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   "source": [
    "# Reading ISMN data (`ismn.interface.ISMN_Interface`)\n",
    "This example shows the basic functionality to read data downloaded from the International Soil Moisture Network (ISMN).\n",
    "Data for your study area can be selected and downloaded for free from <http://ismn.earth> after registration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, data (all available variables) for ISMN networks 'REMEDHUS', 'SMOSMANIA', 'FMI', 'WEGENERNET', 'GTK', 'VAS' and 'RSMN' between 2009-08-04 and 2020-12-12 was downloaded from the ISMN website. Note that - depending on when you download the data  - available stations and measurement values can vary slightly from those shown in this example.\n",
    "\n",
    "<img src=\"ismn.png\" style=\"height: 450px;\"/>\n",
    "\n",
    "ISMN files are downloaded as a compressed `.zip` file after selecting the data from the website. You can extract it (with any zip software) locally into one (root) folder (in this case 'Data_separate_files'). The archive will be organised like this:\n",
    "```shell\n",
    "Data_separate_files/\n",
    "├── network/\n",
    "│   ├── station/\n",
    "│   │   ├── sensor.stm\n",
    "│   │   ├── sensor.stm\n",
    "│   │   ├── ...\n",
    "│   │   ├── sensor.stm\n",
    "│   │   ├── static_variables.csv\n",
    "│   ├── station/\n",
    "│   │   ├── ...\n",
    "├── network/\n",
    "│   ├── ...\n",
    "├── ISMN_qualityflags_description.txt\n",
    "├── Metadata.xml\n",
    "├── Provider_qualityflags_description.txt\n",
    "└── Readme.txt\n",
    "```\n",
    "You can either read from this extracted root folder, or read from the .zip file directly (the reader will then unzip files temporarily when needed). Reading from zip is therefore slower than reading the extracted files. Extracted files are (much) larger than compressed files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class for reading data from extracted or compressed files is `ismn.interface.ISMN_Interface`. It provides functions to access single networks, stations and sensors and the measured time series for each sensor as well as metadata for each station/sensor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ISMN_Interface` expects the path to the downloaded and locally stored ISMN data (zip-file or extracted root folder) as the only required argument."
   ]
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     "text": [
      "Using the existing ismn metadata in /tmp/python_metadata/Data_separate_files_header_20090101_20201231_9289_Cwpc_20221201.csv to set up ISMN_Interface. \n",
      "If there are issues with the data reader, you can remove the metadata csv file to repeat metadata collection.\n"
     ]
    }
   ],
   "source": [
    "from ismn.interface import ISMN_Interface\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # matplotlib is not installed automatically\n",
    "%matplotlib inline\n",
    "\n",
    "# Either a .zip file or one folder that contains all networks, here we read from .zip\n",
    "data_path = \"/tmp/Data_separate_files_header_20090101_20201231_9289_Cwpc_20221201.zip\"\n",
    "ismn_data = ISMN_Interface(data_path, parallel=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first time you initialize `ISMN_Interface` for a dataset, it will collect metadata for all sensors in your data collection from various files (this is **always** done for **all** available networks). The program will iterate through **all** files and collect information such as station names, sensor time coverage, measurement depths, landcover/climate classes, soil properties etc. for each sensor. Depending on the number of files and whether zipped/extracted files are used, this step can take a while. A log file is created in the displayed path. Parallel processing for this step can be activated manually by choosing `ISMN_Interface(data_path, parallel=True)` and will speed up metadata collection significantly. By default this step will create a folder called `python_metadata` (which contains the collected metadata as a `.csv` file) and place it inside the passed root directory. The next time the reader is created it will use `python_metadata` (if it is found) instead of generating it again (a different path to generate and search the metadata in can be passed via `ISMN_Interface(data_path, meta_path='/custom/meta/path')`.\n",
    "\n",
    "**Note: When changing the data (e.g. if you add or remove folders from the collection that is passed to the reader) make sure to delete the `python_metadata` folder and its content to force re-generating it. Otherwise data and metadata won't match anymore!!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To limit reading to a selection of networks from the beginning (e.g. to load one network from a large collection, which is faster than loading all networks and then selecting one), their names can be passed to the reader (e.g. `ISMN_Interface(data_path, network=['REMEDHUS', 'SMOSMANIA'])`. Otherwise, all networks stored under the given path are loaded. Limiting the number of networks when calling the reader will result in faster initialization, because less files are loaded (this does **not** affect the one-time metadata collection, which is always done for all networks in `data_path`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can call `ISMN_Interface(data_path, keep_loaded_data=True)`, which will cause that all sensor time series - once read - are kept in memory for faster subsequent calls. This can fill up your memory and is only recommended for small data collections, respectively if only a few networks are initialized.\n",
    "\n",
    "Finally, you can define a custom temporary root folder, e.g. `ISMN_Interface(data_path, temp_root='/tmp/my_tempdata')`. By default this depends on your OS (e.g. `/tmp` for Linux), and is used by the reader to store some temporary files, e.g. when extracting while reading from `.zip`. The default `temp_root` is cleared automatically by your OS, so we recommend not to change this (unless necessary e.g. because of access restrictions)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ISMN Components (Overview)\n",
    "Here we give a short overview over the components that build a ISMN data set. Each component provides various functions to work with its data and access sub-components. See the python module docs for more details. This is just a basic overview of how to pick data out of the collection. In practice, it is often required to loop over certain stations. This is described later in this tutorial. The components (hierarchically ordered) are `NetworkCollection` <- `Network(s)` <- `Station(s)` <- `Sensor(s)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collection\n",
    "The `ISMN_Interface` object holds all loaded networks (e.g. 'FMI', 'REMEDHUS', ...). Each Network contains multiple Stations (e.g. 'SAA111', 'SA112', ... for the 'FMI' network). Each Station contains multiple Sensors (names not shown in this overview). You can access Networks directly from the reader, and subsequently access Stations and their Sensors."
   ]
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     "data": {
      "text/plain": "ismn.base.IsmnRoot Zip at /tmp/Data_separate_files_header_20090101_20201231_9289_Cwpc_20221201.zip\nwith Networks[Stations]:\n------------------------\n  FMI: ['SAA111', 'SAA112', 'SAA120', 'SOD011', 'SOD012', 'SOD013', 'SOD021', 'SOD022', 'SOD023', 'SOD031', 'SOD032', 'SOD033', 'SOD071', 'SOD072', 'SOD073', 'SOD081', 'SOD082', 'SOD083', 'SOD091', 'SOD092', 'SOD093', 'SOD101', 'SOD102', 'SOD103', 'SOD130', 'SOD140', 'SODAWS'],\n  GTK: ['IlomantsiII', 'Kuusamo', 'PoriII', 'Suomussalmi'],\n  REMEDHUS: ['Canizal', 'Carramedina', 'Carretoro', 'CasaPeriles', 'ConcejodelMonte', 'ElCoto', 'ElTomillar', 'Granja-g', 'Guarena', 'Guarrati', 'LaAtalaya', 'LaCruzdeElias', 'LasArenas', 'LasBodegas', 'LasBrozas', 'LasEritas', 'LasTresRayas', 'LasVacas', 'LasVictorias', 'LlanosdelaBoveda', 'Paredinas', 'Zamarron'],\n  RSMN: ['Adamclisi', 'Alexandria', 'Bacles', 'Banloc', 'Barlad', 'Calarasi', 'ChisineuCris', 'Corugea', 'Cotnari', 'Darabani', 'Dej', 'Dumbraveni', 'Iasi', 'Oradea', 'RosioriideVede', 'SannicolauMare', 'SatuMare', 'Slatina', 'Slobozia', 'Tecuci'],\n  SMOSMANIA: ['Barnas', 'Berzeme', 'CabrieresdAvignon', 'Condom', 'CreondArmagnac', 'LaGrandCombe', 'Lahas', 'LezignanCorbieres', 'Mazan-Abbaye', 'Mejannes-le-Clap', 'Montaut', 'Mouthoumet', 'Narbonne', 'PeyrusseGrande', 'Pezenas', 'Pezenas-old', 'Prades-le-Lez', 'Sabres', 'SaintFelixdeLauragais', 'Savenes', 'Urgons', 'Villevielle'],\n  VAS: ['MelbexI', 'MelbexII'],\n  WEGENERNET: ['15', '19', '27', '34', '50', '54', '6', '77', '78', '84', '85', '99']"
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   ],
   "source": [
    "ismn_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can select a specific network from the collection above via its name. Here we pick the network 'SMOSMANIA' from our loaded set. Networks are sorted alphabetically, so you can also pass a number here, e.g. `ismn_data[4]` to get the fourth network from the list. This will (again) display all stations for that network.\n",
    "Note that we can call a network directly from `ismn_data` without using the collection."
   ]
  },
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   "cell_type": "code",
   "execution_count": 37,
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     "start_time": "2024-06-15T21:55:15.991694Z"
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   "outputs": [
    {
     "data": {
      "text/plain": "Network 'SMOSMANIA' with Stations: ['Barnas', 'Berzeme', 'CabrieresdAvignon', 'Condom', 'CreondArmagnac', 'LaGrandCombe', 'Lahas', 'LezignanCorbieres', 'Mazan-Abbaye', 'Mejannes-le-Clap', 'Montaut', 'Mouthoumet', 'Narbonne', 'PeyrusseGrande', 'Pezenas', 'Pezenas-old', 'Prades-le-Lez', 'Sabres', 'SaintFelixdeLauragais', 'Savenes', 'Urgons', 'Villevielle']"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['SMOSMANIA']  #  overview over stations in SMOSMANIA network; same as ismn_data[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "You can also convert all data in a network into an xarray Dataset object. You might have to run `conda install xarray dask`. You can also filter certain variables or depths in this processing step."
   ],
   "metadata": {
    "collapsed": false
   }
  },
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   "cell_type": "code",
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     "name": "stderr",
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     "text": [
      "100%|██████████| 22/22 [00:06<00:00,  3.44it/s]\n"
     ]
    },
    {
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      "text/plain": "<xarray.Dataset> Size: 316MB\nDimensions:                  (sensor: 125, date_time: 104958)\nCoordinates:\n  * date_time                (date_time) datetime64[ns] 840kB 2009-01-01 ... ...\nDimensions without coordinates: sensor\nData variables: (12/25)\n    soil_moisture            (sensor, date_time) float64 105MB dask.array<chunksize=(1, 104958), meta=np.ndarray>\n    soil_moisture_flag       (sensor, date_time) object 105MB dask.array<chunksize=(1, 104958), meta=np.ndarray>\n    soil_moisture_orig_flag  (sensor, date_time) object 105MB dask.array<chunksize=(1, 104958), meta=np.ndarray>\n    depth_from               (sensor) float64 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    depth_to                 (sensor) float64 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    clay_fraction            (sensor) float64 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    ...                       ...\n    saturation               (sensor) float64 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    silt_fraction            (sensor) float64 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    station                  (sensor) <U21 10kB dask.array<chunksize=(1,), meta=np.ndarray>\n    timerange_from           (sensor) datetime64[ns] 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    timerange_to             (sensor) datetime64[ns] 1kB dask.array<chunksize=(1,), meta=np.ndarray>\n    variable                 (sensor) <U13 6kB dask.array<chunksize=(1,), meta=np.ndarray>\nAttributes:\n    n_sensors:   125\n    n_stations:  22\n    network:     SMOSMANIA",
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div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 316MB\nDimensions:                  (sensor: 125, date_time: 104958)\nCoordinates:\n  * date_time                (date_time) datetime64[ns] 840kB 2009-01-01 ... ...\nDimensions without coordinates: sensor\nData variables: (12/25)\n    soil_moisture            (sensor, date_time) float64 105MB dask.array&lt;chunksize=(1, 104958), meta=np.ndarray&gt;\n    soil_moisture_flag       (sensor, date_time) object 105MB dask.array&lt;chunksize=(1, 104958), meta=np.ndarray&gt;\n    soil_moisture_orig_flag  (sensor, date_time) object 105MB dask.array&lt;chunksize=(1, 104958), meta=np.ndarray&gt;\n    depth_from               (sensor) float64 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    depth_to                 (sensor) float64 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    clay_fraction            (sensor) float64 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    ...                       ...\n    saturation               (sensor) float64 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    silt_fraction            (sensor) float64 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    station                  (sensor) &lt;U21 10kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    timerange_from           (sensor) datetime64[ns] 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    timerange_to             (sensor) datetime64[ns] 1kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n    variable                 (sensor) &lt;U13 6kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nAttributes:\n    n_sensors:   125\n    n_stations:  22\n    network:     SMOSMANIA</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-79604ec0-3393-4ca7-9796-9020f4f11fac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79604ec0-3393-4ca7-9796-9020f4f11fac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sensor</span>: 125</li><li><span class='xr-has-index'>date_time</span>: 104958</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ab4e3fdf-918b-4ebc-a9c2-c424e91ba5d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab4e3fdf-918b-4ebc-a9c2-c424e91ba5d8' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date_time</span></div><div class='xr-var-dims'>(date_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-5188424e-612c-4824-95c0-306a97ec2b5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5188424e-612c-4824-95c0-306a97ec2b5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec0205a9-affa-4fa6-b348-3234a3e65153' class='xr-var-data-in' type='checkbox'><label for='data-ec0205a9-affa-4fa6-b348-3234a3e65153' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T01:00:00.000000000&#x27;,\n       &#x27;2009-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b778630-6911-4730-a9fa-d05efce79d94' class='xr-section-summary-in' type='checkbox'  ><label for='section-3b778630-6911-4730-a9fa-d05efce79d94' class='xr-section-summary' >Data variables: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 104958), meta=np.ndarray&gt;</div><input id='attrs-4a935547-461d-4582-a142-618b417c369b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a935547-461d-4582-a142-618b417c369b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aef0678a-fdd7-48fa-bb9f-2047a65445b7' class='xr-var-data-in' type='checkbox'><label for='data-aef0678a-fdd7-48fa-bb9f-2047a65445b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.10 MiB </td>\n                        <td> 819.98 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125, 104958) </td>\n                        <td> (1, 104958) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 163 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >104958</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,12.706308)\">125</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_flag</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 104958), meta=np.ndarray&gt;</div><input id='attrs-c5fc520b-e2e1-4b82-a98d-4094e0d9352f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5fc520b-e2e1-4b82-a98d-4094e0d9352f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce561a6-641f-43c8-8c08-ee78e78ced0b' class='xr-var-data-in' type='checkbox'><label for='data-8ce561a6-641f-43c8-8c08-ee78e78ced0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.10 MiB </td>\n                        <td> 819.98 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125, 104958) </td>\n                        <td> (1, 104958) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 163 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> object numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >104958</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,12.706308)\">125</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_orig_flag</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 104958), meta=np.ndarray&gt;</div><input id='attrs-4a5bf30e-27e2-467e-ba82-986fe04bfd8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a5bf30e-27e2-467e-ba82-986fe04bfd8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46357552-8679-4917-bf74-20b1db629989' class='xr-var-data-in' type='checkbox'><label for='data-46357552-8679-4917-bf74-20b1db629989' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.10 MiB </td>\n                        <td> 819.98 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125, 104958) </td>\n                        <td> (1, 104958) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 163 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> object numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >104958</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,12.706308)\">125</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_from</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-119ac77b-899e-4501-b902-f8d67b674756' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-119ac77b-899e-4501-b902-f8d67b674756' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d87b9c22-88bb-4c06-99dc-f12efd6cf0a9' class='xr-var-data-in' type='checkbox'><label for='data-d87b9c22-88bb-4c06-99dc-f12efd6cf0a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 11 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_to</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-34c9d885-4095-478c-8e65-8083fa6e2a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34c9d885-4095-478c-8e65-8083fa6e2a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f0af5e-ad96-4dff-80cd-763a364a9f07' class='xr-var-data-in' type='checkbox'><label for='data-a4f0af5e-ad96-4dff-80cd-763a364a9f07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 11 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-eec65235-79fa-40e1-9289-5ee634e3021a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eec65235-79fa-40e1-9289-5ee634e3021a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c1a888-a392-4a17-a2ed-c7e5f9296626' class='xr-var-data-in' type='checkbox'><label for='data-07c1a888-a392-4a17-a2ed-c7e5f9296626' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>climate_KG</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-a731bc6e-bfbd-4d6a-8b50-e0ca721219a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a731bc6e-bfbd-4d6a-8b50-e0ca721219a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-800838c6-da6a-4861-82ad-29746d461a7d' class='xr-var-data-in' type='checkbox'><label for='data-800838c6-da6a-4861-82ad-29746d461a7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.46 kiB </td>\n                        <td> 12 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 15 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U3 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>climate_insitu</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-150b3234-2664-4b6f-98a0-32340d0c29a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-150b3234-2664-4b6f-98a0-32340d0c29a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5da25cb-f8de-4cc3-947a-c57a64ba9038' class='xr-var-data-in' type='checkbox'><label for='data-f5da25cb-f8de-4cc3-947a-c57a64ba9038' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 3.42 kiB </td>\n                        <td> 28 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U7 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bcc4faee-51bd-4da1-8918-2e0d57b4b538' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcc4faee-51bd-4da1-8918-2e0d57b4b538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cf3ea0b-1a04-4757-8a18-8ba824839832' class='xr-var-data-in' type='checkbox'><label for='data-8cf3ea0b-1a04-4757-8a18-8ba824839832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-939e5429-2313-40ae-9185-84f7e030b4a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-939e5429-2313-40ae-9185-84f7e030b4a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-336b082d-91cd-47ad-9ae8-1f0804964279' class='xr-var-data-in' type='checkbox'><label for='data-336b082d-91cd-47ad-9ae8-1f0804964279' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.32 kiB </td>\n                        <td> 60 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U15 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-5220dcd9-9704-4808-bde1-d749aedc824a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5220dcd9-9704-4808-bde1-d749aedc824a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cd4c7ab-4ca7-41f2-8c97-58faed5c72d2' class='xr-var-data-in' type='checkbox'><label for='data-8cd4c7ab-4ca7-41f2-8c97-58faed5c72d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_2000</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-9a57851c-94c1-46b9-a775-98d39b5f4fde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a57851c-94c1-46b9-a775-98d39b5f4fde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a48811e0-5da9-4c65-a72f-ed0eaf5c6ac6' class='xr-var-data-in' type='checkbox'><label for='data-a48811e0-5da9-4c65-a72f-ed0eaf5c6ac6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 14 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_2005</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-7d5b08b0-2b80-4d36-978f-60b6424c3a69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d5b08b0-2b80-4d36-978f-60b6424c3a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-134b6545-6217-4c6d-92b7-c3ab5f4f8b85' class='xr-var-data-in' type='checkbox'><label for='data-134b6545-6217-4c6d-92b7-c3ab5f4f8b85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 14 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_2010</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d505d587-9026-4f4b-9a93-c7a174bffe4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d505d587-9026-4f4b-9a93-c7a174bffe4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b2f8727-59c1-4111-b3b2-7b5d6d8dce1b' class='xr-var-data-in' type='checkbox'><label for='data-0b2f8727-59c1-4111-b3b2-7b5d6d8dce1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 14 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_insitu</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-53b366c8-2449-40b7-8566-fbfc6052f847' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53b366c8-2449-40b7-8566-fbfc6052f847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71c1e88d-2c57-4cd1-ac90-f81f296991a4' class='xr-var-data-in' type='checkbox'><label for='data-71c1e88d-2c57-4cd1-ac90-f81f296991a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 3.42 kiB </td>\n                        <td> 28 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U7 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8541ab7b-6be0-4a14-951c-7b4cdaea4c28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8541ab7b-6be0-4a14-951c-7b4cdaea4c28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd445eb8-7502-400f-9e60-efab94a99302' class='xr-var-data-in' type='checkbox'><label for='data-dd445eb8-7502-400f-9e60-efab94a99302' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-de0c90f9-3a56-4ccb-861e-f2ee5dd4f7d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de0c90f9-3a56-4ccb-861e-f2ee5dd4f7d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dac4180e-3891-4c47-acfa-cac52d730e6b' class='xr-var-data-in' type='checkbox'><label for='data-dac4180e-3891-4c47-acfa-cac52d730e6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 4.39 kiB </td>\n                        <td> 36 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U9 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>organic_carbon</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-20690faa-16d7-4caf-88d9-8913ca1903af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20690faa-16d7-4caf-88d9-8913ca1903af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90199605-ed00-43e9-ad4e-78bf302ec5db' class='xr-var-data-in' type='checkbox'><label for='data-90199605-ed00-43e9-ad4e-78bf302ec5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sand_fraction</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-82c6dbfc-8b15-4e9c-9942-1cbda69dfb67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82c6dbfc-8b15-4e9c-9942-1cbda69dfb67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d4829fc-2cd9-41d9-b283-37f95b987c41' class='xr-var-data-in' type='checkbox'><label for='data-9d4829fc-2cd9-41d9-b283-37f95b987c41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>saturation</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-252a014f-c994-4893-b490-88a9774531b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-252a014f-c994-4893-b490-88a9774531b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fcc53f9-64e5-46d7-b90a-c6cbc5367a1d' class='xr-var-data-in' type='checkbox'><label for='data-1fcc53f9-64e5-46d7-b90a-c6cbc5367a1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silt_fraction</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e4aec492-9482-4374-97cb-97cae9fbbce1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4aec492-9482-4374-97cb-97cae9fbbce1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-883a075d-d47f-43c0-b9b1-a00e949421f5' class='xr-var-data-in' type='checkbox'><label for='data-883a075d-d47f-43c0-b9b1-a00e949421f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 22 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U21</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-7b8b6eca-adb3-4380-a12d-4fb30f7cacc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b8b6eca-adb3-4380-a12d-4fb30f7cacc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-895583d8-53e8-419b-aee7-a89116bbf2a0' class='xr-var-data-in' type='checkbox'><label for='data-895583d8-53e8-419b-aee7-a89116bbf2a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 10.25 kiB </td>\n                        <td> 84 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 44 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U21 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>timerange_from</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-6488e499-f4bf-47de-90ac-a9df7c7e022d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6488e499-f4bf-47de-90ac-a9df7c7e022d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd9b619-004b-4766-ac27-e6479d1b4418' class='xr-var-data-in' type='checkbox'><label for='data-bfd9b619-004b-4766-ac27-e6479d1b4418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 19 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>timerange_to</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-eeb811f2-ab09-45f1-a5c3-ef805c92d869' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeb811f2-ab09-45f1-a5c3-ef805c92d869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec0165c9-4fde-468e-b92b-b13e2214999f' class='xr-var-data-in' type='checkbox'><label for='data-ec0165c9-4fde-468e-b92b-b13e2214999f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 0.98 kiB </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 19 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>variable</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e88fa1b4-5f93-44df-84d4-5f59ee30b553' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e88fa1b4-5f93-44df-84d4-5f59ee30b553' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-420d2e55-5eaf-47ad-900f-b94412b553ad' class='xr-var-data-in' type='checkbox'><label for='data-420d2e55-5eaf-47ad-900f-b94412b553ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 6.35 kiB </td>\n                        <td> 52 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (125,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 125 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> <U13 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >125</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8169dcc-f85f-44c2-bf63-6165f26e93ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-a8169dcc-f85f-44c2-bf63-6165f26e93ba' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>date_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed1e9df9-018f-4337-ab62-af76213301fd' class='xr-index-data-in' type='checkbox'/><label for='index-ed1e9df9-018f-4337-ab62-af76213301fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-01-01 00:00:00&#x27;, &#x27;2009-01-01 01:00:00&#x27;,\n               &#x27;2009-01-01 02:00:00&#x27;, &#x27;2009-01-01 03:00:00&#x27;,\n               &#x27;2009-01-01 04:00:00&#x27;, &#x27;2009-01-01 05:00:00&#x27;,\n               &#x27;2009-01-01 06:00:00&#x27;, &#x27;2009-01-01 07:00:00&#x27;,\n               &#x27;2009-01-01 08:00:00&#x27;, &#x27;2009-01-01 09:00:00&#x27;,\n               ...\n               &#x27;2020-12-31 14:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n               &#x27;2020-12-31 16:00:00&#x27;, &#x27;2020-12-31 17:00:00&#x27;,\n               &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 19:00:00&#x27;,\n               &#x27;2020-12-31 20:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;,\n               &#x27;2020-12-31 22:00:00&#x27;, &#x27;2020-12-31 23:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date_time&#x27;, length=104958, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e995fd6-0c16-42b8-bc17-e61c59071f49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e995fd6-0c16-42b8-bc17-e61c59071f49' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>n_sensors :</span></dt><dd>125</dd><dt><span>n_stations :</span></dt><dd>22</dd><dt><span>network :</span></dt><dd>SMOSMANIA</dd></dl></div></li></ul></div></div>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['SMOSMANIA'].to_xarray(variable='soil_moisture')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:23.200844Z",
     "start_time": "2024-06-15T21:55:15.996858Z"
    }
   },
   "execution_count": 38
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A network consists of multiple stations, multiple variables can be measured by different sensors at a station. You can select a specific station for a network via its name (stations are also sorted alphabetically and can be accessed by index). Here we access the Station 'Saint_Felix_de_Lauragais' from the 'SMOSMANIA' network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:23.205558Z",
     "start_time": "2024-06-15T21:55:23.202028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Station 'SaintFelixdeLauragais' with Sensors: ['ThetaProbe-ML2X_soil_moisture_0.050000_0.050000', 'PT-100_soil_temperature_0.050000_0.050000', 'ThetaProbe-ML2X_soil_moisture_0.100000_0.100000', 'PT-100_soil_temperature_0.100000_0.100000', 'ThetaProbe-ML2X_soil_moisture_0.200000_0.200000', 'PT-100_soil_temperature_0.200000_0.200000', 'ThetaProbe-ML2X_soil_moisture_0.300000_0.300000', 'PT-100_soil_temperature_0.300000_0.300000', 'ThetaProbe-ML3_soil_moisture_0.200000_0.200000']"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['SMOSMANIA']['SaintFelixdeLauragais']  #  overview over sensors at Saint_Felix_de_Lauragais; same as ismn_data[4][18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each station has a metadata attribute. The station metadata contains all metadata variables from all sensors that measure at the station (such as the sensor type, soil properties etc. per sensor). Therefore, the station metadata can be different for different depths. You can call `MetaData` directly, or convert it to either a DataFrame (`MetaData.to_pd()`) or a dictionary (`MetaData.to_dict()`) of form:\n",
    "\n",
    "```\n",
    "{var_name: [(value, depth_from, depth_to), \n",
    "            ...], \n",
    "...}\n",
    "```\n",
    "\n",
    "In the example below, we read metadata without conversion. The first value in each Variable is the name of the metadata variable, the second the actual value for the variable. The third value is the depth range (depth_from, depth_to) that the value applies to - e.g. for soil properties (taken from the Harmonized World Soil Data Base) multiple layers are provided together with the ISMN data and during metadata generation the best matching depth for a sensor is selected. Some values apply to a specific depth (depth_from=depth_to) while others may apply to a depth range (usually depends on the network)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:23.216509Z",
     "start_time": "2024-06-15T21:55:23.206367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "MetaData([\n  MetaVar([clay_fraction, 22.8, Depth([0.05, 0.05])]),\n  MetaVar([climate_KG, Cfb, None]),\n  MetaVar([climate_insitu, unknown, None]),\n  MetaVar([elevation, 337.0, None]),\n  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.05, 0.05])]),\n  MetaVar([latitude, 43.4417, None]),\n  MetaVar([lc_2000, 10, None]),\n  MetaVar([lc_2005, 10, None]),\n  MetaVar([lc_2010, 10, None]),\n  MetaVar([lc_insitu, unknown, None]),\n  MetaVar([longitude, 1.88, None]),\n  MetaVar([network, SMOSMANIA, None]),\n  MetaVar([organic_carbon, 1.15, Depth([0.05, 0.05])]),\n  MetaVar([sand_fraction, 43.5, Depth([0.05, 0.05])]),\n  MetaVar([saturation, 0.44, Depth([0.05, 0.05])]),\n  MetaVar([silt_fraction, 33.7, Depth([0.05, 0.05])]),\n  MetaVar([station, SaintFelixdeLauragais, None]),\n  MetaVar([timerange_from, 2009-01-01 00:00:00, None]),\n  MetaVar([timerange_to, 2020-12-31 23:00:00, None]),\n  MetaVar([variable, soil_moisture, Depth([0.05, 0.05])]),\n  MetaVar([instrument, PT-100, Depth([0.05, 0.05])]),\n  MetaVar([variable, soil_temperature, Depth([0.05, 0.05])]),\n  MetaVar([clay_fraction, 22.4, Depth([0.1, 0.1])]),\n  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.1, 0.1])]),\n  MetaVar([organic_carbon, 0.84, Depth([0.1, 0.1])]),\n  MetaVar([sand_fraction, 40.3, Depth([0.1, 0.1])]),\n  MetaVar([saturation, 0.43, Depth([0.1, 0.1])]),\n  MetaVar([silt_fraction, 37.3, Depth([0.1, 0.1])]),\n  MetaVar([variable, soil_moisture, Depth([0.1, 0.1])]),\n  MetaVar([instrument, PT-100, Depth([0.1, 0.1])]),\n  MetaVar([variable, soil_temperature, Depth([0.1, 0.1])]),\n  MetaVar([clay_fraction, 23.9, Depth([0.2, 0.2])]),\n  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.2, 0.2])]),\n  MetaVar([organic_carbon, 0.97, Depth([0.2, 0.2])]),\n  MetaVar([sand_fraction, 39.7, Depth([0.2, 0.2])]),\n  MetaVar([saturation, 0.44, Depth([0.2, 0.2])]),\n  MetaVar([silt_fraction, 36.4, Depth([0.2, 0.2])]),\n  MetaVar([timerange_to, 2017-09-01 11:00:00, None]),\n  MetaVar([variable, soil_moisture, Depth([0.2, 0.2])]),\n  MetaVar([instrument, PT-100, Depth([0.2, 0.2])]),\n  MetaVar([variable, soil_temperature, Depth([0.2, 0.2])]),\n  MetaVar([clay_fraction, 29.4, Depth([0.3, 0.3])]),\n  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.3, 0.3])]),\n  MetaVar([organic_carbon, 0.7, Depth([0.3, 0.3])]),\n  MetaVar([sand_fraction, 32.0, Depth([0.3, 0.3])]),\n  MetaVar([saturation, 0.44, Depth([0.3, 0.3])]),\n  MetaVar([silt_fraction, 38.6, Depth([0.3, 0.3])]),\n  MetaVar([variable, soil_moisture, Depth([0.3, 0.3])]),\n  MetaVar([instrument, PT-100, Depth([0.3, 0.3])]),\n  MetaVar([variable, soil_temperature, Depth([0.3, 0.3])]),\n  MetaVar([instrument, ThetaProbe-ML3, Depth([0.2, 0.2])]),\n  MetaVar([timerange_from, 2017-10-16 13:00:00, None])\n])"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['SMOSMANIA']['SaintFelixdeLauragais'].metadata  #  Get metadata for the station"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Also the station object can be transformed into a xarray Dataset. It will contain all measurement time series at the station."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<xarray.Dataset> Size: 45MB\nDimensions:                     (sensor: 9, date_time: 102713)\nCoordinates:\n  * date_time                   (date_time) datetime64[ns] 822kB 2009-01-01 ....\nDimensions without coordinates: sensor\nData variables: (12/28)\n    soil_moisture               (sensor, date_time) float64 7MB 0.2957 ... 0.214\n    soil_moisture_flag          (sensor, date_time) object 7MB 'G' 'G' ... 'G'\n    soil_moisture_orig_flag     (sensor, date_time) object 7MB 'M' 'M' ... 'M'\n    depth_from                  (sensor) float64 72B 0.05 0.05 0.1 ... 0.3 0.2\n    depth_to                    (sensor) float64 72B 0.05 0.05 0.1 ... 0.3 0.2\n    clay_fraction               (sensor) float64 72B 22.8 22.8 ... 29.4 23.9\n    ...                          ...\n    timerange_from              (sensor) datetime64[ns] 72B 2009-01-01 ... 20...\n    timerange_to                (sensor) datetime64[ns] 72B 2020-12-31T23:00:...\n    variable                    (sensor) <U16 576B 'soil_moisture' ... 'soil_...\n    soil_temperature            (sensor, date_time) float64 7MB nan nan ... nan\n    soil_temperature_flag       (sensor, date_time) object 7MB nan nan ... nan\n    soil_temperature_orig_flag  (sensor, date_time) object 7MB nan nan ... nan\nAttributes:\n    station_name:  SaintFelixdeLauragais\n    lat:           43.4417\n    lon:           1.88\n    n_sensors:     9",
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div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 45MB\nDimensions:                     (sensor: 9, date_time: 102713)\nCoordinates:\n  * date_time                   (date_time) datetime64[ns] 822kB 2009-01-01 ....\nDimensions without coordinates: sensor\nData variables: (12/28)\n    soil_moisture               (sensor, date_time) float64 7MB 0.2957 ... 0.214\n    soil_moisture_flag          (sensor, date_time) object 7MB &#x27;G&#x27; &#x27;G&#x27; ... &#x27;G&#x27;\n    soil_moisture_orig_flag     (sensor, date_time) object 7MB &#x27;M&#x27; &#x27;M&#x27; ... &#x27;M&#x27;\n    depth_from                  (sensor) float64 72B 0.05 0.05 0.1 ... 0.3 0.2\n    depth_to                    (sensor) float64 72B 0.05 0.05 0.1 ... 0.3 0.2\n    clay_fraction               (sensor) float64 72B 22.8 22.8 ... 29.4 23.9\n    ...                          ...\n    timerange_from              (sensor) datetime64[ns] 72B 2009-01-01 ... 20...\n    timerange_to                (sensor) datetime64[ns] 72B 2020-12-31T23:00:...\n    variable                    (sensor) &lt;U16 576B &#x27;soil_moisture&#x27; ... &#x27;soil_...\n    soil_temperature            (sensor, date_time) float64 7MB nan nan ... nan\n    soil_temperature_flag       (sensor, date_time) object 7MB nan nan ... nan\n    soil_temperature_orig_flag  (sensor, date_time) object 7MB nan nan ... nan\nAttributes:\n    station_name:  SaintFelixdeLauragais\n    lat:           43.4417\n    lon:           1.88\n    n_sensors:     9</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d50adde1-30b7-4d17-a5f3-a96a22efe3a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d50adde1-30b7-4d17-a5f3-a96a22efe3a0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sensor</span>: 9</li><li><span class='xr-has-index'>date_time</span>: 102713</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cd5a5bd5-6893-4b26-a837-54e127324fb4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd5a5bd5-6893-4b26-a837-54e127324fb4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date_time</span></div><div class='xr-var-dims'>(date_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-22972022-56e8-414f-9c02-a19d4a18e1f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22972022-56e8-414f-9c02-a19d4a18e1f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06077d20-d6f4-437a-8398-ba6047a299a2' class='xr-var-data-in' type='checkbox'><label for='data-06077d20-d6f4-437a-8398-ba6047a299a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T01:00:00.000000000&#x27;,\n       &#x27;2009-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20348300-5c4e-4acf-a614-d36acd7733e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-20348300-5c4e-4acf-a614-d36acd7733e7' class='xr-section-summary' >Data variables: <span>(28)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2957 0.2957 ... 0.204 0.214</div><input id='attrs-6d70883a-358b-4acc-bc71-cd5f30f8209a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d70883a-358b-4acc-bc71-cd5f30f8209a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2eec053-caf6-445f-8dfe-dbae3cef3970' class='xr-var-data-in' type='checkbox'><label for='data-a2eec053-caf6-445f-8dfe-dbae3cef3970' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2957, 0.2957, 0.2957, ..., 0.278 , 0.289 , 0.298 ],\n       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n       [0.3139, 0.3139, 0.3139, ..., 0.325 , 0.335 , 0.348 ],\n       ...,\n       [0.3053, 0.3053, 0.3053, ..., 0.354 , 0.354 , 0.358 ],\n       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n       [   nan,    nan,    nan, ..., 0.203 , 0.204 , 0.214 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_flag</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;G&#x27; &#x27;G&#x27; &#x27;G&#x27; &#x27;G&#x27; ... &#x27;G&#x27; &#x27;G&#x27; &#x27;G&#x27; &#x27;G&#x27;</div><input id='attrs-eb6ffb91-2a14-4de1-81f5-f88a037b7010' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb6ffb91-2a14-4de1-81f5-f88a037b7010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aedc03d3-9ede-4723-b91f-9aa4a4b67b88' class='xr-var-data-in' type='checkbox'><label for='data-aedc03d3-9ede-4723-b91f-9aa4a4b67b88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;, ..., &#x27;D05&#x27;, &#x27;D05&#x27;, &#x27;D05&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan],\n       [&#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;, ..., &#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;],\n       ...,\n       [&#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;, ..., &#x27;D10&#x27;, &#x27;D10&#x27;, &#x27;D10&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan],\n       [nan, nan, nan, ..., &#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_orig_flag</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;M&#x27; &#x27;M&#x27; &#x27;M&#x27; &#x27;M&#x27; ... &#x27;M&#x27; &#x27;M&#x27; &#x27;M&#x27; &#x27;M&#x27;</div><input id='attrs-8b5d7780-27ef-4dc9-9b82-3d160acb3ae1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b5d7780-27ef-4dc9-9b82-3d160acb3ae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c59df63e-6d01-40eb-925f-44a9a3d2d075' class='xr-var-data-in' type='checkbox'><label for='data-c59df63e-6d01-40eb-925f-44a9a3d2d075' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;, ..., &#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan],\n       [&#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;, ..., &#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;],\n       ...,\n       [&#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;, ..., &#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan],\n       [nan, nan, nan, ..., &#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_from</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05 0.05 0.1 0.1 ... 0.3 0.3 0.2</div><input id='attrs-e8c84a82-2543-403b-8dca-74814ff7f05d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8c84a82-2543-403b-8dca-74814ff7f05d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1628ee-386f-4253-881b-4d055a58d654' class='xr-var-data-in' type='checkbox'><label for='data-1e1628ee-386f-4253-881b-4d055a58d654' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.05, 0.05, 0.1 , 0.1 , 0.2 , 0.2 , 0.3 , 0.3 , 0.2 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_to</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05 0.05 0.1 0.1 ... 0.3 0.3 0.2</div><input id='attrs-3c130d82-76df-474f-9f1d-504a9057c189' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c130d82-76df-474f-9f1d-504a9057c189' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b88e75a8-66fa-4ded-898d-76df46f90e5f' class='xr-var-data-in' type='checkbox'><label for='data-b88e75a8-66fa-4ded-898d-76df46f90e5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.05, 0.05, 0.1 , 0.1 , 0.2 , 0.2 , 0.3 , 0.3 , 0.2 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.8 22.8 22.4 ... 29.4 29.4 23.9</div><input id='attrs-f1f46c05-3460-4ab4-bcd5-05e43225db8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1f46c05-3460-4ab4-bcd5-05e43225db8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-037d7292-ad3f-4238-9f7d-b6d2d438c37e' class='xr-var-data-in' type='checkbox'><label for='data-037d7292-ad3f-4238-9f7d-b6d2d438c37e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([22.8, 22.8, 22.4, 22.4, 23.9, 23.9, 29.4, 29.4, 23.9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>climate_KG</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cfb&#x27; &#x27;Cfb&#x27; &#x27;Cfb&#x27; ... &#x27;Cfb&#x27; &#x27;Cfb&#x27;</div><input id='attrs-85e0e8eb-aee8-47b6-a720-313a9884e0f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85e0e8eb-aee8-47b6-a720-313a9884e0f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-859f42ae-1db0-4d4e-b985-09ed598396fb' class='xr-var-data-in' type='checkbox'><label for='data-859f42ae-1db0-4d4e-b985-09ed598396fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;, &#x27;Cfb&#x27;],\n      dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>climate_insitu</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;unknown&#x27; &#x27;unknown&#x27; ... &#x27;unknown&#x27;</div><input id='attrs-fc27bd2f-12a4-4355-a218-f3c674dd490a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc27bd2f-12a4-4355-a218-f3c674dd490a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-190068f8-c5cc-4f6d-a4e4-9352e5f1de98' class='xr-var-data-in' type='checkbox'><label for='data-190068f8-c5cc-4f6d-a4e4-9352e5f1de98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;], dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>337.0 337.0 337.0 ... 337.0 337.0</div><input id='attrs-e03b6c88-dc7f-4d39-9f3a-a78cadd5000d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e03b6c88-dc7f-4d39-9f3a-a78cadd5000d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c38b246a-4f6e-4b40-aa1b-e97d35a7193c' class='xr-var-data-in' type='checkbox'><label for='data-c38b246a-4f6e-4b40-aa1b-e97d35a7193c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([337., 337., 337., 337., 337., 337., 337., 337., 337.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;ThetaProbe-ML2X&#x27; ... &#x27;ThetaProb...</div><input id='attrs-9d1e54ef-6031-4ed3-9c62-e37971b3ed5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d1e54ef-6031-4ed3-9c62-e37971b3ed5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fead0d31-5f15-43dc-ae70-09460a9f73d7' class='xr-var-data-in' type='checkbox'><label for='data-fead0d31-5f15-43dc-ae70-09460a9f73d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ThetaProbe-ML2X&#x27;, &#x27;PT-100&#x27;, &#x27;ThetaProbe-ML2X&#x27;, &#x27;PT-100&#x27;,\n       &#x27;ThetaProbe-ML2X&#x27;, &#x27;PT-100&#x27;, &#x27;ThetaProbe-ML2X&#x27;, &#x27;PT-100&#x27;,\n       &#x27;ThetaProbe-ML3&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.44 43.44 43.44 ... 43.44 43.44</div><input id='attrs-5770de73-1f36-4775-ab5d-a244d1dbbf01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5770de73-1f36-4775-ab5d-a244d1dbbf01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b07f919e-ac24-4c4b-b7e1-c3f27b9f233f' class='xr-var-data-in' type='checkbox'><label for='data-b07f919e-ac24-4c4b-b7e1-c3f27b9f233f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([43.4417, 43.4417, 43.4417, 43.4417, 43.4417, 43.4417, 43.4417,\n       43.4417, 43.4417])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_2000</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10 10 10 10 10 10 10 10 10</div><input id='attrs-4f17c0de-c2b9-472b-b220-77707f0a495a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f17c0de-c2b9-472b-b220-77707f0a495a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5873540f-37a7-4c50-8d2e-b159f35f60cc' class='xr-var-data-in' type='checkbox'><label for='data-5873540f-37a7-4c50-8d2e-b159f35f60cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10, 10, 10, 10, 10, 10, 10, 10, 10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_2005</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10 10 10 10 10 10 10 10 10</div><input id='attrs-25c9b341-0676-42d7-9747-82c220418159' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25c9b341-0676-42d7-9747-82c220418159' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf5c08d7-81fe-4afe-a407-9f02a26447d8' class='xr-var-data-in' type='checkbox'><label for='data-bf5c08d7-81fe-4afe-a407-9f02a26447d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10, 10, 10, 10, 10, 10, 10, 10, 10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_2010</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10 10 10 10 10 10 10 10 10</div><input id='attrs-e8a76f20-4e4f-47da-bbeb-8f7a07462135' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8a76f20-4e4f-47da-bbeb-8f7a07462135' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60e0fddb-246f-4b83-a89b-5b85ef87f1b5' class='xr-var-data-in' type='checkbox'><label for='data-60e0fddb-246f-4b83-a89b-5b85ef87f1b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10, 10, 10, 10, 10, 10, 10, 10, 10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_insitu</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;unknown&#x27; &#x27;unknown&#x27; ... &#x27;unknown&#x27;</div><input id='attrs-7ecdb095-66d6-491f-b5ba-7571b6a71a5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ecdb095-66d6-491f-b5ba-7571b6a71a5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92d5522e-6ba7-4e50-ad37-376a05578587' class='xr-var-data-in' type='checkbox'><label for='data-92d5522e-6ba7-4e50-ad37-376a05578587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;], dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.88 1.88 1.88 ... 1.88 1.88 1.88</div><input id='attrs-43bc4d6c-1b22-4dde-983e-0987a8eee250' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43bc4d6c-1b22-4dde-983e-0987a8eee250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59264e75-8444-448c-a67a-fa5cec3e405a' class='xr-var-data-in' type='checkbox'><label for='data-59264e75-8444-448c-a67a-fa5cec3e405a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.88, 1.88, 1.88, 1.88, 1.88, 1.88, 1.88, 1.88, 1.88])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;SMOSMANIA&#x27; ... &#x27;SMOSMANIA&#x27;</div><input id='attrs-ea0a5a93-b958-4af8-ac6b-3c3237d0b619' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea0a5a93-b958-4af8-ac6b-3c3237d0b619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5720fcc0-6993-4ad8-8218-419d2bb3e13f' class='xr-var-data-in' type='checkbox'><label for='data-5720fcc0-6993-4ad8-8218-419d2bb3e13f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;,\n       &#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;, &#x27;SMOSMANIA&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>organic_carbon</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.15 1.15 0.84 ... 0.7 0.7 0.97</div><input id='attrs-8a78e178-b66c-4f71-bae5-791b2822ccf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a78e178-b66c-4f71-bae5-791b2822ccf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62e918b9-f703-4815-a1d7-254b3e3e2093' class='xr-var-data-in' type='checkbox'><label for='data-62e918b9-f703-4815-a1d7-254b3e3e2093' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.15, 1.15, 0.84, 0.84, 0.97, 0.97, 0.7 , 0.7 , 0.97])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sand_fraction</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>43.5 43.5 40.3 ... 32.0 32.0 39.7</div><input id='attrs-5dc5fd30-3125-484e-a1c6-c9e929d2abbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5dc5fd30-3125-484e-a1c6-c9e929d2abbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16d6cfd3-5e6a-4dde-be60-985a3a4f8ca3' class='xr-var-data-in' type='checkbox'><label for='data-16d6cfd3-5e6a-4dde-be60-985a3a4f8ca3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([43.5, 43.5, 40.3, 40.3, 39.7, 39.7, 32. , 32. , 39.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>saturation</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.44 0.44 0.43 ... 0.44 0.44 0.44</div><input id='attrs-ed050bed-76c0-4bb0-a9e3-926d5997bfb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed050bed-76c0-4bb0-a9e3-926d5997bfb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753e7bc8-bf2a-459d-97e9-3caf10551f24' class='xr-var-data-in' type='checkbox'><label for='data-753e7bc8-bf2a-459d-97e9-3caf10551f24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.44, 0.44, 0.43, 0.43, 0.44, 0.44, 0.44, 0.44, 0.44])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silt_fraction</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>33.7 33.7 37.3 ... 38.6 38.6 36.4</div><input id='attrs-43abf8ca-dc84-4717-b69e-be90b4291d94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43abf8ca-dc84-4717-b69e-be90b4291d94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a7a1528-5d68-4103-b9f5-3369a0832912' class='xr-var-data-in' type='checkbox'><label for='data-2a7a1528-5d68-4103-b9f5-3369a0832912' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([33.7, 33.7, 37.3, 37.3, 36.4, 36.4, 38.6, 38.6, 36.4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U21</div><div class='xr-var-preview xr-preview'>&#x27;SaintFelixdeLauragais&#x27; ... &#x27;Sai...</div><input id='attrs-ac9b715e-c8bd-407c-9c45-609dd22b6eba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac9b715e-c8bd-407c-9c45-609dd22b6eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68f85a8d-f7c7-4d6e-bd90-68823e784f45' class='xr-var-data-in' type='checkbox'><label for='data-68f85a8d-f7c7-4d6e-bd90-68823e784f45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SaintFelixdeLauragais&#x27;, &#x27;SaintFelixdeLauragais&#x27;,\n       &#x27;SaintFelixdeLauragais&#x27;, &#x27;SaintFelixdeLauragais&#x27;,\n       &#x27;SaintFelixdeLauragais&#x27;, &#x27;SaintFelixdeLauragais&#x27;,\n       &#x27;SaintFelixdeLauragais&#x27;, &#x27;SaintFelixdeLauragais&#x27;,\n       &#x27;SaintFelixdeLauragais&#x27;], dtype=&#x27;&lt;U21&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>timerange_from</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-01-01 ... 2017-10-16T13:00:00</div><input id='attrs-1ef7f612-cea8-40bf-8ac9-46c6c18af797' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ef7f612-cea8-40bf-8ac9-46c6c18af797' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87df6f9f-d30e-455a-bc9e-73c96db8f833' class='xr-var-data-in' type='checkbox'><label for='data-87df6f9f-d30e-455a-bc9e-73c96db8f833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n       &#x27;2017-10-16T13:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>timerange_to</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-12-31T23:00:00 ... 2020-12-...</div><input id='attrs-afc9c2da-6c41-4fb5-b225-509e81c6537b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afc9c2da-6c41-4fb5-b225-509e81c6537b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-304ea763-c3d8-4bb1-8722-785a5334e2ad' class='xr-var-data-in' type='checkbox'><label for='data-304ea763-c3d8-4bb1-8722-785a5334e2ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-12-31T23:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;,\n       &#x27;2020-12-31T23:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;,\n       &#x27;2017-09-01T11:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;,\n       &#x27;2020-12-31T23:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;,\n       &#x27;2020-12-31T23:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>variable</span></div><div class='xr-var-dims'>(sensor)</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;soil_moisture&#x27; ... &#x27;soil_moisture&#x27;</div><input id='attrs-d0bf690b-e667-42a1-bce5-f411ab511245' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0bf690b-e667-42a1-bce5-f411ab511245' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33ed0a47-11fe-437a-9921-7cac1253a3a3' class='xr-var-data-in' type='checkbox'><label for='data-33ed0a47-11fe-437a-9921-7cac1253a3a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;soil_moisture&#x27;, &#x27;soil_temperature&#x27;, &#x27;soil_moisture&#x27;,\n       &#x27;soil_temperature&#x27;, &#x27;soil_moisture&#x27;, &#x27;soil_temperature&#x27;,\n       &#x27;soil_moisture&#x27;, &#x27;soil_temperature&#x27;, &#x27;soil_moisture&#x27;], dtype=&#x27;&lt;U16&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_temperature</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a72105e3-7904-467a-ae27-675afe7ae481' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a72105e3-7904-467a-ae27-675afe7ae481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26d37518-0bef-4603-9514-011cafc70c87' class='xr-var-data-in' type='checkbox'><label for='data-26d37518-0bef-4603-9514-011cafc70c87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n       [6.9, 7. , 7.1, ..., 7. , 6.9, 6.8],\n       [nan, nan, nan, ..., nan, nan, nan],\n       ...,\n       [nan, nan, nan, ..., nan, nan, nan],\n       [6.9, 6.9, 6.9, ..., 7.7, 7.7, 7.8],\n       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_temperature_flag</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-36cfb618-316e-41f8-969a-cf4492c88358' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36cfb618-316e-41f8-969a-cf4492c88358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bbab0df-1cb0-4f6b-8dae-f3fe07ce3162' class='xr-var-data-in' type='checkbox'><label for='data-8bbab0df-1cb0-4f6b-8dae-f3fe07ce3162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n       [&#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;, ..., &#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan],\n       ...,\n       [nan, nan, nan, ..., nan, nan, nan],\n       [&#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;, ..., &#x27;G&#x27;, &#x27;G&#x27;, &#x27;G&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_temperature_orig_flag</span></div><div class='xr-var-dims'>(sensor, date_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-06ef4b57-136a-4300-bb54-a03935170c16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06ef4b57-136a-4300-bb54-a03935170c16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6aeb5711-c446-46d0-bbdc-1b6ad7f2dab3' class='xr-var-data-in' type='checkbox'><label for='data-6aeb5711-c446-46d0-bbdc-1b6ad7f2dab3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n       [&#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;, ..., &#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan],\n       ...,\n       [nan, nan, nan, ..., nan, nan, nan],\n       [&#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;, ..., &#x27;M&#x27;, &#x27;M&#x27;, &#x27;M&#x27;],\n       [nan, nan, nan, ..., nan, nan, nan]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17c20821-74fb-4c09-8ea2-3945b8fca7dc' class='xr-section-summary-in' type='checkbox'  ><label for='section-17c20821-74fb-4c09-8ea2-3945b8fca7dc' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>date_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4198ef4f-9450-48c0-ab30-c94a86dae5e8' class='xr-index-data-in' type='checkbox'/><label for='index-4198ef4f-9450-48c0-ab30-c94a86dae5e8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-01-01 00:00:00&#x27;, &#x27;2009-01-01 01:00:00&#x27;,\n               &#x27;2009-01-01 02:00:00&#x27;, &#x27;2009-01-01 03:00:00&#x27;,\n               &#x27;2009-01-01 04:00:00&#x27;, &#x27;2009-01-01 05:00:00&#x27;,\n               &#x27;2009-01-01 06:00:00&#x27;, &#x27;2009-01-01 07:00:00&#x27;,\n               &#x27;2009-01-01 08:00:00&#x27;, &#x27;2009-01-01 09:00:00&#x27;,\n               ...\n               &#x27;2020-12-31 14:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n               &#x27;2020-12-31 16:00:00&#x27;, &#x27;2020-12-31 17:00:00&#x27;,\n               &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 19:00:00&#x27;,\n               &#x27;2020-12-31 20:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;,\n               &#x27;2020-12-31 22:00:00&#x27;, &#x27;2020-12-31 23:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date_time&#x27;, length=102713, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57c61c55-8af3-499e-939e-389dc045a385' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57c61c55-8af3-499e-939e-389dc045a385' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>station_name :</span></dt><dd>SaintFelixdeLauragais</dd><dt><span>lat :</span></dt><dd>43.4417</dd><dt><span>lon :</span></dt><dd>1.88</dd><dt><span>n_sensors :</span></dt><dd>9</dd></dl></div></li></ul></div></div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['SMOSMANIA']['SaintFelixdeLauragais'].to_xarray()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:23.783450Z",
     "start_time": "2024-06-15T21:55:23.217928Z"
    }
   },
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing sensors at a station works similar to accessing stations in a network. By default, the name is created from the instrument type, the measured variable and the depth layer that the sensor operates in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:23.787066Z",
     "start_time": "2024-06-15T21:55:23.784418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "ThetaProbe-ML2X_soil_moisture_0.050000_0.050000"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['SMOSMANIA']['SaintFelixdeLauragais']['ThetaProbe-ML2X_soil_moisture_0.050000_0.050000'] #  equivalent to ismn_data[4][18][4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each sensor has a measurement time series (access via `Sensor.data`) and sensor specific metadata (via `Sensor.metadata`) assigned. Here we convert metadata to a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.317768Z",
     "start_time": "2024-06-15T21:55:23.787707Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable        key       \n",
      "clay_fraction   val                            22.8\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "climate_KG      val                             Cfb\n",
      "climate_insitu  val                         unknown\n",
      "elevation       val                           337.0\n",
      "instrument      val                 ThetaProbe-ML2X\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "latitude        val                         43.4417\n",
      "lc_2000         val                              10\n",
      "lc_2005         val                              10\n",
      "lc_2010         val                              10\n",
      "lc_insitu       val                         unknown\n",
      "longitude       val                            1.88\n",
      "network         val                       SMOSMANIA\n",
      "organic_carbon  val                            1.15\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "sand_fraction   val                            43.5\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "saturation      val                            0.44\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "silt_fraction   val                            33.7\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "station         val           SaintFelixdeLauragais\n",
      "timerange_from  val             2009-01-01 00:00:00\n",
      "timerange_to    val             2020-12-31 23:00:00\n",
      "variable        val                   soil_moisture\n",
      "                depth_from                     0.05\n",
      "                depth_to                       0.05\n",
      "Name: data, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Soil Moisture [$m^3 m^{-3}$]')"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sensor = ismn_data['SMOSMANIA']['SaintFelixdeLauragais']['ThetaProbe-ML2X_soil_moisture_0.050000_0.050000']\n",
    "print(sensor.metadata.to_pd())\n",
    "ax = sensor.data.plot(figsize=(12,4))\n",
    "ax.set_xlabel(\"Time [year]\")\n",
    "ax.set_ylabel(\"Soil Moisture [$m^3 m^{-3}$]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some metadata is different for each sensor (e.g. time series range), some depends on the location of the station and is therefore shared by multiple sensors at one station (landcover and climate classes etc.). Sometime metadata is missing (if not provided, indicated by 'unknown', or NaN). Some meta data depends on the depth of a sensor (e.g. soil properties), during metadata collection (in the beginning) these values were collected and assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.323521Z",
     "start_time": "2024-06-15T21:55:24.318633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "{'clay_fraction': [(22.8, 0.05, 0.05)],\n 'climate_KG': [('Cfb', None, None)],\n 'climate_insitu': [('unknown', None, None)],\n 'elevation': [(337.0, None, None)],\n 'instrument': [('ThetaProbe-ML2X', 0.05, 0.05)],\n 'latitude': [(43.4417, None, None)],\n 'lc_2000': [(10, None, None)],\n 'lc_2005': [(10, None, None)],\n 'lc_2010': [(10, None, None)],\n 'lc_insitu': [('unknown', None, None)],\n 'longitude': [(1.88, None, None)],\n 'network': [('SMOSMANIA', None, None)],\n 'organic_carbon': [(1.15, 0.05, 0.05)],\n 'sand_fraction': [(43.5, 0.05, 0.05)],\n 'saturation': [(0.44, 0.05, 0.05)],\n 'silt_fraction': [(33.7, 0.05, 0.05)],\n 'station': [('SaintFelixdeLauragais', None, None)],\n 'timerange_from': [(Timestamp('2009-01-01 00:00:00'), None, None)],\n 'timerange_to': [(Timestamp('2020-12-31 23:00:00'), None, None)],\n 'variable': [('soil_moisture', 0.05, 0.05)]}"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor.metadata.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some other important functions\n",
    "Each component (network, station, sensor) contains different functions to handle its data. `ISMN_Interface` provides general functions to filter and iterate over its components and visualize them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find nearest station\n",
    "The data collection in `ISMN_Interface` contains a grid object that lists the locations of all **stations** in all active networks. For more details see https://github.com/TUW-GEO/pygeogrids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.346320Z",
     "start_time": "2024-06-15T21:55:24.324519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "gpi       0         1         2         3         4         5         6    \\\nlon  27.55062  27.55076  27.53543  26.63378  26.63378  26.63378  26.65176   \nlat  68.33019  68.33025  68.33881  67.36187  67.36179  67.36195  67.36691   \n\ngpi       7         8         9    ...       99        100       101  \\\nlon  26.65162  26.65196  26.65064  ...  15.81499  15.94361  15.96578   \nlat  67.36685  67.36691  67.36702  ...  46.97233  46.97125  46.95952   \n\ngpi       102       103       104       105       106       107       108  \nlon  15.75960  15.85507  15.90710  15.92462  16.04056  15.78112  16.03337  \nlat  46.94327  46.99726  46.93296  46.93291  46.93428  46.91691  46.92135  \n\n[2 rows x 109 columns]",
      "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>gpi</th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>99</th>\n      <th>100</th>\n      <th>101</th>\n      <th>102</th>\n      <th>103</th>\n      <th>104</th>\n      <th>105</th>\n      <th>106</th>\n      <th>107</th>\n      <th>108</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>lon</th>\n      <td>27.55062</td>\n      <td>27.55076</td>\n      <td>27.53543</td>\n      <td>26.63378</td>\n      <td>26.63378</td>\n      <td>26.63378</td>\n      <td>26.65176</td>\n      <td>26.65162</td>\n      <td>26.65196</td>\n      <td>26.65064</td>\n      <td>...</td>\n      <td>15.81499</td>\n      <td>15.94361</td>\n      <td>15.96578</td>\n      <td>15.75960</td>\n      <td>15.85507</td>\n      <td>15.90710</td>\n      <td>15.92462</td>\n      <td>16.04056</td>\n      <td>15.78112</td>\n      <td>16.03337</td>\n    </tr>\n    <tr>\n      <th>lat</th>\n      <td>68.33019</td>\n      <td>68.33025</td>\n      <td>68.33881</td>\n      <td>67.36187</td>\n      <td>67.36179</td>\n      <td>67.36195</td>\n      <td>67.36691</td>\n      <td>67.36685</td>\n      <td>67.36691</td>\n      <td>67.36702</td>\n      <td>...</td>\n      <td>46.97233</td>\n      <td>46.97125</td>\n      <td>46.95952</td>\n      <td>46.94327</td>\n      <td>46.99726</td>\n      <td>46.93296</td>\n      <td>46.93291</td>\n      <td>46.93428</td>\n      <td>46.91691</td>\n      <td>46.92135</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 109 columns</p>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "grid = ismn_data.collection.grid\n",
    "gpis, lons, lats, _ = grid.get_grid_points()\n",
    "pd.DataFrame(index=pd.Index(gpis, name='gpi'), \n",
    "             data={'lon': lons, 'lat': lats}).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.351236Z",
     "start_time": "2024-06-15T21:55:24.347366Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station SAA111 is 43348 metres away from the passed coordinates:\n"
     ]
    }
   ],
   "source": [
    "# Using the GPI or coordinates, a station from **all** stations in **all** networks in the collection can be selected.\n",
    "station, dist = ismn_data.collection.get_nearest_station(27.0, 68.0)\n",
    "print(f'Station {station.name} is {int(dist)} metres away from the passed coordinates:')\n",
    "assert ismn_data.collection.station4gpi(0) == station # same result when selecting with GPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.355762Z",
     "start_time": "2024-06-15T21:55:24.352938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Station 'SAA111' with Sensors: ['5TE_soil_moisture_0.050000_0.050000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.100000_0.100000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_moisture_0.400000_0.400000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_temperature_0.800000_0.800000', 'CS215_air_temperature_-2.000000_-2.000000']"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find network for a specific station\n",
    "`ISMN_Interface` provides a function to find the network when only the name of a station is known. Here we simply read data for the first available sensor at the fist station in the found network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.404387Z",
     "start_time": "2024-06-15T21:55:24.356521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Network 'FMI' with Stations: ['SAA111', 'SAA112', 'SAA120', 'SOD011', 'SOD012', 'SOD013', 'SOD021', 'SOD022', 'SOD023', 'SOD031', 'SOD032', 'SOD033', 'SOD071', 'SOD072', 'SOD073', 'SOD081', 'SOD082', 'SOD083', 'SOD091', 'SOD092', 'SOD093', 'SOD101', 'SOD102', 'SOD103', 'SOD130', 'SOD140', 'SODAWS']"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "                     soil_moisture soil_moisture_flag soil_moisture_orig_flag\ndate_time                                                                    \n2012-10-14 00:00:00          0.180                D02                       M\n2012-10-14 01:00:00          0.180                D02                       M\n2012-10-14 02:00:00          0.179                D02                       M\n2012-10-14 03:00:00          0.178                D02                       M\n2012-10-14 04:00:00          0.178                D02                       M\n...                            ...                ...                     ...\n2020-12-31 19:00:00          0.101            D01,D02                       M\n2020-12-31 20:00:00          0.101            D01,D02                       M\n2020-12-31 21:00:00          0.101            D01,D02                       M\n2020-12-31 22:00:00          0.101            D01,D02                       M\n2020-12-31 23:00:00          0.101            D01,D02                       M\n\n[54322 rows x 3 columns]",
      "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>soil_moisture</th>\n      <th>soil_moisture_flag</th>\n      <th>soil_moisture_orig_flag</th>\n    </tr>\n    <tr>\n      <th>date_time</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-10-14 00:00:00</th>\n      <td>0.180</td>\n      <td>D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2012-10-14 01:00:00</th>\n      <td>0.180</td>\n      <td>D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2012-10-14 02:00:00</th>\n      <td>0.179</td>\n      <td>D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2012-10-14 03:00:00</th>\n      <td>0.178</td>\n      <td>D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2012-10-14 04:00:00</th>\n      <td>0.178</td>\n      <td>D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2020-12-31 19:00:00</th>\n      <td>0.101</td>\n      <td>D01,D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2020-12-31 20:00:00</th>\n      <td>0.101</td>\n      <td>D01,D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2020-12-31 21:00:00</th>\n      <td>0.101</td>\n      <td>D01,D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2020-12-31 22:00:00</th>\n      <td>0.101</td>\n      <td>D01,D02</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2020-12-31 23:00:00</th>\n      <td>0.101</td>\n      <td>D01,D02</td>\n      <td>M</td>\n    </tr>\n  </tbody>\n</table>\n<p>54322 rows × 3 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network = ismn_data.network_for_station('SAA111', name_only=False)\n",
    "display(network)\n",
    "display(network[0][0].data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata overview\n",
    "To get an overview over all metadata for the currently loaded networks, the attribute `ISMN_Interface.metadata` can be called. This will return a pandas DataFrame with all available metadata variables for different depths as (multi-index) columns and sensors as rows. The first column `idx` is the index of the sensor/filehandler, in line with what `ISMN_Interface.get_dataset_ids()` returns, and can therefore be used to read data via `ISMN_Interface.read()` as shown in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.420542Z",
     "start_time": "2024-06-15T21:55:24.405256Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "variable clay_fraction                climate_KG               climate_insitu  \\\nkey         depth_from depth_to   val depth_from depth_to  val     depth_from   \n0                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n1                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n2                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n3                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n4                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n..                 ...      ...   ...        ...      ...  ...            ...   \n667                0.0      0.3  20.0        NaN      NaN  Dfb            NaN   \n668                NaN      NaN   NaN        NaN      NaN  Dfb            NaN   \n669                0.0      0.3  20.0        NaN      NaN  Dfb            NaN   \n670                0.0      0.3  20.0        NaN      NaN  Dfb            NaN   \n671                NaN      NaN   NaN        NaN      NaN  Dfb            NaN   \n\nvariable                    elevation  ... timerange_from                      \\\nkey      depth_to      val depth_from  ...       depth_to                 val   \n0             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n1             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n2             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n3             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n4             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n..            ...      ...        ...  ...            ...                 ...   \n667           NaN  unknown        NaN  ...            NaN 2009-01-01 00:00:00   \n668           NaN  unknown        NaN  ...            NaN 2009-01-16 11:00:00   \n669           NaN  unknown        NaN  ...            NaN 2013-10-23 11:00:00   \n670           NaN  unknown        NaN  ...            NaN 2013-10-23 11:00:00   \n671           NaN  unknown        NaN  ...            NaN 2009-01-01 00:00:00   \n\nvariable timerange_to                                variable           \\\nkey        depth_from depth_to                 val depth_from depth_to   \n0                 NaN      NaN 2020-12-31 23:00:00       0.05     0.05   \n1                 NaN      NaN 2020-12-31 23:00:00       0.05     0.05   \n2                 NaN      NaN 2020-12-31 23:00:00       0.10     0.10   \n3                 NaN      NaN 2020-12-31 23:00:00       0.10     0.10   \n4                 NaN      NaN 2014-07-04 04:00:00       0.20     0.20   \n..                ...      ...                 ...        ...      ...   \n667               NaN      NaN 2013-06-18 16:00:00       0.30     0.30   \n668               NaN      NaN 2020-12-31 16:00:00      -1.50    -1.50   \n669               NaN      NaN 2020-12-31 23:00:00       0.20     0.20   \n670               NaN      NaN 2020-12-31 23:00:00       0.20     0.20   \n671               NaN      NaN 2020-12-31 23:00:00      -2.00    -2.00   \n\nvariable                                                            file_path  \\\nkey                    val                                                val   \n0            soil_moisture  FMI/SAA111/FMI_FMI_SAA111_sm_0.050000_0.050000...   \n1         soil_temperature  FMI/SAA111/FMI_FMI_SAA111_ts_0.050000_0.050000...   \n2            soil_moisture  FMI/SAA111/FMI_FMI_SAA111_sm_0.100000_0.100000...   \n3         soil_temperature  FMI/SAA111/FMI_FMI_SAA111_ts_0.100000_0.100000...   \n4            soil_moisture  FMI/SAA111/FMI_FMI_SAA111_sm_0.200000_0.200000...   \n..                     ...                                                ...   \n667          soil_moisture  WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.30...   \n668          precipitation  WEGENERNET/99/WEGENERNET_WEGENERNET_99_p_-1.50...   \n669          soil_moisture  WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.20...   \n670       soil_temperature  WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.20...   \n671        air_temperature  WEGENERNET/99/WEGENERNET_WEGENERNET_99_ta_-2.0...   \n\nvariable      file_type  \nkey                 val  \n0         header_values  \n1         header_values  \n2         header_values  \n3         header_values  \n4         header_values  \n..                  ...  \n667       header_values  \n668       header_values  \n669       header_values  \n670       header_values  \n671       header_values  \n\n[672 rows x 62 columns]",
      "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 tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th>variable</th>\n      <th colspan=\"3\" halign=\"left\">clay_fraction</th>\n      <th colspan=\"3\" halign=\"left\">climate_KG</th>\n      <th colspan=\"3\" halign=\"left\">climate_insitu</th>\n      <th>elevation</th>\n      <th>...</th>\n      <th colspan=\"2\" halign=\"left\">timerange_from</th>\n      <th colspan=\"3\" halign=\"left\">timerange_to</th>\n      <th colspan=\"3\" halign=\"left\">variable</th>\n      <th>file_path</th>\n      <th>file_type</th>\n    </tr>\n    <tr>\n      <th>key</th>\n      <th>depth_from</th>\n      <th>depth_to</th>\n      <th>val</th>\n      <th>depth_from</th>\n      <th>depth_to</th>\n      <th>val</th>\n      <th>depth_from</th>\n      <th>depth_to</th>\n      <th>val</th>\n      <th>depth_from</th>\n      <th>...</th>\n      <th>depth_to</th>\n      <th>val</th>\n      <th>depth_from</th>\n      <th>depth_to</th>\n      <th>val</th>\n      <th>depth_from</th>\n      <th>depth_to</th>\n      <th>val</th>\n      <th>val</th>\n      <th>val</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2012-10-14 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>0.05</td>\n      <td>0.05</td>\n      <td>soil_moisture</td>\n      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.050000_0.050000...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2012-10-14 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>0.05</td>\n      <td>0.05</td>\n      <td>soil_temperature</td>\n      <td>FMI/SAA111/FMI_FMI_SAA111_ts_0.050000_0.050000...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2012-10-14 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>0.10</td>\n      <td>0.10</td>\n      <td>soil_moisture</td>\n      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.100000_0.100000...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2012-10-14 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>0.10</td>\n      <td>0.10</td>\n      <td>soil_temperature</td>\n      <td>FMI/SAA111/FMI_FMI_SAA111_ts_0.100000_0.100000...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2012-10-14 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-07-04 04:00:00</td>\n      <td>0.20</td>\n      <td>0.20</td>\n      <td>soil_moisture</td>\n      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.200000_0.200000...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>667</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>20.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfb</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2009-01-01 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013-06-18 16:00:00</td>\n      <td>0.30</td>\n      <td>0.30</td>\n      <td>soil_moisture</td>\n      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.30...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>668</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfb</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2009-01-16 11:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 16:00:00</td>\n      <td>-1.50</td>\n      <td>-1.50</td>\n      <td>precipitation</td>\n      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_p_-1.50...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>669</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>20.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfb</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2013-10-23 11:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>0.20</td>\n      <td>0.20</td>\n      <td>soil_moisture</td>\n      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.20...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>670</th>\n      <td>0.0</td>\n      <td>0.3</td>\n      <td>20.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfb</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2013-10-23 11:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>0.20</td>\n      <td>0.20</td>\n      <td>soil_temperature</td>\n      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.20...</td>\n      <td>header_values</td>\n    </tr>\n    <tr>\n      <th>671</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Dfb</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>2009-01-01 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2020-12-31 23:00:00</td>\n      <td>-2.00</td>\n      <td>-2.00</td>\n      <td>air_temperature</td>\n      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_ta_-2.0...</td>\n      <td>header_values</td>\n    </tr>\n  </tbody>\n</table>\n<p>672 rows × 62 columns</p>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting and reading specific sensors <a id='read_from_meta'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can filter the dataset a priori and get IDs of sensors that measure a specific variable. The ID can then be used to read the data directly. Here we extract the IDs of sensors in our data set that measure 'soil_temperature' in 0 to 1 meter depth and within a specific land cover and climate class (all conditions must be fulfilled)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.432209Z",
     "start_time": "2024-06-15T21:55:24.421629Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[461, 463, 465, 467]"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = ismn_data.get_dataset_ids(variable='soil_temperature', \n",
    "                                max_depth=1, \n",
    "                                filter_meta_dict={'lc_2005': 130, 'climate_KG': 'Csb'})\n",
    "ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the same IDs from the full metadata frame held by ISMN_Interface. Filtering the full metadata dataframe is more flexible but needs some some knowlegde on filtering with pandas. In the end we find the same IDs as for the other method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:24.438993Z",
     "start_time": "2024-06-15T21:55:24.433098Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[461, 463, 465, 467]"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditions = (ismn_data.metadata['variable'].val == 'soil_temperature') & \\\n",
    "             (ismn_data.metadata['instrument'].depth_to <= 1) & \\\n",
    "             (ismn_data.metadata['lc_2005'].val == 130) & \\\n",
    "             (ismn_data.metadata['climate_KG'].val == 'Csb') \n",
    "\n",
    "ismn_data.metadata[conditions].index.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the so found IDs to read data from the according sensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:26.284797Z",
     "start_time": "2024-06-15T21:55:24.439897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 1 Axes>",
      "image/png": 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CgUAgEAgEAoGgopOQkICPj49OH82PCq+UZ7usu7q6CqVcIBAIBAKBQCAQCASlhjkh1CLRm0AgEAgEAoFAIBAIBBZCKOUCgUAgEAgEAoFAIBBYCKGUCwQCgUAgEAgEAoFAYCEqfEy5uajVajIyMiwthkBQrrGxsUGpVFpaDIFAIBAIBAKBoNxg9Uq5LMtER0cTFxdnaVEEggqBm5sb3t7eZiW1EAgEAoFAIBAIrB2rV8qzFXIvLy8cHR2FIiEQFBFZlklOTub+/fsAVKtWzcISCQQCgUAgEAgEZR+rVsrVarVOIa9SpYqlxREIyj0ODg4A3L9/Hy8vL+HKLhAIBAKBQCAQFIBVJ3rLjiF3dHS0sCQCQcUh+34SORoEAoFAIBAIBIKCsWqlPBvhsi4QFB/ifhIIBCVNeqaGd1eEsexwpKVFEQgEAoHgiRFKuUAgEAgEgnLFptN32HE+msmbz1taFEE5RJZlbsUmI8uypUURCAQCQCjlglx07NiRjz76yORyPz8/fvzxx1KTpzAUJHteQkJCkCQp36z7wcHBuLm5PbFsAoFAICheElMzLS2CoBwzL+QaL3y7j5k7LlpaFIFAIACsPNGbQJ+NGzdiY2NjaTGKRHmWXSAQCAQCQekxa+clABbuv86EHg0tLI1AIBAIpVyQC3d3d0uLUGgyMjKwsbEpl7ILBAKBQCAQCAQCgXBfF+jI7QJ+//59Xn75ZRwcHPD39+e3337T6xsSEoKtrS0HDx7UtX333Xd4eHgQFRVlsO34+HgcHBzYsWOHXvvGjRtxcnIiMTERgM8++4z69evj6OhI7dq1mTRpkl4W76CgIJo3b87SpUupXbs2dnZ2yLJs4L6+cuVKWrdujYuLC97e3gwaNEhXPzs3//zzD08//TT29vY8++yznD17Nt9ztGXLFlq1aoW9vT21a9dmypQpZGYKN0qBQCAoTS5EJei+J6eLZzDAjYdJrA29RaZaY2lRBAKBQFBIhFIuMMrw4cOJjIxk7969rF+/nnnz5ukptdlK8NChQ4mPj+fMmTNMnDiRRYsWUa1aNYPtVapUiV69ehko97///jt9+vTB2dkZABcXF4KDg7lw4QI//fQTixYt4ocfftBb5+rVq6xdu5YNGzZw+vRpo/Knp6czbdo0zpw5w6ZNm4iIiGD48OEG/caOHcvs2bMJDQ3Fy8uLV155xWQpr507dzJkyBBGjRrFhQsXWLhwIcHBwXz11Vf5nUqBQCAQFDPrw27rvk//K9yCkpQdXpwVwrgN/7L8yA1LiyIQCASCQiLc1wUGXL58me3bt3P06FGeffZZAJYsWULDhvpxV9OnT+fvv/9m5MiRnD9/nqFDh/Lqq6+a3O7gwYN58803SU5OxtHRkYSEBP766y82bNig6/PFF1/ovvv5+fHpp5+yZs0axo0bp2tPT09nxYoVeHp6mtxXYGCg7nvt2rWZM2cObdq0ITExUTcBADB58mS6du0KwLJly6hZsyZ//PEH//nPfwy2+dVXXzF+/HiGDRum2+60adMYN24ckydPNimLQCAQCEqOPeH34NWmlhajzHDk+kMCn/e3tBhlhtuPknmYmM7TPm6WFkUgEAhMUmYs5TNmzECSJD0XZFmWCQoKonr16jg4ONCxY0fOnxflT0qa8PBwVCoVrVu31rU99dRTBpnIbW1tWblyJRs2bCAlJaXAzOy9evVCpVKxefNmADZs2ICLiwvdunXT9Vm/fj3PP/883t7eODs7M2nSJG7evKm3HV9f33wVcoBTp07Rp08ffH19cXFxoWPHjgAG22rbtq3uu7u7Ow0aNCA83LjVJSwsjKlTp+Ls7Kz7vP3220RFRZGcnJyvPAKBQCAoGe4lpFlahDLF4asxlhahTPH8N/vo879/iIhJsrQoAoFAYJIyoZSHhobyyy+/0KxZM732b7/9lu+//56ff/6Z0NBQvL296dq1K48fP7aQpNZBdt1OSZIK7Hv48GEAYmNjiY2Nzbevra0tAwYM4Pfffwe0ruuvv/46KpXWYePo0aO88cYb9OjRg61bt3Lq1CkmTpxIenq63nacnJzy3U9SUhLdunXD2dmZlStXEhoayh9//AFgsC1jmDpujUbDlClTOH36tO5z9uxZrly5gr29fYHbFQgEAoGgpMnQiNrbxrhwN6HgTgKBQGAhLK6UJyYmMnjwYBYtWkTlypV17bIs8+OPPzJx4kT69etHkyZNWLZsGcnJyTqlTlAyNGzYkMzMTE6cOKFru3TpkkFN72vXrvHxxx+zaNEinnvuOd588000mvwTzAwePJgdO3Zw/vx59u3bx+DBg3XL/vnnH3x9fZk4cSKtW7emXr163LhR+Ni4ixcvEhMTw8yZM3nhhRd46qmnjCZ5A+1EQDaPHj3i8uXLPPXUU0b7tmzZkkuXLlG3bl2Dj0Jh8VtJIBAIrILsiWOBCcTpEQgEgnKHxTWJDz74gF69etGlSxe99oiICKKjo/Vcm+3s7HjxxRd11lljpKWlkZCQoPcRFI4GDRrQvXt33n77bY4dO0ZYWBhvvfUWDg4Ouj5qtZqhQ4fSrVs3/u///o9ff/2Vc+fO8d133+W77RdffJGqVasyePBg/Pz8eO6553TL6taty82bN1m9ejXXrl1jzpw5Ogt3YahVqxa2trbMnTuX69evs3nzZqZNm2a079SpU9mzZw/nzp1j+PDheHh40LdvX6N9v/zyS5YvX05QUBDnz58nPDycNWvW6MXBCwQCgaBkOXBFuGcLBAKBoGJhUaV89erVnDx5khkzZhgsi46OBqBq1ap67VWrVtUtM8aMGTOoVKmS7uPj41O8QlsJv/76Kz4+Prz44ov069ePkSNH4uXlpVv+1VdfERkZyS+//AKAt7c3ixcv5osvvtBlRA8ODjZwBZckiYEDB3LmzBk9KzlAnz59+Pjjj/nwww9p3rw5hw8fZtKkSYWW3dPTk+DgYNatW0ejRo2YOXMms2fPNtp35syZjB49mlatWhEVFcXmzZuxtbU12jcgIICtW7eye/dunnnmGZ577jm+//57fH19Cy2jQCAQCIrGnUcplhZBUA6RhQuBQCAow0iyhfzAbt26RevWrdm1axdPP/00oC2z1bx5c3788UcOHz5M+/btuXv3rl6Jrbfffptbt24Z1LvOJi0tjbS0nKQvCQkJ+Pj4EB8fj6urq17f1NRUIiIi8Pf3FzHBJUBQUBAhISGEhIRYWhRBKSLuK4FAUJL4jf/LoC1yZi8LSFJ2uBWbzAvf7tP9nt63Ca+2qIGTnSiyk329/DyoBb2bVddrA3HtCASCkiMhIYFKlSoZ1UPzYjFLeVhYGPfv36dVq1aoVCpUKhX79+9nzpw5qFQqnYU8r1X8/v37Btbz3NjZ2eHq6qr3EViGnTt38u2331paDIFAIBAIKjTf7ryk9/uLTecI2iyq1QgEAkF5wWJTqC+99BJnz57Va/u///s/nnrqKT777DNq166Nt7c3u3fvpkWLFoA2c/b+/fv55ptvLCGyoJAcOXLE0iIIBAKBQGCVrAu7jUopMaNfs4I7WwESBVeUEQgEAkthMaXcxcWFJk2a6LU5OTlRpUoVXftHH33E119/Tb169ahXrx5ff/01jo6ODBo0yBIiCwQCQbEgyzKfrD2Dj7sjn3Stb2lxBIJyw63YZEuLUK5YdfwWX/VtikIhFNLsmPKF+69ZWBKBQCAwpEwHG40bN46UlBTef/99Hj16xLPPPsuuXbtwcXGxtGgCgUBQZE7fiuOPU3cAhFIuEBSCK/cfG23PVGtQKS1eUKZMIgl9XI8Z2y/q/Y6KT6FaJQcTva2Lw1djWHnsBkGvNMbLReSEEQhKkzKllOdNCCZJEkFBQQQFBVlEHoFAICgJ0jI1lhZBICiX/LTnqtH2P0/fpX+rmqUsTdlB6N36qDUyn649TUBjb11bQkomGWrDZ294VIJQyrMYtPgYABoNLBjaysLSCATWRZlSygUCgUAgEAgAUjPU2Nsodb+v3n/MmVtxRvuuCb3Fw6Q0RnaoU0rSlS02n7lrcpksW5+1/MPfT7L9XDSbTuecl8//OMvnf5zNZy1BNpEPkywtgkBgdQhfL4FAIChlcheiPHnzkeUEEQjKKDO2h/PUpB2E3YjVtT1OzTTZ/3hkLF9vu2hSaRdYF9vPRRfcSWCSi9GPiU/JsLQYAoFVIZRygUAgKEWOXHvIwEVHdb9jHqdZUBqBoGyycP91AGbmif8tiIdJ4n4SFI7ck6SCHEIu3be0CAKBVSGUcoFAIChFAoNDLS2CQFBuSM0wnX+ho+IUdaQ7pShN+UTonAJzEK79AoFlEUp5OeXhw4d4eXkRGRlpaVGKnZCQECRJIi4uDoDg4GDc3NwsKpMA0tLSqFWrFmFhYZYWRSCoUJy6+YibD0WpL2OcvRNvtP1p6SrBtrPYYzdWr11YPQWCovH7sZuWFkEgsGqEUl5OmTFjBi+//DJ+fn4Gyx4+fEjNmjX1FNtsZFlm9uzZ1K9fHzs7O3x8fPj66691y6Oiohg0aBANGjRAoVDw0UcfleyBGKFdu3ZERUVRqVKlQq8bFBRE8+bNi1+oMkjeyYuSxs7OjjFjxvDZZ5+Vyv6sBaFDWDc3Hybz6rzDdJi1z9KilCsaK25YWoRyw7UHiZy9Hc8NK0jedfZ2PG/8cqRQ63y1LZzxG/4tIYkEAoHAPIRSXg5JSUlhyZIlvPXWW0aXjxgxgmbNmhldNnr0aBYvXszs2bO5ePEiW7ZsoU2bNrrlaWlpeHp6MnHiRJ5++ukSkb8gbG1t8fb2RrK2dLFZpKenl/o+MzLMS+gyePBgDh48SHh4eAlLZD1sOxtlaRHKBLIsc/1BIhqNdU1TXIxO0H2XhZn3ibHG18asnfnH3Xf74QAv/3yIF2eFVPjkXa8tPMzR67EFd8zF9QdJrA69JbxVBMiyTERMEmorew8JygZCKc+DLMskp2eW+qcwg7Ht27ejUqlo27atwbL58+cTFxfHmDFjDJaFh4czf/58/vzzT1555RX8/f1p3rw5Xbp00fXx8/Pjp59+4s033yySpTqbefPmUa9ePezt7alatSoDBgzQLUtLS2PUqFF4eXlhb2/P888/T2hoTpxtUS3AwcHBTJkyhTNnziBJEpIkERwcDEB8fDwjR47Ey8sLV1dXOnfuzJkzZ3TrZlvYly5dSq1atXB2dua9995DrVbz7bff4u3tjZeXF1999RUAmWoNt2OTkSSJ+fPn06NHDxwcHPD392fdunV6ct25c4fXX3+dypUrU6VKFfr06aMXdjB8+HD69u3LjBkzqF69OvXr1wdg5cqVtG7dGhcXF7y9vRk0aBD372sTr0RGRtKpUycAKleujCRJDB8+HND+hz/++KOeDM2bNycoKEj3W5IkFixYQJ8+fXBycmL69OkAbNmyhVatWmFvb0/t2rWZMmUKmZk5GY+rVKlCu3btWLVqVaH+G4Fp/jxtupSRNbH8yA06f7efseut12J1SmQOz5c7cSlG26uSo4RZ27zGw8Q0/rfvmtn9K7q1PL8cBAXxx6k7TNly3uomBgHikyv2ZI25/HbsJp1mh1Dn822ERyUUvIJAUIyIOuV5SMlQ0+jLnaW+3wtTA3C0Ne/vOHDgAK1btzbcxoULTJ06lWPHjnH9+nWD5Vu2bKF27dps3bqV7t27I8syXbp04dtvv8Xd3f2JjyGbEydOMGrUKFasWEG7du2IjY3l4MGDuuXjxo1jw4YNLFu2DF9fX7799lsCAgK4evXqE8nx+uuvc+7cOXbs2MHff/8NQKVKlZBlmV69euHu7s62bduoVKkSCxcu5KWXXuLy5cu6fV67do3t27ezY8cOrl27xoABA4iIiKB+/frs37+fw4cPExgYyEsvvUS1ek11FodJkyYxc+ZMfvrpJ1asWMHAgQNp0qQJDRs2JDk5mU6dOvHCCy9w4MABVCoV06dPp3v37vz777/Y2toCsGfPHlxdXdm9e7dugiY9PZ1p06bRoEED7t+/z8cff8zw4cPZtm0bPj4+bNiwgf79+3Pp0iVcXV1xcHAo1PmaPHkyM2bM4IcffkCpVLJz506GDBnCnDlzeOGFF7h27RojR47U9c2mTZs2ev+noHCkZKgtLUKZZPLm8wBsOHmb7/5jGS8dSxB2I6ckXkq6uDby48PfT+m+d1Kc1n3/0WYeAzO+sIBElidDXTgFMvifSL7u15Qf/75C10ZetPItvnd/eeeHvy8D0MbPnR5Nq1lYmtJBo5G59iCRrj8csLQoZYIvNp3Tfe/x00EiZ/ayoDQCa0Mo5eWQyMhIqlevrteWlpbGwIEDmTVrFrVq1TKqlF+/fp0bN26wbt06li9fjlqt5uOPP2bAgAHs3bu32OS7efMmTk5O9O7dGxcXF3x9fWnRogUASUlJzJ8/n+DgYHr06AHAokWL2L17N0uWLGHs2LH5bTpfHBwccHZ2RqVS4e3trWvfu3cvZ8+e5f79+9jZ2QEwe/ZsNm3axPr163WKp0ajYenSpbi4uNCoUSM6derEpUuX2LZtGwqFggYNGvDNN98QEhJCP//Guu2/9tprulCCadOmsXv3bubOncu8efNYvXo1CoWCxYsX69zxf/31V9zc3AgJCaFbt24AODk5sXjxYp2SDhAYGKj7Xrt2bebMmUObNm1ITEzE2dlZN5ng5eVVpER4gwYN0tvH0KFDGT9+PMOGDdPtc9q0aYwbN05PKa9Ro0aFTDAoEFiCxYciLC1CuaSrMifhZF3FbQtKUr64G5/C/JBrLNiv/Qilw5CYpNIPIbMUT03aQbq66N4FAoGg+BBKeR4cbJRcmBpgkf2aS0pKCvb29nptEyZMoGHDhgwZMsTkehqNhrS0NJYvX65zkV6yZAmtWrXi0qVLNGjQoGjC56Fr1674+vpSu3ZtunfvTvfu3Xn11VdxdHTk2rVrZGRk0L59e11/Gxsb2rRpU2JxymFhYSQmJlKlShW99pSUFK5dy3H78/Pzw8XFRfe7atWqKJVKFAqFXlu2C3k2ecMI2rZty+nTp3X7vnr1qt52AVJTU/X23bRpUz2FHODUqVMEBQVx+vRpYmNj0Wi0L86bN2/SqFEjcw/fJHm9LcLCwggNDdW56AOo1WpSU1NJTk7G0dER0E5+JCeL2DtByZGeqcFWJaKrBOZhhWHkOgobQ3/0eizuTrYFdxRYBUIhFwjKDkIpz4MkSWa7kVsKDw8PHj16pNeWbQ1ev349kJMwyMPDg4kTJzJlyhSqVauGSqXSKeQADRs2BLSKXnEp5S4uLpw8eZKQkBB27drFl19+SVBQEKGhoTq58iZxk2W5xBK7aTQaqlWrRkhIiMGy3BZmGxsbvWWSJBlt02g0SMi4YTo2L/tYNBoNrVq14rfffjPo4+npqfvu5OSktywpKYlu3brRrVs3Vq5ciaenJzdv3iQgIKDARHAKhcIgR4GxRG5596nRaJgyZQr9+vUz6Jt7Eig2NlZPdoHgSXl3hX6ZvcPXYujYwMtC0pQuIqGQeeSXd6Uyj0tRkrJFUWLoI2LEpGq+WEliglux4joQFA61Rqb5lF0oFBJnJneztDgVjrKtfQqM0qJFC1auXKnXtmHDBlJScpLghIaGEhgYyMGDB6lTpw4A7du3JzMzk2vXrunaLl/WxlD5+vrmu09ZlklMy8TeRomNsmALlkqlokuXLnTp0oXJkyfj5ubG3r17CQgIwNbWlkOHDjFo0CBAqzCeOHGiWMqv2draolbrx2W2bNmS6OhoVCqV0RJyRcE1M5aqCu3EyNGjR3nzzTd1y44ePapz12/ZsiVr1qzRJZgzl4sXLxITE8PMmTPx8fEBtLH6ucm2rOc9Xk9PT6KicjJ6JyQkEBFRsItsy5YtuXTpEnXr1s2337lz53THJygcY9adKbiTlSHLMjvOR+u1XXuQRMfimSMsVzx4nGZpEcosf4fneCjZkKm3TClZhxJljKT0zII75UEksBIA3EtIzXe5tVbAycuiA9fZ+u9dVrz1LK72NgWvUIH593Ycj9O0z5ygzecJeqVxAWsICoPwDyyHBAQEcP78eT1reZ06dWjSpInu4+/vD2gt4V5eWotTly5daNmyJYGBgZw6dYqwsDDeeecdunbtqmc9Dzt5iiOhJ0hMTOTBgwecPn2aYyf/JSImicvRBVsktm7dypw5czh9+jQ3btxg+fLlaDQaGjRogJOTE++99x5jx45lx44dXLhwgbfffpvk5GRGjBjxxOfGz8+PiIgITp8+TUxMDGlpaXTp0oW2bdvSt29fdu7cSWRkJIcPH+aLL74wUHTNxYWcGeZ169axdOlSLl++zOTJkzl+/DgffvghoC0h5uHhQZ8+fTh48CARERHs37+f0aNHc/u26TjIWrVqYWtry9y5c7l+/TqbN29m2rRpen18fX2RJImtW7fy4MEDEhMTAejcuTMrVqzg4MGDnDt3jmHDhqFUFhwe8eWXX7J8+XKCgoI4f/484eHhrFmzhi++0E+gdPDgQV0svMB80jM1rA8Tsa8C08zedcnSIpRZQiNzMqzPs/nJZL9HVpZF+v9+zalc0ktxlN9tpuNJnOUEqggIZRQQJRqz+WpbOGdux/P5xrOWFsXi5HbsCj4cid/4v8R1UowIpbwc0rRpU1q3bs3atWsLtZ5CoWDLli14eHjQoUMHevXqRcOGDVm9erVev9atWtKuzTOEhYXx+++/06JFCwa8+goAalnWlSy7dvoQstpwAOTm5sbGjRvp3LkzDRs2ZMGCBaxatYrGjbUzajNnzqR///4MHTqUli1bcvXqVXbu3EnlypWLeEZy6N+/P927d6dTp054enqyatUqJEli27ZtdOjQgcDAQOrXr88bb7xBZGQkVatWLfQ+8j6ApkyZwurVq2nWrBnLli3jt99+08V8Ozo6cuDAAWrVqkW/fv1o2LAhgYGBpKSk5Gs59/T0JDg4mHXr1tGoUSNmzpzJ7Nmz9frUqFGDKVOmMH78eKpWraqbCJgwYQIdOnSgd+/e9OzZk759++o8I/IjICCArVu3snv3bp555hmee+45vv/+ez0viiNHjhAfH69X4k5gmhnbwvl+t9YbRUa8uMzF2l7yLynCaCRFkioy85vklwM5yUtzJ3nLyw9Z95u1cDOXC/L/bOfQTnmBCTa/m71+RS+RJjBNtsVTYB5b/40quJMVsuxwJCOXnyA6Pn/PC0HBSHIFH/0kJCRQqVIl4uPjDZSg1NRUIiIi8Pf3N0icVtbZtm0bY8aM4dy5c3qJyIqDf2/HAeBspyIpLRMlajQoqCY9JEF2ZP/uHXw1NYgLIeu1MdfVngbJeuZ3ElIyUMVewVFKQ6rRkj/++IO+fftaWqxS4bXXXqNFixZ8/vnnJvuU5/uqOLmXkMqzX+8B4OK07jxMSqf9zJwqBx+p1hMjV2KluqtVZ0CWZRn/Cdv02r7o1ZC3XqhtIYlKl48+n8CPtvMAaJU6n6CBHXn56eoFrFXx8Rv/l+575Mxe+r/tBxn2T/1dr7+1YOy87FU3JzBjnNnbWPtOW9r4V4zSaLnPR1GZ1qcxQ9v6PbkwZZzXFhwmNPKRyeVzBrbgFSt7FuW9fgyePVb0bDFG2I1H9J9/2Oiyjg08Cf6/NqUsUdknPz00L9ajSVUwevbsyTvvvMOdO3dKbB+Zahl/KYpGipvUlB5QRXqMv+Ie2//awtfjP8xJgqaxruydalnGFutykQRt2b2nn36ajz/+2NKilAvSM3Pui//tu6qnkNeR7vCRaiPTbX4FK7egG5sWXpgVw2cNfGWzRPe9sSKS/646lU9vgTnP3ktmhFlVZKRCPlP+OFVy44jyyN6L9wvuVAGISbSe0m/mYMxTKVNkp9cjI5/zERUnLOVPilDKyzGjR4/WJQErCVIz1ThL2pvMTcpxcVv6yzxee7lrTkd16SUnaty4Mc7OzkY/xjKclxQqyfoe1HZ2dnzxxRc4ODhYWpRyx9y9V/V+u5CTlPFZ6WJpi1PmefA4jQ9/F8qptXInLsVouy0ZXLQbbnTZa8oQuim08dWJVu6W21pROBf+2CSRYDA3+y49sLQIpUJEjAhdyE2mkUoY1j1lrs/3uy7xxi9HTS6/dO8xe8LvlaJEFQ+RfV1QaBTpeawQMZeheulk4962bZvR8l5AkeLDi0Lu2VT5zslSO3ZBxcRTirO0CAILIueqsi3SS2l5nGr8GV9HuovCRKb1WTa/APBU6q8lJld5wUUyPqlhip3nxUBaYEgFj24tErFJ6Ww+fYc+zWtQ2cnW0uKUKpvPFOy9NmLZCfZ8+iJ1PJ1LQaKKh1DKBQZUlx7iTApX5RpGlztoLDe7WlDpttIg5nEq1YSPiUAgKGHSMzVsPxdF29pV8HK13vwMhaGj4gzQ0dJiCCzAzYei7rbgybAlgx6KYxzSNOUhlUhO13dpf3dlGMcjYtlxPprVI9taSMqyzawdl1gwtJWlxSiXCNVCYICHlIC9lIGblGhpUcokrhTOCiEQ5KahdINNdl9aWowyw9k78ZYWocyyYP81Rq8+Tc85By0tSqkS/wRlzQobTy2oOEQXUHdbICiIT1Tr+cl2HmH271FbumtgHT4eoS3NePR6rLHVBWgrzcSnZLDv4n0Rk19IhFIu0CO3u5IHYrCcF+350R/0qa0s0Z3APPLOsANMUq1gu90Evbaqkunst9ZAdlKuFxVnuGI3lIbSDQtLVLpk5+2AHIUyJeva2XEuGrC+hEwL9l8jQBHKGtupVCemCFsQgQAAvlI0NSXriI8WFI78Yn/fVW5msc0sJI11JbSVgHdVW3S/99qNES78uSjMmXh94RH+LziUBfuvlZg8FRGhlAv0SM3IUTDtJet6IJtDUlomNSX9QaI6QyTJERiy7Eik3m8FGkaothv0m2SzspQkKtsss/0GG0ltMGlhTQRkJSpbcTQSgAtRCRaUxnKkZmhYaPsDzyouMs2mKDHi1jGQzs8K5UAq++0+4ZDdaBSIiWOBPiOWnTC5bLzNarooT1Ht7t+lKFHZ5C9RmxyAyX+e44aZ4SEnIh9xMWuy/c/T1lFFpbgQSrlAD1k2tO4JctAAyryZ18VMqsAIianWnQH6SXDAOt1QB6r2YUMmiWniOZxNZcl0ebN4hQLr8iHQJzVX2cXnFBf0llWRciZ0rLGEpzl4Eoe1TOCYS09FTnbt8JvRFpSkbHAsQripAyw7Yr4H28OknKey2khGe4FphFIu0ENSi5d3figzRTy5wDykQnjQJpjINm2tKKx4oHzF/k1Li1CmqC49NNr+QKnged+a9PKpXsoSlR00uSaEX1UcyrM0d1Z/672fTDFY+Teh9u8zTrXG0qKUKebZztF9t7Z8H0W9S2ISrctb0o50Jqh+4xkzyrleF2X3CoVQysspDx8+xMvLi8jIyGLdbmEUiZIiJCQESZKIi4sDIDg4GDc3N4vKlI0kG7oBloVzVhqkpaVRq1YtwsLCLC1KheNilGlrYEVHNjIUEkqEIBvvXDkXeiqP6b4fddBmo49WWW8RmV25Spn5KfStmsKBK3++slkKwPuqzRaWpPSJTzE+CbzU5ttSlqRssfOccc+A3oojhNsNN5pnqd2MPbSe/jfDfz1e0uJZjLxx9e8ot/KO6i/W2U0VoTHFjFDKyykzZszg5Zdfxs/Pz2DZw4cPqVmzpp5im40sy8yePZv69etjZ2eHj48PX3/9tW75P4cP077P/1GlcScc6rTlqQ79+OGX0o15bdeuHVFRUVSqVKnQ6wYFBdG8efPiF6oMknfyoqSxs7NjzJgxfPbZZ6Wyv/KOGBSbTzvFOb3fLylOApCaYZ1u3FYyz2eSxDT90A9Nlgvkf1Wbchpz3V+PrWVmNA9RcTmeW5XQt0iNt1ml+24tZ8dKL4NC8+HvJ422d1aeLl1ByhhR8cY9IX+2nYuDlM4J+/cMlt2N14ZahVyynoSKn9is132/bj+E5xQXGKbcabVhZ8WJ9U4xl2NSUlJYsmQJ27ZtM7p8xIgRNGvWjDt37hgsGz16NLt27WL27Nk0bdqU+Ph4YmJyEpc5OTrx4f+9TrOG9XBydODQ8VO889lXODk6MHJI/xI7ptzY2tri7e1dKvsqi6Snp2Nra1uq+8zIyMDGxqbAfoMHD2bs2LGEh4fTsGHDUpCs/CIGiObzu+3Xer/9JK0F8KlJOzj++UtWV6M7W9+0IZOeiqMc0TS2qDylzdk78ZDrL88do2iMKR7uzH6gdXPX1im3PhzzDIhfVh410VNg7Ry8kn9Fgys2NtyyUWFtBXgk8dI2Sm4DgxeG1WJW204HwF+KIihzeClJVTERlvJyyPbt21GpVLRt29Zg2fz584mLi2PMmDEGy8LDw5k/fz5//vknr7zyCv7+/jRv3pwuXbro+jRv3pyBfbvTuEEd/HyqM6R/LwI6tuXgsVOFknHevHnUq1cPe3t7qlatyoABA3TL0tLSGDVqFF5eXtjb2/P8888TGhqqW15UC3BwcDBTpkzhzJkzSJKEJEkEBwcDEB8fz8iRI/Hy8sLV1ZXOnTtz5kzO4C3bwr506VJq1aqFs7Mz7733Hmq1mm+//RZvb2+8vLyY+tUMvX1KNVqyYOEievTogYODA/7+/qxbt06vz507d3j99depXLkyVapUoU+fPnphB8OHD6dv377MmDGD6tWrU79+fQBWrlxJ69atcXFxwdvbm0GDBnH//n0AIiMj6dSpEwCVK1dGkiSGDx8OgJ+fHz/++KOeDM2bNycoKChHbkliwYIF9OnTBycnJ6ZP1z5Ut2zZQqtWrbC3t6d27dpMmTKFzMwcq1WVKlVo164dq1atQlAAsszPNj8xx2YuIFyyC8PHNhuItB8EwE97rlhYmtLnVqw2y+17ys38ZDuPbVackR5ySsTl5q5Njk1hp7OT7vvrqhBsE2+XilyWJrcOUUvxgEcKBQcc7Ml7tr5ULS9VuQRll7xeKMboV7Mao6t6Emf/iLtxKRwqQIm3VqzFk+vUrTjd99wJJPPyfB6PN0HhEUp5XmQZ0pNK/1MIX9cDBw7QunVrg/YLFy4wdepUli9fjkJh+Ndu2bKF2rVrs3XrVvz9/fHz8+Ott94iNjYnu2TeTImnzl3k8Il/ebFtK7PlO3HiBKNGjWLq1KlcunSJHTt20KFDB93ycePGsWHDBpYtW8bJkyepW7cuAQEBenIUhddff51PP/2Uxo0bExUVRVRUFK+//jqyLNOrVy+io6PZtm0bYWFhtGzZkpdeeklvn9euXWP79u3s2LGDVatWsXTpUnr16sXt27fZv38/33zzDd988y1Hw/7V2++UqdPo378/Z86cYciQIQwcOJDw8HAAkpOT6dSpE87Ozhw4cIBDhw7h7OxM9+7dSU/Psf7s2bOH8PBwdu/ezdatWwGtxXzatGmcOXOGTZs2ERERoVO8fXx82LBhAwCXLl0iKiqKn376qVDna/LkyfTp04ezZ88SGBjIzp07GTJkCKNGjeLChQssXLiQ4OBgvvrqK7312rRpw8GDB41u837yfe4m3jUaJ2xtuKgf0Vt5jFeUR3hRcYZr9kMtLVKZY/OZu3y24Wy+fawxeWv1h4cB6KzUToZ65DMQqpjo/+mrQ28a9Pi5spvJtW0TrbMMz8Dq3nzg7cVqV2e99jdUIXyqWqv7/aLiDPtsP2aAcn9pi1hmUAMn7exIszLj6DsrDEuhKdCwyOY7AKKUSl17ot1j2s3cy5Alx/jnqlDM8/LUpB2WFqFUuBitff8MVe5ihe0Mk/3qKu7ymjKkdISqoAj39bxkJMPXFsjm+vldsHUquB9aK2n16voypqWlMXDgQGbNmkWtWrW4fv26wXrXr1/nxo0brFu3juXLl6NWq/n4448ZMGAAe/fuBSA5MQ4noGar7jyIfURmppqgT97hrUGvmn0oN2/exMnJid69e+Pi4oKvry8tWrQAICkpifnz5xMcHEyPHj0AWLRoEbt372bJkiWMHTvW7P3kxcHBAWdnZ1QqlZ77+969ezl79iz379/Hzs4OgNmzZ7Np0ybWr1/PyJEjAdBoNCxduhQXFxcaNWpEp06duHTpEtu2bUOhUNCgQQO++WoqIUfCeK5VM932+/V7lbfeeguAadOmsXv3bubOncu8efNYvXo1CoWCxYsX61yjfv31V9zc3AgJCaFbt24AODk5sXjxYj239cDAQN332rVrM2fOHNq0aUNiYiLOzs64u7sD4OXlVaREeIMGDdLbx9ChQxk/fjzDhg3T7XPatGmMGzeOyZMn6/rVqFHDZILBB8kP0GRoUGdaxwxyfuS2jC+z/caCkpRdRq0qnAeOteBw9yjwvtUm0fGV7un9jk/JwC5X8bN7uRQH41jHTE7eufw7Wd4Du5wcGZyQqLfsv6pNVCKJLzOH86wiHH/FPd5gH+vVL5aWuGWKeW6V+KVyJTonJYPhnE+F5Z+rhtUMOitO0VWpTeDarVYNo+sdi4ilfV2PEpXN4ohEMPkyzSa4wD6zbH7hb3VLHuFa8gJVQIRSXg5JSUnB3l4/xnLChAk0bNiQIUOGmFxPo9GQlpbG8uXLdS7SS5YsoVWrVly6dIkGDRrgoNEmizn4xxISk5I5evIs47+eS11/Hwb27W6WfF27dsXX15fatWvTvXt3unfvzquvvoqjoyPXrl0jIyOD9u3b6/rb2NjQpk0bnXW5uAkLCyMxMZEqVarotaekpHDt2jXdbz8/P1xcXHS/q1atilKp1PM6qOrpzv0YfYv+c8+20fvdtm1bTp8+rdv31atX9bYLkJqaqrfvpk2bGsSRnzp1iqCgIE6fPk1sbCwajXaAfvPmTRo1amTu4Zskr7dFWFgYoaGhepZxtVpNamoqycnJODo6AtrJj+Tk5Hy3LSzlsONcNNPMDIWWNdapfAmM01OhzTLeTBFhYUksgyqPA/Zvx24yQrlb9/uqbf75L6xlbJ3tvu6EeaU631Tt5hHOuKFV2JUVbNKnMEbvlZW07+S9To4lI0w5oqZkKklZrhvJKm4qazjGwlPYv368ajWfZY4sGWEqOEIpz4uNo9ZqbYn9momHhwePHuknW8i2Bq9fr82KmF3CwMPDg4kTJzJlyhSqVauGSqXSKeSALlnXzZs3adCgga7dP2u2tGnDetx7EEvQdwvNVspdXFw4efIkISEh7Nq1iy+//JKgoCBCQ0N1cuVNqCHLcokl2dBoNFSrVo2QkBCDZbktzHkTnUmSpNemTk9BkiSdcpwfEkD8bTSZGbRq1YrffvvNoI+np6fuu5OTvpdEUlIS3bp1o1u3bqxcuRJPT09u3rxJQECAntu7MRQKhUEJi4wMwxIoefep0WiYMmUK/fr1M+ibexIoNjZWT3bBk+P08F+o09nSYlgIMRDKS22F8dI81kJDydB0WVnKsfyG2tuVpjhlFkmSUJHJefsRZq8zWvWH7nsLxdWSEMtiFOZJkmwkxM8ama5awhDVHgCu2eirBLKUM9ZJU1esCRxTxCsUzHerRJ/ERBqmZyDeTzlc0dSgnsIwgXRevKRHBfYRGEco5XmRJLPdyC1FixYtWLlSv0zZhg0bSEnJmS0PDQ0lMDCQgwcPUqdOHQDat29PZmYm165d07VdvnwZAF9fX5P7k2WZtAIUwbyoVCq6dOlCly5dmDx5Mm5ubuzdu5eAgABsbW05dOgQgwZpEzllZGRw4sQJPvroo0Ltwxi2trao1fpWlpYtWxIdHY1KpTJaQs5c0jOMJ0g5djyUwBFv634fPXqUFk/5Q9IDWtarxpp163UJ5szl4sWLxMTEMHPmTHx8fABtrH5usi3reY/X09OTqKgo3e+EhAQiIgq2uLVs2ZJLly5Rt27dfPudO3dOF44gKB5UGdZbp/w5Rcl4yJQHUtLVOFhaiDJIf+UBgzZ3tHGNR+3tWOKWf7lMa0miLMsybnlKoQGctDe/WoESNWoKCgcQVETsSNcp5AB9a+qHRdqSM+Z58Dit1OSyFBIyM6pU5i9nJ36r5MK0Bw/pkP5vwSsKjCKeLYVHTBWWQwICAjh//ryetbxOnTo0adJE9/H39we0lnAvLy8AunTpQsuWLQkMDOTUqVOEhYXxzjvv0LVrV531/Jdff2PLrv1cuX6TK9dv8uuaP5m9cAVD+vU0W76tW7cyZ84cTp8+zY0bN1i+fDkajYYGDRrg5OTEe++9x9ixY9mxYwcXLlzg7bffJjk5mREjzJ/tN4Wfnx8RERGcPn2amJgY0tLS6NKlC23btqVv377s3LmTyMhIDh8+zBdffGGg6BaFjRv/YOnSpVy+fJnJkydz/PhxPhimLR83+NUAPDw86NOnDwcPHiQiIoL9+/czevRobt82nSG4Vq1a2NraMnfuXK5fv87mzZuZNm2aXh9fX18kSWLr1q08ePCAxEStJalz586sWLGCgwcPcu7cOYYNG4aywBhM+PLLL1m+fDlBQUGcP3+e8PBw1qxZwxdffKHX7+DBg7pY+NykZooalbkpVHIuq3ANNI5HgXV3Ku65uRBlbQnczEM24og8SLUPgLerVS14fSu5n87cfvKaVZfshtFFEVYM0lieos7F5C0nZy3Uk/KvUtBdkVMVJ8mMrO0Vgb9yVXKY5FmFdorzHLG34+Ua1YjTeVdYx/MlNx0UZ8yykmfzpnInF+2G86xkvZPuRUEo5eWQpk2b0rp1a9auXVtw51woFAq2bNmCh4cHHTp0oFevXjRs2JDVq1fr+mg0GibM/Jnm3d6gdc8hzF26hpkT/svUse/p+oQcPoFUoyXnb0cZ2w1ubm5s3LiRzp0707BhQxYsWMCqVato3Fhba3fmzJn079+foUOH0rJlS65evcrOnTupXLlyEc6GPv3796d79+506tQJT09PVq1ahSRJbNu2jQ4dOhAYGEj9+vV54403iIyMpGrVggd4BTHpi4msXr2aZs2asWzZMn777Tca1asNgKODAwcOHKBWrVr069ePhg0bEhgYSEpKSr6Wc09PT4KDg1m3bh2NGjVi5syZzJ49W69PjRo1mDJlCuPHj6dq1ap8+OGHgDa/QIcOHejduzc9e/akb9++Os+I/AgICGDr1q3s3r2bZ555hueee47vv/9ez4viyJEjxMfH65W4yyZTYx0vbXMZpNxTcCcrJjreOgfCgqITJ1yO9dh94R6vKA8/0TZUkobFtt8Vk0Tlk84KkXCyIHaev1dwp3LOtTuGx/iuaisjq1Ul0taGF3xr8pPNz0TaD2ZIrhwX2UTEGHqtVARkYHkhktVWleKYarMMG0nND7b/KznBKiDCfb2cMmnSJMaMGcPbb79ttPxZx44djVoLqlevriulpYdGAw+vMCawH2MCDWOKcxN56y61/GuR4VOd6yol/hmZejPUzz//vNH47Wzs7e2ZM2cOc+bMMbo8r+zDhw/XlQIrCDs7O11cfW5cXFzy3WdQUJBeHW9AV+M8NyHrFxm0VatWjV27duk33s15yXt7e7Ns2TKTMhvbD8DAgQMZOHCgXlve/3TSpElMmjRJr83V1ZU1a9botWVnVDe1nWwCAgIICAgwKev333/P2LFjcXAwdLq9n3Lf5HrWiDFrn+m+1kfXHwoux1RHusOhq8LB29qwxTAHBsDjfJTypv61AAiLuMm5Owk4P0qmZuWKn8TrS5sVRttfqVGNaTEPeTqtcKFnAuvBXco/bEq2kjCQbBwuboT6+ffpkzUJNt3mV1aqu+otC9p8nmWBbYytVqGQgX8c7GmQno6nWsMOJ0fmu1Xiu/sx1M3IoJHihq6vlV1CT4yYdi6n9OzZk3feeYc7d8x3J8mXlFhtOTgz+CvkMKMnjsbGxoYUSUGClVgvrCVO0RRpaWk8/fTTfPzxx0aXp2SYlwFYYIg1XlqPUwv2rNhjN5aE2Afsv/wAWZYJu/GIx6nGFbaKzo2HFdMKY4znleeLvO5uJ0fWnbjN89/sK0aJyh8Rtja84+1laTEsisLuLpIqAdsqITj4LAWMP3MkK5wW7ag4rWf9vGxjWNGgMBPLFYEnvQ40FTRs5m6c/thul6MD73l70c1HmxB6rJcH121tGO9ZxWDdalKsQZvANMJSXo4ZPXp0MW7N+MMkXqEgXqGgWmYmd1QqHGWZRQu/5Y4q59K5rVJRSZZLRWtt3LgxN27cMLps4cKFDB48uMRlsFbs7OwM4ssB0tXpRCUZD2WwVjKsJFNtaTBK9QfDljozNqABs3ZeAiByZi8LS1X6vDgrhMvTe2Crso5J0KKSYaWzp4+NHHeSlUyYG0Nhex+n2vqecapKZ8iMb2W0vwOp1JaieYwDN+UnD2sr6wTbfqv7flelpH/NagZ9KqaKKSgsO/Yf4rNcBS92ZsXdZ0oSEbky9l+ysyVFknCooJMTpYFQygUGxCkUJEsS1dRqbmcp34+zMn0nAR55sn0DZCY/QuXkXuKybdu2zWh5L6BY4sPzIzldTd6ctvKdk6S7NzDa31q4k3iHZDO9LKyFlAx1oQY01vQKi45PxcvF/JJWzlk1mLMV8opEembhJm9S0tUVXimPjk/F29JClEN2OBt31V9UyZW34yt2QsHo+FS+2XERH/ecc2Dn/adBP0kyPnboqgxjru3Put9d0r412q8ikg5sdjZeceiRsmI/a/JijR4T5tBc0i+duNsp5z57JU/G/hdr1eD4jfwTCApMI5RygZaUON3XbCu4vYnZrhgjmbzTMzJK5WLKr3RbSeOQdMc6/YwLIENjne7ET8qwal7cUqnYc+uu1VxW289G8d5vJ3nl6ZwXee6BULIkYS/LenFVFXmgNHDRUSLNr16FXIHPRTbPzdhj8pwcdSh4MkcGJEkGWeuxYmMlisUuJ+NK+Rx3N15//BhXTcW9dsasO8OhqzF6bSqnawb9VJVOISlTyExsqNf+svKI3u8XFWeKX8gySqusXAzGWORWCaJknEglCQdkWUayMk8UY3dNkiThZGR8bA1l46ILqOSTolBwwMGeDikikWtRsI63laBg0hMNmqJUYs4mNw6SSJhjjAy1UMrzcj8h/xfSXZWSk/b2PFCpOOhQCK2snDN3r3bGffOZuwA0lG4wWrURgHtKJc/6+fB0nkGiUjL0zLFG+igO6c6ftTLVwzBmMT8SUqzn2ZSfqpRZwaf98irkplA53sDOaydOtX/Ua/8nzzP4RcW/VlFWL82My2KRzXectx9BHekO/hO26do1GhlNBZ7oyeaEvf5E4B/OTjzn58NyVxc6Kk7rLbsYnX/ivPJKdm6B03a2zK1cqcD+v1YyXVlIkD9CKRcIBIJi5q9/o/Nd/ljKefQecLTe7OLb7SZQV6FV0Cd75IS/JChyRov9lYf4XPVbhaslrFFrGKbcaXL5oSv6isZPtvNYfegCG8JuczxCP3lOWqaaX/+J4PoDw8lVa6Piqwk5qHIlLssvM701nZOikNf7r4PyrJ4CWtFoKV0GINJIcre8dFWeBGCQcq+uTaORaf/NXmp/vo07cRUnwauxOYqHea6NL7OSmc2qUplg229xJYkRym1UpeInNBta3ZvNLs6WFqNCY1GlfP78+TRr1gxXV1dcXV1p27Yt27dv1y2XZZmgoCCqV6+Og4MDHTt25Pz5omdlFZQgVjCrLBDkx8ErDzgRmfNitjdR1um8rS0DciXVWePqUuKylRXy83z8J9fkRFqejiNVfzFR9VtJiWURwrYvZYqN6VKJQ5YcM2izI4NP153hPwv13W3nh1xjypYLdP6u4DJzFR1zFI2KgpKcnATn7Ey79u9zst6JP3OwttHLRrsg7ZciHvjDpHSi4rWTpJ1nhxSLTGWVpQVYff+1f5tJNis5Zv9hKUlU9jlnZ2tpEcotFlXKa9asycyZMzlx4gQnTpygc+fO9OnTR6d4f/vtt3z//ff8/PPPhIaG4u3tTdeuXXn8uGK6iJQFiuooap/6oFjlKKvIQKRKxR1V/nE1AusiNimdoUuOM2DBEZ3b4+uqEIN+V2xseKOG9aaxuleAW382iUasfu0U54pbHIuSfvffQq9jKr7+pz1XnlScMkecQsE+RwcyMF233Bi/urlW6DwEReGaFU1UKBxu4Fj7O0uLUWbx5JHue0Ih8i1k34Nfbwun2w85k39phUxWWZZRGRkBhwsFs9CkKhQ09a9FU/9ahNvakKnWsPvCPWKTRAhoQVhUKX/55Zfp2bMn9evXp379+nz11Vc4Oztz9OhRZFnmxx9/ZOLEifTr148mTZqwbNkykpOT+f333y0pdoWmqI9XhVxwzeGKQIokkaRQEKcQSnl+WENCqtzEJpmX4KWfkbIz1kRMonkv5TUuht4DFTsq1jzsMX7+KqKjUs+a1RlV1ZPgSq64kkxYPpZga6WpdN3SIpQ5HGstRmlXOCOBBnikUPCvFShgB+0+0n1fVojY36GqvwH45cB1HiVXzFwNk2xWWlqEMktRXzEfeXkSfDiSt5efoOW03VyMrtiVIJ6UMhNTrlarWb16NUlJSbRt25aIiAiio6Pp1q2bro+dnR0vvvgihw8fNrmdtLQ0EhIS9D4VkYcPH+Ll5UVkZOSTbyyzbMVqhoSEIEkScXFxAAQHB+Pm5mZRmZ6Ih1cho/zHXaWlpVGrVi3CwsIsLUqZR5JFcrIn4ZaNitg81vKq0iN6K47gUEFiy93SogBtfemm/rVo7udT4KToQtsfjLa7k8Bim1l0VZwoZiktx+MsK94cdzeGqnazvwi5F4b/Gsqpm48K7lgO2XY2iqcVhhnGjZE3LrYiIykKrzBud3aig29NBlf3pql/LRNTX+WfiJgk7HOVhTtlBZMQguJhq4mSiwWRrJDYfi4nx85byyrOO6oksLhSfvbsWZydnbGzs+Pdd9/ljz/+oFGjRkRHa//EvLWnq1atqltmjBkzZlCpUiXdx8fHp0TltxQzZszg5Zdfxs/Pz2DZw4cPqVmzpp5im40sy8yePZv69etjZ2eHj48PX381Xbd8/+ETNPFsYvC5fqX0ZuTbtWtHVFQUlSoVnOUxL0FBQTRv3rz4hXoS0h5rFfNiJu/kRUljZ2fHmDFj+Oyzzwrsm66uqMOagpFlqJJcuPulAho6n4gDjg686FtTr81BSudn27mE2wdaSKripfHDXQAsdtNaq9SSpJeJP3cSr2yaKCKNbmuC6ne6KE+xyPb74he0DDBatRGNEVeJwJ1q3vtLbeAqUIU4bMjk7J14Xp1nehK/PPP+bycx139ku4k61AItx/JkX3+9goYYrQ+7pfc7wYomawpi2NLjRV43WqnUvcMjY5KISSz/pdHuP04lPSs0QULmc0+PIm0nTqnkxsMk3e/bj8q/gaoksbhS3qBBA06fPs3Ro0d57733GDZsGBcuXNAtz1sTsaA6iRMmTCA+Pl73uXXrlsm+5ZWUlBSWLFnCW2+9ZXT5iBEjaNasmdFlo0ePZvHixcyePZuLFy+yefNmWrZsadBv69GthJwL0X18a5defXBbW1u8vb3LZD3MlELIpGcr1Zjv3p+eXvoKbUaGedaFwYMHc/DgQcLDwwFMlo1JzkguNtnKIzUeHQVgtYszK11duK1SMsGzcOWcBKa5cq/i5BU54pBjAb6fK1eFqWzzryoOoswT+/ia6kDJCFeG2J6nDrfvPZnuJ2U6/SszdK++j8EIl0Xsth1bmuKVeda7OBGtVJKa9Q5LlCpG5H1JlC67alsxLcj/22eeZ0XryxoG7VMjVcS4GBPsv/ygwBrcxviqSmW61qrBmKz3e8fZIbSe/jdpmeXXW+7q/ce0+WoPPeccBOAH2/lPtD1zw9YEZUApt7W1pW7durRu3ZoZM2bw9NNP89NPP+HtrZ2pzGsVv3//voH1PDd2dna6bO7Zn4rG9u3bUalUtG3b1mDZ/PnziYuLY8yYMQbLwsPDmT9/Pn/++SevvPIK/v7+OFWvS6MWzxr0dfdwx6Oqh+6jLOTDat68edSrVw97e3uqVq3KgAEDdMvS0tIYNWoUXl5e2Nvb8/zzzxMaGqpbXlQLcHBwMFOmTOHMmTNIkoQkSQQHBwMQHx/PyJEj8fLywtXVlc6dO3PmzBndutkW9qVLl1KrVi2cnZ157733UKvVfPvtt3h7e+PV7CWmzFmit0+pRksW/rKIHj164ODggL+/P+vWrSNOoeCirS03VCruRN3n9Xc/o3LlylSpUoU+ffrohR0MHz6cvn37MmPGDKpXr079+vUBWLlyJa1bt8bFxQVvb28GDRrE/fv3AYiMjKRTp04AVK5cGUmSGD58OAB+fn78+OOPenI2b96coKCgHLkliQULFtCnTx+cnJyYPl3rLbFlyxZatWqFvb09tWvXZsqUKWRm5kwoVKlShXbt2rFq1SoAbj02PullKqZ8xM4RNF3WlEepFdOlFLRW74NXYkiVJL7ycOebKpXp4VODrflYq8re9FPpUtihX3J6+R3w7DgXxYqjN3S/k3OVf5tb2U33/VXlPwCk5lGefrCdzw7b8dof6kxY+2ZJiltmuK9S6f12yDXOe/m4/hWUqFDgp7hXGmKVCfaa4do/xaMKXWvV4Bk/Hy7Y2tDWz4dRXkWzfpUldp7Pv/ykwDjXbFQml43boKHvUZlnL1Z8pfxS9GO+33UJgKN5PCYKYr6bK6uzqqfsyvN+f/C4/FrLt/6rDa26el+U2CxtLK6U50WWZdLS0vD398fb25vdu3frlqWnp7N//37atWtXovtPzkgu9U9hZnsPHDhA69atDdovXLjA1KlTWb58OQoj2Yu3bNlC7dq12bp1K/7+/vj5+TH+ow+Ie5SjIGVnPX6t82t0bNyREf1GcPxQ4dx6Tpw4wahRo5g6dSqXLl1ix44ddOjQQbd83LhxbNiwgWXLlnHy5Enq1q1LQEAAsbFPVufx9ddf59NPP6Vx48ZERUURFRXF66+/jizL9OrVi+joaLZt20ZYWBgtW7bkpZde0tvntWvX2L59Ozt27GDVqlUsXbqUXr16cfv2bfaHhPDNxFHMnTGXMyfO6O13ytRp9O/fnzNnzjBkyBAGDhzIgevaQfeD1DQ6vTYSZydHDhw4wKFDh3B2dqZ79+56FvE9e/YQHh7O7t272bp1K6C93qdNm8aZM2fYtGkTEREROsXbx8eHDRs2AHDp0iWioqL46aefCnW+Jk+eTJ8+fTh79iyBgYHs3LmTIUOGMGrUKC5cuMDChQsJDg7mq6++0luvTZs2HDyonUF9nG6+xTJNncbxaO211GFNhwJ6l1/2X9ZOnBRKbbQii4QxhlfzKlT/8ny23l15kkmbcjLJ38iVGfuRUqlLaDbFZhkn7ex4xs+HZv619LZRT3FH++VkMFz4s8RlLk3qSbfN6uf9yPRVkL3ElSSTfSoK0Uolo6t6Fmqdz7OseiFORYsTLUtkl+YqCa7erzgeOccj9MdXfWtWN9ovt3W8SsU5fJME/HiAOXu14YWFfa/MyzWJmpfy+kqXZZkf/6541TzKC6anykqBzz//nB49euDj48Pjx49ZvXo1ISEh7NixA0mS+Oijj/j666+pV68e9erV4+uvv8bR0ZFBgwaVmEwpmSk8+7uh5bikOTboGI425r0gIyMjqV5d/4GalpbGwIEDmTVrFrVq1eL6dcOY1uvXr3Pjxg3WrVvH8uXLyczM5L3/jubdd9/h8LqfAXD39iTo+yAaNWtEeno6W9ZtYUS/Efy66VdatzOcCMhGDWTb0m/evImTkxO9e/fGxcUFX19fWrRoAUBSUhLz588nODiYHj16ALBo0SJ2797NkiVLGDu26C6HDg4OODs7o1KpdJ4WAHv37uXs2bPcv38fu6wB7+zZs9m0aRPr169n5MiRAGg0GpYuXYqLiwuNGjWiU6dOXLp0iW3btiGlxtHg9T5Mm7ec0H9Cebr107rt9+v3qi6UYNq0aezevZvfF//OpG8nsf2P7SgUChbP/hKpRlMAfv31V9zc3AgJCdElMnRycmLx4sXY5nKbCwzMiZ2tXbs2c+bMoU2bNiQmJuLs7Iy7uzsAXl5eRUqEN2jQIL19DB06lPHjxzNs2DDdPqdNm8a4ceOYPHmyrl+NGjWKlGAwsxAu/OWNNaE5HgN3HqVQWNt3OX1/F4oMtb6L8SDlHt33k/bGLRTnbG1pYiScoyRcVssKw6tX5WzETQCGVTftFXb0+kOei6l4g6fdduPM6jdye8G1QuxJJ4GKHU/dtVaNQq9zrQK5Z5ekl9FPe64yd2CLEtxD8RCblM7fF+7Rs1k1nO2MD+v/s/CIWdsauz7nvnJOqbjPWQCNpniPz4ZMMiyrVj0xey/e1/t95lYcKlvrKaloaSx69dy7d4+hQ4fqkno1a9aMHTt20LVrV0BrUU1JSeH999/n0aNHPPvss+zatQsXI+VyrImUlBTs8wxiJ0yYQMOGDRkyZIjJ9TQaDWlpaSxfvpz69euTlJbJlFlzeaNnRy5djaRBXT/q1PWjSv06unWaP9Oc6DvRBM8Lzlcpj1cocM/63rVrV3x9falduzbdu3ene/fuvPrqqzg6OnLt2jUyMjJo3769bl0bGxvatGmji1MubsLCwkhMTKRKFf2Y3pSUFK5dy4mx8vPz07u2qlatilKpRKFQkJb8GFugimcVYmO0M87Zj/Pnnm2jt922bdvyT6jW9fTCmQtcjbyFS/3nQcrxXkhNTdXbd9OmTfUUcoBTp04RFBTE6dOniY2NRaPRvixv3rxJo0aNinYycpHX2yIsLIzQ0FA9y7harSY1NZXk5GQcHbWTRg4ODiQnFz5mPOxexc3avuhghO57xR7GFJ1FB/UnCr+2WWKiZw63bVRGlXKBNjHRxXZyhQp9yFRrUAHJBeTukGQZVcUpj/wEPPnT5ncXZxqlp9P8yYUpdzS7riHKXeKBW/m+i/7v1+OcuR3Pgv3XkIEJPZ6iWU03zt+Np/NTXoXKz9P6qulrqjox3KX8hzxk88HvJ3Xf60h3TPazS5dpFiFzprZEuo3pc/m+8k9+UvcvVhlLm4vR+u4RN2OTsVeV74mG8oRFz/SSJfkPyiRJIigoSC8WtqRxUDlwbNCxUttf7v2ai4eHB48e6cfkZluD169fD+RYkjw8PJg4cSJTpkyhWrVqqFQq6tevT0RMEo9TM/Cvq41fjrwbjf1TdXXu67lp1qoZW9dvzVemB0qlTil3cXHh5MmThISEsGvXLr788kuCgoIIDQ3VyVXYBH5PgkajoVq1aoSEhBgsy21htrHRnw2UJEnXplZnkI4EEjrlOD+yj0Uja2jVrCG/zZ0OVRvr9fH0zHE5dHLSt+YkJSXRrVs3unXrxsqVK/H09OTmzZsEBAQUmAhOoVAYWBKNJXLLu0+NRsOUKVPo16+fQd/ck0CxsbF6spvLlUcVz6pXXJTvIaF5HLoSY2kRLI46M5PFNrO4KNcquHMBaGSZG7Ep+D25WGWC0MhYJm06xw4gSZH/HVG/AA/37KffJrtJfJLxPtCrOEQscxTHc2OGhzuBcfHlVilXF3FeouFNmS/WaN/l/5lgfCisKSceOWduxwNwPUYbrjFyRRiSpHWh/umN5vRpru9NYWoE0eiG/tjGMQ3Wujjzl7Mjc+894LD9KPxSfy92+S1F7lJde+zG8ocJr5oJa9U0ugUHG0nM7WM6v1IH5b86pfzAlQcMfrb0EiRbEpdkGe9YuFLTGkYyJYuY/siDJElmu5FbihYtWrBy5Uq9tg0bNpCSklNqIDQ0lMDAQA4ePEidOlrLd/v27cnMzOTS5SukOWqVqvhIbXy0h08Nowo5wMWzF/EsIGYtM49CrVKp6NKlC126dGHy5Mm4ubmxd+9eAgICsLW15dChQ7owhIyMDE6cOMFHH31k/kkwga2tLWq1fjRvy5YtiY6ORqVSGS0hZw75ucseOx5K4Ii3db+PHj2Kf0N/ABo1a8TuTTvw8nDHtW5ds/d38eJFYmJimDlzpq6s34kT+vUdsy3reY/X09OTqKgo3e+EhAQiIiIoiJYtW3Lp0iXqFiDnuXPnaNGiBQ+SH+TbL+85K4vZ9EuS84WoAVs+hn5PjidxfGczX8+lOCqfJJIV7bycPfgHXZSn6MKpYtleRExShVHKX1uQ5V5rD6ezwoxMMTjEvIwN1aVYVttOBypmJvYhyr9ZQ/7nyhzK85P5t1xJEwvDx38UfA2dvFF+E5Jmv34PXYmhrpez3rIlbsYTIAf9rq+U9wiT+U83rbllaSVXPnoUX/yCliGy3zeecTJNI2VC60s8dpRolBWd9sIFmbl9zNtWaERsuVbK/aUoYuRKhN14xPMF9F3yk/Ze+qW7gr9bGNcjWkhX+Ei1gamZQ4tZ0opFmUv0JiiYgIAAzp8/r2ctr1OnDk2aNNF9/P21SmHDhg3x8tImUerSpQstW7bk7bfeIvzcv1z49zSTPhtL1w7PUaeO9uGxYsEK9mzbw41rN7h68So/TPuB3Vt3M3DEQLPl27p1K3PmzOH06dPcuHGD5cuXo9FoaNCgAU5OTrz33nuMHTuWHTt2cOHCBd5++22Sk5MZMWLEE58bPz8/IiIiOH36NDExMaSlpdGlSxfatm1L37592blzJ5GRkRw+fJgvvvjCQNE1jelhy8aNf7B06VIuX77M5MmTOX78OINGaCccevXvhUdlN/r83yccPHiQiIgI9u/fz+jRo7l927S5p1atWtja2jJ37lyuX7/O5s2bmTZtml4fX19fJEli69atPHjwgMREbabMzp07s2LFCg4ePMi5c+cYNmyYWdnzv/zyS5YvX05QUBDnz58nPDycNWvW8MUXXwBaq3+GOoODBw/SrVs37iffz3d7kfGRJGckM/HQRPbd3IdUrod+hUUm1ESctLUiSRBkE0wH5Vl6K4/q2q/kE69myielvCrr6nTzMvL+7uJccCdAnVoxMzHllwVZqZZ5ysijU5Up45ZYXq+MopE3w7wky1SJL/w52OdYtg0R+ZFtHS4sbmZEYGUU1QxfhlgXdptecw7pteWXoMwUVypQHoL88L0n87/5at7drtEpm0WhPF45sUnpzNp5ie9s5rHP7lOO2X1A8OFIk/2bRmj47+acczRyh/E3thuP+cNuMi8q/yXY5tviFrtCIZTyckjTpk1p3bo1a9euLdR6CoWCLVu2UMWjCoEDevHh8NdpWM+f1fNm6DJFZ2RkMHvybPp17MebL7/JqWOnmPf7PLr27qrbzvF/jtPEswl3buaJwUlNALQu4Rs3bqRz5840bNiQBQsWsGrVKho31rpvz5w5k/79+zN06FBatmzJ1atX2blzJ5UrVy7yOcmmf//+dO/enU6dOuHp6cmqVauQJIlt27bRoUMHAgMDqV+/Pm+88QaRkZH5ltcriOyH7qQvJrJ69WqaNWvGsmXL+O2336jTQOud4ODowIGNi6lVw5t+/frRsGFDAgMDSUlJybdcn6enJ8HBwaxbt45GjRoxc+ZMZs+erdenRo0aTJkyhfHjx1O1alU+/PBDQJtfoEOHDvTu3ZuePXvSt29fnbdEfgQEBLB161Z2797NM888w3PPPcf333+Pr692wib8YThrd60lPj5er8RdbpxSZSolac9MmiaN4PPBbL62mVH7RhGXFlegDBUBWQY3KalQUxBxmRW/9Mi9hDQ8pcJZWsrjwKY4mOHhXmAfWYb6GRdLQZrSJ++9k3uoN/NX4wPl32ep+WWumhoxMhUr0t583tmmYf48NR3OFi7g/rpI5GSCivcEKuoRHTCj7F55J1GhoMO5oierkMr59TL9rwsoUdNfqZ3EcZSMTyIr1TJv/q1m0moNL5zXP+aPNqlxTtZvm6BapfteXRJhbPlhlvt6y5YtC7VRSZLYvHkzNWoUPiuowDwmTZrEmDFjePvtt42WP+vYsaNRl+vq1auzdt16wqO0CnQzhdat+UJWIofA/wYS+N9Ag/Vyc/fmXWr516K6lxdVEmTinCXUCshMfIDK3pXnn3/eaPx2Nvb29syZM4c5c+YYXZ5X9uHDh+tKgRWEnZ2dLq4+Ny4uLvnu0yB3gawheM7XYF9J+xNIlyD4z2CDdatVq8auXbv02s7HnNd99/byYNlPU6G68Syu2bXU8zJw4EAGDtT3UMj7n06aNIlJkybptbm6urJmzRq9tuyM6qa2k01AQAABAQFGlwEsn7+ckaNG4uDggLFqQ1XjIE0DdhnafeS2pi89t9Tkdsszd+JS9H4fvBLDYtUm/kcls7dxKe0mbYtbsDLG1fuJYMTYkpFPWIOpIY5DzFmoVc7K6kWfo975wpUtzI9pil+opb5p0K5OiEbp6m1kjfJL7uvAN/+oGbqc1kCTEhWnzJD3/uj8r7blPwc1HGgqbC7mImlk5ALyGFQU8pZXzEZpplfA28qtLFL3RpE1VaapQLa9nyq78d/4oivl9ZU5yXvLSToCPW4/SuElxUmD9pQ898Ynf2h45orxA2wXLuOSrGHaoBzvzGaKnCSvSqkcnphSxCyl/PTp03z66ac4OxfsUifLMjNnziQtzTw3PUHR6NmzJ1euXOHOnTu6mOMnoTC3yaG9hxjz2SjqxGkvn0rJMte9JVTpCU8sR5kh5ipkJEHiPZ0yfcvGelMwyLJMelo6DRo3IPD9/CdtABRW9NzdfPqu3u8HieLZZ4o2ikt6v+e7ueZf69WEwq4qj14XC9pj2jem8AxU7TPafvr7V2n+5WGUFUjJOGGvjZs2R3HoFSpzz0qU8mP2xuPJ3Sp+efZixTENkowYgmMS01Fr5Ap1LxnDJkPmf/PMc9WeaPM7rRWXeUlxkii5Ci+m/8Dq4zd5o82TJ68sTRJSDZPfpisknr1U9MHLd+5uYDqJe5nneEQsg5WG3mzTq+h7bplSyLNpekN/eUOF4eSxwDhmaxljx47VxSYXxHfffVdkgQTmM3r06CKvm20hLwqzF82mdrSJmzI5FhwqawNIS4DGjRtz44bxxC4LFy5k8ODBxbOjDP1RjZ0m1WpdIgGSM5OxtbPlnU/fKVTQi0au+HWLTt7MkwzIzCnyphEa+h6RmflaxbE0FJaCYhs3OTvRMzHJ4EVVHq0QT8phe3vapaYW2K8VF7gdn0LNyuU3Tjgvb1XThhmZO9knW8mjOtpEqSLbzFIWpMxQtPdNm8sy+542ftH8b99VvF3teaV5dextCs7NUh7pHiabFWOfTYBSm4vHR3pAc+kq4zcqyp1SnppuZrLIh3keOrLMC+dkrlWXuFtF/5o5nytBZXl9RRkb5yabSAItKH7MUsojIiIKVQLpwoULVK9evchCCUqYJ1SUVPk9y+KyFGbHgmMii8K2bduMlvcCnig+vCBsyMCY76185yTplfPPVp6gUOCk0VCeX+dp6hzrb6bG+Igv93WR7aG0I3JHSYpVJth9QZtsyU+KoqYUQ0JWrtLMAhSDSau19+HsJWpuTi5REcstoQ72rHNxZuDjih9zXxDvVPPi0I1bVNIUPNwr75MWppL/OQonFD3uy/mHyPjek7HPgEtWUqpIYasf29DwpkyiA9zy1D/+vB4Xte6bvmG+330ZgIiHSXzW/alikrTs8PZ2NV1PF/2BsdEuqEKVScvLfw7qj5eH7NPwyrGsMBEjpfQ22X7BcU1DzpbTig9O6IfjtZCucDXXb5uMcv5yKeOYNf2RneXZXHx8fMzK9iwoG5h7i/nEyNSOlrHLLGCNjEJMuRYSX19f6tata/Tj4uJSYvs1RvZZUKTlnwH5lkrFXRMWjYqEY5r+dSEjk5KZYqK3ccatP8PTU3ahMUPxKGuE2H3KStsZuD06B8BiN9MD5tyDwmqPoPzOqxcv05dlMm15pp5WGWbCRdcaeWyuxaIcauW2ZNBWoc3FcdJE5YKBIeZNKN9Uqbhqk6PYH74aQ3pmxffayc3Xv2Yya6maaSvUeMeWv+vhSfGMk5nym5rvFhtaEd7dpn8tKI1cGq8pQ/R+779UQDIDC/Gk78onUcjLNRI4ksoJu3eZrlpislu7cP3zk62QGyPczpbmiuuMVP1VLp/B3jxkoo3+BMsfdjkWA/cEmcFmPoMbR2r0xjnH7e14o3pVzoukkvlitk/ClStXGDhwIAkJhnHD8fHxDBo0iOvXrxtZU1DWSTJjwsU2A2yyDKRVCyrdmVmwi2VFQpFWcEbphHLu/pPXOp6uTjfoUymXx7+dcWeGAll74jbxKRlM3XqhaBsoA/ikXsq/gyxT927+XayRGjEy9e9CgztQM1eC1qtGX+Llb8BTWJKNPJfNPWpVfGSxylIazLBZxCrbr/Ltk53ILD80wOwqlXm1ZjUSs87hoMXHmFaOnynm4JCqf27qRud8H77buiYkAPoeyTlmpVrGL1qm9zENH21S8+I5/XOlMnJ6Ztn8UtIiFgtrT9wq8rqVCllCcIOzU5H3VdY4eSOOd1Rb8ZASGKLaU+zbr5Je/oLL19hOy3f5gv+p6XnCvGtm8ioNU1aqkWSZhwoFI6pV5bydHSO9zQuDtlbM1hRmzZqFj4+P0TJOlSpVwsfHh1mzZhWrcILSIb9bzCFNxitOptoj8x/epjJ7l1syDRXQ3CjUqZBZsf0qHyTrWwlyu7NnY5PLIKHSPNl1kF9tzLKIPTnnQ5FPXGPNBzJrZ6qZtlLfelPRbpmi0OpqzkloG67RKRnXbG1NJrSqyBibyDM7r4WJEJOySkJqhq4MT2H4+f8MraAXfHPOUUwuj70VR43nIimvDFPu1CsxmF+8vb0Vupy2vZhzzKu+VfPtr2re3KsxsHxCrtArWcYzTjb6QC6rZ3D8xrNFXrfhrcIdVZBnFd33x5JEfDk2Nhy5FkNrqYAJ9CcgJqHkPEZLCl/FfZPLGt4s/B1Q/y68dlDDD+5uurYE4UWdL2bfUQcOHOC1114zufw///kPe/fuLRahBCVLXHLBZkxFll5R7RE4pxp378rL4yyrhJReweI/zXHHTzW0lldJkPVcuivcZEU+aCTj1vT8KM/nxyPX4NiWTKJMvHi+N+JKaS10UJzJd/mQfTkPmdf+kVn2g3aWHXISfVkTq1wNq518aUb98vLIj7uvFNjHPcHw+VDNMZ27lfXbmtwwrlRVNGpJ+gPo/CoNWWMVIudCOOw1uikze1Ema2eq+d98NX2OVswTVku6p/f7k00FD+waR2r4v11qbHNN7MQrFLTz8+F535qkllBS35JGkiTaK88X3DE/8nnOhEcV7EFZnpjyW9HGLgP+kblsa6QWqsAoZivlN27cyDf7uoeHB7duFd2NRlB6qDX5P4grJ8r43ZdxSjX/xSTJcNPGimNFUuP0frqkyFRKBu9crv5X71ewyYosJBmDbPzpKkPrulEeXoOdEyEphlO34kpGwBIiLVNtMk71qIPxmFhrZrhyZ6HXscll8N3ulJNN/OZDbaxERY4TXmokJ0FYBb2uohMKzj3RLNLwfTQ6No7dLQ2HMdleFxU5C/sI1XaSc5XqcsnnFOZNzirJMv3+0dA4sqLdP0X7w73ioVaukBlz42bLGytsZui+B+40T8mavEpDjzCZYX9rz8mHVT153rembvnYXNZza8OYd8oWZ0fWuBRcPrqsk0HxeYeE2wml3FzMVsorVarEtWvXTC6/evWqUdd2Qfmjcpbu6FGIsuOuyRVzZhkgTV3wC1pOTwJ1jgeCs5EBUtXMihNILOUa/NSMMfzvMxXaRG8FMrclHPkZZtXhr3+jilPEEiU1Q02DL3ZQ/4vtAEi5jtVVMvSskDQyPY9XzIGeuTRV6OccuaUq2I2tTa6asTOq5JhEO594n0/Wnqb+F9s5ERlbfEKWMg+L4P65xcmRf/Io5xnAclcXLusmRive87jBbcNjcpFlMo2cQmup0z0v18TNfzebVrLqRYFrkswrRzVUfizT+YzMGwc0TF6lsQqvgqJQEc9Kbvfk7icLPsL7ueYFsxPC7XfUL+ge4lQxSi829S98SbfWV2Ts0mVePqqhalYyxc89PZju4Y6sKr+W8jiFgud8fRjl5WFpUawOs0cEHTp0YO7cuSaXz5kzhxdeeKFYhBIUzMOHD/Hy8iIyMrLQ69qpzRuxmOOynk2V/BOQF4qQkBAkSSIuLg6A4OBg3Nzcim8HhURtIrtpdmuybKtVUXO5sDsY8dx2lZLLZVbxbNLT0unSvAvnz+i7fNkYGQtKwL8x/xZq+0sORTyBdKXL6TxW/Srk3AAdFacNBnS9QmWG77FepTwhNQNPSX+W77Ua1QpczyvXuCYTiY+9PNjs7IRCktl4UptIJ2jLE7ogWpCi1H/93MuDd/Mky1nkVolZVSrTv6b2nD4yI0SpvPHSGcNn513ZnQZ3DNuz3bXL79PWPHLHZ+ZO7GaMxXPUDNmnYeHPat7ZnvMsWjtTzfPnrPfZZIq8Csnj1Ip3T5lC7VOxc+QAFIfX/ZiNGkZt1jB0n4a5C/UHQp1tDj/5DizETidH0hVShZlwKU+YPSKYMGEC27dvZ8CAARw/fpz4+Hji4+M5duwY/fv3Z+fOnUyYMKEkZRXkYsaMGbz88sv4+fkZLHv48CE1a9bUU2yzkWWZeXPnUP/5vtj5P4tP6x7MnpNTDmLihxNxbNrU4NOqb1+z5EpQKJ54INSuXTuioqKoVCn/GqzGCAoKonnz5k8oQf7YZehnF09Am5FUNhJXnpeo+OJL/pF38qKksbWzZfj7w/lh6g9m9T94+2AJS1R2eEu1Ld/lb+41PegNr2lyUYXhkzWG8eRJZiikuWNhHysV/O3kyMQ87pLn7hTCpaeCMr+y/rNyfdhtC0lSNCS5qEqhhGe8aaV8p5Mj8YoK7MNeTIzaUrGUcqkYJr9DnBypTo5P++1HKWSa4TVXEdDU1SrllStmxB2g7+33JDxzxfi15i8VMEtWRpBlmb8v3ONWrPGxaflKGVr+MVspb9GiBevXr+fAgQO0bdsWd3d33N3dadeuHQcPHmTt2rW0bNmyJGUVZJGSksKSJUt46623jC4fMWIEzZo1M7ps9OjRLPttNbO//JiL+zeyMfhH/Frl9B3/9Xiu79un+1zevRv3SpV4tVs3s2S7pVI9cfkvW1tbvL29kcpQApHsV7EkQ42HMjUeyiBDqmxDYpZSTtpj0OQfp2WXci/f5QDp6YVLkFYcZGSYZwXoPaA3YUfDuBh+sUj7scmQaRqhrV85aOsbJhOilTe6KsJ03yXzHPd1pNhJVHSb3t/hBV/3xlDmM7gOUBwvqjhljq+DM1k7I5Na9/WPV5Jlxq1TM3SP4XPldAWK03v28a5CrzNmhBJJArWR1022l9e8ym68Xr0a7RVnsaXiWjrrGXHttwbW5SkHJiOh0MiM21A8yvNEm5V6v8tbVRBT+N4r4Hqx1Z4/Y15wpjh3p3y5ay/9p2Q988pLPos94fd5a/kJXvh2n64tt+gn7O10CVcFJU+htKfevXtz48YN1q9fz8yZM5kxYwYbNmwgMjKSV155paRkFORh+/btqFQq2rZta7Bs/vz5xMXFMWbMGINl4eHhzJ8/n9XLl/BKtxfxr1UDx5ZNadOxna6Pi6sL3h4eus/J8+d5lJDAm2ZaykFb93zevHnUq1cPe3t7qlatyoABA3TL09LSGDVqFF5eXtjb2/P8888TGhqqW15UC3BwcDBTpkzhzJkzSJKEJEkEBwcDEB8fz8iRI/Hy8sLV1ZXOnTtz5kyO9S7bwr506VJq1aqFc732vDf+a9RqNd//OAfv5l3p0LADv3y/ULdOhKYaDjWbsmHN7wQM/i+OdZ7Dv3Zt1q1bpyfX/bv3+PStT6nc6EUaNG5Jnz599MIOhg8fTt++fZkxYwbVq1enfv36AKxcuZLWrVvj4uKCt7c3gwYN4v59bUxYZGQknTp1AqBy5cpIksTw4cMB8PPz48cff9SToXnz5gQFBel+S5LEggUL6NOnD05OTkyfPh2ALVu20KpVK+zt7alduzZTpkwhMzNnrtTN3Y3mzzRn/dr1hfpvsvlttppJqzW8/5eGsw/P83UV/dTJNaUHBChCKU9Kqi0Z2Ek5A36pkLLXeiCbVOMT0zIJDA4td5ZPU8jARVsbUvJMuDmbyEmRX5mnhbY/Fp9gFkN7gHWz0inMXqI/Cm5wG1pflXn5uOGJGFrdG4ADFSD5W8OknOd/9rXR6IaGL1apqfbQ+EUwKyOaxziTaWReL3fo1R0bFb/ZziBItaxYZS5LfLXCOqs6jF2fHSIlo7C7g8r5IkG/qfXKKz4JLyr0Q7Cm/xVe7pRPYwTuyv96KYpZpffcwpc0tBQp6WocMEzP72HE66aoXHAsH5OABU2YB3lUoUHFGH6UC1SFXcHBwYFXX321JGQpE8iyjJxScCbY4kZycDDbMnzgwAFat25t0H7hwgWmTp3KsWPHuH79usHyLVu2ULt2bXbs+pv+S5YiyzKtXmzLp5M/pVJl467iy/74g87PPUet6tXNPpaTZy4watQoVqxYQbt27YiNjeXgwRxX5nHjxrFhwwaWLVuGr68v3377LQEBAVy9ehV396KX/Hn99dc5d+4cO3bs4O+//wa0CQplWaZXr164u7uzbds2KlWqxMKFC3nppZe4fPmybp/Xrl1j+/bt7Nixg2vHdzHgnXFE3LpD7QaN2fjHUkLC/mXS6Em82uRZ2jz9NHLWq2vW19OY+NmnzJ06huBNfzNw4EBCN27kqdq1SU5J4f/eCKTlcy05sGExstKWbxato3v37vz777/YZpWK2LNnD66uruzevVtXGiw9PZ1p06bRoEED7t+/z8cff8zw4cPZtm0bPj4+bNiwgf79+3Pp0iVcXV1xcHAwclZMM3nyZGbMmMEPP/yAUqlk586dDBkyRJcf4tq1a4wcORKAAR/kTKo0bdmUfQf2MfTjoSa3XVAJnhfOy8x9BRLzeFUcshsNwISMEaRm9MDepuxb0l9T7tf73VQRyVFlTtJLz7j8T4ZHAty8Z3ygtzQknF7XgthzuSUDWk17cmEtzC5HB8ZU9TRoX/qT8UGiqbwWUUol1dRqakoPuC0bbq/M8ch4jewIGxts8qkh/czlgi1+H3gbVkUp7MSQpckt7fqszMVBv2uP/aM/1XwWqD9USa+XytPpGWyVfFGprxpsL2+2cYBBqgpaslVYsbBxC8W+2kYAnipGBcLGiPPu6wuPcH5q9+LbSSkiAzUfyDS0ciVLRsZYoKXPg+K7l847lg/H79Wh+VfNumOjorGVhG2UBQqllB85coT4+HgCAgLKlGtxcSKnpHCpZatS32+Dk2FIjuYlVYiMjKR6HiU5LS2NgQMHMmvWLGrVqmVUKb9+/To3btzgjy1/seynqWSoNXw45Xs+DvyYpX8sNegf9eABuw4dInjmTKNyyDYyUobhdXD1bjROTk707t0bFxcXfH19adGiBQBJSUnMnz+f4OBgevToAcCiRYvYvXs3S5YsYezYsWadA2M4ODjg7OyMSqXC29tb1753717Onj3L/fv3sbOzA2D27Nls2rSJ9evX6xRPjUbD0qVLcXFxoZFbGp3atebStRus/2MTiWnRVG5QhxU/LOFAaChtnn5aN/B97bXXeHPEO1RJvcG0T99m7z8n+N+q35k78QvW7diBQqFg6o9TaZLlIv7rr7/i5uZGSEgI3bLCApycnFi8eLFOSQcIDAzUfa9duzZz5syhTZs2JCYm4uzsrJtM8PLyKlIivEGDBuntY+jQoYwfP55hw4bp9jlt2jTGjRunp5R7eXtx5+adfLdtVCkvxODxM9VqXpzVk2OfdzF7HUvhgmEs1hx3NwDan9cwenPBLzS3i8Yzzz91cxXdlIforzwElH+l/M9Cloq56278PXPBzpZqySkcshvNqPQPgF7FIF0Jsm64QVOEjYoPvL1ofk3/+uh0RsO+p7WTVS+cz/+eOWpvZ7TdJ63gut9liaj4NMiaf/s2j/eMvxFDzlP1tZNYSklBkpG5yMIkKS3vZJeqelI+WX2K2/Gp/LdzXV6oVw4munKRrZAXN8Ymt5LS1SzYf413X6xTIvssKZSo2ersyPeLC/aqWCu/yEAM84BIGhm5AuRokJCQs5y0l7u6PHHIpTFcpGTiktNxcyxfYUYyMM2j6MYxwZNh9pU4ffp0Ro8ezYIFC3jttddKUiZBAaSkpGBvr++yOGHCBBo2bMiQIUNMrqfRaEhLS2Puj99R5YXn8O7Yjqk/TuX4oeNEXNXG1+RWplb++SduLi68/NJLRrensjE+GGjXsR2+vr7Url2boUOH8ttvv5GcrFVcrl27RkZGBu3bt9f1t7GxoU2bNoSHh5t1/IUlLCyMxMREqlSpgrOzs+4TERGhV+bPz88PFxcX3e+qHlVoVK82CinnNvGqUoUHsdoSTNXdtbHkbdu2pVIlNzJkJUo0tG7VgktZkyKnzp/nZsRN2vi1wblee5zrtcfd3Z3U1FS9fTdt2lRPIQc4deoUffr0wdfXFxcXFzp27AjAzZs3i+W85PW2CAsLY+rUqXrn6O233yYqKoqU5BzvEXsHe1IK8CZxyMCgzn1DIxOyuXu8rdyq++4mJXEvoXxkgK0kma5mYI5CDuBwxfgkh3PGwyLJVFGIN2Oe8j3VlpIX5AnRPDQsJxqSVVqo7UX9++S9bRpUmVl1tgvY7lZnJ6PtrR/vKbyQ5Qg7F61icde7I1ufM1QSOpzV0MtKShD2OlE81r3NJ29wPCKWoUsqTq6GopJWgLlq5vaL3EswdH8uy2y1/ZzPPc0rcRUtVTbaPq0ChUlUkx7yWJKYVaUyC7M8RfMLlyosMtB86m4OXYkpsK+lUaJGifa/PWJf/sOhyjNmW8p/+eUX/vzzT5o0aYKzszMPHz6kSpUqBa9YzpAcHGhwMqzgjiWwX3Px8PDg0aNHem3Z1uD167Wxvtku0B4eHkycOJEpU6ZQrVo1VCoVvv7+xKJVJGrXrw1A1O0o/Ov667YnyzLL//iDgb17Y6urfatPnJ0LLsmGVkInZydOnjxJSEgIu3bt4ssvvyQoKIjQ0FCdXHk9LWRZLjHvC41GQ7Vq1QgJCTFYltvCbJPnOCVJwsZGZdCmyToGm1xKtEqpINnGBZvMODQZaSiyZmE1skyjZo34ZsE31MvQJnBLrdwAe1sVnp451ggnJ/3BdVJSEt26daNbt26sXLkST09Pbt68SUBAQIGJ4BQKhe48Z2MskVvefWo0GqZMmUK/fv0M+qbY5yjh8Y/ica9S8ExqvTsyV32gbbiGTKVxl9LcyVAm2vxe4DbLItlK4Wk7Wy7Y2jLwceFT1lY7f7/gThWAa0aeJUq16ZGQeYOksu++m5KuJq/6nF+IR9Bvar4YpsIl19yXfZpMqp3+M9LUrHpB4SMVhTPOLxKpWmTQXv0RDNuj4V9/iVue5d+yVxooNWoyFcbf9dbG7SzdNS2fSyc1o3wpqA0VtwBza3EbP/D6d433biJd55xcu0hyWYJHyel0V4QyJk/ZO5sn9Dh3SJVJsdeeu+xH8Jy9V3i+Xtmt961EzTX7nFDEWKWRt4p4hJYaZivltWrV4sSJE2RmZmJvb4+rq2vBK5VDJEky243cUrRo0YKVK/Wzgm7YsEHPehkaGkpgYCAHDx6kTh2tm1X79u3JzMwkMvIGrg20D4nIa5EAVPfRusM7pGsfJQdPnODazZsMM6KgAUh2Gp37TzbOKTKJDto2lUpFly5d6NKlC5MnT8bNzY29e/cSEBCAra0thw4dYtCgQYBWYTxx4gQfffTRE5wVLba2tqjV+i/Lli1bEh0djUqlMlpCzhyMjXFtVNoBzNGjR3nzzTexc3aHuDhOhoXRrF5DAJo3bMi6nTtw93Snbla25H813tSt6Zbv/i5evEhMTAwzZ87Ex8cHgBMnTuj1ybas5z1eT09PoqJy3KETEhKIiCg402jLli25dOkSdevWNVh2PianFvSVi1d4qulTWhnyyWUiydoEXh9v0lqs5vcsvItYfHIGlRzL3kDxcWomXRUniJVzPCuyE29Vy/4/ChnrmXgrAmcf/4I7lmPu2hi+cmrmY0gYt0FDqo2GD95X8tjR+MhAO9gs22RqZJMDGzsj91D24FeVy9i7/Hs1b3ymRJPLffSQkSRvKZJUbjL/msszl0xYvSUJpWS6eoFrskxFHVHeUhVvvg2lXL6UzLTMkpM3ezJwTNUqYDwdRIUO5W/la9xSboqtdl/gl1p+JtW3ndWOjw476hvDej+hd837f2n4rn+e+7KMXyc/2fxcYB+vuKJvP6PspwUqU5g9Sl66dClr167lgw8+YPXq1QZWRUHpERAQwPnz5/Ws5XXq1KFJkya6j7+/dnDfsGFDvLy8SM/U4NPkWZo1b8Enn44j/N9wzp85z9QxU2nbsS1+dfyo/lDGO2uTyzZu5JlmzWhcr55uHw9y54KzN3x4eWXlqgrZFcKcOXM4ffo0N27cYPny5Wg0Gho0aICTkxPvvfceY8eOZceOHVy4cIG3336b5ORkRowY8cTnxs/Pj4iICE6fPk1MTAxpaWl06dKFtm3b0rdvX3bu3ElkZCSHDx/miy++MFB0s8kEEhQSaVnW+1il0sD6lG3ZX7duHUuXLuXarSi+nL2A0NPneHfgQADe6NULD7fKjBr6Xw4eO0nEzTucOPIPo0eP5vZt09lWatWqha2tLXPnzuX69ets3ryZadP0Y4p9fX2RJImtW7fy4MEDEhO1FtrOnTuzYsUKDh48yLlz5xg2bBhKM0qPffnllyxfvpygoCDOnz9PeHg4a9asYcLnE/T6nTx6knZZGftrmsiMDGCXqR8Tayz2Ma0A74ik9LKZLOX7tTtZZPs9G+ymAPrv3YgsxbMga2VMnnnNc3/8atAnKb18DZRLAvsMWJInEVwZH+eYxRVb7Tu0Xbj5R+Ofp/TtA5XhJEfHWjXK1QmKik+hj/Jwvn3GbjQ+WJak/O8zVZ7HR2BwqIEXUXmlp0+NYt2egrL9rMlUa9h+Nor7j7Vu44evPXloT7LxlAwos07FYUfTrrwV4yoyzlPeFdPolk1UfKrR/8+UJ4C5PHs5Z6tnTeT7KGv0Vh7TfdcAyyoZ/vfvbTN8/rrVTSKkacETnpdqVMxJ0ZLCbKW8fv367N69m+PHj+sSdAksQ9OmTWndujVr1641e517CalkaGS+W/Qb7u7uDHtlGO8PfJ/a9Woz+5fZ2KfL2GdZbOIfP2bT338zLE+W/ezHzYHQUBxqtSAiOsGoRcbV1ZWNGzfSuXNnGjZsyIIFC1i1ahWNGzcGYObMmfTv35+hQ4fSsmVLrl69ys6dO6lcuXCzs8bo378/3bt3p1OnTnh6erJq1SokSWLbtm106NCBwMBA6tevzxtvvEFkZCRVq1Y13Ig6kxilEg0S6ZKEY1L+lrgpU6awevVqmj3dnOXr/+K3n7+ifgOtd4KjgwO7g4Px96xGv7fG0LBjfyaP+ZCUlJR8vU08PT0JDg5m3bp1NGrUiJkzZzJ79my9PjVq1GDKlCmMHz+eqlWr8uGHHwLa/AIdOnSgd+/e9OzZk759++q8JfIjICCArVu3snv3bp555hmee+45vv/+e9y83XR9ToeeJjEhkW4vF1y3PkMJfrnqoToa8bo/b5f/i6usDnxc0/TdzR/mShQTn/W91oP8t3HLQ//mSd4bYtAnPqV8lFUpKo6pMm/vME8ZaBKZMzAoq9eFKYzlHdhcQNK7SolFO8pkhQIk0GhkDl+LIS45/3AXS3PfSO6I/OriJlXL5Ii6EZ3TZvN+x7r5ZprXZN2W2T32XrzPg8flI1dFaZA7drqsW8qX/hPBe7+dpOdPBwvunA9f/0d7URx5SiLdhK+ofqJA49fX7gvRRtvLGwm1jTwfKrgeteRQBBX+IIvARhcnLtqZl5jO1SeF6MqG5zBdBQPH5RiBsktW1r8tM3NpJk/dknUTawJDCl0STVA2mDRpEmPGjOHtt99GYSRzZMeOHfUsAtlfvbyrsXzRXKLt9AcmUnpO30ouLjzMVTc8G3tZ6wp4484d6vj5UMu/No/jbuCaJ6y85XMtGRpiulyWvb09c+bMYc6cOUaX55V9+PDhuhrcBWFnZ6eLq8+Ni4tLvvsMCgrKqeOtySRVkvjq56+0v7NiuO0yYOevWdbMXJbn6tWrs2vXLu2PlEfwKJJbChmPGO0Dy9vDg1+nfYWNewYq4JbGE5+aNXXrZ9dSz8vAgQMZmGVxzyavlWfSpElMmjRJr83V1ZU1a9botWVnVDe1nWwCAgIICAjQa4uMjyQpQ6tULJ+/nOEfDMfeiNuswt4eTWrOw1aScwbFFZ5c76bssI5a9wvInP2URIvrOX28Lj0g9WEM9lVy4s+apIQVrWhsGUOBcUtn8A/mKwJfrtLwnwnak5F3a2E3YmnlW7EyxlY2kpqg1VUN16oX7PUiyzIbT91hzLozVKtkz5EJxpN1WgyNGtb/H9RoDbXeNFjsl0/pXE31DN7J+JgEnGjg7UIljYZvBijpfVxD4zw5MF88K3PLU0aDLrm7IIvwmjBtoJLfZ2nvQUUZV8r/DtdOhMYkat/HRVWpTtdRMHisRIYSup6SeXtnztPki6FKpq9Q6ynlX6mW4itFMz5zpF75xa+3XWRkh/KVgd0Yh7zq0vN6nhsnn5M7dr2a719VoFbmdHpcDqsx5Q2/zI/Emhk4387xDp40RMm0lWX7fikKBwqR2+qnyv2IqhwCB/IMUGRQKyV+7q3gw60aXSjI9KwkgVNXqmmTuIfImWW8YoqFMGu4N2fOHFJTzZ/ZWLBgAY8fPy6yUIKC6dmzJ++88w537uRfmsoYlSXD/8bRjL/XLkuR2/3PP4wZ9xk2Nraklq9qD2aTZGSiwyHXxIWUVfLKADtXZCTSlIYP/EtZMeA+igJMqGWMbIU8PS2dBo0b8Oa7hoNoAEUey78C48nddGRdTy/XqMZxE65e5e9VD0ezJizyS1L2axcFdg76vrVKGf7dkuPCfj8hlafKQby0OXyqMt+rxxwS8iSj6T//CA8TK5YFdOB+w4mMAf/IIMt4x8r5BrVGpd9h7HptSaOo+DJolbi0DS78CbsnoUyLM1jc4LbpY6sjpeHh6cUf72tDaDolpxBWT8GUQYZqd4fzMr/MVevZOtMyK1BW9idwxT9RT0GmSiIz61ZSyuXzvEg2sWb3/eo/CpJvvEWGSkKdVp3EXHPLS7opUGedC+84GHBQez4Gq/bwvPI8s20WFKPUpYeKTL6zmW9yuRr9hM0bu8hI+bx5n7ki89ZODc7JOddeOz+fJxe0lJEL4W/l0Cle7/comwdmuW4DRD40XZ2lrLHPyfx8Wr+oX+ZlTYJBe1yWA1i2F4pKLeP1qLz5tlkOs5Tyjz/+uFBK9rhx43jwoHwpHuWR0aNH65KAFQZjNRmdCxi3JdvlKEjLZ83ixVf6k+9jLe4mZORfNqsoNG7cWK9kV+7Pb7/9Vuz7gxznNefch6My4XatUCLZuVBZXTFmUTW5Bmq2dra89/E7Oiu5c2r+D1qFGl48V3Bm7UhbG0ZUMxJGQPlxU87MNYhJybIatL5qWvrtzyhwrqpVIjMVcKiDNnQjbvcuXZ9zd+ONrlse+UC1uVi2M3ifmh6hGublqpqQzbUH5WPwk4F5lqXcXhS5Cf5ezZyFaobuzbk3lWqZD7aoaZOVEM1b+aBsJ6JKz/mvbBMMM2kp8tEPK9VK4bvXnqZFLe098+6jrPskn3P6o7sbW5wdeVu5ld+PF09JybJAvcLPyes456s9X9mKqEIum/k7CkJpb/7E5Zk6CtTJdUm88hnJER9wtXrONXO1mqRztQX4zyENdrkm4j2JKw5xS50BygP0V5p2+d9atYve76tN1HplYI3x0hmZpT+psU8ryw+ZgiiE7HkeLR1SUtGYaTG4X1HDZSSJRpnpjBitZMZrOdfLuucVKGSZV5O0rl5KDUxYWzHGw6WBWe7rsizz0ksvoTKSVMYYBdUwFlgWY7eHMp9B0D03SLaTqJHrb5VkFY62KvIOg90fyzxyliD5odaVu9rTxSBxDtu2bTNa3gswHh9eDCRmDfZscp0426z6wEbdwO0r4Z6WQKoRh8lMylfMSExKTmrsqnEyTqlwt4pEqo1hRk5FnqoF+V1T2cvLq3t73n89d73olKzs2M9eyv+l76PO5D8TtFfDuEc+cOARnv/eJj05kZuJMu+sCONKrnyaJVk2sDRY72K8pnZh6HNUBmR2tsp7b8llM4FX7HU4oZ/Ar3fN6kaz0JtLdm6Gl4/LrMjySh8UouHFczIvnpP5zwSF3iRRWefkzUfUz9OWn5fJrMzXGO/jpvvtYsb/vsrRhfRKEmcTf6d1yAt81v2poglbxrDP0D/2FFtwMDOFwK0sT2y1zlJe8QbOZ/wkno40vD7kTO2ETu7ktcbCrcp6acHzBUzctpIu8ZVqCWBaBb0p5YybEu1hesxDIsxM77P8ezWBo5UkmqiMUVFYre7EYM7qtZkau0iyjFINmaqKfU46NvDkyK0mPHaM5VSuKI4ERzhw8w6HJa3nZIMnmDi0RswaGUyePLlQG+3Tpw/u7hUrvs+aScqqu6jJMw52tFUaJHpzSwJZksEWKAF3OF9f32LfZkFkGlGEFPklKLOvhBRvfPb+jkqFb2b5sUhkqHMmQJyyvCncHstEuxs5Jw4OPHYA26yZmnbh+f//+VnDAJ6RLgKdCyOuxXiQK8fAPTMnL3PTLvFvol2r4p4g888fy/j85lNkqtWgp5Tnawws88x3q1RwJzN55rIMuW7B/9n8BLQttu0/MUkxEBYMe6cZLHoShdwULx/XH3KvcXWGcuJo8duxW7xhBzEKBX9kJb+rd9e0NnRX5VXoySmlGt299LpyHzCoiNKWDTI0hhPTW9pI3HOTeGtXzoP1vQ+UzP+fVtn+ZoCCz9bnLGuemsYJZ3udcqEq45by4xH6buqXorO8N3NdCqrMnOtmTjdPfONjeDoyZ7kmM6eEpXbdnJVt1DYo1TK5zRYFTSxbGmOJQCU0yFlOsNnVQQA+qOpp0Bdgap/G7L8q8eI5mb2t4bNMNRGFuL2W/qTWTS6XJ56xPYm5vpUnNfV0SvmiAAXfA7WjjT+j1szUXj9DP1VSEYN4xwYqSVfBrv9rw6sT+tA16X+ctLNj03MS/tFQyzOZSrEaNPnMrKoo288aS1IiSnl5o0xaWIoZdRGOMaoyZOaKjZaVMlHuoFZI2EsKkwOjyomANc/JKG3IUNhjzCchWVG+tKo4I/Ge9vkkBE9XZY19ZZnq+ZRLA+geJrOtNWTYZLlRop+MaZ3dVO7wfmFFtggxSvNN/nN7K1hzJ4rj9jkBjTZAjJ8G938lLm3cQFS9T3nKJUPr65xFuXlK3T6hDV9p0g+AW7HJFHfEoVcckMsxppfyOP8+CgeeL+Y9FZG1w+DGIYPmG0WYsCkskixzw0aFM8n0Vh7liKZRie+zsFx9kETdrO8zbBYD8ElVD05l3RNtL5q+2ns29S70/nKPD32k+6Y7lhN+PmW8trA6z2PooauUR2HK0TLraDI4gb3ODXe9zRSwc+TZtHmkZaqxK+Y66MXNnD1Xsr7l/Lm5/+fY+N60jQ3W/T5fC5KvjzLYzv4mEl5xMhGpgXSL/xPIMe21uSxDVqoUN8lI5sUyRkvpMr/afstXmYNZq+6kt+ygo/EkXl4udgR1UXCyjkykv4bBUR5IRRin+EtRXL3/mLpeLgV3LgNEu0STe6Da6YzpGZgP2jeBVRu1/ZKT+cG5P62Tj+S7/UY3ZS7yiPs8eVUhS9DugoZ7bhLXqutfC19q7lMpVXuuEnHi+/sxhDg48N9O2kmfxVFaq0ycyvSYyEcS4c2mKKfOo8VDdq315OTkAnqWfx6nFr60krtSrc0+BaiylPoUW20ZkWqVtA94F03+qkJiEfZrMTRquH8RTbzp+uHmkungYbRdU47cSk2Rn2spEqTKMnJGBlJcXL7bGRyi4Y0DOS/C6zY2Bn3K4tkyllBsl7N5rtmj3lFysKmCRukZeseW6lgVr2raefXm16PxdlYxb3BLvXVljQbWvgnLXnmiBE8lzuKXtJm1o7SJxmKTjGdLzo5/zsv8ngW/lowlELRNKkMliowo5AAxyuJXdJ7L45HimKpVU87Zv8VMm8Xst/uk2PdZZDRqWNqduodyZGqiiATQKeQFoWzYu9C7zW3xfEMVwrk75cSNwAiH7x7m13+X8N5WNZNW5xzYs8mpPJtqfuhg69Q0Zt6P0SnyMlBViuMZ6SKHrsTku25ZIMcokHMOcntfyZLEM1dynpOhdRXIakOF8X8vK5k8VIVGoTBIcPbeNg3RWfdsFSMJcssKHRWnOWQ3io12QVSSkvnWZhHVMKzjrjAyXuvYwIskB4kjjRSk20g8lh3zTfRmin12nxotb1gWeVq6atCWtxb3NwMUJNrDzAEKfF1yxnMuTpn8pO5fYEx55UQ4bv8BdaSy6b/994V7tJ+51+iyOndlPvpTw4xlatwT9K+ZNqlpNEjXjuuNjUIkoGfa15x2MO1N+j8b41WQBOUrvLXYUSqVuLm5cf++dubc0dGxXMdsmkKWZeTMnECzdFnWS+BlkwlpRh7W9ulqqqMmQaHEWaMmTrLTrZeRnoaMhFpWk2YkuCY5E+yRSb13BU0VX2zL+Kw7AMmPIFU7QaPJpSCmZ2rPV+5zJBVQjUC2cSJdo/+Q12Ror63UTBkKUc3AkmgytMdgnyGTpsndLhlcM3JKCsmpatIexSGF7C/wHAG0uJYTF2vszrt+O4rqbrWLKv4TcfneY2QZGnjrD+T6zT+Mj5TjfpVmRHBTdZZzu/0nyzmWi+ttZtEgcRyxdhoqpchMr38N70rafAwn7O2olZFJZU2mNmM1QMwV8MwbiVvG2D0Z3tyk+5nXrX/MRuNK+b6nFQYDpLy4pJThSYlS5pNN+ueq32ENYXkcBvaE38NGqaBdnSqoCuHVUezcPQU387cwFUTbp2oatDVNTeOsvR2bn5V45ZjhtZF3IrH33EPlsiTPw5SHTDw0kYH7NXQ6q39Qdk5qUlzM/2/bJ6fgIsscVLgB8Bgl3vw/e1cdJ0X5h5+Z2bruPi7o7u4OBSlJQUBEVEJERDEBAwkBBZUQEEUUA0SlBKW7u7kD7rju2pqZ3x+zMbM7s3G3u3f64/l80L2Z2XnfnXnjm88XqE1W3CjtCRhXUkWweTzZMhh3uM5iSxtbd2RRqrQWiVcEBeCjbC50PpbIRAob7nxn3YxvFIusjh1VWkcFjN9rva6q5GbZzJiOWB6l/N+EluQtu9ecrUXiuRkEQBAgQOC9MRTiMln0itYDuRbEvyIYepTBmlH+WJi3CsALrum4C/H8t2dAgQZEbKH8KEex0pxGvDqiL7K2fYRQ2vwwNHp/XGMT4KOUwbp4KYd6pDW552Nw+L9WygEgMpILhTMq5v9F6GgGmYUaKKCHHhR0VC4KeAzsMlpAhmuCvMSseBQAyCZU0FLcMcILoCgZSguzUECQCLUwImdpuBJqITQNXZ4acqXj9Q8rDZpioIzbfDN5CoSWYZBPkmB51R/kIl5dPliWhd6iAkGWwYgs0+uBkiTX9NnNyCzm5oVCD5TxAkqy1QBr8c7lcjmyMh8hcNdByH53jG3b0uM5PDoS15UK/PMgBWE0g49/OYqdDT2vlKt1NHovOwQAuPFBX4Hgcj+nFGsV35r+/tPH2kte2wG5lmHN9/zycBmyS2fjjfhPUPsWoP5jFciufXFapcRzBmb6s3wjj7YKeW2ybwOqAMDXQli9tx+Ae/Lg/co4lnsvnvGjqqvpGgK4qnRtDckZv1mHDAw4xeJMRwuP38bjqEs8QK8efTCtZx2X9sEpuIBnxE8lXHsLWG9sSstAqozCOkUUxEaCZW7wG7IfcDerC2qE+Va4P54CwzJ4++jbqHMmE4NOWP/GJlGFOBbhL/JNcVyj66ANeQMKwzb/m48PXkcZrjIJVY7JIylburIC5WXmb+F7ygsVQuLRWmnWz6xpTiQuhHARNgSA3ABr795Bby9MjAzHe9m5+Ihej3G6N53sfeWAFGGp63tOfJWckF+IDYH+eDMnD4B4lJ8j2HUlHe1rlv/7nsK78k3YBAfC7A2bF60MwI1qBG5UI9DToCp48wgV9zYl0OuC8NmGFQJTggPxDKtBS1d13MWQidI+C1HTgt9jvm4s0thgfAUgLjQQ7TQr0FN3Do3VP0DOspBpuDB2WyS+W/x8MbIiHf8P4/9eKScIAlFRUQgPD5dk9f6341ZGEXb+8T0WyL9GOhuEU75afM0jXYrOYfH6r9aTM/FJoaFitaodroVwFq4NvX5GiK8Se9ePw8rAACxdK/z+whe4ofV76iPcbrcIiXWr2jYvgiu/AkcXAABeiYk2HR5dWIjN/v5YtsZspEjctdPmrTRaLVKmTBUc4z8TTD3jql67DSXaEryy4xUAQJ0UBi/yvJc/DqTwynbzO9/XIwSTZ32H1/aNxafbHS9N5cNzpu/18cJ1g9LSPS4Wl5MeoFhTOYQgJbx2SzR6gVIOALVJc0iaRkTrlNHSKuLAIs707MebMmkFauSRYdB16gPc2gP/2zrItk0Q5J2zfHbk418CT69z+Pe4DQUpwEqDyDHXsZDgsHzWpAhY4nB9xzT4O9EEWhtq4w4oKsHH2TlO1Z2tDEyNCMMJL9caJ9tfF//N1y2U/9XypehGXcTKk/eAnstd2gdnwLKsqA+utAKWm7NMbXSnLqCanpYUBBMyWOT4m9t4SfYH/kj95F+llG+6tgmPzhzBBxJRJAqStZvvU6oEvA3G4Q36vmijuAF/g5Mr/AEJJAI6yKpUdkxOsQbdlhxw6FoZ79HcCLJPCpuirQOAn/Zi/QALKAqnvCjMDg/BGyn/vlRHHYD3Q0NEz+Ub7BYz8/LxXEEhAhkG0/UDMJqXU/4oGIh2oBS8DlxEzgeDGla8025EZqEajsQ6yFkWOoJAjE4PTaCZXvx7ugfiQ4QGn/jwUgDia/sBPwJV04zDIpHgxn4aRSGIZ/RvmmReAIYeE6436+l+ps8EAeghAwMC36dlAADG0+MBWPNb8PF5UOBjpVwC//dKuREURYFyQ75fVQAp06B9yd9QyR4iAQ+xQx6ANK15c/EtZEGmCZXqvJZlUBXnCY4V6esjzS8NACBTKKFSqUCXpuCRd6nV99O03NBSFT/Ed0dv44M2fao8cQygAYo5q3ua1rwpfaoCSHWx4Deq7OU/EgTItDTBIf4zgYP5k5WJeyX3kKblfkP3KzRInqeBSCNBppkX6/ETN0ClUuGhv3M5ZX5qILiQRZ4f8GVQoNX5ylK0WInPjkIqjHLnw1TE6Llx5FucgCFFN9FUrcHHAJYMa4Je1Vvjzjd7EJYP3Lx7E0S0Wbli+Kz96iqSE2vIG7eF8w/y0djwuVYqi4++tTYAfvI0iZRQAlmB1t/f2IPEuL+FgsGYfxjsacHt+n/4+eD9nBzcyShG1aM0M0NMISfscHKUGxZaVTeKe08DNX8CWO6eNh3AvewS1BA5viHAMQ/vp4NJfG1xjK/mn6lJYNRB6++1ucnibC3hsfD0gwCecajdysa1nGtYd2gpPvyVhkIP+HbpguKDwh9KKVhQdmp4ZQYACQZbex4r9BTWv0IBiS7ttktwN0vcyFuk0cNyde5zlrdOEAQehAJxNtLjswvaAlHccyTAwhbNUjZFmZSYqgJHwsz/9PXBHxLlKHNrmR1RgQal7F5UPwDnTMez/AlE59pfp5onxsH3kd3LKhW5JVq0/vhvJKvE0874+OFROlYHBmBaXj4CeMa7MlaBPTM6I4lnE1eJkZwYIK+iTONvy77HJNlO3JXLMCg2WnCu01Xz+w604WNRGsjc+GuwGpzMIvV4832AospMoaricNmT2b59O7799lv7Fz5GpYA/aVYFCUsTPXna2vKeqkuwOhZTFIaeJaWYnZNnkvko2K/TNK54Hb47Yk2sUfUg/Tta3HFOeLbkJtDwzF8sULWJugz48caPps+WoVmWVlAZaTuc3xZWfUFj3ndVq0buwl03MJzaj1HU3+X6vpQnuJqeNi266WwY5mXnYnBxCZ7vmIhBzWLgExCK9Ppc+NfddB/8xhOmvD41e340+ipSq+f+MbuX/HbBHFXQ5bJ4v7NDWWQGEWBF1pIDjayPKS2eLwsC285XTUIdW7BJmlgB9DtTNdcXjU78/ec4KKSdrGt9XRHPQ/UwnMAHI62v6XqZRftrwrbluiqUAmIDpbpSzPnndcz4VYPQIkCRmIjoKQNEr7W1E48oLMKyQRQuJRCYO5q0Mjbej+eOsKhaqSC2SPlW8gijnj7MYOgxYc/50RE/Na4ncgeLsWJZ45UHGgSCqhgDuz2jNQsgw4YzJC9CuG/fZGJBgBCIdGonMm6qqlijoxnoaAabTphzmZcHS7Oi349Soo5Wh6WZ2UjU6RHsY34IJEtYRc5poqTlFxtDqlIxScZFe74iUSpPDIWBDAY1jcas3hyfTc1wX4xsVQ1hvua0Dz3LzSmFRLSgLSX/MVyolL/xxhuYMGGCq273GC6EvcjAsHzryZOnj8QQzVzBMRIklmVmY2yhWZixY5hHPkmiAXkfQZcs/RtVD0U2QqUDeXtxyAv2STsIgsSp2uYHX+wF0441KywErBtquLsa6Tw2a3tMoxX9PXWrmD7159k7WCRfiwXydSDV+TavPeNlHfXQ/5Tt57Fe3xfeCrOlZkQrc9Ew725dAQCKFC+k8bgN+Hek3eVhdRbHLUoz7ZtrdUkYbU6D8ZMgx8kJMA+wRmphtEWJl32phgXwjmyT3euqGtxVB3nEYfEbVyOzqs7YkUDdh87172Od0Nt9OVFcrJmxveqvuWL46MSH6PVLEuqmAISfL2IXzwe15xWr6zbo+2CX3sxktrcZgQ4lnCfUn6bxRk4e0kIIfDiKwrV48zO6Z6gw9yiaMa0xValM7Hc8RcqIu1nchtyfOgkA6HWOwfAj1u/3ejXz2pGtCrQ6r4ZZmSiBCpbJFf80Nv+dY1Buw2COIGz54V6UaqumJxQA3g8NxhciEWhGHAjl4lae087CJSYRU3QcORxfZtzW/t/t1WQYFp0W7ke7Bf/g68P3rC9gWStiVp+AQKvLJucVoG1ZGV6u1woAUMzb9uU2jCP37fAPVTYs+1ddhHvBiPQAJZaPbIap3bmwI4Ig8MnQxniqhdlhcJatjVNv9QBrY9jEZlWd9aWqwWWz7caNG6DpquXtegwOjA1ZZNxeGrXSrI9n+/vjHFsbGtY8YfkbVoQ/t5kZQ1UsQRmsZJ3iY6EhgEbF4iWCqhJsWeT5TJuKOPtVlwmCwDc9zdMrpAh41cCS/JevD9gqLhgDAEmY+19soXf6W6TWUQ7+nGwbUapxmVXnmfBDzli97ZD8vT5cfllsFovuFxgQLIsGD6yvO1vDPH/m65+Fvxdv7vCEgiaDJ4IBEP+IRgivHIkw77bqPCsBjiyzOuTDmk3jUvWn+R7jzWkZWPg0iYxA4J2xjqW8sABqkCILWRXHxD3uURQVOuk8bdbWhuBmKErtx7eO3ystRyzPsK5v60wd4G0ipIxVGX/e+xOan7ejx0UWLEkgdskSKM/OB0qsn8N2ugOKYM511ZPAnMwSLMnIwt6Hj2CpGhj381xf7v8FJIUucTGoE7HBbb+nPBAbxV8fFhKlThKZR7UjfPFPE/O37wcGCs6fmNMDOlYFn8JEeBfHoFQXBdZCkygRyTLboFhs+pxdrMXSv+wzebsLhWV6yGyER2/zs82ZoGS4FIZ/mOZ4SvsR7rCxnPed595tCMfL7KUXqiuNB0YKBWU6pBeqkV2sQaGa61uZcW1kWby3mcGCb4RrjoK0Vo2m5hdgbXoWgg313vkRBD1K/n1cA1KokyItWyglSDuyw9vjHFMTm/Q9sHpsK4T7207PHHji32kg9QT+3Sawx3AIm07ch4rQip57UiLM8XRoXQDAON0byGN9MU07VaAGGMOz99ItAAAZgcLvV+elXp34F+RPc+B+k9gT4Xu0/Pr2E7nC8lYEsgMIzJpoVir4CkmJVofeyw7iwz+vlbez7gdPGrIMx7YUglSRMQCA5xo+Z/OWmurRkueq6kJNWIyIEIgbb5Z+TePFXQw6XhWfU/uaCcXLYF9xg5Z/VDwyqnMpJi15dXb5d80qqir1YO17sTupRZJ8LbAiIwsBNI0FmVwC6NlaJKa9JMOtWAIv5+VjSyfbW1X/2Cj8G2k6LctaSeHVSc7xcRytT2BxcKD4Sbryxk70xRWix/lkidUzpL/fo9RaQWgeF2j63MuOcPxBcLDJG3w9rWqHrz8sfIhffphrKmMV8dpr8CVOAUkHAbmQaCrHD7jA1gTBUyrvhxPwYYE+pWXwFvF8G48Y9a8ckkI+RWFfkEbUO12VcPa+feaxv17tgkIfAj90IfFXMwLXIiIE5yMDOLkkPXUyMh5OA5ehLVzPulxm4aU2PzsdgIZksuCag7esDSSewowt57FQvtb0txbALh9v5IoolWKIIKxDl1vGB4MsMIcG+jpQvbW2QZGrQzzAe79dcahtT+Dw7SxcTy+0Or7foFh7aYCGD1iBvAoAVKsauM1wMs0hprHFt7nfejmeGyvFKuBnfQ+rNkqly3RXWQQWs5iwT1oOU7aUcEhRcgzRzsc7+okIMYSy20rNis9kcTmlivDiVDGUSynPy8vDkiVLMHHiRDz//PNYsmQJcnMdoGd8jErBljMP8RQlrA3b4zyDTzZIWzS94poBAE4w9dFMsxp/MO3RpZb1Ai7Tc56HaS/J8MI0s+DIn5AsCCvFpiojU4Twr+Ud80JF+dr3thi3dsuw7x4XuPtsO5eCWxnF+PrIv6M0WrCdVDqjkUYls22AUUcESJ5rcdt6jFRWOpY3zIrL6STh2rZV8T4A4KpCjiS5NVemZQkRI6w3KfOvszwl69oBANDqlvkMw1NcytSOey/cC/vzurr+Nq4pbIfwtdBocPhBKvqLKFXN1Rr82pHE8DeF85Ifcpglk+GwdxUpu1jq2F5YzYnIEGdzz7tcYXFI6nlUYmhyYZn4nvO7HY+eLXgpKFxkqiOP9YWXnSgAmR64b5izV+6n4ZezVbMmt47W4eNtr+ClX0pAsYBf/ycR3KMusP9j7oInlgiuTw43rw1vTKDwTQ8S+xsLV88N+j6Cv8+ytVFH/Q0og5NQxbNqHb5tgx2tCuBWBrch5TugfG5rT+LrvhQYvf0SWJbh6/5lwAebzF7Ur4Ks96/bmZWXZ67WMRhKHTb9/XlwIGaHh2J8VITVtXK9cN6/O4ZCy0bWTOmz+9YRcOJE0vY93x8aOGFiiUzsvW7DquZB3M0qxth1pzB67Umrc8ZxY8ksbsSdGgPwjPYtfKIbiVf1wio6xvVzcw9gUzcSb0ygcJ2Ns7pHctUrZw+tnsGCndeRLFFicOhR2+sn62OfGzzIm9vr+8mtjSFG+JYBA1ZW/ejZyoDTSvnBgweRmJiIzz//HHl5ecjNzcWKFSuQmJiIgwfte0Qeo2pg8m7GyjrIR/+msby/uAVaLrdWVkPKAvFSHmfxKuQZ79U8GZwlgFra6xXprkfBgtvA3vyJRp8z3CKV4OQ+Y9zULEk+Ju/i7vfzmartiQAcY3YV+865GtLfe9CltuS59GDh30Zrf2XkwK5WmMOwrz4Sbi7xZCaySRIjY6LwlJG1lKfoxOSI3/MaL7/xy2eag+aT48mEZvUGg8YDABons/hpgR6TdtGCiqJ1y847/mMqHQRejpCWUCJb5Buusncb4RWRFrpvlYmzOLLUocviJPLqfm9DYPFQ4dac7kCE9rJBwu+UEeLbOytx3BNwR/5t9VBfDNLORyvNl6Zj29uKjyYZA6gN42iBfB1m/Wy/ekBl4KsTyzDw6+vwLwOo+nUQ/dZrILZO4uq8Nx4BNB0tuL6WyRDIIimSwM7WJFhSaAyfpx8n+A4LEhoo0Pwed41lhYOqgns2apQPjYm0+V1GZ1aimTIu7/WDQQ3x+ahmotdbhq8DQBzPEb7DkP6QrBqNqpRCdEmpwITIcGw0VDFIUsit0le+WikM0b5ZjUBhVDfBsZhAL3grZAKlvA8lHlHy4bNiR9kq81iSJFj7AWBBKCdsPHVSvLPKajWRiSCsop9CHis0GGqVQYb/E/i9LYmsQMLKmAMANgjZKw19lx/C6kP30FWixGBiuu2Xdyeknd02qhuY6uNspDOHVu0gpUqF07vzlClTMHz4cCQlJWHr1q3YunUr7t27h5EjR2LKlCnu6ONjVAJig71xaW5vu9exIDGhgFNaGJJAocE5E5HPQqHjJvj0iDCUVKAOradBAOh+kUXzuywmGkIHnfVUSXnKjbiZnl/e7nkMGgfDXJcMMS8jBAgU2XBYsmDxIFT83IFGJEiGRYvbXF52l/hYxBJZ0Hs4B5ZlWTQhzYQwfMH2V4Nn7ZGFh5w/PigLI8Ko2RSenUnhpdJ807EnGkVBT5ijChiVUOsKqd0IOZFmK1evCyxoXRWfQ8WZoofVOhr5Nti1vSM00LHS4dl1teKB6c/+w2DoEQZj/+Y2/yoiCwI6x6IYpIS2Ah9CYNQEAJ3M/O6/7i3+LE/UFY4PqbIzbAUqJVQU4lXKHcPu5uLffb1vHbAgoYfMJNBs6URiVwvr6+V6YLO/fY9pZeJYyhH4LfoG8VkAHeSHxC++Arl7BlCYCoTUBJ781MpA5V+OwJkW8bYsPQwyixyIW65kZMrsee6sx8DYtvF4qolUGhWJC4nSY5S/7lel8mjPREdaEY4+Ey30louNEcLCQGesv52rzTdfI7E0Tyi29oTm+6bZJMz9N0CWWIY+zarjuQ5cfcA5/TjW/unaKVipH4jcsDZW37FU3AFAXgWVclsGLgCobYfyQxtnGcrPoXUCZ+TwUZgHC1NpcY7/bjitlN+9exevvfaaoKY3RVGYOXMm7t6969S9FixYgFatWsHPzw/h4eEYNGgQbt68KbiGZVnMnTsX0dHR8PLyQteuXXH16lVnu/1/i68P30MsIS4s2wJLUvBXyTGte00AwLAWsVZlvgBOyOIXVzHm0czaymDTEvOqtEwqv7Eqgff7VLwU/PIwRRpvJcGLAYJg8Y7sOzxFHnX63p7C6fTTDl13qo7wR9oqAVIa7I3XJ1L4cIT1g2EB/LiQxhu/MNjyCTd2Ppev9Hi0raVjnmTN4/g1g2fNsupAFM9rG2sR+UlTBNRKAo01GvxJt8VAzXzuhJ09i2nfXPA3q6niFCBLaoke1uoZ0AQBpVb8RU6Sv2ZToQ6QMMq0uMNixGEGA06xCM+rrIr25Ufbm8IeLx1E4mBDArtbEKIs9av7crmxeyWUU7FycrkkaRVBILaO/xuwqbv4+PdX8YwMek6p0MsIbOhtrU0s3ECjhOQbEauWdzinLAdHPnoVbW+yoGUkqn+xGvLkrcCt3QClBJ7eACjFjQqn3uqBdsHmcGTvgtqmfNi9dHOr671EIt/8SrkxGe57CncqMSzbGfiUSc98usTxwut1I/3g76XAJ8NJ7GglnCMv/UkjPE/YjqKKs1jcUdiuZRZE02AMBrpfX2qH/o2j8OnwJgAAmrfm3mKj8cwsCi9Mo3CcZ/gLgrXWqae4hasqsPdLLXO+sM078WHNYfBWqfBu/3o4PLsbnu/EjaHfmQ5Yoh9hMizybx/XcbjVfRIzbI/NfyNGNKsrejzcX4XTb/fEmXd6mY4dZxp4qlv/KTgt5TVv3hzXr1uHIl+/fh1NmzZ16l4HDx7ElClTcOLECezduxd6vR69e/dGSYnZmrNo0SIsXboUK1euxOnTpxEZGYlevXqhqOhx/IM9qHU0PtxxHUonNo/vu5J4bSKFWlFc2NerPWvjz2kdsWBII6QHNBX9jpK37ugt9nkjC/txkbJRlY2b6UX4ZPs55JYISfAIAAEl5h/Fz+t1BvU1Gqt63kaMoP7B87Jd+FzxBbRVpeY0Dzra8TEzt91c0+c2UW1wvobQq/f6c+ZB0SyxA1iSwJUE6x3TGJXAh1Ze+UUtm+TstDpm2fsm98xjxFfCa8WCwFTddFxka0rcRYja/UcJ/s5SkPjDxxvfGT192tKqWxiWh1CC86iES/C6HIG49Z2PcD3nfVkwTHxCdb/EcEq5phh7rqZjxd+3K1EwtH6veSK5ry3uCPt3oh6JLwZQ0MkIkBZT4fWcPPzdjMuNZQkCK/vb37qDirhok/FRwtQBuYM1wd2BGqx12k62g/3Ryu0bExjadm56QClA8cbFF7w615UNhmWw7svJGLCfU4Yj3nsX3hEMsJfjsECfj4Ao81yxDNEP91chkhK+61yWC2e+zcbCEm2rB1sdW/cZjSb3GFSXiZSOqqKIE+FZuxbFhZmrMwaA1oRDnd7f7n22TG6HuBAfMCSBbH/hs+12mcXKVUIldJF8Tfk7XQWw8+EjKGXc3GsRH4yVo5sjKoALc+NXU7nFxkAnJ5DvS2BDLxLnqxNY+DQJmZ/weSi1LPLDziAceZj3R9UksPWGGldUz9u8pl4Nbq4QBIFqwd5WRkyFzFopD/MT5xnasJxGUBGLI7ezsXjPDejpqifrOQPKxhIc5qeEF89TfompITg/fI79fPTHKIdSPn36dLzyyitYsmQJjhw5giNHjmDJkiV49dVXMWPGDFy6dMn0zx52796N8ePHo0GDBmjSpAk2bNiABw8e4OzZswA4a9vy5cvx9ttvY8iQIWjYsCE2btyI0tJSbN682flf+38GvUQurmVNRj62tyPxMJxAdCC3OJMkgYYxAZBRJHJ9a2OgZj7aqM21iZvFCzd2S6V8hqEM2IMqWKtx+4qZePN8N/zw3WoAgIzmtCkCLPqfNj+jZnedX0gJgoAPw0qGrz/JI97LKq4qTNpm/Hb3N9NnKQ+nEUNrDzV9bhreFMfrEkgPBM7WJPBXCxL3IwjMGUfhxSkUmoY3BWC/7jnAjdOySvDqWRppfHX2SY8E4eu8zylPmo2HlmSHNMEvN2iNiLZdBX8f8/LCW+GhWBQShHtyGfBxFLBtst2+VTbqk5wi1uWy+DwK81ehFGaj3QjNu2hiqFU+qoB7fltT07EhLUNg8OGjdgrwcWgwkHkNk787i0/33sLxuxLJ/ZWAD0KtFSBbsDTmWbKKH2pEYsIMykQmZDRWHKtnfj6vbOeE5vMqFVJkFFYF+qOAJKRdSJUENa8/cp3tteYZ7Ryb59sVeKFFme2w60sac1rIE9QpB3roGfy6exl6buSiAInhAxD2VD/g5wkAowPqPQW0EioT6YHW71H4am0/S4VMXPx7ewsjiA6qTGQUSr9LJcQrygCAxvjbGG+U3psJXV5HB1vkvjfikPSev9aQt81PcarqGHbY+n36siyebBwlej3Bmn8/P/It35fAghEUztYi4VMmXNNkNJBLUVijWIpvjiVXurfckgsGABoQyXa/17eBOE/B1G410bVOGLrU5hZd/lSjSOk1ddavNMasO4kv9t+tssSSjoJWOJ76wzigXn7+9+0qVEmmasBppXzUqFF4+PAhZs+ejc6dO6Nz586YPXs27t+/j1GjRqFZs2Zo2rQpmjUTJ9KwhYICzpUSHMxN9qSkJKSnp6N3b3Nus1KpRJcuXXDs2DHRe2g0GhQWFgr+PQZHgvSLnw+uKhSobqecb4JEDidBABfZmsiAeTEO9uWE6VpaboO0LJ/R5hYLb3XV9ObNlv8EAJiS/g4AoOltzthgubzWTS3f/d/LzpUMX7csq1LVMP/4fNPn552tpUwQmP6SDAuHmS00d6MJ5PK9Dw4oBjJ9xXJQy4s9V4W5gjkiRhPLXo3ZL/6MtDwDemMNN0fWPtsSAHAxtD+uMvFYqR8o6vAmLLyr/JD5dQbBEJe2iLZbVbDxWLLpc0S++DU7XumM8do3kMREYKL2NZxk6+HLjEwszcjCa3l5WKgbiQCGQUu1RnLcNHzAIpeigHW98KFsHQDgz8tVp275NV4oafU0Fp0lDBRGWK4bUSKkOSVeBGZPlGH4HBnO1ySxLCMLcl46Z12e/NevWgy+CArEvNCQcvXfE/DSsHh5h/RzWZKRhaNMI5v3ULIkvkk3p2v90sF6vCRkVL396Nq9kwiftw5eWqC4cSLqvPMh8Pt0IP8+EBgHPLVCMPY1ehrH6lv/NhK8CiiMArvpVnhfNw77DOHrgYUc2VmMWo7RbeJRN9IPa/pab1KhhZUfoQQARWrp/OS6NhSszb3iRY8vG9HEZnuEQSRWSQSKESyLz/8NqXg81E5hMeyI+JhXSESpEGpzWJNUisd7emH5U2PZ1KYkl8aqrWSv8Ip/blsdGyPbZ/d7JCve71l96uCbCa3NCjjvkdaJ9Je8Xy3eNvQwr+rUM88jSWx2suoFq7BfeciIDiLVmiyxdO8tvPz9Waf68F+H0/EESUnuKeHEsixmzpyJjh07omFDLi8qPZ0TjiMs6ktGRETg/n1x9uoFCxZg3rx5bunjvxl/+vqYBLLWN6UXy2CaxpJMcc+gGBs3a2AB2ZqajltyOehC64nY4yKLP9pULe+MGOR0KQ55qaBgWUgX7nIcCXo9NqSlQwfhM1m4Xg+mu/nvyrYo20OXK+7p35V4Ag3vszhZh0Cbm9ZtSAlG7oZliJmYUm7ZWykiwAh1EKprdair1YICkLTgCVM4nF7mhSe1CwAAIxzpV4oSMJQJvW0nX7CqIGnHp4AhIEAqOiLUV4kLbE1005oZ7/0ZFr0MNanvsNK17flokMy9tzGyv9GSvIWBJz/Ax4NtK3FuQfYtm6c/+ca+F9JWJEnbsjKc8BKyKTZWa9CjtAzreDu62Jjc6+NtfbASoANHTlVfqzWRgE7cw6DDdem1prdIjXIjfnmxHQrVOui/Fx6/H07AcrYOOcoAPcvbc9ejpKwQd6a9hFr5LApDvNBi1fcgLm0Crv0GkDIuj9wr0HQ9y7J4a+sVDFQXAxCOAzlJwpjqS9JynGbr4jRtzgN9mPYc5CUXcbOoPnyVMuye0RlzJ1n3qfqDpCrBnPjHRWnmKY2Kk1P8S80d3diDxI7WJPxL61hd/93E1uhkS1FggTpkPMAA+T5AoIhdoud5FnubE7gpl6OOrmrnlAMAwbB4+oi1vHc3GpiqWQwpFZXiucd/KxsAgKuF3qukFGdVSryUV4DtFvKgwvA4jKzvX/xzBzN7W78HT4GTV4WDeCAl7szjg5VQyi3BN2e0TAjCgRAg1k5wVuWLeiz6kqdxjY3HyOhIK8Jaewjzdbz4epxPCA41IND5KosZL4ixBbIACJxOznOqD/91OO0pj4+Pd/ifM5g6dSouXbqEH374weqcZU4Hy7KSZDVz5sxBQUGB6d/Dhw+d6sd/CaeSuBWCBYErDgjxBYEMDjxIdWqz4b+FRInvjf2HQURupa9GdnFDIceUyHBMEqnxWV6IVR9KzACu8MJ1bXkDKgN6xtyfRkmutXbzDRDzR1MY/iaFjEDxa/1LK0cuXHXQMizRuhfbJXLILHE9qCN+S03DwixuLvLXrUAvc/h6iI/9+emVbR5MVX82cZgr/9b0mc82rhidiQ09Sex/mgutcUVEdQKPz7Iu+RBvyb6XvtidSHJBaVAbz2NCgTWfymcZWSDgWFpIVcARby9cVyrwq58vdvtyc6nlHetRnWmwjt6I5R7JJ0PEjSwtE4LRvW6EVWRNjp9IiDeAd5xMJ3An9rw+GrXulkGjIFBj9VrItKnArje5kz3eB2JbCq5fdfAefj2XApWohM9PBRDx3jFK6PJbg+Xl3kfmWd+nWiZdJdaYq48kiChgXgOfPG3eo34JehXqjCfho+5ufb0DPygluBN3nw7iovEkQ9TY07FRppKdlY1zSmlF6YkzLJomWf9wPz2Du2yMpBzNp80sgHkcPVNQhAMPUjGyyJoEcMVqboGfHxoMP5Ti83/uOPwbKgNGviNLhMgCHfr+h5n5AIA3c3JBgHMyVHX0Is9ilWI5DilfdVohB5wjCY1RRGDlU5yM9yiEwBs5Fso3+ThsXQzlyrxPTU3F0aNHkZmZCcaCGXf69OlO32/atGn4/fffcejQIcTGmglJIiO53I709HRERZlzXzIzM62850YolUoobSxS/0+4nMKF7ntB41AQsEZlm3pKbD6WKc31rWxljX/0LQ286kAnKhF33JD3LrWGJdEKtCM5z8/Ydadw5p2q47rZeHWj6XP9BxUXzVSUCmqaU76CLEp/gSCgkyi5MvYfBmz7CjfvNNIL1YAdXsIfeWWVCBt11BNq1QVxhfv8uu4FLOadI3l5aKSNnDQjSngWnluKqsfRYA/1HpqfUw1Gj49CU0xrRuOYAFxMERfAHU1h6HWeAXhVHMfJ9gK594Dg6uXtssthi5+hRZkaZ71UWJqRhXerWdcNXJ6RhXQZhToaYS5tfY0GoYZ9eFt7Ep2u2fbEa2gNlFTl7pGWpj65joW3hYyWW12HeX1U6HmBwdFmLAbnAiNbx9m8r7dvAMBzqN+JIaAnufrkRlTLBrb7+eKtnDx4V7Lr6uCq91Fvn6FqzbvTEVqrDrC2G0BrgFq9gXZTBdfvuZqORXtu2LznhrQMnFEp8WdBbcFxlZyEWmdtZD3UkLCKhqr/EMipClq5jbnvQ3B7ioJn09bro4DcKBAR1qJtgJf9NVMn54wVagcCkbrEx6Ljzcs4Yielwt14PVw6JUWq9jzDSJegBAC1IW/6URDQt344Oj/KRJJcjhYa8yT1CQgDQwgjcuqksNgR64PLqueRoK5k3ife0IlGNuSE0PnR7K74AJd5OxbS3a5Mg3NJDyAHwBAENnclUaJicD+cwMzfhM+9OXELF9ka4jfyIIZT1gZjmZ7F5sX2o7fSIhnUc6ItkwJv+P+YwiK83D8A0/7kno0MelQtd1TVgNOmvg0bNqB69eqYOHEilixZgmXLlpn+LV++3Kl7sSyLqVOnYuvWrfjnn3+QmCgsX5GYmIjIyEjs3bvXdEyr1eLgwYNo374SpPV/GYxz4jnZbsHWppUyxdjZhINFvHkpIR2wWW9tlbZEeeqoehrusHNKTTBSSyCNolBCEFCVVK1ojp1JZrbxoceEgyLFQtixrKdsiaOjjuLttm+b/paR1oOPllBIm9/liN5YvWfDBK2LSNlG42TpiUORZuHnBmNboRDD4QY8zzovnJKpYmRdtpBuKJ9pyTfBHzpfjWmBMW3jMKefdcmV0W0ce27RuSIHM20rMC5HoTCP/bpCjmnhoUg1eCWkmPm1MmB9eiaOJz9Er9IytGGEF6awoehRWoZnCosht1io+UaLlDD746JUV/l5jZa97HnBeg5pAhlkBRL4oSuFfF/Hxnu+zNqYMfoNGXa0tP7+bkMof5Kd2r3uQvKRPQhawfGaJD3dBk2HvQjsfJ1Lf/CLAgatAnje2CupBZjx4wWwLPBsu3gEM9aCtJryQ0u1Bi/mF4K0eMpS9ofMAPve0qqAIBRilXwZupHnAQDDqEMArMtTAuJ9b1It0Ob9WbCmFL3LItVBxLBJscCh69yJMrFwPDs4W9/2xq33UeLZmRRmvkABYNG5TI1xhcIInf69e2PkG0LlvuajqjNmzG+QxTHVdBxUzhScl6ojTsWIE71ZggFp2sNIkit7uqULhbM1rcfOVuVcvCH70aH7uhO9KOv87U5XHXtnYvPMFmiFdaTO5UTu2TAEMFW23bkb/p/A6dn83nvv4b333kNBQQGSk5ORlJRk+nfvnnNslFOmTMGmTZuwefNm+Pn5IT09Henp6SgrM7BgEwRmzJiBjz/+GNu2bcOVK1cwfvx4eHt7Y/To0c52/f8OxqVhKHVYcDwx3fpawDzpLjHitT2fahKNka2qYelwM1kKSZL4WP/feBeOTAZGKnlYAoTESpbPUOgdF4NO8bGQi9T7rEzYEsYeBAv7WhJmHcbdv7q5/Iy/wh/9q/fHkFpD8HHHj0Xv2eectBL8ARMBVuc5oTmzSI3e5BnJ871J69rtChvmXn64lyX7uiNYOcA8KpvdY9HtIoMpf9BVITlNEjoA+TxlQu+AfBsd6IUPBzXC5C41cPRNoZGviFd72UuiZrkUCso8nPfJCNsbExWJAw7kcb/9LAUSHCMyAHSwYBB/RzfB9NmfYfFyXr7p75GF1uHsRvy0QI8la/UIKGERVMTd+27+Xbv9cTcsh4SllxwA8uPNE4sFgZ/0Xeze96wXZ6wfbRHiL7ZslxmMge98s8PufV2NskcpyHx1FuQ0cLNxEHrNXQNc+AG4uJnLeRq6DvAxe0AzC9WY9O0ZlOlodKoVivf618eoQnMIcaEhtTxbFYc/6TY4ydRFiUW4j7GiiiXyJAiVaRsRQJWBmYrvcTziLib7c2XsfMDNkUuJIlw35ey60T6c50dY1So3YuDxqlXWqqgcJQ4zI2x7yhkAaiUBhiTgpxB/DkE+CqtQQGOKUtUaOUJ8FcgpiwqJrUHvbW3YEwNjsYrF6Lj1SidRuvEF2Q6cTs7Fi9+dRXqB7QoRnoRY+gpg7bwjncyNKgwWkip+ph9sqipCsoAq6LDItx7D6dlcWlqKkSNHgnRBPs1XX32FgoICdO3aFVFRUaZ/W7aYGYVnz56NGTNm4OWXX0bLli2RmpqKv/76C35+jlPzPwaQy1u4e50X31R0BrOfsbapJWQUiU+GNsaQ5uYUAzlFohhVgzyooojS2w+mKagtXYZFDAQJaGSA3mK6HFFxApKOIMqlrLkTt/OsWUuNsCzVpFNae74jfYSWZhkpw7z28zCgxgAAwOlnTuOn/j+ZzgfYcNwp7ZRIciXWH0lC64/+hh8h7BCfHGaNYpnl1+BtY3/lb2MprJBkyEtuWzACANZC6HlpJ4MuV1gMPl4FxoxavLLFgNhodIqPRYbBQ55sSAvJ5UUF/qzvLHnbmEAvjNS+gww2ED00iyEPjEERy80XuZPS9v3cyvUKay2iQKTEmvsRwjMkWBw1lDfb0YqABsIopZfyC3E26QF+SUnD4GLbRqu4bGDt5zRWr6Txzg80DqdWvjCkthjXfmXW77Wun/ndsQQwW/+C3fveVdZDD81ivJGbh59TzVELYlUwSgkSLIB3Cj9wvOMuAKNW4+Lzo+FXpEdKOIXWKzdBnpcM7HiNu6DrHCChg+l6tY7GpG/PIK1AjRphPlg5ujlkFIkl2tE4V517jn815/5PAJiqewUjtO/hLhuD36Z0gI+CQqS/ylT1wRJS9d9nbLngqp9cYUygduFR6GVs9fM1cb7kKrnxUf++9diJD3FMJnm3f33B3/w0oiP1xWXcZw4wpjSUW5VY6rVUW/7g32ZFtvOz6vmYZbwn24kz1hMAlmYIi8T7Gubx1IgwUB52Nlg+D42ek3P58tU9uQxfBgUCAKZIVHpgZY6NnRwLOdmRvel8chZ2X01H2wV/O9SGJyAlS5yoI1wXMmLt5PRZgD+XwvV6LNMPE3CerAoIgHUi02M4rVlPnDgRP//8s0saZ1lW9N/48eNN1xAEgblz5yItLQ1qtRoHDx40sbM/huPY5Wv2aIZKOFYutmBwjqmJdyxKXdjCvyiKVgAxxnOFA4uqXum4UnCXiQJBsnh9IoXXJwoVMH6OY1VTyo1QiCjElsaFi/1qWl0zoeEENA9vjrfbvG11DgBUMhXqBJtZWR/x0sw39hA2kJjhRIcriPl/XgMAtCaEIc8+hG1CkmEi7LZGFPiZn08uhJv4qNZxaFs9GG8/YTtTy7g5HuaVQBp1kKn8+IqMK6KHjaHaH4dwL3anD7f2HGrI9f9AIwJrlc/iDFMbr2lfFL3HCaY+2mi+xF02BgDQSLMOCerNGFpUgo+GS29bpQSBUyolNvn7cdyulRhRIPZ+pLwSliBZ4Mv+JOaNJrGpG4lG7ftZXaMAUEenA8MS6KlZBACYn2Wb/rdxMlvpYcnbfX0wO1zojXrijHWf6luV5rS/2RAEgbtsDEgAdXnfvxEr/O6AEww+DwrAvJBg1CMfONz3ioJlWVya9RIC7mWh0AuQL34X0UFRwC8TAF0JkNgZ6PSa6XqGYfHazxdxMaUAQd5yrB/fypQb7QUNlg4hMXc0iV86is+JptUCcXV+X5x4qwdqhkvnympEUtryS3VVwlsejWy8L/8OKTJhJ/f4U/AtZQUlp4wIdZAlekATM1eR5VJxN1p6vH33KY2mdxkMjeW+X6T2bETOPzcyUP+9PTavicmWfnf36WjMH9hA8nycKgRbUtOw90EqFD7mDXqhbiTOMzUxWTsDiaE+pgoZRgw8ySI8j8Uhby/4w3MRbucf5KH+e3vQ8sO9+NWiFjjFU/xMpURtwN8B7gEAeEE3ExeYGhir5UgZfXis7ZOnCuW980rOqPoMVXnKuGVVGXuwHP1+1Zwrn1Y7wuw4nZ7H8cXoeY+FeqyPi8JppXzBggU4ePAgunbtimnTpmHmzJmCf49RdWBUmG2NfX7OapAuCkO08608eo7gFsMJz7taSG9ktEgOXGXBshY1YA65sinIO2GEKAUnGKQHE0gNFQ/zAoAYIhNVETKR10VbOHe7NxhgdY2/wh8b+23EyLojJe9NEiQ+6/YZnlRHITPQ/Gz+aSJ8Ts8cYHD4lmefzzAZl6t4zEuFf7w5D+3tDOkQ4XBpgmAU+SZilPZt9NAstjrnpaDw4wvtMKmzbSKyu1HcM+l0TTguO8XFYmEVrpn7jyFsmwCLWqksBp3g+h+dw2J41xZ4WjsXW1lpj7kR/BExLS8fF2uQeGMChb3NrCfjVaUCE6MisDAkCMuDXFHY0FlwfTrmpUKb+FjBmQl/0XjvB8ckEQKATkbgajwJmiIwrFWC6dwordDYdZKphztsLGqrN2KQHa85APjfzbJ7jbuQQVF4J8yxWukb9H2cvr/U8nyyrlDUGbufQdsbLH71d07QrChS1nwB5b4ToAngwvQe6NpmBLDnbc7A5R0KDFkL8Hgolv99GzsupUFOEVg1pgXiQ8zG9QgiD1o5gWvxJBiDV4pvJH+xi+PEUs+9SmHJEBK/tuducN7gga/33m58f/J+RX5yhWEZuWQEwQLrP7MtU9hzGvjyIr1UcgqkE16Gt34yz+XTyWKEFu7D29vEDaIAEJfJosd5BsvWSj8bbe0OeLZdgs026mt1iKSF93jAhmOwdj72MK1RLVjco7xyFfcdTxr/lu/jovuyi7V47eeLyCziwtf+UryOO6pnTdf9bqjLXS1Tum++ItF/YrjJxmGQ9gMcZhoDAD7OykGCVocxBYXI8yNMlSMAYJ6h2kNcJcp6mUXOsZ1bZl56Uc554BJCzWtVsGEc8SOWOKX8sWZuCaeV8o8//hh79uxBRkYGLl++jPPnz5v+XbhwwQ1dfIzywpjPmklJh8mu4OWsqgyxJY1jHRdmjdO0t3YxBmrm44825vuVWvDCpeXcdPi+7kZqhtjiyP0a0sY6keeT4HAbH8qmSgqJfIX3Td/lDt/Tk/ASidSnLX6QD1X+1IXucd3h7fcGjvAMQ2UirLcn7uXi94uPPOq1oQFMjgzHKxFhyCFJ9Fp2yHQuxyJ1p4TnlLEsi1It1B/HmQa4y8Zg28vlI6fUS0zfIorEJgcs/1UBU/8wD/jaj4Dx7ROwZFgTHHq9m+j1FC/0jS8nG30YSZEE1va1Hf6/PrAylHIOUyPCoLEYJ/3OSo/fL9JtC2u1IvwwSDMfz2ln4SEbbnW+WVwgtJCDAHCwoW3hqfbP1rwInkKBE2lvgdVbmD5b5m9KIchb6OVqWSadWzLYw7nBRYcPoXD5FwCAHU9FYNzYJcDVbcCZddwFQ1YDfubUn+0XUvH535yy8dHgRmhTXWjMYHjiW6BB6CV4z0nuhBCtkxE4VYdEtj/3HT3FsSNr9YxNBdAduJJagP03ufmg1tEIIbg0Gctfc1kl7Q032tXtKdneChk2P98Gmye1gZeCAkkAU3l8DY6isgJyLElJvdUslqyjMXm3+Nh+eyyFBcNIFAfYSf/k/SAWLH7Wd8ZdJgr7mOYO922y8if7F7kJRWo9ahCpqE2mip4PLBF/YVLlWcVgObSq6/T4IzUN/Q2G0e+6m+fnXUNJYv77upJqw5pfBVDLqwy/tzH/yFyZ9b5jD/2KS1BTq0XbMjVaJwTjg2xzJBfJAHMVa+GLyicerUpwuiTa0qVLsX79ekGI+WP8e/F0UTEAzhtIK7hFxJHSIUYMaBKNBbtuoFTLCQXZAQSWDyRRouRCMOf8bF6E6JT7QFh9qVt5FPXSrJkfjcu0rbCatLa1pU9aIMe3Np7NmwPgW6tzfKW88gMExdH5sgiDrUXNU6K+489DDFGxifhFS8BLQ+JWLCHq2vj1XAo2nMvHoVtZWDJMPL/N1eAPgUILIp1no7mcxrB8FqGFgJIXufh3EwINeTmOzRPD8WSjKIT6KtAsLgjlgT1+FT3NQFYOsh9XgAbwUmQY4nV6vJ2TJzmWoyxKlMooEk+3iBW/GECYr5IrTQcgwl86l+3X9oSgQkBVCcbROZnX09lCeeR/O8CgcF1gawIs8P3TUcCf5vOB3nJsfak9p8j8CPzUiUSXK9IPgqmksZJbLF6aU6pecEKIF5Bv/ttbYZ+DIcxPqKg9WVKCM17i48eTqTGapCQkvzIdMhbY31SGwW+sgaowDfjdUEK246tATXNZzHMP8vD6L5cAAJM7V8fwltWs7mmMxAIAXxECxPIoiqzhBQUVs2hHXjN5AT2J/iuOAAD+fq0LjtzJxvfy7biqkOOq0oE6ZRZwZBa2r2lOpaBIAs/nF2KlIe/YHnxLWUjHULkHmYVqpBmIwpoQd3EHgFzPolESixd32jY03Y4lABDoY2dwKIKiTZ9jAr3wuv5FcJKK4+vaMNlBh6+tKCyXW5YFXpFtNf39XmgwcnnOqUCJgCKx6EApkAQBWuQ51jakzaQHcZ3KMziM28XHYtfDvZinHwcAeJBbioYxnjMcO8tBQNUswymFD546yT2Ueyrn04YXZeWYRs2WyW1xfoH5AVMMcCLqBhamfQFgmNP3/q/C6d1ZqVSiQ4cO9i98jCqBd2Tf2Tz/Sl4BPh1MYm0fEr95twPg3GYe6K3Axfe54sA3WU5wOFafxMUaJM7XIEzhcABQlPTIyd67DwzDgAFwQalAmTGiQMYt2raU8rpBtRxu4+0n60nWWG59i8VPC/R4cwsN9xRjqzgUeuuBEJlv/kwTAFEOQYmPce0TwBIE9rQkkRTJPYfrErraLxa5YpWFBwZyny++ojHve1rAD3CRxwRcqgBkJIEvnmmOeQPLz4OhspOuePrU8XLfu2IgcF6lxHEvL1Pd9vsyazvvQ7nTtl/MfYrLd3y5aw2bxowtXSis6G/exi5nCT1AKbpsp9v2NO5FCP/+le4EH154hNFLvGpMc3wwsAHigq0rHhAEge51uRtlBdpeTzIaR9s87w58d+I+nl/+k+hKJxeRFTOq0QALyAybUU0NjeNv9rDbTmyQMHKHqgIWT7qoCHdfnARZqQY3YoCwd+agtn8C8MtzgKYQqNYG6GZOSUjNL8ML356FVs+gZ70IzO5rXSYQAL7T9zJ9NioCfOWkPOHDLW8bnneaMBe3MvD8Rq4KRkP5NYyMiUImf22xw/Nh/O1BImVcbYEgCPxFt7B/oQEffkuDAONRTzmfJMzIR/PsPgZv/sIg0I7T0Y9mQLAsqvvZXgOIaq3B9P4Y9MgfeQZfx+WU+AwWWzxIxnw7o9jiCIu25HXTX9v8fHHQ21x9YNof4mP7ZDPHX6Tl00hiuPVXDqCmVmuKcDPKk8UkifUB/rignIS3ZN873I6r8MK35nJo2XYilna2JBBCqThHiQH+qvKRGhIALjLVQRAEvqN7maItKQY46u2FAxGe4/T4N8BppfyVV17BihUr3NGXx3A1WBbPy3bZXEp9GAYn65LY25xEKstZjGMkSqdIQU6R8FfJoIZFOBlB4KdO5iHG+FkLk5WFwjIN5oSFYGx0JJ40kLUsCuaEf1vh6/4yx39DtzrheLe/OJlKK4Pw0/wei7N65563O3Av/x6e3PokLmZdNB0Lz7f9nayAiueNKWTCJWhWTh7eHysz5WPpKE+aLFjUJKwVfzVBoD8pVHzlEqzwxd4Ekg1RXh+MogQl0cqLJ0+JD8jwPBYqDQuvHM+Gl/Kxz1uoBJ2w8EqyAM6rhMeOtLEvKPdtGIlLc3ubFJLbH/XD9fl9Ra89xWOJvaPj2gosZuFXyiJZJ1H/sQqhegthGMEVJgHHi8xcDTMM4bR9G0ZhbLsEqwnhLFFkibd9j7Or8e5vV5BfIh5K3uWKdf8JBtDKA/BzajpGFBbhncxSBHjbFwpHt4nDxI6J6Kj5DNvoDmjHi0C4HWXji24CS9N4+NpMEPdTke0HnJreFU83GAX8PQ94dA5QBXLlzyjutxVr9Jj4zWlkF2tQN9IPn41sKkjl4CMVYfg5NQ0jCovwXjaX08y/0lFFkdGYPcUhReYvETyqwrWHnCt36woY68fniKTfeQc75oV9spFzL50AR+AlhnW9rcXl6Dxgpfxzp9qoKMSyuPqct/2yi1TAxFcoHHyQglP3UzCwsXSUkhFk+ymg6goJJt8fUB/PtovHn9M6AgCGad7Dj52tn8ucn2is8iCnR2q+kHCOZR1PeeEjL7r8oVbL9Jy39y4ThV9S002VavzLAMLw0jYE+kNBluIF2Q6PpzzcyzaHB3SLjwVYVpIMcH9jEqv0AwXHGtVwnmvKiC8M90pmo0zPJSqXa3u3rw9YlsXtjKIqQSxZ2XBaKT916hQ2btyI6tWrY8CAARgyZIjg32NUHbAGNsibCmlhhr9sxfh7Y3CzGMx5QtwybwsX3uuNdeOsS66wJMGrd1h1JtyVBznYaWCkzzJY4PMNFmF+TVt+XhDg/C/w4ZGGvP+MuDB8vKjyy/sN3D4QD4oeYMzOMaZjlsRi1xoJN9nvu5Euf6W1DB4fI3manAaqU3dc24gEplPbsE85G4AwZHyzvx9WKlZgJq9EkI+Eo+bV3DzMGU9h8lTKJnuvM/AV0WWKVRyhzrdLaTCV6NH6y0doUPrIQGhjhFjPqqU6JvjwLfNyioSXRPgyv0zfuL8ZdL7MYM0KGus+o8FUlXh2G6iu4l5wKcsZNfUJXcGCxKWkB/jrQSqGFwm9QKy3UDjiK+UjNO/abS/W2/PaaR/yNP5QvI0rIpE1E/+yHiUpNWgUxPdCTZ0O7+TkIdRB5mA5ReLd/vWRwobhVd0URNI0DtxPgZxlcbq2tbjT4rZ7507WZ5+j7NARaGXAhjHheLPvJyBu7QGOr+QuGPQlEMhFmNEMixk/nseN9CKE+iqxbnwrwf5hiScbRaGulns+IYbw9e71zGEXji7Npfcnmz6vGGCeY2VKMyHgRzuvIyXP87mfSmhxRWmdO15DdUn0+uph3J7+VBOOeHZm79oY0iwGG59r7VB7nP3DvG6v78WNmdO1CJyrIb6eP0mdcuje7oBUJJ4lZk+kMDYvAHIAKpZ1XvA3ICbQC/MHNjSFXZ9m62Jre+s+BFs6rj2MZftumZ6NI/NgXW8SO1oReEIpXuZTDK/04KImhxnSsH5n2qOdegVe100GBaCeziwkbFlI48uVekz5g8Z6n8rhgZGDC0kycuLM30RLkgE+xRZASSvQukyN9b1I/N6GAFu9/BFWxvJxBCMzjdi5mxnEGQj3vjp4F72WHcLb2y6Xu43/Cpyem4GBgRgyZAi6dOmC0NBQBAQECP49RtUAy7KgirlaIQd4YTt81skLtX1xn4nCpPwCjCooQvfqNbFsRFMEejsfkkySBLrXFSeCKDI0z+rKX1fTlWBZFv4SjK4A4G1YSxmSwB9tSEEIPuFsKC5hnmLX4wjsb2S9gVXN4HVrEH6+2NbO3NtTdQiEeZffeiqGtmprDbStzDOe4JnyX0yfP+PlFOYbNrGt582kMVJ53goWoCkCeX6ueasr0zNF2+Ir6ozGOVZVV6FYS9sdu2Iqj49ITeqKwLIiwNQ/ea3qPFCqiKEBHeep0Ui056WW/s3GZ9ha8wW6aj5F7Uat0L1eJAgAUbS10CRTeqGNeiVyWY5J+IiXOay7IKI1GtoZDwGk5yOWViuWwYfQ4C8fx4ghL0WGoUd9s/GAqkDESQjDYEJ+IXa2sr7HG7+4Tykv2LEDOWvWAABWP0lhysilCFAXA78ZygC2eRGo+6Tp+oW7b2Df9UwoZCTWPNvCbsSaSm5tpKofZRb4xTxxf0ztiOgAFbZPMacgsrTZKJzN0xdIixDxM8kWxBAeQCgKRMdMsn82vEXm1I5pnbBvZmd0rMV5//1Vciwd0RRdaju2T5EWUQm7WxCYNZHC0sEksmyIt0fuVO00meS0+fi2cDLvSPnWYMvINgBVsi7uzsvpYAF84++HLX72KyzsaUFiY08KmU5UHprSrSZ2Tu+EBUMamY6lIcRkDOioFnrvQ4u4qCCfy1w0F8t6xphuzCX3QynuyWXoaqgKUtdGNuALZYV4pKqJVemZ2N2SxKbuVLn6u0g3Aj/puyDdn+MC6ptYF3Leltb5CnfP5Xs5QssfTz90uo3/GpxO9tuwYYM7+vEYFQTLslh7+B4axwaibfUQzPr5EhQXfwfknNBHsCwGnGQF+dIKWo5SKDE9j1PeU/tXLNdQKlQ3xMiE8rBq5JTvv5mJF2V/4AvEmQ/yJJieF7iHJKtbB8AdXIvjkUmRzk0ZS/ZXsfxgRdWwVQig0lhv2g/rBuGn0HRcTGRxO5oASxDwU7jWy2/c8i8kEmhqIJWj9J73dn5nh9XcslwIAFyNA5qoXasgdylT46IdmYehK2cAJWXbd4eIiX5+dHn9NBJt2BAKwy+mup9DZk1XIP0S8EYyyk5/Z5nEAwAYs19aoCENic/F8EYx621Yi3gVCVgF+OpZdKAXVCGx6JyzHPWIB1D6tMPzhnO/vNQe76yjAVgrbCkhQGxO5Y0XQGiA7H6BMRGL8XG0HoGmkY0swrYrZsh5Kb8AazwYTlt29SoevcXliW9vS6DRqCloEdoE2DgAKMsDopoAveabrt9y+gHWGELEFz/dGM0dIIWMCjCnhdxnwhEPwE9l3p/8vaz3qkaxATg2Rzo3nz81qUqIMjl8W1iujyBYQS4wH3KR7nkpKNQML/+eZCXDEAQeOEA6/c2xZBMPhifhiKd8xQASYJRIg9nZwjhpTJ/arSaupxWiUy3XGuHdiYdyEp+GOEeuWgbHHVIkSaB+tFlOaJ0QjFPJuaaSwhJZJ4hII4BEICTzOICnneqfs3jnt8vYdOIBpnarCQB4MyzUzjfMGNajA2R/mf9myxFv/yXNha2PqsNNohbxwki6p06y2NTd6dv+p1Eu6Uiv12Pfvn1YvXo1ioo4jevRo0coLq7kmJX/Y/x1LQMf77yBkWtOgGVZ/HrObAY7p1Ki7Q0WY/YzGHXQLBxG14rHG7oXTH8rxaygTuLDrBzpkzeT8O3xZNzNqtxxklForTiVXN2FEpKETxmL3ue4xcd3zCj0LS7B5UQSH40g8fLLFDKinVtBKAtCjXY3rBe2+IyqE9ZvxDMHhErEgmEkbjcPB01xdXF1cgLP1n9W4tsVRwkvDXlozlH8o5iJMHjeUyOFjtes39m5miQaac115MbnOx4KZwti9bj5uKmuPOuyvRWDJggQFpt5fqvyK0cFrDcWZ2YjQavD1pQ0vJNtuz6w3yMPlJ1JN4TSJh2CrChFVH2My5Ke45YpsywA8NaNElizhx98vRuK4Y3TbF2AMN/AVykTfSdTXqKQGmoYR7rKC+kvNkiqEXksXtzF4CULtujRr1P4bBCFEAtuArKCSrnzVIPlhz47GylTpgIaDc5VJ3BjWEtMajwJOPgJ8OAYoPADnt4AyDjzzYl7OaayY9N71MLApjEOtdOkWqDpc5nBFKSSU+hhiFgbZ6cOtRj4dYSVOpGamG7G2HXCUPAB5AmHv/vMrIpzJRhX2jkS68pNw6u5lMBdaVki1NPwJyRoxHnI0RtZ/Qn00yzAYM080ErnlNVZfepg3fhWovwGb+bk4ob9FHWPo9iJ8otGRLLll4G/n9QGY9vG4+UB7fGa9kUQpPiaRdMkjnqpoFRniZ53JTad4EjUVu6/g7rkA1x3kphXyFFR/jX4qSacw4+wGD93jMFQvMN3Mj1dz6BqwekReP/+fTRq1AgDBw7ElClTkJXFDaxFixZh1qxZLu/gYziGBznmcOzEOTsBmMf5HYUCkSJ7DFMvGjle1c1/Kyvu9WzK8xT2KuH6lGuIHvpLWYj3tl9Fj089VypDDBRtrZRfu8YJ1k+cYeClBZR160Leri0WZ+VgcFExLlYnkR1AoFt953afYEUwWpWp0cZGvdy+51iny1W4EmKLbZ9zwmPna5JIDKwuOPZ6q9dd0n7XEutUAv5+pj7gj+pkOk6rprikPWfB98TmGTb6aiJKlqWAJndRwv23PWwv00UFNgxhboSyNBPpImzrfLwUEWZVZuZaWLtyt/ms9k30LSnFH6lpqKXTYURRMSblSyveLCM971wOwzzaLRJuK+YRNsLyVPVQX8HRQxJlqd7rXx/hfkrMGyj00JEsi5deprCyP4lRsykMnyNDViABvWEYsXoPhPRL4JyB9M+3TPy8XiZ8GqksV5v7BOFcKcRvJrRCiI8CdxjHor8KylzzTFitFimvzIA+PR2pwcCGYQFY0GUhZMlHgENLuIsGLAdCagAAkrNL8OKms9AzLJ5sHIUZPRyv7tGzXjje0E1CFhuAmbqXTMfXjW+F5E+etJmPbolXcvMBCJXyJ1OF+Z0VJfUsDxIJ8TrTgHAMvTuGQnGeOBGkMzBGtvUqJLEkw1ppevdZGYbPkeHXDtyDSg8Cbtjg7HE3ZqnWIKDY9nspJs317a+z8TjP1nLpu3ymsBjzR1kbRCyNse7C9TRx43cJ5fic/v5ROr5Mz8QZ/7Hl7oecIvHBoIZoEB2AX5nOQiGGhxO1SbwYGe5xvpPNio+duj4mUIUhmrmmvwNk5c+FN4tQQlkm38d6Y+y59FC52/kvoFzs6y1btkReXh68vMxhRYMHD8bff/9t45uPUZkIFlm4GUUMaFKGCdrXMVk7A4wqROSbziFer8fCzGysTcvAWwZrs7HUVX5JOrqQFxHGLz5bCfC6t9Pq2O2MYsh1LPqd4Z5T6OQXQBjywb15jJBieXy2QJAk1qVnYm16ps3rNLrKI+vKKBUW7JXaTBMDEt3S/qzcfCgYFs8WmDdXe7W5PQm+0PViJBeaJub5NOY2DykqhophMKLQNREhLM+6fKiB9YOJO1M5aSGxlz4T/H3BYIUPKWDRMJkbz2e9VFBZONwKfIUhbM7gIlvT6piE7AMA0Pskl7ut8uK6wtobIZW/t6sF9z5f1M7A1pfb45MhjdChZojAI/Webrzod5/rmIiTb/VAjTBhzmQ+64ecAAKHGpGgKfN9jKHJrEieuqdhf35zL3WoZi4+0I3BAvIFO9cL0bVOOM680xNb6Y6C4z90sRZ59np5oURTcaMoy7JI//AjlJ09ixIlsOhpCnO6f4goUMCvkwCwQPNxQCMuZLWgTIeJG08jv1SHJrEB+HRYE6ucZlsgCAJb6G5opfkS19iECvX9+YJCXEp6ABVv7Y+1iPTxNFs0ABTZqE/a+5z53N0oQJvTpcLtGR9/F81y1CwOlLzOWO5KxgDPRUZIXudukLIS9LLDvH5dZb1muupd7n21MwDOmDb8TaFsFO0hW3G/zw6LHv8+SNwrnJgm/PHaPvlorNGiU5kaeWT59yYjQn25ds+x4gY2L2NqYBUmIb3FxKBuYjWcZ2tidXomFmdmI1QZWO77GZ9JUUgjwXFjpaMqJO5VOpxWyo8cOYJ33nkHCgvBIz4+Hqmp0lbNx6hciOXAptXsi3pR/tjPNMMexjF2UkfwREkp2qo1CGUYnE5+aGJfT9CkYqNiIQ4oX3VZW+VBxM3vcUsutG7fTC9EWCFHoFWmAPx69zaZ96pVwLNEgOBy+g1/az0ZR+kgSvVCT3X3i9aDZVqzaRhYY6DVcVcgXq/HifsP8XpuPu4z4die8gg7W5qXpsJKrhiXYfAG/6KYi2sGJuCaadbXebMMVuoHYl52Lo7dT0GEC5WfN8dT+Kkjic1drZdsXSWxrxOs8PeNjY4EAHz1JY33fmCw5GtO0el41TyerlUD6rZ1vrqDLfQrKZEcIy5OX7eNlNMAy6KAMjdK0SzqPhTOp2M8ueR+OIGa6m/RY8jzaB4XhJGt4wx5rWYxpWWdeMkmxXg8nigRJzYyPQt95ZNYtJRgPfejGXgxDPxZbs6lIwTr6CdQQjhPTkcQhCms24ht7Uk894pQeXg9LNQlWkr+jz8i/6efwBDAZwNJdG4/Ej2qdQO2vgCUZAJh9YC+nwAA9DSDqZvP4W5WCaICVFj7bEunDb5muEakJQDIeI/B94YSLYkbUIGLLDtdCURvyd7CqLYaj1gTUSQ/pzzv9gKUMxtTAKOnvBQq9NB+KnmdzvCqIvKBhEoss0wAqJtie+wytHW6kKvsK7UiuOjKJmqNFeFbZRvWxUqiqTQsFn5jHjh0qA4JgbzvuGAdqB7miw8HNUQqIZ6/HWqwdV0udZ8xvVSrxzNfO576YYk+2oVgCRIsCLQvU6NvSWm51sg1Y1vgg0ENzTwPMmFaUvN7LHqdY6DRV55TqqrB6VWMYRjQIsJmSkoK/Pwqv7TT/yvEuI4IsFgdyIWc9BaxptIKbwFzpJxy/SqqYlnTBiYzlLXxISqHLdqI1uRNXLPIralOPDIZLnQUQFAUFApO0h9eWIxxBYVYnWbb2y0GwiKvqSqSuv1w/QfB35N3CRfIE/UpvND4BVCk++obG00keVQwquv0uB3L8w43NH+e/8c1t/XBHlqSt2yeb1/KIJ3lLO2uDmi8F0Xgl04kykRYxJICKyd80l5J0bgsTgjikxsuG0yhaUSCS/sRrqfhLxEOLaeB7GIPrTfHV8L3xs/YymP7HX2AwfxNwv2yWnAZPhrOlZk50JjA+blPYFjLaoJrUkM5D28qG4LFTzsXul2dFvcQGSM52CqglDdJsh48ehI48CAFhx+kQCe39FiVT1jey7SwOlbsLdznWt1iQRVWTLMqOXUK6R9x4aGbu5IoblEbs1rOAo4uA+7tB2RewLBvAAWX2jDvj2s4fDsbXnIKa59tiXB/a94AZ+DnRKi6LVim3ETEfYWe8bPROXw5fjjlee3zgcqcftIoicGCjTQ+W83Np/B8/pWusb45SiSu522F7/7I7ZcVybktP1hE5jnfriv4g/hYkZGFXiWleP0584PpcbHqKVpBFsFrKhWNVB7jepeGCS5pZ0zbeEkC0gjD+9r16K5L2hLDt8fv4+idHPijBKvlS9GXdK5sHwsSFEHgw0Fm/UDnL20clkLvBpEY29b8PTEj8qQ9VW+cVCacnpm9evXC8uXLTX8TBIHi4mK8//77eOKJJ1zZt8dwEGVaGvml1t5cAixWBgVKW7gIErFB3pjdtw5m9qqNEF8x3uCKQ2eQF8gqFK1juQyMk+01hcEaLbwBQcE4nvAy5OBCrNuLlOuyB8JRL4a+8gwVlp5yS+RN6G/6PKHBBABwG8nbTa9mps/GUnT8CMb1R5Pw3DenQdvTCCsBZ5iG+IE2EwFmsoEub0Ms0kKZne/ydhxBfon1mCUs3gvFAEFF5mM7slOgIp0vuWgLFLg6wmLw1bF4kOv5+soA97sHnLIepyzJ4mINrsxMfZ0Wfipro0qJVxRaqL9CN81ShPk5ty4zhLXxbHhhEYIN4ZKH7x8A46FyPACsiD0pmkWgCD/VnPEUFACULPAo7inBufJO98Hd2qOF+ivobRA4zfyNQdH1/Ti0aCjuJN13ug1tSipSX5kB6PU4XJ/AnvYqLOq8CKpHF4B/PuIuemIxEM5FiGw8lozvTtwHQQDLRzY11XyuCGKCXBNOJLeQFQ55e+GgtxfOh6SDgmeJ37yghsJQW1mlYdH5Ctc3owGucbLr9wCpCjIAMLrATEClF7FPX33kGmJPZ0AACHeQy3J8+wS89URdTO9RC9F2yu05g/Ha1xHEMFiamY0y3tL+1EkWYDyzzviiFItlq9CHPA1/cOuN2Jvsf1rYH3mcBt+jD+bpxuIL/VNIrNvUZX1qFCmeDlqq4nrmRbiP78SYjvOTYj76UGewSrFccJ6ibc+d2X3rwEcpw9MtYlFfvR6N1WvAyh0rZ2kLltWIjIhCDuaqPkdDwn2Gin8LHFbKKYpCZmYmli1bhoMHD6J+/fpQq9UYPXo0EhISkJqaioULF7qzr48hgSbz/8LK/XesjhvJPKTSsozT4+WuNTHdCYIZW3hW+4bVMeMGFpRTdTJHDluUWUmSy0yecj4pU7vxCyrWkMUilMmTv4z5pACgL3U/E6cUjIYDsTJoANCuhTlsfUaLGfj1qV/xWsvX3NKXn71Hmj7rDZEbMhpolBhnyu3+50Ym/rxUNcrr8RERHoLnO5vn0Xcm1lvXgRaJZkn1rxylM4axfgeW+eMKPUzVDAAgoILGlKdbCIkW99LNcVFfD/sbi68te1XeiDm/vEJtlgej99NYvVLcCsnwBPoupeIufoIgkIMAaMsVc2H9LPwYBqTB6jjokAb/JO8rx33Lh8O3hGvbD4toUUXifoS530804d6zsb70s+2c99IAAEVyz3E13d9mucKYk3PQuXQfLq1/2an7M6WlSJkyBXReHu5FElj1BInZrd9ALWUI8MtEgKWBRsOBZmMAAAdvZWHeH1cBALP71EWfBpHl+l2WCPR2TbRMmI2UmxjS+Uix8uAJ8gSmU1txXfUcEsgMEAyLb5fS6HLFvHZ4ikSMjzta6bSbSCYb6kqoauCoRDW2bTze618fL3SugZm9aru0DwcYsyE9M8iiR4wetzOKMPf3q8gscp8SOl22DcNkh7BasQyXVC/AG2oQFlEfBMNa5d9/GDgKV8MHYgPdD4v1I13Km9BcXk30OEUbz19yXWMWML6FuqR4ZZYVX9keqy93NfMQlEKFQvi6JOVBijJjeMgyfJr4CG2jlwIAPv3rJvZcTXdBi/8+OKyUG0NzoqOjceHCBcyaNQuTJ09Gs2bN8Mknn+D8+fMID3egoONjuBRqHQ2tRD5Gjlc+ADOZgidwmGmE3XQrrNSbFbkQgwG52BdYHeiPyyJESB6DwVu0z8cbch2L4YdoJKSz2OvtbXpOtpiSnQXJY6d+OS8f7zxrlsgPNjJPv5zk665r1EkcSDmA5rcZfLuUxrDD1ot1m6g2ps8kQaJ2UG2QhHuSdYe1MTO8GyMsjLmDw2KiTOdyS1zrtdHT5klSILJzpPPqVim14ttTXJgvetY3k/44UkPWFVDpqk7UgKWypdCJbDIV6O7CoUIm8vd0E5DMRkjmjmtAIuL8cqBAgmnNxTDOnkEnpH9kWZw5qknqKkVFwksl5mYJL9ed3idOjuRuSBn+AODbR2YhTG6Yg6vHtsBPk9thWvfyGY2Dfbi9Zql+GGropLlBHhrW6XgiQ/IaS7Asi0dz3oLm5k0U+VJYNJRE5xo9MazW08D2KUBhChBcA+i/FCAI3M4owtTvz4FhgaHNY/Fil+r2G7GDNWNboEV8EBYNdS7FgQ8jCRMAvJOVLzg34iCNoCIWBMOitr9niHy/VHyOmfJfAHBcOCqR19b6pvU4qtCcMcDWip1fVsf0ucjC0dyZviQph7kTu3xscy0YKy40iPZ3ikTQWSzXDwEAfG7BWE+zNPp9dhjfHEvGzC0X3dZ+DCFsty95CrkWtSa9RWxyj3TR6NfILFc4mr7gCCiCwtQXrUMqmiSz6HaRwdeBFY+QkUK2Hfko1MGqY3LenmFcSyuCuBBxb/v6MG6Sb/HnUqBX/HMHk787W+H2/o0o1yrm5eWF5557DitXrsSXX36J559/XsDE/hiew6d/3ZQ8p6G4VUgq37J2pOs5AD4a3AQv6l7FEv0I07EHBluNjiGwMigQo2Nc4x1wGnot2M/MQv2i9TSePspi0QbaEAbGbfTBFnlHV5iEcjcZFZOIk8r22Mm2x0v5hcj3JfDqJArvjqWQw68wUWi/3qi7UKApwJu/cALFsCPWwo67FHAxjGgVZ/ps5CKQ81Jg/cE9J7UL2epn/3IRNd/eZfr7hKFsU4NkBu2vce3s4ZW5EiPCA4AiLYtWCeZc2AZRnuHYqJNTyXkhPPdCp6vC96IUTV8uv1bOZyW/wiQgDSFgQQhKOfFhIoPSuIYJ3x6KHKiN+1Spea57S0QODG4WgybVAjG1mzVzsj1IGYOieY4H75uVU9vesjyeEX+0JtBMYy1IquQUWicGi9ZHdgRPt4hF49gA0KDQwoanHLnO82XkrFqFoj17QFMEFg0GFFFRmNd+HohTq4GbOwFKweWRK/2QW6LFxI1nUKTRo3VCMD4e0tBmqLSj6N0gEr++1F5S2HUEfRpEIovlFIQLmpaCc0OPsVi9ksaWhTQ0ZJnLjaF8zP39qtUxAoBYIFDbG9bzZnLnihs5bL2SW6WtTJ9LvIQXylgaG44lV7h9e7DMW98QaLtMlcywHA9qFuOuLgEAVuoHYaL2NdS3mMOl2lLoDWvclUcOxtmXA5Zr3lLFKkG5zg836rFhufXiExfoj4kdEzGsRSy61glDnQhX7tkEMoMIDJ9jnXP20k4GbW64z4hTprX+rRrjI3IiHIAiCex6pRO2T+mAAK+KR+NEBYjriTLauX79l+GUtL1nzx78/vvvNv89hmex9nCS6PHnqR0YLuPqgXe+Yj3YdRTQrFqgy/vTlHfPXFbIAkwVu48ozBHQqedB8DxmMbza7cUkgaFHxRfJi0yNcrdJkCTazNmFJ+aZlb7UUAI3YwkU8OQotbxyogc0IjXbqwqMaQ8+aqDLJQa+pSwuqSYBAHS06za0n84IvajGvev9HxjM2M6g/n0Gf/qaPRIT9om3zVrUWEZEvQr3bb5uLBrbUiYAxIhswG5HEudpJVgW8zfReHMLDbAsfCwiFIOLWBxoZMHKqyh/vVM+jOGJLAjJ8jsdrzK44s7IHI3Q5eCImuUIJZdKTmH7lA6Y1aeO/YstQRB4LScPETxCt9paHRJTzb2Tn7/h/H3LCTlttgpLRSEVWyg5riLN8lbI8PvUjpBTBPoXSxs+WTUnCgWiGAdv2U8lKvr7b2R99jkAYE0fArerUfik0ycIyL4L/PUud1Gfj4GoxtDoabz43Vk8yC1FtWAvrBrbAkpZ5e6FfEzvUQutNF8hQb0ZH+vGYGV/cbGw+Y08vPDtGbf14xsLpfbV8FBsDvBD3zPW622H69bjo7yGGz4slfL4+30QrGdQJ7UtLGf35Xjz34FlRdh7LQMf/Fl5RKRSWD++Isz+jkEPGf5mWlhVHGGKSxCEQjxNHYQX69rw9awiDX4561gEVG2JbLc5T7eAjCKxeFgTfDOhtUsMZUYQdpwZr21zn1Iu9iv0hqNiBi0+1vYR9rtelD+auEhXIAD82Nn6uXS+wmLNChp1HrLoFLbCJW39W+GUUj5u3DgMGjRI8t/gwYPd1c/HcBLvyL83+aPEwtdvxtkmNSkv6kf7Y9mIJtjyQlt00nyGW0wMul7ietLijnkxWLDTM+Ha9M2/UPJpMxTePobbBsKhO3Jri98ZlUoyzP9a9YkoYxXYoO/j2s7xnv+Zc/dce28H8dWFr2ye39/I9WPEEdTUak1cCE2TWEzZwWD9ZzTOqJT4Qf6hW9tOkwnVpuf3MLihtK/YUTTn0umv+RBv6SYiKbRbhfuynu4HJU9BaVNmLdgwMg9bmDXFwEaO/C88j6vB3fweCxkN9LCIInjrJwZdL5uPTdbOAOMtXirGWRiNZV5RdRFaKP4MEjOAnDQVXFcEyALfD7c4YLudgw3dP58eKmpifGER9j58hJ9T0/Budi6qFQmfeUCq2WvFMCz+uZHh0pzPvBItdl9JR8KbO3B1z9em41I15a3SD1zsNVnzbEubwo5Rfq5BpmHcettMxZrbt/Ho9dkAgH2tFNjfhMSLjV9Ei4CawC8TAEYH1O0PtHoeLMvi7W1XcCo5F35KGdaPa+WSMFBXIoLH/E6DwvG64mM0Pq0MZ+67vyzap0GBGBMVgX2G6KQnzzg2FoY0i7V/kR30qi+M4rtS2g33b3+CM4WDrLyxH4w2K7oDLhwFAKw7Iu4kcTWaEbeRrBrt0LUNo90XJs1HvEi0hr6wAGuVH+OJgG/xKta5tL1WH+3DrJ8rFhLvW6O+i3ojAh7hplrCyfzQhSSkj/LL0GfZIWw+yVVJ6EBeFpxXG+TN5ndtz6dn4L45ThDAb+2s15cXdzEILAFe/5XGhdD/79LaTinl6enpYBhG8p9YqbTHqDwYp14/C0vzzx0IrH/CfSHJg5vFok31EJTAC721i63CwQHgt0Pus7jzQf0wDD5F9yD7fjAoDZfcfl9u7au6pFKKMgIDgN6/Ghpq1mGefpzb+ln96BG33dsWdiTtsHn+XpT7lYibDCdMpbBmxYFkgR4XrK0kB66Eoh11DSpdvkvaFvO4LwkJErCIO1rGTp2ZDAC4wlbHZrqHy3LK+XeJF8mLzWM8XIpSy03oXJKENy9aUYpQko/DsnaIDKhY+acemsVYphuKj/TPYEizGFz07yIZvg4AZAHlvtC4B8ecunxTN66jrQzGlT4lrifp2xnAkSUSAOpqdRheVIy/6NZW1+1eNA0A8NuFVDz3zRl0XXzAZX1o9sFevLjprKEf5mdfO1X8PQSUcMcLWW8UsN5gVa6JpjCiWx07fDc8a0FzQrr0IZ2fj4dTpoIpLUVSDV+s60ajeXhzTGr0PPDHDCAvGQiIAwauBAgCqw/dwy9nU0ASwIrRzUx1nasqWJYUZRYHgDvxFZu3tmBkiwaAbwL9cVHlfCWYWBcw0PuKlpbj5mwJa75/gtZiHc4jEAL3hWdfeJiP3BKtaRnbpnzf4e+6OyrjtykdMKt3beyb2QXf6nsJzt1LycKaqDK8EhGGK0HW6QnlhWV4dkMiCReUChRaREu0uM3gxwXSG7jMjc+G5KUyzX6Owt9NrOWBR/kSuaXlwEc7r+NmRhHe2nYZNUov4nuFkKT4nbAQyPWswEguBm+5+/Q4giDAkARyfcXPG1Ntw6p9iaDQP1GsqfzynZ6Gw5qZO7yqj+FeGKeeZU75z50pBMrd7107+VYPAMC63twwU8uBmVtpdL/A4KRqqtvb58MbaoRd/BIAcMJLZSWkN0piECAhH49uEwcaFNokWtbOrRhUvHIhOtozOa+WSC9JtypjxUfthJaS51yF57SvY4O+D0Zr3wYATNC+DhLiyrDRaxJe7JpIC6kw+Ma8OsoC8jIbyp28TNhhPoFSRRDLC0MeVmQ9TnwhTWDlTmTIKDzBKzEjlS9sRJ4PcP69XgLymPLgLhuDz+ihKIEX5g9qCJpU4Xhd8z33NSWwn897RQDFGs88I3u75MyyXHygewZfp2fiZPJD7PKd7PI+6CVKzlkKQvHrOQb2AzezUINIBa11nYDIgcUC2Vq8JPsdZQSBMyol6qSIzx9j6biWmq/QSvMVSMo1dbcdBUGY+7VVOVf0GlavR+rMmdA9eIDSMD982L8MPl4BWNh5IWQXvgeubgVIGfD0esArCHuupmPhbi5N4L3+9dHVnmGgCkALOViJMPBIhfsqhBSUcfOzvIptjTAftxKZAQALBXRpA6FPfwLTcq2Zsl5mN7ul3RP3cjDoi6No/sFep+N9drQiEOAiZn4pNK0WiKnda0FOkfiV7iQ4997Wszhu4Jv6w991Rh3W4knc9i3C2OhIdIivhkaJcSgy6Ctv/MLYVHL8RcpRugrpIWZDaHowgdX9rHuSX+a6fUnNM1RUL7Nmdj/i7YXOEgp5RiDw+QASP3QhMc/PsQiMiuDnjrZlALXvA+jDjuDrw5UTQVqZcJp9/TGqNpTQYpFsNQBOKe91Tqh0vDuGwoCiErye4f6aoxH+KiR/8iSyDU4PlQ5oe5PFi7u4PhW4cEFyBHl5XOLpj/5+SLAg2R1wUnp8N6kWiFNv98DmSW1d2p8BvBzH6zUrp6wVIK1MnahDIO6pEeInXYh0Mhzz9OPwgOWYy88qWiFBp8ODMHEhq4ggEJvv3kiLjtfEx8PTFkR480eRyDKM75vtudDHlaObYXSbOAxtXvFwSgCYmZuPp4qKsS4tw6qOMAAoaM/WDzaCAODFa1pmx1NeqnSt16ZJbAB8lTKUyQORFmQ+fjuaQE6geewQFIucYs9wJzAgbBq5hhSXoGP9BJAAvFkWjeJcE8rPR7hIXXOSIPAoRHw+BaYdwd/K17Fd8a7L+hBLZOKa8jmMku1HLJGN4dGRmBAVYdNosVI/EFN7NcBr/Rq5LQe2mVo8RJ9g7Ct0mYuXoOTYcbAqJd59qhRF3gTmt5+PyOIcYJehFGiP94BqrXD1UQFm/HgBLAuMaRuHce0TXPgr3AfWBuNBg5vuj4T8VL5K8LetucTH7hmdXdaHxFBpRnN1fjuU5XVGGcNZuPY1NY8b1R33kCfyOQ42n3qA7uQ5h7+b5e9ZZ9qw3l1xKYHHXcFoAZZFSCELvQsde3p+JBt02OsjDJ1vn1ANoQWVq7NoVGHCAyK/f+aW8y5rj397Xz0Xgl5KEHghMgz/GEoAP3naepOePJXCtBcpHGlIYlt7EjlskNU1rkapg4EwN9IcpIn/D8FhpXzcuHGPGdb/BRhP7cFw2UFcUirwXlgIJu0RTsLnlTn4ODsHoR7MNNCJyFc7fbyRdsuzJQ/0vN/Mz293BOF+KpcQyfDRVKM11S33L6wcxQqQVqZWDCDBitTFdjX+nNYR3euGY8f0jgCAY3N6wI9hsLOV+PLUPqEa9HAtSYovOKNIsWFn6yJCjggAw48I202PZTFligzD58igkHE7Tf/G0fh4cCPIKugRBrg8vQCGwUfZuWhtIHybN5rE2ZoEtrY3vBuPV+IhTP/lORgRlm/7W3xiRVfA2LSfSgaat8ZUy2JR84H5bz1l7VlxF3b4ekuGB+YZZf0QM6O6ZS1dV+CNvtb1lNvXDMHiIdbjccPRJDTJ2w1AuqZteXBEOQPehNkQkqzgPFLtRAi6AOBISxZrZKMxvUctTO5SfmJNe/g6LRPbUx6ZDGkmMOa5L4b8bb8hd+NGAMCagSo8DCcwos4I9IhqC/w8HtCrgZq9gHbTkFmoxvMbz6BMR6NjzVC8P6DBvyjSULqf3veFERhF6oob1bedT8HKf26bFIqulDBH2JGUmNO1iApH3/Cx65VOdq/5heaMAHwemsh819fhphkWXx24a/r73d+uYL1iicPfb1/s2ZTSGnGx+HCk+V2oWA1GHGLw1Rc0+okohOXFsK+Omz7/o3xNtOqFo2ln7kKzOPvKbR2de4gBKZZ7720SquG4lxdeieAMBLEWhKinahPI8yMAgsDUvHyMKCyCTOPaiFAx2Eo342P3/2GtcodXsg0bNsDPr2rnQz0GEEFwFrJnoiNBieyZxhzGUrgvP8wSOktWagBvhYSAULufNIaPYCbb9HnEYeEGcb2a54UmFubQ6HZHiiotGiVQInJeJ/fMM6kX5Y/141uhgYGQxlcpQ4cyNVIknIgkw6LIhSHb38s/whXV8/jD1xvtEqqJXvPUCQbf+1snQvFJ2NrKElzWJyP+nNZR8HcoTeNqPImFwyhkGLzBhIeVcmb/x9wHFmh12/z7P/7WtgAolUdWXngZvKlTu9dEDG2WwLa3I9GUl36gLCXge/sP1zYugRyKQrCEcf8fQ06hT3wz80E3TPkgMSIxlkWZihB4sQCgZNf78IfnonQs69gbkVVPhc2T2rm17btMFBQAquv0eH0ihbeepZDEBefgnFKFdgnVsEqkxFTZhQtIf+89AMCJfvH4u3oJagbWxKyWs4Cds4HsW4BfFDB4FdQ0i0nfnUVagRrVw3zwxTPNXaowugufj2qG1k6kZ/105iEazf2rwuGlr265iCV/3cKFB/mi5+2RUgFAWjXXOoscidIoZa3bLPXh+nr1UQGW7b0lWpbKWWw7L056xQJYE+BvN5KA0nvWkUYSAAgCKSHc33JGi6HHuD5KVS0pD25mmBfZWCIbN0QqbHS7aLu9sI4uthJboHq4P/56kIrtKRLU7wAo1QPJcxWCg6Ib06UQXgyDo/cfgs3ujMaZdTFx6ED39IkHqShIgJPv+Ch0gfHv34Sqv1s8RrlR85H1gs2yBG4xMfhQOdNj/RDzlP+wiMb9RzdwJjkXP51y08JkARWrlpSBRx3yuLsRoRbEiM98fdLjfQCAliJRA3rDylBZhoLupWUo8hZfuH9cSKOAdk3+KwECHSiOgOatMOlQ4jH7GXwSYi20zskxb+w+hOsNXX4qOfSseZkO4G1YJsZqDw9d8tw35fpeWmfXRIN8NrIp6kT44ZOhjQEAob5KVKd1mDWRwhvjKRT4ELjLI1HO0CgQet69ZVaMb6WMIKwMfkb8Ysija1U3wfw9O2Vzyot3deNxnYmzOr6rpXBOTZVtRy/KHA6r1Xt+HQSAfowPGsa4lyX6Kptg+lyqInAnhjDNoR1ePgDL4otAYR90GZlImTYdrE6HvDa1saxJCpSUEos6L4Lq6nbgwiaOun3o12C9QzDr54u4+DAfgd5yrB/XyiW1fT2Bp5pE46fJ7eCj4DZrMVIqPmb/wuWsfrij/Nwe/L3lpe+5MfjIIr0lpND+fQ7U8nyuvpHEU873xhrG0pOfH8Fnf9/G5//crnA7UkRgS4MCsSI40KoEpSUi73o2Ai8myAsb0jKgM2RBKBnPpA1li6RFhdoYO6mxDEJjXR/ZwAdBEIiiaVTX6RGtE3fb18hyZcoDAfDKhDqCCUVFOHU/Bed0jfGJfjSm66ahc+0w+1+sINJCCLw7Rtz49eNCGqMO0IjJZtEu5iPoKmlPqiw8Vsr/Y2hK9KHnaAAAqC1JREFU3jF9FqsJ+zvTDr21i5Hh5b4QQUtIhqrsP4rrX09C6z974uxt9yvmNAiMiYpwezuOorZWiztR3OekRKDk3kn8ee9PvHn4TTCs5xYimch+cc+g1FTzE/ccuxvGoauVSHG8luOazWz5PmmmZUfQpUyNTzOysD3lEYr8ElzSJ0tcYRNFj5uUcqniz+6Gk81mRrgmf3pg0xjsebWzIPeTBPAgnECSoVrA3GfMG36dKxQekG7IUS7hIm/mhQThqZgolBEENgWIs4afrkWApggcoht5JJT5O7o3+mk/sTqeHmS77Q1HPVPSyRKUB8awmKBqND42SWLx0yc0fvqENnE5MhoNUqZNgz4rC2z1OMzsfB8sQWB2q9moRRPAn69yF3Z5A0joiOX7buPPS2mQkQS+eqYFEmzkJldVkIaxKcUz0o10TQ6snmYwYKV1xZFf/IThNFIl9Pi4mfOMS/rkDIxjyZunc1JqoaBz7ZEDFoVy4htDRAd/2uxubj2+tV6e3Rtig7zRUq0xOWI6J98QnFfrqkaFpvRqDFqpv3BrG/w65d+npePjzGy8OonCrWjzNQH6iskffNy7dRUnlFMxmfoDAIGFwYEOf3c/0xTbXm6P7ya2RrgLCfls4aaN6NTBx1ksW0sji3bfHKqqeKyU/8fQnLyDI17cpBIb8sv1QwG4rWqvKMokSKh1OhpjZfuQQGaAufCT2/uRoaBxqRylVlyJehqz5TqMZhBpiOBPTAK2K9/DnMNzsOPeDqy5tMZjfWqSZG0A2PtEJD7u+DEahTXyWD/EcLqW+MJd46ZrQm5XHyp/+OUBQw333qVlqK7TIyu4hUv6ZAtXmATTZ6NS7tHwdbV5k9zjY12X1hZ0Nfu4ujcmkBYRHRoFgWSeA63gpBu8sGu5OvS/+PshWSG3+TweBQPROj0yAyzmk5uMb5M7Vzd5PQHzXpAaaj2fWABfBgbgoJcK9+/ddEt/7OGhsrrb21iqf9rqmJGLoN9Z8/hhac6Lm/7ee1BfugQyIACfDAVKZDR6xvXEsMQBXB65rgRI6AR0fh2/X3yEz/7mPKMfDW6IdjVC3P573AGjvUgnYQx9SbECaQUVj1K6mJKPK6nWAvdai0iFqFzze3kkEl1/Owpg9O4nprKEcRbxHQ6JycK55Qrbm9gtMinevOYte8frkVjdl8TsCebz2TUqJ1KjtiFiu82DZMFxqWonFcFDCfJQW9VArjajseqlJ13eFz74xtdQmsGAklKkhhKYx6tv713guufxKrkZkUQe5sh/AGwYiMUgD01Es7ggdKrlfi/5nw+lw/ktsWIVDeb/jGT8sVL+H4EcenwrX4BjKhVeiuSkUVqEmCyZjfJ01yTrniqz85FNkriiUODk3QzxiyqIc0olnomKwFWFwmSIMNbElYIDRLzlwnjtbPgwwkX4Mi/HU1tkflC/3/3dPZ0QQX0Rp7NvmzYYUGOAx/oghS/6k8j2A7a1E76UMBdVD5Cj/GwwdyxquIcFuDhp2gC+h4/hfTYqFAQDzHt7KtikwwBDIzW/zKVemsxCNS6nGJKBvzELMheV0gYuNU8W3NOcwIg3KcT5u09R4W9kLQw1wA82Mh+V3XWD9T9fGN1z2obB71AjEr4Mg/v1hSXQ/NxUkmfOE/VwaW4f7Ke52nA3Is1z+cMR5ufCMsDvvj74KigAUyPDMTD7K7f0xx62hTzr9jbus5FWxwiRrYBlCeR+sxEF238HKAp7nm+E87JHiPSJxNz2c0HsfRfIuAx4hwJD1uJ8SiFm/cwRlE3qlIgRrazTBv4tMKYQJEWIb4J+PwbjTmYxAshsDPfdABXKp6A7ImfHZrHofd584dvPWgsSP3V1D0u/WAUDPoyFth64X4exQt9qZlcrxXuOxV7A381IJEea352Mqlp1nl2hXt1IF+5tT1SLEb2uzS3p1hoWRKBFvHuNOVIRUXqewYs/visKkpfHptY5puyP0r6ND3XPIKHtYJf1wx7i9Xr051UesoeMgv+vsmgOFQMdMmSIwzfcunVruTvzGOVHf/I4OlOXsUJltjQ3uC+c8Dt4Fb08mSssFYIWczkd3fpzZaM+SdkJYLHL2x4XzYWrj4yJxK8paQDs56nta0qggct7AgwZMQHBuz/HGS8V+hSX4G3988iqsxvtbnAP6O6OCCjrsojIBx6wnsmzl0KIqnI9PY9Yzi2ilxF4eSq3TA0+bhYwCJp7ZsnZJQjyUZQ7d7Mi7NcMCbyuewGL5WtwgamBLh7IxeIr6MbQW1kZgef0W0Bs/A4FbWejw4GmAIBjb3ZHdGDFiX5af/w3AGDP9A6ok26uf2rLdnWqNoHOV7lnu64PJzjXVnomFaJjmRpnvVRQebigwe9+viAk1tX0QGBRbgHaj28IACj1qQbvkoeo3dF9whBFEpigmw0vnQZTVWZj7M1YniGwmMKuKG8EFrOQ64GU0hy0cVuPhNjdnEDfc9zzCg9xfWk4SzzRKBKwSPMVM0hez/QBtY3bi7Im9cdaxQ6QBIlPOn2CgLsHgNNruQsHr0YqE4hJ3x6FVs+gR91wvNmvnnt/hJsx76kGGLIb2NeMQFQugf6nrcfz6oP30CD+I+xSKTGmcBYA6wiE8kBmMJC2vc5g9AEGkfnC8yUiodi6ECVGRbveCELacXOTNLffbGtPYugxz4Zk63h9q/vQ/H5C/TR4aCDwvR3FMW3fi3Q/k7YYUoPFq22kl6TDX1WxfeDonRz7F9mBl9b9z0VMKa+r0eKG0hw66mhpMEfAlw2OleYADohwx5kGOI4G+MDD1SGaq9X409fB9B7N/1dZNIeU8oAA9xKwPEbFoSI4CXSLgSG6ZiprRTjUWG62ak/q5P5wQSNseZ4pmoV/KXDDtxhPsKxb8y23+XGLAJ+cJTWCRUyGsM29zUi84ob2n2oSDWxTY/+DFATTDH6OD8Nli6pw333KbfBbOpHAeDd0wgIhFrU8Z0+gcD8C+K3WUPc3bgP2iEpuR8pw//A9fLjjOiiSwN2Pn3BZ276ljinqJSrgBN0S/9DNIPMJwUk3jV2VnAIMch+fydkYvu5XSKJ4eyiynsoCcWo9RlMDUYtIwe2MVhVWylPzy6CEFn4oA7P7TYe/t6810PkqUMCL6HZH+S8j+HduV1aGzxCIQcc9TxBDWjT50ssUdDLgh4x01NHpAAW35Xq/eg7QFIHwcbfxi0AZVAIjLD80ObNYiWySwprPuAG24dksuHrmP5JRIBgWQRZVHtb3obC3GYtHIcA/va3LuLkahIMkCNRhfwAMZAP6YHbYfkAPvNj4RbRQhADbDUaUDjNQEtcVz686juxiDepG+uGzUc1cXjbT0wj0VoAoTgTrm4Rve1Lof9ra01qk1uKewZO8xd8P77io7ePKaeiOAMz8zfF5qyXkeH9AfRf1wIwp3Wrg3e1XBcd61gvHvuuZAAAvbRAm5RdgbWAAvu9K4pkDDK7UF/bbFSPB3pbS7ZJ5XpPqCEDFRTTNHUNBoQMCM9tgqgv64SxuVCMQk2u93rPFmUBIxZTyD/6sWBmxX9sTaOCBBE6xV7flUToOenuBJoJAscC2diTckfR2z8e6pE54nvA3LxpKmuQKf5VDqqDLYHTUnahDoO1NO5GrDx4BnqPAqnQ49CY2bNjg7n48RgVhVGIKDPlGYuWJ2HgdxjdOwJi28agR5jkSmgIbTb3/PY26qcBbz/qh89WbaNXQfcKZ0TL3wSbzsymM0yMmQ+hl/bAs0219ALj8IgCoHuaDcxLRd1IMzq7GSAvWeWPoW6SPdainJ2HPU5EbQOFrA/MvbacsjLOIkTDEb14oFFDDo0vxWZ9u8FfJ0SDa8fwtZ8F/FEfl7QAcBMAjejMg7aYfIpqz+Fi+DgDww8V9QJ3RFWpbq2dwVDkdoUQhYBG8cdJLBUiE/9+MIDHlJaCQn2btxugc/miJNzDdHmhEoI8LwwMdQahFya+cAK5nCVkWZV1kCkDmuWgUb4V5q2d4iuONq4FAPAWjdDbhWxnwVsXaOnI7G/xCfheUSrzxM4Pm96zfxcNwri+B3hLEIy7EgCZRVp5yKaiaNsW7HdJQUlCK5uHNMan+OGBjf0BTAMS2Bt31bbyy+QKupxUi1FeBr8e1hK/Ss4KtO0ASgPrRcChrL5S8JrH4TzAlLAaeYLCxZ/kyIMWW9zCiAIBzDqC7Bb0cKmHmLMa0jUe7GiHoufSQ6djcpxqYlPJbTCy+z+OUciMZaQHrnlB6W+CXjMsragkE/g1vhkGpjIROBvg4JuK7FLXVG/EyKb6IMLTrwpfk0MMLFhV1WBZeGqBMJS0//NyJRKss9/MQEASwWDccYUQ+xsv+AsClWXUoLcNdlmt/6FH3yHl8AyTBsFDogQYPhOvviMBc9OzcEKeTcvFkI8+mtRp7dznBvlJ+PKUAjd3fpSqDxznl/2LcTDeHdThSAuFIySDM6FkLNcN9PcIAbITWRr3ruoYynDN/oyF7dNqt/RDrhU5lvSDEkp6pi0iwLK4kVJ5nJbU4FV2uiC+I3nLnSLxcjQew3iT4ubB+JZzixYU8ll/xEps3UkTQMt7++SAU6KouQ6iPEi3ig9wiGJr6w+vjGaqp6TNNCTtK3hXGwhVe21fxxmkdp5CLwLKeqCWyAgloFOY+eko9Nrb4INz9c8tSxGzKUzwPNjS3r6wkrpq5A+qjU61QjG4ThwT/BNPxLIMNKTYH+OA714XfsiyLMevMpR1/8vPFXh9vUYXc0+jTIBI5rJ9D1+6c1BAXC67BX+GPhZ0XQnbgYyD1LKAKAJ5eh0V772Lf9QwoZCTWPNsSsUGVu166CgRBQMvajqmtffc2Fm2g0eE6izUrXDd2HsicVyALC92TbEEQBGqGC8eKQmbef7IQhB4aLsXBGHlii1isvHDG3nyxuBeikgdDe8dc7rYypAst5Ca+E0u4Mm3yhHIKLqleEBx7awuDjctotLsuruy+/DKF7mVlOBE3WfS8K0EQBL6gB2GufrzgOH+Uq9wlavKEmI++pfHdpzT6nxI+kz4lpXimTTyWj2wGGeU5VTCLDYDCMA4ONLY/Qv+8eMXdXapScOhNNG/eHHl5HE10s2bN0Lx5c8l/j+E55JeaRUJLa6EYsokQj3gkLBGj0wtKWy0YZj3sQguB25oUl7Z79ZHQbVVEWrdbSArDe6e8RAGM5xjaxYiGjNh5d4db2+77a1+33r8iyCKCIbMYx5eqm99fzxMl+FT+Jc4rJ2Ot/NNytxMAa8KRiHz7gsPhhiRYABH+7h8rNG8b5xvTLD3lv7SjUAwgSS7DRn8/jCD+qHDbspI00eMsgAAbXC3LMrMFf8/JzkWZb3yF+yMFllfL3fiEjOz4AEylB12NFLlQkVDwBPP1vYUvSAvPMyGP75CI7ya2gUpOYV2fdabjlmPHVfjrWgaMu9FDGYUPQoOxzwGWftJN9dr5IAgCvTSLMU77ht1rV6X8CACY334+IlMvAccMde4Hfomf7hCmqg2Ln26M5nGeZ/92FwgAYG2P0ybXkivczoNc6+oZQ2Mi4aV2VmnznNppmf5wl+UIxozlv+QWQUOucHwUlDmntd0qa4N82sxtEumB/UkMUuS+bKlrqqYAQAhRZGXobZrEHXlVJAWiSAVkBxD4KCsHaoVn52wKa+bMcNVKV6al8fXhe0jO5jbiHuRpbPL3w225ef4SLIuahi28mnBLrhSDDQD00SwEWVgXzdVq6GT2e+Hl/Vgpt8LAgQOhNDDtDho0CAMHDpT89xieQa+lBzFizQnT30ZvmlLL1Vu1RLYf8IANtzruCegI4KMRFHJ9gcVDSdROldh4b7mWZfGTXcIamXqRTVJnoahnBRLwq+u6/GRb8IqxTSfnM3oWHqU7GG/pAuxrSiDcOxwb+270WJtS8FbIQNmwqpMsgaHUEfgRZehFnUN2sUbyWlv4S/m64O9qmSym/WE/pCywmAULeKSm5xe+U5HKhuBt3XPII83ChN5i9e5xkcEGP2BcQBTOpgTgG1/3eO9YAC9EhtnkiuhRWoaxBYWYl5WDy0kPMLqoGGU8T62r4a81M98TAEYVFEEnJ/DBSO4hucOLBQBlFspk/5PmsfNpThaWZ2ThtxSuBMw9RW33dMJBhHuHY377+QCkheaK4mFuKYZTBwAAhXZqwzdVl2/OVgS58MdBpolD146oMwI9guoB2wxetdaTcULZDm9vuwwAmN69JgY2FWd+/reCJAiBUr66H1f9QnBNpHW+qrNYfdB6r1eTJIJt3Lok6WXMG2Web1fi3K9WLBvBjZVVY1pI5ncbPeUKGvhQtk78onLC1i9cukY8dYjlifXRARUn+nQWT7eIlTb6rf/ZRa2weCYqAo0TDSR/LIupv9te5N94jkKf4hJ4syxm9Kzlon7YRr+GkWgZH4TBmvl4Rfuy6fhtFxiJl+27hQ93XEfXJQdw+HYWfg3wwcKQIAyJjTLxt/Q/WfkRSpbIhT+OMM2wMY1LBSnkDdHV/awHzpPXxR0D/1U4FC/0/vvvi35+jMpBkVqH25nm3esD2XqMkO3D6KgIDBYhN9rTjMC+ZiSu5iZ6spsmyFngehyBF6dxw+1RMCvKWOqTLh4mW144YqkulZkXrf2GUBpdt/fhCfuyOqwhHtog7A4vAAqOHEL0067fQHSMtQX+6LC6+Hto1aie4KOUwZ7e8NfNEPSuwyWArzl0D2894TzzcTAhlAI/XScclwXeQICIcT8tmMDLhtJb7kaqrBo6aDhP3Wc9m+IdrvqSoEYuwEWbhJ1T4otzNGQM8HOHivNGiM2grwIDcMLLC/42Sgv+RrfH7NxjgmOtEtzHeNuimMJPIdy7IwDMzMvHDwF+0BtC/BVuqgy0IETocQniRQ90VKsFgrwUM7snYfRI+1a81LQoCIJAM+IOACDPRkhkoTcwuKgYF2yUkXMnni0oxDZf6RKGNQNrYlazGcDm4UBZLhDVBPdbvokXV52BjmbxZKMozOhZuUYWd0ApF76zv5uS+LspiREHaQw9xo1fjY/rx7EKnIFmwl5xg+iioSQYdRyu1iMBQ+mnU3UINK0W6PK+8DG4WSwGN+MqxJRorBeRalra5O2T61m0Uh0EiicCAPTl5DrJKdbgzP089KhrdqIMJg9jquw3vKCbiUwAMj2LWEkScvOiE+Hn+cjIJcOaYMvvOoipF/raromWikE2LvHWjt7nWFO1DymsepSJOioN2qlX4LgHjOkA8NUYjsYt4c08bGc64jN8CQCQ88SM7GINLqXko26kv1PErCeTzPT2Y9edwqhq5ncdQHAb0dj94vNpX1MClVkngv+mPh1CYc5PNL7pSeJkHQKTdwmvDcr2cBmVSka5IynOnj2LTZs24fvvv8f58+dd2afHsIMitXBzGCvbhwPeXrisUmLIMeuFaV8zEvcjCCg8mDfCB2URZFQo4cCr/scl8RPlBENbL0g/LRA+u9IAs2J1rgaB9WkZYJSO5R1WFIG+PgLSJTFQlHtCXg+nHLY6Nqdz1TG4/RMyEk8X2fbIVDuvxDWFHOkUBcbFZG9G7JfIeTpZl4C3h5Qsfg9iAr3wW8ojTM3Lx9PF1qVCep8x5753vlXx+S72C78K4siYlBKRlTdigGJv6zJF7sy752OBbiRUhndjDC11uadcx2m1F1VKNExm8NMCPUYeFDZiaRMs8PeMd8YWwr05QT/QRhQpq6tYomMTkvOCrg2UJj+cN47AwOISvJqbh02P0ivUnjMY0ZJjfn49Nx+HH6Tgjgif5XfdSCzuvBiqY58D948CCl8U9V+D5767hPxSHRrHBmDJsCYg/+VM62IwzlFNdlfB8XuRfOuS8DuuyBP2NijljZPF73WmtvVaRrBAfIjncvl9lDJ8PqoZPh/VzHRMxhKm1Lw6qcBzfmYX6JHbWaL3ySnW4Oz9XBRr9DhxL8dq7+q/4ggmf3cWG44mAwC8oMYyxVeoQabhb0Nk1xNnhN/Z11R8LDau4fpycQ5Bog7uz1fScOJe+Uua0QwLEgyOqoT1cVrbqEluRCjNrc9pjtQKcxM267sDABJ4XMI/nnqA5745g/af/IOeSw+W+978VMhEwrZ3OSmCQCv1l+Vuq6LYQ7cyfb4eR2DcTAr/NCVRImIrOVr//4v6zOlfm5mZie7du6NVq1aYPn06pk6dihYtWqBHjx7IyhJfhB7DtSjVWltsGRte4XSDMycmyPOhTIC1t81doZOWCEnaLvhbqRUu3PfDgBMBZmvrS+o8tFJr4OPnvnyjW1RN0+caYZynZvJU6QdClIP8xhHMOz7P6liTMMfCOj2BDGUipuUVWB2/HC8cTSNiotArLsYlJGINkq2NOD93FF8i387Nxac619TndQYEAdTQ6TE5vxDBebaVAlrlCi+JdBs9L4hb4eOygfjuE13QtuPwp8xzaDPdy/TZGFpqme/JR5mWFpBmOgSt2SX+3g/ccxAziBpxImIU6o1d5lwbbkDbqLZ4ofELNq9hNOX3TIQU3UB98j50AM6pDBKWiNKW7U+AAvBcQRGaVKA9Z/HJ0Ea4Nr8PAIACcLG6eXy//wyFsa9R+KMtiZp5qcDBRQAA+slleHl3Ae5mlSDSX4Wvn20JL4XnmbY9CW1WX0Q/6GP6m69jERZK5PZzznPBWFbXYMqR4Vrk5daCDqJ4qkk0nmoSjViDLEWygIZHZPvFVzR6UCfxoc9HCCHyRe/R/pN/MPSr42j4/h6MXHMC3x5PFpxPK+AcBX9dS8ev51IwhrIm7Bxj4QG1TCWS0dzCFx/byZmf5zJYzo5rhipotTTXMHLNCYxddxK3MpyvP33yXg46kZexw4KnwhY3jxEyb04pn/eU7bRBd+It/fNopP4au1qYX9iRO+Zk7zuGCNik7BL0WnoQf156JH0zhkFd4gGUBrpRy5KjXhrphxISWYosBJbjF7gGufBHHfU36FbCWYdZo4GTIKAx7Nl3DQbTUhVwP8cGgc1/DE4r5dOmTUNhYSGuXr2K3Nxc5OXl4cqVKygsLMT06dPd0cfHsAC/TIe/gajKkhTLiCkvUSYG5Fd6VI6nxnLB1HlInnmSOin429JbppFzJXly/AC1HJCHcR4i99aatb53nh+Bh6EilwKgNe7Ju8xV59q/qBIxrGU1qFgWbSxCxPnkXXyk5pU/HpcFkCqj8MYv1kpmICHuYu2iLsNF1jPFM6VC2tQhtt2/voUVj9m2NRO8JaL3f+hCIiHMfSXixBDIUpiWm4/Xc/IA1mzIMinlNh7V4C+Pos/yQ/j7eob9hmg9UOC8EtL2pVXwC6w8D40RBEFgalPblYtZTfnTMiLzuai55olmD101EVu95yvIcyAIQlAeLt/HPMKvx/GqBWydBIAFmo3FvOT6OHw7G15yCl+Pa+kRHomqgEd0rOnzrRjzc9IxwuitTYduOX1vxiCveEGNEBSAccKsumwQid3NCRytTyDUt3LSH4yyFAmzEmFEELUF6nP5GKH4SvS7Gr1w9M/94xr23xQvw5pXqoM3NMikKKtKD4I2LYLK8m6/j6Kbc+FFea70LR8Jl4QPxZhG5MNwStjh29kYv/6U0/fV0gxekf2KN8OFAlNIofT4OV2L88QaLQX9Gnm+3OuvL7XHsBbcfCqCF87W5J7H/TCApDVYIl+FvqT5eUz85jRuZxZj6ubzklGACWk7sVv5JjYpPhY9v+Zz6U2vA1GGmb0qN/1GAwVqikRlTZxBYdyrFGoYAqhe3sGgy+IDSM13U85VFYPTSvnu3bvx1VdfoV49c0ZC/fr18cUXX2DXrl02vvkY7sAl1SQA0i8yK5Cb/GF6PVonui+n0xnoHWBcdCX6nGEw7BBtlVfqQ3Gb40tTZXh2lgwMAejZygmVsWTGNILWeiZvuaqhQ01u0x1YzEkbdTRafJ2WYRWmNuoAjS6XGPx91XlFKd3gkRgYE4W+1WJEy5P4SWyIpENFCF2DeQMboE+DCHw3sTUSQ815sHpv26pNib97a9Tya4AvHmKeNxmBAOPlaQWUwAsFhXi2sAiDm3PCTxBNmwyAvjam0Y30QvijBFvPp9pthf1+KLCsAbDdtmJrxBm/HjjT9EOHrvUU7HFtsBUwBJZqrQVBsRKDDEEgm/Ws4UYMfzclcLQegZX9Ldb94gwgrC6+D5mCb4/fBwAsG9EUDWOcq6P9b0aRLtr0Oc+PwDlDVEESHYUSni58K8V5A++N9CKooMEl5SScVb2EICpP8tpSBRCZNNT09/F6JNb3ocCSBF7xEGGXJfg16bUWGWYTfyLR4TqLqHOOP5cJG06jSC2eNpIvL0OPuBgMjeFC4+ukWO9JrW5bHGPlAFN5xiOfMuGkN5KSHggqBKngjJ+PCpyXbQiCQHPyjvAYyyJaevhAKwPKlATOMLWxUDcS4X6efy4t4oOweFgTJIb6ACBMJePis4CGKT/iaeoQVimWm66/l12CjqGr0SF4vcmAZYnRsn8AAK3IW7ijHINHFlGVShs2+c7qMkzrXlP6Ag+hc6lQ0a6n0UIrJ0TrzF98mO+hXlUunNZAGIaBXG6d5yqXy8EwlWX//v8GC0AjImjt6MFg58NU/Jiahu0paU6RSLgSPqzj48KVdSybkXcQl8li4l4Gw46yGH1A2I/jXYSr1mf6wWipEbduuwqWv85PJO+dDybTMykhTCXxDdhD/+JS/JCajm/TMtBGrUFwkfAJDj7OYsoOBs9Sfzl9b2NuW5JCOm9fxTL4ra313KJkLLrXlQhvcDHC/VRYPbYlOtUKQ7CPOSRdZueVaZUVD0l5mOdYCZs70eZnpJETCA4KRGfNMizSjQAAFLDuzf3km0gWDG4EAHguv1CQKkPniQvJy+Vf4JJqEhLLrtlth7h3gPtwy7YB+kYM0Fi9Fi1f24qWg6bZvW9VAl1ajKw/5+PXHzegWITcyhb23xJaF6dtp7H0a2tFvVtJKXbz8gorC3oZgc8GUTjUyGIyybxwptVSvLczCQAwu28d9G3oeQ9bpYIWelkzjFldtLA4mIxxPv1ACS1uqCZAbohEWif/FIHFEtF+L1O4rbYeKw3SGyLAy/NlBoUgoJHoQkiec7JMo7l/Qa0zzxWjKJTsl8/9XyHHTh9vfPCdm0pJuBBpFhmARiVUxgDeiZ+7tK0tItWGxDBD9zK+op9yadvO4o9pHbF+fEvE8sS6QCoHqwL9cZpHXFdddhMXw5JwKeIWNBrp0G2jGUdGMDjjZTY2iBlC+SDhmpJ95cFnI5uaPje1SF36Lk2aX6RMxOD7X4TTknj37t3xyiuv4NEjc65DamoqXn31VfTo0cOlnXsMx9A4MQ6zw0PR5oZQwUtMKEI1PY0GWh38KpH5d6xFaHEALT25tMnJLms3EMWYt8ncVpcrwmcQ4KfDc/nmvOV0KhIv9G3psvbFcEbZFgCQZfAStVXbthazds6XB2czzgIA2l03j5fs3s1d3o4rQABoqNWaSNWk+AjqE8luaV/GApu7CpfJ4XNkIAE0aNBM/EsegiWBoiVcEfSx8zK3SRpbah8XK3qdmpe+XuQF+Knk+HLqUJS2no4x2jnopVta8c7YAF8pJwgC3TVLMLyoWDBeHo4fJfrdQRTHEt8zb4tTbdIAKFr8HeTV0OHNwW2dul9Vge7Mzwg78ymG3piBouXO/Qb+e/BWs+h0Tfh8PhpOYuQbFJ7OpaBUVd0w8IyO8zFhRzFohsWQ5jF4qYtnUlWqMoxziWJYULwt/JlHe52+V3UiDbt9vHHQoEjEEDl4a4u4XFDiZR5T4XqzkYhl3RsJZAt8fUYr0Q1doPNKxJ1Ma3JTfq7wG+HOGYIrS+x7FGLhKTcq5XqAIGkogg+CoJwvrXc7owgb/Z0j4mVIoF9xCUjaC+PbJzjdpivhq5ShXfVQePN00WuhN/FFUCCei4rAu7LvAADvKNej9U0GrW4yoFlxw2iygkHzxDh8HmQdvfO9n3RlCQA4z1Sel3xg0xj8NqWD6W8FLxpRgh8QAIvlfzufJvNvhNNi28qVK1FUVISEhATUqFEDNWvWRGJiIoqKirBixQp39PExeNDqxT2rfqUsXtsmPKctaIk3dc97ols2EWARAtzWIk+Y5q3fd5940mXtMiDgYyMS89mCIjxRYvYErhzQBy93de9i1XrMfLwnn4ljPX8DAAy0wzBOu8GYOX73eADAq7+ZxwurqDwBxxn83lZ8yXpKc9wt7cnACiQwY23cxboRUPtXTolBI+z5wR0hv7ELAhgSE4nGiXEoIAkUUSQi8lhM3ikUOMsUXPWCK3EE4g3J5g1jAvDugAYYOfJZbJs9yAWdsdVP4US5x0bDm2XxYZ7Zc1t284FLm/zR31eSQC4ooRSj21QS87ED2NJJeusvyb5v+hxVdrvcbYix81+sQYIhCSQywD9hY5HMRGCxbni523AXhp2siSKNHq0SgrBgSKNK8ypVJRgVKxWjFfCz1M556PS9aKoUr4eHYmpkOHb5eGOrn4+AkVoKg9PMIfVEpTETcK0D3BorpZSrZRXPd/dDKTqQV+1e90dr8fHJuoQC1Xlcr2buz6ZupCl8feJeBo2SGCgjdkEVs9np+y7fdxtLLMpQ2kN+izIsysrBnhkdMLcSSd6M8FJQiOSVpFWTZqfVYMUeAIAK2Zi1lcHrWxnoS8Wj1X4J4TT7tYHWSrnGjmr3jb630/12JZpWC8S6cS3xk74Luht+Xw2tVrLXYTU+wMP8fABARqEaP556IIgq+S/BaaW8WrVqOHfuHHbs2IEZM2Zg+vTp2LlzJ86ePYvYWHEvihQOHTqEAQMGIDo6GgRB4LfffhOcZ1kWc+fORXR0NLy8vNC1a1dcvWp/gfovQ2+RIpBtCDtWWUSQHehA4xYbh5/orpijm4iemkWe6qI1LPYFy+3jT96GUtFavpdS8nH+QZ6hWduClJoOQB2tDosys/Hdo3QQHsgSrhUdjPlvv4+Bnbj6lXYVqyTnBR5buJotPn+qqqfcEg9Dxd/RnT8i3NIeZTEcjXXlV9FPVZrAY4QUuaMRpIQBzxkw0OK2gnODbwzgojtWrKLR46LlpCbwyXAK85+hsCjbrAhTJIH+jaMR4/bUGULwuWc9rvTXZR/7DPTZJIm/vb1Ag8WDnFJJ0iUUCsvMrAwKFOUhAAB95fBPOYxfO5IYPkdcm7iVVmh9UF0IfNke2C9OKmSEDGZBqek96fFJgEVgWAy6apfhC3qQQ332JB7klaFasBdWjWkBpey/zbTuKIyKVbPrOYJ1MTPYcTHyZnoRui7eD5oyW8tnh4fikxDH+G5WFb9o+pwL295AT0EnoZT7Kiq2CJy5n4dh1EGHpJIuVxlsfr4NAGBUa7Mx0E2VQu0iubEOO1oR+L0NgT9bE4KIpWf/5vYlmc89p0uZWtrGLCsBWOJiAoFSA3VFoKrqOB7UNcwCe+1rJBZ/ref4ceI5HertAHNExMV79ktGNkoUGoDrPpR+LteqAQ/ZcGe77HL0qBeBE0w9vJediznZufg6PRNv6CZjdVomVqRn4dPB5nXl208LUdfvewBAm4//xptbL+P97f9NXbDcAY69evXCtGnTMH36dPTs2bNc9ygpKUGTJk2wcuVK0fOLFi3C0qVLsXLlSpw+fRqRkZHo1asXioqcL6XwX4Gl4jjIQP5hSWLWsXoevqN7gQGJH+geuMM6ZzBxLYR9rqYXdlatcI0yrNbReGrlUQz+8hguPsyHirBdb/dHuhuSQjqjX0kpl9tSCbFexgm4rjf3aXsb4bNQHjjj0vam7+cqJFiyleoDPFfvtSLIDCLw5ZPOLVsMw+L43RwUlDlff9lSKSdNem7le85kAFb3k34WpB2+AkdQvfS06bOYRR4ANvQksSXVrLDKK0EQFIavA0tHNAUA3FAIlXJWb+3aHh4TiRkRYdjnVYTOi/dj5TebcPrKTetG/hESthWTJN78WdxaXyAb5uQvqBycrWE9jpkMax4L7Ym1QOZV4OBCyXtdSS3AZNmfpr/7nZEefwRYVA+tPKVqB93a5nk/pQzrxrVCSCWxe1cmRrWuJnrcT4L8uP0lx0k2Z2y5gOScUofNmTl+wAsPzQpEMcz71F1dHYfbdTWMyqEmsx9oiSWYUVmHWbvLu3e3oS/a1wzFvY+fwDtPmkmYK22XIoGNPSls6k4hhKEFSnkkj5TtdmYxCsp0OH7Xul67GPi/JyabxaDjtr/z0UgSLIBMNhDwqhpExwBAq8z9HvAPifgsjh/HCC9ehGdymnhdd39Cmu8lzgYV0Ym6JDp2e8LxzroR25iO8GNZjC4qRijN4A+6Hdqr1ehaVoZCb+Hoff9njvPFG2q0J6/glzPJldBj98Nh6fbkyZNW7OrffvstEhMTER4ejhdeeAEaJ1lb+/Xrhw8//BBDhgyxOseyLJYvX463334bQ4YMQcOGDbFx40aUlpZi82bnw17+qyigKLS7xmDZWuFin6ZuBD2qhmXQ0pAwsEhIXKHSuUaK7/HpQbQlr+E7+cd4e91vuClCSGjExh4kfqfb40HtcaZjlUFUSBoMAXtakBgzi8L33Sm89rz7PDMampujX30hHC/tq1VOPVNbsMx7+pPmPAEHGpM42NBa3LiTKW6s++VcCkatPYH+Kw5bnbNFY3WtGhBq4QbhK+mNYwNtfNv9yCVJ/N2UGzfPzKIwarZw3KhKKy4AVlNft3tNvYesIOLDngffLeC7UAgC/iq5qS9lPL08e80awdcKcrORZWCtPacoxRr5p/hVOQ+tfhFR2mjr/a26hBOjZa1GzvW/kiBWKs73prVEd+G+RHkIHq4+KkAUwZHpNbvD2AxHJsBiXPsEvNS1Bra84Pnc+ym6V1BH/Y3ouR9T07BidDPUjnAud/W/glm9zcqub2F10+e+52zP69T8MmQU2uZA0ei5AdeYciw/9Eh9Ap8WvwoAWDiUm1OlD5+FJqsn6JLKK+lkXG2ulHaxdt8aIFYmujxZEHkOkLAe8eOI8EiSEJR0pSspqZzm7QjdS8sEhgv+lkozDAZ9cRSj1p7AT2fsRwXy00iWraUx6pAdmY0gcJeJQnvN52CJqhPxItWTkAIWOcUahBWY3xutcd4JaSt17Vg9ospUkehQU+ix1/OejGXp5ECDDWK9/0uQ+XyD971tR239W+GwUj537lxcunTJ9Pfly5cxceJE9OzZE2+++Sb++OMPLFiwwGUdS0pKQnp6Onr3Nuc+KJVKdOnSBceOHZP8nkajQWFhoeDffwl3s6xzkF/dbr0wZbKBHuiNY4jTmvvnzTCg/tfefYdHUbV9AP7NbEkvpBOSEBIgoUOQjvSOCEgRQQFBEMECKgq+gCCK7RMVBVRQiqLYX0VE4cWCFAEpUgWREkglvW6bOd8fs222pGDIFp77urjI1pzdnJk59XnA8HNby8m13UVmXhr3b2QUVWKr+nncrjiFV8SVeDrKeVqm7Z15jB7QBwWRXcz36ULqf4+w9cfWqaTv5Gokh/RIy/2ac3UX4MLZEv2Gka7dH+1IuTJUdvth/aPmn20Dvv3UlsMDm/7A0m9P4/9+PIeHPz6Kg8bo6ttPSLO4VwssUz2MMcz99DiWRzgfPc8J5XBPgfzqVmycqDmyaIDLI/9mqKTWjU7FQa/iICjkf9uQwtqvDLDFatCK7HSewddqlsMVQ4H7fHubf+Y4+clkb0vLZ8hbJY97cmbrM+BEhsgiBkFkGKQ4UiflcfXWhppSOxgQDW1YiP9EhOH7AMuspN5q1YWz1E2mc0s5x2Hh51U3ljkwqJU8nh6Sii5JrsjfzkEL+SqKPy+lY8+Va2il06NPiuuXd7qK9eqArIwZKDu/CO1yk1DqJC7ft104LP32NHq89BO6rNgNoYoZT9ORWNbgVI3KIs2Wcdj9RG/c3UlaoiuUtYQubwDcYbVSVYrLdcgtlQ9S1HaLHAMgGF8TUMkQUOn4uw2qsEx0qK068SreNVlVOKvw32GCKLteB2qAqEIGMIaLJedxKU8q+7YTmbZvI8MYA18pXdOdRep3pGVpMAxQOk0t5gq8k17z2jUC/m/nefhbjf9ez/3E7nlFFTqU8/Z1KbyEOQ0+ajKjogjt40NrVd6bJTUmCIJVXRGgwFL9ZLyqH2+XatDkl/MRWPghgMMldZqtyV3U+Ig9fvy4LLr61q1b0aVLF6xbtw6PP/44Vq1ahc8++6zOCpadLU1BREfL94pGR0ebH3PkxRdfREhIiPlffLzjpVie6sA/jpey2HrVcDd+e6ovXh3bFgCQEOa65ckBjGFdVg46VWqwOSsHPICvuluq3vEkDsvvkc7aOaHAy99IS7YZYyiu0KO4Ql+jgy8MlgGYxlw2Squ5IAX4KJHWOAxtNe+hk2YNYqPrvyGWrHPcwF11p+UqdmnMGIfPuRFF2iLM+MEzAmSIdsvNLCdv2z3N/U4wXM6vwMb9l/H2zxfw3Yks3P3e707f+8iVQgAMX1lFKdXYXAQ0asBXlOrQa6N5HEjl8I0x0Jw7LGtVVXNMVPr9+waZUINLhIIBjQ0G3FtcgkcKilzSVP5HlWp33xzdo7i9UoOIKsZlxdIcTP5JxOq1AjpdKHb+RE0xUMPUjudj4bqwx7V0oIXl72saCNSfCMT4d3yh+m+odEd5Pnqkv2N+Xsel2x2/GSd1IlaGhVb5O3nG8LOiR5XPcQUeQANK6woAuPDCUCwf2QoAByYEAuCwpa+TIJsHGTbuv2y+ra/BtplKnkGll/4FVtgfK2/eyWNfCw4/dJTOJsmR7rF/3MQ28J/o4KSnEBnOZdvPcnIQEQznaa7kGLaEBCGgkmHDGwI2vOH42h0Wbvl+eJ7Dk4Oa44GeTZAQ7pp2n3VNmVpcYjeI/vY7Aj54Q4DeKp1edafMOR8fRVNO6ri3qGLPtMmRZA7fX81A40rpO/CpLodoPaqqJNrSAtlWkaIi+1SeL/9wDqd9fKRBGuMXl5zJsHa1gBc2OW/fXQsHfDmGqGD3yHwR4qdCFuSDshuFIVgtjLKrMycSpYNs5O/S5+30N/OUy2yt1HhSo7CwUNZB/vXXXzFkyBDz7U6dOuHq1boNSgXYn/wYY1VGQl24cCEef/xx8+2SkhKv6ZiXaw34v53yvY5JWY5rZe92zREf5o+4Bn5o0TAYiREBDp9XHxh4dNVo0TVbWs+Yo1DIcnuW+3KoMPZxoouADp+Px6OFm+AbGIzP/pD2q93XtTGWj2pd5e856msJAhPAaaFmDI5G0x94VDraJ3ZJgK9KgdXT+8FPpUCof/VBoepauCji44xsTGwkz4FbaN0GcbAPtrZ+uPQDirRFAICBx+zrjMKNlnaZfBb6AHqVW/KPRwf7oGZZs6tXqjWgC/cXzkBaMjbkqGgXtEvVUAfwfnigqBjrU0Nw0NjvezavZgNjN8s1FoE4Lg9KB4f+tXAgzli8oLKadzAO/JOPg5fy8Ui/ZrLlj63K9gANLINVtlHXrT1dUAQAKGCBqO/de7JLgvHGdrErHtGuxcYmHDoYg45dahmPFlZPLanQYvhh6bEuh3gcGeKD3/188WBRMUSDCLWSh5B3EYq3HaS/c9Ii+Lkdj/s499g6VJ3jSZYvznofotog1aOfOX/03TFf9ppDPrMBjJLd9/vWF5Gfr8ABP198FhyEsU42hnSp1OD56/l41W8OPGPX/a1JqeDRxGrPvwEKlNSwf1fV4hpT203FlWPDG7w5Fo51NoBfW3PY14rHvlZAyzIfHKx16W++FKutDd0rKvHYTF+89a783KgQRLtThE4Q8Z5qJXorjmKk9kWcZY2r/D05AbkIKWdYt6rqgfSWI9rJbj/cr1kNPsXNc3uZiBN+CsTp9fBnzGGWikANoChJBxAMNVeJvLKqt79+fzIbk5QX8RfkmWOsFQQC64bwGH6Y4f3BPO4tEBAVFoK99/eFsgbbAOpLphgJOGnNxGStRMcjls/X7JT9qPInh9KRFszwwmYB2aHA3lYcgo1vl5QDfOlkzPPpaQr8klHTAaGbb1rPJhj9v/lYptyElYaxsse0NpfQK1H2WxRPZ+WjTaPapQl0dzWupdHR0bh06RIAQKfT4ejRo+jWrZv58dLSUqiq2MNbWzExUifFdlY8NzfXbvbcmo+PD4KDg2X/vMX/7TwHrU1E5ce/dnyyVhob1hwn7R8J9HFdI1G06Rif0rVGkdUYQamffDly/B8+aPjjamOHnAFg+PD3K6itICfL6EoCOCgYg69K+qW3N4vEbYmuCQLys9AObXQ6u/tL/et2vnH+nvl44eALDh97bKYCPOc+FyyTMmUD9NS+gVNiIh7TzcaOx3rBt4qZLB8He34B543EUE46wS/5RDCPvlpTNtKC4xV4rNAygzqzsBhjS117URuuXYF7dQsd5ilffJ8Ca4dJf0uRQ42Xd92z7ne88b+/8eURq6BNeg04GNAkm+GOgyI4kdlHXQfwVR/57QHa/6vxZ6kr5WrHS6AzxQhZ/IF0tTwoVTKsziuMw9TYaLzTIARfBQWi4/O7UKkTcG7nOvNTjvj44PaERrimVCDUSTU4kMohoN2dN/5h6lFWGHCwOYef2jo+SPjvQ1GeIY9yG8qVywYkLp8/ga5/vYSHrr+AgmpWJ63PzkWMIGDAINd+P4ee6Y9PZnhmHvn6Yn3e/FNshqZOJgBqKqdEY87DfYGpZMFp7/7Ncl7f38Lyi1Wam5NV499KCPfH17O7AwBmFJcgJ8z++FHYnHu/OHINrZ/9EYcjL6FjkwQM9fvO7jW2p2uNUocO/zj/3o8mc3hspgKsTvJf1p2h5cC72bn4JDMHf4jNMfwPx+XL+WM/eke9CZ/UZVCU7nD4HGu9/HZX+bjIAUea8XhuogJ5IdLf5FjiA4hr4F6BbMtE5ys/skouI9YqGF6xbWolo9H7pWMmpggYu49hkNVki7M0fXol53Aw31UCfJQ4z+Jxj34RDrNUfDTdsp20qWj/uT//Q3793vTlkptexvpW45b4kCFDsGDBAvz2229YuHAh/P39cfvtluBQJ06cQHJycp0VrEmTJoiJicGuXbvM9+l0Ovz666/o3r17nf0eT/H4p8exYd9lu/ujnKy4fHKw6yKT2hJtqtkplgxwHL7rxCE9AtjXkpMFYwKAUcf+xBTFj7jsOwmXfSdhsuLHWv9eBtjtg8sOlf7vV+EklGw92yr0rdffpzTYn5Gzwt1zb16XJuG4xqJwh24FvhF7IixAXeXS6PtKdmEg/wfSOPke/FMZVSxLBtCwUH5bpwSeeEABxnEQbVYQMDf4qooRiL1iG4dRzsv9OBxqLhWSZwD0tdtXfinfqqcp6mHggJc3CJj8k4hPX3Y8APizzSRyAep/IHRi3w4Yr12MO7XLzfd9+3APcAwo8+dwzDQjbPMR9gVZBnI4jqFRHkP/YyI+CwhEqUaPw5cLUFRhaRxMjY1GkUKBofGN0OuU/A/wzGQFltyrQIUvh1RPGb3nOLw2RoF3hitke+9NooqB7GL74F1MtHyRZYWWKXahhpGsWjWqXa7huhYV7Ituya7Yy+45rP+SlUWd8U9M1X/b9oE/om3gLpRrHZ8nJq2v2Zx3jiwqv/MehCnwm6t0SJDq8Oe6/g4ft93b++TnfwIAPgmRZtl/jag+MTsDoKhiknzVnTyywjmE8a5bCekIxynQvVKDUFHEGdH5aoC/Lu7H0XAp5osy+vvq37eaDuXXPS119L/XMrHF0B/lCvcIamatfXEoPu/p+Hi6Z4cOF63GokTR8aojnyou7eN/cz55USa63/dh0qmJ5bowrkI+K64QYRcXoNuvB+qlXPWpxp3y559/HgqFAr1798a6deuwbt06qK3SzXzwwQeyoGw1UVZWhuPHj+P48eMApOBux48fR3p6OjiOw9y5c7FixQp8/fXXOHXqFKZOnQp/f39MnDixVr/H031x5Bq+OpYhu2+N6g1sVjkPrBd70/MC19wqUb5Q8ULkQADA5gEKPDlDCa2ac5jrc5lqk/nn56x+duRsVgkuK5Vo0yQBbZok4KRa6sAF2bQnXx+lQK+KSiy77tolyCYxoc5HTDf3kw7P87HAhcILGPrlUGz7ZxuO5BzBkC+H4Ldr9tHEHdEJlk6FoxlhdzW9p33wOdMJ65uu9he0hzVfY516Jb7yWYpf1PMwTvELACCvzPFIszNv3snjaiQHBkCsNpO86zjbU24dIEXUavHH5QKcvFaM/RfyHO5xBIBGuI6B/B8QbfaDGmqwQ7xSpcTHhn41L/hN0MBfjUOsBU4wy8Bw27hQKIwztyeaGAcqbBq456xXEDFg8ScCHvxBxPI3eNzOHa8yONC9P8u/qwuNOPwV7wajNjfItMLCGQZgVnQkHo+KALNesWLVEd8Q4jxi+WWrkB2u2utqa4SDVS+L9Pe7oCTuR7Z6jPnItjrYCuQL8U/8z7gUvxtfHXKcscE0Sw4AzTOdH1dXrj1hdYvhf4/3dvg8f7Xrt4goeA4XnHQ6FcaVekfTC3EsvdDu8fMBNclWxEmDq05U+Ep/kwbxQ5w/yQWso69XhDbH1l6Ozy23n7QKvlqD2f4iBQ+uinPyPw0tdbSJ3oD/GKa7ZcjNBkpfpyutgiqlJegmdxxmDoMntrni/JM1y3J8///SM7DG4F6ruG5vJg1gJ4b7w0epwIfTO2PD1E7w5+TXV6UAMEE+EqHSueNf99+p8VktMjISv/32G4qLixEYGAiFQt5Y/fzzzxEYWLtgHH/88Qf69rXMFJr2gk+ZMgUbN27EU089hcrKSsyePRuFhYXo0qULdu7ciaCgWytViWmE1cQHOgxTHAIAnEWs3fN/T+Fk+yZdLQuRsttjOybgl3/kz7EN6mAiAjjk64OWOl2V829D3/wNacmW2amJjWKQoLUcwDMfUaDYH2A8h62X8+DvJhEihrSJgbNNc6aBiuaZwJL9S3Ct7Bqe2fsMlJwSBmbA7N2zcXLKyWp/x89Xfzb/3PGC/ES3dKL7LVs3USt5TOySgI8PppvvMy0JvBzFwXYWJetgA4Q2kS7yiXwOXuXfA/AqOIjoyp/FGbEx8su0CA/0wfUSLZxF7zW1lRgAwWam3J3CQDkbLtArpHLyAIoKSpD1/jSUMj804XLAccXA0lOA9TJjgxb7fB8DAHxZEA6gpfmh6kIC/tyGQ5wBaBcfCjhpCNQH5xO00gOm5XxhxkwQjDE8tvU41HoBpm8yy1+BdrmWOvUmW4nzJQMhGutZTdccjC+pfQobd6BXOf4Sk8XLAIBrSiX2+UuDvTpBB1+VNChvsBrI+UftPC7H7vY8htZRWetKK50W2yDNMk7XPYFkLhOfCP3wfDWvuxWobQJj6VUcXhvN44mvpb93dqi0dBYAQvg83PO9AD8dsDL1JIanNUfDEOcTA20vOr/+ClAjrcwHJwO1OFc4Ak2jLO1KtYKHzljf3CGatkrBQRAd13mFKGLyB4fs7m+awbBis4DVw3kU+pzEAbGVeTXhY58eQxAq0Ia/iN/FlmCQAo46svheyxVA4F2bCcQWs9oOV5A6AfqL3zh8XopVwDaeq+4My/BdmB6fvuS42/LOUB4hIToA0vLI5/XS5F3jcPdaRQAAkcG+qGkEriNNOXz0zSnoDCIMIsPrd7fHMN55EFtbv7bm0Nu4qitaEJDF3CdfOwC8OaEDPj54BXelxQGQtpMCwG82lyOlAPjlfiW7r0GhZwQtro1aDzWGhDhe+hAWVvs/dJ8+farc88hxHJYuXYqlS5fW+r29GQeGlQ1CUcZzSIsBkq223Rf7S4Eu3Gmsn7PZZxgX5g9YdcqjDAbkK+27GFoOeDEsDF8GByJZp8N/q/gdcxT/xWabBmEeU8LUrSj1kzrkAODPGMqZD9zhVK3knc/Etky3HBuVBsuIsoHVPPAbYwxP73nafNt6r9GCqQpcbOjeM3sLh6YiyFeJEW2lwacwQUSJQmGe+ayJu/i9eE39Dq6xCNzxVgQOLOyPl3/4C0M4x8vaS41tSSmagbzuur4ZCNzZLhbf/plpt2fRjJNWnvgYgOIrZzBCYXMBv3YYSLDs3cIpy4WucYlVSrDM4xBErsq1AmuH81iUx6HZhMeA9V9hj+CaJaWm6My+Kvnfy9e438A0CdPqgnT7WmElOp5+AcU5lgOi3WX59/ligzCMu37W3PhPa5KAxjkMS7cIeGWs/FvJCbX87F9FSihPprc65AwGDQDpO9+4/zLesHpeqk1k5K29eKgNDP9rzwHpcCvjS8qQr1CgW6UG48SO2I2Ori6SWzuYysM0NPm/Drx5tcjiE1vQ+Lz0d/8ooQjPf3cWqyelOX2fwQ6CjZqIHI/9V5+FD1eJUia/SreIDcafV4sAwCWBWW2peB4G0XEmDj+N4yHcFZulNsmc7SLEya/hm/LReE8YCQC4eL0cO9TPoQWfjuX6e/EPIIu/Y+2c1aoc/kYSoN9EIaIlOJkAJeKvO/97t7osYvQBhvWDLefiqwUVUCl4xIRIHew5Hx9FMpdpTg/nyE/teWzKLMY5dQUCmYhHhOEApCDBbofj8VtrHoOP2XcqS/yAYKvdlRejgS1WExPf/pmJg75v40BMDJo6T0Rltn4wjzJfEXtb8fivFki5ve6y+dSFsAC1w8CE11g0IqyGwpUCkMjkH7g41H1XMt4o950mI2Y8RCRwlvUs9yh2YUNoMD4PDpJ1yAFgxmPKOg8S9m/1TZWnGuNs8ivOLiyG4KAmzomOwpfBgWh+jSFbqHokeL7KPh1foNWJzZTDuXOl/f5IV3KUSWDLsC0AgN3tnPwdGUN0IatyGZfJvsx9EJgAHx1DaBlDklV98ddKr183aJ2TV7tekK8KC4e2QOtG0mBgvwopxKizOp7H8yiz+U6HKqSlCHFcHrKM+2PLtHq8oPoADfPtv0PrDj/jbDvlrj+23pzQHkcXD0S3Kuqyj3HchjPmr2UA/pMTh1V/NYJBK4+nkFtkFd3VOu1X+u8wOMr1Y/TyWB7gOLTUMqjj2uPFNt9jiv5pp8+/mXxVCpxaNhjHl8i3UIULUvkHHpM3kLmybExR7gJX6vyi3n2nCudzdsnue/UDAQFaYNkWeWNqwVTL+wR7aVqtXIUC434TcM8vAsS/LGnRTHmGAcBPw/DcR5bvZlcHDl/14LG1t8J8DnYnKgCPFhajk6YmS4kJIKWH/P42Djs6Wv6ejc9blqbzEFGqdTxwrIQWseoLVb6/yHEAeGiZfW90zaQ0+Ch59G4eiV7NXB+3QangYGDS4IBtSs343OrbGhMaxaAgejfu0FuCnDXn03FNqcBoxV4AzOkyZxProLXu4r3ABwEAu4Q0jGgXiwOpzo/9Zz8R0fYyw8PbpPNGmdaA21/5GV1f3G2etNt+IguPKL+G0smwuPE0D8Y43FNahhFlFQA4+KsVdis+3AHjFMhyMo8ZXClFkTfp/ydDOCwTCILI0LdxXI065ACgVXPYNFCBf2KlL2ncbQk3Wux69b2hm+y2SgAUSnmWooJI91ohUhfcr7YSOwuVH2OPzzwM4KVZrHuVuxw+792h0p+zrZs1MOyD+sirXQBjDtefHlf4oNtZEc9/6Dw/Z1W6nnPeac1lobV+v5vC2Om7s1Rq1NxfVIK2kVJu+VI/y3ei1gp44AcBLa+IGHyU4a13BEzdJcIgGvDyoZexO91xVNKH/vcQAGDtagHvvSXI0n6dTpDev2tDz4lCPLOoBEPKnLdSHuIboVuiPAViPHfd7nntOalh+OZ79vXKNEB0kTVEMR8qeyzQDTpcHMchLECNRjap8l7OzTP/XGJaOaqTzgWXFUrc+7OIgccZduzcJnvdheuW77PSasuHwBgEreNLhFYpRbkFgFDjzPDsoZ0xrG0jvD/lthv7YP9SoI/SrnFazEmbXn5rLf8cnDF4zoDjzs8RqRlA6OF9ECHi5Wpyb5dbHat3VlE/3UXvOMf7dB1hDEhXKvFweBTG7WUYfYBBm2tpEcZylvgcwTZZfrIa2J/XvxbcL0e5tTl96y5grTc6mMpj40CFwzgwAMAxAXvO259zASAt8VmUJq+v8v1tB0KtNQr1w7nnh2LTtM5VpsatLyoFD51xwenX3ezL7WOoui324PcCRn6kxpztu9GsQtqmOD8qAkPjG+GfwHwwDjBU0Up/+VIA7rjQU7bE3x0c8uuJXtrXMUs/D+3jQ3GsKY8vu1f992oupSDHD6cs5xbreYdRiv046iuP3Gu6zr0/WPqSYq3aN/1To/DHogE3/iFuIgbO6ZZNALIUcmFlwFd+UlrKttzfWKR+91/97rgI9w30Zk0L+QqU0DKGy9fkOdtDBNfHlahr1Cn3AAP8dmJlg1BMUUvLTBvyeeAYk0X3zA+S9uy5I02wJWDXF0IviAGRuN96dg7AgPIKnGosP2n76Zzno3RGZRVd/KqDgXR/UYSWKTFHnG//oCsopVH2pXkF+DgjG48VFgEAwnzDEFxp+SwDd+Zh0DGGpR+LmGRcMjj0CMOWs1vw0dmPMPfnuVX+mkAHg/aMd32jprYCGMOrxiB9Mx9R4NEH5Ve25z4ScPspEToYo+9r9EjhLWk0WnGXpfeB8+j7pslhVt4EGwJnAgCWXc9Hz4pK3OtG+4Wtj/aVOdcxrNzSIzItwYdW+sNrrZailF46COjsO45aDlCIloqiN4hgGsfnlGnzLN87Z5y9CPFXYfXENPRv4T5pjLaqRiPKYMCxZEtdL972HYQa7kdtWMhQWPwZPgoJRudzNTsXOQvA505W9lmJ8kuzEVCDQaZSkcPoRg1lDUXBKo3jWvWb5p/VNhGnbTtuw7UvYJ5+9o0V+iZ76a426N08ErN6U6e8Rpx0iv1EqSN68pr99qCzxvPSeftQOA7d3yPxRkpWb/zVChiMG3y+78TZzZYPSj9s95oyq35l/z8ZIoxNoXGZUoq0XQFSEMRF0Q3AgcGvijilszWL8ZFhxI1/gJtkVPtGSGfRSIy0RAKqaRBM6xhK1meTAp63yxn3/AQFFkxV4O9WArZfzYRatPwB3r2vo1sEA3SI46F30ilPj5Rmha3dHd0Ao/lf0Tn2DaxpfBEd/76xyYHmmk1uMZhVEyKTtz1aXAOa/HRUdp9Q+x3Ybs89e3FEZnxsDDaEBmN9lHQgCgCe3SLgvVWWI/doU8uBVsm7T+R1AND7R2OM9lks0t+PPc2fAXyCMaxc3ikIEUW0tokm6aMDfnOQpseZhFyGLa8K+OxFA0LKLe/1TwwwtagEAaKIhQWFSNVuxFWleyzhKYqU9vaqALTR6cz7dxd3XYz8IMtnb5hpGXG3nu3+v8OvYvARES3SnXcEHC3RBoC0qDQ82uHRGy67qxUFcsgO43CwubyOPLJNRI/4RngoKhJtlu6UPbbd5xkAAFfVMnTjRathZQiKleFI1HyMu8rKsTbnOvwZwxL9lLr9IDfI+uSdqJfPmmuNbRPDwSP4IcAf+2CJdt3mhzxgRSxQeBmANPP+clgobktMQAUzDmAc3QzllT0wONm2qVdavj/OrcLfyVXyAVAyy0ALAGTOny+bjamK/zF/5CikC/+TX1X9OSMMAgaXlUMtuH6va3XUCjVETQJ61yA15JbAYOh4TpZX+up1x4NTo/fLzzXhjS3n+aFl5TjNmuDJQe6TrtPahM4J2DStM4J8vW9J5I16ZWxbtI0LweRuNd+X2+vKKQBAicY+cFfDfIaRB0QUBjo///6+0JJiLN7N8kvb4nkOeiadH7RqDpOfVErbeowiKu0HJhwNkANAY1WO4/tz3X+Qz9a9XRvjo+ld8NVDllUxjlbNXIixu0uGMYb9F6QVYL0bx9l1Vi9HAxcbcogRBCQYDHifs+yXVirct3sj8Co0NciPjzxj/Gq1HrJzLSClJb3Y5FuU/RWALa8KePqLml9zE3WW36OD55zbmINoNs2z5DPlEN33b3yjvO8TeSGNMVDaRR/p5HxerUbLq/J0X/taWE54ohvse7UWH+aPIywFHwkD8eL4TkiKCLDLN6lgzC6nuK9eHnmUVTOr8/xmyxn7zXcFKI1PNyiAJwqL8PuVa+D1wVLwLje5zjWODEJXzVt29yeFJOGa1Uy/r1XQGOuR9tZXGKbvFLFsi4DPz38uS39m4miJNgBsGroJM9rOuPHCu4mP+tqfxl57l6HLj7XvHP3d0PJz1+QwuyPpNf1YbBYG1/p9b4aNhsEYUlaOTpUaJNvkI48xZuAxfLcL86Mi8I2fJWOF0iB9KvbTC8Z7OHwUIs1o7AjIA7JOAN8+AuXVfbjCVT8SfV7pnp0sQFoKO7y8XBbgEACuFWpwwNdxgCZrZb7VPgUAcFulBj9fzcD/Xc/HOjaq9gV1gd1P9MbzxlUn3SorEa/X45su9teOyH1+AGN4Z7XlPCKcvCJ7zq9+vvgkKBBJWfITawer5btJej0uvzTcYVAf4p7G3xaPbx/uiejgGh4IABLgPP/2m+8JmPSLiC7nnV+AY0J8Mat3MppHB2J8p3inz3MHCo4zR043sf5kf4fG2b0mz0kaGU7JZLGDAIAXGIYecfxdPVToOFCpO1DwHHo2i0CIv9QJbKrTIddBp9x6X7R1B70Pfwzd+NNgABb995T5fqVtU8Y4gG76ho4gFQeEltgmuPeWvAPhY5Fs0ymPMI5zxhTBLg1eVDFwUa3ClN21GwC/FA34W8WJUXrQ6sipbZwHijRRMvedELhR1Cl3E9+dyETPl39yuOTLT8PQ47QI3ngMT4+JtHuOaWlhsCDgwSL3Gl1uEhGAd+5NwxezuiHARwme59DAqoP9vmEIxpWW4XebYCAjDkp7YE2YwXnU8b9VKtkMsr8WUBhP4NZB5M6L0kVynYv2vdpqFh2E+wZ3t7s/KTQJYX7h5tuxmZYRGOuR9l56y9aA5w48h3UnLUHbcityEVnkJqMPN5HBQb8xssQSU0AEsMvfD9lWaRw5jsG2Nt29QIH/TFXixdw8fJqRBT8lb7dC050uaVqo8Or1fHyQnWs+kc82bn/wtxqb+c8nAl7eIG/N7DgZiQrT/nGrD6nnOOD6OfPtP1TOG+NLr+dj29VMfOE3/l99jptpXKcEzCksxvVQ+V9OJRZjBRfl5FUW2zvx1Y7ffdGDw+ocyx7ars0bVvFs95EcGQgVgJOX0vFe9nVszsxxOIPZ6jxv3jJjorNKM3RNqcDDMVFYEREGhe2XZfV23n8mIgCQFS9doLYcvIJ/rpfh57+kTjpnd8YFNgzgketgi+uCoanYOa83An3ce3mqgucgCvKAdNdDLJXex5hX2Rda3MEfQBDKoHaW+StDjXvZdigNDN3OiAiqYEjKKnPyZECZ1/Nfl7++TKzBti+RB5795hRCUYqN6lfxifoFMEG6bkWiCIC8U/5TWw5P5hciRBDwTL40g9o4zB/36BfhEb17rwAsV4cjVVvFvoQ6ciCVx3N5BQgWBCzIL8CFFcNu+u+sK+HBAfgzseoWF2fwvpRo1Cl3Ew9/fAzXCisx++MjsvvLtQY8/J2Ix74Vcf8uqVmj1ttX1A5cJT7MzMZv6RloUlVkEBcZ0rohbku0hJuMEiwHUyxLRqgg4sN+PLb0sZS912l5M47pnFzNRBF3xdk3hE3LvlraJITcOa8XuiaF2z3fVeb0berwfq3gOEhMhdUEcHbuJdljv2da0l9dKbmC1Wu976Rlq6CKGDc9+ZP4JjAAj0dHYmBCI+QaO+ajuF9xzGamlBk7p3eUV6Clsa7ZLnMPUHP47hH3aAw5igT/UFEJ9l2RV3jbVF8AkHhahT27/wIg9Zv8NAxtLongRIAdfMf8vF559g0H0+zxmLJyJBoMGD9s4L/4FDdXRKAvFIBdpoK0ku14ZUP1x4a/lgEG4NktzgcEdUoO/lbvr/SQPXu2IkTRvlNtNPKg/AHRavvCfyIt59LoIvnrrsAygBzrhQ2oWwXHAak2y0Yi9Y4ri6KyEqkFl/H9yWz0f+1X3L/xMI5cKUR3xUm752aGA488ZBkszXSvFMrVWnZnKwAcvr+aYb7vapTl2EgwSDPfs/zeRUz2V4iPWC5Ld2UtPpNDyt7TuHuPiHnfiFi0VUCs6rLD5x5J5rDCMKmuPsZNd1dpOdpqtNh+m/TdrB5u30aNKgY2/3EMoVwZFkSG45mIcDBmwMW8chz2leJQWGfUeWe4AlNKSrEnPQPJxu1bwzu3vPkfpg7wHDC8vHYBQWMKbmxYM0Wnx2/pGZhU4nyAxx1xvBKrRvLIso0Tbf0cgWbKyU0m2ATKqdQL6PS3dF+X01KjxjYPLAAM8SlFe60OPNxrNq8qJy6l48ildNx3WxIAQOPD4ZtuPI4kO/4ETO9kZFF03GAeccjxSax5dJDD+91Nub4cZxys3nP29211WYTBaknUtB+nOX3vtXe6/75XRw6KqXb3MZ7D8gmOT2Ufql7EAT+pMdk4h2FElBRhqCvkDUSdw6ArDI/2ly+1TUsINadnczVn21R8ahhoLOxPKcIQA4fFnwhYvFVEr58V4DL+MD/n+wD7VTef9JZ/172b26/ccRtOOsi84LyTbW3EIYbIi0q0qiK/9sEU29/huXPCdstDnWBWHWxTRGQ/rf3n3qnrhXVZOZhVWIw73DAq/WaD+w4ouZMRbWMBUR6rRiU6jlQ14EAZXt/zNhKLs8z3ncooxsNBq+ye27BAGgx94gEFvunK4T+T3Su1V3W6JIVDCzXibQacTOlMuxccxI7/PonBn6RjzH6G19ZXfYBF5XHoc1I6jprkAD/7O171uLW3ZzXdFQCmFpdg00AFJs1XyAJvmvwTwyGw6SvQBJ3D9sAAbAsKQIlWmmH/OEgaeV/4meX78zGutjR9E0v0UxDVOAXL7myFNye0v5kf51/jOQ4qm9Plkaby76TEJjTUqndrP6jZUiWNYnhWbZEYfEJR6s/h017OS0+dcnLT8TZ7PmxPXTxEdHGQ6ksta4h7RqOQA6AG4K9SQMMsASjCS52U38Hy9YyiSmh0jjvrtsEyACCbedZQvFZlf/Gy3o8frbOcuZ/9RETcLqmzmVmWKYvOb2tvG89q/JjkM8cb8irVjjtfpdd8ccTXB/fvFPDqBwI2vi5d2Bjkx9afSY5f37NZBI4ttmq4u1FkbcHJ6VtZwyIWhPLILdFAoxfMe/sSL1rekwHwMdh/L5eiOXyWkWV3vzvijOmVlIAsJU+uUOHkFfb+RtUDWNk2I/nuHPiuOs4iAtuxGdS45xcBj35r/7m7hzZFV40Wc4qKHYTtcb0XDJOw2TAQ9+kWuLoobi0+zB9nKjrL7vMvb1Tla1oVXDT/LDKGmQ3tszKYMq5cjeSwpa9CllrQU5TAPp96hXERVty1mp9nTEKsXlLj49EDmK4seiUn21JoYorZVd7oBwRUMgRVMOSWSlshXoyQ2m0xRZbnb7tmuQb9KrTFZmEwGAOmdE/EyPZV101Xm9A5HkqbtsQhm4C1zlZU1IaisXulR64NTYi0grS0irjV1CknN50pEMOnh9Oxcuc5nM20pA4TeGAg/wdKHAyeNrMK9PRLgOfsGwEAgyhir9jGfPvvWMcXZkEnP8GcyihGj5d+wujVv1X7OybrnsZ3QlfsT3rs3xX2JtMzy1W4aWhThDkYoLDulFcY5Bf9abukk9RTe56CfxXnY+ZGncvacFZqZzk/M/aFISRb4SBYDif7Hm0DD0q/TLqzQYClU6ZT2jfAXCU5KtTh/TVtxyVeE9B5xW5sumAJpKOzGhRkABrl2X8xjANaONtK4mZ0QVKWBRVj2NdSutxV+jDkVjrOo+zIHT/LL5PXbHa+pGnlB1pWg043UFL3UNMukWjcfnT1ehHirku5yztesK8rg1taOmLvG4bWRRHrlBZqLDHcj9/Etq4uitsrL7wdXJklXZxSX/WKs0g/SxQvkcHhgGaWZ42RO5Wk+Uh229kgcW1V+jh+n2aam78fua61stpDrXEwzumjl1LaciLDynUCXn9PwN2rfgEABFQyPLRdPlPcULCfOW7UwL0yDznTKjbELplXoYNteNedBAWsKY5n+M4Y9O7/9OP+3ZvVM4VCqvsnHewrNwVu9dGW2D3m6ahT7mZMaRye/vIkVv10AW/8aFlmyziG5tw1jPpdfnF7bwiPu7WL0UmzBsO1K3C8gXtEh67Km4bR5p85JiIiyLJfzdEJGwAMNp3yzQcuAwAy8qUlTlWNme0R2+Fh/aMoU/zLs9xN8j+hAwBgkzDIfN+I5BG4GONgptzq5+F/OO6mXi29ivjrjh/b2osH85DVFLYijfXkyfxC2f3aKjJ9vPCh/OLNRBEMHLRWS5sdLafLDLUEA1ygfwC7hQ44EeM+FzY/m9RNWqvVJvc8VfMpFgNvmfVkAHRWPzMHbcJOFU5y+rgh0VfaaqBgljqiEIDYiqsOn2/ggXkzqv7ufm4nv2yOKZPv1YtP7XiDpa1/1nXmd7EFztQwlzBvkGqJ3/5XsfgTx8sqs0Plg385LPSGy0ncgRIlVy3ZOv6q6Iq9VaQsjSm1HGOMMfg4GMcTFJ43M+6IbQT2yuoTO9RITKHj63Sxn9SO+emJ3ji62DO2YMRYdaId/d3bXmZYvVpAwnWgQbk0U9xAU4ogvhCTfxLR94TzNovSLwi/PdUXIX6ek/LrkhCLIqsx/gYOtnxv7n/jXbRHH1RAzYC5+tm4Q/s8Vgsjb/i9XMGcT53j8EUPeX2pNPYROBE4dMkmTZqHo065G+EgQmGzB/JcZqb5Z5EH+ij+tHtdTihwkLXAdYTiNEvE4hGtbnZR/zV1S8tsviiKyFJbZspt0xeZn2fTKf/8j3RMV2xHR/48GuUxp5XZdlmQO3pY/ygm6RbiXdV95vvUvFq2lM3E4ayu7XPAobFNZprMMODVMTy+7sZB9NBUEmf8pVlIP5tZF2cDOY4wjdSp/CDUMkBztKkUyfX7q5bjLTu0vfnnrUI/TNfPh15R89RANx1n6TwWsEB011r2bAoKDperCS7+YxoHDiKacpbP3KAcyFFK77vXz9ec1cHauGLL3uByVketz5uEA/Cl0BMqMOiM7TW1gUPqIceNN6UIZERwuFLFNnnrvX6npYl4vBj7Fr4RuuNTQx+0blxN8l03sku0pJ2ZppuPf2I5LLpPgQcfrnpgIib/d4iVxci9dBoNnGwVnz9d/h5pCe4Ri4H8O2V/L0D5pdkwaBti7TDeaXC23qevwU+Q1uAyBll2FG+yaHgLu/tCy29s0Ns2Ev2go5br9LbOlnZMOCedhJIiAxEW4P7xYWq6Sia0Anhxo6XzznNAh5g1DldsHRBaYq5uNo6LSfg6ag7iw9wr61B1LrJYWWBjfWP71Q9FDrJhOHPfE5bzbZkvkB3GYX/ReLRoFI5TLAl3pSX8uwLXM96qL3Siibx1b2oXN7jO4709F+FNqFPuJu7i9+BPn5loJ5yS3d9addb8s07BQeF3xfalMFiNOj7YKwmxoe6/hKdzW8vAQVlAPC75dzHf1jnYRw0Agl6HH05l4fBlaWRsifJDLFZtwfvqV/H6OudBMKI8IC3Ym/d1R3poZ7x7vyU9mpJXIu0f+7KrahDvI1+Tj/v/Z7mgixww90ElDjfnwXjOY2fK9wcNwYO6eRhuEzSqqplyW1l/HIcA4KBV9PXB5eWYUlKKeKu4BY5W+LvVqn/echEep3sW+ZC36HytrvHXg4HxC5Wy5XDRhcD/1PPR2yYqslgqjYplK5VIvWb/gRX+UgV8UDcXfbUr/+2nuKliQnzxin4CknV6aKzqiH9G1RUmO8x5Y8g6sNvPbXmI4DDhrjFoNH0LWszaZF7t5AlW6CfhohiDZ/VTUAFpwOl8HIfCIA7vDXH+OVimGg8ufwMX8ysgOvmqtGoO1htOwvzdbxZr6QjPiNbsTpghFKImASJTQK+SgrQ5s/zC+wCAExnFuM1BbvK0641ltxUeOFacGmO/+i6+5rtjZAI08n3kcXmWnz/uw+NCQ2BnBw5cWfMb+wUustxwL74TumJXegbeyLmODzOznT5XaVUHuvjsxXW/UjTPtH/eVP1T+K/YE6N0zyNf4cbBRp3QQY1f23D4pguH10bzaCvar0CrKpGS9Xl3+20ctGoOr4yRUgy+OE6qRAbRBx9N74LVE9PwwujWdf0Rbirr8Fp/xzp+jq+GQ3D2ofopUD3xnNaDl1vMv4djOSo8UbxCdn8D1d/mnwM1wDZ/+z2t8wssyzfmDfSMk7XBPwZvGkZjg2EwOna+HXd1jIfK2OMJrHTc88m4XoxZHx3FuHcOAADuV/4IAKiuj9pAw/D0kFSEB6jxn+Hu2Qgb3CoGvz3VD2kJlqhRSl55QyGjGGN2Qd4efdBLIsZwPH4UO8lSUAG165R/c64Qf/koZJGmZ5QVm3/OYOHYZBiIpEj7Y82dBjMK/RLNP2uhwv8e7y17/FycVf5x4+qTV8dY6kH7SwzJfBZ4jbxXZajkcV/DaDwfEYZEm9UWAMAHSV/cj2Jn5KKKfCVuoGGIH/L4cIwsK69VHdncz/mlUePDYcMAHntbctjTmsPA8go0iQjAbYlhaBsX+u8LXY/0QY3QT7cSm4TB5ngmJv/rwJtXAtjiLvpinXol7lAcxAUHadl/SDO+F2PYYBiMTBaGvxqNrePS/3v3dUvEK2PbYvcTvat/MpFJjpIGAQUFh7XDeJQ5WEQUWiL1prb9mYkHf5BfzTYM4JFntaVBLEpD0fnnb1p5bxYFbz8qtbND1TOclVaT23/FWX4O0Mq3DJmuUZlh0vf8zFQl1g9R4Beh3b8psgtw+EHohBhBQP+KSoQKIvJqkATnePRJBOQ6vuaqfSwz4+3iQ+uonPVntWEkwHHY0k+Bg6k8Knke2aHy5wRXOG9vHGnK4cGHFXh3CI9PjDPufzTn8fBsJf42XvvbxYcgxF+F4W0bwlflWW3Azk3CEGWcJKlqm8uAHe/VV5HqBXXK3cTZndGI+iUAR47LOwKiTVS3iEP2s+CBBZY/o6cceOGBarxuGIdlhinwVSkQGqA2R6PMCHd8AOYWFOED1StYqtwIAKjgONzbMBrrQqteFmlQMjzUJxl/LBqAJhHuE6irOo0CG+GDQbU/RCsNlRhhk1c4t4H8O+0cI4+k6ynM24wAvJV9HS/l5kEtMofRXJ2pKJdGpK1XHIRxlhs9tKvwrOF+DLIKUnVH24bgOGBCJ/dZAnY11LK6ZOGQFDSNCsSHhgHm+6yX9Ju2PFy2iVFg0HLgbVbNnVD64Liv82Xpppr1zLBU7JzX64bKXp++e6Qn/ETRaXq0h2bbnzOvhzp+7rtDpYq2oxOPVSMVAMfZDRB5knZxlnPnrsd7o0ulfLYmutD2FfbSo+y/q2+6St9TYXR3LDNMQXftW9Cp3C+eh4LnMP62eCRHOoiyRKpkymwASHEWps1VYNlE+YmYOclNf/cCBXZ04nGm6A5wjENAaQLKs8YDzMneNTemdNBhKPGvulP+0jgFNgzg8cxkBTb1l59/rLPG+BvPzZej5e/niWecbGYZwOUBpEdWvzT7wR0Cln9oX4f+25XD3gX9sGteLzwzLBWzeic7eLV7s80EpATDo7PkdSGwivAtIg8UBnHY3YF3uLo0Qa9H61j3O+fWlFrB47OMbMQac9DnhDp+XpNsz40w7wh1yt1EgxLpoGryt/xPEsjyZLebXrZ/bUGCgLkDmuHjGV3sH3RTTaOCsHRES7w9sYPxHt4cNdrZnvKwze+gn+I4pip3os2zP+LVsFD86euDtQ2q7pQfTZNG6DknjXJ31TmmM3ZVM+Juy5SKpfP5qufY3+7/9o0Wy6Vm9rJcfPtUVmJ4eQVeuZ7ntMPlSNzvuwFAtl+akx120ntZ15e37umAv5YPQUyI++wp5xSWC3iXRKnBs9gwDZ01qwFAFugvyCq9yprhlg+bc8r+ov2NfyAiixhapDtu+pnundkrGc2jazDd4WItGgbbRbo1MfBAfkjN644p/7C3MDVmR7SLRZOIAMTr5Rt/rVdb2GLGtrKPg0DQpu/UoDINgnLutfWD/GsiZ7OXmeNwurG8/RKX47hTzoznViYEoeTccmRfe+imlLE+mFaY7ErPMN8XoKm6smvU0sDehUYc/onlsKNj1ecV29RovId1y58b2Qp/sFTzbX8mgq/BR2jpOB4nPu6rQIifCs2igzCzV7LHTEZZ654iX5OtYkzWjin2BwqquLw6So1s7fGCIuRE9fxXZXQljuMQLoqYVSStYjzeRP7deCvqlLuh3FIN7lVtw0KfdxBWIo+0FJdv//z16kGYO6A5uidH1FMJ68bUHk1wR1vpxMTxHJKMaZZ0TpaZqs9dxsthoVgfEoxXxVfxRXAQOJGhYX7VJ6fLoZ45C8Jz0v7vmviuk/Q8Uxq0JKs00tYnMwBoG9EWfkr3jzvgSMfGDfDnkkGy+yIcpEapSurZvfDjy9DBar9+dX16juPgo3SvCz9ntadcUEgHTbv4UGgg/WzdoLHeX259QSv5OwC8zfhNWCmweq2AZVvsv9cvenBg4DBE+9K//wD1qJ3G8Wi6af/i192kCvBR32ouiRyHl3PlA6WzdY/+6/K5ym2JYfhzySCsmtAegH2DwNkAKQD89XksSjN80CxTfv61HrhoGxeKRsYYJ/1bVBN5kHiUvqnV/z2vxfAYkvN71cE4mBI1T8bnfpS8dNTECAJOXkrHk/mF+KuaLAa2e4XzgqvplNsch7yjtBhubHK3RCy70xJHKEIQq+xw3gr8fOQD/ErjIWLa+rC1Nw9E1zw6om3e8/4VldD4elafwBFTK6er1SDEGyO9t+vqeWuFbgEnrhZDqPwVpdkcggOdDwlt78ShxJ9DNhdaf4W7SThweOV6HlY2CEVuFR3GgH0B6PEnA5CBxtMUePUD+47DwRRONoro46FpV2ozs5/dwBJUSdDrZI3rL3rKT2DjUtwnrdeNCLEJGOXsW1o4RYEXN9nXj6J2CnwSEoy7v3G8muD3hf0R6oZBqWzxCiVe0d+NAK4SowMbAQC+mNUNf12+hol7pYafqdNpHTwnySbGzgsNwvGaVWSGaTvtv5dlE3lcD+aQGwp8f+lpZDH3WcZfE+Fi1StHPunNY3c7IDfUct89TynQMp1h8Vb5a9va5CV/4PakuiqmS1gfT7bHUlWdcgC49ls48hI4xDgJphkR6IPdT/RGUYXerVaZkH8vIcwfyLG//5uuHEYa07bGZYt4LPsLDI06UM+lqz+2y9eDRdHpxIKJaNOn2NOGw30/O3++7Uy5QgjC2klpjp/spvqlRiH9h0gk8NexTeiKLX0z4KMX8VM7zu4cW50gwQMjAtq4r3sTRG4rxafB0ujEWX0KgBy8P5jHt12kLYePFlQCqNmk0ieZ2RjXSArwYfp+/FSe38XrYMyWs7MDh3H7pPNKmZ9ntulrwnuHGzzM6WZSR/RKQxUWbN6Jyd9JOajjLjvIiWW0aYACX3fnoYB7zeDdCNEvHI0MAl67nm+XG7nCantr/z8tjT9HHXIAKLXp0zvbH+oprNNmOFNstVV+11/bZI+ZcqZ+eeeX+HzE5xiZ7Fn5KqvTyLjn6Fq4dPvrbhymP6awyxVrCkR0TatHvkKBhlb7ZU373ebrZyImxNcjlsN1SgzDGmEkXjVMMC9mVCl4xDaQKsOqO+X1xjTDe6Sp/HgYekTewFE46F9djuKkuAQcBx8xAJ/O7Fo3H6IeDSkrR76z2RnO8vneyJFCJwsKDieb8Fg6kce1cGCpcb+s9ffzYGGxxy0lrYpdp7wGY1NKUf75/2gmvUsLrbQ8w1eloA65F2hkk9XFWa3/uLf99ap5boY5oNmqEdLjiTrvyJFmWr4+UPsKclko/BgD4zh82Z3DT205rHcQF8Y2BoqjIHnWUmyyYEzskoChbRxEWHRj8WH+GKJ7GYO1L+Etw2iU+nN4c5QCJ5vUrhvyQxqHDdkORoM8jJ9agd4V0r4yf1FEcpnUBmGmaxEARTXXlneyc9G9ohI/Xs1Aqk6PHsb3G19aCgAYaBUXxxN9K3RDvEHA4LJyFFitJinyzMWvNUKdcjfxV5J0wVMKDIG8ZY16tIP8jACwq72lggZWeP6yQN7HcpS14eTRLVbfUbtqqra51lcGenY1r262CpCiuZpG04sKsmSPmQJkNG/QHKlhqR63t7464aKIt7KvY/F9UqChrb15lPpzKLKJ6WdaMijAPvXOIO3LmKBbhC8E9w9cZtLGKkiXKFovxZc+6O+p8r/zsHJpgM+2ozXgePWdSus6+NkDfdAlKby2xXWpaywCE0tKMX+aAs9OqnrApX9FpXl0HgDONObx+Ewlzhj3y1q/up1Wi7LgpjejyC5hu8/Tx+Zc+sJ4+3Np6jXp/7fu4PHsJAWONOXQuVKDTVme33AmFn1Ta5Z2ivEcNva3rycK4xh6pQ/waEER/C9PrsviuYwp+vrfLA6dtWvwU9EEAMCnvRV4Z7gCpxvbX29t0wgKCg5ZVSSySLI5lJok2edG9wQV8MU5lgDxBrcrHEjl8MFgBVK8YECnZcNg9KzUYGNmDnZczURqVKjdcxwNkFvrUanBuznXEWsMqLgyNw/rsnIwp7AYp8REh5kBPMmT+ln4QeiEFJ0el6yCHRYHcPi5rXS7wMs66J7dW/EiwUqpwRxg0CFRsHSqQsscP/94sqWCuv+cXvXiw/xQwaSpzQCFfAa8pJZLVa7bxH0bUeG5ESgBIL8GxdepOHMeZlWxPIe3Tgk81/25m1Ay11lrGCG73VKnQ7mfFGjIFESowldeb6zrRaBNqpESBOJ3sSWYB50SVVb5sIN9rZYgq42jERwHwfgVXA+TPm8LrQ5ZYbAbsKiO3iq6KxM9b2Z4reFOXFMqUebP4WxC9ecTZ8+YV1CISEFAE50ePqKI2ys1KA1uVreFdSHbWf84q0Hhgykc/kx2fnwUBEP6bjkOnTUa+FFkN6/SsbG815gY7vwkEu1gO4NpINSgAF7IeQcHHaT1Gu5hs7+A/DwMAAKTB8DLDAf+TORwyuq808JgH+PCURYDaw8UWdJ2dvawQVGT/Qv6Yd3k2/DS2Pay+51F1rZ7fQvP7mRa4zgOHICOWi3CRBEKhdruObZn21yrNszyCfbnYn/G0FWjhQrA43rPDZ5oooMKe8XWGFVWhosNOSyfwGPeDKnH885QHv93F4+n7/eGHpCF57RAvVysUpodDyngMFj8qdrn66zqYV5Yx5tVrHrDcZxl9FQln8YsqUWkxa29eGjU8hN3kO2wtIepbmkbIHW8g4yTe8FnM2SPPdZxLkY3G30TSuY6x0T57KRtkBNH8o3Ln5QGZjcCvWBoKiIC1fj5yT51VcSbTqXg8cmMrtg8rbN8X7BVdV87XMofvL2fdEw9k18Axkn5TW8UY563n6+SqVFoFa3etKT01btqdwmcVlwKDsC3GVn444o0RVyTgFeewvZMmW2VSvGkgxk/a0FWO62oO+59RrZrhP8b1w5v3N0eayeloWUV6ZYCHKRyMuXcNlidemKC5Re31R62TxoAeJvZyO2iPAsO4zi8cI8CK+62nGtUVktSmumkbR6Xq+mU97BKV+iB46IAgNhQPwxsGY3A0EjZvvBnpijwec/q22knEjnzthhvw3EqfGC1uqiBIOBvMR7/xEi3Tzbm8PBsJcYvlP5Vtew/nwXhPIu/2UWuNyHGunKyCY+MCA5P5heC8RwOpfAoDvTs9r0t6pS7Cd54kubAIYM3VPNsoNSYB7O9RosXxnp+p1xiTJNisxe4ohbbEb/pyiHTZhCZwfM6EdbKfas/6RQFAOnG1YV+OUXm+6fMU2B66+k3qWSu87PYQXZbWYNugKkdFFLC7NI4zeqdjMP/8aw89gDQLTkcvZrLl5VyHCfl5Qawp42UP/ia8frc3tigqWlUf0eYB86Atk+Mku3P29mRx/gFChxOcXwJ5GrxET0h/kBNJdmkRPuwn+X7GXS06vNosFWnfEypkyVexGPxPIexHeMwqkMjDG3TsMqMFV/2sD+uTJ3yAI3lfDWhs6Xj0DTKM9ehBqrl+8s08HH4PIOSwytjeLw2mke+r2WmwXRd+m83+RdqnTVlR0cOHSp1eDYvHxszc8A8fdgrIMK8iguQ2rOf367AoeZVX5f6llfis8xsj8544YzIq9DJKktIoCjiuhiOl8Yp8FFfHm+Mqrq7li5ajisODCkekK60Jr4TusJ2DUEbLx2YAahT7jYuq6UDSuAZKiuqX8LVBFocvZSOTVk5HjtqamuJKHUeVTYt4kr7VT0OiZACqGSGyU/ssQbPbjTbLsd3pMIH5uXrglUjodKX87o95AAAhQqP6WZjgf4BfCncbk4nYuuJ6Za/valaBZcBHU5ZvpPvb7PPS+7JgnyU+CzDKsQ6x9Vo0KLGPLBTnhAZgp4VNtN3xr93pMEyCBpsTK/3kNVSUVsTdc/UfQHdxOjScswuLMIG44xNudXWocvGPX3bOjs+TkzbbO4tLkGkF0RHJlXjOQ5+eZ0dPpYVZn+fj7FTLpRalq1zVmszPHGwD5CyF7wypm2NnvtHcx4HU3kE6y2NmrEF0jVKUHB4bKblehVWavk+jiVzmKefjbGl5eio1XriKViG5zik6uw7Vt90rbpLMqdMis76veh5gUYd2SlYJtREXmrATSkuAQA8WVCEhmVhKA7k8G1X3jwRZy1RswV9tK9hjeFOTNQvwirDKADAUv1UPD005eZ/gJvsrrRGKEIQlurl8Sc8fPFrlahT7iZO8skAAIXIQdTZ/1kO2ARtmm/IhwrSH1D09DO00V8RQ9BW8x5UNp9HrwAON6v+KOQBgOPsGgR/Bg6ou0K6QIXjgXezvxoBI9tOMOcyDcgsAgBcqVlcHo+07M7W+Ebsia1CP3xq6GNXZ0yuWi0JPJNg2msOaKzC324c6NmDNrZ4nkOiQb7a5pTaB8dFeequEpssBdUtTzYxGNOveZLCiDSn+ew/yMo1/2yqCV2c5DUHgBnDeph/3mwYWCflcxcKAA8VleA2jRadjctlLxkD+H7fSTpmPuyvwOzZ9sfM8STO/B7E+3EAcq/fhdJzS9C2VFrO1iLTuPzcwQCnn/GQ0hs7Hw/1ScawNjHmxx/u57kBE8d3ki8V7ljpYP2+lduL/DGpuBTvZucivDwGnYzPzwrnsKUPj3WDeVyItXyHAg98K3bHWsMIPKR7zBzx3VPFN/DHK7n5GF9Siq+vWWIo/d2Iw+c9pECBpVYrJI8lcXjkQQViROkcvnBoan0X+aZYY5Ay4RwXk3Eh7HYAUmd875Wr6FdRiSif6ma7OVxmDfGKYQKusUisNIxHW817+IHviX6pnh15HQBWjm+POX2T8aXQy5xlBwB+Ems2COaJqFPuJpLiI8w/JxYX2D1uG+DCOkquWukdf8Z37+uIXm2b4YzYRBZRkfEcPupr+YzOZmpM0cdFmwtWQZBnRio1q2IGV6cAlkxWIto/2hwUMPLYFQD2uU29ydiOceafGThUFaD+vicUmPGowry8nzMwnGvoHcdMTel4Dgt9lwCAeQDDdgGJdfBIa+djLT+HCgJElZ/D57kzwT/KaWfRegCjquHNO41LshtHWBpKAU061UHp3MfnBkv2gXezc9FUp8MzUxSYNUeBiw0t9SMvxEFEaeMhFVRNTnjiHcyXJdEfB64tQsDfM3CoeLzT5/ubOuWcCr891RdPDU5Bs+ggHFjYDz8/2QejO8Q5fa2nae1gFthaiRiMBQWF6F6pwTP6aYi0GjD8phuPXWk8tFZZMqQxZA4vG+7BDrELwgOrGal3c35qBaIFAYvzC9HUZsvM570U+L4zb46RAwAv3q1ATphlXcXMXvIBZk91nDVFV81bGKt7FuUBieb7Q4zLX1MaOg/Jf39Riey2qdlbgkA08K9BLksPEeqnRjl8MaLMEsD4APP8VQDO3FotUzfGFJaTbKJon0rGNi0W44C3DSORpnkHHeJDb3Lp6kd8mD/enpgGiEpcjZA3+vKslnAPPuq46WwKiHanzX7GUe09L6JrTe02psbrn9AffzeSf2e2aVS8iVrJ4+Az/bF/QT9M7dGkygQrWjWH4gAOGmME8YTrQKlxS19mFWlovM3amYMAAAnGRlCYzbbfxjmOj6vd7XlszchGt8pKrM/ORVhADfeTuJFAHwW+NfSo9nmhgohZurkAgOev56NPeQV+v3wVn2VkYVleAdYbhgIKS4NHn3rnzSqySwhWTQIlgDi9AYKCk+WINXl9pLz5MKS8Ar0rKnFfsZQjd5TWuzI+EDnearBYhBLZhuQavU7HqxAf5m/eLtQwxM/jYnlUR13N6sUNwlDsFjpgrm42rqMBSg0Rds/RyTrlnj0zXp3NmdnVPwmWDou3bDUDgGyEwwAllEolOmlWyx4TecfX2gHlFZhbWIRFw6UJp65JYfh6dg90M0bln9i58c0tdD0TwaNHpZSDnWcMQVrLrN3b2bnOXuaRqFPuJs4VWVrIpZVShbNest3Ypt79LcbiLcNoFCDYq05QANCGu2IX3E2vtHzGLX14fNPF/jOblm+H2+xnVIR47jKejUM2AgBmz1bgKZvUD8/dw2PTAOkQ9lf5mwPpmNjmHPY20cG+iA31Q3Tj5g4ftw02ZWrkGHhLdODAqlcZepWoYB9ksjCojfXios1hEVIOvO4gmMxfcRxa6XR4L/s6UnR6BPl63ih8/xbRyGLO0witzc5Fa60WK3Pz0P+uB3BcTMbIsnK8lZuHAMbQQqeXVmP4hoCFxOO/QndsMfRHaoJ3DfjZ5g8u5+X1wXrA03rmHABGlJXj7Zzr8GcMz+nvw3HmucuRSfVuNMCh3i5sk/f5S2hS5eOlzA/T9fPxX7EnAGCAg7yn1hMx00qcx7jwVCXMsuKqg1aHtTadqzkPWVJfmdynewZtNe/VTwHrwTirFX9pCQ1wHZZZgtNiYzDecq31EUWsz8rB4ctX8XpuHngAD9yehMsvDcfWmd3QLj4U70+9DR/P6II5fWs2QOYJ0hqHApCC1H6UmY3dVzOgNgTg04ws/HA1A72r2SriaahT7iayfTOQZTweo7OlmSzr5aUHU6Q9Nb+14jD5cQXm6R+C1ksvbiHQmPefAcAbOdcBAA8+rMBro3n82JEz71+0ZlAAL+XmIcGgx2ujpar90lgegq+DqDMeomN0R4xIGoG8EA6XYzj8nmL53KcSefNSfT+ln8OAcNtHb6+vorqM3r8hvhRut7t/aJk8X3uxcTJGITIMOywN3ARX3vTiuQ1/tRIzdY+jk0a6iJ1MlB9Db4/gcaAFj/ELlXjBKn2Po6BNnkal4MGq2O3cs1KDTzJz0Eyvx7jb4uGjcrwh4p75q5EUGYg3gp/Caz4Pob2XrFIyCfSTX1MO+8lHR1voLEtNC6oIlr1V6Iu0hNC6LBrxEM2N59T1g3iHdWRqXw/fTlYD4ZrqZv7lTe/9QhpSbSJK+1u1ga7pvWO5tjXR5jvoWamRpTu7Hsph/EIlfmovPe+La1k4KLZACTwzSr8j84ekoFlUIBYNb4EuSdKF9lHdw/hHbIh5+tnID2yO1dm5SNDrsT47F100WvhWsQrDX61E9+QIKBXe07Xr2DgMd3VohJ+E9min1SHCOOnWUqdHI4PjODGezHv+ch5PgXDjFpFAY9TNbn8xPDtJgRfG88gK55ATxuGtOxXQ+HDgRM/eU1QVFWO4ahWkrIlxuW1hEIeDqVJHtMJBmjCeYxheXoG2Gh0Opkqdi6PNeDQJ9+ylcdsubjP/vLW3dMj+1kr++f2Ujvf5JgQn3LyCuYnoYB8cEFuab7fUavFFRpZdRPaOBqkzyoHDpRjvWl3iyNP5heafW2ulFt4lVTPMKygCAHzVnceGATzmzVBg/EKlLN/nn0lS6p6n7ldUGdPAk7SO+/ejCwE+SnAch1/m98XRxd4V5A0AIgKrjhcw0mqmXK+y1IsHH1aYE0++abgLCTGReH+Kd+23JzXzz9W5AKS0g7MeUeKMTbrkLs1j7V/kZaIrLTPfY0tK7R5nTD5A+IXQy27J+xWrIKU5nPfts3pA9wTyWRAe1c0x39fBSYDNPy+lI0WvBwOHRzw4IKCtqCBf7Hq8Nx64PQkqBY+tM7viW7E7+uteg6phK+SFtkWvSg22X8sypzM1maxb4KJS179Fd7TE4/qHkMHCsc4wDKM6WM4hi/T3u7BkdY865W4ivry9OTq0tbMJHP5Mtv8z8UItknd7GD3HyVYJqBwMDF6134Jlnl1PtgocEmEQwHv4fqxpraeZf84M53D3AgXeulN+UVfxKuzqcGsezkmRgWC8Cl9fy8L8/EJ8lJmDFJ0e4TbRtgWrXrrWOBH6SxsObxpXYnibe60ag6YO09uT0qAEcPjyVVT6ctjRiUdGhOPj42Aqj8teNHjBK6oKBygRmfR5/1Y2u9nFcUu5vs6X3U4oKUWQTcdh/EIlxi9QoDCIQ5h52xDDuNvi0cADYw+Qf69IiJHdDpEvWILSKke3txKYpe7PKC5BlE02DMZsz0UcpuXKJ1qsEoSA8/A2jCN/sFR01L6Db8Ue2GTMYvFEgTSQbJtNxfRVXHhhGJ4Y5L1BvromWbZYiQwOZ7wPiSmYpFuIPV4cgdxWqJ8KRQhCD+0qvGC4F6qmfZHHgrFPaIWPBO8aHL81W/FuSFsRhFKbSYqXx1r+PGE2HQweDLP7JGPd5Nvqo3j1KtZgQLFVTkZH6a4MSg6/tuZky+NCyuxT8uQpPT8EeZeGXWS3mYOZS47jkBfCYdJ8Be6fq8C2zhyefySqvorocnemJaCpXo/JJaUw7cKyTQt23WoPZJBxiaVBAfSr8P417AbjXuG+KVKdqGoJnK3GNtFxPVW5TxQ+yszGQ4WW/ZlJOj2W6e8z385FKABgk/8Uu9eXMO/vTByOuBOv68dgjPZZu8ec1hjj+aiNMeI0B4aoIO9dyUWqp7A6v/xps9VM5aWd8hYNLbPj24QeeCU3D0uv5+OjyjH48Wom9l65an68Waz9aoE3K2Yg0uqaVWS9wE/hrRkNpLrxrEGa7VQDOHYpHX9cvur42bz3d1nu75EIAHh6SApUNp3yZprNGK97FvvENmjTyMF+RS9lmViT/tcr/NBV+zYm6Z9xXaFuEu+v4R5CFH1xKEV+8TrSzPLnmWXVkASAbx/ujqeGpGJgS88NYubMbn0X/HAbhx0dObw1hreb8WxnXOK0eoQCsx6xn/3aauhTH8WsN86Wppu83e9tAEDbiLbQKzmU+3H4sL8CJwLtU+t5K15R/azcaR8f5IRKPzctkxo/Icz79iQBwMeGvgCAEaXSNNWM4pKqng4AaKJz3Pm+3UsGLS416Il2Wh1mFxWbv5cHi4oR2vcx83M4Y9dzer825vs+NvTFVkMfjBeW12+BXYFX4U1hDI6wFBwSUzC7sMj8UImxQfzNtUzcYROvwdp3QjcMb+NdAfBIzTSPlkbJO2dZthNxVqM5/2vPQeVXXe5lz7Ruckfc2zUBU7o1xlUWhaHlFRhTVo7Phd5oo/kAgytW4eDlqzh2KR0RwfZb6k6wZMyySnPVs8iylPuaj+Ngpp6sWZRUV0z9rUHalwFIWR94AOONK70GGc813wjdvS6osSPPjmiFE0sHoU9KFKKCffC+YSgAYLF+KvRWyV/fua+jq4roEtHBloFexgADlECVeXc8E3XK3QTj/HEwxXkFG1ZeIbvtGxTp5JmeL98QA5HnsGGQAqeSOKggz8lovVcWAB59UIG/4oAPJggYq12C/xim13OJb64Y/xinjw1sPBC943sDAHyV3ruloTqC2j56rSOmZevBhdKxFg7v7JQ/Y3gAAPB8Xj52pmdgiM35w5EEm5UFJgGil4TxV1oi2Zq+l2HlFXi0v2WPYgEv7YsZ1qYhZunm4nNDLywzTMECw0y8Psd5DmZvYZ3/+B7dIlknIVIQsFI/Fkl6A168nm/32qm6+eisWY07B/b3+C1DpGZsJwVC/aXB0Z+LJ5nv+197SzNzwwAeKpV3rqKIa+CP50e1weI7WkILNV7WT8AqwyhcRwNUwhfZCMftlWvQWrMB1odHYrhl5cCVgiEYWlaOJ/MLkRdjmR0v5CK8KqI2AKyfchtGtIvFtkekCPTnWTyOi5bPuCi/ED9ezcBr1/PxmG42HtM/7Kqi1rtgY4aToa0bYrnhXvTQvIkPhUGy5zQKrXqyxtv8Or8vuiWFY+2kNNmqrc8e7OayMt0M1Cl3E2qOAzgp2uRLY3lzOgiTImYZWZ1SXALRmztggmVNeqGSwyrDKDxeWIQJJaW4r7gEzfXygBfZYRyW3KfE+Rh//MFSIVgtYPcXPX/ZV5S/82XogmjpVBZobp2ZcVuFUZ3t7rvOgnHYahlcmkZjzs3NC1KrSPTaM6D0+XgADW1WmlwVHQ/oOTtSRpaVY5vQFQCwwTC4rgpY79KaWI4j6++F4zjcrV2MX4R2WKKaZ37Obq4L5htmYeOMXrj80nDZ8lRv9WCvJAxoEY3X724HAQrZPETfikqUwNKB6G8z0CNAgVw0uOFUWcTzrJmUhu8ftWS+CPaVRj2tZ/SuRnF4YroCD81RQK/ioPLmtgss+4DXCndipUE+kJePEGjgA4VVr9w69sJqYTTuyWmA0oJ+yOQtgXM6NYvGMC9bfdI4PABv3dMBrWJD8PcL0mzwOsNw8+McgFhjdO1vjKnjbjVSPeGQAema3at5JN6c0B7HvDDIaHV8VQp8MrMrhrZpKLsudW7iBelhrHhtk9TTcByHBsZG4tFmPK6HymcapmsX4tm8fKRpNJhRVAKxFntCPU2rOPl+q42GIQCA/+QX4qmCIvgw+Z41Ew4cUmOCMKVbY2zOzEYbjRbrbHJfeiIFr8DE1IkYkDDA7rFBiZbRU4PoeKbzVqByEDvgJyENvozh5KV0nLyUjplFJTjdWDqufCql/5mXngFb2nQgNcwyS5xtlQvV2tCycuy/fBUbM3OgtpodDxMEPKmfhQm6RXjBMMnhaz1Bi1jnF++DrAWm6p9GvtqSN/b3hf3x1ezu6JbsPL+5twnwUWL9lNswukOcOej+r1eu4aPMbNym0SLH3xIAb6rNlggfpQLt4kIwqUvj+iwycSGVgkfL2GC8PbED0hJCsWxka4fPuxrFIT/YOFDIVx9w0duZVhQAwOvj21s9wmG07jn8n+Fu2YjxwNZxaBUbgm8f7oFD/+lffwWtJyoFjw+m3obtYlc8rZ+Bkdrn7J7z1ezuLiiZe9k8rTNGtm90ywfRbNHQO7fAANQpdxu8UgU/m2WiaRoNtmZkY2tGNq6JDTG2tBybsnIRIorOg+54gSYx8kawwUF+4ZccLJ/kGYetM7ti2cjW6KDV4eOsHLS1SSPhqRZ2WYjX+74uu++Nvm9gcKJl5tJf5Z0BdGqiaZRldcUaw53opX0d2yEfXfcTmWx/IwAwBcPvovflzf1kRlfZ7ZPMElV7lm6e7dPxYGEx7iivQBBj6KjV4pf0awCAVK0OAYxBCzV+F1tC4Dy3Qa1QOi/72klpSIoIwFsTO5jvCw/0QVqC96UiqqkDC6TGf5goop3xPNpzwEhcEKVB0xCrVUghgoD/PPwgvnm4J/zUNFN+q7mjbSy+mt2jRktqFdQpx7yBzdA8OhBL7miJxIgA3NPZPnVpha8Sh5tx+D2FgypUCurVNi4UUUHeudKgX2o07u2agE+FvviTNUUGs7QD372v4y19LgaA8Fu8I26taVQQPpnRFbuf6O3qotQ56pS7CaVCAX8mX0D6wvV8tNLp0EqnA7NasPGz0M6rZ8pFpR9CrZbcvnmPPML8LN1cc9AhazqVfATaG6l4y4xn/4T+UFo1cJgX14nqpEQH4U7tckzXPYFXDBOQzqKRw8mXaasYw3WbgKUiD9yvm1+PJa0fIf4qtNR8YL7NWw3jxTZKQKLmYyTrLANWDxcVy5aEBRlXGHyemY11hmHmoDz9Ujw3or/CwT7nS6K0J3Zom4b46ck+aBV760S0rU5MiLzxv8ZwJxqHBWCcbgnm62eisd6yMmfbtSw0iaLvjlTvVgjW5ehcYy0qyBc75/XGtJ7SYOn8wfZpvsqVwKtjFVh5lwKKGgQy9QaP9muGmGBfPNq/GYZqX8R8/Uy00azHIC8MaFxTb05oj/AANd69xQK7VadbcjiSIwOrf6KHoU65mwiNS4HG5mIVrLcEROGsGtW7xTT4eEGqL2fKDfI0aAkNI3FcTAIATNU9hQNiSwwut4/+e0VliR79jyjtvzoiele+4XcGvAMVr8LklpPtHhvY2LLPKNIvEv8d+d96LJlrcRyHEywZu0XpwtUuPhTjBvWSPSdMFJAZJj/GLnIxWDqmU72Vsz5VwNKpClJbPvfI9tJM52OFxVAxhsV5UiyCSua44RcXFogtD3TBf4a1wGvj293EEt9cCp7DZsNA2efUwDuDTtWV1/RjzT/7QocuSWEoRDA+F/qAB3DiUjpOXEpHAy+I3UHqRgN/aeA46J/paHe9Cfys6obyFqkmY9Ia1er5YQFq7JnfV3ZfbKVl1YGS9972nrWoYF8cWNgPjw9sjhIE4nOhD0rhf0sM5Dgzsn0j/LFoAG5L9K6908Qx6pS7ift6NMVD+VrZfe8bhqKC+aCQBeLd+3vgLu1SvKYfi0+EfgjxUzl5J8/34Sktkq1yIwf6qjBa9xySNB/hF7E9vpnbHyEiw4rreVjmYBk7ANynW4i3DKPwkG5uPZW6fnRu2BkHJh7A/E72s7uTW03GjDYz8NGwj/DT+J+QHOpd0Vqr0yRCCob455JB+O/s7rIl7YC0fP1UY/nF3ddPhbs72S8d9AYfTLWsMMlSW/b5TumeiHkDmqNvRSUOXr6K8aVS9LvPBMdLwYqT7kBUsC9m9Ery6JUoPMdhieF+tNGuxzrDMADA+373u7hU7m29MMz8c5i/EioFD7UxkNVqw52QwhARYvHfOT0AAJm6Ztib9yDSNJZ2TeAl+8Fkb+RopnxAi6pnexPC/WVR2ZVWAU9upSX/pg646bvwp+0wt/SgxK3m1jnS3ZyvikdaBY8frmbg2YhwTC8qxlRhBN4RRoCBw9lmUZjCmuOo4H35Km0VCb4ItbodHeyL/wxvhQ37LuPxgc2h9g3AMbEpRpRdAAB00miwqkEoemgse4Pv6tsVr/0cgf8M8779wj4Kx7N7PgofPJr2aD2Xxn3smtcLOkGEv1o6rQX5qnBUbIo0Xqonfowhx2ZbmkHpveOS/VKjMUL7PMYo9mBv8AyYutwqBY/HBjQD9gLWQ3uvGu7Gi4aJiOBKsNfHkru7UO0dUX+VxlaeAUq8YLgXbxruQvOo2GpedWurtFptUdzqPgDA9kd7YvOBK/jmYA/MUX7rqqIRN9U43D4Ht0mm6Dy9pzdRKeTXlUsvDkNOiRb/O5uDhiHO94T7qRQo10lb90K0lhgxt2K6012P98bGfZfxUJ9ba3KB3Nq8t0XqYeIa+KMBStHIIGB9di66abR4YlhbaKGGDvJZ8W5J3h0NeHCXdni0oBgA0Ng4Y/7A7UnYt6AfxnSMQ1iAGt8KltyE8QYBr17PR3utJdL6E4OaY/+CfpjRK6l+C09cRqngzR1yAEhLCMVY3VIM1b6IFM1GHDOkQuQ5/K+9ZdT5aoL37UmydpIlYalhKsr4qqOVttO8h1cm9YQGPrjGItFJs9r82G0tm1bxSs+htGkol8HfqwNm1oU+KZFoqtmMTpo1yFInAgCaRQdh+ajWuMosMRte149xUQmJO7LeI+1ntRVNMHh/WkEAiAqyDJwH+ijBcRxiQnzxx6IB+PnJPk5f1yTSMqBxVtfW/LPa99aL15AcGYjlo1oj9hbLx01ubdQpdxOBPkq8LwyV3TezVzKmdk/Ew32bypZDefvIYUrDELTR6fDttUx8lpFt97ivSoEzQd3s7k/UnTf/zHEcncxvcRzHQQSPs6wxtFBjoyCl1ntvCI+TzRT4qS2H0uBbYwYi2Lfq7S7FCESPphG4/NJw7JrXC9fRAImaj5Go+RhNvSiYypnnBuOnJ3qjdSOpczAmLa6aV9za1k7qCAOUuI5Q+KrkzYV3pvXCMO0KLNA/gDeFu1xUQuKOZlu1Uf6XOxsAoBAVuFUWZ060SgsY5Gv5zBGBPvBVOV+O/dH0LgCANo1CcFDfCZEFKfDL7wh/31tjMIOQW51HnCHXrFmDV199FVlZWWjVqhXeeOMN3H777a4uVp1baRiHg2ILhKIcv4ltcALA0jtbmR8/tnggLuWXe31qCNO4ehO987zb/lFN8b9LHZDPQnC38hcAwN9iI3hXWDfyb/mrFajQCQj1V6GT7hz+MRiQq1Ri8x1KXPE1oLvWu/dqvT2xAzbuuyw7j5iUMj8EcZUApKX/pjgVzaKD8MPc2zHkjd8AwKtyovqrlUiKDMTWmd1wNqsEHb38XPpvWac3654cIXssKsgHZ1gizgiJ6JAQWs8lI+7Meg9sqSYVisuzIOoiqniFdwkLUCM2xBeZxRp89ECXGr8u1F+Nyy8NBwAkLtiOizlSzAvb5fCEEO/k9p3yTz/9FHPnzsWaNWvQo0cPvPvuuxg6dCjOnDmDhATvCtDUpUk49l1qAwBYNNx+L3SDALVXNZCdaRFT9VJbAJjRKxkTz0vBzpYaJqMXfxJ7xDY4e7MLRzzKL/P74FRGMfo0j8Lzi781L6UsUkr79piXLxa6o20s7mjreN/0/br5eFm1Ds8ZJmNTtPyYS40JNjcOvVGgjxKdKJptjfz2VF9czi9H5yby7yvFqs58/EDX+i4W8SBCZaKri1Dv9i/sX2fvVV2KNUKId3D7TvnKlSsxffp0PPDAAwCAN954Az/++CPWrl2LF1980cWlq1ufPmi/JPtW1Cy6+k55l6RwtIsLQXSwL3aeycGPYidz9G1CTKKCfNEvVVqivktMQ5HqVwBAsVLqnHO3cOzoP1gq+utec3UxiJuLD/NHfJi/3f08z3n1wA2pe7ZbIAghhFi4dadcp9PhyJEjWLBggez+QYMGYf/+/Q5fo9VqodVaUnCUlJTc1DIS11DwHP47pwc4jsM3xzPw/t5L2Dyts6uLRdwYc9ABNwjUSCSEkPqwc67jtIuEEELcPNBbXl4eBEFAdLQ8v2N0dDSys+0DgAHAiy++iJCQEPO/+Pj4+igqqWODtS8BAKbqnnL6HNO+tZHtG+Hbh3t6dA5lcvOlRNoH/ovwoQA6hBBS1z6a3gXPDEuV3ZcQbr/ighBCiMStO+Um1kFDAIAxZnefycKFC1FcXGz+d/Xq1fooIqlj51gCEjUf4xexvauLQrzEdT7S7r5JrYY6eOat4ZF+UqqzpSNaurgkhBBv07NZBGb28u5MMYQQUpfcevl6REQEFAqF3ax4bm6u3ey5iY+PD3x8fBw+Rgi5dY1Ia4JVl+T3BTkZ3LsVPD6wOSZ2SUDDEEodSAghhBDiSm49U65Wq9GxY0fs2rVLdv+uXbvQvXt3F5WKEOKJuiTZR9sWlNUHFfRWHMdRh5wQQgghxA24daccAB5//HGsX78eH3zwAc6ePYt58+YhPT0ds2bNcnXRyE3Uu7m01HhSF+9Ke0dchwOH97NyZPeVRbR1UWkIIcT77ZnfFwDwxt3tXVsQD5NqTA0bfgukwSWESNx6+ToA3H333cjPz8dzzz2HrKwstG7dGt9//z0aN27s6qKRm2j1pDQc+CcftzeLcHVRiJfgOCBBb5DdFxXs66LSEEKI90sI96fUeTfg24d74tfz1x2u8CKEeCeOMcZcXYibqaSkBCEhISguLkZwMEVaJuRWdSqjGK3XJWCPny/2+/lhckkJYhcVuLpYhBBCCCHEC9WmH+r2M+WEEFIXwozLAHtVatCrUuPi0hBCCCGEECJx+z3lhBBSF2JDKagZIYQQQghxP9QpJ4QQQgghhBBCXIQ65YQQQgghhBBCiItQp5wQQgghhBBCCHER6pQTQgghhBBCCCEuQp1yQgghhBBCCCHERahTTgghhBBCCCGEuAh1ygkht4x5uodcXQRCCCGEEEJklK4uACGE1JevxdsRoi/HKMVeLNDPxA+uLhAhhBBCCLnlUaecEHJL2SgMwUZhiKuLQQghhBBCCABavk4IIYQQQgghhLgMdcoJIYQQQgghhBAXoU45IeSWtHpimquLQAghhBBCCHXKCSG3nq5JYRjetqGri0EIIYQQQgh1ygkhtw6Ok/7v3CTctQUhhBBCCCHEiKKvE0JuGb8+2Re/ns/F+E7xri4KIYQQQgghAKhTTgi5hSSE++O+bomuLgYhhBBCCCFmtHydEEIIIYQQQghxEeqUE0IIIYQQQgghLkKdckIIIYQQQgghxEWoU04IIYQQQgghhLgIdcoJIYQQQgghhBAXoU45IYQQQgghhBDiItQpJ4QQQgghhBBCXIQ65YQQQgghhBBCiItQp5wQQgghhBBCCHERpasLcLMxxgAAJSUlLi4JIYQQQgghhJBbgan/aeqPVsXrO+WlpaUAgPj4eBeXhBBCCCGEEELIraS0tBQhISFVPodjNem6ezBRFJGZmYmgoCBwHOfq4pAbUFJSgvj4eFy9ehXBwcGuLg7xAFRnSG1RnSG1QfWF1BbVGVJbVGc8H2MMpaWliI2NBc9XvWvc62fKeZ5HXFycq4tB6kBwcDCdlEitUJ0htUV1htQG1RdSW1RnSG1RnfFs1c2Qm1CgN0IIIYQQQgghxEWoU04IIYQQQgghhLgIdcqJ2/Px8cGzzz4LHx8fVxeFeAiqM6S2qM6Q2qD6QmqL6gypLaoztxavD/RGCCGEEEIIIYS4K5opJ4QQQgghhBBCXIQ65YQQQgghhBBCiItQp5wQQgghhBBCCHER6pQTQgghhBBCCCEuQp1y4lIUZ5DUFtUZQgghhHg6as8Qa9QpJy6Tm5uL0tJS8206OZHqFBcXQxAE822qM6Q6Fy5cwK5du1xdDOJBzp8/j1mzZuG3335zdVGIh7h69SqOHDmCzMxMVxeFeAhqAxNb1Ckn9c5gMGD69Ono3LkzBgwYgEmTJiEvLw8cx7m6aMRN6fV6zJkzB8OGDcOwYcOwfPlyCIJAdYZU6cSJE2jevDnuueceXLlyxdXFIW5OFEXMmzcP7du3R3l5uazBTIgjer0eDz74INLS0jBt2jS0a9cO+/btc3WxiBujNjBxhjrlpF4ZDAZMnToVZ86cwaZNm3DPPffgxIkTuOuuu3D27FlXF4+4oV27dqFly5Y4ffo05s+fj/j4eGzZsgVLly4FQKPLxDmdTofBgwdDpVLhlVdecXVxiJvbsWMHDh8+jB07duDDDz/EsGHDzI/ReYbYKisrw9ixY/H3339j586d+Oyzz5CWlobFixcDoDpD7FEbmFSFOuWkXmVlZeHQoUOYM2cOevfujXnz5mHXrl24ePEi1q5di5ycHFcXkbiRkpISfPbZZxg8eDB27dqFUaNGYe3atZgwYQIOHz6MiooKGl0mTh09ehQNGjTAli1b8N577+HQoUOuLhJxY+vXr0f79u3Ru3dv/Prrr1i8eDE2btyI9PR0Os8QO2fOnMHZs2exePFidOjQASkpKRg3bhyCgoIgiiLVGWKH2sCkKtQpJ/UqPz8f165dQ9euXQEAWq0WMTExWLhwIXbu3Ik9e/a4uITEnTDG0LNnTzzwwANQqVRgjEGtVkOj0aCyshL+/v40G0Gc8vHxQePGjdGvXz906tQJy5YtAyAN9hBirbS0FHl5eejfvz+ef/55TJgwASdPnsSSJUvQr18/bNu2zdVFJG5Gr9fjwoUL8PHxAQDk5eVh9erViI2NxQcffIDKykoXl5C4G2oDk6pQp5zcNN9//z0A+RKulJQUxMTE4KOPPgIA8LxUBefMmYOgoCDs2LEDWq22/gtL3IKpzoiiCAAICQnBlClT0L59e9n9xcXFSEpKAgCajbjFOTrPmBw9ehRlZWUAgC1btuCHH37A0KFDMXjwYPz111/1Wk7iPhzVmaCgIOj1eqxfvx7nz5/HV199hS+++AJXrlxBcnIyPvjgA6oztzBHdaZHjx7o06cP7r//fgwdOhTR0dGIiYmBWq3GwoULMWXKFJw8edJVRSYu9t5772HdunWyjnazZs2oDUycok45qXPbt29HXFwc7rjjDuzfvx8cx5k7UwAwbtw4fPLJJ8jNzYVKpYJGowEAPPLII/j6669p5vMWZFtneJ6X1RkT0wXs2LFj6NmzJwDat3ercnSeMdUF0/+5ubkYNWoUAGD37t3w8fHB7t278eSTTyI1NdVVRScu4qzOmOrLzJkzsWPHDhw8eBBNmzaFUqkEx3FYtGgRDh48iMLCQhd/AlLfnLVnTNenbdu2Yfv27SgpKcErr7yCHTt24M0338SuXbtw5MgRGsi5BX3yySeIjo7Gxo0bsWbNGowdOxYrVqwAAAiCQG1g4hR1ykmd2rt3L95++22MHj0aQ4YMwWOPPQbA0pny8/PDoEGDEBISYl5Kalr6lZCQALVajfPnz7um8MQlqqsz1jiOw+XLl3Hx4kVzp5zjOFy8eBEAHHbkifdxVmdMqyZMjRofHx9s2rQJnTt3xjPPPINnnnkGgYGBuHz5squKTlykqjpjqjfdunVD7969oVQqZakXO3XqhNLSUmRkZLik7MQ1qro2ma5PAQEBKC0tRX5+PiZPnmw+97Rp0waFhYVIT093WflJ/fv444/x8ssv47nnnsP+/fuxfft2PPvss1ixYgVKSkoQHByMgQMHUhuYOESdclInTBei6OhoDBo0CI8//jiWL1+OM2fO4P333wcgRZ0EpCVfEydOxKZNm/D1119Dr9cDAPbt24eWLVuiTZs2rvkQpF7VpM446mT/8MMPiI+PR0pKCo4dO4YuXbqga9euMBgMDjvyxHvUtM7wPI/KykqUlJTg+++/R+fOnXHs2DEsWrQITz/9NObPn08d81tETeqMqQPevHlzzJ07F//88w/eeecdcyf822+/RZs2bdCrVy/XfAhSr2p7bfL398fff/+Nq1evmgd4tm3bhiZNmqBfv371/wFIvTPVGb1ejy5dumDy5MkAgNjYWLRv3x6NGjXCmTNnAAA9e/akNjBxjBHyLxw5coQVFRXJ7jMYDIwxxvR6PXviiSdYZGQk02g0ssdKSkrYU089xYKCgljv3r3ZuHHjmJ+fH1u9ejVjjDFRFOvxU5D6VNs6Y2KqE4888ggbO3YsmzdvHuN5nk2fPt3uucS73Oh55tChQ+z06dOy12k0GvbKK68wQRDqoeTEVWpbZ6zrw6pVq1hsbCxLSUlho0ePZgEBAeyFF16ov8ITl6htnTFdk/Lz89k999zD/P392axZs9jkyZNZUFAQW7JkCbVlvNyRI0dYYWGh+XZRUZG5zpgcP36cxcTEsIKCAvN91AYmjlCnnNyQL774gsXFxbHk5GSWkJDAlixZwrKyshhj0snEdEK5ePEii4+PZ0888QRjjNk1hD/77DP27LPPslmzZrGzZ8/W74cg9epG64z1xUkQBNa4cWPGcRzr06ePXYeLeJcbrTO2jSJy66ira9Pvv//O1qxZwxYuXMjOnTtXvx+C1Ku6qDMVFRVs/vz5bOrUqWzy5MlUZ7ycbZ1ZvHgxy87ONj9uXTdWrlzJevTowRhjTKvVyt7n888/pzYwMaNOOam1w4cPs9TUVPbGG2+wP//8k61Zs4ZFRkayhx56iOXn5zPGLI1iURTZmjVrmFKpZBcvXmSMSSel4uJil5Wf1L+6qDPl5eWssrKSrVixgv34448u+yykftRVnTE9TrxfXdSZkpISl5Wf1L9/W2c0Go2szuj1+vr/EKRe1aTOCIJgrgujR49mc+bMcWWRiYegTjmpMVPDdu3atSwuLk7WsX777bdZ165d2fLly+1el5+fz7p3785GjhzJjhw5wgYNGsQ+/PBDaijfAuqqzgwcOJB9+OGH9VZu4jp0niG1RXWG1BbVGVJbta0zgiAwURRZcnIy++677xhjjJ07d45NmDCBpaen12/hiUegqEikxkwBTC5duoTmzZtDqVSaH5s6dSo6duyIHTt24PTp0wAswXPCwsIwY8YMfPvtt+jUqRPUajXGjBlD+aVvAXVVZ3x8fHDXXXfV/wcg9Y7OM6S2qM6Q2qI6Q2qrtnWG53kcPnwY/v7+SEtLw9y5c9G2bVvk5+cjKirKJZ+BuDfqlBOndu3ahUcffRRvvvkmDh06ZL6/R48e2L9/P7KzswFIF6uAgACMHDkSHMdh586dAACFQgGdToc1a9Zg+vTp6NWrF06cOIFt27bBz8/PJZ+J3Fw3s874+/u75DORm4vOM6S2qM6Q2qI6Q2rr39YZAPj+++9x6tQppKSkYNeuXdi3bx927txpToNGiDXqlBM7WVlZGDFiBO69914UFBTg/fffx6BBg8wnpUGDBiExMREvv/wyAMvo4cCBA8HzPC5cuGB+r8LCQpw/fx4bNmzAL7/8glatWtX/ByI3HdUZUltUZ0htUZ0htUV1htRWXdYZlUqFiIgIbNy4EadPn0bHjh3r/wMRz+Hq9fPEvZSXl7MpU6awu+++2xzIhDHGOnXqxKZOncoYk4KebN68mfE8z/bt2yd7/aRJk1jfvn3rtczEtajOkNqiOkNqi+oMqS2qM6S26qLO9OnTx3w7Nze3fgpOvALNlBMZf39/+Pj4YOrUqWjSpAkMBgMA4I477sDZs2cBSMu4xo8fj5EjR+KBBx7Ar7/+CsYYsrOz8ffff2PSpEmu/AiknlGdIbVFdYbUFtUZUltUZ0ht1UWduffee83vFxkZ6ZLPQTwTxxhjri4EcS96vR4qlQoAwBgDx3G477774Ofnh/fee898n0ajwdChQ3HmzBm0b98ep06dQkJCAj777DPEx8e7+FOQ+kR1htQW1RlSW1RnSG1RnSG1RXWGuAp1ykmN9OrVC9OmTcPUqVPBGIMoilAoFMjJycGJEydw+PBhJCYmYuLEia4uKnETVGdIbVGdIbVFdYbUFtUZUltUZ0h9oE45qdbFixfRvXt3bN++3RykQqfTQa1Wu7hkxF1RnSG1RXWG1BbVGVJbVGdIbVGdIfWF9pQTp0zjNXv37kVgYKD5ZLRs2TI89thjyM3NdWXxiBuiOkNqi+oMqS2qM6S2qM6Q2qI6Q+qbsvqnkFuVKc3DoUOHMGbMGOzatQszZ85ERUUFPvzwQ0RFRbm4hMTdUJ0htUV1htQW1RlSW1RnSG1RnSH1jZavkyppNBq0adMG//zzD9RqNZYtW4ann37a1cUibozqDKktqjOktqjOkNqiOkNqi+oMqU/UKSfVGjhwIJo1a4aVK1fC19fX1cUhHoDqDKktqjOktqjOkNqiOkNqi+oMqS/UKSfVEgQBCoXC1cUgHoTqDKktqjOktqjOkNqiOkNqi+oMqS/UKSeEEEIIIYQQQlyEoq8TQgghhBBCCCEuQp1yQgghhBBCCCHERahTTgghhBBCCCGEuAh1ygkhhBBCCCGEEBehTjkhhBBCCCGEEOIi1CknhBBCCCGEEEJchDrlhBBCiJtbunQp2rdvX++/95dffgHHceA4DqNGjar33+/I0qVLzWV64403XF0cQggh5F+jTjkhhBDiQqYOprN/U6dOxZNPPondu3e7rIznzp3Dxo0bXfb7rT355JPIyspCXFycq4tCCCGE1AmlqwtACCGE3MqysrLMP3/66adYsmQJzp07Z77Pz88PgYGBCAwMdEXxAABRUVEIDQ112e8HAEEQwHGc+btQKBQuLQ8hhBBSV2imnBBCCHGhmJgY87+QkBBwHGd3n+3y9alTp2LUqFFYsWIFoqOjERoaimXLlsFgMGD+/PkICwtDXFwcPvjgA9nvysjIwN13340GDRogPDwcI0eOxOXLl2tV3s2bNyM8PBxarVZ2/5gxYzB58mTz7W3btqFjx47w9fVFUlKSuXwmK1euRJs2bRAQEID4+HjMnj0bZWVl5sc3btyI0NBQfPfdd2jZsiV8fHxw5cqVWpWVEEII8QTUKSeEEEI80E8//YTMzEzs2bMHK1euxNKlS3HHHXegQYMGOHjwIGbNmoVZs2bh6tWrAICKigr07dsXgYGB2LNnD/bu3YvAwEAMGTIEOp2uxr933LhxEAQB3377rfm+vLw8fPfdd7j//vsBAD/++CPuvfdePProozhz5gzeffddbNy4ES+88IL5NTzPY9WqVTh16hQ2bdqEn376CU899ZTsd1VUVODFF1/E+vXrcfr0aURFRf2br4wQQghxS9QpJ4QQQjxQWFgYVq1ahZSUFEybNg0pKSmoqKjAM888g2bNmmHhwoVQq9XYt28fAGDr1q3geR7r169HmzZt0KJFC2zYsAHp6en45Zdfavx7/fz8MHHiRGzYsMF835YtWxAXF4c+ffoAAF544QUsWLAAU6ZMQVJSEgYOHIjly5fj3XffNb9m7ty56Nu3L5o0aYJ+/fph+fLl+Oyzz2S/S6/XY82aNejevTtSUlIQEBBw418YIYQQ4qZoTzkhhBDigVq1agWet4ytR0dHo3Xr1ubbCoUC4eHhyM3NBQAcOXIEFy5cQFBQkOx9NBoN/vnnn1r97hkzZqBTp07IyMhAo0aNsGHDBkydOhUcx5l/1+HDh2Uz44IgQKPRoKKiAv7+/vj555+xYsUKnDlzBiUlJTAYDNBoNCgvLzd3vtVqNdq2bVu7L4YQQgjxMNQpJ4QQQjyQSqWS3eY4zuF9oigCAERRRMeOHbFlyxa794qMjKzV7+7QoQPatWuHzZs3Y/DgwTh58iS2bdtmflwURSxbtgx33XWX3Wt9fX1x5coVDBs2DLNmzcLy5csRFhaGvXv3Yvr06dDr9ebn+vn5mTv6hBBCiLeiTjkhhBByC0hLS8Onn36KqKgoBAcH/+v3e+CBB/D6668jIyMDAwYMQHx8vOx3nTt3Dk2bNnX42j/++AMGgwGvvfaaebbfduk6IYQQcqugPeWEEELILWDSpEmIiIjAyJEj8dtvv+HSpUv49ddf8dhjj+HatWs39H4ZGRlYt24dpk2bJntsyZIl2Lx5M5YuXYrTp0/j7Nmz+PTTT7Fo0SIAQHJyMgwGA9566y1cvHgRH374Id555506+ZyEEEKIp6FOOSGEEHIL8Pf3x549e5CQkIC77roLLVq0wLRp01BZWXlDM+fBwcEYM2YMAgMDMWrUKNljgwcPxnfffYddu3ahU6dO6Nq1K1auXInGjRsDANq3b4+VK1fi5ZdfRuvWrbFlyxa8+OKLdfExCSGEEI/DMcaYqwtBCCGEEPfzyy+/oG/fvigsLERoaKjd4wMHDkSLFi2watWqei9bYmIi5s6di7lz59b77yaEEELqEs2UE0IIIaRKcXFxuOeee8y3CwoKsHXrVvz000+YM2dOvZZlxYoVCAwMRHp6er3+XkIIIeRmoZlyQgghhDhUWVmJjIwMAEBgYCBiYmIASLPUhYWFWLx4MZ588sl6LVNBQQEKCgoASFHjQ0JC6vX3E0IIIXWNOuWEEEIIIYQQQoiL0PJ1QgghhBBCCCHERahTTgghhBBCCCGEuAh1ygkhhBBCCCGEEBehTjkhhBBCCCGEEOIi1CknhBBCCCGEEEJchDrlhBBCCCGEEEKIi1CnnBBCCCGEEEIIcRHqlBNCCCGEEEIIIS5CnXJCCCGEEEIIIcRF/h+kynZiMLzmpwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts, meta = ismn_data.read(ids, return_meta=True)\n",
    "ax = ts.plot(figsize=(12,4), title=f'Time series for ID {ids[1]}', xlabel=\"Time [year]\", ylabel=\"Soil Temp. [°C]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot station locations\n",
    "We can define a similar query to plot station locations for a specific variable on a map. If a min/max depth is passed, only stations with a sensor that measures within the passed range are included. Note that this kind of visualisation needs additional (optional) packages installed (use `conda` to install `matplotlib` and `cartopy`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.322267Z",
     "start_time": "2024-06-15T21:55:26.285602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x1000 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cartopy.crs as ccrs\n",
    "#plot available station on a map\n",
    "fig, axs = plt.subplots(1, 2, figsize=(16,10), subplot_kw={'projection': ccrs.Robinson()})\n",
    "ismn_data.plot_station_locations('soil_moisture', min_depth=0., max_depth=0.1, ax=axs[0], markersize=5, text_scalefactor=2)\n",
    "ismn_data.plot_station_locations('soil_temperature', min_depth=0.5, ax=axs[1], markersize=5, text_scalefactor=3)\n",
    "axs[0].set_extent([-10, 35, 34, 71])\n",
    "axs[1].set_extent([-10, 35, 34, 71])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network data citations/references\n",
    "When using ISMN data in a publication, please cite:\n",
    "\n",
    "- Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: **The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.**\n",
    "\n",
    "In addition, it is required that you cite the networks you use. Information on how to cite a network can be found on https://ismn.earth/en/networks/ and in the downloaded README file that is provided together with the data.\n",
    "The ISMN package provides functions to export citations for a single network, and for all networks in a collection (e.g. in case you don't use all the networks you downloaded before) as plain text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.325886Z",
     "start_time": "2024-06-15T21:55:27.323128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C. & Truhetz, H. (2014), ‘Wegenernet: A pioneering high-resolution network for monitoring weather and climate’, Bulletin of the American Meteorological Society 95.',\n 'Fuchsberger, J., Kirchengast, G. & Kabas, T. (2020), ‘Wegenernet high-resolution weather and climate data 2007 to 2019’, Earth System Science Data Discussions 2020, 1–49.']"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data['WEGENERNET'].get_citations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.337889Z",
     "start_time": "2024-06-15T21:55:27.326695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "OrderedDict([('FMI',\n              ['Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J., Salminen, M. & Pulliainen, J. (2018), ‘Spatially distributed evaluation of esa cci soil moisture products in a northern boreal forest environment’, Geosciences 8(2), 51.',\n               'Ikonen, J., Vehvilaeinen, J., Rautiainen, K., Smolander, T., Lemmetyinen, J., Bircher, S. & Pulliainen, J. (2015), ‘The sodankylae in-situ soil moisture observation network: an example application to earth observation data product evaluation’, GID 5(2), 599–629.']),\n             ('GTK',\n              ['We acknowledge the work of Pekka Haenninen, Raimo Sutinen in support of the ISMN.']),\n             ('REMEDHUS',\n              ['Gonzalez-Zamora, A., Sanchez, N., Pablos, M. & Martinez-Fernandez, J. (2018), ‘Cci soil moisture assessment with smos soil moisture and in situ data under different environmental conditions and spatial scales in spain’, Remote Sensing of Environment 225.']),\n             ('RSMN', ['No reference(s) for network RSMN available.']),\n             ('SMOSMANIA',\n              ['Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W. & Meurey, C. (2016), ‘Deriving pedotransfer functions for soil quartz fraction in southern france from reverse modeling’, SOIL 2(4), 615–629.',\n               'Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet, B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network, International Geoscience and Remote Sensing Symposium, IGARSS, Barcelona, Spain, 23-28 July 2007, 1196-1199, doi:10.1109/IGARSS.2007.4423019, 2007.',\n               'Albergel, C., Ruediger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on insitu observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, 2008.']),\n             ('VAS',\n              ['We acknowledge the work of Mike Schwank, Jean-Pierre Wigneron, Yann H. Kerr, Diego Intrigliolo, Jennifer Grant, Ernesto Lopez-Baeza (http://nimbus.uv.es/) in support of the ISMN.']),\n             ('WEGENERNET',\n              ['Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C. & Truhetz, H. (2014), ‘Wegenernet: A pioneering high-resolution network for monitoring weather and climate’, Bulletin of the American Meteorological Society 95.',\n               'Fuchsberger, J., Kirchengast, G. & Kabas, T. (2020), ‘Wegenernet high-resolution weather and climate data 2007 to 2019’, Earth System Science Data Discussions 2020, 1–49.'])])"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismn_data.collection.export_citations(out_file='/tmp/citations_for_my_subset.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting and iterating over sensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often desired to iterate over all sensors that fulfill certain requirements (e.g. that measure soil moisture in a certain depth, and/or for a certain land cover class). For these cases the `collection` (and other components) provide iterators that take keywords and values for filtering the loaded networks/stations/sensor and iterating over the filtered data (of a whole collection, a network, or a station)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select by variable and depth\n",
    "In this example we iterate over all sensors in the previously loaded collection (i.e. over all activated networks) that measure 'soil_moisture' in any depth (range) between 0 and 0.05 meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.597391Z",
     "start_time": "2024-06-15T21:55:27.338801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Network 'FMI' with Stations: ['SAA111', 'SAA112', 'SAA120', 'SOD011', 'SOD012', 'SOD013', 'SOD021', 'SOD022', 'SOD023', 'SOD031', 'SOD032', 'SOD033', 'SOD071', 'SOD072', 'SOD073', 'SOD081', 'SOD082', 'SOD083', 'SOD091', 'SOD092', 'SOD093', 'SOD101', 'SOD102', 'SOD103', 'SOD130', 'SOD140', 'SODAWS']"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Station 'SAA111' with Sensors: ['5TE_soil_moisture_0.050000_0.050000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.100000_0.100000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_moisture_0.400000_0.400000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_temperature_0.800000_0.800000', 'CS215_air_temperature_-2.000000_-2.000000']"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "5TE_soil_moisture_0.050000_0.050000"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1mMetadata for sensor 5TE_soil_moisture_0.050000_0.050000:\u001B[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": "variable        key       \nclay_fraction   val                           4.0\n                depth_from                    0.0\n                depth_to                      0.3\nclimate_KG      val                           Dfc\nclimate_insitu  val                       unknown\nelevation       val                         441.0\ninstrument      val                           5TE\n                depth_from                   0.05\n                depth_to                     0.05\nlatitude        val                      68.33019\nlc_2000         val                           110\nlc_2005         val                           110\nlc_2010         val                           110\nlc_insitu       val                       unknown\nlongitude       val                      27.55062\nnetwork         val                           FMI\norganic_carbon  val                          2.18\n                depth_from                    0.0\n                depth_to                      0.3\nsand_fraction   val                          87.0\n                depth_from                    0.0\n                depth_to                      0.3\nsaturation      val                          0.49\n                depth_from                    0.0\n                depth_to                      0.3\nsilt_fraction   val                           9.0\n                depth_from                    0.0\n                depth_to                      0.3\nstation         val                        SAA111\ntimerange_from  val           2012-10-14 00:00:00\ntimerange_to    val           2020-12-31 23:00:00\nvariable        val                 soil_moisture\n                depth_from                   0.05\n                depth_to                     0.05\nName: data, dtype: object"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for network, station, sensor in ismn_data.collection.iter_sensors(variable='soil_moisture', \n",
    "                                                                  depth=[0., 0.05]):\n",
    "    display(network)\n",
    "    display(station)\n",
    "    display(sensor)\n",
    "\n",
    "    print('\\033[1m' + f'Metadata for sensor {sensor}:'+ '\\033[0m')\n",
    "    display(sensor.metadata.to_pd())\n",
    "    ax = sensor.data.plot(figsize=(12,4), title=f'Time series for sensor {sensor.name}')\n",
    "    ax.set_xlabel(\"Time [year]\")\n",
    "    ax.set_ylabel(\"Soil Moisture [$m^3 m^{-3}$]\")\n",
    "    break  # for this example we stop after the first sensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting by variable and other metadata (1)\n",
    "In this example we iterate over all sensors for the network 'RMSN' and filter out those that measure precipitation within an ESA CCI Landcover pixel that is marked as 'Cropland, rainfed' (10) or 'Grassland' (130). Available land cover classes to choose are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.600809Z",
     "start_time": "2024-06-15T21:55:27.598356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCI Landcover Classification\n",
      "----------------------------\n",
      "Cropland, rainfed: 10\n",
      "Cropland, rainfed / Herbaceous cover: 11\n",
      "Cropland, rainfed / Tree or shrub cover: 12\n",
      "Cropland, irrigated or post-flooding: 20\n",
      "Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous: 30\n",
      "Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%): 40\n",
      "Tree cover, broadleaved, evergreen, Closed to open (>15%): 50\n",
      "Tree cover, broadleaved, deciduous, Closed to open (>15%): 60\n",
      "Tree cover, broadleaved, deciduous, Closed (>40%): 61\n",
      "Tree cover, broadleaved, deciduous, Open (15-40%): 62\n",
      "Tree cover, needleleaved, evergreen, closed to open (>15%): 70\n",
      "Tree cover, needleleaved, evergreen, closed (>40%): 71\n",
      "Tree cover, needleleaved, evergreen, open (15-40%): 72\n",
      "Tree cover, needleleaved, deciduous, closed to open (>15%): 80\n",
      "Tree cover, needleleaved, deciduous, closed (>40%): 81\n",
      "Tree cover, needleleaved, deciduous, open (15-40%): 82\n",
      "Tree cover, mixed leaf type (broadleaved and needleleaved): 90\n",
      "Mosaic tree and shrub (>50%) / herbaceous cover (<50%): 100\n",
      "Mosaic herbaceous cover (>50%) / tree and shrub (<50%): 110\n",
      "Shrubland: 120\n",
      "Shrubland / Evergreen Shrubland: 121\n",
      "Shrubland / Deciduous Shrubland: 122\n",
      "Grassland: 130\n",
      "Lichens and mosses: 140\n",
      "Sparse vegetation (tree, shrub, herbaceous cover) (<15%): 150\n",
      "Sparse vegetation (tree, shrub, herbaceous cover) (<15%) / Sparse shrub (<15%): 152\n",
      "Sparse vegetation (tree, shrub, herbaceous cover) (<15%) / Sparse herbaceous cover (<15%): 153\n",
      "Tree cover, flooded, fresh or brakish water: 160\n",
      "Tree cover, flooded, saline water: 170\n",
      "Shrub or herbaceous cover, flooded, fresh/saline/brakish water: 180\n",
      "Urban areas: 190\n",
      "Bare areas: 200\n",
      "Consolidated bare areas: 201\n",
      "Unconsolidated bare areas: 202\n",
      "Water: 210\n",
      "Permanent snow and ice: 220\n"
     ]
    }
   ],
   "source": [
    "ismn_data.print_landcover_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.960315Z",
     "start_time": "2024-06-15T21:55:27.602093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Station 'Barlad' with Sensors: ['5TM_soil_moisture_0.000000_0.050000', '5TM_soil_temperature_0.000000_0.050000', 'HMP155-Vaisala_air_temperature_-2.000000_-2.000000', 'QMR102_precipitation_-1.400000_-1.400000', 'HMP155_air_temperature_-2.000000_-2.000000']"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "QMR102_precipitation_-1.400000_-1.400000"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1mMetadata for sensor QMR102_precipitation_-1.400000_-1.400000:\u001B[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": "variable        key       \nclimate_KG      val                           Dfb\nclimate_insitu  val                       unknown\nelevation       val                         172.0\ninstrument      val                        QMR102\n                depth_from                   -1.4\n                depth_to                     -1.4\nlatitude        val                      46.23313\nlc_2000         val                            10\nlc_2005         val                            10\nlc_2010         val                            10\nlc_insitu       val                       unknown\nlongitude       val                      27.64438\nnetwork         val                          RSMN\nstation         val                        Barlad\ntimerange_from  val           2014-06-06 07:00:00\ntimerange_to    val           2020-12-31 23:00:00\nvariable        val                 precipitation\n                depth_from                   -1.4\n                depth_to                     -1.4\nName: data, dtype: object"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for station, sensor in ismn_data['RSMN'].iter_sensors(variable='precipitation', \n",
    "                                                      filter_meta_dict={'lc_2010': [10, 130]}):\n",
    "    display(station)\n",
    "    display(sensor)\n",
    "    \n",
    "    data = sensor.read_data()\n",
    "    metadata = sensor.metadata\n",
    "    \n",
    "    print('\\033[1m' + f'Metadata for sensor {sensor}:' + '\\033[0m')\n",
    "    display(metadata.to_pd())\n",
    "    ax = data.plot(figsize=(12,4), title=f'Time series for sensor {sensor.name}')\n",
    "    ax.set_xlabel(\"Time [year]\")\n",
    "    ax.set_ylabel(\"Precipitation [mm]\")\n",
    "    break # for this example we stop after the first sensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting by variable, depth and metadata (2)\n",
    "In this example we iterate over all sensors in the collection and filter those that measure 'soil_moisture' between 0 and 10 cm within an ESA CCI Landcover pixel that is marked as 'Cropland, rainfed' (10) or 'Grassland' (130), and has one of the following climate classes assigned: 'Csc', 'Cfa', 'Dfc'. In addition, we set all those soil moisture values that are **not** flagged as 'good' (G) to 'NaN'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:27.963895Z",
     "start_time": "2024-06-15T21:55:27.961268Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KOEPPEN GEIGER Climate Classification\n",
      "-------------------------------------\n",
      "Af  : Tropical Rainforest\n",
      "Am  : Tropical Monsoon\n",
      "As  : Tropical Savanna Dry\n",
      "Aw  : Tropical Savanna Wet\n",
      "BWk : Arid Desert Cold\n",
      "BWh : Arid Desert Hot\n",
      "BWn : Arid Desert With Frequent Fog\n",
      "BSk : Arid Steppe Cold\n",
      "BSh : Arid Steppe Hot\n",
      "BSn : Arid Steppe With Frequent Fog\n",
      "Csa : Temperate Dry Hot Summer\n",
      "Csb : Temperate Dry Warm Summer\n",
      "Csc : Temperate Dry Cold Summer\n",
      "Cwa : Temperate Dry Winter, Hot Summer\n",
      "Cwb : Temperate Dry Winter, Warm Summer\n",
      "Cwc : Temperate Dry Winter, Cold Summer\n",
      "Cfa : Temperate Without Dry Season, Hot Summer\n",
      "Cfb : Temperate Without Dry Season, Warm Summer\n",
      "Cfc : Temperate Without Dry Season, Cold Summer\n",
      "Dsa : Cold Dry Summer, Hot Summer\n",
      "Dsb : Cold Dry Summer, Warm Summer\n",
      "Dsc : Cold Dry Summer, Cold Summer\n",
      "Dsd : Cold Dry Summer, Very Cold Winter\n",
      "Dwa : Cold Dry Winter, Hot Summer\n",
      "Dwb : Cold Dry Winter, Warm Summer\n",
      "Dwc : Cold Dry Winter, Cold Summer\n",
      "Dwd : Cold Dry Winter, Very Cold Winter\n",
      "Dfa : Cold Dry Without Dry Season, Hot Summer\n",
      "Dfb : Cold Dry Without Dry Season, Warm Summer\n",
      "Dfc : Cold Dry Without Dry Season, Cold Summer\n",
      "Dfd : Cold Dry Without Dry Season, Very Cold Winter\n",
      "ET  : Polar Tundra\n",
      "EF  : Polar Eternal Winter\n",
      "W   : Water\n"
     ]
    }
   ],
   "source": [
    "ismn_data.print_climate_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:28.399006Z",
     "start_time": "2024-06-15T21:55:27.964858Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Network 'RSMN' with Stations: ['Adamclisi', 'Alexandria', 'Bacles', 'Banloc', 'Barlad', 'Calarasi', 'ChisineuCris', 'Corugea', 'Cotnari', 'Darabani', 'Dej', 'Dumbraveni', 'Iasi', 'Oradea', 'RosioriideVede', 'SannicolauMare', 'SatuMare', 'Slatina', 'Slobozia', 'Tecuci']"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Station 'Dumbraveni' with Sensors: ['5TM_soil_moisture_0.000000_0.050000', '5TM_soil_temperature_0.000000_0.050000', 'HMP45DX_air_temperature_-2.000000_-2.000000', 'QMR102_precipitation_-1.400000_-1.400000']"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "5TM_soil_moisture_0.000000_0.050000"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1mMetadata for sensor 5TM_soil_moisture_0.000000_0.050000:\u001B[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": "variable        key       \nclay_fraction   val                          19.0\n                depth_from                    0.0\n                depth_to                      0.3\nclimate_KG      val                           Dfc\nclimate_insitu  val                       unknown\nelevation       val                         318.0\ninstrument      val                           5TM\n                depth_from                    0.0\n                depth_to                     0.05\nlatitude        val                      46.22786\nlc_2000         val                            10\nlc_2005         val                            10\nlc_2010         val                            10\nlc_insitu       val                       unknown\nlongitude       val                      24.59161\nnetwork         val                          RSMN\norganic_carbon  val                          0.99\n                depth_from                    0.0\n                depth_to                      0.3\nsand_fraction   val                          37.0\n                depth_from                    0.0\n                depth_to                      0.3\nsaturation      val                          0.44\n                depth_from                    0.0\n                depth_to                      0.3\nsilt_fraction   val                          44.0\n                depth_from                    0.0\n                depth_to                      0.3\nstation         val                    Dumbraveni\ntimerange_from  val           2014-06-11 12:00:00\ntimerange_to    val           2020-12-31 23:00:00\nvariable        val                 soil_moisture\n                depth_from                    0.0\n                depth_to                     0.05\nName: data, dtype: object"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x400 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ismn.meta import Depth\n",
    "for network, station, sensor in ismn_data.collection \\\n",
    "    .iter_sensors(variable='soil_moisture',\n",
    "                  depth=Depth(0.,0.05),\n",
    "                  filter_meta_dict={'lc_2010': [10, 130],\n",
    "                                    'climate_KG':['Csc', 'Cfa', 'Dfc']}):\n",
    "    \n",
    "    display(network)\n",
    "    display(station)\n",
    "    display(sensor)\n",
    "    \n",
    "    data = sensor.read_data()\n",
    "    data.loc[data['soil_moisture_flag'] != 'G', 'soil_moisture'] = np.nan\n",
    "    metadata = sensor.metadata\n",
    "\n",
    "    print('\\033[1m' + f'Metadata for sensor {sensor}:'+ '\\033[0m')\n",
    "    display(metadata.to_pd())\n",
    "    ax = data.plot(figsize=(12,4), title=f\"G-flagged SM for '{sensor.name}' at station '{station.name}' in network '{network.name}''\")\n",
    "    ax.set_xlabel(\"Time [year]\")\n",
    "    ax.set_ylabel(\"Soil Moisture [$m^3 m^{-3}$]\")\n",
    "    break # for this example we stop after the first sensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating subset Interface\n",
    "`ISMN_Interface` has a method to create a subset of itself. A subset can consist of any number of sensors from any stations or networks that were in the original object. A subset is again created by passing a number of IDs. IDs can again be retrieved either from the metadata frame (which hold all the information on the sensors) or the `ISMN_Interface.get_dataset_ids` method see chapter [Selecting and reading specific sensors](#read_from_meta)). Here we randomly pick 10 soil moisture sensors. We then create a subset for these ids and use all the available `ISMN_Interface` functions, e.g. to plot the subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:28.416184Z",
     "start_time": "2024-06-15T21:55:28.399720Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the existing ismn metadata in /tmp/python_metadata/Data_separate_files_header_20090101_20201231_9289_Cwpc_20221201.csv to set up ISMN_Interface. \n",
      "If there are issues with the data reader, you can remove the metadata csv file to repeat metadata collection.\n"
     ]
    },
    {
     "data": {
      "text/plain": "ismn.base.IsmnRoot Zip at /tmp/Data_separate_files_header_20090101_20201231_9289_Cwpc_20221201.zip\nwith Networks[Stations]:\n------------------------\n  GTK: ['IlomantsiII', 'PoriII'],\n  SMOSMANIA: ['Mouthoumet', 'Mejannes-le-Clap'],\n  FMI: ['SOD011', 'SOD140', 'SOD083'],\n  REMEDHUS: ['Guarena']"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = ismn_data.metadata[ismn_data.metadata[('variable', 'val')] == 'soil_moisture']\n",
    "np.random.seed(123)\n",
    "ids = np.random.choice(metadata.index, 10)\n",
    "subset = ismn_data.subset_from_ids(ids)\n",
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:28.532565Z",
     "start_time": "2024-06-15T21:55:28.417060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x1000 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cartopy.crs as ccrs\n",
    "#plot available station on a map\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6,10), subplot_kw={'projection': ccrs.Robinson()})\n",
    "subset.plot_station_locations(ax=ax, markersize=7, text_scalefactor=3)\n",
    "ax.set_extent([-10, 35, 34, 71])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-15T21:55:28.534740Z",
     "start_time": "2024-06-15T21:55:28.533344Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}