From bbbdf679ce00c537b2072dca6401909b1df5fe11 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 18 Nov 2025 13:29:47 -0900
Subject: [PATCH 01/19] begin to add xarray capability and add new variables in
 coords

---
 mhkit/river/io/d3d.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 7295d7e11..b4c854264 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -35,8 +35,8 @@ def get_all_time(data: netCDF4.Dataset) -> NDArray:
         simulation conditions at that time.
     """
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be a NetCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be a NetCDF4 object or xarray Dataset")
 
     seconds_run = np.ma.getdata(data.variables["time"][:], False)
 
@@ -244,6 +244,10 @@ def get_layer_data(
                 "name": "mesh2d_nLayers",
                 "coords": data.variables["mesh2d_layer_sigma"][:],
             },
+            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
+                "name": "mesh2d_nLayers",
+                "coords": data.variables["mesh2d_layer_sigma"][:],
+            },
             "mesh2d_edge_x mesh2d_edge_y": {
                 "name": "mesh2d_nInterfaces",
                 "coords": data.variables["mesh2d_interface_sigma"][:],
@@ -637,6 +641,10 @@ def get_all_data_points(
                 "name": "mesh2d_nLayers",
                 "coords": data.variables["mesh2d_layer_sigma"][:],
             },
+            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
+                "name": "mesh2d_nLayers",
+                "coords": data.variables["mesh2d_layer_sigma"][:],
+            },
             "mesh2d_edge_x mesh2d_edge_y": {
                 "name": "mesh2d_nInterfaces",
                 "coords": data.variables["mesh2d_interface_sigma"][:],

From bb27980efaa51deaed38c850db6b0186ba3cea21 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 19 Nov 2025 10:18:15 -0900
Subject: [PATCH 02/19] fix waterdepth name

---
 mhkit/river/io/d3d.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index b4c854264..f0491e69d 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -257,7 +257,7 @@ def get_layer_data(
             data.variables["mesh2d_waterdepth"][time_index, :], False
         )
         waterlevel = np.ma.getdata(data.variables["mesh2d_s1"][time_index, :], False)
-        coords = str(data.variables["waterdepth"].coordinates).split()
+        coords = str(data.variables["mesh2d_waterdepth"].coordinates).split()
 
     elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
         cords_to_layers = {

From 2e78821536e87a3f317df1e05653df4c39734b52 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 28 Jan 2026 09:28:58 -0900
Subject: [PATCH 03/19] calculate_grid_convergence_index

---
 mhkit/river/io/d3d.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index f0491e69d..79b0399e5 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -894,3 +894,30 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
         "data must be a NetCDF4 Dataset, xarray Dataset, or "
         f"xarray DataArray. Got: {type(data)}"
     )
+
+def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,factor_of_safety=1.25, order=2):
+    """
+    Calculate the Grid Convergence Index (GCI) between two grid sizes. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html
+
+    Parameters
+    ----------
+    fine_grid: numpy.ndarray
+        Results from the finer grid.
+    coarse_grid: numpy.ndarray
+        Results from the coarser grid.
+    refinement_ratio: float 
+        Refinement ratio between the grids.
+    order: int
+        Order of accuracy (default is 2).
+
+    Returns
+    -------
+    gci: float
+        Grid Convergence Index (GCI).
+    """
+    # Calculate the approximate relative error
+    error = np.abs((fine_grid - coarse_grid) / fine_grid)
+
+    # Calculate the GCI
+    gci = (factor_of_safety * error) / (refinement_ratio**order - 1)
+    return gci
\ No newline at end of file

From a43446ca94456830054d655fe5c5bdcd5ae6ace4 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 28 Jan 2026 10:05:15 -0900
Subject: [PATCH 04/19] add test for calculate_grid_convergence_index

---
 mhkit/tests/river/test_io_d3d.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/mhkit/tests/river/test_io_d3d.py b/mhkit/tests/river/test_io_d3d.py
index f41ba4962..86caf8bd1 100644
--- a/mhkit/tests/river/test_io_d3d.py
+++ b/mhkit/tests/river/test_io_d3d.py
@@ -296,7 +296,15 @@ def test_turbulent_intensity(self):
         ucx = river.io.d3d.get_all_data_points(data, "ucx", time_index)
         ucx_size = np.size(ucx["ucx"])
         self.assertEqual(TI_size, ucx_size)
-
+    
+    def test_calculate_grid_convergence_index(self):
+        fine_grid = np.array([1.0, 2.0, 3.0])
+        coarse_grid = np.array([0.5, 1.5, 2.5])
+        refinement_ratio = 2.0
+        gci = river.io.d3d.calculate_grid_convergence_index(
+            fine_grid, coarse_grid, refinement_ratio
+        )
+        self.assertAlmostEqual(gci, np.array([0.2083, 0.1042, 0.0694]))
 
 if __name__ == "__main__":
     unittest.main()

From 0f5e015bf48707854c674818a2d8932dd6fdc547 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 28 Jan 2026 10:28:18 -0900
Subject: [PATCH 05/19] black an pylint formating

---
 mhkit/river/io/d3d.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 79b0399e5..847795a82 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -895,7 +895,10 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
         f"xarray DataArray. Got: {type(data)}"
     )
 
-def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,factor_of_safety=1.25, order=2):
+
+def calculate_grid_convergence_index(
+    fine_grid, coarse_grid, refinement_ratio, factor_of_safety=1.25, order=2
+):
     """
     Calculate the Grid Convergence Index (GCI) between two grid sizes. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html
 
@@ -905,7 +908,7 @@ def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,fa
         Results from the finer grid.
     coarse_grid: numpy.ndarray
         Results from the coarser grid.
-    refinement_ratio: float 
+    refinement_ratio: float
         Refinement ratio between the grids.
     order: int
         Order of accuracy (default is 2).
@@ -920,4 +923,4 @@ def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,fa
 
     # Calculate the GCI
     gci = (factor_of_safety * error) / (refinement_ratio**order - 1)
-    return gci
\ No newline at end of file
+    return gci

From d2155a8be08e7776767db6293f790f3f8abf6433 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 28 Jan 2026 10:28:39 -0900
Subject: [PATCH 06/19] black and pulint formatting

---
 mhkit/tests/river/test_io_d3d.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mhkit/tests/river/test_io_d3d.py b/mhkit/tests/river/test_io_d3d.py
index 86caf8bd1..e2e159082 100644
--- a/mhkit/tests/river/test_io_d3d.py
+++ b/mhkit/tests/river/test_io_d3d.py
@@ -296,7 +296,7 @@ def test_turbulent_intensity(self):
         ucx = river.io.d3d.get_all_data_points(data, "ucx", time_index)
         ucx_size = np.size(ucx["ucx"])
         self.assertEqual(TI_size, ucx_size)
-    
+
     def test_calculate_grid_convergence_index(self):
         fine_grid = np.array([1.0, 2.0, 3.0])
         coarse_grid = np.array([0.5, 1.5, 2.5])
@@ -304,7 +304,8 @@ def test_calculate_grid_convergence_index(self):
         gci = river.io.d3d.calculate_grid_convergence_index(
             fine_grid, coarse_grid, refinement_ratio
         )
-        self.assertAlmostEqual(gci, np.array([0.2083, 0.1042, 0.0694]))
+        assert_array_almost_equal(gci, np.array([0.2083, 0.1042, 0.0694]), decimal=3)
+
 
 if __name__ == "__main__":
     unittest.main()

From 3d115173f213212ed9f779b50bcbaa216db205eb Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 28 Jan 2026 10:38:58 -0900
Subject: [PATCH 07/19] add xarray input to error checks

---
 mhkit/river/io/d3d.py | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 847795a82..8a760ee78 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -118,8 +118,8 @@ def _convert_time(
         provided, returns the closest matching time_index.
     """
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be NetCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be NetCDF4 object or xarray Dataset")
 
     if not (time_index or seconds_run):
         raise ValueError("Input of time_index or seconds_run needed")
@@ -199,8 +199,8 @@ def get_layer_data(
     if not isinstance(layer_index, int):
         raise TypeError("layer_index must be an int")
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be NetCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be NetCDF4 object or xarray Dataset")
 
     if variable not in data.variables.keys():
         raise ValueError("variable not recognized")
@@ -538,8 +538,10 @@ def variable_interpolation(
                 f"If a string, points must be cells or faces. Got {points}"
             )
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError(f"data must be netCDF4 object. Got {type(data)}")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError(
+            f"data must be netCDF4 object or xarray Dataset. Got {type(data)}"
+        )
 
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
@@ -620,8 +622,8 @@ def get_all_data_points(
     if not isinstance(time_index, int):
         raise TypeError("time_index must be an int")
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be NetCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be NetCDF4 object or xarray Dataset")
 
     if variable not in data.variables.keys():
         raise ValueError("variable not recognized")
@@ -792,8 +794,8 @@ def turbulent_intensity(
             f"value of the max time index {max_time_index}"
         )
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be netCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be netCDF4 object or xarray Dataset")
 
     for variable in ["turkin1", "ucx", "ucy", "ucz"]:
         if variable not in data.variables.keys():
@@ -886,7 +888,10 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
     >>> print(variables)
     ['time', 'x', 'y', 'waterdepth', 'ucx', 'ucy', 'ucz', 'turkin1']
     """
-    if isinstance(data, netCDF4.Dataset):
+    if isinstance(
+        data,
+        netCDF4.Dataset,
+    ):
         return list(data.variables.keys())
     if isinstance(data, (xr.Dataset, xr.DataArray)):
         return list(data.variables.keys())

From fbb243481005f04a824f6bbb6a0bbc3e442e4b6b Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 3 Feb 2026 15:00:48 -0900
Subject: [PATCH 08/19] added checkes to GCI function

---
 mhkit/river/io/d3d.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 8a760ee78..70afac65c 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -915,6 +915,8 @@ def calculate_grid_convergence_index(
         Results from the coarser grid.
     refinement_ratio: float
         Refinement ratio between the grids.
+    factor_of_safety: float
+        Factor of safety (default is 1.25).
     order: int
         Order of accuracy (default is 2).
 
@@ -923,6 +925,19 @@ def calculate_grid_convergence_index(
     gci: float
         Grid Convergence Index (GCI).
     """
+
+    # Validate inputs
+    if not (np.issubdtype(refinement_ratio.dtype, np.number)):
+        raise TypeError("refinement_ratio must be a numeric values")
+    if not (np.issubdtype(factor_of_safety.dtype, np.number)):
+        raise TypeError("factor_of_safety must be a numeric values")
+    if not (np.issubdtype(order.dtype, np.number)):
+        raise TypeError("order must be a numeric values")
+    if not (np.issubdtype(fine_grid.dtype, np.number) and np.issubdtype(coarse_grid.dtype, np.number)):
+        raise TypeError("fine_grid and coarse_grid must contain numeric values")
+    if fine_grid.shape != coarse_grid.shape:
+        raise ValueError("fine_grid and coarse_grid must have the same shape")
+
     # Calculate the approximate relative error
     error = np.abs((fine_grid - coarse_grid) / fine_grid)
 

From 6209f683c98eed7e37f375476481678da7084a9f Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 3 Feb 2026 15:36:02 -0900
Subject: [PATCH 09/19] added citation

---
 mhkit/river/io/d3d.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 70afac65c..06e2f2b0c 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -905,7 +905,9 @@ def calculate_grid_convergence_index(
     fine_grid, coarse_grid, refinement_ratio, factor_of_safety=1.25, order=2
 ):
     """
-    Calculate the Grid Convergence Index (GCI) between two grid sizes. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html
+    Calculate the Grid Convergence Index (GCI) between two grid sizes. 
+
+    NASA. (n.d.). Examining spatial (grid) convergence. Acessed Febuary 3, 2026. NASA. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html 
 
     Parameters
     ----------

From de92303f79e7cde9ad0ffff93b05603083ce7811 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 3 Feb 2026 15:37:40 -0900
Subject: [PATCH 10/19] fixed typo

---
 mhkit/river/io/d3d.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 06e2f2b0c..f9183e948 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -907,7 +907,7 @@ def calculate_grid_convergence_index(
     """
     Calculate the Grid Convergence Index (GCI) between two grid sizes. 
 
-    NASA. (n.d.). Examining spatial (grid) convergence. Acessed Febuary 3, 2026. NASA. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html 
+         NASA. (n.d.). Examining spatial (grid) convergence. Accessed Febuary 3, 2026. NASA. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html 
 
     Parameters
     ----------

From 8a15145124a854bcbebb8cde1866be02568f38dc Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Wed, 4 Feb 2026 11:13:03 -0900
Subject: [PATCH 11/19] xarray testing still in progress

---
 mhkit/river/io/d3d.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index f9183e948..2544cee03 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -16,15 +16,15 @@
 from mhkit.utils import unorm
 
 
-def get_all_time(data: netCDF4.Dataset) -> NDArray:
+def get_all_time(data: (netCDF4.Dataset, xr.Dataset)) -> NDArray:
     """
     Returns all of the time stamps from a D3D simulation passed to the function
     as a NetCDF object (data)
 
     Parameters
     ----------
-    data: NetCDF4 object
-       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+    data: NetCDF4 object or xarray Dataset
+       A NetCDF4 object or xarray Dataset that contains spatial data, e.g. velocity or shear
        stress generated by running a Delft3D model.
 
     Returns

From f875e5f2e2e6c3a83ca9ba38a824cd702632f70b Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Mon, 9 Feb 2026 08:53:45 -0900
Subject: [PATCH 12/19] update git_layer_function to take xarray

---
 mhkit/river/io/d3d.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 2544cee03..1b056e690 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -224,6 +224,14 @@ def get_layer_data(
     if isinstance(var[0][0], np.ma.core.MaskedArray):
         max_layer = len(var[0][0])
 
+        if abs(layer_index) > max_layer:
+            raise ValueError(f"layer_index must be less than the max layer {max_layer}")
+
+        v = np.ma.getdata(var[time_index, :, layer_index], False)
+        dimensions = 3
+    elif isinstance(var[0][0], xr.core.variable.Variable):
+        max_layer = var[0][0].shape[0]
+
         if abs(layer_index) > max_layer:
             raise ValueError(f"layer_index must be less than the max layer {max_layer}")
 

From 6c5d9d8365e2b7402f0b1673d92f86eea4a15dc9 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 24 Feb 2026 09:54:55 -0900
Subject: [PATCH 13/19] updated convert_time to take xarray

---
 mhkit/river/io/d3d.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 1b056e690..75d78318c 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -23,8 +23,8 @@ def get_all_time(data: (netCDF4.Dataset, xr.Dataset)) -> NDArray:
 
     Parameters
     ----------
-    data: NetCDF4 object or xarray Dataset
-       A NetCDF4 object or xarray Dataset that contains spatial data, e.g. velocity or shear
+    data: NetCDF4 object
+       A NetCDF4 object that contains spatial data, e.g. velocity or shear
        stress generated by running a Delft3D model.
 
     Returns
@@ -36,9 +36,13 @@ def get_all_time(data: (netCDF4.Dataset, xr.Dataset)) -> NDArray:
     """
 
     if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
-        raise TypeError("data must be a NetCDF4 object or xarray Dataset")
+        raise TypeError("data must be a NetCDF4 object Dataset")
 
-    seconds_run = np.ma.getdata(data.variables["time"][:], False)
+    if isinstance(data, netCDF4.Dataset):
+        seconds_run = np.ma.getdata(data.variables["time"][:], False)
+
+    elif isinstance(data, xr.Dataset):
+        seconds_run = (data.time.values - data.time.values[0]).astype('timedelta64[s]').astype(int)
 
     return seconds_run
 
@@ -88,7 +92,7 @@ def seconds_to_index(data: netCDF4.Dataset, seconds_run: Union[int, float]) -> i
 
 
 def _convert_time(
-    data: netCDF4.Dataset,
+    data: netCDF4.Dataset or xr.Dataset,
     time_index: Optional[Union[int, float]] = None,
     seconds_run: Optional[Union[int, float]] = None,
 ) -> Union[int, float]:

From 8229a7bb33d10dcb7ddc304418c2cb8ca51a5606 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 24 Feb 2026 09:57:37 -0900
Subject: [PATCH 14/19] changed function to Second_to_index from convert_time

---
 examples/Delft3D_example.ipynb | 56 +++++++++++++++++-----------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/examples/Delft3D_example.ipynb b/examples/Delft3D_example.ipynb
index 8bb9e8702..c2342fb56 100644
--- a/examples/Delft3D_example.ipynb
+++ b/examples/Delft3D_example.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "id": "030973b3",
    "metadata": {},
    "outputs": [],
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "id": "258b202f",
    "metadata": {},
    "outputs": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 20,
    "id": "5e0dabc1",
    "metadata": {},
    "outputs": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 32,
    "id": "ebbdc284",
    "metadata": {},
    "outputs": [
@@ -180,14 +180,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sterl\\AppData\\Local\\Temp\\ipykernel_21668\\3610353735.py:2: UserWarning: Warning: seconds_run not found. Closest time stampfound 60.0\n",
-      "  time_index = d3d._convert_time(d3d_data, seconds_run=seconds_run)\n"
+      "c:\\Users\\ashly\\anaconda3\\envs\\Updated_MHKiT\\Lib\\site-packages\\mhkit\\river\\io\\d3d.py:87: UserWarning: Warning: seconds_run not found. Closest time stampfound 60.0\n",
+      "  time_index: int\n"
      ]
     }
    ],
    "source": [
     "seconds_run = 62\n",
-    "time_index = d3d._convert_time(d3d_data, seconds_run=seconds_run)\n",
+    "time_index = d3d.seconds_to_index(d3d_data, seconds_run=seconds_run)\n",
     "print(time_index)"
    ]
   },
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 22,
    "id": "197ab8f9",
    "metadata": {},
    "outputs": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "id": "26e1daba",
    "metadata": {},
    "outputs": [
@@ -333,7 +333,7 @@
        "4      16.426020  3.0    1.000931"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "id": "199785ce",
    "metadata": {},
    "outputs": [
@@ -379,13 +379,13 @@
        "Text(0.5, 1.0, 'Centerline Velocity at: 240.0 s')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1200x500 with 1 Axes>"
       ]
@@ -426,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 25,
    "id": "26daf026",
    "metadata": {},
    "outputs": [
@@ -436,13 +436,13 @@
        "Text(0.5, 1.0, 'Velocity on Layer 2 at Time: 240.0 s')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "id": "baf1e542",
    "metadata": {},
    "outputs": [
@@ -613,7 +613,7 @@
        "[4000 rows x 3 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "id": "441f059a",
    "metadata": {},
    "outputs": [],
@@ -660,13 +660,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "id": "d04fa7ad",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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OnVwRRAAAIl6aTSQGAKno7bff9s3jq8ezzz6b6Po//PBDTOnSpX3raw5lzWGdJ0+eWPt58MEHg563WtasWRNz3nnn+dbJnTt3TMGCBX3fa07ocePGJTj3suZu9j9+/vz5Y4oUKRKTJUsW973meA7lfB544AHf63nz5o0pV65cTPny5WN69OgR8BzOnDnjXve2GT9+fExKdevWzbc/vQ+9H81j7j/X88GDB+Nt582jrTmWE5LceaF1rZKaL/uee+5xrxcvXjzmjz/+iEmJu+++23eenTt3TtG+EppHO9RtA80dr3nj/T+rBQoU8M07rznGvc/GyJEjQ/ocehLaXg4cOBBzyy23xPr8699O4cKFfZ9/71wAAIh0tGgDyBDatWsXazqmQN3G/an1Ti3amiv5uuuuc62M6l6t7tsXXniha2HTmNohQ4aEdB4ad6vKz2rJVSu7ijxpnK9aSdWiq9dat24dcFsdWy2oGgur91OuXDnXOq5WPk3ZpC7yX375ZUjn884777hz0bzQsn37dldwKqGiVhqjffvtt7uvk1sELa5Bgwa5FlIVrtOYeXWhV9dszQGtlmSNS09sSqhwUzdodVPXsAH9vBKaL1vXTj0NdK3uueceXwtvcrRp08b3dSQVQfOnHgUao962bVs3pZfer34u+l5V/XUNUrv7usZoq+u4Wvz1+de/HfV+0BRxmtv71Vdf9c2lDQBAJMuitJ3eJwEAiBwK5eourErcr7zySnqfTobw+OOPu5soKoKm0AoAQLCWLVvmbkrrZqgeGuImocY4NSjoZqZuai5evNjtR0ObdDNfhWUfe+yxgMVPVUdm9OjRCe733Xff9Q09i2vKlCnuhvaKFSt8w/00VK5Zs2YJ7s9rsNBsHro5r5veamx44oknYhV6jXT/nU8DAAAz90tNIVu/yCKtYFe00vh6FVrzxvEDABAK1Z9RPZmUUuDt37+/K36pXndXX321m3ViwYIFLsCrmOu3335refPmDbi9as3EnfpRqlatGnD9IUOGuOky1btPdTgU6mfMmOFmhVG9HBUkjWvRokWuRo5m/9CMFBUqVHB1Y7Qf3ahWvRqveGeko0UbAOCoUJl+iarqtLoL65cZUkZdn3WXX0XXypYta7/99puvQB0AAMF4/fXXXVFITYOph8Knfr+E2qKt4Tdq1VZhSQ3P8aj4qoKwhpcF6s3mtWirKKU3c0tSNmzYYBdffLEL2dpOPbpEw/Y0pamGb2kqUbVWezRcTqFdhWM17EzhWtSqreFDCuGawlXnEw0I2gCQyWlaMt3N/vPPP934ZY3LVdhObrVx/Pcuvh67d+92d+Vl3LhxCY7PBwAgWKpJk5ygnRjNDqLu4wrxCropDdqPPfaY61LepUuXePVuBg8e7Ga4UIu2WrY9X3zxhavRcdlll9nKlStjbbN8+XJXS0S1VbxpXCNd9HRyBwCkCgXsnTt3uqmbdEdb3ccJ2SmjFgMVndMfQeqep94BhGwAQKRSuBX/qRVTYtq0ae450O8+b5nGbwe7jcZ2a3pHDW/bunWrRQPGaANAJqdgjfBSERdvDnEAACLd5s2b3XOgMdieCRMmuNlPzpw5427IN2/e3KpVqxbwZvP27dvd1zVr1oz3uoZSaXYL3ZBWHRPNOiHqTeeF6kC0XOe5evVq1/Ie6QjaAAAAAJCJDR061D3feuutCa7j381bnnnmGVfkU9tqLLbHC9lFihRxveUCKVOmjJs+U2HbfwpS77WEthFtEw0yTdDWfKDqCqGxh9FSqQ4AAADIqDS85tChQ3buuedG1bRNcvz4cTt58mSy33fcPKKK3Hqkh/fee89mzpxphQsXtmeffTbe62qVVjGzhg0burCrIWdff/219e7d24YPH+6KfGrctUfFyySh6uXiBXD9/IPdLtA2kSzTBG2FbHVTAAAAABA5duzYkWArZqSG7HLl89me3WeTtX3+/Pl9odLzwgsvpMuQo/nz57uCZQr+I0aMcDc94tLr/tRtXMXO6tev77pzDxs2zBU3I2tl0qCtlmxp/GV7y5GPqVUARLb6xTam9ykAAJCqjh8+bX2vX+D7Oz1aqCVbIXve0pKWP39oPWUPH46x667a7W4ueGOTJT1as1VYTF3F9X7eeusta9myZUjba/quFi1auPm3v//+e9+0W7qRIEePHrWEaLoy8f/Za7t9+/YluF2gbSJZpgnaXvcMhWyCNoBI1rD4+sz03zMAIJOL1mGdCtn5C4Ta5f2/reAK2f5BO61pCi/NTa1gq5b0xx9/PFn7qVy5snvetWuXb5k3R/e+fftcOA40TluznUj58uVjbadt9Nqll14a1DaRLLoGQwAAAAAAkk2huEmTJu5Z3cLVbT25FIzFP0xrrHe5/4XtFStWxNtGrfkqhKbA7H+zwZtiTHNmB+ItDxTCIxFBGwAirjUbAAAg/BSMb7zxRtu0aZPdd999sYqYherEiRO+ua/jTsnVrFkz96xu5XF5yzQ9WLDbKLBraq/q1atHxdReQtAGAAAAgAxCxck0v3XPnj1jLdfYZ4XZNWvWWNu2be2DDz5Istv++vXr7aOPPnKh2t+ePXvszjvvdK3TaomuV69erNe7dOli2bJlcxXNFy9e7Fv+66+/Wv/+/d10YHGLrGmMuAqtaT5t/xsA6n7eqVMn93WPHj0sWjAIEAAiBK3ZAAAgLrUa9+vXz/e9N61Y7dq1fcv69OnjaxFWt+wNGzbEGjctvXr1skWLFrkArKD7wAMPBLzYo0aN8n2tqbzuvfdeF4qvuOIKK1GihJvNadmyZW6aLVWL/+KLL+IF9qpVq9rAgQNdNfJrr73WdVXXNGAzZsywY8eOueJrlSpVirVNjhw57OOPP7bGjRu77caOHeu6l6syut5L69atrX379lHzASFoAwAAAECEUuvxkiVL4i33X6Z1gh1PfebMGfv0008TXM8/aFepUsW6du3qWqXVEv7333+7Cularq7fCuBFihQJuJ9u3bq5MK3ArbAsCutPP/203XLLLQG3qVu3rv34449u3PicOXNc6/YFF1xgTz31lG8asmiRJUYzpmcCBw8etEKFCtlN33Sk6jiAiENrNgAgM07v9eyVc+3AgQPpWoE7ubli+S+lQq46fvjQWat10V9R954ROsZoAwAAAAAQRgRtAEhntGYDAABkLARtAAAAAADCiKANAOmI1mwAAICMh6ANAAAAAEAYEbQBAAAAAAgjgjYApBO6jQMAAGRMBG0ASAeEbAAAgIyLoA0AAAAAQBgRtAEgjdGaDQAAkLERtAEgDRGyAQAAMj6CNgCkEUI2AABA5kDQBgAAAAAgjAjaAJAGaM0GAADIPAjaAJDKCNkAAACZC0EbAFIRIRsAACDzIWgDQCohZAMAAGROBG0ASAWEbAAAgMyLoA0AYUbIBgAA4bJs2TJ77bXX7Pbbb7cyZcpYlixZ3CM55s6day+++KI1a9bMSpQo4fZToUKFoLY9fPiw2/bSSy+1/PnzW6FChax69erWqVMn91pCJk6caE2bNnXHy507t5UtW9ZatmxpP/zwQ6z1Ro0a5XtviT3GjBlj0SB7ep8AAGQkhGwAABBO/fr1s0mTJoVlX126dLFVq1aFvN2WLVusUaNG7vn888+3m266yU6cOGEbNmyw4cOHW8+ePV349nf27Fnr2LGjjRgxwvLly2fXXHONFS5c2LZv327Tp0+3yy+/3C3zVKpUydq3bx/w+AcOHHCBXfy3iWQEbQAAAACIUHXq1HGtyFdeeaV7qAVaITc5brjhBmvTpo3bj1rHL7744iS30bEUrBWQ33vvPXv44Ydjvb527VorWrRovO1eeuklF7KbN2/uWqv919m3b5/t3bs31voK0AmF6HfffdcF7Xr16rmgHw0I2gAQJrRmAwCAcHvmmWfCtq8BAwb4vv7zzz+D2mbo0KGu5fqpp56KF7JF3cfj2rlzp7366qtWrlw5Gzt2rOXJkyfW60WKFHGPYH388cfu+Z577rFowRhtAAgDQjYAAMiIPvjgA/f8+OOPB73N6NGj7eTJk/bggw/GC9mhUnf1hQsXWs6cOa1t27YWLWjRBgAAAADEs2PHDvvtt99cN3MVMVuwYIFNnjzZjZmuWLGitWrVyo2tjmvWrFnuuW7durZr1y775JNP3H5UQO3666+3G2+8MeiCbl5rtgq4hdIKnt4I2gCQQrRmAwCAUBw8eDDW97ly5XKPSPPLL7+453PPPddVF1fhM3+9e/d2FdF79OgRcLtffvnFhXEFc//u6w0aNLCvvvrKFUfLiN3GhaANAClAyAYAIHOacaSa5c4SWpw6fuS0mf3lWof9vfDCC9a3b1+LNCpaJsuXL7effvrJneMDDzxg2bNnd9Ns9erVy5588kmrVq2aa3GOu1337t1dMTeN81bL99KlS10l8jlz5rjncePGJXp8rb9x40ZXSM1//9GAoA0AAAAAadwlu2DBgr7vI7E125uiS06fPm2PPvqouyHgefrpp13l8IEDB9orr7wSKwif/d926ur99ddfu+m9RFOEqeu5qqiPHz/ehegqVaok2Zqtsdkaox1NKIYGAMlEazYAAEgOhWz/R6QGbf+5se+77754r3vLlixZYsePH4+3XZs2bXwh279KuaYXk3nz5iV4bIV7VSyPxm7jQtAGgGQgZAMAgIyufPnyvq81f3dc3rIzZ87YP//8E2+7CgG28V++e/fuBI89Y8YM97rmzVZRtWhD0AYAAAAAxKOx17lz54417tqff7j2b/2uWbNmgtv4b+e/TULdxu++++6o/MkQtAEgRLRmAwCAzEBd2jUVl6iAWVxz5851z2p19h9z3qJFi1iv+zt8+LArruYfyAOtM2nSJPc1QRsAMkHAJmQDAIBINmzYMNcS3bNnz7DsT0XPpF+/fq54mWfLli3Wp08f9/UjjzwSa5vmzZvbhRdeaAsXLow1JZi6mKsSuVq0NVb7mmuuCXjMCRMm2NGjR6127dpWuXJli0ZUHQeAIBCwAQBAepg2bZoLuZ6TJ0+6Z4VQjwKvV/VblcA3bNhgu3btirev//znP+4hp06dcs9az39fCsa1atXyfa/x0c8//7y99NJLrgW6Xr16li1bNluwYIEdOnTIbrrpJhee/WXLls0+/fRTq1+/vpt/+/3333fTe61YscI2b95sxYoVc69nyZIlQ82d7Y+gDQBJIGQDAID0smfPHlfVOy7/ZVonGDt37oy3LwV3/2UHDx6Mt92LL75ol112mQ0ZMsQWL17sKoJXrVrV2rdvb507d3bBOq4aNWrYypUr3dzbKmz2yy+/WKlSpezBBx+03r17xyq05k/Bf9asWZYjRw674447LFpliYmJibFMQB+YQoUK2U3fdLQc+aJrDjYA6YugDQBA+B0/fNqevXKuHThwINb43mjJFa/9WN9y58+eKd4zQkcxNABIBCEbAAAAoSJoA0ACCNkAAABIDoI2AARAyAYAAEByEbQBIA5CNgAAAFKCoA0AfgjZAAAASCmCNgD8DyEbAAAA4UDQBgAAAAAgjAjaAEBrNgAAAMKIoA0g06PLOAAAAMKJoA0gUyNkAwAAINwI2gAAAAAAhBFBG0CmRWs2AAAAUgNBGwAAAACAMCJoA8iUaM0GAABAaiFoAwAAAAAQRgRtAJkOrdkAAABITQRtAAAAAADCiKANIFOhNRsAAESTZcuW2WuvvWa33367lSlTxrJkyeIeybVv3z7r0qWLlS9f3nLlyuWeu3btavv3709wm40bN9p9993n1s2ZM6cVKFDArrzyShs8eLCdPHky3vqTJ0+29u3b2yWXXGLFixe3HDlyWMmSJe3mm2+2qVOnBjzG6tWrrXPnzla7dm0799xz3bkVKlTI6tSpY2+//badOnXKokn29D4BAAAAAEBg/fr1s0mTJoXl8uzdu9cF199++83OP/98u+222+znn3+2oUOH2tdff22LFi2yokWLxtpm4cKF1qRJEzt69KhdeOGFbpsDBw7Y/PnzrXv37u7cZs6cadmz/3+0HDNmjE2YMMEuvvhiu/rqq10w37p1qzuGHj179rRXXnkl1nHmzZtn77zzjgvzF110kZUoUcL27NljCxYssMWLF9uXX35pM2bMcEE/GtCiDSDToDUbAABEGwXjPn36uFbiXbt2uZbe5FLLtUK2Wsc3bNhgY8eOtbVr19rjjz/uWq0VnONSK7NC9quvvmq//PKLffHFF/btt9/6wvrcuXPto48+irVNr169XEhes2aNTZs2zT7//HMXlvXInz+/a6HXa/7U2r1p0yYXyBXcP/vsM/es76tXr+6O8/7771u0iJqg/ffff7vuBuomUalSpfQ+HQAAAABIdc8884y99NJL1rx5cytdunSy96OQrvCqFuHhw4fHaoEeOHCga0H++OOPbffu3b7lhw8fthUrVljevHnt6aefjrU/de9WCJcff/wx1ms1a9a0YsWKxTsHtW7fcccdFhMTY7Nnz471mkK7HnGVKlXKXQOZNWuWRYuoCdo9evRwXR0AIDlozQYAIHiz9laL90B0++abb+zs2bN27bXXuvDqT63kCvJnzpyx6dOn+5ZrbHXWrElHxkChOiHap4TSBTw526S3qAja33//vY0ePdo6duyY3qcCAAAAZNgwnVioJnRHt1WrVrnnWrVqBXzdW66iZP4B/LrrrnNdxwcMGBBr/T/++MONqVYIvueee4I6hzVr1rju6tpG476DLd725ptvuq+bNWtm0SLii6EdO3bMHn74YTcg/sknn4yqfvkAAADRjpZMBPO5oOdYaA4ePBjrewXalIy9Dsb27dvdsyqXB+It37ZtW6zl7733ngvFKmCmImcaL63zV/Gyc845x43BrlKlSsB9TpkyxRUxU8VwHV+F1RSyP/jgA7vgggsCbvPrr79a//79Xev7X3/95bZRF/ZHHnnE2rVrZ9Ei4oP2iy++aJs3b3aD370uAwAQCn75A0guQiaQvH8rCf3u9V/v1BFNCzU3ai/x3L+rWI7joXVl9t5z2bJlYy1/4YUXrG/fvpaaFFZF460DyZcvn3s+dOhQrOVVq1a1H374wVq2bGnLly+3devWueWqnXX99de7yuKJtaKPHj3a932ePHlchfPEWsAVrv23kSeeeMJVXw+mG3ukiOigrW4L6iagOds0lkAV5wAAQHQgpAKZF//+E7djxw4rWLCg7/vUbs1OCRUga9Wqlbs5oK+vuOIKV6j6ww8/dFN0aZjv0qVLXTG1uHr37u0ex48fd1XK3333XXvooYdcBXW1dAcac33NNde4YmkaL65W8K+++so1vmpaME3vVaFCBYsGERu01VXgwQcftMKFC8cbDwAAwaI1G5kBf9ACQHRRyPYP2mlB02qJxlsHcuTIEfesOa89//zzj7Vp08Z1/VbQPe+883zrqIVZ82m//fbb9sYbb9jrr7+e4LFz587tupxrTHe2bNncNnqo4HVCtF7FihXdlGMK1wr7moZM3dGjQcQGbV14lYkfOXJkSFXsPCdOnHCPhMZBAACQGgi9AIBIVK5cOfe8c+fOgK97y8uXL+9bpvHXCtuNGjXyhWx/CuHKbRqvHax77rnHbTNp0qREg7Y/dVvXjQJVTj958mRUVB+PyKCtLgLqYlC/fn3r0KFDsvahCdXVxQBA5kVrNpJCKAYAZBaXXXaZe9Y460C85Zdeemm88F2oUKGA23jLVRk8WMWLF3fPe/bsCXobjQcvWrSoy4k6VtzpySJRRAbtTp06uTsVqnCXXKqKp24G/i3acYsOAACiG0EZAIDgNG3a1BUTmz9/vu3evdtKlizpe009gdUlW921b775Zt/y0qVLu+cVK1a4MdN63Z96IEso46bnzv1vAbyEqo4HouLY3rh2L6hHuogM2lOnTnVjs1XC3Z8G0cvvv/9uDRo0cF9//vnnvg9AWpfIBwCkDEEZAIDwGjZsmHuou7V6+Xo0Fdddd91ln3zyiT322GMuR2XP/t84+PTTT7sW5vbt28cK4ArnylRbtmyxPn362Msvv+yr/L1hwwZ7/vnn3detW7f2baP9TJw40U3FFbfC+XfffeeOJSp47U/dydUVPW6203F0XiqQdu+998YL+5EqIoO27N+/33e3Iy4Fbu81L3wDgD+6jacfwjMAAOGjcdIqPOZRz1+pXbu2b5lCcLNmzdzXe/fudeF0165d8fY1ZMgQW7x4sav4Xa1aNVdB/Oeff7a1a9da5cqVbdCgQbHWVzhXoTNNr6XQPnbsWKtZs6arOr5o0SLXEq4WcP/hviqq9tBDD1nXrl3t8ssvd/Nza9nGjRtt/fr/TvvWrVs3V9zMn2ab0jbq4l6pUiUXrDWn97Jly1yh7Ouuuy7WjYNIF5FBWxc1EE3vpcpz6mag8vAAgLRDgAYAIO2phXjJkiXxlvsvC3a8s7pdayouzdmtVmdNnaXxzgrSqm+lXsVxde7c2VUM1/zXCukqYqaWagVuFTZ7+OGHY7Uyq0V8wIABNmfOHBfif/rpJxeUFdrvvPNOt77XO9lf//79bfr06W79b7/91o4dO+bGZTdp0sS1xOtY0TSPdpaYhFJtBEpJ0NYYbQ3Wv+mbjpYjX+RXqQOQMrRoh4YQDQBIa6eOnLSvm37gpohK66muUiIluSJa3zMySIs2AKQEITthBGoAAIDUR9AGgAyIQA0AAJB+oipoq2x8FPV0B5COITOjtGoTmAEAAKJPVAVtAMhICNEAAAAZE0EbQIYPsWnduk2ABgAAyNwI2gAyvHB0JSc8AwAAIFgEbQCZAkEZAAAAaSV6ZvwGAAAAACAKELQBAAAAAAgjgjYAAAAAAGFE0AYAAAAAIIwI2gAAAAAAhBFBGwAAAACAMCJoAwAAAAAQRgRtAAAAAADCiKANAAAAAEAYEbQBAAAAAAgjgjYAAAAARKhly5bZa6+9ZrfffruVKVPGsmTJ4h7JtW/fPuvSpYuVL1/ecuXK5Z67du1q+/fvT3CbM2fO2ODBg+2SSy6xPHnyWIkSJaxt27a2bt26RI81ZcoUq1+/vhUsWNA9GjRoYNOmTUt0m59//tnatGnjjqFj6ZhDhgyxs2fPWjQhaAMAAABAhOrXr5/17NnTvvrqK/v9999TtK+9e/faVVddZW+99ZZlz57dbrvtNitQoIANHTrUrr76avvnn3/ibaOAq+DbvXt327lzpzVr1swuvvhiGz9+vF1xxRW2dOnSgMcaMmSItWjRwhYuXGj16tWzhg0bunVvueUWGzZsWMBtFi1aZFdeeaXb9/nnn++21zl369bN7rzzTouJibFoQdAGAAAAgAhVp04d69Onj02ePNl27drlWqGTSy3Xv/32m2sd37Bhg40dO9bWrl1rjz/+uG3cuNGF6bhGjBjhQn7lypVt/fr1LgTPmTPHxo0bZ0ePHrV27drZ6dOnY22zYcMGe/LJJ925zps3z77++mubOHGirVy50ooVK+aCs87D36lTp9y+jh07ZoMGDbIlS5a48/v111/dNdDxRo8ebdGCoA0AAAAAEeqZZ56xl156yZo3b26lS5dO9n4U0j/77DPLmTOnDR8+3LVoewYOHOi6an/88ce2e/fuWNsp9MqAAQOsVKlSvuWtWrVyLc4KzJMmTYq1zdChQ11380ceecSFZE+VKlWsV69eLphrHX8K81u2bLHLLrvMBXFP/vz5fS3gb775pkULgjYAAAAAZHDffPON6wZ+7bXXxgrMopZnBXmF4+nTp/uWK/hqHLbGSqvLeFytW7f2jcX2N+1/47C911O6Ta1atVxXcrW+b9261aIBQRsAAAAAMrhVq1b5Qmsg3vLVq1fH26Z69eqWI0eOoLZRUbXt27e7r2vWrBlvm7Jly1rx4sVt27ZtdvDgwRSdXyQjaAMAAABAGlLA9H+cOHEi1Y/phV9VLg/EW64AHI5tihQpYvny5UvVY0Wy/++YDwAAAAAIyoa/Sli2vLlDulpnjh73ter6e+GFF6xv376peuUPHz7snvPmzRvwdS8UHzp0KFW3CeexIhlBGwAAAADS0I4dO9y80p6UVBJHZCJoAwAAAEAaUsj2D9ppQdW7RVNyBXLkyBH3rHm1U3ObxLbbt29fSMeKZIzRBgAAAIAMrly5cu55586dAV/3lpcvXz4s2+zbt88XjlPrWJGMoA0AAAAAGZzmp5bly5cHfN1bfumll8bbRtNqnTp1KqhtChcu7AvNK1asCNhtfu/evS4w+7fqJ+f8IhlBGwAAAAAyuKZNm1rWrFlt/vz5tnv37livqeq55rXOli2b3Xzzzb7lFStWtAsvvNCOHTvmm+fa3/jx492z5uD21+x/c257r6d0GwX2zZs3u2nGKlSoYNGAMdoAkMGt+7NUep8CQnRh6b+4ZgCAZBk2bJh7tGzZ0l599VXf8nPOOcfuuusu++STT+yxxx6zzz//3LJn/28cfPrpp23Pnj3Wvn17K1myZKz9de/e3Tp27OjWqVu3ru/1CRMm2OTJk61SpUp26623xtqmS5cu9v7779t7771nd955p9WuXdst//XXX61///7uuFrHn85XwV7zaQ8ePNi6devmlqv7eadOndzXPXr0iJpPBUEbANIRIRjR9LngBgAApD21JPfr18/3/cmTJ92zF16lT58+vhZhdcvesGGD7dq1K96+hgwZYosXL7Yvv/zSqlWrZldccYX9/PPPrmt45cqVbdCgQfG2uf/++2369On21VdfuW0aNWrkjjF37lzLkyePffzxx77A7qlataoNHDjQhfRrr73WmjRpYjlz5rQZM2a41vG33nrLBXR/OXLkcPtq3Lix227s2LGue7la4PVeWrdu7W4ERAuCNgBkgoAERNvnm1APAP+lluYlS5bEuxz+y7ROMIoXL25Lly51c3ZPnDjRhedSpUrZE088YS+++KIbXx2XupuPGzfOhg4daiNGjLCpU6e6Oa1btWrltrnooosCHqtbt24uTCtwKyyLgr1axm+55ZaA26jF/Mcff3Tzis+ZM8e1bl9wwQX21FNPuRbwLFmyRM3HIktMTEyMZQIHDx60QoUK2U3fdLQc+XKm9+kAiAKEZiBjI8wD6evUkZP2ddMP7MCBA2k+1VU4csX5o5+zbHlzh7TtmaPHbXP7V6LuPSN0tGgDyPQI1EDmlBr/9gnvAAAhaAPI0AjRAKL1/xxCOwBEL4I2gAyHcA0gIyC0A0DyjRkzxsLl3nvvDXkbgjaADIFwDQCp+38kLewAokmHDh3CVjyNoA0g0yFgA0B0/X9LYAeQVi677LJ4c3yHQpXZV69enaxtadEGEJUI2ACQOf//JqgDCFaNGjXcVGHJtXXrVoI2gMyDkA0AmVdKfgcQ0oHMo2DBgpY3b94U7SNPnjzJnoaNFm0AUYOADQBIr98jhHQguuzfvz/F+xg+fLh7JAdBGwAAAAhzSCeYA5kbQRtAVKA1GwCQ0X9vEc6BjIOgDQAAAEQAwjmQev766y/bsGGDVa1a1UqV+v8bYZs2bbJevXrZ2rVrrVy5cvb8889b7dq1U3w8gjaAiEdrNgAAqfM70r8VPbF90dqOaPfaa6/ZW2+9ZevWrfMF7YMHD9o111xju3fvtpiYGPvll19s7ty5tnLlSqtcuXKKjpc1TOcNAAAAIMooXHuPYNfjBjii0Zw5c+yiiy6yKlWq+JaNGjXKtXTfddddrrV70KBBduzYMXvzzTdTfDxatAFENH6ZAwAQeUJt/Q60/pmjx8N+XkBCfv/9d6tTp06sZdOmTbPs2bPbkCFDrHjx4ta1a1cbPXq0a9VOKYI2gAzt9B+B50/Mfu7RkNZPbwmdLwAAkYab5IhEhw4dijWv9pkzZ2zRokV2+eWXu5DtqVatmk2dOjXFxyNoA8iQkgrMkRqoU+N8CekAACCzO/fcc239+vW+73/44Qc7fPiwNWjQINZ6p0+ftpw5c6b4eARtABlKtAXoSLsmhHIAAJAR1alTxz777DPXTbxRo0bWu3dvy5IlizVv3jzWeiqWdt5556X4eMkuhrZ161bXf71bt25299132y233OKe9b2W63UASEuE7PBcw6QeAAAg7alIl6aeUjGv3Llzuxba+++/3409DtXixYvt1ltvdV2mtS/tU1NcHTlyJOD6O3bssOHDh1uHDh3swgsvtKxZs7qQqgJjwZg4caI1bdrUSpQo4Y5XtmxZa9mypWtV9qfiZNpvUo8xY8aE/J579uxpuXLlsh49eliNGjVswYIFrjW7bt26vnWUYVV5/Oqrr7Y0bdHet2+fC9EffPCBr9ldZdDj0psX/RA6duxo9957rxUpUiTFJwsgcwl2jBfhL20Fc71pGQcAIHyOHz9uDRs2dAH5nHPOcSFZoXDkyJFuPLGWn3/++UHt65NPPrH27du7Mcq1atWy8uXL27Jly+yVV15x+5o/f74VLFgw1jZffvmla1AN1dmzZ10eHDFihOXLl89NpVW4cGHbvn27TZ8+3Y2P1jJPpUqV3LkFcuDAARfYxX+bYF188cUu2A8dOtT27t3rjv3UU0/FWufbb7+1yy67zG677TZLk6B99OhRGzBggCtzrrscefLkcW/uqquucoPFixYt6n4YevMK42puX7p0qf3000/uB6Jm+SeffNK9Ef8B6ACQUoTs6Py5EMQBAAjeyy+/7MK0uj/PmDHD8ufP75ZrOiq10KplO5jW5Z07d9qDDz7oQvaHH37otpOTJ0+61mp1rVZm+/e//x1rO4V4VeS+8sor3aNz587uPJLy0ksvuZCt7tlqrVZu9Cg3KvD6U8ZMKES/++67LmjXq1cv6JsKcenGghqOE/Lwww+7RzhkiQnUJB1H6dKl3STeN954o+seroSvOxJJUSifMGGCffzxx/bdd9+5/fzxxx+WHjQZeaFCheymbzpajnwpH9wOIP1bswnZGRdBHAAyPk3vtbn9K66xLm4LaiTzcsX5o5+zbHlzp/p7VgguWbKk22b58uVWs2bNWK+rBXb16tWukVOttEkF9j59+liTJk3iBeV//vnHKlSo4Lqo//nnn1asWLEE96Nu4Gr9nT17drxiYv6h/oILLnAZUL2h1VibEgrYCxcutPfeey+oMKww3qJFCzfEWeeoabzSUlBjtHXnRN0Jvv76a2vXrl1QIVu03j333ON+CPrBh6OvOwAg42OcOAAA/6WxxArZCq1xQ7a0bt3aPU+ZMiXJS6ZMJ4HCsVqbL730Uld1W/NLp9To0aPdTQK1oKc0ZG/ZssWFbFUDb9u2bVDbKFi/9dZbrrFYY9HvvPNO+/TTT11LeloIKtZ/9dVXKT6QmunDsR8AEFqzMze6pgMAMotVq1b58lQg3nK1aifFK3aWUP0srxXbO2ZKzJo1yz2r2NiuXbvc2PDffvvN9Qa4/vrrXQD2anslRT2kpVmzZkHX/tq4caNt2LDBJk2a5G5CaJz5uHHjLFu2bO6c1NqtLu2VK1e21MD0XgCitggaEGoQp0s6ACDaqHCYlClTJuDr3vJt27YluS9V/U5sXbUcB7uvpPzyyy++51atWrlWeY/qf6lVXQ2xKo4WbNBWb+lQVK1a1Z5++mn3+Pvvv11L/eTJk92w5nnz5rnx6AraKi6nLuYaHx5s+E+16b0AIL3Qmo2UfHaYsgwAEAnjvP0fJ06cSHDdw4cPu+eEikp7w3oPHTqU5HGvu+4696yiZ+rW7U9DfdesWRP0vpKy739dtLt37+66pGt8ud7rzJkzrWLFiq54myqSJ0VFttU6ra7tatFOLrXWazas8ePHuyJsGhb96KOPuoruAwcOdMFfY+FV9Vyt3951T5cW7blz57qHugIk9OHQHQFVtAOA9JJ3Z1Y7WuYsPwCEdPOG1m8AQGLO/JnXYnKHVgzt7PH/tnNqHml/L7zwgvXt2zfVL7jqbakgmlrJ1XX6jTfecNN7LVq0yIVejWvWGG3Nk51SZ8/+928vdfVWqPVuCDRq1Mi1Kit8K/QqRGse76RaszU2W2O0wyFHjhyu67oew4YNc93u1cVc05vpeB999JE71ttvvx3UzYCwBW1VpPMmGE+qaDlBG0B6tWYrYAf6WgjeSO5njQAOAEipHTt2xKo6nitXrgTX9aby0pTLiY27LlCgQJLH1b4UJtVNWgWr9fCfw1pThb3++utBj4NO6lj79u2zNm3axCumXb16dTdNmFqr1YU7oaCt0D927NhkdRsPhUK/HqrIrorrGtOth3939zQJ2pobWxOZa9Lvhx56yJVO9z4AABAJ4gbrUF+Pi2COYG72EMIBAMFQyA52eq9y5cr5pssKxFuululgaDowFQn74osvXHduzamtgmqqyv3qq6+6dZTzUqp8+fIuaGvKsEC0XEFb00gnRFOQ6XXlTRUwSwuajkyt2MltyU5R0Fa616B7dTEgYAOIJKEG6HDtlyAOoQs6ACDcFIxFoTgQb7laZIOl8d4dOnRwD3+aQksSmhs7FDVr1rSVK1cmOJ2WeklLYnnS6zZ+9913W7hs3brVtaInNfxZrdtpHrR110NzaxOyAURKS2JqBexgBTo+4RtCF3QAQErUq1fPTYm1adMmF1xr1KgR63WNcxZNVZUSGqes+ltqzdYxU6pFixY2cuRIt8+4VGjMu0EQaG5wbx2Nmw5X0FbRM7VSay5tSWwIdLoF7csvv9z1XQcAJIzwjVACON3OAQCBqChX586drX///tapUyfXndob8zxo0CAXkOvXr+8ymkcFvvRQXS2vO7hHYV1jpFX4zLNu3To3BZfCpwqAhUPz5s3twgsvdK3kw4cPt8cee8zXaKtK5GrR1nloSq1AJkyY4Mal165dOyxzXT/zzDNuLm9VFldRuNQe/pysoN27d29r2rSpffPNN+4ZANJTerdmh4IK6EgseBO2AQAJ5S9Ni6XQqtB57bXXurmulyxZ4ubGHjFiRKz1NX2VxmGre3RcXbt2dXNbq0u6tlVhNg0JVivuv//9b7v++uvjbaP9KLR71q9f754Vnr2x5pp6y78VOFu2bK71WDcBdIPg/fffdwXXVqxYYZs3b3bTben1hOatTu7c2QlRUbXixYu7Gw0ah53akhW0GzZs6C6K5iG7+eabrUmTJnbeeeclWAbem68NAJIrI82dTdhGQgjbAIBAcufObbNnz3at08phEydOdPNKa4x1v379XP2sYKkbtkLsqlWrbP/+/S5s33HHHfbUU0/F65bu0Vhmhfq41BLuqVatWrzXa9So4YKtpi5TS7wCfqlSpezBBx90Nw8SKuCmYD9r1iw3DZfOLRzUFV2NxGkRsiVLTFLzcyVg1KhR1qtXr6C6kKt7QHrT5Oga23DTNx0tR77wzL8GIHWs+7NUVI3PTi7GcCMhtGwDyAzOHD1um9u/4qZQCrYCdyTwckX511+2rCHPo33ctj3TO+rec0ZQu3Ztd80V+NNC9uSG7AceeMD14dfg9dTo367xBpqne82aNa6kuwav6+6Duh7obssll1wS1uMBQFqjZRsJoWUbAIDw0hzhGputrusJFWBL96A9YMAAN6n69OnTw1L6PZBXXnnFTb6uMvVeqP7555/to48+ss8//9wNjtdE6wAQzQjbSIjXi4PWbQAAUq5NmzZuznENe1ZxuaSGP3vzl6dp0NbcY2pZTq2QLSrlrsp5Go/gTxXrNJhe/fp1ofyr5QFANCJsIzG0bgMAEB5qxNXYdo1r1yMhKtB2+vTpFB0rWSlVyV+TnKemhOZuU2U7dSvXPHIaTB/KxOwAEKkI20gMYRsAgJSZOnWq3X777S5Aq/q4CrFF3PReqjY+ePBgN/eZ7gikNVWf8+aUAwAAAAAgMS+88IKrMTZy5EiXZxOaVixcklWq97nnnnPzq+kxZ84cd8JpRWO0NSec5o8Lx8TlAAAAAICMbd26dW7a6fbt26d6yE52i7YXcDVJeqNGjVwLsyqCBxpIrjehbt7JNXDgQFcETYXRdHH09bnnnmufffaZmwQdAAAAAIDEqLu4Hmkl2cXQ/J08edK2b99uqeHbb7+177//3ve9+tKPGTPGFUpLjCZV18N/vjsAAAAAQObTunVr11iraaPjFtyOmK7jZ8+eDemREjNnznRd0/ft22fz5s1zremqeN6/f/9Et3v11VfdRPLeo2zZsik6DwAAAABAdHr55ZetQoUK1qJFixT1uA5W1MyNVbhwYbv22mvd3N116tSxPn362A033GBXXnllwPV79uxp3bt3j9WiTdgGAAAAgMznlltucUOP1Vu6WrVqLnQnNI+2hj/796rO0EHbo/Hgd9xxhy1btsymTJmSYNDOlSuXewAAAAAAMrc5c+b4vj5z5oxr1U6oZTscxdKCCtoqQHbxxRen+GDh2o83iH3Pnj0p3hcAAAAAIGPbsmVLmh4vqKB96aWXWtu2bV13bH0dqhUrVrgx0xMmTHAThKfU3Llz3fMFF1yQ4n0BQCQ4WiZl9SyQsWU/92h6nwIAAFGtfPnyaXq8rMFO7j1t2jSrWbOm1ahRw15//XVbvHhxrKre/lTJbdGiRS5cX3LJJXbFFVfYN9984/YTjAULFrj14xZSO3XqlL399ttuLu08efK4LuQAEO0I2UgMIRsAgOgTVIv2888/b4888oir9K2ptdSyrX7r2bNndwXGihQpYgUKFLBDhw7ZP//8Yzt27HD93lUtXBW/u3Tp4rYpUaJEUCf166+/2n333ee6iGsar2LFitnevXttzZo1tmvXLleOfdSoURQ3AxD1CNlIDCEbAIDkUS5VbsybN28y92B29OhR14hctGjR1CuGVrJkSRs6dKi99tpr9sUXX9jUqVPthx9+sM2bN8dbt3Tp0q5CeLNmzVyX81DnKdP0Xc8995zrIr569WoXsnPmzOkqw2n+syeeeMIqVaoU0j4BINIQspEYQjYAAMmnRt4OHTrYhx9+mOx9dOrUyfWmTs7w55CrjqvLdvv27d3DK0i2e/duO3DggGu9ViAPtuU6IRUrVkxynmwAiGaEbCSGkA0AQMqod7UeKZXcfaR4ei+F6pQGawAIJnic/iP5XX8iCSEbiSFkAwACOXbsmKuB9fnnn9v27dtdd+amTZtav3793HzQofjuu+9syJAhtnTpUtu/f78VLFjQDdl99NFHrWXLlvHWHzRokOvNrKG8amRVd2r1YlZP5KeeesrV5Ypr8uTJ9uWXX9ry5cvd8F81zGrIsep3PfbYY25e67jUm/n999+3n376yb3Hv//+2/WOvuiii+xf//qXG86s6Z6DpXO+//77Q7o2cbdPriwx4Yj5UeDgwYOuxf2mbzpajnw50/t0ACRi3Z+lAi5PKGjn3RlUXceIQMhGYgjZADKTM0eP2+b2r7gApqAXbbmi/OsvW9YQh8iePX7ctj3TO+T3rGB7/fXXu4LU55xzjhumu3XrVheU1eip5eeff35Q+1LA7tatm6u5VadOHVf3SjW2VMxa0VBDeOP2LlbtrCNHjrgZqLxQr6mbN27c6IKvZpeKG5xbt27tlmt653LlyrmaXjrnJUuWuNdVw+uVV16Jtc2wYcPs8ccfdxXCNVRY7009qFUsW9dAwX7GjBluWHFSsmYNz9+Huk6qPxbydgRtANEetKMhbBOwkRDCNYDMiqAdfNDu3bu3C78Kxgqa+fPn97U09+jRwwXQOXPmJLkfhVYFa83upFZtbeeZN2+e3XDDDXby5En77bffYgV3BV21eMetvTV8+HA3jrlUqVK2c+dOVyzbf4rncuXKucLW/hS0Gzdu7IL7qlWrYrWGe/W/4t40+Ouvv9w2a9eudbNQde7cOegpocPB/zqlWddxAIiUIJveYZswjaQQqgEAoVLwVUuvvPPOO76QLd27d7fRo0e7ULls2TIXhhOjkKspmm+88cZ44fG6665zy9XlW123/cNuvXr1Au5PXcAV9jdt2mS//PKLa/H21KxZM+A2V199tZumWUXKZs+eHStoJ9QqryD/zDPP2D333GOzZs0KKmgnJxyHE0EbQMS5sPRfCbZqBxN0UztwE6iRFAI1ACBc1JqsruYXXHBBwPCqLtoa2zxlypQkg3auXLmCOmbcVujEeGOmg+nOndbbpCeCNoAMVxAtpYGbII1QP5cAAKQWda+WWrVqBXzdW66wnZSrrrrKChcu7FqF1Qoet+v4t99+a5UrV3ZjwIOhqa82bNjgttEjGGvWrLGxY8e64NykSZOgttm3b5+9+eab7mtNIR0NCNoAMl13coI0QkWYBgCkF1XfljJlygR83Vu+bdu2JPelIm7qsq0K3iquVrduXbe9xlcvXLjQdREfM2ZMgq3GAwcOdEXQNL563bp17utzzz3XPvvsM8uWLVvAbaZMmeKqj586dcq9Fx1HIfuDDz5wrfSB/Prrr25MusaSa3y2tjl8+LCrOt6uXTuLBgRtABkaoRqhIFADANKqcnncLt0JdetWwJS8eQP36suXL597PnToUFDHvv322+3rr7+2tm3bum7pHlVBVzG0xKYKU4v3999/7/te1cEVzBPrsr5q1So3jtyTJ08eGzp0qBtvnRCFa/9t5IknnnBTmYWrmnhqS9ZZvvjii+6uBwCkNYIQwvlZivsAACBYeX7P6nrOhfLQNqLK32pd9h6aHzutqAu2Knir+Jm6myvI67lhw4b2/PPPuyCekJkzZ7opwNSVW13N1V1c3c/jTgcWt2K6ttE84Oo2ft9999lDDz1kt956qyv0Fsg111zjtjl9+rSrRK5zHjVqlJuDW1OEZeigXbFiRWvevLmrSqcmfQBIKwQihPp5IVQDACKJ5q1WgTPvoTmlE+JVGT96NPANYXXjFs1TnRRNAfbkk09ajRo1bNy4ca7it1rE9Tx+/Hi3fNq0aa7FOzEa561x3NOnT3et2X369LEff/wx0W1y585t1atXd5XTNVf21KlT3VRdiVF3dOVOVVcfOXKk61KubTNs0H755ZfdnGj6IbRs2dLdkdHFjZa7CwCiH2Eb/p+FxB4AAEQaddP2fyRWDVy5SxLqUewtVzfuYIqXiTJc3C7YCrVea7Zaq4OhsdaaqkutzxqLHax7/tdtfNKkSUFvo3PWTYdvvvkmwZbwqA/azz33nJsrTZOlt2nTxv7++2/XXaBSpUrWtGlTN9hdzfwAAIQLQRoAkBlddtll7nn58uUBX/eW+89hnRAvlKu7eiDecnUND1bx4sXd8549e1J1myxZsljRokVdzgzl/JLyySefWGpI0Uhy9e3//PPP7ffff7c33njDqlat6sK3Btaret2zzz7rmvcBIDlzaSeF1srM0SJNyzQAIDNTJXAFYDV0rly5Mt7r6vItGtablNKlS7vnn376KeDrXvfvChUqBH1+miZMEqogHq5tNFZbXe7VA8AL6uFw//33u3HkCQmmmnsgYSnZpgnN1W9e5d1/+OEHu+uuu2z37t2u/Hu1atWsUaNG9tVXX4XjUAAQC2E7OhCgAQBIHk211blzZ/d1p06dfGOyZdCgQa6QmQqS+Vf+HjZsmMthccd+33bbbb5WXI2R9qdu3J9++qnrUq5u2h5VJld37bh1uTRdl8ZYqzu6KomrC7lnz549bvquQOPKv/vuO3v66afd1yqM5k/7+/PPP+Nto7m6NSWZuqjfe++9CU4llhxqKH7//fetVatWsc5XNzYUwqtUqZL+03vpZNQ337/ku1q2Z8+e7Qbe64evbuUa0w0A4Qxxp/8IPOUF0g43PQAASB1qcVXFb80nrUrfKkSmltYlS5ZYiRIlbMSIEbHW37t3rwunu3btihe0NfRXhdDUAq4q3io2tmXLFl8rt4YEq6eyRz2UFYjViqw8p0ZW7V8VxLV/FTlTRXD/jHfkyBFXWbxr165uG2VCLdu4caOtX7/erdOtWzcXbv2puri2UXd5DUtWsNb7XLZsmQv6qpQe7grtukmh66jroWrnOgddT/Xc1rVR8bZ0Cdq6k6HwrDsWCtO6GF4L98MPP+w+CIsWLXI/MFWl092YUAa9A8jc3cfX/VkqqHUJ26mHAA0AQPpSmFXjpUKmWp0nTpzoxit36NDBzS2tIBvsOOexY8e6ulqap1qt4eqOririN998s6vordfiBlHV6FJ3b62vkK1WdnUvb926tZvfWqHYX8mSJW3AgAEuH6rXs0K8gvI555xjd955p8uJDRo0iHd+XmbU+pqzW1OC6X02adLE9ZpWEbXUmEdbgVrXQ+9Vw6OVYdXqr/eX3ONliVEyToZ169a5cK2uAv/8848L2HXr1rVHHnnE3SUJVDmvdu3a7g7G/v37LT0mhdfYhpu+6Wg58uVM8+MDSJ5gg7Y/WreTRngGAKS3M0eP2+b2r7jprTTuNlp4uaLaE69Ytly5Q9r2zInjtv6t56LuPWdky5cvt9dee80Nddb0Yxo3rt4DuiFx4403Jnu/yWrRVpO6WqkVrvUBefTRR13A1okl5uKLL05yfjUACFeIzCyBm9AMAACQPOo+r8eECRN8BeVef/11a9GihQvg6uKeZkFbYwNq1arlwrUGpefNG9wfsw8++KDrVw8AaR24kxtGQw3rSR0n7v4IyQAAAOlHXdXjdpd/5pln7MILL7S7777bjUWPOwY+1YK2WqX9q9oFq06dOu4BAGkpJWE23EGYYA0AyIxTcoZjaBiQGuKGbI9atFVxXc/JkaygnZyQDQCpXRANAABEXqhOaD+njpy0zWHZGxDcWGwNdVYht2BdcsklyR76HNbpvQAAAABknEAMZBRXXHGFZc+e3U1dpunDatSo4Z71UJX0hGhas+QgaAOIeLRqAwAyO4IzkDItW7Z0U5lpujE9NE2apjuTUqVK+UK3F8A1RjslCNoAAABAGiEwA+njyy+/dM+aWm3FihWuK7mely5dar/++qv9+eefNmPGDDezlubOPn36dIqOR9AGEBVo1QYARBpCMxB9ChUqZA0aNHAPz9q1a61Xr142depUV2n877//TvFxCNoAogZhGwCQmr9jAGRO1atXt0mTJlnXrl3t22+/da3dKUXQBhBVCNsAgMR+RwBAcr3++utu7PbAgQPt+eeft5QgaAOIOoRtAMh4CMkA0luuXLnsqquuss8//5ygDSBzImwDQGQhKAOIZL169bJatWpZzZo17fzzz09wvX379tm2bdtSfDxatAFkmD/q1v1ZKt3OBQCiGSEZQEb36quv+qbzKliwoJvGS6FbD43R1nzZKoa2aNGiRIN4sAjaADLkH4qEbgCZBSEZAJI2ZswYN4+2pvTS89y5c93DC9/+HnroIUspgjaADInQDSBSEYwBIO1p2i49PNu3b3ehW481a9bYzp07rUSJEtayZUt74IEHUnw8gjaADC+UP2ppCQeQ3P8/AADRo1y5cu5x6623psr+CdoAEOIf1YRxIHIQhAEAkYigDQBp8Ic94RyZDQEYAJCZEbQBIEpDB+Edaf2ZAwCkn2PHjrnK2ZrjWeOLixYtak2bNrV+/frZeeedF9Q+Ro0aZffdd1+S640ePdruvfde3/cdOnRwyxLy7rvv2iOPPBLwtSlTptgbb7zhxkKLpth66qmnrFmzZgnu7+eff7a+ffvanDlz7PDhw1apUiU3bvqJJ56wrFmzWjQgaANAlIqmIJVWNwUSuiahHj+ari0AIOM7fvy4NWzY0BYvXmznnHOOG1e8detWGzlypJuSSsuDmZJKgbV9+/YBXztw4IBNnDjRfX3NNdcEXOfGG2+00qVLx1tetWrVgOsPGTLEunXrZtmzZ7fGjRtbrly5bMaMGXbLLbfY22+/bZ07d463jabXatSokbuxcNVVV1mFChVs3rx5bj8LFy60sWPHBqwUHmkI2gCAVJfewTW9jw8AQEq8/PLLLkzXqVPHBdX8+fO75YMGDbIePXrY/fff71p/k6IAnVCIVqu0gna9evUSDO3PPvusNWjQIKhz3rBhgz355JMuXM+ePdudu2zcuNHq1q3rgrNa5BX+PadOnbJ27dq5kK33pnVErdo33HCDjRs3zm6++WbXwh7poqPdHQAAAAAyoZMnT9qwYcPc1++8844vZEv37t3t0ksvdfNBL1u2LEXH+fjjj93zPffcY+EwdOhQO3PmjOtS7oVsqVKlivXq1ctOnz7t1vH31Vdf2ZYtW+yyyy7zhWzRe/auwZtvvmnRgKANAAAAABFqwYIFrlv3BRdcYDVr1oz3euvWrX1joZNL4VbdsnPmzGlt27a1cJg2bVqs8wvmnBPbRmO71dK+du1a120+0tF1HAAAAAAi1KpVq3xBMxBv+erVq1Pcmq0CZUWKFElwvQkTJtiXX37pWqorVqxozZs3t2rVqsVbb//+/a5gmwS6OVC2bFkrXry4bdu2zQ4ePGgFCxYM+r1u3rzZvVeN3Y5kBG0AAAAASEMKl/40jlmPQLzAWqZMmYCve8sVWlO727gKmPl75pln7NFHH3VdwFXwLO45FylSxPLly5fgee/du9ed9yWXXJJm7zWtELQBAAAAIET5dp217DnOhrTN6VNnfS26/l544QU3nVUgKgQmefPmDXwe/wuyhw4dsuRYunSpK1Cm6cISmnJLrdIaZ63K5wq7f/75p3399dfWu3dvGz58uOtyPnjw4KDPOaHzTu33mpYI2gAAAACQhnbs2OHrLi0JtWanBa81W2OzFZgD6dKlS6zv1W38scces/r167vu3CpUpsJscW8gZGYUQwMAAACANKSQ7f9ILGh7VcaPHj0a8PUjR4645wIFCoR8Hqr8rXmpk1tt/OKLL7YWLVq4/Xz//fdBn3NC552a7zWtEbQBAAAAIEKVK1fOPe/cuTPg697y8uXLh7xvzcm9e/duV81bc1snR+XKld3zrl274p3zvn37fOE4mPNOzfea1gjaAAAAABChNKe0LF++PODr3nLNp53cbuN33313ss9PYVr8i54VLlzYF5pXrFgRsOu8CqEpMPt3oU/N95rWCNoAAAAAEKHq1atnhQoVsk2bNtnKlSvjvT5+/Hj3rKm2QqHCY5MmTUpR0D5x4oRv7uu4U3I1+19hNe/8gjnnxLZRYNfUXtWrV4/4qb2EoA0AAAAAEUoFyjp37uy+7tSpU6yu2IMGDXJzSqso2eWXX+5bruJkmt+6Z8+eic6JrbHQtWvX9nX/DmT9+vX20UcfuVDtb8+ePXbnnXe61mm1ROuGQNwCatmyZbP33nvPFi9e7Fv+66+/Wv/+/d10YHGLrLVs2dIVWtN82v5VzPWe9d6lR48eFg2oOg4AAAAAEUzTaM2cOdMWLlzoQvG1117r5pJesmSJlShRwkaMGBFrfXXL3rBhQ6xx08mdO1tTed17770uFF9xxRXueH/88YctW7bMTbOl6b6++OILy5IlS6ztqlatagMHDnTVyHW+TZo0cTcNNC782LFj9tZbb1mlSpVibZMjRw53Xo0bN3bbqVCbupfPnz/fvZfWrVtb+/btLRrQog0AAAAAESx37tw2e/Zs69Onj5tjeuLEiS5od+jQwY1bVjGzUCi0zpo1ywXbO+64I9F1q1SpYl27dnXBec2aNTZu3Dj76aefXODX/N9qUdc6gXTr1s0mT57s5uBWWFZlcoX1KVOm2OOPPx5wGxVl+/HHH61Vq1b222+/ue01x7da7xW84wb6SJUlJiYmxjKBgwcPurENN33T0XLkCzw/HAAAAIC0cerISfu66Qd24MCBWAWxoiVXXN7mZcueI3dI254+ddyWjesdde8ZoaNFGwAAAACAMCJoAwAAAAAQRgRtAAAAAADCiKANAAAAAEAYEbQBAAAAAAgjgjYAAAAAAGFE0AYAAAAAIIwI2gAAAAAAhFH2cO4MACJZw+Lrk1xn1t5qaXIuAAAAyLgI2gAyhWBCdijrCaEcAAAAgRC0AWRYoYTm1N4/oRwAACDzIGgDyJBSO2SHilAOAACQeRC0ASDCEMoBAACiW0QG7aNHj9qMGTNsypQp9sMPP9i2bdssW7ZsVqlSJWvVqpV1797d8ufPn96nCSBCRVprdmoilAMAAESeiAzan376qXXs2NF9feGFF1qLFi3s4MGDtnDhQnvhhRfss88+s7lz51rJkiXT+1QBIKpDOWPHAQAAMknQzpEjhz300EPWtWtXF7Q9u3btsmbNmtmKFSvcawrkABAoPGamVu2USOw6EcIBAAAyUNBu3769e8R1zjnn2DvvvGN169a1CRMm2MmTJy1nzpzpco4AkNHRLR0AACB5slqUueyyy9zziRMn7O+//07v0wEQoWiNTftQntgDAACkzLFjx+z555+3KlWqWO7cue3cc8+1+++/337//fdk7W/r1q32yCOPWMWKFS1XrlxWvHhxq1Onjg0cODDBbU6dOmVDhgyxq666ygoWLOjqZul8kjqPuXPnulpbpUuXdsfSud900002efLkWOvNmTPHsmTJkuTjpZdeskgXkS3aidm8ebOve3nRokXT+3QAAEFISdjmpgkAILM7fvy4NWzY0BYvXux6+d56660uKI8cOdKmTp3qlp9//vlB7+/rr7+21q1bu/Beq1Ytq127tmvEXLNmjf373/+2p556Kt42//zzj91www22bNkydw6NGzd2y3/77Td3Hgrb5513Xrzt+vbtay+++KIL2PXq1XN1thTK58+f79ZXPS6Pgnigns1y5swZ+/jjj93X1157rUW6qAvaQ4cOdc9NmzZ1PywAQMaW3JBOQAcAZBQvv/yyC9NqcdbsTN4MTIMGDbIePXq4kKvW4GCsX7/ebr/9ditQoIB99913bliu5+zZs7Z8+fJ428TExLhgrpCt4tS9e/e27Nmzx2oMVQt3XKNGjXIh++qrr7bx48dbmTJlYs005TWieqpVq+a2SejmgIJ22bJlrUGDBhbpoipoT58+3T788EPXmt2vX79E11XXcj08qloOIHOhKFrmllpd1gnwAIC0pLpUw4YNc1+rXpX/NMea9nj06NGua7ZC8OWXX57k/rSNWsi//PLLWCFbsmbNaldccUW8bcaNG2ezZ8+2Nm3auBbquAK1ph87dsyefPJJF+gnTpzoWqv95c2b16pXr27B8lqz27Vr57qPR7qoCdq683L33Xe7uykaN+CN1U7Iq6++6u6eAAAQTmkx5pwwDwDwLFiwwA4cOGAXXHCB1axZM96FUUvz6tWrbcqUKUkG7R07dti3337rgvHNN98c9EX+4IMP3PPjjz8e9DYTJkxw3dHV2h43ZIfqyJEjNmnSJPf1PffcY9EgKoK2+vCrq/i+ffvcHZguXbokuU3Pnj3duv4t2upmAABApEvvAnIEfQCIHKtWrXLPGksdiLdcYTsp6l6u7uFqyT59+rQLwwryGv+s1uU77rjDihQpEq8A2g8//OC6iqsImo6jFu7du3e7MdYaLx6oEXTWrFnuWcfav3+/ffLJJ7Z27VrLkyePW3bbbbfF6n6eGJ2nwrZuNFx00UUWDSI+aHuD7rdt22b33XefvfHGG0Ftp/HbjOEGQPdxIPqCfii4KQAgGsUd1ppYdtm+fbt79h/f7M9brryUlF9++cU9q/u5Copp3Le/Xr16ubHU119/vW+ZxlGrq3mpUqVs8ODBbh2FdY+6kqshVK8FOtaePXtcON61a5fvNa17ySWX2LRp04JqDPW6jUdLa3bEB+3Dhw+7su/6IWnAvrosREN/fAAAkDai6aZASnFTARnt3+Tx3Kfta4teBbYes+zZY0La5vTp4+45brhUgbFAY5+9TOSNaQ4kX7587vnQoUNJHl89hOU///mPC9uffvqp6zmsMKwaWAq0LVu2tJ9//tlXQdzbRt3A1Wv4sccecwXYChUq5Lpzqzu5pvyqVKmSderUKd6x+vTpY1WrVrUvvvjCtXyvW7fOHn30UVd0TVN+LVmyJNGMp4D+/fffW7Zs2eyuu+6yaBGxQVuFzNQNYenSpXbjjTfaZ5995i4uAISKVm0AGUFmuqmQHriRkTx8LpNHY6X9q3SnVU9cryVa3cY1jVfbtm3d9+ou/tFHH9mGDRvsxx9/tOHDh1v//v3jbaNGUBVk82j8tVq7FbBVI8s/aJ/933bKcKoY7t1cUPdzfa9x4jrWzJkzrUmTJgmes3KgurbrhkBKx3pbZg/aupC6W6F+/erSoD75OXPmTO/TAgAAQAZFYAzuhgPXKTwUsgNNhxWIV2Vc02EForHLourewe5Lz6ogHpeG6ir8qop53G281+Pq0KGDC9iqq6U5tdWy7b9do0aN4rXgay7tZs2auVZuHSuxoB2N3cYjNmirfP1XX33lvi5evLjrnhCIxmvrdQBICq3aAAAkjSAdecqVK+eed+7cGfB1b3n58uWT3Je3jvYZqLt2hQoV3LMKncXdxv91f+rSruCsbfTwgnb58uVtxYoVAbdJ6FhxqZu59qHQruJp0SQig7bXn1+8wB2IxjEQtAEAAABkVF5Fb41pDsRbfumllya5L296MP+8FbcQddxWbI3Frlixom3ZsiXgduoirqricberWbOmmz87lGPFpe7sonpdCY1Rj1RZLQIpQGu+7KQeCd0dAYBAGH8HAACiTb169VzY3bRpk61cuTLe66oSLs2bN09yX5pWq1ixYvbnn3+68dhxeV3G487X3aJFC9/0YHGpcvnJkyfdtF0qehZ3m4ULF7opwuKGc00ZFuhYHuU9FWuLxm7jERu0AQAAAADmalV17tzZXQqNhfbGZMugQYPcvNb169e3yy+/PNZQ3GrVqrkq4f40b3X37t1diNW+/KcZU1GyUaNGuS7lDz/8cKztunbt6s5D+/WfEmzv3r3uNW/8tn9Rtxo1arix15p2rHfv3u6YnpdfftnWr1/vupyrtTqQ+fPnu21V/bxhw4ZR91GIyK7jAAAAAID/UlBVEFbrcOXKlV3BaIVQTY1VokQJGzFiRKxLpQCsFmv/uas9Tz31lM2ePdvtr0qVKla7dm23vgK0ilKr2rgqg/tTT+J3333XHnzwQbvuuuusTp06rpVd56Npv2rVqmWvv/56vGN9+OGHbt0BAwa4buSaO1vjrjV9s1rAP/nkE9/0ZAkVQfvXv/5lWbNGX/tw9J0xAKQA3ccBAEC0yZ07twvHmpNaY5UVWhW0VfFbY7Q1VVawcuTIYdOnT3fBWPWuvv32W1uzZo1rFZ8yZYo999xzAbfTVF6aFUqty2pFnzFjhmuR1rDfefPmBRxrXbZsWVfMTK3nmgZs8uTJLtTfeeedbhrnxo0bJzjVs9cl/u6777ZolCXGvw0/A1O3CN11uembjpYjH1OFAZkZFVUBAEh/xw+ftmevnGsHDhwIeqqrSMoVDa7sZdmz5w5p29Onj9ucH/tH3XtG6GjRBpDp0KoNAACA1ETQBpApEbYBAACQWgjaAAAAAACEEUEbQKZFqzYAAABSA0EbAAAAAIAwImgDyNRo1QYAAEC4EbQBZHqEbQAAAIQTQRsAaNkGAABAGBG0AQAAAAAII4I2APwPXcgBAAAQDgRtAPBD2AYAAEBKEbQBIA7CNgAAAFKCoA0AARC2AQAAQNAGAAAAACAC0KINAAmgVRsAAADJQdAGgEQQtgEAABAqgjYAJIGwDQAAgFAQtAEgCIRtAACQno4dO2bPP/+8ValSxXLnzm3nnnuu3X///fb777+HtJ8KFSpYlixZEnysX78+4HYbN260++67z8qXL285c+a0AgUK2JVXXmmDBw+2kydPxlt/8uTJ1r59e7vkkkusePHiliNHDitZsqTdfPPNNnXq1IDHWL16tXXu3Nlq167t3l+uXLmsUKFCVqdOHXv77bft1KlTFi2yp/cJAEA0he2GxQP/8gEAAEgtx48ft4YNG9rixYvtnHPOsVtvvdW2bt1qI0eOdKFVy88///yQ9qkQHIiCbVwLFy60Jk2a2NGjR+3CCy+02267zQ4cOGDz58+37t2726RJk2zmzJmWPfv/x8sxY8bYhAkT7OKLL7arr77aBXOd89dff+0ePXv2tFdeeSXWcebNm2fvvPOOC/MXXXSRlShRwvbs2WMLFixw7/HLL7+0GTNmuKAf6bLExMTEWCZw8OBB96G56ZuOliNf5P9gAEQuwjYAACl3/PBpe/bKuS6wFSxYMOpyRYMre1n27LlD2vb06eM258f+Ib/n3r17W//+/V3LroJm/vz53fJBgwZZjx49rH79+jZnzpygW7S3bdtmocTAWrVq2YoVK+zVV1+1Z5991rf8jz/+sGuvvdY2b95sI0aMcC3enhUrVli5cuWsWLFisfa1ZMkSa9y4sR05csRWrVrlWrw92o/EvWnw119/uW3Wrl3rWrbV6h3p6DoOACGiGzkAAEgr6pY9bNgw97Vae72QLWpNvvTSS23u3Lm2bNmyVDn+4cOHXWjOmzevPf3007FeU/duL/T++OOPsV6rWbNmvJAtat2+4447XNCfPXt2rNcUsAO1zJcqVcqeeeYZ9/WsWbMsGhC0AQAAACBCqdu0WsAvuOACF17jat26tXueMmVKqhxfY6uzZk06NgYK1YntU0LpAp6cbdITQRsAkoFWbQAAkBbUvdrrvh2It1yFxEIxcOBAe+SRR6xLly72/vvvu7HQgagg2XXXXefGZw8YMCDWa+o6rlZ2heB77rknqOOuWbPGxo4d67bRuO9g7Nu3z9588033dbNmzSwaUAwNAJKJ4mgAACC1bd++3T2XKVMm4Oveco27DkXcbuDdunVz459VyTyu9957z4ViFTBTkbPq1au7seoqXqbibNOmTXPV0AOZMmWKK2KmiuF6LyqsppD9wQcfuFb6QH799Vc3Jv3s2bNufLa2URd23Rho166dRQOCNgCkAGEbAACESiE1bquxHoEoYIrGSAeSL18+93zo0KGgjt2iRQu7/vrr7fLLL3dVvb1CZkOHDrUHH3zQdQFXVXN/VatWtR9++MFatmxpy5cvt3Xr1rnlmg5M+1Jl8cRa5EePHu37Pk+ePO5YibWAK1z7byNPPPGE9evXL6hu7JEgOs4SAAAAACJI9i27LPum30N7bNnlti1btqyrXO49VM07rbz11lsuMKsiuEKvQrK6Zb/77ruuQJlXdMyfCpBddtllrlVaX+tGwZYtW6xXr15uirF69eol2PW8d+/ebr+aB1zdxlWZ/KGHHnJhPtD823LNNde4bU6fPu1uBOj8Ro0aZVdccYWbIiwaELQBIIUYrw0AAEKxY8cOV+DMe6hLdkK8KuMaIx2IpskSzVOdEg888ICVLFnSNmzYECvM/vPPP9amTRsXsjX/tVqwdSxNE6YW5k6dOrn133jjjUT3nzt3btflXGO6H3/8cTf/t7qqJyZbtmxWsWJFV11dgV5dyrVtNCBoA0AYELYBAECwNIe2/yOhbuOilmfZuXNnwNe95eXLl0/RD0Bdsr0x07t2/bflXTT+WmG7du3adt5558XbTiFcNF47WPf8r9v4pEmTgt5GrfC66fDNN98k2BIeSQjaAAAAABCh1GVbNDY6EG+55tNOKVX39h/37R/k1cU9EG+5t20wihcv7p4T6m4eiMaDFy1a1HUnD+VY6YWgDQBhQqs2AAAIN41/VpjdtGmTrVy5Mt7r48ePd8/NmzdP0XF+/vln121cRdeqVavmW166dGn3vGLFCjtz5ky87X788Uf3rK7kwZo7d657TqjqeCAaq60u9+oB4AX1SEbQBoAwImwDAIBwypkzp3Xu3Nl9rfHQ3phsGTRokJs/u379+q6KuGfYsGEuLMcd+z19+nRXzCwu7UNdwFWATJXHdUxP06ZNXdd2FT/r06ePm3LLo2D+/PPPu69bt27tW75nzx43fVegceXfffedb2oxFUbzpzHbf/75Z7xtdJx//etf7vzuvfdeN3Y70jG9FwCEGVN+AQCAcFLl7pkzZ7r5pCtXrmzXXnutmzd7yZIlboouTc/lb+/evS6c+o+1lqVLl9qLL77oxnOrS7par9VSrO7n6pLdoEEDe+2112Jto3myVehM02upOvrYsWOtZs2a9vfff9uiRYvsxIkTdvPNN1uHDh182xw5csRVFu/atau7AaC5vrVs48aNtn79et+83a1atYp1LFUX1zY6t0qVKrlgrfe5bNkyF/Cvu+66NK3QnhIEbQBIBYRtAAAQLqrYPXv2bBcyP/30U5s4caIbr6xwq8rfCrLBuPHGG133a3X3XrBggat4rq7Ymk6rXbt2roU5UGuxWtRVMVzzXy9evNgVMVNIV+BWYbOHH3441nYlS5a0AQMG2Jw5c1yX9J9++skFZYX2O++8062vUB9X//79Xau71v/222/dlGB6n02aNLG77rrLHSta5tHOEqPbBJmA5nrT2IabvuloOfL9f1cIAEhNDYv/964tAACI7fjh0/bslXN9YS/ackXj4vdb9qyh5YrTZ0/azL0jou49I3TRcTsAAKIUY7YBAAAyH4I2AKQywjYAAEDmQtAGgDRA2AYAAMg8CNoAAAAAAIQRQRsA0git2gAAAJkDQRsA0hBhGwAAIOMjaAMAAAAAEEYEbQBIY7RqAwAAZGwEbQBIB4RtAACAjIugDQDphLANAACQMRG0AQAAAAAII4I2AAAAAABhRNAGgHRE93EAAICMh6ANAAAAAEAYEbQBAAAAAAgjgjYApDO6jwMAAGQsBG0AAAAAAMKIoA0AEYBWbQAAkJhjx47Z888/b1WqVLHcuXPbueeea/fff7/9/vvvIV24ChUqWJYsWRJ8rF+/PuB2Z86cscGDB9sll1xiefLksRIlSljbtm1t3bp1iR5vypQpVr9+fStYsKB7NGjQwKZNm5boNj///LO1adPGHUPH0jGHDBliZ8+etWiRPb1PAADw/2G7YfHAv9wAAEDmdfz4cWvYsKEtXrzYzjnnHLv11ltt69atNnLkSJs6dapbfv7554e0z/bt2wdcXqhQoXjLFHAVfL/66isrXLiwNWvWzPbu3Wvjx493oXn27Nl21VVXxdtuyJAh1q1bN8uePbs1btzYcuXKZTNmzLBbbrnF3n77bevcuXO8bRYtWmSNGjVyNxa0T90YmDdvntvPwoULbezYse6GQKQjaAMAAABABHv55ZddmK5Tp44Lqvnz53fLBw0aZD169HAt23PmzAlpn6NGjQp63REjRriQXblyZZs/f76VKlXKLf/yyy+tdevW1q5dO9eyrUDt2bBhgz355JMuXCuI69xl48aNVrduXRecmzZtapUqVfJtc+rUKbcvhWy9N60jhw8fthtuuMHGjRtnN998s3Xo0MEiHV3HASCC0IUcAAD4O3nypA0bNsx9/c477/hCtnTv3t0uvfRSmzt3ri1btizVLpxCrwwYMMAXsqVVq1bWokUL++2332zSpEmxthk6dKjrbv7II4/4Qrao63uvXr3s9OnTbh1/CvNbtmyxyy67zBeyRe/ZuwZvvvmmRQOCNgAAAABEqAULFtiBAwfsggsusJo1a8Z7XS3K3ljo1KDgq9ZqjZVWl/Fgjz/tf+OwvddTuk2tWrVc9/i1a9e6bvORjq7jABBhGKsNAAA8q1at8gXNQLzlq1evDumiDRw40DZt2uS6dl988cXWsmVLV3wsoeNXr17dcuTIEdTx9+/fb9u3b3dfB7o5ULZsWStevLht27bNDh486IqkBfteN2/e7I6lsduRjKANAAAAABHKC6xlypQJ+Lq3XKE1FE8//XSs79VVWwXKNN47pcff/r9tihQpYvny5UtwOxVU03aqKp7cY0WqTBO0Y2Ji3POpIyfT+1QAIEnfHjnf6hfbyJUCAGRYxw+fjvV3erQ5HXPS7GwytjFzrbj+1KqsRyAqBCZ58+YN+LoXZA8dOhTUOWhM9fXXX2+XX365a8FWC7GKnWm89IMPPmjFihVzVc1TcvzDSWyT3O1Cfa/pKdMEbe+HMbPV6PQ+FQAIytdcJwBAJvk7PdCUUpEqZ86cVrp0aZvz58fJ2l6FvdR12t8LL7xgffv2tbTw1ltvxfpe3cZVYKxatWr20EMP2TPPPBMraCN5Mk3Q1oTuO3bssAIFCkTFvGuRQnfb9B+Brp03dgJcz0jB55PrGan4bHI9IxmfT65npFBLtkK2/k6PJrlz53YFwlQNPLnvO24eSag1W7wq40ePHg34+pEjR9yzck5KPPDAA9a7d283LZeKjXljoJNz/PxJbJPYdvv27Uv195oWMk3Qzpo1a4J9/ZE0hWyCdvhwPcOL68n1jFR8NrmekYzPJ9czEkRTS3bcsK1HWihXrpx73rlzZ8DXveXly5dPcV5SZfPdu3fbrl27fEE7Occv979tFJoVjgON005oO22j1zRtWWq917TA9F4AAAAAEKE0p7QsX7484Ove8kDBNFQKueIfjL3ja1qtU6dOBXX8woUL+8L2ihUr4m2j3rIqhKbA7N+Yl5bvNbURtAEAAAAgQtWrV8+1/GsqrpUrV8Z7ffz48e65efPmKTrOzz//7LqNqxCZxmt7KlasaBdeeKEdO3bMN891MMdv9r85t73XU7qNArsKt2masUif2ksI2kiUxouoOENi40YQPK5neHE9uZ6Ris8m1zOS8fnkeiK6qPha586d3dedOnXyjVOWQYMGuTml69ev76qIe4YNG+bCcs+ePWPta/r06TZr1qx4x9A+2rRp48aPq/K4jumve/fuvinB1LXcM2HCBJs8ebJVqlQpXgG1Ll26WLZs2ey9996zxYsX+5b/+uuv1r9/f8uePbtbx5/m8law13zagwcP9i3Xe9Z7lx49elg0yBITrfX0AQAAACATOH78uDVo0MCWLFli55xzjl177bVuLml9rym6FGTPP/983/qqYP7iiy9a+/btbdSoUfGWq8u2ummr9VqtxOqSffr0aXcMhfE8efLEOv7Zs2etdevW9tVXX7m5sRs1auS6fs+dO9eNVZ89e7ZdffXV8c578ODBLqQrVDdp0sQF+BkzZrjWcVU/f/zxx+Nts3DhQmvcuLFbR/vUuc6fP9+NG9c5fPHFF1FR3JoWbQAAAACIYF6Y7dOnjwvHEydOdEG7Q4cOLiT7h+zE3HjjjXb//fe7cdELFixwXbR/++03u+aaa+yDDz6wmTNnxgvZXqG0cePGuWnAVCV+6tSptmbNGmvVqpX99NNPAUO2dOvWzbV416lTx4Xl77//3q644gqbMmVKwJAtdevWtR9//NHtW+em7YsWLepa78eOHRsVIVto0QYAAAAAIIxo0QYAAAAAIIwI2ohFk8OrK4omrK9atarrpqLy/hrD8dJLL9nhw4e5Yinw999/W8mSJV2XFxWNQPLt2bPHnnzySfc5VRcndSmqVauWPfXUU1zWEKhrVtu2bV03sBw5crjpODTua+TIka4gCuJbtmyZvfbaa3b77bdbmTJl3L/nYLqxaYzcVVddZfnz53ef15tvvtmNQ8vMQrmWGh+obocqxKOCPwUKFHBFvTTn6yOPPGJbtmyxzC65n01/GhfpbZfQnLmZRXKvp6Y/GjJkiPv3ru65+jdfpUoV1133999/T5NzB5D+6DqOWP7zn/9Yx44d3dcq46/y+QcPHnR/DB46dMhVL1TRA4VFhE7jaMaMGeMCjP441LgTJO+PH40x0o2Liy++2Pc5/eWXX9wfhirmgaR9+eWXdscdd9iZM2fcTQrd/NENDIUZXcN//etf9sknn3Ap47jtttts0qRJ8a5LYjcmunbtakOHDnU3hW644QZX1Ebj1LSNxsdpn5lRKNdS/19WrlzZfV26dGkXYlTNdunSpS68KHirgI/GGWZWyflsxr0ZdN9997kwqW00z60CZmaVnOv5zz//uH/j+j2lglW1a9f2fX41nlX/v2bmzyiQqajqOOAZNWpUzEMPPRTzyy+/xLoof/zxR0zNmjX1myXmrrvu4oIlw8yZM9310/XV8wUXXMB1TIbdu3fHFC9ePCZv3rwxkyZNivf6kiVLuK5BOHXqVEzJkiXdZ/GTTz6J9Zr+/RctWtS9NmvWLK5nHK+99lpMnz59YiZPnhyza9eumFy5crlrlZDvvvvOvV6sWLGYjRs3+pYvXLgwJmfOnDGFCxeO2bdvX6a8zqFcy99++y2mSZMmMd9//33M2bNnfcuPHz8e06FDB7dduXLlYk6ePBmTWYX62Yz7f6v+3d9www0x5cuXd9vt2LEjJjML9Xrqc3n99de7dV544QX3/6y/TZs2xezZsycNzhxAJCBoI2j6o1C/PPSL5sSJE1y5EBw9etQF64suusj9oU3QTr5HH33UXb933nmHz2AKrFmzxl3HqlWrBnz9iSeecK+//vrrXOckJPXH90033eReHzx4cILX+Y033uA6B3EtE/s/tlChQm7bOXPmcC2D/Gz6+9e//hWTO3dud0ODoJ286zl27Fj3eps2bfgMAohhjDaCpnHacuLECddlF8HTfIWao/C9995z42CRPJpP8eOPP3Z1A9S9Ecmnsa3BKFasGJc5hZ/ZWbNmua8192dc3jJNc4LkU5d8jYGVP/74g0sZom+++cY+/fRT69WrlxvWhOTR1EiS0JRFADKX7Ol9AogeCoqioKhCPgjO6tWr3ZyDCoYqMrV161YuXTJpnkbVCtD4Nv1h/fXXX9t3333nxrvqj2yvqBeSpvk29Qf1hg0b3B/YGo/tWbdunbuhUaRIEWvZsiWXMwV0fXVzskSJEgHHumpsvPf/BJJPhdI0n6w3fhvBO3LkiD366KOuBosKzSF5VADthx9+sOzZs7v6Afo3rTmHd+/ebeedd57deuutvgYLAJkDQRtBUyEfadq0adCtYZmd/vh78MEHXSXnAQMGpPfpRD0VOxMV4wtUpOa5556zDz/80O666650OsPooSJSo0ePtltuucXatWvnbgap0JT+KFSxnosuusgVRuKmWsps377dPSdUUEq9M/T/w759+9xNJBX0Qug+++wz99nVDY26detyCUPw/PPPuxvAc+bMsZw5c3LtUtAYoZu+pUqVssGDB7veAfobwNO3b1/r0qWLew1A5kDXcQRFlVwVYNSa3a9fP65akN5++203fdLAgQPpghsGCiMyefJk19XxnXfecX9c649ETfWlbrrt27e3lStX8hkNQr169dwsAmrdXr58uY0dO9Zmz55tWbNmtSZNmrjlSBlvSsS8efMmuI7CtihoI3SqjK2q7qJpKLkRHDz9u9dNdP2/Wb9+fT5+Yfj9pKF1PXv2dFPObdq0yfbu3ev+flIvLE35pd9bADIHgjaStH79erv77rvddBYKjHR9Cr4lq3fv3u6PF03rhZTzWgc09ZT+oH7sscdcC1b58uXdZ7NNmzau+56+RnCtgOriWLZsWVuyZIkLhRs3bnSfV7VwN2zY0HV7BiK527PmOFaYUS8XhRsER9P6eT2u3njjDS5bGH8/3XTTTS5Q62al6lxo/mzv99Krr77KtQYyCYI2EqW5SdVVXHdqu3fv7ro9ITidOnWykydPugJoCI/8+fP7vg5UDM1bplZaJO7XX391rVjFixe3qVOnusCtllV1H//3v//tupSrtWvEiBFcyjB8Zo8ePZpoWBS6jYdGN9V0c021G1S3QbUGEDy1rq5YscINa9L/A0jd30/eDXf9XaU5tQFkfIzRRoL++ecfu+GGG1yBGf3S4I53aBRe1FIQt4VFY7i8X7YNGjRwX3/++ecU8AmCWq69brhqyY6rQoUK7lndyZE4feYUVHQjzf8PRI8Ky+kzPG/ePFcoCclTrlw597xz584EQ/b+/ftd4TmCdmith7pRpIKINWrUcFXb1TUXwdM1y5Ili6vVMGbMmFiv/fnnn+5ZNzLUFf/ZZ591/1cg6d9P/r+L/On3luqL6PeTHpUqVeJyAhkcQRsBqQupuj6p+JS65WnKCv1CRmj0B3RCrasK3N5rXvhG4mrWrOmeNRZbXZrjjsXUzSEJFBwRmxf8ChUqFPDSeMu9cYdInqpVq7rP6Z49e9zNNVUf9qdeA3LppZdyiUOg6ZM09EGzDXz77bfupiZCpyFhupmWkMWLF7tnhj8lTf9nVqxY0bZs2RLw/03dHNLfBMLvKCBzoOs44lGA0TQUS5cutRtvvNH9MaMKxQj9D5hAD/0SFk2t5C0LdPcbgVsHVSNA1yzQDQxvmRfIkTBvCiR1uw1ERfyEz2bKqJVVY91FU/3ENX78ePfcvHlzPq5BUu2L4cOHu/8PNL2fWgkROlUZT+j3lNc6q0Jz+p6gHZwWLVr4rm2gmxYaTqb/E3QDDkDGR9BGvOIomhpp1qxZbs7nCRMmMN0HIoo3z6uqjO/atcu3XJXGVcBLKIiUNN1ME7Vmvfvuu/H+IPSmoGndunUYf3qZk+pbyMsvv+zGxnsWLVrkxsOrNfaBBx5IxzOMHvpc9u/f390omjlzpq9rPhAJVP1eU6QNGzbM1xtAVKzPq4yvoXhUxgcyhywxulUJ/I+m+fB+GbRs2dIKFiwY8NpovDbFU5JHU1Gpe5latCmIkjxqXdG4QgUUzZmrruQLFy50vTE6duxo77//fjL3nLk89dRTvtoLF198sZs7+48//nABUN0cH3roIRcEEdu0adNiTXOo3j/6VXr11Vf7lvXp08eaNWvm+17/r+r/V43T1NRpatlSa6y2U6u2KmZnRqFcS91Mq1Wrlnu9Tp06rtt4IKqkreJomVFyPpuBqCeL6rOoRTuhOeAzg+RcTxWQ1Gcwe/bs7nOqLuX6/aRpv/T5Vc8ruo4DmQNjtBGL/7iir776KsGr07dvX4I20s3IkSPdHNAKgeqip/oB+gPm4YcfdgWSEBxNN6MbFaqMv2zZMtuwYYMryKUp6XTDQr1bEJ/GW2s6tLj8l2mduBWeVbRLLV0K2Gr1aty4sfsjXT+DzCqUa6nxrV7bgG4G6RGIikxm1qCdnM8mwns9NZWXpvV67bXX3Hq6EazvVVdAPbE0uwOAzIEWbQAAAAAAwogx2gAAAAAAhBFBGwAAAACAMCJoAwAAAAAQRgRtAAAAAADCiKANAAAAAEAYEbQBAAAAAAgjgjYAAAAAAGFE0AYAAAAAIIwI2gAAAAAAhBFBGwAQ0V566SXLmjWrrVmzJlWPc9ttt1mpUqXs8OHDqXocAACQ8RG0AQAR66+//rKBAwda69at7ZJLLknVYz3//PO2e/duGzBgQKoeBwAAZHxZYmJiYtL7JAAACKRLly721ltv2fLly61mzZqpfpGaNm1q8+fPt+3bt1uxYsX4oQAAgGShRRsAEJGOHj1qo0ePturVq6dJyJa7777bd1wAAIDkImgDAMLijjvusCxZstjTTz8d77WNGzda/vz53ePXX38Nan/jxo2zAwcO2F133RXwdR2rQoUKdvr0aevXr59VqlTJ8uTJYxdeeKGNHDnSt96sWbPs+uuvt4IFC1qRIkXs3nvvtb///jvBcdraxwcffBD0+wYAAIiLoA0ACIv33nvPypQpY2+++abNnj3bt/zUqVPWrl07O3LkiA0ZMsQqV64c1P6mTp3qnhs0aJDoem3btnXH1Bju6667zrZs2WL333+/C9vjx4+3G2+80YVxPefLl88++ugjF6gDjZzSjYArrrjC1q9fb5s3bw75GgAAAEh2LgMAIBzUWjxmzBhr3LixazVevXq1W9a3b1/76aefXLh98MEHg96fxkpnz5490W7j27ZtswIFCrhW8hIlSrhlCvkNGza0Xr162cmTJ23ixInWrFkz99rBgwetbt269sMPP9icOXNcS3dcV111lTv23Llz7fzzz0/WtQAAAJkbLdoAgLBRcO3Ro4ft3LnTHnnkERdYX3vtNTvnnHNC6o6t6t+qOF62bFnXlTsxaiX3QrZ3Dgrnu3btsptuuskXskXdxx966CH3tYJ0INWqVXPPK1euDPp8AQAA/BG0AQBh9fLLL1uNGjXsiy++sFtuucV10VY37uLFi4cUtEUt4onJkSNHwK7lXkv0DTfckOBrCuKBFC1a1D3v2bMn6PMFAADwR9AGAIRVzpw5fVW71VVbLdsaHx0KFUETdQtPTOnSpS1btmwBx1rLeeedl+BrJ06cCLhPtXrL/v37QzpnAAAAD0EbABB2Y8eO9X2tLthnzpwJaftChQq550OHDiW6XtasWVP0emIhv3DhwiFvCwAA4P4G4TIAAMJJhcZef/1119qswmiLFi2y/v37h7SPkiVLuud//vknzX84+/btc8/+474BAABCQdAGAISNuorfc889rgVb47I//vhjF1g1z/WSJUtCCtoK6jt27LCjR4+m6U9o3bp17lnjzAEAAJKDoA0ACJvOnTvb1q1b3XPTpk2tVKlS9p///MfNY3333Xe7ubSDde2117rAvmLFijT9CS1dutQ9169fP02PCwAAMg6CNgAgLMaNG2cfffSRXXTRRTZgwADf8hYtWljHjh3tt99+sy5dugS9P29aLs13nVYOHz7s5vzWFF/MoQ0AAJKLoA0ASLHff//dHn74YVdx/JNPPok39/XgwYOtcuXK9uGHH9pXX30V1D7btm3riqJ9+umnafYT0rkdP37c3RgAAABIriwxmuAUAIAI1K1bNxsyZIhrZb788stT/XiahkzF3LZv327FihVL9eMBAICMiRZtAEDE6tmzp5v3+tVXX031Yy1fvtxmzJhhPXr0IGQDAIAUoUUbABDRXnrpJevbt6+tWrXKLrnkklQ7zm233eamItu0aZML9wAAAMlF0AYAAAAAIIzoOg4AAAAAQBgRtAEAAAAACCOCNgAAAAAAYUTQBgAAAAAgjAjaAAAAAACEEUEbAAAAAACCNgAAAAAAkYkWbQAAAAAAwoigDQAAAACAhc//Aassm1USj1U5AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -711,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "id": "857acdd2",
    "metadata": {},
    "outputs": [
@@ -923,13 +923,13 @@
        "[4000 rows x 8 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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0uZi4uDiXn2P79u3SsWNHE3ZHjx5tm4vqzMaNG2Xr1q2SP39+efTRRyWYRUxA1cqpql+wo0RHpd/dCgCASJNUtlhm7wKACJN0/aqs3DjG9jk9VGjlVMPp8rVFJE8e90ZmxsenSN17T5hQnjdvXtv17lRPjxw5Yob0nj17Vvr27Su9e/d2uXratm1btyu1gRYxAdUqo2s4JaACACCSVL5E5H0gABB0QnX6nYbTPDe5OzT5n6qrhlP7gOqqM2fOSOPGjU239K5du8qYMWMyfIyuRf3111+by1p1DXb8PQIAIAIDKQAgtMTHx5sGRzpUt3Xr1mZNU1fC/aJFi0zX3tKlS0udOnUk2IXWbGQAAOARwikAhK6rV69KixYtZO3atdKkSROzvnTWrFldeqw1vFerp6FQrSagAgAQ5ginABC6rl+/Lu3bt5fFixebCuiMGTMkJsa1njqXL1+WmTNnhkT3XgtDfAEACLGwGb3niFv3BwCEro8//tgWMmNjY6VXr14O76fzUfV2ez/++KMZGnzPPffIbbfdJqGAgAoAQIiwwmaq5kbphFXCKQCEvrNnz9ouW0HVEV2mJm1AtR/eGyoIqAAAhGFFlXAKAOEhLi7OrTVS7c2bN09CDXNQAQAIcWnDKOEUABCqqKACABAGCKUAgHBABRUAAAAAEBQIqAAAAACAoEBABQAAAAAEBQIqAAAhgDmmAIBIQEAFACDIEU4BAJGCgAoAQBAjnAIAIgnLzAAAkAkhM3rPEbcfAwBAuCOgAgDgJ+mFTL3NUUglmAIAIhkBFQCAdDgLjOlVQD0JmQRTAAAIqAAAeFwBdRRY3QmahFIAAFKjggoACElWuMtoLqc3z+3vxwAAgNQIqACAkOZOMKQxEQAAwY2ACgAIOZ5WK+0f58mQXAAA4F8EVABASPFVoCSYAgAQfKIyewcAAHAVoRIAgPBGBRUAEPQNjQimAABEBgIqAMCv7MOlXrYPqQRPAADSd/nyZVmwYIHMnj1bVq5cKQcOHJCsWbNKhQoV5PHHH5e+fftKnjx5MjyMDRs2lEWLFpnLhw4dkpIlSwbloSegAgD8xlEAJZQCAOC6adOmSY8ePczlypUry2OPPSYXLlyQVatWyZAhQ2T69OmybNkyKVKkiNPnmDRpkgmnWbJkkZSUlKA+/ARUAIDPED4BAPCtbNmySc+ePaVPnz4moFqOHTsmzZs3lz/++MPcpkHWkZMnT8rLL78sjRs3lh07dpgKbDCjSRIAwCfBlHAKAIDvde7cWcaPH58qnKpixYrJuHHjzOUZM2ZIYmKiw8dreNVhwp988klI/HoIqAAArxBMAQDIHHfddZc5v3r1qpw+ffqG23/55RdTWX3jjTekfPnyEgoIqAAAAAAQgvbu3WsbBlywYMFUt126dEmee+45qVSpkrz22msSKpiDCgAAAAABpE2O7GXPnt2c3PXhhx+a86ZNm97w+MGDB8v+/ftl6dKlEhMTI6GCgAoAAAAAblpwqZLkyOJenEq4lCQif0upUqVSXa/deOPi4tx6rnnz5smECRNM9XT48OGpbtu4caMJrzp/tV69ehJKCKgAAAAAEEC6DmnevHltP7tbPd2+fbt07NjRLBkzevRo21xUdf36denevbvkz59fxowZI6GGgAoAAAAAAaTh1D6guuPIkSNmSO/Zs2elb9++0rt371S3f/DBB2bpGa2uxsbGSqghoAIAvPtDsucInXwBAAiAM2fOmPVMdS3Trl27OqyQzp49W7JkySKTJ0+WKVOmpLrt+PHj5rxt27amatuvXz8TdoMJARUAAAAAglx8fLw0a9ZMtm7dKq1bt5bPPvvMBFFHdOjv8uXLnT7X6tWrzXmXLl0c3l6uXDmf7LPu3549e9x6DAEVAOAR1j8FACAwrl69Ki1atJC1a9dKkyZNZPr06ZI1a1aH99Wuvc6UKVPGVF91DmzJkiWd3k+7//qCswCdHtZBBQC4HUwJpwAABMb169elffv2snjxYqlTp47MmDEjIMvGaHU1OTnZ45N2EPYEFVQAiCBWsNR5o67czx5zTQEACLyPP/5YZs6caS5r06NevXo5vJ/ORw3FpkhpEVABIAKkDZyeVECpmgIAEHhnz561XbaCqiO6jqqvAur7778vVapU8eo5OnToINWqVXP7cVlSdAZtBLhw4YLky5dPGsZ2k+go/5fEASBYECwBAMEoKSlBlq4bIefPn/d4yZXMzBVvr6snOfK4V+9LiE+SfvcsC7nXHEjMQQWAMEY4BQAAoYQhvgAQpginAADAn3QJmW3btklUVJQZEqxdgr1FQAWAMEQ4BQAA/nLu3Dnp3r27bU6szhrVJWW02/D48eMld+7cHj83ARUAwgzhFAAA+NMzzzwj8+fPl8GDB8u9994rly5dkmnTppmThlMNqZ4ioAIAAAAAbBITE52utaprnP7444+m06/9kjdt2rSR++67T77//nuvAipNkgAgDCqm9icAAABv6HzSOXPmOLwtKSlJrl27JkWKFLnhNl3m5urVq15tm4AKACGKQAoAAPyhQoUK0qJFC3n00Udl7969qW7TyqoO6x0wYICsXLlSEhIS5MyZM/LRRx+ZYb8PPPCAV9smoAJAiCGYAgAAf/rll1/MUN3//e9/cvvtt5u5phpELf/5z38kPj5e6tWrZ+acFi5cWHr37i0lSpSQDz/80KttMwcVAEIEw3cBAECgtGrVSpo1ayYjR46U0aNHy5QpU8y8U72+evXqsnv3bpk6dars2LHD3F+DbIcOHSRHjhxebZeACgBBEDSj9xxx6/4AAAD+pmFz6NCh0qVLF1Mhffzxx6Vx48YyduxYufXWW6Vnz54+3yZDfAEgCDhqcsRQXgAAEAzKli0rP/30k8ydO9fMSb3zzjulX79+ZnkZXyOgAkAAuVINJZgCAIBgpEN+dV6qzkn9+OOPpVKlSvLNN9/4dBsEVAAIEIbqAgCAUHHw4EF58cUXpW7dulK1alVp3ry5aY6UJUsW08F327ZtUrNmTWnfvr00aNBAtm7d6pPtElABIAAIpwAAIFQsW7bMVEe1SnrixAnJkyePrF+/Xl544QUTSrWjb6lSpeS7776ThQsXyvHjx6VatWry8ssvy8WLF73aNgEVAAAAAGDzyiuvmPVOV6xYIdu3b5fff/9djh49Kv3795c//vhDvvjiC9t9H3roIdm8ebO89dZb8vnnn5tg6w0CKgD4GdVTAAAQSrZs2WKG7dauXdt2XdasWeW1116TlJQUE0jtRUdHm1CrYVYf5w0CKgAAAADApnjx4qZSmna4rg79VcWKFRNH9Povv/xSvME6qAAAAAAQpC5fviwLFiyQ2bNny8qVK+XAgQOmmlmhQgWzLmnfvn3NHFF7hw4dMvdfu3atrFmzRnbs2GEqn0uWLJH69etnuE19Tm2QpMN1W7RoIQUKFJCdO3fKnDlzJH/+/NKtWze/vV4CKgD4EcN7AQCAN6ZNmyY9evQwlytXriyPPfaYXLhwQVatWiVDhgyR6dOnm8pmkSJFbI/54Ycf5KWXXvJ4m9oMqVChQvLmm2/Kf//7X0lOTpYcOXLIww8/LCNGjDANkvyFIb4AAAAAEKSyZcsmPXv2NMu46Onbb7+VX375xVRFq1evbuZ99unTJ9VjypUrZ6776quvTOWzcePGbm9Xl4/RNU+1gvv333+bcw2+3jZBCpsK6unTp803BidPnpTy5cvL7t27M3uXACBdVE8BAIC3OnfubE6O5nuOGzdO7r//fpkxY4YkJiaazrtKq6x6sujapZ7S5yxcuLAESshUUHVNnVOnTmX2bgCASwinAADA3+666y5zfvXqVVPQCwchEVAXLVokkydPto29BoBgRjgFAACBsHfvXtsw4IIFC/rsebX6OnbsWK+e48MPP0xVxQ2bgHrlyhV55plnpEqVKmZtHQAI5mBKOAUAABnRJkf2J62AehoCVdOmTSV79uziK9qtd9OmTV49hz5+7ty54TcHdejQoeabAe1Mpd8MAEAwIpgCABBZlp2+VbIl/DPn01XXLiXqI2/ogqvdeOPi4tx6rnnz5smECRNMRho+fLj42vHjx2X58uVePd4TQR1QN2/eLO+++6507dpV6tSpI/v378/sXQKAGxBOAQCAO3Sd0rx589p+drf6uX37dunYsaNZ23T06NG2uai+NH/+fHPylO6bJ82Zgjag6lo73bt3NwvBjho1KrN3BwAcIpwCAAB3aTi1D6juOHLkiBnSe/bsWenbt6/07t3b578AR12DAyVoA+pHH30k69atk4kTJ5pFYt2l47jtx3Lr2G4A8CSARu854vQ2AACAQDlz5oxZ0/TAgQNmlOmYMWP8sh3NYJklKJskHTx4UAYOHCj16tWTLl26ePQcI0eOlHz58tlOacd5A4CrDY8IogAAILPFx8dLs2bNZOvWrdK6dWv57LPPvFrfNFgFZUB9/vnnzUKzn376qcfP0b9/fzl//rztpOO8AcAVjgIpIRUAAGSWq1evSosWLWTt2rXSpEkTmT59umTNmjUsfyFBOcRX2xrr3NNnn3021fUJCQm2cdf169c3l7/++mspWrToDc+hE4192WoZQPjLKISmN9wXAADAH65fvy7t27eXxYsXm8axM2bMkJgY97oHh5KgDKjq3LlzZmkZRzSoWrdZoRUAPOVOdZSQCgAAAunjjz+WmTNnmsuxsbHSq1cvh/fT+ah6uzp27Ji0atUqVddfpY+1mjM1b95cBg0aJMEmKAOqtiR2RJeZKVu2rJQvX152794d8P0CEH48GbpLSAUAAIFy9uxZ22UrqDqi66haAVWHBK9Zs+aG+2zbts12uVKlShKMgjKgAkAgeDOvlDmpAAAgEOLi4szJHWXKlHFa9At2QdkkCQD8jYAJAAAQfAioACJOMITTi2VzmhMAAECw279/f8C2FVJDfEO5VA0AFvtgal2+ad8Vrw5QemHX2+cGAACRrUKFCtKwYUPp0aOHWe4mOtp/MZIKKoCIEgzVU0c8raa6Uom17uPoflRyAQBARrRJ7YIFC6Rdu3ZSsmRJ6devn+zatUv8gYAKAAGUXph0Jyx6Giztw2raSi5hFQAAOLJjxw5ZsmSJWY/1woULMmrUKNMFuEGDBvL1119LYmKiROQQXwAIleqpN/NL9bE6LDez5qjab5fhwQAAQNWrV8+czp07J19++aV8/vnnsnTpUlm2bJkUKFBAOnXqZIYAV65cWbxBBRUAvJS2KumLYBksDZTsX4+vXyMAAAg9+fPnl3//+9/y559/yurVq6Vbt26mgvrhhx/KHXfcIXXq1DEBVtdi9QQVVAARwdPqKUGM4wAAABy79957JV++fJIlSxZTUVW//fabrFq1Sl555RUZNGiQvPDCC+IOKqgAwh7h1D+opAIAEJkSEhJMlbRu3bpSpUoVE06LFi0qAwYMkF9//VW6d+8u8fHx0rt3bxk+fLhbz01ABQB4haAKAEBk2Lx5sxneW7x4cenSpYuplj744IPy7bffysGDB+XNN980jZPGjx8vW7dulSJFish///tft7bBEF8AYStYl5QJV75a0xUAAAQXrZB+9tlnsn79eklJSZFChQrJSy+9JM8884xUrFjR4WNKly4tjRo1kq+++sqtbRFQAYQlwmnmIagCABBeevbsac7vv/9+efbZZ6Vt27aSPXv2DB935513yqFDh9zaFgEVQNghnAYHgioAAOHh+eefN9VS7dLrDm2UpCd3EFABwInMXIs0nDhbV9XZsWWIMAAAweXVV1+VPHnyZHi/s2fPysWLF+WWW27xeFs0SQIQVnxdPSUs+ZYr66g6WleWLwoAAMg8ZcuWNSE1I6+99pqUK1fOq20RUAEAIYGQCgBA5tDGSHpy9b7eYIgvgLDhy+opYSg4Ma8VAIDgderUKcmZ07vpUQRUABEdTAmioUl/bwy/BgBEgg0bNsjChQtl7dq15nTkyJEMK5VHjx6Vt956S3755RfTRTdr1qxSoUIFadWqlWladNNNN2W43eXLl6f6+fjx4zdcZ0lKSpIdO3bI/Pnz5fbbbxdvEFABRFSVlEAaPhz9LgmtAIBwM3z4cJk1a5bL99+1a5fUrl1bTp48KWXKlJFHHnlEEhISZNWqVTJs2DD5/vvvzeV8+fKl+zz169eXLFmy2H7W8KknZzQw6/1ffvll8QYBFUDIIJwiI3QGBgCEm1q1apn1RO+55x5z0tB59epVp/d//fXXTTjt1auXjB071lRP1fnz56Vp06ayevVqee+992To0KHpbrdTp062gDp58mQpX768Cb6OxMTESPHixeXRRx+VGjVqePV6CagAgLCXUeWcyisAIFi9/vrrbt3fGoY7aNAgWzhVWjHVLrutW7eWdevWZfg8kyZNsl3WgPrAAw/IF198If5GQAUARDyaLwEAwkX27NkzvE+hQoXces7k5GQJFJaZARCx4ktE3XBCZGPtVQBAqGvcuLFt7ur169dt1+sQ31GjRpnL3bp1k2BFBRVARHC1OZIVUvMcCdw3hQid9w1DgQEAvnDhwoUbqp6uVD5dMXLkSNP595NPPpF58+bJ3XffbZok/fbbb5IjRw6ZOnWqPPjgg+k+hzZT0vmnzz//vBQsWND87Cp9nA4v9lSWFG9XUg2hN4GOu24Y202io2Iye3cAZPJyMu5USwmrcISwCgDeSUpKkKXrRpjKXt68eUMuV5SbPECy5srh1mOvX06QvZ3fuuH6IUOGSFxcnEvPoSFTmySlF+POnDkj7du3lwULFqS6XuefjhgxQipVqpTuNqKiokzQ3LZtm9x66622n12Jjno/+8qtu6igAgh73oRT6/6EVDh7XxFUAQDu0rVJ7UO5r6qnavPmzdK8eXPTIEmXp6lbt65cunTJLC/Tv39/Wbp0qVlm5rbbbhNnJk6caM6LFSuW6udAIKACCHmBWNuUob9I7/1HSAUAuEPDqT+qxteuXZM2bdrI0aNHTadea8mX/PnzS+/evU1lU9cpHTx4sHzzzTdOn6dz587p/uxPdAQBEFHh1NtGSDRTgi/ehwAA+MPq1atl165dUrZsWYfrkbZt2zbVUjTBiAoqgIjhyy69DPtFWlRSAQCZ7fDhw+Zc58g6Yl1/9uxZn2wvKSlJPv/8c/nrr7+kVKlS0rNnTylQoIBXz0kFFUBEVK38sYQM1VSkRSUVAJCZihYtas537NghFy9evOF2HfarypQp49bzahdfndNqX3nVtVHr169vOv1qx+ABAwbIPffcI+fOnfPqNRBQAYSkYAoCrKOKYH1vAgAiS61ataRIkSKmKdILL7xguv1adF7qSy+9ZC7rPFV3LFy4UEqWLGkaLlm06ZI2W6pataqMHz9eWrRoIXv37pVx48Z59RoIqADCfokZf1RP09sWgRWEVACAr8ydO1dq1qxpOyUmJprr7a/T+1hL0GhYjI6OlilTpkj58uWlZcuW0qRJE7O0zJYtW8zc1H79+rm1Dxo8K1eunOq6GTNmmCVlpk+fLj169JAffvjBDPPV4OoN5qACCLlQav/h39vweblksjnPdTgqaLr/Mr81MkIqnX8BAK44efKkrFmz5obr7a/T+1g0kK5du1bGjBljhuTOmzdPYmJipGLFitKuXTvp06eP5Mzp3mif06dPS2xsbKrrli1bZp7TCq4aVnWI75IlS7z6xRJQAYRMldTb9UwzCqr+CqmuBNW0r8WV18barKGNdVQBAK7o0qWLObmjevXq8tVXX/nsAGs4PXLkiO3nrVu3yt9//22G9drTIGxVeD1FQAUQEnxZNU1bPQ1USPVHJ2FFUA2f9zZVVQBAMNIqqVZM//jjDxN+33vvPVMxffjhh1Pdb//+/VKsWDGvtkVABRBSQyP9PZ/U30N+/YGgGj6oqgIAgpE2V1q0aJEZwps/f36zTI2utdq0aVPbfc6fPy8bNmyQxx57zKtthc4nMAARL5DNjjSoWqdQwbI34RVU7U8AAGQmrZR+9NFHUqJECbly5YrUrl1bZs6caYb0WrQp07Vr1+Shhx7yaltUUAFEZCh1J3iGWlXV/pgx/Dc8eBpSGTIMAPAVXe9UT850795dOnXqJHny5PFqOwRUACEbTtOGTH8HSPvthVJYJaRGLoYMAwACRTsDu9sd2BECKoCQkVHVMxBNjlzZl2ALr4RUaFClmgoACAXB9SkKABzw9VxQf88rtZ+/GkpzWBHemM8KAPCGLi2jy92UK1fOVEqzZs3q8BQd7V0NlAoqAEQAqqiwUE0FALjr999/l4YNG5oGSapgwYJStGhR8QcCKoCwEshhvqG2TyxHAwshFQDgjv79+5tw2qdPHxk4cKAJqP5CQAWACAqpGTWfoqFS5CCkAgBctX79eqlWrZq899574m8EVAAIkFDoAswSNZGFLr8AAFfoeqeVKlWSQCCgAgg7wVStDOW1VZ1VWqmyhh+qqQCA9DzwwAOmSVIgBO8nIwDwQ8fdYOuqG2z7g8hldflNe0p7OwAg8rz11luye/duGTdunN+3RQUVQFgKhSpqKO6rYhhwZEkbSqm2AkDk2bhxo3Tt2lVefPFF+fbbb6VRo0ZSsmRJiYpy/PmlU6dOHm+LgAogKKXXyAfBg+VrIhNzVwEgsnTp0kWyZMkiKSkpsmLFCnPSn9PS2/V6AioAuIDhtP5BSI1c9tXVm/b9szYeACD8DB482GEg9QcqqACCmjX0lXAZ3AipYOgvAISvuLi4gG3L44C6f/9+WbZsmWzatElOnjwp586dk/z580vhwoXNGjn16tWTMmXK+HZvAUR0UHUUUqOLXzbnSUdzpTu3k4Drf4RUEFIBwPc2bNggCxculLVr15rTkSNHbMNpnYXJoUOHOn2+119/Xd5+++2g/VW5FVDPnj0rkydPls8++0y2b9/u9MBY5d/KlStLjx49zBjkAgUK+GqfAUSAtEuZaPhJG1KtcIrgmzvMUjSRi5AKAL41fPhwmTVrltuPq127tlSoUOGG6++++26v9ufPP/80QfnUqVNy++23y2OPPWauv3r1qjnlzZvX/wH18uXLMmrUKHn33Xfl0qVLkjNnTrMWzr333msWbC1YsKDZkfPnz5sQu23bNrPT69evl5deekkGDhwor7zyirz66quSK9eNVQ4AyIgGHkch1Vsach1VX+EdgmpkI6QCgO/UqlVL7rzzTrnnnnvMSUepahDMSPfu3U1zI1/ZsWOH6eS7Zs0a23WdO3e2BdRp06aZbc6dO1eaNm3q34Barlw5OXHihDRp0kQ6duwoLVu2lNy5c2f4OA2zM2bMkKlTp5oy8/jx4+Xo0aMe7yyAyOCo2Yp+4E0vpHoSNK0KLCHVfwiqkYuQCgC+8frrr2f6oTx06JDUrVvXTO3UQFqnTh1TfLTXrl076dWrl/zwww9eBdQoV1O7jn3++eef5cknn3QpnCq931NPPSXz58831dT77rvP4x0FENms0Go/dNSVQKpB1pWKa2YPFw73ObIsGxSZ0q6hCgAITcOGDTNDej///HP58ccf5eWXX3aY/bQXkX2F1W8V1JkzZ4q3atSo4ZPnARDZ0gs63lZCM7uSah9SreZO4YRqamSikgoAmWPx4sWmoW1CQoKULFlSmjVr5vH8019++cUMM+7WrVu699PhxwsWLBBvsMwMgJD5kGsFHCvIOap6uhoynVVMMzukRkJYJahGHtZLBYDA+/LLL1P9PGjQIHn88cdl0qRJkidPHreeS6d7atOljFy7ds30L/IGARVASAwJtA+n9uGyctG/bZe3Hb85LCqpzsJquAbVtOgAHN4IqwAgcuHChVSHIXv27ObkC9q5d8yYMaZiWrp0adPEdvny5fLaa6+Z+aHXr193e2RroUKF5ODBgxneb+fOnVKsWLHMC6i6Dqqejh075rSTlC45M2HCBG82AyDCOAsu6YVT62dXQqor802DLaSGe1U1vd8/gTV8uTNH1VHzNADITNeP55KUHDncekxywj9/40qVKpXq+iFDhpj1S31Bm9qmnRvaoUMHefDBB6Vq1apmDunq1aulZs2aLj+nVk/1cTpkWOeZOqK58K+//vK6c7BHAfXMmTPSqlUrWblypdMFYi0EVAC+YB9O7YNpg9h/1mRWi09VcjmkuiIYQ6r98QjnkOossBJWI5cVZgmqAMKBdsW1Xy/UV9XT9GhlU5eJ0eqqzil1J6DqkqFadW3RooV8+umn0rhx4xvmu2owjY6Olj59+kjAA6qubbpixQqzMGvPnj3NMjTujmMGAHcqhhmFU3v2IdXbTr3WY4IxqIbr0N/0MH8VBFUA4UDDqX1ADZSKFSuacx0B6w5djWXs2LHSu3dveeSRRyRXrlymEKlDhjW46pBl/fmTTz4xzZQCHlBnz55tOkH9/vvvBFMAfuVsSK99OH04z1aZF1/FXKdVVPuQ6qvlY+yfJ9jCaiRVU+2DKtXUyEZQBQD36XxU5eqyofZ0jVNdmeXtt982FVMdSXvx4kXJkSOHNGnSRN544w2XGin5JaDqxFpdG5WqKYBASS+cZhRSfc1RWHUWhAMVZiM1pCqCamQjqAKAazRQWs2RNGh6QocF61xUfS5dFzU5OVliY2Mla9as4isefZrR9XOOHz/us50AgFClwdRXVVpfDokGAACR5+TJkzJu3DhT2bQXHx8vzz33nKxZs0aKFi0qrVu3dut5tQuwdui16HDewoULy80335wqnO7atcvcN+ABdeDAgWZ4r06uBQAEj0gMqc66PiOyuNMRGABCydy5c03l0jolJiaa6+2v0/uoS5cuyQsvvCDFixeXBg0ayJNPPmkaGpUpU0bGjx8v+fPnl++//97MIXVH/fr15Z133snwfqNGjTLdggM+xFdf7LRp06RTp07y8MMPS6NGjaREiRISFeX4Q0LdunW92kkAkUuHyGqFUofqWsN8dQivNcxXh/Vaw3uDTWbPVU0bVsN5CDDDfQEA4VwVXbNmzQ3X21+n97HWK3399dfNMjJa8Vy1apWpcJYtW9Z02dVmt5rbPJHR6i2u3sdv66BqmThbtmzy5ZdfmlNGc1YBwFvphVREdhXVQlCNbFpFZRkaAOGmS5cuLq8tetNNN5kmRpnl6NGjXvcp8iigTpo0SZ5++mmTkKtXr+6XZWbee+89s87qli1b5MSJE5KQkGDGS9erV09effVVs8gsgPCmFT8NW1YVFQAAAIExZcqUVD/v3r37hussSUlJsmPHDvn111/dWl/VkSwpHtRhq1SpIvv375d58+aZ8cj+oN2gdAy1rqNjlaH/97//mVK1Vm5nzJhh1uBxla7Nky9fPmkY202io2L8ss8AvJNUvoRtHplVCXN3LVSrg6/FH118Q2F4ryPhPMQ3LTr7Ri4qqEDoSEpKkKXrRsj58+czZU1QT1m5ovQ7b0pUjhxuPTY5IUEOvD4wJF5zVFSUaYbkKo2VuuTMTz/9JA0bNgxsBVXDqVYy/RVO1axZs0y3YH2R9nTx1+eff166d+8uhw8fluhoj0cpAwjygKEh1aqiOhvq62049ddSNAAAAKFs8ODBJqBq8Bw2bJhUq1ZNWrRo4fC+MTExpjGTNmQqVqyYV9v1KN1pRdPdzk/ucrbIqy4Qq8N/9+zZI1u3bjUVVgDhVf1I240zvaG+1lzUtOHUHb4OqcFYPY00+uUGVdTIxDxUAPCNuLi4VFM8tSo6ZMgQ8TePxntp994lS5bImTNnJDPoEF8rqQMIX44ChhX+7AOlo3BKVRQAAMA3dAStLiETCB4F1AEDBpj1bfS0dOlSn7QTdpV2DNYJuBUrVjQnAAAAAEB48GiIrxUMDxw4IA899JCpaGqHXUfroOq4ZR2O66nRo0eb5kjaMGnbtm3mso5vnj59ulnTBwAAAADgX7qyivYDWr58uRw7dkyuXr3q8H7e5j+PmyTZS0xMlIMHD4o/zJ8/XxYtWmT7uXTp0qa9sTZQSo8eMPuDpt22AAAAAADu0UKhNsk9ffq030fPejTENzk52a2TN3QtHT0IZ8+eNWldq7d6cEaMGJHu40aOHGnaP1unUqVKebUfAAAAABCJXn31VTl16pS0bt1a1q9fb4p//sp/IbMoXv78+aVOnTpm7VWtng4aNEjWrVvn9P79+/c36wtZp0OHDgV0fwEAAAAgHKxYsUJuu+02+fbbb6VGjRqSJ08ev20rZAKqRee7PvHEE6aqOnv2bKf3y549u1n81v4EAAAAAHCPZi9dB1Xnl/qbSwFVGxP5gq+eJzY21pyfPHnSJ88HIHSkXQcVAAAA/vWvf/3LNMgNBJcC6p133int27eXzZs3e7SRP/74Q9q1ayd33XWX+MKyZcvMefny5X3yfACC2+WSN85lqFz0b9vlBrHbvXp+1kyNjDV0ERlu2ncls3cBAMJOXFycmV6Z3ghWX3Gpi++QIUNkzJgxZsxx1apVTVjVRkXVq1c3Q2nTSkhIMKFU10idNm2abN26VXLnzm2exxW//fabXLx4URo3bpxq6Zpr167Jp59+atZCzZkzpxnqCyDyqqeOwqmeLz5VKSjCqe5n0tFcPn9eAOkjnAKA//Tu3ds0SerQoYM0atRISpYs6XCZUVW3bl3/BtTBgwfLs88+azrn6hIv2oBIxx9HR0eb7rgFChSQm266yYTKM2fOmIZE169fN2OVtYOuvhh9TOHChV3aqV27dknXrl3NUF5tiFSoUCHTNWrLli1mzZ0cOXLIpEmT6MwLhLn4ElHpDu31tnKK8ET1NDIRTgHAf+rXr2/yn+Y7LRZOnTo13ftrFvT7OqhFihSRDz/8UN5++21TSZ0zZ46sXLlS9u7de8N9ixYtajruNm/e3Azt1UDpDq3ODhgwwAzl1WHFGk5jYmKkTJky0qZNG3nxxRelQoUKbj0ngPAY3mtfPU3L0yqqP1BFzRyEUwAAfK9Tp04BaZDkVkC16NDazp07m5PVqOjEiRNmKRetlmqQdbVS6kzZsmUzXOcUQORVT3057xThh3AKAIB/6OjVQHE7oKalYdTbQAoArjZHcuThPFtlXnyVVFVUDbOZ3fwos6qouQ6H3ApiXiOcAgDC1YYNG2ThwoWydu1aczpy5Ii5XofbppWcnGz6+Wgzo0WLFsnOnTslMTHRzBfVeaOvv/66KQYGM68DKgD40sWyOVP9nFH1VMNp2pAaySIxnAIAEM6GDx8us2bNcum+Ov3SalCk0y4bNGggWbNmNcF2/PjxpoHtvHnz5IEHHpBgRUAFENTDe70RiCqqVkiDZW3WSA2nVE8BAOGsVq1aZtnPe+65x5y0L8/Vq1cd3lfniWqltF+/fvLggw/a5o3q/bXprQ7VffLJJ2X37t2SLVs2p9scNmyYx/ur2xw0aJDnj09xVBsOQxcuXDBzZBvGdpPoqJjM3h0ADiSVL2GrompItYb4ulpFtSqoVqMkf4bTYFxGJhIDKuEUig6+QGhKSkqQpetGmF42efPmlVDLFaXfeVOi3GwGm5yQIAdeH+jVa86RI4cJnO7GuCtXrkixYsXMtnU5UG1M64wuH2N17XWVdX89D0gXXwDwt+g9R2wh9Z/g8U9ItaqUVuDUoGqFUA2q9kN7/R1OCabBgWAKRTAFAPea3d56662ybt06OXr0aLr3HTJkiGQWAiqAoAupN0kJU0VNG1LN7WmCqj1H4dT+ceEUSiO1akowhSKYAoD7tIHSgQMHbPNT00NABYAMQqpKG1RV2nVPHYVT67InITVYwynBFJGIYAoAnps+fbpZHlRXYLn//vslWFFBBRASIVXnpFqhzJqbqmHUvorqLJx6GlKDLZxGWihVVEzDH6ETQCTSeaz2smfPbk7+cujQIenTp4+tAZI/t5UpAXXo0KHy9NNPm/V0ACBwldR/OvxqULss/39eatpuvekFS1dDajCF00gIpgTRyEIoBRAOch6JkqzZ3fsbff3qP/cvVarUDUNq4+LixB8uXbokrVu3llOnTknLli1NN99g5nFAffPNN6Vp06bSo0cPeeSRR0ynJwDwV0hV9tVUlbZ5knVdRjIKqcESTsM9mBJKIwuhFABSVzTtu/j6q6J57do1adu2raxfv96sfarroAY7jz79aDi95ZZbZO7cudKqVSvzDYCudbN//37f7yGAiKchNe0HXEfhzZ1g6ey+wRBO9bWFczjVYEo4jRz675ZwCgCpaTi1P/kjoCYnJ0vnzp3l559/lmrVqsns2bNNJ99g59EnoAEDBsiePXtkwYIFJpGfPn1aRowYIRUqVDBV1R9++EGSkpJ8v7cAwpouMePsZL8+qv08VFe4GvYyO5wSTBFuCKYAkHn+/e9/m8ZIurTM/PnzJX/+/CHx6/DqK/qGDRvK119/LUeOHJExY8bIbbfdZkJru3btzPzUfv36ya5du3y3twAilqNw6spc0vTCaWYHUntUTBFuCKcAkHkGDhwon3zyiRn1unDhQilSpEjI/Dp8MoasUKFC0rdvX/nf//4nK1eulPbt25sWxqNHj5ZKlSrJQw89JDNnzvTFpgBESBhNe7I4q5x6Gjb1cdYps4RjOLWG8TKUNzIRTgEg87z//vtmdKuudfrrr7+akOqtKVOmyKpVqzK83+rVq819g2aZGR32q2ObFy1aZLtOK6lLliyRpUuXyt13322G/6btWgUAGdHqadpw6s6yMabzrxvDggNJ9ytcQiqBFIRTAPCtuXPnyvDhw20/JyYmmvOaNWvartN+QM2bN5dNmzbJyy+/bK4rW7asCaqOdO/e3TRNclWXLl3MKaP1UydMmCBffPGFdOrUSTItoGpnKA2dn332mQmhKSkptorqM888IxUrVpTff//dHJx58+bJCy+8ILNmzfJ2swDCnDWc15XKqTtrm8J/CKcgnAKA7508eVLWrFlzw/X21+l91Llz50weU5rB9ORI/fr13Qqo7jRmypIli1fP4XFA3bZtmwmlX375pZw5c8YcCE3Uuq6ONk6y70RVq1YtmTNnjkn5y5Yt82qHASAtZ8Nzw6UqGQoIpyCcAoB/dPl/1UtXaPC0Ampm2Lt3b6rlcwIWUDVtaxrXF6878Nxzz5lgescdd6T7uNtvv13WrVvn6b4CiEC+HJYbzMN8QxnhNLIRTAEgPA0bNizVzzp8OO11Fl3BZceOHbJ8+XJp1KhR4AOqTpCtUaOGCaUdOnSQXLlcay6iY53r1q3rySYBAEGIcBq5CKYAEN7i4uLMcF0tSuq5BlQ9pUe7Bb/11luBD6haBdWGR+7Sob56AgCENoJpZCOcAkD4mzhxojnXgNqtWzczivbpp592eN+YmBgpXry4mdJpP9UzYAHVk3AKAMEgGIf5htpcWcJpeAdPXdaJAAoA6Ny5s+0gTJ48WZo1a5bqOn/x6TIzABAJgTKSgql9GNXOyoTT8GWFUsIpACAtXTY0UAioAIKSBqG0S82EU/gL5v1zFkIJp+GJQAoACCbB+wkJAMKQBtPMDqeOgqZeZ50QOQinAABXbd261Sx3U65cOcmZM6dkzZrV4Sk62rsaKBVUAAiQzA6mygqgBFEQTgEArtIlRhs2bChXrvwzHaRgwYJStGhR8QcCKoCwEwxBMBj3iVAKRTAFALirf//+Jpz26dNHBg4caAKqvxBQAYRVo6RgCILBuD+EUyjCKQDAE+vXr5dq1arJe++9J/5GQAUQNKL3HJGk8iVsS13YN0pKL+i52+XXV52BgyV8ZoRwCoIpAMAbus5ppUqVJBAIqABCJqQGS1AMlWCqCKeRjWAKAPCFBx54wDRJCoTQ+ZQFIKJCqv2Ha/sOs5kZuIKhA68rguFYwT/034Sjk7P7AQDgC2+99Zbs3r1bxo0bJ/5GBRVAULMqqfas4OXtOqmhOq81PYTS8OJqyCSMAgD8aePGjdK1a1d58cUX5dtvv5VGjRpJyZIlJSrK8WekTp06ebwtAiqAoB7qmxkCHUhdGcbsynMgfBA4AQDBRNc/zZIli6SkpMiKFSvMSX9OS2/X6wmoAMJ+PqrFvprqi2CXGcHUUZj09LUQTMMP4RQAEGwGDx7sMJD6AxVUACFVSU075NebkGo/zDcQ4TSjMOnO0GWCafggkAIAgl1cXFzAtkVABRAWIdWeO4HV38HUkyBJ+Aw/hFAAAFxDQAUQdrwJrP7cD0QOAikAwNe2bdsmb775pixevFjOnDkjxYoVk0ceecRUN2NjYwN2wP/8809Zu3atnDp1Sm6//XZ57LHHzPVXr141p7x583r1/KHTmhIAQigoEk4jF+EUAOBrixcvln/9618ybdo0yZ8/vwmm2bNnN8u+VK9eXQ4fPuz3g75jxw65//77pUaNGvLss8/KwIED5ccff7TdrvtWoEAB+eWXX7zaDgEVQEQIVGBk/dHIRjgFAPja5cuXpUOHDuZcmxVpJfWHH36Q7du3yyuvvGLC6dNPP+3XA3/o0CGpW7eurF69Wh599FEZNWqU6dhrr127dhITE2P2zRsEVAARw1ch1Qqhjk6IXIRTAIA/zJgxQ/7++2+57bbbZMiQIbbrtavuW2+9JWXKlJEFCxaYobf+MmzYMDOk9/PPPzdV05dffvmG++TOnVuqVasma9as8WpbBFQAISkzwgAhFM4QTgEA/rJhwwZzrhXMqKjU8S1btmxSu3Ztc3nWrFl+2wcdtnvnnXdKt27d0r2fhuUjR454tS0CKoCQDgXOgoF1W9rbPa1yUh1Feu9DAAD85dKlS+Zc53c6UqhQIXPuzwrqiRMnTAU3I9euXTNDkb1BF18AYR8QvF07lXAKT997AAA4cuHChVQ/a8MjPTlSuHBhc37gwAGHt+/bty/d231BQ/DBgwczvN/OnTtNd2FvUEEFEDJrofqSq6GTcAp3q/cAgMiQ+5jznhTOTvoYVapUKcmXL5/tNHLkSKfbqVu3rjmfO3eumQdqT4fTLly40Fy+ePGi316rDiNet26dbNq0yel9li1bJn/99ZfUr1/fq20RUAFEREh1NUzQ9AiO3jtpTwAAeNsV9/z587ZT//79nd63cePGZmmX+Ph4adasmVmDVC///vvv5uekpCRzv7TzU31JuwVr194WLVrIzz//LNevX79hGZynnnpKoqOjpU+fPl5ti4AKIGIrqXThhTOEUQCAP+XNmzfVydnwXqtbr3byvf3222X9+vVy3333yU033WTWJNW5oXFxcenOUfUF3ebYsWPl6NGjZg1WXYtV90uXlNHtNmrUyNz28ccfm2ZK3mAOKoCQDKlJ5Utk9m4gzFAZBQAEq9KlS5vhtTNnzpRVq1bJlStXTGB98sknTXhV+rM/9erVy1Ry3377bVMx1YqqDivOkSOHNGnSRN544w1bR2FvEFABhCRCKrwNoFbjLIIpACAUREdHS9u2bc3JngZW5e3cT1fUrFnTrIOq4VTnwyYnJ0tsbKxkzZrVZ9sgoAIIWYRUZCS98EkwBQCEuuPHj8v3339vuuy2bt06YNvV4b1Wd2FfI6ACiJiQah9I7JedQWgjaAIAwt1ff/0lFSpUMMNpLYcPH5bHH3/cDLOdNGmS5MwZHp9tCKgAIrKSml6oIbwGP0IpACCSjBkzxsw/1Tmgus6oNkdauXKlXL16VQYNGiSdO3f26fYaNGjgVXV10aJFHj+egAogrLr7+qJ5EpXW4EY4BQBEmpYtW5rhvH/++af89ttvpnNu06ZNzZIu/ph7unTpUq8CqjcIqACAkEE4BQBEakBt2bJlwLa3b98+ySwEVABAUCOUAgAQ+GVtMgsBFUBY8WVnX+aiBg4hFAAAKAIqAIDgCQAAggIBFUDY8WXDJHiGiigAAOEja9asbjVJSkpK8nhbBFQAYcuboMrwXs8QTAEACD+lSpVy2J03OTnZdBe+du2az+auElABwEnQIqS6h3AKAEB42r9/v9PbNKQuXrxYevfuLVWrVpXp06d7ta0orx4NACFUSYXvA6n9CQAARJ6oqChp2LChzJkzR+bOnSvvvPOOV89HBRUAnHAldEVilZUwCgAA0ipbtqzUrFlTvvjiC+nXr594ioAKICL4cvmZ9MJauAdWwikAAHAmV65ccujQIfEGARUAAhjgnAVYV4NfZgZgwikAAHDmwIEDsnz5crn55pvFGwRUABHDX1VUb5svuRP87O/r67BqPbej5yWcAgAQuaZMmeL0tvj4eNm5c6dMnTpVLly4IL169fJqWwRUABElGEKqr4JfeoHSm+0QRgEAgL0uXbo4XGbGkpKSYs47deokQ4cOFW8QUAEgxJewSRtUrecnaAIAAF8YPHiw04AaExMjxYoVk7p160q5cuW83lZQBtTLly/LggULZPbs2bJy5Uoznjlr1qxSoUIFefzxx6Vv376SJ0+ezN5NACEqGKqo/giP9s9JOAUAAL4SFxcngRKU66BOmzZNWrVqZVoUazB97LHHpE6dOrJv3z4ZMmSI3HPPPXLixInM3k0AAAAAgA8FZQU1W7Zs0rNnT+nTp49UrlzZdv2xY8ekefPm8scff5jbNMgCQKRWYQEAAAJt1apVsmLFCjl69Kj5uXjx4vLAAw9I7dq1wzegdu7c2ZzS0rHN48aNk/vvv19mzJghiYmJZswzAAQyYOpjAQAAIsmWLVtMs6RNmzalaoxkzU296667ZNKkSXLnnXeGX0BNj75wdfXqVTl9+rQJrQDgbdB0NawSTgEAQGY5efKkvPPOO6ZXz8GDByVnzpxSpkwZeeihh2T06NF+2+6OHTukXr16cu7cOSlZsqS0adPGbFdpv6AffvjBBNf69eubCmulSpXCaw5qevbu3WsbBlywYMHM3h0AYcKV4Ek4BQAAmWXDhg1m+uO7775rslCLFi2kZs2acubMGXn//ff9uu0BAwaYcNqvXz+Tx9577z158cUXzUn3Z8+ePdK/f39znzfeeCOyKqgffvihOW/atKlkz549s3cHQBixAqijairhFAAAZGbltGnTpnLlyhWZNWuWaSJrb+3atX7d/pIlS+T222+Xt956y+Ht2th2xIgR8tNPP5n7RkxAnTdvnkyYMMF8YzB8+PB076tDgPVkuXDhQgD2EEA4BlXCKQAAyExDhgyRU6dOmX48acOpuvfee/26/WvXrrk0t1Tvs3//fq+2FTJDfLdv3y4dO3Y0k3F1fLU1F9WZkSNHSr58+WynUqVKBWxfAYQHDaaEUwAAkJmuXLkiU6dOldy5c0vXrl0zZR80e+kw3ozofTLKaWERUI8cOWJK2mfPnpW+fftK7969M3yMjoE+f/687XTo0KGA7CuAyEF4BQAA/rZ+/Xq5ePGiVK9e3TRF+vnnn00m6tWrl3zwwQe25V78SeeVrlu3Tr744gun95k4caK5j85XDeshvjrpt3HjxqY7lH5jMGbMGJcep/NTmaMKwN9YDxUAALgr7fTD9LLL1q1bzXmRIkWkZcuWZg6qPQ2EOg2yffv2PvtFLF++PNXPWr197rnnpEePHmYpmSeeeEJKly5tbtOc9u2338rKlSvNffLkyRO+ATU+Pl6aNWtmfimtW7eWzz77zLbODgAAAABklpv2X5Ho6H/WAnVVUlKCOU87/VDnmMbFxTl8jI4iVdqASJsR6TzUtm3byuXLl+Xjjz82BbzOnTubDr/VqlUTX9DlYhzlLp1uqUH0t99+u+F69Z///Mecrl+/Hn4BVRscaetk7UjVpEkTmT59uvmFAECwoYoKAADcodMP8+bNa/s5vZGfycnJ5jwpKcl0ytWhvRbtzaMVzO+++85c/uqrr3zyi+jUqVOmFQaDMqBq4tYS9eLFi6VOnToyY8YMiYmJyezdAgCnCKkAAMBVGk7tA2p68tgNmXXUJEmv04C6bNkyn/0CdBhvZgnKgKql6pkzZ5rLsbGxqb4lsKflbL0dAIKxaZKj9VQBAADcUfr/zfXMlSuXFC5c+Ibby5QpY85PnDjhtwM7duxYs/3u3btLRAZUa5y1soKqIzpOm4AKAAAAIFxVr17dttyMToNMOxxYm8oqb5sTpefll1+Whx9+OCABNSiXmdHgqRNtMzpZ3xYAQDBiGRoAAOCtW265xawtqvnH0TBe6zoryPpD0aJFJUeOHBIIQRlQASBcEFIBAIC3XnvtNXP+yiuvyLFjx2zXb9q0Sd59911z+dlnn/Xbgdamtdq9NzExUfyNgAoAAAAAQaxDhw5mKZktW7ZIlSpVpHnz5tKgQQOpWbOmGeKr65Pq0jP+ot2DdUWVJ598MlVAjpg5qAAQTujwCwAAvDVx4kSpXbu2jB8/XpYuXWqWgalRo4Y888wzJrz6U//+/c0wY11dZe7cuWa7OvTY0bBf3a8JEyZ4vK0sKdaqqmHuwoULki9fPmkY202io1iyBkBg0dEXAIA0fxuTEmTpuhFy/vx5l5dcCaZcUf+eNyQ6OkdEvOaoKNcH3mpA1WVDPUUFFQACgCoqAAAIVUuWLAnYtgioABCo/3D3HKGSCgAAQk69evUCti2aJAEAAAAAggIBFQACiGVnAAAAnGOILwAEcYCluRIAAMhsusSMO02SkpKSPN4WARUAMnEuakYVVet2gioAAMgspUqVMsEzreTkZDl+/Lhcu3bN/Fy6dGmvt0VABYAQGOpLUAUAAJll//79Tm/TkLp48WLp3bu3VK1aVaZPn+7VtpiDCgAhRIOqdQIAAAiGNVIbNmwoc+bMkblz58o777zj3fP5bM8AAAFFWAUAAMGibNmyUrNmTfniiy+8eh4CKgCEAaqqAAAgs+XKlUsOHTrk1XMQUAEgjBBUAQBAZjhw4IAsX75cbr75Zq+ehyZJAAAAAACnpkyZ4vS2+Ph42blzp0ydOlUuXLggvXr1Em8QUAEgzJeyAQAAcEe5cuWkbdu2toZHXbp0cbjMjCUlJcWcd+rUSYYOHSreIKACQJgipAIAAE+XlTl58qTt58GDBzsNqDExMVKsWDGpW7euCbbeIqACQBgjpAIAAG/FxcVJoNAkCQDCHGumAgCAUEFABYAIQHdfAAAQChjiCwARxN1qqtVoyXocjZcAAAi89957T1auXClbtmyREydOSEJCghQtWlTq1asnr776qlStWtXn29y0aZMMGzbMo8fqnFVPZUmxWi6FOW15nC9fPmkY202io2Iye3cAIKQRVAEAXv8tSUqQpetGyPnz5yVv3rwhlyvq3/OGREfnCMhrjo2NlUuXLsmdd94pJUr88+Xx//73P7O8S7Zs2WTGjBnyyCOPiK9ERUWl27XXGY2W+rjr1697vG0qqAAAryqxhFUAAPxr1qxZcvfdd0uOHKkD8SeffCLPP/+8dO/eXQ4fPizR0b6Ld+XLl5fatWtLoBFQAQDe/SFhzVUAAPyqtpOg2KtXLzP8d8+ePbJ161ZTYfWVBx54QL744gsJNJokAQAAAECIypYtm2090nBAQAUAAACAEPTll1/Kjh07pGLFiuYUDhjiCwAAAAAhYPTo0aY5kjZM2rZtm7lcvHhxmT59umTNmlXCAQEVAAAAAALcCdhe9uzZzSkj8+fPl0WLFtl+Ll26tEyZMsU0UAoXBFQAAAAAcDdI7Tvm/vKVyYnmrFSpUqmuHjJkiMTFxWX48F9//dWcnzt3zqyJquuU6lqob775przxxhvu7Ut6u5mcLJmFgAoAAAAAAXTo0KFU66C6Uj21lz9/fqlTp47MmzdPatWqJYMGDZLGjRvLPffcI6GOJkkAAAAAEEAaTu1P7gZU+w6+TzzxhKSkpMjs2bMlHBBQAQA+WQsVAAAEXmxsrDk/efJkWBx+AioAwCcIqQAABN6yZcvMefny5cPi8BNQAQA+DakEVQAAfOe3336TX3755YbGRdeuXZOPPvrIrIWaM2dOM9Q3HNAkCQDgtyG/SeVL+OX+AABEil27dknXrl3NUF5dTqZQoUJy6tQp08X32LFjkiNHDpk0adINnYFDFQEVAOC/PzIZBM+01daMqq8EWABApKlXr54MGDDADOXdvHmzCacxMTFSpkwZadOmjbz44otSoUIFCRcEVABAQIOqN0OA9bGEVABAJClbtqyMGDFCIgVzUAEAAeOL+am+nOPKfFkAAIILFVQAQMjxppLqbFgxlVkAADIfFVQAQEjydfWTDsQAAGQ+KqgAgLAPqVZ11JX7U1EFACDzEFABAGHPk2qrq0HV0XOzvA4AAJ4hoAIA4MZ8V3eqsGlltNwO82ABAJGOgAoAQIDmu9qHXUfP6U01FgCAcECTJAAAAsjdZkwshQMAiCQEVAAAAABAUCCgAgAQ5KiiAgAiBQEVAIAQQEgFAEQCAioAAAAAICgQUAEAAAAAQYGACgAAAAAICqyDCgBAGM5TZf1UAEAoooIKAEAYoIkSACAcEFABAAjxYOoonBJYAQChiIAKAECIIoQCQOS4cuWKDB48WG699VbJkSOHFC9eXLp16yZHjtz4JWUoYw4qAABhGkj1/sxFBYDQl5CQIA0aNJDVq1dLsWLFpEWLFrJ//36ZOHGizJkzx1xfrlw5CQdUUAEACGNUWQEg9L355psmhNaqVUt27twp33zzjaxZs0beffddOXnypKmkhgsCKgAAETpPFQAQ/BITE+Xjjz82l8eNGyd58uSx3da3b1+58847ZdmyZbJhwwYJBwRUAAAiBCEVAELPb7/9JufPn5fy5ctL9erVb7i9TZs25nz27NkSDpiDCgBABGGtVAAILX/++ac5r1GjhsPbres3b94s4YCACgBAhHJWUaWxEgAEj4MHD5rzkiVLOrzduv7AgQMSDiImoKakpJjzpOTEzN4VAACC2659qX5MKlss03YFQPhKun411ef0UJOUkiiS7MFjROTChQuprs+ePbs5ORIfH2/Oc+XK5fD23Llzm/OLFy9KOIiYgGr9wpaemZrZuwIAQGg5ldk7ACDcP6fny5dPQkVMTIwULVpUlh73LFdok6NSpUqlum7IkCESFxfnoz0MbRETUHUh20OHDslNN90kWbJkyezdCRn67Y7+A9Jjlzdv3szenZDH8eR4BjPenxzLYMV7k+MZzHh/ek4rpxpO9XN6KMmRI4fs27fPdNf19HWnzSPOqqfK6tp7+fJlceTSpUvmXHNOOIiYgBoVFeV03DYypuGUgOo7HE/f4nhyPIMV702OZzDj/cnxDAahVDlNG1L1FAi33HKLOT98+LDD263rS5cuLeGAZWYAAAAAIEjddddd5nzjxo0Ob7eu1/VQwwEBFQAAAACCVO3atU2lec+ePbJp06Ybbv/+++/N+aOPPirhgICKdOl4eJ20nd64eLiO4+lbHE+OZ7DivcnxDGa8PzmeCC0xMTHywgsvmMvPP/+8bc6peu+998z6p/Xq1ZO7775bwkGWlFDt6wwAAAAAESAhIUHq168va9askWLFikmdOnXMuqf6c+HChWX16tVSrlw5CQcEVAAAAAAIcleuXJGRI0fKtGnTzAobBQsWlKZNm8rw4cPDqhksARUAAAAAEBSYgwoAAAAACAoEVKSiCwD/+OOP8vTTT8ttt91m1nfKnTu3aW89bNgwiY+P54h54fTp01KkSBGzOHOFChU4ll44efKkvPLKK+Z9mjNnTjPMpUaNGvLqq69yXN2wbt06adeunVkkPVu2bJI/f34zr2XixIlmIXHcaMOGDfL2229L69atzZAq/fecdsF1RyZNmiT33nuvWXBd368PP/ywrFq1KqIPsTvHMjk5WVasWCGvvfaaaQSiC9Jrs5/y5cvLs88+K/v27ZNI5+l7017Dhg1tj3O25mKk8PR4Xrt2TT744APz713XmtV/87feeqt069ZNjhw5EpB9B0IZQ3yRyueffy49evQwlytXrix33HGHXLhwwXyIunjxolSqVEmWLVtmQhbc16VLF5kyZYr54K8fqnbv3s1h9PBDQ5MmTUzgv/32223v061bt5oPVElJSRxXF/zwww/yxBNPyPXr10241y9NNPhrCNBj2KFDB/nqq684lmm0bNlSZs2adcNxSS/Q9+nTRz788EPzZUrjxo1Ns4tFixaZx+jyAPqckcidY6n/X1asWNFcLlq0qPnwnzVrVlm7dq350K+Bdd68efLAAw9IpPLkvZn2S5SuXbuaEKaP0Tlu4TSvLRDH88yZM+bfuP6d0kY2NWvWtL1/t2zZYv5/jeT3KOAS7eILWCZNmpTSs2fPlK1bt6Y6KEePHk2pXr26/o+c0r59ew6YB3799Vdz/PT46nn58uU5jh44ceJESmxsbEquXLlSZs2adcPta9as4bi64Nq1aylFihQx78Wvvvoq1W36779gwYLmtsWLF3M803j77bdTBg0alPLTTz+lHDt2LCV79uzmWDmzcOFCc3uhQoVSdu7cabt+1apVKTExMSn58+dPOXv2bEQeZ3eO5e7du1MaNWqUsmjRopTk5GTb9QkJCSldunQxj7vllltSEhMTUyKVu+/NtP+36r/7xo0bp5QuXdo87tChQymRzN3jqe/LBx980NxnyJAh5v9Ze3v27Ek5efJkAPYcCG0EVLhMP0zpf7r6H/TVq1c5cm64fPmyCaRVqlQxH1AJqJ577rnnzPEbN24c70EvbNmyxRzH2267zeHtL774orn9nXfe4ThnIKMPrc2aNTO3v//++06P85gxYzjOLhzL9P6PzZcvn3ns0qVLOZYuvjftdejQISVHjhzmiwACqmfH85tvvjG3t23blvcg4AXmoMJlOg9VXb161QythOuGDh0qe/fulU8//dTM84Pn7dWnTp1q5kXrMDR4TufuuaJQoUIcZi/fs4sXLzaX27Rpc8Pt1nWzZ8/mOHtBh07rHD919OhRjqWbfvnlF7NsxRtvvGGmn8Azn332mTn/97//zSEEvBDtzYMRWTRgKQ1Y2uADrtm8ebO8++67JlBp85n9+/dz6Dy0fv16Mxda5+/oB9Kff/5ZFi5caObz6YdTq9kPMqaLeesH0R07dpgPpjrf1LJt2zbzRUCBAgWkVatWHE4v6PHVL/V0EXVHc/l07q/1/wQ8pw2UdMF6a34qXHfp0iV57rnnTI8JbUAFz2hjpJUrV0p0dLSZH63/pr/77js5ceKElChRQlq0aGH7oh9A+giocJk2+FC6ILCr1ZdIpx+aunfvbjqjjho1KrN3J+RpEySlTbocNa8YMGCATJgwQdq3b59Jexg6tLnM5MmT5ZFHHpEnn3zSfImiDWj0w5Q28ahSpYppmMKXUd45ePCgOXfWaEZHA+j/D2fPnjVfvmijH7hv+vTp5r2rXwTcf//9HEI3DB482HxxunTpUomJieHYefElvn5ZevPNN8v7779vqtH6GcASFxcnvXv3NrcBSB9DfOES7YyoH/y1ejp8+HCOmos++ugjs4zH6NGjGSrpA/ohXv30009mSNq4cePMh1L9cKVLzuhwys6dO8umTZt4j7qgdu3apiu3VlM3btwo33zzjSxZskSioqKkUaNG5np4x1qaK1euXE7voyFVaUCF+7TTrHZJVrocGl+guk7/3euXz/r/Zr169Xj7+eDvk06B6t+/v1n6aM+ePXLq1Cnz+UlH/ejSM/p3C0D6CKjI0Pbt26Vjx46mrboGLYaouF45GThwoPmjr8vLwHvWt9G6BIp+EO3Vq5epmJQuXdq8N9u2bWuGWelluFZ10qFopUqVkjVr1pgwtXPnTvN+1YpqgwYNzPBUIJiHp+oalRoCdFSFhgK4RpeXskb4jBkzhsPmw79PzZo1M0FUv+TTefy6/qn1d2nkyJEcayADBFSkS9eW0yG9+s1g3759zfAUuOb555+XxMRE0xgJvqGLnVscNUmyrtOqINK3a9cuUzWJjY2VOXPmmKCqlTwd5jt+/Hgz9FerK1988QWH0gfv2cuXL6cbshTDe92jX0bpl1I6N13npetcarhOq3l//PGHmX6i/w/Av3+frC+q9XMVa6AD6WMOKjJcbFobT+h/tnzD6h790K/fTKf9Rl/nqFh/pOrXr28uf/311zT2cIFWSq3hklo5TatMmTLmXIf9In36ntMP+PoFlP0HK4s2nNL38PLly00DFXjmlltuMeeHDx92Gk7PnTtnGlIRUN2rVukXLNoorVq1aqYLsg6hhOv0mGXJksXMRZ8yZUqq244fP27O9QsAHTLdr18/838FMv77ZP+3yJ7+3dL+Cfr3SU8VKlTgcAJOEFDhkA710yEq2pRGh09p63T9Qwb36AdPZ9U8DarWbVZoRfqqV69uznWuqQ49TTvXTL9UUY4CF1KzAlO+fPkcHhrremteFTxz2223mffpyZMnzZdS2s3Tnlap1Z133skhdoMu46FD1LV79/z5882XgXCfTt3RL6GcWb16tTlnmkrG9P/MsmXLyr59+xz+v6lfquhnAsXfKCB9DPHFDfSDv7ZDX7t2rTRp0sR8CNCOn3D/D7+jk/7xUrrEh3Wdo29b4bgapXOg9Zg5Cv7WdVaQhXPWUhw6PNIRbe6leG96R6t6OpdX6ZITaX3//ffm/NFHH+Xt6iKd2//JJ5+Y/w90mSmtSsF92rXX2d8pqxqoDaj0ZwKqax577DHbsXUU9nXaj/6foF9cAXCOgIobmiboEh26sLyu2TljxgzaziOoWOv0adfeY8eO2a7Xzr3a2EfRKCVj+iWU0urJf/7znxs+SFlLIbRp08aHv73IpPP31Ztvvmnm/lp+//13M99Xq39PP/10Ju5h6ND35YgRI8wXLL/++qttCDUQDLSbtC7V8/HHH9uqz0qbeFmdpnXKFJ2mgfRlSdGvxoD/R9vNW/+JtmrVSvLmzevw2Oh8VJoqeEaXRNFhQFpBpVGCZ/TbfJ03pR/sdc1DHfK7atUqU/3v0aOH/Pe///XwmSPLq6++aptbfvvtt5u1T48ePWqCkw5H69mzpwlQSG3u3LmpltvS0Sb6p/S+++6zXTdo0CBp3ry57Wf9f1X/f9V5aLqEj1ZStPqnj9MqqnagjUTuHEv9EqpGjRrm9lq1apnhvY5oZ1ptmhSJPHlvOqIjJ7T/hFZQna3hGwk8OZ7aWE7fg9HR0eZ9qkN/9e+TLj+j718d6cMQXyB9zEFFKvbzJmbOnOn06OiC0wRUZJaJEyeaNTw1POlQKp0frX/4n3nmGdM4Ba7RZQ804Gun6Q0bNsiOHTtMox5dGkmDvo6mwI10Pqkuy5OW/XV6n7QdU7WZj1ZWNJhqlaVhw4bmw63+DiKVO8dS5+9Z36nrlyh6ckSbz0VqQPXkvQnfHk9dUkaXl3n77bfN/fQLVP1Z503ryB9r3WMAzlFBBQAAAAAEBeagAgAAAACCAgEVAAAAABAUCKgAAAAAgKBAQAUAAAAABAUCKgAAAAAgKBBQAQAAAABBgYAKAAAAAAgKBFQAAAAAQFAgoAIAAAAAggIBFQAQ1IYNGyZRUVGyZcsWv26nZcuWcvPNN0t8fLxftwMAAJwjoAIAgtbff/8to0ePljZt2kjVqlX9uq3BgwfLiRMnZNSoUX7dDgAAcC5LSkpKSjq3AwCQaXr37i1jx46VjRs3SvXq1f2+vaZNm8qKFSvk4MGDUqhQIb9vDwAApEYFFQAQlC5fviyTJ0+WO+64IyDhVHXs2NG2XQAAEHgEVACATzzxxBOSJUsWee211264befOnZInTx5z2rVrl0vP991338n58+elffv2Dm/XbZUpU0aSkpJk+PDhUqFCBcmZM6dUrlxZJk6caLvf4sWL5cEHH5S8efNKgQIFpFOnTnL69Gmn81D1OT777DOXXzcAAPAdAioAwCc+/fRTKVmypLz77ruyZMkS2/XXrl2TJ598Ui5duiQffPCBVKxY0aXnmzNnjjmvX79+uvdr166d2abOUa1bt67s27dPunXrZkLq999/L02aNDEhVs9z584tX375pQmijma4aID+17/+Jdu3b5e9e/e6fQwAAIB3or18PAAAhlYnp0yZIg0bNjRVys2bN5vr4uLiZP369SYUdu/e3eWjpXNBo6Oj0x3ee+DAAbnppptMVbZw4cLmOg3HDRo0kDfeeEMSExPlxx9/lObNm5vbLly4IPfff7+sXLlSli5daiqrad17771m28uWLZNy5crx2wUAIICooAIAfEYD38svvyyHDx+WZ5991gS9t99+W4oVK+bWsFntpqsdfEuVKmWG3KZHq7JWOLX2QUPtsWPHpFmzZrZwqnSYb8+ePc1lDaCOVKpUyZxv2rTJ5f0FAAC+QUAFAPjUm2++KdWqVZNvv/1WHnnkETOUVofbxsbGuhVQlVZg05MtWzaHQ4Ctymfjxo2d3qYB1pGCBQua85MnT7q8vwAAwDcIqAAAn4qJibF1wdUhtVpJ1fmf7tDmSEqH76anaNGikjVrVodzSVWJEiWc3nb16lWHz6lVVnXu3Dm39hkAAHiPgAoA8LlvvvnGdlmHyl6/ft2tx+fLl8+cX7x4Md37RUVFeXV7euE4f/78bj8WAAB4h4AKAPApbUD0zjvvmOqmNkz6/fffZcSIEW49R5EiRcz5mTNnAv7bOXv2rDm3n9cKAAACg4AKAPAZHdL71FNPmYqpzjudOnWqCXq6TumaNWvcCqgacA8dOiSXL18O6G9o27Zt5lzn0QIAgMAioAIAfOaFF16Q/fv3m/OmTZvKzTffLJ9//rlZh7Rjx45mLVRX1alTxwTdP/74I6C/obVr15rzevXqBXS7AACAgAoA8JHvvvtOvvzyS6lSpYqMGjXKdv1jjz0mPXr0kN27d0vv3r1dfj5reRhdrzRQ4uPjzZqtutQMa6ACABB4VFABAF47cuSIPPPMM6aD71dffXXD2qXvv/++VKxYUSZMmCAzZ8506TnbtWtnmiVNmzYtYL8h3beEhAQTqAEAQOBlSdEF6gAACEIvvfSSfPDBB6aqeffdd/t9e7ocjjZ5OnjwoBQqVMjv2wMAAKlRQQUABK3+/fubdUtHjhzp921t3LhRFixYIC+//DLhFACATEIFFQAQ1IYNGyZxcXHy559/StWqVf22nZYtW5olcfbs2WNCMQAACDwCKgAAAAAgKDDEFwAAAAAQFAioAAAAAICgQEAFAAAAAAQFAioAAAAAICgQUAEAAAAAQYGACgAAAAAICgRUAAAAAEBQIKACAAAAAIICARUAAAAAIMHg/wIZRQD5QWG5XQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -983,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "id": "941d76b7",
    "metadata": {},
    "outputs": [
@@ -1201,7 +1201,7 @@
        "[5760 rows x 9 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1256,7 +1256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "id": "75433d08",
    "metadata": {
     "scrolled": true
@@ -1271,7 +1271,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x440 with 2 Axes>"
       ]

From 861d9d1ab4df9e09fc9ced362991c8698305d213 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 24 Feb 2026 11:18:10 -0900
Subject: [PATCH 15/19] updated get_layer_data for xarray

---
 mhkit/river/io/d3d.py | 176 ++++++++++++++++++++++++++----------------
 1 file changed, 111 insertions(+), 65 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 75d78318c..1087bc443 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -161,23 +161,23 @@ def _convert_time(
 # pylint: disable=too-many-branches
 # pylint: disable=too-many-statements
 def get_layer_data(
-    data: netCDF4.Dataset,
+    data: Union[netCDF4.Dataset, xr.Dataset],
     variable: str,
     layer_index: int = -1,
     time_index: int = -1,
     to_pandas: bool = True,
 ) -> Union[pd.DataFrame, xr.Dataset]:
     """
-    Get variable data from the NetCDF4 object at a specified layer and timestep.
+    Get variable data from the NetCDF4 or xarray object at a specified layer and timestep.
     If the data is 2D the layer_index is ignored.
 
     Parameters
     ----------
-    data: NetCDF4 object
-       A NetCDF4 object that contains spatial data, e.g. velocity or shear
+    data: Union[netCDF4.Dataset, xr.Dataset]
+       A NetCDF4 or xarray Dataset object that contains spatial data, e.g. velocity or shear
        stress, generated by running a Delft3D model.
     variable: string
-        Delft3D outputs many vairables. The full list can be
+        Delft3D outputs many variables. The full list can be
         found using "data.variables.keys()" in the console.
     layer_index: int
          An integer to pull out a layer from the dataset. 0 being closest
@@ -204,7 +204,7 @@ def get_layer_data(
         raise TypeError("layer_index must be an int")
 
     if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
-        raise TypeError("data must be NetCDF4 object or xarray Dataset")
+        raise TypeError("data must be a NetCDF4 Dataset or xarray Dataset")
 
     if variable not in data.variables.keys():
         raise ValueError("variable not recognized")
@@ -212,9 +212,14 @@ def get_layer_data(
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    coords = str(data.variables[variable].coordinates).split()
-    var = data.variables[variable][:]
-    max_time_index = data["time"].shape[0] - 1  # to account for zero index
+    if isinstance(data, netCDF4.Dataset):
+        coords = str(data.variables[variable].coordinates).split()
+        var = data.variables[variable][:]
+        max_time_index = data.variables["time"].shape[0] - 1
+    elif isinstance(data, xr.Dataset):
+        coords = list(data[variable].coords)
+        var = data[variable].values
+        max_time_index = data["time"].shape[0] - 1
 
     if abs(time_index) > max_time_index:
         raise ValueError(
@@ -222,8 +227,12 @@ def get_layer_data(
             f"max time index {max_time_index}"
         )
 
-    x = np.ma.getdata(data.variables[coords[0]][:], False)
-    y = np.ma.getdata(data.variables[coords[1]][:], False)
+    if isinstance(data, netCDF4.Dataset):
+        x = np.ma.getdata(data.variables[coords[0]][:], False)
+        y = np.ma.getdata(data.variables[coords[1]][:], False)
+    elif isinstance(data, xr.Dataset):
+        x = data[coords[0]].values
+        y = data[coords[1]].values
 
     if isinstance(var[0][0], np.ma.core.MaskedArray):
         max_layer = len(var[0][0])
@@ -233,7 +242,7 @@ def get_layer_data(
 
         v = np.ma.getdata(var[time_index, :, layer_index], False)
         dimensions = 3
-    elif isinstance(var[0][0], xr.core.variable.Variable):
+    elif isinstance(var[0][0], np.ndarray):
         max_layer = var[0][0].shape[0]
 
         if abs(layer_index) > max_layer:
@@ -241,7 +250,6 @@ def get_layer_data(
 
         v = np.ma.getdata(var[time_index, :, layer_index], False)
         dimensions = 3
-
     else:
         if not isinstance(var[0][0], np.float64):
             raise TypeError("data not recognized")
@@ -250,63 +258,97 @@ def get_layer_data(
         v = np.ma.getdata(var[time_index, :], False)
 
     # waterdepth
-    if "mesh2d" in variable:
-        cords_to_layers = {
-            "mesh2d_face_x mesh2d_face_y": {
-                "name": "mesh2d_nLayers",
-                "coords": data.variables["mesh2d_layer_sigma"][:],
-            },
-            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
-                "name": "mesh2d_nLayers",
-                "coords": data.variables["mesh2d_layer_sigma"][:],
-            },
-            "mesh2d_edge_x mesh2d_edge_y": {
-                "name": "mesh2d_nInterfaces",
-                "coords": data.variables["mesh2d_interface_sigma"][:],
-            },
-        }
-        bottom_depth = np.ma.getdata(
-            data.variables["mesh2d_waterdepth"][time_index, :], False
-        )
-        waterlevel = np.ma.getdata(data.variables["mesh2d_s1"][time_index, :], False)
-        coords = str(data.variables["mesh2d_waterdepth"].coordinates).split()
-
-    elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
-        cords_to_layers = {
-            "FlowElem_xcc FlowElem_ycc": {
-                "name": "laydim",
-                "coords": data.variables["LayCoord_cc"][:],
-            },
-            "FlowLink_xu FlowLink_yu": {
-                "name": "wdim",
-                "coords": data.variables["LayCoord_w"][:],
-            },
-        }
-        bottom_depth = np.ma.getdata(data.variables["waterdepth"][time_index, :], False)
-        waterlevel = np.ma.getdata(data.variables["s1"][time_index, :], False)
-        coords = str(data.variables["waterdepth"].coordinates).split()
-    else:
-        cords_to_layers = {
-            "FlowElem_xcc FlowElem_ycc LayCoord_cc LayCoord_cc": {
-                "name": "laydim",
-                "coords": data.variables["LayCoord_cc"][:],
-            },
-            "FlowLink_xu FlowLink_yu": {
-                "name": "wdim",
-                "coords": data.variables["LayCoord_w"][:],
-            },
-        }
-        bottom_depth = np.ma.getdata(data.variables["waterdepth"][time_index, :], False)
-        waterlevel = np.ma.getdata(data.variables["s1"][time_index, :], False)
-        coords = str(data.variables["waterdepth"].coordinates).split()
+    if isinstance(data, netCDF4.Dataset):
+        if "mesh2d" in variable:
+            cords_to_layers = {
+                "mesh2d_face_x mesh2d_face_y": {
+                    "name": "mesh2d_nLayers",
+                    "coords": data.variables["mesh2d_layer_sigma"][:],
+                },
+                "mesh2d_edge_x mesh2d_edge_y": {
+                    "name": "mesh2d_nInterfaces",
+                    "coords": data.variables["mesh2d_interface_sigma"][:],
+                },
+            }
+            bottom_depth = np.ma.getdata(
+                data.variables["mesh2d_waterdepth"][time_index, :], False
+            )
+            waterlevel = np.ma.getdata(data.variables["mesh2d_s1"][time_index, :], False)
+            coords = str(data.variables["waterdepth"].coordinates).split()
+
+        elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
+            cords_to_layers = {
+                "FlowElem_xcc FlowElem_ycc": {
+                    "name": "laydim",
+                    "coords": data.variables["LayCoord_cc"][:],
+                },
+                "FlowLink_xu FlowLink_yu": {
+                    "name": "wdim",
+                    "coords": data.variables["LayCoord_w"][:],
+                },
+            }
+            bottom_depth = np.ma.getdata(data.variables["waterdepth"][time_index, :], False)
+            waterlevel = np.ma.getdata(data.variables["s1"][time_index, :], False)
+            coords = str(data.variables["waterdepth"].coordinates).split()
+        else:
+            cords_to_layers = {
+                "FlowElem_xcc FlowElem_ycc LayCoord_cc LayCoord_cc": {
+                    "name": "laydim",
+                    "coords": data.variables["LayCoord_cc"][:],
+                },
+                "FlowLink_xu FlowLink_yu": {
+                    "name": "wdim",
+                    "coords": data.variables["LayCoord_w"][:],
+                },
+            }
+            bottom_depth = np.ma.getdata(data.variables["waterdepth"][time_index, :], False)
+            waterlevel = np.ma.getdata(data.variables["s1"][time_index, :], False)
+            coords = str(data.variables["waterdepth"].coordinates).split()
+
+        layer_dim = str(data.variables[variable].coordinates)
 
-    layer_dim = str(data.variables[variable].coordinates)
+    elif isinstance(data, xr.Dataset):
+        if "mesh2d" in variable:
+            cords_to_layers = {
+                "mesh2d_face_x mesh2d_face_y": {
+                    "name": "mesh2d_nLayers",
+                    "coords": data.variables["mesh2d_layer_sigma"][:],
+                },
+                "mesh2d_edge_x mesh2d_edge_y": {
+                    "name": "mesh2d_nInterfaces",
+                    "coords": data.variables["mesh2d_interface_sigma"][:],
+                },
+                }
+            bottom_depth = data["mesh2d_waterdepth"].values[time_index, :]
+            waterlevel = data["mesh2d_s1"].values[time_index, :]
+            coords = list(data["waterdepth"].coords)
+        elif str(list(data[variable].coords)) == "['FlowElem_xcc', 'FlowElem_ycc', 'time']":
+            cords_to_layers = {
+                "FlowElem_xcc FlowElem_ycc": {
+                    "name": "laydim",
+                    "coords": data.variables["LayCoord_cc"][:],
+                },
+                "FlowLink_xu FlowLink_yu": {
+                    "name": "wdim",
+                    "coords": data.variables["LayCoord_w"][:],
+                    },
+                }
+            bottom_depth = data["waterdepth"].values[time_index, :]
+            waterlevel = data["s1"].values[time_index, :]
+            coords = list(data["waterdepth"].coords)
+            
+        layer_dim = " ".join(map(str, list(data[variable].coords)[0:2]))
 
     try:
         cord_sys = cords_to_layers[layer_dim]["coords"]
     except KeyError as exc:
         raise ValueError("Coordinates not recognized.") from exc
-    layer_percentages = np.ma.getdata(cord_sys, False)  # accumulative
+    
+    if isinstance(data, netCDF4.Dataset):
+        layer_percentages = np.ma.getdata(cord_sys, False)  # accumulative
+    elif isinstance(data, xr.Dataset):
+        layer_percentages= cord_sys.values  # accumulative
+        
 
     if layer_dim == "FlowLink_xu FlowLink_yu":
         # interpolate
@@ -348,7 +390,11 @@ def get_layer_data(
             z = [bottom_depth * layer_percentages[layer_index]]
     waterdepth = np.append(waterdepth, z)
 
-    time = np.ma.getdata(data.variables["time"][time_index], False) * np.ones(len(x))
+    if isinstance(data, netCDF4.Dataset):
+        time = np.ma.getdata(data.variables["time"][time_index], False) * np.ones(len(x))
+    elif isinstance(data, xr.Dataset):
+        time= [data.time.values[time_index]] * len(x)
+        
 
     index = np.arange(0, len(time))
     layer_data = xr.Dataset(

From ed1273c3ea6e255a50b5427321a45e08fbb45c60 Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Tue, 24 Feb 2026 14:44:57 -0900
Subject: [PATCH 16/19] updated get_all_data for xarray

---
 mhkit/river/io/d3d.py | 183 ++++++++++++++++++------------------------
 1 file changed, 80 insertions(+), 103 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 1087bc443..a502b18f1 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -94,7 +94,7 @@ def seconds_to_index(data: netCDF4.Dataset, seconds_run: Union[int, float]) -> i
 def _convert_time(
     data: netCDF4.Dataset or xr.Dataset,
     time_index: Optional[Union[int, float]] = None,
-    seconds_run: Optional[Union[int, float]] = None,
+    seconds_run: Optional[Union[int, float, np.datetime64]] = None,
 ) -> Union[int, float]:
     """
     Converts a time index to seconds or seconds to a time index. The user
@@ -393,8 +393,7 @@ def get_layer_data(
     if isinstance(data, netCDF4.Dataset):
         time = np.ma.getdata(data.variables["time"][time_index], False) * np.ones(len(x))
     elif isinstance(data, xr.Dataset):
-        time= [data.time.values[time_index]] * len(x)
-        
+        time=(data.time.values[time_index] - data.time.values[0]).astype('timedelta64[s]').astype(int) * np.ones(len(x))
 
     index = np.arange(0, len(time))
     layer_data = xr.Dataset(
@@ -596,10 +595,8 @@ def variable_interpolation(
                 f"If a string, points must be cells or faces. Got {points}"
             )
 
-    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
-        raise TypeError(
-            f"data must be netCDF4 object or xarray Dataset. Got {type(data)}"
-        )
+    if not isinstance(data, netCDF4.Dataset):
+        raise TypeError(f"data must be netCDF4 object. Got {type(data)}")
 
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
@@ -648,7 +645,7 @@ def variable_interpolation(
 
 
 def get_all_data_points(
-    data: netCDF4.Dataset, variable: str, time_index: int = -1, to_pandas: bool = True
+    data: (netCDF4.Dataset, xr.Dataset), variable: str, time_index: int = -1, to_pandas: bool = True
 ) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Get data points for a passed variable for all layers at a specified time from
@@ -694,53 +691,83 @@ def get_all_data_points(
         raise ValueError(
             f"time_index must be less than the max time index {max_time_index}"
         )
+    if isinstance(data, netCDF4.Dataset):
+        if "mesh2d" in variable:
+            cords_to_layers = {
+                "mesh2d_face_x mesh2d_face_y": {
+                    "name": "mesh2d_nLayers",
+                    "coords": data.variables["mesh2d_layer_sigma"][:],
+                },
+                "mesh2d_edge_x mesh2d_edge_y": {
+                    "name": "mesh2d_nInterfaces",
+                    "coords": data.variables["mesh2d_interface_sigma"][:],
+                },
+            }
 
-    if "mesh2d" in variable:
-        cords_to_layers = {
-            "mesh2d_face_x mesh2d_face_y": {
-                "name": "mesh2d_nLayers",
-                "coords": data.variables["mesh2d_layer_sigma"][:],
-            },
-            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
-                "name": "mesh2d_nLayers",
-                "coords": data.variables["mesh2d_layer_sigma"][:],
-            },
-            "mesh2d_edge_x mesh2d_edge_y": {
-                "name": "mesh2d_nInterfaces",
-                "coords": data.variables["mesh2d_interface_sigma"][:],
-            },
-        }
-
-    elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
-        cords_to_layers = {
-            "FlowElem_xcc FlowElem_ycc": {
-                "name": "laydim",
-                "coords": data.variables["LayCoord_cc"][:],
-            },
-            "FlowLink_xu FlowLink_yu": {
-                "name": "wdim",
-                "coords": data.variables["LayCoord_w"][:],
-            },
-        }
-    else:
-        cords_to_layers = {
-            "FlowElem_xcc FlowElem_ycc LayCoord_cc LayCoord_cc": {
-                "name": "laydim",
-                "coords": data.variables["LayCoord_cc"][:],
-            },
-            "FlowLink_xu FlowLink_yu": {
-                "name": "wdim",
-                "coords": data.variables["LayCoord_w"][:],
-            },
-        }
-
-    layer_dim = str(data.variables[variable].coordinates)
+        elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
+            cords_to_layers = {
+                "FlowElem_xcc FlowElem_ycc": {
+                    "name": "laydim",
+                    "coords": data.variables["LayCoord_cc"][:],
+                },
+                "FlowLink_xu FlowLink_yu": {
+                    "name": "wdim",
+                    "coords": data.variables["LayCoord_w"][:],
+                },
+            }
+        else:
+            cords_to_layers = {
+                "FlowElem_xcc FlowElem_ycc LayCoord_cc LayCoord_cc": {
+                    "name": "laydim",
+                    "coords": data.variables["LayCoord_cc"][:],
+                },
+                "FlowLink_xu FlowLink_yu": {
+                    "name": "wdim",
+                    "coords": data.variables["LayCoord_w"][:],
+                },
+            }
+
+        layer_dim = str(data.variables[variable].coordinates)
+    elif isinstance(data, xr.Dataset):
+            if "mesh2d" in variable:
+                cords_to_layers = {
+                    "mesh2d_face_x mesh2d_face_y": {
+                        "name": "mesh2d_nLayers",
+                        "coords": data.variables["mesh2d_layer_sigma"][:],
+                    },
+                    "mesh2d_edge_x mesh2d_edge_y": {
+                        "name": "mesh2d_nInterfaces",
+                        "coords": data.variables["mesh2d_interface_sigma"][:],
+                    },
+                    }
+                bottom_depth = data["mesh2d_waterdepth"].values[time_index, :]
+                waterlevel = data["mesh2d_s1"].values[time_index, :]
+                coords = list(data["waterdepth"].coords)
+            elif str(list(data[variable].coords)) == "['FlowElem_xcc', 'FlowElem_ycc', 'time']":
+                cords_to_layers = {
+                    "FlowElem_xcc FlowElem_ycc": {
+                        "name": "laydim",
+                        "coords": data.variables["LayCoord_cc"][:],
+                    },
+                    "FlowLink_xu FlowLink_yu": {
+                        "name": "wdim",
+                        "coords": data.variables["LayCoord_w"][:],
+                        },
+                    }
+                bottom_depth = data["waterdepth"].values[time_index, :]
+                waterlevel = data["s1"].values[time_index, :]
+                coords = list(data["waterdepth"].coords)
+                
+            layer_dim = " ".join(map(str, list(data[variable].coords)[0:2]))
 
     try:
         cord_sys = cords_to_layers[layer_dim]["coords"]
     except KeyError as exc:
         raise ValueError("Coordinates not recognized.") from exc
-    layer_percentages = np.ma.getdata(cord_sys, False)
+    if isinstance(data, netCDF4.Dataset):
+        layer_percentages = np.ma.getdata(cord_sys, False)  # accumulative
+    elif isinstance(data, xr.Dataset):
+        layer_percentages= cord_sys.values  # accumulative
 
     x_all = []
     y_all = []
@@ -758,7 +785,7 @@ def get_all_data_points(
         depth_all = np.append(depth_all, layer_data.waterdepth)
         water_level_all = np.append(water_level_all, layer_data.waterlevel)
         v_all = np.append(v_all, layer_data.v)
-        time_all = np.append(time_all, layer_data.time)
+        time_all = np.append(time_all, np.asarray(layer_data.time))
 
     index = np.arange(0, len(time_all))
     all_data = xr.Dataset(
@@ -852,8 +879,8 @@ def turbulent_intensity(
             f"value of the max time index {max_time_index}"
         )
 
-    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
-        raise TypeError("data must be netCDF4 object or xarray Dataset")
+    if not isinstance(data, netCDF4.Dataset):
+        raise TypeError("data must be netCDF4 object")
 
     for variable in ["turkin1", "ucx", "ucy", "ucz"]:
         if variable not in data.variables.keys():
@@ -946,10 +973,7 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
     >>> print(variables)
     ['time', 'x', 'y', 'waterdepth', 'ucx', 'ucy', 'ucz', 'turkin1']
     """
-    if isinstance(
-        data,
-        netCDF4.Dataset,
-    ):
+    if isinstance(data, netCDF4.Dataset):
         return list(data.variables.keys())
     if isinstance(data, (xr.Dataset, xr.DataArray)):
         return list(data.variables.keys())
@@ -957,50 +981,3 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
         "data must be a NetCDF4 Dataset, xarray Dataset, or "
         f"xarray DataArray. Got: {type(data)}"
     )
-
-
-def calculate_grid_convergence_index(
-    fine_grid, coarse_grid, refinement_ratio, factor_of_safety=1.25, order=2
-):
-    """
-    Calculate the Grid Convergence Index (GCI) between two grid sizes. 
-
-         NASA. (n.d.). Examining spatial (grid) convergence. Accessed Febuary 3, 2026. NASA. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html 
-
-    Parameters
-    ----------
-    fine_grid: numpy.ndarray
-        Results from the finer grid.
-    coarse_grid: numpy.ndarray
-        Results from the coarser grid.
-    refinement_ratio: float
-        Refinement ratio between the grids.
-    factor_of_safety: float
-        Factor of safety (default is 1.25).
-    order: int
-        Order of accuracy (default is 2).
-
-    Returns
-    -------
-    gci: float
-        Grid Convergence Index (GCI).
-    """
-
-    # Validate inputs
-    if not (np.issubdtype(refinement_ratio.dtype, np.number)):
-        raise TypeError("refinement_ratio must be a numeric values")
-    if not (np.issubdtype(factor_of_safety.dtype, np.number)):
-        raise TypeError("factor_of_safety must be a numeric values")
-    if not (np.issubdtype(order.dtype, np.number)):
-        raise TypeError("order must be a numeric values")
-    if not (np.issubdtype(fine_grid.dtype, np.number) and np.issubdtype(coarse_grid.dtype, np.number)):
-        raise TypeError("fine_grid and coarse_grid must contain numeric values")
-    if fine_grid.shape != coarse_grid.shape:
-        raise ValueError("fine_grid and coarse_grid must have the same shape")
-
-    # Calculate the approximate relative error
-    error = np.abs((fine_grid - coarse_grid) / fine_grid)
-
-    # Calculate the GCI
-    gci = (factor_of_safety * error) / (refinement_ratio**order - 1)
-    return gci

From e1b0d1afabe8823b456cbce56013fa898a66a79b Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Thu, 26 Feb 2026 10:58:55 -0900
Subject: [PATCH 17/19] finished adding xarray input analysis

---
 mhkit/river/io/d3d.py | 104 ++++++++++++++++++++++++++++++++----------
 1 file changed, 79 insertions(+), 25 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index a502b18f1..a4e000ace 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -322,7 +322,8 @@ def get_layer_data(
             bottom_depth = data["mesh2d_waterdepth"].values[time_index, :]
             waterlevel = data["mesh2d_s1"].values[time_index, :]
             coords = list(data["waterdepth"].coords)
-        elif str(list(data[variable].coords)) == "['FlowElem_xcc', 'FlowElem_ycc', 'time']":
+        elif str(list(data[variable].coords)) == "['FlowElem_xcc', 'FlowElem_ycc', 'time']" or \
+             str(list(data[variable].coords)) == "['FlowLink_xu', 'FlowLink_yu', 'time']":
             cords_to_layers = {
                 "FlowElem_xcc FlowElem_ycc": {
                     "name": "laydim",
@@ -336,6 +337,20 @@ def get_layer_data(
             bottom_depth = data["waterdepth"].values[time_index, :]
             waterlevel = data["s1"].values[time_index, :]
             coords = list(data["waterdepth"].coords)
+        else:
+            cords_to_layers = {
+                "FlowElem_xcc FlowElem_ycc LayCoord_cc LayCoord_cc": {
+                    "name": "laydim",
+                    "coords": data.variables["LayCoord_cc"][:],
+                },
+                "FlowLink_xu FlowLink_yu": {
+                    "name": "wdim",
+                    "coords": data.variables["LayCoord_w"][:],
+                },
+            }
+            bottom_depth = data["waterdepth"].values[time_index, :]
+            waterlevel = data["s1"].values[time_index, :]
+            coords = list(data["waterdepth"].coords)
             
         layer_dim = " ".join(map(str, list(data[variable].coords)[0:2]))
 
@@ -349,30 +364,54 @@ def get_layer_data(
     elif isinstance(data, xr.Dataset):
         layer_percentages= cord_sys.values  # accumulative
         
+    if isinstance(data, netCDF4.Dataset):
+        if layer_dim == "FlowLink_xu FlowLink_yu":
+            # interpolate
+            x_laydim = np.ma.getdata(data.variables[coords[0]][:], False)
+            y_laydim = np.ma.getdata(data.variables[coords[1]][:], False)
+            points_laydim = np.array([[x, y] for x, y in zip(x_laydim, y_laydim)])
 
-    if layer_dim == "FlowLink_xu FlowLink_yu":
-        # interpolate
-        x_laydim = np.ma.getdata(data.variables[coords[0]][:], False)
-        y_laydim = np.ma.getdata(data.variables[coords[1]][:], False)
-        points_laydim = np.array([[x, y] for x, y in zip(x_laydim, y_laydim)])
+            coords_request = str(data.variables[variable].coordinates).split()
+            x_wdim = np.ma.getdata(data.variables[coords_request[0]][:], False)
+            y_wdim = np.ma.getdata(data.variables[coords_request[1]][:], False)
+            points_wdim = np.array([[x, y] for x, y in zip(x_wdim, y_wdim)])
 
-        coords_request = str(data.variables[variable].coordinates).split()
-        x_wdim = np.ma.getdata(data.variables[coords_request[0]][:], False)
-        y_wdim = np.ma.getdata(data.variables[coords_request[1]][:], False)
-        points_wdim = np.array([[x, y] for x, y in zip(x_wdim, y_wdim)])
+            bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth, points_wdim)
+            water_level_wdim = interp.griddata(points_laydim, waterlevel, points_wdim)
 
-        bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth, points_wdim)
-        water_level_wdim = interp.griddata(points_laydim, waterlevel, points_wdim)
+            idx_bd = np.where(np.isnan(bottom_depth_wdim))
 
-        idx_bd = np.where(np.isnan(bottom_depth_wdim))
+            for i in idx_bd:
+                bottom_depth_wdim[i] = interp.griddata(
+                    points_laydim, bottom_depth, points_wdim[i], method="nearest"
+                )
+                water_level_wdim[i] = interp.griddata(
+                    points_laydim, waterlevel, points_wdim[i], method="nearest"
+                )
+    elif isinstance(data, xr.Dataset):
+        if layer_dim == "FlowLink_xu FlowLink_yu":
+            # interpolate
+            x_laydim = data[coords[0]].values
+            y_laydim = data[coords[1]].values
+            points_laydim = np.array([[x, y] for x, y in zip(x_laydim, y_laydim)])
 
-        for i in idx_bd:
-            bottom_depth_wdim[i] = interp.griddata(
-                points_laydim, bottom_depth, points_wdim[i], method="nearest"
-            )
-            water_level_wdim[i] = interp.griddata(
-                points_laydim, waterlevel, points_wdim[i], method="nearest"
-            )
+            coords_request = list(data[variable].coords)[0:2]
+            x_wdim = data[coords_request[0]].values
+            y_wdim = data[coords_request[1]].values
+            points_wdim = np.array([[x, y] for x, y in zip(x_wdim, y_wdim)])
+
+            bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth, points_wdim)
+            water_level_wdim = interp.griddata(points_laydim, waterlevel, points_wdim)
+
+            idx_bd = np.where(np.isnan(bottom_depth_wdim))
+
+            for i in idx_bd:
+                bottom_depth_wdim[i] = interp.griddata(
+                    points_laydim, bottom_depth, points_wdim[i], method="nearest"
+                )
+                water_level_wdim[i] = interp.griddata(
+                    points_laydim, waterlevel, points_wdim[i], method="nearest"
+                )
 
     waterdepth = []
 
@@ -595,8 +634,8 @@ def variable_interpolation(
                 f"If a string, points must be cells or faces. Got {points}"
             )
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError(f"data must be netCDF4 object. Got {type(data)}")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError(f"data must be netCDF4 or xarray Dataset object. Got {type(data)}")
 
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
@@ -743,7 +782,8 @@ def get_all_data_points(
                 bottom_depth = data["mesh2d_waterdepth"].values[time_index, :]
                 waterlevel = data["mesh2d_s1"].values[time_index, :]
                 coords = list(data["waterdepth"].coords)
-            elif str(list(data[variable].coords)) == "['FlowElem_xcc', 'FlowElem_ycc', 'time']":
+            elif str(list(data[variable].coords)) == "['FlowElem_xcc', 'FlowElem_ycc', 'time']" or \
+                str(list(data[variable].coords)) == "['FlowLink_xu', 'FlowLink_yu', 'time']":
                 cords_to_layers = {
                     "FlowElem_xcc FlowElem_ycc": {
                         "name": "laydim",
@@ -757,6 +797,20 @@ def get_all_data_points(
                 bottom_depth = data["waterdepth"].values[time_index, :]
                 waterlevel = data["s1"].values[time_index, :]
                 coords = list(data["waterdepth"].coords)
+            else:
+                cords_to_layers = {
+                    "FlowElem_xcc FlowElem_ycc LayCoord_cc LayCoord_cc": {
+                        "name": "laydim",
+                        "coords": data.variables["LayCoord_cc"][:],
+                    },
+                    "FlowLink_xu FlowLink_yu": {
+                        "name": "wdim",
+                        "coords": data.variables["LayCoord_w"][:],
+                    },
+                }
+                bottom_depth = data["waterdepth"].values[time_index, :]
+                waterlevel = data["s1"].values[time_index, :]
+                coords = list(data["waterdepth"].coords)
                 
             layer_dim = " ".join(map(str, list(data[variable].coords)[0:2]))
 
@@ -879,8 +933,8 @@ def turbulent_intensity(
             f"value of the max time index {max_time_index}"
         )
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be netCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be netCDF4 object or xarray Dataset")
 
     for variable in ["turkin1", "ucx", "ucy", "ucz"]:
         if variable not in data.variables.keys():

From 665f23c81bf2e1a60b59589a50cd92b5b8ad7ecf Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Thu, 26 Feb 2026 11:14:10 -0900
Subject: [PATCH 18/19] consolodated get_layer_data depth interpolation

---
 mhkit/river/io/d3d.py | 51 ++++++++++++++-----------------------------
 1 file changed, 16 insertions(+), 35 deletions(-)

diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index a4e000ace..be69fd3cb 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -364,54 +364,35 @@ def get_layer_data(
     elif isinstance(data, xr.Dataset):
         layer_percentages= cord_sys.values  # accumulative
         
-    if isinstance(data, netCDF4.Dataset):
-        if layer_dim == "FlowLink_xu FlowLink_yu":
-            # interpolate
+    if layer_dim == "FlowLink_xu FlowLink_yu":
+        # interpolate
+        if isinstance(data, netCDF4.Dataset):
             x_laydim = np.ma.getdata(data.variables[coords[0]][:], False)
             y_laydim = np.ma.getdata(data.variables[coords[1]][:], False)
-            points_laydim = np.array([[x, y] for x, y in zip(x_laydim, y_laydim)])
-
             coords_request = str(data.variables[variable].coordinates).split()
             x_wdim = np.ma.getdata(data.variables[coords_request[0]][:], False)
             y_wdim = np.ma.getdata(data.variables[coords_request[1]][:], False)
-            points_wdim = np.array([[x, y] for x, y in zip(x_wdim, y_wdim)])
-
-            bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth, points_wdim)
-            water_level_wdim = interp.griddata(points_laydim, waterlevel, points_wdim)
-
-            idx_bd = np.where(np.isnan(bottom_depth_wdim))
-
-            for i in idx_bd:
-                bottom_depth_wdim[i] = interp.griddata(
-                    points_laydim, bottom_depth, points_wdim[i], method="nearest"
-                )
-                water_level_wdim[i] = interp.griddata(
-                    points_laydim, waterlevel, points_wdim[i], method="nearest"
-                )
-    elif isinstance(data, xr.Dataset):
-        if layer_dim == "FlowLink_xu FlowLink_yu":
-            # interpolate
+        elif isinstance(data, xr.Dataset):
             x_laydim = data[coords[0]].values
             y_laydim = data[coords[1]].values
-            points_laydim = np.array([[x, y] for x, y in zip(x_laydim, y_laydim)])
-
             coords_request = list(data[variable].coords)[0:2]
             x_wdim = data[coords_request[0]].values
             y_wdim = data[coords_request[1]].values
-            points_wdim = np.array([[x, y] for x, y in zip(x_wdim, y_wdim)])
 
-            bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth, points_wdim)
-            water_level_wdim = interp.griddata(points_laydim, waterlevel, points_wdim)
+        points_laydim = np.array([[x, y] for x, y in zip(x_laydim, y_laydim)])
+        points_wdim = np.array([[x, y] for x, y in zip(x_wdim, y_wdim)])
 
-            idx_bd = np.where(np.isnan(bottom_depth_wdim))
+        bottom_depth_wdim = interp.griddata(points_laydim, bottom_depth, points_wdim)
+        water_level_wdim = interp.griddata(points_laydim, waterlevel, points_wdim)
 
-            for i in idx_bd:
-                bottom_depth_wdim[i] = interp.griddata(
-                    points_laydim, bottom_depth, points_wdim[i], method="nearest"
-                )
-                water_level_wdim[i] = interp.griddata(
-                    points_laydim, waterlevel, points_wdim[i], method="nearest"
-                )
+        idx_bd = np.where(np.isnan(bottom_depth_wdim))
+        for i in idx_bd:
+            bottom_depth_wdim[i] = interp.griddata(
+                points_laydim, bottom_depth, points_wdim[i], method="nearest"
+            )
+            water_level_wdim[i] = interp.griddata(
+                points_laydim, waterlevel, points_wdim[i], method="nearest"
+            )
 
     waterdepth = []
 

From f8a4483e4c7718072f87b131fd05c258256f86df Mon Sep 17 00:00:00 2001
From: Emily Browning <eabrowning@alaska.edu>
Date: Fri, 27 Feb 2026 09:52:43 -0900
Subject: [PATCH 19/19] updated test to test xarray input

---
 mhkit/tests/river/test_io_d3d.py | 76 ++++++++++++++++++++++++++++++--
 1 file changed, 73 insertions(+), 3 deletions(-)

diff --git a/mhkit/tests/river/test_io_d3d.py b/mhkit/tests/river/test_io_d3d.py
index e2e159082..575a9b1d9 100644
--- a/mhkit/tests/river/test_io_d3d.py
+++ b/mhkit/tests/river/test_io_d3d.py
@@ -26,6 +26,7 @@ def setUpClass(self):
 
         filename = "turbineTest_map.nc"
         self.d3d_flume_data = netCDF4.Dataset(join(d3ddatadir, filename))
+        self.d3d_flume_data_xr = xr.open_dataset(join(d3ddatadir, filename))
 
     @classmethod
     def tearDownClass(self):
@@ -33,24 +34,33 @@ def tearDownClass(self):
 
     def test_get_all_time(self):
         data = self.d3d_flume_data
+        data_xr = self.d3d_flume_data_xr
         seconds_run = river.io.d3d.get_all_time(data)
+        seconds_run_xr = river.io.d3d.get_all_time(data_xr)
         seconds_run_expected = np.ndarray(
             shape=(5,), buffer=np.array([0, 60, 120, 180, 240]), dtype=int
         )
         np.testing.assert_array_equal(seconds_run, seconds_run_expected)
+        np.testing.assert_array_equal(seconds_run_xr, seconds_run_expected)
 
     def test_convert_time(self):
         data = self.d3d_flume_data
+        data_xr = self.d3d_flume_data_xr
         time_index = 2
         seconds_run = river.io.d3d.index_to_seconds(data, time_index=time_index)
+        seconds_run_xr = river.io.d3d.index_to_seconds(data_xr, time_index=time_index)
         seconds_run_expected = 120
         self.assertEqual(seconds_run, seconds_run_expected)
+        self.assertEqual(seconds_run_xr, seconds_run_expected)
         seconds_run = 60
         time_index = river.io.d3d.seconds_to_index(data, seconds_run=seconds_run)
+        time_index_xr = river.io.d3d.seconds_to_index(data_xr, seconds_run=seconds_run)
         time_index_expected = 1
         self.assertEqual(time_index, time_index_expected)
+        self.assertEqual(time_index_xr, time_index_expected)
         seconds_run = 62
         time_index = river.io.d3d.seconds_to_index(data, seconds_run=seconds_run)
+        time_index_xr = river.io.d3d.seconds_to_index(data_xr, seconds_run=seconds_run)
         time_index_expected = 1
         output_expected = f"ERROR: invalid seconds_run. Closest seconds_run found {time_index_expected}"
         self.assertWarns(UserWarning)
@@ -60,13 +70,17 @@ def test_convert_time_from_tidal(self):
         Test the conversion of time from using tidal import of d3d
         """
         data = self.d3d_flume_data
+        data_xr = self.d3d_flume_data_xr
         time_index = 2
         seconds_run = tidal.io.d3d.index_to_seconds(data, time_index=time_index)
+        seconds_run_xr = tidal.io.d3d.index_to_seconds(data_xr, time_index=time_index)
         seconds_run_expected = 120
         self.assertEqual(seconds_run, seconds_run_expected)
+        self.assertEqual(seconds_run_xr, seconds_run_expected)
 
     def test_layer_data(self):
         data = self.d3d_flume_data
+        data_xr = self.d3d_flume_data_xr
         variable = ["ucx", "s1"]
         for var in variable:
             layer = 2
@@ -77,10 +91,22 @@ def test_layer_data(self):
             layer_data_expected = river.io.d3d.get_layer_data(
                 data, var, layer_compare, time_index_compare
             )
-
             assert_array_almost_equal(layer_data.x, layer_data_expected.x, decimal=2)
             assert_array_almost_equal(layer_data.y, layer_data_expected.y, decimal=2)
             assert_array_almost_equal(layer_data.v, layer_data_expected.v, decimal=2)
+        for var in variable:
+            layer = 2
+            time_index = 3
+            layer_data_xr = river.io.d3d.get_layer_data(data_xr, var, layer, time_index)
+            layer_compare = 2
+            time_index_compare = 4
+            layer_data_expected_xr = river.io.d3d.get_layer_data(
+                data_xr, var, layer_compare, time_index_compare
+            )
+            assert_array_almost_equal(layer_data_xr.x, layer_data_expected_xr.x, decimal=2)
+            assert_array_almost_equal(layer_data_xr.y, layer_data_expected_xr.y, decimal=2)
+            assert_array_almost_equal(layer_data_xr.v, layer_data_expected_xr.v, decimal=2)
+            
 
     def test_create_points_three_points(self):
         """
@@ -209,6 +235,32 @@ def test_variable_interpolation(self):
             np.size(transformes_data["ucx"]), np.size(transformes_data["turkin1"])
         )
 
+    def test_variable_interpolation_xr(self):
+        data_xr = self.d3d_flume_data_xr
+        variables = ["ucx", "turkin1"]
+        transformes_data = river.io.d3d.variable_interpolation(
+            data_xr, variables, points="faces", edges="nearest"
+        )
+        self.assertEqual(
+            np.size(transformes_data["ucx"]), np.size(transformes_data["turkin1"])
+        )
+        transformes_data = river.io.d3d.variable_interpolation(
+            data_xr, variables, points="cells", edges="nearest"
+        )
+        self.assertEqual(
+            np.size(transformes_data["ucx"]), np.size(transformes_data["turkin1"])
+        )
+        x = np.linspace(1, 3, num=3)
+        y = np.linspace(1, 3, num=3)
+        waterdepth = 1
+        points = river.io.d3d.create_points(x, y, waterdepth)
+        transformes_data = river.io.d3d.variable_interpolation(
+            data_xr, variables, points=points
+        )
+        self.assertEqual(
+            np.size(transformes_data["ucx"]), np.size(transformes_data["turkin1"])
+        )
+
     def test_get_all_data_points(self):
         data = self.d3d_flume_data
         variable = "ucx"
@@ -222,6 +274,19 @@ def test_get_all_data_points(self):
         size_output_expected = np.size(output_expected)
         self.assertEqual(size_output, size_output_expected)
 
+    def test_get_all_data_points_xr(self):
+        data_xr = self.d3d_flume_data_xr    
+        variable = "ucx"
+        time_step = 3
+        output = river.io.d3d.get_all_data_points(data_xr, variable, time_step)
+        size_output = np.size(output)
+        time_step_compair = 4
+        output_expected = river.io.d3d.get_all_data_points(
+            data_xr, variable, time_step_compair
+        )
+        size_output_expected = np.size(output_expected)
+        self.assertEqual(size_output, size_output_expected)
+
     def test_unorm(self):
         x = np.linspace(1, 3, num=3)
         y = np.linspace(1, 3, num=3)
@@ -236,6 +301,7 @@ def test_unorm(self):
 
     def test_turbulent_intensity(self):
         data = self.d3d_flume_data
+        data_xr = self.d3d_flume_data_xr
         time_index = -1
         x_test = np.linspace(1, 17, num=10)
         y_test = np.linspace(3, 3, num=10)
@@ -250,7 +316,7 @@ def test_turbulent_intensity(self):
         points = pd.DataFrame(test_points, columns=["x", "y", "waterdepth"])
 
         TI = river.io.d3d.turbulent_intensity(data, points, time_index)
-
+        TI_xr = river.io.d3d.turbulent_intensity(data_xr, points, time_index)
         TI_vars = ["turkin1", "ucx", "ucy", "ucz"]
         TI_data_raw = {}
         for var in TI_vars:
@@ -284,12 +350,16 @@ def test_turbulent_intensity(self):
         assert_array_almost_equal(
             TI.turbulent_intensity, turbulent_intensity_expected, decimal=2
         )
-
+        assert_array_almost_equal(
+            TI_xr.turbulent_intensity, turbulent_intensity_expected, decimal=2
+        )
         TI = river.io.d3d.turbulent_intensity(data, points="faces")
+        TI_xr = river.io.d3d.turbulent_intensity(data_xr, points="faces")
         TI_size = np.size(TI["turbulent_intensity"])
         turkin1 = river.io.d3d.get_all_data_points(data, "turkin1", time_index)
         turkin1_size = np.size(turkin1["turkin1"])
         self.assertEqual(TI_size, turkin1_size)
+        self.assertEqual(TI_size, np.size(TI_xr["turbulent_intensity"]))
 
         TI = river.io.d3d.turbulent_intensity(data, points="cells")
         TI_size = np.size(TI["turbulent_intensity"])