From 84e2537bb0ba72a1ce232419bd3419195781c95d Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Wed, 31 Jan 2024 09:12:39 +0100
Subject: [PATCH 01/21] feat: added parameter to include the uncertainties in
 the output result

---
 src/fusets/_xarray_utils.py                   | 13 +++++++++
 src/fusets/mogpr.py                           | 27 ++++++++++++-------
 src/fusets/openeo/mogpr_udf.py                |  2 ++
 src/fusets/openeo/services/helpers.py         |  6 +++++
 src/fusets/openeo/services/publish_mogpr.py   | 10 +++++--
 .../openeo/services/publish_mogpr_s1_s2.py    | 14 +++++++---
 6 files changed, 56 insertions(+), 16 deletions(-)

diff --git a/src/fusets/_xarray_utils.py b/src/fusets/_xarray_utils.py
index 80d4f86..dc8cefc 100644
--- a/src/fusets/_xarray_utils.py
+++ b/src/fusets/_xarray_utils.py
@@ -44,3 +44,16 @@ def _output_dates(prediction_period, start_date, end_date):
     period = pd.Timedelta(prediction_period)
     range = pd.date_range(start_date, end_date, freq=period)
     return [_topydate(d) for d in range.values]
+
+
+def _suffix_variables(array, suffix):
+    """
+    Rename variables in a data array by appending the suffix to the variable names.
+    """
+    renamed_data_array = array.copy()
+    renamed_variables = []
+    for variable in renamed_data_array['variable'].values:
+        renamed_variables.append(f"{variable}{suffix}")
+
+    renamed_data_array['variables'] = np.array(renamed_variables)
+    return renamed_data_array
diff --git a/src/fusets/mogpr.py b/src/fusets/mogpr.py
index 134f15b..fa4aa13 100644
--- a/src/fusets/mogpr.py
+++ b/src/fusets/mogpr.py
@@ -137,7 +137,8 @@ def fit_transform(self, X: Union[xarray.Dataset, DataCube], y=None, **fit_params
 
 
 def mogpr(
-    array: xarray.Dataset, variables: List[str] = None, time_dimension: str = "t", prediction_period: str = None
+        array: xarray.Dataset, variables: List[str] = None, time_dimension: str = "t", prediction_period: str = None,
+        include_uncertainties: bool = False
 ) -> xarray.Dataset:
     """
     MOGPR (multi-output gaussian-process regression) integrates various timeseries into a single values. This allows to
@@ -150,6 +151,7 @@ def mogpr(
         variables: The list of variable names that should be included, or None to use all variables
         time_dimension: The name of the time dimension of this datacube. Only needs to be specified to resolve ambiguities.
         prediction_period: The duration specified as ISO-8601, e.g. P5D: 5-daily, P1M: monthly. Defaults to input dates.
+        include_uncertainties: Flag indicating if the uncertainties should be added to the output of the mogpr process.
 
     Returns: A gapfilled datacube.
 
@@ -174,7 +176,7 @@ def mogpr(
         raise Exception("The result does not contain any output times, please select a larger range")
 
     def callback(timeseries):
-        out_mean, _, _, _ = mogpr_1D(
+        out_mean, out_std, _, _ = mogpr_1D(
             timeseries,
             list([dates_np for _ in timeseries]),
             0,
@@ -182,19 +184,24 @@ def callback(timeseries):
             nt=1,
             trained_model=None,
         )
-        result = np.array(out_mean)
-        return result
+        return np.array(out_mean), np.array(out_std)
 
     # setting vectorize to true is convenient, but has performance similar to for loop
-    result = xarray.apply_ufunc(
+    result, std = xarray.apply_ufunc(
         callback,
         array.to_array(dim="variable"),
         input_core_dims=[["variable", time_dimension]],
-        output_core_dims=[["variable", output_time_dimension]],
+        output_core_dims=[["variable", output_time_dimension], ["variable", output_time_dimension]],
         vectorize=True,
     )
 
     result = result.assign_coords({output_time_dimension: output_dates})
+
+    if include_uncertainties:
+        std = std.assign_coords({output_time_dimension: output_dates})
+        std['variable'] = [f"{variable}_STD" for variable in std['variable'].values]
+        result = xarray.concat([result, std], dim=output_time_dimension)
+
     result = result.rename({output_time_dimension: time_dimension, "variable": "bands"})
 
     return result.to_dataset(dim="bands")
@@ -303,11 +310,11 @@ def _MOGPR_GPY_retrieval(data_in, time_in, master_ind, output_timevec, nt):
                     else:
                         for ind in range(noutput_timeseries):
                             out_mean[ind][:, None, x, y] = (
-                                out_mean[ind][:, None, x, y]
-                                + (Yp[:, None, ind] * Y_std_vec[ind] + Y_mean_vec[ind]) / nt
+                                    out_mean[ind][:, None, x, y]
+                                    + (Yp[:, None, ind] * Y_std_vec[ind] + Y_mean_vec[ind]) / nt
                             )
                             out_std[ind][:, None, x, y] = (
-                                out_std[ind][:, None, x, y] + (Vp[:, None, ind] * Y_std_vec[ind]) / nt
+                                    out_std[ind][:, None, x, y] + (Vp[:, None, ind] * Y_std_vec[ind]) / nt
                             )
 
                     del Yp, Vp
@@ -423,7 +430,7 @@ def mogpr_1D(data_in, time_in, master_ind, output_timevec, nt, trained_model=Non
                     out_std[out][:, None] = (Vp[:, None, out] * Y_std_vec[out]) / nt
                 else:
                     out_mean[out][:, None] = (
-                        out_mean[out][:, None] + (Yp[:, None, out] * Y_std_vec[out] + Y_mean_vec[out]) / nt
+                            out_mean[out][:, None] + (Yp[:, None, out] * Y_std_vec[out] + Y_mean_vec[out]) / nt
                     )
                     out_std[out][:, None] = out_std[out][:, None] + (Vp[:, None, out] * Y_std_vec[out]) / nt
 
diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 4bedaa9..b8a18bb 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -56,6 +56,7 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
     variables = context.get("variables")
     time_dimension = context.get("time_dimension", "t")
     prediction_period = context.get("prediction_period", "5D")
+    include_uncertainties = context.get("include_uncertainties", False)
 
     dims = cube.get_array().dims
     result = mogpr(
@@ -63,6 +64,7 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
         variables=variables,
         time_dimension=time_dimension,
         prediction_period=prediction_period,
+        include_uncertainties=include_uncertainties
     )
     result_dc = XarrayDataCube(result.to_array(dim="bands").transpose(*dims))
     set_home(home)
diff --git a/src/fusets/openeo/services/helpers.py b/src/fusets/openeo/services/helpers.py
index 6a5b1c4..fa12b90 100644
--- a/src/fusets/openeo/services/helpers.py
+++ b/src/fusets/openeo/services/helpers.py
@@ -1,7 +1,9 @@
 import json
 import os
+from typing import Any, Union
 
 import openeo
+from openeo.api.process import Parameter
 
 try:
     from importlib.resources import files
@@ -64,3 +66,7 @@ def publish_service(id: str, summary: str, description: str, parameters: list, p
 }
 
 GEOJSON_SCHEMA = {"type": "object", "subtype": "geojson"}
+
+
+def get_context_value(p: Union[Parameter, Any]) -> Union[dict, Any]:
+    return {"from_parameter": p.name} if isinstance(p, Parameter) else p
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index be08c4c..529aaad 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -1,11 +1,12 @@
 # Reads contents with UTF-8 encoding and returns str.
+
 import openeo
 from openeo.api.process import Parameter
 from openeo.processes import apply_neighborhood
 from openeo.udf import execute_local_udf
 
 from fusets.openeo import load_mogpr_udf
-from fusets.openeo.services.helpers import publish_service, read_description
+from fusets.openeo.services.helpers import publish_service, read_description, get_context_value
 
 NEIGHBORHOOD_SIZE = 32
 
@@ -68,9 +69,14 @@ def generate_mogpr_udp():
 
     input_cube = Parameter.raster_cube()
 
+    include_uncertainties = Parameter.boolean(
+        "include_uncertainties", "Flag to include the uncertainties in the output results", False)
+
     process = apply_neighborhood(
         input_cube,
-        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context=dict()),
+        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context={
+            'include_uncertainties': get_context_value(include_uncertainties)
+        }),
         size=[
             {"dimension": "x", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
             {"dimension": "y", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index 5bebe49..7991b95 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -4,7 +4,8 @@
 from openeo.processes import apply_neighborhood, eq, if_, merge_cubes, process
 
 from fusets.openeo import load_mogpr_udf
-from fusets.openeo.services.helpers import DATE_SCHEMA, GEOJSON_SCHEMA, publish_service, read_description
+from fusets.openeo.services.helpers import DATE_SCHEMA, GEOJSON_SCHEMA, publish_service, read_description, \
+    get_context_value
 
 NEIGHBORHOOD_SIZE = 32
 
@@ -280,7 +281,7 @@ def load_s2_collection(connection, collection, polygon, date):
     return collections
 
 
-def generate_cube(connection, s1_collection, s2_collection, polygon, date):
+def generate_cube(connection, s1_collection, s2_collection, polygon, date, include_uncertainties):
     # Build the S1 and S2 input data cubes
     s1_input_cube = load_s1_collection(connection, s1_collection, polygon, date)
     s2_input_cube = load_s2_collection(connection, s2_collection, polygon, date)
@@ -291,7 +292,9 @@ def generate_cube(connection, s1_collection, s2_collection, polygon, date):
     # Apply the MOGPR UDF to the multi source datacube
     return apply_neighborhood(
         merged_cube,
-        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context=dict()),
+        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context={
+            'include_uncertainties': get_context_value(include_uncertainties)
+        }),
         size=[
             {"dimension": "x", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
             {"dimension": "y", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
@@ -322,8 +325,11 @@ def generate_mogpr_s1_s2_udp(connection):
     s2_collection = Parameter.string(
         "s2_collection", "S2 data collection to use for fusing the data", S2_COLLECTIONS[0], S2_COLLECTIONS
     )
+    include_uncertainties = Parameter.boolean(
+        "include_uncertainties", "Flag to include the uncertainties in the output results", False)
+
     process = generate_cube(connection=connection, s1_collection=s1_collection, s2_collection=s2_collection,
-                            polygon=polygon, date=date)
+                            polygon=polygon, date=date, include_uncertainties=include_uncertainties)
     return publish_service(
         id="mogpr_s1_s2",
         summary="Integrates timeseries in data cube using multi-output gaussian "

From 78a744a42c6128da134d5c5f06aedabab5d68b6f Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Wed, 31 Jan 2024 16:49:25 +0100
Subject: [PATCH 02/21] feat(#123): updated code to generate std as additional
 output bands

---
 src/fusets/mogpr.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/fusets/mogpr.py b/src/fusets/mogpr.py
index fa4aa13..635b247 100644
--- a/src/fusets/mogpr.py
+++ b/src/fusets/mogpr.py
@@ -195,16 +195,16 @@ def callback(timeseries):
         vectorize=True,
     )
 
-    result = result.assign_coords({output_time_dimension: output_dates})
-
     if include_uncertainties:
-        std = std.assign_coords({output_time_dimension: output_dates})
         std['variable'] = [f"{variable}_STD" for variable in std['variable'].values]
-        result = xarray.concat([result, std], dim=output_time_dimension)
+        merged = xarray.concat([result, std], dim='variable')
+    else:
+        merged = result
 
-    result = result.rename({output_time_dimension: time_dimension, "variable": "bands"})
+    merged = merged.assign_coords({output_time_dimension: output_dates})
+    merged = merged.rename({output_time_dimension: time_dimension, "variable": "bands"})
 
-    return result.to_dataset(dim="bands")
+    return merged.to_dataset(dim="bands")
 
 
 def _MOGPR_GPY_retrieval(data_in, time_in, master_ind, output_timevec, nt):

From b8864d546d3242250b0a9d934e0afa9e8e6ac250 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Wed, 31 Jan 2024 16:50:14 +0100
Subject: [PATCH 03/21] chore(#123): code cleanup

---
 src/fusets/_xarray_utils.py | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/src/fusets/_xarray_utils.py b/src/fusets/_xarray_utils.py
index dc8cefc..80d4f86 100644
--- a/src/fusets/_xarray_utils.py
+++ b/src/fusets/_xarray_utils.py
@@ -44,16 +44,3 @@ def _output_dates(prediction_period, start_date, end_date):
     period = pd.Timedelta(prediction_period)
     range = pd.date_range(start_date, end_date, freq=period)
     return [_topydate(d) for d in range.values]
-
-
-def _suffix_variables(array, suffix):
-    """
-    Rename variables in a data array by appending the suffix to the variable names.
-    """
-    renamed_data_array = array.copy()
-    renamed_variables = []
-    for variable in renamed_data_array['variable'].values:
-        renamed_variables.append(f"{variable}{suffix}")
-
-    renamed_data_array['variables'] = np.array(renamed_variables)
-    return renamed_data_array

From 3e9c9a6107d933d28408fd96de2cb37f13302a3f Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 08:02:33 +0100
Subject: [PATCH 04/21] test(#123): added new parameter to test script

---
 src/fusets/openeo/services/publish_mogpr_s1_s2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index 7991b95..9dfb1ca 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -44,7 +44,7 @@ def execute_udf():
     }
     temp_ext = ["2023-01-01", "2023-12-31"]
     mogpr = connection.datacube_from_flat_graph(
-        generate_cube(connection, 'RVI DESC', 'NDVI', spat_ext, temp_ext).flat_graph())
+        generate_cube(connection, 'RVI DESC', 'NDVI', spat_ext, temp_ext, True).flat_graph())
     mogpr.execute_batch(
         "./result_mogpr_s1_s2_outputs.nc",
         title=f"FuseTS - MOGPR S1 S2 - Local - Outputs - DESC",

From 9cbdd71f7336321582a357e594e98c4dc6383ffc Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 08:04:33 +0100
Subject: [PATCH 05/21] chore(#123): updated the text to make it clear that std
 is included

---
 src/fusets/openeo/services/publish_mogpr_s1_s2.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index 9dfb1ca..995b67d 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -326,7 +326,8 @@ def generate_mogpr_s1_s2_udp(connection):
         "s2_collection", "S2 data collection to use for fusing the data", S2_COLLECTIONS[0], S2_COLLECTIONS
     )
     include_uncertainties = Parameter.boolean(
-        "include_uncertainties", "Flag to include the uncertainties in the output results", False)
+        "include_uncertainties", "Flag to include the uncertainties, expressed as the standard deviation, "
+                                 "in the output results", False)
 
     process = generate_cube(connection=connection, s1_collection=s1_collection, s2_collection=s2_collection,
                             polygon=polygon, date=date, include_uncertainties=include_uncertainties)

From 94e6ddda3d9abd0faa956f48d343646228e8e7b5 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 08:17:33 +0100
Subject: [PATCH 06/21] fix(#123): include the new parameter in the UDP

---
 src/fusets/openeo/services/publish_mogpr_s1_s2.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index 995b67d..74e68dd 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -4,6 +4,7 @@
 from openeo.processes import apply_neighborhood, eq, if_, merge_cubes, process
 
 from fusets.openeo import load_mogpr_udf
+from fusets.openeo.services.dummies import DummyConnection
 from fusets.openeo.services.helpers import DATE_SCHEMA, GEOJSON_SCHEMA, publish_service, read_description, \
     get_context_value
 
@@ -341,6 +342,7 @@ def generate_mogpr_s1_s2_udp(connection):
             date.to_dict(),
             s1_collection.to_dict(),
             s2_collection.to_dict(),
+            include_uncertainties.to_dict(),
         ],
         process_graph=process,
     )
@@ -352,5 +354,5 @@ def generate_mogpr_s1_s2_udp(connection):
 if __name__ == "__main__":
     # Using the dummy connection as otherwise Datatype errors are generated when creating the input datacubes
     # where bands are selected.
-    # generate_mogpr_s1_s2_udp(connection=DummyConnection())
-    execute_udf()
+    generate_mogpr_s1_s2_udp(connection=DummyConnection())
+    # execute_udf()

From 6b455dda67e431ab44a6ef9b6d83fcee63dab6f3 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 08:27:04 +0100
Subject: [PATCH 07/21] chore(#123): started notebook on showing the
 uncertainties

---
 .../FuseTS - MOGPR Multi Source Fusion.ipynb  | 287 ++++++++++++------
 1 file changed, 202 insertions(+), 85 deletions(-)

diff --git a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
index 7a2f988..2d335c2 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
@@ -50,25 +50,25 @@
      "output_type": "stream",
      "text": [
       "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
+      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
       "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
       "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
-      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
+      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
       "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
       "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
-      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
       "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
       "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
       "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
       "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
       "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
       "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
       "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
       "\n",
-      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.3.1\u001B[0m\n",
-      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
      ]
     }
    ],
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "37b1970f",
    "metadata": {
     "id": "37b1970f",
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b2bf0d05",
    "metadata": {
     "colab": {
@@ -192,7 +192,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0ebdc33c9af64e0788e83a9f8a7628b8",
+       "model_id": "41f71eecac2a4f368aed62845954d2e1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -200,7 +200,7 @@
        "Map(center=[51.249352711712234, 5.173686031746518], controls=(ZoomControl(options=['position', 'zoom_in_text',…"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "25dd29b3-856b-4b47-b814-834036262ce2",
    "metadata": {},
    "outputs": [
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "dbd5f5dd-69e6-4d16-ad76-51827f262d2f",
    "metadata": {},
    "outputs": [],
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "369304a6-5020-413c-9a0f-162cc242a0ad",
    "metadata": {},
    "outputs": [],
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "ffcfafe2-40a1-44ff-a738-fb86b66ce65c",
    "metadata": {},
    "outputs": [],
@@ -344,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "299a18e3-63d3-4e05-83e3-9460fe059705",
    "metadata": {},
    "outputs": [],
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "faec8adf-2a1d-486e-a363-544d2158f56b",
    "metadata": {},
    "outputs": [],
@@ -375,7 +375,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
+   "id": "2b27ec81-2543-4fbb-b6c5-2ac796df9e76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service = 'mogpr'\n",
+    "namespace = 'u:bramjanssen'\n",
+    "mogpr = connection.datacube_from_process(service,\n",
+    "                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n",
+    "                                         data=merged_datacube, include_uncertainties=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "77b3f539-b919-43fb-b7c5-31c2853c4c7c",
    "metadata": {},
    "outputs": [],
@@ -398,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "id": "632a7088-67c2-4f89-95ff-813e4587f2be",
    "metadata": {},
    "outputs": [],
@@ -408,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "db4ac328-5413-4304-b1a5-b1c6dd6710fc",
    "metadata": {},
    "outputs": [
@@ -416,53 +430,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:00:00 Job 'j-2312141e96e747528c195bf8569c05db': send 'start'\n",
-      "0:00:35 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:00:40 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:00:47 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:00:55 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:01:05 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:01:18 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:01:34 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
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-      "0:02:18 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
-      "0:02:48 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:03:26 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
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-      "0:11:14 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:12:14 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
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-      "0:14:15 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:15:15 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:16:16 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:17:17 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
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-      "0:24:20 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:25:20 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:26:20 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:27:21 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:28:21 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:29:22 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:30:22 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:31:23 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:32:23 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:33:24 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:34:24 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:35:24 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:36:25 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:37:25 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
-      "0:38:26 Job 'j-2312141e96e747528c195bf8569c05db': finished (progress N/A)\n"
+      "0:00:00 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': send 'start'\n",
+      "0:00:22 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
+      "0:00:27 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
+      "0:00:34 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
+      "0:00:42 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
+      "0:00:52 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
+      "0:01:04 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
+      "0:01:20 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:01:39 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:02:03 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:02:33 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:03:11 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:03:58 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:04:56 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:05:57 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:06:57 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:07:57 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
+      "0:08:58 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n"
      ]
     }
    ],
@@ -487,10 +472,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "id": "ec95ceb1-9027-4305-a40a-6bcf9905c7a2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[                NDVI        date\n",
+       " t                               \n",
+       " 2023-01-02  0.578209  2023-01-02\n",
+       " 2023-01-07  0.567402  2023-01-07\n",
+       " 2023-01-12  0.555066  2023-01-12\n",
+       " 2023-01-17  0.539590  2023-01-17\n",
+       " 2023-01-22  0.530717  2023-01-22\n",
+       " ...              ...         ...\n",
+       " 2023-11-08  0.781755  2023-11-08\n",
+       " 2023-11-13  0.770954  2023-11-13\n",
+       " 2023-11-18  0.759216  2023-11-18\n",
+       " 2023-11-23  0.747641  2023-11-23\n",
+       " 2023-11-28  0.735555  2023-11-28\n",
+       " \n",
+       " [67 rows x 2 columns],\n",
+       "                  RVI        date\n",
+       " t                               \n",
+       " 2023-01-02  0.398482  2023-01-02\n",
+       " 2023-01-07  0.387325  2023-01-07\n",
+       " 2023-01-12  0.375429  2023-01-12\n",
+       " 2023-01-17  0.353655  2023-01-17\n",
+       " 2023-01-22  0.343858  2023-01-22\n",
+       " ...              ...         ...\n",
+       " 2023-11-08  0.405838  2023-11-08\n",
+       " 2023-11-13  0.412925  2023-11-13\n",
+       " 2023-11-18  0.406477  2023-11-18\n",
+       " 2023-11-23  0.404311  2023-11-23\n",
+       " 2023-11-28  0.396419  2023-11-28\n",
+       " \n",
+       " [67 rows x 2 columns]]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cubes_dfs = []\n",
     "ds = xarray.load_dataset(mogpr_output_file)\n",
@@ -500,18 +525,99 @@
     "        var_df = var_df.to_dataframe()\n",
     "        var_df.index = pd.to_datetime(var_df.index)\n",
     "        var_df['date'] = var_df.index.date\n",
-    "        var_df = var_df.set_index('date')\n",
-    "        cubes_dfs.append(var_df)"
+    "        cubes_dfs.append(var_df)\n",
+    "cubes_dfs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "84dab365-5139-4e62-9f1d-438329614d21",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[                NDVI        date\n",
+       " t                               \n",
+       " 2023-01-02  0.578209  2023-01-02\n",
+       " 2023-01-07  0.567402  2023-01-07\n",
+       " 2023-01-12  0.555066  2023-01-12\n",
+       " 2023-01-17  0.539590  2023-01-17\n",
+       " 2023-01-22  0.530717  2023-01-22\n",
+       " ...              ...         ...\n",
+       " 2023-11-08  0.781755  2023-11-08\n",
+       " 2023-11-13  0.770954  2023-11-13\n",
+       " 2023-11-18  0.759216  2023-11-18\n",
+       " 2023-11-23  0.747641  2023-11-23\n",
+       " 2023-11-28  0.735555  2023-11-28\n",
+       " \n",
+       " [67 rows x 2 columns],\n",
+       "                  RVI        date\n",
+       " t                               \n",
+       " 2023-01-02  0.398482  2023-01-02\n",
+       " 2023-01-07  0.387325  2023-01-07\n",
+       " 2023-01-12  0.375429  2023-01-12\n",
+       " 2023-01-17  0.353655  2023-01-17\n",
+       " 2023-01-22  0.343858  2023-01-22\n",
+       " ...              ...         ...\n",
+       " 2023-11-08  0.405838  2023-11-08\n",
+       " 2023-11-13  0.412925  2023-11-13\n",
+       " 2023-11-18  0.406477  2023-11-18\n",
+       " 2023-11-23  0.404311  2023-11-23\n",
+       " 2023-11-28  0.396419  2023-11-28\n",
+       " \n",
+       " [67 rows x 2 columns],\n",
+       " 2023-01-02    0.524968\n",
+       " 2023-01-06    0.514087\n",
+       " 2023-01-09    0.447531\n",
+       " 2023-01-11    0.441934\n",
+       " 2023-01-14    0.449542\n",
+       "                 ...   \n",
+       " 2023-11-17    0.419882\n",
+       " 2023-11-19    0.408779\n",
+       " 2023-11-22    0.421535\n",
+       " 2023-11-26    0.431283\n",
+       " 2023-11-29    0.359894\n",
+       " Length: 110, dtype: float64,\n",
+       " 2023-01-17    0.538890\n",
+       " 2023-02-14    0.486261\n",
+       " 2023-03-01    0.520513\n",
+       " 2023-03-11         NaN\n",
+       " 2023-03-28         NaN\n",
+       " 2023-04-05    0.324869\n",
+       " 2023-04-30         NaN\n",
+       " 2023-05-17    0.282205\n",
+       " 2023-05-27    0.314834\n",
+       " 2023-05-30    0.321425\n",
+       " 2023-06-01    0.338727\n",
+       " 2023-06-04    0.402168\n",
+       " 2023-06-06    0.459086\n",
+       " 2023-06-09    0.550603\n",
+       " 2023-06-11    0.565090\n",
+       " 2023-06-14    0.592962\n",
+       " 2023-06-16    0.584990\n",
+       " 2023-06-24    0.713546\n",
+       " 2023-08-10    0.858731\n",
+       " 2023-08-20    0.848861\n",
+       " 2023-08-23    0.847072\n",
+       " 2023-09-04         NaN\n",
+       " 2023-09-07    0.857140\n",
+       " 2023-09-09    0.826571\n",
+       " 2023-09-24    0.833558\n",
+       " 2023-10-07    0.779410\n",
+       " 2023-10-17    0.828085\n",
+       " 2023-11-28         NaN\n",
+       " dtype: float64]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cols = ['RVI - Raw', 'NDVI - Raw']\n",
     "for result in ['mogpr-multisource-s1-base.json', 'mogpr-multisource-s2-base.json']:\n",
@@ -521,29 +627,40 @@
     "        cubes_dfs.append(df)\n",
     "        result_file.close()\n",
     "\n",
-    "joined_df = pd.concat(cubes_dfs, axis=1)\n",
-    "joined_df = joined_df.rename(columns={0: cols[0], 1: cols[1]})"
+    "cubes_dfs\n",
+    "# joined_df = pd.concat(cubes_dfs, axis=1)\n",
+    "# joined_df = joined_df.rename(columns={0: cols[0], 1: cols[1]})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "id": "8826c305-1f4c-481f-88aa-291d80e38448",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x12769f670>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "cannot reindex on an axis with duplicate labels",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m joined_df\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39m values:\n\u001b[0;32m----> 3\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mjoined_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mjoined_df\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misna\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mplot(values\u001b[38;5;241m.\u001b[39mindex, values[col], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRaw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m col \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-.\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39mcol)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mgrid(\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:3748\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3746\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) DataFrame?\u001b[39;00m\n\u001b[1;32m   3747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, DataFrame):\n\u001b[0;32m-> 3748\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3750\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[1;32m   3751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:11527\u001b[0m, in \u001b[0;36mDataFrame.where\u001b[0;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[1;32m  11518\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwhere\u001b[39m(\n\u001b[1;32m  11519\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  11520\u001b[0m     cond,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  11525\u001b[0m     level: Level \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m  11526\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m> 11527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  11528\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcond\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11529\u001b[0m \u001b[43m        \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11530\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11531\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11532\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11533\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/generic.py:9933\u001b[0m, in \u001b[0;36mNDFrame.where\u001b[0;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[1;32m   9795\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   9796\u001b[0m \u001b[38;5;124;03mReplace values where the condition is {cond_rev}.\u001b[39;00m\n\u001b[1;32m   9797\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   9930\u001b[0m \u001b[38;5;124;03m4  True  True\u001b[39;00m\n\u001b[1;32m   9931\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   9932\u001b[0m other \u001b[38;5;241m=\u001b[39m common\u001b[38;5;241m.\u001b[39mapply_if_callable(other, \u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m-> 9933\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_where\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcond\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/generic.py:9660\u001b[0m, in \u001b[0;36mNDFrame._where\u001b[0;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[1;32m   9657\u001b[0m     cond \u001b[38;5;241m=\u001b[39m cond\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mbool\u001b[39m)\n\u001b[1;32m   9659\u001b[0m cond \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mcond \u001b[38;5;28;01mif\u001b[39;00m inplace \u001b[38;5;28;01melse\u001b[39;00m cond\n\u001b[0;32m-> 9660\u001b[0m cond \u001b[38;5;241m=\u001b[39m \u001b[43mcond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis_number\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   9662\u001b[0m \u001b[38;5;66;03m# try to align with other\u001b[39;00m\n\u001b[1;32m   9663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, NDFrame):\n\u001b[1;32m   9664\u001b[0m     \u001b[38;5;66;03m# align with me\u001b[39;00m\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:5055\u001b[0m, in \u001b[0;36mDataFrame.reindex\u001b[0;34m(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5036\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[1;32m   5037\u001b[0m     NDFrame\u001b[38;5;241m.\u001b[39mreindex,\n\u001b[1;32m   5038\u001b[0m     klass\u001b[38;5;241m=\u001b[39m_shared_doc_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mklass\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5053\u001b[0m     tolerance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   5054\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[0;32m-> 5055\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5056\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5057\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5058\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5059\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5060\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5061\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5062\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5063\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5064\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5065\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5066\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/generic.py:5360\u001b[0m, in \u001b[0;36mNDFrame.reindex\u001b[0;34m(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5357\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_multi(axes, copy, fill_value)\n\u001b[1;32m   5359\u001b[0m \u001b[38;5;66;03m# perform the reindex on the axes\u001b[39;00m\n\u001b[0;32m-> 5360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reindex_axes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5361\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\n\u001b[1;32m   5362\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreindex\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:4890\u001b[0m, in \u001b[0;36mDataFrame._reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   4888\u001b[0m columns \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   4889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4890\u001b[0m     frame \u001b[38;5;241m=\u001b[39m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reindex_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4891\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m   4892\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4894\u001b[0m index \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   4895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:4932\u001b[0m, in \u001b[0;36mDataFrame._reindex_columns\u001b[0;34m(self, new_columns, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   4922\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_reindex_columns\u001b[39m(\n\u001b[1;32m   4923\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   4924\u001b[0m     new_columns,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4930\u001b[0m     tolerance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   4931\u001b[0m ):\n\u001b[0;32m-> 4932\u001b[0m     new_columns, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4933\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnew_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m   4934\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4935\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[1;32m   4936\u001b[0m         {\u001b[38;5;241m1\u001b[39m: [new_columns, indexer]},\n\u001b[1;32m   4937\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[1;32m   4938\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[1;32m   4939\u001b[0m         allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   4940\u001b[0m     )\n",
+      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/indexes/base.py:4275\u001b[0m, in \u001b[0;36mIndex.reindex\u001b[0;34m(self, target, method, level, limit, tolerance)\u001b[0m\n\u001b[1;32m   4272\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot handle a non-unique multi-index!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   4273\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_unique:\n\u001b[1;32m   4274\u001b[0m     \u001b[38;5;66;03m# GH#42568\u001b[39;00m\n\u001b[0;32m-> 4275\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot reindex on an axis with duplicate labels\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   4276\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   4277\u001b[0m     indexer, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_indexer_non_unique(target)\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot reindex on an axis with duplicate labels"
+     ]
     },
     {
      "data": {
-      "image/png": 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7drZ7CxORZklhoRtdeOGF7Nu3j927d/Pss8/y0ksvMXPmTKZMmcI777xDSUnJCcfMnTuXK664gujoaDdULCIiIiLSuHILSnlm0Xbyi+2ubUNjHVzbO55Ppw7i46mD+F2fRAJ8LW6sUhrblb3iaRHiR1Z+KZ9u3O/ucs5Z+Z706qCwisNBeXqGewryMvbsbIp+XKVwVaSeKCx0Iz8/P2w2G4mJiVx55ZWMHDmSRYsWMXHiREpKSvjggw9q7J+WlsaSJUuYMmWKmyoWEREREWlcU+at5bnFO3h3bXVocl60k79d2YVuCWFurEzcyd9qYfIFKVgtJg6mZXpdUPTj7kOkfrqZ+ev2AuCbkgzmmh/PnWYzlsREd5TnVfLmz2fniAvJmDSJnSMuJG/+fHeXJOL1mm5YWF5U90tlRfXxlRXGNntJ7c57jjZt2sSKFSvw9fUlOjqasWPHMmfOnBr7vPbaayQkJDBq1Khzvj0REREREU9T6XDy1aYsisqq35dPPD+JvikRtG8Z4sbKxBPdeH4y33TMZ8iMWzwiKLJXOkg/VMTyHQd5e3UGT321lT++/RNjX/iB3o8u4ue9ea59N+8vYO4Pe/h2aw5grFwdOyvVFRhWmkzM7n41t3yRwcGjZe64O15Bw7dFGkbTXeDkb3F1P+ba16DLVcb/t34K70+C5EEw+fPqfWZ3g+JDJx77SH6db27hwoWEhoZSUVFBWVkZZrOZ559/HoApU6ZwySWXkJaWRqtWrXA6ncybN4+bb74Zs7npZrwiIiIi0vzkFZczf91eXluxh71HSnh0bBduHJACwO/6JHJd3yT3FigeyT/vEMWPP1YjKNr315lkte9Bp+7tGux2Kyod/LIvn/UZeWzNKiDzSDGZh0vIyi/B4Tz1cRmHi+meEA5An+QIbhvSml6J4a7rw6+5hqBBgyhPz2BRvpXvl2VTuvMgl/5zGc+N78X5raMa7D55q9MN39Zq0iJnr+mGhV5g8ODBvPTSS5SUlPDss8/i4+PD1VdfDcBFF11EQkICc+fOZdasWSxevJiMjAwmT57s5qpFRERERM5dpcPJil0HeW/tXhZuznYtUhERaK0RuGixEjmVkwVFZuexef6OhYWl9kpMJvDzOfs5LQ8dLePg0XI62IzerfZKJ9e8uJLKkySDfj5mEiICSIwMJCkykMSIQBIjA0iICKR1iyDXfj0Sw+lxXFBYxWqzYbXZuBLo3KOQO99cz87co0z474/8v1EduGNoG8xm/U5U8UlKwmkyYXJWt4XTbKbcFkvQaY4TkdNrumHhQ2cxya3Fr/r/HccY5zD9phffn345t7qOExgYSNu2bTGbzcyZM4cePXrw6quvMmXKFMxmM5MmTWLevHk88sgjzJ07l+HDh9O6det6u30RERERkcaWcaiY+esymb9uL/vzS13bO9pCuHlgClf2jNdiJVIrrnn+jgsMnWYznfp0dv2c+umvvL06Az8fM6EBVkL9fY79az3Jzz6EBVhpFxPiCga/3pzNba+vo3tCGJ9MHQRAgK+FQW2jsVpMdIsPJznKCAQTIwKJDvartzCvfcsQPpl6AQ9/tIkF6/fx9MJtrEo7zLO/60FUsN+ZT9DEVVQ6eGh5LoU9ruHujfOxOJ1Umkw81/1q1v5vKz88EKfXEpGz1HTDQt9z/B7B4mNc6vu8p2A2m3nooYeYNm0aEyZMICAggMmTJ/PYY4+xYMECPvzwQ1555ZUGuW0RERERkYZUUl7JV5uzeG/NXlburp7SJyzAypU947i2TyJd4kLVi1DqpGqeP9ecdWYzcbNS8Y2NBYxehZ//bHQiKatwcKCwjAOFZ57/7/cXtGLGGCNw7BJvLKJjr3RS6XBiORYEzvt9v4a4SycI9PXhH9f24PxWUfz14018v/0Alz23nH9N6EXflMhGqcETlVc4uPfdDXz+SxaW1udzxS3jGBRYwneFfuz5tZARCeE1gsKMQ8UkRQW6sWIR79J0w0IvdO211/LnP/+ZF154gfvuu49WrVoxYsQIbrvtNvz8/Bg3bpy7SxQRERERqZNN+/IZ//KPFB5btMRkgkFto/ldn0Qu6twSf6t6/sjZO36eP9/kpBrz1PlbLWyYMYqj5RUUlNgpKKmgoNROfond+Ln02PZS47r8Y/8/frhwXJg/G2eOIizA6o67BxhD8X/XN5HuiWHc+eZ6dh8o4pFPNvPp1EHNckhyqb2SO99cz7dbc/G1mHlufC8u7mq0+1XAlaOcFJVXuvbfml3AxbOXMaxDC165qQ8+Fq0BIHImCgs9iI+PD1OnTuWpp57ijjvuICgoiClTprB48WLuvPNO/P393V2iiIiIiMhpHTxaRubhYnolRQDQrmUwFouJxMgAru2dyNW9E4gPD3BzldKUVM3zdzJms8kYYuxvhYi6n9tkMrk1KDxeR1son04dxKOf/cqtQ1o3y6CwqKyCW/+3lhW7DuHnY+blm/owtH2LGvuYTCaC/aqjjjV7jmA2QZCvT42g0OFwNsvHUKQ2FBa6ydy5cykoKDhh+/Tp05k+fbrr5/HjxzN+/PiTnmPYsGE4nadZbktEREREpBGt2HmQm+asJi48gCX3DcNsNuHnY+GjOy8gKTJQH8xFzlGQnw9PXN29xrY5y9NoExN8QmjW1OSX2Jk8dzXrM/II8rXw6qS+tVoh+sbzkxnWvgWO4z47px0s4sZXVzFpYAqju9hIiAjQNAgix1FYKCIiIiIiZyW3oJT9+aX0PLaqa8+kcAKsFiICrRwqKqdFiLEIQ0q01iUVaQjrM47w2Oe/4nDCZ38cRNdjcyw2RUVlFeQUlBHq78P/pvR3ve7URmJkzfkK31qVzt4jJTz2+RYe+3wLsWH+9E2JpG+rSPqlRNIuJlhfbkizprBQRERERERqrdLh5PvtB3h7dQaLt+bSOjqIr+8dgslkItDXh8X3DSUmRNPniDSGzrGh3NA/maLyiiYdFALEhQfw5i39KS6vpHNc6Dmd6/+N6kCbFsG8uzaTX/bmk5Vfyicb9/PJRmNBnLAAK32SI+jbKpK+KZF0iw/D10dzHUrzobBQRERERETOKCu/hPfW7OXdNRnszy91bQ8LsJJXbCciyBdAQaFII/K3Wnj0yq5UOqqH2B46WsbqtMNc0i3WjZXVj71HitmeU8iIji2B+uul7G+1cH2/JK7vl0RxeQUbMvJYvecwa/YcZn16HvkldhZvzWXx1txj+5sZ3y+JmWO61Mvti3g6hYUiIiIiInJSTqeTlbsOMW/lHhb9mkNVHhEWYGXcefGM75dE+5Yh7i1SRLAcGzLrdDq57/2NfLftAOP7JTLj8i4E+HrniuOFpXZ+9+JKDhwtY86kvgxu1zBzMgb6+jCwbTQD20YDYK90sHl/AWv3HGZ12mHWph/hcFE5EYG+rmOKyyv4alM2Izu3NBbPEWliFBaKiIiIiEgNR8sq+HD9XuatTGdn7lHX9n6tIpnQL4mLu9rwt3pnACHSlDmc0CUujCXbD/D26kzW7jnCvyb0oqPt3IbtukOIv5VeSRFszS6gXUzjfSlhtZjpmRhOz8RwbhncGqfTybacQiKPCwu/23qAae9tpG1MMN9MG9potYk0FoWFIiIiIiLi8uyi7cxZnkZhWQUAgb4Wrj4vgRsHJKsXoYiHs5hN3De6AwPbRPGndzewI/coY/61nFsGt+YPg5LdXd5pVVQ6eHNVBhd3tdEy1JjOIHVsF6xmM2GB7uu9ZzKZThq2to0JZmSnlq6fyysc3Pf+Ri7sFMOFnVoS7Ke4RbzXWc3Q+cILL5CSkoK/vz/9+/dn9erVp91/9uzZdOjQgYCAABITE7n33nspLS097TEiIiIiItLwnE4nTmf1fGdlFQ4KyypoHR3EI2M68+NDF/LolV0VFIp4kYFto/nynsGM7NQSe6WT/yzZxejnfmD9QVON33dPsT7jCFc8/wMzP9nMY59vcW2PDvZza1B4Kpd1j2XRvUOYdlF717Yfdh7kk437ueedDZz36CL+8PpaPt6wj6PHvngR8SZ1jrrfffddpk2bxosvvkj//v2ZPXs2o0ePZtu2bcTExJyw/1tvvcX06dOZM2cOAwcOZPv27UyaNAmTycQzzzxTL3dCRERERETq7rutufzr2x3cN6qDa76umwcmc0HbKC5oE4352DxoIuJ9ooL9eOXmPnzzaw6pn20m83AJ8wosbJu7lllXdvOILwAKS+387YstvL06EzDmQ+3fKhKn04nJ5NmvPyaTCV+f6hrbxgRz94i2fPZzFrsPFrFwcw4LN+fg52NmeIcYLu0ey4UdYwhSj0PxAnV+lj7zzDPceuutTJ48GYAXX3yRzz//nDlz5jB9+vQT9l+xYgUXXHABEyZMACAlJYXx48ezatWqcyxdRERERETOxZJtuazPyOOV5WmusDA2LIDYsAA3VyYi9WVk55YMahfNf77bwb+/28mPaUe45J/LmDQwhXtGtnPbAh2r0w4z7b0N7D1SAsC1vROYfklHooL93FLPuUqMDGTaqA7ce1F7tmYX8vnPWXz28372HCrmq83ZfLU5G18fM0PateCSrjZGdmrpkb0mRaCOYWF5eTnr1q3jwQcfdG0zm82MHDmSlStXnvSYgQMH8sYbb7B69Wr69evH7t27+eKLL7jxxhtPeTtlZWWUlZW5fi4oKADAbrdjt9tr7Gu323E6nTgcDhwOR13ujltVdf2uql3qzuFw4HQ6sdvtWCz1P8F21XPtt885aRx6/D2f2sgzqV28g9rJMzX1dikosfP2mr0MbBNJt/gwAG4ekIi/j5mbBiR5zf1u6u3UFKiNPI8F+MOgJMKObGNFiY1vth7k1eVpfLpxP5/cNYCoIN8znqO+lFc4+Nd3u3hpWRpOJySE+/Pk1V3plxIJNI3nTdvoAO4Z0Zq7h7diS3YhX27K4ctNOaQfLuabLTl8syUHH7OJmwckMf3iDjWO1e+PZ2oq7VLb+k3OOkxYsH//fuLj41mxYgUDBgxwbb///vtZunTpKXsLPvfcc9x33304nU4qKiq4/fbb+c9//nPK23nkkUdITU09Yftbb71FYGBgjW0+Pj7YbDYSExPx9W28F7hzdeedd/L2228Dxn2Ii4tj7NixPPTQQ/j7+zNw4ED69+/Ps88+e8Kx77zzDvfccw+//vorW7ZsYcyYMezZs4ewsLAGq8cTlZeXk5mZSXZ2NhUVmgdCRERE5GQKyuG7/WZ+yDVRVmmie6SDKR30ZbVIc7Ylz8QHaWYSgpxMat94rwfZxfD6Tgt7i4zhu/1aOLg6xYF/MxiZ63TC/mLYeNjMxkMmsktMXJVSybBYI5I5aod1B030iHQS7p2dK8ULFBcXM2HCBPLz8wkNPfUq6Q3+K7lkyRL+9re/8e9//5v+/fuzc+dO7rnnHh599FH++te/nvSYBx98kGnTprl+LigoIDExkVGjRp1wZ0pLS8nMzCQ4ONhjQ62TsVqtXHjhhcybN4+KigrWrVvH5MmT8ff354knnuDWW28lNTWV559/noCAmsNA3nvvPcaMGUOrVq1IT08HICQk5LQNXZt6Ro8ezZw5c7Db7SfU44lKS0sJCAhgyJAhDdL2drudRYsWcdFFF2G1qnt4Y9Pj7/nURp5J7eId1E6eqam1y5Hicv67bA+v/5xBqd0IA9rHBHPT4BQu7Rnn5urOXlNrp6ZIbeSZjm+XS61WptorKbE7CD82FDanoJSPNmQxeWAyvj5ntRbqKTmdTt5cnckza7ZTVuEgPMDKo2M7c3GXlmc+uInafaCI8EArkcd6db6/bi8L1v7K1rJg/pCSp98fD9NUXteqRu6eSZ3CwujoaCwWCzk5OTW25+TkYLPZTnrMX//6V2688UZuueUWALp160ZRURG33XYbf/nLXzCbT3wR8vPzw8/vxCjdarWe0CiVlZWYTCbMZvNJz1UX2UXZZBRkkBSahC3o5PenPvn5+REbG4vZbCY5OZk333yTb775BrPZzI033sj06dP58MMPmThxouuYtLQ0lixZwhdffFHjPp/r/TeZTPj7+xMXZ7xx/G09AIcOHWLq1Kl8//33HDlyhDZt2vDQQw8xfvx4AD777DMmTpzIoUOHsFgsbNiwgV69evHAAw+4AsdbbrmF0tJS3njjjbOutYrZbMZkMp30eVGfGvr8cnp6/D2f2sgzqV28g9rJM3l7uxSU2nllWRpzlqe5VuHslRTO3SPaMaxDC49fNKC2vL2dmgO1kWeqaher1UrwcdsfX/gLn/+cxa6DxTx7Xc96u72S8kpuf2MdS7cfAGBwu2j+fm0PWoZ6T2efhtAhLrzGz9EhAfRJjmBouygoysNqtVLmMHHDK6sY1bkll3WLJSU6yD3Fiou3v67VtvY6pUu+vr707t2bxYsXu7Y5HA4WL15cY1jy8YqLi08Isarml/OkJdsX7FjA6A9GM+XrKYz+YDQLdixo1NvftGkTK1ascA2ljo6OZuzYscyZM6fGfq+99hoJCQmMGjWqUesBoydf7969+fzzz9m0aRO33XYbN954I6tXrwZg8ODBFBYW8tNPPwGwdOlSoqOjWbJkiescS5cuZdiwYQ1au4iIiEhzVFRWwQvf7WTwk9/x3OIdHC2roHNsKHMm9WHBHQMZ3jGmyQSFIlL/RnVuSctQP24Z3Kpez+tvNePnY1weGdOZeZP7Nfug8GRGdbEx/46B/GFI9eP/3dZcNmbm8fTCbQz7+xIu/9cyXly6i8zDxW6sVJqDOg9DnjZtGjfffDN9+vShX79+zJ49m6KiItfqyDfddBPx8fE8/vjjAIwZM4ZnnnmGXr16uYYh//Wvf2XMmDENsijF2cguyiZ1ZSoOpzE8w+F0kLoylYFxAxu0h+HChQsJDQ2loqKCsrIyzGYzzz//vOv6KVOmcMkll5CWlkarVq1wOp3MmzePm2+++Zx7UZ7MZ599RnBw8CnriY+P57777nP9/Mc//pGFCxfy3nvv0a9fP8LCwujZsydLliyhT58+LFmyhHvvvZfU1FSOHj1Kfn4+O3fuZOjQofVeu4iIiEhzVWqv5I0f0/nPkl0cKioHoF1MMNMuas/oLjbMZgWEInJmY3vGc0nX2BpDkP+9ZCdRQb5c2zuxTq8lR8sqcDidhPpbMZlMPHF1dw4dLaNdy5CGKL1JOf5LncHtonny6m589nMWK3YdYtO+AjbtK+CJL7fSMzGcy7vHcln3WK1gL/WuzmHhddddx4EDB5gxYwbZ2dn07NmTr776ipYtjbkGMjIyagRZDz/8MCaTiYcffph9+/bRokULxowZw//93//V3704RxkFGa6gsIrD6SCzMLNBw8LBgwfz0ksvUVJSwrPPPouPjw9XX3216/qLLrqIhIQE5s6dy6xZs1i8eDEZGRmuYLY2br/99hpDfo8ePXrKfYcPH85//vMfioqKTlpPZWUlf/vb33jvvffYt28f5eXllJWV1Vh0ZujQoSxZsoT/9//+H8uWLePxxx/nvffeY/ny5Rw+fJi4uDjatWtX6/pFRERE5PTun/8zn2zcD0ByVCD3jmzPmB5xWBQSikgdHR8U7sw9yjNfb6fC4WT+ur08Pq4bbWPOHPZt3p/P1Ld+olt8GM+N7wVAZJCva24+qb3wQF+u65vEdX2TOHS0jC83ZfPZz/tZlXaYDZl5bMjM47HPt9A3JYLLu8dxabdYWoRodRQ5d2e1wMnUqVOZOnXqSa87fsgpGCvrzpw5k5kzZ57NTTWKpNAkzCZzjcDQbDKTGJLYoLcbGBhI27ZtMZvNzJkzhx49evDqq68yZcoUowazmUmTJjFv3jweeeQR5s6dy/Dhw2ndunWtb2PWrFk1egOeTlBQEG3btgU4aT1PP/00//znP5k9ezbdunUjKCiIP/3pT5SXl7vOMWzYMObMmcPGjRuxWq107NiRYcOGsWTJEo4cOaJehSIiIiLnqKLSQVmFgyA/46385AtSWJd+hLsvbMu48xKwWup/BIqIND8pUYFMv6Qjzyzazpo9R7j0n8u5c3gb7hjWBj+fU48SLLU7yDhcTKm9kkNHy4gKVnhVH6KC/Zh4fjITz08mt6CUL37J4rOfs1ibfoQ1e4zLrM9+5Y6hbbhvdAd3lyteTu8kAFuQjZkDZmI2HVssxGRm5oCZjbLISRWz2cxDDz3Eww8/TElJiWv75MmTyczMZMGCBXz44Yeu4K62YmJiaNu2retyLvX88MMPjB07lokTJ9KjRw9at27N9u3baxxXNW/hs88+6woGq8LCJUuWaL5CERERkXOwbMcBLnr2e55ZVP0erFdSBEv/PIzr+iYpKBSReuNjMXPL4NYsmjaUER1jKK90MPubHVz+3HLWpR+usW+lo3o9gt7JEbww4Ty+vGewgsIGEhPqz6QLWjH/joGsfHAED1/WiR4JYVQ6nCRHVY/8O3S0jBW7DuJweM56EeId9G7imHHtxrHw6oXMGT2HhVcvZFy7cY1ew7XXXovFYuGFF15wbWvVqhUjRozgtttuw8/Pj3HjGq+u39bTrl07Fi1axIoVK9iyZQt/+MMfTlgZOyIigu7du/Pmm2+6gsEhQ4awfv16tm/frp6FIiIiIuco7WARX/6SRVlFpWubj0JCEWkg8eEBvHpzH/41vhfRwb7syD3KNS+uZMbHmygstbNy1yEu/McStucUuo65uKuN8EANO24MsWEB3DK4NR9PHcTi/zeUS7vFuq778Kd9TPjvKv749k9urFC8kd5VHMcWZKOvrW+j9ig8no+PD1OnTuWpp56iqKjItX3KlCkcOXKECRMm4O/feKtG/baehx9+mPPOO4/Ro0czbNgwbDYbV1555QnHDR06lMrKSldYGBkZSefOnbHZbHTooO7QIiIiIrVRaq/k9ZV7mPtDmmvboLbRPDGuG19PG3raYYAiIvXJZDIxpkcc30wbyrW9E3A64X8r0xmX+iFPPv4GR/fu55mvt5/5RNKg2rQIdk1RAVBe6SDE34d+rSJd2w4UljHj402s2HWQikrHyU4jcnZzFsq5mzt3LgUFBSdsnz59OtOnT6+xbfz48YwfP/6k5xk2bBhO57l3KX7ttddOuv34eoKCgvjoo4/OeK7Zs2cze/bsGts2bNhwbgWKiIiINBPF5RW8tSqDl7/fTW5hGSF+Pow7L4GwAGNV0ev7Jbm7RBFppsIDfXn62h5c2Suez554kYnL38KME4fJRPSAR4De7i5RjnPnsLb8/oJWOI7LDL7+NZv/rUznfyvTiQzyZXSXllzcNZaBbaI0lYW4KCwUEREREfEABaV2Xl+ZzqvL0zhcZCwgFxfmz+3D2uDnow9wIuI5+gVXELnibcAIocxOJ4dnpRI5bAhWm3tG6snJ+Vtr9kLvHBvK7/ok8PWvORwuKuft1Zm8vTqTUH8fRnZuyVW94rmgTTRms8lNFYsnUFgoIiIiIuJGR8sqePn73cz9IY3C0goAkqMCuXNYG67qlYCvgkIR8TDle9LB8ZshrA4H5ekZCgs9XK+kCHolRfB/lQ5W7T7Ml5uyWLg5m4NHy1mwfh8L1u8jISKA6/okcm2fRGxhjTcVmngOhYUiIiIiIm7gcDhZ8NM+nvxqKwcKywBoGxPM1OFtubx7rBYtERGP5ZuSDGZzzcDQbMY3WdMkeAurxcygdtEMahfNrLFdWbvnMJ/+vJ+PN+xn75ES/rFoO89+s53hHWK4c3gbeidHnvmk0mQoLBQRERERaWTrM46Q+umvbMzMA4yehPeP7sglXW0a+iUiHs9qsxE7K5WsGTONwNBsJnZWqnoVeimL2UT/1lH0bx3FXy7tzJebsnhndSar9xxm8dZcrumd4NrX6XRiMunvVFOnsFBEREREpBEt3pLDlHlrAQj28+GPI9oy6YIUrW4sIl4l/JprCBo0iPL0DHyTkxQUNhEBvhbGnZfAuPMS2HXgKB+u38eFnVq6rn/+252s2HWIP45oy8C20W6sVBqSwkIRERERkUY0qF00raOD6J0cwZ8v7kBMiOaDEhHvZLXZFBI2YW1aBHPf6A6un51OJ++v20vG4WKu65tYY7t6GzYtCgtFRERERBrQnoNFvLJ8N4+M6YKPxYyfj4XP7h5EoK/eiouIiPcwmUy8fdv5LFi3l4u7VofEc3/YwzdbcrhlcCuGtY/RdBpNgN6hiIiIiIg0kPIKB9e//CPZBaXYQv2ZOqIdgIJCERHxSvHhAfzxwnaun51OJ6//mE7awSJW7DpE6xZBTBnUinG9Egjw1fQa3kpLrImIiIiINBBfHzMPXtqR81tHctV5CWc+QERExIuYTCbeuKU/tw1pTYifD7sPFPGXDzcx8InF/H3hNnILSt1dopwFfaUpIiIiIlKPvt2ag9ViZnC7FgCM7RnPmO5xGpYlIiJNUnx4AA9d2om7L2zH+2szmfNDGpmHS3j+u5289P0urugRz5RBregcF+ruUqWW1LPQTSZPnkxERARPPvlkje0fffRRjYlBlyxZgslkwmQyYTabCQsLo1evXtx///1kZWW59uvWrRu33377SW/r9ddfx8/Pj4MHD7rOl5eXd071T5o0yVWX1WqlVatW3H///ZSW6lsDERERaZ6Kyyv4y4e/8PvX1jLtvY0cLip3XaegUEREmrpgPx8mX9CKJfcN58WJ59EnOQJ7pZMP1u/l0ueWMeG/P/LVpmwqKh3uLlXOQGGhG/n7+/PUU09x5MiRM+67bds29u/fz5o1a3jggQf45ptv6Nq1K7/88gsAU6ZM4Z133qGkpOSEY+fOncsVV1xBdHT9Lmt+8cUXk5WVxe7du3n22Wd56aWXmDlzZr3ehoiIiIg32JiZx+XPLefNVRkAjOkeR6DmahIRkWbIYjZxcddY5t8xkA/vHMjl3WOxmE2s2HWI299Yx9UvrnR3iXIGCguPY8/OpujHVdizsxvl9oYOHYrNZuPxxx8/474xMTHYbDbat2/P9ddfzw8//ECLFi244447AJg4cSIlJSV88MEHNY5LS0tjyZIlTJkypd7r9/Pzw2azkZiYyJVXXsnIkSNZtGiR6/pDhw4xfvx44uPjCQwMpFu3brz99tuu6z/77DPCw8OprKwEYMOGDZhMJqZPn+7a55ZbbmHixIn1XruIiIhIfaiodPDc4h1c/Z8V7D5YhC3Unzem9GfGmM74WxUWiohI89YrKYLnJ5zH9/cP5/ahbYgK8mVY+xau6ysqHfyw8yAOh9ONVcpvKSw8Jm/+fHaOuJCMSZPYOeJC8ubPb/DbtFgsPPbYY/zrX/9i7969dTo2ICCA22+/nR9++IHc3Fyio6MZO3Ysc+bMqbHfa6+9RkJCAqNGjarP0k+wadMmVqxYga+vr2tbaWkpvXv35vPPP2fTpk3cdttt3HjjjaxevRqAwYMHU1hYyE8//QTA0qVLiY6OZsmSJa5zLF26lGHDhjVo7SIiIiJnI/1QEb97aSXPLNpOhcPJZd1j+epPgxnUrn5Hc4iIiHi7+PAApl/SkRUPjuDWIa1d27/dmssNr6xiwis/urE6+S2FhRg9CrNmzATHsXHzDgdZM2Y2Sg/Dq666ip49e57V8N2OHTsCsGfPHsAYirxkyRLS0tIAYwnzefPmcfPNN2M2139Tf/bZZwQHB+Pv70+3bt3Izc3lz3/+s+v6+Ph47rvvPnr27Enr1q354x//yMUXX8x7770HQFhYGD179nSFg0uWLOHee+/lp59+4ujRo+zbt4+dO3cydOjQeq9dRERE5Gw5nU7eXZPBpf9cxvqMPEL8fHj2uh48P74X4YG+Zz6BiIhIM+XnYyHYr3qt3QNHywjx86FbfJhrW0Wlg1eW7WbvkWJ3lCgoLASgfE96dVBYxeGgPD2jUW7/ySefZN68eWzZsqVOxzmdRjfdqgVRLrroIhISEpg7dy4AixcvJiMjg8mTJ9f6nLfffjvBwcGuy+kMHz6cDRs2sGrVKm6++WYmT57M1Vdf7bq+srKSRx99lG7duhEZGUlwcDALFy4kI6P6cR06dChLlizB6XSybNkyxo0bR6dOnVi+fDlLly4lLi6Odu3a1bp+ERERkYZ06GgZf3h9HQ988AtF5ZX0axXJl38azFW9EmosUiciIiJndkP/ZFb95ULuGNbWte2HXYd47PMtDHryO8b9+wde+yGN3EItptqYFBYCvinJ8Nued2YzvslJjXL7Q4YMYfTo0Tz44IN1Oq4qXExJSQHAbDYzadIk5s2bh8PhYO7cuQwfPpzWrVuf5iw1zZo1iw0bNrgupxMUFETbtm3p0aMHc+bMYdWqVbz66quu659++mn++c9/8sADD/Ddd9+xYcMGRo8eTXl59cqAw4YNY/ny5WzcuBGr1UrHjh0ZNmwYS5YsYenSpepVKCIiIh5jybZcRs9exte/5mC1mJh+SUfevvV8EiIC3V2aiIiI1wr09SEyqLpnvq/FzIDWUZhMsD4jj0c+/ZXz/7aYG175kXfXZFBYandjtc2DwkLAarMROyu1OjA0m4mdlYrVZmu0Gp544gk+/fRTVq6s3apAJSUlvPzyywwZMoQWLaonB508eTKZmZksWLCADz/8sM4Lm8TExNC2bVvXpbbMZjMPPfQQDz/8sGtF5h9++IGxY8cyceJEevToQevWrdm+fXuN46rmLXz22WddwWBVWLhkyRLNVygiIiIeIyu/lINHy2gXE8xHd13A7UPbYDGrN6GIiEh9GtAmirdvO58fH7yQv17emZ6J4Tic8MPOQzzwwS+c/7fFPPzRL2zNLnB3qU2Wz5l3aR7Cr7mGoEGDKE/PwDc5qVGDQoBu3bpxww038Nxzz530+tzcXEpLSyksLGTdunU89dRTHDx4kAULFtTYr1WrVowYMYLbbrsNPz8/xo0b1xjlA3Dttdfy5z//mRdeeIH77ruPdu3aMX/+fFasWEFERATPPPMMOTk5dO7c2XVMREQE3bt358033+T5558HjJ6Wv/vd77Db7epZKCIiIm5VVlGJn4+xqvH1fRMBuKpXvFY6FhERaWAtQ/2ZMqgVUwa1IuNQMZ/+vJ8P1u9l94Ei3vgxgzd+zKBvSgQTz0/mkq6x+PqoP1x90SN5HKvNRlD/fo0eFFaZNWsWjt/OnXhMhw4diIuLo3fv3jzxxBOMHDmSTZs21QjeqkyZMoUjR44wYcIE/P39G7psFx8fH6ZOncpTTz1FUVERDz/8MOeddx6jR49m2LBh2Gw2rrzyyhOOGzp0KJWVla5ehJGRkXTu3BmbzUaHDh0arX4RERGRKhWVDp7/dgejnv2egmPDnUwmE+P7JSkoFBERaWRJUYHcNbwti6cN5a1b+nNJVxsWs4k1e45wzzsbmPNDmrtLbFLUs9BN5s6dS0FBzS6zKSkplJWV1dg2bNgw10ImtTV+/HjGjx9/0uvO5nwn89prr510+/Tp05k+fTpgzGn40UcfnfFcs2fPZvbs2TW2nWm+RBEREZGGVFbh4L21e8k4XMyH6/dx88AUd5ckIiLS7JlMJga2jWZg22iy80t5Z00G89ftZdx58a591u45TFF5JYPbRmPWdCFnRWGhiIiIiAjgdDqp+k41yM+HZ6/ryZ6DRTU+gIiIiIhnsIX586eR7bnnwnaYTNWh4D++3s7K3YeYfklHbh/axo0Vei8NQxYRERGRZq+orIJ73v2ZZdnVHzZ6J0dwde+EGh9ARERExLMc/3e60uGkY2wI4YFWrugR59q+aV8+GzLz6mWkZXOgnoUiIiIi0qxlHi7m1v+tZWt2If4WM38ptRNptbq7LBEREakji9nEzDFdePCSTjUWPPnH19v4btsBusWHMfH8JK7oEU+Ar+YgPhX1LBQRERGRZmt12mHGvvADW7MLiQ725fZOlYT4KygUERHxZscHhZUOJ1HBfvj6mPllXz4PfPAL/f/2DbM+/ZXdB466sUrP1WTCQnUlbX7U5iIiInIu3lmdwQ2v/MjhonK6xIWy4PbzaRXi7qpERESkPlnMJv5+bQ9+fPBCHrykI4mRARSUVjDnhzRG/GMpE19ZxVebsqmodLi7VI/h9cOQLRaj22h5eTkBAQFurkYaU3FxMQBWDRMSERGROqiodPDY51t4bcUeAC7rFsvT13bHanLyk3tLExERkQYSGeTLH4a24dbBrVm64wBvrEzn2225LN95kOU7D2IL9WfaRe35Xd9Ed5fqdl4fFvr4+BAYGMiBAwewWq2Yzd7RWdLhcFBeXk5paanX1OwpnE4nxcXF5ObmEh4e7gqMRURERM4kr7icqW/9xPKdBwGYdlF7/jiiLSaTCbvd7ubqREREpKGZzSaGd4hheIcYMg8X8/bqDN5dk0l2QSkOjWAEmkBYaDKZiI2NJS0tjfT0dHeXU2tOp5OSkhICAgK0wt5ZCg8Px2azubsMERER8RI7cwu5Zd5a9hwqJsBq4dnrenBx11h3lyUiIiJukhgZyP0Xd+Seke34alM2F3Vu6e6SPILXh4UAvr6+tGvXjvLycneXUmt2u53vv/+eIUOGaBjtWbBarepRKCIiIrX23dZc7n77JwrLKogPD+C/N/Whc1you8sSERERD+DnY2Fsz3h3l+ExmkRYCGA2m/H393d3GbVmsVioqKjA399fYaGIiIhIA8o8XMyt/1tLhcNJv5RI/j3xPKKD/dxdloiIiIhHajJhoYiIiIjIySRGBnLvRe3JPFzMrLFd8fXRfNEiIiIip6KwUERERESanNyCUuwOJ/HhAQDcOawNgOaKFhERETkDfa0qIiIiIk3KlqwCrnj+B6a8toaisgrACAkVFIqIiIicmcJCEREREWlSwgKsVDic2Csd5JXY3V2OiIiIiFfRMGQRERER8XpOp9PVczAuPIDXp/QjPiKAUH8tJCciIiJSF+pZKCIiIiJewZ6dTdGPq7BnZ9fYnnm4mCnz1vLlL1mubZ1iQxUUioiIiJwF9SwUEREREY+XN38+WTNmgsMBZjOxs1LxH3sV//1+N89/t5OyCgfrM44wuH0Lgv30FldERETkbOmdlIiIiIh4NHt2dnVQCOBwsH/GTB7aZGZDqR8A/VtF8uiVXRUUioiIiJwjvZsSEREREY9Wvie9Oig8xuRwUJmZSUzrLvzlsk5c0SNOqx2LiIiI1AOFhSIiIiLi0XxTksFsrhEYVppMDBt+Hrddcz4hmptQREREpN5ogRMRERER8WhWm42WqalUHus56DCZ8bnvIf7fxMEKCkVERETqmXoWioiIiIjH29F7OHeN+gtty48w5y/jCEyIc3dJIiIiIk3SWfUsfOGFF0hJScHf35/+/fuzevXqU+47bNgwTCbTCZfLLrvsrIsWERERkebl81+yOBgQTsKIwQoKRURERBpQncPCd999l2nTpjFz5kzWr19Pjx49GD16NLm5uSfdf8GCBWRlZbkumzZtwmKxcO21155z8SIiIiLS9FU6nHzxSzYAl3WPdXM1IiIiIk1bncPCZ555hltvvZXJkyfTuXNnXnzxRQIDA5kzZ85J94+MjMRms7kuixYtIjAwUGGhiIiIiNTKkeJyOseFEhXkywVtot1djoiIiEiTVqc5C8vLy1m3bh0PPviga5vZbGbkyJGsXLmyVud49dVXuf766wkKCjrlPmVlZZSVlbl+LigoAMBut2O32+tSssequh9N5f40RWoj99Lj7/nURp5J7eId1E51E+Zn5tUbe1Fmr8TkrMRur2yQ21G7eAe1k+dTG3kmtYt3UDt5pqbSLrWt3+R0Op21Pen+/fuJj49nxYoVDBgwwLX9/vvvZ+nSpaxateq0x69evZr+/fuzatUq+vXrd8r9HnnkEVJTU0/Y/tZbbxEYGFjbckVERERERERERAQoLi5mwoQJ5OfnExoaesr9GnU15FdffZVu3bqdNigEePDBB5k2bZrr54KCAhITExk1atRp74w3sdvtLFq0iIsuugir1erucuQk1Ebupcff86mNPJPaxTuonWpvf14JJpOJ2DD/Br8ttYt3UDt5PrWRZ1K7eAe1k2dqKu1SNXL3TOoUFkZHR2OxWMjJyamxPScnB5vNdtpji4qKeOedd5g1a9YZb8fPzw8/P78TtlutVq9ulJNpivepqVEbuZcef8+nNvJMahfvoHY6s//+sJU3fszgvlHtmTqiXaPcptrFO6idPJ/ayDOpXbyD2skzeXu71Lb2Oi1w4uvrS+/evVm8eLFrm8PhYPHixTWGJZ/M+++/T1lZGRMnTqzLTYqIiIhIM3boaDkmE3SND3N3KSIiIiLNQp2HIU+bNo2bb76ZPn360K9fP2bPnk1RURGTJ08G4KabbiI+Pp7HH3+8xnGvvvoqV155JVFRUfVTuYiIiIg0ef+Z2Jvs/FKign3dXYqIiIhIs1DnsPC6667jwIEDzJgxg+zsbHr27MlXX31Fy5YtAcjIyMBsrtlhcdu2bSxfvpyvv/66fqoWERERkWbD1gjzFYqIiIiI4awWOJk6dSpTp0496XVLliw5YVuHDh2ow6LLIiIiItLM5RfbKbFXKigUERERaWR1mrNQRERERKQxvLEqnYFPLObphVvdXYqIiIhIs6KwUEREREQ8isPh5J01GTic0Do62N3liIiIiDQrCgtFRERExKP8sOsgmYdLCPX34bLuse4uR0RERKRZUVgoIiIiIh7l7dUZAIw7LwF/q8XN1YiIiIg0LwoLRURERMRjHCgs4+vNOQBc3y/RzdWIiIiIND8KC0VERETEY8xft5cKh5PzksLpaAt1dzkiIiIizY7CQhERERHxCFULmwCM75fk5mpEREREmieFhSIiIiLiEVbsOkT6oWJC/H24vHucu8sRERERaZYUFoqIiIiIR6ha2OSqXvEE+GphExERERF3UFgoIiIiIm53oLCMhZuzAbi+r4Ygi4iIiLiLwkIRERERcbsP1hsLm/RMDKdznBY2EREREXEXhYUiIiIi4lYOh5N3jg1BnqCFTURERETcSmGhiIiIiLjdrLFdGdszjst7xLq7FBEREZFmzcfdBYiIiIhI82Y2mxjSvgVD2rdwdykiIiIizZ56FoqIiIiIiIiIiAigsFBERERE3Ojt1Rk8/sUWMg4Vu7sUEREREUHDkEVERETETZxOJy9/v5u0g0W0ig4iKUqLm4iIiIi4m3oWioiIiIhbOJ3w4CUdubiLjTE94txdjoiIiIignoUiIiIi4iZms4lRXWyM6mJzdykiIiIicox6FoqIiIiIiIiIiAigsFBERERE3OC9NZk8s2g7+/NK3F2KiIiIiBxHw5BFREREpFE5nU5eXLqL3QeLiA3zZ3w/LWwiIiIi4inUs1BEREREGtWqtMPsPlhEkK9FC5uIiIiIeBiFhSIiIiLSqN5enQHAFT3jCfbTQBcRERERT6KwUEREREQazZGicr78JRuACRp+LCIiIuJxFBaKiIiISKP5YP1eyisddI0PpVtCmLvLEREREZHfUFgoIiIiIo3C6XS6hiBrURMRERERz6SwUEREREQaxZo9R9h1oIhAXwtje8a7uxwREREROQmFhSIiIiLSKKp6FY7tGaeFTUREREQ8lMJCEREREWlwecXlfP5LFqAhyCIiIiKeTGGhiIiIiDS4Bev3UV7hoEtcKN3itbCJiIiIiKdSWCgiIiIiDeq3C5uYTCY3VyQiIiIip6KwUEREREQa1IGjZdgrHQRYLYztGefuckRERETkNDSztIiIiIg0qJgQf779f8PYfbCIEH+ru8sRERERkdNQz0IRERERaXBms4m2McHuLkNEREREzkBhoYiIiIg0mN0HjlJqr3R3GSIiIiJSSwoLRURERKRBOJ1Obn9jHec/vpi1ew67uxwRERERqQWFhSIiIiLSIHILyzhaWkGpvZL2thB3lyMiIiIitaAFTkRERESkQbQM9WfZAyPYnlNIqBY2EREREfEK6lkoIiIiIg3GYjbRKTbU3WWIiIiISC0pLBQRERGRepd5uBh7pcPdZYiIiIhIHSksFBEREZF65XQ6ufV/axn4xLesSz/i7nJEREREpA40Z6GIiIiI1Kv1GXlszS7Ez8dM25hgd5cjIiIiInWgnoUiIiIiUq/eXp0BwOXd4wgL0MImIiIiIt5EYaGIiIiI1Jv8Ejuf/bwfgAn9E91cjYiIiIjUlcJCEREREak3H2/YR6ndQfuWwZyXFOHuckRERESkjhQWioiIiEi9cDqdvLXKGII8vl8SJpPJzRWJiIiISF2dVVj4wgsvkJKSgr+/P/3792f16tWn3T8vL4+77rqL2NhY/Pz8aN++PV988cVZFSwiIiIinmlDZvXCJuN6Jbi7HBERERE5C3VeDfndd99l2rRpvPjii/Tv35/Zs2czevRotm3bRkxMzAn7l5eXc9FFFxETE8P8+fOJj48nPT2d8PDw+qhfRERERDxE1cIml3WPJSxQC5uIiIiIeKM6h4XPPPMMt956K5MnTwbgxRdf5PPPP2fOnDlMnz79hP3nzJnD4cOHWbFiBVar8aYxJSXl3KoWEREREY9SUGrn041ZAEzol+TmakRERETkbNUpLCwvL2fdunU8+OCDrm1ms5mRI0eycuXKkx7zySefMGDAAO666y4+/vhjWrRowYQJE3jggQewWCwnPaasrIyysjLXzwUFBQDY7XbsdntdSvZYVfejqdyfpkht5F56/D2f2sgzqV28Q1NspwXrMimxV9K2RRDd44K98r41xXZpitROnk9t5JnULt5B7eSZmkq71LZ+k9PpdNb2pPv37yc+Pp4VK1YwYMAA1/b777+fpUuXsmrVqhOO6dixI3v27OGGG27gzjvvZOfOndx5553cfffdzJw586S388gjj5CamnrC9rfeeovAwMDalisiIiIijcDphKd/trCv2MRVKZUMi63120sRERERaSTFxcVMmDCB/Px8QkNDT7lfnYch15XD4SAmJoaXX34Zi8VC79692bdvH08//fQpw8IHH3yQadOmuX4uKCggMTGRUaNGnfbOeBO73c6iRYu46KKLXMOzxbOojdxLj7/nUxt5JrWLd/C2dqrIzqY8IwPfpCR8bLYTrv95bz77flyFr4+Zh8YPJ9xL5yv0tnZprtROnk9t5JnULt5B7eSZmkq7VI3cPZM6hYXR0dFYLBZycnJqbM/JycF2kjeOALGxsVit1hpDjjt16kR2djbl5eX4+vqecIyfnx9+fn4nbLdarV7dKCfTFO9TU6M2ci89/p5PbeSZ1C7ewRvaKW/+fLJmzASHA8xmYmelEn7NNTX2adMylIcv60ResZ0WYd4/CsQb2kXUTt5AbeSZ1C7eQe3kmby9XWpbu7kuJ/X19aV3794sXrzYtc3hcLB48eIaw5KPd8EFF7Bz504cDodr2/bt24mNjT1pUCgiIiIinsGenV0dFAI4HGTNmIk9O7vGfuGBvtwyuDX3je7ghipFREREpD7VKSwEmDZtGv/973+ZN28eW7Zs4Y477qCoqMi1OvJNN91UYwGUO+64g8OHD3PPPfewfft2Pv/8c/72t79x11131d+9EBEREZF6V74nvToorOJwUJ6e4Z6CRERERKTB1XnOwuuuu44DBw4wY8YMsrOz6dmzJ1999RUtW7YEICMjA7O5OoNMTExk4cKF3HvvvXTv3p34+HjuueceHnjggfq7FyIiIiJS73xTksFsrhEYOs1mfJOTAFifcYRx/17B09d0Z0yPOPytllOdSkRERES8xFktcDJ16lSmTp160uuWLFlywrYBAwbw448/ns1NiYiIiIibWG02vh9zCxd88l8sTicOk5nSqfdjPTZX9d4jJUSX5PHBKx8z/P6x+LdOdHPFIiIiInKuGnw1ZBERERHxTj/sPMjjpvZEj/4Lr46MpXOfzq6gEKDP5u/539d/w+R0cOD7/+BzksVPRERERMS71HnOQhERERFp+krtlTz04S8AXDa8Bz0uH1EjKLRnZ5P/2KOYnKdf/EREREREvIvCQhERERE5wb++3UH6oWJsof4nXeVYi5+IiIiINE0KC0VERESkhm3Zhby0dDcAqWO7EOJvPWEf1+Inxztu8RMRERER8U4KC0VERETExeFw8uCCn6lwOBnVuSWju9hOup/VZiN2Vmp1YGg2EzsrtcZQZRERERHxPlrgxEPM/SGNqGA/hrZrQVjgid/ei4iIiDSGN1dnsD4jj2A/H1LHdjntvuHXXEPQoEGUp2fgm5ykoFBERESkCVBY6AHKKir5+8JtFJVXYjGb6JsSwchOLRnRMYbWLYLdXZ6IiIg0EzkFpTz15VYA/jy6A7FhAWc8xmqzKSQUERERaUIUFnqAUruDiQOS+XZLLjtyj/Lj7sP8uPswj32+hdbRQVzYKYYRHVvSJyUCq0Ujx0VERKRhrE8/QlmFg56J4Uw8P9nd5YiIiIiIGygs9ABhAVYevKQTD17SifRDRSzeksu3W3NZlXaI3QeL2L0sjf8uSyPU34dhHWK4sFMMw9rHaLiyiIiI1KtLusXS3haC0+nEYja5uxwRERERcQOFhR4mOSqI3w9qxe8HtaKw1M732w+yeGsO323N5UixnU827ueTjfvx9THz018vIshPTSgiIiL1p42mQBERERFp1pQ0ebAQfyuXdY/lsu6xVDqc/JRxhMVbc1m8JYeIQN8aQeHbqzPomxJJ2xi9wRcREZG6eXV5Gr2TI+iZGO7uUkRERETEzRQWegmL2USflEj6pETywMUdKSmvdF23L6+Ev3z4Cw4nLH9gOAkRgW6sVERERLyFPTubrWs38+I32RwKCGfRtKHqWSgiIiLSzCks9FIBvhbX/0vtlVzYqSWl9soaQeGXv2TROzmCmFB/d5QoIiIiHixv/nyyZszEx+HgNZOJZWNupU2Ly9xdloiIiIi4mcLCJqBNi2D+e1MfKiodrm0HCsu4+52fcDphdFcbkwam0Cc5ApNJk5WLiIg0d/bsbLJmzASH8d7B7HQy9LNXsE8bj9Vmc3N1IiIiIuJOZncXIPXHx1LdnIeLyumREE6Fw8nnP2dx7Ysrufo/K/h6czYOh9ONVYqIiIi7le9JdwWFLg4H5ekZ7ilIRERERDyGwsImqoMthPl3DOSLuwdzfd9EfH3MrM/I47bX1zFq9ve8vzaT8grHmU8kIiIiTY4pIRHHb0cbmM34Jie5pyARERER8RgKC5u4znGhPHF1d5Y/MJw7hrUhxM+HnblH+fP8nxn69He8smw3R8sq3F2miIiINKLnfingnz2uobIqMDSbiZ2VqiHIIiIiIqI5C5uLmBB/Hri4I3cOa8NbqzJ4dXkaWfmlPPb5Fp5bvIObBqQw6YIUooP93F2qiIiINKCVuw7x0ve7cKb059o7rmWgfwm+yUkKCkVEREQEUFjY7IT4W/nD0DZMuiCFj37ax0vf72b3gSKe/24nPRLDuahzS3eXKCIiIg0kv9jOtPc24HTC9X0TuWhoN3eXJCIiIiIeRmFhM+XnY+G6vklc2zuRr3/N4atNWVzYMcZ1/cLN2cSHB9A1PsyNVYqIiMi5smdnU74nHWtyEg99l0VWfikpUYH89fLO7i5NRERERDyQwsJmzmw2cXFXGxd3rR56VGqv5KEFv3CoqJzXp/RjcLsWbqxQREREzlbe/PlkzZgJDgdOkxl7j6vxaX0+/7y+F0F+ehsoIiIiIifSAidygsLSCga1iyYlKpABraNc23MLS3E6nW6sTERERGrLnp3tCgoBTE4Hd2+cz/Q+kfRIDHdvcSIiIiLisfSVspygRYgf/7y+F6X2SnwsRp5caq/kmv+sJCEigBljOtPRFurmKkVEROR0yveku4LCKhank+viTG6qSERERES8gXoWyin5Wy2u//+8N5/sglJW7DrEpf9cxoyPN5FXXO7G6kREROR0fFOSwfybt3pmM/4pye4pSERERES8gsJCqZV+rSJZPG0ol3S14XDC/1amM+zvS3h95R4qKh1nPoGIiIg0KqvNhi01lUqT0ZPQaTYTOysVq812hiNFREREpDnTMGSptcTIQP4zsTcrdh4k9dNf2ZZTyF8/3sybqzKYMaYzA9tEu7tEEREROU7RyEuZtLyMhOJDvDHjaoIT491dkoiIiIh4OIWFUmcD20bz+d2DeGt1Bv/4ejtbswuZ8N9VXNLVxvQ+kUTn5eKbkqyeCyIiIm5mNpm4YmQvjpbZFRSKiIiISK0oLJSz4mMxc9OAFMZ0j+PZb7bzxo/pVH72MUcfm08xTpwmM3GPphJ+zTXuLlVERKTZigs3FiYTEREREaktzVko5yQiyJdZY7vy+fj23LNxPmacAJicDrJmzMSenQ3AL3vzKbVXurNUERERERERERE5A4WFUi+Sig9jdjprbnQ4KE/PoKS8kutfXknf//uG9ENF7ilQRESkmXE6nfy4+xBHyyrcXYqIiIiIeBGFhVIvfFOSwfybp5PZjG9yEmkHiwjxtxIWYCUpMtB19cLN2WzPKWzkSkVERJqH9EPFXP/yj/R5bBEVlQ53lyMiIiIiXkJzFkq9sNpsxM5KJWvGTHA4wGwmdlYqVpuNzsCK6SPYn1+CyWQCoLzCwQMf/ExesZ3OsaFc1SueS7vHEh8e4N47IiIi0kQcOFpGfHgALUL88LHo+2ERERERqR2FhVJvwq+5hqBBgyhPz8A3OanGashms4mEiOpehXkl5fRLieS7bbn8mlXAr1kF/N8XW+hoC+HCTjGM6NiSnonhWMwmd9wVERERr9c3JZIfpo+gpFxzBouIiIhI7SkslHpltdlqhISnEhPiz8s39SGvuJzPf8ni45/2szb9MFuzC9maXcgL3+0iMsiXYR1acGHHlgxuH02ov7UR7oGIiEjTEuBrcXcJIiIiIuJFFBZ6gOyibDIKMkgKTcIWdOagrSkJD/Tlhv7J3NA/mSNF5SzdfoDFW3NZsi2Xw0XlLFi/jwXr9+FjNnF+6yhem9xXQ6lERETOwHls0bGq6T9ERERERGpLYaGbfbTrIx5b/RgOpwOzyczMATMZ126cu8tyi4ggX67sFc+VveKxVzpYl36Eb7fmsnhLDrsOFFFcXlEjKJy3Yg/tWgbTNyUSqwJEERERl10HjnLtiyvp3yqKF2/s7e5yRERERMSLKCx0o3xHPv9Y9Q8cGCsUOpwOUlemMjBuYLPrYfhbVouZ81tHcX7rKB66tBN7DhaRX2J3XZ9fbGfWZ79S6XCy7P7hJB5bZbmi0qGehyIi0uxtzMznSLGdQ0Vl7i5FRERERLyMwkI3OlR5yBUUVnE4HWQWZjb7sPC3UqKDavxcVF7BVb3i2Xuk2BUUAtz+xnpyC0sZ2r4FQ9u3oGdiuMJDL1WRnU3Arl1UZGdjTUx0dzkiIl7ll335AHSLD3dvISIiIiLidRQWulGUJQoz5hqBodlkJjFEwciZxIUH8Pdre9TYZq90sGLXQYrLK/l5bz7/+nYnIf4+DG4XzdD2LRjSvgWxYQFuqljqIm/+fLJmzCTR4WDPK68SOyuV8GuucXdZIiJe4+e9eQB0TwhzbyEiIiIi4nUUFrpRmDmMh/s/fMKchepVeHasFjPf3TeM77cfYOn2AyzbcZD8Ejtf/JLNF79kA9ChZQhDOxi9DvukRODnoxUiPc2R9Eyy/joTk/NYiO5wkDVjJkGDBtVqpW0RkeauotLB5v0FAHRTWCgiIiIidaSw0M2ubHMlgxMHk1mYSWJIYp2Cwua8ivKptAz159o+iVzbJ5FKh5ONe/NYus0IDzfuzWNbTiHbcgp5+fvdBFgtDGwTxSNXdKkxlFkaV6m9knXpR1ix6yArdh3C+dM6HnfWHJ6Pw0F5eobCQhGRWtiRe5SyCgchfj60igo68wEiIiIiIsdRWOgBbEG2Ood9C3YsIHVlqlZRPg2L2cR5SRGclxTBvRe150hROct2HnSFhwePlrF0+wEignxdx3y7NQcTJvq3jsRqcmPxTVh5hYONe/NYsfMQK3Yd5KeMPMorq8PB6MAoHCYTZqez+iCzGd/kJDdUKyLifaqGIHeND8Ns1h8zEREREakbhYVeKLso2xUUglZRrq2IIF+u6BHHFT3icDicbMkuYHtOIcF+1b8Gs7/Zwc978/n7tT0Y270lYMyF6OPjxGTSB65zkVdczh1vrGdDZh4l9soa19lC/RnYJooBxy7Bg/3ImjETHA4wm4mdlapehSIitfTzXmNxE81XKCIiIiJnQ2GhF8ooyHAFhVW0inLdmM0musSF0SWu+oOUw+GkR0I4h4vKGdIu2rX9fz9mMG9lBkPbt+DCTi0Z3C4af6vmOjydrdkFvL4ynbAAK/df3BGAUH8rW7ILKLFXEhXky/ltohjYJoqBbaJJiQqsGcZecw1+/fuz7P33GXzttVjj4rnv/Y1c1zeRvimRbrpXIiLewbUSssJCERERETkLCgu9UFJoEmaTuUZgqFWUz53ZbOLRK7u6frbb7QCs3HWYrPxS3lmTyTtrMgn0tTCsQwtGd7ExomMMIf5Wd5XsEfKKy/lyUzYdbCGclxQBwOGj5by5KoO4MH9XWGg2m5h9XU/iwgNoFxN8xp6aPjYbJW3a4GOz8fKy3cxft5dvtuSw7P7hzf4xFxE5lbKKSrZkGYubdI8Pd28xIiIiIuKVFBZ6IVuQjZkDZp4wZ6F6FTaM58f3YP3eQr7bmsvXm7PZn1/qWmHZ12JmYNsoRnexMbxDDLYwf3eX2yiKyytY9GsOn27cz9LtB7BXOpkyqJUrLOyZFM4tg1rRJyUCh8PpmjNrWIeYs7q9yQNbsXbPEW7on6SgUETkNLZnH8Ve6SQ80EpiZIC7yxERERERL6Sw0EuNazeOgXEDz2oVZakbf6uFoe1bMLR9C2aO6cwv+/JZuDmbrzZls+tAEUu2HWDJtgMAdLSFMKR9C37XJ5G2McFurrx+lVc4+H77AT7ZuJ9Fv+bUmHewU2woHW0hrp8DfX14+PLO9XbbAb4WXr25T43eiOUVDnx9zPV2GyIiTcHGY4ubdIsP01y7IiIiInJWFBZ6sbNZRVnOjclkontCON0Twvnz6I7szC1k4eYcFv2aw8a9eWzNLmRrdiGD2ka7wsJ9eSXYKxykRAe5ufq6q3Q4WZV2iE837ueLX7LJL7G7rkuKDGRsT2PBmHYtQ05zlvpx/Ife9ENF3DRnNTMu78yFnVo2+G2LiHiLX7S4iYiIiIico7MKC1944QWefvppsrOz6dGjB//617/o16/fSfd97bXXmDx5co1tfn5+lJaWns1Ni3iUtjEhtI0J4a7hbTlSVM6ynQdZvuMA/VpVL8Lx2g9p/HdZGrcNac1Dl3ZyY7W1ty+vhDnL0/h0435yC8tc21uE+DGmexxX9IyjR4L7eq3MWZ5G+qFibn9jHS9MOI9RXRSai4gATL+kI5d2jyU+XEOQRUREROTs1DksfPfdd5k2bRovvvgi/fv3Z/bs2YwePZpt27YRE3Py+chCQ0PZtm2b62cNi5GmKCLIlyt6GD3tjldYWoGP2VSjl8fPe/P468ebuaBNFBe0jaZ3coTbV1i2VzqwWoxhvSXlFby6PA2AUH8fLukay9iecfRvHYXF7P7f34cv78zBonI+/zmLO99cz7/G9+KSbrHuLktExO0ignwZ2r6Fu8sQERERES9W57DwmWee4dZbb3X1FnzxxRf5/PPPmTNnDtOnTz/pMSaTCZtNPX+keXri6u48fHlnfI4L2ZbtOMjGzDw2Zubx7yW78PUx0yc5ggvaRjOwTRTd4sPwsTTOfHzr0o/w5JdbaRnmz7/G9wKMHpN/GNqaPsmRDGkfjZ+Pe4PM37JazPzzup74mE18vGE/U9/+idkOJ2N+E9SKiIiIiIiISN3UKSwsLy9n3bp1PPjgg65tZrOZkSNHsnLlylMed/ToUZKTk3E4HJx33nn87W9/o0uXLqfcv6ysjLKy6qGPBQUFANjtdux2+6kO8ypV96Op3J+mqD7byM8M4MRudwBwZQ8b0UE+rNx1mJW7D5NTWMaKXYdYsesQAMF+PvRLiWBAm0gGto6kXUxwvfXIrah0cLSskvBAY1Vhs9PB6j2HCbCaKSgqJcDXCAbvG9nWOMDpcNXdmGrz+D95VRfMOPlwQxb3vPMT5fYKruihHoaNRa9jnknt4h0aop0Wb8nlp8x8RnRswXlJ4fV23uZEvz/eQe3k+dRGnknt4h3UTp6pqbRLbes3OZ1OZ21Pun//fuLj41mxYgUDBgxwbb///vtZunQpq1atOuGYlStXsmPHDrp3705+fj5///vf+f7779m8eTMJCQknvZ1HHnmE1NTUE7a/9dZbBAYG1rZcEa/gdEJuKWzPN7E938SOfBMllTWDwTBfJ6PiHQyy1frX9QR7i2DNATPrDproHO5kQluH6/ZX5JroEu4k3O+c7opbOJzwzi4zqw6YMWHcr34tzv5xEhHxVm/sNLPmgJmLExxcktj4X/KIiIiIiGcrLi5mwoQJ5OfnExoaesr9Gnw15AEDBtQIFgcOHEinTp146aWXePTRR096zIMPPsi0adNcPxcUFJCYmMioUaNOe2e8id1uZ9GiRVx00UVYrVZ3lyMn4a42qnQ42ZJVyIrdh1i5+zBr04+QX+4gNK41l17coc7nyy4o5eGPf2Xp9oOubfsrArn44kGYjw2Nvqzeqq8/dXn8L3U4mfHpFt5du5e3dlno0rUL1/aOb6RKmy+9jnkmtYt3aIh2Mm/OodWOg1zZM46+KRH1cs7mRr8/3kHt5PnURp5J7eId1E6eqam0S9XI3TOpU1gYHR2NxWIhJyenxvacnJxaz0lotVrp1asXO3fuPOU+fn5++Pmd2MXJarV6daOcTFO8T01NY7eRFeiVEkWvlCjuGgFlFZV8sG4fF3e1uerYtC+frPxSRnaKOeXwZKfTyQfr95H66WYKSyuwWkyM6mxj3HnxDGnfwrWYiaer7eP/+Lju+PpYeP3HdB76aDOYzEzon9QIFYpexzyT2sU71Gc7jemZwJieJx+1IXWj3x/voHbyfGojz6R28Q5qJ8/k7e1S29rrlBb4+vrSu3dvFi9e7NrmcDhYvHhxjd6Dp1NZWckvv/xCbKzmFROpDT8fCxP6JxEZ5AsYIeD/fb6FW/+3lue/PXnonlNQypR5a7nv/Y0UllbQIzGcL+8ZzAs3nMeFnVp6TVBYF2aziVljuzBpYAoAD334C/9bucetNYmIiIiIiIh4mzoPQ542bRo333wzffr0oV+/fsyePZuioiLX6sg33XQT8fHxPP744wDMmjWL888/n7Zt25KXl8fTTz9Neno6t9xyS/3eE5FmotLh5LzkcH7NKmBc7+oeJBWVDixmEx/+tI9HPtlMQWkFvhYzf7qoHbcNbt1oqyu7k8lkYuYYY+XpV5an8eFP+5jQL6lZ3HcRad62ZBVQUemkgy0EXx+95omIiIjI2atzWHjddddx4MABZsyYQXZ2Nj179uSrr76iZcuWAGRkZGA2V79JPXLkCLfeeivZ2dlERETQu3dvVqxYQefOnevvXog0Iz4WM38e3ZGpw9u5Vi4GuOedDRzYnUnpnj34BkfTvV0yf7+2B+1bhrix2sZnMpn4y2WdSIoKZGzPeAWFItIs/GfJLj7ZuJ/7RrVn6oh27i5HRERERLzYWS1wMnXqVKZOnXrS65YsWVLj52effZZnn332bG5GRE7j+KAw83AxFZ99xMyf5mPGidNkouUFqUS1HOTGCt3HZDJx04CUGtt+yjhCryRN+C8iTdOm/fkAdI0Pc3MlIiIi0pxkF2WTUZBBUmgStqDarWUhnk9dbkSaAFt5Afds/AAzTgBMTie5jzyCPTvbzZV5hv9+v5ur/r2Cfy3e4e5SRETqXVFZBWkHiwDoEqewUERERBrHgh0LGP3BaKZ8PYXRH4xmwY4F7i5J6onCQpEmoHxPOiaHo+ZGh4Py9Az3FORhyioqAah0Ot1ciYhI/duSVYDTCS1D/WgR4ufuckRERKQZyC7KJnVlKg6n8TnU4XSQujKV7CJ1WGkKzmoYsoh4Ft+UZDCb4fjA0GzGNznJfUV5kKkj2tGvVRR9UzQMWUSank37jg1BVq9CERERaSQZBRmuoLCKw+kgszBTw5GbAPUsFGkCrDYbsbNSjcAQwGwmdlYqVptepKv0axWJyWQCoLi8grdWZeBUT0MRaQI27y8AoIvmKxQREZFGkhSahNlUM1Iym8wkhiS6qSKpT+pZKNJEhF9zDUGDBlGenoFvcpKCwlOodDiZ8tpaVu4+RNrBozx0aSdXiCgi4o02VYWFcaFurkRERESaC1uQjZkDZrqGIptNZmYOmKlehU2EwkKRJsRqsykkPAOL2cSl3Wys3H2I/y5Lo8LhZMblnRUYiohXKrVXsiOnENBKyCIiItK4xrUbx8C4gWQWZpIYkqigsAlRWCgizc6NA1KwmM089OEvzP1hDxWVTlKv6ILZrMBQRLzL9pxCKhxOIgKtxIX5u7ucZiu7KJuMggySQpP0QUlERJoVW5BNf/uaIIWFItIsTeifhI/ZxAMLfub1H9OpcDj5vyu7KjAUEa/imq8wLkw9pN1kwY4FJwzBGtdunLvLEhERETlrWuBERJqt3/VN5O/X9MBkgrdXZzB9wc9UOrToiYh4j6qVkLvEa75Cd8guynYFhWCsApm6MpXsomw3VyYiIiJy9hQWikizdnXvBGZf1xOzCd5bu5c/v79RgaGIeI2qxU26xmm+QnfIKMhwBYVVHE4HmYWZbqpIRERE5NwpLBSRZm9sz3j+eX0vLGYTC37ax7T3NlBR6TjzgSIibuR0OrGaTVgtprNeCTm7KJvVWavVE+4sJYUmYTbVfDttNplJDEl0U0UiIiIi505zFoqIAGN6xOFjNvHHt3/i4w37sVc6mH1dL3x99J2KiHgmk8nE/DsGUl7hwOcs5lvVXHvnzhZkY+aAmSc8jproXURERLyZwkIRkWMu6RbLv80mpr71E1/8kk2o/yaeuLq7u8sSETmts/lS41Rz7Q2MG6igq47GtRvHwLiBZBZmkhiSqMdPREREvJ66zIiIHGdUFxsv39Sb+PAAbhncyt3liIicUnlWFkU/rsKeXfchxJprr37Zgmz0tfU9p6BQQ8JFRETEU6hnoYjIbwzrEMO39w3Fz8fi7lJERE4qb/589v11BmanE6fZTNysVMKvuabWx1fNtXd8YKi59txHQ8JFRETEk6hnoYjISRwfFK7YeZC73/4JuxY9EREPYM/OJmvGTMxOY+V2k8NB1oyZdephWDXXXtXiHJprz31ONSRcPQxFRETEXdSzUETkNApK7dz+xjoKSivoHBfK7UPbuLskEWnmyvekg+M3X144HJSnZ2C11T7s01x7nuF0Q8LVJiIiIuIOCgtFRE4j1N/KP6/vxfx1e5k0MMXd5YiI4JuSDGZzzcDQbMY3OanO57IF2RRIuZmGhIuIiIin0TBkEZEzGN4xhhduOA9/qzE02el04nA43VyViDRXVpuN2FmpRmAIYDYTOyu1Tr0KxXNoSLiIiIh4GvUslFrJLsomoyCDpNAkvXmVZs3pdJL66a/kl9h5+pru+Fj0nYuINL4XAjrT8rFXuSSygpiObRUUejkNCRcRERFPorCwGTjXoE8r9IlU25JVyOs/plPpcFJWUcns63rh66PAUEQaT6m9ktdW7MFe6WT0/cOxRga6uySpBxoSLiIiIp5Cn3CbuAU7FjD6g9FM+XoKoz8YzYIdC+p0vFboE6mpc1wo/7nhPHwtZr74JZs731xHqb3S3WWJiAfKLspmddbqev+buXl/AfZKJ9HBviREBNTruT1RQz2OIiIiInJyCgubsPoI+k63Qp9IczWqi42Xb+qNn4+Zb7bkcuv/1lJSrsBQRKqd65d1p7MhMw+AnonhmEymejvv2WrIMK8hH0cREREROTmFhU1YfQR9VSv0HU8r9InAsA4xzJ3cl0BfC8t2HOTmuas5Wlbh7rJExAM0dK/8jceFhe7WkGGeRjeIiIiIuIfCwiasNkHfmXoDaIU+kVMb2Caa//2+HyF+PqxOO8yNr64iv8Tu7rJExM0auld+dc/CCNc2dwzVbegwT6MbRERERNxDYWETdqagr7a9Aca1G8fCqxcyZ/QcFl69UIubiBynT0okb97an7AAKz9l5DHhvz9yuKjc3WWJiBs1ZK/8Q0fLyDhcDEC3hDDAfUN1GzrM0+iG+uXpcz96en0iIiLNicLCJu5UQV9dewPYgmz0tfVVj0KRk+ieEM47t51PVJAvm/cXMP7lH8ktLHV3WSLiJg3ZK3/j3jwA2rQIIizA6tahug0d5ml0Q/3x9LkfPb0+ERGR5sbH3QVIw7MF2U54Y3263gB6Ey5Sd51iQ3n3D+cz4b+r2JZTyPUv/chbt56PLczf3aWJiBuMazeOgXEDySzMJDEksd7+tm7IyAOqhyC78+95VZhXFVY2RJjXUI9jc3KqQHlg3ECPeDw9vT4REXfKLsomoyCDpNAkvSZKo1JY2ExV9QY4/gOGhvaInJu2MSG894cB3PDKKjIOF7Mlq0BhoUgzdrIv687VT1XzFSaFA+7/e94YYV5DPI7Niad/Qezp9YmIuMtHuz7isdWP1fhCTlOCSWPRMORmSkN7RBpGSnQQ79x2Pv+9qQ/DO8a4uxwRaUIcDqdrJeRex1ZC9oS/55qqxLN5+tyPZ1NfTnEOu+27ySnOaejyRETcIt+Rz2OrHnPLNCMioJ6FzZqG9og0jMTIQBIjA10/78srwd/HTFSwnxurEhFvt+dQEQWlFfj5mOlgC3Ft199zOZ3GGC5+Lupa34IdC0hdkYoDB6999BozB6qnjYg0PYcqD+FAva7ri4Zz153CwmZOQ3tEGlbm4WKuf/lHQgOsvH1rf8IDfd1dkoh4qQ3HehV2jQ/DaqnZE0t/z+V0PD1Qrm19rvkNj32AdqD5DUWkaYqyRGHGXCMw9KRe4d5kwY4FJ3whpS+ZzkzDkEVEGlB5pYOyCgel9kpK7Y4zHyAicgqdYkO5a3gbxp0X7+5SxAt5+nDx2tR3uvkNRUSakjBzGA/3f1jThp2jUy2ipeHcZ6aehSIiDahNi2Deua0/If5WWoZqsRMROXudYkPpFBvq7jKkGXP3MC53L+gjItKYrmxzJYMTB3tsr3BvoEW0zp56FoqINLC2MSE1gsIl23IpLLW7sSIREZG6WbBjAaM/GM2Ur6cw+oPRLNixoNFrqM8FfbKLslmdtVq9S9xAj714I3c9bz29V7in8/RFvjyZwkIRkUb0ycb9/P61NUyeu4aisgp3lyMiXmLvkWK+25rL4aJyd5cizZAnDeMa124cn4/9nN8H/Z7Px35+VvNOeULw2VzpsZeT8fQAWc9bz1KX50t9fsnU3CgsFBFpRK2iggjy82Ft+hF+/9oaissVGIrImS3cnMPk19bw5/c3ursUaQibP4T/joAlT7q7kpNq8LkCnU7I3wuOyuptpflQsB9K8qCy5t/KloEtaW1tTcvAlnW+KU8KPpsbPfZyMp4exNXn89bTQ1Fo2Brr49xn83wZ124cC69eyJzRc1h49UItblJLmrNQRKQRdUsI4/Up/bnxlVWsSjvMLfPWMmdSX/ytFneXJiIeqGqOuDKnhdYtguiVFO7ukpq0nOIcdtt3k1OcQ0JYQuPd8OaPYN866H/HGXc9p3kDC/ZDcEsw1+1vTr3PFWgvBeux6TmcTniuFxxJgztXQUxHY/uGt+GrB6qPsfiBbyD4BuPjG0T/Mn/M366Bll2MY6Lbg2/QGW9a81e5jx57+a1TBXGetMJ5fT1vvWFF3oassT7OfS7PF1uQzWOeU95CPQtFRBpZz8RwXvt9P4J8LazYdYhb/7eWUnvlmQ8UkWbl+G/PX0r7PVOvOMxdw9u6u6wma8GOBVz20WXMKZrDZR9dVrfeLfl74aO7YNkzsOVTyN0KFWUn7ObqVbFnKeTvq77iwhnQayJ0vqJ625pX4eO74HBajRpr1aOi5IgRtq38d83tb18PT7WG9yfDhregMKdWd++chnGVF0H6Slj5Asz/PfyzJ7w0pPp6kwlCYsFkgUM7q7c77GA+rl9DZZlxv/IzMR3Yiq1gA5aV/4KPboeXh8Hf4uHl4TVvO2PVsbaoHr5fp/mrnE4oOwrFh0+8T43AG3oh1UVTnDusqbVRY6vPXssN1Rb18bz1hl619VLjb18r6/PcNEIvd6lBPQtFRNygd3IEcyf34+Y5q1m24yB3vLGOF2/sjZ+PehiKiHf0tmhKXI83xx5vzvB4O51gLzF6ugHk/Aob3qi5j8kM4UkQ1Rai2rHAUkpq1rc4cGJ2Opnp35Zx139k7BvVBsa+UH1spR2+fxoKs2DjO9BjPNl9J536OWENMUKtkGPDcg+nGSGaXyj0uxUsVuP6vAwozYPNC4wLgK07tLsI2o6EhH5gOfnHg3HtxjEwbuDJV+V0OKD4IBTsM3ov5u+D7J9h/0+Q+yv85sMdJrNRj1/wsZO/DEHRYA2o3mfgH41LRTmUHzUCOnsxlB+lougIm7//hK4tfbAc2g4HtkLRAbAG1rydD6ZAfiZMWQSJ/Yy7m7OVmRF9ST282mgLTMwM7YHtm8eMMLLkiPGBt+Sw8f/KcmhzIdx4XDD7n4HGc2D820bPxtqotBuPSeZqOLQL/MOgw6WQ0Nu43l4CRQchMAp8A0/eC6fNWOOxKCs0zheWYLStNyjNx5a5npnBXUgt+AWHCeOx7/Unr31N84aeYp6uvnotN2RbVH1Z8tvz1+V56w29autUY2EObPoAks6H+POMbb9+YnzBddk/oPvvzv7cp1HvvdzltBQWioi4Sb9WkcyZ1JfJr63mu20HuOvN9fz7ht74+qjTt0hz5w0fLJqSOj3eGT/Clw+ArRuMfd7YFtkKhj0Eh3YYveMO7oTyQjiyB47sITvtO1IT43CYTMa5TSZSy3YxsHA/tpC4EwuyWOF3r8PSJ2DnN/DT62Rs/QCHLfrEGt+fiC1zI3T7HVx5LHCM7QGthkJ8byOEsliNYO6+ncZw552LYMciyNpgBFjZP8Oyf4BfGLQZZgScPv7g4wd9prhCPVt+NraMdZBogarHZc9yeP0qI1Q7lWCbUUt8L+PfuF7VQSFA+Gk+6Pn4gk8kBEa6NjntdvZsKaLzxZdisR4Ly4oOGUGo68FxGMFbaT5EpFRv37GIceveZ6DFQqbVh0R7BbbK9FPfPhjhXJXiw3AkHXBCaHz19iVPwO6lkNjXCF1bdoYD2yHzRyMg3LceKkpqnjc0rjos3LsG5o2B6A5kT/7kxGD4hxkMfOcWbJXHjUQw+0BEK4hud+zSHqLaQVxPo+0AcjYbbdv1Guh46envZ32yl9CiYBPm79ZD+jIjOHY6GAc1H/vgFdBtsnGM49jvoNnz3wfpC536UR9BXGO0xWm/LKkFbwi56lTjt4/CT6/DeTdXh4Ub34ayAlhwK+z42ggN/cPqfu7TqI/ni9SewkIRETca0CaKV2/uy+9fW8M3W3KZ+tZ6/jWhl3oYijRzJ3tjbcKzPlg0JXX7IGMyQrYjaXDxE0boFd0Ohh03v57TCUdzjgWHO8jY/yOOIytqnMUBZBbtO3lYCEboNPEDyFwDSx4nac8SzE6nK3AEMDudJGauh8pKI6h0XWGBmz858ZwWH0jqb1xGPAxHc2HXt0ZwuGux0ZPu149rHtNzYnWwt/5/sPZV49iqD4iBUceCQpMxH2JonHGJbn8sIDzP+LmhBUUZlypmM/xhqdEWx0vsD2UF2PIysPn4Q0AEBERCYMRx/4+s+f/jeywGRsL0dCOECwiv3p72PWSsMC6n4h9u3H7LzkZPydge1deVHQWLLwRGnTy8NpnItPoYYaHZarRxRemxgHoHbDtu5xs/gjbHhmRv/sjoAVRRVjMsPL5nZ33J+RW2fg5pS/HJXMXAynLYddz1kW2g9VBsrYZgKyuEn940ht9XSV8O70+CzmPh8mfrt7Z6pi906s+5BnGN1RbnMuedN4RcJ62x30PYMtbAL+/DBX+qft3v/jujR/exHtuA8QXX8meML05+ed+YBuLq/0LS+fV6/8/1+SK1p7BQRMTNLmgbzcs39eHW/63l619zuGXeWl66sTeBvnqJFmmufvvG2uk0cbHtLr0pbiCn/CDjF2kEZCVH4IJ7jJ2T+sOYf0LHMacOW0wmCLEZl5RBJBVdgvmD0WfXqyKxL9y4AFvmamZ+9yCpjhwcJpMxlNknAdvoe6D1cGMoc10Fx0CP642Lo9Lo/bXrO2NIcUWpETAdPzQ4Ihk6X2n0PKwS1Rb+tMm4r544JPa4cBUw5oU8fm7Is+EfBskDa24b80/IXGX0Ity7xvggHdXO+DCd2N+4RLU9da+5jpfCw7lQUUZSed7Jw+spSyGitdFr0Ok0hnwf3H4slN5+7LLTaMeqsLDzFUZbHl/v4d3wwrGazBYoLzbCy/Kjx4Z6FxnPB78Q8A81/vULhYkLjJ6eYAw5PLLHuB1bN2Pbjq/hu8cAMAEl1gj8Oo7C3GYYtBpiDJs+3nk31fw5fSUUH4LSAuDYYj4HNpH07u+xYTZC0padjXOlDDEWtNmzzAhqs342At+wRON2Rjxs1A3GatqnGF5/tryhp5g3OZcgrkHbwuEwfifsxcbvUUjsWb/OeUPINa7dOAbazidz5xck7lmF7cM/Q1m+cWVIbHVY2GoI3PJNzYMtPjD0fmg9DD64BfLSYe4lMOTPMOT+er3/WqykceiTqIiIBxjavgVzJ/Xl1v+tZdmOg9z46mreurW/ehiKNGPj2o1jQOwARr/wIUcLw7lp5GXuLqlJG9duHP1i+vH+ove5dtgYEnYthk97QcFe8AmAHhMguIWxc+9JdTp3vfSqSOzHuJsWMzD9ezLz95CYMgJbaD2u2Gy2QEIf43IqVYHp8SzW0w8jbi6qhgJX9ZRzOk8MKs/EZAKrPzbrKZ4vLTrV3Dcs3rhUBYMnY+tWHeZV2fWd0Rt0z7LT11NcZgTHYAx5Pj4k+fld2PoZ+P6j+vxtR8L+9dBqCPbEC/j6x21cetllmK21DFcG3WvcFx+/mnPQtQxl5sHDjDt6BHbnwu4lJz8+PwOyNgImuOjR6u1f/8W4z8OmQ9d6mscuIIaZ7SeSuv0Nj+0p1lzYAlsys8NNpG59DQfGCq412uKTu41FqC6aBbauxrZtXxqLSFWxl4C9yPi3vNj4f3lxzakDzD7w/7bX7MF8Jk6nEapnbYSsjdhyNmErLzZ+f00mY/7W8++C9qOM/bM2wrePYQ5LBgZXn+frhyGmszHP6fE9mutTWSGsnYtt1YvYCo5bgCskDrpdDd2vr915EvvB7cvhy/uNoclLnzR6sI/7L7bIVqf8HckuyiajIIOk0CT9HnkIhYUiIh7igrbRvD6lP5PnrmZgmygFhSJCSUkIBUeS8fMx0zE2xG11NJc38S0tgYw6vIX4uf+GolxjY7DNWGjDN+iczl1fvSpsyUOwMeTMO4p7nSIorO3vUoP2Quo7BZIvgL2rweJnPLd9A8E32Bhy7RtkhMdlhUYvv7ICI0Q5/j6lDDb2a9GxeputK/zuf8b/7XYwba9bXT6+kNjPmINu0U3Vc9CZTKS2aMHAca9jO7gb0pZC2jKj52vyAKOXU0JfY2h1fiaU5Ll6QGYXZZOR/i1JR3ZhO3517ZxfYftX0GaEschPXedJ/PAPjPvlPQYO/hOZXS432sg/ukF6Mcop2EuNIfarX2Zc1obquTBDk7Edv7hJxo9wcBsMnla9LX+vMXdrXbQdWTMofH+yEdaffxeExhrbDmw/FgxuMP7N/tmYN/V0uhxXa9EB2PE15pbdIO64sHDLZ7DiX0bv2tbDoMuVRnB43FyuZ634MKx+GX78T/W8r/5hRi/ybtcavZLNdfxM4h8KV71oPGafTTN6W784CC79u9GT/Tevj1osyDPplUxExIP0To5g4b1DsIX6u7sUEfEAGzLzAOgaH4bV4p5J/5v8m/jKCkhbAhvfxWfrZ3SxFxvbw5Jg0J+g5w1grZ/XZA2dalieFGqfrJa6/i416PMlpqNxOVvn315/tfzGSeegw0FmYCi2frcaK3zXYjEU1+Md4MCclMBMCnE92ls+gSWPw+JUY97N1sOM4LD1cCMAqlJRZixcs+VjGPqAscI5GAHl9oXYzP7YbH2Nbctnw6b5cPk/qxeuqYtDu4zeayGx1cO95UR5mbB2DqyfZwxbB7D4Yet2DbZWQ42g6ngjHzEC76h21dtaDYGx/67+2eoP1mOhuevfgOr/+wTUfK7lZR5bUd4E599Zvf3Nq41V549n8TV6BcZ2N4LpwMjquVSdDmNu1yoxneGK56n0Da0532ef3xu99HJ/NULOnYuM50qrocYcnx0vr1uPRzBWNP7xBaOHZflRY1tkGyNU7XZt9SJJ56LbNUZPwwV/MOZz/eh2Y7qCC+42FrpCiwV5MoWFIiIeJjasen6okvJKHlzwM/eMbE+r6HPr1SIi3qcqLOyREO6W22+yb+KdTsj+xRhK+cv7xmIkGPOsFfrFEnDRQ/j0Gu+Zc/DJSXlSqH2yWgbGDWyav0sNoFZz0J2hJ+AJr104SV37FAOTLzQe75ZdoMNlxnyHxYeMHmqbPjAObtHRCA2LDxm9D8uMORSJ6QwD7jL+3+1a6H5ddahXUW70zirYB69caIQ7F86oHjLqdBq92Q5sNQKf3C1GSDn6/6qLfv0qY563Kd8Yc5WCMRTWx98rVoiuE4fDeHwd9poLIB3YbvTS8/tNT3qn0xg2v/plYxGdqudGaILRU/a8m08dlp1sFfAWHYwLZ/klQ3AMXPcm5GyqWX/SQKM3emyP6kuLjrUPf0Pj4LwbcdrtsOuL6u0X3G1cDmw3FqH69SPjtnctNi6f3QutBhu9AVMGGaHf6Z4zeZnwfB9jHkaAll2NkLDzlXXvRXgm4Ukw6TNj8ZPvHjdC1vy9cIvRs1OLBXmuswoLX3jhBZ5++mmys7Pp0aMH//rXv+jXr98Zj3vnnXcYP348Y8eO5aOPPjqbmxYRaVb+9sUWPtqwn5/35fP1n4bg46aeRSLiHmlbdtP9QBp9gupxbro6cPub+H3r4dO74Uj6sZ4YTuODc0wnY560ll2NoY8tOpJddqT2H/i+uA/WvFL9c0AkdB1HRZdr+HZDDpf2uExBoRfxpFD7VLU8OfhJfSCupfqY4/OMr12dxhiXSjvsXWvMqbbrW2POxQNbjUuV4GP7H79IzG97G/v4wh++N+aW2/g2rH2V7G2fkpHcj6S8LGy526G8sOYx4ck1w0IfP2NY+PELJ614znitan+xMey09TCjp5snOLwbIltX/7z+ddj2hfF6GhBu9KDzDzOGhRdmQ2HWsX+z4Wg2OCqMHnHXv2kc73TCqyONYbt3rqru+brzG/j6r0bIWqXVEOh3G7S/5JyGfZ/1lww+ftDpcuNyvHEvnXUttdKiPQz9s3E5uNMIDX/9yPjya/eS6vk8/cLgzzurQ8qC/WCyQEhL4+fwRKPHn70EBt8H7UfXfY7VujBbjIVOWo+AZX+vMTdukl8EZqcTx3G3bzaZSVz/Nhx49Fhdppr/mixG2B7cAoJijPA2qIXxfIxIbrj70czU+Tfr3XffZdq0abz44ov079+f2bNnM3r0aLZt20ZMTMwpj9uzZw/33XcfgwcPPuU+UguOSuNbhAPbjCXLRaRJu2dkO7ZkFTD9ko4KCkWamfTX32b6a49ixgkrXyZvVirh11zTqDW4fcVP/zDjA9Hxk8yXHzV6mBy3OMOC0FBSI8NxmI5Nbt93OuM632BcuWORsTpszwnVq7EmDTA+2Ha42Ji0ve1I8PE1enNsPK43RzPkSUN5a8vtoXYtajGZTI3yu+SN7Xcy5zpnY61fuyxWY97D5AEw4i/G/G1p3xuvL9ZAI8xK6Fu7nn1B0cY8bT0nsGDhPaT623EU/YzZx8lMXwfjKnyMobAxHY1eijGdax4/dc2J50z73pjH7qfXjYuPv9HrscMlRoBYFf40pko7vDQUcjfD3RsgspWxPWujERbWmskIq6qUFRhz8mGqGfj8+rERFFoDjfnu+t1mfGF0jjzpS4azEt0WhtxnXA7tMobWb/vSWBk8NK5mb8b3bjLmDbxvZ/VCXde9afTgbMiQ8LcSesP4t2tsspUWMbOgjNRQf+NveNWXA8tfhfTldTt/70nGyvRghM6vXWaEiRPe01yiZ6HOj9gzzzzDrbfeyuTJkwF48cUX+fzzz5kzZw7Tp08/6TGVlZXccMMNpKamsmzZMvLy8s6p6Gal0m78wqcvhz0/GBO0luUbaXqHS07soi0iTUp0sB/v3z4A03F/yEvKKwnw1eInIk2ZPTubor8dCwoBHA6yZswkaNAgrLbG+xBTL6v41tWRPRCRYvw/qg1MeAcCo435o0wm4wNAzmbI3gQ5m8jO3URqZDCOYy+TDqg53HDLJ7D+f8axQ/5s7NRpjBEQNtSqkl7Kk4by1oXbQ+1a1NKjRY8G/13y1vY7lXOZs/GsX7sCI43FI7pceVa3C5Ad057UAAcOjBcl1wItV32GLbSOz8kbPzLmetv2JWz9wljxefuXxgWM10rXpZXxb0LfmvMunq2qodN71xi9AQccm5vPYjWG/JqtRkBYFRb2uB5adoaSI0boWpJnLJjhHwYhNmMuRtfFZvQGO74Ht38Y3L/LeI23Vk/JQ0QKjH7c+MKnHl+zPelLhnMW1cZYTXzQvUbnoqID1dc5nZB/bHXjHV9Dr2NfpP12bkd3ievJuKnbGHhgM5nO0uovByyRcHQS4KweWVD1r6MCig4a9/NoDhzNNf4f0ar6vEdzjR6XviE1g8KN7xphc2z3xr2fXqhOYWF5eTnr1q3jwQcfdG0zm82MHDmSlStXnvK4WbNmERMTw5QpU1i2bNkp96tSVlZGWVmZ6+eCAmOeCLvdjt1ur0vJHqvqfpxwfyrKMGVtwJSxwrhkrsZkL6qxi9M3GGfi+VQW5EK4P9hLMC99HMeg/2e8yEq9OGUbSaPQ439yv2YVMOV/63n0is6M7HTq3tyNQW3kmdQu3uFM7VS8axemqgnQqzgcFO/eTWBUHScxP0djUsbQL6afq4dPy8CWDfP8cjowL/or5rWvUDnxI5xJA4ztiRfU3C8kEVp0ha7XAbA7ezWOb2sutuBwOkg7kkaUbxSmuD6YK8pxBEQbPQcBMINPkLFi63Ga8+9PTnEOqStScVCzl02/mH60DHRD76XT+G07RflG8XC/h3ls9WOuYOjhfg8T5RvV6G15uloa8nepru2XU5xDRmEGSSFJJ1x/uutqyxN+lxrttes3dh/Z7WqHKg4cpBXsJSqgriGUyXgNTLwALnwUDmzBvP0rTNu/xJz1k/HlypE9NY6ouPQZnL1uMn7I/hnLyudwxp2Ho/+dp28XezGmrI2Y9q3BtG8dpn1rMR2bz9WJiYqOV0DwsefDxU9DYAsjcKo6V8sexqW2HBhzFv6WJbDma/P5dx9XY/21X1xgHGbMNdrKbDITGxDr9r8B5/z74x9V47HKmfI1GXk7SYpoR0sP/fsWFdmRqnc3drv9xL/9tVV1//yjMF33DtiLq//2H96Nz6d3Q6UdR/87cAy53+ixWutTu/91rT7Utn6T0/nbd6Kntn//fuLj41mxYgUDBgxwbb///vtZunQpq1atOuGY5cuXc/3117Nhwwaio6OZNGkSeXl5p52z8JFHHvn/7d13eFRl+v/x90x6BxKSUBJ6UYogTWJDpdhp9q7Y5av+UHd1dUEUF10V61rW3hsi6FpQURQFRekgJSCQUBIgSBqQNs/vj0MaKUySSWbO5PO6rrnInJw585xzM5PMnft5bqZOnVpl+7vvvkt4uI+s0eBBASUH6brrS2Lz1tEqfyMBpnLwCgMiyIrsTlZkT/ZE9iQnLBnjKK8q6rf1ZTrs/ZGsiG781O2+pi0lFpEm9f4mJ4t2OXFiuLybi2Pj3H4LFxEbyc3Mpv+Mh8srCwHjcLD57rspbuG/fxjsl/YKHbJ+YF3iGNa3ca8iKtuVzWM5j2EqXCsHDu6MvpMYp/9eK0/7s+hPXs1/tcr2ayKuoXNQ52oe4XuyXdlklWQRGxDr9dg39VjqEr/fC35nzoE5GAwOHIwOG83AkIFH/J4v8aVYH66p3pOCi3KILNhJRMEuIgp3EV6wm4jCXaxpexF7I7sDkJz1I/3TXmZXVG8Wdf1b2WOHbnyEwoAocsPaElqUTcv8TUQfSMNZJckZQE5YEnsjurIpfhT7Q3zrDwcNZZf/7+6q7nXhb+fYECFF++iz7W3a7VsMQH5wa1YkXcnu6OZVZbh//34uueQSsrOziY6uucK0UZOFubm59O3bl+eee44zzjgDwK1kYXWVhUlJSezZs6fWk7GToqIivvnmG0aMGEFQgIPAx7vgKLQqCE14HCY5BZM8FFdyilUm66hlnYydKwicNYGSs5/EdDihic7A/1WKUZAWOW9quv7VKy5xcc8na5i9YidOBzw6vg/nHtPGK2NRjHyT4mIPR4rTfxdsZsV/3+a2FR/jNC5wOomfMpnocT44pbD0V8n6/LGyaL/V7TMizrp/MAfHjqWYzsPqdJjZm2ZXqeQa02VM2ffdrZRqzq+fzP2ZnDX7rCpVNp+P/rzO1WWeqEyrTXOOU03cjV9t+wEe+z/QmDGavWk2036dhgsXTpzcN6Ty690XHOk9qcnsXo9z07eY6LaYo8dSVFTE/C9nc+aqm6rd3UQmYNoNwrQbgGk/CJPYt06VV3aUuT+zUvWpL6jP66e618XQNkM99pr2J47UuQR89TccOdb0bFfv8ygZ/qDVJKUW/vKzJycnh7i4uCMmC+s0DTkuLo6AgAAyMzMrbc/MzCSxmvVzNm3axJYtWzjnnHPKtrlc1n/UwMBA1q9fT5cuXao8LiQkhJCQkCrbg4KCbB2U6pSd04l3WlOIO56AI6572fpkbq1KljwQJv5GYMVFTPf+ac3ZV5Vhg/nj/zs70fWvLCgIHr+wP0GBAXy0ZBt3fbwKZ4CTsf290ynVGpNi5IsUF3uoKU45B0uY32UoI64czZmtSgjukNykaxVWqyAX9qRaayOVLnvy2ysw916rQ+MNP9bteGm/wOybrfWoLvvY+p0lKBZ6jKjz0M7veT4nJp1YbTOE+qzj1hxfP+1j2jMlpeoab+1j6vbzpSnXzWuOcaqJu/HbsX9H1SmyxsXOAzsxxtT4vbr+Pyjl6Rhl5GdYSbjS6da4mLZ4GicmnehT68zV9p50uEZtStO2t3WroMQZRPGF7xGYtd5qmhnWCpIGQftBOKLbVVonu6Hs0HCnfUz7ev//bmzuvn5qel08cuIjHn9N+4Wjz4Yuw+D7h+DXF3Cunolz60K45svyNZNrYfefPe6OvU7JwuDgYAYMGMC8efMYM2YMYCX/5s2bx8SJE6vs37NnT1atWlVp23333Udubi5PPfUUSUlNv+iwzzpxUsMeXzFRuGsdvDwcep5ldQMKCm3YsUXEpwQ4HTwyvi8BTgfv/5bOpA9XUOKC8wY04x/6In7mnjOP4uZTuhIU4CA82Msd/LYvgW+mlHcfvuRD6D7K+jqspdWpOOm48v1LimHOLdDlVOg+0tqnoqID8N00WPQfwFjVhTnbyzsV11N1zRBs3+2yiTW0A62/X29fT3y4E78jNYPxlUYxNbFTUwp3GrR4oymNyxmM6ToCjjqzUZ/H3xru+DJvd2G3pZBIOH069DkPZt0AWanw1li4Zq7VfEdwowd8ZZMmTeKll17ijTfeYO3atdx0003k5+eXdUe+4ooryhqghIaG0rt370q3Fi1aEBUVRe/evQkODq7tqaS+di63fvFe+T68fibk7PT2iETEw5xOB/8a24dLhyRjDNw1cwUf/pbu7WGJiAfFhAV5N1G490/46Gp46dTyRGFEPBTmle/T9TSY+Duc8o/ybWkLrd9BPrkeHu0Kb46GxS9Z3RjTfoUXToBFzwIG+l0KN//S4ERhTWpLLEj1EiMSGZQ4qF6JF3++3rNSZzHq41FM+HoCoz4exazUWd4eUrWOFL/SLsHOQ0scVewSXNv3qpORn8HinYvJyM9onJOpRmmysyK7Jj9qSq435fVsLP58br6optdFaRd2d1/TzVK7AXDlZ9Ai2fq9561xVidvqVtlIcCFF17I7t27mTx5MhkZGfTr14+vvvqKhARrzntaWhpOZ51zkOJJx1xktaP/8EqrGuC/w+CCNyF5iLdHJiIe5HQ6mDamNwFOB28u2srfPl5JsctwyZBkbw9NxO80ZUXRtr/2076lF9eIys+CHx+F314+1KnSAcdcDKfcY/0yXVFoTPmU5FItkuGku2Dd57DrD/hzvnX74k7rWBiITIRzny6vUGwkR6qiEs/y1+vtbxWTtVUgultd6q2qsdKE5uHPbcc42KlKsq78+dx8UW2vi4ZWjDcL0W3g8tnw6umQuQreuwiu/B8EeHlmh5fV6+wnTpxY7bRjgPnz59f62Ndff70+Tyl11XkYXP89vHcJ7F4Lr460pgOl3Gp9T2sZivgFh8PB1HN7EeB08NrPW/jHJ6socbm4fGhHbw9NxG805Yfi9Rm5jHryRwZ1bMkH1w/F6WzCn9dFB+CX5+GnJ6Agx9rW5TQYMRUS+7h/nJYd4dT7rFvWJitpuO5zSP8VMFbi8fTpVacnNwJ/SizYgb9eb39MfNQ2RfZI02e9nTz1l+SHvybXwb/PzVfV9rpwZ0p8sxfbBS6fBa+fDUePafaJQqhnslBsolVnuPYb+OJv1nSgTd9Zt8S+cPxtehGI+AmHw8Hks48mwOHg5Z828885a9iTV8jtw7t5dJFqkeaoqT8Ur0jfh9MBLcKDmy5R6CqBFe9bC30f6gxIYh8Y8YD1h8aGiO0Cx99q3fJ2WcukuLF4uCf5S2LBLvzxeivxUZkvJE/9Ifnhr8l18Oy5+fpaob7EH14XXpXYB25dBuGtvD0Sn6BMkb8LiYKxz8Owv8Oi52Dpm5CxEj6eAPOmwtCJ1npBIZHeHqmINIDD4eDes44iLDiAZ77byJodObgMBChXKNIgTfGhuOIHoQsGJXHqUfHkHiz2yLGPqKQYXj7NWu8YICYJTv0n9DkfPL2sjBcXDNcHqKblb9fbn5M69aHkqef4Y3K9lCfOzS5NUpTQ9CNKFJZRsrC5aNkRzvw3nPx3aw2ixS/CvjT48m+wex2c/YS3RygiDeRwOLhjZA+OahPNKT3iCWjK6YsifqqxPxTP3jSbaYunVfkgFBcZ4pHjH1FAICQNgb82w4l3wuDrISi0aZ5bxEb8OalTV0qeepa/Jdcrasi5eXu6u7vsktAUqSt1ImluImKtKsPbV8NZj0OrLtD3ovLv71wJPzwK++zftU6kuTqzTxvCggMAMMbw3PyNZOUVeHlUIvZU1+6gdZHtymbar9O83y3ylHvg1uXWVOFGTBR6o3OqiCc1pFO0vxnXbRxzx8/l1VGvMnf8XL9Ljuj9yvvs0F1dXZ/Fn6mysLkKDodB18LACZW3L3kNfn8V9myA8S95Z2wi4jHPzd/Eo3PX8/GSbXx520kEB+pvRCJ11VgVRVklWbio+kFoyfZUzureiMmI7UutJiZjnreWIWmCRiOqvBDxP/5aEWeX9yt3p75W3C82OLYJR9gwdpju7gvrd4o0Fn1qbO4cjsqdkTudBB1PhP6XlW/L/AM+vdX6V0Rs5fTeibRvGca1J3ZWolCkARqjoig2IBbnYb+KGeOgd0KXuh8sf4/VpGT/3tr3Ky6ED6+EtZ/C9/+q+/PUgyovRMQu7PJ+NSt1FqM+HsWErycw6uNRzEqd5dZ+szfNrtfzeaPSsjEr+z2lNKFZka8lNEXqS58cpbJeY+Gq/0Hnk8u3LXsblr5hLYCuhKGIrXRpHcnX/+8kLh6cXLatqMRVyyNEpKnEOGO4b8h9ZR80jHFwdNA1dIhpW/eDzboePrkBnuwL302DA39Vv19gMJz3qtXl+JR7GjB699lhKpmICNjj/crdhGZ1+01bPI1sV3adns/dxGRj8PXp7nZIaIrUl5KFcmRHj7YWPy/aDx9eAQW53h6R+Bit6+LbwoPLV5zIyivgzKcW8NHvvvNLr0hzNqbLGL4c+xVBu28hf+Pd3HDsxXU/yJ5U2DTP+rowF3581Eoafv8vOLCv6v5Jg+DyTyAkqkFjd5cqL0TELuzwfuVuQrOm/bJKstx+Ll+otPT1tUJ9PaEpUl9KFsqRJQ+Bi96F6HaQlQpzJoIx3h6VRyjJ1XDe/Guj1N37v6WTuiuPu2au5Jl5qRg/eS2L2NnWXcHs3ZNEVGAsJ3ZrXfcD/Pay9W/30+GCtyC+FxTkwA+PHEoaTod3zocdyz06bnep8kJE7MIO71fuJjRr2i82wP11C+1QaekLfD2hKVIfanAi7omIg/Nfh9fOgD9mw68vwHE3eXtUDWKXxYt9WU1/bUxpm6Iflj7q5mFdyCso5vn5m3j8mw38uSef6eP6EBoU4O2hiTRb/1u5A4Azerep+9qixYWw8gPr68HXQdfh0PNsa03C+Q/D7rXww8PW9/dsgIm/Q0CQB0fvnsZqEiMi4mm+/n5VmtA8/HPM4eOsbr/7Bt9H8Ppgt5/LDk1GRKRxKFko7ksaDCMfgq/+Dl/fB22PtaoObUhJLs9QBzD7cTgc/P30nrRtEcb9n67hk2Xb2bQ7j/9ePpDEmFBvD0+kWfp1s9WUZPjRCXV/cGAw3PgzrJ4JnU+1tjmd0GsMHHWu9Qe+Hx+FvF1w3mteSRSW8tfOqSLif3z9/crdhObh+8UGx/LF+i/cfh53E5Mi4n+ULJS6GXIDpP8Caz6Bj66CG36EyHpMmWoEGfkZpOWkkRydfMQfYEpyeYb+2mhflx/XgS6tI7jlnaWs3JbNOc/+xAuXDWBAh5beHppIs5J7sIhNu/MA6J/con4HiWkHx99WdbvTCb3HWTeXy7ovIiJ+wd2EZsX9ioqK6vw8vl5pKSKNQ781St04HHDuMxDXHXJ3wMcTwFXi7VHVed08OyxebAd2WNdFapbSJY5PJ55Az8QoducWcPF/f2HW0m3eHpZIs7Jqew7GQPuWYcRFhtTtwSXF7u+rRKGIiNST1uQTaX70m6PUXUgUXPAmBIXD5h9g/nSvDqc+XbqU5PIcdQCzt6RW4Xx8Uwojj06gsMTFpA9X8MhX63C51PhEfJu/NKhasS0bgH5JLer+4FnXwdvjYedKzw5KRESaBX/5WSriKXpNlNM0ZKmf+KPgnKdh1rXWWkjtB0P3kV4ZSn2nFKuk3nN8fV0XqV1ESCAvXDaAx79Zz3++38Tz8zexcVceT17Yj4gQ/ZgQ3+NPDapW1jdZ6CqBnStg7yYYPtXzAxMREb/mTz9LRTxBr4nKVFko9df3fBh0rfX1omfAeKcSqSFTilVSL2JxOh3cNaonT17Yj+BAJ9/8kcnDX67z9rBEqqhPNbmvMqYBlYXOADj/NRg6ERJ7e35wIiLitzL3Z/rNz1IRT/Cn3y89RclCaZhR/4JT/wmXfGitZ+gFmlIs4jlj+rfj/euPY3DHVtwxsru3hyNSRW3V5HaTXwz5hSUEOh30bhdT+87GwIr34df/lm9rcwyMeqhxBykiIn4nLdd/fpaKeII//X7pKZpfJg0TGAIn3entUWhKsYgHHZvckg9uOA5HhT8ALEv7i/7J6pQs3udPXdgjg2DJP05hZ24RoUEBNe94MBs+vwNWfQTOIOh0orUciIiISD0kR/nPz1IRT/Cn3y89RZWF4jkuF8x/GFZ84JWn15RiEc+pmCh865etjH1uIY98pWnJ4n3+Vk0eGOCkc+vImnfY8jO8cIKVKHQEwMl/hzhV/YqISP0lhCf41c9SkYbyt98vPUGVheI5Kz+wOiMHR0LX4RAR6+0RiYgHZOUVABCpZifiI5pFNXnmHzDvAdjwpXW/RQcY/wokDfLuuERExC80i5+lInWg10Rl+uQnntP3AljzCRx1jhKFIn7k9uHdOaFrHAM6lE9DNl5qaCRSyu5d2ItLXDy6MoD5B1YxdUwfokODrG/sS4Pvp8OK9wBjVRMeewWMeABCo706ZhER8S92/1kq4ml6TZRTslA8xxkAF78Pzgqz212uyvdFxJYGdmxV9nV+QTGXvfIrVw9N9uKIROxt4+58tuU7+GvtLh67IBDy98CCx+G3l6Gk0Nrp6NFWE7G4bt4drIiIiIg0K0oWimdVTAzm7YZ3zrM+7PS7FKISvDcuEfGY137ezLK0fSxL28ewNk5GlLgICvL2qER8TNEBmDMR9qyHQdfBMRdZTcEOSWoZxvU9S+h0VC8Clr0Jc++Fwlzrm51OguH3Q7sB3hm7iIiIiDRrKvmSxvPTDNi5HOZNhRlHwTvnW9OUiw56e2Qi0gA3ntyFG07qDMD8nU6ueO13duXodS1SpjAf3r0AVs+EjFXw2a2HphWXiwgJpFdLw3l9WsLPT1mJwjbHwOWfwJWfKVEoIiIiIl6jykJpPCMegNY9YNk7sG0xpH5t3UJjoPd4OOYSaD8QKnRddUtJEeRlQm4G5O6s8G+mdezW3SGuh9UtUmsninhcYICTe848ij5to7jjo+X8vnUfZz79E89e0p/jOus1J83cwRwrUZi2yGr4Nfh62DQP+l5Yvs+OZRB+qNo+KBwu+xj+mAMpt2rpDhERERHxOiULpfEEBMGAq6zbnlSrqmLF+5CzHX5/1brFdoN+F0PfiyDq0EKizgDr3+1LYP1X1lpNfS+wth3YB490cH8M4bFW4nDkg1ZiEqC4EJyBHvtAlpGfQVpOGsnRyVoMVZqVUb0S2LmuhI92tGDDrjwufflX/n56D647sTOOuv4RQMQfHNgHb4+H7b9DSIyVBEwaBKdNLv/DmMtFyczr2FkUyboWN3OGMdCqE5xwuzdHLiIiIiJSRn++lqYR1836sHT7Krh8tlVhERgGWakw7wF4ohc8GAdpv5Q/Zscy+PHfsGZ2+bbQGAgMBWcQxCRB+0FW9+XB18Op98FxN0PX4RBzqPHC/ixIW1iegARY8hpMbwdf/aPBpzUrdRajPh7FhK8nMOrjUcxKndXgY4rYSXwYfHTDYMb2b0eJy/CvL9Zx09tLyT1Y5O2hiTSt/Cx44xwrURjWEq781EoUQuUK+vzdZBQEEZ+xkqAlP1CSmemd8YqIiIiI1ECVhdK0nAHQ5RTrduZj1rSrFe/B1p/BGGs6cak2/WDgNdD22PJtDgdMWguhLY5cGViYb1U07km1qgtL7dkARfshMLh8mzFWYjEizu1TycjPYOqiqbiMCwCXcTF10VRS2qaowlCalfDgQGZccAzHdmjJA5+t4as1GazPzOWFywbQIzHK28MTaXx5u+DN0bDrDwiPsxKFCb2q3TW9KIrpy0Zyy9JszuA7tsyfT5sHptLivPOaeNAiIiIiItVTslC8JzQajr3cuuVmAgYiWpd/v/3A8qnDFYW3cu/4wRHQtp91q+j0R2DITRAUWr5twWPw63/hkg+g3bG4Iy0nrSxRWMplXKTnpitZKM2Ow+Hg8uM60LttNLe8s5TNe/IZ//xC3pwwmGOTW3p7eCKNJ2cHvHGuVSkfmWglClv3qHH3F2cu4palH+HEWBtcLnZOnkLECScQlKifHSIiIiLifZqGLL4hKsFas7DidOHGEhAIcV0hpr11v7jAqnDM3wWvnwXrvnDrMMnRyTgdlV9CToeTpKgkT49YPCAjP4PFOxeTkZ/h7aH4tf7JLfnfrScyuFMr8gqKeejztRhjvD0skcaxLw1eO8NKFMYkwdVf1JooXL09m5W/ri5PFJZyuSjcmtbIgxURERERcY+ShSKBIXD1l9Zah0X74YNLrSrDI0iMSGTK0CllCUOnw8mUoVNUVeiDtLZk02oVEczrVw/i4sHJvHj5ADU7Ef+Vmwl5u6FlRytRGNul1t0f/nIdOyLjcB3+mnA6Ce6Q3HjjFBERERGpA01DFgEIiYKL34fP74Clb8CXd+HM2gTmuFofNq7bOFLappCem05SVJIShQ3QWF2ltbakd4QHBzJ9XJ9K23IPFhEVGuSlEYl4QNFBeHsc9LsE+l5kNTC57GNokQwx7Wp96I8bdvPTxj0ER7Yi7O77KHjkIXC5wOmkzQNTNQVZRERERHyGKgtFSgUEwTlPwWlTrLuLX2DQ5metasNaJEYkMihxkBJPDdCYlX+1rS0pTefD39I55bH5rMvI8fZQxA11mbbv11P8XS747DZI/826v/xtqyHX99PBlFjbOgw9YqKwxGWY/uU6AC4f2oHOV15Cx7lfkX79dXSc+5Wam4iIiIiIT1GyUKQihwNOnATjX8EEBNM2+3cC3h5rTTM7nDGwbQksf7fpx+lHaqr8y9yf6ZHja21J7ysucfHO4jT25BXy2Yod3h6OHEFdkvd+P8V/0TOw5HV4aywc+AuOuRhGPAinTbaWsHDT7GXbWbszh6jQQCae0hWAwMREDnTpQqAqCkVERETExyhZKFKdPudRcsnHFAZE4NyxBF4ZDntSK++zay28fCp8djsU5JVvVzOHOmnsyj+tLel9gQFO3rx6MPeddRR3juxBUUYGuYt+4eCOnd4emhympuR9dVWDddnXltIXw7dTra9HPghhLSE4Ao6/FY650O3DHCwq4fGv1wNw87CutIwIbozRioiIiIh4jNYsFKmBSR7Kgu6TOXXnczj+2gIvn2ZVlZzxiLVD/FGQ2Nda0P7gPgiJhP174d0L4NR/QueTvTn8pnNgHyx4DLK3wekPW12t66C08q9iwrC08m8XuzwyRK0t6X0x4UFce2Jn9s2cyc7JU8DlwoWDL0ddhfPsMQzo0JJ+SS2ICdOaht5UW/L+8NdNXfa1nf17YeY11lTj3uNhwFX1PtTrC7ewI/sgbWJCufr4jh4booiIiIhIY1GyUKQWeaFtKL7qK4I+uhy2/25NOR7xgDX9zOGAG360/i3146Ow7Td481wYfAMMvx+Cw702/ibhKoYlb0BBDmxfApfNgrhubj+8tPKvtEKptPIvITzBo8NMjEi0fwLD5ooyMsoShQBODKd//TpX0Y6nw1rgcEDX1pEM6NCSY5NbcmyHlnSOi8DpVDflplJb8r4h+9qKMTDnFshOh1ad4ewnK7/P10FO2jZ+eP9L4oJbcsf5JxMaFODZsYqIiIiINAIlC0WOJKI1XPkZrHjP+uDorPCyOfwD5Cn3QvFB+P1VWPwibJoHY1+E9gPr99x7NsLX91pVLn0vgH6Xej/5aAxsWQAdT7TOPyLOWufx2/thXxq8MhIu+dDqEuqm6ir/ioqKGu8cxCsKt2wtSxSWCjCGC9s4+DwgnC1Z+0ndlUfqrjze/82ahp4QHcL4Y9tz/sAkOsVFeGPYjaKxun83VE3J++rGWJd9j8Snrscvz8P6LyAgGM5/HUKj63WY0irayS4XLoeDNieEwAA1MhERERER36dkoYg7gsNh0IQj7xcSCWc/AT3PgjkTIWsjvDICTpgEJ/8dAmtZqyrtF1g1E9r0hWOvOHS8KNjwlfX1tsXw/b9g0LUw+DqIjG/4edWVy2VVTW5ZAJfOhG4jrO0n/D/odxm8ez7sWAZvnAPnvwY9znD70Kr883/BHTuA01k5Yeh0cvtVp3JXYiJ78gpYlraPJVv/YmnaX6xI30dmTgHPzd/Ec/M3Mf7Y9jx+wTHeOwEPmZU6q0qCbVy3cd4eVpm6TNv3xBR/n7oe25fAN5Otr0f9C9rU7f9bcYmLr//IZMHCNVzxVIUqWmPInDKFqBNPIEgNTURERETEx6nBiUhj6Docbl4EfS4A47LW9Hv5VMhcA0UHYOsi+Pkp+Gtr+WN2roTfXoI1s8u3RSVYU+BGTYcWHeDAXvjx3/BEb/j01qpNVxqb02l9eA4Kt9YorCiyNVz5P+g6AooPwPuXWNOTRQ4JSkykzQNTrf9HAE4nbR6YWpY8iYsMYcTRCdx9Rk8+vGEoK+8fyXOXHsuwHq1xOqBLfHll4cGiEpam/YWxWUMhuzQFSYxIZFDiILeSf3XZ93A+dT0O7IOPrgJXERw92vrDTD1M+XQNaxavqVJFi8tF4da0Bg9TRERERKSxqbJQpLGEtYTxL8FRZ1sdkzNWwYuHmp64Dk2xDW0BA660vu58Mhx3szW9t6KBV1v/DrkB1n4GC5+2ql+WvmHdepwJKf8HyUPrva5WjQ7sgwWPQ6+x0O5Ya9tJd8HQWyC6bdX9QyLh4ves813+Nnx2K+TutKoqPT02saUW551HxAknULg1jeAOybVWWYUEBnBmnzac2acNO7MPEFZhvbe5azK47f3lnNy9NW9cM7gphu4Rft0UpB585noYA5/+n7WUQosOcO4ztb5nFWVksHPNBr7ODmbeHnj72iEEOB0EBji55vhOFHYJhUVVq2iDOyQ3wcmIiIiIiDSMkoUije3o0VYi77PbrHWwACLiIWkwRLcr3691Dzh9epWHV1rLq9cY63hpv1hJw/VflN/aDYAhN1rJw5BI68H798KBvyAk2qr8c1dJEfz+GsyfblUzbl8CV31ufXgOa2HdahIQBKOfheg2VsOX+dMhZwecNQMCannL+WsLpH4DA66ufT+xvaDExDpPxWwTE1bpfmbOQUKDnByT1KJsW1GJix837Obk7q0JDPDNwnm/bQpSTz5zPX57GdZ+Cs4gawmF0JhK3/7lzyzeWLiF7ANF9F7+A6O/ewOnMQzFweJ+5/HzsC6c1N16j71pWBegC/sCp5Y39DmsilZERERExJfpE7lIU4iMh4vehV1/QHCEVbniRqVdjWt5dRhq3fakwqJnYfl7VkJv1nVw7XfQfoB1gGVvWetvHXMxjH3B2uYqgeePtxqTRMZbicvI0luC1dX4++mQdWiKc1wPOP72up2vwwGn3gdRbeCLO60KyLxdcN6rlRu0lBSXJwbD42DeA7B9KYz+T92eT5qd60/qwkWDk6lYlPbdul3c8NYSWkeFcELXOPq0i6Fv+xiObhtNeLBv/LjzZFMQf+AT12P3Bpj7D+vrEQ9Yf3g55GBRCY98tY7Xft4CQNyBfdw77w2cWNPfnRhuX/kxbUOvr3LYulTRioiIiIj4Et/49CTSHDgckNDL7d1rWssrpW1K+QfpuG5wzlNwyn3WeoerZ0FQaPlBnEEQEmOtMVhqfxbsXgu7jzCA8Dg45R9w7JX1r/QbNAGiEmHmNbDhSyuZedE7kL3d6p68ZwNcP9+6Nvm7rArIFe9aVT2nPVC/55RmIzo0qNL9nANFxEYEszu3gE+WbeeTZdsBcDqga3wkfdq1oG/7GHq3i+HoNtGEBQdUd1igcbvzeqIpiD+p1/UwBlZ9BFsXQtv+0OkkaNWpfgOI7WotlbBzBRx3U9nm5en7mPThcv7cnQ/ABQPbM7wAnHMrr5PpcLkI2rkDkttXOXR9qmhFRERERLxNyULxCY35wdyu6rSWV2RrK7F3yj8qbx96Mwy92bq+Oxdb1zekBVzxKeTvhrxMq+Ivb5eVrMvbBYV5cPQYq8NxaHTDT6TnWdbzzbrW+kAOVnXhuv9B0X6re3K7Y6FVZ6sacfaN8OvzOENigKPLj2MMuIqhuAAwVqdokQrOH5jE6H7tWPRnFivS97FyWzartlsdlTdk5rEhM4+Pl1qNeQKcDrrFR3Jqz3j+dnrPSsdpiu686v5dWZ2uR95umHMLpM617i95zfq3RTJ0Ohk6D7PWfo1KAKr5+VJcCFt/stYnHHCV1XDnpDut9xiHg8JiF0/PS+W5+RtxGYiPCuGR8X05pWc8RRkJbHxIaxGKiIiIiH9TslC8rik+mNuRp9by8onrmzwE/m+ptZ4hWM1fzpphrdNY2jgFoN/FcDAbvvo7AT8+zKjAGALX/p+VICxNEpZq29+qSiy15WeIaG0lHbXmYbMVHOjk5O6tObl7+Rqdu3IOsmp79qHkofXvnrwC1mXk0qV1ZKXHr87ceuSKXvEuZ4BVBRgQbC2xsHs9bP/dSv4te8u6AbQ+illtOjM1dzUuTPn7X0RneGssBEVA34vKq7EPLQ2xans2z36/EYDR/doy9dxetAgPBso7emstQhERERHxZ/pELV7l1lTbZsoTa3n51PUNqDxllH4XV7/fcTceWjfxIUKLs6G4huNFxFe+/8FlVjOWGxZAm77Wti0/Q9ZGa7p2bDdrnUZ1ZW524qNDOS06lNOOsirNjDFk5Bxkedo+2rUsb5yycVcuo1+cQ3gHH+jOKxaXC9bOgXWfW39gCI2G8FbW+qdhLSHhUPVxQS5sXQSbf7BuGavI2LuBqRF5uA695sve/8Z9RWLb/pDQ26qkrrh0AzCgQ0tuH96N7glRnNmnTZUhaS1CEREREfF3ShZKozrS9OI6TbVthhq6tpltr+/Jf6Oo13n89O3nnDDsNIJCIiAwFAKDISAETAkU5pfvX7gfWiRZXZxju5ZvX/UhLHm9/H77QXDZLM9MrxbbcjgctIkJo02fyh2Wf9/yF6YoDowDHOVVrA6c5OREUxJvCHAq2dzoig5A0KHYOBzw3TQr6d/jDOg93tre8fjKjwmJgu4jrRtAfhZpK1/HteH1Sru5jIv0vG0kVqhK/nN3Hv+cs5qHxvShY1wEALcP717rELUWoYiIiIj4M2d9HvSf//yHjh07EhoaypAhQ1i8eHGN+86aNYuBAwfSokULIiIi6NevH2+99Va9Byz2MSt1FqM+HsWErycw6uNRzEqdVWWf0qm2FdVnqq0/S4xIZFDioHol92x9fWOSyAlLtioCW3aw1h8La2mtdxgSZTVOKRUcDjf8CPekV+62nNAbupxmrWWGA7b9Vt71VOQwFw1O5uc7x3JLn7vLXjdOh5MDO8Yy4dWNDJz2Dbe9v4xPlm0jK6/Ay6P1M7kZ8NvL8OZoeLwnFB20tjscMOhaOP42SOjj/vEiYknue6lb73/TPl/LzxuzmPrZmoaehYiIiIiIX6hzZeEHH3zApEmTeOGFFxgyZAhPPvkko0aNYv369cTHx1fZv1WrVtx777307NmT4OBg/ve//3H11VcTHx/PqFGjPHIS4nvcnf7qiam2UrNmd30Pn2I8+DrrBtaU5NfPstYzO+oc6K73H6mqTUwYNw64hDE9TyU9N52Sglje/OkvFqTu4a/9RcxZvoM5y3fgcEDfdjGc3COeYT1ac0z7Fs236vDQ2n31sn8vfP8Q/P4qVKyCTv8VOp9sfV2hQ3FduPv+98DoXgQFOJhyjvvd6kVERERE/Fmdk4UzZszguuuu4+qrrwbghRde4PPPP+fVV1/l7rvvrrL/sGHDKt2/7bbbeOONN/jpp59qTBYWFBRQUFBetZGTkwNAUVERRUVFdR2yTyo9D385n8P9+def1U5/3fzXZmKDYyttP6fjOQyOH1w21TYhPMEnrou/xMhXr++RePz6txuMc8iNBPz6PGbORIqv/8la+0zqzV9eI9WJDY4lNtZ6rxpwQRJFJS6Wpe/jxw1Z/JC6h3UZuazYls2Kbdk8PS+VluFBHN8llpO7x3Fi11hiI0O8NvYmicv+vTjWf45z7RwcW3/G9B5Pycjp7ncpd5XgXP4Wzvn/wnFgr7Wp7QBMz7Nx9TjLalTkgfFX9/6XnpXLDxv2cOHA9gAkRAbx7EXHAE37f9mfXz92prjYg+Lk+xQj36S42IPi5Jv8JS7ujt9hjDFH3s1SWFhIeHg4M2fOZMyYMWXbr7zySvbt28ecOXNqfbwxhu+++45zzz2X2bNnM2LEiGr3u//++5k6dWqV7e+++y7h4eHVPEJ8TbYrm8dyHsNQcd0vB3dG30mMM8aLI5PmzOkqZNj6yUQd3MG2FkNY0ukWbw9JvCjblU1WSRaxAbF1fl/KLoS1+xysys7hz4NZHDjYGlNsHaNFsOH+Y0vKCl2N8Y++OkHFubTJXkq7v34lLvcPnFT+g9Afbc4nNfGcIx6nZV4qfbe9RYsDWwDICW3HqvaXsyfq6MYYdiV/5sCrGwLILXJwTfcSjol1+1cgERERERHb279/P5dccgnZ2dlER9e8ln+dKgv37NlDSUkJCQkJlbYnJCSwbt26Gh+XnZ1Nu3btKCgoICAggOeee67GRCHAPffcw6RJk8ru5+TkkJSUxMiRI2s9GTspKirim2++YcSIEQQFBR35ATYUtimMaYunlU3/um/wfYzpMsbbw3Jbc4iRL6vp+mfuzyQtN43kqGQSwhNqOUL1HMd2wLx+Ou33/Upix2sxR4/15LCbFTu/RmZvms3jvz6OCxdOnNw3pO7vT7M3zeaLXx8lABdROBkceR3b0/vSp100Z51lTWktcRnOfOZnjm4TzT/P6kmriOBGOJvKPBqXA/twbPgC5x9zcGz5AYervD25SeiD66jRmNhuONd8TLcxT9Pt8K7nFeVmEPD9AzhTP7QeHxKN66S/EzbgGgbX9jgPMMbw3m/beG7xOopKDN3jI7nkrH6EhuY26P2kIez8+vFnios9KE6+TzHyTYqLPShOvslf4lI6c/dImqQbclRUFMuXLycvL4958+YxadIkOnfuXGWKcqmQkBBCQqpO4QoKCrJ1UKrjj+dU6vye53Ni0on17uTrK/w5RnZQ8frPSp1VZf2xcd3G1e2AHQbDSXfCD48Q+NXfrHXRopo2SeBv7PYaycjPsP6QcagyzoWLaYuncWLSiW6/Tx1+DIOL3/JfYu51c2kdllC2fuHqtL/4c89+ducV8mRUGIEB1tp+89ZmEhESSP/kFoQEBjTCWTYwLpsXwMKnYdP34KowVSGhD/QaDUePxRHXlbKR9xlT3jGtpAi+exBSboOIWCguhF9fgB/+DYW51j79L8Nx2v0ERLamcc6+XEFxCfd/tob3FqcDcGafRB497xjmpn3K1LkNfD/xALu9fpoLxcUeFCffpxj5JsXFHhQn32T3uLg79jolC+Pi4ggICCAzM7PS9szMTBITa/6A5XQ66dq1KwD9+vVj7dq1TJ8+vcZkofiPxIhE2yYJxbe42zTHLSfeCeu/hIyV8NmtcPH7/jFPVNySlpNW7Zqq6bnpbv9fcvcYfdrF8NGNQ9mx70BZotAYw/2frSF97wFCAp0M6NCSoZ1jGdollr7tWxAcWM9mITXIyM8gLSeN5Ojkms/vYLbVYCSspXU/fzekfm19Hd8Leo2FXmMgrtuRn/D7f8HPT8GGr+GmhVB8EBY+YyUK2x4LZz4G7Qd45NyOZEX6Pu6auYINmXk4HHDXqB7cdHIXMvdneu79RERERETEz9QpWRgcHMyAAQOYN29e2ZqFLpeLefPmMXHiRLeP43K5KjUwERE5Ek8keMoEBsPYF+G/J0P6YtiXBi07eHC04suSo5NxOpyV/j85HU6SopI8fozAACeDOh5qpGMMpC2iILwd/ZJacqDQxZ68AhZuymLhpiz4BsKCAhjYsSVDu8QytHMsfdrFlCUZ68Otatz5j8CCx+Ckv8HJd1nbuo+CYf+wEoSte9TtSXuPg3X/g1P+YXVJDo2GM/8NBXnQ79L6d06ug4NFJTw1L5UXf9iEy0BcZDCPnX8Mw3rEAx5+PxERERER8TN1noY8adIkrrzySgYOHMjgwYN58sknyc/PL+uOfMUVV9CuXTumT58OwPTp0xk4cCBdunShoKCAL774grfeeovnn3/es2ciIn7NEwmeShKOhvNehfaDNQ25mUmMSGTK0ClVkmh1SRLV6xif3QZL3yD0+Nt55uKpGGNIX/cbQd/+k0+Cz+GVzG5k7S9mQeoeFqTuASAyJJBBh5KH445tT1wduizXWD2X/ReJ3c+E6DbWjjHtoKQQMlaUPzg4Aob93e3nqiSxj1VRWHEdwl5Ntzbo0rS/+NvMlWzclQfA6H5tmXJOr0rrRXr8/URERERExI/UOVl44YUXsnv3biZPnkxGRgb9+vXjq6++Kmt6kpaWhrNC1UB+fj4333wz27ZtIywsjJ49e/L2229z4YUXeu4sRMTveSLBU8VRR+7cKv5pXLdxpLRNadCaqrUeo+igVV2XfBzEtLe2dR0Oq2aW7eJwOEhOfQuyfuFmfuGmVp3ZNfRKvgk+jR+3FvDr5r3kFO3hx/T1zN8Ux+m92kCk9dg1O7IxBjq3jiA8uPof5Wm5NVTPfXsPiUUHYeihbuBHnQvtBkD8UXW+BjVq5IYl1TlYVMKMbzbw8oI/cRloHRXCQ2N6M7JX1dg2yvuJiIiIiIifqFeDk4kTJ9Y47Xj+/PmV7k+bNo1p06bV52lERCrxRIKnRms/s9Zt63+Z544pPs0Ta6pWOoYxsH0pLH8bVn9s/X8adg8Mu9v6fo8z4M4NEBJZfoAT74TQGFj6Jo69f5Lw8xQuC36My/pfxszenXhg1XMYXICD37JCSI4dD8CT36byzR+ZPHVRP0b3awfA6u3Z/OuLtfRqE0VRloPRfx3ECVRMFzqNISmiHQRXGENotHWzuaz8Qt79NQ2XgXHHtmPy2UfTIrzm7tON+n4iIiIiImJjTdINWUTEUxqlac7GefDBZRAYBslDIbaLZ48v9uIqgZdOheICaw2/3laCjv17YedyiG4P0W3Lk355u2DF+7D8Xdi9tvw4MUkQ1qr8fkBQ1Yq7lh1g5DQ4+W5Y+T78+iLs2UDG7//lwaS2mLLGO4YHfnmA49sdT2JEImFBAcSEBREWVN5LeGnaXyzctAc2/8A1AV/SP20ZUyIjmBrXCpfDgROY0O5ynINvxUSHYseWPkUZGRRu2Upwxw4EJSZSVOIi6NCaju1ahPHQ2N5EhQZyak/3lhZQEy4RERERkaqULBQR6XwKdDkV2hxTPmVUmpe9m6FVJ+trZwAkDYbF/7USh6W2/QbvXlB+PzQGIhMhayOYQ/sFhlrT2/tdCp1Odr+ZR0gkDLoWBlwDf35H2qIZuFzplXap2IDj6Yv7V3580UHOLvmOc1s/T4vcDWWbo7O70y3nZJYFxuIqjGPG2hhmzPuOiOAAOrWO4Jj2LXhobJ+y/Q8WlRBaIQHpS/bNnMnOyVPA5QKnk9yb7+SuAx2ZNqY3J3ZrDVBWZSkiIiIiIvWnZKGIiNMJl860kkRgrTeXs93qkpy/BzoPg8jWXh2iNAJj4M/58MtzkPo1TPjGShKC1Rm4++mQ0KvyY1ofZf3fKMixphkfzLa2txsI/S+FXuMgrEX9x+R0QtfhJLfpjXPmSFyY8m/hICmimmR2bga8ejqt/tpsnVZQOJtjUkgY+yDxpi1jtmXTZ3cem/fks3lPPul795NfWMLq7TllVXmlTn/yR/IKinn96sH0bhcDwK6cgxS7DG1iQnE4vFOPmLU5ncx/TsFRugajy0Xkc4+RP+IfPD0vpCxZKCIiIiIiDadkoYgfycjPIC0njeToZE2tqytnhWqq7x6ERc+W3+9yGlw+q+nHJI0nNwPeuxh2LD20wQFpv5QnCyNbQ9fTKj+m+yjrBnAwB3J2WInDmCRo3d2jw0uMSGRKyv3lDTiMYcqeLBIXzIAzH628c9F+a3pzeByk/B/FfS9l1fcLSYrvRt+gIPq2b1Fp98JiF2l797N5Tz6BzvLkX1GJi/S/DlDiMrSOKu+6/PrCLTw3fxNRIYF0TYikW3wk3ROi6JYQRbf4yEZJIh4oLOG3LXv5edMeFm7MwrliCQ8f1qzF4XIxsUco468a5NHnFhERERFp7pQsFPETs1JnVensOa7bOG8Py55ikiAoAlokwe71sGle5WmqYm95u+CNc2DPBivO/S+DITfUba3K0qYg8T0bbZiVGnBsW0ni9w/DoOuq7tiqM1z7LRzYZ62BWFRU63GDA510jY+ka3xkpe1BAU6WTx7B5j35xFdIFuYeLCbQ6SC3oJhlaftYlrav0uMiQwJpExNKfHQI8VGhtI4KoX9SC87o0wYAl8vwx84cQgKddG4dScChBGVBcQkOHAQFOChxGVZsy2bhxj38tHEPy9L2UVhSnhyMi4iz1l005ZWWOJ1cOu54gkKbvvOyiIiIiIg/U7JQxA9k5GeUJQrBWtts6qKppLRNUYVhfQy+zkoeORzw1ljY9B0sewtOm+ztkUlD5e8pTxRGt4erP4eWHb09qhqVNeBIHAR9L4bg8PJv7lgGbQ+tXRgaY90aKCq0aiXig2N688+zj2ZLVj4bMnPZkJnHxl3Wv1v25JNXUEzqrjxSd+WVPWZMv7b07+wgLSeN+LD2nP3MMgDWTB1FRIj1q8e9n6xm5pJtOBzgdFgJw4raxISS0iWO47vGcnzXOEJODKm0ZmGbB6YSlKj3NxERERERT1OyUMQPpOWklSUKS1VshuArbDNNuuKU5GOvPJQsfAeG/QMC9LZpW/v3wpujYfc6iGoDV37q04nCKiomCtd/Be9dCCMegJRbrcR2Yz51oJPuCVF0T4iqtN2a0pxPRnYBu3IPsju3gF25BRSGLWLUx09blc44iU08H1fOYIIDnZUeC9bSkSXG0CI8iKGdY0npGsfxXWLpFBdReXrzeecRccIJFG5NI7hDshKFIiIiIiKNRJ96RfxAcnQyToezUsLQ6XCSFJXkxVFVZttp0j3OtNaCy8uwmmD0PNPbI5L6OPCXlSjMXA2RCXDlZ3WbduxrNs2z/t37p1eHYU1pjqJrfHkSMSM/oyxRCODCRXGrj5h73bWVGqo8en5fHhzTm4LiEopLDAnRoWVTlGsSlJioJKGIiIiISCNzHnkXEfF1iRGJTBk6BafDekmXJuN8pXqvpmnSGfkZXh6ZGwKDod/F1tdL3/TuWKR+DuyzppNnrLQSv1d8CnHdvD2qhjnzUbhjPZz9ZKNXFdZVbZXOFYUEBhATFkR8VChtW4QdMVEoIiIiIiJNQ5WFIm7y9Sm0lZohRCX51BjtMk26Rv2vgIXPQOpcqwNudNvK3z+0hlqZORNh/ZfQ8yzofzm0H+hzCZ1m42AOvD3eWt8vrJVVUdiITUmaVJRvvnbsUOksIiIiIiI1U2WhiBtmpc5i1MejmPD1BEZ9PIpZqbO8PaRqJUYkMihxUIMScBn5GSzeudijVX+lyYOKbJU8aN0dklPAuKy1C0uVFMObY+DhZKvDbqnotrB/Dyx9A14ZDv8ZDD8/BbmZTT70Zm/rQti+BEJbwBVzIOFob4/I7/l6pbOIiIiIiNROyUKRI8jcn2nfKbR11FhJUb9IHhx7hfXv99OsjgxgNTvJzYDCXNj2W/m+g6+HSz6CYy6GoHCr8+43k2HGUfDuRbD2MygubPpzqMnezfDFXbBqpm+Ny5iGj6fH6TDuJbhiNrTp65FhyZGN6zaOuePn8uqoV5k7fq491icVERERERFA05BFjigt1+ZTaN1U07qCKW1TPHKevjxN2i1Hj4av74PCfHAVQ0CQtf2sx6yqtfijyveNiIPuI63bGf+GNZ/A8ncg/VfY8KV1C4+DYy6Cfpd6t9ot8w9rPb+8DFj8X4iIhwFXwoCrIKa998a1ez18eIXVuTgi3hpLiySISbK+LrslQ3irytO8iw5YcYqIs+73Pd8759DMJUYk2u91LiIiIiIiShaKHElyVPNYf6sp1hW0dfIgOByu/caqwquo4wm1Py40+lDy7UrYvQGWvw0r3oe8TFj0rHVr2x/OfwNadmi88Vdn+xJrPb8Df0GrLlaCLS8DfnwUFjxudYIedC10Hta0ay5uXgAfXAoHs637+bus246l1e8fHAl3p4EzwKpGnDkBslKt9Ql9dF0/ERERERERX6VkocgRJIQnMGXolLKqO1tOoXWDmhK4oVVn61ZfrbvDiAfg1Mmw8Vsrcbj+S9iXDlFtyvdb+RGEREKHFAiNafi4q7Ppe/jgMijMg3YD4dKPICQK1n0Ov70MWxbAuv9Zt9huMPpZSD6uccZSUWE+fHSVlShsPxjGvgAFuZC97dAt/dDt0P28TOsaOQOsxzscVvIzezvsS1OyUEREREREpI6ULBRxg+2n0LqhdF1Bf0+K+oSAQGstvR6nQ/4ea6ptYHD597/5J+TuhGvmlifoMlZBzk5o2w8i4xs+ht3rrERhp5PgonetRCFArzHWbdda+O0VWPEeZG2snHTL2Vk+xdfTgiPgvFdh2dtw7tMQFGZtb9uv+v2LC2B/VuVtXU+D06fX/BgRERERERGpkZKFIm6y9RRaNzWHpKjPiYiDiApTmYsLrGm/O1dCYp/y7UvfgsUvWl9Ht7OqDgffAEmDjvwcJcWwZpa1tmL3kda2Y6+AnO1wyn0QFFr1MfFHWesxDp8CW36Glh3Lvzf7Rti2BMfo5+p4sjUoLrASkgm9rPudT7Zu7ggMsbpPV3TSnZ4Zl4iIiIiISDOkZKGIVNIckqI+LTDEmnp7uIjWENfD6qycsx1WfWTdkodCyq3Q/XRw1tDgfvF/Ye49EH80dB1u7RccASOnHXk8IVFWBWQpVwnsSYXCXEyrLsBGa/ua2bBpHnQ+xUp2hrdy73wP7LOmQ2eshAnfQOse7j1OREREREREGoWShSIidnDyXdatIBd2roDl78HKDyBtkXWL6w5DJ0LfC6GkAPJ2QVw367H9LrEShr3HgasInCH1H4czAG5fDZmrrLUMy5KFn8Afs2Hpm4DDmgLcbST0Hl97AjAwFEoKweWC3AwlC0VERERERLxMyUIRETsJibI6MHc8AU69D359AX5/1ao4/OxW+G4aFB+0EoXXzrMafoS1gP9bWnPlYV05ndDmGCgqKt82aII1PfrP72HXH7BjmXX74RFI6AN9xluJwxbJlY8VFAoXvWc1Kkk42jPjExERERERkXpTslCkCWXkZ5CWk0ZydLKm+krDRbeBEVPhxDtg6Rvwy/PWFGWwugrvzypvROKpRGFNOp1k3cBqgLLpO/hjjjU1OXOVdfv2fkgaYq23GBACp9xj7R8Ra91ERERERETE65QsFGkis1JnVek0PK7bOG8PS/xBaDSk/B8MuRFSv7am9nY+pfEThDWJbgP9L7Vu+/daScPVH8OWnyD9V+sG0H4gdBvhnTGKiIiIiIhItZQsFGkCGfkZZYlCAJdxMXXRVFLapqjCUDwnIAh6nuXtUVQW3goGXm3dcnZYaxuu+8JKFHY51dujExERERERkcMoWSjSBNJy0soShaVcxkV6brqShdJ8RLeFobdYNxEREREREfFJXpqjJtK8JEcn43RUfrk5HU6SopK8NCIRERERERERkaqULBRpAokRiUwZOqUsYVi6ZqGqCkVERERERETEl2gaskgTGddtHCltU0jPTScpKkmJQhERERERERHxOUoWijShxIhEJQlFRERERERExGdpGrKIiIiIiIiIiIgAShaKiIiPysjPYPHOxWTkZ3h7KCIiIiIiIs2GpiGLiIjPmZU6i6mLpuIyrrKGQOO6jfP2sERERERERPyeKgtFRMSnZORnlCUKAVzGxdRFU1VhKCIiIiIi0gSULBQREZ+SlpNWligs5TIu0nPTvTQiERERERGR5kPJQhER8SnJ0ck4HZV/PDkdTpKikrw0IhERERERkeZDyUIREfEpiRGJTBk6pSxhWLpmYWJEopdHJiIiIiIi4v/U4ERERHzOuG7jSGmbQnpuOklRSUoUioiIiIiINBElC0VEKsjIzyAtJ43k6GQlqLwsMSJRMRAREREREWliShaKiBwyK3VWWRfe0qmv47qN8/awRERERERERJqM1iwUEcGqKCxNFILVfXfqoqlk5Gd4eWQiIiIiIiIiTUfJQhERIC0nrSxRWMplXKTnpntpRCIiIiIiIiJNT8lCEREgOTq5rPtuKafDSVJUkpdGJCIiIiIiItL0lCwUEcFqpjFl6JSyhGHpmoVqsCG+KiM/g8U7F2uqvIiIiIiIeJQanIiIHDKu2zhS2qaQnptOUlSSEoXis9SMR0REREREGosqC0VEKkiMSGRQ4iC/TxSqKs2+1IxHREREREQakyoLRUSaGVWl2VttzXj8PcktIiIiIiKNT5WFIiLNiKrS7E/NeEREREREpDEpWSgi0ozUVpUm9qBmPCIiIiIi0pjqlSz8z3/+Q8eOHQkNDWXIkCEsXry4xn1feuklTjzxRFq2bEnLli0ZPnx4rfuLiEjjUVWafxjXbRxzx8/l1VGvMnf8XE0jFxERERERj6lzsvCDDz5g0qRJTJkyhaVLl3LMMccwatQodu3aVe3+8+fP5+KLL+b7779n0aJFJCUlMXLkSLZv397gwYuI+CJfbh6iqjT/0Vya8YiIiIiISNOqc4OTGTNmcN1113H11VcD8MILL/D555/z6quvcvfdd1fZ/5133ql0/+WXX+bjjz9m3rx5XHHFFfUctoiIb7JD85Bx3caR0jaF9Nx0kqKSlGwSERERERGRMnVKFhYWFrJkyRLuueeesm1Op5Phw4ezaNEit46xf/9+ioqKaNWqVY37FBQUUFBQUHY/JycHgKKiIoqKiuoyZJ9Veh7+cj7+SDHyLjte/8z9mUxdOBUXlZuHDI4fTEJ4gpdHV1lscCyxsbFA/a+xHWPUHCgu9qA4+SbFxR4UJ9+nGPkmxcUeFCff5C9xcXf8DmOMcfegO3bsoF27dixcuJChQ4eWbf/b3/7GDz/8wK+//nrEY9x8883MnTuXNWvWEBoaWu0+999/P1OnTq2y/d133yU8PNzd4YqINKk/i/7k1fxXq2y/JuIaOgd19sKIRERERERERCz79+/nkksuITs7m+jo6Br3q/M05IZ4+OGHef/995k/f36NiUKAe+65h0mTJpXdz8nJKVvrsLaTsZOioiK++eYbRowYQVBQkLeHI9VQjLzLjtc/c38mr89+vayyEKw1Ac8fcb7PVRZ6gh1j1BwoLvagOPkmxcUeFCffpxj5JsXFHhQn3+QvcSmduXskdUoWxsXFERAQQGZmZqXtmZmZJCbWvubVY489xsMPP8y3335L3759a903JCSEkJCQKtuDgoJsHZTq+OM5+RvFyLvsdP3bx7RnSsqUKmsWto9p7+2hNSo7xag5UVzsQXHyTYqLPShOvk8x8k2Kiz0oTr7J7nFxd+x1ShYGBwczYMAA5s2bx5gxYwBwuVzMmzePiRMn1vi4f//73zz00EPMnTuXgQMH1uUpRURsRc1DRERERERExM7qPA150qRJXHnllQwcOJDBgwfz5JNPkp+fX9Yd+YorrqBdu3ZMnz4dgEceeYTJkyfz7rvv0rFjRzIyMgCIjIwkMjLSg6ciIuIbEiMSlSQUERERERERW6pzsvDCCy9k9+7dTJ48mYyMDPr168dXX31FQoK1HldaWhpOp7Ns/+eff57CwkLOO++8SseZMmUK999/f8NGLyIicpiM/AzSctJIjk5W0lZERERERKSO6tXgZOLEiTVOO54/f36l+1u2bKnPU4iIiA+qmIiLDY719nCqmJU6q8qakeO6jfP2sERERERERGyjSbshi4iIfR2eiLtv8H0EE+ztYZXJyM8oGx+Ay7iYumgqKW1TVGEoIiIiIiLiJueRdxERkeauukTctMXTyHZle3lk5dJy0srGV8plXKTnpntpRCIiIiIiIvajZKGIiBxRTYm4rJIsL42oquToZJyOyj/WnA4nSVFJXhqRiIiIiIiI/ShZKCJiMxn5GSzeuZiM/Iwme86aEnGxAb6zbmFiRCJThk4pG2fpmoWagiwiIiIiIuI+rVkoImIj3mrgUZqIq7Jm4XrfWbMQYFy3caS0TSE9N52kqCQlCkVEREREROpIyUIREZvwdgOPwxNxscGxfLH+i0Z/3rpKjEhUklBERERERKSelCwUEbGJ2hp4NFVyrGIirqioqMb9MvIzSMtJIzk6WYk7ERERERERG1GyUETEJkrXDayYMPTFBh7emiotIiIiIiIiDacGJyIiNmGHBh41TZVuymYsIiIiIiIiUn+qLBQRsRFfb+DhC1OlRUREREREpP6ULBQRsRlfbuBhl6nSIiIiIiIiUj1NQxYREY+xw1RpERERERERqZkqC0VExKN8faq0iIiIiIiI1EzJQhER8ThfniotIiIiIiIiNdM0ZBEREREREREREQGULBQREREREREREZFDlCwUERERERERERERQMlCEREREREREREROUTJQhEREREREREREQGULBQR8RkZ+Rks3rmYjPwMbw9FREREREREmqlAbw9ARERgVuospi6aisu4cDqcTBk6hXHdxnl7WCIiIiIiItLMqLJQRMTLMvIzyhKFAC7jYuqiqaowFBERERERkSanZKGIiJel5aSVJQpLuYyL9Nx0L42ocWm6tYiIiIiIiO/SNGQRES9Ljk7G6XBWShg6HU6SopK8OKrGoenWIiIiIiIivk2VhSIiXpYYkciUoVNwOqy35NIkWmJEYp2P5ctVe5puLSIiIiIi4vtUWSgi4qaM/AzSctJIjk6uVyKvNuO6jSOlbQrpuekkRSXV6/i+XrVX23RrT19PERERERERqR8lC0VE3NAUibjEiMR6J81qqtpLaZviM4m45jTdWkRERERExK40DVlE5AjsMH3WDk1SPDndWkRERERERBqHKgtFRI7ADtNn7VK154np1iIiIiIiItJ4VFkoInIEpYm4inwtEWenqr3EiEQGJQ7yybGJiIiIiIg0d6osFBE5gtJE3OFrFvpasktVeyIiIiIiItJQShaKiLjBLom4hjRJEREREREREVGyUETETUrEiYiIiIiIiL/TmoUiIiIiIiIiIiICKFkoIiIiIiIiIiIihyhZKCIiIiIiIiIiIoCShSIiIiIiIiIiInKIkoUiIiIiIiIiIiICKFkoIiIiIiIiIiIihyhZKCIiIiIiIiIiIoCShSIiIiIiIiIiInKIkoUiIiIiIiIiIiICKFkoIiIiIiIiIiIihyhZKCIiIiIiIiIiIoCShSIiIiIiIiIiInKIkoUiIiIiIiIiIiICKFkoIiIiIiIiIiIihyhZKCIiIiIiIiIiIgAEensA7jDGAJCTk+PlkXhOUVER+/fvJycnh6CgIG8PR6qhGHmXrr/vU4x8k+JiD4qTb1Jc7EFx8n2KkW9SXOxBcfJN/hKX0rxaaZ6tJrZIFubm5gKQlJTk5ZGIiIiIiIiIiIjYV25uLjExMTV+32GOlE70AS6Xix07dhAVFYXD4fD2cDwiJyeHpKQk0tPTiY6O9vZwpBqKkXfp+vs+xcg3KS72oDj5JsXFHhQn36cY+SbFxR4UJ9/kL3ExxpCbm0vbtm1xOmtemdAWlYVOp5P27dt7exiNIjo62tb/0ZoDxci7dP19n2LkmxQXe1CcfJPiYg+Kk+9TjHyT4mIPipNv8oe41FZRWEoNTkRERERERERERARQslBEREREREREREQOUbLQS0JCQpgyZQohISHeHorUQDHyLl1/36cY+SbFxR4UJ9+kuNiD4uT7FCPfpLjYg+Lkm5pbXGzR4EREREREREREREQanyoLRUREREREREREBFCyUERERERERERERA5RslBEREREREREREQAJQtFRERERERERETkECULRUREREREREREBFCysJLp06czaNAgoqKiiI+PZ8yYMaxfv77SPgcPHuSWW24hNjaWyMhIxo8fT2ZmZtn3V6xYwcUXX0xSUhJhYWEcddRRPPXUU5WO8dNPP3H88ccTGxtLWFgYPXv25Iknnjji+IwxTJ48mTZt2hAWFsbw4cNJTU2ttM9DDz1ESkoK4eHhtGjRov4Xw4fZPU5btmxhwoQJdOrUibCwMLp06cKUKVMoLCxs4JVpfHa/9gDnnnsuycnJhIaG0qZNGy6//HJ27NjRgKvie/whTqUKCgro168fDoeD5cuX1/1i+BB/iEvHjh1xOByVbg8//HADropv8YcYAXz++ecMGTKEsLAwWrZsyZgxY+p3QXyE3eMyf/78Kq+b0ttvv/3WwKvjO+weJ4ANGzYwevRo4uLiiI6O5oQTTuD7779vwFXxPf4Qp6VLlzJixAhatGhBbGws119/PXl5eQ24Kt7n63GZNWsWI0eOJDY2tsbfyY40Prvzhxj997//ZdiwYURHR+NwONi3b1+9roUvsXtc9u7dy//93//Ro0cPwsLCSE5O5tZbbyU7O7v+F8VTjJQZNWqUee2118zq1avN8uXLzZlnnmmSk5NNXl5e2T433nijSUpKMvPmzTO///67Oe6440xKSkrZ91955RVz6623mvnz55tNmzaZt956y4SFhZlnnnmmbJ+lS5ead99916xevdps3rzZvPXWWyY8PNy8+OKLtY7v4YcfNjExMWb27NlmxYoV5txzzzWdOnUyBw4cKNtn8uTJZsaMGWbSpEkmJibGcxfHh9g9Tl9++aW56qqrzNy5c82mTZvMnDlzTHx8vLnjjjs8fKU8z+7X3hhjZsyYYRYtWmS2bNlifv75ZzN06FAzdOhQD14l7/OHOJW69dZbzRlnnGEAs2zZsoZfHC/yh7h06NDBPPDAA2bnzp1lt4rjtzt/iNHMmTNNy5YtzfPPP2/Wr19v1qxZYz744AMPXqWmZ/e4FBQUVHrN7Ny501x77bWmU6dOxuVyefhqeY/d42SMMd26dTNnnnmmWbFihdmwYYO5+eabTXh4uNm5c6cHr5R32T1O27dvNy1btjQ33nijWbdunVm8eLFJSUkx48eP9/CValq+Hpc333zTTJ061bz00ks1/k52pPHZnT/E6IknnjDTp08306dPN4D566+/GnxdvM3ucVm1apUZN26c+fTTT83GjRvNvHnzTLdu3XziPU3Jwlrs2rXLAOaHH34wxhizb98+ExQUZD766KOyfdauXWsAs2jRohqPc/PNN5tTTjml1ucaO3asueyyy2r8vsvlMomJiebRRx8t27Zv3z4TEhJi3nvvvSr7v/baa36bLDycneNU6t///rfp1KlTrc/ti/zh2s+ZM8c4HA5TWFhY6/PbmV3j9MUXX5iePXuaNWvW+EWy8HB2jEuHDh3ME088caRT8xt2i1FRUZFp166defnll906P7uyW1wOV1hYaFq3bm0eeOCBWp/b7uwWp927dxvA/Pjjj2X75OTkGMB88803tZ+sjdktTi+++KKJj483JSUlZfusXLnSACY1NbX2k7URX4pLRZs3b672d7L6js/O7Bajir7//nu/SRYezs5xKfXhhx+a4OBgU1RU5NaxG4umIdeitPSzVatWACxZsoSioiKGDx9etk/Pnj1JTk5m0aJFtR6n9BjVWbZsGQsXLuTkk0+ucZ/NmzeTkZFR6bljYmIYMmRIrc/dHPhDnI703L7K7td+7969vPPOO6SkpBAUFFTjse3OjnHKzMzkuuuu46233iI8PPzIJ2lDdowLwMMPP0xsbCz9+/fn0Ucfpbi4uPYTtTG7xWjp0qVs374dp9NJ//79adOmDWeccQarV69274Rtwm5xOdynn35KVlYWV199dY3H9Qd2i1NsbCw9evTgzTffJD8/n+LiYl588UXi4+MZMGCAeydtQ3aLU0FBAcHBwTid5R9jw8LCAGuaoL/wpbi4o77jszO7xai58Ie4ZGdnEx0dTWBgoMePXRfefXYf5nK5uP322zn++OPp3bs3ABkZGQQHB1dZCzAhIYGMjIxqj7Nw4UI++OADPv/88yrfa9++Pbt376a4uJj777+fa6+9tsbxlB4/ISHB7eduDvwhThs3buSZZ57hscceq/G4vsjO1/7vf/87zz77LPv37+e4447jf//73xHP167sGCdjDFdddRU33ngjAwcOZMuWLe6erm3YMS4At956K8ceeyytWrVi4cKF3HPPPezcuZMZM2a4dd52YscY/fnnnwDcf//9zJgxg44dO/L4448zbNgwNmzYYMs/Sh3OjnE53CuvvMKoUaNo3759jce1OzvGyeFw8O233zJmzBiioqJwOp3Ex8fz1Vdf0bJlS7fP3U7sGKdTTz2VSZMm8eijj3LbbbeRn5/P3XffDcDOnTvdO3Ef52txcUd9xmdndoxRc+APcdmzZw8PPvgg119/vUePWx+qLKzBLbfcwurVq3n//ffrfYzVq1czevRopkyZwsiRI6t8f8GCBfz++++88MILPPnkk7z33nsAvPPOO0RGRpbdFixYUO8x+Du7x2n79u2cfvrpnH/++Vx33XX1PgdvsPO1v+uuu1i2bBlff/01AQEBXHHFFRhj6n0evsyOcXrmmWfIzc3lnnvuqfeYfZ0d4wIwadIkhg0bRt++fbnxxht5/PHHeeaZZygoKKj3efgqO8bI5XIBcO+99zJ+/HgGDBjAa6+9hsPh4KOPPqr3efgSO8alom3btjF37lwmTJhQ7/HbgR3jZIzhlltuIT4+ngULFrB48WLGjBnDOeec4zdJqMPZMU69evXijTfe4PHHHyc8PJzExEQ6depEQkJCpWpDO7NjXJobxcg32T0uOTk5nHXWWRx99NHcf//99T4Hj/HqJGgfdcstt5j27dubP//8s9L2efPmVTu3Pzk52cyYMaPStjVr1pj4+Hjzj3/8w63nfPDBB0337t2NMdb6KKmpqWW3/fv3m02bNlU7x/2kk04yt956a5XjNYc1C+0ep+3bt5tu3bqZyy+/vNK6K3Zg92tfUXp6ugHMwoUL3RqHndg1TqNHjzZOp9MEBASU3QATEBBgrrjiijpcAd9k17hUZ/Xq1QYw69atc2scdmHXGH333XcGMAsWLKi0z+DBg90ehy+za1wqeuCBB0zr1q39ep1cu8bp22+/NU6n02RnZ1fap2vXrmb69OlujcNO7BqnijIyMkxubq7Jy8szTqfTfPjhh26Nw5f5YlwqqmndtbqMz+7sGqOK/HHNQrvHJScnxwwdOtScdtpp1TZ99AYlCytwuVzmlltuMW3btjUbNmyo8v3SxTFnzpxZtm3dunVVFsdcvXq1iY+PN3fddZfbzz116lTToUOHWseWmJhoHnvssbJt2dnZzbLBiT/Eadu2baZbt27moosuMsXFxW4/v7f5w7U/3NatWw1gvv/+e7fH4uvsHqetW7eaVatWld3mzp1rADNz5kyTnp7u9lh8jd3jUp23337bOJ1Os3fvXrfH4svsHqPS+xUbnBQWFpr4+PgjduvzZXaPS8V9O3XqZO644w63n99O7B6nTz/91DidTpObm1vpsd27dzcPPfSQ22PxdXaPU3VeeeUVEx4ebuvEhy/HpaIjNTg50vjszO4xqsifkoX+EJfs7Gxz3HHHmZNPPtnk5+e7/fyNTcnCCm666SYTExNj5s+fb3bu3Fl2q5gVvvHGG01ycrL57rvvzO+//26GDh1qhg4dWvb9VatWmdatW5vLLrus0jF27dpVts+zzz5rPv30U7NhwwazYcMG8/LLL5uoqChz77331jq+hx9+2LRo0cLMmTPHrFy50owePdp06tSpUuZ569atZtmyZWbq1KkmMjLSLFu2zCxbtqzKLz52Zvc4bdu2zXTt2tWcdtppZtu2bZWe39fZ/dr/8ssv5plnnjHLli0zW7ZsMfPmzTMpKSmmS5cu5uDBgx6+Wt5j9zgdri7dw3yZ3eOycOFC88QTT5jly5ebTZs2mbffftu0bt3aL6o9S9k9RsYYc9ttt5l27dqZuXPnmnXr1pkJEyaY+Ph4Wyd0/SEuxliVa4BZu3ath66Mb7F7nHbv3m1iY2PNuHHjzPLly8369evNnXfeaYKCgszy5cs9fLW8x+5xMsaYZ555xixZssSsX7/ePPvssyYsLMw89dRTHrxKTc/X45KVlWWWLVtmPv/8cwOY999/3yxbtqzS55cjjc/u/CFGO3fuNMuWLTMvvfRSWff3ZcuWmaysLA9eqaZl97hkZ2ebIUOGmD59+piNGzdWen5vFxUpWVgBUO3ttddeK9vnwIED5uabbzYtW7Y04eHhZuzYsZVegFOmTKn2GBUzzk8//bTp1auXCQ8PN9HR0aZ///7mueeeO+JUVJfLZf75z3+ahIQEExISYk477TSzfv36SvtceeWV1T6/P1VN2T1Or732Wo3n4Ovsfu1XrlxpTjnlFNOqVSsTEhJiOnbsaG688Uazbds2j10jX2D3OB3OX5KFdo/LkiVLzJAhQ0xMTIwJDQ01Rx11lPnXv/7lV4l2u8fIGKuS8I477jDx8fEmKirKDB8+3Kxevdoj18db/CEuxhhz8cUXm5SUlAZfD1/lD3H67bffzMiRI02rVq1MVFSUOe6448wXX3zhkevjK/whTpdffrlp1aqVCQ4ONn379jVvvvmmR66NN/l6XGr6/DJlyhS3x2d3/hCjmp6/4jnYjd3jUlrlWd1t8+bNHrxSdecwxk9X9RcREREREREREZE68Y+WUSIiIiIiIiIiItJgShaKiIiIiIiIiIgIoGShiIiIiIiIiIiIHKJkoYiIiIiIiIiIiABKFoqIiIiIiIiIiMghShaKiIiIiIiIiIgIoGShiIiIiIiIiIiIHKJkoYiIiIiIiIiIiABKFoqIiIiIiIiIiMghShaKiIiIiIiIiIgIoGShiIiIiIiIiIiIHPL/AZU/X6Nu+VRsAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]

From 8df0815c5131628093c82548472af6b277586d3d Mon Sep 17 00:00:00 2001
From: JANSSENB <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 10:20:10 +0100
Subject: [PATCH 08/21] fix(#123): added parameter to mogpr UDP

---
 src/fusets/openeo/services/mogpr.json       | 17 +++++++++++++++--
 src/fusets/openeo/services/publish_mogpr.py |  2 +-
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/src/fusets/openeo/services/mogpr.json b/src/fusets/openeo/services/mogpr.json
index da8eb7f..6929e6c 100644
--- a/src/fusets/openeo/services/mogpr.json
+++ b/src/fusets/openeo/services/mogpr.json
@@ -12,12 +12,16 @@
                         "runudf1": {
                             "process_id": "run_udf",
                             "arguments": {
-                                "context": {},
+                                "context": {
+                                    "include_uncertainties": {
+                                        "from_parameter": "include_uncertainties"
+                                    }
+                                },
                                 "data": {
                                     "from_parameter": "data"
                                 },
                                 "runtime": "Python",
-                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.udf import XarrayDataCube\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in ['tmp/venv_static', 'tmp/venv']:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ['HOME'] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv('HOME')\n    set_home('/tmp')\n    user_file = Path.home() / '.config' / 'GPy' / 'user.cfg'\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config['plotting'] = {\n        'library': 'none'\n    }\n    with open(user_file, 'w') as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n    variables = context.get('variables')\n    time_dimension = context.get('time_dimension', 't')\n    prediction_period = context.get('prediction_period', '5D')\n\n    dims = cube.get_array().dims\n    result = mogpr(cube.get_array().to_dataset(dim=\"bands\"), variables=variables, time_dimension=time_dimension, prediction_period=prediction_period)\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n    return Path(os.path.realpath(__file__)).read_text()\n"
+                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.udf import XarrayDataCube\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv_static\", \"tmp/venv\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
                             },
                             "result": true
                         }
@@ -50,6 +54,15 @@
                 "type": "object",
                 "subtype": "raster-cube"
             }
+        },
+        {
+            "name": "include_uncertainties",
+            "description": "Flag to include the uncertainties in the output results",
+            "schema": {
+                "type": "boolean"
+            },
+            "optional": true,
+            "default": false
         }
     ]
 }
\ No newline at end of file
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 529aaad..3096242 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -88,7 +88,7 @@ def generate_mogpr_udp():
         id="mogpr",
         summary="Integrates timeseries in data cube using multi-output gaussian " "process regression.",
         description=description,
-        parameters=[input_cube.to_dict()],
+        parameters=[input_cube.to_dict(), include_uncertainties.to_dict()],
         process_graph=process,
     )
 

From d7a388fd8e475b98cdf847b8bae0cd018caee8f0 Mon Sep 17 00:00:00 2001
From: JANSSENB <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 14:57:41 +0100
Subject: [PATCH 09/21] feat(#123): updated mogpr code to add uncertainties

---
 .../FuseTS - MOGPR Multi Source Fusion.ipynb  | 322 +++++++-----------
 src/fusets/openeo/mogpr_udf.py                |   4 +-
 src/fusets/openeo/services/mogpr.json         |   2 +-
 src/fusets/openeo/services/publish_mogpr.py   | 126 ++++---
 4 files changed, 202 insertions(+), 252 deletions(-)

diff --git a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
index 2d335c2..40f5f07 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
@@ -49,26 +49,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
-      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
-      "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
-      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
-      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
-      "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
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-      "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
-      "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
+      "Requirement already satisfied: openeo in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (0.21.0)\n",
+      "Requirement already satisfied: deprecated>=1.2.12 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (1.2.14)\n",
+      "Requirement already satisfied: numpy>=1.17.0 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (1.23.5)\n",
+      "Requirement already satisfied: shapely>=1.6.4 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2.0.1)\n",
+      "Requirement already satisfied: oschmod>=0.3.12 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (0.3.12)\n",
+      "Requirement already satisfied: pandas>0.20.0 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2.0.3)\n",
+      "Requirement already satisfied: xarray>=0.12.3 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2023.7.0)\n",
+      "Requirement already satisfied: requests>=2.26.0 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2.31.0)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
+      "Requirement already satisfied: pywin32 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from oschmod>=0.3.12->openeo) (306)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (3.4)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
+      "Requirement already satisfied: packaging>=21.3 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "[notice] A new release of pip available: 22.3.1 -> 23.3.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
      ]
     }
    ],
@@ -192,7 +200,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41f71eecac2a4f368aed62845954d2e1",
+       "model_id": "ae8e2d9034e349c182e3bfac66f2675e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -344,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "299a18e3-63d3-4e05-83e3-9460fe059705",
    "metadata": {},
    "outputs": [],
@@ -355,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "faec8adf-2a1d-486e-a363-544d2158f56b",
    "metadata": {},
    "outputs": [],
@@ -375,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "id": "2b27ec81-2543-4fbb-b6c5-2ac796df9e76",
    "metadata": {},
    "outputs": [],
@@ -387,6 +395,16 @@
     "                                         data=merged_datacube, include_uncertainties=True)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4c84fa2-629b-4a23-b368-9d18538638e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mogpr."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -412,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "id": "632a7088-67c2-4f89-95ff-813e4587f2be",
    "metadata": {},
    "outputs": [],
@@ -422,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "db4ac328-5413-4304-b1a5-b1c6dd6710fc",
    "metadata": {},
    "outputs": [
@@ -430,24 +448,48 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:00:00 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': send 'start'\n",
-      "0:00:22 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
-      "0:00:27 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
-      "0:00:34 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
-      "0:00:42 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
-      "0:00:52 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
-      "0:01:04 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': queued (progress N/A)\n",
-      "0:01:20 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:01:39 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:02:03 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:02:33 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:03:11 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:03:58 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:04:56 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:05:57 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:06:57 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:07:57 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n",
-      "0:08:58 Job 'j-240201f10aed4e3b8c9ab9b1faed9e76': running (progress N/A)\n"
+      "0:00:00 Job 'j-240201c8cf414edb8be1b939d596ffbb': send 'start'\n",
+      "0:01:27 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:01:32 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:01:39 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:01:47 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:01:57 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:02:10 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:02:26 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:02:45 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:03:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:03:40 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:04:18 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:05:04 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:06:03 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
+      "0:07:03 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:08:04 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:09:04 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:10:05 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:11:05 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:12:05 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:13:05 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:14:06 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:15:06 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:16:06 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:17:07 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:18:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:19:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:20:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:21:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:22:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:23:10 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:24:11 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:25:11 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:26:11 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:27:12 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:28:12 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:29:13 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:30:14 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:31:15 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:32:15 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:33:16 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
+      "0:34:16 Job 'j-240201c8cf414edb8be1b939d596ffbb': finished (progress N/A)\n"
      ]
     }
    ],
@@ -457,7 +499,7 @@
     "        'executor-memory': '8g',\n",
     "        'udf-dependency-archives': [ \n",
     "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n",
-    "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n",
+    "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_mogpr_update.zip#tmp/venv_static'\n",
     "        ]\n",
     "    })"
    ]
@@ -472,50 +514,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 123,
    "id": "ec95ceb1-9027-4305-a40a-6bcf9905c7a2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[                NDVI        date\n",
-       " t                               \n",
-       " 2023-01-02  0.578209  2023-01-02\n",
-       " 2023-01-07  0.567402  2023-01-07\n",
-       " 2023-01-12  0.555066  2023-01-12\n",
-       " 2023-01-17  0.539590  2023-01-17\n",
-       " 2023-01-22  0.530717  2023-01-22\n",
-       " ...              ...         ...\n",
-       " 2023-11-08  0.781755  2023-11-08\n",
-       " 2023-11-13  0.770954  2023-11-13\n",
-       " 2023-11-18  0.759216  2023-11-18\n",
-       " 2023-11-23  0.747641  2023-11-23\n",
-       " 2023-11-28  0.735555  2023-11-28\n",
-       " \n",
-       " [67 rows x 2 columns],\n",
-       "                  RVI        date\n",
-       " t                               \n",
-       " 2023-01-02  0.398482  2023-01-02\n",
-       " 2023-01-07  0.387325  2023-01-07\n",
-       " 2023-01-12  0.375429  2023-01-12\n",
-       " 2023-01-17  0.353655  2023-01-17\n",
-       " 2023-01-22  0.343858  2023-01-22\n",
-       " ...              ...         ...\n",
-       " 2023-11-08  0.405838  2023-11-08\n",
-       " 2023-11-13  0.412925  2023-11-13\n",
-       " 2023-11-18  0.406477  2023-11-18\n",
-       " 2023-11-23  0.404311  2023-11-23\n",
-       " 2023-11-28  0.396419  2023-11-28\n",
-       " \n",
-       " [67 rows x 2 columns]]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cubes_dfs = []\n",
     "ds = xarray.load_dataset(mogpr_output_file)\n",
@@ -523,144 +525,61 @@
     "    if var[0] != 'crs':\n",
     "        var_df = var[1].mean(dim=['x', 'y'])\n",
     "        var_df = var_df.to_dataframe()\n",
-    "        var_df.index = pd.to_datetime(var_df.index)\n",
-    "        var_df['date'] = var_df.index.date\n",
-    "        cubes_dfs.append(var_df)\n",
-    "cubes_dfs"
+    "        var_df.index = pd.to_datetime(var_df.index).date\n",
+    "        cubes_dfs.append(var_df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 124,
    "id": "84dab365-5139-4e62-9f1d-438329614d21",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[                NDVI        date\n",
-       " t                               \n",
-       " 2023-01-02  0.578209  2023-01-02\n",
-       " 2023-01-07  0.567402  2023-01-07\n",
-       " 2023-01-12  0.555066  2023-01-12\n",
-       " 2023-01-17  0.539590  2023-01-17\n",
-       " 2023-01-22  0.530717  2023-01-22\n",
-       " ...              ...         ...\n",
-       " 2023-11-08  0.781755  2023-11-08\n",
-       " 2023-11-13  0.770954  2023-11-13\n",
-       " 2023-11-18  0.759216  2023-11-18\n",
-       " 2023-11-23  0.747641  2023-11-23\n",
-       " 2023-11-28  0.735555  2023-11-28\n",
-       " \n",
-       " [67 rows x 2 columns],\n",
-       "                  RVI        date\n",
-       " t                               \n",
-       " 2023-01-02  0.398482  2023-01-02\n",
-       " 2023-01-07  0.387325  2023-01-07\n",
-       " 2023-01-12  0.375429  2023-01-12\n",
-       " 2023-01-17  0.353655  2023-01-17\n",
-       " 2023-01-22  0.343858  2023-01-22\n",
-       " ...              ...         ...\n",
-       " 2023-11-08  0.405838  2023-11-08\n",
-       " 2023-11-13  0.412925  2023-11-13\n",
-       " 2023-11-18  0.406477  2023-11-18\n",
-       " 2023-11-23  0.404311  2023-11-23\n",
-       " 2023-11-28  0.396419  2023-11-28\n",
-       " \n",
-       " [67 rows x 2 columns],\n",
-       " 2023-01-02    0.524968\n",
-       " 2023-01-06    0.514087\n",
-       " 2023-01-09    0.447531\n",
-       " 2023-01-11    0.441934\n",
-       " 2023-01-14    0.449542\n",
-       "                 ...   \n",
-       " 2023-11-17    0.419882\n",
-       " 2023-11-19    0.408779\n",
-       " 2023-11-22    0.421535\n",
-       " 2023-11-26    0.431283\n",
-       " 2023-11-29    0.359894\n",
-       " Length: 110, dtype: float64,\n",
-       " 2023-01-17    0.538890\n",
-       " 2023-02-14    0.486261\n",
-       " 2023-03-01    0.520513\n",
-       " 2023-03-11         NaN\n",
-       " 2023-03-28         NaN\n",
-       " 2023-04-05    0.324869\n",
-       " 2023-04-30         NaN\n",
-       " 2023-05-17    0.282205\n",
-       " 2023-05-27    0.314834\n",
-       " 2023-05-30    0.321425\n",
-       " 2023-06-01    0.338727\n",
-       " 2023-06-04    0.402168\n",
-       " 2023-06-06    0.459086\n",
-       " 2023-06-09    0.550603\n",
-       " 2023-06-11    0.565090\n",
-       " 2023-06-14    0.592962\n",
-       " 2023-06-16    0.584990\n",
-       " 2023-06-24    0.713546\n",
-       " 2023-08-10    0.858731\n",
-       " 2023-08-20    0.848861\n",
-       " 2023-08-23    0.847072\n",
-       " 2023-09-04         NaN\n",
-       " 2023-09-07    0.857140\n",
-       " 2023-09-09    0.826571\n",
-       " 2023-09-24    0.833558\n",
-       " 2023-10-07    0.779410\n",
-       " 2023-10-17    0.828085\n",
-       " 2023-11-28         NaN\n",
-       " dtype: float64]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cols = ['RVI - Raw', 'NDVI - Raw']\n",
     "for result in ['mogpr-multisource-s1-base.json', 'mogpr-multisource-s2-base.json']:\n",
     "    with open(result, 'r') as result_file:\n",
-    "        df = timeseries_json_to_pandas(json.load(result_file))\n",
+    "        df = timeseries_json_to_pandas(json.load(result_file)).to_frame()\n",
     "        df.index = pd.to_datetime(df.index).date\n",
+    "        df.index.name = 't'\n",
+    "        df.columns = [f'{result.split(\"-\")[2].upper()}-RAW']\n",
     "        cubes_dfs.append(df)\n",
     "        result_file.close()\n",
-    "\n",
-    "cubes_dfs\n",
-    "# joined_df = pd.concat(cubes_dfs, axis=1)\n",
-    "# joined_df = joined_df.rename(columns={0: cols[0], 1: cols[1]})"
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "a1de65f1-9a5c-4c20-91ea-5c0eddcbdc5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined_df = pd.concat(cubes_dfs, axis=1)\n",
+    "joined_df = joined_df.rename(columns={'S1-RAW': cols[0], 'S2-RAW': cols[1]})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 132,
    "id": "8826c305-1f4c-481f-88aa-291d80e38448",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "cannot reindex on an axis with duplicate labels",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m joined_df\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39m values:\n\u001b[0;32m----> 3\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mjoined_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mjoined_df\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misna\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mplot(values\u001b[38;5;241m.\u001b[39mindex, values[col], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRaw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m col \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-.\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39mcol)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mgrid(\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:3748\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3746\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) DataFrame?\u001b[39;00m\n\u001b[1;32m   3747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, DataFrame):\n\u001b[0;32m-> 3748\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3750\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[1;32m   3751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:11527\u001b[0m, in \u001b[0;36mDataFrame.where\u001b[0;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[1;32m  11518\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwhere\u001b[39m(\n\u001b[1;32m  11519\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  11520\u001b[0m     cond,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  11525\u001b[0m     level: Level \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m  11526\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m> 11527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  11528\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcond\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11529\u001b[0m \u001b[43m        \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11530\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11531\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11532\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11533\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/generic.py:9933\u001b[0m, in \u001b[0;36mNDFrame.where\u001b[0;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[1;32m   9795\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   9796\u001b[0m \u001b[38;5;124;03mReplace values where the condition is {cond_rev}.\u001b[39;00m\n\u001b[1;32m   9797\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   9930\u001b[0m \u001b[38;5;124;03m4  True  True\u001b[39;00m\n\u001b[1;32m   9931\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   9932\u001b[0m other \u001b[38;5;241m=\u001b[39m common\u001b[38;5;241m.\u001b[39mapply_if_callable(other, \u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m-> 9933\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_where\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcond\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/generic.py:9660\u001b[0m, in \u001b[0;36mNDFrame._where\u001b[0;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[1;32m   9657\u001b[0m     cond \u001b[38;5;241m=\u001b[39m cond\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mbool\u001b[39m)\n\u001b[1;32m   9659\u001b[0m cond \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mcond \u001b[38;5;28;01mif\u001b[39;00m inplace \u001b[38;5;28;01melse\u001b[39;00m cond\n\u001b[0;32m-> 9660\u001b[0m cond \u001b[38;5;241m=\u001b[39m \u001b[43mcond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis_number\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   9662\u001b[0m \u001b[38;5;66;03m# try to align with other\u001b[39;00m\n\u001b[1;32m   9663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, NDFrame):\n\u001b[1;32m   9664\u001b[0m     \u001b[38;5;66;03m# align with me\u001b[39;00m\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:5055\u001b[0m, in \u001b[0;36mDataFrame.reindex\u001b[0;34m(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5036\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[1;32m   5037\u001b[0m     NDFrame\u001b[38;5;241m.\u001b[39mreindex,\n\u001b[1;32m   5038\u001b[0m     klass\u001b[38;5;241m=\u001b[39m_shared_doc_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mklass\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5053\u001b[0m     tolerance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   5054\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[0;32m-> 5055\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5056\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5057\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5058\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5059\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5060\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5061\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5062\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5063\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5064\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5065\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5066\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/generic.py:5360\u001b[0m, in \u001b[0;36mNDFrame.reindex\u001b[0;34m(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5357\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_multi(axes, copy, fill_value)\n\u001b[1;32m   5359\u001b[0m \u001b[38;5;66;03m# perform the reindex on the axes\u001b[39;00m\n\u001b[0;32m-> 5360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reindex_axes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5361\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\n\u001b[1;32m   5362\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreindex\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:4890\u001b[0m, in \u001b[0;36mDataFrame._reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   4888\u001b[0m columns \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   4889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4890\u001b[0m     frame \u001b[38;5;241m=\u001b[39m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reindex_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4891\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m   4892\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4894\u001b[0m index \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   4895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/frame.py:4932\u001b[0m, in \u001b[0;36mDataFrame._reindex_columns\u001b[0;34m(self, new_columns, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   4922\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_reindex_columns\u001b[39m(\n\u001b[1;32m   4923\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   4924\u001b[0m     new_columns,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4930\u001b[0m     tolerance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   4931\u001b[0m ):\n\u001b[0;32m-> 4932\u001b[0m     new_columns, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4933\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnew_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m   4934\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4935\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[1;32m   4936\u001b[0m         {\u001b[38;5;241m1\u001b[39m: [new_columns, indexer]},\n\u001b[1;32m   4937\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[1;32m   4938\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[1;32m   4939\u001b[0m         allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   4940\u001b[0m     )\n",
-      "File \u001b[0;32m~/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages/pandas/core/indexes/base.py:4275\u001b[0m, in \u001b[0;36mIndex.reindex\u001b[0;34m(self, target, method, level, limit, tolerance)\u001b[0m\n\u001b[1;32m   4272\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot handle a non-unique multi-index!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   4273\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_unique:\n\u001b[1;32m   4274\u001b[0m     \u001b[38;5;66;03m# GH#42568\u001b[39;00m\n\u001b[0;32m-> 4275\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot reindex on an axis with duplicate labels\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   4276\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   4277\u001b[0m     indexer, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_indexer_non_unique(target)\n",
-      "\u001b[0;31mValueError\u001b[0m: cannot reindex on an axis with duplicate labels"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1c816169840>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -673,7 +592,10 @@
     "plt.figure(figsize=(16, 6))\n",
     "for col in joined_df.columns. values:\n",
     "    values = joined_df[~joined_df[col].isna()]\n",
-    "    plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
+    "    if 'unkown' in col:\n",
+    "        plt.fill_between(values.index, values['NDVI'] - values[col], values['NDVI'] + values[col], alpha=0.2, label='Standard Deviation Range')\n",
+    "    else:\n",
+    "        plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
     "plt.grid(True)\n",
     "plt.legend()"
    ]
@@ -706,7 +628,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.17"
+   "version": "3.10.11"
   },
   "vscode": {
    "interpreter": {
diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index b8a18bb..8dec550 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -4,7 +4,7 @@
 from pathlib import Path
 from typing import Dict
 
-from openeo.udf import XarrayDataCube
+from openeo.udf import XarrayDataCube, inspect
 
 
 def load_venv():
@@ -12,7 +12,7 @@ def load_venv():
     Add the virtual environment to the system path if the folder `/tmp/venv_static` exists
     :return:
     """
-    for venv_path in ["tmp/venv_static", "tmp/venv"]:
+    for venv_path in ["tmp/venv", "tmp/venv_static"]:
         if Path(venv_path).exists():
             sys.path.insert(0, venv_path)
 
diff --git a/src/fusets/openeo/services/mogpr.json b/src/fusets/openeo/services/mogpr.json
index 6929e6c..a96aefd 100644
--- a/src/fusets/openeo/services/mogpr.json
+++ b/src/fusets/openeo/services/mogpr.json
@@ -21,7 +21,7 @@
                                     "from_parameter": "data"
                                 },
                                 "runtime": "Python",
-                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.udf import XarrayDataCube\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv_static\", \"tmp/venv\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
+                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.udf import XarrayDataCube\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv\", \"tmp/venv_static\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
                             },
                             "result": true
                         }
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 3096242..24a0492 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -1,6 +1,8 @@
 # Reads contents with UTF-8 encoding and returns str.
+from typing import Union
 
 import openeo
+from openeo import DataCube
 from openeo.api.process import Parameter
 from openeo.processes import apply_neighborhood
 from openeo.udf import execute_local_udf
@@ -11,68 +13,82 @@
 NEIGHBORHOOD_SIZE = 32
 
 
-def test_udf():
-    connection = openeo.connect("openeo-dev.vito.be").authenticate_oidc()
+def execute_udf():
+    connection = openeo.connect("openeo.vito.be").authenticate_oidc()
     spat_ext = {
         "type": "Polygon",
         "coordinates": [
             [
-                [5.170012098271149, 51.25062964728295],
-                [5.17085904378298, 51.24882567194015],
-                [5.17857421368097, 51.2468515482926],
-                [5.178972704726344, 51.24982704376254],
-                [5.170012098271149, 51.25062964728295],
+                [
+                    5.170012098271149,
+                    51.25062964728295
+                ],
+                [
+                    5.17085904378298,
+                    51.24882567194015
+                ],
+                [
+                    5.17857421368097,
+                    51.2468515482926
+                ],
+                [
+                    5.178972704726344,
+                    51.24982704376254
+                ],
+                [
+                    5.170012098271149,
+                    51.25062964728295
+                ]
             ]
-        ],
+        ]
     }
-    temp_ext = ["2022-05-01", "2022-07-30"]
-    base = connection.load_collection(
-        "SENTINEL2_L2A_SENTINELHUB", spatial_extent=spat_ext, temporal_extent=temp_ext, bands=["B04", "B08", "SCL"]
-    )
-    base_cloudmasked = base.process("mask_scl_dilation", data=base, scl_band_name="SCL")
-    base_ndvi = base_cloudmasked.ndvi(red="B04", nir="B08")
-
-    mogpr = base_ndvi.apply_neighborhood(
-        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context=dict()),
-        size=[
-            {"dimension": "x", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
-            {"dimension": "y", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
-        ],
-        overlap=[],
-    )
+    temp_ext = ["2023-01-01", "2023-03-31"]
+
+    # Setup NDVI cube
+    base_s2 = connection.load_collection('SENTINEL2_L2A',
+                                         spatial_extent=spat_ext,
+                                         temporal_extent=temp_ext,
+                                         bands=["B04", "B08", "SCL"])
+    base_s2 = base_s2.process("mask_scl_dilation", data=base_s2, scl_band_name="SCL")
+    base_s2 = base_s2.ndvi(red="B04", nir="B08", target_band='NDVI')
+    base_s2 = base_s2.filter_bands(bands=['NDVI'])
+    base_s2 = base_s2.mask_polygon(spat_ext)
+
+    # Setup RVI cube
+    base_s1 = connection.load_collection('SENTINEL1_GRD',
+                                         spatial_extent=spat_ext,
+                                         temporal_extent=temp_ext,
+                                         bands=["VH", "VV"])
+
+    VH = base_s1.band("VH")
+    VV = base_s1.band("VV")
+    base_s1 = (VH + VH) / (VV + VH)
+    base_s1 = base_s1.add_dimension(name="bands", label="RVI", type="bands")
+
+    # Merge input source
+    merged_datacube = base_s2.merge(base_s1)
+
+    # Execute MOGPR
+    mogpr = connection.datacube_from_flat_graph(
+        generate_cube(merged_datacube, True).flat_graph())
     mogpr.execute_batch(
         "./result_mogpr.nc",
         title=f"FuseTS - MOGPR - Local",
         job_options={
             "udf-dependency-archives": [
                 "https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv",
-                "https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static",
+                "https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_mogpr_update.zip#tmp/venv_static",
             ],
-            "executor-memory": "7g",
+            "executor-memory": "8g",
         },
     )
 
 
-def test_udf_locally():
-    """
-    Test the UDF locally using NetCDF files.
-    :return:
-    """
-    mogpr_udf = load_mogpr_udf()
-    result = execute_local_udf(mogpr_udf, "./s2_field_ndvi.nc", fmt="netcdf")
-    result.get_datacube_list()[0].save_to_file("./result_mogpr_local.nc")
-    print(result)
-
-
-def generate_mogpr_udp():
-    description = read_description("mogpr")
-
-    input_cube = Parameter.raster_cube()
-
-    include_uncertainties = Parameter.boolean(
-        "include_uncertainties", "Flag to include the uncertainties in the output results", False)
-
-    process = apply_neighborhood(
+def generate_cube(
+        input_cube: Union[DataCube, Parameter],
+        include_uncertainties: Union[bool, Parameter]
+):
+    mogpr = apply_neighborhood(
         input_cube,
         lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context={
             'include_uncertainties': get_context_value(include_uncertainties)
@@ -84,16 +100,28 @@ def generate_mogpr_udp():
         overlap=[],
     )
 
+    return mogpr
+
+
+def generate_mogpr_udp():
+    description = read_description("mogpr")
+
+    input_cube = Parameter.raster_cube()
+
+    include_uncertainties = Parameter.boolean(
+        "include_uncertainties", "Flag to include the uncertainties in the output results", False)
+
+    mogpr = generate_cube()
+
     return publish_service(
         id="mogpr",
         summary="Integrates timeseries in data cube using multi-output gaussian " "process regression.",
         description=description,
         parameters=[input_cube.to_dict(), include_uncertainties.to_dict()],
-        process_graph=process,
+        process_graph=mogpr,
     )
 
 
 if __name__ == "__main__":
-    # test_udf_locally()
-    # test_udf()
-    generate_mogpr_udp()
+    execute_udf()
+    # generate_mogpr_udp()

From 855b28812359bf2018a13b49a82a2837620e0e3f Mon Sep 17 00:00:00 2001
From: JANSSENB <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 15:00:24 +0100
Subject: [PATCH 10/21] feat(#123): updated notebook to visualize the
 uncertainties

---
 .../FuseTS - MOGPR Multi Source Fusion.ipynb       | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
index 40f5f07..b9372ed 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
@@ -563,23 +563,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 135,
    "id": "8826c305-1f4c-481f-88aa-291d80e38448",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1c816169840>"
+       "<matplotlib.legend.Legend at 0x1c813caf850>"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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f5dnX8d5772XwSg1ewJgsXdUfAAAAAAAAwKBkx5ZRAAAAAAAAAIaNsBAAAAAAAACAJMJCAAAAAAAAAG0ICwEAAAAAAABIIiwEAAAAAAAA0IawEAAAAAAAAIAkwkIAAAAAAAAAbQgLAQAAAAAAAEgiLAQAAAAAAADQhrAQAAAAAAAAgCTCQgAAAAAAAABtCAsBAAAAAAAASJL+P9dmvAKZrvqhAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -590,10 +590,14 @@
    ],
    "source": [
     "plt.figure(figsize=(16, 6))\n",
+    "std_col_mapping = {\n",
+    "    'unkown_band_2': 'NDVI',\n",
+    "    'unkown_band_3': 'RVI'\n",
+    "}\n",
     "for col in joined_df.columns. values:\n",
     "    values = joined_df[~joined_df[col].isna()]\n",
     "    if 'unkown' in col:\n",
-    "        plt.fill_between(values.index, values['NDVI'] - values[col], values['NDVI'] + values[col], alpha=0.2, label='Standard Deviation Range')\n",
+    "        plt.fill_between(values.index, values[std_col_mapping[col]] - values[col], values[std_col_mapping[col]] + values[col], alpha=0.2, label=f'Uncertainty {std_col_mapping[col]}')\n",
     "    else:\n",
     "        plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
     "plt.grid(True)\n",

From 9d473c64334464843d589cd101af27ef34ba91ef Mon Sep 17 00:00:00 2001
From: JANSSENB <bram.janssen@vito.be>
Date: Thu, 1 Feb 2024 16:43:05 +0100
Subject: [PATCH 11/21] feat(#123): added metadata to UDF

---
 src/fusets/openeo/mogpr_udf.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 8dec550..a526623 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -4,6 +4,7 @@
 from pathlib import Path
 from typing import Dict
 
+from openeo.metadata import CollectionMetadata
 from openeo.udf import XarrayDataCube, inspect
 
 
@@ -71,6 +72,13 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
     return result_dc
 
 
+def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
+    return metadata.rename_labels(
+        dimension="bands",
+        target=metadata.bands + [f"{x}_STD" for x in metadata.bands]
+    )
+
+
 def load_mogpr_udf() -> str:
     """
     Loads an openEO udf that applies mogpr.

From 4c53f82455ce61e192df57956b9c45668f604529 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Fri, 2 Feb 2024 08:55:21 +0100
Subject: [PATCH 12/21] fix(#123): tried adding metadata

---
 .../FuseTS - MOGPR Multi Source Fusion.ipynb  | 169 +++++++-----------
 src/fusets/openeo/mogpr_udf.py                |  11 +-
 src/fusets/openeo/services/publish_mogpr.py   |  15 +-
 .../openeo/services/publish_mogpr_s1_s2.py    |  18 +-
 4 files changed, 85 insertions(+), 128 deletions(-)

diff --git a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
index b9372ed..58e9eca 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
@@ -49,34 +49,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: openeo in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (0.21.0)\n",
-      "Requirement already satisfied: deprecated>=1.2.12 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (1.2.14)\n",
-      "Requirement already satisfied: numpy>=1.17.0 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (1.23.5)\n",
-      "Requirement already satisfied: shapely>=1.6.4 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2.0.1)\n",
-      "Requirement already satisfied: oschmod>=0.3.12 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (0.3.12)\n",
-      "Requirement already satisfied: pandas>0.20.0 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2.0.3)\n",
-      "Requirement already satisfied: xarray>=0.12.3 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2023.7.0)\n",
-      "Requirement already satisfied: requests>=2.26.0 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from openeo) (2.31.0)\n",
-      "Requirement already satisfied: wrapt<2,>=1.10 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
-      "Requirement already satisfied: pywin32 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from oschmod>=0.3.12->openeo) (306)\n",
-      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (3.4)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
-      "Requirement already satisfied: packaging>=21.3 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
-      "Requirement already satisfied: six>=1.5 in c:\\users\\janssenb\\projects\\vito\\fusets\\venv_310\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
+      "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
+      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
+      "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
+      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
+      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
+      "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
+      "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
       "\n",
-      "[notice] A new release of pip available: 22.3.1 -> 23.3.2\n",
-      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
      ]
     }
    ],
@@ -200,7 +192,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae8e2d9034e349c182e3bfac66f2675e",
+       "model_id": "25445fa79df34e1c9db45e1a5c56b957",
        "version_major": 2,
        "version_minor": 0
       },
@@ -383,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "2b27ec81-2543-4fbb-b6c5-2ac796df9e76",
    "metadata": {},
    "outputs": [],
@@ -395,16 +387,6 @@
     "                                         data=merged_datacube, include_uncertainties=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4c84fa2-629b-4a23-b368-9d18538638e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mogpr."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -430,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "632a7088-67c2-4f89-95ff-813e4587f2be",
    "metadata": {},
    "outputs": [],
@@ -440,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "db4ac328-5413-4304-b1a5-b1c6dd6710fc",
    "metadata": {},
    "outputs": [
@@ -448,48 +430,50 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:00:00 Job 'j-240201c8cf414edb8be1b939d596ffbb': send 'start'\n",
-      "0:01:27 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:01:32 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:01:39 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:01:47 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:01:57 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:02:10 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:02:26 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:02:45 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:03:09 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:03:40 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:04:18 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:05:04 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:06:03 Job 'j-240201c8cf414edb8be1b939d596ffbb': queued (progress N/A)\n",
-      "0:07:03 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
-      "0:08:04 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
-      "0:09:04 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
-      "0:10:05 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
-      "0:11:05 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
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-      "0:16:06 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
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-      "0:28:12 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
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-      "0:33:16 Job 'j-240201c8cf414edb8be1b939d596ffbb': running (progress N/A)\n",
-      "0:34:16 Job 'j-240201c8cf414edb8be1b939d596ffbb': finished (progress N/A)\n"
+      "0:00:00 Job 'j-2402018ad22342929ac0127cf8fdd990': send 'start'\n",
+      "0:00:22 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
+      "0:00:28 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
+      "0:00:34 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
+      "0:00:42 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
+      "0:00:53 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
+      "0:01:05 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:01:21 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:01:40 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:02:05 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:02:35 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:03:13 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:03:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:04:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:05:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:06:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:08:22 Job 'j-2402018ad22342929ac0127cf8fdd990': Connection error while polling job status: ('Connection aborted.', OSError(65, 'No route to host'))\n",
+      "0:08:53 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:09:53 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:10:54 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:11:54 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:12:55 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:13:55 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:14:55 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:15:56 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:16:56 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:17:57 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:18:57 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:19:57 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:20:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:21:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:22:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:23:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:24:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:25:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:26:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:28:00 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:29:00 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:30:01 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:31:01 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:32:02 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:33:02 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:34:03 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
+      "0:35:03 Job 'j-2402018ad22342929ac0127cf8fdd990': finished (progress N/A)\n"
      ]
     }
    ],
@@ -512,23 +496,6 @@
     "# Explore the results¶"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "ec95ceb1-9027-4305-a40a-6bcf9905c7a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cubes_dfs = []\n",
-    "ds = xarray.load_dataset(mogpr_output_file)\n",
-    "for var in ds.data_vars.items():\n",
-    "    if var[0] != 'crs':\n",
-    "        var_df = var[1].mean(dim=['x', 'y'])\n",
-    "        var_df = var_df.to_dataframe()\n",
-    "        var_df.index = pd.to_datetime(var_df.index).date\n",
-    "        cubes_dfs.append(var_df)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 124,
@@ -632,7 +599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.8.17"
   },
   "vscode": {
    "interpreter": {
diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index a526623..0b63caa 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -4,7 +4,7 @@
 from pathlib import Path
 from typing import Dict
 
-from openeo.metadata import CollectionMetadata
+from openeo.metadata import CollectionMetadata, Band
 from openeo.udf import XarrayDataCube, inspect
 
 
@@ -73,10 +73,11 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
 
 
 def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
-    return metadata.rename_labels(
-        dimension="bands",
-        target=metadata.bands + [f"{x}_STD" for x in metadata.bands]
-    )
+    extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
+    for band in extra_bands:
+        metadata = metadata.append_band(band)
+    inspect(data=metadata, message='Collection metadata of result')
+    return metadata
 
 
 def load_mogpr_udf() -> str:
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 24a0492..5695fba 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -4,8 +4,7 @@
 import openeo
 from openeo import DataCube
 from openeo.api.process import Parameter
-from openeo.processes import apply_neighborhood
-from openeo.udf import execute_local_udf
+from openeo.processes import apply_neighborhood, ProcessBuilder
 
 from fusets.openeo import load_mogpr_udf
 from fusets.openeo.services.helpers import publish_service, read_description, get_context_value
@@ -70,7 +69,7 @@ def execute_udf():
 
     # Execute MOGPR
     mogpr = connection.datacube_from_flat_graph(
-        generate_cube(merged_datacube, True).flat_graph())
+        generate_mogpr_cube(merged_datacube, True).flat_graph())
     mogpr.execute_batch(
         "./result_mogpr.nc",
         title=f"FuseTS - MOGPR - Local",
@@ -84,11 +83,11 @@ def execute_udf():
     )
 
 
-def generate_cube(
-        input_cube: Union[DataCube, Parameter],
+def generate_mogpr_cube(
+        input_cube: Union[DataCube, ProcessBuilder, Parameter],
         include_uncertainties: Union[bool, Parameter]
 ):
-    mogpr = apply_neighborhood(
+    return apply_neighborhood(
         input_cube,
         lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context={
             'include_uncertainties': get_context_value(include_uncertainties)
@@ -100,8 +99,6 @@ def generate_cube(
         overlap=[],
     )
 
-    return mogpr
-
 
 def generate_mogpr_udp():
     description = read_description("mogpr")
@@ -111,7 +108,7 @@ def generate_mogpr_udp():
     include_uncertainties = Parameter.boolean(
         "include_uncertainties", "Flag to include the uncertainties in the output results", False)
 
-    mogpr = generate_cube()
+    mogpr = generate_mogpr_cube()
 
     return publish_service(
         id="mogpr",
diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index 74e68dd..e98186b 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -1,12 +1,11 @@
 # Reads contents with UTF-8 encoding and returns str.
 import openeo
 from openeo.api.process import Parameter
-from openeo.processes import apply_neighborhood, eq, if_, merge_cubes, process
+from openeo.processes import eq, if_, merge_cubes, process
 
-from fusets.openeo import load_mogpr_udf
 from fusets.openeo.services.dummies import DummyConnection
-from fusets.openeo.services.helpers import DATE_SCHEMA, GEOJSON_SCHEMA, publish_service, read_description, \
-    get_context_value
+from fusets.openeo.services.helpers import DATE_SCHEMA, GEOJSON_SCHEMA, publish_service, read_description
+from fusets.openeo.services.publish_mogpr import generate_mogpr_cube
 
 NEIGHBORHOOD_SIZE = 32
 
@@ -291,16 +290,9 @@ def generate_cube(connection, s1_collection, s2_collection, polygon, date, inclu
     merged_cube = merge_cubes(s1_input_cube, s2_input_cube)
 
     # Apply the MOGPR UDF to the multi source datacube
-    return apply_neighborhood(
+    return generate_mogpr_cube(
         merged_cube,
-        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context={
-            'include_uncertainties': get_context_value(include_uncertainties)
-        }),
-        size=[
-            {"dimension": "x", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
-            {"dimension": "y", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
-        ],
-        overlap=[],
+        include_uncertainties,
     )
 
 

From c62acb97829fcfa7e709276006f21d6944568171 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Fri, 2 Feb 2024 16:52:07 +0100
Subject: [PATCH 13/21] fix(#123): updated to python-jep environment for
 updating the metadata in the UDF

---
 src/fusets/openeo/services/publish_mogpr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 5695fba..3c63b10 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -89,7 +89,7 @@ def generate_mogpr_cube(
 ):
     return apply_neighborhood(
         input_cube,
-        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python", context={
+        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python-Jep", context={
             'include_uncertainties': get_context_value(include_uncertainties)
         }),
         size=[

From 0547c6e597cb2a1bcbaf05e4a9bfbb8a9f25572a Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Fri, 2 Feb 2024 16:54:57 +0100
Subject: [PATCH 14/21] chore(#123): removed inspect as it wasn't working

---
 src/fusets/openeo/mogpr_udf.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 0b63caa..7312bf6 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -5,7 +5,7 @@
 from typing import Dict
 
 from openeo.metadata import CollectionMetadata, Band
-from openeo.udf import XarrayDataCube, inspect
+from openeo.udf import XarrayDataCube
 
 
 def load_venv():
@@ -76,7 +76,6 @@ def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMet
     extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
     for band in extra_bands:
         metadata = metadata.append_band(band)
-    inspect(data=metadata, message='Collection metadata of result')
     return metadata
 
 

From 0fff8f0b8c6ac804ce13c13d69131a77588bf3f9 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Mon, 19 Feb 2024 13:32:19 +0100
Subject: [PATCH 15/21] fix: trying to update the band names

---
 src/fusets/openeo/mogpr_udf.py              | 22 +++----
 src/fusets/openeo/services/publish_mogpr.py | 66 ++++++++-------------
 2 files changed, 37 insertions(+), 51 deletions(-)

diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 7312bf6..891ab09 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -4,8 +4,8 @@
 from pathlib import Path
 from typing import Dict
 
-from openeo.metadata import CollectionMetadata, Band
-from openeo.udf import XarrayDataCube
+from openeo.metadata import Band, CollectionMetadata
+from openeo.udf import XarrayDataCube, inspect
 
 
 def load_venv():
@@ -41,6 +41,14 @@ def write_gpy_cfg():
     return home
 
 
+def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
+    extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
+    inspect(data=metadata, message="MOGPR metadata")
+    for band in extra_bands:
+        metadata = metadata.append_band(band)
+    return metadata
+
+
 def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
     """
     Apply mogpr integration to a datacube.
@@ -65,20 +73,14 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
         variables=variables,
         time_dimension=time_dimension,
         prediction_period=prediction_period,
-        include_uncertainties=include_uncertainties
+        include_uncertainties=include_uncertainties,
     )
     result_dc = XarrayDataCube(result.to_array(dim="bands").transpose(*dims))
+    inspect(data=result_dc, message="MOGPR result")
     set_home(home)
     return result_dc
 
 
-def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
-    extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
-    for band in extra_bands:
-        metadata = metadata.append_band(band)
-    return metadata
-
-
 def load_mogpr_udf() -> str:
     """
     Loads an openEO udf that applies mogpr.
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 3c63b10..c38016d 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -4,10 +4,10 @@
 import openeo
 from openeo import DataCube
 from openeo.api.process import Parameter
-from openeo.processes import apply_neighborhood, ProcessBuilder
+from openeo.processes import ProcessBuilder, apply_neighborhood
 
 from fusets.openeo import load_mogpr_udf
-from fusets.openeo.services.helpers import publish_service, read_description, get_context_value
+from fusets.openeo.services.helpers import get_context_value, publish_service, read_description
 
 NEIGHBORHOOD_SIZE = 32
 
@@ -18,46 +18,29 @@ def execute_udf():
         "type": "Polygon",
         "coordinates": [
             [
-                [
-                    5.170012098271149,
-                    51.25062964728295
-                ],
-                [
-                    5.17085904378298,
-                    51.24882567194015
-                ],
-                [
-                    5.17857421368097,
-                    51.2468515482926
-                ],
-                [
-                    5.178972704726344,
-                    51.24982704376254
-                ],
-                [
-                    5.170012098271149,
-                    51.25062964728295
-                ]
+                [5.170012098271149, 51.25062964728295],
+                [5.17085904378298, 51.24882567194015],
+                [5.17857421368097, 51.2468515482926],
+                [5.178972704726344, 51.24982704376254],
+                [5.170012098271149, 51.25062964728295],
             ]
-        ]
+        ],
     }
     temp_ext = ["2023-01-01", "2023-03-31"]
 
     # Setup NDVI cube
-    base_s2 = connection.load_collection('SENTINEL2_L2A',
-                                         spatial_extent=spat_ext,
-                                         temporal_extent=temp_ext,
-                                         bands=["B04", "B08", "SCL"])
+    base_s2 = connection.load_collection(
+        "SENTINEL2_L2A", spatial_extent=spat_ext, temporal_extent=temp_ext, bands=["B04", "B08", "SCL"]
+    )
     base_s2 = base_s2.process("mask_scl_dilation", data=base_s2, scl_band_name="SCL")
-    base_s2 = base_s2.ndvi(red="B04", nir="B08", target_band='NDVI')
-    base_s2 = base_s2.filter_bands(bands=['NDVI'])
+    base_s2 = base_s2.ndvi(red="B04", nir="B08", target_band="NDVI")
+    base_s2 = base_s2.filter_bands(bands=["NDVI"])
     base_s2 = base_s2.mask_polygon(spat_ext)
 
     # Setup RVI cube
-    base_s1 = connection.load_collection('SENTINEL1_GRD',
-                                         spatial_extent=spat_ext,
-                                         temporal_extent=temp_ext,
-                                         bands=["VH", "VV"])
+    base_s1 = connection.load_collection(
+        "SENTINEL1_GRD", spatial_extent=spat_ext, temporal_extent=temp_ext, bands=["VH", "VV"]
+    )
 
     VH = base_s1.band("VH")
     VV = base_s1.band("VV")
@@ -68,8 +51,7 @@ def execute_udf():
     merged_datacube = base_s2.merge(base_s1)
 
     # Execute MOGPR
-    mogpr = connection.datacube_from_flat_graph(
-        generate_mogpr_cube(merged_datacube, True).flat_graph())
+    mogpr = connection.datacube_from_flat_graph(generate_mogpr_cube(merged_datacube, True).flat_graph())
     mogpr.execute_batch(
         "./result_mogpr.nc",
         title=f"FuseTS - MOGPR - Local",
@@ -84,14 +66,15 @@ def execute_udf():
 
 
 def generate_mogpr_cube(
-        input_cube: Union[DataCube, ProcessBuilder, Parameter],
-        include_uncertainties: Union[bool, Parameter]
+    input_cube: Union[DataCube, ProcessBuilder, Parameter], include_uncertainties: Union[bool, Parameter]
 ):
     return apply_neighborhood(
         input_cube,
-        lambda data: data.run_udf(udf=load_mogpr_udf(), runtime="Python-Jep", context={
-            'include_uncertainties': get_context_value(include_uncertainties)
-        }),
+        lambda data: data.run_udf(
+            udf=load_mogpr_udf(),
+            runtime="Python-Jep",
+            context={"include_uncertainties": get_context_value(include_uncertainties)},
+        ),
         size=[
             {"dimension": "x", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
             {"dimension": "y", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
@@ -106,7 +89,8 @@ def generate_mogpr_udp():
     input_cube = Parameter.raster_cube()
 
     include_uncertainties = Parameter.boolean(
-        "include_uncertainties", "Flag to include the uncertainties in the output results", False)
+        "include_uncertainties", "Flag to include the uncertainties in the output results", False
+    )
 
     mogpr = generate_mogpr_cube()
 

From 834e3db2d0e035838dd6476823fcd102b064b300 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 29 Feb 2024 15:22:48 +0100
Subject: [PATCH 16/21] fix: updated readmes

---
 .../openeo/services/descriptions/mogpr.md     |  2 +-
 .../services/descriptions/mogpr_s1_s2.md      | 19 +++++++++++--------
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/src/fusets/openeo/services/descriptions/mogpr.md b/src/fusets/openeo/services/descriptions/mogpr.md
index 3430607..e407284 100644
--- a/src/fusets/openeo/services/descriptions/mogpr.md
+++ b/src/fusets/openeo/services/descriptions/mogpr.md
@@ -39,7 +39,7 @@ base_ndvi = s2.ndvi(red="B04", nir="B08", target_band='NDVI').band('NDVI')
 ## Creation mogpr  data cube
 mogpr = connection.datacube_from_process(service,
                                               namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',
-                                              data=base_ndvi)
+                                              data=base_ndvi, include_uncertainties=True)
 ## Calculate the average time series value for the given area of interest
 mogpr = mogpr.aggregate_spatial(spat_ext, reducer='mean')
 
diff --git a/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md b/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md
index 0a2600b..452565a 100644
--- a/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md
+++ b/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md
@@ -5,19 +5,22 @@
 Compute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. 
 
 ## Parameters
-| Name | Description | Type | Default |
-|---|---|---|---------|
-| polygon | Polygon representing the AOI on which to apply the data fusion | GeoJSON |         | 
-| date | Date range for which to apply the data fusion | Array |         |
-| s1_collection | S1 data collection to use for the fusion | Text | RVI     |
-| s2_collection | S2 data collection to use for fusing the data | Text | NDVI       | 
+| Name | Description                                                          | Type | Default |
+|---|----|---|---|
+| polygon | Polygon representing the AOI on which to apply the data fusion       | GeoJSON |   | 
+| date | Date range for which to apply the data fusion                        | Array |   |
+| s1_collection | S1 data collection to use for the fusion                             | Text | RVI |
+| s2_collection | S2 data collection to use for fusing the data                        | Text | NDVI | 
+| include_uncertainties | Flag that indicated if the uncertainties should be included in the result | Boolean | False | 
 
 ### Supported collections
 
 #### Sentinel-1
 
-* RVI
-* GRD
+* RVI ASC
+* RVI DESC
+* GRD ASC
+* GRD DESC
 * GAMMA0
 * COHERENCE (only Europe)
 

From d39f01ee4c99de660ee538039f59b42b9e395e20 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 29 Feb 2024 15:23:02 +0100
Subject: [PATCH 17/21] fix: fixed typo in s1 collection

---
 .../openeo/services/publish_mogpr_s1_s2.py    | 87 ++++++++++---------
 1 file changed, 46 insertions(+), 41 deletions(-)

diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index e98186b..a28cfb7 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -9,7 +9,7 @@
 
 NEIGHBORHOOD_SIZE = 32
 
-S1_COLLECTIONS = ["RVI ASC", "RVI DESC", "GRD ASC", "RVI DESC", "GAMMA0", "COHERENCE"]
+S1_COLLECTIONS = ["RVI ASC", "RVI DESC", "GRD ASC", "GRD DESC", "GAMMA0", "COHERENCE"]
 S2_COLLECTIONS = ["NDVI", "FAPAR", "LAI", "FCOVER", "EVI", "CCC", "CWC"]
 
 
@@ -19,32 +19,18 @@ def execute_udf():
         "type": "Polygon",
         "coordinates": [
             [
-                [
-                    12.502373837196238,
-                    42.06404350608216
-                ],
-                [
-                    12.502124488464212,
-                    42.03089916587777
-                ],
-                [
-                    12.571692784699895,
-                    42.031269589226014
-                ],
-                [
-                    12.57156811033388,
-                    42.06663507169753
-                ],
-                [
-                    12.502373837196238,
-                    42.06404350608216
-                ]
+                [12.502373837196238, 42.06404350608216],
+                [12.502124488464212, 42.03089916587777],
+                [12.571692784699895, 42.031269589226014],
+                [12.57156811033388, 42.06663507169753],
+                [12.502373837196238, 42.06404350608216],
             ]
         ],
     }
     temp_ext = ["2023-01-01", "2023-12-31"]
     mogpr = connection.datacube_from_flat_graph(
-        generate_cube(connection, 'RVI DESC', 'NDVI', spat_ext, temp_ext, True).flat_graph())
+        generate_cube(connection, "RVI DESC", "NDVI", spat_ext, temp_ext, True).flat_graph()
+    )
     mogpr.execute_batch(
         "./result_mogpr_s1_s2_outputs.nc",
         title=f"FuseTS - MOGPR S1 S2 - Local - Outputs - DESC",
@@ -76,12 +62,17 @@ def _load_s1_grd_bands(connection, polygon, date, bands, orbit_direction):
     :param orbit_direction: Orbit direction to use
     :return:
     """
-    s1_grd = connection.load_collection("SENTINEL1_GRD", spatial_extent=polygon, temporal_extent=date, bands=bands,
-                                        properties={
-                                            "sat:orbit_state": lambda orbit_state: orbit_state == orbit_direction,
-                                            "resolution": lambda x: eq(x, 'HIGH'),
-                                            "sar:instrument_mode": lambda x: eq(x, 'IW')
-                                        })
+    s1_grd = connection.load_collection(
+        "SENTINEL1_GRD",
+        spatial_extent=polygon,
+        temporal_extent=date,
+        bands=bands,
+        properties={
+            "sat:orbit_state": lambda orbit_state: orbit_state == orbit_direction,
+            "resolution": lambda x: eq(x, "HIGH"),
+            "sar:instrument_mode": lambda x: eq(x, "IW"),
+        },
+    )
     return s1_grd.mask_polygon(polygon)
 
 
@@ -232,18 +223,24 @@ def load_s1_collection(connection, collection, polygon, date):
     for option in [
         {
             "label": "grd desc",
-            "function": _load_s1_grd_bands(connection=connection, polygon=polygon, date=date, bands=["VV", "VH"],
-                                           orbit_direction='DESCENDING'),
+            "function": _load_s1_grd_bands(
+                connection=connection, polygon=polygon, date=date, bands=["VV", "VH"], orbit_direction="DESCENDING"
+            ),
         },
         {
             "label": "grd asc",
-            "function": _load_s1_grd_bands(connection=connection, polygon=polygon, date=date, bands=["VV", "VH"],
-                                           orbit_direction='ASCENDING'),
+            "function": _load_s1_grd_bands(
+                connection=connection, polygon=polygon, date=date, bands=["VV", "VH"], orbit_direction="ASCENDING"
+            ),
+        },
+        {
+            "label": "rvi desc",
+            "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction="DESCENDING"),
+        },
+        {
+            "label": "rvi asc",
+            "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction="ASCENDING"),
         },
-        {"label": "rvi desc",
-         "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction='DESCENDING')},
-        {"label": "rvi asc",
-         "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction='ASCENDING')},
         {"label": "gamma0", "function": _load_gamma0(connection=connection, polygon=polygon, date=date)},
         {"label": "coherence", "function": _load_coherence(connection=connection, polygon=polygon, date=date)},
     ]:
@@ -319,15 +316,23 @@ def generate_mogpr_s1_s2_udp(connection):
         "s2_collection", "S2 data collection to use for fusing the data", S2_COLLECTIONS[0], S2_COLLECTIONS
     )
     include_uncertainties = Parameter.boolean(
-        "include_uncertainties", "Flag to include the uncertainties, expressed as the standard deviation, "
-                                 "in the output results", False)
+        "include_uncertainties",
+        "Flag to include the uncertainties, expressed as the standard deviation, " "in the output results",
+        False,
+    )
 
-    process = generate_cube(connection=connection, s1_collection=s1_collection, s2_collection=s2_collection,
-                            polygon=polygon, date=date, include_uncertainties=include_uncertainties)
+    process = generate_cube(
+        connection=connection,
+        s1_collection=s1_collection,
+        s2_collection=s2_collection,
+        polygon=polygon,
+        date=date,
+        include_uncertainties=include_uncertainties,
+    )
     return publish_service(
         id="mogpr_s1_s2",
         summary="Integrates timeseries in data cube using multi-output gaussian "
-                "process regression with a specific focus on fusing S1 and S2 data.",
+        "process regression with a specific focus on fusing S1 and S2 data.",
         description=description,
         parameters=[
             polygon.to_dict(),

From cd3170460f053d2d70b5861d8b38777886b02869 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 29 Feb 2024 17:06:38 +0100
Subject: [PATCH 18/21] feat: updated mogpr process and notebooks

---
 .../FuseTS - MOGPR Multi Source Fusion.ipynb  | 161 ++++++++-------
 .../OpenEO/FuseTS - MOGPR S1 and S2.ipynb     | 186 ++++++++----------
 src/fusets/openeo/mogpr_udf.py                |  10 +-
 src/fusets/openeo/services/mogpr.json         |   4 +-
 src/fusets/openeo/services/publish_mogpr.py   |   8 +-
 5 files changed, 172 insertions(+), 197 deletions(-)

diff --git a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
index 58e9eca..cb03ef9 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
@@ -50,24 +50,24 @@
      "output_type": "stream",
      "text": [
       "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
-      "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
-      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
       "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
       "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
-      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
+      "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
+      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
       "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
+      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
       "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
       "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
       "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
       "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
       "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
       "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
       "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
       "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
       "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
      ]
     }
@@ -192,7 +192,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "25445fa79df34e1c9db45e1a5c56b957",
+       "model_id": "ffe4d7eb5f4247468a3b9ef6e98042aa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -375,30 +375,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "2b27ec81-2543-4fbb-b6c5-2ac796df9e76",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "service = 'mogpr'\n",
-    "namespace = 'u:bramjanssen'\n",
-    "mogpr = connection.datacube_from_process(service,\n",
-    "                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n",
-    "                                         data=merged_datacube, include_uncertainties=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77b3f539-b919-43fb-b7c5-31c2853c4c7c",
-   "metadata": {},
-   "outputs": [],
    "source": [
     "service = 'mogpr'\n",
     "namespace = 'u:fusets'\n",
     "mogpr = connection.datacube_from_process(service,\n",
     "                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n",
-    "                                         data=merged_datacube)"
+    "                                         data=merged_datacube, include_uncertainties=True)"
    ]
   },
   {
@@ -412,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "632a7088-67c2-4f89-95ff-813e4587f2be",
    "metadata": {},
    "outputs": [],
@@ -422,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "db4ac328-5413-4304-b1a5-b1c6dd6710fc",
    "metadata": {},
    "outputs": [
@@ -430,50 +416,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:00:00 Job 'j-2402018ad22342929ac0127cf8fdd990': send 'start'\n",
-      "0:00:22 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
-      "0:00:28 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
-      "0:00:34 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
-      "0:00:42 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
-      "0:00:53 Job 'j-2402018ad22342929ac0127cf8fdd990': queued (progress N/A)\n",
-      "0:01:05 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:01:21 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:01:40 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:02:05 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:02:35 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:03:13 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:03:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:04:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:05:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:06:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:08:22 Job 'j-2402018ad22342929ac0127cf8fdd990': Connection error while polling job status: ('Connection aborted.', OSError(65, 'No route to host'))\n",
-      "0:08:53 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:09:53 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:10:54 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:11:54 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:12:55 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:13:55 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:14:55 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:15:56 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
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-      "0:18:57 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:19:57 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:20:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:21:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
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-      "0:23:58 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:24:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:25:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:26:59 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:28:00 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
-      "0:29:00 Job 'j-2402018ad22342929ac0127cf8fdd990': running (progress N/A)\n",
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-      "0:35:03 Job 'j-2402018ad22342929ac0127cf8fdd990': finished (progress N/A)\n"
+      "0:00:00 Job 'j-2402293289fa4f10ad53fdd013e085dd': send 'start'\n",
+      "0:03:47 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:03:52 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:03:59 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:04:07 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:05:02 Job 'j-2402293289fa4f10ad53fdd013e085dd': Connection error while polling job status: ('Connection aborted.', TimeoutError(60, 'Operation timed out'))\n",
+      "0:05:33 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:05:46 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
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+      "0:07:16 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:07:54 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:08:41 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:09:39 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:10:40 Job 'j-2402293289fa4f10ad53fdd013e085dd': queued (progress N/A)\n",
+      "0:11:40 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:12:41 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:13:41 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:14:42 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:15:42 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:16:43 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:17:43 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:18:43 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:19:44 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:20:44 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:21:44 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:22:45 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:23:46 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:24:46 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:25:47 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:26:47 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:27:48 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:28:48 Job 'j-2402293289fa4f10ad53fdd013e085dd': running (progress N/A)\n",
+      "0:29:49 Job 'j-2402293289fa4f10ad53fdd013e085dd': finished (progress N/A)\n"
      ]
     }
    ],
@@ -483,7 +460,7 @@
     "        'executor-memory': '8g',\n",
     "        'udf-dependency-archives': [ \n",
     "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n",
-    "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_mogpr_update.zip#tmp/venv_static'\n",
+    "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n",
     "        ]\n",
     "    })"
    ]
@@ -498,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 41,
    "id": "84dab365-5139-4e62-9f1d-438329614d21",
    "metadata": {
     "scrolled": true
@@ -506,6 +483,7 @@
    "outputs": [],
    "source": [
     "cols = ['RVI - Raw', 'NDVI - Raw']\n",
+    "cubes_dfs = []\n",
     "for result in ['mogpr-multisource-s1-base.json', 'mogpr-multisource-s2-base.json']:\n",
     "    with open(result, 'r') as result_file:\n",
     "        df = timeseries_json_to_pandas(json.load(result_file)).to_frame()\n",
@@ -519,34 +497,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 42,
+   "id": "3c917463-41ac-403e-8595-f1e34a6382ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xarray.load_dataset(mogpr_output_file)\n",
+    "for var in ds.data_vars.items():\n",
+    "    if var[0] != 'crs':\n",
+    "        var_df = var[1].mean(dim=['x', 'y'])\n",
+    "        var_df = var_df.to_dataframe()\n",
+    "        var_df.index = pd.to_datetime(var_df.index)\n",
+    "        var_df['date'] = var_df.index.date\n",
+    "        var_df = var_df.set_index('date')\n",
+    "        cubes_dfs.append(var_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
    "id": "a1de65f1-9a5c-4c20-91ea-5c0eddcbdc5d",
    "metadata": {},
    "outputs": [],
    "source": [
     "joined_df = pd.concat(cubes_dfs, axis=1)\n",
-    "joined_df = joined_df.rename(columns={'S1-RAW': cols[0], 'S2-RAW': cols[1]})"
+    "joined_df = joined_df.rename(columns={'S1-RAW': cols[0], 'S2-RAW': cols[1], 'NDVI': 'NDVI - Smoothed', 'RVI': 'RVI - Smoothed', 'unkown_band_2': 'NDVI - Uncertainty', 'unkown_band_3': 'RVI - Uncertainty'})\n",
+    "joined_df = joined_df.sort_index()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 45,
    "id": "8826c305-1f4c-481f-88aa-291d80e38448",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1c813caf850>"
+       "<matplotlib.legend.Legend at 0x138ce7d90>"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -558,13 +555,13 @@
    "source": [
     "plt.figure(figsize=(16, 6))\n",
     "std_col_mapping = {\n",
-    "    'unkown_band_2': 'NDVI',\n",
-    "    'unkown_band_3': 'RVI'\n",
+    "    'NDVI - Uncertainty': 'NDVI - Smoothed',\n",
+    "    'RVI - Uncertainty': 'RVI - Smoothed'\n",
     "}\n",
-    "for col in joined_df.columns. values:\n",
+    "for col in sorted(joined_df.columns.values):\n",
     "    values = joined_df[~joined_df[col].isna()]\n",
-    "    if 'unkown' in col:\n",
-    "        plt.fill_between(values.index, values[std_col_mapping[col]] - values[col], values[std_col_mapping[col]] + values[col], alpha=0.2, label=f'Uncertainty {std_col_mapping[col]}')\n",
+    "    if 'Uncertainty' in col:\n",
+    "        plt.fill_between(values.index, values[std_col_mapping[col]] - values[col], values[std_col_mapping[col]] + values[col], alpha=0.2, label=col)\n",
     "    else:\n",
     "        plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
     "plt.grid(True)\n",
diff --git a/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb b/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb
index 852719f..1aa0bd7 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb	
@@ -51,24 +51,24 @@
      "text": [
       "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
       "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
-      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
       "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
       "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
-      "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
+      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
       "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
+      "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
       "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
       "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
       "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
       "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
       "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
       "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
       "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
       "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
      ]
     }
    ],
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 2,
    "id": "35b24fd9",
    "metadata": {
     "id": "35b24fd9",
@@ -86,19 +86,14 @@
    },
    "outputs": [],
    "source": [
-    "import json\n",
+    "\n",
     "import warnings\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
-    "from array import array\n",
-    "\n",
     "import openeo\n",
-    "\n",
+    "import pandas as pd\n",
     "import xarray\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "from ipyleaflet import GeoJSON, Map, basemaps\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")"
@@ -191,7 +186,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e42ba8cc8a8b47b3a46250eea2c1c908",
+       "model_id": "1985e72ec2284df8a86044fb026fd391",
        "version_major": 2,
        "version_minor": 0
       },
@@ -295,12 +290,12 @@
        "    }\n",
        "    </script>\n",
        "    <openeo-process>\n",
-       "        <script type=\"application/json\">{\"show-graph\": true, \"provide-download\": false, \"process\": {\"description\": \"# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description | Type | Default |\\n|---|---|---|---------|\\n| polygon | Polygon representing the AOI on which to apply the data fusion | GeoJSON |         | \\n| date | Date range for which to apply the data fusion | Array |         |\\n| s1_collection | S1 data collection to use for the fusion | Text | RVI     |\\n| s2_collection | S2 data collection to use for fusing the data | Text | NDVI       | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI\\n* GRD\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\\\"2021-01-01\\\", \\\"2021-12-31\\\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\\\"openeo.vito.be\\\").authenticate_oidc()\\nservice = 'mogpr_s1_s2'\\nnamespace = 'u:fusets'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch('./result_mogpr_s1_s2.nc', title=f'FuseTS - MOGPR S1 S2', job_options={\\n    'udf-dependency-archives': [\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\\n    ],\\n    'executor-memory': '8g'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\\\"GTIFF\\\", job_options={\\\"executor-memory\\\": \\\"8g\\\"})\\n```\\n\", \"id\": \"mogpr_s1_s2\", \"parameters\": [{\"description\": \"Polygon representing the AOI on which to apply the data fusion\", \"name\": \"polygon\", \"schema\": {\"subtype\": \"geojson\", \"type\": \"object\"}}, {\"description\": \"Date range for which to apply the data fusion\", \"name\": \"date\", \"schema\": {\"examples\": [[\"2015-01-01T00:00:00Z\", \"2016-01-01T00:00:00Z\"], [\"2015-01-01\", \"2016-01-01\"]], \"items\": {\"anyOf\": [{\"format\": \"date-time\", \"subtype\": \"date-time\", \"type\": \"string\"}, {\"format\": \"date\", \"subtype\": \"date\", \"type\": \"string\"}, {\"maxLength\": 4, \"minLength\": 4, \"pattern\": \"^\\\\d{4}$\", \"subtype\": \"year\", \"type\": \"string\"}, {\"type\": \"null\"}]}, \"maxItems\": 2, \"minItems\": 2, \"subtype\": \"temporal-interval\", \"type\": \"array\"}}, {\"default\": \"RVI\", \"description\": \"S1 data collection to use for fusing the data\", \"name\": \"s1_collection\", \"optional\": true, \"schema\": {\"enum\": [\"RVI\", \"GRD\", \"GAMMA0\", \"COHERENCE\"], \"type\": \"string\"}}, {\"default\": \"NDVI\", \"description\": \"S2 data collection to use for fusing the data\", \"name\": \"s2_collection\", \"optional\": true, \"schema\": {\"enum\": [\"NDVI\", \"FAPAR\", \"LAI\", \"FCOVER\", \"EVI\", \"CCC\", \"CWC\"], \"type\": \"string\"}}], \"summary\": \"Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.\"}}</script>\n",
+       "        <script type=\"application/json\">{\"show-graph\": true, \"provide-download\": false, \"process\": {\"description\": \"# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description                                                          | Type | Default |\\n|---|----|---|---|\\n| polygon | Polygon representing the AOI on which to apply the data fusion       | GeoJSON |   | \\n| date | Date range for which to apply the data fusion                        | Array |   |\\n| s1_collection | S1 data collection to use for the fusion                             | Text | RVI |\\n| s2_collection | S2 data collection to use for fusing the data                        | Text | NDVI | \\n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result | Boolean | False | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI ASC\\n* RVI DESC\\n* GRD ASC\\n* GRD DESC\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\\\"2021-01-01\\\", \\\"2021-12-31\\\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\\\"openeo.vito.be\\\").authenticate_oidc()\\nservice = 'mogpr_s1_s2'\\nnamespace = 'u:fusets'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch('./result_mogpr_s1_s2.nc', title=f'FuseTS - MOGPR S1 S2', job_options={\\n    'udf-dependency-archives': [\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\\n    ],\\n    'executor-memory': '8g'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\\\"GTIFF\\\", job_options={\\\"executor-memory\\\": \\\"8g\\\"})\\n```\\n\", \"id\": \"mogpr_s1_s2\", \"parameters\": [{\"description\": \"Polygon representing the AOI on which to apply the data fusion\", \"name\": \"polygon\", \"schema\": {\"subtype\": \"geojson\", \"type\": \"object\"}}, {\"description\": \"Date range for which to apply the data fusion\", \"name\": \"date\", \"schema\": {\"examples\": [[\"2015-01-01T00:00:00Z\", \"2016-01-01T00:00:00Z\"], [\"2015-01-01\", \"2016-01-01\"]], \"items\": {\"anyOf\": [{\"format\": \"date-time\", \"subtype\": \"date-time\", \"type\": \"string\"}, {\"format\": \"date\", \"subtype\": \"date\", \"type\": \"string\"}, {\"maxLength\": 4, \"minLength\": 4, \"pattern\": \"^\\\\d{4}$\", \"subtype\": \"year\", \"type\": \"string\"}, {\"type\": \"null\"}]}, \"maxItems\": 2, \"minItems\": 2, \"subtype\": \"temporal-interval\", \"type\": \"array\"}}, {\"default\": \"RVI ASC\", \"description\": \"S1 data collection to use for fusing the data\", \"name\": \"s1_collection\", \"optional\": true, \"schema\": {\"enum\": [\"RVI ASC\", \"RVI DESC\", \"GRD ASC\", \"GRD DESC\", \"GAMMA0\", \"COHERENCE\"], \"type\": \"string\"}}, {\"default\": \"NDVI\", \"description\": \"S2 data collection to use for fusing the data\", \"name\": \"s2_collection\", \"optional\": true, \"schema\": {\"enum\": [\"NDVI\", \"FAPAR\", \"LAI\", \"FCOVER\", \"EVI\", \"CCC\", \"CWC\"], \"type\": \"string\"}}, {\"default\": false, \"description\": \"Flag to include the uncertainties, expressed as the standard deviation, in the output results\", \"name\": \"include_uncertainties\", \"optional\": true, \"schema\": {\"type\": \"boolean\"}}], \"summary\": \"Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.\"}}</script>\n",
        "    </openeo-process>\n",
        "    "
       ],
       "text/plain": [
-       "{'description': '# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description | Type | Default |\\n|---|---|---|---------|\\n| polygon | Polygon representing the AOI on which to apply the data fusion | GeoJSON |         | \\n| date | Date range for which to apply the data fusion | Array |         |\\n| s1_collection | S1 data collection to use for the fusion | Text | RVI     |\\n| s2_collection | S2 data collection to use for fusing the data | Text | NDVI       | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI\\n* GRD\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\\nservice = \\'mogpr_s1_s2\\'\\nnamespace = \\'u:fusets\\'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f\\'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}\\',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch(\\'./result_mogpr_s1_s2.nc\\', title=f\\'FuseTS - MOGPR S1 S2\\', job_options={\\n    \\'udf-dependency-archives\\': [\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv\\',\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static\\'\\n    ],\\n    \\'executor-memory\\': \\'8g\\'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"8g\"})\\n```\\n',\n",
+       "{'description': '# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description                                                          | Type | Default |\\n|---|----|---|---|\\n| polygon | Polygon representing the AOI on which to apply the data fusion       | GeoJSON |   | \\n| date | Date range for which to apply the data fusion                        | Array |   |\\n| s1_collection | S1 data collection to use for the fusion                             | Text | RVI |\\n| s2_collection | S2 data collection to use for fusing the data                        | Text | NDVI | \\n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result | Boolean | False | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI ASC\\n* RVI DESC\\n* GRD ASC\\n* GRD DESC\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\\nservice = \\'mogpr_s1_s2\\'\\nnamespace = \\'u:fusets\\'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f\\'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}\\',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch(\\'./result_mogpr_s1_s2.nc\\', title=f\\'FuseTS - MOGPR S1 S2\\', job_options={\\n    \\'udf-dependency-archives\\': [\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv\\',\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static\\'\\n    ],\\n    \\'executor-memory\\': \\'8g\\'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"8g\"})\\n```\\n',\n",
        " 'id': 'mogpr_s1_s2',\n",
        " 'parameters': [{'description': 'Polygon representing the AOI on which to apply the data fusion',\n",
        "   'name': 'polygon',\n",
@@ -323,18 +318,28 @@
        "    'minItems': 2,\n",
        "    'subtype': 'temporal-interval',\n",
        "    'type': 'array'}},\n",
-       "  {'default': 'RVI',\n",
+       "  {'default': 'RVI ASC',\n",
        "   'description': 'S1 data collection to use for fusing the data',\n",
        "   'name': 's1_collection',\n",
        "   'optional': True,\n",
-       "   'schema': {'enum': ['RVI', 'GRD', 'GAMMA0', 'COHERENCE'],\n",
+       "   'schema': {'enum': ['RVI ASC',\n",
+       "     'RVI DESC',\n",
+       "     'GRD ASC',\n",
+       "     'GRD DESC',\n",
+       "     'GAMMA0',\n",
+       "     'COHERENCE'],\n",
        "    'type': 'string'}},\n",
        "  {'default': 'NDVI',\n",
        "   'description': 'S2 data collection to use for fusing the data',\n",
        "   'name': 's2_collection',\n",
        "   'optional': True,\n",
        "   'schema': {'enum': ['NDVI', 'FAPAR', 'LAI', 'FCOVER', 'EVI', 'CCC', 'CWC'],\n",
-       "    'type': 'string'}}],\n",
+       "    'type': 'string'}},\n",
+       "  {'default': False,\n",
+       "   'description': 'Flag to include the uncertainties, expressed as the standard deviation, in the output results',\n",
+       "   'name': 'include_uncertainties',\n",
+       "   'optional': True,\n",
+       "   'schema': {'type': 'boolean'}}],\n",
        " 'summary': 'Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.'}"
       ]
      },
@@ -356,7 +361,8 @@
    "source": [
     "mogpr = connection.datacube_from_process(service,\n",
     "                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n",
-    "                                         polygon=spat_ext, date=temp_ext, s1_collection='COHERENCE', s2_collection='NDVI')"
+    "                                         polygon=spat_ext, date=temp_ext, s1_collection='RVI ASC', s2_collection='NDVI',\n",
+    "                                         include_uncertainties=True)"
    ]
   },
   {
@@ -390,64 +396,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:00:00 Job 'j-231221f6753f4aefb8d1f59aa499f74e': send 'start'\n",
-      "0:00:27 Job 'j-231221f6753f4aefb8d1f59aa499f74e': queued (progress N/A)\n",
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-      "0:42:16 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:43:16 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:44:17 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:45:18 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:46:19 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:47:19 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:48:20 Job 'j-231221f6753f4aefb8d1f59aa499f74e': running (progress N/A)\n",
-      "0:49:20 Job 'j-231221f6753f4aefb8d1f59aa499f74e': finished (progress N/A)\n"
+      "0:00:00 Job 'j-2402291f76034a6d8e171135d40694f1': send 'start'\n",
+      "0:00:53 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:00:58 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:01:05 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:01:13 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:01:23 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:01:36 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:01:52 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:02:11 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:02:35 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:03:06 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:03:43 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
+      "0:04:30 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:05:29 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:06:35 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:07:35 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:08:35 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:09:36 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:10:36 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:11:39 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
+      "0:12:40 Job 'j-2402291f76034a6d8e171135d40694f1': finished (progress N/A)\n"
      ]
     }
    ],
@@ -455,7 +424,7 @@
     "mogpr_job = mogpr.execute_batch(mogpr_output_file, out_format=\"netcdf\",\n",
     "                                title=f'FuseTS - MOGPR - S1/S2 Data Fusion', job_options={\n",
     "        'executor-memory': '8g',\n",
-    "        'udf-dependency-archives': [ \n",
+    "        'udf-dependency-archives': [\n",
     "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n",
     "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n",
     "        ]\n",
@@ -472,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 36,
    "id": "ec95ceb1-9027-4305-a40a-6bcf9905c7a2",
    "metadata": {},
    "outputs": [],
@@ -486,32 +455,43 @@
     "        var_df.index = pd.to_datetime(var_df.index)\n",
     "        var_df['date'] = var_df.index.date\n",
     "        var_df = var_df.set_index('date')\n",
-    "        cubes_dfs.append(var_df)\n",
-    "\n",
-    "joined_df = pd.concat(cubes_dfs, axis=1)"
+    "        cubes_dfs.append(var_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1b2f4652-46b5-40b3-8e4d-5d2a861c4264",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined_df = pd.concat(cubes_dfs, axis=1)\n",
+    "joined_df = joined_df.rename(\n",
+    "    columns={'NDVI': 'NDVI - Smoothed', 'RVI': 'RVI - Smoothed', 'unkown_band_2': 'RVI - Uncertainty',\n",
+    "             'unkown_band_3': 'NDVI - Uncertainty'})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 38,
    "id": "8826c305-1f4c-481f-88aa-291d80e38448",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x11339ae80>"
+       "<matplotlib.legend.Legend at 0x12f555340>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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ayHJ4IosTsLnx/LW1r9/6b/eTLZb58GsvZfMApHJF7njoGP99/2H++/7DAPSGg5y7LsH56xOcty7B9pEeHNVMERERERHxVaHkcjyV53gqTywcIBl1SMZCtS7a0t0UFraiYtYEg9XZg97s+oL5UqX+YK5MrlT2YZAi7cUq5wml9lLoOwsshVVVnucxninWAsHDE1lzeTLHkYkspYbpyZv6o7Ww0LIstg/HyRTKFMpl1vSFGYqHef1Vm1nfF+HuJ0/y8JEUU/kSP9w7xg/3jgEQCtjsXNPDeesSnLc+wTlrE/RoCYSIiIiIiG8y+TKZfJkj1fqGsRCJiOobdjN9QmsV6ZOmKUlhGtzSnDfJFspMF0qk82WKZTUoEVksu5TGmTpAMbHF76GsuqlckXAwUGtO8t0nTvDvPz7AkYkc2eL8BxyCtsW6vgjrk1G2DMabvvZnr76I/niINb3hWt3Cy7cOcPnWAQDGpvN85/ET3H9wgoePpHj4cIpUrsRDh1M8dDgFPwYLePWlG3nj07cCJrzUmxIRERERkdXXWN8wYFv0xRz6Yw6xkKKjbqP/8VbgujC5f87NmVKJdM4EhGUV+xI5Y4HCJEwfptiz3u+hLLtsoczhySwTmSKXbemvbf+Dz/2Uh4+kePdLz69tL5VdnhpNA2BbMNJrAsH1yQgbktHK5SjDPeE5lyIkokHWJCJEnPmXdQ/0hHnZJeu5Zscgx1N5XNfj0ES2Fhw+fCTFkckcI4lw7T77TmZ475ce5tLN/bz5OTuW60cjIiIiIiKLUHY9xqYLjE0XCDt2bZlydfKBdDaFhS2m5Hpk8uVagxLFgyLLL5A7gWc7lGLDfg9l0Ypll2OTmeYlwxNZDk/kGMsUAIg4Nv/2K9fUZuj1RR0ARqfq3dIv2pjkj158LuuTUdYmIguuIxgN2aztiy546bBlWYz0RkhGQxydzGFZFhv7Y9xwnlnOPJ4u4DS84Xj4SIrRqTzHUrmm/fzd1x9nTSLCeesT7Bzp1ZsUEREREZFVki+6HCvmOZbKEw8HSMZC9EUd1TfsYAoLW8R4psh0vkS+WFZAKLIKgpkjeLZDOZL0eyindGAsw+0PHOHgRIb9xwNM/OCHp+xynogE2ZCMki2Wa8sFfv3a7dx6/a6mWYAD8RBXnTW44HE4QYu1iQjJ2NI6SoeCNpsHY0zlihyZzJEvmlIK/fHm/T33nBE29EexG5YiT2aLfOXhY7XrQdti50gP51WappyzNkGiEoiKiIiIiMjKSefLpPNmwkIi4pCMO/SGVd+w0ygsbAGu53FgPEckiH7BRFaRM30Azw7ihnpW/bFdz2M8XWCwp74E919/uJ9vPTbKqy/dwPWVmXdT+RJffOBI5Rbm9SHqBCpLhSO15cIbklHW90Xpicx+WZ8ZyC2GbcNwb5jhnvCyvD71Rhx6wkFOTBc4lsoxs7pCxAlw8cZk07aAZXHLs86qLV0ezxR55OgUjxyd4nMcAmDTQMw0Tamc1iSWZ7wiIiIiIjKb55mD+pPZIsGARV/UoT8WIhqav0yRtA+FhS2g7Hq84vNThAIwELEZjFoMRq3a5YGoxWDUZiBizuOOQkWR5eERmtpHvm87XjCy/Hv3PFK50ozlwubykckc+ZLL//3lq2qz4qbzJQ5NZNk/lq3tY1N/lFdfuoF1iTD26GNcevmVDPREV/w1wLLM7MORhuYly7dvi+HeMMmYw9HJHBOZ4ilv3xMJ8rKLN/CyizfgeR5HU7lacPjwkRQHx7McGMtwYCzDVx46CsBALMSHbrrkjIJSERERERE5vVLZ4+R0gZPTBSKOXWmMElpwqSNpPQoLW8BYuoDrQa4Eh6ddDk+f+vbhAAxELN56dZQLhs1/4b7JMo+NldnSF2DXgJJ8kQXzyoQn95BPbscLLD1YyhRK/GjveC0QPDxpQsF0fv5Ow7YFx6fytbDwhvPWcMXWAbYMxGq36Y04/OLTz8Itl9ibf4z+WGjFg8JENMjavgjh4Mq+ljgBm00DMfrjJY5MZMkVT9/l3bIs1vVFWdcX5XnnrgHMEc1HKsHhw4dTPDE6TdnzSMbqS5P/5q7HOTGd56YrNnH++r4V+55ERERERLpZruiSm8xzbNLUN+yv1De0Vd+wrSgsbAHDvWH++9W9nMy5jGU9xnIeJ7Pm8sls5XJlW7oI+TIcSXs4gfov2w+PlPjYfXmesznIHz7dBA1l1+OXbp8mWZ2t2DBTsXEGY2/I0kxF6W5ekVBqD/m+HWCfPiD7yf5xvvv4Cc5e28uN55vlwplCmb/86u45bz/cG2Z9X/OS4Q3J6KxZe1sG42xZeBnBZRcNBVjXFyG+wOYly6UnHGTHSA8npgscn8rhnj4zbNIXdbh62yBXbzM/vFyxzLFUrva65nkeP943zlimwM9dvql2v/sPTHDvvjGzdHl9X60RjIiIiIiInLlqfcNDE1n6og7JmENvRO+524HCwhZgWxab+iP0Zgps7D11e5NcyasEii6bE/WQYTBqc+maADsbZhWO5zwOT3scnp5/ZhOAY5uZigNREyC+4YIwZyXNfk5mXVJ5j+GYTU9IgaJ0Lqucx57Yw0FrPYcmc81LhydzvONF57J92NQ2PDCW4c5HjpEplGph4UA8xEUb+xjpDTfVEFzbF2lqLNKKQkGbtYkIfTH//nAvdmnyqUScAFsG403b3v2y83n48CQ719TrU/5gz0m++NMj/Od9hwHYkIzW6x6uT7CuL6IDKSIiIiIiZ8jzYCJTZCJj6hsmK8uUW/1zUjdTWNgi+mMOfRGHyVyR8XSB8syq/xWRoMX6Xov1vc1r/5+7xeG5W5o/6CfCFh98XuwUMxU9pgoeRReOZTyOZUyo+Jrz6g0Xvr6vyMfuy/O8LQ5vuyYKQMn1+JO7s7UaioNNNRUtEmGrqZOpSCvxPI/RjMfBKZdDU645nzbnR6dTlL2jc97v8ES2FhZesKGP1165mV1remtfty2LP33FhavyPSwX24aR3ghDPSu/tHmhqkuTB+Km1uNCliafjmVZnDUU56yh5gDx8i0DlF2Phw+n2DeW4VAlIL7zEdN5ORl1OLcSHJ63LsG2ofiy128UEREREekmpbLHiakCJ6YKREM2fdEQyZij+oYtRmFhC7Htemg4kSswkS7OGxouRChg1WoazqdQ9hivhIknK2Hi+p76L6nrQV/YhIBV4zmP7x0szbvPgEUtOKzOVhyI2NxwlsNI3K49btBGoaKsqONpl58cKxEOWDynEqa7HrzxS9OU5smgwgFYnwixfqC3tmx4fTLK1sF6HcHtwz214LAdWRYM9oQY6Y0QaNHaIfHK0uSTadM1ebFLkxfisi39XLalH4CpXJFHj07VGqc8dmyKiWyR7z91ku8/dRKAcNDmZy7byM9fsXn5ByMiIiIi0mWyBZdsIcexVI54OEh/zCERUX3DVqCwsAXZtunk2RcJMZktMJ4xDVBWQihgsSZusSY+d4p/07lhbjo3jNcQWkaCFr95WaQ+WzFngsaxrMdE3qPswWjWYzTrAfVP+FdvCDJSmdjz/x4r8PGf5nnZjhD/32WmC22x7PGZRwqVcLEyWzFqkQxbLRtoiL+mC15tduDBqTKHplxevD3ExWvMS9tj42X+6oc5zh6wa2FhwLbY1GtTcmFDr82GXpuNDeeDUTMzthQbohQb9vPbWxF9UYc1feEVb16yHCzLYqgnTF/0zJcmn05vxOGKrQNcsXUAgELJ5YnR6Up4OMkjR6aYzpfoaajneHA8w19+dTeXbOrnF5++dcXGJiIiIiLSyTwPpnMlpnMlbDtLIuLQHw81vfeW1aWffAsL2KYOWl80xES2wMQKhoan07hEsTdk8bKdc3eNLbnVmYoeY1mXkw1LoEdi9X2czHq4HoQa8orxnMc/P5iftU/bgmRlduNgbaZifdbiRcNB4qqn6Kuy61FwTeBrzqHoehQq58Uy9cuVr589YLMxYZ4AR6Zdvra3SG/I4hW76s+tj/5PjmMZt7Y/c25mpp6shNMzbe8P1MLCrQlTy3Nmh/CPviB+2mW3wcwRPNuhHEme4U+nNcTCpnlJLNR+L/uNS5OPTGbJFlZgmuEMoaBdq18IG3E9jwNjmaYmKA8fSfHkaJrojForn7x7L2v7Ipy3LsHG/mjLLPEWEREREWl1rluvb+gELZKVZcqqb7i62u9TYxcK2DAYD5GMhhjPmNDQp8zwtIK2xXDMYjgGMP8v869eEubnzgnRWJbAtuBF25xajcVqGOR6MJYzXaIfH58dEnzkxjjbK6njfz9e4MtPFXjeFoefOcfUXiyWPX50pFQLF/sjFsE2n6noeZXQrRLQFV2zXDxU6ZB9IutyMOXSG7LY3h+o3efLTxWbA7wZAVzj/qq3e935YS4eMS8V9xwu8rf35tg1GOCdz6gvy/3Z/5xmqrC4Z+VvXhaphYXH0i7//GCeLQm7KSz84ZES+1OnDoYGIlbTzMBL19Zf1jYmAvz5c+Kz7rPQ8MaZPoBnB3FD7bvkOBS0WdsX6YhOv/FwkO3DPYylCxxdoaXJ87Eta1bTlCu3DvDWG89umqU5nSvxHz85WLveGwnWm6asS7B9pEf1WEREREREFqBY8hidyjM6lScasknGQiSjjuqIrwKFhW0kYMNQj0nVTdLeuqHh6QRsi6FYc2AzFLP53SujTdvKrgkMx2Y0Zqlfdpv2c2DK5Ylxl8vX1n8yJ7Me7/putnbdwgRrA9WZijPrK0YtBiPmcuPyZ9fz8Dxq24plj2Np1wRr1Vl1DbPnCuV68FadZZcveeyK1L+/7x8q8p0DJS4aCfCCbSYkSxc8/vDbmVmh3cxwb6a/eE6Mp1Vm1H3/YIm/+XGOZ2wM8u5n1kO9D/0ot+jnzAvOqt+j2gxnMNa8F2dG6BuywQmAY1s4NjgBi1DA3M6xLZwATXUwh2M2L9nhMBRtftH/+XNDZErV+5ll89X99kcs1vfYxJyVDH49QlP7yPdtxwtGTn/zFhKwLUYSYQbjrdO8ZDlYlsVgdWlyKsd4euWWJp9OMhbiWTubl6qXPY+bLt/Ew0dS7D42xVSuxD17xrhnzxgAoYDNzjU9tY7L56xNaHmFiIiIiMhpVOsbHp3M0RMO0h8LkYgGO+qzTivRJ5Q2FLStWmg4nikwmSm2bWh4OgG7uvwYdp5ipmLVq3aFuHxtoKkGY9H1OHvAZixrZieWPZjImxDyqYn5pyZ97AVxzkqax/zI/+T43O4CP39uiF++2IRGxzIuN9+eXvT39PsNDXP3TrrcubdIwIIXbKtstODhE+VF7TNg0dSwoy9isTlhM9wQvlmWxTM3BrGsemBXC+AazqthXKhyft5Q/ed+0XCQv70+Ts+MSWr/9KIeArYJCZdSX3JDr81vXx6dtf36s+Ze7r6qvDLhyT3kk9vxAi0wntOwLBjqCTPcG+7oWp/BgM3G/nrX5NVYmrwQfVGH11+9BYBi2eWp0TQPH5nk4SMpHj6cIpUr8dDhFA8dTsGPzcGLLYMx3vvyC+iPtf7zS0RERETET54HU7kSU7kStm3ef/fHQsR1AH5Z6afZKo49BMNng73w/5KgbTHcEyYZCzGRLjCZ7dzQcKHW9dis62menbYpEeDvbjDLSF3PY/KUMxVNrcWxnMdAw8y3yurephl9Idsi5pjz5oCtMpNuxuy6UMAiaEMsWO8kfcmaALdcHK6FkgCRALzrmdHaLLxQoGF/TbP06uHezK7Sz97k8OxNs5edvrNhluFSJMIWifDs0Lan02tGekVCqT3k+3aA3bq1MpIxhzWJCKFg90zLj4WC7Bjp5eR0nmOpPGW/CrvOwQnYnL22l7PX9vLKp5lSAIcmsrXg8OEjKY5M5jg5XWhaJv5P332K8UyRl128nl1ren38DkREREREWpfrwni6yHi6SChok4w5JGNOWzRzbHUKC1vBo1+E//4tOOtZ8Nw/WlRgCCY0Gu41oeF4pkBKoeG8bMssX+2PUKvlNxfX82iMv157fpifPSdENFjfOhK3+a9XJxb1+GXXY/eB+pLocweDnDvY/P8dsC2eubH968t1GqucJ5TaS6Fvm5m+10Li4QDr+qJEQ937R7G6NPnYVJ6x6YLfw5mTZVls7I+xsT/GDeetBWA8XeBIKtcU+H/vyZOMTuW5/tw1tW2PHEnx4KFJzlufYOdIb1cFwiIiIiIip1MouRxP5TmeyhMNBeiPOSRjoY5ebbWSFBa2AjtowoenvglY8Nx3LDowBDPjbKQ3TH8sxFg6z1SupNBwiWbO1Is7FqxobTxpB3YpjTO1n2Jii99DASDs2KxJdEbzkuUQDNhsSEYZiIU4NJElW1jccn4/9MdD9Mfry489z+O3n7uTh4+kmmYV3v3kCf7zvsOAmVW+c6SH89abpinnrE2Q0HNARERERASAbKFMtlDmyGSO3kiQZCxEIqL6houhsLAV7HoBXP9euPOd8NQ3THD4nD9cUmAIJjRck4jQH3cZSxeYVmgosmwChUmYPkyxZ71/Y7At1iTCDHRY85LlEg0F2DFS6Zo8mWuppcmnY1kWF29KcvGmZNP2XWt6ecb2QR4+kmI8U+SRo1M8cnSKz3EIgE0DsXrX5fUJ1vSG9dwQERERka7meZDKlkhlSwRsi76YQ3/MIRZSFHY6+gm1ii1Ph+e/B772Lnjy64AFz3n7kgNDMF031yYiFCqh4VSudPo7ichpBXIn8GyHUmz49DdeZkO9YdYm45pOvwAD8VCta3KrLk1eqGftHOZZO4fxPI+jqVyt5uHDR1IcHM9yYCzDgbEMX3noKGC+91desoFXPG2DzyMXEREREfFf2fUYmy4wNl0g7Ngko2aZssr7zE1hYSvZ+gx4/rvgznfDk3eZGYbXvf2MGypUQ8P+WGWmYV6hociZCmaO4NkO5UhyVR6vutR4pMO7HC+3gG3VliYfnsySybf+0uRTsSyLdX1R1vVFeV6lpuFktsgjleDw4cMpnhidZixdoLHw6tFUjvfdF2DH8cd42wvPq23/X//1oLktTTefsyxndabiyy9eX3vsfSfTfPBrjzEQD/HOl5xfu+0H7tzNgfFs036r+7SYvXPLgut2DfPii8yM3bF0gT+741HCQZs/fvkFtdv9n+/tYfexqeb7zhhf0/diwaWb+3n1pRsByJfK/MmXHjHf+4vPq705/PxPDnL/wYlZe238Ocwc9a41vbzmys216392x6OUyi5vfs4OkpXO1l975Bj37Dl5yu/bsiyijk3UCRBxAqxJRLjx/LW12zxyJFXpmh2v1SV1PW9WuQwRERERWZh80eVY0TRIjIUD9MfMJAN9zqpTWNhqtj4Lnv9u+Nq74YmvARZc97Zl6cAaDtqs64uQLyk0FFkOzvQBPDuIG+pZscfoiQRZm4gQtFzuX7FH6XzRUIDtwz2moUibLU0+nb6ow9XbBrl62yAAuWKZx49Ps64vUrvNw4enOJa1SEzlm+57aCLL6IxtpzOZLdYu50suT46mZ81cPzCW5YnR6UXt97x19YZRhbLLI0dSRJzmI737xjI8dDi1qP0O94Rrl10X7jswYS579efAvpMZfrJ/YlH7nelHe8colFx+5Vlu035/8NTYovazc6SnKSx8/1d3MzqV569+9uJaHcv//J9DfOoH+4g4AaIhEzLGapfrwWOs8rWoEyAZC3Htrvps6EPjWSwLBntC6hgoIiIiXSuTL5PJZzk8kSURcViXjOAENNtQYWErOutZ8Lx3wl3vgSfuNFMPrv2DZQkMoR4a5oou4xmFhiJL5xGa2ke+bzteMHL6my9CxLFZ0xchETEzCotF9zT3kIXoj4dIRB2OpXKMpQt4nZMZ1kScABdu6GvaduGGBL95Xomt553VtP0tN5xNsezW69p6TWfmcuWHVN22IRmtfW19Msq7X3o+oUDzUdhbnr2NTL5eL9eb+QANl6pfaww3k1GHP3zhOdgzju7+7GUbuf7cNXOOj6Zt9ctrEvX9OgGL37t+V+Vy/U3gjResrdSJ9Gbdf/bPxlwYbAghAX792dspex49kfpbq2dsH2R9cvZrQ+P+y65HrlgmWzkNzdjvSG8Y24JYQ7fzbLFMyfWYzpcW/Dd8QzLaFBb+2R2PsPdkhve+7HyetrkfgK8/eoxP3r2vKXSMhkzYWA0ga9cr5z3hIJdvHajtdzJbJGBbRJ2Ajs6LiIhI2/A88z5msCeksBCFha1r27XAO+Gu98LjXzWB4bPfumyBIZgwohoajqULpAsKDUUWzSsTntxDPrkdLxA6/e1PI1htUBRz1KBihQRsi/XJKANx0zW53ZcmL8Rwb5gdfbB1uHkW7LkNs/mWoicc5LIt/bO2n3eG+404Aa7ZPjRr+/nr++a49cIFAzbXnT0ya3u1OcyZeP55a2ZtO2ddgnPOcL9/9qqLZm179aUbueG8tU0hY7ZQrl8vzN5WXRpdVZ1xGG0IIVO5EmOZAmQWPr5k1OFTv3xV7fptX36Ehw6n+IMXnMMzd5j/w3v3jfHJu/fWHq/xsecMIiuXz1nbW3sdLLsetoVeF0VERERWgcLCVrbtOnN+13vhsa8ANlz7FrCWN+WOODbrkxGyxTJj6SIZhYYii+MVCaX2ku/bvuRA37JMoDPcE541m0pWRsQxS5MnMmZpcqncgdMMpSNFKuHamXj/z1w8a9tzzx7hwg19JmAszBNEFl2yhRK5oku2WG6a8QhQKLmVMdbfq4ynC+w9uYgEEjML9PO//oza9du+/Aj37hvnN5+zo1Yz87FjU/zz9/fOChrnCh5rAaUTYF0yopqPIiIiIqegsLDVbbsOPBe+/icQijG7xPryiToBNiQDZAtlTqYLZIudP9tGZLlY5Ryh1F4Kfdvm7g5xCv1xhzUJ1cbwSzIWojficHwqx8npzlyaLLIQiahDotJMaak+8HOXUCy7TWHcZVsGeO/Lzm8KHjPFsgkcC6WGENIEkrlimcCM19FsoUzZ9ZpeJ09M57n/4OSix/i5X3s6oaDZ/9994wl+uOckr7tqS61W5KHxLJ++Zy+FSZvE+JMEA2ZJdcC2sC2rdjlgmZnKtm0RsCxedOG6Woj76JEUByeybBuKs60yozdTKPHgoVTT/e3a5Yb9ztjWF3UIVr7vUqVsQHUcIiIiIitBYWE72P5cSGyAoV2LDiGWIhoKsDEUJVMJDXMKDUUWxC6lcab2U0xsWdDteyJB1vVFzniGkJy5gG26DPfHQhyeyJLugqXJIitl5oGPgXiIgfiZlWn4oxefR6ZQIhaqv3XdOdLL712/a57Zj+U5l2kXSi5OQ43NyWyB8UyxqeHNiek8333iJGDD6OiCx/i8c9fUXs+/vvs4X37wKK+5YlMtLBydyvPHX3p40d/7373maWwZjAPwb/ce4F9/dIAXXrCW37huR+V7KHLLP9/bFDKaEJKmELIaalaDxl959ja2V8Z2774xvvrQMc5d18srn2a6h3uex4e/+WRTMDo7MG3ed8C2eNrmJOv6TG3T41M5Hjs2TTLqcEFDLdWHj6TwPK9hrPMHptWvh4O2DqqJiIisEoWF7WL47PrlcgF2fxnOfemyL0luFAsFiIWipAslxqaL5Er68CxyOoHCJEwfptizft7bRBybtX0ReiNnNoNHll/ECbBNS5NFWk401FxfEUzphrlqUC7Grz17O6+5YnNTmLmuL8KbnrGF4weeom/dVlwsyq5H2fVwPa92ueyZWopu5XqoIcjaMhDjsi39rG9oCOQEbHaO9FD26vcx+/Eou5htXvN21/WaZhBWX5KatrneklaDNB4MPjyR4/tPnSTYEKS6HnzloaOL3u/bXnBOLSx89MgU7//qbi7a2Mefbriwdps/+eLDTC2ywd6vX7udF124DoCHDk/yni88zNahOH/x6npdz7+6czfj6QKhoE0oYJvzYIBww/Vq6Fi9fNZQvBbG5ktlDk/kiDoB1jY0XfI8T/UyReSUXM80LMsUzClbKJMplMzlYnVbqXb5BeevrR1MevRoiv+67zCb+qO89qr6pIO//frjpAuV12pvdpO42nXP/JOetHlF/zhXbDMNzfadTPN/79nPYE+IX3329trtP/qtJxmdzp/y+5m50uZZO4dqf3OPT+X4h28+SSwU5C031nOKT9y9l70n0/PuY67RX7F1gJdcZD43TedL/MUdjwLwnpedX3vd/eyP9vPg4VTDfudobjfj+gXr+3jNlZtr19/x/x7AA/7whefWmtH9532H+MFTJ2ft649ffgGXzlGTu9soLGw3ngdfew/s+x5M7Ien/+aKP2Q8FCQ+EGS6UGJ8ukCupK6sIqcSyJ3Asx1KseGm7cGAxdpEhP4znGEjKy8ZC5GIOBzT0mSRjjbYE57V2XokEeGlF61jr/skWy/ZgB1Y/NvlF1+0nhdf1HzQaH0yygd+7pIzGS4/f8UmXvW0DU1hYV/U4WO/cNmMQJOGILIhnPQ8SpXrG/tjtX1ctKGP37hue1P3cIDXX7W5KRidHZg27L9yubGjdyLqcMH6BFsrYVzjz2I6X6rtqzRjjI2P5c4RkFZrZuZnHMh+9MgUR1O5Rf1MX3/1llpYeHgiy2995j76Yw7//Ev1xj1/8PkHePzYlAkdgw0hZKB+vfo1x4b8hM2zEmM8Y+dIZbxl7nr0OJGgXau5CXBgLEOuWK7vM2ATDgbMfgKWAkqRFeZ5HvmS2xzuFauXTbh31bbB2uvaT/aN8+WHjrBjpJebLt8EmNfG1/zjDxZ90ObCDX21sPDEdIHvPnGC89cneG3Dbe7ZM8ZktriIvdpcPVUPAVO5Et9/6iSb+qNNt7r/0CQHxhZXS3jbcP11PFd0uXffOIlI89/Hx45N8cChxZUHafy7U3Y9/ufAxKzb7DmZ4b45tp9Kb7h5bD+tjKvk1rOMI5M5HmoIIaumF3kwq1MpLGw3lmU6JR/6MWy+ZlUfuicUpGcgyFS+xHi6QF6hoci8gpkjuAEHN5zEtmG4J8yQmpe0FbthafKRyRzTOb1xEBF/OYHZS3GrZRTOxNahOFuHmgO9gG1x0xWb57nHwlyyKcklm5Kztv/lz85usDMftzLDsjE4u2BDgo++/rJZ1Xl+7drtTOWKFMouhZI55UsuxbI5r20ruxRKZln6uoYPqq5nOnz3zajdWSiVKbkepUK5PsvnlGyGj07VwsLJbJGPfOtJQjPCwo9/bw/37hufcw8W4ATtWYHk1dsGeV1l5lGx7PLXdz1OKGDza9duJxQ0z417941xaDzbEGIGKkGkjVMLJZv3Gw4GVAdT2oLneRTLXmWGXqkW9G0bjtdKVTxyJMWP94+zdTDOM3cMAaZu7Dv/66FZs/zc0xwQXtsXrYWFJ9J5fvDUWNPKk4BtUW44qhy0rVqTLbNSL0g0VLlcaboVCwXZ1HDAZttQnF951rZZJTvecM0WijM/c8944ate89wyJw8+yfnrErWvre+L8BvXbadnRnD22is3k15AINb4UNuGemqXB+Ihfuu5O2b9PXr1pRt57jn1Gf+NI5197MNs2NAwAz/qBLj1+l2zxvHSi9Zx9VkDM8Y2+/WqcctQb/OBwLdWZkA2ljO5/tw1XNhQIqN6/20z/h52K4WF7WjnDbDxSogmfXn43nCQ3nCQqVyJ8YxCQ5H5hKcPEOuNMTI4WCtOL+0n4gQ4ayjOZKbIkVSWYknTDEVEVottWdiB5g+F4WCgaYl31WVnuGxs+3APn/rlq2Zt/+OXX0C2UvOydirXw8jGy/likeMH93HJpvoH0KBtcc22wVkHDBNRh6GesAkuK/uoBhce1B6LhtWCu9b01i7nSy7feszU1fz16+pLDL+1e5RvPrbwepsAV2zt550vOb92/dc+/WMA3vfKC2sBxp0PH+XefeMmYJy5zLsyGzJo2wRsCydgEbBt+mMOF21M1vb72LEpPA+2DMZqNT7T+RK5YplgwCZoWwQr+7GtuQMBaU9l1yNbKBMN1YPpA2MZ9p5MsyYRqT23p3JFPnn3XjLFuZfzZgsmvJ/p/T9zEeesNUHZ7qNTfPZHB7hu13AtLHQCNruPTc05NgtTgsuUvAg2hHoBehtmz523LsFvXLedtTNmYX/4tZcSdUxAuJRZweuT0Tlf0244b+2C9+GWS+wtPcHWwXoIOdgT5oUXrJt12+rPZKl6wkGun2NsZ/oaHAraPGeO8iLnr++b49aL86ydw7O27RjpYcdIz6ztWgVmLCks/PCHP8z73/9+jh49ysUXX8zf/u3fcuWVV85520984hPcfPPNTdvC4TC53OKWCMgMjUHh+D544mtw+S+tSgOUqt5IkN6ICQ3HMgXzZkZEALN8f7AnRLh8BNweCJzZrA/xX1/MoTcS5PhUnhPTeS1NFhHpEr0RZ8F1ht1yib3WHrY2zKgc7Anzhy86d9Ztf/f5zTNovMoy8eYZkM3hZH+sPo6gbfHLzzyLQskl2BBEnrMugetBoVyuB5oN+5l5HWiquel5HocmsgA05ptPjqa5+8nZ9b1O5bx1iaaw8I+/9DATmSJ/8/NP46zK7J0v/PQw//ee/bPua0EtOAzaFoHKZSdgsT4Z5d0vrYebf3PX44xO57n56VtrSzt/enCCb+w+bu4fsEwQadsEAhaObREMVILNhstRJ8AzGoKUJ0enyRbKbBqI1WacZgtlJnNFnErznVrIWbneiQHnVK7IRKZIPByshcdTuSLf3D3asGy3VOtqn2m4Xg39qp8V//51l9Zm1X3r8VE++6MDvOjCdbWw0PXgKw8fW9C4Io5NzAnOqmm7fTjOiy9cx6419SDICdi840XnmlCvEgSa2X9BIo69oP+3jf2xphIOVTPDQ5FOsOiw8LOf/Sy33norH/nIR7jqqqv40Ic+xI033sju3bsZGZm7yHQikWD37t216534AuqbYga+dCtkTkIhbWoYrvLPtzE0PJkuUCwrNJTuFQnaDPaEiVXftHhlGHsKBndCUEep2p1tW6zti5CMOVqaLCIiy8qyzIw8J2ATD5/+9hEnwCsu2TBr+4svXMeLL5w9m2gunudRKLuzDoD91c9eTKHkNi1ffNbOITb1R2fNpqwGjyXXpViu1qF0KZU9tgw2ByvDPeHaMuj6GCpLOWfMFvOAYtmjWJ699Hvm8sdHjqY4OJ5tWia+fyzD1x45vqCfQ1Vf1GkKC//3d57iwcMp/uAF59RmY/14/zh/XmnCMJfG2ZHVywHb4h9/4fLa5+BP/2AfDx6e5OWXbOCabYO18X72R/vITdgkJ57CqSwNb5yxGQxYOJXw0wSdNs/aNUQ4aN537juZZixdYF0yWguwMoUSTxyfbgjxmsO9prCv4TYf+rlLajOs/vWH+/nCT4/ws5dt5A3XbAVgKlfiY995alE/XzBha9X6vigXrE80lQOIhQK87qrNlWW7wVqDq9qS3sry3ogz/9L5CzcmubAhpK66uvKzFpHTW3RY+IEPfIBbbrmlNlvwIx/5CF/60pf4+Mc/ztve9rY572NZFmvXLnwKbT6fJ59vKMyZMkUni8UixeJiCnwuv+rjL/s4AnEozD0t+pTsCNZlv0TwO++Hhz5PGXCv+v9WPTAEc2RnfV+E6VyJ8WyBorqIzlJ9EzTzzZC0v2AABmPhWnetptC8nIPjj8PgdrAD8+zh1FbstUeWJABs7AuRClscbYOuyW651HQuIqem3xlZqnZ87jgWYDWPecdQdUWEi1t5T3Pumjjnrll8La/G/f7lqy+Ytf2my9Zz02XrmxrelMqVwLFyuRpAVsPIYMBq2u/N12xmOl9mQyJU275rJMYvXLWJUqVhTqlc2V9t//VQs/q40VCgab9DPSE2JCPEgo3/t2XCQbt2n5lKte3194K2ZWrKVW+99+Q0Dx1O8ewdg7X9npjK8u3HTwI2jC485LxiSx+OZfb8hfsP8ZWHj/PaKzZy0+UbAThwcpp3/OeDC95f1XQuT1/EhLI9oQA94QAWXm28ccfiGdsHiIWClVl6di3Mi4ZsYk61Xp9dm8EXdWyCAbu2j+t2DnDdTlOLrrotAPzcpetnD2gmr4y7+EbwHasdX3taValUpGi39nv7M7HQz5OWN1ff6XkUCgVisRj/8R//wSte8Yra9je+8Y1MTEzwX//1X7Pu84lPfII3velNbNiwAdd1ufTSS3nf+97H+eefP+u2Ve9+97t5z3veM2v7v/zLvxCLzZ72K7D55Ld42v5/AuDJ4Rt5cMNrfQkMRUREREREuoHnmWWzJQ/TNdyDsttw3a1v31ovN8neKRgvWGyKewxVJtWN5eH+k1bDfqzm/Tbs2/WgVNn3zbtcqgta7jhgcf+YzbPWujx9jfmYfyIHH3s0QCQA4YBHJMCMk0e44Xq4sm1tDByV3BbpOJlMhte+9rVMTk6SSCTmvd2iwsLDhw+zYcMG7r77bq65pt6J961vfSvf+ta3uOeee2bd5/vf/z6PP/44F110EZOTk/zlX/4l3/72t3nooYfYuHHjnI8z18zCTZs2ceLEiVN+M6uhWCxy5513cv311+M4C6tdsmCju6GcP/3t5mE9+kWC3/sAAOULfhb3yl/zPTD0PEhli0xkC6ikoZlR+MShCXZsSKrjXJuzgEQkSDIeaqoTdFrhPujfsujHW9HXHlkWhVKZo5N5phfQXW61ueUS+x/4AZsvvBo7oN5mIqej3xlZKj135Ezo+SNLpefOmbFtSEQckjGnqWNyJ0qlUgwNDZ02LFzxn8I111zTFCw+/elP59xzz+WjH/0of/zHfzznfcLhMOHw7EIdjuO0zIfkFRlL3zqYnF3cd8HOf5mZ4/6dvyLw4L+bMOqqX/c9MBzqDTMQDzOZKzKRKcw5Xb/bBCoFkKU99VSKOzfW21mw0hRkjkPf7BpDC9FKr4PSzHEctkcjpHJFjkzkWrLpkx0I6g2kyCLod0aWSs8dORN6/shS6bmzcJZlPtf1x0IkosGu6a2x0M+Si3oWDQ0NEQgEOHasuTvRsWPHFlyT0HEcnva0p/HEE08s5qG7Q7Qfpg6DewazUs59qZnO990PwE//DbDhql/1PTC0beiPOfRFHCZzRcbTBcpqJSptJhIMMNQTmtVxbdHSxyHgQM/cTaGkvSUiDj0jQU5M5zk+pa7JIiIiIiKtIhqyScZCJKMOwYDW2s9nUT+ZUCjEZZddxl133VXb5roud911V9PswVMpl8s88MADrFu3sA5dXcW2ITZ0+tudznkvg2f+rrn808/ADz9Gq3xarYaGWwfjDPaECHRJei/tzQnYrE1E2DQQPfOgsCp1CLLjy7MvaTm2bTGSiLBzTQ+JqI7uioiIiIj4JRiwGOoNsXNNDztGehnqCSsoPI1Ff4K59dZbeeMb38jll1/OlVdeyYc+9CHS6XStO/Ib3vAGNmzYwG233QbAe9/7Xq6++mp27NjBxMQE73//+9m3bx9vetOblvc76RTxIZg+BpxhuHfey8Fz4Xt/Dff/q5lZeMUtvs8wrLJtGIiF6IuEmMwWGM8U0OpkaTW2BQPxMMmoszK/OuP7wA5CuPf0t5W2FA4G2DIYb+mlySIiIiIincayoC9q6hD2RlTGabEWHRbedNNNjI6O8s53vpOjR49yySWXcMcdd7BmzRoA9u/fj23XE9rx8XFuueUWjh49Sn9/P5dddhl3330355133vJ9F50k4EA0uTwzjs5/pZlRePffwH3/Ar3r4dyXnPl+l1HAhoF4iL5oiIlsgQmFhtICLMwflv7FNi9ZNA/G9sDQTnCiK/g44rdExKF3TZDRKS1NFhERERFZKfFwgP5YiL6og60+AUu2pLVRb37zm3nzm98859e++c1vNl3/4Ac/yAc/+MGlPEz3io8s3/LEC15lzvd8C3Y8d3n2uQICNgzGQySjIcYzJjTUZ2nxQ084yGBPiNBqTUv3yjD2FAzuhGBodR5TfGFZZmlyMhbiyGSWVLb1uiaLiIiIiLSbsGOTjDkkoyFCS2lCKbOokFIrCsUg1AOF6eXZ3wWvMnUM7db/7w7YMNQTIhlzGM8UmMwUFRrKqog4leYlzjLVJFyMcsEEhkM7wfbh8WVVhYI2WwbjTOWKHJnMkS9qabKIiIiIyGIEbIu+mEN/zCEWav2so93oJ9qq4sPLFxZCc1D4408CHlz2i8u3/2UWtC2Ge8IkYyEmFBrKCnICNoPxEL0Rn18OS1mzJHlwe8vUFpWV1Rtx6AkHGZ3OczylpckiIiIiIqdiWdAbCZKMhUhEglj63LRiFBa2qmgSUmEo55d3v8cegh//H3N5w+Ww9oLl3f8ycxpDw3SByaxCQ1keAcuiPx5aueYlS1GYgvG9MHCW3yORVWJZFiO9EZLREEcnc0xmi34PSURERESkpURDAfpjDn1RR12MV4nCwlYWH4bUweXd55rz4apfM5dbPChs5NgWw70mNBzPFEgpNJQlsoC+mMNALExL/p3JTcDkIejb4PdIZBWFgjabB2NM50scnshqabKIiIiIdDUnaNUalUT8KBXV5RQWtrLYAEwdMQ0QltPFP998vVw0XZjbgBOwGOkN0x8LMZbOM5UrKTSUBeuNBBmMh3ECrTKVcB7p4xAIQc+w3yORVdYTDrJzpIcT0wWOT+VwlRmKiIiISJewbUhEHPrjIXrCiqv8pJ9+K7MDJjBMj67cYxSm4fa3wpZnwNNet3KPs8ycgMWaRIT+uMtYusC0QkM5BV+blyxV6iAEghDt93skssosqzqT2uHoZI6JjJYmi4iIiEhnsiyIh4P0xxwSEQfbbvGJHV1CYWGriw+vbFi497tw/GFzsoBL2icwBAgFbNYmIuRjLuOZAlO5kt9DkhYSCtoMxEP0tutRqfF9pjlRuNfvkYgPnIDNpoEY/fESRyay5LQ0WUREREQ6RMSxScZCJGMOTkvWh+pubfoJuosEwxDpg9zkyux/1wtgehTu/Sf44T8CNlzympV5rBUUDprQsD9WmWmYV2jYzQKWxUDc1LdomeYlS+KZDslDO9HLdffqCQfZMdLDyXSBYyktTRYRERGR9hQMWCRjDsloiGiojVZ9dSF9+mwH8ZGVCwsBLv0FwIN7Pw4//KiZBzyzrmGbCAdt1vVFyJcUGnYjC0jGQvTHQq3ZvGQpvDKMPQV96pDczSzLYqgnTF9US5NFREREpH1YlqlDmIw79IaDWO09m6NrKCxsB+EecGJQzKzcY1z6BvA8+PH/gXs+ApYNF/3cyj3eCquGhrmiCQ3TBYWGna5tmpcsRblgZhhK16suTR6Im67JWposIiIiIq0oFg7UuhkHVIew7SgsbBfxYZjYt7KPcdkbwXPhJ5+EH/w9YMFFP7uyj7nCIo7N+mSEbLHMWLpIRqFhx4k6AYZ6wkScTplKOI9yzpx7auUjpgi0liaLiIiISCsJBW36Yw59MYdwUMuM25nCwnYR7YfUYXBXeOnZ5Teb8598En7wYTNn+MKfWdnHXAVRJ8CGZEChYQcJBW0Ge0L0hLrsZWzyAAxv93sU0gKqS5OTUYcjWposIiIiIj6wbVMKKhl1iLdrY0mZRf+T7cKyID4EU0dW/rEu+0Uzw/B/PgXf/zvz2Be8euUfdxXUQsNCmZPpAtli2e8hdS3bAtuysG0LGwvLgoBtzm2rct2ysCzL3M4yq+MDmG3hoN3mzUuWKDcBk4egb4PfI5EWEawsTR7sMUuTswVNMxQRERGRlWNZpglffyxEIqo6hJ1IYWE7iQ3B9DET5K0ky4LLf8ksd7zv03D33wIWXPCqlX3cVRQNBdgYipKphIY5hYandabhnm2bmVABzGU5A+njEAhBz7DfI5EWEgsF2THSy8npPMdSecqulqyLiIiIyPKJhuzaLMJgx3SUlLkoLGwngaBZjpw5ufKPZVlwxS8DHtz3f+H+f4VdL4BQbOUfexXFQgFioSjpQomx6SK5UueEhs3hXiWoU7jXOVIH668JIg0Gq12TUznG01qaLCIiIiJLFwxY9MdCJGMOEUd1CLuFwsJ2Ex9enbAQKoHhm0xAeNa1HRcUNoqHgsQHgkwXSoxPF8iVVn8Zn8I9WbTxfWA7pmO6SINgwGZjf7Vrco50VnVaRURERGRhLAv6og7JmENvxPF7OOIDhYXtxolCOAH51Oo8nmXBJa9r3pYeNaFlB+oJBekZCDKVLzGeLpA/RWg4V7hnV0K7+cK9suey+wBsTEYJObbCPTlDHow9BUM7zWuDyAxmaXIPxyct9mLe9AWC9T/9jc21PZqXLTd/rXG7N8/2pnsvYD9zP3bT9gXcZr4xi4iIiMjixMMB+mMh+qIOtq06hN1MYWE7ig+tXlg40/7vw53vgqf/Jpz7Un/GsAp6w0F6w0GyhTIe1ELAMw33imUTPoYdG0c1HmQ5eOVKYLgLAjrqJ3Prj4UA2NAfxXG653nSFGyeQfi52KBy3vBzjsf1ANf1cD0oux6u51F2vabL5rz+dYWiIiIislzCjk0y5pCMhggF9RlVDIWF7SjSB8EIlHKr/9hHfgrlAhy8F855CZ3ejjYaUk0GaQPlApx80swwtPWcFalq7Mw3/5+r9vs75lUDRc/DdaFcuT7Xdtdt3Fb/mkJHERGR7hWwLRMQxhxiIcVCMpueFe0qPgyTB1b/ca/8FejfCjue1/FBoUhbKWVhbA8MbtfvpkiHsyyLYMA64zdx1fCweRYjDaFiw9cbQknXa7j96pf4FRERkSWwLEhEHPpiDolIsOmgqshMCgvbVXQApo6Au8pF6y0Ldt1Yv+65cOwhWHvh6o5DRGYrTMHEPhPoi4ichqm5a3GmjQ3nWjY9M1yc8zZe/b6a5SgiIrIyoqEA/TGHZCxEQHUIZYEUFrYr24bYIEwf828Mngvf+SA8+kW49q1w9gv9G4uIGNlx0yG5b4PfIxGRLhGwrTP+8DFzCbXreXMso569tNpTPUcREZFZnKBFfyxEMuYQDqpMkSyewsJ2Fh+G6eOAX++MLQgEzeN/6y8qsw5f4NNYRKQmfRwCIejpzK7lIh2hVDD1RvEg1NP15QNWYmm15zXMbjzF0mp3ju0iIiLtKBlzGEzE6Akr6pEzo2dQOws4EE2amUR+sCx4+m+ZVo8P/yd888+BGcuURcQfqYMmzI/2+z0Ske7jeSYILBegXDTnpXz9cjUkrLKDpnlZtF/B4RlarqXVuXyBvcCuNT0EKx3E5+p4PddMxtPdzqvdbnYn7qb9LdN+at2557zvEh7jFN/zXJ3A5+sCPnt/p7uvOXc9j1JZU0hFRMC8ZYiHg/SGguwF1iejOI5iHjlzeha1u/iwf2EhmFenZ/w24MHD/wXf/DOzbecN/o1JRIzxfWZJcrjH75GIdJZqGNgUADZeLrKoWf9uCTInzcl26sGhfnd9U11WHQzYOAHb59HITJ7nUSi7FMsexZJLsezWrhcq17UkXUQ6WcSxSVaWGTsBm2Kx6PeQpMMoLGx3obiZhVCY9m8M1cDQc+GRL5jAEAt2Xu/fmEQE8GDsKRjaBU7E78GItA/XnSMALNSXDrsr+IbcLULmhDkFQhBJVmYcxlbuMUXajGVZhIMBwkEgPPdtimUTGhZL1WDRrQWJhbKr5eYi0naCAYtkzKE/FiJyplPoRU5DYWEniA/5GxYCWDY883fNbItHvwjfvM2EiDue7++4RLqdV4axJ01gGHD8Ho1Ia3DLswPAxpNb8nuERrlgapCmj0MgbEqPRPvBifo9MpGW51RnhYbm/nrZ9eozEkv1WYnVYFFLnUWkFVgW9EUd+mIOveEglkqVyCpRWNgJIkkz+6Bc8Hcclg3PuhXw4NEvwTfeB1iw43n+jkuk25ULcPJJGNoJto5CShcol2bUDMzXL5fyJkRvN+U8TB8zp2C0HhwG55lWJSKnZLp4B+adnTNzqXOhYWZisexpqbOIrKhYOEB/LERf1KmVxhBZTQoLO4FlmdqFqUN+j6QSGP6emWG4+3b4xp+a8W1/rt8jE+lupSyM7YHB7WqeIO2vXJodADY2E2nHMHAxSlmYysLUEXBi9aXKwXmmUInIoi12qXO+XKZY9sjlzNdslboUkUUKBW36Yw7JWIhQUC8i4i+FhZ0iNmg+NHgtUIDFsuHZv2/G8tgd8PU/Mdu2Xef3yES6W2EKJvZB/1a/RyJyavN1EK6eWuFvXasoZsxp6jA4cRMaRpMqOyCyCpqXOpvfuWIxyIPAOWsT2IEgxbJLvjYjsbmGopY6i4htQzIWoj/mEAspnpHWoWdjp7ADJjBMj/o9EsOy4dlvATx44usQVHMFkZaQHTfdVvs2+D0S6VaeN3cA2DhDcDGdhKWumDan1EEI9ZrQMJKEgN7uifjhdEudXdej6FaXN3tNTVi01FmkvViWOdmWVT/HzFJuvF77um3REwqSiKoOobQmvXvsJPHh1gkLwQSYz34rXPAzplaaiLSG9HFT57Rn2O+RSCfyvLkDwFrtwCIKA1dBYcqcJg9CuNfMOIz0qW6pSAuxbYuwHSAcnP/3stAwK7GxhmJ1xqLCROl21ZDOwsK2K+fVbTMDukooZ9vN22v3txq3zR3yNd7WsurnIp1GYWEnCYbNB4HcpN8jqbMDzUHh5EGY2A9bnu7fmETEzDwKBE2AILIYrjt/85DqdmkhHuRT5oQFkYSZbRhJqqiaSBsIBe1T1i4rVQLE6tLm+lLnMoWSR9ld+TTRKhcI5E4CFq4Tww3GdWCiSzTOpqsHcsDMMA1rUTPvqOxnVsg387Zq/CGyYhQWdpr4cGuFhY3So/DF34HMOLzgNth0pd8jEuluE/vNkuRwj98jkVZSCwPzc9cOdIt+j1CWzDPvEXKTYB2AcKI+41CzIkTaUjBgEwxAlPmXOteDxPpS58a6iUudnWiVsgSzowTyk9RmjGcBLLxABNeJU3ZiuE4P2PrYuRwaZ7Y1Bm8WzTPpZs6os2YEd7ZFJZCbebv5Z9PNdVsR6Vx61e404V4IRk2nxFYTHYB1l8DJJ2Bwh9+jERHPhbGnYGgXOKor2jXc8qmbh7glv0coq8FzITdhTlbABIbRpAkQ9QFQpGPYtkXkFHUTPc+rhYjVpc4zayjODBPtwhTB7Ch2cXqeR/WwylkC5SyBSndoLxDGDcZxnR5cJ4YXaL3u7dWXvplLWuthXP1yPaRrDuOqoV01WGsM5BoDPGqhX/O+yqUie4EdIz2EQk7DfRXOicjqUljYiXpGTMfTVmMH4Lq3QSFjlkGJiP+8Mow9aQJDdU/tLG650ik3C5mU2XbsIbBV4Epm8MqQHTMnO1gJDvvNAUgR6WiWZREKWqdf6lxyKaZPUk4do1TMULI9SkGbYtmjvICpiVY5T7Cch/yYCb4CIbxQHM/pxQvFsJwI0Dyrbb4wrnk2XfPl6sy5ahhnzwjwaNivbTUHfK0QxhUtFzDLz52ASkWIiH8UFnaiaD+kDrfmUjE72BwU7v6y+VCiGoYi/ikX4OSTpr6oagy1J9etB4PFtDkv5epfL5sPH3hlQB8+5BTcEmROmpPt1Dsqq1yBSHdyywQzJwimR4m6RQgBoXDzTVwoum7tetMsO6q15ebaeREYMyc3CKG46eQeikMotmLfkogvyqXKe7XKqZSH/rO0ukdalsLCTmRZEB+CqSN+j+TUDv0YvvUXJkC84Y9h89V+j0ike5WyMLYHBrdrCWKr87xKKFh5s1nIVIJBzRiUZeYWTb3h9KjpoB5JmgOS+hAv0vlKBfO7nzlZOdA0P9uG8Jk2THJL9ZqqYMojhOIQ6qmcx/X+RNpHdXVHoSEcnKsB3Pges7pHB+ulBSks7FSxIZg6Skt/eFx3MZz1bNjzLfjq/4Ib/gQ2X+X3qES6V2HKlDDo3+r3SKTK80wQ2Phms5ilpV/bpTOVC5A+bk7BSD041IwIkc5SzML0MchO4OvfGq/c0MkdsGxw4vXgMNSjju7SGmqrOxoO4pbzC7tvKWfeew9sW9kxiiyBwsJOFQiaN/HZMb9HMj87CM/7X3CXB3u+DXf+kQkMNykwFPFNdtzMIEqs93sk3amYmzFjMGsaUYi0klIOpo+aUzBqlipH+yEYPu1dRaRF5VJmJmE1nGs1nmsOahamKhsscGL18DDcq9lZsvJc17w3azyI21j2ZSlyk2aST+/a5RmjyDJRWNjJekZaOyyESmD4Tvjae2Dvd+CrfwQ3/ClsutLvkYl0r+ljplZZz7DfI+lspXzDEpXKsuLTLPUSaTmlLExlTekTJ2ZCw0gSgq3X6VREZvA8c5AwPWr+BrUVr1KjNw3pyqZgtBIc9piZh2rcJmdiNcu+VP+GqgmotBCFhZ3MiZoiwbUjcC2qGhje9R7Y+1346jvgxvfBxiv8HplI90odNG+yo0m/R9IZSoXmZcTFjKnPJNJJqs/x1CHzQT2SNK8h+sAu0lrcsqlFmB6du45auyplzSlzwlwPhOuzDkNxzX6W+bVC2ZfxvTB8tp6n0jIUFna6nmEYa/GwEMwHiee9C772btj3PfhKNTC83O+RiXSviX0mzFcX1MUpF2cUtc62Znd6kZVUmDananAY7YdInymTIiL+KFebFp3ojpns5Txk8/WVVrbTHB46UX/HJ/5pLPtSW93hc9kXr2yaDQ7tUj1OaQl6x9bpIn3mqNpCi6z6KeDA898Nd74L9t8NX/lDeMFtsOEyv0cm0p0813RpG9ypJgbzKZca3mimzXknzdIQOWNevc7YpGU+pFeDQ9UXE1kdxSxMHzdLjru5QZZbhNyEOYE5INrYcdmJqeNyJyrloVB5j9bqZV9KWZjcr2aD0hIUFnaDnhGYPOD3KBYm4MD1764Eht+HO6qB4aV+j0ykO7klGHvSHOXs9qWEbrkeDFbfdLbDgRiRluHVu5tadj04DPdpFoXISshPmZCwVZuW+M0tmeYSuUlz3bLrwWEobrov67Wpvcws+1JIt24wOJ/suAmue0b8Hol0OYWF3SA6AKnD7fNCGQjB9e+BO98J+38Ad7wdfu4T0LvO75GJdKdyAU4+CUM7u2cmkOvOnjF4pt3uRKTOc+sf0i3bzDSMJM25ZvaILF1bNy3xmefWD2gAYNWDw+oMxG55H9QOZpV96aB60KnDZpl8uNfvkUgXU1jYDWwbYoOQPu73SBYuEILr3wtffSesOU9BoYjfSllTeHlgW+d9kF/NbnciMpvnmnAjOw5WwASG0X7zIanTXm9EVorrVpqWHFc5jGXj1euvAmBVGkhWly73qA7raqmVfWl4r9bR9aA987576GwIhvwejHQpvbp1i/iwOcLYTh9+AyG48U9MPRER8V8+ZZqetHMdlVbodici8/PKphlBdsz8/a92VNbsCpG5dVvTEl959fcO6VGzKRhpDg8V7Jy5atmXxvdq3RiAu6VKYLhTB87EF0phukUwZI7UVwv6tovGoLCYga+9Fy55Lay7yL8xiXSz7LgJ8hPr/R7JwjR2uytkzAxJv7vdicjCuCXInDAn2zGhYbTffDAX6XbFnJlFmBlDB7x8VMqZU+akuR4IVcLDasdlNYg7pVrZl4b3aqoHXVdMm94Dyc1+j0S6kMLCbtIz0n5hYaOffAoO/ADGnoCb/i8Ew36PSKQ7TR8zb4bjQ36PpFkp33AkusW73YnI4rjV2VOjEAib4DCShFDM75GJrK78tPk7rKYlralcgGyh0nmaho7LlfCwm1+zXNcctC00rOwoZf0eVevLnDQNT1rtfbd0PIWF3aTa1auY9nskS3PZL5pirxf/vIJCEb9NHjBvgKNJfx5/Zre7TipqLSKnVs6bsGT6mFkCGEmaGYeawSOdyvPMAf/p0fZ9H9+tZnVcDjQsW640TunEJaa1etANjeJU9mXpJg/W62WKrBKFhd2mZxjG2/RNRjBsuiQ3Khch4PgzHpFuN7HPBIbhnpV9nFnd7rIdXtRaRBaslIPpo+YUjJrQMJrUQUXpDK5r6ndOH9fSzE7hlZs7Llu2mcxRCw7b8LWrWg+6mIVCun4QV8HgMvJgbA8Mn63PvrJqFBZ2m0jSLB/shCKxxx+Fr70LnvdOWHO+36MR6T6eC+N7YHDn8s3oqXW7azgS3QmvVyKy8kpZmMrC1GHz4bta41AfrKTdlEtmyX3mhGbNdzrPhcKUOQGUKwFb6gjEEqa5kx3wb3xzaawHXSv7onrQK84tmoYngzs6czaqtByFhd3GsiA2ZN5It7v7Pm2WIN3+FnjR+xUYivjBLcHYkzC0a/EfyKvd7hqPRGvmhIgsh2LanFKHzHK/aH/lgKne+koLqzYtyY4rfOlalbAwMwr5StOUYGX5abjScXk1D4CoHnRrKUybv2t9G/0eiXQBvWPqRvEhs1yn3d+EPOcdcMfb4ch9cPtb4cV/CSPn+j0qke5TLsDYU+ZI53xHv2vd7hpmDJZyqztOEelOhWlzmjxoZulEkmbWYavN1pHulZ82IWG1rp1Io1KlEUjmhLkeCFfCw0rTlOUqu6B60O0hPWoansQG/B6JdDiFhd3IDkB0oP4Hp105UXjBbXDH2+DI/XD778OL/gpGzvF7ZCLdp5gxSyMGtlWuZ+tvOAuZSjCo2jUi4ievXits8oD5oF2dcWjbfg9OulF2wtQjVNMSWYxyHrJ5U88SwHaaw0MnuoB9qB50W5s8YBp8dXN3bVlxCgu7VXy4/cNCqAeGX34bHP0p3P578OK/gmEFhiKrLp+C4w+bN6AKBkWkpTUEh9YBCCfMbMNwn4JDWVlqWiLLzS2abtm5CXPdDjZ3XA6EG2YMZlQPuhNU64YPna3yGrJi9MzqVk7EvDGuduJqZ04MXvhn8OU/gKMPwJd+vxIYnu33yES6j958iki78dz6B20rAJEEOL1+j0o6TblkDtSnR7W0U1aWWzJL2rWsvbOVCzCxz6zqUcMTWQE6dNrN4sN+j2D5ODF4wZ/DmgtMXaLbfx9OPOb3qERERKSdeGXTXGJ8r7k+uhvG98H0qGnE5GnWtCxSKQ8TB+D4QzB1REGhiCyffMq8roisAIWF3SySMN21OkUoBi/8CxMY5qfgS78HJx73e1QiIiLSrsqVumCpg+Yg5JH7TYA4cQDSJ81yPgWIMpdCBsb2mPIcmRPt31hQRFrT9DFzkEtkmSks7HadNLsQKoHhn8Oa8xUYioiIyDLzTM2vzAmY3A+jj5qaySceN92WM2NQVKf3rlZd+jn2RL2GnIjISprYr789suwUFna7aL8pgttJQnEzw3DkPDM1+4H/8HtEIiIi0qk815RASY+a+lGjj8CRn8KJJyB12HS8Lamea0fzPDPT9Pgj5jkgIrKaPLdePkNkmXRYSiSLZtsQG4Lpo36PZHmF4vCiv4D7/hUue6PfoxEREZFu4pWhMGVOVbYDTtS8R3Gi4MTVxbLd1ZqWnDAdaUVE/OLqoJQsL71DEYgPmVoHdFjNnVAPXHlL/brnmTd0nbb0WkRERFqfW4R80ax6qAqETJO2WoAYAzvg3xhlYUp5M5M0c1K1CEWktUwfg/6Nfo9COoDCQoGAY5YjZ8f8HsnK8Tz4/t/BE1+Dl3zQtJgXERER8VO5YE6Nte2CERMaOjFTizkYNStBxH+FjPkgnpuk4w6yi0hnmD4G0V6I9Pk9EmlzCgvFiA93dlhYysLRB8ybuxOPKywUERGR1lTKmVPtfZlVmXVYWbociplA0bJ8HWZXyU3C9GjzsnIRkVY1vg+Gz4Zg2O+RSBtTWChGKAah3s59E+TE4MV/BYf/B856tt+jEREREVmgSgfmYgY4aTZZdmX2YbQ+C9GJ+DrKjuN5kB2H6ePmoLOISLvwyjC2B4Z2aWa6LJnCQqmLD3VuWAgQ7m0OCrPjpm5Qcot/YxIRERFZrGoH5sJ0fZsVqC9drs5CDIb8G2O7csumYUl6VE1LRKR9lbKmO/vAWX6PRNqUwkKpiyYhFYZy3u+RrLzsOHzxd82ykpd+SIGhiIiItLc5OzAHZzRQUQfmeZUKDU1Lyn6PRkTkzOUmzOzonhG/RyJtSO8WpFl8GFIH/R7FyrNs020wOw5f+N1KYLjZ71GJiIiILB+3ZFZRzNWBuTYLscs7MBcykD4O2QnUtEREOk7qsKlzG0n4PRJpM1rALs1ig2YZS6eL9JkahgPbTQHxL/4uTBzwe1QiIiIiK6vafXnqMJx8Ao7+FI4/AuN7K0080uC6fo9y5eVScOIJOLHbHDxWUCgiHckzy5FLBb8HIm1GYaE0s20TGHaDSBJe8lemM3LmpAkMJ7tgVqWIiIhIo1LOBGapg3DiMRMgju6Gif2QPmlm33kdEKZ5HmTG4PijMPZkZ9fqFhGpckswvqc7DgTJslFYKLPFhwHL71GsjkgSXvwB6D8LMifgC7+jwFBERES6XKUDc+YkTO43s++O/hRGHzPvkzJjUMz5PciFc8umbtfxhyszbNTdWES6TDEDk1pJJwunmoUyWzBklunmJvweyeqIJuElHzAzC8f3whd/B17yIejb6O+4RFZLKQ/FbOWUMeelbMO2hu3Vr215Bmx5urn/2FNw57tMx/FX/L2/34uIiKwMz4Vi2pyqWr0Dc7loQkI1LRERMeW3nBj0DPs9EmkDCgtlbvHh7gkLAaL98JIPNgSGlaYniQ1+j0ykzvPMUrFCfnaANyvgq3ztrGth3UXm/scfhe9+wPx+3/in9f1+7pcXP6M2PlwPCy3bHKmM9C3P9ykiIu3hVB2YayGiDx2Yi1kTEqoWoYhIs9Qhc3An3OP3SKTFKSyUuYV7zJu7xqPHnS7ab5Ykf/F3zRKVapfkxHq/RybtyHNNsDdvmNewrZCFbdfC0E5z3yM/hR/9b0hugme/pbbLGx/8LZz7Jhc3jt619bDQLZlaVIXp5tsEo5XzSGVmSOUUrF6Ozd6+7uLmx3jpX5vbVU0cgO/8FVz7Vv0OiYh0Ez87MOdSkB5tfmwREWngmckxw2dDwPF7MNLCFBbK/OJDMNFFYSFAbKC+JHliP3zp9+GmT9Xf0N7/GUifgHNfCv1bzLYTj8P+H5jb2AGzJMeym69Xzi1s1k5MYB04BFuvqT/u2B4zEyyxwSyLhkqtoLH6vqwZ+2vapvKjZ2SuYG++gG/btfUl6gfvhQf+HQZ3wJW31Pf3yZdBfopFzWbo21APC4sZUxuq1FwPqmyH61eawry5Ar5KyDd8dv0+/VvgBX8++0jiy//OvFlY6vMoGGkODwG++0E4ch98/ha47u2w9ZlL27eIiLS/cqHehbmqdoAqXv+bZS/h75DnmRmE6VHz91NERE7NLZrPn0M7weqSXgWyaAoLZX7RfkgdNi8m3SQ2aJYkf+F3TEHsxiPfT9wFJx+HTVc0hIWPwb3/tKBdB4GrAO9gFG7+cv0LP/h7OPgjE6rsutFsO3wffOUPFzhoqx4qvvELEKyESt/7G9j7Hbj0jXDuS8y2k0/CN993mvBxju3X/EZ9mene78CRB2DDpbD5arMtPwUP//cc+7Tnfww7AGsvgFAlvEqPmlO0H3rXmW2eC1NHZu+nXJp/6W011ItX6nHsvwd23w5rzoOLbjLbygX4l5sq+1hEkfbk5npYmJuEA/eYmkizVINCa3aYF4rNDvqSm+t3HdoJz3+3+Tk0+NbZ7+L6p23FCUeXFuyFe2HzVbO3B8Ozt52p694Gd70Hjj0EX/0juOjn4co3meVpIiIipVy9CzMAlgkQq0uXq38f5/sg67qmOV161PxNFxGRhSumTRmi5Ca/RyItSp/aZH6WZcKWqcN+j2T1xQbNbKuD9zZvP/uFkLmiuZZh3yY45yUmWPTKM87dpuuuW2J8Kkt/oqe533Rs0CzVDMXr2yzLXK/d3z1FcW7PfK08I9zMTZo30Y1hWDFjAsPFuvJN9cuH74MHP2dmo1XDwtwk/OgfF7/fV/1jfUbdY18xy2/PfpFZvlod72det/j9Dm6vh4XTR2HPt8zsg8qKXGzHjNlz6/ex7Lln6zXO2Is3FAQeOQ+u/QPoWTPje/qYWXIVikEgvPgjdrFB2HbdrM3FYG/lg1MbzCTtGTGNgu75KDz4H/DTz8Dxh+B57zKzlkVERJp45gBgKQucNJuqf5erS5ery5fTo2alh5qWiIgsXeaEeV2ND/o9EmlBCgvl1GKDJmhpDFS6RaQPdjyvedsFr5p9u3UX1WvCnUa57PLdnxzkRZdupCnuue5ts2+8+Rr4xS81b/M8838xM5RsvGw1hIVX3gIX/VxzwNW/FV70/tn3m2tfjWFnY5C54TIThjV+307UhHxz7qdhXzO3O9GGfcTMjMLqUmwwIakTa7hfyWyvfYCYazluzMyiq1p7ETzjt02wW2VZ8Or/3Vynb7HBXmKdOc3Uu3bh++hkAQee/mZYeyF868/h6ANmWfJz/5eZlSoiInIqc3VgFhGR5TN5AJxI82c9ERQWyukEghAdMEcdxH+WVQkDA7CQuuC9a2cHV+Fe2HjFmY1jy9PrnXCrYoP12YBLdcGrZgeykQTcfHvzNrdcWZa8wGBv4CxzmrV929LGKYuz7Voz0/POd8HYk3D778NlN8PTXtcesyRFRERERDpSpeHJ0Nmr37leWpo+pcnpNc5KE2kFdkDFeNtN30Z4xd9XZp+6ps7nHW83S8FFRERERMQf5YIJDL1FNGeUjqewUE7PiUA44fcoRKTdBcNm9um1f2CWsR+4Bz7/K3D8Yb9HJiIiIiLSvQpTprmpSIXCQlkYzS4UkeVy9gvhFf9gZhtOH4PHv+b3iEREREREulv6OGTG/B6FtAiFhbIwkYRpBCEishwGt8MrPwpPez1c/Wt+j0ZERERERCYPQDHr9yikBSwpLPzwhz/M1q1biUQiXHXVVfzwhz9c0P0+85nPYFkWr3jFK5bysOK3+IjfIxCRThKKwxVvMkuSwXS6/vb7Yewpf8clIiIiItKNPBfG9piGktLVFh0Wfvazn+XWW2/lXe96Fz/5yU+4+OKLufHGGzl+/Pgp77d3715+//d/n2c961lLHqz4LNoPtjokicgKuf+z8OiX4Eu/B6W836MREREREek+5bxpeCJdbdFh4Qc+8AFuueUWbr75Zs477zw+8pGPEIvF+PjHPz7vfcrlMq973et4z3vew7Zt285owOIj24bYoN+jEJFOde6LYeMV8PTfNM1QRERERERk9eVTkDri9yjER4uaJlYoFPjxj3/M29/+9to227Z5/vOfz/e///157/fe976XkZERfvmXf5nvfOc7p32cfD5PPl+fVZJKpQAoFosUi8XFDHnZVR/f73H4JpSE8lFAbdWXolh2m85FFqornjtOAm74M7AsqHyf1vGH8SJ9kNjg8+DaW1c8f0SWkX5nZKn03JEzoeePLNWKPHcmD4PlQKRv+fYpvltolrWosPDEiROUy2XWrFnTtH3NmjU8+uijc97nu9/9Lv/0T//Efffdt+DHue2223jPe94za/tXv/pVYrHYYoa8Yu68806/hyBt7M771ZZelqabnjvh4gTXPfq/CHhFfrL5Fo4mL/N7SG2vm54/IstBvzOyVHruyJnQ80eWavmfOweXeX/it0wms6DbrWgBuqmpKX7hF36Bf/zHf2RoaGjB93v729/OrbfeWrueSqXYtGkTN9xwA4lEYiWGumDFYpE777yT66+/HsdxfB2LbwpZGHvc71G0pWLZ5c77D3P9xetxAmpGLgvXlc+ddITA8Y3Yxx/iqj1/TfmCn8W94hbVTl2Crnz+iJwB/c7IUum5I2dCzx9ZqhV97gTCMLgD7MDy7ld8UV25ezqL+sQ1NDREIBDg2LFjTduPHTvG2rVrZ93+ySefZO/evbz0pS+tbXNdMy02GAyye/dutm/fPut+4XCYcHh2vSrHcVomoGulsaw6x4FsAgrTfo+kbTkBW28AZEm66rmTGIGX/TXc8zF44N8IPPjvBEYfgee/C+LDfo+uLXXV80dkGeh3RpZKzx05E3r+yFKtzHOnCOnDMKD+E51goTnWop5FoVCIyy67jLvuuqu2zXVd7rrrLq655ppZtz/nnHN44IEHuO+++2qnl73sZTznOc/hvvvuY9OmTYt5eGkl+qAuIjMFQtC3zK/rdhCu+Q24/r3gxOHYg/C5W+Dgvcv7OCIiIiIiMrfcJEwdO/3tpGMsei3Xrbfeyhvf+EYuv/xyrrzySj70oQ+RTqe5+eabAXjDG97Ahg0buO2224hEIlxwwQVN908mkwCztkubiSYhFTZt1UVELBv6z4JQzLyZyC9sevuCnfVsczTza++Ck0/C7W+By2+Gp73ePLaIiIiIiKycqcPgRCHib2k4WR2LDgtvuukmRkdHeec738nRo0e55JJLuOOOO2pNT/bv349t64NbV4gPQeqQ36MQkVbQt8kEhQCJ9TA6xbJ3Te/bCC//e/jeX8Pu2+Hej8PRB+C574BIcnkfS0REREREmk3sg6FdEJxdNk46y5KqxL/5zW/mzW9+85xf++Y3v3nK+37iE59YykNKK4oNwtRR8Mp+j0RE/NSzBmID9etO1Lw+ZE4s/2MFw3DtW2HthfDdD8HBH5llyc9/N6w5f/kfT0REREREDLcEY3tMYKhJYh1N/7uydHagOSAQke4TTpiZhDP1rlvZrsVnvxBe+Q9mtmF6FP77t+CEurSLiIiIiKyoUhYm9/s9CllhCgvlzKjRiUj3Ckagf+vcXwsEoWftyj7+wDZ45Udh23Ww9ZkwuGNlH09ERERERCA7DtOjfo9CVtAKTvuQrhAMQ6TPNDQQke5hB01YZwfmv018yCxFLuVWbhyhODzvXVAugGWZbYU0TB8z4xMRERERkeWXOgROBMK9fo9EVoBmFsqZi4/4PQIRWVWWmVF4usLGlgWJDaswHKs+Fs+Db78f/t+vw5PfWPnHFhERERHpSh6M74VSwe+ByApQWChnLtwDTszvUYjIaklsWPgRxEjC1DVcLeU8FDOm+HKPDmSIiIiIiKwYt2QCQ8/zeySyzBQWyvJQ7UKR7hAbhJ5F/r73bQSsFRnOLMEIvODP4OV/19wduZRfnccXEREREekmxTRMHvB7FLLMFBbK8oj2g+34PQoRWUmhHujbtPj7BcOre0DBsmHk3Pr1k0/Cv74G9nx79cYgIiIiItItMichfdLvUcgyUlgoy8OyTDMDEelMgRD0n1VvIrJYvWv9O6DwwL9BdgzufCd8/+/NcgkREREREVk+kwdMo0HpCAoLZfnEhsyMHhHpLJZtgsJAcOn7sAPQu275xrQYz34LXHSTufzAv8EXfgemj/szFhERERGRjuTB2B4oF/0eiCwDJTuyfAJBsxxZRDpLcjOElqGJUXzQn2ZIdhCu/nW44Y8hFIdjD8Lnb4GD967+WEREREREOpVbVMOTDqGwUJaXGp2IdJaetct7ECCxYfn2tVhbnwWv+hgM7oTcJNz+FvjxJ8At+zcmEREREZFOUpiG1CG/RyFnSGGhLC8nCuGE36MQkeUQ6YPEMi8dDvdAJLm8+1yMxAbTKfmclwKeCQvveBtkJ/wbk4iIiIhIJ0mPQmbM71HIGVBYKMtPjU5E2l8wCsmtK7PvxAZ/65sGw/Ds34Pr3g6BMBz8EXz+TXD0Qf/GJCIiIiLSSSYPQCHj9yhkiRQWyvKL9EEw4vcoRGSp7CAMnAX2Cv2JCIYgPrIy+16MXTfCK/8B+jZB+gR84bfhgX/3e1QiIiIiIu3Pc2F8D5RLfo9ElkBhoawM1S4UaVMW9G81s+9WUs8aCIRW9jEWYmAbvPKjsO054JW1XEJEREREZLmUCzCxTw1P2lDQ7wFIh4oOwNQRcHUUQaSt9G2EcO/KP45tQ+868+bBb6EYPO+dsO1a2PrM+nbPA8vyb1wiIiIiIu0unzLZQGK93yORRdDMQlkZtg2xQb9HISKLERta3ZqjsQEI9aze452KZcG268wSbIByEb74u/Do7b4OS0RERESk7U0fg+y436OQRVBYKCsnPgxoVo5IWwj1mFmFqy2xYfUfcyF23w5H7oN7/kGdkkVEREREztTEfijm/B6FLJCWIcvKCTgQTeoIgkirC4Sg/yx/ltyGYqZsQbbFagWe+1LIT5mahtGk36MREREREWlv1YYnQ7vADvg9GjkNzSyUlaVGJyKtzbJNIBbw8dhRYj1YLfaGwbLhaa+HLU+vbztwDzz1Td+GJCIiIiLS1ko5GN/r9yhkARQWysoKxVunJpmIzJbcAk7U3zEEHNMduZWlR+Hrfwpfezfc/bempqGIiIiIiCxOPgVTR/0ehZyGwkJZeavZMEFEFq5nbessse0ZgUDY71HML9oP57zYXH7wc/CF3zaFmkVEREREZHGmjkBu0u9RyCkoLJSVF0mammgi0joiSUis83sUdZZlliO3KjsIV/0q3PCnZrb08Yfhc7eYpckiIiIiIrI44/uglPd7FDIPhYWy8ixLtQtFWkkwapYft5poEsIJv0dxalufAa/6R1OYOZ+CL78N7v04uGW/RyYiIiIi0j68MoztAdf1eyQyB4WFsjpig6ZhgIj4yw6ahiZ2i/4+JtYDPnRlXozEOnjZ38K5LwM8+Mk/w5ffos7vIiIiIiKLUcrCxD6/RyFzaNFPi9Jx7IAJDEXERxb0nwXBFi4L4ETb47UiGIZn3QrPeQcEI3DoJ2ZZ8tEH/B6ZiIiIiEj7yE3A9HG/RyEzKCyU1aOlyCL+6tsE4TboTt67zsyAbAc7r4dXfgSSmyFzwjQ++elnwfP8HpmIiIiISHtIHYb8lN+jkAYKC2X1BMOtX49MpFPFhiDeBjP2AAJB06m5XfRvNYHh9ueB58IP/gEO/sjvUYmIiIiItAkPxvdCqeD3QKSiTaZuSMfoGTFNAURk9YR6oW+j36NYnPgQZE6aOibtwInBc/8I1l0EJx6HjVf4PSIRERERkfbhlmB8DwzubN366l1E/wOyusK9phOriKyOQNjMfLNavGnITJZVaXbSRiwLzns5PPv36z/v3CQ89lUtSxYREREROZ1iBiYP+D0KQWGh+KFnxO8RiHQHy4aBs8yy3nYUSUCkz+9RLJ3nwjdug2++D+79J79HIyIiIiLS+rJjkD7h9yi6nsJCWX3RfrAdv0ch0vmSW0x34XaW2AC02azIRusuNN2Sz7rW75GIiIiIiLSHyYOQn/Z7FF1NYaGsPssy9chEZOX0roNo0u9RnLlguH07qVs2XPI6eM1nYGhnfXvqsH9jEhERERFpeZWGJ+Wi3wPpWgoLxR+xIdp6tpBIK4skobeNugmfTu/a9p6N3BjaHn8E/u0N8L2/0ZsfEREREZH5uEUTGKr2ty8UFoo/AkGzHFlElpcTM8uPO4kdMDMlO8HRB0ynt4c+D1/4LZg+5veIRERERERaU2HaLEmWVaewUPyjRiciy8sOQv9ZYHfgS3t80ASh7e6in4Mb32c6wx9/BD53C+y/x+9RiYiIiIi0pswJyIz5PYqu04GfKKVtOFEI9fo9CpEOYZmgMBjyeyArJ7HB7xEsjy1Ph1d9DIbPhnwK7vgD+NE/gVv2e2QiIiIiIq1n8gAUMn6PoqsoLBR/9bRp4wKRVtO3CcI9fo9iZYV7Oqd8Qe86eNnfwnmvMNf/51Nw+1t01FREREREZCbPhfE9UC75PZKuobBQ/BXpg0DY71GItLf4sFmm2w1615suw50gEIJn/g48948gGIHDP4HP3wJHfur3yEREREREWku5oIYnq6hDPnFJW1PtQpGlCyc6Z3nuQgRDEO+w14wdz4dXftQ0psmchC/+Dtz/Gb0REhERERFpVJiC1GG/R9EVFBaK/6IDYAX8HoVI+wmETcBkWX6PZHX1rDGz8jpJ/xZ45T+Y4NBz4Z6PwJ3vMpdFRERERMRIH1fpnlWgsFD8Z9sQ65IllCLLxQrAwDYIBP0eyeqzbVPzr9M4MXjOO+CZvwu2A0M7OmfJtYiIiIjIcpk8AMWs36PoaF34KVNaUnwY0qOAlt2JLEj/FnAifo/CP7EBs2S3MO33SJaXZcF5L4d1l0ByU317YRqcePfNIhURERERmclzYWwPDJ8NtlYprgRNWZDWEAyZZicicnq96/X7Ap1dq7F/S31WYSkP//3b8M3bdAS1E6k2pYiIiMjilfOm4YmsCM0slNbRMwK5Cb9HIdLaIknoXeP3KFpDKGZKGGRO+j2SlXXkPhjfA9kxKKTBifo9Ilkot2yKcKePm9nz09XzUXOePg6lAmx5OpzzIlh/qY6Oi4iIiCxUPgWpI5DowBJFPlNYKK0jFDfL7Ippv0ci0roSG/0eQWvpXQfZCfDKfo9k5Wy6Cl7yQbPcIj7k92ikqpQDO2hOAIf/B578OgzuMEvJAYoZ+LdfOP2+nvqGOcWHYdeNcOHPmAMDIiIiInJq00fNwfRo0u+RdBSFhdJa4kMwobBQZBbLMee2qkc0CTimO/LUYb9HsrLWXdx8/alvwuH74Jrf6LzO0K2glGue/Tc9Wp8dWJ0ZmE/BSz4E6y8x95k4AI98ATY/vR4WhnrM7NdQjwkCe0Yq58PmPD5ilpk//hV44mtm3/d/xoSFVZ6nWpUiIiIipzKxH4KR7q7pvswUFkprifbD1BEoF/weiUgLsUwNO/b4PZDW1DNiliKX836PZHXkp+Db7zdLkkcfhee/G3rX+j2q9lHKm1DO8+pNZMoFuPOdzUHgQmRO1C+vOR8ufaPpYl1lWfD6z51+PyPnwFW/BvvuhtSh5lmFd7zNBI6XvkH/zyIiIiJz8cqmbM/QLpV0WSYKC6W1WBbEhjp/lpDIYiQ3gxPzexSty7Igsd68QegG4V547h/BN95nwsLP3wLPeQdsvtrvkfmvGgQ21Qc8bmb6DW43t3n8q/Cdv4LN18ALbjPbbAeO3N/cQCYYqcwEHGmeFdg4MzDUU7/94Pb6YyxFMAzbn9O8LXUIDtxjmt1c/kv17W6pvvxZRERERMzKkIl9MLDN75F0BL3TlNYTHzJ1BzzX75GI+C8+ArEBKBb9HklriyYhk1j4jLB2t/kaeNU/wtfeA6OPmNlnl7weLv/Fzg6RPM80fGlcFtwYCs73/7/2gnqQFx82QWDjUWfLgme/xdTNbQwC/V7+27seXvo3MLq7uV7lV/4QsODsF5rmKFqKLiIiIgK5SZg6poaQy6CDP1FI27IDEB1oXt4l0o3CCTNjThYmsR5GpwDP75Gsjt618LK/gR/8Azz0ebjv03D8ITPrMDbo9+gWpzojMNJnZk4CHP0p3PcZ093u6b9ptlkWfOUdpnHIfIKRGfUBR6D/rPrXN10JN395dhC4/bnL+z0tB8uCdReZU1VmDA7eaw6oHbjHvE7seL4JDod2+jdWERERkVYwddg0PIkk/B5JW1NYKK0pPqywULpbIAz9W/2f2dROnKiZfZUe9XskqyfgwDN+y8yc+/b7TUfez90Cz3/X7KYofmlaGjxP05DcpLntc/4Qdt5gLhcysP/u2Ut7111kagzOXB5cPT/djECrzZsExQbgZz8Jj90Bj33F/K186PPmNLjDhIY7nm+CVxEREZFuNLHP1C8Mhv0eSdtSWCityYmY2RLdsqRQpJEVMLU2VJx38XrWQnbc1HTrJtufa4KiO98J43vhi78LV9wCF9+08uFYfgpOPmGWCG+4tL79C79txlINAk8nGGmuGTi0E571e7Nn177gz854yG0vuQmuvMXUMTx0L+z+Muz9nvl/uPtv4QcfMcuTz34RbLxcryUiIiLSXdySeR86uBPsNj9Q7BOFhdK64sMKC6U79W8xgbksXiBoAsPUQb9HsvqSm+EV/wDf/aBp4vHDj8KxB+G6t0Ewvvj9lQsNswGPN88MPPclpm4iwPGH4ct/AAPb4Wf+qX7/3GQ9KAyEzcy/aj3AuWYFzpwRGBuEc1+69J9HN7ADsOkqc8pNwhN3meDw5OOw51vmFBuC815muimLiIiIdItiBib3m9VasmgKC6V1RRIQjEIpe/rbinSKxAYtHzxT8SHInOzO1w4nCte9HdZeBHf/Nez7nlmavPmZ89/nqW+arru1JcKVZiG5ifnvM3JuPSzsWQt9m2bPALz2rSYkjA+bOoRaUr+yIn1wwavM6eQTJjR8/E6zTHniQPNti1nzXBERERHpZNnxegM7WRSFhdLa4sPmaIBIN4j2mxlWcmYsywRXY0/6PRJ/WJaZ+Td8Nuz/AZz1bCi79GX2EvzP95rg7iUfrN/+x58wyzTmEghVZv9VZgDGh82brTUX1G/TvwVu+tTs+46ct5zflSzG4A7TFOaqX4V9328Ock8+Cf/1/8GO6+FZtyrEFRERkc6WOmQOkoZ7/B5JW1FYKK0t2m+6GXVb/THpPk4c+jb7PYrOEUmYmVYLrZfXiYZ2NnXHda0g1skn6t2GqzZfA0Nn14PAxuXB4YTCpHYWCMG2a5u37fselHKQn2z+v82Om7+5IiIiIh3FMwfGh882zQFlQRQWSmuzbVNvafqo3yMRWTm2AwNnqfjucktsgFwK8PweSUtIh0coXf8+gokZs1ev+lV/BiT+eNovwPpLm7sDThyAf38jbLgMdr0Atj5T3QNFRESkc7hFGNtjDqTrQPiCKCyU1hcfgulj6AO/dCTLNkGhjnItv2Clqcb0Mb9H0hJcO4S3+WoIKJTuapYFay9o3nb4f8Bz4eCPzCnUAzueD2e/EIZ26U21iIiItL9iGiYPmKaAcloKC6X1BRyzNCo75vdIRJZf3yYILaFTrSxMzxrIjJmjiSIyt/NeBhsvg913wGNfMd2vH/5PcxrYBrteCDuvh2jS54GKiIiInIHMSVP+KT7o90hanqYXSHuIq3uRdKD4CMQG/B5FZ7MD0LvO71GItL7EBrjil+E1/wov+kvY/lxzsG7sKfjBh+HTr4av/i/Yd7fqCIuIiEj7mjwAhbTfo2h5mlko7SEUg1AvFKb8HonI8ggnoG+D36PoDvFByJyAYsbvkYi0PjsAGy83p/wUPPl12H07jO6Gvd8xp+gAPPN3TKdtERERkbbimfqFw+dAQJHYfDSzUNpHfMjvEYgsj2AE+rf6PYruklAwK7Jo4V447+Xwyo/Cz3wcLvw5iCRNWZBYw/KdzEkoTPs2TBEREZFFcYswvgc89UWYj8JCaR/RJATUnVHanBWA/rPM7B1ZPeEeU/tURJZmYBtc8xvwun+HF/w5jJxX/9qPPwGfejU8/N++DU9ERERkUQrTkDrk9yhalsJCaS+qXShtzTIzCp2I3wPpTr3rTfdpEVm6gAObr6p3SPY8U9ewnIfkpvrt0qMwdcSfMYqIiIgsRHrUNEOUWbRAW9pLbNB8+PDKfo9EZPES6yGS8HsU3SsYMt2RFWCILB/Lgpf9nalpOLyrvv3+z8KD/wEbLjXdlM96NgS1OkBERERazOQBUyYqFPN7JC1FUyykvdh2c50kkXYRHYCeEb9HIfERCIT8HoVIZ7EsGDmneeZu5qQ5P/QT+MafwqdfBd/5Kzj+iOoDiYiISOvwXBjfC+WS3yNpKQoLpf3EhwHL71GILJwTh+Rmv0chYA449K7zexQine/574Kf/1e49I1mRm8hDY98Af7z1+E/bob7P6NlPyIiItIaynmY2Of3KFqKwkJpP8EQRPr8HoXIwtgODJxVr+8l/osNQKjH71GIdL7EOrj8ZnjNv8KLPwA7nm9m9o7vhXs+Av/3Z+Ar74C93wVXR/NFRETER/kUpA77PYqWoZqF0p7iw5Cb8HsUIqdm2aaDaMDxeyQyU2IDnNjt9yhEuoNlm9qFGy41nQef/Absvt0sSd73PXOK9sOr/8mE+SIiIiJ+mD5m3pM4Ub9H4juFhdKewj3gxKCY8XskIvPr26RCua0qFDP1T6t11URkdYR64NyXmtP4Xth9Bzz+FYgNNQeFh34MQ7sg3OvbUEVERKQLuWqmCgoLpZ3Fh1VXQFpXzxrNkGl1vesgO6Hu6iJ+6d8KV/8aXPkmSJ+oby+kzfJkz4Wf+Tj0bfRtiCIiIiLdSDULpX1F+009OJFWE05AYr3fo5DTCTjQu9bvUYiIHWz+XZw+ZsL83rWmZEDV3u+plpCIiIjIKtDMQmlflmVmF07pg4O0kGDEzJaR9hAfNjOaynm/RyIiVQPbzIzC/GS9OVQpB994HxTTsO4SOPuFcNazVVNIREREZAVoZqG0t9igKZwu0grsoPmQawf8HokslGVpFqhIK7IsiCTr17MTMHIuYMGR++Cbt8GnXw3ffj8cewg8z59xioiIiHQgzSyU9hYImuXIalIgvrMguQWCYb8HIosVTUImAfmU3yMRkfn0roUX/6VZovzYV0xjlKnD8OiXzKlvk5ltuOtGcyBRRERERJZMYaG0v/iIwkLxX2IDRBJ+j0KWKrEBRqcAzU4SaWk9a+DSN8DTXg9Hfgq7vwx7vgWTB+CHH4Mf/W/YdCWc/SLYfI2pTSoiIiIii6KwUNqfEzENJTQrSPwSG4SeYb9HIWfCiUB8CNKjfo9ERBbCsmH9Jeb0jN+Cp75pgsNjD8L+H5jTpqvhhX/m80BFRERE2o/CQukM8WGFheKPUI9Z/ibtr3cdZMfBLfk9EhFZjFAcznmxOU3sh8fuMEuVz3pW/Ta5SXjy67D9eZoFLiIiInIa6gwhnSGSMF1oRVZTIAT9Z9W7dUp7swMmMBSR9pXcDFf+Crz2s7Dzhvr2J74G3/truOMP/BubiIiISJtQWCidIz7i9wikm1i2CQoDmqDdUWKDEIz6PQoROVN2sLleYXQABrfDjuvr2wppU+Nw8uDqj09ERESkhelTrnSOaL/pjKglhLIakpshFPN7FLLcLAv6NsDJJ/weiYgsp+3PMSe3XN/25Dfgfz5tTmsvMt2Ut10Ljl7bRUREpLspLJTOYdtmVtD0Mb9HIp2uZ60Jp6UzhXsh0mdqnIlIZ7ED9cuJ9aZz8sF74ehPzel7fw3bnoO180bw9DovIiIi3UlhoXSW+DBMHwc8v0cinSrSBwnVtet4iQ2QS6HXEpEOtuFSc5o+Do9/1XRTTh2Cx75M8LEv88JAjMCR7TC4DQa2wcBZ5jzU4/fIRURERFaUwkLpLAEHoknT0VRkuQWjkNzi9yhkNQTD0DOimcoi3aBnBJ72erjkdXD0Adj9ZbynvkGolIFjD5hT1SWvgytvMZfzU7D/B6YW4sA2f8YuIiIisgKW1ODkwx/+MFu3biUSiXDVVVfxwx/+cN7bfv7zn+fyyy8nmUwSj8e55JJL+NSnPrXkAYuclhqdyEqwg2ZWSeMSNulsPWvAdk5/OxHpDJYF6y6C6/6A0uv/k2+c/SeUrn07XPwa2HSVWb3QGAqO7oZv/Cnc+a7m/ez+Muz9HqSOgOeu7vcgIiIisgwWPbPws5/9LLfeeisf+chHuOqqq/jQhz7EjTfeyO7duxkZmR3SDAwM8I53vINzzjmHUCjEF7/4RW6++WZGRka48cYbl+WbEGkSipklQoVpv0ciHcOC/q1mtpl0DztgappN7PN7JCKy2gIhUrHNeDs2QqDh2LrXUJrAsmDthdC3qfnrd/8dFNPmuhOF/rOalzEPnAWR5Kp8GyIi4rNi1pS48FwY2mW2eR586VZT3mhoV/0USfg7VpEGiw4LP/CBD3DLLbdw8803A/CRj3yEL33pS3z84x/nbW9726zbX3fddU3Xf/u3f5tPfvKTfPe731VYKCsnPqywUJZP30bT9EK6T2wA0qNQzPg9EhFpBZZVv7zhMnNqVMrBlqfD+FMwvt98SDz+sDk1ig5UgsNKeLjpKvN6IyIi7aNchPQJU7Zm+nj9fNeNsPYCc5sD98DX3g1rLoCX/53ZZlmQOgyH/wee+mZ9f71rK8Hh2TC0E4Z36eCS+GZRYWGhUODHP/4xb3/722vbbNvm+c9/Pt///vdPe3/P8/j617/O7t27+fM///N5b5fP58nn87XrqVQKgGKxSLFYXMyQl1318f0eh5xGMA5eENyC3yNpUiy7TefSBqIDEOoDvfZ0r9gaGHvS71GcEb32iCzOkn9n7DBcW3mf7JZg8iDW+B6ssaewxvdijT+FNXUEsmNwaAwO3QtA6UUfxAsnAbAO/w/W0fvx1l+Kt/ai5fqWZJXo9VbOhJ4/Lejkk1jTR7Gmj0H6ONb0cZg+jpU+BpkxrDma4ZWTW3CHzwPAig0TiCTxnDjlhv9X6xm3Yo09iXXiMXOaOgxTR81pz7drt/PiI3hDu/CGduFufx70zt1oUc+dZVQqgd25n7kW+nnS8jxvwa0eDx8+zIYNG7j77ru55ppratvf+ta38q1vfYt77rlnzvtNTk6yYcMG8vk8gUCAv//7v+eXfumX5n2cd7/73bznPe+Ztf1f/uVfiMViCx2uiIiIiEhLCZRzJHKH6M0eIJE7SCJ7gB+d9ZsUg6bL8vkH/4Udo3fw5PANPLjx9QAEy1kuPPhpUtGNpCKbSEU3kg/2Nc90FBGRBbPdAhYeZduUGerJHWL78Tso2xEe3Pi62u2uf+hWYoUT8+6nbDlkQ4NkQoNknQGyoUGOJS5mIr59UeMJltIks/voy+wlmdlLMruHnnxzo73v7HwHYz1nAzA49QiD6ccY7b2A8UU+lnS3TCbDa1/7WiYnJ0kk5l/6virdkHt7e7nvvvuYnp7mrrvu4tZbb2Xbtm2zlihXvf3tb+fWW2+tXU+lUmzatIkbbrjhlN/MaigWi9x5551cf/31OI4K37c0twyjj4JX9nskNcWyy533H+b6i9fjBJbUX0hWix2CwR0QaI2m8Xrt8VmpACcfa9tmBXrtEVmclf+d2dF07fqGy9bANbj7YcuWZ7B5y0az7diDBH/6nab7eOEE3sA2vP6z8Cp1Eb3kVlO7WXyj11s5E3r+LBPPg9xkZSbgMTMrcPo4VtosFbbSx7Gy45SvfjPu+a8CwBqdIvjIt/Diw2y+9A9quwqMnYObOQHxEbyeNZXz+mUifYQtizDQX7nP0qO7s5uuFQvTWCefwDrxONbJx7j6mqebWriA/f3/JPDE59k1AO6l11Isu3zjJ09yvX039vDZeEM7ITa45JF0tYHtEIr7PYoVU125ezqL+hQ8NDREIBDg2LHmhPvYsWOsXbt23vvZts2OHeZN0SWXXMIjjzzCbbfdNm9YGA6HCYdnNxJwHKdlPiS30lhkPg70Dpl6Yy3GCdh6A9DKLBuGdtT+GLcSvfb4xHGgbx1MHfF7JGdErz0ii+PL78z2a2H7tTQ9as8QXPaLMPYUjO2B1CGsfArryH1w5L7m+/euqzdVufT1EIys2tClTq+3cib0/DmNUt6EgT0NDVbv+SiceKxeO7B8+nJUgcwJAtWfc3ITXHYzVu/a5p/9jX+yzINfhGgCNl5qTtD8d2H9RVBIEdh4We176MvsI/TEJ+u3iQ3Wm6cMn23O40OrN/52FQya9/4daqGfJRcVFoZCIS677DLuuusuXvGKVwDgui533XUXb37zmxe8H9d1m2oSiqyY+HBLhoXS4pKbWzIoFJ/FRyBzckFvPkVEllVivQkLq0p5GN8L43vqAeLYU+Y1auqIOR3+CVzRUPbnhx8zH6IveBWMnLfa34GIyMJ4LmTHmxuGDJ8N6y42Xx/dDf/vV83nvNf9e/1+xx6Eow807MgyYVnPCPSsmfs83LBqMZKAy964Kt/isth2nTk1KAUiuDuuxz75OEzsN38T9n/fnKqiA5XwsKELc3xYZS1klkWvr7v11lt54xvfyOWXX86VV17Jhz70IdLpdK078hve8AY2bNjAbbfdBsBtt93G5Zdfzvbt28nn89x+++186lOf4h/+4R+W9zsRmUswbFrS5yb9Hom0i561EO0//e2k+9i2+cA+vtfvkYhItwuGzYfn4eYla+Qm6+FhMWNmylftu9u8fu24vnnbff/S0Jm50p053Lsq34a0Ac+DYhpyKfP8yqUgXz1PweZrYORcc9uJ/fCj/206ul79G/V9PPl1KObMEvlgFELRynmsfh4IK6zoFqVcJQg8PruLcKWJCOUZDRgu+rl6WFidGVfImLJTdqB+m3NeWg8D40MQ6NzZYXOZjG2lfOkzsQM2FLNw8gkz23L0MXM+sc802DrwA3MCM0HiF78EVH7/Rh81gaICxK636LDwpptuYnR0lHe+850cPXqUSy65hDvuuIM1a9YAsH//fmy7/sYknU7zG7/xGxw8eJBoNMo555zDpz/9aW666abl+y5ETiU+rLBQFibSB4m5O4yJACZITp+AwrTfIxERmS3SB+ufZk4zXfVr5oPj8K76ttFHzWycYw823zY+3BweDmwzs+4DoZUdv6yO/JSZlWoFYM359e13/60Ja6pBYDUcPFX970hfPSxMnzBdXPu3NoeFP/nn0x9os2wTHDqV0wWvhvNfYb6WOQn3ftz8Db7iTfX7HPqxCcVr94vV7+/Eui4oajm5FDz2ZchPwxW/XN9++1tmzACcg2VXZgWuMa9Hgw11XqMD8Ib/Ngc1GsOsrc9a3vG3OycKay80p6pSDk4+aWZnnqgEiJG+5gNLX/9TmDwAL3o/bLzCbPv/27vv+Kbq/X/gr5OdNE33pIWWVYYFCipXUGaZXpYICii0VBCBnyCCXPCrDL0I3AqIuK5IWxeoV0BFHFgpeitXQYaCCIJFQFmFltLSkTbn98dp0qR7cprk9Xw8zoM0OeOd8OnIK5+RlwlYiqX/DwaIbqNeM/fPnj27ymHHaWlpDl8/++yzePZZGcf5E2k9pT8YzDfkroSaM5Ue8I6QuwpyBqYWQOZxuasgIqqbln+TNnvth0kh4NXfy3ok5l6UpnDJuwyc/b5sX0Eh7Rt2O3CHXRAkinzzKAfrnG32oV6Vt68Bt08vG7J44Wfgi8VSz9Qxr5Wd84/vqp6bV6WThmzqSjetlxQy+ESW7eMVBvSeU3Eql5Bu0siN4nypN1hxvtTryXxD+heQhp6a86QNKPsXkMLCXz+VeorZh4X7NwEXj1b9GilU5QJEvdSz9hZpQQsU5QEH3pJ6NnafXHZc5gmpzgoBpN4xVHFH5htV9wjMvQS06S+1NQCwmIH/vSK9Zj2mSP8fgBQ4qU+VDgWuYniwh3/Z/uUJgtQGqe5UOukDAvsPCewX77MUSz3XBQXg167s/qPbgEPvSD8DrHMf+rcD/KOknsT8HeCSmscyn0RNzSNA6nZNVBmFSuo1oXDzPwCpdjQG6dPuG1fkroSIqGFMIRV71Bflls2BaPv3d+n+rNOAV3jZvqIIvDseMPgDg58pGx5YYmavrtpyGOabI73x1ntLj2X+Bvy6Q/o7NuaBsmPeHlv330F5mWW39b7StBoegY77xDwoBTw6L7tgsPS2quLikxUYA4HOYyref+fcqo8RLVJvJ3O+42a/cIXeB7g1oWKb8mkt/Vs+gLQOYbUUS6Fpod3Kn/a9bguuAT9tkQIU+7Bw3xuOQbk9lc4xPLSGieF/KwshLcXA4fek+zuNLAu9rp2TnqstgDRIvXWbS9AiWhzD0JOpUrCcewnIKw0DC69Xf46cv8pu632AtrFS+y0xl70OfRfy50NzYv9/rlABYzdKH0bYf88X5Uo9kQtzgHP7pM1KayoNDu0WUfEMaT7tmuqNYSG5B72P9MvLYq55X3IzgjRcRsWhVVQHnqFAfnb1Q7OIiJyRxlhx6JooSj0Nr2ZI881ZWXsg3rgqhUpW/10rTajvGykFOrYhzRGuvTqzKDp+/cd30u8K+x6AhTll8/9Zb9v/Lum3CGg/RLp9IxP45SPpzbd9WKgsfRMvKB0DPZ2p9F+vcoGfyTHkDewA3P9uxfo7DG+Ul6FOBEVpeGaoeh+PAKD7gxXv7/N45ftbisuFjzfKbtuH4yqdNM8dyoUaHgFSL1r7Y629r4oLpC0/y/EYY3DZ7aI8YN/r0u1OI8vu379Jmr/RnnX4dek8jkq1Ab0KFFBe8ZZek+BbHAPYYzukECfyrrLvpfwsafE1awhZVY+8oryyHoAao3RuQGqD26ZLAXT8Z2XHn9kLnPyq4nk0xip6BAZIoy/sn9uA/6t4PIPC5q/8hwN3PiZNLZCVUTb/YeYJ6YOkwhxpSoA/fyzbX2OUphK4VVrXQvrZKLJnrpNhWEjuQRCkT7urGlpB7ssrjBO5U90pVVLvj5w/5a6EiKjpCUJpIFCuJ5rBDxifAuRccAwArmZIAcafWcCfB+xPJPVoKz8foqlF2SIFzYXDMF/7Yb2lQ3vbD5F60wBAxrfAnlXS3H1DVpWdY/eK2s9xqyxdlM8+uPKOkHq8eZbr/TnyhdKeaR7svVMZhUr6266mv+8Mvo5zK1r1me/4tShKYVyF8NHuX6+wsv0FhTTEv6TIMbhTG6QODOZ8KXAEKgy/VgAIAABbsxHLwkJLMfBtonQ7/KOysHB/EnDs47LrKNWOC8gA0qIhRXZDuyP7loWFWmPZnHR5mdLfNwDQqrcUgjqEgoGAxqP615Vck0oLBHSQNquSIunnfaZdgHiltCe6/e+E6xeArQ8BgZ2AYav5c8tJMCwk92Hwlz5Ns5+Xgdybwb9syBRRXXkESH9UlxTKXQkRkTwUSsC7lbTZ+/vzQNYfjnMhXv0dKMiWPmTJ+RM4/W3Z/kqNNKdZt0nS1yVmKZQz+DXum8obV6RFXlR6IKSLdJ8oAl8/I9VWYBcMWsOcqvi3KwsLlSrpzXH5BfVCu0tvpm09/irp9WftCVjZMF9TCHDr1Ir3ewTU+alTAwiC9P+j0pYNEa+O1hPot7Di/X3mAygNIkWLtEJ0uXkciwvycPC3c4gJ00NVUuDYU89SIi3iYc4vNy+kCCjUZSOoSszSZj/82r42YyDgGWT3/BTAqJek8NTgV3Z/m/7SRlQVpUYaehwQVXZfiVmassK+t3nmCSmsLrjm+DN9x2PS94J/lLT4ln97KXhnD8RmgWEhuQ+lSvo0j/OMEQBoPB0/BSaqK0EAvFpIb4CJiKiM2iD1srOukmt146o0jM0+QMw6LQVzGrteYJkngI9mSdOEjEsuuz/rD6lXU1GetGhHQSW9/sr3BOy7sGxhlz9/lHr7tegO3L1Guk8QgHP7Kw9WBIVdsOflGO7ZB6TBXYFxKYDey/H4wcvr+QKSyxMUUs8/6zzIpcQSC/7KPIduUWGAslxgotJKc4OWd9fj0lZiLu21WG4ItmiR5qc0BlQ93Ns+7CFqCKW67IMUq4je0lyI1gWNAOmDlAs/Sz1azx8uu19tkI73a1caIEZJ79maW+9zN8CwkNyLRwDDQpKG+vhEsAs8NZz1TWRlbzKJiMiRwVfaWvQou0+0SPNK2w8ZvX5BClMM5Xr/f/q4NI9fXdjPLecRAPi1deytBQB/e0R6I6r1suv9Z6r9MF+NAdCUhoclHMFCMlGqS4d+cqVgamYUKulnb/n7xrwmfTh0+XjpEOZTUsB9/rBjgKjSlS2i0mk04B0OanoMC8m9qPV8Y+/uBIU0R5KSP/6okZhaAJevAxBr3JWIiMoRFBV7+rcdCETc6bjyqjm/bDo/jUfphzVeNQ/xtV/QIjRG6t1SXtSwRn9aRERUDUEB+LWRNuvPYEsxkH2mNEC0zoF4Uup9fuFnaWs3uOwcp/8rzYvbqhcQdqs8z8OF8d0yuR8Pf4aF7sy7Vbl5XogaSK2Tfq7kXZa7EiIi12GdI85KrQcm/Ufqicj5rIiIXI9CVbYAVvuh0n2WEuDa2dLw8LjU6cPqzF7g10+l3w/WsLAgG9ifLA2t928P+LSqeoVwqhZfNXI/Oi+pK3NNE1eT6/EMqd3E1ER15RkiDXWzFMtdCRGRa2NQSETkPhRKafoonwig/WDHxyL7SHMcht9edt/l48Av28u+Vmqk3ov+7cs230gGiLXAV4jck0eA9AkFuQ+dN+AZLHcV5KoUSikw5M8VIiIiIqKmF95T2ux5BABdxpf2RPwNMOcBl45Jm5VSDfiWBojWRVT82nI++3IYFpJ70vsC18+zF5C7UBscVy0kagoGPyAvU1qFkIiIiIiIbi7f1sDfZkq3rQtoWRdQsW5FecDlX6XtGKSRhw9uLztHQQ6gNcpRfbPCsJDck0IhvbHPvSh3JdTUFCrAJ1L6PydqSoIAeLWQJmImIiIiIiL5WBfQ8gqTFs4CygJE6yIqV05Ii2XZ9yq8cQXwCpWn5maEYSG5L4M/kHsJXMHUlQlSUKjSyF0IuQutp/TpZME1uSshIiIiIiJ79gFimwEVHy8pAkyBN7+uZohdbch9qTRc7MLVeYWzCzndfKYWADjnCRERERGRU1FqpA/+iWEhuTmPALkroKbiEQB4+MldBbkjlRYw8hNJIiIiIiJyTgwLyb1pPAC1h9xVUGPTeJb27iKSiTEYUKjlroKIiIiIiKjOGBYSGdm70KUotYBPhOMktUQ3m0IBmDgxMhEREREROR+GhUQ6b2luAnJ+ghLwjQSUXLuJmgGDL6A2yF0FERERERFRnTAsJBIEzl3oKnxaAWq93FUQlfEKk7sCIiIiIiKiOmFYSAQABj9pGXVyXp6hXLmKmh+NB6D3kbsKIiIiIiKiWmM6QgQACqUUGJJz0vsAnkFyV0FUOc9QfhhBREREREROg+9eiKw4FNk5qQ2AV0u5qyCqmkoDGBlmExERERGRc2BYSGSl0gJak9xVUF0o1IBPpLTyLFFz5hHIhZSIiIiIiMgp8B02kT1joNwVUK0J0srHKgYw5AQUCsAUKncVRERERERENWJYSGRP6wmouJquU/BuKS0eQeQs9D6Axih3FURERERERNVSyV0AUbNjDASy/5C7CvchKABBCShU0kIzCmXp16X32W7bf60ClPzxRU7I1ALIPC53FURERERERFXiu22i8vQ+QM5fgMUsdyXOoy6Bn8N+KkAQ5K6e6ObRGKSV129ckbsSIiIiIiKiSjEsJCpPEAAPf+D6ebkrubnKB36Colyox8CPqFF4hgL52YBYInclREREREREFTAsJKqMwR+4fgGAKHcldWML/KwhHgM/omZHqQI8g4GcP+WuhIiIiIiIqAKGhUSVUaqk4cj5V2/+tasL/Cod6mv/GNcsInIKHgHSUOTiArkrISIiIiIicsCwkKgqxsAGhIVCuV57pYGfBQDOScMQNdoq5vZj4Efk8gQBMIUCV3+XuxIiIiIiIiIHDAuJqqLWAzovoLjQLvArtyJvlYt5VBH4mc0ADklzIqrVN/PZEFFzo/MCtCagMEfuSoiIiIiIiGwYFhJVx7e13BUQkSsztQAuX4fTzY9KREREREQui+MdiYiI5KLWST2NiYiIiIiImgmGhURERHLyDJGmMCAiIiIiImoGGBYSERHJSaGUAkMiIiIiIqJmgGEhERGR3Ax+gEovdxVEREREREQMC4mIiGQnCIBXC7mrICIiIiIiYlhIRETULGg9AZ2X3FUQEREREZGbY1hIRETUXJhaABDkroKIiIiIiNwYw0IiIqLmQqUFjIFyV0FERERERG6MYSEREVFzYgwGFGq5qyAiIiIiIjfFsJCIiKg5USgAU6jcVRARERERkZtiWEhERNTcGHwBtYfcVRARERERkRtiWEhERNQcebWQuwIiIiIiInJDDAuJiIiaI40HoPeRuwoiIiIiInIzDAuJiIiaK1MLQOCvaiIiIiIiunn4DoSIiKi5UqoBY5DcVRARERERkRthWEhERNSceQQCSo3cVRARERERkZtgWEhERNScKRSAKVTuKoiIiIiIXJ8gyF1Bs8CwkIiIqLnT+wAao9xVEBERERG5Ls9QaZFBYlhIRETkFEwt5K6AiIiIiMgFCYBPBODJucKtGBYSERE5A40BMPjJXQURERERketQqAC/ttJIHrJhWEhEROQsPEMBQSl3FUREREREzk+pBfzbA1pO91Mew0IiIiJnoVQBnsFyV0FERERE5Nw0RikoVGnlrqRZYlhIRETkTDwCAJVO7iqIiIiIiJyT3kcaeqxUyV1Js8WwkIiIyJkIAmAKlbsKIiIiIiLnYwySFjMRBLkradYYFhIRETkbnRegNcldBRERERGRkxAAr5b80L2WGBYSERE5I1MLAPxElIiIiIioWoIS8G0NePjJXYnTYFhIRETkjNQ6wMNf7iqIiIiIiJovpQbwbwfoOCqnLhgWEhEROSvPEEDBiZmJiIiIiCpQG6QVj9V6uStxOgwLiYiInJVCKQWGRERERERURmsC/NoBSrXclTglhoVERETOzOAHqPhpKRERERERAMAjQJqjUMHIq774yhERETkzQQC8WshdBRERERGR/EwtAK8w6W9kqjeGhURERM5O6wnovOSugoiIiIhIHoIC8IkEjIFyV+ISGBYSERG5AlMLAPwElYiIiIjcjEIN+LUF9N5yV+IyGBYSERG5ApWWn6QSERERkXtR6aQVjzUeclfiUlRyF0BERESNxBgM3LgKWMxyV0IVCNJqfAq19K/1NkTp/6ykUO4CiYiIiJyLxhPwjQQUSrkrcTkMC4mIiFyFQgGYQoHsP+SuxH0IirIAUKFyDAQdvq7mTy7PYKAgB8i7DBTm3LzaiYiIiJyV3hfwbsmFTJoIw0IiIiJXYvAF8jIBc57clTi3GkNATentRvokW2eStuJC4MYVabMUN865iYiIiFyJZ4j0YSs1GYaFRERErsarBZB5Qu4qmqfqQkCHQFCm4SwqrdQ71BgMFGQz+CUiIiKyEaTehAZfuQtxeQwLiYiIXI3GA9D7APlZcldy81QIATVV9Ap0kjltFArpD2GDL1CUJ4WGBdmAaJG7MiIiIqKbT1BK8xNqPeWuxC0wLCQiInJFphZAwTXnD5cEhRT8Wef9q3JosJOEgPWh8ZC2khalQ5QzgZIiuasiIiIiujmUWsC3NaDWyV2J22BYSERE5IqUasAYBFw/L3cllROUjot/VBoCaqQediRRqgDPIGkruCb1NuSCKEREROTK1B5SUFjdYnHU6PhqExERuSqPQKknWknBzbtmtSGgpuwxhoANo/OStuJCKTS8cQUQS+SuioiIiKjx6LwB71b8u1EGDAuJiIhclUIhLZaR+XvDz1VpCKgpFwgyBLzpVFppQRvPEGmOyhuZgPmG3FURERERNYxHoPQ3DsmCYSEREZEr0/sAamPVj1tDQKWm3BBghoBORaEAPPykrSgPyLsM5GcDEOWujIiIiKgOBMArDPDwl7sQt8awkIiIyNWZQgH8CniGAlodQ0BXZ10QxVTMBVGIiIjIeQgKwCdCmmqFZMWwkIiIyNVZV47z8AfUanlroZvHuiCKMVBaEOXGFS6IQkRERM2TQi0tZKIxyF0JgWEhERERkWsTBEDvLW3mAqmn4Y2rXBCFiIiImgeVXgoKVRq5K6FSDAuJiIiI3IVaJ80D5BkqLYiSdxkozpe7KiIiInJXWpM09FihlLsSsuMyYaHFYkFRUdPPx2M2m6FSqVBQUICSEn4iT3VTVftRq9VQKvnDkYiIbhL7BVEKc6XehlwQhYiIiG4mgx/gFS6NgqBmxSXCwqKiImRkZMBisTT5tURRRHBwMM6ePQuBDZrqqLr24+3tjeDgYLYrIiK6ubRGaTOZSxdEucIFUYiIiKhpeYZKcytTs+T0YaEoijh//jyUSiXCw8OhaOJVHS0WC3Jzc2E0Gpv8WuR6Kms/oijixo0buHTpEgAgJCREzhKJiMhdKdWAZzBgDJIWRMnLBIquy10VERERuRJBAXi3BPQ+cldC1XD6sLC4uBg3btxAaGgoDIamXzXHOtxZp9MxLKQ6q6r96PV6AMClS5cQGBjIIclERCQfLohCRERETUGhAnwipREN1KzVK+166aWXEBERAZ1Oh549e+KHH36oct/XX38dd911F3x8fODj44PY2Nhq968r67xvGg1XzSHnZg27zWazzJUQERGVsi6IEnSLNKeQSi93RUREROSMVDrAvz2DQidR57Dwvffew7x587BkyRIcOHAAXbt2xZAhQ2xDKMtLS0vDhAkTsHv3buzduxfh4eEYPHgw/vzzzwYXb4/zvJGzYxsmIqJmS6EAPPyBwA6AX1tA5w2Av7eIiIioFjRGwK8doNLKXQnVUp3DwjVr1mDatGmIj49Hp06d8Oqrr8JgMGDTpk2V7v/OO+9g5syZ6NatGzp06ICNGzfCYrEgNTW1wcUTERER0U2m9QR8I4GgzoBnCKBQy10RERERNVd6H+mDRqXTz4LnVur0v1VUVIQff/wRixYtst2nUCgQGxuLvXv31uocN27cgNlshq+vb5X7FBYWorCw0PZ1Tk4OAGl4ZvkhmmazGaIowmKx3LTVkK3/3ozrkWuprv1YLBaIogiz2cw5C6kC688+DlOn+mD7oSaj8wO0vkDhNSDvKmDOlbuiRmEusTj8S1RbbDvUEGw/VF/Ntu14BEkLpxUXy10Jlart+4E6hYWZmZkoKSlBUJDj8tZBQUH49ddfa3WOhQsXIjQ0FLGxsVXu89xzz2HZsmUV7v/yyy8rLGKiUqkQHByM3NxcFBUV1aqGxnD9OlcHdDY+Pj54++23cffddzfqef/+978jOjoazz33XK2Pqaz9FBUVIT8/H9988w2K+cOUqrBr1y65SyAnxvZDVDe7Dv8ldwnkpNh2qCHYfqi+ml/bOSd3AVTOjRs3arXfTe0HunLlSmzZsgVpaWnQ6XRV7rdo0SLMmzfP9nVOTo5trkOTyeSwb0FBAc6ePQuj0VjtORuLKIq4fv06PD09GzTHXHx8PN58802sWLECCxcutN2/fft2jB071rZwS1paGgYOHAhAmtPO09MTrVu3RmxsLObOnYuQkBAAQNeuXdGrVy+88sorFa711ltvYfr06Th79iyOHDmCgQMH4sqVK/D29q53/ZcvX8aSJUuwc+dOXLx4ET4+PujSpQueeuop9O7du97nbQzLli3DRx99hAMHDlR4TK/XV2hDDaVSqaDRaGp13uraT0FBAfR6Pfr06XNT2jI5F7PZjF27dmHQoEFQqznkj+qG7YduOksJkJ8F3LgClBTWvH8zYy6xYNfhvzCoayjUynqtB0huim2HGoLth+qrWbUdQQn4tJLmKaRmxzpytyZ1Cgv9/f2hVCpx8eJFh/svXryI4ODgao9NTEzEypUr8dVXX6FLly7V7qvVaqHVVpz4Uq1WV3iTU1JSAkEQoFAooFA0/TeFdeio9Zr1JQgCdDodVq9ejRkzZsDHxwcAbOcs/+/x48dhMpmQk5ODAwcOYPXq1di0aRPS0tIQHR2NhIQELF26FOvWrYNe77hSYUpKCkaOHInAwECH8zak/nHjxqGoqAgpKSlo3bo1Ll68iNTUVGRlZd2U/4fqWEO4yupoqnZS2/ZQXftRKBQQBKHSdk5kxfZBDcH2QzePGtCGAN4hQOF1IO8yUJADQJS7sDpRKxXyv+kip8S2Qw3B9kP1JXvbUWoA39aAWl/zviSL2r4XqFMr0mg06NGjh8PiJNbFSu64444qj1u9ejWeeeYZfP7557j11lvrcsl6u1FUXOet2G58f3GJBTeKilFgLqlw3vyikgrH1kdsbCyCg4NrNXw1MDAQwcHBaN++Pe6//36kp6cjICAAjzzyCADggQceQH5+Pj788EOH4zIyMpCWloaEhIR61ViZ7OxsfPvtt1i1ahX69++PVq1a4fbbb8eiRYswcuRI236CIOC1117D3//+dxgMBnTs2BF79+7FyZMn0a9fP3h4eKBXr144deqUw/lfeeUVtGnTBhqNBlFRUXjrrbccHj9z5gxGjRoFo9EIk8mE8ePH2wLs5ORkLFu2DIcPH4YgCBAEAcnJybZjMzMzMWbMGBgMBrRr1w4ff/yxw7mPHDmCYcOGwWg0IigoCA8++CAyMzNtj+fl5WHy5MkwGo0ICQnB888/31gvKxERkWvSekpvHAI7AcZgLohCRETkitQGwL89g0IXUedhyPPmzcOUKVNw66234vbbb8e6deuQl5eH+Ph4AMDkyZPRokULWwC2atUqPP3003j33XcRERGBCxcuAACMRiOMxqbrltrp6S/qfMxLE7vj7i7SsN4vjl7ErHcPoGekL957uCwI7bM6DVdvVJwQ8vTKus+Dp1QqsWLFCkycOBGPPvoowsLCan2sXq/HjBkz8Nhjj+HSpUsIDAzEqFGjsGnTJjzwwAO2/ZKTkxEWFobBgwfXub6qWP/vtm/fjr/97W+V9gK1euaZZ7BmzRqsWbMGCxcuxMSJE9G6dWssWrQILVu2xNSpUzF79mx89tlnAIBt27Zhzpw5WLduHWJjY7Fjxw7Ex8cjLCwM/fv3h8VisQWFe/bsQXFxMWbNmoX77rsPaWlpuO+++3DkyBF8/vnn+OqrrwAAXl5etnqWLVuG1atX41//+hdefPFFTJo0CX/88Qd8fX2RnZ2NAQMG4KGHHsLatWuRn5+PhQsXYvz48fj6668BAAsWLMCePXvw0UcfITAwEIsXL8aBAwfQrVu3Rnt9iYiIXJJKA5hCAM/gsiHKRa6xIAoREZFb03kB3hGAzKMMqfHU+X/yvvvuQ2JiIp5++ml069YNhw4dwueff25b9OTMmTM4f/68bf9XXnkFRUVFuPfeexESEmLbEhMTG+9ZOLExY8agW7duWLJkSZ2P7dChAwDg9OnTAICEhASkpaUhIyMDgDQ/XkpKCqZMmdKoQ29VKhWSk5ORkpICb29v9O7dG4sXL8ZPP/1UYd/4+HiMHz8e7du3x8KFC3H69GlMmjQJQ4YMQceOHTFnzhykpaXZ9k9MTERcXBxmzpyJ9u3bY968ebjnnnts7SU1NRU///wz3n33XfTo0QM9e/bEm2++iT179mDfvn3Q6/UwGo22hW+Cg4MdhmXHxcVhwoQJaNu2LVasWIHc3Fz88MMPAIANGzYgJiYGK1asQIcOHRATE4NNmzZh9+7dOHHiBHJzc/HGG28gMTERAwcORHR0NFJSUrgYCRERUV0IAmDwBfzbAQEdAIMfIPDNBRERkVPyCJBGEDAodCn1WuBk9uzZmD17dqWP2Qc/QFmQdbP9snxInY/R2I3tH9I5CL8sHwJFuUUovnmiH67nXIenybPRArhVq1ZhwIABmD9/fp2OE0Vp3h/rHH2DBg1CWFgYkpKSsHz5cqSmpuLMmTO2Xp+1MWPGDLz99tu2r3NzK//Ef+zYsbj77rvx7bff4n//+x8+++wzrF69Ghs3bkRcXJxtP/v5Ka2BcnR0tMN9BQUFyMnJgclkwrFjxzB9+nSHa/Xu3RsvvPACAODYsWMIDw9HeHi47fFOnTrB29sbx44dw2233Vbt87Ovx8PDAyaTCZcuXQIAHD58GLt37660x+upU6eQn5+PoqIi9OzZ03a/r68voqKiqr0mERERVUGtB7xbAqYWwI2rwI1MoLhA7qqIiIioNkxhgDFA7iqoCbhs9GvQqOq8qezCQpVSAYNGBZ1aWeG8eo2ywrEN0adPHwwZMgSLFi2q03HHjh0DAERERACQFsiIi4tDSkoKLBYLkpKS0L9/f7Ru3brW51y+fDkOHTpk26qj0+kwaNAgPPXUU/juu+8QFxdXoYek/eSZ1lCzsvusC380tfKTeQqCYLt2bm4uRowY4fD8Dx06hN9++w19+vS5KfURERG5JYVSerMR2BHwbSMNZ4JQ42FEREQkA0EB+EQyKHRhLhsWOpuVK1fik08+wd69e2u1f35+Pv7973+jT58+CAgo+waNj4/H2bNnsXXrVmzbtq3OC5sEBgaibdu2tq0uOnXqhLy8vDodU17Hjh2Rnp7ucF96ejo6depke/zs2bM4e/as7fFffvkF2dnZtn00Gg1KShwXpqmN7t274+jRo4iIiHB4Ddq2bQsPDw+0adMGarUa33//ve2YrKwsnDhxoj5PlYiIiCqjM9ktiBIEKBr2oSwRERE1IoUa8GsH6L3lroSaEP/6aiaio6MxadIkrF+/vtLHL126hIKCAly/fh0//vgjVq9ejczMTGzdutVhv8jISAwYMADTp0+HVqvFPffc0+i1XrlyBePGjcPUqVPRpUsXeHp6Yv/+/Vi9ejVGjRrVoHMvWLAA48ePR0xMDGJjY/HJJ59g69attsVKYmNjba/VunXrUFxcjJkzZ6Jv3762lbYjIiKQkZGBQ4cOISwsDJ6entUuwmI1a9YsvP7665gwYQKeeOIJ+Pr64uTJk9iyZQs2btwIo9GIhIQELFiwAH5+fggMDMSTTz7ZqPNBEhERUSmVBjCFAp4h0oIoeZmAuWEfShIREVEDqPTSB3oqjdyVUBNjytGMLF++vMrhuFFRUQgNDUWPHj2wcuVKxMbG4siRI7bedPYSEhKQlZWFiRMnQqfTNXqdRqMRPXv2xNq1a9GnTx/ccssteOqppzBt2jRs2LChQecePXo0XnjhBSQmJqJz58547bXXkJSUhH79+gGQhg1/9NFH8PHxQZ8+fRAbG4vWrVvjvffes51j7NixGDp0KPr374+AgABs3ry5VtcODQ1Feno6SkpKMHjwYERHR2Pu3Lnw9va2BYL/+te/cNddd2HEiBGIjY3FnXfeiR49ejToORMREVE1rAuiBLQH/KO4IAoREZEcNJ7S4mQMCt2CIFpXyWjGcnJy4OXlhWvXrsFkMjk8VlBQgIyMDERGRjZJMFaexWKxLcbBHmVUV9W1n5vdlsm5mM1m7Ny5E8OHD68w9yZRTdh+yOWUFAP5V6XehiWFjX56c4kFOw+cw/DuYVAr+fce1R7bDjUE2w/VV5O3Hb2vtCCZwPmEnV11+Zo9DkMmIiIiIueiVAHGQGkruCaFhoU5cldFRETkejxDAM9guaugm4xhIRERERE5L52XtBUXAjeuSJulWO6qiIiInJwg9SY0+MpdCMmAYSEREREROT+VVloQxRgMFGRzQRQiIqL6UqgAn0hAa5S7EpIJw0IiIiIich0KhdQLwuALFOVJoWFBNiBWvogcERER2VFqpRWP1ZxH350xLCQiIiIi16TxkLaSFqVDlDOBkiK5qyIiImqe1B5SUKhkVOTu2AKIiIiIyLUpVYBnkLRxQRQiIqKKdN6Adyuphz65PYaFREREROQ+7BdEycuUehyKJXJXRUREJB+PQMCrhdxVUDPCsJCIiIiI3I9KK70x8gwB8rOkIcrmG3JXRUREdBMJgFcY4OEvdyHUzDAsJCIiIiL3pVAAHn7SVpQH5F0Gcq/KXRUREVHTEpSATwSgM8ldCTVDLhsW/nzuWpOc1yJakJeXB4/rIhRC2Vj+6DCvJrke3XwRERGYO3cu5s6dK3cpREREdDNZF0TRBwE4C3iGAiiWFkUpLgRKCuWukIiIqOEUasCvDaDWy10JNVOcuVImcXFxEAQBK1eudLh/+/btEATB9nVaWhoEQYAgCFAoFPDy8kJMTAyeeOIJnD9/3rZfdHQ0ZsyYUem13nrrLWi1WmRmZtrOl52d3aD6+/XrV2mYlpycDG9v7wadu7EIgoDt27fX+bh9+/Zh+vTptd6/sV5TIiIiaiasq0B6+APe4dIbqqBOQEg3IKCjtFKkqQVg8Ae0JkCpkbVcIiKiWlMbgIAoBoVULYaFMtLpdFi1ahWysrJq3Pf48eP466+/sG/fPixcuBBfffUVbrnlFvz8888AgISEBGzZsgX5+fkVjk1KSsLIkSPh7+8e8xAUFRU16PiAgAAYDIZGqoaIiIhchiAAap20QIox0C5I7FwWJPpElgWJGk8GiURE1HxoTYBfO0CplrsSauYYFsooNjYWwcHBeO6552rcNzAwEMHBwWjfvj3uv/9+pKenIyAgAI888ggA4IEHHkB+fj4+/PBDh+MyMjKQlpaGhISEJnkONYmLi8Po0aORmJiIkJAQ+Pn5YdasWTCbzbZ9CgsLsXDhQoSHh0Or1aJt27Z44403bI8fOXIEw4YNg9FoRFBQEB588EFkZmbaHu/Xrx9mz56NuXPnwt/fH0OGDEFERAQAYMyYMRAEwfb1qVOnMGrUKAQFBcFoNOK2227DV1995VBzREQE1q1bZ/taEARs3LgRY8aMgcFgQLt27fDxxx8DAE6fPo3+/fsDAHx8fCAIAuLi4vDmm2/Cz88PhYWOw5XGjBmDhx9+uMGvKxERETUz1iBR710WJPq3lYLE4K5AQAe7INFPChIVfLNGREQ3icFf6hmvYAxENWMrkZFSqcSKFSvw4osv4ty5c3U6Vq/XY8aMGUhPT8elS5fg7++PUaNGYdOmTQ77JScnIywsDIMHD27M0utk9+7dOHXqFHbv3o2UlBQkJycjOTnZ9vjkyZOxefNmrF+/HseOHcNrr70Go9EIAMjOzsaAAQMQExOD/fv34/PPP8fFixcxfvx4h2ukpKRAo9EgPT0dr776Kvbt2wdA6lV5/vx529e5ubkYPnw4UlNTcfDgQQwdOhQjRozAmTNnqn0Oy5Ytw/jx4/HTTz9h+PDhmDRpEq5evYrw8HBbQHv8+HGcP38eL7zwAsaNG4eSkhJbqAgAly5dws6dO/HAAw80+DUlIiIiJ6JQSMO9bEFiSylIDL7FMUj0DC0NEo0MEomIqPGYWkgfYtlNeUZUHZdd4MRZjBkzBt26dcOSJUscetPVRocOHQBIvdsCAwORkJCAYcOGISMjA5GRkRBFESkpKZgyZQoUMn564OPjgw0bNkCpVKJDhw64++67kZqaimnTpuHEiRN4//33sWvXLsTGxgIAWrdubTt2w4YNiImJwYoVK2z3bdq0CeHh4Thx4gTat28PAGjXrh1Wr15d4dre3t4IDg62fd21a1d07drV9vUzzzyDbdu24eOPP8bs2bOrfA5xcXGYMGECAGDFihVYv349fvjhBwwdOhS+vr4ApN6f9vM1Tpw4EUlJSRg3bhwA4O2330bLli1x55131vq1IyIiIhenUAAKfeVzR1ks0qIqxQVAcZH0r3WxFYu54v5ERET2BIX0AZXeR+5KyMmwZ2EzsGrVKqSkpODYsWN1Ok4URQCwLYgyaNAghIWFISkpCQCQmpqKM2fOID4+vtbnnDFjBoxGo21rDJ07d4ZSqbR9HRISgkuXLgEADh06BKVSib59+1Z67OHDh7F7926Hmqwh6alTp2z79ejRo1a15ObmYv78+ejYsSO8vb1hNBpx7NixGnsWdunSxXbbw8MDJpPJ9hyqMm3aNHz55Zf4888/AUi9PKdMmeKwgA0RERFRlWw9En0AzyDApxXg3660R2IXwD8K8IkAPEMAvW9pj0T2BSAiIki/D/zaMiikeuFfE81Anz59MGTIECxatAhxcXG1Ps4aLlrn41MoFIiLi0NKSgqWLl2KpKQk9O/f36GnXk2WL1+O+fPn17ifyWTCtWvXKtyfnZ0NLy8vh/vUasdhNIIgwGKxAJCGU1cnNzcXI0aMwKpVqyo8FhISYrvt4eFRY80AMH/+fOzatQuJiYlo27Yt9Ho97r333hoXRanuOVQlJiYGXbt2xZtvvonBgwfj6NGj+OSTT2pVJxEREVG1FEpAYwBQyaJslhKp96F9T8TiQqmXoqX4ppdKREQ3mUonzU+o0spdCTkphoXNxMqVK9GtWzdERUXVav/8/Hz8+9//Rp8+fRAQEGC7Pz4+Hs8++yy2bt2Kbdu2YePGjXWqIzAwEIGBgTXuFxUVhS+//LLC/QcOHLANDa6N6OhoWCwW7NmzxzYM2V737t3x4YcfIiIiAipV3ZqrWq1GSUmJw33p6emIi4vDmDFjAEhh5OnTp+t03vI0GmmVw/LXAoCHHnoI69atw59//onY2FiEh4cjJyenQdcjIiIiqpY1SNRUFSQWlIaHRWVDnBkkEhG5Bo2xdCETZc37ElWBw5CbiejoaEyaNAnr16+v9PFLly7hwoUL+O2337Blyxb07t0bmZmZeOWVVxz2i4yMxIABAzB9+nRotVrcc889TVLvI488ghMnTuDRRx/FTz/9hOPHj2PNmjXYvHkzHn/88VqfJyIiAlOmTMHUqVOxfft22+rN77//PgBg1qxZuHr1KiZMmIB9+/bh1KlT+OKLLxAfH19pOFf+3Kmpqbhw4QKysrIASHMbbt26FYcOHcLhw4cxceLEGnsI1qRVq1YQBAE7duzA5cuXkZuba3ts4sSJOHfuHF5//XVMnTq1QdchIiIiajCFEtB4AAZfwDNYGsYc0B4IjgaCogH/9oB3K8AYLA1dU3sAAt9wEhE5BZ23NPSYQSE1kMv2LIwO86p5p3qwWCzIyRFgMpkafdGQ5cuX47333qv0saioKAiCAKPRiNatW2Pw4MGYN2+ew+IdVgkJCUhNTcXMmTOh0+katUar1q1b45tvvsGTTz6J2NhYFBUVoUOHDvjggw8wdOjQOp3rlVdeweLFizFz5kxcuXIFLVu2xOLFiwEAoaGhSE9Px8KFCzF48GAUFhaiVatWGDp0aI2v//PPP4958+bh9ddfR4sWLXD69GmsWbMGU6dORa9eveDv74+FCxc2uKdfixYtsGzZMvzjH/9AfHw8Jk+ebFvt2cvLC2PHjsWnn36K0aNHN+g6RERERE1KqZI2TSXTu5QUly62UlhxiLNY/Qe4RER0k3i35IrH1CgE0bpKRjOWk5MDLy8vXLt2DSaTyeGxgoIC2+q/TRWM2ZPCwpwmCQvJNQ0cOBCdO3fG+vXrq20/N7stk3Mxm83YuXMnhg8fXmEOTaKasP0Q1Q2/Z+qopNguPCw3xFls2AgOZ2MusWDngXMY3j0MaiXfK1DdsP1Q/QgwG0Oxc/de/t6iGlWXr9lz2Z6FRHLLyspCWloa0tLS8PLLL8tdDhEREVHTUKoApbHyx0rMjgus2N92syCRiKjRCUrANxJQsLMJNS6GhURNJCYmBllZWVi1alWtF64hIiIicilKtbRpKwkTS8yOC6zY32aQSERUPaUG8G0DqHWA2Sx3NeRiGBYSNZGGrrJMRERE5NJsQWIlj9kCxNLhzPa30exnUSIialpqD6lHoZJDjqlpMCwkIiIiIqLmRaWRtvJBosUCFOZIW8E1wFIsS3lERLLReQHeEQDXUKAmxLCQiIiIiIicg0IB6L2lDQAKc8uCw+ICOSsjImp6HgGAV5jcVZAbYFhIRERERETOSWuUNlOotHBKwTVpK8oDhysTkesQAFMLwBggdyHkJhgWEhERERGR81NpAWOgtJUUl/U4LMzhgilE5LwEBeATIQ0/JrpJGBYSEREREZFrUaoAg6+0iSJQeL2s16GFq4YSkZNQqAHf1oDGIHcl5GYYFhIRERERkesSBEBnkjaEA0U3yoLD4ny5qyMiqpxKLwWFKo3clZAb4vI55LQEQcD27dsb/bz9+vXD3LlzG/28RERERNQMaAyAKQQI7AAEdgZMYYDWBECQuzIiIonWBPi3Y1BIsmFYKJO4uDgIggBBEKBWqxEZGYknnngCBQXSKm7R0dGYMWNGpce+9dZb0Gq1yMzMRFpaGgRBQHZ2doPquXz5Mh555BG0bNkSWq0WwcHBGDJkCNLT0xt03sawdOlSdOvWTe4yiIiIiMjVqDTSggF+bYDgaGleML0PICjlroyI3JXBT+pRqODPIZIPhyHLaOjQoUhKSoLZbMaPP/6IKVOmQBAErFq1CgkJCVi6dCnWrl0LvV7vcFxSUhJGjhwJf3//Rqtl7NixKCoqQkpKClq3bo2LFy8iNTUVV65cabRrEBERERE1WwqlFBTqfaR5DotyS4cr5wAlhXJXR0TuwDMU8AySuwoiF+5ZWJRX962kuOz4kmLpPnN+xfOab1Q8th6sPfjCw8MxevRoxMbGYteuXQCABx54APn5+fjwww8djsnIyEBaWhoSEhLqdc3KZGdn49tvv8WqVavQv39/tGrVCrfffjsWLVqEkSNH2vYTBAGvvfYa/v73v8NgMKBjx47Yu3cvTp48iX79+sHDwwO9evXCqVOnHM7/yiuvoE2bNtBoNIiKisJbb73l8PiZM2cwatQoGI1GmEwmjB8/HhcvXgQAJCcnY9myZTh8+LCtJ2ZycrLt2MzMTIwZMwYGgwHt2rXDxx9/7HDuI0eOYNiwYTAajQgKCsKDDz6IzMxM2+N5eXmYPHkyjEYjQkJC8PzzzzfWy0pEREREzkoQAK0n4BUGBHUCAjoAniGA2kPuyojIJQmAdysGhdRsuG5YuCK07tuvn5Qd/+sn0n1v3+twWmF9V3i/1BGKlWGOxzbQkSNH8N1330GjkeYk8Pf3x6hRo7Bp0yaH/ZKTkxEWFobBgwc3+JpWRqMRRqMR27dvR2Fh9Z+aPvPMM5g8eTIOHTqEDh06YOLEiXj44YexaNEi7N+/H6IoYvbs2bb9t23bhjlz5uDxxx/HkSNH8PDDDyM+Ph67d+8GAFgsFowaNQpXr17Fnj17sGvXLvz++++47777AAD33XcfHn/8cXTu3Bnnz5/H+fPnbY8BwLJlyzB+/Hj89NNPGD58OCZNmoSrV68CkELQAQMGICYmBvv378fnn3+OixcvYvz48bbjFyxYgD179uCjjz7Cl19+ibS0NBw4cKDRXlsiIiIicgFqPeAZDAS0B4JuAbxaSnOKCa77doqIbhKFCvBrK63eTtRMcBiyjHbs2AGj0Yji4mIUFhZCoVBgw4YNtscTEhIwbNgwZGRkIDIyEqIoIiUlBVOmTIFC0Xh/mKhUKiQnJ2PatGl49dVX0b17d/Tt2xf3338/unTp4rBvfHy8LWxbuHAh7rjjDjz11FMYMmQIAGDOnDmIj4+37Z+YmIi4uDjMnDkTADBv3jz873//Q2JiIvr374/U1FT8/PPPyMjIQHh4OADgzTffROfOnbFv3z7cdtttMBqNUKlUCA4OrlB7XFwcJkyYAABYsWIF1q9fjx9++AFDhw7Fhg0bEBMTgxUrVtj237RpE8LDw3HixAmEhobijTfewNtvv42BAwcCAFJSUhAWFtZYLy0RERERuRqlGvDwkzaLBSjMkYYrF+YAJUVyV0dEzkSpleZMVWnlroTIgeuGhYv/qvsxSrtv0A4jpHOU+7RQfPQwrl2/DpOnZ4MDu/79++OVV15BXl4e1q5dC5VKhbFjx9oeHzRoEMLCwpCUlITly5cjNTUVZ86ccQjjajJjxgy8/fbbtq9zc3Mr3W/s2LG4++678e233+J///sfPvvsM6xevRobN25EXFycbT/78DAoSOoiHR0d7XBfQUEBcnJyYDKZcOzYMUyfPt3hWr1798YLL7wAADh27BjCw8NtQSEAdOrUCd7e3jh27Bhuu+22ap+ffT0eHh4wmUy4dOkSAODw4cPYvXs3jEZjheNOnTqF/Px8FBUVoWfPnrb7fX19ERUVVe01iYiIiIgAAAoFoPeWNgDIywZwrvR9hVm2sojICWiMgE8koHTdWIacl+u2Sk0D5xNRqir/ptV4AOoS6d8GhoUeHh5o27YtAKnHW9euXfHGG2/Y5iNUKBSIi4tDSkoKli5diqSkJPTv3x+tW7eu9TWWL1+O+fPn12pfnU6HQYMGYdCgQXjqqafw0EMPYcmSJQ5hoVqttt0WBKHK+ywWS61rbAj7a1uvb712bm4uRowYgVWrVlU4LiQkBCdPnrwpNRIRERGRm7C+BwmIAlBS1uuwKA+AKGdlRNRoBKlTkaCQ5jcVFHb3CWUdjuy/rrCPUlr1uBFHDBI1JtcNC52MQqHA4sWLMW/ePEycONG2AnJ8fDyeffZZbN26Fdu2bcPGjRvrdN7AwEAEBgbWq6ZOnTph+/bt9TrWqmPHjkhPT8eUKVNs96Wnp6NTp062x8+ePYuzZ8/aehf+8ssvyM7Otu2j0WhQUlJS52t3794dH374ISIiIqBSVWzqbdq0gVqtxvfff4+WLVsCALKysnDixAn07du3ztcjIiIiIrJR66TNGCgtnliYAxRkA4XXAfHmfLBO5HIqDebKh3dCNfuUD++EqvepLBQs7RxD5OoYFjYj48aNw4IFC/DSSy/ZegNGRkZiwIABmD59OrRaLe65555Gv+6VK1cwbtw4TJ06FV26dIGnpyf279+P1atXY9SoUQ0694IFCzB+/HjExMQgNjYWn3zyCbZu3YqvvvoKABAbG4vo6GhMmjQJ69atQ3FxMWbOnIm+ffvi1ltvBQBEREQgIyMDhw4dQlhYGDw9PaHV1jynw6xZs/D6669jwoQJeOKJJ+Dr64uTJ09iy5Yt2LhxI4xGIxISErBgwQL4+fkhMDAQTz75ZKPOB0lEREREBKVKWrzA4AuIYmlwWNrr0MLhyuQkKu0hV/5r+6Cuup535YK6Gs/LoI7oZmJY2IyoVCrMnj0bq1evxiOPPAIPD2kYQ0JCAlJTUzFz5kzodLpGv67RaETPnj2xdu1anDp1CmazGeHh4Zg2bRoWL17coHOPHj0aL7zwAhITEzFnzhxERkYiKSkJ/fr1AyANG/7oo4/w//7f/0OfPn2gUCgwdOhQvPjii7ZzjB07Flu3bkX//v2RnZ2NpKQkh6HRVQkNDUV6ejoWLlyIwYMHo7CwEK1atcLQoUNtgeC//vUv23BlT09PPP7447h27VqDnjMRERERUZUEAdB5SRvCpSHK1uCwOF/u6sgVCEpApQNUGkCpQeXhHWoR5pXvrUdE7kIQRbHZT56Rk5MDLy8vXLt2DSaTyeGxgoIC22rBTRGklWexWGyLd7AHGtVVde3nZrdlci5msxk7d+7E8OHDK8zVSVQTth+iuuH3DNVXg9tOcZEUGhZcA4pywXkO3Yu5xIKdB85hePcwqJU1vNcUlNIKuiqttKCO/W0umOF2+HuLaqu6fM0ef4oQERERERE1ByoNYAyQNkuJFBpahyyLdZ/Dm5ycNRBUakp7CtrdZiBIRE2IP2GIiIiIiIiaG4XScZ7DotyyXoclRXJXR41FUJSGfxpIb8/PAb5tAJ0HoGQPMSKSB8NCIiIiIiKi5kwQAK2ntHmFAeb80uAwBzDnyV0d1URQVBwqbLttFwiaSxe70TAoJCJ5MSwkIiIiIiJyJmq9tHkGAyXmsh6HhdfBeQ5lYgsErcOE7W8z+CMi58KwkIiIiIiIyFkp1YCHv7RZLKVzHJbOdWgplrs61yIoSucMtPYOtK44XBoMEhG5CIaFRERERERErkChAPTe0iaKQFFeWXBYXCB3dU5CqLioiC0cZCBIRO6BYSEREREREZGrEQRAa5Q2tADMBWXBYVGu3NXJrJJA0HabgSAREcNCIiIiIiIiV6fWSZtnEFBSXDpcOVua51C0yF1dExDKhgyrtGUrDluDQUGQu0AiomaLYSEREREREZE7UaoAg6+0WSxA0fWy1ZUtZrmrq4NygaDDKsMMBImI6st1w8K/DjbNeS0ilHl5QK4HoLD75RMa0zTXowaLiIjA3LlzMXfuXLlLISIiIiJqXhQKQOclbUDpPIeli6QU58tbGwCHQNB+qLC1pyADQSKiRqeQuwB3FRcXB0EQIAgC1Go1IiMj8cQTT6CgQJp4ODo6GjNmzKj02LfeegtarRaZmZlIS0uDIAjIzs5uUD39+vWrNExLTk6Gt7d3g87dWARBwPbt2+t83L59+zB9+vRa799YrykRERERkdPReACmECCwAxDYCTCFARpPAE0cyik1gNYEGPwBUwvAtzUQ0BEI6QoEdQL82gDe4YAxQAo2VVoGhURETcR1exY6gaFDhyIpKQlmsxk//vgjpkyZAkEQsGrVKiQkJGDp0qVYu3Yt9Hq9w3FJSUkYOXIk/P39Zar85ioqKoJGU/+JhgMCAhqxGiIiIiIiN6HSSuGcMQCwlJQOVb5WOs9hSd3Pp9Q4DhW2HzrM4I+IqNlgz0IZabVaBAcHIzw8HKNHj0ZsbCx27doFAHjggQeQn5+PDz/80OGYjIwMpKWlISEhQY6SERcXh9GjRyMxMREhISHw8/PDrFmzYDaXzW1SWFiIhQsXIjw8HFqtFm3btsUbb7xhe/zIkSMYNmwYjEYjgoKC8OCDDyIzM9P2eL9+/TB79mzMnTsX/v7+GDJkCCIiIgAAY8aMgSAItq9PnTqFUaNGISgoCEajEbfddhu++uorh5ojIiKwbt0629eCIGDjxo0YM2YMDAYD2rVrh48//hgAcPr0afTv3x8A4OPjA0EQEBcXhzfffBN+fn4oLCx0OPfo0aPx4IMPNvh1JSIiIiJq1hRKaY5D30ggOBrwbQN4BEgBoD2lRuqJaPCTegj6REo9BIO7AkGdAf+2pT0EA6Uegmodg0IiomaGYWEzceTIEXz33Xe2HnT+/v4YNWoUNm3a5LBfcnIywsLCMHjwYDnKBADs3r0bp06dwu7du5GSkoLk5GQkJyfbHp88eTI2b96M9evX49ixY3jttddgNBoBANnZ2RgwYABiYmKwf/9+fP7557h48SLGjx/vcI2UlBRoNBqkp6fj1Vdfxb59+wBIvSrPnz9v+zo3NxfDhw9HamoqDh48iKFDh2LEiBE4c+ZMtc9h2bJlGD9+PH766ScMHz4ckyZNwtWrVxEeHm4LaI8fP47z58/jhRdewLhx41BSUmILFQHg0qVL+PTTTzF16tQGv6ZERERERE5DEACdCfAKkwLAgA7S5hAItpQCQb23FAgq+NaTiMhZcBiyjHbs2AGj0Yji4mIUFhZCoVBgw4YNtscTEhIwbNgwZGRkIDIyEqIoIiUlBVOmTIFCxl+2Pj4+2LBhA5RKJTp06IC7774bqampmDZtGk6cOIH3338fu3btQmxsLACgdevWtmM3bNiAmJgYrFixwnbfpk2bEB4ejhMnTqB9+/YAgHbt2mH16tUVru3t7Y3g4GDb1127dkXXrl1tXz/zzDPYtm0bPv74Y8yePbvK5xAXF4cJEyYAAFasWIH169fjhx9+wNChQ+Hr6wsACAwMdJivceLEiUhKSsK4ceMAAG+//TZatmyJfv361falIyIiIiJyPWp9zfsQEZHT4Mc7Murfvz8OHTqE77//HlOmTEF8fDzGjh1re3zQoEEICwtDUlISACA1NRVnzpxBfHx8ra8xY8YMGI1G29YYOnfuDKVSafs6JCQEly5dAgAcOnQISqUSffv2rfTYw4cPY/fu3Q41dejQAYA0pNiqR48etaolNzcX8+fPR8eOHeHt7Q2j0Yhjx47V2LOwS5cuttseHh4wmUy251CVadOm4csvv8Sff/4JQOrlaV2ohoiIiIiIiIjIFbBnoYw8PDzQtm1bAFLvuq5du+KNN96wzUeoUCgQFxeHlJQULF26FElJSejfv79DT72aLF++HPPnz69xP5PJhGvXrlW4Pzs7G15eXg73qdVqh68FQYDFYgGACouxlJebm4sRI0Zg1apVFR4LCQmx3fbw8KixZgCYP38+du3ahcTERLRt2xZ6vR733nsvioqKqj2uuudQlZiYGHTt2hVvvvkmBg8ejKNHj+LTTz+tVZ1ERERERERERM6AYWEzoVAosHjxYsybNw8TJ060hW7x8fF49tlnsXXrVmzbtg0bN26s03kDAwMRGBhY435RUVH48ssvK9x/4MAB29Dg2oiOjobFYsGePXtsw5Dtde/eHR9++CEiIiKgUtWt+anVapSUOK66lp6ejri4OIwZMwaAFEaePn26TuctzzpvZPlrAcBDDz2EdevW4c8//0RsbCzCw8MbdC0iIiIiIiIiouaEw5CbkXHjxkGpVOKll16y3RcZGYkBAwZg+vTp0Gq1uOeee5rk2o888ghOnDiBRx99FD/99BOOHz+ONWvWYPPmzXj88cdrfZ6IiAhMmTIFU6dOxfbt222rN7///vsAgFmzZuHq1auYMGEC9u3bh1OnTuGLL75AfHx8peFc+XOnpqbiwoULyMrKAiDNbbh161YcOnQIhw8fxsSJE2vsIViTVq1aQRAE7NixA5cvX0Zubq7tsYkTJ+LcuXN4/fXXubAJEREREREREbkc1w0LQ2OaaOuGkqBoILSb4/2NQKVSYfbs2Vi9ejXy8vJs9yckJCArKwsTJ06ETqdrlGuV17p1a3zzzTf49ddfERsbi549e+L999/HBx98gKFDh9bpXK+88gruvfdezJw5Ex06dMC0adNszyc0NBTp6ekoKSnB4MGDER0djblz58Lb27vGRVuef/557Nq1C+Hh4YiJkV7zNWvWwMfHB7169cKIESMwZMgQdO/evX4vQqkWLVpg2bJl+Mc//oGgoCCHhVK8vLwwduxYGI1GjB49ukHXISIiIiIiIiJqbgRRFEW5i6hJTk4OvLy8cO3aNZhMJofHCgoKbKsFN1WQZs9isSAnJwcmk0nWFYlJPgMHDkTnzp2xfv36Oh9bXfu52W2ZnIvZbMbOnTsxfPjwCnNuEtWE7Yeobvg9Q/XFtkMNwfZD9cW2Q7VVXb5mj3MWEtVSVlYW0tLSkJaWhpdfflnucoiIiIiIiIiIGh3DQqJaiomJQVZWFlatWoWoqCi5yyEiIiIiIiIianQMC4lqqaGrLBMRERERERERNXecdI+IiIiIiIiIiIgAuFBY6ATrtBBVy2KxyF0CEREREREREbk5px+GrFarIQgCLl++jICAAAiC0KTXs1gsKCoqQkFBAVdDpjqrrP2IooiioiJcvnwZCoUCGo1G5iqJiIiIiIiIyF05fVioVCoRFhaGc+fO3ZQ55URRRH5+PvR6fZMHk+R6qms/BoMBLVu2ZAhNRERERERERLJx+rAQAIxGI9q1awez2dzk1zKbzfjmm2/Qp08fqNXqJr8euZaq2o9SqYRKpWIATURERERERESycomwEJDCFqVSeVOuU1xcDJ1Ox7CQ6ozth4iIiIiIiIiaM453JCIiIiIiIiIiIgAMC4mIiIiIiIiIiKgUw0IiIiIiIiIiIiIC4CRzFoqiCADIycmRuRJpgYobN24gJyeHc85RnbH9UH2x7VBDsP0Q1Q2/Z6i+2HaoIdh+qL7Ydqi2rLmaNWerilOEhdevXwcAhIeHy1wJERERERERERGR87p+/Tq8vLyqfFwQa4oTmwGLxYK//voLnp6eEARB1lpycnIQHh6Os2fPwmQyyVoLOR+2H6ovth1qCLYforrh9wzVF9sONQTbD9UX2w7VliiKuH79OkJDQ6FQVD0zoVP0LFQoFAgLC5O7DAcmk4nfhFRvbD9UX2w71BBsP0R1w+8Zqi+2HWoIth+qL7Ydqo3qehRacYETIiIiIiIiIiIiAsCwkIiIiIiIiIiIiEoxLKwjrVaLJUuWQKvVyl0KOSG2H6ovth1qCLYforrh9wzVF9sONQTbD9UX2w41NqdY4ISIiIiIiIiIiIiaHnsWEhEREREREREREQCGhURERERERERERFSKYSEREREREREREREBYFhIREREREREREREpRgWEhEREREREREREYBmHBY+99xzuO222+Dp6YnAwECMHj0ax48fd9inoKAAs2bNgp+fH4xGI8aOHYuLFy/aHj98+DAmTJiA8PBw6PV6dOzYES+88ILDOf773/+id+/e8PPzg16vR4cOHbB27doa6xNFEU8//TRCQkKg1+sRGxuL3377zWGff/7zn+jVqxcMBgO8vb1r/dx/+ukn3HXXXdDpdAgPD8fq1asdHj969CjGjh2LiIgICIKAdevW1frc7oLtp+r2AwDr1q1DVFQU9Ho9wsPD8dhjj6GgoKDW13Bl7tp2CgoKEBcXh+joaKhUKowePbrCPmlpaRAEocJ24cKFWl3DHbhr+0lLS8OoUaMQEhICDw8PdOvWDe+8847DPq+//jruuusu+Pj4wMfHB7Gxsfjhhx9qdX5yXc7+PXP69GkkJCQgMjISer0ebdq0wZIlS1BUVFTjudPS0tC9e3dotVq0bdsWycnJDo9/8803GDFiBEJDQyEIArZv317jOd0J207VbaekpARPPfWUw7mfeeYZiKJY47ndhbu2n/Pnz2PixIlo3749FAoF5s6dW2Gf5OTkCn/r6XS6Gmt2F+7adrZu3YpBgwYhICAAJpMJd9xxB7744os6vzbkRMRmasiQIWJSUpJ45MgR8dChQ+Lw4cPFli1birm5ubZ9ZsyYIYaHh4upqani/v37xb/97W9ir169bI+/8cYb4qOPPiqmpaWJp06dEt966y1Rr9eLL774om2fAwcOiO+++6545MgRMSMjQ3zrrbdEg8Egvvbaa9XWt3LlStHLy0vcvn27ePjwYXHkyJFiZGSkmJ+fb9vn6aefFtesWSPOmzdP9PLyqtXzvnbtmhgUFCROmjRJPHLkiLh582ZRr9c71PPDDz+I8+fPFzdv3iwGBweLa9eurdW53QnbT9Xt55133hG1Wq34zjvviBkZGeIXX3whhoSEiI899litruHq3LXt5ObmijNmzBD//e9/i0OGDBFHjRpVYZ/du3eLAMTjx4+L58+ft20lJSW1uoY7cNf2889//lP8v//7PzE9PV08efKkuG7dOlGhUIiffPKJbZ+JEyeKL730knjw4EHx2LFjYlxcnOjl5SWeO3euVtcg1+Ts3zOfffaZGBcXJ37xxRfiqVOnxI8++kgMDAwUH3/88WrP+/vvv4sGg0GcN2+e+Msvv4gvvviiqFQqxc8//9y2z86dO8Unn3xS3Lp1qwhA3LZtW11eWpfHtlN12/nnP/8p+vn5iTt27BAzMjLEDz74QDQajeILL7xQp9fYlblr+8nIyBAfffRRMSUlRezWrZs4Z86cCvskJSWJJpPJ4W+9Cxcu1OZldQvu2nbmzJkjrlq1Svzhhx/EEydOiIsWLRLVarV44MCBOr025DyabVhY3qVLl0QA4p49e0RRFMXs7GxRrVaLH3zwgW2fY8eOiQDEvXv3VnmemTNniv3796/2WmPGjBEfeOCBKh+3WCxicHCw+K9//ct2X3Z2tqjVasXNmzdX2D8pKanWb7hefvll0cfHRywsLLTdt3DhQjEqKqrS/Vu1asWwsBbYfsraz6xZs8QBAwY4HDdv3jyxd+/etbqGu3GXtmNvypQp1YaFWVlZdT6nu3LH9mM1fPhwMT4+vsrHi4uLRU9PTzElJaXe1yDX48zfM1arV68WIyMjq732E088IXbu3Nnhvvvuu08cMmRIpfszLKwZ205Z27n77rvFqVOnOuxzzz33iJMmTar23O7MXdqPvb59+1YZFjbk97+7cce2Y9WpUydx2bJlVT5e/rUh59JshyGXd+3aNQCAr68vAODHH3+E2WxGbGysbZ8OHTqgZcuW2Lt3b7XnsZ6jMgcPHsR3332Hvn37VrlPRkYGLly44HBtLy8v9OzZs9pr18bevXvRp08faDQa231DhgzB8ePHkZWV1aBzuzO2n7L206tXL/z444+24X+///47du7cieHDhzfo2q7KXdpOXXTr1g0hISEYNGgQ0tPTb9p1nZE7t5+aar5x4wbMZnO1+5D7cYXvmZquDUi/r+3PC0i/r2/mz3JXw7ZTdt5evXohNTUVJ06cACANefzvf/+LYcOGVXtud+Yu7ae2cnNz0apVK4SHh2PUqFE4evRoo5zXFblr27FYLLh+/Xq1x5V/bci5qOQuoDYsFgvmzp2L3r1745ZbbgEAXLhwARqNpsJ8SkFBQVXOn/Xdd9/hvffew6efflrhsbCwMFy+fBnFxcVYunQpHnrooSrrsZ4/KCio1teurQsXLiAyMrLCea2P+fj4NOj87ojtx7H9TJw4EZmZmbjzzjshiiKKi4sxY8YMLF68uEHXdkXu1HZqIyQkBK+++ipuvfVWFBYWYuPGjejXrx++//57dO/evcmv72zcuf28//772LdvH1577bUq91m4cCFCQ0MrvOkl9+UK3zMnT57Eiy++iMTExCrPaz13ZefNyclBfn4+9Hp9tceTI7Ydx7bzj3/8Azk5OejQoQOUSiVKSkrwz3/+E5MmTar23O7KndpPbURFRWHTpk3o0qULrl27hsTERPTq1QtHjx5FWFhYg8/vSty57SQmJiI3Nxfjx4+v9PHKXhtyLk7Rs3DWrFk4cuQItmzZUu9zHDlyBKNGjcKSJUswePDgCo9/++232L9/P1599VWsW7cOmzdvBgC88847MBqNtu3bb7+tdw3lde7c2XZeftLXdNh+HKWlpWHFihV4+eWXceDAAWzduhWffvopnnnmmUarzVWw7TiKiorCww8/jB49eqBXr17YtGkTevXqVavJlt2Ru7af3bt3Iz4+Hq+//jo6d+5c6TlWrlyJLVu2YNu2bZw0nWyc/Xvmzz//xNChQzFu3DhMmzbNdr/9eWfMmFHv50ZVY9tx9P777+Odd97Bu+++iwMHDiAlJQWJiYlISUmpc23ugO3H0R133IHJkyejW7du6Nu3L7Zu3YqAgIBqPwB0V+7adt59910sW7YM77//PgIDAys9d2O8NiSvZt+zcPbs2dixYwe++eYbh08ygoODUVRUhOzsbIfU/uLFiwgODnY4xy+//IKBAwdi+vTp+L//+79Kr2PtjRUdHY2LFy9i6dKlmDBhAkaOHImePXva9mvRogXOnz9vu1ZISIjDtbt161br57Zz506YzWYAsH2CHBwc7LBSkvW81seobth+Krafp556Cg8++KDtU6no6Gjk5eVh+vTpePLJJ6FQOMVnCE3O3dpOfd1+++3473//26BzuCJ3bT979uzBiBEjsHbtWkyePLnS4xMTE7Fy5Up89dVX6NKlS62vS67N2b9n/vrrL/Tv3x+9evXCv//9b4fHDh06ZLttMplsz6uy39cmk4m9CuuIbadi21mwYAH+8Y9/4P7777fV/Mcff+C5557DlClTKn1+7srd2k99qNVqxMTE4OTJk/U+hyty17azZcsWPPTQQ/jggw+qHB1S1WtDTkbuSROrYrFYxFmzZomhoaHiiRMnKjxunTj0P//5j+2+X3/9tcLEoUeOHBEDAwPFBQsW1Pray5YtE1u1alVtbcHBwWJiYqLtvmvXrjXqAhVFRUW2+xYtWsQFTuqI7afq9tO9e3fxiSeecDju3XffFfV6vVhcXFyr67gyd2079qpa4KQysbGx4pgxY+p8DVflzu1n9+7dooeHh7hhw4Yq91m1apVoMpmqneCb3IsrfM+cO3dObNeunXj//ffX+vfoE088Id5yyy0O902YMIELnNQB206Z8m3H19dXfPnllx32WbFihdiuXbtaXcMduGv7sVfVAiflFRcXi1FRUeJjjz1W52u4InduO++++66o0+nE7du3V3n96l4bci7NNix85JFHRC8vLzEtLc1h2fYbN27Y9pkxY4bYsmVL8euvvxb3798v3nHHHeIdd9xhe/znn38WAwICxAceeMDhHJcuXbLts2HDBvHjjz8WT5w4IZ44cULcuHGj6OnpKT755JPV1rdy5UrR29tb/Oijj8SffvpJHDVqlMOS5KIoin/88Yd48OBBcdmyZaLRaBQPHjwoHjx4ULx+/XqV583OzhaDgoLEBx98UDxy5Ii4ZcuWCkukFxYW2s4VEhIizp8/Xzx48KD422+/1ek1dmVsP1W3nyVLloienp7i5s2bxd9//1388ssvxTZt2ojjx4+v02vsqty17YiiKB49elQ8ePCgOGLECLFfv36246zWrl0rbt++Xfztt9/En3/+WZwzZ46oUCjEr776qrYvr8tz1/bz9ddfiwaDQVy0aJFDzVeuXHG4tkajEf/zn/847FNTuyTX5uzfM+fOnRPbtm0rDhw4UDx37pzD9avz+++/iwaDQVywYIF47Ngx8aWXXhKVSqX4+eef2/a5fv267fsPgLhmzRrx4MGD4h9//FGn19hVse1U3XamTJkitmjRQtyxY4eYkZEhbt26VfT396/wYbE7c9f2I4qi7edKjx49xIkTJ4oHDx4Ujx49ant82bJl4hdffCGeOnVK/PHHH8X7779f1Ol0Dvu4M3dtO++8846oUqnEl156yeGY7OzsOr025DyabVgIoNItKSnJtk9+fr44c+ZM0cfHRzQYDOKYMWMcGvmSJUsqPYd9Gr9+/Xqxc+fOosFgEE0mkxgTEyO+/PLLYklJSbX1WSwW8amnnhKDgoJErVYrDhw4UDx+/LjDPlOmTKn0+rt376723IcPHxbvvPNOUavVii1atBBXrlzp8HhGRkal5+3bt2+153UnbD9Vtx+z2SwuXbpUbNOmjajT6cTw8HBx5syZYlZWVrXndRfu3HZatWpV6XFWq1atsrUbX19fsV+/fuLXX39d84vqRty1/VR1jP3vpara15IlS2rz0pKLcvbvmaSkpCqfQ012794tduvWTdRoNGLr1q0dnrP18crOO2XKlBrP7Q7YdqpuOzk5OeKcOXPEli1bijqdTmzdurX45JNPioWFhTWe2124c/upqea5c+eKLVu2FDUajRgUFCQOHz5cPHDgQM0vqptw17bTt2/fGn8n1ea1IechiKIogoiIiIiIiIiIiNweVzIgIiIiIiIiIiIiAAwLiYiIiIiIiIiIqBTDQiIiIiIiIiIiIgLAsJCIiIiIiIiIiIhKMSwkIiIiIiIiIiIiAAwLiYiIiIiIiIiIqBTDQiIiIiIiIiIiIgLAsJCIiIiIiIiIiIhKMSwkIiIiIiIiIiIiAAwLiYiIiIiIiIiIqBTDQiIiIiIiIiIiIgIA/H+Y+n7Ip65TqAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -519,22 +499,20 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(16, 6))\n",
-    "ndvi_ax = ax.twinx()\n",
-    "ax.set_ylabel(\"VV & VH\")\n",
-    "ndvi_ax.set_ylabel(\"NDVI\")\n",
-    "ndvi_ax.set_ylim(-0.08, 0.92)\n",
-    "plots = []\n",
-    "for col in joined_df.columns.values:\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "std_col_mapping = {\n",
+    "    'NDVI - Uncertainty': 'NDVI - Smoothed',\n",
+    "    'RVI - Uncertainty': 'RVI - Smoothed'\n",
+    "}\n",
+    "for col in sorted(joined_df.columns.values):\n",
     "    values = joined_df[~joined_df[col].isna()]\n",
-    "    if col.lower() == 'ndvi':\n",
-    "        ndvi_ax.plot(values.index, values[col], 'go-', label=col)\n",
+    "    if 'Uncertainty' in col:\n",
+    "        plt.fill_between(values.index, values[std_col_mapping[col]] - values[col],\n",
+    "                         values[std_col_mapping[col]] + values[col], alpha=0.2, label=col)\n",
     "    else:\n",
-    "        ax.plot(values.index, values[col], 'o-', label=col)\n",
-    "        \n",
-    "handles, labels = ax.get_legend_handles_labels()\n",
-    "ndvi_handles, ndvi_labels = ndvi_ax.get_legend_handles_labels()\n",
-    "ax.legend(handles + ndvi_handles, labels + ndvi_labels)"
+    "        plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
+    "plt.grid(True)\n",
+    "plt.legend()"
    ]
   },
   {
diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 891ab09..24baa40 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -4,7 +4,7 @@
 from pathlib import Path
 from typing import Dict
 
-from openeo.metadata import Band, CollectionMetadata
+from openeo.metadata import CollectionMetadata
 from openeo.udf import XarrayDataCube, inspect
 
 
@@ -42,10 +42,10 @@ def write_gpy_cfg():
 
 
 def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
-    extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
-    inspect(data=metadata, message="MOGPR metadata")
-    for band in extra_bands:
-        metadata = metadata.append_band(band)
+    # extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
+    # inspect(data=metadata, message="MOGPR metadata")
+    # for band in extra_bands:
+    #     metadata = metadata.append_band(band)
     return metadata
 
 
diff --git a/src/fusets/openeo/services/mogpr.json b/src/fusets/openeo/services/mogpr.json
index a96aefd..5a14bb0 100644
--- a/src/fusets/openeo/services/mogpr.json
+++ b/src/fusets/openeo/services/mogpr.json
@@ -21,7 +21,7 @@
                                     "from_parameter": "data"
                                 },
                                 "runtime": "Python",
-                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.udf import XarrayDataCube\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv\", \"tmp/venv_static\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
+                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.metadata import CollectionMetadata\nfrom openeo.udf import XarrayDataCube, inspect\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv\", \"tmp/venv_static\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    # extra_bands = [Band(f\"{x}_STD\", None, None) for x in metadata.bands]\n    # inspect(data=metadata, message=\"MOGPR metadata\")\n    # for band in extra_bands:\n    #     metadata = metadata.append_band(band)\n    return metadata\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties,\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    inspect(data=result_dc, message=\"MOGPR result\")\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
                             },
                             "result": true
                         }
@@ -45,7 +45,7 @@
     },
     "id": "mogpr",
     "summary": "Integrates timeseries in data cube using multi-output gaussian process regression.",
-    "description": "# Multi output gaussian process regression\n\n## Description\n\nCompute an integrated timeseries based on multiple inputs.\nFor instance, combine Sentinel-2 NDVI with Sentinel-1 RVI into one integrated NDVI.\n\n## Usage\n\nUsage examples for the MOGPR process.\n\n### Python\n\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\nThe result of this batch job will consist of individual GeoTIFF files per date.\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\n\n```python\nimport openeo\n\n## Setup of parameters\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\n\n## Setup connection to openEO\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\nservice = 'mogpr'\nnamespace = 'u:fusets'\n\n## Creation of the base NDVI data cube upon which the mogpr is executed\ns2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n                                spatial_extent=spat_ext,\n                                temporal_extent=temp_ext,\n                                bands=[\"B04\", \"B08\", \"SCL\"])\ns2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\nbase_ndvi = s2.ndvi(red=\"B04\", nir=\"B08\", target_band='NDVI').band('NDVI')\n\n## Creation mogpr  data cube\nmogpr = connection.datacube_from_process(service,\n                                              namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n                                              data=base_ndvi)\n## Calculate the average time series value for the given area of interest\nmogpr = mogpr.aggregate_spatial(spat_ext, reducer='mean')\n\n## Execute the service through an openEO batch job\nmogpr_job = mogpr.execute_batch('./mogpr.json', out_format=\"json\",\n                                          title=f'FuseTS - MOGPR', job_options={\n        'udf-dependency-archives': [\n            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n        ]\n    })\n```\n\n## Limitations\n\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\n\n## Configuration & Resource Usage\n\nRun configurations for different ROI/TOI with memory requirements and estimated run durations.\n\n### Synchronous calls\n\nTODO: Replace with actual measurements!!!\n\n| Spatial extent | Run duration |\n|----------------|--------------|\n| 100 m x 100 m  | 1 minute     |\n| 500m x 500 m   | 1 minute     |\n| 1 km x 1 km    | 1 minute     |\n| 5 km x 5 km    | 2 minutes    |\n| 10 km x 10 km  | 3 minutes    |\n| 50 km x 50 km  | 9 minutes    |\n\nThe maximum duration of a synchronous run is 15 minutes.\nFor long running computations, you can use batch jobs.\n\n### Batch jobs\n\nTODO: Replace with actual measurements!!!\n\n| Spatial extent  | Temporal extent | Executor memory | Run duration |\n|-----------------|-----------------|-----------------|--------------|\n| 100 m x 100 m   | 1 month         | default         | 7 minutes    |\n| 500 m x 100 m   | 1 month         | default         | 7 minutes    |\n| 1 km x 1 km     | 1 month         | default         | 7 minutes    |\n| 5 km x 5 km     | 1 month         | default         | 10 minutes   |\n| 10 km x 10 km   | 1 month         | default         | 11 minutes   |\n| 50 km x 50 km   | 1 month         | 6 GB            | 20 minutes   |\n| 100 km x 100 km | 1 month         | 7 GB            | 34 minutes   |\n| 100m x 100 m    | 7 months        | default         | 10 minutes   |\n| 500 m x 500 m   | 7 months        | default         | 10 minutes   |\n| 1 km x 1 km     | 7 months        | default         | 14 minutes   |\n| 5 km x 5 km     | 7 months        | default         | 14 minutes   |\n| 10 km x 10 km   | 7 months        | default         | 19 minutes   |\n| 50 km x 50 km   | 7 months        | 6 GB            | 45 minutes   |\n| 100 km x 100 km | 7 months        | 8 GB            | 65 minutes   |\n\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\n\n```python\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"7g\"})\n```\n",
+    "description": "# Multi output gaussian process regression\n\n## Description\n\nCompute an integrated timeseries based on multiple inputs.\nFor instance, combine Sentinel-2 NDVI with Sentinel-1 RVI into one integrated NDVI.\n\n## Usage\n\nUsage examples for the MOGPR process.\n\n### Python\n\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\nThe result of this batch job will consist of individual GeoTIFF files per date.\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\n\n```python\nimport openeo\n\n## Setup of parameters\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\n\n## Setup connection to openEO\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\nservice = 'mogpr'\nnamespace = 'u:fusets'\n\n## Creation of the base NDVI data cube upon which the mogpr is executed\ns2 = connection.load_collection('SENTINEL2_L2A_SENTINELHUB',\n                                spatial_extent=spat_ext,\n                                temporal_extent=temp_ext,\n                                bands=[\"B04\", \"B08\", \"SCL\"])\ns2 = s2.process(\"mask_scl_dilation\", data=s2, scl_band_name=\"SCL\")\nbase_ndvi = s2.ndvi(red=\"B04\", nir=\"B08\", target_band='NDVI').band('NDVI')\n\n## Creation mogpr  data cube\nmogpr = connection.datacube_from_process(service,\n                                              namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n                                              data=base_ndvi, include_uncertainties=True)\n## Calculate the average time series value for the given area of interest\nmogpr = mogpr.aggregate_spatial(spat_ext, reducer='mean')\n\n## Execute the service through an openEO batch job\nmogpr_job = mogpr.execute_batch('./mogpr.json', out_format=\"json\",\n                                          title=f'FuseTS - MOGPR', job_options={\n        'udf-dependency-archives': [\n            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n        ]\n    })\n```\n\n## Limitations\n\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\n\n## Configuration & Resource Usage\n\nRun configurations for different ROI/TOI with memory requirements and estimated run durations.\n\n### Synchronous calls\n\nTODO: Replace with actual measurements!!!\n\n| Spatial extent | Run duration |\n|----------------|--------------|\n| 100 m x 100 m  | 1 minute     |\n| 500m x 500 m   | 1 minute     |\n| 1 km x 1 km    | 1 minute     |\n| 5 km x 5 km    | 2 minutes    |\n| 10 km x 10 km  | 3 minutes    |\n| 50 km x 50 km  | 9 minutes    |\n\nThe maximum duration of a synchronous run is 15 minutes.\nFor long running computations, you can use batch jobs.\n\n### Batch jobs\n\nTODO: Replace with actual measurements!!!\n\n| Spatial extent  | Temporal extent | Executor memory | Run duration |\n|-----------------|-----------------|-----------------|--------------|\n| 100 m x 100 m   | 1 month         | default         | 7 minutes    |\n| 500 m x 100 m   | 1 month         | default         | 7 minutes    |\n| 1 km x 1 km     | 1 month         | default         | 7 minutes    |\n| 5 km x 5 km     | 1 month         | default         | 10 minutes   |\n| 10 km x 10 km   | 1 month         | default         | 11 minutes   |\n| 50 km x 50 km   | 1 month         | 6 GB            | 20 minutes   |\n| 100 km x 100 km | 1 month         | 7 GB            | 34 minutes   |\n| 100m x 100 m    | 7 months        | default         | 10 minutes   |\n| 500 m x 500 m   | 7 months        | default         | 10 minutes   |\n| 1 km x 1 km     | 7 months        | default         | 14 minutes   |\n| 5 km x 5 km     | 7 months        | default         | 14 minutes   |\n| 10 km x 10 km   | 7 months        | default         | 19 minutes   |\n| 50 km x 50 km   | 7 months        | 6 GB            | 45 minutes   |\n| 100 km x 100 km | 7 months        | 8 GB            | 65 minutes   |\n\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\n\n```python\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"7g\"})\n```\n",
     "parameters": [
         {
             "name": "data",
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index c38016d..2ab05db 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -72,7 +72,7 @@ def generate_mogpr_cube(
         input_cube,
         lambda data: data.run_udf(
             udf=load_mogpr_udf(),
-            runtime="Python-Jep",
+            runtime="Python",
             context={"include_uncertainties": get_context_value(include_uncertainties)},
         ),
         size=[
@@ -92,7 +92,7 @@ def generate_mogpr_udp():
         "include_uncertainties", "Flag to include the uncertainties in the output results", False
     )
 
-    mogpr = generate_mogpr_cube()
+    mogpr = generate_mogpr_cube(input_cube=input_cube, include_uncertainties=include_uncertainties)
 
     return publish_service(
         id="mogpr",
@@ -104,5 +104,5 @@ def generate_mogpr_udp():
 
 
 if __name__ == "__main__":
-    execute_udf()
-    # generate_mogpr_udp()
+    # execute_udf()
+    generate_mogpr_udp()

From 4c398c9b149abf1be403d71cd33bb5a6ae560684 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Wed, 6 Mar 2024 16:27:37 +0100
Subject: [PATCH 19/21] feat: added parameter to include the raw input signals

---
 .../OpenEO/FuseTS - MOGPR S1 and S2.ipynb     |  100 +-
 src/fusets/mogpr.py                           |   36 +-
 src/fusets/openeo/mogpr_udf.py                |    2 +
 .../services/descriptions/mogpr_s1_s2.md      |   15 +-
 src/fusets/openeo/services/mogpr.json         |   14 +-
 src/fusets/openeo/services/mogpr_s1_s2.json   | 1344 +++++++++++++++++
 src/fusets/openeo/services/publish_mogpr.py   |   22 +-
 .../openeo/services/publish_mogpr_s1_s2.py    |   33 +-
 8 files changed, 1484 insertions(+), 82 deletions(-)
 create mode 100644 src/fusets/openeo/services/mogpr_s1_s2.json

diff --git a/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb b/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb
index 1aa0bd7..f865901 100644
--- a/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb	
+++ b/notebooks/OpenEO/FuseTS - MOGPR S1 and S2.ipynb	
@@ -51,19 +51,19 @@
      "text": [
       "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
       "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
-      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
-      "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
-      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
       "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
       "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
+      "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
+      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
+      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
       "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
       "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
       "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
       "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
       "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
       "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
       "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
       "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
       "\n",
@@ -186,7 +186,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1985e72ec2284df8a86044fb026fd391",
+       "model_id": "24afa9b6e1774aa6ad9a80467730ad24",
        "version_major": 2,
        "version_minor": 0
       },
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "04c3233f-32c6-4ad5-9de1-fa4dad0fd60b",
    "metadata": {},
    "outputs": [],
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "a0aae336-216f-4829-ba17-a4b6bb710c4b",
    "metadata": {},
    "outputs": [
@@ -290,12 +290,12 @@
        "    }\n",
        "    </script>\n",
        "    <openeo-process>\n",
-       "        <script type=\"application/json\">{\"show-graph\": true, \"provide-download\": false, \"process\": {\"description\": \"# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description                                                          | Type | Default |\\n|---|----|---|---|\\n| polygon | Polygon representing the AOI on which to apply the data fusion       | GeoJSON |   | \\n| date | Date range for which to apply the data fusion                        | Array |   |\\n| s1_collection | S1 data collection to use for the fusion                             | Text | RVI |\\n| s2_collection | S2 data collection to use for fusing the data                        | Text | NDVI | \\n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result | Boolean | False | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI ASC\\n* RVI DESC\\n* GRD ASC\\n* GRD DESC\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\\\"2021-01-01\\\", \\\"2021-12-31\\\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\\\"openeo.vito.be\\\").authenticate_oidc()\\nservice = 'mogpr_s1_s2'\\nnamespace = 'u:fusets'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch('./result_mogpr_s1_s2.nc', title=f'FuseTS - MOGPR S1 S2', job_options={\\n    'udf-dependency-archives': [\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\\n    ],\\n    'executor-memory': '8g'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\\\"GTIFF\\\", job_options={\\\"executor-memory\\\": \\\"8g\\\"})\\n```\\n\", \"id\": \"mogpr_s1_s2\", \"parameters\": [{\"description\": \"Polygon representing the AOI on which to apply the data fusion\", \"name\": \"polygon\", \"schema\": {\"subtype\": \"geojson\", \"type\": \"object\"}}, {\"description\": \"Date range for which to apply the data fusion\", \"name\": \"date\", \"schema\": {\"examples\": [[\"2015-01-01T00:00:00Z\", \"2016-01-01T00:00:00Z\"], [\"2015-01-01\", \"2016-01-01\"]], \"items\": {\"anyOf\": [{\"format\": \"date-time\", \"subtype\": \"date-time\", \"type\": \"string\"}, {\"format\": \"date\", \"subtype\": \"date\", \"type\": \"string\"}, {\"maxLength\": 4, \"minLength\": 4, \"pattern\": \"^\\\\d{4}$\", \"subtype\": \"year\", \"type\": \"string\"}, {\"type\": \"null\"}]}, \"maxItems\": 2, \"minItems\": 2, \"subtype\": \"temporal-interval\", \"type\": \"array\"}}, {\"default\": \"RVI ASC\", \"description\": \"S1 data collection to use for fusing the data\", \"name\": \"s1_collection\", \"optional\": true, \"schema\": {\"enum\": [\"RVI ASC\", \"RVI DESC\", \"GRD ASC\", \"GRD DESC\", \"GAMMA0\", \"COHERENCE\"], \"type\": \"string\"}}, {\"default\": \"NDVI\", \"description\": \"S2 data collection to use for fusing the data\", \"name\": \"s2_collection\", \"optional\": true, \"schema\": {\"enum\": [\"NDVI\", \"FAPAR\", \"LAI\", \"FCOVER\", \"EVI\", \"CCC\", \"CWC\"], \"type\": \"string\"}}, {\"default\": false, \"description\": \"Flag to include the uncertainties, expressed as the standard deviation, in the output results\", \"name\": \"include_uncertainties\", \"optional\": true, \"schema\": {\"type\": \"boolean\"}}], \"summary\": \"Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.\"}}</script>\n",
+       "        <script type=\"application/json\">{\"show-graph\": true, \"provide-download\": false, \"process\": {\"description\": \"# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description                                                                   | Type | Default |\\n|---|-------------------------------------------------------------------------------|---|---|\\n| polygon | Polygon representing the AOI on which to apply the data fusion                | GeoJSON |   | \\n| date | Date range for which to apply the data fusion                                 | Array |   |\\n| s1_collection | S1 data collection to use for the fusion                                      | Text | RVI |\\n| s2_collection | S2 data collection to use for fusing the data                                 | Text | NDVI | \\n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result     | Boolean | False | \\n| include_raw_inputs | Flag that indicated if the raw input signals should be included in the result | Boolean | False | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI ASC\\n* RVI DESC\\n* GRD ASC\\n* GRD DESC\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\\\"2021-01-01\\\", \\\"2021-12-31\\\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\\\"openeo.vito.be\\\").authenticate_oidc()\\nservice = 'mogpr_s1_s2'\\nnamespace = 'u:fusets'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch('./result_mogpr_s1_s2.nc', title=f'FuseTS - MOGPR S1 S2', job_options={\\n    'udf-dependency-archives': [\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\\n    ],\\n    'executor-memory': '8g'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\\\"GTIFF\\\", job_options={\\\"executor-memory\\\": \\\"8g\\\"})\\n```\\n\", \"id\": \"mogpr_s1_s2\", \"parameters\": [{\"description\": \"Polygon representing the AOI on which to apply the data fusion\", \"name\": \"polygon\", \"schema\": {\"subtype\": \"geojson\", \"type\": \"object\"}}, {\"description\": \"Date range for which to apply the data fusion\", \"name\": \"date\", \"schema\": {\"examples\": [[\"2015-01-01T00:00:00Z\", \"2016-01-01T00:00:00Z\"], [\"2015-01-01\", \"2016-01-01\"]], \"items\": {\"anyOf\": [{\"format\": \"date-time\", \"subtype\": \"date-time\", \"type\": \"string\"}, {\"format\": \"date\", \"subtype\": \"date\", \"type\": \"string\"}, {\"maxLength\": 4, \"minLength\": 4, \"pattern\": \"^\\\\d{4}$\", \"subtype\": \"year\", \"type\": \"string\"}, {\"type\": \"null\"}]}, \"maxItems\": 2, \"minItems\": 2, \"subtype\": \"temporal-interval\", \"type\": \"array\"}}, {\"default\": \"RVI ASC\", \"description\": \"S1 data collection to use for fusing the data\", \"name\": \"s1_collection\", \"optional\": true, \"schema\": {\"enum\": [\"RVI ASC\", \"RVI DESC\", \"GRD ASC\", \"GRD DESC\", \"GAMMA0\", \"COHERENCE\"], \"type\": \"string\"}}, {\"default\": \"NDVI\", \"description\": \"S2 data collection to use for fusing the data\", \"name\": \"s2_collection\", \"optional\": true, \"schema\": {\"enum\": [\"NDVI\", \"FAPAR\", \"LAI\", \"FCOVER\", \"EVI\", \"CCC\", \"CWC\"], \"type\": \"string\"}}, {\"default\": false, \"description\": \"Flag to include the uncertainties, expressed as the standard deviation in the final result\", \"name\": \"include_uncertainties\", \"optional\": true, \"schema\": {\"type\": \"boolean\"}}, {\"default\": false, \"description\": \"Flag to include the raw input signals in the final result\", \"name\": \"include_raw_inputs\", \"optional\": true, \"schema\": {\"type\": \"boolean\"}}], \"summary\": \"Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.\"}}</script>\n",
        "    </openeo-process>\n",
        "    "
       ],
       "text/plain": [
-       "{'description': '# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description                                                          | Type | Default |\\n|---|----|---|---|\\n| polygon | Polygon representing the AOI on which to apply the data fusion       | GeoJSON |   | \\n| date | Date range for which to apply the data fusion                        | Array |   |\\n| s1_collection | S1 data collection to use for the fusion                             | Text | RVI |\\n| s2_collection | S2 data collection to use for fusing the data                        | Text | NDVI | \\n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result | Boolean | False | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI ASC\\n* RVI DESC\\n* GRD ASC\\n* GRD DESC\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\\nservice = \\'mogpr_s1_s2\\'\\nnamespace = \\'u:fusets\\'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f\\'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}\\',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch(\\'./result_mogpr_s1_s2.nc\\', title=f\\'FuseTS - MOGPR S1 S2\\', job_options={\\n    \\'udf-dependency-archives\\': [\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv\\',\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static\\'\\n    ],\\n    \\'executor-memory\\': \\'8g\\'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"8g\"})\\n```\\n',\n",
+       "{'description': '# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\\n\\n## Description\\n\\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \\n\\n## Parameters\\n| Name | Description                                                                   | Type | Default |\\n|---|-------------------------------------------------------------------------------|---|---|\\n| polygon | Polygon representing the AOI on which to apply the data fusion                | GeoJSON |   | \\n| date | Date range for which to apply the data fusion                                 | Array |   |\\n| s1_collection | S1 data collection to use for the fusion                                      | Text | RVI |\\n| s2_collection | S2 data collection to use for fusing the data                                 | Text | NDVI | \\n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result     | Boolean | False | \\n| include_raw_inputs | Flag that indicated if the raw input signals should be included in the result | Boolean | False | \\n\\n### Supported collections\\n\\n#### Sentinel-1\\n\\n* RVI ASC\\n* RVI DESC\\n* GRD ASC\\n* GRD DESC\\n* GAMMA0\\n* COHERENCE (only Europe)\\n\\n#### Sentinel-2\\n\\n* NDVI\\n* FAPAR\\n* LAI\\n* FCOVER\\n* EVI\\n* CCC\\n* CWC\\n\\n\\n## Usage\\n\\nUsage examples for the MOGPR process.\\n\\n### Python\\n\\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\\nThe result of this batch job will consist of individual GeoTIFF files per date.\\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\\n\\n```python\\nimport openeo\\n\\n## Setup of parameters\\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\\n\\n## Setup connection to openEO\\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\\nservice = \\'mogpr_s1_s2\\'\\nnamespace = \\'u:fusets\\'\\n\\nmogpr = connection.datacube_from_process(service,\\n                                         namespace=f\\'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}\\',\\n                                         polygon=spat_ext, date=temp_ext)\\n\\nmogpr.execute_batch(\\'./result_mogpr_s1_s2.nc\\', title=f\\'FuseTS - MOGPR S1 S2\\', job_options={\\n    \\'udf-dependency-archives\\': [\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv\\',\\n        \\'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static\\'\\n    ],\\n    \\'executor-memory\\': \\'8g\\'\\n})\\n\\n```\\n\\n## Limitations\\n\\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\\n\\n## Configuration & Resource Usage\\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\\n\\n```python\\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"8g\"})\\n```\\n',\n",
        " 'id': 'mogpr_s1_s2',\n",
        " 'parameters': [{'description': 'Polygon representing the AOI on which to apply the data fusion',\n",
        "   'name': 'polygon',\n",
@@ -336,14 +336,19 @@
        "   'schema': {'enum': ['NDVI', 'FAPAR', 'LAI', 'FCOVER', 'EVI', 'CCC', 'CWC'],\n",
        "    'type': 'string'}},\n",
        "  {'default': False,\n",
-       "   'description': 'Flag to include the uncertainties, expressed as the standard deviation, in the output results',\n",
+       "   'description': 'Flag to include the uncertainties, expressed as the standard deviation in the final result',\n",
        "   'name': 'include_uncertainties',\n",
        "   'optional': True,\n",
+       "   'schema': {'type': 'boolean'}},\n",
+       "  {'default': False,\n",
+       "   'description': 'Flag to include the raw input signals in the final result',\n",
+       "   'name': 'include_raw_inputs',\n",
+       "   'optional': True,\n",
        "   'schema': {'type': 'boolean'}}],\n",
        " 'summary': 'Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -354,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "77b3f539-b919-43fb-b7c5-31c2853c4c7c",
    "metadata": {},
    "outputs": [],
@@ -362,7 +367,7 @@
     "mogpr = connection.datacube_from_process(service,\n",
     "                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n",
     "                                         polygon=spat_ext, date=temp_ext, s1_collection='RVI ASC', s2_collection='NDVI',\n",
-    "                                         include_uncertainties=True)"
+    "                                         include_uncertainties=True, include_raw_inputs=True)"
    ]
   },
   {
@@ -376,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "632a7088-67c2-4f89-95ff-813e4587f2be",
    "metadata": {},
    "outputs": [],
@@ -386,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "db4ac328-5413-4304-b1a5-b1c6dd6710fc",
    "metadata": {
     "scrolled": true
@@ -396,27 +401,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:00:00 Job 'j-2402291f76034a6d8e171135d40694f1': send 'start'\n",
-      "0:00:53 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:00:58 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
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-      "0:01:36 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:01:52 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:02:11 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:02:35 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:03:06 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:03:43 Job 'j-2402291f76034a6d8e171135d40694f1': queued (progress N/A)\n",
-      "0:04:30 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:05:29 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:06:35 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:07:35 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:08:35 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:09:36 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:10:36 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:11:39 Job 'j-2402291f76034a6d8e171135d40694f1': running (progress N/A)\n",
-      "0:12:40 Job 'j-2402291f76034a6d8e171135d40694f1': finished (progress N/A)\n"
+      "0:00:00 Job 'j-240306c902384df689b88b5554e2c868': send 'start'\n",
+      "0:00:26 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:00:31 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:00:38 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:00:46 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:00:56 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:01:08 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:01:24 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:01:44 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:02:08 Job 'j-240306c902384df689b88b5554e2c868': queued (progress N/A)\n",
+      "0:02:38 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:03:15 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:04:02 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:05:01 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:06:01 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:07:03 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:08:04 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:09:04 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:10:05 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:11:05 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:12:06 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:13:06 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:14:06 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:15:06 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:16:07 Job 'j-240306c902384df689b88b5554e2c868': running (progress N/A)\n",
+      "0:17:07 Job 'j-240306c902384df689b88b5554e2c868': finished (progress N/A)\n"
      ]
     }
    ],
@@ -441,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 15,
    "id": "ec95ceb1-9027-4305-a40a-6bcf9905c7a2",
    "metadata": {},
    "outputs": [],
@@ -460,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 18,
    "id": "1b2f4652-46b5-40b3-8e4d-5d2a861c4264",
    "metadata": {},
    "outputs": [],
@@ -468,28 +478,28 @@
     "joined_df = pd.concat(cubes_dfs, axis=1)\n",
     "joined_df = joined_df.rename(\n",
     "    columns={'NDVI': 'NDVI - Smoothed', 'RVI': 'RVI - Smoothed', 'unkown_band_2': 'RVI - Uncertainty',\n",
-    "             'unkown_band_3': 'NDVI - Uncertainty'})"
+    "             'unkown_band_3': 'NDVI - Uncertainty', 'unkown_band_4': 'RVI - Raw', 'unkown_band_5': 'NDVI - Raw'})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 22,
    "id": "8826c305-1f4c-481f-88aa-291d80e38448",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x12f555340>"
+       "<matplotlib.legend.Legend at 0x159915220>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -509,6 +519,8 @@
     "    if 'Uncertainty' in col:\n",
     "        plt.fill_between(values.index, values[std_col_mapping[col]] - values[col],\n",
     "                         values[std_col_mapping[col]] + values[col], alpha=0.2, label=col)\n",
+    "    elif 'Raw' in col:\n",
+    "        plt.plot(values.index, values[col], '-x' if 'Raw' in col else '-.', label=col)\n",
     "    else:\n",
     "        plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
     "plt.grid(True)\n",
diff --git a/src/fusets/mogpr.py b/src/fusets/mogpr.py
index 635b247..062e1cf 100644
--- a/src/fusets/mogpr.py
+++ b/src/fusets/mogpr.py
@@ -137,8 +137,12 @@ def fit_transform(self, X: Union[xarray.Dataset, DataCube], y=None, **fit_params
 
 
 def mogpr(
-        array: xarray.Dataset, variables: List[str] = None, time_dimension: str = "t", prediction_period: str = None,
-        include_uncertainties: bool = False
+    array: xarray.Dataset,
+    variables: List[str] = None,
+    time_dimension: str = "t",
+    prediction_period: str = None,
+    include_uncertainties: bool = False,
+    include_raw_inputs: bool = False,
 ) -> xarray.Dataset:
     """
     MOGPR (multi-output gaussian-process regression) integrates various timeseries into a single values. This allows to
@@ -152,6 +156,7 @@ def mogpr(
         time_dimension: The name of the time dimension of this datacube. Only needs to be specified to resolve ambiguities.
         prediction_period: The duration specified as ISO-8601, e.g. P5D: 5-daily, P1M: monthly. Defaults to input dates.
         include_uncertainties: Flag indicating if the uncertainties should be added to the output of the mogpr process.
+        include_raw_inputs: Flag indicating if the raw inputs should be added to the output of the mogpr process.
 
     Returns: A gapfilled datacube.
 
@@ -194,14 +199,23 @@ def callback(timeseries):
         output_core_dims=[["variable", output_time_dimension], ["variable", output_time_dimension]],
         vectorize=True,
     )
+    result["variable"] = [f"{variable}_FUSED" for variable in result["variable"].values]
 
+    # Assign coordinates to the time dimensions
+    result = result.assign_coords({output_time_dimension: output_dates})
+    std = std.assign_coords({output_time_dimension: output_dates})
+
+    merged = result
     if include_uncertainties:
-        std['variable'] = [f"{variable}_STD" for variable in std['variable'].values]
-        merged = xarray.concat([result, std], dim='variable')
-    else:
-        merged = result
+        std["variable"] = [f"{variable}_STD" for variable in std["variable"].values]
+        merged = xarray.concat([merged, std], dim="variable")
+
+    if include_raw_inputs:
+        variables_renames = {a: f"{a}_RAW" for a in array.data_vars if a != "crs"}
+        variables_renames[time_dimension] = output_time_dimension
+        array = array.rename(variables_renames)
+        merged = xarray.concat([merged, array.to_array(dim="variable")], dim="variable", compat="no_conflicts")
 
-    merged = merged.assign_coords({output_time_dimension: output_dates})
     merged = merged.rename({output_time_dimension: time_dimension, "variable": "bands"})
 
     return merged.to_dataset(dim="bands")
@@ -310,11 +324,11 @@ def _MOGPR_GPY_retrieval(data_in, time_in, master_ind, output_timevec, nt):
                     else:
                         for ind in range(noutput_timeseries):
                             out_mean[ind][:, None, x, y] = (
-                                    out_mean[ind][:, None, x, y]
-                                    + (Yp[:, None, ind] * Y_std_vec[ind] + Y_mean_vec[ind]) / nt
+                                out_mean[ind][:, None, x, y]
+                                + (Yp[:, None, ind] * Y_std_vec[ind] + Y_mean_vec[ind]) / nt
                             )
                             out_std[ind][:, None, x, y] = (
-                                    out_std[ind][:, None, x, y] + (Vp[:, None, ind] * Y_std_vec[ind]) / nt
+                                out_std[ind][:, None, x, y] + (Vp[:, None, ind] * Y_std_vec[ind]) / nt
                             )
 
                     del Yp, Vp
@@ -430,7 +444,7 @@ def mogpr_1D(data_in, time_in, master_ind, output_timevec, nt, trained_model=Non
                     out_std[out][:, None] = (Vp[:, None, out] * Y_std_vec[out]) / nt
                 else:
                     out_mean[out][:, None] = (
-                            out_mean[out][:, None] + (Yp[:, None, out] * Y_std_vec[out] + Y_mean_vec[out]) / nt
+                        out_mean[out][:, None] + (Yp[:, None, out] * Y_std_vec[out] + Y_mean_vec[out]) / nt
                     )
                     out_std[out][:, None] = out_std[out][:, None] + (Vp[:, None, out] * Y_std_vec[out]) / nt
 
diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 24baa40..40103b6 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -66,6 +66,7 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
     time_dimension = context.get("time_dimension", "t")
     prediction_period = context.get("prediction_period", "5D")
     include_uncertainties = context.get("include_uncertainties", False)
+    include_raw_inputs = context.get("include_raw_inputs", False)
 
     dims = cube.get_array().dims
     result = mogpr(
@@ -74,6 +75,7 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
         time_dimension=time_dimension,
         prediction_period=prediction_period,
         include_uncertainties=include_uncertainties,
+        include_raw_inputs=include_raw_inputs,
     )
     result_dc = XarrayDataCube(result.to_array(dim="bands").transpose(*dims))
     inspect(data=result_dc, message="MOGPR result")
diff --git a/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md b/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md
index 452565a..ffacf88 100644
--- a/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md
+++ b/src/fusets/openeo/services/descriptions/mogpr_s1_s2.md
@@ -5,13 +5,14 @@
 Compute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. 
 
 ## Parameters
-| Name | Description                                                          | Type | Default |
-|---|----|---|---|
-| polygon | Polygon representing the AOI on which to apply the data fusion       | GeoJSON |   | 
-| date | Date range for which to apply the data fusion                        | Array |   |
-| s1_collection | S1 data collection to use for the fusion                             | Text | RVI |
-| s2_collection | S2 data collection to use for fusing the data                        | Text | NDVI | 
-| include_uncertainties | Flag that indicated if the uncertainties should be included in the result | Boolean | False | 
+| Name | Description                                                                   | Type | Default |
+|---|-------------------------------------------------------------------------------|---|---|
+| polygon | Polygon representing the AOI on which to apply the data fusion                | GeoJSON |   | 
+| date | Date range for which to apply the data fusion                                 | Array |   |
+| s1_collection | S1 data collection to use for the fusion                                      | Text | RVI |
+| s2_collection | S2 data collection to use for fusing the data                                 | Text | NDVI | 
+| include_uncertainties | Flag that indicated if the uncertainties should be included in the result     | Boolean | False | 
+| include_raw_inputs | Flag that indicated if the raw input signals should be included in the result | Boolean | False | 
 
 ### Supported collections
 
diff --git a/src/fusets/openeo/services/mogpr.json b/src/fusets/openeo/services/mogpr.json
index 5a14bb0..54ecc1e 100644
--- a/src/fusets/openeo/services/mogpr.json
+++ b/src/fusets/openeo/services/mogpr.json
@@ -15,13 +15,16 @@
                                 "context": {
                                     "include_uncertainties": {
                                         "from_parameter": "include_uncertainties"
+                                    },
+                                    "include_raw_inputs": {
+                                        "from_parameter": "include_raw_inputs"
                                     }
                                 },
                                 "data": {
                                     "from_parameter": "data"
                                 },
                                 "runtime": "Python",
-                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.metadata import CollectionMetadata\nfrom openeo.udf import XarrayDataCube, inspect\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv\", \"tmp/venv_static\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    # extra_bands = [Band(f\"{x}_STD\", None, None) for x in metadata.bands]\n    # inspect(data=metadata, message=\"MOGPR metadata\")\n    # for band in extra_bands:\n    #     metadata = metadata.append_band(band)\n    return metadata\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties,\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    inspect(data=result_dc, message=\"MOGPR result\")\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
+                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.metadata import CollectionMetadata\nfrom openeo.udf import XarrayDataCube, inspect\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv\", \"tmp/venv_static\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    # extra_bands = [Band(f\"{x}_STD\", None, None) for x in metadata.bands]\n    # inspect(data=metadata, message=\"MOGPR metadata\")\n    # for band in extra_bands:\n    #     metadata = metadata.append_band(band)\n    return metadata\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n    include_raw_inputs = context.get(\"include_raw_inputs\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties,\n        include_raw_inputs=include_raw_inputs\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    inspect(data=result_dc, message=\"MOGPR result\")\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
                             },
                             "result": true
                         }
@@ -63,6 +66,15 @@
             },
             "optional": true,
             "default": false
+        },
+        {
+            "name": "include_raw_inputs",
+            "description": "Flag to include the raw input signals in the final result",
+            "schema": {
+                "type": "boolean"
+            },
+            "optional": true,
+            "default": false
         }
     ]
 }
\ No newline at end of file
diff --git a/src/fusets/openeo/services/mogpr_s1_s2.json b/src/fusets/openeo/services/mogpr_s1_s2.json
new file mode 100644
index 0000000..283da12
--- /dev/null
+++ b/src/fusets/openeo/services/mogpr_s1_s2.json
@@ -0,0 +1,1344 @@
+{
+    "process_graph": {
+        "loadcollection1": {
+            "process_id": "load_collection",
+            "arguments": {
+                "id": "TERRASCOPE_S1_SLC_COHERENCE_V1",
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskpolygon1": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection1"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "loadcollection2": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "VH",
+                    "VV"
+                ],
+                "id": "SENTINEL1_GAMMA0_SENTINELHUB",
+                "properties": {
+                    "polarization": {
+                        "process_graph": {
+                            "eq1": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "DV"
+                                },
+                                "result": true
+                            }
+                        }
+                    }
+                },
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "sarbackscatter1": {
+            "process_id": "sar_backscatter",
+            "arguments": {
+                "coefficient": "gamma0-terrain",
+                "contributing_area": false,
+                "data": {
+                    "from_node": "loadcollection2"
+                },
+                "elevation_model": null,
+                "ellipsoid_incidence_angle": false,
+                "local_incidence_angle": false,
+                "mask": false,
+                "noise_removal": true
+            }
+        },
+        "maskpolygon2": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "sarbackscatter1"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "loadcollection3": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "VV",
+                    "VH"
+                ],
+                "id": "SENTINEL1_GRD",
+                "properties": {
+                    "sat:orbit_state": {
+                        "process_graph": {
+                            "eq2": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "ASCENDING"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "resolution": {
+                        "process_graph": {
+                            "eq3": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "HIGH"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "sar:instrument_mode": {
+                        "process_graph": {
+                            "eq4": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "IW"
+                                },
+                                "result": true
+                            }
+                        }
+                    }
+                },
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskpolygon3": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection3"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "reducedimension1": {
+            "process_id": "reduce_dimension",
+            "arguments": {
+                "data": {
+                    "from_node": "maskpolygon3"
+                },
+                "dimension": "bands",
+                "reducer": {
+                    "process_graph": {
+                        "arrayelement1": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 1
+                            }
+                        },
+                        "add1": {
+                            "process_id": "add",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "arrayelement1"
+                                },
+                                "y": {
+                                    "from_node": "arrayelement1"
+                                }
+                            }
+                        },
+                        "arrayelement2": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 0
+                            }
+                        },
+                        "add2": {
+                            "process_id": "add",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "arrayelement2"
+                                },
+                                "y": {
+                                    "from_node": "arrayelement1"
+                                }
+                            }
+                        },
+                        "divide1": {
+                            "process_id": "divide",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "add1"
+                                },
+                                "y": {
+                                    "from_node": "add2"
+                                }
+                            },
+                            "result": true
+                        }
+                    }
+                }
+            }
+        },
+        "adddimension1": {
+            "process_id": "add_dimension",
+            "arguments": {
+                "data": {
+                    "from_node": "reducedimension1"
+                },
+                "label": "RVI",
+                "name": "bands",
+                "type": "bands"
+            }
+        },
+        "loadcollection4": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "VV",
+                    "VH"
+                ],
+                "id": "SENTINEL1_GRD",
+                "properties": {
+                    "sat:orbit_state": {
+                        "process_graph": {
+                            "eq5": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "DESCENDING"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "resolution": {
+                        "process_graph": {
+                            "eq6": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "HIGH"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "sar:instrument_mode": {
+                        "process_graph": {
+                            "eq7": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "IW"
+                                },
+                                "result": true
+                            }
+                        }
+                    }
+                },
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskpolygon4": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection4"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "reducedimension2": {
+            "process_id": "reduce_dimension",
+            "arguments": {
+                "data": {
+                    "from_node": "maskpolygon4"
+                },
+                "dimension": "bands",
+                "reducer": {
+                    "process_graph": {
+                        "arrayelement3": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 1
+                            }
+                        },
+                        "add3": {
+                            "process_id": "add",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "arrayelement3"
+                                },
+                                "y": {
+                                    "from_node": "arrayelement3"
+                                }
+                            }
+                        },
+                        "arrayelement4": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 0
+                            }
+                        },
+                        "add4": {
+                            "process_id": "add",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "arrayelement4"
+                                },
+                                "y": {
+                                    "from_node": "arrayelement3"
+                                }
+                            }
+                        },
+                        "divide2": {
+                            "process_id": "divide",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "add3"
+                                },
+                                "y": {
+                                    "from_node": "add4"
+                                }
+                            },
+                            "result": true
+                        }
+                    }
+                }
+            }
+        },
+        "adddimension2": {
+            "process_id": "add_dimension",
+            "arguments": {
+                "data": {
+                    "from_node": "reducedimension2"
+                },
+                "label": "RVI",
+                "name": "bands",
+                "type": "bands"
+            }
+        },
+        "loadcollection5": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "VV",
+                    "VH"
+                ],
+                "id": "SENTINEL1_GRD",
+                "properties": {
+                    "sat:orbit_state": {
+                        "process_graph": {
+                            "eq8": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "ASCENDING"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "resolution": {
+                        "process_graph": {
+                            "eq9": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "HIGH"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "sar:instrument_mode": {
+                        "process_graph": {
+                            "eq10": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "IW"
+                                },
+                                "result": true
+                            }
+                        }
+                    }
+                },
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskpolygon5": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection5"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "loadcollection6": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "VV",
+                    "VH"
+                ],
+                "id": "SENTINEL1_GRD",
+                "properties": {
+                    "sat:orbit_state": {
+                        "process_graph": {
+                            "eq11": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "DESCENDING"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "resolution": {
+                        "process_graph": {
+                            "eq12": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "HIGH"
+                                },
+                                "result": true
+                            }
+                        }
+                    },
+                    "sar:instrument_mode": {
+                        "process_graph": {
+                            "eq13": {
+                                "process_id": "eq",
+                                "arguments": {
+                                    "x": {
+                                        "from_parameter": "value"
+                                    },
+                                    "y": "IW"
+                                },
+                                "result": true
+                            }
+                        }
+                    }
+                },
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskpolygon6": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection6"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "eq14": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s1_collection"
+                },
+                "y": "grd desc"
+            }
+        },
+        "if1": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon6"
+                },
+                "reject": null,
+                "value": {
+                    "from_node": "eq14"
+                }
+            }
+        },
+        "eq15": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s1_collection"
+                },
+                "y": "grd asc"
+            }
+        },
+        "if2": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon5"
+                },
+                "reject": {
+                    "from_node": "if1"
+                },
+                "value": {
+                    "from_node": "eq15"
+                }
+            }
+        },
+        "eq16": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s1_collection"
+                },
+                "y": "rvi desc"
+            }
+        },
+        "if3": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "adddimension2"
+                },
+                "reject": {
+                    "from_node": "if2"
+                },
+                "value": {
+                    "from_node": "eq16"
+                }
+            }
+        },
+        "eq17": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s1_collection"
+                },
+                "y": "rvi asc"
+            }
+        },
+        "if4": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "adddimension1"
+                },
+                "reject": {
+                    "from_node": "if3"
+                },
+                "value": {
+                    "from_node": "eq17"
+                }
+            }
+        },
+        "eq18": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s1_collection"
+                },
+                "y": "gamma0"
+            }
+        },
+        "if5": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon2"
+                },
+                "reject": {
+                    "from_node": "if4"
+                },
+                "value": {
+                    "from_node": "eq18"
+                }
+            }
+        },
+        "eq19": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s1_collection"
+                },
+                "y": "coherence"
+            }
+        },
+        "if6": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon1"
+                },
+                "reject": {
+                    "from_node": "if5"
+                },
+                "value": {
+                    "from_node": "eq19"
+                }
+            }
+        },
+        "BIOPAR1": {
+            "process_id": "BIOPAR",
+            "arguments": {
+                "biopar_type": "CWC",
+                "date": {
+                    "from_parameter": "date"
+                },
+                "polygon": {
+                    "from_parameter": "polygon"
+                }
+            },
+            "namespace": "vito"
+        },
+        "maskpolygon7": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "BIOPAR1"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "BIOPAR2": {
+            "process_id": "BIOPAR",
+            "arguments": {
+                "biopar_type": "CCC",
+                "date": {
+                    "from_parameter": "date"
+                },
+                "polygon": {
+                    "from_parameter": "polygon"
+                }
+            },
+            "namespace": "vito"
+        },
+        "maskpolygon8": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "BIOPAR2"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "loadcollection7": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "B02",
+                    "B04",
+                    "B08",
+                    "SCL"
+                ],
+                "id": "SENTINEL2_L2A",
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskscldilation1": {
+            "process_id": "mask_scl_dilation",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection7"
+                },
+                "scl_band_name": "SCL"
+            }
+        },
+        "reducedimension3": {
+            "process_id": "reduce_dimension",
+            "arguments": {
+                "data": {
+                    "from_node": "maskscldilation1"
+                },
+                "dimension": "bands",
+                "reducer": {
+                    "process_graph": {
+                        "arrayelement5": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 2
+                            }
+                        },
+                        "arrayelement6": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 1
+                            }
+                        },
+                        "subtract1": {
+                            "process_id": "subtract",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "arrayelement5"
+                                },
+                                "y": {
+                                    "from_node": "arrayelement6"
+                                }
+                            }
+                        },
+                        "multiply1": {
+                            "process_id": "multiply",
+                            "arguments": {
+                                "x": 2.5,
+                                "y": {
+                                    "from_node": "subtract1"
+                                }
+                            }
+                        },
+                        "multiply2": {
+                            "process_id": "multiply",
+                            "arguments": {
+                                "x": 6.0,
+                                "y": {
+                                    "from_node": "arrayelement6"
+                                }
+                            }
+                        },
+                        "add5": {
+                            "process_id": "add",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "arrayelement5"
+                                },
+                                "y": {
+                                    "from_node": "multiply2"
+                                }
+                            }
+                        },
+                        "arrayelement7": {
+                            "process_id": "array_element",
+                            "arguments": {
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "index": 1
+                            }
+                        },
+                        "multiply3": {
+                            "process_id": "multiply",
+                            "arguments": {
+                                "x": 7.5,
+                                "y": {
+                                    "from_node": "arrayelement7"
+                                }
+                            }
+                        },
+                        "subtract2": {
+                            "process_id": "subtract",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "add5"
+                                },
+                                "y": {
+                                    "from_node": "multiply3"
+                                }
+                            }
+                        },
+                        "add6": {
+                            "process_id": "add",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "subtract2"
+                                },
+                                "y": 1.0
+                            }
+                        },
+                        "divide3": {
+                            "process_id": "divide",
+                            "arguments": {
+                                "x": {
+                                    "from_node": "multiply1"
+                                },
+                                "y": {
+                                    "from_node": "add6"
+                                }
+                            },
+                            "result": true
+                        }
+                    }
+                }
+            }
+        },
+        "maskpolygon9": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "reducedimension3"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "adddimension3": {
+            "process_id": "add_dimension",
+            "arguments": {
+                "data": {
+                    "from_node": "maskpolygon9"
+                },
+                "label": "EVI",
+                "name": "bands",
+                "type": "bands"
+            }
+        },
+        "BIOPAR3": {
+            "process_id": "BIOPAR",
+            "arguments": {
+                "biopar_type": "FCOVER",
+                "date": {
+                    "from_parameter": "date"
+                },
+                "polygon": {
+                    "from_parameter": "polygon"
+                }
+            },
+            "namespace": "vito"
+        },
+        "maskpolygon10": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "BIOPAR3"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "BIOPAR4": {
+            "process_id": "BIOPAR",
+            "arguments": {
+                "biopar_type": "LAI",
+                "date": {
+                    "from_parameter": "date"
+                },
+                "polygon": {
+                    "from_parameter": "polygon"
+                }
+            },
+            "namespace": "vito"
+        },
+        "maskpolygon11": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "BIOPAR4"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "BIOPAR5": {
+            "process_id": "BIOPAR",
+            "arguments": {
+                "biopar_type": "FAPAR",
+                "date": {
+                    "from_parameter": "date"
+                },
+                "polygon": {
+                    "from_parameter": "polygon"
+                }
+            },
+            "namespace": "vito"
+        },
+        "maskpolygon12": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "BIOPAR5"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "loadcollection8": {
+            "process_id": "load_collection",
+            "arguments": {
+                "bands": [
+                    "B04",
+                    "B08",
+                    "SCL"
+                ],
+                "id": "SENTINEL2_L2A",
+                "spatial_extent": {
+                    "from_parameter": "polygon"
+                },
+                "temporal_extent": {
+                    "from_parameter": "date"
+                }
+            }
+        },
+        "maskscldilation2": {
+            "process_id": "mask_scl_dilation",
+            "arguments": {
+                "data": {
+                    "from_node": "loadcollection8"
+                },
+                "scl_band_name": "SCL"
+            }
+        },
+        "ndvi1": {
+            "process_id": "ndvi",
+            "arguments": {
+                "data": {
+                    "from_node": "maskscldilation2"
+                },
+                "nir": "B08",
+                "red": "B04",
+                "target_band": "NDVI"
+            }
+        },
+        "filterbands1": {
+            "process_id": "filter_bands",
+            "arguments": {
+                "bands": [
+                    "NDVI"
+                ],
+                "data": {
+                    "from_node": "ndvi1"
+                }
+            }
+        },
+        "maskpolygon13": {
+            "process_id": "mask_polygon",
+            "arguments": {
+                "data": {
+                    "from_node": "filterbands1"
+                },
+                "mask": {
+                    "from_parameter": "polygon"
+                }
+            }
+        },
+        "eq20": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "ndvi"
+            }
+        },
+        "if7": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon13"
+                },
+                "reject": null,
+                "value": {
+                    "from_node": "eq20"
+                }
+            }
+        },
+        "eq21": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "fapar"
+            }
+        },
+        "if8": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon12"
+                },
+                "reject": {
+                    "from_node": "if7"
+                },
+                "value": {
+                    "from_node": "eq21"
+                }
+            }
+        },
+        "eq22": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "lai"
+            }
+        },
+        "if9": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon11"
+                },
+                "reject": {
+                    "from_node": "if8"
+                },
+                "value": {
+                    "from_node": "eq22"
+                }
+            }
+        },
+        "eq23": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "fcover"
+            }
+        },
+        "if10": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon10"
+                },
+                "reject": {
+                    "from_node": "if9"
+                },
+                "value": {
+                    "from_node": "eq23"
+                }
+            }
+        },
+        "eq24": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "evi"
+            }
+        },
+        "if11": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "adddimension3"
+                },
+                "reject": {
+                    "from_node": "if10"
+                },
+                "value": {
+                    "from_node": "eq24"
+                }
+            }
+        },
+        "eq25": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "ccc"
+            }
+        },
+        "if12": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon8"
+                },
+                "reject": {
+                    "from_node": "if11"
+                },
+                "value": {
+                    "from_node": "eq25"
+                }
+            }
+        },
+        "eq26": {
+            "process_id": "eq",
+            "arguments": {
+                "case_sensitive": false,
+                "x": {
+                    "from_parameter": "s2_collection"
+                },
+                "y": "cwc"
+            }
+        },
+        "if13": {
+            "process_id": "if",
+            "arguments": {
+                "accept": {
+                    "from_node": "maskpolygon7"
+                },
+                "reject": {
+                    "from_node": "if12"
+                },
+                "value": {
+                    "from_node": "eq26"
+                }
+            }
+        },
+        "mergecubes1": {
+            "process_id": "merge_cubes",
+            "arguments": {
+                "cube1": {
+                    "from_node": "if6"
+                },
+                "cube2": {
+                    "from_node": "if13"
+                }
+            }
+        },
+        "applyneighborhood1": {
+            "process_id": "apply_neighborhood",
+            "arguments": {
+                "data": {
+                    "from_node": "mergecubes1"
+                },
+                "overlap": [],
+                "process": {
+                    "process_graph": {
+                        "runudf1": {
+                            "process_id": "run_udf",
+                            "arguments": {
+                                "context": {
+                                    "include_uncertainties": {
+                                        "from_parameter": "include_uncertainties"
+                                    },
+                                    "include_raw_inputs": {
+                                        "from_parameter": "include_raw_inputs"
+                                    }
+                                },
+                                "data": {
+                                    "from_parameter": "data"
+                                },
+                                "runtime": "Python",
+                                "udf": "import os\nimport sys\nfrom configparser import ConfigParser\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom openeo.metadata import CollectionMetadata\nfrom openeo.udf import XarrayDataCube, inspect\n\n\ndef load_venv():\n    \"\"\"\n    Add the virtual environment to the system path if the folder `/tmp/venv_static` exists\n    :return:\n    \"\"\"\n    for venv_path in [\"tmp/venv\", \"tmp/venv_static\"]:\n        if Path(venv_path).exists():\n            sys.path.insert(0, venv_path)\n\n\ndef set_home(home):\n    os.environ[\"HOME\"] = home\n\n\ndef create_gpy_cfg():\n    home = os.getenv(\"HOME\")\n    set_home(\"/tmp\")\n    user_file = Path.home() / \".config\" / \"GPy\" / \"user.cfg\"\n    if not user_file.exists():\n        user_file.parent.mkdir(parents=True, exist_ok=True)\n    return user_file, home\n\n\ndef write_gpy_cfg():\n    user_file, home = create_gpy_cfg()\n    config = ConfigParser()\n    config[\"plotting\"] = {\"library\": \"none\"}\n    with open(user_file, \"w\") as cfg:\n        config.write(cfg)\n        cfg.close()\n    return home\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    # extra_bands = [Band(f\"{x}_STD\", None, None) for x in metadata.bands]\n    # inspect(data=metadata, message=\"MOGPR metadata\")\n    # for band in extra_bands:\n    #     metadata = metadata.append_band(band)\n    return metadata\n\n\ndef apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:\n    \"\"\"\n    Apply mogpr integration to a datacube.\n    MOGPR requires a full timeseries for multiple bands, so it needs to be invoked in the context of an apply_neighborhood process.\n    @param cube:\n    @param context:\n    @return:\n    \"\"\"\n    load_venv()\n    home = write_gpy_cfg()\n\n    from fusets.mogpr import mogpr\n\n    variables = context.get(\"variables\")\n    time_dimension = context.get(\"time_dimension\", \"t\")\n    prediction_period = context.get(\"prediction_period\", \"5D\")\n    include_uncertainties = context.get(\"include_uncertainties\", False)\n    include_raw_inputs = context.get(\"include_raw_inputs\", False)\n\n    dims = cube.get_array().dims\n    result = mogpr(\n        cube.get_array().to_dataset(dim=\"bands\"),\n        variables=variables,\n        time_dimension=time_dimension,\n        prediction_period=prediction_period,\n        include_uncertainties=include_uncertainties,\n        include_raw_inputs=include_raw_inputs\n    )\n    result_dc = XarrayDataCube(result.to_array(dim=\"bands\").transpose(*dims))\n    inspect(data=result_dc, message=\"MOGPR result\")\n    set_home(home)\n    return result_dc\n\n\ndef load_mogpr_udf() -> str:\n    \"\"\"\n    Loads an openEO udf that applies mogpr.\n    @return:\n    \"\"\"\n    import os\n\n    return Path(os.path.realpath(__file__)).read_text()\n"
+                            },
+                            "result": true
+                        }
+                    }
+                },
+                "size": [
+                    {
+                        "dimension": "x",
+                        "value": 32,
+                        "unit": "px"
+                    },
+                    {
+                        "dimension": "y",
+                        "value": 32,
+                        "unit": "px"
+                    }
+                ]
+            },
+            "result": true
+        }
+    },
+    "id": "mogpr_s1_s2",
+    "summary": "Integrates timeseries in data cube using multi-output gaussian process regression with a specific focus on fusing S1 and S2 data.",
+    "description": "# Sentinel-1 and Sentinel-2 data fusion through multi output gaussian process regression\n\n## Description\n\nCompute a temporal dense timeseries based on the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) using MOGPR. \n\n## Parameters\n| Name | Description                                                                   | Type | Default |\n|---|-------------------------------------------------------------------------------|---|---|\n| polygon | Polygon representing the AOI on which to apply the data fusion                | GeoJSON |   | \n| date | Date range for which to apply the data fusion                                 | Array |   |\n| s1_collection | S1 data collection to use for the fusion                                      | Text | RVI |\n| s2_collection | S2 data collection to use for fusing the data                                 | Text | NDVI | \n| include_uncertainties | Flag that indicated if the uncertainties should be included in the result     | Boolean | False | \n| include_raw_inputs | Flag that indicated if the raw input signals should be included in the result | Boolean | False | \n\n### Supported collections\n\n#### Sentinel-1\n\n* RVI ASC\n* RVI DESC\n* GRD ASC\n* GRD DESC\n* GAMMA0\n* COHERENCE (only Europe)\n\n#### Sentinel-2\n\n* NDVI\n* FAPAR\n* LAI\n* FCOVER\n* EVI\n* CCC\n* CWC\n\n\n## Usage\n\nUsage examples for the MOGPR process.\n\n### Python\n\nThis code example highlights the usage of the MOGPR process in an OpenEO batch job.\nThe result of this batch job will consist of individual GeoTIFF files per date.\nGenerating multiple GeoTIFF files as output is only possible in a batch job.\n\n```python\nimport openeo\n\n## Setup of parameters\nminx, miny, maxx, maxy = (15.179421073198585, 45.80924633589998, 15.185336903822831, 45.81302555710934)\nspat_ext = dict(west=minx, east=maxx, north=maxy, south=miny, crs=4326)\ntemp_ext = [\"2021-01-01\", \"2021-12-31\"]\n\n## Setup connection to openEO\nconnection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()\nservice = 'mogpr_s1_s2'\nnamespace = 'u:fusets'\n\nmogpr = connection.datacube_from_process(service,\n                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n                                         polygon=spat_ext, date=temp_ext)\n\nmogpr.execute_batch('./result_mogpr_s1_s2.nc', title=f'FuseTS - MOGPR S1 S2', job_options={\n    'udf-dependency-archives': [\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n    ],\n    'executor-memory': '8g'\n})\n\n```\n\n## Limitations\n\nThe spatial extent is limited to a maximum size equal to a Sentinel-2 MGRS tile (100 km x 100 km).\n\n## Configuration & Resource Usage\nThe executor memory defaults to 5 GB. You can increase the executor memory by specifying it as a job option, eg:\n\n```python\njob = cube.execute_batch(out_format=\"GTIFF\", job_options={\"executor-memory\": \"8g\"})\n```\n",
+    "parameters": [
+        {
+            "name": "polygon",
+            "description": "Polygon representing the AOI on which to apply the data fusion",
+            "schema": {
+                "type": "object",
+                "subtype": "geojson"
+            }
+        },
+        {
+            "name": "date",
+            "description": "Date range for which to apply the data fusion",
+            "schema": {
+                "type": "array",
+                "subtype": "temporal-interval",
+                "minItems": 2,
+                "maxItems": 2,
+                "items": {
+                    "anyOf": [
+                        {
+                            "type": "string",
+                            "format": "date-time",
+                            "subtype": "date-time"
+                        },
+                        {
+                            "type": "string",
+                            "format": "date",
+                            "subtype": "date"
+                        },
+                        {
+                            "type": "string",
+                            "subtype": "year",
+                            "minLength": 4,
+                            "maxLength": 4,
+                            "pattern": "^\\d{4}$"
+                        },
+                        {
+                            "type": "null"
+                        }
+                    ]
+                },
+                "examples": [
+                    [
+                        "2015-01-01T00:00:00Z",
+                        "2016-01-01T00:00:00Z"
+                    ],
+                    [
+                        "2015-01-01",
+                        "2016-01-01"
+                    ]
+                ]
+            }
+        },
+        {
+            "name": "s1_collection",
+            "description": "S1 data collection to use for fusing the data",
+            "schema": {
+                "type": "string",
+                "enum": [
+                    "RVI ASC",
+                    "RVI DESC",
+                    "GRD ASC",
+                    "GRD DESC",
+                    "GAMMA0",
+                    "COHERENCE"
+                ]
+            },
+            "optional": true,
+            "default": "RVI ASC"
+        },
+        {
+            "name": "s2_collection",
+            "description": "S2 data collection to use for fusing the data",
+            "schema": {
+                "type": "string",
+                "enum": [
+                    "NDVI",
+                    "FAPAR",
+                    "LAI",
+                    "FCOVER",
+                    "EVI",
+                    "CCC",
+                    "CWC"
+                ]
+            },
+            "optional": true,
+            "default": "NDVI"
+        },
+        {
+            "name": "include_uncertainties",
+            "description": "Flag to include the uncertainties, expressed as the standard deviation in the final result",
+            "schema": {
+                "type": "boolean"
+            },
+            "optional": true,
+            "default": false
+        },
+        {
+            "name": "include_raw_inputs",
+            "description": "Flag to include the raw input signals in the final result",
+            "schema": {
+                "type": "boolean"
+            },
+            "optional": true,
+            "default": false
+        }
+    ]
+}
\ No newline at end of file
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 2ab05db..03a51f8 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -51,7 +51,7 @@ def execute_udf():
     merged_datacube = base_s2.merge(base_s1)
 
     # Execute MOGPR
-    mogpr = connection.datacube_from_flat_graph(generate_mogpr_cube(merged_datacube, True).flat_graph())
+    mogpr = connection.datacube_from_flat_graph(generate_mogpr_cube(merged_datacube, True, False).flat_graph())
     mogpr.execute_batch(
         "./result_mogpr.nc",
         title=f"FuseTS - MOGPR - Local",
@@ -66,14 +66,19 @@ def execute_udf():
 
 
 def generate_mogpr_cube(
-    input_cube: Union[DataCube, ProcessBuilder, Parameter], include_uncertainties: Union[bool, Parameter]
+    input_cube: Union[DataCube, ProcessBuilder, Parameter],
+    include_uncertainties: Union[bool, Parameter],
+    include_raw_inputs: Union[bool, Parameter],
 ):
     return apply_neighborhood(
         input_cube,
         lambda data: data.run_udf(
             udf=load_mogpr_udf(),
             runtime="Python",
-            context={"include_uncertainties": get_context_value(include_uncertainties)},
+            context={
+                "include_uncertainties": get_context_value(include_uncertainties),
+                "include_raw_inputs": get_context_value(include_raw_inputs),
+            },
         ),
         size=[
             {"dimension": "x", "value": NEIGHBORHOOD_SIZE, "unit": "px"},
@@ -91,14 +96,21 @@ def generate_mogpr_udp():
     include_uncertainties = Parameter.boolean(
         "include_uncertainties", "Flag to include the uncertainties in the output results", False
     )
+    include_raw_inputs = Parameter.boolean(
+        "include_raw_inputs",
+        "Flag to include the raw input signals in the final result",
+        False,
+    )
 
-    mogpr = generate_mogpr_cube(input_cube=input_cube, include_uncertainties=include_uncertainties)
+    mogpr = generate_mogpr_cube(
+        input_cube=input_cube, include_uncertainties=include_uncertainties, include_raw_inputs=include_raw_inputs
+    )
 
     return publish_service(
         id="mogpr",
         summary="Integrates timeseries in data cube using multi-output gaussian " "process regression.",
         description=description,
-        parameters=[input_cube.to_dict(), include_uncertainties.to_dict()],
+        parameters=[input_cube.to_dict(), include_uncertainties.to_dict(), include_raw_inputs.to_dict()],
         process_graph=mogpr,
     )
 
diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index a28cfb7..6e25b43 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -19,17 +19,17 @@ def execute_udf():
         "type": "Polygon",
         "coordinates": [
             [
-                [12.502373837196238, 42.06404350608216],
-                [12.502124488464212, 42.03089916587777],
-                [12.571692784699895, 42.031269589226014],
-                [12.57156811033388, 42.06663507169753],
-                [12.502373837196238, 42.06404350608216],
+                [5.170012098271149, 51.25062964728295],
+                [5.17085904378298, 51.24882567194015],
+                [5.17857421368097, 51.2468515482926],
+                [5.178972704726344, 51.24982704376254],
+                [5.170012098271149, 51.25062964728295],
             ]
         ],
     }
-    temp_ext = ["2023-01-01", "2023-12-31"]
+    temp_ext = ["2023-01-01", "2023-06-30"]
     mogpr = connection.datacube_from_flat_graph(
-        generate_cube(connection, "RVI DESC", "NDVI", spat_ext, temp_ext, True).flat_graph()
+        generate_cube(connection, "RVI DESC", "NDVI", spat_ext, temp_ext, True, True).flat_graph()
     )
     mogpr.execute_batch(
         "./result_mogpr_s1_s2_outputs.nc",
@@ -278,19 +278,16 @@ def load_s2_collection(connection, collection, polygon, date):
     return collections
 
 
-def generate_cube(connection, s1_collection, s2_collection, polygon, date, include_uncertainties):
+def generate_cube(connection, s1_collection, s2_collection, polygon, date, include_uncertainties, include_raw_inputs):
     # Build the S1 and S2 input data cubes
     s1_input_cube = load_s1_collection(connection, s1_collection, polygon, date)
     s2_input_cube = load_s2_collection(connection, s2_collection, polygon, date)
 
     # Merge the inputs to a single datacube
-    merged_cube = merge_cubes(s1_input_cube, s2_input_cube)
+    input_cube = merge_cubes(s1_input_cube, s2_input_cube)
 
     # Apply the MOGPR UDF to the multi source datacube
-    return generate_mogpr_cube(
-        merged_cube,
-        include_uncertainties,
-    )
+    return generate_mogpr_cube(input_cube, include_uncertainties, include_raw_inputs)
 
 
 def generate_mogpr_s1_s2_udp(connection):
@@ -317,7 +314,13 @@ def generate_mogpr_s1_s2_udp(connection):
     )
     include_uncertainties = Parameter.boolean(
         "include_uncertainties",
-        "Flag to include the uncertainties, expressed as the standard deviation, " "in the output results",
+        "Flag to include the uncertainties, expressed as the standard deviation in the final result",
+        False,
+    )
+
+    include_raw_inputs = Parameter.boolean(
+        "include_raw_inputs",
+        "Flag to include the raw input signals in the final result",
         False,
     )
 
@@ -328,6 +331,7 @@ def generate_mogpr_s1_s2_udp(connection):
         polygon=polygon,
         date=date,
         include_uncertainties=include_uncertainties,
+        include_raw_inputs=include_raw_inputs,
     )
     return publish_service(
         id="mogpr_s1_s2",
@@ -340,6 +344,7 @@ def generate_mogpr_s1_s2_udp(connection):
             s1_collection.to_dict(),
             s2_collection.to_dict(),
             include_uncertainties.to_dict(),
+            include_raw_inputs.to_dict(),
         ],
         process_graph=process,
     )

From b4c83ab75192f94b2b2068f034fa6f5bd52ff7f6 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Wed, 6 Mar 2024 17:09:21 +0100
Subject: [PATCH 20/21] chore: fixed formatting

---
 src/fusets/temporal_outliers.py               |   2 +-
 tests/conftest.py                             |   5 +-
 tests/fusets_openeo_tests/conftest.py         |  13 +-
 tests/fusets_openeo_tests/test_performance.py | 191 +++++++++---------
 tests/helpers.py                              |   8 +-
 tests/test_temporal_outliers.py               |   5 +-
 6 files changed, 112 insertions(+), 112 deletions(-)

diff --git a/src/fusets/temporal_outliers.py b/src/fusets/temporal_outliers.py
index 3bf9c7a..f2c06a3 100644
--- a/src/fusets/temporal_outliers.py
+++ b/src/fusets/temporal_outliers.py
@@ -67,4 +67,4 @@ def temporal_outliers_f(x: Sequence[datetime], y: np.ndarray, window: Union[int,
     ts_zscore = timeseries.sub(ts_mean).div(ts_std)
     ts_mask = ts_zscore.between(-threshold, threshold)
 
-    return timeseries.where(ts_mask, ts_mean).to_numpy(dtype='float32')
+    return timeseries.where(ts_mask, ts_mean).to_numpy(dtype="float32")
diff --git a/tests/conftest.py b/tests/conftest.py
index fcaf288..693b715 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -71,12 +71,13 @@ def generate_data(xs: np.array):
 @pytest.fixture
 def outlier_timeseries():
     rng = np.random.default_rng(42)
-    dates = pd.date_range('2019-01-01', '2019-12-31', periods=300)
-    values = np.sin(np.linspace(0, 4*np.pi, len(dates))) + rng.random(len(dates))*0.2
+    dates = pd.date_range("2019-01-01", "2019-12-31", periods=300)
+    values = np.sin(np.linspace(0, 4 * np.pi, len(dates))) + rng.random(len(dates)) * 0.2
     values[rng.choice(range(len(dates)), 4).astype(int)] += rng.choice([-1, 1], 4) * 5
 
     return xarray.DataArray(data=values, dims=["time"], coords=dict(time=dates))
 
+
 @pytest.fixture
 def areas():
     return {
diff --git a/tests/fusets_openeo_tests/conftest.py b/tests/fusets_openeo_tests/conftest.py
index 8e9ea49..4fb8db6 100644
--- a/tests/fusets_openeo_tests/conftest.py
+++ b/tests/fusets_openeo_tests/conftest.py
@@ -9,18 +9,15 @@
 
 @pytest.fixture
 def benchmark_features():
-    aoi_dir = RESOURCES / 'aois'
-    geojson_files = [file for file in os.listdir(aoi_dir) if file.endswith('.geojson')]
+    aoi_dir = RESOURCES / "aois"
+    geojson_files = [file for file in os.listdir(aoi_dir) if file.endswith(".geojson")]
     result = []
     for file in geojson_files:
-        with open(aoi_dir / file, 'r') as input:
+        with open(aoi_dir / file, "r") as input:
             data = json.load(input)
-            aois = data['features']
+            aois = data["features"]
             for feature in aois:
-                feature["properties"] = {
-                    **feature["properties"],
-                    "jobname": file.split('.')[0]
-                }
+                feature["properties"] = {**feature["properties"], "jobname": file.split(".")[0]}
             result += aois
             input.close()
     return gpd.GeoDataFrame.from_features(result)
diff --git a/tests/fusets_openeo_tests/test_performance.py b/tests/fusets_openeo_tests/test_performance.py
index 59c7242..e3124ac 100644
--- a/tests/fusets_openeo_tests/test_performance.py
+++ b/tests/fusets_openeo_tests/test_performance.py
@@ -12,25 +12,18 @@
 
 from tests.helpers import read_test_json
 
-logging.basicConfig(
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    level=logging.INFO
-)
+logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO)
 
 job_options = {
-    'executor-memory': '8g',
-    'udf-dependency-archives': [
-        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',
-        'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'
-    ]
+    "executor-memory": "8g",
+    "udf-dependency-archives": [
+        "https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv",
+        "https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static",
+    ],
 }
 
 
-def start_job(
-        data,
-        context: dict,
-        **kwargs
-) -> openeo.BatchJob:
+def start_job(data, context: dict, **kwargs) -> openeo.BatchJob:
     """
     Callback function for the openEO MultiBackendJobManager to start a new job.
     :param data: Dictionary containing the general information for launching the job
@@ -38,24 +31,31 @@ def start_job(
     :param kwargs:
     :return: OpenEO Batch Job
     """
-    row = data['row']
-    connection = data['connection']
-    aoi = geojson.Feature(geometry=row['geometry'])
-
-    if 'params' in context and 'skipData' in context['params'] and context['params']['skipData']:
-        service_dc = connection.datacube_from_process(polygon=aoi, **context['jobinfo'], )
+    row = data["row"]
+    connection = data["connection"]
+    aoi = geojson.Feature(geometry=row["geometry"])
+
+    if "params" in context and "skipData" in context["params"] and context["params"]["skipData"]:
+        service_dc = connection.datacube_from_process(
+            polygon=aoi,
+            **context["jobinfo"],
+        )
     else:
-        base = connection.load_collection('SENTINEL2_L2A',
-                                          spatial_extent=aoi,
-                                          temporal_extent=context['params']['temp-ext'],
-                                          bands=["B04", "B08", "SCL"])
+        base = connection.load_collection(
+            "SENTINEL2_L2A",
+            spatial_extent=aoi,
+            temporal_extent=context["params"]["temp-ext"],
+            bands=["B04", "B08", "SCL"],
+        )
         base_cloudmasked = base.process("mask_scl_dilation", data=base, scl_band_name="SCL")
         base_ndvi = base_cloudmasked.ndvi(red="B04", nir="B08")
-        service_dc = connection.datacube_from_process(data=base_ndvi, **context['jobinfo'])
+        service_dc = connection.datacube_from_process(data=base_ndvi, **context["jobinfo"])
 
-    return service_dc.create_job(title=f'FuseTS - Benchmark - {context["jobinfo"]["process_id"]} - {row["jobname"]}',
-                                 job_options=job_options,
-                                 format='netcdf')
+    return service_dc.create_job(
+        title=f'FuseTS - Benchmark - {context["jobinfo"]["process_id"]} - {row["jobname"]}',
+        job_options=job_options,
+        format="netcdf",
+    )
 
 
 def get_job_cost(connection, jobId) -> float:
@@ -66,7 +66,7 @@ def get_job_cost(connection, jobId) -> float:
     :return: Floating number representing the total cost of the job in credits
     """
     job = connection.job(jobId).describe_job()
-    return job['costs'] if 'costs' in job else np.nan
+    return job["costs"] if "costs" in job else np.nan
 
 
 def read_job_info(connection, path) -> pd.DataFrame:
@@ -78,18 +78,18 @@ def read_job_info(connection, path) -> pd.DataFrame:
     :return: Dataframe containing the job information extended with additional information
     """
     job_data = pd.read_csv(path)
-    geometry = [loads(poly) for poly in job_data['geometry']]
+    geometry = [loads(poly) for poly in job_data["geometry"]]
     job_data = gpd.GeoDataFrame(job_data, geometry=geometry, crs=4326)
     job_data = job_data.to_crs(epsg=3857)
-    for col in ['cpu', 'memory', 'duration', 'sentinelhub']:
-        job_data[col] = job_data[col].str.extract('(\d+)').astype(float)
-
-    job_data['area_hectares'] = job_data['geometry'].apply(lambda x: x.area / 10000)
-    job_data['cost'] = job_data['id'].apply(lambda x: get_job_cost(connection, x))
-    job_data['cost_ha'] = job_data['cost'] / job_data['area_hectares']
-    job_data['cpu_ha'] = job_data['cpu'] / job_data['area_hectares']
-    job_data['memory_ha'] = job_data['memory'] / job_data['area_hectares']
-    job_data['duration_ha'] = job_data['duration'] / job_data['area_hectares']
+    for col in ["cpu", "memory", "duration", "sentinelhub"]:
+        job_data[col] = job_data[col].str.extract("(\d+)").astype(float)
+
+    job_data["area_hectares"] = job_data["geometry"].apply(lambda x: x.area / 10000)
+    job_data["cost"] = job_data["id"].apply(lambda x: get_job_cost(connection, x))
+    job_data["cost_ha"] = job_data["cost"] / job_data["area_hectares"]
+    job_data["cpu_ha"] = job_data["cpu"] / job_data["area_hectares"]
+    job_data["memory_ha"] = job_data["memory"] / job_data["area_hectares"]
+    job_data["duration_ha"] = job_data["duration"] / job_data["area_hectares"]
     return job_data
 
 
@@ -99,7 +99,7 @@ def get_service_metrics(service) -> dict:
     :param service: Name of the service for which to read the base metrics
     :return: Dictionary containing the base metrics for cost, cpu, duration, memory and sentinelhub
     """
-    metrics = read_test_json('benchmarks/performance.json')
+    metrics = read_test_json("benchmarks/performance.json")
     return metrics[service]
 
 
@@ -113,74 +113,74 @@ def check_performance(service, jobs):
     metrics = get_service_metrics(service)
 
     # Check performance benchmarks
-    assert (jobs['cpu_ha'].mean() == pytest.approx(metrics['cpu'], abs=metrics['cpu'] * 0.25))
-    assert (jobs['memory_ha'].mean() == pytest.approx(metrics['memory'], abs=metrics['memory'] * 0.25))
-    assert (jobs['duration_ha'].mean() == pytest.approx(metrics['duration'], abs=metrics['duration'] * 0.25))
-    assert (jobs['sentinelhub'].mean() == pytest.approx(metrics['sentinelhub'], abs=metrics['sentinelhub'] * 0.25))
-    assert (jobs['cost_ha'].mean() == pytest.approx(metrics['cost_ha'], abs=metrics['cost_ha'] * 0.25))
+    assert jobs["cpu_ha"].mean() == pytest.approx(metrics["cpu"], abs=metrics["cpu"] * 0.25)
+    assert jobs["memory_ha"].mean() == pytest.approx(metrics["memory"], abs=metrics["memory"] * 0.25)
+    assert jobs["duration_ha"].mean() == pytest.approx(metrics["duration"], abs=metrics["duration"] * 0.25)
+    assert jobs["sentinelhub"].mean() == pytest.approx(metrics["sentinelhub"], abs=metrics["sentinelhub"] * 0.25)
+    assert jobs["cost_ha"].mean() == pytest.approx(metrics["cost_ha"], abs=metrics["cost_ha"] * 0.25)
 
 
 @pytest.mark.parametrize(
     "context",
     [
         (
-                {
-                    "params": {
-                        "temp-ext": ["2023-01-01", "2023-12-31"],
-                    },
-                    "jobinfo": {
-                        "process_id": "whittaker",
-                        "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/whittaker",
-                        "smoothing_lambda": 10000
-                    }
-                }
+            {
+                "params": {
+                    "temp-ext": ["2023-01-01", "2023-12-31"],
+                },
+                "jobinfo": {
+                    "process_id": "whittaker",
+                    "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/whittaker",
+                    "smoothing_lambda": 10000,
+                },
+            }
         ),
         (
-                {
-                    "params": {
-                        "temp-ext": ["2023-01-01", "2023-12-31"],
-                    },
-                    "jobinfo": {
-                        "process_id": "mogpr",
-                        "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/mogpr",
-                    }
-                }
+            {
+                "params": {
+                    "temp-ext": ["2023-01-01", "2023-12-31"],
+                },
+                "jobinfo": {
+                    "process_id": "mogpr",
+                    "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/mogpr",
+                },
+            }
         ),
         (
-                {
-                    "params": {
-                        "temp-ext": ["2023-01-01", "2023-12-31"],
-                    },
-                    "jobinfo": {
-                        "process_id": "peakvalley",
-                        "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/peakvalley",
-                    }
-                }
+            {
+                "params": {
+                    "temp-ext": ["2023-01-01", "2023-12-31"],
+                },
+                "jobinfo": {
+                    "process_id": "peakvalley",
+                    "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/peakvalley",
+                },
+            }
         ),
         (
-                {
-                    "params": {
-                        "temp-ext": ["2023-01-01", "2023-12-31"],
-                    },
-                    "jobinfo": {
-                        "process_id": "phenology",
-                        "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/phenology",
-                    }
-                }
+            {
+                "params": {
+                    "temp-ext": ["2023-01-01", "2023-12-31"],
+                },
+                "jobinfo": {
+                    "process_id": "phenology",
+                    "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/phenology",
+                },
+            }
         ),
         (
-                {
-                    "params": {
-                        "skipData": True,
-                    },
-                    "jobinfo": {
-                        "process_id": "mogpr_s1_s2",
-                        "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/mogpr_s1_s2",
-                        "date": ["2023-01-01", "2023-12-31"],
-                    }
-                }
+            {
+                "params": {
+                    "skipData": True,
+                },
+                "jobinfo": {
+                    "process_id": "mogpr_s1_s2",
+                    "namespace": "https://openeo.vito.be/openeo/1.1/processes/u:fusets/mogpr_s1_s2",
+                    "date": ["2023-01-01", "2023-12-31"],
+                },
+            }
         ),
-    ]
+    ],
 )
 def test_benchmark_fusets_service(benchmark_features, context):
     """
@@ -195,8 +195,9 @@ def test_benchmark_fusets_service(benchmark_features, context):
     manager.run_jobs(
         df=benchmark_features,
         start_job=lambda **x: start_job(x, context=context),
-        output_file=Path(f"benchmark_fusets_{context['jobinfo']['process_id']}.csv"))
+        output_file=Path(f"benchmark_fusets_{context['jobinfo']['process_id']}.csv"),
+    )
 
     # Evaluate the performance metrics
     job_data = read_job_info(connection, output_file)
-    check_performance(context['jobinfo']['process_id'], job_data)
+    check_performance(context["jobinfo"]["process_id"], job_data)
diff --git a/tests/helpers.py b/tests/helpers.py
index 7e4cbaf..6bc6b5d 100644
--- a/tests/helpers.py
+++ b/tests/helpers.py
@@ -1,9 +1,11 @@
 import json
 from pathlib import Path
 
-RESOURCES = Path(__file__).parent / 'resources'
+RESOURCES = Path(__file__).parent / "resources"
 
 
-def read_test_json(name, ):
-    with open(f'{RESOURCES}/{name}', 'r') as file:
+def read_test_json(
+    name,
+):
+    with open(f"{RESOURCES}/{name}", "r") as file:
         return json.load(file)
diff --git a/tests/test_temporal_outliers.py b/tests/test_temporal_outliers.py
index 22bf6bd..71dcdcb 100644
--- a/tests/test_temporal_outliers.py
+++ b/tests/test_temporal_outliers.py
@@ -1,16 +1,15 @@
 import numpy as np
+from numpy.testing import assert_almost_equal
 
 from fusets._xarray_utils import _extract_dates
 from fusets.temporal_outliers import temporal_outliers_f
-from numpy.testing import assert_almost_equal
 
 
 def test_temporal_outlier_filtering(outlier_timeseries):
     dates = np.array(_extract_dates(outlier_timeseries))
     vals = outlier_timeseries.values
 
-    filtered_vals = temporal_outliers_f(dates, vals, window='20D', threshold=3)
+    filtered_vals = temporal_outliers_f(dates, vals, window="20D", threshold=3)
 
     assert_almost_equal(filtered_vals.mean(), 0.09904716, decimal=6)
     assert_almost_equal(filtered_vals.std(), 0.71552783, decimal=6)
-

From 6ac55c29e4fac9849ecb8475166e30da8de8ab34 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Wed, 13 Mar 2024 17:29:46 +0100
Subject: [PATCH 21/21] feat: first test with renaming bands

---
 src/fusets/openeo/mogpr_udf.py                | 20 +++++++++++++------
 src/fusets/openeo/services/publish_mogpr.py   |  2 +-
 .../openeo/services/publish_mogpr_s1_s2.py    |  7 +++----
 3 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/src/fusets/openeo/mogpr_udf.py b/src/fusets/openeo/mogpr_udf.py
index 40103b6..8ef1dfa 100644
--- a/src/fusets/openeo/mogpr_udf.py
+++ b/src/fusets/openeo/mogpr_udf.py
@@ -4,7 +4,7 @@
 from pathlib import Path
 from typing import Dict
 
-from openeo.metadata import CollectionMetadata
+from openeo.metadata import Band, CollectionMetadata
 from openeo.udf import XarrayDataCube, inspect
 
 
@@ -42,10 +42,18 @@ def write_gpy_cfg():
 
 
 def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
-    # extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
-    # inspect(data=metadata, message="MOGPR metadata")
-    # for band in extra_bands:
-    #     metadata = metadata.append_band(band)
+    include_uncertainties = context.get("include_uncertainties", False)
+    include_raw_inputs = context.get("include_raw_inputs", False)
+    extra_bands = []
+
+    if include_uncertainties:
+        extra_bands += [Band(f"{x.name}_STD", None, None) for x in metadata.bands]
+    if include_raw_inputs:
+        extra_bands += [Band(f"{x.name}_RAW", None, None) for x in metadata.bands]
+    for band in extra_bands:
+        metadata = metadata.append_band(band)
+    inspect(data=metadata, message="MOGPR metadata")
+
     return metadata
 
 
@@ -77,7 +85,7 @@ def apply_datacube(cube: XarrayDataCube, context: Dict) -> XarrayDataCube:
         include_uncertainties=include_uncertainties,
         include_raw_inputs=include_raw_inputs,
     )
-    result_dc = XarrayDataCube(result.to_array(dim="bands").transpose(*dims))
+    result_dc = XarrayDataCube(result.to_array(dim="bands").transpose(*dims).astype("float32"))
     inspect(data=result_dc, message="MOGPR result")
     set_home(home)
     return result_dc
diff --git a/src/fusets/openeo/services/publish_mogpr.py b/src/fusets/openeo/services/publish_mogpr.py
index 03a51f8..29fc7f7 100644
--- a/src/fusets/openeo/services/publish_mogpr.py
+++ b/src/fusets/openeo/services/publish_mogpr.py
@@ -74,7 +74,7 @@ def generate_mogpr_cube(
         input_cube,
         lambda data: data.run_udf(
             udf=load_mogpr_udf(),
-            runtime="Python",
+            runtime="Python-Jep",
             context={
                 "include_uncertainties": get_context_value(include_uncertainties),
                 "include_raw_inputs": get_context_value(include_raw_inputs),
diff --git a/src/fusets/openeo/services/publish_mogpr_s1_s2.py b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
index 6e25b43..3e8a754 100644
--- a/src/fusets/openeo/services/publish_mogpr_s1_s2.py
+++ b/src/fusets/openeo/services/publish_mogpr_s1_s2.py
@@ -3,7 +3,6 @@
 from openeo.api.process import Parameter
 from openeo.processes import eq, if_, merge_cubes, process
 
-from fusets.openeo.services.dummies import DummyConnection
 from fusets.openeo.services.helpers import DATE_SCHEMA, GEOJSON_SCHEMA, publish_service, read_description
 from fusets.openeo.services.publish_mogpr import generate_mogpr_cube
 
@@ -14,7 +13,7 @@
 
 
 def execute_udf():
-    connection = openeo.connect("openeo.vito.be").authenticate_oidc()
+    connection = openeo.connect("openeo-dev.vito.be").authenticate_oidc()
     spat_ext = {
         "type": "Polygon",
         "coordinates": [
@@ -356,5 +355,5 @@ def generate_mogpr_s1_s2_udp(connection):
 if __name__ == "__main__":
     # Using the dummy connection as otherwise Datatype errors are generated when creating the input datacubes
     # where bands are selected.
-    generate_mogpr_s1_s2_udp(connection=DummyConnection())
-    # execute_udf()
+    # generate_mogpr_s1_s2_udp(connection=DummyConnection())
+    execute_udf()