.15,34.59,33.0,2111.0,429.0,1067.0,397.0,3.7344,111400.0,INLAND\n-118.16,34.6,5.0,7294.0,1139.0,3123.0,930.0,4.9904,154100.0,INLAND\n-118.21,34.56,12.0,2472.0,408.0,1048.0,380.0,4.7097,262100.0,INLAND\n-118.22,34.63,4.0,14348.0,2145.0,5839.0,1806.0,5.3799,222400.0,INLAND\n-118.21,34.64,16.0,2573.0,427.0,1273.0,426.0,5.9508,181100.0,INLAND\n-118.12,34.58,13.0,2614.0,650.0,1949.0,537.0,2.0547,102600.0,INLAND\n-118.13,34.58,29.0,2370.0,475.0,1746.0,483.0,3.7464,113500.0,INLAND\n-118.14,34.57,6.0,9882.0,1892.0,4892.0,1621.0,3.7636,167600.0,INLAND\n-118.12,34.56,5.0,6446.0,1154.0,3427.0,1104.0,3.9936,148500.0,INLAND\n-118.1,34.58,32.0,1489.0,306.0,774.0,267.0,3.275,103500.0,INLAND\n-118.1,34.58,29.0,2843.0,603.0,1517.0,573.0,2.6658,106900.0,INLAND\n-118.1,34.57,7.0,20377.0,4335.0,11973.0,3933.0,3.3086,138100.0,INLAND\n-118.09,34.57,4.0,9761.0,1683.0,4970.0,1535.0,4.5266,142900.0,INLAND\n-118.08,34.58,5.0,1113.0,186.0,631.0,168.0,4.1719,146600.0,INLAND\n-118.07,34.58,34.0,3416.0,601.0,1929.0,567.0,4.0147,107400.0,INLAND\n-118.06,34.58,36.0,1493.0,258.0,899.0,260.0,3.86,109300.0,INLAND\n-118.08,34.58,12.0,3851.0,857.0,2169.0,811.0,3.0101,116300.0,INLAND\n-118.07,34.56,5.0,10264.0,1821.0,5871.0,1790.0,4.2329,145500.0,INLAND\n-118.08,34.56,14.0,5144.0,887.0,2846.0,824.0,4.5615,137200.0,INLAND\n-118.03,34.58,4.0,9849.0,1780.0,4546.0,1598.0,4.0729,154300.0,INLAND\n-118.02,34.57,4.0,10655.0,1706.0,5391.0,1529.0,5.083,151300.0,INLAND\n-118.01,34.55,2.0,2701.0,530.0,1368.0,430.0,4.071,137400.0,INLAND\n-118.08,34.55,5.0,16181.0,2971.0,8152.0,2651.0,4.5237,141800.0,INLAND\n-118.07,34.51,14.0,2798.0,459.0,1236.0,404.0,4.8667,239900.0,INLAND\n-118.37,34.43,11.0,17339.0,2866.0,8721.0,2803.0,5.9507,225200.0,INLAND\n-118.4,34.41,22.0,4443.0,560.0,1573.0,496.0,10.0285,500001.0,<1H OCEAN\n-118.35,34.52,14.0,3490.0,592.0,1710.0,580.0,5.9171,333300.0,INLAND\n-118.22,34.52,7.0,4524.0,735.0,2298.0,717.0,6.5538,311600.0,INLAND\n-118.26,34.5,6.0,5813.0,908.0,2275.0,790.0,4.7778,340400.0,INLAND\n-118.27,34.46,10.0,2184.0,405.0,1119.0,370.0,4.7437,294000.0,INLAND\n-118.13,34.44,10.0,2726.0,465.0,1773.0,459.0,4.8295,319100.0,INLAND\n-117.89,34.49,12.0,3449.0,598.0,1502.0,540.0,3.7043,150800.0,INLAND\n-117.96,34.48,32.0,1896.0,342.0,806.0,299.0,4.5769,159400.0,INLAND\n-117.79,34.45,18.0,2986.0,597.0,1355.0,472.0,3.2765,165000.0,INLAND\n-118.46,34.4,12.0,25957.0,4798.0,10475.0,4490.0,4.542,195300.0,<1H OCEAN\n-118.5,34.45,25.0,1290.0,190.0,689.0,216.0,6.0097,220200.0,<1H OCEAN\n-118.5,34.52,3.0,6577.0,1056.0,3032.0,1004.0,5.9263,251800.0,INLAND\n-118.48,34.47,36.0,84.0,12.0,29.0,17.0,3.375,187500.0,<1H OCEAN\n-118.5,34.46,17.0,10267.0,,4956.0,1483.0,5.5061,239400.0,<1H OCEAN\n-118.52,34.44,26.0,934.0,148.0,519.0,162.0,5.3209,185000.0,<1H OCEAN\n-118.53,34.44,19.0,3013.0,507.0,1356.0,484.0,5.1163,233200.0,<1H OCEAN\n-118.53,34.45,26.0,828.0,149.0,508.0,158.0,5.2374,185500.0,<1H OCEAN\n-118.53,34.44,19.0,1285.0,195.0,650.0,193.0,6.0398,217800.0,<1H OCEAN\n-118.52,34.46,5.0,15341.0,2527.0,7270.0,2320.0,6.1281,236200.0,<1H OCEAN\n-118.53,34.45,10.0,5509.0,969.0,3002.0,959.0,5.5981,220100.0,<1H OCEAN\n-118.51,34.43,15.0,8510.0,1258.0,3733.0,1233.0,6.1082,253700.0,<1H OCEAN\n-118.46,34.42,25.0,2988.0,525.0,1884.0,513.0,4.7007,169500.0,<1H OCEAN\n-118.44,34.5,5.0,1514.0,220.0,1355.0,215.0,8.1344,359000.0,INLAND\n-118.45,34.44,16.0,13406.0,2574.0,7030.0,2440.0,4.6861,187900.0,<1H OCEAN\n-118.49,34.43,15.0,8244.0,1409.0,4453.0,1357.0,5.4829,199600.0,<1H OCEAN\n-118.49,34.42,23.0,4166.0,756.0,2082.0,743.0,4.4107,213400.0,<1H OCEAN\n-118.48,34.42,21.0,1375.0,259.0,728.0,258.0,5.0166,229000.0,<1H OCEAN\n-118.47,34.42,17.0,913.0,228.0,530.0,201.0,3.038,238500.0,<1H OCEAN\n-118.47,34.42,25.0,3223.0,524.0,1763.0,508.0,5.2887,183000.0,<1H OCEAN\n-118.43,34.42,13.0,3600.0,580.0,1799.0,576.0,6.2971,218300.0,<1H OCEAN\n-118.43,34.43,5.0,21113.0,4386.0,9842.0,3886.0,4.2037,194600.0,<1H OCEAN\n-118.55,34.44,14.0,15348.0,2366.0,7087.0,2169.0,6.3277,237700.0,INLAND\n-118.56,34.42,2.0,966.0,270.0,233.0,169.0,1.9667,450000.0,<1H OCEAN\n-118.61,34.59,5.0,4028.0,896.0,2062.0,826.0,4.0579,167100.0,INLAND\n-118.7,34.53,5.0,14275.0,2474.0,7158.0,2311.0,5.4284,236300.0,INLAND\n-118.66,34.43,9.0,2356.0,469.0,1556.0,386.0,3.775,155000.0,INLAND\n-118.59,34.47,5.0,538.0,98.0,8733.0,105.0,4.2391,154600.0,INLAND\n-118.61,34.31,4.0,1949.0,458.0,868.0,398.0,5.0151,285200.0,<1H OCEAN\n-118.52,34.39,21.0,5477.0,1275.0,3384.0,1222.0,3.6625,228100.0,<1H OCEAN\n-118.53,34.38,18.0,2288.0,607.0,2305.0,597.0,3.227,136100.0,<1H OCEAN\n-118.54,34.38,18.0,2096.0,309.0,1044.0,328.0,6.8299,262100.0,<1H OCEAN\n-118.52,34.4,5.0,7748.0,1557.0,4768.0,1393.0,5.305,311200.0,<1H OCEAN\n-118.53,34.37,8.0,3839.0,852.0,1342.0,593.0,3.9118,333700.0,<1H OCEAN\n-118.54,34.37,27.0,2051.0,301.0,917.0,287.0,7.6059,323700.0,<1H OCEAN\n-118.52,34.36,5.0,4222.0,712.0,2024.0,646.0,5.8703,500001.0,<1H OCEAN\n-118.55,34.37,21.0,7010.0,1063.0,3331.0,1038.0,6.776,278100.0,<1H OCEAN\n-118.56,34.37,23.0,3927.0,728.0,1984.0,707.0,4.8536,202600.0,<1H OCEAN\n-118.55,34.41,8.0,21086.0,3945.0,9936.0,3790.0,5.8602,265100.0,<1H OCEAN\n-118.55,34.38,24.0,6246.0,1028.0,2803.0,999.0,6.3002,282900.0,<1H OCEAN\n-118.56,34.41,4.0,17313.0,3224.0,6902.0,2707.0,5.6798,320900.0,<1H OCEAN\n-118.55,34.39,16.0,8726.0,1317.0,3789.0,1279.0,6.8419,323300.0,<1H OCEAN\n-118.61,34.38,2.0,5989.0,883.0,1787.0,613.0,6.6916,329500.0,INLAND\n-117.86,34.24,52.0,803.0,267.0,628.0,225.0,4.1932,14999.0,INLAND\n-118.12,34.23,52.0,433.0,69.0,147.0,53.0,3.9583,162500.0,INLAND\n-118.35,34.32,52.0,102.0,29.0,54.0,32.0,1.9875,191700.0,<1H OCEAN\n-118.38,34.3,39.0,1622.0,355.0,903.0,314.0,4.1125,183000.0,<1H OCEAN\n-118.29,34.36,34.0,503.0,99.0,275.0,68.0,4.5491,375000.0,INLAND\n-119.53,37.34,26.0,4047.0,702.0,571.0,199.0,2.3482,179500.0,INLAND\n-119.51,37.32,14.0,362.0,78.0,88.0,39.0,3.5893,214300.0,INLAND\n-119.56,37.29,14.0,2391.0,451.0,798.0,308.0,3.0924,114600.0,INLAND\n-119.45,37.21,17.0,3538.0,726.0,1603.0,629.0,2.9449,95600.0,INLAND\n-119.65,37.09,17.0,1280.0,254.0,707.0,267.0,3.55,106300.0,INLAND\n-119.59,37.39,19.0,3273.0,611.0,1164.0,481.0,3.5446,106500.0,INLAND\n-119.66,37.39,10.0,2106.0,410.0,1003.0,397.0,2.7813,124100.0,INLAND\n-119.68,37.35,13.0,2307.0,386.0,925.0,347.0,3.1326,119800.0,INLAND\n-119.72,37.38,16.0,2131.0,424.0,989.0,369.0,2.6071,103700.0,INLAND\n-119.65,37.32,11.0,2161.0,448.0,820.0,405.0,2.3565,122300.0,INLAND\n-119.64,37.31,15.0,2654.0,530.0,1267.0,489.0,2.8393,104400.0,INLAND\n-119.6,37.29,13.0,1722.0,325.0,712.0,269.0,2.625,137500.0,INLAND\n-119.62,37.33,7.0,3389.0,621.0,1268.0,474.0,3.0224,147800.0,INLAND\n-119.91,37.23,17.0,2171.0,389.0,1042.0,375.0,3.625,94400.0,INLAND\n-119.85,37.1,8.0,828.0,168.0,413.0,146.0,3.375,80700.0,INLAND\n-119.68,37.19,10.0,3113.0,589.0,1508.0,512.0,2.8167,96100.0,INLAND\n-119.77,37.19,8.0,5212.0,872.0,2383.0,857.0,4.1099,113600.0,INLAND\n-119.67,37.27,13.0,5087.0,981.0,2284.0,913.0,2.7413,123100.0,INLAND\n-120.31,37.11,38.0,1696.0,301.0,985.0,278.0,2.4054,112500.0,INLAND\n-120.35,37.04,37.0,1495.0,292.0,858.0,275.0,2.9306,46300.0,INLAND\n-120.25,37.04,21.0,1724.0,317.0,1006.0,290.0,3.2868,91700.0,INLAND\n-120.16,37.12,29.0,1995.0,392.0,1261.0,354.0,1.9073,79200.0,INLAND\n-120.27,37.11,18.0,1277.0,234.0,674.0,238.0,2.6694,75900.0,INLAND\n-120.25,37.11,20.0,2062.0,466.0,1285.0,456.0,1.5319,50500.0,INLAND\n-120.26,37.11,33.0,1097.0,254.0,627.0,253.0,1.2794,50700.0,INLAND\n-120.27,37.12,42.0,1142.0,236.0,597.0,210.0,1.7279,52300.0,INLAND\n-120.27,37.12,36.0,1219.0,258.0,639.0,245.0,1.9464,57000.0,INLAND\n-120.27,37.12,17.0,3328.0,628.0,1580.0,619.0,2.9861,81500.0,INLAND\n-120.26,37.13,33.0,1239.0,250.0,648.0,227.0,2.0278,58800.0,INLAND\n-120.43,36.99,16.0,1027.0,199.0,673.0,193.0,2.9688,63800.0,INLAND\n-120.29,36.88,34.0,1391.0,297.0,943.0,281.0,2.4219,83900.0,INLAND\n-120.06,36.95,24.0,646.0,134.0,454.0,149.0,2.125,61900.0,INLAND\n-120.06,36.94,19.0,901.0,183.0,700.0,190.0,2.2375,64300.0,INLAND\n-120.04,36.93,11.0,3606.0,699.0,2074.0,644.0,2.6941,63300.0,INLAND\n-120.05,36.95,18.0,2287.0,534.0,1339.0,505.0,2.2527,65200.0,INLAND\n-120.21,36.98,21.0,1667.0,303.0,861.0,276.0,2.6012,92200.0,INLAND\n-120.16,36.96,18.0,508.0,104.0,393.0,114.0,3.0,156300.0,INLAND\n-120.11,36.96,17.0,3344.0,570.0,1624.0,536.0,3.8952,95300.0,INLAND\n-120.08,37.06,18.0,402.0,76.0,213.0,71.0,1.9063,95800.0,INLAND\n-120.09,37.0,11.0,3761.0,675.0,2374.0,673.0,3.4598,74600.0,INLAND\n-120.06,37.02,13.0,6301.0,1080.0,3840.0,1033.0,3.5258,84900.0,INLAND\n-119.94,37.04,14.0,1636.0,253.0,766.0,225.0,3.125,88500.0,INLAND\n-120.09,37.02,9.0,1608.0,297.0,1057.0,295.0,3.7143,81600.0,INLAND\n-119.95,36.96,18.0,1996.0,379.0,1327.0,356.0,2.6087,96000.0,INLAND\n-120.02,36.95,25.0,2115.0,482.0,1976.0,474.0,1.8431,53900.0,INLAND\n-119.9,36.94,11.0,2513.0,408.0,1360.0,415.0,4.277,98500.0,INLAND\n-119.85,36.97,13.0,2872.0,477.0,1607.0,481.0,4.475,102400.0,INLAND\n-119.88,36.93,12.0,3174.0,520.0,1590.0,488.0,4.5347,101200.0,INLAND\n-119.87,36.93,13.0,1429.0,209.0,702.0,205.0,4.3625,111800.0,INLAND\n-120.07,36.98,12.0,1790.0,379.0,1399.0,397.0,2.5388,59600.0,INLAND\n-120.06,36.98,12.0,2710.0,575.0,1724.0,516.0,1.4712,60400.0,INLAND\n-120.05,36.98,16.0,3705.0,739.0,2463.0,697.0,2.5288,61800.0,INLAND\n-120.05,36.97,20.0,2029.0,427.0,983.0,401.0,1.8444,47100.0,INLAND\n-120.06,36.97,38.0,1542.0,364.0,1220.0,334.0,1.625,52800.0,INLAND\n-120.07,36.97,28.0,1563.0,403.0,1564.0,408.0,1.5662,48000.0,INLAND\n-120.07,36.96,34.0,1457.0,239.0,557.0,226.0,3.6181,96500.0,INLAND\n-120.08,36.97,13.0,3356.0,589.0,1458.0,601.0,3.8257,94200.0,INLAND\n-120.1,36.96,20.0,2100.0,317.0,910.0,274.0,4.8187,90900.0,INLAND\n-120.09,36.95,16.0,3222.0,511.0,1425.0,503.0,4.1544,119400.0,INLAND\n-120.08,36.96,36.0,2074.0,349.0,954.0,363.0,3.1136,73800.0,INLAND\n-120.08,36.95,41.0,1164.0,211.0,476.0,171.0,2.4196,70700.0,INLAND\n-120.07,36.97,27.0,968.0,240.0,587.0,231.0,1.6071,55000.0,INLAND\n-120.06,36.97,35.0,1859.0,428.0,1208.0,399.0,1.4044,61700.0,INLAND\n-120.05,36.96,37.0,1000.0,261.0,1092.0,233.0,1.4267,52300.0,INLAND\n-120.05,36.95,31.0,696.0,254.0,913.0,248.0,1.4,52500.0,INLAND\n-120.06,36.96,44.0,1288.0,295.0,723.0,287.0,1.6534,61400.0,INLAND\n-120.07,36.96,42.0,963.0,216.0,471.0,211.0,2.2898,66100.0,INLAND\n-120.07,36.96,32.0,1268.0,283.0,549.0,273.0,1.4511,65200.0,INLAND\n-120.04,36.97,20.0,2129.0,526.0,1845.0,522.0,1.8973,51600.0,INLAND\n-120.04,36.96,23.0,2126.0,506.0,2091.0,491.0,1.3713,51800.0,INLAND\n-120.04,36.95,36.0,1528.0,347.0,1334.0,304.0,1.3594,48300.0,INLAND\n-119.98,36.9,26.0,1284.0,239.0,820.0,254.0,2.5833,62300.0,INLAND\n-119.81,36.92,14.0,4795.0,710.0,2047.0,640.0,4.665,121300.0,INLAND\n-119.98,36.86,31.0,2366.0,482.0,1933.0,433.0,3.0234,65000.0,INLAND\n-120.13,36.87,32.0,2089.0,468.0,1765.0,427.0,2.234,61700.0,INLAND\n-122.49,38.1,43.0,1226.0,244.0,491.0,205.0,4.9286,307000.0,NEAR BAY\n-122.54,38.14,16.0,4431.0,603.0,1659.0,630.0,7.5412,392100.0,NEAR BAY\n-122.55,38.1,26.0,5188.0,892.0,2341.0,906.0,5.0029,255600.0,NEAR BAY\n-122.62,38.15,14.0,2259.0,341.0,1127.0,346.0,6.4092,334900.0,<1H OCEAN\n-122.59,38.13,20.0,1589.0,231.0,601.0,224.0,5.3755,290900.0,<1H OCEAN\n-122.58,38.15,9.0,1302.0,177.0,682.0,190.0,7.5,423200.0,<1H OCEAN\n-122.57,38.11,24.0,2863.0,734.0,1583.0,682.0,3.1981,215300.0,NEAR BAY\n-122.57,38.11,32.0,3521.0,748.0,1706.0,723.0,3.4705,228600.0,NEAR BAY\n-122.59,38.12,25.0,7784.0,1145.0,3445.0,1166.0,6.0132,287900.0,<1H OCEAN\n-122.58,38.12,13.0,5027.0,871.0,1912.0,770.0,4.9286,309500.0,NEAR BAY\n-122.6,38.11,19.0,1752.0,328.0,873.0,336.0,3.8068,201600.0,<1H OCEAN\n-122.6,38.11,23.0,8642.0,1294.0,3594.0,1253.0,5.3962,301500.0,<1H OCEAN\n-122.65,38.11,21.0,3891.0,616.0,1968.0,632.0,5.5524,279200.0,<1H OCEAN\n-122.61,38.09,18.0,6205.0,821.0,2311.0,756.0,6.9081,368700.0,<1H OCEAN\n-122.58,38.1,22.0,11872.0,2300.0,5600.0,2200.0,4.6463,276300.0,NEAR BAY\n-122.58,38.08,27.0,10839.0,1637.0,4406.0,1623.0,5.615,285600.0,NEAR BAY\n-122.55,38.07,5.0,1495.0,235.0,555.0,201.0,6.7232,345000.0,NEAR BAY\n-122.56,38.09,17.0,9614.0,2123.0,4684.0,2060.0,4.1705,209800.0,NEAR BAY\n-122.56,38.06,19.0,15622.0,2721.0,6109.0,2615.0,5.0965,295300.0,NEAR BAY\n-122.55,38.07,38.0,3392.0,709.0,1894.0,713.0,3.0573,350800.0,NEAR BAY\n-122.41,38.07,20.0,4536.0,708.0,1812.0,701.0,6.0433,435900.0,NEAR BAY\n-122.51,38.06,24.0,9493.0,1935.0,5162.0,1880.0,3.0742,118800.0,NEAR BAY\n-122.53,38.01,16.0,1495.0,292.0,472.0,284.0,3.4432,67500.0,NEAR BAY\n-122.44,38.03,13.0,4284.0,1042.0,2146.0,937.0,4.1289,179200.0,NEAR BAY\n-122.45,38.01,36.0,4501.0,832.0,2196.0,800.0,4.3182,252700.0,NEAR BAY\n-122.53,38.01,27.0,3121.0,531.0,1318.0,489.0,5.4781,310900.0,NEAR BAY\n-122.51,38.0,17.0,2449.0,536.0,1157.0,543.0,3.9519,274200.0,NEAR BAY\n-122.49,38.0,26.0,48.0,8.0,19.0,8.0,7.7197,400000.0,NEAR BAY\n-122.55,38.03,29.0,7174.0,1169.0,3063.0,1172.0,6.0902,293200.0,NEAR BAY\n-122.57,38.03,24.0,2330.0,322.0,911.0,320.0,6.5253,387700.0,NEAR BAY\n-122.56,38.03,34.0,1887.0,290.0,815.0,283.0,6.5249,324800.0,NEAR BAY\n-122.59,38.04,25.0,3412.0,455.0,1238.0,406.0,8.3646,397300.0,NEAR BAY\n-122.57,38.02,33.0,9531.0,1487.0,3798.0,1409.0,5.6512,314000.0,NEAR BAY\n-122.56,38.01,21.0,2144.0,400.0,840.0,398.0,4.6,239500.0,NEAR BAY\n-122.55,38.02,27.0,4985.0,711.0,1928.0,742.0,6.4978,361500.0,NEAR BAY\n-122.55,38.01,27.0,3966.0,577.0,1657.0,611.0,6.3314,342200.0,NEAR BAY\n-122.54,38.0,28.0,3416.0,826.0,1694.0,800.0,3.18,277000.0,NEAR BAY\n-122.55,38.0,18.0,3119.0,803.0,1395.0,722.0,3.9265,301100.0,NEAR BAY\n-122.54,37.99,32.0,2236.0,348.0,818.0,330.0,7.3521,444000.0,NEAR BAY\n-122.52,37.98,31.0,6555.0,1571.0,2962.0,1464.0,2.8903,324200.0,NEAR BAY\n-122.54,37.98,52.0,1758.0,316.0,607.0,264.0,5.5083,371900.0,NEAR BAY\n-122.55,37.99,34.0,3306.0,555.0,1398.0,585.0,4.8993,319900.0,NEAR BAY\n-122.53,37.98,32.0,2390.0,336.0,810.0,354.0,8.5759,500001.0,NEAR BAY\n-122.55,37.98,31.0,3807.0,828.0,1581.0,795.0,3.293,337500.0,NEAR BAY\n-122.51,37.99,32.0,4138.0,632.0,1541.0,626.0,5.5791,433300.0,NEAR BAY\n-122.51,37.98,37.0,4801.0,699.0,1830.0,679.0,6.0762,487800.0,NEAR BAY\n-122.51,37.97,37.0,4296.0,1089.0,2100.0,1025.0,3.2462,329400.0,NEAR BAY\n-122.47,37.99,22.0,7274.0,1002.0,2468.0,957.0,7.494,439200.0,NEAR BAY\n-122.46,37.98,10.0,1325.0,189.0,427.0,162.0,12.0933,500001.0,NEAR BAY\n-122.49,37.99,27.0,5470.0,755.0,1916.0,764.0,6.994,420800.0,NEAR BAY\n-122.49,37.98,34.0,1256.0,178.0,460.0,174.0,6.4271,451700.0,NEAR BAY\n-122.53,37.97,52.0,205.0,119.0,228.0,132.0,1.9063,200000.0,NEAR BAY\n-122.53,37.97,52.0,1560.0,451.0,700.0,419.0,2.5125,270800.0,NEAR BAY\n-122.53,37.97,44.0,3595.0,953.0,1831.0,910.0,2.6036,287500.0,NEAR BAY\n-122.54,37.97,39.0,4193.0,762.0,1833.0,737.0,5.6263,352100.0,NEAR BAY\n-122.52,37.97,33.0,563.0,194.0,265.0,169.0,2.75,231300.0,NEAR BAY\n-122.51,37.96,39.0,3302.0,684.0,1574.0,653.0,3.6863,263800.0,NEAR BAY\n-122.52,37.96,35.0,2012.0,346.0,818.0,352.0,5.2818,331000.0,NEAR BAY\n-122.53,37.97,37.0,1340.0,322.0,621.0,314.0,3.5588,268800.0,NEAR BAY\n-122.52,37.95,37.0,350.0,57.0,179.0,69.0,6.2862,500001.0,NEAR BAY\n-122.53,37.96,35.0,908.0,194.0,413.0,197.0,3.9917,290800.0,NEAR BAY\n-122.47,37.95,16.0,3769.0,839.0,1986.0,815.0,3.9712,187500.0,NEAR BAY\n-122.5,37.96,16.0,100.0,20.0,45.0,25.0,6.1359,212500.0,NEAR BAY\n-122.5,37.97,25.0,6526.0,1902.0,5917.0,1812.0,2.7273,187500.0,NEAR BAY\n-122.64,38.01,36.0,1336.0,258.0,678.0,249.0,5.5789,292000.0,NEAR OCEAN\n-122.64,38.01,36.0,1199.0,232.0,551.0,229.0,3.7321,266700.0,NEAR OCEAN\n-122.68,38.01,41.0,1865.0,392.0,825.0,369.0,4.2011,255400.0,NEAR OCEAN\n-122.7,38.03,42.0,1410.0,308.0,624.0,292.0,4.1379,309100.0,NEAR OCEAN\n-122.65,38.01,40.0,1428.0,280.0,708.0,255.0,5.0766,305400.0,NEAR OCEAN\n-122.59,37.97,46.0,4036.0,856.0,1872.0,833.0,4.5625,275200.0,NEAR OCEAN\n-122.61,37.99,40.0,7737.0,1488.0,3108.0,1349.0,4.4375,289600.0,NEAR OCEAN\n-122.62,37.97,52.0,370.0,62.0,150.0,56.0,7.7006,316700.0,NEAR OCEAN\n-122.59,37.99,36.0,4869.0,871.0,1899.0,827.0,4.1659,302000.0,NEAR BAY\n-122.6,38.0,21.0,2198.0,462.0,1100.0,449.0,4.1098,246600.0,<1H OCEAN\n-122.59,38.01,35.0,8814.0,1307.0,3450.0,1258.0,6.1724,414300.0,NEAR BAY\n-122.57,37.99,45.0,2404.0,425.0,926.0,400.0,4.9674,320100.0,NEAR BAY\n-122.57,37.99,38.0,5587.0,996.0,2466.0,1027.0,4.1711,336900.0,NEAR BAY\n-122.57,37.98,49.0,2860.0,552.0,1178.0,522.0,4.625,355000.0,NEAR BAY\n-122.58,37.98,52.0,3675.0,649.0,1553.0,639.0,4.6905,316300.0,NEAR BAY\n-122.58,37.98,52.0,1180.0,216.0,467.0,197.0,4.9615,292200.0,NEAR BAY\n-122.56,37.98,36.0,2649.0,542.0,1111.0,557.0,4.8056,345700.0,NEAR BAY\n-122.56,37.97,52.0,1833.0,324.0,735.0,306.0,4.6944,398900.0,NEAR BAY\n-122.57,37.97,47.0,5416.0,1115.0,2177.0,1027.0,3.5055,382100.0,NEAR BAY\n-122.55,37.97,52.0,2232.0,291.0,731.0,253.0,7.1155,500001.0,NEAR BAY\n-122.57,37.96,52.0,3458.0,468.0,1449.0,471.0,9.1834,500001.0,NEAR BAY\n-122.56,37.95,34.0,2677.0,411.0,933.0,410.0,6.1444,500001.0,NEAR OCEAN\n-122.56,37.94,36.0,2023.0,242.0,653.0,241.0,10.6272,500001.0,NEAR BAY\n-122.64,37.96,29.0,377.0,58.0,151.0,67.0,9.5551,500001.0,NEAR OCEAN\n-122.54,37.96,44.0,1552.0,204.0,596.0,208.0,10.129,500001.0,NEAR BAY\n-122.54,37.96,33.0,2534.0,495.0,996.0,449.0,4.3083,500001.0,NEAR BAY\n-122.54,37.95,38.0,2310.0,400.0,971.0,386.0,5.697,435700.0,NEAR BAY\n-122.52,37.95,33.0,4448.0,631.0,1675.0,628.0,7.8904,468800.0,NEAR BAY\n-122.53,37.95,22.0,7446.0,1979.0,2980.0,1888.0,3.5838,271300.0,NEAR BAY\n-122.53,37.96,36.0,4385.0,620.0,1549.0,626.0,8.3935,470500.0,NEAR BAY\n-122.53,37.93,42.0,2171.0,362.0,887.0,347.0,6.6125,393200.0,NEAR BAY\n-122.54,37.93,43.0,2998.0,470.0,970.0,430.0,5.5385,431800.0,NEAR BAY\n-122.54,37.94,39.0,3670.0,775.0,1519.0,788.0,4.4081,435200.0,NEAR BAY\n-122.54,37.94,26.0,3990.0,804.0,1550.0,792.0,5.1834,405500.0,NEAR BAY\n-122.52,37.94,18.0,1804.0,284.0,600.0,241.0,5.9582,500001.0,NEAR BAY\n-122.53,37.94,18.0,878.0,255.0,384.0,247.0,4.7344,200000.0,NEAR BAY\n-122.52,37.93,34.0,2782.0,502.0,1219.0,507.0,5.0779,333900.0,NEAR BAY\n-122.51,37.92,32.0,2622.0,541.0,1022.0,464.0,3.7647,375000.0,NEAR BAY\n-122.52,37.92,24.0,421.0,64.0,163.0,75.0,14.5833,500001.0,NEAR BAY\n-122.53,37.92,42.0,1741.0,301.0,723.0,306.0,5.5379,410500.0,NEAR BAY\n-122.53,37.93,37.0,1722.0,352.0,648.0,337.0,4.125,310300.0,NEAR BAY\n-122.53,37.92,45.0,1530.0,324.0,608.0,328.0,3.875,390800.0,NEAR BAY\n-122.52,37.92,47.0,793.0,163.0,334.0,151.0,5.8509,317800.0,NEAR BAY\n-122.5,37.92,32.0,2639.0,415.0,1013.0,408.0,6.1632,349200.0,NEAR BAY\n-122.5,37.92,30.0,2270.0,359.0,974.0,351.0,5.5926,300900.0,NEAR BAY\n-122.49,37.92,26.0,2170.0,347.0,849.0,318.0,6.2953,386200.0,NEAR BAY\n-122.51,37.91,2.0,647.0,136.0,203.0,118.0,6.641,310000.0,NEAR BAY\n-122.48,37.93,16.0,2947.0,802.0,1385.0,743.0,3.6731,318000.0,NEAR BAY\n-122.46,37.88,35.0,2492.0,409.0,812.0,373.0,8.8386,500001.0,NEAR BAY\n-122.47,37.87,36.0,4471.0,618.0,1315.0,582.0,11.5706,500001.0,NEAR BAY\n-122.45,37.91,27.0,2682.0,382.0,935.0,369.0,10.0791,500001.0,NEAR BAY\n-122.5,37.91,31.0,7001.0,1282.0,2755.0,1267.0,5.4851,441100.0,NEAR BAY\n-122.49,37.89,23.0,1650.0,403.0,541.0,336.0,6.0238,500001.0,NEAR BAY\n-122.47,37.89,23.0,10774.0,1736.0,3895.0,1683.0,7.2905,500001.0,NEAR BAY\n-122.45,37.9,30.0,3763.0,717.0,1292.0,632.0,8.4888,500001.0,NEAR BAY\n-122.5,37.88,28.0,5448.0,1089.0,2100.0,1023.0,4.7475,474600.0,NEAR BAY\n-122.51,37.89,27.0,2674.0,565.0,1233.0,547.0,3.4485,458300.0,NEAR BAY\n-122.53,37.91,37.0,2524.0,398.0,999.0,417.0,7.9892,500001.0,NEAR BAY\n-122.53,37.9,44.0,2846.0,551.0,1232.0,537.0,3.8839,327200.0,NEAR BAY\n-122.54,37.9,48.0,2491.0,460.0,937.0,455.0,4.4375,370000.0,NEAR BAY\n-122.54,37.91,48.0,2924.0,489.0,1159.0,505.0,5.6302,489000.0,NEAR BAY\n-122.55,37.92,52.0,2303.0,350.0,859.0,359.0,6.1085,500001.0,NEAR BAY\n-122.52,37.91,30.0,4174.0,739.0,1818.0,705.0,5.5951,402900.0,NEAR BAY\n-122.52,37.89,17.0,4363.0,1041.0,1640.0,989.0,3.9531,417600.0,NEAR BAY\n-122.52,37.9,16.0,1704.0,402.0,689.0,348.0,4.4239,267100.0,NEAR BAY\n-122.54,37.9,41.0,3170.0,622.0,1091.0,528.0,3.7813,389200.0,NEAR BAY\n-122.55,37.9,34.0,1431.0,224.0,503.0,220.0,7.9606,453400.0,NEAR BAY\n-122.56,37.9,48.0,1550.0,253.0,641.0,276.0,8.634,463500.0,NEAR BAY\n-122.56,37.91,52.0,1972.0,327.0,755.0,345.0,7.1924,500001.0,NEAR BAY\n-122.56,37.92,37.0,1926.0,290.0,721.0,298.0,8.9248,500001.0,NEAR BAY\n-122.55,37.91,48.0,1283.0,278.0,567.0,255.0,3.2794,460000.0,NEAR BAY\n-122.53,37.88,25.0,4921.0,866.0,1913.0,834.0,6.8742,413100.0,NEAR BAY\n-122.54,37.88,30.0,4382.0,732.0,1775.0,745.0,6.7809,414400.0,NEAR BAY\n-122.53,37.87,20.0,1814.0,282.0,658.0,253.0,7.9977,400000.0,NEAR BAY\n-122.53,37.88,36.0,2688.0,485.0,1064.0,449.0,4.4583,308600.0,NEAR BAY\n-122.56,37.9,36.0,1760.0,283.0,562.0,246.0,6.7546,402400.0,NEAR BAY\n-122.54,37.89,33.0,4971.0,836.0,1907.0,795.0,6.1275,424400.0,NEAR BAY\n-122.53,37.89,35.0,4127.0,689.0,1596.0,707.0,5.9073,400400.0,NEAR BAY\n-122.56,37.9,24.0,221.0,41.0,75.0,38.0,5.1292,362500.0,NEAR BAY\n-122.51,37.87,21.0,3904.0,980.0,1949.0,919.0,2.862,258400.0,NEAR BAY\n-122.47,37.85,19.0,1926.0,593.0,881.0,546.0,2.9145,140400.0,NEAR BAY\n-122.5,37.87,17.0,4333.0,947.0,1650.0,919.0,6.3066,346100.0,NEAR BAY\n-122.49,37.86,35.0,2729.0,538.0,969.0,528.0,6.7669,500001.0,NEAR BAY\n-122.49,37.86,52.0,2175.0,510.0,809.0,503.0,4.5398,442000.0,NEAR BAY\n-122.48,37.86,52.0,3914.0,752.0,1177.0,670.0,6.2113,500001.0,NEAR BAY\n-122.48,37.85,42.0,6297.0,1307.0,2096.0,1205.0,6.4752,500001.0,NEAR BAY\n-122.49,37.85,38.0,240.0,29.0,63.0,34.0,12.2547,500001.0,NEAR BAY\n-122.52,37.88,52.0,176.0,50.0,88.0,46.0,3.8068,275000.0,NEAR BAY\n-122.62,37.85,30.0,833.0,164.0,358.0,143.0,6.8198,493800.0,NEAR OCEAN\n-122.53,37.86,38.0,1183.0,196.0,628.0,205.0,3.75,478600.0,NEAR BAY\n-122.66,37.93,42.0,1505.0,324.0,553.0,277.0,4.1792,350000.0,NEAR OCEAN\n-122.71,37.88,21.0,2845.0,552.0,599.0,250.0,4.3125,495800.0,NEAR OCEAN\n-122.69,37.91,43.0,1730.0,326.0,629.0,279.0,4.3194,238700.0,NEAR OCEAN\n-122.71,37.9,23.0,1250.0,257.0,437.0,188.0,3.115,242600.0,NEAR OCEAN\n-122.86,38.1,44.0,2602.0,509.0,691.0,343.0,4.3125,261500.0,NEAR OCEAN\n-122.84,38.07,31.0,1858.0,367.0,701.0,297.0,3.8269,270700.0,NEAR OCEAN\n-122.93,38.02,28.0,1284.0,265.0,628.0,219.0,3.5469,200000.0,NEAR OCEAN\n-122.68,38.07,26.0,1445.0,244.0,510.0,207.0,5.6305,430000.0,NEAR OCEAN\n-122.9,38.28,52.0,1275.0,218.0,627.0,185.0,2.3482,163500.0,NEAR OCEAN\n-122.96,38.26,20.0,1982.0,358.0,308.0,132.0,3.1429,240900.0,NEAR OCEAN\n-122.81,38.08,19.0,1615.0,366.0,815.0,337.0,3.4609,238800.0,NEAR OCEAN\n-122.8,38.18,36.0,2378.0,476.0,957.0,362.0,3.625,253100.0,NEAR OCEAN\n-120.19,37.53,25.0,1470.0,341.0,706.0,283.0,1.7614,71300.0,INLAND\n-120.02,37.57,17.0,2116.0,425.0,909.0,319.0,2.7188,113100.0,INLAND\n-119.99,37.51,14.0,2878.0,617.0,1011.0,509.0,1.398,103800.0,INLAND\n-119.95,37.47,32.0,1312.0,315.0,600.0,265.0,1.5,91500.0,INLAND\n-119.98,37.43,12.0,2776.0,592.0,1236.0,489.0,2.5551,105000.0,INLAND\n-120.07,37.34,16.0,1667.0,372.0,762.0,283.0,1.75,87500.0,INLAND\n-120.31,37.64,11.0,2403.0,497.0,890.0,344.0,3.0,120800.0,INLAND\n-120.15,37.69,13.0,866.0,252.0,369.0,165.0,2.875,70200.0,INLAND\n-120.02,37.72,17.0,2806.0,600.0,990.0,410.0,2.3818,88100.0,INLAND\n-119.81,37.67,24.0,172.0,42.0,79.0,30.0,3.8333,93800.0,INLAND\n-119.82,37.57,13.0,1713.0,340.0,643.0,241.0,2.662,92400.0,INLAND\n-119.9,37.49,13.0,2230.0,443.0,920.0,361.0,3.0,112000.0,INLAND\n-119.84,37.48,17.0,2582.0,553.0,1087.0,423.0,2.5,104200.0,INLAND\n-119.8,37.5,15.0,989.0,184.0,406.0,151.0,3.1771,121900.0,INLAND\n-119.72,37.46,13.0,1999.0,375.0,750.0,308.0,2.875,96000.0,INLAND\n-119.85,37.39,14.0,2744.0,555.0,1153.0,474.0,2.753,111100.0,INLAND\n-119.55,37.75,30.0,2165.0,536.0,1500.0,414.0,3.5391,55900.0,INLAND\n-119.64,37.61,30.0,2857.0,661.0,291.0,135.0,2.6838,164600.0,INLAND\n-123.15,39.74,23.0,608.0,143.0,281.0,108.0,2.9306,70000.0,INLAND\n-123.24,39.81,25.0,1435.0,304.0,746.0,259.0,1.7788,57900.0,INLAND\n-123.23,39.77,25.0,2075.0,435.0,991.0,377.0,1.2281,60300.0,INLAND\n-123.47,39.8,18.0,2130.0,545.0,863.0,346.0,2.3571,79200.0,INLAND\n-123.71,39.88,42.0,1518.0,383.0,656.0,303.0,1.4952,69800.0,NEAR OCEAN\n-123.84,39.83,19.0,1461.0,340.0,515.0,227.0,1.5278,145800.0,NEAR OCEAN\n-123.58,39.66,15.0,1839.0,489.0,887.0,332.0,2.2429,100000.0,<1H OCEAN\n-123.5,39.67,22.0,2124.0,450.0,1122.0,446.0,2.1793,71500.0,INLAND\n-123.64,39.45,21.0,3359.0,677.0,1908.0,642.0,3.0433,140700.0,<1H OCEAN\n-123.79,39.5,24.0,1421.0,291.0,588.0,274.0,2.325,157300.0,<1H OCEAN\n-123.8,39.47,28.0,2492.0,507.0,1202.0,460.0,2.7857,150300.0,<1H OCEAN\n-123.73,39.44,32.0,790.0,151.0,380.0,142.0,2.7,165000.0,<1H OCEAN\n-123.84,39.46,47.0,1150.0,244.0,552.0,201.0,2.5192,110400.0,<1H OCEAN\n-123.8,39.46,35.0,1718.0,345.0,698.0,299.0,2.9243,131600.0,<1H OCEAN\n-123.8,39.44,52.0,1533.0,336.0,754.0,340.0,1.9213,95000.0,<1H OCEAN\n-123.79,39.44,49.0,2290.0,482.0,1201.0,479.0,3.5,113300.0,<1H OCEAN\n-123.8,39.44,33.0,2024.0,459.0,1019.0,422.0,1.9208,93600.0,<1H OCEAN\n-123.79,39.44,36.0,1330.0,273.0,761.0,286.0,2.7813,105800.0,<1H OCEAN\n-123.79,39.44,16.0,2017.0,423.0,1177.0,414.0,3.2171,116200.0,<1H OCEAN\n-123.85,39.42,11.0,1804.0,506.0,895.0,451.0,1.7574,150000.0,<1H OCEAN\n-123.34,39.5,15.0,2342.0,535.0,1064.0,433.0,1.8967,96600.0,INLAND\n-123.4,39.46,10.0,4086.0,831.0,2111.0,758.0,3.2156,104400.0,<1H OCEAN\n-123.32,39.42,22.0,2085.0,432.0,1133.0,402.0,2.3906,92600.0,<1H OCEAN\n-123.38,39.37,18.0,3946.0,813.0,1899.0,730.0,2.6424,124600.0,<1H OCEAN\n-123.35,39.42,18.0,1619.0,346.0,904.0,295.0,2.1625,77200.0,<1H OCEAN\n-123.37,39.43,32.0,2780.0,470.0,1281.0,479.0,3.588,96000.0,<1H OCEAN\n-123.36,39.41,46.0,1748.0,362.0,808.0,330.0,2.9183,76900.0,<1H OCEAN\n-123.35,39.4,27.0,1321.0,338.0,779.0,327.0,1.85,71800.0,<1H OCEAN\n-123.36,39.4,21.0,1081.0,254.0,715.0,275.0,1.5625,71500.0,<1H OCEAN\n-123.34,39.39,18.0,2821.0,628.0,1636.0,615.0,2.3333,84000.0,<1H OCEAN\n-123.1,39.36,19.0,1056.0,248.0,611.0,226.0,1.746,105000.0,INLAND\n-123.23,39.33,20.0,804.0,121.0,448.0,140.0,3.9632,147100.0,<1H OCEAN\n-123.15,39.31,19.0,1026.0,205.0,424.0,152.0,2.8833,154200.0,INLAND\n-123.11,39.32,20.0,2745.0,504.0,1421.0,430.0,3.3431,137500.0,INLAND\n-123.18,39.26,25.0,3066.0,570.0,1558.0,535.0,3.788,134200.0,<1H OCEAN\n-123.22,39.28,16.0,5569.0,1106.0,3148.0,1088.0,3.1455,142900.0,<1H OCEAN\n-123.36,39.25,17.0,1087.0,254.0,522.0,202.0,2.5875,144500.0,<1H OCEAN\n-123.18,39.23,18.0,243.0,55.0,115.0,54.0,2.125,175000.0,<1H OCEAN\n-123.21,39.2,17.0,3145.0,693.0,1560.0,647.0,2.2926,149300.0,<1H OCEAN\n-123.19,39.21,22.0,1542.0,291.0,821.0,285.0,3.5917,118800.0,<1H OCEAN\n-123.2,39.23,26.0,786.0,168.0,494.0,161.0,2.3583,105400.0,<1H OCEAN\n-123.75,39.37,16.0,1377.0,296.0,830.0,279.0,3.25,151400.0,<1H OCEAN\n-123.85,39.39,23.0,4671.0,912.0,2095.0,857.0,3.184,140500.0,<1H OCEAN\n-123.81,39.34,17.0,1981.0,371.0,773.0,325.0,3.1563,277000.0,<1H OCEAN\n-123.7,39.32,18.0,1652.0,352.0,711.0,292.0,3.1071,213200.0,<1H OCEAN\n-123.81,39.31,23.0,2754.0,577.0,887.0,432.0,3.3654,225000.0,NEAR OCEAN\n-123.73,39.17,20.0,4620.0,1042.0,1745.0,794.0,2.375,158800.0,NEAR OCEAN\n-123.53,38.93,38.0,1706.0,355.0,506.0,211.0,2.5625,165600.0,NEAR OCEAN\n-123.69,38.9,17.0,2206.0,478.0,1140.0,428.0,2.1985,95300.0,NEAR OCEAN\n-123.59,38.8,17.0,5202.0,1037.0,1742.0,803.0,3.1201,176100.0,NEAR OCEAN\n-123.54,39.17,18.0,2251.0,510.0,1032.0,369.0,2.2946,101000.0,<1H OCEAN\n-123.39,38.99,28.0,1416.0,294.0,812.0,258.0,3.4063,109400.0,<1H OCEAN\n-123.36,39.01,35.0,1551.0,321.0,857.0,288.0,2.7232,115400.0,<1H OCEAN\n-123.21,39.18,17.0,2772.0,576.0,1501.0,584.0,2.6275,142100.0,<1H OCEAN\n-123.34,39.1,24.0,5372.0,1051.0,3002.0,992.0,3.0652,131100.0,<1H OCEAN\n-123.21,39.07,17.0,1890.0,342.0,877.0,312.0,3.7833,159800.0,<1H OCEAN\n-123.22,39.15,45.0,1348.0,265.0,639.0,270.0,3.3667,115200.0,<1H OCEAN\n-123.22,39.16,32.0,1149.0,187.0,499.0,208.0,3.6587,154600.0,<1H OCEAN\n-123.23,39.13,33.0,1176.0,211.0,529.0,217.0,3.8958,144000.0,<1H OCEAN\n-123.22,39.15,36.0,1166.0,216.0,504.0,203.0,3.5938,122100.0,<1H OCEAN\n-123.21,39.15,52.0,1370.0,258.0,617.0,228.0,2.55,112900.0,<1H OCEAN\n-123.21,39.14,39.0,1419.0,262.0,661.0,278.0,3.0,114600.0,<1H OCEAN\n-123.21,39.13,27.0,1531.0,266.0,822.0,234.0,4.0469,127400.0,<1H OCEAN\n-123.2,39.16,14.0,1908.0,484.0,1195.0,467.0,1.7929,82300.0,<1H OCEAN\n-123.22,39.16,29.0,6121.0,1222.0,3595.0,1189.0,2.631,109600.0,<1H OCEAN\n-123.2,39.15,27.0,990.0,238.0,592.0,225.0,2.0074,96200.0,<1H OCEAN\n-123.19,39.15,16.0,2577.0,495.0,1232.0,488.0,2.6012,125600.0,<1H OCEAN\n-123.21,39.15,31.0,2685.0,675.0,1367.0,626.0,1.6571,108900.0,<1H OCEAN\n-123.2,39.14,17.0,1620.0,396.0,878.0,399.0,1.8042,109200.0,<1H OCEAN\n-123.2,39.13,26.0,1474.0,417.0,1065.0,401.0,1.375,84400.0,<1H OCEAN\n-123.21,39.14,15.0,2235.0,545.0,1376.0,516.0,1.9032,100000.0,<1H OCEAN\n-123.1,39.15,32.0,1143.0,208.0,454.0,188.0,3.8333,116100.0,<1H OCEAN\n-123.17,39.18,14.0,2240.0,327.0,1030.0,308.0,5.9585,214900.0,<1H OCEAN\n-123.17,39.15,30.0,1904.0,331.0,816.0,325.0,4.425,161900.0,<1H OCEAN\n-123.16,39.13,33.0,1320.0,303.0,1048.0,303.0,1.7813,94700.0,<1H OCEAN\n-123.16,39.1,31.0,418.0,82.0,327.0,81.0,2.775,120800.0,<1H OCEAN\n-123.19,39.12,38.0,267.0,57.0,196.0,60.0,2.3125,70000.0,<1H OCEAN\n-123.15,38.94,22.0,2163.0,436.0,1048.0,358.0,2.7171,95800.0,<1H OCEAN\n-123.1,38.97,36.0,1211.0,247.0,697.0,251.0,2.5761,94900.0,<1H OCEAN\n-120.46,37.51,22.0,2704.0,497.0,1432.0,399.0,2.9,83100.0,INLAND\n-120.68,37.47,33.0,1028.0,226.0,658.0,197.0,2.3043,66300.0,INLAND\n-120.75,37.44,27.0,2295.0,424.0,1252.0,350.0,3.6182,123200.0,INLAND\n-120.76,37.44,18.0,2003.0,398.0,1333.0,411.0,2.7562,90500.0,INLAND\n-120.77,37.42,27.0,949.0,224.0,888.0,241.0,2.3333,72800.0,INLAND\n-120.79,37.43,17.0,2534.0,517.0,1764.0,502.0,2.8519,80700.0,INLAND\n-120.79,37.41,35.0,2436.0,466.0,1730.0,469.0,2.2071,85900.0,INLAND\n-120.71,37.39,11.0,1479.0,341.0,1476.0,327.0,3.2721,73800.0,INLAND\n-120.71,37.39,40.0,680.0,160.0,785.0,175.0,2.6058,72700.0,INLAND\n-120.69,37.4,46.0,860.0,130.0,496.0,147.0,3.5167,137500.0,INLAND\n-120.74,37.33,30.0,2390.0,470.0,1409.0,428.0,2.1484,81300.0,INLAND\n-120.75,37.37,32.0,1656.0,317.0,1037.0,286.0,2.4964,88800.0,INLAND\n-120.73,37.38,37.0,653.0,176.0,827.0,176.0,1.9236,64400.0,INLAND\n-120.72,37.38,22.0,1311.0,319.0,1455.0,340.0,2.2813,67300.0,INLAND\n-120.73,37.38,23.0,1451.0,292.0,1052.0,265.0,2.8698,72900.0,INLAND\n-120.71,37.38,14.0,1979.0,432.0,1756.0,382.0,2.6923,71400.0,INLAND\n-120.84,37.4,7.0,2773.0,530.0,1374.0,505.0,2.6214,103800.0,INLAND\n-120.86,37.4,17.0,3511.0,636.0,1904.0,617.0,3.1111,113900.0,INLAND\n-120.84,37.43,32.0,2892.0,521.0,1580.0,484.0,3.7784,164500.0,INLAND\n-120.94,37.4,32.0,1175.0,208.0,774.0,222.0,3.0,109400.0,INLAND\n-120.88,37.37,24.0,1294.0,222.0,684.0,221.0,2.6908,103100.0,INLAND\n-120.89,37.33,27.0,2692.0,481.0,1518.0,447.0,2.0417,94200.0,INLAND\n-120.64,37.38,21.0,3157.0,637.0,2268.0,620.0,2.567,70400.0,INLAND\n-120.64,37.38,19.0,2256.0,449.0,1469.0,435.0,2.5129,84600.0,INLAND\n-120.65,37.33,25.0,1731.0,311.0,810.0,266.0,4.1058,107600.0,INLAND\n-120.59,37.39,16.0,4717.0,1119.0,3589.0,1017.0,2.1061,72800.0,INLAND\n-120.63,37.41,27.0,2083.0,444.0,1462.0,479.0,2.6439,69100.0,INLAND\n-120.57,37.43,39.0,2235.0,412.0,1268.0,402.0,2.6758,74600.0,INLAND\n-120.6,37.37,10.0,3075.0,498.0,1368.0,487.0,4.6667,130100.0,INLAND\n-120.6,37.36,27.0,2521.0,484.0,1307.0,456.0,3.0911,86900.0,INLAND\n-120.63,37.36,16.0,1605.0,282.0,866.0,284.0,4.0694,110200.0,INLAND\n-120.62,37.35,18.0,874.0,203.0,572.0,190.0,1.6833,71000.0,INLAND\n-120.62,37.36,15.0,3455.0,729.0,2014.0,659.0,3.2656,83500.0,INLAND\n-120.62,37.37,8.0,2608.0,428.0,1530.0,435.0,3.968,102100.0,INLAND\n-120.59,37.36,11.0,3946.0,978.0,2814.0,953.0,2.0179,87300.0,INLAND\n-120.6,37.35,34.0,1722.0,316.0,904.0,315.0,2.4653,66100.0,INLAND\n-120.61,37.35,34.0,1900.0,401.0,1009.0,385.0,2.2222,75000.0,INLAND\n-120.62,37.35,20.0,1457.0,372.0,1000.0,346.0,1.4615,69200.0,INLAND\n-120.61,37.32,18.0,5009.0,826.0,2497.0,805.0,4.25,146300.0,INLAND\n-120.6,37.34,34.0,1830.0,431.0,1304.0,415.0,2.1182,68900.0,INLAND\n-120.61,37.36,16.0,638.0,,380.0,132.0,1.9135,87500.0,INLAND\n-120.57,37.35,18.0,704.0,176.0,520.0,154.0,3.003,101300.0,INLAND\n-120.6,37.35,19.0,3874.0,676.0,2441.0,707.0,3.2955,88600.0,INLAND\n-120.59,37.35,15.0,3249.0,613.0,1569.0,595.0,3.5393,88000.0,INLAND\n-120.58,37.36,33.0,3564.0,716.0,2603.0,696.0,2.2179,67500.0,INLAND\n-120.64,37.2,16.0,2236.0,438.0,1361.0,393.0,2.0066,125000.0,INLAND\n-120.55,37.32,21.0,1410.0,229.0,590.0,205.0,3.3194,141400.0,INLAND\n-120.49,37.26,28.0,2159.0,416.0,1283.0,378.0,1.8939,83000.0,INLAND\n-120.5,37.37,18.0,8606.0,1678.0,5303.0,1644.0,2.4012,79700.0,INLAND\n-120.41,37.16,21.0,1684.0,341.0,1052.0,312.0,2.0809,95800.0,INLAND\n-120.51,37.32,13.0,2109.0,477.0,1108.0,466.0,2.3099,89600.0,INLAND\n-120.49,37.32,10.0,1275.0,255.0,620.0,240.0,3.0263,118300.0,INLAND\n-120.5,37.32,13.0,1936.0,384.0,1158.0,367.0,2.75,83200.0,INLAND\n-120.49,37.32,13.0,3474.0,927.0,2149.0,821.0,1.9528,85300.0,INLAND\n-120.48,37.32,13.0,3641.0,897.0,1737.0,788.0,2.1418,130600.0,INLAND\n-120.47,37.32,15.0,3952.0,984.0,2024.0,1026.0,2.558,121600.0,INLAND\n-120.5,37.34,16.0,1245.0,231.0,956.0,219.0,3.4559,108000.0,INLAND\n-120.48,37.34,8.0,6146.0,1017.0,2821.0,987.0,4.67,127600.0,INLAND\n-120.47,37.34,9.0,2934.0,511.0,1227.0,501.0,3.6742,117200.0,INLAND\n-120.43,37.32,16.0,1170.0,178.0,566.0,181.0,5.2522,125300.0,INLAND\n-120.45,37.32,21.0,1318.0,202.0,618.0,197.0,4.8214,117800.0,INLAND\n-120.45,37.32,19.0,3136.0,466.0,1631.0,484.0,3.6471,101400.0,INLAND\n-120.46,37.33,4.0,786.0,116.0,368.0,109.0,6.3215,138200.0,INLAND\n-120.46,37.33,17.0,6111.0,1171.0,2950.0,1104.0,3.2852,98800.0,INLAND\n-120.44,37.31,16.0,3369.0,532.0,1770.0,574.0,5.2662,126200.0,INLAND\n-120.45,37.31,20.0,4379.0,753.0,2055.0,716.0,3.7652,133500.0,INLAND\n-120.46,37.31,26.0,3170.0,572.0,1524.0,565.0,3.48,95300.0,INLAND\n-120.48,37.31,42.0,2361.0,512.0,1684.0,511.0,2.355,75600.0,INLAND\n-120.5,37.31,36.0,2162.0,433.0,1048.0,451.0,2.6797,81800.0,INLAND\n-120.49,37.31,45.0,1834.0,421.0,1405.0,407.0,2.0521,72400.0,INLAND\n-120.48,37.3,49.0,2919.0,719.0,1956.0,679.0,1.5427,88500.0,INLAND\n-120.48,37.3,39.0,1015.0,356.0,875.0,313.0,1.5,67000.0,INLAND\n-120.49,37.3,50.0,985.0,309.0,621.0,250.0,1.3125,60900.0,INLAND\n-120.44,37.31,21.0,6911.0,1341.0,3967.0,1297.0,3.0515,95200.0,INLAND\n-120.46,37.31,35.0,2042.0,378.0,953.0,356.0,2.7344,87800.0,INLAND\n-120.47,37.3,40.0,3693.0,771.0,2102.0,742.0,2.1838,75000.0,INLAND\n-120.46,37.3,36.0,3346.0,739.0,2151.0,713.0,2.3095,68300.0,INLAND\n-120.51,37.29,20.0,4927.0,1042.0,4205.0,1009.0,1.7679,79800.0,INLAND\n-120.5,37.3,29.0,1572.0,456.0,1697.0,429.0,1.76,63200.0,INLAND\n-120.49,37.3,35.0,1313.0,324.0,1350.0,343.0,1.75,57600.0,INLAND\n-120.49,37.29,17.0,2414.0,594.0,2487.0,582.0,1.0955,62700.0,INLAND\n-120.47,37.29,16.0,749.0,222.0,1277.0,224.0,1.2054,60900.0,INLAND\n-120.48,37.29,17.0,2266.0,693.0,3200.0,664.0,1.5635,60400.0,INLAND\n-120.47,37.28,19.0,1548.0,319.0,1227.0,309.0,1.7756,73300.0,INLAND\n-120.49,37.28,11.0,1721.0,381.0,1708.0,373.0,1.9535,57100.0,INLAND\n-120.44,37.28,12.0,2855.0,598.0,1658.0,586.0,2.3929,81100.0,INLAND\n-120.44,37.29,18.0,1260.0,268.0,576.0,263.0,1.7222,101500.0,INLAND\n-120.46,37.29,30.0,2972.0,635.0,1940.0,590.0,2.3594,72300.0,INLAND\n-120.43,37.35,15.0,1613.0,203.0,673.0,213.0,5.9378,212200.0,INLAND\n-120.4,37.3,28.0,1401.0,,967.0,257.0,1.5917,89400.0,INLAND\n-120.3,37.34,33.0,993.0,186.0,556.0,175.0,2.4286,103600.0,INLAND\n-120.32,37.29,38.0,576.0,,478.0,112.0,2.3382,59600.0,INLAND\n-120.31,37.29,40.0,1542.0,341.0,1283.0,341.0,1.6929,55900.0,INLAND\n-120.31,37.29,36.0,969.0,206.0,732.0,175.0,1.5938,57600.0,INLAND\n-120.32,37.29,9.0,695.0,188.0,810.0,190.0,1.6172,56300.0,INLAND\n-120.25,37.23,34.0,1656.0,328.0,1110.0,332.0,2.1845,59900.0,INLAND\n-120.24,37.21,31.0,2447.0,465.0,1313.0,352.0,3.3929,93800.0,INLAND\n-120.35,37.31,17.0,605.0,159.0,416.0,83.0,2.0,87500.0,INLAND\n-121.0,37.25,31.0,1923.0,341.0,806.0,349.0,3.1738,97600.0,INLAND\n-121.0,37.26,45.0,1750.0,371.0,847.0,354.0,1.7062,77400.0,INLAND\n-121.0,37.25,21.0,1937.0,389.0,1002.0,373.0,2.6087,96200.0,INLAND\n-121.01,37.25,16.0,2216.0,458.0,1135.0,424.0,2.7316,97500.0,INLAND\n-121.06,37.18,30.0,2603.0,507.0,1491.0,473.0,3.0909,123400.0,INLAND\n-120.89,37.21,25.0,3301.0,678.0,994.0,306.0,3.2262,97200.0,INLAND\n-121.02,37.09,17.0,1118.0,270.0,560.0,244.0,2.0216,112500.0,INLAND\n-121.02,36.94,33.0,1541.0,313.0,880.0,272.0,2.5074,117700.0,INLAND\n-120.77,37.01,28.0,1689.0,378.0,1057.0,267.0,3.125,156300.0,INLAND\n-120.95,37.09,43.0,1116.0,222.0,801.0,207.0,2.875,97200.0,INLAND\n-120.85,37.08,3.0,3425.0,611.0,1588.0,459.0,4.1779,147800.0,INLAND\n-120.85,37.07,16.0,1795.0,362.0,1642.0,340.0,2.5363,86300.0,INLAND\n-120.84,37.07,24.0,1520.0,335.0,882.0,306.0,2.2019,100000.0,INLAND\n-120.84,37.06,14.0,1506.0,380.0,1096.0,352.0,1.1301,78500.0,INLAND\n-120.85,37.06,31.0,2609.0,645.0,1796.0,629.0,1.5479,82000.0,INLAND\n-120.87,37.07,26.0,2036.0,401.0,1343.0,414.0,3.6331,88600.0,INLAND\n-120.87,37.05,29.0,4176.0,779.0,2092.0,741.0,2.595,104200.0,INLAND\n-120.85,37.05,32.0,2893.0,481.0,1198.0,466.0,3.1719,140600.0,INLAND\n-120.84,37.05,8.0,1944.0,283.0,814.0,276.0,5.3988,165500.0,INLAND\n-120.79,37.08,9.0,97.0,20.0,91.0,22.0,2.9063,55000.0,INLAND\n-120.83,37.07,16.0,3736.0,761.0,1942.0,730.0,2.5598,120200.0,INLAND\n-120.82,37.05,15.0,1385.0,288.0,775.0,255.0,1.933,140600.0,INLAND\n-120.65,37.09,22.0,886.0,173.0,595.0,161.0,2.4398,150000.0,INLAND\n-120.61,37.03,34.0,1841.0,354.0,1019.0,356.0,1.7841,67500.0,INLAND\n-120.7,36.99,32.0,320.0,73.0,222.0,78.0,2.9271,87500.0,INLAND\n-120.65,36.98,26.0,1787.0,364.0,1548.0,362.0,1.7188,49500.0,INLAND\n-120.63,36.98,20.0,2380.0,489.0,1581.0,505.0,2.0595,61300.0,INLAND\n-120.64,36.99,23.0,2363.0,449.0,1168.0,410.0,2.2794,75700.0,INLAND\n-120.62,36.99,32.0,2455.0,508.0,1344.0,492.0,1.9732,69400.0,INLAND\n-120.67,37.37,18.0,164.0,30.0,104.0,32.0,1.6607,87500.0,INLAND\n-121.16,41.78,42.0,2918.0,576.0,1182.0,440.0,2.1434,44000.0,INLAND\n-121.18,41.31,22.0,2124.0,432.0,829.0,313.0,2.4519,57500.0,INLAND\n-120.87,41.54,21.0,1091.0,208.0,660.0,188.0,2.2321,34600.0,INLAND\n-120.51,41.35,16.0,2843.0,564.0,892.0,386.0,2.5074,69100.0,INLAND\n-120.48,41.82,20.0,1367.0,284.0,429.0,181.0,2.0227,47500.0,INLAND\n-120.08,41.79,34.0,1355.0,262.0,434.0,178.0,2.0903,56100.0,INLAND\n-120.12,41.4,33.0,2820.0,515.0,976.0,403.0,2.6062,52600.0,INLAND\n-120.55,41.61,22.0,9047.0,1831.0,4276.0,1622.0,2.0802,47900.0,INLAND\n-119.54,38.51,14.0,1250.0,272.0,721.0,234.0,2.35,95700.0,INLAND\n-119.44,38.53,20.0,1963.0,434.0,682.0,273.0,1.5817,97800.0,INLAND\n-119.3,38.26,19.0,3325.0,660.0,750.0,286.0,2.9509,114800.0,INLAND\n-118.98,38.03,15.0,991.0,277.0,419.0,170.0,3.5469,82500.0,INLAND\n-119.07,37.8,12.0,1736.0,352.0,330.0,123.0,3.5294,160700.0,INLAND\n-119.08,37.78,17.0,1631.0,335.0,285.0,128.0,2.7656,130000.0,INLAND\n-118.45,37.7,15.0,2199.0,453.0,899.0,347.0,2.35,107800.0,INLAND\n-118.74,37.58,20.0,3301.0,779.0,1085.0,448.0,3.7315,159300.0,INLAND\n-118.96,37.64,11.0,3934.0,697.0,901.0,345.0,4.2381,242700.0,INLAND\n-119.02,37.64,14.0,5919.0,1278.0,265.0,112.0,3.2431,221400.0,INLAND\n-118.98,37.65,18.0,1795.0,416.0,483.0,208.0,4.5375,169800.0,INLAND\n-118.99,37.65,20.0,2474.0,625.0,338.0,141.0,5.01,195500.0,INLAND\n-118.98,37.64,17.0,3769.0,908.0,1160.0,453.0,3.05,188900.0,INLAND\n-118.97,37.64,13.0,1907.0,544.0,575.0,234.0,3.0685,162500.0,INLAND\n-118.97,37.64,14.0,2284.0,622.0,342.0,137.0,3.0921,87500.0,INLAND\n-118.97,37.64,14.0,1847.0,439.0,238.0,98.0,3.6042,137500.0,INLAND\n-118.99,37.63,10.0,7744.0,1573.0,483.0,224.0,3.2917,231800.0,INLAND\n-121.64,36.72,17.0,4203.0,816.0,2900.0,827.0,4.1742,159900.0,<1H OCEAN\n-121.63,36.71,19.0,5015.0,1013.0,3251.0,940.0,3.9818,152900.0,<1H OCEAN\n-121.62,36.71,24.0,4195.0,706.0,2200.0,647.0,4.3451,177800.0,<1H OCEAN\n-121.62,36.74,30.0,1337.0,253.0,838.0,247.0,5.0374,165400.0,<1H OCEAN\n-121.64,36.7,32.0,4089.0,735.0,2927.0,713.0,4.1675,142500.0,<1H OCEAN\n-121.65,36.7,29.0,4964.0,1056.0,2773.0,1036.0,3.0827,148100.0,<1H OCEAN\n-121.66,36.71,27.0,4131.0,886.0,2002.0,815.0,3.2929,157500.0,<1H OCEAN\n-121.66,36.7,33.0,3252.0,630.0,2010.0,641.0,3.4222,158100.0,<1H OCEAN\n-121.65,36.69,21.0,7884.0,2011.0,4907.0,1919.0,2.7367,160300.0,<1H OCEAN\n-121.64,36.68,16.0,6568.0,1603.0,6012.0,1565.0,2.3463,156100.0,<1H OCEAN\n-121.63,36.68,24.0,2591.0,739.0,3243.0,702.0,2.1766,108500.0,<1H OCEAN\n-121.61,36.68,37.0,3149.0,833.0,3456.0,788.0,2.8542,127600.0,<1H OCEAN\n-121.62,36.68,43.0,2534.0,592.0,2448.0,603.0,2.4884,130500.0,<1H OCEAN\n-121.61,36.69,19.0,9899.0,2617.0,11272.0,2528.0,2.0244,118500.0,<1H OCEAN\n-121.61,36.67,39.0,3260.0,821.0,3130.0,793.0,2.5224,119200.0,<1H OCEAN\n-121.62,36.67,45.0,1827.0,408.0,1507.0,410.0,2.8942,129000.0,<1H OCEAN\n-121.62,36.67,31.0,2697.0,690.0,2220.0,665.0,2.5329,135200.0,<1H OCEAN\n-121.63,36.67,34.0,2486.0,560.0,2443.0,557.0,2.5263,130400.0,<1H OCEAN\n-121.64,36.67,28.0,256.0,66.0,214.0,60.0,3.0197,137500.0,<1H OCEAN\n-121.63,36.65,44.0,309.0,121.0,315.0,80.0,1.6071,112500.0,<1H OCEAN\n-121.64,36.66,24.0,3174.0,506.0,1466.0,535.0,5.2285,248100.0,<1H OCEAN\n-121.65,36.66,30.0,3745.0,767.0,1762.0,748.0,3.2355,214200.0,<1H OCEAN\n-121.65,36.66,42.0,4261.0,840.0,2013.0,801.0,3.5288,221000.0,<1H OCEAN\n-121.65,36.67,52.0,2351.0,459.0,1169.0,439.0,2.8924,169600.0,<1H OCEAN\n-121.65,36.67,28.0,1926.0,556.0,1717.0,535.0,1.9385,123200.0,<1H OCEAN\n-121.66,36.68,10.0,913.0,265.0,508.0,251.0,0.9914,147500.0,<1H OCEAN\n-121.66,36.67,40.0,2878.0,592.0,1444.0,564.0,3.1439,192300.0,<1H OCEAN\n-121.66,36.67,40.0,2497.0,520.0,1275.0,508.0,3.1071,193100.0,<1H OCEAN\n-121.67,36.66,19.0,9371.0,1980.0,4259.0,1882.0,3.6875,189700.0,<1H OCEAN\n-121.67,36.67,24.0,3071.0,544.0,1477.0,560.0,3.9219,222500.0,<1H OCEAN\n-121.68,36.67,26.0,5745.0,1017.0,2780.0,996.0,5.0,202300.0,<1H OCEAN\n-121.67,36.68,38.0,5561.0,1292.0,3523.0,1253.0,2.8289,168300.0,<1H OCEAN\n-121.66,36.69,6.0,10613.0,2485.0,7249.0,2375.0,3.7912,168900.0,<1H OCEAN\n-121.79,36.85,28.0,1049.0,235.0,705.0,208.0,2.7321,150000.0,NEAR OCEAN\n-121.77,36.87,37.0,424.0,65.0,266.0,64.0,3.3472,293800.0,NEAR OCEAN\n-121.73,36.9,23.0,2392.0,721.0,3074.0,718.0,2.5195,136900.0,<1H OCEAN\n-121.71,36.9,16.0,1680.0,285.0,1103.0,275.0,4.6125,253800.0,<1H OCEAN\n-121.68,36.9,13.0,833.0,130.0,405.0,127.0,5.2729,322900.0,<1H OCEAN\n-121.66,36.89,15.0,2608.0,458.0,1531.0,457.0,5.5148,253500.0,<1H OCEAN\n-121.76,36.83,28.0,1445.0,268.0,1017.0,284.0,3.6693,211000.0,<1H OCEAN\n-121.73,36.86,28.0,827.0,178.0,703.0,144.0,4.4271,175700.0,<1H OCEAN\n-121.71,36.88,19.0,2528.0,554.0,2332.0,492.0,3.7766,177000.0,<1H OCEAN\n-121.73,36.85,22.0,1304.0,278.0,887.0,227.0,3.6607,206300.0,<1H OCEAN\n-121.7,36.84,19.0,2511.0,465.0,1551.0,450.0,4.9107,231900.0,<1H OCEAN\n-121.65,36.85,20.0,2606.0,424.0,1361.0,426.0,4.5787,245100.0,<1H OCEAN\n-121.69,36.8,19.0,2164.0,410.0,1309.0,426.0,3.338,185300.0,<1H OCEAN\n-121.74,36.79,16.0,3841.0,620.0,1799.0,611.0,4.3814,245300.0,<1H OCEAN\n-121.72,36.81,18.0,1984.0,379.0,1078.0,359.0,3.2969,229900.0,<1H OCEAN\n-121.69,36.81,18.0,2837.0,522.0,1454.0,458.0,4.5272,221000.0,<1H OCEAN\n-121.66,36.82,17.0,3921.0,654.0,1895.0,641.0,5.0092,238700.0,<1H OCEAN\n-121.64,36.82,18.0,1819.0,283.0,919.0,295.0,4.1696,222500.0,<1H OCEAN\n-121.76,36.75,21.0,1141.0,257.0,671.0,195.0,3.8424,155700.0,<1H OCEAN\n-121.7,36.77,21.0,2294.0,478.0,1306.0,430.0,3.0347,227200.0,<1H OCEAN\n-121.76,36.77,27.0,1608.0,503.0,2031.0,498.0,2.3384,121000.0,<1H OCEAN\n-121.75,36.77,25.0,1851.0,418.0,1678.0,390.0,3.2937,135300.0,<1H OCEAN\n-121.75,36.76,32.0,1740.0,399.0,1563.0,389.0,2.7694,132400.0,<1H OCEAN\n-121.64,36.74,30.0,2628.0,444.0,1372.0,432.0,4.1696,175000.0,<1H OCEAN\n-121.64,36.8,18.0,5915.0,1000.0,2975.0,975.0,4.5812,255200.0,<1H OCEAN\n-121.6,36.81,18.0,1575.0,230.0,751.0,219.0,5.2203,286500.0,<1H OCEAN\n-121.65,36.77,15.0,2191.0,358.0,1150.0,330.0,4.7969,227500.0,<1H OCEAN\n-121.68,36.72,12.0,19234.0,4492.0,12153.0,4372.0,3.2652,152800.0,<1H OCEAN\n-121.7,36.67,37.0,641.0,129.0,458.0,142.0,3.3456,252600.0,<1H OCEAN\n-121.62,36.63,52.0,1437.0,298.0,836.0,257.0,3.6286,165500.0,<1H OCEAN\n-121.54,36.7,12.0,6758.0,1241.0,3918.0,1100.0,3.525,201700.0,<1H OCEAN\n-121.62,36.69,11.0,4712.0,1098.0,5982.0,1105.0,2.5986,135700.0,<1H OCEAN\n-121.62,36.69,12.0,512.0,144.0,767.0,149.0,2.2667,72900.0,<1H OCEAN\n-121.69,36.62,19.0,1907.0,323.0,681.0,270.0,6.0332,244900.0,<1H OCEAN\n-121.7,36.6,19.0,3562.0,530.0,1607.0,505.0,5.0162,283100.0,<1H OCEAN\n-121.67,36.58,11.0,5892.0,837.0,2327.0,812.0,6.1551,291800.0,<1H OCEAN\n-121.59,36.55,34.0,737.0,140.0,362.0,138.0,5.1788,270000.0,<1H OCEAN\n-121.7,36.55,21.0,3905.0,583.0,1528.0,586.0,7.6245,336600.0,<1H OCEAN\n-121.69,36.52,15.0,4037.0,586.0,1596.0,557.0,8.0922,390100.0,<1H OCEAN\n-121.43,36.5,14.0,1835.0,468.0,1867.0,461.0,2.3879,129800.0,<1H OCEAN\n-121.44,36.51,31.0,1636.0,380.0,1468.0,339.0,3.2219,114700.0,<1H OCEAN\n-121.45,36.51,29.0,1045.0,311.0,1245.0,273.0,1.775,112500.0,<1H OCEAN\n-121.42,36.57,13.0,2685.0,621.0,2474.0,573.0,2.8775,134100.0,<1H OCEAN\n-121.48,36.49,28.0,1006.0,228.0,738.0,193.0,1.9722,210700.0,<1H OCEAN\n-121.74,36.49,33.0,2952.0,565.0,1264.0,517.0,4.4209,274600.0,<1H OCEAN\n-121.73,36.5,27.0,3184.0,520.0,1121.0,493.0,5.6383,377000.0,<1H OCEAN\n-121.74,36.47,28.0,1973.0,343.0,970.0,349.0,4.25,279100.0,<1H OCEAN\n-121.7,36.48,19.0,2150.0,479.0,1052.0,428.0,3.5039,288400.0,<1H OCEAN\n-121.62,36.43,20.0,1335.0,290.0,717.0,243.0,4.7891,230600.0,<1H OCEAN\n-121.66,36.37,9.0,1580.0,287.0,465.0,208.0,6.1668,405800.0,<1H OCEAN\n-121.31,36.42,21.0,2740.0,615.0,2630.0,564.0,2.6629,102700.0,<1H OCEAN\n-121.32,36.42,20.0,1054.0,269.0,1219.0,273.0,3.0437,76600.0,<1H OCEAN\n-121.32,36.43,22.0,2927.0,637.0,2546.0,618.0,2.7153,114300.0,<1H OCEAN\n-121.33,36.43,40.0,622.0,194.0,902.0,196.0,2.625,109100.0,<1H OCEAN\n-121.4,36.38,39.0,2288.0,529.0,1449.0,410.0,3.3289,190600.0,<1H OCEAN\n-121.23,36.33,23.0,2095.0,536.0,1858.0,457.0,3.0543,92400.0,<1H OCEAN\n-121.25,36.32,12.0,4776.0,1082.0,4601.0,1066.0,2.9184,100500.0,<1H OCEAN\n-121.26,36.32,30.0,146.0,41.0,164.0,40.0,2.3,206300.0,<1H OCEAN\n-121.24,36.33,13.0,1642.0,418.0,1534.0,388.0,3.1222,125500.0,<1H OCEAN\n-121.24,36.34,33.0,1691.0,308.0,792.0,262.0,2.6648,164600.0,<1H OCEAN\n-121.12,36.21,16.0,1720.0,473.0,1427.0,291.0,2.1107,76200.0,<1H OCEAN\n-121.13,36.21,27.0,1476.0,352.0,1156.0,358.0,3.1929,137900.0,<1H OCEAN\n-121.13,36.21,30.0,1484.0,414.0,1200.0,351.0,1.7548,95800.0,<1H OCEAN\n-121.13,36.21,34.0,2504.0,550.0,1810.0,547.0,3.4821,113700.0,<1H OCEAN\n-121.13,36.2,16.0,1868.0,443.0,1323.0,436.0,2.9559,163200.0,<1H OCEAN\n-121.02,36.24,12.0,2198.0,507.0,1971.0,502.0,2.6801,100000.0,<1H OCEAN\n-121.2,36.14,12.0,3738.0,710.0,2337.0,664.0,3.9647,135000.0,<1H OCEAN\n-121.39,36.16,28.0,1057.0,249.0,288.0,130.0,3.0526,146900.0,NEAR OCEAN\n-121.32,35.95,31.0,372.0,68.0,479.0,67.0,3.5547,200000.0,NEAR OCEAN\n-121.0,35.94,16.0,3077.0,628.0,1479.0,536.0,3.3724,114600.0,<1H OCEAN\n-120.79,36.06,29.0,1916.0,386.0,1019.0,314.0,2.4881,87500.0,<1H OCEAN\n-120.51,35.91,39.0,768.0,162.0,264.0,118.0,5.3245,250000.0,INLAND\n-121.91,36.42,14.0,1078.0,261.0,382.0,171.0,3.7083,210000.0,NEAR OCEAN\n-121.84,36.25,20.0,958.0,245.0,590.0,189.0,2.6094,362500.0,NEAR OCEAN\n-121.62,36.14,25.0,726.0,274.0,411.0,214.0,3.2375,450000.0,NEAR OCEAN\n-121.87,36.55,20.0,10053.0,1768.0,3083.0,1621.0,5.1506,387500.0,NEAR OCEAN\n-121.82,36.54,22.0,1746.0,363.0,886.0,364.0,5.5469,378800.0,<1H OCEAN\n-121.77,36.53,18.0,2321.0,358.0,803.0,341.0,10.1854,426000.0,<1H OCEAN\n-121.84,36.52,18.0,3165.0,533.0,1312.0,434.0,6.5234,357400.0,NEAR OCEAN\n-121.88,36.49,28.0,2830.0,458.0,898.0,370.0,5.8142,500001.0,NEAR OCEAN\n-121.92,36.57,42.0,3944.0,738.0,1374.0,598.0,4.174,394400.0,NEAR OCEAN\n-121.91,36.55,39.0,5468.0,834.0,1782.0,712.0,5.7248,398800.0,NEAR OCEAN\n-121.92,36.54,33.0,5323.0,887.0,1670.0,740.0,3.9792,468000.0,NEAR OCEAN\n-121.92,36.56,40.0,2124.0,449.0,643.0,341.0,5.5164,369100.0,NEAR OCEAN\n-121.94,36.55,30.0,2722.0,584.0,628.0,384.0,3.4048,487100.0,NEAR OCEAN\n-121.92,36.56,39.0,2144.0,538.0,749.0,419.0,2.7039,364000.0,NEAR OCEAN\n-121.92,36.55,37.0,2190.0,419.0,490.0,300.0,3.7852,465800.0,NEAR OCEAN\n-121.92,36.55,44.0,3494.0,635.0,693.0,415.0,3.6,452800.0,NEAR OCEAN\n-121.92,36.55,52.0,2616.0,483.0,582.0,313.0,3.275,500001.0,NEAR OCEAN\n-121.95,36.61,31.0,1736.0,250.0,497.0,170.0,6.3835,407800.0,NEAR OCEAN\n-121.93,36.59,25.0,2201.0,353.0,622.0,295.0,5.0621,386500.0,NEAR OCEAN\n-121.95,36.6,32.0,3152.0,504.0,793.0,426.0,7.1198,469900.0,NEAR OCEAN\n-121.95,36.59,22.0,3553.0,530.0,1108.0,441.0,5.8505,417100.0,NEAR OCEAN\n-121.94,36.58,23.0,4911.0,693.0,1480.0,606.0,6.777,500000.0,NEAR OCEAN\n-121.94,36.57,28.0,3153.0,409.0,569.0,271.0,14.4113,500001.0,NEAR OCEAN\n-121.93,36.6,33.0,3455.0,683.0,1704.0,663.0,4.0154,225700.0,NEAR OCEAN\n-121.92,36.61,29.0,3735.0,808.0,1873.0,757.0,3.1543,253800.0,NEAR OCEAN\n-121.93,36.62,34.0,2351.0,,1063.0,428.0,3.725,278000.0,NEAR OCEAN\n-121.92,36.61,27.0,3044.0,661.0,1229.0,618.0,3.1359,268000.0,NEAR OCEAN\n-121.93,36.62,39.0,869.0,173.0,406.0,165.0,4.0313,253800.0,NEAR OCEAN\n-121.92,36.62,52.0,974.0,190.0,403.0,181.0,4.3281,236500.0,NEAR OCEAN\n-121.92,36.62,52.0,1001.0,191.0,425.0,184.0,3.7614,241700.0,NEAR OCEAN\n-121.92,36.62,47.0,1811.0,401.0,948.0,375.0,3.0379,249300.0,NEAR OCEAN\n-121.91,36.62,52.0,1431.0,300.0,657.0,293.0,3.2865,240100.0,<1H OCEAN\n-121.92,36.62,52.0,867.0,199.0,391.0,187.0,2.6713,234600.0,NEAR OCEAN\n-121.92,36.62,52.0,1410.0,303.0,578.0,285.0,2.5625,235400.0,NEAR OCEAN\n-121.92,36.62,52.0,728.0,161.0,313.0,142.0,3.4327,254500.0,NEAR OCEAN\n-121.91,36.62,40.0,1292.0,271.0,504.0,230.0,2.475,258300.0,<1H OCEAN\n-121.91,36.62,30.0,724.0,167.0,325.0,155.0,3.3333,247900.0,<1H OCEAN\n-121.71,36.78,19.0,2371.0,324.0,944.0,332.0,5.9175,240200.0,<1H OCEAN\n-121.92,36.62,52.0,2584.0,599.0,790.0,444.0,2.5263,286400.0,NEAR OCEAN\n-121.91,36.62,52.0,541.0,157.0,240.0,145.0,3.5865,290000.0,<1H OCEAN\n-121.91,36.62,52.0,1220.0,267.0,488.0,265.0,3.7454,243800.0,<1H OCEAN\n-121.91,36.63,42.0,817.0,194.0,391.0,193.0,2.1776,279200.0,<1H OCEAN\n-121.93,36.63,28.0,3983.0,852.0,1582.0,778.0,3.5147,313900.0,NEAR OCEAN\n-121.93,36.63,33.0,1740.0,342.0,638.0,329.0,3.1912,319800.0,NEAR OCEAN\n-121.92,36.63,40.0,1076.0,193.0,406.0,180.0,3.4943,311100.0,<1H OCEAN\n-121.93,36.63,41.0,1049.0,198.0,428.0,183.0,4.3571,287500.0,NEAR OCEAN\n-121.92,36.63,36.0,877.0,175.0,349.0,168.0,3.4167,339100.0,<1H OCEAN\n-121.89,36.63,20.0,1834.0,554.0,971.0,514.0,3.0383,217300.0,<1H OCEAN\n-121.9,36.61,29.0,3412.0,827.0,1574.0,759.0,2.9309,217100.0,NEAR OCEAN\n-121.91,36.61,30.0,2755.0,597.0,1519.0,554.0,3.2952,234600.0,NEAR OCEAN\n-121.92,36.61,27.0,1619.0,352.0,831.0,344.0,4.3,226400.0,NEAR OCEAN\n-121.92,36.61,21.0,1242.0,340.0,834.0,362.0,2.4922,243800.0,NEAR OCEAN\n-121.9,36.6,39.0,1629.0,423.0,804.0,386.0,2.4663,236500.0,NEAR OCEAN\n-121.9,36.6,33.0,2461.0,649.0,1234.0,601.0,2.8727,225000.0,NEAR OCEAN\n-121.9,36.6,45.0,2249.0,412.0,944.0,429.0,3.0625,260300.0,NEAR OCEAN\n-121.9,36.59,42.0,2689.0,510.0,1023.0,459.0,4.6182,301000.0,NEAR OCEAN\n-121.9,36.58,31.0,1431.0,,704.0,393.0,3.1977,289300.0,NEAR OCEAN\n-121.91,36.59,31.0,2034.0,335.0,966.0,322.0,4.6964,291300.0,NEAR OCEAN\n-121.91,36.6,35.0,2605.0,410.0,1110.0,406.0,5.5519,329500.0,NEAR OCEAN\n-121.91,36.59,17.0,5039.0,833.0,1678.0,710.0,6.2323,339100.0,NEAR OCEAN\n-121.89,36.6,40.0,626.0,164.0,337.0,150.0,2.7917,225000.0,<1H OCEAN\n-121.89,36.6,19.0,656.0,200.0,248.0,173.0,1.2656,500000.0,<1H OCEAN\n-121.88,36.6,30.0,1671.0,469.0,760.0,375.0,2.5164,178100.0,<1H OCEAN\n-121.88,36.59,30.0,1822.0,505.0,932.0,496.0,2.6894,179500.0,<1H OCEAN\n-121.89,36.59,18.0,2700.0,937.0,1042.0,744.0,3.1364,150000.0,NEAR OCEAN\n-121.89,36.59,32.0,784.0,112.0,262.0,114.0,6.918,500001.0,NEAR OCEAN\n-121.88,36.58,29.0,4910.0,871.0,3438.0,904.0,4.0432,450000.0,NEAR OCEAN\n-121.86,36.58,20.0,6332.0,991.0,2668.0,955.0,5.7578,347700.0,<1H OCEAN\n-121.81,36.57,13.0,3030.0,413.0,1027.0,363.0,6.9615,500001.0,<1H OCEAN\n-121.87,36.61,21.0,1616.0,400.0,688.0,384.0,4.2109,278800.0,<1H OCEAN\n-121.86,36.6,31.0,1044.0,285.0,762.0,301.0,3.038,195300.0,<1H OCEAN\n-121.86,36.6,21.0,3634.0,1011.0,1985.0,917.0,2.9085,156300.0,<1H OCEAN\n-121.86,36.6,33.0,1409.0,307.0,633.0,290.0,3.5568,191200.0,<1H OCEAN\n-121.85,36.6,21.0,2381.0,701.0,1264.0,659.0,2.5372,218000.0,<1H OCEAN\n-121.85,36.59,42.0,891.0,203.0,525.0,212.0,3.3156,186300.0,<1H OCEAN\n-121.84,36.59,34.0,3852.0,733.0,1661.0,696.0,4.3269,221300.0,<1H OCEAN\n-121.83,36.61,27.0,5665.0,1281.0,3612.0,1191.0,3.0542,142100.0,<1H OCEAN\n-121.83,36.6,30.0,2748.0,502.0,1491.0,535.0,4.3472,185000.0,<1H OCEAN\n-121.84,36.61,26.0,2902.0,761.0,2258.0,719.0,2.5663,128900.0,<1H OCEAN\n-121.84,36.6,30.0,2958.0,691.0,1616.0,666.0,3.4643,191800.0,<1H OCEAN\n-121.84,36.61,21.0,2876.0,802.0,2487.0,795.0,2.2007,112800.0,<1H OCEAN\n-121.84,36.61,15.0,2190.0,586.0,1570.0,510.0,1.875,122300.0,<1H OCEAN\n-121.82,36.61,24.0,2437.0,438.0,1430.0,444.0,3.8015,169100.0,<1H OCEAN\n-121.83,36.61,26.0,3723.0,789.0,2563.0,747.0,3.4531,133100.0,<1H OCEAN\n-121.83,36.61,27.0,2248.0,466.0,1644.0,453.0,3.2545,131200.0,<1H OCEAN\n-121.83,36.62,33.0,2938.0,576.0,1516.0,568.0,3.5,162400.0,<1H OCEAN\n-121.83,36.62,33.0,1771.0,398.0,1037.0,388.0,2.7708,161800.0,<1H OCEAN\n-121.85,36.6,41.0,3138.0,717.0,1890.0,642.0,2.4864,140400.0,<1H OCEAN\n-121.85,36.61,38.0,238.0,,191.0,67.0,1.3897,125000.0,<1H OCEAN\n-121.86,36.63,37.0,338.0,109.0,231.0,100.0,2.5313,108300.0,<1H OCEAN\n-121.84,36.62,26.0,32.0,8.0,27.0,10.0,2.225,150000.0,<1H OCEAN\n-121.79,36.64,11.0,32627.0,6445.0,28566.0,6082.0,2.3087,118800.0,<1H OCEAN\n-121.8,36.68,18.0,8581.0,1957.0,6071.0,1889.0,3.0,162200.0,<1H OCEAN\n-121.79,36.68,22.0,6912.0,1513.0,3794.0,1455.0,3.0608,168300.0,<1H OCEAN\n-121.8,36.72,14.0,2493.0,407.0,1296.0,418.0,5.4508,190000.0,<1H OCEAN\n-121.8,36.69,12.0,3877.0,914.0,2274.0,858.0,3.4239,194800.0,<1H OCEAN\n-121.77,36.71,18.0,6601.0,1395.0,3562.0,1299.0,3.512,174800.0,<1H OCEAN\n-122.29,38.3,52.0,144.0,54.0,89.0,48.0,1.0096,162500.0,NEAR BAY\n-122.29,38.3,52.0,935.0,224.0,315.0,207.0,1.8287,146900.0,NEAR BAY\n-122.3,38.3,21.0,1108.0,269.0,524.0,274.0,2.7619,154600.0,NEAR BAY\n-122.3,38.3,52.0,1128.0,207.0,450.0,197.0,3.3542,154600.0,NEAR BAY\n-122.29,38.3,52.0,1219.0,288.0,847.0,283.0,1.6691,183300.0,NEAR BAY\n-122.28,38.29,23.0,1398.0,388.0,1112.0,406.0,2.2366,140200.0,NEAR BAY\n-122.29,38.29,52.0,3217.0,742.0,1670.0,671.0,2.4398,163100.0,NEAR BAY\n-122.3,38.29,48.0,2278.0,477.0,1219.0,453.0,2.9643,154000.0,NEAR BAY\n-122.3,38.29,40.0,1739.0,318.0,744.0,312.0,2.6518,156100.0,NEAR BAY\n-122.29,38.28,38.0,2308.0,425.0,1272.0,406.0,3.6083,134200.0,NEAR BAY\n-122.26,38.29,10.0,969.0,160.0,482.0,180.0,6.5799,218100.0,NEAR BAY\n-122.27,38.29,36.0,1446.0,306.0,678.0,295.0,2.8409,153000.0,NEAR BAY\n-122.28,38.3,23.0,526.0,152.0,245.0,130.0,2.0134,142500.0,NEAR BAY\n-122.28,38.29,19.0,531.0,112.0,139.0,80.0,1.9875,325000.0,NEAR BAY\n-122.27,38.29,20.0,3870.0,795.0,2088.0,774.0,3.3021,152700.0,NEAR BAY\n-122.26,38.28,24.0,2831.0,502.0,1462.0,503.0,4.5,158300.0,NEAR BAY\n-122.27,38.28,37.0,1170.0,303.0,766.0,302.0,2.6618,136200.0,NEAR BAY\n-122.26,38.31,33.0,4518.0,704.0,1776.0,669.0,5.2444,281100.0,NEAR BAY\n-122.27,38.32,31.0,1267.0,319.0,545.0,297.0,1.9946,206800.0,NEAR BAY\n-122.27,38.31,44.0,3030.0,589.0,1373.0,582.0,2.9054,155200.0,NEAR BAY\n-122.28,38.32,12.0,4609.0,1005.0,2293.0,960.0,3.4543,194500.0,NEAR BAY\n-122.29,38.32,23.0,4312.0,993.0,2317.0,934.0,2.7667,153200.0,NEAR BAY\n-122.3,38.32,20.0,2063.0,486.0,1045.0,460.0,2.5035,153200.0,NEAR BAY\n-122.3,38.31,34.0,1797.0,395.0,1162.0,407.0,3.455,137500.0,NEAR BAY\n-122.29,38.31,25.0,4927.0,1005.0,2756.0,998.0,2.7325,162900.0,NEAR BAY\n-122.28,38.31,52.0,58.0,18.0,48.0,22.0,1.76,166700.0,NEAR BAY\n-122.29,38.31,45.0,3075.0,754.0,1635.0,723.0,2.2721,139800.0,NEAR BAY\n-122.3,38.3,44.0,3690.0,809.0,1922.0,736.0,2.6346,139800.0,NEAR BAY\n-122.31,38.34,19.0,4187.0,684.0,1827.0,605.0,4.5293,210400.0,NEAR BAY\n-122.31,38.33,21.0,1922.0,344.0,1051.0,342.0,3.6042,183300.0,NEAR BAY\n-122.3,38.33,15.0,4741.0,956.0,2043.0,856.0,4.1862,183600.0,NEAR BAY\n-122.31,38.33,26.0,2155.0,339.0,956.0,365.0,4.0132,174700.0,NEAR BAY\n-122.31,38.32,33.0,2463.0,421.0,1235.0,465.0,3.7045,161500.0,NEAR BAY\n-122.32,38.33,17.0,851.0,118.0,370.0,123.0,5.0877,209300.0,NEAR BAY\n-122.33,38.33,15.0,3193.0,468.0,1303.0,426.0,5.3017,202600.0,NEAR BAY\n-122.32,38.32,19.0,2922.0,417.0,1221.0,442.0,5.8002,238700.0,NEAR BAY\n-122.32,38.32,22.0,2483.0,528.0,1478.0,492.0,4.0878,164400.0,NEAR BAY\n-122.31,38.32,35.0,3997.0,762.0,2074.0,703.0,3.285,138100.0,NEAR BAY\n-122.32,38.32,26.0,2710.0,498.0,1439.0,484.0,5.0,175200.0,NEAR BAY\n-122.33,38.31,14.0,6778.0,947.0,2768.0,1014.0,6.1953,258900.0,NEAR BAY\n-122.31,38.3,25.0,3883.0,740.0,1641.0,676.0,3.9,187300.0,NEAR BAY\n-122.31,38.31,32.0,2577.0,458.0,1172.0,447.0,3.8796,175500.0,NEAR BAY\n-122.31,38.3,45.0,3023.0,659.0,1789.0,657.0,3.6039,126000.0,NEAR BAY\n-122.32,38.29,21.0,1607.0,356.0,834.0,352.0,2.3787,177900.0,NEAR BAY\n-122.33,38.29,14.0,3541.0,499.0,1577.0,459.0,5.3351,269900.0,NEAR BAY\n-122.3,38.29,20.0,1789.0,434.0,1113.0,398.0,2.4728,139700.0,NEAR BAY\n-122.3,38.29,25.0,1701.0,427.0,1021.0,399.0,3.0404,142100.0,NEAR BAY\n-122.3,38.28,31.0,1633.0,316.0,944.0,300.0,3.3977,158700.0,NEAR BAY\n-122.31,38.27,34.0,1748.0,284.0,783.0,303.0,4.3585,194400.0,NEAR BAY\n-122.3,38.27,4.0,1051.0,263.0,455.0,248.0,3.6389,130200.0,NEAR BAY\n-122.3,38.25,18.0,3548.0,880.0,1476.0,699.0,3.7188,163400.0,NEAR BAY\n-122.21,38.28,35.0,1273.0,210.0,555.0,181.0,4.4861,269300.0,NEAR BAY\n-122.24,38.25,33.0,213.0,36.0,91.0,33.0,4.9167,187500.0,NEAR BAY\n-122.28,38.22,42.0,106.0,18.0,40.0,25.0,7.5197,275000.0,NEAR BAY\n-122.29,38.19,13.0,7065.0,1259.0,3864.0,1221.0,4.7472,148600.0,NEAR BAY\n-122.25,38.17,34.0,778.0,137.0,406.0,136.0,4.2955,121300.0,NEAR BAY\n-122.23,38.17,45.0,350.0,,225.0,72.0,1.8942,216700.0,NEAR BAY\n-122.25,38.16,17.0,4459.0,944.0,1812.0,888.0,2.9375,106700.0,NEAR BAY\n-122.26,38.16,23.0,2840.0,491.0,1586.0,466.0,4.0337,130400.0,NEAR BAY\n-122.39,38.37,33.0,1066.0,191.0,403.0,163.0,6.8,240800.0,<1H OCEAN\n-122.37,38.33,29.0,1868.0,291.0,764.0,284.0,4.825,195100.0,NEAR BAY\n-122.35,38.3,18.0,3735.0,557.0,1504.0,521.0,5.6304,243100.0,NEAR BAY\n-122.33,38.21,33.0,2017.0,370.0,949.0,342.0,4.625,228600.0,NEAR BAY\n-122.4,38.34,33.0,1408.0,273.0,520.0,212.0,3.5781,242500.0,<1H OCEAN\n-122.33,38.38,28.0,1020.0,169.0,504.0,164.0,4.5694,287500.0,INLAND\n-122.4,38.41,20.0,4867.0,1015.0,1725.0,1015.0,2.5685,267600.0,INLAND\n-122.32,38.35,20.0,3494.0,549.0,1673.0,541.0,5.5718,185200.0,INLAND\n-122.36,38.41,18.0,1808.0,400.0,968.0,370.0,3.7067,175000.0,INLAND\n-122.36,38.4,16.0,2716.0,546.0,898.0,500.0,2.2536,201200.0,INLAND\n-122.18,38.35,24.0,407.0,68.0,175.0,61.0,6.0266,216700.0,INLAND\n-122.21,38.41,12.0,4270.0,654.0,1624.0,598.0,5.5266,331300.0,INLAND\n-122.29,38.4,28.0,2024.0,340.0,844.0,309.0,4.7833,361100.0,INLAND\n-122.33,38.39,36.0,831.0,122.0,272.0,109.0,6.3427,304800.0,INLAND\n-122.28,38.34,44.0,1066.0,190.0,416.0,174.0,3.6389,304000.0,NEAR BAY\n-122.26,38.36,25.0,1821.0,344.0,349.0,179.0,6.9931,398800.0,INLAND\n-122.26,38.33,34.0,2048.0,316.0,780.0,267.0,5.815,339200.0,NEAR BAY\n-122.23,38.33,31.0,3440.0,574.0,1538.0,537.0,5.5368,325900.0,NEAR BAY\n-122.24,38.31,38.0,1938.0,301.0,823.0,285.0,6.1089,280800.0,NEAR BAY\n-122.52,38.53,35.0,1227.0,236.0,548.0,207.0,4.875,336700.0,INLAND\n-122.48,38.48,29.0,2278.0,397.0,765.0,322.0,4.6379,348200.0,INLAND\n-122.4,38.46,33.0,2542.0,466.0,1099.0,420.0,4.635,248500.0,INLAND\n-122.47,38.51,18.0,2487.0,516.0,980.0,503.0,3.5506,187500.0,INLAND\n-122.47,38.51,25.0,928.0,195.0,413.0,184.0,3.4904,196900.0,INLAND\n-122.48,38.51,49.0,1977.0,393.0,741.0,339.0,3.1312,247600.0,INLAND\n-122.48,38.5,37.0,3049.0,,1287.0,439.0,4.3125,276500.0,INLAND\n-122.47,38.5,23.0,2191.0,415.0,959.0,361.0,4.1993,214500.0,INLAND\n-122.45,38.51,18.0,1297.0,337.0,610.0,312.0,1.9441,184400.0,INLAND\n-122.4,38.53,24.0,1741.0,289.0,564.0,231.0,3.6118,248400.0,INLAND\n-122.44,38.57,26.0,2101.0,390.0,2171.0,360.0,3.6429,159700.0,INLAND\n-122.45,38.58,34.0,2517.0,483.0,1324.0,464.0,3.0938,189400.0,INLAND\n-122.47,38.6,20.0,1036.0,202.0,589.0,194.0,5.3698,303300.0,INLAND\n-122.48,38.54,37.0,1898.0,359.0,973.0,340.0,4.2096,256600.0,INLAND\n-122.46,38.53,32.0,1735.0,331.0,785.0,309.0,3.6641,275800.0,INLAND\n-122.27,38.68,18.0,742.0,142.0,343.0,119.0,3.1563,98400.0,INLAND\n-122.26,38.57,22.0,509.0,103.0,139.0,73.0,2.1979,152800.0,INLAND\n-122.27,38.53,22.0,678.0,137.0,336.0,103.0,4.4,142500.0,INLAND\n-122.18,38.49,15.0,1743.0,366.0,655.0,264.0,3.3393,146900.0,INLAND\n-122.52,38.67,35.0,1705.0,321.0,708.0,253.0,3.4539,300000.0,INLAND\n-122.59,38.56,43.0,2088.0,379.0,721.0,293.0,4.65,245000.0,<1H OCEAN\n-122.57,38.58,18.0,2083.0,506.0,926.0,487.0,1.9925,225000.0,INLAND\n-122.58,38.59,33.0,1239.0,262.0,539.0,246.0,3.5208,195800.0,INLAND\n-122.58,38.58,32.0,2723.0,637.0,1549.0,556.0,2.3942,183100.0,INLAND\n-122.59,38.58,18.0,3753.0,752.0,1454.0,668.0,3.7585,185700.0,<1H OCEAN\n-121.07,39.15,15.0,6828.0,1319.0,3002.0,1318.0,2.4726,143400.0,INLAND\n-121.07,39.09,17.0,1878.0,345.0,892.0,299.0,2.8864,143100.0,INLAND\n-121.1,39.08,13.0,1110.0,216.0,602.0,209.0,2.5887,144400.0,INLAND\n-121.11,39.09,16.0,1000.0,197.0,508.0,190.0,2.3062,138800.0,INLAND\n-121.12,39.03,17.0,838.0,161.0,388.0,142.0,3.6563,163500.0,INLAND\n-121.05,39.13,10.0,3063.0,497.0,1168.0,507.0,4.4375,185100.0,INLAND\n-121.03,39.14,10.0,3138.0,524.0,1275.0,511.0,4.0775,164500.0,INLAND\n-121.07,39.13,8.0,4839.0,832.0,1977.0,762.0,4.0848,155900.0,INLAND\n-121.05,39.11,7.0,2767.0,423.0,1143.0,382.0,3.6333,170200.0,INLAND\n-121.04,39.08,8.0,2870.0,526.0,1307.0,451.0,3.463,201700.0,INLAND\n-121.0,39.09,7.0,439.0,84.0,246.0,80.0,3.0781,162500.0,INLAND\n-121.08,39.02,13.0,1839.0,275.0,752.0,270.0,4.2031,209600.0,INLAND\n-121.07,39.05,10.0,1813.0,311.0,827.0,287.0,3.6087,182100.0,INLAND\n-121.07,39.04,9.0,2374.0,372.0,884.0,333.0,4.5042,206400.0,INLAND\n-121.03,39.05,12.0,1875.0,307.0,806.0,283.0,3.9185,195200.0,INLAND\n-121.06,39.04,14.0,1651.0,279.0,633.0,261.0,4.2802,194800.0,INLAND\n-121.06,39.04,15.0,1999.0,287.0,585.0,246.0,5.5161,361900.0,INLAND\n-121.06,39.03,11.0,1887.0,303.0,775.0,283.0,3.8417,187200.0,INLAND\n-121.22,39.11,14.0,1405.0,269.0,660.0,228.0,3.0804,156800.0,INLAND\n-121.19,39.05,14.0,1131.0,193.0,520.0,178.0,3.9,180400.0,INLAND\n-121.14,39.1,13.0,1085.0,227.0,629.0,214.0,5.0389,171500.0,INLAND\n-121.12,39.2,9.0,1431.0,254.0,681.0,221.0,3.045,170400.0,INLAND\n-121.1,39.15,10.0,680.0,143.0,354.0,140.0,4.0333,161500.0,INLAND\n-121.08,39.18,19.0,2323.0,397.0,963.0,379.0,3.7426,162700.0,INLAND\n-121.18,39.26,14.0,811.0,161.0,352.0,121.0,3.5938,140300.0,INLAND\n-121.18,39.25,9.0,3415.0,562.0,1208.0,479.0,4.3646,185900.0,INLAND\n-121.2,39.25,5.0,906.0,144.0,376.0,141.0,4.3523,188200.0,INLAND\n-121.18,39.23,8.0,2112.0,360.0,782.0,344.0,3.7125,175000.0,INLAND\n-121.21,39.24,7.0,4194.0,673.0,1355.0,566.0,4.3702,226100.0,INLAND\n-121.2,39.23,9.0,2802.0,447.0,955.0,418.0,5.2359,213300.0,INLAND\n-121.18,39.19,16.0,1528.0,351.0,729.0,319.0,2.4688,138800.0,INLAND\n-121.16,39.18,14.0,1006.0,187.0,462.0,185.0,3.1042,152000.0,INLAND\n-121.25,39.17,9.0,999.0,189.0,411.0,176.0,2.125,151800.0,INLAND\n-121.2,39.2,16.0,1039.0,182.0,554.0,184.0,2.9688,128300.0,INLAND\n-121.15,39.23,13.0,3883.0,763.0,1816.0,682.0,2.8102,144400.0,INLAND\n-121.24,39.22,14.0,983.0,163.0,399.0,161.0,2.2917,145100.0,INLAND\n-121.23,39.27,11.0,1265.0,224.0,573.0,205.0,3.3603,162500.0,INLAND\n-121.07,39.23,39.0,2099.0,433.0,929.0,423.0,1.9886,113800.0,INLAND\n-121.04,39.24,48.0,1188.0,227.0,471.0,219.0,2.3125,125700.0,INLAND\n-121.05,39.23,20.0,1634.0,374.0,1053.0,390.0,1.5313,154900.0,INLAND\n-121.06,39.23,10.0,2229.0,537.0,982.0,512.0,2.186,132700.0,INLAND\n-121.06,39.22,52.0,1749.0,422.0,837.0,391.0,2.325,109700.0,INLAND\n-121.09,39.23,35.0,2637.0,511.0,1181.0,480.0,2.7813,109200.0,INLAND\n-121.07,39.22,52.0,2432.0,495.0,928.0,435.0,2.425,121100.0,INLAND\n-121.08,39.22,30.0,2188.0,,1033.0,437.0,2.1419,105200.0,INLAND\n-121.08,39.21,17.0,3033.0,590.0,1319.0,583.0,2.4811,111800.0,INLAND\n-121.09,39.22,25.0,2200.0,439.0,1045.0,419.0,2.6042,116700.0,INLAND\n-121.07,39.2,45.0,204.0,62.0,133.0,51.0,1.0,90600.0,INLAND\n-121.02,39.23,16.0,1427.0,319.0,642.0,333.0,1.4241,125000.0,INLAND\n-121.04,39.22,14.0,1889.0,471.0,853.0,399.0,2.25,112500.0,INLAND\n-121.03,39.21,28.0,2843.0,535.0,1310.0,525.0,3.2337,123100.0,INLAND\n-121.05,39.2,48.0,1759.0,389.0,716.0,350.0,2.3125,108300.0,INLAND\n-121.06,39.21,52.0,1452.0,309.0,637.0,299.0,2.2083,103900.0,INLAND\n-121.05,39.21,43.0,1264.0,273.0,611.0,260.0,2.535,117100.0,INLAND\n-120.94,39.22,12.0,1321.0,268.0,661.0,232.0,4.0062,153800.0,INLAND\n-120.83,39.27,14.0,3338.0,608.0,1373.0,562.0,3.67,160100.0,INLAND\n-121.0,39.23,15.0,2809.0,450.0,1267.0,408.0,4.0426,191700.0,INLAND\n-120.99,39.22,16.0,1497.0,275.0,737.0,243.0,2.8942,182500.0,INLAND\n-120.99,39.2,15.0,2993.0,562.0,1296.0,518.0,3.3009,156800.0,INLAND\n-120.93,39.17,13.0,2331.0,464.0,1110.0,419.0,3.6563,164900.0,INLAND\n-120.99,39.13,14.0,770.0,116.0,285.0,116.0,3.6434,155400.0,INLAND\n-120.99,39.18,23.0,2550.0,457.0,1016.0,405.0,3.6607,153000.0,INLAND\n-121.0,39.16,10.0,1170.0,225.0,537.0,194.0,3.2813,163200.0,INLAND\n-121.04,39.19,17.0,856.0,167.0,518.0,170.0,3.5859,144300.0,INLAND\n-121.02,39.17,17.0,2277.0,459.0,1149.0,476.0,3.2303,149500.0,INLAND\n-121.02,39.27,52.0,3720.0,707.0,1424.0,609.0,3.2,155000.0,INLAND\n-121.03,39.26,49.0,3739.0,759.0,1422.0,606.0,2.4283,143100.0,INLAND\n-121.0,39.26,14.0,810.0,151.0,302.0,138.0,3.1094,136100.0,INLAND\n-121.02,39.25,52.0,1549.0,275.0,604.0,249.0,2.2278,155400.0,INLAND\n-121.06,39.29,14.0,1864.0,331.0,894.0,332.0,3.4028,171800.0,INLAND\n-120.94,39.32,14.0,3120.0,595.0,1569.0,556.0,3.5385,157400.0,INLAND\n-121.06,39.25,17.0,3127.0,539.0,1390.0,520.0,3.9537,172800.0,INLAND\n-120.89,39.3,17.0,2282.0,431.0,974.0,371.0,3.5417,155100.0,INLAND\n-120.99,39.26,16.0,2616.0,422.0,1090.0,425.0,3.7917,179200.0,INLAND\n-120.91,39.39,16.0,352.0,105.0,226.0,82.0,1.6094,79500.0,INLAND\n-121.03,39.37,15.0,1337.0,326.0,1172.0,306.0,2.6341,85000.0,INLAND\n-121.13,39.31,17.0,3442.0,705.0,1693.0,619.0,2.8102,128900.0,INLAND\n-120.74,39.39,18.0,453.0,117.0,152.0,77.0,1.3523,85700.0,INLAND\n-120.09,39.4,17.0,1076.0,283.0,171.0,64.0,2.125,83900.0,INLAND\n-120.17,39.33,18.0,1046.0,204.0,486.0,179.0,4.119,110900.0,INLAND\n-120.2,39.33,26.0,1988.0,379.0,905.0,321.0,3.7841,109500.0,INLAND\n-120.19,39.32,16.0,1536.0,298.0,646.0,208.0,2.3594,155700.0,INLAND\n-120.17,39.32,14.0,2421.0,489.0,1000.0,354.0,3.5652,119800.0,INLAND\n-120.17,39.33,10.0,614.0,141.0,195.0,95.0,0.9283,116300.0,INLAND\n-120.1,39.37,10.0,2325.0,410.0,1016.0,373.0,4.5208,117300.0,INLAND\n-120.1,39.33,9.0,2738.0,510.0,1193.0,412.0,4.3958,124800.0,INLAND\n-120.15,39.36,9.0,2254.0,400.0,694.0,243.0,5.6856,138100.0,INLAND\n-120.26,39.32,24.0,6012.0,1227.0,780.0,358.0,3.0043,122100.0,INLAND\n-120.35,39.34,29.0,1986.0,474.0,337.0,100.0,4.0278,95800.0,INLAND\n-120.27,39.35,11.0,2520.0,401.0,397.0,165.0,4.665,145600.0,INLAND\n-120.25,39.34,9.0,2739.0,555.0,294.0,110.0,3.1842,162500.0,INLAND\n-120.24,39.35,8.0,4195.0,725.0,291.0,115.0,3.4792,180800.0,INLAND\n-120.23,39.36,7.0,2045.0,358.0,245.0,92.0,4.0481,152300.0,INLAND\n-120.22,39.35,8.0,1872.0,281.0,203.0,71.0,4.5882,198400.0,INLAND\n-120.21,39.35,7.0,914.0,159.0,85.0,34.0,4.7917,187500.0,INLAND\n-120.19,39.35,7.0,2611.0,395.0,482.0,159.0,5.0622,174100.0,INLAND\n-117.95,33.94,28.0,2851.0,496.0,1287.0,496.0,5.0782,264100.0,<1H OCEAN\n-117.96,33.94,34.0,2228.0,399.0,1159.0,378.0,4.8906,228900.0,<1H OCEAN\n-117.97,33.94,36.0,1870.0,338.0,947.0,324.0,4.1205,217000.0,<1H OCEAN\n-117.97,33.94,34.0,1632.0,263.0,690.0,268.0,5.5608,255800.0,<1H OCEAN\n-117.97,33.94,35.0,1928.0,360.0,1056.0,366.0,4.0893,215700.0,<1H OCEAN\n-117.97,33.93,31.0,1975.0,373.0,918.0,347.0,4.4107,202000.0,<1H OCEAN\n-117.97,33.93,33.0,1700.0,369.0,981.0,362.0,4.5461,194000.0,<1H OCEAN\n-117.96,33.93,31.0,1471.0,321.0,841.0,330.0,3.46,232800.0,<1H OCEAN\n-117.96,33.93,29.0,2316.0,522.0,1275.0,501.0,3.776,192600.0,<1H OCEAN\n-117.96,33.94,31.0,2397.0,518.0,1407.0,476.0,2.6641,185200.0,<1H OCEAN\n-117.94,33.93,33.0,1770.0,370.0,1346.0,366.0,4.0833,162500.0,<1H OCEAN\n-117.94,33.93,34.0,1475.0,319.0,698.0,293.0,3.8194,186000.0,<1H OCEAN\n-117.94,33.93,14.0,999.0,232.0,1037.0,244.0,2.7125,166100.0,<1H OCEAN\n-117.95,33.93,25.0,3445.0,801.0,2400.0,750.0,3.4702,161900.0,<1H OCEAN\n-117.95,33.93,37.0,2633.0,630.0,1904.0,630.0,2.6123,161300.0,<1H OCEAN\n-117.96,33.93,15.0,2014.0,419.0,839.0,390.0,4.7446,175400.0,<1H OCEAN\n-117.97,33.92,32.0,2620.0,398.0,1296.0,429.0,5.7796,241300.0,<1H OCEAN\n-117.97,33.93,35.0,1887.0,328.0,989.0,351.0,4.1321,198100.0,<1H OCEAN\n-117.97,33.92,24.0,2017.0,416.0,900.0,436.0,3.0,251400.0,<1H OCEAN\n-117.96,33.92,18.0,3744.0,1027.0,1654.0,912.0,3.2158,215000.0,<1H OCEAN\n-117.95,33.92,18.0,2825.0,660.0,1590.0,643.0,3.6106,153600.0,<1H OCEAN\n-117.94,33.92,28.0,639.0,179.0,1062.0,169.0,3.0588,145200.0,<1H OCEAN\n-117.95,33.92,32.0,1661.0,312.0,1201.0,302.0,4.0,178200.0,<1H OCEAN\n-117.95,33.92,11.0,3127.0,706.0,1594.0,694.0,4.3426,141300.0,<1H OCEAN\n-117.94,33.92,32.0,1053.0,207.0,1038.0,222.0,4.6696,165500.0,<1H OCEAN\n-117.95,33.92,13.0,2312.0,592.0,2038.0,559.0,3.1378,137000.0,<1H OCEAN\n-117.94,33.94,25.0,3250.0,546.0,1452.0,501.0,5.1084,303800.0,<1H OCEAN\n-117.94,33.94,30.0,1596.0,307.0,845.0,309.0,4.5096,241100.0,<1H OCEAN\n-117.95,33.94,31.0,2237.0,431.0,1135.0,434.0,4.45,267900.0,<1H OCEAN\n-117.94,33.94,26.0,1962.0,540.0,1236.0,520.0,2.2156,145000.0,<1H OCEAN\n-117.93,33.94,30.0,2658.0,382.0,1135.0,392.0,6.0516,245000.0,<1H OCEAN\n-117.93,33.94,28.0,3664.0,719.0,1820.0,657.0,4.225,224700.0,<1H OCEAN\n-117.93,33.93,25.0,2431.0,534.0,1702.0,523.0,3.7933,184400.0,<1H OCEAN\n-117.92,33.94,27.0,4566.0,620.0,2045.0,664.0,5.583,267700.0,<1H OCEAN\n-117.92,33.94,30.0,2506.0,394.0,1255.0,421.0,4.7813,198200.0,<1H OCEAN\n-117.93,33.92,34.0,2271.0,437.0,1393.0,433.0,4.2443,174400.0,<1H OCEAN\n-117.93,33.93,37.0,1128.0,273.0,931.0,234.0,2.8,137500.0,<1H OCEAN\n-117.93,33.93,33.0,1626.0,378.0,1062.0,356.0,2.1944,139600.0,<1H OCEAN\n-117.92,33.93,12.0,4415.0,890.0,1532.0,854.0,3.75,166300.0,<1H OCEAN\n-117.91,33.93,21.0,2578.0,363.0,1207.0,350.0,6.2452,291700.0,<1H OCEAN\n-117.91,33.94,15.0,5799.0,842.0,2314.0,787.0,6.3433,350500.0,<1H OCEAN\n-117.89,33.94,20.0,3349.0,685.0,1822.0,675.0,4.7216,227000.0,<1H OCEAN\n-117.88,33.93,17.0,6100.0,861.0,2771.0,866.0,7.6486,306700.0,<1H OCEAN\n-117.9,33.93,12.0,4325.0,1191.0,1897.0,1080.0,3.3173,247400.0,<1H OCEAN\n-117.89,33.92,17.0,2936.0,555.0,1381.0,535.0,5.4617,190300.0,<1H OCEAN\n-117.9,33.92,27.0,698.0,116.0,391.0,126.0,5.9177,267600.0,<1H OCEAN\n-117.91,33.92,21.0,380.0,91.0,398.0,70.0,4.7222,208300.0,<1H OCEAN\n-117.91,33.91,27.0,2181.0,501.0,1555.0,488.0,3.6106,196400.0,<1H OCEAN\n-117.91,33.91,34.0,1763.0,303.0,894.0,297.0,5.0096,221700.0,<1H OCEAN\n-117.91,33.91,32.0,1530.0,301.0,666.0,276.0,4.125,230200.0,<1H OCEAN\n-117.91,33.91,24.0,2249.0,379.0,1015.0,385.0,4.9766,267100.0,<1H OCEAN\n-117.89,33.92,34.0,1473.0,312.0,1025.0,315.0,3.8333,170400.0,<1H OCEAN\n-117.89,33.91,33.0,1264.0,224.0,527.0,227.0,3.7321,216500.0,<1H OCEAN\n-117.9,33.91,36.0,1376.0,257.0,687.0,221.0,3.5403,195400.0,<1H OCEAN\n-117.89,33.92,8.0,2120.0,544.0,1281.0,470.0,3.4954,159500.0,<1H OCEAN\n-117.89,33.92,14.0,1562.0,373.0,609.0,328.0,2.3935,125000.0,<1H OCEAN\n-117.89,33.9,16.0,1426.0,216.0,652.0,226.0,6.5284,288700.0,<1H OCEAN\n-117.89,33.9,23.0,1533.0,226.0,693.0,230.0,7.898,258200.0,<1H OCEAN\n-117.9,33.9,19.0,2176.0,414.0,1002.0,402.0,4.9743,193500.0,<1H OCEAN\n-117.9,33.91,26.0,2885.0,476.0,1227.0,439.0,4.9524,226600.0,<1H OCEAN\n-117.9,33.91,33.0,4181.0,804.0,2049.0,834.0,4.3103,201600.0,<1H OCEAN\n-117.92,33.92,19.0,2181.0,400.0,1272.0,337.0,5.1952,302100.0,<1H OCEAN\n-117.92,33.91,33.0,2868.0,382.0,1204.0,412.0,6.1825,336900.0,<1H OCEAN\n-117.92,33.91,27.0,2558.0,310.0,891.0,316.0,9.5561,411800.0,<1H OCEAN\n-117.91,33.9,27.0,829.0,114.0,383.0,133.0,9.3125,293500.0,<1H OCEAN\n-117.92,33.89,18.0,2895.0,487.0,1116.0,429.0,5.4716,400000.0,<1H OCEAN\n-117.92,33.9,13.0,1814.0,320.0,1010.0,313.0,6.3489,337900.0,<1H OCEAN\n-117.93,33.91,24.0,1698.0,297.0,676.0,273.0,5.2017,364600.0,<1H OCEAN\n-117.9,33.9,18.0,3821.0,576.0,1430.0,568.0,6.9399,349600.0,<1H OCEAN\n-117.9,33.88,28.0,2696.0,346.0,947.0,356.0,9.0055,375400.0,<1H OCEAN\n-117.89,33.89,17.0,1671.0,192.0,678.0,206.0,13.1107,467600.0,<1H OCEAN\n-117.92,33.89,12.0,1859.0,393.0,622.0,316.0,5.0258,161800.0,<1H OCEAN\n-117.97,33.91,19.0,8096.0,1318.0,3853.0,1313.0,6.0076,269500.0,<1H OCEAN\n-117.96,33.9,9.0,1899.0,284.0,1070.0,293.0,7.2532,381500.0,<1H OCEAN\n-117.95,33.9,15.0,3057.0,479.0,1679.0,498.0,6.8429,372600.0,<1H OCEAN\n-117.96,33.9,10.0,2423.0,356.0,1213.0,347.0,6.5635,346900.0,<1H OCEAN\n-117.98,33.9,6.0,1537.0,347.0,506.0,280.0,4.8264,146800.0,<1H OCEAN\n-117.96,33.88,25.0,3578.0,461.0,1588.0,466.0,6.2556,341300.0,<1H OCEAN\n-117.95,33.89,17.0,1665.0,247.0,755.0,254.0,6.5764,349000.0,<1H OCEAN\n-117.96,33.89,24.0,1332.0,252.0,625.0,230.0,4.4375,334100.0,<1H OCEAN\n-117.94,33.91,18.0,8836.0,1527.0,3946.0,1451.0,5.6441,313000.0,<1H OCEAN\n-117.93,33.9,30.0,2629.0,331.0,956.0,319.0,9.9071,500001.0,<1H OCEAN\n-117.94,33.89,30.0,2577.0,404.0,1076.0,374.0,6.7528,459600.0,<1H OCEAN\n-117.94,33.88,35.0,2159.0,343.0,833.0,335.0,5.3738,365100.0,<1H OCEAN\n-117.94,33.9,27.0,2029.0,242.0,711.0,254.0,9.7956,500001.0,<1H OCEAN\n-117.98,33.87,25.0,2037.0,515.0,1435.0,496.0,3.3199,188800.0,<1H OCEAN\n-117.98,33.87,29.0,1310.0,332.0,937.0,294.0,3.8068,158700.0,<1H OCEAN\n-117.98,33.86,25.0,1025.0,266.0,726.0,183.0,3.875,137500.0,<1H OCEAN\n-117.98,33.86,26.0,1240.0,285.0,781.0,315.0,4.1287,205800.0,<1H OCEAN\n-117.97,33.87,28.0,1784.0,440.0,1255.0,433.0,3.7054,169200.0,<1H OCEAN\n-117.97,33.86,34.0,2138.0,490.0,1682.0,463.0,3.6006,161700.0,<1H OCEAN\n-117.97,33.86,35.0,1691.0,367.0,1265.0,378.0,3.5855,174300.0,<1H OCEAN\n-117.97,33.86,12.0,1370.0,367.0,1022.0,296.0,3.6471,141700.0,<1H OCEAN\n-117.96,33.86,35.0,2146.0,430.0,1230.0,429.0,3.7813,184900.0,<1H OCEAN\n-117.96,33.86,35.0,2181.0,371.0,1249.0,358.0,4.2937,183200.0,<1H OCEAN\n-117.96,33.87,35.0,1972.0,367.0,1152.0,356.0,3.7222,187500.0,<1H OCEAN\n-117.95,33.86,35.0,2375.0,439.0,1343.0,424.0,4.53,193500.0,<1H OCEAN\n-117.95,33.86,35.0,2478.0,431.0,1333.0,427.0,5.2099,191400.0,<1H OCEAN\n-117.96,33.86,32.0,2366.0,505.0,1283.0,477.0,3.3516,190000.0,<1H OCEAN\n-117.94,33.88,46.0,1747.0,312.0,770.0,296.0,5.4217,256000.0,<1H OCEAN\n-117.95,33.87,34.0,1599.0,296.0,938.0,307.0,4.285,184900.0,<1H OCEAN\n-117.95,33.87,35.0,1854.0,383.0,1115.0,381.0,4.4784,185200.0,<1H OCEAN\n-117.95,33.87,22.0,1432.0,335.0,746.0,296.0,2.0227,55000.0,<1H OCEAN\n-117.96,33.87,27.0,890.0,289.0,416.0,200.0,3.141,167500.0,<1H OCEAN\n-117.96,33.87,37.0,1785.0,360.0,1155.0,403.0,4.7984,175800.0,<1H OCEAN\n-117.95,33.88,34.0,1939.0,355.0,817.0,314.0,3.6705,275000.0,<1H OCEAN\n-117.94,33.86,36.0,2824.0,493.0,1394.0,507.0,4.6477,194700.0,<1H OCEAN\n-117.94,33.86,33.0,1013.0,312.0,706.0,266.0,2.1432,197500.0,<1H OCEAN\n-117.95,33.86,36.0,2038.0,343.0,1066.0,346.0,5.197,195700.0,<1H OCEAN\n-117.93,33.86,36.0,1672.0,318.0,1173.0,337.0,4.5774,182100.0,<1H OCEAN\n-117.93,33.86,35.0,1216.0,225.0,893.0,228.0,4.0288,184000.0,<1H OCEAN\n-117.94,33.86,35.0,1235.0,227.0,875.0,220.0,4.6964,183100.0,<1H OCEAN\n-117.94,33.86,35.0,2127.0,417.0,1247.0,378.0,4.75,185600.0,<1H OCEAN\n-117.93,33.88,52.0,2157.0,362.0,1001.0,373.0,5.1237,240000.0,<1H OCEAN\n-117.93,33.87,10.0,1277.0,488.0,730.0,417.0,1.4803,137500.0,<1H OCEAN\n-117.94,33.87,46.0,2066.0,450.0,1275.0,448.0,3.9375,187000.0,<1H OCEAN\n-117.94,33.88,35.0,1694.0,296.0,679.0,282.0,4.3333,239300.0,<1H OCEAN\n-117.92,33.87,44.0,308.0,87.0,301.0,100.0,2.1991,191100.0,<1H OCEAN\n-117.92,33.87,36.0,1125.0,285.0,966.0,257.0,2.8438,162500.0,<1H OCEAN\n-117.93,33.87,52.0,950.0,229.0,429.0,185.0,2.315,182100.0,<1H OCEAN\n-117.93,33.88,45.0,1306.0,293.0,585.0,260.0,4.0812,241700.0,<1H OCEAN\n-117.93,33.88,32.0,2458.0,359.0,967.0,409.0,7.2893,293500.0,<1H OCEAN\n-117.92,33.88,52.0,1270.0,276.0,609.0,211.0,3.75,232500.0,<1H OCEAN\n-117.91,33.88,34.0,1851.0,291.0,784.0,290.0,5.2336,235600.0,<1H OCEAN\n-117.92,33.88,32.0,1683.0,273.0,719.0,263.0,5.3649,243600.0,<1H OCEAN\n-117.92,33.88,32.0,1632.0,244.0,575.0,235.0,5.3986,318700.0,<1H OCEAN\n-117.91,33.89,30.0,1631.0,212.0,523.0,216.0,7.875,351900.0,<1H OCEAN\n-117.9,33.88,35.0,2062.0,353.0,991.0,357.0,5.2897,230400.0,<1H OCEAN\n-117.9,33.88,34.0,1396.0,245.0,661.0,261.0,4.675,215400.0,<1H OCEAN\n-117.9,33.87,28.0,2315.0,538.0,1360.0,504.0,2.9861,218600.0,<1H OCEAN\n-117.9,33.87,34.0,1411.0,292.0,1040.0,299.0,3.4338,195200.0,<1H OCEAN\n-117.91,33.87,29.0,1121.0,,762.0,276.0,2.5,143800.0,<1H OCEAN\n-117.91,33.87,52.0,2031.0,506.0,1191.0,463.0,2.9076,177300.0,<1H OCEAN\n-117.88,33.89,18.0,1616.0,532.0,866.0,496.0,3.6435,119100.0,<1H OCEAN\n-117.88,33.89,16.0,959.0,176.0,353.0,185.0,4.5,173300.0,<1H OCEAN\n-117.88,33.88,15.0,957.0,360.0,615.0,370.0,3.0263,162500.0,<1H OCEAN\n-117.89,33.88,15.0,1655.0,626.0,1549.0,582.0,1.9127,175000.0,<1H OCEAN\n-117.89,33.88,33.0,1582.0,256.0,771.0,240.0,5.3836,229600.0,<1H OCEAN\n-117.89,33.88,27.0,2091.0,336.0,1037.0,332.0,5.7519,243400.0,<1H OCEAN\n-117.89,33.87,25.0,1492.0,439.0,755.0,389.0,3.0893,188200.0,<1H OCEAN\n-117.89,33.87,32.0,1133.0,216.0,693.0,228.0,3.3594,202100.0,<1H OCEAN\n-117.88,33.87,35.0,1919.0,349.0,1302.0,345.0,5.6409,190900.0,<1H OCEAN\n-117.89,33.87,32.0,1569.0,422.0,835.0,386.0,3.0465,148900.0,<1H OCEAN\n-117.93,33.87,45.0,1006.0,230.0,1237.0,237.0,3.3472,168000.0,<1H OCEAN\n-117.93,33.86,36.0,931.0,279.0,778.0,303.0,2.6563,155000.0,<1H OCEAN\n-117.93,33.86,17.0,1627.0,398.0,1216.0,369.0,3.3438,186600.0,<1H OCEAN\n-117.93,33.86,35.0,931.0,181.0,516.0,174.0,5.5867,182500.0,<1H OCEAN\n-117.93,33.87,29.0,1221.0,371.0,1822.0,326.0,1.7935,162500.0,<1H OCEAN\n-117.89,33.86,28.0,1395.0,398.0,1220.0,362.0,3.3008,193800.0,<1H OCEAN\n-117.91,33.86,26.0,2296.0,570.0,1415.0,527.0,2.4732,165800.0,<1H OCEAN\n-117.92,33.86,26.0,745.0,161.0,247.0,151.0,3.6375,133900.0,<1H OCEAN\n-117.92,33.87,33.0,1597.0,,1888.0,423.0,3.055,157800.0,<1H OCEAN\n-117.87,33.91,16.0,2434.0,455.0,1017.0,476.0,4.2188,176300.0,<1H OCEAN\n-117.88,33.9,21.0,3180.0,434.0,1413.0,391.0,6.5945,277300.0,<1H OCEAN\n-117.88,33.9,15.0,1705.0,478.0,759.0,479.0,3.5333,114400.0,<1H OCEAN\n-117.88,33.89,19.0,3583.0,911.0,2300.0,871.0,3.0214,218400.0,<1H OCEAN\n-117.88,33.89,17.0,3218.0,923.0,1701.0,824.0,3.6946,265500.0,<1H OCEAN\n-117.87,33.89,25.0,1142.0,162.0,486.0,150.0,7.1472,270100.0,<1H OCEAN\n-117.86,33.91,16.0,2889.0,423.0,1227.0,401.0,6.4514,270800.0,<1H OCEAN\n-117.86,33.9,17.0,1452.0,188.0,630.0,194.0,6.9113,285200.0,<1H OCEAN\n-117.86,33.9,25.0,3205.0,409.0,1291.0,408.0,7.2478,299200.0,<1H OCEAN\n-117.87,33.9,21.0,3181.0,447.0,1416.0,469.0,6.8268,280300.0,<1H OCEAN\n-117.86,33.89,22.0,4386.0,593.0,1915.0,592.0,6.6897,289800.0,<1H OCEAN\n-117.86,33.89,24.0,2002.0,253.0,820.0,241.0,6.9612,274500.0,<1H OCEAN\n-117.87,33.89,19.0,1674.0,243.0,786.0,234.0,6.4218,275000.0,<1H OCEAN\n-117.86,33.88,19.0,1621.0,328.0,871.0,322.0,3.7361,201400.0,<1H OCEAN\n-117.87,33.89,17.0,1441.0,530.0,769.0,456.0,2.425,171700.0,<1H OCEAN\n-117.87,33.89,22.0,2340.0,664.0,1382.0,546.0,3.343,184600.0,<1H OCEAN\n-117.87,33.88,25.0,1808.0,440.0,1342.0,454.0,3.025,156900.0,<1H OCEAN\n-117.87,33.88,24.0,2655.0,702.0,1519.0,708.0,3.3036,183900.0,<1H OCEAN\n-117.87,33.88,28.0,3333.0,752.0,2026.0,722.0,3.5667,190700.0,<1H OCEAN\n-117.87,33.88,28.0,2612.0,602.0,1682.0,563.0,3.6417,204300.0,<1H OCEAN\n-117.86,33.87,12.0,1600.0,251.0,685.0,256.0,5.1784,254000.0,<1H OCEAN\n-117.87,33.85,33.0,45.0,11.0,34.0,10.0,5.2949,350000.0,<1H OCEAN\n-117.85,33.86,18.0,329.0,72.0,209.0,71.0,4.6806,187500.0,<1H OCEAN\n-117.85,33.89,22.0,4020.0,655.0,1486.0,635.0,5.9639,262300.0,<1H OCEAN\n-117.85,33.88,14.0,4753.0,681.0,2138.0,678.0,7.3658,288500.0,<1H OCEAN\n-117.86,33.88,20.0,3977.0,540.0,1886.0,541.0,6.5843,272200.0,<1H OCEAN\n-117.85,33.87,15.0,5078.0,806.0,2569.0,759.0,5.6549,301900.0,<1H OCEAN\n-117.85,33.9,20.0,4026.0,648.0,1997.0,650.0,5.5918,260500.0,<1H OCEAN\n-117.85,33.9,25.0,1548.0,256.0,811.0,263.0,5.2037,242200.0,<1H OCEAN\n-117.84,33.89,24.0,3935.0,625.0,1912.0,593.0,5.7951,226900.0,<1H OCEAN\n-117.85,33.89,24.0,3326.0,503.0,1616.0,494.0,5.7457,240600.0,<1H OCEAN\n-117.88,33.87,21.0,1519.0,388.0,1203.0,366.0,3.2083,145300.0,<1H OCEAN\n-117.87,33.86,19.0,2232.0,448.0,1149.0,417.0,3.1534,324400.0,<1H OCEAN\n-117.87,33.87,7.0,2663.0,642.0,1367.0,677.0,4.6563,162400.0,<1H OCEAN\n-117.87,33.87,15.0,1898.0,476.0,1766.0,455.0,2.4929,158500.0,<1H OCEAN\n-117.87,33.87,16.0,1332.0,368.0,1534.0,295.0,3.0227,297100.0,<1H OCEAN\n-117.86,33.87,19.0,1591.0,279.0,891.0,237.0,5.6573,216000.0,<1H OCEAN\n-117.88,33.85,18.0,2705.0,713.0,2726.0,674.0,2.7759,200000.0,<1H OCEAN\n-117.87,33.86,28.0,2292.0,531.0,2197.0,509.0,3.4856,142800.0,<1H OCEAN\n-117.88,33.85,22.0,1105.0,241.0,971.0,249.0,3.1667,113900.0,<1H OCEAN\n-117.81,33.89,13.0,3252.0,583.0,1546.0,557.0,5.8243,297900.0,<1H OCEAN\n-117.82,33.88,18.0,1982.0,300.0,1027.0,324.0,7.0526,327500.0,<1H OCEAN\n-117.82,33.89,24.0,2168.0,421.0,1050.0,397.0,4.6172,238300.0,<1H OCEAN\n-117.81,33.88,19.0,2968.0,503.0,1430.0,459.0,5.3339,371700.0,<1H OCEAN\n-117.82,33.89,21.0,3079.0,509.0,1431.0,480.0,4.0714,278900.0,<1H OCEAN\n-117.78,33.86,16.0,3471.0,708.0,1769.0,691.0,4.1064,246100.0,<1H OCEAN\n-117.76,33.87,16.0,3973.0,595.0,1971.0,575.0,6.4265,263700.0,<1H OCEAN\n-117.83,33.9,23.0,2446.0,360.0,1196.0,359.0,6.5755,272800.0,<1H OCEAN\n-117.84,33.9,24.0,1723.0,223.0,707.0,219.0,7.0352,299600.0,<1H OCEAN\n-117.83,33.89,25.0,1737.0,270.0,840.0,265.0,4.625,245700.0,<1H OCEAN\n-117.83,33.88,23.0,2365.0,355.0,1209.0,347.0,6.0132,259400.0,<1H OCEAN\n-117.84,33.89,19.0,3544.0,542.0,1787.0,560.0,6.7837,264300.0,<1H OCEAN\n-117.83,33.88,18.0,2112.0,340.0,1048.0,315.0,6.9308,231700.0,<1H OCEAN\n-117.82,33.88,15.0,5392.0,895.0,2531.0,827.0,6.2185,280300.0,<1H OCEAN\n-117.83,33.87,5.0,6971.0,1449.0,3521.0,1423.0,5.2131,243900.0,<1H OCEAN\n-117.81,33.88,19.0,2265.0,283.0,904.0,279.0,9.2327,461300.0,<1H OCEAN\n-117.81,33.87,19.0,4491.0,680.0,2457.0,702.0,6.0591,233500.0,<1H OCEAN\n-117.8,33.87,16.0,5954.0,1281.0,3107.0,1209.0,4.2566,206100.0,<1H OCEAN\n-117.81,33.86,18.0,133.0,29.0,95.0,23.0,3.5625,235000.0,<1H OCEAN\n-117.87,33.92,14.0,4039.0,669.0,1905.0,670.0,6.3303,303000.0,<1H OCEAN\n-117.88,33.92,13.0,3292.0,727.0,1565.0,698.0,5.457,308800.0,<1H OCEAN\n-117.85,33.92,11.0,3331.0,410.0,1460.0,416.0,8.0287,371800.0,<1H OCEAN\n-117.87,33.92,17.0,4575.0,764.0,2054.0,737.0,6.0571,272400.0,<1H OCEAN\n-117.83,33.93,14.0,1956.0,282.0,671.0,269.0,6.5841,306400.0,<1H OCEAN\n-117.8,33.92,16.0,5819.0,986.0,2306.0,914.0,4.6315,277500.0,<1H OCEAN\n-117.8,33.89,25.0,3121.0,381.0,1278.0,389.0,7.0217,357900.0,<1H OCEAN\n-117.79,33.88,17.0,8562.0,1351.0,3822.0,1316.0,6.0829,252600.0,<1H OCEAN\n-117.78,33.87,16.0,5609.0,952.0,2624.0,934.0,5.3307,169600.0,<1H OCEAN\n-117.78,33.88,16.0,1800.0,238.0,871.0,234.0,6.6678,301900.0,<1H OCEAN\n-117.78,33.9,14.0,6239.0,901.0,2923.0,904.0,6.5437,268200.0,<1H OCEAN\n-117.82,33.9,25.0,1137.0,170.0,524.0,164.0,7.5744,259300.0,<1H OCEAN\n-117.8,33.9,22.0,3760.0,482.0,1485.0,461.0,7.8537,354900.0,<1H OCEAN\n-117.74,33.89,4.0,37937.0,5471.0,16122.0,5189.0,7.4947,366300.0,<1H OCEAN\n-117.76,33.88,9.0,4838.0,759.0,2090.0,695.0,6.6536,307800.0,<1H OCEAN\n-117.77,33.88,8.0,4874.0,627.0,2070.0,619.0,8.1117,410200.0,<1H OCEAN\n-117.78,33.89,7.0,9729.0,1210.0,4160.0,1214.0,8.9088,415300.0,<1H OCEAN\n-117.8,33.85,23.0,3038.0,470.0,1568.0,438.0,5.6403,233000.0,<1H OCEAN\n-117.82,33.85,21.0,2603.0,404.0,1350.0,390.0,6.057,235900.0,<1H OCEAN\n-117.82,33.85,18.0,1810.0,305.0,1189.0,326.0,5.2227,213500.0,<1H OCEAN\n-117.8,33.85,16.0,4151.0,637.0,1558.0,604.0,5.806,304900.0,<1H OCEAN\n-117.82,33.84,25.0,1788.0,203.0,676.0,217.0,10.1299,454300.0,<1H OCEAN\n-117.81,33.84,17.0,4343.0,515.0,1605.0,484.0,10.5981,460100.0,<1H OCEAN\n-117.79,33.84,9.0,10484.0,1603.0,4005.0,1419.0,8.3931,365300.0,<1H OCEAN\n-117.78,33.85,16.0,3781.0,504.0,1665.0,499.0,7.2554,335600.0,<1H OCEAN\n-117.78,33.86,16.0,4390.0,660.0,2146.0,633.0,6.1504,266000.0,<1H OCEAN\n-117.76,33.87,16.0,3182.0,429.0,1663.0,428.0,7.0592,288200.0,<1H OCEAN\n-117.78,33.81,23.0,1986.0,278.0,826.0,260.0,7.7752,380000.0,<1H OCEAN\n-117.77,33.8,16.0,3973.0,483.0,1373.0,452.0,9.8074,417000.0,<1H OCEAN\n-117.8,33.79,13.0,2021.0,362.0,1081.0,341.0,4.3269,231400.0,<1H OCEAN\n-117.79,33.8,11.0,10535.0,1620.0,4409.0,1622.0,6.67,283200.0,<1H OCEAN\n-117.77,33.85,13.0,5415.0,827.0,2061.0,714.0,7.3681,353100.0,<1H OCEAN\n-117.76,33.84,15.0,3764.0,510.0,1448.0,468.0,8.7124,410500.0,<1H OCEAN\n-117.75,33.84,16.0,3491.0,502.0,1496.0,509.0,6.6207,270500.0,<1H OCEAN\n-117.76,33.83,15.0,3086.0,457.0,1262.0,436.0,6.4415,300700.0,<1H OCEAN\n-117.77,33.84,5.0,4380.0,715.0,1913.0,741.0,6.7274,266400.0,<1H OCEAN\n-117.76,33.86,14.0,3666.0,442.0,1400.0,433.0,10.1316,500001.0,<1H OCEAN\n-117.75,33.83,14.0,2452.0,296.0,954.0,275.0,8.2375,388300.0,<1H OCEAN\n-117.74,33.85,4.0,5416.0,820.0,1753.0,583.0,6.9544,314000.0,<1H OCEAN\n-117.78,33.82,12.0,6208.0,750.0,2443.0,739.0,9.1808,413700.0,<1H OCEAN\n-117.76,33.81,2.0,582.0,70.0,199.0,64.0,7.1193,500001.0,<1H OCEAN\n-117.81,33.8,25.0,2765.0,475.0,1666.0,474.0,6.0531,230700.0,<1H OCEAN\n-117.81,33.79,25.0,5950.0,1155.0,4528.0,1064.0,4.2564,204600.0,<1H OCEAN\n-117.8,33.81,14.0,1206.0,142.0,572.0,149.0,8.847,388700.0,<1H OCEAN\n-117.8,33.78,18.0,3548.0,474.0,1506.0,449.0,6.925,290300.0,<1H OCEAN\n-117.8,33.78,17.0,4138.0,805.0,2442.0,780.0,4.7804,242000.0,<1H OCEAN\n-117.69,33.8,5.0,3178.0,631.0,1467.0,581.0,5.2541,237100.0,<1H OCEAN\n-117.66,33.61,16.0,2022.0,254.0,789.0,270.0,8.4112,286900.0,<1H OCEAN\n-117.66,33.62,16.0,4065.0,661.0,1962.0,636.0,6.2177,256600.0,<1H OCEAN\n-117.67,33.61,24.0,3859.0,661.0,1972.0,624.0,5.7871,227400.0,<1H OCEAN\n-117.66,33.61,17.0,3464.0,519.0,1713.0,530.0,6.0471,248400.0,<1H OCEAN\n-117.66,33.61,21.0,1932.0,266.0,860.0,286.0,7.1497,274000.0,<1H OCEAN\n-117.66,33.6,24.0,1684.0,232.0,781.0,230.0,6.8667,279600.0,<1H OCEAN\n-117.66,33.6,25.0,3745.0,522.0,1648.0,496.0,7.5488,278100.0,<1H OCEAN\n-117.67,33.61,23.0,3588.0,577.0,1695.0,569.0,6.1401,243200.0,<1H OCEAN\n-117.68,33.63,16.0,5218.0,1187.0,2701.0,1125.0,3.929,143100.0,<1H OCEAN\n-117.68,33.63,13.0,5830.0,921.0,2897.0,891.0,6.2403,257400.0,<1H OCEAN\n-117.67,33.63,9.0,5774.0,1320.0,3086.0,1265.0,4.4063,202200.0,<1H OCEAN\n-117.62,33.77,43.0,1911.0,439.0,930.0,433.0,4.6369,186400.0,<1H OCEAN\n-117.6,33.72,36.0,1317.0,228.0,531.0,214.0,5.6346,272500.0,<1H OCEAN\n-117.67,33.6,25.0,3164.0,449.0,1517.0,453.0,6.7921,266000.0,<1H OCEAN\n-117.66,33.59,18.0,4552.0,706.0,1918.0,671.0,7.5791,288100.0,<1H OCEAN\n-117.67,33.6,20.0,1213.0,171.0,565.0,170.0,7.2592,314800.0,<1H OCEAN\n-117.66,33.58,16.0,3016.0,394.0,1172.0,382.0,7.5196,315600.0,<1H OCEAN\n-117.67,33.57,18.0,1614.0,210.0,692.0,209.0,7.9294,280300.0,<1H OCEAN\n-117.66,33.57,16.0,4277.0,565.0,1642.0,549.0,8.0082,286600.0,<1H OCEAN\n-117.7,33.62,16.0,9653.0,2000.0,4732.0,1922.0,3.7361,197200.0,<1H OCEAN\n-117.69,33.62,18.0,4265.0,581.0,2025.0,544.0,6.4598,282700.0,<1H OCEAN\n-117.68,33.61,19.0,2962.0,405.0,1295.0,440.0,6.0689,248000.0,<1H OCEAN\n-117.69,33.61,16.0,3010.0,580.0,1649.0,538.0,4.0221,236200.0,<1H OCEAN\n-117.68,33.6,19.0,3913.0,460.0,1646.0,454.0,7.2147,303900.0,<1H OCEAN\n-117.65,33.65,16.0,1538.0,260.0,835.0,259.0,5.5779,234800.0,<1H OCEAN\n-117.66,33.64,17.0,3173.0,501.0,1555.0,520.0,6.7079,250800.0,<1H OCEAN\n-117.67,33.64,11.0,2722.0,554.0,1565.0,508.0,5.1645,164100.0,<1H OCEAN\n-117.66,33.65,13.0,8527.0,1364.0,4597.0,1393.0,6.2242,237900.0,<1H OCEAN\n-117.65,33.65,15.0,3485.0,519.0,1740.0,485.0,6.7543,251900.0,<1H OCEAN\n-117.65,33.63,16.0,3388.0,425.0,1395.0,427.0,8.4471,351300.0,<1H OCEAN\n-117.65,33.65,15.0,4713.0,671.0,2197.0,673.0,7.5426,294800.0,<1H OCEAN\n-117.66,33.63,16.0,3299.0,610.0,1967.0,604.0,5.5085,223300.0,<1H OCEAN\n-117.63,33.65,10.0,3580.0,491.0,1688.0,467.0,7.7814,288700.0,<1H OCEAN\n-117.62,33.64,2.0,7826.0,893.0,2985.0,790.0,10.1531,484100.0,<1H OCEAN\n-117.64,33.63,10.0,4814.0,643.0,1808.0,588.0,8.798,436600.0,<1H OCEAN\n-117.64,33.64,11.0,2422.0,429.0,810.0,395.0,6.1935,293200.0,<1H OCEAN\n-117.64,33.65,4.0,6842.0,1512.0,3256.0,1439.0,5.4132,216600.0,<1H OCEAN\n-117.63,33.62,9.0,4257.0,785.0,1293.0,745.0,3.7139,196700.0,<1H OCEAN\n-117.63,33.63,6.0,3068.0,549.0,985.0,536.0,4.2009,238000.0,<1H OCEAN\n-117.65,33.62,15.0,2708.0,410.0,1140.0,389.0,6.2899,275000.0,<1H OCEAN\n-117.64,33.62,16.0,3970.0,771.0,1202.0,734.0,3.4115,184800.0,<1H OCEAN\n-117.66,33.62,16.0,5175.0,799.0,2717.0,813.0,6.1493,257800.0,<1H OCEAN\n-117.65,33.6,15.0,5736.0,,2529.0,762.0,6.4114,278700.0,<1H OCEAN\n-117.64,33.61,14.0,5232.0,810.0,3041.0,839.0,5.826,247900.0,<1H OCEAN\n-117.65,33.6,13.0,2319.0,430.0,1004.0,380.0,5.133,316100.0,<1H OCEAN\n-117.64,33.59,4.0,3274.0,383.0,1312.0,390.0,8.1611,348000.0,<1H OCEAN\n-117.66,33.58,6.0,4186.0,,1794.0,541.0,9.6986,357600.0,<1H OCEAN\n-117.65,33.57,5.0,1998.0,500.0,1185.0,446.0,4.3542,195600.0,<1H OCEAN\n-117.65,33.58,2.0,2411.0,354.0,703.0,217.0,7.8061,331400.0,<1H OCEAN\n-117.66,33.57,16.0,2483.0,443.0,1357.0,400.0,5.5545,214200.0,<1H OCEAN\n-117.65,33.59,8.0,2649.0,340.0,1238.0,354.0,8.0409,337900.0,<1H OCEAN\n-117.67,33.54,16.0,2102.0,350.0,1003.0,328.0,4.7981,170800.0,<1H OCEAN\n-117.67,33.54,15.0,2423.0,435.0,1366.0,423.0,4.8906,181800.0,<1H OCEAN\n-117.67,33.55,6.0,3157.0,721.0,1695.0,710.0,3.7609,222300.0,<1H OCEAN\n-117.67,33.56,4.0,3289.0,728.0,1345.0,632.0,4.6863,184400.0,<1H OCEAN\n-117.64,33.51,15.0,1743.0,254.0,943.0,274.0,5.9339,286000.0,<1H OCEAN\n-117.64,33.51,14.0,1343.0,175.0,650.0,184.0,7.2648,363200.0,<1H OCEAN\n-117.55,33.52,15.0,426.0,62.0,133.0,45.0,5.136,447400.0,<1H OCEAN\n-117.59,33.61,3.0,2993.0,429.0,991.0,390.0,10.0765,378200.0,<1H OCEAN\n-117.58,33.6,5.0,5348.0,659.0,1862.0,555.0,11.0567,495400.0,<1H OCEAN\n-117.49,33.64,3.0,8874.0,1302.0,3191.0,1027.0,6.8588,302000.0,<1H OCEAN\n-117.59,33.67,29.0,1223.0,215.0,633.0,204.0,6.5143,279800.0,<1H OCEAN\n-117.53,33.69,6.0,454.0,102.0,213.0,43.0,10.9704,483300.0,<1H OCEAN\n-117.59,33.66,3.0,1206.0,256.0,563.0,287.0,5.1589,167800.0,<1H OCEAN\n-117.58,33.66,4.0,3305.0,644.0,1693.0,597.0,5.2497,215000.0,<1H OCEAN\n-117.58,33.65,4.0,2000.0,422.0,833.0,386.0,5.7709,190300.0,<1H OCEAN\n-117.59,33.66,4.0,1318.0,218.0,673.0,225.0,6.0722,260800.0,<1H OCEAN\n-117.59,33.65,2.0,4860.0,1193.0,2332.0,1073.0,4.5022,151900.0,<1H OCEAN\n-117.6,33.65,4.0,3134.0,504.0,1599.0,485.0,6.2464,233900.0,<1H OCEAN\n-117.61,33.67,3.0,4541.0,720.0,1600.0,583.0,6.8004,284900.0,<1H OCEAN\n-117.64,33.66,6.0,5221.0,1217.0,2597.0,1119.0,4.6076,204000.0,<1H OCEAN\n-117.59,33.65,4.0,1793.0,390.0,897.0,386.0,4.2463,182800.0,<1H OCEAN\n-117.58,33.65,4.0,1606.0,498.0,815.0,426.0,3.375,500001.0,<1H OCEAN\n-117.61,33.63,2.0,4678.0,817.0,1970.0,712.0,6.1078,219000.0,<1H OCEAN\n-117.65,33.53,7.0,6814.0,785.0,2175.0,681.0,10.49,500001.0,<1H OCEAN\n-117.62,33.42,23.0,2656.0,515.0,998.0,435.0,4.0294,500001.0,NEAR OCEAN\n-117.63,33.38,12.0,5744.0,1054.0,2104.0,847.0,5.1482,500001.0,NEAR OCEAN\n-117.61,33.41,35.0,2556.0,404.0,946.0,399.0,6.1557,402900.0,NEAR OCEAN\n-117.61,33.42,31.0,3959.0,856.0,1919.0,775.0,4.0313,282000.0,NEAR OCEAN\n-117.6,33.41,29.0,2193.0,389.0,922.0,387.0,4.5476,309200.0,NEAR OCEAN\n-117.62,33.47,4.0,1812.0,255.0,661.0,211.0,6.487,294200.0,NEAR OCEAN\n-117.63,33.45,5.0,3549.0,604.0,1571.0,534.0,5.3705,363500.0,NEAR OCEAN\n-117.63,33.47,4.0,1969.0,280.0,805.0,271.0,7.6012,310800.0,NEAR OCEAN\n-117.61,33.45,6.0,950.0,184.0,426.0,186.0,4.7237,220700.0,NEAR OCEAN\n-117.63,33.46,7.0,7684.0,1088.0,2812.0,1057.0,6.3401,387300.0,NEAR OCEAN\n-117.64,33.45,26.0,1528.0,,607.0,218.0,6.2871,325500.0,NEAR OCEAN\n-117.65,33.42,25.0,2174.0,428.0,603.0,352.0,3.3967,249400.0,NEAR OCEAN\n-117.64,33.45,27.0,334.0,56.0,130.0,46.0,4.875,284100.0,NEAR OCEAN\n-117.62,33.43,27.0,1835.0,413.0,1221.0,377.0,3.2232,247100.0,NEAR OCEAN\n-117.62,33.43,27.0,3858.0,1062.0,2321.0,873.0,3.3155,231000.0,NEAR OCEAN\n-117.62,33.43,24.0,1296.0,384.0,850.0,367.0,2.7545,231300.0,NEAR OCEAN\n-117.65,33.4,17.0,2737.0,654.0,910.0,492.0,3.5729,370800.0,NEAR OCEAN\n-117.62,33.43,23.0,4052.0,955.0,1950.0,859.0,4.0647,240600.0,NEAR OCEAN\n-117.61,33.43,33.0,1150.0,383.0,604.0,317.0,2.3545,187500.0,NEAR OCEAN\n-117.62,33.42,27.0,1005.0,266.0,460.0,243.0,3.1029,190600.0,NEAR OCEAN\n-117.62,33.42,27.0,1444.0,412.0,597.0,311.0,3.1395,310000.0,NEAR OCEAN\n-117.6,33.45,4.0,2369.0,566.0,996.0,435.0,5.4031,243800.0,NEAR OCEAN\n-117.59,33.44,3.0,5813.0,1264.0,2363.0,1041.0,4.3897,341300.0,NEAR OCEAN\n-117.59,33.4,22.0,3167.0,743.0,1797.0,642.0,4.0076,252100.0,NEAR OCEAN\n-117.59,33.41,17.0,2248.0,448.0,878.0,423.0,5.1298,246000.0,NEAR OCEAN\n-117.6,33.42,23.0,2482.0,461.0,1048.0,425.0,4.665,280600.0,NEAR OCEAN\n-117.61,33.43,24.0,2303.0,399.0,851.0,379.0,3.9875,346500.0,NEAR OCEAN\n-117.6,33.43,21.0,3951.0,562.0,1392.0,543.0,5.1439,414000.0,NEAR OCEAN\n-117.61,33.44,17.0,2036.0,272.0,713.0,265.0,6.5954,346200.0,NEAR OCEAN\n-117.59,33.43,14.0,3223.0,484.0,1230.0,488.0,6.5964,358800.0,NEAR OCEAN\n-117.67,33.47,22.0,2728.0,616.0,1081.0,566.0,1.6393,500001.0,<1H OCEAN\n-117.67,33.46,24.0,3571.0,722.0,1409.0,543.0,4.6518,277800.0,<1H OCEAN\n-117.67,33.44,25.0,2994.0,519.0,903.0,410.0,6.6852,500001.0,<1H OCEAN\n-117.67,33.46,18.0,1679.0,271.0,783.0,257.0,5.3999,300000.0,<1H OCEAN\n-117.66,33.46,26.0,2073.0,370.0,952.0,340.0,5.0877,288100.0,<1H OCEAN\n-117.66,33.46,28.0,1261.0,233.0,609.0,242.0,5.1024,312700.0,<1H OCEAN\n-117.66,33.48,22.0,809.0,180.0,334.0,157.0,2.3846,500001.0,<1H OCEAN\n-117.65,33.49,16.0,2223.0,454.0,628.0,382.0,4.3603,248800.0,<1H OCEAN\n-117.64,33.49,3.0,2516.0,429.0,781.0,337.0,5.6197,271600.0,<1H OCEAN\n-117.65,33.48,10.0,3484.0,582.0,1469.0,556.0,5.4188,402200.0,<1H OCEAN\n-117.65,33.48,6.0,1638.0,188.0,572.0,174.0,13.0502,500001.0,<1H OCEAN\n-117.63,33.5,12.0,3619.0,536.0,1506.0,492.0,7.2013,353600.0,<1H OCEAN\n-117.63,33.47,4.0,2320.0,405.0,1408.0,477.0,6.3369,256000.0,NEAR OCEAN\n-117.65,33.46,19.0,7034.0,1139.0,2824.0,1068.0,6.0873,277300.0,<1H OCEAN\n-117.64,33.48,12.0,2007.0,397.0,1033.0,373.0,5.6754,275900.0,<1H OCEAN\n-117.65,33.45,15.0,7468.0,1275.0,3033.0,1217.0,5.49,239300.0,NEAR OCEAN\n-117.73,33.49,31.0,5112.0,778.0,1530.0,648.0,10.3983,500001.0,<1H OCEAN\n-117.76,33.48,38.0,3832.0,809.0,1332.0,636.0,5.0044,381200.0,<1H OCEAN\n-117.74,33.51,29.0,1720.0,269.0,612.0,258.0,7.8239,500001.0,<1H OCEAN\n-117.73,33.53,3.0,6388.0,920.0,2129.0,819.0,7.8915,420600.0,<1H OCEAN\n-117.72,33.54,13.0,4866.0,812.0,1909.0,733.0,4.9821,244800.0,<1H OCEAN\n-117.72,33.53,14.0,1672.0,295.0,704.0,293.0,5.1129,251300.0,<1H OCEAN\n-117.72,33.51,17.0,3617.0,597.0,1176.0,571.0,5.133,324000.0,<1H OCEAN\n-117.72,33.49,4.0,3623.0,734.0,1129.0,530.0,5.7281,500001.0,<1H OCEAN\n-117.73,33.49,17.0,2168.0,290.0,654.0,279.0,9.8321,500001.0,<1H OCEAN\n-117.73,33.51,5.0,4549.0,786.0,1238.0,632.0,6.1785,295900.0,<1H OCEAN\n-117.69,33.6,19.0,3562.0,439.0,1584.0,470.0,6.4211,288100.0,<1H OCEAN\n-117.68,33.6,24.0,1956.0,262.0,969.0,256.0,6.8154,265900.0,<1H OCEAN\n-117.69,33.6,17.0,2150.0,361.0,1194.0,335.0,5.4622,227000.0,<1H OCEAN\n-117.7,33.6,16.0,2092.0,489.0,877.0,392.0,3.0461,216900.0,<1H OCEAN\n-117.69,33.6,16.0,2205.0,393.0,1333.0,402.0,3.475,279500.0,<1H OCEAN\n-117.69,33.6,12.0,3258.0,421.0,1464.0,435.0,6.5413,332000.0,<1H OCEAN\n-117.69,33.59,13.0,3320.0,426.0,1432.0,431.0,7.9283,348100.0,<1H OCEAN\n-117.68,33.59,12.0,3473.0,466.0,1569.0,450.0,8.8636,314000.0,<1H OCEAN\n-117.69,33.58,5.0,6678.0,1011.0,2877.0,982.0,7.5177,330000.0,<1H OCEAN\n-117.68,33.59,8.0,2327.0,263.0,899.0,236.0,14.9009,500001.0,<1H OCEAN\n-117.69,33.58,8.0,2887.0,351.0,1176.0,351.0,10.3953,500001.0,<1H OCEAN\n-117.68,33.47,7.0,4458.0,731.0,1731.0,704.0,6.126,285600.0,<1H OCEAN\n-117.68,33.48,15.0,1786.0,299.0,727.0,293.0,5.0527,231400.0,<1H OCEAN\n-117.68,33.49,17.0,2232.0,372.0,1072.0,385.0,4.245,214500.0,<1H OCEAN\n-117.68,33.49,16.0,3084.0,724.0,2557.0,690.0,2.8357,106300.0,<1H OCEAN\n-117.67,33.49,15.0,2782.0,579.0,983.0,525.0,2.1935,183300.0,<1H OCEAN\n-117.67,33.49,10.0,366.0,61.0,128.0,61.0,8.163,250000.0,<1H OCEAN\n-117.68,33.51,19.0,2930.0,428.0,1481.0,430.0,6.323,480800.0,<1H OCEAN\n-117.68,33.49,18.0,4173.0,625.0,1649.0,634.0,6.3568,294300.0,<1H OCEAN\n-117.69,33.48,25.0,3240.0,481.0,1462.0,497.0,6.1815,288500.0,<1H OCEAN\n-117.69,33.47,13.0,2020.0,378.0,679.0,290.0,5.756,305600.0,<1H OCEAN\n-117.66,33.51,18.0,2626.0,,1302.0,522.0,4.0167,189600.0,<1H OCEAN\n-117.66,33.5,16.0,1956.0,346.0,862.0,326.0,4.4732,186300.0,<1H OCEAN\n-117.67,33.51,17.0,2112.0,480.0,1893.0,433.0,4.0388,120400.0,<1H OCEAN\n-117.67,33.51,18.0,1645.0,393.0,1490.0,355.0,3.4792,126400.0,<1H OCEAN\n-117.67,33.51,19.0,1258.0,246.0,545.0,227.0,2.9762,184400.0,<1H OCEAN\n-117.69,33.47,23.0,3499.0,722.0,1480.0,634.0,3.86,300000.0,<1H OCEAN\n-117.7,33.47,20.0,1577.0,363.0,764.0,333.0,4.1563,320800.0,<1H OCEAN\n-117.7,33.47,21.0,2208.0,534.0,1423.0,482.0,3.5915,305600.0,<1H OCEAN\n-117.69,33.47,19.0,2595.0,621.0,1728.0,571.0,3.668,243800.0,<1H OCEAN\n-117.7,33.47,21.0,1857.0,399.0,881.0,380.0,3.8403,350000.0,<1H OCEAN\n-117.72,33.43,5.0,1889.0,359.0,616.0,246.0,3.8992,500001.0,<1H OCEAN\n-117.68,33.55,5.0,2262.0,427.0,1016.0,402.0,6.065,315500.0,<1H OCEAN\n-117.68,33.54,5.0,2840.0,403.0,1363.0,403.0,7.618,341400.0,<1H OCEAN\n-117.68,33.52,5.0,3621.0,632.0,1546.0,567.0,5.753,322800.0,<1H OCEAN\n-117.69,33.55,9.0,3856.0,571.0,1646.0,576.0,6.8007,318300.0,<1H OCEAN\n-117.69,33.54,20.0,1767.0,280.0,801.0,284.0,6.5394,272000.0,<1H OCEAN\n-117.69,33.53,17.0,5041.0,778.0,2396.0,801.0,6.0868,282900.0,<1H OCEAN\n-117.69,33.52,4.0,2142.0,625.0,1176.0,483.0,3.4455,325000.0,<1H OCEAN\n-117.7,33.51,2.0,5261.0,763.0,1460.0,599.0,6.8279,279000.0,<1H OCEAN\n-117.69,33.52,3.0,7374.0,1444.0,3214.0,1279.0,4.538,278200.0,<1H OCEAN\n-117.7,33.53,5.0,6698.0,1254.0,2834.0,1139.0,5.9088,288500.0,<1H OCEAN\n-117.71,33.52,17.0,2486.0,417.0,876.0,361.0,6.1007,340900.0,<1H OCEAN\n-117.71,33.51,11.0,2198.0,252.0,883.0,281.0,13.1477,487000.0,<1H OCEAN\n-117.71,33.51,12.0,4676.0,698.0,1543.0,598.0,6.379,500001.0,<1H OCEAN\n-117.68,33.57,2.0,10008.0,1453.0,3550.0,1139.0,10.1122,500001.0,<1H OCEAN\n-117.69,33.55,4.0,1764.0,220.0,705.0,224.0,8.3275,384200.0,<1H OCEAN\n-117.69,33.55,3.0,1618.0,266.0,710.0,246.0,6.0743,274300.0,<1H OCEAN\n-117.7,33.54,4.0,4956.0,693.0,1802.0,625.0,7.9338,354700.0,<1H OCEAN\n-117.7,33.55,12.0,2459.0,390.0,1054.0,391.0,7.1736,262100.0,<1H OCEAN\n-117.7,33.56,2.0,2112.0,305.0,703.0,261.0,6.9343,298500.0,<1H OCEAN\n-117.7,33.56,3.0,2443.0,637.0,1033.0,548.0,4.1379,183300.0,<1H OCEAN\n-117.7,33.57,4.0,3283.0,911.0,1512.0,782.0,3.3125,138500.0,<1H OCEAN\n-117.71,33.54,7.0,4907.0,577.0,1883.0,556.0,10.4415,453800.0,<1H OCEAN\n-117.71,33.54,15.0,2460.0,368.0,962.0,320.0,7.3878,318300.0,<1H OCEAN\n-117.71,33.58,2.0,2530.0,562.0,1066.0,510.0,4.6336,187500.0,<1H OCEAN\n-117.7,33.57,9.0,1204.0,355.0,469.0,293.0,3.6196,119900.0,<1H OCEAN\n-117.71,33.57,4.0,3289.0,753.0,1285.0,651.0,4.045,226000.0,<1H OCEAN\n-117.71,33.57,6.0,3673.0,881.0,1846.0,768.0,4.877,144300.0,<1H OCEAN\n-117.68,33.51,4.0,2428.0,401.0,959.0,386.0,6.2661,268500.0,<1H OCEAN\n-117.69,33.51,4.0,1223.0,275.0,505.0,244.0,4.6607,173000.0,<1H OCEAN\n-117.7,33.5,4.0,2351.0,445.0,834.0,397.0,5.5677,245400.0,<1H OCEAN\n-117.71,33.49,5.0,1680.0,254.0,617.0,231.0,8.583,397700.0,<1H OCEAN\n-117.7,33.5,4.0,7474.0,1037.0,2969.0,1007.0,8.7591,434700.0,<1H OCEAN\n-117.7,33.48,10.0,3458.0,638.0,1156.0,470.0,6.3579,336700.0,<1H OCEAN\n-117.7,33.48,6.0,16590.0,2696.0,6223.0,2357.0,6.3088,340300.0,<1H OCEAN\n-117.71,33.47,14.0,3894.0,672.0,1490.0,629.0,6.5206,368500.0,<1H OCEAN\n-117.74,33.46,9.0,6564.0,1316.0,1720.0,904.0,4.89,454100.0,<1H OCEAN\n-117.71,33.47,17.0,2681.0,454.0,830.0,410.0,5.5507,345700.0,<1H OCEAN\n-117.7,33.68,29.0,5650.0,1084.0,3985.0,1056.0,2.8192,162500.0,<1H OCEAN\n-117.76,33.71,14.0,4321.0,582.0,2025.0,578.0,8.3634,355100.0,<1H OCEAN\n-117.75,33.71,15.0,2849.0,537.0,878.0,520.0,3.2841,158300.0,<1H OCEAN\n-117.76,33.7,12.0,4025.0,574.0,2042.0,588.0,7.9125,344900.0,<1H OCEAN\n-117.77,33.71,15.0,2102.0,295.0,1060.0,303.0,7.3141,337100.0,<1H OCEAN\n-117.77,33.7,3.0,3636.0,749.0,1486.0,696.0,5.5464,207500.0,<1H OCEAN\n-117.77,33.7,4.0,2446.0,622.0,1315.0,560.0,3.7147,137500.0,<1H OCEAN\n-117.69,33.65,16.0,5805.0,852.0,2356.0,795.0,6.1062,274600.0,<1H OCEAN\n-117.71,33.65,16.0,3774.0,456.0,1587.0,430.0,8.6088,307400.0,<1H OCEAN\n-117.7,33.65,16.0,3388.0,492.0,1249.0,463.0,6.1863,355600.0,<1H OCEAN\n-117.71,33.64,14.0,2945.0,356.0,1293.0,335.0,8.111,308900.0,<1H OCEAN\n-117.71,33.63,16.0,1565.0,274.0,950.0,280.0,5.8399,220600.0,<1H OCEAN\n-117.71,33.63,16.0,2497.0,500.0,1357.0,456.0,4.5909,241800.0,<1H OCEAN\n-117.71,33.63,16.0,1641.0,354.0,945.0,318.0,3.4261,219700.0,<1H OCEAN\n-117.7,33.63,16.0,4428.0,745.0,1525.0,682.0,5.2325,286800.0,<1H OCEAN\n-117.72,33.64,16.0,1230.0,242.0,380.0,246.0,2.2969,67500.0,<1H OCEAN\n-117.7,33.62,19.0,2957.0,492.0,1639.0,495.0,5.0686,225600.0,<1H OCEAN\n-117.7,33.63,23.0,3038.0,473.0,1501.0,436.0,5.5584,241700.0,<1H OCEAN\n-117.71,33.62,22.0,2520.0,387.0,1338.0,391.0,5.8898,242800.0,<1H OCEAN\n-117.76,33.72,11.0,4508.0,618.0,1993.0,573.0,10.4498,386100.0,<1H OCEAN\n-117.76,33.72,14.0,3011.0,388.0,1359.0,371.0,7.9739,368700.0,<1H OCEAN\n-117.75,33.72,10.0,2464.0,347.0,1241.0,366.0,8.7603,362500.0,<1H OCEAN\n-117.76,33.72,15.0,941.0,266.0,366.0,248.0,4.3636,148400.0,<1H OCEAN\n-117.76,33.71,15.0,1010.0,350.0,470.0,342.0,3.2229,108300.0,<1H OCEAN\n-117.74,33.73,18.0,328.0,68.0,391.0,60.0,4.1167,87500.0,<1H OCEAN\n-117.69,33.66,11.0,2630.0,327.0,1256.0,352.0,8.2953,350500.0,<1H OCEAN\n-117.69,33.66,5.0,4246.0,689.0,1933.0,722.0,6.9501,225700.0,<1H OCEAN\n-117.67,33.67,5.0,10534.0,2035.0,4656.0,1863.0,5.7797,309200.0,<1H OCEAN\n-117.67,33.66,4.0,10175.0,2181.0,4762.0,1929.0,4.7341,237400.0,<1H OCEAN\n-117.68,33.65,6.0,10395.0,1915.0,4783.0,1811.0,5.928,239900.0,<1H OCEAN\n-117.64,33.68,4.0,5687.0,970.0,2677.0,938.0,6.5069,243400.0,<1H OCEAN\n-117.7,33.72,6.0,211.0,51.0,125.0,44.0,1.9659,500001.0,<1H OCEAN\n-117.69,33.65,15.0,5394.0,748.0,2383.0,706.0,7.5619,302000.0,<1H OCEAN\n-117.7,33.64,15.0,5743.0,773.0,2380.0,773.0,8.1926,326600.0,<1H OCEAN\n-117.69,33.63,23.0,1444.0,260.0,792.0,253.0,4.9079,273800.0,<1H OCEAN\n-117.69,33.64,18.0,3783.0,654.0,1843.0,623.0,5.7559,215800.0,<1H OCEAN\n-117.69,33.64,16.0,2592.0,372.0,1279.0,383.0,6.9741,262000.0,<1H OCEAN\n-117.82,33.71,9.0,5206.0,992.0,4660.0,978.0,2.885,162500.0,<1H OCEAN\n-117.82,33.72,24.0,3260.0,458.0,1383.0,442.0,6.5987,272800.0,<1H OCEAN\n-117.81,33.73,17.0,1063.0,189.0,363.0,183.0,2.1719,261300.0,<1H OCEAN\n-117.8,33.72,16.0,2617.0,506.0,1317.0,511.0,4.821,201400.0,<1H OCEAN\n-117.81,33.71,16.0,2666.0,387.0,1227.0,347.0,7.3769,302400.0,<1H OCEAN\n-117.82,33.72,24.0,3477.0,462.0,1593.0,484.0,6.8634,276500.0,<1H OCEAN\n-117.77,33.7,15.0,1392.0,267.0,681.0,263.0,5.4248,187200.0,<1H OCEAN\n-117.77,33.69,14.0,1413.0,372.0,744.0,338.0,3.7988,184100.0,<1H OCEAN\n-117.77,33.69,15.0,500.0,113.0,261.0,116.0,5.0631,154000.0,<1H OCEAN\n-117.77,33.69,16.0,1666.0,341.0,479.0,336.0,2.1406,55000.0,<1H OCEAN\n-117.78,33.69,16.0,4702.0,806.0,2529.0,814.0,5.1299,238900.0,<1H OCEAN\n-117.78,33.68,19.0,2500.0,331.0,1027.0,327.0,6.115,315600.0,<1H OCEAN\n-117.78,33.69,16.0,3400.0,501.0,1575.0,488.0,6.0961,295500.0,<1H OCEAN\n-117.8,33.69,16.0,2745.0,447.0,1429.0,411.0,6.8219,325500.0,<1H OCEAN\n-117.79,33.69,16.0,1532.0,240.0,679.0,248.0,5.7115,313900.0,<1H OCEAN\n-117.79,33.68,16.0,1998.0,308.0,818.0,299.0,6.8722,326100.0,<1H OCEAN\n-117.8,33.68,14.0,2635.0,516.0,1150.0,499.0,4.4391,306700.0,<1H OCEAN\n-117.8,33.69,14.0,1800.0,362.0,874.0,373.0,4.2083,251000.0,<1H OCEAN\n-117.8,33.69,15.0,2099.0,322.0,873.0,307.0,7.9887,328000.0,<1H OCEAN\n-117.8,33.68,5.0,623.0,146.0,396.0,136.0,3.631,225000.0,<1H OCEAN\n-117.8,33.68,8.0,2032.0,349.0,862.0,340.0,6.9133,274100.0,<1H OCEAN\n-117.81,33.67,8.0,2098.0,342.0,908.0,329.0,7.7589,342900.0,<1H OCEAN\n-117.81,33.67,8.0,2440.0,502.0,1113.0,483.0,4.6019,242500.0,<1H OCEAN\n-117.81,33.67,9.0,1567.0,299.0,675.0,294.0,5.2124,199600.0,<1H OCEAN\n-117.81,33.67,9.0,2435.0,396.0,1194.0,385.0,7.2025,275000.0,<1H OCEAN\n-117.81,33.67,9.0,3279.0,530.0,1447.0,510.0,7.4581,296600.0,<1H OCEAN\n-117.81,33.68,8.0,1964.0,413.0,913.0,406.0,5.1583,192200.0,<1H OCEAN\n-117.78,33.68,15.0,1834.0,330.0,841.0,309.0,6.0634,234300.0,<1H OCEAN\n-117.78,33.68,14.0,1750.0,336.0,852.0,300.0,4.6793,236800.0,<1H OCEAN\n-117.79,33.68,13.0,2636.0,416.0,1137.0,404.0,7.2118,311500.0,<1H OCEAN\n-117.78,33.68,11.0,1994.0,477.0,849.0,411.0,4.0187,235600.0,<1H OCEAN\n-117.79,33.68,9.0,1633.0,295.0,928.0,297.0,5.7858,265900.0,<1H OCEAN\n-117.79,33.68,10.0,2106.0,319.0,1002.0,332.0,8.735,375300.0,<1H OCEAN\n-117.8,33.67,5.0,2638.0,521.0,1179.0,480.0,5.7759,240000.0,<1H OCEAN\n-117.8,33.67,5.0,2487.0,388.0,1147.0,397.0,8.284,302500.0,<1H OCEAN\n-117.8,33.67,4.0,3345.0,552.0,1525.0,539.0,6.7962,329100.0,<1H OCEAN\n-117.81,33.69,5.0,1256.0,256.0,880.0,288.0,2.4233,450000.0,<1H OCEAN\n-117.81,33.68,4.0,1545.0,304.0,788.0,296.0,4.5469,500001.0,<1H OCEAN\n-117.82,33.68,4.0,1346.0,213.0,603.0,219.0,8.7974,360600.0,<1H OCEAN\n-117.82,33.68,3.0,3068.0,494.0,1357.0,486.0,7.9187,333600.0,<1H OCEAN\n-117.82,33.67,17.0,2895.0,439.0,1588.0,450.0,6.276,290700.0,<1H OCEAN\n-117.83,33.68,4.0,3226.0,838.0,1666.0,800.0,4.1652,184500.0,<1H OCEAN\n-117.82,33.68,3.0,7105.0,1459.0,3068.0,1241.0,6.1395,358000.0,<1H OCEAN\n-117.77,33.67,12.0,4329.0,1068.0,1913.0,978.0,4.5094,160200.0,<1H OCEAN\n-117.77,33.72,10.0,2815.0,431.0,1181.0,398.0,6.5743,278700.0,<1H OCEAN\n-117.77,33.72,9.0,2153.0,316.0,954.0,324.0,7.8139,304700.0,<1H OCEAN\n-117.77,33.71,13.0,1939.0,247.0,928.0,244.0,8.1111,379800.0,<1H OCEAN\n-117.77,33.71,5.0,4050.0,584.0,1986.0,598.0,7.5847,375700.0,<1H OCEAN\n-117.77,33.71,4.0,1646.0,321.0,859.0,300.0,5.5631,227800.0,<1H OCEAN\n-117.78,33.71,16.0,2207.0,291.0,1081.0,308.0,7.3518,331200.0,<1H OCEAN\n-117.78,33.71,4.0,974.0,232.0,428.0,203.0,4.6141,195400.0,<1H OCEAN\n-117.79,33.73,3.0,8240.0,1410.0,3318.0,1270.0,7.2074,291300.0,<1H OCEAN\n-117.78,33.7,16.0,1663.0,250.0,597.0,204.0,5.409,233900.0,<1H OCEAN\n-117.79,33.7,16.0,6259.0,1098.0,3785.0,1114.0,6.3298,247100.0,<1H OCEAN\n-117.79,33.71,16.0,3114.0,463.0,1641.0,469.0,6.2162,283200.0,<1H OCEAN\n-117.79,33.71,16.0,6339.0,862.0,3132.0,825.0,7.1069,313400.0,<1H OCEAN\n-117.79,33.7,6.0,1593.0,371.0,832.0,379.0,4.4286,239500.0,<1H OCEAN\n-117.8,33.7,5.0,1549.0,378.0,735.0,355.0,5.2923,194000.0,<1H OCEAN\n-117.79,33.7,16.0,1416.0,249.0,636.0,244.0,5.1741,227700.0,<1H OCEAN\n-117.79,33.69,15.0,1875.0,316.0,890.0,316.0,6.5783,244800.0,<1H OCEAN\n-117.79,33.69,16.0,3067.0,396.0,1275.0,372.0,8.7385,340000.0,<1H OCEAN\n-117.8,33.69,13.0,1161.0,289.0,630.0,296.0,3.3438,333300.0,<1H OCEAN\n-117.8,33.55,38.0,1757.0,464.0,821.0,426.0,4.1304,433300.0,<1H OCEAN\n-117.79,33.56,36.0,2057.0,329.0,658.0,309.0,7.866,500001.0,<1H OCEAN\n-117.81,33.56,24.0,6258.0,1003.0,1730.0,752.0,10.9601,500001.0,<1H OCEAN\n-117.8,33.55,35.0,2067.0,428.0,724.0,377.0,5.8371,500001.0,<1H OCEAN\n-117.77,33.6,33.0,247.0,80.0,167.0,70.0,3.7059,237500.0,<1H OCEAN\n-117.8,33.53,41.0,2017.0,489.0,783.0,403.0,4.1591,500001.0,<1H OCEAN\n-117.78,33.54,29.0,1421.0,462.0,520.0,339.0,2.2969,450000.0,<1H OCEAN\n-117.79,33.55,39.0,5066.0,1292.0,1915.0,1117.0,3.821,452100.0,<1H OCEAN\n-117.75,33.54,21.0,8711.0,1544.0,3173.0,1396.0,5.0907,378200.0,<1H OCEAN\n-117.77,33.51,29.0,3590.0,772.0,1070.0,603.0,4.4464,500001.0,<1H OCEAN\n-117.77,33.55,28.0,2024.0,297.0,617.0,274.0,6.7861,499100.0,<1H OCEAN\n-117.73,33.57,5.0,11976.0,2495.0,4327.0,2009.0,4.8488,194400.0,<1H OCEAN\n-117.86,33.67,16.0,20.0,5.0,15.0,5.0,3.875,450000.0,<1H OCEAN\n-117.82,33.67,15.0,1010.0,274.0,649.0,261.0,2.5197,350000.0,<1H OCEAN\n-117.83,33.67,17.0,2634.0,641.0,1454.0,560.0,3.7976,275000.0,<1H OCEAN\n-117.83,33.66,15.0,2355.0,438.0,747.0,450.0,6.5356,272800.0,<1H OCEAN\n-117.83,33.66,16.0,1574.0,385.0,515.0,363.0,5.3423,291700.0,<1H OCEAN\n-117.81,33.67,24.0,3930.0,661.0,1831.0,616.0,6.3767,269000.0,<1H OCEAN\n-117.8,33.66,16.0,2542.0,498.0,1022.0,494.0,4.0,223400.0,<1H OCEAN\n-117.81,33.66,16.0,1414.0,191.0,635.0,230.0,10.0757,383900.0,<1H OCEAN\n-117.81,33.66,20.0,2851.0,490.0,1192.0,463.0,5.8752,274200.0,<1H OCEAN\n-117.82,33.66,24.0,4227.0,641.0,1605.0,589.0,6.4238,278400.0,<1H OCEAN\n-117.84,33.64,11.0,6840.0,1689.0,6083.0,1629.0,2.4132,198300.0,<1H OCEAN\n-117.87,33.61,25.0,2267.0,359.0,866.0,348.0,7.79,500001.0,<1H OCEAN\n-117.86,33.61,15.0,3191.0,482.0,930.0,447.0,8.6001,500001.0,<1H OCEAN\n-117.88,33.55,27.0,2278.0,316.0,772.0,304.0,10.1275,500001.0,<1H OCEAN\n-117.84,33.6,21.0,4281.0,582.0,1443.0,576.0,9.0519,500001.0,<1H OCEAN\n-117.86,33.62,23.0,3166.0,411.0,1092.0,345.0,7.9367,500001.0,<1H OCEAN\n-117.86,33.62,17.0,2975.0,371.0,1247.0,398.0,10.1989,500001.0,<1H OCEAN\n-117.85,33.62,18.0,729.0,105.0,316.0,108.0,10.3893,500001.0,<1H OCEAN\n-117.85,33.61,14.0,4340.0,741.0,1505.0,670.0,7.5674,500001.0,<1H OCEAN\n-117.85,33.62,13.0,5192.0,658.0,1865.0,662.0,15.0001,500001.0,<1H OCEAN\n-117.86,33.63,17.0,3095.0,551.0,1175.0,534.0,5.3099,500001.0,<1H OCEAN\n-117.77,33.54,28.0,3404.0,497.0,1134.0,466.0,7.2217,500001.0,<1H OCEAN\n-117.76,33.54,28.0,2250.0,329.0,826.0,323.0,6.9257,466400.0,<1H OCEAN\n-117.77,33.54,47.0,3090.0,652.0,1105.0,582.0,4.1699,373700.0,<1H OCEAN\n-117.77,33.53,46.0,1033.0,223.0,462.0,224.0,3.2708,384700.0,<1H OCEAN\n-117.8,33.52,50.0,1152.0,341.0,519.0,225.0,3.053,500001.0,<1H OCEAN\n-117.76,33.53,28.0,3085.0,499.0,1176.0,480.0,7.9794,426100.0,<1H OCEAN\n-117.76,33.53,18.0,3224.0,561.0,1310.0,580.0,8.4614,391900.0,<1H OCEAN\n-117.76,33.53,24.0,2105.0,346.0,712.0,332.0,10.6349,500001.0,<1H OCEAN\n-117.77,33.53,32.0,3116.0,661.0,1105.0,543.0,5.1837,445600.0,<1H OCEAN\n-117.78,33.51,44.0,1833.0,331.0,515.0,268.0,6.6178,500001.0,<1H OCEAN\n-117.73,33.63,15.0,2874.0,592.0,1382.0,586.0,5.5137,161800.0,<1H OCEAN\n-117.74,33.62,16.0,4134.0,740.0,2103.0,745.0,5.6877,231400.0,<1H OCEAN\n-117.75,33.64,9.0,2499.0,492.0,1111.0,542.0,5.5342,182300.0,<1H OCEAN\n-117.72,33.62,21.0,2322.0,518.0,662.0,457.0,3.1679,110000.0,<1H OCEAN\n-117.72,33.62,19.0,1144.0,268.0,365.0,279.0,2.8583,105800.0,<1H OCEAN\n-117.71,33.61,25.0,3004.0,718.0,891.0,626.0,2.395,80300.0,<1H OCEAN\n-117.72,33.63,15.0,1362.0,255.0,378.0,202.0,1.9,162500.0,<1H OCEAN\n-117.72,33.62,19.0,5777.0,1261.0,1711.0,1225.0,2.7634,86900.0,<1H OCEAN\n-117.73,33.61,16.0,590.0,130.0,178.0,121.0,4.8611,186800.0,<1H OCEAN\n-117.74,33.62,16.0,1889.0,590.0,686.0,537.0,3.4706,241700.0,<1H OCEAN\n-117.74,33.61,16.0,2753.0,576.0,857.0,546.0,3.7422,229800.0,<1H OCEAN\n-117.74,33.61,17.0,2116.0,474.0,662.0,443.0,3.5625,180800.0,<1H OCEAN\n-117.75,33.61,17.0,2499.0,566.0,781.0,522.0,3.1779,186500.0,<1H OCEAN\n-117.75,33.6,5.0,4944.0,1164.0,1727.0,948.0,4.9,255600.0,<1H OCEAN\n-117.75,33.61,16.0,2270.0,488.0,709.0,489.0,3.2845,227600.0,<1H OCEAN\n-117.73,33.61,17.0,2612.0,582.0,832.0,564.0,2.6759,120600.0,<1H OCEAN\n-117.7,33.61,16.0,2371.0,725.0,1738.0,686.0,3.6484,322600.0,<1H OCEAN\n-117.7,33.6,26.0,2283.0,506.0,634.0,469.0,2.3774,74300.0,<1H OCEAN\n-117.71,33.6,8.0,3329.0,753.0,1312.0,629.0,3.5521,229800.0,<1H OCEAN\n-117.71,33.6,25.0,3011.0,714.0,893.0,654.0,2.3387,74800.0,<1H OCEAN\n-117.72,33.61,26.0,2033.0,463.0,618.0,450.0,2.5685,80400.0,<1H OCEAN\n-117.72,33.61,26.0,2653.0,621.0,774.0,584.0,2.49,81100.0,<1H OCEAN\n-117.71,33.61,26.0,3046.0,726.0,888.0,663.0,2.6848,74100.0,<1H OCEAN\n-117.71,33.61,26.0,2280.0,550.0,669.0,502.0,2.3438,72300.0,<1H OCEAN\n-117.71,33.6,25.0,1949.0,459.0,602.0,428.0,2.7601,72500.0,<1H OCEAN\n-117.7,33.6,26.0,1021.0,230.0,301.0,208.0,2.625,80600.0,<1H OCEAN\n-117.7,33.6,25.0,1321.0,295.0,396.0,278.0,3.1131,77100.0,<1H OCEAN\n-117.7,33.59,11.0,8039.0,1717.0,3445.0,1571.0,4.1678,190900.0,<1H OCEAN\n-117.84,33.66,5.0,665.0,171.0,384.0,171.0,4.5833,230400.0,<1H OCEAN\n-117.84,33.66,5.0,1688.0,430.0,857.0,402.0,3.7857,231600.0,<1H OCEAN\n-117.84,33.65,4.0,1649.0,456.0,1030.0,411.0,2.2262,225000.0,<1H OCEAN\n-117.83,33.66,4.0,1011.0,198.0,511.0,198.0,7.9217,296200.0,<1H OCEAN\n-117.83,33.65,9.0,638.0,266.0,426.0,234.0,3.7875,187500.0,<1H OCEAN\n-117.83,33.65,8.0,2149.0,426.0,950.0,399.0,4.1103,250400.0,<1H OCEAN\n-117.82,33.66,15.0,2460.0,447.0,1049.0,398.0,6.4967,387500.0,<1H OCEAN\n-117.81,33.63,17.0,4477.0,610.0,1798.0,612.0,8.1093,410400.0,<1H OCEAN\n-117.82,33.65,18.0,2105.0,302.0,830.0,286.0,6.3822,362500.0,<1H OCEAN\n-117.8,33.63,8.0,32.0,9.0,26.0,11.0,4.1944,270800.0,<1H OCEAN\n-117.8,33.64,8.0,4447.0,713.0,1680.0,705.0,8.8693,450400.0,<1H OCEAN\n-117.8,33.63,15.0,3236.0,451.0,1289.0,416.0,11.1121,493000.0,<1H OCEAN\n-117.82,33.64,18.0,1974.0,260.0,808.0,278.0,9.8589,500001.0,<1H OCEAN\n-117.81,33.64,16.0,2404.0,349.0,868.0,329.0,11.0138,442100.0,<1H OCEAN\n-117.81,33.64,4.0,1741.0,225.0,811.0,233.0,12.3411,500001.0,<1H OCEAN\n-117.88,33.6,31.0,5488.0,1055.0,1938.0,964.0,8.8742,500001.0,<1H OCEAN\n-117.87,33.6,20.0,3212.0,572.0,1064.0,526.0,6.6155,500001.0,<1H OCEAN\n-117.87,33.6,35.0,1598.0,398.0,782.0,411.0,5.1155,500000.0,<1H OCEAN\n-117.87,33.59,44.0,2499.0,396.0,910.0,374.0,6.6544,500001.0,<1H OCEAN\n-117.87,33.6,33.0,3120.0,602.0,1155.0,553.0,5.2949,500001.0,<1H OCEAN\n-117.86,33.6,30.0,1891.0,364.0,635.0,314.0,6.6265,500001.0,<1H OCEAN\n-117.87,33.6,34.0,3415.0,779.0,1275.0,718.0,4.498,482900.0,<1H OCEAN\n-117.89,33.6,36.0,1496.0,247.0,441.0,203.0,7.8164,500001.0,<1H OCEAN\n-117.9,33.61,41.0,1521.0,328.0,527.0,275.0,4.0764,500001.0,<1H OCEAN\n-117.91,33.61,40.0,2790.0,531.0,952.0,424.0,4.8,500001.0,<1H OCEAN\n-117.91,33.6,37.0,2088.0,510.0,673.0,390.0,5.1048,500001.0,<1H OCEAN\n-117.92,33.57,37.0,3355.0,492.0,921.0,366.0,7.2988,500001.0,NEAR OCEAN\n-117.9,33.6,25.0,2465.0,585.0,906.0,472.0,3.6538,500001.0,<1H OCEAN\n-117.92,33.61,23.0,1808.0,408.0,539.0,300.0,3.5682,500001.0,<1H OCEAN\n-117.91,33.61,38.0,1232.0,178.0,410.0,171.0,11.075,500001.0,<1H OCEAN\n-117.92,33.61,37.0,1244.0,173.0,394.0,154.0,10.3682,500001.0,<1H OCEAN\n-117.92,33.61,36.0,1025.0,150.0,316.0,126.0,10.3048,500001.0,<1H OCEAN\n-117.91,33.61,36.0,3082.0,455.0,771.0,365.0,11.216,500001.0,<1H OCEAN\n-117.88,33.63,21.0,9565.0,2289.0,3162.0,1831.0,4.7024,345400.0,<1H OCEAN\n-117.89,33.62,24.0,1016.0,238.0,465.0,236.0,3.0625,93800.0,<1H OCEAN\n-117.88,33.64,16.0,3615.0,570.0,1209.0,559.0,8.5574,392200.0,<1H OCEAN\n-117.9,33.61,19.0,2897.0,413.0,860.0,367.0,13.1738,500001.0,<1H OCEAN\n-117.89,33.61,16.0,2413.0,559.0,656.0,423.0,6.3017,350000.0,<1H OCEAN\n-117.89,33.61,41.0,1790.0,361.0,540.0,284.0,6.0247,500001.0,<1H OCEAN\n-117.89,33.61,42.0,1301.0,280.0,539.0,249.0,5.0,500001.0,<1H OCEAN\n-117.89,33.61,44.0,2126.0,423.0,745.0,332.0,5.1923,500001.0,<1H OCEAN\n-117.89,33.6,40.0,1639.0,352.0,498.0,278.0,5.6336,500001.0,<1H OCEAN\n-117.89,33.61,45.0,1883.0,419.0,653.0,328.0,4.2222,500001.0,<1H OCEAN\n-117.9,33.61,44.0,1469.0,312.0,507.0,266.0,3.4937,500001.0,<1H OCEAN\n-117.87,33.64,26.0,3521.0,455.0,1336.0,451.0,10.2849,500001.0,<1H OCEAN\n-117.86,33.65,4.0,3618.0,767.0,1326.0,714.0,5.4284,500001.0,<1H OCEAN\n-117.87,33.63,9.0,6163.0,1004.0,1912.0,903.0,10.8289,500001.0,<1H OCEAN\n-117.87,33.62,15.0,2209.0,275.0,735.0,274.0,15.0001,500001.0,<1H OCEAN\n-117.87,33.62,8.0,1266.0,,375.0,183.0,9.802,500001.0,<1H OCEAN\n-117.88,33.65,24.0,4879.0,756.0,1777.0,754.0,5.9055,477300.0,<1H OCEAN\n-117.9,33.63,26.0,4486.0,554.0,1598.0,549.0,10.1454,500001.0,<1H OCEAN\n-117.91,33.63,20.0,3442.0,1526.0,1427.0,977.0,3.1985,106300.0,<1H OCEAN\n-117.9,33.63,32.0,3556.0,521.0,1381.0,537.0,6.1426,450700.0,<1H OCEAN\n-117.9,33.63,26.0,1632.0,376.0,598.0,375.0,3.2125,455000.0,<1H OCEAN\n-117.9,33.63,28.0,2370.0,352.0,832.0,347.0,7.1148,500001.0,<1H OCEAN\n-117.88,33.66,26.0,6017.0,1244.0,2673.0,1135.0,3.5426,295400.0,<1H OCEAN\n-117.89,33.66,33.0,3595.0,785.0,1621.0,732.0,4.1372,265200.0,<1H OCEAN\n-117.9,33.66,13.0,1642.0,423.0,841.0,368.0,3.6042,226000.0,<1H OCEAN\n-117.9,33.65,24.0,4496.0,877.0,1928.0,855.0,4.6808,245500.0,<1H OCEAN\n-117.89,33.66,32.0,2736.0,550.0,1279.0,534.0,5.5422,253100.0,<1H OCEAN\n-117.9,33.65,27.0,3310.0,598.0,1402.0,563.0,6.632,441100.0,<1H OCEAN\n-117.9,33.64,28.0,2466.0,507.0,1081.0,465.0,3.9375,339800.0,<1H OCEAN\n-117.9,33.65,28.0,2043.0,430.0,1108.0,452.0,5.2549,261800.0,<1H OCEAN\n-117.9,33.65,30.0,2196.0,486.0,1131.0,460.0,4.4135,272300.0,<1H OCEAN\n-117.91,33.65,24.0,885.0,321.0,590.0,254.0,2.625,217900.0,<1H OCEAN\n-117.9,33.65,30.0,1634.0,373.0,771.0,364.0,3.4125,284100.0,<1H OCEAN\n-117.91,33.65,17.0,1328.0,377.0,762.0,344.0,2.2222,276800.0,<1H OCEAN\n-117.91,33.64,37.0,1998.0,472.0,1030.0,436.0,3.9306,268400.0,<1H OCEAN\n-117.9,33.64,36.0,2017.0,357.0,850.0,348.0,5.0532,310900.0,<1H OCEAN\n-117.91,33.64,29.0,1652.0,310.0,832.0,326.0,4.8098,325400.0,<1H OCEAN\n-117.91,33.64,40.0,1958.0,333.0,876.0,364.0,3.6406,326100.0,<1H OCEAN\n-117.91,33.63,32.0,1901.0,400.0,946.0,418.0,2.7264,311100.0,<1H OCEAN\n-117.92,33.63,34.0,2479.0,491.0,1131.0,490.0,4.9643,317900.0,<1H OCEAN\n-117.91,33.63,30.0,2071.0,412.0,1081.0,412.0,4.9125,335700.0,<1H OCEAN\n-117.92,33.64,25.0,2224.0,580.0,985.0,516.0,3.1305,268800.0,<1H OCEAN\n-117.91,33.64,38.0,2222.0,542.0,1067.0,512.0,2.8553,307600.0,<1H OCEAN\n-117.92,33.63,24.0,1562.0,441.0,696.0,347.0,3.5161,236400.0,<1H OCEAN\n-117.91,33.63,32.0,1122.0,233.0,557.0,223.0,3.5388,407000.0,<1H OCEAN\n-117.92,33.62,37.0,2038.0,379.0,837.0,381.0,5.2416,471300.0,<1H OCEAN\n-117.92,33.63,39.0,1469.0,226.0,553.0,225.0,7.8496,490800.0,<1H OCEAN\n-117.92,33.62,35.0,1821.0,335.0,727.0,316.0,6.5842,458500.0,<1H OCEAN\n-117.91,33.61,27.0,1797.0,343.0,435.0,203.0,5.9196,500001.0,<1H OCEAN\n-117.91,33.62,32.0,1997.0,427.0,944.0,426.0,4.4063,500001.0,<1H OCEAN\n-117.91,33.62,35.0,2426.0,359.0,937.0,387.0,9.2175,500001.0,<1H OCEAN\n-117.92,33.61,18.0,1538.0,425.0,425.0,288.0,5.3369,312500.0,<1H OCEAN\n-117.93,33.62,33.0,1890.0,416.0,859.0,329.0,4.5658,500001.0,<1H OCEAN\n-117.93,33.62,34.0,2125.0,498.0,1052.0,468.0,5.6315,484600.0,<1H OCEAN\n-117.94,33.62,28.0,1765.0,390.0,832.0,349.0,6.5928,439100.0,<1H OCEAN\n-117.95,33.63,29.0,1496.0,282.0,463.0,215.0,6.0516,500001.0,<1H OCEAN\n-117.96,33.6,34.0,959.0,230.0,384.0,197.0,5.2333,471400.0,NEAR OCEAN\n-117.93,33.61,27.0,1806.0,465.0,791.0,358.0,3.8125,366700.0,<1H OCEAN\n-117.93,33.62,37.0,2204.0,428.0,807.0,410.0,7.0516,500001.0,<1H OCEAN\n-117.94,33.62,25.0,1188.0,264.0,569.0,249.0,3.6607,500001.0,<1H OCEAN\n-117.94,33.65,20.0,5476.0,1073.0,2327.0,963.0,5.6637,222100.0,<1H OCEAN\n-117.93,33.65,35.0,2133.0,413.0,1473.0,402.0,4.4211,215200.0,<1H OCEAN\n-117.93,33.64,31.0,1291.0,356.0,1252.0,373.0,2.7143,185400.0,<1H OCEAN\n-117.94,33.64,18.0,1867.0,426.0,871.0,399.0,2.6221,272000.0,<1H OCEAN\n-117.93,33.64,24.0,1395.0,396.0,1478.0,404.0,2.5301,192900.0,<1H OCEAN\n-117.92,33.64,24.0,2539.0,695.0,1623.0,611.0,3.0708,188700.0,<1H OCEAN\n-117.94,33.64,24.0,1097.0,307.0,470.0,333.0,1.6389,225000.0,<1H OCEAN\n-117.93,33.64,15.0,1707.0,514.0,1335.0,434.0,2.7543,177800.0,<1H OCEAN\n-117.95,33.63,27.0,2489.0,481.0,1082.0,443.0,5.8777,358800.0,<1H OCEAN\n-117.95,33.63,27.0,891.0,183.0,513.0,171.0,6.0,381500.0,<1H OCEAN\n-117.93,33.63,10.0,2766.0,732.0,1332.0,646.0,4.6161,226300.0,<1H OCEAN\n-117.95,33.63,17.0,6745.0,1547.0,2688.0,1535.0,3.9917,271600.0,<1H OCEAN\n-117.91,33.65,19.0,1589.0,421.0,1118.0,394.0,4.1029,213400.0,<1H OCEAN\n-117.92,33.64,5.0,949.0,287.0,497.0,244.0,2.75,225000.0,<1H OCEAN\n-117.92,33.65,15.0,1309.0,477.0,1330.0,424.0,3.4417,182500.0,<1H OCEAN\n-117.93,33.65,29.0,1253.0,375.0,1198.0,362.0,3.5179,225000.0,<1H OCEAN\n-117.91,33.65,24.0,1494.0,494.0,814.0,459.0,2.1074,181300.0,<1H OCEAN\n-117.92,33.65,28.0,1087.0,423.0,807.0,425.0,0.9702,225400.0,<1H OCEAN\n-117.92,33.65,25.0,1679.0,470.0,1314.0,473.0,4.1026,211500.0,<1H OCEAN\n-117.92,33.65,20.0,1391.0,393.0,856.0,360.0,3.184,220000.0,<1H OCEAN\n-117.93,33.65,27.0,1283.0,406.0,1063.0,376.0,2.75,275000.0,<1H OCEAN\n-117.92,33.68,28.0,3397.0,597.0,1397.0,560.0,4.8125,244600.0,<1H OCEAN\n-117.93,33.67,27.0,3512.0,472.0,1391.0,481.0,8.1001,336500.0,<1H OCEAN\n-117.94,33.67,26.0,2552.0,314.0,925.0,323.0,8.1839,367000.0,<1H OCEAN\n-117.94,33.66,16.0,2095.0,450.0,963.0,411.0,5.5,224100.0,<1H OCEAN\n-117.94,33.65,15.0,2016.0,443.0,1015.0,419.0,5.2732,209700.0,<1H OCEAN\n-117.93,33.65,34.0,2141.0,425.0,1559.0,429.0,4.2036,220100.0,<1H OCEAN\n-117.93,33.69,19.0,2602.0,439.0,1156.0,424.0,5.01,263800.0,<1H OCEAN\n-117.93,33.69,26.0,2822.0,473.0,1258.0,469.0,6.4441,261000.0,<1H OCEAN\n-117.92,33.68,25.0,2017.0,454.0,1024.0,428.0,4.4732,245600.0,<1H OCEAN\n-117.93,33.68,33.0,2664.0,432.0,1197.0,429.0,5.069,264200.0,<1H OCEAN\n-117.94,33.68,26.0,4183.0,539.0,1504.0,520.0,7.4056,374200.0,<1H OCEAN\n-117.92,33.67,14.0,6224.0,1679.0,3148.0,1589.0,4.2071,430900.0,<1H OCEAN\n-117.93,33.66,18.0,2043.0,250.0,702.0,246.0,9.6062,414700.0,<1H OCEAN\n-117.93,33.65,26.0,5831.0,1546.0,4738.0,1477.0,3.1483,213000.0,<1H OCEAN\n-117.91,33.69,30.0,2704.0,426.0,1289.0,423.0,5.2815,229500.0,<1H OCEAN\n-117.91,33.67,16.0,7961.0,2276.0,5014.0,2116.0,3.512,218400.0,<1H OCEAN\n-117.9,33.68,25.0,7060.0,1159.0,3903.0,1139.0,4.8359,249200.0,<1H OCEAN\n-117.9,33.67,26.0,2507.0,393.0,1333.0,392.0,6.1601,266100.0,<1H OCEAN\n-117.89,33.68,26.0,2905.0,504.0,1452.0,491.0,5.0853,260300.0,<1H OCEAN\n-117.9,33.67,25.0,639.0,98.0,311.0,93.0,6.6833,275900.0,<1H OCEAN\n-117.91,33.67,32.0,3058.0,562.0,1475.0,569.0,4.4625,253500.0,<1H OCEAN\n-117.91,33.66,26.0,5761.0,1326.0,2681.0,1116.0,4.0341,243300.0,<1H OCEAN\n-117.91,33.65,14.0,2598.0,759.0,1584.0,703.0,4.0417,180900.0,<1H OCEAN\n-117.91,33.66,21.0,1708.0,505.0,1099.0,434.0,3.225,193800.0,<1H OCEAN\n-117.9,33.66,22.0,3568.0,938.0,1952.0,938.0,3.1667,161000.0,<1H OCEAN\n-117.9,33.66,4.0,456.0,91.0,623.0,84.0,6.6369,192600.0,<1H OCEAN\n-117.9,33.69,13.0,9947.0,1675.0,4071.0,1582.0,5.422,316600.0,<1H OCEAN\n-117.87,33.69,4.0,2337.0,768.0,983.0,655.0,3.7174,275000.0,<1H OCEAN\n-117.89,33.68,8.0,5278.0,1575.0,2389.0,1371.0,3.3409,181300.0,<1H OCEAN\n-117.88,33.69,20.0,5330.0,976.0,2734.0,1000.0,5.2138,233100.0,<1H OCEAN\n-117.86,33.71,21.0,1795.0,406.0,2246.0,400.0,3.152,152800.0,<1H OCEAN\n-117.86,33.71,36.0,191.0,42.0,208.0,37.0,3.375,157500.0,<1H OCEAN\n-117.87,33.71,16.0,3397.0,686.0,1924.0,621.0,4.9148,155500.0,<1H OCEAN\n-117.87,33.71,13.0,1087.0,340.0,817.0,342.0,3.5326,262500.0,<1H OCEAN\n-117.87,33.7,17.0,3216.0,607.0,1916.0,618.0,4.9153,266400.0,<1H OCEAN\n-117.87,33.7,21.0,3648.0,654.0,2266.0,628.0,5.0956,246000.0,<1H OCEAN\n-117.88,33.71,27.0,1596.0,297.0,1703.0,289.0,4.1,184900.0,<1H OCEAN\n-117.88,33.71,30.0,1739.0,359.0,1914.0,369.0,3.5551,185200.0,<1H OCEAN\n-117.88,33.71,20.0,1738.0,509.0,1403.0,411.0,3.1742,245000.0,<1H OCEAN\n-117.88,33.7,18.0,2135.0,373.0,1464.0,405.0,5.4836,225800.0,<1H OCEAN\n-117.88,33.7,17.0,5122.0,1544.0,2966.0,1339.0,3.4835,116700.0,<1H OCEAN\n-117.88,33.7,24.0,534.0,88.0,249.0,74.0,5.3254,240500.0,<1H OCEAN\n-117.88,33.7,16.0,1505.0,358.0,835.0,339.0,3.8029,205400.0,<1H OCEAN\n-117.9,33.72,36.0,443.0,117.0,577.0,115.0,3.6875,137500.0,<1H OCEAN\n-117.91,33.72,32.0,2436.0,504.0,2839.0,516.0,4.5607,182100.0,<1H OCEAN\n-117.9,33.72,33.0,2613.0,562.0,3150.0,543.0,4.3899,180700.0,<1H OCEAN\n-117.9,33.73,31.0,1171.0,306.0,1690.0,301.0,3.2639,155200.0,<1H OCEAN\n-117.89,33.72,25.0,4343.0,847.0,3872.0,850.0,4.65,197800.0,<1H OCEAN\n-117.89,33.72,23.0,2305.0,538.0,2493.0,502.0,3.6618,183500.0,<1H OCEAN\n-117.88,33.73,32.0,1947.0,355.0,1786.0,332.0,4.5726,177500.0,<1H OCEAN\n-117.88,33.72,38.0,1421.0,300.0,1236.0,263.0,3.9844,165300.0,<1H OCEAN\n-117.88,33.72,36.0,1910.0,352.0,1593.0,329.0,3.89,170000.0,<1H OCEAN\n-117.9,33.71,16.0,4208.0,630.0,2592.0,662.0,6.1966,260500.0,<1H OCEAN\n-117.89,33.71,23.0,1422.0,260.0,1092.0,263.0,4.7422,202400.0,<1H OCEAN\n-117.89,33.71,24.0,4365.0,804.0,2663.0,753.0,4.5814,233300.0,<1H OCEAN\n-117.9,33.71,16.0,1917.0,317.0,1324.0,351.0,6.2488,252000.0,<1H OCEAN\n-117.89,33.71,16.0,1591.0,225.0,926.0,239.0,6.2452,266300.0,<1H OCEAN\n-117.9,33.71,15.0,539.0,71.0,287.0,66.0,6.3427,305200.0,<1H OCEAN\n-117.92,33.7,15.0,3201.0,,1510.0,622.0,4.2708,161700.0,<1H OCEAN\n-117.91,33.71,16.0,3113.0,783.0,1719.0,715.0,3.6505,145700.0,<1H OCEAN\n-117.89,33.7,13.0,1857.0,572.0,838.0,525.0,3.2386,129200.0,<1H OCEAN\n-117.9,33.7,12.0,4695.0,1110.0,2153.0,989.0,4.6483,190800.0,<1H OCEAN\n-117.9,33.7,15.0,2289.0,686.0,982.0,634.0,4.5757,162500.0,<1H OCEAN\n-117.86,33.73,31.0,1115.0,268.0,1369.0,259.0,3.5694,150500.0,<1H OCEAN\n-117.86,33.72,31.0,1194.0,297.0,1602.0,306.0,2.3333,157700.0,<1H OCEAN\n-117.87,33.72,39.0,3167.0,669.0,2789.0,619.0,3.5902,165900.0,<1H OCEAN\n-117.87,33.72,37.0,2216.0,497.0,2445.0,506.0,3.8421,174000.0,<1H OCEAN\n-117.86,33.72,37.0,1429.0,428.0,2089.0,399.0,3.413,150600.0,<1H OCEAN\n-117.86,33.72,32.0,1461.0,340.0,1909.0,346.0,3.5511,159100.0,<1H OCEAN\n-117.85,33.73,28.0,1499.0,574.0,3328.0,595.0,2.4539,115000.0,<1H OCEAN\n-117.84,33.73,20.0,2572.0,732.0,1534.0,669.0,2.4211,175000.0,<1H OCEAN\n-117.84,33.74,22.0,6072.0,1802.0,4715.0,1666.0,3.1353,121400.0,<1H OCEAN\n-117.84,33.74,24.0,1752.0,407.0,910.0,427.0,3.3611,134600.0,<1H OCEAN\n-117.84,33.74,25.0,1818.0,577.0,1426.0,532.0,3.2104,112500.0,<1H OCEAN\n-117.83,33.74,23.0,1818.0,522.0,958.0,485.0,2.6771,131500.0,<1H OCEAN\n-117.86,33.75,5.0,187.0,49.0,207.0,51.0,1.8,154200.0,<1H OCEAN\n-117.86,33.75,31.0,1761.0,515.0,1810.0,468.0,1.9309,173400.0,<1H OCEAN\n-117.86,33.75,6.0,1565.0,599.0,3157.0,629.0,2.9271,123200.0,<1H OCEAN\n-117.86,33.76,15.0,851.0,297.0,1326.0,254.0,2.8289,117500.0,<1H OCEAN\n-117.85,33.74,19.0,1248.0,357.0,1214.0,328.0,2.7059,159800.0,<1H OCEAN\n-117.85,33.75,27.0,2311.0,632.0,2936.0,609.0,2.5651,171400.0,<1H OCEAN\n-117.86,33.74,9.0,525.0,171.0,1257.0,165.0,3.375,165300.0,<1H OCEAN\n-117.86,33.74,32.0,691.0,151.0,926.0,148.0,4.125,175900.0,<1H OCEAN\n-117.85,33.74,26.0,2589.0,1003.0,5756.0,983.0,2.1992,170800.0,<1H OCEAN\n-117.86,33.73,23.0,407.0,108.0,647.0,96.0,3.775,177400.0,<1H OCEAN\n-117.86,33.73,30.0,2651.0,572.0,3249.0,552.0,3.7202,182100.0,<1H OCEAN\n-117.86,33.73,26.0,1702.0,456.0,2776.0,463.0,2.6385,180200.0,<1H OCEAN\n-117.87,33.74,31.0,2338.0,652.0,3289.0,631.0,2.6734,158500.0,<1H OCEAN\n-117.87,33.73,45.0,2264.0,,1970.0,499.0,3.4193,177000.0,<1H OCEAN\n-117.87,33.74,52.0,2411.0,526.0,2165.0,521.0,3.415,172500.0,<1H OCEAN\n-117.86,33.74,38.0,2415.0,642.0,3242.0,599.0,3.425,165600.0,<1H OCEAN\n-117.86,33.73,38.0,2284.0,511.0,2451.0,504.0,3.3125,159100.0,<1H OCEAN\n-117.86,33.74,34.0,2254.0,630.0,2984.0,625.0,2.5,162500.0,<1H OCEAN\n-117.89,33.73,33.0,1308.0,375.0,2175.0,347.0,3.0824,177400.0,<1H OCEAN\n-117.9,33.73,32.0,2930.0,833.0,5116.0,854.0,3.7147,164100.0,<1H OCEAN\n-117.9,33.73,30.0,746.0,172.0,1048.0,163.0,4.1,166400.0,<1H OCEAN\n-117.88,33.73,33.0,2291.0,594.0,3232.0,589.0,3.2037,163500.0,<1H OCEAN\n-117.88,33.73,36.0,2471.0,498.0,2594.0,475.0,3.75,170500.0,<1H OCEAN\n-117.89,33.75,31.0,1205.0,280.0,1476.0,301.0,4.0231,139200.0,<1H OCEAN\n-117.89,33.74,32.0,1562.0,365.0,2145.0,347.0,2.9167,158400.0,<1H OCEAN\n-117.89,33.74,34.0,1759.0,353.0,2083.0,330.0,3.2292,160600.0,<1H OCEAN\n-117.9,33.75,28.0,1346.0,291.0,1575.0,278.0,3.425,159500.0,<1H OCEAN\n-117.9,33.74,19.0,1566.0,379.0,1032.0,330.0,2.2105,180400.0,<1H OCEAN\n-117.9,33.74,18.0,1884.0,442.0,1915.0,442.0,2.3783,166700.0,<1H OCEAN\n-117.91,33.74,25.0,4273.0,965.0,2946.0,922.0,2.9926,183200.0,<1H OCEAN\n-117.91,33.73,26.0,2413.0,512.0,2867.0,509.0,4.7639,179900.0,<1H OCEAN\n-117.9,33.73,26.0,1324.0,314.0,1804.0,311.0,3.9659,178500.0,<1H OCEAN\n-117.9,33.74,24.0,2932.0,955.0,5516.0,911.0,2.7535,111000.0,<1H OCEAN\n-117.9,33.74,25.0,808.0,163.0,1066.0,189.0,4.7679,173100.0,<1H OCEAN\n-117.9,33.74,24.0,1435.0,494.0,3171.0,504.0,3.0833,151700.0,<1H OCEAN\n-117.89,33.74,32.0,660.0,145.0,959.0,113.0,3.75,159000.0,<1H OCEAN\n-117.89,33.74,33.0,619.0,139.0,1217.0,146.0,4.6875,154400.0,<1H OCEAN\n-117.89,33.73,32.0,728.0,134.0,837.0,135.0,4.0769,163900.0,<1H OCEAN\n-117.88,33.75,10.0,1823.0,590.0,2176.0,548.0,1.5026,151800.0,<1H OCEAN\n-117.88,33.74,29.0,720.0,174.0,1045.0,181.0,3.1964,151900.0,<1H OCEAN\n-117.88,33.74,16.0,1444.0,446.0,2329.0,441.0,3.1691,159400.0,<1H OCEAN\n-117.88,33.74,31.0,1120.0,296.0,1718.0,268.0,2.8077,140300.0,<1H OCEAN\n-117.87,33.74,16.0,1243.0,365.0,1925.0,376.0,2.7632,158900.0,<1H OCEAN\n-117.88,33.74,19.0,2261.0,642.0,3545.0,635.0,2.5224,148500.0,<1H OCEAN\n-117.88,33.74,25.0,1799.0,557.0,3416.0,538.0,3.0083,163500.0,<1H OCEAN\n-117.87,33.76,6.0,2992.0,1194.0,3800.0,1130.0,2.246,183300.0,<1H OCEAN\n-117.87,33.75,14.0,5526.0,1916.0,6799.0,1796.0,2.6561,144400.0,<1H OCEAN\n-117.87,33.75,18.0,697.0,255.0,812.0,221.0,2.6635,162500.0,<1H OCEAN\n-117.86,33.75,39.0,275.0,87.0,554.0,103.0,3.5972,158000.0,<1H OCEAN\n-117.86,33.75,13.0,1632.0,598.0,3356.0,659.0,1.5054,137500.0,<1H OCEAN\n-117.87,33.75,12.0,2782.0,1077.0,1968.0,795.0,0.971,102500.0,<1H OCEAN\n-117.87,33.75,26.0,411.0,114.0,448.0,95.0,1.7019,350000.0,<1H OCEAN\n-117.88,33.76,37.0,2988.0,677.0,2354.0,666.0,3.4345,235500.0,<1H OCEAN\n-117.88,33.76,17.0,1768.0,474.0,1079.0,436.0,1.7823,205300.0,<1H OCEAN\n-117.88,33.75,50.0,1344.0,228.0,747.0,234.0,4.5125,195400.0,<1H OCEAN\n-117.88,33.75,34.0,3004.0,673.0,5477.0,640.0,2.8342,187200.0,<1H OCEAN\n-117.9,33.76,26.0,2678.0,702.0,3262.0,685.0,3.6953,176800.0,<1H OCEAN\n-117.9,33.75,32.0,1893.0,431.0,2245.0,426.0,3.7143,163000.0,<1H OCEAN\n-117.89,33.76,36.0,2656.0,572.0,2370.0,571.0,3.8056,177200.0,<1H OCEAN\n-117.89,33.75,34.0,2753.0,654.0,3117.0,631.0,3.1713,170100.0,<1H OCEAN\n-117.88,33.78,26.0,1813.0,421.0,1235.0,343.0,3.5972,187500.0,<1H OCEAN\n-117.88,33.77,31.0,2549.0,355.0,1044.0,362.0,6.9737,288800.0,<1H OCEAN\n-117.88,33.78,26.0,3141.0,670.0,1572.0,724.0,3.3472,237400.0,<1H OCEAN\n-117.89,33.77,32.0,2342.0,570.0,1445.0,453.0,4.1951,195000.0,<1H OCEAN\n-117.89,33.77,35.0,1799.0,343.0,1239.0,368.0,3.9219,189600.0,<1H OCEAN\n-117.89,33.76,34.0,1050.0,210.0,723.0,201.0,4.8,192700.0,<1H OCEAN\n-117.87,33.77,52.0,2512.0,356.0,978.0,365.0,8.0784,320300.0,<1H OCEAN\n-117.87,33.76,37.0,4943.0,851.0,2164.0,788.0,4.1071,311300.0,<1H OCEAN\n-117.86,33.77,39.0,4159.0,655.0,1669.0,651.0,4.6111,240300.0,<1H OCEAN\n-117.86,33.76,34.0,3153.0,561.0,1679.0,532.0,4.7083,205300.0,<1H OCEAN\n-117.84,33.76,14.0,1458.0,423.0,615.0,365.0,4.2798,218800.0,<1H OCEAN\n-117.85,33.76,26.0,2312.0,525.0,1273.0,437.0,2.8828,204700.0,<1H OCEAN\n-117.84,33.76,16.0,238.0,51.0,93.0,50.0,5.375,215700.0,<1H OCEAN\n-117.84,33.76,22.0,378.0,78.0,196.0,81.0,3.6806,219400.0,<1H OCEAN\n-117.84,33.75,16.0,4367.0,1161.0,2164.0,1005.0,4.0214,139500.0,<1H OCEAN\n-117.85,33.76,33.0,1866.0,327.0,1053.0,371.0,4.5461,213800.0,<1H OCEAN\n-117.84,33.77,14.0,4412.0,952.0,1656.0,874.0,4.3292,206500.0,<1H OCEAN\n-117.85,33.77,23.0,5928.0,1204.0,3570.0,1150.0,4.0398,233100.0,<1H OCEAN\n-117.84,33.77,26.0,3350.0,581.0,1314.0,550.0,3.5195,249100.0,<1H OCEAN\n-117.84,33.76,26.0,2110.0,409.0,1146.0,407.0,4.3698,229600.0,<1H OCEAN\n-117.83,33.75,22.0,6433.0,1174.0,2703.0,1125.0,4.9957,296400.0,<1H OCEAN\n-117.82,33.75,30.0,2910.0,535.0,1270.0,489.0,4.6161,236500.0,<1H OCEAN\n-117.82,33.75,24.0,893.0,209.0,342.0,197.0,2.8261,146500.0,<1H OCEAN\n-117.82,33.74,25.0,2720.0,680.0,1559.0,631.0,3.0958,137800.0,<1H OCEAN\n-117.83,33.75,34.0,2660.0,601.0,1475.0,567.0,3.4152,210200.0,<1H OCEAN\n-117.81,33.75,23.0,3498.0,636.0,1574.0,642.0,5.021,252200.0,<1H OCEAN\n-117.8,33.74,30.0,3569.0,551.0,1540.0,537.0,5.2998,247200.0,<1H OCEAN\n-117.81,33.73,19.0,5471.0,1345.0,2828.0,1247.0,3.5719,252800.0,<1H OCEAN\n-117.81,33.74,24.0,2696.0,649.0,1908.0,626.0,3.3047,216900.0,<1H OCEAN\n-117.81,33.73,19.0,4022.0,975.0,2334.0,954.0,3.0305,140600.0,<1H OCEAN\n-117.81,33.73,23.0,3056.0,556.0,1508.0,555.0,4.7273,234200.0,<1H OCEAN\n-117.82,33.73,23.0,2542.0,772.0,1720.0,675.0,3.8703,137000.0,<1H OCEAN\n-117.82,33.73,24.0,845.0,190.0,482.0,190.0,4.7039,225000.0,<1H OCEAN\n-117.82,33.73,27.0,1270.0,258.0,809.0,264.0,5.0162,223000.0,<1H OCEAN\n-117.83,33.74,23.0,6114.0,1623.0,4088.0,1521.0,3.0382,183600.0,<1H OCEAN\n-117.83,33.73,20.0,5768.0,1597.0,4853.0,1465.0,3.5387,160400.0,<1H OCEAN\n-117.8,33.76,27.0,2655.0,345.0,1017.0,335.0,6.9014,366800.0,<1H OCEAN\n-117.79,33.76,25.0,2037.0,252.0,796.0,249.0,11.0546,487200.0,<1H OCEAN\n-117.79,33.75,26.0,2893.0,345.0,983.0,326.0,13.466,500001.0,<1H OCEAN\n-117.79,33.75,26.0,2955.0,377.0,1074.0,373.0,9.3845,500001.0,<1H OCEAN\n-117.8,33.75,29.0,3058.0,488.0,1197.0,474.0,5.3903,286600.0,<1H OCEAN\n-117.8,33.74,33.0,2890.0,453.0,1300.0,452.0,6.5616,290200.0,<1H OCEAN\n-117.81,33.75,25.0,2365.0,471.0,1197.0,458.0,3.7031,227800.0,<1H OCEAN\n-117.79,33.77,23.0,3596.0,451.0,1292.0,458.0,8.5403,451300.0,<1H OCEAN\n-117.79,33.77,21.0,4349.0,553.0,1680.0,519.0,6.9014,439000.0,<1H OCEAN\n-117.78,33.76,25.0,2260.0,261.0,719.0,254.0,11.4537,500001.0,<1H OCEAN\n-117.78,33.78,6.0,9792.0,1283.0,3744.0,1179.0,10.1714,481500.0,<1H OCEAN\n-117.76,33.79,4.0,8974.0,1268.0,3754.0,1241.0,8.2653,374000.0,<1H OCEAN\n-117.77,33.76,19.0,3532.0,402.0,1200.0,426.0,11.0124,500001.0,<1H OCEAN\n-117.82,33.77,32.0,2308.0,301.0,967.0,320.0,7.0565,324600.0,<1H OCEAN\n-117.83,33.77,22.0,2956.0,642.0,1342.0,558.0,4.1151,203200.0,<1H OCEAN\n-117.83,33.77,26.0,4931.0,853.0,2249.0,818.0,4.275,285400.0,<1H OCEAN\n-117.82,33.76,27.0,3230.0,449.0,1193.0,448.0,6.5308,287800.0,<1H OCEAN\n-117.82,33.77,27.0,2578.0,314.0,976.0,340.0,7.1882,359200.0,<1H OCEAN\n-117.82,33.76,33.0,2774.0,428.0,1229.0,407.0,6.2944,265600.0,<1H OCEAN\n-117.81,33.76,32.0,2053.0,339.0,835.0,323.0,5.5654,281800.0,<1H OCEAN\n-117.8,33.77,29.0,5436.0,707.0,2046.0,685.0,8.7496,349500.0,<1H OCEAN\n-117.81,33.77,31.0,4624.0,624.0,1852.0,635.0,7.2392,334600.0,<1H OCEAN\n-117.81,33.83,8.0,7326.0,884.0,2569.0,798.0,10.157,477100.0,<1H OCEAN\n-117.83,33.83,13.0,3759.0,489.0,1496.0,499.0,8.3818,377600.0,<1H OCEAN\n-117.83,33.83,23.0,2775.0,547.0,1226.0,510.0,3.6707,231400.0,<1H OCEAN\n-117.82,33.8,15.0,3207.0,647.0,1414.0,595.0,4.0484,165600.0,<1H OCEAN\n-117.83,33.79,29.0,1454.0,236.0,724.0,262.0,4.8542,218100.0,<1H OCEAN\n-117.82,33.79,26.0,2641.0,633.0,3657.0,617.0,4.1339,222300.0,<1H OCEAN\n-117.83,33.8,30.0,4713.0,758.0,2271.0,730.0,5.8622,221000.0,<1H OCEAN\n-117.83,33.8,31.0,2016.0,409.0,1095.0,405.0,3.8681,196000.0,<1H OCEAN\n-117.84,33.79,37.0,2733.0,460.0,1378.0,476.0,5.3041,235700.0,<1H OCEAN\n-117.83,33.79,25.0,2070.0,513.0,1078.0,460.0,2.9312,220100.0,<1H OCEAN\n-117.84,33.79,34.0,2590.0,603.0,1658.0,608.0,2.378,199600.0,<1H OCEAN\n-117.84,33.78,26.0,2577.0,434.0,1086.0,432.0,4.6125,229200.0,<1H OCEAN\n-117.84,33.78,24.0,3817.0,787.0,1656.0,713.0,4.25,248000.0,<1H OCEAN\n-117.81,33.79,23.0,3114.0,610.0,2045.0,577.0,3.75,211900.0,<1H OCEAN\n-117.82,33.78,25.0,4977.0,645.0,2061.0,646.0,6.58,318500.0,<1H OCEAN\n-117.81,33.78,27.0,3589.0,507.0,1484.0,495.0,5.7934,270500.0,<1H OCEAN\n-117.82,33.78,28.0,4485.0,667.0,2048.0,685.0,5.4562,274700.0,<1H OCEAN\n-117.81,33.82,20.0,2819.0,319.0,1019.0,319.0,12.2092,500001.0,<1H OCEAN\n-117.8,33.83,17.0,2971.0,350.0,1180.0,346.0,11.1228,500001.0,<1H OCEAN\n-117.82,33.82,22.0,3173.0,372.0,1181.0,355.0,8.3637,500001.0,<1H OCEAN\n-117.81,33.81,19.0,3154.0,390.0,1404.0,384.0,8.9257,431800.0,<1H OCEAN\n-117.81,33.82,22.0,2898.0,335.0,1057.0,324.0,10.8111,500001.0,<1H OCEAN\n-117.82,33.81,19.0,2556.0,304.0,822.0,260.0,9.9055,456900.0,<1H OCEAN\n-117.83,33.82,26.0,3259.0,456.0,1354.0,459.0,5.7817,267600.0,<1H OCEAN\n-117.83,33.82,23.0,1100.0,285.0,940.0,267.0,3.6953,150000.0,<1H OCEAN\n-117.83,33.81,24.0,3550.0,895.0,2828.0,834.0,2.8403,225600.0,<1H OCEAN\n-117.82,33.81,25.0,2662.0,402.0,1247.0,401.0,5.4395,244000.0,<1H OCEAN\n-117.83,33.81,28.0,1972.0,315.0,970.0,326.0,5.4298,234200.0,<1H OCEAN\n-117.82,33.81,30.0,2260.0,345.0,1182.0,341.0,6.0705,236700.0,<1H OCEAN\n-117.85,33.79,46.0,1846.0,383.0,867.0,336.0,3.4234,200000.0,<1H OCEAN\n-117.85,33.79,52.0,2102.0,403.0,898.0,365.0,3.6827,236800.0,<1H OCEAN\n-117.86,33.79,31.0,3523.0,922.0,2660.0,949.0,3.1792,146400.0,<1H OCEAN\n-117.85,33.79,52.0,1963.0,430.0,1197.0,415.0,3.8929,211000.0,<1H OCEAN\n-117.85,33.78,23.0,3187.0,870.0,1977.0,852.0,3.3939,212100.0,<1H OCEAN\n-117.86,33.78,25.0,2635.0,660.0,1710.0,634.0,3.125,215000.0,<1H OCEAN\n-117.85,33.77,16.0,2186.0,511.0,908.0,466.0,4.575,225000.0,<1H OCEAN\n-117.85,33.79,40.0,1251.0,336.0,729.0,343.0,2.4688,236400.0,<1H OCEAN\n-117.86,33.79,34.0,1883.0,408.0,1227.0,424.0,3.8929,187500.0,<1H OCEAN\n-117.86,33.78,21.0,2713.0,731.0,1952.0,722.0,2.6959,178800.0,<1H OCEAN\n-117.87,33.78,21.0,2487.0,573.0,1515.0,494.0,4.3039,168500.0,<1H OCEAN\n-117.87,33.78,19.0,2813.0,567.0,1334.0,596.0,4.7208,173500.0,<1H OCEAN\n-117.87,33.78,30.0,2022.0,522.0,1196.0,463.0,3.7454,186000.0,<1H OCEAN\n-117.86,33.8,35.0,1683.0,347.0,1242.0,335.0,3.5172,190400.0,<1H OCEAN\n-117.86,33.79,42.0,1024.0,191.0,483.0,187.0,4.105,194500.0,<1H OCEAN\n-117.87,33.79,25.0,2546.0,545.0,1543.0,521.0,4.192,219900.0,<1H OCEAN\n-117.88,33.79,32.0,1484.0,295.0,928.0,295.0,5.1418,190300.0,<1H OCEAN\n-117.89,33.78,16.0,6352.0,1747.0,5085.0,1649.0,2.8835,193800.0,<1H OCEAN\n-117.9,33.78,25.0,10336.0,2596.0,7111.0,2419.0,3.3627,197900.0,<1H OCEAN\n-117.85,33.84,26.0,2095.0,280.0,793.0,261.0,6.6719,271700.0,<1H OCEAN\n-117.86,33.84,19.0,1725.0,392.0,920.0,400.0,3.0087,159400.0,<1H OCEAN\n-117.85,33.84,17.0,2830.0,502.0,1370.0,459.0,5.1785,247300.0,<1H OCEAN\n-117.85,33.83,26.0,1904.0,292.0,945.0,303.0,5.6784,232400.0,<1H OCEAN\n-117.86,33.83,23.0,2377.0,403.0,1101.0,408.0,5.3439,227100.0,<1H OCEAN\n-117.84,33.84,23.0,4388.0,864.0,2526.0,846.0,4.5217,219400.0,<1H OCEAN\n-117.86,33.8,34.0,1793.0,480.0,1722.0,441.0,2.8235,153100.0,<1H OCEAN\n-117.85,33.81,26.0,4186.0,767.0,2447.0,777.0,4.9917,248100.0,<1H OCEAN\n-117.85,33.81,32.0,1766.0,322.0,876.0,330.0,4.0417,234500.0,<1H OCEAN\n-117.85,33.8,34.0,1593.0,283.0,872.0,255.0,3.825,216700.0,<1H OCEAN\n-117.85,33.8,40.0,1461.0,286.0,1322.0,264.0,4.3269,194100.0,<1H OCEAN\n-117.84,33.81,26.0,5574.0,1025.0,2607.0,988.0,4.0324,244900.0,<1H OCEAN\n-117.84,33.8,35.0,1490.0,251.0,629.0,257.0,4.3661,222100.0,<1H OCEAN\n-117.84,33.8,34.0,2004.0,331.0,843.0,328.0,3.59,230600.0,<1H OCEAN\n-117.86,33.82,9.0,1682.0,291.0,1015.0,271.0,6.6603,230900.0,<1H OCEAN\n-117.84,33.82,24.0,10281.0,1689.0,4926.0,1629.0,4.7946,251200.0,<1H OCEAN\n-117.84,33.84,23.0,6157.0,1129.0,2817.0,1073.0,5.0629,232600.0,<1H OCEAN\n-117.89,33.84,35.0,3315.0,744.0,2425.0,687.0,3.5521,182800.0,<1H OCEAN\n-117.89,33.83,35.0,2984.0,446.0,1435.0,455.0,5.6276,200800.0,<1H OCEAN\n-117.9,33.83,33.0,3065.0,611.0,2204.0,606.0,3.8456,211800.0,<1H OCEAN\n-117.89,33.82,21.0,1591.0,298.0,904.0,297.0,4.8906,179100.0,<1H OCEAN\n-117.89,33.8,38.0,51.0,12.0,41.0,10.0,6.0224,187500.0,<1H OCEAN\n-117.89,33.82,24.0,1268.0,210.0,700.0,224.0,5.0605,216200.0,<1H OCEAN\n-117.89,33.82,18.0,3197.0,809.0,1894.0,726.0,3.6761,140500.0,<1H OCEAN\n-117.87,33.81,15.0,3082.0,536.0,1268.0,531.0,3.7604,280100.0,<1H OCEAN\n-117.87,33.84,10.0,3381.0,729.0,1584.0,636.0,5.3812,235400.0,<1H OCEAN\n-117.88,33.84,26.0,1499.0,290.0,755.0,277.0,3.5893,238500.0,<1H OCEAN\n-117.88,33.84,31.0,3301.0,712.0,1532.0,682.0,3.7303,223800.0,<1H OCEAN\n-117.87,33.83,27.0,2287.0,,1140.0,351.0,5.6163,231000.0,<1H OCEAN\n-117.88,33.83,22.0,3522.0,543.0,1706.0,524.0,6.4685,241200.0,<1H OCEAN\n-117.88,33.83,25.0,1785.0,248.0,750.0,251.0,6.8407,266700.0,<1H OCEAN\n-117.88,33.82,17.0,2247.0,705.0,1382.0,618.0,3.8631,225000.0,<1H OCEAN\n-117.88,33.82,26.0,1783.0,298.0,1048.0,306.0,6.0488,232000.0,<1H OCEAN\n-117.87,33.82,26.0,2435.0,346.0,1088.0,350.0,5.9397,249400.0,<1H OCEAN\n-117.88,33.85,26.0,3924.0,781.0,2332.0,725.0,3.7772,223900.0,<1H OCEAN\n-117.88,33.85,34.0,1127.0,185.0,588.0,181.0,4.375,224700.0,<1H OCEAN\n-117.88,33.84,34.0,1410.0,214.0,837.0,240.0,6.1168,213900.0,<1H OCEAN\n-117.88,33.84,33.0,1526.0,237.0,906.0,245.0,5.1782,225000.0,<1H OCEAN\n-117.9,33.85,31.0,3413.0,764.0,2326.0,728.0,4.325,187100.0,<1H OCEAN\n-117.9,33.85,35.0,1756.0,328.0,1026.0,332.0,3.6,193500.0,<1H OCEAN\n-117.89,33.85,18.0,2036.0,414.0,1292.0,380.0,3.875,273000.0,<1H OCEAN\n-117.9,33.85,32.0,1605.0,314.0,986.0,306.0,3.3375,186200.0,<1H OCEAN\n-117.89,33.85,13.0,1583.0,474.0,1672.0,432.0,3.2303,201300.0,<1H OCEAN\n-117.9,33.84,31.0,2043.0,468.0,1524.0,454.0,3.5329,187400.0,<1H OCEAN\n-117.89,33.84,33.0,1587.0,374.0,1159.0,331.0,2.8021,195100.0,<1H OCEAN\n-117.88,33.85,25.0,1234.0,351.0,507.0,285.0,2.3173,225000.0,<1H OCEAN\n-117.88,33.84,25.0,1781.0,349.0,918.0,378.0,3.9286,262700.0,<1H OCEAN\n-117.87,33.84,23.0,1678.0,369.0,912.0,347.0,4.5,237300.0,<1H OCEAN\n-117.87,33.84,25.0,1928.0,414.0,961.0,385.0,4.0724,231400.0,<1H OCEAN\n-117.87,33.84,16.0,1545.0,354.0,730.0,350.0,4.5112,139000.0,<1H OCEAN\n-117.87,33.84,17.0,2395.0,410.0,1224.0,399.0,5.1182,249200.0,<1H OCEAN\n-117.85,33.85,17.0,4678.0,1065.0,2427.0,1020.0,4.2276,254100.0,<1H OCEAN\n-117.86,33.85,17.0,1131.0,236.0,622.0,244.0,4.9306,158500.0,<1H OCEAN\n-117.92,33.85,38.0,2082.0,532.0,1592.0,510.0,2.3704,166100.0,<1H OCEAN\n-117.92,33.84,45.0,2019.0,394.0,1549.0,377.0,4.6111,223000.0,<1H OCEAN\n-117.92,33.85,44.0,1231.0,258.0,682.0,244.0,3.2344,170100.0,<1H OCEAN\n-117.91,33.85,35.0,932.0,258.0,1147.0,267.0,2.7014,156700.0,<1H OCEAN\n-117.91,33.84,35.0,1244.0,324.0,1603.0,322.0,2.9583,175400.0,<1H OCEAN\n-117.91,33.85,22.0,1178.0,289.0,865.0,294.0,3.025,180000.0,<1H OCEAN\n-117.91,33.84,29.0,1570.0,482.0,1849.0,430.0,2.6563,162500.0,<1H OCEAN\n-117.93,33.85,27.0,1962.0,544.0,1492.0,481.0,1.9621,118100.0,<1H OCEAN\n-117.94,33.85,26.0,1888.0,429.0,1550.0,458.0,3.3393,168600.0,<1H OCEAN\n-117.93,33.85,33.0,2489.0,546.0,1857.0,444.0,2.9474,178400.0,<1H OCEAN\n-117.93,33.85,31.0,2149.0,465.0,966.0,302.0,3.875,183900.0,<1H OCEAN\n-117.93,33.85,25.0,1026.0,288.0,1646.0,283.0,4.2019,163900.0,<1H OCEAN\n-117.93,33.85,36.0,2147.0,416.0,1011.0,392.0,3.2188,196900.0,<1H OCEAN\n-117.94,33.85,37.0,588.0,121.0,436.0,104.0,4.275,186200.0,<1H OCEAN\n-117.93,33.84,23.0,2870.0,653.0,1680.0,598.0,3.2301,189900.0,<1H OCEAN\n-117.95,33.85,13.0,6963.0,1426.0,3892.0,1375.0,4.1325,203500.0,<1H OCEAN\n-117.96,33.85,35.0,1175.0,191.0,568.0,186.0,4.125,189200.0,<1H OCEAN\n-117.97,33.85,30.0,2513.0,476.0,1611.0,472.0,4.0061,182900.0,<1H OCEAN\n-117.96,33.85,36.0,1951.0,365.0,1254.0,358.0,4.8438,185700.0,<1H OCEAN\n-117.95,33.84,32.0,1378.0,492.0,1202.0,448.0,3.4028,183700.0,<1H OCEAN\n-117.95,33.84,19.0,1749.0,406.0,969.0,391.0,3.75,173400.0,<1H OCEAN\n-117.95,33.84,34.0,1229.0,215.0,1035.0,218.0,3.5455,180000.0,<1H OCEAN\n-117.94,33.84,25.0,4016.0,831.0,2166.0,774.0,3.1884,135400.0,<1H OCEAN\n-117.94,33.84,28.0,604.0,207.0,615.0,212.0,3.6214,182100.0,<1H OCEAN\n-117.98,33.85,23.0,2089.0,377.0,1085.0,362.0,4.765,181500.0,<1H OCEAN\n-117.98,33.84,33.0,2291.0,439.0,1187.0,405.0,3.9539,191100.0,<1H OCEAN\n-117.98,33.84,35.0,984.0,179.0,661.0,199.0,5.0747,189600.0,<1H OCEAN\n-117.97,33.85,45.0,818.0,147.0,546.0,152.0,5.1057,170700.0,<1H OCEAN\n-117.96,33.84,31.0,2265.0,537.0,1617.0,507.0,3.4583,186300.0,<1H OCEAN\n-117.97,33.84,34.0,874.0,153.0,549.0,153.0,4.8667,186800.0,<1H OCEAN\n-117.97,33.84,35.0,793.0,128.0,589.0,137.0,5.25,190200.0,<1H OCEAN\n-117.97,33.84,25.0,2471.0,518.0,1539.0,500.0,4.2679,191700.0,<1H OCEAN\n-117.97,33.84,18.0,1063.0,209.0,462.0,223.0,2.8348,219000.0,<1H OCEAN\n-117.99,33.84,31.0,2982.0,547.0,1895.0,570.0,4.9115,255500.0,<1H OCEAN\n-117.97,33.83,16.0,2035.0,564.0,1118.0,503.0,3.2546,187500.0,<1H OCEAN\n-117.98,33.84,31.0,1252.0,225.0,714.0,226.0,4.6042,220700.0,<1H OCEAN\n-117.98,33.83,17.0,3506.0,992.0,2104.0,893.0,3.3006,185800.0,<1H OCEAN\n-118.0,33.82,21.0,2253.0,580.0,1536.0,500.0,3.2326,204700.0,<1H OCEAN\n-118.01,33.82,31.0,1960.0,380.0,1356.0,356.0,4.0625,225900.0,<1H OCEAN\n-118.01,33.83,24.0,4639.0,1374.0,3093.0,1257.0,2.5577,202300.0,<1H OCEAN\n-118.01,33.83,23.0,1086.0,268.0,825.0,250.0,2.4609,219600.0,<1H OCEAN\n-118.0,33.83,26.0,1718.0,385.0,1022.0,368.0,3.9333,196100.0,<1H OCEAN\n-118.0,33.83,24.0,2578.0,580.0,1217.0,529.0,2.2401,212500.0,<1H OCEAN\n-118.0,33.82,18.0,2947.0,559.0,1820.0,551.0,4.5294,224800.0,<1H OCEAN\n-117.99,33.83,25.0,3434.0,835.0,1749.0,657.0,3.2539,199000.0,<1H OCEAN\n-117.99,33.82,19.0,1991.0,528.0,1202.0,460.0,3.1538,252100.0,<1H OCEAN\n-117.99,33.83,35.0,1484.0,252.0,916.0,248.0,5.2657,191400.0,<1H OCEAN\n-117.99,33.82,33.0,2342.0,475.0,1367.0,509.0,4.1167,215500.0,<1H OCEAN\n-117.98,33.83,17.0,3419.0,932.0,2460.0,766.0,3.2823,228500.0,<1H OCEAN\n-117.98,33.82,34.0,1038.0,175.0,578.0,174.0,4.9219,200000.0,<1H OCEAN\n-117.98,33.82,34.0,1290.0,220.0,867.0,241.0,5.5486,218100.0,<1H OCEAN\n-117.98,33.83,32.0,1133.0,166.0,523.0,187.0,6.213,230800.0,<1H OCEAN\n-117.97,33.83,22.0,3310.0,688.0,1807.0,674.0,4.0185,200900.0,<1H OCEAN\n-117.97,33.82,26.0,2335.0,504.0,1121.0,502.0,2.9891,205200.0,<1H OCEAN\n-117.97,33.82,26.0,4013.0,985.0,2442.0,922.0,3.7655,197700.0,<1H OCEAN\n-117.96,33.83,30.0,2838.0,649.0,1758.0,593.0,3.3831,197400.0,<1H OCEAN\n-117.96,33.83,18.0,2067.0,770.0,870.0,541.0,3.1315,137500.0,<1H OCEAN\n-117.96,33.83,29.0,1194.0,176.0,474.0,170.0,6.1001,298900.0,<1H OCEAN\n-117.95,33.84,18.0,3418.0,815.0,1961.0,773.0,3.65,171400.0,<1H OCEAN\n-117.94,33.83,20.0,812.0,192.0,494.0,172.0,3.25,350000.0,<1H OCEAN\n-117.95,33.83,31.0,2421.0,389.0,1348.0,413.0,4.9394,217800.0,<1H OCEAN\n-117.94,33.82,34.0,1347.0,212.0,676.0,201.0,3.8828,215400.0,<1H OCEAN\n-117.94,33.82,27.0,1366.0,326.0,878.0,325.0,3.4,196900.0,<1H OCEAN\n-117.95,33.82,29.0,2929.0,640.0,1618.0,584.0,3.6875,213200.0,<1H OCEAN\n-117.95,33.83,35.0,1107.0,207.0,641.0,210.0,5.0599,216700.0,<1H OCEAN\n-117.96,33.83,34.0,982.0,148.0,498.0,156.0,6.3214,220800.0,<1H OCEAN\n-117.95,33.83,36.0,1380.0,237.0,690.0,234.0,3.8214,210900.0,<1H OCEAN\n-117.93,33.83,32.0,1792.0,411.0,1131.0,381.0,2.4942,186300.0,<1H OCEAN\n-117.92,33.82,36.0,2360.0,405.0,1479.0,386.0,4.3583,187200.0,<1H OCEAN\n-117.94,33.82,29.0,1422.0,409.0,1057.0,390.0,2.3347,208100.0,<1H OCEAN\n-117.94,33.82,24.0,4735.0,955.0,2600.0,868.0,5.0764,228600.0,<1H OCEAN\n-117.93,33.82,28.0,2444.0,555.0,1848.0,567.0,3.0179,198800.0,<1H OCEAN\n-117.92,33.84,38.0,1316.0,263.0,671.0,278.0,3.2969,220000.0,<1H OCEAN\n-117.92,33.83,6.0,3136.0,990.0,1894.0,859.0,2.5564,171300.0,<1H OCEAN\n-117.93,33.83,30.0,1561.0,381.0,1104.0,391.0,3.375,201900.0,<1H OCEAN\n-117.93,33.84,34.0,2160.0,298.0,852.0,305.0,6.0531,287100.0,<1H OCEAN\n-117.93,33.84,26.0,2811.0,612.0,1374.0,566.0,3.475,282500.0,<1H OCEAN\n-117.91,33.84,26.0,1156.0,393.0,1880.0,400.0,2.2716,350000.0,<1H OCEAN\n-117.9,33.83,23.0,2459.0,689.0,2720.0,598.0,2.8072,164700.0,<1H OCEAN\n-117.91,33.83,9.0,1160.0,368.0,735.0,325.0,1.119,175000.0,<1H OCEAN\n-117.92,33.83,17.0,382.0,86.0,272.0,81.0,1.425,212500.0,<1H OCEAN\n-117.91,33.84,16.0,919.0,253.0,912.0,249.0,1.5903,165400.0,<1H OCEAN\n-117.91,33.84,25.0,1021.0,252.0,975.0,258.0,3.125,168100.0,<1H OCEAN\n-117.91,33.83,47.0,504.0,113.0,375.0,109.0,3.6607,160600.0,<1H OCEAN\n-117.91,33.83,37.0,1039.0,260.0,719.0,243.0,3.0288,161400.0,<1H OCEAN\n-117.92,33.83,36.0,1072.0,193.0,639.0,196.0,5.0275,179300.0,<1H OCEAN\n-117.92,33.83,52.0,1514.0,301.0,855.0,293.0,3.6042,166400.0,<1H OCEAN\n-117.91,33.83,32.0,1855.0,527.0,2568.0,504.0,2.5509,170800.0,<1H OCEAN\n-117.9,33.82,32.0,1187.0,302.0,1003.0,275.0,2.4931,166900.0,<1H OCEAN\n-117.92,33.82,10.0,1548.0,506.0,1535.0,424.0,4.5057,152400.0,<1H OCEAN\n-117.91,33.82,32.0,2696.0,640.0,2330.0,626.0,2.9479,184600.0,<1H OCEAN\n-117.91,33.82,29.0,1444.0,326.0,1038.0,271.0,2.3843,182900.0,<1H OCEAN\n-117.91,33.81,18.0,1181.0,353.0,781.0,340.0,2.5625,153100.0,<1H OCEAN\n-117.91,33.82,32.0,1408.0,307.0,1331.0,284.0,3.7014,179600.0,<1H OCEAN\n-117.93,33.81,18.0,3291.0,587.0,1640.0,563.0,4.8981,166300.0,<1H OCEAN\n-117.92,33.81,34.0,988.0,173.0,759.0,184.0,5.6047,205100.0,<1H OCEAN\n-117.93,33.8,34.0,3903.0,717.0,2054.0,716.0,4.2731,218000.0,<1H OCEAN\n-117.92,33.8,17.0,1317.0,256.0,679.0,272.0,4.6696,159500.0,<1H OCEAN\n-117.9,33.8,21.0,1342.0,326.0,748.0,335.0,2.9231,45000.0,<1H OCEAN\n-117.9,33.8,22.0,2964.0,829.0,2639.0,771.0,2.4833,157500.0,<1H OCEAN\n-117.9,33.8,23.0,1368.0,397.0,1940.0,358.0,3.0789,350000.0,<1H OCEAN\n-117.9,33.8,27.0,2176.0,442.0,1440.0,418.0,4.375,212500.0,<1H OCEAN\n-117.94,33.81,33.0,1891.0,334.0,932.0,343.0,4.2759,238000.0,<1H OCEAN\n-117.94,33.81,25.0,1731.0,482.0,1127.0,455.0,3.256,214300.0,<1H OCEAN\n-117.94,33.81,34.0,1290.0,203.0,664.0,204.0,5.8461,227400.0,<1H OCEAN\n-117.94,33.81,26.0,1589.0,259.0,735.0,315.0,4.5714,243200.0,<1H OCEAN\n-117.94,33.8,28.0,2914.0,489.0,1500.0,499.0,4.9429,254800.0,<1H OCEAN\n-117.94,33.81,24.0,4602.0,1131.0,3003.0,1014.0,3.6771,172200.0,<1H OCEAN\n-117.93,33.8,29.0,1672.0,267.0,891.0,281.0,4.8611,231900.0,<1H OCEAN\n-117.95,33.82,35.0,1068.0,190.0,514.0,174.0,4.0735,208700.0,<1H OCEAN\n-117.95,33.82,35.0,1117.0,181.0,496.0,168.0,4.3269,224700.0,<1H OCEAN\n-117.96,33.82,32.0,2856.0,622.0,1499.0,601.0,3.63,183400.0,<1H OCEAN\n-117.96,33.82,32.0,2726.0,556.0,1513.0,531.0,3.7917,197400.0,<1H OCEAN\n-117.96,33.8,30.0,729.0,131.0,488.0,139.0,4.7667,195200.0,<1H OCEAN\n-117.96,33.81,35.0,1153.0,192.0,884.0,208.0,5.2384,177400.0,<1H OCEAN\n-117.96,33.81,34.0,1416.0,277.0,980.0,284.0,4.7772,182500.0,<1H OCEAN\n-117.96,33.81,35.0,1996.0,326.0,1409.0,330.0,4.7738,180000.0,<1H OCEAN\n-117.96,33.82,29.0,2176.0,468.0,1632.0,428.0,3.707,180400.0,<1H OCEAN\n-117.96,33.82,19.0,1199.0,251.0,730.0,276.0,3.6422,209400.0,<1H OCEAN\n-117.95,33.81,33.0,1724.0,291.0,943.0,285.0,5.118,195200.0,<1H OCEAN\n-117.96,33.81,34.0,1941.0,356.0,1021.0,339.0,4.4663,183900.0,<1H OCEAN\n-117.95,33.81,24.0,2749.0,498.0,1367.0,460.0,4.025,240700.0,<1H OCEAN\n-118.0,33.81,33.0,2970.0,547.0,1869.0,539.0,4.3636,201800.0,<1H OCEAN\n-118.0,33.81,22.0,2642.0,640.0,1702.0,588.0,3.5268,174700.0,<1H OCEAN\n-118.01,33.81,25.0,1831.0,345.0,809.0,339.0,4.5179,177100.0,<1H OCEAN\n-118.0,33.82,24.0,3002.0,644.0,1495.0,634.0,3.1087,202800.0,<1H OCEAN\n-118.0,33.81,13.0,2782.0,605.0,1749.0,628.0,4.1276,153800.0,<1H OCEAN\n-118.0,33.81,17.0,2142.0,436.0,946.0,412.0,3.7059,146300.0,<1H OCEAN\n-117.99,33.8,18.0,383.0,94.0,487.0,98.0,3.975,162500.0,<1H OCEAN\n-118.0,33.81,17.0,1530.0,404.0,883.0,344.0,2.8835,196500.0,<1H OCEAN\n-117.99,33.82,21.0,2281.0,557.0,1510.0,460.0,2.8625,189600.0,<1H OCEAN\n-117.99,33.81,23.0,3284.0,795.0,3257.0,758.0,2.4526,182900.0,<1H OCEAN\n-117.99,33.81,42.0,161.0,40.0,157.0,50.0,2.2,153100.0,<1H OCEAN\n-117.99,33.81,46.0,38.0,8.0,66.0,14.0,4.1667,162500.0,<1H OCEAN\n-117.98,33.81,28.0,3528.0,816.0,2304.0,764.0,2.582,181800.0,<1H OCEAN\n-117.98,33.81,18.0,3751.0,878.0,2281.0,815.0,3.7201,183100.0,<1H OCEAN\n-117.98,33.81,35.0,897.0,156.0,479.0,161.0,5.152,215600.0,<1H OCEAN\n-117.97,33.81,26.0,4022.0,1081.0,2457.0,1001.0,2.8042,206300.0,<1H OCEAN\n-117.97,33.81,30.0,2406.0,462.0,1753.0,456.0,4.485,180600.0,<1H OCEAN\n-117.99,33.8,25.0,3179.0,639.0,2526.0,623.0,3.3281,180800.0,<1H OCEAN\n-117.99,33.79,33.0,2064.0,324.0,1384.0,315.0,4.5263,169000.0,<1H OCEAN\n-117.99,33.79,29.0,2470.0,560.0,1589.0,513.0,3.1801,190500.0,<1H OCEAN\n-117.99,33.79,35.0,2301.0,467.0,2272.0,454.0,3.9566,167800.0,<1H OCEAN\n-117.98,33.8,35.0,2114.0,341.0,1077.0,343.0,5.4876,227500.0,<1H OCEAN\n-117.98,33.79,35.0,2356.0,478.0,1659.0,480.0,4.1115,179700.0,<1H OCEAN\n-117.98,33.8,32.0,2161.0,432.0,1503.0,402.0,4.3036,191400.0,<1H OCEAN\n-117.97,33.8,35.0,2985.0,474.0,1614.0,453.0,5.4631,225600.0,<1H OCEAN\n-117.97,33.79,33.0,3268.0,641.0,1704.0,591.0,3.6849,211400.0,<1H OCEAN\n-118.0,33.79,18.0,3679.0,694.0,1820.0,652.0,3.6531,143500.0,<1H OCEAN\n-117.99,33.78,15.0,4273.0,993.0,2300.0,946.0,3.5313,213000.0,<1H OCEAN\n-117.99,33.79,21.0,2695.0,707.0,1888.0,683.0,3.2857,213300.0,<1H OCEAN\n-117.98,33.78,31.0,2825.0,546.0,1908.0,563.0,3.9798,187500.0,<1H OCEAN\n-117.97,33.79,34.0,2456.0,410.0,1289.0,442.0,4.1818,224200.0,<1H OCEAN\n-117.99,33.78,19.0,7399.0,1698.0,3554.0,1593.0,3.1049,173900.0,<1H OCEAN\n-117.97,33.78,35.0,3148.0,597.0,2110.0,587.0,3.9479,203800.0,<1H OCEAN\n-117.98,33.78,22.0,4255.0,971.0,2901.0,920.0,3.2636,180200.0,<1H OCEAN\n-117.96,33.8,33.0,1984.0,420.0,1119.0,387.0,3.4821,231300.0,<1H OCEAN\n-117.96,33.79,36.0,2398.0,403.0,1261.0,402.0,5.2816,221800.0,<1H OCEAN\n-117.96,33.8,35.0,1493.0,267.0,811.0,272.0,5.244,218000.0,<1H OCEAN\n-117.96,33.8,33.0,2362.0,394.0,1185.0,387.0,4.425,188400.0,<1H OCEAN\n-117.95,33.79,34.0,2584.0,408.0,1233.0,405.0,5.6935,218300.0,<1H OCEAN\n-117.95,33.78,26.0,4115.0,883.0,2184.0,825.0,3.9536,191000.0,<1H OCEAN\n-117.96,33.79,29.0,1813.0,501.0,1170.0,482.0,2.0677,214500.0,<1H OCEAN\n-117.96,33.78,35.0,1330.0,201.0,658.0,217.0,6.37,229200.0,<1H OCEAN\n-117.94,33.8,23.0,2757.0,734.0,1811.0,707.0,2.8,214300.0,<1H OCEAN\n-117.95,33.79,34.0,2912.0,520.0,1625.0,501.0,4.4667,190600.0,<1H OCEAN\n-117.95,33.8,34.0,1654.0,285.0,905.0,292.0,4.6389,214600.0,<1H OCEAN\n-117.95,33.8,32.0,1219.0,192.0,634.0,197.0,5.237,215700.0,<1H OCEAN\n-117.94,33.79,24.0,4179.0,784.0,1902.0,733.0,4.7986,236500.0,<1H OCEAN\n-117.93,33.78,36.0,2169.0,359.0,1018.0,370.0,4.3906,231300.0,<1H OCEAN\n-117.94,33.78,34.0,2627.0,468.0,1409.0,450.0,4.7731,199200.0,<1H OCEAN\n-117.93,33.79,36.0,2363.0,403.0,1240.0,391.0,4.0909,190800.0,<1H OCEAN\n-117.93,33.79,34.0,3592.0,616.0,2138.0,605.0,5.2129,193400.0,<1H OCEAN\n-117.92,33.79,35.0,1785.0,288.0,1033.0,297.0,4.5739,190500.0,<1H OCEAN\n-117.92,33.79,29.0,3692.0,969.0,2683.0,881.0,3.1726,198700.0,<1H OCEAN\n-117.92,33.79,26.0,2737.0,614.0,1877.0,606.0,2.8622,184300.0,<1H OCEAN\n-117.91,33.79,22.0,4417.0,1054.0,2759.0,983.0,4.25,170300.0,<1H OCEAN\n-117.91,33.78,33.0,2729.0,549.0,2223.0,535.0,4.0362,177900.0,<1H OCEAN\n-117.93,33.78,28.0,4380.0,820.0,2187.0,835.0,3.9018,182300.0,<1H OCEAN\n-117.92,33.77,28.0,3614.0,960.0,3282.0,889.0,3.522,190300.0,<1H OCEAN\n-117.91,33.78,26.0,4297.0,1037.0,3596.0,967.0,3.045,184000.0,<1H OCEAN\n-117.92,33.78,35.0,1654.0,323.0,1065.0,354.0,3.4837,186500.0,<1H OCEAN\n-117.95,33.78,9.0,3553.0,1035.0,2017.0,986.0,2.9726,133800.0,<1H OCEAN\n-117.94,33.77,32.0,714.0,142.0,654.0,154.0,4.5052,170800.0,<1H OCEAN\n-117.94,33.77,33.0,2964.0,747.0,2235.0,718.0,3.2591,175900.0,<1H OCEAN\n-117.94,33.78,11.0,2880.0,745.0,1806.0,722.0,3.8056,171100.0,<1H OCEAN\n-117.94,33.78,40.0,299.0,68.0,163.0,70.0,3.0125,166100.0,<1H OCEAN\n-117.93,33.77,36.0,3157.0,582.0,1842.0,561.0,4.5833,190700.0,<1H OCEAN\n-117.96,33.78,26.0,2136.0,557.0,1528.0,537.0,2.4931,236100.0,<1H OCEAN\n-117.95,33.78,32.0,2296.0,560.0,1376.0,532.0,3.7303,188500.0,<1H OCEAN\n-117.96,33.78,33.0,1520.0,,658.0,242.0,4.875,269300.0,<1H OCEAN\n-117.95,33.77,38.0,1476.0,308.0,1114.0,309.0,4.1917,181800.0,<1H OCEAN\n-117.95,33.77,38.0,989.0,246.0,691.0,204.0,3.2632,180900.0,<1H OCEAN\n-117.96,33.77,32.0,4398.0,905.0,2777.0,884.0,4.1321,222800.0,<1H OCEAN\n-117.97,33.77,20.0,1988.0,424.0,1277.0,425.0,2.9414,162200.0,<1H OCEAN\n-117.97,33.77,25.0,1295.0,417.0,856.0,342.0,2.7157,350000.0,<1H OCEAN\n-117.97,33.77,22.0,2244.0,575.0,1543.0,533.0,2.6618,179600.0,<1H OCEAN\n-117.99,33.77,15.0,2081.0,531.0,1617.0,561.0,3.4955,160900.0,<1H OCEAN\n-117.98,33.77,7.0,2252.0,570.0,1576.0,550.0,3.6333,169800.0,<1H OCEAN\n-117.97,33.76,18.0,1862.0,399.0,1301.0,369.0,3.1771,194000.0,<1H OCEAN\n-117.98,33.77,22.0,3236.0,673.0,2034.0,662.0,4.0955,174200.0,<1H OCEAN\n-117.98,33.76,29.0,1518.0,312.0,1086.0,317.0,4.32,196900.0,<1H OCEAN\n-117.97,33.76,28.0,1386.0,272.0,901.0,294.0,4.7464,187500.0,<1H OCEAN\n-117.96,33.76,24.0,1328.0,290.0,1012.0,306.0,4.2813,189500.0,<1H OCEAN\n-117.96,33.76,22.0,2520.0,556.0,2126.0,527.0,3.7734,193900.0,<1H OCEAN\n-117.96,33.75,14.0,2509.0,611.0,1814.0,547.0,2.7986,176100.0,<1H OCEAN\n-117.95,33.76,29.0,1829.0,366.0,1703.0,343.0,4.1295,188000.0,<1H OCEAN\n-117.94,33.76,27.0,2512.0,506.0,1861.0,511.0,4.2386,184200.0,<1H OCEAN\n-117.94,33.76,33.0,1441.0,337.0,1233.0,331.0,3.7232,176200.0,<1H OCEAN\n-117.95,33.76,24.0,3956.0,812.0,3196.0,795.0,4.3512,191400.0,<1H OCEAN\n-117.94,33.75,30.0,5268.0,1093.0,4480.0,1050.0,4.015,186700.0,<1H OCEAN\n-117.96,33.75,25.0,1323.0,208.0,852.0,229.0,4.6167,237300.0,<1H OCEAN\n-117.96,33.75,22.0,2300.0,539.0,1625.0,542.0,2.78,196300.0,<1H OCEAN\n-117.95,33.75,19.0,1983.0,283.0,1098.0,275.0,6.6355,276100.0,<1H OCEAN\n-117.95,33.75,24.0,2027.0,358.0,1405.0,341.0,5.1416,231400.0,<1H OCEAN\n-117.97,33.76,27.0,1712.0,325.0,1036.0,345.0,4.0508,183900.0,<1H OCEAN\n-117.97,33.75,26.0,3361.0,722.0,2709.0,648.0,3.9107,190700.0,<1H OCEAN\n-117.97,33.75,32.0,1564.0,270.0,973.0,290.0,3.75,190400.0,<1H OCEAN\n-117.93,33.76,24.0,3202.0,703.0,3308.0,714.0,4.1577,174100.0,<1H OCEAN\n-117.93,33.75,24.0,1380.0,339.0,1472.0,304.0,4.2219,162800.0,<1H OCEAN\n-117.93,33.75,15.0,2448.0,602.0,1666.0,575.0,3.5967,141600.0,<1H OCEAN\n-117.93,33.76,17.0,3341.0,803.0,3381.0,825.0,3.371,161800.0,<1H OCEAN\n-117.93,33.76,21.0,2884.0,662.0,2613.0,645.0,4.05,177900.0,<1H OCEAN\n-117.92,33.75,8.0,2325.0,598.0,1511.0,565.0,3.3629,137500.0,<1H OCEAN\n-117.92,33.75,19.0,1920.0,471.0,1413.0,432.0,4.0313,147500.0,<1H OCEAN\n-117.89,33.77,29.0,2577.0,445.0,1849.0,470.0,4.4732,194800.0,<1H OCEAN\n-117.9,33.77,35.0,2002.0,378.0,1726.0,387.0,3.9613,182300.0,<1H OCEAN\n-117.91,33.77,26.0,5556.0,1398.0,4545.0,1333.0,3.0902,190400.0,<1H OCEAN\n-117.91,33.76,22.0,7531.0,1569.0,5254.0,1523.0,3.8506,167400.0,<1H OCEAN\n-117.92,33.76,26.0,784.0,177.0,662.0,169.0,2.8438,174300.0,<1H OCEAN\n-117.91,33.76,20.0,4413.0,,4818.0,1063.0,2.8594,215100.0,<1H OCEAN\n-117.92,33.75,23.0,893.0,223.0,1149.0,216.0,2.6442,156300.0,<1H OCEAN\n-117.92,33.75,32.0,790.0,199.0,1196.0,201.0,3.0625,142800.0,<1H OCEAN\n-117.91,33.75,8.0,2346.0,679.0,3842.0,674.0,3.0635,160000.0,<1H OCEAN\n-117.93,33.74,15.0,1206.0,282.0,677.0,270.0,3.9219,142600.0,<1H OCEAN\n-117.93,33.74,30.0,1654.0,434.0,1843.0,467.0,3.1403,153000.0,<1H OCEAN\n-117.93,33.74,5.0,639.0,197.0,666.0,197.0,3.3017,87500.0,<1H OCEAN\n-117.92,33.74,13.0,4620.0,1265.0,3385.0,1109.0,3.1773,186500.0,<1H OCEAN\n-117.92,33.74,18.0,1639.0,491.0,2513.0,458.0,2.1838,159700.0,<1H OCEAN\n-117.91,33.74,15.0,715.0,214.0,1394.0,244.0,3.3846,162500.0,<1H OCEAN\n-117.92,33.74,24.0,5321.0,1063.0,4011.0,1047.0,4.3882,189300.0,<1H OCEAN\n-117.93,33.73,27.0,3662.0,834.0,3009.0,743.0,3.9816,179500.0,<1H OCEAN\n-117.94,33.74,24.0,4248.0,840.0,3118.0,798.0,4.2222,207200.0,<1H OCEAN\n-117.94,33.73,24.0,4197.0,718.0,2468.0,714.0,5.2563,211400.0,<1H OCEAN\n-117.95,33.74,16.0,2768.0,600.0,1182.0,563.0,3.7162,201200.0,<1H OCEAN\n-117.95,33.74,25.0,1393.0,243.0,976.0,245.0,5.4485,225200.0,<1H OCEAN\n-117.95,33.74,21.0,3576.0,554.0,1846.0,538.0,5.9838,271900.0,<1H OCEAN\n-117.98,33.7,17.0,1989.0,411.0,1401.0,453.0,4.1603,160500.0,<1H OCEAN\n-117.98,33.71,24.0,2308.0,464.0,1101.0,407.0,4.4766,230000.0,<1H OCEAN\n-117.99,33.71,19.0,1967.0,487.0,1251.0,404.0,3.6696,218800.0,<1H OCEAN\n-117.98,33.71,26.0,1905.0,373.0,1098.0,368.0,4.8611,229600.0,<1H OCEAN\n-117.96,33.68,26.0,1374.0,234.0,731.0,244.0,6.0905,224800.0,<1H OCEAN\n-117.97,33.68,23.0,1722.0,316.0,865.0,309.0,4.6452,273800.0,<1H OCEAN\n-117.96,33.68,25.0,2004.0,349.0,1085.0,343.0,4.7656,230700.0,<1H OCEAN\n-117.96,33.68,18.0,2594.0,539.0,817.0,485.0,2.3674,219200.0,<1H OCEAN\n-117.96,33.68,24.0,6517.0,1279.0,3441.0,1198.0,4.25,152100.0,<1H OCEAN\n-117.97,33.68,26.0,3653.0,568.0,1930.0,585.0,5.7301,260900.0,<1H OCEAN\n-117.95,33.69,26.0,1417.0,264.0,817.0,261.0,4.875,230400.0,<1H OCEAN\n-117.95,33.68,26.0,2249.0,344.0,1311.0,373.0,5.0287,265000.0,<1H OCEAN\n-117.95,33.68,27.0,1732.0,303.0,1115.0,308.0,5.5312,239200.0,<1H OCEAN\n-117.95,33.68,19.0,1028.0,191.0,340.0,159.0,3.6364,252800.0,<1H OCEAN\n-117.95,33.67,25.0,1611.0,383.0,554.0,327.0,3.0417,137300.0,<1H OCEAN\n-117.95,33.67,25.0,1799.0,233.0,810.0,265.0,8.289,372400.0,<1H OCEAN\n-117.95,33.66,26.0,1787.0,227.0,639.0,224.0,6.8226,329800.0,<1H OCEAN\n-117.95,33.66,22.0,2785.0,441.0,1086.0,392.0,7.3719,337400.0,<1H OCEAN\n-117.98,33.65,22.0,3335.0,754.0,1500.0,719.0,3.7315,197900.0,<1H OCEAN\n-117.98,33.65,18.0,1027.0,206.0,436.0,180.0,4.2159,211300.0,<1H OCEAN\n-117.98,33.65,22.0,3592.0,527.0,1598.0,523.0,6.5501,294900.0,<1H OCEAN\n-117.98,33.64,20.0,1851.0,495.0,792.0,363.0,3.8187,137500.0,NEAR OCEAN\n-117.97,33.74,16.0,1735.0,380.0,784.0,360.0,4.2566,139200.0,<1H OCEAN\n-117.97,33.74,18.0,2814.0,539.0,1439.0,493.0,3.599,262000.0,<1H OCEAN\n-117.97,33.73,27.0,2097.0,325.0,1217.0,331.0,5.7121,222500.0,<1H OCEAN\n-117.97,33.73,26.0,1694.0,260.0,885.0,279.0,5.0875,224200.0,<1H OCEAN\n-117.96,33.74,19.0,1783.0,415.0,1025.0,383.0,4.1484,230000.0,<1H OCEAN\n-117.96,33.73,22.0,3479.0,455.0,1454.0,488.0,6.6324,347600.0,<1H OCEAN\n-117.97,33.73,19.0,4154.0,560.0,2130.0,589.0,7.2845,301800.0,<1H OCEAN\n-117.97,33.72,24.0,2991.0,500.0,1437.0,453.0,5.4286,273400.0,<1H OCEAN\n-117.95,33.72,21.0,3107.0,483.0,1688.0,503.0,5.9582,288000.0,<1H OCEAN\n-117.96,33.72,23.0,3929.0,559.0,1858.0,538.0,6.8645,318200.0,<1H OCEAN\n-117.93,33.73,19.0,4021.0,557.0,1872.0,545.0,6.7919,295600.0,<1H OCEAN\n-117.93,33.72,17.0,4461.0,585.0,2095.0,580.0,7.6709,319500.0,<1H OCEAN\n-117.92,33.73,17.0,1692.0,293.0,934.0,280.0,4.4728,205800.0,<1H OCEAN\n-117.92,33.73,14.0,5147.0,1182.0,3171.0,1126.0,3.9929,225800.0,<1H OCEAN\n-117.92,33.72,17.0,3318.0,502.0,1520.0,498.0,5.5501,274200.0,<1H OCEAN\n-117.96,33.71,19.0,4328.0,849.0,2243.0,808.0,5.5702,342600.0,<1H OCEAN\n-117.95,33.71,16.0,6058.0,1715.0,3285.0,1495.0,3.4133,290900.0,<1H OCEAN\n-117.96,33.71,19.0,1624.0,221.0,782.0,228.0,4.5962,304500.0,<1H OCEAN\n-117.95,33.71,20.0,2781.0,407.0,1242.0,408.0,6.1092,306500.0,<1H OCEAN\n-117.94,33.71,18.0,3695.0,602.0,1779.0,572.0,5.9449,276500.0,<1H OCEAN\n-117.93,33.71,10.0,2775.0,717.0,1581.0,633.0,4.1366,158800.0,<1H OCEAN\n-117.94,33.7,18.0,4827.0,718.0,2471.0,716.0,6.1181,284500.0,<1H OCEAN\n-117.95,33.69,24.0,4269.0,618.0,1954.0,597.0,6.9261,284600.0,<1H OCEAN\n-117.95,33.7,17.0,5781.0,924.0,2585.0,915.0,5.343,231900.0,<1H OCEAN\n-117.98,33.69,22.0,3957.0,520.0,1774.0,527.0,7.0907,350200.0,<1H OCEAN\n-117.97,33.69,21.0,4112.0,580.0,1886.0,581.0,6.799,292000.0,<1H OCEAN\n-117.98,33.7,16.0,5127.0,631.0,2142.0,596.0,7.8195,390500.0,<1H OCEAN\n-117.96,33.7,23.0,4417.0,740.0,1865.0,693.0,5.3428,279300.0,<1H OCEAN\n-117.96,33.7,23.0,2622.0,445.0,1103.0,407.0,4.725,289600.0,<1H OCEAN\n-117.96,33.69,20.0,3123.0,441.0,1319.0,432.0,6.091,290400.0,<1H OCEAN\n-117.96,33.69,17.0,2500.0,343.0,1242.0,368.0,7.7313,316700.0,<1H OCEAN\n-117.98,33.7,24.0,3451.0,504.0,1736.0,493.0,6.3749,278000.0,<1H OCEAN\n-117.98,33.71,24.0,3430.0,548.0,1601.0,512.0,5.6825,264600.0,<1H OCEAN\n-117.97,33.71,26.0,2553.0,405.0,1337.0,411.0,5.3737,252900.0,<1H OCEAN\n-117.97,33.71,25.0,3273.0,478.0,1645.0,497.0,5.8195,286100.0,<1H OCEAN\n-117.99,33.7,25.0,2017.0,357.0,1063.0,369.0,4.0345,229400.0,<1H OCEAN\n-117.98,33.7,17.0,1997.0,340.0,952.0,341.0,4.4148,239200.0,<1H OCEAN\n-117.99,33.69,17.0,3386.0,729.0,1715.0,666.0,3.7479,213000.0,<1H OCEAN\n-117.98,33.69,16.0,2437.0,438.0,986.0,422.0,5.7117,247200.0,<1H OCEAN\n-117.97,33.67,25.0,3906.0,660.0,1809.0,622.0,5.6765,265100.0,<1H OCEAN\n-117.97,33.66,22.0,3914.0,600.0,1871.0,607.0,5.8541,281500.0,<1H OCEAN\n-117.96,33.67,16.0,5143.0,652.0,2209.0,637.0,7.0173,382100.0,<1H OCEAN\n-117.96,33.66,19.0,5925.0,744.0,2302.0,729.0,7.5699,333300.0,<1H OCEAN\n-117.96,33.65,23.0,5379.0,684.0,1826.0,555.0,7.0151,350600.0,<1H OCEAN\n-117.97,33.65,26.0,2379.0,336.0,988.0,346.0,5.3674,339300.0,<1H OCEAN\n-117.97,33.63,25.0,2482.0,360.0,960.0,352.0,6.1572,344000.0,NEAR OCEAN\n-117.96,33.65,24.0,4462.0,689.0,1943.0,712.0,5.7395,289800.0,<1H OCEAN\n-117.96,33.65,21.0,2030.0,318.0,910.0,311.0,7.8453,343300.0,<1H OCEAN\n-117.96,33.65,18.0,3603.0,879.0,1549.0,756.0,4.0229,363100.0,<1H OCEAN\n-117.98,33.61,17.0,2054.0,291.0,836.0,288.0,6.8939,383900.0,NEAR OCEAN\n-117.97,33.73,18.0,3698.0,574.0,2046.0,614.0,6.2984,269800.0,<1H OCEAN\n-117.98,33.73,18.0,3833.0,,2192.0,996.0,3.4679,219700.0,<1H OCEAN\n-117.98,33.72,24.0,2826.0,547.0,1738.0,546.0,6.0494,240400.0,<1H OCEAN\n-117.98,33.72,28.0,3109.0,561.0,1891.0,562.0,5.2655,243100.0,<1H OCEAN\n-117.98,33.67,7.0,5664.0,1174.0,2493.0,1101.0,5.8252,264700.0,<1H OCEAN\n-117.98,33.66,26.0,3527.0,547.0,1615.0,542.0,6.1624,279400.0,<1H OCEAN\n-117.97,33.67,17.0,4466.0,640.0,2166.0,666.0,6.979,330700.0,<1H OCEAN\n-117.97,33.66,14.0,6090.0,1338.0,1974.0,1248.0,2.8061,180300.0,<1H OCEAN\n-117.98,33.68,14.0,3396.0,477.0,1542.0,472.0,7.3982,369100.0,<1H OCEAN\n-117.98,33.68,24.0,4177.0,,1704.0,606.0,6.2473,281900.0,<1H OCEAN\n-117.98,33.68,17.0,2603.0,373.0,1265.0,382.0,6.8039,332900.0,<1H OCEAN\n-117.97,33.68,16.0,4508.0,598.0,2221.0,623.0,7.3731,390800.0,<1H OCEAN\n-117.97,33.68,26.0,1616.0,292.0,700.0,241.0,5.5105,232100.0,<1H OCEAN\n-118.01,33.67,13.0,2902.0,536.0,1125.0,490.0,5.888,447700.0,NEAR OCEAN\n-118.01,33.67,16.0,3581.0,780.0,1644.0,774.0,5.041,397600.0,NEAR OCEAN\n-118.01,33.66,19.0,4559.0,1045.0,1949.0,910.0,4.355,429200.0,NEAR OCEAN\n-118.0,33.66,25.0,4041.0,903.0,1689.0,784.0,4.2289,442700.0,NEAR OCEAN\n-118.02,33.65,38.0,2548.0,646.0,755.0,399.0,2.8352,408300.0,NEAR OCEAN\n-117.99,33.68,13.0,4000.0,883.0,1999.0,881.0,4.7245,273600.0,<1H OCEAN\n-117.99,33.68,18.0,2024.0,462.0,1047.0,451.0,3.5848,186900.0,<1H OCEAN\n-117.99,33.67,15.0,3141.0,664.0,1729.0,633.0,4.2165,234600.0,<1H OCEAN\n-117.99,33.68,14.0,3305.0,841.0,2272.0,769.0,3.4899,216700.0,<1H OCEAN\n-117.99,33.67,19.0,3808.0,790.0,1776.0,756.0,4.625,282200.0,<1H OCEAN\n-117.99,33.67,12.0,2228.0,479.0,1122.0,488.0,4.0385,350000.0,<1H OCEAN\n-117.99,33.67,17.0,1692.0,427.0,903.0,423.0,3.5859,262500.0,<1H OCEAN\n-118.0,33.66,16.0,2809.0,708.0,1260.0,638.0,3.2353,252900.0,NEAR OCEAN\n-117.99,33.66,29.0,1330.0,293.0,613.0,236.0,4.6591,353100.0,<1H OCEAN\n-117.99,33.66,14.0,3155.0,653.0,951.0,575.0,3.0625,268800.0,<1H OCEAN\n-118.02,33.62,11.0,3969.0,834.0,1508.0,754.0,4.3409,271400.0,NEAR OCEAN\n-118.05,33.65,5.0,7017.0,935.0,2427.0,867.0,10.1154,477700.0,NEAR OCEAN\n-118.0,33.68,12.0,5241.0,985.0,2048.0,943.0,6.4858,285400.0,<1H OCEAN\n-118.0,33.67,34.0,3712.0,667.0,1521.0,632.0,4.8125,387800.0,<1H OCEAN\n-118.0,33.71,19.0,4808.0,1029.0,2422.0,971.0,4.0121,279700.0,<1H OCEAN\n-117.99,33.71,18.0,1994.0,578.0,3031.0,577.0,2.7614,237500.0,<1H OCEAN\n-117.99,33.71,17.0,1600.0,458.0,1803.0,432.0,2.7865,216700.0,<1H OCEAN\n-118.02,33.71,23.0,5554.0,995.0,2408.0,936.0,5.3886,331900.0,<1H OCEAN\n-118.02,33.7,23.0,5069.0,770.0,2473.0,769.0,6.3047,285700.0,<1H OCEAN\n-118.02,33.72,22.0,8844.0,1706.0,4404.0,1594.0,4.4453,267800.0,<1H OCEAN\n-118.01,33.73,23.0,4095.0,578.0,1766.0,589.0,6.7418,302500.0,<1H OCEAN\n-118.02,33.73,24.0,6393.0,1141.0,2743.0,1057.0,5.1384,336900.0,<1H OCEAN\n-118.03,33.72,24.0,5203.0,957.0,2465.0,946.0,5.163,261000.0,<1H OCEAN\n-118.04,33.72,24.0,7141.0,1330.0,3418.0,1268.0,4.6649,237800.0,<1H OCEAN\n-117.99,33.73,24.0,2104.0,421.0,1181.0,414.0,3.8365,250900.0,<1H OCEAN\n-118.0,33.73,26.0,2236.0,280.0,809.0,282.0,6.7395,342800.0,<1H OCEAN\n-117.99,33.73,20.0,3182.0,884.0,1770.0,817.0,3.1912,220800.0,<1H OCEAN\n-117.99,33.72,26.0,1787.0,275.0,801.0,270.0,5.5514,255700.0,<1H OCEAN\n-117.99,33.72,17.0,2801.0,649.0,1473.0,535.0,4.2875,134800.0,<1H OCEAN\n-117.99,33.72,14.0,2127.0,537.0,1338.0,475.0,3.628,188500.0,<1H OCEAN\n-118.01,33.71,18.0,6565.0,1357.0,3079.0,1248.0,4.7515,295600.0,<1H OCEAN\n-118.01,33.7,24.0,3856.0,567.0,1741.0,588.0,7.248,302700.0,<1H OCEAN\n-118.01,33.69,3.0,945.0,115.0,337.0,123.0,11.5199,500001.0,<1H OCEAN\n-117.99,33.7,13.0,4013.0,903.0,1999.0,859.0,4.625,248800.0,<1H OCEAN\n-117.99,33.69,16.0,1476.0,294.0,886.0,270.0,5.3259,216400.0,<1H OCEAN\n-117.99,33.69,12.0,2480.0,858.0,1441.0,788.0,1.6705,350000.0,<1H OCEAN\n-118.02,33.71,24.0,2598.0,443.0,1184.0,435.0,5.8623,287800.0,<1H OCEAN\n-118.03,33.7,15.0,3244.0,421.0,1259.0,413.0,7.7854,395300.0,<1H OCEAN\n-118.03,33.71,26.0,1483.0,251.0,738.0,235.0,6.0,271400.0,<1H OCEAN\n-118.07,33.67,13.0,5126.0,711.0,2429.0,718.0,9.5268,437900.0,NEAR OCEAN\n-118.04,33.72,14.0,4494.0,1048.0,2222.0,963.0,4.7821,169400.0,<1H OCEAN\n-118.05,33.72,14.0,2673.0,687.0,1192.0,656.0,4.1862,188900.0,<1H OCEAN\n-118.04,33.72,15.0,1836.0,490.0,942.0,477.0,4.0238,182500.0,<1H OCEAN\n-118.09,33.7,13.0,4770.0,969.0,2261.0,972.0,5.883,295100.0,NEAR OCEAN\n-118.04,33.71,12.0,4014.0,868.0,1605.0,769.0,6.0102,396900.0,<1H OCEAN\n-118.09,33.75,32.0,6239.0,974.0,2615.0,950.0,6.6188,380000.0,NEAR OCEAN\n-118.1,33.74,37.0,997.0,262.0,531.0,282.0,4.7773,400000.0,NEAR OCEAN\n-118.11,33.75,15.0,2569.0,812.0,785.0,477.0,5.4011,346400.0,NEAR OCEAN\n-118.11,33.74,43.0,1222.0,303.0,565.0,309.0,4.8482,500001.0,NEAR OCEAN\n-118.09,33.74,44.0,1671.0,390.0,871.0,367.0,4.6369,422200.0,NEAR OCEAN\n-118.11,33.75,24.0,1608.0,314.0,592.0,314.0,5.0926,390500.0,NEAR OCEAN\n-118.1,33.74,33.0,2119.0,524.0,872.0,465.0,4.537,495500.0,NEAR OCEAN\n-118.1,33.74,31.0,1310.0,342.0,563.0,310.0,4.6528,457100.0,NEAR OCEAN\n-118.11,33.73,32.0,1258.0,333.0,645.0,334.0,5.0476,500001.0,NEAR OCEAN\n-118.1,33.74,32.0,2035.0,,934.0,512.0,4.2287,500001.0,NEAR OCEAN\n-118.07,33.72,24.0,1240.0,296.0,513.0,254.0,4.9044,485000.0,NEAR OCEAN\n-118.07,33.72,32.0,1179.0,250.0,369.0,209.0,5.1824,500001.0,NEAR OCEAN\n-118.09,33.71,19.0,1397.0,271.0,491.0,197.0,8.7397,500001.0,NEAR OCEAN\n-118.06,33.72,17.0,4573.0,937.0,1619.0,796.0,5.7704,500001.0,NEAR OCEAN\n-118.05,33.71,25.0,4150.0,570.0,1424.0,547.0,8.8281,461600.0,NEAR OCEAN\n-118.06,33.72,22.0,4311.0,531.0,1426.0,533.0,9.8177,500001.0,NEAR OCEAN\n-118.07,33.73,13.0,1822.0,313.0,643.0,303.0,9.8346,401700.0,NEAR OCEAN\n-118.06,33.72,14.0,2665.0,331.0,964.0,319.0,15.0001,500001.0,NEAR OCEAN\n-118.06,33.73,16.0,4392.0,602.0,1490.0,578.0,10.5424,500001.0,<1H OCEAN\n-118.08,33.72,14.0,2021.0,396.0,696.0,367.0,7.1673,340700.0,NEAR OCEAN\n-118.05,33.73,25.0,2472.0,450.0,1301.0,467.0,5.0699,266100.0,<1H OCEAN\n-118.05,33.72,17.0,1875.0,472.0,900.0,406.0,5.2589,226100.0,<1H OCEAN\n-118.05,33.72,22.0,5416.0,1271.0,2260.0,1184.0,3.8038,174500.0,<1H OCEAN\n-118.09,33.77,26.0,1388.0,409.0,515.0,392.0,1.8015,62000.0,<1H OCEAN\n-118.08,33.77,26.0,2461.0,562.0,971.0,544.0,2.1944,87500.0,<1H OCEAN\n-118.08,33.77,26.0,2013.0,551.0,664.0,510.0,2.2708,67500.0,<1H OCEAN\n-118.08,33.77,26.0,3083.0,806.0,960.0,723.0,1.9074,68500.0,<1H OCEAN\n-118.08,33.76,25.0,1995.0,637.0,743.0,597.0,1.4617,46900.0,<1H OCEAN\n-118.09,33.76,26.0,1625.0,440.0,533.0,414.0,1.808,58500.0,<1H OCEAN\n-118.09,33.77,26.0,5359.0,1508.0,1829.0,1393.0,1.7675,61300.0,<1H OCEAN\n-118.08,33.76,26.0,1967.0,577.0,692.0,538.0,1.6111,54300.0,<1H OCEAN\n-118.08,33.76,26.0,996.0,364.0,366.0,313.0,1.2813,46700.0,<1H OCEAN\n-118.08,33.76,27.0,529.0,159.0,193.0,155.0,2.0952,71300.0,<1H OCEAN\n-118.09,33.77,27.0,2301.0,640.0,847.0,627.0,1.7208,67500.0,<1H OCEAN\n-118.0,33.76,26.0,1876.0,455.0,1499.0,436.0,2.925,176000.0,<1H OCEAN\n-118.01,33.75,30.0,3380.0,722.0,2269.0,652.0,4.525,186000.0,<1H OCEAN\n-118.0,33.75,26.0,1382.0,387.0,1977.0,368.0,2.7589,137500.0,<1H OCEAN\n-118.0,33.74,25.0,2767.0,346.0,1148.0,372.0,6.394,316700.0,<1H OCEAN\n-118.02,33.76,27.0,2905.0,587.0,1781.0,561.0,4.25,214800.0,<1H OCEAN\n-118.02,33.75,26.0,2989.0,479.0,1596.0,475.0,5.7157,231200.0,<1H OCEAN\n-118.03,33.76,25.0,4650.0,849.0,2503.0,790.0,5.742,221900.0,<1H OCEAN\n-118.04,33.76,16.0,2070.0,263.0,878.0,297.0,7.0879,338800.0,<1H OCEAN\n-118.04,33.75,16.0,3757.0,650.0,1291.0,614.0,5.2001,235600.0,<1H OCEAN\n-118.04,33.74,26.0,2532.0,421.0,1274.0,441.0,5.3559,235800.0,<1H OCEAN\n-118.02,33.74,26.0,3842.0,609.0,1961.0,595.0,6.128,248200.0,<1H OCEAN\n-118.02,33.73,26.0,3711.0,610.0,1902.0,597.0,5.5599,234100.0,<1H OCEAN\n-118.01,33.74,25.0,8110.0,1264.0,3613.0,1232.0,6.0609,264900.0,<1H OCEAN\n-117.98,33.75,24.0,3865.0,802.0,2670.0,772.0,3.8158,180000.0,<1H OCEAN\n-117.98,33.75,37.0,1264.0,274.0,783.0,273.0,3.3438,199600.0,<1H OCEAN\n-117.99,33.75,30.0,1859.0,462.0,1314.0,418.0,3.0909,184400.0,<1H OCEAN\n-117.98,33.74,29.0,3443.0,635.0,2257.0,620.0,4.7404,207500.0,<1H OCEAN\n-117.99,33.74,26.0,4065.0,741.0,1960.0,739.0,4.506,240000.0,<1H OCEAN\n-117.98,33.74,16.0,4636.0,908.0,2341.0,825.0,4.4261,304700.0,<1H OCEAN\n-117.99,33.73,17.0,5239.0,1045.0,2440.0,985.0,4.375,248100.0,<1H OCEAN\n-117.98,33.73,22.0,4232.0,624.0,2408.0,660.0,6.6539,284900.0,<1H OCEAN\n-117.99,33.77,29.0,1312.0,267.0,922.0,255.0,3.1902,202400.0,<1H OCEAN\n-117.98,33.76,28.0,3215.0,652.0,2066.0,636.0,4.0194,197400.0,<1H OCEAN\n-117.99,33.76,30.0,1572.0,362.0,1351.0,359.0,3.369,190900.0,<1H OCEAN\n-117.99,33.76,17.0,2545.0,737.0,1468.0,699.0,1.9439,177700.0,<1H OCEAN\n-117.99,33.75,22.0,3024.0,754.0,2357.0,743.0,3.3125,191800.0,<1H OCEAN\n-117.98,33.76,23.0,1553.0,518.0,1988.0,474.0,2.1375,150000.0,<1H OCEAN\n-117.98,33.76,24.0,1880.0,405.0,967.0,418.0,4.4545,192500.0,<1H OCEAN\n-117.98,33.75,27.0,2343.0,415.0,1537.0,426.0,5.1345,210600.0,<1H OCEAN\n-118.02,33.77,34.0,2115.0,352.0,1253.0,338.0,5.1507,207500.0,<1H OCEAN\n-118.02,33.77,33.0,2683.0,436.0,1520.0,456.0,5.0091,211500.0,<1H OCEAN\n-118.02,33.76,25.0,1759.0,404.0,1404.0,385.0,3.6289,195800.0,<1H OCEAN\n-118.01,33.77,33.0,1387.0,238.0,890.0,264.0,5.422,204100.0,<1H OCEAN\n-118.01,33.77,32.0,1771.0,296.0,995.0,272.0,5.8362,217500.0,<1H OCEAN\n-118.01,33.76,35.0,2072.0,349.0,1249.0,317.0,3.9855,191900.0,<1H OCEAN\n-118.01,33.76,26.0,2141.0,597.0,2038.0,585.0,2.2981,177700.0,<1H OCEAN\n-118.0,33.77,24.0,1324.0,267.0,687.0,264.0,3.4327,192800.0,<1H OCEAN\n-118.0,33.76,29.0,1982.0,503.0,1426.0,502.0,3.0263,194200.0,<1H OCEAN\n-118.0,33.77,28.0,2401.0,503.0,1155.0,456.0,3.5139,211700.0,<1H OCEAN\n-118.0,33.76,14.0,1120.0,319.0,982.0,307.0,2.9083,179200.0,<1H OCEAN\n-118.0,33.76,12.0,1250.0,331.0,1047.0,334.0,3.0625,208800.0,<1H OCEAN\n-118.03,33.77,21.0,3803.0,898.0,1511.0,829.0,3.0,221200.0,<1H OCEAN\n-118.02,33.77,7.0,586.0,118.0,232.0,107.0,5.2077,181300.0,<1H OCEAN\n-118.03,33.77,27.0,2000.0,310.0,880.0,294.0,5.635,218900.0,<1H OCEAN\n-118.03,33.77,24.0,3810.0,579.0,1818.0,590.0,5.8053,255900.0,<1H OCEAN\n-118.03,33.76,32.0,2980.0,494.0,1370.0,481.0,5.0866,223500.0,<1H OCEAN\n-118.04,33.76,25.0,4061.0,545.0,1623.0,527.0,7.1572,294900.0,<1H OCEAN\n-118.03,33.79,26.0,5321.0,889.0,2932.0,896.0,5.8914,237600.0,<1H OCEAN\n-118.03,33.79,32.0,3191.0,634.0,1718.0,611.0,4.1548,216600.0,<1H OCEAN\n-118.01,33.79,30.0,2460.0,403.0,1277.0,395.0,5.4372,223200.0,<1H OCEAN\n-118.02,33.78,28.0,3375.0,559.0,1754.0,554.0,5.5446,228900.0,<1H OCEAN\n-118.01,33.78,19.0,2648.0,478.0,1160.0,452.0,5.9357,207400.0,<1H OCEAN\n-118.03,33.78,26.0,2001.0,302.0,836.0,298.0,5.1061,257500.0,<1H OCEAN\n-118.01,33.78,26.0,2343.0,377.0,1166.0,373.0,6.0,233100.0,<1H OCEAN\n-118.03,33.78,25.0,3554.0,528.0,1600.0,537.0,6.6453,270100.0,<1H OCEAN\n-118.04,33.78,26.0,3642.0,557.0,1623.0,569.0,5.8426,259400.0,<1H OCEAN\n-118.04,33.78,25.0,3715.0,575.0,1640.0,572.0,5.7705,247100.0,<1H OCEAN\n-118.08,33.8,29.0,3675.0,613.0,1457.0,591.0,6.0553,369400.0,<1H OCEAN\n-118.07,33.8,34.0,3486.0,507.0,1311.0,503.0,7.1221,384500.0,<1H OCEAN\n-118.08,33.79,34.0,2840.0,395.0,1127.0,396.0,7.6144,376200.0,<1H OCEAN\n-118.09,33.78,26.0,2146.0,298.0,852.0,296.0,6.6137,342700.0,<1H OCEAN\n-118.07,33.79,34.0,2473.0,383.0,967.0,353.0,5.8283,362400.0,<1H OCEAN\n-118.09,33.79,31.0,4231.0,617.0,1694.0,623.0,6.6312,360100.0,<1H OCEAN\n-118.08,33.78,34.0,2287.0,347.0,1051.0,346.0,5.5767,372000.0,<1H OCEAN\n-118.08,33.78,30.0,2879.0,403.0,1109.0,414.0,6.9324,364700.0,<1H OCEAN\n-118.08,33.78,25.0,5321.0,967.0,1969.0,903.0,5.0102,340100.0,<1H OCEAN\n-118.02,33.79,23.0,6368.0,1030.0,3281.0,1001.0,6.1142,240400.0,<1H OCEAN\n-118.01,33.79,23.0,2663.0,430.0,1499.0,403.0,5.7837,258000.0,<1H OCEAN\n-118.02,33.8,24.0,84.0,14.0,32.0,8.0,5.875,193800.0,<1H OCEAN\n-118.02,33.8,16.0,2956.0,393.0,1379.0,429.0,8.4952,359600.0,<1H OCEAN\n-118.01,33.8,16.0,4021.0,701.0,1488.0,650.0,5.32,219500.0,<1H OCEAN\n-118.05,33.78,18.0,3414.0,434.0,1272.0,454.0,8.7015,390900.0,<1H OCEAN\n-118.05,33.78,25.0,3112.0,435.0,1098.0,401.0,6.0,353500.0,<1H OCEAN\n-118.05,33.78,25.0,2356.0,330.0,937.0,326.0,6.6264,359100.0,<1H OCEAN\n-118.06,33.78,22.0,4048.0,562.0,1637.0,541.0,7.3463,355600.0,<1H OCEAN\n-118.04,33.8,33.0,2685.0,466.0,1359.0,476.0,5.0261,245100.0,<1H OCEAN\n-118.06,33.8,21.0,2196.0,504.0,1215.0,477.0,4.8,196900.0,<1H OCEAN\n-118.07,33.79,26.0,4422.0,624.0,1936.0,625.0,6.4288,320700.0,<1H OCEAN\n-118.06,33.8,22.0,1892.0,442.0,1015.0,404.0,4.1379,212500.0,<1H OCEAN\n-118.06,33.8,20.0,1379.0,333.0,937.0,304.0,3.6217,195300.0,<1H OCEAN\n-118.07,33.8,17.0,2439.0,554.0,1161.0,532.0,3.6442,193100.0,<1H OCEAN\n-118.05,33.79,19.0,1863.0,355.0,1260.0,317.0,3.2465,277400.0,<1H OCEAN\n-118.04,33.84,21.0,6623.0,1204.0,3193.0,1129.0,4.5395,256000.0,<1H OCEAN\n-118.05,33.84,21.0,4890.0,653.0,2295.0,654.0,6.983,329700.0,<1H OCEAN\n-118.04,33.83,19.0,4526.0,830.0,2318.0,748.0,4.6681,320700.0,<1H OCEAN\n-118.04,33.82,26.0,4105.0,637.0,2072.0,648.0,5.844,273900.0,<1H OCEAN\n-118.04,33.83,20.0,1488.0,312.0,972.0,283.0,4.055,201900.0,<1H OCEAN\n-118.05,33.82,25.0,1548.0,279.0,732.0,265.0,5.123,159600.0,<1H OCEAN\n-118.06,33.82,25.0,2637.0,462.0,965.0,415.0,4.5833,190900.0,<1H OCEAN\n-118.06,33.81,25.0,3497.0,513.0,1839.0,544.0,5.4216,263000.0,<1H OCEAN\n-118.07,33.81,22.0,2711.0,352.0,1305.0,368.0,8.5407,398800.0,<1H OCEAN\n-118.07,33.8,22.0,1391.0,338.0,810.0,295.0,3.8792,218200.0,<1H OCEAN\n-118.08,33.81,21.0,1189.0,281.0,577.0,264.0,3.3155,237500.0,<1H OCEAN\n-118.03,33.82,17.0,1851.0,346.0,770.0,310.0,5.6093,244400.0,<1H OCEAN\n-118.03,33.82,17.0,2178.0,477.0,1077.0,457.0,3.6815,245300.0,<1H OCEAN\n-118.03,33.82,20.0,2662.0,464.0,1275.0,472.0,6.0162,318500.0,<1H OCEAN\n-118.02,33.82,19.0,2485.0,437.0,1286.0,431.0,4.7466,258300.0,<1H OCEAN\n-118.02,33.83,16.0,1139.0,328.0,665.0,290.0,3.2933,260000.0,<1H OCEAN\n-118.03,33.83,34.0,3203.0,653.0,2072.0,691.0,4.225,198400.0,<1H OCEAN\n-118.03,33.83,25.0,3030.0,532.0,1668.0,509.0,4.625,229600.0,<1H OCEAN\n-118.03,33.83,25.0,768.0,195.0,529.0,184.0,3.175,132800.0,<1H OCEAN\n-118.06,33.83,22.0,5290.0,1054.0,2812.0,1021.0,4.53,226400.0,<1H OCEAN\n-118.05,33.83,24.0,4316.0,678.0,2286.0,665.0,5.7018,286700.0,<1H OCEAN\n-118.06,33.83,21.0,3941.0,655.0,1897.0,670.0,4.88,343900.0,<1H OCEAN\n-118.05,33.82,21.0,2997.0,372.0,1323.0,372.0,8.6123,386700.0,<1H OCEAN\n-118.06,33.83,17.0,1973.0,516.0,1112.0,501.0,3.8512,163800.0,<1H OCEAN\n-118.06,33.82,24.0,3983.0,675.0,1568.0,638.0,4.6458,213400.0,<1H OCEAN\n-118.04,33.81,22.0,4057.0,624.0,2204.0,643.0,5.8527,241000.0,<1H OCEAN\n-118.04,33.81,27.0,2990.0,515.0,1849.0,497.0,5.6846,216100.0,<1H OCEAN\n-118.05,33.81,26.0,2523.0,437.0,1377.0,450.0,5.2542,234600.0,<1H OCEAN\n-118.03,33.81,26.0,3635.0,567.0,1779.0,543.0,5.7089,237400.0,<1H OCEAN\n-118.03,33.86,19.0,1795.0,328.0,1014.0,322.0,4.535,289300.0,<1H OCEAN\n-118.04,33.85,24.0,2233.0,347.0,1162.0,355.0,5.6094,279200.0,<1H OCEAN\n-118.04,33.85,23.0,3132.0,469.0,1646.0,478.0,5.777,315900.0,<1H OCEAN\n-118.05,33.85,25.0,2856.0,388.0,1212.0,362.0,6.1737,313100.0,<1H OCEAN\n-118.04,33.85,18.0,3628.0,546.0,1922.0,544.0,7.5057,328500.0,<1H OCEAN\n-118.03,33.85,16.0,1831.0,390.0,1347.0,389.0,3.8426,344400.0,<1H OCEAN\n-118.01,33.83,29.0,3963.0,772.0,2104.0,743.0,4.9803,208600.0,<1H OCEAN\n-118.01,33.84,29.0,3740.0,691.0,1724.0,638.0,3.9628,215600.0,<1H OCEAN\n-118.03,33.84,28.0,3857.0,857.0,2328.0,830.0,4.0156,196000.0,<1H OCEAN\n-118.02,33.83,26.0,3616.0,892.0,2257.0,821.0,3.1497,217600.0,<1H OCEAN\n-118.02,33.82,21.0,2052.0,456.0,1173.0,432.0,3.7885,204500.0,<1H OCEAN\n-118.01,33.82,10.0,3897.0,893.0,1992.0,693.0,4.1591,192300.0,<1H OCEAN\n-118.01,33.81,18.0,5238.0,1083.0,3032.0,1065.0,4.4583,190100.0,<1H OCEAN\n-118.02,33.81,34.0,3482.0,614.0,2227.0,641.0,5.1155,200900.0,<1H OCEAN\n-118.02,33.86,26.0,2342.0,383.0,1290.0,394.0,5.6677,220700.0,<1H OCEAN\n-118.02,33.85,31.0,1922.0,329.0,1030.0,353.0,5.3416,213000.0,<1H OCEAN\n-118.03,33.85,30.0,2320.0,448.0,1434.0,452.0,4.0865,203700.0,<1H OCEAN\n-118.03,33.85,23.0,5495.0,1141.0,2873.0,1004.0,3.9156,224100.0,<1H OCEAN\n-118.01,33.85,29.0,2064.0,447.0,1265.0,400.0,3.8864,209300.0,<1H OCEAN\n-118.01,33.85,29.0,3061.0,612.0,2396.0,640.0,4.6326,195200.0,<1H OCEAN\n-118.0,33.85,34.0,1078.0,205.0,575.0,206.0,4.5083,188000.0,<1H OCEAN\n-118.01,33.86,29.0,2307.0,452.0,1218.0,402.0,3.4306,194200.0,<1H OCEAN\n-118.01,33.84,28.0,4097.0,838.0,2112.0,803.0,4.5,202100.0,<1H OCEAN\n-118.01,33.84,35.0,4166.0,713.0,2354.0,709.0,5.1775,213400.0,<1H OCEAN\n-118.02,33.84,35.0,3473.0,563.0,2091.0,580.0,4.4821,214100.0,<1H OCEAN\n-118.03,33.84,30.0,4781.0,831.0,2568.0,797.0,5.4746,226400.0,<1H OCEAN\n-117.99,33.86,36.0,1138.0,228.0,725.0,219.0,3.4167,187200.0,<1H OCEAN\n-117.99,33.85,34.0,1948.0,306.0,957.0,304.0,4.9777,212600.0,<1H OCEAN\n-118.0,33.85,33.0,2053.0,418.0,1154.0,405.0,4.0455,197200.0,<1H OCEAN\n-118.0,33.86,32.0,1162.0,196.0,563.0,178.0,3.875,203000.0,<1H OCEAN\n-117.99,33.85,35.0,1661.0,272.0,949.0,276.0,5.2548,192600.0,<1H OCEAN\n-117.99,33.84,34.0,2079.0,343.0,1379.0,352.0,5.103,207000.0,<1H OCEAN\n-118.0,33.84,30.0,1549.0,325.0,885.0,299.0,4.0039,195100.0,<1H OCEAN\n-118.0,33.84,29.0,2641.0,637.0,2413.0,619.0,2.8169,165100.0,<1H OCEAN\n-118.01,33.87,25.0,6348.0,1615.0,4188.0,1497.0,3.139,185700.0,<1H OCEAN\n-117.99,33.86,20.0,3540.0,906.0,2898.0,876.0,3.0252,178000.0,<1H OCEAN\n-118.0,33.88,28.0,1624.0,289.0,755.0,280.0,4.7083,268100.0,<1H OCEAN\n-118.01,33.88,19.0,1434.0,391.0,1088.0,341.0,3.369,269600.0,<1H OCEAN\n-117.99,33.88,15.0,2298.0,567.0,1261.0,527.0,4.2422,159400.0,<1H OCEAN\n-117.99,33.88,42.0,1461.0,302.0,986.0,314.0,3.9559,161100.0,<1H OCEAN\n-118.0,33.88,18.0,2628.0,720.0,2276.0,649.0,2.735,170800.0,<1H OCEAN\n-117.99,33.87,16.0,1689.0,499.0,1260.0,453.0,3.1205,174000.0,<1H OCEAN\n-118.0,33.87,13.0,2086.0,544.0,1356.0,462.0,2.95,165600.0,<1H OCEAN\n-117.99,33.87,17.0,2334.0,537.0,1662.0,535.0,3.0147,217000.0,<1H OCEAN\n-117.99,33.87,34.0,1239.0,307.0,869.0,291.0,3.59,161900.0,<1H OCEAN\n-117.99,33.86,20.0,2303.0,612.0,1607.0,564.0,2.9,176100.0,<1H OCEAN\n-117.98,33.89,18.0,2939.0,437.0,1278.0,435.0,7.1425,393700.0,<1H OCEAN\n-117.99,33.88,25.0,3401.0,509.0,1503.0,498.0,6.6704,240600.0,<1H OCEAN\n-117.99,33.89,21.0,5195.0,1020.0,2539.0,988.0,4.5033,160500.0,<1H OCEAN\n-117.99,33.89,23.0,2111.0,306.0,979.0,288.0,8.5621,347800.0,<1H OCEAN\n-117.97,33.89,17.0,1851.0,344.0,764.0,339.0,5.1315,181800.0,<1H OCEAN\n-117.97,33.89,17.0,1740.0,445.0,1158.0,412.0,2.8649,137500.0,<1H OCEAN\n-117.97,33.88,9.0,1344.0,279.0,530.0,265.0,5.0731,185100.0,<1H OCEAN\n-117.97,33.88,11.0,1454.0,247.0,635.0,236.0,6.2427,218500.0,<1H OCEAN\n-117.97,33.89,15.0,3801.0,542.0,1992.0,526.0,9.0683,367400.0,<1H OCEAN\n-117.97,33.88,16.0,2003.0,300.0,1172.0,318.0,6.0394,321600.0,<1H OCEAN\n-117.97,33.89,14.0,923.0,136.0,420.0,130.0,10.2252,462800.0,<1H OCEAN\n-120.1,39.17,33.0,1849.0,384.0,218.0,92.0,1.7083,143800.0,INLAND\n-120.06,39.09,30.0,2979.0,583.0,316.0,124.0,2.1987,124000.0,INLAND\n-120.06,39.15,22.0,2213.0,372.0,98.0,42.0,1.1912,170000.0,INLAND\n-120.2,39.12,15.0,2146.0,361.0,197.0,76.0,4.1316,200000.0,INLAND\n-120.18,39.14,25.0,2171.0,386.0,248.0,116.0,3.0375,171900.0,INLAND\n-120.16,39.14,21.0,2484.0,460.0,309.0,144.0,3.9722,127800.0,INLAND\n-120.18,39.17,18.0,1703.0,360.0,354.0,163.0,3.6563,146900.0,INLAND\n-120.15,39.17,32.0,1684.0,359.0,454.0,209.0,2.9125,145800.0,INLAND\n-120.15,39.15,25.0,1669.0,348.0,163.0,78.0,5.75,176600.0,INLAND\n-120.15,39.2,14.0,1382.0,242.0,141.0,66.0,4.1016,283300.0,INLAND\n-120.12,39.18,17.0,2839.0,525.0,390.0,189.0,3.5667,179200.0,INLAND\n-120.1,39.19,18.0,3824.0,559.0,241.0,106.0,5.5456,360000.0,INLAND\n-120.1,39.19,17.0,1480.0,241.0,202.0,80.0,3.9375,213200.0,INLAND\n-120.1,39.2,20.0,1703.0,294.0,409.0,174.0,3.087,196900.0,INLAND\n-120.11,39.21,18.0,2245.0,392.0,421.0,162.0,4.5795,158300.0,INLAND\n-120.11,39.24,21.0,3005.0,574.0,385.0,150.0,3.1193,153300.0,INLAND\n-120.08,39.23,19.0,1746.0,306.0,251.0,104.0,4.8182,146900.0,INLAND\n-120.07,39.24,20.0,3729.0,614.0,365.0,152.0,4.962,169500.0,INLAND\n-120.06,39.25,21.0,2459.0,525.0,584.0,233.0,3.01,163500.0,INLAND\n-120.04,39.24,30.0,2369.0,469.0,510.0,213.0,2.65,123800.0,INLAND\n-120.04,39.27,24.0,2237.0,491.0,264.0,95.0,4.1364,154500.0,INLAND\n-120.02,39.24,24.0,1602.0,426.0,751.0,257.0,1.7609,99300.0,INLAND\n-120.02,39.24,22.0,2309.0,571.0,919.0,342.0,3.0057,93600.0,INLAND\n-120.02,39.24,32.0,1347.0,444.0,825.0,303.0,1.8269,225000.0,INLAND\n-120.01,39.26,26.0,1930.0,391.0,307.0,138.0,2.6023,139300.0,INLAND\n-120.91,38.98,13.0,7689.0,1415.0,3264.0,1198.0,3.653,146800.0,INLAND\n-120.81,39.02,30.0,806.0,189.0,326.0,146.0,2.8155,101000.0,INLAND\n-120.69,39.12,19.0,1048.0,262.0,493.0,184.0,2.2917,118200.0,INLAND\n-120.83,39.02,15.0,1117.0,242.0,551.0,229.0,2.6319,97700.0,INLAND\n-121.06,38.91,18.0,6501.0,1416.0,2954.0,1373.0,2.5373,143000.0,INLAND\n-121.08,38.9,27.0,3436.0,755.0,1568.0,709.0,2.4273,138400.0,INLAND\n-121.06,38.88,17.0,7635.0,1284.0,3096.0,1227.0,4.2917,184300.0,INLAND\n-121.08,38.89,41.0,3471.0,753.0,1680.0,710.0,2.6701,139000.0,INLAND\n-121.07,38.9,52.0,1280.0,281.0,523.0,266.0,1.7375,122200.0,INLAND\n-121.15,38.89,20.0,2024.0,313.0,879.0,309.0,5.2903,239400.0,INLAND\n-121.12,38.86,17.0,3949.0,717.0,1683.0,686.0,3.3802,216500.0,INLAND\n-121.13,38.87,48.0,1127.0,,530.0,186.0,3.0917,128100.0,INLAND\n-121.15,38.91,23.0,1654.0,299.0,787.0,299.0,4.2723,193100.0,INLAND\n-121.08,38.85,10.0,2509.0,422.0,1037.0,389.0,6.0,220100.0,INLAND\n-121.16,38.74,17.0,3353.0,463.0,1417.0,447.0,5.1721,237100.0,INLAND\n-121.14,38.77,15.0,10282.0,1333.0,3868.0,1300.0,6.4789,287800.0,INLAND\n-121.16,38.75,27.0,771.0,108.0,315.0,111.0,8.4882,276600.0,INLAND\n-121.14,38.84,22.0,2750.0,433.0,1161.0,428.0,4.2143,236500.0,INLAND\n-121.14,38.82,22.0,1816.0,278.0,832.0,278.0,5.07,233000.0,INLAND\n-121.15,38.8,20.0,2104.0,370.0,745.0,314.0,4.1685,217500.0,INLAND\n-121.18,38.78,13.0,3480.0,528.0,1432.0,532.0,6.1642,277800.0,INLAND\n-121.18,38.8,18.0,2541.0,414.0,1276.0,405.0,5.1857,220100.0,INLAND\n-121.21,38.76,16.0,1608.0,296.0,792.0,286.0,3.1583,239200.0,INLAND\n-121.21,38.75,11.0,4552.0,639.0,2006.0,623.0,4.3962,264400.0,INLAND\n-121.17,38.76,14.0,2028.0,255.0,781.0,251.0,6.5322,394000.0,INLAND\n-121.2,38.73,11.0,4897.0,636.0,1931.0,616.0,7.7499,334800.0,INLAND\n-121.18,38.73,16.0,1584.0,264.0,613.0,226.0,6.0302,273100.0,INLAND\n-121.18,38.75,16.0,2807.0,459.0,1201.0,429.0,4.7941,247600.0,INLAND\n-121.24,38.75,5.0,9137.0,1368.0,3667.0,1294.0,5.4896,229600.0,INLAND\n-121.24,38.72,12.0,3605.0,576.0,1556.0,549.0,4.9,203700.0,INLAND\n-121.27,38.74,19.0,3869.0,887.0,2086.0,685.0,2.6065,154900.0,INLAND\n-121.26,38.73,14.0,3323.0,499.0,1527.0,540.0,5.3451,172100.0,INLAND\n-121.26,38.74,22.0,7173.0,1314.0,3526.0,1316.0,3.3941,135900.0,INLAND\n-121.28,38.73,6.0,4223.0,672.0,1747.0,631.0,5.419,267400.0,INLAND\n-121.27,38.72,6.0,4664.0,644.0,2105.0,663.0,6.0804,198700.0,INLAND\n-121.25,38.72,10.0,7277.0,1168.0,3507.0,1131.0,4.485,179400.0,INLAND\n-121.27,38.75,21.0,4812.0,1117.0,1985.0,1045.0,2.5083,128500.0,INLAND\n-121.28,38.74,33.0,4384.0,778.0,1775.0,789.0,4.05,134700.0,INLAND\n-121.3,38.73,9.0,5558.0,1099.0,2717.0,1043.0,3.6455,139200.0,INLAND\n-121.36,38.73,21.0,2253.0,416.0,1050.0,411.0,3.141,220100.0,INLAND\n-121.32,38.74,14.0,1449.0,228.0,670.0,232.0,4.3897,186300.0,INLAND\n-121.31,38.75,7.0,4185.0,750.0,2147.0,706.0,4.0519,129200.0,INLAND\n-121.3,38.75,36.0,3903.0,885.0,2313.0,804.0,2.655,86300.0,INLAND\n-121.28,38.75,52.0,493.0,89.0,189.0,94.0,2.108,83800.0,INLAND\n-121.3,38.74,41.0,4374.0,1039.0,2387.0,959.0,2.3611,87900.0,INLAND\n-121.33,38.77,3.0,20214.0,3559.0,8361.0,3112.0,4.2259,169300.0,INLAND\n-121.28,38.76,47.0,2901.0,631.0,1276.0,578.0,2.1366,101900.0,INLAND\n-121.27,38.75,43.0,1292.0,307.0,647.0,249.0,2.7188,85300.0,INLAND\n-121.29,38.76,12.0,1198.0,174.0,443.0,170.0,6.0097,187500.0,INLAND\n-121.28,38.77,6.0,3819.0,550.0,1738.0,587.0,5.8718,201400.0,INLAND\n-121.28,38.8,7.0,9003.0,1739.0,4445.0,1591.0,3.816,147900.0,INLAND\n-121.24,38.78,11.0,1851.0,352.0,1049.0,369.0,3.5288,141100.0,INLAND\n-121.24,38.78,18.0,549.0,143.0,249.0,136.0,0.8691,136500.0,INLAND\n-121.23,38.78,13.0,3813.0,871.0,1513.0,783.0,2.0807,142600.0,INLAND\n-121.22,38.78,8.0,3418.0,514.0,1312.0,409.0,6.3914,218000.0,INLAND\n-121.24,38.82,5.0,12259.0,1643.0,4819.0,1582.0,5.4498,217300.0,INLAND\n-121.25,38.8,14.0,5094.0,729.0,1974.0,705.0,5.5205,188700.0,INLAND\n-121.24,38.79,15.0,2615.0,485.0,1063.0,428.0,3.7904,173200.0,INLAND\n-121.24,38.79,23.0,1419.0,261.0,706.0,269.0,3.1875,110200.0,INLAND\n-121.23,38.79,45.0,907.0,176.0,463.0,190.0,2.2292,92000.0,INLAND\n-121.22,38.8,11.0,2521.0,521.0,1390.0,477.0,3.5265,124800.0,INLAND\n-121.19,38.87,20.0,3118.0,500.0,1405.0,519.0,6.0,209400.0,INLAND\n-121.21,38.83,21.0,3691.0,640.0,1758.0,603.0,3.5607,151900.0,INLAND\n-121.19,38.85,8.0,4114.0,710.0,2268.0,716.0,4.4085,139400.0,INLAND\n-121.18,38.83,15.0,4488.0,859.0,2114.0,805.0,2.9484,124400.0,INLAND\n-121.39,38.85,19.0,3568.0,646.0,1714.0,590.0,4.0862,162700.0,INLAND\n-121.3,39.0,16.0,3155.0,541.0,1630.0,540.0,4.0282,126400.0,INLAND\n-121.31,38.97,16.0,1210.0,228.0,726.0,222.0,2.7083,82100.0,INLAND\n-121.22,38.92,19.0,2531.0,461.0,1206.0,429.0,4.4958,192600.0,INLAND\n-121.27,38.87,16.0,2094.0,358.0,1092.0,357.0,4.4769,191400.0,INLAND\n-121.29,38.9,45.0,2019.0,394.0,1104.0,407.0,3.1691,108700.0,INLAND\n-121.28,38.9,31.0,1297.0,259.0,765.0,240.0,2.7656,93600.0,INLAND\n-121.29,38.89,10.0,653.0,120.0,407.0,146.0,3.3889,110800.0,INLAND\n-121.3,38.89,23.0,1750.0,297.0,1012.0,315.0,3.4706,99300.0,INLAND\n-121.3,38.89,45.0,1529.0,317.0,793.0,281.0,2.9866,91300.0,INLAND\n-121.32,38.89,9.0,5927.0,1269.0,3369.0,1176.0,2.8194,111300.0,INLAND\n-121.14,38.92,16.0,2069.0,312.0,889.0,299.0,4.6771,212000.0,INLAND\n-121.1,38.92,21.0,4064.0,871.0,1847.0,859.0,3.0321,135500.0,INLAND\n-121.11,38.91,24.0,2558.0,423.0,1149.0,403.0,4.0679,190500.0,INLAND\n-121.1,38.94,42.0,410.0,117.0,706.0,112.0,1.0179,125000.0,INLAND\n-121.07,38.92,15.0,5301.0,884.0,2335.0,831.0,4.515,164000.0,INLAND\n-121.05,38.92,34.0,2144.0,372.0,899.0,378.0,3.3021,158800.0,INLAND\n-121.15,39.0,15.0,4145.0,691.0,1872.0,680.0,4.3553,220600.0,INLAND\n-121.19,38.95,16.0,2544.0,431.0,1199.0,412.0,4.5129,196300.0,INLAND\n-121.11,38.95,14.0,3888.0,890.0,1830.0,844.0,1.8238,158600.0,INLAND\n-121.1,38.95,17.0,1475.0,403.0,943.0,363.0,2.1287,55300.0,INLAND\n-121.06,38.98,14.0,2267.0,355.0,1140.0,369.0,4.7019,212800.0,INLAND\n-121.05,38.97,12.0,3676.0,550.0,1572.0,510.0,4.8214,201900.0,INLAND\n-121.09,38.97,13.0,1467.0,221.0,688.0,231.0,5.2536,191900.0,INLAND\n-121.1,39.0,16.0,1106.0,195.0,505.0,187.0,5.0126,192300.0,INLAND\n-121.08,38.95,18.0,1931.0,380.0,1271.0,377.0,2.7463,156100.0,INLAND\n-121.08,38.93,14.0,4239.0,824.0,1729.0,794.0,2.4278,167700.0,INLAND\n-121.07,38.94,14.0,1710.0,294.0,839.0,297.0,4.7143,150700.0,INLAND\n-121.04,38.95,22.0,1931.0,445.0,1009.0,407.0,2.75,153200.0,INLAND\n-120.99,39.04,17.0,2289.0,450.0,1182.0,397.0,2.3696,166800.0,INLAND\n-120.98,38.99,17.0,3403.0,661.0,1540.0,622.0,3.6354,162900.0,INLAND\n-121.04,39.0,21.0,4059.0,730.0,1874.0,693.0,4.8051,174300.0,INLAND\n-121.02,39.01,17.0,4786.0,799.0,2066.0,770.0,3.9734,185400.0,INLAND\n-121.0,39.0,4.0,170.0,23.0,93.0,27.0,10.9891,312500.0,INLAND\n-120.87,39.18,25.0,2691.0,598.0,964.0,373.0,3.9196,142700.0,INLAND\n-120.87,39.15,17.0,1819.0,389.0,736.0,283.0,2.8603,128900.0,INLAND\n-120.58,39.27,15.0,4126.0,903.0,723.0,266.0,3.0147,118800.0,INLAND\n-120.33,39.3,16.0,868.0,178.0,44.0,21.0,3.0,175000.0,INLAND\n-120.18,39.28,14.0,10098.0,1545.0,701.0,254.0,4.0819,141300.0,INLAND\n-120.22,39.2,22.0,8259.0,1409.0,845.0,353.0,3.3699,244000.0,INLAND\n-120.96,39.12,24.0,2069.0,436.0,909.0,374.0,2.5326,139100.0,INLAND\n-120.98,39.08,20.0,4570.0,906.0,2125.0,815.0,3.0403,148000.0,INLAND\n-120.94,39.05,8.0,3758.0,717.0,1744.0,661.0,3.1972,151500.0,INLAND\n-120.98,39.93,25.0,2220.0,511.0,912.0,449.0,1.8914,87800.0,INLAND\n-120.95,39.93,26.0,2023.0,385.0,922.0,365.0,2.8125,83500.0,INLAND\n-120.93,39.9,20.0,1511.0,328.0,791.0,320.0,2.0221,70900.0,INLAND\n-120.9,39.93,23.0,2679.0,546.0,1424.0,529.0,2.8812,81900.0,INLAND\n-120.9,39.95,20.0,1349.0,238.0,601.0,203.0,3.5417,96600.0,INLAND\n-120.93,39.96,15.0,1666.0,351.0,816.0,316.0,2.9559,118800.0,INLAND\n-120.92,40.02,35.0,383.0,92.0,202.0,72.0,2.6458,102500.0,INLAND\n-120.74,39.9,23.0,1017.0,218.0,387.0,152.0,2.2656,88200.0,INLAND\n-120.57,39.78,15.0,1291.0,283.0,582.0,242.0,2.1216,102000.0,INLAND\n-120.66,39.72,15.0,3763.0,784.0,717.0,348.0,2.2019,130500.0,INLAND\n-120.74,39.82,9.0,1955.0,398.0,294.0,122.0,3.9583,126500.0,INLAND\n-121.0,39.75,8.0,1116.0,214.0,27.0,39.0,2.5893,83000.0,INLAND\n-121.14,39.86,16.0,2534.0,557.0,638.0,244.0,2.2101,88800.0,INLAND\n-120.53,39.79,18.0,1234.0,266.0,543.0,201.0,2.5156,71900.0,INLAND\n-120.48,39.78,11.0,513.0,104.0,204.0,86.0,2.375,100000.0,INLAND\n-120.45,39.8,47.0,2149.0,456.0,965.0,419.0,1.7829,55900.0,INLAND\n-120.46,39.83,18.0,3406.0,673.0,1567.0,617.0,2.2717,75900.0,INLAND\n-120.38,39.82,10.0,1262.0,258.0,510.0,209.0,2.1667,92800.0,INLAND\n-120.15,39.8,19.0,785.0,151.0,366.0,140.0,3.0625,82500.0,INLAND\n-120.94,40.17,22.0,1334.0,261.0,597.0,222.0,2.2132,89200.0,INLAND\n-120.91,40.08,24.0,1629.0,313.0,641.0,274.0,2.2067,69600.0,INLAND\n-120.94,40.14,31.0,3127.0,664.0,1345.0,580.0,1.5774,58000.0,INLAND\n-121.23,40.01,38.0,725.0,190.0,219.0,115.0,1.625,75000.0,INLAND\n-120.71,40.13,19.0,897.0,180.0,276.0,110.0,2.9554,89400.0,INLAND\n-121.25,40.27,25.0,958.0,245.0,28.0,16.0,2.625,67500.0,INLAND\n-121.24,40.31,36.0,1597.0,301.0,632.0,262.0,3.5962,93600.0,INLAND\n-121.23,40.29,21.0,3229.0,667.0,1501.0,582.0,2.1524,77100.0,INLAND\n-121.19,40.23,10.0,1572.0,232.0,247.0,104.0,5.8453,193800.0,INLAND\n-121.08,40.19,11.0,919.0,199.0,69.0,43.0,1.6944,137500.0,INLAND\n-121.06,40.23,23.0,1127.0,225.0,215.0,85.0,3.4844,143800.0,INLAND\n-121.09,40.3,15.0,1717.0,336.0,501.0,206.0,3.6477,113400.0,INLAND\n-121.14,40.29,17.0,1944.0,394.0,384.0,172.0,1.6875,111500.0,INLAND\n-121.15,40.25,14.0,5156.0,880.0,616.0,281.0,3.3462,145200.0,INLAND\n-117.35,34.0,38.0,1214.0,254.0,723.0,236.0,2.5469,87800.0,INLAND\n-117.36,33.99,42.0,1178.0,261.0,804.0,283.0,2.9688,92900.0,INLAND\n-117.37,34.0,36.0,730.0,155.0,476.0,142.0,2.4306,88900.0,INLAND\n-117.37,34.0,41.0,1248.0,278.0,770.0,250.0,3.025,90600.0,INLAND\n-117.36,34.0,19.0,4592.0,895.0,2769.0,838.0,3.3622,105100.0,INLAND\n-117.37,34.01,15.0,1386.0,247.0,703.0,185.0,3.6415,124200.0,INLAND\n-117.38,34.0,45.0,2881.0,514.0,1470.0,515.0,3.3687,123800.0,INLAND\n-117.38,33.99,52.0,1797.0,332.0,905.0,313.0,2.7054,141700.0,INLAND\n-117.38,33.98,52.0,2274.0,571.0,1167.0,504.0,2.0284,101600.0,INLAND\n-117.39,33.98,37.0,2337.0,452.0,948.0,437.0,3.145,169100.0,INLAND\n-117.41,33.97,24.0,950.0,183.0,383.0,182.0,3.0694,125000.0,INLAND\n-117.37,33.98,43.0,2862.0,772.0,1878.0,675.0,2.1151,96700.0,INLAND\n-117.38,33.97,29.0,1157.0,297.0,2027.0,253.0,1.6389,155000.0,INLAND\n-117.38,33.98,10.0,642.0,176.0,462.0,186.0,2.1528,162500.0,INLAND\n-117.37,33.98,27.0,1342.0,547.0,844.0,484.0,1.1194,95800.0,INLAND\n-117.37,33.99,44.0,917.0,224.0,666.0,220.0,1.685,114200.0,INLAND\n-117.36,33.98,46.0,1680.0,453.0,1570.0,435.0,2.0436,82300.0,INLAND\n-117.37,33.97,34.0,3676.0,697.0,2653.0,682.0,2.5804,92400.0,INLAND\n-117.37,33.97,38.0,1156.0,241.0,877.0,200.0,1.4514,79900.0,INLAND\n-117.37,33.97,40.0,1166.0,250.0,976.0,244.0,1.95,84800.0,INLAND\n-117.37,33.98,52.0,201.0,44.0,130.0,24.0,2.025,125000.0,INLAND\n-117.35,33.99,45.0,131.0,28.0,89.0,31.0,2.6071,112500.0,INLAND\n-117.35,33.98,31.0,4163.0,1242.0,3928.0,1076.0,1.6943,85900.0,INLAND\n-117.35,33.97,27.0,3960.0,886.0,2807.0,838.0,3.024,122500.0,INLAND\n-117.36,33.97,32.0,1625.0,335.0,1212.0,327.0,2.7596,82200.0,INLAND\n-117.36,33.98,33.0,2070.0,469.0,1851.0,467.0,2.4667,80700.0,INLAND\n-117.37,33.96,33.0,3974.0,548.0,1398.0,528.0,7.2519,216600.0,INLAND\n-117.35,33.96,25.0,2396.0,316.0,951.0,314.0,8.2405,235200.0,INLAND\n-117.35,33.95,28.0,1650.0,210.0,557.0,211.0,7.6632,204800.0,INLAND\n-117.37,33.94,14.0,9286.0,1269.0,3565.0,1238.0,6.6635,219600.0,INLAND\n-117.36,33.92,7.0,9376.0,1181.0,3570.0,1107.0,8.5326,315200.0,INLAND\n-117.39,33.97,52.0,3307.0,553.0,1269.0,529.0,4.3176,136200.0,INLAND\n-117.38,33.97,30.0,2953.0,703.0,1406.0,580.0,2.6895,150000.0,INLAND\n-117.39,33.96,52.0,1992.0,345.0,948.0,358.0,3.2917,129300.0,INLAND\n-117.4,33.96,51.0,1806.0,322.0,709.0,298.0,3.575,125500.0,INLAND\n-117.39,33.97,48.0,1915.0,348.0,1060.0,376.0,3.4044,117900.0,INLAND\n-117.4,33.97,38.0,1383.0,238.0,649.0,232.0,5.0194,148900.0,INLAND\n-117.4,33.97,41.0,1707.0,276.0,660.0,269.0,3.8618,134800.0,INLAND\n-117.41,33.96,32.0,2837.0,617.0,1393.0,595.0,2.3798,118800.0,INLAND\n-117.41,33.96,24.0,4481.0,901.0,2398.0,823.0,3.864,123400.0,INLAND\n-117.41,33.97,34.0,2316.0,365.0,956.0,389.0,4.337,157800.0,INLAND\n-117.44,33.96,29.0,124.0,22.0,50.0,18.0,12.5381,112500.0,INLAND\n-117.43,33.96,28.0,3747.0,651.0,2399.0,646.0,3.8682,116500.0,INLAND\n-117.44,33.95,31.0,914.0,177.0,556.0,161.0,3.7344,115300.0,INLAND\n-117.4,33.95,46.0,2189.0,423.0,866.0,389.0,3.1384,111500.0,INLAND\n-117.4,33.95,32.0,1979.0,491.0,954.0,444.0,2.4408,117300.0,INLAND\n-117.41,33.95,37.0,1586.0,283.0,675.0,305.0,2.9583,132100.0,INLAND\n-117.41,33.95,37.0,1462.0,257.0,849.0,287.0,3.0542,123900.0,INLAND\n-117.42,33.95,32.0,4251.0,848.0,2494.0,798.0,2.8173,110800.0,INLAND\n-117.42,33.96,33.0,2275.0,469.0,1691.0,459.0,2.7452,98100.0,INLAND\n-117.41,33.96,27.0,2341.0,418.0,1272.0,415.0,3.0208,112700.0,INLAND\n-117.38,33.96,30.0,3153.0,623.0,1544.0,575.0,3.4491,133800.0,INLAND\n-117.39,33.95,36.0,1380.0,269.0,598.0,262.0,3.1667,122900.0,INLAND\n-117.39,33.95,35.0,1599.0,284.0,721.0,287.0,4.125,120700.0,INLAND\n-117.4,33.95,43.0,633.0,166.0,292.0,135.0,1.1601,121400.0,INLAND\n-117.39,33.96,49.0,2527.0,461.0,1344.0,451.0,4.0833,114400.0,INLAND\n-117.37,33.95,32.0,2215.0,351.0,771.0,311.0,4.3542,142600.0,INLAND\n-117.37,33.94,20.0,1682.0,296.0,706.0,291.0,4.0966,140100.0,INLAND\n-117.38,33.94,21.0,2468.0,380.0,1164.0,385.0,4.0625,136800.0,INLAND\n-117.39,33.93,26.0,3014.0,494.0,1832.0,485.0,4.8333,127900.0,INLAND\n-117.39,33.95,35.0,3306.0,680.0,1742.0,673.0,3.7109,109100.0,INLAND\n-117.4,33.94,30.0,1198.0,251.0,1019.0,214.0,3.0509,82700.0,INLAND\n-117.4,33.93,35.0,1468.0,298.0,1168.0,261.0,2.2222,81300.0,INLAND\n-117.41,33.93,35.0,793.0,150.0,669.0,128.0,4.0156,89300.0,INLAND\n-117.4,33.94,37.0,987.0,187.0,551.0,191.0,3.5865,112000.0,INLAND\n-117.4,33.94,42.0,943.0,171.0,466.0,203.0,3.1458,116000.0,INLAND\n-117.41,33.94,33.0,2074.0,476.0,911.0,420.0,2.87,117600.0,INLAND\n-117.41,33.94,29.0,3181.0,714.0,1603.0,706.0,3.25,112500.0,INLAND\n-117.42,33.93,32.0,2885.0,595.0,1509.0,590.0,3.1795,125600.0,INLAND\n-117.41,33.94,22.0,4179.0,1081.0,2096.0,1013.0,2.4435,118500.0,INLAND\n-117.42,33.94,26.0,2420.0,532.0,1383.0,469.0,3.5403,113500.0,INLAND\n-117.42,33.94,35.0,1764.0,325.0,1094.0,353.0,4.1528,113900.0,INLAND\n-117.43,33.95,36.0,2284.0,444.0,1425.0,405.0,4.0526,104500.0,INLAND\n-117.43,33.93,36.0,2386.0,396.0,1176.0,374.0,4.5122,113300.0,INLAND\n-117.43,33.93,15.0,4836.0,1368.0,3012.0,1240.0,2.1865,129300.0,INLAND\n-117.43,33.93,31.0,1273.0,262.0,686.0,254.0,2.4922,109400.0,INLAND\n-117.44,33.93,34.0,1577.0,272.0,880.0,284.0,4.6327,116000.0,INLAND\n-117.45,33.94,12.0,3539.0,869.0,1987.0,859.0,2.1023,103700.0,INLAND\n-117.44,33.94,32.0,2349.0,452.0,1479.0,425.0,3.9118,114100.0,INLAND\n-117.44,33.94,30.0,2992.0,516.0,1521.0,507.0,3.9128,126900.0,INLAND\n-117.44,33.94,32.0,1814.0,320.0,903.0,306.0,4.1776,118700.0,INLAND\n-117.44,33.93,33.0,1371.0,236.0,715.0,227.0,4.375,129900.0,INLAND\n-117.45,33.93,20.0,5998.0,1320.0,3185.0,1199.0,3.2731,113900.0,INLAND\n-117.44,33.92,33.0,2433.0,525.0,1466.0,517.0,3.0437,110800.0,INLAND\n-117.45,33.91,29.0,2320.0,422.0,1358.0,415.0,3.7333,121400.0,INLAND\n-117.45,33.92,35.0,2552.0,588.0,1840.0,551.0,2.2548,113300.0,INLAND\n-117.39,33.92,25.0,2886.0,583.0,2327.0,577.0,2.3851,113700.0,INLAND\n-117.4,33.9,32.0,1263.0,178.0,508.0,180.0,3.6667,314100.0,INLAND\n-117.44,33.9,23.0,4487.0,754.0,2609.0,778.0,4.2788,148700.0,INLAND\n-117.43,33.91,15.0,14281.0,2511.0,7540.0,2245.0,4.3222,138000.0,INLAND\n-117.42,33.89,4.0,80.0,10.0,55.0,13.0,7.7197,193800.0,INLAND\n-117.4,34.01,25.0,1858.0,366.0,1311.0,331.0,2.7083,87800.0,INLAND\n-117.41,34.01,34.0,1231.0,216.0,841.0,199.0,2.6442,92000.0,INLAND\n-117.43,34.02,33.0,3084.0,570.0,1753.0,449.0,3.05,97800.0,INLAND\n-117.42,34.02,9.0,5455.0,882.0,3015.0,858.0,4.2321,162800.0,INLAND\n-117.4,34.0,24.0,2316.0,599.0,1829.0,532.0,1.6955,86800.0,INLAND\n-117.4,34.0,31.0,1192.0,307.0,1013.0,283.0,2.0742,76200.0,INLAND\n-117.41,34.0,38.0,2228.0,571.0,1697.0,530.0,1.9052,83400.0,INLAND\n-117.43,33.98,21.0,2634.0,421.0,1376.0,406.0,4.2589,152200.0,INLAND\n-117.42,33.98,16.0,10072.0,2043.0,5913.0,1909.0,3.0606,119500.0,INLAND\n-117.45,34.01,26.0,3042.0,598.0,1720.0,551.0,2.76,95200.0,INLAND\n-117.43,34.01,34.0,2101.0,426.0,1150.0,377.0,3.0909,98300.0,INLAND\n-117.41,34.0,26.0,2372.0,621.0,1647.0,612.0,1.4719,88600.0,INLAND\n-117.42,34.0,32.0,1617.0,346.0,1153.0,385.0,3.016,96600.0,INLAND\n-117.43,33.99,18.0,3307.0,547.0,1738.0,457.0,4.566,116900.0,INLAND\n-117.44,33.99,12.0,9966.0,1517.0,5008.0,1492.0,4.5625,171300.0,INLAND\n-117.5,34.0,15.0,1929.0,317.0,1237.0,316.0,4.4063,128500.0,INLAND\n-117.49,33.99,21.0,2050.0,392.0,1153.0,336.0,4.84,116400.0,INLAND\n-117.48,33.98,20.0,2451.0,475.0,1785.0,456.0,3.3966,115000.0,INLAND\n-117.49,33.98,17.0,2727.0,462.0,1691.0,448.0,4.8371,160600.0,INLAND\n-117.5,33.98,21.0,2394.0,416.0,1291.0,381.0,4.2099,138700.0,INLAND\n-117.47,33.98,8.0,12106.0,1913.0,5810.0,1717.0,4.9886,158100.0,INLAND\n-117.48,34.01,23.0,2000.0,376.0,1361.0,388.0,4.369,121100.0,INLAND\n-117.49,34.02,35.0,2051.0,427.0,1466.0,425.0,3.6711,108200.0,INLAND\n-117.49,34.02,21.0,3736.0,738.0,2021.0,640.0,4.4545,142400.0,INLAND\n-117.51,34.02,24.0,7779.0,1835.0,3996.0,1765.0,2.1764,135300.0,INLAND\n-117.48,34.0,12.0,6751.0,1153.0,3266.0,1134.0,3.8529,145500.0,INLAND\n-117.51,34.0,36.0,3791.0,746.0,2258.0,672.0,3.2067,124700.0,INLAND\n-117.52,33.99,14.0,13562.0,2057.0,7600.0,2086.0,5.2759,182900.0,INLAND\n-117.53,33.97,34.0,1293.0,215.0,774.0,217.0,3.8906,141000.0,INLAND\n-117.53,33.97,29.0,1430.0,273.0,872.0,283.0,4.0833,141000.0,INLAND\n-117.51,33.97,35.0,352.0,62.0,184.0,57.0,3.6691,137500.0,INLAND\n-117.53,34.02,19.0,256.0,34.0,101.0,28.0,5.3269,375000.0,INLAND\n-117.6,33.94,26.0,2925.0,575.0,1921.0,501.0,3.1859,153100.0,INLAND\n-117.55,34.0,17.0,3583.0,700.0,1587.0,719.0,2.6979,75000.0,INLAND\n-117.53,33.94,21.0,5675.0,935.0,2834.0,865.0,4.2263,203200.0,INLAND\n-117.55,33.94,30.0,5398.0,926.0,2672.0,864.0,4.4762,163900.0,INLAND\n-117.56,33.94,29.0,266.0,42.0,136.0,40.0,1.625,164300.0,INLAND\n-117.56,33.94,6.0,575.0,73.0,318.0,88.0,7.0215,257100.0,INLAND\n-117.57,33.93,3.0,1240.0,151.0,519.0,146.0,7.5408,271900.0,INLAND\n-117.55,33.95,17.0,3196.0,444.0,1581.0,462.0,5.9333,229400.0,INLAND\n-117.59,33.91,7.0,10223.0,1491.0,5205.0,1509.0,5.4872,203400.0,INLAND\n-117.57,33.9,7.0,3797.0,850.0,2369.0,720.0,3.5525,137600.0,INLAND\n-117.56,33.89,16.0,693.0,185.0,365.0,176.0,2.3417,191700.0,INLAND\n-117.55,33.89,25.0,2999.0,439.0,1396.0,458.0,5.6973,164800.0,INLAND\n-117.52,33.89,2.0,17978.0,3217.0,7305.0,2463.0,5.1695,220800.0,INLAND\n-117.6,33.91,15.0,1864.0,271.0,1006.0,288.0,7.2379,251000.0,INLAND\n-117.55,33.93,25.0,5187.0,934.0,2725.0,860.0,4.1865,154300.0,INLAND\n-117.55,33.92,24.0,2807.0,501.0,1653.0,509.0,4.8167,163300.0,INLAND\n-117.55,33.9,21.0,1839.0,324.0,871.0,307.0,3.4459,198800.0,INLAND\n-117.53,33.92,12.0,2290.0,319.0,728.0,228.0,6.1561,233500.0,INLAND\n-117.58,33.92,16.0,4157.0,586.0,2036.0,594.0,6.155,246400.0,INLAND\n-117.57,33.91,22.0,2620.0,396.0,1324.0,362.0,5.3735,214600.0,INLAND\n-117.59,33.93,17.0,338.0,47.0,200.0,46.0,7.8118,244200.0,INLAND\n-117.5,33.93,19.0,4741.0,835.0,2903.0,796.0,4.3723,135600.0,INLAND\n-117.49,33.91,17.0,5364.0,1020.0,3754.0,936.0,3.2857,139100.0,INLAND\n-117.5,33.92,31.0,2529.0,513.0,1504.0,426.0,2.9821,115600.0,INLAND\n-117.5,33.92,28.0,2101.0,337.0,1061.0,348.0,4.55,146800.0,INLAND\n-117.51,33.95,12.0,9016.0,1486.0,4285.0,1457.0,4.9984,169100.0,INLAND\n-117.46,33.95,34.0,1565.0,296.0,1142.0,328.0,3.6979,99600.0,INLAND\n-117.47,33.95,15.0,6248.0,1249.0,3795.0,1128.0,4.1264,124600.0,INLAND\n-117.5,33.96,12.0,7923.0,1470.0,4861.0,1385.0,4.2985,139200.0,INLAND\n-117.5,33.95,29.0,932.0,153.0,711.0,172.0,4.8214,143400.0,INLAND\n-117.46,33.94,26.0,2481.0,620.0,2411.0,552.0,1.7059,85800.0,INLAND\n-117.47,33.94,34.0,559.0,139.0,532.0,137.0,3.0687,88500.0,INLAND\n-117.48,33.94,19.0,1891.0,465.0,1693.0,416.0,2.7813,112900.0,INLAND\n-117.48,33.94,29.0,1625.0,336.0,1046.0,320.0,3.1985,117300.0,INLAND\n-117.49,33.94,28.0,2787.0,490.0,1684.0,467.0,4.0256,127100.0,INLAND\n-117.46,33.93,16.0,4112.0,880.0,2821.0,857.0,3.0122,114700.0,INLAND\n-117.46,33.93,19.0,4780.0,861.0,3043.0,766.0,3.7431,132800.0,INLAND\n-117.46,33.92,21.0,713.0,142.0,476.0,142.0,3.5208,121100.0,INLAND\n-117.47,33.93,33.0,919.0,208.0,724.0,235.0,3.4028,110500.0,INLAND\n-117.47,33.94,34.0,2086.0,417.0,1501.0,395.0,3.2311,105600.0,INLAND\n-117.46,33.94,35.0,1566.0,294.0,1056.0,279.0,3.5227,105400.0,INLAND\n-117.48,33.93,31.0,2191.0,459.0,1564.0,450.0,2.6776,122000.0,INLAND\n-117.47,33.92,18.0,3869.0,773.0,2500.0,726.0,3.6583,126100.0,INLAND\n-117.49,33.93,26.0,2970.0,576.0,2156.0,558.0,3.9522,124600.0,INLAND\n-117.47,33.91,21.0,3491.0,760.0,1920.0,669.0,2.2241,127300.0,INLAND\n-117.46,33.9,10.0,9738.0,2130.0,4936.0,1840.0,3.3187,144800.0,INLAND\n-117.48,33.89,14.0,10395.0,1799.0,6295.0,1855.0,4.7295,149900.0,INLAND\n-117.49,33.9,7.0,10235.0,2238.0,5271.0,2094.0,3.6071,159100.0,INLAND\n-117.48,33.91,22.0,3611.0,666.0,1869.0,649.0,4.2207,141100.0,INLAND\n-117.44,33.88,5.0,2589.0,351.0,1109.0,360.0,6.8089,334100.0,INLAND\n-117.51,33.89,16.0,5418.0,1005.0,2690.0,1088.0,4.0556,158000.0,INLAND\n-117.51,33.88,24.0,3044.0,602.0,2541.0,564.0,4.131,123800.0,INLAND\n-117.52,33.88,21.0,722.0,178.0,770.0,165.0,2.5656,102500.0,INLAND\n-117.53,33.88,22.0,2855.0,667.0,2453.0,624.0,3.1312,91000.0,INLAND\n-117.5,33.87,4.0,6755.0,1017.0,2866.0,850.0,5.0493,239800.0,INLAND\n-117.56,33.88,36.0,838.0,210.0,722.0,180.0,2.4861,96200.0,INLAND\n-117.58,33.89,14.0,1731.0,404.0,1269.0,351.0,2.3654,107900.0,INLAND\n-117.55,33.88,19.0,2472.0,618.0,2143.0,610.0,2.2372,108800.0,INLAND\n-117.57,33.87,33.0,2076.0,517.0,1374.0,480.0,2.2197,138200.0,INLAND\n-117.56,33.88,40.0,1196.0,294.0,1052.0,258.0,2.0682,113000.0,INLAND\n-117.57,33.88,35.0,1755.0,446.0,1453.0,428.0,2.316,119400.0,INLAND\n-117.57,33.88,39.0,679.0,164.0,769.0,179.0,2.3036,110600.0,INLAND\n-117.58,33.87,42.0,765.0,171.0,590.0,177.0,1.6875,113500.0,INLAND\n-117.59,33.88,7.0,3586.0,959.0,2695.0,877.0,2.4387,117000.0,INLAND\n-117.59,33.88,13.0,3239.0,849.0,2751.0,813.0,2.6111,107000.0,INLAND\n-117.58,33.88,16.0,1739.0,478.0,1235.0,420.0,2.2969,116100.0,INLAND\n-117.57,33.87,37.0,621.0,156.0,443.0,135.0,2.3333,122800.0,INLAND\n-117.57,33.87,27.0,1786.0,287.0,939.0,278.0,5.1929,165000.0,INLAND\n-117.58,33.87,17.0,2772.0,449.0,1685.0,461.0,5.0464,163900.0,INLAND\n-117.58,33.87,30.0,701.0,131.0,356.0,125.0,3.2917,144300.0,INLAND\n-117.58,33.87,34.0,1511.0,272.0,773.0,265.0,3.5313,142100.0,INLAND\n-117.55,33.85,4.0,8207.0,1373.0,3887.0,1304.0,4.8686,195300.0,INLAND\n-117.56,33.83,28.0,895.0,127.0,346.0,115.0,5.4788,339300.0,INLAND\n-117.58,33.85,6.0,16431.0,2640.0,8222.0,2553.0,5.2861,195100.0,INLAND\n-117.54,33.82,6.0,202.0,29.0,75.0,28.0,4.125,216700.0,INLAND\n-117.55,33.83,6.0,502.0,76.0,228.0,65.0,4.2386,500001.0,INLAND\n-117.6,33.85,9.0,6538.0,955.0,2928.0,892.0,5.3006,221400.0,<1H OCEAN\n-117.56,33.86,25.0,6964.0,1066.0,3240.0,1036.0,5.2898,177100.0,INLAND\n-117.55,33.87,18.0,8136.0,1584.0,4976.0,1516.0,3.9414,137100.0,INLAND\n-117.64,33.88,13.0,8010.0,1366.0,3920.0,1309.0,5.536,204800.0,<1H OCEAN\n-117.6,33.87,18.0,6450.0,1165.0,3716.0,1113.0,4.2721,150300.0,INLAND\n-117.6,33.86,23.0,2949.0,473.0,1671.0,477.0,5.195,161000.0,INLAND\n-117.6,33.87,15.0,7626.0,1570.0,3823.0,1415.0,3.4419,138100.0,INLAND\n-117.64,33.87,2.0,17470.0,2727.0,5964.0,1985.0,6.2308,257900.0,<1H OCEAN\n-117.52,33.84,20.0,688.0,146.0,575.0,144.0,3.55,111000.0,INLAND\n-117.52,33.83,22.0,2397.0,400.0,1347.0,403.0,4.46,189800.0,INLAND\n-117.53,33.83,7.0,2191.0,324.0,1156.0,310.0,5.5362,195600.0,INLAND\n-117.54,33.76,5.0,5846.0,1035.0,3258.0,1001.0,4.7965,160800.0,<1H OCEAN\n-117.52,33.82,14.0,3776.0,580.0,1877.0,559.0,5.1365,215000.0,INLAND\n-117.36,33.88,15.0,2857.0,421.0,1361.0,382.0,4.6875,189800.0,INLAND\n-117.33,33.9,2.0,12837.0,1842.0,4636.0,1453.0,5.1512,187800.0,INLAND\n-117.34,33.89,17.0,2678.0,394.0,1225.0,367.0,5.363,211300.0,INLAND\n-117.38,33.89,12.0,3964.0,524.0,1707.0,549.0,5.1624,267900.0,INLAND\n-117.36,33.88,10.0,5600.0,848.0,2573.0,788.0,5.0346,240500.0,INLAND\n-117.26,33.86,16.0,1171.0,235.0,659.0,216.0,3.1103,110000.0,INLAND\n-117.33,33.87,14.0,2300.0,335.0,1001.0,311.0,5.1045,161300.0,INLAND\n-117.3,33.85,15.0,3991.0,751.0,2317.0,657.0,2.9542,127900.0,INLAND\n-117.28,33.85,16.0,3498.0,702.0,2372.0,672.0,2.3229,118000.0,INLAND\n-117.43,33.81,13.0,4770.0,718.0,1985.0,662.0,4.2273,295200.0,INLAND\n-117.4,33.76,8.0,1954.0,330.0,973.0,321.0,4.4875,249100.0,INLAND\n-117.4,33.85,9.0,7538.0,1125.0,3450.0,1077.0,5.4625,223600.0,INLAND\n-117.26,33.84,12.0,1159.0,209.0,523.0,159.0,2.7232,123200.0,INLAND\n-117.32,33.87,15.0,826.0,138.0,440.0,134.0,4.8125,173900.0,INLAND\n-117.28,33.89,33.0,6982.0,1371.0,5650.0,1195.0,2.5379,152700.0,INLAND\n-117.34,33.96,15.0,6437.0,1298.0,2805.0,1205.0,4.1883,184500.0,INLAND\n-117.32,33.96,19.0,3216.0,666.0,1363.0,629.0,3.7585,144500.0,INLAND\n-117.34,33.94,20.0,4589.0,594.0,1660.0,595.0,7.4141,236500.0,INLAND\n-117.34,33.94,13.0,7910.0,,3382.0,1176.0,5.5563,214500.0,INLAND\n-117.31,33.94,7.0,11232.0,1791.0,4218.0,1644.0,5.2713,216500.0,INLAND\n-117.33,33.98,52.0,1417.0,353.0,881.0,300.0,1.9531,162500.0,INLAND\n-117.33,33.97,8.0,152.0,19.0,1275.0,20.0,1.625,162500.0,INLAND\n-117.34,34.0,27.0,321.0,64.0,214.0,67.0,3.175,101600.0,INLAND\n-117.34,33.98,10.0,17286.0,4952.0,9851.0,4616.0,1.7579,103400.0,INLAND\n-117.32,33.99,27.0,5464.0,850.0,2400.0,836.0,4.711,133500.0,INLAND\n-117.31,33.97,28.0,3420.0,691.0,1502.0,656.0,3.4896,140300.0,INLAND\n-117.29,33.97,4.0,18767.0,3032.0,8805.0,2723.0,4.6667,160600.0,INLAND\n-117.35,34.01,23.0,3707.0,769.0,1938.0,658.0,2.725,95300.0,INLAND\n-117.34,34.02,28.0,2683.0,708.0,2047.0,636.0,2.275,85400.0,INLAND\n-117.32,34.01,23.0,3021.0,527.0,1580.0,533.0,4.4063,129900.0,INLAND\n-117.24,33.95,11.0,6617.0,1118.0,3710.0,1087.0,4.7877,132600.0,INLAND\n-117.23,33.94,7.0,13195.0,2696.0,6763.0,2437.0,3.5851,142000.0,INLAND\n-117.25,33.95,5.0,13096.0,2208.0,6780.0,2180.0,4.2775,138700.0,INLAND\n-117.23,33.96,5.0,9179.0,1361.0,4573.0,1294.0,5.253,163300.0,INLAND\n-117.21,33.95,5.0,8403.0,1240.0,3962.0,1150.0,5.2174,155500.0,INLAND\n-117.14,33.94,5.0,4873.0,639.0,1947.0,568.0,6.3223,223200.0,INLAND\n-117.21,33.97,3.0,18356.0,2537.0,8437.0,2342.0,5.6409,197700.0,INLAND\n-117.28,33.94,10.0,972.0,212.0,773.0,219.0,1.3125,135700.0,INLAND\n-117.28,33.92,35.0,3623.0,841.0,2721.0,766.0,2.1574,86900.0,INLAND\n-117.27,33.92,13.0,8443.0,1744.0,4885.0,1470.0,3.0907,127200.0,INLAND\n-117.27,33.93,2.0,337.0,55.0,115.0,49.0,3.1042,164800.0,INLAND\n-117.24,33.94,15.0,1569.0,423.0,1123.0,369.0,1.6111,113900.0,INLAND\n-117.24,33.93,12.0,7105.0,1447.0,4520.0,1333.0,3.2705,113200.0,INLAND\n-117.25,33.93,8.0,10110.0,1761.0,5804.0,1703.0,4.2654,137600.0,INLAND\n-117.25,33.92,7.0,9812.0,1914.0,5595.0,1729.0,4.1482,124600.0,INLAND\n-117.23,33.91,9.0,11654.0,2100.0,7596.0,2127.0,4.0473,127200.0,INLAND\n-117.23,33.89,5.0,11775.0,2031.0,6686.0,1911.0,4.1953,136600.0,INLAND\n-117.23,33.94,8.0,2405.0,537.0,1594.0,517.0,3.0789,114200.0,INLAND\n-117.22,33.93,14.0,5104.0,1026.0,3513.0,972.0,3.2148,117000.0,INLAND\n-117.22,33.92,5.0,16884.0,2865.0,9509.0,2688.0,4.0938,130900.0,INLAND\n-117.21,33.93,4.0,10002.0,1468.0,5439.0,1397.0,5.0223,152600.0,INLAND\n-117.16,33.92,12.0,3236.0,502.0,1610.0,502.0,4.7568,143500.0,INLAND\n-117.22,33.9,8.0,8302.0,1461.0,5155.0,1370.0,4.0467,121500.0,INLAND\n-117.13,33.89,4.0,1611.0,239.0,275.0,84.0,3.5781,244400.0,INLAND\n-117.19,33.9,3.0,21060.0,3366.0,9623.0,2812.0,4.189,143000.0,INLAND\n-117.22,33.87,16.0,56.0,7.0,39.0,14.0,2.625,500001.0,INLAND\n-117.24,33.85,8.0,1031.0,201.0,606.0,179.0,2.8194,136300.0,INLAND\n-117.17,33.83,7.0,77.0,12.0,64.0,15.0,4.6,187500.0,INLAND\n-117.2,33.83,14.0,1265.0,230.0,621.0,173.0,3.6618,161300.0,INLAND\n-117.23,33.83,2.0,1424.0,251.0,681.0,192.0,4.0833,100000.0,INLAND\n-117.21,33.82,2.0,4198.0,805.0,1943.0,673.0,3.9052,122100.0,INLAND\n-117.22,33.81,4.0,9911.0,1946.0,5145.0,1661.0,3.4237,113700.0,INLAND\n-117.21,33.71,16.0,8476.0,1758.0,2711.0,1427.0,2.1848,97900.0,<1H OCEAN\n-117.2,33.72,8.0,5528.0,1073.0,1674.0,918.0,2.5335,110100.0,<1H OCEAN\n-117.2,33.72,16.0,5373.0,1079.0,1573.0,933.0,1.9912,98600.0,<1H OCEAN\n-117.2,33.71,24.0,4210.0,920.0,1283.0,829.0,2.0881,83300.0,<1H OCEAN\n-117.2,33.7,23.0,6323.0,1196.0,1984.0,1124.0,2.3276,92400.0,<1H OCEAN\n-117.19,33.7,24.0,5783.0,1256.0,1990.0,1151.0,1.9014,83500.0,<1H OCEAN\n-117.19,33.69,3.0,6484.0,1037.0,3295.0,1074.0,4.5881,136400.0,<1H OCEAN\n-117.27,33.68,8.0,26322.0,4072.0,9360.0,3361.0,5.3238,228900.0,<1H OCEAN\n-117.25,33.7,10.0,5156.0,941.0,2294.0,747.0,3.58,113400.0,<1H OCEAN\n-117.23,33.68,10.0,3659.0,650.0,1476.0,515.0,3.8869,125900.0,<1H OCEAN\n-117.22,33.66,12.0,1869.0,356.0,1007.0,323.0,3.125,117200.0,<1H OCEAN\n-117.17,33.66,2.0,7401.0,1187.0,2826.0,839.0,4.1386,177300.0,<1H OCEAN\n-117.07,33.67,11.0,939.0,187.0,557.0,190.0,2.375,145800.0,INLAND\n-117.11,33.83,14.0,2715.0,500.0,1540.0,464.0,3.8036,139600.0,INLAND\n-117.11,33.78,13.0,1914.0,339.0,930.0,304.0,4.1875,161200.0,INLAND\n-117.08,33.82,6.0,1771.0,293.0,935.0,279.0,4.065,148200.0,INLAND\n-117.18,33.78,7.0,1697.0,424.0,808.0,354.0,1.3417,169300.0,INLAND\n-117.11,33.75,17.0,4174.0,851.0,1845.0,780.0,2.2618,96100.0,INLAND\n-117.16,33.76,11.0,4934.0,929.0,2508.0,840.0,2.625,155400.0,INLAND\n-117.22,33.74,7.0,1810.0,386.0,931.0,355.0,2.5221,109200.0,<1H OCEAN\n-117.06,33.78,17.0,2813.0,565.0,1345.0,488.0,2.5847,145300.0,INLAND\n-117.14,33.81,13.0,4496.0,756.0,2044.0,695.0,3.2778,148800.0,INLAND\n-117.22,33.8,3.0,5284.0,920.0,2703.0,729.0,4.0717,126500.0,INLAND\n-117.09,33.71,13.0,1974.0,426.0,1276.0,408.0,1.972,90500.0,INLAND\n-117.07,33.72,16.0,4928.0,960.0,2132.0,853.0,2.7983,112500.0,INLAND\n-117.11,33.74,18.0,4799.0,1035.0,1966.0,944.0,2.1182,71300.0,INLAND\n-117.15,33.7,2.0,6305.0,1265.0,2489.0,1152.0,3.1319,111500.0,INLAND\n-117.16,33.73,10.0,2381.0,454.0,1323.0,477.0,2.6322,140700.0,INLAND\n-117.23,33.79,17.0,3318.0,759.0,2016.0,673.0,2.2969,89300.0,INLAND\n-117.23,33.78,23.0,3465.0,703.0,2672.0,607.0,1.9767,81500.0,INLAND\n-117.23,33.77,5.0,2108.0,496.0,1666.0,461.0,2.0,83000.0,INLAND\n-117.29,33.83,15.0,4173.0,804.0,2393.0,713.0,2.4662,118300.0,INLAND\n-117.32,33.8,11.0,3196.0,576.0,1757.0,552.0,4.0982,173300.0,INLAND\n-117.26,33.81,22.0,4249.0,922.0,2405.0,846.0,2.1549,146500.0,INLAND\n-117.24,33.77,9.0,6907.0,1379.0,3665.0,1290.0,2.8401,104200.0,INLAND\n-117.31,33.75,19.0,3173.0,678.0,2204.0,606.0,2.1484,129200.0,<1H OCEAN\n-117.27,33.77,16.0,2876.0,576.0,1859.0,545.0,2.0878,101300.0,<1H OCEAN\n-117.29,33.72,19.0,2248.0,427.0,1207.0,368.0,2.817,110000.0,<1H OCEAN\n-117.28,33.72,11.0,1161.0,235.0,640.0,210.0,2.1667,114600.0,<1H OCEAN\n-117.39,33.69,5.0,6529.0,997.0,3464.0,1006.0,5.3275,168700.0,<1H OCEAN\n-117.37,33.7,8.0,4345.0,865.0,2425.0,785.0,3.2481,123800.0,<1H OCEAN\n-117.34,33.71,10.0,2591.0,486.0,1255.0,425.0,3.1513,154300.0,<1H OCEAN\n-117.31,33.67,9.0,981.0,169.0,596.0,156.0,3.1832,157400.0,<1H OCEAN\n-117.35,33.69,11.0,1229.0,236.0,581.0,190.0,3.102,111300.0,<1H OCEAN\n-117.33,33.67,27.0,4376.0,1003.0,2667.0,870.0,1.9194,100600.0,<1H OCEAN\n-117.35,33.68,10.0,516.0,107.0,282.0,96.0,4.2788,125000.0,<1H OCEAN\n-117.38,33.67,9.0,13288.0,2728.0,7235.0,2350.0,3.375,131800.0,<1H OCEAN\n-117.38,33.67,17.0,10145.0,2306.0,4776.0,1749.0,2.2423,132600.0,<1H OCEAN\n-117.35,33.64,23.0,6859.0,1535.0,3405.0,1351.0,2.5395,109200.0,<1H OCEAN\n-117.29,33.63,7.0,16010.0,2726.0,7139.0,2426.0,3.8056,162200.0,<1H OCEAN\n-117.28,33.66,15.0,4573.0,928.0,2513.0,832.0,2.6949,163600.0,<1H OCEAN\n-117.43,33.55,8.0,446.0,62.0,188.0,68.0,9.4356,465600.0,<1H OCEAN\n-117.36,33.6,10.0,4097.0,813.0,2082.0,731.0,3.2258,159300.0,<1H OCEAN\n-117.25,33.65,10.0,1652.0,316.0,725.0,233.0,3.5125,155600.0,<1H OCEAN\n-117.19,33.64,12.0,1481.0,265.0,757.0,243.0,3.235,210700.0,<1H OCEAN\n-117.21,33.61,7.0,7722.0,1324.0,2975.0,1161.0,3.6273,150900.0,<1H OCEAN\n-117.2,33.58,2.0,30450.0,5033.0,9419.0,3197.0,4.5936,174300.0,<1H OCEAN\n-117.16,33.61,3.0,2744.0,428.0,1223.0,366.0,4.7944,215300.0,<1H OCEAN\n-117.12,33.61,2.0,2569.0,431.0,1232.0,388.0,4.3651,145600.0,<1H OCEAN\n-117.16,33.57,2.0,20391.0,3245.0,7132.0,2716.0,3.9443,187300.0,<1H OCEAN\n-117.16,33.54,4.0,4952.0,1000.0,2912.0,943.0,3.7538,147500.0,<1H OCEAN\n-117.05,33.52,5.0,3471.0,530.0,1541.0,502.0,4.8083,347700.0,<1H OCEAN\n-116.96,33.52,9.0,2802.0,471.0,1155.0,421.0,4.125,392100.0,INLAND\n-117.1,33.56,6.0,1868.0,289.0,750.0,247.0,4.3833,307600.0,<1H OCEAN\n-117.02,33.6,7.0,1972.0,352.0,964.0,317.0,3.244,337200.0,INLAND\n-116.96,33.62,8.0,1003.0,167.0,388.0,140.0,4.2917,221900.0,INLAND\n-117.22,33.48,5.0,1585.0,247.0,510.0,181.0,6.9136,493300.0,<1H OCEAN\n-117.19,33.53,6.0,108.0,18.0,43.0,17.0,3.475,187500.0,<1H OCEAN\n-117.18,33.51,13.0,270.0,42.0,120.0,42.0,6.993,500001.0,<1H OCEAN\n-117.12,33.49,4.0,21988.0,4055.0,8824.0,3252.0,3.9963,191100.0,<1H OCEAN\n-117.12,33.52,4.0,30401.0,4957.0,13251.0,4339.0,4.5841,212300.0,<1H OCEAN\n-117.15,33.45,4.0,9089.0,1413.0,3886.0,1243.0,4.6904,174200.0,<1H OCEAN\n-116.99,33.46,13.0,1614.0,410.0,846.0,270.0,2.83,43000.0,<1H OCEAN\n-117.23,33.57,6.0,13724.0,2269.0,5860.0,1986.0,3.9617,183000.0,<1H OCEAN\n-117.27,33.55,4.0,6112.0,890.0,2088.0,712.0,5.5351,429000.0,<1H OCEAN\n-117.32,33.51,4.0,966.0,133.0,311.0,92.0,5.2066,500001.0,<1H OCEAN\n-117.02,33.71,6.0,8278.0,1579.0,3062.0,1446.0,3.0043,134700.0,INLAND\n-116.91,33.71,19.0,6807.0,1164.0,2703.0,1055.0,3.1591,189700.0,INLAND\n-116.89,33.73,15.0,2094.0,316.0,937.0,277.0,5.3623,201300.0,INLAND\n-116.95,33.68,11.0,1183.0,178.0,543.0,147.0,4.4792,173900.0,INLAND\n-116.9,33.65,15.0,652.0,149.0,248.0,97.0,2.1071,93800.0,INLAND\n-116.92,33.63,18.0,397.0,89.0,239.0,80.0,2.8125,143800.0,INLAND\n-116.99,33.73,13.0,16148.0,3474.0,6159.0,3232.0,1.9961,97800.0,INLAND\n-116.9,33.74,14.0,2281.0,426.0,894.0,430.0,2.3712,127900.0,INLAND\n-116.95,33.75,19.0,2238.0,573.0,1190.0,507.0,2.0714,85800.0,INLAND\n-116.95,33.75,23.0,4676.0,1096.0,2770.0,1057.0,1.7847,109500.0,INLAND\n-116.93,33.75,14.0,6027.0,1148.0,3136.0,1036.0,2.964,121500.0,INLAND\n-116.93,33.74,15.0,3757.0,666.0,1693.0,654.0,3.6806,112800.0,INLAND\n-116.95,33.74,20.0,2233.0,431.0,1024.0,399.0,2.4554,89400.0,INLAND\n-116.96,33.73,20.0,4735.0,973.0,2306.0,904.0,3.069,87000.0,INLAND\n-116.95,33.73,21.0,4587.0,810.0,2233.0,765.0,3.2371,94500.0,INLAND\n-116.95,33.74,18.0,1996.0,405.0,1270.0,400.0,2.7083,91200.0,INLAND\n-116.94,33.74,19.0,2901.0,445.0,1414.0,475.0,4.6406,118900.0,INLAND\n-116.94,33.73,17.0,5160.0,851.0,2344.0,781.0,3.7175,120000.0,INLAND\n-116.93,33.73,13.0,3603.0,573.0,1644.0,515.0,4.0433,132300.0,INLAND\n-116.96,33.75,35.0,3269.0,757.0,2328.0,705.0,2.5898,76300.0,INLAND\n-116.97,33.74,31.0,2712.0,628.0,1519.0,629.0,1.942,86200.0,INLAND\n-116.97,33.75,22.0,3740.0,965.0,2011.0,824.0,1.3039,77500.0,INLAND\n-116.99,33.75,18.0,9601.0,2401.0,4002.0,2106.0,1.4366,77000.0,INLAND\n-116.98,33.74,25.0,4952.0,1062.0,1589.0,1024.0,1.8446,85700.0,INLAND\n-116.97,33.74,20.0,3674.0,792.0,1498.0,758.0,2.2161,76900.0,INLAND\n-116.96,33.74,19.0,3649.0,755.0,1717.0,696.0,2.2115,87600.0,INLAND\n-116.94,33.77,14.0,7240.0,1410.0,2708.0,1240.0,2.4145,137600.0,INLAND\n-117.01,33.75,15.0,2873.0,903.0,1094.0,659.0,1.8015,105100.0,INLAND\n-116.95,33.76,10.0,6890.0,1702.0,3141.0,1451.0,1.7079,95900.0,INLAND\n-116.98,33.77,12.0,5829.0,1309.0,2711.0,1118.0,1.9707,107900.0,INLAND\n-116.99,33.76,12.0,7626.0,1704.0,2823.0,1554.0,2.1722,69400.0,INLAND\n-117.0,33.74,8.0,5330.0,1529.0,2143.0,1107.0,2.1103,94400.0,INLAND\n-117.02,33.73,14.0,3700.0,750.0,1171.0,695.0,1.9476,112500.0,INLAND\n-116.99,33.77,7.0,10352.0,2007.0,3559.0,1689.0,2.2925,113100.0,INLAND\n-117.02,33.76,20.0,1317.0,203.0,453.0,158.0,2.8393,120700.0,INLAND\n-116.98,33.83,15.0,2228.0,472.0,653.0,350.0,2.683,139300.0,INLAND\n-117.02,33.81,10.0,6317.0,1335.0,2625.0,1094.0,2.3,108900.0,INLAND\n-116.95,33.79,8.0,10997.0,2205.0,5060.0,1949.0,2.1979,95300.0,INLAND\n-116.95,33.79,20.0,2399.0,546.0,1726.0,542.0,1.8845,77700.0,INLAND\n-116.96,33.79,21.0,2990.0,691.0,2108.0,660.0,2.0135,83000.0,INLAND\n-116.97,33.78,24.0,2680.0,606.0,1728.0,527.0,2.535,74800.0,INLAND\n-116.95,33.78,24.0,3409.0,804.0,1939.0,739.0,1.7303,74000.0,INLAND\n-116.89,33.79,12.0,701.0,130.0,434.0,110.0,2.0577,56700.0,INLAND\n-116.87,33.76,5.0,4116.0,761.0,1714.0,717.0,2.5612,130800.0,INLAND\n-116.88,33.74,20.0,3111.0,623.0,1000.0,508.0,1.5982,140000.0,INLAND\n-116.89,33.75,23.0,2719.0,538.0,930.0,485.0,2.0154,81700.0,INLAND\n-116.91,33.75,13.0,10886.0,2127.0,4266.0,1955.0,2.3169,123400.0,INLAND\n-116.86,33.73,13.0,2604.0,443.0,978.0,417.0,2.933,170700.0,INLAND\n-117.04,34.0,25.0,3750.0,781.0,1594.0,785.0,2.4167,104900.0,INLAND\n-117.06,34.0,33.0,1575.0,326.0,879.0,282.0,2.5357,94400.0,INLAND\n-117.04,34.0,21.0,4624.0,852.0,2174.0,812.0,3.5255,132100.0,INLAND\n-116.99,33.99,22.0,4227.0,658.0,1849.0,619.0,4.7356,195900.0,INLAND\n-117.01,33.97,18.0,4775.0,886.0,1868.0,836.0,2.3355,118800.0,INLAND\n-116.95,33.97,14.0,5320.0,974.0,1947.0,843.0,3.1393,116300.0,INLAND\n-116.97,33.96,12.0,5876.0,1222.0,2992.0,1151.0,2.4322,112100.0,INLAND\n-117.11,33.98,25.0,1254.0,312.0,715.0,301.0,2.7344,149000.0,INLAND\n-117.02,33.95,5.0,1822.0,367.0,798.0,313.0,2.8783,105200.0,INLAND\n-117.03,33.89,6.0,78.0,11.0,27.0,10.0,3.125,187500.0,INLAND\n-116.96,33.94,22.0,1999.0,497.0,1304.0,479.0,1.4063,81900.0,INLAND\n-116.99,33.92,26.0,503.0,69.0,293.0,59.0,3.7083,147500.0,INLAND\n-116.86,33.97,11.0,658.0,131.0,376.0,120.0,2.3977,58000.0,INLAND\n-116.79,33.99,16.0,319.0,68.0,212.0,67.0,1.4688,90000.0,INLAND\n-116.77,33.92,19.0,2307.0,525.0,1266.0,437.0,1.6875,63000.0,INLAND\n-116.81,33.9,17.0,2009.0,469.0,820.0,381.0,1.3286,81800.0,INLAND\n-116.89,33.86,2.0,6900.0,1238.0,1950.0,980.0,3.0417,146300.0,INLAND\n-116.95,33.86,1.0,6.0,2.0,8.0,2.0,1.625,55000.0,INLAND\n-116.86,33.84,18.0,521.0,118.0,174.0,74.0,2.7788,91100.0,INLAND\n-116.98,33.94,27.0,3459.0,640.0,1760.0,654.0,3.4545,89800.0,INLAND\n-116.98,33.93,40.0,2277.0,498.0,1391.0,453.0,1.9472,73200.0,INLAND\n-116.97,33.94,29.0,3197.0,632.0,1722.0,603.0,3.0432,91200.0,INLAND\n-116.97,33.93,29.0,2793.0,722.0,1583.0,626.0,1.424,73200.0,INLAND\n-116.98,33.93,33.0,376.0,83.0,267.0,88.0,2.1581,68300.0,INLAND\n-116.9,33.98,30.0,3915.0,672.0,1820.0,643.0,3.6339,98600.0,INLAND\n-116.93,33.93,13.0,7804.0,1594.0,3297.0,1469.0,2.0549,95600.0,INLAND\n-116.89,33.93,29.0,4549.0,916.0,2494.0,912.0,2.0976,72600.0,INLAND\n-116.88,33.93,37.0,1495.0,429.0,865.0,342.0,1.2188,55000.0,INLAND\n-116.9,33.93,34.0,3183.0,738.0,1820.0,647.0,2.2321,71800.0,INLAND\n-116.91,34.0,18.0,553.0,100.0,215.0,82.0,5.5,193800.0,INLAND\n-116.87,33.94,35.0,4448.0,906.0,2736.0,843.0,2.218,73400.0,INLAND\n-116.87,33.93,32.0,3141.0,812.0,2589.0,721.0,1.4556,54600.0,INLAND\n-116.87,33.91,37.0,1858.0,361.0,1632.0,310.0,2.7536,73100.0,INLAND\n-116.89,33.92,10.0,2653.0,621.0,1967.0,598.0,2.6643,81000.0,INLAND\n-116.75,33.83,16.0,5277.0,1070.0,657.0,276.0,3.3333,143400.0,INLAND\n-116.8,33.8,35.0,324.0,63.0,158.0,39.0,3.4167,100000.0,INLAND\n-116.71,33.75,25.0,10665.0,2161.0,1874.0,852.0,3.0625,150500.0,INLAND\n-116.68,33.71,21.0,3460.0,711.0,658.0,255.0,3.5882,161100.0,INLAND\n-116.74,33.62,11.0,2385.0,661.0,682.0,242.0,2.9141,214300.0,INLAND\n-116.8,33.52,3.0,830.0,145.0,272.0,104.0,3.8281,163500.0,INLAND\n-116.89,33.48,14.0,1016.0,219.0,443.0,169.0,2.8071,137500.0,INLAND\n-116.87,33.57,12.0,1153.0,265.0,446.0,195.0,3.038,128100.0,INLAND\n-116.72,33.56,13.0,3166.0,682.0,1250.0,475.0,2.355,122900.0,INLAND\n-116.48,33.61,8.0,1294.0,272.0,457.0,199.0,2.9167,115300.0,INLAND\n-116.57,33.64,10.0,489.0,82.0,183.0,74.0,6.2702,345500.0,INLAND\n-116.76,33.46,6.0,1251.0,268.0,544.0,216.0,3.0694,173400.0,INLAND\n-116.42,33.51,26.0,186.0,48.0,102.0,39.0,2.5625,103100.0,INLAND\n-116.6,33.49,16.0,3730.0,827.0,1346.0,592.0,2.183,113500.0,INLAND\n-116.69,33.5,13.0,1187.0,255.0,442.0,179.0,1.9107,155700.0,INLAND\n-116.39,33.82,15.0,11115.0,2257.0,4122.0,1653.0,2.7219,74400.0,INLAND\n-116.71,33.94,12.0,549.0,109.0,209.0,90.0,3.0208,66300.0,INLAND\n-116.51,33.89,21.0,1284.0,306.0,537.0,233.0,1.95,61000.0,INLAND\n-116.61,33.93,35.0,321.0,71.0,157.0,61.0,2.8056,68100.0,INLAND\n-116.57,33.94,29.0,551.0,166.0,224.0,107.0,1.1917,50000.0,INLAND\n-116.57,34.0,20.0,260.0,67.0,69.0,50.0,3.5208,76600.0,INLAND\n-116.44,33.93,17.0,5293.0,1266.0,1201.0,599.0,1.6849,88400.0,INLAND\n-116.36,33.88,11.0,12557.0,3098.0,2453.0,1232.0,1.7844,78500.0,INLAND\n-116.52,33.97,13.0,3921.0,754.0,1902.0,665.0,3.3616,89600.0,INLAND\n-116.51,33.96,16.0,4913.0,1395.0,2518.0,1132.0,1.4665,61100.0,INLAND\n-116.53,33.95,18.0,2990.0,648.0,1280.0,532.0,2.625,68200.0,INLAND\n-116.5,33.95,10.0,7249.0,1882.0,4274.0,1621.0,1.6983,66600.0,INLAND\n-116.51,33.94,12.0,3369.0,780.0,1315.0,584.0,1.7388,66000.0,INLAND\n-116.48,33.96,11.0,1381.0,300.0,644.0,248.0,2.3382,89400.0,INLAND\n-116.47,33.94,18.0,2233.0,471.0,919.0,388.0,3.2578,85200.0,INLAND\n-116.48,33.94,10.0,3254.0,913.0,923.0,486.0,1.8,81000.0,INLAND\n-116.5,33.98,5.0,4332.0,868.0,1420.0,567.0,4.0417,146400.0,INLAND\n-116.51,33.84,16.0,980.0,193.0,454.0,185.0,4.0729,100000.0,INLAND\n-116.63,33.89,22.0,1540.0,364.0,610.0,268.0,1.5227,71000.0,INLAND\n-116.52,33.84,17.0,4465.0,859.0,853.0,445.0,3.6875,130400.0,INLAND\n-116.54,33.87,16.0,3648.0,1035.0,1687.0,581.0,1.9167,70400.0,INLAND\n-116.57,33.84,18.0,7962.0,1652.0,2009.0,921.0,3.3897,230200.0,INLAND\n-116.53,33.85,16.0,10077.0,2186.0,3048.0,1337.0,2.9647,110900.0,INLAND\n-116.55,33.84,28.0,2992.0,562.0,676.0,346.0,5.7613,500001.0,INLAND\n-116.53,33.84,28.0,8399.0,1839.0,3470.0,1340.0,2.5885,159000.0,INLAND\n-116.52,33.85,13.0,7559.0,1444.0,3189.0,1105.0,3.4886,112500.0,INLAND\n-116.5,33.82,16.0,343.0,85.0,29.0,14.0,2.1042,87500.0,INLAND\n-116.49,33.82,27.0,3316.0,636.0,2362.0,532.0,2.9569,65900.0,INLAND\n-116.52,33.82,21.0,10227.0,2315.0,3623.0,1734.0,2.5212,145200.0,INLAND\n-116.54,33.82,12.0,9482.0,2501.0,2725.0,1300.0,1.5595,115600.0,INLAND\n-116.56,33.83,36.0,1765.0,399.0,451.0,264.0,2.6083,321900.0,INLAND\n-116.48,33.8,15.0,3004.0,615.0,437.0,210.0,3.6667,90000.0,INLAND\n-116.5,33.81,26.0,5032.0,1229.0,3086.0,1183.0,2.5399,94800.0,INLAND\n-116.52,33.81,12.0,12396.0,2552.0,2548.0,1265.0,3.4394,162200.0,INLAND\n-116.54,33.81,31.0,6814.0,1714.0,2628.0,1341.0,2.1176,124100.0,INLAND\n-116.54,33.81,24.0,6087.0,1217.0,1721.0,833.0,3.1493,199400.0,INLAND\n-116.49,33.8,13.0,8789.0,1875.0,1274.0,688.0,3.7396,148900.0,INLAND\n-116.54,33.8,22.0,6050.0,1387.0,1432.0,890.0,2.2216,183900.0,INLAND\n-116.57,33.76,25.0,2616.0,547.0,581.0,343.0,3.1364,301600.0,INLAND\n-116.5,33.69,20.0,4810.0,1074.0,1304.0,740.0,2.25,248100.0,INLAND\n-116.54,33.79,18.0,9374.0,1780.0,1678.0,919.0,3.9737,235600.0,INLAND\n-116.53,33.78,18.0,2547.0,463.0,411.0,214.0,2.5489,220500.0,INLAND\n-116.53,33.88,5.0,4423.0,763.0,1906.0,667.0,4.6855,125200.0,INLAND\n-116.48,33.84,5.0,5480.0,1371.0,1050.0,485.0,1.7204,137500.0,INLAND\n-116.48,33.79,14.0,9425.0,2020.0,1711.0,1000.0,2.6298,145200.0,INLAND\n-116.47,33.84,3.0,9169.0,1512.0,3838.0,1270.0,4.3111,142500.0,INLAND\n-116.46,33.82,6.0,4863.0,920.0,3010.0,828.0,3.9508,104200.0,INLAND\n-116.47,33.81,7.0,10105.0,2481.0,6274.0,2095.0,2.4497,90900.0,INLAND\n-116.46,33.79,10.0,6960.0,1487.0,1130.0,661.0,2.1411,136400.0,INLAND\n-116.43,33.81,8.0,6710.0,1343.0,2069.0,781.0,3.5223,115100.0,INLAND\n-116.42,33.79,12.0,7095.0,1260.0,1179.0,570.0,4.9444,285000.0,INLAND\n-116.43,33.78,17.0,4293.0,712.0,1091.0,464.0,6.1437,232100.0,INLAND\n-116.45,33.78,16.0,5228.0,992.0,1177.0,639.0,3.0859,134600.0,INLAND\n-116.42,33.76,14.0,16921.0,2837.0,2524.0,1262.0,7.6281,341700.0,INLAND\n-116.4,33.78,8.0,3059.0,500.0,612.0,208.0,6.8729,259200.0,INLAND\n-116.45,33.8,9.0,5534.0,1206.0,2283.0,1008.0,3.6161,99100.0,INLAND\n-116.36,33.78,6.0,24121.0,4522.0,4176.0,2221.0,3.3799,239300.0,INLAND\n-116.38,33.74,7.0,17579.0,3479.0,3581.0,1820.0,4.084,194500.0,INLAND\n-116.33,33.75,5.0,19107.0,3923.0,2880.0,1376.0,4.036,158500.0,INLAND\n-116.31,33.73,19.0,12467.0,2508.0,4086.0,1761.0,3.2846,131900.0,INLAND\n-116.46,33.78,25.0,1137.0,414.0,604.0,240.0,1.3801,55000.0,INLAND\n-116.46,33.78,33.0,2565.0,745.0,2301.0,638.0,2.5477,83000.0,INLAND\n-116.47,33.77,26.0,4300.0,767.0,1557.0,669.0,4.4107,122500.0,INLAND\n-116.47,33.78,27.0,1781.0,441.0,759.0,340.0,3.3162,113600.0,INLAND\n-116.38,33.73,10.0,11836.0,2405.0,3811.0,1570.0,4.0079,134500.0,INLAND\n-116.37,33.72,17.0,8626.0,1859.0,3497.0,1337.0,3.312,121300.0,INLAND\n-116.39,33.72,19.0,7646.0,1618.0,2496.0,1075.0,3.5569,128000.0,INLAND\n-116.38,33.71,17.0,12509.0,2460.0,2737.0,1423.0,4.5556,258100.0,INLAND\n-116.39,33.69,10.0,11659.0,2007.0,2186.0,1083.0,6.9833,238800.0,INLAND\n-116.37,33.72,19.0,6190.0,1355.0,2242.0,1043.0,3.0021,152300.0,INLAND\n-116.44,33.77,18.0,4872.0,1110.0,955.0,656.0,2.2439,97500.0,INLAND\n-116.43,33.75,24.0,2596.0,438.0,473.0,237.0,3.7727,500001.0,INLAND\n-116.44,33.74,5.0,846.0,249.0,117.0,67.0,7.9885,403300.0,INLAND\n-116.41,33.74,25.0,2475.0,476.0,910.0,387.0,3.3639,168800.0,INLAND\n-116.41,33.74,17.0,4289.0,893.0,958.0,440.0,2.4659,177800.0,INLAND\n-116.42,33.68,15.0,3895.0,782.0,900.0,529.0,2.2208,138300.0,INLAND\n-116.37,33.69,7.0,8806.0,1542.0,858.0,448.0,7.8005,318100.0,INLAND\n-116.33,33.72,11.0,12327.0,2000.0,2450.0,1139.0,7.4382,353100.0,INLAND\n-116.29,33.67,12.0,5048.0,842.0,883.0,391.0,5.6918,231300.0,INLAND\n-116.3,33.68,10.0,2387.0,481.0,863.0,304.0,2.8882,137500.0,INLAND\n-116.31,33.67,15.0,2214.0,410.0,1152.0,350.0,2.9187,93400.0,INLAND\n-116.31,33.67,11.0,4686.0,851.0,2466.0,731.0,3.3333,91800.0,INLAND\n-116.31,33.66,7.0,4497.0,831.0,2248.0,713.0,3.6354,98000.0,INLAND\n-116.31,33.65,8.0,3079.0,558.0,1572.0,474.0,4.5938,102600.0,INLAND\n-116.26,33.72,10.0,9404.0,1827.0,3208.0,1283.0,3.1086,105800.0,INLAND\n-116.25,33.69,5.0,1664.0,444.0,907.0,374.0,2.7667,92900.0,INLAND\n-116.22,33.7,9.0,3861.0,849.0,825.0,401.0,3.2833,124700.0,INLAND\n-116.24,33.76,9.0,1961.0,595.0,966.0,275.0,3.8125,96700.0,INLAND\n-116.29,33.74,6.0,12991.0,2555.0,4571.0,1926.0,4.7195,199300.0,INLAND\n-116.29,33.72,5.0,3584.0,760.0,1097.0,470.0,3.1771,167400.0,INLAND\n-116.25,33.81,24.0,880.0,187.0,507.0,169.0,3.4583,67500.0,INLAND\n-116.25,33.75,33.0,278.0,91.0,375.0,81.0,2.025,50000.0,INLAND\n-116.24,33.72,25.0,5236.0,1039.0,2725.0,935.0,3.775,93400.0,INLAND\n-116.24,33.71,10.0,9033.0,2224.0,5525.0,1845.0,2.7598,95000.0,INLAND\n-116.24,33.73,14.0,2774.0,566.0,1530.0,505.0,3.0682,104100.0,INLAND\n-116.22,33.74,26.0,4120.0,858.0,2918.0,815.0,3.3107,69400.0,INLAND\n-116.21,33.75,22.0,894.0,,830.0,202.0,3.0673,68200.0,INLAND\n-116.21,33.72,28.0,2488.0,714.0,2891.0,676.0,2.3169,68900.0,INLAND\n-116.22,33.73,38.0,1695.0,352.0,1279.0,305.0,2.1217,68500.0,INLAND\n-116.23,33.73,29.0,1133.0,221.0,918.0,239.0,2.8648,72100.0,INLAND\n-116.21,33.71,19.0,3114.0,787.0,3157.0,772.0,1.7083,82200.0,INLAND\n-116.22,33.72,28.0,826.0,258.0,979.0,245.0,1.7172,58800.0,INLAND\n-116.2,33.7,26.0,2399.0,625.0,2654.0,535.0,2.2989,60600.0,INLAND\n-116.23,33.72,32.0,4981.0,1326.0,3779.0,1186.0,1.7805,76900.0,INLAND\n-116.23,33.71,17.0,4874.0,1349.0,5032.0,1243.0,2.444,90000.0,INLAND\n-116.15,33.69,22.0,197.0,54.0,331.0,70.0,2.9286,112500.0,INLAND\n-116.11,33.64,20.0,1273.0,354.0,1548.0,355.0,2.0871,84700.0,INLAND\n-116.15,33.64,10.0,1711.0,499.0,1896.0,443.0,1.6557,65400.0,INLAND\n-116.2,33.63,23.0,1152.0,273.0,1077.0,235.0,2.5,96300.0,INLAND\n-116.21,33.66,19.0,1596.0,295.0,1201.0,282.0,3.8846,100900.0,INLAND\n-116.21,33.68,34.0,584.0,176.0,625.0,166.0,1.5809,100000.0,INLAND\n-116.25,33.68,16.0,926.0,189.0,238.0,118.0,3.0114,366700.0,INLAND\n-116.26,33.65,3.0,7437.0,1222.0,574.0,302.0,10.2948,382400.0,INLAND\n-116.17,33.53,13.0,1713.0,512.0,1978.0,442.0,2.1287,58600.0,INLAND\n-116.12,33.53,17.0,2421.0,820.0,2971.0,685.0,1.654,100000.0,INLAND\n-116.01,33.51,24.0,2985.0,958.0,4042.0,905.0,1.7344,66400.0,INLAND\n-115.84,33.49,20.0,1660.0,379.0,637.0,250.0,2.0347,68900.0,INLAND\n-116.16,33.68,12.0,1230.0,277.0,1334.0,260.0,2.2679,61400.0,INLAND\n-116.17,33.67,18.0,3585.0,800.0,3873.0,788.0,2.5714,65900.0,INLAND\n-116.17,33.66,22.0,639.0,203.0,664.0,153.0,1.9306,47500.0,INLAND\n-116.19,33.67,16.0,1859.0,476.0,1994.0,477.0,1.7297,67500.0,INLAND\n-116.18,33.67,25.0,2888.0,654.0,2940.0,660.0,2.2141,66700.0,INLAND\n-116.18,33.69,17.0,89.0,19.0,79.0,21.0,2.375,155000.0,INLAND\n-116.19,33.69,11.0,5692.0,1346.0,5682.0,1273.0,2.5383,74000.0,INLAND\n-116.08,33.86,16.0,381.0,89.0,182.0,75.0,2.425,76100.0,INLAND\n-115.22,33.54,18.0,1706.0,397.0,3424.0,283.0,1.625,53500.0,INLAND\n-114.67,33.92,17.0,97.0,24.0,29.0,15.0,1.2656,27500.0,INLAND\n-115.58,33.88,21.0,1161.0,282.0,724.0,186.0,3.1827,71700.0,INLAND\n-114.98,33.82,15.0,644.0,129.0,137.0,52.0,3.2097,71300.0,INLAND\n-114.49,33.97,17.0,2809.0,635.0,83.0,45.0,1.6154,87500.0,INLAND\n-114.65,33.6,28.0,1678.0,322.0,666.0,256.0,2.9653,94900.0,INLAND\n-114.68,33.49,20.0,1491.0,360.0,1135.0,303.0,1.6395,44400.0,INLAND\n-114.56,33.69,17.0,720.0,174.0,333.0,117.0,1.6509,85700.0,INLAND\n-114.57,33.64,14.0,1501.0,337.0,515.0,226.0,3.1917,73400.0,INLAND\n-114.57,33.57,20.0,1454.0,326.0,624.0,262.0,1.925,65500.0,INLAND\n-114.57,33.52,27.0,173.0,35.0,117.0,34.0,2.0833,45000.0,INLAND\n-114.59,33.61,34.0,4789.0,1175.0,3134.0,1056.0,2.1782,58400.0,INLAND\n-114.61,33.62,16.0,1187.0,261.0,1115.0,242.0,2.1759,61500.0,INLAND\n-114.6,33.62,16.0,3741.0,801.0,2434.0,824.0,2.6797,86500.0,INLAND\n-114.58,33.63,29.0,1387.0,236.0,671.0,239.0,3.3438,74000.0,INLAND\n-114.62,33.62,26.0,18.0,3.0,5.0,3.0,0.536,275000.0,INLAND\n-114.58,33.61,25.0,2907.0,680.0,1841.0,633.0,2.6768,82400.0,INLAND\n-114.6,33.6,21.0,1988.0,483.0,1182.0,437.0,1.625,62000.0,INLAND\n-121.43,38.58,35.0,5298.0,954.0,1933.0,918.0,3.9167,155700.0,INLAND\n-121.43,38.57,38.0,2507.0,446.0,888.0,448.0,4.0972,163700.0,INLAND\n-121.42,38.57,38.0,1878.0,338.0,710.0,342.0,3.7731,161400.0,INLAND\n-121.44,38.58,43.0,1806.0,339.0,764.0,341.0,3.9271,147100.0,INLAND\n-121.43,38.57,46.0,2443.0,476.0,939.0,457.0,3.5893,142000.0,INLAND\n-121.44,38.57,52.0,3080.0,545.0,975.0,495.0,3.776,164500.0,INLAND\n-121.44,38.58,42.0,2334.0,435.0,892.0,446.0,3.0208,148800.0,INLAND\n-121.45,38.58,44.0,2314.0,415.0,796.0,388.0,3.4914,153900.0,INLAND\n-121.45,38.57,48.0,1962.0,356.0,704.0,362.0,3.5313,147900.0,INLAND\n-121.46,38.58,52.0,4408.0,807.0,1604.0,777.0,3.8914,181600.0,INLAND\n-121.46,38.58,40.0,1394.0,397.0,689.0,353.0,1.7765,109800.0,INLAND\n-121.47,38.58,44.0,2092.0,555.0,878.0,528.0,1.5922,115100.0,INLAND\n-121.47,38.58,43.0,3807.0,952.0,1484.0,850.0,2.3266,137500.0,INLAND\n-121.47,38.58,52.0,2035.0,483.0,904.0,459.0,2.6976,109300.0,INLAND\n-121.48,38.58,52.0,576.0,146.0,273.0,127.0,2.01,94300.0,INLAND\n-121.48,38.58,48.0,2434.0,744.0,1281.0,662.0,1.6277,140600.0,INLAND\n-121.49,38.58,52.0,2151.0,664.0,1146.0,603.0,1.4034,90300.0,INLAND\n-121.48,38.59,52.0,1186.0,341.0,1038.0,320.0,1.6116,70500.0,INLAND\n-121.49,38.59,20.0,463.0,180.0,486.0,190.0,1.0313,85000.0,INLAND\n-121.49,38.58,52.0,1000.0,324.0,456.0,250.0,1.4375,168800.0,INLAND\n-121.5,38.58,5.0,761.0,306.0,2031.0,295.0,0.7526,162500.0,INLAND\n-121.5,38.58,20.0,4018.0,1220.0,1570.0,1122.0,2.5821,125000.0,INLAND\n-121.5,38.57,9.0,745.0,175.0,297.0,160.0,3.358,77500.0,INLAND\n-121.49,38.58,52.0,569.0,405.0,509.0,367.0,0.9196,137500.0,INLAND\n-121.48,38.58,42.0,1823.0,566.0,761.0,503.0,1.245,137500.0,INLAND\n-121.48,38.57,47.0,2438.0,804.0,1148.0,747.0,1.4301,141700.0,INLAND\n-121.48,38.57,52.0,567.0,193.0,272.0,187.0,1.625,187500.0,INLAND\n-121.49,38.57,49.0,1334.0,492.0,634.0,421.0,1.7568,93800.0,INLAND\n-121.49,38.57,38.0,2410.0,967.0,1091.0,829.0,1.2209,87900.0,INLAND\n-121.47,38.57,39.0,1360.0,368.0,589.0,338.0,2.1691,150000.0,INLAND\n-121.47,38.57,48.0,3136.0,926.0,1443.0,877.0,1.9034,123900.0,INLAND\n-121.48,38.57,38.0,2809.0,805.0,1243.0,785.0,1.8512,114100.0,INLAND\n-121.47,38.57,52.0,438.0,103.0,176.0,99.0,3.0217,200000.0,INLAND\n-121.47,38.57,50.0,3233.0,968.0,1223.0,837.0,1.2041,168100.0,INLAND\n-121.48,38.58,52.0,2501.0,757.0,1081.0,708.0,1.5872,157500.0,INLAND\n-121.45,38.57,52.0,2006.0,412.0,825.0,384.0,3.2963,236100.0,INLAND\n-121.45,38.56,52.0,3170.0,476.0,1027.0,457.0,4.63,233800.0,INLAND\n-121.46,38.56,52.0,1750.0,372.0,764.0,369.0,2.9191,111800.0,INLAND\n-121.46,38.57,52.0,810.0,172.0,326.0,151.0,3.1583,140000.0,INLAND\n-121.46,38.57,52.0,893.0,159.0,367.0,160.0,3.2386,213200.0,INLAND\n-121.46,38.57,52.0,1917.0,367.0,722.0,358.0,3.1484,158900.0,INLAND\n-121.46,38.57,52.0,1625.0,419.0,614.0,383.0,2.0549,156700.0,INLAND\n-121.43,38.56,41.0,1105.0,227.0,443.0,210.0,3.1827,131700.0,INLAND\n-121.44,38.56,45.0,2423.0,466.0,873.0,438.0,3.7167,131900.0,INLAND\n-121.44,38.56,52.0,906.0,165.0,257.0,166.0,2.8542,139400.0,INLAND\n-121.43,38.56,50.0,1533.0,288.0,532.0,257.0,2.5417,125900.0,INLAND\n-121.43,38.56,46.0,1316.0,244.0,452.0,245.0,3.0938,137800.0,INLAND\n-121.45,38.57,52.0,3994.0,635.0,1295.0,625.0,5.1169,232500.0,INLAND\n-121.45,38.56,52.0,3420.0,555.0,1301.0,530.0,4.0417,173800.0,INLAND\n-121.42,38.55,35.0,182.0,39.0,115.0,43.0,2.6417,98900.0,INLAND\n-121.43,38.55,44.0,3514.0,714.0,1509.0,656.0,2.7333,100100.0,INLAND\n-121.45,38.55,19.0,3374.0,808.0,1412.0,753.0,1.4889,77600.0,INLAND\n-121.46,38.56,52.0,1878.0,393.0,722.0,381.0,3.3348,122800.0,INLAND\n-121.45,38.56,51.0,1250.0,235.0,452.0,232.0,2.625,121200.0,INLAND\n-121.44,38.55,46.0,1698.0,383.0,726.0,386.0,2.9821,97000.0,INLAND\n-121.47,38.56,52.0,889.0,162.0,273.0,145.0,3.125,85600.0,INLAND\n-121.46,38.56,52.0,907.0,180.0,479.0,177.0,2.2125,104000.0,INLAND\n-121.46,38.55,52.0,3126.0,648.0,1789.0,558.0,1.7616,84100.0,INLAND\n-121.46,38.55,52.0,2094.0,463.0,1364.0,407.0,1.2235,68500.0,INLAND\n-121.47,38.55,48.0,1091.0,403.0,926.0,336.0,1.1458,65400.0,INLAND\n-121.47,38.56,52.0,1532.0,408.0,782.0,369.0,1.8911,85900.0,INLAND\n-121.47,38.56,51.0,2083.0,559.0,874.0,524.0,2.0221,95800.0,INLAND\n-121.47,38.56,44.0,1986.0,573.0,1044.0,490.0,1.7328,88100.0,INLAND\n-121.48,38.56,44.0,1151.0,263.0,518.0,258.0,2.0089,113600.0,INLAND\n-121.48,38.56,46.0,1476.0,344.0,688.0,353.0,2.7316,134700.0,INLAND\n-121.48,38.57,38.0,1145.0,324.0,596.0,288.0,1.78,114300.0,INLAND\n-121.49,38.56,42.0,900.0,239.0,506.0,231.0,1.2813,87500.0,INLAND\n-121.49,38.56,35.0,1521.0,457.0,987.0,455.0,1.9013,86900.0,INLAND\n-121.49,38.57,51.0,1492.0,385.0,736.0,365.0,1.7155,108800.0,INLAND\n-121.5,38.57,41.0,1124.0,344.0,807.0,316.0,1.4712,94600.0,INLAND\n-121.5,38.57,44.0,1375.0,351.0,766.0,321.0,2.1719,87500.0,INLAND\n-121.51,38.57,36.0,613.0,166.0,425.0,147.0,2.2031,93800.0,INLAND\n-121.5,38.57,45.0,858.0,254.0,510.0,200.0,1.0114,80000.0,INLAND\n-121.5,38.56,46.0,2646.0,645.0,1684.0,616.0,1.128,123100.0,INLAND\n-121.51,38.56,43.0,1048.0,312.0,1320.0,294.0,1.0649,137500.0,INLAND\n-121.51,38.55,45.0,3032.0,631.0,1341.0,597.0,2.8417,137900.0,INLAND\n-121.51,38.55,46.0,1485.0,278.0,531.0,291.0,2.7885,137200.0,INLAND\n-121.49,38.56,52.0,1844.0,392.0,667.0,353.0,3.0033,103500.0,INLAND\n-121.49,38.55,52.0,2515.0,460.0,836.0,442.0,3.3844,151100.0,INLAND\n-121.5,38.55,52.0,2784.0,455.0,957.0,448.0,5.6402,209800.0,INLAND\n-121.49,38.56,52.0,1777.0,368.0,624.0,350.0,3.6729,137800.0,INLAND\n-121.49,38.54,47.0,2313.0,536.0,779.0,442.0,2.5639,123000.0,INLAND\n-121.5,38.54,52.0,1145.0,133.0,334.0,138.0,8.338,405800.0,INLAND\n-121.49,38.55,51.0,4280.0,632.0,1486.0,621.0,5.0359,224100.0,INLAND\n-121.5,38.55,49.0,4094.0,634.0,1363.0,659.0,5.2362,236800.0,INLAND\n-121.5,38.54,44.0,1167.0,201.0,452.0,209.0,3.7344,179800.0,INLAND\n-121.48,38.55,52.0,2508.0,360.0,832.0,345.0,7.1035,228700.0,INLAND\n-121.48,38.55,52.0,2037.0,358.0,811.0,375.0,4.3929,162500.0,INLAND\n-121.48,38.56,50.0,1587.0,448.0,877.0,380.0,2.0833,94300.0,INLAND\n-121.48,38.55,52.0,1684.0,309.0,675.0,296.0,4.1467,175000.0,INLAND\n-121.48,38.55,52.0,2216.0,333.0,714.0,327.0,4.8603,191900.0,INLAND\n-121.48,38.56,52.0,814.0,216.0,327.0,181.0,2.8542,125000.0,INLAND\n-121.47,38.55,48.0,968.0,310.0,706.0,274.0,0.9948,65400.0,INLAND\n-121.47,38.55,29.0,1303.0,308.0,861.0,263.0,1.0208,55800.0,INLAND\n-121.47,38.54,47.0,2085.0,464.0,1346.0,402.0,1.2679,56700.0,INLAND\n-121.47,38.55,24.0,979.0,287.0,546.0,291.0,1.186,67000.0,INLAND\n-121.47,38.55,52.0,1384.0,295.0,561.0,244.0,2.0242,94600.0,INLAND\n-121.45,38.54,47.0,1159.0,250.0,810.0,244.0,2.7787,56000.0,INLAND\n-121.45,38.54,41.0,1278.0,308.0,839.0,280.0,1.4702,58300.0,INLAND\n-121.46,38.54,48.0,1001.0,205.0,605.0,175.0,1.8333,58200.0,INLAND\n-121.46,38.55,40.0,2077.0,435.0,1454.0,385.0,2.0074,57000.0,INLAND\n-121.42,38.54,29.0,1407.0,265.0,556.0,235.0,3.0521,108000.0,INLAND\n-121.43,38.54,43.0,2084.0,417.0,912.0,410.0,2.9769,92700.0,INLAND\n-121.43,38.54,44.0,1879.0,359.0,791.0,345.0,3.15,101500.0,INLAND\n-121.44,38.54,44.0,2570.0,509.0,1145.0,503.0,2.5694,92400.0,INLAND\n-121.45,38.54,48.0,3421.0,734.0,1441.0,727.0,1.9485,86600.0,INLAND\n-121.42,38.54,18.0,2525.0,501.0,1726.0,468.0,2.398,87600.0,INLAND\n-121.42,38.54,29.0,2358.0,493.0,1071.0,470.0,2.925,94300.0,INLAND\n-121.43,38.54,42.0,3321.0,688.0,1346.0,658.0,2.4618,101300.0,INLAND\n-121.44,38.54,39.0,2855.0,,1217.0,562.0,3.2404,93600.0,INLAND\n-121.44,38.54,47.0,2518.0,501.0,1308.0,471.0,2.5389,75700.0,INLAND\n-121.41,38.53,35.0,2061.0,371.0,1110.0,342.0,3.1944,79000.0,INLAND\n-121.41,38.53,37.0,1058.0,224.0,588.0,231.0,2.9737,72100.0,INLAND\n-121.42,38.53,37.0,1958.0,367.0,1171.0,366.0,2.8298,71200.0,INLAND\n-121.42,38.53,36.0,1581.0,288.0,832.0,291.0,3.4083,71800.0,INLAND\n-121.43,38.53,36.0,1488.0,294.0,846.0,279.0,3.1208,82700.0,INLAND\n-121.43,38.53,36.0,2430.0,426.0,1199.0,437.0,3.1667,81900.0,INLAND\n-121.44,38.53,37.0,1951.0,432.0,1089.0,411.0,2.3272,80600.0,INLAND\n-121.41,38.52,25.0,3087.0,720.0,2529.0,708.0,1.8689,66800.0,INLAND\n-121.42,38.51,21.0,3249.0,666.0,2611.0,663.0,1.9423,87800.0,INLAND\n-121.42,38.52,32.0,2828.0,556.0,1655.0,485.0,2.5574,72600.0,INLAND\n-121.43,38.52,43.0,2089.0,399.0,955.0,385.0,2.5898,72100.0,INLAND\n-121.43,38.52,30.0,3657.0,945.0,2925.0,899.0,1.3927,78300.0,INLAND\n-121.44,38.52,38.0,2080.0,388.0,995.0,380.0,2.7697,76600.0,INLAND\n-121.5,38.53,39.0,3184.0,593.0,1188.0,572.0,4.6923,192000.0,INLAND\n-121.5,38.53,37.0,3642.0,684.0,1508.0,657.0,3.5231,114300.0,INLAND\n-121.51,38.53,34.0,1613.0,265.0,631.0,266.0,4.25,191900.0,INLAND\n-121.51,38.53,36.0,2603.0,408.0,966.0,419.0,5.3135,216600.0,INLAND\n-121.5,38.52,37.0,2008.0,466.0,1261.0,427.0,2.2574,59100.0,INLAND\n-121.51,38.51,33.0,2918.0,439.0,1085.0,427.0,5.5208,171300.0,INLAND\n-121.51,38.51,31.0,1595.0,217.0,542.0,236.0,6.6112,251600.0,INLAND\n-121.51,38.52,30.0,3236.0,588.0,1167.0,569.0,4.0972,181400.0,INLAND\n-121.48,38.53,38.0,1451.0,315.0,786.0,340.0,2.3487,101600.0,INLAND\n-121.49,38.53,40.0,2966.0,536.0,1225.0,505.0,3.125,130600.0,INLAND\n-121.49,38.54,37.0,1655.0,393.0,841.0,355.0,1.6932,78400.0,INLAND\n-121.48,38.53,43.0,1378.0,280.0,708.0,280.0,2.3542,103900.0,INLAND\n-121.48,38.52,36.0,1824.0,357.0,906.0,356.0,2.9911,96400.0,INLAND\n-121.49,38.52,37.0,1902.0,413.0,955.0,384.0,3.1014,96800.0,INLAND\n-121.49,38.53,42.0,1468.0,281.0,571.0,271.0,3.3906,124200.0,INLAND\n-121.48,38.54,41.0,3364.0,685.0,1841.0,626.0,2.1975,73500.0,INLAND\n-121.48,38.53,37.0,1704.0,361.0,902.0,356.0,1.9837,62300.0,INLAND\n-121.47,38.54,36.0,2099.0,510.0,1845.0,483.0,1.4138,52500.0,INLAND\n-121.47,38.53,43.0,3215.0,725.0,2400.0,625.0,1.4625,54400.0,INLAND\n-121.47,38.53,44.0,543.0,146.0,506.0,125.0,1.3646,65400.0,INLAND\n-121.49,38.51,18.0,700.0,169.0,260.0,128.0,2.9219,152900.0,INLAND\n-121.49,38.5,32.0,2364.0,439.0,1331.0,449.0,3.319,84500.0,INLAND\n-121.49,38.5,30.0,1715.0,271.0,842.0,263.0,3.0313,87900.0,INLAND\n-121.5,38.5,29.0,2049.0,330.0,787.0,309.0,3.7414,98500.0,INLAND\n-121.49,38.51,30.0,3166.0,607.0,1857.0,579.0,3.1768,79500.0,INLAND\n-121.51,38.54,34.0,2815.0,479.0,1075.0,471.0,3.9792,164800.0,INLAND\n-121.52,38.53,31.0,3089.0,585.0,1366.0,561.0,4.2885,160300.0,INLAND\n-121.52,38.53,30.0,3377.0,623.0,1289.0,594.0,3.5737,171200.0,INLAND\n-121.52,38.51,23.0,6876.0,1456.0,2942.0,1386.0,3.0963,156900.0,INLAND\n-121.51,38.49,21.0,4426.0,790.0,1856.0,761.0,4.1,158300.0,INLAND\n-121.51,38.5,25.0,4719.0,745.0,1857.0,739.0,5.0371,180200.0,INLAND\n-121.55,38.51,14.0,5490.0,851.0,2415.0,837.0,6.5253,216800.0,INLAND\n-121.55,38.5,9.0,4868.0,738.0,2036.0,750.0,5.7621,204600.0,INLAND\n-121.54,38.5,15.0,6093.0,1051.0,2415.0,997.0,4.2075,183600.0,INLAND\n-121.54,38.51,17.0,8482.0,1590.0,3362.0,1513.0,4.2216,217900.0,INLAND\n-121.53,38.5,17.0,3087.0,477.0,1365.0,495.0,6.4667,216800.0,INLAND\n-121.53,38.51,20.0,6132.0,1324.0,2595.0,1174.0,3.1607,178900.0,INLAND\n-121.52,38.5,19.0,4900.0,805.0,2519.0,855.0,4.8497,184400.0,INLAND\n-121.53,38.48,5.0,27870.0,5027.0,11935.0,4855.0,4.8811,212200.0,INLAND\n-121.52,38.49,5.0,3344.0,800.0,1341.0,670.0,3.6196,152800.0,INLAND\n-121.54,38.49,6.0,9104.0,1535.0,3759.0,1481.0,5.1442,174500.0,INLAND\n-121.48,38.52,34.0,2561.0,497.0,1583.0,530.0,3.1583,95800.0,INLAND\n-121.48,38.51,24.0,979.0,201.0,723.0,205.0,2.5926,72300.0,INLAND\n-121.48,38.5,23.0,2679.0,792.0,1740.0,659.0,1.3679,70300.0,INLAND\n-121.49,38.49,26.0,1557.0,301.0,986.0,300.0,2.6613,77700.0,INLAND\n-121.5,38.49,29.0,3606.0,690.0,2317.0,696.0,2.7368,78200.0,INLAND\n-121.5,38.49,32.0,4013.0,725.0,2032.0,675.0,3.3689,83400.0,INLAND\n-121.48,38.49,26.0,3165.0,806.0,2447.0,752.0,1.5908,78600.0,INLAND\n-121.49,38.49,26.0,4629.0,832.0,2902.0,816.0,2.735,74600.0,INLAND\n-121.47,38.49,17.0,3595.0,790.0,2760.0,770.0,2.3233,78800.0,INLAND\n-121.47,38.48,25.0,2969.0,551.0,1745.0,487.0,2.6382,76200.0,INLAND\n-121.49,38.47,26.0,6121.0,1185.0,4224.0,1105.0,2.3496,68000.0,INLAND\n-121.47,38.48,24.0,2359.0,462.0,2048.0,476.0,3.2702,67300.0,INLAND\n-121.45,38.54,38.0,1865.0,384.0,1052.0,354.0,1.7891,60500.0,INLAND\n-121.45,38.53,38.0,1746.0,388.0,1142.0,315.0,1.7714,69900.0,INLAND\n-121.45,38.53,34.0,1893.0,415.0,884.0,395.0,2.1679,75400.0,INLAND\n-121.45,38.53,34.0,1717.0,354.0,848.0,306.0,2.4741,87000.0,INLAND\n-121.46,38.54,39.0,1453.0,324.0,843.0,281.0,1.7692,63900.0,INLAND\n-121.46,38.53,37.0,2745.0,588.0,1607.0,556.0,1.8007,65400.0,INLAND\n-121.46,38.54,36.0,1825.0,411.0,1226.0,391.0,1.5292,55700.0,INLAND\n-121.47,38.52,26.0,2177.0,638.0,1971.0,560.0,1.2575,66800.0,INLAND\n-121.46,38.51,18.0,2123.0,606.0,1576.0,599.0,1.5735,110000.0,INLAND\n-121.45,38.5,25.0,3033.0,665.0,1559.0,627.0,2.7101,99500.0,INLAND\n-121.47,38.5,17.0,1895.0,424.0,620.0,417.0,1.7188,137500.0,INLAND\n-121.47,38.51,52.0,20.0,4.0,74.0,9.0,3.625,80000.0,INLAND\n-121.47,38.52,42.0,2316.0,515.0,1597.0,522.0,1.8205,60400.0,INLAND\n-121.44,38.52,36.0,3446.0,950.0,2460.0,847.0,1.6521,69700.0,INLAND\n-121.45,38.51,29.0,4221.0,901.0,2294.0,850.0,2.2245,75900.0,INLAND\n-121.45,38.52,37.0,1705.0,325.0,827.0,326.0,2.6288,71200.0,INLAND\n-121.45,38.52,37.0,1477.0,321.0,888.0,312.0,2.5592,70300.0,INLAND\n-121.46,38.52,29.0,3873.0,797.0,2237.0,706.0,2.1736,72100.0,INLAND\n-121.46,38.51,32.0,2437.0,592.0,1596.0,557.0,1.68,84100.0,INLAND\n-121.46,38.52,34.0,1279.0,285.0,963.0,268.0,2.71,65600.0,INLAND\n-121.44,38.51,27.0,7212.0,1606.0,4828.0,1549.0,2.214,82400.0,INLAND\n-121.44,38.5,27.0,2527.0,439.0,1089.0,415.0,4.088,96800.0,INLAND\n-121.44,38.5,20.0,2033.0,586.0,1281.0,521.0,1.4007,97500.0,INLAND\n-121.42,38.51,15.0,7901.0,1422.0,4769.0,1418.0,2.8139,90400.0,INLAND\n-121.42,38.5,24.0,7740.0,1539.0,4333.0,1397.0,3.025,87900.0,INLAND\n-121.46,38.49,15.0,10211.0,1995.0,5656.0,1752.0,2.575,107900.0,INLAND\n-121.45,38.49,34.0,3573.0,662.0,1540.0,620.0,3.5323,109800.0,INLAND\n-121.44,38.49,31.0,4297.0,788.0,2083.0,771.0,3.3878,109300.0,INLAND\n-121.45,38.48,28.0,2780.0,510.0,1638.0,533.0,2.9571,103100.0,INLAND\n-121.43,38.48,12.0,4602.0,930.0,2299.0,860.0,3.0625,90500.0,INLAND\n-121.44,38.48,12.0,4929.0,1010.0,2621.0,870.0,2.7262,109800.0,INLAND\n-121.45,38.48,24.0,1766.0,340.0,1028.0,372.0,3.5402,98700.0,INLAND\n-121.46,38.48,8.0,3593.0,659.0,1710.0,530.0,3.5227,93100.0,INLAND\n-121.42,38.49,17.0,13180.0,2444.0,7235.0,2335.0,3.363,103000.0,INLAND\n-121.42,38.48,13.0,7880.0,1992.0,4749.0,1882.0,1.9657,116000.0,INLAND\n-121.4,38.49,12.0,7290.0,1283.0,3960.0,1248.0,3.5968,106300.0,INLAND\n-121.38,38.49,11.0,8537.0,1643.0,4224.0,1648.0,2.9647,108900.0,INLAND\n-121.39,38.51,19.0,1808.0,375.0,758.0,320.0,2.0062,92000.0,INLAND\n-121.42,38.56,21.0,2066.0,748.0,2548.0,734.0,1.3571,55000.0,INLAND\n-121.39,38.56,19.0,8507.0,1470.0,3517.0,1453.0,4.3644,137400.0,INLAND\n-121.38,38.55,23.0,2790.0,430.0,1407.0,460.0,4.3288,133700.0,INLAND\n-121.38,38.55,26.0,1532.0,264.0,781.0,285.0,4.6944,130900.0,INLAND\n-121.39,38.55,25.0,2171.0,431.0,1053.0,422.0,3.5278,126200.0,INLAND\n-121.39,38.55,18.0,1734.0,467.0,783.0,447.0,1.9044,154300.0,INLAND\n-121.4,38.55,26.0,2697.0,398.0,1088.0,389.0,5.0,142500.0,INLAND\n-121.41,38.55,14.0,2534.0,705.0,1495.0,583.0,1.9167,156300.0,INLAND\n-121.4,38.55,19.0,2497.0,494.0,748.0,442.0,2.925,142400.0,INLAND\n-121.4,38.53,38.0,152.0,30.0,65.0,35.0,0.9274,67500.0,INLAND\n-121.48,38.59,43.0,987.0,240.0,1253.0,237.0,0.9204,82100.0,INLAND\n-121.5,38.59,43.0,88.0,21.0,119.0,19.0,1.725,67500.0,INLAND\n-121.44,38.6,16.0,2987.0,864.0,1240.0,755.0,2.8231,137500.0,INLAND\n-121.41,38.56,17.0,7228.0,1369.0,2455.0,1365.0,5.1385,179500.0,INLAND\n-121.37,38.57,22.0,4899.0,847.0,1701.0,826.0,5.2449,387000.0,INLAND\n-121.39,38.57,33.0,2648.0,357.0,863.0,359.0,8.4016,338700.0,INLAND\n-121.4,38.57,25.0,2022.0,295.0,639.0,278.0,5.8416,297600.0,INLAND\n-121.41,38.57,16.0,4429.0,1124.0,1538.0,960.0,3.2443,190700.0,INLAND\n-121.4,38.56,22.0,2623.0,357.0,838.0,368.0,7.143,327800.0,INLAND\n-121.42,38.6,23.0,3713.0,1078.0,2194.0,1018.0,1.7451,89600.0,INLAND\n-121.42,38.6,35.0,1166.0,193.0,574.0,190.0,2.2452,102800.0,INLAND\n-121.42,38.6,36.0,1327.0,209.0,613.0,230.0,3.8672,111400.0,INLAND\n-121.43,38.61,33.0,2289.0,576.0,1100.0,503.0,2.1694,95700.0,INLAND\n-121.44,38.61,34.0,172.0,38.0,149.0,55.0,2.6442,55000.0,INLAND\n-121.41,38.61,36.0,3099.0,605.0,1322.0,623.0,3.4784,105500.0,INLAND\n-121.41,38.6,16.0,5407.0,1467.0,2523.0,1265.0,2.0471,104200.0,INLAND\n-121.41,38.59,18.0,5527.0,1446.0,2883.0,1305.0,2.6485,114500.0,INLAND\n-121.41,38.59,17.0,12355.0,3630.0,5692.0,3073.0,2.5245,99100.0,INLAND\n-121.41,38.58,18.0,6955.0,1882.0,2803.0,1740.0,3.089,141400.0,INLAND\n-121.39,38.61,36.0,2396.0,536.0,1225.0,515.0,2.9559,136600.0,INLAND\n-121.39,38.6,22.0,5773.0,1320.0,2607.0,1250.0,2.5238,118800.0,INLAND\n-121.4,38.61,37.0,1994.0,347.0,782.0,355.0,4.1488,136400.0,INLAND\n-121.38,38.59,36.0,1239.0,237.0,764.0,222.0,3.0156,103000.0,INLAND\n-121.39,38.59,33.0,2091.0,468.0,1053.0,470.0,2.2264,108100.0,INLAND\n-121.39,38.59,34.0,1151.0,234.0,563.0,251.0,2.8,113600.0,INLAND\n-121.4,38.59,25.0,2228.0,534.0,1130.0,481.0,2.5363,124600.0,INLAND\n-121.4,38.59,18.0,2614.0,624.0,1181.0,616.0,2.0432,156800.0,INLAND\n-121.38,38.59,36.0,2253.0,434.0,1018.0,426.0,3.2596,98700.0,INLAND\n-121.39,38.58,36.0,2019.0,369.0,878.0,356.0,2.8462,93400.0,INLAND\n-121.39,38.58,41.0,2577.0,365.0,913.0,339.0,6.3406,448300.0,INLAND\n-121.37,38.6,35.0,3137.0,544.0,1312.0,549.0,3.788,136800.0,INLAND\n-121.37,38.6,36.0,1119.0,144.0,414.0,150.0,5.8336,283300.0,INLAND\n-121.38,38.6,36.0,1249.0,159.0,362.0,143.0,6.8469,446400.0,INLAND\n-121.38,38.61,34.0,2888.0,496.0,1168.0,479.0,3.6053,148600.0,INLAND\n-121.37,38.59,36.0,2523.0,401.0,927.0,398.0,3.5179,207800.0,INLAND\n-121.37,38.58,37.0,2839.0,390.0,1006.0,400.0,7.3343,280400.0,INLAND\n-121.38,38.58,38.0,2968.0,475.0,1176.0,454.0,5.0497,191700.0,INLAND\n-121.38,38.59,38.0,1839.0,287.0,685.0,276.0,4.5313,189400.0,INLAND\n-121.37,38.59,36.0,2388.0,369.0,838.0,356.0,4.775,194100.0,INLAND\n-121.35,38.61,27.0,3900.0,776.0,1549.0,761.0,2.7788,115700.0,INLAND\n-121.35,38.6,27.0,4314.0,611.0,1662.0,575.0,5.0997,170100.0,INLAND\n-121.36,38.6,35.0,1930.0,328.0,805.0,338.0,4.4643,133000.0,INLAND\n-121.36,38.6,36.0,1275.0,227.0,530.0,245.0,3.875,133600.0,INLAND\n-121.36,38.61,35.0,2355.0,365.0,993.0,354.0,5.0492,144100.0,INLAND\n-121.35,38.59,29.0,1285.0,193.0,460.0,206.0,5.3243,265700.0,INLAND\n-121.36,38.59,32.0,3303.0,480.0,1185.0,436.0,5.0508,225700.0,INLAND\n-121.36,38.58,25.0,3196.0,406.0,978.0,419.0,8.4699,344000.0,INLAND\n-121.35,38.58,20.0,2992.0,378.0,1105.0,368.0,8.6572,320200.0,INLAND\n-121.34,38.58,18.0,1631.0,228.0,599.0,228.0,7.8031,267200.0,INLAND\n-121.34,38.59,23.0,2912.0,421.0,1132.0,410.0,5.9174,225900.0,INLAND\n-121.34,38.59,22.0,3273.0,480.0,1151.0,463.0,8.05,380000.0,INLAND\n-121.36,38.64,24.0,6540.0,1008.0,2667.0,1031.0,5.5632,175200.0,INLAND\n-121.36,38.63,28.0,6119.0,985.0,2631.0,934.0,4.875,146400.0,INLAND\n-121.35,38.62,28.0,4175.0,796.0,2032.0,830.0,3.4299,164000.0,INLAND\n-121.35,38.61,25.0,4916.0,1243.0,2140.0,1136.0,2.5511,134100.0,INLAND\n-121.36,38.61,37.0,2191.0,394.0,951.0,362.0,3.8882,159500.0,INLAND\n-121.36,38.62,34.0,2447.0,503.0,1077.0,456.0,3.058,133000.0,INLAND\n-121.36,38.63,30.0,2619.0,370.0,940.0,359.0,4.7283,164500.0,INLAND\n-121.37,38.64,36.0,322.0,48.0,133.0,59.0,4.6111,139300.0,INLAND\n-121.38,38.64,19.0,4563.0,1069.0,2256.0,926.0,2.1472,143400.0,INLAND\n-121.37,38.63,32.0,3658.0,797.0,1452.0,715.0,2.6623,120700.0,INLAND\n-121.37,38.63,37.0,494.0,86.0,253.0,99.0,4.8194,141100.0,INLAND\n-121.37,38.63,30.0,5996.0,1018.0,2532.0,1049.0,4.6127,151800.0,INLAND\n-121.37,38.62,27.0,1743.0,380.0,697.0,368.0,1.6678,166100.0,INLAND\n-121.38,38.62,34.0,2352.0,610.0,1127.0,592.0,2.2,116500.0,INLAND\n-121.38,38.62,41.0,774.0,144.0,356.0,150.0,3.5625,115300.0,INLAND\n-121.37,38.62,43.0,1077.0,199.0,447.0,182.0,3.0139,115600.0,INLAND\n-121.37,38.61,42.0,945.0,193.0,460.0,193.0,3.7569,127100.0,INLAND\n-121.37,38.61,39.0,823.0,146.0,329.0,144.0,3.0833,114100.0,INLAND\n-121.38,38.61,27.0,2375.0,537.0,863.0,452.0,3.0086,126900.0,INLAND\n-121.39,38.63,30.0,2930.0,739.0,1661.0,668.0,2.7813,118900.0,INLAND\n-121.39,38.63,34.0,1226.0,180.0,359.0,167.0,3.8068,150400.0,INLAND\n-121.39,38.61,35.0,2024.0,359.0,786.0,364.0,2.4632,156900.0,INLAND\n-121.39,38.62,27.0,5693.0,1487.0,2334.0,1387.0,2.2844,170500.0,INLAND\n-121.39,38.62,45.0,2696.0,624.0,1059.0,582.0,1.8176,160900.0,INLAND\n-121.4,38.63,30.0,3626.0,834.0,1577.0,806.0,2.517,130400.0,INLAND\n-121.4,38.63,31.0,1540.0,452.0,1079.0,444.0,1.8571,98700.0,INLAND\n-121.4,38.62,28.0,3671.0,886.0,1733.0,820.0,2.2292,113200.0,INLAND\n-121.4,38.61,33.0,3512.0,825.0,1515.0,782.0,1.9908,118800.0,INLAND\n-121.41,38.62,21.0,3260.0,763.0,1735.0,736.0,2.5162,97500.0,INLAND\n-121.42,38.62,33.0,3171.0,832.0,1591.0,695.0,2.0786,88600.0,INLAND\n-121.42,38.61,34.0,1126.0,256.0,589.0,243.0,2.1776,84400.0,INLAND\n-121.43,38.61,40.0,1134.0,252.0,675.0,249.0,1.3696,65200.0,INLAND\n-121.42,38.62,41.0,1087.0,272.0,462.0,219.0,2.0224,64900.0,INLAND\n-121.42,38.63,42.0,1385.0,273.0,740.0,274.0,2.6055,78000.0,INLAND\n-121.43,38.63,43.0,1009.0,225.0,604.0,218.0,1.6641,67000.0,INLAND\n-121.42,38.63,42.0,2217.0,536.0,1203.0,507.0,1.9412,73100.0,INLAND\n-121.43,38.62,36.0,1765.0,438.0,1008.0,382.0,2.0639,73000.0,INLAND\n-121.44,38.61,41.0,1404.0,313.0,765.0,330.0,1.8792,63300.0,INLAND\n-121.41,38.64,41.0,1578.0,317.0,897.0,333.0,2.3214,66800.0,INLAND\n-121.41,38.64,38.0,1384.0,287.0,682.0,280.0,1.9167,64400.0,INLAND\n-121.42,38.64,42.0,1720.0,382.0,1069.0,362.0,1.8611,60500.0,INLAND\n-121.42,38.64,44.0,1728.0,367.0,1042.0,349.0,1.6033,58500.0,INLAND\n-121.42,38.65,21.0,2274.0,495.0,1157.0,445.0,2.098,49800.0,INLAND\n-121.43,38.65,18.0,909.0,198.0,661.0,176.0,3.1696,77400.0,INLAND\n-121.43,38.64,34.0,2010.0,411.0,1501.0,422.0,2.0417,65900.0,INLAND\n-121.44,38.64,18.0,1756.0,442.0,837.0,320.0,1.125,70500.0,INLAND\n-121.44,38.64,25.0,1678.0,367.0,971.0,307.0,1.0398,62100.0,INLAND\n-121.44,38.65,28.0,1219.0,240.0,559.0,212.0,3.8295,122200.0,INLAND\n-121.44,38.63,38.0,1673.0,399.0,1116.0,382.0,1.3302,62200.0,INLAND\n-121.44,38.63,33.0,1077.0,271.0,753.0,236.0,1.3462,55900.0,INLAND\n-121.44,38.63,38.0,1402.0,370.0,970.0,382.0,1.6343,71000.0,INLAND\n-121.44,38.62,37.0,1607.0,385.0,972.0,354.0,1.9107,64700.0,INLAND\n-121.44,38.62,37.0,3009.0,733.0,1513.0,588.0,1.4387,61000.0,INLAND\n-121.45,38.65,5.0,2680.0,502.0,1885.0,498.0,2.6369,110000.0,INLAND\n-121.46,38.65,8.0,3746.0,767.0,2161.0,744.0,3.2039,103400.0,INLAND\n-121.46,38.65,14.0,3167.0,551.0,1787.0,533.0,3.8125,92600.0,INLAND\n-121.45,38.64,23.0,1481.0,343.0,1079.0,315.0,1.867,60600.0,INLAND\n-121.46,38.64,20.0,1517.0,323.0,1287.0,328.0,1.6607,67000.0,INLAND\n-121.46,38.63,26.0,3185.0,658.0,2444.0,626.0,1.56,67600.0,INLAND\n-121.45,38.63,28.0,1246.0,295.0,884.0,258.0,1.4397,51700.0,INLAND\n-121.45,38.62,37.0,1534.0,315.0,1147.0,322.0,2.5643,59800.0,INLAND\n-121.45,38.62,38.0,2419.0,605.0,1696.0,503.0,1.4861,63100.0,INLAND\n-121.45,38.61,32.0,2436.0,612.0,1509.0,618.0,1.0424,81400.0,INLAND\n-121.46,38.61,43.0,705.0,178.0,464.0,159.0,2.4205,60900.0,INLAND\n-121.46,38.62,35.0,3326.0,696.0,2511.0,649.0,1.9871,60900.0,INLAND\n-121.44,38.61,33.0,1591.0,466.0,1000.0,418.0,1.0467,70100.0,INLAND\n-121.45,38.6,44.0,2324.0,413.0,823.0,375.0,4.6625,158900.0,INLAND\n-121.45,38.61,46.0,1758.0,511.0,1094.0,484.0,1.0685,70000.0,INLAND\n-121.46,38.61,43.0,1111.0,269.0,613.0,290.0,1.2917,66300.0,INLAND\n-121.46,38.6,29.0,1978.0,538.0,823.0,490.0,1.9688,135600.0,INLAND\n-121.45,38.61,34.0,438.0,116.0,263.0,100.0,0.9379,67500.0,INLAND\n-121.47,38.63,29.0,2197.0,520.0,1374.0,483.0,2.1889,69300.0,INLAND\n-121.47,38.61,31.0,1072.0,,781.0,281.0,1.6563,65800.0,INLAND\n-121.47,38.61,35.0,1372.0,360.0,850.0,328.0,1.6331,67500.0,INLAND\n-121.52,38.65,17.0,1269.0,233.0,494.0,231.0,3.9615,331300.0,INLAND\n-121.53,38.61,5.0,8149.0,1913.0,2933.0,1616.0,3.6788,178800.0,INLAND\n-121.49,38.63,6.0,12197.0,2617.0,5634.0,2329.0,3.7449,129300.0,INLAND\n-121.5,38.62,8.0,16679.0,3457.0,7919.0,3329.0,3.7188,134500.0,INLAND\n-121.5,38.61,5.0,1395.0,373.0,638.0,322.0,2.6745,225000.0,INLAND\n-121.5,38.63,6.0,693.0,143.0,276.0,151.0,3.1944,117000.0,INLAND\n-121.49,38.62,8.0,15309.0,2996.0,7463.0,2885.0,3.9143,129700.0,INLAND\n-121.49,38.61,6.0,4391.0,974.0,1982.0,914.0,3.4291,105300.0,INLAND\n-121.48,38.62,23.0,7709.0,1279.0,4147.0,1262.0,3.8272,96600.0,INLAND\n-121.48,38.61,18.0,1511.0,315.0,1062.0,304.0,2.3438,89400.0,INLAND\n-121.51,38.69,28.0,800.0,149.0,450.0,158.0,2.1029,108600.0,INLAND\n-121.59,38.69,32.0,541.0,82.0,229.0,98.0,8.0379,383300.0,INLAND\n-121.42,38.72,10.0,3054.0,528.0,1932.0,510.0,3.0903,91900.0,INLAND\n-121.44,38.73,25.0,1287.0,224.0,727.0,236.0,4.7396,135500.0,INLAND\n-121.47,38.72,26.0,1708.0,299.0,911.0,290.0,4.0227,99800.0,INLAND\n-121.44,38.71,25.0,2336.0,406.0,1172.0,408.0,3.5129,101200.0,INLAND\n-121.42,38.7,10.0,2562.0,460.0,1478.0,433.0,4.0625,96200.0,INLAND\n-121.45,38.7,24.0,2159.0,369.0,1141.0,355.0,3.9853,90400.0,INLAND\n-121.46,38.7,32.0,965.0,183.0,568.0,188.0,3.8611,93900.0,INLAND\n-121.43,38.69,28.0,927.0,165.0,542.0,148.0,2.5,96200.0,INLAND\n-121.44,38.68,19.0,2476.0,534.0,1355.0,463.0,2.0625,94400.0,INLAND\n-121.42,38.68,32.0,2118.0,345.0,1019.0,338.0,3.725,112200.0,INLAND\n-121.41,38.69,28.0,1601.0,308.0,848.0,305.0,3.6429,105200.0,INLAND\n-121.44,38.69,24.0,3124.0,556.0,1512.0,555.0,3.1942,94900.0,INLAND\n-121.45,38.69,32.0,2962.0,526.0,1542.0,521.0,2.2243,89200.0,INLAND\n-121.46,38.68,35.0,1299.0,254.0,705.0,245.0,2.8333,103000.0,INLAND\n-121.47,38.68,19.0,946.0,182.0,474.0,173.0,5.0155,97300.0,INLAND\n-121.46,38.69,11.0,3335.0,658.0,1963.0,622.0,3.3125,96800.0,INLAND\n-121.47,38.7,31.0,1007.0,181.0,563.0,185.0,3.625,91300.0,INLAND\n-121.43,38.66,35.0,1814.0,367.0,1076.0,372.0,2.7179,81100.0,INLAND\n-121.46,38.66,3.0,3438.0,603.0,1602.0,554.0,3.9914,120500.0,INLAND\n-121.4,38.66,50.0,880.0,150.0,1148.0,148.0,2.5062,112500.0,INLAND\n-121.37,38.69,29.0,2103.0,380.0,1124.0,387.0,3.0833,87000.0,INLAND\n-121.37,38.68,34.0,1086.0,187.0,663.0,190.0,3.3074,84200.0,INLAND\n-121.38,38.68,35.0,1643.0,298.0,831.0,305.0,4.0673,84200.0,INLAND\n-121.37,38.68,36.0,1775.0,296.0,937.0,305.0,3.1786,83400.0,INLAND\n-121.37,38.69,35.0,1851.0,327.0,1007.0,286.0,3.2361,84000.0,INLAND\n-121.38,38.69,35.0,2943.0,554.0,1460.0,510.0,2.6713,84400.0,INLAND\n-121.39,38.69,38.0,300.0,47.0,154.0,51.0,4.0909,108300.0,INLAND\n-121.37,38.69,35.0,1093.0,192.0,590.0,190.0,2.7009,80200.0,INLAND\n-121.37,38.68,29.0,3757.0,646.0,2022.0,611.0,3.5429,88200.0,INLAND\n-121.37,38.68,35.0,1620.0,276.0,939.0,277.0,2.5542,72900.0,INLAND\n-121.38,38.68,35.0,1565.0,290.0,861.0,277.0,2.4844,77000.0,INLAND\n-121.38,38.68,40.0,67.0,17.0,50.0,32.0,1.7596,93800.0,INLAND\n-121.37,38.67,36.0,1354.0,258.0,771.0,267.0,2.2723,78800.0,INLAND\n-121.37,38.67,36.0,1786.0,338.0,974.0,319.0,2.555,72700.0,INLAND\n-121.38,38.67,37.0,2176.0,460.0,1067.0,357.0,2.3958,78400.0,INLAND\n-121.38,38.67,38.0,1001.0,228.0,597.0,226.0,2.2788,73400.0,INLAND\n-121.39,38.67,35.0,562.0,174.0,240.0,106.0,0.9338,112500.0,INLAND\n-121.37,38.7,18.0,3938.0,649.0,1861.0,606.0,3.6484,95000.0,INLAND\n-121.38,38.7,25.0,3919.0,764.0,2203.0,783.0,2.2402,89500.0,INLAND\n-121.39,38.69,30.0,2897.0,506.0,1508.0,478.0,3.865,88400.0,INLAND\n-121.37,38.7,26.0,2230.0,410.0,1155.0,377.0,3.4911,88200.0,INLAND\n-121.34,38.69,16.0,2686.0,516.0,1553.0,529.0,3.7857,112700.0,INLAND\n-121.34,38.69,17.0,1968.0,364.0,996.0,331.0,3.7031,114300.0,INLAND\n-121.35,38.68,20.0,7085.0,1222.0,3455.0,1229.0,4.3118,120000.0,INLAND\n-121.35,38.68,18.0,7923.0,1558.0,3789.0,1473.0,3.5403,98600.0,INLAND\n-121.35,38.72,2.0,21897.0,3513.0,8652.0,2873.0,4.5432,151300.0,INLAND\n-121.38,38.71,7.0,4842.0,935.0,2857.0,907.0,3.9318,133000.0,INLAND\n-121.4,38.71,15.0,4680.0,758.0,2626.0,729.0,3.8355,107000.0,INLAND\n-121.36,38.69,13.0,6850.0,1400.0,4251.0,1421.0,3.6989,93300.0,INLAND\n-121.35,38.7,5.0,14414.0,2979.0,7608.0,2832.0,3.5802,129600.0,INLAND\n-121.36,38.67,17.0,2770.0,684.0,1471.0,624.0,2.3683,82500.0,INLAND\n-121.36,38.67,5.0,5819.0,1507.0,3237.0,1356.0,2.2339,116600.0,INLAND\n-121.37,38.66,9.0,3184.0,779.0,1929.0,769.0,2.3844,86000.0,INLAND\n-121.36,38.66,22.0,2878.0,599.0,1362.0,541.0,2.7955,96500.0,INLAND\n-121.37,38.66,17.0,4866.0,1056.0,2371.0,1030.0,2.4574,103300.0,INLAND\n-121.38,38.66,17.0,3778.0,939.0,2393.0,862.0,1.8972,100500.0,INLAND\n-121.38,38.65,34.0,825.0,173.0,355.0,130.0,3.1858,109500.0,INLAND\n-121.39,38.64,33.0,1503.0,282.0,652.0,229.0,3.6937,99300.0,INLAND\n-121.34,38.68,28.0,3379.0,552.0,1543.0,556.0,4.2743,124000.0,INLAND\n-121.34,38.67,34.0,1503.0,264.0,731.0,285.0,4.0352,118500.0,INLAND\n-121.34,38.67,35.0,643.0,117.0,331.0,134.0,3.0417,120700.0,INLAND\n-121.34,38.66,16.0,3154.0,860.0,1837.0,793.0,1.9805,92900.0,INLAND\n-121.35,38.66,8.0,3322.0,805.0,1694.0,774.0,2.7011,130700.0,INLAND\n-121.34,38.66,17.0,1149.0,257.0,583.0,243.0,2.8092,137500.0,INLAND\n-121.34,38.66,18.0,4164.0,963.0,2032.0,898.0,2.119,133100.0,INLAND\n-121.35,38.66,24.0,3313.0,769.0,1631.0,681.0,2.5556,105700.0,INLAND\n-121.36,38.66,14.0,756.0,141.0,424.0,155.0,3.6953,116100.0,INLAND\n-121.37,38.64,27.0,1672.0,299.0,757.0,282.0,3.6786,159700.0,INLAND\n-121.35,38.65,20.0,2498.0,546.0,1185.0,506.0,3.2243,107900.0,INLAND\n-121.33,38.66,15.0,4371.0,908.0,1842.0,818.0,2.7797,105500.0,INLAND\n-121.33,38.65,23.0,2446.0,523.0,1132.0,513.0,2.6266,198500.0,INLAND\n-121.34,38.65,27.0,1595.0,246.0,610.0,253.0,4.6,199000.0,INLAND\n-121.33,38.65,24.0,3533.0,741.0,1496.0,723.0,2.8106,183200.0,INLAND\n-121.34,38.64,12.0,2772.0,578.0,1335.0,565.0,3.8068,161000.0,INLAND\n-121.33,38.64,27.0,2203.0,493.0,1158.0,492.0,2.4342,119500.0,INLAND\n-121.34,38.64,17.0,2761.0,501.0,1128.0,482.0,3.7562,139700.0,INLAND\n-121.34,38.63,13.0,3033.0,540.0,1363.0,519.0,4.0036,161700.0,INLAND\n-121.33,38.63,23.0,1947.0,409.0,866.0,400.0,2.7181,156800.0,INLAND\n-121.33,38.62,19.0,1853.0,415.0,772.0,397.0,2.2574,135800.0,INLAND\n-121.34,38.62,20.0,4021.0,864.0,1658.0,730.0,2.4537,153000.0,INLAND\n-121.34,38.61,22.0,1778.0,408.0,875.0,375.0,2.6023,142200.0,INLAND\n-121.34,38.61,11.0,1716.0,404.0,722.0,415.0,2.0926,166100.0,INLAND\n-121.33,38.61,21.0,2453.0,518.0,1326.0,505.0,2.7079,148000.0,INLAND\n-121.34,38.61,20.0,5801.0,1148.0,2586.0,1063.0,3.9063,162100.0,INLAND\n-121.33,38.6,25.0,4260.0,607.0,1635.0,640.0,6.2817,288200.0,INLAND\n-121.29,38.63,24.0,2868.0,527.0,1284.0,487.0,3.3182,213000.0,INLAND\n-121.3,38.63,31.0,1817.0,372.0,992.0,339.0,3.0972,150000.0,INLAND\n-121.32,38.63,20.0,7003.0,1409.0,3107.0,1315.0,3.0348,150500.0,INLAND\n-121.31,38.62,31.0,3114.0,430.0,1121.0,456.0,6.244,240000.0,INLAND\n-121.32,38.62,29.0,2430.0,448.0,1087.0,394.0,3.0864,177900.0,INLAND\n-121.32,38.62,33.0,898.0,190.0,470.0,201.0,2.6897,148300.0,INLAND\n-121.32,38.61,22.0,3902.0,845.0,1870.0,763.0,2.774,190200.0,INLAND\n-121.31,38.61,17.0,992.0,151.0,316.0,159.0,6.6238,326700.0,INLAND\n-121.3,38.64,20.0,5001.0,830.0,2330.0,830.0,4.0833,160000.0,INLAND\n-121.31,38.64,19.0,5407.0,838.0,1927.0,804.0,4.6302,195400.0,INLAND\n-121.32,38.64,19.0,8501.0,1558.0,3576.0,1467.0,3.6523,158500.0,INLAND\n-121.31,38.66,27.0,1713.0,282.0,761.0,295.0,5.2081,136400.0,INLAND\n-121.31,38.66,26.0,1604.0,245.0,751.0,267.0,4.7381,140500.0,INLAND\n-121.32,38.66,26.0,1149.0,193.0,500.0,194.0,5.078,163400.0,INLAND\n-121.32,38.66,21.0,1276.0,208.0,501.0,205.0,3.95,143600.0,INLAND\n-121.32,38.65,23.0,2628.0,499.0,1210.0,453.0,3.0952,157700.0,INLAND\n-121.31,38.65,21.0,2759.0,409.0,1053.0,374.0,5.5,165700.0,INLAND\n-121.3,38.66,21.0,3824.0,634.0,1818.0,600.0,3.712,139000.0,INLAND\n-121.3,38.66,28.0,3391.0,550.0,1546.0,553.0,4.2188,139200.0,INLAND\n-121.3,38.65,26.0,3192.0,447.0,1132.0,418.0,4.5278,144300.0,INLAND\n-121.3,38.65,36.0,1665.0,293.0,846.0,306.0,3.5852,121600.0,INLAND\n-121.21,38.66,15.0,6940.0,1019.0,2829.0,990.0,5.4889,232300.0,INLAND\n-121.21,38.65,14.0,3443.0,510.0,1413.0,505.0,5.6529,196000.0,INLAND\n-121.24,38.64,13.0,4491.0,689.0,1657.0,667.0,5.259,249400.0,INLAND\n-121.23,38.65,19.0,2926.0,476.0,1349.0,480.0,4.6437,212900.0,INLAND\n-121.23,38.66,19.0,3243.0,546.0,1334.0,515.0,4.8088,169500.0,INLAND\n-121.24,38.66,14.0,3335.0,440.0,1329.0,429.0,6.2082,250300.0,INLAND\n-121.28,38.66,17.0,7741.0,1401.0,3153.0,1331.0,3.7869,216100.0,INLAND\n-121.25,38.66,26.0,3670.0,556.0,1616.0,550.0,5.02,169600.0,INLAND\n-121.26,38.66,19.0,3170.0,444.0,1344.0,452.0,6.1183,221600.0,INLAND\n-121.27,38.66,19.0,1891.0,266.0,678.0,255.0,6.1872,188700.0,INLAND\n-121.27,38.66,15.0,2642.0,520.0,1032.0,475.0,4.1382,189800.0,INLAND\n-121.25,38.64,21.0,2764.0,363.0,902.0,360.0,5.6864,258700.0,INLAND\n-121.26,38.65,17.0,2655.0,421.0,991.0,384.0,4.6484,270600.0,INLAND\n-121.27,38.65,25.0,2787.0,601.0,1247.0,522.0,2.9016,159800.0,INLAND\n-121.27,38.64,22.0,1597.0,280.0,657.0,273.0,4.3098,213500.0,INLAND\n-121.26,38.64,40.0,1098.0,175.0,415.0,160.0,4.8375,217400.0,INLAND\n-121.27,38.65,33.0,1984.0,289.0,842.0,276.0,5.2949,173300.0,INLAND\n-121.29,38.65,27.0,2744.0,464.0,1340.0,452.0,3.8816,147300.0,INLAND\n-121.28,38.64,24.0,3459.0,573.0,1336.0,544.0,4.8661,186200.0,INLAND\n-121.28,38.63,36.0,120.0,16.0,30.0,14.0,10.2264,350000.0,INLAND\n-121.28,38.64,19.0,3574.0,669.0,1373.0,643.0,3.6298,242100.0,INLAND\n-121.28,38.71,8.0,4053.0,912.0,2033.0,897.0,2.8973,117100.0,INLAND\n-121.28,38.7,14.0,5827.0,1246.0,2578.0,1038.0,3.0212,112900.0,INLAND\n-121.28,38.71,35.0,3095.0,594.0,1550.0,576.0,3.575,113500.0,INLAND\n-121.28,38.7,15.0,5828.0,1051.0,2868.0,1037.0,3.7813,143200.0,INLAND\n-121.28,38.68,14.0,11442.0,2690.0,6068.0,2435.0,2.6016,121200.0,INLAND\n-121.3,38.7,18.0,7334.0,1332.0,3339.0,1271.0,3.235,124700.0,INLAND\n-121.3,38.69,21.0,6575.0,1105.0,3358.0,1098.0,4.0739,115400.0,INLAND\n-121.32,38.69,11.0,13796.0,2372.0,6000.0,2250.0,3.8776,124500.0,INLAND\n-121.33,38.68,13.0,5826.0,1411.0,2244.0,1219.0,1.9093,142900.0,INLAND\n-121.3,38.71,17.0,5434.0,1106.0,2755.0,1047.0,2.8226,99900.0,INLAND\n-121.29,38.71,32.0,1875.0,361.0,1027.0,343.0,3.5769,103800.0,INLAND\n-121.3,38.72,15.0,2514.0,482.0,1166.0,503.0,2.2813,131900.0,INLAND\n-121.31,38.72,11.0,2306.0,420.0,1308.0,418.0,3.9506,122200.0,INLAND\n-121.32,38.71,14.0,4594.0,774.0,2474.0,782.0,4.5245,127500.0,INLAND\n-121.31,38.71,18.0,3998.0,744.0,2071.0,660.0,4.3836,102000.0,INLAND\n-121.32,38.7,17.0,3214.0,551.0,1879.0,562.0,4.3643,124900.0,INLAND\n-121.32,38.7,16.0,2966.0,578.0,1365.0,480.0,3.2444,118400.0,INLAND\n-121.32,38.7,16.0,3592.0,642.0,1740.0,629.0,3.0703,126000.0,INLAND\n-121.33,38.7,15.0,2226.0,421.0,1004.0,417.0,2.7868,117800.0,INLAND\n-121.33,38.69,15.0,3137.0,509.0,1635.0,544.0,4.6923,122700.0,INLAND\n-121.32,38.68,25.0,1252.0,207.0,587.0,217.0,3.5893,146400.0,INLAND\n-121.32,38.67,10.0,3908.0,890.0,1898.0,798.0,2.8426,130600.0,INLAND\n-121.32,38.67,21.0,3455.0,706.0,1605.0,704.0,3.1382,91600.0,INLAND\n-121.31,38.67,26.0,1387.0,226.0,807.0,244.0,4.1563,135700.0,INLAND\n-121.33,38.67,17.0,2683.0,704.0,1410.0,659.0,1.962,130200.0,INLAND\n-121.33,38.66,17.0,2767.0,584.0,1275.0,568.0,2.5909,125400.0,INLAND\n-121.31,38.67,27.0,1998.0,353.0,970.0,343.0,4.8224,115500.0,INLAND\n-121.32,38.67,31.0,2532.0,479.0,1396.0,467.0,4.0417,114500.0,INLAND\n-121.29,38.68,12.0,5098.0,1094.0,2029.0,1065.0,3.5444,132500.0,INLAND\n-121.3,38.69,13.0,2135.0,429.0,779.0,432.0,3.6995,134900.0,INLAND\n-121.31,38.68,16.0,5179.0,1271.0,2181.0,1151.0,2.1009,82500.0,INLAND\n-121.31,38.68,22.0,1194.0,207.0,545.0,223.0,3.8603,134300.0,INLAND\n-121.3,38.67,15.0,4018.0,850.0,2070.0,814.0,3.0733,119800.0,INLAND\n-121.3,38.68,19.0,2655.0,438.0,1253.0,454.0,5.2817,140600.0,INLAND\n-121.3,38.67,20.0,1234.0,208.0,649.0,211.0,4.8523,143000.0,INLAND\n-121.3,38.66,32.0,2915.0,492.0,1292.0,454.0,3.3188,117100.0,INLAND\n-121.3,38.67,23.0,2145.0,340.0,1022.0,349.0,4.2037,125400.0,INLAND\n-121.27,38.7,16.0,3747.0,586.0,1817.0,590.0,4.6488,145300.0,INLAND\n-121.27,38.69,16.0,3389.0,597.0,1674.0,568.0,4.4489,145600.0,INLAND\n-121.26,38.68,4.0,3080.0,827.0,1195.0,683.0,2.7477,133000.0,INLAND\n-121.24,38.7,13.0,3243.0,488.0,1585.0,480.0,5.7133,166800.0,INLAND\n-121.25,38.7,16.0,3262.0,533.0,1794.0,543.0,4.2464,144400.0,INLAND\n-121.26,38.69,17.0,3917.0,638.0,1809.0,564.0,5.2586,137000.0,INLAND\n-121.25,38.69,17.0,3050.0,481.0,1490.0,489.0,4.5562,134500.0,INLAND\n-121.25,38.69,24.0,1014.0,185.0,606.0,194.0,4.1607,112800.0,INLAND\n-121.24,38.68,20.0,1402.0,236.0,676.0,236.0,3.7426,135500.0,INLAND\n-121.25,38.68,15.0,1497.0,243.0,730.0,242.0,4.9688,135600.0,INLAND\n-121.26,38.68,13.0,4256.0,619.0,1948.0,622.0,5.2051,167400.0,INLAND\n-121.25,38.68,13.0,503.0,70.0,267.0,77.0,6.1943,276100.0,INLAND\n-121.25,38.67,14.0,6155.0,1034.0,2407.0,941.0,4.2262,244300.0,INLAND\n-121.26,38.67,18.0,1830.0,313.0,905.0,361.0,4.2273,141800.0,INLAND\n-121.27,38.67,16.0,3185.0,886.0,1550.0,802.0,2.5199,149000.0,INLAND\n-121.26,38.66,8.0,1145.0,241.0,447.0,216.0,4.0781,124300.0,INLAND\n-121.27,38.67,15.0,2116.0,524.0,866.0,519.0,2.7388,111600.0,INLAND\n-121.27,38.67,15.0,1701.0,346.0,723.0,352.0,3.8906,128700.0,INLAND\n-121.28,38.68,16.0,3467.0,615.0,1478.0,601.0,3.75,147300.0,INLAND\n-121.28,38.67,23.0,1727.0,264.0,833.0,258.0,5.4797,160000.0,INLAND\n-121.28,38.67,29.0,1087.0,174.0,430.0,174.0,4.3625,158800.0,INLAND\n-121.29,38.68,20.0,1881.0,378.0,921.0,360.0,1.8589,144000.0,INLAND\n-121.29,38.67,20.0,1992.0,363.0,889.0,346.0,3.6516,130500.0,INLAND\n-121.25,38.72,15.0,6838.0,941.0,3166.0,926.0,5.2177,162700.0,INLAND\n-121.27,38.71,16.0,4082.0,666.0,1912.0,652.0,4.4609,142900.0,INLAND\n-121.25,38.71,14.0,3713.0,637.0,1845.0,635.0,4.3009,143400.0,INLAND\n-121.26,38.7,9.0,7812.0,1348.0,3275.0,1178.0,4.3826,146600.0,INLAND\n-121.17,38.69,5.0,7138.0,1227.0,2623.0,1139.0,5.6902,243200.0,INLAND\n-121.19,38.71,11.0,4415.0,,1520.0,627.0,3.2321,390800.0,INLAND\n-121.18,38.69,7.0,7104.0,970.0,2772.0,920.0,6.3528,274500.0,INLAND\n-121.2,38.7,28.0,2970.0,471.0,1379.0,463.0,4.3214,131700.0,INLAND\n-121.2,38.69,26.0,3077.0,607.0,1603.0,595.0,2.7174,137500.0,INLAND\n-121.22,38.68,10.0,6262.0,1278.0,2954.0,1169.0,3.4506,139000.0,INLAND\n-121.22,38.71,23.0,1843.0,273.0,818.0,276.0,4.4695,214700.0,INLAND\n-121.23,38.71,18.0,4947.0,714.0,2227.0,675.0,4.8542,170500.0,INLAND\n-121.23,38.69,19.0,5268.0,849.0,2357.0,849.0,3.9226,148700.0,INLAND\n-121.23,38.67,27.0,5266.0,971.0,2432.0,948.0,3.8954,133000.0,INLAND\n-121.24,38.67,28.0,3558.0,589.0,1742.0,581.0,4.0182,131700.0,INLAND\n-121.2,38.68,9.0,2200.0,422.0,938.0,369.0,3.4896,143800.0,INLAND\n-121.21,38.67,11.0,5500.0,956.0,2827.0,946.0,4.1071,145800.0,INLAND\n-121.22,38.67,20.0,1412.0,226.0,700.0,227.0,4.05,130700.0,INLAND\n-121.21,38.67,19.0,2987.0,626.0,1610.0,605.0,3.0533,112100.0,INLAND\n-121.19,38.67,16.0,1754.0,284.0,773.0,277.0,4.817,147000.0,INLAND\n-121.2,38.67,26.0,1546.0,287.0,773.0,299.0,2.9803,115400.0,INLAND\n-121.2,38.67,10.0,3875.0,668.0,1632.0,593.0,4.6902,171000.0,INLAND\n-121.19,38.66,26.0,1937.0,286.0,769.0,274.0,6.1185,179200.0,INLAND\n-121.2,38.66,17.0,1605.0,217.0,732.0,241.0,5.47,204800.0,INLAND\n-121.15,38.69,52.0,240.0,44.0,6675.0,29.0,6.1359,225000.0,INLAND\n-121.17,38.71,15.0,3084.0,557.0,1040.0,562.0,2.5183,293300.0,INLAND\n-121.17,38.68,37.0,1252.0,267.0,686.0,256.0,3.0,121900.0,INLAND\n-121.18,38.67,42.0,2101.0,480.0,945.0,426.0,2.3333,116000.0,INLAND\n-121.16,38.67,21.0,6198.0,1223.0,2827.0,1179.0,3.7796,159000.0,INLAND\n-121.15,38.68,6.0,9798.0,1551.0,4583.0,1494.0,5.0159,189600.0,INLAND\n-121.16,38.68,21.0,3517.0,667.0,1687.0,655.0,3.1161,131600.0,INLAND\n-121.13,38.66,2.0,12360.0,1747.0,4438.0,1470.0,6.2503,222500.0,INLAND\n-121.13,38.55,8.0,530.0,109.0,398.0,96.0,4.2031,212500.0,INLAND\n-121.06,38.51,6.0,6873.0,959.0,2354.0,931.0,6.8869,263100.0,INLAND\n-121.13,38.47,16.0,2574.0,441.0,1041.0,428.0,3.6645,203400.0,INLAND\n-121.24,38.63,4.0,11021.0,1565.0,3857.0,1494.0,7.2582,273200.0,INLAND\n-121.22,38.58,25.0,394.0,94.0,155.0,83.0,2.233,55000.0,INLAND\n-121.28,38.55,35.0,7088.0,1279.0,4885.0,1272.0,2.6981,112500.0,INLAND\n-121.31,38.59,35.0,3295.0,560.0,1454.0,536.0,3.1711,101900.0,INLAND\n-121.32,38.59,21.0,9774.0,1777.0,4674.0,1712.0,3.6817,136100.0,INLAND\n-121.32,38.59,24.0,4378.0,910.0,2149.0,812.0,2.5035,123700.0,INLAND\n-121.29,38.61,17.0,13553.0,2474.0,6544.0,2359.0,3.9727,132700.0,INLAND\n-121.3,38.61,25.0,2707.0,464.0,1423.0,490.0,4.3235,116900.0,INLAND\n-121.3,38.6,32.0,9534.0,1819.0,4951.0,1710.0,3.3926,103400.0,INLAND\n-121.27,38.62,13.0,2321.0,539.0,1066.0,497.0,2.7652,95600.0,INLAND\n-121.28,38.61,22.0,2938.0,619.0,1501.0,561.0,2.7356,96100.0,INLAND\n-121.28,38.61,23.0,2547.0,504.0,1235.0,469.0,2.4722,103300.0,INLAND\n-121.29,38.61,26.0,1814.0,299.0,963.0,317.0,4.4519,110500.0,INLAND\n-121.27,38.61,17.0,6663.0,1369.0,2840.0,1299.0,2.9452,115600.0,INLAND\n-121.28,38.6,25.0,1122.0,198.0,564.0,213.0,3.1654,111600.0,INLAND\n-121.28,38.6,17.0,1671.0,378.0,848.0,351.0,3.1194,112500.0,INLAND\n-121.29,38.6,29.0,1276.0,225.0,600.0,223.0,4.0938,109100.0,INLAND\n-121.32,38.57,15.0,3369.0,499.0,1733.0,470.0,5.31,127500.0,INLAND\n-121.33,38.56,17.0,3608.0,682.0,1694.0,666.0,3.311,109400.0,INLAND\n-121.32,38.57,25.0,692.0,146.0,504.0,167.0,3.6897,101100.0,INLAND\n-121.32,38.56,18.0,1169.0,186.0,614.0,192.0,4.5766,108700.0,INLAND\n-121.32,38.54,13.0,4715.0,1090.0,2420.0,1059.0,2.9693,104400.0,INLAND\n-121.28,38.53,18.0,224.0,38.0,95.0,41.0,3.1042,165000.0,INLAND\n-121.3,38.58,19.0,2653.0,680.0,1419.0,579.0,2.3787,91300.0,INLAND\n-121.3,38.58,16.0,1537.0,,1125.0,375.0,2.6471,90700.0,INLAND\n-121.31,38.58,10.0,2421.0,580.0,962.0,497.0,2.5035,112500.0,INLAND\n-121.31,38.57,9.0,2748.0,521.0,1663.0,565.0,3.5192,113300.0,INLAND\n-121.31,38.57,22.0,1229.0,253.0,733.0,250.0,2.5,101600.0,INLAND\n-121.33,38.57,17.0,1621.0,350.0,706.0,338.0,2.3684,150000.0,INLAND\n-121.28,38.59,3.0,4188.0,1136.0,2081.0,995.0,3.0481,92500.0,INLAND\n-121.29,38.59,19.0,2460.0,470.0,1346.0,480.0,3.6563,95600.0,INLAND\n-121.3,38.59,25.0,3002.0,718.0,1660.0,613.0,2.1116,89600.0,INLAND\n-121.3,38.58,29.0,2748.0,563.0,1619.0,525.0,2.8966,92400.0,INLAND\n-121.34,38.56,12.0,2975.0,628.0,1440.0,593.0,2.9896,118600.0,INLAND\n-121.34,38.55,11.0,2838.0,498.0,1701.0,504.0,4.1447,114000.0,INLAND\n-121.35,38.54,12.0,16239.0,3358.0,8656.0,3234.0,3.5691,116300.0,INLAND\n-121.37,38.56,19.0,6308.0,1167.0,3012.0,1112.0,2.9464,113500.0,INLAND\n-121.37,38.57,16.0,3895.0,896.0,1762.0,855.0,2.6635,135800.0,INLAND\n-121.37,38.56,18.0,2129.0,363.0,815.0,347.0,2.7679,118000.0,INLAND\n-121.36,38.57,26.0,1793.0,244.0,653.0,235.0,5.6485,129500.0,INLAND\n-121.35,38.56,16.0,2629.0,491.0,1265.0,485.0,4.5066,140200.0,INLAND\n-121.34,38.58,17.0,1605.0,258.0,748.0,262.0,5.0917,134100.0,INLAND\n-121.34,38.57,14.0,5737.0,1008.0,2731.0,983.0,4.4602,134500.0,INLAND\n-121.35,38.55,18.0,4481.0,780.0,2211.0,775.0,3.9934,123300.0,INLAND\n-121.35,38.55,22.0,2607.0,411.0,1216.0,407.0,5.0427,126900.0,INLAND\n-121.36,38.55,33.0,1191.0,198.0,554.0,191.0,2.8021,118800.0,INLAND\n-121.35,38.56,16.0,2278.0,370.0,1203.0,371.0,5.0622,132400.0,INLAND\n-121.36,38.56,17.0,6225.0,938.0,3064.0,947.0,5.2881,138000.0,INLAND\n-121.37,38.55,21.0,2713.0,432.0,1287.0,440.0,4.5815,125500.0,INLAND\n-121.36,38.56,20.0,1232.0,332.0,667.0,288.0,1.8288,32500.0,INLAND\n-121.37,38.56,27.0,1827.0,509.0,852.0,450.0,2.0901,52500.0,INLAND\n-121.3,38.51,19.0,822.0,134.0,457.0,133.0,4.15,157500.0,INLAND\n-121.35,38.51,29.0,2337.0,391.0,1054.0,352.0,4.2206,157700.0,INLAND\n-121.4,38.47,4.0,20982.0,3392.0,10329.0,3086.0,4.3658,130600.0,INLAND\n-121.38,38.47,4.0,14418.0,2282.0,6578.0,2140.0,4.5604,145900.0,INLAND\n-121.39,38.43,3.0,2696.0,384.0,990.0,316.0,5.4445,237600.0,INLAND\n-121.27,38.44,19.0,2780.0,414.0,1320.0,404.0,5.8831,209900.0,INLAND\n-121.35,38.46,2.0,6992.0,1132.0,2816.0,984.0,4.3879,144400.0,INLAND\n-121.34,38.44,14.0,3205.0,465.0,1439.0,456.0,5.7452,240900.0,INLAND\n-121.32,38.41,17.0,4401.0,655.0,1970.0,639.0,5.8239,247500.0,INLAND\n-121.36,38.42,6.0,3254.0,465.0,1168.0,345.0,5.1811,188400.0,INLAND\n-121.37,38.41,14.0,3727.0,685.0,1741.0,646.0,3.5625,125700.0,INLAND\n-121.37,38.42,18.0,2643.0,502.0,1755.0,541.0,3.3281,91200.0,INLAND\n-121.38,38.41,7.0,6091.0,921.0,2916.0,886.0,4.7557,150400.0,INLAND\n-121.38,38.41,10.0,3425.0,629.0,1538.0,587.0,4.45,138700.0,INLAND\n-121.38,38.4,15.0,4155.0,637.0,1722.0,616.0,4.8831,154400.0,INLAND\n-121.37,38.39,15.0,1883.0,254.0,893.0,256.0,6.2575,143500.0,INLAND\n-121.35,38.4,11.0,2322.0,459.0,1373.0,424.0,3.175,94400.0,INLAND\n-121.36,38.4,18.0,4813.0,849.0,2333.0,843.0,4.175,144400.0,INLAND\n-121.36,38.39,10.0,5121.0,763.0,2568.0,758.0,5.2447,148100.0,INLAND\n-121.13,38.37,10.0,1034.0,153.0,478.0,155.0,7.0326,241100.0,INLAND\n-121.2,38.36,14.0,2634.0,463.0,1402.0,432.0,3.8897,175700.0,INLAND\n-121.22,38.4,14.0,2655.0,441.0,1277.0,422.0,4.6989,213800.0,INLAND\n-121.22,38.43,20.0,2054.0,339.0,934.0,336.0,4.5368,219300.0,INLAND\n-121.29,38.36,17.0,2193.0,386.0,1148.0,372.0,4.5272,191700.0,INLAND\n-121.27,38.31,17.0,1144.0,202.0,626.0,178.0,4.4107,151600.0,INLAND\n-121.1,38.33,14.0,1357.0,247.0,695.0,224.0,4.1974,157800.0,INLAND\n-121.2,38.28,20.0,1732.0,307.0,999.0,305.0,3.9808,160200.0,INLAND\n-121.26,38.27,20.0,1314.0,229.0,712.0,219.0,4.4125,144600.0,INLAND\n-121.29,38.28,11.0,1554.0,260.0,793.0,233.0,4.8073,156700.0,INLAND\n-121.35,38.28,17.0,2756.0,557.0,1986.0,530.0,3.2234,82000.0,INLAND\n-121.33,38.28,14.0,980.0,171.0,659.0,183.0,4.4306,170100.0,INLAND\n-121.31,38.28,16.0,1708.0,391.0,687.0,378.0,1.9485,155400.0,INLAND\n-121.3,38.26,19.0,1403.0,276.0,901.0,290.0,3.215,104600.0,INLAND\n-121.31,38.26,22.0,1768.0,396.0,1005.0,420.0,1.8846,88300.0,INLAND\n-121.32,38.26,4.0,6125.0,1063.0,3077.0,953.0,4.1179,134600.0,INLAND\n-121.3,38.25,27.0,2475.0,548.0,1703.0,517.0,2.5727,86100.0,INLAND\n-121.48,38.46,8.0,10505.0,1777.0,6002.0,1694.0,4.0516,121200.0,INLAND\n-121.42,38.47,11.0,5665.0,1507.0,3422.0,1299.0,2.3343,97800.0,INLAND\n-121.43,38.46,18.0,1378.0,235.0,818.0,262.0,4.0625,100300.0,INLAND\n-121.43,38.47,21.0,1787.0,291.0,988.0,301.0,4.35,96200.0,INLAND\n-121.44,38.47,16.0,1215.0,223.0,787.0,233.0,4.1597,95900.0,INLAND\n-121.44,38.47,5.0,5666.0,1178.0,3139.0,1131.0,3.3608,108900.0,INLAND\n-121.44,38.46,10.0,4446.0,897.0,2499.0,884.0,3.5461,103600.0,INLAND\n-121.44,38.43,3.0,39320.0,6210.0,16305.0,5358.0,4.9516,153700.0,INLAND\n-121.41,38.34,24.0,1605.0,277.0,1966.0,250.0,3.0833,162500.0,INLAND\n-121.45,38.37,32.0,1441.0,261.0,629.0,249.0,4.4519,137500.0,INLAND\n-121.5,38.34,36.0,1212.0,255.0,569.0,256.0,2.0048,72900.0,INLAND\n-121.54,38.29,47.0,1396.0,254.0,630.0,218.0,2.8616,92500.0,INLAND\n-121.56,38.26,43.0,1906.0,327.0,996.0,314.0,2.9744,136800.0,INLAND\n-121.51,38.26,52.0,910.0,212.0,429.0,212.0,1.6458,52800.0,INLAND\n-121.57,38.19,36.0,1395.0,264.0,700.0,244.0,2.4353,162500.0,INLAND\n-121.7,38.1,19.0,4896.0,1083.0,2150.0,905.0,3.3398,89700.0,INLAND\n-121.29,36.9,17.0,3610.0,593.0,1734.0,559.0,5.8324,374200.0,INLAND\n-121.37,36.89,21.0,2471.0,473.0,1753.0,451.0,4.025,293800.0,INLAND\n-121.47,36.92,27.0,2049.0,417.0,1230.0,336.0,4.6477,265900.0,INLAND\n-121.53,36.85,23.0,3359.0,725.0,1862.0,651.0,2.6719,193600.0,INLAND\n-121.51,36.86,36.0,1019.0,168.0,602.0,169.0,2.625,210000.0,INLAND\n-121.6,36.88,21.0,3416.0,624.0,1862.0,595.0,4.7813,241500.0,<1H OCEAN\n-121.5,36.81,20.0,1345.0,230.0,731.0,217.0,4.2333,363300.0,INLAND\n-121.45,36.86,11.0,1613.0,335.0,1617.0,342.0,3.1375,146200.0,INLAND\n-121.42,36.86,41.0,440.0,106.0,389.0,94.0,2.6818,225000.0,INLAND\n-121.41,36.85,11.0,1708.0,394.0,1474.0,372.0,2.8839,145900.0,INLAND\n-121.4,36.85,50.0,2666.0,613.0,1768.0,555.0,2.6598,157300.0,INLAND\n-121.4,36.84,52.0,1860.0,400.0,1215.0,367.0,2.9554,136500.0,INLAND\n-121.4,36.84,40.0,2352.0,536.0,1430.0,535.0,3.0912,155300.0,INLAND\n-121.41,36.84,23.0,1771.0,356.0,1105.0,338.0,3.7049,192200.0,INLAND\n-121.4,36.86,36.0,1256.0,270.0,910.0,255.0,1.9405,145400.0,INLAND\n-121.38,36.85,13.0,4115.0,782.0,2903.0,747.0,3.7316,192400.0,INLAND\n-121.38,36.84,6.0,3769.0,669.0,2061.0,648.0,4.1875,217600.0,INLAND\n-121.37,36.84,11.0,1996.0,382.0,1023.0,358.0,3.5714,243000.0,INLAND\n-121.38,36.84,17.0,2625.0,512.0,1487.0,481.0,3.6354,221200.0,INLAND\n-121.37,36.83,14.0,3658.0,612.0,1951.0,600.0,4.76,216000.0,INLAND\n-121.4,36.83,11.0,3701.0,739.0,1749.0,654.0,3.067,207900.0,INLAND\n-121.44,36.84,7.0,1644.0,338.0,1143.0,331.0,4.005,180400.0,INLAND\n-121.42,36.85,7.0,1626.0,325.0,677.0,304.0,2.3125,170800.0,INLAND\n-121.32,36.79,30.0,516.0,90.0,288.0,95.0,3.6333,202500.0,INLAND\n-121.36,36.81,7.0,4609.0,741.0,1660.0,720.0,5.0871,290500.0,INLAND\n-121.34,36.76,15.0,2638.0,429.0,1289.0,357.0,4.1528,336800.0,INLAND\n-121.06,36.72,23.0,395.0,70.0,166.0,52.0,2.2132,100000.0,INLAND\n-120.95,36.47,52.0,1691.0,301.0,618.0,239.0,3.2292,225000.0,<1H OCEAN\n-117.75,34.01,4.0,22128.0,3522.0,10450.0,3258.0,6.1287,289600.0,<1H OCEAN\n-117.78,33.97,2.0,556.0,63.0,179.0,54.0,8.4411,500001.0,<1H OCEAN\n-117.72,33.99,14.0,5622.0,861.0,3108.0,821.0,5.7763,206700.0,INLAND\n-117.76,33.98,3.0,9662.0,1385.0,2497.0,856.0,6.7172,292400.0,<1H OCEAN\n-117.74,33.97,4.0,9755.0,1748.0,4662.0,1583.0,5.6501,254900.0,<1H OCEAN\n-117.72,33.97,16.0,13290.0,2062.0,6931.0,2023.0,5.228,187800.0,<1H OCEAN\n-117.71,33.97,10.0,10856.0,2278.0,6474.0,2199.0,3.851,137200.0,INLAND\n-117.7,33.92,4.0,8301.0,1333.0,3941.0,1236.0,6.2141,252200.0,<1H OCEAN\n-117.75,33.95,13.0,984.0,127.0,364.0,119.0,7.5839,426900.0,<1H OCEAN\n-117.76,33.94,40.0,1092.0,213.0,457.0,190.0,5.1165,184200.0,<1H OCEAN\n-117.69,34.09,28.0,1437.0,295.0,724.0,262.0,2.725,140200.0,INLAND\n-117.7,34.08,10.0,1979.0,454.0,1117.0,389.0,3.7802,107300.0,INLAND\n-117.7,34.09,25.0,1719.0,331.0,1098.0,324.0,3.625,121800.0,INLAND\n-117.71,34.08,29.0,1276.0,283.0,1216.0,316.0,2.5972,134300.0,INLAND\n-117.71,34.07,31.0,1840.0,380.0,1187.0,357.0,3.8875,129200.0,INLAND\n-117.69,34.08,30.0,4255.0,773.0,2129.0,730.0,4.5185,142500.0,INLAND\n-117.69,34.07,35.0,3222.0,559.0,1970.0,550.0,3.7083,131000.0,INLAND\n-117.69,34.07,34.0,4055.0,739.0,2470.0,753.0,3.8586,136000.0,INLAND\n-117.69,34.08,14.0,4136.0,886.0,2026.0,788.0,3.2344,128200.0,INLAND\n-117.71,34.07,24.0,1948.0,362.0,1286.0,364.0,3.6,139300.0,INLAND\n-117.7,34.08,33.0,4674.0,791.0,2769.0,784.0,4.1448,137300.0,INLAND\n-117.7,34.07,33.0,1552.0,288.0,1326.0,303.0,3.7969,128400.0,INLAND\n-117.69,34.06,29.0,873.0,226.0,649.0,198.0,2.7986,114400.0,INLAND\n-117.69,34.06,25.0,1881.0,433.0,1337.0,417.0,2.5536,144000.0,INLAND\n-117.7,34.06,25.0,2054.0,609.0,2271.0,564.0,2.3049,150000.0,INLAND\n-117.7,34.06,7.0,732.0,145.0,431.0,132.0,2.9107,95300.0,INLAND\n-117.71,34.06,27.0,2127.0,628.0,1970.0,534.0,1.4722,91300.0,INLAND\n-117.68,34.05,25.0,1859.0,463.0,1070.0,374.0,2.5395,187500.0,INLAND\n-117.69,34.05,10.0,1875.0,366.0,1055.0,363.0,4.3264,128900.0,INLAND\n-117.7,34.05,24.0,2834.0,470.0,1815.0,471.0,4.7357,162500.0,INLAND\n-117.71,34.06,16.0,1458.0,295.0,912.0,331.0,3.625,160400.0,INLAND\n-117.71,34.05,20.0,2281.0,444.0,1545.0,481.0,2.5735,130500.0,INLAND\n-117.72,34.05,31.0,2220.0,526.0,1662.0,472.0,2.7321,104300.0,INLAND\n-117.72,34.05,8.0,1841.0,409.0,1243.0,394.0,4.0614,107000.0,INLAND\n-117.69,34.04,5.0,4459.0,896.0,2028.0,881.0,4.0096,182600.0,INLAND\n-117.7,34.04,13.0,5301.0,1025.0,2870.0,984.0,3.5954,163000.0,INLAND\n-117.71,34.04,20.0,1950.0,310.0,1054.0,312.0,4.625,222100.0,INLAND\n-117.71,34.04,17.0,4098.0,733.0,1859.0,713.0,2.9811,231800.0,INLAND\n-117.72,34.03,17.0,2902.0,476.0,1652.0,479.0,5.6029,161800.0,INLAND\n-117.72,34.02,17.0,1781.0,262.0,860.0,256.0,6.5958,236800.0,INLAND\n-117.73,34.01,36.0,2340.0,392.0,1213.0,388.0,4.125,213000.0,INLAND\n-117.72,34.0,15.0,4363.0,690.0,2410.0,666.0,5.4824,179700.0,INLAND\n-117.68,34.0,5.0,3761.0,580.0,2335.0,648.0,5.7338,225400.0,INLAND\n-117.69,34.0,28.0,707.0,154.0,561.0,129.0,2.5781,111600.0,INLAND\n-117.7,34.0,15.0,4905.0,711.0,2711.0,762.0,5.7021,193100.0,INLAND\n-117.71,34.02,17.0,12689.0,2426.0,7343.0,2230.0,3.6361,157700.0,INLAND\n-117.71,34.03,11.0,3467.0,749.0,2163.0,676.0,3.4267,164400.0,INLAND\n-117.67,34.03,20.0,8561.0,1411.0,4861.0,1450.0,4.7056,165500.0,INLAND\n-117.68,34.03,16.0,2859.0,668.0,1946.0,591.0,3.0396,124300.0,INLAND\n-117.69,34.03,20.0,6374.0,1412.0,3690.0,1350.0,3.4184,162500.0,INLAND\n-117.68,34.01,20.0,7326.0,1555.0,5718.0,1538.0,3.2073,123500.0,INLAND\n-117.69,34.01,30.0,2598.0,573.0,2170.0,518.0,2.3,95600.0,INLAND\n-117.68,34.15,24.0,1033.0,189.0,486.0,204.0,4.1719,213500.0,INLAND\n-117.66,34.15,25.0,3430.0,485.0,1284.0,438.0,8.5282,360100.0,INLAND\n-117.64,34.15,16.0,2896.0,404.0,1165.0,379.0,6.4559,392900.0,INLAND\n-117.68,34.15,4.0,4082.0,578.0,1996.0,580.0,6.7813,286300.0,INLAND\n-117.68,34.14,4.0,4791.0,695.0,1871.0,659.0,6.9532,277000.0,INLAND\n-117.66,34.14,11.0,3628.0,469.0,1488.0,463.0,7.0844,325000.0,INLAND\n-117.66,34.14,8.0,1692.0,253.0,857.0,251.0,6.9418,310500.0,INLAND\n-117.66,34.15,20.0,2524.0,311.0,965.0,285.0,8.0103,395500.0,INLAND\n-117.65,34.14,16.0,2196.0,287.0,949.0,289.0,8.6573,354000.0,INLAND\n-117.69,34.13,8.0,2915.0,371.0,1271.0,354.0,7.9627,345400.0,INLAND\n-117.67,34.13,10.0,2846.0,362.0,1221.0,355.0,7.7234,304100.0,INLAND\n-117.66,34.13,17.0,3229.0,405.0,1289.0,407.0,6.3842,307100.0,INLAND\n-117.67,34.12,15.0,3162.0,495.0,1145.0,473.0,5.3525,191700.0,INLAND\n-117.66,34.13,19.0,3995.0,554.0,1523.0,509.0,6.075,254100.0,INLAND\n-117.66,34.12,22.0,2272.0,278.0,933.0,305.0,8.8204,390500.0,INLAND\n-117.65,34.14,9.0,3877.0,490.0,1815.0,490.0,8.4839,406700.0,INLAND\n-117.65,34.13,24.0,2121.0,296.0,913.0,302.0,5.9328,255900.0,INLAND\n-117.65,34.12,17.0,3006.0,427.0,1291.0,406.0,6.2083,242700.0,INLAND\n-117.63,34.12,4.0,4323.0,775.0,1479.0,663.0,6.0758,226800.0,INLAND\n-117.64,34.12,18.0,3605.0,534.0,1682.0,480.0,5.8407,202900.0,INLAND\n-117.65,34.11,29.0,2927.0,634.0,1710.0,623.0,3.6219,176600.0,INLAND\n-117.64,34.11,16.0,2129.0,420.0,932.0,379.0,2.5868,146900.0,INLAND\n-117.63,34.11,30.0,2674.0,428.0,1404.0,456.0,4.2969,165600.0,INLAND\n-117.69,34.11,16.0,2427.0,522.0,794.0,491.0,2.6929,119300.0,INLAND\n-117.68,34.12,16.0,2181.0,321.0,1133.0,350.0,5.7214,259400.0,INLAND\n-117.68,34.11,16.0,3190.0,471.0,1414.0,464.0,5.5292,208600.0,INLAND\n-117.66,34.12,16.0,3853.0,541.0,1726.0,497.0,6.1195,251100.0,INLAND\n-117.66,34.11,19.0,3445.0,661.0,1635.0,580.0,5.0681,230500.0,INLAND\n-117.65,34.12,27.0,2298.0,340.0,1071.0,369.0,6.5587,239000.0,INLAND\n-117.65,34.11,28.0,2788.0,370.0,1140.0,385.0,5.3368,233500.0,INLAND\n-117.69,34.1,17.0,3759.0,1035.0,1722.0,847.0,2.6074,137500.0,INLAND\n-117.68,34.1,11.0,7392.0,1796.0,3841.0,1621.0,2.8326,163000.0,INLAND\n-117.67,34.1,28.0,1263.0,248.0,601.0,219.0,3.875,174000.0,INLAND\n-117.67,34.1,19.0,2969.0,605.0,1326.0,573.0,4.3438,155700.0,INLAND\n-117.65,34.1,19.0,1688.0,365.0,622.0,322.0,3.6,136400.0,INLAND\n-117.66,34.1,37.0,1971.0,345.0,939.0,358.0,3.4634,145300.0,INLAND\n-117.66,34.1,26.0,1855.0,553.0,1109.0,536.0,2.2429,150000.0,INLAND\n-117.65,34.1,30.0,1461.0,341.0,1014.0,345.0,2.4667,106000.0,INLAND\n-117.68,34.09,22.0,1547.0,334.0,773.0,316.0,2.9812,148800.0,INLAND\n-117.67,34.09,17.0,4418.0,1256.0,2417.0,1094.0,2.7266,101000.0,INLAND\n-117.66,34.09,26.0,1151.0,200.0,593.0,188.0,3.6667,166300.0,INLAND\n-117.66,34.09,20.0,2462.0,496.0,1117.0,458.0,3.2321,162500.0,INLAND\n-117.66,34.09,23.0,1426.0,313.0,954.0,319.0,3.0357,151500.0,INLAND\n-117.65,34.09,33.0,2446.0,396.0,1209.0,412.0,4.3958,145600.0,INLAND\n-117.62,34.13,20.0,3216.0,516.0,1655.0,524.0,5.1261,158800.0,INLAND\n-117.62,34.11,17.0,1869.0,311.0,831.0,262.0,6.148,243900.0,INLAND\n-117.62,34.11,31.0,2561.0,414.0,1204.0,435.0,4.4637,192800.0,INLAND\n-117.63,34.1,15.0,4799.0,1209.0,2554.0,1057.0,2.6582,122800.0,INLAND\n-117.63,34.09,8.0,3557.0,890.0,2251.0,765.0,2.6818,114100.0,INLAND\n-117.64,34.09,34.0,2839.0,659.0,1822.0,631.0,3.05,121300.0,INLAND\n-117.65,34.09,46.0,1214.0,281.0,701.0,294.0,2.7083,116300.0,INLAND\n-117.65,34.1,44.0,1526.0,337.0,831.0,326.0,3.0284,115800.0,INLAND\n-117.65,34.1,44.0,2808.0,585.0,1444.0,550.0,2.7159,139300.0,INLAND\n-117.68,34.08,21.0,5662.0,1185.0,3067.0,1055.0,3.3482,137300.0,INLAND\n-117.68,34.08,28.0,2459.0,492.0,1230.0,498.0,3.0978,137200.0,INLAND\n-117.67,34.07,29.0,1840.0,388.0,1278.0,368.0,3.5036,123400.0,INLAND\n-117.68,34.07,24.0,2626.0,692.0,2204.0,647.0,1.7806,135000.0,INLAND\n-117.68,34.07,32.0,1775.0,314.0,1067.0,302.0,4.0375,121300.0,INLAND\n-117.65,34.08,35.0,2621.0,391.0,1074.0,391.0,4.7176,166400.0,INLAND\n-117.65,34.08,38.0,2750.0,572.0,1410.0,483.0,3.3836,144900.0,INLAND\n-117.66,34.07,37.0,2454.0,511.0,1165.0,504.0,2.9474,139600.0,INLAND\n-117.66,34.06,24.0,4043.0,952.0,2174.0,859.0,2.2244,114900.0,INLAND\n-117.66,34.07,36.0,2072.0,408.0,964.0,395.0,2.8702,137000.0,INLAND\n-117.66,34.07,33.0,2081.0,409.0,1008.0,375.0,2.587,138100.0,INLAND\n-117.66,34.08,36.0,1485.0,236.0,623.0,261.0,3.3036,141000.0,INLAND\n-117.66,34.08,33.0,3659.0,590.0,1773.0,615.0,3.9227,157200.0,INLAND\n-117.64,34.08,37.0,2576.0,468.0,1284.0,428.0,3.3958,130400.0,INLAND\n-117.64,34.07,43.0,1970.0,379.0,1036.0,391.0,3.2083,122800.0,INLAND\n-117.65,34.08,52.0,2264.0,439.0,1031.0,437.0,3.375,144300.0,INLAND\n-117.65,34.08,40.0,1609.0,258.0,624.0,242.0,5.4689,158200.0,INLAND\n-117.64,34.08,35.0,1254.0,241.0,729.0,253.0,3.495,118000.0,INLAND\n-117.64,34.08,33.0,1987.0,455.0,1369.0,475.0,2.4464,122600.0,INLAND\n-117.63,34.08,38.0,1810.0,371.0,1257.0,354.0,3.8355,111700.0,INLAND\n-117.63,34.07,39.0,2650.0,511.0,1537.0,495.0,3.4432,106700.0,INLAND\n-117.62,34.07,15.0,4061.0,811.0,2884.0,734.0,3.3936,127000.0,INLAND\n-117.62,34.08,30.0,1372.0,235.0,1047.0,225.0,3.1597,116300.0,INLAND\n-117.63,34.09,19.0,3490.0,816.0,2818.0,688.0,2.8977,126200.0,INLAND\n-117.62,34.09,26.0,3271.0,595.0,2259.0,566.0,4.0139,132000.0,INLAND\n-117.62,34.08,22.0,2626.0,631.0,1678.0,557.0,3.125,111800.0,INLAND\n-117.62,34.08,24.0,2801.0,554.0,2064.0,529.0,4.4946,136000.0,INLAND\n-117.61,34.08,20.0,3550.0,736.0,2229.0,681.0,3.0199,128800.0,INLAND\n-117.61,34.09,23.0,1945.0,362.0,1483.0,383.0,4.4205,135500.0,INLAND\n-117.61,34.09,11.0,2000.0,391.0,1503.0,426.0,4.6167,144000.0,INLAND\n-117.61,34.08,12.0,4427.0,,2400.0,843.0,4.7147,158700.0,INLAND\n-117.6,34.08,15.0,2700.0,460.0,1432.0,449.0,4.9063,159800.0,INLAND\n-117.65,34.07,35.0,2501.0,651.0,1182.0,591.0,1.4464,113200.0,INLAND\n-117.65,34.07,52.0,1041.0,252.0,558.0,231.0,1.9236,117200.0,INLAND\n-117.65,34.06,41.0,465.0,130.0,349.0,138.0,2.0893,112500.0,INLAND\n-117.62,34.07,16.0,6009.0,1599.0,5110.0,1389.0,2.5677,128900.0,INLAND\n-117.64,34.07,30.0,2787.0,713.0,2647.0,693.0,2.2765,98100.0,INLAND\n-117.64,34.07,38.0,2450.0,544.0,1823.0,536.0,2.837,111200.0,INLAND\n-117.64,34.07,52.0,1644.0,372.0,1269.0,355.0,2.6913,108300.0,INLAND\n-117.67,34.06,26.0,1592.0,429.0,1182.0,365.0,2.4583,110400.0,INLAND\n-117.66,34.06,39.0,1405.0,339.0,1489.0,336.0,1.608,91800.0,INLAND\n-117.65,34.06,41.0,1171.0,334.0,1479.0,334.0,2.25,90500.0,INLAND\n-117.64,34.06,43.0,763.0,219.0,851.0,198.0,1.7292,79200.0,INLAND\n-117.64,34.06,50.0,637.0,143.0,590.0,147.0,1.9659,85700.0,INLAND\n-117.63,34.06,39.0,1210.0,310.0,1294.0,303.0,2.3636,88300.0,INLAND\n-117.66,34.05,36.0,2341.0,520.0,2138.0,523.0,2.3347,104000.0,INLAND\n-117.66,34.05,33.0,960.0,216.0,831.0,222.0,2.5391,108600.0,INLAND\n-117.66,34.05,6.0,5129.0,1119.0,2533.0,949.0,3.625,113600.0,INLAND\n-117.67,34.04,16.0,3260.0,501.0,1973.0,535.0,4.6563,162000.0,INLAND\n-117.66,34.04,16.0,2081.0,348.0,1332.0,356.0,4.7872,147600.0,INLAND\n-117.66,34.05,14.0,2644.0,525.0,2021.0,511.0,3.6467,147500.0,INLAND\n-117.65,34.04,15.0,3393.0,,2039.0,611.0,3.9336,151000.0,INLAND\n-117.66,34.04,10.0,3657.0,695.0,2079.0,663.0,4.2054,159900.0,INLAND\n-117.67,34.05,6.0,2833.0,628.0,1717.0,589.0,3.2062,167500.0,INLAND\n-117.67,34.04,13.0,2295.0,374.0,1284.0,378.0,5.2551,194300.0,INLAND\n-117.67,34.04,13.0,1543.0,,776.0,358.0,3.0598,99700.0,INLAND\n-117.68,34.04,27.0,574.0,103.0,321.0,103.0,3.9107,186500.0,INLAND\n-117.65,34.02,9.0,2107.0,411.0,1138.0,389.0,4.4042,159100.0,INLAND\n-117.66,34.03,14.0,2137.0,345.0,1151.0,352.0,5.753,185500.0,INLAND\n-117.67,34.02,16.0,3042.0,524.0,1516.0,475.0,4.8906,178500.0,INLAND\n-117.66,34.02,11.0,3358.0,504.0,1690.0,482.0,6.7544,207900.0,INLAND\n-117.65,34.03,15.0,4420.0,903.0,2373.0,858.0,3.449,149100.0,INLAND\n-117.65,34.04,28.0,2360.0,607.0,2623.0,592.0,2.5048,100000.0,INLAND\n-117.64,34.05,32.0,1129.0,251.0,1378.0,268.0,3.0057,96900.0,INLAND\n-117.64,34.05,27.0,1407.0,362.0,1684.0,350.0,2.1944,95700.0,INLAND\n-117.64,34.03,11.0,2050.0,382.0,1044.0,371.0,4.8281,137000.0,INLAND\n-117.64,34.04,21.0,1801.0,507.0,2556.0,484.0,2.4716,102500.0,INLAND\n-117.64,34.03,10.0,3194.0,579.0,2088.0,549.0,4.1779,159100.0,INLAND\n-117.64,34.02,10.0,4887.0,930.0,2637.0,831.0,4.0611,158000.0,INLAND\n-117.63,34.02,13.0,4864.0,729.0,2780.0,723.0,5.6168,175400.0,INLAND\n-117.62,34.02,9.0,4265.0,587.0,2280.0,589.0,5.5632,195100.0,INLAND\n-117.62,34.03,15.0,3942.0,661.0,2240.0,621.0,4.8311,176000.0,INLAND\n-117.62,34.02,16.0,2040.0,325.0,1207.0,324.0,5.0431,164100.0,INLAND\n-117.61,34.02,15.0,1791.0,346.0,1219.0,328.0,3.8125,170300.0,INLAND\n-117.6,34.02,16.0,2103.0,348.0,1305.0,356.0,5.2849,160400.0,INLAND\n-117.6,34.03,16.0,1499.0,232.0,918.0,239.0,5.5677,175400.0,INLAND\n-117.62,34.05,33.0,883.0,211.0,1007.0,210.0,2.8281,103600.0,INLAND\n-117.61,34.04,8.0,4116.0,766.0,1785.0,745.0,3.1672,150200.0,INLAND\n-117.66,34.02,12.0,5616.0,871.0,3019.0,782.0,5.5425,202300.0,INLAND\n-117.64,34.02,6.0,248.0,47.0,119.0,42.0,2.125,416700.0,INLAND\n-117.61,34.01,25.0,352.0,41.0,99.0,34.0,3.9696,500000.0,INLAND\n-117.61,34.02,8.0,63.0,9.0,25.0,7.0,7.7197,275000.0,INLAND\n-117.64,33.99,29.0,1005.0,152.0,513.0,149.0,2.4375,181300.0,INLAND\n-117.66,34.0,5.0,1387.0,236.0,855.0,270.0,5.411,201100.0,INLAND\n-117.6,33.98,26.0,1225.0,199.0,717.0,204.0,2.7284,225000.0,INLAND\n-117.63,33.94,36.0,447.0,95.0,2886.0,85.0,4.2578,183300.0,INLAND\n-117.58,34.0,2.0,7544.0,1516.0,2801.0,1001.0,4.0037,245200.0,INLAND\n-117.57,34.15,3.0,12806.0,2219.0,4249.0,1499.0,5.485,343100.0,INLAND\n-117.57,34.13,5.0,6135.0,879.0,2795.0,781.0,5.9369,225200.0,INLAND\n-117.54,34.12,4.0,17577.0,2819.0,7766.0,2473.0,5.1333,181800.0,INLAND\n-117.54,34.11,16.0,2114.0,374.0,1463.0,399.0,3.9241,131500.0,INLAND\n-117.51,34.14,21.0,2455.0,381.0,1094.0,327.0,4.6437,191700.0,INLAND\n-117.5,34.12,2.0,11965.0,1802.0,4436.0,1296.0,5.285,191700.0,INLAND\n-117.51,34.16,2.0,718.0,98.0,119.0,50.0,4.1,315000.0,INLAND\n-117.55,34.14,3.0,5710.0,919.0,2874.0,886.0,5.3638,206300.0,INLAND\n-117.59,34.16,10.0,9467.0,1181.0,3819.0,1122.0,7.8252,361400.0,INLAND\n-117.58,34.14,7.0,11818.0,1745.0,5499.0,1600.0,5.3678,231700.0,INLAND\n-117.62,34.15,16.0,13556.0,1704.0,5669.0,1668.0,6.5138,311500.0,INLAND\n-117.61,34.14,14.0,15809.0,2485.0,7363.0,2410.0,5.5198,245600.0,INLAND\n-117.61,34.13,21.0,8416.0,1386.0,4308.0,1341.0,4.4611,164600.0,INLAND\n-117.61,34.12,17.0,6709.0,1198.0,3954.0,1161.0,4.6997,156900.0,INLAND\n-117.59,34.13,10.0,20263.0,3915.0,9716.0,3744.0,3.8505,169600.0,INLAND\n-117.58,34.11,14.0,11635.0,2055.0,6443.0,2009.0,4.7547,157600.0,INLAND\n-117.6,34.11,18.0,6025.0,1062.0,3360.0,1028.0,4.8889,155700.0,INLAND\n-117.56,34.12,4.0,6956.0,1271.0,3455.0,1228.0,4.7193,178700.0,INLAND\n-117.56,34.12,4.0,5351.0,1210.0,2988.0,1101.0,3.7973,181300.0,INLAND\n-117.61,34.1,9.0,18956.0,4095.0,10323.0,3832.0,3.6033,132600.0,INLAND\n-117.59,34.1,17.0,3646.0,1035.0,1987.0,895.0,2.3603,139300.0,INLAND\n-117.58,34.1,4.0,6360.0,1584.0,3359.0,1396.0,3.5186,127800.0,INLAND\n-117.58,34.09,27.0,754.0,200.0,746.0,185.0,1.9531,100800.0,INLAND\n-117.59,34.09,16.0,2401.0,465.0,1757.0,500.0,3.9755,120400.0,INLAND\n-117.57,34.07,4.0,2152.0,580.0,1083.0,441.0,3.1458,118800.0,INLAND\n-117.59,34.03,16.0,3443.0,562.0,2130.0,564.0,5.0769,161400.0,INLAND\n-117.59,34.02,14.0,1463.0,261.0,881.0,245.0,4.7857,152500.0,INLAND\n-117.58,34.02,4.0,5998.0,1092.0,3182.0,1042.0,5.2692,174800.0,INLAND\n-117.57,34.02,5.0,6933.0,1311.0,3845.0,1285.0,4.6727,158900.0,INLAND\n-117.53,34.06,18.0,5605.0,1303.0,4028.0,1145.0,2.9386,116400.0,INLAND\n-117.53,34.1,5.0,2185.0,488.0,1379.0,458.0,3.7917,103000.0,INLAND\n-117.42,34.13,4.0,11587.0,1796.0,5804.0,1705.0,4.8283,141900.0,INLAND\n-117.43,34.12,7.0,5954.0,1071.0,3567.0,1070.0,3.2056,134100.0,INLAND\n-117.45,34.13,16.0,5058.0,965.0,3388.0,878.0,3.6417,119000.0,INLAND\n-117.46,34.14,10.0,714.0,131.0,381.0,119.0,0.8926,116100.0,INLAND\n-117.47,34.12,6.0,10565.0,1767.0,5690.0,1555.0,4.1797,141000.0,INLAND\n-117.46,34.1,35.0,908.0,226.0,667.0,203.0,2.5833,93500.0,INLAND\n-117.46,34.1,7.0,1759.0,473.0,1064.0,328.0,1.9583,108800.0,INLAND\n-117.46,34.09,8.0,4711.0,963.0,3310.0,988.0,3.5488,101600.0,INLAND\n-117.48,34.09,32.0,3170.0,630.0,2612.0,580.0,3.6394,99200.0,INLAND\n-117.48,34.09,32.0,1650.0,328.0,1124.0,290.0,3.1838,98600.0,INLAND\n-117.48,34.1,30.0,2287.0,531.0,1796.0,503.0,2.5833,90600.0,INLAND\n-117.46,34.08,18.0,3830.0,750.0,2767.0,702.0,3.6602,120700.0,INLAND\n-117.46,34.07,19.0,3155.0,572.0,2482.0,642.0,2.9973,113400.0,INLAND\n-117.48,34.08,28.0,1922.0,382.0,1565.0,340.0,3.915,117400.0,INLAND\n-117.48,34.08,17.0,1834.0,390.0,1253.0,357.0,3.1028,106400.0,INLAND\n-117.47,34.07,24.0,1017.0,227.0,568.0,187.0,1.5972,112500.0,INLAND\n-117.42,34.06,27.0,2532.0,495.0,1305.0,436.0,2.9107,143100.0,INLAND\n-117.46,34.06,24.0,2831.0,478.0,1582.0,435.0,4.3397,195600.0,INLAND\n-117.47,34.06,33.0,1379.0,273.0,884.0,229.0,2.7574,125000.0,INLAND\n-117.49,34.05,20.0,1483.0,249.0,660.0,194.0,3.9464,207700.0,INLAND\n-117.5,34.04,5.0,3958.0,665.0,2456.0,666.0,5.1647,154700.0,INLAND\n-117.5,34.04,4.0,3428.0,649.0,2158.0,632.0,5.0175,143400.0,INLAND\n-117.49,34.04,4.0,6034.0,1170.0,3527.0,1098.0,4.1775,143700.0,INLAND\n-117.46,34.04,3.0,12870.0,2315.0,5820.0,1759.0,4.2429,147300.0,INLAND\n-117.38,34.14,11.0,10804.0,1493.0,5221.0,1482.0,5.246,161400.0,INLAND\n-117.4,34.15,4.0,12156.0,1864.0,5020.0,1524.0,4.7909,149200.0,INLAND\n-117.4,34.18,16.0,1769.0,254.0,1778.0,251.0,5.3671,181800.0,INLAND\n-117.38,34.2,16.0,193.0,45.0,312.0,76.0,3.7578,137500.0,INLAND\n-117.45,34.11,7.0,6356.0,1244.0,4052.0,1164.0,2.9112,121700.0,INLAND\n-117.45,34.1,9.0,4288.0,1017.0,3156.0,900.0,2.7827,105800.0,INLAND\n-117.45,34.1,6.0,5571.0,1316.0,4048.0,1154.0,2.0308,91100.0,INLAND\n-117.42,34.11,25.0,4261.0,893.0,2319.0,702.0,3.3958,111900.0,INLAND\n-117.42,34.1,18.0,3977.0,809.0,2231.0,742.0,4.1399,115400.0,INLAND\n-117.43,34.1,34.0,1345.0,265.0,834.0,290.0,3.7011,99500.0,INLAND\n-117.43,34.11,17.0,4109.0,884.0,2544.0,780.0,2.7757,109800.0,INLAND\n-117.43,34.1,43.0,1898.0,418.0,971.0,366.0,2.4735,89900.0,INLAND\n-117.44,34.1,43.0,1614.0,400.0,926.0,349.0,2.075,95100.0,INLAND\n-117.44,34.09,24.0,3477.0,831.0,2541.0,753.0,2.3682,97400.0,INLAND\n-117.44,34.09,12.0,3598.0,828.0,2588.0,781.0,2.375,113800.0,INLAND\n-117.44,34.08,15.0,5024.0,992.0,3208.0,981.0,3.6025,116400.0,INLAND\n-117.42,34.09,28.0,3193.0,525.0,1750.0,523.0,4.1375,128300.0,INLAND\n-117.42,34.08,28.0,2300.0,419.0,1312.0,444.0,3.4844,127700.0,INLAND\n-117.43,34.08,31.0,3207.0,560.0,1582.0,538.0,4.263,127400.0,INLAND\n-117.43,34.09,18.0,3172.0,632.0,1621.0,573.0,2.7437,120200.0,INLAND\n-117.42,34.08,21.0,4460.0,930.0,2657.0,839.0,2.7569,127500.0,INLAND\n-117.43,34.08,13.0,4563.0,1187.0,2475.0,1019.0,2.1189,121700.0,INLAND\n-117.45,34.07,21.0,3465.0,639.0,2292.0,628.0,3.3553,113500.0,INLAND\n-117.43,34.07,18.0,2453.0,537.0,1503.0,500.0,2.3768,95300.0,INLAND\n-117.41,34.11,12.0,6758.0,1550.0,3204.0,1279.0,2.5181,105500.0,INLAND\n-117.41,34.1,5.0,4937.0,1139.0,2204.0,812.0,2.5272,92000.0,INLAND\n-117.4,34.08,21.0,3622.0,667.0,2503.0,720.0,3.8531,105400.0,INLAND\n-117.41,34.08,38.0,1541.0,290.0,861.0,299.0,3.5655,95600.0,INLAND\n-117.41,34.09,21.0,3300.0,587.0,1896.0,572.0,3.6466,130600.0,INLAND\n-117.41,34.1,29.0,1362.0,251.0,776.0,253.0,3.1287,102000.0,INLAND\n-117.41,34.11,29.0,3999.0,772.0,2602.0,760.0,3.5481,105500.0,INLAND\n-117.4,34.11,14.0,1933.0,347.0,1443.0,376.0,4.2121,128100.0,INLAND\n-117.39,34.11,5.0,2987.0,457.0,1821.0,485.0,4.8889,138900.0,INLAND\n-117.39,34.11,16.0,1140.0,181.0,627.0,206.0,4.9444,132700.0,INLAND\n-117.38,34.11,36.0,1497.0,264.0,894.0,275.0,3.3066,96300.0,INLAND\n-117.38,34.11,32.0,3179.0,662.0,1878.0,661.0,3.1375,101200.0,INLAND\n-117.39,34.1,12.0,7184.0,1516.0,4862.0,1235.0,2.4492,103800.0,INLAND\n-117.39,34.1,19.0,1000.0,211.0,572.0,230.0,2.4028,112500.0,INLAND\n-117.39,34.13,9.0,2228.0,398.0,1316.0,370.0,4.1632,119800.0,INLAND\n-117.38,34.13,13.0,2903.0,510.0,1844.0,510.0,3.7198,112900.0,INLAND\n-117.37,34.13,17.0,2681.0,470.0,1621.0,459.0,3.875,118500.0,INLAND\n-117.38,34.13,23.0,1326.0,300.0,722.0,263.0,2.1856,107800.0,INLAND\n-117.37,34.13,12.0,1893.0,493.0,1054.0,389.0,2.3456,140800.0,INLAND\n-117.38,34.12,17.0,5959.0,1208.0,4115.0,1088.0,2.4053,105200.0,INLAND\n-117.39,34.12,7.0,5059.0,780.0,3253.0,801.0,4.9196,140500.0,INLAND\n-117.38,34.09,8.0,3955.0,815.0,2184.0,725.0,3.3438,127600.0,INLAND\n-117.36,34.09,32.0,3616.0,631.0,2131.0,593.0,3.2879,95500.0,INLAND\n-117.37,34.08,17.0,2029.0,404.0,1190.0,437.0,2.9554,115000.0,INLAND\n-117.38,34.08,11.0,5684.0,1139.0,3095.0,1036.0,3.6875,112600.0,INLAND\n-117.38,34.07,6.0,1156.0,191.0,910.0,234.0,4.9091,122400.0,INLAND\n-117.37,34.07,52.0,50.0,9.0,60.0,16.0,4.125,262500.0,INLAND\n-117.36,34.08,4.0,8866.0,1832.0,4775.0,1554.0,3.7348,125800.0,INLAND\n-117.4,34.09,5.0,6190.0,993.0,3615.0,963.0,4.4034,133200.0,INLAND\n-117.39,34.09,10.0,5736.0,945.0,3528.0,932.0,4.3958,130700.0,INLAND\n-117.39,34.07,15.0,1966.0,331.0,1118.0,323.0,3.8558,122700.0,INLAND\n-117.39,34.07,26.0,1387.0,277.0,664.0,239.0,3.0278,96800.0,INLAND\n-117.4,34.07,28.0,2879.0,659.0,1661.0,554.0,2.066,88100.0,INLAND\n-117.37,34.1,44.0,2087.0,447.0,1270.0,423.0,2.3889,86100.0,INLAND\n-117.37,34.1,10.0,3404.0,855.0,1656.0,675.0,1.6977,91300.0,INLAND\n-117.35,34.12,22.0,5640.0,889.0,3157.0,887.0,4.1581,126500.0,INLAND\n-117.36,34.11,35.0,2969.0,521.0,1555.0,503.0,3.25,107100.0,INLAND\n-117.37,34.12,32.0,3190.0,568.0,1614.0,512.0,3.8398,118200.0,INLAND\n-117.37,34.13,18.0,5877.0,1043.0,3114.0,1002.0,4.0294,133200.0,INLAND\n-117.35,34.13,26.0,3920.0,570.0,1862.0,552.0,3.7286,132000.0,INLAND\n-117.36,34.1,29.0,2819.0,637.0,1683.0,608.0,2.3205,87600.0,INLAND\n-117.36,34.1,31.0,2587.0,531.0,1227.0,489.0,2.3578,88600.0,INLAND\n-117.36,34.1,33.0,1904.0,343.0,1366.0,338.0,3.6227,92800.0,INLAND\n-117.38,34.06,17.0,3139.0,569.0,1612.0,516.0,3.3571,112300.0,INLAND\n-117.4,34.06,17.0,5451.0,1008.0,3533.0,940.0,3.9191,101600.0,INLAND\n-117.4,34.04,17.0,1906.0,334.0,1550.0,338.0,3.025,81800.0,INLAND\n-117.39,34.04,27.0,2919.0,549.0,1841.0,564.0,2.8682,96400.0,INLAND\n-117.35,34.04,14.0,2991.0,522.0,1729.0,537.0,3.5139,146800.0,INLAND\n-117.35,34.17,28.0,1905.0,372.0,1480.0,341.0,2.9844,79200.0,INLAND\n-117.34,34.16,31.0,1606.0,354.0,1049.0,335.0,2.1935,72700.0,INLAND\n-117.33,34.15,28.0,1473.0,333.0,1196.0,312.0,1.6993,67800.0,INLAND\n-117.32,34.14,32.0,1691.0,353.0,1457.0,329.0,1.8438,66600.0,INLAND\n-117.33,34.14,29.0,1646.0,391.0,1296.0,351.0,1.9423,69700.0,INLAND\n-117.34,34.14,37.0,1834.0,393.0,1198.0,348.0,2.225,81600.0,INLAND\n-117.35,34.15,32.0,2699.0,552.0,2086.0,551.0,2.2974,84500.0,INLAND\n-117.35,34.16,36.0,1717.0,348.0,1054.0,279.0,2.4444,73400.0,INLAND\n-117.32,34.13,41.0,1837.0,409.0,1430.0,344.0,2.4524,70400.0,INLAND\n-117.32,34.12,37.0,2868.0,574.0,2055.0,563.0,2.3508,70500.0,INLAND\n-117.32,34.12,39.0,2210.0,498.0,1752.0,477.0,1.4066,66400.0,INLAND\n-117.33,34.12,33.0,933.0,219.0,838.0,211.0,1.3417,69000.0,INLAND\n-117.33,34.13,30.0,2335.0,363.0,1214.0,311.0,2.2449,93200.0,INLAND\n-117.33,34.13,18.0,3009.0,740.0,2317.0,659.0,1.6375,72400.0,INLAND\n-117.34,34.13,29.0,1494.0,286.0,991.0,280.0,2.125,70600.0,INLAND\n-117.34,34.13,29.0,331.0,85.0,341.0,107.0,0.7069,70300.0,INLAND\n-117.32,34.11,38.0,1462.0,337.0,1208.0,324.0,2.2604,68100.0,INLAND\n-117.32,34.11,41.0,1229.0,302.0,994.0,270.0,1.4891,67300.0,INLAND\n-117.33,34.12,38.0,1703.0,385.0,1356.0,363.0,2.0391,70400.0,INLAND\n-117.34,34.12,26.0,1008.0,164.0,568.0,196.0,3.3516,105600.0,INLAND\n-117.34,34.11,29.0,2912.0,566.0,2188.0,518.0,3.2656,90600.0,INLAND\n-117.35,34.11,34.0,2104.0,388.0,1578.0,365.0,3.0833,88400.0,INLAND\n-117.34,34.1,14.0,11827.0,2445.0,6640.0,2299.0,2.4878,103800.0,INLAND\n-117.35,34.09,14.0,5983.0,1224.0,3255.0,1150.0,2.5902,111500.0,INLAND\n-117.35,34.2,5.0,9269.0,1605.0,4916.0,1519.0,4.4367,133200.0,INLAND\n-117.38,34.22,16.0,774.0,122.0,489.0,136.0,5.7628,221300.0,INLAND\n-117.41,34.24,20.0,1160.0,181.0,543.0,188.0,5.2072,164300.0,INLAND\n-117.41,34.23,17.0,889.0,131.0,439.0,141.0,6.1426,155000.0,INLAND\n-117.28,34.17,26.0,3106.0,603.0,1396.0,576.0,3.1736,122200.0,INLAND\n-117.28,34.17,26.0,3728.0,888.0,1765.0,727.0,1.7456,86800.0,INLAND\n-117.29,34.17,35.0,4174.0,847.0,2127.0,778.0,3.2232,88300.0,INLAND\n-117.3,34.18,28.0,2685.0,425.0,1304.0,420.0,4.3676,111100.0,INLAND\n-117.3,34.17,30.0,2483.0,573.0,1172.0,438.0,1.875,89700.0,INLAND\n-117.31,34.17,25.0,2795.0,596.0,1650.0,569.0,3.0078,87100.0,INLAND\n-117.32,34.17,6.0,5661.0,1287.0,2943.0,1162.0,3.6362,106500.0,INLAND\n-117.3,34.18,19.0,2526.0,381.0,1176.0,381.0,5.5136,137100.0,INLAND\n-117.32,34.19,6.0,1068.0,182.0,999.0,188.0,4.7222,109000.0,INLAND\n-117.34,34.18,7.0,2914.0,481.0,1584.0,499.0,4.6312,124900.0,INLAND\n-117.33,34.17,5.0,4718.0,1140.0,2564.0,1056.0,2.9877,119900.0,INLAND\n-117.33,34.17,13.0,3616.0,665.0,2189.0,620.0,3.7949,106300.0,INLAND\n-117.32,34.16,9.0,711.0,139.0,316.0,152.0,4.0156,131000.0,INLAND\n-117.31,34.15,7.0,5747.0,1307.0,2578.0,1147.0,3.3281,122200.0,INLAND\n-117.3,34.15,33.0,1607.0,282.0,608.0,260.0,4.3438,115000.0,INLAND\n-117.3,34.15,38.0,740.0,163.0,332.0,138.0,2.4107,88000.0,INLAND\n-117.3,34.15,40.0,961.0,199.0,509.0,182.0,2.06,85500.0,INLAND\n-117.3,34.14,39.0,1781.0,335.0,841.0,320.0,1.9432,89000.0,INLAND\n-117.31,34.14,44.0,1487.0,273.0,972.0,281.0,3.2292,86100.0,INLAND\n-117.31,34.14,38.0,2011.0,448.0,1190.0,403.0,1.8654,89400.0,INLAND\n-117.31,34.15,34.0,2037.0,385.0,1195.0,391.0,3.9231,96000.0,INLAND\n-117.3,34.15,45.0,942.0,166.0,401.0,174.0,3.8594,90800.0,INLAND\n-117.31,34.13,38.0,1287.0,284.0,1047.0,269.0,2.2865,65500.0,INLAND\n-117.3,34.12,34.0,1127.0,275.0,971.0,249.0,2.0583,64800.0,INLAND\n-117.31,34.12,37.0,1412.0,343.0,1127.0,351.0,1.1667,70900.0,INLAND\n-117.31,34.13,35.0,1622.0,393.0,1296.0,362.0,1.9286,68500.0,INLAND\n-117.31,34.13,36.0,1076.0,283.0,773.0,224.0,2.6307,66400.0,INLAND\n-117.3,34.11,42.0,525.0,111.0,444.0,120.0,2.6771,67000.0,INLAND\n-117.31,34.11,38.0,1208.0,321.0,1225.0,317.0,1.4663,64000.0,INLAND\n-117.31,34.11,52.0,851.0,190.0,731.0,190.0,1.9044,64900.0,INLAND\n-117.31,34.11,41.0,1105.0,257.0,816.0,197.0,1.9375,64100.0,INLAND\n-117.31,34.1,52.0,1457.0,415.0,1238.0,341.0,2.0089,68100.0,INLAND\n-117.3,34.1,44.0,589.0,130.0,504.0,137.0,1.775,63400.0,INLAND\n-117.31,34.1,28.0,2899.0,755.0,2406.0,655.0,1.5208,69500.0,INLAND\n-117.32,34.1,27.0,2053.0,461.0,1737.0,463.0,3.1213,78800.0,INLAND\n-117.32,34.1,42.0,801.0,176.0,711.0,183.0,1.8681,59700.0,INLAND\n-117.3,34.09,40.0,1051.0,244.0,745.0,243.0,2.1842,75200.0,INLAND\n-117.31,34.09,34.0,1336.0,345.0,1009.0,311.0,1.608,73700.0,INLAND\n-117.27,34.16,32.0,2894.0,427.0,1151.0,446.0,6.2236,159700.0,INLAND\n-117.28,34.15,36.0,1734.0,280.0,604.0,259.0,3.8292,122200.0,INLAND\n-117.28,34.15,38.0,1981.0,343.0,796.0,344.0,3.8125,97400.0,INLAND\n-117.29,34.15,42.0,1811.0,345.0,856.0,352.0,2.9667,97000.0,INLAND\n-117.29,34.16,38.0,2458.0,488.0,1135.0,453.0,2.875,99100.0,INLAND\n-117.28,34.16,26.0,2469.0,532.0,1068.0,501.0,1.9832,122100.0,INLAND\n-117.28,34.16,35.0,2028.0,456.0,972.0,398.0,2.3778,90700.0,INLAND\n-117.28,34.15,32.0,2170.0,430.0,815.0,401.0,3.1765,135000.0,INLAND\n-117.28,34.14,40.0,2364.0,438.0,968.0,416.0,3.4906,93300.0,INLAND\n-117.29,34.14,52.0,1683.0,266.0,646.0,256.0,4.0481,97300.0,INLAND\n-117.29,34.15,49.0,1820.0,321.0,757.0,324.0,3.2976,102600.0,INLAND\n-117.28,34.14,40.0,2190.0,496.0,1214.0,493.0,2.3947,81900.0,INLAND\n-117.29,34.14,45.0,1598.0,314.0,771.0,319.0,2.5417,82900.0,INLAND\n-117.29,34.14,48.0,1717.0,307.0,610.0,267.0,3.125,97600.0,INLAND\n-117.29,34.14,39.0,1989.0,401.0,805.0,341.0,2.425,90000.0,INLAND\n-117.3,34.14,37.0,1454.0,261.0,761.0,248.0,2.3438,88100.0,INLAND\n-117.28,34.13,44.0,2469.0,568.0,1363.0,517.0,1.8396,77100.0,INLAND\n-117.28,34.13,29.0,2077.0,577.0,1418.0,524.0,1.8281,76800.0,INLAND\n-117.3,34.13,52.0,1859.0,395.0,968.0,333.0,1.2034,79500.0,INLAND\n-117.29,34.13,52.0,2424.0,528.0,1171.0,455.0,1.4815,77900.0,INLAND\n-117.28,34.12,36.0,2991.0,822.0,2378.0,751.0,1.3571,70600.0,INLAND\n-117.29,34.12,40.0,2198.0,612.0,1517.0,531.0,1.0951,65800.0,INLAND\n-117.29,34.13,44.0,2337.0,563.0,1238.0,467.0,1.5156,75800.0,INLAND\n-117.3,34.13,42.0,2115.0,557.0,1532.0,494.0,1.4531,71500.0,INLAND\n-117.3,34.12,43.0,1018.0,261.0,736.0,215.0,2.6,66900.0,INLAND\n-117.28,34.12,47.0,2456.0,611.0,1653.0,512.0,1.3973,66100.0,INLAND\n-117.29,34.12,47.0,1648.0,432.0,1308.0,385.0,1.2069,68200.0,INLAND\n-117.29,34.12,45.0,1369.0,351.0,1046.0,274.0,1.8438,72100.0,INLAND\n-117.3,34.12,50.0,1629.0,437.0,1581.0,394.0,2.2019,63500.0,INLAND\n-117.29,34.11,48.0,1498.0,448.0,1586.0,455.0,1.1687,70800.0,INLAND\n-117.29,34.11,35.0,2014.0,677.0,1714.0,612.0,0.7075,78800.0,INLAND\n-117.29,34.11,35.0,2426.0,715.0,1920.0,586.0,1.5561,68000.0,INLAND\n-117.28,34.11,39.0,1573.0,418.0,1258.0,359.0,1.4896,69500.0,INLAND\n-117.29,34.1,19.0,1204.0,405.0,844.0,424.0,0.7235,71300.0,INLAND\n-117.29,34.09,24.0,1451.0,387.0,1178.0,330.0,1.1806,68300.0,INLAND\n-117.3,34.1,49.0,60.0,11.0,76.0,13.0,2.5625,75000.0,INLAND\n-117.28,34.09,44.0,376.0,,273.0,107.0,2.2917,90800.0,INLAND\n-117.3,34.07,34.0,567.0,143.0,387.0,138.0,1.7981,73300.0,INLAND\n-117.25,34.16,31.0,1516.0,238.0,596.0,255.0,4.3362,159400.0,INLAND\n-117.25,34.16,37.0,1709.0,278.0,744.0,274.0,3.7188,116600.0,INLAND\n-117.25,34.16,35.0,2707.0,481.0,1595.0,479.0,3.9018,91500.0,INLAND\n-117.25,34.15,30.0,1770.0,380.0,990.0,348.0,3.3,97600.0,INLAND\n-117.26,34.15,33.0,2271.0,389.0,1100.0,380.0,3.5978,88300.0,INLAND\n-117.26,34.16,31.0,3538.0,658.0,1715.0,663.0,3.7125,98000.0,INLAND\n-117.26,34.17,30.0,1937.0,351.0,945.0,344.0,3.8906,123700.0,INLAND\n-117.27,34.17,16.0,30.0,3.0,49.0,8.0,4.625,250000.0,INLAND\n-117.25,34.15,22.0,7935.0,1976.0,4523.0,1791.0,2.1465,88800.0,INLAND\n-117.25,34.14,19.0,5163.0,1229.0,2680.0,1141.0,2.2482,114500.0,INLAND\n-117.27,34.14,36.0,3795.0,676.0,1742.0,585.0,4.1,96400.0,INLAND\n-117.27,34.14,35.0,1517.0,257.0,658.0,245.0,4.4435,97600.0,INLAND\n-117.27,34.15,35.0,1490.0,253.0,705.0,253.0,3.3616,95300.0,INLAND\n-117.25,34.13,33.0,2898.0,503.0,1374.0,487.0,3.6856,90000.0,INLAND\n-117.25,34.13,37.0,2498.0,472.0,1291.0,487.0,3.0,83400.0,INLAND\n-117.26,34.13,37.0,2403.0,550.0,1234.0,493.0,2.0,72100.0,INLAND\n-117.26,34.13,39.0,3521.0,747.0,2256.0,721.0,2.1375,87500.0,INLAND\n-117.27,34.12,31.0,2209.0,636.0,1314.0,562.0,1.7235,78800.0,INLAND\n-117.27,34.13,40.0,1298.0,254.0,793.0,268.0,3.0721,83800.0,INLAND\n-117.27,34.13,36.0,3337.0,687.0,2388.0,589.0,2.9628,87800.0,INLAND\n-117.25,34.13,35.0,861.0,148.0,381.0,138.0,2.5234,88200.0,INLAND\n-117.25,34.12,17.0,3107.0,752.0,2160.0,643.0,1.8463,72600.0,INLAND\n-117.25,34.11,32.0,2910.0,641.0,2011.0,614.0,2.7473,70800.0,INLAND\n-117.27,34.12,27.0,2896.0,684.0,1514.0,668.0,1.462,70200.0,INLAND\n-117.27,34.12,52.0,954.0,246.0,943.0,256.0,0.8658,87500.0,INLAND\n-117.25,34.11,30.0,2173.0,560.0,1509.0,486.0,1.4079,67700.0,INLAND\n-117.26,34.11,33.0,1210.0,288.0,850.0,238.0,1.2171,59300.0,INLAND\n-117.27,34.11,44.0,567.0,134.0,565.0,150.0,1.8281,62900.0,INLAND\n-117.27,34.1,9.0,3904.0,1042.0,3688.0,896.0,1.8022,78000.0,INLAND\n-117.33,34.09,29.0,1960.0,415.0,1681.0,435.0,2.9292,84500.0,INLAND\n-117.33,34.08,35.0,2240.0,423.0,1394.0,396.0,3.1799,86700.0,INLAND\n-117.33,34.08,37.0,2267.0,405.0,1175.0,403.0,3.75,95200.0,INLAND\n-117.33,34.07,32.0,2086.0,458.0,1355.0,412.0,2.5238,89200.0,INLAND\n-117.34,34.07,46.0,1851.0,425.0,1100.0,377.0,2.0461,90500.0,INLAND\n-117.34,34.08,35.0,1380.0,248.0,730.0,264.0,3.2305,93700.0,INLAND\n-117.34,34.08,33.0,4924.0,1007.0,3502.0,953.0,3.233,99400.0,INLAND\n-117.32,34.09,38.0,1585.0,345.0,1347.0,368.0,2.375,75300.0,INLAND\n-117.32,34.09,30.0,1129.0,251.0,1034.0,237.0,2.3917,78600.0,INLAND\n-117.32,34.08,41.0,1359.0,264.0,786.0,244.0,2.5208,85500.0,INLAND\n-117.32,34.08,46.0,1308.0,276.0,576.0,244.0,3.1875,84000.0,INLAND\n-117.32,34.07,52.0,1226.0,269.0,693.0,272.0,1.9963,76900.0,INLAND\n-117.32,34.06,52.0,802.0,160.0,564.0,131.0,2.1591,63500.0,INLAND\n-117.33,34.05,26.0,613.0,149.0,431.0,130.0,1.3977,73100.0,INLAND\n-117.33,34.06,42.0,530.0,123.0,390.0,124.0,1.0469,67000.0,INLAND\n-117.33,34.06,48.0,732.0,149.0,486.0,139.0,2.5673,68200.0,INLAND\n-117.33,34.06,36.0,755.0,157.0,625.0,152.0,2.0242,65000.0,INLAND\n-117.32,34.06,46.0,476.0,102.0,476.0,91.0,1.4511,73100.0,INLAND\n-117.31,34.08,43.0,1697.0,387.0,1181.0,352.0,1.9234,74600.0,INLAND\n-117.31,34.08,40.0,2011.0,495.0,1528.0,469.0,1.9375,69900.0,INLAND\n-117.31,34.07,40.0,2936.0,732.0,2024.0,676.0,2.1139,70900.0,INLAND\n-117.32,34.07,26.0,971.0,245.0,592.0,207.0,2.1125,84000.0,INLAND\n-117.31,34.08,37.0,953.0,231.0,611.0,230.0,1.9926,81500.0,INLAND\n-117.31,34.05,6.0,7423.0,2111.0,4092.0,1789.0,2.7002,88300.0,INLAND\n-117.33,34.04,18.0,1837.0,388.0,727.0,336.0,2.5187,116700.0,INLAND\n-117.33,34.03,14.0,1582.0,347.0,825.0,259.0,2.8281,106300.0,INLAND\n-117.31,34.04,29.0,2481.0,383.0,1188.0,385.0,4.7344,134600.0,INLAND\n-117.31,34.04,5.0,2785.0,577.0,1310.0,536.0,3.39,149500.0,INLAND\n-117.3,34.05,6.0,2155.0,,1039.0,391.0,1.6675,95800.0,INLAND\n-117.3,34.05,7.0,4672.0,1121.0,2534.0,1046.0,3.4228,115700.0,INLAND\n-117.29,34.06,7.0,1971.0,403.0,1336.0,423.0,4.5066,111500.0,INLAND\n-117.28,34.06,2.0,1658.0,290.0,868.0,304.0,5.1365,136700.0,INLAND\n-117.32,34.02,17.0,1779.0,292.0,1006.0,293.0,4.6708,123100.0,INLAND\n-117.33,34.03,18.0,2342.0,402.0,1264.0,382.0,4.7986,123700.0,INLAND\n-117.32,34.03,13.0,3853.0,761.0,1685.0,669.0,3.9024,122400.0,INLAND\n-117.31,34.03,24.0,1966.0,299.0,786.0,302.0,5.0318,134500.0,INLAND\n-117.31,34.03,9.0,1199.0,187.0,629.0,207.0,5.7393,151600.0,INLAND\n-117.31,34.02,18.0,1634.0,274.0,899.0,285.0,5.2139,129300.0,INLAND\n-117.29,34.03,9.0,8185.0,1525.0,3630.0,1466.0,4.1667,197700.0,INLAND\n-117.27,34.09,36.0,848.0,186.0,737.0,169.0,0.9838,79300.0,INLAND\n-117.27,34.08,38.0,1093.0,256.0,856.0,212.0,1.4279,73000.0,INLAND\n-117.25,34.08,30.0,2981.0,605.0,1784.0,573.0,2.45,85800.0,INLAND\n-117.25,34.07,21.0,3067.0,706.0,2140.0,687.0,2.4432,78800.0,INLAND\n-117.27,34.07,21.0,418.0,132.0,401.0,120.0,1.7206,82100.0,INLAND\n-117.24,34.04,17.0,3362.0,507.0,1520.0,496.0,6.1986,214500.0,INLAND\n-117.25,34.04,18.0,5761.0,1063.0,2763.0,1058.0,4.4472,161100.0,INLAND\n-117.27,34.05,34.0,1703.0,395.0,849.0,359.0,3.1607,138200.0,INLAND\n-117.27,34.06,20.0,5258.0,1514.0,3780.0,1404.0,2.025,85700.0,INLAND\n-117.25,34.06,23.0,4503.0,1156.0,3264.0,937.0,1.9821,93000.0,INLAND\n-117.25,34.06,18.0,5009.0,1108.0,2948.0,963.0,3.0042,106500.0,INLAND\n-117.24,34.04,5.0,1775.0,234.0,726.0,222.0,7.978,223900.0,INLAND\n-117.24,34.04,4.0,4289.0,682.0,1981.0,705.0,5.3366,165100.0,INLAND\n-117.24,34.06,9.0,3603.0,786.0,1782.0,718.0,3.2604,93300.0,INLAND\n-117.23,34.15,17.0,5036.0,817.0,2084.0,833.0,4.6445,137200.0,INLAND\n-117.23,34.14,16.0,2577.0,521.0,956.0,472.0,2.5625,129400.0,INLAND\n-117.24,34.14,6.0,2383.0,606.0,1301.0,488.0,3.016,107500.0,INLAND\n-117.24,34.15,23.0,3847.0,608.0,1621.0,630.0,4.6111,128400.0,INLAND\n-117.24,34.15,26.0,2041.0,293.0,936.0,375.0,6.0,140200.0,INLAND\n-117.21,34.14,16.0,1613.0,245.0,811.0,267.0,5.2591,140700.0,INLAND\n-117.22,34.15,9.0,2524.0,352.0,1139.0,362.0,6.2516,159300.0,INLAND\n-117.2,34.15,18.0,1859.0,251.0,747.0,256.0,7.732,173200.0,INLAND\n-117.2,34.14,14.0,2647.0,524.0,989.0,479.0,3.1513,160000.0,INLAND\n-117.2,34.14,18.0,1920.0,333.0,890.0,323.0,5.159,144800.0,INLAND\n-117.24,34.13,24.0,1203.0,310.0,594.0,187.0,1.1522,87500.0,INLAND\n-117.24,34.13,26.0,3774.0,716.0,1913.0,620.0,3.3534,98900.0,INLAND\n-117.23,34.12,6.0,4464.0,1093.0,2364.0,952.0,2.3848,81600.0,INLAND\n-117.23,34.13,10.0,1145.0,293.0,726.0,251.0,1.645,68700.0,INLAND\n-117.22,34.13,10.0,5951.0,1330.0,3204.0,1159.0,2.7011,110200.0,INLAND\n-117.2,34.13,14.0,3998.0,711.0,1509.0,665.0,3.4138,126700.0,INLAND\n-117.22,34.12,34.0,2457.0,499.0,1538.0,507.0,2.809,82500.0,INLAND\n-117.21,34.13,31.0,3037.0,565.0,1834.0,575.0,3.3445,92900.0,INLAND\n-117.2,34.12,24.0,3532.0,618.0,1681.0,590.0,3.5,113900.0,INLAND\n-117.24,34.12,29.0,2654.0,667.0,1822.0,593.0,2.1563,72300.0,INLAND\n-117.24,34.11,23.0,1920.0,454.0,1161.0,358.0,2.2109,73200.0,INLAND\n-117.23,34.11,22.0,1162.0,221.0,995.0,244.0,2.5875,81300.0,INLAND\n-117.23,34.12,18.0,1439.0,319.0,699.0,310.0,2.1071,73500.0,INLAND\n-117.23,34.11,33.0,2170.0,500.0,1425.0,472.0,2.0133,78300.0,INLAND\n-117.22,34.12,30.0,2512.0,597.0,1390.0,523.0,2.3725,77200.0,INLAND\n-117.22,34.11,26.0,2972.0,,1972.0,532.0,2.0388,80400.0,INLAND\n-117.21,34.12,32.0,1677.0,354.0,1021.0,339.0,3.6853,90900.0,INLAND\n-117.21,34.11,27.0,1245.0,229.0,692.0,234.0,3.2176,89400.0,INLAND\n-117.21,34.11,26.0,1757.0,304.0,905.0,281.0,3.4103,90900.0,INLAND\n-117.19,34.1,5.0,2167.0,384.0,1174.0,358.0,4.0114,97700.0,INLAND\n-117.21,34.08,5.0,5749.0,1385.0,2382.0,1088.0,3.0587,143100.0,INLAND\n-117.22,34.07,8.0,3065.0,692.0,1440.0,666.0,3.2368,129200.0,INLAND\n-117.17,34.12,2.0,3867.0,573.0,1275.0,433.0,5.4138,164400.0,INLAND\n-117.12,34.1,40.0,96.0,14.0,46.0,14.0,3.2708,162500.0,INLAND\n-117.17,34.12,3.0,15695.0,2248.0,6080.0,1920.0,6.2178,173900.0,INLAND\n-117.18,34.08,28.0,2243.0,399.0,1464.0,379.0,3.2105,90300.0,INLAND\n-117.19,34.08,22.0,2467.0,555.0,1567.0,494.0,2.6536,84700.0,INLAND\n-117.19,34.08,5.0,4458.0,751.0,2392.0,773.0,4.5938,126500.0,INLAND\n-117.18,34.07,14.0,1258.0,245.0,752.0,264.0,3.3924,97400.0,INLAND\n-117.18,34.07,28.0,1306.0,279.0,885.0,255.0,2.1154,75300.0,INLAND\n-117.18,34.07,7.0,1347.0,301.0,799.0,276.0,2.9485,112500.0,INLAND\n-117.18,34.06,26.0,1953.0,446.0,1284.0,414.0,1.3485,85100.0,INLAND\n-117.18,34.06,28.0,699.0,180.0,432.0,168.0,2.1875,81900.0,INLAND\n-117.19,34.06,37.0,1467.0,348.0,1316.0,339.0,1.448,72800.0,INLAND\n-117.19,34.07,40.0,2374.0,500.0,1772.0,455.0,2.189,72500.0,INLAND\n-117.18,34.06,52.0,954.0,233.0,533.0,239.0,1.3021,100000.0,INLAND\n-117.19,34.06,21.0,6107.0,1559.0,2805.0,1444.0,2.5643,102700.0,INLAND\n-117.18,34.05,29.0,3436.0,731.0,1323.0,676.0,2.4943,122300.0,INLAND\n-117.17,34.05,29.0,4007.0,700.0,1576.0,696.0,3.1801,149300.0,INLAND\n-117.18,34.04,41.0,1766.0,288.0,753.0,278.0,4.9125,140700.0,INLAND\n-117.18,34.05,52.0,1820.0,342.0,601.0,315.0,2.6129,137000.0,INLAND\n-117.19,34.05,52.0,1949.0,432.0,767.0,392.0,2.5143,117600.0,INLAND\n-117.19,34.05,33.0,1688.0,313.0,808.0,298.0,3.2188,117800.0,INLAND\n-117.19,34.05,33.0,3007.0,498.0,1252.0,488.0,3.8816,134600.0,INLAND\n-117.2,34.04,24.0,1587.0,222.0,676.0,234.0,6.0715,173400.0,INLAND\n-117.2,34.04,23.0,1762.0,267.0,1132.0,279.0,5.9915,153200.0,INLAND\n-117.21,34.04,14.0,3063.0,426.0,1570.0,419.0,6.2917,224700.0,INLAND\n-117.21,34.05,4.0,2904.0,764.0,1250.0,664.0,3.2131,137500.0,INLAND\n-117.18,34.04,38.0,2492.0,381.0,1003.0,369.0,3.6875,152800.0,INLAND\n-117.19,34.03,36.0,2223.0,361.0,942.0,331.0,4.6806,152400.0,INLAND\n-117.19,34.03,25.0,2513.0,340.0,900.0,320.0,6.4962,182400.0,INLAND\n-117.19,34.03,20.0,856.0,124.0,395.0,145.0,10.8634,381800.0,INLAND\n-117.16,34.08,9.0,5306.0,993.0,2630.0,925.0,4.51,135800.0,INLAND\n-117.17,34.07,24.0,6573.0,1235.0,2904.0,1202.0,3.0651,108000.0,INLAND\n-117.17,34.08,5.0,1473.0,228.0,842.0,257.0,4.875,138100.0,INLAND\n-117.15,34.07,15.0,1852.0,316.0,906.0,298.0,5.3526,129800.0,INLAND\n-117.14,34.07,3.0,5542.0,828.0,2506.0,806.0,5.5875,162000.0,INLAND\n-117.14,34.06,15.0,3057.0,510.0,1154.0,460.0,3.9741,141100.0,INLAND\n-117.15,34.06,25.0,3670.0,644.0,1815.0,634.0,4.0658,127400.0,INLAND\n-117.14,34.05,5.0,2634.0,359.0,1173.0,372.0,6.746,204100.0,INLAND\n-117.15,34.05,9.0,1442.0,219.0,633.0,230.0,5.0227,162300.0,INLAND\n-117.16,34.05,23.0,3215.0,462.0,1411.0,435.0,6.0701,149900.0,INLAND\n-117.17,34.05,24.0,2877.0,507.0,1141.0,474.0,4.2059,121500.0,INLAND\n-117.15,34.04,14.0,2845.0,420.0,1172.0,377.0,7.5822,283100.0,INLAND\n-117.16,34.06,33.0,2101.0,468.0,1997.0,412.0,2.8125,117200.0,INLAND\n-117.16,34.06,17.0,2285.0,554.0,1412.0,541.0,1.8152,94300.0,INLAND\n-117.14,34.01,26.0,7561.0,1051.0,2909.0,1012.0,7.2972,269600.0,INLAND\n-117.15,34.03,32.0,2832.0,393.0,1033.0,385.0,6.5648,237200.0,INLAND\n-117.15,34.03,26.0,5305.0,701.0,1818.0,676.0,6.1461,217100.0,INLAND\n-117.17,34.03,33.0,4583.0,648.0,1760.0,638.0,6.3308,230600.0,INLAND\n-117.09,34.01,37.0,106.0,18.0,27.0,12.0,4.0556,131300.0,INLAND\n-117.09,34.07,24.0,6260.0,1271.0,3132.0,1189.0,2.5156,103000.0,INLAND\n-117.12,34.06,38.0,281.0,55.0,151.0,52.0,1.3906,120800.0,INLAND\n-117.13,34.06,4.0,3078.0,510.0,1341.0,486.0,4.9688,163200.0,INLAND\n-117.13,34.07,34.0,2405.0,541.0,1342.0,514.0,2.8031,86900.0,INLAND\n-117.08,34.08,34.0,45.0,11.0,39.0,14.0,3.0625,500001.0,INLAND\n-117.12,34.04,25.0,2495.0,438.0,1071.0,405.0,4.8173,146600.0,INLAND\n-117.07,34.05,14.0,5764.0,1006.0,1876.0,841.0,1.9694,173200.0,INLAND\n-117.03,34.07,16.0,3784.0,577.0,1615.0,525.0,4.2333,220300.0,INLAND\n-117.02,34.03,19.0,4415.0,648.0,1627.0,619.0,4.2361,191600.0,INLAND\n-117.03,34.02,26.0,3909.0,670.0,1884.0,665.0,4.1361,121000.0,INLAND\n-117.01,34.01,15.0,5592.0,891.0,2419.0,840.0,4.7193,135200.0,INLAND\n-116.98,34.05,6.0,2290.0,312.0,957.0,274.0,7.2708,316700.0,INLAND\n-117.05,34.04,23.0,3967.0,766.0,1518.0,698.0,2.29,111800.0,INLAND\n-117.1,34.03,24.0,4144.0,826.0,2127.0,772.0,2.5172,96000.0,INLAND\n-117.08,34.03,23.0,3862.0,699.0,2082.0,652.0,3.154,115700.0,INLAND\n-117.08,34.02,20.0,3111.0,563.0,1453.0,538.0,3.3365,122800.0,INLAND\n-117.06,34.02,24.0,3912.0,809.0,1926.0,762.0,2.6875,116300.0,INLAND\n-117.05,34.02,21.0,3098.0,646.0,1351.0,614.0,2.598,106700.0,INLAND\n-117.05,34.01,27.0,5484.0,1205.0,2645.0,1131.0,2.1927,116700.0,INLAND\n-117.04,34.02,24.0,4663.0,1213.0,1851.0,1116.0,1.4418,103500.0,INLAND\n-117.05,34.03,28.0,3009.0,698.0,1200.0,626.0,1.3993,104600.0,INLAND\n-117.06,34.03,27.0,1945.0,446.0,859.0,418.0,1.5203,126200.0,INLAND\n-117.03,34.03,26.0,3501.0,664.0,1860.0,681.0,3.0403,113500.0,INLAND\n-117.04,34.03,29.0,3375.0,795.0,1760.0,699.0,2.7028,92000.0,INLAND\n-117.04,34.04,30.0,3474.0,735.0,1674.0,691.0,2.5863,98300.0,INLAND\n-117.29,35.54,35.0,7922.0,1636.0,3431.0,1329.0,3.4145,40400.0,INLAND\n-117.37,34.59,39.0,8193.0,1747.0,6852.0,1597.0,2.3832,35000.0,INLAND\n-117.62,34.44,6.0,8884.0,1687.0,3767.0,1334.0,3.599,140200.0,INLAND\n-117.56,34.42,6.0,4264.0,749.0,2005.0,666.0,3.4695,138800.0,INLAND\n-117.51,34.41,5.0,2884.0,567.0,1396.0,465.0,3.7361,119600.0,INLAND\n-117.44,34.45,6.0,6068.0,1137.0,3094.0,947.0,3.5167,130900.0,INLAND\n-117.54,34.47,4.0,6712.0,1200.0,3126.0,1026.0,3.2277,126500.0,INLAND\n-117.5,34.66,20.0,1319.0,309.0,486.0,196.0,2.0184,84900.0,INLAND\n-117.42,34.59,8.0,5445.0,1360.0,3220.0,1214.0,1.7567,69500.0,INLAND\n-117.41,34.58,14.0,859.0,212.0,541.0,181.0,1.6838,57900.0,INLAND\n-117.4,34.58,18.0,755.0,169.0,483.0,165.0,1.4196,64700.0,INLAND\n-117.41,34.58,10.0,2964.0,668.0,1853.0,609.0,1.6047,73400.0,INLAND\n-117.54,34.55,5.0,2949.0,671.0,1620.0,530.0,2.9479,83300.0,INLAND\n-117.36,34.54,7.0,3940.0,764.0,2140.0,711.0,3.0357,91300.0,INLAND\n-117.63,34.37,20.0,7052.0,1306.0,2197.0,810.0,3.7252,167100.0,INLAND\n-117.61,34.34,18.0,5210.0,912.0,1301.0,464.0,4.8623,176900.0,INLAND\n-117.53,34.28,35.0,1529.0,338.0,688.0,256.0,4.1083,108000.0,INLAND\n-117.55,34.25,39.0,1578.0,317.0,872.0,322.0,4.555,153100.0,INLAND\n-117.03,34.91,27.0,2718.0,583.0,1472.0,509.0,2.825,76600.0,INLAND\n-117.01,34.9,36.0,2181.0,555.0,1404.0,492.0,2.3077,55500.0,INLAND\n-117.02,34.9,37.0,1199.0,351.0,782.0,296.0,1.6515,61600.0,INLAND\n-117.06,34.9,36.0,2828.0,916.0,1762.0,736.0,1.4318,59600.0,INLAND\n-117.01,34.9,34.0,2194.0,519.0,1326.0,515.0,2.1056,72000.0,INLAND\n-117.0,34.89,29.0,2637.0,512.0,1188.0,446.0,2.99,69400.0,INLAND\n-117.01,34.89,26.0,2599.0,498.0,1332.0,443.0,2.7198,70400.0,INLAND\n-117.02,34.89,29.0,3111.0,661.0,1530.0,608.0,2.8281,69300.0,INLAND\n-117.04,34.89,37.0,1884.0,366.0,1052.0,353.0,3.175,66800.0,INLAND\n-117.05,34.89,36.0,1199.0,260.0,665.0,229.0,3.7065,62000.0,INLAND\n-117.22,34.44,5.0,4787.0,910.0,1944.0,806.0,2.6576,98500.0,INLAND\n-117.2,34.46,7.0,8414.0,1584.0,5146.0,1517.0,3.2794,92500.0,INLAND\n-117.12,34.46,17.0,1613.0,326.0,765.0,300.0,2.6827,110400.0,INLAND\n-117.11,34.43,14.0,3026.0,556.0,1349.0,485.0,2.8021,111200.0,INLAND\n-117.13,34.39,29.0,2251.0,464.0,855.0,315.0,3.4183,104100.0,INLAND\n-117.25,34.49,4.0,2372.0,361.0,1017.0,322.0,5.1112,170900.0,INLAND\n-117.23,34.49,9.0,4055.0,536.0,1458.0,478.0,5.4201,170600.0,INLAND\n-117.21,34.49,14.0,2125.0,348.0,1067.0,360.0,3.6333,116200.0,INLAND\n-117.2,34.5,10.0,4201.0,850.0,2378.0,808.0,2.1781,92200.0,INLAND\n-117.17,34.49,13.0,4460.0,925.0,2225.0,840.0,2.0136,94100.0,INLAND\n-117.15,34.48,31.0,265.0,55.0,186.0,55.0,2.125,64800.0,INLAND\n-117.18,34.48,8.0,3561.0,691.0,2156.0,659.0,2.7778,86900.0,INLAND\n-117.2,34.48,7.0,4998.0,953.0,2764.0,891.0,3.205,101900.0,INLAND\n-117.22,34.48,7.0,2449.0,447.0,1217.0,408.0,3.6646,109900.0,INLAND\n-117.26,34.53,10.0,3103.0,520.0,1283.0,464.0,3.071,151600.0,INLAND\n-117.25,34.51,7.0,3200.0,477.0,1522.0,470.0,4.6914,142200.0,INLAND\n-117.23,34.51,9.0,5756.0,807.0,2158.0,758.0,5.5875,167800.0,INLAND\n-117.21,34.51,17.0,4379.0,629.0,1720.0,595.0,5.086,148400.0,INLAND\n-117.25,34.53,13.0,5841.0,955.0,2455.0,915.0,4.1333,158200.0,INLAND\n-117.22,34.54,8.0,12526.0,2495.0,6133.0,2324.0,2.9072,119200.0,INLAND\n-117.2,34.52,12.0,4476.0,761.0,2255.0,735.0,3.925,118500.0,INLAND\n-117.17,34.51,15.0,5151.0,942.0,2896.0,897.0,3.4875,90800.0,INLAND\n-117.18,34.54,5.0,3772.0,619.0,2097.0,635.0,3.8194,98500.0,INLAND\n-117.3,34.53,38.0,1643.0,489.0,1196.0,406.0,1.2275,64100.0,INLAND\n-117.3,34.54,31.0,1174.0,360.0,1161.0,328.0,1.06,56500.0,INLAND\n-117.3,34.54,25.0,2546.0,488.0,1338.0,487.0,3.2596,85400.0,INLAND\n-117.31,34.53,26.0,2299.0,496.0,1259.0,441.0,2.6125,79900.0,INLAND\n-117.32,34.55,18.0,279.0,59.0,188.0,60.0,0.8246,91700.0,INLAND\n-117.32,34.54,9.0,5904.0,1165.0,3489.0,1063.0,3.125,92800.0,INLAND\n-117.33,34.53,10.0,3781.0,712.0,2044.0,685.0,3.0943,97100.0,INLAND\n-117.34,34.51,6.0,5667.0,1385.0,2447.0,1199.0,2.3617,103100.0,INLAND\n-117.35,34.5,10.0,2163.0,392.0,1174.0,362.0,3.375,98000.0,INLAND\n-117.34,34.49,9.0,3293.0,585.0,1678.0,530.0,3.2941,98300.0,INLAND\n-117.36,34.48,3.0,16533.0,2549.0,7588.0,2285.0,3.9792,122100.0,INLAND\n-117.3,34.52,34.0,4493.0,838.0,2335.0,779.0,3.1635,74300.0,INLAND\n-117.32,34.51,16.0,3072.0,612.0,1283.0,604.0,2.8929,115600.0,INLAND\n-117.31,34.51,18.0,2704.0,698.0,1611.0,597.0,2.0243,82300.0,INLAND\n-117.28,34.51,10.0,4676.0,884.0,2845.0,812.0,3.0181,100400.0,INLAND\n-117.31,34.5,14.0,2443.0,447.0,883.0,465.0,2.1111,116700.0,INLAND\n-117.32,34.49,7.0,4584.0,1051.0,2049.0,918.0,1.6232,93400.0,INLAND\n-117.29,34.49,3.0,7689.0,1545.0,3804.0,1399.0,3.3871,111800.0,INLAND\n-117.32,34.48,8.0,4627.0,887.0,2739.0,846.0,3.0204,93100.0,INLAND\n-117.27,34.5,7.0,2045.0,342.0,878.0,292.0,6.0296,194100.0,INLAND\n-117.27,34.49,7.0,2344.0,351.0,846.0,314.0,4.7361,174500.0,INLAND\n-117.27,34.48,8.0,1794.0,276.0,690.0,271.0,3.662,165300.0,INLAND\n-117.26,34.48,6.0,4632.0,753.0,1851.0,694.0,4.1933,163100.0,INLAND\n-117.27,34.5,8.0,3567.0,543.0,1133.0,419.0,5.3733,302600.0,INLAND\n-117.25,34.41,13.0,3682.0,668.0,1606.0,668.0,2.1875,119700.0,INLAND\n-117.28,34.41,14.0,2105.0,396.0,960.0,396.0,2.9934,118200.0,INLAND\n-117.29,34.41,11.0,5934.0,1380.0,2756.0,1239.0,1.5758,108300.0,INLAND\n-117.3,34.39,11.0,3572.0,592.0,1876.0,507.0,3.6615,105100.0,INLAND\n-117.27,34.4,8.0,6042.0,979.0,3031.0,991.0,3.3438,124400.0,INLAND\n-117.27,34.39,6.0,6988.0,1121.0,3660.0,1092.0,4.2224,125700.0,INLAND\n-117.31,34.35,9.0,2404.0,390.0,1074.0,359.0,5.0198,151900.0,INLAND\n-117.31,34.41,14.0,3019.0,643.0,1639.0,582.0,1.5288,103400.0,INLAND\n-117.32,34.41,13.0,2032.0,348.0,1038.0,344.0,4.2891,120100.0,INLAND\n-117.33,34.41,13.0,3684.0,604.0,1767.0,585.0,3.7478,113500.0,INLAND\n-117.34,34.39,8.0,3579.0,672.0,2216.0,630.0,3.4038,100500.0,INLAND\n-117.31,34.39,15.0,1703.0,273.0,847.0,266.0,3.7917,123400.0,INLAND\n-117.39,34.38,4.0,7151.0,1295.0,3527.0,1170.0,3.5696,129700.0,INLAND\n-117.3,34.46,8.0,6246.0,1273.0,3883.0,1264.0,2.7917,98200.0,INLAND\n-117.31,34.44,10.0,1731.0,299.0,1056.0,312.0,3.6007,104000.0,INLAND\n-117.31,34.43,16.0,5130.0,1172.0,3126.0,1046.0,1.6784,71900.0,INLAND\n-117.35,34.44,9.0,11810.0,2181.0,6716.0,2081.0,3.1821,95600.0,INLAND\n-117.38,34.44,4.0,5083.0,867.0,2541.0,856.0,4.2414,121400.0,INLAND\n-117.34,34.46,9.0,5983.0,1122.0,3515.0,1064.0,3.1505,102000.0,INLAND\n-117.27,34.42,9.0,5643.0,1005.0,3166.0,957.0,3.2077,93300.0,INLAND\n-117.26,34.43,11.0,4597.0,782.0,2534.0,776.0,3.3368,99300.0,INLAND\n-117.27,34.45,8.0,6463.0,1095.0,3213.0,1031.0,3.2215,108800.0,INLAND\n-116.72,34.89,14.0,4527.0,875.0,1640.0,590.0,2.8594,81700.0,INLAND\n-115.93,35.55,18.0,1321.0,272.0,754.0,226.0,3.4028,67500.0,INLAND\n-115.75,35.23,5.0,208.0,78.0,132.0,56.0,2.5333,75000.0,INLAND\n-115.53,34.91,12.0,807.0,199.0,246.0,102.0,2.5391,40000.0,INLAND\n-116.51,34.85,15.0,3149.0,713.0,1281.0,486.0,2.0,64700.0,INLAND\n-116.57,35.43,8.0,9975.0,1743.0,6835.0,1439.0,2.7138,22500.0,INLAND\n-116.14,34.45,12.0,8796.0,1721.0,11139.0,1680.0,2.2612,137500.0,INLAND\n-116.32,34.1,10.0,4256.0,861.0,1403.0,686.0,2.6618,81000.0,INLAND\n-116.27,34.13,37.0,452.0,109.0,184.0,59.0,3.7292,65800.0,INLAND\n-116.31,34.13,20.0,2352.0,556.0,1217.0,481.0,1.6063,55400.0,INLAND\n-116.35,34.13,9.0,1969.0,406.0,805.0,349.0,1.5491,62300.0,INLAND\n-116.32,34.14,18.0,1880.0,487.0,994.0,425.0,1.69,54200.0,INLAND\n-116.33,34.15,13.0,1808.0,411.0,735.0,320.0,1.5489,57400.0,INLAND\n-116.29,34.18,15.0,4203.0,966.0,1756.0,695.0,2.182,60800.0,INLAND\n-116.62,34.23,14.0,6438.0,1719.0,1586.0,691.0,1.6136,67400.0,INLAND\n-116.73,34.52,16.0,1247.0,315.0,433.0,159.0,1.0568,75000.0,INLAND\n-116.51,34.45,21.0,8502.0,2634.0,2330.0,991.0,1.3811,51300.0,INLAND\n-116.56,34.06,15.0,6928.0,1529.0,2568.0,1075.0,2.5405,69600.0,INLAND\n-116.44,34.16,19.0,1867.0,361.0,758.0,321.0,2.8929,98100.0,INLAND\n-116.38,34.2,14.0,4985.0,1238.0,2517.0,954.0,2.0674,65000.0,INLAND\n-116.39,34.15,15.0,5583.0,1149.0,2709.0,964.0,1.9779,73300.0,INLAND\n-116.44,34.12,18.0,5584.0,1303.0,2250.0,1158.0,1.5823,72400.0,INLAND\n-116.47,34.07,22.0,5473.0,1234.0,2581.0,1098.0,1.9375,65300.0,INLAND\n-116.43,34.1,17.0,6683.0,1296.0,2677.0,1227.0,2.4828,84000.0,INLAND\n-116.4,34.09,9.0,4855.0,872.0,2098.0,765.0,3.2723,97800.0,INLAND\n-116.4,34.12,16.0,5648.0,1089.0,2524.0,1008.0,2.6739,78000.0,INLAND\n-116.38,34.1,6.0,2104.0,348.0,841.0,320.0,4.1458,116300.0,INLAND\n-116.22,34.21,23.0,1175.0,468.0,355.0,151.0,2.2083,42500.0,INLAND\n-116.14,34.22,32.0,3298.0,1228.0,763.0,360.0,1.875,47800.0,INLAND\n-116.06,34.2,29.0,1202.0,290.0,383.0,156.0,1.3371,66900.0,INLAND\n-116.06,34.15,15.0,10377.0,2331.0,4507.0,1807.0,2.2466,66800.0,INLAND\n-116.09,34.15,13.0,9444.0,1997.0,4166.0,1482.0,2.6111,65600.0,INLAND\n-116.15,34.14,18.0,3312.0,705.0,1251.0,512.0,3.0139,82600.0,INLAND\n-116.05,34.12,19.0,301.0,65.0,150.0,56.0,3.125,65600.0,INLAND\n-116.02,34.18,8.0,569.0,97.0,312.0,96.0,4.3021,94500.0,INLAND\n-115.85,34.2,34.0,3868.0,1257.0,890.0,423.0,1.3571,41000.0,INLAND\n-115.52,34.22,30.0,540.0,136.0,122.0,63.0,1.3333,42500.0,INLAND\n-116.0,34.12,32.0,3163.0,712.0,1358.0,544.0,2.125,57700.0,INLAND\n-114.94,34.55,20.0,350.0,95.0,119.0,58.0,1.625,50000.0,INLAND\n-114.47,34.4,19.0,7650.0,1901.0,1129.0,463.0,1.82,80100.0,INLAND\n-114.31,34.19,15.0,5612.0,1283.0,1015.0,472.0,1.4936,66900.0,INLAND\n-114.59,34.83,41.0,812.0,,375.0,158.0,1.7083,48500.0,INLAND\n-114.65,34.89,17.0,2556.0,587.0,1005.0,401.0,1.6991,69100.0,INLAND\n-114.6,34.83,46.0,1497.0,309.0,787.0,271.0,2.1908,48100.0,INLAND\n-114.61,34.84,48.0,1291.0,248.0,580.0,211.0,2.1571,48600.0,INLAND\n-114.61,34.83,31.0,2478.0,464.0,1346.0,479.0,3.212,70400.0,INLAND\n-114.64,34.83,10.0,2502.0,573.0,1152.0,481.0,1.7062,86800.0,INLAND\n-117.36,34.28,18.0,3903.0,715.0,1388.0,428.0,4.2386,157200.0,INLAND\n-117.28,34.26,18.0,3895.0,,1086.0,375.0,3.3672,133600.0,INLAND\n-117.31,34.25,29.0,4610.0,,1569.0,592.0,2.7663,97900.0,INLAND\n-117.32,34.24,29.0,1290.0,263.0,323.0,113.0,1.9265,103300.0,INLAND\n-117.3,34.24,38.0,4116.0,949.0,1196.0,422.0,3.5625,96500.0,INLAND\n-117.28,34.24,16.0,3474.0,633.0,853.0,315.0,5.2185,128600.0,INLAND\n-117.27,34.24,34.0,3687.0,756.0,941.0,367.0,2.875,117600.0,INLAND\n-117.26,34.24,10.0,4750.0,844.0,1220.0,428.0,4.5536,132400.0,INLAND\n-117.27,34.23,26.0,6339.0,1244.0,1177.0,466.0,3.7708,110400.0,INLAND\n-117.16,34.26,27.0,9285.0,1621.0,1135.0,410.0,2.5446,135200.0,INLAND\n-117.18,34.3,33.0,399.0,87.0,71.0,27.0,1.875,71300.0,INLAND\n-117.17,34.28,13.0,4867.0,718.0,780.0,250.0,7.1997,253800.0,INLAND\n-117.19,34.27,16.0,7961.0,1147.0,879.0,280.0,5.2146,255200.0,INLAND\n-117.21,34.28,16.0,3326.0,569.0,527.0,192.0,5.7421,167600.0,INLAND\n-117.2,34.26,17.0,9419.0,1455.0,1382.0,459.0,6.2233,230900.0,INLAND\n-117.22,34.26,16.0,8020.0,1432.0,1749.0,540.0,4.9716,162500.0,INLAND\n-117.2,34.24,22.0,8106.0,1665.0,1062.0,423.0,3.0434,137200.0,INLAND\n-117.17,34.25,15.0,4236.0,753.0,703.0,255.0,3.5625,165500.0,INLAND\n-117.21,34.22,31.0,5454.0,1053.0,1408.0,504.0,5.0,161100.0,INLAND\n-117.07,34.24,21.0,4773.0,1047.0,337.0,130.0,3.9375,115000.0,INLAND\n-117.13,34.24,17.0,2828.0,506.0,673.0,274.0,5.2563,144100.0,INLAND\n-117.15,34.22,10.0,1039.0,174.0,317.0,109.0,7.2371,171900.0,INLAND\n-117.12,34.21,19.0,4641.0,994.0,1334.0,474.0,4.5972,123900.0,INLAND\n-117.1,34.21,22.0,4397.0,931.0,1145.0,445.0,4.5268,108400.0,INLAND\n-117.09,34.22,16.0,1347.0,327.0,271.0,91.0,4.0,87500.0,INLAND\n-117.06,34.17,21.0,2520.0,582.0,416.0,151.0,2.712,89000.0,INLAND\n-117.13,34.17,17.0,1181.0,271.0,248.0,114.0,5.5762,150000.0,INLAND\n-116.94,34.24,27.0,12342.0,2630.0,1300.0,566.0,1.998,153500.0,INLAND\n-116.91,34.24,23.0,6379.0,1636.0,1350.0,568.0,1.6336,124500.0,INLAND\n-116.9,34.25,16.0,3018.0,523.0,556.0,244.0,3.5288,189700.0,INLAND\n-116.88,34.24,13.0,4137.0,796.0,573.0,218.0,4.6394,226500.0,INLAND\n-116.88,34.25,11.0,1089.0,198.0,230.0,90.0,4.9643,176000.0,INLAND\n-116.87,34.24,15.0,4419.0,822.0,622.0,267.0,3.9688,182800.0,INLAND\n-116.86,34.24,19.0,5411.0,1042.0,441.0,185.0,3.1324,132000.0,INLAND\n-116.86,34.23,13.0,4760.0,938.0,309.0,132.0,5.4618,147800.0,INLAND\n-116.99,34.3,29.0,5055.0,1036.0,410.0,191.0,3.5104,157100.0,INLAND\n-116.89,34.27,13.0,3661.0,770.0,567.0,228.0,4.4063,152600.0,INLAND\n-116.86,34.31,19.0,1649.0,328.0,382.0,151.0,4.0556,133000.0,INLAND\n-116.85,34.26,18.0,6988.0,1635.0,2044.0,726.0,2.4308,90600.0,INLAND\n-116.85,34.26,19.0,5395.0,1220.0,981.0,366.0,2.6094,92400.0,INLAND\n-116.85,34.25,5.0,5806.0,1030.0,569.0,219.0,4.0132,163100.0,INLAND\n-116.83,34.25,15.0,8948.0,1985.0,1316.0,514.0,2.7375,90800.0,INLAND\n-116.82,34.24,11.0,5799.0,1527.0,713.0,262.0,2.5147,84700.0,INLAND\n-116.76,34.29,14.0,3959.0,849.0,1064.0,376.0,2.8214,111400.0,INLAND\n-116.76,34.23,10.0,4374.0,989.0,1020.0,376.0,2.6071,89000.0,INLAND\n-116.98,34.13,16.0,2098.0,449.0,342.0,143.0,4.0333,133900.0,INLAND\n-116.98,34.07,21.0,739.0,125.0,199.0,82.0,4.8958,117500.0,INLAND\n-116.88,34.08,52.0,3419.0,777.0,710.0,265.0,3.9028,128600.0,INLAND\n-116.76,34.14,4.0,42.0,10.0,9.0,3.0,0.536,42500.0,INLAND\n-116.88,34.19,38.0,898.0,259.0,106.0,52.0,1.6875,225000.0,INLAND\n-117.46,34.85,7.0,9759.0,1816.0,2933.0,1168.0,3.4912,157700.0,INLAND\n-117.28,35.13,32.0,671.0,166.0,856.0,114.0,2.6477,53300.0,INLAND\n-116.85,34.98,26.0,3606.0,792.0,1683.0,608.0,2.6587,57400.0,INLAND\n-117.14,34.75,33.0,552.0,120.0,347.0,97.0,1.8158,100000.0,INLAND\n-117.28,34.68,28.0,1932.0,421.0,1156.0,404.0,1.8958,55600.0,INLAND\n-117.06,34.87,14.0,3348.0,619.0,1756.0,557.0,3.5987,91400.0,INLAND\n-117.13,34.88,21.0,3254.0,669.0,1548.0,545.0,2.3373,57100.0,INLAND\n-117.15,34.83,30.0,5370.0,1062.0,2778.0,944.0,3.099,66800.0,INLAND\n-117.19,34.94,31.0,2034.0,444.0,1097.0,367.0,2.1522,60800.0,INLAND\n-117.16,34.9,16.0,1579.0,327.0,934.0,298.0,2.7305,73800.0,INLAND\n-117.08,34.96,28.0,1777.0,307.0,721.0,259.0,3.6343,79800.0,INLAND\n-116.96,34.94,20.0,2355.0,467.0,1198.0,428.0,3.9934,88500.0,INLAND\n-116.99,34.89,24.0,2741.0,577.0,1551.0,522.0,3.474,70500.0,INLAND\n-116.99,34.88,23.0,6060.0,1165.0,2920.0,1072.0,3.1528,69000.0,INLAND\n-117.0,34.87,16.0,6862.0,1292.0,3562.0,1126.0,3.6091,87200.0,INLAND\n-117.02,34.88,18.0,2127.0,443.0,1168.0,401.0,3.0313,80000.0,INLAND\n-117.03,34.87,7.0,2245.0,407.0,1016.0,364.0,3.9464,101500.0,INLAND\n-116.96,34.83,30.0,1211.0,289.0,611.0,230.0,1.6667,44700.0,INLAND\n-116.9,34.69,10.0,337.0,102.0,108.0,50.0,0.4999,55000.0,INLAND\n-117.24,34.59,4.0,5027.0,797.0,1869.0,686.0,3.5507,186100.0,INLAND\n-117.29,34.57,22.0,1054.0,239.0,428.0,239.0,1.2548,68300.0,INLAND\n-117.1,34.57,6.0,5110.0,1044.0,1938.0,724.0,3.1917,112800.0,INLAND\n-116.9,34.52,20.0,3481.0,840.0,1694.0,587.0,1.4,77700.0,INLAND\n-116.94,34.4,20.0,6541.0,1401.0,2631.0,980.0,2.1339,78500.0,INLAND\n-117.18,32.76,52.0,2023.0,301.0,649.0,285.0,4.7396,441700.0,NEAR OCEAN\n-117.18,32.75,52.0,1539.0,212.0,535.0,224.0,5.392,408500.0,NEAR OCEAN\n-117.18,32.75,52.0,1504.0,208.0,518.0,196.0,8.603,459600.0,NEAR OCEAN\n-117.19,32.75,52.0,1495.0,230.0,459.0,190.0,8.1548,500001.0,NEAR OCEAN\n-117.19,32.75,52.0,1388.0,213.0,513.0,211.0,6.1309,411600.0,NEAR OCEAN\n-117.19,32.76,52.0,1294.0,175.0,434.0,180.0,5.7914,500001.0,NEAR OCEAN\n-117.17,32.76,45.0,3149.0,639.0,1160.0,661.0,2.7266,354200.0,NEAR OCEAN\n-117.17,32.75,38.0,5430.0,1176.0,2357.0,1100.0,3.654,249000.0,NEAR OCEAN\n-117.18,32.74,39.0,3132.0,738.0,1200.0,690.0,2.5288,274000.0,NEAR OCEAN\n-117.18,32.75,36.0,2282.0,534.0,918.0,531.0,2.7222,284700.0,NEAR OCEAN\n-117.17,32.75,52.0,1052.0,,381.0,201.0,3.0726,289600.0,NEAR OCEAN\n-117.16,32.75,23.0,2474.0,594.0,1107.0,536.0,2.9705,245500.0,NEAR OCEAN\n-117.16,32.75,49.0,1566.0,494.0,643.0,419.0,1.9637,137500.0,NEAR OCEAN\n-117.16,32.74,52.0,852.0,262.0,389.0,249.0,2.6042,225000.0,NEAR OCEAN\n-117.16,32.74,21.0,1882.0,486.0,903.0,482.0,3.06,243800.0,NEAR OCEAN\n-117.16,32.74,27.0,2335.0,604.0,982.0,590.0,3.1921,261500.0,NEAR OCEAN\n-117.17,32.75,28.0,1514.0,384.0,540.0,352.0,2.1532,240000.0,NEAR OCEAN\n-117.16,32.75,19.0,5430.0,1593.0,2496.0,1484.0,2.9112,199100.0,NEAR OCEAN\n-117.16,32.75,34.0,1785.0,558.0,804.0,490.0,2.2687,200000.0,NEAR OCEAN\n-117.15,32.76,43.0,2361.0,489.0,824.0,470.0,3.4196,302200.0,NEAR OCEAN\n-117.15,32.76,36.0,2644.0,674.0,1211.0,654.0,3.0445,214800.0,NEAR OCEAN\n-117.15,32.76,40.0,1809.0,474.0,826.0,456.0,2.6518,179800.0,NEAR OCEAN\n-117.15,32.76,37.0,1921.0,502.0,811.0,472.0,2.75,175000.0,NEAR OCEAN\n-117.15,32.75,40.0,2261.0,579.0,903.0,525.0,2.465,198700.0,NEAR OCEAN\n-117.15,32.75,9.0,2818.0,821.0,851.0,555.0,2.6181,204200.0,NEAR OCEAN\n-117.15,32.75,27.0,3166.0,867.0,1332.0,817.0,2.6742,171400.0,NEAR OCEAN\n-117.15,32.74,43.0,2383.0,607.0,962.0,587.0,2.2578,263600.0,NEAR OCEAN\n-117.15,32.74,26.0,3149.0,832.0,1320.0,808.0,3.0259,211700.0,NEAR OCEAN\n-117.14,32.75,35.0,1391.0,329.0,726.0,317.0,2.6818,159400.0,NEAR OCEAN\n-117.14,32.74,47.0,1494.0,327.0,689.0,304.0,3.125,172700.0,NEAR OCEAN\n-117.14,32.74,16.0,6075.0,1816.0,2592.0,1634.0,2.5553,178100.0,NEAR OCEAN\n-117.14,32.75,29.0,1961.0,565.0,1002.0,569.0,2.2813,118100.0,NEAR OCEAN\n-117.14,32.75,25.0,1908.0,513.0,956.0,467.0,2.4828,133300.0,NEAR OCEAN\n-117.14,32.75,37.0,1832.0,525.0,955.0,488.0,2.7852,129200.0,NEAR OCEAN\n-117.14,32.75,19.0,1358.0,613.0,766.0,630.0,1.0353,150000.0,NEAR OCEAN\n-117.14,32.75,20.0,1182.0,379.0,678.0,326.0,2.1937,162500.0,NEAR OCEAN\n-117.14,32.75,27.0,1551.0,464.0,880.0,400.0,2.4167,131300.0,NEAR OCEAN\n-117.14,32.76,28.0,3025.0,756.0,1328.0,695.0,2.694,164100.0,NEAR OCEAN\n-117.14,32.76,35.0,2539.0,661.0,1308.0,629.0,2.6777,146400.0,NEAR OCEAN\n-117.14,32.76,32.0,2587.0,681.0,1246.0,650.0,2.1727,145500.0,NEAR OCEAN\n-117.14,32.76,35.0,1785.0,493.0,965.0,506.0,2.0792,160000.0,NEAR OCEAN\n-117.13,32.77,30.0,2582.0,650.0,1098.0,603.0,2.8281,171700.0,NEAR OCEAN\n-117.13,32.76,29.0,2568.0,682.0,1191.0,642.0,2.1094,162500.0,NEAR OCEAN\n-117.13,32.76,41.0,1545.0,420.0,747.0,415.0,2.375,154400.0,NEAR OCEAN\n-117.13,32.76,27.0,2280.0,695.0,1235.0,664.0,1.9392,142900.0,NEAR OCEAN\n-117.13,32.76,33.0,1591.0,461.0,794.0,425.0,2.6333,140000.0,NEAR OCEAN\n-117.13,32.76,22.0,2623.0,732.0,1283.0,718.0,2.1563,127100.0,NEAR OCEAN\n-117.14,32.76,24.0,3523.0,991.0,1775.0,873.0,2.1273,142300.0,NEAR OCEAN\n-117.13,32.75,24.0,1877.0,519.0,898.0,483.0,2.2264,112500.0,NEAR OCEAN\n-117.13,32.75,28.0,2279.0,671.0,1166.0,623.0,1.95,150000.0,NEAR OCEAN\n-117.13,32.75,23.0,3999.0,1182.0,2051.0,1130.0,2.1292,135000.0,NEAR OCEAN\n-117.13,32.75,31.0,2336.0,656.0,1186.0,609.0,2.5872,130600.0,NEAR OCEAN\n-117.13,32.75,50.0,1476.0,354.0,698.0,354.0,3.0,168800.0,NEAR OCEAN\n-117.13,32.74,52.0,1512.0,321.0,651.0,321.0,3.6852,185300.0,NEAR OCEAN\n-117.13,32.75,37.0,4142.0,1031.0,1936.0,968.0,2.693,174100.0,NEAR OCEAN\n-117.12,32.75,37.0,2344.0,546.0,1134.0,513.0,2.4394,118300.0,NEAR OCEAN\n-117.12,32.74,46.0,1898.0,441.0,978.0,439.0,3.2708,155200.0,NEAR OCEAN\n-117.12,32.74,52.0,1969.0,389.0,877.0,424.0,3.79,163400.0,NEAR OCEAN\n-117.13,32.75,20.0,2271.0,602.0,992.0,520.0,2.2599,157600.0,NEAR OCEAN\n-117.12,32.75,15.0,2671.0,724.0,1800.0,646.0,2.1394,106700.0,NEAR OCEAN\n-117.12,32.75,17.0,2060.0,633.0,1251.0,602.0,1.9886,119200.0,NEAR OCEAN\n-117.12,32.75,20.0,1406.0,413.0,850.0,412.0,2.3261,114600.0,NEAR OCEAN\n-117.12,32.75,25.0,2222.0,634.0,1025.0,568.0,1.64,130000.0,NEAR OCEAN\n-117.12,32.76,23.0,2681.0,717.0,1279.0,648.0,2.1597,116100.0,NEAR OCEAN\n-117.12,32.76,33.0,2279.0,591.0,1250.0,576.0,2.4297,139000.0,NEAR OCEAN\n-117.12,32.76,43.0,2336.0,644.0,1203.0,614.0,2.3594,127800.0,NEAR OCEAN\n-117.12,32.76,26.0,1221.0,331.0,620.0,296.0,2.4821,123600.0,NEAR OCEAN\n-117.11,32.76,29.0,2030.0,545.0,1014.0,518.0,2.2409,114200.0,NEAR OCEAN\n-117.12,32.76,28.0,2160.0,608.0,1339.0,571.0,1.9152,128100.0,NEAR OCEAN\n-117.12,32.76,27.0,1426.0,364.0,792.0,353.0,2.0673,118800.0,NEAR OCEAN\n-117.12,32.76,28.0,1605.0,501.0,936.0,460.0,2.5991,147500.0,NEAR OCEAN\n-117.12,32.76,17.0,1559.0,462.0,821.0,428.0,2.0139,150000.0,NEAR OCEAN\n-117.12,32.76,41.0,1469.0,421.0,803.0,395.0,2.1856,120500.0,NEAR OCEAN\n-117.11,32.77,48.0,1502.0,272.0,590.0,265.0,2.5952,190300.0,NEAR OCEAN\n-117.11,32.77,50.0,1729.0,355.0,617.0,337.0,3.6705,167000.0,NEAR OCEAN\n-117.12,32.77,48.0,2012.0,422.0,893.0,394.0,2.7928,175000.0,NEAR OCEAN\n-117.12,32.77,43.0,2167.0,464.0,977.0,461.0,3.125,192200.0,NEAR OCEAN\n-117.11,32.77,52.0,1484.0,224.0,498.0,223.0,6.6053,331400.0,NEAR OCEAN\n-117.11,32.77,52.0,1506.0,233.0,478.0,240.0,4.3875,300000.0,NEAR OCEAN\n-117.1,32.77,49.0,4449.0,711.0,1606.0,709.0,5.7768,281600.0,NEAR OCEAN\n-117.1,32.76,52.0,2606.0,426.0,883.0,380.0,4.2813,270800.0,NEAR OCEAN\n-117.09,32.77,38.0,2065.0,374.0,812.0,343.0,3.125,216500.0,NEAR OCEAN\n-117.09,32.76,44.0,1139.0,214.0,470.0,217.0,3.5481,203100.0,NEAR OCEAN\n-117.09,32.76,43.0,3889.0,711.0,1466.0,663.0,3.5529,223000.0,NEAR OCEAN\n-117.11,32.76,31.0,2293.0,549.0,1108.0,557.0,3.3854,204400.0,NEAR OCEAN\n-117.1,32.76,30.0,1835.0,474.0,934.0,415.0,2.875,139600.0,NEAR OCEAN\n-117.11,32.76,28.0,1457.0,397.0,672.0,342.0,1.9799,122700.0,NEAR OCEAN\n-117.11,32.76,21.0,2226.0,600.0,1085.0,533.0,2.2604,126300.0,NEAR OCEAN\n-117.11,32.76,19.0,2188.0,616.0,1304.0,607.0,2.0852,114400.0,NEAR OCEAN\n-117.1,32.75,17.0,871.0,379.0,955.0,351.0,1.4375,96400.0,NEAR OCEAN\n-117.1,32.75,11.0,2393.0,726.0,1905.0,711.0,1.3448,91300.0,NEAR OCEAN\n-117.11,32.75,11.0,1607.0,478.0,1384.0,450.0,2.05,100000.0,NEAR OCEAN\n-117.11,32.75,21.0,2127.0,658.0,1812.0,603.0,1.6896,100000.0,NEAR OCEAN\n-117.11,32.75,18.0,1943.0,587.0,1329.0,522.0,1.7696,103100.0,NEAR OCEAN\n-117.09,32.76,29.0,1650.0,496.0,882.0,445.0,2.2287,140000.0,NEAR OCEAN\n-117.09,32.76,31.0,1235.0,387.0,816.0,397.0,1.5517,122500.0,NEAR OCEAN\n-117.1,32.76,31.0,987.0,267.0,619.0,250.0,2.9286,151800.0,NEAR OCEAN\n-117.1,32.75,16.0,2426.0,799.0,1505.0,754.0,1.6444,103400.0,NEAR OCEAN\n-117.1,32.75,21.0,2063.0,609.0,1686.0,558.0,1.4828,94800.0,NEAR OCEAN\n-117.1,32.75,20.0,2355.0,722.0,1848.0,576.0,2.0036,99200.0,NEAR OCEAN\n-117.1,32.75,15.0,2422.0,774.0,2120.0,715.0,1.0617,92400.0,NEAR OCEAN\n-117.1,32.75,23.0,1858.0,551.0,1506.0,492.0,1.7446,85200.0,NEAR OCEAN\n-117.11,32.75,46.0,695.0,182.0,601.0,195.0,2.4219,90600.0,NEAR OCEAN\n-117.11,32.75,20.0,1667.0,469.0,1292.0,445.0,2.0893,101100.0,NEAR OCEAN\n-117.11,32.75,34.0,2131.0,594.0,1373.0,562.0,2.113,102100.0,NEAR OCEAN\n-117.12,32.75,13.0,2795.0,773.0,1869.0,690.0,2.1767,101800.0,NEAR OCEAN\n-117.1,32.74,14.0,2361.0,601.0,1831.0,526.0,1.6102,93400.0,NEAR OCEAN\n-117.11,32.74,25.0,2846.0,644.0,2272.0,632.0,2.2,98700.0,NEAR OCEAN\n-117.11,32.74,25.0,684.0,190.0,665.0,187.0,2.4524,90300.0,NEAR OCEAN\n-117.11,32.74,33.0,1126.0,267.0,621.0,241.0,3.2422,123100.0,NEAR OCEAN\n-117.11,32.73,35.0,1689.0,397.0,1135.0,366.0,2.3269,97300.0,NEAR OCEAN\n-117.1,32.73,24.0,2927.0,704.0,2005.0,668.0,2.2375,102900.0,NEAR OCEAN\n-117.11,32.73,34.0,1096.0,221.0,574.0,223.0,3.8355,126700.0,NEAR OCEAN\n-117.11,32.73,27.0,3160.0,627.0,1628.0,612.0,3.8864,132600.0,NEAR OCEAN\n-117.09,32.75,30.0,1899.0,546.0,1620.0,493.0,1.6034,84400.0,NEAR OCEAN\n-117.09,32.74,23.0,3130.0,779.0,2472.0,744.0,2.32,93200.0,NEAR OCEAN\n-117.1,32.74,20.0,3854.0,1046.0,3555.0,966.0,1.6747,100000.0,NEAR OCEAN\n-117.1,32.74,30.0,1772.0,500.0,1389.0,447.0,2.3641,94100.0,NEAR OCEAN\n-117.1,32.75,11.0,1976.0,548.0,1528.0,512.0,1.4886,89800.0,NEAR OCEAN\n-117.08,32.75,20.0,1886.0,586.0,1134.0,525.0,1.5029,100000.0,NEAR OCEAN\n-117.08,32.75,16.0,1111.0,328.0,930.0,303.0,1.2347,128100.0,NEAR OCEAN\n-117.08,32.75,15.0,1821.0,516.0,1385.0,439.0,2.5101,95300.0,NEAR OCEAN\n-117.09,32.75,24.0,1245.0,376.0,1230.0,362.0,1.875,95000.0,NEAR OCEAN\n-117.09,32.75,28.0,1220.0,391.0,1286.0,396.0,1.2286,105000.0,NEAR OCEAN\n-117.09,32.75,20.0,1701.0,503.0,1482.0,465.0,1.6789,95500.0,NEAR OCEAN\n-117.09,32.76,10.0,1922.0,577.0,1595.0,545.0,1.5208,118800.0,NEAR OCEAN\n-117.08,32.76,18.0,1704.0,596.0,1639.0,548.0,1.7391,125000.0,NEAR OCEAN\n-117.07,32.76,14.0,2523.0,545.0,1297.0,525.0,2.3886,138100.0,NEAR OCEAN\n-117.07,32.75,37.0,2690.0,549.0,1219.0,524.0,2.3148,154200.0,NEAR OCEAN\n-117.07,32.75,14.0,3073.0,851.0,2000.0,782.0,2.3824,144700.0,NEAR OCEAN\n-117.07,32.75,9.0,3464.0,749.0,1687.0,645.0,3.3026,119100.0,NEAR OCEAN\n-117.06,32.75,34.0,1917.0,419.0,1181.0,426.0,3.0208,129200.0,NEAR OCEAN\n-117.05,32.74,34.0,2178.0,455.0,1193.0,446.0,3.1719,115300.0,NEAR OCEAN\n-117.07,32.74,37.0,1042.0,205.0,589.0,208.0,2.6629,116900.0,NEAR OCEAN\n-117.07,32.75,31.0,2036.0,501.0,1263.0,442.0,2.5583,120700.0,NEAR OCEAN\n-117.08,32.75,20.0,1989.0,508.0,1452.0,462.0,2.0077,118300.0,NEAR OCEAN\n-117.08,32.74,26.0,2359.0,622.0,2067.0,581.0,1.8103,124700.0,NEAR OCEAN\n-117.09,32.75,19.0,2739.0,707.0,2004.0,622.0,1.6318,117700.0,NEAR OCEAN\n-117.09,32.74,42.0,1986.0,472.0,1472.0,475.0,2.1757,110100.0,NEAR OCEAN\n-117.08,32.74,33.0,3260.0,673.0,1784.0,666.0,3.5078,126500.0,NEAR OCEAN\n-117.08,32.73,36.0,3331.0,643.0,1903.0,622.0,3.6974,122000.0,NEAR OCEAN\n-117.07,32.74,38.0,1901.0,392.0,1099.0,406.0,2.7661,113900.0,NEAR OCEAN\n-117.08,32.74,35.0,1434.0,253.0,753.0,228.0,2.3812,135100.0,NEAR OCEAN\n-117.08,32.73,36.0,1158.0,218.0,619.0,233.0,3.6125,122500.0,NEAR OCEAN\n-117.08,32.73,19.0,2935.0,763.0,1953.0,720.0,1.4254,111300.0,NEAR OCEAN\n-117.07,32.73,18.0,2968.0,656.0,1149.0,581.0,2.6452,154200.0,NEAR OCEAN\n-117.07,32.78,22.0,922.0,240.0,1524.0,235.0,1.6815,218800.0,NEAR OCEAN\n-117.09,32.77,31.0,3062.0,,1263.0,539.0,3.0875,291500.0,NEAR OCEAN\n-117.08,32.77,31.0,1070.0,155.0,426.0,153.0,6.1628,219200.0,NEAR OCEAN\n-117.07,32.77,34.0,2245.0,394.0,1849.0,429.0,3.5446,185500.0,NEAR OCEAN\n-117.07,32.77,38.0,3779.0,614.0,1495.0,614.0,4.3529,184000.0,NEAR OCEAN\n-117.07,32.76,42.0,1827.0,378.0,880.0,380.0,2.5125,176600.0,NEAR OCEAN\n-117.08,32.77,25.0,3911.0,849.0,1580.0,767.0,2.7778,184100.0,NEAR OCEAN\n-117.08,32.76,27.0,1221.0,254.0,606.0,259.0,3.0833,155400.0,NEAR OCEAN\n-117.09,32.76,31.0,2567.0,624.0,1255.0,582.0,2.5909,159100.0,NEAR OCEAN\n-117.08,32.76,20.0,2547.0,785.0,1199.0,643.0,1.7743,140300.0,NEAR OCEAN\n-117.04,32.77,21.0,1824.0,447.0,962.0,431.0,2.7826,143800.0,<1H OCEAN\n-117.05,32.77,23.0,2556.0,662.0,1200.0,548.0,1.8899,147700.0,NEAR OCEAN\n-117.06,32.77,18.0,2269.0,682.0,1329.0,581.0,1.7951,161800.0,NEAR OCEAN\n-117.06,32.77,34.0,1730.0,373.0,730.0,350.0,2.0284,161800.0,NEAR OCEAN\n-117.07,32.77,38.0,1130.0,228.0,699.0,241.0,2.65,167600.0,NEAR OCEAN\n-117.06,32.77,32.0,3888.0,827.0,3868.0,841.0,3.0755,166800.0,NEAR OCEAN\n-117.05,32.77,33.0,3535.0,683.0,1568.0,672.0,2.8097,158300.0,NEAR OCEAN\n-117.06,32.76,36.0,2785.0,577.0,1275.0,527.0,2.3015,156800.0,NEAR OCEAN\n-117.06,32.76,38.0,1549.0,288.0,636.0,278.0,3.2188,150500.0,NEAR OCEAN\n-117.05,32.76,46.0,1887.0,359.0,795.0,358.0,3.25,159600.0,NEAR OCEAN\n-117.06,32.76,37.0,2356.0,476.0,1231.0,499.0,2.965,155700.0,NEAR OCEAN\n-117.06,32.76,24.0,1629.0,587.0,1012.0,488.0,1.7452,156800.0,NEAR OCEAN\n-117.05,32.75,36.0,2024.0,,1030.0,390.0,3.8233,139800.0,NEAR OCEAN\n-117.06,32.75,34.0,2516.0,611.0,1317.0,594.0,2.2308,125900.0,NEAR OCEAN\n-117.05,32.75,35.0,2144.0,388.0,1003.0,383.0,3.0938,137300.0,NEAR OCEAN\n-117.07,32.72,18.0,1758.0,286.0,987.0,277.0,4.6875,141800.0,NEAR OCEAN\n-117.08,32.72,32.0,2286.0,468.0,1741.0,467.0,3.0446,101900.0,NEAR OCEAN\n-117.07,32.71,36.0,2448.0,475.0,1268.0,450.0,2.5682,109100.0,NEAR OCEAN\n-117.06,32.73,33.0,3444.0,619.0,1884.0,582.0,3.7891,126700.0,NEAR OCEAN\n-117.06,32.72,31.0,2669.0,514.0,1626.0,499.0,3.1923,116900.0,NEAR OCEAN\n-117.05,32.72,35.0,1777.0,369.0,1158.0,353.0,3.4107,117000.0,NEAR OCEAN\n-117.06,32.71,25.0,2681.0,596.0,1947.0,553.0,2.8964,104300.0,NEAR OCEAN\n-117.07,32.71,39.0,2754.0,652.0,2263.0,619.0,2.2454,89600.0,NEAR OCEAN\n-117.08,32.7,35.0,1477.0,264.0,852.0,279.0,3.1786,100600.0,NEAR OCEAN\n-117.08,32.7,37.0,2176.0,418.0,1301.0,375.0,2.875,98900.0,NEAR OCEAN\n-117.08,32.7,36.0,2103.0,390.0,1279.0,392.0,2.4135,97000.0,NEAR OCEAN\n-117.06,32.71,21.0,1864.0,388.0,1498.0,389.0,3.8194,125700.0,NEAR OCEAN\n-117.06,32.71,11.0,2397.0,523.0,1566.0,514.0,3.8687,145200.0,NEAR OCEAN\n-117.08,32.71,27.0,2204.0,598.0,1656.0,521.0,1.4821,86200.0,NEAR OCEAN\n-117.07,32.71,26.0,4151.0,823.0,2822.0,697.0,2.8372,123400.0,NEAR OCEAN\n-117.07,32.7,14.0,2763.0,456.0,1914.0,465.0,4.1645,143000.0,NEAR OCEAN\n-117.07,32.69,20.0,2192.0,406.0,1766.0,393.0,4.0921,135000.0,NEAR OCEAN\n-117.06,32.7,12.0,3943.0,737.0,3280.0,751.0,4.112,141400.0,NEAR OCEAN\n-117.04,32.71,28.0,5274.0,991.0,3727.0,961.0,3.57,109800.0,NEAR OCEAN\n-117.05,32.71,25.0,3292.0,608.0,2266.0,592.0,3.2986,119200.0,NEAR OCEAN\n-117.03,32.71,33.0,3126.0,627.0,2300.0,623.0,3.2596,103000.0,NEAR OCEAN\n-117.03,32.71,34.0,2328.0,444.0,1684.0,429.0,3.25,99600.0,NEAR OCEAN\n-117.02,32.71,20.0,4050.0,745.0,2870.0,761.0,3.7366,121800.0,NEAR OCEAN\n-117.02,32.7,22.0,2756.0,516.0,1849.0,486.0,4.1837,125400.0,NEAR OCEAN\n-117.02,32.7,18.0,1643.0,283.0,1134.0,269.0,5.1769,133000.0,NEAR OCEAN\n-117.02,32.71,30.0,3187.0,592.0,2082.0,631.0,3.5388,118500.0,NEAR OCEAN\n-117.01,32.7,25.0,2321.0,398.0,1434.0,386.0,3.5341,120800.0,NEAR OCEAN\n-117.05,32.69,8.0,831.0,158.0,740.0,154.0,5.3908,165500.0,NEAR OCEAN\n-117.06,32.69,9.0,1520.0,269.0,1250.0,265.0,4.8875,157700.0,NEAR OCEAN\n-117.06,32.69,13.0,705.0,149.0,718.0,155.0,4.4375,154900.0,NEAR OCEAN\n-117.06,32.69,9.0,521.0,111.0,491.0,110.0,5.1305,158900.0,NEAR OCEAN\n-117.03,32.7,19.0,2304.0,572.0,2010.0,556.0,2.2866,109900.0,NEAR OCEAN\n-117.04,32.69,27.0,1790.0,356.0,1286.0,347.0,3.5437,115800.0,NEAR OCEAN\n-117.04,32.7,7.0,9311.0,1703.0,7302.0,1694.0,4.419,156900.0,NEAR OCEAN\n-117.07,32.68,18.0,1475.0,267.0,1149.0,268.0,5.0827,142200.0,NEAR OCEAN\n-117.06,32.68,38.0,1481.0,317.0,1080.0,291.0,2.85,125800.0,NEAR OCEAN\n-117.06,32.68,36.0,3815.0,796.0,2945.0,728.0,2.0959,125000.0,NEAR OCEAN\n-117.05,32.69,21.0,991.0,210.0,695.0,203.0,3.625,144300.0,NEAR OCEAN\n-117.06,32.68,41.0,2665.0,515.0,1664.0,512.0,2.375,113500.0,NEAR OCEAN\n-117.06,32.67,29.0,4047.0,754.0,2353.0,730.0,4.0505,125000.0,NEAR OCEAN\n-117.05,32.68,35.0,3414.0,580.0,1761.0,522.0,3.9922,129800.0,NEAR OCEAN\n-117.05,32.67,32.0,4227.0,785.0,2842.0,795.0,3.9646,137800.0,NEAR OCEAN\n-117.05,32.68,19.0,1469.0,275.0,1010.0,292.0,3.5664,150400.0,NEAR OCEAN\n-117.05,32.67,16.0,2168.0,343.0,1589.0,338.0,5.4863,153800.0,NEAR OCEAN\n-117.06,32.66,33.0,3425.0,511.0,1528.0,479.0,5.6889,234600.0,NEAR OCEAN\n-117.06,32.66,24.0,2587.0,491.0,1617.0,458.0,3.5066,133400.0,NEAR OCEAN\n-117.04,32.66,22.0,3362.0,630.0,1471.0,612.0,4.1442,303900.0,NEAR OCEAN\n-117.03,32.67,15.0,5094.0,818.0,2118.0,758.0,5.3505,266600.0,NEAR OCEAN\n-117.02,32.68,14.0,3986.0,675.0,2065.0,702.0,5.7192,267400.0,NEAR OCEAN\n-117.05,32.69,14.0,1689.0,555.0,1319.0,527.0,3.16,143800.0,NEAR OCEAN\n-117.05,32.68,15.0,1828.0,359.0,955.0,248.0,3.2174,165100.0,NEAR OCEAN\n-117.04,32.68,13.0,2132.0,425.0,1345.0,432.0,4.0,89300.0,NEAR OCEAN\n-117.04,32.68,14.0,1320.0,270.0,943.0,260.0,5.0947,152700.0,NEAR OCEAN\n-117.04,32.67,14.0,3464.0,683.0,2139.0,734.0,4.0668,137500.0,NEAR OCEAN\n-117.04,32.69,9.0,3417.0,860.0,2521.0,828.0,3.02,158900.0,NEAR OCEAN\n-117.04,32.68,9.0,3087.0,609.0,1530.0,556.0,3.775,125000.0,NEAR OCEAN\n-117.04,32.68,11.0,1875.0,357.0,1014.0,386.0,4.375,115000.0,NEAR OCEAN\n-117.01,32.7,7.0,2327.0,490.0,1304.0,445.0,3.3553,132200.0,NEAR OCEAN\n-117.02,32.69,7.0,6055.0,1004.0,3031.0,952.0,4.436,135000.0,NEAR OCEAN\n-117.03,32.69,5.0,3201.0,532.0,2061.0,536.0,5.0829,179400.0,NEAR OCEAN\n-117.03,32.69,10.0,901.0,163.0,698.0,167.0,4.6648,156100.0,NEAR OCEAN\n-117.03,32.69,8.0,2460.0,397.0,1784.0,390.0,4.5662,175500.0,NEAR OCEAN\n-117.09,32.71,12.0,3375.0,945.0,2357.0,808.0,1.5,106300.0,NEAR OCEAN\n-117.09,32.7,15.0,869.0,217.0,887.0,216.0,1.4583,84200.0,NEAR OCEAN\n-117.09,32.7,22.0,2409.0,582.0,1887.0,578.0,1.4089,94200.0,NEAR OCEAN\n-117.09,32.69,18.0,1645.0,430.0,1221.0,410.0,1.3269,108000.0,NEAR OCEAN\n-117.09,32.69,20.0,1102.0,205.0,852.0,217.0,3.1833,108300.0,NEAR OCEAN\n-117.1,32.69,11.0,3071.0,911.0,2812.0,774.0,1.2413,83100.0,NEAR OCEAN\n-117.1,32.7,28.0,633.0,137.0,525.0,170.0,3.6042,95600.0,NEAR OCEAN\n-117.1,32.7,42.0,2002.0,488.0,1505.0,464.0,1.5057,86300.0,NEAR OCEAN\n-117.1,32.71,9.0,1931.0,472.0,1628.0,445.0,2.085,92600.0,NEAR OCEAN\n-117.09,32.73,26.0,3114.0,686.0,1948.0,660.0,2.8942,124100.0,NEAR OCEAN\n-117.1,32.72,5.0,1615.0,387.0,1094.0,394.0,2.2024,137200.0,NEAR OCEAN\n-117.09,32.72,39.0,1273.0,246.0,770.0,242.0,2.0938,102500.0,NEAR OCEAN\n-117.09,32.72,33.0,1096.0,240.0,716.0,224.0,1.6944,111800.0,NEAR OCEAN\n-117.09,32.73,28.0,3109.0,594.0,1559.0,589.0,3.5789,120300.0,NEAR OCEAN\n-117.09,32.71,29.0,2238.0,523.0,2061.0,504.0,2.5559,96800.0,NEAR OCEAN\n-117.1,32.71,29.0,3422.0,713.0,2775.0,644.0,1.7075,86900.0,NEAR OCEAN\n-117.1,32.71,25.0,939.0,247.0,1003.0,240.0,1.75,87900.0,NEAR OCEAN\n-117.11,32.72,25.0,1491.0,348.0,1183.0,316.0,1.9583,88600.0,NEAR OCEAN\n-117.11,32.71,30.0,1729.0,457.0,1673.0,460.0,1.7,85900.0,NEAR OCEAN\n-117.11,32.7,34.0,2028.0,522.0,1797.0,464.0,1.7402,79400.0,NEAR OCEAN\n-117.11,32.7,37.0,2045.0,502.0,1920.0,472.0,1.8125,83300.0,NEAR OCEAN\n-117.11,32.7,33.0,1980.0,488.0,1626.0,428.0,1.4856,86400.0,NEAR OCEAN\n-117.12,32.7,14.0,819.0,237.0,827.0,237.0,1.3194,90500.0,NEAR OCEAN\n-117.12,32.7,36.0,1011.0,253.0,763.0,226.0,1.8187,84100.0,NEAR OCEAN\n-117.11,32.71,29.0,1040.0,291.0,1054.0,297.0,1.1818,83200.0,NEAR OCEAN\n-117.1,32.69,37.0,1269.0,340.0,1369.0,302.0,2.2102,87200.0,NEAR OCEAN\n-117.1,32.69,35.0,1292.0,272.0,1183.0,272.0,2.0547,98000.0,NEAR OCEAN\n-117.11,32.69,36.0,1421.0,367.0,1418.0,355.0,1.9425,93400.0,NEAR OCEAN\n-117.11,32.69,34.0,1144.0,295.0,1271.0,302.0,2.09,91800.0,NEAR OCEAN\n-117.11,32.69,37.0,2395.0,627.0,2489.0,599.0,1.5933,86300.0,NEAR OCEAN\n-117.11,32.69,39.0,395.0,159.0,620.0,162.0,2.725,86500.0,NEAR OCEAN\n-117.12,32.69,46.0,200.0,77.0,180.0,65.0,1.0658,93800.0,NEAR OCEAN\n-117.12,32.69,37.0,1082.0,294.0,1146.0,265.0,2.0673,88500.0,NEAR OCEAN\n-117.12,32.7,38.0,818.0,217.0,953.0,231.0,1.0531,65700.0,NEAR OCEAN\n-117.12,32.7,37.0,1361.0,348.0,1398.0,328.0,1.1681,78100.0,NEAR OCEAN\n-117.13,32.69,36.0,1469.0,400.0,1271.0,340.0,1.043,90100.0,NEAR OCEAN\n-117.13,32.7,48.0,786.0,230.0,917.0,231.0,1.875,75600.0,NEAR OCEAN\n-117.13,32.7,42.0,1210.0,292.0,945.0,258.0,0.8991,78900.0,NEAR OCEAN\n-117.13,32.7,38.0,1445.0,392.0,1286.0,357.0,1.4632,80200.0,NEAR OCEAN\n-117.13,32.7,35.0,1179.0,344.0,1372.0,330.0,1.9509,70200.0,NEAR OCEAN\n-117.12,32.71,33.0,1256.0,331.0,1315.0,321.0,1.9286,78500.0,NEAR OCEAN\n-117.13,32.71,38.0,993.0,246.0,760.0,205.0,1.1563,82700.0,NEAR OCEAN\n-117.13,32.71,42.0,1145.0,314.0,1114.0,307.0,1.2614,87500.0,NEAR OCEAN\n-117.13,32.71,44.0,1697.0,413.0,1396.0,363.0,1.5474,83300.0,NEAR OCEAN\n-117.13,32.72,32.0,2197.0,623.0,1784.0,599.0,1.901,120300.0,NEAR OCEAN\n-117.12,32.71,24.0,421.0,101.0,396.0,113.0,0.6433,111300.0,NEAR OCEAN\n-117.13,32.72,9.0,2436.0,720.0,1780.0,653.0,1.8299,137500.0,NEAR OCEAN\n-117.13,32.72,19.0,1341.0,435.0,1048.0,360.0,1.975,117900.0,NEAR OCEAN\n-117.12,32.72,36.0,6096.0,1285.0,3093.0,1229.0,3.37,159100.0,NEAR OCEAN\n-117.12,32.73,50.0,2307.0,424.0,887.0,356.0,3.5156,168800.0,NEAR OCEAN\n-117.12,32.73,52.0,3976.0,718.0,1750.0,769.0,3.4151,175400.0,NEAR OCEAN\n-117.13,32.74,46.0,3355.0,768.0,1457.0,708.0,2.6604,170100.0,NEAR OCEAN\n-117.13,32.73,52.0,1148.0,214.0,481.0,215.0,5.454,240900.0,NEAR OCEAN\n-117.13,32.73,52.0,2676.0,557.0,1181.0,537.0,3.6058,213100.0,NEAR OCEAN\n-117.13,32.74,50.0,1527.0,338.0,728.0,322.0,2.625,203200.0,NEAR OCEAN\n-117.13,32.73,43.0,2706.0,667.0,1531.0,614.0,2.1513,145000.0,NEAR OCEAN\n-117.13,32.72,43.0,2160.0,504.0,1221.0,452.0,2.4821,140600.0,NEAR OCEAN\n-117.13,32.72,52.0,1560.0,307.0,757.0,315.0,2.7083,199100.0,NEAR OCEAN\n-117.13,32.73,52.0,1911.0,415.0,777.0,412.0,2.2429,221100.0,NEAR OCEAN\n-117.14,32.72,42.0,1558.0,458.0,1227.0,407.0,2.2804,139100.0,NEAR OCEAN\n-117.13,32.72,17.0,1285.0,423.0,1208.0,409.0,1.758,126600.0,NEAR OCEAN\n-117.14,32.71,34.0,1694.0,455.0,1467.0,425.0,2.1164,139400.0,NEAR OCEAN\n-117.14,32.71,32.0,719.0,251.0,894.0,208.0,1.8456,103100.0,NEAR OCEAN\n-117.14,32.72,43.0,1073.0,344.0,660.0,279.0,2.0529,168800.0,NEAR OCEAN\n-117.14,32.72,45.0,1140.0,310.0,840.0,339.0,1.6156,156300.0,NEAR OCEAN\n-117.14,32.72,34.0,2533.0,862.0,2011.0,778.0,2.1199,160400.0,NEAR OCEAN\n-117.14,32.71,52.0,1225.0,332.0,955.0,321.0,1.6011,106300.0,NEAR OCEAN\n-117.14,32.71,52.0,979.0,314.0,975.0,297.0,1.2375,100000.0,NEAR OCEAN\n-117.14,32.71,52.0,800.0,313.0,1337.0,282.0,1.5594,87500.0,NEAR OCEAN\n-117.14,32.71,52.0,500.0,,480.0,108.0,1.8696,91100.0,NEAR OCEAN\n-117.13,32.71,37.0,1220.0,325.0,1472.0,323.0,1.825,81500.0,NEAR OCEAN\n-117.13,32.71,35.0,614.0,180.0,691.0,164.0,1.6953,81300.0,NEAR OCEAN\n-117.14,32.71,43.0,966.0,255.0,857.0,208.0,1.2841,72000.0,NEAR OCEAN\n-117.14,32.71,39.0,1647.0,478.0,2176.0,479.0,1.7642,82900.0,NEAR OCEAN\n-117.14,32.7,32.0,1280.0,353.0,1335.0,330.0,1.6023,77300.0,NEAR OCEAN\n-117.13,32.7,35.0,365.0,98.0,463.0,112.0,2.5588,78800.0,NEAR OCEAN\n-117.14,32.7,43.0,1126.0,289.0,1132.0,294.0,2.1875,87000.0,NEAR OCEAN\n-117.14,32.7,40.0,1227.0,330.0,1199.0,316.0,1.2188,92500.0,NEAR OCEAN\n-117.14,32.7,36.0,633.0,148.0,557.0,139.0,1.5729,82700.0,NEAR OCEAN\n-117.14,32.7,48.0,510.0,180.0,545.0,132.0,1.8008,86500.0,NEAR OCEAN\n-117.14,32.7,44.0,658.0,218.0,869.0,212.0,1.9338,89400.0,NEAR OCEAN\n-117.14,32.7,47.0,552.0,161.0,593.0,174.0,0.9589,90000.0,NEAR OCEAN\n-117.15,32.7,50.0,475.0,172.0,483.0,120.0,1.3657,162500.0,NEAR OCEAN\n-117.15,32.71,52.0,402.0,183.0,557.0,172.0,1.3125,87500.0,NEAR OCEAN\n-117.15,32.7,52.0,458.0,148.0,1283.0,166.0,1.2863,86300.0,NEAR OCEAN\n-117.15,32.71,52.0,217.0,82.0,531.0,93.0,1.6607,137500.0,NEAR OCEAN\n-117.15,32.72,52.0,344.0,177.0,460.0,147.0,1.2292,137500.0,NEAR OCEAN\n-117.16,32.72,52.0,788.0,463.0,805.0,391.0,0.9142,162500.0,NEAR OCEAN\n-117.16,32.71,52.0,845.0,451.0,1230.0,375.0,1.0918,22500.0,NEAR OCEAN\n-117.16,32.71,5.0,2508.0,827.0,2066.0,761.0,1.3092,325000.0,NEAR OCEAN\n-117.17,32.71,7.0,2493.0,693.0,951.0,641.0,4.2375,205000.0,NEAR OCEAN\n-117.17,32.71,39.0,311.0,181.0,206.0,113.0,0.7685,187500.0,NEAR OCEAN\n-117.14,32.73,26.0,450.0,132.0,317.0,109.0,4.0,137500.0,NEAR OCEAN\n-117.15,32.72,51.0,1321.0,,781.0,499.0,1.3071,250000.0,NEAR OCEAN\n-117.16,32.72,24.0,1232.0,663.0,1184.0,626.0,1.0391,162500.0,NEAR OCEAN\n-117.16,32.73,52.0,1218.0,471.0,821.0,429.0,1.9597,200000.0,NEAR OCEAN\n-117.16,32.72,27.0,1245.0,471.0,653.0,451.0,1.2668,225000.0,NEAR OCEAN\n-117.17,32.73,52.0,408.0,143.0,313.0,143.0,1.815,116700.0,NEAR OCEAN\n-117.17,32.72,44.0,626.0,256.0,572.0,229.0,1.5909,262500.0,NEAR OCEAN\n-117.16,32.73,52.0,1682.0,617.0,873.0,534.0,2.0972,112500.0,NEAR OCEAN\n-117.16,32.73,52.0,1863.0,559.0,906.0,493.0,1.9203,195800.0,NEAR OCEAN\n-117.17,32.73,52.0,1578.0,487.0,879.0,446.0,2.4069,215000.0,NEAR OCEAN\n-117.16,32.74,49.0,1815.0,495.0,601.0,410.0,3.0571,418800.0,NEAR OCEAN\n-117.16,32.74,43.0,1437.0,406.0,692.0,379.0,3.1979,466700.0,NEAR OCEAN\n-117.17,32.74,39.0,3803.0,806.0,1567.0,775.0,3.7039,361500.0,NEAR OCEAN\n-117.17,32.74,38.0,5054.0,1168.0,2366.0,1103.0,2.9422,289400.0,NEAR OCEAN\n-117.17,32.73,52.0,55.0,18.0,65.0,22.0,1.6591,112500.0,NEAR OCEAN\n-117.2,32.76,40.0,581.0,157.0,298.0,156.0,2.4,255000.0,NEAR OCEAN\n-117.19,32.75,33.0,1115.0,316.0,583.0,269.0,2.5882,258300.0,NEAR OCEAN\n-117.18,32.74,20.0,1165.0,269.0,459.0,244.0,3.175,191700.0,NEAR OCEAN\n-117.19,32.75,52.0,25.0,5.0,13.0,5.0,0.536,162500.0,NEAR OCEAN\n-117.21,32.75,15.0,1716.0,702.0,914.0,672.0,1.0612,300000.0,NEAR OCEAN\n-117.21,32.74,45.0,3025.0,583.0,1980.0,550.0,2.2982,87500.0,NEAR OCEAN\n-117.21,32.75,27.0,2072.0,534.0,1118.0,510.0,2.8043,262100.0,NEAR OCEAN\n-117.22,32.75,24.0,3914.0,985.0,2147.0,874.0,2.9735,225000.0,NEAR OCEAN\n-117.22,32.75,26.0,696.0,185.0,384.0,184.0,2.6121,125000.0,NEAR OCEAN\n-117.23,32.75,23.0,2415.0,653.0,1275.0,596.0,3.1389,101800.0,NEAR OCEAN\n-117.22,32.75,26.0,617.0,112.0,251.0,110.0,3.8036,162000.0,NEAR OCEAN\n-117.23,32.75,11.0,4304.0,1245.0,1960.0,1105.0,3.3456,159800.0,NEAR OCEAN\n-117.21,32.74,52.0,1245.0,174.0,468.0,193.0,6.9322,334500.0,NEAR OCEAN\n-117.22,32.74,52.0,1283.0,173.0,436.0,190.0,7.4029,345700.0,NEAR OCEAN\n-117.22,32.74,52.0,1260.0,202.0,555.0,209.0,7.2758,345200.0,NEAR OCEAN\n-117.22,32.74,41.0,2621.0,542.0,1074.0,471.0,2.4016,287500.0,NEAR OCEAN\n-117.22,32.75,34.0,6001.0,1111.0,2654.0,1072.0,4.5878,291000.0,NEAR OCEAN\n-117.22,32.73,38.0,3966.0,768.0,1640.0,729.0,3.8409,291400.0,NEAR OCEAN\n-117.23,32.73,35.0,2914.0,683.0,1562.0,638.0,2.5259,240200.0,NEAR OCEAN\n-117.23,32.73,44.0,1168.0,263.0,509.0,256.0,2.7273,269700.0,NEAR OCEAN\n-117.23,32.72,43.0,952.0,209.0,392.0,210.0,2.1635,244200.0,NEAR OCEAN\n-117.23,32.74,44.0,1404.0,229.0,513.0,217.0,4.1806,263800.0,NEAR OCEAN\n-117.24,32.74,43.0,2216.0,375.0,918.0,388.0,5.5289,297700.0,NEAR OCEAN\n-117.24,32.73,37.0,2260.0,354.0,809.0,351.0,5.9113,388300.0,NEAR OCEAN\n-117.23,32.73,36.0,2052.0,287.0,699.0,265.0,7.5557,441400.0,NEAR OCEAN\n-117.23,32.72,38.0,2827.0,581.0,972.0,558.0,3.2361,500001.0,NEAR OCEAN\n-117.24,32.71,32.0,4164.0,701.0,1277.0,607.0,6.6661,500001.0,NEAR OCEAN\n-117.24,32.72,39.0,3089.0,431.0,1175.0,432.0,7.5925,466700.0,NEAR OCEAN\n-117.24,32.72,39.0,3819.0,594.0,1361.0,583.0,6.6013,396400.0,NEAR OCEAN\n-117.25,32.73,38.0,1840.0,291.0,633.0,283.0,4.9125,383600.0,NEAR OCEAN\n-117.25,32.72,36.0,2632.0,450.0,2038.0,419.0,6.5319,345800.0,NEAR OCEAN\n-117.25,32.72,33.0,1677.0,228.0,629.0,239.0,6.597,496400.0,NEAR OCEAN\n-117.25,32.73,37.0,2224.0,331.0,821.0,341.0,6.3331,400000.0,NEAR OCEAN\n-117.28,32.73,44.0,1934.0,325.0,783.0,316.0,4.8684,358600.0,NEAR OCEAN\n-117.28,32.74,33.0,4168.0,1112.0,1785.0,984.0,2.7515,247700.0,NEAR OCEAN\n-117.25,32.74,36.0,3548.0,956.0,1648.0,866.0,2.6962,288200.0,NEAR OCEAN\n-117.25,32.74,36.0,1830.0,430.0,755.0,419.0,2.9904,286800.0,NEAR OCEAN\n-117.25,32.74,40.0,2186.0,549.0,953.0,515.0,2.8007,257100.0,NEAR OCEAN\n-117.24,32.74,44.0,1686.0,285.0,712.0,298.0,4.0268,298600.0,NEAR OCEAN\n-117.24,32.74,44.0,1488.0,259.0,667.0,281.0,4.0862,321800.0,NEAR OCEAN\n-117.25,32.73,39.0,1688.0,256.0,635.0,272.0,4.5938,367400.0,NEAR OCEAN\n-117.23,32.75,5.0,1824.0,,892.0,426.0,3.4286,137500.0,NEAR OCEAN\n-117.23,32.75,21.0,2050.0,608.0,1131.0,550.0,2.4779,165000.0,NEAR OCEAN\n-117.23,32.74,16.0,735.0,139.0,299.0,134.0,4.6354,179200.0,NEAR OCEAN\n-117.23,32.74,16.0,1953.0,404.0,798.0,385.0,4.8167,169800.0,NEAR OCEAN\n-117.23,32.74,35.0,2615.0,525.0,1312.0,547.0,4.1339,238200.0,NEAR OCEAN\n-117.24,32.74,45.0,1718.0,293.0,757.0,329.0,4.05,284900.0,NEAR OCEAN\n-117.24,32.75,36.0,2831.0,669.0,1279.0,660.0,2.9896,252700.0,NEAR OCEAN\n-117.24,32.75,36.0,1856.0,475.0,822.0,416.0,2.3042,220600.0,NEAR OCEAN\n-117.24,32.75,41.0,1989.0,514.0,1015.0,489.0,2.79,226000.0,NEAR OCEAN\n-117.25,32.74,36.0,1240.0,310.0,577.0,319.0,2.6625,248200.0,NEAR OCEAN\n-117.25,32.75,32.0,3551.0,1037.0,1731.0,935.0,2.2017,208300.0,NEAR OCEAN\n-117.25,32.75,36.0,1929.0,526.0,974.0,491.0,1.7622,205800.0,NEAR OCEAN\n-117.24,32.75,33.0,1980.0,614.0,1057.0,567.0,2.2042,231300.0,NEAR OCEAN\n-117.25,32.75,37.0,1189.0,377.0,645.0,377.0,2.4672,216700.0,NEAR OCEAN\n-117.28,32.75,34.0,981.0,313.0,508.0,304.0,2.2328,266700.0,NEAR OCEAN\n-117.25,32.79,37.0,1467.0,442.0,651.0,354.0,2.375,340400.0,NEAR OCEAN\n-117.25,32.79,43.0,906.0,240.0,458.0,205.0,1.8365,328600.0,NEAR OCEAN\n-117.25,32.78,36.0,1527.0,427.0,710.0,312.0,2.7857,291700.0,NEAR OCEAN\n-117.25,32.78,21.0,1479.0,484.0,658.0,384.0,2.45,350000.0,NEAR OCEAN\n-117.28,32.77,38.0,1267.0,340.0,442.0,250.0,4.3403,500000.0,NEAR OCEAN\n-117.25,32.77,32.0,2021.0,524.0,973.0,485.0,3.18,362500.0,NEAR OCEAN\n-117.25,32.77,35.0,2494.0,690.0,1126.0,624.0,4.0313,385300.0,NEAR OCEAN\n-117.25,32.76,38.0,2331.0,493.0,836.0,433.0,4.9125,452600.0,NEAR OCEAN\n-117.22,32.78,22.0,2020.0,466.0,1010.0,429.0,3.4527,175000.0,NEAR OCEAN\n-117.23,32.79,23.0,2578.0,665.0,989.0,622.0,3.5484,238000.0,NEAR OCEAN\n-117.23,32.79,28.0,2453.0,648.0,1082.0,617.0,3.625,266700.0,NEAR OCEAN\n-117.23,32.78,35.0,1649.0,355.0,746.0,360.0,4.6293,356500.0,NEAR OCEAN\n-117.24,32.78,44.0,2172.0,431.0,892.0,420.0,4.1742,342200.0,NEAR OCEAN\n-117.24,32.79,17.0,1149.0,266.0,403.0,228.0,4.1652,241700.0,NEAR OCEAN\n-117.24,32.79,18.0,2539.0,616.0,964.0,526.0,3.4306,275000.0,NEAR OCEAN\n-117.24,32.79,20.0,961.0,278.0,525.0,254.0,3.1838,245800.0,NEAR OCEAN\n-117.24,32.79,25.0,2135.0,691.0,566.0,320.0,2.6902,212500.0,NEAR OCEAN\n-117.24,32.79,18.0,1741.0,602.0,508.0,283.0,3.2625,193800.0,NEAR OCEAN\n-117.21,32.8,19.0,786.0,282.0,525.0,229.0,1.7273,137500.0,NEAR OCEAN\n-117.22,32.8,23.0,1906.0,525.0,1029.0,491.0,2.93,183300.0,NEAR OCEAN\n-117.23,32.8,22.0,2981.0,873.0,1751.0,745.0,2.3482,190600.0,NEAR OCEAN\n-117.23,32.8,27.0,1297.0,355.0,776.0,337.0,2.4643,244400.0,NEAR OCEAN\n-117.23,32.81,28.0,1508.0,263.0,996.0,267.0,3.8026,270000.0,NEAR OCEAN\n-117.22,32.81,21.0,1703.0,335.0,902.0,369.0,3.7813,362500.0,NEAR OCEAN\n-117.25,32.8,37.0,1096.0,260.0,490.0,267.0,3.2663,270600.0,NEAR OCEAN\n-117.25,32.8,32.0,1601.0,468.0,731.0,429.0,2.5568,258300.0,NEAR OCEAN\n-117.25,32.8,30.0,2061.0,631.0,1007.0,577.0,2.5813,253100.0,NEAR OCEAN\n-117.25,32.8,31.0,2182.0,630.0,1069.0,599.0,2.9781,212500.0,NEAR OCEAN\n-117.25,32.79,27.0,848.0,300.0,455.0,298.0,3.0774,275000.0,NEAR OCEAN\n-117.25,32.79,25.0,1627.0,375.0,735.0,378.0,3.6429,317100.0,NEAR OCEAN\n-117.28,32.8,20.0,1838.0,540.0,615.0,325.0,3.5486,193800.0,NEAR OCEAN\n-117.23,32.8,21.0,2429.0,579.0,1011.0,538.0,3.225,229400.0,NEAR OCEAN\n-117.23,32.8,31.0,1403.0,388.0,724.0,371.0,2.6403,216100.0,NEAR OCEAN\n-117.23,32.8,28.0,3379.0,918.0,1849.0,849.0,3.0293,241700.0,NEAR OCEAN\n-117.24,32.8,26.0,3433.0,873.0,1492.0,798.0,2.9258,234800.0,NEAR OCEAN\n-117.24,32.8,28.0,1072.0,331.0,692.0,321.0,2.1357,187500.0,NEAR OCEAN\n-117.24,32.8,18.0,2205.0,661.0,874.0,580.0,3.8018,112500.0,NEAR OCEAN\n-117.24,32.8,19.0,1863.0,497.0,868.0,503.0,2.288,210000.0,NEAR OCEAN\n-117.24,32.8,30.0,1917.0,462.0,828.0,437.0,2.4671,276300.0,NEAR OCEAN\n-117.24,32.8,29.0,3376.0,882.0,1513.0,843.0,3.101,238200.0,NEAR OCEAN\n-117.25,32.81,32.0,2402.0,551.0,1020.0,532.0,3.3942,307400.0,NEAR OCEAN\n-117.25,32.8,35.0,2281.0,506.0,1005.0,496.0,4.2296,275000.0,NEAR OCEAN\n-117.25,32.8,26.0,2442.0,659.0,1134.0,624.0,3.3274,295500.0,NEAR OCEAN\n-117.26,32.81,25.0,2076.0,586.0,1060.0,554.0,2.8421,227800.0,NEAR OCEAN\n-117.26,32.81,30.0,1328.0,346.0,577.0,328.0,2.3284,290600.0,NEAR OCEAN\n-117.26,32.81,37.0,1616.0,421.0,650.0,395.0,2.92,326500.0,NEAR OCEAN\n-117.29,32.81,35.0,1878.0,308.0,598.0,257.0,6.9553,500001.0,NEAR OCEAN\n-117.26,32.8,30.0,1446.0,385.0,650.0,344.0,3.744,450000.0,NEAR OCEAN\n-117.24,32.81,33.0,1588.0,289.0,683.0,301.0,5.4103,332400.0,NEAR OCEAN\n-117.24,32.81,34.0,2420.0,391.0,917.0,392.0,6.5881,394400.0,NEAR OCEAN\n-117.25,32.81,39.0,1846.0,350.0,765.0,329.0,3.9187,311900.0,NEAR OCEAN\n-117.27,32.84,34.0,1655.0,450.0,870.0,411.0,3.2109,376000.0,NEAR OCEAN\n-117.28,32.84,41.0,1420.0,338.0,640.0,314.0,2.9306,360300.0,NEAR OCEAN\n-117.27,32.83,39.0,1877.0,426.0,805.0,409.0,3.875,410000.0,NEAR OCEAN\n-117.28,32.83,34.0,2392.0,653.0,933.0,619.0,3.7306,500000.0,NEAR OCEAN\n-117.31,32.83,38.0,2367.0,480.0,891.0,428.0,4.1477,500001.0,NEAR OCEAN\n-117.27,32.82,35.0,2908.0,595.0,1068.0,529.0,4.1793,500001.0,NEAR OCEAN\n-117.27,32.82,42.0,2820.0,488.0,1175.0,500.0,4.5083,405200.0,NEAR OCEAN\n-117.31,32.82,42.0,2785.0,389.0,833.0,333.0,11.3074,500001.0,NEAR OCEAN\n-117.27,32.85,34.0,2105.0,444.0,780.0,406.0,2.3187,488900.0,NEAR OCEAN\n-117.28,32.84,21.0,2455.0,660.0,1015.0,597.0,3.7596,381300.0,NEAR OCEAN\n-117.27,32.85,26.0,1373.0,,608.0,268.0,4.425,475000.0,NEAR OCEAN\n-117.3,32.85,28.0,2334.0,694.0,770.0,552.0,3.1324,500001.0,NEAR OCEAN\n-117.23,32.81,24.0,3271.0,508.0,1496.0,482.0,5.9359,422200.0,NEAR OCEAN\n-117.23,32.81,22.0,3205.0,429.0,1083.0,410.0,8.1844,406300.0,NEAR OCEAN\n-117.24,32.82,20.0,2467.0,332.0,731.0,335.0,7.2559,392300.0,NEAR OCEAN\n-117.25,32.84,19.0,1759.0,214.0,659.0,195.0,10.7751,500001.0,NEAR OCEAN\n-117.26,32.85,30.0,3652.0,499.0,978.0,462.0,8.2374,500001.0,NEAR OCEAN\n-117.25,32.83,17.0,2075.0,262.0,704.0,241.0,10.9529,500001.0,NEAR OCEAN\n-117.27,32.84,26.0,3940.0,657.0,1180.0,600.0,6.1025,500001.0,NEAR OCEAN\n-117.23,32.88,18.0,5566.0,1465.0,6303.0,1458.0,1.858,205000.0,NEAR OCEAN\n-117.22,32.85,26.0,1647.0,261.0,694.0,259.0,4.6875,274400.0,NEAR OCEAN\n-117.22,32.84,19.0,2691.0,347.0,1154.0,366.0,8.051,363600.0,NEAR OCEAN\n-117.23,32.85,25.0,4229.0,601.0,1634.0,574.0,6.3955,316700.0,NEAR OCEAN\n-117.21,32.86,24.0,3596.0,494.0,1573.0,492.0,6.5382,326000.0,NEAR OCEAN\n-117.21,32.86,26.0,1352.0,202.0,654.0,217.0,5.3693,260700.0,NEAR OCEAN\n-117.21,32.85,15.0,2593.0,521.0,901.0,456.0,4.2065,277800.0,NEAR OCEAN\n-117.21,32.85,26.0,2012.0,315.0,872.0,335.0,5.4067,277500.0,NEAR OCEAN\n-117.24,32.83,18.0,3109.0,501.0,949.0,368.0,7.4351,445700.0,NEAR OCEAN\n-117.25,32.82,19.0,5255.0,762.0,1773.0,725.0,7.8013,474000.0,NEAR OCEAN\n-117.25,32.82,23.0,6139.0,826.0,2036.0,807.0,9.5245,500001.0,NEAR OCEAN\n-117.26,32.83,24.0,1663.0,199.0,578.0,187.0,10.7721,500001.0,NEAR OCEAN\n-117.26,32.82,34.0,5846.0,785.0,1817.0,747.0,8.496,500001.0,NEAR OCEAN\n-117.27,32.83,35.0,1420.0,193.0,469.0,177.0,8.0639,500001.0,NEAR OCEAN\n-117.29,32.92,25.0,2355.0,381.0,823.0,358.0,6.8322,500001.0,NEAR OCEAN\n-117.25,32.86,30.0,1670.0,219.0,606.0,202.0,12.4429,500001.0,NEAR OCEAN\n-117.25,32.86,25.0,2911.0,533.0,1137.0,499.0,5.1023,500001.0,NEAR OCEAN\n-117.25,32.86,27.0,2530.0,469.0,594.0,326.0,7.2821,500001.0,NEAR OCEAN\n-117.26,32.85,42.0,1761.0,329.0,480.0,255.0,5.3787,500001.0,NEAR OCEAN\n-117.24,32.85,18.0,3117.0,475.0,904.0,368.0,6.7587,388500.0,NEAR OCEAN\n-117.24,32.85,22.0,3479.0,448.0,1252.0,440.0,10.0707,500001.0,NEAR OCEAN\n-117.19,32.86,18.0,4231.0,728.0,2030.0,720.0,6.1805,272400.0,NEAR OCEAN\n-117.19,32.86,19.0,3716.0,563.0,1788.0,587.0,5.2113,267400.0,NEAR OCEAN\n-117.2,32.85,22.0,3501.0,631.0,1297.0,581.0,4.7891,295300.0,NEAR OCEAN\n-117.2,32.85,26.0,2298.0,549.0,980.0,555.0,2.4207,213500.0,NEAR OCEAN\n-117.19,32.85,15.0,2895.0,498.0,1164.0,443.0,5.102,417500.0,NEAR OCEAN\n-117.23,32.87,15.0,2290.0,662.0,1034.0,594.0,3.0104,204200.0,NEAR OCEAN\n-117.23,32.86,16.0,1675.0,354.0,604.0,332.0,5.2326,188300.0,NEAR OCEAN\n-117.23,32.86,15.0,1199.0,301.0,510.0,296.0,3.6083,180100.0,NEAR OCEAN\n-117.23,32.86,15.0,1703.0,320.0,587.0,282.0,5.0855,209800.0,NEAR OCEAN\n-117.23,32.86,16.0,1200.0,468.0,648.0,443.0,3.045,100000.0,NEAR OCEAN\n-117.23,32.87,11.0,3123.0,740.0,1223.0,634.0,5.417,196800.0,NEAR OCEAN\n-117.21,32.86,16.0,2800.0,566.0,1267.0,518.0,3.2794,148600.0,NEAR OCEAN\n-117.22,32.87,14.0,3512.0,807.0,1835.0,792.0,3.35,171000.0,NEAR OCEAN\n-117.22,32.86,4.0,16289.0,4585.0,7604.0,4176.0,3.6287,280800.0,NEAR OCEAN\n-117.22,32.87,5.0,3511.0,1008.0,1599.0,918.0,3.8542,176600.0,NEAR OCEAN\n-117.21,32.89,14.0,3114.0,773.0,1592.0,776.0,3.3176,156100.0,NEAR OCEAN\n-117.2,32.86,4.0,4308.0,1095.0,1923.0,932.0,3.9356,267000.0,NEAR OCEAN\n-117.21,32.87,12.0,1428.0,303.0,528.0,269.0,4.1429,254400.0,NEAR OCEAN\n-117.15,32.91,15.0,1613.0,303.0,702.0,240.0,4.875,169300.0,<1H OCEAN\n-117.15,32.91,14.0,1259.0,238.0,889.0,247.0,4.9464,174800.0,<1H OCEAN\n-117.16,32.91,5.0,1619.0,272.0,1063.0,296.0,6.0891,214600.0,<1H OCEAN\n-117.15,32.91,10.0,2349.0,431.0,1598.0,435.0,4.8229,183200.0,<1H OCEAN\n-117.15,32.9,12.0,1681.0,381.0,1050.0,362.0,4.2008,176100.0,<1H OCEAN\n-117.16,32.89,5.0,8576.0,1952.0,5006.0,1827.0,4.3598,189100.0,<1H OCEAN\n-117.12,32.91,8.0,3405.0,961.0,1742.0,918.0,2.8728,114600.0,<1H OCEAN\n-117.13,32.91,15.0,1450.0,266.0,747.0,290.0,3.6111,196300.0,<1H OCEAN\n-117.13,32.91,16.0,3230.0,579.0,1825.0,576.0,4.2969,151200.0,<1H OCEAN\n-117.13,32.91,16.0,2715.0,581.0,1619.0,584.0,4.0,154700.0,<1H OCEAN\n-117.14,32.91,14.0,3014.0,710.0,2165.0,705.0,3.7837,160300.0,<1H OCEAN\n-117.14,32.9,16.0,3217.0,,2054.0,687.0,4.2234,162100.0,<1H OCEAN\n-117.13,32.9,15.0,2785.0,644.0,1798.0,630.0,3.7156,175200.0,<1H OCEAN\n-117.12,32.9,14.0,3249.0,937.0,1929.0,838.0,2.8588,92500.0,<1H OCEAN\n-117.12,32.9,13.0,1743.0,363.0,854.0,353.0,4.6667,138200.0,<1H OCEAN\n-117.13,32.92,16.0,2173.0,399.0,1460.0,393.0,4.2614,169600.0,<1H OCEAN\n-117.13,32.92,17.0,1481.0,315.0,1002.0,300.0,3.6196,163400.0,<1H OCEAN\n-117.14,32.92,7.0,1308.0,418.0,766.0,390.0,3.2151,106300.0,<1H OCEAN\n-117.14,32.92,6.0,3069.0,750.0,1541.0,736.0,3.814,132500.0,<1H OCEAN\n-117.14,32.92,15.0,3242.0,595.0,1936.0,593.0,4.9706,184700.0,<1H OCEAN\n-117.13,32.92,16.0,1580.0,241.0,917.0,261.0,4.7266,191100.0,<1H OCEAN\n-117.13,32.92,16.0,1565.0,257.0,893.0,239.0,5.5036,192300.0,<1H OCEAN\n-117.13,32.94,15.0,4846.0,825.0,2797.0,823.0,4.9375,180400.0,<1H OCEAN\n-117.13,32.93,16.0,2918.0,444.0,1697.0,444.0,5.3062,195500.0,<1H OCEAN\n-117.14,32.93,14.0,1946.0,463.0,1205.0,390.0,4.2109,171200.0,<1H OCEAN\n-117.14,32.93,12.0,1474.0,364.0,1009.0,372.0,4.0521,166700.0,<1H OCEAN\n-117.14,32.93,16.0,2412.0,419.0,1612.0,422.0,4.5086,171100.0,<1H OCEAN\n-117.12,32.93,7.0,1427.0,243.0,927.0,239.0,5.3625,218900.0,<1H OCEAN\n-117.15,32.93,16.0,2718.0,438.0,1515.0,431.0,5.1433,185300.0,<1H OCEAN\n-117.14,32.92,15.0,1558.0,314.0,949.0,332.0,5.2864,174400.0,<1H OCEAN\n-117.15,32.92,16.0,1972.0,402.0,1377.0,413.0,4.4615,168300.0,<1H OCEAN\n-117.15,32.92,16.0,2366.0,392.0,1482.0,407.0,4.9024,182900.0,<1H OCEAN\n-117.15,32.92,16.0,2969.0,514.0,1594.0,465.0,4.5221,168300.0,<1H OCEAN\n-117.18,32.92,4.0,15025.0,2616.0,7560.0,2392.0,5.196,210700.0,<1H OCEAN\n-117.25,32.96,18.0,4773.0,743.0,1970.0,716.0,6.6199,406200.0,NEAR OCEAN\n-117.26,32.95,15.0,1036.0,149.0,395.0,157.0,5.8343,500001.0,NEAR OCEAN\n-117.26,32.95,15.0,1882.0,233.0,704.0,219.0,6.9794,500001.0,NEAR OCEAN\n-117.25,32.94,16.0,4755.0,807.0,1829.0,756.0,6.7694,425900.0,NEAR OCEAN\n-117.25,32.94,15.0,1804.0,339.0,673.0,296.0,5.9806,370500.0,NEAR OCEAN\n-117.24,32.95,18.0,1591.0,268.0,547.0,243.0,5.9547,329300.0,NEAR OCEAN\n-117.24,32.94,12.0,2165.0,437.0,792.0,386.0,5.2648,294400.0,NEAR OCEAN\n-117.13,32.98,5.0,2276.0,311.0,1158.0,317.0,6.4321,271900.0,<1H OCEAN\n-117.13,32.97,10.0,3486.0,469.0,1700.0,483.0,6.4696,249500.0,<1H OCEAN\n-117.18,32.95,4.0,19001.0,2688.0,8980.0,2441.0,6.3237,260900.0,<1H OCEAN\n-117.14,32.96,12.0,5949.0,799.0,2936.0,781.0,6.3721,241500.0,<1H OCEAN\n-117.13,32.96,15.0,2267.0,292.0,1180.0,289.0,6.712,240200.0,<1H OCEAN\n-117.24,32.98,4.0,6423.0,1042.0,2607.0,983.0,7.6348,337000.0,NEAR OCEAN\n-117.21,32.96,3.0,6251.0,988.0,2330.0,893.0,8.4355,467600.0,NEAR OCEAN\n-117.22,32.95,4.0,18123.0,3173.0,7301.0,2964.0,6.357,322500.0,NEAR OCEAN\n-117.22,32.83,34.0,2936.0,597.0,1512.0,571.0,3.7841,176900.0,NEAR OCEAN\n-117.22,32.83,31.0,3958.0,727.0,1924.0,728.0,5.4605,190200.0,NEAR OCEAN\n-117.22,32.83,17.0,1124.0,187.0,553.0,205.0,5.7451,237300.0,NEAR OCEAN\n-117.22,32.83,31.0,2558.0,512.0,1164.0,492.0,3.4318,200400.0,NEAR OCEAN\n-117.22,32.82,35.0,756.0,135.0,423.0,136.0,3.5234,183900.0,NEAR OCEAN\n-117.2,32.84,34.0,3353.0,544.0,1583.0,571.0,4.55,187700.0,NEAR OCEAN\n-117.2,32.84,32.0,2033.0,394.0,989.0,389.0,3.2583,181400.0,NEAR OCEAN\n-117.21,32.84,34.0,2158.0,366.0,1046.0,335.0,4.5402,182100.0,NEAR OCEAN\n-117.2,32.83,36.0,1089.0,240.0,623.0,226.0,2.5909,176000.0,NEAR OCEAN\n-117.21,32.83,36.0,1475.0,328.0,806.0,327.0,3.5078,166000.0,NEAR OCEAN\n-117.21,32.83,28.0,3241.0,533.0,1334.0,513.0,4.1806,199600.0,NEAR OCEAN\n-117.21,32.83,35.0,2259.0,501.0,1340.0,511.0,3.4482,162500.0,NEAR OCEAN\n-117.21,32.81,33.0,4773.0,873.0,1954.0,845.0,4.3862,184800.0,NEAR OCEAN\n-117.21,32.82,31.0,2035.0,383.0,866.0,360.0,3.8529,212000.0,NEAR OCEAN\n-117.21,32.81,27.0,1318.0,216.0,495.0,191.0,5.2837,283800.0,NEAR OCEAN\n-117.22,32.82,22.0,3738.0,795.0,1476.0,728.0,3.7963,303100.0,NEAR OCEAN\n-117.22,32.81,24.0,730.0,196.0,335.0,203.0,3.5078,362500.0,NEAR OCEAN\n-117.2,32.83,35.0,1377.0,350.0,792.0,313.0,2.8472,161400.0,NEAR OCEAN\n-117.2,32.82,35.0,1217.0,220.0,643.0,237.0,3.9464,171600.0,NEAR OCEAN\n-117.2,32.82,35.0,2772.0,537.0,1392.0,521.0,3.337,172300.0,NEAR OCEAN\n-117.19,32.82,34.0,3850.0,608.0,1619.0,602.0,5.0465,208200.0,NEAR OCEAN\n-117.19,32.82,35.0,1074.0,180.0,442.0,173.0,5.253,204000.0,NEAR OCEAN\n-117.19,32.82,35.0,2197.0,353.0,945.0,357.0,4.9219,192900.0,NEAR OCEAN\n-117.18,32.84,31.0,3064.0,575.0,1476.0,549.0,3.6667,175900.0,NEAR OCEAN\n-117.18,32.84,30.0,2290.0,523.0,1272.0,472.0,3.5606,165100.0,NEAR OCEAN\n-117.18,32.84,32.0,1351.0,237.0,823.0,269.0,4.2768,167800.0,NEAR OCEAN\n-117.19,32.84,30.0,2492.0,406.0,1250.0,431.0,5.5277,197100.0,NEAR OCEAN\n-117.19,32.84,35.0,2263.0,427.0,1001.0,408.0,3.875,172000.0,NEAR OCEAN\n-117.18,32.83,23.0,2105.0,525.0,1218.0,484.0,3.375,184100.0,NEAR OCEAN\n-117.19,32.83,30.0,2288.0,448.0,1240.0,469.0,4.0114,169800.0,NEAR OCEAN\n-117.19,32.83,30.0,3225.0,555.0,1601.0,532.0,4.3317,173300.0,NEAR OCEAN\n-117.17,32.83,24.0,3541.0,530.0,1591.0,530.0,5.3538,212500.0,NEAR OCEAN\n-117.17,32.82,24.0,1623.0,417.0,911.0,397.0,2.7401,198100.0,NEAR OCEAN\n-117.17,32.82,21.0,2869.0,596.0,1471.0,577.0,3.0375,197600.0,NEAR OCEAN\n-117.18,32.83,27.0,2346.0,399.0,1105.0,373.0,4.2708,182800.0,NEAR OCEAN\n-117.18,32.83,31.0,1772.0,353.0,1090.0,350.0,3.9265,162000.0,NEAR OCEAN\n-117.16,32.82,28.0,2291.0,371.0,1098.0,382.0,4.6875,188000.0,NEAR OCEAN\n-117.17,32.81,26.0,2424.0,388.0,974.0,375.0,4.739,184100.0,NEAR OCEAN\n-117.17,32.81,26.0,788.0,127.0,346.0,125.0,5.0603,185700.0,NEAR OCEAN\n-117.18,32.81,19.0,6823.0,1509.0,3784.0,1509.0,3.1032,179500.0,NEAR OCEAN\n-117.17,32.82,24.0,1569.0,377.0,715.0,321.0,3.1146,187500.0,NEAR OCEAN\n-117.16,32.81,14.0,4328.0,1100.0,2046.0,1044.0,2.2899,159000.0,NEAR OCEAN\n-117.16,32.81,35.0,1213.0,200.0,532.0,181.0,3.6806,172400.0,NEAR OCEAN\n-117.16,32.8,37.0,422.0,79.0,211.0,80.0,3.0625,159700.0,NEAR OCEAN\n-117.16,32.8,22.0,2259.0,634.0,1213.0,601.0,2.5,177800.0,NEAR OCEAN\n-117.16,32.81,34.0,2275.0,375.0,1021.0,379.0,3.6371,176300.0,NEAR OCEAN\n-117.17,32.81,33.0,3064.0,506.0,1355.0,488.0,4.22,178700.0,NEAR OCEAN\n-117.14,32.83,25.0,2161.0,462.0,896.0,468.0,2.2284,177500.0,NEAR OCEAN\n-117.18,32.82,25.0,1756.0,301.0,722.0,312.0,4.5625,162300.0,NEAR OCEAN\n-117.18,32.81,28.0,3436.0,537.0,1503.0,498.0,4.7679,204000.0,NEAR OCEAN\n-117.19,32.81,33.0,5226.0,833.0,2221.0,839.0,5.1491,207000.0,NEAR OCEAN\n-117.18,32.8,10.0,3821.0,631.0,1605.0,609.0,5.5454,217100.0,NEAR OCEAN\n-117.18,32.8,30.0,2456.0,390.0,1022.0,393.0,3.8542,198500.0,NEAR OCEAN\n-117.18,32.79,30.0,5201.0,1104.0,2961.0,1064.0,3.2661,140400.0,NEAR OCEAN\n-117.17,32.79,44.0,2262.0,647.0,3009.0,657.0,2.2663,123600.0,NEAR OCEAN\n-117.17,32.79,43.0,1269.0,297.0,946.0,285.0,2.1447,133300.0,NEAR OCEAN\n-117.16,32.8,25.0,1399.0,329.0,1308.0,355.0,2.5682,187500.0,NEAR OCEAN\n-117.17,32.8,20.0,2827.0,554.0,1822.0,536.0,3.4706,157600.0,NEAR OCEAN\n-117.15,32.8,27.0,1937.0,537.0,1211.0,482.0,2.75,87500.0,NEAR OCEAN\n-117.15,32.8,23.0,2395.0,476.0,2284.0,488.0,3.7292,146300.0,NEAR OCEAN\n-117.16,32.78,24.0,3566.0,765.0,1697.0,722.0,3.6375,178600.0,NEAR OCEAN\n-117.16,32.79,32.0,1731.0,413.0,1569.0,427.0,3.3375,154300.0,NEAR OCEAN\n-117.17,32.78,17.0,3845.0,1051.0,3102.0,944.0,2.3658,164100.0,NEAR OCEAN\n-117.16,32.78,34.0,2515.0,488.0,1594.0,515.0,3.7381,165000.0,NEAR OCEAN\n-117.17,32.78,42.0,1104.0,305.0,892.0,270.0,2.2768,145200.0,NEAR OCEAN\n-117.17,32.77,35.0,1399.0,274.0,695.0,281.0,3.767,166800.0,NEAR OCEAN\n-117.17,32.77,6.0,3856.0,875.0,1547.0,816.0,4.5481,164800.0,NEAR OCEAN\n-117.18,32.77,16.0,2374.0,780.0,913.0,705.0,2.7386,87500.0,NEAR OCEAN\n-117.18,32.76,8.0,3694.0,997.0,1297.0,807.0,3.6492,158900.0,NEAR OCEAN\n-117.18,32.76,17.0,711.0,254.0,327.0,227.0,2.6493,67500.0,NEAR OCEAN\n-117.18,32.78,21.0,4185.0,1018.0,3122.0,993.0,3.0481,210000.0,NEAR OCEAN\n-117.18,32.77,23.0,1215.0,225.0,592.0,224.0,3.4,200600.0,NEAR OCEAN\n-117.21,32.81,26.0,2496.0,407.0,1062.0,380.0,5.5413,302100.0,NEAR OCEAN\n-117.2,32.8,34.0,4854.0,912.0,2089.0,854.0,3.8542,200000.0,NEAR OCEAN\n-117.2,32.79,16.0,2079.0,394.0,746.0,383.0,5.0958,300000.0,NEAR OCEAN\n-117.2,32.8,33.0,2573.0,436.0,1084.0,443.0,4.2417,294100.0,NEAR OCEAN\n-117.21,32.8,34.0,1398.0,222.0,532.0,244.0,3.7102,289600.0,NEAR OCEAN\n-117.19,32.8,16.0,2593.0,794.0,1235.0,684.0,3.1304,166300.0,NEAR OCEAN\n-117.2,32.8,36.0,4018.0,1067.0,1620.0,842.0,2.3599,168400.0,NEAR OCEAN\n-117.19,32.79,36.0,1514.0,258.0,665.0,278.0,3.8571,235100.0,NEAR OCEAN\n-117.19,32.79,35.0,1788.0,378.0,777.0,374.0,3.3713,238400.0,NEAR OCEAN\n-117.2,32.79,31.0,3417.0,533.0,1245.0,532.0,4.7788,276000.0,NEAR OCEAN\n-117.2,32.79,29.0,1213.0,,654.0,246.0,4.5987,255600.0,NEAR OCEAN\n-117.2,32.79,34.0,757.0,212.0,409.0,222.0,3.2312,192200.0,NEAR OCEAN\n-117.19,32.78,34.0,4108.0,664.0,1659.0,644.0,4.4097,252000.0,NEAR OCEAN\n-117.2,32.78,38.0,2662.0,498.0,1132.0,496.0,4.0057,241600.0,NEAR OCEAN\n-117.19,32.77,30.0,2747.0,640.0,3185.0,657.0,3.765,238000.0,NEAR OCEAN\n-117.19,32.77,16.0,3273.0,670.0,1305.0,671.0,4.1368,151000.0,NEAR OCEAN\n-117.19,32.77,9.0,634.0,152.0,248.0,133.0,3.8571,143800.0,NEAR OCEAN\n-117.19,32.77,14.0,3575.0,992.0,1645.0,839.0,2.4397,140600.0,NEAR OCEAN\n-117.2,32.77,30.0,156.0,45.0,77.0,40.0,3.2679,137500.0,NEAR OCEAN\n-117.2,32.77,31.0,1952.0,471.0,936.0,462.0,2.8621,196900.0,NEAR OCEAN\n-117.15,32.81,34.0,1629.0,318.0,900.0,282.0,3.1458,178300.0,NEAR OCEAN\n-117.15,32.8,41.0,1413.0,261.0,1070.0,259.0,2.3578,166700.0,NEAR OCEAN\n-117.14,32.8,41.0,2423.0,469.0,1813.0,466.0,2.1157,156900.0,NEAR OCEAN\n-117.14,32.8,33.0,2670.0,435.0,1256.0,431.0,3.9417,179800.0,NEAR OCEAN\n-117.14,32.8,35.0,1267.0,212.0,710.0,204.0,2.5368,169600.0,NEAR OCEAN\n-117.14,32.79,35.0,3578.0,582.0,1568.0,553.0,4.7813,188600.0,NEAR OCEAN\n-117.15,32.78,25.0,1577.0,266.0,611.0,284.0,5.25,205100.0,NEAR OCEAN\n-117.14,32.79,31.0,984.0,161.0,422.0,158.0,5.282,183000.0,NEAR OCEAN\n-117.13,32.81,19.0,2157.0,554.0,1349.0,535.0,2.7652,177400.0,NEAR OCEAN\n-117.14,32.81,34.0,1748.0,294.0,800.0,294.0,4.4886,179100.0,NEAR OCEAN\n-117.13,32.8,15.0,1606.0,375.0,784.0,342.0,3.7237,108300.0,NEAR OCEAN\n-117.13,32.8,33.0,2731.0,456.0,1263.0,445.0,4.5568,175300.0,NEAR OCEAN\n-117.12,32.8,29.0,2863.0,534.0,1392.0,522.0,3.8719,174200.0,NEAR OCEAN\n-117.13,32.81,26.0,2119.0,444.0,1202.0,440.0,3.2308,166500.0,NEAR OCEAN\n-117.12,32.8,31.0,1727.0,342.0,879.0,345.0,3.8125,166300.0,NEAR OCEAN\n-117.13,32.8,35.0,2129.0,382.0,1044.0,350.0,3.9732,174900.0,NEAR OCEAN\n-117.13,32.79,35.0,1458.0,262.0,723.0,257.0,4.2098,174100.0,NEAR OCEAN\n-117.13,32.79,35.0,1362.0,243.0,698.0,255.0,3.6458,173800.0,NEAR OCEAN\n-117.12,32.78,4.0,2782.0,817.0,1309.0,787.0,4.2621,124200.0,NEAR OCEAN\n-117.15,32.77,16.0,2056.0,631.0,847.0,569.0,2.9576,92200.0,NEAR OCEAN\n-117.09,32.81,7.0,6100.0,1185.0,2710.0,1040.0,5.5673,288200.0,NEAR OCEAN\n-117.11,32.82,16.0,3980.0,682.0,3174.0,647.0,2.6607,175000.0,NEAR OCEAN\n-117.11,32.82,17.0,1787.0,330.0,1341.0,314.0,2.875,112500.0,NEAR OCEAN\n-117.11,32.81,15.0,3428.0,491.0,2303.0,486.0,2.5953,67500.0,NEAR OCEAN\n-117.11,32.8,17.0,3890.0,586.0,2791.0,595.0,3.2197,67500.0,NEAR OCEAN\n-117.09,32.8,15.0,666.0,152.0,247.0,164.0,2.15,131300.0,NEAR OCEAN\n-117.11,32.84,16.0,4608.0,629.0,2020.0,636.0,6.04,243000.0,<1H OCEAN\n-117.11,32.82,16.0,4241.0,892.0,1771.0,864.0,4.375,166500.0,NEAR OCEAN\n-117.1,32.83,16.0,1049.0,154.0,467.0,160.0,6.2047,248100.0,<1H OCEAN\n-117.1,32.83,16.0,4214.0,744.0,1820.0,699.0,4.3783,179500.0,<1H OCEAN\n-117.09,32.83,15.0,4138.0,636.0,2001.0,677.0,4.8419,264000.0,<1H OCEAN\n-117.08,32.82,16.0,1787.0,236.0,770.0,228.0,7.1298,278600.0,<1H OCEAN\n-117.08,32.82,10.0,5177.0,856.0,2190.0,816.0,5.9734,271700.0,<1H OCEAN\n-117.08,32.83,7.0,13703.0,2352.0,4446.0,1856.0,6.4335,260600.0,<1H OCEAN\n-117.04,32.83,8.0,2205.0,348.0,777.0,341.0,6.0266,177400.0,<1H OCEAN\n-117.07,32.91,5.0,2234.0,256.0,894.0,253.0,10.3354,477600.0,<1H OCEAN\n-117.04,32.9,6.0,6525.0,826.0,3146.0,806.0,9.2858,436100.0,<1H OCEAN\n-117.08,32.8,25.0,2963.0,552.0,1162.0,556.0,3.625,184500.0,NEAR OCEAN\n-117.09,32.79,20.0,2183.0,534.0,999.0,496.0,2.8631,169700.0,NEAR OCEAN\n-117.09,32.8,36.0,2163.0,367.0,915.0,360.0,4.7188,174100.0,NEAR OCEAN\n-117.08,32.8,32.0,1587.0,268.0,635.0,249.0,3.375,178100.0,NEAR OCEAN\n-117.11,32.79,16.0,2574.0,771.0,1129.0,721.0,3.3849,96900.0,NEAR OCEAN\n-117.11,32.79,16.0,1791.0,518.0,1006.0,491.0,3.5179,129300.0,NEAR OCEAN\n-117.11,32.78,16.0,2470.0,830.0,1170.0,724.0,3.5562,73500.0,NEAR OCEAN\n-117.11,32.78,16.0,2220.0,512.0,930.0,527.0,3.6528,133200.0,NEAR OCEAN\n-117.09,32.79,36.0,1529.0,266.0,683.0,260.0,4.0982,171200.0,NEAR OCEAN\n-117.09,32.79,36.0,1936.0,345.0,861.0,343.0,3.8333,170000.0,NEAR OCEAN\n-117.09,32.79,31.0,2019.0,417.0,872.0,386.0,3.1964,177700.0,NEAR OCEAN\n-117.09,32.78,28.0,1708.0,393.0,816.0,393.0,2.9881,165300.0,NEAR OCEAN\n-117.07,32.8,31.0,2550.0,395.0,1017.0,405.0,5.1488,181000.0,<1H OCEAN\n-117.07,32.8,36.0,2028.0,349.0,820.0,352.0,3.9828,168900.0,<1H OCEAN\n-117.07,32.79,36.0,3583.0,642.0,1711.0,602.0,3.9745,170800.0,NEAR OCEAN\n-117.07,32.8,23.0,2698.0,410.0,1094.0,411.0,5.1782,195100.0,<1H OCEAN\n-117.07,32.81,15.0,2000.0,402.0,778.0,369.0,4.3594,224200.0,<1H OCEAN\n-117.06,32.81,17.0,3939.0,550.0,1694.0,553.0,6.7927,234700.0,<1H OCEAN\n-117.05,32.81,17.0,1885.0,292.0,771.0,301.0,5.6402,228600.0,<1H OCEAN\n-117.05,32.8,17.0,1475.0,308.0,549.0,293.0,3.7167,180400.0,<1H OCEAN\n-117.06,32.8,17.0,2247.0,340.0,973.0,318.0,5.5,222000.0,<1H OCEAN\n-117.07,32.79,25.0,2489.0,314.0,911.0,309.0,7.8336,277600.0,NEAR OCEAN\n-117.07,32.78,26.0,3725.0,623.0,1516.0,627.0,4.7143,268300.0,NEAR OCEAN\n-117.08,32.78,21.0,2919.0,496.0,984.0,443.0,4.625,222800.0,NEAR OCEAN\n-117.04,32.79,23.0,2491.0,350.0,863.0,348.0,6.7196,306800.0,<1H OCEAN\n-117.04,32.8,25.0,2504.0,345.0,1067.0,350.0,5.7416,243400.0,<1H OCEAN\n-117.05,32.8,16.0,1561.0,378.0,574.0,350.0,3.0035,94600.0,<1H OCEAN\n-117.05,32.8,25.0,1905.0,250.0,865.0,253.0,6.4797,249000.0,<1H OCEAN\n-117.05,32.8,23.0,3309.0,401.0,1116.0,386.0,7.916,330600.0,<1H OCEAN\n-117.06,32.79,17.0,2524.0,332.0,771.0,317.0,8.7604,331800.0,<1H OCEAN\n-117.06,32.79,21.0,3787.0,492.0,1246.0,457.0,9.6023,391300.0,<1H OCEAN\n-117.06,32.78,31.0,2485.0,436.0,942.0,448.0,4.8,220800.0,NEAR OCEAN\n-117.02,32.8,27.0,2369.0,370.0,927.0,374.0,4.1162,177200.0,<1H OCEAN\n-117.03,32.79,26.0,3859.0,513.0,1469.0,538.0,5.8683,220500.0,<1H OCEAN\n-117.03,32.8,19.0,3866.0,775.0,1554.0,703.0,4.3281,220000.0,<1H OCEAN\n-117.04,32.8,11.0,1802.0,440.0,630.0,428.0,2.0337,146700.0,<1H OCEAN\n-117.01,32.8,17.0,1042.0,210.0,650.0,215.0,3.1,84200.0,<1H OCEAN\n-117.01,32.8,17.0,1558.0,479.0,803.0,431.0,2.6934,160200.0,<1H OCEAN\n-117.01,32.8,20.0,2705.0,545.0,1198.0,497.0,3.7159,168900.0,<1H OCEAN\n-117.01,32.79,33.0,4015.0,663.0,1864.0,664.0,4.3152,159300.0,<1H OCEAN\n-117.02,32.8,29.0,1232.0,243.0,665.0,247.0,3.65,168900.0,<1H OCEAN\n-117.02,32.8,31.0,2692.0,445.0,1129.0,450.0,4.4583,170000.0,<1H OCEAN\n-117.01,32.81,26.0,4499.0,645.0,1866.0,626.0,5.516,185100.0,<1H OCEAN\n-117.02,32.81,27.0,1950.0,317.0,950.0,320.0,4.0656,164000.0,<1H OCEAN\n-117.02,32.81,26.0,1998.0,301.0,874.0,305.0,5.4544,180900.0,<1H OCEAN\n-117.01,32.81,21.0,4203.0,618.0,1620.0,600.0,5.3441,193500.0,<1H OCEAN\n-117.02,32.81,14.0,3173.0,599.0,1451.0,585.0,3.7292,182200.0,<1H OCEAN\n-117.03,32.82,16.0,1765.0,289.0,743.0,280.0,4.9744,209700.0,<1H OCEAN\n-117.04,32.81,12.0,2880.0,406.0,1381.0,418.0,6.5412,254200.0,<1H OCEAN\n-117.05,32.82,16.0,4046.0,731.0,1684.0,701.0,4.2312,197000.0,<1H OCEAN\n-117.05,32.59,26.0,1919.0,345.0,1326.0,341.0,4.2679,131900.0,NEAR OCEAN\n-117.06,32.59,13.0,3920.0,775.0,2814.0,760.0,4.0616,148800.0,NEAR OCEAN\n-117.05,32.58,22.0,2101.0,399.0,1551.0,371.0,4.1518,136900.0,NEAR OCEAN\n-117.06,32.58,17.0,2724.0,567.0,2213.0,554.0,3.8529,147700.0,NEAR OCEAN\n-117.06,32.58,13.0,3435.0,708.0,1761.0,699.0,3.4792,107600.0,NEAR OCEAN\n-117.06,32.58,11.0,2879.0,679.0,2098.0,673.0,3.5125,142400.0,NEAR OCEAN\n-117.06,32.57,16.0,1269.0,282.0,1609.0,298.0,2.6985,156500.0,NEAR OCEAN\n-117.04,32.58,14.0,2355.0,406.0,1883.0,401.0,5.0311,152100.0,NEAR OCEAN\n-117.04,32.58,20.0,2029.0,357.0,1497.0,353.0,4.0089,132100.0,NEAR OCEAN\n-117.05,32.58,23.0,1918.0,339.0,1392.0,340.0,4.087,134800.0,NEAR OCEAN\n-117.05,32.58,25.0,2185.0,370.0,1558.0,369.0,5.3072,132700.0,NEAR OCEAN\n-117.05,32.57,22.0,2857.0,516.0,2412.0,496.0,4.7337,127600.0,NEAR OCEAN\n-117.06,32.57,25.0,1268.0,282.0,991.0,299.0,3.0284,123600.0,NEAR OCEAN\n-117.06,32.57,17.0,2252.0,378.0,1776.0,365.0,4.6364,141100.0,NEAR OCEAN\n-117.05,32.57,13.0,2880.0,576.0,2450.0,567.0,3.1696,138000.0,NEAR OCEAN\n-117.05,32.56,18.0,1215.0,320.0,1195.0,349.0,1.9875,114900.0,NEAR OCEAN\n-117.05,32.56,17.0,985.0,233.0,811.0,223.0,2.875,134500.0,NEAR OCEAN\n-117.06,32.57,18.0,1384.0,311.0,1429.0,287.0,1.3362,95000.0,NEAR OCEAN\n-116.97,32.56,23.0,1262.0,294.0,5176.0,275.0,2.5625,153300.0,NEAR OCEAN\n-117.04,32.55,15.0,2206.0,648.0,2511.0,648.0,1.6348,93200.0,NEAR OCEAN\n-117.05,32.56,22.0,2172.0,563.0,2049.0,524.0,2.0159,139300.0,NEAR OCEAN\n-117.06,32.56,17.0,2803.0,683.0,2768.0,676.0,1.7958,140400.0,NEAR OCEAN\n-117.06,32.56,5.0,2706.0,925.0,3148.0,855.0,1.7301,125000.0,NEAR OCEAN\n-117.06,32.55,5.0,3223.0,940.0,3284.0,854.0,1.4384,108800.0,NEAR OCEAN\n-117.04,32.54,7.0,938.0,297.0,1187.0,282.0,1.2667,67500.0,NEAR OCEAN\n-117.1,32.59,21.0,2350.0,667.0,1621.0,613.0,2.0734,87500.0,NEAR OCEAN\n-117.1,32.58,23.0,1662.0,377.0,1318.0,386.0,2.3,120800.0,NEAR OCEAN\n-117.1,32.58,17.0,2046.0,559.0,1585.0,530.0,2.25,132800.0,NEAR OCEAN\n-117.1,32.58,33.0,393.0,76.0,330.0,80.0,4.1029,122700.0,NEAR OCEAN\n-117.1,32.58,29.0,1061.0,202.0,759.0,206.0,4.8646,136800.0,NEAR OCEAN\n-117.1,32.57,26.0,2343.0,371.0,1221.0,372.0,4.3601,144900.0,NEAR OCEAN\n-117.1,32.56,16.0,2687.0,501.0,1502.0,480.0,3.75,146800.0,NEAR OCEAN\n-117.07,32.57,17.0,2961.0,634.0,1911.0,615.0,2.5859,131400.0,NEAR OCEAN\n-117.08,32.57,18.0,2203.0,544.0,1943.0,497.0,2.25,103200.0,NEAR OCEAN\n-117.07,32.57,14.0,1527.0,357.0,1224.0,363.0,2.7361,93600.0,NEAR OCEAN\n-117.07,32.56,9.0,3648.0,895.0,3293.0,840.0,3.0992,142600.0,NEAR OCEAN\n-117.08,32.59,8.0,2888.0,662.0,2441.0,683.0,2.7048,153000.0,NEAR OCEAN\n-117.08,32.58,15.0,1462.0,274.0,1002.0,271.0,3.9698,142700.0,NEAR OCEAN\n-117.08,32.58,22.0,2128.0,477.0,1420.0,450.0,3.2687,131000.0,NEAR OCEAN\n-117.07,32.58,25.0,1607.0,280.0,899.0,260.0,3.8194,134400.0,NEAR OCEAN\n-117.09,32.58,12.0,2565.0,567.0,1785.0,545.0,3.0273,135300.0,NEAR OCEAN\n-117.08,32.57,9.0,6298.0,1512.0,4451.0,1456.0,2.569,88300.0,NEAR OCEAN\n-117.09,32.57,23.0,1817.0,323.0,1371.0,327.0,3.6736,139500.0,NEAR OCEAN\n-117.09,32.57,10.0,2198.0,368.0,1645.0,350.0,4.5547,160700.0,NEAR OCEAN\n-117.09,32.57,17.0,444.0,83.0,357.0,87.0,5.1478,138900.0,NEAR OCEAN\n-117.09,32.56,8.0,864.0,156.0,626.0,172.0,4.8984,151500.0,NEAR OCEAN\n-117.09,32.55,8.0,6533.0,1217.0,4797.0,1177.0,3.9583,144400.0,NEAR OCEAN\n-117.12,32.58,34.0,2003.0,466.0,1226.0,443.0,3.0613,136700.0,NEAR OCEAN\n-117.12,32.57,21.0,1738.0,295.0,983.0,298.0,4.8274,174100.0,NEAR OCEAN\n-117.13,32.58,27.0,2511.0,615.0,1427.0,576.0,3.1645,156000.0,NEAR OCEAN\n-117.13,32.58,32.0,1870.0,437.0,1142.0,426.0,2.3194,159400.0,NEAR OCEAN\n-117.13,32.58,27.0,1417.0,373.0,814.0,348.0,2.3603,195300.0,NEAR OCEAN\n-117.12,32.56,20.0,2524.0,682.0,1819.0,560.0,2.9286,257700.0,NEAR OCEAN\n-117.11,32.58,28.0,1869.0,407.0,1074.0,344.0,2.5988,135600.0,NEAR OCEAN\n-117.11,32.57,32.0,2723.0,586.0,1702.0,562.0,3.3371,140500.0,NEAR OCEAN\n-117.12,32.57,35.0,1450.0,256.0,930.0,286.0,2.6715,133300.0,NEAR OCEAN\n-117.12,32.58,26.0,1360.0,309.0,869.0,328.0,3.0217,131600.0,NEAR OCEAN\n-117.11,32.58,21.0,2894.0,685.0,2109.0,712.0,2.2755,125000.0,NEAR OCEAN\n-117.1,32.58,27.0,2616.0,591.0,1889.0,577.0,2.3824,127600.0,NEAR OCEAN\n-117.1,32.57,14.0,5058.0,1299.0,3662.0,1193.0,2.3253,133700.0,NEAR OCEAN\n-117.11,32.59,17.0,2020.0,534.0,1529.0,500.0,2.1773,143200.0,NEAR OCEAN\n-117.11,32.58,12.0,1086.0,294.0,870.0,290.0,2.4213,132500.0,NEAR OCEAN\n-117.11,32.59,18.0,2329.0,580.0,1538.0,567.0,2.1179,153100.0,NEAR OCEAN\n-117.12,32.59,28.0,2793.0,706.0,1825.0,676.0,2.6724,144500.0,NEAR OCEAN\n-117.16,32.58,36.0,1940.0,399.0,1076.0,382.0,3.3906,147800.0,NEAR OCEAN\n-117.13,32.63,10.0,7374.0,1157.0,1900.0,794.0,8.7991,478500.0,NEAR OCEAN\n-117.17,32.63,26.0,1617.0,279.0,2745.0,250.0,3.5357,67500.0,NEAR OCEAN\n-117.17,32.68,16.0,5895.0,1424.0,873.0,522.0,7.3669,187500.0,NEAR OCEAN\n-117.18,32.68,29.0,1539.0,344.0,556.0,289.0,3.25,500001.0,NEAR OCEAN\n-117.18,32.69,52.0,1837.0,313.0,668.0,300.0,5.1009,500001.0,NEAR OCEAN\n-117.18,32.69,37.0,3112.0,716.0,1304.0,674.0,3.2121,320800.0,NEAR OCEAN\n-117.18,32.69,44.0,2819.0,514.0,1258.0,503.0,4.4777,452800.0,NEAR OCEAN\n-117.17,32.69,40.0,2236.0,331.0,767.0,316.0,5.3177,500001.0,NEAR OCEAN\n-117.18,32.69,48.0,2764.0,491.0,978.0,449.0,5.1249,432400.0,NEAR OCEAN\n-117.17,32.69,19.0,2802.0,802.0,1159.0,597.0,4.7891,334600.0,NEAR OCEAN\n-117.17,32.69,45.0,3168.0,598.0,1341.0,562.0,4.5189,422200.0,NEAR OCEAN\n-117.17,32.7,33.0,4084.0,897.0,1804.0,833.0,4.0488,409700.0,NEAR OCEAN\n-117.18,32.7,44.0,2655.0,514.0,1102.0,489.0,3.6759,368800.0,NEAR OCEAN\n-117.18,32.7,42.0,1691.0,286.0,761.0,281.0,5.1386,404500.0,NEAR OCEAN\n-117.19,32.69,35.0,2921.0,438.0,1042.0,415.0,6.3612,482700.0,NEAR OCEAN\n-117.11,32.68,36.0,26.0,14.0,58.0,23.0,1.9107,125000.0,NEAR OCEAN\n-117.11,32.67,43.0,515.0,146.0,445.0,140.0,1.6094,93000.0,NEAR OCEAN\n-117.11,32.67,46.0,928.0,236.0,790.0,235.0,1.6806,92500.0,NEAR OCEAN\n-117.11,32.67,52.0,204.0,74.0,248.0,57.0,1.7961,47500.0,NEAR OCEAN\n-117.12,32.66,52.0,16.0,4.0,8.0,3.0,1.125,60000.0,NEAR OCEAN\n-117.11,32.67,52.0,280.0,71.0,217.0,71.0,1.4844,83300.0,NEAR OCEAN\n-117.11,32.66,52.0,25.0,5.0,14.0,9.0,1.625,118800.0,NEAR OCEAN\n-117.09,32.67,37.0,1157.0,332.0,983.0,306.0,2.0972,117000.0,NEAR OCEAN\n-117.09,32.66,38.0,833.0,206.0,570.0,182.0,1.8333,127100.0,NEAR OCEAN\n-117.09,32.66,37.0,1232.0,330.0,1086.0,330.0,1.6389,114300.0,NEAR OCEAN\n-117.1,32.66,27.0,1782.0,560.0,1785.0,560.0,2.1542,106300.0,NEAR OCEAN\n-117.1,32.67,26.0,2629.0,763.0,2721.0,767.0,2.0982,109100.0,NEAR OCEAN\n-117.1,32.67,22.0,1690.0,541.0,1669.0,494.0,2.0213,110600.0,NEAR OCEAN\n-117.09,32.68,30.0,2662.0,653.0,1997.0,605.0,2.8089,120600.0,NEAR OCEAN\n-117.09,32.67,31.0,2051.0,549.0,1581.0,538.0,2.052,108900.0,NEAR OCEAN\n-117.1,32.67,15.0,1635.0,553.0,1347.0,597.0,1.2745,92900.0,NEAR OCEAN\n-117.1,32.68,20.0,1012.0,269.0,837.0,240.0,2.0488,88900.0,NEAR OCEAN\n-117.1,32.69,29.0,4174.0,1195.0,3675.0,1124.0,1.8112,103600.0,NEAR OCEAN\n-117.1,32.68,42.0,2013.0,568.0,1920.0,557.0,2.0724,107600.0,NEAR OCEAN\n-117.1,32.68,47.0,771.0,224.0,637.0,212.0,2.0156,90300.0,NEAR OCEAN\n-117.1,32.68,49.0,1412.0,350.0,1200.0,332.0,2.0398,93600.0,NEAR OCEAN\n-117.1,32.68,47.0,1044.0,274.0,1003.0,280.0,1.7802,97800.0,NEAR OCEAN\n-117.1,32.68,45.0,1183.0,289.0,900.0,266.0,2.4943,99600.0,NEAR OCEAN\n-117.07,32.69,29.0,1429.0,293.0,1091.0,317.0,3.4609,118000.0,NEAR OCEAN\n-117.08,32.69,31.0,2558.0,487.0,1938.0,492.0,3.4875,117000.0,NEAR OCEAN\n-117.08,32.69,36.0,1571.0,284.0,1001.0,268.0,3.6875,111400.0,NEAR OCEAN\n-117.09,32.69,34.0,1469.0,267.0,1031.0,267.0,3.4583,112700.0,NEAR OCEAN\n-117.09,32.68,29.0,1792.0,449.0,1650.0,396.0,2.2201,100000.0,NEAR OCEAN\n-117.07,32.69,28.0,1485.0,275.0,820.0,283.0,4.069,153300.0,NEAR OCEAN\n-117.08,32.68,26.0,3071.0,615.0,2156.0,568.0,2.9318,112400.0,NEAR OCEAN\n-117.08,32.68,19.0,3635.0,1078.0,3127.0,1098.0,1.324,122600.0,NEAR OCEAN\n-117.09,32.68,20.0,2569.0,737.0,2341.0,705.0,2.0114,104900.0,NEAR OCEAN\n-117.07,32.67,28.0,2758.0,623.0,2179.0,631.0,2.3814,112300.0,NEAR OCEAN\n-117.08,32.67,31.0,3008.0,764.0,2088.0,757.0,2.5662,118200.0,NEAR OCEAN\n-117.09,32.66,46.0,844.0,147.0,423.0,161.0,3.375,136300.0,NEAR OCEAN\n-117.08,32.66,43.0,1004.0,236.0,839.0,235.0,2.81,103400.0,NEAR OCEAN\n-117.07,32.67,35.0,3200.0,725.0,1723.0,610.0,1.8977,95600.0,NEAR OCEAN\n-117.07,32.65,12.0,4131.0,891.0,2272.0,840.0,3.4701,204900.0,NEAR OCEAN\n-117.07,32.64,32.0,5135.0,1025.0,2152.0,944.0,4.1325,172800.0,NEAR OCEAN\n-117.08,32.64,43.0,1005.0,230.0,548.0,252.0,1.8672,145800.0,NEAR OCEAN\n-117.08,32.65,17.0,2633.0,712.0,1487.0,694.0,2.5392,147000.0,NEAR OCEAN\n-117.08,32.64,38.0,917.0,256.0,494.0,233.0,1.9241,150000.0,NEAR OCEAN\n-117.08,32.64,11.0,1651.0,533.0,947.0,515.0,1.6806,141700.0,NEAR OCEAN\n-117.09,32.65,20.0,1445.0,323.0,573.0,334.0,2.619,145800.0,NEAR OCEAN\n-117.09,32.65,25.0,3509.0,985.0,2359.0,899.0,2.6296,150000.0,NEAR OCEAN\n-117.08,32.65,28.0,2296.0,603.0,1277.0,550.0,2.3562,123800.0,NEAR OCEAN\n-117.09,32.64,38.0,2095.0,536.0,1240.0,550.0,2.7218,145900.0,NEAR OCEAN\n-117.09,32.64,30.0,3171.0,862.0,2126.0,800.0,2.507,142700.0,NEAR OCEAN\n-117.1,32.64,29.0,1578.0,460.0,1236.0,461.0,2.5658,134700.0,NEAR OCEAN\n-117.09,32.64,20.0,1999.0,651.0,1302.0,592.0,1.6321,57500.0,NEAR OCEAN\n-117.09,32.64,19.0,2571.0,791.0,1205.0,783.0,1.62,131300.0,NEAR OCEAN\n-117.09,32.63,27.0,2920.0,770.0,1935.0,746.0,2.4148,67500.0,NEAR OCEAN\n-117.11,32.64,23.0,1619.0,447.0,1025.0,415.0,1.858,67500.0,NEAR OCEAN\n-117.11,32.62,27.0,1846.0,509.0,1078.0,482.0,2.1719,131500.0,NEAR OCEAN\n-117.09,32.62,37.0,1538.0,298.0,867.0,285.0,3.0729,128700.0,NEAR OCEAN\n-117.09,32.62,37.0,1925.0,428.0,1344.0,426.0,2.4866,129700.0,NEAR OCEAN\n-117.09,32.62,34.0,1576.0,364.0,1153.0,381.0,2.1955,129700.0,NEAR OCEAN\n-117.08,32.63,33.0,2891.0,793.0,1607.0,754.0,2.1281,139800.0,NEAR OCEAN\n-117.09,32.64,24.0,3613.0,973.0,2002.0,931.0,1.947,147500.0,NEAR OCEAN\n-117.09,32.63,33.0,620.0,161.0,420.0,164.0,1.8417,150000.0,NEAR OCEAN\n-117.07,32.64,38.0,1486.0,269.0,745.0,295.0,4.6477,150400.0,NEAR OCEAN\n-117.07,32.63,37.0,2303.0,379.0,1026.0,357.0,3.455,156900.0,NEAR OCEAN\n-117.07,32.64,30.0,2873.0,774.0,1593.0,731.0,2.24,129500.0,NEAR OCEAN\n-117.06,32.63,37.0,1326.0,234.0,612.0,240.0,4.125,160200.0,NEAR OCEAN\n-117.06,32.62,36.0,786.0,125.0,408.0,138.0,3.9167,189700.0,NEAR OCEAN\n-117.07,32.63,40.0,1706.0,322.0,796.0,303.0,3.5583,154900.0,NEAR OCEAN\n-117.07,32.63,37.0,2372.0,444.0,1056.0,419.0,3.7583,145500.0,NEAR OCEAN\n-117.08,32.63,30.0,2504.0,559.0,1827.0,490.0,2.6146,159400.0,NEAR OCEAN\n-117.08,32.63,28.0,2080.0,427.0,1266.0,434.0,2.2788,146300.0,NEAR OCEAN\n-117.08,32.62,28.0,2468.0,506.0,1353.0,522.0,3.0771,158600.0,NEAR OCEAN\n-117.08,32.62,36.0,1674.0,309.0,818.0,307.0,3.4773,150400.0,NEAR OCEAN\n-117.09,32.61,23.0,1157.0,309.0,640.0,313.0,2.1548,118800.0,NEAR OCEAN\n-117.09,32.61,21.0,1945.0,430.0,1335.0,419.0,3.6467,113000.0,NEAR OCEAN\n-117.08,32.62,16.0,5192.0,1381.0,3261.0,1321.0,2.2685,151900.0,NEAR OCEAN\n-117.08,32.61,27.0,2264.0,525.0,1485.0,468.0,3.3514,149100.0,NEAR OCEAN\n-117.07,32.62,19.0,5016.0,1173.0,2750.0,1081.0,2.7838,155900.0,NEAR OCEAN\n-117.06,32.61,34.0,4325.0,1015.0,2609.0,979.0,2.8489,128300.0,NEAR OCEAN\n-117.07,32.61,22.0,5016.0,1331.0,3222.0,1196.0,2.1441,135500.0,NEAR OCEAN\n-117.07,32.6,18.0,2602.0,551.0,1042.0,550.0,1.9267,67500.0,NEAR OCEAN\n-117.07,32.59,21.0,1779.0,466.0,1327.0,488.0,1.6007,96200.0,NEAR OCEAN\n-117.08,32.59,30.0,144.0,52.0,220.0,48.0,2.3929,134400.0,NEAR OCEAN\n-117.08,32.6,24.0,1901.0,490.0,1334.0,476.0,2.2544,121900.0,NEAR OCEAN\n-117.07,32.61,10.0,1686.0,414.0,1000.0,391.0,2.1765,128400.0,NEAR OCEAN\n-117.06,32.61,24.0,4369.0,1353.0,3123.0,1247.0,2.0571,152300.0,NEAR OCEAN\n-117.06,32.61,23.0,1630.0,362.0,1267.0,418.0,2.5625,131100.0,NEAR OCEAN\n-117.07,32.6,13.0,1607.0,435.0,983.0,400.0,2.2903,106300.0,NEAR OCEAN\n-117.06,32.6,25.0,1075.0,238.0,434.0,234.0,1.7472,94600.0,NEAR OCEAN\n-117.06,32.6,24.0,1088.0,268.0,1095.0,246.0,2.4191,107300.0,NEAR OCEAN\n-117.06,32.6,33.0,905.0,205.0,989.0,222.0,2.7014,108200.0,NEAR OCEAN\n-117.05,32.63,31.0,4911.0,861.0,2334.0,843.0,4.1958,160100.0,NEAR OCEAN\n-117.05,32.62,34.0,3928.0,686.0,2315.0,681.0,4.2851,144500.0,NEAR OCEAN\n-117.04,32.62,26.0,3620.0,607.0,2000.0,593.0,4.9962,156000.0,NEAR OCEAN\n-117.05,32.61,31.0,4033.0,715.0,2585.0,715.0,3.5096,139900.0,NEAR OCEAN\n-117.05,32.61,21.0,6034.0,1205.0,3795.0,1146.0,3.2633,129700.0,NEAR OCEAN\n-117.05,32.61,26.0,1563.0,286.0,1145.0,313.0,3.8615,139300.0,NEAR OCEAN\n-117.04,32.6,20.0,8052.0,1461.0,5094.0,1430.0,4.2241,139800.0,NEAR OCEAN\n-117.04,32.6,18.0,4747.0,846.0,3002.0,872.0,3.9076,152900.0,NEAR OCEAN\n-117.05,32.59,16.0,4683.0,929.0,3073.0,865.0,3.0495,98300.0,NEAR OCEAN\n-117.04,32.63,26.0,2074.0,356.0,1228.0,335.0,4.1154,160200.0,NEAR OCEAN\n-117.04,32.62,27.0,1710.0,282.0,1089.0,297.0,4.6793,151900.0,NEAR OCEAN\n-117.03,32.61,23.0,1553.0,216.0,778.0,229.0,5.1538,171300.0,NEAR OCEAN\n-117.03,32.61,22.0,1028.0,148.0,523.0,152.0,6.0086,166900.0,NEAR OCEAN\n-117.03,32.6,26.0,1335.0,224.0,742.0,215.0,5.152,143400.0,NEAR OCEAN\n-117.02,32.59,19.0,1902.0,335.0,1102.0,313.0,3.0365,98100.0,NEAR OCEAN\n-116.98,32.62,6.0,7995.0,1458.0,4771.0,1376.0,4.7068,184300.0,NEAR OCEAN\n-117.06,32.64,30.0,4494.0,667.0,1883.0,680.0,5.766,186100.0,NEAR OCEAN\n-117.06,32.63,29.0,4168.0,742.0,2096.0,713.0,4.2204,169800.0,NEAR OCEAN\n-116.98,32.68,22.0,2346.0,380.0,1217.0,382.0,5.5248,156300.0,NEAR OCEAN\n-117.01,32.67,17.0,2319.0,348.0,1125.0,337.0,5.551,266900.0,NEAR OCEAN\n-117.0,32.67,16.0,2238.0,307.0,1002.0,303.0,6.6143,264100.0,NEAR OCEAN\n-117.02,32.67,20.0,1505.0,184.0,635.0,182.0,6.5772,245200.0,NEAR OCEAN\n-117.01,32.66,11.0,9992.0,1368.0,4495.0,1316.0,6.9664,293900.0,NEAR OCEAN\n-117.02,32.66,19.0,771.0,,376.0,108.0,6.6272,273600.0,NEAR OCEAN\n-116.97,32.65,4.0,16450.0,2833.0,7985.0,2683.0,5.6631,233400.0,NEAR OCEAN\n-116.99,32.64,15.0,4331.0,699.0,2046.0,627.0,3.9519,193500.0,NEAR OCEAN\n-117.04,32.65,8.0,8806.0,1401.0,3159.0,1059.0,4.2155,247800.0,NEAR OCEAN\n-117.03,32.65,14.0,1111.0,142.0,472.0,145.0,7.6344,290500.0,NEAR OCEAN\n-117.0,32.64,11.0,3098.0,490.0,1391.0,484.0,4.9792,170400.0,NEAR OCEAN\n-117.01,32.63,7.0,6483.0,976.0,3269.0,1005.0,5.7358,221600.0,NEAR OCEAN\n-117.04,32.64,5.0,2329.0,542.0,1213.0,514.0,4.0298,225600.0,NEAR OCEAN\n-117.02,32.64,5.0,260.0,41.0,157.0,42.0,6.5151,281700.0,NEAR OCEAN\n-117.03,32.63,13.0,2087.0,313.0,1165.0,330.0,5.7789,227700.0,NEAR OCEAN\n-117.03,32.63,14.0,2796.0,476.0,1466.0,464.0,5.2489,213700.0,NEAR OCEAN\n-117.04,32.63,26.0,2756.0,422.0,1166.0,398.0,5.1354,181600.0,NEAR OCEAN\n-116.99,32.74,17.0,3101.0,547.0,1410.0,486.0,3.1771,189900.0,<1H OCEAN\n-116.98,32.74,17.0,3943.0,843.0,1995.0,766.0,2.6944,158300.0,<1H OCEAN\n-116.98,32.74,24.0,977.0,147.0,454.0,169.0,4.9286,173700.0,<1H OCEAN\n-116.98,32.74,16.0,3361.0,537.0,1754.0,578.0,5.1098,162300.0,<1H OCEAN\n-116.98,32.72,15.0,4209.0,680.0,1914.0,641.0,4.5135,158300.0,<1H OCEAN\n-116.98,32.73,16.0,952.0,143.0,530.0,143.0,5.0864,175300.0,<1H OCEAN\n-116.98,32.72,4.0,1078.0,158.0,571.0,184.0,4.6667,223300.0,<1H OCEAN\n-116.97,32.74,14.0,7410.0,1344.0,3597.0,1274.0,4.2192,176100.0,<1H OCEAN\n-116.95,32.74,7.0,2722.0,578.0,1429.0,574.0,3.9583,141700.0,<1H OCEAN\n-116.96,32.71,18.0,2413.0,533.0,1129.0,551.0,2.4567,155000.0,<1H OCEAN\n-116.95,32.73,17.0,1635.0,272.0,960.0,279.0,5.2671,157100.0,<1H OCEAN\n-116.97,32.76,26.0,2460.0,313.0,838.0,299.0,5.9878,270700.0,<1H OCEAN\n-116.98,32.75,25.0,4137.0,662.0,1905.0,630.0,4.375,214000.0,<1H OCEAN\n-116.97,32.75,28.0,3519.0,583.0,1720.0,590.0,4.7973,186900.0,<1H OCEAN\n-116.98,32.75,18.0,1519.0,369.0,802.0,347.0,2.9886,170800.0,<1H OCEAN\n-116.97,32.76,33.0,3071.0,466.0,1348.0,513.0,6.1768,228900.0,<1H OCEAN\n-116.95,32.76,13.0,5543.0,857.0,2074.0,737.0,4.9528,266200.0,<1H OCEAN\n-116.94,32.75,4.0,14934.0,2479.0,6945.0,2418.0,5.1221,229700.0,<1H OCEAN\n-116.92,32.76,9.0,1859.0,307.0,947.0,304.0,5.9202,181300.0,<1H OCEAN\n-116.92,32.76,7.0,1659.0,237.0,862.0,242.0,5.2741,249400.0,<1H OCEAN\n-116.91,32.75,5.0,8710.0,1614.0,4372.0,1527.0,4.7813,240900.0,<1H OCEAN\n-117.0,32.76,31.0,1989.0,280.0,805.0,301.0,6.5645,189100.0,<1H OCEAN\n-116.99,32.76,21.0,3833.0,595.0,1645.0,589.0,4.625,273500.0,<1H OCEAN\n-116.99,32.74,18.0,3341.0,611.0,1952.0,602.0,3.9844,215300.0,<1H OCEAN\n-117.0,32.74,17.0,2357.0,599.0,1423.0,510.0,1.8856,118800.0,<1H OCEAN\n-116.99,32.73,30.0,1856.0,339.0,1103.0,379.0,4.0357,153800.0,<1H OCEAN\n-117.01,32.75,26.0,4038.0,706.0,2065.0,687.0,3.9545,178100.0,<1H OCEAN\n-117.01,32.75,34.0,2105.0,340.0,973.0,357.0,4.3088,152500.0,<1H OCEAN\n-117.01,32.74,31.0,3473.0,,2098.0,677.0,2.6973,135200.0,<1H OCEAN\n-117.01,32.73,22.0,2526.0,530.0,1556.0,529.0,2.8646,120800.0,NEAR OCEAN\n-117.0,32.73,17.0,6050.0,1143.0,3424.0,1131.0,3.7647,127600.0,<1H OCEAN\n-117.0,32.72,10.0,3817.0,943.0,2352.0,875.0,2.1362,143200.0,NEAR OCEAN\n-117.01,32.72,12.0,2914.0,734.0,2104.0,703.0,2.3068,132300.0,NEAR OCEAN\n-116.99,32.7,15.0,3660.0,622.0,2629.0,612.0,4.0444,150100.0,NEAR OCEAN\n-117.0,32.7,23.0,2785.0,468.0,1456.0,449.0,4.3714,131000.0,NEAR OCEAN\n-116.98,32.71,18.0,2355.0,444.0,1277.0,433.0,3.4551,121400.0,<1H OCEAN\n-116.99,32.71,21.0,3049.0,582.0,2355.0,585.0,3.8904,113800.0,NEAR OCEAN\n-117.0,32.71,22.0,2263.0,441.0,1395.0,416.0,3.725,123500.0,NEAR OCEAN\n-117.01,32.71,20.0,3506.0,692.0,1977.0,668.0,2.981,129100.0,NEAR OCEAN\n-116.99,32.72,15.0,825.0,130.0,334.0,131.0,4.0391,169500.0,<1H OCEAN\n-116.99,32.72,14.0,1771.0,301.0,1046.0,284.0,4.775,143300.0,<1H OCEAN\n-116.99,32.72,13.0,1330.0,216.0,719.0,215.0,3.8295,149600.0,<1H OCEAN\n-116.99,32.72,11.0,1112.0,164.0,441.0,174.0,4.7679,169500.0,<1H OCEAN\n-117.02,32.74,30.0,4205.0,772.0,2012.0,734.0,3.5,144700.0,NEAR OCEAN\n-117.03,32.73,34.0,2061.0,,1169.0,400.0,3.5096,142000.0,NEAR OCEAN\n-117.03,32.73,32.0,1750.0,333.0,997.0,335.0,3.4784,154400.0,NEAR OCEAN\n-117.03,32.74,37.0,821.0,150.0,404.0,135.0,3.0125,130400.0,NEAR OCEAN\n-117.02,32.73,22.0,5201.0,865.0,3280.0,817.0,4.7952,141400.0,NEAR OCEAN\n-117.02,32.72,36.0,2030.0,369.0,1142.0,357.0,3.7763,126900.0,NEAR OCEAN\n-117.03,32.73,38.0,3174.0,606.0,1557.0,619.0,3.5861,123600.0,NEAR OCEAN\n-117.03,32.72,38.0,886.0,176.0,505.0,188.0,3.5938,125400.0,NEAR OCEAN\n-117.03,32.72,37.0,2192.0,455.0,1515.0,446.0,3.0588,120600.0,NEAR OCEAN\n-117.04,32.72,24.0,5474.0,955.0,3020.0,904.0,4.0813,137000.0,NEAR OCEAN\n-117.04,32.73,36.0,2084.0,400.0,1097.0,398.0,3.2717,130700.0,NEAR OCEAN\n-117.04,32.73,25.0,1375.0,267.0,1032.0,278.0,3.5492,125400.0,NEAR OCEAN\n-117.05,32.72,35.0,3669.0,617.0,1694.0,585.0,3.9485,133900.0,NEAR OCEAN\n-117.05,32.73,27.0,3184.0,588.0,1763.0,571.0,3.5529,133900.0,NEAR OCEAN\n-117.03,32.74,35.0,1878.0,454.0,991.0,409.0,2.4345,129700.0,NEAR OCEAN\n-117.04,32.74,33.0,3880.0,770.0,2288.0,805.0,3.6848,140700.0,NEAR OCEAN\n-117.02,32.74,12.0,3301.0,963.0,2000.0,879.0,1.8594,119200.0,NEAR OCEAN\n-117.04,32.74,5.0,2878.0,785.0,1727.0,758.0,1.7179,132000.0,NEAR OCEAN\n-117.04,32.75,36.0,2297.0,418.0,1070.0,392.0,3.5192,144000.0,NEAR OCEAN\n-117.05,32.75,29.0,2767.0,612.0,1437.0,587.0,2.8306,142900.0,NEAR OCEAN\n-117.05,32.75,43.0,1718.0,344.0,826.0,336.0,2.7014,133700.0,NEAR OCEAN\n-117.03,32.77,34.0,1796.0,428.0,918.0,424.0,2.875,161200.0,<1H OCEAN\n-117.02,32.76,15.0,1204.0,326.0,543.0,326.0,1.0278,154200.0,<1H OCEAN\n-117.02,32.76,40.0,2523.0,488.0,976.0,470.0,3.11,185700.0,<1H OCEAN\n-117.02,32.75,33.0,3296.0,537.0,1345.0,556.0,5.2835,217100.0,<1H OCEAN\n-117.03,32.75,24.0,7879.0,1655.0,3898.0,1534.0,3.0897,187300.0,NEAR OCEAN\n-117.04,32.77,26.0,4263.0,1037.0,2199.0,1010.0,2.734,148900.0,<1H OCEAN\n-117.04,32.76,43.0,3171.0,665.0,1534.0,625.0,3.141,141400.0,NEAR OCEAN\n-117.05,32.76,37.0,4879.0,906.0,2076.0,871.0,3.6625,154800.0,NEAR OCEAN\n-117.04,32.76,37.0,2979.0,557.0,1285.0,564.0,3.7368,152200.0,NEAR OCEAN\n-117.04,32.77,16.0,7963.0,1881.0,3769.0,1804.0,2.9624,144700.0,<1H OCEAN\n-117.03,32.77,19.0,4819.0,1492.0,2572.0,1336.0,2.3393,118200.0,<1H OCEAN\n-117.05,32.78,37.0,1184.0,178.0,529.0,192.0,4.7941,161700.0,<1H OCEAN\n-117.03,32.78,17.0,5481.0,1618.0,2957.0,1537.0,2.5707,171300.0,<1H OCEAN\n-117.02,32.78,33.0,3481.0,708.0,1726.0,719.0,3.3675,158200.0,<1H OCEAN\n-117.03,32.79,31.0,2366.0,383.0,1077.0,387.0,4.2992,174400.0,<1H OCEAN\n-117.03,32.79,17.0,7352.0,1699.0,3331.0,1634.0,2.7006,166300.0,<1H OCEAN\n-117.0,32.77,30.0,1802.0,401.0,776.0,386.0,2.8125,173500.0,<1H OCEAN\n-117.01,32.76,22.0,3626.0,824.0,1800.0,769.0,2.8594,189600.0,<1H OCEAN\n-117.01,32.76,34.0,3415.0,608.0,1464.0,593.0,4.0549,223700.0,<1H OCEAN\n-117.01,32.77,24.0,2311.0,536.0,1005.0,525.0,2.9,185200.0,<1H OCEAN\n-117.01,32.77,34.0,3330.0,723.0,1592.0,656.0,2.6678,164200.0,<1H OCEAN\n-117.01,32.77,43.0,841.0,192.0,496.0,207.0,3.0179,149300.0,<1H OCEAN\n-117.01,32.79,31.0,3776.0,815.0,1886.0,799.0,3.4421,155300.0,<1H OCEAN\n-117.01,32.78,20.0,2616.0,597.0,1532.0,579.0,2.9896,235600.0,<1H OCEAN\n-117.02,32.78,31.0,2567.0,,1198.0,499.0,3.4659,163000.0,<1H OCEAN\n-117.02,32.79,36.0,2211.0,384.0,868.0,329.0,4.0491,147900.0,<1H OCEAN\n-116.99,32.79,33.0,2420.0,393.0,1003.0,397.0,4.0658,165100.0,<1H OCEAN\n-116.99,32.79,26.0,3623.0,703.0,1609.0,669.0,3.744,165800.0,<1H OCEAN\n-116.99,32.78,29.0,1114.0,163.0,385.0,154.0,5.4333,222800.0,<1H OCEAN\n-117.0,32.79,31.0,3256.0,726.0,1595.0,706.0,3.4773,155800.0,<1H OCEAN\n-116.99,32.77,35.0,2306.0,334.0,828.0,310.0,6.1103,301600.0,<1H OCEAN\n-116.98,32.77,29.0,3558.0,447.0,1097.0,445.0,8.093,379600.0,<1H OCEAN\n-117.0,32.76,31.0,2545.0,373.0,956.0,342.0,4.3977,226800.0,<1H OCEAN\n-117.0,32.77,35.0,2114.0,317.0,881.0,320.0,5.5,241400.0,<1H OCEAN\n-116.95,32.78,33.0,2432.0,443.0,1147.0,427.0,3.3906,138100.0,<1H OCEAN\n-116.96,32.78,26.0,2807.0,630.0,1785.0,580.0,2.1638,132800.0,<1H OCEAN\n-116.97,32.78,26.0,8902.0,1413.0,3941.0,1387.0,4.7943,226900.0,<1H OCEAN\n-116.92,32.78,21.0,4192.0,752.0,2101.0,710.0,4.4306,159100.0,<1H OCEAN\n-116.91,32.78,15.0,4058.0,511.0,1580.0,473.0,7.5,316400.0,<1H OCEAN\n-116.92,32.77,16.0,2770.0,406.0,1269.0,429.0,6.6783,275000.0,<1H OCEAN\n-116.9,32.77,8.0,3600.0,492.0,1421.0,482.0,6.2609,307100.0,<1H OCEAN\n-116.95,32.78,20.0,3425.0,448.0,1489.0,443.0,6.2552,296400.0,<1H OCEAN\n-116.95,32.77,25.0,3308.0,421.0,1201.0,414.0,6.3191,303400.0,<1H OCEAN\n-116.94,32.78,17.0,13559.0,2656.0,6990.0,2533.0,3.434,193200.0,<1H OCEAN\n-116.85,32.83,17.0,4234.0,770.0,2191.0,725.0,3.6445,197600.0,<1H OCEAN\n-116.88,32.81,35.0,2926.0,562.0,1590.0,506.0,4.2014,143200.0,<1H OCEAN\n-116.86,32.8,19.0,1747.0,291.0,848.0,290.0,4.875,187200.0,<1H OCEAN\n-116.83,32.83,6.0,3123.0,495.0,1513.0,480.0,5.4288,167800.0,<1H OCEAN\n-116.83,32.81,18.0,2367.0,402.0,1021.0,395.0,4.8125,210500.0,<1H OCEAN\n-116.92,32.79,24.0,4055.0,742.0,2123.0,744.0,4.5224,142000.0,<1H OCEAN\n-116.93,32.79,23.0,5759.0,1258.0,3108.0,1202.0,3.0927,140600.0,<1H OCEAN\n-116.91,32.8,32.0,1943.0,287.0,1081.0,292.0,5.6846,208800.0,<1H OCEAN\n-116.9,32.79,21.0,3770.0,491.0,1410.0,446.0,6.7685,294700.0,<1H OCEAN\n-116.94,32.8,21.0,7906.0,2292.0,4868.0,2117.0,1.8937,109800.0,<1H OCEAN\n-116.93,32.79,19.0,3354.0,,1948.0,682.0,3.0192,142300.0,<1H OCEAN\n-116.95,32.79,19.0,11391.0,3093.0,7178.0,2905.0,2.0326,123200.0,<1H OCEAN\n-116.96,32.8,16.0,3920.0,1094.0,2612.0,1023.0,1.3291,120800.0,<1H OCEAN\n-116.96,32.79,19.0,3008.0,693.0,2341.0,689.0,2.6087,123800.0,<1H OCEAN\n-116.96,32.79,35.0,1081.0,266.0,691.0,259.0,2.6324,133700.0,<1H OCEAN\n-116.96,32.8,24.0,2493.0,693.0,1420.0,643.0,1.8357,104200.0,<1H OCEAN\n-116.97,32.79,19.0,4108.0,1101.0,2971.0,1006.0,1.9893,112500.0,<1H OCEAN\n-116.96,32.79,17.0,5236.0,1325.0,3308.0,1233.0,2.3221,138800.0,<1H OCEAN\n-116.97,32.78,37.0,1255.0,238.0,671.0,278.0,3.7019,138600.0,<1H OCEAN\n-116.97,32.78,35.0,1113.0,236.0,681.0,246.0,2.9784,136400.0,<1H OCEAN\n-116.97,32.79,32.0,1255.0,338.0,782.0,302.0,2.6635,113600.0,<1H OCEAN\n-116.98,32.79,32.0,3756.0,662.0,1611.0,598.0,3.8667,189700.0,<1H OCEAN\n-116.98,32.8,28.0,5721.0,1029.0,2672.0,1054.0,3.963,164400.0,<1H OCEAN\n-116.99,32.8,34.0,3657.0,538.0,1513.0,562.0,5.2907,195800.0,<1H OCEAN\n-117.0,32.8,33.0,1816.0,325.0,768.0,316.0,4.5662,150300.0,<1H OCEAN\n-117.0,32.8,29.0,2045.0,398.0,912.0,368.0,3.0189,144100.0,<1H OCEAN\n-116.99,32.81,25.0,4436.0,758.0,1997.0,738.0,4.2386,201000.0,<1H OCEAN\n-116.99,32.81,18.0,10968.0,1521.0,4439.0,1501.0,6.2787,250000.0,<1H OCEAN\n-116.97,32.83,23.0,149.0,32.0,101.0,34.0,2.6458,112500.0,<1H OCEAN\n-116.97,32.81,19.0,1573.0,471.0,844.0,414.0,2.1422,125000.0,<1H OCEAN\n-116.97,32.8,15.0,3927.0,1018.0,2204.0,977.0,2.4367,111400.0,<1H OCEAN\n-116.94,32.81,22.0,4266.0,1010.0,2766.0,985.0,2.8175,135200.0,<1H OCEAN\n-116.94,32.8,28.0,3042.0,729.0,1964.0,703.0,2.4141,137500.0,<1H OCEAN\n-116.96,32.8,19.0,4574.0,1152.0,3045.0,1057.0,2.065,124100.0,<1H OCEAN\n-116.96,32.8,25.0,3421.0,803.0,1681.0,742.0,3.369,134400.0,<1H OCEAN\n-116.92,32.82,34.0,1765.0,284.0,772.0,282.0,5.0118,165300.0,<1H OCEAN\n-116.92,32.81,23.0,2668.0,528.0,1510.0,524.0,3.3669,158900.0,<1H OCEAN\n-116.93,32.82,26.0,4129.0,714.0,1820.0,718.0,4.2586,171000.0,<1H OCEAN\n-116.91,32.81,22.0,4331.0,637.0,1952.0,654.0,5.4834,232000.0,<1H OCEAN\n-116.92,32.8,33.0,1518.0,268.0,857.0,272.0,3.5586,160400.0,<1H OCEAN\n-116.92,32.81,17.0,1312.0,394.0,836.0,337.0,1.6686,112500.0,<1H OCEAN\n-116.93,32.8,19.0,1867.0,538.0,1219.0,468.0,2.0685,130000.0,<1H OCEAN\n-116.93,32.81,18.0,2447.0,466.0,1573.0,472.0,2.6429,125400.0,<1H OCEAN\n-116.95,32.82,19.0,5308.0,1058.0,2852.0,1092.0,2.9161,135700.0,<1H OCEAN\n-116.95,32.82,12.0,5535.0,1434.0,3112.0,1262.0,2.5949,108300.0,<1H OCEAN\n-116.95,32.81,31.0,1277.0,293.0,698.0,237.0,3.1106,147700.0,<1H OCEAN\n-116.96,32.81,8.0,2378.0,638.0,1410.0,623.0,2.9097,152500.0,<1H OCEAN\n-116.94,32.82,35.0,1737.0,285.0,826.0,294.0,3.2411,159200.0,<1H OCEAN\n-116.94,32.81,8.0,2517.0,632.0,1686.0,613.0,2.136,143500.0,<1H OCEAN\n-116.95,32.81,15.0,2619.0,599.0,1513.0,537.0,2.543,100000.0,<1H OCEAN\n-116.95,32.82,18.0,3038.0,592.0,1904.0,595.0,3.8024,144900.0,<1H OCEAN\n-116.97,32.83,17.0,6659.0,1402.0,3183.0,1378.0,2.949,117300.0,<1H OCEAN\n-116.99,32.83,20.0,6696.0,1326.0,3687.0,1291.0,3.1979,154600.0,<1H OCEAN\n-116.99,32.85,32.0,5211.0,949.0,3025.0,948.0,4.0931,134200.0,<1H OCEAN\n-116.98,32.85,12.0,3570.0,713.0,3321.0,666.0,4.0882,134500.0,<1H OCEAN\n-117.01,32.83,17.0,15401.0,3280.0,7302.0,3176.0,3.3067,121900.0,<1H OCEAN\n-117.01,32.84,23.0,1951.0,395.0,901.0,378.0,3.1023,143300.0,<1H OCEAN\n-117.02,32.84,17.0,4013.0,673.0,2263.0,661.0,5.131,148300.0,<1H OCEAN\n-116.96,32.85,11.0,9724.0,1796.0,5247.0,1777.0,4.1716,166100.0,<1H OCEAN\n-116.96,32.86,14.0,3064.0,496.0,1681.0,503.0,4.4347,160300.0,<1H OCEAN\n-116.96,32.87,17.0,4713.0,740.0,2531.0,723.0,4.8286,158500.0,<1H OCEAN\n-116.98,32.88,12.0,7320.0,1279.0,4048.0,1249.0,4.3952,151700.0,<1H OCEAN\n-116.98,32.86,19.0,2121.0,341.0,1236.0,353.0,4.7717,153200.0,<1H OCEAN\n-116.98,32.86,16.0,7718.0,1423.0,4383.0,1394.0,4.0693,146400.0,<1H OCEAN\n-117.0,32.87,18.0,11544.0,1979.0,6296.0,1923.0,4.4904,150400.0,<1H OCEAN\n-117.01,32.85,23.0,2592.0,414.0,1401.0,431.0,5.4903,151400.0,<1H OCEAN\n-117.0,32.85,24.0,1888.0,319.0,950.0,319.0,5.282,140800.0,<1H OCEAN\n-116.95,32.83,14.0,12517.0,2506.0,6389.0,2333.0,3.3081,168700.0,<1H OCEAN\n-116.95,32.84,31.0,1307.0,,752.0,231.0,3.4286,129400.0,<1H OCEAN\n-116.93,32.85,15.0,3273.0,895.0,1872.0,842.0,2.5388,119000.0,<1H OCEAN\n-116.93,32.85,5.0,4116.0,990.0,2770.0,905.0,3.1142,150000.0,<1H OCEAN\n-116.94,32.85,31.0,1293.0,232.0,599.0,228.0,4.7578,161000.0,<1H OCEAN\n-116.94,32.84,32.0,1607.0,253.0,778.0,262.0,4.5278,166300.0,<1H OCEAN\n-116.94,32.83,38.0,1701.0,317.0,872.0,304.0,3.7831,147800.0,<1H OCEAN\n-116.88,32.86,9.0,3049.0,471.0,1527.0,515.0,5.0733,196600.0,<1H OCEAN\n-116.86,32.87,17.0,5799.0,921.0,2630.0,843.0,5.0524,285400.0,<1H OCEAN\n-116.84,32.86,16.0,2502.0,532.0,1211.0,494.0,3.2516,202100.0,<1H OCEAN\n-116.89,32.85,16.0,1743.0,333.0,652.0,322.0,2.8906,158300.0,<1H OCEAN\n-116.91,32.87,14.0,3048.0,597.0,1690.0,576.0,4.3818,147100.0,<1H OCEAN\n-116.91,32.86,10.0,3699.0,838.0,2310.0,759.0,2.5365,139500.0,<1H OCEAN\n-116.91,32.86,15.0,3153.0,628.0,1633.0,527.0,3.6898,131000.0,<1H OCEAN\n-116.92,32.86,11.0,2204.0,518.0,1472.0,497.0,2.3693,127000.0,<1H OCEAN\n-116.93,32.83,19.0,3038.0,529.0,1463.0,509.0,3.944,172500.0,<1H OCEAN\n-116.93,32.83,21.0,1283.0,278.0,684.0,289.0,2.3203,163500.0,<1H OCEAN\n-116.92,32.82,17.0,2492.0,494.0,1278.0,439.0,2.8875,155700.0,<1H OCEAN\n-116.92,32.85,23.0,1378.0,269.0,767.0,266.0,4.0625,145000.0,<1H OCEAN\n-116.92,32.84,16.0,4675.0,834.0,2188.0,817.0,4.6674,178000.0,<1H OCEAN\n-116.91,32.83,16.0,5203.0,,2515.0,862.0,4.105,174400.0,<1H OCEAN\n-116.92,32.82,16.0,2784.0,468.0,1458.0,465.0,4.0048,184600.0,<1H OCEAN\n-116.91,32.85,21.0,4152.0,703.0,2255.0,697.0,4.5096,159500.0,<1H OCEAN\n-116.89,32.85,15.0,3560.0,593.0,1757.0,574.0,5.1185,185300.0,<1H OCEAN\n-116.9,32.84,18.0,3612.0,737.0,1864.0,713.0,2.7069,153800.0,<1H OCEAN\n-116.89,32.82,18.0,2515.0,443.0,1442.0,449.0,5.0201,154400.0,<1H OCEAN\n-116.9,32.84,18.0,4215.0,810.0,2104.0,773.0,4.0873,146900.0,<1H OCEAN\n-116.91,32.82,14.0,1978.0,424.0,1085.0,387.0,3.8073,170100.0,<1H OCEAN\n-116.96,32.9,16.0,3047.0,495.0,1507.0,499.0,5.3008,186500.0,<1H OCEAN\n-116.94,32.89,24.0,2541.0,381.0,1078.0,372.0,5.2542,227800.0,<1H OCEAN\n-116.94,32.87,24.0,2824.0,441.0,1480.0,471.0,5.2614,177200.0,<1H OCEAN\n-116.93,32.87,17.0,3722.0,670.0,1561.0,604.0,3.6027,211900.0,<1H OCEAN\n-116.9,32.9,19.0,3090.0,552.0,1621.0,520.0,4.0806,189200.0,<1H OCEAN\n-116.84,32.92,20.0,1066.0,219.0,536.0,173.0,3.1607,119300.0,<1H OCEAN\n-117.05,33.01,19.0,3558.0,588.0,1439.0,578.0,4.625,211100.0,<1H OCEAN\n-117.05,33.01,17.0,3430.0,425.0,1468.0,433.0,10.6186,429300.0,<1H OCEAN\n-117.04,33.01,28.0,922.0,107.0,314.0,97.0,8.4721,342300.0,<1H OCEAN\n-117.03,33.0,6.0,6139.0,793.0,2693.0,770.0,7.7569,387400.0,<1H OCEAN\n-117.04,32.99,6.0,9518.0,1418.0,4413.0,1275.0,6.6012,314900.0,<1H OCEAN\n-117.04,32.98,16.0,1332.0,196.0,640.0,193.0,6.0226,192900.0,<1H OCEAN\n-117.06,32.99,16.0,1306.0,196.0,713.0,222.0,6.2683,180700.0,<1H OCEAN\n-117.05,32.97,17.0,9911.0,1436.0,4763.0,1414.0,5.5882,194300.0,<1H OCEAN\n-117.05,32.96,18.0,3593.0,661.0,1992.0,626.0,4.8295,165800.0,<1H OCEAN\n-117.04,32.97,13.0,6711.0,1256.0,3683.0,1220.0,4.5746,175700.0,<1H OCEAN\n-117.03,32.97,16.0,3936.0,694.0,1935.0,659.0,4.5625,231200.0,<1H OCEAN\n-117.03,32.96,16.0,3424.0,698.0,1940.0,645.0,4.121,182100.0,<1H OCEAN\n-116.99,32.96,17.0,5509.0,866.0,2748.0,817.0,4.8854,181300.0,<1H OCEAN\n-117.02,32.95,25.0,1909.0,334.0,1043.0,322.0,3.7784,160100.0,<1H OCEAN\n-117.03,32.95,19.0,4500.0,815.0,2456.0,782.0,4.5032,168900.0,<1H OCEAN\n-117.05,32.95,17.0,3039.0,555.0,1297.0,552.0,3.9531,178600.0,<1H OCEAN\n-117.05,32.95,17.0,4814.0,1091.0,3013.0,1078.0,3.2369,167800.0,<1H OCEAN\n-117.06,33.02,24.0,830.0,190.0,279.0,196.0,1.9176,121100.0,<1H OCEAN\n-117.06,33.01,24.0,2618.0,485.0,726.0,443.0,3.5192,159100.0,<1H OCEAN\n-117.07,33.01,25.0,2120.0,381.0,588.0,359.0,3.1187,169400.0,<1H OCEAN\n-117.07,33.02,17.0,2863.0,665.0,715.0,467.0,2.6048,148200.0,<1H OCEAN\n-117.06,33.04,17.0,1785.0,255.0,667.0,277.0,5.7382,278000.0,<1H OCEAN\n-117.06,33.03,23.0,2023.0,309.0,678.0,340.0,7.0913,265400.0,<1H OCEAN\n-117.07,33.03,14.0,6665.0,1231.0,2026.0,1001.0,5.09,268500.0,<1H OCEAN\n-117.07,33.03,15.0,1095.0,158.0,361.0,176.0,6.8099,328200.0,<1H OCEAN\n-117.08,33.03,10.0,2296.0,450.0,818.0,405.0,4.3424,160600.0,<1H OCEAN\n-117.07,33.04,4.0,2271.0,578.0,926.0,391.0,3.6437,210100.0,<1H OCEAN\n-117.08,33.03,15.0,3023.0,623.0,1283.0,559.0,3.3724,137900.0,<1H OCEAN\n-117.08,33.03,17.0,987.0,142.0,463.0,152.0,5.8747,229300.0,<1H OCEAN\n-117.08,33.03,18.0,1339.0,284.0,761.0,290.0,5.3074,137200.0,<1H OCEAN\n-117.09,33.03,17.0,2786.0,396.0,1228.0,396.0,6.4211,220700.0,<1H OCEAN\n-117.08,33.04,10.0,2577.0,347.0,1193.0,365.0,6.53,264100.0,<1H OCEAN\n-117.15,33.02,4.0,15029.0,2279.0,5613.0,1696.0,7.2731,450400.0,NEAR OCEAN\n-117.1,32.97,17.0,3167.0,861.0,2098.0,828.0,2.4459,85800.0,<1H OCEAN\n-117.1,32.96,7.0,3619.0,770.0,1134.0,482.0,4.1279,167600.0,<1H OCEAN\n-117.05,33.05,6.0,7916.0,1293.0,2741.0,1204.0,5.6436,278600.0,<1H OCEAN\n-117.05,33.04,12.0,1840.0,254.0,580.0,234.0,6.7769,400000.0,<1H OCEAN\n-117.05,33.03,14.0,5180.0,1051.0,1639.0,991.0,4.5,222200.0,<1H OCEAN\n-117.05,33.03,16.0,87.0,20.0,32.0,21.0,4.3571,144600.0,<1H OCEAN\n-117.06,33.02,17.0,2635.0,389.0,994.0,359.0,5.8966,261500.0,<1H OCEAN\n-117.05,33.02,18.0,917.0,121.0,388.0,131.0,6.3517,260100.0,<1H OCEAN\n-117.01,33.04,13.0,4595.0,567.0,1643.0,544.0,7.7684,362300.0,<1H OCEAN\n-117.04,33.03,16.0,2852.0,435.0,1083.0,448.0,6.3761,296200.0,<1H OCEAN\n-117.01,32.99,8.0,3372.0,430.0,1536.0,448.0,8.4284,378300.0,<1H OCEAN\n-116.99,33.01,11.0,1412.0,185.0,529.0,166.0,7.7517,500001.0,<1H OCEAN\n-117.09,32.91,9.0,2012.0,316.0,802.0,289.0,6.5706,255700.0,<1H OCEAN\n-117.09,32.91,16.0,2005.0,266.0,827.0,270.0,7.0546,282200.0,<1H OCEAN\n-117.09,32.9,16.0,1989.0,290.0,814.0,291.0,6.2715,255100.0,<1H OCEAN\n-117.1,32.9,16.0,2994.0,445.0,1047.0,437.0,5.149,184300.0,<1H OCEAN\n-117.11,32.9,16.0,2043.0,388.0,705.0,352.0,4.4766,161500.0,<1H OCEAN\n-117.11,32.91,15.0,1840.0,235.0,855.0,241.0,7.5992,310600.0,<1H OCEAN\n-117.08,32.91,16.0,1653.0,228.0,690.0,224.0,6.5853,248400.0,<1H OCEAN\n-117.08,32.93,5.0,14944.0,2490.0,6600.0,2407.0,6.0857,308300.0,<1H OCEAN\n-117.08,32.91,9.0,1547.0,218.0,683.0,231.0,7.5604,327900.0,<1H OCEAN\n-117.07,33.01,5.0,5870.0,977.0,1917.0,842.0,5.1998,294100.0,<1H OCEAN\n-117.06,33.01,9.0,2470.0,417.0,904.0,427.0,4.4219,209200.0,<1H OCEAN\n-117.07,33.0,4.0,6242.0,1258.0,2211.0,1116.0,4.25,281600.0,<1H OCEAN\n-117.07,33.0,4.0,9153.0,1866.0,3775.0,1698.0,4.955,241500.0,<1H OCEAN\n-117.08,33.01,5.0,5659.0,931.0,2565.0,902.0,6.1949,238700.0,<1H OCEAN\n-117.09,32.99,16.0,2175.0,327.0,1037.0,326.0,5.1909,201400.0,<1H OCEAN\n-117.09,32.99,18.0,3215.0,588.0,1618.0,509.0,4.6028,216800.0,<1H OCEAN\n-117.09,32.98,23.0,1125.0,273.0,687.0,308.0,2.3182,268800.0,<1H OCEAN\n-117.12,32.96,16.0,3050.0,559.0,1444.0,512.0,5.2463,156300.0,<1H OCEAN\n-117.11,32.97,9.0,1531.0,242.0,850.0,240.0,6.0862,263600.0,<1H OCEAN\n-117.1,33.0,5.0,15502.0,2613.0,7417.0,2358.0,5.9094,261100.0,<1H OCEAN\n-117.11,32.95,11.0,4694.0,824.0,2223.0,783.0,4.9485,231800.0,<1H OCEAN\n-117.12,32.95,8.0,3670.0,536.0,1723.0,592.0,6.3542,218100.0,<1H OCEAN\n-117.12,32.96,15.0,2869.0,405.0,1526.0,402.0,6.0175,238300.0,<1H OCEAN\n-117.12,32.95,4.0,9018.0,1572.0,4438.0,1498.0,4.988,263700.0,<1H OCEAN\n-117.08,32.97,3.0,17466.0,3336.0,7644.0,2895.0,5.4584,246500.0,<1H OCEAN\n-117.06,32.97,17.0,4754.0,877.0,2412.0,832.0,4.3548,192300.0,<1H OCEAN\n-117.25,33.06,6.0,9859.0,1448.0,4194.0,1401.0,6.439,296200.0,NEAR OCEAN\n-117.25,33.05,16.0,2794.0,476.0,1387.0,442.0,4.3286,213400.0,NEAR OCEAN\n-117.26,33.05,14.0,2323.0,373.0,1057.0,372.0,6.2513,240900.0,NEAR OCEAN\n-117.26,33.06,11.0,2660.0,352.0,1226.0,366.0,7.6832,319800.0,NEAR OCEAN\n-117.24,33.05,15.0,3029.0,555.0,1559.0,546.0,5.3129,169200.0,NEAR OCEAN\n-117.24,33.05,11.0,5827.0,882.0,2588.0,842.0,6.4027,344200.0,NEAR OCEAN\n-117.2,33.07,5.0,10394.0,1617.0,4496.0,1553.0,5.9289,411300.0,NEAR OCEAN\n-117.16,33.06,16.0,1988.0,279.0,770.0,252.0,5.8661,404500.0,NEAR OCEAN\n-117.21,33.03,20.0,3370.0,433.0,1020.0,408.0,11.0911,500001.0,NEAR OCEAN\n-117.18,33.02,15.0,3540.0,453.0,1364.0,425.0,13.6623,500001.0,NEAR OCEAN\n-117.21,33.02,26.0,3194.0,454.0,1032.0,406.0,10.156,500001.0,NEAR OCEAN\n-117.23,33.01,18.0,3961.0,511.0,1541.0,470.0,11.1118,500001.0,NEAR OCEAN\n-117.26,32.97,25.0,2582.0,495.0,1088.0,471.0,6.4651,500001.0,NEAR OCEAN\n-117.26,32.96,36.0,1721.0,264.0,710.0,282.0,10.1768,500001.0,NEAR OCEAN\n-117.26,32.95,34.0,1651.0,273.0,650.0,271.0,5.6582,500001.0,NEAR OCEAN\n-117.26,32.95,22.0,5484.0,1227.0,1947.0,1012.0,4.4375,500001.0,NEAR OCEAN\n-117.3,32.96,30.0,1226.0,205.0,380.0,151.0,4.2875,500001.0,NEAR OCEAN\n-117.25,33.01,16.0,3892.0,520.0,1454.0,524.0,7.7317,396000.0,NEAR OCEAN\n-117.24,33.0,16.0,2512.0,356.0,795.0,353.0,7.5975,369100.0,NEAR OCEAN\n-117.23,32.99,17.0,2718.0,326.0,1011.0,319.0,15.0001,500001.0,NEAR OCEAN\n-117.25,32.99,10.0,4926.0,749.0,1478.0,634.0,7.472,439900.0,NEAR OCEAN\n-117.25,33.0,14.0,2518.0,458.0,931.0,414.0,5.8393,485300.0,NEAR OCEAN\n-117.26,33.0,31.0,2695.0,491.0,1059.0,451.0,4.7841,393500.0,NEAR OCEAN\n-117.27,33.0,36.0,2426.0,454.0,1085.0,420.0,5.1523,387800.0,NEAR OCEAN\n-117.31,33.0,30.0,1631.0,310.0,665.0,297.0,6.8443,492500.0,NEAR OCEAN\n-117.26,32.99,16.0,2127.0,512.0,1532.0,499.0,2.7348,231300.0,NEAR OCEAN\n-117.27,32.99,21.0,3318.0,578.0,1273.0,538.0,5.5922,382100.0,NEAR OCEAN\n-117.26,32.98,12.0,3900.0,977.0,1690.0,892.0,4.125,226900.0,NEAR OCEAN\n-117.31,32.98,17.0,2789.0,648.0,849.0,345.0,4.1012,244700.0,NEAR OCEAN\n-117.27,32.98,17.0,1853.0,392.0,351.0,208.0,5.2742,230700.0,NEAR OCEAN\n-117.32,33.01,29.0,3584.0,712.0,1619.0,667.0,4.125,394400.0,NEAR OCEAN\n-117.28,33.02,21.0,2736.0,585.0,1251.0,576.0,4.2356,347700.0,NEAR OCEAN\n-117.27,33.02,13.0,5723.0,1242.0,2450.0,1140.0,4.7179,376700.0,NEAR OCEAN\n-117.26,33.04,18.0,2229.0,346.0,1088.0,352.0,6.3525,278300.0,NEAR OCEAN\n-117.26,33.04,16.0,3109.0,450.0,1433.0,453.0,6.6319,269600.0,NEAR OCEAN\n-117.24,33.04,13.0,3498.0,663.0,1412.0,618.0,3.212,147600.0,NEAR OCEAN\n-117.25,33.03,6.0,3416.0,493.0,1319.0,467.0,6.9326,324600.0,NEAR OCEAN\n-117.27,33.03,25.0,1787.0,311.0,1108.0,311.0,3.9826,215800.0,NEAR OCEAN\n-117.27,33.03,16.0,2240.0,443.0,1104.0,416.0,3.5313,148700.0,NEAR OCEAN\n-117.26,33.02,9.0,4632.0,759.0,1724.0,685.0,6.3712,369800.0,NEAR OCEAN\n-117.27,33.03,19.0,2899.0,499.0,1356.0,512.0,4.87,220900.0,NEAR OCEAN\n-117.27,33.02,21.0,2144.0,340.0,928.0,344.0,5.798,286100.0,NEAR OCEAN\n-117.29,33.05,28.0,1146.0,338.0,672.0,292.0,3.1667,300000.0,NEAR OCEAN\n-117.28,33.04,12.0,4459.0,928.0,2471.0,888.0,3.5179,252700.0,NEAR OCEAN\n-117.27,33.04,27.0,1839.0,392.0,1302.0,404.0,3.55,214600.0,NEAR OCEAN\n-117.29,33.04,30.0,2750.0,555.0,1281.0,520.0,4.7333,286900.0,NEAR OCEAN\n-117.33,33.03,31.0,1171.0,321.0,603.0,267.0,2.8611,314300.0,NEAR OCEAN\n-117.3,33.07,14.0,2670.0,426.0,1034.0,407.0,6.4247,295100.0,NEAR OCEAN\n-117.29,33.08,18.0,3225.0,515.0,1463.0,476.0,5.7787,346700.0,NEAR OCEAN\n-117.27,33.08,7.0,2949.0,447.0,1335.0,426.0,6.0922,342400.0,NEAR OCEAN\n-117.29,33.06,20.0,2110.0,335.0,1008.0,325.0,6.1509,338700.0,NEAR OCEAN\n-117.28,33.06,8.0,4172.0,1022.0,2585.0,941.0,4.0118,245800.0,NEAR OCEAN\n-117.27,33.06,7.0,3686.0,733.0,1612.0,672.0,3.197,367100.0,NEAR OCEAN\n-117.27,33.05,15.0,3333.0,808.0,1371.0,737.0,2.9083,122400.0,NEAR OCEAN\n-117.3,33.08,24.0,2628.0,527.0,1389.0,520.0,4.0,343200.0,NEAR OCEAN\n-117.34,33.06,17.0,2718.0,518.0,815.0,403.0,4.3182,357100.0,NEAR OCEAN\n-117.31,33.07,21.0,2035.0,534.0,948.0,467.0,3.2984,369400.0,NEAR OCEAN\n-117.3,33.07,16.0,3147.0,765.0,2165.0,690.0,3.5585,284800.0,NEAR OCEAN\n-117.3,33.06,24.0,2171.0,511.0,870.0,442.0,3.194,276300.0,NEAR OCEAN\n-117.3,33.06,31.0,2128.0,520.0,1049.0,485.0,4.027,290000.0,NEAR OCEAN\n-117.3,33.05,34.0,1797.0,458.0,775.0,391.0,3.2308,331300.0,NEAR OCEAN\n-117.33,33.17,11.0,10923.0,2041.0,4773.0,1858.0,4.0791,281300.0,NEAR OCEAN\n-117.34,33.16,31.0,2851.0,458.0,1286.0,467.0,4.5694,243700.0,NEAR OCEAN\n-117.33,33.16,29.0,3559.0,552.0,1533.0,545.0,4.0585,245500.0,NEAR OCEAN\n-117.32,33.15,15.0,13245.0,2212.0,5495.0,2060.0,5.4904,262100.0,NEAR OCEAN\n-117.32,33.12,25.0,2670.0,527.0,936.0,461.0,2.7717,354000.0,NEAR OCEAN\n-117.31,33.1,15.0,2392.0,446.0,747.0,421.0,3.5341,500001.0,NEAR OCEAN\n-117.29,33.13,4.0,617.0,105.0,224.0,105.0,3.9205,183000.0,NEAR OCEAN\n-117.31,33.11,7.0,7974.0,1703.0,2904.0,1550.0,4.1282,188100.0,NEAR OCEAN\n-117.29,33.12,4.0,1380.0,322.0,755.0,286.0,4.7961,168800.0,NEAR OCEAN\n-117.29,33.1,6.0,6091.0,1018.0,2064.0,957.0,5.1837,259800.0,NEAR OCEAN\n-117.28,33.1,13.0,2644.0,422.0,1197.0,399.0,6.5338,267900.0,NEAR OCEAN\n-117.35,33.17,16.0,4595.0,1341.0,2849.0,1197.0,2.478,185600.0,NEAR OCEAN\n-117.34,33.16,24.0,1006.0,277.0,610.0,246.0,2.25,187500.0,NEAR OCEAN\n-117.34,33.15,19.0,5710.0,1423.0,4163.0,1406.0,3.0306,178500.0,NEAR OCEAN\n-117.35,33.16,22.0,1331.0,305.0,580.0,193.0,3.975,500001.0,NEAR OCEAN\n-117.35,33.16,10.0,1684.0,515.0,902.0,449.0,3.7891,206300.0,NEAR OCEAN\n-117.34,33.15,17.0,4505.0,1140.0,2111.0,1062.0,3.3536,283300.0,NEAR OCEAN\n-117.36,33.18,26.0,5550.0,1153.0,2372.0,1058.0,2.5509,181800.0,NEAR OCEAN\n-117.36,33.18,39.0,1546.0,291.0,833.0,308.0,2.8893,185400.0,NEAR OCEAN\n-117.35,33.17,36.0,1977.0,423.0,812.0,387.0,3.625,198000.0,NEAR OCEAN\n-117.36,33.17,24.0,2046.0,442.0,812.0,367.0,2.3182,500001.0,NEAR OCEAN\n-117.37,33.18,19.0,1931.0,509.0,855.0,394.0,2.6979,266700.0,NEAR OCEAN\n-117.37,33.19,23.0,4104.0,1274.0,4729.0,1187.0,1.8214,173800.0,NEAR OCEAN\n-117.37,33.19,33.0,2205.0,453.0,1138.0,439.0,2.8819,208600.0,NEAR OCEAN\n-117.37,33.19,18.0,975.0,382.0,650.0,286.0,1.9562,192500.0,NEAR OCEAN\n-117.37,33.19,38.0,861.0,213.0,486.0,204.0,4.1875,185000.0,NEAR OCEAN\n-117.38,33.19,35.0,928.0,264.0,538.0,248.0,2.4583,197900.0,NEAR OCEAN\n-117.38,33.14,14.0,5039.0,1373.0,1298.0,696.0,3.209,313300.0,NEAR OCEAN\n-117.38,33.19,26.0,4123.0,1145.0,1703.0,895.0,1.9891,500000.0,NEAR OCEAN\n-117.38,33.2,17.0,1877.0,581.0,1288.0,426.0,1.9386,106300.0,NEAR OCEAN\n-117.37,33.2,19.0,928.0,317.0,845.0,319.0,1.6318,187500.0,NEAR OCEAN\n-117.38,33.19,17.0,353.0,112.0,359.0,118.0,1.5625,162500.0,NEAR OCEAN\n-117.38,33.2,26.0,1427.0,386.0,974.0,317.0,1.3903,184400.0,NEAR OCEAN\n-117.36,33.2,19.0,1926.0,557.0,1190.0,483.0,1.3269,166100.0,NEAR OCEAN\n-117.36,33.2,19.0,2129.0,562.0,1323.0,525.0,2.9539,169900.0,NEAR OCEAN\n-117.36,33.2,26.0,2447.0,482.0,1405.0,486.0,3.2917,150800.0,NEAR OCEAN\n-117.35,33.2,23.0,3297.0,728.0,1793.0,622.0,2.5754,169700.0,NEAR OCEAN\n-117.35,33.21,24.0,1586.0,262.0,912.0,298.0,4.25,150300.0,NEAR OCEAN\n-117.34,33.21,23.0,2062.0,376.0,1302.0,379.0,4.0109,145700.0,NEAR OCEAN\n-117.35,33.2,32.0,1251.0,220.0,700.0,232.0,3.9875,142900.0,NEAR OCEAN\n-117.34,33.21,12.0,5963.0,1372.0,3015.0,1124.0,2.7386,216100.0,NEAR OCEAN\n-117.35,33.19,28.0,2823.0,476.0,1189.0,433.0,5.1733,198100.0,NEAR OCEAN\n-117.34,33.19,23.0,3546.0,553.0,1533.0,518.0,5.276,224500.0,NEAR OCEAN\n-117.33,33.19,15.0,3672.0,845.0,1827.0,796.0,2.9716,173600.0,NEAR OCEAN\n-117.34,33.19,19.0,3575.0,525.0,1654.0,559.0,5.7409,274100.0,NEAR OCEAN\n-117.29,33.24,5.0,3109.0,634.0,1823.0,578.0,3.1875,153800.0,<1H OCEAN\n-117.3,33.23,13.0,3619.0,791.0,1759.0,806.0,2.765,98500.0,<1H OCEAN\n-117.32,33.23,24.0,2580.0,604.0,982.0,569.0,1.6402,169300.0,NEAR OCEAN\n-117.31,33.24,6.0,1580.0,288.0,792.0,265.0,4.0469,162400.0,<1H OCEAN\n-117.32,33.22,15.0,4784.0,1039.0,1810.0,986.0,2.4375,108900.0,NEAR OCEAN\n-117.33,33.22,21.0,2868.0,602.0,855.0,559.0,2.7846,91200.0,NEAR OCEAN\n-117.32,33.22,16.0,1057.0,232.0,316.0,221.0,2.7417,91700.0,NEAR OCEAN\n-117.33,33.21,17.0,1246.0,300.0,424.0,288.0,2.2882,85800.0,NEAR OCEAN\n-117.3,33.22,4.0,14960.0,2988.0,6666.0,2612.0,3.7568,184100.0,NEAR OCEAN\n-117.31,33.19,11.0,20944.0,3753.0,8738.0,3441.0,4.3762,215500.0,NEAR OCEAN\n-117.29,33.2,16.0,2150.0,461.0,1428.0,407.0,2.4754,157300.0,NEAR OCEAN\n-117.31,33.18,16.0,1835.0,430.0,599.0,399.0,2.0147,87700.0,NEAR OCEAN\n-117.29,33.19,18.0,6235.0,1233.0,4127.0,1162.0,3.0704,151600.0,NEAR OCEAN\n-117.28,33.2,20.0,4835.0,854.0,2983.0,834.0,4.3428,152100.0,NEAR OCEAN\n-117.37,33.22,35.0,2204.0,482.0,1435.0,462.0,3.676,125600.0,NEAR OCEAN\n-117.35,33.23,4.0,1837.0,287.0,934.0,277.0,3.8958,189800.0,NEAR OCEAN\n-117.35,33.21,18.0,2971.0,606.0,2051.0,493.0,2.675,117100.0,NEAR OCEAN\n-117.38,33.21,31.0,1502.0,367.0,1514.0,342.0,2.6442,103300.0,NEAR OCEAN\n-117.37,33.2,29.0,1315.0,311.0,1425.0,306.0,2.0272,99600.0,NEAR OCEAN\n-117.28,33.28,13.0,6131.0,1040.0,4049.0,940.0,3.8156,150700.0,<1H OCEAN\n-117.25,33.3,14.0,2513.0,351.0,1151.0,357.0,6.3054,359000.0,<1H OCEAN\n-117.26,33.26,9.0,4609.0,798.0,2582.0,746.0,4.3429,173900.0,<1H OCEAN\n-117.3,33.26,23.0,1678.0,275.0,1227.0,264.0,4.1713,133800.0,<1H OCEAN\n-117.3,33.25,22.0,2329.0,419.0,1456.0,381.0,3.7933,131000.0,<1H OCEAN\n-117.31,33.25,14.0,3483.0,764.0,2140.0,687.0,3.125,102300.0,<1H OCEAN\n-117.31,33.25,13.0,3075.0,630.0,1843.0,674.0,2.8558,97100.0,<1H OCEAN\n-117.32,33.25,7.0,2499.0,420.0,1314.0,398.0,4.85,186900.0,<1H OCEAN\n-117.32,33.25,7.0,8206.0,1523.0,4399.0,1423.0,3.6301,170900.0,<1H OCEAN\n-117.34,33.23,11.0,3737.0,757.0,2212.0,727.0,3.1062,141000.0,NEAR OCEAN\n-117.33,33.23,15.0,2919.0,592.0,1130.0,579.0,2.5872,155600.0,NEAR OCEAN\n-117.33,33.23,15.0,1905.0,416.0,1258.0,388.0,3.33,127900.0,NEAR OCEAN\n-117.33,33.24,13.0,4543.0,881.0,2298.0,870.0,2.9386,143400.0,NEAR OCEAN\n-117.42,33.35,14.0,25135.0,4819.0,35682.0,4769.0,2.5729,134400.0,<1H OCEAN\n-117.24,33.34,17.0,2866.0,442.0,1354.0,431.0,4.5764,257300.0,<1H OCEAN\n-117.22,33.36,16.0,3165.0,482.0,1351.0,452.0,4.605,263300.0,<1H OCEAN\n-117.22,33.31,12.0,2924.0,433.0,1193.0,394.0,6.2475,331300.0,<1H OCEAN\n-117.21,33.34,10.0,5294.0,817.0,2312.0,810.0,5.4563,325700.0,<1H OCEAN\n-117.19,33.34,15.0,3310.0,488.0,1104.0,460.0,6.1009,314400.0,<1H OCEAN\n-117.17,33.34,15.0,3313.0,679.0,1022.0,564.0,2.7986,189900.0,<1H OCEAN\n-117.2,33.29,12.0,6358.0,1182.0,2778.0,1020.0,4.0357,295900.0,<1H OCEAN\n-117.17,33.28,16.0,1921.0,312.0,862.0,280.0,5.1786,376800.0,<1H OCEAN\n-117.25,33.38,16.0,3536.0,765.0,2007.0,687.0,3.0,146700.0,<1H OCEAN\n-117.24,33.4,16.0,2704.0,463.0,1322.0,424.0,3.7857,227000.0,<1H OCEAN\n-117.23,33.38,18.0,3339.0,704.0,1727.0,652.0,2.8393,173200.0,<1H OCEAN\n-117.24,33.38,16.0,2792.0,525.0,1696.0,516.0,3.668,171200.0,<1H OCEAN\n-117.25,33.38,17.0,1614.0,431.0,1031.0,389.0,2.0956,134400.0,<1H OCEAN\n-117.25,33.39,22.0,2699.0,543.0,1425.0,491.0,2.375,137300.0,<1H OCEAN\n-117.26,33.37,7.0,2221.0,548.0,1440.0,501.0,2.2368,154600.0,<1H OCEAN\n-117.25,33.37,8.0,1755.0,530.0,1687.0,511.0,1.995,146900.0,<1H OCEAN\n-117.24,33.37,14.0,4687.0,793.0,2436.0,779.0,4.5391,180900.0,<1H OCEAN\n-117.24,33.36,11.0,2786.0,480.0,1250.0,450.0,4.5,222600.0,<1H OCEAN\n-117.25,33.36,6.0,3725.0,960.0,2833.0,915.0,2.3214,247000.0,<1H OCEAN\n-117.34,33.46,14.0,1902.0,338.0,848.0,304.0,5.5395,273300.0,<1H OCEAN\n-117.19,33.41,16.0,3031.0,554.0,1301.0,518.0,4.0882,296100.0,<1H OCEAN\n-117.14,33.39,17.0,2889.0,587.0,1931.0,510.0,3.8547,208300.0,<1H OCEAN\n-117.2,33.38,14.0,5392.0,821.0,2350.0,810.0,5.0507,291500.0,<1H OCEAN\n-117.1,33.36,19.0,3518.0,658.0,2091.0,610.0,3.2614,168800.0,<1H OCEAN\n-116.95,33.31,16.0,2921.0,639.0,1838.0,540.0,2.2393,117000.0,<1H OCEAN\n-117.03,33.32,14.0,1088.0,209.0,601.0,193.0,3.8438,243800.0,<1H OCEAN\n-117.05,33.29,17.0,1800.0,312.0,891.0,281.0,7.0177,267600.0,<1H OCEAN\n-117.05,33.26,14.0,3103.0,569.0,1704.0,539.0,3.7644,264700.0,<1H OCEAN\n-117.12,33.27,11.0,3016.0,601.0,1727.0,541.0,4.9375,232800.0,<1H OCEAN\n-117.0,33.29,17.0,2073.0,313.0,573.0,221.0,8.2531,419200.0,<1H OCEAN\n-116.98,33.26,12.0,5898.0,1002.0,3129.0,945.0,4.7647,254100.0,<1H OCEAN\n-116.99,33.2,17.0,2980.0,539.0,1531.0,505.0,3.1553,250000.0,<1H OCEAN\n-116.9,33.22,11.0,4132.0,773.0,2012.0,703.0,3.1906,234500.0,<1H OCEAN\n-117.14,33.23,11.0,4068.0,829.0,918.0,500.0,3.1272,281300.0,<1H OCEAN\n-117.11,33.23,13.0,5819.0,919.0,2228.0,866.0,4.9335,298100.0,<1H OCEAN\n-117.08,33.23,14.0,3337.0,571.0,1385.0,512.0,4.15,272200.0,<1H OCEAN\n-117.2,33.24,12.0,4992.0,,2106.0,801.0,6.2079,307300.0,<1H OCEAN\n-117.15,33.2,16.0,2690.0,459.0,1253.0,393.0,4.0328,294600.0,<1H OCEAN\n-117.2,33.2,16.0,4409.0,629.0,1875.0,609.0,5.543,286400.0,<1H OCEAN\n-117.22,33.22,17.0,3675.0,672.0,1693.0,597.0,3.3882,190800.0,<1H OCEAN\n-117.22,33.22,16.0,2134.0,643.0,1555.0,560.0,1.7217,175000.0,<1H OCEAN\n-117.22,33.22,15.0,1430.0,343.0,704.0,322.0,1.9571,162500.0,<1H OCEAN\n-117.23,33.24,26.0,1991.0,330.0,1014.0,304.0,4.3068,240100.0,<1H OCEAN\n-117.24,33.23,13.0,3756.0,648.0,1767.0,614.0,4.0776,196000.0,<1H OCEAN\n-117.24,33.23,21.0,1718.0,308.0,1194.0,312.0,3.4359,150900.0,<1H OCEAN\n-117.23,33.23,13.0,2899.0,657.0,1946.0,579.0,2.9875,172000.0,<1H OCEAN\n-117.23,33.22,18.0,2334.0,573.0,962.0,557.0,1.808,97000.0,<1H OCEAN\n-117.23,33.22,16.0,3224.0,729.0,1036.0,608.0,2.0246,148800.0,<1H OCEAN\n-117.24,33.22,20.0,1962.0,334.0,1173.0,349.0,4.1316,162500.0,<1H OCEAN\n-117.24,33.21,9.0,2486.0,626.0,1938.0,525.0,2.1293,151400.0,<1H OCEAN\n-117.23,33.21,34.0,544.0,108.0,348.0,127.0,4.125,164600.0,<1H OCEAN\n-117.25,33.25,6.0,6160.0,993.0,2997.0,1029.0,4.6187,205000.0,<1H OCEAN\n-117.27,33.23,5.0,20908.0,3933.0,9690.0,3510.0,4.1405,198500.0,<1H OCEAN\n-117.27,33.22,5.0,2283.0,337.0,999.0,325.0,5.0249,196700.0,<1H OCEAN\n-117.27,33.22,16.0,1420.0,311.0,470.0,313.0,1.8849,90800.0,<1H OCEAN\n-117.28,33.22,13.0,2832.0,542.0,1065.0,531.0,2.3844,98600.0,<1H OCEAN\n-117.27,33.21,5.0,5764.0,996.0,3161.0,1012.0,4.4531,177500.0,<1H OCEAN\n-117.25,33.22,26.0,2010.0,347.0,1160.0,331.0,3.9815,142600.0,<1H OCEAN\n-117.25,33.22,19.0,2167.0,443.0,1654.0,435.0,3.5,135800.0,<1H OCEAN\n-117.26,33.21,26.0,1906.0,408.0,1325.0,427.0,3.0197,136000.0,<1H OCEAN\n-117.28,33.2,11.0,1472.0,261.0,1012.0,285.0,4.21,175600.0,NEAR OCEAN\n-117.27,33.2,23.0,2145.0,379.0,1360.0,404.0,4.2054,150700.0,<1H OCEAN\n-117.26,33.2,13.0,3163.0,725.0,1675.0,629.0,2.8214,121900.0,<1H OCEAN\n-117.27,33.2,34.0,1852.0,322.0,978.0,332.0,4.3542,156900.0,<1H OCEAN\n-117.27,33.19,8.0,973.0,289.0,663.0,209.0,2.724,139300.0,NEAR OCEAN\n-117.28,33.19,5.0,2697.0,639.0,1633.0,580.0,3.4456,165800.0,NEAR OCEAN\n-117.25,33.21,9.0,1944.0,488.0,1992.0,453.0,2.066,127200.0,<1H OCEAN\n-117.24,33.21,19.0,1872.0,489.0,1859.0,446.0,2.1875,121700.0,<1H OCEAN\n-117.24,33.21,18.0,1846.0,419.0,1581.0,387.0,3.0982,111300.0,<1H OCEAN\n-117.24,33.2,25.0,1631.0,415.0,1045.0,386.0,2.4505,147500.0,<1H OCEAN\n-117.24,33.2,26.0,1701.0,404.0,989.0,367.0,2.5119,171700.0,<1H OCEAN\n-117.25,33.2,22.0,2361.0,618.0,1472.0,596.0,2.0625,124500.0,<1H OCEAN\n-117.25,33.2,10.0,2050.0,473.0,1302.0,471.0,2.7961,131300.0,<1H OCEAN\n-117.25,33.21,13.0,1203.0,292.0,1035.0,293.0,2.6339,117000.0,<1H OCEAN\n-117.23,33.21,21.0,1934.0,386.0,861.0,381.0,3.6181,213800.0,<1H OCEAN\n-117.22,33.21,19.0,4400.0,828.0,1901.0,735.0,3.6375,198800.0,<1H OCEAN\n-117.22,33.2,31.0,1736.0,277.0,801.0,292.0,4.4844,205500.0,<1H OCEAN\n-117.23,33.2,29.0,3372.0,720.0,1770.0,693.0,3.5109,166000.0,<1H OCEAN\n-117.23,33.2,21.0,2284.0,360.0,999.0,356.0,4.8929,212500.0,<1H OCEAN\n-117.21,33.2,22.0,3337.0,518.0,1288.0,466.0,5.04,253700.0,<1H OCEAN\n-117.22,33.19,16.0,3004.0,656.0,1948.0,606.0,2.7019,216900.0,<1H OCEAN\n-117.27,33.18,4.0,3371.0,773.0,1481.0,627.0,2.9133,215700.0,NEAR OCEAN\n-117.26,33.19,4.0,2342.0,595.0,1518.0,545.0,2.9469,216100.0,NEAR OCEAN\n-117.26,33.19,2.0,2629.0,509.0,1044.0,522.0,4.2361,158500.0,NEAR OCEAN\n-117.25,33.19,18.0,1891.0,306.0,830.0,279.0,4.5764,207000.0,<1H OCEAN\n-117.24,33.19,19.0,1569.0,351.0,1035.0,352.0,2.9191,159400.0,<1H OCEAN\n-117.23,33.19,22.0,2554.0,447.0,1147.0,422.0,3.6346,192500.0,<1H OCEAN\n-117.24,33.18,19.0,3337.0,565.0,1646.0,554.0,5.0195,200200.0,<1H OCEAN\n-117.3,33.17,6.0,7880.0,1533.0,3760.0,1460.0,4.1807,182600.0,NEAR OCEAN\n-117.32,33.17,18.0,2143.0,299.0,828.0,283.0,4.2383,239000.0,NEAR OCEAN\n-117.31,33.17,7.0,2349.0,312.0,809.0,282.0,5.552,283900.0,NEAR OCEAN\n-117.31,33.16,4.0,5846.0,894.0,2282.0,801.0,5.5956,247800.0,NEAR OCEAN\n-117.31,33.16,17.0,1704.0,263.0,781.0,281.0,5.6605,224400.0,NEAR OCEAN\n-117.29,33.18,15.0,3780.0,792.0,1632.0,721.0,2.7644,111400.0,NEAR OCEAN\n-117.29,33.18,17.0,821.0,176.0,436.0,168.0,3.1667,160600.0,NEAR OCEAN\n-117.28,33.18,16.0,3002.0,591.0,842.0,538.0,2.1205,157300.0,NEAR OCEAN\n-117.28,33.18,14.0,676.0,118.0,384.0,126.0,6.2096,178100.0,NEAR OCEAN\n-117.26,33.18,9.0,4540.0,793.0,2235.0,746.0,4.5781,225600.0,NEAR OCEAN\n-117.27,33.15,4.0,23915.0,4135.0,10877.0,3958.0,4.6357,244900.0,NEAR OCEAN\n-117.29,33.15,11.0,2560.0,445.0,952.0,448.0,4.0625,87500.0,NEAR OCEAN\n-117.24,33.17,4.0,9998.0,1874.0,3925.0,1672.0,4.2826,237500.0,<1H OCEAN\n-117.22,33.17,6.0,1487.0,362.0,810.0,322.0,3.625,135700.0,<1H OCEAN\n-117.23,33.16,4.0,3066.0,625.0,1164.0,582.0,4.0,228100.0,NEAR OCEAN\n-117.23,33.16,2.0,4624.0,946.0,2091.0,808.0,3.6736,214500.0,NEAR OCEAN\n-117.22,33.18,13.0,4273.0,886.0,2328.0,801.0,3.3444,183900.0,<1H OCEAN\n-117.21,33.17,16.0,1787.0,361.0,1446.0,362.0,3.75,163800.0,<1H OCEAN\n-117.21,33.19,21.0,3765.0,612.0,1722.0,593.0,4.8152,218500.0,<1H OCEAN\n-117.19,33.18,7.0,3561.0,722.0,1921.0,657.0,4.1128,209700.0,<1H OCEAN\n-117.21,33.16,13.0,2937.0,698.0,1246.0,579.0,2.6487,196000.0,<1H OCEAN\n-117.2,33.16,13.0,4503.0,1137.0,3094.0,1091.0,2.3159,91600.0,<1H OCEAN\n-117.2,33.15,11.0,4091.0,864.0,1927.0,765.0,3.0139,199000.0,<1H OCEAN\n-117.2,33.14,19.0,2025.0,414.0,1663.0,403.0,3.8147,139200.0,<1H OCEAN\n-117.22,33.14,5.0,4576.0,848.0,2314.0,705.0,5.0123,210400.0,NEAR OCEAN\n-117.21,33.14,12.0,4839.0,954.0,1708.0,952.0,2.8586,163300.0,<1H OCEAN\n-117.21,33.13,15.0,1889.0,368.0,754.0,409.0,2.2278,132800.0,NEAR OCEAN\n-117.17,33.18,25.0,596.0,115.0,426.0,137.0,3.0221,214300.0,<1H OCEAN\n-117.18,33.16,15.0,5923.0,1206.0,3943.0,1006.0,3.1793,159900.0,<1H OCEAN\n-117.15,33.16,5.0,4750.0,962.0,2726.0,905.0,3.5839,158500.0,<1H OCEAN\n-117.14,33.16,16.0,1660.0,,733.0,214.0,5.6874,202700.0,<1H OCEAN\n-117.13,33.15,15.0,2241.0,381.0,997.0,390.0,3.4833,193200.0,<1H OCEAN\n-117.14,33.15,17.0,1149.0,182.0,702.0,192.0,5.5696,168400.0,<1H OCEAN\n-117.14,33.15,16.0,1129.0,198.0,758.0,178.0,5.0346,174600.0,<1H OCEAN\n-117.15,33.14,15.0,1070.0,208.0,470.0,217.0,2.3062,158900.0,<1H OCEAN\n-117.13,33.15,16.0,3907.0,671.0,1759.0,663.0,3.1776,172600.0,<1H OCEAN\n-117.13,33.14,16.0,1649.0,278.0,993.0,277.0,4.8526,170700.0,<1H OCEAN\n-117.13,33.14,5.0,2618.0,539.0,1320.0,512.0,4.1053,171400.0,<1H OCEAN\n-117.12,33.14,16.0,1710.0,272.0,1025.0,267.0,4.1641,163600.0,<1H OCEAN\n-117.12,33.14,12.0,2363.0,408.0,1211.0,396.0,3.8967,172600.0,<1H OCEAN\n-117.13,33.14,12.0,2258.0,456.0,1147.0,433.0,4.0495,153900.0,<1H OCEAN\n-117.19,33.14,12.0,3652.0,923.0,1677.0,728.0,2.3267,92000.0,<1H OCEAN\n-117.18,33.15,7.0,6225.0,1683.0,5410.0,1580.0,2.32,117500.0,<1H OCEAN\n-117.21,33.13,19.0,3068.0,596.0,912.0,554.0,3.775,168000.0,NEAR OCEAN\n-117.2,33.12,18.0,4372.0,736.0,1473.0,675.0,5.1194,247800.0,NEAR OCEAN\n-117.21,33.12,4.0,3261.0,689.0,926.0,561.0,4.3672,258900.0,NEAR OCEAN\n-117.18,33.11,16.0,3470.0,601.0,1197.0,552.0,5.1814,279900.0,NEAR OCEAN\n-117.25,33.12,8.0,8552.0,1437.0,3335.0,1323.0,5.311,255800.0,NEAR OCEAN\n-117.24,33.11,10.0,3487.0,545.0,1410.0,557.0,6.0336,240300.0,NEAR OCEAN\n-117.23,33.1,4.0,1862.0,291.0,685.0,248.0,7.745,237400.0,NEAR OCEAN\n-117.25,33.1,14.0,3676.0,720.0,1176.0,614.0,3.9464,171900.0,NEAR OCEAN\n-117.26,33.09,22.0,2398.0,407.0,349.0,169.0,7.0423,500001.0,NEAR OCEAN\n-117.26,33.09,13.0,3730.0,761.0,1335.0,603.0,4.1667,227100.0,NEAR OCEAN\n-117.23,33.09,7.0,5320.0,855.0,2015.0,768.0,6.3373,279600.0,NEAR OCEAN\n-117.25,33.08,13.0,3651.0,465.0,1311.0,435.0,7.5402,340300.0,NEAR OCEAN\n-117.25,33.08,14.0,7208.0,1012.0,3097.0,1014.0,6.617,285200.0,NEAR OCEAN\n-117.26,33.08,12.0,5080.0,814.0,1958.0,716.0,5.3905,299600.0,NEAR OCEAN\n-117.09,33.13,9.0,5685.0,1442.0,3773.0,1250.0,3.0426,129900.0,<1H OCEAN\n-117.08,33.14,15.0,1497.0,250.0,827.0,239.0,4.3846,154200.0,<1H OCEAN\n-117.08,33.14,11.0,1430.0,292.0,921.0,294.0,4.2357,160900.0,<1H OCEAN\n-117.07,33.14,16.0,2546.0,429.0,1683.0,408.0,4.7426,160600.0,<1H OCEAN\n-117.07,33.15,17.0,1893.0,297.0,936.0,287.0,5.1842,157700.0,<1H OCEAN\n-117.06,33.15,24.0,2155.0,379.0,1158.0,360.0,4.7941,147500.0,<1H OCEAN\n-117.11,33.19,15.0,3154.0,488.0,1656.0,429.0,5.0461,222400.0,<1H OCEAN\n-117.03,33.18,17.0,5391.0,886.0,2732.0,830.0,5.1771,212800.0,<1H OCEAN\n-117.06,33.17,4.0,5465.0,974.0,2844.0,950.0,4.4477,174800.0,<1H OCEAN\n-117.1,33.17,12.0,2465.0,412.0,1226.0,428.0,5.4819,183800.0,<1H OCEAN\n-117.08,33.16,11.0,6341.0,1030.0,2697.0,977.0,4.8554,206700.0,<1H OCEAN\n-117.07,33.15,15.0,2994.0,522.0,1231.0,503.0,3.2024,180400.0,<1H OCEAN\n-117.08,33.14,19.0,2629.0,494.0,1444.0,503.0,3.5462,156800.0,<1H OCEAN\n-117.09,33.15,13.0,3958.0,865.0,1981.0,840.0,3.4764,137500.0,<1H OCEAN\n-117.07,33.13,17.0,6817.0,1632.0,4526.0,1474.0,2.6152,135300.0,<1H OCEAN\n-117.07,33.13,33.0,555.0,165.0,612.0,176.0,2.1786,137500.0,<1H OCEAN\n-117.06,33.14,27.0,3819.0,674.0,2447.0,717.0,3.8185,137200.0,<1H OCEAN\n-117.05,33.13,20.0,7746.0,2035.0,5370.0,1838.0,2.3762,98500.0,<1H OCEAN\n-117.04,33.15,15.0,13814.0,2888.0,6583.0,2789.0,2.8247,150000.0,<1H OCEAN\n-117.05,33.14,16.0,4552.0,1166.0,2737.0,1051.0,2.25,136300.0,<1H OCEAN\n-117.06,33.13,12.0,8742.0,2114.0,4854.0,1957.0,2.8015,143500.0,<1H OCEAN\n-117.08,33.13,17.0,8466.0,2628.0,7014.0,2267.0,2.1437,113700.0,<1H OCEAN\n-117.08,33.12,43.0,107.0,44.0,107.0,48.0,0.7054,137500.0,<1H OCEAN\n-117.11,33.15,14.0,8374.0,1407.0,2916.0,1295.0,4.7019,191100.0,<1H OCEAN\n-117.1,33.15,5.0,3159.0,685.0,1398.0,581.0,3.1467,161100.0,<1H OCEAN\n-117.12,33.15,7.0,2810.0,464.0,1564.0,457.0,4.4655,182800.0,<1H OCEAN\n-117.14,33.18,11.0,5546.0,974.0,2300.0,970.0,3.7109,199800.0,<1H OCEAN\n-117.1,33.14,7.0,10665.0,2576.0,4917.0,2424.0,2.3171,159500.0,<1H OCEAN\n-117.11,33.14,10.0,3208.0,636.0,1395.0,582.0,3.4455,190500.0,<1H OCEAN\n-117.14,33.12,7.0,6126.0,1032.0,2662.0,923.0,4.9005,264000.0,<1H OCEAN\n-117.13,33.13,17.0,3164.0,652.0,1123.0,699.0,2.082,80000.0,<1H OCEAN\n-117.11,33.12,46.0,52.0,13.0,59.0,13.0,3.875,200000.0,<1H OCEAN\n-117.1,33.12,12.0,961.0,342.0,315.0,201.0,0.813,275000.0,<1H OCEAN\n-117.11,33.11,17.0,2641.0,627.0,1167.0,647.0,2.2875,132400.0,<1H OCEAN\n-117.14,33.07,12.0,9302.0,1603.0,4074.0,1504.0,4.3513,199600.0,<1H OCEAN\n-117.12,33.07,45.0,1032.0,235.0,363.0,177.0,3.6389,186600.0,<1H OCEAN\n-117.1,33.07,16.0,2402.0,336.0,1080.0,365.0,8.6803,347300.0,<1H OCEAN\n-117.07,33.07,8.0,2756.0,343.0,1045.0,340.0,8.5957,444100.0,<1H OCEAN\n-117.1,33.09,5.0,12045.0,2162.0,5640.0,1997.0,4.4375,353000.0,<1H OCEAN\n-117.08,33.08,23.0,3400.0,501.0,1383.0,488.0,4.9844,249100.0,<1H OCEAN\n-117.09,33.1,21.0,2876.0,539.0,1387.0,499.0,3.8292,177000.0,<1H OCEAN\n-117.08,33.09,23.0,3792.0,624.0,1988.0,658.0,4.7566,178300.0,<1H OCEAN\n-117.09,33.12,11.0,567.0,184.0,620.0,163.0,2.5284,122500.0,<1H OCEAN\n-117.08,33.11,31.0,1356.0,324.0,1301.0,331.0,2.5331,115100.0,<1H OCEAN\n-117.08,33.11,28.0,2094.0,585.0,1556.0,563.0,2.2,127700.0,<1H OCEAN\n-117.09,33.11,32.0,1713.0,321.0,891.0,286.0,3.1429,171600.0,<1H OCEAN\n-117.08,33.12,37.0,1060.0,268.0,823.0,229.0,1.8363,145500.0,<1H OCEAN\n-117.07,33.11,31.0,2055.0,473.0,1326.0,427.0,3.0915,139900.0,<1H OCEAN\n-117.08,33.11,31.0,1832.0,444.0,1669.0,463.0,2.2146,116700.0,<1H OCEAN\n-117.08,33.12,33.0,674.0,208.0,565.0,188.0,1.875,114300.0,<1H OCEAN\n-117.07,33.12,21.0,4578.0,927.0,2818.0,900.0,3.1458,187700.0,<1H OCEAN\n-117.07,33.12,32.0,2474.0,499.0,1224.0,461.0,2.7216,146300.0,<1H OCEAN\n-117.07,33.12,12.0,2453.0,599.0,1251.0,529.0,2.4122,127000.0,<1H OCEAN\n-117.07,33.11,17.0,5565.0,1237.0,3004.0,1139.0,3.0054,142300.0,<1H OCEAN\n-117.06,33.09,11.0,7483.0,1276.0,3516.0,1261.0,4.0484,262500.0,<1H OCEAN\n-117.03,33.13,15.0,7000.0,1185.0,3555.0,1118.0,4.7022,172800.0,<1H OCEAN\n-116.97,33.13,10.0,5149.0,851.0,2177.0,783.0,6.7957,287500.0,<1H OCEAN\n-117.05,33.13,17.0,2385.0,372.0,1118.0,369.0,4.2813,169900.0,<1H OCEAN\n-117.05,33.11,18.0,4393.0,642.0,2095.0,677.0,5.4786,223500.0,<1H OCEAN\n-117.05,33.13,22.0,2427.0,390.0,1099.0,362.0,5.2323,167500.0,<1H OCEAN\n-117.05,33.1,13.0,5516.0,746.0,2119.0,662.0,6.1868,320200.0,<1H OCEAN\n-117.03,33.12,25.0,3142.0,446.0,1286.0,419.0,5.4663,248300.0,<1H OCEAN\n-117.04,33.09,16.0,4677.0,581.0,1902.0,566.0,6.1834,335600.0,<1H OCEAN\n-116.93,33.06,16.0,3490.0,545.0,1628.0,535.0,4.8836,239900.0,<1H OCEAN\n-116.89,32.99,14.0,2816.0,501.0,1448.0,452.0,5.0278,210900.0,<1H OCEAN\n-116.95,32.96,18.0,2087.0,353.0,992.0,329.0,4.5,222600.0,<1H OCEAN\n-116.84,33.08,15.0,2755.0,519.0,1474.0,460.0,4.0408,225900.0,<1H OCEAN\n-116.77,33.08,13.0,1406.0,260.0,737.0,279.0,5.5842,239100.0,<1H OCEAN\n-116.78,33.0,7.0,12480.0,1946.0,5102.0,1697.0,5.5102,221000.0,<1H OCEAN\n-116.84,33.01,5.0,5673.0,855.0,2592.0,797.0,5.4155,199200.0,<1H OCEAN\n-116.88,33.05,11.0,7217.0,1583.0,4197.0,1502.0,2.8827,166700.0,<1H OCEAN\n-116.88,33.02,16.0,3204.0,541.0,1818.0,529.0,5.2596,171500.0,<1H OCEAN\n-116.9,33.03,11.0,3213.0,634.0,1975.0,579.0,3.475,167200.0,<1H OCEAN\n-116.86,33.05,17.0,9044.0,1689.0,5030.0,1596.0,3.6348,164500.0,<1H OCEAN\n-116.86,33.02,17.0,401.0,68.0,251.0,69.0,4.6518,170200.0,<1H OCEAN\n-116.84,33.02,11.0,1227.0,202.0,645.0,198.0,5.6674,209800.0,<1H OCEAN\n-116.74,33.33,17.0,4190.0,946.0,1802.0,673.0,2.4744,158200.0,INLAND\n-116.68,33.16,26.0,1820.0,374.0,1001.0,324.0,2.1797,156300.0,INLAND\n-116.6,33.06,23.0,1731.0,365.0,612.0,258.0,2.7813,172900.0,INLAND\n-116.58,33.09,36.0,992.0,224.0,334.0,126.0,3.0089,134400.0,INLAND\n-116.56,33.05,15.0,1985.0,361.0,536.0,209.0,4.125,148200.0,INLAND\n-116.61,33.04,11.0,2522.0,538.0,616.0,269.0,3.875,198100.0,INLAND\n-116.66,33.09,24.0,1378.0,272.0,532.0,188.0,1.5909,221900.0,INLAND\n-116.67,32.97,16.0,349.0,74.0,120.0,43.0,5.359,193800.0,<1H OCEAN\n-116.52,32.9,18.0,4454.0,852.0,1754.0,656.0,4.57,189900.0,INLAND\n-116.34,33.36,24.0,2746.0,514.0,731.0,295.0,3.3214,176400.0,INLAND\n-116.32,33.28,19.0,1791.0,367.0,327.0,185.0,3.3625,100000.0,INLAND\n-116.26,33.07,17.0,934.0,284.0,452.0,184.0,1.9875,83700.0,INLAND\n-116.28,32.84,18.0,382.0,128.0,194.0,69.0,2.5179,58800.0,INLAND\n-116.37,33.19,12.0,4890.0,1152.0,1289.0,570.0,2.5795,98700.0,INLAND\n-116.58,32.69,19.0,4085.0,876.0,2133.0,718.0,2.919,116500.0,INLAND\n-116.45,32.65,22.0,2680.0,643.0,1644.0,516.0,2.1949,127100.0,INLAND\n-116.35,32.74,16.0,2595.0,606.0,1046.0,367.0,1.7137,110700.0,INLAND\n-116.2,32.64,28.0,1608.0,409.0,567.0,254.0,1.4648,61800.0,INLAND\n-116.75,32.85,17.0,4863.0,845.0,2266.0,769.0,4.2321,217400.0,<1H OCEAN\n-116.79,32.84,12.0,4281.0,786.0,1891.0,721.0,3.5769,231800.0,<1H OCEAN\n-116.76,32.84,16.0,3311.0,702.0,1627.0,624.0,3.1196,187200.0,<1H OCEAN\n-116.77,32.82,16.0,3688.0,817.0,1969.0,767.0,2.875,222900.0,<1H OCEAN\n-116.75,32.82,17.0,3348.0,481.0,1222.0,443.0,6.6361,308600.0,<1H OCEAN\n-116.8,32.8,11.0,3874.0,565.0,1672.0,546.0,6.1481,274600.0,<1H OCEAN\n-116.62,32.86,18.0,4115.0,847.0,2032.0,745.0,4.0159,169100.0,<1H OCEAN\n-116.66,32.79,13.0,843.0,,918.0,152.0,6.2152,240600.0,<1H OCEAN\n-116.87,32.75,15.0,2053.0,321.0,993.0,309.0,5.5164,248900.0,<1H OCEAN\n-116.91,32.73,8.0,4630.0,624.0,2048.0,575.0,6.4745,300300.0,<1H OCEAN\n-116.87,32.72,13.0,3268.0,491.0,1431.0,503.0,5.7652,259900.0,<1H OCEAN\n-116.89,32.67,9.0,2652.0,393.0,1355.0,362.0,6.2578,293100.0,<1H OCEAN\n-116.76,32.74,14.0,4085.0,751.0,2129.0,688.0,4.7367,214500.0,<1H OCEAN\n-116.79,32.61,19.0,2652.0,520.0,1421.0,491.0,3.5227,206100.0,<1H OCEAN\n-122.41,37.81,25.0,1178.0,545.0,592.0,441.0,3.6728,500001.0,NEAR BAY\n-122.41,37.81,31.0,3991.0,1311.0,2305.0,1201.0,1.8981,500001.0,NEAR BAY\n-122.42,37.81,52.0,1314.0,317.0,473.0,250.0,4.3472,500001.0,NEAR BAY\n-122.42,37.8,52.0,2852.0,581.0,838.0,510.0,8.0755,500001.0,NEAR BAY\n-122.42,37.8,52.0,4985.0,1355.0,1848.0,1255.0,4.9211,500001.0,NEAR BAY\n-122.42,37.8,50.0,2494.0,731.0,958.0,712.0,3.2356,500001.0,NEAR BAY\n-122.42,37.8,52.0,741.0,170.0,277.0,165.0,4.4712,500001.0,NEAR BAY\n-122.41,37.8,52.0,2583.0,672.0,1335.0,613.0,3.1477,500001.0,NEAR BAY\n-122.41,37.8,52.0,2618.0,611.0,1328.0,559.0,4.1607,350000.0,NEAR BAY\n-122.41,37.8,52.0,3278.0,775.0,1279.0,709.0,5.4378,500001.0,NEAR BAY\n-122.41,37.8,52.0,812.0,252.0,629.0,247.0,2.5875,500001.0,NEAR BAY\n-122.41,37.8,52.0,4088.0,946.0,1906.0,863.0,3.6065,433300.0,NEAR BAY\n-122.41,37.8,52.0,1288.0,309.0,437.0,272.0,6.3245,500001.0,NEAR BAY\n-122.4,37.8,52.0,1860.0,575.0,679.0,448.0,4.775,500001.0,NEAR BAY\n-122.4,37.8,52.0,2094.0,568.0,920.0,503.0,4.2015,412500.0,NEAR BAY\n-122.4,37.81,12.0,1349.0,349.0,536.0,334.0,7.7852,250000.0,NEAR BAY\n-122.41,37.8,52.0,2450.0,741.0,1415.0,664.0,2.8229,375000.0,NEAR BAY\n-122.4,37.8,52.0,1642.0,570.0,1432.0,513.0,1.9063,300000.0,NEAR BAY\n-122.41,37.8,52.0,1394.0,395.0,1700.0,400.0,2.75,168800.0,NEAR BAY\n-122.41,37.8,52.0,1724.0,416.0,1016.0,395.0,3.3839,400000.0,NEAR BAY\n-122.41,37.8,52.0,1866.0,748.0,2957.0,710.0,1.8295,243800.0,NEAR BAY\n-122.41,37.8,30.0,1821.0,738.0,1648.0,684.0,0.8836,450000.0,NEAR BAY\n-122.41,37.8,52.0,3697.0,837.0,1446.0,711.0,5.866,500001.0,NEAR BAY\n-122.41,37.8,52.0,2892.0,751.0,1785.0,733.0,3.5746,350000.0,NEAR BAY\n-122.41,37.79,52.0,3598.0,1011.0,2062.0,966.0,2.9871,380000.0,NEAR BAY\n-122.42,37.8,52.0,2797.0,685.0,1156.0,651.0,4.3472,500001.0,NEAR BAY\n-122.42,37.8,52.0,3823.0,1040.0,1830.0,977.0,4.2458,450000.0,NEAR BAY\n-122.42,37.8,52.0,3321.0,1115.0,1576.0,1034.0,2.0987,458300.0,NEAR BAY\n-122.42,37.8,52.0,1777.0,486.0,932.0,427.0,3.3643,420000.0,NEAR BAY\n-122.42,37.8,52.0,3067.0,870.0,2122.0,850.0,2.5603,287500.0,NEAR BAY\n-122.42,37.79,52.0,3457.0,1021.0,2286.0,994.0,2.565,225000.0,NEAR BAY\n-122.42,37.79,52.0,3364.0,1100.0,2112.0,1045.0,2.1343,400000.0,NEAR BAY\n-122.42,37.79,52.0,3511.0,1232.0,2452.0,1131.0,2.5013,275000.0,NEAR BAY\n-122.41,37.79,52.0,2909.0,851.0,1711.0,830.0,3.0296,500001.0,NEAR BAY\n-122.41,37.79,52.0,3302.0,869.0,1178.0,727.0,3.3681,500001.0,NEAR BAY\n-122.41,37.79,52.0,2161.0,544.0,904.0,431.0,3.5066,350000.0,NEAR BAY\n-122.41,37.8,52.0,1999.0,642.0,1846.0,620.0,1.9145,225000.0,NEAR BAY\n-122.41,37.79,52.0,2302.0,938.0,1515.0,861.0,1.3668,55000.0,NEAR BAY\n-122.41,37.8,52.0,3260.0,1535.0,3260.0,1457.0,0.9,500001.0,NEAR BAY\n-122.39,37.8,25.0,4561.0,1474.0,1525.0,1169.0,4.5581,500001.0,NEAR BAY\n-122.4,37.79,52.0,1185.0,660.0,1007.0,623.0,1.4552,450000.0,NEAR BAY\n-122.41,37.79,52.0,1436.0,738.0,1688.0,662.0,1.5156,237500.0,NEAR BAY\n-122.41,37.79,52.0,3610.0,1286.0,1504.0,1047.0,3.2059,500001.0,NEAR BAY\n-122.41,37.79,52.0,6016.0,2509.0,3436.0,2119.0,2.5166,275000.0,NEAR BAY\n-122.42,37.79,52.0,2737.0,1241.0,1761.0,1029.0,1.8068,225000.0,NEAR BAY\n-122.41,37.79,52.0,5783.0,2747.0,4518.0,2538.0,1.724,225000.0,NEAR BAY\n-122.42,37.78,26.0,812.0,507.0,628.0,445.0,2.3304,500001.0,NEAR BAY\n-122.42,37.78,27.0,1728.0,884.0,1211.0,752.0,0.8543,500001.0,NEAR BAY\n-122.43,37.81,39.0,3275.0,837.0,1137.0,725.0,3.7679,500001.0,NEAR BAY\n-122.44,37.8,52.0,3830.0,,1310.0,963.0,3.4801,500001.0,NEAR BAY\n-122.44,37.8,52.0,1724.0,412.0,540.0,319.0,4.2857,500001.0,NEAR BAY\n-122.43,37.81,52.0,4309.0,942.0,1297.0,798.0,4.6781,500001.0,NEAR BAY\n-122.45,37.81,52.0,1375.0,322.0,287.0,184.0,3.9028,500001.0,NEAR BAY\n-122.44,37.8,52.0,2869.0,594.0,500.0,335.0,5.0376,500001.0,NEAR BAY\n-122.44,37.8,52.0,3257.0,735.0,1045.0,620.0,4.5523,500001.0,NEAR BAY\n-122.44,37.8,52.0,1580.0,470.0,714.0,448.0,3.2447,500001.0,NEAR BAY\n-122.44,37.8,52.0,1006.0,291.0,445.0,257.0,2.7717,500000.0,NEAR BAY\n-122.44,37.8,52.0,3149.0,719.0,1145.0,658.0,4.625,500001.0,NEAR BAY\n-122.44,37.8,52.0,3161.0,472.0,842.0,410.0,7.9761,500001.0,NEAR BAY\n-122.44,37.8,52.0,2865.0,593.0,1029.0,577.0,5.2539,500001.0,NEAR BAY\n-122.44,37.8,52.0,1603.0,487.0,727.0,464.0,3.9856,500001.0,NEAR BAY\n-122.43,37.8,52.0,3172.0,848.0,1259.0,806.0,4.1047,466700.0,NEAR BAY\n-122.43,37.8,52.0,2788.0,813.0,1302.0,764.0,4.199,400000.0,NEAR BAY\n-122.43,37.8,52.0,2994.0,821.0,1240.0,779.0,3.3715,500000.0,NEAR BAY\n-122.43,37.8,52.0,1976.0,726.0,1045.0,669.0,3.6893,475000.0,NEAR BAY\n-122.43,37.8,52.0,1006.0,251.0,349.0,233.0,3.2235,500000.0,NEAR BAY\n-122.43,37.8,52.0,1380.0,322.0,553.0,288.0,4.0417,500001.0,NEAR BAY\n-122.42,37.8,52.0,2657.0,772.0,1014.0,685.0,4.038,500001.0,NEAR BAY\n-122.43,37.8,52.0,2696.0,572.0,925.0,552.0,5.0365,500000.0,NEAR BAY\n-122.43,37.8,52.0,2520.0,649.0,959.0,607.0,5.7934,500001.0,NEAR BAY\n-122.43,37.8,52.0,2802.0,622.0,954.0,572.0,4.5399,500001.0,NEAR BAY\n-122.42,37.8,52.0,4079.0,1112.0,1466.0,1024.0,4.5913,500001.0,NEAR BAY\n-122.42,37.79,48.0,4506.0,1342.0,1980.0,1239.0,4.0156,500001.0,NEAR BAY\n-122.43,37.79,52.0,3565.0,892.0,1377.0,852.0,3.8068,500001.0,NEAR BAY\n-122.43,37.79,52.0,3486.0,847.0,1248.0,813.0,7.2623,500001.0,NEAR BAY\n-122.43,37.79,52.0,6186.0,1566.0,2065.0,1374.0,5.8543,500001.0,NEAR BAY\n-122.44,37.79,52.0,1979.0,359.0,648.0,370.0,5.3124,500001.0,NEAR BAY\n-122.44,37.79,52.0,1335.0,151.0,402.0,157.0,10.8783,500001.0,NEAR BAY\n-122.44,37.79,52.0,2045.0,353.0,722.0,327.0,8.0755,500001.0,NEAR BAY\n-122.44,37.79,52.0,1447.0,186.0,483.0,181.0,15.0001,500001.0,NEAR BAY\n-122.45,37.79,52.0,2196.0,280.0,668.0,291.0,10.0914,500001.0,NEAR BAY\n-122.45,37.79,52.0,3069.0,579.0,1107.0,536.0,5.5634,500001.0,NEAR BAY\n-122.45,37.79,52.0,1734.0,482.0,731.0,429.0,1.4804,425000.0,NEAR BAY\n-122.45,37.79,52.0,1457.0,215.0,495.0,208.0,10.7097,500001.0,NEAR BAY\n-122.46,37.79,52.0,899.0,96.0,304.0,110.0,14.2959,500001.0,NEAR BAY\n-122.46,37.79,52.0,2106.0,373.0,743.0,348.0,5.2909,500001.0,NEAR BAY\n-122.44,37.79,52.0,1817.0,535.0,800.0,487.0,3.975,500001.0,NEAR BAY\n-122.44,37.79,52.0,3640.0,840.0,1525.0,796.0,4.4375,500001.0,NEAR BAY\n-122.44,37.79,52.0,3785.0,808.0,1371.0,799.0,6.4209,500001.0,NEAR BAY\n-122.43,37.79,52.0,3522.0,938.0,1319.0,887.0,4.3986,500001.0,NEAR BAY\n-122.43,37.79,52.0,3219.0,969.0,1152.0,830.0,4.2042,500001.0,NEAR BAY\n-122.42,37.79,52.0,2511.0,895.0,1202.0,804.0,2.6607,87500.0,NEAR BAY\n-122.42,37.79,6.0,670.0,301.0,655.0,284.0,3.4423,117500.0,NEAR BAY\n-122.43,37.79,50.0,3312.0,1095.0,1475.0,997.0,2.7165,500001.0,NEAR BAY\n-122.43,37.79,52.0,3020.0,842.0,1294.0,769.0,3.4375,500001.0,NEAR BAY\n-122.43,37.79,25.0,1637.0,394.0,649.0,379.0,5.0049,460000.0,NEAR BAY\n-122.44,37.79,52.0,1903.0,461.0,831.0,433.0,4.4464,500001.0,NEAR BAY\n-122.44,37.79,52.0,2083.0,491.0,1224.0,483.0,4.0882,468800.0,NEAR BAY\n-122.44,37.79,52.0,1726.0,384.0,614.0,356.0,3.6812,500000.0,NEAR BAY\n-122.44,37.78,52.0,2695.0,657.0,1243.0,573.0,2.8569,372200.0,NEAR BAY\n-122.45,37.78,45.0,2747.0,699.0,1320.0,693.0,3.1576,333300.0,NEAR BAY\n-122.45,37.79,46.0,2009.0,464.0,761.0,453.0,3.7188,500001.0,NEAR BAY\n-122.45,37.78,52.0,3975.0,716.0,1515.0,691.0,5.0156,500001.0,NEAR BAY\n-122.43,37.79,24.0,2459.0,1001.0,1362.0,957.0,2.6782,450000.0,NEAR BAY\n-122.43,37.78,10.0,2380.0,843.0,1245.0,789.0,1.3062,220000.0,NEAR BAY\n-122.44,37.78,39.0,1181.0,310.0,901.0,281.0,1.4866,237500.0,NEAR BAY\n-122.45,37.78,52.0,1345.0,291.0,560.0,294.0,3.7159,494400.0,NEAR BAY\n-122.45,37.78,52.0,2686.0,606.0,1392.0,578.0,4.0,355300.0,NEAR BAY\n-122.46,37.78,47.0,1682.0,379.0,837.0,375.0,5.2806,400000.0,NEAR BAY\n-122.44,37.78,37.0,1235.0,314.0,481.0,297.0,3.6875,492300.0,NEAR BAY\n-122.44,37.78,52.0,3510.0,791.0,1703.0,657.0,2.8654,280000.0,NEAR BAY\n-122.44,37.78,44.0,1545.0,334.0,561.0,326.0,3.875,412500.0,NEAR BAY\n-122.45,37.78,43.0,1452.0,397.0,897.0,393.0,4.1319,322700.0,NEAR BAY\n-122.45,37.78,48.0,1013.0,194.0,464.0,205.0,3.2011,428300.0,NEAR BAY\n-122.45,37.78,52.0,2033.0,438.0,2198.0,418.0,3.6667,418400.0,NEAR BAY\n-122.44,37.78,16.0,883.0,236.0,601.0,219.0,2.151,146900.0,NEAR BAY\n-122.43,37.78,49.0,2246.0,587.0,1277.0,546.0,2.9792,350000.0,NEAR BAY\n-122.44,37.78,52.0,3017.0,851.0,1588.0,800.0,3.3882,471400.0,NEAR BAY\n-122.44,37.78,52.0,2747.0,736.0,1309.0,653.0,2.943,341700.0,NEAR BAY\n-122.44,37.78,52.0,1118.0,279.0,514.0,284.0,2.4196,346200.0,NEAR BAY\n-122.44,37.78,31.0,1364.0,386.0,707.0,379.0,3.1607,293800.0,NEAR BAY\n-122.43,37.78,26.0,3587.0,1034.0,1821.0,936.0,2.6392,287500.0,NEAR BAY\n-122.43,37.78,2.0,1205.0,468.0,577.0,363.0,3.6437,275000.0,NEAR BAY\n-122.42,37.78,19.0,4065.0,1645.0,2079.0,1470.0,3.1462,187500.0,NEAR BAY\n-122.42,37.78,17.0,1257.0,339.0,1093.0,384.0,1.8438,72500.0,NEAR BAY\n-122.43,37.78,17.0,2728.0,908.0,1670.0,893.0,1.077,115000.0,NEAR BAY\n-122.43,37.78,24.0,2037.0,696.0,1371.0,585.0,0.9355,112500.0,NEAR BAY\n-122.42,37.78,52.0,989.0,425.0,634.0,341.0,2.4414,275000.0,NEAR BAY\n-122.42,37.78,52.0,1254.0,469.0,895.0,456.0,2.1516,187500.0,NEAR BAY\n-122.43,37.78,29.0,1310.0,364.0,1009.0,379.0,1.3844,177500.0,NEAR BAY\n-122.43,37.78,52.0,1952.0,628.0,1284.0,576.0,2.105,316700.0,NEAR BAY\n-122.43,37.78,52.0,4014.0,1069.0,2070.0,927.0,2.8202,442900.0,NEAR BAY\n-122.43,37.77,52.0,3944.0,1072.0,1913.0,973.0,2.9567,425000.0,NEAR BAY\n-122.44,37.78,52.0,2911.0,753.0,1696.0,676.0,2.5721,475000.0,NEAR BAY\n-122.44,37.77,52.0,2705.0,647.0,1355.0,628.0,2.0161,364300.0,NEAR BAY\n-122.45,37.77,52.0,2339.0,548.0,1090.0,507.0,3.3679,350000.0,NEAR BAY\n-122.45,37.77,52.0,3188.0,708.0,1526.0,664.0,3.3068,500001.0,NEAR BAY\n-122.45,37.77,52.0,1722.0,465.0,885.0,437.0,3.0906,500001.0,NEAR BAY\n-122.44,37.77,52.0,3475.0,807.0,1518.0,777.0,3.6186,500001.0,NEAR BAY\n-122.45,37.77,52.0,2296.0,509.0,1039.0,472.0,4.1417,500000.0,NEAR BAY\n-122.45,37.77,52.0,2191.0,627.0,1100.0,585.0,3.0409,500000.0,NEAR BAY\n-122.45,37.77,52.0,2645.0,626.0,1275.0,553.0,3.35,492900.0,NEAR BAY\n-122.43,37.77,52.0,3563.0,832.0,1712.0,787.0,3.3702,335700.0,NEAR BAY\n-122.43,37.77,52.0,1760.0,366.0,742.0,318.0,4.445,400000.0,NEAR BAY\n-122.44,37.77,52.0,2002.0,520.0,939.0,501.0,3.2239,488900.0,NEAR BAY\n-122.44,37.77,52.0,2994.0,736.0,1428.0,700.0,3.0766,438900.0,NEAR BAY\n-122.42,37.77,52.0,1086.0,349.0,589.0,361.0,2.5186,250000.0,NEAR BAY\n-122.42,37.77,52.0,1925.0,568.0,867.0,515.0,2.879,450000.0,NEAR BAY\n-122.43,37.77,52.0,1567.0,482.0,654.0,425.0,2.6914,366700.0,NEAR BAY\n-122.43,37.77,52.0,2514.0,729.0,1428.0,597.0,2.3977,412500.0,NEAR BAY\n-122.43,37.77,52.0,2416.0,620.0,1188.0,591.0,2.3887,337500.0,NEAR BAY\n-122.43,37.77,52.0,1862.0,472.0,872.0,471.0,3.2981,222700.0,NEAR BAY\n-122.43,37.77,52.0,4397.0,1116.0,1939.0,1053.0,2.7587,354500.0,NEAR BAY\n-122.43,37.77,52.0,2685.0,629.0,1170.0,614.0,3.6894,418800.0,NEAR BAY\n-122.44,37.77,52.0,2537.0,559.0,849.0,530.0,5.1788,476900.0,NEAR BAY\n-122.44,37.77,52.0,5604.0,1268.0,2023.0,1196.0,4.4085,400000.0,NEAR BAY\n-122.44,37.76,38.0,2202.0,452.0,833.0,435.0,6.8939,455900.0,NEAR BAY\n-122.44,37.77,52.0,3225.0,667.0,1494.0,619.0,4.4875,500001.0,NEAR BAY\n-122.44,37.77,52.0,3505.0,745.0,1374.0,714.0,4.3667,500001.0,NEAR BAY\n-122.45,37.76,50.0,2518.0,507.0,979.0,516.0,4.6912,500001.0,NEAR BAY\n-122.45,37.76,52.0,1457.0,292.0,621.0,315.0,4.6477,450000.0,NEAR BAY\n-122.45,37.77,52.0,3095.0,682.0,1269.0,639.0,3.575,500001.0,NEAR BAY\n-122.45,37.77,52.0,3939.0,852.0,1737.0,797.0,4.5052,500001.0,NEAR BAY\n-122.41,37.78,52.0,1928.0,836.0,2124.0,739.0,1.1185,55000.0,NEAR BAY\n-122.41,37.78,52.0,1534.0,763.0,1520.0,614.0,1.4554,375000.0,NEAR BAY\n-122.41,37.78,52.0,254.0,72.0,153.0,29.0,3.8625,350000.0,NEAR BAY\n-122.41,37.77,52.0,361.0,76.0,168.0,55.0,3.2292,275000.0,NEAR BAY\n-122.41,37.77,52.0,1963.0,565.0,1628.0,524.0,2.6083,193800.0,NEAR BAY\n-122.4,37.78,32.0,352.0,132.0,313.0,105.0,2.5742,350000.0,NEAR BAY\n-122.41,37.77,52.0,849.0,276.0,582.0,222.0,3.4671,250000.0,NEAR BAY\n-122.41,37.78,52.0,1014.0,422.0,1055.0,382.0,1.8519,32500.0,NEAR BAY\n-122.39,37.78,3.0,3464.0,1179.0,1441.0,919.0,4.7105,275000.0,NEAR BAY\n-122.39,37.78,5.0,1405.0,515.0,725.0,392.0,3.6037,187500.0,NEAR BAY\n-122.39,37.79,52.0,94.0,24.0,113.0,27.0,4.6563,350000.0,NEAR BAY\n-122.37,37.81,26.0,5416.0,1045.0,4531.0,962.0,2.7909,250000.0,NEAR BAY\n-122.4,37.78,52.0,464.0,202.0,286.0,148.0,1.6125,112500.0,NEAR BAY\n-122.4,37.77,52.0,144.0,63.0,1061.0,68.0,4.3958,225000.0,NEAR BAY\n-122.42,37.77,52.0,759.0,323.0,421.0,255.0,2.0548,162500.0,NEAR BAY\n-122.42,37.77,52.0,1176.0,493.0,1136.0,436.0,1.375,312500.0,NEAR BAY\n-122.42,37.76,37.0,1291.0,588.0,1846.0,557.0,1.3365,225000.0,NEAR BAY\n-122.42,37.77,52.0,2185.0,656.0,1266.0,626.0,2.7794,350000.0,NEAR BAY\n-122.42,37.77,52.0,4226.0,1315.0,2619.0,1242.0,2.5755,325000.0,NEAR BAY\n-122.42,37.76,52.0,3587.0,1030.0,2259.0,979.0,2.5403,250000.0,NEAR BAY\n-122.43,37.77,52.0,2714.0,779.0,1438.0,733.0,3.6031,275000.0,NEAR BAY\n-122.43,37.76,52.0,1582.0,353.0,868.0,329.0,3.8261,250000.0,NEAR BAY\n-122.43,37.76,52.0,2250.0,566.0,1051.0,562.0,2.8458,350000.0,NEAR BAY\n-122.44,37.76,52.0,2959.0,683.0,1145.0,666.0,4.2222,361600.0,NEAR BAY\n-122.44,37.76,30.0,5089.0,1210.0,1935.0,1139.0,4.6053,386100.0,NEAR BAY\n-122.44,37.75,28.0,4930.0,1381.0,2232.0,1321.0,4.3232,316200.0,NEAR BAY\n-122.44,37.76,35.0,1581.0,422.0,580.0,388.0,4.05,423100.0,NEAR BAY\n-122.44,37.76,50.0,2589.0,569.0,945.0,544.0,5.2519,376600.0,NEAR BAY\n-122.45,37.76,51.0,2564.0,457.0,810.0,442.0,5.6235,500001.0,NEAR BAY\n-122.44,37.76,52.0,1968.0,472.0,784.0,430.0,3.3702,370000.0,NEAR BAY\n-122.44,37.76,52.0,2110.0,454.0,816.0,438.0,3.9079,370000.0,NEAR BAY\n-122.44,37.76,52.0,2509.0,496.0,855.0,478.0,5.0731,405400.0,NEAR BAY\n-122.43,37.76,52.0,3771.0,1017.0,1575.0,921.0,3.5655,427300.0,NEAR BAY\n-122.43,37.76,52.0,2242.0,459.0,751.0,464.0,4.75,500001.0,NEAR BAY\n-122.43,37.76,52.0,2356.0,501.0,909.0,481.0,4.2569,455400.0,NEAR BAY\n-122.43,37.76,52.0,3708.0,849.0,1531.0,822.0,3.3565,386400.0,NEAR BAY\n-122.42,37.76,52.0,4407.0,1192.0,2280.0,1076.0,3.3937,270000.0,NEAR BAY\n-122.42,37.76,52.0,4001.0,1084.0,2129.0,1037.0,3.5052,391200.0,NEAR BAY\n-122.42,37.76,52.0,2088.0,487.0,1082.0,488.0,2.6803,490000.0,NEAR BAY\n-122.42,37.76,52.0,1190.0,400.0,1270.0,332.0,2.0329,225000.0,NEAR BAY\n-122.42,37.76,52.0,2038.0,629.0,2007.0,596.0,2.5701,266700.0,NEAR BAY\n-122.42,37.76,52.0,1494.0,610.0,1630.0,590.0,1.65,265000.0,NEAR BAY\n-122.42,37.76,46.0,2150.0,817.0,2075.0,807.0,1.3824,212500.0,NEAR BAY\n-122.42,37.75,52.0,1855.0,611.0,1715.0,614.0,2.1289,250000.0,NEAR BAY\n-122.42,37.75,52.0,1207.0,302.0,1008.0,269.0,3.3816,262500.0,NEAR BAY\n-122.42,37.75,52.0,801.0,272.0,639.0,259.0,2.1971,275000.0,NEAR BAY\n-122.42,37.75,52.0,1609.0,510.0,1155.0,439.0,2.2328,250000.0,NEAR BAY\n-122.42,37.75,52.0,1974.0,525.0,935.0,465.0,2.7173,300000.0,NEAR BAY\n-122.42,37.75,52.0,2112.0,528.0,1227.0,513.0,3.5536,400000.0,NEAR BAY\n-122.42,37.75,52.0,2708.0,762.0,1460.0,741.0,2.9052,400000.0,NEAR BAY\n-122.42,37.75,52.0,1564.0,396.0,1162.0,374.0,3.0,275000.0,NEAR BAY\n-122.43,37.75,52.0,2700.0,595.0,1181.0,575.0,3.575,396800.0,NEAR BAY\n-122.43,37.75,52.0,1970.0,495.0,871.0,474.0,4.0625,355600.0,NEAR BAY\n-122.43,37.75,52.0,2459.0,507.0,1012.0,475.0,4.0568,387900.0,NEAR BAY\n-122.43,37.76,52.0,2332.0,434.0,861.0,406.0,4.4318,437500.0,NEAR BAY\n-122.43,37.75,52.0,2285.0,509.0,839.0,456.0,4.7946,355600.0,NEAR BAY\n-122.44,37.75,52.0,3114.0,637.0,1144.0,591.0,4.0,375000.0,NEAR BAY\n-122.44,37.75,52.0,2082.0,425.0,801.0,411.0,4.2708,368900.0,NEAR BAY\n-122.43,37.75,52.0,2721.0,581.0,1043.0,519.0,3.7545,383700.0,NEAR BAY\n-122.44,37.75,46.0,1519.0,291.0,573.0,289.0,4.2667,338800.0,NEAR BAY\n-122.44,37.75,52.0,1573.0,334.0,725.0,338.0,5.0505,380400.0,NEAR BAY\n-122.43,37.75,52.0,1615.0,393.0,633.0,378.0,3.5114,347500.0,NEAR BAY\n-122.43,37.75,52.0,3521.0,767.0,1415.0,687.0,4.875,362200.0,NEAR BAY\n-122.43,37.75,52.0,2960.0,623.0,1191.0,589.0,3.95,347700.0,NEAR BAY\n-122.42,37.75,52.0,2164.0,533.0,1122.0,469.0,3.2632,306000.0,NEAR BAY\n-122.42,37.74,52.0,2713.0,624.0,1370.0,594.0,4.6547,325700.0,NEAR BAY\n-122.42,37.74,52.0,1786.0,427.0,856.0,394.0,3.0833,328100.0,NEAR BAY\n-122.43,37.74,52.0,2229.0,498.0,1079.0,472.0,5.0196,324300.0,NEAR BAY\n-122.43,37.75,52.0,2155.0,468.0,962.0,490.0,3.775,325900.0,NEAR BAY\n-122.43,37.75,40.0,4850.0,977.0,1824.0,952.0,5.0519,356100.0,NEAR BAY\n-122.44,37.75,21.0,5457.0,1247.0,2304.0,1180.0,4.5469,409700.0,NEAR BAY\n-122.44,37.74,23.0,6291.0,1269.0,2818.0,1198.0,4.2672,391900.0,NEAR BAY\n-122.44,37.74,52.0,2074.0,366.0,909.0,394.0,4.8382,294900.0,NEAR BAY\n-122.44,37.74,23.0,184.0,44.0,118.0,40.0,4.5375,350000.0,NEAR BAY\n-122.43,37.74,52.0,3328.0,653.0,1260.0,614.0,4.7437,331000.0,NEAR BAY\n-122.43,37.74,52.0,2637.0,539.0,1159.0,497.0,3.8846,333100.0,NEAR BAY\n-122.43,37.74,52.0,1514.0,314.0,724.0,301.0,5.3292,300900.0,NEAR BAY\n-122.43,37.73,52.0,1142.0,224.0,494.0,206.0,5.0602,298900.0,NEAR BAY\n-122.43,37.73,52.0,1029.0,205.0,461.0,212.0,5.0782,310800.0,NEAR BAY\n-122.38,37.76,52.0,248.0,68.0,124.0,51.0,1.4886,450000.0,NEAR BAY\n-122.39,37.76,52.0,624.0,170.0,410.0,148.0,4.0042,208300.0,NEAR BAY\n-122.39,37.76,52.0,157.0,28.0,88.0,27.0,3.675,162500.0,NEAR BAY\n-122.39,37.76,52.0,1877.0,427.0,712.0,398.0,3.9722,290900.0,NEAR BAY\n-122.39,37.76,52.0,2316.0,468.0,1047.0,476.0,4.5057,321600.0,NEAR BAY\n-122.39,37.76,52.0,3390.0,691.0,1645.0,596.0,3.7051,253700.0,NEAR BAY\n-122.4,37.75,44.0,6848.0,1584.0,3269.0,1383.0,2.8679,243300.0,NEAR BAY\n-122.4,37.76,52.0,4265.0,912.0,1555.0,836.0,4.119,298300.0,NEAR BAY\n-122.4,37.76,52.0,1495.0,311.0,506.0,275.0,4.4375,320000.0,NEAR BAY\n-122.4,37.76,52.0,1185.0,246.0,480.0,253.0,4.4074,277300.0,NEAR BAY\n-122.41,37.76,52.0,1427.0,281.0,620.0,236.0,1.9944,262500.0,NEAR BAY\n-122.4,37.76,52.0,1529.0,385.0,1347.0,348.0,2.9312,239100.0,NEAR BAY\n-122.41,37.76,52.0,3452.0,784.0,2987.0,753.0,2.8135,260300.0,NEAR BAY\n-122.41,37.76,52.0,2064.0,496.0,1726.0,466.0,3.4028,233300.0,NEAR BAY\n-122.41,37.76,52.0,492.0,139.0,316.0,168.0,3.0865,225000.0,NEAR BAY\n-122.41,37.76,52.0,351.0,81.0,308.0,75.0,2.6667,325000.0,NEAR BAY\n-122.41,37.76,52.0,2479.0,515.0,1816.0,496.0,3.0774,300000.0,NEAR BAY\n-122.41,37.76,52.0,2605.0,678.0,2071.0,611.0,3.2964,265000.0,NEAR BAY\n-122.4,37.75,52.0,1182.0,307.0,1029.0,306.0,2.0577,214600.0,NEAR BAY\n-122.41,37.75,52.0,1057.0,276.0,837.0,292.0,2.4531,229000.0,NEAR BAY\n-122.41,37.75,52.0,1919.0,404.0,1483.0,421.0,3.4063,253900.0,NEAR BAY\n-122.41,37.75,52.0,1892.0,415.0,1442.0,371.0,4.2891,230000.0,NEAR BAY\n-122.41,37.75,52.0,1678.0,386.0,1220.0,357.0,2.5809,255300.0,NEAR BAY\n-122.41,37.75,52.0,2164.0,606.0,2034.0,513.0,2.0325,178100.0,NEAR BAY\n-122.41,37.75,52.0,2452.0,623.0,1932.0,549.0,2.3903,236100.0,NEAR BAY\n-122.41,37.75,9.0,1282.0,334.0,1176.0,305.0,2.6538,206300.0,NEAR BAY\n-122.39,37.74,42.0,4110.0,846.0,2147.0,674.0,2.5694,201000.0,NEAR BAY\n-122.39,37.73,43.0,4864.0,972.0,3134.0,959.0,4.3393,217300.0,NEAR BAY\n-122.4,37.73,45.0,3490.0,712.0,2337.0,781.0,3.4472,225400.0,NEAR BAY\n-122.4,37.74,45.0,2462.0,509.0,1587.0,450.0,2.59,211800.0,NEAR BAY\n-122.38,37.73,18.0,4037.0,990.0,2722.0,834.0,1.4282,140400.0,NEAR BAY\n-122.38,37.73,15.0,6804.0,1664.0,4737.0,1581.0,1.4133,203400.0,NEAR BAY\n-122.39,37.74,45.0,1462.0,308.0,924.0,302.0,2.1767,185300.0,NEAR BAY\n-122.38,37.73,20.0,120.0,27.0,55.0,19.0,2.625,187500.0,NEAR BAY\n-122.38,37.73,40.0,543.0,,259.0,89.0,2.2167,193800.0,NEAR BAY\n-122.38,37.73,38.0,1388.0,276.0,871.0,265.0,2.1667,193800.0,NEAR BAY\n-122.39,37.73,46.0,1517.0,299.0,879.0,309.0,2.2222,195100.0,NEAR BAY\n-122.39,37.73,52.0,1931.0,329.0,1025.0,293.0,2.9063,192000.0,NEAR BAY\n-122.39,37.73,52.0,1070.0,224.0,567.0,207.0,2.8603,189000.0,NEAR BAY\n-122.4,37.73,48.0,1489.0,326.0,1115.0,356.0,2.6364,199300.0,NEAR BAY\n-122.39,37.72,28.0,1609.0,340.0,1064.0,290.0,1.1125,206300.0,NEAR BAY\n-122.39,37.72,45.0,2893.0,570.0,1923.0,535.0,3.6607,192300.0,NEAR BAY\n-122.39,37.72,52.0,135.0,34.0,93.0,26.0,2.1484,181300.0,NEAR BAY\n-122.41,37.75,52.0,3065.0,622.0,1405.0,606.0,3.7813,275900.0,NEAR BAY\n-122.41,37.74,38.0,1754.0,382.0,928.0,354.0,4.1417,270800.0,NEAR BAY\n-122.41,37.74,34.0,1403.0,262.0,839.0,255.0,4.7031,255200.0,NEAR BAY\n-122.41,37.75,52.0,2524.0,559.0,1430.0,476.0,3.4073,254700.0,NEAR BAY\n-122.41,37.75,52.0,2515.0,576.0,1209.0,540.0,3.5912,284900.0,NEAR BAY\n-122.41,37.74,48.0,409.0,86.0,148.0,70.0,3.6687,335000.0,NEAR BAY\n-122.41,37.74,52.0,831.0,175.0,415.0,159.0,1.9464,249000.0,NEAR BAY\n-122.41,37.74,52.0,1842.0,339.0,1032.0,357.0,5.5563,250800.0,NEAR BAY\n-122.42,37.74,52.0,2019.0,418.0,999.0,448.0,4.2212,271300.0,NEAR BAY\n-122.42,37.74,52.0,1916.0,432.0,889.0,424.0,4.0391,279900.0,NEAR BAY\n-122.42,37.74,52.0,1674.0,346.0,734.0,335.0,3.8864,281300.0,NEAR BAY\n-122.42,37.74,52.0,1271.0,353.0,1076.0,324.0,2.9911,263900.0,NEAR BAY\n-122.42,37.75,52.0,2163.0,607.0,1447.0,546.0,3.3555,275000.0,NEAR BAY\n-122.41,37.74,43.0,1663.0,330.0,935.0,335.0,4.1552,240900.0,NEAR BAY\n-122.41,37.74,47.0,1728.0,398.0,1178.0,315.0,3.2813,229600.0,NEAR BAY\n-122.41,37.74,52.0,2058.0,399.0,1208.0,399.0,3.6429,230000.0,NEAR BAY\n-122.42,37.73,50.0,3426.0,769.0,2261.0,671.0,2.888,246400.0,NEAR BAY\n-122.42,37.74,52.0,1651.0,351.0,973.0,366.0,3.4583,240900.0,NEAR BAY\n-122.42,37.74,52.0,2084.0,550.0,1438.0,516.0,2.3087,258600.0,NEAR BAY\n-122.42,37.74,52.0,1540.0,370.0,1136.0,363.0,4.3125,243000.0,NEAR BAY\n-122.42,37.73,52.0,3230.0,654.0,1765.0,611.0,3.3333,292300.0,NEAR BAY\n-122.43,37.73,52.0,1583.0,347.0,935.0,341.0,4.6786,263200.0,NEAR BAY\n-122.43,37.73,49.0,1435.0,322.0,1008.0,329.0,4.0,264000.0,NEAR BAY\n-122.43,37.73,52.0,1386.0,276.0,729.0,274.0,3.6694,275500.0,NEAR BAY\n-122.43,37.72,48.0,1289.0,280.0,782.0,235.0,3.6719,259800.0,NEAR BAY\n-122.44,37.72,52.0,1775.0,347.0,1102.0,367.0,4.3125,267200.0,NEAR BAY\n-122.44,37.73,52.0,2381.0,492.0,1485.0,447.0,4.3898,270000.0,NEAR BAY\n-122.44,37.73,52.0,866.0,205.0,587.0,171.0,5.0224,261900.0,NEAR BAY\n-122.42,37.73,46.0,1819.0,411.0,1534.0,406.0,4.0132,229400.0,NEAR BAY\n-122.42,37.73,35.0,1871.0,342.0,1055.0,310.0,4.625,279300.0,NEAR BAY\n-122.42,37.73,35.0,1791.0,322.0,988.0,304.0,4.5769,254500.0,NEAR BAY\n-122.43,37.73,52.0,1985.0,401.0,1337.0,424.0,4.1071,240900.0,NEAR BAY\n-122.41,37.73,42.0,2604.0,573.0,1703.0,507.0,3.4231,230200.0,NEAR BAY\n-122.4,37.73,50.0,1947.0,411.0,1170.0,384.0,3.4769,238700.0,NEAR BAY\n-122.4,37.73,42.0,1413.0,406.0,1027.0,362.0,2.3625,233000.0,NEAR BAY\n-122.41,37.73,52.0,1931.0,358.0,1092.0,356.0,3.7835,271300.0,NEAR BAY\n-122.41,37.73,41.0,2115.0,378.0,1168.0,365.0,4.0642,272500.0,NEAR BAY\n-122.42,37.73,48.0,1474.0,308.0,998.0,330.0,4.0781,250300.0,NEAR BAY\n-122.4,37.72,47.0,1465.0,306.0,1119.0,315.0,4.2672,219400.0,NEAR BAY\n-122.4,37.72,37.0,971.0,248.0,647.0,208.0,2.1187,239300.0,NEAR BAY\n-122.41,37.73,33.0,2789.0,567.0,1682.0,552.0,3.8643,276200.0,NEAR BAY\n-122.41,37.72,35.0,2104.0,434.0,1225.0,410.0,4.8214,242900.0,NEAR BAY\n-122.41,37.72,32.0,1650.0,316.0,904.0,295.0,4.0583,236200.0,NEAR BAY\n-122.42,37.72,37.0,2638.0,546.0,1789.0,521.0,4.0071,244700.0,NEAR BAY\n-122.43,37.72,50.0,2912.0,562.0,1989.0,537.0,3.6667,252600.0,NEAR BAY\n-122.43,37.72,49.0,3427.0,696.0,2363.0,661.0,3.6885,254000.0,NEAR BAY\n-122.43,37.72,52.0,2206.0,478.0,1583.0,456.0,3.7105,250500.0,NEAR BAY\n-122.44,37.72,52.0,2890.0,571.0,1769.0,541.0,3.8274,252000.0,NEAR BAY\n-122.43,37.72,52.0,3351.0,719.0,2101.0,706.0,3.0107,242000.0,NEAR BAY\n-122.43,37.73,52.0,3602.0,738.0,2270.0,647.0,3.8934,251800.0,NEAR BAY\n-122.43,37.73,52.0,1494.0,306.0,1463.0,360.0,3.1786,222600.0,NEAR BAY\n-122.44,37.72,49.0,1557.0,405.0,1173.0,385.0,3.4605,265000.0,NEAR BAY\n-122.44,37.72,48.0,2675.0,585.0,1773.0,540.0,3.9565,268500.0,NEAR BAY\n-122.45,37.72,52.0,1729.0,319.0,890.0,300.0,4.3036,261800.0,NEAR BAY\n-122.44,37.72,52.0,1380.0,272.0,847.0,284.0,3.7143,260000.0,NEAR BAY\n-122.45,37.71,46.0,2559.0,506.0,1562.0,498.0,4.3846,270600.0,NEAR OCEAN\n-122.45,37.71,52.0,1658.0,322.0,1086.0,326.0,3.8583,261600.0,NEAR OCEAN\n-122.45,37.71,41.0,1578.0,351.0,1159.0,299.0,3.9167,243600.0,NEAR OCEAN\n-122.46,37.71,52.0,1580.0,337.0,1425.0,330.0,4.0547,246200.0,NEAR OCEAN\n-122.46,37.71,47.0,1527.0,283.0,1102.0,282.0,4.0,231600.0,NEAR OCEAN\n-122.43,37.71,35.0,2878.0,564.0,1633.0,528.0,4.5,266900.0,NEAR BAY\n-122.43,37.71,52.0,1508.0,278.0,1138.0,304.0,4.0234,266500.0,NEAR BAY\n-122.43,37.71,52.0,1410.0,286.0,879.0,282.0,3.1908,255600.0,NEAR BAY\n-122.44,37.72,52.0,1507.0,282.0,929.0,281.0,3.8958,247700.0,NEAR BAY\n-122.44,37.71,52.0,2711.0,591.0,1848.0,524.0,3.9567,251500.0,NEAR BAY\n-122.44,37.71,46.0,1230.0,247.0,895.0,257.0,5.3913,248900.0,NEAR BAY\n-122.44,37.71,31.0,2370.0,441.0,1524.0,470.0,5.0201,264100.0,NEAR BAY\n-122.45,37.71,34.0,3131.0,669.0,2204.0,600.0,3.5536,251000.0,NEAR OCEAN\n-122.4,37.72,40.0,1948.0,413.0,1434.0,396.0,3.0313,219100.0,NEAR BAY\n-122.4,37.71,40.0,1883.0,397.0,1411.0,438.0,3.0469,238000.0,NEAR BAY\n-122.41,37.71,47.0,2289.0,481.0,1697.0,465.0,3.4773,226300.0,NEAR BAY\n-122.41,37.71,28.0,5015.0,1240.0,3900.0,1029.0,1.2269,181900.0,NEAR BAY\n-122.41,37.71,40.0,2054.0,433.0,1738.0,429.0,4.9926,213900.0,NEAR BAY\n-122.41,37.71,49.0,1852.0,429.0,1615.0,447.0,3.495,217800.0,NEAR BAY\n-122.4,37.72,41.0,1975.0,440.0,1528.0,424.0,3.8625,218300.0,NEAR BAY\n-122.4,37.72,47.0,1167.0,250.0,953.0,253.0,4.2727,241900.0,NEAR BAY\n-122.45,37.77,52.0,2602.0,,1330.0,647.0,3.5435,278600.0,NEAR BAY\n-122.46,37.76,52.0,2236.0,545.0,1186.0,532.0,3.4531,414300.0,NEAR BAY\n-122.46,37.77,52.0,1824.0,388.0,799.0,363.0,3.75,435700.0,NEAR BAY\n-122.46,37.76,52.0,1817.0,449.0,948.0,380.0,3.93,390000.0,NEAR BAY\n-122.45,37.76,31.0,5283.0,1330.0,2659.0,1269.0,3.5744,500000.0,NEAR BAY\n-122.46,37.76,28.0,1072.0,165.0,363.0,168.0,6.1636,367700.0,NEAR BAY\n-122.46,37.75,26.0,2192.0,438.0,954.0,456.0,4.5352,374200.0,NEAR BAY\n-122.47,37.76,52.0,2941.0,783.0,1545.0,726.0,2.9899,406500.0,NEAR BAY\n-122.47,37.76,52.0,2680.0,740.0,1587.0,713.0,2.5933,359600.0,NEAR BAY\n-122.47,37.76,49.0,2842.0,670.0,1396.0,648.0,3.2679,345700.0,NEAR BAY\n-122.47,37.76,52.0,2465.0,489.0,1170.0,498.0,4.0793,306700.0,NEAR BAY\n-122.47,37.76,48.0,2064.0,484.0,1055.0,467.0,2.8711,329600.0,NEAR BAY\n-122.47,37.76,40.0,3525.0,941.0,1675.0,857.0,3.2083,330000.0,NEAR BAY\n-122.47,37.76,52.0,4001.0,809.0,1886.0,756.0,3.3239,350000.0,NEAR BAY\n-122.47,37.76,39.0,3200.0,689.0,1391.0,618.0,3.6346,338000.0,NEAR BAY\n-122.47,37.75,45.0,2399.0,426.0,911.0,423.0,4.4312,361000.0,NEAR BAY\n-122.47,37.75,51.0,2413.0,431.0,1095.0,437.0,4.0089,357000.0,NEAR BAY\n-122.47,37.75,49.0,2747.0,472.0,1281.0,448.0,5.482,366300.0,NEAR BAY\n-122.47,37.76,34.0,2807.0,487.0,1152.0,445.0,5.1893,420300.0,NEAR BAY\n-122.47,37.76,48.0,2464.0,459.0,1179.0,458.0,4.4946,358600.0,NEAR BAY\n-122.46,37.75,52.0,1590.0,236.0,622.0,232.0,5.8151,500001.0,NEAR BAY\n-122.46,37.75,52.0,1207.0,152.0,465.0,162.0,10.7569,500001.0,NEAR BAY\n-122.47,37.75,51.0,2713.0,396.0,1090.0,401.0,9.3603,500001.0,NEAR BAY\n-122.47,37.75,46.0,3238.0,544.0,1293.0,470.0,6.1592,381700.0,NEAR BAY\n-122.47,37.75,52.0,1598.0,285.0,689.0,265.0,4.6071,337400.0,NEAR BAY\n-122.47,37.74,52.0,1538.0,305.0,819.0,319.0,4.0846,333600.0,NEAR BAY\n-122.45,37.75,36.0,2303.0,381.0,862.0,371.0,6.0274,349000.0,NEAR BAY\n-122.45,37.75,36.0,1997.0,356.0,772.0,348.0,4.95,322600.0,NEAR BAY\n-122.45,37.75,35.0,1363.0,302.0,1786.0,301.0,3.0804,313400.0,NEAR BAY\n-122.46,37.75,52.0,1849.0,287.0,695.0,258.0,6.5372,394000.0,NEAR BAY\n-122.45,37.74,52.0,1596.0,276.0,642.0,273.0,4.375,349500.0,NEAR BAY\n-122.46,37.74,51.0,1905.0,291.0,707.0,284.0,6.2561,431000.0,NEAR BAY\n-122.45,37.74,46.0,6429.0,1093.0,2535.0,1109.0,5.0887,335100.0,NEAR BAY\n-122.44,37.73,43.0,3700.0,684.0,1488.0,623.0,5.5622,313600.0,NEAR BAY\n-122.45,37.74,38.0,5688.0,930.0,2263.0,908.0,6.203,346800.0,NEAR BAY\n-122.46,37.74,52.0,2180.0,326.0,856.0,326.0,5.3961,416900.0,NEAR BAY\n-122.46,37.74,52.0,2053.0,281.0,791.0,287.0,10.959,500001.0,NEAR BAY\n-122.47,37.74,52.0,2055.0,265.0,735.0,252.0,8.1189,500001.0,NEAR BAY\n-122.47,37.74,52.0,3797.0,668.0,1633.0,658.0,5.6787,363600.0,NEAR BAY\n-122.47,37.74,52.0,3688.0,640.0,1605.0,567.0,4.9537,365600.0,NEAR BAY\n-122.46,37.73,52.0,2673.0,349.0,876.0,338.0,7.8476,500001.0,NEAR BAY\n-122.46,37.73,52.0,3547.0,506.0,1276.0,491.0,8.0069,426800.0,NEAR BAY\n-122.46,37.72,52.0,2951.0,406.0,1115.0,397.0,6.7228,405200.0,NEAR OCEAN\n-122.47,37.73,52.0,2134.0,277.0,936.0,285.0,5.9245,484600.0,NEAR OCEAN\n-122.47,37.73,52.0,2151.0,280.0,762.0,274.0,10.7309,500001.0,NEAR OCEAN\n-122.47,37.73,50.0,1653.0,252.0,641.0,224.0,10.6605,500001.0,NEAR OCEAN\n-122.47,37.72,46.0,1836.0,319.0,767.0,302.0,5.9114,399000.0,NEAR OCEAN\n-122.46,37.73,52.0,2401.0,346.0,812.0,328.0,6.8322,394100.0,NEAR BAY\n-122.45,37.73,52.0,2510.0,438.0,1153.0,407.0,5.1238,335100.0,NEAR BAY\n-122.46,37.73,52.0,2857.0,469.0,1431.0,496.0,5.2088,344200.0,NEAR BAY\n-122.44,37.73,39.0,1912.0,,970.0,406.0,4.7813,275500.0,NEAR BAY\n-122.44,37.73,52.0,2838.0,567.0,1411.0,526.0,3.8304,261400.0,NEAR BAY\n-122.45,37.73,52.0,1350.0,241.0,752.0,246.0,3.2448,266200.0,NEAR BAY\n-122.45,37.73,52.0,2035.0,424.0,1193.0,430.0,5.0634,264200.0,NEAR BAY\n-122.44,37.73,46.0,3581.0,758.0,1670.0,703.0,4.1932,269200.0,NEAR BAY\n-122.45,37.72,52.0,982.0,197.0,653.0,171.0,4.2167,231900.0,NEAR BAY\n-122.45,37.72,46.0,1406.0,235.0,771.0,239.0,4.7143,219300.0,NEAR BAY\n-122.45,37.72,47.0,1773.0,345.0,1083.0,315.0,4.475,221200.0,NEAR BAY\n-122.45,37.72,51.0,2690.0,554.0,1795.0,539.0,3.6581,225000.0,NEAR BAY\n-122.46,37.72,47.0,1723.0,389.0,1216.0,399.0,3.3208,238600.0,NEAR OCEAN\n-122.46,37.72,49.0,1207.0,255.0,658.0,220.0,4.0859,228600.0,NEAR OCEAN\n-122.46,37.72,48.0,1690.0,339.0,962.0,317.0,3.2875,221500.0,NEAR OCEAN\n-122.46,37.72,45.0,2399.0,419.0,1225.0,399.0,4.0855,244100.0,NEAR OCEAN\n-122.46,37.72,39.0,2254.0,,1388.0,404.0,2.9688,232000.0,NEAR OCEAN\n-122.47,37.71,44.0,2547.0,511.0,1577.0,516.0,4.1939,237900.0,NEAR OCEAN\n-122.47,37.71,42.0,1961.0,427.0,1211.0,409.0,3.5156,239400.0,NEAR OCEAN\n-122.47,37.72,43.0,968.0,199.0,434.0,162.0,2.5333,239300.0,NEAR OCEAN\n-122.47,37.72,46.0,1453.0,306.0,817.0,310.0,3.0,246700.0,NEAR OCEAN\n-122.47,37.72,49.0,1690.0,307.0,770.0,294.0,4.5913,259700.0,NEAR OCEAN\n-122.45,37.72,45.0,964.0,173.0,613.0,201.0,2.9119,228900.0,NEAR BAY\n-122.45,37.71,45.0,2253.0,431.0,1382.0,392.0,4.2562,221600.0,NEAR OCEAN\n-122.46,37.71,52.0,1642.0,351.0,1138.0,340.0,4.1406,219500.0,NEAR OCEAN\n-122.46,37.71,49.0,1711.0,348.0,1138.0,325.0,2.875,225000.0,NEAR OCEAN\n-122.46,37.72,37.0,1833.0,388.0,1093.0,363.0,3.0703,211800.0,NEAR OCEAN\n-122.48,37.76,48.0,2660.0,616.0,1491.0,602.0,3.9758,348600.0,NEAR BAY\n-122.48,37.76,48.0,2304.0,558.0,1273.0,512.0,3.275,332100.0,NEAR BAY\n-122.48,37.76,52.0,2684.0,574.0,1395.0,549.0,3.9097,323800.0,NEAR BAY\n-122.48,37.76,50.0,2236.0,484.0,1171.0,467.0,4.0977,322100.0,NEAR BAY\n-122.48,37.76,52.0,1845.0,336.0,1015.0,337.0,4.1397,331300.0,NEAR BAY\n-122.48,37.76,52.0,3260.0,653.0,1594.0,632.0,4.4094,336100.0,NEAR BAY\n-122.49,37.76,52.0,1382.0,230.0,708.0,279.0,5.8096,339800.0,NEAR BAY\n-122.49,37.76,52.0,2564.0,502.0,1092.0,459.0,3.5302,329600.0,NEAR BAY\n-122.49,37.76,52.0,2245.0,425.0,1091.0,409.0,3.5909,331200.0,NEAR BAY\n-122.49,37.76,52.0,1792.0,305.0,782.0,287.0,4.0391,332700.0,NEAR BAY\n-122.49,37.76,48.0,1351.0,270.0,650.0,265.0,3.5278,339800.0,NEAR BAY\n-122.49,37.76,49.0,1724.0,295.0,795.0,297.0,4.3977,353600.0,NEAR BAY\n-122.49,37.76,49.0,1637.0,304.0,729.0,281.0,4.3281,323100.0,NEAR BAY\n-122.48,37.75,51.0,2095.0,410.0,1126.0,429.0,4.4,318400.0,NEAR BAY\n-122.48,37.75,52.0,2515.0,494.0,1583.0,477.0,4.3393,317600.0,NEAR BAY\n-122.48,37.74,52.0,2453.0,508.0,1056.0,453.0,3.6859,311800.0,NEAR OCEAN\n-122.48,37.75,49.0,2203.0,407.0,1052.0,405.0,4.4375,329200.0,NEAR BAY\n-122.48,37.75,48.0,2555.0,548.0,1285.0,482.0,3.7734,314700.0,NEAR BAY\n-122.48,37.75,52.0,2074.0,401.0,1136.0,409.0,4.7703,331000.0,NEAR BAY\n-122.49,37.75,48.0,2181.0,419.0,1041.0,379.0,3.7361,320200.0,NEAR OCEAN\n-122.49,37.75,47.0,2140.0,425.0,1105.0,401.0,3.7054,308500.0,NEAR OCEAN\n-122.49,37.75,45.0,2341.0,461.0,1092.0,438.0,4.8036,297800.0,NEAR OCEAN\n-122.49,37.75,43.0,2044.0,393.0,979.0,378.0,3.9205,319100.0,NEAR OCEAN\n-122.49,37.75,48.0,2387.0,424.0,1041.0,408.0,3.7562,321200.0,NEAR OCEAN\n-122.49,37.75,52.0,2226.0,385.0,1177.0,416.0,4.8516,323800.0,NEAR OCEAN\n-122.49,37.74,52.0,2189.0,433.0,1147.0,420.0,3.4583,321300.0,NEAR OCEAN\n-122.48,37.74,52.0,2285.0,435.0,1211.0,442.0,4.0208,323100.0,NEAR OCEAN\n-122.48,37.74,52.0,2166.0,423.0,1072.0,370.0,4.131,314300.0,NEAR OCEAN\n-122.48,37.74,52.0,2841.0,517.0,1372.0,517.0,3.9236,335000.0,NEAR OCEAN\n-122.49,37.73,48.0,1190.0,182.0,497.0,199.0,6.2642,438500.0,NEAR OCEAN\n-122.49,37.74,48.0,1186.0,213.0,487.0,207.0,3.8333,340800.0,NEAR OCEAN\n-122.49,37.74,52.0,2442.0,449.0,1188.0,436.0,4.3909,317700.0,NEAR OCEAN\n-122.49,37.74,52.0,2302.0,457.0,1154.0,424.0,4.5744,315200.0,NEAR OCEAN\n-122.48,37.73,52.0,1597.0,240.0,566.0,231.0,5.1681,500001.0,NEAR OCEAN\n-122.48,37.73,47.0,2382.0,392.0,867.0,376.0,5.2598,371500.0,NEAR OCEAN\n-122.49,37.73,39.0,1937.0,336.0,742.0,307.0,5.1991,369400.0,NEAR OCEAN\n-122.49,37.73,37.0,1399.0,224.0,530.0,235.0,3.9219,433300.0,NEAR OCEAN\n-122.49,37.73,36.0,1821.0,292.0,742.0,298.0,5.6204,406200.0,NEAR OCEAN\n-122.48,37.73,38.0,3195.0,828.0,2410.0,778.0,3.1359,350000.0,NEAR OCEAN\n-122.47,37.72,47.0,1176.0,286.0,564.0,258.0,3.2059,350000.0,NEAR OCEAN\n-122.48,37.71,43.0,3850.0,1018.0,1497.0,829.0,3.5296,400000.0,NEAR OCEAN\n-122.48,37.72,45.0,1405.0,338.0,733.0,342.0,4.1116,187500.0,NEAR OCEAN\n-122.48,37.72,46.0,2403.0,638.0,1281.0,603.0,3.2321,112500.0,NEAR OCEAN\n-122.5,37.76,52.0,2018.0,422.0,1142.0,463.0,3.7083,307700.0,NEAR OCEAN\n-122.5,37.76,43.0,2108.0,456.0,1299.0,447.0,3.1406,316200.0,NEAR OCEAN\n-122.5,37.76,50.0,1993.0,410.0,1009.0,374.0,3.9464,295600.0,NEAR OCEAN\n-122.5,37.76,48.0,1408.0,295.0,891.0,269.0,3.8333,296300.0,NEAR OCEAN\n-122.5,37.76,46.0,1491.0,285.0,841.0,306.0,4.5329,278800.0,NEAR OCEAN\n-122.5,37.75,45.0,1672.0,344.0,838.0,314.0,4.1419,291500.0,NEAR OCEAN\n-122.5,37.75,43.0,2373.0,481.0,1247.0,454.0,4.0985,283200.0,NEAR OCEAN\n-122.51,37.76,43.0,2345.0,624.0,1439.0,614.0,2.8448,268900.0,NEAR OCEAN\n-122.51,37.76,43.0,2527.0,619.0,1332.0,558.0,3.0465,274200.0,NEAR OCEAN\n-122.51,37.76,40.0,2320.0,562.0,1499.0,521.0,3.2792,260800.0,NEAR OCEAN\n-122.5,37.76,46.0,2226.0,480.0,1272.0,468.0,4.2644,284100.0,NEAR OCEAN\n-122.5,37.76,45.0,1673.0,377.0,1078.0,393.0,3.3393,272300.0,NEAR OCEAN\n-122.5,37.75,45.0,1620.0,,941.0,328.0,4.3859,270200.0,NEAR OCEAN\n-122.5,37.75,44.0,1819.0,,1137.0,354.0,3.4919,271800.0,NEAR OCEAN\n-122.54,37.76,45.0,1592.0,325.0,920.0,322.0,3.96,272200.0,NEAR OCEAN\n-122.5,37.75,44.0,1739.0,343.0,872.0,330.0,2.9632,286300.0,NEAR OCEAN\n-122.5,37.74,40.0,2310.0,445.0,1266.0,490.0,3.7969,297800.0,NEAR OCEAN\n-122.5,37.74,45.0,1771.0,349.0,1098.0,342.0,3.7552,296600.0,NEAR OCEAN\n-122.49,37.74,44.0,1472.0,275.0,820.0,310.0,5.6826,300000.0,NEAR OCEAN\n-122.5,37.74,44.0,2374.0,496.0,1087.0,426.0,3.5,275700.0,NEAR OCEAN\n-122.5,37.74,44.0,2527.0,518.0,1434.0,444.0,3.875,275700.0,NEAR OCEAN\n-122.5,37.75,46.0,2298.0,457.0,1429.0,477.0,4.0217,272400.0,NEAR OCEAN\n-122.5,37.74,44.0,2792.0,615.0,1640.0,579.0,4.0625,272800.0,NEAR OCEAN\n-122.5,37.74,42.0,1667.0,395.0,1041.0,387.0,3.9583,273700.0,NEAR OCEAN\n-122.5,37.74,44.0,2082.0,470.0,1154.0,403.0,4.3611,268100.0,NEAR OCEAN\n-122.54,37.74,42.0,2006.0,415.0,1230.0,435.0,4.1786,271100.0,NEAR OCEAN\n-122.46,37.79,52.0,2005.0,359.0,847.0,356.0,4.1029,500001.0,NEAR BAY\n-122.46,37.78,52.0,2165.0,580.0,1067.0,530.0,2.9293,350000.0,NEAR BAY\n-122.46,37.78,52.0,2594.0,622.0,1421.0,593.0,3.0265,350000.0,NEAR BAY\n-122.46,37.79,52.0,2059.0,416.0,999.0,402.0,3.7419,500001.0,NEAR BAY\n-122.46,37.78,52.0,3429.0,773.0,1584.0,696.0,3.7887,500001.0,NEAR BAY\n-122.47,37.78,51.0,1485.0,386.0,880.0,385.0,2.7431,307100.0,NEAR BAY\n-122.47,37.78,52.0,2635.0,587.0,1302.0,577.0,3.7292,416700.0,NEAR BAY\n-122.47,37.79,52.0,2844.0,623.0,1380.0,596.0,4.75,500001.0,NEAR BAY\n-122.47,37.79,52.0,437.0,105.0,194.0,87.0,2.8125,500001.0,NEAR BAY\n-122.47,37.79,52.0,2383.0,477.0,990.0,464.0,3.9688,483300.0,NEAR BAY\n-122.47,37.78,52.0,2388.0,507.0,1078.0,494.0,3.5221,443300.0,NEAR BAY\n-122.47,37.78,52.0,1941.0,436.0,955.0,425.0,4.1339,396400.0,NEAR BAY\n-122.48,37.78,48.0,2835.0,728.0,1674.0,684.0,3.129,375000.0,NEAR BAY\n-122.48,37.79,52.0,4683.0,1055.0,2246.0,975.0,4.1148,457800.0,NEAR BAY\n-122.48,37.78,44.0,3371.0,794.0,1738.0,753.0,3.1653,335300.0,NEAR BAY\n-122.49,37.78,32.0,3028.0,815.0,1704.0,718.0,3.2028,322900.0,NEAR BAY\n-122.49,37.78,46.0,3304.0,792.0,1783.0,777.0,3.6148,352200.0,NEAR BAY\n-122.48,37.79,52.0,1647.0,236.0,546.0,227.0,9.1881,500001.0,NEAR BAY\n-122.49,37.79,52.0,3146.0,478.0,1143.0,455.0,6.1407,500001.0,NEAR BAY\n-122.49,37.79,52.0,2488.0,281.0,805.0,295.0,10.7058,500001.0,NEAR BAY\n-122.46,37.78,52.0,4140.0,984.0,2030.0,892.0,3.4236,376800.0,NEAR BAY\n-122.46,37.78,52.0,2632.0,542.0,1364.0,544.0,3.4605,441700.0,NEAR BAY\n-122.46,37.78,52.0,2051.0,552.0,1400.0,510.0,3.2396,375000.0,NEAR BAY\n-122.46,37.78,52.0,3088.0,727.0,1636.0,662.0,2.8553,360700.0,NEAR BAY\n-122.46,37.77,52.0,3193.0,688.0,2099.0,681.0,3.9375,402900.0,NEAR BAY\n-122.47,37.77,52.0,2241.0,443.0,1042.0,377.0,4.1635,398400.0,NEAR BAY\n-122.47,37.78,52.0,2275.0,412.0,1166.0,424.0,4.0652,421300.0,NEAR BAY\n-122.47,37.78,52.0,2169.0,522.0,1220.0,505.0,3.1989,446900.0,NEAR BAY\n-122.47,37.78,52.0,2951.0,647.0,1448.0,591.0,3.1392,422400.0,NEAR BAY\n-122.47,37.78,52.0,3021.0,569.0,1479.0,514.0,4.0208,414600.0,NEAR BAY\n-122.47,37.78,52.0,2042.0,378.0,1153.0,408.0,4.1856,404700.0,NEAR BAY\n-122.47,37.77,52.0,3143.0,635.0,1350.0,623.0,3.8571,366700.0,NEAR BAY\n-122.48,37.78,52.0,3047.0,641.0,1427.0,620.0,3.4883,337200.0,NEAR BAY\n-122.48,37.78,52.0,2666.0,515.0,1362.0,494.0,4.218,393800.0,NEAR BAY\n-122.48,37.77,52.0,2556.0,595.0,1202.0,568.0,3.8899,348500.0,NEAR BAY\n-122.48,37.78,50.0,2159.0,437.0,1111.0,417.0,3.5588,346400.0,NEAR BAY\n-122.48,37.78,52.0,2910.0,611.0,1508.0,515.0,3.5865,311400.0,NEAR BAY\n-122.49,37.78,47.0,2695.0,643.0,1505.0,644.0,3.0877,329100.0,NEAR BAY\n-122.49,37.78,52.0,3440.0,722.0,1663.0,665.0,3.0278,356300.0,NEAR BAY\n-122.49,37.77,52.0,2342.0,458.0,1170.0,458.0,3.7036,369200.0,NEAR BAY\n-122.5,37.77,52.0,2433.0,454.0,1070.0,420.0,4.125,359500.0,NEAR BAY\n-122.49,37.78,52.0,2050.0,439.0,1109.0,437.0,2.6719,318500.0,NEAR BAY\n-122.49,37.78,42.0,2723.0,579.0,1419.0,519.0,3.6429,328400.0,NEAR BAY\n-122.49,37.78,49.0,2176.0,441.0,1040.0,448.0,4.2414,500001.0,NEAR BAY\n-122.5,37.78,50.0,1922.0,427.0,1049.0,443.0,3.5833,348500.0,NEAR BAY\n-122.5,37.77,52.0,1769.0,414.0,1032.0,380.0,3.9954,324700.0,NEAR BAY\n-122.5,37.77,52.0,2299.0,441.0,1252.0,415.0,5.0562,336700.0,NEAR BAY\n-122.5,37.77,52.0,2739.0,569.0,1312.0,531.0,3.5833,322900.0,NEAR BAY\n-122.5,37.78,46.0,2646.0,607.0,1418.0,563.0,3.7167,332800.0,NEAR BAY\n-122.51,37.78,45.0,2564.0,499.0,1056.0,460.0,4.7328,351100.0,NEAR BAY\n-122.51,37.78,47.0,2496.0,494.0,1201.0,454.0,4.0353,342200.0,NEAR BAY\n-122.55,37.79,32.0,2131.0,625.0,1229.0,572.0,2.9201,322200.0,NEAR OCEAN\n-122.47,37.81,45.0,6927.0,1258.0,4715.0,1165.0,3.4051,500001.0,NEAR BAY\n-122.5,37.79,52.0,8.0,1.0,13.0,1.0,15.0001,500001.0,NEAR BAY\n-122.54,37.72,17.0,2975.0,968.0,1453.0,828.0,3.527,318900.0,NEAR OCEAN\n-122.42,37.72,42.0,4219.0,1125.0,3549.0,993.0,1.2387,212800.0,NEAR BAY\n-122.36,37.72,10.0,479.0,125.0,355.0,108.0,2.7083,180400.0,NEAR BAY\n-122.39,37.74,52.0,126.0,24.0,37.0,27.0,10.2264,225000.0,NEAR BAY\n-122.4,37.75,26.0,54.0,9.0,23.0,9.0,6.1359,225000.0,NEAR BAY\n-122.38,37.71,47.0,1088.0,190.0,558.0,166.0,4.2708,207100.0,NEAR BAY\n-122.4,37.71,47.0,1649.0,328.0,1183.0,356.0,3.3625,223700.0,NEAR BAY\n-121.28,37.96,28.0,1942.0,724.0,1618.0,638.0,0.9365,52500.0,INLAND\n-121.28,37.95,49.0,1200.0,364.0,1448.0,318.0,1.1094,52500.0,INLAND\n-121.28,37.95,46.0,1026.0,330.0,1109.0,333.0,1.2904,63300.0,INLAND\n-121.29,37.96,52.0,888.0,324.0,630.0,258.0,1.2411,112500.0,INLAND\n-121.29,37.96,52.0,287.0,119.0,154.0,85.0,0.8738,75000.0,INLAND\n-121.29,37.95,16.0,761.0,306.0,438.0,282.0,0.7714,87500.0,INLAND\n-121.3,37.95,9.0,674.0,242.0,575.0,193.0,2.2024,45000.0,INLAND\n-121.29,37.95,52.0,107.0,79.0,167.0,53.0,0.7917,22500.0,INLAND\n-121.3,37.96,24.0,1212.0,366.0,1202.0,343.0,1.7875,76800.0,INLAND\n-121.31,37.96,48.0,1112.0,227.0,583.0,216.0,2.3393,77600.0,INLAND\n-121.29,37.97,52.0,1610.0,480.0,1025.0,440.0,1.2962,110200.0,INLAND\n-121.29,37.96,48.0,1778.0,541.0,1237.0,462.0,1.3438,103100.0,INLAND\n-121.29,37.96,50.0,1669.0,558.0,1340.0,484.0,1.3191,92300.0,INLAND\n-121.3,37.96,31.0,2668.0,812.0,1398.0,721.0,1.125,110400.0,INLAND\n-121.3,37.96,52.0,1475.0,238.0,736.0,260.0,3.6591,105100.0,INLAND\n-121.3,37.96,52.0,1354.0,314.0,679.0,311.0,1.7788,97400.0,INLAND\n-121.31,37.96,52.0,2654.0,468.0,1157.0,494.0,3.226,107600.0,INLAND\n-121.31,37.96,52.0,1829.0,301.0,694.0,319.0,3.3466,92600.0,INLAND\n-121.28,37.97,47.0,2348.0,507.0,1455.0,479.0,1.65,66000.0,INLAND\n-121.27,37.96,43.0,1624.0,448.0,1805.0,440.0,1.425,61300.0,INLAND\n-121.27,37.95,43.0,557.0,165.0,573.0,144.0,1.7212,59000.0,INLAND\n-121.27,37.95,52.0,1318.0,308.0,1368.0,310.0,1.8261,54600.0,INLAND\n-121.27,37.94,38.0,512.0,133.0,676.0,124.0,1.7386,52000.0,INLAND\n-121.28,37.94,44.0,1406.0,357.0,1489.0,386.0,1.4688,56800.0,INLAND\n-121.28,37.94,48.0,1766.0,444.0,1406.0,421.0,1.7039,52700.0,INLAND\n-121.29,37.95,52.0,288.0,86.0,272.0,54.0,0.696,42500.0,INLAND\n-121.29,37.94,40.0,2827.0,655.0,2037.0,574.0,2.0303,63800.0,INLAND\n-121.32,37.95,40.0,964.0,230.0,742.0,209.0,1.2625,43000.0,INLAND\n-121.31,37.94,41.0,375.0,108.0,323.0,98.0,1.9531,45000.0,INLAND\n-121.3,37.94,40.0,452.0,109.0,412.0,97.0,1.3417,60800.0,INLAND\n-121.3,37.94,52.0,24.0,6.0,23.0,5.0,2.375,67500.0,INLAND\n-121.32,37.94,36.0,40.0,10.0,64.0,14.0,2.625,55000.0,INLAND\n-121.31,37.96,52.0,1938.0,332.0,788.0,320.0,3.6094,118400.0,INLAND\n-121.32,37.96,47.0,1700.0,344.0,922.0,357.0,3.1845,87200.0,INLAND\n-121.32,37.96,46.0,1832.0,365.0,975.0,373.0,2.0398,88100.0,INLAND\n-121.33,37.96,42.0,1619.0,340.0,906.0,339.0,2.5488,80300.0,INLAND\n-121.34,37.96,27.0,1839.0,442.0,2010.0,416.0,2.1284,59400.0,INLAND\n-121.32,37.95,36.0,747.0,189.0,338.0,145.0,1.7885,62100.0,INLAND\n-121.32,37.96,5.0,123.0,21.0,50.0,20.0,2.7656,50000.0,INLAND\n-121.34,37.97,33.0,2493.0,454.0,1203.0,436.0,3.765,94600.0,INLAND\n-121.34,37.96,23.0,2830.0,659.0,1554.0,654.0,3.0354,113700.0,INLAND\n-121.35,37.97,33.0,3656.0,681.0,1698.0,671.0,3.1406,93900.0,INLAND\n-121.36,37.96,32.0,614.0,95.0,227.0,107.0,3.9922,247400.0,INLAND\n-121.35,37.96,21.0,1343.0,183.0,462.0,193.0,5.8995,189900.0,INLAND\n-121.32,37.98,37.0,3247.0,643.0,1737.0,665.0,3.066,94100.0,INLAND\n-121.33,37.98,36.0,3113.0,576.0,1746.0,544.0,3.4625,84600.0,INLAND\n-121.33,37.97,38.0,3166.0,575.0,1351.0,561.0,3.5404,91600.0,INLAND\n-121.32,37.97,46.0,2270.0,427.0,1097.0,453.0,3.3235,87800.0,INLAND\n-121.32,37.97,43.0,2453.0,490.0,1093.0,438.0,2.9107,88800.0,INLAND\n-121.33,37.96,20.0,1727.0,386.0,730.0,342.0,2.5195,92600.0,INLAND\n-121.33,37.97,43.0,1511.0,292.0,721.0,320.0,3.5703,87400.0,INLAND\n-121.33,37.97,36.0,1953.0,492.0,999.0,371.0,2.0043,90800.0,INLAND\n-121.31,37.98,47.0,3386.0,663.0,1228.0,619.0,3.0625,141500.0,INLAND\n-121.3,37.97,52.0,2980.0,537.0,1128.0,510.0,4.061,113600.0,INLAND\n-121.31,37.97,42.0,1824.0,277.0,720.0,309.0,5.1915,183700.0,INLAND\n-121.31,37.97,45.0,2604.0,454.0,988.0,442.0,3.6667,123100.0,INLAND\n-121.29,37.98,42.0,625.0,143.0,533.0,159.0,2.625,65400.0,INLAND\n-121.29,37.97,52.0,2995.0,555.0,1392.0,503.0,1.7794,98800.0,INLAND\n-121.29,37.98,49.0,2501.0,565.0,1171.0,550.0,2.5043,76700.0,INLAND\n-121.3,37.98,47.0,2373.0,461.0,990.0,444.0,4.175,98300.0,INLAND\n-121.3,37.97,52.0,2259.0,417.0,766.0,385.0,2.2981,105400.0,INLAND\n-121.29,37.99,41.0,930.0,191.0,463.0,185.0,3.4141,90600.0,INLAND\n-121.29,37.99,30.0,1271.0,528.0,2019.0,524.0,1.5152,81300.0,INLAND\n-121.3,37.99,38.0,2375.0,494.0,1167.0,471.0,2.6673,87500.0,INLAND\n-121.3,37.98,39.0,3375.0,659.0,1388.0,631.0,2.6364,93800.0,INLAND\n-121.28,37.99,42.0,495.0,116.0,284.0,97.0,2.8854,55700.0,INLAND\n-121.26,37.98,32.0,3274.0,820.0,2050.0,738.0,2.1265,55700.0,INLAND\n-121.27,37.98,43.0,2608.0,516.0,1322.0,528.0,2.5714,70000.0,INLAND\n-121.27,37.98,43.0,1005.0,200.0,492.0,172.0,2.6812,72800.0,INLAND\n-121.28,37.98,52.0,941.0,184.0,414.0,171.0,2.1429,69900.0,INLAND\n-121.29,37.99,45.0,965.0,198.0,498.0,195.0,1.6944,75200.0,INLAND\n-121.27,37.97,39.0,1023.0,243.0,550.0,224.0,1.1141,54400.0,INLAND\n-121.26,37.96,43.0,527.0,133.0,367.0,152.0,2.5,63600.0,INLAND\n-121.27,37.96,43.0,948.0,221.0,749.0,208.0,1.962,52700.0,INLAND\n-121.27,37.96,52.0,583.0,114.0,310.0,93.0,2.5625,54200.0,INLAND\n-121.26,37.98,41.0,1633.0,433.0,885.0,413.0,0.9782,54200.0,INLAND\n-121.25,37.98,39.0,1765.0,414.0,1056.0,414.0,1.5329,48300.0,INLAND\n-121.26,37.97,31.0,1189.0,295.0,891.0,292.0,2.5536,50500.0,INLAND\n-121.25,37.97,34.0,1288.0,344.0,846.0,293.0,1.7895,63100.0,INLAND\n-121.26,37.96,43.0,940.0,208.0,690.0,181.0,2.3056,62300.0,INLAND\n-121.26,37.97,41.0,2398.0,448.0,1143.0,444.0,3.0352,69800.0,INLAND\n-121.25,37.97,41.0,855.0,189.0,716.0,206.0,2.0375,75000.0,INLAND\n-121.26,37.96,35.0,1511.0,316.0,892.0,304.0,1.7898,63500.0,INLAND\n-121.25,37.95,40.0,1703.0,362.0,1208.0,373.0,2.0817,55300.0,INLAND\n-121.26,37.95,44.0,819.0,184.0,677.0,183.0,1.725,59300.0,INLAND\n-121.26,37.95,39.0,1841.0,428.0,1368.0,390.0,2.1583,62000.0,INLAND\n-121.27,37.96,41.0,461.0,101.0,382.0,79.0,1.275,54000.0,INLAND\n-121.26,37.96,40.0,535.0,105.0,335.0,102.0,2.5234,62800.0,INLAND\n-121.25,37.96,26.0,2205.0,478.0,1730.0,472.0,2.4866,68300.0,INLAND\n-121.24,37.96,29.0,874.0,217.0,788.0,222.0,1.9187,57700.0,INLAND\n-121.25,37.95,46.0,2001.0,428.0,1384.0,401.0,1.9402,62200.0,INLAND\n-121.24,37.95,36.0,361.0,63.0,169.0,62.0,3.7734,63800.0,INLAND\n-121.25,37.94,30.0,1509.0,308.0,967.0,278.0,1.7798,65900.0,INLAND\n-121.24,37.94,5.0,2232.0,488.0,1857.0,435.0,2.8705,113600.0,INLAND\n-121.24,37.93,21.0,1185.0,237.0,960.0,245.0,2.0893,65000.0,INLAND\n-121.25,37.94,28.0,964.0,232.0,782.0,218.0,2.3269,55900.0,INLAND\n-121.25,37.93,31.0,1673.0,382.0,1734.0,400.0,2.0833,48300.0,INLAND\n-121.26,37.93,33.0,2109.0,531.0,2248.0,588.0,1.4583,53000.0,INLAND\n-121.26,37.94,43.0,1610.0,412.0,1409.0,365.0,1.7574,51700.0,INLAND\n-121.27,37.93,24.0,1451.0,320.0,1413.0,283.0,2.125,61200.0,INLAND\n-121.28,37.94,35.0,2680.0,634.0,2188.0,611.0,1.9375,56700.0,INLAND\n-121.28,37.92,36.0,499.0,115.0,451.0,124.0,2.1705,60300.0,INLAND\n-121.28,37.94,40.0,2806.0,685.0,2268.0,635.0,1.8814,57700.0,INLAND\n-121.29,37.93,37.0,2488.0,578.0,1854.0,514.0,2.551,59100.0,INLAND\n-121.29,37.93,24.0,1438.0,351.0,1294.0,342.0,2.7829,61800.0,INLAND\n-121.28,37.93,23.0,1491.0,346.0,1223.0,343.0,2.1591,67800.0,INLAND\n-121.28,37.92,30.0,1061.0,230.0,851.0,195.0,2.4412,61600.0,INLAND\n-121.29,37.92,12.0,1096.0,240.0,1175.0,278.0,3.1053,73100.0,INLAND\n-121.28,37.91,31.0,820.0,179.0,576.0,155.0,1.69,65900.0,INLAND\n-121.31,37.93,21.0,1556.0,314.0,1140.0,304.0,2.4667,81400.0,INLAND\n-121.3,37.92,28.0,3308.0,766.0,3201.0,720.0,1.7694,73900.0,INLAND\n-121.24,37.98,33.0,450.0,123.0,236.0,103.0,1.1964,80400.0,INLAND\n-121.24,37.97,47.0,886.0,196.0,517.0,188.0,2.1991,67200.0,INLAND\n-121.24,37.96,37.0,1175.0,260.0,951.0,267.0,2.875,57700.0,INLAND\n-121.23,37.96,37.0,2351.0,564.0,1591.0,549.0,1.6563,57200.0,INLAND\n-121.23,37.96,44.0,2204.0,473.0,1277.0,435.0,1.5539,59200.0,INLAND\n-121.23,37.95,36.0,811.0,168.0,514.0,152.0,2.625,89200.0,INLAND\n-121.22,37.97,37.0,1514.0,337.0,1121.0,337.0,2.401,58400.0,INLAND\n-121.22,37.96,30.0,1737.0,381.0,1177.0,347.0,1.9875,56400.0,INLAND\n-121.22,37.96,31.0,1484.0,314.0,1163.0,336.0,2.625,72100.0,INLAND\n-121.25,37.92,19.0,2109.0,427.0,1742.0,426.0,2.4097,66000.0,INLAND\n-121.23,37.92,28.0,590.0,129.0,315.0,99.0,1.8958,85700.0,INLAND\n-121.36,38.0,17.0,4535.0,762.0,1562.0,743.0,5.3224,225800.0,INLAND\n-121.35,38.0,22.0,3564.0,730.0,1539.0,699.0,3.675,152400.0,INLAND\n-121.35,38.0,6.0,1649.0,369.0,732.0,350.0,3.4231,123800.0,INLAND\n-121.37,38.01,15.0,2430.0,315.0,1016.0,314.0,10.0088,242000.0,INLAND\n-121.35,38.01,15.0,2682.0,599.0,1520.0,601.0,3.5982,94400.0,INLAND\n-121.36,38.01,16.0,926.0,230.0,451.0,198.0,4.0221,173300.0,INLAND\n-121.36,38.01,16.0,2178.0,667.0,1192.0,579.0,2.3339,87100.0,INLAND\n-121.36,38.01,16.0,1080.0,166.0,507.0,182.0,4.5278,166900.0,INLAND\n-121.34,38.01,17.0,2033.0,452.0,1114.0,446.0,3.2872,175000.0,INLAND\n-121.33,38.01,27.0,1612.0,234.0,630.0,255.0,5.318,155100.0,INLAND\n-121.34,38.0,32.0,3877.0,687.0,1642.0,647.0,4.0444,129200.0,INLAND\n-121.33,38.0,32.0,4474.0,929.0,2177.0,884.0,3.2889,98900.0,INLAND\n-121.33,38.01,36.0,1383.0,207.0,531.0,203.0,5.9191,151900.0,INLAND\n-121.32,38.01,20.0,1903.0,395.0,919.0,359.0,2.6765,96400.0,INLAND\n-121.32,38.01,36.0,391.0,74.0,171.0,79.0,2.7045,102800.0,INLAND\n-121.32,38.0,21.0,1795.0,482.0,1114.0,472.0,2.0091,101500.0,INLAND\n-121.32,38.0,22.0,2105.0,521.0,781.0,483.0,2.213,87500.0,INLAND\n-121.33,38.0,14.0,3731.0,772.0,1679.0,750.0,3.1369,119600.0,INLAND\n-121.33,37.99,15.0,4472.0,1079.0,1837.0,976.0,2.5,175900.0,INLAND\n-121.34,37.99,11.0,4487.0,868.0,2195.0,780.0,3.9615,194600.0,INLAND\n-121.34,37.99,14.0,3111.0,498.0,1178.0,525.0,6.556,234700.0,INLAND\n-121.31,37.99,15.0,3103.0,965.0,3061.0,861.0,1.3729,110300.0,INLAND\n-121.32,37.98,20.0,1591.0,589.0,1916.0,536.0,1.3531,94600.0,INLAND\n-121.33,37.98,9.0,2370.0,424.0,1129.0,386.0,5.143,176500.0,INLAND\n-121.33,37.98,10.0,1564.0,397.0,643.0,347.0,2.7031,150000.0,INLAND\n-121.36,37.99,8.0,1801.0,380.0,684.0,350.0,4.2589,134900.0,INLAND\n-121.34,37.98,8.0,2628.0,428.0,1158.0,393.0,5.3002,191700.0,INLAND\n-121.33,38.02,33.0,2854.0,489.0,1109.0,452.0,4.3008,136400.0,INLAND\n-121.34,38.02,30.0,4375.0,689.0,2038.0,709.0,5.1202,133800.0,INLAND\n-121.33,38.02,31.0,1466.0,,608.0,254.0,3.1827,162100.0,INLAND\n-121.33,38.02,37.0,1964.0,315.0,915.0,335.0,4.3008,119800.0,INLAND\n-121.34,38.03,20.0,4213.0,751.0,2071.0,714.0,4.4063,130800.0,INLAND\n-121.33,38.03,19.0,1708.0,291.0,906.0,288.0,4.918,130600.0,INLAND\n-121.36,38.04,4.0,2477.0,359.0,1234.0,377.0,5.5427,162100.0,INLAND\n-121.35,38.04,5.0,4303.0,613.0,2206.0,621.0,5.5842,159100.0,INLAND\n-121.35,38.04,12.0,6217.0,1019.0,3771.0,961.0,3.7206,146000.0,INLAND\n-121.36,38.04,9.0,2167.0,370.0,1290.0,351.0,5.0285,148200.0,INLAND\n-121.34,38.05,16.0,667.0,92.0,267.0,90.0,5.6147,244700.0,INLAND\n-121.34,38.04,16.0,3295.0,565.0,2279.0,576.0,3.6083,146400.0,INLAND\n-121.33,38.04,15.0,1933.0,280.0,965.0,260.0,4.6477,142700.0,INLAND\n-121.33,38.03,10.0,629.0,140.0,635.0,146.0,2.2961,126700.0,INLAND\n-121.32,38.03,16.0,4045.0,623.0,1862.0,625.0,4.875,143100.0,INLAND\n-121.33,38.04,15.0,2903.0,440.0,1325.0,423.0,4.5179,145600.0,INLAND\n-121.33,38.04,10.0,1421.0,204.0,657.0,209.0,5.1878,153900.0,INLAND\n-121.35,38.03,8.0,1904.0,255.0,895.0,242.0,5.7201,155700.0,INLAND\n-121.34,38.03,12.0,2707.0,433.0,1200.0,380.0,4.9861,133500.0,INLAND\n-121.35,38.02,16.0,1665.0,311.0,1301.0,259.0,2.8403,132300.0,INLAND\n-121.35,38.03,16.0,3158.0,515.0,1596.0,528.0,4.1739,131300.0,INLAND\n-121.36,38.03,14.0,2356.0,438.0,1378.0,481.0,3.7375,138800.0,INLAND\n-121.35,38.02,15.0,3583.0,644.0,2183.0,643.0,3.428,140700.0,INLAND\n-121.36,38.02,5.0,2229.0,543.0,1010.0,474.0,4.1719,206100.0,INLAND\n-121.36,38.03,7.0,3461.0,859.0,1518.0,741.0,3.5684,78700.0,INLAND\n-121.32,38.04,30.0,249.0,44.0,167.0,45.0,4.5,92800.0,INLAND\n-121.31,38.03,24.0,3050.0,568.0,1743.0,549.0,3.7413,105300.0,INLAND\n-121.32,38.03,25.0,2474.0,513.0,1947.0,524.0,2.5742,98400.0,INLAND\n-121.32,38.02,26.0,2851.0,533.0,1544.0,499.0,3.5379,99100.0,INLAND\n-121.31,38.03,18.0,4893.0,1008.0,3036.0,997.0,2.5212,110000.0,INLAND\n-121.29,38.0,12.0,4038.0,1074.0,3440.0,942.0,1.9698,112300.0,INLAND\n-121.31,37.99,23.0,3135.0,707.0,1650.0,680.0,1.886,105300.0,INLAND\n-121.3,38.0,23.0,3706.0,1106.0,3785.0,1019.0,1.7774,100000.0,INLAND\n-121.3,38.01,29.0,2289.0,449.0,1215.0,435.0,3.2788,100000.0,INLAND\n-121.3,38.0,27.0,2918.0,580.0,1338.0,544.0,2.6495,116200.0,INLAND\n-121.31,38.0,35.0,2097.0,351.0,977.0,358.0,4.3958,108400.0,INLAND\n-121.31,38.0,19.0,908.0,158.0,306.0,154.0,3.9792,131900.0,INLAND\n-121.31,38.02,24.0,4157.0,951.0,2734.0,879.0,2.7981,92100.0,INLAND\n-121.3,38.01,30.0,2547.0,485.0,1547.0,501.0,3.994,95500.0,INLAND\n-121.32,38.02,23.0,3251.0,689.0,1890.0,668.0,3.0729,104800.0,INLAND\n-121.31,38.01,22.0,2101.0,514.0,1304.0,511.0,2.8348,101600.0,INLAND\n-121.31,38.01,22.0,2575.0,680.0,1367.0,645.0,1.4274,90500.0,INLAND\n-121.3,38.04,8.0,2668.0,447.0,1713.0,444.0,4.0156,117600.0,INLAND\n-121.3,38.03,13.0,1014.0,200.0,712.0,197.0,3.1471,102800.0,INLAND\n-121.3,38.03,10.0,1409.0,248.0,782.0,222.0,4.0227,107700.0,INLAND\n-121.29,38.04,16.0,2128.0,441.0,1860.0,459.0,3.1779,97300.0,INLAND\n-121.29,38.03,16.0,4356.0,881.0,1629.0,818.0,2.2672,91100.0,INLAND\n-121.28,38.03,11.0,3585.0,729.0,2769.0,715.0,3.0907,94100.0,INLAND\n-121.29,38.03,7.0,2021.0,441.0,1615.0,406.0,2.5842,111300.0,INLAND\n-121.28,38.03,11.0,826.0,150.0,684.0,166.0,3.9265,107400.0,INLAND\n-121.29,38.02,12.0,2006.0,426.0,1849.0,396.0,2.5437,99000.0,INLAND\n-121.3,38.02,16.0,2717.0,621.0,3343.0,643.0,2.5473,106300.0,INLAND\n-121.3,38.03,11.0,2866.0,654.0,1404.0,525.0,2.505,95000.0,INLAND\n-121.3,38.02,4.0,1515.0,384.0,491.0,348.0,2.8523,87500.0,INLAND\n-121.29,38.0,4.0,1392.0,322.0,1784.0,309.0,2.375,124500.0,INLAND\n-121.29,38.01,2.0,6403.0,1116.0,3327.0,957.0,4.4871,137900.0,INLAND\n-121.28,38.02,8.0,1868.0,392.0,1258.0,389.0,3.175,95900.0,INLAND\n-121.29,38.01,10.0,69.0,16.0,50.0,20.0,3.75,120800.0,INLAND\n-121.27,38.05,26.0,378.0,75.0,164.0,65.0,3.4107,82800.0,INLAND\n-121.27,38.02,32.0,342.0,58.0,138.0,52.0,2.9821,155000.0,INLAND\n-121.3,38.05,52.0,122.0,26.0,62.0,25.0,1.15,112500.0,INLAND\n-121.25,38.05,25.0,1967.0,362.0,1035.0,361.0,3.5735,106800.0,INLAND\n-121.25,38.04,26.0,3080.0,473.0,1257.0,465.0,4.9861,201800.0,INLAND\n-121.25,38.01,16.0,2397.0,501.0,1053.0,557.0,2.6994,112500.0,INLAND\n-121.23,38.04,32.0,1829.0,262.0,677.0,243.0,6.1805,247900.0,INLAND\n-121.22,38.04,42.0,343.0,50.0,116.0,49.0,5.5376,212500.0,INLAND\n-121.25,38.03,29.0,2465.0,327.0,859.0,315.0,6.6605,220700.0,INLAND\n-121.22,38.0,35.0,1841.0,300.0,783.0,285.0,2.8167,162100.0,INLAND\n-121.24,38.01,22.0,1526.0,299.0,790.0,300.0,2.4342,125000.0,INLAND\n-121.25,38.0,21.0,446.0,73.0,182.0,57.0,2.8958,135000.0,INLAND\n-121.26,37.99,27.0,429.0,102.0,179.0,90.0,2.3333,87500.0,INLAND\n-121.24,38.0,25.0,1471.0,300.0,721.0,304.0,2.4688,126800.0,INLAND\n-121.23,37.99,38.0,523.0,80.0,226.0,72.0,5.5693,153100.0,INLAND\n-121.23,37.98,27.0,849.0,137.0,373.0,131.0,5.0362,181300.0,INLAND\n-121.2,37.97,39.0,440.0,83.0,270.0,97.0,6.0582,157700.0,INLAND\n-121.19,38.04,35.0,703.0,117.0,290.0,107.0,3.225,177100.0,INLAND\n-121.16,38.03,28.0,253.0,50.0,201.0,51.0,1.4732,156300.0,INLAND\n-121.2,38.02,44.0,608.0,108.0,287.0,83.0,3.3882,125000.0,INLAND\n-121.18,37.99,31.0,2450.0,559.0,1459.0,478.0,2.4674,130900.0,INLAND\n-121.17,37.97,28.0,1374.0,248.0,769.0,229.0,3.6389,130400.0,INLAND\n-121.23,37.95,32.0,2081.0,472.0,1342.0,411.0,2.7958,59000.0,INLAND\n-121.19,37.93,27.0,1621.0,363.0,909.0,345.0,2.1513,99700.0,INLAND\n-121.23,37.94,20.0,268.0,78.0,77.0,49.0,1.125,150000.0,INLAND\n-121.22,37.93,21.0,336.0,68.0,206.0,73.0,4.75,121400.0,INLAND\n-121.22,37.95,30.0,1055.0,211.0,629.0,170.0,2.8676,76900.0,INLAND\n-121.18,37.96,35.0,411.0,74.0,193.0,59.0,2.5625,146900.0,INLAND\n-121.24,37.9,16.0,50.0,10.0,20.0,6.0,2.625,137500.0,INLAND\n-121.26,37.88,42.0,465.0,93.0,256.0,93.0,3.1719,158300.0,INLAND\n-121.28,37.9,28.0,371.0,71.0,171.0,70.0,0.9614,55700.0,INLAND\n-121.27,37.88,43.0,968.0,249.0,664.0,240.0,1.6458,83600.0,INLAND\n-121.27,37.87,34.0,1010.0,206.0,678.0,234.0,2.9531,104000.0,INLAND\n-121.31,37.9,38.0,226.0,44.0,125.0,38.0,2.9,125000.0,INLAND\n-121.29,37.89,26.0,161.0,27.0,1542.0,30.0,5.7485,162500.0,INLAND\n-121.29,37.87,29.0,488.0,108.0,308.0,115.0,2.6563,103100.0,INLAND\n-121.23,37.87,49.0,98.0,24.0,59.0,26.0,3.65,162500.0,INLAND\n-121.49,37.94,31.0,1860.0,394.0,1848.0,293.0,2.2891,162500.0,INLAND\n-121.38,37.88,44.0,1158.0,226.0,1094.0,224.0,2.6842,156300.0,INLAND\n-121.47,38.13,13.0,3192.0,715.0,1768.0,626.0,2.2619,123500.0,INLAND\n-121.42,38.22,35.0,1507.0,313.0,868.0,283.0,2.0284,96300.0,INLAND\n-121.36,38.15,42.0,2051.0,334.0,878.0,318.0,4.3553,185700.0,INLAND\n-121.32,38.16,14.0,2049.0,398.0,1071.0,369.0,3.5,240800.0,INLAND\n-121.32,38.13,5.0,3136.0,501.0,1327.0,467.0,5.5942,186900.0,INLAND\n-121.35,38.09,32.0,1706.0,292.0,923.0,284.0,5.5057,147200.0,INLAND\n-121.32,38.15,5.0,5428.0,994.0,2725.0,902.0,3.9323,130100.0,INLAND\n-121.23,38.12,22.0,393.0,58.0,134.0,57.0,3.95,178100.0,INLAND\n-121.23,38.11,48.0,561.0,81.0,240.0,69.0,3.6312,202800.0,INLAND\n-121.23,38.09,23.0,633.0,91.0,236.0,83.0,6.4562,230000.0,INLAND\n-121.26,38.09,35.0,930.0,186.0,525.0,201.0,2.0625,155000.0,INLAND\n-121.3,38.09,31.0,335.0,53.0,154.0,55.0,2.0694,175000.0,INLAND\n-121.29,38.07,21.0,1185.0,207.0,533.0,213.0,3.1917,204500.0,INLAND\n-121.25,38.07,28.0,2103.0,422.0,1167.0,391.0,3.0592,152800.0,INLAND\n-121.29,38.14,27.0,836.0,132.0,303.0,133.0,3.875,127400.0,INLAND\n-121.3,38.14,17.0,3507.0,696.0,1867.0,709.0,3.2123,120700.0,INLAND\n-121.29,38.14,34.0,2770.0,544.0,1409.0,535.0,3.2338,101800.0,INLAND\n-121.29,38.14,34.0,1500.0,337.0,674.0,282.0,2.515,110800.0,INLAND\n-121.29,38.13,31.0,1008.0,212.0,453.0,195.0,2.3917,113500.0,INLAND\n-121.3,38.13,27.0,1004.0,192.0,470.0,192.0,2.8942,116700.0,INLAND\n-121.3,38.13,23.0,2864.0,504.0,1298.0,499.0,3.2303,131800.0,INLAND\n-121.29,38.15,23.0,4183.0,633.0,1886.0,628.0,4.8787,175300.0,INLAND\n-121.28,38.14,37.0,3278.0,623.0,1431.0,575.0,3.3987,99500.0,INLAND\n-121.27,38.14,33.0,3557.0,894.0,2659.0,894.0,2.2883,86900.0,INLAND\n-121.27,38.14,40.0,929.0,257.0,576.0,229.0,2.125,137500.0,INLAND\n-121.28,38.14,38.0,2803.0,500.0,1223.0,509.0,4.119,128800.0,INLAND\n-121.28,38.13,48.0,1892.0,333.0,804.0,352.0,4.0625,143200.0,INLAND\n-121.27,38.13,52.0,1081.0,257.0,437.0,225.0,2.1979,114100.0,INLAND\n-121.27,38.13,40.0,2402.0,509.0,1197.0,486.0,2.1771,98200.0,INLAND\n-121.28,38.13,32.0,3366.0,676.0,1916.0,697.0,2.5401,125400.0,INLAND\n-121.28,38.12,34.0,3268.0,640.0,1906.0,628.0,2.8237,110700.0,INLAND\n-121.27,38.12,44.0,2356.0,482.0,1043.0,443.0,2.4949,108000.0,INLAND\n-121.29,38.13,20.0,3168.0,514.0,1390.0,490.0,5.0,154800.0,INLAND\n-121.3,38.13,26.0,2256.0,360.0,937.0,372.0,5.0528,153700.0,INLAND\n-121.29,38.12,18.0,1534.0,275.0,741.0,263.0,3.9607,132500.0,INLAND\n-121.3,38.12,11.0,1792.0,252.0,767.0,263.0,7.6889,229300.0,INLAND\n-121.3,38.11,5.0,5979.0,1190.0,2679.0,1084.0,4.196,171700.0,INLAND\n-121.27,38.11,11.0,3163.0,794.0,2106.0,762.0,2.4482,103000.0,INLAND\n-121.28,38.11,10.0,2974.0,588.0,1559.0,568.0,3.8825,136800.0,INLAND\n-121.27,38.11,15.0,2039.0,384.0,1178.0,375.0,3.8672,120100.0,INLAND\n-121.28,38.1,13.0,2432.0,586.0,1441.0,606.0,2.5556,133100.0,INLAND\n-121.29,38.1,14.0,1551.0,297.0,785.0,281.0,3.775,163300.0,INLAND\n-121.27,38.13,39.0,2614.0,634.0,1862.0,654.0,1.9238,70700.0,INLAND\n-121.27,38.12,37.0,2232.0,504.0,1455.0,471.0,2.5587,87800.0,INLAND\n-121.26,38.13,38.0,1419.0,411.0,1226.0,397.0,2.2188,68800.0,INLAND\n-121.26,38.12,27.0,1818.0,459.0,1182.0,428.0,1.8575,73800.0,INLAND\n-121.25,38.13,25.0,1305.0,270.0,789.0,235.0,3.2993,91100.0,INLAND\n-121.26,38.11,4.0,2058.0,366.0,933.0,316.0,4.2448,150900.0,INLAND\n-121.26,38.11,8.0,2770.0,642.0,1611.0,633.0,3.1284,115100.0,INLAND\n-121.25,38.14,16.0,1174.0,242.0,464.0,261.0,2.3,133300.0,INLAND\n-121.26,38.14,10.0,3371.0,665.0,1823.0,654.0,3.5333,116800.0,INLAND\n-121.27,38.13,35.0,2607.0,685.0,2016.0,618.0,1.75,82900.0,INLAND\n-121.26,38.13,25.0,2549.0,675.0,2053.0,648.0,2.0875,83100.0,INLAND\n-121.24,38.22,28.0,2593.0,487.0,1365.0,457.0,3.3929,113000.0,INLAND\n-121.22,38.16,24.0,4411.0,776.0,2038.0,732.0,3.475,151200.0,INLAND\n-121.28,38.17,19.0,1337.0,236.0,744.0,225.0,4.0924,244200.0,INLAND\n-121.32,38.21,27.0,2643.0,467.0,1455.0,444.0,3.6398,146700.0,INLAND\n-121.16,38.16,31.0,1953.0,366.0,999.0,316.0,2.4906,122500.0,INLAND\n-121.14,38.16,14.0,2591.0,497.0,1371.0,479.0,3.5774,113900.0,INLAND\n-121.06,38.25,13.0,651.0,102.0,301.0,104.0,3.6528,200000.0,INLAND\n-121.15,38.21,18.0,4176.0,700.0,2164.0,699.0,4.0365,174200.0,INLAND\n-121.05,38.14,19.0,3326.0,561.0,1544.0,511.0,2.9875,166300.0,INLAND\n-121.09,38.19,23.0,762.0,140.0,358.0,141.0,2.4545,105000.0,INLAND\n-121.19,38.13,27.0,2400.0,435.0,1085.0,444.0,3.7687,165200.0,INLAND\n-121.18,38.07,21.0,2333.0,377.0,1073.0,332.0,4.8125,161100.0,INLAND\n-120.97,38.0,27.0,1683.0,288.0,873.0,258.0,4.7069,176900.0,INLAND\n-121.05,37.93,17.0,2474.0,480.0,1649.0,453.0,3.275,156500.0,INLAND\n-121.11,38.04,32.0,1083.0,188.0,471.0,178.0,2.9241,187500.0,INLAND\n-121.09,38.03,21.0,2064.0,342.0,1021.0,359.0,4.517,152200.0,INLAND\n-121.12,38.0,36.0,683.0,159.0,505.0,141.0,3.4265,158900.0,INLAND\n-120.99,37.8,32.0,2564.0,513.0,1198.0,459.0,2.9083,113400.0,INLAND\n-121.0,37.8,13.0,4030.0,744.0,2248.0,766.0,3.6107,141300.0,INLAND\n-120.98,37.79,20.0,2458.0,491.0,1227.0,481.0,2.7857,110900.0,INLAND\n-120.97,37.84,28.0,2368.0,430.0,1231.0,403.0,2.883,141900.0,INLAND\n-120.96,37.77,32.0,2262.0,416.0,1156.0,404.0,3.8534,157600.0,INLAND\n-121.04,37.78,32.0,2916.0,528.0,1466.0,473.0,2.5643,200000.0,INLAND\n-121.06,37.86,24.0,1713.0,328.0,1258.0,324.0,2.683,169400.0,INLAND\n-121.13,37.74,31.0,677.0,144.0,523.0,159.0,2.4598,97100.0,INLAND\n-121.13,37.74,28.0,409.0,104.0,244.0,98.0,3.4643,90900.0,INLAND\n-121.12,37.73,35.0,1107.0,227.0,573.0,210.0,2.3924,102200.0,INLAND\n-121.13,37.74,21.0,2376.0,475.0,1175.0,441.0,3.6016,134600.0,INLAND\n-121.13,37.73,40.0,1126.0,220.0,667.0,235.0,3.3158,125900.0,INLAND\n-121.11,37.74,11.0,3886.0,599.0,1605.0,529.0,4.4213,182700.0,INLAND\n-121.1,37.8,35.0,1853.0,331.0,958.0,340.0,3.3578,149000.0,INLAND\n-121.11,37.76,22.0,2606.0,411.0,1252.0,397.0,4.1833,192100.0,INLAND\n-121.16,37.73,7.0,4956.0,941.0,3006.0,915.0,3.4426,139000.0,INLAND\n-121.25,37.76,22.0,2430.0,417.0,1292.0,391.0,3.4009,182400.0,INLAND\n-121.22,37.72,34.0,2123.0,387.0,1310.0,368.0,2.6368,165600.0,INLAND\n-121.22,37.81,17.0,2879.0,542.0,1802.0,530.0,3.6378,126100.0,INLAND\n-121.22,37.8,28.0,2608.0,576.0,1719.0,554.0,2.1186,94400.0,INLAND\n-121.22,37.8,37.0,1038.0,222.0,521.0,211.0,2.125,91900.0,INLAND\n-121.21,37.81,18.0,2404.0,498.0,1531.0,506.0,2.995,124300.0,INLAND\n-121.21,37.8,31.0,699.0,186.0,460.0,170.0,2.7443,94200.0,INLAND\n-121.21,37.8,44.0,300.0,72.0,160.0,73.0,2.1786,120800.0,INLAND\n-121.21,37.8,45.0,370.0,84.0,167.0,70.0,1.4853,101800.0,INLAND\n-121.21,37.79,33.0,811.0,185.0,446.0,198.0,1.6724,96900.0,INLAND\n-121.21,37.8,33.0,1862.0,429.0,971.0,389.0,2.6053,99200.0,INLAND\n-121.21,37.81,8.0,1883.0,298.0,999.0,301.0,5.193,172100.0,INLAND\n-121.2,37.8,28.0,3264.0,576.0,1512.0,567.0,3.7546,135300.0,INLAND\n-121.2,37.8,24.0,1698.0,344.0,927.0,313.0,3.5625,130800.0,INLAND\n-121.2,37.79,36.0,866.0,160.0,502.0,149.0,2.4798,101500.0,INLAND\n-121.2,37.8,37.0,311.0,61.0,171.0,54.0,4.0972,101800.0,INLAND\n-121.21,37.81,12.0,3667.0,640.0,2173.0,652.0,5.0369,163900.0,INLAND\n-121.17,37.88,22.0,1283.0,256.0,3082.0,239.0,3.5365,111800.0,INLAND\n-121.17,37.82,35.0,2506.0,406.0,1316.0,398.0,3.8472,197600.0,INLAND\n-121.2,37.83,18.0,3415.0,580.0,1912.0,562.0,4.4423,161400.0,INLAND\n-121.2,37.81,26.0,395.0,74.0,193.0,72.0,7.3718,212500.0,INLAND\n-121.19,37.81,8.0,4019.0,857.0,1959.0,782.0,2.7321,175000.0,INLAND\n-121.18,37.79,16.0,1326.0,286.0,509.0,297.0,1.9464,112500.0,INLAND\n-121.2,37.78,4.0,58.0,29.0,79.0,29.0,3.375,106300.0,INLAND\n-121.23,37.78,20.0,273.0,49.0,149.0,49.0,4.8229,158300.0,INLAND\n-121.24,37.79,7.0,5151.0,867.0,2553.0,805.0,4.075,195000.0,INLAND\n-121.27,37.79,16.0,1853.0,390.0,1013.0,362.0,2.7083,173900.0,INLAND\n-121.22,37.8,13.0,335.0,89.0,247.0,77.0,1.6111,74100.0,INLAND\n-121.22,37.79,36.0,1052.0,221.0,712.0,212.0,1.7228,105000.0,INLAND\n-121.22,37.79,38.0,2152.0,451.0,1320.0,457.0,2.5025,101900.0,INLAND\n-121.23,37.79,21.0,1922.0,373.0,1130.0,372.0,4.0815,117900.0,INLAND\n-121.22,37.79,5.0,3107.0,477.0,1549.0,443.0,4.4766,169400.0,INLAND\n-121.23,37.79,23.0,1985.0,424.0,1198.0,389.0,2.7734,116800.0,INLAND\n-121.23,37.8,11.0,2451.0,665.0,1155.0,533.0,2.2254,130800.0,INLAND\n-121.24,37.81,6.0,3883.0,800.0,2319.0,787.0,3.5595,161000.0,INLAND\n-121.23,37.81,15.0,2906.0,537.0,1886.0,557.0,4.2431,137100.0,INLAND\n-121.22,37.81,20.0,1811.0,352.0,1191.0,327.0,4.0125,121500.0,INLAND\n-121.22,37.82,13.0,4452.0,949.0,2740.0,937.0,3.1964,141500.0,INLAND\n-121.23,37.81,16.0,2085.0,342.0,1236.0,345.0,5.5591,149300.0,INLAND\n-121.23,37.82,8.0,1289.0,235.0,867.0,239.0,4.6821,138500.0,INLAND\n-121.23,37.82,14.0,1847.0,325.0,1030.0,309.0,4.9271,155300.0,INLAND\n-121.23,37.84,28.0,1347.0,241.0,713.0,225.0,4.0208,155700.0,INLAND\n-121.24,37.82,9.0,6169.0,959.0,3378.0,945.0,5.1047,157900.0,INLAND\n-121.3,37.85,35.0,1034.0,206.0,604.0,192.0,2.2391,120000.0,INLAND\n-121.31,37.81,36.0,284.0,53.0,130.0,47.0,3.1429,179200.0,INLAND\n-121.29,37.8,6.0,110.0,26.0,69.0,24.0,3.7292,475000.0,INLAND\n-121.27,37.82,26.0,1170.0,238.0,830.0,216.0,2.6458,127500.0,INLAND\n-121.28,37.83,32.0,696.0,151.0,443.0,144.0,2.5156,86300.0,INLAND\n-121.28,37.82,10.0,9205.0,1774.0,5935.0,1673.0,3.65,119400.0,INLAND\n-121.37,37.77,19.0,2610.0,474.0,1290.0,452.0,4.1298,222800.0,INLAND\n-121.4,37.74,20.0,2706.0,477.0,1236.0,474.0,4.15,322400.0,INLAND\n-121.43,37.78,24.0,807.0,174.0,585.0,166.0,2.6181,163500.0,INLAND\n-121.48,37.77,19.0,2364.0,373.0,1264.0,390.0,5.0176,274200.0,INLAND\n-121.52,37.75,18.0,1544.0,272.0,825.0,286.0,4.3229,327300.0,INLAND\n-121.46,37.73,20.0,2039.0,373.0,862.0,330.0,5.1629,222900.0,INLAND\n-121.45,37.72,2.0,2239.0,321.0,766.0,219.0,5.75,240200.0,INLAND\n-121.44,37.7,5.0,1365.0,196.0,591.0,156.0,6.0389,215100.0,INLAND\n-121.42,37.71,7.0,8297.0,1433.0,4189.0,1271.0,4.3696,170700.0,INLAND\n-121.42,37.76,18.0,5501.0,1051.0,2964.0,1009.0,4.1855,162100.0,INLAND\n-121.42,37.75,33.0,1999.0,368.0,1061.0,390.0,3.5242,121400.0,INLAND\n-121.42,37.74,45.0,818.0,144.0,340.0,138.0,4.8021,133500.0,INLAND\n-121.42,37.74,38.0,773.0,147.0,320.0,134.0,2.825,152500.0,INLAND\n-121.42,37.74,35.0,796.0,132.0,313.0,152.0,3.15,153200.0,INLAND\n-121.42,37.75,33.0,1329.0,266.0,683.0,233.0,4.3687,128700.0,INLAND\n-121.43,37.75,34.0,1280.0,268.0,754.0,294.0,3.1333,132000.0,INLAND\n-121.43,37.75,41.0,1717.0,325.0,855.0,303.0,2.75,127300.0,INLAND\n-121.43,37.75,42.0,1207.0,278.0,699.0,279.0,3.3611,117600.0,INLAND\n-121.43,37.74,52.0,876.0,170.0,426.0,179.0,3.0865,119800.0,INLAND\n-121.43,37.74,40.0,859.0,196.0,427.0,176.0,3.5789,110400.0,INLAND\n-121.43,37.75,30.0,1912.0,451.0,1065.0,388.0,2.1424,125000.0,INLAND\n-121.43,37.76,7.0,2125.0,508.0,1358.0,464.0,3.6312,147600.0,INLAND\n-121.44,37.76,5.0,7264.0,1285.0,3670.0,1146.0,5.0443,194800.0,INLAND\n-121.45,37.75,15.0,3846.0,677.0,2360.0,635.0,4.6173,164800.0,INLAND\n-121.44,37.74,33.0,1875.0,363.0,970.0,381.0,3.5096,141700.0,INLAND\n-121.44,37.75,29.0,918.0,159.0,417.0,166.0,4.2768,151300.0,INLAND\n-121.44,37.75,16.0,2229.0,458.0,1199.0,445.0,3.4821,170600.0,INLAND\n-121.42,37.74,19.0,1393.0,367.0,915.0,355.0,1.1957,103100.0,INLAND\n-121.43,37.74,52.0,966.0,247.0,589.0,228.0,1.6937,108300.0,INLAND\n-121.43,37.74,52.0,994.0,258.0,623.0,264.0,1.725,111500.0,INLAND\n-121.44,37.74,25.0,456.0,116.0,370.0,106.0,3.1319,112500.0,INLAND\n-121.44,37.73,7.0,8363.0,1314.0,3907.0,1068.0,5.3321,208100.0,INLAND\n-121.43,37.73,40.0,1718.0,391.0,1312.0,388.0,2.9955,134700.0,INLAND\n-121.42,37.73,2.0,2682.0,393.0,883.0,271.0,5.9934,196700.0,INLAND\n-121.29,37.72,22.0,1630.0,404.0,4402.0,358.0,1.9792,63000.0,INLAND\n-121.32,37.67,21.0,1494.0,271.0,781.0,255.0,4.3015,250000.0,INLAND\n-121.47,37.58,14.0,1594.0,292.0,887.0,287.0,4.6625,294000.0,INLAND\n-120.9,35.69,14.0,5020.0,909.0,2105.0,796.0,3.8158,248700.0,<1H OCEAN\n-120.93,35.76,11.0,8997.0,1698.0,1825.0,756.0,3.23,154300.0,<1H OCEAN\n-120.7,35.76,15.0,1914.0,425.0,1130.0,421.0,2.2165,90600.0,<1H OCEAN\n-120.69,35.65,14.0,3487.0,889.0,2352.0,796.0,1.6303,144900.0,<1H OCEAN\n-120.69,35.64,38.0,2564.0,546.0,1301.0,481.0,2.0076,114000.0,<1H OCEAN\n-120.69,35.62,43.0,3044.0,652.0,1456.0,608.0,2.4567,140000.0,<1H OCEAN\n-120.69,35.62,35.0,3451.0,713.0,1550.0,653.0,2.9167,161700.0,<1H OCEAN\n-120.72,35.63,31.0,3476.0,644.0,1476.0,567.0,3.3472,195200.0,<1H OCEAN\n-120.67,35.63,8.0,2690.0,410.0,1085.0,381.0,4.2841,256700.0,<1H OCEAN\n-120.67,35.62,6.0,12779.0,2441.0,6085.0,2157.0,3.8661,168100.0,<1H OCEAN\n-120.63,35.59,9.0,5782.0,1184.0,3026.0,1130.0,2.6528,113500.0,<1H OCEAN\n-120.64,35.65,9.0,3466.0,673.0,2356.0,619.0,2.9926,158200.0,<1H OCEAN\n-120.47,35.74,9.0,4267.0,785.0,2065.0,691.0,3.7303,162700.0,<1H OCEAN\n-120.29,35.56,15.0,4760.0,871.0,2459.0,734.0,2.811,142100.0,<1H OCEAN\n-120.6,35.6,13.0,4461.0,764.0,1795.0,640.0,4.475,206900.0,<1H OCEAN\n-121.11,35.52,9.0,6044.0,1222.0,2239.0,972.0,3.24,264600.0,NEAR OCEAN\n-121.14,35.55,13.0,5383.0,1070.0,1880.0,796.0,3.8019,271200.0,NEAR OCEAN\n-121.12,35.58,16.0,4109.0,798.0,1298.0,626.0,3.4799,320800.0,NEAR OCEAN\n-120.92,35.4,23.0,2059.0,354.0,636.0,278.0,3.6908,278800.0,NEAR OCEAN\n-120.85,35.38,27.0,3493.0,909.0,1481.0,666.0,2.3075,184200.0,NEAR OCEAN\n-120.86,35.39,23.0,1664.0,355.0,629.0,279.0,2.7344,188300.0,NEAR OCEAN\n-120.86,35.4,21.0,2787.0,641.0,1106.0,501.0,2.7043,186200.0,NEAR OCEAN\n-120.87,35.41,16.0,2168.0,444.0,782.0,374.0,3.0187,278100.0,NEAR OCEAN\n-120.94,35.42,18.0,3418.0,686.0,970.0,453.0,3.7738,279400.0,NEAR OCEAN\n-120.95,35.44,30.0,6346.0,1410.0,1769.0,887.0,2.6864,283600.0,NEAR OCEAN\n-120.85,35.37,21.0,1033.0,195.0,588.0,187.0,2.8173,226900.0,NEAR OCEAN\n-120.83,35.36,28.0,4323.0,886.0,1650.0,705.0,2.7266,266800.0,NEAR OCEAN\n-120.84,35.35,27.0,2863.0,711.0,930.0,533.0,2.6205,221100.0,NEAR OCEAN\n-120.84,35.37,34.0,3279.0,714.0,1397.0,646.0,2.5552,200000.0,NEAR OCEAN\n-120.89,35.37,29.0,2046.0,588.0,846.0,410.0,1.65,227300.0,NEAR OCEAN\n-120.84,35.33,15.0,3276.0,670.0,1520.0,613.0,3.6412,207800.0,NEAR OCEAN\n-120.83,35.33,14.0,4155.0,787.0,2112.0,755.0,4.4766,192700.0,NEAR OCEAN\n-120.82,35.32,12.0,3522.0,683.0,1780.0,662.0,3.3958,215800.0,NEAR OCEAN\n-120.83,35.32,11.0,3252.0,701.0,1814.0,660.0,3.2226,183200.0,NEAR OCEAN\n-120.84,35.32,15.0,2419.0,538.0,1279.0,522.0,3.4762,189600.0,NEAR OCEAN\n-120.9,35.33,16.0,1576.0,287.0,595.0,262.0,3.588,266300.0,NEAR OCEAN\n-120.84,35.32,17.0,4197.0,802.0,1656.0,732.0,3.526,183900.0,NEAR OCEAN\n-120.84,35.31,23.0,3100.0,603.0,1515.0,609.0,2.8493,196100.0,NEAR OCEAN\n-120.84,35.3,15.0,2062.0,327.0,781.0,316.0,4.9359,317700.0,NEAR OCEAN\n-120.82,35.31,16.0,3924.0,699.0,1325.0,638.0,2.5172,293900.0,NEAR OCEAN\n-120.8,35.33,20.0,2200.0,393.0,996.0,365.0,3.587,330000.0,NEAR OCEAN\n-121.1,35.6,20.0,3389.0,704.0,1309.0,520.0,3.2112,204500.0,NEAR OCEAN\n-120.65,35.29,36.0,1940.0,315.0,850.0,298.0,3.1818,249600.0,NEAR OCEAN\n-120.66,35.29,23.0,1932.0,487.0,1380.0,472.0,1.9647,253600.0,NEAR OCEAN\n-120.66,35.29,16.0,2272.0,629.0,1689.0,649.0,1.7031,195000.0,NEAR OCEAN\n-120.67,35.3,19.0,1540.0,715.0,1799.0,635.0,0.7025,500001.0,NEAR OCEAN\n-120.65,35.32,20.0,626.0,212.0,3574.0,261.0,1.0298,300000.0,NEAR OCEAN\n-120.62,35.28,28.0,3952.0,592.0,1469.0,571.0,6.3144,328800.0,NEAR OCEAN\n-120.65,35.29,29.0,1785.0,481.0,1344.0,472.0,1.4492,222900.0,NEAR OCEAN\n-120.65,35.28,32.0,896.0,240.0,548.0,231.0,2.5455,165900.0,NEAR OCEAN\n-120.65,35.27,27.0,2034.0,341.0,768.0,316.0,4.2411,258900.0,NEAR OCEAN\n-120.65,35.27,15.0,2365.0,538.0,1446.0,490.0,2.5129,225900.0,NEAR OCEAN\n-120.64,35.26,21.0,3298.0,716.0,1862.0,687.0,2.1507,221500.0,NEAR OCEAN\n-120.63,35.27,23.0,1630.0,253.0,704.0,263.0,5.156,251300.0,NEAR OCEAN\n-120.66,35.29,39.0,2163.0,652.0,1153.0,599.0,2.084,233300.0,NEAR OCEAN\n-120.66,35.28,31.0,2773.0,844.0,1358.0,794.0,1.4036,209600.0,NEAR OCEAN\n-120.66,35.28,46.0,2054.0,502.0,1170.0,494.0,2.1786,206300.0,NEAR OCEAN\n-120.65,35.27,52.0,2254.0,642.0,1237.0,590.0,2.6208,227100.0,NEAR OCEAN\n-120.66,35.27,46.0,2217.0,544.0,1107.0,527.0,2.8009,192600.0,NEAR OCEAN\n-120.66,35.27,33.0,1664.0,455.0,1077.0,461.0,1.6875,174200.0,NEAR OCEAN\n-120.66,35.27,17.0,2719.0,589.0,1386.0,570.0,3.7337,208200.0,NEAR OCEAN\n-120.66,35.26,15.0,5540.0,1319.0,2383.0,1165.0,2.2656,226200.0,NEAR OCEAN\n-120.67,35.3,32.0,4202.0,986.0,2309.0,956.0,2.2165,231700.0,NEAR OCEAN\n-120.7,35.31,24.0,3504.0,521.0,1490.0,506.0,4.6719,337000.0,NEAR OCEAN\n-120.68,35.29,37.0,1354.0,293.0,753.0,290.0,3.25,225000.0,NEAR OCEAN\n-120.69,35.28,26.0,4225.0,886.0,1795.0,704.0,2.2847,247000.0,NEAR OCEAN\n-120.67,35.29,44.0,2236.0,411.0,1036.0,437.0,3.0833,219300.0,NEAR OCEAN\n-120.69,35.26,20.0,1248.0,231.0,722.0,225.0,4.625,221800.0,NEAR OCEAN\n-120.7,35.28,14.0,3768.0,682.0,1884.0,664.0,4.6071,239900.0,NEAR OCEAN\n-120.71,35.27,9.0,2568.0,421.0,1149.0,398.0,5.4287,331600.0,NEAR OCEAN\n-120.68,35.26,26.0,1704.0,315.0,918.0,310.0,3.2464,208000.0,NEAR OCEAN\n-120.69,35.25,15.0,4210.0,899.0,1933.0,867.0,2.794,262500.0,NEAR OCEAN\n-120.7,35.32,46.0,118.0,17.0,6532.0,13.0,4.2639,350000.0,NEAR OCEAN\n-120.52,35.24,5.0,4413.0,804.0,2003.0,725.0,5.0267,253300.0,<1H OCEAN\n-120.68,35.25,16.0,4208.0,897.0,1634.0,806.0,2.2868,233700.0,NEAR OCEAN\n-120.69,35.34,16.0,164.0,30.0,542.0,32.0,1.6563,42500.0,NEAR OCEAN\n-120.81,35.19,14.0,3414.0,802.0,1236.0,632.0,3.7635,336200.0,NEAR OCEAN\n-120.66,35.2,13.0,5138.0,713.0,1838.0,645.0,5.9676,380000.0,NEAR OCEAN\n-120.7,35.14,17.0,5805.0,1097.0,1919.0,932.0,3.5352,357800.0,NEAR OCEAN\n-120.68,35.14,34.0,3100.0,617.0,1155.0,542.0,3.0938,245900.0,NEAR OCEAN\n-120.66,35.13,41.0,2666.0,751.0,940.0,507.0,1.9653,236100.0,<1H OCEAN\n-120.63,35.13,16.0,2680.0,704.0,975.0,619.0,1.7878,55000.0,<1H OCEAN\n-120.64,35.15,7.0,7922.0,1442.0,2863.0,1197.0,4.849,275000.0,<1H OCEAN\n-120.59,35.13,8.0,6638.0,1054.0,2710.0,966.0,4.6776,295500.0,<1H OCEAN\n-120.56,35.13,15.0,5818.0,924.0,2324.0,845.0,4.4033,267600.0,<1H OCEAN\n-120.57,35.12,39.0,1656.0,333.0,866.0,317.0,2.8824,195200.0,<1H OCEAN\n-120.57,35.11,18.0,2920.0,556.0,1068.0,552.0,3.5242,156800.0,<1H OCEAN\n-120.59,35.11,25.0,3642.0,726.0,1729.0,673.0,3.155,205400.0,<1H OCEAN\n-120.6,35.12,22.0,3342.0,644.0,1342.0,593.0,3.4509,217700.0,<1H OCEAN\n-120.61,35.12,16.0,1671.0,354.0,935.0,340.0,2.5792,163800.0,<1H OCEAN\n-120.59,35.12,27.0,3055.0,677.0,1407.0,610.0,2.1702,179700.0,<1H OCEAN\n-120.6,35.11,17.0,2495.0,524.0,1292.0,501.0,2.2625,153000.0,<1H OCEAN\n-120.59,35.11,20.0,3098.0,571.0,1449.0,611.0,3.5744,197800.0,<1H OCEAN\n-120.61,35.12,31.0,1486.0,345.0,823.0,322.0,2.6974,165400.0,<1H OCEAN\n-120.61,35.13,16.0,3431.0,721.0,1777.0,701.0,2.7301,190400.0,<1H OCEAN\n-120.61,35.12,12.0,3430.0,793.0,1840.0,720.0,2.9821,162000.0,<1H OCEAN\n-120.61,35.11,11.0,3733.0,831.0,1839.0,739.0,3.3062,158500.0,<1H OCEAN\n-120.65,35.12,19.0,2949.0,662.0,1425.0,548.0,2.9615,178100.0,<1H OCEAN\n-120.62,35.13,26.0,3971.0,803.0,1792.0,723.0,2.7128,209900.0,<1H OCEAN\n-120.62,35.11,18.0,2241.0,544.0,1521.0,509.0,2.0292,155800.0,<1H OCEAN\n-120.62,35.12,22.0,1240.0,294.0,768.0,288.0,2.655,160000.0,<1H OCEAN\n-120.66,35.1,19.0,1583.0,392.0,704.0,269.0,2.1042,268300.0,<1H OCEAN\n-120.61,35.1,14.0,2919.0,691.0,1896.0,577.0,2.4003,142100.0,<1H OCEAN\n-120.61,35.1,17.0,2799.0,637.0,2015.0,592.0,3.0536,143600.0,<1H OCEAN\n-120.6,35.1,16.0,3290.0,686.0,1497.0,655.0,2.6875,178200.0,<1H OCEAN\n-120.58,35.0,37.0,523.0,119.0,374.0,95.0,1.4726,106300.0,<1H OCEAN\n-120.61,35.06,13.0,2364.0,421.0,1257.0,380.0,4.6167,273100.0,<1H OCEAN\n-120.56,35.07,14.0,6788.0,1216.0,2866.0,1036.0,3.3603,280200.0,<1H OCEAN\n-120.52,35.06,11.0,1317.0,234.0,655.0,243.0,4.3611,329700.0,<1H OCEAN\n-120.3,35.1,16.0,2819.0,479.0,1068.0,365.0,4.5461,270800.0,<1H OCEAN\n-120.43,35.17,16.0,947.0,163.0,477.0,137.0,3.851,315000.0,<1H OCEAN\n-120.57,35.18,16.0,5209.0,917.0,2284.0,809.0,4.0403,346100.0,<1H OCEAN\n-120.48,35.05,24.0,2314.0,468.0,1549.0,463.0,2.8203,152600.0,<1H OCEAN\n-120.47,35.04,29.0,1315.0,279.0,926.0,249.0,2.9375,144800.0,<1H OCEAN\n-120.5,35.03,10.0,10463.0,1756.0,4660.0,1715.0,3.5682,277300.0,<1H OCEAN\n-120.48,35.02,17.0,2721.0,477.0,1672.0,492.0,2.9798,204800.0,<1H OCEAN\n-120.69,35.52,26.0,2758.0,571.0,1291.0,522.0,2.925,181400.0,<1H OCEAN\n-120.68,35.51,17.0,1701.0,298.0,941.0,293.0,4.3218,209100.0,<1H OCEAN\n-120.68,35.5,19.0,3369.0,673.0,1834.0,646.0,3.7672,173800.0,<1H OCEAN\n-120.67,35.5,15.0,2752.0,546.0,1422.0,545.0,3.2813,175000.0,<1H OCEAN\n-120.66,35.5,19.0,1861.0,364.0,1040.0,363.0,3.3125,163900.0,<1H OCEAN\n-120.66,35.49,17.0,4422.0,945.0,2307.0,885.0,2.8285,171300.0,<1H OCEAN\n-120.65,35.48,19.0,2310.0,471.0,1341.0,441.0,3.225,166900.0,<1H OCEAN\n-120.64,35.46,6.0,5876.0,1406.0,2877.0,1304.0,2.5437,146400.0,<1H OCEAN\n-120.69,35.49,16.0,2666.0,450.0,1203.0,429.0,4.1375,222400.0,<1H OCEAN\n-120.68,35.48,15.0,2608.0,525.0,1351.0,502.0,2.7798,205800.0,<1H OCEAN\n-120.67,35.48,18.0,2339.0,443.0,1097.0,416.0,3.3438,176100.0,<1H OCEAN\n-120.66,35.47,18.0,2474.0,449.0,1269.0,431.0,3.9063,184800.0,<1H OCEAN\n-120.66,35.46,17.0,3748.0,609.0,1860.0,612.0,4.5179,225600.0,<1H OCEAN\n-120.7,35.55,10.0,3979.0,761.0,1834.0,671.0,3.5,172100.0,<1H OCEAN\n-120.76,35.52,7.0,9613.0,1666.0,4487.0,1653.0,3.6667,250600.0,<1H OCEAN\n-120.71,35.5,12.0,3098.0,453.0,1433.0,434.0,5.2508,292900.0,<1H OCEAN\n-120.56,35.48,12.0,4161.0,731.0,1609.0,615.0,5.0947,267500.0,<1H OCEAN\n-119.93,35.2,29.0,1649.0,342.0,671.0,264.0,3.0602,69800.0,INLAND\n-120.49,35.35,17.0,3043.0,608.0,1457.0,545.0,3.1641,158600.0,<1H OCEAN\n-120.65,35.41,15.0,6725.0,1111.0,3139.0,1029.0,4.1875,261600.0,<1H OCEAN\n-120.64,35.47,8.0,416.0,121.0,936.0,97.0,2.1154,117200.0,<1H OCEAN\n-122.4,37.68,36.0,3595.0,815.0,1649.0,755.0,3.3816,253400.0,NEAR BAY\n-122.4,37.68,41.0,2267.0,486.0,1045.0,459.0,4.1146,272200.0,NEAR BAY\n-122.32,37.69,48.0,592.0,122.0,340.0,143.0,5.966,315200.0,NEAR BAY\n-122.41,37.7,23.0,1817.0,400.0,1376.0,382.0,2.4113,214200.0,NEAR BAY\n-122.42,37.71,44.0,2080.0,489.0,1781.0,478.0,3.6827,215300.0,NEAR BAY\n-122.43,37.71,24.0,4299.0,857.0,2249.0,788.0,4.6098,290400.0,NEAR BAY\n-122.43,37.7,19.0,1733.0,354.0,959.0,348.0,4.7708,281700.0,NEAR BAY\n-122.44,37.7,6.0,3523.0,664.0,1705.0,608.0,4.9318,258100.0,NEAR OCEAN\n-122.45,37.7,46.0,2193.0,499.0,1814.0,489.0,4.0125,230100.0,NEAR OCEAN\n-122.45,37.71,50.0,1441.0,283.0,1159.0,286.0,4.5417,233700.0,NEAR OCEAN\n-122.45,37.71,49.0,2244.0,442.0,1948.0,423.0,4.7639,251500.0,NEAR OCEAN\n-122.45,37.7,16.0,6457.0,1336.0,4375.0,1231.0,5.1788,267000.0,NEAR OCEAN\n-122.46,37.7,37.0,1028.0,275.0,904.0,261.0,3.5035,238600.0,NEAR OCEAN\n-122.46,37.69,35.0,1983.0,385.0,1577.0,414.0,4.0833,266700.0,NEAR OCEAN\n-122.46,37.7,37.0,3029.0,738.0,2436.0,700.0,3.3214,243200.0,NEAR OCEAN\n-122.47,37.7,45.0,3290.0,693.0,2466.0,666.0,3.6588,238600.0,NEAR OCEAN\n-122.46,37.71,44.0,364.0,102.0,339.0,98.0,2.483,214300.0,NEAR OCEAN\n-122.46,37.71,45.0,1799.0,394.0,1436.0,389.0,3.65,239900.0,NEAR OCEAN\n-122.46,37.7,42.0,876.0,216.0,713.0,203.0,3.84,235900.0,NEAR OCEAN\n-122.47,37.7,44.0,2034.0,423.0,1491.0,373.0,4.5341,236500.0,NEAR OCEAN\n-122.47,37.71,37.0,1046.0,251.0,822.0,239.0,3.5,224400.0,NEAR OCEAN\n-122.46,37.71,39.0,2076.0,482.0,1738.0,445.0,3.1958,232100.0,NEAR OCEAN\n-122.48,37.7,33.0,4167.0,1398.0,2923.0,1314.0,3.049,307000.0,NEAR OCEAN\n-122.48,37.7,33.0,4492.0,,3477.0,1537.0,3.0546,297900.0,NEAR OCEAN\n-122.48,37.71,39.0,3615.0,632.0,1571.0,615.0,5.1149,314200.0,NEAR OCEAN\n-122.48,37.71,29.0,1048.0,150.0,455.0,152.0,6.1278,417600.0,NEAR OCEAN\n-122.54,37.7,36.0,3988.0,732.0,1793.0,708.0,4.2472,292500.0,NEAR OCEAN\n-122.49,37.67,35.0,5275.0,903.0,2892.0,842.0,4.6771,266400.0,NEAR OCEAN\n-122.49,37.68,35.0,2405.0,461.0,1583.0,471.0,5.0659,238000.0,NEAR OCEAN\n-122.49,37.67,29.0,3795.0,675.0,2494.0,696.0,5.2848,260300.0,NEAR OCEAN\n-122.49,37.7,36.0,1946.0,340.0,828.0,313.0,5.2811,287700.0,NEAR OCEAN\n-122.49,37.69,36.0,1344.0,258.0,782.0,265.0,4.5,275600.0,NEAR OCEAN\n-122.48,37.69,33.0,2347.0,512.0,1259.0,481.0,3.4492,264300.0,NEAR OCEAN\n-122.49,37.69,35.0,2644.0,456.0,1465.0,430.0,4.9375,277000.0,NEAR OCEAN\n-122.49,37.69,35.0,2576.0,443.0,1273.0,433.0,4.7391,272800.0,NEAR OCEAN\n-122.47,37.7,47.0,737.0,126.0,370.0,136.0,3.775,281300.0,NEAR OCEAN\n-122.47,37.69,27.0,2447.0,720.0,2104.0,657.0,3.449,239100.0,NEAR OCEAN\n-122.47,37.69,30.0,837.0,213.0,606.0,199.0,4.875,258800.0,NEAR OCEAN\n-122.48,37.69,42.0,2993.0,512.0,1594.0,546.0,4.4821,252400.0,NEAR OCEAN\n-122.48,37.69,43.0,2661.0,455.0,1384.0,456.0,4.2421,257500.0,NEAR OCEAN\n-122.46,37.69,26.0,4302.0,1125.0,3320.0,1100.0,3.4375,277700.0,NEAR OCEAN\n-122.46,37.68,23.0,2812.0,769.0,1983.0,681.0,2.9413,229400.0,NEAR OCEAN\n-122.47,37.69,35.0,1720.0,421.0,1452.0,425.0,3.5909,256100.0,NEAR OCEAN\n-122.47,37.69,34.0,1954.0,357.0,1130.0,367.0,4.6447,304500.0,NEAR OCEAN\n-122.47,37.68,31.0,4077.0,777.0,2544.0,738.0,4.5337,306700.0,NEAR OCEAN\n-122.48,37.68,31.0,3506.0,653.0,2296.0,645.0,5.5647,268700.0,NEAR OCEAN\n-122.48,37.67,15.0,2897.0,728.0,2340.0,720.0,3.3906,303700.0,NEAR OCEAN\n-122.48,37.67,31.0,2609.0,433.0,1746.0,464.0,5.1054,294500.0,NEAR OCEAN\n-122.49,37.68,34.0,3718.0,676.0,2510.0,632.0,5.3311,270800.0,NEAR OCEAN\n-122.48,37.67,14.0,3395.0,1059.0,2258.0,945.0,2.964,319700.0,NEAR OCEAN\n-122.45,37.69,17.0,2359.0,501.0,884.0,504.0,3.0625,87500.0,NEAR OCEAN\n-122.45,37.67,35.0,491.0,98.0,274.0,97.0,4.4286,238600.0,NEAR OCEAN\n-122.47,37.67,20.0,5689.0,992.0,3752.0,1002.0,5.5845,304300.0,NEAR OCEAN\n-122.47,37.66,18.0,4172.0,806.0,3226.0,790.0,5.7535,297900.0,NEAR OCEAN\n-122.46,37.67,16.0,3372.0,1101.0,2049.0,1021.0,4.1303,146500.0,NEAR OCEAN\n-122.46,37.66,15.0,6082.0,1284.0,3861.0,1198.0,5.4221,284700.0,NEAR OCEAN\n-122.46,37.65,21.0,2751.0,502.0,2027.0,491.0,5.2573,322900.0,NEAR OCEAN\n-122.45,37.67,36.0,1664.0,326.0,963.0,322.0,4.7813,246400.0,NEAR OCEAN\n-122.45,37.66,35.0,2738.0,509.0,1545.0,493.0,5.3446,263300.0,NEAR OCEAN\n-122.46,37.66,36.0,2535.0,451.0,1390.0,436.0,5.3398,260900.0,NEAR OCEAN\n-122.45,37.66,36.0,5456.0,926.0,2761.0,916.0,4.7755,280700.0,NEAR OCEAN\n-122.44,37.65,38.0,5277.0,1008.0,2695.0,997.0,3.9722,276200.0,NEAR OCEAN\n-122.43,37.66,29.0,3541.0,786.0,2259.0,770.0,4.3039,278400.0,NEAR OCEAN\n-122.44,37.66,21.0,5108.0,1510.0,3288.0,1405.0,3.1927,252600.0,NEAR OCEAN\n-122.44,37.66,36.0,1447.0,276.0,799.0,275.0,4.7639,265600.0,NEAR OCEAN\n-122.44,37.67,35.0,1814.0,365.0,1025.0,384.0,4.425,268400.0,NEAR OCEAN\n-122.42,37.67,42.0,2274.0,429.0,1255.0,397.0,5.1205,226300.0,NEAR OCEAN\n-122.41,37.66,37.0,2155.0,446.0,1255.0,428.0,3.8438,250700.0,NEAR OCEAN\n-122.42,37.66,41.0,2189.0,414.0,1063.0,409.0,4.7361,302600.0,NEAR OCEAN\n-122.42,37.66,28.0,3520.0,672.0,1746.0,602.0,4.9236,273500.0,NEAR OCEAN\n-122.42,37.66,36.0,725.0,121.0,335.0,140.0,4.125,327600.0,NEAR OCEAN\n-122.41,37.66,32.0,1385.0,356.0,1096.0,353.0,4.475,246700.0,NEAR OCEAN\n-122.41,37.66,34.0,1075.0,318.0,906.0,294.0,3.0052,242500.0,NEAR OCEAN\n-122.41,37.66,40.0,1294.0,308.0,1177.0,301.0,3.6667,218800.0,NEAR OCEAN\n-122.41,37.66,37.0,694.0,188.0,658.0,225.0,4.6103,237500.0,NEAR OCEAN\n-122.41,37.66,44.0,431.0,195.0,682.0,212.0,3.2833,233300.0,NEAR OCEAN\n-122.41,37.65,32.0,3436.0,868.0,2583.0,817.0,3.5039,232400.0,NEAR OCEAN\n-122.42,37.66,26.0,3253.0,932.0,2246.0,855.0,2.6631,244000.0,NEAR OCEAN\n-122.43,37.66,43.0,1769.0,387.0,1102.0,377.0,4.5493,281500.0,NEAR OCEAN\n-122.42,37.65,39.0,4402.0,894.0,2941.0,887.0,3.8565,239800.0,NEAR OCEAN\n-122.43,37.64,34.0,8400.0,1812.0,4101.0,1717.0,4.1033,301000.0,NEAR OCEAN\n-122.43,37.64,42.0,4091.0,757.0,1861.0,771.0,4.207,272700.0,NEAR OCEAN\n-122.45,37.64,19.0,6326.0,1025.0,3444.0,984.0,6.2498,353300.0,NEAR OCEAN\n-122.46,37.64,26.0,2806.0,375.0,1617.0,396.0,5.3922,353700.0,NEAR OCEAN\n-122.46,37.65,16.0,8676.0,1633.0,5130.0,1574.0,4.8096,262000.0,NEAR OCEAN\n-122.46,37.64,17.0,3523.0,669.0,2150.0,666.0,4.5938,251200.0,NEAR OCEAN\n-122.47,37.65,27.0,8103.0,1655.0,5023.0,1605.0,4.6452,236200.0,NEAR OCEAN\n-122.53,37.66,25.0,7778.0,1493.0,4674.0,1451.0,5.4694,272400.0,NEAR OCEAN\n-122.53,37.65,20.0,4582.0,1124.0,2325.0,1040.0,4.0556,275000.0,NEAR OCEAN\n-122.48,37.65,39.0,3348.0,666.0,1817.0,668.0,4.2593,227400.0,NEAR OCEAN\n-122.49,37.63,31.0,3109.0,621.0,1472.0,618.0,5.155,263900.0,NEAR OCEAN\n-122.49,37.63,34.0,696.0,145.0,398.0,162.0,3.525,254100.0,NEAR OCEAN\n-122.49,37.63,31.0,1256.0,328.0,785.0,297.0,3.2446,234600.0,NEAR OCEAN\n-122.53,37.63,27.0,2589.0,658.0,1386.0,608.0,2.9087,228200.0,NEAR OCEAN\n-122.48,37.64,7.0,120.0,21.0,50.0,27.0,12.5,281000.0,NEAR OCEAN\n-122.47,37.61,34.0,4551.0,837.0,2208.0,834.0,5.4364,279300.0,NEAR OCEAN\n-122.54,37.62,35.0,1481.0,277.0,747.0,254.0,4.4286,262100.0,NEAR OCEAN\n-122.49,37.59,35.0,2683.0,475.0,1498.0,484.0,5.1282,262500.0,NEAR OCEAN\n-122.5,37.59,36.0,1521.0,253.0,736.0,241.0,4.3542,237500.0,NEAR OCEAN\n-122.5,37.6,35.0,2197.0,369.0,971.0,326.0,4.25,241700.0,NEAR OCEAN\n-122.55,37.59,31.0,1331.0,245.0,598.0,225.0,4.1827,345500.0,NEAR OCEAN\n-122.51,37.58,20.0,64.0,21.0,59.0,21.0,2.2375,450000.0,NEAR OCEAN\n-122.49,37.6,33.0,3507.0,669.0,1697.0,660.0,4.0795,270600.0,NEAR OCEAN\n-122.48,37.59,29.0,5889.0,959.0,2784.0,923.0,5.3991,273000.0,NEAR OCEAN\n-122.48,37.57,34.0,4648.0,806.0,2282.0,814.0,4.5556,249000.0,NEAR OCEAN\n-122.46,37.59,21.0,12902.0,2118.0,6160.0,2082.0,5.7653,325800.0,NEAR OCEAN\n-122.46,37.63,22.0,6728.0,1382.0,3783.0,1310.0,5.0479,280400.0,NEAR OCEAN\n-122.45,37.62,26.0,3507.0,512.0,1712.0,509.0,6.7206,344600.0,NEAR OCEAN\n-122.45,37.63,28.0,4946.0,848.0,2683.0,824.0,5.748,302100.0,NEAR OCEAN\n-122.44,37.63,35.0,5113.0,959.0,3004.0,964.0,4.7625,281300.0,NEAR OCEAN\n-122.43,37.63,34.0,4135.0,687.0,2154.0,742.0,4.9732,342300.0,NEAR OCEAN\n-122.43,37.61,21.0,10252.0,2595.0,4790.0,2428.0,4.1692,344500.0,NEAR OCEAN\n-122.41,37.62,49.0,1464.0,302.0,636.0,259.0,4.25,284100.0,NEAR OCEAN\n-122.41,37.61,46.0,2975.0,643.0,1479.0,577.0,3.8214,273600.0,NEAR OCEAN\n-122.42,37.61,37.0,1866.0,300.0,822.0,305.0,4.7,341300.0,NEAR OCEAN\n-122.42,37.62,39.0,1355.0,214.0,682.0,246.0,6.3443,324700.0,NEAR OCEAN\n-122.42,37.62,40.0,1545.0,264.0,756.0,282.0,4.4643,308100.0,NEAR OCEAN\n-122.42,37.61,17.0,1040.0,432.0,669.0,405.0,4.1513,137500.0,NEAR OCEAN\n-122.42,37.63,46.0,1811.0,337.0,796.0,333.0,3.43,292900.0,NEAR OCEAN\n-122.42,37.62,43.0,2367.0,409.0,1141.0,400.0,4.8295,319000.0,NEAR OCEAN\n-122.42,37.62,36.0,1017.0,165.0,407.0,159.0,4.8,306800.0,NEAR OCEAN\n-122.42,37.62,36.0,1538.0,256.0,671.0,247.0,4.4091,317900.0,NEAR OCEAN\n-122.41,37.62,39.0,3119.0,758.0,1807.0,696.0,3.2216,242700.0,NEAR OCEAN\n-122.4,37.62,32.0,3586.0,921.0,2249.0,911.0,3.1058,253000.0,NEAR OCEAN\n-122.41,37.63,39.0,4220.0,1055.0,2720.0,1046.0,2.639,242500.0,NEAR OCEAN\n-122.42,37.64,41.0,98.0,20.0,68.0,19.0,2.225,212500.0,NEAR OCEAN\n-122.43,37.63,15.0,2748.0,997.0,1447.0,901.0,3.5214,144200.0,NEAR OCEAN\n-122.42,37.63,46.0,66.0,11.0,30.0,12.0,2.375,275000.0,NEAR OCEAN\n-122.41,37.63,37.0,1252.0,275.0,878.0,287.0,4.2262,228500.0,NEAR OCEAN\n-122.41,37.63,35.0,865.0,226.0,602.0,217.0,3.0,229100.0,NEAR OCEAN\n-122.4,37.62,44.0,1619.0,362.0,1064.0,335.0,4.0238,224200.0,NEAR OCEAN\n-122.41,37.64,38.0,1204.0,268.0,921.0,247.0,4.4464,215400.0,NEAR OCEAN\n-122.4,37.61,35.0,2084.0,549.0,1077.0,545.0,3.1628,318400.0,NEAR OCEAN\n-122.38,37.6,33.0,2577.0,590.0,1867.0,566.0,3.3632,265100.0,NEAR OCEAN\n-122.39,37.6,36.0,1770.0,499.0,1225.0,459.0,2.56,273100.0,NEAR OCEAN\n-122.41,37.61,42.0,1602.0,262.0,705.0,255.0,5.7398,336400.0,NEAR OCEAN\n-122.41,37.61,43.0,1934.0,303.0,847.0,300.0,4.7381,347400.0,NEAR OCEAN\n-122.41,37.6,31.0,4424.0,834.0,1915.0,817.0,4.1364,412000.0,NEAR OCEAN\n-122.42,37.6,34.0,3562.0,565.0,1542.0,563.0,5.8783,405100.0,NEAR OCEAN\n-122.41,37.6,26.0,2754.0,402.0,1128.0,395.0,6.3719,466900.0,NEAR OCEAN\n-122.41,37.59,34.0,3931.0,622.0,1717.0,621.0,6.2946,450000.0,NEAR OCEAN\n-122.41,37.59,40.0,2401.0,383.0,894.0,356.0,5.6493,422400.0,NEAR OCEAN\n-122.4,37.6,30.0,5351.0,1134.0,2558.0,1074.0,3.5817,369300.0,NEAR OCEAN\n-122.4,37.6,52.0,1380.0,203.0,530.0,210.0,6.221,420300.0,NEAR OCEAN\n-122.39,37.6,44.0,2304.0,384.0,986.0,379.0,4.652,387100.0,NEAR OCEAN\n-122.39,37.6,34.0,707.0,,381.0,156.0,4.375,340900.0,NEAR OCEAN\n-122.39,37.59,32.0,4497.0,,1846.0,715.0,6.1323,500001.0,NEAR OCEAN\n-122.4,37.59,22.0,2754.0,477.0,1163.0,479.0,6.2306,500001.0,NEAR OCEAN\n-122.38,37.59,31.0,3052.0,844.0,1581.0,788.0,3.0744,457700.0,NEAR OCEAN\n-122.38,37.59,44.0,2089.0,348.0,837.0,317.0,4.6628,459200.0,NEAR OCEAN\n-122.39,37.58,36.0,6026.0,852.0,2314.0,892.0,7.8997,500001.0,NEAR OCEAN\n-122.4,37.58,26.0,3281.0,,1145.0,480.0,6.358,500001.0,NEAR OCEAN\n-122.39,37.59,33.0,2064.0,299.0,813.0,303.0,6.0374,500001.0,NEAR OCEAN\n-122.39,37.57,35.0,520.0,83.0,185.0,76.0,6.4865,450000.0,NEAR OCEAN\n-122.37,37.6,26.0,15.0,3.0,11.0,3.0,5.048,350000.0,NEAR OCEAN\n-122.37,37.59,39.0,4645.0,1196.0,2156.0,1113.0,3.4412,353800.0,NEAR OCEAN\n-122.38,37.59,49.0,1657.0,266.0,613.0,270.0,5.7837,378100.0,NEAR OCEAN\n-122.37,37.59,52.0,2272.0,403.0,963.0,376.0,5.7245,500000.0,NEAR OCEAN\n-122.37,37.58,52.0,2188.0,361.0,917.0,357.0,4.4,500000.0,NEAR OCEAN\n-122.38,37.58,52.0,1704.0,226.0,671.0,243.0,8.4704,500001.0,NEAR OCEAN\n-122.38,37.58,52.0,2039.0,299.0,772.0,303.0,6.471,500001.0,NEAR OCEAN\n-122.36,37.58,52.0,3084.0,595.0,1324.0,571.0,5.0756,374200.0,NEAR OCEAN\n-122.36,37.58,37.0,3325.0,734.0,1468.0,692.0,4.0987,434000.0,NEAR OCEAN\n-122.37,37.58,43.0,2506.0,432.0,967.0,428.0,4.7404,500001.0,NEAR OCEAN\n-122.37,37.58,52.0,1900.0,290.0,665.0,276.0,4.5486,500001.0,NEAR OCEAN\n-122.34,37.59,44.0,1395.0,269.0,736.0,288.0,5.6206,386400.0,NEAR OCEAN\n-122.33,37.58,40.0,2362.0,468.0,992.0,425.0,4.7917,359900.0,NEAR OCEAN\n-122.34,37.58,50.0,2784.0,743.0,1622.0,698.0,3.8413,372200.0,NEAR OCEAN\n-122.35,37.58,52.0,2495.0,458.0,1081.0,471.0,4.0855,410800.0,NEAR OCEAN\n-122.36,37.59,20.0,2638.0,854.0,1352.0,718.0,3.5125,350600.0,NEAR OCEAN\n-122.35,37.58,30.0,5039.0,1564.0,2129.0,1536.0,3.3469,345000.0,NEAR OCEAN\n-122.35,37.58,26.0,854.0,246.0,396.0,231.0,2.8393,375000.0,NEAR OCEAN\n-122.34,37.57,39.0,2647.0,616.0,1254.0,555.0,4.2407,433800.0,NEAR OCEAN\n-122.35,37.57,52.0,2059.0,345.0,800.0,308.0,4.97,500001.0,NEAR OCEAN\n-122.37,37.58,34.0,2697.0,313.0,810.0,279.0,12.4291,500001.0,NEAR OCEAN\n-122.36,37.57,35.0,1774.0,205.0,588.0,207.0,10.7339,500001.0,NEAR OCEAN\n-122.36,37.56,32.0,4684.0,540.0,1512.0,511.0,15.0001,500001.0,NEAR OCEAN\n-122.37,37.56,21.0,7189.0,874.0,2440.0,846.0,11.6833,500001.0,NEAR OCEAN\n-122.34,37.56,39.0,3562.0,391.0,1139.0,391.0,12.6417,500001.0,NEAR OCEAN\n-122.34,37.55,25.0,4470.0,518.0,1507.0,504.0,13.3913,500001.0,NEAR OCEAN\n-122.36,37.54,23.0,6184.0,747.0,2165.0,700.0,10.1675,500001.0,NEAR OCEAN\n-122.35,37.56,52.0,1659.0,191.0,519.0,201.0,14.4219,500001.0,NEAR OCEAN\n-122.34,37.57,52.0,2635.0,408.0,967.0,374.0,7.0422,500001.0,NEAR OCEAN\n-122.34,37.57,52.0,2547.0,373.0,876.0,359.0,8.2598,500001.0,NEAR OCEAN\n-122.35,37.57,52.0,2170.0,269.0,784.0,274.0,10.4286,500001.0,NEAR OCEAN\n-122.34,37.57,28.0,3751.0,949.0,1691.0,846.0,3.9728,300000.0,NEAR OCEAN\n-122.33,37.57,43.0,2543.0,621.0,1301.0,606.0,3.1111,318400.0,NEAR OCEAN\n-122.33,37.57,27.0,3085.0,876.0,1453.0,896.0,3.4333,290000.0,NEAR OCEAN\n-122.33,37.58,27.0,5144.0,1481.0,2518.0,1447.0,3.4836,287900.0,NEAR OCEAN\n-122.33,37.58,28.0,2784.0,736.0,1534.0,685.0,3.38,285400.0,NEAR OCEAN\n-122.31,37.6,34.0,3225.0,726.0,1958.0,656.0,3.6811,273000.0,NEAR BAY\n-122.31,37.58,44.0,1990.0,442.0,1141.0,424.0,3.9696,258300.0,NEAR BAY\n-122.31,37.57,45.0,1165.0,236.0,845.0,251.0,4.1875,267300.0,NEAR OCEAN\n-122.33,37.58,43.0,1772.0,422.0,1573.0,401.0,2.7474,233100.0,NEAR OCEAN\n-122.32,37.57,42.0,2574.0,614.0,2377.0,588.0,3.2891,237900.0,NEAR OCEAN\n-122.32,37.57,33.0,3384.0,819.0,2626.0,793.0,3.2285,234800.0,NEAR OCEAN\n-122.32,37.57,52.0,499.0,148.0,318.0,145.0,2.9934,256300.0,NEAR OCEAN\n-122.31,37.57,42.0,3157.0,676.0,1603.0,629.0,3.7422,292600.0,NEAR OCEAN\n-122.32,37.56,44.0,537.0,173.0,355.0,194.0,2.8571,250000.0,NEAR OCEAN\n-122.33,37.57,20.0,2126.0,643.0,1112.0,597.0,3.625,283300.0,NEAR OCEAN\n-122.32,37.56,9.0,1150.0,287.0,377.0,243.0,3.8317,237500.0,NEAR OCEAN\n-122.33,37.56,34.0,6394.0,1619.0,2400.0,1496.0,3.4902,500001.0,NEAR OCEAN\n-122.32,37.56,49.0,2016.0,299.0,691.0,288.0,5.549,500001.0,NEAR OCEAN\n-122.32,37.56,26.0,2339.0,704.0,1283.0,654.0,3.162,415000.0,NEAR OCEAN\n-122.33,37.56,50.0,1975.0,245.0,644.0,251.0,10.0743,500001.0,NEAR OCEAN\n-122.32,37.55,50.0,2501.0,433.0,1050.0,410.0,4.6406,500001.0,NEAR OCEAN\n-122.33,37.55,51.0,2565.0,332.0,870.0,309.0,9.3694,500001.0,NEAR OCEAN\n-122.34,37.55,44.0,2465.0,328.0,843.0,324.0,6.9533,500001.0,NEAR OCEAN\n-122.31,37.56,52.0,2351.0,494.0,1126.0,482.0,3.9688,356900.0,NEAR OCEAN\n-122.31,37.55,52.0,900.0,183.0,371.0,166.0,3.25,296400.0,NEAR OCEAN\n-122.32,37.55,46.0,1437.0,266.0,607.0,263.0,4.8068,369700.0,NEAR OCEAN\n-122.32,37.55,44.0,2151.0,411.0,849.0,370.0,4.4583,397100.0,NEAR OCEAN\n-122.33,37.55,33.0,2199.0,312.0,827.0,319.0,6.1349,500001.0,NEAR OCEAN\n-122.32,37.54,34.0,3661.0,692.0,1608.0,656.0,5.0774,407200.0,NEAR OCEAN\n-122.34,37.53,27.0,3339.0,481.0,1354.0,458.0,7.3081,464600.0,NEAR OCEAN\n-122.33,37.53,18.0,4493.0,760.0,1784.0,725.0,6.7042,413000.0,NEAR OCEAN\n-122.35,37.53,27.0,2169.0,305.0,905.0,319.0,7.7743,453100.0,NEAR OCEAN\n-122.34,37.52,34.0,3559.0,560.0,1747.0,550.0,6.6959,411200.0,NEAR OCEAN\n-122.33,37.53,25.0,1729.0,383.0,769.0,352.0,4.0417,458500.0,NEAR OCEAN\n-122.32,37.52,17.0,6645.0,1034.0,2557.0,1032.0,6.3892,480800.0,NEAR OCEAN\n-122.3,37.53,37.0,1338.0,215.0,535.0,221.0,5.4351,376600.0,NEAR OCEAN\n-122.3,37.53,40.0,1833.0,308.0,751.0,306.0,6.0,384200.0,NEAR OCEAN\n-122.3,37.53,38.0,984.0,171.0,429.0,157.0,5.3261,376800.0,NEAR OCEAN\n-122.31,37.52,35.0,1817.0,262.0,659.0,262.0,6.8336,457200.0,NEAR OCEAN\n-122.31,37.53,39.0,1160.0,191.0,508.0,185.0,5.9539,379100.0,NEAR OCEAN\n-122.3,37.54,39.0,4292.0,1097.0,1758.0,987.0,2.9405,340500.0,NEAR OCEAN\n-122.3,37.53,43.0,1748.0,366.0,984.0,371.0,4.5116,337800.0,NEAR OCEAN\n-122.29,37.53,41.0,839.0,190.0,419.0,215.0,5.012,368200.0,NEAR OCEAN\n-122.31,37.54,45.0,1222.0,220.0,492.0,205.0,5.539,396900.0,NEAR OCEAN\n-122.31,37.54,46.0,2444.0,397.0,952.0,402.0,4.75,388200.0,NEAR OCEAN\n-122.31,37.53,41.0,1608.0,269.0,676.0,267.0,4.6125,361700.0,NEAR OCEAN\n-122.32,37.53,39.0,2795.0,464.0,1183.0,443.0,5.779,387100.0,NEAR OCEAN\n-122.31,37.55,27.0,3931.0,933.0,1877.0,851.0,3.9722,354100.0,NEAR OCEAN\n-122.31,37.54,49.0,1340.0,281.0,660.0,284.0,4.163,393800.0,NEAR OCEAN\n-122.31,37.54,38.0,1946.0,407.0,975.0,417.0,4.0726,385400.0,NEAR OCEAN\n-122.31,37.54,42.0,1159.0,261.0,465.0,247.0,3.1842,352800.0,NEAR OCEAN\n-122.3,37.55,35.0,3675.0,735.0,1930.0,715.0,3.9833,342800.0,NEAR OCEAN\n-122.31,37.55,45.0,507.0,140.0,305.0,139.0,2.6159,272900.0,NEAR OCEAN\n-122.31,37.56,45.0,1685.0,321.0,815.0,314.0,4.2955,309700.0,NEAR OCEAN\n-122.31,37.56,36.0,1727.0,340.0,952.0,337.0,4.7917,316000.0,NEAR OCEAN\n-122.31,37.56,45.0,1792.0,301.0,829.0,318.0,4.9013,330100.0,NEAR OCEAN\n-122.31,37.56,40.0,1351.0,330.0,701.0,297.0,3.32,292900.0,NEAR OCEAN\n-122.31,37.57,37.0,1437.0,305.0,979.0,331.0,4.0,273700.0,NEAR OCEAN\n-122.31,37.57,31.0,2197.0,477.0,1193.0,394.0,4.6371,271100.0,NEAR OCEAN\n-122.3,37.57,36.0,1973.0,352.0,1169.0,370.0,5.033,270900.0,NEAR BAY\n-122.3,37.56,37.0,1962.0,367.0,1267.0,382.0,4.7344,271800.0,NEAR OCEAN\n-122.3,37.57,36.0,2406.0,436.0,1189.0,403.0,4.7917,276100.0,NEAR BAY\n-122.29,37.56,34.0,1693.0,281.0,846.0,291.0,5.3683,339400.0,NEAR BAY\n-122.3,37.56,36.0,1379.0,228.0,750.0,227.0,5.5381,282000.0,NEAR OCEAN\n-122.29,37.56,36.0,805.0,140.0,445.0,139.0,5.8221,289400.0,NEAR BAY\n-122.3,37.56,35.0,1873.0,351.0,945.0,333.0,5.5184,274800.0,NEAR OCEAN\n-122.29,37.56,12.0,6474.0,1467.0,2516.0,1390.0,5.0353,305800.0,NEAR BAY\n-122.26,37.55,17.0,4576.0,814.0,1941.0,807.0,5.9572,443800.0,NEAR BAY\n-122.26,37.55,17.0,1321.0,425.0,683.0,408.0,4.7045,500001.0,NEAR BAY\n-122.27,37.55,16.0,4789.0,816.0,1840.0,763.0,6.7474,338200.0,NEAR BAY\n-122.26,37.54,16.0,2118.0,333.0,770.0,318.0,7.2477,376000.0,NEAR BAY\n-122.27,37.55,15.0,1958.0,282.0,811.0,284.0,8.1221,483300.0,NEAR BAY\n-122.28,37.55,17.0,4199.0,629.0,2020.0,630.0,6.1228,375700.0,NEAR BAY\n-122.27,37.56,17.0,3211.0,847.0,1553.0,812.0,4.9434,292100.0,NEAR BAY\n-122.27,37.56,5.0,4921.0,1179.0,1810.0,1073.0,5.6936,322200.0,NEAR BAY\n-122.26,37.54,13.0,1422.0,295.0,395.0,195.0,5.3247,327800.0,NEAR BAY\n-122.26,37.54,5.0,3264.0,442.0,1607.0,453.0,9.1415,500001.0,NEAR BAY\n-122.26,37.54,5.0,1649.0,388.0,779.0,376.0,6.9635,417300.0,NEAR BAY\n-122.27,37.54,5.0,2140.0,420.0,990.0,394.0,6.035,438800.0,NEAR BAY\n-122.27,37.54,16.0,3913.0,565.0,1752.0,557.0,7.3644,419700.0,NEAR BAY\n-122.27,37.54,15.0,2126.0,310.0,905.0,306.0,8.9083,500001.0,NEAR BAY\n-122.26,37.56,23.0,7283.0,1342.0,3399.0,1298.0,5.6683,391000.0,NEAR BAY\n-122.26,37.57,23.0,7995.0,1254.0,3484.0,1198.0,6.5948,404000.0,NEAR BAY\n-122.25,37.56,19.0,7976.0,1406.0,3437.0,1338.0,5.6396,430300.0,NEAR BAY\n-122.29,37.55,27.0,3789.0,874.0,2243.0,866.0,4.39,270100.0,NEAR OCEAN\n-122.28,37.54,24.0,5114.0,1357.0,3169.0,1268.0,3.9699,293200.0,NEAR OCEAN\n-122.29,37.54,43.0,2268.0,438.0,1151.0,449.0,4.9091,293200.0,NEAR OCEAN\n-122.28,37.54,37.0,991.0,180.0,463.0,177.0,5.1701,294200.0,NEAR OCEAN\n-122.29,37.54,39.0,1459.0,285.0,761.0,291.0,5.0081,298100.0,NEAR OCEAN\n-122.29,37.54,41.0,1743.0,349.0,811.0,349.0,4.9464,282400.0,NEAR OCEAN\n-122.29,37.53,35.0,2043.0,511.0,1089.0,504.0,3.0278,310600.0,NEAR OCEAN\n-122.28,37.53,15.0,5417.0,1199.0,2593.0,1098.0,4.8047,438000.0,NEAR OCEAN\n-122.28,37.53,34.0,1980.0,385.0,970.0,391.0,5.1207,310900.0,NEAR OCEAN\n-122.27,37.53,43.0,1145.0,230.0,586.0,254.0,3.5,267400.0,NEAR OCEAN\n-122.28,37.53,25.0,3710.0,1015.0,2068.0,958.0,3.5445,286700.0,NEAR OCEAN\n-122.29,37.52,33.0,4104.0,751.0,1837.0,771.0,5.3506,388100.0,NEAR OCEAN\n-122.28,37.52,27.0,2958.0,655.0,1285.0,577.0,4.0801,397800.0,NEAR OCEAN\n-122.28,37.52,29.0,1526.0,355.0,724.0,315.0,4.0313,435200.0,NEAR OCEAN\n-122.28,37.52,38.0,2197.0,357.0,1228.0,373.0,5.4719,397900.0,NEAR OCEAN\n-122.29,37.52,38.0,3767.0,603.0,1455.0,615.0,6.8787,386800.0,NEAR OCEAN\n-122.3,37.52,38.0,2769.0,387.0,994.0,395.0,5.5902,417000.0,NEAR OCEAN\n-122.3,37.51,35.0,2789.0,445.0,1156.0,404.0,5.4322,391000.0,NEAR OCEAN\n-122.3,37.52,32.0,2297.0,347.0,871.0,342.0,8.1039,382200.0,NEAR OCEAN\n-122.31,37.52,24.0,2328.0,335.0,969.0,354.0,7.7364,435800.0,NEAR OCEAN\n-122.32,37.52,26.0,4042.0,591.0,1611.0,578.0,8.4693,419200.0,NEAR OCEAN\n-122.31,37.5,22.0,14034.0,3020.0,6266.0,2952.0,4.3939,491200.0,NEAR OCEAN\n-122.27,37.52,35.0,1051.0,259.0,517.0,234.0,3.7,339700.0,NEAR OCEAN\n-122.27,37.51,36.0,1406.0,224.0,598.0,237.0,5.8964,414800.0,NEAR OCEAN\n-122.29,37.51,35.0,3040.0,520.0,1374.0,518.0,6.1004,426400.0,NEAR OCEAN\n-122.26,37.52,34.0,483.0,131.0,291.0,157.0,3.0833,256300.0,NEAR OCEAN\n-122.26,37.51,46.0,672.0,149.0,351.0,136.0,5.3264,258100.0,NEAR OCEAN\n-122.25,37.51,45.0,989.0,174.0,504.0,180.0,4.8382,289400.0,NEAR OCEAN\n-122.25,37.5,44.0,348.0,79.0,154.0,73.0,4.7708,253800.0,NEAR OCEAN\n-122.27,37.51,39.0,3996.0,793.0,1744.0,761.0,4.5075,364900.0,NEAR OCEAN\n-122.26,37.51,29.0,3703.0,1075.0,1611.0,1025.0,2.7075,323800.0,NEAR OCEAN\n-122.26,37.5,24.0,2307.0,510.0,842.0,507.0,3.6111,341500.0,NEAR OCEAN\n-122.26,37.5,52.0,878.0,186.0,393.0,186.0,3.7045,360500.0,NEAR OCEAN\n-122.25,37.5,45.0,1812.0,336.0,752.0,329.0,4.95,345000.0,NEAR OCEAN\n-122.25,37.49,43.0,2607.0,477.0,1225.0,461.0,4.224,349600.0,NEAR OCEAN\n-122.25,37.49,40.0,2709.0,521.0,1156.0,510.0,4.6366,395500.0,NEAR OCEAN\n-122.25,37.49,44.0,4420.0,743.0,1790.0,735.0,6.142,394700.0,NEAR OCEAN\n-122.26,37.5,44.0,6983.0,1131.0,2818.0,1115.0,5.6271,374800.0,NEAR OCEAN\n-122.28,37.51,33.0,4719.0,,1980.0,757.0,6.1064,405000.0,NEAR OCEAN\n-122.28,37.5,33.0,6499.0,998.0,2694.0,957.0,7.4787,431300.0,NEAR OCEAN\n-122.28,37.49,25.0,7335.0,1157.0,2626.0,1049.0,6.5475,500001.0,NEAR OCEAN\n-122.28,37.49,29.0,4148.0,635.0,1638.0,627.0,6.912,457200.0,NEAR OCEAN\n-122.27,37.48,26.0,3542.0,507.0,1392.0,524.0,8.5184,500001.0,NEAR OCEAN\n-122.26,37.48,34.0,4453.0,682.0,1805.0,672.0,5.6038,451300.0,NEAR OCEAN\n-122.29,37.48,15.0,5480.0,892.0,2009.0,831.0,7.4678,500001.0,NEAR OCEAN\n-122.27,37.47,44.0,3022.0,473.0,1235.0,477.0,6.7058,495900.0,NEAR OCEAN\n-122.26,37.46,26.0,5067.0,750.0,1996.0,728.0,7.0001,500001.0,NEAR OCEAN\n-122.28,37.47,44.0,863.0,114.0,281.0,99.0,6.8879,500001.0,NEAR OCEAN\n-122.24,37.47,41.0,1183.0,203.0,455.0,171.0,5.1071,314100.0,NEAR OCEAN\n-122.25,37.48,37.0,3507.0,569.0,1663.0,608.0,5.0863,440300.0,NEAR OCEAN\n-122.24,37.47,40.0,1504.0,270.0,689.0,287.0,6.1244,308800.0,NEAR OCEAN\n-122.25,37.47,38.0,645.0,124.0,265.0,103.0,5.4688,305000.0,NEAR OCEAN\n-122.25,37.47,35.0,3183.0,515.0,1313.0,487.0,5.9062,383200.0,NEAR OCEAN\n-122.25,37.48,45.0,2743.0,390.0,974.0,400.0,7.1621,500001.0,NEAR OCEAN\n-122.24,37.48,45.0,4126.0,696.0,1722.0,668.0,4.8966,362100.0,NEAR OCEAN\n-122.24,37.48,40.0,4459.0,1027.0,2080.0,982.0,3.5322,361900.0,NEAR OCEAN\n-122.24,37.48,47.0,2423.0,407.0,1010.0,407.0,6.2154,362700.0,NEAR OCEAN\n-122.24,37.49,30.0,2956.0,590.0,1191.0,594.0,3.7463,427600.0,NEAR OCEAN\n-122.24,37.49,38.0,4105.0,950.0,2561.0,909.0,3.8684,265600.0,NEAR OCEAN\n-122.21,37.48,39.0,1535.0,340.0,1204.0,370.0,2.8482,247200.0,NEAR BAY\n-122.23,37.49,11.0,840.0,329.0,1338.0,345.0,2.3333,241700.0,NEAR OCEAN\n-122.21,37.49,24.0,2528.0,947.0,2437.0,861.0,2.2746,225000.0,NEAR BAY\n-122.22,37.48,47.0,2570.0,783.0,3107.0,724.0,2.8058,229500.0,NEAR OCEAN\n-122.22,37.48,34.0,1541.0,584.0,1564.0,558.0,2.56,250000.0,NEAR OCEAN\n-122.24,37.49,19.0,322.0,112.0,191.0,102.0,2.5833,500001.0,NEAR OCEAN\n-122.24,37.55,3.0,6164.0,1175.0,2198.0,975.0,6.7413,435900.0,NEAR BAY\n-122.25,37.53,16.0,4428.0,664.0,1677.0,623.0,7.6864,422500.0,NEAR BAY\n-122.26,37.53,4.0,5233.0,1109.0,1690.0,907.0,6.2007,311800.0,NEAR BAY\n-122.25,37.52,14.0,1472.0,291.0,876.0,292.0,4.3594,366000.0,NEAR BAY\n-122.21,37.52,18.0,2962.0,945.0,1639.0,851.0,2.7399,87500.0,NEAR BAY\n-122.19,37.48,35.0,7067.0,1646.0,5380.0,1597.0,4.1776,265300.0,NEAR BAY\n-122.21,37.48,20.0,505.0,216.0,326.0,216.0,2.9286,237500.0,NEAR BAY\n-122.21,37.48,37.0,1326.0,335.0,1771.0,335.0,3.0147,218100.0,NEAR BAY\n-122.2,37.48,32.0,640.0,166.0,991.0,160.0,1.9844,270000.0,NEAR BAY\n-122.2,37.48,41.0,733.0,155.0,652.0,140.0,5.1654,233600.0,NEAR BAY\n-122.2,37.48,30.0,1170.0,258.0,610.0,243.0,3.4427,263500.0,NEAR BAY\n-122.19,37.48,45.0,886.0,165.0,492.0,173.0,4.2708,267000.0,NEAR BAY\n-122.19,37.48,38.0,1300.0,269.0,608.0,292.0,4.5568,286900.0,NEAR BAY\n-122.19,37.47,44.0,1371.0,263.0,589.0,301.0,4.8068,312300.0,NEAR BAY\n-122.2,37.47,44.0,1927.0,332.0,846.0,362.0,4.2083,278200.0,NEAR BAY\n-122.2,37.47,37.0,1053.0,266.0,939.0,267.0,3.1989,320800.0,NEAR BAY\n-122.2,37.47,37.0,1403.0,369.0,1587.0,331.0,2.8258,232800.0,NEAR BAY\n-122.21,37.47,33.0,1266.0,415.0,1991.0,334.0,2.92,202800.0,NEAR OCEAN\n-122.21,37.47,26.0,1777.0,555.0,1966.0,497.0,3.0472,211000.0,NEAR OCEAN\n-122.21,37.47,43.0,733.0,162.0,497.0,175.0,3.2708,255300.0,NEAR OCEAN\n-122.22,37.47,23.0,7740.0,1943.0,4124.0,1743.0,3.3268,322800.0,NEAR OCEAN\n-122.22,37.47,35.0,367.0,113.0,398.0,109.0,2.5,166700.0,NEAR OCEAN\n-122.22,37.47,28.0,5956.0,1612.0,3571.0,1549.0,3.1864,272800.0,NEAR OCEAN\n-122.23,37.48,33.0,3108.0,805.0,1895.0,717.0,3.3015,267700.0,NEAR OCEAN\n-122.23,37.47,39.0,5264.0,1259.0,3057.0,1265.0,3.623,276600.0,NEAR OCEAN\n-122.23,37.48,38.0,1578.0,399.0,879.0,388.0,2.7969,298400.0,NEAR OCEAN\n-122.24,37.47,35.0,2283.0,491.0,1148.0,436.0,4.5556,318600.0,NEAR OCEAN\n-122.24,37.47,36.0,2021.0,433.0,1117.0,432.0,3.929,303100.0,NEAR OCEAN\n-122.23,37.46,33.0,2643.0,464.0,1015.0,427.0,4.2232,363700.0,NEAR OCEAN\n-122.23,37.46,26.0,4670.0,1039.0,2103.0,933.0,4.4167,333800.0,NEAR OCEAN\n-122.25,37.46,33.0,6841.0,950.0,2681.0,980.0,7.1088,443300.0,NEAR OCEAN\n-122.24,37.46,36.0,4686.0,781.0,2254.0,845.0,6.1043,343500.0,NEAR OCEAN\n-122.26,37.45,17.0,2742.0,441.0,986.0,421.0,5.9285,496000.0,NEAR OCEAN\n-122.23,37.46,36.0,6090.0,1057.0,3081.0,1075.0,5.6629,343600.0,NEAR OCEAN\n-122.22,37.46,37.0,2586.0,495.0,1208.0,502.0,4.3214,342700.0,NEAR OCEAN\n-122.23,37.45,34.0,4177.0,723.0,1586.0,660.0,5.0457,395100.0,NEAR OCEAN\n-122.23,37.45,29.0,1617.0,235.0,758.0,246.0,7.7932,469900.0,NEAR OCEAN\n-122.22,37.46,13.0,2888.0,546.0,1182.0,504.0,6.0255,409300.0,NEAR OCEAN\n-122.2,37.46,40.0,1723.0,208.0,976.0,209.0,9.8892,500001.0,NEAR OCEAN\n-122.2,37.44,31.0,2328.0,270.0,722.0,247.0,15.0001,500001.0,NEAR OCEAN\n-122.22,37.44,32.0,4281.0,501.0,1318.0,484.0,15.0001,500001.0,NEAR OCEAN\n-122.21,37.46,48.0,2560.0,322.0,921.0,301.0,10.8758,500001.0,NEAR OCEAN\n-122.21,37.46,40.0,1777.0,207.0,577.0,207.0,15.0001,500001.0,NEAR OCEAN\n-122.18,37.46,40.0,2529.0,293.0,831.0,258.0,15.0001,500001.0,NEAR BAY\n-122.2,37.47,40.0,2959.0,389.0,985.0,365.0,9.9025,500001.0,NEAR BAY\n-122.18,37.47,37.0,2848.0,328.0,852.0,327.0,13.367,500001.0,NEAR BAY\n-122.16,37.47,44.0,2581.0,437.0,1006.0,414.0,5.397,341700.0,NEAR BAY\n-122.17,37.48,39.0,2427.0,401.0,1178.0,408.0,5.9629,352700.0,NEAR BAY\n-122.16,37.47,33.0,3687.0,852.0,3091.0,852.0,2.6506,162600.0,NEAR BAY\n-122.16,37.48,36.0,2238.0,479.0,1949.0,457.0,2.3769,157300.0,NEAR BAY\n-122.14,37.5,46.0,30.0,4.0,13.0,5.0,15.0001,500001.0,NEAR BAY\n-122.14,37.48,36.0,1210.0,236.0,981.0,239.0,4.0039,148900.0,NEAR BAY\n-122.14,37.47,36.0,2081.0,412.0,1931.0,373.0,3.7917,160600.0,NEAR BAY\n-122.12,37.48,36.0,880.0,177.0,795.0,188.0,3.8194,159400.0,NEAR BAY\n-122.13,37.47,25.0,1630.0,353.0,1546.0,371.0,5.0893,173400.0,NEAR BAY\n-122.13,37.46,37.0,1576.0,334.0,1385.0,323.0,2.5294,159400.0,NEAR BAY\n-122.13,37.46,35.0,1321.0,300.0,1133.0,287.0,3.7312,159600.0,NEAR BAY\n-122.12,37.45,38.0,1276.0,314.0,955.0,287.0,2.0096,155700.0,NEAR BAY\n-122.13,37.46,31.0,2247.0,573.0,1711.0,511.0,3.2642,185600.0,NEAR BAY\n-122.13,37.47,30.0,1480.0,294.0,1126.0,301.0,4.983,166700.0,NEAR BAY\n-122.14,37.47,37.0,3373.0,815.0,2909.0,705.0,2.8868,156600.0,NEAR BAY\n-122.15,37.47,37.0,1844.0,382.0,1634.0,417.0,2.7993,145500.0,NEAR BAY\n-122.15,37.47,38.0,1560.0,301.0,1331.0,316.0,3.0521,151500.0,NEAR BAY\n-122.15,37.46,30.0,4198.0,1244.0,2678.0,1147.0,3.6712,308600.0,NEAR BAY\n-122.14,37.46,27.0,5580.0,2009.0,4165.0,1763.0,2.4375,189000.0,NEAR BAY\n-122.15,37.47,39.0,1295.0,239.0,566.0,242.0,5.6407,326400.0,NEAR BAY\n-122.15,37.46,42.0,1995.0,412.0,794.0,374.0,5.6234,379600.0,NEAR BAY\n-122.16,37.46,45.0,2068.0,348.0,844.0,366.0,6.227,417800.0,NEAR BAY\n-122.16,37.45,50.0,196.0,41.0,76.0,42.0,7.6129,412500.0,NEAR BAY\n-122.16,37.46,32.0,2663.0,661.0,1403.0,733.0,4.2667,410200.0,NEAR BAY\n-122.17,37.46,47.0,2312.0,332.0,1044.0,282.0,9.459,500001.0,NEAR BAY\n-122.19,37.46,34.0,5419.0,1183.0,2002.0,1138.0,4.1985,500001.0,NEAR BAY\n-122.17,37.45,33.0,1828.0,396.0,766.0,378.0,4.4531,500001.0,NEAR BAY\n-122.17,37.45,35.0,1025.0,242.0,388.0,232.0,5.1995,500001.0,NEAR BAY\n-122.18,37.45,43.0,2061.0,437.0,817.0,385.0,4.4688,460200.0,NEAR BAY\n-122.18,37.45,37.0,5257.0,1360.0,2128.0,1264.0,4.0,394300.0,NEAR BAY\n-122.19,37.45,18.0,1636.0,414.0,853.0,439.0,5.1032,464600.0,NEAR OCEAN\n-122.18,37.44,44.0,2237.0,347.0,948.0,346.0,8.2436,500001.0,NEAR OCEAN\n-122.19,37.44,38.0,3383.0,456.0,1203.0,465.0,9.3198,500001.0,NEAR OCEAN\n-122.19,37.44,39.0,4402.0,618.0,1616.0,631.0,8.9955,500001.0,NEAR OCEAN\n-122.2,37.43,38.0,3626.0,528.0,1350.0,532.0,7.3681,500001.0,NEAR OCEAN\n-122.21,37.44,35.0,1140.0,193.0,486.0,199.0,4.6908,500001.0,NEAR OCEAN\n-122.2,37.43,40.0,2223.0,412.0,1050.0,417.0,5.2421,444500.0,NEAR OCEAN\n-122.19,37.43,39.0,2392.0,420.0,937.0,406.0,6.6136,472800.0,NEAR OCEAN\n-122.2,37.43,22.0,3294.0,744.0,1337.0,655.0,5.2391,500001.0,NEAR OCEAN\n-122.21,37.43,33.0,1606.0,254.0,727.0,271.0,8.6963,500001.0,NEAR OCEAN\n-122.21,37.43,23.0,5741.0,1012.0,1843.0,888.0,5.7211,500001.0,NEAR OCEAN\n-122.23,37.42,16.0,1945.0,320.0,512.0,300.0,7.4542,500001.0,NEAR OCEAN\n-122.19,37.42,47.0,932.0,167.0,295.0,116.0,8.4375,500001.0,NEAR OCEAN\n-122.21,37.43,20.0,975.0,134.0,324.0,146.0,9.7796,500001.0,NEAR OCEAN\n-122.21,37.42,28.0,564.0,72.0,191.0,79.0,11.9666,500001.0,NEAR OCEAN\n-122.2,37.4,37.0,1296.0,194.0,540.0,192.0,8.2782,500001.0,NEAR OCEAN\n-122.2,37.4,30.0,2612.0,338.0,980.0,324.0,10.0481,500001.0,NEAR OCEAN\n-122.21,37.38,28.0,4518.0,578.0,1489.0,559.0,11.3176,500001.0,NEAR OCEAN\n-122.2,37.35,17.0,3095.0,442.0,1173.0,424.0,13.2986,500001.0,NEAR BAY\n-122.21,37.37,34.0,1476.0,217.0,613.0,223.0,8.2883,500001.0,NEAR OCEAN\n-122.22,37.37,26.0,440.0,202.0,322.0,218.0,5.1831,350000.0,NEAR OCEAN\n-122.22,37.36,34.0,1559.0,243.0,600.0,242.0,8.7382,500001.0,NEAR OCEAN\n-122.22,37.4,32.0,2297.0,287.0,814.0,283.0,15.0001,500001.0,NEAR OCEAN\n-122.25,37.45,34.0,2999.0,365.0,927.0,369.0,10.2811,500001.0,NEAR OCEAN\n-122.27,37.45,41.0,830.0,136.0,353.0,153.0,6.3824,500001.0,NEAR OCEAN\n-122.24,37.43,36.0,2410.0,361.0,934.0,377.0,7.652,500001.0,NEAR OCEAN\n-122.27,37.43,33.0,1601.0,223.0,629.0,215.0,15.0001,500001.0,NEAR OCEAN\n-122.25,37.39,33.0,370.0,42.0,153.0,53.0,10.6514,500001.0,NEAR OCEAN\n-122.26,37.38,28.0,1103.0,164.0,415.0,154.0,7.8633,500001.0,NEAR OCEAN\n-122.29,37.41,30.0,6373.0,854.0,2149.0,798.0,10.6868,500001.0,NEAR OCEAN\n-122.46,37.51,23.0,949.0,151.0,399.0,149.0,5.6286,411300.0,NEAR OCEAN\n-122.47,37.5,18.0,2297.0,416.0,1086.0,381.0,4.875,334600.0,NEAR OCEAN\n-122.47,37.5,25.0,950.0,259.0,404.0,195.0,3.1937,319200.0,NEAR OCEAN\n-122.48,37.51,22.0,1564.0,278.0,761.0,270.0,4.7578,318500.0,NEAR OCEAN\n-122.47,37.51,15.0,4974.0,764.0,2222.0,774.0,6.7606,364300.0,NEAR OCEAN\n-122.44,37.52,16.0,7077.0,1179.0,3502.0,1148.0,5.9919,345100.0,NEAR OCEAN\n-122.53,37.5,19.0,4768.0,807.0,2199.0,805.0,6.1896,331100.0,NEAR OCEAN\n-122.49,37.54,15.0,3456.0,545.0,1527.0,535.0,6.3256,368000.0,NEAR OCEAN\n-122.51,37.53,17.0,1574.0,262.0,672.0,241.0,7.2929,355800.0,NEAR OCEAN\n-122.5,37.51,11.0,749.0,137.0,355.0,124.0,8.2364,371800.0,NEAR OCEAN\n-122.49,37.5,21.0,1209.0,309.0,801.0,259.0,4.5625,500000.0,NEAR OCEAN\n-122.43,37.43,17.0,11999.0,2249.0,5467.0,1989.0,4.8405,354300.0,NEAR OCEAN\n-122.34,37.46,21.0,1799.0,293.0,576.0,277.0,7.439,500001.0,NEAR OCEAN\n-122.38,37.34,33.0,1054.0,209.0,400.0,161.0,7.7773,456300.0,NEAR OCEAN\n-122.33,37.39,52.0,573.0,102.0,232.0,92.0,6.2263,500001.0,NEAR OCEAN\n-122.27,37.32,37.0,2607.0,534.0,1346.0,507.0,5.3951,277700.0,NEAR OCEAN\n-122.27,37.24,30.0,2762.0,593.0,1581.0,502.0,5.1002,319400.0,NEAR OCEAN\n-122.38,37.18,52.0,1746.0,315.0,941.0,220.0,3.3047,286100.0,NEAR OCEAN\n-119.77,34.44,24.0,5652.0,1313.0,2312.0,1294.0,2.4717,295300.0,NEAR OCEAN\n-119.78,34.45,9.0,1830.0,353.0,1515.0,220.0,4.2109,450000.0,NEAR OCEAN\n-119.79,34.45,24.0,2746.0,433.0,1076.0,380.0,5.8635,348700.0,NEAR OCEAN\n-119.75,34.45,26.0,3578.0,677.0,1504.0,618.0,4.1375,395000.0,NEAR OCEAN\n-119.75,34.44,28.0,1080.0,298.0,524.0,251.0,1.8432,327300.0,NEAR OCEAN\n-119.76,34.44,28.0,1985.0,582.0,1092.0,548.0,2.4701,290900.0,NEAR OCEAN\n-119.75,34.45,6.0,2864.0,,1404.0,603.0,5.5073,263800.0,NEAR OCEAN\n-119.78,34.48,21.0,2377.0,322.0,1007.0,328.0,7.9248,500001.0,NEAR OCEAN\n-119.75,34.5,26.0,3563.0,579.0,1479.0,575.0,5.9522,438400.0,<1H OCEAN\n-119.78,34.45,23.0,2077.0,306.0,705.0,256.0,6.4744,500001.0,NEAR OCEAN\n-119.73,34.44,38.0,1729.0,,801.0,395.0,3.1364,357500.0,NEAR OCEAN\n-119.73,34.43,35.0,2703.0,654.0,1383.0,631.0,4.5278,340400.0,NEAR OCEAN\n-119.74,34.44,26.0,4257.0,1031.0,1861.0,950.0,3.4047,294500.0,NEAR OCEAN\n-119.72,34.43,30.0,2491.0,656.0,1091.0,576.0,2.5139,279500.0,<1H OCEAN\n-119.72,34.43,27.0,984.0,299.0,777.0,313.0,2.5694,275000.0,<1H OCEAN\n-119.72,34.43,46.0,1332.0,329.0,746.0,310.0,3.6719,357400.0,<1H OCEAN\n-119.72,34.43,36.0,1156.0,309.0,521.0,304.0,2.6014,320600.0,<1H OCEAN\n-119.72,34.43,33.0,1028.0,377.0,753.0,356.0,2.3454,243800.0,<1H OCEAN\n-119.71,34.43,18.0,1170.0,372.0,681.0,346.0,2.1974,255000.0,<1H OCEAN\n-119.71,34.42,31.0,1643.0,499.0,1253.0,499.0,3.1563,267000.0,<1H OCEAN\n-119.71,34.43,47.0,1572.0,417.0,790.0,384.0,2.6429,279200.0,<1H OCEAN\n-119.72,34.44,50.0,3265.0,509.0,1256.0,443.0,6.3997,500001.0,<1H OCEAN\n-119.71,34.44,52.0,1837.0,343.0,711.0,355.0,4.1316,443000.0,<1H OCEAN\n-119.7,34.43,37.0,1462.0,306.0,678.0,322.0,5.1545,418400.0,<1H OCEAN\n-119.71,34.43,48.0,2408.0,536.0,1005.0,497.0,3.5213,458600.0,<1H OCEAN\n-119.7,34.43,39.0,1486.0,467.0,758.0,409.0,2.6875,320600.0,<1H OCEAN\n-119.7,34.43,52.0,977.0,289.0,412.0,272.0,2.125,300000.0,<1H OCEAN\n-119.7,34.47,32.0,3725.0,569.0,1304.0,527.0,7.7261,500001.0,<1H OCEAN\n-119.72,34.47,34.0,3262.0,533.0,1265.0,502.0,5.8411,381800.0,<1H OCEAN\n-119.71,34.45,35.0,2183.0,363.0,988.0,351.0,5.5922,384400.0,<1H OCEAN\n-119.71,34.44,41.0,2220.0,367.0,927.0,355.0,5.3184,376000.0,<1H OCEAN\n-119.72,34.44,43.0,1781.0,342.0,663.0,358.0,4.7,293800.0,<1H OCEAN\n-119.72,34.44,39.0,1489.0,304.0,700.0,268.0,3.8819,289900.0,<1H OCEAN\n-119.73,34.44,48.0,2114.0,390.0,973.0,367.0,4.8021,351100.0,NEAR OCEAN\n-119.73,34.45,44.0,2261.0,328.0,763.0,294.0,6.7449,415600.0,<1H OCEAN\n-119.74,34.44,27.0,1251.0,282.0,503.0,283.0,2.8,353000.0,NEAR OCEAN\n-119.74,34.45,29.0,2526.0,388.0,1092.0,409.0,6.0597,383100.0,<1H OCEAN\n-119.69,34.44,41.0,1989.0,271.0,666.0,269.0,6.8406,500001.0,<1H OCEAN\n-119.69,34.43,44.0,2440.0,485.0,1011.0,442.0,4.149,443600.0,<1H OCEAN\n-119.7,34.43,35.0,1402.0,369.0,654.0,385.0,2.6205,318800.0,<1H OCEAN\n-119.69,34.43,43.0,1257.0,311.0,671.0,263.0,2.875,280600.0,<1H OCEAN\n-119.69,34.43,37.0,2801.0,497.0,1150.0,476.0,5.8311,387700.0,<1H OCEAN\n-119.68,34.44,23.0,2600.0,398.0,917.0,374.0,8.7394,500001.0,<1H OCEAN\n-119.67,34.47,35.0,2700.0,422.0,1995.0,383.0,4.9757,500001.0,<1H OCEAN\n-119.66,34.44,26.0,2790.0,413.0,1014.0,397.0,6.5631,500001.0,<1H OCEAN\n-119.66,34.43,27.0,5509.0,1059.0,2591.0,979.0,3.8456,500001.0,<1H OCEAN\n-119.67,34.44,32.0,3202.0,537.0,1316.0,538.0,5.2888,463800.0,<1H OCEAN\n-119.69,34.43,30.0,1273.0,343.0,1082.0,325.0,2.5104,228100.0,<1H OCEAN\n-119.68,34.43,33.0,1961.0,462.0,1693.0,445.0,2.9896,236000.0,<1H OCEAN\n-119.68,34.43,49.0,1785.0,386.0,1267.0,380.0,3.5208,251200.0,<1H OCEAN\n-119.67,34.38,28.0,1814.0,526.0,849.0,420.0,3.1625,364300.0,<1H OCEAN\n-119.68,34.42,38.0,1452.0,354.0,1139.0,340.0,2.707,236800.0,<1H OCEAN\n-119.67,34.43,39.0,1467.0,381.0,1404.0,374.0,2.3681,241400.0,<1H OCEAN\n-119.67,34.42,37.0,1673.0,444.0,1477.0,446.0,2.0643,246700.0,<1H OCEAN\n-119.67,34.42,23.0,1333.0,393.0,1369.0,381.0,2.5947,232600.0,<1H OCEAN\n-119.7,34.43,52.0,1364.0,460.0,804.0,400.0,2.375,293800.0,<1H OCEAN\n-119.7,34.42,52.0,329.0,109.0,291.0,102.0,1.4722,350000.0,<1H OCEAN\n-119.7,34.42,41.0,725.0,239.0,582.0,214.0,3.1667,362500.0,<1H OCEAN\n-119.69,34.42,17.0,1826.0,544.0,1325.0,532.0,1.2762,253600.0,<1H OCEAN\n-119.69,34.42,52.0,302.0,112.0,392.0,114.0,2.5978,258300.0,<1H OCEAN\n-119.71,34.42,52.0,1838.0,692.0,851.0,576.0,1.4851,237500.0,<1H OCEAN\n-119.71,34.42,49.0,1560.0,436.0,1041.0,411.0,2.925,246900.0,<1H OCEAN\n-119.71,34.42,39.0,1172.0,322.0,606.0,316.0,2.16,259100.0,<1H OCEAN\n-119.71,34.42,50.0,840.0,279.0,488.0,270.0,2.2097,258300.0,<1H OCEAN\n-119.7,34.42,43.0,1802.0,557.0,1490.0,538.0,2.675,247900.0,<1H OCEAN\n-119.7,34.41,52.0,1526.0,458.0,1633.0,449.0,2.2069,226500.0,NEAR OCEAN\n-119.72,34.42,37.0,1635.0,427.0,1027.0,408.0,3.5905,264700.0,NEAR OCEAN\n-119.72,34.42,31.0,1524.0,383.0,1257.0,398.0,2.6019,250000.0,NEAR OCEAN\n-119.72,34.42,49.0,1610.0,370.0,961.0,351.0,2.6983,260100.0,NEAR OCEAN\n-119.72,34.42,52.0,1759.0,387.0,980.0,402.0,4.0125,261000.0,NEAR OCEAN\n-119.71,34.42,52.0,1411.0,324.0,1091.0,306.0,4.1062,252900.0,<1H OCEAN\n-119.71,34.42,23.0,2068.0,658.0,1898.0,570.0,2.5506,230800.0,<1H OCEAN\n-119.71,34.41,31.0,1034.0,319.0,997.0,308.0,2.6538,231800.0,NEAR OCEAN\n-119.73,34.35,20.0,1648.0,319.0,905.0,307.0,4.375,335200.0,NEAR OCEAN\n-119.71,34.36,34.0,1706.0,276.0,628.0,243.0,4.1842,364000.0,NEAR OCEAN\n-119.7,34.36,35.0,1604.0,334.0,904.0,337.0,4.7411,336400.0,NEAR OCEAN\n-119.71,34.4,36.0,1846.0,358.0,748.0,329.0,4.2283,326800.0,NEAR OCEAN\n-119.7,34.4,25.0,1858.0,493.0,865.0,460.0,3.0938,312500.0,NEAR OCEAN\n-119.72,34.41,35.0,871.0,145.0,354.0,154.0,4.3214,341800.0,NEAR OCEAN\n-119.71,34.4,27.0,3782.0,771.0,1742.0,751.0,4.0451,395100.0,NEAR OCEAN\n-119.71,34.41,18.0,1225.0,317.0,694.0,306.0,3.6823,255000.0,NEAR OCEAN\n-119.7,34.41,19.0,1215.0,360.0,1349.0,423.0,2.6607,226500.0,NEAR OCEAN\n-119.7,34.41,19.0,2086.0,575.0,1701.0,530.0,2.8042,236100.0,NEAR OCEAN\n-119.69,34.38,39.0,1383.0,459.0,677.0,362.0,2.25,281300.0,NEAR OCEAN\n-119.69,34.41,44.0,1208.0,357.0,603.0,297.0,2.6103,500000.0,<1H OCEAN\n-119.75,34.43,23.0,2982.0,837.0,1317.0,787.0,3.3776,283200.0,NEAR OCEAN\n-119.74,34.43,26.0,3119.0,562.0,1459.0,562.0,5.0434,340400.0,NEAR OCEAN\n-119.75,34.4,31.0,1997.0,299.0,826.0,301.0,6.8927,500001.0,NEAR OCEAN\n-119.73,34.43,27.0,1448.0,404.0,978.0,338.0,2.303,261000.0,NEAR OCEAN\n-119.73,34.42,23.0,1364.0,227.0,638.0,238.0,5.3279,413900.0,NEAR OCEAN\n-119.73,34.42,25.0,2024.0,312.0,907.0,335.0,5.4127,392800.0,NEAR OCEAN\n-119.72,34.41,35.0,1853.0,375.0,878.0,338.0,4.9044,335300.0,NEAR OCEAN\n-119.74,34.41,30.0,2365.0,417.0,1053.0,409.0,5.5959,346200.0,NEAR OCEAN\n-119.73,34.41,29.0,1769.0,297.0,703.0,269.0,4.4375,350000.0,NEAR OCEAN\n-119.72,34.41,26.0,1648.0,378.0,954.0,405.0,3.2895,335000.0,NEAR OCEAN\n-119.74,34.35,34.0,1664.0,292.0,705.0,257.0,5.0,329400.0,NEAR OCEAN\n-119.74,34.38,32.0,1479.0,287.0,830.0,288.0,5.345,322600.0,NEAR OCEAN\n-119.63,34.42,42.0,1765.0,263.0,753.0,260.0,8.5608,500001.0,<1H OCEAN\n-119.64,34.43,34.0,3045.0,570.0,1002.0,488.0,5.623,500001.0,<1H OCEAN\n-119.63,34.4,29.0,3865.0,814.0,1266.0,613.0,6.0069,500001.0,<1H OCEAN\n-119.64,34.43,32.0,1872.0,318.0,749.0,296.0,4.625,500001.0,<1H OCEAN\n-119.61,34.45,33.0,3597.0,519.0,1207.0,479.0,5.3963,500001.0,<1H OCEAN\n-119.63,34.44,37.0,3188.0,442.0,984.0,376.0,9.4522,500001.0,<1H OCEAN\n-119.61,34.43,16.0,2665.0,391.0,794.0,311.0,9.0267,500001.0,<1H OCEAN\n-119.53,34.41,8.0,1705.0,400.0,886.0,391.0,3.9659,297400.0,<1H OCEAN\n-119.52,34.41,20.0,4489.0,800.0,2867.0,765.0,4.806,279700.0,<1H OCEAN\n-119.51,34.4,24.0,3422.0,596.0,1763.0,601.0,5.2039,301300.0,NEAR OCEAN\n-119.51,34.4,15.0,1112.0,256.0,411.0,245.0,2.0625,314300.0,NEAR OCEAN\n-119.52,34.39,23.0,1414.0,365.0,889.0,345.0,2.6397,250000.0,NEAR OCEAN\n-119.51,34.39,32.0,1921.0,394.0,951.0,334.0,3.233,346000.0,NEAR OCEAN\n-119.52,34.4,20.0,1834.0,477.0,1305.0,417.0,3.2125,251000.0,NEAR OCEAN\n-119.53,34.38,22.0,2323.0,727.0,1301.0,478.0,2.7864,300000.0,NEAR OCEAN\n-119.53,34.4,14.0,1671.0,383.0,1079.0,365.0,3.1389,248700.0,<1H OCEAN\n-119.51,34.46,28.0,3506.0,563.0,1362.0,483.0,6.091,500001.0,<1H OCEAN\n-119.55,34.38,17.0,1951.0,368.0,681.0,350.0,2.7275,500001.0,<1H OCEAN\n-119.57,34.38,22.0,2512.0,426.0,919.0,341.0,5.759,425000.0,<1H OCEAN\n-119.59,34.43,28.0,2718.0,542.0,1066.0,442.0,4.2059,500001.0,<1H OCEAN\n-119.59,34.39,35.0,622.0,170.0,278.0,139.0,3.6969,335000.0,<1H OCEAN\n-119.5,34.35,39.0,308.0,38.0,59.0,21.0,11.7794,500001.0,NEAR OCEAN\n-119.49,34.39,17.0,4617.0,982.0,2303.0,923.0,3.9224,230600.0,NEAR OCEAN\n-119.72,34.77,35.0,2469.0,553.0,1168.0,427.0,2.4583,62100.0,<1H OCEAN\n-120.18,34.75,17.0,2074.0,382.0,1035.0,359.0,3.7958,400000.0,<1H OCEAN\n-120.27,34.72,14.0,1289.0,277.0,693.0,237.0,3.2569,230800.0,<1H OCEAN\n-120.18,34.62,25.0,1337.0,219.0,671.0,225.0,3.1912,226400.0,NEAR OCEAN\n-120.2,34.63,14.0,2647.0,515.0,1487.0,488.0,4.4519,227900.0,NEAR OCEAN\n-120.2,34.61,15.0,2958.0,690.0,1348.0,617.0,3.8582,215200.0,NEAR OCEAN\n-120.13,34.63,11.0,2137.0,339.0,916.0,338.0,5.5221,394900.0,NEAR OCEAN\n-120.12,34.6,10.0,2426.0,426.0,966.0,419.0,5.5106,290900.0,NEAR OCEAN\n-120.14,34.6,22.0,2136.0,465.0,1143.0,409.0,2.9479,243100.0,NEAR OCEAN\n-120.16,34.61,17.0,921.0,189.0,434.0,219.0,3.0185,500001.0,NEAR OCEAN\n-120.14,34.59,9.0,2536.0,499.0,832.0,385.0,2.5743,309800.0,NEAR OCEAN\n-120.14,34.59,24.0,1601.0,282.0,731.0,285.0,4.2026,259800.0,NEAR OCEAN\n-120.04,34.72,13.0,3942.0,585.0,1542.0,515.0,6.6054,500001.0,NEAR OCEAN\n-120.11,34.66,18.0,1348.0,238.0,631.0,247.0,5.3154,289400.0,NEAR OCEAN\n-120.08,34.64,18.0,2375.0,429.0,1048.0,369.0,4.2222,375000.0,NEAR OCEAN\n-120.08,34.62,11.0,3478.0,588.0,1693.0,582.0,4.6554,272300.0,NEAR OCEAN\n-120.09,34.62,18.0,2708.0,382.0,988.0,359.0,5.5194,367000.0,NEAR OCEAN\n-120.11,34.62,16.0,2943.0,394.0,959.0,359.0,6.2094,440000.0,NEAR OCEAN\n-120.09,34.61,11.0,586.0,125.0,317.0,74.0,2.8906,84400.0,NEAR OCEAN\n-120.08,34.59,24.0,1874.0,319.0,820.0,315.0,5.1909,390200.0,NEAR OCEAN\n-120.01,34.54,30.0,2992.0,609.0,1288.0,465.0,3.9375,292900.0,NEAR OCEAN\n-120.44,34.91,12.0,3189.0,463.0,1200.0,442.0,5.299,226800.0,<1H OCEAN\n-120.45,34.91,16.0,712.0,147.0,355.0,162.0,2.56,150000.0,<1H OCEAN\n-120.48,34.9,20.0,3842.0,630.0,2490.0,662.0,3.0559,120100.0,<1H OCEAN\n-120.45,34.88,15.0,2143.0,286.0,929.0,315.0,5.7306,269700.0,<1H OCEAN\n-120.44,34.88,9.0,3124.0,415.0,1169.0,407.0,6.7694,275100.0,<1H OCEAN\n-120.45,34.87,4.0,1533.0,221.0,545.0,191.0,7.5696,328700.0,<1H OCEAN\n-120.44,34.87,13.0,2312.0,352.0,1084.0,388.0,5.038,194000.0,<1H OCEAN\n-120.45,34.86,23.0,3415.0,778.0,1492.0,633.0,2.2791,114800.0,NEAR OCEAN\n-120.4,34.86,11.0,1633.0,348.0,504.0,327.0,2.0508,275000.0,<1H OCEAN\n-120.4,34.85,26.0,2384.0,385.0,1323.0,400.0,4.8185,157900.0,<1H OCEAN\n-120.41,34.86,15.0,978.0,187.0,407.0,182.0,4.375,158000.0,<1H OCEAN\n-120.43,34.86,17.0,3172.0,506.0,1538.0,473.0,4.3125,168100.0,<1H OCEAN\n-120.43,34.86,17.0,1932.0,347.0,874.0,312.0,3.8203,141500.0,<1H OCEAN\n-120.37,34.9,17.0,2649.0,386.0,1057.0,362.0,4.7813,326800.0,<1H OCEAN\n-120.33,34.87,24.0,2590.0,404.0,1093.0,338.0,3.9375,341200.0,<1H OCEAN\n-120.42,34.91,4.0,6986.0,1217.0,2801.0,1212.0,3.2135,212700.0,<1H OCEAN\n-120.43,34.9,27.0,2019.0,354.0,1029.0,346.0,3.5391,144700.0,<1H OCEAN\n-120.43,34.9,30.0,2388.0,393.0,1117.0,375.0,4.1058,164000.0,<1H OCEAN\n-120.41,34.88,8.0,3119.0,620.0,1159.0,544.0,3.5288,165500.0,<1H OCEAN\n-120.42,34.89,24.0,2020.0,307.0,855.0,283.0,5.0099,162500.0,<1H OCEAN\n-120.43,34.89,30.0,1979.0,342.0,999.0,320.0,5.0286,158000.0,<1H OCEAN\n-120.43,34.89,28.0,2862.0,478.0,1384.0,463.0,4.6694,158200.0,<1H OCEAN\n-120.43,34.88,22.0,2580.0,381.0,1149.0,372.0,5.0113,158600.0,<1H OCEAN\n-120.42,34.87,18.0,2505.0,376.0,1162.0,382.0,4.8359,195700.0,<1H OCEAN\n-120.43,34.87,21.0,2131.0,329.0,1094.0,353.0,4.6648,193000.0,<1H OCEAN\n-120.43,34.87,26.0,1699.0,272.0,799.0,266.0,3.9871,157700.0,<1H OCEAN\n-120.41,34.88,4.0,3680.0,559.0,1678.0,569.0,5.0639,201700.0,<1H OCEAN\n-120.4,34.87,10.0,2197.0,329.0,1064.0,319.0,4.9766,199600.0,<1H OCEAN\n-120.41,34.87,32.0,1997.0,317.0,866.0,281.0,5.062,158900.0,<1H OCEAN\n-120.41,34.87,15.0,1534.0,251.0,761.0,240.0,4.9028,193600.0,<1H OCEAN\n-120.42,34.95,52.0,1391.0,287.0,632.0,276.0,1.7431,131500.0,<1H OCEAN\n-120.42,34.95,33.0,3404.0,711.0,1579.0,639.0,3.1078,146700.0,<1H OCEAN\n-120.43,34.95,50.0,1966.0,413.0,985.0,403.0,2.3506,136100.0,<1H OCEAN\n-120.43,34.95,43.0,2020.0,344.0,692.0,310.0,3.6815,181800.0,<1H OCEAN\n-120.42,34.94,32.0,2844.0,551.0,1337.0,516.0,2.7188,133700.0,<1H OCEAN\n-120.43,34.93,4.0,2866.0,648.0,1311.0,578.0,2.8649,186500.0,<1H OCEAN\n-120.43,34.93,10.0,2980.0,585.0,1593.0,562.0,3.285,218300.0,<1H OCEAN\n-120.43,34.98,21.0,2725.0,514.0,1466.0,488.0,3.6639,128600.0,<1H OCEAN\n-120.43,34.97,28.0,1433.0,270.0,1001.0,278.0,4.0125,130100.0,<1H OCEAN\n-120.42,34.96,20.0,1678.0,307.0,840.0,316.0,4.4342,160700.0,<1H OCEAN\n-120.43,34.96,24.0,2739.0,414.0,1171.0,413.0,4.8155,162900.0,<1H OCEAN\n-120.43,34.96,24.0,1799.0,470.0,1416.0,408.0,2.0673,136900.0,<1H OCEAN\n-120.42,34.96,31.0,3518.0,608.0,1386.0,572.0,3.6212,151400.0,<1H OCEAN\n-120.42,34.96,19.0,2298.0,511.0,1246.0,513.0,2.212,132000.0,<1H OCEAN\n-120.43,34.96,19.0,2350.0,631.0,1291.0,515.0,1.0349,130800.0,<1H OCEAN\n-120.42,34.97,18.0,1932.0,350.0,1071.0,346.0,4.125,139800.0,<1H OCEAN\n-120.41,34.96,16.0,2299.0,403.0,1245.0,395.0,4.2125,148300.0,<1H OCEAN\n-120.42,34.96,14.0,2069.0,343.0,1240.0,338.0,4.5066,149800.0,<1H OCEAN\n-120.38,34.96,9.0,2813.0,492.0,1144.0,490.0,4.0431,226800.0,<1H OCEAN\n-120.41,34.96,21.0,1774.0,263.0,724.0,237.0,4.65,152500.0,<1H OCEAN\n-120.41,34.96,9.0,2712.0,428.0,1116.0,415.0,4.5536,190100.0,<1H OCEAN\n-120.4,34.95,8.0,1885.0,286.0,835.0,290.0,5.0206,261000.0,<1H OCEAN\n-120.47,34.98,6.0,5762.0,1115.0,2551.0,919.0,3.0723,137300.0,<1H OCEAN\n-120.44,34.97,22.0,1619.0,360.0,1509.0,384.0,1.7941,110300.0,<1H OCEAN\n-120.44,34.97,26.0,1705.0,344.0,1605.0,307.0,3.7589,113700.0,<1H OCEAN\n-120.45,34.97,25.0,1920.0,380.0,1434.0,388.0,3.0368,129300.0,<1H OCEAN\n-120.45,34.97,10.0,1897.0,354.0,1353.0,357.0,3.7679,131300.0,<1H OCEAN\n-120.44,34.96,29.0,2374.0,562.0,1617.0,463.0,2.6531,108300.0,<1H OCEAN\n-120.44,34.96,30.0,1685.0,315.0,1290.0,368.0,3.4722,112500.0,<1H OCEAN\n-120.45,34.96,21.0,2121.0,445.0,2211.0,463.0,4.0603,117600.0,<1H OCEAN\n-120.45,34.96,11.0,1299.0,280.0,1158.0,223.0,2.5556,129200.0,<1H OCEAN\n-120.44,34.96,39.0,1228.0,379.0,851.0,341.0,1.899,113300.0,<1H OCEAN\n-120.44,34.96,34.0,1248.0,284.0,986.0,272.0,2.9167,104200.0,<1H OCEAN\n-120.45,34.96,26.0,1949.0,396.0,1575.0,377.0,2.875,121400.0,<1H OCEAN\n-120.45,34.95,32.0,1574.0,447.0,1772.0,463.0,1.8625,90200.0,<1H OCEAN\n-120.44,34.94,24.0,2481.0,476.0,1101.0,474.0,3.1576,147200.0,<1H OCEAN\n-120.47,34.94,17.0,1368.0,308.0,642.0,303.0,1.8633,109400.0,<1H OCEAN\n-120.44,34.93,15.0,868.0,244.0,1133.0,253.0,2.0995,87500.0,<1H OCEAN\n-120.44,34.93,16.0,2098.0,558.0,1252.0,492.0,2.1509,67500.0,<1H OCEAN\n-120.44,34.95,38.0,3004.0,794.0,2601.0,747.0,2.2743,106400.0,<1H OCEAN\n-120.45,34.95,10.0,2207.0,644.0,2232.0,543.0,2.375,98500.0,<1H OCEAN\n-120.44,34.94,29.0,1877.0,516.0,1634.0,492.0,1.6875,122700.0,<1H OCEAN\n-120.45,34.95,7.0,1479.0,532.0,1057.0,459.0,2.2538,162500.0,<1H OCEAN\n-120.45,34.94,26.0,1058.0,232.0,891.0,243.0,3.6422,120600.0,<1H OCEAN\n-120.45,34.94,24.0,1702.0,447.0,1240.0,417.0,2.4091,115500.0,<1H OCEAN\n-120.54,34.97,23.0,1353.0,345.0,1322.0,336.0,1.8185,97800.0,<1H OCEAN\n-120.57,34.96,27.0,1401.0,294.0,1306.0,286.0,2.5809,83200.0,NEAR OCEAN\n-120.57,34.96,38.0,1145.0,297.0,1107.0,296.0,2.1776,89100.0,NEAR OCEAN\n-120.64,34.97,5.0,2090.0,469.0,1911.0,482.0,2.4318,86100.0,NEAR OCEAN\n-120.6,34.91,44.0,711.0,140.0,384.0,116.0,2.1094,73800.0,NEAR OCEAN\n-120.59,34.7,29.0,17738.0,3114.0,12427.0,2826.0,2.7377,28300.0,NEAR OCEAN\n-120.48,34.7,26.0,3069.0,518.0,1524.0,539.0,4.3162,136400.0,NEAR OCEAN\n-120.46,34.64,11.0,562.0,164.0,504.0,147.0,2.0161,118800.0,NEAR OCEAN\n-120.46,34.65,22.0,1298.0,358.0,1272.0,363.0,1.6488,117500.0,NEAR OCEAN\n-120.46,34.64,26.0,2364.0,604.0,1520.0,529.0,1.9444,129400.0,NEAR OCEAN\n-120.47,34.64,8.0,2482.0,586.0,1427.0,540.0,3.071,120400.0,NEAR OCEAN\n-120.47,34.65,31.0,1438.0,320.0,816.0,270.0,2.4583,128100.0,NEAR OCEAN\n-120.46,34.64,16.0,686.0,217.0,614.0,200.0,0.8106,83300.0,NEAR OCEAN\n-120.44,34.64,8.0,787.0,126.0,446.0,133.0,4.6023,163400.0,NEAR OCEAN\n-120.45,34.64,30.0,2330.0,422.0,1255.0,449.0,3.8512,134600.0,NEAR OCEAN\n-120.45,34.64,27.0,2696.0,622.0,1322.0,543.0,3.0352,135400.0,NEAR OCEAN\n-120.45,34.64,40.0,1051.0,235.0,574.0,201.0,2.0865,111500.0,NEAR OCEAN\n-120.46,34.65,10.0,2143.0,593.0,1167.0,548.0,2.0819,103300.0,NEAR OCEAN\n-120.46,34.65,14.0,885.0,223.0,533.0,224.0,2.5966,109300.0,NEAR OCEAN\n-120.47,34.65,32.0,2193.0,430.0,1074.0,377.0,2.3333,130200.0,NEAR OCEAN\n-120.47,34.65,16.0,2549.0,428.0,1486.0,432.0,4.2875,150700.0,NEAR OCEAN\n-120.45,34.65,27.0,2253.0,382.0,1197.0,384.0,3.3203,134700.0,NEAR OCEAN\n-120.44,34.65,30.0,2265.0,512.0,1402.0,471.0,1.975,134000.0,NEAR OCEAN\n-120.45,34.65,27.0,2215.0,578.0,1544.0,527.0,1.9257,135300.0,NEAR OCEAN\n-120.45,34.65,21.0,1182.0,243.0,733.0,251.0,3.1442,131600.0,NEAR OCEAN\n-120.45,34.65,25.0,980.0,276.0,896.0,245.0,2.0,87500.0,NEAR OCEAN\n-120.46,34.63,48.0,1408.0,301.0,682.0,279.0,2.9271,146600.0,NEAR OCEAN\n-120.46,34.64,37.0,1697.0,334.0,740.0,272.0,2.3804,148000.0,NEAR OCEAN\n-120.47,34.64,16.0,1912.0,406.0,1009.0,417.0,3.4063,138000.0,NEAR OCEAN\n-120.45,34.64,17.0,1226.0,277.0,484.0,224.0,3.2167,112500.0,NEAR OCEAN\n-120.45,34.64,34.0,2571.0,499.0,1105.0,451.0,3.7778,150000.0,NEAR OCEAN\n-120.45,34.63,32.0,1840.0,309.0,828.0,333.0,4.5486,172400.0,NEAR OCEAN\n-120.44,34.66,22.0,3231.0,549.0,1739.0,581.0,4.5417,142400.0,NEAR OCEAN\n-120.45,34.66,7.0,3329.0,504.0,1462.0,452.0,4.7875,198300.0,NEAR OCEAN\n-120.46,34.66,5.0,2904.0,702.0,1302.0,618.0,3.0081,135200.0,NEAR OCEAN\n-120.47,34.66,4.0,3376.0,525.0,1684.0,535.0,4.9237,166600.0,NEAR OCEAN\n-120.39,34.52,40.0,2162.0,395.0,1010.0,332.0,2.5667,239300.0,NEAR OCEAN\n-120.48,34.66,4.0,1897.0,331.0,915.0,336.0,4.1563,172600.0,NEAR OCEAN\n-120.48,34.65,26.0,1933.0,316.0,1001.0,319.0,4.4628,134400.0,NEAR OCEAN\n-120.45,34.63,25.0,2445.0,368.0,983.0,363.0,4.9286,180100.0,NEAR OCEAN\n-120.47,34.63,23.0,2441.0,463.0,1392.0,434.0,3.7917,142200.0,NEAR OCEAN\n-120.46,34.74,15.0,2185.0,386.0,827.0,336.0,5.3765,223100.0,NEAR OCEAN\n-120.45,34.71,21.0,1868.0,268.0,522.0,255.0,6.4678,249300.0,NEAR OCEAN\n-120.46,34.71,17.0,2830.0,430.0,1035.0,416.0,4.9292,207200.0,NEAR OCEAN\n-120.47,34.7,24.0,2387.0,385.0,1051.0,382.0,4.4595,152700.0,NEAR OCEAN\n-120.47,34.71,21.0,2535.0,383.0,1012.0,368.0,5.6177,183800.0,NEAR OCEAN\n-120.37,34.69,18.0,1868.0,315.0,747.0,265.0,4.7946,290600.0,NEAR OCEAN\n-120.43,34.7,26.0,2353.0,389.0,1420.0,389.0,3.87,125800.0,NEAR OCEAN\n-120.43,34.69,33.0,2054.0,373.0,1067.0,358.0,3.6023,128300.0,NEAR OCEAN\n-120.44,34.68,6.0,2187.0,277.0,697.0,273.0,6.2685,307400.0,NEAR OCEAN\n-119.86,34.42,23.0,1450.0,642.0,1258.0,607.0,1.179,225000.0,NEAR OCEAN\n-119.88,34.4,25.0,2741.0,623.0,2272.0,624.0,2.2647,216700.0,NEAR OCEAN\n-119.85,34.4,14.0,2307.0,650.0,5723.0,615.0,2.1652,37500.0,NEAR OCEAN\n-119.88,34.43,14.0,2472.0,685.0,1292.0,621.0,3.3026,229500.0,NEAR OCEAN\n-119.88,34.43,16.0,1734.0,365.0,962.0,391.0,4.4777,282500.0,NEAR OCEAN\n-119.88,34.43,16.0,2206.0,541.0,1227.0,554.0,3.75,223100.0,NEAR OCEAN\n-119.88,34.42,22.0,2367.0,492.0,1333.0,488.0,3.6304,312200.0,NEAR OCEAN\n-119.91,34.4,24.0,2001.0,365.0,1170.0,330.0,6.0992,268800.0,NEAR OCEAN\n-119.83,34.45,24.0,2168.0,373.0,934.0,366.0,5.4197,280900.0,NEAR OCEAN\n-119.84,34.44,28.0,977.0,162.0,537.0,159.0,4.2404,274300.0,NEAR OCEAN\n-119.84,34.45,26.0,4424.0,616.0,1839.0,601.0,6.3654,331200.0,NEAR OCEAN\n-119.85,34.44,28.0,1765.0,301.0,1173.0,297.0,6.0256,276800.0,NEAR OCEAN\n-119.81,34.47,26.0,4382.0,618.0,1728.0,587.0,7.4734,432200.0,NEAR OCEAN\n-119.81,34.46,22.0,3488.0,452.0,1479.0,458.0,7.1687,384400.0,NEAR OCEAN\n-119.85,34.48,23.0,1915.0,277.0,724.0,267.0,6.2987,348200.0,NEAR OCEAN\n-119.88,34.44,27.0,4724.0,793.0,2394.0,738.0,5.5954,261400.0,NEAR OCEAN\n-119.89,34.44,25.0,2786.0,470.0,1669.0,462.0,5.5184,268300.0,NEAR OCEAN\n-119.89,34.44,25.0,3160.0,507.0,1514.0,523.0,5.0767,271200.0,NEAR OCEAN\n-119.92,34.44,17.0,2143.0,324.0,1073.0,330.0,6.0321,402600.0,NEAR OCEAN\n-120.05,34.47,21.0,1241.0,248.0,746.0,211.0,3.8056,425000.0,NEAR OCEAN\n-119.86,34.38,28.0,1062.0,309.0,1058.0,305.0,1.5071,316700.0,NEAR OCEAN\n-119.86,34.41,24.0,1576.0,580.0,1630.0,531.0,1.24,325000.0,NEAR OCEAN\n-119.86,34.38,26.0,1626.0,375.0,1580.0,359.0,2.1471,187500.0,NEAR OCEAN\n-119.81,34.45,24.0,3678.0,567.0,1554.0,570.0,6.5173,334000.0,NEAR OCEAN\n-119.81,34.44,23.0,3172.0,588.0,1467.0,559.0,4.6806,288900.0,NEAR OCEAN\n-119.82,34.45,24.0,3592.0,533.0,1683.0,528.0,6.7247,333800.0,NEAR OCEAN\n-119.83,34.44,26.0,1739.0,402.0,599.0,368.0,3.0875,198400.0,NEAR OCEAN\n-119.82,34.44,22.0,2239.0,475.0,1016.0,434.0,4.875,295400.0,NEAR OCEAN\n-119.81,34.44,14.0,961.0,305.0,662.0,286.0,3.2115,206300.0,NEAR OCEAN\n-119.82,34.43,15.0,1482.0,345.0,669.0,379.0,3.0773,112500.0,NEAR OCEAN\n-119.82,34.44,16.0,1414.0,463.0,793.0,439.0,3.6034,150000.0,NEAR OCEAN\n-119.82,34.44,28.0,1992.0,531.0,1622.0,509.0,2.7689,228200.0,NEAR OCEAN\n-119.83,34.44,35.0,796.0,281.0,567.0,257.0,2.1389,260000.0,NEAR OCEAN\n-119.83,34.43,31.0,798.0,346.0,699.0,301.0,2.1417,205000.0,NEAR OCEAN\n-119.77,34.43,22.0,2552.0,443.0,1066.0,424.0,5.1271,342500.0,NEAR OCEAN\n-119.77,34.43,28.0,3318.0,441.0,1604.0,404.0,9.7821,500001.0,NEAR OCEAN\n-119.79,34.4,20.0,3104.0,415.0,1061.0,380.0,9.6885,500001.0,NEAR OCEAN\n-119.8,34.44,27.0,2674.0,419.0,1176.0,416.0,5.0294,280200.0,NEAR OCEAN\n-119.8,34.43,27.0,3143.0,537.0,1760.0,570.0,4.6957,271500.0,NEAR OCEAN\n-119.8,34.43,22.0,2845.0,500.0,1456.0,454.0,5.6604,276400.0,NEAR OCEAN\n-119.79,34.44,25.0,1479.0,314.0,977.0,309.0,4.1797,271800.0,NEAR OCEAN\n-119.78,34.44,28.0,2864.0,495.0,1364.0,482.0,4.835,353400.0,NEAR OCEAN\n-119.79,34.43,26.0,3611.0,563.0,2089.0,540.0,5.1615,276200.0,NEAR OCEAN\n-121.9,37.36,38.0,1141.0,333.0,1028.0,291.0,2.7333,182500.0,<1H OCEAN\n-121.89,37.36,37.0,1525.0,363.0,1075.0,374.0,2.8971,186100.0,<1H OCEAN\n-121.88,37.36,30.0,2453.0,573.0,1845.0,530.0,3.7396,210700.0,<1H OCEAN\n-121.89,37.35,44.0,1668.0,380.0,1143.0,365.0,3.2083,181900.0,<1H OCEAN\n-121.9,37.36,47.0,1007.0,245.0,581.0,240.0,2.9545,237500.0,<1H OCEAN\n-121.9,37.35,52.0,1034.0,239.0,531.0,223.0,2.7411,227100.0,<1H OCEAN\n-121.9,37.35,42.0,2082.0,626.0,1396.0,610.0,3.25,185300.0,<1H OCEAN\n-121.9,37.34,50.0,1345.0,287.0,791.0,254.0,3.5966,245800.0,<1H OCEAN\n-121.9,37.34,52.0,241.0,69.0,385.0,64.0,2.619,212500.0,<1H OCEAN\n-121.92,37.34,35.0,357.0,120.0,377.0,99.0,3.0139,204200.0,<1H OCEAN\n-121.92,37.34,42.0,2101.0,524.0,1212.0,526.0,3.6389,239200.0,<1H OCEAN\n-121.92,37.34,52.0,2584.0,491.0,1087.0,433.0,4.4,391300.0,<1H OCEAN\n-121.92,37.33,52.0,2009.0,338.0,841.0,338.0,5.5259,295800.0,<1H OCEAN\n-121.92,37.33,52.0,2962.0,557.0,1215.0,506.0,4.7768,301100.0,<1H OCEAN\n-121.93,37.33,44.0,1449.0,291.0,676.0,282.0,3.575,292200.0,<1H OCEAN\n-121.93,37.33,44.0,2142.0,358.0,846.0,375.0,5.4273,421000.0,<1H OCEAN\n-121.94,37.33,34.0,1809.0,317.0,863.0,302.0,4.3,330500.0,<1H OCEAN\n-121.91,37.34,35.0,2189.0,607.0,1193.0,562.0,2.8042,240900.0,<1H OCEAN\n-121.91,37.33,28.0,454.0,147.0,366.0,140.0,2.9853,187500.0,<1H OCEAN\n-121.91,37.33,52.0,2562.0,410.0,973.0,398.0,4.8854,330600.0,<1H OCEAN\n-121.92,37.33,52.0,2125.0,382.0,930.0,387.0,5.2831,299500.0,<1H OCEAN\n-121.9,37.33,34.0,197.0,44.0,152.0,47.0,4.05,200000.0,<1H OCEAN\n-121.91,37.33,52.0,2212.0,563.0,1195.0,532.0,2.894,209500.0,<1H OCEAN\n-121.89,37.33,49.0,658.0,318.0,467.0,316.0,0.7068,200000.0,<1H OCEAN\n-121.9,37.33,11.0,1283.0,390.0,718.0,345.0,4.226,166700.0,<1H OCEAN\n-121.9,37.33,52.0,1009.0,231.0,929.0,210.0,2.5,162500.0,<1H OCEAN\n-121.88,37.34,40.0,1547.0,625.0,1493.0,543.0,1.2887,212500.0,<1H OCEAN\n-121.89,37.33,6.0,1495.0,552.0,1087.0,557.0,2.8798,225000.0,<1H OCEAN\n-121.88,37.33,36.0,1904.0,689.0,3561.0,632.0,2.0972,187500.0,<1H OCEAN\n-121.89,37.35,48.0,1562.0,439.0,1469.0,424.0,2.5673,177500.0,<1H OCEAN\n-121.89,37.34,46.0,1197.0,416.0,898.0,370.0,2.1714,190600.0,<1H OCEAN\n-121.89,37.34,20.0,1106.0,494.0,851.0,448.0,0.8894,350000.0,<1H OCEAN\n-121.89,37.34,43.0,1423.0,467.0,1013.0,428.0,1.6708,204200.0,<1H OCEAN\n-121.88,37.34,52.0,1390.0,365.0,921.0,352.0,2.1442,188900.0,<1H OCEAN\n-121.88,37.36,42.0,2087.0,402.0,1342.0,423.0,4.2149,199000.0,<1H OCEAN\n-121.88,37.35,49.0,1728.0,350.0,1146.0,391.0,3.5781,193000.0,<1H OCEAN\n-121.89,37.35,47.0,2879.0,631.0,2229.0,606.0,3.2599,183100.0,<1H OCEAN\n-121.89,37.35,43.0,1185.0,296.0,933.0,244.0,2.925,170800.0,<1H OCEAN\n-121.89,37.35,44.0,2019.0,615.0,1243.0,518.0,2.0549,193800.0,<1H OCEAN\n-121.88,37.35,43.0,1086.0,219.0,715.0,226.0,4.2381,193500.0,<1H OCEAN\n-121.87,37.35,37.0,1566.0,375.0,1223.0,346.0,3.2793,174500.0,<1H OCEAN\n-121.88,37.34,44.0,1267.0,353.0,1018.0,327.0,2.4196,194400.0,<1H OCEAN\n-121.88,37.35,52.0,1704.0,418.0,1336.0,411.0,2.8167,183500.0,<1H OCEAN\n-121.87,37.34,52.0,1170.0,215.0,604.0,207.0,2.6667,325900.0,<1H OCEAN\n-121.87,37.34,52.0,1087.0,166.0,650.0,194.0,6.6345,309000.0,<1H OCEAN\n-121.87,37.34,39.0,2479.0,541.0,1990.0,506.0,2.4306,289100.0,<1H OCEAN\n-121.88,37.34,52.0,867.0,232.0,1264.0,227.0,2.6312,302900.0,<1H OCEAN\n-121.87,37.35,27.0,3500.0,1036.0,3019.0,955.0,2.9222,153700.0,<1H OCEAN\n-121.86,37.35,46.0,1448.0,330.0,1094.0,331.0,2.4968,174100.0,<1H OCEAN\n-121.87,37.35,52.0,1557.0,424.0,1580.0,434.0,2.3277,183700.0,<1H OCEAN\n-121.86,37.34,29.0,5274.0,1625.0,6234.0,1639.0,2.5947,177300.0,<1H OCEAN\n-121.86,37.34,40.0,2277.0,508.0,1718.0,434.0,3.0089,185200.0,<1H OCEAN\n-121.88,37.33,41.0,395.0,164.0,549.0,184.0,2.375,175000.0,<1H OCEAN\n-121.88,37.33,35.0,3300.0,1154.0,3120.0,1075.0,1.473,213600.0,<1H OCEAN\n-121.87,37.33,37.0,3137.0,685.0,2048.0,651.0,3.0156,270300.0,<1H OCEAN\n-121.88,37.33,45.0,1192.0,371.0,1084.0,345.0,2.8594,205900.0,<1H OCEAN\n-121.89,37.33,42.0,1279.0,358.0,1254.0,340.0,2.2583,192500.0,<1H OCEAN\n-121.89,37.32,34.0,1014.0,246.0,952.0,215.0,2.8864,172500.0,<1H OCEAN\n-121.88,37.32,30.0,1242.0,338.0,1438.0,325.0,2.6607,169300.0,<1H OCEAN\n-121.88,37.32,40.0,1331.0,374.0,1276.0,389.0,2.7546,172500.0,<1H OCEAN\n-121.89,37.32,43.0,1105.0,241.0,982.0,206.0,2.1149,184900.0,<1H OCEAN\n-121.89,37.32,41.0,977.0,265.0,865.0,253.0,3.2317,184800.0,<1H OCEAN\n-121.89,37.31,52.0,1994.0,404.0,1014.0,389.0,4.3882,223600.0,<1H OCEAN\n-121.9,37.31,52.0,2125.0,431.0,1014.0,443.0,5.8186,281100.0,<1H OCEAN\n-121.9,37.32,48.0,1274.0,313.0,971.0,291.0,3.7738,220600.0,<1H OCEAN\n-121.91,37.32,42.0,1067.0,256.0,608.0,280.0,3.0096,180800.0,<1H OCEAN\n-121.91,37.31,16.0,2962.0,898.0,1555.0,795.0,2.5804,216300.0,<1H OCEAN\n-121.93,37.32,52.0,1460.0,492.0,1165.0,455.0,2.5833,167500.0,<1H OCEAN\n-121.94,37.33,37.0,818.0,269.0,576.0,261.0,2.1902,250000.0,<1H OCEAN\n-121.94,37.32,46.0,2451.0,472.0,1163.0,448.0,4.8519,225800.0,<1H OCEAN\n-121.93,37.32,51.0,2711.0,728.0,1607.0,724.0,3.0,184700.0,<1H OCEAN\n-121.92,37.32,39.0,836.0,254.0,704.0,272.0,3.5256,192600.0,<1H OCEAN\n-121.92,37.32,31.0,1902.0,554.0,1485.0,494.0,2.4207,165600.0,<1H OCEAN\n-121.92,37.32,28.0,2089.0,641.0,1666.0,587.0,2.3633,198400.0,<1H OCEAN\n-121.93,37.32,50.0,1135.0,215.0,500.0,207.0,4.2614,211300.0,<1H OCEAN\n-121.94,37.31,5.0,2364.0,578.0,1102.0,502.0,5.2642,246400.0,<1H OCEAN\n-121.94,37.31,30.0,1680.0,312.0,858.0,310.0,4.0474,280500.0,<1H OCEAN\n-121.94,37.3,26.0,4348.0,814.0,2347.0,810.0,4.7275,293000.0,<1H OCEAN\n-121.93,37.3,16.0,1111.0,226.0,317.0,199.0,2.7153,233300.0,<1H OCEAN\n-121.93,37.31,26.0,2182.0,704.0,1638.0,704.0,2.8981,229800.0,<1H OCEAN\n-121.92,37.31,34.0,876.0,150.0,424.0,163.0,5.2769,241100.0,<1H OCEAN\n-121.92,37.31,26.0,3242.0,780.0,2100.0,741.0,3.1107,247900.0,<1H OCEAN\n-121.93,37.3,16.0,2111.0,485.0,1285.0,499.0,5.0477,292500.0,<1H OCEAN\n-121.93,37.31,29.0,1377.0,430.0,969.0,399.0,2.6573,252800.0,<1H OCEAN\n-121.92,37.31,13.0,6035.0,1551.0,2946.0,1481.0,4.0524,213900.0,<1H OCEAN\n-121.93,37.3,14.0,6277.0,1742.0,3025.0,1630.0,4.0653,234200.0,<1H OCEAN\n-121.92,37.3,36.0,2088.0,358.0,772.0,347.0,4.2762,310100.0,<1H OCEAN\n-121.92,37.3,35.0,1335.0,296.0,635.0,296.0,3.6053,345800.0,<1H OCEAN\n-121.91,37.31,28.0,3104.0,811.0,1488.0,754.0,3.6429,332600.0,<1H OCEAN\n-121.91,37.31,46.0,3052.0,587.0,1373.0,590.0,4.7287,340000.0,<1H OCEAN\n-121.9,37.3,39.0,3627.0,666.0,1531.0,635.0,4.537,345900.0,<1H OCEAN\n-121.91,37.3,43.0,828.0,151.0,446.0,145.0,4.4375,327600.0,<1H OCEAN\n-121.91,37.3,31.0,2095.0,427.0,829.0,405.0,3.6563,344700.0,<1H OCEAN\n-121.91,37.3,31.0,616.0,131.0,185.0,107.0,3.625,265000.0,<1H OCEAN\n-121.89,37.31,40.0,1844.0,340.0,719.0,305.0,3.3682,235200.0,<1H OCEAN\n-121.88,37.31,28.0,3085.0,552.0,1277.0,512.0,4.5795,262800.0,<1H OCEAN\n-121.89,37.3,47.0,1604.0,284.0,639.0,278.0,5.8415,283300.0,<1H OCEAN\n-121.89,37.3,52.0,2071.0,362.0,825.0,364.0,4.2414,284800.0,<1H OCEAN\n-121.89,37.31,47.0,2986.0,627.0,1399.0,613.0,3.7455,247400.0,<1H OCEAN\n-121.9,37.3,52.0,1456.0,269.0,582.0,277.0,5.036,296600.0,<1H OCEAN\n-121.9,37.3,52.0,1575.0,284.0,629.0,284.0,5.6437,312000.0,<1H OCEAN\n-121.88,37.3,42.0,1867.0,398.0,927.0,389.0,4.325,247000.0,<1H OCEAN\n-121.88,37.29,44.0,2026.0,396.0,908.0,383.0,4.1406,274100.0,<1H OCEAN\n-121.89,37.3,47.0,2355.0,426.0,961.0,428.0,5.3955,282300.0,<1H OCEAN\n-121.89,37.3,46.0,2639.0,448.0,938.0,424.0,5.0662,331600.0,<1H OCEAN\n-121.89,37.29,38.0,1568.0,351.0,710.0,339.0,2.7042,286600.0,<1H OCEAN\n-121.9,37.29,26.0,1797.0,244.0,626.0,244.0,7.8575,424600.0,<1H OCEAN\n-121.9,37.29,36.0,1389.0,225.0,623.0,223.0,6.6331,283300.0,<1H OCEAN\n-121.92,37.29,34.0,943.0,135.0,378.0,139.0,5.1765,344600.0,<1H OCEAN\n-121.91,37.29,36.0,945.0,149.0,371.0,158.0,5.6266,320500.0,<1H OCEAN\n-121.92,37.29,35.0,2189.0,307.0,800.0,320.0,7.6659,426900.0,<1H OCEAN\n-121.91,37.29,18.0,3597.0,664.0,1321.0,593.0,5.3077,351400.0,<1H OCEAN\n-121.93,37.29,36.0,2241.0,437.0,989.0,442.0,3.9625,288200.0,<1H OCEAN\n-121.92,37.29,32.0,1260.0,199.0,560.0,207.0,6.5858,346700.0,<1H OCEAN\n-121.93,37.28,34.0,2422.0,370.0,1010.0,395.0,5.6494,376200.0,<1H OCEAN\n-121.93,37.28,10.0,3163.0,832.0,1537.0,797.0,4.1674,214000.0,<1H OCEAN\n-121.94,37.28,18.0,4356.0,1334.0,1968.0,1245.0,3.6294,240000.0,<1H OCEAN\n-121.94,37.28,27.0,2859.0,464.0,1144.0,430.0,5.0822,327500.0,<1H OCEAN\n-121.93,37.27,30.0,2862.0,544.0,1387.0,542.0,5.1104,278100.0,<1H OCEAN\n-121.94,37.27,23.0,1932.0,552.0,997.0,482.0,3.662,211900.0,<1H OCEAN\n-121.94,37.27,39.0,1030.0,191.0,537.0,175.0,3.9265,236900.0,<1H OCEAN\n-121.94,37.26,43.0,2104.0,388.0,1137.0,403.0,4.9236,238000.0,<1H OCEAN\n-121.94,37.26,21.0,3843.0,716.0,1850.0,705.0,4.6758,264200.0,<1H OCEAN\n-121.95,37.26,10.0,3611.0,803.0,1599.0,716.0,5.2,248700.0,<1H OCEAN\n-121.95,37.26,34.0,1482.0,255.0,584.0,246.0,5.5121,264700.0,<1H OCEAN\n-121.94,37.25,16.0,3942.0,749.0,1894.0,737.0,5.2894,332800.0,<1H OCEAN\n-121.93,37.26,39.0,1103.0,175.0,446.0,163.0,2.8125,291300.0,<1H OCEAN\n-121.92,37.26,33.0,1306.0,259.0,762.0,237.0,4.5208,230700.0,<1H OCEAN\n-121.92,37.25,36.0,3874.0,656.0,1826.0,639.0,4.9662,258500.0,<1H OCEAN\n-121.91,37.25,31.0,1944.0,343.0,975.0,334.0,4.9205,240500.0,<1H OCEAN\n-121.92,37.28,27.0,3028.0,486.0,1284.0,498.0,4.5833,308800.0,<1H OCEAN\n-121.92,37.27,33.0,3280.0,569.0,1583.0,559.0,4.5625,253500.0,<1H OCEAN\n-121.93,37.27,35.0,1855.0,345.0,985.0,329.0,6.0196,255100.0,<1H OCEAN\n-121.93,37.27,28.0,3428.0,753.0,1753.0,729.0,4.1033,281000.0,<1H OCEAN\n-121.92,37.28,26.0,6201.0,783.0,2381.0,819.0,7.9819,397000.0,<1H OCEAN\n-121.92,37.27,29.0,5536.0,862.0,2651.0,881.0,5.6358,282100.0,<1H OCEAN\n-121.91,37.27,30.0,4412.0,862.0,2168.0,772.0,5.0062,232000.0,<1H OCEAN\n-121.91,37.28,29.0,5650.0,817.0,2098.0,813.0,6.4285,337300.0,<1H OCEAN\n-121.9,37.28,26.0,3756.0,,1408.0,535.0,5.6427,320000.0,<1H OCEAN\n-121.9,37.27,33.0,3410.0,583.0,1739.0,588.0,5.0714,255600.0,<1H OCEAN\n-121.9,37.25,28.0,2714.0,502.0,1389.0,490.0,5.7385,240400.0,<1H OCEAN\n-121.9,37.25,20.0,5483.0,1079.0,2892.0,1057.0,4.6845,250000.0,<1H OCEAN\n-121.9,37.27,28.0,4538.0,685.0,1996.0,667.0,5.4609,263600.0,<1H OCEAN\n-121.9,37.26,20.0,4447.0,661.0,2062.0,660.0,6.8088,283300.0,<1H OCEAN\n-121.91,37.26,25.0,4258.0,719.0,2290.0,743.0,5.1461,267200.0,<1H OCEAN\n-121.91,37.26,32.0,3776.0,620.0,1790.0,612.0,5.4675,261100.0,<1H OCEAN\n-121.91,37.26,32.0,3983.0,876.0,1989.0,794.0,3.5625,255200.0,<1H OCEAN\n-121.89,37.25,23.0,2705.0,449.0,1180.0,442.0,6.0791,316500.0,<1H OCEAN\n-121.89,37.26,26.0,1864.0,331.0,956.0,325.0,5.5,231700.0,<1H OCEAN\n-121.88,37.26,25.0,3025.0,689.0,1755.0,661.0,3.8893,218600.0,<1H OCEAN\n-121.88,37.26,13.0,1893.0,487.0,1018.0,464.0,3.8047,204700.0,<1H OCEAN\n-121.88,37.26,13.0,1676.0,471.0,710.0,406.0,3.8936,225900.0,<1H OCEAN\n-121.88,37.25,24.0,968.0,240.0,631.0,250.0,2.8636,240300.0,<1H OCEAN\n-121.89,37.25,21.0,2080.0,352.0,1040.0,325.0,5.2887,264500.0,<1H OCEAN\n-121.89,37.25,26.0,1741.0,323.0,1007.0,339.0,4.7069,234800.0,<1H OCEAN\n-121.89,37.29,36.0,2959.0,529.0,1125.0,520.0,4.2614,268800.0,<1H OCEAN\n-121.89,37.28,35.0,2418.0,375.0,988.0,374.0,6.0936,365400.0,<1H OCEAN\n-121.9,37.28,34.0,4613.0,749.0,2050.0,725.0,5.3922,302900.0,<1H OCEAN\n-121.88,37.28,33.0,2951.0,529.0,1288.0,521.0,4.1554,313100.0,<1H OCEAN\n-121.89,37.28,32.0,4308.0,717.0,2002.0,695.0,4.1645,281900.0,<1H OCEAN\n-121.88,37.27,27.0,2019.0,335.0,1020.0,351.0,5.8178,267400.0,<1H OCEAN\n-121.88,37.27,24.0,4567.0,688.0,2102.0,695.0,5.6895,289000.0,<1H OCEAN\n-121.89,37.26,25.0,3319.0,531.0,1560.0,502.0,5.8479,263300.0,<1H OCEAN\n-121.89,37.27,28.0,1481.0,256.0,688.0,221.0,5.2088,240900.0,<1H OCEAN\n-121.87,37.32,39.0,1839.0,471.0,1528.0,456.0,2.6818,184900.0,<1H OCEAN\n-121.87,37.32,36.0,1471.0,360.0,1182.0,326.0,2.7031,175800.0,<1H OCEAN\n-121.88,37.32,38.0,1787.0,508.0,2113.0,530.0,2.6386,177600.0,<1H OCEAN\n-121.88,37.32,45.0,2213.0,564.0,1920.0,514.0,3.2806,164200.0,<1H OCEAN\n-121.86,37.31,24.0,1939.0,652.0,1808.0,625.0,2.2259,112500.0,<1H OCEAN\n-121.87,37.3,28.0,859.0,199.0,455.0,211.0,2.3293,215900.0,<1H OCEAN\n-121.87,37.31,6.0,3797.0,984.0,2437.0,904.0,3.6802,152400.0,<1H OCEAN\n-121.87,37.3,14.0,360.0,124.0,134.0,84.0,2.7411,112500.0,<1H OCEAN\n-121.88,37.3,16.0,2692.0,749.0,1674.0,681.0,2.6763,191100.0,<1H OCEAN\n-121.86,37.32,13.0,2519.0,581.0,2094.0,530.0,4.3621,173400.0,<1H OCEAN\n-121.85,37.33,19.0,2228.0,559.0,2845.0,551.0,2.6,172800.0,<1H OCEAN\n-121.84,37.32,14.0,5762.0,1538.0,3979.0,1389.0,3.6953,192600.0,<1H OCEAN\n-121.85,37.33,16.0,2987.0,874.0,4241.0,841.0,2.8024,127900.0,<1H OCEAN\n-121.85,37.33,19.0,735.0,158.0,597.0,134.0,4.5208,188100.0,<1H OCEAN\n-121.84,37.32,22.0,3015.0,581.0,2491.0,530.0,4.3419,176300.0,<1H OCEAN\n-121.84,37.32,16.0,1866.0,364.0,1835.0,412.0,5.3363,212800.0,<1H OCEAN\n-121.87,37.29,18.0,1892.0,568.0,974.0,553.0,2.3715,228000.0,<1H OCEAN\n-121.87,37.28,21.0,3305.0,749.0,2459.0,701.0,3.9688,249600.0,<1H OCEAN\n-121.86,37.29,14.0,6160.0,1222.0,2472.0,1204.0,4.1444,178400.0,<1H OCEAN\n-121.85,37.28,17.0,4208.0,954.0,1476.0,904.0,2.3971,87500.0,<1H OCEAN\n-121.83,37.3,16.0,5684.0,1386.0,4203.0,1318.0,3.1964,185800.0,<1H OCEAN\n-121.85,37.3,19.0,6275.0,1546.0,4312.0,1466.0,2.7768,178600.0,<1H OCEAN\n-121.84,37.29,24.0,3403.0,656.0,2829.0,612.0,4.775,191900.0,<1H OCEAN\n-121.84,37.29,4.0,2937.0,648.0,1780.0,665.0,4.3851,160400.0,<1H OCEAN\n-121.84,37.28,18.0,2749.0,633.0,1779.0,561.0,3.925,166100.0,<1H OCEAN\n-121.83,37.31,19.0,11181.0,1895.0,7817.0,1853.0,5.6058,232700.0,<1H OCEAN\n-121.83,37.3,17.0,1299.0,211.0,825.0,217.0,4.5,235800.0,<1H OCEAN\n-121.81,37.29,15.0,5957.0,1037.0,3838.0,997.0,6.2907,253100.0,<1H OCEAN\n-121.81,37.28,17.0,2277.0,428.0,1887.0,422.0,5.7078,217000.0,<1H OCEAN\n-121.82,37.28,33.0,2873.0,489.0,1946.0,475.0,5.0709,176400.0,<1H OCEAN\n-121.83,37.29,10.0,1828.0,453.0,1356.0,409.0,4.5943,123500.0,<1H OCEAN\n-121.83,37.29,20.0,1649.0,408.0,1729.0,362.0,3.3833,115200.0,<1H OCEAN\n-121.83,37.28,33.0,1115.0,250.0,1168.0,261.0,3.9009,178600.0,<1H OCEAN\n-121.83,37.28,19.0,2644.0,833.0,2317.0,780.0,3.1042,183100.0,<1H OCEAN\n-121.82,37.28,31.0,1340.0,235.0,1336.0,270.0,4.2361,179500.0,<1H OCEAN\n-121.83,37.29,20.0,2308.0,461.0,2223.0,456.0,4.2589,191000.0,<1H OCEAN\n-121.82,37.29,16.0,2085.0,394.0,1705.0,391.0,5.0225,222800.0,<1H OCEAN\n-121.83,37.32,17.0,1887.0,664.0,1906.0,597.0,2.5652,165300.0,<1H OCEAN\n-121.82,37.31,22.0,2044.0,402.0,1925.0,429.0,3.7102,177500.0,<1H OCEAN\n-121.82,37.31,15.0,1504.0,294.0,1267.0,291.0,5.5145,219400.0,<1H OCEAN\n-121.81,37.31,14.0,2731.0,578.0,1109.0,551.0,3.1382,139700.0,<1H OCEAN\n-121.81,37.31,15.0,1898.0,395.0,1527.0,381.0,4.4792,212500.0,<1H OCEAN\n-121.81,37.32,26.0,2528.0,511.0,2677.0,512.0,4.1165,164900.0,<1H OCEAN\n-121.82,37.32,10.0,2506.0,623.0,2634.0,622.0,3.135,231400.0,<1H OCEAN\n-121.82,37.33,23.0,3279.0,647.0,2582.0,630.0,4.3782,175800.0,<1H OCEAN\n-121.83,37.32,26.0,1125.0,210.0,943.0,214.0,4.825,181000.0,<1H OCEAN\n-121.81,37.33,4.0,5532.0,778.0,3651.0,770.0,7.2982,343000.0,<1H OCEAN\n-121.8,37.34,25.0,1642.0,297.0,1146.0,279.0,5.2088,231400.0,<1H OCEAN\n-121.8,37.34,20.0,2686.0,414.0,1507.0,405.0,5.8068,263900.0,<1H OCEAN\n-121.79,37.34,20.0,2018.0,328.0,1196.0,323.0,4.9318,262400.0,<1H OCEAN\n-121.8,37.35,15.0,2781.0,498.0,1389.0,475.0,5.614,223300.0,<1H OCEAN\n-121.78,37.34,21.0,1959.0,292.0,891.0,300.0,7.375,338400.0,<1H OCEAN\n-121.75,37.35,18.0,1947.0,250.0,605.0,184.0,8.1871,500001.0,<1H OCEAN\n-121.77,37.33,8.0,3088.0,474.0,1799.0,456.0,7.2707,355300.0,<1H OCEAN\n-121.77,37.33,9.0,3160.0,468.0,1675.0,470.0,7.5443,348400.0,<1H OCEAN\n-121.78,37.34,11.0,3195.0,410.0,1774.0,418.0,7.0671,378200.0,<1H OCEAN\n-121.79,37.33,13.0,2978.0,505.0,1794.0,485.0,6.6813,277800.0,<1H OCEAN\n-121.79,37.33,18.0,3611.0,614.0,2381.0,642.0,5.6345,231000.0,<1H OCEAN\n-121.79,37.33,10.0,3283.0,550.0,2491.0,522.0,6.6633,283700.0,<1H OCEAN\n-121.78,37.33,10.0,2829.0,394.0,1510.0,386.0,6.68,359500.0,<1H OCEAN\n-121.8,37.32,20.0,2473.0,476.0,2228.0,501.0,5.6817,224200.0,<1H OCEAN\n-121.8,37.32,23.0,1829.0,346.0,1277.0,324.0,4.8092,217400.0,<1H OCEAN\n-121.8,37.32,14.0,4412.0,924.0,2698.0,891.0,4.7027,227600.0,<1H OCEAN\n-121.79,37.32,6.0,2850.0,561.0,2160.0,581.0,5.5336,241900.0,<1H OCEAN\n-121.79,37.32,20.0,3034.0,451.0,1669.0,430.0,6.2742,241300.0,<1H OCEAN\n-121.76,37.33,5.0,4153.0,719.0,2435.0,697.0,5.6306,286200.0,<1H OCEAN\n-121.81,37.3,14.0,1870.0,348.0,1214.0,347.0,4.9769,186500.0,<1H OCEAN\n-121.81,37.31,15.0,1794.0,366.0,1533.0,371.0,5.7843,209900.0,<1H OCEAN\n-121.8,37.31,15.0,1807.0,378.0,1277.0,341.0,4.5045,164500.0,<1H OCEAN\n-121.8,37.3,16.0,906.0,149.0,605.0,148.0,4.8173,235600.0,<1H OCEAN\n-121.81,37.3,15.0,1929.0,345.0,1683.0,347.0,5.5248,235600.0,<1H OCEAN\n-121.8,37.31,21.0,2630.0,446.0,1789.0,389.0,5.0543,232000.0,<1H OCEAN\n-121.79,37.31,22.0,2199.0,361.0,1270.0,386.0,5.1149,235700.0,<1H OCEAN\n-121.78,37.31,25.0,2093.0,297.0,983.0,338.0,6.4664,271500.0,<1H OCEAN\n-121.79,37.3,10.0,5469.0,950.0,3083.0,906.0,5.9399,241900.0,<1H OCEAN\n-121.76,37.28,17.0,660.0,129.0,431.0,123.0,4.9097,241000.0,<1H OCEAN\n-121.74,37.3,12.0,1961.0,280.0,985.0,269.0,6.7159,362700.0,<1H OCEAN\n-121.78,37.31,7.0,1973.0,328.0,1047.0,303.0,6.234,292200.0,<1H OCEAN\n-121.77,37.31,16.0,1649.0,228.0,769.0,230.0,6.6455,302600.0,<1H OCEAN\n-121.76,37.29,15.0,2267.0,348.0,1150.0,327.0,7.1267,277900.0,<1H OCEAN\n-121.76,37.3,6.0,3526.0,559.0,1378.0,491.0,6.1463,335500.0,<1H OCEAN\n-121.75,37.3,23.0,1801.0,415.0,548.0,393.0,2.5052,133700.0,<1H OCEAN\n-121.74,37.29,6.0,7292.0,1295.0,2468.0,1262.0,5.6411,294700.0,<1H OCEAN\n-121.75,37.29,15.0,1486.0,310.0,455.0,296.0,4.3365,221000.0,<1H OCEAN\n-121.84,37.34,33.0,1019.0,191.0,938.0,215.0,4.0929,165000.0,<1H OCEAN\n-121.84,37.33,28.0,1535.0,330.0,1937.0,317.0,4.1146,160100.0,<1H OCEAN\n-121.84,37.33,26.0,1934.0,408.0,2059.0,416.0,3.6765,163600.0,<1H OCEAN\n-121.83,37.32,21.0,4559.0,1163.0,5124.0,1124.0,3.2052,179000.0,<1H OCEAN\n-121.82,37.35,24.0,2298.0,575.0,2409.0,569.0,3.4509,182400.0,<1H OCEAN\n-121.82,37.34,23.0,7609.0,1446.0,6034.0,1414.0,4.8424,195300.0,<1H OCEAN\n-121.81,37.35,28.0,3477.0,671.0,2990.0,648.0,4.4671,172600.0,<1H OCEAN\n-121.81,37.35,29.0,2396.0,452.0,2000.0,481.0,4.375,185500.0,<1H OCEAN\n-121.8,37.35,27.0,2358.0,415.0,1562.0,383.0,5.2297,192800.0,<1H OCEAN\n-121.8,37.35,17.0,2529.0,423.0,1756.0,429.0,5.1017,240700.0,<1H OCEAN\n-121.84,37.34,27.0,2512.0,526.0,3033.0,526.0,4.25,162900.0,<1H OCEAN\n-121.83,37.33,27.0,3127.0,610.0,3257.0,604.0,4.6333,173600.0,<1H OCEAN\n-121.83,37.34,21.0,6404.0,1232.0,6047.0,1235.0,4.2098,193400.0,<1H OCEAN\n-121.83,37.34,26.0,1848.0,339.0,1952.0,327.0,4.087,182500.0,<1H OCEAN\n-121.87,37.36,34.0,938.0,242.0,769.0,226.0,3.5625,194500.0,<1H OCEAN\n-121.86,37.35,35.0,2391.0,605.0,1886.0,595.0,2.5551,182100.0,<1H OCEAN\n-121.86,37.35,43.0,1536.0,371.0,1256.0,357.0,2.8,153300.0,<1H OCEAN\n-121.85,37.34,27.0,1481.0,409.0,1505.0,391.0,2.5769,137500.0,<1H OCEAN\n-121.84,37.35,22.0,2914.0,768.0,2962.0,762.0,2.2031,164000.0,<1H OCEAN\n-121.85,37.35,29.0,3515.0,807.0,3572.0,776.0,2.7562,160100.0,<1H OCEAN\n-121.84,37.35,20.0,3375.0,867.0,4610.0,860.0,2.6894,182200.0,<1H OCEAN\n-121.86,37.37,15.0,8162.0,2124.0,8793.0,2086.0,3.3306,210300.0,<1H OCEAN\n-121.84,37.37,15.0,3315.0,1042.0,2749.0,1059.0,2.3199,140100.0,<1H OCEAN\n-121.85,37.36,15.0,3148.0,1116.0,3556.0,1037.0,3.0466,159600.0,<1H OCEAN\n-121.85,37.36,18.0,1525.0,485.0,1705.0,448.0,3.7198,128600.0,<1H OCEAN\n-121.85,37.36,11.0,2109.0,592.0,2744.0,607.0,4.0452,205900.0,<1H OCEAN\n-121.86,37.36,31.0,1602.0,358.0,1179.0,354.0,4.4896,156800.0,<1H OCEAN\n-121.84,37.38,34.0,762.0,182.0,611.0,193.0,3.5625,201800.0,<1H OCEAN\n-121.85,37.38,12.0,12980.0,2568.0,8190.0,2515.0,5.2415,286500.0,<1H OCEAN\n-121.84,37.39,31.0,5524.0,914.0,2848.0,879.0,5.5592,229900.0,<1H OCEAN\n-121.83,37.38,15.0,4430.0,992.0,3278.0,1018.0,4.5533,209900.0,<1H OCEAN\n-121.84,37.38,33.0,835.0,181.0,781.0,169.0,5.1082,195800.0,<1H OCEAN\n-121.83,37.38,31.0,3633.0,843.0,2677.0,797.0,3.2222,184800.0,<1H OCEAN\n-121.83,37.37,43.0,821.0,149.0,370.0,135.0,4.25,209100.0,<1H OCEAN\n-121.83,37.37,43.0,1461.0,284.0,800.0,258.0,3.2279,182400.0,<1H OCEAN\n-121.84,37.37,42.0,1237.0,232.0,900.0,241.0,3.8571,187500.0,<1H OCEAN\n-121.84,37.37,28.0,1579.0,339.0,1252.0,353.0,4.1615,214800.0,<1H OCEAN\n-121.82,37.37,40.0,802.0,149.0,445.0,143.0,4.0446,196300.0,<1H OCEAN\n-121.83,37.36,29.0,4045.0,885.0,3036.0,845.0,3.1982,171700.0,<1H OCEAN\n-121.83,37.36,22.0,3936.0,860.0,3508.0,877.0,4.2312,183800.0,<1H OCEAN\n-121.83,37.35,31.0,2914.0,715.0,3547.0,645.0,3.7143,178600.0,<1H OCEAN\n-121.82,37.37,41.0,1558.0,281.0,970.0,304.0,4.4167,215200.0,<1H OCEAN\n-121.81,37.37,26.0,2987.0,539.0,1931.0,518.0,5.1099,213100.0,<1H OCEAN\n-121.82,37.36,33.0,1624.0,337.0,1412.0,323.0,4.0385,167600.0,<1H OCEAN\n-121.82,37.36,34.0,1834.0,377.0,1450.0,347.0,3.7188,161500.0,<1H OCEAN\n-121.82,37.37,42.0,2913.0,577.0,1873.0,580.0,3.7214,167900.0,<1H OCEAN\n-121.81,37.39,34.0,2218.0,286.0,827.0,299.0,7.4559,456500.0,<1H OCEAN\n-121.82,37.39,37.0,4137.0,636.0,1569.0,578.0,6.1008,286200.0,<1H OCEAN\n-121.82,37.38,32.0,3747.0,665.0,1687.0,649.0,5.4949,330800.0,<1H OCEAN\n-121.81,37.38,29.0,570.0,76.0,244.0,72.0,12.3292,416700.0,<1H OCEAN\n-121.82,37.38,32.0,1650.0,246.0,768.0,263.0,6.8462,320900.0,<1H OCEAN\n-121.81,37.37,24.0,962.0,146.0,492.0,155.0,7.2861,328000.0,<1H OCEAN\n-121.79,37.38,22.0,3650.0,527.0,1637.0,520.0,5.3774,325600.0,<1H OCEAN\n-121.81,37.36,20.0,3189.0,420.0,1234.0,389.0,7.5813,374100.0,<1H OCEAN\n-121.87,37.39,9.0,2522.0,547.0,1591.0,481.0,4.9091,259700.0,<1H OCEAN\n-121.87,37.39,16.0,2655.0,487.0,1862.0,448.0,6.057,246800.0,<1H OCEAN\n-121.87,37.38,16.0,1050.0,245.0,722.0,228.0,4.5187,163500.0,<1H OCEAN\n-121.87,37.38,16.0,3275.0,529.0,1863.0,527.0,5.5429,269100.0,<1H OCEAN\n-121.88,37.39,13.0,3334.0,565.0,2240.0,561.0,7.105,273900.0,<1H OCEAN\n-121.87,37.41,17.0,3719.0,588.0,2089.0,561.0,6.7867,273700.0,<1H OCEAN\n-121.87,37.4,16.0,1767.0,268.0,1061.0,280.0,6.9584,351600.0,<1H OCEAN\n-121.86,37.4,19.0,4043.0,764.0,2196.0,708.0,6.1504,268400.0,<1H OCEAN\n-121.87,37.39,16.0,1334.0,389.0,1103.0,415.0,3.7153,229800.0,<1H OCEAN\n-121.86,37.39,17.0,1777.0,328.0,1235.0,329.0,5.4225,258100.0,<1H OCEAN\n-121.85,37.39,15.0,8748.0,1547.0,4784.0,1524.0,5.8322,276600.0,<1H OCEAN\n-121.82,37.42,13.0,3752.0,572.0,1581.0,526.0,6.1091,329400.0,<1H OCEAN\n-121.81,37.41,25.0,2496.0,351.0,1034.0,367.0,7.0544,320700.0,<1H OCEAN\n-121.84,37.4,25.0,1866.0,271.0,840.0,275.0,6.8677,288500.0,<1H OCEAN\n-121.83,37.4,27.0,1145.0,150.0,492.0,160.0,5.716,348300.0,<1H OCEAN\n-121.9,37.4,16.0,2998.0,603.0,1606.0,615.0,3.7622,150000.0,<1H OCEAN\n-121.9,37.39,42.0,42.0,14.0,26.0,14.0,1.7361,500001.0,<1H OCEAN\n-121.9,37.37,20.0,78.0,72.0,120.0,69.0,1.0938,187500.0,<1H OCEAN\n-121.88,37.37,14.0,6016.0,1404.0,3258.0,1316.0,3.5745,333700.0,<1H OCEAN\n-121.87,37.38,14.0,3851.0,534.0,2052.0,478.0,7.0735,335600.0,<1H OCEAN\n-121.86,37.38,15.0,2052.0,405.0,1380.0,409.0,5.8686,181100.0,<1H OCEAN\n-121.89,37.39,2.0,1136.0,365.0,535.0,257.0,4.375,425000.0,<1H OCEAN\n-121.88,37.4,9.0,6751.0,,4240.0,1438.0,5.34,257400.0,<1H OCEAN\n-121.89,37.38,3.0,4778.0,1047.0,2522.0,990.0,5.7695,271400.0,<1H OCEAN\n-121.88,37.37,3.0,4430.0,841.0,2559.0,801.0,6.0959,272700.0,<1H OCEAN\n-121.86,37.4,16.0,2391.0,369.0,1419.0,373.0,5.8721,267800.0,<1H OCEAN\n-121.86,37.4,21.0,1386.0,260.0,946.0,257.0,6.5226,258500.0,<1H OCEAN\n-121.85,37.41,25.0,1837.0,278.0,1006.0,271.0,6.6842,265300.0,<1H OCEAN\n-121.85,37.4,23.0,1793.0,319.0,1145.0,310.0,5.5968,243200.0,<1H OCEAN\n-121.86,37.41,16.0,2938.0,589.0,1718.0,568.0,5.5073,178900.0,<1H OCEAN\n-121.85,37.41,17.0,2156.0,435.0,1400.0,393.0,5.6096,199100.0,<1H OCEAN\n-121.86,37.41,16.0,1603.0,287.0,1080.0,296.0,6.1256,266900.0,<1H OCEAN\n-121.86,37.41,16.0,1489.0,262.0,945.0,263.0,7.3861,267000.0,<1H OCEAN\n-121.86,37.41,16.0,2411.0,420.0,1671.0,442.0,6.5004,263600.0,<1H OCEAN\n-121.9,37.44,12.0,4228.0,734.0,2594.0,732.0,6.6086,299400.0,<1H OCEAN\n-121.9,37.44,4.0,1646.0,408.0,853.0,410.0,5.0821,265500.0,<1H OCEAN\n-121.9,37.44,9.0,957.0,139.0,532.0,142.0,8.6675,441000.0,<1H OCEAN\n-121.89,37.44,8.0,2534.0,,1527.0,364.0,7.8532,422800.0,<1H OCEAN\n-121.88,37.43,17.0,3469.0,896.0,2762.0,808.0,3.3884,245800.0,<1H OCEAN\n-121.88,37.43,31.0,2573.0,474.0,1898.0,475.0,5.6651,204100.0,<1H OCEAN\n-121.87,37.43,22.0,3805.0,596.0,2118.0,621.0,6.2892,254200.0,<1H OCEAN\n-121.85,37.44,8.0,426.0,61.0,241.0,55.0,7.309,367900.0,<1H OCEAN\n-121.87,37.42,19.0,12128.0,2112.0,6810.0,2040.0,6.4419,264500.0,<1H OCEAN\n-121.87,37.41,24.0,3308.0,548.0,1891.0,544.0,5.6683,248700.0,<1H OCEAN\n-121.88,37.41,23.0,3224.0,652.0,2183.0,655.0,4.3807,226900.0,<1H OCEAN\n-121.86,37.42,20.0,5032.0,808.0,2695.0,801.0,6.6227,264800.0,<1H OCEAN\n-121.87,37.42,25.0,4430.0,729.0,2685.0,721.0,5.6965,261100.0,<1H OCEAN\n-121.88,37.44,17.0,1621.0,299.0,1028.0,293.0,5.2722,186900.0,<1H OCEAN\n-121.88,37.44,20.0,1351.0,372.0,1427.0,394.0,4.4712,144000.0,<1H OCEAN\n-121.88,37.44,26.0,1471.0,348.0,1089.0,320.0,5.3057,226400.0,<1H OCEAN\n-121.88,37.44,23.0,1310.0,267.0,910.0,261.0,5.3994,237900.0,<1H OCEAN\n-121.89,37.46,5.0,1519.0,186.0,705.0,186.0,10.3798,500001.0,<1H OCEAN\n-121.88,37.46,5.0,1819.0,245.0,802.0,228.0,10.9722,500001.0,<1H OCEAN\n-121.89,37.45,15.0,2428.0,513.0,1687.0,519.0,4.75,254400.0,<1H OCEAN\n-121.88,37.44,14.0,2073.0,343.0,1107.0,330.0,6.7093,311200.0,<1H OCEAN\n-121.87,37.46,43.0,91.0,12.0,58.0,16.0,15.0001,500001.0,<1H OCEAN\n-121.9,37.46,29.0,2385.0,513.0,1788.0,510.0,3.8421,220700.0,<1H OCEAN\n-121.91,37.46,26.0,2762.0,496.0,1716.0,459.0,5.6062,226800.0,<1H OCEAN\n-121.9,37.45,18.0,4900.0,814.0,2984.0,758.0,6.6176,276200.0,<1H OCEAN\n-121.9,37.45,16.0,2952.0,446.0,1525.0,460.0,5.6063,320500.0,<1H OCEAN\n-121.91,37.42,19.0,1684.0,387.0,1224.0,376.0,4.1389,174100.0,<1H OCEAN\n-121.9,37.41,24.0,4759.0,921.0,3188.0,902.0,5.6344,228700.0,<1H OCEAN\n-121.89,37.42,26.0,40.0,8.0,52.0,7.0,7.7197,225000.0,<1H OCEAN\n-121.92,37.45,10.0,3937.0,1054.0,2032.0,1002.0,3.2617,252200.0,<1H OCEAN\n-121.91,37.44,26.0,1669.0,276.0,951.0,278.0,4.7794,225800.0,<1H OCEAN\n-121.91,37.44,19.0,2174.0,484.0,1645.0,484.0,5.0362,255100.0,<1H OCEAN\n-121.91,37.44,24.0,1212.0,251.0,799.0,242.0,5.0808,212500.0,<1H OCEAN\n-121.91,37.43,33.0,2791.0,496.0,1714.0,485.0,4.8304,224900.0,<1H OCEAN\n-122.07,37.44,21.0,4599.0,986.0,2756.0,943.0,2.9817,225000.0,NEAR BAY\n-121.96,37.43,18.0,2514.0,578.0,2205.0,545.0,3.3859,158000.0,<1H OCEAN\n-121.97,37.44,17.0,127.0,28.0,219.0,22.0,4.5179,112500.0,<1H OCEAN\n-121.99,37.4,24.0,3217.0,689.0,1196.0,684.0,3.4896,226700.0,<1H OCEAN\n-121.99,37.4,35.0,1845.0,325.0,1343.0,317.0,5.3912,235300.0,<1H OCEAN\n-121.99,37.39,25.0,3495.0,834.0,2484.0,797.0,4.8145,230700.0,<1H OCEAN\n-122.02,37.4,33.0,2015.0,484.0,1285.0,419.0,4.0655,226800.0,<1H OCEAN\n-122.01,37.4,14.0,4841.0,1130.0,813.0,517.0,3.7614,137500.0,<1H OCEAN\n-122.0,37.4,17.0,5121.0,1017.0,1470.0,968.0,2.9706,81300.0,<1H OCEAN\n-122.0,37.4,17.0,4324.0,854.0,1656.0,885.0,3.6619,232400.0,<1H OCEAN\n-122.0,37.4,35.0,1542.0,298.0,1164.0,318.0,5.9145,236900.0,<1H OCEAN\n-122.01,37.4,24.0,1297.0,297.0,441.0,282.0,3.1439,47500.0,<1H OCEAN\n-122.0,37.39,33.0,2154.0,405.0,1655.0,434.0,5.7962,229800.0,<1H OCEAN\n-121.96,37.41,17.0,3208.0,617.0,2286.0,602.0,5.2937,238000.0,<1H OCEAN\n-121.97,37.4,17.0,2937.0,558.0,1662.0,533.0,5.8792,255500.0,<1H OCEAN\n-121.96,37.39,20.0,1032.0,229.0,658.0,238.0,4.5062,219300.0,<1H OCEAN\n-121.95,37.39,24.0,5230.0,934.0,3795.0,970.0,5.4228,264100.0,<1H OCEAN\n-121.95,37.38,22.0,765.0,198.0,390.0,176.0,3.1812,87500.0,<1H OCEAN\n-121.95,37.41,13.0,2164.0,412.0,1087.0,411.0,4.7625,137500.0,<1H OCEAN\n-121.94,37.42,16.0,3936.0,788.0,1910.0,769.0,4.7049,112500.0,<1H OCEAN\n-121.93,37.4,34.0,148.0,28.0,132.0,13.0,3.375,67500.0,<1H OCEAN\n-121.91,37.36,42.0,3224.0,708.0,1940.0,674.0,3.2153,199700.0,<1H OCEAN\n-121.92,37.36,42.0,198.0,32.0,158.0,32.0,3.1563,137500.0,<1H OCEAN\n-121.95,37.37,39.0,446.0,129.0,317.0,127.0,3.0357,208300.0,<1H OCEAN\n-121.95,37.36,27.0,3236.0,832.0,2230.0,798.0,3.5625,208600.0,<1H OCEAN\n-121.95,37.36,25.0,3472.0,956.0,2267.0,944.0,2.7727,235600.0,<1H OCEAN\n-121.93,37.35,36.0,1823.0,410.0,1589.0,387.0,3.1065,234100.0,<1H OCEAN\n-121.93,37.34,48.0,2068.0,495.0,946.0,452.0,3.0375,218500.0,<1H OCEAN\n-121.98,37.37,36.0,1651.0,344.0,1062.0,331.0,4.575,215400.0,<1H OCEAN\n-121.98,37.36,32.0,1199.0,229.0,814.0,238.0,4.6719,252100.0,<1H OCEAN\n-121.98,37.37,35.0,995.0,202.0,615.0,199.0,5.0942,217500.0,<1H OCEAN\n-121.99,37.37,27.0,1797.0,538.0,1610.0,531.0,4.2422,237500.0,<1H OCEAN\n-121.97,37.36,24.0,4841.0,894.0,2656.0,920.0,6.0573,254500.0,<1H OCEAN\n-121.97,37.36,34.0,884.0,153.0,534.0,154.0,6.0116,271200.0,<1H OCEAN\n-121.97,37.35,35.0,1880.0,370.0,926.0,321.0,4.2273,269900.0,<1H OCEAN\n-121.96,37.36,16.0,5040.0,1325.0,3150.0,1196.0,4.2837,264500.0,<1H OCEAN\n-121.96,37.36,33.0,2581.0,623.0,1598.0,628.0,3.5199,261400.0,<1H OCEAN\n-121.97,37.35,36.0,815.0,126.0,353.0,122.0,6.3191,258300.0,<1H OCEAN\n-121.97,37.36,36.0,765.0,134.0,306.0,136.0,4.575,247600.0,<1H OCEAN\n-121.98,37.36,34.0,1735.0,318.0,1019.0,301.0,4.5625,242700.0,<1H OCEAN\n-121.98,37.36,33.0,1582.0,272.0,809.0,267.0,5.7059,287200.0,<1H OCEAN\n-121.98,37.36,35.0,1293.0,223.0,701.0,216.0,7.8543,281900.0,<1H OCEAN\n-121.98,37.36,35.0,1440.0,267.0,743.0,259.0,5.0866,254600.0,<1H OCEAN\n-121.99,37.36,32.0,1754.0,324.0,917.0,330.0,4.6761,298300.0,<1H OCEAN\n-121.99,37.36,33.0,2321.0,480.0,1230.0,451.0,4.9091,270300.0,<1H OCEAN\n-121.99,37.36,33.0,2677.0,644.0,1469.0,633.0,3.2048,261800.0,<1H OCEAN\n-121.99,37.36,33.0,2545.0,467.0,1287.0,458.0,5.5,282200.0,<1H OCEAN\n-121.98,37.35,41.0,1150.0,249.0,729.0,260.0,3.5491,261100.0,<1H OCEAN\n-121.99,37.35,18.0,1712.0,509.0,972.0,467.0,4.3971,238900.0,<1H OCEAN\n-121.99,37.35,16.0,3249.0,947.0,1637.0,841.0,4.5427,198400.0,<1H OCEAN\n-121.99,37.35,25.0,1527.0,325.0,707.0,339.0,4.375,212200.0,<1H OCEAN\n-121.98,37.35,36.0,1054.0,193.0,546.0,187.0,4.5625,240000.0,<1H OCEAN\n-121.97,37.35,30.0,1955.0,,999.0,386.0,4.6328,287100.0,<1H OCEAN\n-121.98,37.34,33.0,3570.0,776.0,1922.0,761.0,4.9562,238700.0,<1H OCEAN\n-121.98,37.34,18.0,6649.0,1712.0,3604.0,1651.0,4.5368,307400.0,<1H OCEAN\n-121.99,37.34,26.0,3637.0,933.0,2249.0,905.0,3.9625,262900.0,<1H OCEAN\n-121.96,37.35,37.0,1755.0,325.0,699.0,321.0,3.925,251300.0,<1H OCEAN\n-121.97,37.34,33.0,3162.0,,1553.0,686.0,3.6682,266100.0,<1H OCEAN\n-121.97,37.35,35.0,1249.0,232.0,556.0,247.0,3.925,287100.0,<1H OCEAN\n-121.96,37.35,32.0,1484.0,274.0,673.0,272.0,5.2019,279900.0,<1H OCEAN\n-121.95,37.35,36.0,832.0,211.0,545.0,211.0,3.2813,244400.0,<1H OCEAN\n-121.94,37.35,52.0,906.0,227.0,1662.0,219.0,3.1667,231600.0,<1H OCEAN\n-121.94,37.35,18.0,1922.0,561.0,1096.0,545.0,2.3713,244000.0,<1H OCEAN\n-121.95,37.35,48.0,1246.0,294.0,697.0,284.0,3.6118,235500.0,<1H OCEAN\n-121.95,37.35,42.0,1421.0,330.0,659.0,303.0,3.3333,237900.0,<1H OCEAN\n-121.95,37.35,52.0,2382.0,523.0,1096.0,492.0,4.2656,236100.0,<1H OCEAN\n-121.94,37.34,42.0,2174.0,420.0,1304.0,464.0,3.1429,286500.0,<1H OCEAN\n-121.94,37.34,29.0,3377.0,853.0,1674.0,792.0,3.4233,229300.0,<1H OCEAN\n-121.94,37.34,41.0,2151.0,473.0,1092.0,469.0,3.7321,250000.0,<1H OCEAN\n-121.94,37.33,36.0,1893.0,359.0,797.0,360.0,3.6818,257600.0,<1H OCEAN\n-121.94,37.33,37.0,1822.0,329.0,845.0,348.0,4.75,251100.0,<1H OCEAN\n-121.94,37.34,36.0,3142.0,632.0,1372.0,560.0,5.0175,246100.0,<1H OCEAN\n-121.95,37.34,25.0,5236.0,1320.0,2529.0,1213.0,3.1702,256100.0,<1H OCEAN\n-121.95,37.33,31.0,1866.0,465.0,821.0,447.0,2.3547,275900.0,<1H OCEAN\n-121.95,37.33,36.0,1683.0,286.0,740.0,324.0,4.7604,294700.0,<1H OCEAN\n-121.96,37.33,26.0,3269.0,788.0,1427.0,696.0,4.2136,288300.0,<1H OCEAN\n-121.96,37.33,35.0,2294.0,411.0,1054.0,449.0,4.0667,276900.0,<1H OCEAN\n-121.96,37.34,42.0,2001.0,402.0,942.0,375.0,4.4453,255400.0,<1H OCEAN\n-121.96,37.34,36.0,844.0,153.0,373.0,160.0,5.791,254100.0,<1H OCEAN\n-121.96,37.34,18.0,4438.0,939.0,1901.0,895.0,5.3873,288300.0,<1H OCEAN\n-121.96,37.34,34.0,1461.0,299.0,739.0,276.0,3.4375,252600.0,<1H OCEAN\n-121.96,37.34,37.0,663.0,127.0,293.0,132.0,3.7813,247800.0,<1H OCEAN\n-121.98,37.33,30.0,2645.0,462.0,1506.0,480.0,6.3716,330500.0,<1H OCEAN\n-121.98,37.33,30.0,3742.0,633.0,1721.0,631.0,6.1388,302400.0,<1H OCEAN\n-121.99,37.34,27.0,3353.0,653.0,1571.0,621.0,5.273,315600.0,<1H OCEAN\n-121.97,37.33,21.0,8275.0,1566.0,3636.0,1524.0,5.1506,302100.0,<1H OCEAN\n-121.98,37.33,25.0,3223.0,612.0,1529.0,602.0,5.121,287600.0,<1H OCEAN\n-121.98,37.33,35.0,1907.0,326.0,912.0,313.0,5.9567,294300.0,<1H OCEAN\n-121.99,37.33,35.0,1802.0,291.0,841.0,315.0,4.8365,313900.0,<1H OCEAN\n-121.99,37.33,33.0,2023.0,425.0,1016.0,405.0,3.9417,285800.0,<1H OCEAN\n-121.98,37.31,28.0,3840.0,629.0,1883.0,662.0,6.4095,335900.0,<1H OCEAN\n-121.99,37.3,28.0,4863.0,901.0,2110.0,868.0,5.1483,342000.0,<1H OCEAN\n-122.0,37.3,28.0,5096.0,1011.0,2588.0,954.0,5.357,355200.0,<1H OCEAN\n-121.98,37.32,17.0,9789.0,2552.0,4748.0,2206.0,4.2531,279800.0,<1H OCEAN\n-121.99,37.32,20.0,4461.0,864.0,2042.0,808.0,4.7083,217700.0,<1H OCEAN\n-121.98,37.31,32.0,2248.0,460.0,1191.0,419.0,5.606,288900.0,<1H OCEAN\n-121.98,37.31,34.0,2034.0,359.0,1016.0,375.0,5.8127,288300.0,<1H OCEAN\n-121.99,37.31,26.0,3285.0,502.0,1443.0,530.0,5.7833,339600.0,<1H OCEAN\n-121.97,37.32,19.0,4620.0,1404.0,2672.0,1308.0,3.699,237500.0,<1H OCEAN\n-121.96,37.32,11.0,1711.0,493.0,1094.0,543.0,3.73,227700.0,<1H OCEAN\n-121.95,37.32,39.0,1164.0,199.0,619.0,231.0,4.6304,263200.0,<1H OCEAN\n-121.95,37.32,20.0,1145.0,198.0,431.0,173.0,3.1103,281900.0,<1H OCEAN\n-121.95,37.31,27.0,4140.0,,2135.0,893.0,3.6292,264600.0,<1H OCEAN\n-121.96,37.31,26.0,4310.0,678.0,1819.0,686.0,7.0469,365500.0,<1H OCEAN\n-121.96,37.31,31.0,3890.0,711.0,1898.0,717.0,5.2534,290900.0,<1H OCEAN\n-121.97,37.31,21.0,7628.0,2166.0,3637.0,1749.0,3.6401,267500.0,<1H OCEAN\n-121.97,37.31,25.0,5775.0,1225.0,3580.0,1138.0,3.9187,314900.0,<1H OCEAN\n-121.97,37.3,25.0,5463.0,1351.0,2758.0,1310.0,3.0079,277300.0,<1H OCEAN\n-121.95,37.32,33.0,726.0,168.0,351.0,147.0,3.1458,270500.0,<1H OCEAN\n-121.94,37.31,30.0,4238.0,1010.0,1914.0,972.0,3.7632,307000.0,<1H OCEAN\n-121.95,37.31,27.0,2462.0,570.0,1278.0,565.0,3.5652,329500.0,<1H OCEAN\n-121.94,37.3,30.0,1758.0,248.0,814.0,256.0,6.623,332500.0,<1H OCEAN\n-121.95,37.3,21.0,4193.0,1068.0,2487.0,1011.0,3.7188,293000.0,<1H OCEAN\n-121.94,37.3,25.0,1455.0,370.0,734.0,331.0,3.2727,262500.0,<1H OCEAN\n-121.95,37.3,25.0,5641.0,1517.0,3786.0,1410.0,3.3958,267500.0,<1H OCEAN\n-121.96,37.3,20.0,4228.0,1006.0,2334.0,1007.0,4.3081,227300.0,<1H OCEAN\n-121.94,37.29,22.0,2593.0,637.0,1249.0,623.0,3.75,212500.0,<1H OCEAN\n-121.94,37.29,20.0,710.0,188.0,363.0,176.0,4.0962,214100.0,<1H OCEAN\n-121.95,37.27,17.0,1330.0,271.0,408.0,258.0,1.7171,181300.0,<1H OCEAN\n-121.95,37.28,52.0,777.0,148.0,362.0,144.0,4.0208,262500.0,<1H OCEAN\n-121.95,37.29,9.0,1503.0,381.0,715.0,349.0,4.6371,234300.0,<1H OCEAN\n-121.95,37.29,30.0,3734.0,813.0,1834.0,824.0,3.4505,260000.0,<1H OCEAN\n-121.95,37.28,19.0,7027.0,1847.0,3759.0,1753.0,3.1509,242900.0,<1H OCEAN\n-121.98,37.3,30.0,3404.0,693.0,1794.0,633.0,4.6312,283200.0,<1H OCEAN\n-121.98,37.29,33.0,2120.0,349.0,907.0,336.0,7.5443,283000.0,<1H OCEAN\n-121.98,37.29,31.0,2750.0,664.0,1459.0,660.0,3.2287,264900.0,<1H OCEAN\n-121.96,37.28,33.0,1940.0,327.0,877.0,314.0,5.4386,280400.0,<1H OCEAN\n-121.97,37.28,25.0,4707.0,695.0,1995.0,642.0,6.6437,296100.0,<1H OCEAN\n-121.97,37.28,27.0,2427.0,403.0,1301.0,438.0,5.0385,277300.0,<1H OCEAN\n-121.98,37.28,26.0,1182.0,309.0,620.0,306.0,3.3922,269100.0,<1H OCEAN\n-121.98,37.28,28.0,3688.0,633.0,1877.0,620.0,5.7251,272600.0,<1H OCEAN\n-121.98,37.28,27.0,3526.0,589.0,1725.0,553.0,5.7812,275000.0,<1H OCEAN\n-121.99,37.27,27.0,2937.0,497.0,1454.0,511.0,5.4051,273500.0,<1H OCEAN\n-121.99,37.29,32.0,2930.0,481.0,1336.0,481.0,6.4631,344100.0,<1H OCEAN\n-121.96,37.3,23.0,4040.0,843.0,2181.0,843.0,4.0403,303400.0,<1H OCEAN\n-121.97,37.3,31.0,3340.0,735.0,1891.0,686.0,4.8542,275000.0,<1H OCEAN\n-121.96,37.29,24.0,1240.0,263.0,690.0,276.0,5.0,283000.0,<1H OCEAN\n-121.97,37.29,25.0,4096.0,743.0,2027.0,741.0,5.3294,300300.0,<1H OCEAN\n-121.97,37.29,29.0,2721.0,682.0,1602.0,646.0,3.337,265300.0,<1H OCEAN\n-121.99,37.27,17.0,1527.0,267.0,775.0,260.0,5.9658,278000.0,<1H OCEAN\n-121.98,37.27,25.0,3075.0,564.0,1633.0,543.0,5.2528,269400.0,<1H OCEAN\n-121.98,37.27,29.0,2658.0,484.0,1318.0,498.0,5.3561,298900.0,<1H OCEAN\n-121.96,37.28,28.0,5018.0,1066.0,2846.0,998.0,4.0174,273900.0,<1H OCEAN\n-121.96,37.27,22.0,6114.0,1211.0,2983.0,1163.0,5.2533,269100.0,<1H OCEAN\n-121.96,37.27,31.0,3347.0,589.0,1566.0,597.0,5.5151,286800.0,<1H OCEAN\n-121.97,37.26,19.0,2174.0,454.0,998.0,426.0,4.6827,255100.0,<1H OCEAN\n-121.96,37.26,22.0,1408.0,351.0,636.0,294.0,1.8542,333300.0,<1H OCEAN\n-121.95,37.25,30.0,3298.0,634.0,1532.0,602.0,5.0863,332000.0,<1H OCEAN\n-121.95,37.24,37.0,3109.0,541.0,1566.0,544.0,6.0235,413500.0,<1H OCEAN\n-121.95,37.24,32.0,1382.0,239.0,705.0,251.0,6.0957,405400.0,<1H OCEAN\n-121.96,37.24,26.0,3032.0,605.0,1208.0,562.0,5.4683,430900.0,<1H OCEAN\n-121.96,37.25,35.0,1018.0,169.0,484.0,174.0,6.1648,371900.0,<1H OCEAN\n-121.95,37.25,34.0,2906.0,544.0,1282.0,522.0,5.5127,268200.0,<1H OCEAN\n-121.93,37.25,36.0,1089.0,182.0,535.0,170.0,4.69,252600.0,<1H OCEAN\n-121.93,37.25,21.0,1354.0,289.0,639.0,273.0,4.5333,234200.0,<1H OCEAN\n-121.92,37.25,34.0,2231.0,360.0,1035.0,365.0,4.7917,243200.0,<1H OCEAN\n-121.93,37.25,32.0,1555.0,287.0,779.0,284.0,6.0358,260100.0,<1H OCEAN\n-121.94,37.24,35.0,1484.0,244.0,664.0,238.0,4.675,245300.0,<1H OCEAN\n-121.91,37.24,24.0,5046.0,1001.0,2449.0,968.0,4.7118,274600.0,<1H OCEAN\n-121.91,37.24,30.0,2327.0,419.0,1114.0,372.0,4.7279,272000.0,<1H OCEAN\n-121.92,37.24,26.0,6777.0,1051.0,3319.0,1061.0,6.3663,279400.0,<1H OCEAN\n-121.94,37.24,19.0,1741.0,294.0,632.0,279.0,5.5944,290500.0,<1H OCEAN\n-121.94,37.24,26.0,2561.0,388.0,1165.0,393.0,7.3522,363800.0,<1H OCEAN\n-121.93,37.24,26.0,2574.0,414.0,1096.0,428.0,6.0738,335900.0,<1H OCEAN\n-121.92,37.24,27.0,1265.0,216.0,660.0,232.0,5.3911,281200.0,<1H OCEAN\n-121.91,37.23,22.0,2614.0,453.0,1240.0,462.0,6.0712,271800.0,<1H OCEAN\n-121.91,37.23,27.0,4866.0,668.0,1956.0,659.0,7.3843,405000.0,<1H OCEAN\n-121.93,37.22,21.0,4872.0,594.0,1811.0,560.0,9.3834,500001.0,<1H OCEAN\n-121.96,37.23,36.0,4423.0,632.0,1719.0,608.0,7.8407,476400.0,<1H OCEAN\n-121.97,37.23,22.0,2781.0,523.0,1291.0,516.0,4.6065,445900.0,<1H OCEAN\n-121.98,37.22,46.0,10088.0,1910.0,3728.0,1781.0,5.2321,500001.0,<1H OCEAN\n-121.96,37.22,35.0,4709.0,723.0,1866.0,694.0,8.492,500001.0,<1H OCEAN\n-121.95,37.21,20.0,2345.0,322.0,890.0,276.0,10.0187,500001.0,<1H OCEAN\n-121.97,37.24,35.0,2553.0,533.0,1117.0,498.0,4.4063,436100.0,<1H OCEAN\n-121.98,37.23,33.0,3585.0,935.0,1511.0,835.0,3.1176,396300.0,<1H OCEAN\n-122.0,37.23,36.0,3191.0,430.0,1234.0,440.0,9.0704,500001.0,<1H OCEAN\n-121.96,37.25,19.0,1858.0,359.0,790.0,347.0,4.5156,339300.0,<1H OCEAN\n-121.97,37.25,32.0,2892.0,496.0,1193.0,492.0,6.131,367800.0,<1H OCEAN\n-121.98,37.26,27.0,2331.0,461.0,1178.0,447.0,4.6654,340700.0,<1H OCEAN\n-121.99,37.27,21.0,1214.0,192.0,500.0,185.0,7.598,347800.0,<1H OCEAN\n-121.99,37.26,17.0,4034.0,611.0,1158.0,560.0,8.2069,442500.0,<1H OCEAN\n-121.98,37.25,19.0,755.0,93.0,267.0,99.0,15.0,500001.0,<1H OCEAN\n-121.99,37.25,25.0,1743.0,212.0,604.0,200.0,10.7582,500001.0,<1H OCEAN\n-121.97,37.25,21.0,2775.0,389.0,856.0,350.0,7.9135,496400.0,<1H OCEAN\n-121.99,37.25,22.0,4240.0,532.0,1480.0,514.0,11.2463,500001.0,<1H OCEAN\n-121.98,37.24,35.0,3574.0,485.0,1325.0,476.0,8.5425,500001.0,<1H OCEAN\n-122.0,37.27,33.0,1664.0,271.0,759.0,272.0,5.7876,415800.0,<1H OCEAN\n-122.01,37.27,27.0,3340.0,451.0,1220.0,447.0,8.8178,500001.0,<1H OCEAN\n-122.02,37.26,24.0,2411.0,299.0,847.0,299.0,10.2666,500001.0,<1H OCEAN\n-122.02,37.26,34.0,1764.0,243.0,692.0,223.0,8.0331,500001.0,<1H OCEAN\n-122.01,37.25,31.0,1574.0,193.0,551.0,191.0,10.2311,500001.0,<1H OCEAN\n-121.99,37.26,29.0,2718.0,365.0,982.0,339.0,7.9234,500001.0,<1H OCEAN\n-122.01,37.26,14.0,2561.0,404.0,1172.0,378.0,7.6107,500001.0,<1H OCEAN\n-122.03,37.25,34.0,2892.0,413.0,903.0,365.0,7.8711,500001.0,<1H OCEAN\n-122.04,37.24,24.0,1521.0,209.0,539.0,192.0,11.1557,500001.0,<1H OCEAN\n-122.02,37.24,28.0,2796.0,365.0,1085.0,363.0,10.6834,500001.0,<1H OCEAN\n-122.03,37.29,22.0,3118.0,438.0,1147.0,425.0,10.3653,500001.0,<1H OCEAN\n-122.02,37.29,18.0,2550.0,312.0,999.0,320.0,8.7939,500001.0,<1H OCEAN\n-122.02,37.29,25.0,3845.0,492.0,1461.0,475.0,10.3979,500001.0,<1H OCEAN\n-122.01,37.29,31.0,3136.0,431.0,1190.0,412.0,7.5,500001.0,<1H OCEAN\n-122.01,37.28,22.0,2038.0,260.0,773.0,281.0,9.1569,500001.0,<1H OCEAN\n-122.0,37.28,35.0,3133.0,541.0,1449.0,555.0,5.7295,346100.0,<1H OCEAN\n-122.0,37.28,33.0,2170.0,311.0,854.0,303.0,8.3605,500001.0,<1H OCEAN\n-122.0,37.28,32.0,2782.0,495.0,1092.0,455.0,5.4103,335900.0,<1H OCEAN\n-122.03,37.27,32.0,4350.0,645.0,1551.0,609.0,7.8279,500001.0,<1H OCEAN\n-122.03,37.28,29.0,3752.0,468.0,1320.0,471.0,9.8937,500001.0,<1H OCEAN\n-122.02,37.28,25.0,3437.0,428.0,1198.0,411.0,9.3464,500001.0,<1H OCEAN\n-122.01,37.27,28.0,3825.0,473.0,1415.0,480.0,10.675,500001.0,<1H OCEAN\n-122.06,37.27,16.0,1612.0,221.0,567.0,208.0,10.5793,500001.0,<1H OCEAN\n-122.04,37.29,19.0,3625.0,432.0,1252.0,409.0,12.2145,500001.0,<1H OCEAN\n-122.03,37.27,25.0,4460.0,553.0,1608.0,561.0,10.7958,500001.0,<1H OCEAN\n-122.04,37.26,24.0,4973.0,709.0,1692.0,696.0,7.8627,500001.0,<1H OCEAN\n-122.06,37.33,29.0,1945.0,269.0,826.0,275.0,8.248,498800.0,<1H OCEAN\n-122.06,37.32,30.0,3033.0,540.0,1440.0,507.0,6.2182,380800.0,<1H OCEAN\n-122.05,37.31,25.0,4601.0,696.0,2003.0,666.0,8.0727,455500.0,<1H OCEAN\n-122.05,37.31,25.0,4111.0,538.0,1585.0,568.0,9.2298,500001.0,<1H OCEAN\n-122.06,37.3,11.0,5488.0,706.0,1947.0,641.0,10.7326,500001.0,<1H OCEAN\n-122.07,37.33,13.0,2173.0,349.0,891.0,345.0,8.0158,420000.0,<1H OCEAN\n-122.06,37.33,23.0,4507.0,751.0,2167.0,722.0,7.0102,500001.0,<1H OCEAN\n-122.07,37.31,24.0,4401.0,698.0,1818.0,685.0,7.2986,500001.0,<1H OCEAN\n-122.08,37.3,30.0,2268.0,404.0,1197.0,372.0,7.0813,485300.0,<1H OCEAN\n-122.08,37.31,17.0,2560.0,396.0,959.0,400.0,7.8528,368900.0,<1H OCEAN\n-122.04,37.34,20.0,4475.0,1048.0,2271.0,1021.0,4.8836,396200.0,<1H OCEAN\n-122.06,37.34,13.0,2057.0,466.0,790.0,436.0,5.0081,288300.0,<1H OCEAN\n-122.05,37.33,21.0,2052.0,346.0,933.0,351.0,5.3167,416300.0,<1H OCEAN\n-122.04,37.33,26.0,2690.0,401.0,1264.0,429.0,7.7643,474700.0,<1H OCEAN\n-122.05,37.33,17.0,3674.0,824.0,1364.0,694.0,6.3131,436400.0,<1H OCEAN\n-122.04,37.33,22.0,4011.0,963.0,2206.0,879.0,4.5721,351200.0,<1H OCEAN\n-122.04,37.32,27.0,2826.0,451.0,1259.0,439.0,5.7528,431400.0,<1H OCEAN\n-122.04,37.31,29.0,2476.0,434.0,1217.0,416.0,6.2045,393800.0,<1H OCEAN\n-122.04,37.31,24.0,3388.0,633.0,1627.0,585.0,5.154,355100.0,<1H OCEAN\n-122.04,37.3,26.0,1714.0,270.0,778.0,262.0,6.075,417000.0,<1H OCEAN\n-122.04,37.3,25.0,2366.0,417.0,1076.0,398.0,6.9238,345900.0,<1H OCEAN\n-122.03,37.31,25.0,2131.0,410.0,1132.0,395.0,5.3508,409100.0,<1H OCEAN\n-122.04,37.3,25.0,3807.0,600.0,1678.0,600.0,6.6818,411300.0,<1H OCEAN\n-122.01,37.3,25.0,4044.0,551.0,1699.0,533.0,8.0837,380600.0,<1H OCEAN\n-122.01,37.31,23.0,6846.0,1078.0,2951.0,1063.0,6.3702,332000.0,<1H OCEAN\n-122.0,37.31,28.0,3811.0,585.0,1795.0,581.0,7.8383,372700.0,<1H OCEAN\n-122.0,37.3,29.0,3429.0,524.0,1518.0,520.0,7.218,400700.0,<1H OCEAN\n-122.03,37.31,19.0,2885.0,859.0,1520.0,784.0,3.375,275700.0,<1H OCEAN\n-122.03,37.3,30.0,3007.0,554.0,1551.0,616.0,5.8521,326300.0,<1H OCEAN\n-122.03,37.3,22.0,3583.0,758.0,1792.0,695.0,5.4842,335300.0,<1H OCEAN\n-122.02,37.31,34.0,2629.0,433.0,1301.0,431.0,6.083,341400.0,<1H OCEAN\n-122.02,37.31,35.0,2355.0,384.0,1248.0,378.0,5.9714,332500.0,<1H OCEAN\n-122.02,37.3,26.0,1983.0,301.0,924.0,297.0,6.7123,354600.0,<1H OCEAN\n-122.02,37.3,32.0,2134.0,328.0,903.0,322.0,6.359,341900.0,<1H OCEAN\n-122.02,37.32,27.0,4336.0,754.0,2009.0,734.0,6.3923,348300.0,<1H OCEAN\n-122.02,37.31,33.0,2563.0,434.0,1230.0,418.0,6.3197,340100.0,<1H OCEAN\n-122.03,37.32,15.0,5132.0,1059.0,2156.0,982.0,5.6511,404800.0,<1H OCEAN\n-122.0,37.32,34.0,3450.0,731.0,1915.0,689.0,4.7402,244500.0,<1H OCEAN\n-122.01,37.31,26.0,1391.0,241.0,700.0,236.0,6.6766,332700.0,<1H OCEAN\n-122.01,37.32,32.0,3108.0,613.0,1577.0,603.0,4.6613,284000.0,<1H OCEAN\n-122.0,37.31,33.0,4211.0,918.0,2389.0,861.0,4.7235,242200.0,<1H OCEAN\n-122.02,37.33,25.0,3823.0,584.0,1689.0,571.0,7.3693,373600.0,<1H OCEAN\n-122.03,37.33,23.0,4221.0,671.0,1782.0,641.0,7.4863,412300.0,<1H OCEAN\n-122.03,37.34,16.0,1755.0,410.0,674.0,410.0,5.1602,231200.0,<1H OCEAN\n-122.02,37.34,30.0,1036.0,151.0,467.0,156.0,6.448,360600.0,<1H OCEAN\n-122.0,37.33,30.0,4033.0,794.0,1788.0,807.0,5.6932,338700.0,<1H OCEAN\n-122.02,37.35,26.0,2785.0,418.0,1221.0,422.0,8.1078,365700.0,<1H OCEAN\n-122.02,37.35,22.0,3219.0,756.0,1479.0,667.0,4.1473,354400.0,<1H OCEAN\n-122.02,37.34,26.0,1992.0,328.0,980.0,342.0,6.2475,350000.0,<1H OCEAN\n-122.02,37.34,28.0,2488.0,396.0,1190.0,410.0,5.7881,344700.0,<1H OCEAN\n-122.03,37.34,25.0,5404.0,906.0,2338.0,883.0,6.0577,451800.0,<1H OCEAN\n-122.03,37.35,25.0,3095.0,514.0,1251.0,507.0,5.5388,352100.0,<1H OCEAN\n-122.01,37.35,33.0,2517.0,496.0,1158.0,443.0,5.0785,289500.0,<1H OCEAN\n-122.0,37.35,20.0,4304.0,851.0,2059.0,835.0,5.1674,333000.0,<1H OCEAN\n-122.0,37.34,27.0,1716.0,290.0,817.0,301.0,5.9158,343100.0,<1H OCEAN\n-122.0,37.34,31.0,3344.0,620.0,1604.0,572.0,5.2108,351500.0,<1H OCEAN\n-122.01,37.34,31.0,3080.0,526.0,1493.0,582.0,6.3052,344200.0,<1H OCEAN\n-122.05,37.35,34.0,2494.0,375.0,1399.0,382.0,7.3753,388100.0,<1H OCEAN\n-122.05,37.34,34.0,2515.0,401.0,1079.0,399.0,7.7865,423900.0,<1H OCEAN\n-122.05,37.34,31.0,1443.0,215.0,627.0,222.0,6.6087,416500.0,<1H OCEAN\n-122.06,37.34,20.0,3435.0,593.0,1293.0,553.0,6.7578,451400.0,<1H OCEAN\n-122.04,37.35,28.0,3250.0,485.0,1328.0,473.0,7.4729,431600.0,<1H OCEAN\n-122.04,37.34,28.0,3081.0,460.0,1260.0,461.0,7.5372,432600.0,<1H OCEAN\n-122.03,37.35,16.0,1156.0,198.0,455.0,216.0,7.2779,292900.0,<1H OCEAN\n-122.04,37.35,28.0,1582.0,264.0,696.0,270.0,5.678,370100.0,<1H OCEAN\n-122.04,37.34,25.0,1994.0,287.0,704.0,283.0,7.7799,447300.0,<1H OCEAN\n-122.03,37.34,17.0,1165.0,278.0,598.0,287.0,4.0129,342400.0,<1H OCEAN\n-122.04,37.34,23.0,2590.0,725.0,1795.0,680.0,3.16,225000.0,<1H OCEAN\n-122.04,37.34,19.0,3694.0,1036.0,2496.0,986.0,3.6991,271500.0,<1H OCEAN\n-122.05,37.37,35.0,1365.0,256.0,662.0,262.0,5.6533,291400.0,<1H OCEAN\n-122.05,37.36,34.0,2400.0,419.0,1017.0,384.0,4.1369,316900.0,<1H OCEAN\n-122.05,37.36,27.0,2621.0,513.0,1063.0,523.0,3.9848,409700.0,<1H OCEAN\n-122.06,37.35,31.0,1795.0,281.0,872.0,282.0,8.0599,381800.0,<1H OCEAN\n-122.06,37.36,35.0,2693.0,493.0,1343.0,455.0,6.0777,327500.0,<1H OCEAN\n-122.06,37.37,32.0,2510.0,578.0,1160.0,581.0,4.9087,322700.0,<1H OCEAN\n-122.04,37.37,23.0,5135.0,911.0,2351.0,863.0,5.2319,430100.0,<1H OCEAN\n-122.03,37.36,28.0,2490.0,345.0,948.0,361.0,6.4913,411900.0,<1H OCEAN\n-122.04,37.35,20.0,2016.0,313.0,767.0,310.0,6.837,383000.0,<1H OCEAN\n-122.05,37.36,29.0,1733.0,255.0,679.0,278.0,7.5337,406800.0,<1H OCEAN\n-122.04,37.36,26.0,3298.0,460.0,1241.0,472.0,6.8753,403000.0,<1H OCEAN\n-122.05,37.37,27.0,3885.0,661.0,1537.0,606.0,6.6085,344700.0,<1H OCEAN\n-122.03,37.36,16.0,2697.0,803.0,1369.0,723.0,4.4699,367400.0,<1H OCEAN\n-122.02,37.35,17.0,2975.0,608.0,1465.0,577.0,5.8779,362200.0,<1H OCEAN\n-122.02,37.35,18.0,1221.0,255.0,507.0,271.0,5.3679,228400.0,<1H OCEAN\n-122.03,37.35,19.0,3811.0,1227.0,1930.0,1153.0,3.5154,311400.0,<1H OCEAN\n-122.03,37.37,16.0,3402.0,1193.0,1479.0,1043.0,3.5861,500001.0,<1H OCEAN\n-122.02,37.36,21.0,2471.0,677.0,1486.0,689.0,3.9038,243800.0,<1H OCEAN\n-122.02,37.36,25.0,2074.0,387.0,1273.0,383.0,4.7609,378000.0,<1H OCEAN\n-122.01,37.36,16.0,1105.0,354.0,499.0,324.0,4.2061,253600.0,<1H OCEAN\n-122.02,37.36,24.0,1709.0,437.0,892.0,408.0,4.9671,335200.0,<1H OCEAN\n-122.01,37.36,25.0,2796.0,429.0,1267.0,426.0,6.6329,349000.0,<1H OCEAN\n-122.01,37.36,21.0,2483.0,396.0,1194.0,424.0,7.1273,346300.0,<1H OCEAN\n-122.01,37.35,16.0,3716.0,916.0,1551.0,759.0,4.5,323600.0,<1H OCEAN\n-122.0,37.36,19.0,2237.0,433.0,1158.0,426.0,6.7718,368300.0,<1H OCEAN\n-122.0,37.36,17.0,2070.0,,797.0,275.0,8.6155,411200.0,<1H OCEAN\n-122.0,37.36,25.0,3534.0,949.0,1880.0,849.0,3.4238,337000.0,<1H OCEAN\n-122.0,37.36,17.0,6012.0,1737.0,3539.0,1625.0,3.8464,239400.0,<1H OCEAN\n-122.03,37.37,9.0,2966.0,770.0,1430.0,740.0,3.0047,256000.0,<1H OCEAN\n-122.03,37.37,41.0,2123.0,425.0,1032.0,435.0,4.6957,284800.0,<1H OCEAN\n-122.03,37.37,30.0,1269.0,290.0,556.0,266.0,3.8125,325000.0,<1H OCEAN\n-122.04,37.37,42.0,1125.0,273.0,616.0,258.0,3.6765,252800.0,<1H OCEAN\n-122.04,37.37,33.0,2757.0,489.0,1201.0,481.0,5.0453,311600.0,<1H OCEAN\n-122.04,37.38,38.0,2850.0,550.0,1518.0,514.0,4.2028,273600.0,<1H OCEAN\n-122.03,37.38,21.0,2667.0,798.0,1433.0,727.0,3.8732,252400.0,<1H OCEAN\n-122.02,37.38,43.0,1261.0,317.0,836.0,333.0,4.0911,224600.0,<1H OCEAN\n-122.01,37.38,32.0,726.0,204.0,538.0,203.0,4.505,230400.0,<1H OCEAN\n-122.02,37.37,8.0,5686.0,1489.0,3250.0,1329.0,4.2782,327700.0,<1H OCEAN\n-122.01,37.37,11.0,2559.0,694.0,1309.0,668.0,4.1847,167300.0,<1H OCEAN\n-122.01,37.37,25.0,2213.0,360.0,1066.0,390.0,7.2165,360900.0,<1H OCEAN\n-122.0,37.37,16.0,1434.0,372.0,804.0,361.0,3.7045,178100.0,<1H OCEAN\n-122.03,37.39,22.0,3280.0,933.0,1842.0,795.0,4.4107,232700.0,<1H OCEAN\n-122.02,37.38,32.0,1889.0,487.0,1321.0,508.0,3.2574,254400.0,<1H OCEAN\n-122.01,37.39,26.0,2500.0,962.0,2374.0,879.0,3.5586,222200.0,<1H OCEAN\n-122.01,37.39,36.0,1976.0,361.0,1348.0,371.0,5.6447,252600.0,<1H OCEAN\n-122.0,37.39,36.0,1236.0,229.0,880.0,247.0,5.791,239400.0,<1H OCEAN\n-122.03,37.39,34.0,2600.0,650.0,1994.0,650.0,4.0223,250200.0,<1H OCEAN\n-122.02,37.4,35.0,2348.0,531.0,1475.0,498.0,4.35,261000.0,<1H OCEAN\n-122.01,37.39,16.0,3015.0,829.0,1769.0,807.0,4.0068,249500.0,<1H OCEAN\n-122.02,37.39,35.0,2297.0,497.0,1428.0,497.0,4.7431,239700.0,<1H OCEAN\n-122.04,37.39,5.0,8745.0,2211.0,3959.0,2019.0,4.7685,280100.0,<1H OCEAN\n-122.05,37.38,23.0,3200.0,907.0,2029.0,866.0,3.5649,450000.0,<1H OCEAN\n-122.05,37.38,24.0,2424.0,501.0,1367.0,507.0,4.072,364200.0,<1H OCEAN\n-122.05,37.38,29.0,1875.0,340.0,816.0,350.0,5.4351,336500.0,<1H OCEAN\n-122.06,37.38,20.0,4293.0,1272.0,2389.0,1210.0,4.2719,270800.0,NEAR BAY\n-122.05,37.37,27.0,2687.0,768.0,1362.0,725.0,3.4028,324200.0,<1H OCEAN\n-122.05,37.39,25.0,347.0,82.0,148.0,77.0,4.4531,350000.0,<1H OCEAN\n-122.06,37.4,21.0,12855.0,3226.0,7273.0,3052.0,4.3351,267400.0,NEAR BAY\n-122.06,37.39,26.0,18.0,4.0,8.0,4.0,3.75,375000.0,NEAR BAY\n-122.06,37.39,22.0,1236.0,290.0,413.0,274.0,3.6875,40000.0,NEAR BAY\n-122.06,37.38,20.0,3401.0,768.0,1497.0,747.0,4.2188,500001.0,NEAR BAY\n-122.06,37.38,21.0,1798.0,399.0,837.0,410.0,5.6999,470000.0,NEAR BAY\n-122.06,37.37,18.0,3058.0,661.0,1377.0,675.0,6.1299,500001.0,<1H OCEAN\n-122.06,37.38,20.0,2976.0,766.0,1227.0,634.0,4.8625,262500.0,NEAR BAY\n-122.07,37.39,19.0,1465.0,342.0,646.0,345.0,4.712,289300.0,NEAR BAY\n-122.08,37.4,23.0,806.0,158.0,373.0,156.0,5.9291,284600.0,NEAR BAY\n-122.07,37.4,16.0,3352.0,813.0,1440.0,729.0,3.7359,262500.0,NEAR BAY\n-122.07,37.41,26.0,1184.0,225.0,815.0,218.0,5.7657,322300.0,NEAR BAY\n-122.07,37.4,15.0,2940.0,910.0,943.0,711.0,4.359,192200.0,NEAR BAY\n-122.08,37.4,19.0,3565.0,858.0,1639.0,744.0,4.1544,277000.0,NEAR BAY\n-122.08,37.4,25.0,1750.0,341.0,999.0,319.0,5.806,308700.0,NEAR BAY\n-122.08,37.41,20.0,1896.0,456.0,1069.0,436.0,4.6875,288900.0,NEAR BAY\n-122.09,37.4,36.0,1575.0,379.0,1036.0,382.0,5.1408,264700.0,NEAR BAY\n-122.1,37.41,33.0,6277.0,1274.0,3025.0,1211.0,5.4721,343300.0,NEAR BAY\n-122.09,37.41,8.0,1480.0,414.0,856.0,445.0,2.8203,284100.0,NEAR BAY\n-122.09,37.41,14.0,753.0,193.0,421.0,153.0,4.2463,266700.0,NEAR BAY\n-122.09,37.41,18.0,1476.0,473.0,838.0,415.0,3.575,274000.0,NEAR BAY\n-122.09,37.4,22.0,1489.0,436.0,662.0,470.0,3.5179,197200.0,NEAR BAY\n-122.09,37.4,17.0,748.0,184.0,412.0,180.0,3.4375,290600.0,NEAR BAY\n-122.09,37.42,23.0,4874.0,1251.0,2699.0,1163.0,3.8003,229800.0,NEAR BAY\n-122.11,37.41,27.0,5110.0,1599.0,2764.0,1482.0,3.4198,351900.0,NEAR BAY\n-122.09,37.4,26.0,3218.0,1021.0,2087.0,964.0,3.2875,182700.0,NEAR BAY\n-122.1,37.4,27.0,3410.0,1156.0,2314.0,1086.0,3.4868,165600.0,NEAR BAY\n-122.1,37.4,23.0,1755.0,508.0,1374.0,506.0,4.3077,293500.0,NEAR BAY\n-122.1,37.4,19.0,1085.0,288.0,1009.0,305.0,3.9091,276000.0,NEAR BAY\n-122.11,37.4,16.0,1994.0,489.0,1173.0,472.0,4.1875,266400.0,NEAR BAY\n-122.11,37.4,15.0,255.0,63.0,138.0,74.0,4.6591,175000.0,NEAR BAY\n-122.1,37.4,23.0,514.0,210.0,367.0,206.0,3.1736,181300.0,NEAR BAY\n-122.08,37.4,52.0,766.0,203.0,448.0,196.0,2.5208,316700.0,NEAR BAY\n-122.09,37.39,43.0,2065.0,535.0,1029.0,500.0,3.7318,327700.0,NEAR BAY\n-122.09,37.39,30.0,1722.0,490.0,1057.0,517.0,3.725,261300.0,NEAR BAY\n-122.09,37.4,24.0,3983.0,1126.0,2645.0,1072.0,3.6742,275000.0,NEAR BAY\n-122.08,37.39,44.0,1498.0,430.0,848.0,400.0,2.8438,307100.0,NEAR BAY\n-122.08,37.39,46.0,1115.0,248.0,543.0,248.0,3.2083,334300.0,NEAR BAY\n-122.08,37.39,4.0,2292.0,,1050.0,584.0,4.8036,340000.0,NEAR BAY\n-122.07,37.39,30.0,1695.0,480.0,932.0,447.0,3.5045,352500.0,NEAR BAY\n-122.07,37.39,37.0,1169.0,239.0,589.0,249.0,5.0131,330300.0,NEAR BAY\n-122.07,37.38,26.0,1272.0,306.0,562.0,284.0,4.5644,280200.0,NEAR BAY\n-122.08,37.39,39.0,2210.0,483.0,1023.0,450.0,4.5833,342400.0,NEAR BAY\n-122.1,37.39,31.0,1117.0,304.0,591.0,302.0,3.5909,353100.0,NEAR BAY\n-122.1,37.39,36.0,1860.0,367.0,794.0,366.0,5.0871,354500.0,NEAR BAY\n-122.09,37.39,34.0,1508.0,483.0,774.0,443.0,2.7279,365600.0,NEAR BAY\n-122.09,37.39,36.0,1035.0,196.0,475.0,205.0,5.5385,359000.0,NEAR BAY\n-122.09,37.38,34.0,1959.0,342.0,849.0,357.0,6.2884,414700.0,NEAR BAY\n-122.09,37.38,36.0,2587.0,416.0,1055.0,410.0,6.1995,407200.0,NEAR BAY\n-122.08,37.38,33.0,2771.0,659.0,1496.0,581.0,3.4042,353600.0,NEAR BAY\n-122.08,37.38,36.0,857.0,156.0,448.0,168.0,5.0086,366700.0,NEAR BAY\n-122.08,37.38,36.0,1199.0,198.0,485.0,199.0,5.0796,373400.0,NEAR BAY\n-122.08,37.38,36.0,782.0,130.0,348.0,128.0,6.828,383900.0,NEAR BAY\n-122.08,37.37,29.0,1229.0,,707.0,194.0,7.1108,465000.0,NEAR BAY\n-122.09,37.37,27.0,1269.0,186.0,464.0,182.0,6.8374,500001.0,NEAR BAY\n-122.09,37.37,34.0,2165.0,355.0,776.0,339.0,5.2971,442100.0,NEAR BAY\n-122.08,37.38,25.0,830.0,228.0,368.0,174.0,3.3917,342900.0,NEAR BAY\n-122.07,37.37,22.0,3770.0,727.0,1657.0,762.0,4.8021,457500.0,NEAR BAY\n-122.07,37.37,30.0,2937.0,407.0,1097.0,407.0,7.9813,473500.0,NEAR BAY\n-122.07,37.36,21.0,3244.0,426.0,1158.0,415.0,7.5,500001.0,<1H OCEAN\n-122.06,37.36,34.0,1747.0,250.0,662.0,257.0,6.8268,500001.0,<1H OCEAN\n-122.07,37.36,28.0,4612.0,608.0,1686.0,567.0,10.0346,500001.0,<1H OCEAN\n-122.08,37.36,31.0,2717.0,376.0,1001.0,381.0,9.281,500001.0,NEAR BAY\n-122.09,37.36,37.0,1550.0,238.0,805.0,250.0,5.0222,500001.0,NEAR BAY\n-122.09,37.36,37.0,2269.0,325.0,930.0,321.0,7.5274,500001.0,NEAR BAY\n-122.08,37.36,28.0,2181.0,284.0,728.0,238.0,8.2266,500001.0,NEAR BAY\n-122.06,37.35,30.0,2040.0,294.0,787.0,278.0,8.758,500001.0,<1H OCEAN\n-122.07,37.34,33.0,1208.0,198.0,495.0,216.0,5.4659,500001.0,<1H OCEAN\n-122.07,37.34,35.0,1172.0,184.0,512.0,175.0,7.3561,500001.0,<1H OCEAN\n-122.07,37.35,35.0,1447.0,205.0,619.0,206.0,9.8144,500001.0,<1H OCEAN\n-122.07,37.35,35.0,1579.0,210.0,570.0,196.0,8.5888,500001.0,<1H OCEAN\n-122.08,37.35,35.0,1347.0,207.0,548.0,189.0,7.7068,500001.0,NEAR BAY\n-122.07,37.34,30.0,1851.0,238.0,631.0,236.0,10.1007,500001.0,<1H OCEAN\n-122.08,37.34,23.0,2597.0,335.0,922.0,338.0,10.5142,500001.0,<1H OCEAN\n-122.08,37.34,28.0,1643.0,216.0,594.0,205.0,12.367,500001.0,<1H OCEAN\n-122.08,37.35,33.0,2398.0,317.0,832.0,314.0,10.3591,500001.0,NEAR BAY\n-122.1,37.38,37.0,4167.0,612.0,1577.0,597.0,7.5655,500001.0,NEAR BAY\n-122.1,37.37,37.0,2511.0,354.0,945.0,348.0,8.3924,500001.0,NEAR BAY\n-122.1,37.37,40.0,2224.0,354.0,929.0,345.0,8.1064,500001.0,NEAR BAY\n-122.11,37.37,22.0,1477.0,195.0,520.0,187.0,10.3329,500001.0,NEAR BAY\n-122.11,37.38,36.0,3598.0,500.0,1296.0,533.0,7.8177,500001.0,NEAR BAY\n-122.11,37.38,22.0,3638.0,719.0,1329.0,650.0,5.0804,500001.0,NEAR BAY\n-122.11,37.37,49.0,1068.0,190.0,410.0,171.0,7.2045,500001.0,NEAR BAY\n-122.12,37.37,18.0,1617.0,231.0,555.0,222.0,8.9021,500001.0,NEAR BAY\n-122.11,37.4,31.0,2836.0,490.0,1138.0,481.0,4.9519,500001.0,NEAR BAY\n-122.11,37.39,36.0,1660.0,261.0,655.0,249.0,6.3967,500001.0,NEAR BAY\n-122.1,37.39,35.0,2471.0,349.0,881.0,342.0,7.6229,500001.0,NEAR BAY\n-122.12,37.4,31.0,2356.0,405.0,921.0,358.0,7.0245,500001.0,NEAR BAY\n-122.12,37.39,34.0,3561.0,497.0,1336.0,501.0,8.9172,500001.0,NEAR BAY\n-122.12,37.38,34.0,1443.0,218.0,504.0,200.0,8.4709,500001.0,NEAR BAY\n-122.12,37.4,32.0,3514.0,473.0,1583.0,480.0,10.3894,500001.0,NEAR BAY\n-122.13,37.4,29.0,6027.0,1195.0,2687.0,1171.0,5.1335,461200.0,NEAR BAY\n-122.13,37.41,36.0,4787.0,900.0,2039.0,890.0,5.4063,440900.0,NEAR BAY\n-122.14,37.41,35.0,2419.0,426.0,949.0,433.0,6.4588,437100.0,NEAR BAY\n-122.14,37.42,46.0,206.0,44.0,134.0,51.0,4.15,265000.0,NEAR BAY\n-122.13,37.42,36.0,3982.0,1045.0,2251.0,995.0,3.5364,314100.0,NEAR BAY\n-122.12,37.41,33.0,2892.0,617.0,1250.0,581.0,5.3727,360900.0,NEAR BAY\n-122.11,37.43,35.0,3584.0,535.0,1405.0,538.0,7.3023,451300.0,NEAR BAY\n-122.11,37.43,35.0,3905.0,565.0,1562.0,553.0,7.313,463700.0,NEAR BAY\n-122.11,37.42,32.0,3058.0,595.0,1267.0,540.0,6.4949,417800.0,NEAR BAY\n-122.11,37.41,35.0,2712.0,428.0,1084.0,425.0,7.1382,443800.0,NEAR BAY\n-122.11,37.41,33.0,1641.0,284.0,659.0,282.0,6.0884,432900.0,NEAR BAY\n-122.12,37.42,35.0,2445.0,533.0,1187.0,519.0,5.2803,362100.0,NEAR BAY\n-122.12,37.42,36.0,2607.0,551.0,1165.0,523.0,5.1524,373100.0,NEAR BAY\n-122.13,37.43,40.0,3454.0,648.0,1498.0,647.0,5.2114,438400.0,NEAR BAY\n-122.13,37.43,32.0,4398.0,878.0,1799.0,792.0,4.7375,431900.0,NEAR BAY\n-122.12,37.43,36.0,3212.0,553.0,1455.0,574.0,6.46,425500.0,NEAR BAY\n-122.12,37.44,33.0,2974.0,623.0,1435.0,588.0,5.485,406300.0,NEAR BAY\n-122.13,37.44,42.0,2390.0,462.0,1146.0,468.0,6.3111,397400.0,NEAR BAY\n-122.12,37.44,33.0,1509.0,303.0,748.0,268.0,4.875,373400.0,NEAR BAY\n-122.11,37.44,35.0,2016.0,349.0,1023.0,376.0,5.6413,376600.0,NEAR BAY\n-122.12,37.43,33.0,3262.0,668.0,1411.0,626.0,5.316,398100.0,NEAR BAY\n-122.13,37.45,37.0,2295.0,332.0,933.0,332.0,6.7257,500001.0,NEAR BAY\n-122.13,37.45,37.0,1287.0,197.0,510.0,206.0,7.9029,500001.0,NEAR BAY\n-122.13,37.45,41.0,3233.0,540.0,1251.0,506.0,6.6354,500001.0,NEAR BAY\n-122.13,37.44,43.0,3004.0,440.0,1088.0,427.0,9.1508,500001.0,NEAR BAY\n-122.13,37.44,38.0,2835.0,447.0,1148.0,446.0,5.9277,446600.0,NEAR BAY\n-122.15,37.46,39.0,906.0,109.0,353.0,112.0,10.3942,500001.0,NEAR BAY\n-122.16,37.45,52.0,1135.0,219.0,441.0,200.0,7.5418,492000.0,NEAR BAY\n-122.15,37.46,52.0,1803.0,257.0,683.0,259.0,10.9508,500001.0,NEAR BAY\n-122.14,37.45,52.0,3841.0,537.0,1391.0,540.0,7.8647,500001.0,NEAR BAY\n-122.15,37.45,52.0,2117.0,353.0,734.0,328.0,6.767,500001.0,NEAR BAY\n-122.14,37.45,48.0,2074.0,297.0,700.0,279.0,8.7051,500001.0,NEAR BAY\n-122.15,37.45,52.0,568.0,91.0,219.0,75.0,6.1575,500001.0,NEAR BAY\n-122.16,37.45,47.0,4234.0,,1808.0,1093.0,4.2297,425000.0,NEAR BAY\n-122.16,37.45,19.0,2207.0,810.0,1304.0,775.0,2.1406,402500.0,NEAR BAY\n-122.16,37.45,37.0,2926.0,874.0,1363.0,815.0,4.5987,356000.0,NEAR BAY\n-122.16,37.44,34.0,2199.0,529.0,1193.0,532.0,4.2972,405900.0,NEAR BAY\n-122.15,37.44,52.0,2063.0,320.0,774.0,309.0,7.2543,500001.0,NEAR BAY\n-122.15,37.44,52.0,1400.0,217.0,522.0,227.0,4.9861,500001.0,NEAR BAY\n-122.15,37.44,52.0,1945.0,293.0,708.0,275.0,6.1655,500001.0,NEAR BAY\n-122.14,37.44,52.0,3117.0,468.0,1114.0,421.0,6.6756,500001.0,NEAR BAY\n-122.14,37.43,52.0,1944.0,308.0,696.0,293.0,8.2664,500001.0,NEAR BAY\n-122.14,37.43,52.0,1327.0,190.0,467.0,189.0,12.5902,500001.0,NEAR BAY\n-122.14,37.43,52.0,1383.0,227.0,551.0,249.0,6.5829,500001.0,NEAR BAY\n-122.15,37.43,47.0,2600.0,490.0,1149.0,465.0,5.0203,476300.0,NEAR BAY\n-122.15,37.42,44.0,3558.0,839.0,1779.0,832.0,3.9243,404800.0,NEAR BAY\n-122.14,37.43,18.0,2060.0,563.0,1144.0,600.0,4.0686,378600.0,NEAR BAY\n-122.15,37.43,20.0,11709.0,,7604.0,3589.0,1.9045,375000.0,NEAR BAY\n-122.16,37.42,34.0,4448.0,610.0,2571.0,581.0,11.0492,500001.0,NEAR OCEAN\n-122.15,37.41,15.0,2577.0,360.0,979.0,364.0,10.476,500001.0,NEAR BAY\n-122.17,37.43,24.0,3924.0,1142.0,7174.0,950.0,4.0972,387500.0,NEAR OCEAN\n-122.15,37.41,29.0,473.0,103.0,359.0,87.0,7.0309,475000.0,NEAR BAY\n-122.18,37.41,21.0,1034.0,117.0,323.0,117.0,10.7237,500001.0,NEAR OCEAN\n-122.13,37.39,27.0,3385.0,427.0,1248.0,409.0,12.0372,500001.0,NEAR BAY\n-122.12,37.37,37.0,1446.0,181.0,549.0,190.0,10.7355,500001.0,NEAR BAY\n-122.11,37.36,34.0,1575.0,183.0,511.0,180.0,13.1867,500001.0,NEAR BAY\n-122.13,37.37,30.0,2139.0,260.0,742.0,242.0,11.806,500001.0,NEAR BAY\n-122.14,37.38,26.0,2859.0,343.0,951.0,336.0,10.4277,500001.0,NEAR BAY\n-122.1,37.36,32.0,1433.0,199.0,498.0,201.0,9.3586,500001.0,NEAR BAY\n-122.1,37.36,35.0,2063.0,266.0,676.0,252.0,8.5294,500001.0,NEAR BAY\n-122.09,37.35,37.0,1795.0,285.0,791.0,261.0,7.5794,500001.0,NEAR BAY\n-122.09,37.35,30.0,1502.0,186.0,501.0,180.0,10.0259,500001.0,NEAR BAY\n-122.14,37.36,23.0,11294.0,1377.0,3840.0,1367.0,12.1387,500001.0,NEAR BAY\n-122.15,37.35,23.0,3814.0,485.0,1344.0,464.0,12.9792,500001.0,NEAR BAY\n-122.11,37.31,7.0,189.0,26.0,84.0,29.0,13.8093,500001.0,<1H OCEAN\n-122.12,37.29,11.0,436.0,70.0,212.0,75.0,8.6196,500001.0,<1H OCEAN\n-122.12,37.28,21.0,349.0,64.0,149.0,56.0,5.8691,360000.0,<1H OCEAN\n-122.08,37.24,21.0,427.0,63.0,182.0,70.0,11.3283,500001.0,<1H OCEAN\n-122.01,37.18,37.0,3852.0,652.0,1534.0,567.0,5.8596,318700.0,<1H OCEAN\n-121.98,37.16,42.0,2533.0,433.0,957.0,398.0,5.3468,279900.0,<1H OCEAN\n-121.9,37.16,43.0,1529.0,311.0,570.0,250.0,5.2366,293300.0,<1H OCEAN\n-121.93,37.13,37.0,1150.0,203.0,511.0,179.0,5.7415,398500.0,<1H OCEAN\n-121.88,37.24,24.0,4420.0,996.0,2981.0,975.0,3.506,226400.0,<1H OCEAN\n-121.9,37.24,24.0,7521.0,1364.0,3970.0,1318.0,4.4004,255800.0,<1H OCEAN\n-121.86,37.22,18.0,7245.0,1029.0,2893.0,1049.0,6.9508,361200.0,<1H OCEAN\n-121.86,37.23,24.0,4337.0,670.0,1936.0,652.0,5.8904,271400.0,<1H OCEAN\n-121.85,37.22,21.0,6203.0,798.0,2494.0,800.0,7.7201,362700.0,<1H OCEAN\n-121.89,37.21,14.0,5636.0,697.0,2281.0,680.0,8.4262,459200.0,<1H OCEAN\n-121.84,37.18,6.0,9176.0,1201.0,3637.0,1138.0,8.3837,473400.0,<1H OCEAN\n-121.87,37.22,17.0,2825.0,365.0,1052.0,345.0,8.0595,485000.0,<1H OCEAN\n-121.87,37.22,26.0,1921.0,250.0,725.0,253.0,7.6933,405900.0,<1H OCEAN\n-121.86,37.21,23.0,2552.0,305.0,916.0,316.0,9.1974,500001.0,<1H OCEAN\n-121.87,37.21,18.0,1080.0,122.0,382.0,121.0,9.08,500001.0,<1H OCEAN\n-121.83,37.23,7.0,5389.0,903.0,2232.0,825.0,6.6659,500001.0,<1H OCEAN\n-121.8,37.19,45.0,1797.0,303.0,870.0,281.0,4.5417,434500.0,<1H OCEAN\n-121.84,37.21,15.0,5468.0,676.0,2221.0,675.0,8.3792,418300.0,<1H OCEAN\n-121.83,37.2,6.0,3694.0,574.0,1554.0,527.0,6.8333,348000.0,<1H OCEAN\n-121.83,37.21,14.0,2714.0,361.0,1259.0,375.0,7.7738,387500.0,<1H OCEAN\n-121.89,37.23,16.0,3574.0,466.0,1503.0,487.0,8.1988,355900.0,<1H OCEAN\n-121.89,37.23,20.0,7754.0,976.0,3094.0,941.0,8.19,361600.0,<1H OCEAN\n-121.88,37.24,14.0,7174.0,950.0,2782.0,899.0,8.3065,394200.0,<1H OCEAN\n-121.87,37.23,19.0,7357.0,963.0,3018.0,981.0,6.9473,361400.0,<1H OCEAN\n-121.87,37.27,16.0,3298.0,451.0,1542.0,423.0,6.7064,305600.0,<1H OCEAN\n-121.87,37.27,25.0,1730.0,226.0,721.0,243.0,7.5845,279300.0,<1H OCEAN\n-121.86,37.27,19.0,1852.0,268.0,866.0,272.0,5.6139,279500.0,<1H OCEAN\n-121.87,37.27,18.0,3561.0,560.0,1753.0,553.0,5.0292,269400.0,<1H OCEAN\n-121.86,37.27,17.0,4393.0,709.0,2292.0,692.0,5.6876,246500.0,<1H OCEAN\n-121.77,37.23,15.0,4713.0,769.0,2519.0,778.0,5.6958,253800.0,<1H OCEAN\n-121.77,37.22,16.0,1617.0,306.0,667.0,264.0,4.5221,191100.0,<1H OCEAN\n-121.77,37.22,16.0,3992.0,540.0,2097.0,555.0,6.7287,299300.0,<1H OCEAN\n-121.76,37.23,16.0,4274.0,715.0,2240.0,704.0,5.4218,233900.0,<1H OCEAN\n-121.84,37.25,25.0,5939.0,989.0,3275.0,954.0,5.6488,234600.0,<1H OCEAN\n-121.83,37.23,22.0,5507.0,841.0,2785.0,848.0,6.0889,245200.0,<1H OCEAN\n-121.84,37.24,18.0,3574.0,504.0,1803.0,536.0,6.7836,274100.0,<1H OCEAN\n-121.84,37.24,24.0,7991.0,1286.0,4017.0,1213.0,5.4741,238800.0,<1H OCEAN\n-121.85,37.24,17.0,6425.0,1268.0,3934.0,1238.0,5.1228,237600.0,<1H OCEAN\n-121.86,37.25,16.0,6958.0,1300.0,2965.0,1217.0,4.2885,262400.0,<1H OCEAN\n-121.83,37.28,17.0,3057.0,606.0,2030.0,602.0,5.2166,230900.0,<1H OCEAN\n-121.81,37.27,20.0,3244.0,520.0,1769.0,469.0,5.9214,224000.0,<1H OCEAN\n-121.8,37.27,17.0,3912.0,737.0,2693.0,746.0,5.0772,221500.0,<1H OCEAN\n-121.82,37.27,16.0,2030.0,321.0,1343.0,365.0,6.3566,279100.0,<1H OCEAN\n-121.81,37.27,22.0,2996.0,695.0,2169.0,607.0,4.3438,209700.0,<1H OCEAN\n-121.8,37.26,26.0,1394.0,238.0,990.0,315.0,4.8862,195000.0,<1H OCEAN\n-121.8,37.26,18.0,3631.0,947.0,2357.0,757.0,2.875,184400.0,<1H OCEAN\n-121.85,37.27,17.0,1957.0,261.0,863.0,269.0,7.3339,294200.0,<1H OCEAN\n-121.84,37.27,9.0,3624.0,812.0,1856.0,721.0,4.2083,198400.0,<1H OCEAN\n-121.84,37.27,17.0,2795.0,482.0,1904.0,506.0,5.0186,250800.0,<1H OCEAN\n-121.83,37.27,8.0,4454.0,1058.0,2595.0,1027.0,4.5615,282600.0,<1H OCEAN\n-121.84,37.27,16.0,2429.0,387.0,1293.0,363.0,5.5,253200.0,<1H OCEAN\n-121.83,37.27,14.0,2855.0,380.0,1420.0,383.0,6.6712,311500.0,<1H OCEAN\n-121.82,37.26,10.0,3030.0,574.0,1623.0,589.0,5.1356,218700.0,<1H OCEAN\n-121.83,37.26,11.0,2394.0,403.0,1393.0,409.0,5.5875,259300.0,<1H OCEAN\n-121.81,37.26,14.0,3379.0,683.0,1465.0,620.0,4.0547,236200.0,<1H OCEAN\n-121.81,37.26,16.0,1911.0,327.0,1158.0,332.0,5.9359,249500.0,<1H OCEAN\n-121.81,37.25,20.0,3398.0,771.0,1231.0,744.0,2.0288,350000.0,<1H OCEAN\n-121.81,37.25,12.0,2070.0,587.0,1216.0,532.0,4.1926,244500.0,<1H OCEAN\n-121.83,37.26,7.0,3609.0,751.0,1739.0,682.0,4.5033,213100.0,<1H OCEAN\n-121.82,37.25,16.0,2650.0,600.0,1969.0,586.0,3.9461,194300.0,<1H OCEAN\n-121.83,37.25,17.0,2332.0,637.0,1636.0,623.0,3.1932,123400.0,<1H OCEAN\n-121.87,37.26,17.0,1051.0,172.0,446.0,173.0,5.6652,234500.0,<1H OCEAN\n-121.86,37.26,16.0,2814.0,485.0,1305.0,465.0,5.5121,224100.0,<1H OCEAN\n-121.87,37.26,24.0,2383.0,343.0,1146.0,341.0,5.6223,265700.0,<1H OCEAN\n-121.85,37.26,16.0,1982.0,280.0,1030.0,297.0,6.4339,289200.0,<1H OCEAN\n-121.85,37.26,16.0,2312.0,303.0,1158.0,295.0,7.4323,311800.0,<1H OCEAN\n-121.85,37.26,16.0,1816.0,241.0,793.0,234.0,6.8194,291200.0,<1H OCEAN\n-121.84,37.26,5.0,1808.0,340.0,825.0,339.0,5.0509,184800.0,<1H OCEAN\n-121.83,37.26,15.0,3243.0,551.0,1752.0,551.0,5.5849,257400.0,<1H OCEAN\n-121.84,37.25,17.0,2363.0,473.0,1369.0,442.0,4.8355,141600.0,<1H OCEAN\n-121.87,37.25,4.0,2498.0,775.0,1213.0,631.0,3.7844,183900.0,<1H OCEAN\n-121.85,37.25,20.0,3773.0,624.0,1965.0,607.0,5.4939,241200.0,<1H OCEAN\n-121.83,37.24,23.0,2543.0,388.0,1297.0,385.0,5.9164,237400.0,<1H OCEAN\n-121.81,37.24,21.0,3250.0,610.0,1978.0,568.0,4.5,234400.0,<1H OCEAN\n-121.82,37.24,20.0,3671.0,567.0,1944.0,589.0,6.0538,241000.0,<1H OCEAN\n-121.82,37.25,24.0,3344.0,531.0,1768.0,541.0,5.8305,245600.0,<1H OCEAN\n-121.82,37.25,25.0,4021.0,634.0,2178.0,650.0,5.1663,241200.0,<1H OCEAN\n-121.81,37.25,5.0,1975.0,520.0,861.0,440.0,4.4565,159000.0,<1H OCEAN\n-121.81,37.25,25.0,4096.0,623.0,2128.0,618.0,6.2957,251800.0,<1H OCEAN\n-121.82,37.23,23.0,4487.0,584.0,2024.0,580.0,7.5218,291500.0,<1H OCEAN\n-121.82,37.23,25.0,2349.0,394.0,1266.0,383.0,4.9688,233100.0,<1H OCEAN\n-121.81,37.23,16.0,1674.0,281.0,850.0,254.0,5.3157,253300.0,<1H OCEAN\n-121.81,37.23,19.0,2635.0,427.0,1497.0,410.0,6.3178,248000.0,<1H OCEAN\n-121.81,37.23,24.0,2413.0,369.0,1237.0,381.0,6.4328,257200.0,<1H OCEAN\n-121.81,37.23,17.0,2319.0,324.0,1076.0,338.0,6.4664,278300.0,<1H OCEAN\n-121.78,37.24,17.0,2123.0,341.0,1067.0,339.0,6.0062,262700.0,<1H OCEAN\n-121.78,37.22,18.0,2127.0,387.0,1547.0,402.0,5.0958,217100.0,<1H OCEAN\n-121.78,37.23,18.0,1747.0,317.0,1055.0,285.0,5.898,229100.0,<1H OCEAN\n-121.79,37.23,16.0,2240.0,300.0,1221.0,305.0,6.0198,289600.0,<1H OCEAN\n-121.79,37.23,17.0,2281.0,359.0,1226.0,394.0,5.4193,259500.0,<1H OCEAN\n-121.8,37.23,18.0,3179.0,526.0,1663.0,507.0,5.9225,265800.0,<1H OCEAN\n-121.8,37.23,18.0,2581.0,358.0,1284.0,377.0,6.7385,272400.0,<1H OCEAN\n-121.8,37.27,10.0,3301.0,593.0,2190.0,575.0,6.223,260700.0,<1H OCEAN\n-121.8,37.26,16.0,1868.0,285.0,995.0,284.0,5.9053,260500.0,<1H OCEAN\n-121.76,37.26,17.0,250.0,52.0,141.0,51.0,4.6458,500001.0,<1H OCEAN\n-121.77,37.24,12.0,10236.0,1878.0,5674.0,1816.0,4.747,261100.0,<1H OCEAN\n-121.7,37.2,15.0,531.0,154.0,469.0,155.0,4.65,385700.0,<1H OCEAN\n-121.74,37.19,11.0,1290.0,197.0,881.0,191.0,4.2039,500001.0,<1H OCEAN\n-121.72,37.16,21.0,1062.0,179.0,631.0,185.0,4.7386,394100.0,<1H OCEAN\n-121.75,37.11,18.0,3167.0,,1414.0,482.0,6.8773,467700.0,<1H OCEAN\n-121.68,37.0,19.0,3754.0,588.0,1692.0,550.0,6.7644,412600.0,<1H OCEAN\n-121.61,37.15,16.0,5498.0,729.0,2051.0,694.0,7.8601,416300.0,<1H OCEAN\n-121.6,37.13,14.0,9483.0,1361.0,4108.0,1281.0,7.5,344500.0,<1H OCEAN\n-121.64,37.15,13.0,4780.0,798.0,2795.0,764.0,6.1684,288100.0,<1H OCEAN\n-121.64,37.14,14.0,5487.0,1024.0,2823.0,979.0,4.175,229800.0,<1H OCEAN\n-121.63,37.12,17.0,1830.0,398.0,1110.0,388.0,2.4821,248200.0,<1H OCEAN\n-121.69,37.14,12.0,4077.0,590.0,1618.0,540.0,5.2951,386200.0,<1H OCEAN\n-121.66,37.13,20.0,4477.0,924.0,2656.0,871.0,3.8788,226900.0,<1H OCEAN\n-121.65,37.12,14.0,4721.0,999.0,2648.0,888.0,3.6895,239300.0,<1H OCEAN\n-121.66,37.11,19.0,3785.0,611.0,2198.0,610.0,5.1514,436700.0,<1H OCEAN\n-121.65,37.11,14.0,6006.0,914.0,2915.0,898.0,5.9356,321700.0,<1H OCEAN\n-121.63,37.1,14.0,5034.0,797.0,2124.0,790.0,4.9028,335000.0,<1H OCEAN\n-121.67,37.13,19.0,3269.0,483.0,1383.0,452.0,5.6205,300800.0,<1H OCEAN\n-121.56,37.08,17.0,6725.0,1051.0,3439.0,1027.0,6.4313,393100.0,<1H OCEAN\n-121.61,37.06,21.0,5322.0,908.0,3011.0,895.0,5.5936,386800.0,<1H OCEAN\n-121.6,37.09,24.0,1511.0,318.0,1052.0,292.0,3.625,350000.0,<1H OCEAN\n-121.62,37.09,37.0,1593.0,303.0,1030.0,287.0,3.9306,260700.0,<1H OCEAN\n-121.58,37.01,44.0,3192.0,565.0,1439.0,568.0,4.3693,234000.0,INLAND\n-121.57,37.0,18.0,7241.0,1225.0,4168.0,1138.0,4.5714,260300.0,INLAND\n-121.57,36.98,14.0,5231.0,817.0,2634.0,799.0,4.9702,279800.0,INLAND\n-121.58,37.01,15.0,2873.0,547.0,1582.0,567.0,5.1519,264700.0,INLAND\n-121.59,36.97,16.0,865.0,123.0,403.0,130.0,5.7396,308700.0,INLAND\n-121.59,37.01,16.0,6637.0,1171.0,3575.0,1162.0,4.3227,251500.0,INLAND\n-121.61,37.03,5.0,6529.0,1010.0,3071.0,977.0,5.6754,298500.0,<1H OCEAN\n-121.58,37.03,16.0,3120.0,685.0,2383.0,681.0,3.5551,198600.0,INLAND\n-121.58,37.02,27.0,2303.0,471.0,1447.0,467.0,3.2019,203600.0,INLAND\n-121.59,37.02,14.0,6355.0,1279.0,3704.0,1224.0,4.4233,228600.0,INLAND\n-121.57,37.02,17.0,2889.0,624.0,2681.0,608.0,2.9417,178000.0,INLAND\n-121.57,37.01,44.0,1448.0,393.0,1066.0,357.0,2.0625,170300.0,INLAND\n-121.56,37.0,20.0,3976.0,953.0,3866.0,950.0,2.5387,160100.0,INLAND\n-121.54,36.99,27.0,2361.0,449.0,1782.0,397.0,3.2614,305000.0,INLAND\n-121.51,37.02,19.0,2372.0,394.0,1142.0,365.0,4.0238,374600.0,INLAND\n-121.74,37.35,34.0,440.0,90.0,217.0,93.0,5.2327,500001.0,<1H OCEAN\n-121.55,37.37,39.0,759.0,141.0,252.0,106.0,3.6964,262500.0,INLAND\n-121.59,37.19,52.0,220.0,32.0,55.0,26.0,15.0001,131300.0,<1H OCEAN\n-121.37,37.06,25.0,474.0,92.0,300.0,104.0,3.8062,340900.0,INLAND\n-122.03,37.18,10.0,212.0,38.0,78.0,21.0,6.0622,390000.0,<1H OCEAN\n-121.96,37.13,26.0,50.0,5.0,17.0,4.0,15.0001,400000.0,<1H OCEAN\n-121.98,37.14,37.0,74.0,19.0,63.0,17.0,9.5908,350000.0,<1H OCEAN\n-122.0,37.0,16.0,32.0,4.0,36.0,5.0,2.625,137500.0,NEAR OCEAN\n-122.01,36.99,29.0,227.0,45.0,112.0,41.0,6.4469,271400.0,NEAR OCEAN\n-122.01,36.99,28.0,1321.0,240.0,652.0,239.0,4.9808,263100.0,NEAR OCEAN\n-121.99,36.99,29.0,3119.0,507.0,1476.0,487.0,5.8123,281500.0,NEAR OCEAN\n-121.99,36.98,40.0,1104.0,224.0,669.0,215.0,4.3409,256300.0,NEAR OCEAN\n-122.0,36.98,43.0,1636.0,324.0,792.0,325.0,3.5562,239200.0,NEAR OCEAN\n-122.01,36.99,41.0,2548.0,508.0,1290.0,488.0,3.6902,233000.0,NEAR OCEAN\n-122.01,36.98,47.0,1250.0,249.0,607.0,234.0,4.0417,265300.0,NEAR OCEAN\n-122.02,36.99,30.0,2156.0,487.0,1023.0,458.0,2.7875,245000.0,NEAR OCEAN\n-122.02,36.98,21.0,1484.0,394.0,984.0,380.0,2.1734,187500.0,NEAR OCEAN\n-122.02,36.98,44.0,1153.0,238.0,657.0,219.0,3.2368,212500.0,NEAR OCEAN\n-122.04,36.98,35.0,2155.0,355.0,866.0,335.0,5.6188,404700.0,NEAR OCEAN\n-122.04,36.98,33.0,797.0,125.0,385.0,133.0,6.7974,367600.0,NEAR OCEAN\n-122.04,36.97,30.0,2695.0,424.0,1098.0,420.0,5.3972,362300.0,NEAR OCEAN\n-122.06,37.0,14.0,1547.0,374.0,4731.0,348.0,2.4732,131300.0,NEAR OCEAN\n-122.05,36.97,20.0,2428.0,473.0,1145.0,454.0,3.6797,263800.0,NEAR OCEAN\n-122.05,36.97,16.0,3363.0,611.0,1603.0,556.0,4.2542,294100.0,NEAR OCEAN\n-122.06,36.98,15.0,3385.0,669.0,1571.0,615.0,4.2254,320900.0,NEAR OCEAN\n-122.04,36.98,51.0,1076.0,206.0,495.0,201.0,2.9286,258300.0,NEAR OCEAN\n-122.04,36.97,45.0,1302.0,245.0,621.0,258.0,5.1806,266400.0,NEAR OCEAN\n-122.04,36.97,52.0,1901.0,335.0,955.0,301.0,3.8259,253100.0,NEAR OCEAN\n-122.04,36.97,49.0,792.0,136.0,331.0,137.0,5.2128,238600.0,NEAR OCEAN\n-122.04,36.96,44.0,1294.0,269.0,645.0,259.0,3.2437,223900.0,NEAR OCEAN\n-122.03,36.98,37.0,2817.0,716.0,1341.0,662.0,2.1553,255400.0,NEAR OCEAN\n-122.01,36.98,47.0,2403.0,517.0,1144.0,455.0,2.5954,229400.0,NEAR OCEAN\n-122.02,36.98,35.0,1053.0,263.0,552.0,237.0,2.7125,217500.0,NEAR OCEAN\n-122.02,36.98,21.0,607.0,155.0,226.0,136.0,1.9063,166700.0,NEAR OCEAN\n-122.02,36.97,29.0,2568.0,747.0,1743.0,659.0,1.9286,195300.0,NEAR OCEAN\n-122.01,36.97,43.0,2162.0,509.0,1208.0,464.0,2.5417,260900.0,NEAR OCEAN\n-122.01,36.97,52.0,920.0,202.0,525.0,264.0,2.9444,232800.0,NEAR OCEAN\n-122.01,36.97,35.0,1605.0,392.0,743.0,382.0,2.5368,240000.0,NEAR OCEAN\n-122.0,36.98,20.0,2502.0,454.0,981.0,399.0,4.3,275000.0,NEAR OCEAN\n-122.01,36.98,27.0,2820.0,730.0,1511.0,745.0,2.589,242400.0,NEAR OCEAN\n-122.0,36.97,30.0,1029.0,242.0,753.0,249.0,3.1205,240500.0,NEAR OCEAN\n-122.0,36.93,51.0,1616.0,374.0,608.0,302.0,3.1932,400000.0,NEAR OCEAN\n-122.01,36.95,52.0,1217.0,325.0,508.0,237.0,2.0547,326700.0,NEAR OCEAN\n-122.03,36.97,51.0,924.0,232.0,488.0,228.0,2.1964,234400.0,NEAR OCEAN\n-122.03,36.97,36.0,337.0,69.0,223.0,68.0,3.2404,225000.0,NEAR OCEAN\n-122.03,36.97,52.0,403.0,72.0,200.0,73.0,1.6923,262500.0,NEAR OCEAN\n-122.04,36.97,40.0,1193.0,227.0,570.0,204.0,4.4659,237500.0,NEAR OCEAN\n-122.03,36.96,18.0,2677.0,785.0,1391.0,656.0,2.5067,232600.0,NEAR OCEAN\n-122.02,36.97,44.0,594.0,169.0,325.0,139.0,1.1552,250000.0,NEAR OCEAN\n-122.02,36.97,39.0,2124.0,661.0,1365.0,606.0,1.6642,281300.0,NEAR OCEAN\n-122.02,36.96,52.0,775.0,305.0,1054.0,305.0,2.0172,112500.0,NEAR OCEAN\n-122.03,36.96,40.0,584.0,126.0,316.0,139.0,3.5938,243500.0,NEAR OCEAN\n-122.03,36.96,28.0,1607.0,421.0,926.0,385.0,2.425,216100.0,NEAR OCEAN\n-122.04,36.96,32.0,1438.0,306.0,802.0,293.0,4.1964,202000.0,NEAR OCEAN\n-122.04,36.96,42.0,538.0,107.0,200.0,104.0,2.1667,196400.0,NEAR OCEAN\n-122.04,36.96,42.0,1149.0,264.0,703.0,232.0,2.5865,206400.0,NEAR OCEAN\n-122.03,36.96,32.0,2182.0,406.0,1122.0,370.0,3.52,284200.0,NEAR OCEAN\n-122.04,36.95,36.0,1862.0,364.0,1080.0,364.0,4.4567,263800.0,NEAR OCEAN\n-122.01,36.91,19.0,691.0,191.0,324.0,167.0,3.1312,388500.0,NEAR OCEAN\n-122.05,36.96,30.0,971.0,185.0,644.0,173.0,4.2045,226500.0,NEAR OCEAN\n-122.06,36.96,52.0,65.0,17.0,24.0,10.0,4.5,258300.0,NEAR OCEAN\n-122.05,36.87,18.0,2232.0,440.0,1091.0,458.0,3.8269,276000.0,NEAR OCEAN\n-122.04,36.95,27.0,1987.0,374.0,961.0,343.0,3.9667,265800.0,NEAR OCEAN\n-122.04,37.0,52.0,3365.0,644.0,796.0,333.0,2.9712,116600.0,NEAR OCEAN\n-121.75,36.91,32.0,1461.0,422.0,1494.0,416.0,2.8056,173200.0,<1H OCEAN\n-121.74,36.92,29.0,1210.0,281.0,863.0,262.0,3.1062,156000.0,<1H OCEAN\n-121.74,36.92,17.0,2648.0,589.0,1193.0,540.0,2.4461,151700.0,<1H OCEAN\n-121.74,36.92,14.0,3355.0,695.0,1350.0,697.0,2.6506,164600.0,<1H OCEAN\n-121.75,36.93,24.0,4026.0,881.0,2264.0,863.0,3.1327,218100.0,<1H OCEAN\n-121.75,36.92,48.0,1801.0,353.0,1071.0,361.0,3.6,194500.0,<1H OCEAN\n-121.75,36.92,46.0,1362.0,321.0,1068.0,305.0,2.4615,177800.0,<1H OCEAN\n-121.76,36.92,36.0,2096.0,409.0,1454.0,394.0,3.2216,238300.0,<1H OCEAN\n-121.76,36.92,46.0,947.0,257.0,1120.0,264.0,3.4125,160700.0,<1H OCEAN\n-121.75,36.91,52.0,1211.0,447.0,1102.0,392.0,1.6875,161400.0,<1H OCEAN\n-121.76,36.91,23.0,1276.0,437.0,1359.0,376.0,1.9609,155000.0,<1H OCEAN\n-121.75,36.91,42.0,1368.0,468.0,2312.0,484.0,2.5599,151400.0,<1H OCEAN\n-121.77,36.91,8.0,2715.0,750.0,2580.0,718.0,2.8348,162000.0,<1H OCEAN\n-121.76,36.9,44.0,919.0,309.0,1321.0,301.0,2.0775,121400.0,<1H OCEAN\n-121.77,36.93,20.0,2587.0,547.0,1534.0,540.0,2.4375,190400.0,<1H OCEAN\n-121.77,36.93,24.0,1943.0,447.0,1844.0,461.0,3.0192,184300.0,<1H OCEAN\n-121.77,36.93,33.0,1406.0,317.0,1075.0,301.0,3.2813,190000.0,<1H OCEAN\n-121.76,36.92,20.0,2687.0,637.0,2154.0,610.0,2.6434,169700.0,<1H OCEAN\n-121.77,36.92,9.0,4934.0,1112.0,3198.0,977.0,3.5,194800.0,<1H OCEAN\n-121.79,36.93,19.0,2512.0,509.0,1856.0,537.0,3.1815,189100.0,<1H OCEAN\n-121.78,36.92,19.0,1515.0,253.0,975.0,266.0,4.3906,241200.0,<1H OCEAN\n-121.78,36.93,21.0,2794.0,662.0,2236.0,565.0,2.4053,178400.0,<1H OCEAN\n-121.77,36.94,18.0,1063.0,341.0,1033.0,313.0,2.0192,171300.0,<1H OCEAN\n-121.79,36.95,34.0,2152.0,430.0,1516.0,386.0,3.7863,192200.0,<1H OCEAN\n-121.8,36.94,29.0,2377.0,476.0,1669.0,499.0,2.8214,190100.0,<1H OCEAN\n-122.25,37.08,20.0,1201.0,282.0,601.0,234.0,2.5556,177500.0,NEAR OCEAN\n-122.14,37.08,18.0,2420.0,439.0,1278.0,416.0,5.2101,334000.0,NEAR OCEAN\n-122.11,37.05,18.0,3337.0,549.0,1449.0,519.0,5.1412,315800.0,NEAR OCEAN\n-122.13,36.97,27.0,991.0,194.0,543.0,155.0,4.7188,350000.0,NEAR OCEAN\n-122.07,37.13,26.0,1127.0,199.0,543.0,199.0,4.9792,240000.0,NEAR OCEAN\n-122.09,37.09,46.0,695.0,136.0,408.0,148.0,3.9408,222600.0,NEAR OCEAN\n-122.09,37.11,32.0,2637.0,489.0,1031.0,410.0,3.6474,231600.0,NEAR OCEAN\n-122.12,37.09,36.0,1397.0,289.0,661.0,243.0,4.125,239600.0,NEAR OCEAN\n-122.09,37.07,33.0,3581.0,734.0,1780.0,663.0,4.3429,214300.0,NEAR OCEAN\n-122.08,37.08,35.0,1541.0,297.0,791.0,277.0,4.425,204800.0,NEAR OCEAN\n-122.16,37.17,35.0,6422.0,1380.0,2755.0,1064.0,5.0165,202300.0,NEAR OCEAN\n-122.12,37.12,51.0,2419.0,485.0,1078.0,435.0,2.7933,206900.0,NEAR OCEAN\n-122.18,37.15,17.0,1457.0,289.0,591.0,235.0,5.5785,284100.0,NEAR OCEAN\n-122.12,37.16,32.0,1602.0,317.0,752.0,275.0,5.1664,185100.0,NEAR OCEAN\n-122.1,37.19,18.0,808.0,136.0,420.0,145.0,7.1831,273300.0,NEAR OCEAN\n-122.08,37.15,23.0,506.0,96.0,264.0,89.0,7.1366,273900.0,NEAR OCEAN\n-122.11,37.11,46.0,1993.0,404.0,850.0,327.0,5.208,206800.0,NEAR OCEAN\n-122.11,37.14,29.0,3201.0,640.0,1722.0,570.0,4.4597,204100.0,NEAR OCEAN\n-122.13,37.15,39.0,2854.0,613.0,1338.0,518.0,3.9423,180300.0,NEAR OCEAN\n-122.09,37.21,15.0,1969.0,332.0,822.0,324.0,7.8774,394900.0,<1H OCEAN\n-122.0,37.12,17.0,4413.0,672.0,1674.0,608.0,6.9772,383300.0,<1H OCEAN\n-122.04,37.12,30.0,1427.0,311.0,686.0,251.0,4.0781,154500.0,NEAR OCEAN\n-122.02,37.11,36.0,2066.0,401.0,942.0,344.0,5.2417,196400.0,NEAR OCEAN\n-122.05,37.11,39.0,1065.0,248.0,497.0,208.0,4.5972,146300.0,NEAR OCEAN\n-122.07,37.08,21.0,5639.0,894.0,2670.0,871.0,6.0809,270000.0,NEAR OCEAN\n-122.05,37.05,41.0,2422.0,502.0,915.0,366.0,4.1679,201300.0,NEAR OCEAN\n-122.07,37.06,31.0,1634.0,370.0,939.0,332.0,3.8625,232300.0,NEAR OCEAN\n-122.08,37.04,34.0,2800.0,577.0,1353.0,512.0,4.1161,220900.0,NEAR OCEAN\n-122.08,37.03,36.0,4682.0,899.0,2143.0,832.0,4.5096,203700.0,NEAR OCEAN\n-122.04,37.04,17.0,4977.0,994.0,1987.0,947.0,3.8854,312300.0,NEAR OCEAN\n-122.03,37.03,21.0,4650.0,733.0,2014.0,704.0,5.6233,322000.0,NEAR OCEAN\n-122.02,37.01,20.0,1005.0,138.0,345.0,129.0,10.0968,500001.0,NEAR OCEAN\n-122.03,37.0,30.0,2077.0,342.0,816.0,328.0,5.2078,440500.0,NEAR OCEAN\n-122.04,37.08,20.0,467.0,95.0,229.0,86.0,4.8,261500.0,NEAR OCEAN\n-122.02,37.09,35.0,1818.0,368.0,682.0,254.0,4.8611,240000.0,NEAR OCEAN\n-122.03,37.05,12.0,2010.0,422.0,784.0,407.0,3.9728,190900.0,NEAR OCEAN\n-122.01,37.06,19.0,4113.0,767.0,2006.0,732.0,5.1121,308100.0,NEAR OCEAN\n-122.0,37.08,17.0,4154.0,739.0,2149.0,693.0,5.5919,373400.0,NEAR OCEAN\n-121.95,37.11,21.0,2387.0,357.0,913.0,341.0,7.736,397700.0,<1H OCEAN\n-121.96,37.1,20.0,922.0,155.0,361.0,135.0,6.3617,331500.0,<1H OCEAN\n-121.87,37.1,20.0,1918.0,304.0,798.0,302.0,7.5755,402300.0,<1H OCEAN\n-121.9,37.1,23.0,1708.0,287.0,670.0,238.0,6.4517,356600.0,<1H OCEAN\n-121.93,37.04,36.0,1522.0,230.0,677.0,206.0,5.8642,363500.0,<1H OCEAN\n-121.96,37.03,17.0,1343.0,203.0,511.0,185.0,4.625,386400.0,NEAR OCEAN\n-121.93,37.05,14.0,679.0,108.0,306.0,113.0,6.4214,340600.0,<1H OCEAN\n-121.96,37.06,16.0,1321.0,224.0,650.0,206.0,6.3258,390000.0,NEAR OCEAN\n-121.99,37.05,19.0,2023.0,392.0,955.0,328.0,5.2486,353000.0,NEAR OCEAN\n-122.0,37.06,20.0,2403.0,376.0,1149.0,369.0,6.0621,304400.0,NEAR OCEAN\n-122.01,37.03,21.0,5904.0,956.0,2616.0,916.0,5.9039,355300.0,NEAR OCEAN\n-121.97,37.01,21.0,2073.0,357.0,1044.0,351.0,4.5682,371600.0,NEAR OCEAN\n-121.98,36.99,14.0,6787.0,1454.0,3416.0,1357.0,3.5943,262400.0,NEAR OCEAN\n-121.97,37.0,25.0,990.0,166.0,522.0,185.0,4.8269,272900.0,NEAR OCEAN\n-121.98,36.98,29.0,2681.0,632.0,1652.0,620.0,3.075,215800.0,NEAR OCEAN\n-121.99,36.99,16.0,1592.0,369.0,1039.0,351.0,3.6364,207000.0,NEAR OCEAN\n-121.99,36.98,25.0,2113.0,422.0,1365.0,439.0,4.6484,234600.0,NEAR OCEAN\n-121.99,36.98,19.0,5613.0,1321.0,3018.0,1268.0,3.1914,215600.0,NEAR OCEAN\n-121.98,36.97,21.0,3349.0,737.0,1952.0,718.0,3.7273,251900.0,NEAR OCEAN\n-121.98,36.96,20.0,3495.0,818.0,2186.0,772.0,3.1167,258300.0,NEAR OCEAN\n-121.99,36.97,15.0,3044.0,786.0,1306.0,693.0,2.1771,213200.0,NEAR OCEAN\n-122.0,36.97,39.0,2702.0,646.0,1136.0,491.0,2.8941,256700.0,NEAR OCEAN\n-121.99,36.96,42.0,1275.0,272.0,451.0,200.0,4.7321,422400.0,NEAR OCEAN\n-121.99,36.96,16.0,875.0,201.0,300.0,157.0,2.625,377300.0,NEAR OCEAN\n-121.96,36.88,37.0,2846.0,553.0,939.0,433.0,4.7468,294400.0,NEAR OCEAN\n-121.97,36.97,24.0,3665.0,870.0,1954.0,833.0,2.8036,228500.0,NEAR OCEAN\n-121.97,36.96,27.0,4001.0,999.0,1808.0,945.0,2.561,234600.0,NEAR OCEAN\n-121.98,36.96,31.0,3209.0,723.0,1489.0,692.0,3.6619,245100.0,NEAR OCEAN\n-121.96,36.98,16.0,4907.0,1117.0,2265.0,1048.0,2.6757,229200.0,NEAR OCEAN\n-121.97,36.98,17.0,2813.0,497.0,1337.0,477.0,3.7083,252400.0,NEAR OCEAN\n-121.97,36.97,15.0,2849.0,668.0,1546.0,582.0,2.7587,228600.0,NEAR OCEAN\n-121.96,36.97,23.0,4324.0,1034.0,1844.0,875.0,3.0777,263800.0,NEAR OCEAN\n-121.94,36.98,24.0,3010.0,562.0,1360.0,504.0,4.2006,290700.0,NEAR OCEAN\n-121.94,36.98,21.0,3520.0,831.0,1486.0,753.0,3.0905,264300.0,NEAR OCEAN\n-121.95,36.98,34.0,3745.0,958.0,1622.0,802.0,3.1546,261200.0,NEAR OCEAN\n-121.94,36.97,31.0,1738.0,422.0,746.0,355.0,2.5172,330800.0,NEAR OCEAN\n-121.93,36.99,19.0,6356.0,1100.0,2954.0,1070.0,4.7325,283500.0,NEAR OCEAN\n-121.94,37.0,32.0,2210.0,426.0,1082.0,396.0,4.1587,315200.0,NEAR OCEAN\n-121.96,37.0,20.0,3847.0,727.0,1725.0,737.0,3.3447,305200.0,NEAR OCEAN\n-121.96,36.99,23.0,3209.0,748.0,1423.0,666.0,2.7375,238000.0,NEAR OCEAN\n-121.94,36.99,11.0,4571.0,924.0,2004.0,847.0,4.2898,221700.0,NEAR OCEAN\n-121.86,37.0,16.0,8638.0,1392.0,3706.0,1251.0,5.503,351800.0,<1H OCEAN\n-121.91,36.99,23.0,5675.0,964.0,2197.0,880.0,4.8693,322300.0,NEAR OCEAN\n-121.91,36.98,16.0,1957.0,408.0,865.0,369.0,2.6875,233300.0,NEAR OCEAN\n-121.88,36.98,21.0,4117.0,752.0,2001.0,763.0,4.8953,289500.0,NEAR OCEAN\n-121.91,36.97,19.0,4920.0,1092.0,1807.0,922.0,3.5112,231900.0,NEAR OCEAN\n-121.92,36.95,29.0,3457.0,699.0,1327.0,563.0,3.6597,252300.0,NEAR OCEAN\n-121.88,36.96,18.0,4910.0,817.0,1971.0,773.0,5.8325,308800.0,NEAR OCEAN\n-121.9,36.97,21.0,3707.0,751.0,1420.0,608.0,4.4485,295200.0,NEAR OCEAN\n-121.88,36.96,18.0,6355.0,1100.0,2304.0,972.0,6.0281,321100.0,NEAR OCEAN\n-121.9,36.93,22.0,7281.0,1233.0,1849.0,832.0,5.3276,335500.0,NEAR OCEAN\n-121.87,36.95,7.0,3703.0,679.0,1375.0,608.0,4.9219,368400.0,NEAR OCEAN\n-121.84,36.94,29.0,4921.0,967.0,2319.0,823.0,4.9517,307900.0,NEAR OCEAN\n-121.89,36.89,18.0,2774.0,492.0,1283.0,353.0,5.368,352000.0,NEAR OCEAN\n-121.91,36.85,22.0,2442.0,624.0,1301.0,290.0,3.1563,300000.0,NEAR OCEAN\n-121.82,36.86,17.0,1573.0,272.0,142.0,55.0,2.1719,420000.0,NEAR OCEAN\n-121.79,37.0,28.0,2715.0,451.0,1154.0,386.0,4.8021,290400.0,<1H OCEAN\n-121.83,36.98,19.0,4431.0,705.0,1764.0,679.0,4.3321,298600.0,<1H OCEAN\n-121.82,36.95,16.0,2599.0,430.0,1417.0,445.0,4.6611,349300.0,<1H OCEAN\n-121.83,37.02,22.0,1903.0,350.0,760.0,322.0,2.9559,288400.0,<1H OCEAN\n-121.76,37.0,21.0,1416.0,269.0,779.0,200.0,3.1987,279800.0,<1H OCEAN\n-121.79,37.03,18.0,943.0,213.0,544.0,179.0,3.934,228600.0,<1H OCEAN\n-121.69,36.96,23.0,1229.0,254.0,687.0,232.0,5.1433,305600.0,<1H OCEAN\n-121.75,36.96,19.0,3461.0,634.0,2790.0,607.0,4.7569,190800.0,<1H OCEAN\n-121.75,36.95,27.0,1580.0,303.0,1066.0,306.0,4.7071,202700.0,<1H OCEAN\n-121.77,36.96,20.0,4228.0,816.0,2389.0,844.0,3.525,229100.0,<1H OCEAN\n-121.73,36.93,29.0,2931.0,535.0,1954.0,506.0,3.2917,224700.0,<1H OCEAN\n-121.67,36.93,22.0,569.0,132.0,542.0,125.0,2.1875,187500.0,<1H OCEAN\n-122.39,40.59,26.0,1279.0,438.0,1276.0,420.0,1.2404,81300.0,INLAND\n-122.39,40.58,44.0,1625.0,392.0,944.0,347.0,1.5972,68900.0,INLAND\n-122.37,40.58,25.0,2054.0,495.0,835.0,475.0,2.1538,76900.0,INLAND\n-122.38,40.58,34.0,1262.0,267.0,520.0,259.0,1.6983,72600.0,INLAND\n-122.38,40.58,36.0,1808.0,384.0,807.0,383.0,1.8375,74800.0,INLAND\n-122.36,40.58,17.0,1220.0,275.0,800.0,261.0,1.9181,118800.0,INLAND\n-122.34,40.58,7.0,4843.0,992.0,2223.0,932.0,3.0549,101700.0,INLAND\n-122.35,40.57,22.0,589.0,97.0,338.0,107.0,3.2639,87500.0,INLAND\n-122.34,40.57,24.0,1610.0,307.0,748.0,307.0,2.6591,82800.0,INLAND\n-122.38,40.57,43.0,2251.0,542.0,1479.0,512.0,1.5676,58200.0,INLAND\n-122.38,40.56,23.0,2281.0,408.0,1164.0,420.0,3.5347,101200.0,INLAND\n-122.38,40.54,36.0,1216.0,240.0,647.0,228.0,2.6944,75300.0,INLAND\n-122.37,40.54,28.0,2213.0,390.0,1096.0,378.0,3.6923,86900.0,INLAND\n-122.4,40.58,40.0,3895.0,929.0,1782.0,910.0,1.3329,78200.0,INLAND\n-122.41,40.58,35.0,2072.0,385.0,1029.0,375.0,2.8512,75600.0,INLAND\n-122.4,40.58,43.0,1455.0,300.0,747.0,279.0,2.7857,104200.0,INLAND\n-122.39,40.57,38.0,855.0,172.0,468.0,150.0,1.4091,84400.0,INLAND\n-122.42,40.59,24.0,5045.0,972.0,2220.0,979.0,2.6792,138900.0,INLAND\n-122.45,40.61,17.0,785.0,155.0,417.0,136.0,2.3289,58200.0,INLAND\n-122.45,40.56,17.0,1712.0,307.0,963.0,329.0,3.9375,148700.0,INLAND\n-122.42,40.57,10.0,7949.0,1309.0,3176.0,1163.0,4.1099,120100.0,INLAND\n-122.4,40.57,23.0,1321.0,259.0,749.0,222.0,1.655,90100.0,INLAND\n-122.4,40.62,9.0,4794.0,889.0,2162.0,865.0,3.1439,103100.0,INLAND\n-122.42,40.6,5.0,2614.0,433.0,1275.0,411.0,3.4464,122900.0,INLAND\n-122.39,40.6,22.0,2195.0,489.0,1021.0,460.0,1.4125,66500.0,INLAND\n-122.38,40.61,14.0,4773.0,1133.0,2101.0,1072.0,1.7227,105000.0,INLAND\n-122.37,40.6,7.0,5178.0,1336.0,2557.0,1283.0,2.4079,111400.0,INLAND\n-122.39,40.64,13.0,3604.0,704.0,1598.0,670.0,2.4141,78700.0,INLAND\n-122.36,40.62,11.0,3896.0,886.0,1902.0,843.0,2.2905,94200.0,INLAND\n-122.34,40.63,10.0,2183.0,369.0,1061.0,325.0,3.6853,151600.0,INLAND\n-122.33,40.6,5.0,6383.0,1206.0,2965.0,1141.0,3.8103,111100.0,INLAND\n-122.32,40.58,2.0,1937.0,350.0,756.0,274.0,3.0,114200.0,INLAND\n-122.3,40.58,19.0,1043.0,204.0,505.0,183.0,1.6033,98800.0,INLAND\n-122.36,40.56,20.0,3592.0,868.0,1865.0,781.0,2.0258,64800.0,INLAND\n-122.37,40.55,26.0,1435.0,234.0,544.0,232.0,2.6705,136700.0,INLAND\n-122.36,40.55,21.0,2500.0,466.0,1428.0,502.0,2.6513,113300.0,INLAND\n-122.46,40.52,13.0,2085.0,322.0,1077.0,333.0,5.2149,146500.0,INLAND\n-122.41,40.55,19.0,3753.0,761.0,1952.0,738.0,3.0954,86500.0,INLAND\n-122.41,40.53,28.0,1127.0,245.0,538.0,208.0,2.037,72000.0,INLAND\n-122.39,40.53,28.0,1427.0,304.0,692.0,285.0,2.125,80800.0,INLAND\n-122.39,40.52,24.0,2068.0,346.0,951.0,332.0,3.9306,85900.0,INLAND\n-122.4,40.51,20.0,1750.0,352.0,834.0,340.0,2.485,100600.0,INLAND\n-122.37,40.52,18.0,4547.0,774.0,2269.0,766.0,3.7896,98100.0,INLAND\n-122.35,40.56,16.0,2801.0,614.0,1695.0,563.0,1.9,81600.0,INLAND\n-122.35,40.56,12.0,3900.0,863.0,2145.0,864.0,1.9881,85200.0,INLAND\n-122.35,40.54,17.0,2280.0,453.0,976.0,434.0,2.71,97800.0,INLAND\n-122.36,40.57,31.0,431.0,90.0,231.0,78.0,2.184,77300.0,INLAND\n-122.35,40.57,18.0,2226.0,490.0,859.0,451.0,1.6821,69400.0,INLAND\n-122.34,40.57,26.0,2187.0,472.0,1339.0,463.0,2.0395,67900.0,INLAND\n-122.33,40.57,16.0,2777.0,503.0,1432.0,500.0,2.5592,75900.0,INLAND\n-122.32,40.57,15.0,2524.0,449.0,1374.0,467.0,3.3816,93800.0,INLAND\n-122.31,40.55,11.0,13714.0,2302.0,6511.0,2267.0,3.5522,100100.0,INLAND\n-122.34,40.51,16.0,2247.0,502.0,1206.0,463.0,1.9946,119200.0,INLAND\n-122.33,40.52,23.0,2801.0,507.0,1318.0,454.0,3.5081,116700.0,INLAND\n-122.31,40.49,18.0,4026.0,718.0,1731.0,705.0,3.35,118400.0,INLAND\n-122.28,40.5,21.0,2405.0,476.0,1197.0,412.0,2.6488,83100.0,INLAND\n-122.4,40.71,22.0,2390.0,484.0,1131.0,452.0,2.1458,84700.0,INLAND\n-122.43,40.66,15.0,2532.0,458.0,1183.0,450.0,2.5417,92200.0,INLAND\n-122.42,40.63,23.0,2248.0,489.0,1132.0,444.0,1.6429,80400.0,INLAND\n-122.38,40.69,21.0,1774.0,370.0,875.0,354.0,1.7422,61500.0,INLAND\n-122.36,40.69,32.0,3611.0,772.0,2060.0,759.0,1.7427,60600.0,INLAND\n-122.35,40.68,36.0,1822.0,449.0,930.0,399.0,1.3801,56600.0,INLAND\n-122.36,40.68,28.0,1745.0,379.0,1011.0,370.0,2.0391,59800.0,INLAND\n-122.38,40.67,10.0,2281.0,444.0,1274.0,438.0,2.212,65600.0,INLAND\n-122.36,40.66,17.0,2786.0,559.0,1528.0,517.0,2.0119,75800.0,INLAND\n-122.31,40.65,11.0,3664.0,647.0,1686.0,613.0,2.9338,141600.0,INLAND\n-122.25,40.6,16.0,2753.0,494.0,1414.0,459.0,3.8323,128300.0,INLAND\n-122.23,40.63,16.0,1141.0,220.0,563.0,200.0,2.3287,130700.0,INLAND\n-122.25,40.66,15.0,2771.0,546.0,1423.0,505.0,3.6413,108500.0,INLAND\n-122.32,40.71,18.0,2879.0,578.0,1399.0,586.0,2.4036,105400.0,INLAND\n-122.31,40.75,18.0,1411.0,330.0,494.0,227.0,1.4911,75800.0,INLAND\n-122.27,40.53,17.0,2255.0,416.0,1171.0,411.0,2.875,129800.0,INLAND\n-122.26,40.58,14.0,2539.0,466.0,1271.0,438.0,3.9762,138500.0,INLAND\n-122.23,40.57,18.0,1633.0,243.0,750.0,252.0,5.1585,150800.0,INLAND\n-122.24,40.51,23.0,2216.0,378.0,1006.0,338.0,4.559,116800.0,INLAND\n-122.31,40.45,10.0,1187.0,236.0,728.0,248.0,2.0469,66800.0,INLAND\n-122.31,40.45,25.0,2596.0,557.0,1536.0,549.0,2.0221,60400.0,INLAND\n-122.29,40.44,30.0,1270.0,365.0,840.0,324.0,1.3904,48100.0,INLAND\n-122.29,40.43,21.0,2842.0,640.0,1658.0,608.0,1.9943,59800.0,INLAND\n-122.29,40.47,20.0,2858.0,612.0,1422.0,589.0,1.9657,63000.0,INLAND\n-122.31,40.47,26.0,2723.0,551.0,1326.0,547.0,2.3594,66000.0,INLAND\n-122.3,40.45,32.0,1286.0,271.0,694.0,236.0,1.6579,68500.0,INLAND\n-122.27,40.46,14.0,2633.0,530.0,1324.0,513.0,2.2768,78600.0,INLAND\n-122.24,40.45,27.0,1804.0,321.0,782.0,300.0,3.5978,80600.0,INLAND\n-122.25,40.42,17.0,1429.0,265.0,692.0,245.0,2.8611,98700.0,INLAND\n-122.23,40.4,18.0,2102.0,377.0,1059.0,384.0,3.0556,95500.0,INLAND\n-122.27,40.39,26.0,1833.0,422.0,939.0,408.0,1.3571,59000.0,INLAND\n-122.29,40.39,17.0,1682.0,332.0,887.0,316.0,1.8438,76400.0,INLAND\n-122.33,40.48,26.0,695.0,126.0,319.0,124.0,3.2788,101600.0,INLAND\n-122.33,40.47,30.0,2502.0,523.0,1296.0,481.0,2.125,66100.0,INLAND\n-122.36,40.48,21.0,2333.0,514.0,1308.0,509.0,2.0899,74800.0,INLAND\n-122.37,40.45,18.0,1748.0,337.0,921.0,327.0,3.3315,85400.0,INLAND\n-122.42,40.44,16.0,994.0,,495.0,181.0,2.1875,76400.0,INLAND\n-122.43,40.47,16.0,3552.0,704.0,1801.0,658.0,2.1496,97700.0,INLAND\n-122.45,40.46,16.0,2734.0,501.0,1413.0,484.0,2.8085,105700.0,INLAND\n-122.37,40.39,12.0,3783.0,702.0,1970.0,639.0,3.3005,98500.0,INLAND\n-122.32,40.42,17.0,3019.0,578.0,1538.0,545.0,2.793,76500.0,INLAND\n-122.56,40.75,20.0,1182.0,250.0,512.0,210.0,1.7935,74500.0,INLAND\n-122.57,40.61,27.0,1540.0,315.0,883.0,321.0,2.8036,93400.0,INLAND\n-122.66,40.52,13.0,3013.0,486.0,1361.0,515.0,4.5357,171200.0,INLAND\n-122.76,40.4,22.0,2153.0,461.0,903.0,314.0,2.125,123200.0,INLAND\n-122.34,41.06,33.0,2149.0,498.0,631.0,273.0,1.8816,65800.0,INLAND\n-122.31,40.89,18.0,754.0,161.0,247.0,107.0,2.2583,78800.0,INLAND\n-122.45,40.85,20.0,2701.0,573.0,892.0,358.0,2.7736,107800.0,INLAND\n-122.07,40.95,14.0,2721.0,627.0,1356.0,468.0,3.0299,73200.0,INLAND\n-121.89,40.97,26.0,1183.0,276.0,513.0,206.0,2.225,52000.0,INLAND\n-121.86,40.77,17.0,2816.0,639.0,1027.0,406.0,2.503,65600.0,INLAND\n-121.83,40.69,14.0,821.0,170.0,477.0,129.0,3.15,87500.0,INLAND\n-122.08,40.64,14.0,3099.0,519.0,1447.0,494.0,4.0132,141200.0,INLAND\n-122.06,40.55,17.0,3057.0,577.0,1497.0,556.0,3.5189,101000.0,INLAND\n-121.92,40.52,13.0,4581.0,881.0,1799.0,734.0,2.2993,99500.0,INLAND\n-121.67,40.61,8.0,2411.0,463.0,786.0,297.0,2.1513,80400.0,INLAND\n-121.55,40.48,14.0,2413.0,524.0,805.0,329.0,2.7857,77400.0,INLAND\n-121.64,40.9,24.0,2237.0,434.0,834.0,318.0,1.7538,90300.0,INLAND\n-121.67,40.87,31.0,1581.0,299.0,776.0,287.0,2.9063,77800.0,INLAND\n-121.65,40.88,15.0,2909.0,549.0,1537.0,522.0,3.0179,61300.0,INLAND\n-121.67,40.89,17.0,2548.0,537.0,1118.0,461.0,2.267,57800.0,INLAND\n-121.63,40.92,23.0,1922.0,411.0,872.0,350.0,2.2337,64500.0,INLAND\n-121.41,40.82,16.0,2668.0,516.0,915.0,362.0,2.3393,90300.0,INLAND\n-121.47,41.12,22.0,2737.0,512.0,1168.0,442.0,2.83,88700.0,INLAND\n-121.45,41.04,33.0,2029.0,378.0,936.0,343.0,2.67,77500.0,INLAND\n-120.08,39.61,32.0,1404.0,247.0,544.0,201.0,2.7778,72900.0,INLAND\n-120.23,39.56,14.0,1781.0,346.0,734.0,287.0,2.46,93000.0,INLAND\n-120.48,39.66,32.0,1516.0,289.0,304.0,131.0,1.8839,71000.0,INLAND\n-120.24,39.67,40.0,690.0,129.0,305.0,110.0,2.3625,62500.0,INLAND\n-120.73,39.63,17.0,1791.0,356.0,432.0,190.0,3.8826,92400.0,INLAND\n-120.24,39.67,52.0,296.0,63.0,143.0,56.0,3.625,68600.0,INLAND\n-120.92,39.56,48.0,1276.0,292.0,358.0,145.0,1.875,66600.0,INLAND\n-120.51,39.52,26.0,2286.0,444.0,498.0,216.0,2.065,96100.0,INLAND\n-121.62,41.78,40.0,3272.0,663.0,1467.0,553.0,1.7885,43500.0,INLAND\n-121.93,41.86,28.0,4225.0,835.0,1908.0,686.0,1.74,44000.0,INLAND\n-122.33,41.86,19.0,3599.0,695.0,1572.0,601.0,2.234,58600.0,INLAND\n-122.53,41.81,21.0,2400.0,485.0,1109.0,443.0,1.7639,55400.0,INLAND\n-122.26,41.66,17.0,1885.0,350.0,953.0,328.0,2.1607,61400.0,INLAND\n-122.64,41.95,18.0,1867.0,424.0,802.0,314.0,1.8242,53500.0,INLAND\n-123.26,41.86,25.0,2344.0,532.0,1117.0,424.0,2.7222,64600.0,INLAND\n-123.38,41.8,25.0,1941.0,477.0,1000.0,390.0,2.2976,54400.0,INLAND\n-123.41,41.6,23.0,1654.0,369.0,669.0,273.0,1.965,65400.0,INLAND\n-122.92,41.7,23.0,4017.0,792.0,1634.0,619.0,2.3571,62000.0,INLAND\n-122.56,41.69,21.0,2010.0,360.0,947.0,306.0,2.4107,70100.0,INLAND\n-122.61,41.74,15.0,4206.0,922.0,1863.0,869.0,2.0591,55700.0,INLAND\n-122.64,41.74,33.0,2644.0,459.0,1113.0,483.0,3.3095,81300.0,INLAND\n-122.64,41.73,36.0,3319.0,664.0,1492.0,631.0,1.8694,71200.0,INLAND\n-122.64,41.73,50.0,1525.0,308.0,661.0,285.0,2.2206,63200.0,INLAND\n-122.65,41.72,15.0,3643.0,801.0,1784.0,743.0,1.8533,57500.0,INLAND\n-122.73,41.76,19.0,2200.0,414.0,950.0,367.0,2.5357,94200.0,INLAND\n-122.64,41.63,19.0,2722.0,479.0,1108.0,430.0,3.1062,100000.0,INLAND\n-122.56,41.53,29.0,1729.0,355.0,848.0,328.0,2.2024,60900.0,INLAND\n-122.93,41.48,20.0,4288.0,789.0,1800.0,660.0,2.723,79600.0,INLAND\n-122.9,41.46,31.0,1277.0,263.0,600.0,241.0,1.7292,61700.0,INLAND\n-123.08,41.26,34.0,2773.0,679.0,1066.0,424.0,1.6757,63300.0,INLAND\n-122.38,41.54,14.0,4453.0,797.0,1817.0,685.0,2.7468,81100.0,INLAND\n-122.49,41.43,19.0,3689.0,644.0,1544.0,566.0,3.125,76100.0,INLAND\n-122.38,41.43,45.0,2245.0,448.0,1155.0,421.0,1.6509,46200.0,INLAND\n-122.37,41.41,28.0,1729.0,419.0,929.0,370.0,1.27,53100.0,INLAND\n-122.39,41.41,23.0,910.0,199.0,370.0,169.0,1.7448,80100.0,INLAND\n-122.32,41.31,45.0,1393.0,294.0,521.0,249.0,1.1915,71900.0,INLAND\n-122.3,41.32,13.0,2300.0,513.0,1151.0,488.0,2.1571,81500.0,INLAND\n-122.3,41.31,29.0,4059.0,787.0,1700.0,702.0,2.4526,97100.0,INLAND\n-122.28,41.38,15.0,5266.0,1031.0,2147.0,885.0,2.8036,110100.0,INLAND\n-122.45,41.28,15.0,2740.0,503.0,1188.0,445.0,3.4519,128800.0,INLAND\n-122.34,41.21,26.0,178.0,40.0,55.0,25.0,2.0375,57500.0,INLAND\n-122.27,41.23,40.0,1958.0,386.0,725.0,331.0,2.1898,65500.0,INLAND\n-122.27,41.2,52.0,4513.0,985.0,1926.0,815.0,1.5923,56000.0,INLAND\n-121.76,41.5,31.0,602.0,153.0,112.0,47.0,1.0667,34200.0,INLAND\n-122.09,41.32,52.0,4019.0,824.0,1728.0,706.0,2.2462,62900.0,INLAND\n-122.22,38.12,15.0,14125.0,2344.0,6456.0,2147.0,5.1014,179500.0,NEAR BAY\n-122.19,38.13,5.0,7854.0,1446.0,4361.0,1395.0,4.9504,214800.0,NEAR BAY\n-122.21,38.11,31.0,2766.0,604.0,1441.0,552.0,3.1768,131000.0,NEAR BAY\n-122.21,38.11,35.0,2122.0,400.0,1189.0,408.0,3.0962,124600.0,NEAR BAY\n-122.21,38.1,36.0,3018.0,557.0,1445.0,556.0,3.8029,129900.0,NEAR BAY\n-122.22,38.11,43.0,1939.0,353.0,968.0,392.0,3.1848,112700.0,NEAR BAY\n-122.22,38.1,38.0,931.0,181.0,566.0,207.0,3.0221,93300.0,NEAR BAY\n-122.22,38.1,40.0,2549.0,478.0,1275.0,494.0,2.9469,111600.0,NEAR BAY\n-122.22,38.1,44.0,2256.0,451.0,1057.0,426.0,3.1204,110800.0,NEAR BAY\n-122.22,38.1,44.0,3013.0,563.0,1353.0,512.0,3.4559,111900.0,NEAR BAY\n-122.22,38.09,47.0,2161.0,440.0,966.0,360.0,2.2734,88700.0,NEAR BAY\n-122.21,38.09,37.0,4368.0,779.0,2083.0,741.0,3.8667,127000.0,NEAR BAY\n-122.2,38.09,18.0,6860.0,1079.0,3205.0,1058.0,5.2957,171900.0,NEAR BAY\n-122.22,38.08,37.0,2811.0,,1574.0,516.0,3.1053,96700.0,NEAR BAY\n-122.22,38.08,37.0,4590.0,857.0,2920.0,832.0,3.436,94800.0,NEAR BAY\n-122.22,38.07,4.0,15654.0,2394.0,7025.0,2168.0,5.8171,225200.0,NEAR BAY\n-122.25,38.09,48.0,833.0,188.0,652.0,165.0,2.2417,87900.0,NEAR BAY\n-122.23,38.09,26.0,4397.0,997.0,2539.0,965.0,2.4875,90000.0,NEAR BAY\n-122.24,38.07,13.0,5451.0,1194.0,2957.0,1081.0,2.6098,162500.0,NEAR BAY\n-122.26,38.1,24.0,1213.0,395.0,699.0,386.0,1.3007,94600.0,NEAR BAY\n-122.26,38.1,30.0,3317.0,1058.0,1794.0,990.0,1.1835,133300.0,NEAR BAY\n-122.25,38.1,52.0,248.0,86.0,173.0,69.0,2.3,109400.0,NEAR BAY\n-122.25,38.1,52.0,1780.0,373.0,824.0,317.0,2.75,109900.0,NEAR BAY\n-122.25,38.1,52.0,2315.0,556.0,1113.0,486.0,2.5042,147900.0,NEAR BAY\n-122.25,38.1,52.0,1591.0,372.0,817.0,357.0,2.1411,97200.0,NEAR BAY\n-122.23,38.1,46.0,4143.0,895.0,2240.0,847.0,2.4201,92800.0,NEAR BAY\n-122.24,38.1,49.0,1851.0,356.0,849.0,307.0,2.9432,103500.0,NEAR BAY\n-122.24,38.11,52.0,2050.0,492.0,1277.0,463.0,3.0507,107300.0,NEAR BAY\n-122.24,38.11,42.0,1743.0,388.0,889.0,341.0,2.3241,99200.0,NEAR BAY\n-122.23,38.1,47.0,1303.0,278.0,694.0,269.0,2.5969,92800.0,NEAR BAY\n-122.23,38.12,49.0,2715.0,435.0,1006.0,429.0,4.2308,145800.0,NEAR BAY\n-122.23,38.11,47.0,3007.0,524.0,1152.0,486.0,4.0,141500.0,NEAR BAY\n-122.24,38.11,52.0,2111.0,310.0,772.0,323.0,4.775,148200.0,NEAR BAY\n-122.23,38.13,29.0,5154.0,1084.0,2459.0,1019.0,3.2664,142900.0,NEAR BAY\n-122.24,38.13,37.0,3223.0,564.0,1325.0,539.0,4.0938,126900.0,NEAR BAY\n-122.24,38.12,39.0,2967.0,500.0,1243.0,523.0,4.2902,152400.0,NEAR BAY\n-122.24,38.12,42.0,1625.0,255.0,578.0,243.0,4.0114,166900.0,NEAR BAY\n-122.25,38.12,47.0,1339.0,298.0,794.0,286.0,2.5865,109800.0,NEAR BAY\n-122.25,38.11,49.0,2365.0,504.0,1131.0,458.0,2.6133,103100.0,NEAR BAY\n-122.25,38.11,52.0,2846.0,705.0,1519.0,620.0,2.1976,97900.0,NEAR BAY\n-122.26,38.11,52.0,793.0,216.0,505.0,194.0,1.9667,93800.0,NEAR BAY\n-122.26,38.11,52.0,1560.0,353.0,670.0,287.0,1.7411,98400.0,NEAR BAY\n-122.26,38.11,52.0,2573.0,639.0,1238.0,529.0,2.6708,109700.0,NEAR BAY\n-122.26,38.12,28.0,3102.0,734.0,1623.0,639.0,3.1025,103700.0,NEAR BAY\n-122.27,38.12,45.0,4423.0,1001.0,2109.0,874.0,2.6937,111800.0,NEAR BAY\n-122.27,38.12,42.0,5266.0,1167.0,3124.0,1025.0,2.7375,120000.0,NEAR BAY\n-122.26,38.15,26.0,3699.0,671.0,2388.0,699.0,4.0515,121900.0,NEAR BAY\n-122.26,38.14,34.0,963.0,159.0,392.0,176.0,4.0156,134700.0,NEAR BAY\n-122.26,38.15,16.0,3921.0,727.0,2830.0,680.0,4.5053,123700.0,NEAR BAY\n-122.32,38.12,12.0,5382.0,928.0,3928.0,921.0,5.3785,150600.0,NEAR BAY\n-122.26,38.13,28.0,3072.0,790.0,1375.0,705.0,1.6368,91200.0,NEAR BAY\n-122.24,38.14,15.0,8479.0,1759.0,5008.0,1646.0,3.724,131600.0,NEAR BAY\n-122.24,38.15,10.0,6817.0,1188.0,4163.0,1135.0,4.4529,144100.0,NEAR BAY\n-122.25,38.15,24.0,2917.0,543.0,1878.0,531.0,3.7014,123600.0,NEAR BAY\n-122.22,38.15,7.0,5129.0,,2824.0,738.0,5.5138,171100.0,NEAR BAY\n-122.23,38.15,33.0,1253.0,238.0,753.0,236.0,1.756,86400.0,NEAR BAY\n-122.23,38.14,36.0,1412.0,260.0,792.0,268.0,2.3056,90400.0,NEAR BAY\n-122.15,38.04,14.0,2804.0,587.0,1083.0,573.0,2.6466,168500.0,NEAR BAY\n-122.16,38.05,52.0,1888.0,457.0,830.0,408.0,3.1373,185100.0,NEAR BAY\n-122.16,38.05,34.0,2434.0,428.0,926.0,423.0,4.6776,208300.0,NEAR BAY\n-122.19,38.07,20.0,3000.0,525.0,1207.0,491.0,4.6406,217500.0,NEAR BAY\n-122.18,38.07,10.0,4976.0,849.0,2089.0,803.0,5.3288,201800.0,NEAR BAY\n-122.17,38.07,15.0,2125.0,278.0,857.0,272.0,6.4599,219700.0,NEAR BAY\n-122.16,38.07,14.0,6360.0,1236.0,2876.0,1127.0,4.5321,190300.0,NEAR BAY\n-122.17,38.06,16.0,3515.0,626.0,1764.0,626.0,4.4397,187100.0,NEAR BAY\n-122.15,38.06,10.0,3008.0,532.0,1381.0,522.0,5.3661,195800.0,NEAR BAY\n-122.17,38.08,7.0,18392.0,2782.0,8276.0,2742.0,6.4232,229200.0,NEAR BAY\n-122.2,38.1,5.0,9567.0,1729.0,4620.0,1580.0,4.4821,210000.0,NEAR BAY\n-122.19,38.09,8.0,614.0,118.0,278.0,115.0,6.3735,166300.0,NEAR BAY\n-122.14,38.05,27.0,3794.0,772.0,1756.0,724.0,3.2891,150600.0,NEAR BAY\n-122.14,38.07,31.0,3401.0,616.0,1750.0,602.0,4.6761,143100.0,NEAR BAY\n-122.11,38.09,11.0,673.0,145.0,318.0,137.0,2.3929,122500.0,NEAR BAY\n-122.13,38.26,40.0,1538.0,255.0,669.0,263.0,3.3281,170200.0,NEAR BAY\n-122.15,38.29,17.0,1625.0,239.0,703.0,224.0,6.5891,328800.0,NEAR BAY\n-122.18,38.29,18.0,1953.0,265.0,658.0,270.0,8.0113,393000.0,NEAR BAY\n-122.18,38.23,21.0,2475.0,341.0,812.0,308.0,7.2589,320400.0,NEAR BAY\n-122.14,38.16,4.0,3273.0,495.0,1497.0,454.0,5.3345,176100.0,NEAR BAY\n-122.18,38.17,7.0,4093.0,651.0,2228.0,646.0,5.2523,161300.0,NEAR BAY\n-122.07,38.26,15.0,1173.0,146.0,450.0,154.0,6.0487,197700.0,INLAND\n-122.1,38.24,13.0,7367.0,1042.0,3193.0,983.0,5.3102,195000.0,NEAR BAY\n-122.06,38.27,14.0,6920.0,996.0,3196.0,978.0,5.0672,171300.0,INLAND\n-122.07,38.27,8.0,6761.0,1234.0,3237.0,1177.0,4.3586,173400.0,INLAND\n-122.08,38.3,2.0,6718.0,858.0,2012.0,654.0,6.8872,305200.0,INLAND\n-122.04,38.28,12.0,3861.0,795.0,2129.0,806.0,3.676,135000.0,INLAND\n-122.04,38.28,25.0,3304.0,493.0,1464.0,488.0,5.2527,130600.0,INLAND\n-122.03,38.28,15.0,5114.0,833.0,2418.0,778.0,4.4882,144000.0,INLAND\n-122.03,38.3,5.0,1569.0,199.0,713.0,209.0,6.6779,223900.0,INLAND\n-122.0,38.28,3.0,7030.0,1191.0,3238.0,1055.0,4.962,161700.0,INLAND\n-121.98,38.29,4.0,8778.0,1291.0,4010.0,1188.0,5.4399,187100.0,INLAND\n-121.99,38.15,36.0,263.0,73.0,88.0,42.0,2.5313,162500.0,INLAND\n-122.05,38.26,32.0,1070.0,199.0,631.0,195.0,2.6827,98900.0,INLAND\n-122.05,38.25,37.0,1336.0,251.0,680.0,231.0,3.815,99000.0,INLAND\n-122.06,38.25,36.0,1818.0,323.0,953.0,298.0,3.3153,99000.0,INLAND\n-122.06,38.25,34.0,1562.0,289.0,898.0,307.0,3.3598,107200.0,INLAND\n-122.06,38.26,36.0,1248.0,221.0,672.0,222.0,3.3839,105900.0,INLAND\n-122.07,38.24,15.0,7937.0,1635.0,4390.0,1567.0,3.5464,129800.0,INLAND\n-122.04,38.25,38.0,1214.0,244.0,632.0,254.0,2.8438,94200.0,INLAND\n-122.05,38.25,39.0,199.0,36.0,101.0,38.0,6.2299,105400.0,INLAND\n-122.04,38.26,34.0,3082.0,702.0,1795.0,703.0,2.7885,105900.0,INLAND\n-122.04,38.25,37.0,1176.0,291.0,648.0,271.0,2.7167,92200.0,INLAND\n-122.04,38.25,32.0,1203.0,287.0,571.0,255.0,3.0938,110400.0,INLAND\n-122.04,38.25,52.0,582.0,131.0,241.0,106.0,2.4,125000.0,INLAND\n-122.05,38.26,21.0,7195.0,1416.0,3927.0,1377.0,3.0912,126300.0,INLAND\n-122.04,38.27,16.0,8517.0,1910.0,4508.0,1837.0,3.1853,129600.0,INLAND\n-122.03,38.27,24.0,3580.0,735.0,1959.0,731.0,2.7284,118500.0,INLAND\n-122.03,38.26,25.0,4617.0,1046.0,2685.0,1011.0,2.9576,108500.0,INLAND\n-122.02,38.26,27.0,3440.0,787.0,2085.0,748.0,2.5896,104700.0,INLAND\n-122.03,38.25,35.0,1940.0,384.0,1177.0,403.0,3.1389,101100.0,INLAND\n-122.02,38.27,20.0,2237.0,464.0,1169.0,425.0,3.2115,99100.0,INLAND\n-122.02,38.26,20.0,3899.0,763.0,2198.0,779.0,3.2061,120400.0,INLAND\n-122.01,38.27,17.0,9089.0,1542.0,4758.0,1520.0,4.0619,126600.0,INLAND\n-122.02,38.26,8.0,2894.0,602.0,1566.0,572.0,3.6335,131600.0,INLAND\n-122.0,38.23,1.0,2062.0,343.0,872.0,268.0,5.2636,191300.0,INLAND\n-122.03,38.24,16.0,1104.0,164.0,495.0,156.0,5.4074,157700.0,INLAND\n-122.04,38.24,22.0,2761.0,757.0,2612.0,641.0,1.6875,87500.0,INLAND\n-122.04,38.24,30.0,2081.0,456.0,1005.0,438.0,1.9954,92900.0,INLAND\n-122.03,38.25,13.0,3334.0,541.0,1923.0,538.0,4.0905,134800.0,INLAND\n-122.01,38.25,11.0,6550.0,1149.0,3570.0,1123.0,3.8583,137900.0,INLAND\n-122.01,38.25,16.0,1081.0,181.0,792.0,184.0,4.6779,131300.0,INLAND\n-122.02,38.25,10.0,2237.0,454.0,1255.0,429.0,3.1176,126500.0,INLAND\n-122.0,38.25,7.0,11768.0,1893.0,6657.0,1874.0,4.9222,142900.0,INLAND\n-121.98,38.25,4.0,2487.0,440.0,1545.0,452.0,4.9103,140400.0,INLAND\n-122.01,38.26,12.0,4132.0,710.0,2087.0,633.0,4.5987,139700.0,INLAND\n-121.99,38.27,16.0,4138.0,758.0,1762.0,723.0,3.1979,137500.0,INLAND\n-121.99,38.26,18.0,921.0,126.0,368.0,120.0,6.0842,261100.0,INLAND\n-121.94,38.27,35.0,10869.0,2226.0,9879.0,2152.0,2.5681,81300.0,INLAND\n-121.94,38.37,17.0,7973.0,1591.0,2899.0,1502.0,2.8357,120100.0,INLAND\n-121.94,38.37,14.0,1156.0,216.0,574.0,227.0,3.2396,143800.0,INLAND\n-121.94,38.38,25.0,182.0,48.0,71.0,52.0,1.0208,78600.0,INLAND\n-121.94,38.36,2.0,4953.0,735.0,1791.0,562.0,5.0346,205100.0,INLAND\n-121.99,38.48,17.0,1824.0,348.0,934.0,305.0,4.6719,250000.0,INLAND\n-122.01,38.44,12.0,2344.0,354.0,1035.0,321.0,4.9773,281200.0,INLAND\n-122.0,38.41,11.0,2838.0,429.0,1331.0,426.0,4.945,298400.0,INLAND\n-122.07,38.41,17.0,3053.0,595.0,1434.0,557.0,3.4741,245800.0,INLAND\n-121.95,38.43,19.0,3011.0,551.0,1665.0,535.0,5.1534,232800.0,INLAND\n-121.94,38.41,15.0,1263.0,211.0,665.0,208.0,4.5,260900.0,INLAND\n-121.92,38.37,26.0,2056.0,413.0,933.0,367.0,2.7051,193800.0,INLAND\n-121.96,38.34,15.0,2857.0,373.0,1325.0,359.0,6.0252,151700.0,INLAND\n-121.96,38.34,14.0,3035.0,680.0,1597.0,663.0,3.6036,143500.0,INLAND\n-121.95,38.35,16.0,2084.0,292.0,1099.0,292.0,5.8269,150200.0,INLAND\n-121.94,38.35,8.0,3157.0,559.0,1758.0,569.0,4.412,140100.0,INLAND\n-121.92,38.34,2.0,7747.0,1133.0,3481.0,1083.0,6.1112,181000.0,INLAND\n-121.95,38.34,9.0,4999.0,874.0,2687.0,817.0,4.2324,142100.0,INLAND\n-121.96,38.33,3.0,7985.0,1257.0,3664.0,1215.0,4.976,158300.0,INLAND\n-121.96,38.32,12.0,5127.0,998.0,2749.0,976.0,4.0458,130600.0,INLAND\n-121.93,38.31,25.0,185.0,32.0,85.0,32.0,4.875,250000.0,INLAND\n-121.98,38.32,45.0,19.0,5.0,7460.0,6.0,10.2264,137500.0,INLAND\n-121.98,38.36,33.0,1083.0,217.0,562.0,203.0,2.433,101700.0,INLAND\n-121.99,38.35,45.0,1778.0,339.0,839.0,319.0,2.4659,102900.0,INLAND\n-122.0,38.35,38.0,1918.0,364.0,745.0,348.0,2.5707,126000.0,INLAND\n-122.0,38.35,34.0,1084.0,187.0,561.0,198.0,4.2115,118900.0,INLAND\n-122.0,38.35,24.0,745.0,116.0,300.0,115.0,3.6176,158500.0,INLAND\n-122.01,38.35,18.0,4486.0,723.0,1600.0,697.0,3.8651,189700.0,INLAND\n-122.01,38.36,15.0,476.0,67.0,213.0,73.0,7.1053,315200.0,INLAND\n-121.98,38.36,30.0,140.0,35.0,103.0,35.0,4.163,112500.0,INLAND\n-121.97,38.35,17.0,5678.0,1116.0,3182.0,1135.0,3.7388,122000.0,INLAND\n-121.97,38.34,11.0,1500.0,319.0,899.0,304.0,4.5568,127200.0,INLAND\n-121.96,38.35,20.0,1415.0,266.0,667.0,250.0,4.0938,117300.0,INLAND\n-121.96,38.34,7.0,3292.0,698.0,1911.0,702.0,3.89,140300.0,INLAND\n-121.96,38.36,11.0,3208.0,790.0,1772.0,694.0,2.7434,218800.0,INLAND\n-121.98,38.35,16.0,1697.0,267.0,832.0,277.0,4.4375,132600.0,INLAND\n-121.98,38.34,13.0,3616.0,672.0,2022.0,652.0,4.0536,134800.0,INLAND\n-121.97,38.34,16.0,2331.0,450.0,1074.0,400.0,4.0329,126800.0,INLAND\n-121.98,38.34,18.0,3876.0,916.0,2386.0,867.0,2.5938,129500.0,INLAND\n-121.99,38.34,16.0,1470.0,261.0,748.0,256.0,4.0433,132200.0,INLAND\n-121.99,38.34,13.0,3252.0,610.0,1915.0,631.0,4.2137,151700.0,INLAND\n-122.0,38.35,34.0,432.0,65.0,208.0,71.0,5.5435,136000.0,INLAND\n-122.0,38.38,16.0,2509.0,366.0,1043.0,339.0,6.0704,173400.0,INLAND\n-122.01,38.37,16.0,3996.0,550.0,1673.0,539.0,5.778,175700.0,INLAND\n-122.01,38.36,28.0,1967.0,315.0,734.0,291.0,4.9583,146200.0,INLAND\n-122.02,38.38,16.0,808.0,137.0,371.0,145.0,6.0767,216400.0,INLAND\n-122.02,38.37,16.0,2495.0,331.0,1118.0,338.0,6.4894,198000.0,INLAND\n-122.01,38.36,15.0,1176.0,166.0,485.0,171.0,5.9441,228200.0,INLAND\n-121.98,38.39,3.0,9488.0,1417.0,4095.0,1335.0,5.1781,191900.0,INLAND\n-121.98,38.37,21.0,3027.0,675.0,2018.0,642.0,2.8438,111500.0,INLAND\n-121.98,38.36,24.0,2434.0,630.0,1538.0,574.0,2.1067,101100.0,INLAND\n-122.0,38.37,18.0,1048.0,185.0,469.0,162.0,3.625,125000.0,INLAND\n-121.99,38.36,35.0,2728.0,451.0,1290.0,452.0,3.2768,117600.0,INLAND\n-122.0,38.36,34.0,2735.0,539.0,1390.0,491.0,2.7262,118800.0,INLAND\n-121.99,38.36,33.0,146.0,31.0,75.0,31.0,3.5179,84400.0,INLAND\n-122.0,38.36,34.0,1502.0,282.0,860.0,297.0,3.3438,135600.0,INLAND\n-121.82,38.36,26.0,1974.0,364.0,1002.0,362.0,3.3036,210000.0,INLAND\n-121.81,38.49,18.0,4518.0,827.0,2230.0,715.0,3.9309,178500.0,INLAND\n-121.76,38.41,19.0,686.0,107.0,348.0,109.0,3.9306,93800.0,INLAND\n-121.81,38.45,24.0,1951.0,341.0,1140.0,338.0,3.7061,128500.0,INLAND\n-121.82,38.46,10.0,6331.0,1181.0,3419.0,1110.0,3.7083,154800.0,INLAND\n-121.85,38.43,2.0,790.0,135.0,235.0,87.0,5.0862,166500.0,INLAND\n-121.83,38.45,15.0,5115.0,776.0,2540.0,794.0,4.8611,146400.0,INLAND\n-121.83,38.45,32.0,2139.0,440.0,1154.0,411.0,3.2672,107500.0,INLAND\n-121.83,38.45,36.0,839.0,158.0,446.0,167.0,2.3438,122700.0,INLAND\n-121.83,38.43,24.0,1307.0,314.0,917.0,291.0,2.2244,98100.0,INLAND\n-121.81,38.43,30.0,1674.0,297.0,756.0,292.0,3.9286,133100.0,INLAND\n-121.76,38.25,32.0,1495.0,333.0,905.0,281.0,2.625,212500.0,INLAND\n-121.69,38.16,33.0,1808.0,363.0,824.0,340.0,3.2937,96400.0,INLAND\n-121.69,38.16,46.0,2292.0,472.0,970.0,431.0,2.2888,94900.0,INLAND\n-121.73,38.13,40.0,1266.0,257.0,547.0,247.0,3.0288,164400.0,INLAND\n-121.74,38.15,22.0,1910.0,326.0,1001.0,345.0,4.8173,115800.0,INLAND\n-121.84,38.13,33.0,596.0,105.0,212.0,94.0,4.2813,81300.0,INLAND\n-122.42,38.27,25.0,3282.0,566.0,1244.0,483.0,4.5313,308400.0,NEAR BAY\n-122.49,38.22,33.0,1486.0,290.0,781.0,274.0,3.5647,251800.0,NEAR BAY\n-122.41,38.16,37.0,1549.0,,863.0,275.0,2.7457,254700.0,NEAR BAY\n-122.45,38.3,24.0,1946.0,400.0,718.0,380.0,3.5507,257900.0,NEAR BAY\n-122.47,38.3,15.0,4885.0,988.0,2175.0,924.0,3.4031,209500.0,<1H OCEAN\n-122.42,38.31,18.0,1479.0,246.0,550.0,217.0,4.7356,333300.0,NEAR BAY\n-122.44,38.34,25.0,3106.0,715.0,1262.0,665.0,1.9487,233500.0,<1H OCEAN\n-122.46,38.29,21.0,2423.0,560.0,1098.0,503.0,2.364,173300.0,NEAR BAY\n-122.46,38.29,35.0,1762.0,350.0,686.0,339.0,3.5982,271700.0,NEAR BAY\n-122.45,38.28,20.0,3306.0,503.0,1374.0,460.0,5.7984,297600.0,NEAR BAY\n-122.47,38.29,14.0,3732.0,846.0,1277.0,775.0,2.5658,208000.0,NEAR BAY\n-122.45,38.27,25.0,5024.0,881.0,1994.0,838.0,4.2237,262300.0,NEAR BAY\n-122.52,38.27,18.0,2405.0,390.0,872.0,367.0,5.2155,248300.0,<1H OCEAN\n-122.53,38.32,22.0,3577.0,,1371.0,501.0,5.795,332300.0,<1H OCEAN\n-122.49,38.32,30.0,1631.0,284.0,788.0,284.0,3.3098,195500.0,<1H OCEAN\n-122.49,38.31,27.0,3078.0,597.0,1411.0,586.0,3.25,195500.0,<1H OCEAN\n-122.49,38.3,14.0,2844.0,602.0,1613.0,544.0,3.3571,193600.0,<1H OCEAN\n-122.48,38.3,17.0,2703.0,550.0,1241.0,515.0,2.652,171300.0,<1H OCEAN\n-122.49,38.29,26.0,1726.0,289.0,672.0,251.0,3.8,242100.0,<1H OCEAN\n-122.49,38.27,8.0,5092.0,988.0,1657.0,936.0,3.5625,213200.0,NEAR BAY\n-122.47,38.34,15.0,2411.0,446.0,1144.0,407.0,4.3472,261000.0,<1H OCEAN\n-122.49,38.32,17.0,3308.0,720.0,1587.0,632.0,3.2727,176000.0,<1H OCEAN\n-122.48,38.32,31.0,1701.0,363.0,680.0,324.0,3.1375,192100.0,<1H OCEAN\n-122.48,38.31,29.0,2375.0,560.0,1124.0,502.0,2.3276,166200.0,<1H OCEAN\n-122.48,38.31,19.0,2398.0,521.0,1266.0,471.0,2.7727,186800.0,<1H OCEAN\n-122.48,38.32,42.0,2106.0,533.0,1141.0,445.0,3.1129,149300.0,<1H OCEAN\n-122.55,38.42,24.0,2220.0,411.0,894.0,365.0,4.2891,211700.0,<1H OCEAN\n-122.58,38.38,27.0,3800.0,728.0,1587.0,605.0,4.7237,306600.0,<1H OCEAN\n-122.5,38.4,36.0,1860.0,364.0,777.0,339.0,4.1307,295700.0,<1H OCEAN\n-122.5,38.35,25.0,1566.0,352.0,784.0,362.0,3.075,165100.0,<1H OCEAN\n-122.54,38.36,40.0,2725.0,531.0,1167.0,458.0,3.7969,202800.0,<1H OCEAN\n-122.61,38.24,25.0,2990.0,450.0,1335.0,434.0,4.7,190100.0,<1H OCEAN\n-122.61,38.24,17.0,1728.0,271.0,897.0,284.0,3.4896,185900.0,<1H OCEAN\n-122.61,38.23,18.0,2042.0,420.0,914.0,400.0,2.9871,193800.0,<1H OCEAN\n-122.62,38.24,19.0,1687.0,253.0,893.0,257.0,6.204,201800.0,<1H OCEAN\n-122.61,38.25,18.0,2915.0,418.0,1340.0,421.0,5.2452,204900.0,<1H OCEAN\n-122.6,38.24,16.0,1410.0,209.0,741.0,229.0,4.725,204500.0,<1H OCEAN\n-122.61,38.24,18.0,2933.0,481.0,1279.0,443.0,5.0849,188500.0,<1H OCEAN\n-122.6,38.24,16.0,2621.0,416.0,1247.0,386.0,4.8603,198400.0,<1H OCEAN\n-122.63,38.25,20.0,3460.0,602.0,1707.0,568.0,3.7115,181900.0,<1H OCEAN\n-122.62,38.24,33.0,1369.0,280.0,758.0,246.0,4.0341,156500.0,<1H OCEAN\n-122.62,38.25,33.0,1453.0,250.0,677.0,237.0,4.0962,170200.0,<1H OCEAN\n-122.61,38.26,17.0,2864.0,487.0,1482.0,547.0,4.6833,215200.0,<1H OCEAN\n-122.62,38.25,20.0,1888.0,411.0,826.0,396.0,2.875,189100.0,<1H OCEAN\n-122.62,38.25,24.0,2388.0,358.0,1187.0,362.0,4.6534,196500.0,<1H OCEAN\n-122.57,38.27,7.0,6508.0,1028.0,2902.0,1010.0,5.3707,250500.0,<1H OCEAN\n-122.51,38.17,8.0,5875.0,1115.0,2808.0,1029.0,3.6392,246300.0,NEAR BAY\n-122.65,38.27,9.0,4764.0,816.0,2077.0,755.0,5.1391,234500.0,<1H OCEAN\n-122.63,38.26,7.0,7808.0,1390.0,3551.0,1392.0,4.6069,202300.0,<1H OCEAN\n-122.66,38.27,16.0,1523.0,308.0,477.0,315.0,2.1696,75000.0,<1H OCEAN\n-122.63,38.24,45.0,1615.0,338.0,823.0,327.0,2.5179,145500.0,<1H OCEAN\n-122.63,38.23,45.0,2264.0,504.0,1076.0,472.0,3.0139,194100.0,<1H OCEAN\n-122.64,38.23,52.0,1075.0,249.0,519.0,210.0,3.0769,230900.0,<1H OCEAN\n-122.64,38.23,49.0,2300.0,463.0,1061.0,429.0,4.075,228800.0,<1H OCEAN\n-122.63,38.22,34.0,878.0,160.0,372.0,167.0,4.0417,232100.0,<1H OCEAN\n-122.63,38.22,17.0,2652.0,342.0,1199.0,350.0,5.565,267100.0,<1H OCEAN\n-122.63,38.21,22.0,2933.0,461.0,1283.0,449.0,6.2034,291100.0,<1H OCEAN\n-122.63,38.23,37.0,1966.0,348.0,875.0,381.0,4.0703,223800.0,<1H OCEAN\n-122.65,38.23,52.0,1735.0,347.0,712.0,343.0,3.1711,200800.0,<1H OCEAN\n-122.64,38.23,52.0,2156.0,469.0,1070.0,467.0,3.3011,252300.0,<1H OCEAN\n-122.65,38.23,52.0,1923.0,393.0,910.0,345.0,3.45,200600.0,<1H OCEAN\n-122.66,38.2,39.0,2889.0,517.0,1351.0,489.0,4.3056,251300.0,<1H OCEAN\n-122.64,38.24,52.0,1621.0,393.0,635.0,349.0,2.5202,244000.0,<1H OCEAN\n-122.64,38.24,40.0,1974.0,410.0,1039.0,398.0,3.7917,151600.0,<1H OCEAN\n-122.65,38.25,23.0,4030.0,,1852.0,778.0,3.402,193300.0,<1H OCEAN\n-122.65,38.24,24.0,1948.0,310.0,922.0,313.0,4.95,243600.0,<1H OCEAN\n-122.65,38.24,49.0,3273.0,579.0,1431.0,539.0,4.275,227600.0,<1H OCEAN\n-122.64,38.25,31.0,2554.0,515.0,1507.0,533.0,3.8,162600.0,<1H OCEAN\n-122.69,38.27,32.0,2344.0,434.0,1066.0,384.0,4.0313,285000.0,<1H OCEAN\n-122.67,38.25,32.0,1333.0,235.0,660.0,206.0,4.0729,288500.0,<1H OCEAN\n-122.68,38.25,29.0,1315.0,240.0,650.0,228.0,3.8269,306000.0,<1H OCEAN\n-122.67,38.24,29.0,2644.0,464.0,1372.0,450.0,5.0544,261800.0,<1H OCEAN\n-122.77,38.29,32.0,3201.0,542.0,1869.0,519.0,3.2442,268000.0,<1H OCEAN\n-122.73,38.26,35.0,3941.0,645.0,1668.0,620.0,4.385,317700.0,<1H OCEAN\n-122.7,38.23,47.0,2090.0,387.0,1053.0,377.0,3.5673,310300.0,<1H OCEAN\n-122.72,38.35,16.0,3049.0,609.0,1675.0,618.0,2.4117,162500.0,<1H OCEAN\n-122.72,38.31,26.0,1644.0,294.0,801.0,291.0,4.3906,248000.0,<1H OCEAN\n-122.67,38.31,28.0,1915.0,419.0,930.0,342.0,3.7875,292700.0,<1H OCEAN\n-122.69,38.3,30.0,3919.0,743.0,1693.0,693.0,3.3827,292100.0,<1H OCEAN\n-122.73,38.34,44.0,743.0,155.0,434.0,162.0,2.5819,209600.0,<1H OCEAN\n-122.69,38.32,16.0,3741.0,698.0,1938.0,658.0,4.6324,183600.0,<1H OCEAN\n-122.71,38.33,13.0,4011.0,936.0,2064.0,914.0,3.6953,157600.0,<1H OCEAN\n-122.7,38.31,14.0,3155.0,580.0,1208.0,501.0,4.1964,258100.0,<1H OCEAN\n-122.7,38.33,16.0,1244.0,242.0,696.0,236.0,3.6369,158700.0,<1H OCEAN\n-122.69,38.32,15.0,2536.0,414.0,1400.0,426.0,5.6613,172400.0,<1H OCEAN\n-122.69,38.34,23.0,2846.0,516.0,1526.0,492.0,3.733,163500.0,<1H OCEAN\n-122.7,38.33,26.0,1887.0,381.0,1060.0,364.0,3.0078,160400.0,<1H OCEAN\n-122.71,38.34,22.0,1249.0,335.0,699.0,308.0,2.6033,121600.0,<1H OCEAN\n-122.7,38.33,26.0,1584.0,295.0,846.0,295.0,3.375,156300.0,<1H OCEAN\n-122.69,38.35,12.0,1550.0,187.0,685.0,188.0,7.226,255300.0,<1H OCEAN\n-122.69,38.34,15.0,3091.0,697.0,1602.0,682.0,4.0071,135500.0,<1H OCEAN\n-122.71,38.35,11.0,2242.0,699.0,1203.0,642.0,2.3464,104200.0,<1H OCEAN\n-122.7,38.35,14.0,2313.0,,954.0,397.0,3.7813,146500.0,<1H OCEAN\n-122.7,38.35,14.0,1555.0,369.0,493.0,335.0,1.6033,67500.0,<1H OCEAN\n-122.7,38.34,19.0,2987.0,676.0,1782.0,688.0,2.8261,154500.0,<1H OCEAN\n-122.71,38.34,23.0,2744.0,588.0,1493.0,557.0,3.1781,162000.0,<1H OCEAN\n-122.69,38.37,8.0,6322.0,1001.0,2969.0,1043.0,4.8233,214000.0,<1H OCEAN\n-122.71,38.37,16.0,2355.0,345.0,1014.0,348.0,5.6018,253000.0,<1H OCEAN\n-122.69,38.35,16.0,1689.0,254.0,921.0,270.0,4.4444,191800.0,<1H OCEAN\n-122.69,38.36,6.0,5496.0,1374.0,2502.0,1189.0,2.4827,177500.0,<1H OCEAN\n-122.7,38.35,16.0,1328.0,187.0,607.0,197.0,5.0366,257800.0,<1H OCEAN\n-122.7,38.36,11.0,5817.0,878.0,2538.0,876.0,4.221,227100.0,<1H OCEAN\n-122.63,38.34,15.0,2153.0,345.0,979.0,335.0,5.1966,325400.0,<1H OCEAN\n-122.68,38.36,8.0,7520.0,1336.0,3833.0,1287.0,4.3278,184100.0,<1H OCEAN\n-122.69,38.34,12.0,3876.0,782.0,2146.0,764.0,4.0844,165400.0,<1H OCEAN\n-122.67,38.33,4.0,8072.0,1606.0,4323.0,1475.0,3.9518,220300.0,<1H OCEAN\n-122.69,38.34,16.0,1683.0,341.0,880.0,327.0,3.2857,160200.0,<1H OCEAN\n-122.68,38.4,32.0,2826.0,627.0,1767.0,628.0,3.1047,141400.0,<1H OCEAN\n-122.7,38.43,28.0,1585.0,412.0,1362.0,424.0,1.6685,114100.0,<1H OCEAN\n-122.71,38.42,23.0,1569.0,414.0,1031.0,368.0,1.6267,129200.0,<1H OCEAN\n-122.71,38.4,17.0,1690.0,464.0,833.0,445.0,1.439,140600.0,<1H OCEAN\n-122.7,38.39,16.0,4922.0,1211.0,2557.0,1088.0,2.0915,168100.0,<1H OCEAN\n-122.62,38.4,10.0,9772.0,1308.0,3741.0,1242.0,6.5261,324700.0,<1H OCEAN\n-122.66,38.44,17.0,5815.0,898.0,2614.0,887.0,4.3657,215900.0,<1H OCEAN\n-122.67,38.44,29.0,2551.0,448.0,1165.0,456.0,4.3587,196400.0,<1H OCEAN\n-122.68,38.43,18.0,2723.0,529.0,1150.0,520.0,3.5885,191900.0,<1H OCEAN\n-122.65,38.4,21.0,1059.0,150.0,400.0,154.0,6.8586,343100.0,<1H OCEAN\n-122.65,38.37,15.0,1848.0,280.0,786.0,282.0,5.7204,344100.0,<1H OCEAN\n-122.67,38.43,17.0,1804.0,304.0,750.0,298.0,4.5588,196400.0,<1H OCEAN\n-122.67,38.43,17.0,2007.0,400.0,895.0,403.0,3.2813,202700.0,<1H OCEAN\n-122.66,38.42,14.0,5315.0,1037.0,2228.0,950.0,4.023,208400.0,<1H OCEAN\n-122.68,38.43,29.0,488.0,63.0,161.0,62.0,6.0774,334400.0,<1H OCEAN\n-122.58,38.46,15.0,2936.0,517.0,1182.0,501.0,3.3981,246900.0,<1H OCEAN\n-122.61,38.42,13.0,7731.0,1360.0,2543.0,1249.0,4.6957,259800.0,<1H OCEAN\n-122.58,38.43,10.0,3597.0,661.0,1132.0,639.0,3.9375,269200.0,<1H OCEAN\n-122.59,38.44,14.0,1665.0,390.0,505.0,348.0,3.183,201200.0,<1H OCEAN\n-122.59,38.43,20.0,2791.0,546.0,785.0,512.0,3.4561,216700.0,<1H OCEAN\n-122.56,38.41,20.0,1151.0,211.0,478.0,183.0,5.93,384600.0,<1H OCEAN\n-122.66,38.46,14.0,2364.0,631.0,1300.0,625.0,2.6023,221100.0,<1H OCEAN\n-122.66,38.45,26.0,2081.0,339.0,906.0,323.0,4.4375,293500.0,<1H OCEAN\n-122.67,38.45,24.0,2622.0,525.0,1027.0,510.0,2.9222,242600.0,<1H OCEAN\n-122.67,38.44,32.0,3771.0,741.0,1786.0,721.0,3.2415,172200.0,<1H OCEAN\n-122.68,38.44,29.0,2796.0,588.0,1346.0,562.0,2.9107,169700.0,<1H OCEAN\n-122.7,38.45,47.0,904.0,154.0,310.0,144.0,3.9766,190600.0,<1H OCEAN\n-122.68,38.45,36.0,1686.0,303.0,744.0,304.0,4.0139,163100.0,<1H OCEAN\n-122.69,38.44,40.0,1449.0,281.0,636.0,295.0,2.7222,161200.0,<1H OCEAN\n-122.69,38.44,35.0,1356.0,241.0,620.0,216.0,3.5521,168300.0,<1H OCEAN\n-122.68,38.44,36.0,1311.0,259.0,648.0,268.0,3.4545,161200.0,<1H OCEAN\n-122.7,38.44,45.0,883.0,202.0,401.0,194.0,3.2845,178300.0,<1H OCEAN\n-122.69,38.45,36.0,1943.0,337.0,711.0,318.0,3.9191,183000.0,<1H OCEAN\n-122.69,38.44,31.0,1808.0,315.0,691.0,280.0,3.8583,193200.0,<1H OCEAN\n-122.7,38.44,42.0,709.0,182.0,547.0,172.0,2.1912,165000.0,<1H OCEAN\n-122.7,38.44,35.0,1304.0,343.0,822.0,304.0,3.2935,157800.0,<1H OCEAN\n-122.71,38.43,52.0,1439.0,325.0,738.0,316.0,2.2262,129900.0,<1H OCEAN\n-122.71,38.43,38.0,1689.0,526.0,1071.0,529.0,1.5026,124000.0,<1H OCEAN\n-122.72,38.44,52.0,1059.0,281.0,627.0,273.0,1.5357,137500.0,<1H OCEAN\n-122.71,38.44,52.0,988.0,283.0,475.0,242.0,1.3684,258300.0,<1H OCEAN\n-122.71,38.44,27.0,966.0,251.0,462.0,230.0,1.7,350000.0,<1H OCEAN\n-122.72,38.47,29.0,1706.0,415.0,990.0,394.0,1.9932,164800.0,<1H OCEAN\n-122.72,38.46,35.0,1445.0,309.0,795.0,308.0,2.9073,157000.0,<1H OCEAN\n-122.72,38.45,41.0,1743.0,373.0,780.0,357.0,3.1467,175500.0,<1H OCEAN\n-122.71,38.46,42.0,1574.0,376.0,844.0,369.0,2.314,169400.0,<1H OCEAN\n-122.71,38.46,41.0,1974.0,482.0,965.0,458.0,2.905,159300.0,<1H OCEAN\n-122.71,38.46,36.0,2175.0,516.0,1087.0,477.0,3.0444,167200.0,<1H OCEAN\n-122.71,38.45,39.0,2739.0,573.0,1223.0,569.0,2.9663,185400.0,<1H OCEAN\n-122.71,38.45,48.0,3118.0,561.0,1275.0,530.0,3.455,222100.0,<1H OCEAN\n-122.7,38.45,26.0,2011.0,557.0,855.0,530.0,1.125,233300.0,<1H OCEAN\n-122.71,38.45,52.0,2259.0,537.0,957.0,520.0,2.1827,188800.0,<1H OCEAN\n-122.68,38.48,15.0,1575.0,262.0,716.0,259.0,5.3409,244600.0,<1H OCEAN\n-122.67,38.47,16.0,3452.0,791.0,1567.0,731.0,2.4722,194300.0,<1H OCEAN\n-122.68,38.46,17.0,3201.0,527.0,1244.0,495.0,4.7143,202900.0,<1H OCEAN\n-122.67,38.47,19.0,1848.0,428.0,1130.0,433.0,3.0568,190300.0,<1H OCEAN\n-122.68,38.46,15.0,1811.0,406.0,718.0,403.0,2.3929,141300.0,<1H OCEAN\n-122.71,38.46,23.0,3220.0,603.0,1299.0,591.0,3.9261,213300.0,<1H OCEAN\n-122.7,38.46,29.0,2891.0,459.0,1012.0,441.0,5.0415,240200.0,<1H OCEAN\n-122.69,38.46,32.0,2970.0,504.0,1117.0,512.0,5.0,275900.0,<1H OCEAN\n-122.7,38.45,39.0,2015.0,335.0,640.0,315.0,4.1734,240500.0,<1H OCEAN\n-122.72,38.48,23.0,2296.0,356.0,902.0,334.0,6.0298,289100.0,<1H OCEAN\n-122.68,38.46,19.0,4976.0,711.0,1926.0,625.0,7.3003,381300.0,<1H OCEAN\n-122.71,38.5,15.0,5645.0,830.0,2324.0,769.0,6.6104,330900.0,<1H OCEAN\n-122.66,38.48,16.0,2697.0,490.0,1462.0,515.0,4.2051,190300.0,<1H OCEAN\n-122.66,38.48,16.0,2724.0,593.0,1124.0,586.0,2.825,186200.0,<1H OCEAN\n-122.66,38.47,23.0,2246.0,437.0,1035.0,386.0,3.7617,172600.0,<1H OCEAN\n-122.65,38.46,14.0,2096.0,420.0,926.0,397.0,4.0647,187800.0,<1H OCEAN\n-122.66,38.48,21.0,2066.0,393.0,919.0,395.0,3.267,176200.0,<1H OCEAN\n-122.66,38.47,20.0,2806.0,477.0,1369.0,460.0,4.75,190500.0,<1H OCEAN\n-122.69,38.51,18.0,3364.0,501.0,1442.0,506.0,6.6854,313000.0,<1H OCEAN\n-122.64,38.48,19.0,3244.0,449.0,1174.0,454.0,5.8369,255700.0,<1H OCEAN\n-122.65,38.47,24.0,2268.0,330.0,847.0,296.0,3.858,214400.0,<1H OCEAN\n-122.6,38.48,17.0,1528.0,264.0,606.0,251.0,6.6004,341500.0,<1H OCEAN\n-122.62,38.54,24.0,2409.0,464.0,1006.0,403.0,4.5167,265200.0,<1H OCEAN\n-122.63,38.5,19.0,2107.0,332.0,874.0,341.0,5.7819,265600.0,<1H OCEAN\n-122.65,38.48,17.0,1090.0,164.0,473.0,163.0,5.5061,231800.0,<1H OCEAN\n-122.75,38.54,6.0,6719.0,1016.0,2699.0,997.0,5.4886,254200.0,<1H OCEAN\n-122.78,38.52,23.0,2511.0,549.0,1052.0,527.0,2.4922,192000.0,<1H OCEAN\n-122.79,38.5,18.0,4839.0,918.0,2755.0,841.0,3.75,248300.0,<1H OCEAN\n-122.76,38.52,6.0,2073.0,388.0,826.0,375.0,3.055,224100.0,<1H OCEAN\n-122.75,38.5,16.0,4196.0,638.0,1713.0,615.0,5.449,252100.0,<1H OCEAN\n-122.73,38.46,14.0,4042.0,1298.0,2323.0,1158.0,2.0651,135400.0,<1H OCEAN\n-122.73,38.47,16.0,1834.0,391.0,994.0,390.0,3.7266,156500.0,<1H OCEAN\n-122.74,38.47,16.0,1426.0,287.0,525.0,260.0,3.0714,161700.0,<1H OCEAN\n-122.74,38.46,9.0,2268.0,594.0,1311.0,585.0,2.6607,91500.0,<1H OCEAN\n-122.74,38.48,12.0,4174.0,670.0,1882.0,647.0,4.551,178300.0,<1H OCEAN\n-122.75,38.48,4.0,6487.0,1112.0,2958.0,1131.0,4.5417,197400.0,<1H OCEAN\n-122.79,38.48,7.0,6837.0,,3468.0,1405.0,3.1662,191000.0,<1H OCEAN\n-122.81,38.46,28.0,3580.0,611.0,1634.0,567.0,4.745,248600.0,<1H OCEAN\n-122.75,38.46,13.0,4323.0,1020.0,2566.0,728.0,3.0147,142800.0,<1H OCEAN\n-122.75,38.46,16.0,2653.0,606.0,1693.0,586.0,2.6384,146900.0,<1H OCEAN\n-122.76,38.46,14.0,4742.0,756.0,2149.0,732.0,4.5152,199200.0,<1H OCEAN\n-122.76,38.46,14.0,4794.0,767.0,2252.0,768.0,4.2061,213100.0,<1H OCEAN\n-122.73,38.46,14.0,2324.0,754.0,1026.0,677.0,1.722,150000.0,<1H OCEAN\n-122.74,38.45,17.0,3064.0,588.0,1704.0,590.0,3.9329,170900.0,<1H OCEAN\n-122.74,38.45,25.0,2696.0,496.0,1296.0,514.0,4.0798,179200.0,<1H OCEAN\n-122.74,38.44,23.0,2819.0,612.0,1644.0,546.0,2.6576,147900.0,<1H OCEAN\n-122.73,38.44,20.0,2919.0,508.0,1711.0,500.0,3.875,140300.0,<1H OCEAN\n-122.72,38.44,48.0,707.0,166.0,458.0,172.0,3.1797,140400.0,<1H OCEAN\n-122.73,38.44,35.0,1120.0,297.0,659.0,274.0,2.3824,145000.0,<1H OCEAN\n-122.73,38.44,28.0,1073.0,241.0,652.0,238.0,2.4,146200.0,<1H OCEAN\n-122.72,38.44,52.0,188.0,62.0,301.0,72.0,0.9437,129200.0,<1H OCEAN\n-122.74,38.44,17.0,2287.0,497.0,1240.0,493.0,3.5845,164300.0,<1H OCEAN\n-122.74,38.43,11.0,4670.0,1007.0,2430.0,962.0,3.0341,142300.0,<1H OCEAN\n-122.73,38.43,15.0,3265.0,690.0,1629.0,629.0,3.7132,167600.0,<1H OCEAN\n-122.76,38.45,8.0,5823.0,1104.0,2864.0,1041.0,3.6292,183600.0,<1H OCEAN\n-122.82,38.44,23.0,1551.0,236.0,555.0,243.0,4.6792,304700.0,<1H OCEAN\n-122.76,38.44,11.0,2895.0,524.0,1633.0,534.0,4.7283,170200.0,<1H OCEAN\n-122.76,38.44,14.0,4376.0,797.0,1809.0,746.0,3.8244,180000.0,<1H OCEAN\n-122.78,38.44,14.0,4143.0,656.0,1569.0,629.0,3.9766,345300.0,<1H OCEAN\n-122.79,38.42,9.0,4967.0,885.0,2581.0,915.0,5.038,185600.0,<1H OCEAN\n-122.74,38.42,42.0,2050.0,434.0,1073.0,416.0,2.375,141000.0,<1H OCEAN\n-122.73,38.43,29.0,2677.0,691.0,1880.0,664.0,2.1864,143200.0,<1H OCEAN\n-122.72,38.43,31.0,2020.0,476.0,1408.0,437.0,2.5735,131100.0,<1H OCEAN\n-122.72,38.42,26.0,1168.0,253.0,937.0,248.0,1.9458,146000.0,<1H OCEAN\n-122.72,38.42,26.0,3604.0,734.0,2605.0,704.0,3.0969,143800.0,<1H OCEAN\n-122.73,38.42,26.0,1446.0,296.0,884.0,295.0,4.3523,150000.0,<1H OCEAN\n-122.72,38.42,30.0,2099.0,406.0,1156.0,401.0,2.8036,152300.0,<1H OCEAN\n-122.73,38.4,30.0,3689.0,746.0,2250.0,697.0,2.975,157300.0,<1H OCEAN\n-122.73,38.37,40.0,1389.0,309.0,841.0,288.0,3.1094,183300.0,<1H OCEAN\n-122.75,38.43,36.0,1599.0,345.0,1086.0,314.0,2.6667,149100.0,<1H OCEAN\n-122.78,38.41,43.0,1351.0,277.0,1011.0,297.0,2.5917,144000.0,<1H OCEAN\n-122.77,38.39,35.0,2611.0,475.0,1293.0,463.0,2.75,197500.0,<1H OCEAN\n-122.75,38.41,17.0,3150.0,588.0,1857.0,610.0,3.9688,165000.0,<1H OCEAN\n-122.82,38.41,32.0,701.0,182.0,489.0,168.0,2.785,169300.0,<1H OCEAN\n-122.84,38.42,29.0,2756.0,551.0,1381.0,531.0,2.9625,237300.0,<1H OCEAN\n-122.86,38.42,38.0,1166.0,223.0,584.0,225.0,3.6667,244400.0,<1H OCEAN\n-122.84,38.41,19.0,2191.0,391.0,1065.0,404.0,4.125,204600.0,<1H OCEAN\n-122.83,38.4,37.0,2217.0,451.0,1019.0,428.0,3.1217,178500.0,<1H OCEAN\n-122.84,38.4,15.0,3080.0,617.0,1446.0,599.0,3.6696,194400.0,<1H OCEAN\n-122.78,38.37,21.0,795.0,163.0,414.0,162.0,3.7991,175000.0,<1H OCEAN\n-122.82,38.4,40.0,2406.0,423.0,1054.0,426.0,3.8846,215900.0,<1H OCEAN\n-122.8,38.37,26.0,1634.0,315.0,909.0,317.0,4.1731,257200.0,<1H OCEAN\n-122.8,38.39,26.0,2273.0,474.0,1124.0,420.0,2.9453,166700.0,<1H OCEAN\n-122.84,38.39,16.0,1688.0,292.0,793.0,280.0,4.4357,216900.0,<1H OCEAN\n-122.83,38.39,19.0,1765.0,394.0,868.0,388.0,2.462,260300.0,<1H OCEAN\n-122.82,38.39,22.0,1288.0,243.0,593.0,220.0,3.625,233700.0,<1H OCEAN\n-122.82,38.39,32.0,1437.0,257.0,752.0,245.0,4.7422,240900.0,<1H OCEAN\n-122.82,38.38,27.0,2565.0,479.0,1227.0,467.0,4.5132,259900.0,<1H OCEAN\n-122.76,38.35,30.0,2260.0,374.0,958.0,359.0,5.0323,222400.0,<1H OCEAN\n-122.87,38.39,34.0,1138.0,205.0,541.0,180.0,4.5147,271400.0,<1H OCEAN\n-122.77,38.33,32.0,2054.0,324.0,843.0,306.0,4.5875,290700.0,<1H OCEAN\n-122.82,38.33,25.0,3067.0,569.0,1602.0,550.0,3.9917,244100.0,<1H OCEAN\n-122.88,38.34,20.0,3404.0,628.0,1641.0,585.0,5.0574,276200.0,<1H OCEAN\n-122.81,38.36,18.0,2399.0,389.0,1131.0,391.0,5.2769,293900.0,<1H OCEAN\n-122.85,38.37,16.0,1762.0,293.0,810.0,297.0,4.4437,305000.0,<1H OCEAN\n-122.89,38.38,16.0,2017.0,369.0,931.0,336.0,5.7664,267500.0,<1H OCEAN\n-122.86,38.44,31.0,1534.0,292.0,716.0,288.0,3.4471,209500.0,<1H OCEAN\n-122.89,38.42,28.0,2388.0,437.0,1015.0,381.0,5.1512,268300.0,<1H OCEAN\n-122.87,38.43,36.0,1987.0,387.0,1065.0,347.0,4.0446,172200.0,<1H OCEAN\n-122.91,38.43,19.0,1968.0,350.0,852.0,308.0,4.6705,269800.0,<1H OCEAN\n-122.89,38.4,22.0,2900.0,538.0,1445.0,515.0,4.511,296800.0,<1H OCEAN\n-123.04,38.49,30.0,3977.0,930.0,1387.0,582.0,2.6161,132500.0,NEAR OCEAN\n-123.02,38.46,52.0,2154.0,499.0,524.0,259.0,2.0556,120000.0,NEAR OCEAN\n-122.98,38.44,29.0,4450.0,939.0,1328.0,590.0,3.1,162100.0,<1H OCEAN\n-123.01,38.48,37.0,1179.0,282.0,354.0,176.0,1.3712,118300.0,NEAR OCEAN\n-123.02,38.54,35.0,2157.0,487.0,768.0,322.0,3.2315,136900.0,<1H OCEAN\n-123.0,38.51,33.0,1565.0,390.0,759.0,311.0,2.6726,153100.0,NEAR OCEAN\n-122.97,38.5,44.0,3234.0,746.0,1112.0,470.0,1.9265,132700.0,<1H OCEAN\n-122.97,38.53,48.0,3939.0,860.0,1257.0,571.0,2.1165,98700.0,<1H OCEAN\n-122.94,38.53,49.0,1141.0,239.0,505.0,184.0,3.7143,148800.0,<1H OCEAN\n-122.94,38.5,46.0,2280.0,492.0,807.0,366.0,2.6316,117000.0,<1H OCEAN\n-122.91,38.49,37.0,2469.0,519.0,1137.0,474.0,3.6343,146500.0,<1H OCEAN\n-122.87,38.48,27.0,3894.0,776.0,1832.0,715.0,3.5085,187800.0,<1H OCEAN\n-122.88,38.46,25.0,1563.0,314.0,737.0,305.0,2.5687,249200.0,<1H OCEAN\n-122.85,38.46,22.0,3328.0,550.0,1309.0,512.0,4.7105,266200.0,<1H OCEAN\n-122.94,38.49,37.0,3169.0,719.0,777.0,344.0,2.7072,117100.0,<1H OCEAN\n-122.91,38.46,18.0,2021.0,,912.0,329.0,4.5,251900.0,<1H OCEAN\n-122.72,38.58,4.0,7042.0,1100.0,2936.0,1043.0,5.0555,240800.0,<1H OCEAN\n-122.82,38.55,8.0,6190.0,1088.0,2967.0,1000.0,3.8616,195100.0,<1H OCEAN\n-122.85,38.52,13.0,4808.0,848.0,2568.0,762.0,3.6583,183200.0,<1H OCEAN\n-122.78,38.53,9.0,3659.0,652.0,1889.0,632.0,4.2716,250800.0,<1H OCEAN\n-122.81,38.54,12.0,2289.0,611.0,919.0,540.0,1.1553,139300.0,<1H OCEAN\n-122.82,38.53,27.0,1823.0,360.0,907.0,317.0,3.276,172900.0,<1H OCEAN\n-122.79,38.54,5.0,3986.0,737.0,1887.0,687.0,3.7768,213800.0,<1H OCEAN\n-122.87,38.61,23.0,2676.0,521.0,1456.0,500.0,3.7361,173700.0,<1H OCEAN\n-122.87,38.62,52.0,1514.0,348.0,767.0,354.0,2.1903,160000.0,<1H OCEAN\n-122.86,38.61,52.0,1753.0,380.0,982.0,380.0,3.4013,183300.0,<1H OCEAN\n-122.86,38.62,35.0,2597.0,522.0,1231.0,499.0,2.7432,174000.0,<1H OCEAN\n-122.87,38.62,18.0,2721.0,557.0,1667.0,539.0,3.1875,176100.0,<1H OCEAN\n-122.82,38.64,29.0,2176.0,385.0,1117.0,374.0,3.8681,188600.0,<1H OCEAN\n-122.83,38.58,17.0,5199.0,1023.0,2036.0,890.0,3.2452,168800.0,<1H OCEAN\n-122.82,38.61,41.0,2720.0,501.0,987.0,364.0,4.0294,201700.0,<1H OCEAN\n-122.85,38.62,16.0,4418.0,704.0,1908.0,697.0,4.5913,244600.0,<1H OCEAN\n-123.01,38.67,33.0,914.0,147.0,394.0,132.0,4.6875,246200.0,<1H OCEAN\n-122.94,38.57,33.0,1530.0,266.0,728.0,250.0,5.1005,266700.0,<1H OCEAN\n-122.94,38.64,26.0,4050.0,712.0,2072.0,636.0,4.0781,287800.0,<1H OCEAN\n-122.95,38.73,37.0,1548.0,328.0,863.0,287.0,2.9792,151300.0,<1H OCEAN\n-122.87,38.68,32.0,4073.0,718.0,2053.0,629.0,3.7352,228000.0,<1H OCEAN\n-122.85,38.77,18.0,2856.0,513.0,1027.0,405.0,4.6953,241700.0,<1H OCEAN\n-122.7,38.66,43.0,1384.0,284.0,582.0,224.0,3.9063,210000.0,<1H OCEAN\n-123.03,38.79,16.0,4047.0,769.0,1998.0,673.0,3.375,171900.0,<1H OCEAN\n-123.02,38.81,35.0,956.0,213.0,488.0,215.0,3.025,140600.0,<1H OCEAN\n-123.02,38.81,45.0,1717.0,389.0,916.0,367.0,3.2425,138800.0,<1H OCEAN\n-123.01,38.8,21.0,360.0,96.0,131.0,74.0,3.5156,133300.0,<1H OCEAN\n-123.01,38.79,32.0,2697.0,529.0,1417.0,535.0,3.2546,134100.0,<1H OCEAN\n-123.1,38.79,20.0,3109.0,712.0,1643.0,638.0,2.8344,164400.0,<1H OCEAN\n-123.49,38.7,9.0,5409.0,1019.0,594.0,327.0,3.3125,295400.0,NEAR OCEAN\n-123.24,38.7,38.0,1460.0,311.0,569.0,176.0,2.7171,131300.0,NEAR OCEAN\n-123.25,38.54,27.0,3658.0,764.0,1278.0,518.0,3.3536,157500.0,NEAR OCEAN\n-123.08,38.38,28.0,3297.0,676.0,923.0,373.0,3.9167,232600.0,NEAR OCEAN\n-122.96,38.42,50.0,2530.0,524.0,940.0,361.0,2.9375,122900.0,<1H OCEAN\n-123.07,38.46,31.0,855.0,217.0,280.0,139.0,2.3611,112500.0,NEAR OCEAN\n-122.93,38.38,18.0,2562.0,500.0,1128.0,414.0,3.9336,262500.0,<1H OCEAN\n-123.02,38.36,16.0,1496.0,298.0,778.0,284.0,3.8589,268800.0,NEAR OCEAN\n-123.0,38.33,8.0,3223.0,637.0,851.0,418.0,5.6445,364800.0,NEAR OCEAN\n-120.84,37.92,27.0,471.0,84.0,195.0,72.0,3.3333,208300.0,INLAND\n-120.79,37.82,17.0,4227.0,729.0,1809.0,679.0,3.2667,269500.0,INLAND\n-120.9,37.81,27.0,4213.0,750.0,2142.0,746.0,3.7031,173300.0,INLAND\n-120.9,37.76,20.0,570.0,112.0,304.0,108.0,2.2024,156300.0,INLAND\n-120.86,37.73,27.0,508.0,93.0,263.0,81.0,3.1136,183300.0,INLAND\n-120.79,37.76,14.0,3531.0,508.0,1505.0,497.0,5.5228,275300.0,INLAND\n-120.76,37.73,16.0,1343.0,241.0,732.0,195.0,3.5833,187500.0,INLAND\n-120.69,37.77,46.0,431.0,86.0,239.0,80.0,3.3182,282100.0,INLAND\n-120.87,37.77,9.0,4838.0,920.0,2460.0,923.0,3.5959,142700.0,INLAND\n-120.85,37.77,52.0,436.0,81.0,197.0,68.0,1.8625,85400.0,INLAND\n-120.86,37.77,45.0,621.0,129.0,257.0,124.0,1.7188,109400.0,INLAND\n-120.86,37.77,38.0,1545.0,279.0,662.0,266.0,3.825,96400.0,INLAND\n-120.86,37.77,28.0,1208.0,232.0,535.0,232.0,2.3523,94700.0,INLAND\n-120.87,37.76,16.0,1174.0,249.0,601.0,242.0,1.7143,113300.0,INLAND\n-120.85,37.77,20.0,651.0,157.0,421.0,151.0,2.0833,77300.0,INLAND\n-120.85,37.77,37.0,1738.0,403.0,936.0,366.0,2.4717,77100.0,INLAND\n-120.86,37.76,32.0,964.0,198.0,623.0,201.0,3.0917,88900.0,INLAND\n-120.87,37.76,16.0,2022.0,413.0,1126.0,408.0,2.5655,116400.0,INLAND\n-120.85,37.75,26.0,28.0,4.0,9.0,5.0,1.625,85000.0,INLAND\n-120.85,37.78,15.0,3553.0,659.0,1684.0,611.0,3.3169,131200.0,INLAND\n-120.85,37.78,25.0,421.0,,303.0,106.0,2.2679,71300.0,INLAND\n-120.85,37.77,35.0,404.0,96.0,261.0,100.0,2.4583,75000.0,INLAND\n-120.85,37.78,30.0,1120.0,248.0,609.0,237.0,2.2386,87200.0,INLAND\n-120.83,37.77,20.0,1717.0,403.0,1062.0,401.0,1.6759,116700.0,INLAND\n-120.85,37.77,10.0,423.0,110.0,295.0,94.0,1.3583,85200.0,INLAND\n-120.83,37.76,21.0,435.0,96.0,219.0,83.0,2.9125,112500.0,INLAND\n-120.83,37.79,16.0,893.0,164.0,548.0,155.0,3.6875,121900.0,INLAND\n-120.93,37.74,37.0,1956.0,402.0,1265.0,397.0,2.3023,91900.0,INLAND\n-120.94,37.74,35.0,1166.0,268.0,515.0,266.0,2.3469,90200.0,INLAND\n-120.95,37.74,18.0,3453.0,666.0,1958.0,601.0,3.0043,156500.0,INLAND\n-120.95,37.73,12.0,3609.0,712.0,2650.0,742.0,2.8565,92700.0,INLAND\n-120.93,37.73,14.0,2799.0,,2294.0,596.0,2.6343,81500.0,INLAND\n-120.93,37.72,18.0,391.0,71.0,247.0,71.0,4.3864,179500.0,INLAND\n-120.98,37.71,22.0,434.0,89.0,195.0,86.0,2.4211,268800.0,INLAND\n-120.99,37.7,14.0,9849.0,1887.0,4356.0,1780.0,3.5877,160900.0,INLAND\n-120.97,37.69,14.0,5514.0,909.0,2819.0,970.0,3.8598,174400.0,INLAND\n-120.91,37.74,19.0,1690.0,327.0,855.0,296.0,3.25,176700.0,INLAND\n-120.91,37.73,31.0,840.0,154.0,429.0,150.0,2.4063,170200.0,INLAND\n-120.92,37.7,24.0,527.0,112.0,270.0,112.0,1.6172,156300.0,INLAND\n-120.94,37.7,25.0,1005.0,159.0,390.0,139.0,4.4,174100.0,INLAND\n-120.97,37.73,19.0,3725.0,543.0,1412.0,463.0,5.7476,248600.0,INLAND\n-121.01,37.74,14.0,2368.0,297.0,796.0,301.0,8.7783,435000.0,INLAND\n-121.01,37.72,23.0,1373.0,264.0,677.0,245.0,2.5486,161100.0,INLAND\n-121.06,37.73,5.0,2256.0,420.0,1246.0,397.0,4.9236,155900.0,INLAND\n-121.07,37.71,39.0,223.0,37.0,92.0,37.0,3.375,212500.0,INLAND\n-121.08,37.69,19.0,6473.0,1212.0,3559.0,1123.0,3.2246,129300.0,INLAND\n-121.14,37.7,29.0,1343.0,223.0,751.0,225.0,3.2391,187500.0,INLAND\n-121.09,37.67,30.0,1653.0,285.0,800.0,291.0,3.3482,220000.0,INLAND\n-121.09,37.61,42.0,1787.0,296.0,921.0,287.0,3.8864,171400.0,INLAND\n-121.18,37.64,43.0,1244.0,209.0,611.0,197.0,2.875,187500.0,INLAND\n-121.06,37.7,7.0,9374.0,1847.0,4827.0,1722.0,3.462,151900.0,INLAND\n-121.04,37.7,52.0,349.0,59.0,121.0,40.0,3.3036,197500.0,INLAND\n-121.04,37.69,5.0,9601.0,1639.0,4449.0,1575.0,4.5332,195500.0,INLAND\n-121.02,37.71,25.0,207.0,41.0,87.0,43.0,3.6023,131300.0,INLAND\n-121.03,37.69,6.0,2607.0,557.0,1266.0,475.0,3.4632,137700.0,INLAND\n-121.02,37.7,16.0,3476.0,650.0,2126.0,665.0,3.3438,125400.0,INLAND\n-121.0,37.71,52.0,102.0,23.0,35.0,33.0,2.25,175000.0,INLAND\n-121.01,37.7,12.0,9148.0,1906.0,4656.0,1853.0,3.2447,142200.0,INLAND\n-121.06,37.67,31.0,906.0,146.0,383.0,129.0,3.4167,196900.0,INLAND\n-121.06,37.66,6.0,3655.0,598.0,1993.0,596.0,4.6053,150100.0,INLAND\n-121.05,37.65,5.0,3096.0,545.0,1760.0,519.0,4.5701,146400.0,INLAND\n-121.04,37.66,11.0,1658.0,301.0,913.0,298.0,4.1705,162800.0,INLAND\n-121.04,37.65,8.0,1959.0,379.0,995.0,365.0,3.3567,129100.0,INLAND\n-121.03,37.65,37.0,375.0,58.0,120.0,37.0,3.9844,150000.0,INLAND\n-121.04,37.67,16.0,19.0,19.0,166.0,9.0,0.536,162500.0,INLAND\n-121.0,37.69,18.0,3469.0,661.0,1452.0,628.0,3.4079,147500.0,INLAND\n-121.01,37.69,20.0,3275.0,760.0,1538.0,705.0,2.48,135600.0,INLAND\n-121.01,37.68,33.0,828.0,123.0,373.0,133.0,5.5,146200.0,INLAND\n-121.0,37.68,15.0,1232.0,180.0,408.0,196.0,6.9682,182400.0,INLAND\n-121.0,37.68,29.0,2911.0,445.0,1170.0,460.0,4.9904,158100.0,INLAND\n-121.01,37.68,33.0,3230.0,587.0,1579.0,560.0,3.5775,109700.0,INLAND\n-121.0,37.67,26.0,90.0,18.0,47.0,18.0,1.125,87500.0,INLAND\n-121.04,37.68,18.0,5129.0,1171.0,3622.0,1128.0,2.0272,92700.0,INLAND\n-121.04,37.68,28.0,1909.0,398.0,1140.0,380.0,2.3783,81400.0,INLAND\n-121.02,37.69,19.0,3814.0,790.0,2219.0,804.0,3.5208,145000.0,INLAND\n-121.03,37.69,5.0,4034.0,771.0,1967.0,742.0,3.8065,146000.0,INLAND\n-121.04,37.69,9.0,6333.0,1355.0,3265.0,1265.0,3.0217,160900.0,INLAND\n-121.02,37.68,25.0,3262.0,588.0,1834.0,578.0,3.996,114500.0,INLAND\n-121.03,37.68,20.0,3204.0,625.0,2016.0,605.0,2.6567,110400.0,INLAND\n-121.03,37.68,27.0,1956.0,327.0,1004.0,307.0,3.7857,110500.0,INLAND\n-121.02,37.68,28.0,2875.0,560.0,1608.0,558.0,3.5489,106400.0,INLAND\n-120.94,37.68,4.0,13315.0,2424.0,6420.0,2289.0,4.2471,162100.0,INLAND\n-120.95,37.67,15.0,3062.0,584.0,1624.0,538.0,4.3864,137600.0,INLAND\n-120.96,37.66,16.0,4961.0,902.0,2654.0,804.0,4.2823,138300.0,INLAND\n-120.95,37.66,16.0,4478.0,647.0,1990.0,672.0,5.1473,188400.0,INLAND\n-120.95,37.65,37.0,136.0,20.0,72.0,22.0,2.2279,225000.0,INLAND\n-120.93,37.67,6.0,3491.0,657.0,2075.0,644.0,3.3844,138500.0,INLAND\n-120.93,37.66,10.0,7566.0,1348.0,3227.0,1199.0,4.744,148100.0,INLAND\n-120.94,37.66,17.0,1147.0,140.0,327.0,136.0,6.8654,290500.0,INLAND\n-120.98,37.69,18.0,3176.0,468.0,1296.0,471.0,5.5684,185100.0,INLAND\n-120.99,37.69,25.0,2773.0,384.0,1060.0,381.0,6.4788,199400.0,INLAND\n-120.99,37.68,30.0,1975.0,375.0,732.0,326.0,2.6932,94900.0,INLAND\n-120.98,37.68,24.0,705.0,114.0,347.0,141.0,3.1912,149600.0,INLAND\n-120.97,37.69,16.0,2793.0,476.0,1279.0,477.0,3.4667,160900.0,INLAND\n-120.97,37.69,15.0,4065.0,841.0,1986.0,680.0,3.072,114300.0,INLAND\n-120.97,37.68,16.0,2349.0,446.0,1302.0,392.0,3.1625,130300.0,INLAND\n-120.97,37.68,9.0,1114.0,172.0,529.0,174.0,4.7159,163700.0,INLAND\n-120.98,37.68,27.0,4006.0,762.0,1806.0,718.0,3.1848,112800.0,INLAND\n-120.99,37.68,28.0,3269.0,647.0,1595.0,617.0,2.2336,112700.0,INLAND\n-120.99,37.67,16.0,568.0,124.0,307.0,116.0,2.1518,107400.0,INLAND\n-120.98,37.67,13.0,1221.0,260.0,682.0,275.0,3.65,155500.0,INLAND\n-120.97,37.68,16.0,2493.0,535.0,1370.0,504.0,3.3368,121200.0,INLAND\n-120.98,37.68,18.0,4197.0,1006.0,2203.0,874.0,2.166,118600.0,INLAND\n-120.97,37.67,16.0,1499.0,250.0,1292.0,271.0,4.3851,117300.0,INLAND\n-120.96,37.67,17.0,2434.0,511.0,1558.0,546.0,2.9219,114300.0,INLAND\n-120.96,37.67,18.0,1442.0,229.0,538.0,220.0,4.2969,163200.0,INLAND\n-120.97,37.67,31.0,1648.0,293.0,792.0,294.0,2.4,121500.0,INLAND\n-120.97,37.66,19.0,1974.0,393.0,799.0,377.0,3.1286,137500.0,INLAND\n-120.96,37.66,15.0,2485.0,434.0,1296.0,434.0,3.8542,145200.0,INLAND\n-120.97,37.66,24.0,2930.0,588.0,1448.0,570.0,3.5395,127900.0,INLAND\n-120.97,37.66,21.0,2760.0,632.0,1260.0,576.0,2.0227,179800.0,INLAND\n-120.98,37.66,33.0,1959.0,342.0,984.0,356.0,4.5208,114200.0,INLAND\n-120.98,37.66,40.0,3012.0,616.0,1423.0,595.0,2.6346,100600.0,INLAND\n-120.98,37.65,36.0,826.0,167.0,432.0,150.0,2.5,103100.0,INLAND\n-120.98,37.66,10.0,934.0,,401.0,255.0,0.9336,127100.0,INLAND\n-120.98,37.67,33.0,1433.0,298.0,824.0,302.0,2.7621,109100.0,INLAND\n-120.99,37.67,28.0,1768.0,423.0,1066.0,392.0,1.8315,90500.0,INLAND\n-120.99,37.66,30.0,1718.0,395.0,914.0,400.0,1.933,107000.0,INLAND\n-120.99,37.66,39.0,1748.0,329.0,831.0,302.0,2.5938,135600.0,INLAND\n-120.99,37.66,46.0,1750.0,347.0,754.0,356.0,2.9137,106000.0,INLAND\n-120.99,37.65,44.0,2848.0,623.0,1408.0,576.0,2.1487,86600.0,INLAND\n-121.0,37.65,52.0,3887.0,803.0,1768.0,779.0,2.5089,119000.0,INLAND\n-121.0,37.67,27.0,2278.0,479.0,995.0,449.0,2.5148,110200.0,INLAND\n-121.01,37.67,37.0,2483.0,459.0,1072.0,445.0,3.0721,108100.0,INLAND\n-121.01,37.66,36.0,3679.0,613.0,1366.0,581.0,4.5,151400.0,INLAND\n-121.0,37.66,43.0,2369.0,413.0,944.0,422.0,3.2632,138100.0,INLAND\n-121.0,37.66,43.0,2039.0,331.0,875.0,342.0,3.9844,152000.0,INLAND\n-121.01,37.65,47.0,1713.0,334.0,570.0,297.0,2.1969,149400.0,INLAND\n-121.02,37.67,32.0,3951.0,797.0,1916.0,740.0,2.6722,111500.0,INLAND\n-121.03,37.67,24.0,2162.0,459.0,1468.0,441.0,3.1857,98300.0,INLAND\n-121.03,37.66,31.0,887.0,217.0,614.0,199.0,2.1528,75500.0,INLAND\n-121.02,37.66,36.0,3495.0,641.0,1688.0,684.0,3.1568,109900.0,INLAND\n-121.02,37.66,28.0,1437.0,400.0,806.0,338.0,1.6078,125000.0,INLAND\n-121.02,37.65,20.0,2973.0,620.0,1996.0,570.0,3.0645,106000.0,INLAND\n-121.03,37.64,22.0,2001.0,387.0,1520.0,387.0,3.148,102300.0,INLAND\n-121.03,37.63,5.0,2881.0,584.0,1490.0,570.0,3.0398,120000.0,INLAND\n-121.05,37.64,33.0,1438.0,237.0,569.0,208.0,3.3516,150000.0,INLAND\n-121.05,37.62,37.0,1043.0,196.0,555.0,197.0,3.4125,125000.0,INLAND\n-121.02,37.64,42.0,1437.0,307.0,1035.0,284.0,2.1036,88300.0,INLAND\n-121.02,37.63,35.0,1591.0,364.0,1290.0,352.0,1.564,81800.0,INLAND\n-121.02,37.62,30.0,1721.0,399.0,1878.0,382.0,2.5363,83900.0,INLAND\n-121.02,37.61,33.0,1469.0,370.0,1318.0,349.0,1.7104,59000.0,INLAND\n-121.03,37.62,43.0,1241.0,240.0,612.0,266.0,2.8194,81300.0,INLAND\n-121.03,37.62,46.0,2331.0,508.0,1210.0,484.0,2.5313,77700.0,INLAND\n-121.02,37.62,14.0,5737.0,1286.0,4722.0,1210.0,1.6731,95800.0,INLAND\n-121.01,37.64,52.0,201.0,35.0,74.0,22.0,1.3036,75000.0,INLAND\n-121.01,37.64,36.0,1981.0,507.0,1998.0,468.0,1.9013,69900.0,INLAND\n-121.01,37.64,33.0,693.0,207.0,598.0,192.0,1.0217,81300.0,INLAND\n-121.0,37.64,43.0,311.0,95.0,293.0,94.0,1.2902,67500.0,INLAND\n-120.99,37.64,41.0,1580.0,385.0,881.0,361.0,2.7538,99600.0,INLAND\n-120.99,37.64,50.0,683.0,189.0,459.0,195.0,1.8162,70000.0,INLAND\n-121.0,37.65,17.0,484.0,202.0,198.0,204.0,0.6825,187500.0,INLAND\n-121.01,37.65,52.0,178.0,53.0,152.0,62.0,0.4999,82500.0,INLAND\n-121.0,37.64,19.0,121.0,41.0,658.0,41.0,0.9573,162500.0,INLAND\n-121.0,37.64,52.0,530.0,177.0,325.0,158.0,1.1875,90600.0,INLAND\n-120.97,37.65,16.0,3960.0,716.0,1776.0,724.0,3.9886,137500.0,INLAND\n-120.96,37.64,41.0,1467.0,328.0,673.0,310.0,2.7917,90700.0,INLAND\n-120.97,37.64,42.0,2359.0,504.0,1131.0,480.0,2.0833,95500.0,INLAND\n-120.98,37.65,40.0,422.0,63.0,158.0,63.0,7.3841,172200.0,INLAND\n-120.98,37.64,45.0,1913.0,335.0,839.0,333.0,3.1397,110700.0,INLAND\n-120.98,37.64,40.0,1791.0,359.0,679.0,322.0,2.1458,130300.0,INLAND\n-120.9,37.66,19.0,3377.0,669.0,2426.0,663.0,2.9783,82500.0,INLAND\n-120.91,37.66,36.0,1320.0,255.0,720.0,232.0,2.6523,76300.0,INLAND\n-120.9,37.64,26.0,1762.0,418.0,855.0,308.0,1.6767,81300.0,INLAND\n-120.94,37.65,13.0,5075.0,978.0,3033.0,838.0,3.0577,119000.0,INLAND\n-120.93,37.65,1.0,2254.0,328.0,402.0,112.0,4.25,189200.0,INLAND\n-120.92,37.65,23.0,505.0,124.0,163.0,129.0,1.3696,275000.0,INLAND\n-120.95,37.65,14.0,5200.0,1119.0,3221.0,1102.0,2.6964,107000.0,INLAND\n-120.96,37.65,34.0,1700.0,325.0,972.0,326.0,2.4485,95500.0,INLAND\n-120.95,37.64,32.0,3487.0,740.0,1957.0,685.0,2.7209,88300.0,INLAND\n-120.94,37.63,43.0,244.0,52.0,176.0,60.0,1.425,69400.0,INLAND\n-120.92,37.63,39.0,45.0,8.0,22.0,9.0,1.7679,450000.0,INLAND\n-120.96,37.64,36.0,60.0,12.0,51.0,14.0,3.625,67500.0,INLAND\n-120.98,37.64,39.0,2617.0,659.0,2052.0,642.0,1.6952,65000.0,INLAND\n-120.97,37.63,39.0,2360.0,607.0,2047.0,605.0,1.7054,58800.0,INLAND\n-121.0,37.63,31.0,215.0,62.0,192.0,66.0,1.75,73800.0,INLAND\n-121.0,37.63,49.0,2051.0,500.0,1525.0,467.0,1.59,80900.0,INLAND\n-121.01,37.63,41.0,2764.0,639.0,2122.0,600.0,1.9643,74900.0,INLAND\n-121.01,37.62,35.0,568.0,150.0,622.0,145.0,1.8167,79500.0,INLAND\n-121.01,37.62,35.0,2074.0,477.0,1687.0,431.0,2.0885,73700.0,INLAND\n-120.98,37.62,26.0,3819.0,955.0,3010.0,932.0,1.9206,81300.0,INLAND\n-120.99,37.63,21.0,319.0,120.0,276.0,85.0,2.4792,60000.0,INLAND\n-121.0,37.62,28.0,1153.0,420.0,1043.0,357.0,1.0801,75000.0,INLAND\n-120.99,37.62,37.0,2014.0,505.0,1787.0,515.0,1.5515,54100.0,INLAND\n-121.01,37.61,5.0,3655.0,696.0,2316.0,647.0,3.4703,129300.0,INLAND\n-121.0,37.61,36.0,2647.0,604.0,2045.0,550.0,2.273,62900.0,INLAND\n-121.0,37.6,22.0,4412.0,925.0,3116.0,817.0,2.6899,82100.0,INLAND\n-121.02,37.6,33.0,1009.0,238.0,1027.0,246.0,2.5993,68000.0,INLAND\n-120.98,37.6,36.0,1437.0,,1073.0,320.0,2.1779,58400.0,INLAND\n-120.99,37.61,39.0,512.0,132.0,443.0,127.0,1.2857,60000.0,INLAND\n-120.98,37.59,2.0,5042.0,834.0,2784.0,787.0,4.6484,145900.0,INLAND\n-120.96,37.59,11.0,4236.0,879.0,2410.0,850.0,2.3849,122000.0,INLAND\n-120.95,37.59,29.0,1727.0,439.0,1063.0,386.0,1.8929,63600.0,INLAND\n-120.94,37.61,13.0,3309.0,603.0,1796.0,555.0,3.8372,129300.0,INLAND\n-120.95,37.61,17.0,4054.0,654.0,2034.0,667.0,4.6833,142200.0,INLAND\n-120.96,37.61,23.0,3497.0,887.0,2467.0,816.0,1.9444,93400.0,INLAND\n-120.97,37.61,16.0,1326.0,375.0,884.0,375.0,1.871,103900.0,INLAND\n-120.95,37.6,35.0,1493.0,278.0,729.0,268.0,2.9821,97400.0,INLAND\n-120.94,37.6,30.0,3257.0,574.0,1804.0,588.0,3.5331,102900.0,INLAND\n-120.95,37.59,43.0,1561.0,354.0,862.0,332.0,1.8466,81500.0,INLAND\n-120.94,37.59,16.0,3964.0,824.0,2622.0,766.0,2.3152,111300.0,INLAND\n-120.94,37.58,19.0,1549.0,369.0,770.0,370.0,2.0493,99500.0,INLAND\n-120.95,37.62,11.0,4981.0,814.0,1934.0,686.0,3.7041,174800.0,INLAND\n-120.97,37.62,7.0,8489.0,1673.0,5807.0,1575.0,2.9451,127800.0,INLAND\n-120.54,37.68,18.0,335.0,76.0,189.0,67.0,1.2273,87500.0,INLAND\n-120.64,37.7,16.0,284.0,51.0,239.0,46.0,1.8958,137500.0,INLAND\n-120.75,37.69,24.0,2282.0,423.0,1167.0,398.0,3.8214,116100.0,INLAND\n-120.86,37.69,5.0,6660.0,1217.0,3012.0,1087.0,3.0809,143600.0,INLAND\n-120.87,37.64,40.0,1010.0,155.0,488.0,157.0,3.8984,170500.0,INLAND\n-120.82,37.64,20.0,3375.0,630.0,1505.0,598.0,2.69,201300.0,INLAND\n-120.77,37.64,8.0,3294.0,667.0,2277.0,652.0,2.6417,96800.0,INLAND\n-120.76,37.65,25.0,3214.0,682.0,2319.0,640.0,2.0385,84300.0,INLAND\n-120.46,37.65,17.0,315.0,89.0,130.0,58.0,1.4464,79200.0,INLAND\n-120.59,37.59,36.0,291.0,48.0,124.0,47.0,5.6945,154200.0,INLAND\n-120.69,37.59,27.0,1170.0,227.0,660.0,222.0,2.3906,81800.0,INLAND\n-120.76,37.61,30.0,816.0,159.0,531.0,147.0,3.2604,87900.0,INLAND\n-120.8,37.61,30.0,918.0,154.0,469.0,139.0,3.9688,175000.0,INLAND\n-120.76,37.58,35.0,1395.0,264.0,756.0,253.0,3.6181,178600.0,INLAND\n-120.83,37.58,30.0,1527.0,256.0,757.0,240.0,3.6629,171400.0,INLAND\n-120.85,37.57,27.0,819.0,157.0,451.0,150.0,3.4934,193800.0,INLAND\n-120.88,37.57,22.0,1440.0,267.0,774.0,249.0,3.9821,204300.0,INLAND\n-120.87,37.62,30.0,455.0,70.0,220.0,69.0,4.8958,142500.0,INLAND\n-120.87,37.6,32.0,4579.0,914.0,2742.0,856.0,2.6619,86200.0,INLAND\n-120.86,37.6,25.0,1178.0,206.0,709.0,214.0,4.5625,133600.0,INLAND\n-120.89,37.59,33.0,1016.0,206.0,617.0,209.0,2.151,195800.0,INLAND\n-120.92,37.6,12.0,4485.0,805.0,2445.0,832.0,3.7611,123100.0,INLAND\n-120.92,37.59,26.0,1705.0,279.0,642.0,236.0,2.6591,180500.0,INLAND\n-120.91,37.57,26.0,3396.0,705.0,2446.0,694.0,2.0521,65400.0,INLAND\n-120.93,37.56,17.0,1812.0,361.0,672.0,334.0,1.55,166100.0,INLAND\n-120.95,37.57,29.0,1179.0,249.0,672.0,243.0,3.1125,154800.0,INLAND\n-120.98,37.57,27.0,925.0,176.0,449.0,168.0,2.6406,129700.0,INLAND\n-121.02,37.58,36.0,1285.0,270.0,706.0,273.0,1.7169,121400.0,INLAND\n-121.04,37.6,27.0,958.0,184.0,580.0,177.0,2.1875,82800.0,INLAND\n-121.09,37.56,32.0,1717.0,325.0,1356.0,307.0,2.6705,91900.0,INLAND\n-121.03,37.55,32.0,946.0,198.0,624.0,173.0,1.9728,97900.0,INLAND\n-121.04,37.5,33.0,613.0,123.0,343.0,116.0,3.1875,129200.0,INLAND\n-121.02,37.48,26.0,467.0,,244.0,83.0,4.1346,187500.0,INLAND\n-121.12,37.48,5.0,4109.0,820.0,3062.0,713.0,3.2396,125200.0,INLAND\n-121.14,37.47,38.0,2427.0,450.0,1272.0,474.0,2.8833,115200.0,INLAND\n-121.14,37.48,6.0,1772.0,332.0,1011.0,331.0,3.7045,128100.0,INLAND\n-121.13,37.47,37.0,1995.0,448.0,1559.0,443.0,2.1833,92700.0,INLAND\n-121.11,37.47,12.0,2263.0,410.0,913.0,330.0,3.5795,145600.0,INLAND\n-121.14,37.46,4.0,2919.0,503.0,1592.0,491.0,5.2452,161900.0,INLAND\n-121.2,37.6,30.0,2110.0,406.0,1301.0,345.0,2.3173,86500.0,INLAND\n-121.27,37.56,31.0,1223.0,330.0,1067.0,245.0,2.8558,100000.0,INLAND\n-121.31,37.44,33.0,69.0,28.0,47.0,14.0,0.536,112500.0,INLAND\n-121.21,37.5,34.0,294.0,49.0,147.0,47.0,3.0,162500.0,INLAND\n-121.14,37.52,37.0,1358.0,231.0,586.0,214.0,3.1645,170800.0,INLAND\n-121.06,37.45,33.0,1401.0,299.0,915.0,282.0,3.4464,162500.0,INLAND\n-121.06,37.42,52.0,504.0,96.0,295.0,97.0,3.5,73500.0,INLAND\n-121.11,37.43,42.0,412.0,75.0,227.0,75.0,2.5,74200.0,INLAND\n-121.29,37.33,36.0,48.0,12.0,27.0,8.0,4.0,75000.0,INLAND\n-121.09,37.33,40.0,524.0,112.0,329.0,96.0,1.7188,112500.0,INLAND\n-121.01,37.37,41.0,1045.0,233.0,632.0,230.0,2.3583,95000.0,INLAND\n-121.01,37.33,17.0,1926.0,410.0,1054.0,321.0,1.6214,71500.0,INLAND\n-121.03,37.33,27.0,1333.0,230.0,730.0,229.0,3.06,106000.0,INLAND\n-121.03,37.32,42.0,2905.0,561.0,1457.0,551.0,2.2566,82100.0,INLAND\n-121.04,37.3,6.0,2657.0,486.0,1409.0,392.0,3.3824,115500.0,INLAND\n-120.84,37.53,14.0,3643.0,706.0,2070.0,697.0,3.1523,141800.0,INLAND\n-120.86,37.53,18.0,2829.0,732.0,1751.0,712.0,1.6445,156900.0,INLAND\n-120.88,37.53,18.0,239.0,39.0,92.0,36.0,5.3168,175000.0,INLAND\n-120.85,37.55,45.0,350.0,62.0,187.0,63.0,2.5938,275000.0,INLAND\n-120.89,37.54,30.0,509.0,115.0,275.0,115.0,2.2679,250000.0,INLAND\n-120.89,37.52,42.0,1200.0,221.0,647.0,192.0,2.5402,157500.0,INLAND\n-120.96,37.54,29.0,1468.0,245.0,747.0,231.0,3.4643,125000.0,INLAND\n-120.96,37.51,30.0,1288.0,237.0,720.0,233.0,2.3864,139100.0,INLAND\n-120.96,37.48,32.0,1256.0,212.0,682.0,236.0,2.9844,135900.0,INLAND\n-120.97,37.43,27.0,1380.0,,810.0,262.0,2.1875,137500.0,INLAND\n-120.8,37.55,18.0,1802.0,335.0,1110.0,329.0,3.1641,96300.0,INLAND\n-120.79,37.53,20.0,1417.0,263.0,853.0,263.0,3.3083,108300.0,INLAND\n-120.8,37.53,29.0,1162.0,254.0,726.0,225.0,2.1932,90600.0,INLAND\n-120.81,37.53,15.0,570.0,123.0,189.0,107.0,1.875,181300.0,INLAND\n-120.82,37.54,20.0,707.0,114.0,282.0,86.0,6.1324,164800.0,INLAND\n-120.8,37.52,13.0,2920.0,481.0,1602.0,490.0,3.9286,145800.0,INLAND\n-120.72,37.54,17.0,729.0,134.0,431.0,121.0,4.2188,131300.0,INLAND\n-120.79,37.49,44.0,1186.0,225.0,687.0,234.0,3.4167,160700.0,INLAND\n-120.84,37.47,11.0,2285.0,499.0,1468.0,471.0,2.7857,110300.0,INLAND\n-120.89,37.48,27.0,1118.0,195.0,647.0,209.0,2.9135,159400.0,INLAND\n-120.89,37.45,29.0,1940.0,337.0,1070.0,332.0,3.6597,145600.0,INLAND\n-120.88,37.52,2.0,1871.0,409.0,707.0,256.0,2.6103,133600.0,INLAND\n-120.87,37.5,7.0,4966.0,985.0,2431.0,904.0,3.1042,122500.0,INLAND\n-120.86,37.5,34.0,4272.0,996.0,2916.0,962.0,1.9829,82800.0,INLAND\n-120.85,37.49,39.0,2840.0,733.0,2606.0,737.0,1.9429,76400.0,INLAND\n-120.86,37.49,37.0,1084.0,271.0,893.0,236.0,1.6213,69500.0,INLAND\n-120.86,37.49,22.0,2140.0,445.0,1441.0,409.0,2.4706,89400.0,INLAND\n-120.85,37.49,42.0,264.0,72.0,310.0,70.0,1.4063,61500.0,INLAND\n-120.84,37.48,10.0,2874.0,612.0,1960.0,596.0,2.7381,104600.0,INLAND\n-120.86,37.52,9.0,9885.0,1871.0,5372.0,1843.0,3.4821,127100.0,INLAND\n-120.85,37.51,5.0,2899.0,745.0,1593.0,633.0,2.2292,127500.0,INLAND\n-120.85,37.51,15.0,1131.0,285.0,728.0,281.0,1.5531,93100.0,INLAND\n-120.84,37.5,47.0,2310.0,484.0,1126.0,447.0,2.2083,97300.0,INLAND\n-120.85,37.5,52.0,1724.0,352.0,922.0,348.0,1.7227,85700.0,INLAND\n-120.84,37.49,25.0,2383.0,576.0,1234.0,583.0,1.4529,86100.0,INLAND\n-120.82,37.49,25.0,1611.0,285.0,882.0,261.0,3.5547,122400.0,INLAND\n-120.82,37.51,17.0,1664.0,253.0,736.0,254.0,4.4083,165800.0,INLAND\n-120.83,37.51,34.0,3078.0,477.0,1226.0,487.0,4.601,150000.0,INLAND\n-120.82,37.5,21.0,2974.0,495.0,1313.0,461.0,4.4886,135400.0,INLAND\n-120.83,37.5,30.0,1340.0,244.0,631.0,231.0,3.375,118500.0,INLAND\n-120.84,37.51,8.0,1191.0,242.0,688.0,260.0,2.7243,138400.0,INLAND\n-120.84,37.51,14.0,6337.0,1593.0,3909.0,1480.0,2.0643,106500.0,INLAND\n-120.83,37.52,6.0,1488.0,252.0,773.0,259.0,4.1859,150000.0,INLAND\n-120.84,37.52,16.0,4527.0,887.0,2531.0,825.0,3.7065,124800.0,INLAND\n-120.84,37.51,20.0,1901.0,313.0,1258.0,320.0,3.8958,126800.0,INLAND\n-120.83,37.51,13.0,3795.0,604.0,1639.0,609.0,4.6635,198400.0,INLAND\n-121.62,39.16,7.0,4480.0,776.0,2271.0,767.0,3.809,110700.0,INLAND\n-121.63,39.16,7.0,1879.0,444.0,1065.0,410.0,2.4183,103800.0,INLAND\n-121.63,39.15,27.0,336.0,60.0,195.0,68.0,5.3946,71800.0,INLAND\n-121.62,39.16,16.0,2037.0,464.0,1267.0,451.0,2.4556,97100.0,INLAND\n-121.63,39.15,16.0,1547.0,418.0,940.0,400.0,1.5613,72500.0,INLAND\n-121.63,39.15,27.0,2991.0,637.0,1419.0,606.0,1.8849,73500.0,INLAND\n-121.62,39.15,36.0,2321.0,455.0,1168.0,489.0,3.0962,74000.0,INLAND\n-121.62,39.15,23.0,1984.0,528.0,1043.0,452.0,1.9375,65300.0,INLAND\n-121.63,39.14,39.0,1874.0,411.0,822.0,377.0,2.5038,68300.0,INLAND\n-121.62,39.14,41.0,2183.0,559.0,1202.0,506.0,1.6902,61500.0,INLAND\n-121.61,39.14,44.0,2035.0,476.0,1030.0,453.0,1.4661,65200.0,INLAND\n-121.62,39.13,41.0,1317.0,309.0,856.0,337.0,1.6719,64100.0,INLAND\n-121.63,39.13,26.0,2355.0,531.0,1047.0,497.0,1.8208,79500.0,INLAND\n-121.62,39.13,41.0,1147.0,243.0,583.0,239.0,2.2431,63400.0,INLAND\n-121.61,39.13,21.0,1432.0,328.0,933.0,336.0,1.6823,83800.0,INLAND\n-121.63,39.12,34.0,1991.0,348.0,804.0,344.0,3.4492,98800.0,INLAND\n-121.62,39.12,35.0,2787.0,587.0,1431.0,601.0,2.5469,65900.0,INLAND\n-121.61,39.13,33.0,2559.0,539.0,1583.0,504.0,1.4727,53000.0,INLAND\n-121.6,39.12,21.0,1299.0,338.0,1494.0,311.0,1.3348,225000.0,INLAND\n-121.62,39.12,26.0,1405.0,204.0,627.0,215.0,4.2188,94200.0,INLAND\n-121.63,39.12,32.0,2574.0,425.0,1099.0,391.0,4.3864,117500.0,INLAND\n-121.62,39.11,5.0,2320.0,502.0,1245.0,489.0,3.2465,97200.0,INLAND\n-121.63,39.1,22.0,3585.0,548.0,1757.0,577.0,4.174,100100.0,INLAND\n-121.62,39.11,11.0,3519.0,577.0,1459.0,549.0,4.2792,123800.0,INLAND\n-121.62,39.09,21.0,2693.0,481.0,1337.0,435.0,3.8534,99700.0,INLAND\n-121.68,39.13,17.0,1969.0,297.0,717.0,268.0,3.4698,179700.0,INLAND\n-121.65,39.13,11.0,4833.0,944.0,2336.0,841.0,2.6842,89100.0,INLAND\n-121.68,39.11,19.0,1366.0,220.0,596.0,203.0,4.0625,141700.0,INLAND\n-121.64,39.12,13.0,6408.0,1087.0,3294.0,1106.0,4.2656,110700.0,INLAND\n-121.64,39.11,18.0,3212.0,542.0,1817.0,508.0,3.3793,92900.0,INLAND\n-121.66,39.09,27.0,2098.0,372.0,1090.0,333.0,4.45,96200.0,INLAND\n-121.67,39.18,26.0,2121.0,375.0,1125.0,366.0,3.3958,94600.0,INLAND\n-121.68,39.15,14.0,2774.0,451.0,1292.0,428.0,4.3833,115200.0,INLAND\n-121.67,39.14,22.0,2264.0,390.0,1056.0,403.0,3.6111,112300.0,INLAND\n-121.63,39.18,13.0,1907.0,347.0,821.0,367.0,2.0978,134000.0,INLAND\n-121.66,39.15,22.0,2144.0,376.0,1200.0,370.0,3.4426,102400.0,INLAND\n-121.65,39.16,16.0,5022.0,1103.0,2087.0,956.0,2.3963,114800.0,INLAND\n-121.64,39.15,15.0,2659.0,396.0,1159.0,407.0,5.234,124900.0,INLAND\n-121.71,39.25,37.0,1871.0,321.0,806.0,294.0,4.0,101400.0,INLAND\n-121.68,39.29,29.0,1860.0,400.0,1137.0,365.0,1.5281,61600.0,INLAND\n-121.64,39.28,25.0,2857.0,662.0,2076.0,685.0,1.8095,64100.0,INLAND\n-121.64,39.22,37.0,1189.0,248.0,627.0,219.0,3.8611,100000.0,INLAND\n-121.67,39.26,29.0,3041.0,683.0,2106.0,687.0,1.6315,58000.0,INLAND\n-121.83,39.23,25.0,3819.0,702.0,1983.0,658.0,2.4464,72500.0,INLAND\n-121.74,39.15,20.0,2302.0,412.0,1205.0,399.0,2.8,71200.0,INLAND\n-121.83,39.1,42.0,1282.0,198.0,451.0,159.0,3.2917,97900.0,INLAND\n-121.91,39.14,45.0,845.0,155.0,343.0,136.0,2.125,62000.0,INLAND\n-121.76,38.94,48.0,540.0,110.0,234.0,74.0,3.6111,67500.0,INLAND\n-121.67,38.85,46.0,645.0,131.0,410.0,122.0,1.7417,110400.0,INLAND\n-121.69,38.87,38.0,412.0,93.0,304.0,95.0,2.6597,86000.0,INLAND\n-121.62,38.96,36.0,1826.0,329.0,1068.0,318.0,1.9797,118800.0,INLAND\n-121.7,39.07,26.0,2668.0,510.0,1437.0,505.0,3.3125,100000.0,INLAND\n-121.47,38.95,34.0,2129.0,350.0,969.0,314.0,2.7039,106300.0,INLAND\n-121.52,38.9,32.0,1650.0,313.0,802.0,284.0,2.9048,98200.0,INLAND\n-121.58,38.81,25.0,778.0,135.0,340.0,155.0,1.7857,258300.0,INLAND\n-121.51,38.79,29.0,1716.0,323.0,850.0,282.0,2.9324,137500.0,INLAND\n-122.1,40.05,26.0,633.0,129.0,305.0,140.0,2.1827,72700.0,INLAND\n-122.08,40.09,19.0,2611.0,503.0,1185.0,483.0,2.3657,94000.0,INLAND\n-122.12,40.14,34.0,1950.0,407.0,1029.0,376.0,2.5197,82300.0,INLAND\n-122.17,40.2,28.0,1782.0,334.0,873.0,311.0,3.3594,79100.0,INLAND\n-121.8,40.34,26.0,4815.0,910.0,1341.0,539.0,2.881,79800.0,INLAND\n-121.78,40.12,14.0,388.0,108.0,35.0,17.0,6.1359,106300.0,INLAND\n-122.42,40.32,16.0,1978.0,375.0,961.0,333.0,2.6827,83900.0,INLAND\n-122.34,40.32,12.0,3848.0,689.0,2008.0,683.0,2.6352,92200.0,INLAND\n-122.23,40.32,10.0,2336.0,426.0,1003.0,368.0,3.0833,81300.0,INLAND\n-122.38,40.09,16.0,2077.0,388.0,1155.0,389.0,3.1361,84800.0,INLAND\n-122.57,39.9,15.0,3873.0,810.0,1697.0,627.0,2.4555,55600.0,INLAND\n-122.72,40.17,16.0,396.0,78.0,188.0,72.0,1.3889,87500.0,INLAND\n-122.2,40.26,15.0,2102.0,358.0,957.0,371.0,3.1908,137900.0,INLAND\n-122.38,40.2,16.0,2722.0,511.0,1366.0,495.0,2.8447,87700.0,INLAND\n-122.35,40.25,10.0,1621.0,318.0,866.0,283.0,3.5,104300.0,INLAND\n-122.26,40.19,35.0,2467.0,469.0,1194.0,444.0,2.0425,63700.0,INLAND\n-122.24,40.19,29.0,1912.0,336.0,859.0,325.0,3.7,70500.0,INLAND\n-122.23,40.2,17.0,762.0,138.0,322.0,139.0,4.2917,128800.0,INLAND\n-122.24,40.18,39.0,2191.0,493.0,1307.0,499.0,1.6483,60800.0,INLAND\n-122.24,40.17,51.0,2378.0,584.0,1083.0,494.0,1.577,51900.0,INLAND\n-122.25,40.17,47.0,1554.0,308.0,846.0,301.0,1.8077,54100.0,INLAND\n-122.22,40.18,13.0,3719.0,803.0,1754.0,764.0,2.3517,88900.0,INLAND\n-122.21,40.2,19.0,3404.0,731.0,1421.0,683.0,2.6149,84400.0,INLAND\n-122.19,40.2,30.0,2750.0,476.0,1296.0,464.0,3.5305,73600.0,INLAND\n-122.21,40.18,30.0,744.0,156.0,410.0,165.0,2.1898,63200.0,INLAND\n-122.25,40.17,19.0,3182.0,630.0,1741.0,642.0,1.9727,64900.0,INLAND\n-122.24,40.16,19.0,2500.0,509.0,1293.0,494.0,2.035,55100.0,INLAND\n-122.23,40.17,21.0,1401.0,331.0,651.0,299.0,2.225,64700.0,INLAND\n-122.23,40.15,14.0,2297.0,573.0,1637.0,551.0,1.787,51600.0,INLAND\n-122.25,40.15,15.0,1677.0,346.0,858.0,327.0,2.4375,59200.0,INLAND\n-122.17,40.11,24.0,1631.0,340.0,1042.0,333.0,1.7708,59000.0,INLAND\n-122.19,40.07,21.0,1548.0,290.0,744.0,265.0,1.9773,55000.0,INLAND\n-122.18,40.02,30.0,1952.0,397.0,961.0,333.0,2.25,68200.0,INLAND\n-122.14,40.07,31.0,2053.0,465.0,1193.0,447.0,1.4923,44400.0,INLAND\n-122.13,40.01,21.0,916.0,194.0,451.0,178.0,2.125,63300.0,INLAND\n-122.1,40.03,25.0,2516.0,,1266.0,494.0,1.7566,58400.0,INLAND\n-122.06,40.02,32.0,1435.0,277.0,690.0,254.0,2.3043,68400.0,INLAND\n-122.0,39.94,27.0,852.0,176.0,464.0,148.0,1.7125,58200.0,INLAND\n-122.11,39.82,27.0,1065.0,214.0,508.0,198.0,2.625,91700.0,INLAND\n-122.12,39.91,16.0,4006.0,797.0,2028.0,752.0,2.3929,77200.0,INLAND\n-122.17,39.92,16.0,1566.0,306.0,652.0,287.0,1.9038,60800.0,INLAND\n-122.17,39.94,32.0,2352.0,477.0,1316.0,447.0,2.2292,57400.0,INLAND\n-122.14,39.97,27.0,1079.0,222.0,625.0,197.0,3.1319,62700.0,INLAND\n-122.23,39.95,21.0,2087.0,382.0,888.0,361.0,2.207,86400.0,INLAND\n-122.2,39.93,9.0,1296.0,287.0,768.0,260.0,1.9191,54400.0,INLAND\n-122.23,39.86,21.0,1730.0,350.0,982.0,322.0,1.8375,79800.0,INLAND\n-122.19,39.92,20.0,2563.0,658.0,1363.0,611.0,1.023,54200.0,INLAND\n-122.18,39.93,35.0,1387.0,272.0,610.0,237.0,2.1759,59500.0,INLAND\n-122.19,39.91,39.0,2467.0,529.0,1433.0,502.0,1.8571,53500.0,INLAND\n-122.68,41.15,32.0,817.0,206.0,224.0,89.0,3.631,90400.0,INLAND\n-122.81,40.93,16.0,2050.0,471.0,588.0,195.0,2.7083,88900.0,INLAND\n-122.96,40.77,29.0,1637.0,297.0,753.0,270.0,3.2891,93100.0,INLAND\n-122.93,40.78,20.0,3758.0,798.0,1685.0,757.0,2.3667,91200.0,INLAND\n-122.89,40.76,14.0,712.0,131.0,270.0,90.0,2.3958,102100.0,INLAND\n-122.79,40.75,17.0,3851.0,818.0,1352.0,560.0,2.125,71700.0,INLAND\n-122.86,40.56,12.0,1350.0,300.0,423.0,172.0,1.7393,81300.0,INLAND\n-122.95,40.67,17.0,1498.0,331.0,574.0,242.0,2.0268,94200.0,INLAND\n-122.95,40.71,26.0,2231.0,421.0,987.0,364.0,2.4792,88800.0,INLAND\n-123.35,40.99,23.0,141.0,59.0,47.0,23.0,1.125,66000.0,INLAND\n-123.53,40.88,20.0,2680.0,599.0,918.0,345.0,2.2115,75000.0,INLAND\n-123.48,40.79,15.0,619.0,160.0,287.0,104.0,1.9107,79200.0,INLAND\n-123.28,40.77,25.0,767.0,206.0,301.0,121.0,1.625,79200.0,INLAND\n-123.13,40.85,18.0,1650.0,377.0,675.0,282.0,1.8933,84700.0,INLAND\n-123.41,40.61,17.0,769.0,205.0,301.0,126.0,1.7875,55000.0,INLAND\n-123.11,40.6,23.0,708.0,202.0,316.0,136.0,1.1602,65000.0,INLAND\n-123.18,40.58,18.0,1451.0,278.0,695.0,254.0,1.7262,73700.0,INLAND\n-123.22,40.54,27.0,1573.0,361.0,847.0,330.0,1.9034,49600.0,INLAND\n-123.12,40.54,23.0,1091.0,217.0,539.0,201.0,1.8696,61500.0,INLAND\n-123.21,40.51,16.0,241.0,84.0,152.0,61.0,1.375,48800.0,INLAND\n-123.32,40.43,15.0,661.0,146.0,131.0,57.0,0.4999,56700.0,INLAND\n-123.08,40.4,10.0,365.0,102.0,140.0,49.0,1.7969,37500.0,INLAND\n-123.17,40.31,36.0,98.0,28.0,18.0,8.0,0.536,14999.0,INLAND\n-123.22,40.16,27.0,1848.0,449.0,396.0,150.0,2.8472,41300.0,INLAND\n-123.48,40.34,19.0,518.0,108.0,216.0,80.0,2.7083,64500.0,INLAND\n-123.43,40.22,20.0,133.0,35.0,87.0,37.0,3.625,67500.0,INLAND\n-123.41,40.07,17.0,449.0,151.0,141.0,53.0,0.8362,87500.0,INLAND\n-118.96,36.66,18.0,1302.0,424.0,320.0,133.0,3.1964,80000.0,INLAND\n-119.12,36.54,30.0,2747.0,515.0,1368.0,453.0,2.9828,85200.0,INLAND\n-118.96,36.49,24.0,1268.0,269.0,636.0,183.0,1.742,118800.0,INLAND\n-118.65,36.57,20.0,1431.0,416.0,570.0,225.0,1.4821,143300.0,INLAND\n-118.86,36.41,20.0,2749.0,575.0,1195.0,491.0,3.0391,139700.0,INLAND\n-118.94,36.32,10.0,2271.0,398.0,986.0,358.0,4.0703,147100.0,INLAND\n-119.26,36.61,33.0,560.0,90.0,310.0,113.0,2.5417,118800.0,INLAND\n-119.25,36.56,35.0,1675.0,373.0,1131.0,316.0,1.6722,59100.0,INLAND\n-119.28,36.54,33.0,1470.0,330.0,1222.0,301.0,1.8163,57400.0,INLAND\n-119.29,36.54,18.0,2581.0,628.0,2732.0,592.0,1.8429,58300.0,INLAND\n-119.29,36.55,21.0,2467.0,520.0,1721.0,515.0,2.5521,65600.0,INLAND\n-119.3,36.57,32.0,728.0,,461.0,149.0,3.0156,109100.0,INLAND\n-119.44,36.48,27.0,1546.0,415.0,1704.0,395.0,1.1728,41700.0,INLAND\n-119.37,36.47,26.0,337.0,69.0,277.0,73.0,2.3438,100000.0,INLAND\n-119.45,36.48,38.0,402.0,86.0,311.0,87.0,3.1719,106300.0,INLAND\n-119.48,36.44,22.0,1389.0,290.0,1185.0,271.0,2.0857,49200.0,INLAND\n-119.35,36.52,39.0,3027.0,608.0,2199.0,592.0,2.6445,62000.0,INLAND\n-119.48,36.5,32.0,3451.0,625.0,1968.0,574.0,2.9554,110300.0,INLAND\n-119.43,36.55,27.0,1621.0,323.0,882.0,324.0,2.75,93500.0,INLAND\n-119.48,36.54,28.0,2112.0,363.0,1011.0,335.0,4.2222,108900.0,INLAND\n-119.4,36.55,19.0,3000.0,628.0,2202.0,590.0,2.5141,67400.0,INLAND\n-119.39,36.55,30.0,1669.0,314.0,837.0,325.0,3.3869,80400.0,INLAND\n-119.38,36.56,14.0,3965.0,804.0,1945.0,733.0,2.6906,95300.0,INLAND\n-119.38,36.55,31.0,2342.0,439.0,1411.0,465.0,3.017,72000.0,INLAND\n-119.38,36.56,25.0,1180.0,222.0,611.0,212.0,2.0729,84700.0,INLAND\n-119.38,36.54,33.0,2465.0,536.0,2030.0,522.0,1.5223,51800.0,INLAND\n-119.38,36.53,38.0,1281.0,,1423.0,293.0,1.9602,51400.0,INLAND\n-119.39,36.54,34.0,1590.0,422.0,1272.0,407.0,1.8068,59000.0,INLAND\n-119.39,36.54,30.0,1408.0,326.0,1184.0,324.0,1.7165,59100.0,INLAND\n-119.4,36.53,28.0,2201.0,429.0,1524.0,412.0,2.75,65000.0,INLAND\n-119.28,36.52,19.0,1402.0,324.0,1327.0,316.0,2.25,53200.0,INLAND\n-119.29,36.53,33.0,1509.0,352.0,1734.0,336.0,1.625,50300.0,INLAND\n-119.29,36.52,39.0,858.0,228.0,1222.0,224.0,1.5714,43000.0,INLAND\n-119.26,36.5,35.0,1689.0,371.0,1475.0,329.0,2.5719,74300.0,INLAND\n-119.1,36.43,24.0,1039.0,190.0,643.0,193.0,2.6711,71300.0,INLAND\n-119.1,36.42,26.0,1775.0,416.0,1217.0,383.0,1.8801,57600.0,INLAND\n-119.09,36.42,15.0,1517.0,361.0,1275.0,343.0,1.5875,55800.0,INLAND\n-119.09,36.42,17.0,877.0,219.0,966.0,218.0,2.0,52500.0,INLAND\n-119.1,36.4,31.0,1533.0,361.0,1518.0,386.0,1.5608,51700.0,INLAND\n-119.1,36.4,23.0,1885.0,363.0,1056.0,338.0,3.2159,92800.0,INLAND\n-119.23,36.45,36.0,1508.0,323.0,1283.0,312.0,2.1205,60000.0,INLAND\n-119.18,36.4,39.0,1730.0,310.0,899.0,309.0,2.6648,129200.0,INLAND\n-119.27,36.39,17.0,2076.0,350.0,998.0,340.0,4.3281,145700.0,INLAND\n-119.23,36.39,39.0,1660.0,349.0,1061.0,306.0,1.4812,53500.0,INLAND\n-119.21,36.39,31.0,1465.0,303.0,1013.0,297.0,2.0363,53500.0,INLAND\n-119.21,36.38,18.0,2158.0,413.0,1461.0,395.0,2.0216,58000.0,INLAND\n-119.35,36.42,18.0,1115.0,193.0,1742.0,176.0,2.7969,123800.0,INLAND\n-119.31,36.39,32.0,2293.0,466.0,1538.0,468.0,1.9342,68600.0,INLAND\n-119.45,36.35,22.0,1824.0,333.0,1076.0,282.0,2.3365,69600.0,INLAND\n-119.41,36.35,20.0,1743.0,340.0,1390.0,336.0,2.2222,52900.0,INLAND\n-119.42,36.35,20.0,1469.0,303.0,1031.0,259.0,1.6645,48000.0,INLAND\n-119.29,36.35,15.0,1740.0,319.0,1332.0,308.0,2.5743,60200.0,INLAND\n-119.29,36.34,10.0,1832.0,455.0,1664.0,429.0,2.0227,53300.0,INLAND\n-119.3,36.35,24.0,1855.0,416.0,1520.0,410.0,2.3304,64900.0,INLAND\n-119.3,36.34,27.0,1515.0,358.0,1178.0,309.0,1.4432,48100.0,INLAND\n-119.31,36.34,14.0,2985.0,607.0,2250.0,607.0,2.1602,65200.0,INLAND\n-119.32,36.36,18.0,2060.0,383.0,1348.0,397.0,3.4312,68400.0,INLAND\n-119.37,36.35,20.0,1132.0,177.0,518.0,178.0,5.3767,231300.0,INLAND\n-119.32,36.33,20.0,1896.0,266.0,674.0,277.0,9.0376,239100.0,INLAND\n-119.32,36.33,18.0,2603.0,478.0,1158.0,423.0,4.5938,150500.0,INLAND\n-119.34,36.34,5.0,4505.0,834.0,1917.0,775.0,4.0144,126600.0,INLAND\n-119.34,36.33,17.0,2250.0,430.0,1218.0,468.0,4.1812,93700.0,INLAND\n-119.35,36.33,14.0,1195.0,220.0,568.0,229.0,3.1486,105600.0,INLAND\n-119.36,36.33,11.0,3221.0,617.0,1351.0,565.0,2.9844,132000.0,INLAND\n-119.29,36.34,28.0,1440.0,431.0,2178.0,440.0,1.2634,50600.0,INLAND\n-119.29,36.34,35.0,1235.0,369.0,1246.0,341.0,1.474,71000.0,INLAND\n-119.3,36.34,45.0,3723.0,831.0,2256.0,770.0,1.8299,63100.0,INLAND\n-119.31,36.34,32.0,1893.0,453.0,1744.0,425.0,1.4729,54100.0,INLAND\n-119.29,36.33,19.0,792.0,232.0,641.0,222.0,0.7445,112500.0,INLAND\n-119.31,36.33,46.0,1636.0,338.0,772.0,332.0,2.425,84900.0,INLAND\n-119.25,36.36,16.0,3245.0,469.0,1471.0,450.0,5.8673,154800.0,INLAND\n-119.27,36.34,7.0,3433.0,626.0,1793.0,626.0,3.5296,83700.0,INLAND\n-119.28,36.35,7.0,3598.0,701.0,2080.0,678.0,3.1111,72400.0,INLAND\n-119.28,36.33,10.0,1051.0,297.0,927.0,274.0,0.78,55500.0,INLAND\n-119.27,36.34,26.0,2057.0,472.0,1453.0,439.0,2.4113,58600.0,INLAND\n-119.24,36.33,9.0,3289.0,621.0,1866.0,631.0,3.1599,95000.0,INLAND\n-119.19,36.34,33.0,2199.0,403.0,1245.0,394.0,2.73,96900.0,INLAND\n-119.09,36.35,21.0,3146.0,595.0,1580.0,513.0,2.7857,92700.0,INLAND\n-119.11,36.29,18.0,1666.0,294.0,859.0,301.0,2.6065,93800.0,INLAND\n-119.13,36.3,33.0,3379.0,612.0,1565.0,618.0,2.7321,76500.0,INLAND\n-119.12,36.29,29.0,1638.0,323.0,942.0,322.0,2.1731,66200.0,INLAND\n-119.16,36.28,18.0,2377.0,414.0,1359.0,424.0,4.4,79300.0,INLAND\n-119.16,36.31,7.0,2946.0,664.0,1608.0,622.0,1.6829,80200.0,INLAND\n-119.15,36.29,18.0,1435.0,,657.0,254.0,2.4281,72500.0,INLAND\n-119.14,36.29,32.0,2084.0,482.0,1410.0,420.0,1.5321,48300.0,INLAND\n-119.14,36.29,36.0,788.0,181.0,405.0,180.0,1.47,61900.0,INLAND\n-119.2,36.3,19.0,1427.0,311.0,1026.0,293.0,2.625,57000.0,INLAND\n-119.2,36.3,32.0,1355.0,363.0,1427.0,384.0,1.3444,45600.0,INLAND\n-119.21,36.3,23.0,951.0,235.0,806.0,222.0,1.7734,41400.0,INLAND\n-119.21,36.3,18.0,1433.0,265.0,1092.0,276.0,1.9135,49400.0,INLAND\n-119.22,36.31,17.0,2079.0,459.0,2022.0,462.0,1.5464,54100.0,INLAND\n-119.25,36.27,23.0,1494.0,275.0,678.0,235.0,2.6875,69100.0,INLAND\n-119.2,36.28,22.0,2295.0,508.0,1654.0,478.0,1.684,65900.0,INLAND\n-119.29,36.32,27.0,1513.0,374.0,839.0,350.0,1.2012,64600.0,INLAND\n-119.29,36.32,35.0,1898.0,481.0,1123.0,433.0,1.1419,62900.0,INLAND\n-119.28,36.32,29.0,2274.0,514.0,1234.0,521.0,1.9138,66900.0,INLAND\n-119.29,36.32,33.0,2107.0,451.0,1364.0,442.0,2.2024,67200.0,INLAND\n-119.28,36.32,16.0,2812.0,514.0,1620.0,523.0,3.7404,89200.0,INLAND\n-119.27,36.32,6.0,2881.0,518.0,1432.0,504.0,4.0806,110200.0,INLAND\n-119.27,36.32,9.0,3631.0,635.0,1881.0,628.0,4.7723,113100.0,INLAND\n-119.25,36.32,32.0,1821.0,345.0,812.0,299.0,2.75,72200.0,INLAND\n-119.26,36.3,18.0,3578.0,720.0,1540.0,640.0,2.425,84600.0,INLAND\n-119.3,36.33,44.0,2060.0,414.0,819.0,355.0,2.8795,77000.0,INLAND\n-119.3,36.32,23.0,3521.0,615.0,1712.0,636.0,3.3875,92500.0,INLAND\n-119.31,36.32,44.0,2032.0,308.0,791.0,336.0,4.0298,109000.0,INLAND\n-119.31,36.32,23.0,2945.0,592.0,1419.0,532.0,2.5733,88800.0,INLAND\n-119.29,36.31,14.0,2382.0,377.0,1278.0,386.0,5.1896,101900.0,INLAND\n-119.29,36.31,34.0,1439.0,253.0,607.0,223.0,3.0972,82800.0,INLAND\n-119.3,36.31,16.0,2234.0,357.0,1150.0,361.0,4.2778,97300.0,INLAND\n-119.31,36.31,18.0,3860.0,760.0,1643.0,664.0,2.0714,92600.0,INLAND\n-119.31,36.3,20.0,1256.0,209.0,566.0,195.0,4.0221,86300.0,INLAND\n-119.3,36.3,14.0,3023.0,469.0,1523.0,492.0,5.3602,118600.0,INLAND\n-119.29,36.3,20.0,1157.0,179.0,572.0,191.0,5.3495,177300.0,INLAND\n-119.33,36.32,16.0,3331.0,839.0,1955.0,763.0,1.6148,86600.0,INLAND\n-119.34,36.32,14.0,1204.0,227.0,633.0,247.0,3.925,83800.0,INLAND\n-119.35,36.32,10.0,3817.0,719.0,1686.0,714.0,3.8235,94600.0,INLAND\n-119.34,36.32,6.0,3266.0,604.0,1769.0,580.0,3.1574,89200.0,INLAND\n-119.33,36.32,20.0,2025.0,328.0,1039.0,346.0,3.5313,82800.0,INLAND\n-119.32,36.32,35.0,2316.0,387.0,849.0,378.0,4.3816,88600.0,INLAND\n-119.33,36.32,23.0,3137.0,628.0,1446.0,548.0,2.5,85500.0,INLAND\n-119.33,36.32,19.0,2778.0,431.0,1092.0,451.0,5.2561,121300.0,INLAND\n-119.32,36.32,29.0,2409.0,436.0,1142.0,440.0,3.6895,87700.0,INLAND\n-119.32,36.31,21.0,2309.0,424.0,1047.0,453.0,2.9886,87500.0,INLAND\n-119.33,36.31,17.0,2401.0,409.0,1100.0,409.0,4.0577,107300.0,INLAND\n-119.33,36.31,15.0,1472.0,228.0,892.0,257.0,5.3909,113000.0,INLAND\n-119.33,36.3,11.0,3045.0,,1563.0,516.0,5.4337,133800.0,INLAND\n-119.32,36.3,15.0,2864.0,571.0,1480.0,475.0,2.9698,93400.0,INLAND\n-119.34,36.31,14.0,1635.0,422.0,870.0,399.0,2.7,88900.0,INLAND\n-119.34,36.3,13.0,2394.0,458.0,1177.0,389.0,2.6875,74400.0,INLAND\n-119.33,36.3,12.0,2172.0,352.0,1013.0,354.0,4.9464,115600.0,INLAND\n-119.38,36.3,14.0,1932.0,330.0,997.0,291.0,3.6875,93200.0,INLAND\n-119.33,36.28,16.0,2624.0,527.0,1077.0,520.0,2.125,104200.0,INLAND\n-119.32,36.3,14.0,1680.0,343.0,931.0,350.0,2.7336,89200.0,INLAND\n-119.29,36.28,23.0,1895.0,340.0,749.0,313.0,2.2333,120100.0,INLAND\n-119.46,36.25,32.0,1702.0,348.0,1016.0,350.0,2.5,73600.0,INLAND\n-119.4,36.25,25.0,1696.0,279.0,909.0,291.0,2.3,132800.0,INLAND\n-119.36,36.22,10.0,2445.0,526.0,1262.0,476.0,1.9355,68300.0,INLAND\n-119.37,36.22,19.0,1673.0,318.0,1298.0,343.0,2.706,64800.0,INLAND\n-119.36,36.21,18.0,1082.0,202.0,793.0,213.0,2.4032,60000.0,INLAND\n-119.35,36.22,32.0,1290.0,304.0,852.0,309.0,1.4429,54600.0,INLAND\n-119.35,36.21,26.0,2481.0,586.0,1445.0,498.0,1.6378,60300.0,INLAND\n-119.36,36.21,25.0,1170.0,259.0,804.0,257.0,1.3889,50200.0,INLAND\n-119.37,36.21,35.0,2228.0,476.0,1567.0,449.0,1.4455,54100.0,INLAND\n-119.34,36.23,12.0,4965.0,872.0,2191.0,804.0,3.5611,90200.0,INLAND\n-119.35,36.22,29.0,2051.0,351.0,915.0,343.0,3.1944,106800.0,INLAND\n-119.34,36.22,38.0,2708.0,460.0,1260.0,455.0,3.0905,78200.0,INLAND\n-119.33,36.22,29.0,1735.0,323.0,805.0,293.0,3.5039,89900.0,INLAND\n-119.33,36.21,38.0,3115.0,622.0,1238.0,606.0,2.6083,67000.0,INLAND\n-119.34,36.21,30.0,749.0,214.0,537.0,199.0,0.8229,68400.0,INLAND\n-119.33,36.22,9.0,3748.0,644.0,1955.0,620.0,4.2011,108100.0,INLAND\n-119.32,36.22,5.0,2319.0,438.0,1283.0,423.0,3.6343,95400.0,INLAND\n-119.32,36.21,29.0,1220.0,232.0,619.0,246.0,3.3125,78300.0,INLAND\n-119.32,36.25,21.0,1231.0,,609.0,206.0,2.8365,90000.0,INLAND\n-119.25,36.23,24.0,2015.0,355.0,1031.0,351.0,3.4306,139200.0,INLAND\n-119.27,36.18,23.0,3180.0,547.0,1829.0,498.0,2.6098,66000.0,INLAND\n-119.29,36.12,24.0,1248.0,226.0,641.0,200.0,2.4722,129200.0,INLAND\n-119.14,36.23,22.0,2935.0,523.0,1927.0,530.0,2.5875,70400.0,INLAND\n-119.12,36.19,21.0,2645.0,464.0,1245.0,407.0,2.9145,114200.0,INLAND\n-119.08,36.22,28.0,1606.0,320.0,1158.0,317.0,3.0324,55600.0,INLAND\n-119.08,36.21,20.0,1911.0,389.0,1241.0,348.0,2.5156,59300.0,INLAND\n-119.09,36.22,34.0,1715.0,290.0,780.0,297.0,3.4306,74300.0,INLAND\n-119.09,36.21,43.0,1335.0,280.0,943.0,288.0,1.9861,47700.0,INLAND\n-119.09,36.21,38.0,1901.0,453.0,1613.0,400.0,1.8828,44600.0,INLAND\n-119.1,36.21,38.0,727.0,173.0,559.0,176.0,2.4653,49500.0,INLAND\n-119.11,36.21,10.0,1972.0,455.0,1469.0,442.0,1.5407,58400.0,INLAND\n-118.74,36.23,22.0,1033.0,232.0,442.0,136.0,2.6447,137500.0,INLAND\n-118.93,36.19,30.0,2685.0,546.0,951.0,523.0,2.6184,113900.0,INLAND\n-118.82,36.13,43.0,1281.0,287.0,534.0,231.0,2.8906,65700.0,INLAND\n-118.54,36.12,11.0,4103.0,882.0,356.0,171.0,2.1029,99100.0,INLAND\n-118.37,36.19,10.0,443.0,111.0,48.0,21.0,3.125,71300.0,INLAND\n-118.7,35.82,20.0,4642.0,1300.0,658.0,247.0,2.3937,82100.0,INLAND\n-118.86,35.9,38.0,298.0,55.0,161.0,47.0,4.125,71300.0,INLAND\n-118.73,36.01,14.0,3263.0,651.0,1910.0,594.0,2.8603,128900.0,INLAND\n-119.08,36.2,30.0,1677.0,358.0,1159.0,365.0,2.4554,61200.0,INLAND\n-119.1,36.19,17.0,1564.0,396.0,713.0,362.0,1.6186,77100.0,INLAND\n-119.34,36.21,22.0,3065.0,726.0,2165.0,738.0,1.4792,54400.0,INLAND\n-119.34,36.2,12.0,1632.0,378.0,1303.0,315.0,2.0333,54400.0,INLAND\n-119.33,36.19,27.0,418.0,163.0,332.0,141.0,1.0714,63800.0,INLAND\n-119.31,36.2,23.0,1837.0,332.0,1064.0,335.0,3.1453,74500.0,INLAND\n-119.32,36.21,25.0,2360.0,460.0,1424.0,436.0,2.3152,63100.0,INLAND\n-119.32,36.2,25.0,1427.0,246.0,772.0,221.0,2.2262,64500.0,INLAND\n-119.32,36.2,15.0,1562.0,275.0,961.0,287.0,3.4231,83300.0,INLAND\n-119.32,36.19,11.0,1281.0,291.0,861.0,313.0,1.0962,72300.0,INLAND\n-119.32,36.19,11.0,3136.0,620.0,2013.0,583.0,3.335,69700.0,INLAND\n-119.35,36.2,31.0,1783.0,382.0,1266.0,358.0,2.2264,50800.0,INLAND\n-119.35,36.2,29.0,1938.0,404.0,1487.0,414.0,1.7462,51100.0,INLAND\n-119.36,36.2,33.0,1955.0,398.0,1412.0,397.0,2.25,51500.0,INLAND\n-119.37,36.19,24.0,1306.0,266.0,889.0,276.0,2.4922,66100.0,INLAND\n-119.35,36.19,6.0,958.0,226.0,734.0,230.0,1.0349,67800.0,INLAND\n-119.45,36.16,27.0,2119.0,373.0,1268.0,345.0,2.8152,106900.0,INLAND\n-119.35,36.16,21.0,2751.0,602.0,1496.0,489.0,2.3882,49200.0,INLAND\n-119.45,36.09,18.0,408.0,82.0,253.0,75.0,2.0313,112500.0,INLAND\n-119.31,36.06,20.0,2236.0,434.0,1405.0,412.0,1.8827,48700.0,INLAND\n-119.4,36.04,39.0,915.0,199.0,580.0,175.0,1.8894,112500.0,INLAND\n-119.27,36.05,29.0,1016.0,174.0,481.0,140.0,2.2917,112500.0,INLAND\n-119.21,36.1,30.0,1471.0,373.0,1418.0,357.0,1.7432,42500.0,INLAND\n-119.19,36.06,29.0,1815.0,376.0,1421.0,339.0,1.9091,71300.0,INLAND\n-119.19,36.14,41.0,759.0,140.0,408.0,129.0,3.9,85900.0,INLAND\n-119.13,36.13,28.0,1673.0,385.0,1434.0,371.0,2.0586,40900.0,INLAND\n-119.08,36.13,21.0,2271.0,376.0,1145.0,372.0,3.1528,113700.0,INLAND\n-119.03,36.13,24.0,2259.0,408.0,1169.0,395.0,1.7106,95500.0,INLAND\n-119.06,36.15,20.0,1282.0,273.0,852.0,247.0,1.6354,49000.0,INLAND\n-119.06,36.15,25.0,2402.0,478.0,1527.0,461.0,2.3194,52900.0,INLAND\n-119.14,36.06,32.0,1838.0,441.0,1628.0,425.0,1.6452,41500.0,INLAND\n-119.12,36.05,27.0,1575.0,321.0,1063.0,317.0,2.1477,53900.0,INLAND\n-119.08,36.02,26.0,1748.0,346.0,891.0,303.0,1.9439,62100.0,INLAND\n-119.01,36.02,17.0,3915.0,742.0,1768.0,688.0,2.375,79800.0,INLAND\n-118.92,36.04,28.0,1148.0,233.0,521.0,212.0,2.9208,98500.0,INLAND\n-119.08,36.09,25.0,1880.0,339.0,1003.0,315.0,2.7298,103400.0,INLAND\n-119.06,36.1,21.0,1344.0,249.0,868.0,221.0,2.5893,63600.0,INLAND\n-119.06,36.09,11.0,2572.0,454.0,1402.0,415.0,3.6786,72900.0,INLAND\n-119.04,36.09,15.0,2288.0,401.0,1238.0,429.0,3.0278,77400.0,INLAND\n-119.05,36.09,9.0,3297.0,568.0,1749.0,568.0,4.0217,99200.0,INLAND\n-119.04,36.07,17.0,2623.0,659.0,1912.0,618.0,1.5893,52000.0,INLAND\n-119.06,36.08,19.0,2554.0,443.0,1301.0,419.0,4.1856,72100.0,INLAND\n-119.07,36.08,5.0,2693.0,508.0,1785.0,491.0,3.0,71000.0,INLAND\n-119.07,36.07,11.0,2265.0,382.0,1285.0,387.0,3.2042,76200.0,INLAND\n-119.06,36.07,20.0,2683.0,553.0,1497.0,548.0,1.7031,64600.0,INLAND\n-119.05,36.07,21.0,2472.0,523.0,1238.0,504.0,1.7756,62900.0,INLAND\n-119.05,36.06,23.0,2344.0,407.0,1184.0,406.0,3.1625,70600.0,INLAND\n-119.02,36.09,15.0,2234.0,415.0,1254.0,420.0,3.0234,88600.0,INLAND\n-119.01,36.08,31.0,1620.0,366.0,1154.0,348.0,1.8857,55500.0,INLAND\n-119.03,36.08,19.0,2736.0,549.0,1432.0,503.0,2.6944,67700.0,INLAND\n-119.03,36.08,19.0,2471.0,431.0,1040.0,426.0,3.25,80600.0,INLAND\n-119.04,36.07,26.0,2185.0,435.0,1108.0,419.0,2.2277,78000.0,INLAND\n-119.03,36.07,26.0,3210.0,646.0,1908.0,642.0,2.4167,77600.0,INLAND\n-119.02,36.07,29.0,2610.0,597.0,1659.0,571.0,1.5911,60800.0,INLAND\n-119.02,36.07,39.0,1173.0,269.0,702.0,232.0,1.6146,53100.0,INLAND\n-119.01,36.07,44.0,2450.0,575.0,1330.0,508.0,1.6103,50900.0,INLAND\n-118.93,36.1,19.0,2988.0,681.0,1654.0,576.0,2.3792,90000.0,INLAND\n-118.98,36.06,33.0,2043.0,443.0,1497.0,417.0,2.343,47400.0,INLAND\n-119.0,36.05,24.0,3208.0,691.0,1986.0,662.0,1.5506,52300.0,INLAND\n-118.97,36.06,26.0,1289.0,262.0,1100.0,244.0,1.975,51400.0,INLAND\n-118.99,36.07,21.0,983.0,165.0,672.0,169.0,2.975,63900.0,INLAND\n-118.99,36.06,19.0,2153.0,458.0,1317.0,386.0,1.7564,42600.0,INLAND\n-119.0,36.06,41.0,937.0,158.0,396.0,142.0,4.0833,81300.0,INLAND\n-119.0,36.07,20.0,1042.0,183.0,509.0,175.0,2.9815,73000.0,INLAND\n-119.03,36.06,36.0,1925.0,443.0,1405.0,422.0,2.162,51900.0,INLAND\n-119.02,36.06,41.0,2279.0,538.0,1908.0,511.0,1.3952,43100.0,INLAND\n-119.01,36.06,25.0,1505.0,,1392.0,359.0,1.6812,47700.0,INLAND\n-119.01,36.05,27.0,1127.0,294.0,839.0,276.0,1.3807,53100.0,INLAND\n-119.02,36.05,22.0,2078.0,431.0,1336.0,456.0,2.2202,65200.0,INLAND\n-119.42,35.97,21.0,554.0,121.0,426.0,122.0,2.3516,47500.0,INLAND\n-119.31,35.99,26.0,1460.0,316.0,880.0,286.0,1.3676,47800.0,INLAND\n-119.28,35.99,20.0,2911.0,597.0,1746.0,588.0,1.7372,51000.0,INLAND\n-119.19,35.96,25.0,2014.0,402.0,1160.0,362.0,1.881,52500.0,INLAND\n-119.46,35.86,22.0,1750.0,374.0,1113.0,338.0,1.505,42700.0,INLAND\n-119.3,35.87,20.0,1934.0,377.0,1341.0,336.0,2.1434,62600.0,INLAND\n-119.12,35.85,37.0,736.0,166.0,564.0,138.0,2.4167,58300.0,INLAND\n-119.1,35.79,19.0,1809.0,477.0,2051.0,416.0,1.8144,49800.0,INLAND\n-119.27,35.89,18.0,1855.0,424.0,1839.0,392.0,1.7572,53300.0,INLAND\n-119.27,35.88,32.0,1393.0,343.0,1282.0,336.0,1.5069,43700.0,INLAND\n-119.27,35.87,12.0,972.0,269.0,1134.0,286.0,1.63,49500.0,INLAND\n-119.26,35.87,24.0,1590.0,390.0,1686.0,372.0,1.6469,47600.0,INLAND\n-119.06,35.94,18.0,3501.0,721.0,2009.0,660.0,2.6576,65700.0,INLAND\n-119.04,35.96,18.0,1187.0,308.0,1343.0,277.0,1.875,51700.0,INLAND\n-119.04,35.95,25.0,1009.0,246.0,994.0,222.0,1.8462,55800.0,INLAND\n-118.96,35.87,17.0,1668.0,307.0,888.0,277.0,3.7794,96200.0,INLAND\n-120.4,38.0,17.0,2098.0,370.0,912.0,354.0,2.6544,112600.0,INLAND\n-120.39,38.0,33.0,2177.0,404.0,891.0,383.0,3.212,105200.0,INLAND\n-120.4,37.98,19.0,2010.0,433.0,910.0,390.0,2.6696,121200.0,INLAND\n-120.39,37.98,52.0,1056.0,274.0,584.0,255.0,2.1513,86700.0,INLAND\n-120.37,38.01,30.0,473.0,,242.0,93.0,2.5417,123200.0,INLAND\n-120.35,37.99,3.0,1167.0,306.0,422.0,186.0,2.4191,217500.0,INLAND\n-120.38,37.99,36.0,2864.0,603.0,1155.0,565.0,2.3571,113400.0,INLAND\n-120.37,37.98,29.0,2508.0,591.0,1112.0,550.0,1.6021,91400.0,INLAND\n-120.38,37.97,47.0,1060.0,219.0,496.0,205.0,2.5781,104800.0,INLAND\n-120.26,38.13,17.0,301.0,94.0,122.0,47.0,4.0583,87500.0,INLAND\n-120.28,38.07,13.0,1996.0,410.0,618.0,218.0,2.9083,104600.0,INLAND\n-120.4,38.06,12.0,1430.0,310.0,517.0,240.0,2.6544,128100.0,INLAND\n-120.35,38.04,16.0,1499.0,326.0,733.0,286.0,2.5729,118800.0,INLAND\n-120.39,38.03,20.0,1551.0,309.0,647.0,228.0,2.6094,139100.0,INLAND\n-120.41,38.03,14.0,2061.0,465.0,859.0,462.0,2.1289,115300.0,INLAND\n-120.43,38.02,15.0,1613.0,299.0,655.0,251.0,3.6875,186000.0,INLAND\n-120.31,38.02,11.0,2366.0,398.0,1046.0,387.0,3.8203,139700.0,INLAND\n-120.3,38.04,6.0,1281.0,245.0,422.0,160.0,3.2875,111300.0,INLAND\n-120.28,38.03,13.0,2095.0,391.0,860.0,331.0,3.6838,145700.0,INLAND\n-120.27,38.02,13.0,3839.0,715.0,1486.0,532.0,3.1875,99800.0,INLAND\n-120.29,38.01,12.0,3014.0,560.0,1424.0,485.0,3.0729,105100.0,INLAND\n-120.3,37.99,23.0,1908.0,383.0,984.0,374.0,2.517,153500.0,INLAND\n-120.33,38.0,14.0,1944.0,330.0,822.0,314.0,3.5,170700.0,INLAND\n-120.25,38.04,22.0,4173.0,763.0,1086.0,444.0,2.5562,136200.0,INLAND\n-120.25,38.03,21.0,4924.0,966.0,1175.0,454.0,2.9457,116500.0,INLAND\n-120.22,38.05,14.0,3803.0,689.0,1129.0,477.0,2.7188,137000.0,INLAND\n-120.19,38.07,43.0,102.0,19.0,44.0,13.0,0.4999,162500.0,INLAND\n-120.19,38.03,17.0,8651.0,1579.0,2071.0,757.0,3.1076,115800.0,INLAND\n-120.12,38.12,37.0,3355.0,666.0,338.0,136.0,2.0625,88900.0,INLAND\n-120.03,38.19,26.0,7005.0,1358.0,416.0,189.0,2.125,132500.0,INLAND\n-120.26,37.99,12.0,2726.0,517.0,1351.0,474.0,3.5,107100.0,INLAND\n-120.24,38.01,11.0,1214.0,228.0,633.0,199.0,3.125,148600.0,INLAND\n-120.23,37.98,14.0,1954.0,368.0,917.0,316.0,3.1523,93300.0,INLAND\n-120.28,37.9,17.0,1047.0,212.0,530.0,196.0,2.1538,153300.0,INLAND\n-120.24,37.96,34.0,1747.0,395.0,935.0,362.0,1.625,79400.0,INLAND\n-120.23,37.96,52.0,1230.0,262.0,609.0,243.0,2.0057,68200.0,INLAND\n-120.25,37.93,13.0,493.0,76.0,196.0,68.0,3.375,134100.0,INLAND\n-120.13,37.93,5.0,111.0,26.0,58.0,25.0,1.675,112500.0,INLAND\n-120.35,37.98,4.0,1658.0,301.0,676.0,278.0,3.5714,149500.0,INLAND\n-120.33,37.97,17.0,2530.0,526.0,1024.0,496.0,2.0057,118900.0,INLAND\n-120.3,37.97,17.0,3243.0,619.0,1408.0,566.0,2.474,120100.0,INLAND\n-120.29,37.94,17.0,1459.0,297.0,753.0,271.0,3.05,144800.0,INLAND\n-120.32,37.91,16.0,108.0,18.0,54.0,22.0,4.375,100000.0,INLAND\n-120.35,37.86,25.0,287.0,57.0,118.0,50.0,2.3056,162500.0,INLAND\n-120.23,37.86,16.0,324.0,61.0,135.0,61.0,2.4615,137500.0,INLAND\n-120.2,37.84,9.0,13670.0,2453.0,2811.0,1193.0,3.2589,137900.0,INLAND\n-120.2,37.8,30.0,1189.0,255.0,446.0,165.0,3.4838,112500.0,INLAND\n-119.93,37.85,18.0,473.0,115.0,88.0,41.0,4.0833,137500.0,INLAND\n-119.57,37.94,17.0,346.0,130.0,51.0,20.0,3.4861,137500.0,INLAND\n-120.47,37.96,25.0,2505.0,529.0,1145.0,483.0,2.006,103000.0,INLAND\n-120.42,37.98,18.0,3059.0,609.0,1335.0,581.0,2.5129,115900.0,INLAND\n-120.42,37.95,19.0,2787.0,578.0,1208.0,532.0,2.4922,98700.0,INLAND\n-120.39,37.96,10.0,2554.0,501.0,922.0,439.0,2.1094,164000.0,INLAND\n-120.4,37.92,22.0,1022.0,194.0,517.0,198.0,3.625,99400.0,INLAND\n-120.35,37.95,13.0,2104.0,407.0,960.0,401.0,2.4,177000.0,INLAND\n-120.41,37.88,16.0,744.0,141.0,311.0,122.0,4.4231,87500.0,INLAND\n-120.45,37.79,8.0,2687.0,495.0,5087.0,385.0,3.1719,115400.0,INLAND\n-119.31,34.7,19.0,961.0,218.0,479.0,138.0,3.3438,156300.0,INLAND\n-119.06,34.62,10.0,416.0,110.0,436.0,70.0,2.2222,262500.0,<1H OCEAN\n-118.75,34.42,28.0,1000.0,206.0,545.0,154.0,2.4167,191700.0,<1H OCEAN\n-118.8,34.41,45.0,1610.0,,1148.0,347.0,2.7,120400.0,<1H OCEAN\n-118.88,34.42,20.0,728.0,120.0,360.0,115.0,6.1244,375000.0,<1H OCEAN\n-118.98,34.4,34.0,1328.0,244.0,795.0,227.0,4.4219,338100.0,<1H OCEAN\n-118.93,34.36,33.0,1775.0,309.0,1071.0,296.0,4.6607,187900.0,<1H OCEAN\n-118.93,34.4,17.0,3275.0,599.0,2422.0,637.0,3.7092,190500.0,<1H OCEAN\n-118.92,34.4,23.0,1290.0,283.0,1060.0,279.0,3.3152,198000.0,<1H OCEAN\n-118.92,34.41,22.0,2702.0,655.0,2664.0,571.0,3.0893,173400.0,<1H OCEAN\n-118.9,34.41,35.0,4431.0,739.0,2304.0,720.0,4.2599,209100.0,<1H OCEAN\n-118.91,34.4,30.0,2861.0,613.0,2065.0,586.0,3.2024,176100.0,<1H OCEAN\n-118.9,34.4,16.0,2614.0,575.0,1163.0,524.0,1.5781,134400.0,<1H OCEAN\n-119.06,34.38,33.0,1465.0,262.0,731.0,266.0,3.9464,230300.0,<1H OCEAN\n-119.06,34.37,32.0,3885.0,759.0,2504.0,736.0,3.6453,201700.0,<1H OCEAN\n-119.05,34.36,22.0,1815.0,506.0,2428.0,473.0,2.8417,162500.0,<1H OCEAN\n-119.05,34.4,50.0,1236.0,282.0,1079.0,257.0,2.6991,181300.0,<1H OCEAN\n-119.1,34.31,21.0,2424.0,527.0,1379.0,484.0,2.6786,184000.0,<1H OCEAN\n-119.04,34.34,35.0,462.0,90.0,334.0,96.0,5.3582,281300.0,<1H OCEAN\n-119.06,34.36,52.0,1409.0,359.0,981.0,304.0,2.7951,199300.0,<1H OCEAN\n-119.06,34.36,52.0,1239.0,320.0,934.0,298.0,1.8618,183300.0,<1H OCEAN\n-119.06,34.35,34.0,2426.0,646.0,2116.0,631.0,2.0682,158300.0,<1H OCEAN\n-119.05,34.35,39.0,950.0,300.0,1366.0,312.0,2.2443,146600.0,<1H OCEAN\n-119.09,34.35,20.0,4725.0,881.0,2823.0,869.0,4.0122,214800.0,<1H OCEAN\n-119.08,34.35,24.0,3663.0,828.0,2718.0,778.0,3.2757,186000.0,<1H OCEAN\n-119.08,34.34,23.0,3065.0,723.0,2042.0,698.0,2.7593,194800.0,<1H OCEAN\n-119.11,34.33,14.0,4026.0,769.0,1825.0,671.0,3.5541,191800.0,<1H OCEAN\n-119.12,34.38,28.0,7200.0,1281.0,3793.0,1238.0,4.075,237900.0,<1H OCEAN\n-119.06,34.36,48.0,1459.0,324.0,902.0,350.0,2.4185,189900.0,<1H OCEAN\n-119.23,34.44,34.0,3193.0,664.0,1434.0,627.0,2.4777,260300.0,<1H OCEAN\n-119.26,34.46,30.0,3826.0,691.0,1656.0,657.0,4.0074,434700.0,<1H OCEAN\n-119.23,34.46,34.0,9280.0,1765.0,4514.0,1693.0,3.2026,227600.0,<1H OCEAN\n-119.19,34.46,39.0,2056.0,381.0,939.0,371.0,6.6257,427600.0,<1H OCEAN\n-119.23,34.42,16.0,630.0,117.0,343.0,100.0,5.75,325000.0,<1H OCEAN\n-119.27,34.45,15.0,1659.0,274.0,679.0,253.0,5.0,357900.0,<1H OCEAN\n-119.15,34.44,33.0,2005.0,392.0,1043.0,351.0,5.308,297900.0,<1H OCEAN\n-119.14,34.49,17.0,321.0,44.0,92.0,39.0,7.75,375000.0,<1H OCEAN\n-119.31,34.41,22.0,2612.0,494.0,1361.0,439.0,4.1319,245000.0,NEAR OCEAN\n-119.31,34.38,23.0,282.0,69.0,130.0,57.0,2.4375,225000.0,NEAR OCEAN\n-119.29,34.37,41.0,1408.0,311.0,793.0,264.0,2.5441,161200.0,NEAR OCEAN\n-119.34,34.39,27.0,669.0,131.0,314.0,106.0,2.4659,231300.0,NEAR OCEAN\n-119.31,34.44,5.0,403.0,48.0,208.0,54.0,12.632,500001.0,NEAR OCEAN\n-119.28,34.45,36.0,2376.0,541.0,1505.0,547.0,2.4595,197600.0,<1H OCEAN\n-119.29,34.45,26.0,2849.0,535.0,1383.0,532.0,2.6893,230800.0,<1H OCEAN\n-119.29,34.44,34.0,4314.0,878.0,2361.0,831.0,3.2279,243100.0,NEAR OCEAN\n-119.27,34.44,22.0,3527.0,711.0,1483.0,640.0,2.7019,234700.0,<1H OCEAN\n-119.28,34.42,23.0,4763.0,828.0,2198.0,771.0,4.8105,313000.0,NEAR OCEAN\n-119.3,34.42,18.0,5591.0,1042.0,2860.0,1026.0,3.5822,219900.0,NEAR OCEAN\n-119.29,34.4,22.0,3891.0,657.0,1727.0,581.0,4.2656,241400.0,NEAR OCEAN\n-119.3,34.39,35.0,3079.0,579.0,1807.0,589.0,4.69,199300.0,NEAR OCEAN\n-119.17,34.29,28.0,731.0,155.0,285.0,162.0,3.2917,225000.0,NEAR OCEAN\n-119.17,34.29,18.0,3932.0,724.0,1896.0,680.0,5.2953,279400.0,NEAR OCEAN\n-119.17,34.31,21.0,259.0,38.0,142.0,45.0,5.2681,500001.0,NEAR OCEAN\n-119.15,34.3,21.0,2475.0,502.0,1269.0,505.0,2.98,259200.0,NEAR OCEAN\n-119.19,34.28,28.0,3231.0,524.0,1665.0,540.0,4.8583,224200.0,NEAR OCEAN\n-119.18,34.28,17.0,4526.0,717.0,2088.0,655.0,5.6885,268200.0,NEAR OCEAN\n-119.19,34.3,25.0,2197.0,320.0,934.0,330.0,6.311,283200.0,NEAR OCEAN\n-119.22,34.34,29.0,3128.0,672.0,1815.0,648.0,2.9821,175700.0,NEAR OCEAN\n-119.39,34.32,19.0,3238.0,629.0,1195.0,443.0,4.8472,500001.0,NEAR OCEAN\n-119.32,34.35,16.0,52.0,16.0,51.0,15.0,2.475,225000.0,NEAR OCEAN\n-119.14,34.29,17.0,2754.0,577.0,1349.0,533.0,3.1618,154200.0,<1H OCEAN\n-119.16,34.28,30.0,413.0,98.0,400.0,112.0,4.0,219200.0,NEAR OCEAN\n-119.16,34.28,11.0,5330.0,1056.0,2801.0,1028.0,4.763,232700.0,NEAR OCEAN\n-119.16,34.27,24.0,1824.0,331.0,1049.0,320.0,5.9181,221100.0,NEAR OCEAN\n-119.14,34.28,31.0,790.0,241.0,1095.0,222.0,2.25,75000.0,NEAR OCEAN\n-119.18,34.27,6.0,2307.0,386.0,910.0,364.0,5.215,279500.0,NEAR OCEAN\n-119.17,34.27,24.0,4165.0,646.0,2194.0,658.0,6.0661,234800.0,NEAR OCEAN\n-119.17,34.27,18.0,8010.0,1539.0,3982.0,1483.0,4.0905,236500.0,NEAR OCEAN\n-119.17,34.26,10.0,3654.0,541.0,1638.0,551.0,6.1885,267300.0,NEAR OCEAN\n-119.22,34.27,11.0,4695.0,955.0,2065.0,982.0,3.2158,223600.0,NEAR OCEAN\n-119.21,34.26,31.0,224.0,88.0,326.0,88.0,2.375,55000.0,NEAR OCEAN\n-119.21,34.26,10.0,3150.0,781.0,1582.0,653.0,4.2448,157300.0,NEAR OCEAN\n-119.22,34.26,16.0,2596.0,625.0,1403.0,562.0,3.4018,145200.0,NEAR OCEAN\n-119.2,34.27,8.0,4942.0,1173.0,3012.0,1033.0,3.445,203400.0,NEAR OCEAN\n-119.21,34.26,23.0,2887.0,540.0,1508.0,518.0,3.3452,217600.0,NEAR OCEAN\n-119.2,34.26,13.0,3009.0,588.0,1439.0,607.0,4.1845,199500.0,NEAR OCEAN\n-119.19,34.26,16.0,5018.0,853.0,2524.0,830.0,5.1752,218000.0,NEAR OCEAN\n-119.18,34.26,22.0,2334.0,359.0,1298.0,363.0,5.5275,228900.0,NEAR OCEAN\n-119.19,34.25,12.0,232.0,37.0,79.0,35.0,4.1667,214600.0,NEAR OCEAN\n-119.21,34.26,26.0,2406.0,411.0,1313.0,391.0,4.9079,234100.0,NEAR OCEAN\n-119.2,34.26,25.0,2203.0,367.0,1194.0,377.0,5.4087,223200.0,NEAR OCEAN\n-119.2,34.25,18.0,3208.0,643.0,1973.0,614.0,3.8162,235000.0,NEAR OCEAN\n-119.2,34.25,25.0,195.0,59.0,140.0,43.0,3.8889,187500.0,NEAR OCEAN\n-119.2,34.28,22.0,2362.0,601.0,1127.0,499.0,3.4006,219400.0,NEAR OCEAN\n-119.22,34.27,30.0,1937.0,295.0,695.0,313.0,5.0679,234300.0,NEAR OCEAN\n-119.23,34.27,22.0,3536.0,615.0,1650.0,612.0,4.2381,229300.0,NEAR OCEAN\n-119.21,34.31,22.0,7548.0,1038.0,2855.0,1008.0,6.729,409300.0,NEAR OCEAN\n-119.21,34.28,27.0,2219.0,312.0,937.0,315.0,5.7601,281100.0,NEAR OCEAN\n-119.23,34.3,18.0,1713.0,244.0,690.0,239.0,6.9483,404300.0,NEAR OCEAN\n-119.23,34.28,24.0,4260.0,691.0,1581.0,607.0,5.5048,303600.0,NEAR OCEAN\n-119.22,34.28,24.0,2212.0,332.0,899.0,331.0,5.533,299700.0,NEAR OCEAN\n-119.22,34.28,33.0,2467.0,377.0,1052.0,363.0,4.7333,257500.0,NEAR OCEAN\n-119.25,34.28,36.0,1530.0,341.0,703.0,317.0,3.5819,231900.0,NEAR OCEAN\n-119.26,34.28,41.0,2822.0,564.0,1288.0,541.0,3.0799,254100.0,NEAR OCEAN\n-119.25,34.3,34.0,1189.0,220.0,445.0,203.0,4.8824,396400.0,NEAR OCEAN\n-119.25,34.28,36.0,2232.0,373.0,951.0,368.0,5.2261,303200.0,NEAR OCEAN\n-119.24,34.28,41.0,1280.0,240.0,608.0,252.0,4.4038,229100.0,NEAR OCEAN\n-119.26,34.28,41.0,1835.0,311.0,683.0,308.0,4.8977,358200.0,NEAR OCEAN\n-119.27,34.28,52.0,2239.0,420.0,941.0,397.0,4.125,349000.0,NEAR OCEAN\n-119.27,34.29,32.0,2274.0,406.0,982.0,393.0,5.3254,385200.0,NEAR OCEAN\n-119.29,34.29,33.0,3854.0,982.0,1835.0,894.0,3.5294,323900.0,NEAR OCEAN\n-119.29,34.3,24.0,7637.0,1705.0,4647.0,1623.0,3.5385,186800.0,NEAR OCEAN\n-119.3,34.29,41.0,1445.0,410.0,1052.0,388.0,2.6333,170800.0,NEAR OCEAN\n-119.3,34.29,50.0,3128.0,825.0,2535.0,783.0,2.3669,165300.0,NEAR OCEAN\n-119.3,34.29,26.0,3665.0,932.0,2775.0,870.0,1.9286,160500.0,NEAR OCEAN\n-119.29,34.31,25.0,1092.0,190.0,702.0,215.0,3.9063,192700.0,NEAR OCEAN\n-119.29,34.28,38.0,2387.0,748.0,1537.0,741.0,2.3147,192500.0,NEAR OCEAN\n-119.3,34.27,17.0,1527.0,503.0,688.0,423.0,1.6007,187500.0,NEAR OCEAN\n-119.29,34.26,32.0,3295.0,764.0,1344.0,600.0,3.6007,395500.0,NEAR OCEAN\n-119.27,34.26,23.0,3578.0,753.0,1455.0,649.0,4.1898,359100.0,NEAR OCEAN\n-119.29,34.23,22.0,2486.0,608.0,709.0,523.0,2.9018,275000.0,NEAR OCEAN\n-119.29,34.24,27.0,4742.0,775.0,1682.0,696.0,6.194,500001.0,NEAR OCEAN\n-119.27,34.27,44.0,1312.0,279.0,668.0,278.0,4.09,203800.0,NEAR OCEAN\n-119.27,34.28,50.0,1710.0,412.0,915.0,380.0,3.1757,206300.0,NEAR OCEAN\n-119.27,34.27,52.0,459.0,112.0,276.0,107.0,2.375,198400.0,NEAR OCEAN\n-119.27,34.27,52.0,1577.0,343.0,836.0,335.0,3.5893,206600.0,NEAR OCEAN\n-119.28,34.27,43.0,403.0,77.0,156.0,85.0,4.6667,384600.0,NEAR OCEAN\n-119.28,34.27,44.0,706.0,176.0,399.0,149.0,3.0089,166700.0,NEAR OCEAN\n-119.25,34.27,46.0,679.0,159.0,382.0,143.0,3.5,221200.0,NEAR OCEAN\n-119.24,34.27,32.0,4071.0,888.0,1900.0,874.0,3.2792,220500.0,NEAR OCEAN\n-119.23,34.27,29.0,3298.0,804.0,1509.0,711.0,3.8125,244500.0,NEAR OCEAN\n-119.25,34.26,30.0,2948.0,827.0,1635.0,750.0,2.67,214900.0,NEAR OCEAN\n-119.25,34.27,35.0,2532.0,407.0,1338.0,422.0,4.7727,219000.0,NEAR OCEAN\n-119.26,34.27,36.0,1972.0,382.0,1029.0,411.0,3.7337,209000.0,NEAR OCEAN\n-119.26,34.27,40.0,2528.0,572.0,1318.0,549.0,3.6413,212700.0,NEAR OCEAN\n-119.26,34.27,42.0,918.0,204.0,394.0,204.0,4.0069,214300.0,NEAR OCEAN\n-119.23,34.25,28.0,26.0,3.0,29.0,9.0,8.0,275000.0,NEAR OCEAN\n-119.25,34.21,12.0,15201.0,2418.0,7132.0,2251.0,5.6756,301800.0,NEAR OCEAN\n-119.18,34.24,17.0,629.0,221.0,514.0,186.0,3.2847,112500.0,NEAR OCEAN\n-119.18,34.23,16.0,4609.0,1220.0,2147.0,1007.0,3.375,218800.0,NEAR OCEAN\n-119.19,34.22,26.0,3175.0,736.0,2460.0,775.0,3.125,219900.0,NEAR OCEAN\n-119.19,34.23,17.0,3889.0,748.0,2415.0,739.0,4.5,234300.0,NEAR OCEAN\n-119.18,34.22,15.0,4615.0,1008.0,2549.0,973.0,3.9063,198700.0,NEAR OCEAN\n-119.17,34.22,29.0,4188.0,816.0,2783.0,790.0,4.1949,197100.0,NEAR OCEAN\n-119.17,34.21,33.0,1039.0,256.0,1432.0,272.0,3.1103,143500.0,NEAR OCEAN\n-119.16,34.2,35.0,2183.0,636.0,3504.0,623.0,1.9704,160300.0,NEAR OCEAN\n-119.17,34.2,36.0,2028.0,523.0,2751.0,496.0,3.015,149300.0,NEAR OCEAN\n-119.17,34.2,40.0,1083.0,319.0,1843.0,349.0,2.3077,106900.0,NEAR OCEAN\n-119.18,34.21,30.0,1096.0,231.0,741.0,229.0,3.8625,234700.0,NEAR OCEAN\n-119.18,34.21,29.0,4039.0,680.0,1677.0,644.0,4.3897,257600.0,NEAR OCEAN\n-119.19,34.21,28.0,4194.0,811.0,2556.0,856.0,4.2227,235400.0,NEAR OCEAN\n-119.19,34.21,27.0,1887.0,487.0,1339.0,428.0,2.9185,224500.0,NEAR OCEAN\n-119.19,34.2,36.0,1293.0,312.0,1128.0,335.0,2.1542,253900.0,NEAR OCEAN\n-119.19,34.21,34.0,3413.0,693.0,2223.0,651.0,3.8239,208200.0,NEAR OCEAN\n-119.18,34.21,46.0,2062.0,484.0,1522.0,469.0,3.087,213900.0,NEAR OCEAN\n-119.19,34.2,18.0,3620.0,,3171.0,779.0,3.3409,220500.0,NEAR OCEAN\n-119.18,34.19,19.0,2393.0,,1938.0,762.0,1.6953,167400.0,NEAR OCEAN\n-119.18,34.2,21.0,494.0,127.0,489.0,106.0,2.6964,170800.0,NEAR OCEAN\n-119.18,34.19,5.0,384.0,131.0,410.0,149.0,1.5625,87500.0,NEAR OCEAN\n-119.27,34.17,15.0,11403.0,2131.0,3327.0,1585.0,4.3693,423300.0,NEAR OCEAN\n-119.23,34.19,16.0,5297.0,810.0,1489.0,667.0,6.4522,500001.0,NEAR OCEAN\n-119.23,34.17,18.0,6171.0,1490.0,2164.0,1210.0,3.6875,500001.0,NEAR OCEAN\n-119.23,34.15,18.0,6213.0,1188.0,2679.0,1000.0,3.748,380400.0,NEAR OCEAN\n-119.21,34.19,15.0,3797.0,692.0,2216.0,675.0,4.7443,229500.0,NEAR OCEAN\n-119.21,34.19,15.0,5614.0,989.0,2754.0,994.0,5.035,242900.0,NEAR OCEAN\n-119.22,34.18,17.0,3332.0,762.0,1797.0,673.0,4.4292,231200.0,NEAR OCEAN\n-119.2,34.19,19.0,9503.0,1769.0,6370.0,1718.0,5.0016,218500.0,NEAR OCEAN\n-119.2,34.18,27.0,1035.0,229.0,782.0,222.0,4.2212,185400.0,NEAR OCEAN\n-119.19,34.19,35.0,2599.0,552.0,2726.0,543.0,3.2212,180500.0,NEAR OCEAN\n-119.19,34.18,32.0,3366.0,677.0,2857.0,669.0,4.6186,181100.0,NEAR OCEAN\n-119.18,34.19,36.0,4519.0,1081.0,4818.0,1061.0,2.8561,179100.0,NEAR OCEAN\n-119.18,34.18,31.0,2636.0,638.0,2695.0,614.0,3.2196,175800.0,NEAR OCEAN\n-119.17,34.18,38.0,3221.0,783.0,2792.0,736.0,2.9118,172400.0,NEAR OCEAN\n-119.17,34.17,34.0,2749.0,539.0,2330.0,559.0,4.2137,185600.0,NEAR OCEAN\n-119.17,34.17,32.0,1567.0,304.0,1482.0,308.0,3.5867,182100.0,NEAR OCEAN\n-119.17,34.17,42.0,1411.0,300.0,1295.0,339.0,2.6667,164900.0,NEAR OCEAN\n-119.17,34.17,25.0,1596.0,321.0,1378.0,308.0,4.0074,188000.0,NEAR OCEAN\n-119.17,34.17,21.0,2361.0,464.0,1146.0,396.0,3.6597,195100.0,NEAR OCEAN\n-119.19,34.17,31.0,1872.0,434.0,1511.0,405.0,3.2314,186800.0,NEAR OCEAN\n-119.19,34.17,35.0,4276.0,767.0,3295.0,708.0,4.2583,187300.0,NEAR OCEAN\n-119.18,34.17,32.0,2388.0,467.0,1746.0,483.0,3.9331,187600.0,NEAR OCEAN\n-119.18,34.16,30.0,2053.0,368.0,1496.0,391.0,3.6546,186200.0,NEAR OCEAN\n-119.19,34.17,27.0,2183.0,364.0,1458.0,388.0,4.4567,191100.0,NEAR OCEAN\n-119.19,34.16,34.0,2610.0,466.0,1543.0,433.0,3.9722,189000.0,NEAR OCEAN\n-119.19,34.16,35.0,2733.0,510.0,1814.0,511.0,4.4187,183400.0,NEAR OCEAN\n-119.22,34.15,32.0,3152.0,596.0,3490.0,526.0,2.725,450000.0,NEAR OCEAN\n-119.2,34.18,22.0,6465.0,1397.0,2694.0,1370.0,2.9832,165600.0,NEAR OCEAN\n-119.21,34.18,13.0,6103.0,1319.0,2986.0,1212.0,3.9718,215200.0,NEAR OCEAN\n-119.19,34.15,31.0,4175.0,1004.0,3310.0,954.0,3.1989,185400.0,NEAR OCEAN\n-119.2,34.15,27.0,2076.0,681.0,1904.0,647.0,1.4773,160800.0,NEAR OCEAN\n-119.21,34.12,15.0,5778.0,1285.0,1722.0,829.0,4.3427,305800.0,NEAR OCEAN\n-119.18,34.16,12.0,460.0,101.0,405.0,103.0,5.2783,167400.0,NEAR OCEAN\n-119.18,34.16,27.0,1832.0,415.0,1480.0,414.0,3.9643,186000.0,NEAR OCEAN\n-119.18,34.15,22.0,4769.0,1366.0,5534.0,1318.0,2.4167,192000.0,NEAR OCEAN\n-119.17,34.16,17.0,5276.0,1020.0,4066.0,984.0,4.5828,205400.0,NEAR OCEAN\n-119.17,34.15,18.0,2509.0,688.0,3129.0,677.0,2.6098,146100.0,NEAR OCEAN\n-119.13,34.19,16.0,6389.0,1330.0,6242.0,1340.0,4.0222,206800.0,NEAR OCEAN\n-119.15,34.17,22.0,1612.0,334.0,1431.0,335.0,4.8125,194400.0,NEAR OCEAN\n-119.14,34.17,16.0,1593.0,353.0,836.0,357.0,2.726,67500.0,NEAR OCEAN\n-119.11,34.17,37.0,470.0,105.0,522.0,83.0,2.0368,243800.0,NEAR OCEAN\n-119.17,34.19,28.0,1444.0,508.0,2145.0,437.0,1.6964,175000.0,NEAR OCEAN\n-119.14,34.15,25.0,2202.0,390.0,1415.0,412.0,4.43,207700.0,NEAR OCEAN\n-119.16,34.15,23.0,3204.0,644.0,2295.0,614.0,3.9485,196600.0,NEAR OCEAN\n-119.16,34.12,17.0,224.0,70.0,147.0,71.0,3.6167,280000.0,NEAR OCEAN\n-119.16,34.17,17.0,7982.0,1603.0,6437.0,1596.0,4.1279,223900.0,NEAR OCEAN\n-119.15,34.17,23.0,2239.0,537.0,784.0,497.0,1.6038,194300.0,NEAR OCEAN\n-119.15,34.2,25.0,3445.0,898.0,5558.0,894.0,3.0972,169300.0,NEAR OCEAN\n-119.17,34.25,15.0,1329.0,282.0,1001.0,284.0,3.65,189300.0,NEAR OCEAN\n-119.15,34.25,36.0,3511.0,664.0,2965.0,695.0,4.0878,186800.0,NEAR OCEAN\n-119.16,34.23,26.0,5444.0,1293.0,3700.0,1158.0,2.7556,213200.0,NEAR OCEAN\n-119.14,34.23,8.0,243.0,75.0,102.0,80.0,2.5714,500001.0,NEAR OCEAN\n-119.12,34.23,35.0,2028.0,554.0,2252.0,521.0,2.4643,182000.0,NEAR OCEAN\n-119.12,34.25,31.0,737.0,146.0,1436.0,168.0,3.5625,194100.0,NEAR OCEAN\n-119.04,34.28,21.0,1856.0,276.0,863.0,255.0,4.5833,500001.0,<1H OCEAN\n-118.96,34.3,16.0,3103.0,482.0,1567.0,467.0,6.907,500001.0,<1H OCEAN\n-119.06,34.24,21.0,7436.0,984.0,2982.0,988.0,7.6775,391200.0,<1H OCEAN\n-119.09,34.24,17.0,10214.0,1589.0,3409.0,1327.0,5.3806,452100.0,<1H OCEAN\n-119.02,34.26,40.0,1498.0,292.0,707.0,249.0,3.7974,228700.0,<1H OCEAN\n-119.03,34.25,25.0,3344.0,502.0,1483.0,496.0,6.196,340600.0,<1H OCEAN\n-119.04,34.24,20.0,7794.0,1192.0,4169.0,1188.0,5.9316,311900.0,<1H OCEAN\n-119.05,34.24,24.0,4341.0,646.0,1929.0,703.0,5.4298,279600.0,<1H OCEAN\n-118.99,34.23,9.0,10618.0,1617.0,4830.0,1606.0,6.6246,284200.0,<1H OCEAN\n-119.01,34.23,11.0,5785.0,1035.0,2760.0,985.0,4.693,232200.0,<1H OCEAN\n-118.94,34.24,5.0,10018.0,1233.0,4253.0,1120.0,8.9063,500001.0,<1H OCEAN\n-118.96,34.23,14.0,15207.0,2924.0,6301.0,2829.0,3.9699,217000.0,<1H OCEAN\n-119.03,34.24,25.0,3655.0,545.0,1776.0,544.0,5.687,238100.0,<1H OCEAN\n-119.03,34.23,16.0,5323.0,795.0,2493.0,779.0,5.6762,271300.0,<1H OCEAN\n-119.02,34.24,24.0,4650.0,748.0,2374.0,702.0,5.8838,232600.0,<1H OCEAN\n-119.03,34.23,21.0,3284.0,487.0,1832.0,521.0,5.2773,250800.0,<1H OCEAN\n-119.03,34.22,24.0,3421.0,656.0,2220.0,645.0,4.7831,214200.0,<1H OCEAN\n-119.04,34.23,21.0,9807.0,1614.0,4199.0,1554.0,5.0145,246600.0,<1H OCEAN\n-119.06,34.23,23.0,3471.0,510.0,2002.0,555.0,5.2742,257500.0,<1H OCEAN\n-119.06,34.23,29.0,3511.0,632.0,2591.0,596.0,3.0219,221700.0,<1H OCEAN\n-119.04,34.22,18.0,3117.0,583.0,2079.0,545.0,4.6458,222800.0,<1H OCEAN\n-119.06,34.22,13.0,4175.0,1321.0,2257.0,1271.0,3.1446,177100.0,<1H OCEAN\n-119.0,34.19,5.0,3634.0,718.0,1317.0,743.0,4.2917,227900.0,<1H OCEAN\n-119.05,34.19,39.0,143.0,36.0,113.0,33.0,2.8942,275000.0,NEAR OCEAN\n-119.08,34.17,32.0,166.0,22.0,63.0,29.0,7.3004,125000.0,NEAR OCEAN\n-119.03,34.21,11.0,4528.0,729.0,2398.0,684.0,5.3044,319000.0,<1H OCEAN\n-119.05,34.21,27.0,4357.0,926.0,2110.0,876.0,3.0119,218200.0,<1H OCEAN\n-119.09,34.22,8.0,40.0,10.0,309.0,16.0,4.0208,52500.0,NEAR OCEAN\n-119.05,34.13,12.0,57.0,22.0,69.0,15.0,5.0066,275000.0,NEAR OCEAN\n-118.97,34.18,18.0,7338.0,1020.0,3419.0,1058.0,7.0242,293100.0,<1H OCEAN\n-118.96,34.19,16.0,1807.0,346.0,587.0,296.0,1.9811,162500.0,<1H OCEAN\n-118.96,34.18,16.0,3137.0,462.0,1384.0,436.0,6.1306,258200.0,<1H OCEAN\n-118.98,34.16,16.0,2476.0,402.0,1251.0,387.0,5.7676,241300.0,<1H OCEAN\n-118.95,34.18,25.0,2237.0,331.0,1121.0,365.0,6.0994,254900.0,<1H OCEAN\n-118.95,34.17,9.0,2372.0,312.0,1039.0,321.0,7.6016,344900.0,<1H OCEAN\n-118.94,34.17,16.0,3746.0,508.0,1556.0,452.0,6.3303,299400.0,<1H OCEAN\n-118.94,34.17,15.0,1679.0,271.0,928.0,264.0,5.5681,235600.0,<1H OCEAN\n-118.95,34.17,23.0,2630.0,404.0,1184.0,385.0,5.2955,247600.0,<1H OCEAN\n-118.95,34.16,21.0,2953.0,419.0,1397.0,410.0,6.541,291500.0,<1H OCEAN\n-118.93,34.18,18.0,2730.0,415.0,1248.0,412.0,6.187,287900.0,<1H OCEAN\n-118.92,34.18,17.0,2400.0,352.0,1067.0,323.0,6.3522,259300.0,<1H OCEAN\n-118.92,34.17,17.0,1552.0,246.0,685.0,244.0,5.9836,294800.0,<1H OCEAN\n-118.94,34.16,3.0,1170.0,148.0,493.0,142.0,8.0428,500001.0,<1H OCEAN\n-118.91,34.18,17.0,3220.0,716.0,1381.0,733.0,2.8958,176000.0,<1H OCEAN\n-118.9,34.18,20.0,1288.0,179.0,539.0,181.0,5.8652,302600.0,<1H OCEAN\n-118.9,34.18,14.0,2627.0,328.0,1121.0,328.0,7.0504,333800.0,<1H OCEAN\n-118.9,34.17,14.0,4719.0,734.0,1880.0,731.0,5.3558,313800.0,<1H OCEAN\n-118.88,34.17,15.0,4260.0,,1701.0,669.0,5.1033,410700.0,<1H OCEAN\n-118.85,34.17,42.0,564.0,96.0,220.0,81.0,4.5625,318800.0,<1H OCEAN\n-118.84,34.16,18.0,6075.0,1056.0,2571.0,1018.0,5.22,399400.0,<1H OCEAN\n-118.84,34.15,17.0,3785.0,494.0,1527.0,507.0,8.4443,358500.0,<1H OCEAN\n-118.86,34.16,16.0,1509.0,216.0,578.0,235.0,10.2614,410800.0,<1H OCEAN\n-118.85,34.14,24.0,1999.0,244.0,759.0,247.0,8.7657,366300.0,<1H OCEAN\n-118.82,34.15,9.0,655.0,110.0,222.0,109.0,7.8528,337500.0,NEAR OCEAN\n-118.83,34.15,16.0,3380.0,731.0,1227.0,641.0,4.2857,233200.0,NEAR OCEAN\n-118.83,34.14,16.0,1316.0,194.0,450.0,173.0,10.1597,500001.0,NEAR OCEAN\n-118.83,34.14,16.0,1956.0,312.0,671.0,319.0,6.4001,321800.0,NEAR OCEAN\n-118.85,34.14,16.0,4109.0,543.0,1409.0,560.0,8.1064,423400.0,<1H OCEAN\n-118.95,34.19,24.0,2719.0,434.0,1318.0,424.0,4.675,228800.0,<1H OCEAN\n-118.94,34.18,24.0,3689.0,585.0,1898.0,581.0,5.9224,239400.0,<1H OCEAN\n-118.94,34.18,25.0,3502.0,508.0,1713.0,508.0,5.5181,242100.0,<1H OCEAN\n-118.93,34.2,17.0,2619.0,606.0,1655.0,557.0,3.886,281300.0,<1H OCEAN\n-118.92,34.19,16.0,3631.0,974.0,2585.0,923.0,3.0691,130400.0,<1H OCEAN\n-118.9,34.2,16.0,6510.0,817.0,2304.0,778.0,7.9943,452100.0,<1H OCEAN\n-118.9,34.19,26.0,1582.0,196.0,573.0,182.0,10.0595,500001.0,<1H OCEAN\n-118.91,34.22,15.0,5644.0,757.0,2659.0,783.0,6.7559,312000.0,<1H OCEAN\n-118.89,34.22,20.0,3878.0,665.0,1651.0,591.0,5.5402,264600.0,<1H OCEAN\n-118.87,34.23,14.0,4242.0,746.0,1858.0,689.0,6.0145,287100.0,<1H OCEAN\n-118.88,34.22,16.0,2343.0,393.0,2007.0,383.0,5.756,302700.0,<1H OCEAN\n-118.87,34.22,14.0,3108.0,451.0,1566.0,434.0,6.2423,305400.0,<1H OCEAN\n-118.85,34.25,17.0,5593.0,732.0,1992.0,660.0,7.2965,342900.0,<1H OCEAN\n-118.85,34.23,13.0,5094.0,764.0,2230.0,737.0,6.4823,290900.0,<1H OCEAN\n-118.86,34.22,26.0,1932.0,280.0,886.0,289.0,5.0855,232200.0,<1H OCEAN\n-118.86,34.22,26.0,1775.0,295.0,1004.0,323.0,5.5845,251700.0,<1H OCEAN\n-118.86,34.22,22.0,1230.0,200.0,673.0,195.0,6.2708,251400.0,<1H OCEAN\n-118.85,34.21,25.0,1328.0,209.0,691.0,228.0,4.9234,241400.0,<1H OCEAN\n-118.85,34.21,29.0,2195.0,414.0,1360.0,401.0,3.4773,206700.0,<1H OCEAN\n-118.86,34.21,26.0,3354.0,659.0,2020.0,648.0,4.1576,211800.0,<1H OCEAN\n-118.88,34.22,22.0,3654.0,517.0,1565.0,518.0,6.2748,274800.0,<1H OCEAN\n-118.87,34.21,26.0,4439.0,616.0,1881.0,592.0,6.2935,258000.0,<1H OCEAN\n-118.88,34.21,26.0,1590.0,196.0,654.0,199.0,6.5851,300000.0,<1H OCEAN\n-118.87,34.2,26.0,1924.0,245.0,775.0,244.0,7.001,286800.0,<1H OCEAN\n-118.88,34.2,23.0,4862.0,597.0,1938.0,594.0,7.3409,316000.0,<1H OCEAN\n-118.88,34.19,26.0,2296.0,275.0,842.0,263.0,7.7889,309900.0,<1H OCEAN\n-118.86,34.2,32.0,2399.0,384.0,1199.0,390.0,4.125,264600.0,<1H OCEAN\n-118.86,34.19,26.0,3135.0,480.0,1474.0,458.0,6.1949,243500.0,<1H OCEAN\n-118.87,34.19,23.0,2179.0,423.0,1338.0,406.0,5.5224,240700.0,<1H OCEAN\n-118.88,34.19,16.0,7268.0,1729.0,3232.0,1653.0,3.3703,228700.0,<1H OCEAN\n-118.87,34.18,21.0,5661.0,1369.0,3188.0,1308.0,3.4676,212800.0,<1H OCEAN\n-118.85,34.18,11.0,5873.0,1455.0,3089.0,1365.0,3.5504,173800.0,<1H OCEAN\n-118.84,34.17,16.0,3449.0,820.0,1877.0,816.0,3.2176,187500.0,<1H OCEAN\n-118.85,34.2,28.0,2040.0,297.0,848.0,280.0,6.7612,323700.0,<1H OCEAN\n-118.86,34.19,29.0,1326.0,185.0,586.0,187.0,6.5474,422900.0,<1H OCEAN\n-118.86,34.19,27.0,1931.0,261.0,736.0,244.0,6.7805,392900.0,<1H OCEAN\n-118.85,34.19,27.0,2287.0,320.0,967.0,321.0,6.5162,349400.0,<1H OCEAN\n-118.83,34.18,23.0,5647.0,786.0,2050.0,738.0,6.3586,348300.0,<1H OCEAN\n-118.83,34.17,17.0,4668.0,628.0,1917.0,624.0,8.1397,353900.0,<1H OCEAN\n-118.9,34.14,35.0,1503.0,263.0,576.0,216.0,5.1457,500001.0,<1H OCEAN\n-119.0,34.08,17.0,1822.0,438.0,578.0,291.0,5.4346,428600.0,NEAR OCEAN\n-118.75,34.18,4.0,16704.0,2704.0,6187.0,2207.0,6.6122,357600.0,NEAR OCEAN\n-118.75,34.17,18.0,6217.0,858.0,2703.0,834.0,6.8075,325900.0,NEAR OCEAN\n-118.69,34.18,11.0,1177.0,138.0,415.0,119.0,10.0472,500001.0,<1H OCEAN\n-118.8,34.19,4.0,15572.0,2222.0,5495.0,2152.0,8.6499,500001.0,<1H OCEAN\n-118.83,34.23,6.0,8803.0,1114.0,3385.0,1010.0,8.7288,425800.0,<1H OCEAN\n-118.84,34.22,11.0,3170.0,420.0,1418.0,432.0,7.5118,361900.0,<1H OCEAN\n-118.84,34.21,16.0,4975.0,949.0,2537.0,971.0,5.2361,224700.0,<1H OCEAN\n-118.8,34.21,16.0,1466.0,196.0,661.0,209.0,6.2893,282700.0,<1H OCEAN\n-118.74,34.25,25.0,1815.0,281.0,960.0,284.0,5.4243,214700.0,<1H OCEAN\n-118.74,34.26,22.0,4337.0,673.0,2347.0,636.0,5.4091,222400.0,<1H OCEAN\n-118.7,34.24,28.0,2405.0,462.0,1011.0,378.0,4.504,204300.0,<1H OCEAN\n-118.64,34.25,47.0,1315.0,290.0,581.0,268.0,5.4024,253000.0,<1H OCEAN\n-118.69,34.21,10.0,3663.0,409.0,1179.0,371.0,12.542,500001.0,<1H OCEAN\n-118.81,34.25,4.0,9147.0,1827.0,3950.0,1661.0,5.716,320800.0,<1H OCEAN\n-118.79,34.26,17.0,1986.0,249.0,761.0,241.0,7.2137,401900.0,<1H OCEAN\n-118.8,34.27,12.0,3330.0,600.0,1577.0,584.0,4.6985,264100.0,<1H OCEAN\n-118.77,34.24,6.0,16222.0,2309.0,6700.0,2080.0,6.4963,308100.0,<1H OCEAN\n-118.78,34.26,24.0,4072.0,582.0,1834.0,565.0,6.0487,254500.0,<1H OCEAN\n-118.78,34.25,13.0,1841.0,237.0,833.0,231.0,7.7785,404700.0,<1H OCEAN\n-118.85,34.27,50.0,187.0,33.0,130.0,35.0,3.3438,500001.0,<1H OCEAN\n-118.9,34.3,13.0,5591.0,1013.0,3188.0,971.0,5.5925,208600.0,<1H OCEAN\n-118.83,34.33,6.0,6679.0,1164.0,3196.0,1157.0,5.4493,242600.0,<1H OCEAN\n-118.89,34.29,28.0,1545.0,371.0,1334.0,318.0,3.4375,194100.0,<1H OCEAN\n-118.89,34.33,23.0,366.0,62.0,265.0,66.0,3.125,375000.0,<1H OCEAN\n-118.89,34.28,30.0,917.0,157.0,678.0,171.0,5.8133,195700.0,<1H OCEAN\n-118.88,34.28,22.0,3369.0,771.0,2751.0,710.0,4.0474,182100.0,<1H OCEAN\n-118.91,34.28,6.0,6106.0,1134.0,3246.0,1062.0,5.2206,280200.0,<1H OCEAN\n-118.9,34.26,5.0,25187.0,3521.0,11956.0,3478.0,6.9712,321300.0,<1H OCEAN\n-118.81,34.28,20.0,3678.0,684.0,1882.0,694.0,4.1607,196800.0,<1H OCEAN\n-118.78,34.27,20.0,2743.0,685.0,1798.0,613.0,3.6761,170900.0,<1H OCEAN\n-118.79,34.27,27.0,1146.0,189.0,595.0,197.0,4.5833,198500.0,<1H OCEAN\n-118.77,34.27,7.0,3074.0,794.0,1816.0,654.0,2.7137,196400.0,<1H OCEAN\n-118.77,34.27,10.0,1658.0,310.0,1053.0,333.0,4.7574,209900.0,<1H OCEAN\n-118.77,34.28,6.0,4685.0,965.0,2180.0,909.0,4.5458,208200.0,<1H OCEAN\n-118.77,34.28,27.0,1416.0,251.0,1024.0,268.0,5.1074,185200.0,<1H OCEAN\n-118.77,34.28,26.0,2873.0,480.0,1915.0,475.0,5.3681,187700.0,<1H OCEAN\n-118.75,34.29,17.0,5512.0,,2734.0,814.0,6.6073,258100.0,<1H OCEAN\n-118.75,34.28,22.0,3844.0,537.0,1665.0,492.0,6.2059,239900.0,<1H OCEAN\n-118.76,34.28,21.0,2786.0,342.0,1114.0,322.0,5.8578,266300.0,<1H OCEAN\n-118.75,34.27,20.0,3495.0,449.0,1629.0,428.0,5.8096,264400.0,<1H OCEAN\n-118.75,34.28,27.0,1452.0,251.0,928.0,259.0,4.6908,186600.0,<1H OCEAN\n-118.75,34.27,26.0,966.0,191.0,690.0,191.0,5.1698,188000.0,<1H OCEAN\n-118.75,34.27,24.0,3241.0,461.0,1567.0,446.0,5.5983,233300.0,<1H OCEAN\n-118.75,34.27,25.0,3371.0,502.0,1717.0,506.0,6.1253,225000.0,<1H OCEAN\n-118.71,34.27,26.0,990.0,223.0,719.0,232.0,3.163,179400.0,<1H OCEAN\n-118.73,34.27,23.0,4550.0,762.0,2301.0,744.0,4.556,205300.0,<1H OCEAN\n-118.74,34.27,23.0,2493.0,522.0,1488.0,505.0,4.18,215000.0,<1H OCEAN\n-118.77,34.26,26.0,3038.0,468.0,1825.0,468.0,5.6385,196900.0,<1H OCEAN\n-118.76,34.26,26.0,1750.0,284.0,962.0,278.0,4.5673,190400.0,<1H OCEAN\n-118.76,34.26,26.0,1929.0,293.0,1067.0,320.0,5.4038,222100.0,<1H OCEAN\n-118.75,34.26,26.0,1767.0,265.0,1040.0,250.0,5.4787,198100.0,<1H OCEAN\n-118.75,34.26,24.0,2234.0,373.0,1325.0,383.0,5.4604,193400.0,<1H OCEAN\n-118.74,34.26,27.0,3467.0,545.0,1798.0,493.0,4.8717,204100.0,<1H OCEAN\n-118.74,34.28,21.0,4056.0,637.0,1974.0,634.0,5.9024,221000.0,<1H OCEAN\n-118.73,34.27,25.0,3409.0,493.0,1699.0,484.0,5.653,225800.0,<1H OCEAN\n-118.71,34.28,27.0,2911.0,562.0,1773.0,580.0,4.6528,186600.0,<1H OCEAN\n-118.7,34.28,25.0,2377.0,491.0,1200.0,439.0,4.7083,196100.0,<1H OCEAN\n-118.7,34.28,27.0,727.0,136.0,467.0,144.0,3.7188,250000.0,<1H OCEAN\n-118.72,34.28,17.0,2654.0,478.0,1392.0,451.0,5.4459,223900.0,<1H OCEAN\n-118.72,34.28,18.0,2229.0,371.0,1283.0,379.0,5.5955,217700.0,<1H OCEAN\n-118.72,34.28,17.0,3051.0,,1705.0,495.0,5.7376,218600.0,<1H OCEAN\n-118.67,34.28,21.0,4059.0,598.0,2133.0,634.0,5.6949,235300.0,<1H OCEAN\n-118.68,34.28,17.0,6488.0,1102.0,3199.0,1070.0,5.0962,238000.0,<1H OCEAN\n-118.67,34.3,5.0,6123.0,825.0,2440.0,736.0,7.9013,393000.0,<1H OCEAN\n-118.68,34.28,5.0,6150.0,1265.0,3188.0,1266.0,4.7034,223000.0,<1H OCEAN\n-118.68,34.27,16.0,4637.0,941.0,2476.0,878.0,4.0568,225200.0,<1H OCEAN\n-118.68,34.27,26.0,1561.0,212.0,817.0,242.0,5.477,209100.0,<1H OCEAN\n-118.67,34.27,15.0,3221.0,659.0,1390.0,607.0,3.5313,191800.0,<1H OCEAN\n-118.67,34.27,10.0,3753.0,678.0,1859.0,660.0,4.9946,204600.0,<1H OCEAN\n-118.65,34.27,23.0,1724.0,265.0,934.0,306.0,6.0783,229200.0,<1H OCEAN\n-118.71,34.29,24.0,2983.0,406.0,1203.0,381.0,6.3236,302000.0,<1H OCEAN\n-118.71,34.29,21.0,2751.0,493.0,1432.0,483.0,5.2067,221200.0,<1H OCEAN\n-118.7,34.28,27.0,3536.0,646.0,1837.0,580.0,4.4964,238300.0,<1H OCEAN\n-118.7,34.3,23.0,2831.0,406.0,1284.0,393.0,6.1383,244100.0,<1H OCEAN\n-118.7,34.29,25.0,1678.0,252.0,862.0,268.0,6.1834,229800.0,<1H OCEAN\n-118.71,34.3,23.0,1983.0,280.0,978.0,287.0,6.3199,236700.0,<1H OCEAN\n-118.7,34.3,27.0,1527.0,220.0,756.0,226.0,6.1825,227000.0,<1H OCEAN\n-118.71,34.3,20.0,1586.0,187.0,699.0,209.0,6.5483,335000.0,<1H OCEAN\n-118.68,34.33,45.0,121.0,25.0,67.0,27.0,2.9821,325000.0,<1H OCEAN\n-118.75,34.33,27.0,534.0,85.0,243.0,77.0,8.2787,330000.0,<1H OCEAN\n-118.73,34.29,11.0,5451.0,736.0,2526.0,752.0,7.355,343900.0,<1H OCEAN\n-118.72,34.29,22.0,3266.0,529.0,1595.0,494.0,6.0368,248000.0,<1H OCEAN\n-118.73,34.29,8.0,4983.0,754.0,2510.0,725.0,6.9454,276500.0,<1H OCEAN\n-121.52,38.59,35.0,6418.0,1545.0,3814.0,1496.0,1.6647,69100.0,INLAND\n-121.51,38.58,42.0,1822.0,636.0,1372.0,560.0,1.2542,76000.0,INLAND\n-121.53,38.6,25.0,5154.0,1105.0,3196.0,1073.0,2.7566,80200.0,INLAND\n-121.54,38.59,29.0,2242.0,493.0,1481.0,478.0,2.0781,74800.0,INLAND\n-121.54,38.59,40.0,2120.0,504.0,1304.0,464.0,2.0368,67800.0,INLAND\n-121.55,38.59,36.0,435.0,95.0,285.0,90.0,1.2292,69600.0,INLAND\n-121.63,38.67,34.0,431.0,85.0,391.0,77.0,2.625,225000.0,INLAND\n-121.52,38.58,24.0,938.0,275.0,508.0,253.0,1.642,32500.0,INLAND\n-121.53,38.56,39.0,2438.0,483.0,1103.0,472.0,2.9375,86600.0,INLAND\n-121.52,38.57,43.0,2360.0,471.0,1041.0,452.0,2.89,86200.0,INLAND\n-121.54,38.58,30.0,4648.0,1252.0,2524.0,1089.0,1.3177,74300.0,INLAND\n-121.56,38.58,32.0,2070.0,561.0,2046.0,523.0,1.9426,82300.0,INLAND\n-121.53,38.58,33.0,4988.0,1169.0,2414.0,1075.0,1.9728,76400.0,INLAND\n-121.53,38.58,35.0,1316.0,321.0,732.0,336.0,2.1213,79200.0,INLAND\n-121.53,38.57,34.0,3395.0,592.0,1518.0,627.0,4.0833,118500.0,INLAND\n-121.54,38.54,36.0,1672.0,302.0,969.0,337.0,3.0536,73100.0,INLAND\n-121.55,38.51,22.0,2403.0,431.0,1088.0,421.0,3.9,146900.0,INLAND\n-121.55,38.55,10.0,6227.0,1164.0,2909.0,1077.0,4.106,115900.0,INLAND\n-121.56,38.44,43.0,1485.0,270.0,653.0,251.0,3.0,141700.0,INLAND\n-121.61,38.38,37.0,1365.0,276.0,952.0,268.0,4.037,156900.0,INLAND\n-121.79,38.54,7.0,1777.0,513.0,4479.0,504.0,1.4653,310000.0,INLAND\n-121.8,38.55,11.0,5121.0,899.0,2258.0,901.0,4.7168,223200.0,INLAND\n-121.78,38.55,12.0,10509.0,2186.0,5633.0,2138.0,2.9605,204300.0,INLAND\n-121.76,38.57,11.0,15018.0,3008.0,7984.0,2962.0,3.1371,201800.0,INLAND\n-121.81,38.58,17.0,1964.0,314.0,808.0,286.0,5.9629,286000.0,INLAND\n-121.7,38.6,16.0,2372.0,588.0,1400.0,583.0,2.8922,153600.0,INLAND\n-121.67,38.54,13.0,6141.0,1019.0,2553.0,967.0,4.2432,326500.0,INLAND\n-121.74,38.56,18.0,3960.0,1151.0,2248.0,1144.0,1.7257,179100.0,INLAND\n-121.74,38.55,34.0,2299.0,579.0,1300.0,536.0,1.6435,148500.0,INLAND\n-121.73,38.55,34.0,1717.0,393.0,1224.0,387.0,2.7917,130800.0,INLAND\n-121.73,38.54,18.0,974.0,317.0,521.0,317.0,1.0633,137500.0,INLAND\n-121.73,38.56,30.0,3306.0,629.0,1623.0,648.0,2.8614,145200.0,INLAND\n-121.71,38.56,20.0,8627.0,1516.0,4071.0,1466.0,4.2198,164100.0,INLAND\n-121.72,38.54,16.0,2790.0,624.0,1386.0,636.0,3.1908,194300.0,INLAND\n-121.7,38.54,13.0,6819.0,1158.0,2828.0,1115.0,4.6225,226500.0,INLAND\n-121.75,38.55,33.0,2479.0,382.0,979.0,377.0,4.7308,236200.0,INLAND\n-121.74,38.55,33.0,6861.0,1820.0,3717.0,1767.0,1.7311,182600.0,INLAND\n-121.76,38.55,23.0,8800.0,1857.0,6330.0,1832.0,2.065,219400.0,INLAND\n-121.75,38.55,26.0,4802.0,950.0,2199.0,939.0,3.7452,227700.0,INLAND\n-121.77,38.69,47.0,1697.0,318.0,775.0,276.0,3.4559,123100.0,INLAND\n-121.77,38.68,43.0,2559.0,598.0,1820.0,591.0,2.1927,107900.0,INLAND\n-121.76,38.68,38.0,674.0,178.0,701.0,189.0,1.3942,69400.0,INLAND\n-121.8,38.69,8.0,3544.0,691.0,2118.0,678.0,3.7477,122200.0,INLAND\n-121.79,38.69,23.0,1755.0,321.0,1061.0,313.0,2.8864,103100.0,INLAND\n-121.78,38.69,31.0,2547.0,535.0,1579.0,509.0,2.6774,95800.0,INLAND\n-121.8,38.68,11.0,3851.0,892.0,1847.0,747.0,3.4331,120600.0,INLAND\n-121.79,38.68,24.0,3794.0,848.0,2225.0,864.0,2.8068,95300.0,INLAND\n-121.78,38.68,39.0,2806.0,662.0,1659.0,638.0,1.9787,97800.0,INLAND\n-121.8,38.67,11.0,3251.0,623.0,1700.0,615.0,3.1875,172000.0,INLAND\n-121.79,38.67,17.0,2875.0,810.0,1876.0,749.0,1.951,152500.0,INLAND\n-121.78,38.68,43.0,3766.0,847.0,1855.0,817.0,2.3468,119400.0,INLAND\n-121.8,38.67,10.0,2086.0,380.0,1073.0,378.0,4.5526,154400.0,INLAND\n-121.79,38.67,30.0,2602.0,401.0,981.0,405.0,4.7222,167200.0,INLAND\n-121.78,38.67,38.0,2948.0,478.0,1123.0,460.0,4.0556,146900.0,INLAND\n-121.77,38.67,42.0,2670.0,518.0,1548.0,534.0,2.2794,108900.0,INLAND\n-121.77,38.67,45.0,2438.0,462.0,1415.0,510.0,2.8351,107200.0,INLAND\n-121.75,38.67,9.0,12139.0,2640.0,6837.0,2358.0,3.125,132500.0,INLAND\n-121.71,38.72,32.0,710.0,155.0,550.0,154.0,2.8882,151400.0,INLAND\n-121.7,38.65,22.0,1360.0,282.0,808.0,229.0,2.4167,225000.0,INLAND\n-121.84,38.65,29.0,3167.0,548.0,1554.0,534.0,4.3487,200700.0,INLAND\n-121.79,38.66,15.0,6809.0,1052.0,3060.0,1060.0,5.3064,165000.0,INLAND\n-121.78,38.66,18.0,4224.0,632.0,1907.0,641.0,4.8226,139900.0,INLAND\n-121.76,38.66,17.0,5320.0,984.0,2866.0,928.0,4.1997,133400.0,INLAND\n-121.96,38.54,6.0,1485.0,318.0,894.0,308.0,3.2222,139600.0,INLAND\n-121.99,38.53,6.0,4598.0,834.0,2561.0,812.0,3.4186,127300.0,INLAND\n-121.98,38.52,27.0,3044.0,565.0,1583.0,514.0,2.7989,126700.0,INLAND\n-122.05,38.56,20.0,1005.0,168.0,457.0,157.0,5.679,225000.0,INLAND\n-121.92,38.57,10.0,1320.0,246.0,898.0,228.0,1.9327,193800.0,INLAND\n-121.9,38.72,38.0,575.0,107.0,259.0,109.0,3.75,187500.0,INLAND\n-122.0,38.83,26.0,272.0,49.0,194.0,52.0,3.4187,98400.0,INLAND\n-121.94,38.89,15.0,1462.0,314.0,774.0,271.0,2.5478,91700.0,INLAND\n-121.81,38.84,37.0,352.0,65.0,238.0,67.0,2.8542,275000.0,INLAND\n-121.72,38.8,36.0,1069.0,228.0,567.0,190.0,1.9559,78400.0,INLAND\n-121.77,38.76,32.0,1950.0,385.0,1145.0,363.0,2.8365,87900.0,INLAND\n-122.21,38.83,20.0,1138.0,221.0,459.0,209.0,3.1534,123400.0,INLAND\n-122.16,38.9,33.0,1221.0,236.0,488.0,199.0,3.7574,92700.0,INLAND\n-122.0,38.73,31.0,371.0,74.0,208.0,84.0,3.875,137500.0,INLAND\n-121.95,38.65,19.0,1265.0,228.0,755.0,218.0,3.3472,69800.0,INLAND\n-122.04,38.68,26.0,1113.0,222.0,689.0,234.0,3.0486,83600.0,INLAND\n-122.03,38.69,23.0,1796.0,380.0,939.0,330.0,2.7955,96300.0,INLAND\n-121.6,39.15,19.0,1396.0,336.0,940.0,309.0,1.5208,70300.0,INLAND\n-121.59,39.15,5.0,1922.0,489.0,938.0,439.0,2.0474,61300.0,INLAND\n-121.59,39.15,48.0,1783.0,399.0,938.0,374.0,1.6652,58900.0,INLAND\n-121.59,39.14,41.0,1492.0,350.0,804.0,353.0,1.684,71300.0,INLAND\n-121.58,39.15,34.0,1376.0,376.0,702.0,317.0,1.4946,55500.0,INLAND\n-121.58,39.14,52.0,662.0,160.0,520.0,149.0,0.8928,55000.0,INLAND\n-121.58,39.16,36.0,1206.0,197.0,537.0,204.0,3.3611,79800.0,INLAND\n-121.57,39.16,21.0,1872.0,302.0,870.0,301.0,3.725,84700.0,INLAND\n-121.56,39.16,12.0,3349.0,642.0,2029.0,619.0,2.9647,88800.0,INLAND\n-121.57,39.16,33.0,2033.0,375.0,914.0,330.0,2.6964,68500.0,INLAND\n-121.58,39.16,33.0,1897.0,378.0,888.0,385.0,2.1111,68700.0,INLAND\n-121.58,39.15,38.0,1756.0,396.0,837.0,401.0,1.9122,55500.0,INLAND\n-121.56,39.16,35.0,2157.0,441.0,1009.0,409.0,1.5827,63000.0,INLAND\n-121.57,39.16,18.0,1632.0,367.0,769.0,330.0,3.1029,71700.0,INLAND\n-121.57,39.13,30.0,442.0,103.0,413.0,88.0,1.5694,57900.0,INLAND\n-121.56,39.13,17.0,2277.0,608.0,1607.0,562.0,1.5085,69700.0,INLAND\n-121.54,39.13,18.0,4289.0,1021.0,2707.0,939.0,1.3375,59600.0,INLAND\n-121.54,39.12,17.0,4251.0,899.0,3265.0,934.0,2.3496,65000.0,INLAND\n-121.58,39.12,26.0,2796.0,629.0,2017.0,632.0,1.8355,61200.0,INLAND\n-121.57,39.12,30.0,2601.0,534.0,1702.0,506.0,2.08,56600.0,INLAND\n-121.57,39.1,28.0,1442.0,333.0,832.0,286.0,1.8413,62300.0,INLAND\n-121.59,39.1,24.0,1107.0,261.0,768.0,205.0,1.7167,48800.0,INLAND\n-121.56,39.11,18.0,2171.0,480.0,1527.0,447.0,2.3011,57500.0,INLAND\n-121.56,39.1,28.0,2130.0,484.0,1195.0,439.0,1.3631,45500.0,INLAND\n-121.55,39.1,27.0,1783.0,441.0,1163.0,409.0,1.2857,47000.0,INLAND\n-121.56,39.08,26.0,1377.0,289.0,761.0,267.0,1.4934,48300.0,INLAND\n-121.55,39.09,31.0,1728.0,365.0,1167.0,384.0,1.4958,53400.0,INLAND\n-121.54,39.08,26.0,2276.0,460.0,1455.0,474.0,2.4695,58000.0,INLAND\n-121.54,39.08,23.0,1076.0,216.0,724.0,197.0,2.3598,57500.0,INLAND\n-121.53,39.08,15.0,1810.0,441.0,1157.0,375.0,2.0469,55100.0,INLAND\n-121.53,39.06,20.0,561.0,109.0,308.0,114.0,3.3021,70800.0,INLAND\n-121.55,39.06,25.0,1332.0,247.0,726.0,226.0,2.25,63400.0,INLAND\n-121.56,39.01,22.0,1891.0,340.0,1023.0,296.0,2.7303,99100.0,INLAND\n-121.48,39.05,40.0,198.0,41.0,151.0,48.0,4.5625,100000.0,INLAND\n-121.47,39.01,37.0,1244.0,247.0,484.0,157.0,2.3661,77500.0,INLAND\n-121.44,39.0,20.0,755.0,147.0,457.0,157.0,2.4167,67000.0,INLAND\n-121.37,39.03,32.0,1158.0,244.0,598.0,227.0,2.8235,65500.0,INLAND\n-121.41,39.04,16.0,1698.0,300.0,731.0,291.0,3.0739,87200.0,INLAND\n-121.52,39.12,37.0,102.0,17.0,29.0,14.0,4.125,72000.0,INLAND\n-121.43,39.18,36.0,1124.0,184.0,504.0,171.0,2.1667,93800.0,INLAND\n-121.32,39.13,5.0,358.0,65.0,169.0,59.0,3.0,162500.0,INLAND\n-121.48,39.1,19.0,2043.0,421.0,1018.0,390.0,2.5952,92400.0,INLAND\n-121.39,39.12,28.0,10035.0,1856.0,6912.0,1818.0,2.0943,108300.0,INLAND\n-121.32,39.29,11.0,2640.0,505.0,1257.0,445.0,3.5673,112000.0,INLAND\n-121.4,39.33,15.0,2655.0,493.0,1200.0,432.0,3.5179,107200.0,INLAND\n-121.45,39.26,15.0,2319.0,416.0,1047.0,385.0,3.125,115600.0,INLAND\n-121.53,39.19,27.0,2080.0,412.0,1082.0,382.0,2.5495,98300.0,INLAND\n-121.56,39.27,28.0,2332.0,395.0,1041.0,344.0,3.7125,116800.0,INLAND\n-121.09,39.48,25.0,1665.0,374.0,845.0,330.0,1.5603,78100.0,INLAND\n-121.21,39.49,18.0,697.0,150.0,356.0,114.0,2.5568,77100.0,INLAND\n-121.22,39.43,17.0,2254.0,485.0,1007.0,433.0,1.7,92300.0,INLAND\n-121.32,39.43,18.0,1860.0,409.0,741.0,349.0,1.8672,84700.0,INLAND\n-121.24,39.37,16.0,2785.0,616.0,1387.0,530.0,2.3886,89400.0,INLAND\n"
  }
]