merge_sbae_ceo_phaseI.ipynb

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    "# V - Merging esbae with CEO phase I data \n",
    "### This script serves to merge validated data from CEO with eSBAE points for quick estimation of areas\n",
    "#### This script requires minimum an m16 instance and select the python 3 kernel\n",
    "###### For more information contact aurelie.shapiro@fao.org or remi.dannunzio@fao.org"
   ]
  },
  {
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     "text": [
      "/usr/local/share/jupyter/kernels/venv-eSBAE_notebooks/venv/lib/python3.8/site-packages/bfast/__init__.py:10: UserWarning: cupy is not available in this environment, GPU fonctionnalities won't be available\n",
      "  warn(\"cupy is not available in this environment, GPU fonctionnalities won't be available\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "# Set the display option to a large value to prevent text wrapping\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "\n",
    "from sampling_handler.misc import py_helpers\n",
    "from sampling_handler.ensemble import classification as clf\n",
    "from sampling_handler.ensemble import helpers as h\n",
    "from sampling_handler.sampling import sfc\n",
    "from sampling_handler import KMeansSubSampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b5e710-f28e-4862-ac7f-f072e4672886",
   "metadata": {
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   },
   "source": [
    "#### combine all CEO sample files\n",
    "##### download your CEO sample files from collect.earth and upload to SEPAL using FileZilla.\n",
    "##### add as many files as you have projects"
   ]
  },
  {
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   "execution_count": 2,
   "id": "dbf603c5-ed47-4e36-b271-26e6200a3737",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OID_</th>\n",
       "      <th>PLOTID</th>\n",
       "      <th>CEO_PLOTID</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>ISO</th>\n",
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       "      <th>Ref_LCover</th>\n",
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       "      <th>Ref_Chan_1</th>\n",
       "      <th>countDrive</th>\n",
       "      <th>Ref_InfraR</th>\n",
       "      <th>Ref_Urb</th>\n",
       "      <th>Ref_ArtAg</th>\n",
       "      <th>Ref_IndAg</th>\n",
       "      <th>Ref_ArtFor</th>\n",
       "      <th>Ref_IndFor</th>\n",
       "      <th>Ref_ArtMin</th>\n",
       "      <th>Ref_IndMin</th>\n",
       "      <th>Ref_Other</th>\n",
       "      <th>Source</th>\n",
       "      <th>Ref_ChDate</th>\n",
       "      <th>Change_Bin</th>\n",
       "      <th>UniqueID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.602120</td>\n",
       "      <td>-11.691888</td>\n",
       "      <td>COD</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Deg</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Deg_2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.012978</td>\n",
       "      <td>6.404943</td>\n",
       "      <td>CAF</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Deg</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Deg_2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.791831</td>\n",
       "      <td>7.078410</td>\n",
       "      <td>CAF</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Deg</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Deg_2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.430102</td>\n",
       "      <td>-6.534839</td>\n",
       "      <td>COD</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ceo</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.212148</td>\n",
       "      <td>-9.940982</td>\n",
       "      <td>COD</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
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       "      <td>5</td>\n",
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      "text/plain": [
       "   OID_  PLOTID CEO_PLOTID        LON        LAT  ISO  CEO_Ref_Co  Ref_FNF  \\\n",
       "0     0     311        NaN  25.602120 -11.691888  COD         147        1   \n",
       "1     1      53        NaN  19.012978   6.404943  CAF         149        1   \n",
       "2     2      47        NaN  22.791831   7.078410  CAF         149        1   \n",
       "3     3     279        NaN  27.430102  -6.534839  COD          11        1   \n",
       "4     4     224        NaN  22.212148  -9.940982  COD          11        1   \n",
       "\n",
       "   Ref_LCover Ref_Change  Ref_Chan_1  countDrive  Ref_InfraR  Ref_Urb  \\\n",
       "0          11        Deg      2018.0           1           0        0   \n",
       "1          11        Deg      2020.0           2           0        0   \n",
       "2          11        Deg      2020.0           1           0        0   \n",
       "3          11     Stable         NaN           0           0        0   \n",
       "4          11     Stable         NaN           0           0        0   \n",
       "\n",
       "   Ref_ArtAg  Ref_IndAg  Ref_ArtFor  Ref_IndFor  Ref_ArtMin  Ref_IndMin  \\\n",
       "0          0          0           1           0           0           0   \n",
       "1          1          0           1           0           0           0   \n",
       "2          0          0           1           0           0           0   \n",
       "3          0          0           0           0           0           0   \n",
       "4          0          0           0           0           0           0   \n",
       "\n",
       "   Ref_Other Source Ref_ChDate  Change_Bin  UniqueID  \n",
       "0          0    ceo   Deg_2018           1         1  \n",
       "1          0    ceo   Deg_2020           1         2  \n",
       "2          0    ceo   Deg_2020           1         3  \n",
       "3          0    ceo     Stable           0         4  \n",
       "4          0    ceo     Stable           0         5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "ceo1 = pd.read_csv('/home/sepal-user/CAFI_DDD/all_ceo_validation_TMF_2023_uniqueID.csv', delimiter=',')\n",
    "ceo1.head()"
   ]
  },
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   "id": "aa3ab8ff-9649-44f5-99c4-90089d9a6f51",
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     "text": [
      "['OID_', 'PLOTID', 'CEO_PLOTID', 'LON', 'LAT', 'ISO', 'CEO_Ref_Co', 'Ref_FNF', 'Ref_LCover', 'Ref_Change', 'Ref_Chan_1', 'countDrive', 'Ref_InfraR', 'Ref_Urb', 'Ref_ArtAg', 'Ref_IndAg', 'Ref_ArtFor', 'Ref_IndFor', 'Ref_ArtMin', 'Ref_IndMin', 'Ref_Other', 'Source', 'Ref_ChDate', 'Change_Bin', 'UniqueID']\n"
     ]
    }
   ],
   "source": [
    "column_names = ceo1.columns.tolist()\n",
    "# Print the list of column names\n",
    "print(column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e405fab6-a8a0-4461-96e4-091177c712f3",
   "metadata": {},
   "source": [
    "##### add a column named \"interpreted\" which = 1 when the point has been validated in CEO, otherwise 0\n",
    "##### count the number of validated points per ceo file\n",
    "##### replace forest and non-forest types with codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9c12ded-8591-4487-9494-68b2081e3987",
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       "      <th></th>\n",
       "      <th>OID_</th>\n",
       "      <th>PLOTID</th>\n",
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       "      <th>LON</th>\n",
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       "      <th>Ref_Change_Year_1520</th>\n",
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       "      <th>UniqueID</th>\n",
       "      <th>collection</th>\n",
       "      <th>interpreted</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>25.602120</td>\n",
       "      <td>-11.691888</td>\n",
       "      <td>COD</td>\n",
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       "      <td>Deg</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Deg2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.012978</td>\n",
       "      <td>6.404943</td>\n",
       "      <td>CAF</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Deg</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Deg2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.791831</td>\n",
       "      <td>7.078410</td>\n",
       "      <td>CAF</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Deg</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Deg2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.430102</td>\n",
       "      <td>-6.534839</td>\n",
       "      <td>COD</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Stable</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.212148</td>\n",
       "      <td>-9.940982</td>\n",
       "      <td>COD</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Stable</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OID_  PLOTID CEO_PLOTID        LON        LAT  ISO  CEO_Ref_Co  \\\n",
       "0     0     311        NaN  25.602120 -11.691888  COD         147   \n",
       "1     1      53        NaN  19.012978   6.404943  CAF         149   \n",
       "2     2      47        NaN  22.791831   7.078410  CAF         149   \n",
       "3     3     279        NaN  27.430102  -6.534839  COD          11   \n",
       "4     4     224        NaN  22.212148  -9.940982  COD          11   \n",
       "\n",
       "   Ref_FNF_2015  Ref_LCtype Ref_Change_Type_1520  Ref_Year  countDrivers  \\\n",
       "0             1          11                  Deg    2018.0             1   \n",
       "1             1          11                  Deg    2020.0             2   \n",
       "2             1          11                  Deg    2020.0             1   \n",
       "3             1          11               Stable       NaN             0   \n",
       "4             1          11               Stable       NaN             0   \n",
       "\n",
       "   Ref_InfraR  Ref_Urb  Ref_ArtAg  Ref_IndAg  Ref_ArtFor  Ref_IndFor  \\\n",
       "0           0        0          0          0           1           0   \n",
       "1           0        0          1          0           1           0   \n",
       "2           0        0          0          0           1           0   \n",
       "3           0        0          0          0           0           0   \n",
       "4           0        0          0          0           0           0   \n",
       "\n",
       "   Ref_ArtMin  Ref_IndMin  Ref_Other Source Ref_Change_Year_1520  \\\n",
       "0           0           0          0    ceo              Deg2018   \n",
       "1           0           0          0    ceo              Deg2020   \n",
       "2           0           0          0    ceo              Deg2020   \n",
       "3           0           0          0    ceo               Stable   \n",
       "4           0           0          0    ceo               Stable   \n",
       "\n",
       "   Ref_Change_1520  UniqueID  collection  interpreted  \n",
       "0                1         1           1            1  \n",
       "1                1         2           1            1  \n",
       "2                1         3           1            1  \n",
       "3                0         4           1            1  \n",
       "4                0         5           1            1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add 'collection' column with an increasing number\n",
    "ceo1['collection'] = 1\n",
    "    \n",
    "# Add 'interpreted' column with 1 when 'email' is not null, otherwise 0\n",
    "ceo1['interpreted'] = 1\n",
    "    \n",
    "# Calculate total count for each 'interpreted' value\n",
    "interpreted_counts = ceo1['interpreted'].value_counts().to_dict()\n",
    "    \n",
    "# Create a dictionary to map old column names to new column names\n",
    "column_mapping = {\n",
    "    'Ref_FNF': 'Ref_FNF_2015',\n",
    "    'Ref_LCover': 'Ref_LCtype', \n",
    "    'Ref_Change': 'Ref_Change_Type_1520', \n",
    "    'Ref_Chan_1': 'Ref_Year',\n",
    "    'Change_Bin': 'Ref_Change_1520',\n",
    "    'Ref_ChDate': 'Ref_Change_Year_1520',\n",
    "    'countDrive': 'countDrivers'\n",
    "}\n",
    "\n",
    "# Use the rename() method to rename the columns\n",
    "ceo_phaseI = ceo1.rename(columns=column_mapping)\n",
    "\n",
    "ceo_phaseI['Ref_Change_Year_1520'] = ceo_phaseI['Ref_Change_Year_1520'].str.replace('_', '', regex=True)\n",
    "ceo_phaseI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e2c49c5-65ce-48fe-8983-6e4163dc038f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['OID_', 'PLOTID', 'CEO_PLOTID', 'LON', 'LAT', 'ISO', 'CEO_Ref_Co', 'Ref_FNF_2015', 'Ref_LCtype', 'Ref_Change_Type_1520', 'Ref_Year', 'countDrivers', 'Ref_InfraR', 'Ref_Urb', 'Ref_ArtAg', 'Ref_IndAg', 'Ref_ArtFor', 'Ref_IndFor', 'Ref_ArtMin', 'Ref_IndMin', 'Ref_Other', 'Source', 'Ref_Change_Year_1520', 'Ref_Change_1520', 'UniqueID', 'collection', 'interpreted']\n"
     ]
    }
   ],
   "source": [
    "ceo_column_names = ceo_phaseI.columns.tolist()\n",
    "# Print the list of column names\n",
    "print(ceo_column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d96fd19-6809-413a-9696-c3991d50efbc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Ref_FNF_2015\n",
       "1    8509\n",
       "0    4606\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_phaseI['Ref_FNF_2015'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c29d4ef8-380e-40d1-8348-7dba22f200e2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Ref_Change_1520\n",
       "0    9275\n",
       "1    3840\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_phaseI['Ref_Change_1520'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0097c4de-9bca-4a73-bcf5-1c9afc87f88b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Ref_Change_Type_1520\n",
       "Stable    4669\n",
       "NF        4606\n",
       "Def       2541\n",
       "Deg       1299\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_phaseI['Ref_Change_Type_1520'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04549d39-bee9-4f57-b112-cad1d8b8d259",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Ref_LCtype\n",
       "1     3502\n",
       "13    1453\n",
       "3     1306\n",
       "16    1202\n",
       "15    1068\n",
       "4      893\n",
       "12     812\n",
       "9      686\n",
       "2      529\n",
       "19     477\n",
       "11     428\n",
       "14     187\n",
       "17     181\n",
       "7      166\n",
       "8      166\n",
       "18      33\n",
       "10      21\n",
       "0        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_phaseI['Ref_LCtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eec38754-782c-4810-afab-b66aa9697456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ceo_phaseI['Def2016'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Def2016', 1, 0)\n",
    "ceo_phaseI['Def2017'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Def2017', 1, 0)\n",
    "ceo_phaseI['Def2018'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Def2018', 1, 0)\n",
    "ceo_phaseI['Def2019'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Def2019', 1, 0)\n",
    "ceo_phaseI['Def2020'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Def2020', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6b46afa-fa39-4678-bb0a-98f222e9d4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ceo_phaseI['Deg2016'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Deg2016', 1, 0)\n",
    "ceo_phaseI['Deg2017'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Deg2017', 1, 0)\n",
    "ceo_phaseI['Deg2018'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Deg2018', 1, 0)\n",
    "ceo_phaseI['Deg2019'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Deg2019', 1, 0)\n",
    "ceo_phaseI['Deg2020'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Deg2020', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d71783a-2664-41d0-947c-45572341cc31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ceo_phaseI['Defall'] = np.where(ceo_phaseI['Ref_Change_Type_1520'] == 'Def', 1, 0)\n",
    "ceo_phaseI['Degall'] = np.where(ceo_phaseI['Ref_Change_Type_1520'] == 'Deg', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0926ff1a-84a1-400b-bc07-7c72cfcbbdae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ceo_phaseI['Stable'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'Stable', 1, 0)\n",
    "ceo_phaseI['NF'] = np.where(ceo_phaseI['Ref_Change_Year_1520'] == 'NF', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3dced9a5-9eb6-4f06-bc1a-19375d9ae2e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#export cleaned CEO data\n",
    "ceo_phaseI_out = '/home/sepal-user/CAFI_DDD/phaseI_ceo_clean.csv'\n",
    "ceo_phaseI.to_csv(ceo_phaseI_out,index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e833515d-6228-4f54-a977-1e9db1a1a50f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Index(['OID_', 'PLOTID', 'CEO_PLOTID', 'LON', 'LAT', 'ISO', 'CEO_Ref_Co',\n",
       "       'Ref_FNF_2015', 'Ref_LCtype', 'Ref_Change_Type_1520', 'Ref_Year',\n",
       "       'countDrivers', 'Ref_InfraR', 'Ref_Urb', 'Ref_ArtAg', 'Ref_IndAg',\n",
       "       'Ref_ArtFor', 'Ref_IndFor', 'Ref_ArtMin', 'Ref_IndMin', 'Ref_Other',\n",
       "       'Source', 'Ref_Change_Year_1520', 'Ref_Change_1520', 'UniqueID',\n",
       "       'collection', 'interpreted', 'Def2016', 'Def2017', 'Def2018', 'Def2019',\n",
       "       'Def2020', 'Deg2016', 'Deg2017', 'Deg2018', 'Deg2019', 'Deg2020',\n",
       "       'Defall', 'Degall', 'Stable', 'NF'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_phaseI.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5493dd-37d0-486e-8daf-29b413318e0e",
   "metadata": {},
   "source": [
    "#### read all esbae points with augmented data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "994e19b2-3bd9-400d-9919-b9373beca822",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Index(['geometry', 'UniqueID', 'dates', 'ts', 'images', 'mon_images',\n",
       "       'bfast_change_date', 'bfast_magnitude', 'bfast_means',\n",
       "       'cusum_change_date', 'cusum_confidence', 'cusum_magnitude', 'red_mean',\n",
       "       'red_sd', 'red_min', 'red_max', 'nir_mean', 'nir_sd', 'nir_min',\n",
       "       'nir_max', 'swir1_mean', 'swir1_sd', 'swir1_min', 'swir1_max',\n",
       "       'swir2_mean', 'swir2_sd', 'swir2_min', 'swir2_max', 'ndfi_mean',\n",
       "       'ndfi_sd', 'ndfi_min', 'ndfi_max', 'brightness_mean', 'brightness_sd',\n",
       "       'brightness_min', 'brightness_max', 'greenness_mean', 'greenness_sd',\n",
       "       'greenness_min', 'greenness_max', 'wetness_mean', 'wetness_sd',\n",
       "       'wetness_min', 'wetness_max', 'bs_slope_mean', 'bs_slope_sd',\n",
       "       'bs_slope_max', 'bs_slope_min', 'ccdc_change_date', 'ccdc_magnitude',\n",
       "       'aspect', 'dw_class_mode', 'dw_tree_prob__max', 'dw_tree_prob__min',\n",
       "       'dw_tree_prob__stdDev', 'dw_tree_prob_mean', 'elevation', 'esa_lc20',\n",
       "       'esa_lc21', 'esri_lc17', 'esri_lc18', 'esri_lc19', 'esri_lc20',\n",
       "       'esri_lc21', 'gfc_gain', 'gfc_loss', 'gfc_lossyear', 'gfc_tc00',\n",
       "       'lang_tree_height', 'potapov_tree_height', 'slope', 'tmf_2016',\n",
       "       'tmf_2017', 'tmf_2018', 'tmf_2019', 'tmf_2020', 'tmf_defyear',\n",
       "       'tmf_degyear', 'tmf_main', 'tmf_sub'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae = py_helpers.geojson_to_gdf('/home/sepal-user/module_results/esbae/CAFI/CAFI_esbae.geojson')\n",
    "esbae.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b817a5b-862a-4db6-95a0-f77aa062d75d",
   "metadata": {},
   "source": [
    "##### Rename columns to be same as ceo file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f35df58-6413-4d11-b9ff-7c2f652da0a6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "esbae = esbae.rename(columns={'PLOTID': 'plotid'})\n",
    "esbae = esbae.rename(columns={'LAT': 'lat'})\n",
    "esbae = esbae.rename(columns={'LON': 'lon'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2dfcddf9-c267-4ec8-a062-da9219f39100",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns_to_drop = ['dates', 'ts']\n",
    "esbae = esbae.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0ad0f19-b552-4019-915d-a02890b6183b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "geometry                geometry\n",
      "UniqueID                   int64\n",
      "images                     int64\n",
      "mon_images                 int64\n",
      "bfast_change_date        float64\n",
      "bfast_magnitude          float64\n",
      "bfast_means              float64\n",
      "cusum_change_date        float64\n",
      "cusum_confidence         float64\n",
      "cusum_magnitude          float64\n",
      "red_mean                 float64\n",
      "red_sd                   float64\n",
      "red_min                  float64\n",
      "red_max                  float64\n",
      "nir_mean                 float64\n",
      "nir_sd                   float64\n",
      "nir_min                  float64\n",
      "nir_max                  float64\n",
      "swir1_mean               float64\n",
      "swir1_sd                 float64\n",
      "swir1_min                float64\n",
      "swir1_max                float64\n",
      "swir2_mean               float64\n",
      "swir2_sd                 float64\n",
      "swir2_min                float64\n",
      "swir2_max                float64\n",
      "ndfi_mean                float64\n",
      "ndfi_sd                  float64\n",
      "ndfi_min                 float64\n",
      "ndfi_max                 float64\n",
      "brightness_mean          float64\n",
      "brightness_sd            float64\n",
      "brightness_min           float64\n",
      "brightness_max           float64\n",
      "greenness_mean           float64\n",
      "greenness_sd             float64\n",
      "greenness_min            float64\n",
      "greenness_max            float64\n",
      "wetness_mean             float64\n",
      "wetness_sd               float64\n",
      "wetness_min              float64\n",
      "wetness_max              float64\n",
      "bs_slope_mean            float64\n",
      "bs_slope_sd              float64\n",
      "bs_slope_max             float64\n",
      "bs_slope_min             float64\n",
      "ccdc_change_date         float64\n",
      "ccdc_magnitude           float64\n",
      "aspect                   float64\n",
      "dw_class_mode            float64\n",
      "dw_tree_prob__max        float64\n",
      "dw_tree_prob__min        float64\n",
      "dw_tree_prob__stdDev     float64\n",
      "dw_tree_prob_mean        float64\n",
      "elevation                float64\n",
      "esa_lc20                 float64\n",
      "esa_lc21                 float64\n",
      "esri_lc17                float64\n",
      "esri_lc18                float64\n",
      "esri_lc19                float64\n",
      "esri_lc20                float64\n",
      "esri_lc21                float64\n",
      "gfc_gain                 float64\n",
      "gfc_loss                 float64\n",
      "gfc_lossyear             float64\n",
      "gfc_tc00                 float64\n",
      "lang_tree_height         float64\n",
      "potapov_tree_height      float64\n",
      "slope                    float64\n",
      "tmf_2016                 float64\n",
      "tmf_2017                 float64\n",
      "tmf_2018                 float64\n",
      "tmf_2019                 float64\n",
      "tmf_2020                 float64\n",
      "tmf_defyear              float64\n",
      "tmf_degyear              float64\n",
      "tmf_main                 float64\n",
      "tmf_sub                  float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(esbae.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "449f951d-7aaf-452f-80cf-ad8305b7daaa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "13115"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(esbae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80f1e8b3-709f-4a87-8b2d-5445afc35bab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Index(['geometry', 'UniqueID', 'images', 'mon_images', 'bfast_change_date',\n",
       "       'bfast_magnitude', 'bfast_means', 'cusum_change_date',\n",
       "       'cusum_confidence', 'cusum_magnitude', 'red_mean', 'red_sd', 'red_min',\n",
       "       'red_max', 'nir_mean', 'nir_sd', 'nir_min', 'nir_max', 'swir1_mean',\n",
       "       'swir1_sd', 'swir1_min', 'swir1_max', 'swir2_mean', 'swir2_sd',\n",
       "       'swir2_min', 'swir2_max', 'ndfi_mean', 'ndfi_sd', 'ndfi_min',\n",
       "       'ndfi_max', 'brightness_mean', 'brightness_sd', 'brightness_min',\n",
       "       'brightness_max', 'greenness_mean', 'greenness_sd', 'greenness_min',\n",
       "       'greenness_max', 'wetness_mean', 'wetness_sd', 'wetness_min',\n",
       "       'wetness_max', 'bs_slope_mean', 'bs_slope_sd', 'bs_slope_max',\n",
       "       'bs_slope_min', 'ccdc_change_date', 'ccdc_magnitude', 'aspect',\n",
       "       'dw_class_mode', 'dw_tree_prob__max', 'dw_tree_prob__min',\n",
       "       'dw_tree_prob__stdDev', 'dw_tree_prob_mean', 'elevation', 'esa_lc20',\n",
       "       'esa_lc21', 'esri_lc17', 'esri_lc18', 'esri_lc19', 'esri_lc20',\n",
       "       'esri_lc21', 'gfc_gain', 'gfc_loss', 'gfc_lossyear', 'gfc_tc00',\n",
       "       'lang_tree_height', 'potapov_tree_height', 'slope', 'tmf_2016',\n",
       "       'tmf_2017', 'tmf_2018', 'tmf_2019', 'tmf_2020', 'tmf_defyear',\n",
       "       'tmf_degyear', 'tmf_main', 'tmf_sub'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eSBAE_cols = esbae.columns\n",
    "eSBAE_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d699564-4605-445b-b157-8100ee053f03",
   "metadata": {},
   "source": [
    "### Merge ceo and sbae tables\n",
    "#### replace validated points in esbae points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dc76170a-f34e-4ff7-bbce-aaddc01d8054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "13115"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make list of point_ids of validated points\n",
    "validated_id_list = ceo_phaseI['UniqueID'].tolist()\n",
    "len(validated_id_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c2d4c3d0-a285-4d4d-8d9e-b7235e043a14",
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   "outputs": [
    {
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    "# Merge ceo and sbae on \"pointid\" with an outer join\n",
    "esbae_ceo = pd.merge(esbae, ceo_phaseI, on='UniqueID', how='outer')"
   ]
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   "id": "186fce90-2a65-4d4b-a8ec-6d64709168b5",
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    "len(esbae_ceo)"
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  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2706fbbf-4744-41c5-bd58-03c4b5a4bff2",
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    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>images</th>\n",
       "      <th>mon_images</th>\n",
       "      <th>bfast_change_date</th>\n",
       "      <th>bfast_magnitude</th>\n",
       "      <th>bfast_means</th>\n",
       "      <th>cusum_change_date</th>\n",
       "      <th>cusum_confidence</th>\n",
       "      <th>cusum_magnitude</th>\n",
       "      <th>red_mean</th>\n",
       "      <th>red_sd</th>\n",
       "      <th>red_min</th>\n",
       "      <th>red_max</th>\n",
       "      <th>nir_mean</th>\n",
       "      <th>nir_sd</th>\n",
       "      <th>nir_min</th>\n",
       "      <th>nir_max</th>\n",
       "      <th>swir1_mean</th>\n",
       "      <th>swir1_sd</th>\n",
       "      <th>swir1_min</th>\n",
       "      <th>swir1_max</th>\n",
       "      <th>swir2_mean</th>\n",
       "      <th>swir2_sd</th>\n",
       "      <th>swir2_min</th>\n",
       "      <th>swir2_max</th>\n",
       "      <th>ndfi_mean</th>\n",
       "      <th>ndfi_sd</th>\n",
       "      <th>ndfi_min</th>\n",
       "      <th>ndfi_max</th>\n",
       "      <th>brightness_mean</th>\n",
       "      <th>brightness_sd</th>\n",
       "      <th>brightness_min</th>\n",
       "      <th>brightness_max</th>\n",
       "      <th>greenness_mean</th>\n",
       "      <th>greenness_sd</th>\n",
       "      <th>greenness_min</th>\n",
       "      <th>greenness_max</th>\n",
       "      <th>wetness_mean</th>\n",
       "      <th>wetness_sd</th>\n",
       "      <th>wetness_min</th>\n",
       "      <th>wetness_max</th>\n",
       "      <th>bs_slope_mean</th>\n",
       "      <th>bs_slope_sd</th>\n",
       "      <th>bs_slope_max</th>\n",
       "      <th>bs_slope_min</th>\n",
       "      <th>ccdc_change_date</th>\n",
       "      <th>ccdc_magnitude</th>\n",
       "      <th>aspect</th>\n",
       "      <th>dw_class_mode</th>\n",
       "      <th>dw_tree_prob__max</th>\n",
       "      <th>dw_tree_prob__min</th>\n",
       "      <th>dw_tree_prob__stdDev</th>\n",
       "      <th>dw_tree_prob_mean</th>\n",
       "      <th>elevation</th>\n",
       "      <th>esa_lc20</th>\n",
       "      <th>esa_lc21</th>\n",
       "      <th>esri_lc17</th>\n",
       "      <th>esri_lc18</th>\n",
       "      <th>esri_lc19</th>\n",
       "      <th>esri_lc20</th>\n",
       "      <th>esri_lc21</th>\n",
       "      <th>gfc_gain</th>\n",
       "      <th>gfc_loss</th>\n",
       "      <th>gfc_lossyear</th>\n",
       "      <th>gfc_tc00</th>\n",
       "      <th>lang_tree_height</th>\n",
       "      <th>potapov_tree_height</th>\n",
       "      <th>slope</th>\n",
       "      <th>tmf_2016</th>\n",
       "      <th>tmf_2017</th>\n",
       "      <th>tmf_2018</th>\n",
       "      <th>tmf_2019</th>\n",
       "      <th>tmf_2020</th>\n",
       "      <th>tmf_defyear</th>\n",
       "      <th>tmf_degyear</th>\n",
       "      <th>tmf_main</th>\n",
       "      <th>tmf_sub</th>\n",
       "      <th>OID_</th>\n",
       "      <th>PLOTID</th>\n",
       "      <th>CEO_PLOTID</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>ISO</th>\n",
       "      <th>CEO_Ref_Co</th>\n",
       "      <th>Ref_FNF_2015</th>\n",
       "      <th>Ref_LCtype</th>\n",
       "      <th>Ref_Change_Type_1520</th>\n",
       "      <th>Ref_Year</th>\n",
       "      <th>countDrivers</th>\n",
       "      <th>Ref_InfraR</th>\n",
       "      <th>Ref_Urb</th>\n",
       "      <th>Ref_ArtAg</th>\n",
       "      <th>Ref_IndAg</th>\n",
       "      <th>Ref_ArtFor</th>\n",
       "      <th>Ref_IndFor</th>\n",
       "      <th>Ref_ArtMin</th>\n",
       "      <th>Ref_IndMin</th>\n",
       "      <th>Ref_Other</th>\n",
       "      <th>Source</th>\n",
       "      <th>Ref_Change_Year_1520</th>\n",
       "      <th>Ref_Change_1520</th>\n",
       "      <th>collection</th>\n",
       "      <th>interpreted</th>\n",
       "      <th>Def2016</th>\n",
       "      <th>Def2017</th>\n",
       "      <th>Def2018</th>\n",
       "      <th>Def2019</th>\n",
       "      <th>Def2020</th>\n",
       "      <th>Deg2016</th>\n",
       "      <th>Deg2017</th>\n",
       "      <th>Deg2018</th>\n",
       "      <th>Deg2019</th>\n",
       "      <th>Deg2020</th>\n",
       "      <th>Defall</th>\n",
       "      <th>Degall</th>\n",
       "      <th>Stable</th>\n",
       "      <th>NF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (10.87640 3.64235)</td>\n",
       "      <td>12914</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
       "      <td>2019.082</td>\n",
       "      <td>-557.733582</td>\n",
       "      <td>-1.582820</td>\n",
       "      <td>2018.929</td>\n",
       "      <td>0.177278</td>\n",
       "      <td>3701.562500</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9219.062500</td>\n",
       "      <td>719.520017</td>\n",
       "      <td>7767.333333</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>-97.504722</td>\n",
       "      <td>160.683310</td>\n",
       "      <td>466.831422</td>\n",
       "      <td>-805.785974</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>540.694336</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12912</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>10.876399</td>\n",
       "      <td>3.642354</td>\n",
       "      <td>CMR</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TMF</td>\n",
       "      <td>Stable</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (9.28827 4.23093)</td>\n",
       "      <td>329</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>2017.216</td>\n",
       "      <td>621.118164</td>\n",
       "      <td>0.503695</td>\n",
       "      <td>2017.019</td>\n",
       "      <td>0.051356</td>\n",
       "      <td>3900.995098</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9097.318627</td>\n",
       "      <td>877.824442</td>\n",
       "      <td>6254.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>226.091324</td>\n",
       "      <td>105.140959</td>\n",
       "      <td>673.607419</td>\n",
       "      <td>-8.988100</td>\n",
       "      <td>2020.877</td>\n",
       "      <td>251.387876</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>786.967590</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>328</td>\n",
       "      <td>585</td>\n",
       "      <td>19</td>\n",
       "      <td>9.288266</td>\n",
       "      <td>4.230930</td>\n",
       "      <td>CMR</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Stable</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (9.30363 4.27863)</td>\n",
       "      <td>5438</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>2020.373</td>\n",
       "      <td>-1197.522217</td>\n",
       "      <td>-1.709263</td>\n",
       "      <td>2020.088</td>\n",
       "      <td>0.137395</td>\n",
       "      <td>7826.650000</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>8127.116667</td>\n",
       "      <td>1521.775736</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>9720.0</td>\n",
       "      <td>559.996792</td>\n",
       "      <td>333.678366</td>\n",
       "      <td>1528.989681</td>\n",
       "      <td>-322.507270</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>893.522400</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5437</td>\n",
       "      <td>148636</td>\n",
       "      <td>4980</td>\n",
       "      <td>9.303627</td>\n",
       "      <td>4.278631</td>\n",
       "      <td>CMR</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>Stable</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (20.77547 -3.54965)</td>\n",
       "      <td>13019</td>\n",
       "      <td>124</td>\n",
       "      <td>70</td>\n",
       "      <td>2016.455</td>\n",
       "      <td>115.265793</td>\n",
       "      <td>0.079259</td>\n",
       "      <td>2020.814</td>\n",
       "      <td>0.451355</td>\n",
       "      <td>5902.755102</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9584.633163</td>\n",
       "      <td>344.815773</td>\n",
       "      <td>8441.500000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>91.408013</td>\n",
       "      <td>17.122538</td>\n",
       "      <td>144.963451</td>\n",
       "      <td>35.533267</td>\n",
       "      <td>2016.323</td>\n",
       "      <td>-655.027831</td>\n",
       "      <td>189.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>444.309326</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13017</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>20.775474</td>\n",
       "      <td>-3.549650</td>\n",
       "      <td>COD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TMF</td>\n",
       "      <td>Stable</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (8.74119 -0.67576)</td>\n",
       "      <td>9023</td>\n",
       "      <td>66</td>\n",
       "      <td>37</td>\n",
       "      <td>2020.551</td>\n",
       "      <td>-612.401123</td>\n",
       "      <td>-0.689755</td>\n",
       "      <td>2019.762</td>\n",
       "      <td>0.356480</td>\n",
       "      <td>13092.747748</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9461.756757</td>\n",
       "      <td>1273.425737</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>-164.674283</td>\n",
       "      <td>162.635953</td>\n",
       "      <td>346.338651</td>\n",
       "      <td>-577.550151</td>\n",
       "      <td>2020.551</td>\n",
       "      <td>-4433.883959</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.740432</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9022</td>\n",
       "      <td>256821</td>\n",
       "      <td>8442</td>\n",
       "      <td>8.741192</td>\n",
       "      <td>-0.675758</td>\n",
       "      <td>GAB</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>NF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ceo</td>\n",
       "      <td>NF</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    geometry  UniqueID  images  mon_images  bfast_change_date  \\\n",
       "0   POINT (10.87640 3.64235)     12914      38          16           2019.082   \n",
       "1    POINT (9.28827 4.23093)       329      31          17           2017.216   \n",
       "2    POINT (9.30363 4.27863)      5438      31          20           2020.373   \n",
       "3  POINT (20.77547 -3.54965)     13019     124          70           2016.455   \n",
       "4   POINT (8.74119 -0.67576)      9023      66          37           2020.551   \n",
       "\n",
       "   bfast_magnitude  bfast_means  cusum_change_date  cusum_confidence  \\\n",
       "0      -557.733582    -1.582820           2018.929          0.177278   \n",
       "1       621.118164     0.503695           2017.019          0.051356   \n",
       "2     -1197.522217    -1.709263           2020.088          0.137395   \n",
       "3       115.265793     0.079259           2020.814          0.451355   \n",
       "4      -612.401123    -0.689755           2019.762          0.356480   \n",
       "\n",
       "   cusum_magnitude     red_mean       red_sd      red_min  red_max  \\\n",
       "0      3701.562500  9219.062500   719.520017  7767.333333  10000.0   \n",
       "1      3900.995098  9097.318627   877.824442  6254.000000  10000.0   \n",
       "2      7826.650000  8127.116667  1521.775736  2204.000000   9720.0   \n",
       "3      5902.755102  9584.633163   344.815773  8441.500000  10000.0   \n",
       "4     13092.747748  9461.756757  1273.425737  5146.000000  10000.0   \n",
       "\n",
       "      nir_mean       nir_sd      nir_min  nir_max   swir1_mean     swir1_sd  \\\n",
       "0  9219.062500   719.520017  7767.333333  10000.0  9219.062500   719.520017   \n",
       "1  9097.318627   877.824442  6254.000000  10000.0  9097.318627   877.824442   \n",
       "2  8127.116667  1521.775736  2204.000000   9720.0  8127.116667  1521.775736   \n",
       "3  9584.633163   344.815773  8441.500000  10000.0  9584.633163   344.815773   \n",
       "4  9461.756757  1273.425737  5146.000000  10000.0  9461.756757  1273.425737   \n",
       "\n",
       "     swir1_min  swir1_max   swir2_mean     swir2_sd    swir2_min  swir2_max  \\\n",
       "0  7767.333333    10000.0  9219.062500   719.520017  7767.333333    10000.0   \n",
       "1  6254.000000    10000.0  9097.318627   877.824442  6254.000000    10000.0   \n",
       "2  2204.000000     9720.0  8127.116667  1521.775736  2204.000000     9720.0   \n",
       "3  8441.500000    10000.0  9584.633163   344.815773  8441.500000    10000.0   \n",
       "4  5146.000000    10000.0  9461.756757  1273.425737  5146.000000    10000.0   \n",
       "\n",
       "     ndfi_mean      ndfi_sd     ndfi_min  ndfi_max  brightness_mean  \\\n",
       "0  9219.062500   719.520017  7767.333333   10000.0      9219.062500   \n",
       "1  9097.318627   877.824442  6254.000000   10000.0      9097.318627   \n",
       "2  8127.116667  1521.775736  2204.000000    9720.0      8127.116667   \n",
       "3  9584.633163   344.815773  8441.500000   10000.0      9584.633163   \n",
       "4  9461.756757  1273.425737  5146.000000   10000.0      9461.756757   \n",
       "\n",
       "   brightness_sd  brightness_min  brightness_max  greenness_mean  \\\n",
       "0     719.520017     7767.333333         10000.0     9219.062500   \n",
       "1     877.824442     6254.000000         10000.0     9097.318627   \n",
       "2    1521.775736     2204.000000          9720.0     8127.116667   \n",
       "3     344.815773     8441.500000         10000.0     9584.633163   \n",
       "4    1273.425737     5146.000000         10000.0     9461.756757   \n",
       "\n",
       "   greenness_sd  greenness_min  greenness_max  wetness_mean   wetness_sd  \\\n",
       "0    719.520017    7767.333333        10000.0   9219.062500   719.520017   \n",
       "1    877.824442    6254.000000        10000.0   9097.318627   877.824442   \n",
       "2   1521.775736    2204.000000         9720.0   8127.116667  1521.775736   \n",
       "3    344.815773    8441.500000        10000.0   9584.633163   344.815773   \n",
       "4   1273.425737    5146.000000        10000.0   9461.756757  1273.425737   \n",
       "\n",
       "   wetness_min  wetness_max  bs_slope_mean  bs_slope_sd  bs_slope_max  \\\n",
       "0  7767.333333      10000.0     -97.504722   160.683310    466.831422   \n",
       "1  6254.000000      10000.0     226.091324   105.140959    673.607419   \n",
       "2  2204.000000       9720.0     559.996792   333.678366   1528.989681   \n",
       "3  8441.500000      10000.0      91.408013    17.122538    144.963451   \n",
       "4  5146.000000      10000.0    -164.674283   162.635953    346.338651   \n",
       "\n",
       "   bs_slope_min  ccdc_change_date  ccdc_magnitude  aspect  dw_class_mode  \\\n",
       "0   -805.785974             0.000        0.000000    58.0            1.0   \n",
       "1     -8.988100          2020.877      251.387876    92.0            1.0   \n",
       "2   -322.507270             0.000        0.000000    94.0            1.0   \n",
       "3     35.533267          2016.323     -655.027831   189.0            1.0   \n",
       "4   -577.550151          2020.551    -4433.883959   102.0            1.0   \n",
       "\n",
       "   dw_tree_prob__max  dw_tree_prob__min  dw_tree_prob__stdDev  \\\n",
       "0               75.0               34.0                  18.0   \n",
       "1               74.0               63.0                   3.0   \n",
       "2               75.0               39.0                  12.0   \n",
       "3               75.0               59.0                   3.0   \n",
       "4               63.0                3.0                  16.0   \n",
       "\n",
       "   dw_tree_prob_mean   elevation  esa_lc20  esa_lc21  esri_lc17  esri_lc18  \\\n",
       "0               62.0  540.694336      10.0      10.0        2.0        2.0   \n",
       "1               72.0  786.967590      10.0      10.0        2.0        2.0   \n",
       "2               65.0  893.522400      10.0      10.0        2.0        2.0   \n",
       "3               71.0  444.309326      10.0      10.0        2.0        2.0   \n",
       "4               39.0    1.740432      95.0      95.0        4.0        4.0   \n",
       "\n",
       "   esri_lc19  esri_lc20  esri_lc21  gfc_gain  gfc_loss  gfc_lossyear  \\\n",
       "0        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "1        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "2        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "3        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "4        4.0        4.0        4.0       0.0       0.0           NaN   \n",
       "\n",
       "   gfc_tc00  lang_tree_height  potapov_tree_height  slope  tmf_2016  tmf_2017  \\\n",
       "0      69.0              32.0                 24.0   25.0       1.0       1.0   \n",
       "1      84.0              38.0                 21.0   10.0       1.0       1.0   \n",
       "2      55.0              31.0                  9.0   13.0       1.0       1.0   \n",
       "3     100.0              30.0                 28.0    8.0       1.0       1.0   \n",
       "4      77.0               9.0                  1.0    0.0       1.0       1.0   \n",
       "\n",
       "   tmf_2018  tmf_2019  tmf_2020  tmf_defyear  tmf_degyear  tmf_main  tmf_sub  \\\n",
       "0       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "1       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "2       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "3       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "4       1.0       1.0       1.0          0.0          0.0      10.0     12.0   \n",
       "\n",
       "    OID_  PLOTID CEO_PLOTID        LON       LAT  ISO  CEO_Ref_Co  \\\n",
       "0  12912       0             10.876399  3.642354  CMR           1   \n",
       "1    328     585         19   9.288266  4.230930  CMR           1   \n",
       "2   5437  148636       4980   9.303627  4.278631  CMR           1   \n",
       "3  13017       0             20.775474 -3.549650  COD           1   \n",
       "4   9022  256821       8442   8.741192 -0.675758  GAB          14   \n",
       "\n",
       "   Ref_FNF_2015  Ref_LCtype Ref_Change_Type_1520  Ref_Year  countDrivers  \\\n",
       "0             1           1               Stable       NaN             0   \n",
       "1             1           1               Stable       NaN             0   \n",
       "2             1           1               Stable       NaN             0   \n",
       "3             1           1               Stable       NaN             0   \n",
       "4             0          14                   NF       NaN             0   \n",
       "\n",
       "   Ref_InfraR  Ref_Urb  Ref_ArtAg  Ref_IndAg  Ref_ArtFor  Ref_IndFor  \\\n",
       "0           0        0          0          0           0           0   \n",
       "1           0        0          0          0           0           0   \n",
       "2           0        0          0          0           0           0   \n",
       "3           0        0          0          0           0           0   \n",
       "4           0        0          0          0           0           0   \n",
       "\n",
       "   Ref_ArtMin  Ref_IndMin  Ref_Other Source Ref_Change_Year_1520  \\\n",
       "0           0           0          0    TMF               Stable   \n",
       "1           0           0          0    ceo               Stable   \n",
       "2           0           0          0    ceo               Stable   \n",
       "3           0           0          0    TMF               Stable   \n",
       "4           0           0          0    ceo                   NF   \n",
       "\n",
       "   Ref_Change_1520  collection  interpreted  Def2016  Def2017  Def2018  \\\n",
       "0                0           1            1        0        0        0   \n",
       "1                0           1            1        0        0        0   \n",
       "2                0           1            1        0        0        0   \n",
       "3                0           1            1        0        0        0   \n",
       "4                0           1            1        0        0        0   \n",
       "\n",
       "   Def2019  Def2020  Deg2016  Deg2017  Deg2018  Deg2019  Deg2020  Defall  \\\n",
       "0        0        0        0        0        0        0        0       0   \n",
       "1        0        0        0        0        0        0        0       0   \n",
       "2        0        0        0        0        0        0        0       0   \n",
       "3        0        0        0        0        0        0        0       0   \n",
       "4        0        0        0        0        0        0        0       0   \n",
       "\n",
       "   Degall  Stable  NF  \n",
       "0       0       1   0  \n",
       "1       0       1   0  \n",
       "2       0       1   0  \n",
       "3       0       1   0  \n",
       "4       0       0   1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fdc0d6-d2ff-482b-93e5-736ea6acfd14",
   "metadata": {},
   "source": [
    "#### clean up _x and _y columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2ea74501-e56d-440c-9713-d4a3d134b2da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['geometry', 'UniqueID', 'images', 'mon_images', 'bfast_change_date', 'bfast_magnitude', 'bfast_means', 'cusum_change_date', 'cusum_confidence', 'cusum_magnitude', 'red_mean', 'red_sd', 'red_min', 'red_max', 'nir_mean', 'nir_sd', 'nir_min', 'nir_max', 'swir1_mean', 'swir1_sd', 'swir1_min', 'swir1_max', 'swir2_mean', 'swir2_sd', 'swir2_min', 'swir2_max', 'ndfi_mean', 'ndfi_sd', 'ndfi_min', 'ndfi_max', 'brightness_mean', 'brightness_sd', 'brightness_min', 'brightness_max', 'greenness_mean', 'greenness_sd', 'greenness_min', 'greenness_max', 'wetness_mean', 'wetness_sd', 'wetness_min', 'wetness_max', 'bs_slope_mean', 'bs_slope_sd', 'bs_slope_max', 'bs_slope_min', 'ccdc_change_date', 'ccdc_magnitude', 'aspect', 'dw_class_mode', 'dw_tree_prob__max', 'dw_tree_prob__min', 'dw_tree_prob__stdDev', 'dw_tree_prob_mean', 'elevation', 'esa_lc20', 'esa_lc21', 'esri_lc17', 'esri_lc18', 'esri_lc19', 'esri_lc20', 'esri_lc21', 'gfc_gain', 'gfc_loss', 'gfc_lossyear', 'gfc_tc00', 'lang_tree_height', 'potapov_tree_height', 'slope', 'tmf_2016', 'tmf_2017', 'tmf_2018', 'tmf_2019', 'tmf_2020', 'tmf_defyear', 'tmf_degyear', 'tmf_main', 'tmf_sub', 'OID_', 'PLOTID', 'CEO_PLOTID', 'LON', 'LAT', 'ISO', 'CEO_Ref_Co', 'Ref_FNF_2015', 'Ref_LCtype', 'Ref_Change_Type_1520', 'Ref_Year', 'countDrivers', 'Ref_InfraR', 'Ref_Urb', 'Ref_ArtAg', 'Ref_IndAg', 'Ref_ArtFor', 'Ref_IndFor', 'Ref_ArtMin', 'Ref_IndMin', 'Ref_Other', 'Source', 'Ref_Change_Year_1520', 'Ref_Change_1520', 'collection', 'interpreted', 'Def2016', 'Def2017', 'Def2018', 'Def2019', 'Def2020', 'Deg2016', 'Deg2017', 'Deg2018', 'Deg2019', 'Deg2020', 'Defall', 'Degall', 'Stable', 'NF']\n"
     ]
    }
   ],
   "source": [
    "merged_columns = esbae_ceo.columns.tolist()\n",
    "print(merged_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a413d7d4-c52b-4e7b-a830-9d5f2019a41b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['geometry' 'UniqueID' 'images' 'mon_images' 'bfast_change_date'\n",
      " 'bfast_magnitude' 'bfast_means' 'cusum_change_date' 'cusum_confidence'\n",
      " 'cusum_magnitude' 'red_mean' 'red_sd' 'red_min' 'red_max' 'nir_mean'\n",
      " 'nir_sd' 'nir_min' 'nir_max' 'swir1_mean' 'swir1_sd' 'swir1_min'\n",
      " 'swir1_max' 'swir2_mean' 'swir2_sd' 'swir2_min' 'swir2_max' 'ndfi_mean'\n",
      " 'ndfi_sd' 'ndfi_min' 'ndfi_max' 'brightness_mean' 'brightness_sd'\n",
      " 'brightness_min' 'brightness_max' 'greenness_mean' 'greenness_sd'\n",
      " 'greenness_min' 'greenness_max' 'wetness_mean' 'wetness_sd' 'wetness_min'\n",
      " 'wetness_max' 'bs_slope_mean' 'bs_slope_sd' 'bs_slope_max' 'bs_slope_min'\n",
      " 'ccdc_change_date' 'ccdc_magnitude' 'aspect' 'dw_class_mode'\n",
      " 'dw_tree_prob__max' 'dw_tree_prob__min' 'dw_tree_prob__stdDev'\n",
      " 'dw_tree_prob_mean' 'elevation' 'esa_lc20' 'esa_lc21' 'esri_lc17'\n",
      " 'esri_lc18' 'esri_lc19' 'esri_lc20' 'esri_lc21' 'gfc_gain' 'gfc_loss'\n",
      " 'gfc_lossyear' 'gfc_tc00' 'lang_tree_height' 'potapov_tree_height'\n",
      " 'slope' 'tmf_2016' 'tmf_2017' 'tmf_2018' 'tmf_2019' 'tmf_2020'\n",
      " 'tmf_defyear' 'tmf_degyear' 'tmf_main' 'tmf_sub' 'OID_' 'PLOTID'\n",
      " 'CEO_PLOTID' 'LON' 'LAT' 'ISO' 'CEO_Ref_Co' 'Ref_FNF_2015' 'Ref_LCtype'\n",
      " 'Ref_Change_Type_1520' 'Ref_Year' 'countDrivers' 'Ref_InfraR' 'Ref_Urb'\n",
      " 'Ref_ArtAg' 'Ref_IndAg' 'Ref_ArtFor' 'Ref_IndFor' 'Ref_ArtMin'\n",
      " 'Ref_IndMin' 'Ref_Other' 'Source' 'Ref_Change_Year_1520'\n",
      " 'Ref_Change_1520' 'collection' 'interpreted' 'Def2016' 'Def2017'\n",
      " 'Def2018' 'Def2019' 'Def2020' 'Deg2016' 'Deg2017' 'Deg2018' 'Deg2019'\n",
      " 'Deg2020' 'Defall' 'Degall' 'Stable' 'NF']\n"
     ]
    }
   ],
   "source": [
    "print(esbae_ceo.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ef5a019b-fce8-474e-a75c-4d442a303f69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "interpreted\n",
       "1    13115\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo['interpreted'] = esbae_ceo['interpreted'].fillna(0)\n",
    "esbae_ceo['interpreted'] = esbae_ceo['interpreted'].astype(int)\n",
    "esbae_ceo['interpreted'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dbc6a9bd-8fd3-49e9-8d56-e1420024ecc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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       "            "
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "Ref_Change_Year_1520\n",
       "Stable     4669\n",
       "NF         4606\n",
       "Def2017     765\n",
       "Def2018     734\n",
       "Def2016     539\n",
       "Deg2017     440\n",
       "Def2019     362\n",
       "Deg2018     328\n",
       "Deg2016     280\n",
       "Deg2019     150\n",
       "Def2020     141\n",
       "Deg2020     101\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo['Ref_Change_Year_1520'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337f6b9d-9d22-478f-a241-82cb5e605fb8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a12dbf8a-baa2-472d-ad75-bc528465ec2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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       "            "
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'geometry': 0, 'UniqueID': 0, 'images': 0, 'mon_images': 0, 'bfast_change_date': 0, 'bfast_magnitude': 0, 'bfast_means': 0, 'cusum_change_date': 0, 'cusum_confidence': 0, 'cusum_magnitude': 0, 'red_mean': 0, 'red_sd': 0, 'red_min': 0, 'red_max': 0, 'nir_mean': 0, 'nir_sd': 0, 'nir_min': 0, 'nir_max': 0, 'swir1_mean': 0, 'swir1_sd': 0, 'swir1_min': 0, 'swir1_max': 0, 'swir2_mean': 0, 'swir2_sd': 0, 'swir2_min': 0, 'swir2_max': 0, 'ndfi_mean': 0, 'ndfi_sd': 0, 'ndfi_min': 0, 'ndfi_max': 0, 'brightness_mean': 0, 'brightness_sd': 0, 'brightness_min': 0, 'brightness_max': 0, 'greenness_mean': 0, 'greenness_sd': 0, 'greenness_min': 0, 'greenness_max': 0, 'wetness_mean': 0, 'wetness_sd': 0, 'wetness_min': 0, 'wetness_max': 0, 'bs_slope_mean': 0, 'bs_slope_sd': 0, 'bs_slope_max': 0, 'bs_slope_min': 0, 'ccdc_change_date': 0, 'ccdc_magnitude': 0, 'aspect': 84, 'dw_class_mode': 96, 'dw_tree_prob__max': 96, 'dw_tree_prob__min': 96, 'dw_tree_prob__stdDev': 96, 'dw_tree_prob_mean': 96, 'elevation': 84, 'esa_lc20': 84, 'esa_lc21': 84, 'esri_lc17': 84, 'esri_lc18': 84, 'esri_lc19': 84, 'esri_lc20': 85, 'esri_lc21': 84, 'gfc_gain': 84, 'gfc_loss': 84, 'gfc_lossyear': 7798, 'gfc_tc00': 84, 'lang_tree_height': 220, 'potapov_tree_height': 84, 'slope': 84, 'tmf_2016': 84, 'tmf_2017': 84, 'tmf_2018': 84, 'tmf_2019': 84, 'tmf_2020': 84, 'tmf_defyear': 84, 'tmf_degyear': 84, 'tmf_main': 84, 'tmf_sub': 84}\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store the counts by column\n",
    "missing_counts = {}\n",
    "\n",
    "# Count missing values and sum the counts by column\n",
    "for column in eSBAE_cols:\n",
    "    missing_counts[column] = esbae_ceo[column].isna().sum()\n",
    "\n",
    "# Print the counts\n",
    "print(missing_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cf541d-ffb9-4489-b03a-ba2bcb7b5878",
   "metadata": {},
   "source": [
    "##### plot deforestation points by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f1c32086-c2e8-41ca-b034-fc54d94edeeb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
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       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#select the values you want to plot\n",
    "values_to_plot = ['Def2016','Def2017','Def2018','Def2019','Def2020']\n",
    "\n",
    "# Filter the DataFrame to include only the selected values\n",
    "filtered_df = esbae_ceo[esbae_ceo['Ref_Change_Year_1520'].isin(values_to_plot)]\n",
    "\n",
    "# Get the value counts and sort by index (alphabetically)\n",
    "defvalue_counts = filtered_df['Ref_Change_Year_1520'].value_counts().sort_index()\n",
    "\n",
    "# Plot the value counts as a bar graph\n",
    "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
    "defvalue_counts.plot(kind='bar', color='skyblue')\n",
    "\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('# of def points per year')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c7b947-6152-49cb-90cd-a54b7f7d0e3c",
   "metadata": {},
   "source": [
    "##### plot degradation points by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a0bd70ef-914e-4c48-a377-4ee31526a640",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot DEG \n",
    "#select the values you want to plot\n",
    "values_to_plot = ['Deg2016','Deg2017','Deg2018','Deg2019','Deg2020']\n",
    "\n",
    "# Filter the DataFrame to include only the selected values\n",
    "filtered_df = esbae_ceo[esbae_ceo['Ref_Change_Year_1520'].isin(values_to_plot)]\n",
    "\n",
    "# Get the value counts and sort by index (alphabetically)\n",
    "degvalue_counts = filtered_df['Ref_Change_Year_1520'].value_counts().sort_index()\n",
    "\n",
    "# Plot the value counts as a bar graph\n",
    "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
    "degvalue_counts.plot(kind='bar', color='skyblue')\n",
    "\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('# of deg points per year')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "60b15ae2-0dc1-4edc-896d-028c731faff6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['geometry' 'UniqueID' 'images' 'mon_images' 'bfast_change_date'\n",
      " 'bfast_magnitude' 'bfast_means' 'cusum_change_date' 'cusum_confidence'\n",
      " 'cusum_magnitude' 'red_mean' 'red_sd' 'red_min' 'red_max' 'nir_mean'\n",
      " 'nir_sd' 'nir_min' 'nir_max' 'swir1_mean' 'swir1_sd' 'swir1_min'\n",
      " 'swir1_max' 'swir2_mean' 'swir2_sd' 'swir2_min' 'swir2_max' 'ndfi_mean'\n",
      " 'ndfi_sd' 'ndfi_min' 'ndfi_max' 'brightness_mean' 'brightness_sd'\n",
      " 'brightness_min' 'brightness_max' 'greenness_mean' 'greenness_sd'\n",
      " 'greenness_min' 'greenness_max' 'wetness_mean' 'wetness_sd' 'wetness_min'\n",
      " 'wetness_max' 'bs_slope_mean' 'bs_slope_sd' 'bs_slope_max' 'bs_slope_min'\n",
      " 'ccdc_change_date' 'ccdc_magnitude' 'aspect' 'dw_class_mode'\n",
      " 'dw_tree_prob__max' 'dw_tree_prob__min' 'dw_tree_prob__stdDev'\n",
      " 'dw_tree_prob_mean' 'elevation' 'esa_lc20' 'esa_lc21' 'esri_lc17'\n",
      " 'esri_lc18' 'esri_lc19' 'esri_lc20' 'esri_lc21' 'gfc_gain' 'gfc_loss'\n",
      " 'gfc_lossyear' 'gfc_tc00' 'lang_tree_height' 'potapov_tree_height'\n",
      " 'slope' 'tmf_2016' 'tmf_2017' 'tmf_2018' 'tmf_2019' 'tmf_2020'\n",
      " 'tmf_defyear' 'tmf_degyear' 'tmf_main' 'tmf_sub' 'OID_' 'PLOTID'\n",
      " 'CEO_PLOTID' 'LON' 'LAT' 'ISO' 'CEO_Ref_Co' 'Ref_FNF_2015' 'Ref_LCtype'\n",
      " 'Ref_Change_Type_1520' 'Ref_Year' 'countDrivers' 'Ref_InfraR' 'Ref_Urb'\n",
      " 'Ref_ArtAg' 'Ref_IndAg' 'Ref_ArtFor' 'Ref_IndFor' 'Ref_ArtMin'\n",
      " 'Ref_IndMin' 'Ref_Other' 'Source' 'Ref_Change_Year_1520'\n",
      " 'Ref_Change_1520' 'collection' 'interpreted' 'Def2016' 'Def2017'\n",
      " 'Def2018' 'Def2019' 'Def2020' 'Deg2016' 'Deg2017' 'Deg2018' 'Deg2019'\n",
      " 'Deg2020' 'Defall' 'Degall' 'Stable' 'NF']\n"
     ]
    }
   ],
   "source": [
    "print(esbae_ceo.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b11d713f-fc8b-4fb3-92b8-d8070726fde3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "                    \n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "                \n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "                    \n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Ref_Change_Year_1520\n",
       "Def2016     539\n",
       "Def2017     765\n",
       "Def2018     734\n",
       "Def2019     362\n",
       "Def2020     141\n",
       "Deg2016     280\n",
       "Deg2017     440\n",
       "Deg2018     328\n",
       "Deg2019     150\n",
       "Deg2020     101\n",
       "NF         4606\n",
       "Stable     4669\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_annual_counts = esbae_ceo['Ref_Change_Year_1520'].value_counts()\n",
    "merged_annual_counts = merged_annual_counts.sort_index()\n",
    "merged_annual_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "28ba55f8-c672-43c9-bff1-667f09029210",
   "metadata": {},
   "outputs": [
    {
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       "                .geemap-colab {\n",
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       "                .geemap-colab .jupyter-button {\n",
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>images</th>\n",
       "      <th>mon_images</th>\n",
       "      <th>bfast_change_date</th>\n",
       "      <th>bfast_magnitude</th>\n",
       "      <th>bfast_means</th>\n",
       "      <th>cusum_change_date</th>\n",
       "      <th>cusum_confidence</th>\n",
       "      <th>cusum_magnitude</th>\n",
       "      <th>red_mean</th>\n",
       "      <th>red_sd</th>\n",
       "      <th>red_min</th>\n",
       "      <th>red_max</th>\n",
       "      <th>nir_mean</th>\n",
       "      <th>nir_sd</th>\n",
       "      <th>nir_min</th>\n",
       "      <th>nir_max</th>\n",
       "      <th>swir1_mean</th>\n",
       "      <th>swir1_sd</th>\n",
       "      <th>swir1_min</th>\n",
       "      <th>swir1_max</th>\n",
       "      <th>swir2_mean</th>\n",
       "      <th>swir2_sd</th>\n",
       "      <th>swir2_min</th>\n",
       "      <th>swir2_max</th>\n",
       "      <th>ndfi_mean</th>\n",
       "      <th>ndfi_sd</th>\n",
       "      <th>ndfi_min</th>\n",
       "      <th>ndfi_max</th>\n",
       "      <th>brightness_mean</th>\n",
       "      <th>brightness_sd</th>\n",
       "      <th>brightness_min</th>\n",
       "      <th>brightness_max</th>\n",
       "      <th>greenness_mean</th>\n",
       "      <th>greenness_sd</th>\n",
       "      <th>greenness_min</th>\n",
       "      <th>greenness_max</th>\n",
       "      <th>wetness_mean</th>\n",
       "      <th>wetness_sd</th>\n",
       "      <th>wetness_min</th>\n",
       "      <th>wetness_max</th>\n",
       "      <th>bs_slope_mean</th>\n",
       "      <th>bs_slope_sd</th>\n",
       "      <th>bs_slope_max</th>\n",
       "      <th>bs_slope_min</th>\n",
       "      <th>ccdc_change_date</th>\n",
       "      <th>ccdc_magnitude</th>\n",
       "      <th>aspect</th>\n",
       "      <th>dw_class_mode</th>\n",
       "      <th>dw_tree_prob__max</th>\n",
       "      <th>dw_tree_prob__min</th>\n",
       "      <th>dw_tree_prob__stdDev</th>\n",
       "      <th>dw_tree_prob_mean</th>\n",
       "      <th>elevation</th>\n",
       "      <th>esa_lc20</th>\n",
       "      <th>esa_lc21</th>\n",
       "      <th>esri_lc17</th>\n",
       "      <th>esri_lc18</th>\n",
       "      <th>esri_lc19</th>\n",
       "      <th>esri_lc20</th>\n",
       "      <th>esri_lc21</th>\n",
       "      <th>gfc_gain</th>\n",
       "      <th>gfc_loss</th>\n",
       "      <th>gfc_lossyear</th>\n",
       "      <th>gfc_tc00</th>\n",
       "      <th>lang_tree_height</th>\n",
       "      <th>potapov_tree_height</th>\n",
       "      <th>slope</th>\n",
       "      <th>tmf_2016</th>\n",
       "      <th>tmf_2017</th>\n",
       "      <th>tmf_2018</th>\n",
       "      <th>tmf_2019</th>\n",
       "      <th>tmf_2020</th>\n",
       "      <th>tmf_defyear</th>\n",
       "      <th>tmf_degyear</th>\n",
       "      <th>tmf_main</th>\n",
       "      <th>tmf_sub</th>\n",
       "      <th>OID_</th>\n",
       "      <th>PLOTID</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>CEO_Ref_Co</th>\n",
       "      <th>Ref_FNF_2015</th>\n",
       "      <th>Ref_LCtype</th>\n",
       "      <th>Ref_Year</th>\n",
       "      <th>countDrivers</th>\n",
       "      <th>Ref_InfraR</th>\n",
       "      <th>Ref_Urb</th>\n",
       "      <th>Ref_ArtAg</th>\n",
       "      <th>Ref_IndAg</th>\n",
       "      <th>Ref_ArtFor</th>\n",
       "      <th>Ref_IndFor</th>\n",
       "      <th>Ref_ArtMin</th>\n",
       "      <th>Ref_IndMin</th>\n",
       "      <th>Ref_Other</th>\n",
       "      <th>Ref_Change_1520</th>\n",
       "      <th>collection</th>\n",
       "      <th>interpreted</th>\n",
       "      <th>Def2016</th>\n",
       "      <th>Def2017</th>\n",
       "      <th>Def2018</th>\n",
       "      <th>Def2019</th>\n",
       "      <th>Def2020</th>\n",
       "      <th>Deg2016</th>\n",
       "      <th>Deg2017</th>\n",
       "      <th>Deg2018</th>\n",
       "      <th>Deg2019</th>\n",
       "      <th>Deg2020</th>\n",
       "      <th>Defall</th>\n",
       "      <th>Degall</th>\n",
       "      <th>Stable</th>\n",
       "      <th>NF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>1.311500e+04</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>1.311500e+04</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13019.000000</td>\n",
       "      <td>13019.000000</td>\n",
       "      <td>13019.000000</td>\n",
       "      <td>13019.000000</td>\n",
       "      <td>13019.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13030.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>5317.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>12895.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13031.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>3840.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.0</td>\n",
       "      <td>13115.0</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.00000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "      <td>13115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6558.024552</td>\n",
       "      <td>132.418147</td>\n",
       "      <td>78.613420</td>\n",
       "      <td>1863.019480</td>\n",
       "      <td>-1037.548040</td>\n",
       "      <td>-1.599384e+07</td>\n",
       "      <td>2018.415474</td>\n",
       "      <td>0.352812</td>\n",
       "      <td>52419.001478</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>5539.360354</td>\n",
       "      <td>2745.945325</td>\n",
       "      <td>-206.846761</td>\n",
       "      <td>9642.287276</td>\n",
       "      <td>-80.643652</td>\n",
       "      <td>1.506222e+02</td>\n",
       "      <td>406.517921</td>\n",
       "      <td>-576.152187</td>\n",
       "      <td>1248.913343</td>\n",
       "      <td>-733.984595</td>\n",
       "      <td>178.730719</td>\n",
       "      <td>1.843306</td>\n",
       "      <td>69.742377</td>\n",
       "      <td>12.589907</td>\n",
       "      <td>14.280820</td>\n",
       "      <td>46.794531</td>\n",
       "      <td>748.066131</td>\n",
       "      <td>21.136521</td>\n",
       "      <td>20.633489</td>\n",
       "      <td>4.221165</td>\n",
       "      <td>4.719899</td>\n",
       "      <td>4.833781</td>\n",
       "      <td>4.854259</td>\n",
       "      <td>4.852352</td>\n",
       "      <td>0.007290</td>\n",
       "      <td>0.340649</td>\n",
       "      <td>15.660899</td>\n",
       "      <td>59.066150</td>\n",
       "      <td>15.130050</td>\n",
       "      <td>9.109355</td>\n",
       "      <td>5.002379</td>\n",
       "      <td>3.681222</td>\n",
       "      <td>3.742460</td>\n",
       "      <td>3.803162</td>\n",
       "      <td>3.829714</td>\n",
       "      <td>3.840457</td>\n",
       "      <td>432.714047</td>\n",
       "      <td>302.825167</td>\n",
       "      <td>45.928402</td>\n",
       "      <td>56.539636</td>\n",
       "      <td>6557.000000</td>\n",
       "      <td>167870.325963</td>\n",
       "      <td>19.508425</td>\n",
       "      <td>0.176108</td>\n",
       "      <td>59.902783</td>\n",
       "      <td>0.648799</td>\n",
       "      <td>7.996111</td>\n",
       "      <td>2017.519010</td>\n",
       "      <td>0.776210</td>\n",
       "      <td>0.151735</td>\n",
       "      <td>0.114602</td>\n",
       "      <td>0.254594</td>\n",
       "      <td>0.023332</td>\n",
       "      <td>0.152116</td>\n",
       "      <td>0.026763</td>\n",
       "      <td>0.013725</td>\n",
       "      <td>0.004804</td>\n",
       "      <td>0.034541</td>\n",
       "      <td>0.292795</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041098</td>\n",
       "      <td>0.058330</td>\n",
       "      <td>0.055966</td>\n",
       "      <td>0.027602</td>\n",
       "      <td>0.010751</td>\n",
       "      <td>0.021350</td>\n",
       "      <td>0.033549</td>\n",
       "      <td>0.02501</td>\n",
       "      <td>0.011437</td>\n",
       "      <td>0.007701</td>\n",
       "      <td>0.193748</td>\n",
       "      <td>0.099047</td>\n",
       "      <td>0.356005</td>\n",
       "      <td>0.351201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3786.160211</td>\n",
       "      <td>57.824750</td>\n",
       "      <td>35.421002</td>\n",
       "      <td>536.428645</td>\n",
       "      <td>2550.876589</td>\n",
       "      <td>8.461920e+08</td>\n",
       "      <td>1.414568</td>\n",
       "      <td>0.184333</td>\n",
       "      <td>48135.855925</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>3766.123950</td>\n",
       "      <td>1985.163289</td>\n",
       "      <td>6450.389582</td>\n",
       "      <td>756.504508</td>\n",
       "      <td>623.369932</td>\n",
       "      <td>9.737932e+01</td>\n",
       "      <td>701.044380</td>\n",
       "      <td>713.345538</td>\n",
       "      <td>980.528291</td>\n",
       "      <td>2743.546041</td>\n",
       "      <td>103.971455</td>\n",
       "      <td>1.634584</td>\n",
       "      <td>10.324747</td>\n",
       "      <td>16.201839</td>\n",
       "      <td>5.869407</td>\n",
       "      <td>18.354947</td>\n",
       "      <td>574.081250</td>\n",
       "      <td>17.566163</td>\n",
       "      <td>16.787109</td>\n",
       "      <td>3.774725</td>\n",
       "      <td>4.037883</td>\n",
       "      <td>4.080652</td>\n",
       "      <td>4.085285</td>\n",
       "      <td>4.071803</td>\n",
       "      <td>0.085075</td>\n",
       "      <td>0.473946</td>\n",
       "      <td>4.144757</td>\n",
       "      <td>27.838343</td>\n",
       "      <td>10.122841</td>\n",
       "      <td>9.065614</td>\n",
       "      <td>6.220130</td>\n",
       "      <td>2.213019</td>\n",
       "      <td>2.170437</td>\n",
       "      <td>2.127229</td>\n",
       "      <td>2.106947</td>\n",
       "      <td>2.097312</td>\n",
       "      <td>737.668233</td>\n",
       "      <td>577.164199</td>\n",
       "      <td>24.683768</td>\n",
       "      <td>33.738177</td>\n",
       "      <td>3786.118725</td>\n",
       "      <td>110507.652673</td>\n",
       "      <td>6.627045</td>\n",
       "      <td>6.116608</td>\n",
       "      <td>82.553221</td>\n",
       "      <td>0.477364</td>\n",
       "      <td>6.197371</td>\n",
       "      <td>1.149494</td>\n",
       "      <td>1.372017</td>\n",
       "      <td>0.358777</td>\n",
       "      <td>0.318553</td>\n",
       "      <td>0.435649</td>\n",
       "      <td>0.150962</td>\n",
       "      <td>0.359147</td>\n",
       "      <td>0.161397</td>\n",
       "      <td>0.116350</td>\n",
       "      <td>0.069144</td>\n",
       "      <td>0.182620</td>\n",
       "      <td>0.455062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.198524</td>\n",
       "      <td>0.234376</td>\n",
       "      <td>0.229866</td>\n",
       "      <td>0.163836</td>\n",
       "      <td>0.103132</td>\n",
       "      <td>0.144552</td>\n",
       "      <td>0.180073</td>\n",
       "      <td>0.15616</td>\n",
       "      <td>0.106336</td>\n",
       "      <td>0.087421</td>\n",
       "      <td>0.395249</td>\n",
       "      <td>0.298736</td>\n",
       "      <td>0.478835</td>\n",
       "      <td>0.477364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-225207.500000</td>\n",
       "      <td>-7.491559e+10</td>\n",
       "      <td>2016.005000</td>\n",
       "      <td>-0.055615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-7923.444585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.000000</td>\n",
       "      <td>-1113.000000</td>\n",
       "      <td>-4634.278982</td>\n",
       "      <td>3.827992e-13</td>\n",
       "      <td>-4249.974108</td>\n",
       "      <td>-11448.135198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16163.455442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.018668</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.532603</td>\n",
       "      <td>-13.397789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3279.500000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2016.323000</td>\n",
       "      <td>-1904.274475</td>\n",
       "      <td>-3.672527e+00</td>\n",
       "      <td>2017.101000</td>\n",
       "      <td>0.222787</td>\n",
       "      <td>12434.937525</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>3149.374112</td>\n",
       "      <td>1053.199353</td>\n",
       "      <td>-6423.964286</td>\n",
       "      <td>9581.100000</td>\n",
       "      <td>-350.274504</td>\n",
       "      <td>7.171759e+01</td>\n",
       "      <td>39.122177</td>\n",
       "      <td>-909.432293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2263.366064</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>416.918213</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3278.500000</td>\n",
       "      <td>69580.000000</td>\n",
       "      <td>13.556251</td>\n",
       "      <td>-4.534246</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6558.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>2016.940000</td>\n",
       "      <td>-790.799622</td>\n",
       "      <td>-1.859713e+00</td>\n",
       "      <td>2018.477000</td>\n",
       "      <td>0.366328</td>\n",
       "      <td>38482.233333</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>6566.408333</td>\n",
       "      <td>2283.332998</td>\n",
       "      <td>475.166667</td>\n",
       "      <td>9981.000000</td>\n",
       "      <td>-5.270081</td>\n",
       "      <td>1.411802e+02</td>\n",
       "      <td>286.882414</td>\n",
       "      <td>-408.704840</td>\n",
       "      <td>2017.019000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>598.908875</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>6557.000000</td>\n",
       "      <td>165805.000000</td>\n",
       "      <td>19.798015</td>\n",
       "      <td>0.547480</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9836.500000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>2017.863000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.823984e-01</td>\n",
       "      <td>2019.510000</td>\n",
       "      <td>0.494248</td>\n",
       "      <td>81834.258812</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8664.905938</td>\n",
       "      <td>4342.420491</td>\n",
       "      <td>5474.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>204.383833</td>\n",
       "      <td>2.203411e+02</td>\n",
       "      <td>760.277515</td>\n",
       "      <td>-69.942294</td>\n",
       "      <td>2019.216000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>966.673523</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>504.500000</td>\n",
       "      <td>499.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>9835.500000</td>\n",
       "      <td>264927.000000</td>\n",
       "      <td>25.418999</td>\n",
       "      <td>5.053428</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13116.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>20867.542969</td>\n",
       "      <td>1.773611e+03</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>0.772322</td>\n",
       "      <td>421876.120602</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7958.678801</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>3658.309153</td>\n",
       "      <td>2.714601e+03</td>\n",
       "      <td>5839.279231</td>\n",
       "      <td>1640.634455</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>17509.391634</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>4344.163086</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>13114.000000</td>\n",
       "      <td>359957.000000</td>\n",
       "      <td>31.220543</td>\n",
       "      <td>13.078168</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           UniqueID        images    mon_images  bfast_change_date  \\\n",
       "count  13115.000000  13115.000000  13115.000000       13115.000000   \n",
       "mean    6558.024552    132.418147     78.613420        1863.019480   \n",
       "std     3786.160211     57.824750     35.421002         536.428645   \n",
       "min        1.000000     13.000000      6.000000          -2.000000   \n",
       "25%     3279.500000     85.000000     49.000000        2016.323000   \n",
       "50%     6558.000000    137.000000     82.000000        2016.940000   \n",
       "75%     9836.500000    177.000000    106.000000        2017.863000   \n",
       "max    13116.000000    277.000000    170.000000        2021.000000   \n",
       "\n",
       "       bfast_magnitude   bfast_means  cusum_change_date  cusum_confidence  \\\n",
       "count     13115.000000  1.311500e+04       13115.000000      13115.000000   \n",
       "mean      -1037.548040 -1.599384e+07        2018.415474          0.352812   \n",
       "std        2550.876589  8.461920e+08           1.414568          0.184333   \n",
       "min     -225207.500000 -7.491559e+10        2016.005000         -0.055615   \n",
       "25%       -1904.274475 -3.672527e+00        2017.101000          0.222787   \n",
       "50%        -790.799622 -1.859713e+00        2018.477000          0.366328   \n",
       "75%           0.000000 -1.823984e-01        2019.510000          0.494248   \n",
       "max       20867.542969  1.773611e+03        2021.000000          0.772322   \n",
       "\n",
       "       cusum_magnitude      red_mean        red_sd       red_min  \\\n",
       "count     13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean      52419.001478   5539.360354   2745.945325   -206.846761   \n",
       "std       48135.855925   3766.123950   1985.163289   6450.389582   \n",
       "min           0.000000  -7923.444585      0.000000 -10000.000000   \n",
       "25%       12434.937525   3149.374112   1053.199353  -6423.964286   \n",
       "50%       38482.233333   6566.408333   2283.332998    475.166667   \n",
       "75%       81834.258812   8664.905938   4342.420491   5474.000000   \n",
       "max      421876.120602  10000.000000   7958.678801  10000.000000   \n",
       "\n",
       "            red_max      nir_mean        nir_sd       nir_min       nir_max  \\\n",
       "count  13115.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean    9642.287276   5539.360354   2745.945325   -206.846761   9642.287276   \n",
       "std      756.504508   3766.123950   1985.163289   6450.389582    756.504508   \n",
       "min    -1113.000000  -7923.444585      0.000000 -10000.000000  -1113.000000   \n",
       "25%     9581.100000   3149.374112   1053.199353  -6423.964286   9581.100000   \n",
       "50%     9981.000000   6566.408333   2283.332998    475.166667   9981.000000   \n",
       "75%    10000.000000   8664.905938   4342.420491   5474.000000  10000.000000   \n",
       "max    10000.000000  10000.000000   7958.678801  10000.000000  10000.000000   \n",
       "\n",
       "         swir1_mean      swir1_sd     swir1_min     swir1_max    swir2_mean  \\\n",
       "count  13115.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean    5539.360354   2745.945325   -206.846761   9642.287276   5539.360354   \n",
       "std     3766.123950   1985.163289   6450.389582    756.504508   3766.123950   \n",
       "min    -7923.444585      0.000000 -10000.000000  -1113.000000  -7923.444585   \n",
       "25%     3149.374112   1053.199353  -6423.964286   9581.100000   3149.374112   \n",
       "50%     6566.408333   2283.332998    475.166667   9981.000000   6566.408333   \n",
       "75%     8664.905938   4342.420491   5474.000000  10000.000000   8664.905938   \n",
       "max    10000.000000   7958.678801  10000.000000  10000.000000  10000.000000   \n",
       "\n",
       "           swir2_sd     swir2_min     swir2_max     ndfi_mean       ndfi_sd  \\\n",
       "count  13115.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean    2745.945325   -206.846761   9642.287276   5539.360354   2745.945325   \n",
       "std     1985.163289   6450.389582    756.504508   3766.123950   1985.163289   \n",
       "min        0.000000 -10000.000000  -1113.000000  -7923.444585      0.000000   \n",
       "25%     1053.199353  -6423.964286   9581.100000   3149.374112   1053.199353   \n",
       "50%     2283.332998    475.166667   9981.000000   6566.408333   2283.332998   \n",
       "75%     4342.420491   5474.000000  10000.000000   8664.905938   4342.420491   \n",
       "max     7958.678801  10000.000000  10000.000000  10000.000000   7958.678801   \n",
       "\n",
       "           ndfi_min      ndfi_max  brightness_mean  brightness_sd  \\\n",
       "count  13115.000000  13115.000000     13115.000000   13115.000000   \n",
       "mean    -206.846761   9642.287276      5539.360354    2745.945325   \n",
       "std     6450.389582    756.504508      3766.123950    1985.163289   \n",
       "min   -10000.000000  -1113.000000     -7923.444585       0.000000   \n",
       "25%    -6423.964286   9581.100000      3149.374112    1053.199353   \n",
       "50%      475.166667   9981.000000      6566.408333    2283.332998   \n",
       "75%     5474.000000  10000.000000      8664.905938    4342.420491   \n",
       "max    10000.000000  10000.000000     10000.000000    7958.678801   \n",
       "\n",
       "       brightness_min  brightness_max  greenness_mean  greenness_sd  \\\n",
       "count    13115.000000    13115.000000    13115.000000  13115.000000   \n",
       "mean      -206.846761     9642.287276     5539.360354   2745.945325   \n",
       "std       6450.389582      756.504508     3766.123950   1985.163289   \n",
       "min     -10000.000000    -1113.000000    -7923.444585      0.000000   \n",
       "25%      -6423.964286     9581.100000     3149.374112   1053.199353   \n",
       "50%        475.166667     9981.000000     6566.408333   2283.332998   \n",
       "75%       5474.000000    10000.000000     8664.905938   4342.420491   \n",
       "max      10000.000000    10000.000000    10000.000000   7958.678801   \n",
       "\n",
       "       greenness_min  greenness_max  wetness_mean    wetness_sd   wetness_min  \\\n",
       "count   13115.000000   13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean     -206.846761    9642.287276   5539.360354   2745.945325   -206.846761   \n",
       "std      6450.389582     756.504508   3766.123950   1985.163289   6450.389582   \n",
       "min    -10000.000000   -1113.000000  -7923.444585      0.000000 -10000.000000   \n",
       "25%     -6423.964286    9581.100000   3149.374112   1053.199353  -6423.964286   \n",
       "50%       475.166667    9981.000000   6566.408333   2283.332998    475.166667   \n",
       "75%      5474.000000   10000.000000   8664.905938   4342.420491   5474.000000   \n",
       "max     10000.000000   10000.000000  10000.000000   7958.678801  10000.000000   \n",
       "\n",
       "        wetness_max  bs_slope_mean   bs_slope_sd  bs_slope_max  bs_slope_min  \\\n",
       "count  13115.000000   13115.000000  1.311500e+04  13115.000000  13115.000000   \n",
       "mean    9642.287276     -80.643652  1.506222e+02    406.517921   -576.152187   \n",
       "std      756.504508     623.369932  9.737932e+01    701.044380    713.345538   \n",
       "min    -1113.000000   -4634.278982  3.827992e-13  -4249.974108 -11448.135198   \n",
       "25%     9581.100000    -350.274504  7.171759e+01     39.122177   -909.432293   \n",
       "50%     9981.000000      -5.270081  1.411802e+02    286.882414   -408.704840   \n",
       "75%    10000.000000     204.383833  2.203411e+02    760.277515    -69.942294   \n",
       "max    10000.000000    3658.309153  2.714601e+03   5839.279231   1640.634455   \n",
       "\n",
       "       ccdc_change_date  ccdc_magnitude        aspect  dw_class_mode  \\\n",
       "count      13115.000000    13115.000000  13031.000000   13019.000000   \n",
       "mean        1248.913343     -733.984595    178.730719       1.843306   \n",
       "std          980.528291     2743.546041    103.971455       1.634584   \n",
       "min            0.000000   -16163.455442      0.000000       0.000000   \n",
       "25%            0.000000    -2263.366064     88.000000       1.000000   \n",
       "50%         2017.019000        0.000000    178.000000       1.000000   \n",
       "75%         2019.216000        0.000000    269.000000       1.000000   \n",
       "max         2021.000000    17509.391634    359.000000       8.000000   \n",
       "\n",
       "       dw_tree_prob__max  dw_tree_prob__min  dw_tree_prob__stdDev  \\\n",
       "count       13019.000000       13019.000000          13019.000000   \n",
       "mean           69.742377          12.589907             14.280820   \n",
       "std            10.324747          16.201839              5.869407   \n",
       "min             5.000000           1.000000              0.000000   \n",
       "25%            70.000000           3.000000             11.000000   \n",
       "50%            74.000000           6.000000             15.000000   \n",
       "75%            75.000000          13.000000             18.000000   \n",
       "max            78.000000          74.000000             34.000000   \n",
       "\n",
       "       dw_tree_prob_mean     elevation      esa_lc20      esa_lc21  \\\n",
       "count       13019.000000  13031.000000  13031.000000  13031.000000   \n",
       "mean           46.794531    748.066131     21.136521     20.633489   \n",
       "std            18.354947    574.081250     17.566163     16.787109   \n",
       "min             3.000000     -1.018668     10.000000     10.000000   \n",
       "25%            33.000000    416.918213     10.000000     10.000000   \n",
       "50%            48.000000    598.908875     20.000000     10.000000   \n",
       "75%            63.000000    966.673523     30.000000     30.000000   \n",
       "max            74.000000   4344.163086     95.000000     95.000000   \n",
       "\n",
       "          esri_lc17     esri_lc18     esri_lc19     esri_lc20     esri_lc21  \\\n",
       "count  13031.000000  13031.000000  13031.000000  13030.000000  13031.000000   \n",
       "mean       4.221165      4.719899      4.833781      4.854259      4.852352   \n",
       "std        3.774725      4.037883      4.080652      4.085285      4.071803   \n",
       "min        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "25%        2.000000      2.000000      2.000000      2.000000      2.000000   \n",
       "50%        2.000000      2.000000      2.000000      2.000000      2.000000   \n",
       "75%        5.000000     11.000000     11.000000     11.000000     11.000000   \n",
       "max       11.000000     11.000000     11.000000     11.000000     11.000000   \n",
       "\n",
       "           gfc_gain      gfc_loss  gfc_lossyear      gfc_tc00  \\\n",
       "count  13031.000000  13031.000000   5317.000000  13031.000000   \n",
       "mean       0.007290      0.340649     15.660899     59.066150   \n",
       "std        0.085075      0.473946      4.144757     27.838343   \n",
       "min        0.000000      0.000000      1.000000      0.000000   \n",
       "25%        0.000000      0.000000     15.000000     40.000000   \n",
       "50%        0.000000      0.000000     17.000000     57.000000   \n",
       "75%        0.000000      1.000000     18.000000     87.000000   \n",
       "max        1.000000      1.000000     22.000000    100.000000   \n",
       "\n",
       "       lang_tree_height  potapov_tree_height         slope      tmf_2016  \\\n",
       "count      12895.000000         13031.000000  13031.000000  13031.000000   \n",
       "mean          15.130050             9.109355      5.002379      3.681222   \n",
       "std           10.122841             9.065614      6.220130      2.213019   \n",
       "min            0.000000             0.000000      0.000000      0.000000   \n",
       "25%            9.000000             3.000000      1.000000      1.000000   \n",
       "50%           13.000000             6.000000      3.000000      4.000000   \n",
       "75%           20.000000            12.000000      6.000000      6.000000   \n",
       "max           46.000000            40.000000     49.000000      6.000000   \n",
       "\n",
       "           tmf_2017      tmf_2018      tmf_2019      tmf_2020   tmf_defyear  \\\n",
       "count  13031.000000  13031.000000  13031.000000  13031.000000  13031.000000   \n",
       "mean       3.742460      3.803162      3.829714      3.840457    432.714047   \n",
       "std        2.170437      2.127229      2.106947      2.097312    737.668233   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        1.000000      2.000000      2.000000      2.000000      0.000000   \n",
       "50%        4.000000      4.000000      4.000000      4.000000      0.000000   \n",
       "75%        6.000000      6.000000      6.000000      6.000000    504.500000   \n",
       "max        6.000000      6.000000      6.000000      6.000000   2020.000000   \n",
       "\n",
       "        tmf_degyear      tmf_main       tmf_sub          OID_         PLOTID  \\\n",
       "count  13031.000000  13031.000000  13031.000000  13115.000000   13115.000000   \n",
       "mean     302.825167     45.928402     56.539636   6557.000000  167870.325963   \n",
       "std      577.164199     24.683768     33.738177   3786.118725  110507.652673   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000     20.000000     24.000000   3278.500000   69580.000000   \n",
       "50%        0.000000     50.000000     54.000000   6557.000000  165805.000000   \n",
       "75%      499.500000     70.000000     91.000000   9835.500000  264927.000000   \n",
       "max     2020.000000     70.000000     93.000000  13114.000000  359957.000000   \n",
       "\n",
       "                LON           LAT    CEO_Ref_Co  Ref_FNF_2015    Ref_LCtype  \\\n",
       "count  13115.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean      19.508425      0.176108     59.902783      0.648799      7.996111   \n",
       "std        6.627045      6.116608     82.553221      0.477364      6.197371   \n",
       "min        8.532603    -13.397789      0.000000      0.000000      0.000000   \n",
       "25%       13.556251     -4.534246      7.000000      0.000000      1.000000   \n",
       "50%       19.798015      0.547480     15.000000      1.000000      8.000000   \n",
       "75%       25.418999      5.053428    110.000000      1.000000     13.000000   \n",
       "max       31.220543     13.078168    254.000000      1.000000     19.000000   \n",
       "\n",
       "          Ref_Year  countDrivers    Ref_InfraR       Ref_Urb     Ref_ArtAg  \\\n",
       "count  3840.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean   2017.519010      0.776210      0.151735      0.114602      0.254594   \n",
       "std       1.149494      1.372017      0.358777      0.318553      0.435649   \n",
       "min    2016.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    2017.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%    2017.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%    2018.000000      1.000000      0.000000      0.000000      1.000000   \n",
       "max    2020.000000      7.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          Ref_IndAg    Ref_ArtFor    Ref_IndFor    Ref_ArtMin    Ref_IndMin  \\\n",
       "count  13115.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean       0.023332      0.152116      0.026763      0.013725      0.004804   \n",
       "std        0.150962      0.359147      0.161397      0.116350      0.069144   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          Ref_Other  Ref_Change_1520  collection  interpreted       Def2016  \\\n",
       "count  13115.000000     13115.000000     13115.0      13115.0  13115.000000   \n",
       "mean       0.034541         0.292795         1.0          1.0      0.041098   \n",
       "std        0.182620         0.455062         0.0          0.0      0.198524   \n",
       "min        0.000000         0.000000         1.0          1.0      0.000000   \n",
       "25%        0.000000         0.000000         1.0          1.0      0.000000   \n",
       "50%        0.000000         0.000000         1.0          1.0      0.000000   \n",
       "75%        0.000000         1.000000         1.0          1.0      0.000000   \n",
       "max        1.000000         1.000000         1.0          1.0      1.000000   \n",
       "\n",
       "            Def2017       Def2018       Def2019       Def2020       Deg2016  \\\n",
       "count  13115.000000  13115.000000  13115.000000  13115.000000  13115.000000   \n",
       "mean       0.058330      0.055966      0.027602      0.010751      0.021350   \n",
       "std        0.234376      0.229866      0.163836      0.103132      0.144552   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "            Deg2017      Deg2018       Deg2019       Deg2020        Defall  \\\n",
       "count  13115.000000  13115.00000  13115.000000  13115.000000  13115.000000   \n",
       "mean       0.033549      0.02501      0.011437      0.007701      0.193748   \n",
       "std        0.180073      0.15616      0.106336      0.087421      0.395249   \n",
       "min        0.000000      0.00000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.00000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.00000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.00000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.00000      1.000000      1.000000      1.000000   \n",
       "\n",
       "             Degall        Stable            NF  \n",
       "count  13115.000000  13115.000000  13115.000000  \n",
       "mean       0.099047      0.356005      0.351201  \n",
       "std        0.298736      0.478835      0.477364  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      0.000000  \n",
       "50%        0.000000      0.000000      0.000000  \n",
       "75%        0.000000      1.000000      1.000000  \n",
       "max        1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "14fcaa3e-1f21-43b1-a4ec-671a1950134c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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     "text": [
      "geometry                geometry\n",
      "UniqueID                   int64\n",
      "images                     int64\n",
      "mon_images                 int64\n",
      "bfast_change_date        float64\n",
      "bfast_magnitude          float64\n",
      "bfast_means              float64\n",
      "cusum_change_date        float64\n",
      "cusum_confidence         float64\n",
      "cusum_magnitude          float64\n",
      "red_mean                 float64\n",
      "red_sd                   float64\n",
      "red_min                  float64\n",
      "red_max                  float64\n",
      "nir_mean                 float64\n",
      "nir_sd                   float64\n",
      "nir_min                  float64\n",
      "nir_max                  float64\n",
      "swir1_mean               float64\n",
      "swir1_sd                 float64\n",
      "swir1_min                float64\n",
      "swir1_max                float64\n",
      "swir2_mean               float64\n",
      "swir2_sd                 float64\n",
      "swir2_min                float64\n",
      "swir2_max                float64\n",
      "ndfi_mean                float64\n",
      "ndfi_sd                  float64\n",
      "ndfi_min                 float64\n",
      "ndfi_max                 float64\n",
      "brightness_mean          float64\n",
      "brightness_sd            float64\n",
      "brightness_min           float64\n",
      "brightness_max           float64\n",
      "greenness_mean           float64\n",
      "greenness_sd             float64\n",
      "greenness_min            float64\n",
      "greenness_max            float64\n",
      "wetness_mean             float64\n",
      "wetness_sd               float64\n",
      "wetness_min              float64\n",
      "wetness_max              float64\n",
      "bs_slope_mean            float64\n",
      "bs_slope_sd              float64\n",
      "bs_slope_max             float64\n",
      "bs_slope_min             float64\n",
      "ccdc_change_date         float64\n",
      "ccdc_magnitude           float64\n",
      "aspect                   float64\n",
      "dw_class_mode            float64\n",
      "dw_tree_prob__max        float64\n",
      "dw_tree_prob__min        float64\n",
      "dw_tree_prob__stdDev     float64\n",
      "dw_tree_prob_mean        float64\n",
      "elevation                float64\n",
      "esa_lc20                 float64\n",
      "esa_lc21                 float64\n",
      "esri_lc17                float64\n",
      "esri_lc18                float64\n",
      "esri_lc19                float64\n",
      "esri_lc20                float64\n",
      "esri_lc21                float64\n",
      "gfc_gain                 float64\n",
      "gfc_loss                 float64\n",
      "gfc_lossyear             float64\n",
      "gfc_tc00                 float64\n",
      "lang_tree_height         float64\n",
      "potapov_tree_height      float64\n",
      "slope                    float64\n",
      "tmf_2016                 float64\n",
      "tmf_2017                 float64\n",
      "tmf_2018                 float64\n",
      "tmf_2019                 float64\n",
      "tmf_2020                 float64\n",
      "tmf_defyear              float64\n",
      "tmf_degyear              float64\n",
      "tmf_main                 float64\n",
      "tmf_sub                  float64\n",
      "OID_                       int64\n",
      "PLOTID                     int64\n",
      "CEO_PLOTID                object\n",
      "LON                      float64\n",
      "LAT                      float64\n",
      "ISO                       object\n",
      "CEO_Ref_Co                 int64\n",
      "Ref_FNF_2015               int64\n",
      "Ref_LCtype                 int64\n",
      "Ref_Change_Type_1520      object\n",
      "Ref_Year                 float64\n",
      "countDrivers               int64\n",
      "Ref_InfraR                 int64\n",
      "Ref_Urb                    int64\n",
      "Ref_ArtAg                  int64\n",
      "Ref_IndAg                  int64\n",
      "Ref_ArtFor                 int64\n",
      "Ref_IndFor                 int64\n",
      "Ref_ArtMin                 int64\n",
      "Ref_IndMin                 int64\n",
      "Ref_Other                  int64\n",
      "Source                    object\n",
      "Ref_Change_Year_1520      object\n",
      "Ref_Change_1520            int64\n",
      "collection                 int64\n",
      "interpreted                int64\n",
      "Def2016                    int64\n",
      "Def2017                    int64\n",
      "Def2018                    int64\n",
      "Def2019                    int64\n",
      "Def2020                    int64\n",
      "Deg2016                    int64\n",
      "Deg2017                    int64\n",
      "Deg2018                    int64\n",
      "Deg2019                    int64\n",
      "Deg2020                    int64\n",
      "Defall                     int64\n",
      "Degall                     int64\n",
      "Stable                     int64\n",
      "NF                         int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(esbae_ceo.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bd0119-9d19-461b-854a-7d1345b01984",
   "metadata": {},
   "source": [
    "##### export merged esbae and ceo data to one csv file for next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "44453121-cc91-406e-9f6b-e4d78bb40ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you want to remove all the eSBAE variables\n",
    "#DRC\n",
    "#esbae = esbae[['point_id', 'PLOTID', 'LAT','LON','stratum', 'chg_prob', 'simple_combined','NOM']]\n",
    "#GAB\n",
    "#esbae = esbae[['point_id', 'PLOTID', 'LAT','LON','stratum', 'chg_prob', 'simple_combined']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a3199a69-7b32-4d91-9e59-f2daa370a714",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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   "source": [
    "esbae_ceo_data_out =  '/home/sepal-user/module_results/esbae/CAFI/CAFI_esbae_ceo_phaseI_merge.csv'\n",
    "esbae_ceo.to_csv(esbae_ceo_data_out,index=False)\n",
    "#OR\n",
    "#esbae_ceo[esbae_ceo_columns].to_csv(data_out,index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "718daa5d-6ea8-45eb-a6a9-37f29bc08f03",
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   "source": []
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