Repository: DOV-Vlaanderen/pydov

Issue:
Add the 'mv_mtaw' field to GrondwaterFilter
It would be interesting to add the 'mv_mtaw' field to the GrondwaterFilter type, in order to be able to compare the water level measurements to the ground level.

The information needs to be added to the Filter XML export serverside, and can afterwards be added as a new XmlField in pydov.

I will follow up here once the XML export has been changed.

Discussion:

Patch:
diff --git a/.github/CONTRIBUTING.rst b/.github/CONTRIBUTING.rst
index 9430fc2..ad5c3f1 100644
--- a/.github/CONTRIBUTING.rst
+++ b/.github/CONTRIBUTING.rst
@@ -249,6 +249,48 @@ The workflow is provided for command line usage and using the `Github for Deskto
 
 If any of the above seems like magic to you, please look up the `Git documentation <https://git-scm.com/documentation>`_ on the web, or ask a friend or another contributor for help.
 
+
+Setting up your environment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+To start developing, make sure to setup a development environment. We advice to work with a virtual
+environment such as `virtualenv <https://virtualenv.pypa.io/en/latest/>`_ or
+`conda <https://docs.conda.io/en/latest/miniconda.html>`_. To install the required dependencies
+for development, use the ``requirement_dev.txt`` file:
+
+::
+
+   pip install -r requirements_dev.txt
+
+This will install both the packages pydov relies on as well as the development tools (unit testing,...).
+
+
+.. note::
+   The repository contains multiple ``requirement_*.txt`` files:
+
+   * ``requirement.txt`` required packages to use the pydov API
+   * ``requirement_dev.txt`` required packages to contribute to pydov code
+   * ``requirement_doc.txt`` required packages to contribute to the pydov documentation. This environmnet is used by the readthedocs service for building/hosting the documentation.
+   * ``requirement_appveyor.txt`` requirement specific for Appveyor (If someone has a better way of dealing with Appveyor, contributions welcome)
+   * ``binder/requirement.txt`` requirements setup to setup a Binder environment
+
+   When adding dependencies, make sure to make the appropriate adjustments in the individual file!
+
+Running the unit tests
+^^^^^^^^^^^^^^^^^^^^^^^
+
+To run the unit tests, ``pytest`` is used. In the common line, you can run all the tests from the terminal,
+using the command line. Navigate to the ``pydov`` main directory and do:
+
+::
+
+   pytest
+
+When adding new functionality or adjusting code, make sure to check/update/add the unit tests. Test files
+are grouped by the functionality. Each file name starts with ``test_*`` (required for pytest), followed
+by the module name (e.g. ``search``, ``types``,...).
+
+
 .. _docs-technical:
 
 Creating the documentation
diff --git a/.travis.yml b/.travis.yml
index dcad5d7..6022b46 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,8 +1,5 @@
 language: python
 sudo: false
-env:
-  global:
-  - COVERALLS_PARALLEL=true
 notifications:
   email:
     recipients:
@@ -11,10 +8,8 @@ notifications:
     on_success: change
     on_failure: always
     on_error: always
-  webhooks: https://coveralls.io/webhook
 install:
 - pip install -U tox-travis
-- pip install -U coveralls
 addons:
   apt:
     packages:
@@ -45,8 +40,6 @@ matrix:
     env: TOXENV=docs
   - env: TOXENV=flake8
 script: tox
-after_success:
-- coveralls || echo "coveralls failure"
 deploy:
 - provider: pypi
   distributions: sdist bdist_wheel
diff --git a/README.md b/README.md
index 2e153ad..5554e7c 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # pydov <img src="docs/_static/img/logo.png" align="right" alt="" width="120">
 
-[![Build Status](https://travis-ci.org/DOV-Vlaanderen/pydov.svg?branch=master)](https://travis-ci.org/DOV-Vlaanderen/pydov) [![AppVeyor Build status](https://ci.appveyor.com/api/projects/status/4ljy2a0p661v3d9k/branch/master?svg=true)](https://ci.appveyor.com/project/Roel/pydov) [![Documentation Status](https://readthedocs.org/projects/pydov/badge/?version=latest)](https://pydov.readthedocs.io/en/latest/?badge=latest) [![Coverage Status](https://coveralls.io/repos/github/DOV-Vlaanderen/pydov/badge.svg?branch=master)](https://coveralls.io/github/DOV-Vlaanderen/pydov?branch=master) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2788680.svg)](https://doi.org/10.5281/zenodo.2788680)
+[![Build Status](https://travis-ci.org/DOV-Vlaanderen/pydov.svg?branch=master)](https://travis-ci.org/DOV-Vlaanderen/pydov) [![AppVeyor Build status](https://ci.appveyor.com/api/projects/status/4ljy2a0p661v3d9k/branch/master?svg=true)](https://ci.appveyor.com/project/Roel/pydov) [![Documentation Status](https://readthedocs.org/projects/pydov/badge/?version=latest)](https://pydov.readthedocs.io/en/latest/?badge=latest) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2788680.svg)](https://doi.org/10.5281/zenodo.2788680)
 
 pydov is a Python package to query and download data from [Databank Ondergrond Vlaanderen (DOV)](https://www.dov.vlaanderen.be). It is hosted on [GitHub](https://github.com/DOV-Vlaanderen/pydov) and development is coordinated by Databank Ondergrond Vlaanderen (DOV). DOV aggregates data about soil, subsoil and groundwater of Flanders and makes them publicly available. Interactive and human-readable extraction and querying of the data is provided by a [web application](https://www.dov.vlaanderen.be/portaal/?module=verkenner#ModulePage), whereas the focus of this package is to **support machine-based extraction and conversion of the data**.
 
diff --git a/appveyor.yml b/appveyor.yml
index b1200cf..bd83530 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -38,7 +38,7 @@ install:
     - cmd: conda.exe update -y -q conda
     - cmd: conda.exe config --add channels conda-forge
     - cmd: conda.exe install -y -q python=%CONDA_PY:~0,1%.%CONDA_PY:~1,2%
-    - cmd: conda.exe install -y -q numpy"<1.17.0" pandas"<0.25.0" owslib"<0.19.0"
+    - cmd: conda.exe install -y -q numpy pandas owslib
     - cmd: call %CONDA_INSTALL_LOCN%\python.exe -m pip install --ignore-installed --no-cache-dir -r requirements_appveyor.txt
 
 build: false
diff --git a/docs/index.rst b/docs/index.rst
index c0d4762..1403caa 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -11,9 +11,6 @@ Welcome to pydov's documentation!
     :target: http://pydov.readthedocs.io/en/latest/?badge=latest
     :alt: Documentation Status
 
-.. image:: https://coveralls.io/repos/github/DOV-Vlaanderen/pydov/badge.svg?branch=master
-    :target: https://coveralls.io/github/DOV-Vlaanderen/pydov?branch=master
-
 .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2788680.svg
    :target: https://doi.org/10.5281/zenodo.2788680
 
diff --git a/docs/notebooks/search_grondwaterfilters.ipynb b/docs/notebooks/search_grondwaterfilters.ipynb
index 902ee88..5d0f46e 100644
--- a/docs/notebooks/search_grondwaterfilters.ipynb
+++ b/docs/notebooks/search_grondwaterfilters.ipynb
@@ -23,9 +23,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -35,9 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# check pydov path\n",
@@ -54,9 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pydov.search.grondwaterfilter import GrondwaterFilterSearch\n",
@@ -96,54 +90,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "betrouwbaarheid\n",
-      "boringfiche\n",
-      "grondwaterlichaam_code\n",
-      "start_grondwaterlocatie_mtaw\n",
-      "namen\n",
-      "peilmetingen_van\n",
-      "datum\n",
-      "aquifer\n",
-      "filtergrafiek\n",
-      "kwaliteitsmetingen_tot\n",
-      "methode\n",
-      "kwaliteitsmetingen_van\n",
-      "gw_id\n",
-      "tijdstip\n",
-      "regime\n",
-      "filtertoestand\n",
-      "meetnet_code\n",
-      "beheerder\n",
-      "stijghoogterapport\n",
-      "filtertype\n",
-      "datum_in_filter\n",
-      "grondwaterlichaam\n",
-      "putsoort\n",
-      "filternummer\n",
-      "diepte_onderkant_filter\n",
-      "pkey_filter\n",
-      "putgrafiek\n",
-      "datum_uit_filter\n",
-      "filterstatus\n",
-      "meetnet\n",
-      "peilmetingen_tot\n",
-      "gemeente\n",
-      "recentste_exploitant\n",
-      "pkey_grondwaterlocatie\n",
-      "peil_mtaw\n",
-      "lengte_filter\n",
-      "analyserapport\n",
-      "y\n",
-      "x\n",
-      "aquifer_code\n",
-      "boornummer\n"
+      "gw_id\npkey_grondwaterlocatie\nfilternummer\npkey_filter\nnamen\nfiltergrafiek\nputgrafiek\naquifer\ndiepte_onderkant_filter\nlengte_filter\nputsoort\nfiltertype\nmeetnet\nx\ny\nstart_grondwaterlocatie_mtaw\ngemeente\ngrondwaterlichaam\nregime\ndatum_in_filter\ndatum_uit_filter\nstijghoogterapport\nanalyserapport\nboornummer\nboringfiche\npeilmetingen_van\npeilmetingen_tot\nkwaliteitsmetingen_van\nkwaliteitsmetingen_tot\nrecentste_exploitant\nbeheerder\nmv_mtaw\nmeetnet_code\naquifer_code\ngrondwaterlichaam_code\ndatum\ntijdstip\npeil_mtaw\nbetrouwbaarheid\nmethode\nfilterstatus\nfiltertoestand\n"
      ]
     }
    ],
@@ -169,21 +123,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'cost': 1,\n",
-       " 'definition': 'De aquifer waarin de filter hangt. Als tekst, opgebouwd uit de HCOV code (vier karakters) en de naam gescheiden door \" - \"',\n",
-       " 'name': 'aquifer',\n",
-       " 'notnull': True,\n",
-       " 'query': True,\n",
-       " 'type': 'string'}"
+       "{'name': 'aquifer',\n 'definition': 'De aquifer waarin de filter hangt. Als tekst, opgebouwd uit de HCOV code (vier karakters) en de naam gescheiden door \" - \"',\n 'type': 'string',\n 'notnull': True,\n 'query': True,\n 'cost': 1}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,28 +151,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'Installatie': None,\n",
-       " 'batterijput': None,\n",
-       " 'bodemlus': None,\n",
-       " 'bron, natuurlijke holte': None,\n",
-       " 'bronbemaling': None,\n",
-       " 'draineringsinrichting': None,\n",
-       " 'galerij': None,\n",
-       " 'graverij, mijn, groeve': None,\n",
-       " 'niet-verbuisde boorput': None,\n",
-       " 'onbekend': None,\n",
-       " 'ring- of steenput': None,\n",
-       " 'verbuisde boorput': None,\n",
-       " 'vijver': None}"
+       "{'Installatie': None,\n 'batterijput': None,\n 'bodemlus': None,\n 'bron, natuurlijke holte': None,\n 'bronbemaling': None,\n 'draineringsinrichting': None,\n 'galerij': None,\n 'graverij, mijn, groeve': None,\n 'niet-verbuisde boorput': None,\n 'onbekend': None,\n 'ring- of steenput': None,\n 'verbuisde boorput': None,\n 'vijver': None}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -258,14 +195,203 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[000/026] cccccccccccccccccccccccccc\n"
+      "[000/026] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     },
     {
@@ -297,8 +423,8 @@
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
        "      <th>gemeente</th>\n",
-       "      <th>meetnet_code</th>\n",
        "      <th>...</th>\n",
        "      <th>regime</th>\n",
        "      <th>diepte_onderkant_filter</th>\n",
@@ -323,8 +449,8 @@
        "      <td>94147.0</td>\n",
        "      <td>169582.0</td>\n",
        "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
        "      <td>Wortegem-Petegem</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>NaN</td>\n",
@@ -347,8 +473,8 @@
        "      <td>94147.0</td>\n",
        "      <td>169582.0</td>\n",
        "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
        "      <td>Wortegem-Petegem</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>NaN</td>\n",
@@ -371,8 +497,8 @@
        "      <td>94147.0</td>\n",
        "      <td>169582.0</td>\n",
        "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
        "      <td>Wortegem-Petegem</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>NaN</td>\n",
@@ -395,8 +521,8 @@
        "      <td>94147.0</td>\n",
        "      <td>169582.0</td>\n",
        "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
        "      <td>Wortegem-Petegem</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>NaN</td>\n",
@@ -419,8 +545,8 @@
        "      <td>94147.0</td>\n",
        "      <td>169582.0</td>\n",
        "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
        "      <td>Wortegem-Petegem</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>NaN</td>\n",
@@ -435,183 +561,246 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
+       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                         pkey_filter  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "2  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "3  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "4  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "\n",
-       "                              pkey_grondwaterlocatie    gw_id filternummer  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/put/2018-00...  SWPP006            1   \n",
-       "1  https://www.dov.vlaanderen.be/data/put/2018-00...  SWPP006            1   \n",
-       "2  https://www.dov.vlaanderen.be/data/put/2018-00...  SWPP006            1   \n",
-       "3  https://www.dov.vlaanderen.be/data/put/2018-00...  SWPP006            1   \n",
-       "4  https://www.dov.vlaanderen.be/data/put/2018-00...  SWPP006            1   \n",
-       "\n",
-       "   filtertype        x         y  start_grondwaterlocatie_mtaw  \\\n",
-       "0  peilfilter  94147.0  169582.0                           9.4   \n",
-       "1  peilfilter  94147.0  169582.0                           9.4   \n",
-       "2  peilfilter  94147.0  169582.0                           9.4   \n",
-       "3  peilfilter  94147.0  169582.0                           9.4   \n",
-       "4  peilfilter  94147.0  169582.0                           9.4   \n",
-       "\n",
-       "           gemeente  meetnet_code      ...          regime  \\\n",
-       "0  Wortegem-Petegem             9      ...        onbekend   \n",
-       "1  Wortegem-Petegem             9      ...        onbekend   \n",
-       "2  Wortegem-Petegem             9      ...        onbekend   \n",
-       "3  Wortegem-Petegem             9      ...        onbekend   \n",
-       "4  Wortegem-Petegem             9      ...        onbekend   \n",
-       "\n",
-       "   diepte_onderkant_filter lengte_filter       datum  tijdstip peil_mtaw  \\\n",
-       "0                      NaN           NaN  1999-04-13       NaN      9.22   \n",
-       "1                      NaN           NaN  1999-04-14       NaN      9.41   \n",
-       "2                      NaN           NaN  1999-04-22       NaN      9.29   \n",
-       "3                      NaN           NaN  1999-05-06       NaN      9.11   \n",
-       "4                      NaN           NaN  1999-05-18       NaN      9.01   \n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
        "\n",
-       "   betrouwbaarheid   methode filterstatus filtertoestand  \n",
-       "0         onbekend  peillint     onbekend            1.0  \n",
-       "1         onbekend  peillint     onbekend            1.0  \n",
-       "2         onbekend  peillint     onbekend            1.0  \n",
-       "3         onbekend  peillint     onbekend            1.0  \n",
-       "4         onbekend  peillint     onbekend            1.0  \n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
        "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pydov.util.location import Within, Box\n",
-    "\n",
-    "df = gwfilter.search(location=Within(Box(93378, 168009, 94246, 169873)))\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the *pkey* attributes one can request the details of the corresponding *put* or *filter* in a webbrowser:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://www.dov.vlaanderen.be/data/put/2019-019725\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007292\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007293\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007290\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007291\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007296\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007294\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007295\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007299\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007313\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007312\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007311\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007310\n",
-      "https://www.dov.vlaanderen.be/data/put/2017-002867\n",
-      "https://www.dov.vlaanderen.be/data/put/2017-002866\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007285\n",
-      "https://www.dov.vlaanderen.be/data/put/2019-020544\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007287\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007286\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007289\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007288\n",
-      "https://www.dov.vlaanderen.be/data/put/2017-002868\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007300\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007304\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007305\n",
-      "https://www.dov.vlaanderen.be/data/put/2018-007307\n",
-      "https://www.dov.vlaanderen.be/data/filter/2007-011019\n",
-      "https://www.dov.vlaanderen.be/data/filter/2007-011018\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011012\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011013\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011010\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011011\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011014\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011015\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011030\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011031\n",
-      "https://www.dov.vlaanderen.be/data/filter/1995-061303\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011007\n",
-      "https://www.dov.vlaanderen.be/data/filter/2009-011026\n",
-      "https://www.dov.vlaanderen.be/data/filter/2007-011023\n",
-      "https://www.dov.vlaanderen.be/data/filter/2007-011024\n",
-      "https://www.dov.vlaanderen.be/data/filter/1991-062098\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011032\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-000605\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-000607\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-000606\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011005\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011004\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011029\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011006\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011009\n",
-      "https://www.dov.vlaanderen.be/data/filter/1999-011008\n"
-     ]
-    }
-   ],
-   "source": [
-    "for pkey_grondwaterlocatie in set(df.pkey_grondwaterlocatie):\n",
-    "    print(pkey_grondwaterlocatie)\n",
-    "\n",
-    "for pkey_filter in set(df.pkey_filter):\n",
-    "    print(pkey_filter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get groundwater screens with specific properties"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next to querying groundwater screens based on their geographic location within a bounding box, we can also search for groundwater screens matching a specific set of properties. For this we can build a query using a combination of the 'GrondwaterFilter' fields and operators provided by the WFS protocol.\n",
-    "\n",
-    "A list of possible operators can be found below:"
+       "    .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>pkey_filter</th>\n",
+       "      <th>pkey_grondwaterlocatie</th>\n",
+       "      <th>gw_id</th>\n",
+       "      <th>filternummer</th>\n",
+       "      <th>filtertype</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
+       "      <th>gemeente</th>\n",
+       "      <th>...</th>\n",
+       "      <th>regime</th>\n",
+       "      <th>diepte_onderkant_filter</th>\n",
+       "      <th>lengte_filter</th>\n",
+       "      <th>datum</th>\n",
+       "      <th>tijdstip</th>\n",
+       "      <th>peil_mtaw</th>\n",
+       "      <th>betrouwbaarheid</th>\n",
+       "      <th>methode</th>\n",
+       "      <th>filterstatus</th>\n",
+       "      <th>filtertoestand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>SWPP006</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>94147.0</td>\n",
+       "      <td>169582.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>Wortegem-Petegem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999-04-13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.22</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>SWPP006</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>94147.0</td>\n",
+       "      <td>169582.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>Wortegem-Petegem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999-04-14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.41</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>SWPP006</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>94147.0</td>\n",
+       "      <td>169582.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>Wortegem-Petegem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999-04-22</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.29</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>SWPP006</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>94147.0</td>\n",
+       "      <td>169582.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>Wortegem-Petegem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999-05-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.11</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>SWPP006</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>94147.0</td>\n",
+       "      <td>169582.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>Wortegem-Petegem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999-05-18</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.01</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pydov.util.location import Within, Box\n",
+    "\n",
+    "df = gwfilter.search(location=Within(Box(93378, 168009, 94246, 169873)))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the *pkey* attributes one can request the details of the corresponding *put* or *filter* in a webbrowser:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://www.dov.vlaanderen.be/data/put/2018-007312\nhttps://www.dov.vlaanderen.be/data/put/2018-007296\nhttps://www.dov.vlaanderen.be/data/put/2018-007310\nhttps://www.dov.vlaanderen.be/data/put/2018-007292\nhttps://www.dov.vlaanderen.be/data/put/2017-002868\nhttps://www.dov.vlaanderen.be/data/put/2018-007288\nhttps://www.dov.vlaanderen.be/data/put/2018-007295\nhttps://www.dov.vlaanderen.be/data/put/2018-007294\nhttps://www.dov.vlaanderen.be/data/put/2018-007290\nhttps://www.dov.vlaanderen.be/data/put/2018-007305\nhttps://www.dov.vlaanderen.be/data/put/2018-007307\nhttps://www.dov.vlaanderen.be/data/put/2018-007304\nhttps://www.dov.vlaanderen.be/data/put/2018-007289\nhttps://www.dov.vlaanderen.be/data/put/2018-007285\nhttps://www.dov.vlaanderen.be/data/put/2018-007299\nhttps://www.dov.vlaanderen.be/data/put/2018-007300\nhttps://www.dov.vlaanderen.be/data/put/2019-019725\nhttps://www.dov.vlaanderen.be/data/put/2018-007311\nhttps://www.dov.vlaanderen.be/data/put/2018-007291\nhttps://www.dov.vlaanderen.be/data/put/2018-007293\nhttps://www.dov.vlaanderen.be/data/put/2017-002866\nhttps://www.dov.vlaanderen.be/data/put/2018-007287\nhttps://www.dov.vlaanderen.be/data/put/2019-020544\nhttps://www.dov.vlaanderen.be/data/put/2018-007286\nhttps://www.dov.vlaanderen.be/data/put/2017-002867\nhttps://www.dov.vlaanderen.be/data/put/2018-007313\nhttps://www.dov.vlaanderen.be/data/filter/1999-011031\nhttps://www.dov.vlaanderen.be/data/filter/1999-011006\nhttps://www.dov.vlaanderen.be/data/filter/1999-011015\nhttps://www.dov.vlaanderen.be/data/filter/1999-011029\nhttps://www.dov.vlaanderen.be/data/filter/1999-011013\nhttps://www.dov.vlaanderen.be/data/filter/1995-061303\nhttps://www.dov.vlaanderen.be/data/filter/2007-011023\nhttps://www.dov.vlaanderen.be/data/filter/1999-000605\nhttps://www.dov.vlaanderen.be/data/filter/1999-000606\nhttps://www.dov.vlaanderen.be/data/filter/1999-011004\nhttps://www.dov.vlaanderen.be/data/filter/1999-011010\nhttps://www.dov.vlaanderen.be/data/filter/2007-011024\nhttps://www.dov.vlaanderen.be/data/filter/1999-011012\nhttps://www.dov.vlaanderen.be/data/filter/1999-011011\nhttps://www.dov.vlaanderen.be/data/filter/1999-011007\nhttps://www.dov.vlaanderen.be/data/filter/1999-011030\nhttps://www.dov.vlaanderen.be/data/filter/1999-000607\nhttps://www.dov.vlaanderen.be/data/filter/1999-011005\nhttps://www.dov.vlaanderen.be/data/filter/2009-011026\nhttps://www.dov.vlaanderen.be/data/filter/1999-011009\nhttps://www.dov.vlaanderen.be/data/filter/1999-011032\nhttps://www.dov.vlaanderen.be/data/filter/1991-062098\nhttps://www.dov.vlaanderen.be/data/filter/2007-011019\nhttps://www.dov.vlaanderen.be/data/filter/1999-011008\nhttps://www.dov.vlaanderen.be/data/filter/2007-011018\nhttps://www.dov.vlaanderen.be/data/filter/1999-011014\n"
+     ]
+    }
+   ],
+   "source": [
+    "for pkey_grondwaterlocatie in set(df.pkey_grondwaterlocatie):\n",
+    "    print(pkey_grondwaterlocatie)\n",
+    "\n",
+    "for pkey_filter in set(df.pkey_filter):\n",
+    "    print(pkey_filter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get groundwater screens with specific properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next to querying groundwater screens based on their geographic location within a bounding box, we can also search for groundwater screens matching a specific set of properties. For this we can build a query using a combination of the 'GrondwaterFilter' fields and operators provided by the WFS protocol.\n",
+    "\n",
+    "A list of possible operators can be found below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['PropertyIsBetween',\n",
-       " 'PropertyIsEqualTo',\n",
-       " 'PropertyIsGreaterThan',\n",
-       " 'PropertyIsGreaterThanOrEqualTo',\n",
-       " 'PropertyIsLessThan',\n",
-       " 'PropertyIsLessThanOrEqualTo',\n",
-       " 'PropertyIsLike',\n",
-       " 'PropertyIsNotEqualTo',\n",
-       " 'PropertyIsNull',\n",
-       " 'SortProperty']"
+       "['PropertyIsBetween',\n 'PropertyIsEqualTo',\n 'PropertyIsGreaterThan',\n 'PropertyIsGreaterThanOrEqualTo',\n 'PropertyIsLessThan',\n 'PropertyIsLessThanOrEqualTo',\n 'PropertyIsLike',\n 'PropertyIsNotEqualTo',\n 'PropertyIsNull',\n 'SortProperty']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -629,14 +818,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[000/002] cc\n"
+      "[000/002] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     },
     {
@@ -668,8 +878,8 @@
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
        "      <th>gemeente</th>\n",
-       "      <th>meetnet_code</th>\n",
        "      <th>...</th>\n",
        "      <th>regime</th>\n",
        "      <th>diepte_onderkant_filter</th>\n",
@@ -694,8 +904,8 @@
        "      <td>224798.0</td>\n",
        "      <td>157819.0</td>\n",
        "      <td>130.8</td>\n",
+       "      <td>130.8</td>\n",
        "      <td>Herstappe</td>\n",
-       "      <td>7</td>\n",
        "      <td>...</td>\n",
        "      <td>freatisch</td>\n",
        "      <td>45.0</td>\n",
@@ -718,8 +928,8 @@
        "      <td>224843.0</td>\n",
        "      <td>157842.0</td>\n",
        "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "      <td>Herstappe</td>\n",
-       "      <td>7</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>NaN</td>\n",
@@ -734,43 +944,112 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2 rows × 22 columns</p>\n",
+       "<p>2 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                         pkey_filter  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/1993...   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/1900...   \n",
-       "\n",
-       "                              pkey_grondwaterlocatie     gw_id filternummer  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/put/2019-02...  7-001016            1   \n",
-       "1  https://www.dov.vlaanderen.be/data/put/2019-05...   7-97027            1   \n",
-       "\n",
-       "   filtertype         x         y  start_grondwaterlocatie_mtaw   gemeente  \\\n",
-       "0  pompfilter  224798.0  157819.0                         130.8  Herstappe   \n",
-       "1  pompfilter  224843.0  157842.0                          -1.0  Herstappe   \n",
-       "\n",
-       "   meetnet_code       ...           regime diepte_onderkant_filter  \\\n",
-       "0             7       ...        freatisch                    45.0   \n",
-       "1             7       ...         onbekend                     NaN   \n",
-       "\n",
-       "  lengte_filter  datum  tijdstip  peil_mtaw  betrouwbaarheid  methode  \\\n",
-       "0           5.0    NaN       NaN        NaN              NaN      NaN   \n",
-       "1           NaN    NaN       NaN        NaN              NaN      NaN   \n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
        "\n",
-       "   filterstatus  filtertoestand  \n",
-       "0           NaN             NaN  \n",
-       "1           NaN             NaN  \n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
        "\n",
-       "[2 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+       "    .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>pkey_filter</th>\n",
+       "      <th>pkey_grondwaterlocatie</th>\n",
+       "      <th>gw_id</th>\n",
+       "      <th>filternummer</th>\n",
+       "      <th>filtertype</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
+       "      <th>gemeente</th>\n",
+       "      <th>...</th>\n",
+       "      <th>regime</th>\n",
+       "      <th>diepte_onderkant_filter</th>\n",
+       "      <th>lengte_filter</th>\n",
+       "      <th>datum</th>\n",
+       "      <th>tijdstip</th>\n",
+       "      <th>peil_mtaw</th>\n",
+       "      <th>betrouwbaarheid</th>\n",
+       "      <th>methode</th>\n",
+       "      <th>filterstatus</th>\n",
+       "      <th>filtertoestand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1993...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2019-02...</td>\n",
+       "      <td>7-001016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>pompfilter</td>\n",
+       "      <td>224798.0</td>\n",
+       "      <td>157819.0</td>\n",
+       "      <td>130.8</td>\n",
+       "      <td>130.8</td>\n",
+       "      <td>Herstappe</td>\n",
+       "      <td>...</td>\n",
+       "      <td>freatisch</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1900...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2019-05...</td>\n",
+       "      <td>7-97027</td>\n",
+       "      <td>1</td>\n",
+       "      <td>pompfilter</td>\n",
+       "      <td>224843.0</td>\n",
+       "      <td>157842.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>Herstappe</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 23 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "from owslib.fes import PropertyIsEqualTo\n",
     "\n",
     "query = PropertyIsEqualTo(\n",
@@ -790,15 +1069,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "https://www.dov.vlaanderen.be/data/filter/1900-050992\n",
-      "https://www.dov.vlaanderen.be/data/filter/1993-065801\n"
+      "https://www.dov.vlaanderen.be/data/filter/1900-050992\nhttps://www.dov.vlaanderen.be/data/filter/1993-065801\n"
      ]
     }
    ],
@@ -816,7 +1094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -849,37 +1127,37 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1900...</td>\n",
-       "      <td>110238.18</td>\n",
-       "      <td>205717.638</td>\n",
-       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>98347.0</td>\n",
+       "      <td>191821.0</td>\n",
+       "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>98347.00</td>\n",
-       "      <td>191821.000</td>\n",
+       "      <td>98514.0</td>\n",
+       "      <td>191518.0</td>\n",
        "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>98514.00</td>\n",
-       "      <td>191518.000</td>\n",
+       "      <td>98630.0</td>\n",
+       "      <td>191301.0</td>\n",
        "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>98630.00</td>\n",
-       "      <td>191301.000</td>\n",
+       "      <td>99017.0</td>\n",
+       "      <td>191447.0</td>\n",
        "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>99017.00</td>\n",
-       "      <td>191447.000</td>\n",
+       "      <td>99104.0</td>\n",
+       "      <td>191412.0</td>\n",
        "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -887,57 +1165,6 @@
        "</div>"
       ],
       "text/plain": [
-       "                                         pkey_filter          x           y  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/1900...  110238.18  205717.638   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/1999...   98347.00  191821.000   \n",
-       "2  https://www.dov.vlaanderen.be/data/filter/1999...   98514.00  191518.000   \n",
-       "3  https://www.dov.vlaanderen.be/data/filter/1999...   98630.00  191301.000   \n",
-       "4  https://www.dov.vlaanderen.be/data/filter/1999...   99017.00  191447.000   \n",
-       "\n",
-       "                                             meetnet  \n",
-       "0                         meetnet 7 - winningsputten  \n",
-       "1  meetnet 9 - peilputten INBO en natuurorganisaties  \n",
-       "2  meetnet 9 - peilputten INBO en natuurorganisaties  \n",
-       "3  meetnet 9 - peilputten INBO en natuurorganisaties  \n",
-       "4  meetnet 9 - peilputten INBO en natuurorganisaties  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query = PropertyIsEqualTo(propertyname='gemeente',\n",
-    "                          literal='Gent')\n",
-    "\n",
-    "df = gwfilter.search(query=query,\n",
-    "                     return_fields=('pkey_filter', 'x', 'y', 'meetnet'))\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the 'meetnet' and 'meetnet_code' for groundwater screens in Boortmeerbeek"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[000/050] cccccccccccccccccccccccccccccccccccccccccccccccccc\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
        "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
@@ -957,72 +1184,63 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pkey_filter</th>\n",
-       "      <th>meetnet_code</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
        "      <th>meetnet</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1981...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>98347.0</td>\n",
+       "      <td>191821.0</td>\n",
+       "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/2018...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>98514.0</td>\n",
+       "      <td>191518.0</td>\n",
+       "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1976...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>98630.0</td>\n",
+       "      <td>191301.0</td>\n",
+       "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1980...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>99017.0</td>\n",
+       "      <td>191447.0</td>\n",
+       "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1980...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>99104.0</td>\n",
+       "      <td>191412.0</td>\n",
+       "      <td>meetnet 9 - peilputten INBO en natuurorganisaties</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                                         pkey_filter  meetnet_code  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/1981...             7   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/2018...             7   \n",
-       "2  https://www.dov.vlaanderen.be/data/filter/1976...             7   \n",
-       "3  https://www.dov.vlaanderen.be/data/filter/1980...             7   \n",
-       "4  https://www.dov.vlaanderen.be/data/filter/1980...             7   \n",
-       "\n",
-       "                      meetnet  \n",
-       "0  meetnet 7 - winningsputten  \n",
-       "1  meetnet 7 - winningsputten  \n",
-       "2  meetnet 7 - winningsputten  \n",
-       "3  meetnet 7 - winningsputten  \n",
-       "4  meetnet 7 - winningsputten  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "query = PropertyIsEqualTo(propertyname='gemeente',\n",
-    "                          literal='Boortmeerbeek')\n",
+    "                          literal='Gent')\n",
     "\n",
     "df = gwfilter.search(query=query,\n",
-    "                   return_fields=('pkey_filter', 'meetnet', 'meetnet_code'))\n",
+    "                     return_fields=('pkey_filter', 'x', 'y', 'meetnet'))\n",
     "df.head()"
    ]
   },
@@ -1030,104 +1248,902 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Get all details of groundwaterscreens of 'meetnet 9' within the given bounding box"
+    "### Get the 'meetnet' and 'meetnet_code' for groundwater screens in Boortmeerbeek"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[000/016] cccccccccccccccc\n"
+      "[000/050] "
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pkey_filter</th>\n",
-       "      <th>pkey_grondwaterlocatie</th>\n",
-       "      <th>gw_id</th>\n",
-       "      <th>filternummer</th>\n",
-       "      <th>filtertype</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>start_grondwaterlocatie_mtaw</th>\n",
-       "      <th>gemeente</th>\n",
-       "      <th>meetnet_code</th>\n",
-       "      <th>...</th>\n",
-       "      <th>regime</th>\n",
-       "      <th>diepte_onderkant_filter</th>\n",
-       "      <th>lengte_filter</th>\n",
-       "      <th>datum</th>\n",
-       "      <th>tijdstip</th>\n",
-       "      <th>peil_mtaw</th>\n",
-       "      <th>betrouwbaarheid</th>\n",
-       "      <th>methode</th>\n",
-       "      <th>filterstatus</th>\n",
-       "      <th>filtertoestand</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
-       "      <td>WVSP009</td>\n",
-       "      <td>1</td>\n",
-       "      <td>peilfilter</td>\n",
-       "      <td>89720.046875</td>\n",
-       "      <td>165712.140625</td>\n",
-       "      <td>11.84</td>\n",
-       "      <td>Avelgem</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>5.78</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1999-01-12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.20</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>peillint</td>\n",
-       "      <td>in rust</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
-       "      <td>WVSP009</td>\n",
-       "      <td>1</td>\n",
-       "      <td>peilfilter</td>\n",
-       "      <td>89720.046875</td>\n",
-       "      <td>165712.140625</td>\n",
-       "      <td>11.84</td>\n",
-       "      <td>Avelgem</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>onbekend</td>\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
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+    {
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+     "name": "stdout",
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+      "c"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
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+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pkey_filter</th>\n",
+       "      <th>meetnet</th>\n",
+       "      <th>meetnet_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>meetnet 7 - winningsputten</td>\n",
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+       "      <th>3</th>\n",
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+       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>7</td>\n",
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+       "<div>\n",
+       "<style scoped>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pkey_filter</th>\n",
+       "      <th>meetnet</th>\n",
+       "      <th>meetnet_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1976...</td>\n",
+       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1980...</td>\n",
+       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1980...</td>\n",
+       "      <td>meetnet 7 - winningsputten</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = PropertyIsEqualTo(propertyname='gemeente',\n",
+    "                          literal='Boortmeerbeek')\n",
+    "\n",
+    "df = gwfilter.search(query=query,\n",
+    "                   return_fields=('pkey_filter', 'meetnet', 'meetnet_code'))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get all details of groundwaterscreens of 'meetnet 9' within the given bounding box"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[000/016] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
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+      "c"
+     ]
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+     ]
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+    {
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+    {
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+    {
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+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pkey_filter</th>\n",
+       "      <th>pkey_grondwaterlocatie</th>\n",
+       "      <th>gw_id</th>\n",
+       "      <th>filternummer</th>\n",
+       "      <th>filtertype</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
+       "      <th>gemeente</th>\n",
+       "      <th>...</th>\n",
+       "      <th>regime</th>\n",
+       "      <th>diepte_onderkant_filter</th>\n",
+       "      <th>lengte_filter</th>\n",
+       "      <th>datum</th>\n",
+       "      <th>tijdstip</th>\n",
+       "      <th>peil_mtaw</th>\n",
+       "      <th>betrouwbaarheid</th>\n",
+       "      <th>methode</th>\n",
+       "      <th>filterstatus</th>\n",
+       "      <th>filtertoestand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-01-12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.20</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-01-21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.26</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-02-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.31</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-02-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.29</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
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+       "      <td>11.84</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>12.18</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
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+       "    }\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>pkey_filter</th>\n",
+       "      <th>pkey_grondwaterlocatie</th>\n",
+       "      <th>gw_id</th>\n",
+       "      <th>filternummer</th>\n",
+       "      <th>filtertype</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
+       "      <th>gemeente</th>\n",
+       "      <th>...</th>\n",
+       "      <th>regime</th>\n",
+       "      <th>diepte_onderkant_filter</th>\n",
+       "      <th>lengte_filter</th>\n",
+       "      <th>datum</th>\n",
+       "      <th>tijdstip</th>\n",
+       "      <th>peil_mtaw</th>\n",
+       "      <th>betrouwbaarheid</th>\n",
+       "      <th>methode</th>\n",
+       "      <th>filterstatus</th>\n",
+       "      <th>filtertoestand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-01-12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.20</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
        "      <td>5.78</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1999-01-21</td>\n",
@@ -1140,161 +2156,351 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
-       "      <td>WVSP009</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-02-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.31</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-02-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.29</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
+       "      <td>WVSP009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>89720.046875</td>\n",
+       "      <td>165712.140625</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>11.84</td>\n",
+       "      <td>Avelgem</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>5.78</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1999-02-12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.18</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>in rust</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from owslib.fes import PropertyIsLike\n",
+    "\n",
+    "query = PropertyIsLike(propertyname='meetnet',\n",
+    "                       literal='meetnet 9 %')\n",
+    "df = gwfilter.search(query=query,\n",
+    "                     location=Within(Box(87676, 163442, 91194, 168043)))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get groundwater screens based on a combination of specific properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get all groundwater screens in Hamme that have a value for length_filter and either belong to the primary meetnet of VMM or that have a depth bottom screen less than 3 meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pkey_filter</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>gw_id</th>\n",
+       "      <th>filternummer</th>\n",
+       "      <th>diepte_onderkant_filter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2001...</td>\n",
+       "      <td>130078.000000</td>\n",
+       "      <td>196561.000000</td>\n",
+       "      <td>MORP002</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>131763.200000</td>\n",
+       "      <td>198674.500000</td>\n",
+       "      <td>802/21/3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>131837.656250</td>\n",
+       "      <td>197054.203125</td>\n",
+       "      <td>810/21/1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>133865.921875</td>\n",
+       "      <td>195656.328125</td>\n",
+       "      <td>813/21/2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2000...</td>\n",
+       "      <td>130190.000000</td>\n",
+       "      <td>196378.000000</td>\n",
+       "      <td>MORP001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<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>pkey_filter</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>gw_id</th>\n",
+       "      <th>filternummer</th>\n",
+       "      <th>diepte_onderkant_filter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2001...</td>\n",
+       "      <td>130078.000000</td>\n",
+       "      <td>196561.000000</td>\n",
+       "      <td>MORP002</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>131763.200000</td>\n",
+       "      <td>198674.500000</td>\n",
+       "      <td>802/21/3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>131837.656250</td>\n",
+       "      <td>197054.203125</td>\n",
+       "      <td>810/21/1</td>\n",
        "      <td>1</td>\n",
-       "      <td>peilfilter</td>\n",
-       "      <td>89720.046875</td>\n",
-       "      <td>165712.140625</td>\n",
-       "      <td>11.84</td>\n",
-       "      <td>Avelgem</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>5.78</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1999-02-01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.31</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>peillint</td>\n",
-       "      <td>in rust</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
-       "      <td>WVSP009</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>133865.921875</td>\n",
+       "      <td>195656.328125</td>\n",
+       "      <td>813/21/2</td>\n",
        "      <td>1</td>\n",
-       "      <td>peilfilter</td>\n",
-       "      <td>89720.046875</td>\n",
-       "      <td>165712.140625</td>\n",
-       "      <td>11.84</td>\n",
-       "      <td>Avelgem</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>5.78</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1999-02-06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.29</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>peillint</td>\n",
-       "      <td>in rust</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/1999...</td>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/put/2017-00...</td>\n",
-       "      <td>WVSP009</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2000...</td>\n",
+       "      <td>130190.000000</td>\n",
+       "      <td>196378.000000</td>\n",
+       "      <td>MORP001</td>\n",
        "      <td>1</td>\n",
-       "      <td>peilfilter</td>\n",
-       "      <td>89720.046875</td>\n",
-       "      <td>165712.140625</td>\n",
-       "      <td>11.84</td>\n",
-       "      <td>Avelgem</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>5.78</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1999-02-12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.18</td>\n",
-       "      <td>onbekend</td>\n",
-       "      <td>peillint</td>\n",
-       "      <td>in rust</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.59</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                                         pkey_filter  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "2  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "3  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "4  https://www.dov.vlaanderen.be/data/filter/1999...   \n",
-       "\n",
-       "                              pkey_grondwaterlocatie    gw_id filternummer  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/put/2017-00...  WVSP009            1   \n",
-       "1  https://www.dov.vlaanderen.be/data/put/2017-00...  WVSP009            1   \n",
-       "2  https://www.dov.vlaanderen.be/data/put/2017-00...  WVSP009            1   \n",
-       "3  https://www.dov.vlaanderen.be/data/put/2017-00...  WVSP009            1   \n",
-       "4  https://www.dov.vlaanderen.be/data/put/2017-00...  WVSP009            1   \n",
-       "\n",
-       "   filtertype             x              y  start_grondwaterlocatie_mtaw  \\\n",
-       "0  peilfilter  89720.046875  165712.140625                         11.84   \n",
-       "1  peilfilter  89720.046875  165712.140625                         11.84   \n",
-       "2  peilfilter  89720.046875  165712.140625                         11.84   \n",
-       "3  peilfilter  89720.046875  165712.140625                         11.84   \n",
-       "4  peilfilter  89720.046875  165712.140625                         11.84   \n",
-       "\n",
-       "  gemeente  meetnet_code      ...          regime  diepte_onderkant_filter  \\\n",
-       "0  Avelgem             9      ...        onbekend                     5.78   \n",
-       "1  Avelgem             9      ...        onbekend                     5.78   \n",
-       "2  Avelgem             9      ...        onbekend                     5.78   \n",
-       "3  Avelgem             9      ...        onbekend                     5.78   \n",
-       "4  Avelgem             9      ...        onbekend                     5.78   \n",
-       "\n",
-       "  lengte_filter       datum  tijdstip peil_mtaw  betrouwbaarheid   methode  \\\n",
-       "0           1.0  1999-01-12       NaN     12.20         onbekend  peillint   \n",
-       "1           1.0  1999-01-21       NaN     12.26         onbekend  peillint   \n",
-       "2           1.0  1999-02-01       NaN     12.31         onbekend  peillint   \n",
-       "3           1.0  1999-02-06       NaN     12.29         onbekend  peillint   \n",
-       "4           1.0  1999-02-12       NaN     12.18         onbekend  peillint   \n",
-       "\n",
-       "  filterstatus filtertoestand  \n",
-       "0      in rust            1.0  \n",
-       "1      in rust            1.0  \n",
-       "2      in rust            1.0  \n",
-       "3      in rust            1.0  \n",
-       "4      in rust            1.0  \n",
-       "\n",
-       "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from owslib.fes import PropertyIsLike\n",
+    "from owslib.fes import Or, Not, PropertyIsNull, PropertyIsLessThanOrEqualTo, And\n",
     "\n",
-    "query = PropertyIsLike(propertyname='meetnet',\n",
-    "                       literal='meetnet 9 %')\n",
-    "df = gwfilter.search(query=query,\n",
-    "                     location=Within(Box(87676, 163442, 91194, 168043)))\n",
-    "df.head()"
+    "query = And([PropertyIsEqualTo(propertyname='gemeente',\n",
+    "                               literal='Hamme'),\n",
+    "             Not([PropertyIsNull(propertyname='lengte_filter')]),\n",
+    "             Or([PropertyIsLike(propertyname='meetnet',\n",
+    "                                literal='meetnet 1%'),\n",
+    "                 PropertyIsLessThanOrEqualTo(\n",
+    "                     propertyname='diepte_onderkant_filter',\n",
+    "                     literal='3')])])\n",
+    "df_hamme = gwfilter.search(query=query,\n",
+    "                     return_fields=('pkey_filter', 'x', 'y', 'gw_id', 'filternummer', 'diepte_onderkant_filter'))\n",
+    "df_hamme.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Get groundwater screens based on a combination of specific properties"
+    "## Working with water head time series "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Get all groundwater screens in Hamme that have a value for length_filter and either belong to the primary meetnet of VMM or that have a depth bottom screen less than 3 meter."
+    "For further analysis and visualisation of the time series data, we can use the data analysis library [pandas](https://pandas.pydata.org/) and visualisation library [matplotlib](https://matplotlib.org/). "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query the data of a specific filter using its `pkey`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[000/001] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1317,148 +2523,155 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pkey_filter</th>\n",
+       "      <th>pkey_grondwaterlocatie</th>\n",
        "      <th>gw_id</th>\n",
        "      <th>filternummer</th>\n",
+       "      <th>filtertype</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
+       "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
+       "      <th>gemeente</th>\n",
+       "      <th>...</th>\n",
+       "      <th>regime</th>\n",
        "      <th>diepte_onderkant_filter</th>\n",
+       "      <th>lengte_filter</th>\n",
+       "      <th>datum</th>\n",
+       "      <th>tijdstip</th>\n",
+       "      <th>peil_mtaw</th>\n",
+       "      <th>betrouwbaarheid</th>\n",
+       "      <th>methode</th>\n",
+       "      <th>filterstatus</th>\n",
+       "      <th>filtertoestand</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/2001...</td>\n",
-       "      <td>MORP002</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>ZWAP205</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>218953.0</td>\n",
+       "      <td>198767.0</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>Houthalen-Helchteren</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2003-10-18</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58.26</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
        "      <td>1</td>\n",
-       "      <td>130078.000000</td>\n",
-       "      <td>196561.000000</td>\n",
-       "      <td>1.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
-       "      <td>802/21/3</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>ZWAP205</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>218953.0</td>\n",
+       "      <td>198767.0</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>Houthalen-Helchteren</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2003-11-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58.30</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
        "      <td>1</td>\n",
-       "      <td>131763.200000</td>\n",
-       "      <td>198674.500000</td>\n",
-       "      <td>2.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
-       "      <td>810/21/1</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>ZWAP205</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>218953.0</td>\n",
+       "      <td>198767.0</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>Houthalen-Helchteren</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2003-11-17</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58.31</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
        "      <td>1</td>\n",
-       "      <td>131837.656250</td>\n",
-       "      <td>197054.203125</td>\n",
-       "      <td>2.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
-       "      <td>813/21/2</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>ZWAP205</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>218953.0</td>\n",
+       "      <td>198767.0</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>Houthalen-Helchteren</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2003-11-23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58.31</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
        "      <td>1</td>\n",
-       "      <td>133865.921875</td>\n",
-       "      <td>195656.328125</td>\n",
-       "      <td>2.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>https://www.dov.vlaanderen.be/data/filter/2000...</td>\n",
-       "      <td>MORP001</td>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/filter/2003...</td>\n",
+       "      <td>https://www.dov.vlaanderen.be/data/put/2018-00...</td>\n",
+       "      <td>ZWAP205</td>\n",
+       "      <td>1</td>\n",
+       "      <td>peilfilter</td>\n",
+       "      <td>218953.0</td>\n",
+       "      <td>198767.0</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
+       "      <td>Houthalen-Helchteren</td>\n",
+       "      <td>...</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2003-12-14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58.30</td>\n",
+       "      <td>onbekend</td>\n",
+       "      <td>peillint</td>\n",
+       "      <td>onbekend</td>\n",
        "      <td>1</td>\n",
-       "      <td>130190.000000</td>\n",
-       "      <td>196378.000000</td>\n",
-       "      <td>1.59</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                         pkey_filter     gw_id filternummer  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/2001...   MORP002            1   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/2003...  802/21/3            1   \n",
-       "2  https://www.dov.vlaanderen.be/data/filter/2003...  810/21/1            1   \n",
-       "3  https://www.dov.vlaanderen.be/data/filter/2003...  813/21/2            1   \n",
-       "4  https://www.dov.vlaanderen.be/data/filter/2000...   MORP001            1   \n",
-       "\n",
-       "               x              y  diepte_onderkant_filter  \n",
-       "0  130078.000000  196561.000000                     1.91  \n",
-       "1  131763.200000  198674.500000                     2.50  \n",
-       "2  131837.656250  197054.203125                     2.50  \n",
-       "3  133865.921875  195656.328125                     2.50  \n",
-       "4  130190.000000  196378.000000                     1.59  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from owslib.fes import Or, Not, PropertyIsNull, PropertyIsLessThanOrEqualTo, And\n",
-    "\n",
-    "query = And([PropertyIsEqualTo(propertyname='gemeente',\n",
-    "                               literal='Hamme'),\n",
-    "             Not([PropertyIsNull(propertyname='lengte_filter')]),\n",
-    "             Or([PropertyIsLike(propertyname='meetnet',\n",
-    "                                literal='meetnet 1%'),\n",
-    "                 PropertyIsLessThanOrEqualTo(\n",
-    "                     propertyname='diepte_onderkant_filter',\n",
-    "                     literal='3')])])\n",
-    "df_hamme = gwfilter.search(query=query,\n",
-    "                     return_fields=('pkey_filter', 'x', 'y', 'gw_id', 'filternummer', 'diepte_onderkant_filter'))\n",
-    "df_hamme.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Working with water head time series "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For further analysis and visualisation of the time series data, we can use the data analysis library [pandas](https://pandas.pydata.org/) and visualisation library [matplotlib](https://matplotlib.org/). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Query the data of a specific filter using its `pkey`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[000/001] c\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
        "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
@@ -1485,8 +2698,8 @@
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>start_grondwaterlocatie_mtaw</th>\n",
+       "      <th>mv_mtaw</th>\n",
        "      <th>gemeente</th>\n",
-       "      <th>meetnet_code</th>\n",
        "      <th>...</th>\n",
        "      <th>regime</th>\n",
        "      <th>diepte_onderkant_filter</th>\n",
@@ -1511,8 +2724,8 @@
        "      <td>218953.0</td>\n",
        "      <td>198767.0</td>\n",
        "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
        "      <td>Houthalen-Helchteren</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>0.68</td>\n",
@@ -1535,8 +2748,8 @@
        "      <td>218953.0</td>\n",
        "      <td>198767.0</td>\n",
        "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
        "      <td>Houthalen-Helchteren</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>0.68</td>\n",
@@ -1559,8 +2772,8 @@
        "      <td>218953.0</td>\n",
        "      <td>198767.0</td>\n",
        "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
        "      <td>Houthalen-Helchteren</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>0.68</td>\n",
@@ -1583,8 +2796,8 @@
        "      <td>218953.0</td>\n",
        "      <td>198767.0</td>\n",
        "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
        "      <td>Houthalen-Helchteren</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>0.68</td>\n",
@@ -1607,8 +2820,8 @@
        "      <td>218953.0</td>\n",
        "      <td>198767.0</td>\n",
        "      <td>58.44</td>\n",
+       "      <td>58.44</td>\n",
        "      <td>Houthalen-Helchteren</td>\n",
-       "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>onbekend</td>\n",
        "      <td>0.68</td>\n",
@@ -1623,56 +2836,11 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
+       "<p>5 rows × 23 columns</p>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                                         pkey_filter  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/filter/2003...   \n",
-       "1  https://www.dov.vlaanderen.be/data/filter/2003...   \n",
-       "2  https://www.dov.vlaanderen.be/data/filter/2003...   \n",
-       "3  https://www.dov.vlaanderen.be/data/filter/2003...   \n",
-       "4  https://www.dov.vlaanderen.be/data/filter/2003...   \n",
-       "\n",
-       "                              pkey_grondwaterlocatie    gw_id filternummer  \\\n",
-       "0  https://www.dov.vlaanderen.be/data/put/2018-00...  ZWAP205            1   \n",
-       "1  https://www.dov.vlaanderen.be/data/put/2018-00...  ZWAP205            1   \n",
-       "2  https://www.dov.vlaanderen.be/data/put/2018-00...  ZWAP205            1   \n",
-       "3  https://www.dov.vlaanderen.be/data/put/2018-00...  ZWAP205            1   \n",
-       "4  https://www.dov.vlaanderen.be/data/put/2018-00...  ZWAP205            1   \n",
-       "\n",
-       "   filtertype         x         y  start_grondwaterlocatie_mtaw  \\\n",
-       "0  peilfilter  218953.0  198767.0                         58.44   \n",
-       "1  peilfilter  218953.0  198767.0                         58.44   \n",
-       "2  peilfilter  218953.0  198767.0                         58.44   \n",
-       "3  peilfilter  218953.0  198767.0                         58.44   \n",
-       "4  peilfilter  218953.0  198767.0                         58.44   \n",
-       "\n",
-       "               gemeente  meetnet_code      ...          regime  \\\n",
-       "0  Houthalen-Helchteren             9      ...        onbekend   \n",
-       "1  Houthalen-Helchteren             9      ...        onbekend   \n",
-       "2  Houthalen-Helchteren             9      ...        onbekend   \n",
-       "3  Houthalen-Helchteren             9      ...        onbekend   \n",
-       "4  Houthalen-Helchteren             9      ...        onbekend   \n",
-       "\n",
-       "   diepte_onderkant_filter lengte_filter       datum  tijdstip peil_mtaw  \\\n",
-       "0                     0.68           0.3  2003-10-18       NaN     58.26   \n",
-       "1                     0.68           0.3  2003-11-01       NaN     58.30   \n",
-       "2                     0.68           0.3  2003-11-17       NaN     58.31   \n",
-       "3                     0.68           0.3  2003-11-23       NaN     58.31   \n",
-       "4                     0.68           0.3  2003-12-14       NaN     58.30   \n",
-       "\n",
-       "   betrouwbaarheid   methode filterstatus filtertoestand  \n",
-       "0         onbekend  peillint     onbekend              1  \n",
-       "1         onbekend  peillint     onbekend              1  \n",
-       "2         onbekend  peillint     onbekend              1  \n",
-       "3         onbekend  peillint     onbekend              1  \n",
-       "4         onbekend  peillint     onbekend              1  \n",
-       "\n",
-       "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1695,10 +2863,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
     "df['datum'] = pd.to_datetime(df['datum'])\n",
@@ -1721,24 +2887,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x12992860>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9d50df748>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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iOVp6FGQjL0ttslBEn2HlOtr0DLf7g3h03haM6VeIHxzXD+W11tPBJYsT8kA7\nHUZm7x3tpMetSINZIoHaiDLKR+skooFR29R4KaAoDf/WZAJizE4otEYe33nyA6XiEbLQpKgkI4pZ\nKSqfmaCpPepxb+0IJG088RpjI71xiMZOgPS0g22+QNxzE83JG7+NIg33YU1mmCydeyg/J3bICCao\nCRBlyTCrnySEJPyctvmDqmMTLa9uB7yBEDQhIRD9ZDs0zEb9QSoNcqvyca0BSbnnyG7j8sUlu1DV\n0I4//XACPB5im1zAysc4MRbZeR7N3prRscGdYy9DGswSCdQPtptmub5ASFnGHd4zX/WethADAAy/\nN7rke964PvhYoxu9QKPP1XLB35aiyUC2YQWmTWS8u6oSj326FW/9bBqK86JeQJHxGw+WZYAP8lte\nbk46waebmvCH+crrL7fW4MutNaJDDPnrwu340/xt+NOl43EXl0Js2L3zDI4KFxTg0fPU3vhaVG+s\nlweWTWIoKBpa/Rj/h891v3djVeyS6z8WqwOssjPCHtEMD1G1a3SfQpVEpXTOXNx6ulpaYpYVFYdw\n/alDVNuu/VeZzt6xFOWJPcxa+mpkNtsONqM4P3qsmUH9znfW4q2yStvz1C7aWoPSOXPx3b2zTBnu\nora+t3ofbp01PEYvDwB9uprL4z2gKFaKxPhs4wHceNpQU59jFe1cSy9gNLq/+gQ0tPmxI5Jr/Dcf\nbMCVJwxSfdY7Kysx79ZTMKqPOW00o6apA88t2okzRvfCCUOKAdjneeWv4ctLd+H+jzZh0x/OQq7G\ney7q39PB4m01WLu3HuMHdLN8LKUUg+/+BL84fThuP+MY0xlcWN72mqZ2y9+ZTqTBLJFAYzC7yGLm\nU3htN6FP5dEay2ZgOuCCnAw0tQcwe2wfAMANGsPm7Run4fpXy3QzToiYt/4AAGDuuipMH9ED/y3b\niyml3RVjL5GlXz6LhFM8E8m9+69lFY6lnooWsQD2xNE9Mi3m2cf2xqcbD8S8//yVk9AlK2wweyMG\n81s/m4bCLhkYUpKPVzQFJVjw3sMXjsW6ynp8W35I0TcbUaDxVut5Sq85qRQ1TR3Yc6gVJwwpxowR\nPQGEdbcXT+qPhjY/Hvz+GOxvaEdJfqwRfdW0UhTmZGJtZT1e/SZc+fG0Y3oAAC4/fqApD+1bZVFD\nrkumV8lx/fI1U+Iey/NO5Lk5rHludtY0Y1CxuKAMj55RLcqA8/vzR+PcyPMbj/tmj8b0Y3pizd56\nPPOlOpf022V7U2cwW3zmiwXXd0WF8UR52c46ywbznxdsQ7s/iLvPiQbYWjWYf3PeaGyqasSoPgWY\nNrRYWMznha/DQcp1zT7kFrkt5BrvAAAgAElEQVTHHPtiS3VCBjObzzz9xXbcfsYxlq/vzhpzmmen\ncM8VkkgchH+w3aTI0OtuzOpfE6V3YQ5OGZ6Pv10xUfj+lNIi3Dx9KP74yRbTn8mW233BkPK7KOUW\n4RKRMNt0Gp760QTc+uaahI71eAAEAZ+BsTy0R15KBwO+TLIvaFzwgwV+3n3uSJw4rDimouNZx/ZW\nXmd4CDoAHNMrH91yxR7dxjY/sjI8uGzqQFw2dSCAsHfPqEQzEFsaW1QqGwB+d/6xwu0ZXg+euGS8\n8jer+KbF6yG4aFJ/zBrdSzGYCSHoUZANSq0LMnjDaWqptapkk0uL0D03K8ZgDoWoqawOeve7SEIz\nfkA39NQ5J1pyMr2YNbqX0COYyqA/qwZVFxMFiLTdt9XufNvBJry5Yg+umlaqxG0A1g3miQO74dqT\nByt/3zR9KJ5btFPdNpvHmh4F2agxUdUvHsnmsGaX1eq9k53h7kzH7m6dRJIm3Jo3Xa/jslrFzSoh\nSuNqlc0MXjwsn6svQJWO1B8MqVLEWcUu/ak2mMgKLI+vT5AmjZFqzzM7hyFK4QsYfxdbFcjO8Aoz\nVfCYMRIa2vwxwVtmioF0aDTM2oIodqM1KrMzPOjwhywP6nxJb6vPQPj42IlBiJoLntTbI0tQICWR\ney6ZQMxESEfBCqtG6UNzNyM/OyNGamRVT6z9bXlZsfcK4VaG7MAuzXOigbza1Rqr+nppMEsknQA2\nKIQ9nul3Mbd0BFA6Zy4+3aBeIv9Ap5jEqj3i1Gh2YcYbmm3RWGDL9O+uqlTa7wvSqHeUsyPeWVmJ\nV5bG5lTWok1LlSh5CaaM+nDNPiWI0Oic2Rl5/uEa9T3R0hFQvMS7altw2T+XGx7PyjJnZ3jiBtmx\na2w0gH6y/kBMGWMz2Vg6NBOMlhSXws7SDMZZGR50BEN4eJ5xQRS+cMfhFp/KEE1EvrWvPjbtIAWN\nK6UBwoGpIkST20QKd4i83BV1rXhqwXbMtyEHcW1zB67713dKejyrBlUi9qCVQxZvq8FX22rwi9OH\no7tGI2/1Wmt/mygtHRtrfvHmasPKl2axq59JdJKkPcqq3a19Rt2Gu1snkaQJvn/4yYmlaf/+d1eF\nNZI3vrZStf0hnepmT3y2NeVtqowzgA8siq+51IMVGPEFgkonzy8H//rttfj9R5vifg7LT2uWMf0K\nMaJXAS6Z1F/Z9t8bTsCx/aIaR+2gM6W0u+7nmZVx3HnWCOH2orwsvHbt8TEBnUZov5MvDqOXy5mn\nKWJ0ZWd6VPe610Pw6k+nqvZ9/brj8bPThqCYMx6evuw4wyAxQJ0FZdqQYswa1TNmn1hJRtRgmDak\nGP+5/gSVhjRZtNc1y+uBLxASZuvgKeM0si8siQZHnhrRQdtBMETx2w83xN3vo0hVPC28ofujKQNw\n5QmDLMtFAGD2OLHm+c8LtuH6V80HZOrx2cYDWLC5WpHGTB1cbOq4N64/HgAwYUB39OvWBRMHmtfX\nmrXZgiGKh+ZuxsCiXFw5bVDM+9pVlNLiXMzRuT9/Mm0Qjhuo7jd+OCUsV5o6OHpd2ARgzd563YBe\nEWeO7oWbp8fqyjNsKPMO2FP0BUjt6oQTSA2zRAKA71ZzNUtnhYJUWnZjtfysHTq1eMRblhtckqf6\nW5RB4InPtsYEEfH4gzTpylz9unXBCUOK8eSl4+NGlv/vlpMVT9HjnP6VZ+V9s7DtYLNSaS+ZZeMn\nLhmPiyf1V6WyE52n+befFrON/y3fn9AXXbIyVKXGFeIMSnecNQKPCyZYWV4PMrI8+MMFx+K3H27E\nZVMHxBiBx/QqwN3nqKt+nT++L84f39fwXPMe5kcuGotnvwxrNweX5CnFDDo0HjV+wvTkpePRt1sX\nTBtqzqBKhOxMr6nnjje0mXH9+/NH4+qTBusdYhmVlh9h76SZ9I2j+xTicKtP9TsGFOXilhnDEmpH\nTgISEyswiRnzfhs5RD/++ck47+klGN2nECcOLQEQvhZL58wEoJ9FQnvezNpsb5XtxdaDTXjuiolK\nlhgerYd50R0zAACPf7Y1Rjp3/wVjYo7vkuXF6D6FuuOJURpKLf+4ajIA4FmNJlovHaRVEk0VqT3X\nVvvOdEh0kkF6mCUSqB9U7UOfjkmy1fKzTSnWewLxAz/MLP/FG/P9wRAyIp18IkvIQNiLYXYl0syy\nal52BnIyo12jVjpgBTZ+Jev38XiIrl423iAjGkS9HqKcd/a+1UmbEbzB7CFE8Xzx94PWw6wqbJOG\nTDXZXo+pa8vf52x/uw1L7bOm9+xp5WIZXoJgiKqkNXYZTamABSszGZbZtGNW0N45Zr6huSOAJz/f\nhiml3XH2mN7CffT0wVZkJV4PUV1bPrezHSXI7ZI0JKxh1pxtq5Ibt3uk3ftkSSRphC+XrH3oE32I\nW32BGL2pHryx8r6Obpmnqd0FBrMNASb+YEgxjozK4hqxv6HdVo1wptej8jAlY0jaVezASwiyM8Xd\ndbz7U7RMywfXsEHWzsBE/jsJiRrQ/PnQGsz8fCkjDUZfVobHsod51e6wBEjvWiTKnkOtKs+onsFS\nrclTSwhBiKqNLTufBS21zYmtbO2saca35XVgt9ir3+xGuz+I/60RS0ySoUqjETdjtD3/1U7UNnfg\n3tmjdT37eufVylPjIeF878ybzH8i628Xb6vBAx/Hl6OJsMtgTjRLRrLOJulhlkg6AXe/t155neyy\nEuN3H27ErW+uURnjevDFFRZZLJ5x+fEDVV6l8f27WjpeL0dpvE6Tr/rWWyd91TljjPPA+gIhZSDS\nplyyAqtUd9nUgeiem4nCnAzMGtVLFZleECew76bpQ5V9eA+zHQZzInbzTzgd5SWTB+hmtBjQ3VhL\nfsrwkphtvMF8fKQwwyWT+8fsZ8SQHlFJzjUnlarey1QZzESZFPE2h1aSwa8wpMro61GQjasi5zU7\nw2PKq8e3ZdP+cJBpbZNPb/e4sOAvXof7h4834YzRvZS/9Qxm7ebS4txIZpTo79ggKEyTCKJ+5IJn\njAsf6XH6k1/hh/9YrjJeb/j3St1J8vcn9MXASF7qGwVaXSOenL/N0v5V9W34x+JyXDChLyYY5B7W\nuyfviUiWWKyDVtLHw/Jlv7AkNqCZSdKuemmFEuNhFpbT/NdnimMlrJKohznZzzn7WLF33y1IDbNE\nwiF6vBNNXXagMewNMpMuK146nVeumYLpI3oqlZSAcNDd4jvDOroJA7rhznfCVeY+/L+TcdNrKzFv\nQ2xRCi3De+Zj3q2nYNnO2pjKc/GWSzO9nriVz0b3LYzZh9ce+oIhUynI4sGyNDx84Vg8fOFYZWA2\nW8YbAO46eyTuOjscxMMvuevlBzYDG2QTybxy/wVjVFpIlllAS262sTxgSEm+cg0embcFf/9qp8oT\n1a9bl4Qq2H3xq+m676klGVGPMX8eYjzMOqXT7eS7e2cpr0UeZrPa4WTSGW64/ywAwK/eWqsKWuUD\nP83qWbt1yUSIqiUZya5q3zd7FB6cuxmTBhXFFEQRZfiwAn+N+aDiXQ+fi5tfX6X0WX/50XEAxHp/\nPfSCMOOtwDzx2VZQhLX+RuitqF1/6pCYipV6eEh40lPLYlC4jzSavO3847nwekiMblt0fsr/eC6G\n3BMeI+Lllx/VpxCb98dmGrIrz78VLfSFx/XD6L7WCsykG+lhlkg0JJsaJxHieTFZrl9+MOdToWmN\nTrP5LNlgItJkJpu83gzhoL/kuyGt95UQYslY1sKfv+Q8zAkfKvgsPQ1lnOO4RuRHjOtUp07kr6mH\nEOX+NNYwc8fbFO1vRFaGR2C0x+4neg7s0Alrn1n+W8zKYwgJa2L5e9SOCWiq4H8XP6kihCQtX9KT\nXhg9H+sq6/He6n249uTB6B9npcaOCrBs4siaxH+i0STJyldbOY1ZOvdK4nmY1X9bkXmluraAHUiD\nWSLRkmTy9cMt4ah1lu+2trkDDW1+Q09zPP2uyKbkvXDavLdmtWysXxQFlKXDYA6GaIxxVJeATrIl\nTgEOq9jlYU5GkhGDzmdY0bOzSVa8/MvJwhfSIATCSREfcEcpxa7aqAfdjklUPLK8nqinL4Lonhd5\nKK3mIBehXeLn09eZDYD1eghCIYr6tmjlwHTovxOFz+6zvVrd59nxjIj6ar1ejFKKB+duRnFeljBF\nWyrIjFxz1kyPCd06YG2lzMq+eveKHX1/XXMHKurMVzfV5nJ3I1KSIZFoSNbDfNwD8zFzZE9lOfP2\nt6IlgkVLaBv2NcTXrHFt6JabifpWP07mtKlaA8Oswcw6xu6CssenC3LnpgLe8A8EQ5j04ALLnyFa\nVkyGZD3MWd6wPtZOLa6eB06bu9sINjGqa0lcg2sGrYeZTYpClKIgOwNNHQH4gxTBEIXXQ/Deqn24\n69313DEpbR6AcOCedqIlMhREcwsrebP1WLZTnTP7rbJK5bXZFIteTzjoj9f/j+tnLYYhnbyyrEJ5\nrT3Vo/oU4uN1+xP+bEqBd1fFBkzr+Ts+33QQK3YdwoPfH4OCnExL35VocF1mhgfwBZUJEf9I+4NU\nNWkS0bMgG9UWUoqWFucZvj9tSLEwxibRjEW8VMlqP76jWiw5cxPSYJZINMQG/VmfbX+xpdr0vnod\nBa+x5AeXhbefhm/K6zBrVC9uX60kQ+0Be/WnU/Heqkp8EIlKLy3ORUVdq2Ig9O6ag4/+72Sc/8wS\n5Zh7zx1t+jckA+/lMJtm6riB3bCa03/aXSGK99L4giHT2lZGYZcM1Db7bMuSAURT1Fnhpasnq/4+\naVhsAGAq4FcNPIQoExB/kGLJnJl4asF2vLR0F3yBELpkebGuMnotn/rRhKTkNGbJ8sZ6icOGglew\nTc3kBIqCaGH5qEVoDff87AxVLm8GIbHPzA+nDEi6bU5w3SmDMaQkDycmeI9SUHy3K9bgFOnNfYEQ\nHv5kM4b3zMePEjhf390zK/5OAthKoEiqEAiGhGPBAi5H+4JfnYZWCxUxxw/ohrdvnIbBJXmobe7A\n2X/5WvX+bWccgx9M7IfTn/xKtd2uLBk879w4Df4gBSFAr8IcFORk4LXlu/GXBdsT+i4nkAazRMIh\neuBTnRpSTxunCuThOv3i/GycN66val/m0WOGidaAHFCUi26cF5l5VPiOcawmKr6LQbS3nfDBNGYd\nG3lZ6q4r1fKRjkDIUu7dwi6ZYYPZY58kIxHjW1uNsWuuNU9aovAaX4KohMEXCKFrl0ylUmBHIIgu\nWV7VMzAojlfMtjZqAh/31beJJRmpVa8I0RrBelfeS0hMgFY6JhupIDvDi3PGGmfVMYJSIDMj9reL\n+u/Xlu9GRV0rXr5mSkISlkSfIybJYH07/0z7Q1TYxwzjVjMKczJRaNEbPiUyuRMF8nk9BEN7xK6W\nJGwwG7wnmmT+7NShisGcDglgsrhX7CSROIRQB2fSak4kulgvIwD/lfE+lnkuWKejDfrL8BBhIGAq\nCgdYhZ8MmF0K1HqNUt3ZWi0qwFKHscmAHUF2iRhCWqmOdqKRKjI0hUvYvcfOI1sBYfpwftJkR35v\nM/DPA3st0pGm6hnRK0QDxN7PevrWsCTD+WfYDYSouQDi+lYfnlq4HacML8F0G8ubm0Ep0iToTwJB\na5Nyy1h4rOxKKxcPftKaru9MBmkwSxxl8/5GDLl7rmG6otI5c+OWPLZKTVMHht3zCVbtORzz2aLH\n1uyzfPqfvoq/E4ArX/xW+V2issVa4kXlM0kG63S0HuYML1EFBjJtbXFerHY53fB6zbV71Wms9CYq\nWru6JD/b9nbx/OaDDZb2Z5MgX9C+QJb8OOnjhO3QBFSy656X4tUDVeESDzhJRvjCsUwOlYfDz72X\nl3CkaVTKEhRvEU147UqxZfT9WsxW/mtqD9iexYdN9gp0Sjhf+OxSnPjwQnGZdgFbEyxIZBVKxRlC\ntP3r01/sQGO7H/ecOyrt3njWvg/WVOH3/9uo+v5nF+3Ek5/HHwsSxcoK1dfbay0HuwPWA+T5GI9E\ny3GnE2kwSxzlPyv2IESBBZsOpvV7vymvQyBE8c/F5THviZ55s14cI10iz9fba2OOefqy43BmpHjB\nc1dMVN6/ZFJ/nDDEWDOp9axoNcxeD1EN0IVdMvH4xePwyjVTVfu9cs0UXHH8QDzLfb/dfPbLU1V/\nt3PphN5bVal6T88Y4D3M4/t3xfs3n2hfAwV8aLIi2XNXTMSzV0xUJieHW8LZC+wYl2eMCAdhag2t\noT30JQza7CkA8PjF4/Dqtccn3yAD+FWTsIc5fD/6Ix5lFmTKMseoPMzpiPiDBQ9zZNvPTjOXa9cs\nbNLyvfF9Y96L9TDHGhMf/d/JSiEVhlazngiXTB6A35w3GjfPEGeOWLWnHlUN7apiT0a8XbY3ZtvL\n10xRXts12aWIvwpTUduCV7+pwA8nD9At2GTE81dOwvzbTo2/ow788/jKsooYp+92jYaZL26TLEZn\n5u8/nogLj+uHrl2ico9EPL56R7xxvX5/c9/sUQl/X7qRGmaJo7BZb7qXFdkAbTa9Vjrad/yQIpwv\nGDx/cfrwuANBvLRymR6Pap9QiOKSybHBLtNH9MT0EanNjjGidwEGFHXB3kNh72K7P3wNBhR1ifmd\n/mAIXk+sN5S/HP83czgGFBnnUE0Xw3rmY3ivAnyxpRrf7jpkq1SEEIKrTyzFu5pJxTG9CuAhBEFK\nUV6jnrCJ5D6i6243XpXBHC0lzYKdencNV4ZUJBnc/qkqWqJFbTCH7zHR9WID+cjeBbZ+P5N6TB/R\nA/9bq56Qafsb0W3Ut1tOTDaRmSN7xe5oEa+H4NqTB8fdz+xl0soMThhSpEz+AOC8cYnrllWYeNQe\n/XQLMr0e3H7mMQl9xVlJVqKzml981ujkr6eW7rmZONzqV207e0wfnD2mD8bf/7myzRcICSfcRugN\nkycO1Q/kvGhifzw4d7PUMEskZkn3s8I6e58gWlnUlHTY83qaRjMZIGIKl2g6Oq9X7WF2WveYyXnE\n2yP5eANBGjMI63WifPONtKDphgWvsQkZM4rsMgEzvSQm5RilLNdx7Lc4lZOX9xiLNczhv9nqgkez\nfzoQSTJEXi72rNit/2Zz9URzoHsISZs3XoTZe0sbPKz9vXb9hnjVF1fsOoR5Gw7gxtOGomdBji3f\naRWtARpPfmTns8A+y+gs8ZKKVOdqZ7BnTxrMEokOwRDF7/+3Eav3hHNAVh5uxT8Xl2MnV/73ha/L\nsXhbjfJ3IpoqLXvqWvHcop2KJ1MUfCH6nhMeXohWnzqtU6svgNI5c7EjkoB/bhI5RAFxtT1AvKwe\nbx/m0WNkeIiqqpPTnRPfXmY0BSI5eXn0lun4wbFLlnPdmFZbzgYlpsll7bdLK5nh9aDNH1TJWD7d\neADbDjbDKxh90+Wt1aK9jtp7Wxv053FAkpElkGQEQxQ/feU7/GXBNuU99qzkZ9ttMId/e45AT/7X\nhdtRebg1ZjsPIamv2GhEpsnrtKlKLRvRGtB2zd2/qzhsEPNA8dDcTehdmIPrT7FXWmOFTM0zGu/6\n2Xl12SNmdL759xKqcJrAtdQGrLsZUyMNIaSCELKeELKGEFIW2TaBELKcbSOETDU4vpAQso8Q8oxd\nDZd0bnbXteCVZRVKcY+Xl1bgoU824+LnlgEId3APzt2Mq15aoRyz9WDywSNXv7wCj366BbWRanLa\nWbRe4EF9qx/Pf6XWO//uw40AgFl/WgwAuOWNVUm1Tc8wNuNh1i71aQ25DI/HVR7mhy8aq7xui0gy\nAsEQJgxQa/bMeJiP7ZuaQg33zR6lSukkQuvZZ57Vm04birH9umJ2EmmyRDDDTlSo5bCgGEk6SkyL\nIDoeZgab0LFqf/y9mTaDmcvDnMUFJX6xpVqVG5Y9KyUF9gaW/u3yiTh5WAl6Cj73y601uO5fZcrf\npwqyORDBeU0nZq/T3PVqR8J1GoP1pyeXJvT9v5g5LGabXrf2v7VVWFvZgDvOGpG2dJki7jpnpOrv\nXJ22DCoOS8wSnWdfOrk/Hr5wrPA9I8fTcz+epLxOpMKpyMtfqBM8ysj0EswY0QP/vCp5/X2qsfK0\nzaCUTqCUsl/1GID7KaUTAPw28rceDwAwlz5AclSgVzeeVd4SPdJ2zEC1yf+1CeT9QarqdEvyo1kk\ntN/fqvMbzKCtFHayQbJ+UeS3Fq2BLPIwqzTMDk/mJw7sjr//OBxYqHiYgzSm3aIVACBsxJw8rAQV\nj8xOWSqm604Zgj/+QDzoMDI1BgtzIA0oysVHPz8ZRZEsJHaZgJMHhYM/RYUPjhMECGk9Wk7gIbFB\nqMzQ6/CzwjzR32N3ERo9hEF/IolWZJPd3voTh5XgteuO182A08KtaImMDg+xqeS6AXpGF5D4dRrQ\nXR1v0L97YvEHv5xlXof86KdbMKZfIX5wXL+EvssuRmh08HyQHWP6iB5cv5HYBX7s4vG4bOpA1Tb2\nWUZdP1891i5Jxgs/mWL4PiEEL18zFTNGpqeybDIk0zNRACzMtCsAYRg5IWQSgF4APhe9Lzk6YYFe\nWlj3kGoPqEcn6E+7DMUbmVpvXTISEVHaN919TUgytHpCbRUzj8ZgdsPyFzsHzJPhD4ViPET6kozU\nGwtA/MlKjCZRp1F2tZX3hGoRFcDRK4qTTkQeZjbJYdeevx+zBRX4UoFIw+wXrDAxr1m6tNUMvimi\npyAdKdGMJglmch6LsGsFQXRv6/Vq+xvace+5ox1/HrQ5xkX3lIcQZaJr6yW2+FlWc88DYg+/C7og\n2zB7x1MAnxNCVhJCbohs+yWAxwkhewE8AeBu7UGEEA+AJwHcYUdjJUcO7Tre2Y5ACP9evhutvtj3\n9YxsAFi95zDaBMdoYR0Q00Zv1OjrfMEQnudSzfEG0crdh1X78l6+FkHZWiNiDGaDwcfMwKg17ETe\nHzdJMoCoUV8e0a0HNN59QD+Ha7qaH89I0k5mUi0nYNeZnbODje3Kex1JrHikEkJiVzzYeWOSDN5g\nTpeMRGQwG1X6c7SAnkOGiNG1MHOdGtr8MdtSWZhGr184Y3QvTBtanLLvNYu2mxd1+x4SrViYkkmR\nyb7TH7AnrVxnrTwpwqzBfBKldCKAcwDcQgg5FcBNAG6jlA4AcBuAFwXH3QzgE0ppbCJGDkLIDREd\ndFlNTY3RrpIjBCPj9jcfbMCv3lobs/3Od2K3AUBdcwd+8Owy3PbfNXG/92BjWLv86cYDuvswfTMA\n7DkUDbz5enstNlZFC2vw3uhfmvhuAGhqDw8gWs/Ngs2xeagvtLB8GBP0xxkDrAABb9xdMMHZpUkg\naqQ0tYcnG4EQjTHkr3nlO+GxFOnx+PUqNI6m105U8nQCw+waNNg5+01EP3/8Hxcq7y3YXK28dnrp\nmYdweZgZHg9BltejeJj5654uXa466C/cPqEkI/K/hxCU5GerZFp2oJexgTfeRdpQDyGYbVdKNh2M\nJqZmVr4e+HiT6m9CgJxIkK6dOYYZejEod2u0w04RE/Qn7BeIcm7t7OFYdpLLTxgYZ88wiXmYY2+Y\nI8heNpeHmVJaFfm/mhDyPoCpAH4C4NbILm8DeEFw6DQApxBCbgaQDyCLENJMKZ2j+fx/APgHAEye\nPNl515ck5bBUYnqsq6yP2bazRlwUhOmhRcfYTV1zNLCKXxZfX9kg2j2GNl8QBTmZpiQRT1wyHo9e\nPM7U5xrlYV5+9+kx2356Uqmpz00lIi+4SJsrglKalo64d9cc/GjKAHy0Vly4hJ33XoXZWPir6bZn\nUtBixkgpyc/Gk5eMx2Mm751Usf2hcxSDS2QEZ2d4FA1zIBROKbj5gbPTlgqP93RmKFlNYo0E3pj/\n9p7TbW9H19xMbH3wbFAaDub8wbPLYr5XrwjaD47rjyyvN+mAYz2MDGYzHualO8IFmrIyPFh610xk\nZ3qUyck7N55o+0qXqIz5wKJcDOlhHLybLrSSENEZ9BCARJ4BO1cRsjI82PHQOfB6SEwAO88r10zB\n1S9/Z0tWKiD9UqZUErd3J4TkAfBQSpsir88E8AeENcunAVgEYCaA7dpjKaVXcJ9zNYDJWmNZcnQS\nTz5h5SFjy+DpluXyHma2hBYPRbNpoq0eD4HHpI9BKwXgDRTm9eSNajcsk4mMKD2pjhZK7fW+GFGU\nl6UbMZ6hpESyP+2YCDOBVl6PtXsnVfD3m9BgzvQoE+dQiCLT64nxRKcS/hFg/Y3Iw8ycux5P6vIe\ns9/NX1++JXoe5vCxqZtgGHVTZjTMrI/M9nrQQ5MNJBX3qMgpqpeJwg2IxjlCwGmY7T0/Ziaj7LlN\nZDwVSjKsf4xrMdPD9wLwfuTCZQB4g1L6KSGkGcBThJAMAO0AbgAAQshkADdSSq9LUZslRwDxDCMr\n4xLrdETehVTCL1mZrYjU7g+CUopQiq37eBpmNyBqT7xURr5ACF4PAQVNm+ciO8OLQIgiKMgTzaQ1\nekvBdmPmPnOBPD0G0cCfneFVPMyic5vyNnFDOftqo8Il6Wgdb4TyHj7RNVXy6qawPUZeRjPZexSn\nQpoubbqeQ7sQdWEeEi0y5YRfg31nIt5/cdDfkWMyxzWYKaXlAMYLti8BMEmwvQxAjLFMKX0FwCuJ\nNFJy5KGXVo5R1dAes03vuVMqGMV5wJ/8fKu5xnGM6FWgm/95HSfD0JYk1uOMPy8Wbu/b1d7KUyJj\n1MwAl05E8oKOOFKd7z2zBH265iAUSt9gws6lLxCKyeHKvHupygWt1xYAGPf7z4T7uNBeVhhckqe8\nzs7wYGNVA0rnzMXAotyUBoOJKOwSHf6USbfG4CqdM1d5nY7m8Y9EbbMPi7ZWY/qInsJrytrMUpCl\ngp4GGn6jCc6P/vENlpcfihp+trdMzFtllfF3chG6WTIifXWqzltpcS4q6sSFcVib7nxnHRbfOcPS\n527YFytN5J+zzs6R80sknYrmdmtZJQDgupMHC7ezfjueLvjpL3aY/q7xA7ph5oiemDq4CJf9c7np\n46xQWpyLJy+dgCXba4AYT10AACAASURBVHHtKeLfliiipW0nixyIEEsyxJUXmYdyy4EmbDnQhKE9\n8tK2fG9kMLO/n43klE41vIe5UfMM3Xr6cDy1cLsrPcwA8PaN01RFOrIyPNgSyYKy51CrMCdtKhlU\nHDXeo2km9U9eOjxl2u+Yv+lg2GAWXFRmsE4a1B2/O380ZqYgj+1pXMGU8f27KoWmAGNJxvLyQwCi\nHuZUS8DOG9cHH+tUWnXb81CSn60ElgvPCkl9Sfv3bj4Jew+JDWZ2X+3Red+I5eV1yusbTh2Ckb0L\nVM9ZZ8ddI6jkqEE72MejICdDqE8Doh61eCoHKx7W6cf0wK2zhisZJhiioKBEGd23EJMGdcets4bb\nrn8VGaNmZSPpQizJiPUwi6K12/2hlBUs0aIU2QjGto3ScLR/YU56jD2jSU80o4fLLIQIU0qLVINn\ntub6OZGvddaoXpHvZh5m/XOXjubpeW3jGX3XnDQ45YbJGaN7qf62koY51XONKaVFqf0CGyktjhZq\nEckePCSaJcNsELRVivKyMH6A/VlK+N9z8aT+uHBif9u/w0ncNYJKjhqaLBrMWV6PbuUh9ozG01xZ\nMbBY3ljtAOaL5Ka0I4I4lR4rkdzBbRpmkYdYpGEWDRrt/iC6ZKU3/ViHXrGdNEoJjCY97C23edT0\nyNHcj+nWMANRI539b1TdLB3XWddgTvk3x0fbf1q5Xqn2znfW4hii+RmBcYGiVJNMUSv+0C5pcmik\nE3eNoJKjhuaO2IT2RmR6Pfj38t0onTMX1Y1qfTOLIG9qD+C6f5Up5ZT3HmrFZf9YjvmbDuLm11ei\nwIIXlxmcWuPjgY834cGPN9ky80+lgSCqaNUpPMwCo9QvMKLrWnxo6UhPoQ7m1fUFQ2jzBXHFC8ux\nozosJRBlL0glRveMN+Lyc4NxZQbt9XcicwszAqPZTgw8zGnRMOt5mN13VT2E4MM1+/DbDzfE3TfV\np87o3kn3M2oFsYc5GkzshMEszBRjElU+9Ux3jTd2cOT9IkmnwKqHmU/b9vKyCt39Fmw+iP2RgME/\nL9iGb8rrcP2rZfhk/QGcPUY/yf8956oT2+dmhY3rkb0LVNsJAT5cW6XqyLTpkq443lxieLsN2Mcv\nHofXrzte9323eZhFxoEoP7deAv33V++zvU0isjkN8/JddVi6ow5/+Hiz8r4bnFvPXTFR8TC7oYqj\nGbSTIye8hL87fzRuOHUIzjw2LDfwGxjM6dAw6wU+uqCSPb6rOKT620MIbn1zDV79ZrdDLQIevWgs\n3rj+eEdWJ+xAT5LBxgZfiiQZRiQjO+R/To/8bP0dOynuGkElRw2WDWbOuIzxAun0KVaC3Mb264bv\nT+ir/F1aEtaZ8Z7aikdm46xje6OlI6AymHOzvIph3aswG78+c4Sp77TbYL5k8gCcNKxE930zRS/S\njXZyIfIw+3RSzaVryY9JRzoCISU/qtvSV50ztg+XLcbhxphkTD91ZhEn0k8V52fjnnNHKX1F0EiS\nkYb2iFaGAHesGmglGdpqpUakavXgh1MG4sShJZ1WksF7cy+bGukLSWeWZISP7Zab6Ypc/3bjvhFU\nclTASkSbJcvAYNZ7vLUaWaPqghleoixpA1EPs5a8LC9afUGV1rbDH1IGXH+QwmsyuDDdWSvcJskA\nYtsk8ibreZjTla6Iz5LhVZZKmZY9LU0wBWubG5fvRWhXPJzM18oyPojyMDPS0T49I9QN11Q74dYz\n7kWk+tR1VuOMv9/YteeD/vScBelqk1WYwXwk5V7mcd8IKjkqsNPDrDeWaAfkN77do/v5GR6iGqz0\nvJesat6f528DAORkenCgsV3pZPzBqBcyHunOi+zGZUvWJvZ/hyA/91vf7RUem67MFErQXyCoXLMA\nZ8S7ZWxg96/zppU5sjT3v5PnkT2yojyyCmlonxUjNN1kxkxwHGqIgHTn8E4G/vlUGcyR56G+1a/0\nM057mK0W2GK7u+nesBNpMEscoanDqsEcfQK1GittUAfrZKx4cDM8HlxxQlQeMLAomvpn5siemD0u\nrH9muWLfjBhxLG/wxqpGAMBvZo82bZimWlM8cWA3XDVtUEq/I1nYIMEyJrQLPCrPLy7HAUEhm3vO\nHZXaxkXgNcza4DAXOP4UFK+Oi9pkhBs9zB+sqdLdJx1GQK7ORN1JXfo9545ESX4WLp08QLXdWpYM\nu1ul+fxOZMnwqwW8tKuqvg1AOA4nk1uxTDeTB3VXXn+yQZzbWo9hPfIBAD+fOdzWNrmFTnSbSY4U\nOgJB+AIh3HHWCFQ8MhsXHtfPcP9nr5io8TAbfz6btesZzCX54cpY395zulJhz+shGNe/GyoemY2K\nR2arClS8dPUU/O3ycGEKo9yVEwZ0w6VTBgiXVV+7NjYYL9USifduPgl/uGBMSr8jWZg3nukjRR5m\nINbT8uszj8GMFBRqEMFLMjI0kgxAXWI5HVQ8MhtzzhkZs93byTzM2vvfyRUQM9+djmX/DK8HJw4t\njtnu5MTshlOHouy+MzCmb6Fqu5UJTqqfkc4kAVB5mLl+hH+dqeRhTr+Hma/u2GoxE1FuZNzU5uw+\nUpAGsyTtMDmGtiiIHllej8obpQ240g4mrJPR8+Ay7yBv2GbYII9gfbZoWVU0HrtRU5xu2HnnjVIz\ntPrSk1IOUAf9sfYqHmaHzFPRpKyzaZi197+TJo+ZALZ0tU9kvLvhkmrbZWWCk2p71shgdsO54+Fl\nDrz8gZ+QOSnJUGHxujFnlR3jqRuRI7ZJqurb0K7j/XICSil217U43YyEiDGY4zxbXg8x9DBr+0M2\nU9crnaw81Nw6npWIbz2MPkHkneoshk0qYdeVdbB6z5g2ECWdBjNvzCsllPlJmwNjg2iyxQyYZIJ2\n0ol2Qut3MPOIGeMvXV5Mvq+obgqXUHZDLuFkPOypPnOdycPMP558GkP+kXbSw8xj9awym8SobHpn\n5sj8VSnge88swZOfb3W6GQpfbq3G9CcW6daDdzMsQ0ZBdlgPHLesKVFrmGM9zOrBhGmc9SQZZ47u\nHX4/06MMAmYf8OKInINRwuWaNBpQ9kX0aTzSXo5eVxa0I6r0B8R6nndUN6e2YRwsYr2DG7wCCWbJ\nmDYkdrk9EUQenHjn0G1osy5UHo59RtKF2Kurvrjpssn4Szt/00EA7sjDrMVKm1ItZ+lMQWa8lIwf\ny3ijf1jPsBZ40iBnS35bnYj88+tdAI7MKn+ANJhN0REIorbZhy+31jjdFIU9da2gFNhd1/kM5maN\nh/lHUwbE7POrM46J/kHVHjVtIITWaGHGjJ7k4eELx+Lbe05HTqZXGRTNToh7FuTgrGOj+qwbTxui\nvDbqWmqbO7Dyvln48JaTlG1uHATTDdMuBykFIWpj7+bpQ5XXTnpaWMUqXl+typJh8nO+u3cWXr5m\nii1tEq2IDI0Msp0FrYfZyQmkKMuCtj1pM5hF1p9L+oqv75yhvHbTCpmbs4to+eXpw3HJpP4A1Lpl\n3jgd2bsQS+fMxE9PKk1381Qkes/zMUBHEtJgNkFDW9gjuqO6WRit7wSHWnwAgION7miPFRoVgzns\nYRZ5H44b2F31N++NiudBi7cknZXhQS8usMEqfNt4w8Woc/GQcJGEbrnRVGidpSJbKmEGsy8QzmXd\nweXK7tM1eo20uZiTqUZlFSUnajCkGFGJyB56FGTHFH9IFNGKSJ6F0u9uwE0afpHBpX0+0xXcKfR2\nu8Ri7tuti/LaSvflpIbZbXg8BJMimSjUGmb1fv26dXE8v3QnOq1pwT09lotpaI0W2Vi6o9bBlkQ5\n1Bo2mJnGrTOhSDIMgv60gwa/BN1hUIAEiHojzRikiXRIvNSD/wajz2IdOm8wuWMIdBYlO0YghCyv\nR7V6wBtUWklGII3pltj17vCHFMMlGvTnDCJJhl5KMrfitlLtWrRzonQ5Md0a9Aeo22bFiE+9waz/\nnktOnQo2HvAGsxuN/nRnAHI77u6xXALzMAMuMpg5DzOlFOc+9TWG3/sJLn5umWq/XbUtOPWxL7H3\nUCu+3FKNc5/62pHqQTxmsmTw7+VmeVXGk7Z8snYweWlpRXi7ibb07Rb2YlrxdvFGLx9YaNS1FEby\nN/NLVd26pKfwhpvpoqSTCyFLE6TZlTs/WklGj4JspAs2EVq157CwPU6Mc82CPObMS9o9t3PcV+ku\n3GOVGA9zmi60yHBy42pUrBQuhMF3z8Xr3+6O2TflaeU6kSQDiF7P8tpo4L4bC0tZueVbLNZW6Ix0\nrjU8h2AG88CiXCzZUQtKqeNLJXXNzMPcjsOtfmza34iS/CyU7T6M+lYfuuWGg9OW7qjFnkOt+Ka8\nDpuqGrFpfyMqD7diSA/n9I7MYM7nlpDfvelEZHoJCAh8wSDG9OuKB74/Br5ACFMHF2HehgPKvtoS\n11pPx+JtNeyNGC6d3F/1999/PAlLdtRakmjwHuZBxdECJ6Jb4slLxuN/a6tw8cTw9xbmZOI3543G\nrtpmXCrQbh9tMIPZFwyhiDuvvz9/NE7gAuTYJI+Q8FLlIxeNS2s7CQlfuxhJBoUjWTK2HmgSbn/m\n8uNURXfcjBs9zB4S9SxrDcJ02TMifbqbzOW7zh6JRz/dEmPEdwTCkqUHP94cc0yqJSVu9M4aoZ3w\nvnzNFHy81lqRELexpxMmILCK+3osF1IfkWTMHtcH1U0daY3Q1+NwK/MwdyiZMs4eE87+sIUbTNnA\nuvVAE3bWhNu918FodABo7vAjN8urVE0DgEmDumNc/24Y27+rEhl85QmDcO3Jg0EIUQ2u2tRjes4X\nUSd93SlDVH8X52fjggnGhVO08F5lDyEY268rALEXpVdhDv7106kqD8i1Jw/Gg98fq5owHK3kZEYr\n5/ETkUsmD1Bdc38wBEopKAUumthf5X1OB6N6F6qkQAEHC5cA+gbCeeP6Ylx//eI6bkKbJcNpZo7s\nid7cxDnokIfZK9Cnu8nBfNP0oRjZu0DVJkqpck+K4gtSHXLgQuesIdoYiBkjesJljwOA9ErfOgMu\nvETug3mYZ48Nl0de4gJZBi/JYOmYZo0KZ2/gvU9bDjQq28prwss/lYednQk2tQdMFy1hZKo0zNrS\n2GJEg4wdgzRv2Hk9UWNeNJ46mVu2M8DLW3gDmRC1TIZ5rwBnvElZGR5VG5hR4FQwVmfzqIlwU9Af\nEH6W+by4TskgRFIVt5kthBCVxpvS6PkSlXNO9bkUZTlxM0FB0LAbn+l0Bld3BtzVY7kUZjCP6lOI\n0uJcLNnujMH8wtflWF5eh1CI4nDE613d2KEshUwa1B3dcjMVI5lSqnibN1Q1oKohbFjvPRT1MC8v\nr8PzX+1M589AU3vAsnfVSMPMggi1iLroTBuWgVmaMSCcbD7byGDuJDlxnYLXdKsMZhCV4eAPUmXQ\ndcKblJXhgS8QDfrjxzsnxjmX2ZoJ4TZJhpcQlSFz1YsrHGmHKKDTTSncAGDz/kYs2HxQ+TtEjaeO\nIgPRTpyWSFpFaDC70E2+Zm+96X3dqLO3G3f1WC6loc2PgpwMeD0E00f0xNc7atGoY6SlilCI4rFP\nt+K15bvR0OZHMEQxsCgXvmAIG6oa0C03EwU5mRjRq0Axkvc3tKOpPYAhJXmob/Ur3jHew/zop1vw\n14Xb0/pbGtv9Sko5s/AG85TB6mTu2vZPGBBekk6dh5kL9CNEMZhFHoJThvdI+vvs5M8/HI9bZgyN\nv2Oa6MelqdIWmuEHQV8gpBipTgws2REPs1u4alqp8vp35492riFJ4DoPs5eo8mtbMRbs5Oczh6v+\nZlIknl/OUu/jNBTGBtPzV05K6fcbZslwoSEnSkv5i8h1/9vlE9PdnBjuPXcUAOA/K/aaPmbjvrCj\n7u5zRqakTW7AXT2WS2lo8yuayQsm9IUvEMKnXBBaOqht6YAvGEJFXYuSUm5k7wIAwMqKw+jfPWx4\njOpTiG0HmhAKUcXTzGt0i/OyFA3z7roWrN5Tj1Z/MK2dSiKSDN7Q1QbF+CJLgOMHdMPAolyURgLx\nRD4P2yUZGn21FrclcP/Bcf1xx1nu6dCMJBk8/mDIUQ9GNvMwa5rgVJP4LCE/7KTBo3qVOJ3CS4gr\nyor3KsxBLtdvUBrbl102dWC6m2VISGDUM/5vxrCYvPp248YME0bwFf4uPz58LXt3zUHFI7Mxe1wf\np5qlcGzfQsvHzF2/HwOKuuCGU4fE37mT4q4ey6XwBvOEAd0wqDgXH67Zl9Y2VNWHC5RU1LYq+uVR\nfcI39YHGdgzoHjYSR/QuQIsviMrDbYqn+XsT+iqfc9KwEuyLeJg/XFMFINwht/vT5z1r7gig0LKH\nmV+eF5fGJpH9/AZlizMzku9Y1ZIMonicO9uyoNswmszwqRCd0jBri6cwnJFkRL/UjdpHM7htCTrD\n4w6DGVA7BYyMUbdAqb4nNx23Z2frewOq/MsONkQHq7dbQ6sfS3fU4twxfTrdtbCCNJhNEE7TFq1K\nd8H4vli2sy6tVfaq6sNe4eaOALYdDBvCo/oUKO8zD/OIiNd5y4FGbNnfhH7dumBwSR5K8rPRp2sO\njumVj9pmH9p8QXzAGf0tvvTlUGxq91sP+tNkTBBBCJCV4VWWzkUPvd2SDA/nYT5yu4n0oJa6qN/z\ncR5mRzTMXk9M/nJKKcp2H055BgARGUeAwey2Vns9JOVaW7PwGYQOt/qxsapR9b7bzp3TRr0bjU4j\nQnyFP9ddTet65PmbDyIQojhnrPPe8VQiDWYT8B5mAPjehH6gFGmVZezjUsGt3B0uoDCyd3TZpD/z\nMPcKG8yb9zehrOIQxvQL73Pi0GJMLi3CgEiO1o/WVaG8pgWTIyU623zG1fPsJLEsGdFbVZvqhj3b\nBBpPIPfQ9yoML2HbsXRXnJ+lvA57mGOD/noX5mB4T+dyXXcmCnMyMGFAt5igPx6VhtkBAzE7w4uO\nQFBlFHyyPvz8f1Nel/b28F4cUd7ezgBfJn5Yz3zHl6LdZDBfc2Kp8vqKF5bH7uCySx6izgZ9dTZJ\nxsyRvZTXbmy61edg3vr96NetC8b375qiFrkDmQjWBA1tAZXBPKxnPob0yMOCzQfxE65jSyX76qMG\n8+o94WCU3l1z0C03E/WtfgwoCnuY87IzMLAoF2+V7UVVQzvuigjwn/rRBBBCsHL3IQDAX+ZvQ16W\nFz+cMgBluw+nzcMcCIbQ6gsiP9uaJIP3DBstm2ZneNARydPMdlt53ywU59tXGY6Xk3hI1DPK93vL\n7zndtu870ln3+7MAAL9+e62yjdmDFY/MxrB7PlFpmJ1wqLIsGTyHWtxRlt5t0gaz5GZFh59PfnGK\n44a/m4yun548GE/O3wYA2HbQ+bz/8YiXJSPVGE2i3TEFUjNtaDGG9czHjupmV0oYrMx9Gtv9+Hp7\nLa6aNsiVv8VOpIc5DpRSNLb50bVLlmr7GaN6YXl5nW5KM7upqm/DkJI8ZHgIdtW2IC/Li5xML3oV\nhBPtMw8zEA4G3FffhtwsL84YHZ7JshuZaZ2rGtpxwXH90DOSqL+lIz0eZlbhKBkPs3b2y7pqQghy\nMr1oZ5IMGt2eKjxcHubOujTuFrJVHuYoSkq3iL3qmIaZSyvHtknsISvD47jh7yaDOd4t7rZlfBrS\n9zCnw/HcGbteNkF0Y9utrBYs3HwQvmDoiJdjANLDHJc2fxC+YCimstjpo3rh+cXl+HBNFU4ZXqJ6\nr3fXHGRneOEPhhAMUVUmgESpamjDwOJchChFRV0riiKygJ6F2dh6sEmVnmtk7wJ8vukgzhzdS+XF\nAYCS/Gxl8L986kClal6rjR7mQy0+3YnEgYaw7juZwiXaZOosLzOB2sPMHvlU9kdeLq2cJDnUWTKi\nV81LCFr9QSUfuhMDjCit3JHuTTnacJPBHA+33XqhcCoPx+hM147BJv5udLRYkWR8sv4Aehfm4LgB\nnaPCaDJIgzkObJDWGswTB3ZDUV4W7vtgQ8wxM0f2xEtXT8F972/AhqoGzP3FKUm3Y9/hNqXkbUVd\nK4rywhKDAUW56FWYjTyuEMixkVLNFxwXW/LZ4yEYXJyHLllejOnXFRurGgDY52HeV9+GGU8silm+\n1sLrgM3AB/3xGuZ2fxBlEU33Mb0L0NQeiAb9MW1zCvsjvtKf3yX6x86KOhAmSlNHAG98uwdvfLsH\ngHMe5kCIqgaSO99Zl/Z2aGHa/M5KTqYnrRl6jHBaEsITz4PsnpaGCedhdu77DfsEl3bLzMh327UE\nzF/L5o4AvtpWg8unDnR8hSgdSIM5DnoGc4bXg39fO1VVhhoAFm6pxqcbDmBHdTPeX70PvmAILR0B\nlUFrlVZfAIdb/ejXrUtEy1uDokjAzO1nHKMKEAHCcpH/XH8CThhSFPthAJ778UQlP3BexAPd5rfH\nw/zuykr4AiE89IMx6KLjWc/J9Fou6KGnYW7lghV/PnMY/vT5NsVrzjzRGSkskODhgv5kVb/k6N01\nukpiNP450TGzSZGbipfMv+1UW7X5TrDkrpmob01vESg93DTgu9DpaEhYw+ycZcpfumtPHowXl+xy\nrC1mYfebm+47htm6DF9sqYYvEHI8YDddSIM5Dqwz5yO6Gcf27Ypj+6qjQo/pVYC56/bj5/9ZrWRr\n2HawKanE7SwHc99uOciLGLrMw1ySn40SzaDp8RBMG1qs+3lDekSzN+Rmhz/PDg9zKETxzspKnDi0\nGFccPyjpz+PR0zDzfU2m14PszOjSOcvHnErPUTjoL9w2vTy9EnPw3lIjuYPXoSwZgLsM5uG9CuLv\n5HJE/ZdTuMnDHA+3yYFClLrGw3zPuaM6hcHMVIbuupJhzF7Leev3o2dBNialuDCNW5DiyzjoeZj1\nOLZvIQYW5WLz/kYMjKRw26LxQluF5WDu1y0XpSV5AKxLGvRgHmY7NMzf7jqEPYdaccnk/kl/lha1\nhln8NHsJQU6GN+phjhjMqSzBG9Ywh42peDIUiTF8HmYjnDBsmIdZXuMjFycmYnrED/pzF0aFS9IB\nbzB3lnlPhoelI3Vfg4MmrmWrL4Avt1bj7DG9XeklTwXSYBbw2vLdmPHEIry/utKywUwIwTljewMA\nbjtjOHKzvDGyDSvc8sYq3P7WGgBAn645GBwxmIvy7DGYmWyC9zC3+gK48sVvsau2xdJnvb+6EgXZ\nGTj7WPuXZ3ijd/P+RkXvqvI2eyJZMiKlvgOhEAhJbUCIx0OUyn96BVUk5jAbPMmeyXSS7WWSjPTl\nK5ekF6/HPcOh27JgxCMYcrpwSfR8udEAFcFuNzc2l5/8BHTGtS+31KDdH8I5Y44OOQZg0mAmhFQQ\nQtYTQtYQQsoi2yYQQpazbYSQqYLjBhFCVkb22UgIudHuH2A3/mAIf1mwHbtqW/Dgx5sV725XgSRD\nj2tOHIybpw/F7LF9cUyvAmze3xj/IAHt/iDmrtuPXoU5uPG0oejfvQsGFuXijrNG4DybNEMeD0GX\nTK/Kw1xR24qvt9diVSSYzizlNS0Y06+roo+2E20KL7Y0zjubMyJ64hANe6H9QYrMFA2CzAj3EqLo\nq6UkIzn4kuM82gIw6/Y1pKM5KhQNs0sC1CT2k8KFKMvE9TC7zMhy3GA2uHYujflTPMypXAFNFD6t\nHEsFq+WTDftRkp+FqYPFsVJHIlau1AxK6QRK6eTI348BuJ9SOuH/27vzOMnq6u7jn1NLr9M9M8wO\nDIOAMuwDjKyyahD3LfoYI6jR4IIbxhiTxyVqjD5qjJpEDcYkPiZGg+JjxCj6MomKuGSIuIAgoMgy\ngzM9MDO9d1fVef6491bf7q7eqrvq3qr7fb9e8+rq6qqeX/+q6t5Tp87v/IC3hd/PtAc4L7zN2cCb\nzezwZY24wb5x+28YGBrntZcex/7hCf7x5nvJGazqWHy59+bVXbzp8u10FHKcsKWPO38zWNfHVXvC\nFmy/d/6jePOTtmNmmBlXX3LctL7Ly9XbmZ+2eG4szKKNLTGbdmhsctGZ+KWaeVCZrEQB89S85sMM\nMwRvNibLlWmlHCspH1uwoQzzypirJOPpp00/ZDToIZ1XZwoX/cnKSleGeaGfpytijm9dP1MzFgPO\n7JLRv8S2pUlKY1vS+Klssjz78RudKPOfd+zliSdtbsmWfvVaziPlQLQ382pg96wbuE+4e7QdVucy\n/7+m+MwP7uOINd287gmP4YknbeLAyCT93cW6a3SO39THgZFJ9g4ufVewau3y2u4Fbrk8PR2F6QFz\nWAO81HZPM7cQX0kzA9+olnT6AkCjKwxexyYrlMqVhnXIiOpocwYdedUwr4S5Thwz+5gn2yVDJRnt\nKk2L/lqlrCASbOqTnDT2Ml5I9AYjjQFzvMVnrZ7M3/rFXkYmyjw5A5uVxC32kXLg62F5xVXhda8H\n3m9m9wMfAP641h3NbKuZ/QS4H/g/7j4rsE6LeweGuenuAZ7/2K3kc8YbLzuenMGaZQSBx28O3lPU\nKsu4b/8In7/lASCoG37vV+/gbV/6Ge/72h2Ml8rV7bDjm5I0Qk9HnuHYxy7Rx85R4Axw++5DfPWn\ne+b9PQdHJ5dUurIUHTMzzOXaGeYoS3lwdILJijcuwxweoJ2pUoLfHErHVsmtaq6SjJnXJ9WHGZRh\nbmdpWri04EjSM1SAaVvXJyFFHw4sWjRdK7Gx2UqLP5YzNwqDYLOStT1Fzs5QOQYsPmA+393PAJ4E\nXG1mFwKvBK5x963ANcAna93R3e9391OB44AXmdmmmbcxs6vCOuhd+/btq+sPWQk//NXDANWego/e\n1McrLjqWix6ztJ7BcSce3o8Z/M99B2b97Lpb7ueN1/2YyXKFW379CB//1j18/pYH+Oh/3cPN9+zn\nwUdGMYNN4fbVjdLbOT3DHGXRxmMB8ydv+hVv/dJtc/6O8VKZsclKwz4Km1mSEWVz46/lnEGxEJxJ\n9hwcCzLMDTqSvuc5p3DEmm56ivllvaGSKXOVZHTNuP4l5x/dhNFMV20rV+NTlx0Z2OEqC9KUYV5I\n2hKqk+VKahb97RotiAAAIABJREFUQXrrluPKKc4wX7J9Y/VyqUZJxs/3HOKxRx/W0D0O0mhRf22U\nFXb3vcAXgbOAFwHXhze5Lrxuod9xGzBr2zt3v9bdd7r7zg0b6g9Ol2tgOMgQboltoPCmy7fzjmec\nXPfvXN1d5NQjVnPz3QOzfhYFqUNjpWqG91O/dxZmcOt9B9h9YJSNfZ2zFryttJ6OPMOxRX9RKcZo\nLGA+NDY5LQs906HR4GcNK8koLJxhNjOOXhd0ESlVnFLZKTQow/zUUw/nu2++lEI+l5o+sq2ua5EZ\n5pm9z5uh2lauPLsk4x1PP6nZw5EGiGeYP/Dc0xIcSfoC4oWkqa3cTEmOaz7ROawzhRnmTf1dfOR3\nTgdqt3EtV7zhcUkaLfgXm1mvmfVFl4HLgJ8R1CxfFN7sUuCuGvc90sy6w8trgfOBO1dm6CtvYHCC\n3o78ind5OP+49fzo/gMMjk1vhxWVPATBaHB5Y18nj9nYx633H2D3wVEOb3A5BgQB8+gCNcyDY5OM\nTpan1TbFRa2++ptUwxx9ND6zX2SUiZ4oVZis+KxSjoaMLYMHjkaYK8O82P7MjRQ9j2rV9bdacCO1\npSnDvFANc3pGGqj43FndZixQnPXQpTNGnqYaMKf0/BG9HmqVZJQqnqnFfpHFPFKbgJvM7MfAD4Gv\nuPvXgN8H/iK8/s+BqwDMbKeZ/V143xOAH4S3+RbwAXf/6Ur/EStlYGic9X0rny183HHrKVe8WvIR\niU6+h0ZL1bZuvZ0Fdmxdw48fOMADj4w2vH4Zgs1LpmeYy9O+wlRrmXjWOa7hAXNuZoY5OCLOzB5E\nB59HhieYLFUalmGOa0ZQngVzL/pLfn6jLHet538rLjiS2eIBgB7RpQl2+ksuSq21MC3tolKHtAbM\n0euhVklGWQFzbe7+S3c/Lfx3kru/O7z+Jnc/M7z+bHe/Jbx+l7u/LLz8DXc/NbzNqe5+bWP/nOXZ\nPzzOuhXaECTujG1r6SzkuGlGWUbUtu3Q2CTDYYa3t6PAjqPWcGBkkl/vH2lKwNzTmWdkPN5WrjLt\nK8DgWBAwD8+xI+ChsaVt8LJUMxfkRO/OZ3ZyizLMb77+p5Qqjathnv5/Zu/A0QhznTjW9SZf8hK9\nKfrMD+5LeCTSKPGd/o6b0fs7bdIWHlY82T7MMz8VftqOVHevBabW4aThE7RaovNarTcjFfdU7YzZ\nLK3TrLAJBgYn2LZu5fobR7qKec561GF8d0bAHC2qGxybZGS8hFmQTYsvImpGScZiMsxRwDwyXoa+\n2b/j0BJ3RFyu6qK/mSUZsaBrsty4LhlxrdYCKq3mWkBy8hH91ctffd2sJRBNMV8WSA9/e4h/GrV9\nSx8/fttlHBidYFN/V806ziSlrSzXffaxuPqzJoT3PR0Ffvgnj68Gn+98+kk898wjedZHb274/12v\niWoNc1ozzMG4apVkKMMsDSvJgKCO+Re/GWLvobHqdfGSjOGJMr0dBcyMx2zqoyd8x9yMgLm7I8/Y\nZKX6TnJsRls5d6/WX8+VYa6WZHQ1KWAuz+7DDNPLI0qVxvVhluaJvyFZ06C2hQuZb4FL2jaRkPpM\n214ZY3VPkW3reukq5lnVmbLcUsoC5qQzzAAb+7uqbU0L+Rxrelb+0+KVlPYa5qJKMmZJ5yOVgHLF\neXhkomEdDx533HoAvnvPVJY5vuhvZKJUDZLzOePUI4NOAM2qYYap+syZGebxUqVaMxxfHBh3qFrD\n3JwTy5wZ5liWaLLsqVrII8uX1EF6vo9Nk6zdlJUTP1ak/VODZmRtl6Li6ct6p110Tk1jH2aYOtbW\nKskouwLmTHt4eAJ3WL+qMe9KT9zSz5qeIt+7Z3/1umoN82jQJaM3lsU4/ai1QHMC5p7O4AU7Ei7s\ni/owR5nmqBwDqNZaA/zzD37N5R/6Nu7OwdFJuoq5ptVjTQXM06+PZ5R/+KuHeeCR0aaMR5qjGTXp\ntWSxhVLWtFK8l6ZNViDMMM8xg0mvQUjr45r2DHNUojRZI2AulbMZMKfsc6bkDAwFPZgblWHO5YIe\nwbsP1CjJGCtNyzAD/P4Fx7Bj65qG7ZwXF2WYo2B4ZklGvB3eSKwX8z9//z7ueGiQg6OTDd0Wu5bo\nXW/09epLjgWY9dFptFtio/3ry89lc4M3mJHkMsz5nJHP2axsy7ueeTInbumf417SSjavnnr9pjkU\neOczTmpa6dtcPvz8Hbzus7dWv3f3WcmLyIvOO7o5g5ohzY8hxAPmtGaYg0B+ZivZ4fESQ+OlTO4/\noIA5tH9oAmhcwBz87g4ejAXM46XpfZijwBXgsN4OnnjS5oaNJS5aYRxtTFItyShFAfPsDPO9A8Pc\nHm73vefgGIdGS00NmKNFOFFbuXOOWde0/7uWszK2RWhSkiyx6Szkpu2IuX5VJ1ecsy2x8cjKih+/\n0ryQ99LYLmxJOXPb2mnfVyq1Nwg555jDMpmJXIyoJCO1i/4s6sM8/XGNPrXdetjKN0hIu3Q+UgmI\nMszrGlSSAUEQvD/8f2B2H+aoNKLZ5q5hDsY3FMsqj4aL/r76s4eq1z10aIyDo5NNzXqUZiz6Uy/c\nbEjy5DuzLEP18e1F/dQXb+bxtjJHhjllzUVSJTp3pbUkI6p+m/mp2gOPjABw5NrGl4umTTofqQQ0\nuiQDYN2qzrBWOupGMb0PczzD3ExRoD6VYQ5LMiZml2REGeav/WwPh4cfYT50cKzpJRnRu97otayA\nORuSfJxn1k8rc9ZeirGAWY/s/Ga+Du/aO8RzPlajhZsC5gWl9Y1adHybuag5yjArYM6wgaEJOvI5\n+rsaF7Su6+2gVHEOjYaL66oZ5qAPc88Kb8m9WFGgHn3cHJViTG2sMpVhjmqY7947xBNO3IRZWJIx\n1viA+c+fdQpve+qJwFSGOXoxzxW7/NXvnN7QMcnK+vgLz+Ct4WMc99LHPYruYj7RTWJGYy0Vn3DC\nJv7+xY9NbCyy8tK+sLOnI88V52zj8NXJByozj7fvv/HOmrdLQweZFAyhpi+88jxecdGxqS3/iUoy\nZmaY7394hM5Cjg2qYc6ugaFx1q3qaOiTNyr32D88zqquQrWX8OBY2Ic5oV6fPbNqmINxTZadcsUZ\nmlHD7O6MTJbp7yqyYVUnvwkzzI3aFjvygrOP4uDoJO+84fZYhjn4Ole272mnpX/HJ5ly+clbal7/\n1qeeWDOQbqYztq3lO3cFbSE/9Pwd6evNK8sSfzOWxhjm0Zv6eNczT056GMDia7yTjFXT+BjGnblt\n7axa8DSZL8N85Nru1Ab6jZTut9RNNDA03vBVn1F7nf3DE9UFfzC7D3OzRf9vlGEej+3wNzZZri76\nW9tTZGSixHipgntQyrF5dRe7D44yOFZqeMAMs7frjL5m8cUrzRWvNdSzrf3EM8ypPJ6kKFW62Gqk\nNGSYpT75OTYueeDASCYX/IEC5qr9QxMN68EcqWaYhyaqWdzV3UUGx0pMlj2xDHP0/1ZLMmYFzJN0\nF/Os7i4yMlGuZqJ7ink293dx12+GgOZsi119EVe7ZEy/HhZ/MBdZirS2f5KVUUyox/dipSn0XOxa\nAsXLrSt6jMs+syRjNJP1y6CAuWr/0DjrmpZhHq8GpRtjW3EnlWHuLOTIGYyENZpjpUo12zJWqjA4\nVqKvq0BPR4Hh8XI1sO7pLLB5dRcPhdt9N7L+OxKd1KJ3vVNdMqZu8weXHQ/A9s19DR+PZEc8w5z2\neldZurRtBnL8punHrzQFn4sNmK88N/m2i2nbFbFVVEsyYjXMh8aCPRe2rs1mhllFeKFvv+kSxsPd\n4xrlsN54hjkIOjf0dXLX3iBDm1SXDDOjNwyGIcgqr+kusndwnNGJMkPjUcCcZ2SiVG0/19ORn9bs\nvxkZ5lzOMINyZeaiv6kD+NWXHMfVlxzX8LFItnSGW9huWd01raOCSCPceM2FfPp79/LWL90GpCvw\ns0U8/d/ylBN49hlHNn4w0hDVrbFj79QerHbIyGbArKN+qJDPNbwkoqMQdOF4eHiqJGNDPMOcUB/m\n6P8emSjh7kHAHO4wODZZ5tDYJKu6ivR0FhiOl2R05KftbtestnKFnFW361RbOWmWKMOsZ5o0Tey4\n1ooZZmld1YA5lmG+/+Hs9mAGZZibbt2qTgaGxqst2+IlGUllmIGg3GKizGQ5aEC/pjvIho+XgkV/\n/V0FejvyPHRwlNGoJKOjQFdxKshvxqI/CPrhlmd0yUh5+aG0gWhHrlQuCJO2FH+mpStgXvg2Sb9O\nTG9tlyVqKxe1v4Vs7/IHyjA33fpVHewbjNcwT2Vok6phjv7v0YlStXvHVIa5wuDYZO0a5iQzzDP6\nMOcVxEiDadGfNFtaD2uLyTCndOiySFFN/7v//efV6/YcHKWrmGNtT/M2KUsTBcxNtm1dL78cGK6+\na9vYH8swJ9jXNaphjkpF4iUZQ+Ml+jqL9IZlG8MTsZKMJtcwAxTyNmvRX9LZDGl/UUlG9GZNpNHi\nWdIUJZgXFcin5ZCcpsx8K6m1t8F4qUJXMZ/Z860C5iZ79MZV7BscZ+9g0FkivltOohnmMBiOMt9r\neoKSjNGwD/OqrgLdHXmGJ8rTSjJ6Ogr0dxXI56xp48/ncvO2lRNphChgnlDALE0Sj0s8RZGfMszt\nr1DjnFqueKY/zVXA3GTHbVwFwG27DwHTF/0lnmGeKFdLMqJs8UhYgtHXVaC3o8BE2GYOpgL8Lau7\nWd1dbNq7zmLeqltj12orJ9IIUZeMiQZ30xGJpLeGeREBc4YDq3ZQ6zGuuKeu/WIzKWBusihg/tmD\nB4EgSO4NA88kM8zdHXlGJ2aXZOwbGgegr6tYHd9AeF13+P3m1V1NK8cAGBorcd0tD3Dz3QM128qJ\nNEL0Ji2q4RdptHv3jyQ9hJoWt+iv8eOYTyHcFXZNRuttlyv+qe1Xf7oHUIZZXTKa7Mi1PXQUcvx8\nzyAAXcU8fV1FhifK9CTYJaO3I89wvCQj7JIRBcd9nYVqP8Z9Q+MUckZH2Iv2NZcexyMjk00b62DY\n1u5v/utunnHaEUD6Nh2Q9nN32C9dpFk+edMvq5dT1Ye5BUoyDl/TzTufcRKXnbg54ZG0pnjA/Edf\n+AlPOmULpYpnuvxRAXOT5XPGMet7ueOhKGDO0d9d4MBoLtEnYk9ngZHYor9oFey+wSjDXKjWbg4M\nTdDdMVX4v/PowxIYcbDbX9lVkiHN0V1UlwxprngP3DSVZCxKCjKRV557dNJDaGk7tq7h1vsPTNv1\nL8sBs0oyEhCVZQB0FfL0dxUT7cEMQYZ5olxhaDzIFK/qKpAz2DsYL8kIxjgwOJ5o+Uic2spJs3Sn\n5Dkv2RGLl1OUX16cDMdVbaMYlrVM7fqX7QX2yjAnIAqYO/I5cjmjv7uY6C5/QDUYfng4CJi7inm6\ninkGYhnm6HUyMDTOqgQXKMZV1FZOmqRLGWZJUJq6ZCyGNg5pfdF5NQqSb999MA0fHCRGGeYERAFz\ntHPYpds38uSTtyQ5JHo7py/o6yoEAXO06G9VV4GeMEjePzyRimybM5WByfK7XmmO55xxZNJDkIx5\n/RMeXb3cWuGytIPotBp9gruqq1hNomVROtKEGRMFzFHG6oXnbEtyOAD0d02vWe4q5ugq5Hh4eAII\nMsxRNrdc8dSUZKitnDRLfJMekWY49cjVU98oYpYmi7pP5fNTJ9gdR61NajiJU8CcgEet7yVnQVCa\nFn1hwBxtqNJZzNMVC4r7u4rT+s8m2dGjyqdqmNUlQ0TameJlabZqwBzVYbhnutAmBVFP9nQW8hx1\nWA/FfHoC5v7u4Kmwd1qGOQiYi3mjs5CbtjAxLRnmqKxPfZhFRERWzsxElJOK5ieJSU/EljFnbFvL\nEWu7kx5GVV+sJMMsWJAYZcBXdRYws2kLE9NQwwyorZyIrJgnnZyunr3HbpjqqNRqi/6k9Z1/7DoA\n1vYG+zK4J99fO0kKmBPynmefwt9ecWbSw6jq75rKMHcVgh7LUY11FEx35HPV/eWTboMX0U5/IrJS\n/voFZ3DHuy5PehhV29b1cv2rzgOmt5hrBWnaaEXqc9WFxwBwwpZ+IHhMs9yRSgFzQjoLeToL6cjS\nwlRQPFGqVDPLUwFzEBybWTWznIaSDMerCxEVMIvIcuVzlrr2geui7J4CUGkyM2NjX2f1PAvKMIvQ\nUcjNCpSj76OAGaYyy2koyXBXWzkRaW/qZyxJKuSs2o0q61VBCpilKsoyTwXMwddVncXqbaI65rSU\nZKitnIi0s66O4DR9XKyeOS2KeR14293ug2Ncd8sDQFjDnOGHfFFRj5ndCwwCZaDk7jvNbAfwcaAL\nKAGvcvcfzrjfDuBjQH9433e7++dWbviykvq7CuwbHKezMD3T3J/SDDNMLYTJcl2VNM9/vfFiRibK\nSQ9DMmRjXxeffulZ7Ni6JumhTPPFV53H5tVdnPue/0h6KNIk4dk24VEkZylpwkvcfSD2/fuAd7j7\nV83syeH3F8+4zwhwpbvfZWaHA7eY2Y3ufmBZo5aGmJVhLkyvYYap2uU01DBDUJKhcgxplqPX9yY9\nBMmgCx69IekhzHJ6jQ0sTtzSz+17DiUwGmkGd1eGuU5OkDkGWA3snnUD91/ELu82s73ABkABcwr1\nd0cBc27a1yiQhnjAnJKSDHeVY4iIiDRBlk+3i416HPi6mTnwt+5+LfB64EYz+wBBLfR58/0CMzsL\n6ADuWcZ4pYGi0otZNczxDHNncDnJDLNZtODPqbirQ4aIiEiDZb2GebGL/s539zOAJwFXm9mFwCuB\na9x9K3AN8Mm57mxmW4BPAy9x90qNn19lZrvMbNe+ffuW/EfIyqiWZBTm65KRfEnGl1/9OCBY8Fep\nKGAWEUmDjDdRaEvx87/jme7asqiA2d13h1/3Al8EzgJeBFwf3uS68LpZzKwf+ArwFnf//hy//1p3\n3+nuOzdsSF+tVlZE22NHgXL3jI1LYKoUI8mSjJOPWM0TTtjIZNlVwywiItIgV5yzrbphGSjDPC8z\n6zWzvugycBnwM4Ka5YvCm10K3FXjvh0EAfb/dffrVmrQ0hj9Mxb9dRbTu+ivkMtRqlSoZHwRgoiI\nSKN0FfOUKk6pXMl8H+bFpAk3AV8M23YVgM+4+9fMbAj4sJkVgDHgKgAz2wm8wt1fBjwPuBBYZ2Yv\nDn/fi9391pX9M2QlzFXD3NcZK8lIQQ0zQCFvlMoqyRARSaNW28pbaovazE6UKzjZzjAvGDC7+y+B\n02pcfxNwZo3rdwEvCy//E/BPyx+mNENUetEZlmRs39zH1sO6p7XSOunwfrZv7mNNT0ciY4wU8zkm\nKxXK7irJEBFJAY+lIL/yk91ccc62BEcjKyEKmMcnK0FbuQzXMKejN5ikQrWGOVz095hNfXznTZdO\nu83Fx2/k4uM3Nn1sM+VzRqUSLPwrKGAWEUmVg6OlpIcgKyAqzRwvBRnmDMfL2hpbpszcuCTNCjmj\nVKkwWVbALCIi0ghRhnlssgye6XhZAbNMmVr0l/6nRT5nlCtOueLk81l+CYuIiDRGZ2F6htkyXMSc\n/shImmb9qg4KOWP9qs6kh7KgIMPslCpOMaensYhIGpx+1BoALt2uFrHtoFrDXCqHNczZpRpmqVq3\nqpNvvOEitq7tTnooC8rncpTLQasbLfoTEUmH615+Lt+5a4CLj1fA3A6iJgDjpWDPuQwnmBUwy3SP\ninXESLNCfirDXMgrwywikjR3KORzXLI9+YXhsjKiNU3jk5XM7+SoSENaUryGWYv+REREVt70koxs\nL/pThllaUt6CLhn/ccfepIciIiKAZz4H2X6iRX8v/dQu1vV2aNGfSKvJ50w7SYmIpEiWN7VoV9vW\n9VQv7x+eyPQjrIBZWpLKMERE0iXDyce2NWtfhgw/xgqYpSWp97KISLrkFDG3vSx/iqCAWVqSMswi\nIumilvjtL8vvifT0lpaU15FZRCRVspx9zIosP8KKOqQlxTPMLz7v6OQGIiIiAOiDv/b0lqeckPQQ\nUkEBs7Sk+O5+qpsTEUlelluOtbP4OTbLD7ECZmlJ8YA5yy9gEZG00LG4PU0732a4KEMBs7Sk6S9g\nERFJ2rNPPyLpIUgD5JSgAhQwS4sq6AUsIpIqLzxnW9JDkAaI16Zn+XyrgFlakmqYRUTSRTXM7Wn6\nOTa7j7ECZmlJhXhbuey+fkVERBpKGeaAAmZpSRX36uUsL0IQERFppPgnBzfdNZDgSJKlgFla0lGH\n9VQvZ/kdr4iISLPc9/BI0kNIjAJmaUl9XYXqZTXLFxERkUZSwCwtqbOYr15WSYaIiIg0kgJmaUmd\nhamnrkoyREREGqOY10kWFDBLi5oWMCc4DhERgT958vakhyAN8tRTD096CKmggFlaUmchVpKhFLOI\nSKJ++8ytSQ9BGqSYV6gICpilRcU/IlK8LCKSLB2Gpd0pYJaWpKyyiEh66JAs7U4Bs7S82B4mIiKS\nACUxpN0pYJaWl9OBWkQkUToMS7tTwCwtTxuXiIgkS4dhaXcKmKXl5RQxi4gkSiUZ0u4UMEvLU0mG\niEiylLeQdreogNnM7jWzn5rZrWa2K7xuh5l9P7rOzM6a475fM7MDZnbDSg5cJKIDtYhIskxFGW3t\n3197AQCrOgsJjyQ5S/nLL3H3gdj37wPe4e5fNbMnh99fXON+7wd6gJfXPUqReSjDLCKSLB2G29uG\nvk4APMNtqZZTkuFAf3h5NbC75o3cvwkMLuP/EZmXaphFRJKlgLm9RafZSnbj5UVnmB34upk58Lfu\nfi3weuBGM/sAQeB9XoPGKDKv1d3FpIcgIpJpKslob/kwYq4ow7yg8939DOBJwNVmdiHwSuAad98K\nXAN8st5BmNlVYR30rn379tX7ayRjPv+Kc7n8pM089dQtSQ9FRCTTlGFub1EXlOyGy4sMmN19d/h1\nL/BF4CzgRcD14U2uC6+ri7tf6+473X3nhg0b6v01kjE7jz6Mj19xJl3FfNJDERHJNMXL7a1a+Zjh\niHnBgNnMes2sL7oMXAb8jKBm+aLwZpcCdzVqkCIiIpJeWnzd3nLVDHN2I+bF1DBvAr4YpuMLwGfc\n/WtmNgR82MwKwBhwFYCZ7QRe4e4vC7//DrAdWGVmDwAvdfcbV/5PERERkSQoXm5v1YA5u/HywgGz\nu/8SOK3G9TcBZ9a4fhfwstj3FyxzjCIiIpJi2umvvUUPb4bjZe30JyIiIiJzizLM6pIhIiIiIlJD\n1FYuw/GyAmYRERERmZv2B1PALCIiInV66eMelfQQpAmiGvVXXHRswiNJjqVtX/CdO3f6rl27kh6G\niIiIiLQ5M7vF3XcudDtlmEVERERE5qGAWURERERkHgqYRURERETmoYBZRERERGQeCphFREREROah\ngFlEREREZB4KmEVERERE5qGAWURERERkHgqYRURERETmkbqd/sxsH/DrpMcxh/XAQNKDaFGau/pp\n7uqnuVsezV/9NHf109zVT3O3dNvcfcNCN0pdwJxmZrZrMdsnymyau/pp7uqnuVsezV/9NHf109zV\nT3PXOCrJEBERERGZhwJmEREREZF5KGBemmuTHkAL09zVT3NXP83d8mj+6qe5q5/mrn6auwZRDbOI\niIiIyDyUYRYRERERmYcCZhEREZEWYWaW9BiySAHzDGbWE37VE3KJzOzYpMfQqsysmPQYWpWZ5cOv\nes0ukeZsecxsdfhV59IlMrOTzKwr6XG0qO6kB5BFepETHOzM7DAz+zrwhwCu4u5FM7MzzOzbwHvN\nrD/p8bQSMzvHzD4LvN/MTk56PK3EzM43s08BbzGzw/SaXTwzO9vMPgH8kZkt2LBfpoTni34zuwH4\nCIC7VxIeVssws1PN7Cbgz4B1SY+nlYTniy8Af2Nml0XJAmkOBcxUD3YlYDVwjJk9AZR9WQwz6yA4\n8H3O3Z/r7ofC6zV3CzCz5wIfA24AuoA3hNdr7hZgZscAHwX+E9gGvMvMnpLsqNLPzPJm9h6ClfTf\nBc4A3m5mm5IdWesIzxeDQBE4wsz+FyjLvARvAT7v7s9y9wdBx7zFMLOLCY551wN3Ai8E1iY5pqzR\nC3zKicBDwHeAp5lZtzJWi3IGsN/d/wbAzM41s07N3aI8Gviyu/8T8JcQlGZo7hblTODn7v6PwB8A\ntwJPNbOtiY4q/XLAfcBzw7l7PXAO+oh3qbYTbD/8IeB3zazP3SsK/OYWZuaPBYbc/UPhdb9lZmsA\nlVUt7BTgv939n4FPE7xhG0p2SNmSyYDZzJ5nZm8ws3NiV/8auA34BVABLjezzYkMMMVic3dueNWv\ngePN7Glm9g3g7cAnzOx3khtlOtWYuzuBZ5vZm4DvAYcTfNT22MQGmVLhR5GPiV3138CRZrbV3R8h\nyJYeAJ6VyABTbMbcVYB/cfdfhG9sdwMPAOuTG2G6xecvFtDdDUwAvwr/vcjMjtKb3enicxdm5vcC\nF5jZU8zs/wFvJChrUSnkDDWOed8BnmtmbwP+B9gCfDT8pFKaIFMBc/hx5NuAPwqv+oSZPTu8vAPo\ndfdvE5x4/wr4MzMr6F1vzbm71syeA+wDvkxQTvBed7+c4GPyS81sezKjTZc5nndPJ/ho7XXAhcCV\n4dztA56jN2sBM1tjZl8BvgE8z8xWhT8aA24Cnhd+fydwO7BOC4kCtebO3cvufgDA3cfNrA94FLA7\nybGmUY35640FdDuBQ+5+G0Gi5e3Ax8ysqNKM2nMH4O6DwD8A7wL+3t2fCPwdcM6MBFZmzXXMc/db\ngcuBo4FXufvFBImCy83shISGmymZemG7exk4HvgDd/8gwUHuteG7uN3AsJn9A/ASgkzzT9y9pHe9\nNefuT4FXEnw0+WPgJII6XID/APqA4eaPNH3meN5dAzzG3b9JEPzdGd78S8CpaO4ivcCNwGvCyxeG\n1+8Dvg+cYmZnhXP8IHC+u48lMtL0mTl3F9S4zdnAbe6+28xWmdmjmznAlJvruQdBWUufmX0OeBNw\nC/ALd5/YIOd3AAAHC0lEQVTUAkBg/rm7gSDoi+pvdwG/AcabOL40m/N16+4/BDYA94ZX6VzbRG0f\nMJvZlWZ2UVgnBcELc62ZFdz9eoLswDMInoSXESzmOA14P3C6mR3d/FGnwwJz9wWCNxVPI/io6H3A\n68Lsym8BhxEEgpm0iLm7DXh+mEm+B/jt8Hank+F5g2lz1x8uCroW+FeCeTnLzI4IA+TvAz8C/jLM\nwpwE3Gdha8gsWmDuzjazw8PbFcK7rAHuN7OXEJS57Ehi3Gmx2PkjCPY2EKx7OZ0geXB8ljN9i5i7\nIwDc/ScEJRivNrP1BIvXTgb2JzT0xC3hddsJ3AxcHd718QSdRjJ9zmiWttwaOyyh2Ax8hqBm7x6C\nd2ovB14LFICPuPuB8AD3GYJgeTzW5WELUHL3fQn8CYlZ4txtBz4HXO7ue8zsvQR1uEcCV7v7z5P4\nG5JSx9x9luDNxakEB8DDCRZxvNrd72j+X5Cceebude4+EN7mfIISjF3u/unYfT9I8JzbRlDacicZ\nssS5++9wkWl0308Dvwt8CvjLMJjJlHqfe2a2PvbzVUCHuz+cwJ+QmGW+bt8AHEOw+Pkad7+9ycNP\n1DKedycRfEq5GZgkOF9k6lyblLbLMJtZPiyh6AMedPfHA68CDgIfJmjLcj5wqpn1hE+0u4AXuPsh\nC1by5tx9TwaD5aXO3R3AHUC0wO+PCWqrLs3aC7jOubuLoFvBN4Ergd939ydkMFiea+4eJsi0AODu\n3yX4KPJ4M1sd1t9CkK16qbufncFgealzt92CHsJRLfhXgOe5+0syGizX+9zrdfeBcH1Czt2HMhgs\nL+t1G5anXePuT8xgsFzP3K2xoHvXbcCLgBe7++Ozdq5NUmHhm7SG8CPGdwJ5M/t3oB8oA7h7ycxe\nTfDx2QcJ3tE9n2CV6ecI3qV9P7xt5urPljl3JYKFB9EK50y1uVnm3E0Q1D7i7kPAT5v+ByRoEXP3\nWmC3mV3k7t8K7/YJgr7f3wC2mdnpHnR6GGz+X5CcZc7dN4GjzGyHu382geEnboWfe5myknPn7pMJ\n/AmJWYG5O8rMzgjLNn7Z/L8g29oiw2xmFxEEHmsJ2v28iyAIvsTMzoJqIPwO4P3u/ing68CVZvYj\ngjcOmQpWIpq7+mnu6rfIuXOCk8ufxu76FIJMzI+BUzIasCx37m4lmLs9TRx2aui5Vz/NXf1W8HX7\nYBOHLTFtUcNsZhcAR8dqfD5KEIiMAq9x9zMtWIy2Efhrgo+B7rdgwVWPu2f2nZrmrn6au/otce4+\nArzJ3e81s2cAj3jQ/jGTNHfLo/mrn+aufpq71tcWGWaCd23/alP7qn8XOMqDnazyZvaaMNN3JDDp\n7vcDuPtDWQ5aQpq7+mnu6reUuSu7+70A7v4lnTg0d8uk+auf5q5+mrsW1xYBs7uPuPu4B62mIOg8\nEC3YewlwgpndAPwLwQ45EtLc1U9zV7965s5MGwiB5m65NH/109zVT3PX+tpm0R8EK08BBzYB/xZe\nPQj8CUGfx1+p/qc2zV39NHf1W8rceTvUj60gzd3yaP7qp7mrn+audbVFhjmmAhSBAYL2XTcAbwUq\n7n6TgpZ5ae7qp7mrn+aufpq75dH81U9zVz/NXYtqi0V/cRbsR39z+O8f3P2TCQ+pZWju6qe5q5/m\nrn6au+XR/NVPc1c/zV1raseA+UjgCuCD7q696ZdAc1c/zV39NHf109wtj+avfpq7+mnuWlPbBcwi\nIiIiIiup3WqYRURERERWlAJmEREREZF5KGAWEREREZmHAmYRERERkXkoYBYRERERmYcCZhGRlDKz\nPzWzN87z82ea2YnNHJOISBYpYBYRaV3PBBQwi4g0mPowi4ikiJn9b+BK4H5gH3ALcBC4CugA7ibY\n9GAHcEP4s4PAc4BPAm90911mth7Y5e5Hm9mLCYLrPHAy8Bfh77oCGAee7O4PN+tvFBFpNcowi4ik\nhJmdCTwfOB14NvDY8EfXu/tj3f004OfAS939ZuDfgD909x3ufs8Cv/5k4AXAWcC7gRF3Px34HkGA\nLiIicygkPQAREam6APiiu48AmNm/hdefbGZ/BqwBVgE31vG7/9PdB4FBMzsIfDm8/qfAqcsbtohI\ne1OGWUQkXWrVyf0j8Gp3PwV4B9A1x31LTB3XZ95mPHa5Evu+gpInIiLzUsAsIpIe3waeZWbdZtYH\nPC28vg/YY2ZF4Hdjtx8Mfxa5FzgzvPzbDR6riEhmKGAWEUkJd/8f4HPArcAXgO+EP3or8APgG8Ad\nsbt8FvhDM/uRmR0LfAB4pZndDKxv2sBFRNqcumSIiIiIiMxDGWYRERERkXkoYBYRERERmYcCZhER\nERGReShgFhERERGZhwJmEREREZF5KGAWEREREZmHAmYRERERkXkoYBYRERERmcf/B/977VYPLr74\nAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e41be0>"
+       "<Figure size 864x360 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1758,14 +2924,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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erBzTJCtdrUzNBwDsOVOK/PI67EwvVbZR3xbltSZQShWvCCBJovMz+iZOQUsP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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1274fc18>"
+       "<Figure size 864x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1777,7 +2943,7 @@
     "from matplotlib.ticker import MaxNLocator, MultipleLocator\n",
     "\n",
     "# Get height of ground surface\n",
-    "# ground_surface = df[\"mv_mtaw\"][0]\n",
+    "ground_surface = df[\"mv_mtaw\"][0]\n",
     "\n",
     "# create a plot with 2 subplots\n",
     "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), \n",
@@ -1809,12 +2975,12 @@
     "    ax.yaxis.set_major_locator(MultipleLocator(0.2))\n",
     "    \n",
     "    # Add the ground surface (provided in the data) on the subplots\n",
-    "    # ax.axhline(ground_surface, color = 'brown')\n",
-    "    # ax.annotate('Ground surface', \n",
-    "    #         xy=(0.05, 0.68),\n",
-    "    #         xycoords='axes fraction',\n",
-    "    #         xytext=(-25, -15), textcoords='offset points', \n",
-    "    #         fontsize=12, color='brown')   \n",
+    "    ax.axhline(ground_surface, color = 'brown')\n",
+    "    ax.annotate('Ground surface', \n",
+    "             xy=(0.05, 0.68),\n",
+    "             xycoords='axes fraction',\n",
+    "             xytext=(-25, -15), textcoords='offset points', \n",
+    "             fontsize=12, color='brown')   \n",
     "    \n",
     "fig.tight_layout(h_pad=5)"
    ]
@@ -1842,7 +3008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1958,36 +3124,133 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019-12-31</th>\n",
-       "      <td>58.41</td>\n",
-       "      <td>58.51</td>\n",
+       "      <td>58.09</td>\n",
+       "      <td>58.52</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              min    max\n",
-       "datum                   \n",
-       "2003-12-31  58.26  58.36\n",
-       "2004-12-31  58.22  58.36\n",
-       "2005-12-31  58.18  58.35\n",
-       "2006-12-31  58.10  58.42\n",
-       "2007-12-31  58.28  58.53\n",
-       "2008-12-31  58.34  58.60\n",
-       "2009-12-31  58.27  58.54\n",
-       "2010-12-31  58.21  58.56\n",
-       "2011-12-31  58.32  58.56\n",
-       "2012-12-31  58.32  58.55\n",
-       "2013-12-31  58.22  58.56\n",
-       "2014-12-31  58.21  58.54\n",
-       "2015-12-31  58.08  58.56\n",
-       "2016-12-31  58.24  58.54\n",
-       "2017-12-31  58.27  58.56\n",
-       "2018-12-31  58.08  58.53\n",
-       "2019-12-31  58.41  58.51"
+       "<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>min</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datum</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2003-12-31</th>\n",
+       "      <td>58.26</td>\n",
+       "      <td>58.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004-12-31</th>\n",
+       "      <td>58.22</td>\n",
+       "      <td>58.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-12-31</th>\n",
+       "      <td>58.18</td>\n",
+       "      <td>58.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006-12-31</th>\n",
+       "      <td>58.10</td>\n",
+       "      <td>58.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007-12-31</th>\n",
+       "      <td>58.28</td>\n",
+       "      <td>58.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008-12-31</th>\n",
+       "      <td>58.34</td>\n",
+       "      <td>58.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009-12-31</th>\n",
+       "      <td>58.27</td>\n",
+       "      <td>58.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010-12-31</th>\n",
+       "      <td>58.21</td>\n",
+       "      <td>58.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-12-31</th>\n",
+       "      <td>58.32</td>\n",
+       "      <td>58.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-12-31</th>\n",
+       "      <td>58.32</td>\n",
+       "      <td>58.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-31</th>\n",
+       "      <td>58.22</td>\n",
+       "      <td>58.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31</th>\n",
+       "      <td>58.21</td>\n",
+       "      <td>58.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>58.08</td>\n",
+       "      <td>58.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>58.24</td>\n",
+       "      <td>58.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>58.27</td>\n",
+       "      <td>58.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31</th>\n",
+       "      <td>58.08</td>\n",
+       "      <td>58.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31</th>\n",
+       "      <td>58.09</td>\n",
+       "      <td>58.52</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2005,24 +3268,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x125f0f98>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9d65b6f98>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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dcpYYY8Alnyx+vXU5fR9Cg6WPDdBmejV2AG/4BvCm7wKv/Jfy760RFunPFchK\nX7pJZdKvQOnn8/qkL9So8PRFYUqhOnNK/zOfAbo2I5fnxovZO1z0P2LjFEiLjun3jC+0d5idbmZA\natIl2RaL1tFrxx8rb8+oA7nuBiWTBwAu/DBdy947tQTKOSm4dJyspa7NVA284nLKygkeoe2KSF8a\n3OYt11ezDjcR1OTR0i2GVcjkdKqsL/gQDZDPbVOeG95NhW1dm5HM5DAeSck/mn20LqdZXTatbXct\nlD6gWF+SvbN/OIx0Lk+k3yaRvtrXN9uWIDFF11/MEo0QWEj7M3mNTEG2dwpJf1hKxywThC6EetZW\nDiLdeAZgkf5cQZG9U4XSz8QAcC3pNyxQbnh3I5GIuJmNlD4AdG7CTfc8iw/dXSJ1z99Ogc3bL6Vl\nD7+yBOh/RruNnLI5n4phVr8JWPtOSo0sbKOw4Fzgqe8CX11KlaZ6yOfIQhGevnee9jwWXwQsXEtL\nHn5tmVI1+5uPAj97C60WxXOK33vhR+gcdvwnDTq+AnvH4abBqVu3dpHQuoJsGJG5o+7fXoBDJyM4\n7z8fwp8PFPTMWbCarIzdP1Oek2Y9uUUbcMlX/oLNt+6Qf979Y9V1nrecPtdjf6HP4Nhf6flkSLF1\nZNIne0fk5a/rbkGT14l5fpei9AHzDcjiZapx5f110nUeP0SCxigFtRK4DeydyLBxxk8piIGoXDDX\nKNPrNOGMWBjdggkUkr6cSVOB0hc9adyNivJSKypx409JuchGnj6APs9KPPTiINoa3Mb/z99OWS2Z\nGHDBjVQkdOxRYPGFyjZi0HL6gPfcD8xfRdPrf7in+NzefBvVJjzzQ+CJ/wbO//vilLuDf6CZytmv\no79f+S9au4Ux4G13EgE+8nmlP/zwHmDsRaXBmwgQrngN8LafkGJt6SlO/QOAa39ZmqRWXQ387pPA\nCz+nFEOXz3DTO/52HKlsHrc9egyvXrlA+2LH+ZRfLjDYC8xbhogtgIloCm9YsxAXLW/F3sFp/KJ3\nEHsGpim3XgwyD/0bqd7gEWDZq7Q1FOKzl2y23f3TWNjkQUcTfc96Wn04Xqj0D/6x/GLhellgeggs\nAsCBE09IMYM66FVh7xR2Vg0PG/v5pdC4kIRPsAzpp8JkA1lK30JNSIYAMEXBVuPpi1RFtdJvValO\nofpEAYpeGwbp+dtfpBt9IppCJFnQlkHA306E39ABvPZWutEKPXmh9B0eoGerQhCty5X+/AILzqWA\n2JaPE0H3/a34fz59G9C0mIrXAJotLFqn3aZ1Oe2nebEyqxGqdf/9RO5CpTEGnPdWJRCnh86N2llJ\nIc5/Bw0mwcMlverJWBr37xloYtD4AAAgAElEQVRCq9+FnX2T2DdUsOqTu5GuZz4ntT/uBbo2I5Ik\ny+bSc9rxnouW4HNXrUaD24E7nziunC9A/x9QvgciqC/2DWiU/rpuJXurp82Pvom4ciyBRWQtlusZ\nVK7Zmrw/adAc3VtZgVYplLJ3qplJ2Gw0gJazd+Sgv0X6FmqBCLoJBVSNpy+TfkC52fWU/ugLZLWI\nm0ZA+p+ZhRtx3+4hLAiQypezOgohvvSbP0AKuWsjEZU6DiAGrXLZGmqseRvNQp5RZbMM7KKCoROP\nU4qjmYpb4SOn45QSK5ShUSofgKHpBKbjFS6E7vIBG6+nx5LqPhlO4i8HxzQ/395xCKlsHj98z0b4\nXHa5MErZjyowGR6iorjOTQglaNBt9Djl32/f1IXfvzCCk+GktFCLKoaTjtLAkYlprT3pmgSjKfRP\nxjWkv7TVj9FwEom0lLlitheNXl8mPcg58byk/WUGsVQWA5NxbfZOYpoCsMkwnX819g5gnAmVz1O8\naewlpdHgDNk7FunPFaTC2uBrNXn6osDL3Sg11bKRvy0g1OrI8/pNt5w+wNOElzzrkMzk8c9/R6mC\nGq9XjXnLKGtm4/vo785NpPzUnqis9EvYRIVweoH115KVk0nSTXjH5cBvbqKZ0IbrzO1HThOUslAu\n/igNdktfYfiWa3/0NP7xf3VaJZfD5g/SwNZBVc6f+L/deN+duzQ/dz11Aq84qw2beubhmvWd+O3z\nw0hlVemB6uKyUWmxukXrEJZmWgGvMtC9+6IlyOY5HnpxlD7HhedTHx53E70/HdXus2kxFYG5G7FX\nmmGsVZH+EimY2z8pDfBCKZfL4Inr9GXSg5qEa1T633j4EN7ygyfp+woQ6e/4PPCTK8pXMJdDS4+S\n6qwGY9QnSZ3pZZQddIphefpzBcmQVq0xRiRSkacvlH4DtQe+eb+2D3vH+cBNzxAhiF4jatgdwMee\nxV+fngBwDK87twOf/vUL2qwONS78EPnuDZLiV3doFDd2NUofoJ4pXFpcXCirK75G/d7NtlgIdFLQ\nV6wEtWQrDVAGN+tYOIm+YBx9wTgOnYzg7AWNutvpH2838IkX5K6Nh09G8bpzF+DDr9IS3Ir5pE5f\nsaIN9z7Tj5dGIoriVpO+sFX87QiHyN4JSEofIGVuY8BYWLq+/3APDfLfu4jer7b6AEpRlArKhHe/\nvL1B3t8iydsfCSVwTkejapnDEsFczsu3VRbwtihrMNeYufPM8SDGIymk84DL6VfiGKF+YEBa8bVa\n0nf6qDK7MJbBmGL9iLoJy96xUBPU6XUCDk/1nj6gJXyAvrjzV1IQs7EgiCjQ0I6RSBatfheafE4s\nCLhxfMLA3rE7tQQ6fxUp/8L1AJi98gZowp5Sq9bODUSuZiGIa1iq0Ax00jUxSOXbPaCknBZZL6b+\n3wLA7kA4mUEwlsb6xS1FP8KiWbeYiH53v8ozV5+zKrAvlH6TVyF9m41hnt+NYCwlb6dZPazwu+Bw\ny/GUvokYGtwOtDUoQWsR0B0NibqKDgCstL2TjlJ6r5lALmPK97EGpZ9I53BglM4tlMiQwElHlRnJ\nSw/Q72pJX7Q0z2eLXxMVu3Ln2tbq/keNsJT+XEEyVNwR0GhREyPIU/oSDdBMYDSUVGV1+DX2zq6+\nSbgdNpzfpePj2uxEzIMFpF9C5f+ydwCjoSRaG9x45wXdYEJdyao3rLWtCnB8IobfvzAshxH8bgfe\nc/ESOO02xaIQx1M4CBZgd/80nHaGN6xZiPueG0RHwIOz5jfgijWl31cIMTMSRU96WNjkxYKAW2lp\nDGjPWRTruQOISAOAWukDQFuDCxPRgviDqMUQpO/SuWbBOHrafMq1BjC/0QPGgNGwRPp2J6X7llL6\nJVowHBmL4I97R8EY8KZ1neie55NW0BrVbxNtEnuHQsjl6cOejqfR7vJT9o4YnES6arX/wy5d41xa\neSwwbzmw/wFKCfW2FL9+mmCR/lyBntJ3Vqj0Q4PkWddI+iOhJDqbiaiXtvnxyH7KKU+kc/jgXb1o\ncDvw109dBrtNJ5Wv43yg9yfK37m0oZ//XP8UPvWrF+S/u+d58YqzpCmz2uooVK0q3P7YMfzvTq0H\n2+R14q0buxS1N7hL1YPGGHsGprB6YQAfvWwFHnrxJL75yCHYGPDXT11GpGUSwj5Z2lb6/63vbsHu\nfj3Sl5S+q5FmDlIgt8Gjvd3bGtwIRgu+H+5Gem+JgbJvIobzu7TfNZfDhrYGt6L0ASkQXsLTF1aK\nTvviW+7bi119NCgEY2n85xvPJXvN21w6BbQM9gwoM6PpRIY+0/AQNQkEpJlHW2UxJDVk0tfJWJu3\njGo8RNvmGYJl78wVJMP69k4lnv7Qs0S61fSSV+FkOIkFAUnpt/kRjKURTmZw/54hTMczGJxKYMdL\nBgtyN8yn2YkomCmh9O98og+NHgd6//1ytDW4cOcTfcqLJkk/ksxgaZsfR269AodvvQIr5jfgzieP\nUyWx3NkzWDaFL5fneGEwhHXdzThrQSNe/MLr8LdPXwbGGO5+2mSPdQki9XFJa+mBYt3iZvRPxhXi\nls85qmmhEE5m0OB2FA2yrQ0uBGOFSr+h5DVLZ/MYnIrrDkgdAQ9GNKTfWdreeeY2Ur892sD4vqEQ\ndvVN4d+uXIXOZi9CcYlAL7sFeMfPdHZkHnsGpuUxYyqWpoFxQiwOo9MyuVLYJctLj/SFLTX2okX6\nFmpEPl+cvQNU5unnslS2X6oroQkkMzlMxtJYqLJ3AFKH257ow8qORnQ2e7FNTdBqiJtBBF+zKd2C\np9FQEn/cO4J3bOpGW4Mb77pwCf58YEwpECoifUbxggLEUlk0uB1w2G1w2m1475Ye7BsK49kTU3Q9\nRYZHmen+oZMRxNM52Wu32Ri65/nw+nM78H87+xFP63i8BugLxrCoyQOPs3QbgPVSAFe2eESFaSpC\nKabS9yGcyCLgKR7IW/1uTER0lH4J0h+YiiPP9a2njiZPgdIv0Yph8FmaQV34oaJCq588cRw+lx3v\nuKAbAa9TjknUA7v7p7FWshZlpS/aiqy6WjruGqp9bdJ1zuspfYn0eX7G0jUBi/TnBlJhALx4MRI9\nTz+fBx75T6U1scDYfpriGjTn+vHfjuHJI+XXtD0pebodTVSoJRThv/56Lw6ejOD9lyzFey5egqeO\nBXHDT3fhKw8e0O5AXlxF+l8qpf/1hw7ixrt6ceNdvbjhrl3IcY7rLu4BALz7wsVw2hnueqqP3ldI\n+q4G3SrOWDoHn0sh17ds6ETA48CdT/ZJwUOxSlJp9SeId3231p9+79YehJNZXHfHTvzTz/cYF6qp\ncHwiJvezKYU1XU2w25hi8bgK7B1B+skMAt5i/7i1wYVYOqfk1gNKJ1UD0pfjDTrHt7DJo3j6AA2U\nqVBxxStAHUFdjcC6d2meHo+k8LvnR/C2jV0IeJwIeBwIJ80PmEb48d+O4Yaf7sJIKInLzqHv2HQ8\nrbXsVgvSVwb4fJ7j8w+8iBvv6sW/bd+LrF7fIzVkpa9Tq+FvU6xTHaX/7R2H8P/9wcQaBDXCIv25\ngMIWDAJ6nv7EIWpLvOPz2udFxozOQtBHx6P4r9+/hP/bpbNcXwGE0hNKf1m7H5ee0w7OOV51djuu\nXrsI79y8GBctm4fnB0P4waNHlT7sgGpxFSmXOZsCHG5kc3l89y9HsHtgGv2TcWRzHDe+chkWSxbI\n/IAHW5a34amjkmrTkH5Y19oBgHg6C79bUcE+lwNXrV2Evx4cp4Cf3tJ4OtjdP4UWn7PIktm0pAVv\n3dCFYCyN+3YP0QyiDPqC5kjf53Kgq8WLEyI33u6gmUkqrCX9RKYoiAtAzr6RM3gApZNqUt/TLxVv\n6GjyIJTIKLOa1hX0e/xA0bYY2EldRAv2f88zJ5DO5XH9lh4AIKWfqE3pD07F8aU/vIT9w2Gs627G\nNesXwWFjmI5nlNkRGNWJbLiOOq1K+POBMWx7sg97BqZxzzP9ODKuM4CpIXv6OgOVSNsEdEn/zwfG\nsH8kXMUZVgYrkDsXYET6Dk9xx0lB7ocfBiaOAG3SjTn4LKWQtfQU7f6nUvrhtImbTyg94ek77TZs\ne5+22ZjHacf/3Xgx7n6qD5/7zYuIJLOKlVHYZ19S+qKVwE2XLsf7ti7V/d/L2v3YeXwSnHMwh4em\n2kK1GpF+Kgd/m/Y22Li4Bfc+04+j41Gc3Whe6a/rbtZktAAAYwzf+Pu1GJiM4xVf/YvW/tDBdDyN\n6XgGS0tk7qjR7HNpK4BleyYMeGi93kgyi0XNxXGRVj8FK4PRNLpafMr7AUphdPqL0lP7gjEEPA60\n+IoHkY6Akra5rL1BsQoHe4ttw9hEkWWWzubxs6f7cek57XINQKPHIX/21eLup06AMYZffWQLFjXT\nDLTZ56Tvs0ci/Yb5ZCNe/T+a99755HEsbPLge9duwFu+/yT6JmJY2VEi0UGdvaOH1uXUtK+A9Dnn\nOD4RwzXr6tBIrgwspT8XUIr0swUkM9grWR1OYOftqud3kbVTQFrhZAa/epb6g5tpLyACeSJlsxSE\n5aDxbAv77EtKX64q1VGsAkvb/EhkcjgZTkkLfwgCjBqSfjSVhd+lJTbhy+/pnzZl70SSGRwei2Jd\nt3GRkRgENfaHDvqklhVmlD4ANHudpFgFxDkX2js6163VSOkDRPruhqL39E1QELdwcANUufriHAOL\nqIFcYT+lTIJmEwW+9u/3DmMimtIM6gFPbUo/ns7if3f24/XndsiED1CGlsbe0fl8D52M4IkjQbzn\n4iVyUZxhzYmAsHf0PH1A8fULSH8ylkYkmS0bvK8HLNKfCyhcH1fAiPS7NlOTsD33EDnEJ8n20ekp\n84tdA4inczh7QYOWXApw91N9+Oz2vRgNJdHocaDBXX4SKYhIc1M7PeR7Fij9cEKqKtXxpgVEcFET\nzC2n9NM5+FzaY13a6keT14ndA1MKGegEcr/64AF8e8chvDAYAufKYKEH3ZRGHZwICvvE3M3f4nNi\nOqGj9FVtkcOJDBp1ArmiA6omV19cp/CwfrpmCetpoRTH0Zyj3jKWBg3H7n7qBJa1+/GKFcpgEPA6\nEUll5dz6SnHfc0MIJ7N439YezfMtPhd9n0X/ncZi0r/ziT64HTa8c/NiBDxOtPpd6JuIIZ7O4p23\nP43n1TUSArYSKZuAksHjb0c+z/H+bbvwlwNjci1LuTTdesAi/bmAUp5+RnUDpmOULta1Gbjow1SM\ntfseYPfdADhwzus1b8/lOX76VB8297Rgy/I2TJVQ+n89NI57n+nHY4fH5Wl+OYheMEXTd39bkdKP\nyErfeDARN4xcDKYOSuoQGOccsXQWfrdW6dtsDGu7mylAeu6bgVf/OzB/ddH7t+8ewvf/clROP12n\nV3CmQkeTW5vSqINJKYVSWC/l0OxzYTpWoPQjo5Qh4mlCPs8RSWUNA7kA2TvK+yXrIjxUdM1yeY6R\nUBJdLfoLnojPfaSQ9Kf6FKIHdFcZy+U59g2F8XerFsCmSi0Vn3e0CouHc45tT/bhvM4ANi7RzsKa\nfU5MqUm/QOlPx9PYvnsQb17fiRY/XaeeNj+OB2N47sQ0njoWxKMHdVZ9K5WnDwAr3wBcegvQtQlH\nxqP484Ex3L9nSJ5BmJ3h1QKL9OcCBOkXFlUVpmzKKyltomXkui+iLIqdPwKWXCI3+xL400snMTCZ\nwPu2LkWT14lIMmuYvRBLUTD22HjMlLUDqJR+YUaLf36x0pebhhkr/UXNXrjsNqXXj6uBbAQD0k9k\ncuAcmkCuwPruZhw6GUHM0Qy88lNFmT+JdA4joSTSuTzukhRqk47PrUZHwCtnNxkhLmXSeF2l0zUF\nmn2khOWVsNwBICQF3D1NiKaz4FzfFvO5HPC57JhQF2gJEkxMFV2zYDSFXJ7LmVmF8LrsaPI6tUpf\nzB6HVH3+ZaWvtOAYnk4gncsXKV1dC9AkHj8ygSNjUbxvy9IiO6rZ50KohL3zvzsHkMzk8V7VDKGn\n1Y++iZhc4KXbSLCcp+9uBC79DGB3kn0Iigf1TcRgY0B3i2XvvLygXlDZaJlBPZQkfVXKpmgnIG7E\niz5MKiw0QI8BzfKG257sw6ImD167eoEcuAsZ+KvqPPSFJkm/UbZ39JS+SNmUPH1pGz2bQsBuY+ie\n51Up/dL2jhioCj19gKyaPAdeGAwVvQYAJybpf3idduTyvChVUw8LmzxllX4inYONAW6HuVuz2Vtg\nkbkaKEcfADxN8ixK3WFTjdYGl7YqV32dCr5P4tgXlpjJFaVtimUs1RZPrLi18HGDVFBDYWACdz7R\nh7YGN65aW2zNNXslpS+v/asEULO5PO5+qg8XL2vVBG2XtvkwFknhSSlDTLNojECp4qwCiF5NJ4Jx\nPNc/ha4WH1wmP/daYJH+bMH+3wBfXqysFfuVHuDF+829NzxEN2hhJa3DQ4ojL6nAvsdp1SG/1Ohp\n5RupXW4zLSpy33ODuOBLf0Iyk0PfRAxPHg3i3RcvgcNuQ7OPvsxGGTzRVBbnSF0lO5vNqRVBRMVK\nv70qpQ+QxSMv5uFupNRDg5RNMVAVevqAYtXsHdLxbaHkq3/0MvJoNywp3xNepDRq8uILEEtn4Xc5\ndAOlehDWw5SIt6jP09MkDwZGAfBWv1tblashfe01MxOkXxAoKNBy+ckaG1a1m5btHYX0jTxtYe8U\nCYMyOD4Rw58PjOHaCxfD7Sge1Fv8LiQyOaSdkiXavFh+7YmjQQyHkhqVDygD0lPHgppj1qBUcVYB\ndvdPoVkSU08dC54WawewSH92gHPgb98gj73/KVLkyWng8CPl35sMA/u2K8v/qSGvk5ukHvVHdlAA\nV8DuAK79BfCuXwA2O3b1TWE8kpLK4CnV87WrqZum+HIaZfDE0zmc39WEez94Ia67uLiXih68Tjsc\nNlacneFvpyrJfE5S+h6EExkwBjToELQaosFbPi+t9Rs9CXnd3wJEU0QkevZOi9+l5HLrQHiw123p\nwR3Xb8JbN3SVPd8OExk8iXTOtLUDKJ0zQyKYqyH9gEL6BoNlUdM19ftd2uwdpfDOmPTbG91auwgg\n6ySm8r9jE5QOqiqMOj4Rg89lx/xGbSyjWnunrcGFz121GtdetFj3dXHdpuZfALzz57Q2soST0qB1\n7iLtTEckCnBOy0NOxzPF90Op4iwVYqksDp2M4O0bu2C3MXAOLD0NmTuARfqzA/1P08IkAE2DRYpb\nYaqbHvbcS771hR8pfs2hIv2dP6Kc6803aLdZcC61NIaiXvcMTGP3wDQaPQ4sa6MbX1b6BiQYS1GR\n05blbbL6LAfGmH6Zvb+dYg/xSSJ9uwvhZBaNbocmyKeHnjY/Utk8Eau7sXgxEBWEf14YyBXwuuzy\nNoXom4ihrcGFgMeJ16xaULZlAqDYXiMh435I8YIK4XJokT6XqZie0m+Wq1mNbLFWv7uEvVOs9F12\nG+b5jD9fsovSGpsQnial2AvQXRS8byKGJa3FqaC6GV4m0Ohx4gOXLMX8Rv0BqkWeueYogUH1f4UY\nKMxAUyvxN6+nQb7I4ikXyJWwdyiEPAe2LG+T112wlP7LCc/cRjfGgjVKTxIAGD+o+PV6yOcpENu1\nmZYaLIQg/XgQ2P0zYPU1JdsDi+nq7v5p7OmnYiNBsi2y0i/+MlMWTM6QPEshoFd8IwghOkrTZMne\nKWftAKoMnomY1pNWPR6eTiCczCCWMrZ36Hm7oRVzPBgr2fpYD0IhlwrmxtM5eMvMZtSQZ2CJ6uyd\ntkYXJmNpmhkBRFoOb/G+AIyGEpgfcJcceNsb3Ejn8oikVJ+pp0n7PY6NF6Vr9gXjummqigVYeysG\nNUrNXI1mgA1uB9ob3fA4bXjdeQuk41ZI/8hYFHlmjvRF64y13c1YL6X6WqT/ckEuA7z0W2Dtu4Ce\nS8j7HOyV2s1yYOg54/cOP0e2TaF6FxCkv/9+8rWNtoOSjQIAO/smcfBkRLMGarNXeMfFN0kqm0cu\nzw3JsxQa9YpvBCGEpF7sUiC3VGGWwGKphfHgVEJbXCRZFfF0Flf9z+P42oMH5UCuUU2Bz+VAPGOs\n9Cu9STuadFIaC5DIZCtS+soMTNg72oGuXCyko8mLbJ7jqLq9QOG6uBJGw8myQXrdNFBPgEhfqP8C\n0s/m8hiYjOsOouKzMdOzqBII0p/SETHRVBYeJzXgK8R5iwK4YGkrlrbRymMifnRwNILLv/lX7ByQ\nehaV8fT3DYWweJ4P8/wubFneCoeNyTGxUw1TpM8Y62OM7WWM7WGM9UrPrWOMPS2eY4xdUOL9AcbY\nEGPsu/U68DmDVIR6bLcsoVTKbIL8fEHQpSwe0RJWp18OAMXTH90LgFGapgHE2qZru5sxHqHUvPWq\nYqNGjwM2pp+9I9skFZCVQMCr01BLNF0TqYeS0i+VuSMgCDORyelaFdt3D2EylsbgVBwxOZBrYO84\n7UjodMeMpbIYi6QqLqTxuRwIeBwlC7QqtXca3fS5TBcGcp0+wOGSZ1FG1+6K8zrgsttw11Oq9s8y\n6Ws9bVocRz9dU0Bp7aCyjDxN9B0X7bKjWntnaDqBbJ7rDqIOuw0NbkfFgdxyEINlKFEsYiLJLBrc\n+oPkd965Ht9913q4HXYsalYyxXYep+BuMCElTZTx9I9NxLC8nc73DWsW4m//epmmYvhUohKlfxnn\nfB3nXJRtfhXAFzjn6wD8h/S3Eb4I4K9VHuPchrgRXH5tf5IVrwHazia7xwjBo7Suqc4iFAAUpX/y\nRVrI3Gms0oQ3+Zb1SuraWlWxkc3G0OR16ir9WImAaDnoltnLSp/aP5DSN2fvuCVvPZUtJn3OudzS\nORhLI17muH0Gnr640Su1dwCqWi1F+ol0Dl4T8QGBos9FnLOq2ZrPZddVrQBV5V69bhF+/dygMqDr\nKH3OqTCrI1C6aEwofU1wWBQNJkNkScYnNMtklls0ptHjqGt7ZUCxK42UvtEg2ehxyjNOyhSTLFEp\n/TKZlz47vYZrEjjnOKGqbGaMydXMpwO12DscgJACTQB0G2czxjYCWADg4Rr+19yFmvSblxDhuRqA\n9pWUTz+4S5kW7/u1dlGUyaOUaqbTbx6AQvqTx5TufgYQRPaG8xfCaWdY0upDa4P2BpdL1wsgFHPV\npF94Q3uaKbdbpfQjSXP2jshvT2XyRaT/xJEgDo9FEfA4EIymEZMI3UhZ+9wOfdKXqycrz7boaPLg\nxeEwvveXIxiYLO7jUqnSB6TPRSZsydJyKwuolLtu793Sg3g6h1+ILqo6pB9KZJDK5ssqfdHaQdPP\nR036yWlaP1ZqQ3D30ydw7zO0cpnRIFpr/x09eJ12uOw23e9zVFp0phx6Wv04PhED51wutIrnJEot\nofTHIinE07nT0nJBD2ZJnwN4mDH2LGPsRum5TwL4GmNsAMDXAdxS+CbGmA3ANwB8qtTOGWM3ShZR\n7/i4TmnzXIbILnE1UgbBqquBlVdRps2Cc0kVJaaIuH/1fm3ufvCo0sBJD4L0eb7sYtIiG6WtwY1X\nr5yP153bUbRNk8+pT/qp0uRZCgGvztTdZqNunyeelM7DLQVyy9+IDhuDjVGcodDf/tuRcbjsNrxl\nQxcmoinEUlk4bMywEMrn1A/kDk4RWS+uYAlEgQ2LWzA0ncDXHjqIL/5uf9HrlQZyAfpc5NWlxDlL\nRBtNFbeZKMR5nU1YvTCAxw5L955M+kpMZKSgZbYRRFbMRERH6afCmr47B0Yj+Nz9+/Dw/pNY1u7X\nLLSuBlmA9SV9yhzT36+ZawYAqxYGEE5m8fiRCRyTFH9CKP0Snr5ciFbFTLEeMEv6WznnGwBcAeCj\njLFXAvgIgJs5590AbgZwh877bgLwB855yUbsnPPbOeebOOeb2ttnbhmxGYFM+tIX4KpvAm/5ofSc\nRCrZlMoPlZYZ5ByYPF6azNV2TqnBAdLCHdKX8Ifv2YTPXrmqaJtmb0FzLwkxgxQ3Mwh4nEhkckob\nAYGN11OnRwB5uxvRlDmlzxiD22HXsXcakEzn4HPbsajZg1Q2j7FICj6X3bAQyueyI54pnqYHY2l4\nnLaqzvcTl5+FQ/91BW66dDkeeelkkdpPpCsL5AJEtEb2TkKnoZweFjZ5ivehGjSFJbWgTF8ll8OG\nJq+zoHOnSumrCrPE/7vnhgux4+ZXGX4OAY+z5vbKenA77Nq1HCREUzlDT1+NN65diAa3A7fct1d+\nLp6VzqFE9k5fGTvrVMMU6XPOh6XfYwC2A7gAwPUA7pM2+aX0XCEuBvAxxlgfaDZwHWPsyzUe89yC\n2t4phF2yV3IpICvdkOKmiY1Tfr4ZpQ+UV/omFu5o8bmUfHAVSlW2loPwTotu6g3XyUsVJriT+seY\n8PQBwO20IZ3NK8VFdhfgcCORycHjsMvBxv5gvKQl5TVI2ZyIptDqd5uumi2Ey2HDdRf3wM6YvFYB\nQF5vPFO5vaNpr1xI+hlzxV5NPmdxrr9q0BQFZWZabLRJufoyPHqk3y6Tfntj6TTQei+ZKB+W00Yz\nwgJEU+aSBho9TrxtYxcGpxJgDDTDzIGsyRKkfzwYg8tuO22B20KUJX3GmJ8x1igeA3gtgH0gD/9V\n0mavBnC48L2c82s554s55z0A/gXAXZzzz9Tp2OcGUsbFQ3BIpJ9NKS2SxU0TPEq/S5G5w5zSj6ez\nOBlOoadMRWCTz6mbvSP3sKkmT7+wd4yAtwVY+w+0/xzt18yNCJCvT/aOlrySmTw8TpscbDwxGStJ\n+kaB3GA0bWhFmEVHkwdXrFmIn/cOyINmMpMH5+abrQloFlIRi8fIpJ83FRhu9rpKBnJHQknYGBF0\nObQ2FFTlGpC+GKiaywzmAU/9s3cAUvopPaWfzJqexb13Sw8YA86e3wi/y0EzB7uzpKffNxFD9zxv\n0UL1pwtmlP4CAI8zxp4HsBPA7znnDwL4IIBvSM9/CcCNAMAY28QY+/GpOuA5h0J7Rw016eekm0jc\nNJMS6ZcK0ArSZzbdFfFixz8AACAASURBVLEEhqYoONxdxqNu8bkQTWVJRatQi9Iv2VBr6yeAFX+H\nSf9yzbbl4DIk/Rw8TrscbDwZTpVMM/W6HEhkckrhkoRgLFUU5K4GV57XgUgyi2PjNNsT19Ff4XVs\n9jkRS+foc2EM2PQB4JwrAJBdZIb0W3xO5bNd8XfA+ndrRMN4JIl5fpdhFpAabQ0ubT8fsXZzcprs\nSWYHfK3yQFWuO2mjx4lIMlP0OdQKPaXPOUc0lUWDSYHR0+bHh1+1HO+5eAncTjuSmTzNLEvaO/EZ\ns3YAE8slcs6PAVir8/zjAIoSxDnnvQCKqoA459sAbKvmIOc0zNg72ZTSIlmt9G0O43RNQPH0m7qN\nM3ygDtKVnm42qzptqhVftEyRUykoSl9HybX0AO/+FaakroZmArkAFE/fZqceL4L0s3kN6QOlByqf\nyw7OgWRW64sHo2msKrVknkmIXHEx4FXaVllArpZOpKntwJVK9rRZe0fz2fZsBXq2al6fiKY1160U\nqLVDUHnC4aYqX7FgT2MHYLNjOk7ppHoN0dQIeB3Ic8oSazQ58JuBnqefyuaRyfGKvsv/+vqVAIAf\nPHpU+t45DAO5+TxHXzCGV5zVpvv66YBVkTvTEKTvLKH0c2rSl7IfJo8S4Rd21tS8XyL9Mn6+Wb+2\nqPpTQjydBWOknCqFspCKsTKKmFgqUQ23w0YpmwARvhSQTKZz8DhtmKfqDVTO3gGgsXg452TvmLA5\nykFuMSANeIlMdVlQTaLQSCezKpHOm+oLZPTZCgSjKfOk3+DCVDyjXXtB9N8JD8m966fimbLWDqCe\nDdbX4tFT+kZ9d0zvT1b6+tdxNJxEKps/bS0X9GCR/kwjHZUWoNb5KDT2jiqQyzkQPFaWzIn0WdnM\nHZGZMb9M4Y24QccLuijGUrmK2gGrYaZfurjZm8wGch2qm9ndqFL6ZO+4HDa5ZW+pOISwRdTB3HAy\ni3Quj1aTTeVKofDc42XqBoxQqtAomTFX7FXUw6cAwVhajoWUg7C+JgstnmSIlmGUlp4MJdLyYFMK\nhnGfGqGn9GvJRJNnmHanYXHW6VwW0QgW6c800lF9awdQ2rSqA7n5rJK3X4bMwRjwhq8DF3yw5GYj\noSRa/a6y0+w1nU1wO2z47fMjmudjJvOa9dBool96uaZhhZBvPgB4zX8AW/4RgJYAhWotbe/Qa2ql\nL9oLmFW9pSDITGQuCU/f66yMcJQZifYacs4le6f8ba506zRS+mnTSzi2G1XlJkNAeEResGQqnpEH\nm1IQBBxLnXqlLz4Ls55+4f7I0zcO5Ir4mdGSk6cDFunPNNIxY9IX9oza3gGol04mVl7pA9TDp/2c\nkpuMhhKmljhs8btwzbpObN89qLEBxMIf1cDvot4xpZU+vWb2RnSrb+bVV1MjO5B9IqwOoVpLBXL1\nyFQEKM2q3lIQZCYGtUSVSt9lp+0zOW2gM5PjyOW5KaUvZlF6Sj+ZySGaylas9IuqckODlGYs2TvT\n8bQ82JSCEBQxg46n1cLjLFb6wt5prMreEbEkp6GnLzqslqt3OJWwSH+mkYpqu0GqIds7aa1yGHiG\nfhdk7qSzeUzF0vJPUcGTAUbDKdNLHL7vkh4kM3n89MkTMvHHpaKnamCzMf1OmyqEE5RCZzbFTePp\nqyBSNgFFqZfL0we09o5Q+mZVbynYbQyNbkfN9o5YYq8wq0rECMx4+mINBD1Pf0Ke3Zgkfb9ep80m\nJeNMJv1M2cwdQDXjqrPS19iAEqI1KH23w1Y2e2cklESLz2nqMzlVqE6eWagf0tGiFYpkyKSfVOwd\nQCF9ldLnnOPK7/wNR8aUFrkbFjfjvpu0WRh6GA0lsNHEcn8AsLIjgIuXteJbOw7hWzsO4ZYrVlLZ\nepVKHzDotKlCxGSHTQGNvaNCMpOTLSxZ6ZcYrPQCucKyqDVPXyDgdSqB3Cqzd2TSz2nPWahYM/vz\nu+yGK4UF5XM2N9CJILdm3QBPE7UDAYDAInDOMZ3IyPGIUhAzomjd7R1jpV9dIFdUgjsNSd9Mp9JT\nDYv0ZxrpGOCbp/+aXJGbVipyAWBgF6mJpm75qeFQEkfGonjTukVY392MPx0Yw87jk+CclwywJjM5\nTMUz8lJ+ZvDVt52PPx8Yw/cfPYLd/dOIp7OGKxSZQbPXZZg1Akj2UQU3oZ6CA6gJm2zv+M14+hLp\nZ9RKn45zXh0CuYC2g2S19Q6GSl8MIiZUJWMMzT6nbjBY2DRmaxMCHicWNnmwb1i1Wpa6D1JgESKp\nLHJ5Lq/TUAp6g289IL4n6nskUlMgt7ynb2ZNglMNy96ZaZRU+iKQKyl9mwMAA1IhymG3KTfz7v4p\nAMAHLlmK925dikvPmY9UNq9bQauGsu6pefXRPc+H67f04LxFTegLxhBPVd46QI1mn9MwawSovPOk\n3IZBhVyeI53LqwK5dG1L3dyCfNU99SeiKbT4nHCYKFIyA1L6EulXmbLpspe2d8y2am72uXT7y4vZ\nTSUZS+sXN2PPwJTyhKjKBYDGhXJ6qZlArhjwYzprG9QCt5PqMNIqG7QWe0dW+nYXJVzoYDSUnFE/\nH5jLpB8do0KQemHiSMke2VUjHStB+mKNWyll0+FVZgUFmTt7+qfhctiwUioaWmhilSb169Woj542\nWoQ8UqO902zQslkgXmGPebJ3tASYlP1t+sq3ytk7ldk79arGFVA3E4uncmAMhl0/jSCUfuE5y56+\nyUGk2evU7a0kZjeVBK/XdTdjYDKhtGMQpO9vBxxuue+OmZRNt8MGu43VPXvHrXPdYqksbMz8QFm4\nv2QmT+JMR+mnsjkEY2lL6Z8y/PK9wP06i4VXg1QU+MEW4NEv1Wd/aphJ2cylifgdLmWBkYIg7p6B\naazpbJIJQKiJUgt2qF+vRn30tPmRzOQxHklV1UtfgBqGGds7iUqVvsNW5OknC4KaoqNoqaZXXgNP\nvx45+gIBjzaQW029g1v29AsGugrsHUAafHVmXBNR6kZaie20fnELAMh95mXSl3L0xSBvxtNnjMHn\nsss9nuoF8V1Q+/rRFCUNVFNzolH6Op7+WJgGQDOZcqcSc5f0p/qoH3veXAZLSURPUtpk70+AdPHC\nF1WDc1L6Rtk7jNEXKJuUSN+jkH6rQvqZXB57h0KaNW3NKv1R2d6p/Iu4VNUPvNo8fYBu/FDCuLdK\nPJ2tiHBcDpucriiQlNScUPqrFwXw1C2vxnmdTbr7AMg2sduYNmWzgspUM1DbO4lMtuIgLgC5H46R\nvWN2wGz26Q++wWiq4hTV8xY1wW5j2DMgSF/6bso5+kLpm6u98LscRXUItUKz4I6ESLL6Vg9C6XO7\nQ5f0xb1YSfzsVGBukj7nVLmaCivryNYC0e8mMQXs/UXt+xPIpsj7M1L6ABF9Nk2Djl2t9BV758BI\nBKlsXkP67Y1u2JhC6kYYDSXR6HFUFbhSrxxVTbM1gSafC3mu015ZQiJtrn+MgMjQUZOgCGqqU+XK\n9RpijMHntBfYO7V32FQj4HEgksoin+dVrZoFUOqn3cZq9vRbDBbJoXOubKDzuuxYtbARu4WvL5S+\nlK4pYk1m7B2ARMWpUvrqWWE0ZW7VLD2IpTrzTD+QOxKiwqyZtnfmZvZOKqxc9KFeYP7K2vYnkT53\nBxD963fRuOF6en7//cCKy/XbIpuB3GytAeFkBj/fOSBP0Z12hrdv7EaL3aUUZ2mUvkL64sZSL2Tu\ntNvQ3ujGaEi1vKIK0/E0ftE7gKePBav+Ei5q8sLloKBpQw1KX7R3mIqndfO2K+0xr3i1ymBRaO+Y\nhbqnfiaXx3Q8U19P30trBUTT2YpjF2q47MXB67jOQFcKzT4XEpmc3I1UYCKaRmcVvd/XdTfj/t3D\nyOU57KLTpui7I8UOzLbW8Lsd9Q/kSt+TZCaP4xMxjIQSplfNKrW/HHPArlOcdbKGWXU9MTdJXzQl\nA4DBXmoTW9P+iPR3L/oHbDh+O0aOPo+FDQ6KG1xZvs2BIVRtlR/YM4xb//CS5mWP047rHB6ly6bD\nBXRuBNpXAYEuebs9/dNoa3AX3ZgdAY+hvXPvzn589cGDAIC/39Slu0052GwMS+b5cHgsWpPSb/GX\n7vsSr1TpO4sDdELNVUr66p76z52gwbWaZRKNIPffSWQqjl2o4XLYij39CvL0AW2nTfV1CkZTWNtl\nbIMZYWVHANFUP8YjKXQEOinFuGszAOoI2uB2mGrVDEifwylU+nc+0YffvTCMjiYPllS5jKHYX44Z\n2zt+l72unUKrwdy0d4Qd4/AQ6de8PxpEHnduAQCEDj9FC5YDwETR2jHmIZN+A46Nx+B12nHgi6/H\n3s+/FoBkdzhcSj99uxtY+w7go09rGrTtGZjG+sXNRcGnjiaPtkBGhd390+hp9eHgf70eX3nr+VWf\ngugWWIun3yTlak/p+Mm5PEc6m4evgn40wt5Re7VJ6bGnwswYr0tZHP3OJ/rQ7HPqrh9cLdSdNiuN\nXaghZlxqVJKnD0DOmVd/Dvk8x2QFzdbUEP83nc1T3OrmfcAyWndp2mTfHYEGt6PuxVlqT18s/H4i\nGK+qBYN6f0akT4VZM6vygblO+ssuA8ZeVGyUWvbnacbO2AKEuQ9ssJdsI0ApLa8GKnunLxjDklYf\nPE47GtzUjyaRzhHRy4HcYlthOp7GsYmYxs8XWNjk1VX6nHPs7p/GhsUtcDuM14g1A9EtsCalLxSm\njp+sFCxVZ+8IVFvt6nPZkchkMTAZx8P7R/HOCxZXFWw1glB9kWSm4hmNGi57sdKvpA0DoOq0qfoc\nQokMsnleVdsJp0FWEf2PdEWk7zsFgVw5eyebQzSlnHO1nr7YXxb6KZtUmDWz1bjAXCX96BgAIHfW\n6wCex9Thp6vYxzjw8OcoiBobB/ztOB5MYk9+OQKTzyszCLFs4Z57gSM7KvsfKnunbyImEyilqEkK\n0+FWpWwW33giO2K9DukvCHgQSWaLFNLQNOVPr1tsrvVCKYjUx5pSNn3GSl9uQlbBTEIv/zpZo73z\ns6dPgDGG91xUYtGaKqDuFZ+oYn1cAbee0s/k4JJy3M1AIX3lcxDVuNWsH2BUNAZQh00zzdYE/G57\n3RuuyTZgJo9oKisr/GoKswDle5eFQ7c4azYUZgFzlfQlO+ZQy6uQ5E6MPH535fs4+Afgye8Aw88B\nsQnkfW0YDiWwhy/HgsRRYPwgLdw93U8Dw4O3ADsrXCVSWh836/ChfzKuWVjBKylMONyq4qziL8ye\ngWkwBpyvq/T1c/WVgaKlsuPVwSvPbsNl57TjrAUGaacmIHd41FX6lVepunWyMhR7p8JArpMCuX86\nMIZLVrTVfTFrxd7JVJ29A+jbO8kKA8MBj7bVMwCEpL5AgSqI0OWgwUav8V+swoI+v8tR/9bKDpXS\nT2bxqnPacc26RXjl2e3V7U+61hkdpc85x/yAp6b7pF6Yo4FcsmMG037syV2Ct47+nqpzjXrc6CE8\nTL+DR4HYOGKNy8A5sI+dDRukL/HKqyiF8+ifaf3PrH6mjCEke2cs5UQ2z7V57yKAKEg/m1SKtVTY\n3T+Ns+c36k5JhX94MpzEivkNmve4HTasXFhl1pEKXS0+3Pm+C2rah93GEPA4dHPE5SUEK/L0i/Ov\nCytyzcLnsmMsksJUPI2r1y6q6L1moF5IJZHOVdxLX0AvkJswuYCKgFC+yToEwAGl5bOevZPO5eX/\nZwY+N81883kOW50WFJfPV1L6Aa8TX3rzmqr3J9s7wtPnnGptQLP333y0fPPD04E5qvTJjhkNJbAt\n9zq4eBp4dltl+wgP0e/Jo0B0DFOMshecizcr26x5O/1+4ef0O1Mp6ZPS74vQF0Or9CV7x+6WUjbT\nRfYO5xzPD05rUjXVEEUghb6+qN41mzlxOtDi168GTWRq8PRzOqRfoZL2uhyYjKXBOQyvcy0Q3UND\niQxi6Wz1Sl8nZTORyVcUI5A9bpWNIgbOSltDAJR2DAAZHXsnnc3L9o8ZiHUPEpn6WTweOeCf09g7\n1UKujOZ2ABzI19eOqhdmz11fT8QmAH87RkJJHOSL8UTuXOSe+VHJFeqLEJFWhxo/CCQmMZYjVbxx\n1Vk4kZ+PZNNy3LBD+lAP/gEAMDk9XdlxSkr/eIgqR7XFTlJ+uMOtFGcVkP6JYBzT8YxuEBdQlP7I\ntDIYZXN57Cuo3p0NoFYMxZ+PKMipjPT1snck0q/Q3lH/3/O76n/NHHYbfC47ToaT4LzyQLOAUfZO\nJQpdtjtUxFqT0tcZfAXS2bwc6DWDU9F0TSj9WDqHZCZfdQBXQGPvAIYLqcw05ijpjwMN7RgNJ8EY\ncGfu9bBHh4EDvzO/D2HvSKmZQ+kGzPO7sLa7Cf+VfTe+a78OO/pziDG/3Os+nawwSygdBcBweCoP\nv8uOdlXRDwUQhacvddm0a0l/WCJzo0WWPU472hrcGJhSWkdMRNNIZfNY1j7z3qIa1HSthL1TVZ6+\n1tO3MUV9moUg/RXzG0wXElWKgMeJ371AIqPa2YRRnr63AgvFaafK3qReLKQK0hczyboofbF6Vh1z\n9YU4EE3hqg3gKvsTSl86L4P2yjONOUr6Y5K9k8SaziY8btuIKXcn8PRt5vch7J3oSQDA0YQPPa0+\n9LT68Uh+E747fBYcNhuO5ubLb3HlS7c8KILUYbNPCuKqUye9ovzf7lb66Tu0nv6Uifa0S9t86JtQ\nk77ojV6/VgL1gFF7ZcXeqaD3jl0ne0eqMq00PVX831M5Mwp4HYgkszhnQSMuXtZa1T707Z3KUkAZ\nY/CITpESxMBZjb1j1AgOIPVfyT7F51DPYK7dxuC0M7l1dC0ZaIAyMKa5tJ9KnIXTiLlH+rkM9ciR\nSL+7xYdVi5qx3fkGYOBp8L7H6fXElPGHko7RIs5SR0AAOBjxoKfVj3l+l+z9feFN56IftM0gbzNH\n+tLxxaYnkI5MIO/04fhErEit+zSBXEnpF2TvTEu9z0ulvvW0+nE8qMxAxBqv9ewfUw+0+Fy6i3JX\nl71TTPqJTGVWh4D4v6fCzxcQwdz3be2pumbCyN6ptK2Dx2nX+OZ1UfoFpM85Fdy5KiB9Yb3UeyEV\nj8MuL4FZX08fFumfNsSDAADua8OIVAG3YXELvjlxAaLcA7btDcBXeujntkv09xGW/PyeV8hPHY55\nsFRS48vmN6Cz2Yt3bOqGc8FK5DnD8/Y1cPGU/v7U+P5FwFd64P/2crhe/AWORhw4EYxjWQHpe9V5\n+uk4AF5k7wgPvJTt0NPmx3gkJefq13ON13qiyetEOJnVdMYEqiuqcqsCdALJTL6qvjbi2m5YXHt6\nqxFa/C40+5y4Zn1n1ftw2YtXCyvsoWMGhUsI1qL0jVb0Egu4V2LviMG37j31nba62TtyIDwvSH92\n2jtzL2VTqsaNu+Yhkcnh/2/vzIMkuav8/nmZWVldfU5P99yj0QwSOkDHSBodloQug0CCXWNguVaA\nCBRaYXu9Iow2IBYTq7AJBxhss7vYIFbLhpfFJgTSmiNA0mpZI0ASHrGjAyR0jEbSaM7umT7rrvr5\nj8xfVXZ19XRVZvZ0VtXvEzHR1VlZOa+yM1++3/f3fu9tGunjX+zcwimjGf78yc9ROfAEt7/5DAZf\n+zk89yMo5SHVkP+upZ0db6pV1ZxQw7z1HG/5/efffS6C4NgWl77vUzz59NWU9vwD7lTJm7G3lrjR\nCnMw+QK7+/4Z/7d4FlecPs7R4XO4c+0beft5mxbs6k3klr00zZIfqTuNTr9IJmWf8MbWC772Tcxz\nzpaRxMo7o4G6L8FWhLVIv60mKs0XZ7WTIqh56xs38lc37+LsTcPL7xyST99wFtk2J10bWSpls91s\noHTKalq+Ilz2zlJOv1qzuVVWrHuWY9eaxESdyF2k6S/RPWu16VqnP6mGgQIbR/pYN5Tm5it2sPeM\n93PdlzYxWHo9t5+91nP6swdh7Y6Fx9CZO6dcirIcKlXFuadt44wNXgaP7k4FsGZ8AzuveSd7n37E\n21DKLV0f3z/u38xcwBlv+RiXXXv6kl+j37XJliooJ01twN/g9I+3UL9Er5jdN+k5/cm5Iq5jRb7A\n40avyp3KFhc5fde22mpP2MzpF0qVtjN3wBthXHfWhrY/1w5xTKp7PQQWV9lsd3STaRLpO5aEag9Z\nb9i+cPSmHwJhnH78RdesWsnjoYiRvmUJrm2Rr2pNP5mRfvfJO/5q3INlz0EHGxa8bt0g1565jm8+\n+grFAb9ols7SCeJH+runBplKb2FCDfPRK163eL8AVdv/f0o5T46ZenXJ405a43zgkm0nPF7G9fp3\nliUQkTcszprKlpbNKNFpoPsmvNHCxFyRdYPpSPV2VgL98GpszJ0rtt9YRERwG7pneZp+913umqUm\ncttdl9CXshdl74QdgSxVhqEYJtL3v0f8RddstKI4mI6enZV2LApVnb1jNP2Tg19357WSF+E2VrX7\n6BU7mJgr8NND/tO4qdM/QDE1wnvu3sPDc5s5bG/murPWL94vQNXxl+eXsvDIX8BXLoX5yUXHBXjD\nmWcuiGaboeWMUtDpN0zkTueKy9Yv6XcdNgynecnP4PF6vCZL2oH6ZHTjZG7Y0gRpZ7FMEWehtKTR\nOJGrq5O2P5Fr1eZRwJsXCCPtaJtg8URuLdJvS9PXE7lxF12r2xBV0wevBEiu2gUTuSKyT0SeEpE9\nIrLb37ZTRB7V20Rk0Vp8ETlVRB739/m1iNwW9xdYxPxRsFK8Mp9CBNYPLXSUb3r9OKevH+Tre/xJ\nV63fB5k5yHRqHSKw6fe/ypbb7l126bdKaaef82Sc0jz86q8X7FOZ2g/Amg3LF+3SF3khqMA1Sdls\npVLhqWNeA3PwmlzH2eM1LvTDubHTV7bNtENNY3P0fEh5p1NwHYtyVdVaTtZq6bfr9B27IWUzfKRv\nW4IliyP9Qgh5x3UsUrbEX3QtcE20M2+09PEs8jrS74LFWdcqpXYqpXb5v38BuFMptRP4rP97IweB\ny/19LgU+JSLxFzAJ4q/GPTTj9TJtvLBEhJsv385jB0pUUkN1/T7IzGsclTE2j2S4+KztjK9fvn66\nCkb6hVnv9S//csHTvjR1gGNqkIHB5WveaEdXy/mFxSmb2VJL7eZ2jA0E5J1CrJ2f4mJ8MI1tyaL6\n/zm/WXi7NFadDJPJ0km4DTnxuTYbqGj63EZ5J3ykr+1aKtJv97gD6RUouuZH+oNpJ5aaPn0pi3wl\n2dk7UeQdBegZzRFgkU6ilCoqVctjTEf8/1qiOHOYCTXM7pePL9mA+F0XbmG4z+GIrPUi/XIBHvsa\n/Oy/ev+OvcT+yppa5ksriOuXUCjlPKdvOTB7gIn/953aPtWp/RxSYy1NGOkViAUV1PTrzlop1XJN\n8u3jA0zOF5nOlbxIP4Hyjm0J64fSi+oEZUNo+uBnoTRo02GydzqFxgVpzXoCt0KfYy+QxQrlaq1q\naRhSTVJJw2j6oCttrkykH1diQ9qxyelIf3o/PPwlOL4vlmPHRavfVAEPiIgCvqaUugu4HbhfRL6I\n58wvb/ZBETkF+CFwOnCHUmrRw0FEbgVuBdi27cQTnMsxdfQAz0y7vFCa46bLmh+r33X4nfM3s3fP\nCJtm/PIMP/rjBfs8wnZOHWujLZ7j7VspZrELs7D1Eo6+8iyvPXIP45d9AACZPcAhNVpbjHMidLXF\nvGou78wXK5SrqpbqeCJOW+c9vH718nGKlSrjCcvR12wc6VtUBjpXrDAaQo5qJu+E7T/bCaQbcuJD\nyzspq2FxVrRIP32CSF9X4WyVWmmSGKlF+jHo+fp4uYp/vg49BY/+dzjlMhjdHsvx46DVb3qFUuqA\niKwHHhSRZ4H3AJ9QSn1XRN4L3A28ufGDSqlXgfN8WefvROQ7SqnDDfvcBdwFsGvXLtV4jHZIFSaZ\nts7g2f/wthNerFtH+3mtMkp15jms/bs96eSOF8ByOJ4t8Y3/9DCfaSPSx/XknXJ+HrswA8NbeZ6t\nbMnXs3jsuYMcUjs5vYUaLnryMk9zeUdPeOoWdydClxB46FnvtCcx0gcv0+r5I3MLtmWLFbaMhpzI\nNfJOqBW5C1M2q5GynlJNsor07+3WQfKaoyc70u9L2WTz/vk6/rL3c3jT0h9YBVr6a+roXCl1BLgP\nuAT4CHCvv8s9/rbljvFr4E0n2i8SSjFQOkYhPbZsnZWxQZeDrEXmDsOrj8HmCyA9BKkML017F9b2\nNhokW768Uy7MeYuw0kPsrWxgXXG/V1e7XMAtHOOgWltrnHEitNPPVQP7BlI2p3PL193RrB/uY8ua\nDA8942U2jSdQ04fmkX42ZI1517FqK3KVUuQjOq+k07j6NWx7SJ2nr5QXexVKlQWTnWHsWhTpVyoL\nbG6VgbS9Ypp+1Bx9TdqxyJa109/n/Rxa2WnMdln2rIvIgIgM6dfA9cDTeBr+1f5u1wGLOoSLyFYR\nyfivR4ErgN/GY3oTinO4qkilf3zZXdcNpjms1iKqCq89Dlsuqr2nJz2Xql7ZDO30KwVvIrfiDrK3\nupF+lfVKQ/gTxodY25q849+s2UrghgssztKtBVuZyAUv2td6eVIj/U0jfcwVyszm65Pf2ZA15oOR\nfqmiqFRVd2fv6IYl5Spf+ckLfOmB54AQmn7KoqrqpRJiifSXknfadPr9K9A9S89XhEkWaEZfyiZb\n8YPNqZehf2zxiv9VppVvugG4z4+aHeBbSqkfi8gc8GURcYA8viYvIruA25RStwBnA1/y5wIE+KJS\n6qkV+B4e/mpcGVi+3dnYoMtBFeiktXVX7eW+iXksgW1rW9f07bR2+vNQmKWcGuQl5TvpyRdBedHN\nIbW2paiilpdcDTwgAk5f191pRdMHr2DYD5/yHjxJjfR1/9DDM/law/Dwefo2E2XvwRi2P24nEYz0\n/9vfP8dg2uGCbWtq8zmtos+R7q+bjxrp2xbF8kLFthAye2ckk+LpJpVYo9DnxKvppx2LCR3pF+dg\ndMeJP7AKLPtNnNrXUQAAHPZJREFUlVJ7gfObbP8ZcFGT7buBW/zXDwLnRTezNcqzR3CA1MiJF1IB\njA2mObTA6dc7Yr00mWXLaKatSMTynb7KTUGlQMkZYJ/yRxzHXqxJM4dZ21JUUSswVQnYYAedvufQ\nRlp0+sHSwO00pD6ZbBrx5kUOTuc5ff0QlaqiUA63qCqYvRO2a1YnofXx6VyJUkVx61Wn8fFrTmv7\nOLX+wqUKZFLRI/0mNYHCTuSODbpMzhdRSsW2olx/3zg1/bly4HwNJ0vagS5bkTs94UWy/aPL59WP\nDQQi/cGNMFyvcLhvYr4tPR/AdfsoKRuZ80YbRWuAV9U6KlhepO+vxp1zN7SUD5x2LERgfoG8Ux8m\n6ki/lYlcgHO2jOBYwkgm1faw+mShG7lrGUpPRkZdkVuoNUVP5veOA/03nZz3MqRbmTdqhj5HeoFW\n1Eg/bVuLmqjUqmy2+fcYH0hTLFdjLcWgRxvxavqB+9s4/ZVlbtJz+sNjy5/ovpRNOb3GK3OwdVet\ngTF4xcnadfppxyaHi2S9DJm83U8ZhwOs9yL9qVfIWxns/taqNYoI/SmbuUrzlM3j2RIDrt3yjdOX\nsjl703Bi9XyA9cPeSOaw7/R1el4m1OKsesqmfnh0s7yjnZduCNLKvFEz9KhKS2LRI31pEumHm8jV\n166uihkHKxHpzybc6Ser1GJE8lOHAFi7vrUTPTaY5ruDf8j7r3x7bVu2WGY2X2bzmkxb/7frWORJ\n05/1Cr7lxZN7XmYjpxx5FrKT/DZ9Xls3Y8Z1Fg4Vg/JOrtjyJK7mjreeyWw+meVewXPUYwMuB/1V\nubkQZZXrx6rLOzoyTFpl0TjRUokunR02ctWT3bmil8ETh6bfeM2FXZylV5JPzhfaSrI4EXFr+q5j\nka1Ydc+aQKffVZF+eeYwM6qfjWtHWtp/bMDl+6nrYWt9akJHEe1GxGnHIqdcnJwn7+Qsz+nvrWyA\no8/A/BG+3/e7bTn9ftdmtub0Bez6Z6darLsT5Koz1i2q2580gmmbYbpmaTxN33MuM/7kX1jJoxPQ\nDnRiVss74SL9WiOQkrf4r6pYsTz9dgquAbWaUUdnkxvpu7a1sHTKUPLut65y+mQnOMZwyw2sxwfT\ni4aKOlJqt52g61jkSJPynX7Wj/RfrPrzC+Nn8rPquW1FYP2uzWzJd3hOeoEE1WoJhk5j43AfB6Zy\nzORLtb9N2IJrxXIVpVQt0gwreXQCNafvX79hv6t28PlytTYBHjVPf6mJ3HYXZ40HIv24qEX6MTn9\nlGNRxUKJ71qHw3dDWym6yuk7uUlm7dGWZ/bHBtM1DVSjf283rdGTd1zsqvf5eXynr/zh3aV/wEy+\n3FYElglG+oF0zWpVcXA6n9gsnChsXpPh2UOznPenD3DT3Y8B1NI32yHYSGUmryP9XnD6vqYfdiI3\nEOnrkVKUSL9Znf9CxeuP224Gji5HHqemr2WddqXSpaiNXiz/WkugvNNV491M8RjTbutP1vFBl2Pz\nBapVVcuoqfWQbdPppx2b46r+mTn6gSl+UX0j0+/4OiMXvIuZHzzUVgQ24DrMFssg1gI9/+EXJjg4\nnV+2xn8n8vFrTmP7+EBtRehQn7Mg3bRVtIOfyZeYyfVApG/HFenXnX4t0o/YxrFZ7Z10yE5cI5lU\n7R6Ng0t3jPHVmy7iwpga36f8h6+yU4idgr6Va7MZlq5y+kOV4+wfWrSkYEnGBlyqCqYCfVkn/Zo2\n7dac9+Sd+mdmlZd+WMViesc7GBSbuUK5rQgs49qePXZ6QaT/jZ+/xPhgOvH6fBg2r8nwsSujL2hZ\n4zv9qWyJmXyJlC09UYZBt8MMm6lUk3cCkX6UgmtLafph04bHBl0m5uOL9G1LeNs5y6d4t0o6GOkP\nLr9IdDXomrugWi4xomZRA8uXYNDUsgECkcPEXIHBtNP2TZNe4PSFuUr9AZArVZgLoSvXmqM7dae/\n9+gc//jbo9x02bZIWmu3M1rruVtiJldiqC+VuBaRcVKrslmpMhwhE6Ue6dc1/agN20tNeuSmQkT6\n4OXqxxnpx03K8a6xqpVKpLQDXeT0j00cwhKFM9x6E2udoXM0cBGFrTevUzYBSA+TC0Q3uVKlpiu3\nO5GbLVY8h+/LOz980luL8MFLo5Wg7nbqPXeL3lxKTCl5SSWYCRNFxso00fRjj/Qr0SL9ODX9uNGp\ns5XMGIyfucrWNKdr7oShNWM8e+M97Njy+pY/s64W6dcvosn5Qqh2gq7tpWwCkB5aUJM8V6wwbbU/\nmZhJOV6uen+6tjBrcr7IUJ+zqA2kYSHa6U9nS8zmS109iQtgWYJjCeWqYijCd12JSL9YqS4onRBV\n3nnspeQ6fZ2RtPeGb3H2ttYD0JNJ1zj9dF8/Z11yfVufaSbvTM4V2yq0Vvv//ZRN75ehRc2lFd4Q\nt115J1uqoJw04pdgmMmXunpCMi50NsZUrshMrjfOmetYlIuVSKMa2xJStpArVWrlKyK1S/SdYKmi\ncJ2A0w8p74wNpDmeLVKuVHFCHmMl0Q+znDsKbjwLyOImeWftJLImk8KS+uQthO8hKyIUxI++00ML\nGlHkSpV6BkmbE7mVqkLZbq1g20yuHFudkG5mwLVxLOF4tuSnynb/OdM6edRRjdccvVJb0Rw10gcW\nZPBEkXfGh9IoBceyyYz29cOsUdJKEj3t9C1LWDeUrq0ArVQVx+aLbS/M0pQt7fQHyZUq2H4aaK5Y\n1/TbjfQByuveCBvOAfxIv8ulijgQEdb0u/WJ3HT3nzPtSKPOX/S5NoVypVZ0LaqmDwudYBR5Z3wF\ncvXjpNlDLml0f/izDKeuHWDfpNc0ZSpbpKraT9fUlKy01004PUQ2X2G0P8XEXNEbKvsXfTsOW19A\nU2/9c9b7teZnciW2jrYvP/Uia/pTTGWL/oOy+y91HWVGlbL6Uhb5UnVFI/2wK2DHmszDJYlmD7mk\n0dORPsD28X5emsgCgRz9kE1GSvZCeUenDeZLlVr9l3Yudn0TB3u9zvaIVBEHo/0pjs4WyJeqPaHp\n64g8DnknV4w30i80RvphNX1daTPGUgxx0gmRvnH64wNMzBWYzZcCdXfCOf1KzekPkyvWnb6Wd4bS\nTk3yaYVaQ4vADWMmcltnJOPy6nHvgd4Lklhs8k7KJl+OR9NPN4v0I8k73r3ZWD4lKTR7yCWNnnf6\nO/y6+S9PZmtDxrCafsX2yzH7KZtDfU4tE2Iq274W3zgpVK0qb1WvmchtidH+FIdnojUV6STcuCL9\nlOWXYYhR049pInc447BjfKCWFZQ06g85tcyeq0f33wnLoOtyvzQxX4v0w8o71Qan3+faXqPkYoWJ\nuQLjQ+3W89FRgxdxzRbKKNUbUWscBKuQ9sLoKD5N32Y2X6ZQruBYEik1UttUCvTJjSLviAg/+eQ1\noe1ZaYym3wHoDln7JuaZnCtiSb1uS7tUnICmX6yQSdlkUl762+RckXUhavRD/QKq1YXvAQcWB8HK\niWEqdXYaOnqOmtLb51+z+VI1UpQP9QJkxUo9hTmKvJN0jKbfAWRcm43Dfbw0Oc/hmTxrB9yWetg2\noxbpu17KZiZlk3FtcqWKv9K3/XLNUB8a1+rC94BUEQcLIv0eOGfxyTv1PP2oLSabJSN0s9PXK3JN\npJ9wto/38+zBWR585jAXb18b+jiH+0/n25n3welv9py+60X684VKqJo+uqCaXhkZJte/lwn2G+iF\ncxabvONYfhmG6JG+XoUb1LgLlfDyTtJpDNSSSHee+TbZMT7Abw7OMJUtcfPl20Mfx3FSfCN9E9X0\nCPlSlb6Up+kfmc1Trqq25woaL6B627/ud2BxEJTpeuGc1SP9aKOajKuzd6oxRPre53Xkq5Tq7kjf\nMpp+R6B1/TdsGuaSHeEj/XTKplCu1oayWtPffzwHhGvBCPWJ3Blf3jFlGFpDa/qWeGUZuh3XsXAs\nqVXKDEtd069Eds6pWqRf9X96EX+3RvqWX7vIaPoJ57R1gwDcfMX2SDXXdWs4XWEzk7LIuDbH5sO1\nYDQTudHQmn6319LXDKYdRgfcyN/VSz6ocmy+GJumr69hPWrt1kgfmpeTThLde+bb4Joz1/EXH7yA\nd1+4NdJxXMeiEHD6/a6zoKl3u5q+2+j0Q9Tk72W0pt8Lk7gAt119Gl+96aLIx7n6TK/j0+MvH4+e\nvdOQp18qd7/Tb9YiMkl075lvA8e2eMd5m9taLduMtGNRKFdqZZX7/IlcTbvZO8Hm3uBl7wy4diJL\nyiaRvpSF61g9MzLavCbDRaeORj7OhdtGOd/vSxw10m8crfZMpG+cfm+Qdnx5p6jlnbrTF/FWiLaD\n2+D0Z3KmwmY7iAij/amecfpx8lE/oSFqX2Ed6evIVzv/btX0Qcu8yV2R271nfhVI+/JOtuhNuOo8\nffCkhnYj9MYc55l8yUg7bXLKaD+b12RW24yO48ZzN7F5pC/06nRNo0RZ6BF5J8mRfkseRET2AbNA\nBSgrpXaJyE7gq0AfUAb+lVLqlw2f2wn8D2DY/+znlFLfjs/8ZKEvZL2IKuNateFxmHo+IuJdQLVI\nv2yi1jb5+od34SS0TkuScR2L7/3hlZHlnaUi/ahzBUnGta3a3EUSaSdsvFYpNRH4/QvAnUqpH4nI\njf7v1zR8Jgt8WCn1vIhsBh4XkfuVUlORrE4o2ulP+1k2fQF5p109X5MOZALM5EtsGDa9cdthNGRv\nBEP4arNBGleo9oKmn/RIP8qZV3gRPMAIcGDRDko9p5R63n99ADgCrIvwfyYavYJ2ynf6nqbvneJ2\nM3c0rj85DH4tfSPvGDoIEfE0bj8/v67pd++6iaTn6bfqQRTwgIgo4GtKqbuA24H7ReSLeA+Py090\nABG5BHCBF5u8dytwK8C2bdtatz5hNEb6Gbeu6YeNmtLOwkjfTOQaOo2gRFnsEU2/G+rpX6GUuhC4\nAfjXInIV8HHgE0qpU4BPAHcv9WER2QT8DfBRpdSis6GUuksptUsptWvdus4dCGidctpv2pzxyzBA\n+BaM+gJSSnnZO0bTN3QYwchXV9vsZqefsrsgT9+XZlBKHQHuAy4BPgLc6+9yj79tESIyDPwQ+IxS\n6tGoBieZE2r6oSN9m2K5ynyxQlWZhVmGzqNZpJ/q4sn14Og8iSzr9EVkQESG9GvgeuBpPA3/an+3\n64Dnm3zWxXtI/E+l1D1xGZ1UdIrlVK6EiPfHr8s74SP9YqVqiq0ZOpZg5FvogeydpEf6rYSNG4D7\n/HoeDvAtpdSPRWQO+LKIOEAeX5MXkV3AbUqpW4D3AlcBYyJys3+8m5VSe+L9GslA97Tdve84O8YG\nEBHesGmYK08fZ+e2NaGOqSdy5wpeGmg7jdUNhiTgOhaFRYuzunci1014pL+sB1FK7QXOb7L9Z8Ci\nQh9Kqd3ALf7rbwLfjG5mZ6Aj/blCmX93/RmAJ+t885ZLQx9TDxXnjdM3dCjBvPVeSNn0In2zIrcn\nSPvpmYNph/dcFK14m0ZP5M4XdBG37o2QDN1JMG+9VwqudUP2jqEFtE75e7u2xtaTtRbp+6UdBkyk\nb+gwgqWGeyHSd7tA0ze0yBkbhviDq1/Hx67cEdsxXcdeUM/HOH1Dp9GXsmpFCHNFzxn2dbPT73RN\n39A6Kdvi0zecHesxdWMWLe/0QgcoQ3eRSTlMznlrV7KlMq5tdXV58KSvyO3eM98lpFNa0zeRvqEz\nGUjbtcZCuWKF/nR3By6ubVOuKqrVZE7mGqefcFzbS9mcD9ToNxg6iX7XJutfv9lihf4uv4Z1X+Ck\nFl0zTj/h6IncbKFMv2tjRezuZTCcbDIpJ6DpVxa0EO1G3IYWkUnDOP2EoxuzzBfLRtoxdCRepF9G\nKUW2WKbf7e7ruLFxTNIwTj/h6AtoKlsyk7iGjiTj2lQVfhZa70T6SZ3MNU4/4ega/cezxa6PkAzd\niV5QmC1WyJUqXb/AUHcLM5G+IRQ60j8+X2Kgy7MeDN1J3emXvYncLnf6+p41kb4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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x125f0b38>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2042,24 +3305,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x15b040f0>"
+       "<matplotlib.legend.Legend at 0x1d9d5220dd8>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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QBEEQhAmkMBPEGYqSVo4C/wiCIAjCHFKYCeIMh4qXEARBEIQ5pDATxBkKWZgJgiAIIjRI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\nU1TVO5HRwjR18UD/WxudHtU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xEKHQ6HTji5+OmT6X+qDsRCm6YgZZmAkC3oFI1AT9Od08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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1468b358>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2083,7 +3346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "collapsed": true
    },
@@ -2094,14 +3357,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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zsjAzq0Hvevv2QOVKfjhZmJnVoIZS+/SqThZmZtaNQfVJsrj32eUVOZ+ThZlZ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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x14696438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2134,11 +3397,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'folium'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-29-697717bb5c5c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# import the necessary modules (not included in the requirements of pydov!)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mfolium\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mfolium\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplugins\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMarkerCluster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyproj\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mProj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'folium'"
+     ],
+     "output_type": "error"
+    }
+   ],
    "source": [
     "# import the necessary modules (not included in the requirements of pydov!)\n",
     "import folium\n",
@@ -2205,21 +3480,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/output_fields.rst b/docs/output_fields.rst
index f469125..99ebb32 100644
--- a/docs/output_fields.rst
+++ b/docs/output_fields.rst
@@ -213,6 +213,7 @@ Groundwater screens (grondwaterfilters)
     x,1,float,110490
     y,1,float,194090
     start_grondwaterlocatie_mtaw,1,float,NaN
+    mv_mtaw,10,float,NaN
     gemeente,1,string,Destelbergen
     meetnet_code,10,string,1
     aquifer_code,10,string,0100
diff --git a/pydov/types/grondwaterfilter.py b/pydov/types/grondwaterfilter.py
index 119e9e0..c7b92b4 100644
--- a/pydov/types/grondwaterfilter.py
+++ b/pydov/types/grondwaterfilter.py
@@ -84,6 +84,12 @@ class GrondwaterFilter(AbstractDovType):
         WfsField(name='y', source_field='Y_mL72', datatype='float'),
         WfsField(name='start_grondwaterlocatie_mtaw', source_field='Z_mTAW',
                  datatype='float'),
+        XmlField(name='mv_mtaw',
+                 source_xpath='/grondwaterlocatie/puntligging/'
+                              'oorspronkelijk_maaiveld/waarde',
+                 definition='Maaiveldhoogte in mTAW op dag '
+                            'dat de put/boring uitgevoerd werd',
+                 datatype='float'),
         WfsField(name='gemeente', source_field='gemeente', datatype='string'),
         XmlField(name='meetnet_code',
                  source_xpath='/filter/meetnet',
diff --git a/requirements.txt b/requirements.txt
index 95f63df..6816e55 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-owslib<0.19.0
-pandas<0.25.0
-numpy<1.17.0
+owslib
+pandas
+numpy
 requests


Test patch:
diff --git a/tests/test_search_grondwaterfilter.py b/tests/test_search_grondwaterfilter.py
index a1403af..3eec1aa 100644
--- a/tests/test_search_grondwaterfilter.py
+++ b/tests/test_search_grondwaterfilter.py
@@ -151,7 +151,7 @@ class TestGrondwaterfilterSearch(AbstractTestSearch):
         """
         return ['pkey_filter', 'pkey_grondwaterlocatie', 'gw_id',
                 'filternummer', 'filtertype', 'x', 'y',
-                'start_grondwaterlocatie_mtaw',
+                'start_grondwaterlocatie_mtaw', 'mv_mtaw',
                 'gemeente', 'meetnet_code', 'aquifer_code',
                 'grondwaterlichaam_code', 'regime',
                 'diepte_onderkant_filter', 'lengte_filter',
diff --git a/tests/test_types_grondwaterfilter.py b/tests/test_types_grondwaterfilter.py
index ccccf2f..7008483 100644
--- a/tests/test_types_grondwaterfilter.py
+++ b/tests/test_types_grondwaterfilter.py
@@ -62,7 +62,7 @@ class TestGrondwaterFilter(AbstractTestTypes):
         """
         return ['pkey_filter', 'pkey_grondwaterlocatie', 'gw_id',
                 'filternummer', 'filtertype', 'x', 'y',
-                'start_grondwaterlocatie_mtaw',
+                'start_grondwaterlocatie_mtaw', 'mv_mtaw',
                 'gemeente', 'meetnet_code', 'aquifer_code',
                 'grondwaterlichaam_code', 'regime',
                 'diepte_onderkant_filter', 'lengte_filter',
@@ -93,7 +93,7 @@ class TestGrondwaterFilter(AbstractTestTypes):
         """
         return ['pkey_filter', 'pkey_grondwaterlocatie', 'gw_id',
                 'filternummer', 'filtertype', 'x', 'y',
-                'start_grondwaterlocatie_mtaw',
+                'start_grondwaterlocatie_mtaw', 'mv_mtaw',
                 'gemeente', 'meetnet_code', 'aquifer_code',
                 'grondwaterlichaam_code', 'regime',
                 'diepte_onderkant_filter', 'lengte_filter']