From 005b26c212e69fe741a275dd22e108314b8729dd Mon Sep 17 00:00:00 2001
From: mdtanker <matt.d.tankersley@gmail.com>
Date: Fri, 28 Nov 2025 14:18:04 +0100
Subject: [PATCH 1/3] tesseroid docstrings; fix mistakes, add examples, and
 clarify

---
 harmonica/_forward/tesseroid_layer.py | 104 ++++++++++++++++++++++----
 1 file changed, 91 insertions(+), 13 deletions(-)

diff --git a/harmonica/_forward/tesseroid_layer.py b/harmonica/_forward/tesseroid_layer.py
index 27b7a38be..99f3e61b5 100644
--- a/harmonica/_forward/tesseroid_layer.py
+++ b/harmonica/_forward/tesseroid_layer.py
@@ -21,22 +21,43 @@ def tesseroid_layer(coordinates, surface, reference, properties=None):
     """
     Create a layer of tesseroids of equal size.
 
+    Build a regular grid of tesseroids (spherical prisms) with variable top and bottom
+    boundaries and properties like density, magnetization, etc. The function returns a
+    :class:`xarray.Dataset` containing ``longitude``, ``latitude``, ``top`` and
+    ``bottom`` coordinates, and all physical properties as ``data_var`` s. The
+    ``longitude`` and ``latitude`` coordinates correspond to the location of the center
+    of each tesseroid.
+
+    Unlike the :func:`harmonica.prism_layer`, the top and bottom boundaries of
+    tesseroids, provided via `surface` and `reference`, are not heights relative to a
+    reference level (e.g., sea level). Instead, they are radii from the center of the
+    body (ellipsoid or sphere) to the top and bottom boundaries of the tesseroids. To
+    convert top and bottom boundaries from heights to radii, you can use the
+    :meth:`boule.geocentric_radius` method from :mod:`boule` to calculate the radius
+    based on the latitude, and add these values to your height values.
+
+    The ``tesseroid_layer`` dataset accessor can be used to access special methods
+    and attributes for the layer of tesseroids, like the horizontal dimensions of
+    the tesseroids, getting the boundaries of each tesseroids, etc.
+    See :class:`DatasetAccessorTesseroidLayer` for the definition of these methods
+    and attributes.
+
     Parameters
     ----------
     coordinates : tuple
         List containing the coordinates of the centers of the tesseroids in
         spherical coordinates in the following order ``longitude`` and
-        ``latitude``.
+        ``latitude``. All coordinates should be in degrees.
     surface : 2d-array
-        Array used to create the uppermost boundary of the tesserois layer. All
-        radii should be in meters. On every point where ``surface`` is below
-        ``reference``, the ``surface`` value will be used to set the ``bottom``
+        Array used to create the uppermost boundary of the tesseroids layer. Values
+        should be radii (not heights) in meters. On every point where ``surface`` is
+        below ``reference``, the ``surface`` value will be used to set the ``bottom``
         boundary of that tesseroid, while the ``reference`` value will be used
         to set the ``top`` boundary of the tesseroid.
     reference : 2d-array or float
         Reference surface used to create the lowermost boundary of the
         tesseroids layer. It can be either a plane or an irregular surface
-        passed as 2d array. Radii must be in meters.
+        passed as 2d array. Values must be radii (not heights) in meters.
     properties : dict or None
         Dictionary containing the physical properties of the tesseroids. The
         keys must be strings that will be used to name the corresponding
@@ -56,6 +77,50 @@ def tesseroid_layer(coordinates, surface, reference, properties=None):
     --------
     harmonica.DatasetAccessorsTesseroidLayer
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import boule as bl
+    >>> # Create a synthetic relief
+    >>> longitude = np.linspace(0, 10, 5)
+    >>> latitude = np.linspace(2, 8, 4)
+    >>> mesh_longitude, mesh_latitude = np.meshgrid(longitude, latitude)
+    >>> surface_heights = np.arange(20, dtype=float).reshape((4, 5))
+    >>> # Convert heights to radii
+    >>> reference_radii = bl.WGS84.geocentric_radius(mesh_latitude)
+    >>> surface_radii = surface_heights + reference_radii
+    >>> # Define constant densities
+    >>> density = 2670.0 * np.ones_like(surface_radii)
+    >>> # Define a layer of tesseroids
+    >>> tesseroids = tesseroid_layer(
+    ...     (longitude, latitude),
+    ...     surface_radii,
+    ...     reference=reference_radii,
+    ...     properties={"density": density},
+    ... )
+    >>> print(tesseroids) # doctest: +SKIP
+    <xarray.Dataset>
+    Dimensions:   (latitude: 4, longitude: 5)
+    Coordinates:
+      * latitude   (latitude) float64 2.0 4.0 6.0 8.0
+      * longitude  (longitude) float64 0.0 2.5 5.0 7.5 10.0
+        top       (latitude, longitude) float64 6.378e+06 6.378e+06 ... 6.378e+06
+        bottom    (latitude, longitude) float64 6.378e+06 6.378e+06 ... 6.378e+06
+    Data variables:
+        density   (latitude, longitude) float64 2.67e+03 2.67e+03 ... 2.67e+03
+    Attributes:
+        longitude_units:  degrees
+        latitude_units:   degrees
+        radius_units:     meters
+        properties_units:  SI
+    >>> # Get the boundaries of the layer (will exceed the region)
+    >>> boundaries = tesseroids.tesseroid_layer.boundaries
+    >>> list(float(b) for b in boundaries)
+    [-1.25, 11.25, 1.0, 9.0]
+    >>> # Get the boundaries of one of the tesseroids
+    >>> tesseroid = tesseroids.tesseroid_layer.get_tesseroid((0, 2))
+    >>> list(float(b) for b in tesseroid)
+    [3.75, 6.25, 1.0, 3.0, 6378137.0, 6378138.0]
     """
     dims = ("latitude", "longitude")
     # Initialize data and data_names as None
@@ -183,7 +248,7 @@ def shape(self):
         n_latitude : int
             Number of tesseroids on the latitude direction.
         n_longitude : int
-            Number of tesserods on the longitude direction.
+            Number of tesseroids on the longitude direction.
         """
         return (self._obj.latitude.size, self._obj.longitude.size)
 
@@ -221,15 +286,14 @@ def update_top_bottom(self, surface, reference):
         ----------
         surface : 2d-array
             Array used to create the uppermost boundary of the tesseroid layer.
-            All heights should be in meters. On every point where ``surface``
+            All radii should be in meters. On every point where ``surface``
             is below ``reference``, the ``surface`` value will be used to set
             the ``bottom`` boundary of that tesseroid, while the ``reference``
             value will be used to set the ``top`` boundary of the tesseroid.
-
         reference : 2d-array or float
             Reference surface used to create the lowermost boundary of the
             tesseroid layer. It can be either a plane or an irregular surface
-            passed as 2d array. Height(s) must be in meters.
+            passed as 2d array. Radii must be in meters.
         """
         surface, reference = np.asarray(surface), np.asarray(reference)
         if surface.shape != self.shape:
@@ -261,6 +325,13 @@ def gravity(
         """
         Compute the gravity generated by the layer of tesseroids.
 
+        Uses :func:`harmonica.tesseroid_gravity` for computing the gravity field
+        generated by the tesseroids of the layer.
+        The density of the tesseroids will be assigned from the ``data_var`` chosen
+        through the ``density_name`` argument.
+        Ignores the tesseroids which ``top`` or ``bottom`` boundaries are
+        ``np.nan``s.
+
         Parameters
         ----------
         coordinates : list of arrays
@@ -270,7 +341,7 @@ def gravity(
             ``latitude`` should be in degrees and ``radius`` in meters.
         field : str
             Gravitational field that wants to be computed.
-            The variable fields are:
+            The available fields are:
             - Gravitational potential: ``potential``
             - Downward acceleration: ``g_z``
         progressbar : bool (optional)
@@ -302,7 +373,7 @@ def gravity(
         # Select only the boundaries and density elements for masked tesseroid
         boundaries = boundaries[mask.ravel()]
         density = density[mask]
-        # Return gravity field of tesserids
+        # Return gravity field of tesseroids
         return tesseroid_gravity(
             coordinates,
             tesseroids=boundaries,
@@ -318,6 +389,13 @@ def _get_nonans_mask(self, property_name=None):
 
         Parameters
         ----------
+        property_name : str (optional)
+            Name of the property layer (or ``data_var`` of the
+            :class:`xarray.Dataset`) that will be used for masking the tesseroids
+            in the layer.
+
+        Returns
+        -------
         mask : 2d-array
             Array of bools that can be used as a mask for selecting tesseroids
             with no nans on top boundaries, bottom boundaries ans the passed
@@ -398,12 +476,12 @@ def get_tesseroid(self, indices):
         ----------
         indices : tuple
             Indices of the desired tesseroid of the layer in the following
-            order: ``(index_northing, index_easting)``.
+            order: ``(index_latitude, index_longitude)``.
 
         Returns
         -------
         tesseroid : tuple
-           Boundaries of the prisms in the following order:
+           Boundaries of the tesseroids in the following order:
            ``longitude_w``, ``longitude_e``, ``latitude_s``, ``latitude_n``,
            ``bottom``, ``top``.
         """

From 946bc690583c983177db3ca9ff3fb1a95066df94 Mon Sep 17 00:00:00 2001
From: mdtanker <matt.d.tankersley@gmail.com>
Date: Fri, 28 Nov 2025 14:18:39 +0100
Subject: [PATCH 2/3] docstring typos

---
 harmonica/_forward/_tesseroid_utils.py            | 2 +-
 harmonica/_forward/_tesseroid_variable_density.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/harmonica/_forward/_tesseroid_utils.py b/harmonica/_forward/_tesseroid_utils.py
index e3d7f38ed..073fa05d0 100644
--- a/harmonica/_forward/_tesseroid_utils.py
+++ b/harmonica/_forward/_tesseroid_utils.py
@@ -61,7 +61,7 @@ def gauss_legendre_quadrature(
     glq_nodes : list
         Unscaled location of GLQ nodes for each direction.
     glq_weights : list
-        GLQ weigths for each node for each direction.
+        GLQ weights for each node for each direction.
     kernel : func
         Kernel function for the gravitational field of point masses.
 
diff --git a/harmonica/_forward/_tesseroid_variable_density.py b/harmonica/_forward/_tesseroid_variable_density.py
index 3f05e2136..e91844e28 100644
--- a/harmonica/_forward/_tesseroid_variable_density.py
+++ b/harmonica/_forward/_tesseroid_variable_density.py
@@ -61,7 +61,7 @@ def gauss_legendre_quadrature_variable_density(
     glq_nodes : list
         Unscaled location of GLQ nodes for each direction.
     glq_weights : list
-        GLQ weigths for each node for each direction.
+        GLQ weights for each node for each direction.
     kernel : func
         Kernel function for the gravitational field of point masses.
 
@@ -136,7 +136,7 @@ def density_based_discretization(tesseroids, density):
     Returns
     -------
     discretized_tesseroids : 2d-array
-        Array containing the coordinates of radially discretized tesseriods.
+        Array containing the coordinates of radially discretized tesseroids.
         Each row of the array will have the boundaries for each new tesseroid.
     """
     discretized_tesseroids = []

From 6fe6dd114184ebacd51032dce2b54a278576fe47 Mon Sep 17 00:00:00 2001
From: mdtanker <matt.d.tankersley@gmail.com>
Date: Thu, 11 Dec 2025 14:04:10 +0100
Subject: [PATCH 3/3] WIP

---
 doc/gallery_src/forward/tesseroid_layer.py    |   29 +-
 .../forward_modelling/tesseroid.rst           |   44 +-
 doc/user_guide/forward_modelling/test.ipynb   | 3539 +++++++++++++++++
 harmonica/__init__.py                         |    2 +-
 harmonica/_forward/tesseroid_layer.py         |   80 +-
 harmonica/tests/test_tesseroid.py             |    2 +-
 harmonica/tests/test_tesseroid_layer.py       |  126 +-
 7 files changed, 3805 insertions(+), 17 deletions(-)
 create mode 100644 doc/user_guide/forward_modelling/test.ipynb

diff --git a/doc/gallery_src/forward/tesseroid_layer.py b/doc/gallery_src/forward/tesseroid_layer.py
index 4d26564df..d7deaebc5 100644
--- a/doc/gallery_src/forward/tesseroid_layer.py
+++ b/doc/gallery_src/forward/tesseroid_layer.py
@@ -27,10 +27,20 @@
 ellipsoid = bl.WGS84
 
 longitude, latitude = np.meshgrid(topo.longitude, topo.latitude)
+
+# Compute radius of WGS84 ellipsoid at each lat/lon coordinates
 reference = ellipsoid.geocentric_radius(latitude)
+
+# Compute surface topography with respect to the center of the Earth
 surface = topo + reference
+
+# tesseroids above sea level have density of 2670 kg/m³ and tesseroids located below sea
+# level have density of -1630 kg/m³
 density = xr.where(topo > 0, 2670.0, 1040.0 - 2670.0)
 
+# Create a layer of tesseroids representing the topography
+# These have tops and bottoms defined by Earth's topography with respect to the center
+# of the Earth.
 tesseroids = hm.tesseroid_layer(
     coordinates=(topo.longitude, topo.latitude),
     surface=surface,
@@ -53,8 +63,23 @@
     extra_coords_names="radius",
 )
 
-# Plot gravity field
 fig = pygmt.Figure()
+
+# Plot tesseroid thickness
+fig.grdimage(
+    tesseroids.top - tesseroids.bottom,
+    projection="M15c",
+    nan_transparent=True,
+    cmap="viridis",
+    frame="+tTesseroid thickness",
+)
+fig.basemap(frame=True)
+fig.colorbar(frame="af+lm")
+fig.coast(shorelines="0.5p,black", borders=["1/0.5p,black"])
+
+fig.shift_origin(xshift="16c")
+
+# Plot gravity field
 maxabs = vd.maxabs(gravity.g_z)
 pygmt.makecpt(cmap="polar", series=(-maxabs, maxabs))
 fig.grdimage(
@@ -63,6 +88,6 @@
     nan_transparent=True,
 )
 fig.basemap(frame=True)
-fig.colorbar(frame='af+l"Gravity [mGal]"', position="JCR")
+fig.colorbar(frame='af+l"Gravity [mGal]"')
 fig.coast(shorelines="0.5p,black", borders=["1/0.5p,black"])
 fig.show()
diff --git a/doc/user_guide/forward_modelling/tesseroid.rst b/doc/user_guide/forward_modelling/tesseroid.rst
index 21c4a5c47..357089e9f 100644
--- a/doc/user_guide/forward_modelling/tesseroid.rst
+++ b/doc/user_guide/forward_modelling/tesseroid.rst
@@ -36,6 +36,11 @@ boundaries in the following order: *west*, *east*, *south*, *north*, *bottom*,
 *top*, where the former four are its longitudinal and latitudinal boundaries in
 decimal degrees and the latter two are the two radii given in meters.
 
+These two radii represent the top and bottom surfaces of the tesseroid, and should be
+given as distances from the center of a sphere. Note this is different from the
+vertical boundaries used for **prisms** in Cartesian coordinates, which are given as
+heights above or below some reference level (e.g., mean sea level or an ellipsoid).
+
 .. note::
 
    The :func:`harmonica.tesseroid_gravity` numerically computed the
@@ -45,7 +50,7 @@ decimal degrees and the latter two are the two radii given in meters.
 
 
 Lets define a single tesseroid and compute the gravitational potential
-it generates on a regular grid of computation points located at 10 km  above
+it generates on a regular grid of computation points located at 10 km above
 its *top* boundary.
 
 Get the WGS84 reference ellipsoid from :mod:`boule` so we can obtain its mean
@@ -125,6 +130,15 @@ And finally plot the computed gravitational field
          cmap="viridis",
       )
 
+   # Plot edges of tesseroid
+   fig.plot(
+      x=[tesseroid[0], tesseroid[1], tesseroid[1], tesseroid[0], tesseroid[0]],
+      y=[tesseroid[2], tesseroid[2], tesseroid[3], tesseroid[3], tesseroid[2]],
+      pen="1p,red",
+      label="Tesseroid boundaries",
+   )
+   fig.legend()
+
    fig.colorbar(cmap=True, frame=["a200f50", "x+lmGal"])
    fig.coast(shorelines="1p,black")
 
@@ -179,6 +193,20 @@ And plot the results:
          cmap="viridis",
       )
 
+   # Plot edges of tesseroids
+   for i, tesseroid in enumerate(tesseroids):
+      if i == 0:
+         label="Tesseroid boundaries"
+      else:
+         label=None
+      fig.plot(
+         x=[tesseroid[0], tesseroid[1], tesseroid[1], tesseroid[0], tesseroid[0]],
+         y=[tesseroid[2], tesseroid[2], tesseroid[3], tesseroid[3], tesseroid[2]],
+         pen="1p,red",
+         label=label,
+      )
+   fig.legend()
+
    fig.colorbar(cmap=True, frame=["a1000f500", "x+lmGal"])
    fig.coast(shorelines="1p,black")
 
@@ -265,6 +293,20 @@ Finally, lets plot it:
          cmap="viridis",
       )
 
+   # Plot edges of tesseroids
+   for i, tesseroid in enumerate(tesseroids):
+      if i == 0:
+         label="Tesseroid boundaries"
+      else:
+         label=None
+      fig.plot(
+         x=[tesseroid[0], tesseroid[1], tesseroid[1], tesseroid[0], tesseroid[0]],
+         y=[tesseroid[2], tesseroid[2], tesseroid[3], tesseroid[3], tesseroid[2]],
+         pen="1p,red",
+         label=label,
+      )
+   fig.legend()
+
    fig.colorbar(cmap=True, frame=["a200f100", "x+lmGal"])
    fig.coast(shorelines="1p,black")
 
diff --git a/doc/user_guide/forward_modelling/test.ipynb b/doc/user_guide/forward_modelling/test.ipynb
new file mode 100644
index 000000000..f5f25573c
--- /dev/null
+++ b/doc/user_guide/forward_modelling/test.ipynb
@@ -0,0 +1,3539 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a5dedff4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import boule as bl\n",
+    "import ensaio\n",
+    "import numpy as np\n",
+    "import pygmt\n",
+    "import pyvista as pv\n",
+    "import verde as vd\n",
+    "import xarray as xr\n",
+    "\n",
+    "import harmonica as hm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52a78539",
+   "metadata": {},
+   "source": [
+    "# Plot single tesseroid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "1a149538",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-70,\n",
+       " -50,\n",
+       " -40,\n",
+       " -20,\n",
+       " np.float64(3370994.4018275756),\n",
+       " np.float64(6370994.401827576))"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ellipsoid = bl.WGS84\n",
+    "mean_radius = ellipsoid.mean_radius\n",
+    "tesseroid = (-70, -50, -40, -20, mean_radius - 3000e3, mean_radius)\n",
+    "density = 2670\n",
+    "tesseroid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bee3441",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "width": 500
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coordinates = vd.grid_coordinates(\n",
+    "    region=[-80, -40, -50, -10],\n",
+    "    shape=(80, 80),\n",
+    "    extra_coords=100e3 + mean_radius,\n",
+    ")\n",
+    "\n",
+    "gravity = hm.tesseroid_gravity(coordinates, tesseroid, density, field=\"g_z\")\n",
+    "\n",
+    "grid = vd.make_xarray_grid(\n",
+    "    coordinates, gravity, data_names=\"gravity\", extra_coords_names=\"extra\"\n",
+    ")\n",
+    "\n",
+    "fig = pygmt.Figure()\n",
+    "title = \"Downward component of gravitational acceleration\"\n",
+    "with pygmt.config(FONT_TITLE=\"12p\"):\n",
+    "    fig.grdimage(\n",
+    "        region=[-80, -40, -50, -10],\n",
+    "        projection=\"M-60/-30/10c\",\n",
+    "        grid=grid.gravity,\n",
+    "        frame=[\"a\", f\"+t{title}\"],\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "\n",
+    "# Plot edges of tesseroid\n",
+    "fig.plot(\n",
+    "    x=[tesseroid[0], tesseroid[1], tesseroid[1], tesseroid[0], tesseroid[0]],\n",
+    "    y=[tesseroid[2], tesseroid[2], tesseroid[3], tesseroid[3], tesseroid[2]],\n",
+    "    pen=\"1p,red\",\n",
+    "    label=\"Tesseroid boundaries\",\n",
+    ")\n",
+    "fig.legend()\n",
+    "\n",
+    "fig.colorbar(cmap=True, frame=[\"x+lmGal\"])\n",
+    "fig.coast(shorelines=\"1p,black\")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4adbd979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_tesseroid(tesseroid, plot=True, add_sphere=True, add_edges=True):\n",
+    "    \"\"\"Plot a tesseroid using PyVista.\"\"\"\n",
+    "    lon_min, lon_max, lat_min, lat_max, r_inner, r_outer = tesseroid\n",
+    "\n",
+    "    # phi (latitude) is defined as 0 at north pole and 180 at south pole in PyVista\n",
+    "    def lat_to_phi(lat):\n",
+    "        return 90 - lat\n",
+    "\n",
+    "    min_phi = lat_to_phi(lat_max)\n",
+    "    max_phi = lat_to_phi(lat_min)\n",
+    "\n",
+    "    # Create a solid sphere sector (tesseroid)\n",
+    "    tesseroid_mesh = pv.SolidSphere(\n",
+    "        outer_radius=r_outer,\n",
+    "        inner_radius=r_inner,\n",
+    "        start_theta=lon_min,  # longitude\n",
+    "        end_theta=lon_max,  # longitude\n",
+    "        start_phi=min_phi,  # latitude\n",
+    "        end_phi=max_phi,  # latitude\n",
+    "        # phi_resolution=2,\n",
+    "        # theta_resolution=2,\n",
+    "        # radius_resolution=2,\n",
+    "    )\n",
+    "\n",
+    "    if plot:\n",
+    "        # Plot the tesseroid\n",
+    "        plotter = pv.Plotter()\n",
+    "\n",
+    "        if add_sphere:\n",
+    "            # add wireframe sphere if entire Earth/planet for context\n",
+    "            sphere_mesh = pv.Sphere(\n",
+    "                radius=r_outer,\n",
+    "                # phi_resolution=2, # latitude resolution\n",
+    "                # theta_resolution=2, # longitude resolution\n",
+    "                # radius_resolution=2, # radial resolution\n",
+    "            )\n",
+    "            plotter.add_mesh(\n",
+    "                sphere_mesh,\n",
+    "                color=\"white\",\n",
+    "                style=\"wireframe\",\n",
+    "                opacity=0.1,\n",
+    "            )\n",
+    "\n",
+    "        plotter.add_mesh(\n",
+    "            tesseroid_mesh,\n",
+    "            color=\"lightblue\",\n",
+    "            show_edges=False,\n",
+    "            opacity=1,\n",
+    "        )\n",
+    "\n",
+    "        if add_edges:\n",
+    "            # add wire mesh to show tesseroid edges\n",
+    "            mesh = pv.SolidSphere(\n",
+    "                outer_radius=r_outer,\n",
+    "                inner_radius=r_inner,\n",
+    "                start_theta=lon_min,  # longitude\n",
+    "                end_theta=lon_max,  # longitude\n",
+    "                start_phi=min_phi,  # latitude\n",
+    "                end_phi=max_phi,  # latitude\n",
+    "                phi_resolution=2,\n",
+    "                theta_resolution=2,\n",
+    "                radius_resolution=2,\n",
+    "            )\n",
+    "            # edges = mesh.extract_all_edges()\n",
+    "            edges = mesh.extract_feature_edges()\n",
+    "            plotter.add_mesh(\n",
+    "                edges,\n",
+    "                line_width=10,\n",
+    "                color=\"black\",\n",
+    "            )\n",
+    "\n",
+    "        plotter.add_axes()\n",
+    "\n",
+    "        _mean_phi, mean_theta = (min_phi + max_phi) / 2, (lon_min + lon_max) / 2\n",
+    "\n",
+    "        # set camera to look at equator from prime meridian\n",
+    "        cam_pos = pv.grid_from_sph_coords(\n",
+    "            # theta = [0], # longitude\n",
+    "            phi=[90],  # latitude\n",
+    "            theta=[mean_theta + 20],  # longitude\n",
+    "            # phi = [mean_phi+20], # latitude\n",
+    "            r=[r_outer * 10],  # distance from the origin\n",
+    "        )\n",
+    "\n",
+    "        plotter.set_position(cam_pos.points)\n",
+    "        plotter.set_viewup((0, 0, 1))\n",
+    "        plotter.show()\n",
+    "\n",
+    "    return tesseroid_mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "c257cba2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/pyvista/jupyter/notebook.py:56: UserWarning: Failed to use notebook backend: \n",
+      "\n",
+      "cannot import name 'vtk' from 'trame.widgets' (/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/trame/widgets/__init__.py)\n",
+      "\n",
+      "Falling back to a static output.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=1024x768>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<table style='width: 100%;'>\n",
+       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
+       "<tr><td>N Cells</td><td>3364</td></tr>\n",
+       "<tr><td>N Points</td><td>4500</td></tr>\n",
+       "<tr><td>X Bounds</td><td>8.832e+05, 3.848e+06</td></tr>\n",
+       "<tr><td>Y Bounds</td><td>-5.626e+06, -1.978e+06</td></tr>\n",
+       "<tr><td>Z Bounds</td><td>-4.095e+06, -1.153e+06</td></tr>\n",
+       "<tr><td>N Arrays</td><td>0</td></tr>\n",
+       "</table>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "UnstructuredGrid (0x7fdeb8f9de40)\n",
+       "  N Cells:    3364\n",
+       "  N Points:   4500\n",
+       "  X Bounds:   8.832e+05, 3.848e+06\n",
+       "  Y Bounds:   -5.626e+06, -1.978e+06\n",
+       "  Z Bounds:   -4.095e+06, -1.153e+06\n",
+       "  N Arrays:   0"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_tesseroid(tesseroid, add_sphere=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8f2bc21",
+   "metadata": {},
+   "source": [
+    "# Multiple tesseroids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18db090b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'mean_radius' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m tesseroids = [\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m     [-\u001b[32m70\u001b[39m, -\u001b[32m65\u001b[39m, -\u001b[32m40\u001b[39m, -\u001b[32m35\u001b[39m, \u001b[43mmean_radius\u001b[49m - \u001b[32m100e3\u001b[39m, mean_radius],\n\u001b[32m      3\u001b[39m     [-\u001b[32m55\u001b[39m, -\u001b[32m50\u001b[39m, -\u001b[32m40\u001b[39m, -\u001b[32m35\u001b[39m, mean_radius - \u001b[32m100e3\u001b[39m, mean_radius],\n\u001b[32m      4\u001b[39m     [-\u001b[32m70\u001b[39m, -\u001b[32m65\u001b[39m, -\u001b[32m25\u001b[39m, -\u001b[32m20\u001b[39m, mean_radius - \u001b[32m100e3\u001b[39m, mean_radius],\n\u001b[32m      5\u001b[39m     [-\u001b[32m55\u001b[39m, -\u001b[32m50\u001b[39m, -\u001b[32m25\u001b[39m, -\u001b[32m20\u001b[39m, mean_radius - \u001b[32m100e3\u001b[39m, mean_radius],\n\u001b[32m      6\u001b[39m ]\n\u001b[32m      7\u001b[39m densities = [\u001b[32m2670\u001b[39m , \u001b[32m2670\u001b[39m, \u001b[32m2670\u001b[39m, \u001b[32m2670\u001b[39m]\n\u001b[32m      8\u001b[39m coordinates = vd.grid_coordinates(\n\u001b[32m      9\u001b[39m     region=[-\u001b[32m80\u001b[39m, -\u001b[32m40\u001b[39m, -\u001b[32m50\u001b[39m, -\u001b[32m10\u001b[39m],\n\u001b[32m     10\u001b[39m     shape=(\u001b[32m80\u001b[39m, \u001b[32m80\u001b[39m),\n\u001b[32m     11\u001b[39m     extra_coords=\u001b[32m100e3\u001b[39m + mean_radius,\n\u001b[32m     12\u001b[39m )\n",
+      "\u001b[31mNameError\u001b[39m: name 'mean_radius' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "tesseroids = [\n",
+    "    [-70, -65, -40, -35, mean_radius - 100e3, mean_radius],\n",
+    "    [-55, -50, -40, -35, mean_radius - 100e3, mean_radius],\n",
+    "    [-70, -65, -25, -20, mean_radius - 100e3, mean_radius],\n",
+    "    [-55, -50, -25, -20, mean_radius - 100e3, mean_radius],\n",
+    "]\n",
+    "densities = [2670, 2670, 2670, 2670]\n",
+    "coordinates = vd.grid_coordinates(\n",
+    "    region=[-80, -40, -50, -10],\n",
+    "    shape=(80, 80),\n",
+    "    extra_coords=100e3 + mean_radius,\n",
+    ")\n",
+    "gravity = hm.tesseroid_gravity(\n",
+    "    coordinates,\n",
+    "    tesseroids,\n",
+    "    densities,\n",
+    "    field=\"g_z\",\n",
+    ")\n",
+    "\n",
+    "grid = vd.make_xarray_grid(\n",
+    "    coordinates, gravity, data_names=\"gravity\", extra_coords_names=\"extra\"\n",
+    ")\n",
+    "\n",
+    "fig = pygmt.Figure()\n",
+    "title = \"Downward component of gravitational acceleration\"\n",
+    "with pygmt.config(FONT_TITLE=\"12p\"):\n",
+    "    fig.grdimage(\n",
+    "        region=[-80, -40, -50, -10],\n",
+    "        projection=\"M-60/-30/10c\",\n",
+    "        grid=grid.gravity,\n",
+    "        frame=[\"a\", f\"+t{title}\"],\n",
+    "        cmap=\"viridis\",\n",
+    "    )\n",
+    "\n",
+    "# Plot edges of tesseroids\n",
+    "for i, tesseroid in enumerate(tesseroids):\n",
+    "    label = \"Tesseroid boundaries\" if i == 0 else None\n",
+    "    fig.plot(\n",
+    "        x=[tesseroid[0], tesseroid[1], tesseroid[1], tesseroid[0], tesseroid[0]],\n",
+    "        y=[tesseroid[2], tesseroid[2], tesseroid[3], tesseroid[3], tesseroid[2]],\n",
+    "        pen=\"1p,red\",\n",
+    "        label=label,\n",
+    "    )\n",
+    "fig.legend()\n",
+    "\n",
+    "fig.colorbar(cmap=True, frame=[\"a1000f500\", \"x+lmGal\"])\n",
+    "fig.coast(shorelines=\"1p,black\")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c96c47ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _discard_thin_tesseroids(\n",
+    "    tesseroids,\n",
+    "    density,\n",
+    "    thickness_threshold,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Discard tesseroids with a thickness below a threshold.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    tesseroids : 2d-array\n",
+    "        Array containing the boundaries of the tesseroids in the following order:\n",
+    "        ``longitude_w``, ``longitude_e``, ``latitude_s``, ``latitude_n``,\n",
+    "           ``bottom``, ``top``.\n",
+    "        The array must have the following shape: (``n_tesseroids``, 6), where\n",
+    "        ``n_tesseroids`` is the total number of tesseroids.\n",
+    "    density : 1d-array\n",
+    "        Array containing the density of each tesseroid in kg/m^3. Must have the\n",
+    "        same size as the number of tesseroids.\n",
+    "    thickness_threshold : float\n",
+    "        Tesseroids thinner than this threshold will be discarded.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    tesseroids : 2d-array\n",
+    "        A copy of the ``tesseroids`` array that doesn't include the thin tesseroids.\n",
+    "    density : 1d-array\n",
+    "        A copy of the ``density`` array that doesn't include the density values\n",
+    "        for thin tesseroids.\n",
+    "    \"\"\"\n",
+    "    bottom, top = tesseroids[:, -2], tesseroids[:, -1]\n",
+    "    # Mark tesseroids with thickness < threshold  as null tesseroids\n",
+    "    thickness = top - bottom\n",
+    "    null_tesseroids = thickness < thickness_threshold\n",
+    "    # Keep only thick tesseroids and their densities\n",
+    "    tesseroids = tesseroids[np.logical_not(null_tesseroids), :]\n",
+    "    density = density[np.logical_not(null_tesseroids)]\n",
+    "    return tesseroids, density"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66e2b240",
+   "metadata": {},
+   "source": [
+    "# Tesseroid layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f093d617",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6681c82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1.25, 11.25, 1.0, 9.0]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import boule as bl\n",
+    "\n",
+    "import harmonica as hm\n",
+    "\n",
+    "# Create a synthetic relief\n",
+    "longitude = np.linspace(0, 10, 5)\n",
+    "latitude = np.linspace(2, 8, 4)\n",
+    "mesh_longitude, mesh_latitude = np.meshgrid(longitude, latitude)\n",
+    "surface_heights = np.arange(20, dtype=float).reshape((4, 5))\n",
+    "# Convert heights to radii\n",
+    "reference_radii = bl.WGS84.geocentric_radius(mesh_latitude)\n",
+    "surface_radii = surface_heights + reference_radii\n",
+    "# Define constant densities\n",
+    "density = 2670.0 * np.ones_like(surface_radii)\n",
+    "# Define a layer of tesseroid\n",
+    "tesseroids = hm.tesseroid_layer(\n",
+    "    (longitude, latitude),\n",
+    "    surface_radii,\n",
+    "    reference=reference_radii,\n",
+    "    properties={\"density\": density},\n",
+    ")\n",
+    "# Get the boundaries of the layer (will exceed the region)\n",
+    "boundaries = tesseroids.tesseroid_layer.boundaries\n",
+    "[float(b) for b in boundaries]\n",
+    "# Get the boundaries of one of the tesseroids\n",
+    "tesseroid = tesseroids.tesseroid_layer.get_tesseroid((0, 2))\n",
+    "[float(b) for b in tesseroid]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b264fd56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UJQAA4cTVQ/Fgqi1QU2M3P4wHLZ4AQL6JPAGgJdyebavREwAaiam2QO2UZ4/FgxZPACD3RJ4A0CribFsAoBHERZ6m2gI1FUfahhBKHXvSVgIAUGsiTwBoCV+Mn4z9nYJPAEgum2oLUDvVLZ5pKwEAqAORJwC0hCvj8/NsN/WLPAGgUezaOJy6BCCfpucmY4tnYc02LZ4AQCsQeQJA/lW3eD7UvzN1OQDQ6ky1BWqtMjc/0rbQpsUTAGgJIk8AyD8tngDQOKZvjKcuAci56hbPYtv21OUAANSDyBMAWkiWfQIAqVy8MREPXZ26PIGa0OIJALQgkScA5N+jL+xLXQIAMC9OtQ0hFDv701YC5FJ59pgWTwCgBYk8AQAAoN4s8gRqpNSxJx5i8AkA0CJEngCQf1+Mn4yHKxMG2wJASieuHooHU22B2imsmZ9nW549lrYSAIC6EXkCQM59MX7y7KED8dw7vDdtMQBAZKotUDuljj0x9azcOjc9N5m6HACAehB5AkDOXRmf7+zsHd77UP/OtMUAQIuLizxNtQVqrdA23+hZmTPeFgBoCSJPAGgV8k4ASCubagtQa8W27Ro9AYCWIvIEAACAutq1cTh1CUD+afQEAFqKyBMAAADqwVRbIInKrXPl2WOpqwAAqK321AUAADV05uD+KxPlEMKmvlLqWgCgpU3fGE9dAtBCyrPHKrfmmzsLa7aVOvakrQcAoNZ0eQJAbn0xfjLmnSGER1/Yl7YY6mDqA2+mAzSuizcm4sFUW6CeBtY+Le8EAFqBLk8AyKEzB/eHELK8s3d4b9JyqK2e5/rOj0yEEEZfPRxeDz3P9qeuCIC7iFNtAeqs2LY9dQkAAPWgyxMAcujKRDnLOzf1lR7q35m2Hmqq59n+7qH5tXBT73s/HaARZVNtLfIEAACoBZEnAOTWpr7Spr6SkbatYPcbwzH1PD8ycfyVQ6nLAWChbKptV6fIE6ir6bnJ1CUAANSDyBMA8kze2Tp2vzG/Ge78yISlngANq9hp/DhQD4W2bfEwdvNDqScA0ApEngAAOfHU68/Hw+irh6WeAA0lLvI01Raom2Lb9sKa26ln2mIAAOpA5AkAkBOWegI0phNXjRwHEih17MlSz/LssbTFAADUmsgTACA/qpd6pq4FgIV2bRxOXQLQWrLUs3LrnNQTAMg3kScAQD4df0VTEUBDMNUWAACg1kSeAAC5svsNLUQADWT6huXKQDLl2WOVW+dCCIU120ode1KXAwBQQyJPAIC8MdsWoHFcvDF/NzbVFqiz6bnJmHeGEOSdAEDuiTwBIG/OHNwfD5v6S2krITmzbQGSi1NtAepsem5y7OaH8Tyw9um0xQAA1IHIEwBy66H+nalLIA2zbQEaRDbV1iJPoM4qc/P9nYU124pt29MWAwBQByJPAMibKxPlEMKmPi2eLc1sW4BGkE217eoUeQL1k420tcITAGgdIk8AyJUvxk+mLgEAWKjY2Z+6BKCFaOsEAFpQe+oCAIDV9FD/zrOHDoRvez2BnJn6YHzq/dvNuz3P9fU8K0eBxhUXeZpqCwAAUGu6PAEgb7KRtjo+W1nPc/Nvrx9/5VDaSlhdU+9PnB+5/Wv01cNv7f6dVxka04mr8/82TbUF6q+wZlsIIY63BQBoBSJPAMibR1/YFw9XxjV6ti6df3l11/2s50cmYvAp+4TGZKotAABArRlsCwC5ZbYt5MzUB+Px0D3Ut/uN4RDC8VcOZSFoPLy1+3fdQ309z/WFsCNVnUBkqi2QkP5OAKDViDwBILeyCbdAPmRbPGPemR3igs/q7PP8yMSGJ/7XhidKG5/cmaRUYPrGeOoSgNZVnj0WD3G8LQBAKxB5AgBAc+t5tj+OMo6DbWP2ee3j8rWPyyEEqSckcfHG/E8h7No4nLYSoAVlLZ6ljj1pKwEAqBuRJwAANIeYZXYPfe+QzDubPi/84UCQekIKptoCqWjxBABa05rUBQAAq+yL8ZOpSwBWX7bIc1E9z/bvfmN4wxPzo61j6gnUk6m2QEJZi2ehTeQJALQQkScA5M2V8XI8bOq3y7OlxV7AbL8jze7ORZ739sjP9mWp52e/31+rsoC7yabadnXq8gTqLWvuHLv5YdpKAADqSeQJALly5uD+KxPzkedD/UZZQkvLUs+41BOomzjVNoRQ7OxPWwnQgkode7LUMxtyCwCQe3Z5AkBOVIedIYRNfVo8IVcWXeQJNBqLPIFUSh17yrPHKrfOVW6dK88eK3XsSV0RAEDN6fIEgDz4Yvzkgrzz0Rf2JawHWF1LX+QJJHfi6qF4MNUWSChb5Jmt9gQAyDddngCQBw/177wyXo6pZ+/wXiNtIWeyRZ49zy0zQcnWeT7yMz8PAXViqi2QUGVuPunMhtwCAOSbyBMAcmJTfylGnlfGyyJPyKueZ5eZoGTrPP/y+i/jIa75DEJQWG1xkaeptkCDMNUWAGgRBtsCQE481L8z7u+8MlH+Yvxk6nJIL2sHPP7KobSVsHIrWeS54YlSlm5Wu/ZxOf7KGkCBlcum2gIkND03GefZavEEAFqHLk8AgHzqebZ/NBxOXQWrYCWLPBd0cF7988ms3bP68Nnv9+v1hNW1a+Nw6hKA1nV7qm2byBMAaBUiTwDIIYNtIU9Wvsgzs/HJnRufvH1/uPrnkxf+cCCEcO3j8tU/n6z+FLA8ptoCyVW3eBbbtqcuBwCgTgy2BYD8iLs843hbIH+Wvcjz+2x8cufDf7c3ni/84cDVP5uJDSsyfWP5PdkAq0WLJwDQmkSeAJATZw7axsdCcfXj+ZGJ468cWslwVNJaySLPRW18cme26TMbdQssz8Ub8z3ZXZ26PIFkYotnCEGLJwDQUgy2BYC8efQFC/mYt/uN4eOvHDo/MhF/hddXv02QHHjkZ/s++/3+ax+XLfVsBdX7XO9hwxOllQ86/uz39f5ZnC+uXluVr9PV2VfsXM7dMk61DSEs7+kAK1eePRYPhTVaPAGA1iLyBICcMNWWu8pSzxDC6KuHpZ7c1SM/2/eX138ZLPVsATHbXsrD4p7XahueKGX/qTSm1Qk8Qwgryywt8gQSylo8Sx170lYCAFBnBtvW3DeLeemll1LXCADk2e43hrOZqKOvHjbhtrnU7fWy1JNFxT7g1FU0rhNXD8WDqbZAKtNzk/GgxZPaWfStztQFAtC6dHkCAOSfXs/mNfX+/JzM3W8M1/Qbxc7O2Nh34Q8HNHrmVWzxjP2a93jYndFmfGLWIZqtgL2H+g9J7v2XUyv8CieuHqrMTmTzaZfHVFsglcqcFk8AoHWJPAEAWoLUk0VtfHJnNvXUUs8Wd+erf/XPJ7NRt4smpq0pBqWm2gIAACRhsC0AQKvY/cbwU68/H8+jrx4+/sqhtPXQgB752b7YvWeEaS6tZGTxxid3LqWzs2VlU20BAABIQuQJANBCep7tz1LP8yMTUk/ulMVa1z4uW+qZM9lY2uX1aGaJeI5bPLM1nMuOMHdtrO0MagCayIkr75248t70TJ1WswNAizPYFgCgtfQ82x9eD6OvHg4hnB+ZmPpg3ITbxjT1wfjU+xNxFnH3UP1GZS5Y6pldodld/fPJLPJctkd+ti/fQXixsz9cO7yMJ5pqCzSU6bnJEEKxbXvqQlrRiSvvhRAqN76zFrq4zv/fBoCaE3kCQB6cOWj+JPehOvUcffVwz3FvwTScqQ/G4wuURPVSzwt/OHDt4/J9dfUtiNZy3BHYRBZs4lzJlxKB32n6hvYdoLGM3fywsGabyLNupmfGz/zHyS+//n/v+tkF8ScAUCMiTwBoemcO7r8yMZ8ubOq3aI0l6Xm2f2povoPw+CuHdr9hEmMDSZt3Ro/8bN9nv98fk8trH5f/8vovH/67vffOurKkc0Er4V9e/2V2XknYtvIOxayGVkths5cytOQf/34VOvoqsxOxa3OJLn77XraptkDjqNw6V549VurYs/SnlGePZefsidnF+/pSLWV6ZvzklwcXXCx8Oyk95p3TM+MaPQGg1kSeAND0sryzd3jvQ/2ab1iq3W8Mv7X7d8F42wZTnXfWc57tneII06w1MLZ7Vj9gGRnkasWWKxET3NZM/lrzT10H95WPAtROoW3blVv/60a4Hj+s3Do3PTe5aK/n9NxkZe5c5da56osHZ/61VlXmS3Xe2bnmP21aW+ha91h1uvlu5bchhIszp0WeAFBrIk8AyIlNfSV5J/ere6gvNnrSIBbkncm7bzc+uXPjkzur2z2X+MQsWlsw5HYlkecKx7HevHpl7cZNWQFZ8LnhiVLuJ7XGP7W8s0ayqbYWeQLJFdu2x4CzPHssRpiVuXOVuXNZg2ZMNwtt35l5e2feeVf32zPaCqrzzkJn365NP7nzMYXOvsqNCbNtAaAORJ4AAAQtnmlNfTA+9f5ECCFLoBsh78wsaPfM3JlB3pmoxdC0hsXdv+opr3Fl6Z1LLlshB2VRJ64eWsqg2myqbVenyBNoFKWOPTH1jFnmgpbN22nod5POwpptMdSMw2wrt84V1mzLHs8CS8k7q5248t6ijwEAVkLkCQDQumLAlnZ6ais7/sqhUBVzZhoq74waMLlctpjLfvb7/eGOrtMFUajgszXt2jj87qXfhRAqsxPTN8aLnUv9iZClPxKgDrLU866fHbv54V2fsuAQZenpUsbktoil5527Nv0kzrYFAGpN5AkAze3Mwf3xsKl/RSMfaUExbwsh9Dwn8qyr70s6QwjdQ309z/Vpuq2DRefuxtQzB8Hn1T+fTF1C8yl09MX1nBdvTCwaZMZHmmoLNKCYXGarOmPLZvhu1+ZS+jgLbduyxlCRZ3Rx5nQ8LKW/M5ttOz0zbqMnANSOyBMAcsIiT5ZNwFYHcXTt98WcIQRJZxJ37V6NOWi2vrSpg8/qicQrXIbaUnZtHD5x9VBldqIyO/Hupd8VOvq+b8LtiauH6lwbwP3KFnxmsu7PgbVPLyXCLLZtj6HpgmQ0zr9dsBm0RWS7OZcyq7Zr3WPx8RdnTos8AaB2RJ4AAC3qrvEbq+veDZ0hhEYbYEv4Nge9R/BZPRR3wxOlOzeYNo7qvLMZI9uEujrnGz1DCFnw2dXZ931Nn0vZ+gnQIGLX5hLzzgXKs8dixpnlppVb5ypz5xbMws236ZnxeCgsbYtzcV1/+DKEECo3Jmz0BIDaEXkCALSo7qG+GMUdf+WQ4G11GV2bA98XfGYhYnTt4/JfXv/lw3+3twEDxRjNhobPZRtTsbP/xc7+2OsZr8Smz8KNiVAVcJpqCzSjO/s+F1Xq2HNw5l9DDDhvnVuwCrRy69zBmX+9c0ZuvHK/baCxebSRM9Rsqm3XuseW+JSdP3gh7v6MqWfXuse0ewLAqhN5AgC0qN1vDL+1+3chhPMjE1MfjAvhliHbh5plxvGvdAGja5vXncHnXV34w4EGHH7bFH2oDS5Gm9M3xi/emIjpZvz9xNVDuzYOT98YT1wfQB0NrH06jretvlgdc965EPQebaDTc5NjNz8srNlWfT1bOxpCKM8ea8DU88SV97KRtiG2by5NcV3/znA79azcmNgZXpB6AsDqEnkCALSup15/fvTVwyGE0VcP9xz3nst9mPpgPP7VRW/t/l3MNasZXZsb1cFn+O6Q2M9+v3/B8Fv5Yv4UO/uLnf13Bp8Xs0VuptoCLSD2hsYWzPDd3s1syG34NgSNsouVW+fiYwprtsUnVubOZddjtBlD0Ornxu8Vv8jyGkaXYXpm/PuSyOmZ8eq8c4lTbTPFdf0vrvuHLDQ9+eXBwkyfIbcAsIpEngDQ3K5MlEMIm/pKqQuhKfU82z81NGGp5/06/sqhO//Sqq889frzGjrzJwafCy4+8rN9C4bf/uX1X/7nV/8pRYHfUT3VNm0luZEFnyevHQ4hVM+8BWgdd+28vEc7ZhZk3u74/O5c3DgUt/ophTXb7mwbza6M3fyw8E0Nk8KTXx4MX4adP7hLC2bs0Qzfhp3Lq2HXpp9kqafVngCwutakLgAAgJR6npv/+fRsRiv39tbu32XpZvdQ38+P/333UF91i6e8s9VsfHLnIz/b9/Df7c3CxSxubAQNNWs3B4qd8/+6s7zTIvp+NQsAACAASURBVE+Aeyi2bR9Y+/RdP1XdD1p9sdSxp/pTdz6sa91jJ668927lt+9Wfnviynsnrry34AF3Xjxx5b3pmcWnkWfPOvnlwezx0zPj1V+w0Nm3a9NPVpJT7tr0k50/eCGeKzcm3q38dim1AQCL0uUJANDSep7tHw2HF38cIYTvBsNZtJmNrp36YHzq/Ql5Z2uKPaBxzu21j8uf/X5/wgm31TN4U9WQY4WOvur+zq77HGwI0GqyibiVW+fiQtAQQuXWuULbtlLHnmxSbqiaW5tdz65Uz87Nui1DCLFd8t3Kb8O3zZfxSvXU2ayr8vsm1sYHFDr7qufWxsGz2Recr7BzdRpM42rPizOnDbkFgFUk8gQAIITvzmVlUT8//vd3Xux5tl/e2eIe+dm+v7z+yxDCtY/LV/98sv4dltVTdqmRrs7vRJ4nrx3eWdX9CcBdlTr2TM9NxvhzwfXve/yCD6tTz2hBSFl9DiGcuPJe17rH7rweZSlp9tnskF1c8MRCZ1/8gquiuK6/uK7fkFsAWEUiTwCAVtc91CfvhNXy8N/tvfCHAyGE+HudU8/qvHPDE6WEnaY5Vuzs3xnCxRsTWfB58caEyBNgUQvCzvt1uyV0bXvXuseyls3pmfGLM6fDdxPKu2aWmWyQ7F0fsGvTT6Znxqt7SUMILxb+YSXFf5/q7xVTz+o/GgBwX0SeANDEvhg/mboEcmXqg3FNivcWs+HqzZ2wwMYnd2a544U/HKhn5Fk9z3bDEyVbPGun2Nlf7Ow/cfVQTD13bRxOXRFAS4itn2t+sLH6YmyXjOcYf941yKy+GCPSTGzfjLljnIi7YPBsTcXvlaWelRsThZm+LPjMAt17RKHZYzSJAtDiRJ4A0Ky+GD959tCB1FUAsNAjP9sXl3qGEOq51LO6v1PeCUALykLB2D25ILOcnhlfkBpW927G3DFLDRcMnq112S+u+4fqIbeVGxPhy+88JkahC/5c1etLQwjvVn5757bRpYSmAJAPIk8AaFZXxuff2t7UV3r0BaMLWb5sqq0Wz3ub+mA8dQk0jQ1PlOq/UDNr8ZR3AtCysqbPeIgzbGMP5ckvD4YvF24ArX7ii+sWTq/dtekn9Uk9l/K9Kjcm4grS6it3PubElffu+oDKjYmd4QWpJwA5JvIEgGa1qb90ZaLe76eTP1mMZ1jroqben3/PaPcbhliyiCzvrFuL52e/31+fb8Sdpm+M2+UJ0JhiwleYuR1zZv2RSxwDW89psfF7ZX2csYmza91j4dvUNrOgv7P6w3uEpie/PCj1BCDHRJ4A0Kwe6t95Zbx8ZaIs+GQlshiv5zmRJ6yaVC2eoY4hK5mT1w6Ha4cLHX2WegI0phglZj2US887k6jeTprJJt8uKD7mo9mVBX2iMQqN43CzVlepJwB5JfIEgCYm7GTl4lTb7qE+U20Xlf1dpS6EprHhiVJ9vlHW4lm370gIoSu21Mx+2zY0O/Hupd8VOvq6Ovs0fQI0oEaOOZfirvUvyEezZZ8LHlxc15+1uko9AcirNakLAACW6czB+Te4N/V5g5tlOv7KodQlQA4lDCC1eNZTsbN/18bhnRueL3Tc/kmIyuzEyWuHT1w9NH3D9l8AEiiu679rOLpr009i02cI4eSXB+OWUwDIE5EnADS9R1/wBjfUXLb0FJZu45M76/ON4lRbLZ5JxODzxa1/X+joy7LPyuzExe9fpQYASUg9Acg3kScANKUvxk/GqbZaPFmJ3W/Mr507PzIh0rs3S09ZujoHkKbaNohdG4ermz4rsxMaPQFoNFJPAHJM5AkATemh/jp1DpF7T73+fDxkkR53mvpgPC7yDCFYesq9Xf3zyXi49nE5O9dH3ZpKuYdiZ39X9lbytcNpiwGAO0k9AcgrkScANKvY3xl7PWHZep7t7x7qCxo97ynLg+PfFdxb1m154Q8HshZMWkexsz+bcPvupd9V/0pbGABEUk8AcknkCQBN78xB76ezItmk1tFXNSTdXWzx7B7qy0YBw/fZ+OTOR362L0s9r31clnq2oF0b3SsAaGhSTwDypz11Acz7+PojqUtoUSeu9qQuAept3ee3UpfA6sj6O5/4334RvKz39P99vTZ1CQ1ty9/+567BMxePjIcQjv2fh3/0m5+mrqhBzX3zQBP9t7T5L24LKW3+L7+49IOBT/74Zgih4+o3NX05/vJxOYSw5ZEBL/qtmZnUJdw2sO6Zys2z2YeVucnQYBWSE7e+SV0B1Ns3X8+lLiEndj74/MlvDlVmz4QQLl7/tNDem7oiAFgRXZ4A0JROHT8QD5u7B9JWQj5kMWcMPlmga7A/+MvhPm3tLqUugZSK7b2l9YPxV3bx4Ff7p78+m6wmAPiunRuMJQAgP0SeANDcduzem7oEciKmeiGEj157J20lwNKdGj2QugQWV1o/WGjbHs9jM0elngA0jkLHoyGE2OsJAE1N5AkATenz82NBiyer6ke/+WnWy3hh5HTqchrChZHTH732zr//7T/r76QxnRo9cPmzsRDClkcGdjzlJ2AamtQTAACgpuzyBABgXtfQ4zHbuzjy6cNDj6UuJ5kLI6cvjnx6Z8yZNcLCfbn82dil8+VVn3Mr72w6pfWD5etH4lLPsZmjlbaz1TNvAQAAWAmRJwAA8x4eeuxPv0pdRDr3TjqzdaewdFseGYip5Cd/fHPrS6sZeWZ5ZwhhS4+loU2jtH5w+uuzYzNHQwiVucmDX+0PIRTathfW9hbbexMXBwAA0MxEngDQxOJ4W2CF4vrSu4adkk5WYsdTe7Ns8tTogdXqxbx0vpzlnT/88S9WvX+Umiq294Z1IaaeUWVusjI3ORaOxsm3Wj8BSOLktUM7NwynrgIAlk/kCQBNaXP3QMw7Tx0/sGO3eYawTBdGTv/pV+8uuNg12N819Hgrj/ZlFWWp5+XPxlYl9bx0vvzJH9+MZ3lnkyq29xYf7J3++mzl5tk45zaK59j6+cKD+5LVB0Ar2blh+L3L/zN1FQCwCkSeANCUduzee+r4gc/Pj31+fkzqCctWnXdKOqmRLT2l2JS58qWe8s48KbbfHmZbvn4kfBt5Zle0ewIAACydyBMAmtXWnlJs9DTeFpYnzrMNBthSY1u7S+HHIUaVn/zxzfDjsLyosjrv3PLIgLwzT7J0M9v0CQAAwH1Zk7oAAGCZtvTMv9m9uXsgbSXkz8Uj4xdGTqeuorY+eu2duLxT3kkdbO0ubXlk/l6dxZb3qzrvXK21oDSaYntv3OhZmZuc/vps6nIAAACahi5PAGhWp44fSF0COfQ3v34xznq9OPJpnka8ZgHnneSd1Ee21DOEsIylnqdG5+/58s7WMTZztNJ2trD29vxbAKiFQsejldkzldkzqQsBgBXR5QkATc8iT1bRw0OPdQ32hxAuHhnP5r7m2N/8+sXUJdBCdjy1N/Z6Xv5sLIswlyhmpfGLrH5lNJLC2vlGzxBCZW7SnFsAAIClEHkCQFO6PFWOKzxNtWXVZV2PuRxv2zXYn/36m1+/mKdOVppCNpP88mdjl86Xl/is6hbPmpRFIym295bWDw6seyYLPk24BQAAWJTBtgDQfC5PlT8ZXeYqOFiKvI63DSF0DT2esz8RzWVrdyn8eH4r5yd/fHPrS6VFn3LpfFmLZwuKw2wrc5MhhMrNs2bbAlAH0zcmip19qasAgGXS5QkATaY679zcPWCqLSzF7dbVkU/TVgJbu+djziW2bF6emm8G/eGPf1GrmmhIYk4A6mPnhuF4KH91ePrGRNpiAGDZRJ4A0GTkndRBlgt2DT2ethIgtnhueWQgy0oBAFZXoePReJB6AtC8RJ4A0GSy5Z1be7z3Tc2ZAQur7vZiTrdx7ql8/UjqEgBoFTs3DEs9AWh2Ik8AaDJZ0nnp21GHsOouHhlPXQLkn65N7mH667NxkWcIobR+MGktALSEBannyWuHBJ8ANBeRJwA0GV1B1E3XYH/qEgBaVOXm2XgotG2f/vpsylIAaBnVqWdl9kwMPtOWBABLJ/IEgOYTZ9t+fn4sdSHk04WR06lLqImY4GpgJblsN+dSHpxNwaXVFNb2xkNlbnJs5mj5+hFzbgGog50bhksPPl8dfL53+X9q9wSgKYg8AaCJnTrurXBW38WRT+Oha+jxtJVAi7t0vhzz0RDCjqf2pi2GOiu2977w4L5C2/b4YWVusjI3efCr/Qe/2p+2MAByr9jZF4PP7Ip2TwCagsgTAJrM5amy/k7q4+Ghx1KXAHlzX12bl7/d2bzEllDyp7R+cGDdM9VXshAUAGqq2Nn3ky3/e3W7Z9p6AGBR7akLAADuw+Wp8iejb8bz5u6BrfZ6UgP5G/16YeT0xZFP8/fnoqktZTFzNgJXi2crK7b3Fh/sDSHo7wSg/nZuGD557ZC8E4CmoMsTAJpJ9hb55u6BHbv3LuUdc1ieuPkyBy6MnP7Tr97N8s6/+fWLaeuhxWX37U/++GbaSgAAACBPRJ4AANx2YeR06hJW00evvfOnX70bz12D/f/1//nvpvWS1tbuUjal9r6G3NLipr8+m7oEAACAhibyBADgtosjn8ZD19DjaStZoQsjp//9b/85a+7sGuz/0W9+mrYkiHY8tTemnpc/G7t0vpy6HJpDsb03bvGszE2Wrx9JXQ4AAEDDEXkCQJPZ3D0QQvj8/FjqQsi5pu6GXNDc+Te/flHeSUMx3pZlKK0fjIfK3KSmTwAAgAVEngAA3Ja1RTavj157Z0FzZ1PHt+SS8bYsQxZzFtq2F9t7U5YCAADQeESeANCsTh33Ljm10jXYn7qEZaoeZqu5k0ZWPd721OgBE25ZVOXm2dQlAAAANC6RJwA0mWyk7Y7de9NWAo3mo9feiYc4zFZzJw0uG297+bOxT/74pnZP7q16sK11ngAAAAuIPAGgmWSdnXGjJ1At6+80zJamsLW7tPul/5FNuI3tnmlLosENrHsmHqzzBAAAWEDkCQBN4/JUObZ4bu4e0OIJC1S3eKatBO7Ljqf2/vDHv4jny5+NpS2GBlds781Sz7GZo1JPAGpt+sZEZfZMCKHQ8WjqWgBgESJPAGgan4y+GQ9bv52FCGSqWzzTVgL3a2t3Kev1zNjuyV0V23sLbdvj2XZPAGptenb+/2MXO/xYIQCNTuQJAE1mc/fAFpEnNRNbJLP4sFlo8SR/Lk/NR57u+SxQWj8YU09LPQGoqeoWz2JnX+pyAGARIk8AAJrbhZHTMaPtGuzX4kluZENut3aLPFmotH4wHqSeANSOFk8AmovIEwCaxubugRBCXOcJZC6OfBoPXUOPp60Eli1r5Tw1eiBUTbW9c+AtRNlSz8rcpKWeAKw6LZ4ANB2RJwAATay6xfPhocdSlwPLtKCV01RbFlVs781Sz7GZo1JPAFaXFk8Amk576gIAAGA54v7ObO2oFk/yxFRblqLY3ltpO1uZmwwhVG6eLbb3pq4IgPzQ4glA0xF5AkDzuTxV1vdDrX302js/+s1PL4ycvjjyaYMsyIzFhKqYE3LJVFuWrrR+sHz9SGVuMi71zHZ8AgAAtBqRJwA0jR27946+9Y8hhEsiT2rv4pHxf//bf47nGH/Wv4b7yjj/9Kt3Q3jRbFtyIJtqu+OpvWkroSmU1g8e/Gp/CEHqCcAqKnQ8Wpk9E3s9AaApiDwBALjtR7/56UevvbMgYrx4ZPzCyOn6pIlxXG24Z8zZNdgfQugaejyWlBUs9aSpbXlk4PJnY9lIWy2eLN3AumfGZo6GECpzk9Nfm3ALAAC0IpEnAADfERs6F3RY/ulX7z489N9r8e2W0soZM86stjsLlnoCrazY3hvWhZh6js0cDeuC1BOAFSp29McWz5PXDu3cMJy6HABYnMgTAJrJ5u6Bz8+PfX5+LHUh5N/DQ48t6KHM+i/D90SPSxe/1L3H1S5o5bw3qSf5Y6ot96XY3ltpO1uZmwwhVG5q9ARgpYqdfeWvUhcBAPdD5AkAwL1kaWJ1Qpmt+QxVLZjZ4+/8Iitv5Vx6naGWPalQO3FJcxxsa6oty1BaP1i+fqQyN2mpJwAA0IJEngAALKJr6PF7RJULPrUgDV1KK+cKe0Yz1annR6+9s1pfFupja3dpa3cphHBq9ECMP+F+ldYPHvxqf7DUE4DVUOh4tDJ7Jo63BYDGJ/IEAGARDw89Vt00mbVsRktPQ8N9jqtdhuqeVKknTcpIW1ZiYN0zcamn8bYAAEBLEXkCQNO4PFXOtnieOn5gx27viZNGtubzTndNQ1e3lXNRUk+glRXbe8fC0RBC3OsJAMtW7OiPLZ4nrx3auWE4dTkAsAiRJwA0jUtT5XjY3D2w1cxDGtI90tB6knoCrazQtj3mnWbbArASxc6+8lepiwCAJVuTugAA4L7t2L3Xmje4t66hx+Mhpp5piwGop8La3nio3Dybsg4AAIA6EnkCAJBDDw899je/fjGepZ5AS9HZCQAAtCCRJwA0jbjIc3P3QOpCoDlIPYGWVWjbHqzzBAAAWonIEwCaw+VvF3kCSyf1BFpc+fqR1CUAAADUg8gTAJrDJ6NvxsNWWzzhfixIPS+MnE5bD0AdlNYPpi4BAACgrkSeANAETh0/EA+buwe2iDzhPlWnnn/61btST6AVmG0LAAC0FJEnADSBuMUzhLBj9960lUCTWpB6pi0GoJ7MtgVghSqzZ1KXAACLE3nW3AOLefvtt1PXCEACp44fyHo3F31kPGzuHqhlRZBzDw89lroEgPrJZttW5ialngAsT6Hj0Xg4ee1QPCz6Vme6YgFodSJPAEjg1PEDn58f+/z82Ohb/3h5qnyPR16eKmvxhFWRzbPtGuxPWwlAfQyseyYepJ4ALM/ODcMx9dToCUDjE3kCQL3FvDP78NL3R56Xp8qfjL4Zzz986hc1rwxy7eLIp/HQNfR42koA6qPY3iv1BGCFih1+XhCA5iDyBIC6WpB33luWd27uHtjSU6pZUdASLh4ZjwcTboHWIfUEYIWKnX2lB59PXQUALE7kCQD1U513Ltq1Wb3C00hbWCFTbYGWJfUEYIWKnX3ZUk8AaFjtqQsAgFaxIO/c0lMK3zZx3vnIEEJ8sLwTVkU21fZHv/lp2koA6q/Y3hvWhbGZoyGEytzkwa/2F9q2hxBK6wcTVwYAALBKRJ4AUA93yTu/9fn5sctT5fDtUs/qsbfyTlgtcaqtFk+gZRXbe4sP9pavH6nMTYYQ4u8x+yys7S229yauDwAAYGVEngBQV9V55+bugRhwfnK3ds/N3QNb7e+E1fDRa++kLgGgIZTWD05/fbZy82yMPEMIlbnJytxkWBekngAAQFMTeQJAPWztKcV089JUOYs8d+zeW939GW3uHoifqn+RkHum2gIU2+d7Oquzz7GZo8UHe9MWBgAAsBIiTwCoh+/b3Bmj0Bhzbu0pbdHWCTVgqi3AnWL2mY26LV8/YrUnAADQvESeAJDSlp7SU2JOqCVTbQHuobR+MKaelblJqScAANC81qQuAABaRWzlXDDGFqgbU20B7iqLOWPqOf312ZTVAAAALIvIEwCAPDPVFmBRA+ueiYfK3OTYzNGDX+0vXz+StCIAGsvODcOpSwCARYg8AaDeLk+VU5cArSKbats19HjaSgAaWbG9N0s9o8La3kS1AAAALIfIEwDqZMfuvfFwSeQJ9RJbPEMIDw89lrYSgAZXbO994cF9hbbt8cOxmaMm3AIAAE1E5AkAQD5dGDkdD6baAixRdXPn2MxR420BAIBmIfIEgDoxzxbq7OLIp/Fgqi3AEi3o9azMTUo9AQCApiDyBIB6uDxV/mT0zdRVQGsx1RZgeUrrBxes9gQAAGhwIk8AqLnqvHNz90C21BOoHVNtAVai2N6bugQAAID7IPIEgJqTd0L9ZVNtf/Sbn6atBAAAAIBaE3kCQJ3IO6Ge4lRbLZ4AAAAArUDkCQBA3nz02jupSwBoeoW27SGEytzk9NdnU9cCAACwCJEnAAC5ZaotwMqNzRwtXz+SugoAAIB7EXkCAJA3ptoCrFxp/WBs9AwhVOYmD361X7snAADQsNpTFwAAAKspm2rbNfR42koAml1p/eD012crN89W5iZDCGMzRyttZ0MI8cNC2/bC2t5ie2/SGgEAAEIQeQIAkFcPDz2WugSAplds7y2295avH4kxZ/w9qsxNVuYmx8LRgXXPCD4BAIC0DLYFACA/LoycNtUWYNWV1g8OrHum+ko28zaEMDZz1MxbAAAgLV2eAFAnn58fS10C5N/FkU/jwVRbgNVVbO8tPti74GLW/Tk2c/TOzwIAANSNLk8AqLnN3QPxcOr4gbSVQO7FFs9gqi1AXZTWD2btnuXrR5LWAgAAtDSRJwDU3NaeUjxo9ISaujByOh5MtQWomyz1rMxNSj0BAIBUDLYFgJq7NFWOh6zdE6gFU20Bkiis7Y3jbStzkwe/2p9df+HBfemKAgAAWosuTwCorctT5djcubl7YMfuvanLgTyLU227BvtNtQWop2J778C6ZxZczAbeAgAA1IEuTwCorazFMxtvC9RCNtUWgPortvdW2s7Gc+z4LKztTVgPAADQanR5AkCdbBF5Qi1lU21/9Jufpq0EoDWV1g+W1g+mrgIAAGhRIk8AAPIgm2qbuhAAQgihcvNs6hIAAIAWIvIEAKDpffTaO6lLACCEEErrB+MWz8rcZPn6kdTlAAAArULkCQB1cvnbpZ5A7ZhqC5BctsWzMjd58Kv95etHytePTH99NmVNAABA3ok8AaC2tn67wvOSyBNqxlRbgMZRbO+NjZ5RZW6yMjc5NnNU0ycAAFA7Ik8AqKFTxw98MvpmPH9+fkyjJwDQCuJ42/gru2jULQAAUDvtqQsAAIAVyRZ5dg09nrYSADKl9YPZefrrs2MzR8O3o24H1j1TbO9NVRgAAJBLIk8AqKGtPaXPz4/F8+bugS3fDrkFauHhocdSlwDAXRTbe8O6ULl5tjI3GUIYmzk6Fo7GTxXatleHowAAAMsj8gSAGtrSU3rw1NGvPj8XqpZ6RqeOH8jS0BDCD5/6hUAUlsciT4DGV2zvLbb3lq8fialnJvZ9Cj4BAIAVsssTAGro8lQ55p0LWjwvT5Wr884Qwiejb546fqDe9UHzuzByOnUJACxVaf3gwLpn7rrm8+BX+236BAAAlk3kCQA1dGmqHA8LWjyz65u7BzZ3D8Tz5+fHpJ5wvy6OfBoPFnkCNIVie29p/WD89cKD++4MPhPWBgAANC+RJwDUSnUrZ5ZxRvH65u6BHbv37ti994dP/SK7PvrWP17+7oOBpbDIE6AZ3Rl86vUEAACWQeQJALVSHXNWZ5lZK2fW+rmlp/TDp36RtXt+Mvqm1BOWyCJPgByoDj4XLPsEAABYCpEnANRPXNiZtX5Wb/fc0lPasXtvlnpeEnnCEljkCZAnhbW98VC+fmT667MpSwEAAJqNyBMAamXH7r3ZuXph54Ird31K9jDgHrJFnj/6zU/TVgLAyhXbe7NGz7GZo1JPAABg6USeAFBDcVztUz//H3FhZ3XMWR2IVsseY7YtLCpOtQUgN0rrB7O9nmMzR+31BAAAlqg9dQEAkGdbekpbqhZ2bukpxcG2d23xjHbs3jv61j+GEC5Nlasn3wLfxyJPgDwprR8sXz8SN3pW5iYPfrU/+1ShbXtp/WCyygAAgAamyxMA6iq2e269Z5YZA1GzbeHePnrtndQlAFATpfWDA+ueufN6ZW7Smk8AAOCudHkCQL0tvXfz1PED3zf/FshY5AmQP8X23uKDvdWDbbO+z8rcZFgXiu29qWoDAAAakMgTABpONtsWuIe4yNNUW4AcWzDGNht4OzZztNJ21pBbAAAgY7AtAADN58LI6dQlAFBvpfWDhbbt8RyH3CYtBwAAaCAiTwBoOJenyqlLgEZ3ceTTeOgaejxtJQDUU/WaT6knAACQEXkCQMO59G3kaZEnLOrhocdSlwBAXRXbe7PUEwAAIBJ5AgDQfCzyBGhlxfbeOOE2rvYEAAAQeQIA0GQs8gQAAACgmsgTAIAmky3y/NFvfpq2EgCSm/76bOoSAACA9ESeANBwPj8/FkLY3D2QuhAAAAAAgCYg8gSAxnJ5qpy6BGh0FnkCtLjy9SPZFs9ie2/SWgAAgIYg8gSAxnLp28hza08pbSXQmD567Z3UJQCQWGn9YKFtezyXrx9JWgsAANAQRJ4A0EAuT5WzqbZbRJ5wTxZ5ArSywtreeKjMTUo9AQAAkScANBAtnrCoONUWgBZXbO8dWPdMPFfmJqe/Ppu0HAAAIDGRJwA0Ci2esHQWeQJQnXqOzRyVegIAQCsTeQJAo9DiCYvKFnl2DT2ethIAGoHUEwAAiESeANAQtHjCfXl46LHUJQDQEIrtvYW27fEs9QQAgJYl8gSA9C5PlT8ZfTOetXjCPcRFnqbaAlCttH4wSz0rN88mrQUAAEhD5AkAiVXnnVo84R4ujJxOXQIADSpLPStzk+XrR1KXAwAA1JvIEwBSWpB37ti9N2090MgujnwaDxZ5AnCn0vrBeKjMTSYtBAAASEDkCQDJyDtheSzyBOCusvG2NnoCAECrEXkCQBryTrhfFnkCcG+Ftb3xYKMnAAC0GpEnAKRxaaocD/JOWAqLPAFYVLG9N9voqdETgP+fvfuJkfK880X/VNFYExjpXhq7u2DotiUg6GCokmIZyxhyYOGcWMrudrcjrHBWd2fvspmNLXszm6vZ2Lu7GiyjuLuvziaSc5wFJGC4NiI6Vca+csBS6MKmC0N7RopxxnR33cUbXper/1V3V9VTfz4flUZPV1e//TVJlIjv/H4PAH1lIHYAAOh3+k5oRHqR51NvvBA3CQBdofS3P5TCH5JzuvB2eOsTuYEnomUCAABaxpQnAABdINlqCwCrK/zoxNI3Kws3k1fpb38w/QkAAD3JlCcAAF3DRZ4ArOln//jfQwjFb8+n71QWbqbn0t/+BRK6wAAAIABJREFUUNnyl2WbUQAAoHupPAEgsnvl4s6RQuwU0NGuvvpe7AgAdJmlpWbx2/NJ91lZuFn89rwltwAA0EsstgWACD67dGbuVik53y0X44aBLuIiTwA2rPCjE/l/+K/J2ZJbAADoMaY8AaDdavvOwT35A0dPx80DnS+5yNNWWwA2KTfwRO4fn0jHPZMlt6v/iBW4AADQFVSeANBuB46erm09AQBop8KPTtQuuV39w+//9d/y//BfrcAFAIAOp/IEgAgeHSkklefcrZK7PGF16UWeu07uj5sEgJ5R+NGJ2fm/lP72h0Y+XPrbH8I/hNzAE8VvzyfvJEXp8JbHzYACAECHUHkCQAQ7Rwo/DuHPl98OIfz58ts/DkHrCWvafXJf7AgA9I5kye3qn6ldgVsK9f1oZeHm7PxfDIACAEAnyMYOAADoO2E1LvIEIJbCj04Mb3l8lQ80OCcKAAC0milPAIgjGfEMIfz42V/FTQKd7MtzN2JHAKCvJStwKw/+kn6ZHNIB0OK35623BQCA6FSeABDT4J68EU9Yxe1z15ODizwBiCU38MTS7bWFH514/6//Fqy3BQCAzqDyBIAI7pWLsSNAl3GRJwCdJv8P/zVZbFt70+fwlscNfQIAQPu5yxMAIrj7sPI8cPR03CTQ4VzkCUDHyg08sfSmz8rCzff/+m+z83+JkQgAAPqXyhMAgA7lIk8AOlw60Dm85fHa+rP0tz8Uvz2v+AQAgLax2BYAgA6VXuT51BsvxE0CACv52T/+99ovi9+eryzcDCFUFm5WFm5WtvzFnlsAAGgDlScAAB0q2WoLAF2k8KMTs/N/qTz4S1p8vv/Xf1v6seEtjw9vfSI38ES78wEAQI9SeQIA0NFc5AlAd8kNPJEbeKK2+FzKDCgAADSXyrPlxsbGVv/A5cuXv/jii/aEAQDoFldffS92BADYuKT4LH57ftnv1s6ADm95fMPFZ/HBhRBCpVpOvhzOjBS2Ht/YowAaseZfdU5PT7cnCQDUUXm23NTU1OofGB8f9z8FAABqXX31vXSrrYs8AeheK3WZS5ffNl58Jj8bHvamtSrV8vvfnc0PHMtlRzeRGmBFa/5VZyaTaU8SAKij8gSAmD67dObA0dOxU0AHqS07g622APSodAY0rS0rCzdXGgmttdKm3OHMSDrrWZq/WDHuCQBAn1F5AkAEB46evjz569gpoIN8ee7G7XPXa8vOEMKuE3uNeALQw5KxzrT4XKnOXMnwlsfD4mJttVl8cCEpPo17AgDQb1SeABDH4J783K3S3K1S7CAQmbITgD5X+NGJ2j23qxve8nio3Zf73YMfPGrr8dnFmcrCzaT4NO4JAED/UHkCQGT3ysWdI4XYKSCCuh22IYRdJ/buOrl/98l9sSIBQBTJntvmPCo7msuOGvcEAKDfqDwBII5HRwrJiOddlSf9R9kJAC1l3BMAgH6j8gSAOHaOFMLlt2OngLaywxYA2mbZcc9hxScAAD1K5QkAQMspOwEgirpxz0q1XHxwYXjL4/bcAgDQY1SeAAC0kB22ABDX0nHPynzZnlsAAHqMyhMAIvjs0pnkIk/oYcpOAOgcxj0BAOhtKk8AiOzRkULsCNBMdtgCQGcy7gkAQA9TeQJATIN78jtVnvQKZScAdD7jngAA9CSVJwBEcODo6cuTvw4hWG9Lb7DDFgC6iHFPAAB6j8oTAOIY3JNP+s575aJBT7qXshMAupRxTwAAeonKEwAiuFcupue7Kk+6kB22ANADjHsCANAzVJ4AEMGfL7+dnh/Vd9JVlJ0A0GOMewIA0ANUngAQQbrVdnBP3ogn3cIOWwDoVcY9AQDodipPAIjgwNHTlyd/HTsFNErZCQD9wLgnAADdS+UJAMDy7LAFgH5j3BMAgC6l8gSACO6Vi7EjwGqUnQDQz5KCs7b4NO4JAECHU3kCQLvVrrR91EWedJhld9iGEJSdANBvkj23pfmLwbgnAAAdT+UJADHtVHnSMVzYCQDUyWVHc4+cqh33/MN3/+PAwFPGPQEA6DQqTwBot8E9+blbpeQQOwusuMNW2QkAJGrHPf8zfFuav2jcEwCATqPyBIBoDhw9HTsCfc2FnQBAg5Jxz/e/O5t86XZPAAA6jcoTAKL57NIZrSdRuLATANiA/MCxZNYzuN0TAIAOo/IEgGj0nbSfCzsBgA3LZUcrmZHkXs+EcU8AADqEyhMA2s1FnrSfCzsBgKYobD1efHChrvU07gkAQHQqTwBoq88unYkdgf7iwk4AoLkKW4/PLs5UFm7WFZ8ffvc/Hx/4L8Y9AQCIQuUJAHHYakurXX31vRBCbdnpwk4AoCly2dFcdrSu+PyPcK80f/Fm2PnMI/8tbjwAAPqQyhMA2spWW9rAhZ0AQBusVHz+4bv/cWDgKeOeAAC0k8oTANpqcE9+7lYpKT6huVzYCQC0X1p83lz4//6jei+E8J/h29L8Rbd7AgDQTipPAIjjXrm4c6QQOwU9woWdAEBcafFZmr+YvFOplosPLmg9AQBoD5UnALTPZ5fOmO+kuVzYCQB0jlx2NPfIqeKDC8meW60nAABto/IEgAh+/OyvjHiySS7sBAA6U2Hr8XTcU+sJAEB7qDwBALqMshMA6HC57GgYOJa2nrOLM7nsaOxQAAD0MpUnALTPoyOFZLHtXRd5slF1facLOwGAzlTbepbmL4aBY1pPAABaJxs7AAD0ETUnm5f2nbtO7P3FH1/WdwIAHSuXHc0PHEvOpfmLs4szcfMAANDDVJ4A0FaDe/IhhGTWE9br6qvvJQfDnQBAV8hlR4czI8lZ6wkAQOuoPAEAukY64qnvBAC6RWHr8bT1rCzcjBsGAIBepfIEgLZK5juTWU9Yl9oRz7hJAADWJW09K9Vy8cGF2HEAAOhBKk8AaJ975WLsCHQxI54AQPfSegIA0FIqTwBon7sPK89HRwpxk9B1jHgCAN2usPV4ctB6AgDQdCpPAIhgp8qTdTLiCQD0gPzAseRQqZZnF2fihgEAoJcMxA4AAH3ERZ404stzN26fu552nLWMeAIAXS2XHQ0Dx0rzF0MIpfmLlczI8JbHc9nR2LkAAOh6Kk8AaBMXedKIq6++t2zZmTDiCQB0u9rWs1ItV+bLpRCGMyPp2lsAANgAlScAtEl6keeBo6fjJiGRDFOmX+46uX/3yX0R8yTSvnPpQOeuk/vbHgcAoPmS1rOycLNSLSfvVKrl9787O2zoEwCAjVJ5AgB9auny2OiV55fnbiSHXSf2GugEAHpYLjuaVJvFBxdqi8/KfLli4hMAgPVTeQJAm7jIs6Osvj82lnTqVN8JAPSJpN2cXZxJhz6Tic/8wDHjngAANC4bOwAA9IXPLp2JHYHvfXnuRtp3/uKPL8cNUytJtXSlLQBAb8tlRwtbj+cHjqXvlOYvFh9ciBgJAIDuovIEAPrLl+du/Om13yXnn7z+87hhal199b3k4M5OAKA/5bKjP3vk1HBmJPkyGfecXZyJmwoAgK6g8gSAdjhw9HRymLtVulcuxg3T59LlsbtO7I1+eWetdPC0o1IBALRZMu6ZFp/GPQEAaITKEwDaJL3F867KM570Cs9dJ/Z21H2ZX567kRxstQUASPbcGvcEAKBxKk8AaJMDR08nradBz4jSScqO6jtD7eyprbYAACEE454AAKzHQOwAANBHHh0pzN0qhRDulos7Rwqx4/Sd9LLMTrjCMwmTVLC/+OPL6eyprbYAAKlcdjSXHS0+uFCplsPDcc+0BA0hDG95PJcdjRcQAIBOofIEAPpOba2Y9qBtls6bhhB++9O3omQAAOgKha3HZxdnKgs30+Iz/VZlvlzJjBS2Ho+XDgCAjmCxLQC0T3qL54Gjp+MmIRVl1e1KPWunrdsFAOgQdbd71qpUy8UHF9z0CQDQ50x5AgD9K60ed53Y287fm/asu07srT23MwMAQNepm+acXZwpzV8MIVSqZeOeAAB9zpQnALRPcpHn4J587CB9Kr0vM/nyy3M30nfaOV755bkbyWHXib27Tu5P3689AwCwplx29GePnEpHP5Nxz7iRAACIxZQnALTJZ5fOxI5ACCHcPv/5l+du7D657/a568k7be4a09+b9Ky3z+3ddXJ/7fWiAAA0LrnpMx33fP+7s/mBY7nsaOxcAAC0lSlPAGiHzy6dSUY8QwiPjhTihulbP3n958nh9rnrv/3pW+mIZ5vrxrph06feeEHfCQCwGbnsaH7gWDruWZq/aNwTAKDfqDwBoB0OHD2dHAb35HeqPCPZfXJfUjSm12eGh6OWbfP97aHW2AIANE8uO1rYerxuye3s4kzcVAAAtI3KEwDaJLnCM531JIq0aNx1Yu+uE3vTuc+2SdtWk50AAE1X2Ho8P3AsOVeq5dL8Ra0nAECfcJcnANBHdp/ct/vky7F++/cjng+32gIA0Fy57GgYOFZZuFmplkMIpfmLlcxIYevx2LkAAGgtU54A0G6fXToTOwKRtXmbLgBAX7HkFgCgD6k8AaBN0pW26b2e9JUvz91Ittoa8QQAaANLbgEA+orFtgDQDvfKxeSQ3OhJH7p97npySO8TBQCgpXLZ0dwjp4oPLlhyC7THnj17VvrWs88+284kPezWrVvlcnnZb01MTPzrv/5rm/MAnUPlCQDtcPdh5UnfSkY8Qwi7T+6LmwQAoK8Uth5PW89Ktfz+d2fzA8dy2dHYuYAe9MUXX6z0renp6XYm6U+Tk5MqT+hnFtsCQDuky2znbpXuqT/7z9VX30sOttoCALRfsuQ2vd2zNH+x+OBC3EgAADSXyhMA2uTHz/4qOZj47GdPvfFC7AgAAP0olx0tbD2etp6ValnrCQDQS1SeANAmO0cKyUWec7dKn106EzsO7fPluRvJVlsjngAAcSXjnsk5WXI7uzgTNxIAAE3hLk8AaJ8DR09fnvx17BS02+1z15PDrpP74yYBACCXHQ0DxyoLN5PbPUvzFysPRz9rFbYeb3s0AAA2TuUJANAmu0/uix0BAICQy47msqPFBxeS1jP5v3WKDy4Mb3k8lx1tezoAADbCYlsAgNZKttoCANBRapfcLlWplkvzF933CQDQLUx5AgC0g4s8AQA6TS47mnvkVN2bs4sz6drbSrWctJ4mPgEAOpzKEwCgHcx6AgB0hWXX3lbmy6Uffmy45gZQhSiwpv/jF9tjR+gR/+/Vv31xe2HZb42MLHM3M9A/+qXyHB8fX/rm1NRU+5MAAP1m14m9+k4AgO5S2Hq8dtxzqdr3k0I0KUELW4+3KSLQVd79v3fFjtAjXvw/b/8/v/1m2W/t2bOnzWGAjrJ85Tk1NTU5ORlCmJ6eTt4ZGxsLIUxMTCzbHXas8fHx9B9hqUwmE0IYGxvTfQLQNoN78nO3SnO3SvfKxZ0jhdhxaIddJ/cnlefVV9976o0XYscBAKAhybhncl56qefSKjR55/3vzoYQhjMjRj8BANqpvvJMys6lNWHyzvT09NjY2JrF59TU1MTExLpyVKvVdX1+TY1nmJ6ezmQyk5OT3dXmAtClHh0pzN0qhRD+fPntH4eg9ewHu0/u+9NrsUMAALAJq89uJoXoD+Y+q+XKfDkMHNN6AgC0R7b2i/Hx8YmJiVXGIkMI09PTExMTq49FJhOiTTE1NTU+Pp6p0UgxuYHOtesGWAHoUjtHCj9+9lfJ+c+X344bhrbZdWJvCOH2+c+vvvrel+duxI4DAEAzFbYeL2w9/rNHTuUHjtXe8VmavxgxFQBAX/m+8lx2B+zYQ3Xvr9l6NsWyFWwylLnKb1/ad46NjU1OTlZ/aHJysu6fa3p62oZbANognewc3JOPm4T2u33+8z+99rurr74XOwgAAM2Xy44m3WdafC7diAv0p2qoejXlFftfSaBz/X2x7dTUVG2zuOz22roqcWJiopFttEvr0gbVVrC1D0nenJiYWGkVbW3IVe7pHB8fT3689het8lgAgA176o0Xvjx34/a568mlnrfPf/7bn76VjH7uOrl/98l9sQMCANBMha3Hiw8uVKrlSrU8uzhjvS0AQKv9vfKsXUW7Uk04Pj4+OTlZWyiOj48v+8na9nRjc5NpBbs0TNq8LttNLv1wI7+rtvVUeQLQavfKxdgRiGD3yX27T+778tyNP732u+SdtP7802vhF398OWo6AACabHjL45X5ckjW27rUEwCgxf6+2La2pFzlFsyk9Uy/XP3Wz7CJEc/0tywNMz4+njx22d9e1902+Otqf8ua/1AAsEl3H1aejz7ccEv/2H1y3y/++HIy3wkAQA/LZUfT9baVhZtxwwAA9LxsCKF2qHFsbGz1Gce0cUy06PLLdMRz9dW1S7/bYHdbx1gnAFHcNe7Zr55644Vf/PFl3ScAQG8rbD2etJ6VatmlngAALZXd5M/XTlW2TYMN5bqKzA0PpALAeh04enpwTz6EMHer9NmlM7HjEFOy21bxCQDQqwpbjyeH5FLPNT8/uzjz/ndnG/kk0F2qXk16AaykvvJsfDJyJbVF4+aftqwGR0vXVXlOTEyMPbTRXADQqANHTycHrSchhNvnP//y3I3YKQAAaIn8wLHkUJq/uHqXWXxwoTR/MfmkqVDoOdG7wp55ASwvG364DLa5K143/LT0ts5l2810tHTpdzdcWI6Pj089tLEnAMC6/PjZXyWHuVulezbc9qufvP7z5PCn136n9QQA6EmNXOo5uzhTfHChUi2n79iFCwCwLtkQQrXGen++wTnO8R9q/LGTk5N1HeTU1FR60+fqD5mennZJJwAda+dIIW09/3z5ba1nf9p9cp/WEwCg561+qWcy3Jn2nd/3o1pPAICGDaz3B9LGMbF6pzg1NTU5OVn7+UQmkxkbG5uYmFjpx8fHx8fGxqYfqm0306ct27ZOTU1lMpnaD6/5uwAglp0jhcFyce5WKYRwt1zcOVKInYgIdp/cd/vc3uRSz9vnru8+uS92IgAAmq+w9fj7350NDy/1zGVHk/drhzuHMyPJ3Z+zizPJhtuk9UwvBAW61/qnjVieP0hgJfV3ea4pXSobVpizTCvJpGhc2nemH1u9hpyamkqfP10jjbHSz9YmrP1dmUwmmTG1uhaAznHg6OnBPfngUs/+9tQbL8SOAABAy9Ve6ll8cKH44ML7351N+878wLG02sxlR9MPJ63n6peAAgCwvspzfHy8tsJcvTtcqeys+8zqrefk5GRdsTo2NlatVlf5qfHx8aWtZ/rr6urPNRM2IrOCpjwcgP5x4Ojp2BEAAIBW+cGlntVy7XBnfuBYOveZfvhnj5xK1+GW5i9qPekiK/19qT18ALROo4ttl66oXalWrLN0qWxyTh+VtJ4rtacbKyaT1nPZnbqp5FtJMTk2Nta60c81n3zr1q0W/WoAusJ39/89hLD1R/9b7CDE9L8fHP73TyvJelsAAHpVYevx2k22oWaZ7ZqfL81frGRGhrc8XleOQjvZn7cJFrI2iz9JYHkNVZ51w51h5b2ydW8u+7Hkvxdrnzk9PT01NdXc/x+ftCuta1iXlV75ubH/zl52wW/6S5e9cxQAUn+dmwkhPPj2P2IHIaYfDf3jv39aiZ0CAICWG97yeFgIjTeXha3HP3zwP/+jei8ks6Hz5VLynFW7UmiRBv+qc6W/L+3nvylV0wG02hqV59KyM6x6j2bjH0s6zkbu5tyk2hZz9fpz9XnTBn9FLbttAWhccqMnAADQ23LZ0fWOaT6z9b/VzYaGh3d8GvqkMxkGBaD9Vqw8l26yDQ0sgF3Xf5lNTEzUDno2/oMbtmb9ueHWEwA25l65GDsCneXLczd2n9wXOwUAAJ0lGeicXZypLNwMIST1ZzL0WXl4P2j6MQCAPpRd9t3x8fHaPjKEMDY2Njk52dwucHx8vHbFQZsvr56ampqamqpWq3VrFpLWs51JAOhndx9WngeOno6bhLh2ndyfHP702u++PHcjbhgAADpTLjta2Hq8sPX4zx45Nfyw6axUy+lrdnEmbkIAgFjqK8+pqalMJrPscGevFoHJPGvtO+0ZOQUASO0+ue8nr/88Of/ptd/FDQMAQOcrbD0+XDPfmSjNX4wSBgAguh8stp2amqq7QXrNTba9YXx8fHJysp9vzwYginvl4tytUnCRJyGEEHaf3Hf73N7b5z8PIVx99b2n3nghdiIAADpa7Rrb9LLP4oMLK30GiKgaOwBAz/t+yrOu72zFJttWy9RY78/WzbD26kgrAB3lros8+aGn3nhh14m9IYTb5z+/+up7seMAANA10qHP2j23lWr5/e/O1r5svgUAetXfK8+lfWcPb7IFgE7w2aUz6YinizxJpcOdt89/7lJPAAAaN7zl8TU/U1m42YYkAADt9/fFtrWXWW54mW3tpZgTExPtb0zHxsbSazjHx8c3M6JqyS0AEMtPXv95cp3nn177XQg/331yX+xEAAB0gVx2NPfIqdp3ZhdnajvOZO6z+OCCbbcQg9W2AK01EEKYmppKm8JNXt6ZPic0thu29vMdtUTXhCsArXbg6OnLk78OIczdKt0rF3eOFGInolPsPrkvhO9bz90nX46dCACArpTLjuayo+mX7393NoRQqZZnF2dq3wcA6AHZ8MMRz81MN9bWhNPT02tWmLUfGBsb2/DvXfaB09PT66otaz/clDAA0Lg/X377s0tnYqegg5jsBACg6fIDx5JDaf5i3CTQh6pezXsBLCsb1j+auYrasrC2SV2q7vbQZi2SrQ3QeOtZO+faxDAAsLpnJ/6vwT355Dx3q6T17ENXX33vtz996+qr76XXdl599b3kzeTLXSf2xksHAEBPyWVHhzMjyfn9784WH1yImwcAoImydV9n1qmuU1w6Z7nsrGdd3zk2NtasRbJ1v26VDOnnx8fHWxQGANZ04OjpHz/7q+ScbLiNm4d2uvrqe7fPfx5CuH3+8z+99rvf/vSt3/70rdvnP0/eTOw6uT9eQAAAek1h6/G09Uzu9ZxdnIkbCQCgKQaaXu9NTk6mDeL09PT09HTdntjaecqw6dtDVw+QZgjLrautS9KKMACwpp0jhR+H8OfLb4cQ7rrUs2+kfeeydp3Yu+vkfuttAQBousLW48UHFyrVcgihUi1X5suVzEhh6/HYuQAANmWg6U8cHx9fWjqu9OFWVIxJgMnJybrfu0qM1oUBgEbsHCmEy2+HEOZulWJnoR1q+86fvP7z3Sf3XX31vfS7T73xQqRcAAD0hcLW47OLM5WFm2nx+f53Z/MDx3LZ0djRAAA2qH6xbVMkpePSqco6rasYk2W2awaoTTI5OanvBCCi9FJPN3r2vC/P3ajrO0MIT73xQvqKmg4AgL6Qy44Wth7PDxxL3ynNX4yYBwBgkwZaVzomvePk5GT44YRl0kS2oV9MfsWyGWqTTExMuLwTgOgOHD19efLXwaBnH7h97npySPtOAACIIpcdzT1yKt1zC7RONVRjRwDocc1fbFsrKT5b+iu6IgMANGJwT17f2Q/SEU99JwAAnWB4y+OV+XIIofjggks9AYAu1ZLFtgAALOvLczeSw64Te+MmAQCAhCs8AYAeoPIEAGifdKvtrpP74yYBAIDUcGYkhGC9LQDQvVSeAADtY6stAACdrPjgQuwIAAAbofIEAGgTW20BAOhM6RWelWp5dnEmbhgAgA0YiB0AAKBf2GoLAEDHyg8cK81fDCGU5i+GgWMu+ITmqoZq7Ag9w58ksDxTngDQKeZulWJHoLVstQUAoGPlsqPJjZ4haT0BALqKyhMAOsJnl84kh8E9+bhJaBFbbQEA6HCFrcfT1tOlngBAd1F5AkBHSEc8Dxw9HTcJLWKrLQAAnS9tPSvVstYTAOgiKk8A6CBGPPuBrbYAAHSywtbjyUHrCQB0EZUnAEA7JBd52moLAEDnyw8cSw6Vanl2cSZuGOgNVa/mvQCWpfIEAGi5q6++lxxstQUAoPPlsqNp61mav6j1BAA6n8oTAKB9bLUFAKAr5LKjyaWeIYTKws24YQAA1qTyBABoOVttAQDoOoWtx5PWs1Itx84CALCGgdgBAIAQQhjck5+7VZq7VYodhOaz1RYAAKDPVavuoARoLVOeAABtYqstAABdynWeAECHU3kCQHz3ykXznT3MVlsAALpUYevx5OA6TwCgw6k8ASCOy5O//uzSmRDCvXLxz5ffTt4c3JOPGorms9UWAIAeUKmWiw8uxE4BALAid3kCQDRzt0qfXTqTznf++Nlf7RwpxI1E69hqCwBAN8oPHCvNXwwPW8907hNYFzd5ArSaKU8AiCCZ7wwh6Dt7nq22AAB0tVx2ND9wLDlXquX3vzvrXk8AoAOpPAEgPn1nr0q32gIAQPfKZUd/9sip4cxI8mVp/qLWEwDoNCpPAIhpcE9e39kPnnrjhdgRAABgUwpbj9e2nq72BAA6isoTACJI9tkO7skfOHpa39nDbLUFAKCXFLYer11yq/UEADrHQOwAAAAAAEB3yGVHw8Cx0vzFEEKlWo4dB7pGNXaAnuFPEliJKU8AAAAAoFG57Gi64RYAoEOoPAGg3T67dCZ2BAAAgI1L5jsVnwBA57DYFgDa6rNLZ5KLPEMIB46ejhsGAAAAaAsLWQFaS+UJAG1yr1y8Wy6mfScAAAAAAE2h8gSAVqkd6KwzuCdvxBMAAOhGxQcXksPwlsfjJgEASKk8AaAl7q0w0Kns7EO3z3/+5bkbu0/uix0EAACaKZcdjR0Buoa1tgCtpvIEgJb48+W3k8Pgnnz65qMjhZ0jhUiJiGDXib23z38eQrh97rrKEwAAAABaROUJAM332aUzycFMZ5976o0Xrr763u3zn98+//nVV9976o0XYicCAIDNqlTLsSMAANTLxg4AANDL0pozWW8bNwwAADTLcGYkdgQAgO+pPAGg+Q4cPZ3ss132Ok/6zU9e/3ly+NNrv9N6AgAA9KGqV5NeACtReQIAtNbuk/tqW8+4YQDeG927AAAgAElEQVQAYGOKDy4kr9hBAACWofIEAGi53Sf3xY4AAAAbN7s4U6mWk1fyTmHr8biRAABqqTwBANpn14m9sSMAAMC6VRZuxo4AALCagdgBAAAAAIBO9P53Z4czI8NbHk/fyQ8cU3/CBlRdQ9kk/iSBlag8AQAAAIB6ybWdlWq5Mv/3ZbbDmZFcdjSXHY2aCwBgGRbbAgAAAAD10ms7AQA6n8oTAAAAAPiBZMQzhDCcGUnfLGw9HikOAMAaLLYFgOb77NKZ2BEAAAA2Lh3xTGrOtAEFNsb9kwCtpvIEgKa5Vy7eLRfnbpViBwEAANiI2cWZysLN9Mt0xNN8JwDQ4VSeANA0dX3n4J78oyOFiHkAAAAaUXxwYdmbOzWdAEC3UHkCQPMN7skfOHo6dgo60e3zn8eOAAAAP7Bs3zmcGRne8niUPAAAG6DyBICmSUY89Z0sa9eJvUnfefXV955644XYcQAA4O/SvjNZY2uyEwDoRtnYAQAA+sJTb7yw68TeEMLt859fffW92HEAACCEEIoPLiSH4cxIYetxfSe0SNWrSS+Alag8AaA5Prt0Jjm4v5OV7Dq5PzncPv/5l+duxA0DAAChZsRT2QkAdDWVJwBAm+w+uS8Z9Awh3D53PW4YAACo9f53Z2cXZ2KnAADYIJUnADRHen/n3XIxbhI6mfW2AAB0lPzAseQKzxBCaf5iuucWaLboG2F75gWwPJUnADTZ3K1S7Ah0tKfeeCE53D7/edwkAAAQanbbhhCGtzweMQkAwIapPAGgaQb35GNHoDuk620BACCi2cWZ0vzF5DycGfnZI6dy2dG4kQAANkblCQDN8dmlM8l8p+KTxtltCwBALHV9Z2Hr8bh5oLdF3wbbSy+AZQ3EDgAA3e1euXi3XKxdZvvoSCFiHrrCU2+88NufvhU7BQAA/av44EK6z1bfCQD0AJVnp/jo6ydiR+hTI9u/jh0B2u2vf/kmdoSe8s3cf9b2nfl9E8ML+4I/5A7z8V/+KXaE5f3HNz/q2Gx0u/9SvBM7ArTb4vx87AjQbtXFxdgR6ErFhQ/uPOw789mjw9nRLvr3UuY//zN2BACgE1lsCwCbMjz4ZHIY2nEwv28i/RJWt+3pQyGE+1euxQ4CAEDfqes744YBAGgKlScANI2+EwAA6HxDmZHkoO8EAHqGyhMANqUy90nsCHSlbc/kk8NXb74TNwkAAACtVvVq0gtgJSpPANiU2XsWk7IR248cjh0BAIA+lXs45Vlc+CBuEgCAZlF5AkBz3Pn60+L1d2OnAAAAWIN9tgBA71F5AsCmFPa/OLTjYHK+8/WnccPQXbY9fSiEcP+KQWEAANqnsjjz+/nfJOc71XLcMABNND09ndmo8fHx2PGBzRqIHQAAul5h/4uVuU9KNyZDCMXr7xb2vxg7EQAAQL3K4sxstVxbc+azRyPmgb5SrbqGskn8QQIrMOUJAE0wPPhk7Ah0n23P5JPDV2++EzcJAAA9r7jwQWnxUtp3DmVGnh/4pQ23AEDPMOUJABDH9iOH78bOAABAP0g32YYQhjIjucyIshPoPf/0T//07LPPbuxnJyYmmhsGaD+VJwA0x9COg3e+/tR1nmyA6zwBAGipfPZoafFSCGEoM1LY8lzsOAAt8eyzz05NTcVOAURjsS0ANFll7pPYEega254+FDsCAAC9bzg7OpQZCSHcqZYrizOx40A/qno17wWwLJUnADRHbuffu6vSjUmtJ+v1zUcfx44AAEAvy2VGksPsw+s8AQB6icoTAJpjePDJoR0Hk3PpxmTcMHSLbc/kk8P9D0txkwAA0CcMegIAPUnlCQBNU9j/Ytp6Fq+/GzcMXWH7kcOxIwAA0PsqizPJXZ4hhKHMyHB2NG4eAICmU3kCQDOlreedrz/VetKI5DrP+1euxQ4CAEBvqus7C1uei5sHAKAVVJ4A0GRaTwAAoEPoO6FDVL2a9AJYicoTAJqvsP/F2BHoPt989HHsCAAA9JTavjOEoO8EAHqYyhMAWiId9KzMfRI7Cx3tsVdeSg73PyzFTQIAQG8rLnxQWZyJnQIAoCVUngDQErmdh5LD7D13NAIAABEMZ0efH/jlUGYk+fJOtVxavKT1BAB6ksoTAFpiePDJ5HDn60/jJqHzbXv6UAjh/hXtOAAAzVfY8lw+ezQtPmer5bh5AABaYSB2gN43Nja2+gcuX778xRdftCcMAO00tOOgvhMAAOgEdzSdNMOaf9U5PT3dniRdpxo7AEDPU3m23NTU1OofGB8f9z8FAHqSvpP1+urNd9KrPQEAoCmKCx+kfedQZiT3cNwTNmDNv+rMZDLtSQIAdSy2BYDWGtpxMHYEOp2aEwCAFqnrOwtbnhvOjsaNBADQCqY8AaAlKnOfxI4AAAD0r8rizGy1nPad+exRZScA0MNUngDQfMXr76ZbbQv7X4wbhq6w7elD969cu3/lWuwgAAD0gsriTGnxUvqlvhMA6HkqTwBossrcJ27xBAAAYlm6zDZuHiCEUI0doGf4kwRW4i5PAGiy4cEnk8PQjoPPH3k9bhi6xbZn8snhqzffiZsEAIDuVVmc0XcCAP3JlCcAQHzbjxy+GzsDAABdzTJbAKCfqTwBAAAAoLsZ7gQA+pzKEwCgI2x7+tD9K9fuX7kWOwgAAF2jsjgzWy2HEPSd0OGq7qBsGn+SwPJUngAAneWbjz7efuRw7BQAAHSopTVnyjJbAKBvZWMHAAAghBC2PZNPDvc/LMVNAgBAJystXrpTLdf1nUOZEX0nANDPTHkCQJP9/qPXksOdrz+Nm4Tusv3I4buxMwAA0OGKCx+k56HMSAjBGlsAgKDyBIDmKl5/Nz3n901ETEL3cp0nAADLKi58kAx3urATuo77JwFazWJbAGiaytwn6WTn0I6Dw4NPxs1D19n29KHYEQAA6Fz6TgCAlag8AQA6wjcffWy+EwCAlaQrbXOZkbhJAAA6kMoTAJpmePDJ54+8npzvfP1pZe6TuHnoLvc/LCWHR18+FTcJAAAdpfb+zhDCcHY0VhIAgI6l8gSAJhvacTA5lG5Mxk1CF/nqzXeSEc9tTx/afuRw7DgAAHSKyuLMnWr59/O/SbbaAl2q6tW8F8CyVJ4A0Ey113nm903EDUO3SPvOEMK2Z/JxwwAA0FFmNZ0AAA0YiB0AAHrT0I6Dw4NPxk5Bp/vmo4/vf1hK+85HXz5lxBMAgFq5zIj5TgCANak8AaCZhgefHNpx8M7XnyZ3eWo9WcU3H318962zyXnb04e2PZPXdwIAUGc4O5oPobR4KflyKDMSNw8AQGdSeQJAk+V2Hkp2287eu6byZCV1fedjr7wUNw8AAB2rtvW8Uy1XFmeGs6OxQwHrU3UFJUCLucsTAJosGfQMISSDnrHj0Im+evMdfScAAI2rvdEznfgEACBlyhMAWsigJ0t99eY76eWd+k4AAFZXWZyp7TiHMiM5u20BAJZQeQJA8yWLbUMIuZ2H4iah09T2nY++fMrlnQAArKSyODNbLd+pme/MZ49aaQsAsCyVJwA0WfH6u8lhaMdBI56kvvno4/sflvSdAAA0qG64s7DluYhhgE1ylSdAq6k8AaCZitffTUY8h3YcLOx/MXYcOsU3H31ce3nntmfy+k4AAFY3lBlJRjwNdwIArEnlCQDNlK60vfP1p8Xr7+Z2HjLoSV3f6fJOAADWRd8JALCmbOwAANBT8vsmhnYcTM53vv60dGOyMvdJ3EhEd//DUnLQdwIAsAHFhQ9iRwAA6HQqTwBopuHBJwv7X6wtPmfvXYsbic6h7wQAoHEu7wQAaJzKEwCaLyk+Y6cAAAC6WGVxJnYEoGmqXs17ASxL5QkArZXe7gkAANC42Wo5ORj3BABY00DsAADQg4rX39V0krp/5VoIYdvTh2IHAQCgm9yplkMIQ5mR2EEAALqAKU8AaLLK3Ce1fWd6qScAAECDigsfJIecyhMAoAGmPAGgyWbvXUsOQzsOutETAADYgDsPt9oOZ0fjJgEA6AoqTwBojuL1d5NDMuKp7wQAADamsjiTHGy1BQBokMoTAJqmdp9tbqeLGwEAgI2YfTjiWdjyXNwkQLNUYwcA6Hnu8gSA5hvacXB48MnYKQAAgK6UbLU14gkA0DhTngDQZM8feT12BDrIV2++kxy2PZOPmwQAgK5QXPggOeRUngAADTPlCQDNkW6yTS/1hFrbjxyOHQEAgC5w5+FW2+HsaNwkAABdROUJAM1hky0AANBE6bgn0AOqXk16AaxE5QkAAAAAnSKfPZoc7lTLlcWZuGEAALqFyhMAmmZox8EQwp2vP40dBAAA6FbD2dG09SwtXtJ6AgA0QuUJAAAAAB1k9uF1nsGNngAAjRmIHQAAeof5TgAAYJOKCx/ceVh5puOeQLerhkzsCD3CnySwElOeANAclblPkkOy3hYS969cCyFse/pQ7CAAAHSH2r7TiCcAQINUngDQHLP3riWHwv4X4yYBAAC63VBmRN8JANA4lScANEey1daIJwAAsGGVxZnYEQAAupLKEwCaoHj93dgR6FDJStv7V65989HHsbMAANDpZh9utc1lRuImAZqr6tWkF8BKVJ4A0Ey22lJn2zP55HD/w1LcJAAAdBFbbQEA1kXlCQDNZNyTOtuPHE4HPWNnAQCg0915OOVpwy0AwLqoPAGgCdLhzjtff1qZ+yRuGDpNOuj51ZvvxE0CAECHy2ePJofS4iWtJwBA41SeANAc+X0TyaF0YzJuEjrN9iOHk4NBTwAAVjecHR16eItneq8nAABrUnkCQBNU5j6Zvfd9m2W9LXWS3bbBoCcAAGspbHkuaT3vVMvFhQ9ixwGao+rVpBfASlSeANAEs/eu3fn60+Q8tONg3DB0oMdeeSk53L9y7ZuPPo4bBgCADlfY8lxyuFMtW28LANAIlScANFN+30Rh/4vp1Z6QSgc9739YipsEAIDOV3upZ9wkAABdQeUJAE2QjngODz4ZNwkd67FXXkpaz/tXrllvCwBA4wx6Qi+IvhC2Z14AK1B5AkDTWGnL6rSeAAA0orI4kw535rNHh7OjcfMAAHQ+lScAQPtseyafHFzqCQDASmar5eQwlBnRdwIANELlCQCbVZn7JHYEusb2I4cffflUcr771lmtJwAAidrttbnMSMQkQCtEXwfbSy+AZQ3EDgAAXW/23rXkUNj/YtwkdIXtRw6Hl0/dfetsCOHuW2fDy6e2HzkcOxQAAJHNVsul+UuxUwAAdCtTngAA7bb9yOHkUs8Qwv0PS3HDAADQse483HALAMDqVJ4AABE89spLSet5/8q1r958J3YcAAA6xVBmJHklXxYXPoibBwCgK1hsCwCbdefrT0MIQzsOxg5Cl3nslZe+evOd+1euJa3nY6+8FDsRAADxFbY8FzsCAED3UXkCwMYVr7+b9J2wMdueyd+/ci2EcP/KtW8++tilngAAAD2pGjsAQM+z2BYANqiu78ztPBQxDF1q+5HDj758KjnffevsNx99HDcPAAAAAHQjlScAbNbQjoPPH3l9ePDJ2EHoStuPHE4u9Qwh3P+wFDcMAAAAAHQjlScAbFBh/4uxI9AjHnvlpaT1TNbbxo4DAEAEd6rlEMJQZiR2EACArqTyBICNG9pxMITgOk82b9sz+eRw962zcZMAAADQdNWQ8WrKK/a/kkDnUnkCQBNU5j6JHYHuVrve9qs334kbBgAAAAC6i8oTAJpg9t612BHoetbbAgAAAMDGDMQOAADdpzL3yey9a/bZ0nTbnsnfv3IthHD3rbPbz/xL7DgAALRcceGD5BZPAAA2w5QnAKxbXd85tONgYf+LEfPQM6y3BQDoN/pO6BNVr+a9AJZlyhMA1i3pO4d2HAwhKDtprsdeeemrN9+5f+Vast52+5HDsRMBANByQ5mREEJhy3OxgwAAdCtTngCwPpW5T9KzvpNW2PZMPjncfets3CQAALSNvhMAYDNMeQLA+szeu5YccjsPxU1Cr9p+5PD9pw8ll3p+9eY7j73yUuxEAAA0R2VxZrZmk62ttgAAzWLKEwA2aHjwydgR6FmPvfJScqlnst42dhwAAJpjtlq+U/OKHQdon+j3X/bMC2AlpjwBYH1qL/KE1tn2TD4Z9Lz71tntZ/4ldhwAAJqgsOW538//Jv0yucIzhJB7eACATRofH08OExMT6bkrHu75bJLKEwDWoXj93dgR6BfW2wIA9KShzEgy3/n8wC9jZwGghaampiYnJzf84+vqzJLfNT09Xftm8uXY2FjygQ0naenDPZ8mUnkCQKOK199NRjxDCIX9L8YNQz947JWXbp7+5/Bwve32I4djJwIAoGkqizPD2dHYKYA2qYZM7Ai028TExCaf0GDlOT4+Xte31Uq+lclkJicnNzB32NKHez7N5S5PAGhIbd9pqy1t8+jLp5LD3bfOxk0CAEBTFLY8lxxm3eUJwOZMTU1lMplVKrda61212tKHez6toPIEgHVLu09ote1HDm97+lBy/urNd+KGAQCgKZIrPO+oPAHYhKmpqfUOkk5PTzdYvLX04Z5Pi6g8AWBtdSOezx95PW4e+kp6i2ey3jZuGAAANqy48MHv53/z+/nfpGVnZXEmbiQAWqQN1zcue1Ho2NjY5ORktVqtVqtjY2PJXZK1pqenG8nW0od7Pi3iLk8AWFth/4u1rSe02aMvn0oW297/sORGTwCArlNZnJmtlpeOdc5Wy8PBdZ7QF6qxA/SMbvmTrKvElrZfa1p9iHDpFZJjY2N1dVry5dJ5xImJiWp1tT/Ilj7c89d8Phum8gSAhuR2Hkoqzztff1q8/m5h/4uxE9FHth85fP/pQ/evXLt/5VrsLAAArE9lcaa0eCk5JyttQ82NngD0g6bP9q1ZuaXGx8cnJyfrirfx8fFVIrX04Z6/5vPZMIttAQC6wLZn8snBjZ4AAN2isjhTXPigtu8sbHkuecUNBkBXW3ol5OoV2vj4eN2YaV1p17aHe/6az2czVJ4A0JDhwSfTc27noYhJAACATpY0nb+f/01p8VK6zDbpO+MGA6Bt2llrNbI1d2kt1+CgYUsf7vk0l8oTABqV3/f3NRSz9ywXpd3uf1hKDo+98lLcJAAArKSyOFPXdCby2aP6ToC+tYGLPFdX16durLysu220PQ/3/DWfz2aoPAGgUcODTw7tOBgeXucZOw79JbnFc9vTJowBADrX7A+bzqHMSD579PmBXw5nR2NFAjpE1atJL+oWqzbep9bdKLnsHGpLH+75az6fTRqIHQAAukZl7pM7X3+anAv7X4wbhr6S3t+Z3ugJAEAHSoY77bAF6HOdubZ06SWU3fJwz6dBpjwBADpdMuIZQth+5HDcJAAArKSyOBM7AgAdoaVrS5s4ILi0h2vpwz1/zeezSSpPAGhUutg2hGCxLW3zzUcfJwdbbQEAOtlwdnQoMxIeznoC/EA149W0V7epW2raXOuaKF3vraItfbjn03QW2wLAOhT2v1i8/u6drz9NN9xCq93/sJQcHnvlpbhJAABYXS4zkvSdxYUP7LYFIJHO801NTS07ANqZi3Ch66g8AWB9lJ0AAMCyZs13ArCcpOxcaS1qJpMZGxubmJhYc9lpS8vRVjevnk+rqTxbbs3/GNy6das9SQBoonTDLQAAQCJdaWvEk17lb/w3rBo7AO1UV22Oj4+veQfk9PT09PT0msVnc28JrUvV0od7/prPZ/NUni3X0j3dALRTZe6T0o2//4+b3E63KgIAAN8rLnyQHJIbPaEn+atO2IDGy62k+JycnFxz3DOxgbs5M5lGL0Nt6cM9n1ZQeQLAGipznwwPPplc4Zm+OTz4ZMRIAABAJ6gszgxnR5OzEU+AuC5fvtxgU9iIls40j42NrdKDTkxMNN56AimVJwCsYfbetXS4M/H8kddjhaFPfPPRx/c/LCXn+1euxQ0DAMBKSouXwuKl2rFOI54AsXzxxRedsCx0papybGxsaY260h2fExMT1ap1yLA+Kk8AWLfi9XcL+1+MnYKeknacCk4AgG6RbrJN5zuDEU8Alli27EyMj4+Pj49PTU0t3RqdvN/6dNA7VJ4AsKLi9XdDCLX7bKFZvnrzneSwZse57elDIYRtz+RbngkAgM0x4gmsxLxes3Tdn+QqfWdqfHx8cnKyrvXshIlV6C4qTwBYUWH/i7//6LW6N4d2HDTiyWZ89eY7q9ScScEZQtj2TH77kcPtCgUAwMYlTeedatmIJ0BEjZSLbVDXXDYYadnW06AnrIvKEwDWIb9vYnjwydgp6GJL+86043zslZdiJAIAYIOSfbZDmRFNJwCpZFftxn5wbGzMcCdsmMoTAH6geP3d3M5Daa85tONguth2aMdBfSfpQtpE47OY33z08d23zn7/g08f0nECAHSvyuJM7AgA9Dj1J6yLyhMAvle8/u6drz+98/Wn6TRnbuehtPK0z7bPLbuQ9v6Va3dDCCFse/rQ6vVn2neu+UkAADrfbLWcHHLu7wQaUA2Z2BHoAlNTU5nMOv6tst5OdF3jpy19uOfTCtnYAQCgg6TtZunGZGXukxBC7bhntFh0gJun/3mVCzhD0n2+dfabjz5e9rvpbGgy3KnvBADoRpXFmeLCB8WFD34//5s7DyvP4exo3FQA9LC6nqzuss9NGhsba9vDPX/N57N5pjwB4O+K19+t/bJ0YzK/b2L23motF32idplt3ULa9FtJIXr3rbP3l9tYm9alltkCAHSp4sIHac2ZGjLiCUAbtXRSsNVjiJ5Pq6k8ASCEECpzn9Te2ZmcSzcm03dste1b33z0cVpYPn7mX+q+m1aY6drb+1euffXmO8vWotuePtTyuAAAtMDSvjMpO221BQDoEBbbAkAIIaTTnEm7WbfGVt/Zz+5/WEoOj758apWPPfbKS+kH7l+5dvP0P9cNgAYjngAA3am278xnjz4/8MvnB35Z2PJcYctzttoCDap6NenVb9Zcpto513Ou9+GeT9OpPAHg+xHPdJozaT2HdhzM75t4/sjrsQMSTTriue3pQ2tewLn9yOFHXz6VjnLWFZ9GPAEAulRt36njpA/9r2/P/69vz8dOAX1qaanWxDsgp6am2vlwz1/z+WySyhMAvh/xzO38vpQq7H+xsP/F4cEnI4WiI6QjntueyTfy+e1HDj/2yku17aYRTwCA3jCUGdF30odm5/9SWbhZWbgZOwh0h8wPdVSt1dIwrf4n/f/Zu5vfuO87T/DfIunoIdl4RKWratHmw8Ga2ZE9pGeAJBvLbvuwRh92LwOIdCONuM8DdN9y2DkMgmAPc+lb5h+IjQSWqD3sHgbICgNYsaWB3Visi21l0UMd+ODGVDEmvfY4lhSTVXv4Sl//XA9kkaqqXz28Xig0vlWs+tVHPwpJQ+98Ph/XpxsiTwCA9k7U4pn1J3/zlwtv/vvz338+ZZ9aPAEARlStvp13CZCn8sxi3iXAKGnqArx+/fqJPt7U09m2p7ApPOt+XGpTMYO/uOsfe32ekMgTAL5e1ZnaPSGcvMWzyZ/8zV/GBZ/nv/+8Fk8AgBFVfTzVtlyYy7cSyEtpeiHvEsZB7iswx+kxQk66vrHL9zcFZl1ulGy6eKctoX29uOsfe32ehMgTAEIIoXjhcghh99Pf1fbv5l0LQ+H3v/jV6Vo8m8RRt72rCwCAfKzX7+j4ZDK9cO7VvEsYDwWPHj2GWuv80i4jsbbv7DQNtSkw6yYobe0f7VRYXy/u+sdenych8gSAEDR60sIOTgAAQgjL01eKj/s71+t38i0G8qLRE7rX1AV448aNbvY4rq2tNYVnR8w+bQ3Mjo7QWi9+hL5e3PXpK5EnADySGj3zLoT8/f4Xv4oHOzgBAMimnpXD209yqcrh7fjoRV0wOOWnFvMuAUZG68zS1dXVo1OxlZWVpk9dvXr16KC0aTfkjRs3On3F2tpa6yLJHC/u+t1E4JzOTN4FAED+avt3t/7rnc/+8HF4HHwy4bR4AgCQtTx9pXJ4e7exs9vYqRzeXp6+coqLxCvE882Dt0MIxcJcuTBXmppvfXOtvp3WiEa733waPx5rO0UxcCLlmcVKuJV3FaNttDZQ8iRWVlauXr3a1Nh348aNQqEQGzdj4pXCsLYtgMfuemz9lhi8NcWrKysrrdfP9+Kuf+z1ObVCo+E/bPulUDjBYPH/5bd/3b9KOMLctz/NuwQYtHv/5p/mXcLQufnBz9J56dnV0uxzORZDP/yXv/7Wid6/9ca/DSGc//7zIk9G1z//3/w/OUyc+s4/5l0CDFrj4DDvEiZLyiyLhbkjgsZstNmlYmHupB9p+niYmOxz6uyZvEvor+rBZvWrzRBC+anF8sxiztVk/OaLX3b/5on9N+cj/kH477ba/I8bOIX/9d/8/j/9x/ttfzRU/XNt464uXb9+vctdj6f4liG5uOvTD7o8AeAb5J2EEM5///kv/+6j1OsJAAAhhOXpK7E7s208GcfVni657OZTMdds+8744s2Dt9MA3hBCp/5RhtmH99+pHW7Fc+1wqxJuxSWawxZ/AsdaW1s7RSR29erVY6fgZsWWwe6/5USRW18v7vr0gy7PPtLlORJ0eTKBdHm2ynZ5Fi9cXr70eo7F0A8n7fL8/S9+FfPOhTf/fX8qgr7T5ckE0uXJBNLlOXhpIG1qqeyUdKb08bPGJw/D/aYXW99/JpxLb2u9SHgcYZ6ihbT1UmGUW0LHtcszG3Z2km/8qcuzG0f8g/AHWwuDrGSM/dt/8/v/9B+/bPujoeryjOL02i5TsVPX32W2errIra8Xd316S5cnAIQQwtKzq+v3rocQdj/9Xd61MET+8MHff/sH/yLvKgAAGDpx12bbpLOpvTKmpOG46bWteWf4ZjK629gJ9TunLrjpq7NVhVFOQMdA9WCz8qD9mswz4fzD8HW0EzPR2uFWdXpT3yeMhJWVlZWVlaODz+yCz9NZW1s74iue8HbpLLEAACAASURBVPp9vbjr01u6PPtIl+dI0OXJBNLl2aq2f7c0+1xl41rMOzV6jp+TdnkG6zwZfbo8mUC6PJlAujwHLyWFTZp6NM+Ec/HwdOF7Dxtffhb2BlHcE2vNa4fQOHV5xrWdxzZ3dlKaXnjh3Ks9regoujy7octzAEary7Ot1O3Xp2pjAten6/f14q7PExJ59pHIcySIPJlAIs8jpAm3r/3g5/lWQm+dNPL8wwd//8l/+HUQeTLKRJ5MIJEnE0jkOUinnijbvSO2dZ7ig98NFwshnCmcP8U1hzn7HJvIs2mSbWl6ofzUYqd2z2QmfGs6zGRbP5fPvpLaPT+8/0481A634hTcXmWiR7SitjWx/+Ys8hyAMYg8gT4x2BYAvla8cDk2esa+z7zLITdfvr8eD+d/uJRvJQAADIP+5Z3dzJWN60Kzdhs78YNNwWS2zs/DXgihGM6XM5tHQ3d/lt3Gzm5jp9jYMfC2H5rCzji9tna41U2750H440H4Y/aVyoNbldAmjIxX+80Xv+xJM2j1q80nvAIA9JvIEwCg2Zd/91EI4fz3n7fIEwBgwtXq2+sn36BZLMwtT1+J+z6bfpTSyu7TxJO+M0akMdeM4WWo3yk+Dj5jYd38oXYbOzcP3l6aenE42z1HUduwM9uymcQezSeZeZs+Wzvc+vD+O0+Yep66EpIJ7XvtA3cS6ETkCQCP1Pbvpl2eWjwn2e9/8at40OIJADDhsg2RxcJc262ccQZsCCGlm6nzsjQ1Xwo5hIUx2oyBa6o/5pcx+CxNzS+F0Db1bFpKGkJYr9/R7nmsTls5Y3KZtOadTW+LbyhNLzxo/OGz+ienrqepktrhVvVgM82/PanqweapKwGAgRF5AsAj1b2P4qF88fl8KyFfscUzhKDFEwBgYjXlhTHX3Gz8Q/Y9TQsvc0k3jxAD107BZ7kwtzT1Ymvq+TDcfzpcbEp2tXseIa7P7NQE2fb1Ts2d6c1NeWcMRMtPLWZ3dp607bLy4Fb5O4sn+kgU09xTfBAABkzkCQCPaPEkZFs8vy/5BgCYUE3bLpemXqw2drLpYFPYOcxSp2n2D/VoVWeH1POzsPfdcPHzln5W7Z5Zbds6m3o6Q0vkeWzYGUJ4eup7Ke/stIYzvtiptfSImk/U6HmKYJWjNAp5VwAw5kSeABBCCJWNa/GgxXPCpRbPP/mbv8y3EgAABi+7BTOEEJdujmjY2aR12u1upvuzyedhL/7Zm94Q2z3D4zsTjtwzml1lOuQ3LXZqHu3o8K80vZDtwszKBpPZsDO7bjM5E85n+zuPXsBZnlkszyx2H3xWv+oq8mzbt9q2WgAYKiJPAAjhcYsnE06LJwDAZGpKOqNiYS77yuiGnVltp9229Vnjk6cL32vb7hky9yomoOFxCBpvUev1dxs7oX4nhDBU03FP2iXZVgw7q19tVr/arDy41eX7yzOLKWddeOryg/ofQgi1w62mTLSbAmLw2fr66Xo0qwdtWldj8vqbL3550qsBwCCJPAHgG9bvXX/tBz/PuwryocUTAGDS1OrbrZNdo6a8c5wGuqZpt+8f3AwhtA01H4b7R2eiTVLnaOhwP6MhmY57ijiwNYAsP7UYQug+6QyZrs1ssrj1Vfv/AW68/qm9cO7Vpj/msX/k6sHXf5Yj+lY5nUbeBQCMPZEnAIQQwms/+Hll41rs9axsXFu+9HreFTFoWjwBACZQ69Da1hdzz+f654czr2Wfxm7XqDXvPBPOnQ3nzxTOnygKbbXb2Kkc3s6lZbbTyNbQYYTs0cloN7s8o9aLV7/aPLrU0vTCk8eN5acWmyr8zRe/XD77Sqcrp6o67RAFgGEm8gSAR5YvvX7zg58FQ24n0h8++HstngAAkyYlfCnXzDZ9jnfY2VbbP2/l8HbMOB+G+w/D/WI4f+x1psJUPdSPeENcI7oUQm9Tz5MOqj16AWdT7+YRlz1FQ+SxRT5hi+eji8wsVqebb0jlwa1wNrRWm/JdeScAI0rkCQBfK164rNFzMn35/no8fO+vf5xvJQAADEZK8trmnUO1cjJfy9NXsrs5u2nxPDrvTNbrd0L9Tk+WpJ5oUG3bhDJt1gwd2kDbXv9001+z39X6Rb2NG1sbPUO71DM7aFfeCcCIEnkCwNc0ek6gP3zw95/8h1/H8/nvP//tH/yLfOsBAGAAavXtFN217e+Ud2bF3Z+d9p4WC3MnGnV7Jpw7DIcH4Y/xaez4PF322XZQbQjhTDj/MHzZ+uI/mf6T8lOL1a824+PYlDT1O354/52YRz7hestOBfevsbI8s1gJbVaNVh7c2py6+z+e/5/DN1tal8++0o8yAGAARJ4A8A2p0bO2f7c0+1ze5dBfv//Fr9I82/Pff/78D5fyrQcAgAFo6uYMmY7PMJHzbLtUmpp/bWo+e6+i3cZOsTCXzsde52G43/b17rPPTgNsS9MLDxp/+Kz+SWveGUJ4GL580PhD07jatrIrOZv6R2uHW5Vw66TtmG2TzqenvvdZ/ZN02S4vdQql6YW21/+s/slvvvhl0zuffIEonTTyLgBg7Ik8AeAbUqNnde8jkecYyzZ3hhDOf/95KzwBACZBNrFbmnqx2thZP/i6c1Heeaw457ap3fNEXZ7H6pR9Ppqv+0Xzd50J5/+Hs9/vpmszRYxRNtrMtm+2LvJsEr/oN1/88ojs84jFoul7PwuftP6051449+oRY3uzVRlpC8BIE3kCQLPY6Ln76e80eo6r1uZO82wBAMZea3tiU24n7+xSp3bPk/puuHi2cL7pImfCudQGms0+Q+dg9WH4sm1CmZLFtiHf8tlX2nY0nmgtaGv2eUTSmcbttv1p9WCzfx2WrVlmrDOeYz3yTgBGncgTAJqVLz4fZ9tq9Bx7mjsBACbBseGcsPMUYrtnNaaSHaSBt03NmjFs/jzsnQ3nY69teJxopryzKfvsvrDWzsu2KWblwa3ydxazrxzb3HmElH0e8Z6243a//vav+hh5tirPPNFSUgAYQiJPAGhWmn0uNXrmXQt9EVs85Z0AABOoWJjL5mfCzidRmpovhUftnindDCEcfUtLU/NLj1tsdx8npsXCXMw+02+nNfv8brj4edg76srTC9n5tElsvmyNM2N3ZgxHj23u7LQR80RiHNvXzZ0MrUYo5F3CmLAVFehE5AkAbaRGz8rGteVLr+ddDgAA0AOvzfxFCOHmwdtB2Nk7p7iNMfXMBpwx+ywW5l6b+YvK4e2Q6exM2eexeecRo1nLM4vhbGgdOVs73Dq6NTPKTsFNezGbLvX01Pce1L9sbeVsCmKzazWbKjm2DADgCCJPAGgjzbPV6AkAANBzsUM0fHPs8G5j5+bB2zGNPnZqbvhm02TtcOvD++8cnXqWZxZPtKozXr+pczR9RdOlPqt/0vrZpnqqB+3XfB6d1wIA3RB5AkCzysa1bNKp0RMAAKBPYpNoa/DZ5cdfOPdqih5jy2a2I/Po97c6E87HNs2jL5Iu1doh2jbpTNruCu3muwCAY4k8AaBZmmqbnuZYDAAA0CtxYipDqDX47MbXGedTi2lobeXBrer05hFNk51We54J59O58uBW+TuLxxbQdsFnp69uHWmruXOi2EAJ0G8iTwBoVpp9bunZ1fV710MIxQuX05BbAABgpMVtkcXC3O5xE1PJRQo+j3jD1NkzIYTqwWY24yxNLzxo/CG9LQ65bRpIm1WeWSx/ZzFmkCmzjP2dT099L46oPXpMbtTa6Fk73KoebLZ+b+tIW82dANBbIk8AaKO691E8aPEcP7//xa/yLgEAgNzE1DOea/Xt0tR8vvXQKgafR2tazNnaalk73KodboWz4eght/GQYss4lvaz8EnoYjnoo2rPvtLUMFp5cKv1e5veU5pekHcCQG9N5V0AAAyjONhWi+f4+cMHf//l3z3Ks//kb/4y32IAAMhF+XHkWdXoOeJeOPfq8tlXYk7ZVuXBrdZxsk2qB5vxEK/Tac1nJ+WZxdYC4vemK7fWYJ4tAPScLk8AYIJ8+f56PHzvr3+cbyUAAOSl2tgx23ZsxHbPtrs5o6OH3GY/eNKw8+sanlqMnz0TzsfpuOFxm2kl3Gq775MJZJcnQL/p8gQAJkVq8Tz//ee//YN/kXc5AADkr1bfzrsEeqA8s/jn3/mrTu2etcOttu2ecSFoT749fvXD8OWZcL7121s/cmzvKeOo4NG7B0AbIk8AYFKkFs/zP1zKtxIAAHK029gx23YsxTm3TS+mDDK2e8bzh/ff+fD+O5UHt47uv+y+OzMNqo1dnq3BZ5PyU4tdXhkA6JLBtgDARNDiCQBAUpqaD/U7IQSzbcdMeWax/J3FD++/k9LKh+HL84X/7svGfwsh1A63fvPFL7u/WmuAevSb04zcFHymObdN72w7ZRcAeBIiTwBoo3jh8u6nv9v99Hd5F0LPfPIffh0PWjwBACZWXOFZLMzV6tvxnHdF9MUL516tHmxu/vHuZ/VPQghfNv7b01PfCyHEp1lPT32v9cXopMFkSlvD4/bQlHdms09558RqWObZI24k0InIEwAYf7//xa/iQYsnAMCEW5p6sTQ1H0II9UctnpXD28vTV3Iui14rzyyWZ75u9+yUa/Yq70zihNu4JTTbaRpCKE0vpPm3AEDP2eUJAN9Q27+bdwn0XhxpG0L4k7/5y3wrAQBg8FKimW3rfBR8Mtbids/S9ELrj0rTC21fD71oxCzPLL5w7tU//85fpW+RdwJAv+nyBICvVTauZYfZFi9czrEYeqWycS0ezn//+XwrAQAgL0tTL67X74QQqo2dUhB2TpDY7lk92Kw8uBXTx/JTi20Tzdia2emnpyPmJKOQdwEAY07kCQAdlS9KyEZebf9uirG1eAIATKzS1HyxsbPb2Nlt7NTq26Wp+Vp9O++iGJy4aPP499iySd/YQAnQbyJPACZR6uZcena1NPtc2+bO8sXnS7PP5VYivabFEwBgwpULc3GwbWz0rD4ecmuRJwDAGBB5AjCJUsBZ3fuouvdRNu+Mli+9PvCi6IuUW6d1ngAATKamRs+8ywEAoJdEngBMtLabO82zHTPFC5dbU20AACZQttEz71oAAOglkScAhPB4wm3eVdAX5YvPx8jz97/4lXWeAACTLNvomXctAAD00lTeBQBAblJbZ/HCZXknAABMAps7gVw0QsGjJ4+8f5PA8BJ5AjDRlp5dLV64bHPnGKvt312/dz2erfMEACCEUCzMZZ9WDm/nVQkAAL1isC0AE600+5z+zjFW2biWtnie//7zptoCANDKkFsAgDGgyxOAiVPbv5t3CQyCvBMAgKOlds9afTvfSgAAeEK6PAGYOGnMKRNi6dnVz/7mX+ZdBQAAwyK2dTaNtwUAYKSJPAGYLJWNa/FghefYiy2exQuXS7PPfZZ3MQAADIm2DZ2lqfnBVwJMlEYo5F3CmHAngU4MtgVggqRJp/JOAACYTNXHmzvLujwBAMaIyBOASZHd7CjvHHupnbd88fl8KwEAYDjp7AQAGCciTwAmztKzq3mXwOCUZp/LuwQAAIaIRZ4AAGNJ5AnApMhudsy7FvoudfQCAAAAAGNvJu8CAAD6pXjhct4lAAAwRCqHt+PBIk9gwBp5FwAw9nR5AgDjprZ/N+8SAAAAAIDBEXkCMBFkYBOluvdRPCxfej3fSgAAGCqpubPa2An2egIAjBGRJwATYf3e9XgoX3w+30oAAIC8lKbm8y4BAIC+sMsTgPFX2bgWD8ULl0uzz+VbDAOw++nvgkWeAAC0UyzM7TZ2Yn8nwMA0QiHvEgDGnC5PAMZcZeNaCsCMOZ0EhhgDANCNWn077xIAAOgZkScAMFbSIk9DjAEAAABgQog8ARhzcq+JZYgxAACt0khbez0BAMaJXZ4AjDm516SxyBMAgE7SMNsz4Vy+lQCTppF3AWPDnQQ60eUJwJiz2REAAIiqj1s8H4b77x/cjOdyYS6/igAA6A2RJwBjzmbHiVLZuJZ3CQAADK801TaE8DB8GQ8m3AIAjAGRJwDjrLJxLY05NeF2EsRfdwhh+dLr+VYCAMCwqRzezj59GO7nVQkAAD1nlycA40wANlFSi6dFngAAAAwVGygB+k3kCcDYEoBNGgk3AABHiFNtz4Rzsb/zu+Hi52Ev76KAyVHIuwCAMWewLQBjK420FYBNgtr+3XiQcAMA0CpNtT0bzocQlqZePFs4n2tFAAD0ki5PAGDkpY7eoMUTAIB2YotnCOFM4XwxnC9NzVcPd/ItCQCAHhJ5AgCjKiadaZ5t0OIJAEA7tfp2PBQLc8vTV+I5haAAA9CwzLNX3EmgA5EnADBiWpPOqHjhcvni83lUBADAUKs+TjfLhbl4SHNui49fAQBgpIk8AYDRUNu/W937qG3SGcyzBQCgC6Wp+XhILZ6p6RMAgJEm8gQARkNT3inpBADgdLR4Anko5F3A2HAngfZEnn3XOG5M+8rKyo0bNwZTDACMgeKFy5JOAABOTYsnnNqx/9RZKIij2rOAslfcSaCTqbwLAADoipgTAIAnV6tvx4MWTwCAcSLyBGA8VTau5V0CPeZ3CgDAk6s+bvEsizwBAMaIyBOA8RSXPpqAOjYqG9fSIs/yxefzLQYAgBFSObwdJ9l+N1ys1bfjuViYK03N510aAAA9Y5cnADDssnnn0rOrpdnn8q0HAIBRkTLOEEJBiycdfHj/nXioHW79+Xf+KtdaAIBTEnkCMJ6KFy7vfvq7lJMx0uSdAACcQq2+vV6/k31FiydZv/nil3mXwARp5F0AwNgz2BYAGA3FC5flnQAAdG+z/g/Zpylv0OJJyHR2AgDjQeQJwBjKzkGtbFzLtxiekN8gAADHqhzejo/0ym8P/o/Pw172PfGpFk9CCB/ef6d2uBVCKE0vlKYX8i4HAOgBg20BGCu1/bvVvY9S3lm8cHn50uv5lkSv+FUCAJBVq29XGztpVWd08+DtYmEuhPAw3G/7KS2eVA82Y94ZZc/LZ1/JoyImRCHvAgDGnMgTgLGSzTvtfRwP8RdavHA570IAABguTXs6k6YQtIkWzwlXPdisPLiVnqa8szS9UH5qsTyzmE9ZAMATE3kCMJ7sfRwPptoCANBWdoZtsTBXLsyVpuYrh7ePzjuLWjwnW1PemZSmF1449+rAyxkNaQgwT6hx/FsAeCIiTwDG0+6nv6tsXDMKdaSlnawGFAMAkFWrb8dos1iYW56+kl6P5yOCz+ybmUDlmcXydxbzrmKUyDsBGCEiTwDGyvKl12v7d9fvXc+7EJ5Ubf9umlFcvvh8vsUAADBUqo8TzdbFnJ3yzqZwlHFVPdisfrXZ+rqhtaeQXXpaml6QfQIw5ESeAIwb82zHQDa3tpMVAICsbItn02LOtnlnGns7uBIZiJhuZgfSdppbG4k8TyR7M5fPvlKeWfzNF7/MtyQAOJrIEwAYOvJOAAA6Wa/fiYemFs8UhYYQioW53caOsHNctZ22agRrb6Vm2dL0grQYgJEg8gQAhktl41o8FC9clncCAJBVObwdD00tnrX6dopCl6ZeLE3NVw5vm2Q7ZuJvf/eL5kbepubO0vRCtvVTb+IppPy46WbyJBp5FzA23EmgE5EnAGOoeOHy7qe/S5sgGVq1/bvVvY/KF59vG20uX3p98CUBADASsnFma97Z9AZGXacVreGbzZ2l6QU7O59c9pbKO3uqkHcBAGNO5AkA5Ka691EKp4sXLocQli+9nn0KAADHSnln63ZPxkO5MJciz5hrVr/ajLFcNu9szec+vP/Ooys8tTiQSkdbXI+abuny2VfyrQcATkTkCQAMhZh03vzgZ3kXAgDASCoW5nR2jqvS1HyxsZNSz+wY29Bdc6fWz6PFbDi7DHX57CtuGgCjReQJAOQvziLOvlK++HxexQAAAMMmNXpmY7lw3LLJpjfTpKmtMylNL8g7e84GygG4ceNGoXDKAcJXr15dW1vrbT3AgIk8AYDcpBm2cW1nXO0ZX2y73RMAAJhMTY2epemFY+PMNNW2NL3Q19pGVHZnZ2QZKgAjTeQJwDirbFyLWRojoTT7nKQTAABoKzZ6pl2ej17ssKQz5XlHt4ESHkfC7hIAo07kCcA4k3cOs9r+3bxLAAAAeq9yeDuEEDsye7hjtTQ1/9rU/NTZMyGEFHm2Je88EbdoUE45cJUWHe/kn/7pn/7oRz863UVXV1dPWw8wLESeAIyhNC4170I4SnXvo3iwthMAAMZG5fB2Gj/bP+WnFmOoWf1qs2kQq7zzpKoHzfeQfrDLcwB+9KMf2ccJk2wq7wIAoMcqG9fyLoHjVTaupWTaMFsAABgPrXlnr1o8m5RnFuM41qZtlPLOU6g8uJX2ngLA6BJ5AjC2TLUdZjHvDH5NAAAwFmr17ZsHb6dhtun1yuHtWn27H9+Ytnhm47qUgMo7j1V+6lFsHEKoHW795otfCj4BGGkiTwDGjam2wy914vo1AQDAGKjVt9frd+I5m3eGEHYbO9X+zLlNs1hrh1vVg82QyT6Xz77Sj28cM+WZxRfOvbp89hXBJwDjwS5PAMZEbf9ude+j1DtoPeTQqu3f1eIJAABjoynvXJ6+Ujm8PZivLk0vxLbOyoNblXArvW4zZffKM4vlmcU0EDg8jpDdw56zy7NX3EmgE5EnAOMjBWkhBOshh1Z176N4WHp2Nd9KAACAJ5fNO8uFuZsHbw/sq1849+pvvvhl04upZ5HuxTnAKfisPLgVzkqOe66QdwEAY85gWwDGRGn2uWyEVtu/m2MxtFXbv3vzg5+lycNiaQAAeiJOUt3tz/RUTiTFnyEz4XZ5+kr/vvHPv/NXcTRrfPTviyZBnHMbz5UHt45+MwAMG12eAIyPmHqu37seQli/d33p2VWh2vCo7d+Nv5oQQvHCZZOHAQBgzKTUOY63Hdj3xtGsA/u68VaeWaxOb6YhtwAwQkSeAIyVbOpZ3ftI5DkkKhvX0tjh4oXLVngCAMB4aFrbGWfblqbmn/zKtfr2P9T/n3829S97cjW6F/NOLbM917CCEqDPRJ4AjBsx57CRdwIAwFiqHN7OzhPuSXNnzFDTZdfrd0L9TpyR23rx6sFm9avN9LT8lHbPNtKGztAhyIyLPAFg1Ik8ARg3lY1r8ZBiNvKSHWYbQjBqGAAAxkOtvl1t7GTzzqWpF5+wHfPmwdudfhS/6ObB28XC3O4XHZe21g63qtObArwkG3ZGbSfW/uaLX4YQls++Up5Z/PD+O4OpDQB6TuQJwFhpaijMt5gJp7kTAIC+qtW3QwimnuaiKe8s9miYbavY35m+K/ulbVlCmZW9G7G/84j7U/1qM9sjKznug0LeBQCMOZEnAOMjm7FpKMyXvBMAgL6q1bfX63eKhblSEHnmoOfzbNvKdo6mgbdNo1lTMtfa0TjhUr9maXrhiPwyDgeuHW7Fu+ce9o9VngD9JvIEYAzJO3NU279b3ftI9gwAQF+t1++EdssdGbDB5J0h87ueOnum9c3Vg01ZXSdH92vGzs5497Ipab+rAoCem8q7AADomRizFS9clrHlSN4JAADjLTZc9tx84Z+m84km5VYPNisPbsXz8tlXel/ZuEvzbMXGAIw0XZ4AQF+89oOf510CAADjLG55JF/HLtfs3oNwPx5O1DnalHdmt1HSvdL0QjbvtMgTgFGkyxOAMVHbv5t3CQAAwOCURZ45aYo5a/XtJ79m5fB2vOyJ8s4P778j7+yJF869WppeMM8WgJGmyxOAMVHd+ygeli+9nm8lAABA/xQLczEb637wKb21NPVi9XHq2atFnqfLO1NjYml6Qd75hHR29lsjFPIuYUy4k0AnIk8AAAAARsPy9JWbB28HU21zVZqaL4U88+bsMNugvxMACCGIPAEYaZWNayGE8sXnq3sf7X76uxBC8cLlvIuadH4RAAD0T09mqDLSmps7n1qUdwIAQeQJwEiL6Vr8vwAAwNhL81Qt8pxMaeVnCKE0vWAWayfVg828SwCAQRN5AjCqYotnVvHCZYs8AQBgEmQXeaY1kPmVQ9/V6tvr9TvpqWG2bVUPNqtfbaYuWACYKCJPAMbEaz/4ed4lAAAA/dWabhp1OwmamjsNs22VnfeblKYXcikGAHIh8gRgJNX272bn2S49u5pjMQAAwAC0TTeNuh172byzWJgzzDbrw/vvhBDahp1u1LBphELeJQCMOZEnACNp/d717NPS7HN5VQIAAAxGSjeXp6+0/jQ76pYRtdvYqRzeLhfm4m+zaZjt0tSLfstZv/nil02vaIEFYJKJPAEYSUvPrlb3PoqNnsULl/MuBwAAyIdFnt2o1beHPCwsFubir3K3sbPb2Ck+PqefphyUVpJOAAgiTwBGVGn2udLsczc/+FnehQAAAAPSmm5WDm/nV87IiL2SMURs2yA7DJanr9Tq29XGzu43w84QQrEwN7RlDwMzbAEgEnkCMKoqG9fyLoFmtf27eZcAAMAkEokdoZoJEW8evD20CWJpar4U5rPBZzDMFgDomsgTgNFT2bgWR9oybKp7H8XD8qXX860EAACIsh2T8WkMPodzVGwMPmP/7nBWCKfTyLsAgLEn8gRglLSGncULl6VrAAAAbdXq23mXcBrD2YcKAAwzkScAo0rYCQAAE6VYmNvNjDzlWJXD2623a2j7OwEAnoTIE4CRJO8cNrX9u9W9jwwcBgCAYVCrb6/X77S+XizMhRDknTB4jUYh7xLGhBHBQCdTeRcAACeQYk7R2lCpbFxbv3c9/VKKFy7nWw8AAONtRIe1Dkzl8HbKO2PGmcQ22ZsHb1cOb7uNAMA4EXkCMGJSnFbZuJZvJSTZsPO1H/xcAy4AAP1QfpzeVc227Sw7zDbOsG37tt3Gznr9juBzdH14/528SwCA4SLyBGDEaPQcNil7Nm0YAIC+Mo71pGKuGc+vzfzF0tSLxZYEVH48cqoHmx/ef6d2uBWflp9azLUcABgWdnkCJfBfvQAAIABJREFUMHqKFy7HvLOycU3GlruUPftdAABA7sqFud12KebNg7ezT2MDqBR55FQPNisPbqWny2dfKc8s5lcOJ2ADJUC/iTwBGD3Ll16/+cHPgkbPIZBt8cy3EgAAJkGxMBe3UcanKd6r1beld1Fpav61qfkQQuXwdgihNf4Udo6ubHNnaXrhhXOv5loOAAwXkScAI0zMlq/a/t2YOhtpCwBAvqqNnVKQ4X3D8vSVdK7Vt+MAW2Hn6JJ3AsDRRJ4AjJ7a/t28SyCEEKp7H8VD+eLz+VYCAMBkSosqyy0rKsdb5fD2bmOnWJjL5ppHKE3Ni4RHV/Vgs/rVZso7DbMFgLZEngCMnpS06SzMV2rxLM0+l3ctAABMhDimtViYC48Ht8an4925mP6ky9NXavXtFPTuNnYqh7e7TD0ZXfLO8WCXJ0C/iTwBGD1WeAIAwASq1bfTObY5hhC673QcOSnpTPs4bx683fSecf2zk6R5tobZjr5C3gWMDXcSaE/kCcCIqWxciweLPAEAYKJUHyd/IZMCjvFI293Mn7etpakXB1MJecnu75R3AsDRpvIuAABOoLJxLbV42h8JAACTo1bfThFgOixNvTjeI22TYmGu+M1wd+zH+VI9+MY823yLAYDhp8sTgJGRzTuXnl21PxIAACZEdoFlMq6ZX5pnm2SH96bZtmPc3kpUeXArHuzvHA92efaKOwl0IvIEYDTIOwEAYDKltZ1Z47rCs/UPm/2TpjR0XONekg/vvxMPpekFeScAdMNgWwBGgLwTAAAmVooAs8Ndx7XH8Yj9ndnRvmMZ95KkFZ6l6QUrPAGgS7o8ARh28k4AAJhYqa8xru1MT8eyx7FW346Htj2s1cwG04GWxWBlV3iWn1rMtRZ6q5B3AQBjTuQJwLCTdw6t4oXLu5/+Lv2CAACgf8Yy42xSbdfiWatvVxs72VbXSbgVE6t6sGmF57iygRKg30SeAAy1ysa1eJB3AgAA461cmIvR5m5jJ/azts65HdeJvoTM/s5ghScAnJzIE4DRIO8cQuWLz8cWz8rGteVLr+ddDgAAYyhmftkVnvGVyuHt8dtnWZqaLz5u6GwNO4uFubIWz7ETY840yTaywhMATkHkCcBQi4la8cLlvAuhDTk0AAADVpqaD/U7eVfRR8vTV24evJ2exqx3/MJdPrz/TlPMmcg7AeB0RJ4ADK801bZ88fl8K6ET6zwBAKC3lqZejEs9JZ1jrDXvLE0vhBCEnQBwaiJPAIZUbf9uOusmBACACRT3WU6a0tR8KZheOxHEnBOlEQp5lwAw5qbyLgAA2qhsXFu/d91U2xGSWnIBAKDnWvsdW1ddwqioHmyms7wTAHpFlycAw6WycS07JbV44bKptsMs/bKWL72ebyUAAIyxWn27NDUfMn2fcckljKLqV5vxIO8EgB4SeQIwLNqGnUbaDrM0fFgnLgAA/VAuzMVuzmpjJ856Tc2d9lwCI6aRdwFjw50EOhB5ApC/2v7d6t5HTXmnrsHhV937KB78sgAA6IfS1Hyo30lPtXgCANCJyBOAnLU2d8rPRkJt/65lqwAADEytvq3Fk/FQO9wKIZSmF/IuBADGisgTgGEh7BwtqcXTslUAAPqnWJjbbeyksDOEsDT1Yo71AJxOIxTyLmFMuJNAJ1N5FwDARMt2Cso7R0vqzbVvFQCAPsl2dsZDsTBXmprPtSgAAIaRyBOAPOkUHFGVjWvxYKotAAB9Uqtvr2cWeUZlWzwZcdWDzbxLAIDxJPIEIE86BUed3lwAAPqhbd6pxZMxUP1qMx5eOPdqnnUAwNgReQKQG52CIyo7jjjvWgAAGE8p7ywW5oqPOzu1eAIA0MlM3gUAgE7BEWMcMQAAgxHDTls8GRsf3n+ndrgVQihNL+RdC4PWyLsAgLEn8gQATia1eBpHDADAwCxPX8m7BDi96sFm9avNmHcGU20nVCHvAgDGnMG2AOQvTbhl+KVflhZPAACAbnx4/53Kg1sp79TiCQD9oMsTgNwsX3r95gc/y7sKTia2eIYQtHgCAAAcLU2yjUrTC+WnFsszi/lVBABjS+QJQP5SisaoKF64nHcJAAAAw6t6sFl5cCv7yvLZV4Sdk8wuT4B+E3kCkJs0IlV+BgAAwDipfrWZzqXpBcs7AaDfRJ4A5G/309/d/OBnr/3g53kXAgAAAD0Q59kKO8ko5F0AwJgTeQKQj9r+XfNsAQAAgElgsG2vuJNAJ1N5FwDAhKrufZR3CQAAANB71YPNvEsAgImjy7Pv1tbWjn7Dxx9/PJhKAIbN0rOrpdnnbn7ws2CdJwAA0M5uYyfvEuDE0iLP8Ztqe+w/dQJAXkSefbe6upp3CQBDobZ/N9vZuXzp9RyLAQAAhlOtvl19HHOmvLNYmMuvIuBr/qkTgKEl8gRgQKp7Hx29vLO2f7c0+9zA6gEAAIZN5fB227bO5ekrgy8GoIdsoAToN7s8ARiQtnlnbf9u+qntngAAMMmyeWexMPfazF+kc35FwWnUDrdCCKXphbwLAYAJossTgJwVL1w+uvsTAAAYb7X69nr9TnpaLMwtT1+pHN6OT8siTwAAjiPyBGBAihcuhxDKF5/PTq8tzT5Xmn2usnFt99PfCT4BAGACNTV3lgtzpan5kFnkGZ/CqKgebOZdAgBMIpEnAAOyfOn11hdr+3eP3fEJAACMk1p9u9rYCZlQM4rNnek96cUBlwen9uH9d8LjqbYhhBfOvZpjMQybRqOQdwljw50E2hN5Dou5b3+adwkTavapP+RdAgza589+O+8SHtnbrty7dz37yv/9X//3Sy+/kVc9dOWDEEI4+M7M8PxF6sbC24d5lwAD98c/5l0BDFrj0H/aM3kajbwr6EqtsVOtb4cQdsPHbd9QDM+Up+ZLhbnKwXtN7ykX5kblj8lgNOr1vEtoVnl4K4RQO9xuen0ISwWAMSbyBCBPs3NL+zvr6Wn2DAAAjLTK4e1OGWd0Jpx7OlwMIcTmzlpjp+n9xfBMSZcnw6pT0lmani9NL5ZnFvIoCgAml8gTgNxcnF++OL8czxvvvhnzzr3tSnqRYbPx7pt5lwAAwGg4Nu8MITwdLn49ybaxs16/E8/F8Ew8lG3xZFhVHt5qCjtL0/MhhOUzr+RUEQBMOpEnAPnb266k/s79LZHnMNrbrtx77630dHbB7wgAgI66yTuL4Zm2eefS1Is6Oxly2bxT0kmXTOjuFXcS6ETkCUDOslna7NySXZ5DyO8IAIDupbzzTDj3MNyPL6bGzRSFdurvlHcyzKoHW+t//G16uvStPzPAFgCGhMgTgDzJ0obQ3nZlf6sSz03bVf2OAAA4Qq2xU61vp1Az5Z2pcfPm4bX0SvpUtf6oWy7b9wlDKJt3lqbndXYCwFAReQKQJ3nnENrfqjQlnZHfEQAAR8g2a6b+zmyKWTm8HQ/ZVs7UEirvZMg1DbOVdwLAsBF5ApCn2bmltukaOcr+RmbnltJZ3gkAQCdNw2lDZoBt6uxMP82GoPJORoK8EwCGn8gTgDxdevmN93/90xDC/s763nbl4vxy3hVNur3tRyNt9XQCANCl1rzzs7AXn6bgMzlbOB8PKe8MIZSn5gdRKJxKNu+0vJNTa4RC3iUAjLmpvAsAYNKlPsK0P5K8bLz7Zho1DAAA3cjmnd8NsyGE3fBx2uLZ5Lth9p9N/ct4Tnln2vQJQ6h6sCXvBICRoMsTgGGhpzBHe9uV7ApPLZ4AAHSjKe/8POx/HvaPeP8Pp19LH4yH7F5PGEK1w814KE3PyzsBYJiJPAFg0m28+2Z2f+ezL/3EhGEAAI6VzTvPhHMp7EyLObOja0MIS1MvxkPl8HZ60QpPhlkaaWt/JwAMP5EnAMNi4903dRYOXjbv1NwJAED3qvVH0z7PhHNpkm0xPFOemm8KO0MI3w2z1fr2ergz6CrhCaSRtqXpxVwLAQCOZ5cnADmbXXjUULi/s77x7pv5FjPJnn3pJ/JOAAC6VGvsxFCzKe8MIazX7zTlnSGEz8N+64vx/TDkjLQFgJGgyxOAfMTmwkdthS+Fe++9FR6nnoK3XBhmCwBA99JI25R3Lk29WK1vt+aaWSnjNM8WmDSNUMi7BIAxJ/IEIAdpmGr8vxfnl/fnKvEs7xyw2YXleOeFzQAAdCm7jDNklnfGubWtfZ9BxskIqjy8FQ+m2gLASBB5ApCDh1/+f0+de/qr+599++J8fOW/fbIVDxvvvjm7sKzjcGD2typ5lwAAwIjJtnKmvDPloNm8U9LJGDDVFgBGgsgTgBycOf9P/rC3HUL47//5KyGE93/90/Sj/Z31/Z31/blK0PHZZ3vblf/6/96KvwgAAOhSU4tnJ/JOAAAGSeQJQA7iJNXQeX+kUav9lmYLR8++9BOdtQAAdCO2eBbDM01rO+PTNNVW3gkAwCCJPAEYtL3t9pNUY+q2t13Z36qkTZ9Sz37I5p1PnXv6X/3rf5dvPQAAjLpaYyceYt6ZVnjCiKoebNUOt0MIpen5vGthTDTyLmBsuJNAJyJPAAatdXnk7NxSyjUvzi/H4PPee2+Fx6mn7Z59orkTAICeqNa/sS6hPCUlYrTVDjfjoTS9mGcdjJVC3gWMDXcSaE/kCcCg7e+sz84thRBmFx6Fba19nBfnl8NLIbZ7xkd4qeMUXE4qtnjOzi25pQAAAE2yLZ7lmYW8ywEAuiLyBGDQfvjjv+3mbbHdM41gvffeW1JPAAAYTk17Pdfrd0IIr02/nlM58ES0eALAKJrKuwAAOMqll9+ILaEhhHvvvdVpDygAAJCXyuHtvEuAntHiSZ80Gh69eQB0IvIEYNhlU8/WPaAAAECOdsPHTS2eMNK0eALAiBJ5AjACUuq5v7Ou0RMAAIB+qDy8pcUTAEaUyBOA0XDp5TfiQaMnAAAMg2J45tQ/hWFTPdj6v758K+adQYsnfVHw6NEDoD2RJwCjQXMnAAAMleXpK/Hw3TB7xE9h+FUe3lr/42/T06Vv/ZkWT3qu4dGjB0AnM3kXAABdSc2dqd0TAADI19LUiyGE9fqdpte1eDIq0iTbqDQ9v3zmlRzrAZ7Ef/7P/3llZeV0n11dXT31Z4EhIfIEAAAA4DRKhbnK4e3W18tT84MvBk4qm3eWpudL04uaO2Gk/eM//uONGzdO/XGRJ4w6g20BGA37O+vxsPHum/lWMgZm55ZC5pYCAMCTaOrpLIZnSoW5vIqB7mXzzuUzr8g7AWCk6fIEYMTs76xvvPvm7MLyxfnlvGsZeRvvvmlQMAAAT6I8NV+tfz0XtBiescWT0WKYLYNhCWWvHHEn//RP//RHP/rR6S67urp6ug8Cw0PkCcDoie2JIs9Tu/TyG+//+qd5VwEAwDgoFebWw6NdnktTL+rvBCAvP/rRj9bW1vKuAsiNwbYAjIYf/vhv4zhWesJsWwAAeiLt8pR3MloqD2/lXQIA0EsiTwCYRLMLj3pk7UYFAOBJ7IaPg/2djJrqwVZc5GmqLQNU8OjRA6A9kScAI2N2YfmHP/7bvKsYE2kssEZPAABOLbV4wmipHW7GQ2l6Mc86AIDeEXkCMDIs7+yt71ycj4e97Uq+lQAAMCqaMs7Y4hlCWJ6+kkc5cBqVh7dSi2d5ZiHvcgCA3hB5AsCEKv/zR+Ob9rdEngAAnIyMkxGVRtqGEIy0ZZAaHj16AHQi8gRgZOxtVyye7CGzbQEAOKnU1hkVwzPxUGvs5FEOnFhmpO18roUAAD0m8gRgNGy8++a9995K4dzsgiG3PTA7txQPZtsCANC9lHSmXs9qfTu/cqBb2ZG2WjwBYMyIPAEYMbNzSz/88d/a69kTKTk22xYAgGOlRZ7lqa/b42L82dT9CUMo5Z0hhNL0Yq61AAC9J/IEYMRcevmNvEsYH2bbAgBwCqXCXOuLKRCFIZTNO5e+9WflmYV86wEAek7kCQATzWxbAAC6FFs501TbKNvxCcNJ3skwaHj06AHQyUzeBQAAeZpdWI4tnvtbFeOCAQA4qbTFU/bJEKoebNUON9P+ztL0oryT/BTyLmBsuJNAe7o8AWCimW0LAEA32i7yDJktnm2n3UKOqgdb63/8bco7l8+8Iu8EgDEm8gRgNMwuPErmNt59M99Kxo/ZtgAAdC8bbdYaO/HQNO0WchfzzniOeWe+9QAA/SbyBGA0mLk6APtbIk8AAE7AVFuGk7yTIdRoePTsAdCWyBOAHJyumzA2I+7vrGv07JNLL7+RdwkAAIwkU20ZHvJOAJhMIk8ABu39X//03ntvnSK2TLNt93fWjWAFAACgibwTACbWTN4FADCh9nfWT/qRi/PL4aVw7723Qgj7WxWjbgEAYPBqjR09nQynysNbtcNHw5aXvvVn5ZmFfOuBbyrkXQDAmNPlCUAOZueWnn3pJ6f44MX5ZeNtAQBg8Janr8RD2t8JQ0veyRBqePToAdCJyBOAQZudW7r08hun7tFM+yaNtwUAgHyVp+bjoXJ4O99KIIQQWzxL0/PyTgCYQCJPAAYtZZank40597dEngAAkBsTbgEAGBJ2eQIwSjbefTMtAY3dovnWAwAAwDCoHmzlXQIAkCeRJwCjJOWdz770k1OPxqVJvKtxSSoAAMAoqh1uxsPymVdyLQTas4QSoN9EngCMntm5JXknAADkq9bYCSEUwzO74ePd8HHe5TDRKg9vpUWeedcCnRTyLgBgzIk8AQAAADhGDDizqvVtSSfDIJt3avEEgIk1lXcBAEDO4kjbNDQYAABaVevb8bA8fSUe5J0MA3knABCJPAEYGRvvvhkPswum2vZSup/pDgMAwEm1toFCv6W8M4RQml7MtRY4RsOjdw+AtkSeAIweizx76+L8cmr0lHoCANBW7Okshmfi0xRwpldSGygMRjbvXPrWn5VnFvKtBwDIl8gTAPi60VPqCQBAq8rh7aZXsnNuX5t+PQWfMBjVgy15JwCQJfIEAMLF+eVnX/pJPEs9AQDoJC3ybH2904+g56oHW+t//G08yzsZHQWP3j0A2pjJuwAAYChcnF8OL4V7770VQtjfWc+7HAAAhkjTVNu2r8BgyDsZUTZQAvSbLk8ARoYcrt/SUk8AAEjS2s4jXoHBqDV25J0AQFsiTwBGQxq1KpPrq7TU02xbAACitLazPDXf6RUYgFpjZ71+J55L0/PyTgAgy2BbAEbA3nYltXheevmNfIsZbxfnl++Ft/KuAgCAYVQqzMVDnGqbfQX6qnJ4O2T/4k3PL595JdeKAIChI/IEYMRsvPum1BMAAAamaW1nDJ+CRZ70Tfo7ljLOLHknANCWyBOAEXBxfjm8FO6991YIYX9nXeoJAACDkcKnJKVQy9NXBl4OY6vW2KnWt9tmnFnF8Iy8kxHVyLsAgLEn8gRgNGRTTwAAYMBiwKnFk36oHN5uG3amv2blqXlTlAGAo4k8ARgZ1kwOxuzc0v7OelqeCgDAJGsKorR40ltNzZ0x4xRwMp4ahbwrABhzIk8ARok0bgDi7Z2dW8q7EAAAhkUMorR40ltNzZ1LUy9KOgGAUxN5AjCSNt59c3Zh+eL8ct6FAADA2EoZZ3lqPmjxpKeyeWcxPOMvFQDwhESeAIyS2YXl2IMYez335yqXXn4j76IAAGDMlQpzWjzpE82dTIhG3gWMDXcS6GRSIs+VlZXWF9fW1gZfCQBP4uL8cngp7G9VzLYFAIB+i014MePU4kkP1Ro76W+XvBMA6In2kWcKCG/cuBFCuHr1aghhdXW1bXB4rPipnlzqFF8dv7etQqEQS5J9AoyQi/PLF+eX3//1T/MuBAAAxlmtsZN3CYytan07HiToAECvNEeebTPC+ErKLLsPCFuvli519erVvgafa2trq6ur3bzzxo0bhULh+vXrg0lhAeiJ2bmlONs270IAAGA8NYVSxfBMbMurHN4WU/Eksi2eedcCAIyPqXRaW1s7uicyunHjRjfR4LFXu3Hjxurq6rHpabxOIaPLb+8y70wG1ngKAAAAMPzSJNtoefpKmnCrAZQnkdL08tR8vpUAAOPk68jz+vXrTQnl1Yzs692knp2u1vS2o1PPlZWV1dXV1j7RQqFwxKda886rV69ev3698U3Xr19v/XOZcAsAAACQZPvwUkCVIit4ErZ4MlEaHj16AHTyaLBtU0dm2+m12ffEdLBT8Nl0taaZsU2RZKeJstmLZLPJ+OLq6mqnD2YvfsQY3pWVlfjx7BcdcVkAmAR725W8SwAAIH+Vw9vxkO3DKxXm4njb2OgprwI4iULeBQCMuUddnk0JZduYcG1tLRs9Xr9+ve0V19bWjsg7QwgrKyvZz7btrUwXuXr1aqPRWMtIn21bQNOluuna7PLPBQCTYH/rUeR56eU38q0EAIAhlBLQ9fqdfCthdFnkCQD0w1QIIRtJXr169Ygex2wDZac9ndnIsNPVVlZWjk4Z0yutKznTZ9sW0PTtbSts1c2fC4DctW1A3Hj3zcFXAgAA4215+ko8NM2wjY2e8Zw6QQEAIHdTTc9bI8aspqiybQ9lNjI84mrZz7amjKnF8+jRta0/7fLbm5hkCzD89rYr9957qzX1nF1YzqUeAACYEJXD2+8f3vyH+v8Tn6Y0NI63za8ugFGS+wrMsXkAdDIVvhkTnij8a+3OzAaZRzeMhm92YZ7oe7t884mu2X1LKACDF/PO8Hjs6sa7b+7vrIcQZueWLs6LPAEAoPdiN2fc3Pl52N9u/Jebh9duHl6rNXZSo2dTDygAAORlpreXG8wizG42dIYQVlZWunxnOElLKAADlvLOEML+zvrediXmncG+SQAAGLhqfXt5+krl8HZMQ2uNnVJhLu+iAACYdDPhCRocuw8UO328UCjEc9Ns26tXr964cePGjRtra2utzZopWG0tIH7wFMWsrKwYbwswnFLe2fR0dm4pj3IAAGAipAG2Nw+vZV+PMWd5an63/nEIoVrfLk2LPOlWWgFbnprPtxIYuELeBYwNdxJobyacMLk8OlDM/vRJAtHV1dV4qZhuZsPItbW1tOnz2FJP1OgJwBDaePfNTj/S4gkAAINUDM/sho9DCOv1O69Nvx6f7oaPK4e3Uz4KXdIczKSxhLJX3Emgk5MNts1Gj31dfrmyspIaPW/cuJH9rpSqth1Fm+0cjW8uFApXr15dXV3VxAkw6lJbp7ATAAAGrzw1H+ohpp4x5owNoPEVAADI0VSX71tZWSkUCiluvHr1ak+6J4/ITdfW1tJPb2TEV65fv94pwmzdJ3rjxo3V1dVCoRCn1+r7BBgV2bWd0aWX35B39lW84YYGAwDQKm7xLIZnwuPxtvEcMtNKAQAgFx0jz5hxJtmJtb3KO5u0XnNtbe369etNsejVq1cbjcYRLZsrKyutqWcUQ9Ns/PnkZYcQCh305OIAE2jj3Tff//VP3//1T7NrO5996SfCzn47YowwAADsho/fP7yZVjCu1++kebYaPenGbw//T39VJkSnfy81hw+A/um2yzPpU94Z2nVnhhBiU2Yjo5tvj6nn0aN3Y/zpv2sBht/s3NKll9+4OL+cdyHjL/XUSpcBAGjr87BfrW9/N8yGEM6EcyGEp8PFvItiBNQaO5XD2w/D/fg09QcDAPTKiSPPGzdujMRs2JSVXr169di1ozH77FPw2ThOX7eiAoyiSy+/8dS5p9PT2QVh5yDsbVfiwVRbAACymkLN3fDx52E/hPAw3K81dj4Le0GCxZFqjZ31+p3Y33kmnFuaejH1BzNyjv2nzrwLHF6NUPDoySPv3yQwvGY6/WB1dbXplTTbNvZHHrFNc6hk09lYcHZIb9ap09xO/11uti3Aqf2rf/3vNt59MzYd3nvvrYs/lnr23f7W48hTxgwAQMYPpv+nyuHtlFdlG/Wq9e14TtNuoUn6yxNCKIZnhJ0TQvYJwOB1jDzbrrpcWVlJeeHq6uqopJ7JsfHnqPSwAkyCSy+/8f6vfxo0HQ5KDJhn55bMEAYAoMny9JUYXD0M97OpZ4yyiuGZUmEu1wIZRrXGTrW+Le8EAAbjZINt19bWslNY227fPLUBB41ra2tp8m329Zh6DrISANraePfNvEuYIOlua/EEAKCt1Mf5MNy3v5NuZPNOw2whNDx69ADo4MS7PLMDbztNiO3ek1/hya2trTVlt8NQFQCXXn4j9nfu76ynNZP0SWzxDCFo8QQAoK1SYW5p6sV4/izsZZd3irJolebZFsMzr02/rg8YAOi3E0eeY9kBubKy0tuOVQB6InUcpjWT9MPXLZ5mCAMA0IVieKY8NZ8CrbzLYejUGjupv1MiDgAMxokjzxBCdhJs0zTaI340AIWMk362Kcody2QXYORcnF/W6DlgG+++6VYDANBWtb4dD+Wp+ew5v4oYRrXGznr9TjynzmCgEQoePXnk/ZsEhtfUyspKiglPkfMd0Rx5bN9kNhNtWqgJAFEauEqf7G1X0k3e31nf31m/995b7//6p+//+qfiTwAAktS3VwzPlApzqYfPwFKapDg8/lXJtxgAYHLMnOIz2VWXTa2cq6ur3S/C7Pks2atXr6ZvX1lZeZI20+zKUgDykh24asdknxwxNDgmoPtzlUsvvzHIkgAAGELZts5aYyeeTbWlSXaF54BH2lYPtmqHmyGE2uGjv6ul6fnlM6/E17MvhhBK04vlmYVBlgcA9NvMiULKY2X7RE902WGLGA22BRgqIrf/n72763HzPPMEf5NVthIPNoFL3SQXXWQZiNRnSyYHs71RWRYWWAe906eSkwlg+SsMMPkY3cB+BUtANrZ8OoNFtCd6qcoGc9Ah92hbCuAiNZhiTReNZDB2yy6Se3CXbj9mvaheSD58+f3wwP3wIfnwEquSNPzXdV2Tc/S7TUlz7P7sdVrPntxb22hInQEAllm2xbPZ34oXrWkkazorPJsvH3X77ZhcfvvRrxLNkYu//fL+0Yvxn62vQ/3N9wSfALAwiucNKbOvP3YabfbiKcGrlq0vAAAgAElEQVThgwcP0sfdvn17LBFjtq3zs88+O9c9X/vnAoAlcf3m3XjERaohtnue3AwKAMCySfFn3oUwQ6azwrPZ30qZZfbIvqa8UhsJRNPFkeutrx9PqE4AYPqK4cwhZfhuThlOaM3MXjwlQx37VNso+2c5e+p5lj8XANO3tnHYVpj6Dpmm6zfv/s0v/z4Gn7HXM++KAADIR2rrrBRr6RyyUt45uRWe2S7SETHOrL/53s/e+rBx5Vbjyq2fvfXhqxm2tfqb78WL8Xr9zfdS9tl8+WgSpcJRQ8f4DoBjFcORkPKkmPDBgwfZV57Umnnnzp3XZqh37tzJtnheZunm0SKzD+Mf55T7P3jw4M6dO2f5cwEwfSapzoIUPMc5twAALLNyoTqFyaXMl+6wk4Lwya3w/E4X6ZvvpeTyZ299mGLOkSm1MeA8er2yutG4cuvwtsdNxAUA5tFqeBVSpgzys88+KxQKI8Ndj/ZrntIK+eDBg0KhMHK3+PrY3DnRlspPP/10JMSNH3d0XO3RP9R481cALmC/3ZR0zpSrtUav2ox557Mn9+xVBQAgmGrLK83+VkrBp5Z3xgizsrpxmU2c5ZVazDubLx+lBBQAmF+r8f/EZsdsBHj6Xs9PP/309FbIk3LH897nAu7cufPpp59++umnI5/42k2l8k6A3D17cq/XafWqzZir7bftj5wJ12/e/f2vf5V3FQAA5C874TbfSshdd9jZHbSnkHeGEHYH7fQpl4k5sxpXbv32y/shhG6/LfUEgAVQTGcPHjw4y37N27dvD4fD1+aUMXc82liZNYm8M336gwcPTv/0rNu3b3/66afyToB8xbwzZAao9nYOI880WxUAAJgFE1rWyFxo9rea/a3WYHs6eWdqJB37p6SNnjH13D3YGePNYcQwFBxjOUIo5P3DBGbUavbBnTt3Upw50hMZ48NzhYJ37tyJ0eNIw+UFbnUx8SNSlHvsGNsQwgcffGB5J0DuUt6ZHoZX2edatW7Ubb7ijyPIngEAYFnFBt+UcSalsF4p1iYXgU8u7wwhNK7car58FMfbdvvtbr/d7de0ewLAnFo9emm8YWQMPsd4w7krAICzGBmdmo0/xWyzQ/YMALDkUviUdyFMSXaJ5oj4azC55s4ohawTmqXcuHJr92Cn2/88BZ+//fJ+WhcKAMyRYyJPAJiy/XYzzbAdocUzX/vt5vOn9/OuAgAAmEWTzjuTUlifXCNpZXWjsrqxe7DT+vpxvNL6+nG3XyuvvCP4BIA5IvIEIH+9nWaaYXv95t04RnVtoyHszNfItOFr736YYzEAAMD0paAxzpXNNn0eHXI71yqrG5XVD825ZXKGeRcAsPBEngBMVmwT/Jtf/v1ZXnz95t30T3KX8s4YRedbDAAAkLtyofr+ys/Tfs1J6w47U/iUrKNzbpsvH2n3BIC5UMy7AAAWXJxYGxs3mSP77cNRw/JOAABGTG2cKbMg7uzMpadzd9COJ9P8lausbjSu3CqvHK4O7fbbra8fN18+mloBAMDF6PIEYIJGJqMe+4KQ6SZkdqTtqmsbxgsDACy11NJ3JXz/Zfgq73JgGuI82+yc292DHb2eADDLRJ4ATMp+u5myzFNCTXnnjLNRFQBgmXWHndTe973wlsiTrO6wk5Z9jt3UxueeIs65bX39OITQ7X8u8uQyhqGQdwkLwlZU4CQG2wIwKalNMDp9tu1atX7t3Q8nXBEAAHA+rcF2PKkXb/wp7IdXY05ZHpXi4YjXZn9r5KnWYHvkYnfYedj/5GH/k2Z/69hNnM3+VjxO/9Bs3pnvr1xldSMOuY3tngDAzNLlCcCkxPbNtWr9LH2caxsN3YQzJf348i4EAIDcpFyqFNYn18zHjDv6o2+sbKZIci+8eNj/JBxZ+bkXXuwNXoQQ6sUb5UK1O+zsDtrZrs1mf6tSrMWnYrJeCutxZ2c274xvn/SfEQBYACJPACYi9XTGTZC9Tmsk+NxvN58/vZ8e9naaIk8AAJgdKXZKQRQkjZXNkRTzpCG0rcF2KawffTZmotmn9sKLmLLPWt4Z+ztjrydcnHms4+KbBE4g8gRgsq7WGmnC7e9//atjX3Pt3Q/lnTNlJLEGAGDZZFd4xrmmqeMzjTll2YzEluVCtbxSTb8YKSCPvZshk5qPjKgdaRI99iOy95kR3X77t18e/rXd8kqtceVWvvUAACNEngBMRHYs6vWbd08KO9eq9es37061Ms5DFA0AsJx2B4drC4+OtJ2pFIopyM43PvrsKR3A2WgzfLddOPtUSjdns7G4+fLR0Yvdfrv58pHUEwBmisgTgPE72iN47EpI+zsBAGA2VYq1uIgRUmZ5gRgyRZtHU8w0Gjebg45cmQXllXdGrsQ5t91+e/dgp7K6kUNNzKdhKORdAsCCE3kCMEEp0dTKOUf2281sky4AAEuoXKjGJYt74UV32NHZubROb/E8i0qxFgbHx6VxNO7pV3JXWd0YyTV3D3ZaXz8OIbS+fhzCe1JPAJgRxbwLAGABCczmWtq9CgDAMks7O9OQW5ZNdqXrhTsvy4XqTHVtXl5ldaP+5nvxvPX1492DnXzrAQAikScAY5am2jIj9tvniDCfPbmXEmu9uQAAyyw2eobMXNOoO+zkVBHTltLuevFGvpXMmsrqRnnl8O8ExI5PACB3BtsCwAKKwXMML0MI4d1wlrWpaaRtMIsYAICMZn+rsbL5sP9JCGF30J610aNMQmrxLIV1k42Paly51Xz5KO71bL581LhyK++KAGDZ6fIEYMxEZfl69uTe73/9q16n9W3eGcLzp/df2+u5324+f3o/nl9798MJlggAwLz5U9hPOx3jds9862HSmv2t1mA7nqcRx4xoXLkVez27/Xbz5aO8y2HWDR1jOgBOIvIEgIW1Vq2njarPn94/aebwsyf3nj25l807z9ISCgDA8ngZvtoLL34YrsaHrcG21HOBNftbaZqxFs/TST0BYHYYbAsAC+X6zbu///WvQmYZZ1rP2eu0Yuq5ttG4Wms8e3Lvv/3zzjdf/Sn79rVqXd4JAEDIzDW9Er7/MnwVQvhT2P9huPqnsB9CaA2268UbwrDFk807/YjPorzyThxvG1PP8so7ldWNvIsCgGUk8gRgzFIr4dqG5Cwfa9V6drDt9Zt309DalH0+D/ePviuYSwwAwCu7g3Y8+V54K0aeIYQ/hf0fhLU/h16Qei4ieecFVFY3Qniv9fXjEEK33+72291+zWpPAJg+kScAk6JZMHfPntyLEebVWiO8G3o7zeyCz+iN7//wf/iLDUknAABZqcWzFNZHnvpz6KXUc3fQLq9IxRZBd9jZHbTlnRdTWd2orH7YfPkotXv+9sv79Tff0+5J1jAU8i5hQfgmgZOIPAEYs6OhGlOWZttmXa01Ygi93272dprpldMuDgCAeZBaPCvFWjovhfUYiX0vvPW98NZeeLEXXjT7W42VzdwKZRy6w05rsB3PS2HdD/RiGldu7R7sxHbPEELr68faPQFgmop5FwDAYopTUslL/P6PjZ+v1hrXb96Nx9TrAgBgzpQL1WwAFps+sw2gqS+QOSXvHKPK6sbP3vqwvFKLD+N2z3xLAoDlIfIEYJz22828S+A70mpVAAC4gGZ/K/uwsbKZks6TJt8yR+Sdk9C4cqv+5nvxvNtv7x7s5FsPACwJkScA42Ri6oxY27BIFQCAizsl+qoUa9OshMlp9rfknRNSWd3IpJ6f51oLs2LoGNMBcBKRJwAsoJQ9yz4BALiYk+bWlgtVnZ0LoNnfSj/cevGGvHPsKqsbccKt8bYAMB0iTwDGKS6PtMgzX/vtZtriebUm8gQA4FK6w07eJTBmI3lnuVDNt55FVV55J550++1cCwGApSDyBICF8uzJvedP78dz2TMAABeW2v52B6Npjdm286s77Mg7pyY1eoYQNHoCwKSt5l0AADAe++1mb+fb/s61at1GVQAALm8vvCiF9b3wIuVkQrI51R12LO+cssaVW82Xj7r9drff/u2X98srtcaVW3kXBQCLSeQJAIvg2ZN7KewMIVx790MjbQFgQpoHWyGExqqogMUXk84QwsvwVbzSHXbknXNK3pmX8so7abCt4HOZDUMh7xIAFpzBtgCwCLLNnX/zy7+XdwLAhDQPtvaGnb1hJwafsNjSANthGMaTOOS22ff7P2fknTmqrG787K0P04Tb8Cr4NOoWAMZL5AnAOMXlkdl2Q6bJMFsAmKjuoL037MTzvWHn4Te/6Q7a8XrzYOvhN795+M1vci0Qxiw1dP459E56TZp2y8xq9rfknblrXLl1NPiUegLAGBlsCwBzb7/dzLsEAFh83UG71X+VGRSqMfts9bfDq4tR82DrpJm3I42hRuMyF9Js25F1nsyjevGGucT5ivNs43bP8Krds/7me5XVjbxLY/KGeRewBD777LNC4YIDhG/fvv3gwYPx1gNMmS5PAJh7vZ3DyFOLJwBMSDbvrK/caKxuls4fG8SJuOl4+M1vTMdl9qXZtn8K+/Hk6FTb7qvuZ2acvHNGjHR8tr5+vHuwk29JALAARJ4AjFMcaRvH2wIALIxs3lku1kIIKfUsFaqlQvX9N34RH56UZR6bbgo+mSM/DFdPeiou+GRmpT7dvAvhOxpXbkk9AWCMDLYFYGyePbkXT9Y2GvlWAgAwXmmS7e6gU37V9DYymbZSrO71v7PpM/v27E1GxBeXClWjbplBKc6sFGthEOJs2/dXfv6w/0m+hcECaFy5lYbctr5+HIIJt4tsGC44cJURp0wINpwWlpwuTwDG72pN5AkALJQURh6bWUblYi31eo5Iw2xP+Qgdn8y4kZmo2ZZBCz7hwkZ6PZsvH+VbDwDML5EnAGNjqm1efPMAMAUpyzw9lWysbsbgM3ucftvsCwSfzBozUWHSGldu1d98L553+22pJwBcjMG2AIxHmmoLALAwuoP27mC0NfMs42dPec3Re8YXNw+2UhuoUbfMiGb/xPQ929kpEIVLqqxuhPBe6+vH4VXq2bhyK++iAGDOiDwBGLPrN+/mXQIAwMVlo8ejTm/ZPItysZa2gWYdG3w2D7aknsyCxspmyHR8jkShleN+pYFzkXoCwCWJPAFgvumvBYBx6Q7arf52vjWMBJ+nr/+ESTvjVNvypf8qAFOwF150hx0/rFkWU89u//Nuv93tt/MuhzEb5l0AwMITeQLAHNtvN+Miz6C/FgAuJ5t3Zls5c2mybKxupno0epKX7smJe4xCr4TvvwxfTbEiLqhevNEabIcQWoPtUliPPbvMpsrqRrf/eTzX6LlwCnkXsDB8k8DxRJ4AMK/2283nT+/H82vvfphvMQAwj9JazWwz5Yxs0CwXa6G/HTR6kp/dwWGTWUzIUgKa3eLJXCgXqin13Asvmv0tqecsa1y59dsv7+ddBQDMH5EnAMyr3k4znqxV61drjXyLAYB5cWzMmcxI3hmVCtVYZHfQPnb9J0xZKaxn805dnnNkJPU04XbGlVdqcbbt7sFOZXUj73IYj6HJtgATVsy7AAAWzX67mXcJS+HZk3txpO1atW6kLQCcUZwWuzfsjOSdpUK1VKjWV27MTt4ZMjN1Y0YLeWn2t0II5UK1sbKZ3ev559DLryjOLaae8Ty18DKbyivvxJPW1493D3ZyrQUA5obIE4DxSKlbaj1koqzwBIDzym7rDK9izvff+MX7b/yisbrZWN2cwU7KuFV0b9jpyieYujT7NNvZmVLPK+H78Uo2BGXGpc5O04lnXGV1o7xy+D9Jra8fN18+yrceAJgLBtsCwBxbq9bzLgEAZloaYxsyk2zrKzdmMN08VqVY3et3Qgi7g8681MwiSZNss9sfGyub3WEnjkgNmWSUuZB+pmbbzrjGlVu7Bzutrx+HELr9dvPlo8aVW3kXxSUV8i4AYMHp8gQAAGABPfzmNw+/+U0aYzuPeWcIIZV67OZRmLST4sw0FlWL59xJP1OzbWdfZXWj/uZ78TymnvnWwyUNHeM7AI4l8gQAAGApxG2dc5R3RqVXbVjNg618K2E5xVBzL7zovsrdu8NObBMshXUtnvMo/UzzLoTXq6xu/OytD+N5ty+lBoDTiDwBAABYWDHmTAs75y7vDCE0VkVK5Kny6j81qSkwnVTm8D9QZDX7/iLFfEh7PTV6AsApRJ4AjE3cK9nrtPIuBADg0N6w0+pvNw+24tE1yBEuJ9viaRPknNKbO3fSFk+NngBwCpEnAMwxATMAHGsk2kzrPFv97Yff/CavqmAejfR0tgbb2YfMKbNt545GTwB4LZEnAMyfZ0/uxZPYWQsAjCgXa6VCNR0jz87XUsxUbaWoo448lQvVNAdViydMmUbPBTB0jOkAOMlq3gUAAOez326m5s7rN+/mWwwAzKxjV2DOdYvnPC4iZQGkMbYh0xRoMurC6A470ut5UV6pxbyz+fJReeWdyupG3hUBwGzR5QkAc6a304wn1979MN9KAGDuxI7PvWHHUk84r2yLZ76VcHkptN71X4bzo3HlVhxv2+23W18/3j3YybsiAJgtIk8AxmZtoxFP0thVxi61eK5V61drjbzLAYA5k2bD7g46+VYCc6E7PPxPyr+EL7V4Qu5S6hlCkHrOoYJjTAfA8USeAIyNBG4Knj+9H09SwAwAnF3c8RlC2BuKPOH1Ugvg98Jb8aRevJFfOcBo6tl8+SjfegBgdog8AWD+aPEEgMtrHmzlXcKZxHS2ZNkeU9fsb6XOzrTR095HyF3jyq36m+/F826/LfUEgGg17wIAgLMyMRgALq+xuvnwm9+EEPaGnXgSlQrVxqpxnXCoO+ykvPOH4eqfwn4IoVKs5VoUcKiyuhHCe62vH4dXqWfjyq28i+I1hnkXALDwdHkCwPy5fvNu3iUAwBybo47J7quxojBN3WGnNdiO57XCX8e8U4vngimF9fCqf5d5VFndyPZ65lsMAMwCkScA47RWrYcQep1W3oUAAJyoUqyWCt8eeZfzre6g3TzYah5spaRzd3C4c7RSnKE6WWzZvLNevNEe/lM81+IJs6ayupH2ehpvCwAG2wLA3IhZcsyVAYALKxdr5Ux4k+bcdgft8iRDne6gnSLM8GpJ57H2+p16CLuDTlrkOdHCIMnmnaWwvvsqfdfiCbOpceXWb7+8n3cVADATRJ4AAAAstVKhGpPFVn+7HsJ4w8UUc54ScB6r1d9O55aMMjXZjLOxsvmw/0l82FjxSwgzqrxS6/bb3X5792CnsrqRdzkAkBuRJwDMh2dP7sWTtY1GvpUAwIJprG42D7ZS6lkadM4YMTYPtsIJeWR86rUx59GxutliovrKjbMUA5fX7G/FzY4x72z2t+L1uPQRmE3llXfiLs9u//OxR567Bzvd/ufButBxGOZdwMLwTQInEXkCwJy5WhN5AsCYNVY3u4N2bKzcG3aaB1uV4mtmyXYH7RhMxrm4Kbw8mllG8QVnCVPTHUqF6mvLgHEZyTu7w072Yd7VMUHN/pYf8VyrrG60vg5hAqlk8+UjSedYFfIuYGH4JoHjiTwBmIhnT+5dv3k37yoAAM6qXKzVX42T3Rt29vqd90/OGlM+mqSMMyag0dljzhEx9TTPlqlJeWcIoVKshcyE24rQfUFlBxcz7+Js23n/CAC4pGLeBQCwUMSck9PrtEIIa9V63oUAwMIqF2vvv/GL1K8Zh9MeK67nDCGUCtV4jLygVKjWV240VjcvHFvKO5mmlHfWizfKhWq2xbN85NcbmDUxjCyvjPkvKKTb/uytDxtXbo335gAwdro8AQAA4FuN1c3YqXnSJs40t7ZUqI4EkyklFVgyR7qvftVTwNkaHDYxa/FcYN3XLRtmXjRfPsq7BM5kaAXluPgmgROIPAEAAOA7SoVqDDWPTpdNKzxDCJXiaPebpJN5NDLDttk/TO61eC42s4sXj0ZMAJacwbYAAADwHSm5PNromUba1ldulOUELIQ01bZcqKalnqWw3lgR4S+m7rCT/UELtufdhKbaAsDc0eUJAAAAo442enYH7VZ/Oz0r72QxZKfaphWeQeffIuoOO7uDdvoRR37Q8y5NtS2vvJNrIZxFIe8CABacyBOAMVur1nudVq/T2m83r9YaeZcDAHAR2Y2e3UF7d9DJdnwaYMvCyE43Tec6/xbJsUlnCKEU1kMIftALo7K6kXcJvIYFlACTJvIEYMzWNhq9TiuE0NsReQIAcyw1eqbmznhR3snCyLZ1plTMSNtF8rD/yciVmHT6ETNi92Cn9fXjvKsAgEsReQIwZldrjV61qdETAJh3jdXN5sFWau4sFaqVonm2LI7usNMaHMb5PwhrKfsUhi2SUlhPSXbww+Vk3f7neZcAAJcl8gRg/DR6AgCLoVKshsHhibCTRZLNO2MY9ufQCyHUizfyLItxqxRrewNh9iLr9tshhPLKpf4Xavdg55T72BIKwLwQeQIwfho9J8dXCgDTVC7WJJ0snqN5Z2oEtNlxwZQL1djouRdedIcdP1+OlVo8yyvv2AkKwPwq5l0AAItpbeMwk+vtNPOtZGFce/fDePL86f39tm8VAIALyuadlWLNSNvFVnn19zZ2B+18K2E2ZVs85Z0TNXSM7wA4lsgTgInQhjh2V2uNtWo9nj9/ej/fYgAAmFPdtJ42rDdWNlP8aaTtooqNniGEvfCi2d/KuxxmTrbFM886AODSRJ4AMDeu37ybUs9nT+7lWwwAAPMotfo1VjZTAGak7WJL/bupoxei5stHWjwBWBh2eU7cgwcPTn/Bixf+300Azur6zbvPntyLe1KfPbl3/ebdvCsCAGBudIedtLYznQcjbZdA3OgZQmj2t/y4idJI2xBC48qtM77rtf+qkxMNC3lXALDgRJ4T98EHH+RdAkAO9CBOzvWbd3//61+FEKSeAACcS2rxrBRrRtoulcbK5sP+J0GjJyGEw7Dz85R3lldqZ3+vf9UJwMwSeQIwfrENMYSwVq0L5MYlmyKvVevxG+51Wvvtps2pAAC8VrbFM2WfRtouj9ToyZJLw2yj8krt7C2eADDLRJ4AjNl+uxnTuBDC2oYobjyy3+qI50/vh3eD1BMAgFM0+1sp7vqX8OWfQy+EUArrZpwuoe6wI+deWtm8s7xSK6+8Y4Xn1AzzLgBg4Yk8AZiUtWpdDjcuvZ3myJW1aj2XSgAAmC/dYWd30E555w/CWsw7QwiV4jmmWTLv0mzb3UG7vCLyXEbZ5Z31N98TdgKwYESeAIzZ1VqjV232Oq2TuhK5AIOCAQC4gO6wk3Z2hhDqxRu7g3Zq8dTqB0ul2/88nsg7AVhIIk8AJujZk3siustLWzwNCgYA4FyyOzvjDNvdcHjFSNtl0x128i6BCWq+fHTSU9nNnSGE8kpN3gnAQhJ5AjB+12/e/f2vf5V3FXPp2JA49csaFAwAwNl1h504zza7szNdybMy8pD6fQ00XjwjoebpGlduTa4STmGXJ8CkiTwBIH/PntxLueZ+u5mNNr9t8bS8EwCA80gtniIumv2teGKg8RIqr9Qy5+/kVwiFvAtYGL5J4HgiTwDGb7/dzLuEeZLNO0MIvZ3msd2cRgQDAHB22RbPFHGl3ItlE38ZgoHGCyfFmXo3AUDkCcD49XYOI08p3Wvtt5sp71yr1nudVq/TSo2e6VktngAAnEuaYnpsxCX3WirZFs98K2HsJJ0AkIg8ARg/Kd3ZpXj42rsfhldfXWr0TM+ubdjiCQDAWYm4SI5d6QpMn12e4+KbBE4i8gRgzEy1Pbs00natWj/MOKvN1Oj5/On9+LL0LAAAnMWxU0yb/a0UfeVTFnmw0hUAWBLFvAsAYNFoTDyj7EjboxOAs3mn+cAAAJzdsS2eKe8Mptouk2zOnVa6AgAsJJEnAJOiMfF0IyNto5F0U94JAMB5HY0202jTEEK9eCOfssiVnBtmQMExpgPgeAbbAkAOjo60Tdaq9fjUtXc/FBsDAHAux7Z4tgbb8aRevKHVDyAXNlACTJouTwDGLM2zffbkXr6VzLJTRtrGK/JOAAAu4GiLZzYElXcCALCoRJ4AjJmg7uzWqvVjr//NL//e1wgAwHmldPMHYS1dSascjTYFAGCBGWwLwPjF0aypkREAAJio7rCTpteGEP5m5f3w3RWelWItn8oAAGAqRJ4AAAAAcyybd6ZuzuxFKzwBAFh4BtsCMH7WeQIAwHQ0+1tH884Qwu6gnS7KOwFyN3SM6QA4icgTgPGzhxIAAKYgO7o2m3da4QkAwLIReQIwEWvVegjBOk8AAJic1MpZL95I0WY2B5V3AgCwJOzyBIBp22838y4BAIC51x20UytnGl07ssIzt+IAGDEs5F0BwILT5QnARFjneYrezmHkef3m3XwrAQBgfu0OOvGkUqyli//f4B/jiRWehBBSKJ53IQAAEyfyBGAirPM8RZz3G2f/AgDAxewNO+G70Wazv/UyfBVC+GG4aqQtzf5W3iUAAEyPwbYAMFWp7TU1wgIAwHk1D76TZjX7W2l/Zwjhf17536ZeEbNL/g2zYJh3AQvDNwmcRJcnAExVbPEMGmEBALiE2OIZQqgUayN5pxWehBC6w46ptgDAUtHlCQDTs98+3OJpqi0AABeWWjxLYb012E7XS2FdPx/R7qCddwkAAFMl8gSA6entvIo8TbUFAOCiUotnIuwkKzX++sUAAJaHwbYAMD1xqu1atW6qLQAAF9N91b1XKlTTRbEWibwTAFhOIk8AmLa0zhMAAM5rd3DY4tlY3bSskRHyTgBgaRlsCwDTs1atyzsBALiMONW2VKimjZ4QyTthlg1DIe8SFoZvEjieLk8AmJ60wvPZk3v5VgIAwMIQbjGiUqzlXQIAwLSJPAFgeqzwBABgXA7bPU215YhyZs8rAMCSEHkCMClr1Xqwt/IIXwsAABeWhtnGvDPo5yPDblcAYJmJPAGYuP12M+8SZpHZtgAAnEvzYCslnVteWGAAACAASURBVIl+PgAACCJPACYn7a3s7Yg8v5X6O6/fvJtvJQAAzIvuoJ3NO0uvYk79fAAAEIk8AZiUq7VGGuKq0TNKnZ3xmwEAgLPYHXRS3llfuZGuN1Y2c6qImdM90gEMzJRhKDjGcuT9kwRml8gTgAnS6DlCiycAAOfVHbRj3lkqVN9/4xflYi09zLs0ZkV32GkNtvOuAgAgTyJPACYo2+iZdy350+IJAMAF7A4Ou/cqxWoIoXmwlWs5zJxmfyvlnaWwrvcXAFhOIk8ApiQFfmjxBADgjLItnuViLftUY1WyRWj2t/bCi3gu7wQAlpnIE4DJSvGeRk8AADivkRbPbAKaZ1nMhmzeWS/ekHcCAMtM5AnAxKU5rho9AQDg7EZaPLuDdqt/OL80JqAsre6wM5J3lqXgMNuGjvEdAMcSeQIwcea4RrHP1SJPAADOKNvimc07jw65Zal0h53WYDvmnaWwLu8EAAghrOZdAABLYa1a73VavU5rv928WmvkXQ4AAMyo7qAdk87Y3xleBZwPv/lNemiL55JrDV6F35Z3AgC8IvIEYBrWNhqxx7G3I/IEAIDjNQ+2UtKZVIrV5sFWPJd3kiXvhDkyHBbyLmFRmGwLnMBgWwCm4WqtEQe69jqt3//6V/vtZt4VAQDAbBnJO0uFaqlQff+NX+wOOmmjp7yTEEIprMeTZn8r30oAAGaHyBOAKbl+825aY/n86f18i5k+KS8AAKfoDtop76yv3Hj/jV80Vjcbq5spB5V3klRebXLdCy+kngAAkcgTgOnJpp7LFgH2dg7/vNdv3s23EgAAZlCrf7idsb5yo/wq0Mr2fVaK1XwqY/aUC9V68UY83wsvHvY/6R6ZhwwAsGxEngBM1drG4SLPFAECAMAy6w7a2VWdx+ad2RwUwqvUM024bQ22tXvCjBs6xnQAnGQ17wIAWC5Xa43nYemm2gIAwFHdQTvt6QyZ0bUjSz3lnRyrXKiWV6rN/tZeeBFeDbltrJh+DAAsKV2eAOSj12nlXQIAAOTpaN4ZOz7lnZxdY2UzO+RWrycAsLR0eQIwbWvVurwTAABitFkqVCvFarlY6w7aaaNnyDR9wunikNvWYDuEEDs+AQCWkMgTgGlbzrwz/qnXqvW8CwEAYCak/Z0hhJG8U9jJeZUL1VJYj3mn8bYAwHIy2BaAqXr25F48Ef4BALDM0vTamG7uDkY3esK5pJhToyfMpqFjfAfAsUSeAExVavG8fvNuvpVMWYx4e53WfruZdy0AAMyKUqEaQkj7O+WdXEYprMeTbmYdLADAkhB5AjA9y9ziubbRiCe9HZEnAACHYefesCPvBACAy7PLE4B8xPhzeXo9r9YavWqz12nFRs+rtUbeFQEAMBPShNtKsZpvJSyMcsHvEsyeYSHvChaGbxI4ni5PAKYnBZwx+et1Wqnvcxlo9AQAIITQHbQffvObve+OHq2v3CgXa3mVxGKIWzzTeFsAgKUi8gRgqkZG2qYUcBlcrTXSRs+8awEAIAfdQbt5sNXqb49cl3dyefZ3AgBLTuQJwFRlM85r7364tPNdl6q9FQCAEEIMO/eO5FKlQlXeyeXtDtrxpOLXCWbS0DGmA+AkIk8ApiplnGvV+hLmndnRvvlWAgDANGUn2ZYyexZLhWpjdTOnolhMFnkCAMtJ5AkAU5VG+2r0BABYEs2DrXReX7mRzuWdjJFFngDAkhN5AsBUafQEAJhrcR9n82Cr+2qO6Gtfn/o733/jF7uDTnwo7wSA8frss88KF3Xnzp28ywcuazXvAgBg6axV6zHvfPbkXkpAAQCYfc2DrZRf7vU7ob8dp9SeEl7uDg5fX1+5kY0/K0XTRxmbZv+wk9giTwBgaYk8AWDart+8+/tf/yqE0Ou09tvNJdxpCgAwd7qDdqu/ffR6jDAffvObEEKpUK0Uq+VirTtop6Qz9XSm/s4QQn3lRlk0xQRY5AkALC2RJwBTtd9u5l3CTLj27ofPn94PITx/ej+8G6SeAAAzLkWYKdeMGzpTihnPY+vn0bdnXybvZOws8oTZNwyFvEtYfH/1V3/105/+9GLv/eCDD8ZbDDB9Ik8Apqq3cxh5LvlA16u1Rq/ajONtnz+9f/WXIk8AgJl2dAFnOok9ndlQ8xTyTgCYkJ/+9KcPHjzIuwogN8W8CwCAJXX95t21aj2eP3tyL99iAAA4RXfQPuXZcrHWWN18/41flE6eKVoqVOsrN95/4xfyTsbOIk8AgKDLEwBydP3m3WdP7vU6rV6n9ezJvSXvfAUAmFlpqm2lOBpqprWdR7s8YwIap+BOvkawyBNm2zDvAgAWncgTAPK0ttGI423jPwEAmEEpzhwJL5sHWyclnWnsLUyaRZ4AAEHkCUBeNDVG2aWeAADMspG5td1BO5t3lgpVDZ0AAJAXkScA+eh1Wr//9a/WqnXBJwAAs6x5kBYlfifyTNNuS4Wqnk7ykhZ5AjNuGAp5l7AgTAgGTlLMuwAAlsv1m3fXqvX0MO6wzLEeAAA4o5EOztjiKe9kRjRW/B4CAEtNlycA0xbbOp89uWeaKwAAsy9Fm9mL3UE7noy0fsKUxUWeAADo8gQgH2mereATAICZlaba/mn4z+k8ZKbaWt5JjtJU21JYz7cSAIDciTwByMd+uxlPsnNuAQBgpsQWzxDCy/BVOodZ0B12UotnRfQOACw9g20ByEdv51XkudHIt5LZsd9uXq35NgAAZkW2rTMcmW0L+dp9NV25XrxR9ssJM2+YdwEAC0/kCUA+4jzbtWpdyLe20YjfRm9H5AkAMCu6g7a2TmZQHGab+jtLYV3eCQAQRJ4A5OLZk3t5lzBDrtYavWqz12n1Oi2NngAAMyJt6zxKGsr0jSSdSWNlM49yAABmjsgTgGnbbzdjU2MI4frNu/kWMyM0egIAzJTTQ81WfzueGHXLpHWHnd1B+2jSWQrrlWJNfycAQCLyBGCq9tvN50/vx/O1aj3fYmaHRk8AgJmSbfEsFarZ+DMt+CwVqo1VDXZMUHfYaQ22s1dKYT3o7IT5NAyFvEtYGL5J4HjFvAsAYIlk884QQq/TMuE2Wds4jDl7O818KwEAWHIjLZ6V4reNdM2DrfiUvJMp2B2040kprJfC+vsrP2+sbMo7AQCOJfIEYEpG8s5I6plcrTVi22ts9My7HACA5TWyxbNcrMWTPw3/OUWh8k4mrdnfivNsS2Fd0gkA8FoiTwCmYWSebTziQ6lnkho9nz+9/+zJPcEnAMD0pT7OKLut82X4Kp7UV25MuyyWTMo7gzG2sCiGjvEdAMeyyxOAabhaa1z95eh+ypSD9jqtPIqaOWmjZwgh7vV8Hkb7YqO1av36zbvTrQ4AgBBCKBWqqe8TJqE77KS8s16UrwMAnIkuTwDylzo+uX7z7rV3P3ztF6I1FgBgOo4OsDXSlqkphfVyps8YAIBT6PIEIDe9ncPBrRoWs67WGldrjf12M30/I1Ib6LMn93x1AACwYMqFaims74UXqdcTAIDXEnkCkJsY3WnxPFYMPo99KjsQWOoJADA5cZFn82Ar70JYXs3+ll2eAABnYbAtAPlIc1nXNo4P9jjJ1Vrj2rsfxnMTbgEAxis7t3Zv2GkebO0NOznWw3JKMadGT1gYQ8eYDoCTiDwByEds8QwhnNTLyClGUs/99vEjcAEAuKSUd/4gXM23EpZNKazHk2ZfnzEAwOuJPAHIQTai+/2vf5VjJfMrm3rGObcAAEzID8LVP4f9vKtguZhnCwBwLiJPAHLQ2/lOV6LRrBdztdawCRUAYOziCs/kSvh+yjvrKzfyqIglFRs998KLrunKsACGBceYjrx/lMCsEnkCkI+1al1cd3lxPrBvEgBgQkqF6svwVTovF2v51sNSqbz6fdsdtPOtBABg9ok8AcjB9Zt3r9+8u7ZxuMUz7fUEAIDZUSpUG6ubOjvJS7lQTY2eedcCADDrRJ4A5GO/3UwbKNNOSs4lDQRO4TEAAGNUKVZDCOViLY663Rt2uprtmK7U6Nnsb+VbCXBJQ8eYDoCTiDwByEfKO9eq9as1id2l+AIBAMaosboZT3YHhwsUY/aZvQLTUX61WVajJwDA6USeAOQgtSeGEK7fvJtjJXPNQGAAgHE5vX1Toyc5irNtAQA4nchz4oavc/v27bxrBJi26zfvrlXr8Twbf3IB6ZsEAOBimgdbI+2bRwNOjZ4A4Qz/qjPvAgFYXiJPAHKw326mDkV7KE/3+1//6ve//tXRYHi/3cylHgCABdM82NobjqaYRwNOjZ4AADDLVvMuAIBl1Ns5jOss8jxdSjp7ndbvf/2rtWo9JsTpCwwmAwMAXEJ30D6ad4YYcA46e8NODDjLxVoIoVKs7vU7IYTdQSdeAQAAZoTIEwBmVLYXNup1WvZ3AgCMS3fQbvW343lq60yOBpzH5qAwaXvhRbDRE+bfMBTyLmFBmJ4MnMRgWwByoDHxLJ4/vR9P1qr1eBx9jUWeAAAXk807QwhH88vTJ9na6AkAADNFlycAzKi1aj32dPY6rb/55d+H2Pe50wwyYwCAc2oebI1cOXae7YiRRs/sFNyjXaEAAECORJ4A5CCtqIybKTnW9Zt3nz25F1PPZ0/uXb9592qtYfUpAMAFnCXgPGpkkm3q7Kyv3DDVFoDzMY8VYMIMtgUgTwK8012/eTeOru11WiknBgDgXI62eEalQjWOrj1F6uZs9bdjbloqVOWdAAAwa3R5ApCD2LloD+VZrG004tcFAMAlvf/GL0IIsV+zsboZTk5Dk9Toma7EN8J0dC/UoAwAsIREngBM2367mXcJ8+RqrfE83M+7CgCABdE82Ir5ZXfQLhdrqXEz77rgeLuDdjyp6C2GOTcMhbxLWBi+SeB4Ik8AchOHtfY6rbVq/frNu3mXAwDAgnv4zW/S+e6gk3ZznqI7aF9sDyiMxV54EUIohfWyYB4A4FR2eQKQgzTSNo5sNbj1LKzzBAAYo71h5yxZ5lliUZiQZv81g5cBAEhEngBMW2+nOZJxWup5umxCLPUEADivSrGaRteWCtX6yo30VHx40nrONAUX8tVYsUEWAOA1DLYFIDfm2Z7R9Zt399vN509t9ARYKP/4X/9D98tnf7vx7/IuBBZfuVgrf3cPYmnQ2Rt2SoXqSWFnd9Bu9bdHLpYK1UrRcFGmpDvspKm2edcCADAHRJ4A5EbeeXa9nWY8Wdto5FsJAOf1j//1P4QQul8+K791/Sd/+XfpYvfLZyGE3f/+T5V/9dd51gdL6aSkMzk6z/aUfBQmYXfQjieV7wb2AAAcS+QJAPPkak3kCTBnYrQZT3b/+z+FEP7wz/8xPftfvnwm8oQZFOfZxnG4ZtuSi9jiCSyMYd4FACw8uzwBYA7E7aeWngLMu//y5bNs3hkygSgwmxqrmyn47L7quoMpqBcPl862BttduTsAwOuIPAFgbsTgE4A5EqfaJingLL91vfzW9WNfA8yatL/z6LRbmJxyoZq2eO6K2wEAXkfkCQBzIPV37reb+VYCwMX87ca/S+c//ot/85O//LsUf6YFn8BsknSSl8bKZkw998KLZn8r73KASyo4xnQAHE/kCUBupHdnd/3m3XjS2/GlAcyNf/yv/yE7tzZ2dv74L/5N5V/9dersTL2ewEw5dotnY3Uzp3JYXo2Vw9+6vfDCeFsAgFOIPAGYtrWNRjyR3p1LbPQ02xZgXmTzzh//xb8JIfzkL//uJ3/5d5V/9dchM+FWiyfMEbs8yYWlngAAZ7GadwEALJ2rtUav2ux1Wr1Oa7/d7O001zYaV2uNvOuadWsbjZh3PntyLzV9AjCbsnln+a3r/+XLZ3/45/8YjvR0avGEuVApVvf6nRBCq79dD6FcrOVdEcslLvXcCy9CCLuDdnmlmndFwEUM8y5gYfgmgZPo8gQgT8+f3u91Ws+f3jfk9rWkwgBz5Cd/+XcpzszOtu1++Swe6WU5FAecQaV4mCo1D7bKxVqccxtCaPW39XoyfZZ6AgC8lsgTgBwcbVI05BaABXNS6plo8YRZllo5/2X4ZXfQzm7x1OVJLiz1BAA4ncG2AOQjbqaM4pBb81oBWDA/+cu/+792/o/slb/d+Hd5FQOcS/PgsJHuZfhyd9DZHRwmTKndE6avXrzRGmyHEFqD7fdXfp53OQAAs0XkOSu+7L+ZdwlL6ne77+RdAkzbG6uFvEsIIYQf/a8fpfPho4+/2Gn1Oq1/2rp37dZHp7yLEMKwGAaz8UOcI99/8d/yLgGm7puDvCvgOH4uk+V/HxmbvVdddC/DV3uZjrrG6rs5VXS84bCfdwlMTyn8VVrqORwO8i4nP19/nXcFAMAsMtgWgPyJOQFYVOXv/Sid//jtv82xEuDsmgdPj72uxROACxs6xnQAnESXJwDMmS92Ws8ffRxExQDz4H/8/vXuv/yx/L0f/WTtf8+7FuCy9ixQBACAWSXyBIC58fZG/YudVggh/vM/3fv3b2/U1zZ+vPZOI+/SADhe5fvXyl/JO2GedAdt0SYAAMwdkScAzI1rtz7qfd7s7fwhRp4hhC92Wl/stEK4K/UEmFnyTpgvu4N29mGpUM0moM2Dp7O2zhOAuTAcWjoOMFkiTwCYJ2vvNGK6+fzRxyn47O38QeQJADAWMeC8Er7/MnwVDLMFAIA5Ucy7AAD4jrilkte6duujf333H/KuAgBguVSKtbxLAAAAjiHyBGCGxDGtUs+ze3ujHl6t9gQAYFx+WPiL99/4t++/8W9LhWq6WF/ZLIs8yU9r8Lu98CLvKgAAZpTIE4CZ88VOq/d5M+8q5sPaxo/jiZwYAGDsmgdP02BbeSf5SnlnKaz/T4X/Je9ygHMbOsZ3ABzLLk8AZkKc0dr7vPnHx/dCCH98fC+Eu/ZTvtbaO40/Ps67CACARSfvJEfdYac7fJHyznrxp3lXBAAwi3R5AjBD1t5pxEmtIYTezh/yLQYAACJ5Jzn6f4f/j7wTAOC1RJ4AzJZrtz5K+ykNa30tE4ABAMaoefA0nlRknMyG1uB38UTeCQBwOpEnADMn7ae01PO1Ui/stVsf5VsJAMAi0dbJjIj9nSEEeScAwOns8gRg5mRH2lrnCQAALKdsi2e+lQCXNwyFvEtYGL5J4Hi6PAGYLc8fffzFTiue/+i9u/kWAwDAcuoO2nmXAFo8AQDOQeQJwGzJ5p1aPM/O3lMAgMtrrL4bT3YH7RDC3rATQigVqnnWxLLS4gkAcC4G2wIwi97eqMs7z+WLndbzRx+vbfzY9wYAMBbNg6d5l8BS0+LJlD08+KRUWK8UauWiv+cxGcO8C1gYvkngBLo8AZghvc+beZcwZ67d+ujtjXo8/2Kn9cfH93yHAACXEXs6Y39nlFo/YWq0eDJl3UEnhLA3fNEabDf7W/EhAMwXXZ4AzJDezh/iydrGj/OtZI5cu/VR7/Nmb+cPcSbwHx/f6+3Ur936KO+6AADmm6m25EiLJ1O2O/x2gfHe8MXe8EVpuF4p1NL1xspmTqUBwFmJPAFg7q2901h7p/H80ccx9fxip/Wf7v37f333H/KuCwAAuKAr4ftXwvfzroKlUyqs7w1fhFfBZ7re7G/lV9SCGIZC3iUALDiDbQGYIam5M7V7cnbXbn30o/fupjm3zx99nG89AADzqFKsnfIQpuZl+Op74a28q2ChPDz45CzJZb14o1QYnaicjT8BYDaJPAGYIWvvNPIuYb6tvdNII21jxycAAGfXHbRb380DyiJPpi4t8iwfiZ3gkvaGL45NPdPc2r3hi3Kx2ljZjMHn0ewTAGaWyBOA2RKbFMV1l6HREwDgvLqDdvPg6UjeaZEn+Sr7DWQC9oYvHh580h10mv2teMTrKd2MV2Lwmd4l+wRg9ok8AZgVvc+b2Yiu93kzx2LmWpoPnE4AADhFDDv3hp34UNJJvlJzZ2r3hLFrDbbjts694YvuoBO+2+iZXlYp1I5eBIDZJPIEYFb0dv7wxU4r9Xda53lh6aszKBgA4FxKhWo909XUWH03x2JYWpo7mbLWYDuu+Rxp9AwhlItV420BmBereRcAACGE0Pu8aZgtAAD5ihln67hFdzBNPwhrfw69vaCvjvOJ/Zrl4mmpeamwXinUdoft8N3ezaN9nN1BpzXYTu/S6HlJw7wLWBi+SeAkIk8AZkJqTPzRe3f1JgIAkDvjbcnRn0MvnnSHHU2fnFFMKEuF9XI4/ncmxpZ7wxeVQq2xsjmyxXNknm027wwG2wIwDwy2BWC2yDsBAIBlll3h2ZUzcTYjCeWx0mLO2OJ50p7OUmG9XKzG18SHBtsCMBdEngCwsJ4/+jjvEgAA5knz4GneJcB3mG3LGaW8M5tfdgedZn/r4cEn8WFczBlf0+xvZeffZt/VWNkMmUA0Xnl/9eeTLH85DAuO8RwAJxB5AjAT1jZ+HE+kdJd37dZH8eSLnVbv82a+xQAAzL64wjOEsDfshFcjbeM5wNzpDjox7GwNtmOWmWbYntTZmZQK6/HF2Xw0rggFgBkn8gRgJphnO0bPH3389kY9nv/x8b18iwEAmAtpc2e20bM7aOdUDnwrO+cWsmKumeLMpDXYTmHniBRkhkwOmp1bG5d9Pjz45OHBJ+kOacgtAMwykScAsyKmdF/stPIuZL49f/TxFzut7NeocRYA4LVSo2cIoVJ8te5O5Eke6sWf5l0CMypOqU1RZcw145TaeGVk6WZawxlfE18W59bGi+nZevHGKQs7T2oJ5VyGjjEdACdZzbsAAGAiYoS8tvFjHbQAAGdRKlT3hp29YacSaq9/NUyLdZ6LZCR0PK/GymZsvkzrOY++IH1KpVArF6spDR2ZcJu9GEJoDbbrxRvx7d1B52hbp9m2MFF37txJ5w8ePBjX3T744IPsncdiojd3fy5J5AnAzHn+6GNB3eWljZ4AAJxFpVjb63dCCLuDdoo/8y6KJVUK69mkszvslF/NXmZ+dQedlDueJfU8ez460oWZfUtjZTPeJ73mpJbN1mC7Hm6Ui9VysVoOx/2+DV5bCHARd+7c+eyzz+L57du3L3aTBw8efPrpp+k+UXwY73mZJHWiN3d/xkjkCcCsuHbrozSU9Yud1to7/5B3RXMpjrRNuzwBADijcrFWGrRj0plWe3YH7XJR0yc56w5fiDwXQGuwHU/imNkzZpkjrywV1s87Zja+PTaGpvfWizd2h+30ME613R22jw87gYl58ODBSFR2AdnQ9Kj4VKFQ+PTTTy/QdzjRm7s/42WXJwAz5Nqtj2R1AADkpXIk3bTOk1lgtu0CGJkoe3bZVZ0hhErh8L+m0p7O5JRlnCPSCs/sxcbK5oUn7gIX9sEHH1zm7Q8ePCgUCmcMTc87anWiN3d/JkHkCQAAABBCCKmh00hb8lUv/jTvEhinNNL2YrKpZ7n4bRfmWRLKtIPz/dWf14s3UlCaWk5T/HnhUBa4sEtmYA8ePDhvYvrZZ5+d8UMnenP3Z0JEngAAAACHSt8dHyr7ZEa0Br/LuwQu3qm5OzzsF68Xb1zsDtlIMtujGVPMY9/SHXSa/a3WYDsbl8aUdCR/Pbbpk7EbOsZ0LIzTB6Kexaeffnr04u3btz/99NPhcDgcDm/fvn10Oehnn312ltWSE725+zMhdnkCMFusoryktzfqcRlq3oUAAMylxuq7D7/5P7NXrPMkF/9b8c7/PfBvRWdIs7919h2cR98YQigV1svFahhcsID46ZVCLZtNlovVcqh2B52Uqo586EnFZKV7XuBPB1zM5Vd4Hk1Mb9++PRKnxYdH+xE/+OCD4fC0+HiiN3f/196fC9PlCQAAAPCtkUbPVn+ra6MnLLeUFJ53AGx2pG1jZfNc701rO5O94YuRgbRRbN88Ka3MZpzZ16TmzuywXGA6ju0RPJfXRm7JnTt3jn7c6RNWJ3pz9zfednJEngAAAADfaqy+O3JF6gnLbGQT57lSz9R8OTI89pRmyocHn8QFnCONm1mlwvq52jG7g06ccxsyWWkqptnfMtt2KgqO8R3z7fIjbY9mZqePS71z587IkNVTCpjozd3/tffnMkSeAMyQ3ufNvEtYHM8ffZx3CQAA8yo1eqYTqScsp+6gkxoro3PFjdmMMwaZ4bsNmseKCzizAWSpsJ7dA3pKAc3+1tHpta3BdmuwfdJg3r3hC2EnTM3lR9oedXRn5LGf+9or07+5+zNeIk8AZsgfH9+LJ2sbP863kvl17dZHeZcAALA4KsWa1BOW2WXyzpGMM3Vtnh5YxpOUQZYK6++v/ryxslkuVmPqmc0+j37i6fllTD1T5no0fD3X6F3OZegY3zHXspsdb9++fZbA7KiR0PRi4eVJw3UnenP3f+39uQyRJwCzInUlvr1RX3unkW8xAAAss0rxcIXe7qDdWH03pZ67Ik+mqBRe0wvIpI3kf+cdJ5syzkqhlqbjvrbFc0T2E8vF6vurPz9l9WaKRaN68cbRdHNkSO/7qz8/b0nAhY3MRM3Gnxe+ydlD05GPO7bZdKI3d//X3p9LWs27AACW13+69+/f3qinh1/stEIIb2/U9SkCAJCvcrEWMlHH3vCwVevomk9gUR0dLXuuvDNkwsVysZrS00qh9tq3lArrF8tH42fVw43YnNoabMc7xOLTkN6RNtD454p/XhNuYXJGRtrevn37zp070+z2O7qEcl5u7v6ckS5PAHLz9kb9i51WOuJFI20vLwbJ6SsFAOAy0jDb1OsJ01EurGv0nF/ZqbbZFs9ysdoddNKzWdmm0ss0X2Z7PfeGL+rFGzHUHOkBHdFY2cx2hQLj9eDBg5GRthfe5jjGBsGjOdxEb+7+r70/lyTyBCA3R7s5jbQFAGBGxIBzb9hJw2y1eDJl5UK1Xvxp3lUsr9PbMV8rO9U2e97sb7UG27vDdnfQafa3skd6b0woGyub5+0rTbLpZmuwnRLWNBQ3RZvZzy0Xqxf+ROB0I92cF847jzrXrc67e05AlQAAIABJREFUOnSiN3d/xs5gWwDyFBs9g3m2AADMqjjVVosnLJtysRoG3z68zMTX9N7dYTueZ/854pJ9lt1BZ3fYbqxsppw1ZJLO8GpqrgG2ME137tzJdgdOc5gtLBVdngDk4/mjj0MI1259lKaw9j5v5l3UovGVAgAAnNFJ82YvJk2yzUaPZw8aYw/oSD3dQefhwSenFJm2dWY/65RhtkzT0DGmY+4cu8LzMncbR1E53Nz9mQJdngDkoPd584ud1vNHH+vsnIS1jR/H3tnezh8MCgYAAIhiB2Q8rxRqqfcxDneNGeH7xZ+n18eGyIt91tmbKUc+JY6Wjf2ge8MXYXDY91kp1GKc2Rps18ON1DAawrcviH+6lLbGk2yLJzB94x1pO94O0ZHNlBO9ufu/9v5cni5PAHLQ2/lDONLZKZwbl7V3Gql3NnbTAgBwXjZ3wuJJOeLe8EXKPpv9rWw2mXoojzZTZtdenl12UG29eCP1XJ6ehmafjeWl9s0QQvY8+4Jj7znG1lXgvCY90namdnOeN811f8ZOlycAOYg9iCGEPz6+F09iRMe4XLv10fNHH3+x09JNCwAAEDVWNh8efBLP94Yvmv2txspm9mIIYXfYLodqdkJs9u0X+NBKoRb7L7/tKx2MvmDkPGWrMS49NsjMtngefcHRP9HIR6S3WOo5PcNC3hUsvt/97neXmRk74vIB1XhH2gKvJfIEYNqOXTC5tvHj6Vey2NJ4W6knAMDl7Q21SZGPUljfCy/2glxqPLIB4UlRYuz7HHl9tlnzLFKSujtsN1Y2s6HjiHKxOvJspVBLBcScNY3kbaxsjpQ3cuW1sh93sb5VmFn/+T//55kaFvrBBx+k89u3b2vyg0kz2BaAaYtTbZO3N+r/+u4/mGo7dmvvNH703t14PjJDGACAsygVLMCDBRczv5E4MxsoZlsw01sumRRmP+7YJLVcrKb+zvhZ5WI1NqSGEBormyflryPXS4X1Y/8ISbonMHYjDZ3Z+BOYEJEnANOWGjpj2Kn7cHKyqecfH9+TegLw/7N3fzFynWeemL9T5Iz/BZa67XQRdje7ZXJnbSOsJoLYwxY15IUlDJAE2GDcTSHADonc5SJAgPhmB/HlBh4g8E1uc9WcBQbqpo0EmCTYiEZCSlRz7EXALgKWd0LK3ezyQFVjFyVlx7LWYp1cfOTRUVX/76o61XWeBwXNV6eqTr/ssmWPfn7fF4Djq6nVuB+6Er5W2thp1WVsoMxWfmYjZ+PizNc/fm2fwecu/ZfxR3RdXH9y5/WPX9u9FXXb1LPrbvHp/ImLT6fpUrTUo0+PY6FrhaeRtjAcIk8Ahk1D5zBNzs1ne1KzzakAABxCs/Oo6BIotaadi33SFRb27uyMusLIamWma35sDD5j9pk9sgA1O+zSkbmfDsupZHrbUDb/2ZiAxiuvnHw1PrRvUkKLi4tp/xy6jN4VnkbawnCIPAEoQAzh4qZJBu3s5WtZ6vng1nKxxQAAHCOnKp+aBvmuyJMiZFs8rfMcnPwk221fmkqmuzZodr0ne9Q7b73+8WvZ6s3wrD00L59Q7iJLLndq06xVXsxuIuCE0bGyspJ/Ku+EoRF5AlAkCdxwZKnn48263zkAwD5VKzsuwINCNNOtm51VE26PKEsHewPOrpey0bUxzsy/VKu8GDdlbvsj6p23sqy0K7CMc2v78gcxsRZGUNdI2674ExgokScABbC/c/iy33lMPe31BAA4qFa6ZbYtwzcVPgnV/i5dDybc9kOMKltpo1Z5sevinlNhs27OfA66y5u7Jt/Gj7TSxj5XgQLHSO9I24Ou8Mx/fBDvP1A9A725+zMIJ4suAICSmpitPd6sm207TGcuXY3rPONvvr1Zm5w9b7UqAMD+1Z/cqen+pDgfhQ+DCbd9Va3MTKXTMYacP3HxKP2XWYza+9JOF5udrV06NWMmamItHCO9PZ2HSLl6P5IfjXvlypWDJm27WFxczD8d6M3df8/7c3QiTwAK1t5Yl7oNx+TcfAhX25v3YtIcg89H/+a50//JP/MVAABsayqZaaVb+cO7nUciT4apVlm42eneA1fvrNUqC4XUMx6yqbPhWdK504jaLnEHZ35VZ1ckmV/5udMdsgLqnbdeqby6e5GxNsHnGEhDUnQJYyItuoD9O1w8tvunBtopOOg2RPdn0ESeABRjcvZ8DN7am/fkbUMzOTc/OTff3ljPgs/f//b9h7evT879sOjSAABGXUw9W+nW+sdvzp98qehyKDWNnkeR39CZnzS7nxbPGD1WKzPVsH135vyJi12p5ysnX81+7qnkdGzrzN6z/uROV5yZRarZTWJtgk8A2J1dngAUIx9z2is5ZJNz82cvXztz6WrRhQAAjLos2mylW/mzpZ4MU36dZ6beWRt+JeNh/sTFrKdzp47MWuXFV06+OpVMd3V/NjtbB7p//uPzJy5mY2yz98Q4Mz7iS+tP7lQrM/MnLsYCso9na0H3UwNQHqOznvMQXa3uT3+JPAEozMRsLYTweLP+8Pb1B7eWiy6ndLLUOX4RAABsayp5GlGsf/xm7VmLVf3JHaknQ1NNpr8YJrsuavQ8ivkTF2uVF+M55pr5xysnX43Z5PyJi/n8MoSQzbPd5/1PJTvOwd72pdj9GaPNeJ/e4LPeeWtff0hGTerRv0e59XEHZH5L6BBu7v573p8jEnkCMBIeb9Yf3FrW7jlMP//f/6eiSwAAOAbyzZ3VyuksAX1X5MmwVJOZz4bP917X6HkU1crMKydffeXkqzHXzD+63tmVeu7//rXKi1lb57ZvyN+260fEobuxoTMLPrP3ZLN5gZGyurqaHlBXhLa4uNj7nj6W169bDfnm7s8+2eUJwFDFLZIhhLhIMu/xZv3xZj2Eq1Z7DscffuG5f/x1CCGcvXyt6FoAAEZa3OIZQohbPNc/ftNST0aBRs+hOZWc3mkE7i52yTujuLYz37WZ/ymttNFKG1Pp0xWe8a+xDTQGotlNDloYcKytrq4mSZI93f+41JWVlfzTbRsWB3pz99/z/hyRLk8Ahqq9ee9ZtPkpX/jy6YnZ2plL8s7hid+CqbYAAHvKN3rGp7HXM6aeRVZGuW2745NByLeE9vfO2aTc+RMX872b+W2jcdPn+pM7+TfE4DOffTLi0pB49OURQrL3r3vcdQVm+9wo2ZXPXblyZfg3d/89789RiDwBKMbEbC0+vnX1h9+6+sNv/qf/7dnL1+SdQ2N5KgDAgeQ3eoYQTlWeLuFrpVuWelKIz4TP1SoLRVdBP3XlqbXKi11bPOOj61NSTyihrsBsP72GXcnc4uLiTlndQG/u/nven6MQeQJQjLOXr8VH0YWUna8AAGA/uho9q5XTtWfhRF3YQBE+Ch/a5TlO1p/cef3j17KnrbTxbvoorhHNb/HcltQTyqY3MNs9QltdXd3/CNaB3tz9GSiRJwCUkam2AAAH1dXoWa2c7roCgxPXdk6FacNsx0wMNcOnt3hGMcicP3ExPuJk3Z0SUKknlE3XbsgbN27sFLytrq72LpJcXV0t6ubuv+f9OTSRJwBDNTl7Ph4MVi1Q9svPvg4AAPbU1egZLPWkCIbZjp9TyemdXuoNMrsS0Dj8NgtNm52tgZcLjIalpaWupZIxeOuK05aWlq5cuXLQRZIDvbn773l/Du1k0QUAUC6Tc/MPbxddBM9YngoAcCBTyUzMO9c/fjMmoKcqp1tPZAwM1bnkwv30bgihuuuwU46LamWmFl6sd97quh6DzK4Fn72frYaZARYHjLDV1dWlpaV8onbjxo09Z6iurKzsZ5HkQG/u/gyIyBMASsdUWwCAw5k/+dLrv//rkGv0rFZOT3UetdKt7AoMWjWZaaaNVmhUE1nXmKhWZqbS6TjbNiadp5LT1Yrvd6ykRRfAWIotg/tfFXmgyG2gN3d/BsFgWwAohfbG+oNbyz+7/r2fXf9evGKqLQDAIdjfySioVRYs9RwzcalnrfJinFsr7wT2Iw5T7RqyupODRm4Dvbn7Mwi6PAduz1W0jUb3cnKA8dDeWA89o1Ot8CxEe2P94e3rXRdNtQUAOIT5ky+tf/xmbOtsdh5VK58s4et6CgNlqef42X2G7YjY8x91AgfSl39Pra6urq6urqysbNtxGDO5Q/+ggd7c/emvJE211A9KkiT7f/O1n/5Xg6uEXaz/w1eKLgGG7Q9emxzOD4rdhBOztbOXr8UrD24tx5GqIYQzl66K3IajK++cmK1Nzp4v2y9/8t7jokuAYUt+/V7RJcCwPfmH3xRdAmXR7DyqP7kTQphKZuJGzzjtNns6NOmTj4f54yjQzc5qCGEqTEs6k4qpdcV4/ePX9v/m0v4z513+gfDSnf96mJWMsbXv/5+N/+udbV9aXFyULYUQYgIXBpO0DfTm7s8RiTwHSOR5LIg8KaEhR57RxGwtCzuDvHOI8nlnPn4uG5EnJSTypIREngxTbPQMIdROXKxWTos8GTSRZ0bkWRSR536IPIdA5AnsxGBbAAaia4BtlneWOXUrlt88AEAfnaqcbj3ZCiG8mxtmG0NQ6Lt6Zy0eqon9nQAA2/O/igJgsCZma/mz1G3IdNMCAAxCtXJ6KpkJIcSNnvEMg1b1rzQ4tlKP/j0AtqXLE4CBiG2dMeNsb6y3N++FEOSdhYhThfODhQEAOLp8o2d2sZlr+oS8ZrrVTBs7vVpNpsWZAABHIfIEoP/aG+v5p5Nz83oNR8GDW8tSZwCAfqlWTk91HrXSrVa6lXV5vivyLL1munU/vXvQT7XSRjNtCD4BAA7NYFsA+uDBreX88s7Y0xlCmJw9X1BFfELMCQAwIKekm/SoJjMvV5amwoGXbrbC9j2g9c5afOkQ9wQAKA9dngAcVXtjPQ5NfXBreXL2fL6hU3PniDDbFgBgEPKNnvFKK916/fd/HUKIfZ/zJ18qsj6KU02mW58eYzsVpmuVha635VtCp8I2LZ75vLP34wAAZESeABxJe2P94e3r8RxDtYe3n740MVsrrCwAABiKbKNnlxiCvv77v66duGjUbQlVk5mu8bZdTZxvdP4mhPBR+DD/hpud1Xj+TPjcc+FL+U9VEy2ecLylRRcAMPZEngAAAACHlDV6hhBqJy6+23kUnuWdUf3JnanOI+2eZXYuuZBv39zPss+Pwof5iLTrDgAA9BJ5AnAkk3Pz7c1anJiatXV2jbelcF1fEAAAfZQ1etaf3Mn3dDY7j+pP7oQQWunW+sdvSj3LZipMx9gyBpwxs8wG1e6fvBMAYD9EngAc1dnL1352/XshhMeb9TOXrgo7AQAolWrldC2EmG7Wn9yphRBTz/z1mHr2flYOOsZqlYUs4Gymn2SfmSwTzZ7a1gljLSm6AIAxVym6AADGwZlLV+OhvXmv2Ero9eDWcjxMzp4vthIAgHFVrZyeetaHV39yp9l5lF2vnbgYz610q/eRvZOxlF/AGVPPzLnkQq2ycC65MBWms6fDrg8AYIyIPAHog8m5+Tg09fFmPQvYGDUacAEABmf+5Eu7pJ5TOwwmjT2gjKss5uyKM7NZtdVkplZZeLmyZHQtAMARiTwB6I+zl6/Fw+PNentjvdhiAABg+HZJPeNLtRMXX/mD/zI+snduO/CW8ZPNsJ0K0wJOKKHUo08PgJ2IPAHom9joGYy3HTGPN+sh9+0AADA4O6We8aW447PrnTut+WQMxJgzjq4FAGCgRJ4A9F/M2Bgpum8BAIZjl9Szy6lnCailngAAcEQniy4AgDHx4NZylnSeuXS12GLIO3Pp6sPb10MID29fD+GqjZ4AAIM2f/Kl9Y/fbKVbIW7rfLawM7/R81TldLVyeqrzKL7t3c6jfA8oY6CZbhVdAgBAiejyBKA/srxzYrYmVBspk3Pz2VTbh7ev6/UEABiCfK9nppVuZY/YAJofb1tEmQxQM326vLNWWSi2EgCAMhB5AtAfWWenAaoj6Ozla/nUs9hiAABKIsaZ+UfXG3Yfe8u4igs+AQDoL4NtAeiPybn5EAxQHV1nL1/Lhg8/uLV89vK1oisCABh/8ydf2vZ6s/Oo/uROCKH+5E4Wha5//OZO7+c4Em0CeWnRBYwNv0lgJ7o8Aeib/ABVRlDW6/l4s/7g1vJOb3twa3mXVwEAOLpq5XTtxMV4NtJ2vE2F6aJLAAAoBZEnAH3T3liPTYTWeY6sfOq57fzh2An6eLP+s+vfE3wCAAxOPvVk/DQl2QAAwyXyBKBv2pv3ii6BvU3Ono+H3u8rm3wbxeDTZlYAgAGReo6xZvp0qm010eUJhBBCSBOP/jxCUvR3CYwokScAfZNlaXFuqqhsNGXzh7saPfN5Z35A8cPb132bAAADYsJtOdU7a9pAAQD6S+QJQN9Mzs2fuXQ1i9Me3r4uJxtNvY2e+bzzzKWrZy9f+9bVH2bBZ/w2BZ8AAIOg13Ms1SoL8RDbPeudtfyrrdDI2kABAOiLk0UXAMBYmZybn5ybz/Kz9uY9Sz1H0OTcfHuzFnd2tjfW25v38nln9pWdvXwt5NLQp+/frE3Onve1AgD0UbVyuhZC/cmdoguh/1ph+2gzy0SBkkiLLgBg7OnyBKD/YlTGKMsaPR/9m/81JpoTs7V83pk5e/la1rwbnnV8DrNUAIAy0Os5fqbCNls8t70IAMDRiTwBoIwm5+a/8OXTIYTf//b9EMLEbO3s5Ws79W5Ozs13BZ8m3AIA9F21crroEhiIrqm2AAAMgsgTAErqP/gP5+Ih5p17vn9ybr53CSgAALCtbUfXxjm3ej0BAPpO5AkAJXX6W/8s7DvvjKzwBACA/WimW1lzZys0dtroCQBAv5wsugAAoDDbLu8EAACOot5Zy2ecXwyTH4R2gfUAAJSByBOA/ntwa7noEtiXQ+SdE7O1x5v1x5v1QdQDAABjIMs7p8J0NZm+n97Nv3ouuVBNZoqoCyhSWnQBAGNP5AlAPz24tSwMAwAASiubZxujzTc6f5N/dSpMyzsBAAbBLk8A+qYr7zxz6er+l0RyXEzOno8HvbwAANAra/GsJjN3Ov/HR+HDrlezTBQAgD7S5QlA/03M1oSd42pybv7h7aKLAACAkZTFmV8Mk/XO2ofh3/W+pxUazXRLryeUTpoUXcG4MCMY2IEuTwD6JrZ4yjsBAIByylo8v135zvvhNzu9bTP9u2FVBABQFiJPAOBgJmZr4VnCDQAARFmL51SYDiF0jbTN+yC0f9r5yZDKAgAoB4NtAeiP9sZ60SUAAAAUJmvxrFUW8tenwvTvwm8/CO38xa6nwNgzjbVf/CaBnejyBKA/2pv34mFy9nyxlTBQ7Y31bIJx0bUAAMCoaKZb8RBbPPNaodEbcE6F6ewjAAAcnS5PAPpscm6+6BIYoIe3r8eDbBsAADLN9GmLZzXpjjzzvhgmZ5M/qiYzQykKAKBEdHkC0B/ZZscHt5aLrYTByb7cidmabBsAAKJmuhWn2k6F6a4482T4g/zTb1e+I+8EABgEkScA/XHm0tV4eLxZt9dzLGUjbUMIZy9fK7YYAAAYHbu0eJ749Ig1w2wBAAZE5AlAf0zOzWep58Pb16We4ydb15p90QAAwC4tniGE58KXziUXToY/LKI0AIASEXkC0DdSzzGWtXgaaQsAAHl7bvGsJjMfh38fdshEgZJIPfr0ANiJyBOAfpqcm5+YrcWz1HOcZC2eRtoCAEDeLi2eUb2zFg87ZaIAABydyBOAPjt7+VqWemY5GQAAwPjJ4sydvB9+EzPREIIWTyi1NPHozwNgByJPAPovSz0fb9Yf3FouuhwAAIDB2qmD86PwYXbeMx8FAODQRJ4ADITUEwAAGHtZ0plt9Hz2dCv/dCpMTwVTbQEABkjkCcCgZEsfpZ5j4PFmPYSQjSwGAADCzrNquxLQWmUhPoZSFDCKUo8+PQB2IvIEYIDOXLoaDzEwAwAAGEvZws7ep/o7AQCGQOQJwABNzs3rCxwD7Y31oksAAIAR1Ztodu3s1NwJADAEJ4suAAAYaQ9uLWvSBQCAw9HiCQAwHLo8ARiU9sZ6Pi2zzvPY6foGJ2Zr2X5WAACgS9bc2TXkFgCAIdDlCcCgtDfv5dOyydnzxdbDQeW/wTOXrk7OzRdbDwAAjLI4wDYLPr8YJj8IbfEnAMBwiDwBGJSzl6/97Pr3snOxxXAU37r6w6JLAACA4yHLOD8bPv9BaBdbDDA60qILGBt+k8BODLYFYIAmZmvxYKrtcZQ15vr6AABgP5rpVjxY4QkAMGQiTwAGSHPnsWaSLQAA7Cm2dcaMs5k+bfGsJiJPAIChMtgWgAHSHQgAAJRQNZkJIbTSRgih3lmLaz6BUkuToisYG36TwPZ0eQIwQI836/Gg3fM4klgDAMDuskm2XeqdtZh6AgAwHCJPAIZBeHasSawBAGBb2STb2MepmxMAoCgG2wIA24tNuhOztaILAQAAgOMtLboAgLGnyxOAQWlvrGfnydnzBVbCIeS/PgAA4BCmwnQIoRUaRRcCADD+dHkCMCjtzXvx8K2rPyy2Eg4h+/rE1QAAsJMYZ8ZoEwCAAunyBAB2Mzk3X3QJAABwbOjsBAAohMgTANiGRZ4AALC7Zrq1+6t6QAEAhkbkCcCgxMyMYyqGnY8365Z6AgDAtprp01bOWmVhl1ericgTAGDgRJ4ADJY2wWMqW+H58Pb1YisBAIDjKGvxrCYzRdcCFC/16NMDYCciTwAG4sGt5aJL4Egm5+azuNq3CQAAh6PFEwBgOESeAPRfe2M9m2p79vK1Yovh0LLvznhbAADYRb2zlp27htxm420BABgokScA/dfevBcPZy5dLbYSjij7BrPvFAAAiLJ0M86wBdhV4tGnB8D2RJ4A9NmDW8uxxXNitjY5N190ORxJlnRmbbsAAEBmKjydW9tMt7pe+kz4XJCGAgAMi8gTgL5pb6z/7Pr3jLQdG1l6HTTsAgDAdrJGz10G2PamoQAA9N3JogsAYBy0N9bbm/fyjYASsuMuv5D1zKWrGnYBAGBbU2G6FRqt0GimW9VkJrvyUfgwvqGZNuJ1oMzSogsYG36TwE5EngAcVb4XMIQwMVvT33nctTfWH96+Hs/yTgAA2A/RJgBAgUSeABzGg1vL8dAVdk7OnhePHXfyTgAA2I9munU/vZs9zTd6AgAwZCJPAA7sZ9e/13tRNjY22pv34sF3CgAAu4szbLOn99O799O7U2G6wJIAAMpJ5AnAwWT9ndHEbC2EYJLtWJJ3AgDALqrJTOzprHfWQghZ9vl++E2RZQEAlJLIE4BD+tbVHxZdAgAAQPFqlYWQm3P7Ufiw91UAAAZK5AnAweSXdwIAAJRcM91qpk/7O7vm3AJk0qILABh7laILAOBYivNsGUuTs+fjoWuIMQAA0CV2drZCI3t0vcFeTwCA4RB5AnAAMrAysMITAAD2KU6yBQCgcAbbAnAYZy9fK7oEBs4QYwAA2EW9s7bne6qJLk8ghBBCmhRdwbgwIxjYgS5PAKCbwcUAALB/U2H6XHLh5crSueRC10vZmk8AAAZK5AnAAcS2P3lYebQ31osuAQAARlStshDDzlploZrMhBCqyczLlaX8/s5WaDTTreJqBAAoC5EnAPsl/SqPbHBxe/NesZUAAMAoy8LOXWj0BEIIqUefHgA7EXkCsF9Z+mWRJwAAwE5aQcYJADBsIk8AAAAA6I96Z63oEgAAykjkCcB+WeQJAADQ5aedn9zsrL7R+Zu4s1OLJ0BRbty4kRzW0tJS0eUDR3Wy6AIAgJHz4NZy0SUAAMCoa6Zb99O78fxR+PB+evfv0vXet1WT6eHWBQBQRiJPAPZFBlZO9rYCAMC2ftr5yQeh3XXxo/Bh7zurycxQKgJGWlp0AWPDbxLYicgTgL09uLUcp9oCAAAQQvhs+HwWeU6F6d+F334UPswiz6kwnU24rXfWapWFYqoEKJOvfvWrCwuH/PvtlStX+lsMMHwiTwD20N5Yz+edss8ysLcVAAB2V6ss1DtrMdfcfX9nKzSa6ZZeT4BBW1hYWF1dLboKoDAiTwD28PD29fzTM5euFlUJw9He2Gb/EAAA0CWfenbpuhhXfko9odTSpOgKxobfJLA9kScAu8mv8JyYrU3Onp+cmy+wHoagvXkvO2f/ArDUEwAAesXU8/3wm223eObdT+8204YJtwAAAyLyBGBH+ZG2E7M1oVdJTM6ej997fohxe2Nd2g0AAL16U8x6Zy0e2qH5cfh9dr0VGvXOWjWZ1u4JANB3Ik8AdpSNtD1z6aq4qzwm5+bbm7Wupa0Pb1+fnPthUSUBAMAxkg9Bb3Y+tVWuFRqttKHdE0ooLboAgLFXKboAAEbdxGxN3lk2Zy9fm5itTczWzly6OjFbK7ocAAAYB18Mk/HQCo2bndVmulVsPQAA40SXJwCwjWyOcX61JwAAsIs40rYVGlNhuvfVD0L7dPgnj8L/G5/a7gkA0EciTwD20DXgFAAAgLxmutVMG63QyK7kz5nPhM/9UeX8H4Xz9c5afEPc7in1BAA4OoNtBy7dy+LiYtE1Amzjwa3leDDXFAAAoFcz3ap31m52Vu+nd7fNODOVUAkhfBQ+/GnnJyGEWmXhXHJhSFVCX+35jzqLLhCA8tLlCcA2Htxajs2dE7O1bMApAAAAIYSsTXOfOqETDx+EdjxUk5nYGHqg+wAAsBORJwDdsrwz5BY6AgAAEHbIO+Pyznh9KkzHWbW9A2/jx7sm2ZptC2WgARZg0Ay2BeBT2hvrWd555tLVYosBAAAYNdVkOh6mwvRUmD6XXHi5slSrLNQqCzH4zPLLajLTO8O2FRrNdCu+LQtKb3ZW40UAAA5HlycAn9LevBcPZy5dnZybL7YYAACAUVNNZrK/9rw03Uq7G0CzGbbZlWbaiB+vVRayntH76d2dbgsAwJ5EngBsT94JAACwrZ2CyWoys5/MMjZ6Sj2hXNKk6AoAxpzBtgB8IptqOzGTH9duAAAgAElEQVRbK7oWAACAMdG7+zOmm1E24TZeN+EWAOAQRJ4APPXg1vLD29eLrgIAAGCcfTFMxkO9s5ZdzKeezZ7RuAAA7EnkCUAIuf7OEMLEbO3s5WvF1gMAADAe8l2bJ8MfziZ/FM9xvG32Uq2ykF0fZnnAcKQe/XsAbEvkCUAIIbQ378XDmUtX5Z3kZVk4AABwUM10Kz/D9uPw76vJzLnkwrNXP5Vu5ho9zbYFADgYkScAn1rhOTk3X3Q5jJD2xno8WO8KAAD9Uk1mYrrZ1ehZTcy2BQA4JJEnQNk9uLWctXg+3qxnEReEXPvv5Oz5YisBAIDjqJrMnA7/5DPhcz3Xn6ab+R7QajITD2bbAgAclMgToNQe3Fp+vFnPTy7NIi4Iz6baav8FAIBD+6PK+T+p/Ofx/MUwGQ9Zo2cIod5Zy96cXQQA4EBEngClll/bOTFbm5itaeYj8+DWctElAADAsXezs3qzsxrPnw2fz67XKgvxkO/p1N8JAHA4J4suAICRMDFby8efEEKYnD2f7wAGAAAOKt/BGXLzbKOpMB0zznpnLUtAg15PGEdp0QUAjD1dngBlNzFbC8/ml0JeNszWvzwAAOBwujLObFtnVKssxHSzFRo3O6s/7fwkXm+FRr2zVu+sNdOtoZUKAHCs6fIEAHY0MVuLeeeDW8v6gAEAYP+a6VYz3XtKbTWZbj1720fhw8+Ez30UPgzPJty20sb99G58NWv9rCbTXdEpAAC6PAHKLlveaXEjvbKYU6MnAAAcVCs0unZzds25DSFUk5ksy/wofPhR+HCnqbbxbq3QuJ/e7b0PMOrSxKNPj6K/SmBUiTwByi4bXgrbiqOPg1AcAAAOIp9lZn4XfhsPcW5tPGfjbbOnL1eW4mMqTGeP/H3iINxBlg8AcMyIPAF4Shsf29LoCQAAh9OVZYYQPhs+H0Kod9Ziv2aWelaTT0LNfAdnrbKQPWIIei65kL2q1xMAICPyBOCTNj7YlkZPAAA4nGryqcizVlnI/joVprNXq8lMFmp2faTnhjMvV5YGVi8wKKlHnx4AOxF5AvCJ9sZ60SUwijR6AgDAIdQ7a/fTu9nTz4TPZeeXK0u1ykI1men91LYXAQDYncgTgE8CrfbmvWIrYWRp9AQAgP1oplv1zloz3QohtEIj/1I+8jyiOAU3jsY13hYAIIg8AYD9mJw9Hw8aPQEAYBfVZKYVGvfTu810K7/I81xy4duV7/T9x3XtBAUAKK2TRRcAwEiYmK093qxLs8jLGjrz/8Kw+RUAAPJiQ2cIoZk2qsl0NpY2P9L2XHJhoONqY+oZV4QCAJSTyBMA2MbPrn+v9+LEbC0bgwwAAKUSo82YXNY7a9nQ2qkwnZ1baaOZNvJXnn220d/IM0s3m+lWzFalngBAyYk8AaCk2hvr7/78//7DLzyfNXHGDs6zl691LezMOjvlnQAAlFYzbcSJtflxtaFnYWfX02hwSWRMUrPU82ZndSpMCz4BgBISeQJAibQ31tub90JuVu0//vpR9mq8mO/v/NbVHw63QAAAGFGftHJuF2pWQuXL4Stdr06F6c+Gzz2XfGmgheVTz53KAwAYeyJPACiR9ua93o2tWROnZa4AALCTc8mF/HrOLp3Q+V347bcr34kzb6fCdH6v56B1pZ7NdGtoPxrYpzRNii5hTKTBbxLYnsgTAMooxpyTs+cn5+bz12MbqOwTAAC6VJOZajKT3+LZ5YPQLnCbZjWZiaN3w7PsU+oJAJSKyBMAyminrZyTc/NZCNq10RMAAKhVFprp1k7tnsUOla1VFrJEVuoJAJRNpegCAIARtVMsCgAAZVZNZl6uLE2F6d6Xvhgmh19PXq2ykBXWTC31BABKROQJAAAAAAdTqyycSy50BZ8fhHYz3SqqpChLPVuhUe+sFVsMAMDQiDwBAAAA4MCqyUy+qzK6n96VegIADJ/IEwAAAAAOKWv3HKmJsrXKQjwUu14UAGBoRJ4AhPbG+uPNetFVAAAAHEvVZCbkwsURSRmzCFajJwBQBiJPAMLD29fjYWK2VmwlAAAAY6Dw2bZBoycAUDIniy4AgII9uLUcDxOztbOXrxVbDAAAwLH2cmWp6BI+MRWmY95Z76xlCShQiLToAgDGni5PgFJ7cGs5jrSVdwIAABzOyE6OFXMCAOWhyxOgvLK8M4Qg7wQAADiEm53VokvYTWz0NNsWABh7Ik+AkmpvrGd555lLV4stBgAAYAxMhemiSwBGVlJ0AQBjTuQJUEbtjfWHt6/H85lLVyfn5outh+HI9/UCAABH0Uy37qd381emwrRBsgAARRF5ApSRvLOE8nnnxGyt2GIAAOC4a6bdo2JHPO9splvVZOYoH2+mjVZo7JTsxoWmvS/FD4YQsuG6sRe2mkwfpR4AgC4iT4Aympit6fYrj3xTb5BzAwBAP1ST6danU896Z22UU8/Yk3rQlDFLOrMrrdC42Vk9l1zIbpV/z83Oan6677Y7RGNueog/AhxradEFAIw9kSdAGU3Ono+RZ3vznvRrvOXzzonZ2uTsed84AAAcXTWZqSYz9c5aluq1QuOInZSDUKssZEXeT+8208ZOuWwz3QrPMtHepLNLvFU8d71tl0/l39NKG72Tgbd9s35QAGA/RJ4AZTQ5N//wdtFFMBTtzXvxMDFbO3v5WrHFAADAmKlVFvLpYDNtjGA4l089W6GxbTdq9oauIbRRDCPjp/K36n1P1/X8DNubndXd69wpK22ljV2SWgCASOQJUFJxtq3xtmMv+4rlnQAAMAj5ds/RbPRsplvVZLoapmNXZVeymG9U7X11Kkx3NVl2pbzbvqfeWettzTyXXOhdgJq3S3toTGq1ewIAuxB5ApRUNtv2wa1lYdi4enBrOR4mZmvFVgIAAOMtW+05ao2eWaL5cmUpGyS7Z8Nlb4qZl6W8YYeps9t2ZMZPHbT+ZrqVJbWx3XO+cvGgNwEAykDkCVBSZtuWilQbAAAGqprMxMbH0Wz03NNUmI7tm+HZOs89DWfSbNfO1P0sCgUAyknkCQDjqb2xHht5tXgCAMAQjGyjZ2zZbKZb2brNLll4OVJl5+XXka4/uTN/QqMnx09adAFjw28S2InIE6CkspGnjKv25r14mJw9X2wlAABQBqPZ6JnvxRyRkg6nVlmI83hbuy4EBQBKq1J0AQAUzMjTcZW1eE7OzRddCwAAlEI1edpG2RTLDUDWpbr+5E6xlQAAI0jkCVBSRp6Ot6yLV4snAAAMTTWZibFcbPQsupxxk3WsavTkWEoTj/48AHYg8gQoI1Ntx9uDW8sx0g4haPEEAIBh0ug5UFmj5+sfv6bXEwDIE3kClJqptuMnn3eeuXS12GIAAKBs8o2eNzur9c5a0RWNlfxq0lbaWH9yp9nRTQsAhBDCyaILAKAAptqOq668U4snAAAMXzWZDmlohUYIoRUa9c5aNZmuJjNF1zUmXjn56vqTO3G2bStttNLGVDp9KjldrfgNM9LSogsAGHsiTwAYE/JOAAAYBdVkpprM1DtrWerZShvNtJHvUOQo5k9cDCEIPgGAPINtAcoo9ndm8RhjQN4JAAAjpVZZOJdcyHZPxjm3P+38pNiqxsn8iYu1yotTz5anttJGvfOWObcAUFoiTwA49uSdAAAwgqrJTAw+sysfhPYbnb8psKQxU63MdAWfUk8AKC2RJwAcb+2NdXknAACMrGoy83JlKWv3/Ez4XLH1jJ8YfEo9AaDkRJ4AcLy1N+/Fg7wTAABGVrbI87Ph88VWMq6kngBQciJPgFJrb6wXXQJ9I+8EAIBRFhs9W6FRdCFjKw65jed65631J3eKrQcAGCaRJ0AZnb18LR6yBkGOrzjVdmK2VnQhAADAbrKws95ZK7aSMVatzGSpZyttSD0BoDxEngAAAAAwPK3QkHoOTlfqacItAJTEyaILAAAOz2hiAAA4jlqhcbOzei65UE1miq5lDFUrM7XwYr3zVgjh3fRRNfglU7w0TYouYVz4TQI70OUJUEYPbi3Hw+Ts+WIr4Yiy0cTZsGIAAGDExaWeIYT76d1mqgdxIKqVmalkOhhvCwClIfIEKLXJufmiSwAAACiLqTD9cmWpVlmQeg7BqeR0PMTU04RbABhvBtsClNHjzXoIYWK2VnQhHJWvEgAAjouXK0vZuVZZqHfWWqERQrif3g0hmHDbd9XKzFQ63UobIYRW2miljal0+lRyulrxqwaAMaTLE6B0bH8EAAAoXFevZ72zVmw9Y2n+xMVXTr4aJ9yGEFppo955S8cnAIwlXZ4ApZNtf7TI87jLdrICAADHUa2y0Ey3YpdnKzTqnbVaZaHoosbQ/ImLzc7Wu+mjfMdn6IQYhQ6i73P9yZ34s+KPmD9xsb/3BwB6iTwByssiz7Fx9vK1oksAAAAOI86zlXoOWrUyUw0z+eAzhJAloKETwmDiyfgjXv/4tRBCrfKimboAMDgiT4DSsf0RAABgdEg9hyYffIZneWQmH09ms3D7mIC+mz6qBpEnAAyKyBOgXH75gx8VXQL98eDWsvQaAADGQ0w9m2mjFRqt0LjZWT2XXIgX6bsYfGZP15/cCTvEnyGE1z9+bSqZPmjwGd+fjbfN7rn+5I4ht6WVFl3A2PCbBHYi8gQoKaNQj68s7AQAAMZGNZmpJjP1zlorNMKzpk+p5xDkM8htG0BjVHmIlZ+9wWcrbTQ7W6M53rbZ2QohjGZtALAfIk+Acnn/zV8EfYHHVntjvb15L8s7J2Zrk7Pn7WQFAICxUass5FPPZtqoJtOCz6HpbQDN9n220sZUeuB2zxDC/ImLcVhuNLLjbeOW01qwcBSA40rkCVAiptoea13NnROzNa26AAAwfvKpZys0Wmnjfnp3KkyHEMSfQ9bVptlKG11zbuNQ3JDrCo1LQLuS0alkumtw7siqd946XLLL3tKk6AoAxpzIE6AsfvmDH2UtnqKy42hy9nwIIUs941MAAGD85FPP6GkCmos/a5WFwuormfkTF+PA23zwudOb43viG6aS6UOMwx2Orvm6captdOhBvgBQLJEnQCm898bbMe8MorJja3JufnJuPuv1fHj7entTeg0AAOMpSzTrnbXwLPKM4vlmZzWEIP4cjjjwNr+Vs0ts7gw9S0BbaSN0Pnm1cPnsNs6wzV/JZIN8w6f/RAAwykSeAKWw8Zc/joe5f/Fnk39v9eMxdvbytSz1fLxZb2+s2+UJAABjLIszm+lWM3067TZ7NR9/nksuGHs7UPMnLmap5xfD5GeTz2/bCtkbIo5CathbVb3zVuh86j2xLTXfzzrkIsdbWnQBAGNP5Akw/rIVns+99PXn/+QbYccBPBwPZy9fa2+sP7x9PYTQ3rwn8gQAgDKoJjNZorlt/Hk/vRvfVkh5JZGlgHOVr+809zW2hIZn420L19uc2rtbND+Dt6ufddsGVgAYQSJPgPEXR9o+99LXX/iL7xZdC/0xOTf/8HbRRQAAAAXpjT9j9in1HLQYFk4l04fbczmVTM+fuNj3qnaybdgZo81mZ6veeSvGmb29qtsWOSIJLgDsROQJMOayFs+Jl75ZbCUAAAD0XYw/6501qecQ7D+wbHa2uq4MM+/cNuzM//RqZeaVyqvDKQYAhkPkCVAWz//JN4ougb55cGs5HiZnzxdbCQAAMApqlQWp5+iIPZRdF4eTd/bu7BxyaykAFEXkCTDmsqm2RRdCPz3erMeDRZ4AAEDUlXpupn/32fD5+FK28vPlylJh9ZVGPu+McWNvx+eAdDV31iovHm4ALwAcRyJPgHGWTbVlnLQ31uNhYrZWbCUAAMBIyaeeH4T2B6Hd9YZ6Z61WWSiitBJ5N33UdWU4uWM+79TZCUAJlSXyXFra5n/Ctrq6OvxKAArxwl98t+gS6Jv25r14OHv5WrGVAAAAo6ZWWWimW3G2ba9WaEg9h6mVNl7/+LVBB5Bdc3Q1dwJQTttHnllAeOPGjRDC4uJiCOHKlSvbBoejbGlpKf4RtpUkSQhhcXFR9gmMK1Ntx1KcaqvFEwAA2Fbc4vl36fpH4cPeV1uhcbOzei65YNnngMR0M99zOdDgs6u581RyWt4JQDl1R57bZoTxShZ/7hkQrq6uXrly5UB1pGl6oPfvaf813LhxI0mSlZWVYxfoAuzOVNux9ODWcjxMzp4vthIAAGBkVZOZmGjWO2u/C7/9ILSnwnQ1mW6mjWzZZzNtaPccnJ2Cz94WzGZn69300SGiyq7mTsNsR1yaJkWXADDmKtlpdXV1957I6MaNG3tGgysrK30oLVdVkrOfYPIQmetx7GEF2MV7b7ydnU21HSexxTOEMDk3X2wlAADA6KtVFr5d+c7LlaVqMl1NZmqVhakwHV+KQ26LLW/szZ+4+MrJV6eS6exKPqTMrrTSRr3z1usfv/b6x6+tP7mznzuvP7nTNcxW3glAyX0Sea6srHTlnYs5+ev7ST37Ymlp6cqVK11VxabMXTpNe/POxcXFlZWV9NNWVlZ6/1wm3ALj4b033t74yx+bajt+2hvr8WCqLQAAcCDZGNtaZeFcciGe45DbZrpVXF2l0BV8xlyz2dkKIewz4Mxrdra6htm+cvJVw2wB4Olg267+zm2n1+bfE9PB/QSfXbHi/uV/XP4m8eKVK1d2GkWbzzt3GcO7tLQUP57/QbvcFuAYefzmz+PhuZe+rsVznLQ378XD2cvXiq0EAAA4vmL8acjtkM2fuPj6x6/FcytttNJG6HzyagxEn17PiWNvs0913bN3TC4AlNbTyDOfd+6U+XVNvt0lGszf7XB9k6urqzutDs2aOLctoPfN+/lZ+/xzARw78s4xE6faavEEAACOKC77rHfWYuoZh9xKPQdtKpnujS3j9VPJ6WxQ7fqTO3FKbde2zq6PmGQLAHmVEEI+4VtcXNwl8Ms3UO659TMcocUz2wbau5JzaWkp3nbbAvJrRPf/0w/65wKA4XtwazkeJmfPF1sJAAAwHnqH3NruOVDzJy7Gbs6pZDq/4DOfd4YQWmkjDr/tyjvjp+JD3gkAXSpdz3sjxrwsbowGt/kya/HcfXRt76v5wHL3P0uetk4ARl9s8QwhTM7NF1sJAAAwNqrJzLnkwlR4Gr9lwacFnwNyKjkdA8tswWet8uK76aN8Ahp1bet85eSr8VPxMfTCAWDUnQyfjgkPFP4VNQN2nz/0QLUtLi7q7wRgZLU31uPBVFsAAKC/uobchhBaodFKG820EUIw7ba/qpWZavhk+2YML/NXQgjrT+7k804BJwDsx8m+3zEfNO6/z/JA9tldurS0tP8+1AGVCgB90d68Fw9nL18rthIAAGAsxWizK/iMV6SeQ3YqOR0jT3knAOzfyXCEjZt7BoqH7gGNPZc3btxYXV3tvUm2sLO3gEM3ay4tLRlvC8DIilNttXgCAAADFdPNZrrVTBsx8myFRr2zVk2mq8nMXp+mP6qVmal0OjzrAWU8pEUXMDb8JoGdnAwHXMl56EAx/3TPn3jlypX4g2K6mf/46upqtulzz1IP1OgJAKPpwa3leJicPV9sJQAAQBnEUbfNdOt+ejc8m3MbrxddWlkIOwHgoA422DYfPe6nN3R1dXVlZaU3JU2SZHFx8cqVKzs1Vi4tLWWNnjdu3Mj/rOxu246iXV1dTZIk/+Y9fxbAmHnvjbfff/MXRVdBP8UWzxDC5Nx8sZWU073NH8VD8/23Qwh/Wvt+oeUAAMCQdO34jPGn1BMAGE37jTyXlpbyyeXi4uJO3ZPZ22LQuNMNsyxzp/vEkbbxbr2h6crKyk4R5srKStfPjT8rPItpxZ/AeHv85s+LLoF+am+sx4OptoV4972fx6Qzf+XU898sqh4AABiyWmUhn3o204bVnnAYabL3e9iPnX+Ta2trh/4n/1IDGAM7Rp5dGWfeLjll3n5G4O4+e3Z1dbW3T3TPn760tNSbeuZLyseffRl7m+8rBYD+am/ei4ezl68VW0k5vfvpvDNeEXkCAFAqtcpCfsjtTzs/+XblO0UXxUjb6Z+X7vMfLMPh/OpXvzrcYr5I5AnHXeWgHzjofywtLi6urKykOYuLi12Danf5W0kMRPMf389Pj6nn7qN3Y+tnkiRJkvh7GTBOfv/rD4ougX76d/+wGUL4wpdPF11IScUWz+c+/9U/rX2/+tw34pV339NLDQBAuVSTmXPJhXj+ILTrnbV6Z62ZbhVbFQBA5mC7PMOzhHKnLu+ui9uOn42ZZb6L9MaNG3GM7UGL2cXS0lK8Yfzr7v/jjmzl5yD+R0Z6QIEh+4MvfzGEvw8hnP/f/vuia6EPfv/b90MIf/iF54supIyyLZ5zX/7jEMKp574RE9BtGz3ffe/nsSX0/Ox3h1smAAAMQ9zimfV6hhBaaeN+encqTBt1Wx7+UeehpUUXUAZf/epXFxYO+bejXZb0AcfFjpFn77/D8wnljRs3dtmmGe3+hvyqzj3ffBT5FHP3+HP3Kbu7SNPt/wPLfwMAgGMt2+IZA85Tz3/z3fffbr7/dvP9t+9t/uj87HdjJppf9hk7QQEAYCzlU89MKzRudlbPJRfiqxB2/uelMFALCwsmJ0OZ7bbLszeDzIeUV65c6c0pD/Q3lCtXruRj1P1/8ND2jD8PnXoCAGMma/HMp5jnZ7/7r+v/MoTQfP/teAAAgFKpJjMx2mymW820EZ51fMYcVOoJABTlYLs8V1dX8wsyV1ZWjvKzl5aW8ncb8kLN1dXVuCW0a+Xn7rtFAUbf+2/+IoTw3EtfL7oQON6y3s2uQbXb9nFq7gQAoGyqyUytslCrLEyF6XjlfnrXdk8AoCgHizzDpwfeDqc1c9BWV1e7stvx+HMBAH2RtXtGWQJafe4b1ee+8ae17/9p7fvZq8333373vZ8PtT4AAChUV+pZ76wVWw8AUE4HjjzHsgNyaWnpiB2rAMCYmT/9Z/EQZ9jmg8wYc56f/W4Wf56f/W7W6Ln+6MddKSkAAIy3WmXhXHIhnluhIfUEAIbvwJFnCCE/CXak1l4mOQf9bFeUO5bJLgCwf6ee/+af1r6fDzJ3ad+8t/mjbBBu9blvdM3CBQCAsVdNZqSeAECBKktLS1lMeIicT3MkADDGDtq+OX/6z+SdAACUU0w945DbVmjc7Kxa7QkADM1hujzzqy7zXZ6rq6tLzxTS/ZlvPz1im2Z+ZSnA8fLcS18vugT6pr2xXnQJfCr1bL7/9rapZ2zxrD73jVPPf3OoxQEAwCipJjNdqz2lnhClqUd/HgA7OXnlypV8hHlE+VvtJ3TcKT0tnMG2wDH13htvv//mL4qugr5pb96Lh7OXrxVbScmdeu4b2dzaXrsMvAUAgBKqVRbqnbVWaISnqWejVlkouigAYMxV8tnefrLP/PvzXZVdL924cWPPCDP/hq5bHU7+hjdu3DhQbLnLnwvguPjlD3608Zc/jueJl7SaQd+sP/rxLq+++ywNPfWsGRQAAEou3+tptScAMASVcJB5sKurq/lYtHf6a/5Wu6/5XF1dzX+8X4Nk8wXsP/Xc888FMPp++YMf5fs7n/8T0Qv0TTWXZTbff/tf1/9l/pE1gJpqCwAAmVpl4VxyIZ6lngDAoFXCpxO+XWLCrpBycXGx9529fZbb9nru51aH0/Xjdqkhe//S0tKAigEYjvfeePvef/Y/mGcLg5Nf57mTPd8AAABlU01mulJPqz0BgAE5GUJYWlpaXFzM2hxv3LiRJEnXcNfembc7tUKurKxkL924cePGjRu732pxcbG/WzzzBWQ1hO3G1fb+ofpeDMCg5Zs7n3vp6/Hwwl98t7iKYDydn336b6t33/v5u5/e67nLmk8AACi5ajITQmimjVZotEKjlTbiFQCA/joZ/09sdsxHgLvv9VxZWdmpFXJpaak3dNzpPoOIGGMBKysrXT93z02l8k7g2OnKOyWdMASnnv+mAbYAALB/1WSmmszUO2ut0Agh1DtrtcpC0UUBAOOmkp1WV1d3374ZLS4upmm6++jXGDr2dlX23mpAEWMcZrtnAflKVlZW5J3AMdI1zHbuX/yZvHPMtDfWH9xafrxZL7oQAACAPqhVFqbCdLDXEwAYjJP5J0tLS1mc2Tt+NvRsytzF0tJSzB1jjJq/20FvdWjxR2xbQ76SK1euWN4JHC/vvfH2xl/+OJ6fe+nrEy998/k/sURwrHSFnROztQKLAQAA6ItaZeFmZzWE0AqNZrplwi0A0Ecney/1MYyMwWe/7nZ8awDoI8Nsx1t7Y729eS/LOydma5Oz5yfn5outCgAAoC/OJRfup3dDCPGvUk/KIy26gLHhNwnsZJvIE4DR9N4bbz9+8+f5YbaaO8dMb3Pn2cvXCqwHAACgv6rJTDNtxKWezbQh8gQA+kXkCXA85IfZBnnnMRdbObOnvQs7NXcCAADjqlZZqHfWWqERl3rWKgtFVwQAjAORJ8AxYJjtmMmPru115tJVYScAADDGpJ6UUlJ0AQBjTuQJMOrknWOma3ptCGFitpadTbIFAADKoJpMt9JGCKEVGs10y4RbAOCIRJ4Ao87yznHS3ljP8s5vXf1hscUAAAAUJWac99O78a8iTwDgiCpFFwDAbn75gx/Fw3MvfV3eOQayFZ75zk4AAIASqiYzU2E6nuudtWKLgUFLPfr0ANiJyBNgpGUtnubZjoFspO3EbM0AWwAAgFplIaaecaln0eUAAMeYyBNgdOVbPIutBAAAAAahmjxt9IxLPYstBgA4vkSeAMeAFs/xoLMTAACgSzWZOZdciOf76V2pJwBwOCJPgNEVp9pq8Rw/cbwtAAAA4dNLPZtpo9hiAIBjSuQJAMMzMVuLh/bGerGVAAAAjA5LPQGAIxJ5AsDwTM6ej4eHt68/uLUs+AQAAIhqlYV4kHoCAIdwsugCAKBEJufmH95+en68WX+8WX94+2nrp02fAABAyZ1LLtxP74YQWqHRTLeqyUzRFQEAx4bIE2AUvQlAhroAACAASURBVPfG24/f/HnRVTAQZy5dbW/ey6/zjOefXf+e7BMAACizmHHG1LOZNkSeAMD+iTwBRs57b7y98Zc/zp6+/+YvfvmDH73wF98tsCT6aHJufnJuPp4f3FoOzyLP7PDg1rLUEwAAKKdqMtNMG63Q0OjJmEnTpOgSAMacyBNg5OTzzuj9N39RSCUMWhZt5rPPx5v19sZ6FosCAACUSjWZbqWNoNETADgIkeeoOJl0ii6hpCr/y5eKLgE+5Z2by/Hw/AvzX3v52v/zP/938enGf/M3X3u5P51/f/j/PenLfeijb/7Hfx5C+HV1/d/+9F+FEB7evn7qS/9j0UWNleQff1d0CTBs6T/+tugSYNiSP/D/4VI66RP/3Z4xVE1OZ42e9c5arfJi0RUBAMdApegCAPjEOzeX3/vleniWd4YQpv6jS/Gl9365/vide0UWx+B9+avzX/pKLZ5/8bd/VWwxAAAARclizlZoFFsJAHBciDwBRkWWd4YQsobO6YX/4vkXng44ffzOejGVMSy//tX6b/6+XnQVAAAAxZsK0/FQ77xVbCUAwLEg8gQYCY/fuZflnS9852r+pa+9fC2mntkbGEu/+Nu/ilNtQwhf+krt63/858XWAwAAUKB8o2cz3Sq2GABg9Nl0AlC8x+/c++VPrsfzC9+5OvG1811vmPjafMi1fjJ+fvG3f5X1d/7Tb//zL391vth6AAAACjcVpuNg22a6VU1mii4HABhpujwBipdNrH3+hfnevDOEMPG18/LOkvjSV2ryTgAAgBBCrfJiHG+r0RMA2JPIE2CEyDVLyxhbAACAXllzp8gTANidyBNghDx+517RJVCwbLwtAAAA5tkyNlKP/j0AtiXyBChe1tyZTbilhL70lVrRJQAAAIycbLZt0YUAACNN5AkAo+UXf/tXRZcAAAAAAHCciDwBYCRY5wkAALAL6zw53tLEo28PgO2IPAFgVMTZttZ5AgAAZGqVF+NB5AkA7OJk0QUAAN1+/av1L391vugqAAAAilfvvFV0CdAHadEFAIw9XZ4AxXvn5nLRJTASvjxdi4d/+9N/VWwlAAAAo6CZbrVCI56zdk8AgF4iT4CCPX7n3nu/XI/nia9p7Cu1L391Ps62DSH84m//qthiAAAAitVMt+6na/F8LlkothgAYMSJPAGK9Pide7/8yfV4fuE7Vye+dr7Yeijc1//4z7ONnlJPAACgzLLlnVNhuprMFFsMADDiRJ4AhXnn5vLjd572dz7/wry8k+jrf/zn8fCbv6//+lfrxRYDAABQiHrnrTjSdipMG2kLAOzpZNEFAJTROzeXs2G2IYTnX5j/2svXCqyHUfNPv/3P4zrPXzfqX/6qcccAAEC5WOEJAByULk+AYevKO0MI8k66iDkBAIAys8ITADgoXZ4AQ5Xlnc+/MD/xNcNsAQAA4FPqnbfiwQpPAGD/RJ4AQ/L4nXuP31nP8k6dnQAAANDFCk8A4HBEngBD8sufXI8HeScAAAD0yq/w1N/JmEnToisAGHd2eQIMwzs3l+NB3gn8/+zd32+cV3on+FNsz2Z/dG/IiCbVIuUNC1hYmIarlFxYY/lmACvAXE3QAMkZTFvWxSKYew+8yJ+QSYDcLQZYD7CQ5A4QFgEjuQrQFJAbSSvvjptVQgcSgilimqRapEtDbbcx04O0WXtx5Nfl4q8iWW+dqrc+HxCNU8XiqUcU1Rf8+nkeAADgUDvtzXgw0hYAOC2RJ8AgxHm2IQR5J71obddTlwAAADBQRtoCAOch8gTIXWeLZ9pKGBWtrUY8XLl2M20lAAAAA9A50lbeCQCcgcgTYHC0eAIAAMBBj9sP4+Gt0jtpKwEARpTIEyBfe831ONVWiycAAAAc1Nh/EA9WeAIAZybyBMjXXtNSRgAAADicFZ4AQF+IPAHyZZgtAAAAHKpzhaf+TgDgPESeALkz0pZTaW3XXzxrhBAuXKqkrgUAACBHO+3NeHir9I7IEwA4j9dSFwBQfFPl6suN+suN+l5zPc651frJMVpbjXiYnhd5AgAAhZW1eFrhCQCcny5PgNxNla/GRs+Ne3di9tlcu526KIZXbPEMIUzP6Q8GAAAKK2vxlHcCAOcn8gQYhKmy7AoAAABe0eIJAPSXyBNgELJGz8hgW46RrfB88uhu2koAAABy8rj9MB7knYyJdrvkoz8fqf8qgaEl8gQYkKzRszP7hIOuXLuZugQAAIAcNfYfxIMWTwCgX0SeAAOSNXq+3KinroVhFxs9XzxrtLb9tAAAAEUTR9qGECoT19NWAgAUhsgTIIHm2u3UJTDUpudfzbZtbTXSVgIAANBfnS2eaSsBAIpE5AkwOFZ40qPpuWrW6Jm6FgAAgL7ZaW9q8QQA8iDyBBgos205rSeP7qYuAQAAoD922pvx8FbpnbSVAAAFI/IESMNsW4535drNeNDoCQAAFEPW4jkT5mdLl1OXAwAUisgTYKDMtqV3cbZtCKG1rS0YAAAYbTvtzazFU94JAPSdyBNg0My2pUfT819HnlsaPQEAgFHV2H+wtr/yuP1QiycAkB+RJ0Aye8311CUw1KbnqrHR02xbAABgRK3tr8SkMyPvBADyIPIEGLRstu1eU6MnJ8gaPZ88upu2EgAAgDO4MbH8VumdmTAfH2rxBAByIvIESMZsW040PVeNB42eAADAiJJxAgAD8FrqAgDGTnPtdjwsvPdB2koYCRcuVWLe2dquZwkoAADAKKpMXE9dAqTRbpdSl1AU7dQFAMNKlydAMlPlq6lLYARcuXYzHlpbGj0BAICRFNd5ZuNtAQD6TuQJMGhxnu3kgnY9enXhUiWE8OJZo7VtGDIAADBiGvsP4sGEWwAgPyJPgIHaa66nLoERptETAAAYXSJPACA/dnkCDMhec32vWY8tntC7J4/uxl2eAAAAo8hUWwBgAESeALk7GHZOLlTLN24lLImR0Nqut7YaWd755tvvT8+ZhwwAAIwSU20BgMEQeQLkq7l2uyvsnCpXp8pXE5bEqJB3AgAAhSHyBAByJfIEyFFn3ins5FRa2/WYd164VLly7WbqcgAAAAAAhpfIEyBHWd658N4Hwk5OpbX1qr9zer6SthIAAADgnNqpCwAovInUBQAUVnPtdjxMLmju5HQ6WzzNswUAAAAAOJ7IEyAvWYtn+cattJUwcrR4AgAAAAD0zmBbgFx0tnimrYRRpMUTAAAACqVdSl1BYfhOAofT5QmQCy2eAAAAMFu6HA+N/QdpKwEAik3kCdB/WjzplyeP7j55dLe1XU9dCAAAwFlkkScAQK4MtgXos+ba7djiOblQ1eLJecTxtvEw/UPxOQAAAIyqduoCCsN3EjiKLk+AfsryzhDCVFlGxRlduFS5cKnS+TBhMQAAAAAAQ06XJ0B/7DXXN+7dyR4uvPfBVPlqwnoYaVeu3Wxt12OX54VLlSvXbqauCAAAAABgeIk8AfqgM+80z5a+ePrZJ/EwPa/FEwAAAADgOAbbApyXvJO+e/LobjxcuFSZnjMhGQAAAADgOLo8Ac6lc3mnvJO+yEbahhCMtAUAAAAAOJHIE+DsOvNOyzvpuwuXjLQFAAAKYjds7bQ3Z0uXUxcCABSTwbYAZyfvJA/ZJNus1xMAAGB0zYT5eHjcfrjT3kxbDABQVCJPgPOaXKjKO+mvrL+ztV1PWwkAAMA5VSauZ6mnyBMAyInIE+DsJheqoaPXE/olW+HZ2tLoCQAAjLzKxPXUJUBi7baP/nwAHEXkmbv2SRYXF1PXCMDQiY2eZtsCAAAwPE78VWfqAgEYXyJPABhqZtsCAAAAABxP5AkAw8hsWwAAAACAHok8AfqguXY7dQkUkNm2AABAYcyE+RDCbthq7D9IXQsAUEAiT4A+mCpXU5dAkT15dDd1CQAAAOcyW7ocD7tha6e9mbYYAKB4RJ4AZ9Rcu/1yox5CmFyoTpWvpi6HApqer6QuAQAAoD9mS5djo2cIQeQJAPSdyBPgjGLeGUIo37iVthKKKtviKfsEAAAKoDJx3XhbID+rq6uls1paWkpdPnBeIk+As8iWd04uGGlLLp48uhu3eF64VJme82MGAAAUgfG2AEBORJ4AZ6HFk7zFvDOEcOXazbSVAAAA9IvxtgBATkSeAGenxZOcPHl0Nx4uXDLSFgAAKJTKxPXUJQDFtLi42D6rWq2WunzgvESeADB0tHgCAAAAAPTutdQFAADfosUTAAAYB3GdZ7bdE4qt3S6lLqEo2qkLAIaVLk8AGCKt7boWTwAAoNjeKr0TD4/bD9NWAgAUhi5PABic2MH54lnjwqXKoYlma+tV3vnm2+8PtDIAAIBBmS1d3mlv7oatEEJj/4HtngDA+enyBIABuf/pRy+eNWIT54tnjfuffpTNsI2yFs8LlyrTc9U0VQIAAOSvMnF9JsyHEHbDVmP/QepyAICRp8sTAJKJwWfc2ZnNsw0hTM/b4gkAABTcbOnybnsrWOoJAPSDyBMABiFr6Hzz7fen56pPHt3NMs7OsDNo8QQAAMZDzDjjOs/H7YciTwDgPESeADBQMc6Mizw7g88Qw855eScAADAuLPUEAPpF5AkAg5At6ex8Mgs+4wEAAGDcVCauN/Yf7IatuNRT6gkAnM1E6gIAoPiyqbaHkncCAADjLBtpG1PPtMUAACNK5AkA+Wpt17PptdPzleNfDAAAMG5mS5ffKr0TzzH13Glvpi0JABg5BtsCQI5a2/Wnn30Sz2++/b49nQAAAAfFRs+413M3bO22t0JH9ycAwIl0eQJAXuSdAAAAPZotXa5MXJ8J8/Hh4/ZDvZ4AQO90eQJALjrzzguXKvJOAACAE1Umrjf2H+yGrRDC4/bDoNeTomi3S6lLKIh26gKAoaXLEwD6ryvvvHLtZtp6AAAARkVXr2dj/0HaegCAkaDLEwD6qbVdb201XjxrxIfyTgAAgNOqTFzfaW/GLs/dsNXYfzBbuqzdEwA4hsgTAPqjK+wM8k4AAICzigFnlnrutrd22puVieup6wIAhpTIEwD6I5tkG+Lyznn7OwEAAM6uM/UMIeyGrZ325sXwRl8u/8lv/rIycX12QucoABSEyBMA+knYCQAA0C+zpctZ5BlCeNx+WNov9SWnnCnNN/YfVILUEwAKYiJ1AQCjZ6+5nroEhs6TR3fjQd4JAACQn8b+g/pX9/t4287+Zr9uAwASEnkCnNpesx4PU2XJFt3knQAAAP2y0/4mj5wJ8/Gw296SegIAXUSeAKez11x/uVEPIUwuVKfKV1OXw7B48awRQrhwqZK6EAAAgOLYaW/OhPm3Su+8VXqnMnH9rdI78fnd9tZPfvOX/YoqpZ4AUAAiT4DT0eIJAAAAg1GZuF6ZuD5bujxbuhxCmC1drkxcnym9avfMosrjmz539jfrX92vf3W/M9esfufdztdIPQFg1L2WugCAUaLFk0O1tuupSwAAABgLsxOXZ8Pl+lf3d9tbIYTG/oOZ9nwcdVv9zrs7+5vP2z/fbW/NlOZ/3f4vvwz/ufNrd9tb8cWH3tzYf1AJ12cnLg/ijwEA9JvIE+AUtHhyqNZWIx6m5w22BQAAyF31O+9mqWf2vz/5zV9mLzgq1zzq+eh5++ezQeRJLtqpCwAoPINtAXqlxZOjZElnln0CAACQq+p33q1MXD/zl8+U5v/n8DtdTx4fiAIAw0yXJ0CvtHhylOm5autS48WzxotnIk8AAIABmZ24/AcT/yKeY9PnTKl7bm18Jq7/7Jx8G0K49tof7OxvNvYfdL5+Z3/TbFsAGEUiT4BeafGkF08e3b1y7WbqKgAAAMZLjDNjWpnNvK1MXJ+duNyZYsZVoPE18WElXG/sP8iyUrNtAWBEGWwL0JPm2u140OLJobKYU6MnAABAElmuebH0RmzrjM8c2rVZ/c672VdVJq5nDwGAESXyBDgdLZ4c5cKlrzd6btfTVgIAADDOZicuV7/z7h+89i96f30IIaak1nkCwIgSeQLA4Vrb9fuffvTk0d3UhQAAADA4ceYtADBaRJ4APckWeaYuhMFpbTVCCC+eNU4bfE7P+TkBAAAYMWbbAsBIey11AQAwpDq3csbg88KlyvR8pSvRbG3Xs3A0dIy3BQAAYLTMlOZ321tm2wLAKBJ5Apxsr7meugQGLWvrvHCpkmWfL541XjxrtC59KwpNUBwAAAAAAB1EngAn22vW42GqbGDpuMiyzCvXboavE9D45FExZ+zvjK8HAAAAAGBgRJ4AJ2iu3c4WeU6Vr6Yuh0HobPGMhxhkxhm2nZFnfMHBabcAAAAAAAyMyBPgBDHvDFo8x0lXi2dmeq46PVeNwaduTgAAAKBH7XYpdQmF4TsJHG4idQEAQ625djsetHiOj4Mtnl2m56ryTgAAgOK5WHojHupf3U9bCQBwWro8AY6011zPRtqWb9xKXQ599uTR3djN2RVtHtXiCQAAQLHNTlwO+6mLAADOROQJcKSNe3fiwUjb4rn/6UfZuXM3Z+aoFk8AAAAAAIaNwbYAR5pceJV07jXraSuhv1rb3/yFHhVtavEEAAAAABgVujwBjjRVrsbBthRMa+tVW+e7P/yz7k9t11tbjel5LZ4AAAAAACND5AlwpKy502Db8TE9V52e89cNAAAAADBKRJ4A32iu3T7Y1jm5UJ0qX01SDwAAAAAAcCK7PAFOoMWzeF48a4Sjt3gCAAAAADBaRJ4A3zh0c6cWTwAAAAAAGGYiT4DjTC5o8Sya1vYhwTYAAAAAAKNL5Anwyl5zveuZyYVq+catJMWQn9ZWIx6m5w22BQAA4FtmSvMhhN32VupCAIDTeS11AQBDYa+5vnHvTuczC+99YKRtsU3PaeEFAAAABqKduoDC8J0EjiDyBPhW3hkn2U6Vq/JOAAAAAAAYCSJPgDBVvrrXrL/ceLXi0TDbYnvxrBFCuHDJVFsAAAAAgIKwyxMghBCmyq9mnL7cqB9c6gkAAAAAAAwtXZ7AuGiu3Q7f7uD8/OMPQwiTC9XyjVtxjG0cb7tx746ptgAAAAAAMCp0eQLj4uVG/eVG/fOPP4zZZ/zfTlPlq3GR56GfpTDiSNs43hYAAAAAgAIQeQJjJwaf2ebOTuUbt2Lq+XKjLvUEAAAAAICRIPIExsIZ8stsuycAAAAAADDMRJ7AeMlG1x5qr7keuz8nF6rWeQIAAAAAwEgQeQLj5eA8285uzr1m/eCTFMz0fCUenjy6m7YSAAAAhs3F0hvxUP/qftpKAIBTeS11AQCDkPVuxsM/+h9/+3+a/d3yjVudr9HiOSam56pPwyepqwAAAGAYzU5cDvupiwAATk/kCYyjg3ln0OJZXK3temur8eJZI4Tw5tvvT8/5+wUAAAAGqt0upS6hINqpCwCGlsgTKL695vqpXq/Fs0juf/pR58Onn30S3k5VCwAAAAAAubDLEyi+rH0zc7DFM3Q0dzbXbudeEwPR2u7+qw8x9QwhhBD7PgEAAAAAGHUiT6Dgmmu344bOE2nuLJ7W1qtQ882333/3h3924VKl87Nvvv1+iqIAAAAAAOgzkSdQZJ1558J7Hxz/4tPOv2WExP2dV67dzGJOSz0BAAAAAArDLk9gXGzcu3PMZ/ea68e/gGKYnqvGXZ7yTgAAAACAwhB5AoW111wv37i111zfa9a7Ztt+/vGH8TC58Cr36mwGNeG22ISdAAAAAAAFI/IEiinr2pwqX50qX80yzi5dUai8EwAAAAAARo7IEyigLO/cuHdnr/lNqDm5UM36PuMznZGnvBMAAACYKc3vtrd221upCwEATkHkCRRQZ8zZGWpOlavh677PBGUxcC+eNVKXAAAAAABA7kSeQNE01253jasNX/d3JqmHVFrbr34MLlyqpK0EAAAAAIBciTyBounKOycXqlPlqrbOMdTaetXiOT0v8gQAAAAAKDKRJ1BkmjvHWZxqe+FSZXqumroWAAAAYKy126krACg6kSdQNJML1azR8+VGvbl2Ox4mF6ohBAnomHjy6G7qEgAAAAAAGBCRJ1A0U+XqVLkaQti4dyd0zLmNh88//lD2OQ5ii2cI4cq1m2krAQAAAAAgbxOpCwDos6ny1fgRo82DXm7UX27UP//4wwEXxsC0tl/l3Bcu2eIJAAAAAFB8Ik+gsGKv51GOCkQpgNbWqxbP6XmRJwAAAABA8Yk8gcLqbPTsCjgnF6oG2xZYnGp74VJlek6wDQAAwBnt7G+mLgEA6JXIExgL5Ru3fv+P/nxyoWqRZ+E9eXQ3dQkAAACMsOp33o2H5+2fp60EAOjda6kLABgcSedYMdUWAAAAAGBM6PIExsJecz11CQzIlWs34+HpZ5+0tutpiwEAAAAAYABEnkCRZW2de03R1zh6+tkn5twCAAAAABSeyBOAonn3h3924dKrqbYvnjWkngAAAAAAxSbyBKCArly7+ebb76euAgAAgNGzs7+ZugQA4NReS10AAAAAAMCweN7+eTxcLL2RthKKpN0upS6hKNqpCwCGlS5PAIqptdWIhyvXbqatBAAAgFFR/+r+bnsrhDBTmp+duJy6HACgVyJPoMj2muupSyCZF88aIYRsqScAAAAcb2d/M+adIYTqd95NWwwAcCoiT6DI9pr1eCjfuJW2Egastf3qr/7Fs0Z2BgAAgGNkI20rE9fTVgIAnJbIEyiylxuyrvGV9Xc+/eyTJ4/upi0GAACAIWekLQCMNJEnUFjZVNvJhWraShi86bnqlWs3s9TzxbOG1BMAAICjGGkLAKNO5AkUVjbVdqos8hwjre16lm5Oz3+zyLPzDAAAAJ2ykbYzpfm0lQAAZ/Na6gIAwucffzi5UJ0qV6fKV/O4P6drGUKt7frTzz4JIdz/9KPQMdv2wqXK9JzkGwAAgENkLZ4zpXktngAwonR5Aok1126HEF5u1LOmzH6JizxNtR0rra1G58MXzxohhAuXKleu3UxUEQAAAMOusf8gHi6W3khbCQBwZiJPILEYTPZdTFIhhCDvBAAA4Cj1r+7Hw0xpfnbictpiAIAzE3kCKe011/N+i/KNW3m/BcPjyrWbb779fjbPNmpt5xKrAwAAMOqMtAWAwrDLM3e1Wu34F2xtbQ2mEhhCncNsX27Um2u389voyZiYnqtOz1Vb2/XWViMOtm1tNSzyBAAA4CAjbU/rxF91AkAqIs/cLS8vpy4BhlfXVNuXG/W41LOPrZl7zXUZ6hiKwef9Tz9KXQgAAABDykjbM/CrzrNrl1JXUBi+k8DhDLYFksnWbU4uVBfe+2By4VUf3suN+ucff3jOy6fKr27buHenuXbbas/xFCfcxl5PAAAAyGQjbUMIRtoCQAGIPIFkslQyhDBVvvpyo56lnqEjED3r5d90dsbmUaknAAAAED1v/zweKhPX01YCAPSFyBNIZqp8NWacWR7ZOee2a+btGcTO0c4YlbHV2j7vjxMAAACFUf/qfmzxNNIWAApD5AmklDV6Hhpw7jXXz3f51fKNW31cC8rIuXLtZjy0tsy2BQAAIAQjbQGgoESeQEpZo2cUmzKzZ/aaOvMAAACAfjLSFgAK6bXUBQDjbqpczVo8p8rVbAdnX1Zv7jXXN+7dOf89jK4LlyovnjVePNPlCQAAgJG2AFBYujyBxLJGz8mFb/LOEML5Z9J25p2TC9VsiC7j6f6nH9noCQAAMM6MtAWAAhN5AunlFEZmc3EnF6rlG7c681TGx5VrNy9cqsTz088+kXoCAACMrWyk7UxpPm0lAEDfiTyB9GKj5zl7Og/KLswG5zKeulLPJ4/upq0HAACAwctaPGdK81o8AaB4RJ7AUOh73hnFkbmhT5tBGVGt7XrnLs8XzxpSTwAAgHHT2H8QDxdLb6StBADIw2upCwDI0VS5Gls8NXqOs9bWN3lnbPe8cu1munIAAAAYtPpX9+NhpjQ/O3E5bTGMp3Y7dQVF4RsJHEXkCRRZ5zrPtJWQypNHd2OL54VLFUknAADAGDLSFsbEw4cPl5aWzva1y8vLZ/5aYEiIPIEE4pjZnIbZZvaa67G5M49FoYyEzpG28k4AAIDxZKQtjInt7e3V1dUzf7nIE0adXZ7AoDXXbr/cqL/cqOe9X3Pj3p14mCpr8RxT2UjbOM8WAACAcWOkLQCMCV2ewEDFvDOeY+qZU/9llqdOLlSnylfzeAtGiBZPAACAMZSNtA0hGGkLhTc3N/fOO++c7WuXl5f7WwwweCJPYHA6887JhWrW69n31DMbaRvyH58LAAAADKfn7Z/HQ2XietpKgAF45513arVa6ipgeNVqtZWVlWM+O8hi8iDyBAakM+9ceO+DqfLVzz/+MO83mlww0hYAAADGUf2r+7HF00hbAAghrKysHLPvtlQqhRAWFxfjw1FMQEWewCAczDtzWuTZlXdq8QQAAIAxZKQtAJxBlomOYgIq8gRy1zlmNuadnZ+Ns20P/cKp8inWcO411zfu3ckeyjsBAABgbBlpCwDnd0xX6BASeQK522sekndOlashhBiFZoHoQT1Gnl3NnafKShk5Tx7dDSFMz1em5wwuBgAAoJuRtgAwhkSewOB0xpBT5atT5atdrZldjolCM5o7x01ru/7iWSOeOyPPmINeuXYzTVkAAAAMByNtAWA8iTyB3MXkcnLhkIa8GHwefP7zjz+MhxNXfmruHDetrVd554tnjdZ2PT6MIeiFS5WUlQEAADAEspG2M6X5tJUAwOgarS2ekcgTGDqdMWcvjZ5Bc+c4yVo8QwhPP/vkmFc+eXRXFAoAADBWshbPmdK8Fk+GSzt1AYXhOwn5GMWMs4vIE8jXiW2aB3Wu+eyRvHNMtLaP+6mYnv9WtJmFo6bdAgAAjInG/oN4uFh6I20lADBsRjfL7JHIExiQ3lPJqfLVveY342rFmWSyqbaHOrjaM2jxBAAAGBv1r+7Hw0xpfnbictpiAIABE3kCwyjGnM212/JOOmWNmxcuVTon3IZvR5ut7Xo2ZwfycgAAIABJREFU0laLJwAAwDgw0hYAxpzIE8hXnE87uVA98ZUHyTuJWtv1zv7OLO/MYs6uXDN7cdeoWwAAAIrKSFsAGHMiTyBHe8311CUw8u5/+tFRn4rB57s//LPOJ588upsFop2jbgEAACgqI20BAJEnkKNsJWcIYa+5PlW+mrAYRtSbb7/f2mp0jrHtGmnbKRtpGw60fgIAAFBI2UjbEIKRtgBwlFqttrKycvwLBlZMHkSewCDE8bYiT85geq46PVdtbdeffvZJ9uS7P/yzrJuzUzbStnO1JwAAAAX2vP3zeKhMXE9bCQAMs5WVldXV1WNeUCqVQgiLi4vx4cgloCJPIEflG7c+//jD7Jy2GEba9Fx1+ofVQ2POb71svnL8CwAAACiSrMXTSFsA6IssFu1MQEci/hR5Ajlqrt2Oh8kFKxXpgyzRfPLo7uEvmKs+DZ8c+ikAAACKJ2vxvFh6I20lAFBIxzeGDpWJ1AUAhbXXXI/zbCcXqlo86YvpuZOz8zjSVq8nAABA4WnxBAAyujyBvOw16/HwcqPeXLst9eQ8jmrrPEZru95LRAoAAMCI0uIJAAOQbfccciJPIC9T5WoIITZ6xjOcTWu73tW1eeXazfuffhS+7uk89FOtrYbIEwAAoKi0eDJa2u1S6hIAepVlnCOxwjMj8gTyMlW+unHvTjzHjs+p8tWkFTGqWlvfyjsPxpyHMtsWAACgwBr7D+Kh+p1301YCAKNuRDPOLiJPIC/NtdvZ+eVG/eVGfa9ZN96WM+gKL188a7S268e8/sKlirwTAACgwOpf3Y+HmdJ82koAYFSMdJzZi4nUBQCFVb5xa+G9D0IIkwuvhovGpZ5Ji6IgOvs+nzy6++TR3c4QdHq+kn1q0JUBAACQvzjSNmjxBAC+pssTyNFU+WocZrvXXM+G3MKptLbrXZNsXzxrZE2c2SGLOUMI03PVp+GTYLYtAABAEWnxBAAOEnkCgzBVvrrXrH893nbdUk96Nz1XnZ6rdj7z5NHdg1lm12uy2bZPHt29cu1m3kUCAAAwMFo8AYCDDLYFBmSq/CqR2mset4URTtTZ0Bl1tYGGELKYU6MnAABAkWjxBAAOJfIEBiTr7Hy5IfLkXKbnqm++/f63njkQgoaOHNRGTwAAgMLQ4gkAHErkCQzO5MKrRs/m2u20lTDqusbYtrYOaeXsbPRsbQvaAQAARl5j/0E8VCaup60EABg2Ik9gcMo3bqUugeLoHGZ7VKiZNYMemokCAAAwQnbam7thK4QwU5qfnbicuhwAYLiIPIGBio2eZttyTq3teteSzkNDzem5akxGNXoCAACMup32ZjxcLL2RthIAYAiJPIGBmiqbbct5tbbrTz/7JJ47ez0Pla351OgJAAAwurR4MvLaPvr0AXCE11IXAIyXqfLVjXt3gkZPzuqovDPb3Nlleq7autR48awRGz27loACAAAwErR4AsAwq9VqKysrx3x2ADWIPIE04oRbOK0zNGtOz1fiFNzWVkPkCQAAMHK+afEMWjwBIL2lpaV4WF1d7eX1pVIpOy8uLoYQlpeXs0v6ReQJwKjK1nkeP97WSFsAAICRlrV4zpbknQCQRkwoe8w4jxFvyO6JCWhf2kBFngCMkjjA9smju1needqvBQAAYIR8q8VT5AkAAxTH1Z4/5jxGvLxUKp0/+xR5AjB6uoLP6fnjujwBAAAYUTvtzcfth/FcmbiethgAGB9LS0u5Jp0HnT/7FHkCMKqy4PP4DZ0xFj1++C0AAADDpjPvnAnzaYsBgHEwgLbOE3Vmn6da+SnyBGC0HT+utrVdH1glAAAA9FG2wnMmzGvxBIBcDb6t80Srq6urq6u9B58iT2Cg9prrqUtgvLS2Xq38NPwWAABghDT2H2QrPOWdAJCfs4Wdcfxs5sQ5tJ2Z5anervfgU+QJDMhec32vWX+5oeWONI4ffgsAAMDwkHcCwACcKuzMMs4zbNk86quyCPPEMnoJPkWewCA01253hp2TC9XyjVsJ62F8WOQJAAAwWuSdAJC3Wq22vLx84stizHmqhZqnLaPzvLKyEo6NP48PPkWewCBkeefkQnWqXJ0qX01bDwAAADCE5J0AkKuYLB7fVRmTzrN1c57Z0tJSlmLGw1FFZsFnV4UiT2BwNHcyeBcuVV48a7x41mht1822BQAAGGbyTgqs3S6lLqEo2qkLgFF2/CTbXvZlDkaWZR6Tfa6uri4tLXUWPDGw+gBg8KbnX420bW010lYCAADAMeSdAJCrY/LOxcXFdrtdq9WGIe/sVKvVYltqtkm00+rqqsgTGJy95npz7XbqKuDVUk8AAACGkLwTAPJTq9VKpdLBvHNxcXFlZSWGnUkK69HS0lKtVmu320cFnzH1NNgWyFFz7Xa2xRMG78mju1nSeeFSJW0xAAAAHEreCQC5WllZ6XpmeGbYnkqMZg+2q8aHIk9gECYXqlPl6lT5aupCGCOdeeebb79vkScAAMAQkncCwCCNaNjZ6ajgU+QJ5Chr8SzfuJW2EsaNvBMAAGD4yTsBYGAKEHZ2Ohh8ijyB3E0uSJsYKHknAADA8MvyzhDCbOly2mIAoNgWFxeHfGHn2WTBZxB5AlBg8k4AAIDh1Jl3vlV6R+QJAPkpZNjZKf4BJ1KXAQB9Fls8L1yqyDsBAACGkLwTAOg7kSeQozjSNtvoCQAAAIw5eScAkAeRJzAIzbXbqUsAAAAAEpN3AgA5EXkCOSrfuJW6BAAAAGAoyDsBgPy8lroAoOAmF6ovN+pm2wIAAMA4k3cy7tqpCwAoOl2eQL6mytV4MNsWAAAAxpO8EwDIm8gTyNdU+WrqEgAAAIDB2WlvNvYfNPYfxIfyTgBgAAy2BQAAAAD6Zqe9GTPOnfZmdg7yTgAgTyJPIF/ZPNtswi0MxotnjdQlAAAAjLXH7YfZWd4JACNtaWkphLC6ujqA92q3z7IAWeQJDIgJtwzMhUuVmHe2tuvTc7J2AACAxOSdADCiarXaysrKYJLOc7LLExiQrN0T8nbl2s14aG1p9AQAAEhM3gkAI6pWqy0vL49E3hlEnkDeyjduTS5UQwgvN+p7zfXU5TAuLlyqBLNtAQAAAADOJOadqas4BZEnkLtsi+des562EsZQa9tPHQAAwEBVJq7PhPl4ngnzWjwBYOSMXN4ZRJ7AAEyVr2aNnsbbMhhm2wIAAKSy097cDVvxXJm4nrYYAOAMVlZWUpdwaiJPYBDKN27Fw8sNLXcMiNm2AAAASey0N+Mh6/UEAEbLqOzv7CTyBAYkNnqGEDR6MmBPHt1NXQIAAMC4yFo8Z8K8Fk/oUPLRvw8gX0tLS0d9anFxcXFxsZ2zs5Ut8gQGpHzjVjbedq+5nrocii+bbQsAAMDAPG4/jAcrPAGgYFZWVmq1Wq1WS13I4USewOBMlV81em7cu5O2EsaE2bYAAACD1Nh/EA8zYV7kCQAj6tCptisrK8d0fw4DkScwOFPlq8bbkoTZtgAAAHnLRtqGEIy0BYAiWVxcHPK8M4g8gQEr37gVD8bbMgBm2wIAAAzMTnszHt4qvZO2EgCgv5aXl1OXcDKRJzBoC+99EA97zXraShgHZtsCAAAMQGP/QWzxNNIWAIpn+Fs8g8gTGLxsvK1GTwZger4SD2bbAgAA5MRIWwAgOZEnkMBU+dVGz417d9JWQuFNz1VTlwAAAFBw2UjbmTCfthIAoO8WFxdTl9ATkSeQQNboGUJort1OWwyFl822bW2bpQwAANBnWYvnTJjX4gkAxbO6upq6hJ6IPIE0yjduxYPxtuQtm23b2rLREwAAoM8etx/GgxWeAFAMo9LW2UXkCSSz8N4H8bDX1HtHjrLZti+eiTwBAAD6qbH/IB5mwrzIEwCKqlarpS7hZCJPIJlsvK1GT/IWZ9uGEMy2BQAAyIORtgBQGAcDzpWVlSSVnIrIE0hpqvyq/W7j3p20lVBsZtsCAAAAKbV99OkDGIiu2barq6vD3+gp8gRSyho9QwjNtdtpi6HAzLYFAADIw27YCiHMhPnUhQAA/XQw4FxeXk5SSe9EnkBi5Ru3UpfAWDDbFgAAAACgRweH2S4tLQ1zr6fIE0gv2+iZuhCKzGxbAACA/mrsP4iH2dLltJUAAH23tLTUlXqurq4uLy8Pbeop8gSGiNm25Gd6rhobPc22BQAA6C+RJwAU0sHUM4SwvLy8tLS0tLSUpKRjvJa6AIBQvnHr848/TF0FY6S1Xc+2ewIAAAAAcKiYenYt8lxdXQ0hlEqlEMLi4mLf3/RsjaQiT2AoTC5UX27UX27U95rrU+WrqcuhmK5cu3n/049CCK2thsgTAADgnHbDVghhJsynLgQAyNHBRs9OMf4cBgbbAkNhqvwqf9pr2uhJjsy2BQAA6Iud9mbqEgCA3C0tLQ1PqHk8kScwFLLOzpcbIk8G4cmju6lLAAAAGGFZ5GmRJwAU1QjlnUHkCQyPyYWs0XM9bSUU2JVrN1OXAAAAUCgiTwAopNHKO4PIExgeZtsyGGbbAgAAnJ9FngBQYCOXd4YQXktdAMArU+Wre836y4262bYMxpNHdzV9AgAAnEcMPgHo0dLSUnZeXl7ufNj3t8jj/lwvd/9QGbm8M4g8B2BxcfH4Fzx8+HB7e3swxcCoaK7dLt+4lboKiunKtZv3P/0odRUAAACjbSbMx7yzsf+gMnE9dTkMyIm/6hzFX5EPSDt1AYUxgt/JWq22srJy8F9H9szi4uI5E7JD3yI+jP9sa7XacF7u/uF0/E/j0P65RJ65O/FvfRS7gyEn5Ru3Pv/4w9RVUHwXLlVePGuYbQsAAHBmlYnra/srIYTdsLXT3rTRc0yc+KvOUqk0mEpgJBwVdnZZXV1dXV09c/B5fMQQP1UqlVZWVobtcvePnMXFxSFMOjN2eQLDZXKhGkIw25ZcTc9X4uHJo7tpKwEAABhd2SLPnfZmY/9B2mIAhk2tVlteXu6932l1dXV5eflUeVKtViuVSj2+xWnz1Fwvd/+QO/TPNeR5ZxB5AjCGpueq8aDREwAA4MyyRZ67YUuXJ0CnmHee4Qt7Tz3P8Barq6s9Bm+5Xu7+UTT8eWcQeQJDa6+5nroEiuzCJY2eAAAAZ7fT3ux8KPIEyByVhy0uLq6srLS/trKycuh+3B6ztJWVlePfYnFx8eD9q6urvQRXuV7u/lF0tgh/wErt9ghu+x0RcXL9id/hOOv5f/t/bg2kKLr99P+spi6BbnGd5+RCtXzDv4tc/A+tr1KXMBSePLobuzwvXKpcuXYzdTnk63uNndQlwKC1W/85dQkwaO1/+IfUJcCg7f/6v6UugfHV2H+QdXnOhPnKxPXBvG9pwqrINH7ym78MPfyqs8ffiBbVMatM/5f/608GWUmBffF//Pi//L+PD/3U8HShHdzveHxtp339qb7k0Pz1+H+kuV7u/pH4f8iD/282EmXr8gRgTGUbPV88a7S2rY8FAAA4hSzvDFo8Ab7ttPllrVbr6gg8cUNk72+xtLR0sCXx+AmruV7u/pEebzvkRJ7ACNtrrjfXbseuUDit6bnqm2+/H89PP/skbTEAAAAjZKe9ORPms4ciT4BMV6DVY+/pwV7AY4Kxg586sSW090g118vdf+L9w+nQCcxDSOQJDJ3JhWoI4eVGd9ddTDeba7f3musx7Ny4d+fgy+AMstWeAAAAnGi2dDmbZNuZfQLQpccNiAeDsd718oUHY7kehwDnern76S+RJzCkYvCZaa7djunmy436xr07XWFnc+32oOujEFpbjXjIhtwCAADQi8b+g3jQ4gnQqauHL48ppl1vcbbw8uDA1QFc7v4T7x8SI9qZKvIEhlcWZGZ5Z5euWBRO68WzryPPOT9LAAAAvdppb2ZnkSfAIB0cnNvjF3b1mx4aYuV6uftPvJ9zEnkCQ6d841YI4eVGPcacnXnnwnsf/P4f/fnkQnVyofr7f/TnU+VXMZXxtpzBk0d348FUWwAAgB419h+s7a88bj/cDVvBVFuAbxvmgaV59JsO5nL3D97Bn+SR+COIPIGRMblQ3bh35/OPP5wqV8s3bu011zfu3YmfWnjvg7S1MdJMtQUAAOjF2v5KTDqjmTCvxRN61S756M/HcOsaWHrm9ZzH6GOD4MEQK9fL3X/i/UNlFGfbijyB4dU1tzZr5dy4d6e5drsz75wqXx10cYy+K9duxkO20RMAAICjZMs7QwgzYf7GxHJl4rrIE6BTrVZrdxhA0+ep3uK0EWyul7t/yNVqta6ahz+mFXkCI6lz1K28k3PKNnoCAABwqMb+g2ySbQw7U1cEUDRdXXRd2x9h8A6uIB3y1FPkCQydveZ61zPZ8s6DL5Z3ch62eAIAAJxop72ZzbPV1gmQh64kaXFx8dBsKde20bx7Ut0/cpaWlrpmNQ956inyBIbOXvNVB2f5xq34v/Eji0JjAhrPzbXbSYqkYJ48upu6BAAAgCH1uP0wHt4qvSPyBOivWq22tLTUY4tnV/50Tl1vmuvl7j/x/uF0aOpZKpWGM/h8LXUBAL3KotCpcnWqfPXzjz8MHRNu4QyuXLt5/9OPUlcBAAAwvLIVnjNhXt4JcB61Wu1genTwZSsrKz3mSWfYzVkqlXp8ca6Xu3+ExNRzZWWl88c1Bp/5LSg9W0+tyBMYDXvN9Szd3Lh3J4QwuVCNz+w114235Zys8wQAADioc4Wn/Z3AkHv48GEfO8/ymGLaFRodtLi4uLy8PJz9c4ybXgLaYetVFXkCQycGmdno2ihr8Yw27t3JXrDXrIs8OSdLPQEAALpY4QmMlu3t7WELYE5lcXFxDLdFQh/Z5QmMhoMDbLNnXm7UszWfAAAAwPnttDet8AQYpNXV1aWlJaknnJnIExguh4aXzbXb2XnhvQ8W3vvgwFfZ6MlZPHl0Nx6m53V5AgAAhBDCTnuzsf8gyzut8AQYmNXVVYNt4cwMtgWGSxZeTpWrBz87uVDNZth2vtJgW85peu6QnzcAAIBxky3vjKzwBEbI8A+GPVheTDe75vFq94SzEXkCQ6ozxcy2e5Zv3Mo+K+YEAACAfumcZBu+bu7U3wn90m6nrqAoCvaNzHLNpaWlzuBT6glnIPIEhshec/3gzs4Qwu//0Z83124f2vcJ5/HiWSOEcOGSqbYAAMD42mlv7rQ3O5s7Le8EGLBarXYw9UxYDywuLqYu4dREnsCwaK7dzvLOyYXudDPr7wQAAAD6xSRbgCFRq9VKpVLnMyc2ep42Fj3VltBcL3f/8BvFJuOJ1AUAhHAg7xRwMhixvzP2egIAAIyVnfbm2v5KlnfOhPm3Su/IOwESOrGvbnl5Ob+3y/Vy9594P+enyxNIrzPvXHjvA0s6AQAAID8m2QKMhIN9hLl2Cubdhuh+8qbLExgikwtVeSdJtLYPWSILAABQPDvtzcfth53NnTcmluWdAMOgv32EMG5EnkB6xtiSypVrN+OhtWW2LQAAUHwx74xnk2wB+q70baf98jP0EQ7Pes7TXu5++k7kCQAAAADF15V3Viaua+4EGCq1Wu3E1/RxB+TBt8v1cvefeD/nJPIEhsLkQjWEkG30BAAAAPqosf+gK+9MWw/AODhtrLWyspJTJSHnjC3vAM/99OK11AXwyq/+4b9PXcKYmvnb56lLGF4//cVfxcPvff8P836v7S9+HQ/+Rgbg17/7O6lLGCKvz7z1xe7jF88a/+jLr1LXQo7av/XfpS4BBq396/+WugQYtPZXv0ldAgxcez91BYyARvvhbtjOHs6EuUrpn4zuD8//9y/fSV3CuPrkL1NXACNgcXGxc37pysrKGWbVdt528MlardY5Mrf3caldeergL3f/ifdzTro8gUP8zd//6d/8/Z/ufPk0fmTZZ36+/90342EA7wUAAADjoNF+uNZePZB3igwBhlSPEVpXYNZjqtp1+fLy8uAvd/+J9+dkaWlpaWmpqM2mS0tLMYoWeQInG0Dq+Ysvn+Z6P5xod6eRugQAAID+ODTsvFFalHcC5KorylpdXe09ZDoYnh31tQff5bSXLy4uHpXV5Xq5+0+8Pz+rq6vLy8tJ3jo/tVptaWkp+1sQeQKHmP265/LqxX8eD4Pp9YxvNIB3gcwPqj+Khy+eP05bCQAAwDnttLeEnTCk2j769zHElpaWuroAl5eXe0k9a7VaV3h2zOzTg6nV8TnWwcuPkevl7k9udXW1VCoVIPiMYefy8nLnt1fkCZygM/V8/qsnaYsBAAAADoph5+Pwfws7AdI6OLP0xNQzJjcn3tOpazfk6urqUSFWrVY7uEjy+Hpyvdz9wzBddqSDz0PDzkjkCRzi977/h/Hwiy+fXvzelSz1XH/+1wNIPQWrJPHFri5PAABg9BwMO2fCnLATIJWDjZ4hhDhQNOp8ZdxBeDC5WVlZOT6OOvguMXjritMOTYZOXCSZ6+XuP/H+PNRqtYM/llnwOQwpbC/iP5lDw874pyu128PdBz7K4rrUE7/DcdDw8oN/PZCi6PbLW7+duoQh9Td//6chhNnvvhnjz+e/erL+/K/jp65e/OcXv3elv2/301/8VRxpG2fqZpkrefj17/5O6hKGy8/qP4555z/9gz9JXQt5+a3tX6YuAQat/R//U+oSYNDaX/0mdQkwaO2vvkpdAinttLd2wmZXW+dsuDxbmk9YVd5++SM5bhqfffJvQg+/6uzxN6JFFf/4h3rj3//bQVZSYF/8u0/+6384/D9bH5L+uahzv+Bp9f4HOcO7nBimDuZy9ydxTM0xMhyef0GdYrPsMZVnO0pfG2xhwMiY/e6bO18+zTZrXvzeldmOh/2V5Z1B2AkAAAAnGc+wE2CExNmbZ0g9T5WKxZbB3t9leC53fxK1Wu2o+DA+UyqVOhPE5GIZx3yTu/77AINtgRP89Bd/1fVMf1s8O/PObIIuDNLrF9+Kh5/Vf5y2EgAAgOM12g8PjrF9K/yTSukdeSfAUDl0lOgxFhcXT5uKxZGkPb7LUF3u/lROLHt1dXV5eTntwNvOmc/HNHeurKx0VajLEzjc733/D+Ns24HJY14u9GJmtvJ34S9SVwEAAHCcRvthZ1tn0NkJMPRiHtNLp9p5+uqOad0L5x5Ymuvl7k8l+8k85scyixvjnyLv1s8T/5l0OipCFnkCJ9j58ulPf/FX3//um527NvMg7wQAAICDhJ0AI60z8Yrx2MHnzyl2xXW9Rb/uz/Vy9yd0fF6biZ/NXtOvBDT78lPNfz5+021pbFdJD0CPy7pjkL784F8PpCi6/fLWb6cuYXh1jpzNzH73zf6u28ze5Z/9r/97H6/lGL/+3d9JXcLQ+Vn9x1/sPg4h/NM/+JPUtZCL39r+ZeoSYNDa//E/pS4BBq391W9SlwCD1v7qq9QlkK9Dw85K6Z1U9QyDX/5orP/4CX32yb8JPfyqs8ffiBZV/OMf6o1//28HWUmBffHvPvmv/+HxoZ86PgsBjtJL8HmMUw1wPs+7nPgPXJcncKSss3Mwnv/qiUZPktvdaczMVlJXAQAAjDthJwAwGLFR9czB55lTzB71/l8ziDyBI1383pV/9nUG+dNf/FU89LfFM3QsDf3Fl09FnqTy+sW3YpfnF88fizwBAIBUdtpbO2FT2AkADFg2ofdUazXzc4YdtyJPoCd9TzphqMzMVr6YefzF7uMvdh//rP7jH1R/lLoiAABgvAg7AYBhEFsqzznt9szimNyzDakWeQJACCH8oPqjv/3JH4cQvth9bLwtAAAwMAfDzpkwNxsuz5bmE1YF9Fn7yDWfAEMoa/qM2WfIue/zPElnRuQJAK+8PmO8LQAAMDjCTgBgyGXZZ+j3zNsYc552eu0xRJ5AerPffXPny6c7Xz5NXQjj7gfVH/2s/mPjbQEAgLwJOwGAkdPZhZl1f4beQtAYcB68p49EngDwjdcvft3oabwtAACQg0b74cGFncJOAGC0dHZ/DgmRJwB8Y2a28sXM45h6/l3jL76YeazXEwAA6AthJwBAfiZSFwAAw+UH1R+9PvNWPMcJt2nrAQAARl2j/XCtvdo1xvZGabFSekfeCQDQFyJPIL3vf/fNePjpL/4qbSUQ/aD6o39c+Vfx/MXu47/9yR/v7jTSlgQAAIyiY8LOhFUBABSPwbZAehe/dyU8/+vUVcC3zMxWQiV88dyQWwAA4NR22ls7YfPgGFtJJwBATkSeAHC4mdnKzGzlZ/Ufx9QzDrmVegIAAMcQdgIAJCHyBIDj/KD6o92dxt81/iKEELNPAACAgw6GnTNhLoQg7AQAGACRJzBEdr58mroEOEQcchtTT42eAABAp5h0hhC6ws7ZcHm2NJ+uLgCA8SLyBIbC7HfflHcyzGZmK38X/iJ1FQAAwFA4NOaMhJ3AoUrt1BUURSl1AcDQEnkCQE9en3nri93HZtsCAMDYarQfhsNizkjYCQCQkMgTAE5nd6cxM1tJXQUAAJCjnfZWDC9PjDmDbZ0AAENA5AkMl+e/enLxe1dSVwEAAMD4arQf7obtx0dMoYwxp4ZOAIChIvIEhsLvff8P/+bv/zSE8Isvn4o8GUK7O4040vb1mbe0eAIAQFHFsPPg82JOAIAhJ/IEgJN98fzVCs/XL76VthIAAKDvdtpbO2HzYNhpbi0AwKgQeQLACX5W/7EWTwAAKKRD2zpnwpyYEwBgtIg8AeAEMe8MIfyg+qO0lQAAAH1xaFunnk4AgNEl8gSGxex339z58unOl09TFwLf8rP6j+Ph9RkjbQEAYOQ12g9DCAfDTns6AQBGmsgTAI60u9PQ4gkAAAWgrRMAoNhEngBwpC+ev8o7/3HlX6WtBAAAOBttnQAA40DkCQyd5796cvF7V1JXAd/yxfPHM7OV1FUAAACn0Gg/7Eo6QwgzYU5bJwBA8Yg8gWHx/e++GRd5/uLLpyJPhsQPqj/625/8ceoqAACAUzhqhq22TiClduoCCsN3EjiCyBMYFhe/dyU8/+v7An33AAAgAElEQVQQQgw+4Sg/q/84268ZvT7zVt6LNrveEQAAGELaOgEAxpbIExgis183esIxBpw+vj7zlrwTAP5/9u6ut40sz/P8CcoNlwvU5KJzGEFOSe6+SVBjQaSFAXqQFnqQWLQGvrJuImjA2pu53vuEX0LCL2DfgQyYwRv5yhgNsMZMy4UdYCGTggxxDOygJPXyQV2NyZLQWd6xGHtxrOOTwadgMMgIkt8PjJxQKEges6pRmPrV7/8HgMSSqzoF2zoBAAAWG5EngATZzO28+fhCCHHU2N/M7cR9HCRRu1WL6+ParRrrPAEAAIDYybm1oivjVKh1AgAALCAiTwDJQtETg102p1e47J6gCwAAACAW/aqcOlP8TghB2AkAALCYiDwBJIsseqrUs3l1ml1ei/dISL5JBJPtVk297RR2hQIAAADQDa1yituMUxBzAgAAgMgTQALJomfz6lQI8b752rqu59J5gk9IevTYbtUmV/r8UHspLx4UnjHPFgAAAJiC4FVOlnQCAADAh8gTQOLIomfjui67nvKfRJ7oZlqFCYWRJ9U9eZExN8g7AQAAgAmhygkAAICoEHkCSCJ9o6eVzm/mduI9DxaESjrVSFvm2QIAAADRklXOARmnoMoJAACA0RF5AkgiWfSU163r+lFjn9QTETqp7gVZ//mg8GwKhwEAAADmW/AqJxknAAAAQiPyBJBQetETiEq7VVNLOgdjpC0AAAAQWvAqJ+NqAQAAEAkiTwAJpRc9gcHarVqQeFIvd2bMje4HMlliTgAAEI9W59xKrcZ9CiA8qpwAAAAYwHGc7puu60b4EUSeAJJLFT2Zaot+5PbNy/bxB/EyY270W73ZbtUum8d63smSTgAAkBzVm8O2d2F6K0KI4tJW3McBgqLKCQAj8Iy4TzA3+CaBGeM4TqVS6fkrwzBs2y6VSj0D0VEReQKYAezyRLfufZz91nP6htk+KDyjygkAAJKgenOYNe5bqdW2dyGEkP88+PzKNFZU8Fm9ORTkoEgMmXEKqpwAAAALSQaTKr/0PG/w867rlkqlwc9UKpVKpWLb9viNTyJPAMnFbFt08/U1dQ8Kz/QfZQFUaFEo5U4AAJA0f+jUrdRqIfWo6Z3JyFMI0fYuDj6/ivdggDK0yikzTkGVEwAAYH4NaGr2EyTvVCqViuM4Y6aeRJ4AEk3OtpXjbX2OGvvyIpfOZ5fXpnsuxKZf3rn8zeqH2ssP4mX3ryTyTgAAkDQ/e3/8JH6R6eZdcW/Ak3Q9MX1yN2e/pJMqJwAAwIIYKbnUjfqq8VNPIk8AiZa7XeepZtseNfZ9CWjruv5QCFLPRdA9zFZ6UHimj67t+QDDbAEAQKK0OuefxC/qR/06JVId0bkr7n1jfNs981bOwp3+gbE4at7vu5NOqpwAAAALKHTeGW4355ipJ5EngETLLq+J5mshROu6LofcPv7ux+7Us3FdJ/Kce+1WrV/eedn8OrpW3c9kN4g5AQBAYtU67/r96l8a/6rtXXwSv3zyfvlGfHvXuKfPvG17F6IjyD4RuZ4DbGXSScwJAACwgELnna7rjjoFV6lUKq7rhktMiTwBJJ11W/RUcl13Wtd1VQPFvOrucWbMDZlrqsiT0bUAAGAmyEG1/WSN+5+8X34Wf/xZ/FEIUTAeFZe25Et82ed26ukUTot5JTNOqWetk6QTAABgkZXL5chfaNu2EKJUKslneiaj5XKZyBPAfNIDTiudF0I0eq32JPWcbyfVPXUtq5wy3Wy3am8Pnuv3AQAAEq56c6iSy35k2Ck1vTNLrKpFnvrLqzeHLPhEEIPTTZ0pfseGTgAAADiOM05Ts/umbdv6xFoZavb8lNBFTyJPAEmXXV6zrusy9dzM7TSvTlu9Ik/MMTnSVk86JX21p2x8xnM+AACAsAxheMLz3fwf3j/qP7a9i1bnXM2wlRnnwedX3b8ChBA17/d/Fr/8RtwTw9JNHQNsAQAAEISsaQ5Yt9kvquz5Ehltdqee4YqeRJ4AZslRY19dW+m87HSq1Z65dD62k2HCftj+Sf+x3apdNo/1vJORtgAAYFbIKbWyqdmddwohzrz/dlfc+yR+UXdk0VN/ppB6JLeB1jrvGG+7sFqd86b3B3ntSzf/1P9VMt0UQtDmBAAAQE/9lnH6mprBDRh12zP1DFcwJfIEMAM2cztvPr6Q16riqSbZqjvZ5bV4zocJM62C/qNe7hRCPCg88z0AAACQcHrq2S0lUp/EL3fFvbvi3p/EP4lebU4rtWp6K/IdGG+7UNQi2KHjkQXpJgAAAELpmVAGzDt7ppWDK5ulUin0EF0dkSeA2WCl863reuu6/vi7H1WtU6ae6oFYD4hpoNwJAADmRta43y+y6oiOEOKT+OUb49t/u7Qtw9HuomdxaUuNt53CgRGXVue86Z2JYf9Ck24CAABgooLknT2fkbNwB+gZiDqOM2qjlMgTwIyRzc7m1en75muhlT6Zajv32q3ah9pL9aOv3Cmrn3Lfp0QaCgAAkkyGWIN98n4Rt8s7VbdPZxor5J1zKUiV0zRWhBCWt0LACQCzoccwewBIqO7O5dDYUhowwHYw27bHL3oSeQKYDfpsWyFEdnntoRAy9VR34jgXpkQfZpsxNzLZDd8w2/Xirm/g7duD5+p54k8AAJAorc65irJkcOVLtu6Ke98Y3+rjagePrvWNvcXMCVLlNG+jTf3fDN7NzaTPBgAAAIwj3PrPEIg8AcwMNdtW/phdXhO3kSdTbeeYr9w5IL/MZDf0yFO5bB+/PXhO8AkAAJJDbeI0jRUZX8kRtXfFvU/iF9NYyRr3R4owa513oiMKqUcEnzMkeJVz1H8/AAAAANEKGFtGspIzNCJPALNHzrbV7zDVdl4NLXfqTKtwaf5q06cQQv1I8AkAABKluLRVvTn0dTc/iV9Gii2zxn2hBWYy+BRCqCQViRK6ygkAAADMqIATcbuFSE+JPAHMDN9sWx1TbeeSL+8MElWuF3fVPFv52u7g86S6Nzg6BQAAmA6VabU65+pmrfPO9L6uZtSboN2s1KrMONnomRyysKuqulQ5AQAAsCAcx4n3AESeAGaJb7atuhnXeTBRKqd8UHgWPKF8UHh22TzWM04hRMbc0O9cto8vzWPqngAAICFk808I8Y349s/in/VsbGhf00qtWmJVxmyIkYo2pbZ30fYuZCDdjSonAAAAFsTUFnkKIk8AsyWXzsu8U862lWEnU23nW8YcrZFpWgX5vF4S7d7x2XPrJwAAQCxkxinTTV94GTAS277zVAhRvTmUb9X2Lg4+v2K87YTo6Wbwfi1VTgAAAMy3eBd5CiJPALMlu7wmmq/Vj76NnoBOljjbrZpe+tSdVPcoegIAgNjp+ZmvKVhIPRrprYpLW3JbpB58CsK2MQQZS9tNlTglgmcAAADMOsdxQvQ1Qy/yDPdaIk8AM6bnbFvMn5PqnrzIZDfGeR+99CkodwIAgORRWVrWuF/rvFP3TWOlX0Ipc03RK0uTc25V3VP/CDVnNUQC2uqcL05cqr7eIDGnnm4SbQIAAGA+2LY9amVzzEWekTREiTwBzBjfbNu4j4OJG2mq7QBDS58AAACxKKQeyaRTzzvFwPxM9Tj7Ta+Vd/pFd3oCOjT+/JqedoYvFp1pekG2J5Vu0pcFAADAohkaSfZ8IGAxNKp9n0SeAGaMmm1L0RMhqNKnzD7jPg4AAICwUqumtzLS3FRd1rg/4J0t8TWZ6zmjVcWfpverOFM+3J2VVm8O5yzw6/k3lWTGOccpLwAAANBTqVTqjjAHzLbtWfEMPpm2XC73PEPAlytEngBmj3Vb9GxenWaX1+I+DiZCFjEz5lhTbQdQ2ScAAEDsiktbvlG0d8W9gK+1UqvVm8NP3i93jXuDw0g9uusugMrFnzLk687/TGNFhaMyHw0dfKqP7ne2cAYkl6MyjfB/OwAA+vLiPsDc4JsEJq9nhFmpVFzX7f6V67rjjKXt9/IQk3KJPAHMHjXbtnFdJ/IEAADA/PkkfvHdaXXOhRDdOdzB51dfrrxfzaodHCLqBVB9oKsvMvSFfyqXVcFndzHUJ2AG+fVv0Ye+MjPcRwR5fwqdAAAAgNRznWepVLJtW+96uq7br44ZZFxtv5cHb4jqiDwBzB5m2wIAAGDOdOd2//Xzf/qbO3+ntz9NY0XmlMWlrf/r88GfxD8NeCsZIgYpLMr40/dBolf+V1za8uWjQ6PKqATMNQcko/1Q6AQAAAC6ua5rGEb3/UqlIu/3zESVoZmlLHH2e4cQU20FkecU9Pz3BIAxMdt2fO1WLbGTXU+qe/Iik53UYFsAAIBEUZNjlZ/FHwc8/xvjt3/yekeeup51zJ5UnBmkHuobwztAzwzS9xE9R936DPg4+REklwAwHfxXnQCwOAaHmoOH2fbLLB3HCTIFN8RUW0HkCWBGMdt2fJfN4w+1l3JZ5npxN+7j9JbYUBYAACBaxaUtX2NyQGGx1TnvFwGaxoqviykCVyT1abdDTyt6TbINNxg2+OcCAAAAmBq5uTPEnk7btsNlllK5XA73QiJPADOJ2bZRuWwfCyFOqnuJTT0BAAAWU9a4L4QoLm11VyprnXf9XqWGzW7feSq07ZutznnkJUg2XwIAAADzrVQqhYg8g2zx7GecuDQV+lMBIF5WOi8vmlen8Z5khhw19tXXJcNOdf324Hm7VQv3tu1W7aS6J/9EcEoAAICFpGqdprGyfeepSiiLS1tqdqvo6lbeFfdMY6WQeuRrhbY65+olQoihk2MBAAAAwMdxnHK5PHQxpy50R1MIYdv2OHEpLU8As2ozt/Pm4wvBbNsRvW++lgVZSQ62lfHnh9rLS/N41LrnSXVPT0/fHjz/YfuniA4LAACwiFROqd9pexcyBFVza9WvvoSjnS8Py1pn0zurfe5bBgUAAACAIJxbQ+uetm2XSqXQHc0x805ByxPATJNFT2bbBreZ2+m+uV7clcGnEOKyfRy8qSnLnXreCQAAgHD09ZzdE2it1Goh9Uj/sbi0Jf+I20Knuinrnr5ZuAHXeQIAAABAN9d1Pc/rV/e0bbtcLsvdnyHeXL18vDPS8gQwF5pXpxQ9A3qYffL+tuWZMTcy2Q0hxHpxt92qfai9FLepZya7YVqFAe/jCzsfFJ5dNo8jjz9ZMgoAABaEam365tMqAzZx1jrvREeYxopsfMpK6IC3AgAAAIAQxk8ldTJAHacY6kPkCWCGMds2hOzyWqbz31U2qXJN0yqIglCp52X7WBTEgNRTvYPMTVXeqQqj41gv7r49eD7++wAAAMyE6s2hCillcTM4K7UqR9q2vYu2dyGzz0Lq0YCIFAAAAABiUSqVos1NdUSeAOYBs21Hsl7clR3Ny/bx24PnMrM0rYJpFcztgqpvDljtqYbfZswN9W7qx2n+XQAAAGadyjuFEKPmnZJprOhza2X2aXr+iufXrZ8AAAAAEIeoCp09scsTwGyT6zwxKjnPVrpsH3+ovTyp7rVbNdFrtae835OciPu18ZmNoOIJAACwOPQVnvq2znD0SbYy+NT/1Drv1IeO+UEAAGBkHn8i+gMAfRB5ApgTR439uI8wS0yr8MP2Tw8Kz/R080Pt5duD528PnusrOeX9AamnnIUrhHhQeDZ4/edI5MEiXw4KAACQKCqGlNfVm8MQeaTeDd2+83TACs/qzaH8IFJPAAAAAHOGyBPAbNvM7cR9hBlmWoX14q4efPYja6ADHsiYGxHmnQAAAAvCF0/q82lDkC+XCajc6Ll956n8Iz+o7V3I1LPpnY3zQQAAAACQNOzyBDDzrHS+dV1nnWdocoun0DZ09sTQWgAAgMgVl7YOPr/S74Rbt6nWecpEc/vOU98DWeO+fED/JwAAAADMDSJPAMAX68XduI/QQ7tVoz8KAADmWCH1SI63NY0VfUTtSHyJZjcrtWp6K/pvW53zcPEqAAAAAIzKcZzum67rRvgRRJ4AZl4unZcVz6PGPnNup0Ou2Bw6Dncc68XdtwfPhRCXzWMiTwAAMMes1KrojPsmQQbVFpe2qjeHKvVsemeWIPIEAAAAMFmO41QqlZ6/MgzDtu1SqdQzEB0VuzwBzLzs8lrcRwAAAABCkqNox6TXN337QXV6i7TtXbQ65+N/NAAAAIAF4TiO4zjGraHPu65rGEa/vFOqVCpEngDwlZXOCyFY5zl9g9d/AgAAILjQU219oWnbu6jeHFZvDnsmmoXUI5WJBumGAgAAAIBMOiuVyuD8Uue6bqlUCvhwpVIZP/VksC2AudK8OqX0OTVyvK2UyU5qyK3+KQAAAPNHFjR91Uw5gbaQehRk3abMSvWhtV/3ena+vHPWuC/fykqtWmJVPizD0dBRKwAAAIC5N1JyqRv1VTL1HGe7Jy1PAPMgl87Li8bCFz2PGvtHjf3m1ek0P/RB4dkk1m1OdFcoAABAQshIUqaP8o4KL2udd93P95tGW1za2r7z1DRWfOmpjDZrnXd6GVTFnIy3BQAAANBP6LwzXGVzzK4nLU8A8yC7vCaar+M+RVK0ruut67povrbS+Vw63117Panuyerk+FFlxtxYL+6O8w4AAAALrri0dfD5ldD2cWaN+/K6kHpUvTlUBU2p1nlneiu+m/q7qWuZcerVz1bnXL3KNFbkr5remSWGd0kBAAAALJTQeafrusHn3/pUKhXXdcMFn0SeAOaElc5/ifpwS34h1nVdCLGZ2zlq7Itfbzz9UHspCmLU1LPdqskL8k4AAIBIqPRRjpm1UqvbqadCCBWFyh8VWdw0vZXBM2nVb1udc1kYVXGp6B+FAgAAAIAQolwuR/5C27aFEKVSST7TMxktl8tEngAghBBHjf3N3E7cp0gQmXG++fii528/1F6a26NFnpfNaBqiQ2WyG7KNelLdI1sFAABzrLvoKakotGck2fYuDj6/Mo0hwacQwkqtmt6KWvDp+xQAADANnhH3CQBgBI7jjNPU7L5p27a+p1OGmj0/JXTRk12eAOaEijlb13VZZ1xwj7/78WH2iXW75VSx0vkHhWc/bP+kNmWeVPdOqnvB3zmT3ZhC3inEyPVTAACA2aUWcPo2bprGSiH1SM87u1d1Hnx+pb+qJ/lWPT9RCEHFEwAAzIFKpWKENc76QGCh2LZt27bneZ7n9Xyg3/816XmnflP2Pn3CFUxpeQKYQ63revPqtHuH5aLJLq/JL0FlwDIY/rP1l0KI9eKuXOopm5RvD57LEHRon5IkEgAAIHJ60VPml3JbZ3eDUz2pk68KMudWvZauJwAAAICe+i3j9DU1gxsQYcpCp+/jwhVMaXkCmB96o/F983WMJ4lRz22mm7kd+cd3f724q7qeQggZf749eD5S6RMAAACRULVLOXu21nl38PlVq3Pe/WQh9aj7pspKDz6/kr3P6s1hz5f3+1wAAAAAEH0SyoB5Z8+0cnCRulQqBT/bALQ8Acyn7oGui0C1OYP/9WWns92qfai9VDcz2Y3+rwAAAMBEZI373c3LWuddQTzyDZ7Vd3Pq2t6FyjjV5s7t1FP9mULqUa3zLuKjAwAAJMDvfve777//Ptxro0pcgDkWJO/s+UzP0bW6noGo4zijNkqJPAHMj83czpuPL+I+RWyOGvuy4mml892FzsEum8fqejp7OgEAAKBrdc77JZE9U8/i0lb15rA79Wx6Z/qP3Q1OK7W6nXoqp+D2fAcAAIAZ9f3334cbuQnAp7upOTS2lMLt4JTvH26YrY7BtgDmU27BWp7Nq1M10nbUv7vc6CmvyTsBAACmT887e46Z7ZmGFpe2tu883b7z1DRW5B8hRNu7uCvuDf1EuddT/ycAAAAATMLU/rcItDwBzBUrnZfJX3Z5Le6zTFV2ec26rsu/+/vma9F87Ztt27P3KXd2yrwzY25kshvknQAAALEzja9Da7fvPJVdTNnL7Pm87/5//jzCVvuea0EBAAAAQBcwthy/qTkOIk8Ac0VNdo37IDHYzO2o2bbi9qtQ9JG/mZ83VK3zyx1zQy71BAAAwPTpuzl9k2YPPr+SF23v4uDzK1nlHNzL/Hd3ngghWp3zpneWNe4P/ehxTg4AAAAAAwSciNstRHpK5AkA82Mzt9O8Om1c10VX5KmblbxTllABAAAWQcDNmvIBlYOaxkrWuN8ztrRSq5YgzgQAAAAwJY7jxHsAIk8A8+OosS8vFm2Rpy67vNY91FfloFLrup4xN4QQszLJNpmJLAAAQLSyxv2hkadP27toexemt8I+TgAAAAAJNLVFnoLIEwAWgS8H/fNf/2WMhwEAAEBPTe8syGNytq2kZuHKmbcEnwAAJJbhxX2CucE3CSRVvIs8hRCpeD8eACKkyp2N/jNdAQAAgGQqLm3pcebgJ+WfQuqRutn2Lqo3h63O+cQOCAAAAGBBhRtaG3qRZ7jXEnkCmB/Z5TUrnRcD11gCAAAAiZU17nffNI0V9UcI0fYuVK5ppVa37zxVQWnbuwhYFQUAAACAfkLEjWMu8oykIUrkCWA+qb2emFEn1b3L9nHcpwAAAJiq7sDyX4i/VJ1OFYj6HisubangU3Y9p3NaAAAAAAtiaCTZ84GAizyj2vdJ5AlgrmzmduI+AiKg550PCs/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TY/V6vd9rALrwx6reX84mOILfhenHVJH+X1nbOwIYIl358/yUF707d+70usMYVzwAcdL/eJZwseJnPACDryuXu5YOxc94AAAAQIjGthg47f2x6urq6p07d5rWS/G3/wAGStt/np/moncEvwsDgARprlQrKyv1ej3hYsXPeAAGX1cud+kPxc94AAAAQCSqPHHcrK2thT1/5BMjvwgDcPzIRY8rHoBBFnelunz5cvrf2vMzHoDB15XLXfKhuOIBAAAAcf5/FJHeqR5RRYwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "width": 500
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fname = ensaio.fetch_earth_topography(version=1)\n",
+    "topo = xr.load_dataarray(fname)\n",
+    "\n",
+    "# resample to coarse grid for faster processing\n",
+    "spacing = 3.0  # degrees\n",
+    "topo = topo.coarsen(\n",
+    "    latitude=int(spacing / np.abs(topo.latitude[1] - topo.latitude[0])),\n",
+    "    longitude=int(spacing / np.abs(topo.longitude[1] - topo.longitude[0])),\n",
+    "    boundary=\"trim\",\n",
+    ").mean()\n",
+    "\n",
+    "topo = topo * 100\n",
+    "\n",
+    "region = (-78, -53, -57, -20)\n",
+    "topo = topo.sel(latitude=slice(*region[2:]), longitude=slice(*region[:2]))\n",
+    "\n",
+    "ellipsoid = bl.WGS84\n",
+    "\n",
+    "longitude, latitude = np.meshgrid(topo.longitude, topo.latitude)\n",
+    "reference = ellipsoid.geocentric_radius(latitude)\n",
+    "surface = topo + reference\n",
+    "density = xr.where(topo > 0, 2670.0, 1040.0 - 2670.0)\n",
+    "\n",
+    "tesseroid_layer = hm.tesseroid_layer(\n",
+    "    coordinates=(topo.longitude, topo.latitude),\n",
+    "    surface=surface,\n",
+    "    reference=reference,\n",
+    "    properties={\"density\": density},\n",
+    ")\n",
+    "\n",
+    "tesseroid_layer[\"thickness\"] = tesseroid_layer.top - tesseroid_layer.bottom\n",
+    "\n",
+    "# Plot thickness\n",
+    "fig = pygmt.Figure()\n",
+    "fig.grdimage(\n",
+    "    tesseroid_layer.thickness,\n",
+    "    projection=\"M15c\",\n",
+    "    nan_transparent=True,\n",
+    "    cmap=\"viridis\",\n",
+    ")\n",
+    "fig.basemap(frame=True)\n",
+    "fig.colorbar(frame='af+l\"Thickness (m)\"', position=\"JCR\")\n",
+    "fig.coast(shorelines=\"0.5p,black\", borders=[\"1/0.5p,black\"])\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "46252706",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3kB\n",
+       "Dimensions:    (latitude: 13, longitude: 8)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 104B -54.67 -51.83 -49.0 ... -23.5 -20.67\n",
+       "  * longitude  (longitude) float64 64B -76.58 -73.58 -70.58 ... -58.58 -55.58\n",
+       "    top        (latitude, longitude) float64 832B 6.364e+06 ... 6.408e+06\n",
+       "    bottom     (latitude, longitude) float64 832B 5.965e+06 ... 6.375e+06\n",
+       "Data variables:\n",
+       "    density    (latitude, longitude) float64 832B -1.63e+03 ... 2.67e+03\n",
+       "    thickness  (latitude, longitude) float64 832B 3.992e+05 ... 3.279e+04\n",
+       "Attributes:\n",
+       "    longitude_units:   degrees\n",
+       "    latitude_units:    degrees\n",
+       "    radius_units:      meters\n",
+       "    properties_units:  SI</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cc7e381f-f8d9-4d13-aabe-b46641700542' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc7e381f-f8d9-4d13-aabe-b46641700542' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 13</li><li><span class='xr-has-index'>longitude</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-274aabc5-9b4e-4a78-ae58-2c69905c3fcf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-274aabc5-9b4e-4a78-ae58-2c69905c3fcf' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-54.67 -51.83 ... -23.5 -20.67</div><input id='attrs-27b53a06-0e49-41b6-acea-1e30387f5535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27b53a06-0e49-41b6-acea-1e30387f5535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddad587b-9309-4576-9f1c-e9505a63e566' class='xr-var-data-in' type='checkbox'><label for='data-ddad587b-9309-4576-9f1c-e9505a63e566' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[-90.  90.]</dd></dl></div><div class='xr-var-data'><pre>array([-54.666667, -51.833333, -49.      , -46.166667, -43.333333, -40.5     ,\n",
+       "       -37.666667, -34.833333, -32.      , -29.166667, -26.333333, -23.5     ,\n",
+       "       -20.666667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-76.58 -73.58 ... -58.58 -55.58</div><input id='attrs-273a484d-ddeb-4f87-805a-dd1af1757e97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-273a484d-ddeb-4f87-805a-dd1af1757e97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52b57a62-42e8-4fba-9a5a-b04975608542' class='xr-var-data-in' type='checkbox'><label for='data-52b57a62-42e8-4fba-9a5a-b04975608542' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>actual_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([-76.583333, -73.583333, -70.583333, -67.583333, -64.583333, -61.583333,\n",
+       "       -58.583333, -55.583333])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>top</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.364e+06 6.364e+06 ... 6.408e+06</div><input id='attrs-b73b4f1b-f3a4-4675-8b75-10bbdef6b51f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b73b4f1b-f3a4-4675-8b75-10bbdef6b51f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-675311fb-d345-4ea0-8976-c99feffc41c5' class='xr-var-data-in' type='checkbox'><label for='data-675311fb-d345-4ea0-8976-c99feffc41c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6363944.83842011, 6363944.83842011, 6363944.83842011,\n",
+       "        6372710.19789724, 6363944.83842011, 6363944.83842011,\n",
+       "        6363944.83842011, 6363944.83842011],\n",
+       "       [6364960.77116885, 6390507.503195  , 6386984.30058062,\n",
+       "        6364960.77116885, 6364960.77116885, 6364960.77116885,\n",
+       "        6364960.77116885, 6364960.77116885],\n",
+       "       [6366000.61469342, 6430562.70619669, 6422554.20946466,\n",
+       "        6374007.80423591, 6366000.61469342, 6366000.61469342,\n",
+       "        6366000.61469342, 6366000.61469342],\n",
+       "       [6367054.1590734 , 6415306.77345248, 6439839.77998843,\n",
+       "        6388708.08064202, 6367054.1590734 , 6367054.1590734 ,\n",
+       "        6367054.1590734 , 6367054.1590734 ],\n",
+       "       [6368111.0852034 , 6381331.01984392, 6460237.55579163,\n",
+       "        6422497.68651059, 6370110.75840601, 6368111.0852034 ,\n",
+       "        6368111.0852034 , 6368111.0852034 ],\n",
+       "       [6369161.06645938, 6369161.06645938, 6474799.95534827,\n",
+       "        6437411.06645938, 6377228.38672081, 6369161.06645938,\n",
+       "        6369161.06645938, 6369161.06645938],\n",
+       "       [6370193.86944091, 6370193.86944091, 6498325.24198993,\n",
+       "        6418198.44460431, 6390202.69297032, 6386096.15702261,\n",
+       "        6375928.1831664 , 6370193.86944091],\n",
+       "       [6371199.45281694, 6371199.45281694, 6521874.28941824,\n",
+       "        6442603.04758818, 6397833.11294765, 6380069.71425484,\n",
+       "        6374047.49203262, 6375109.58353589],\n",
+       "       [6372168.06335902, 6372168.06335902, 6540534.07643091,\n",
+       "        6452020.02414333, 6430540.93917601, 6379914.14179039,\n",
+       "        6377417.40976424, 6387798.45551588],\n",
+       "       [6373090.32830943, 6373090.32830943, 6506234.11915911,\n",
+       "        6542877.58321139, 6408450.132231  , 6380659.93615257,\n",
+       "        6378951.11262316, 6387729.87079309],\n",
+       "       [6373957.34329949, 6373957.34329949, 6373957.34329949,\n",
+       "        6757730.87271126, 6452873.35637139, 6388514.53284197,\n",
+       "        6381659.3040838 , 6395065.84003152],\n",
+       "       [6374760.7551041 , 6374760.7551041 , 6374760.7551041 ,\n",
+       "        6758330.03614986, 6532219.25183613, 6393315.00347012,\n",
+       "        6385083.6309211 , 6404308.79431979],\n",
+       "       [6375492.83859063, 6375492.83859063, 6375492.83859063,\n",
+       "        6772482.3810743 , 6600308.52486514, 6404797.74055142,\n",
+       "        6387290.22421155, 6408284.34185861]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bottom</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.965e+06 6.16e+06 ... 6.375e+06</div><input id='attrs-abaea99f-f59f-43d8-8f8d-a7a1307666f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abaea99f-f59f-43d8-8f8d-a7a1307666f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df319f38-4bae-4c6d-bb6d-4998d3516ac5' class='xr-var-data-in' type='checkbox'><label for='data-df319f38-4bae-4c6d-bb6d-4998d3516ac5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5964765.42665541, 6159855.29593645, 6349420.98221096,\n",
+       "        6363944.83842011, 6241039.60966194, 6169241.57044626,\n",
+       "        6173198.75998874, 6106141.57044626],\n",
+       "       [6095044.10450219, 6364960.77116885, 6364960.77116885,\n",
+       "        6358332.3397963 , 6348416.65352179, 6344437.24175709,\n",
+       "        6345729.72541722, 6230052.60123421],\n",
+       "       [6146248.32711172, 6366000.61469342, 6366000.61469342,\n",
+       "        6366000.61469342, 6354950.28789604, 6346090.15717708,\n",
+       "        6285944.07874571, 6154929.69966074],\n",
+       "       [6118654.48587078, 6367054.1590734 , 6367054.1590734 ,\n",
+       "        6367054.1590734 , 6358520.82574006, 6352329.32247209,\n",
+       "        6126209.38783157, 5828951.54469431],\n",
+       "       [6051518.27474588, 6368111.0852034 , 6368111.0852034 ,\n",
+       "        6368111.0852034 , 6368111.0852034 , 6358925.46428837,\n",
+       "        6214995.0721315 , 5877809.77801386],\n",
+       "       [5982794.39979271, 6329069.23639402, 6369161.06645938,\n",
+       "        6369161.06645938, 6369161.06645938, 6366051.58933519,\n",
+       "        6358036.88345284, 6151222.83116526],\n",
+       "       [5959519.0328396 , 6244423.608003  , 6370193.86944091,\n",
+       "        6370193.86944091, 6370193.86944091, 6370193.86944091,\n",
+       "        6370193.86944091, 6343772.62761084],\n",
+       "       [5968716.1194836 , 6067992.26327445, 6371199.45281694,\n",
+       "        6371199.45281694, 6371199.45281694, 6371199.45281694,\n",
+       "        6371199.45281694, 6371199.45281694],\n",
+       "       [5954199.43590804, 5956178.84767274, 6372168.06335902,\n",
+       "        6372168.06335902, 6372168.06335902, 6372168.06335902,\n",
+       "        6372168.06335902, 6372168.06335902],\n",
+       "       [5959558.95576041, 5928229.87079309, 6373090.32830943,\n",
+       "        6373090.32830943, 6373090.32830943, 6373090.32830943,\n",
+       "        6373090.32830943, 6373090.32830943],\n",
+       "       [5994506.68970472, 5976884.14068511, 6303705.38251518,\n",
+       "        6373957.34329949, 6373957.34329949, 6373957.34329949,\n",
+       "        6373957.34329949, 6373957.34329949],\n",
+       "       [5923431.99693417, 5977466.96425443, 6212188.2060845 ,\n",
+       "        6374760.7551041 , 6374760.7551041 , 6374760.7551041 ,\n",
+       "        6374760.7551041 , 6374760.7551041 ],\n",
+       "       [5904019.63597626, 5913395.12617233, 6195528.13270828,\n",
+       "        6375492.83859063, 6375492.83859063, 6375492.83859063,\n",
+       "        6375492.83859063, 6375492.83859063]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c4666c1-4eea-41a2-857d-eebc3550293e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c4666c1-4eea-41a2-857d-eebc3550293e' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.63e+03 -1.63e+03 ... 2.67e+03</div><input id='attrs-acb9cf07-1d03-46c5-b1ae-999d2106219b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acb9cf07-1d03-46c5-b1ae-999d2106219b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fce680da-12bd-4618-a627-bb6287b7080f' class='xr-var-data-in' type='checkbox'><label for='data-fce680da-12bd-4618-a627-bb6287b7080f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1630., -1630., -1630.,  2670., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670., -1630., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670.,  2670., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670.,  2670., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670.,  2670.,  2670., -1630., -1630., -1630.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670., -1630., -1630., -1630.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670., -1630.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thickness</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.992e+05 2.041e+05 ... 3.279e+04</div><input id='attrs-59c0fb27-26c9-4c75-8b78-8f152aa6f684' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59c0fb27-26c9-4c75-8b78-8f152aa6f684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8f9e924-d30b-4d8c-af32-4579e7ec00fc' class='xr-var-data-in' type='checkbox'><label for='data-d8f9e924-d30b-4d8c-af32-4579e7ec00fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[399179.41176471, 204089.54248366,  14523.85620915,\n",
+       "          8765.35947712, 122905.22875817, 194703.26797386,\n",
+       "        190746.07843137, 257803.26797386],\n",
+       "       [269916.66666667,  25546.73202614,  22023.52941176,\n",
+       "          6628.43137255,  16544.11764706,  20523.52941176,\n",
+       "         19231.04575163, 134908.16993464],\n",
+       "       [219752.2875817 ,  64562.09150327,  56553.59477124,\n",
+       "          8007.18954248,  11050.32679739,  19910.45751634,\n",
+       "         80056.53594771, 211070.91503268],\n",
+       "       [248399.67320261,  48252.61437908,  72785.62091503,\n",
+       "         21653.92156863,   8533.33333333,  14724.83660131,\n",
+       "        240844.77124183, 538102.61437908],\n",
+       "       [316592.81045752,  13219.93464052,  92126.47058824,\n",
+       "         54386.60130719,   1999.67320261,   9185.62091503,\n",
+       "        153116.0130719 , 490301.30718954],\n",
+       "       [386366.66666667,  40091.83006536, 105638.88888889,\n",
+       "         68250.        ,   8067.32026144,   3109.47712418,\n",
+       "         11124.18300654, 217938.23529412],\n",
+       "       [410674.83660131, 125770.26143791, 128131.37254902,\n",
+       "         48004.5751634 ,  20008.82352941,  15902.2875817 ,\n",
+       "          5734.31372549,  26421.24183006],\n",
+       "       [402483.33333333, 303207.18954248, 150674.83660131,\n",
+       "         71403.59477124,  26633.66013072,   8870.26143791,\n",
+       "          2848.03921569,   3910.13071895],\n",
+       "       [417968.62745098, 415989.21568627, 168366.0130719 ,\n",
+       "         79851.96078431,  58372.87581699,   7746.07843137,\n",
+       "          5249.34640523,  15630.39215686],\n",
+       "       [413531.37254902, 444860.45751634, 133143.79084967,\n",
+       "        169787.25490196,  35359.80392157,   7569.60784314,\n",
+       "          5860.78431373,  14639.54248366],\n",
+       "       [379450.65359477, 397073.20261438,  70251.96078431,\n",
+       "        383773.52941176,  78916.0130719 ,  14557.18954248,\n",
+       "          7701.96078431,  21108.49673203],\n",
+       "       [451328.75816993, 397293.79084967, 162572.54901961,\n",
+       "        383569.28104575, 157458.49673203,  18554.24836601,\n",
+       "         10322.87581699,  29548.03921569],\n",
+       "       [471473.20261438, 462097.7124183 , 179964.70588235,\n",
+       "        396989.54248366, 224815.68627451,  29304.90196078,\n",
+       "         11797.38562092,  32791.50326797]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20257e1f-5d8a-42f2-8223-946b8552dff8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20257e1f-5d8a-42f2-8223-946b8552dff8' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>longitude_units :</span></dt><dd>degrees</dd><dt><span>latitude_units :</span></dt><dd>degrees</dd><dt><span>radius_units :</span></dt><dd>meters</dd><dt><span>properties_units :</span></dt><dd>SI</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3kB\n",
+       "Dimensions:    (latitude: 13, longitude: 8)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 104B -54.67 -51.83 -49.0 ... -23.5 -20.67\n",
+       "  * longitude  (longitude) float64 64B -76.58 -73.58 -70.58 ... -58.58 -55.58\n",
+       "    top        (latitude, longitude) float64 832B 6.364e+06 ... 6.408e+06\n",
+       "    bottom     (latitude, longitude) float64 832B 5.965e+06 ... 6.375e+06\n",
+       "Data variables:\n",
+       "    density    (latitude, longitude) float64 832B -1.63e+03 ... 2.67e+03\n",
+       "    thickness  (latitude, longitude) float64 832B 3.992e+05 ... 3.279e+04\n",
+       "Attributes:\n",
+       "    longitude_units:   degrees\n",
+       "    latitude_units:    degrees\n",
+       "    radius_units:      meters\n",
+       "    properties_units:  SI"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tesseroid_layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fdf140f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_tesseroids(tesseroids):\n",
+    "    \"\"\"Plot tesseroids using PyVista.\"\"\"\n",
+    "\n",
+    "    # Plot the tesseroid\n",
+    "    plotter = pv.Plotter()\n",
+    "\n",
+    "    # plot tesseroids\n",
+    "    for tesseroid in tesseroids:\n",
+    "        lon_min, lon_max, lat_min, lat_max, r_inner, r_outer = tesseroid\n",
+    "\n",
+    "        # phi (latitude) is defined as 0 at north pole and 180 at south pole in PyVista\n",
+    "        def lat_to_phi(lat):\n",
+    "            return 90 - lat\n",
+    "\n",
+    "        min_phi = lat_to_phi(lat_max)\n",
+    "        max_phi = lat_to_phi(lat_min)\n",
+    "\n",
+    "        # Create a solid sphere sector (tesseroid)\n",
+    "        tesseroid_mesh = pv.SolidSphere(\n",
+    "            outer_radius=r_outer,\n",
+    "            inner_radius=r_inner,\n",
+    "            start_theta=lon_min,  # longitude\n",
+    "            end_theta=lon_max,  # longitude\n",
+    "            start_phi=min_phi,  # latitude\n",
+    "            end_phi=max_phi,  # latitude\n",
+    "            # phi_resolution=2,\n",
+    "            # theta_resolution=2,\n",
+    "            # radius_resolution=2,\n",
+    "        )\n",
+    "\n",
+    "        plotter.add_mesh(\n",
+    "            tesseroid_mesh,\n",
+    "            color=\"lightblue\",\n",
+    "            show_edges=False,\n",
+    "            opacity=1,\n",
+    "        )\n",
+    "\n",
+    "        # # add wire mesh to show tesseroid edges\n",
+    "        # mesh = pv.SolidSphere(\n",
+    "        #     outer_radius=r_outer,\n",
+    "        #     inner_radius=r_inner,\n",
+    "        #     start_theta=lon_min,  # longitude\n",
+    "        #     end_theta=lon_max,    # longitude\n",
+    "        #     start_phi=min_phi,    # latitude\n",
+    "        #     end_phi=max_phi,      # latitude\n",
+    "        #     phi_resolution=2,\n",
+    "        #     theta_resolution=2,\n",
+    "        #     radius_resolution=2,\n",
+    "        # )\n",
+    "        # edges = mesh.extract_all_edges()\n",
+    "        # plotter.add_mesh(\n",
+    "        #     edges,\n",
+    "        #     line_width=2,\n",
+    "        #     color=\"black\",\n",
+    "        # )\n",
+    "\n",
+    "    plotter.add_axes()\n",
+    "\n",
+    "    # mean_phi, mean_theta = (min_phi + max_phi)/2, (lon_min + lon_max)/2\n",
+    "\n",
+    "    # # set camera to look at equator from prime meridian\n",
+    "    # cam_pos = pv.grid_from_sph_coords(\n",
+    "    #     # theta = [0], # longitude\n",
+    "    #     phi = [90], # latitude\n",
+    "    #     theta = [mean_theta+20], # longitude\n",
+    "    #     # phi = [mean_phi+20], # latitude\n",
+    "    #     r = [r_outer*10], # distance from the origin\n",
+    "    # )\n",
+    "\n",
+    "    # plotter.set_position(cam_pos.points)\n",
+    "    # plotter.set_viewup((0,0,1))\n",
+    "    plotter.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "152d976f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract tesseroids from the layer\n",
+    "tesseroids = tesseroid_layer.tesseroid_layer._to_tesseroids()\n",
+    "\n",
+    "# discard thin tesseroids\n",
+    "density = tesseroid_layer.tesseroid_layer._obj.density.values\n",
+    "mask = tesseroid_layer.tesseroid_layer._get_nonans_mask(property_name=\"density\")\n",
+    "density = density[mask]\n",
+    "tesseroids = _discard_thin_tesseroids(\n",
+    "    tesseroids,\n",
+    "    density,\n",
+    "    1,\n",
+    ")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "9564f0c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/pyvista/jupyter/notebook.py:56: UserWarning: Failed to use notebook backend: \n",
+      "\n",
+      "cannot import name 'vtk' from 'trame.widgets' (/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/trame/widgets/__init__.py)\n",
+      "\n",
+      "Falling back to a static output.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=1024x768>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_tesseroids(tesseroids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "id": "fcb6f6b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3kB\n",
+       "Dimensions:    (latitude: 13, longitude: 8)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 104B -54.67 -51.83 -49.0 ... -23.5 -20.67\n",
+       "  * longitude  (longitude) float64 64B -76.58 -73.58 -70.58 ... -58.58 -55.58\n",
+       "    top        (latitude, longitude) float64 832B 6.364e+06 ... 6.408e+06\n",
+       "    bottom     (latitude, longitude) float64 832B 5.965e+06 ... 6.375e+06\n",
+       "Data variables:\n",
+       "    density    (latitude, longitude) float64 832B -1.63e+03 ... 2.67e+03\n",
+       "    thickness  (latitude, longitude) float64 832B 3.992e+05 ... 3.279e+04\n",
+       "Attributes:\n",
+       "    longitude_units:   degrees\n",
+       "    latitude_units:    degrees\n",
+       "    radius_units:      meters\n",
+       "    properties_units:  SI</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-343af5b7-b611-49b3-8a38-267887897196' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-343af5b7-b611-49b3-8a38-267887897196' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 13</li><li><span class='xr-has-index'>longitude</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f811efe9-90d0-477d-94ad-ba33e9a32a1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f811efe9-90d0-477d-94ad-ba33e9a32a1e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-54.67 -51.83 ... -23.5 -20.67</div><input id='attrs-1b7ad1cd-5704-4b3c-9cba-6beb358203e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b7ad1cd-5704-4b3c-9cba-6beb358203e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d19b9f5-0035-4b11-8221-be41c0e9dd22' class='xr-var-data-in' type='checkbox'><label for='data-3d19b9f5-0035-4b11-8221-be41c0e9dd22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[-90.  90.]</dd></dl></div><div class='xr-var-data'><pre>array([-54.666667, -51.833333, -49.      , -46.166667, -43.333333, -40.5     ,\n",
+       "       -37.666667, -34.833333, -32.      , -29.166667, -26.333333, -23.5     ,\n",
+       "       -20.666667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-76.58 -73.58 ... -58.58 -55.58</div><input id='attrs-8cf31eec-90a4-4be3-b678-9d2d5c388de4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cf31eec-90a4-4be3-b678-9d2d5c388de4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dbd639d-aab4-4a02-9a35-9c588e7a979d' class='xr-var-data-in' type='checkbox'><label for='data-7dbd639d-aab4-4a02-9a35-9c588e7a979d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>actual_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([-76.583333, -73.583333, -70.583333, -67.583333, -64.583333, -61.583333,\n",
+       "       -58.583333, -55.583333])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>top</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.364e+06 6.364e+06 ... 6.408e+06</div><input id='attrs-f3c825e3-eee8-4eaa-a0b7-b00a1617063a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3c825e3-eee8-4eaa-a0b7-b00a1617063a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2370e676-3c2f-4e3b-a00a-031370a1479e' class='xr-var-data-in' type='checkbox'><label for='data-2370e676-3c2f-4e3b-a00a-031370a1479e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6363944.83842011, 6363944.83842011, 6363944.83842011,\n",
+       "        6372710.19789724, 6363944.83842011, 6363944.83842011,\n",
+       "        6363944.83842011, 6363944.83842011],\n",
+       "       [6364960.77116885, 6390507.503195  , 6386984.30058062,\n",
+       "        6364960.77116885, 6364960.77116885, 6364960.77116885,\n",
+       "        6364960.77116885, 6364960.77116885],\n",
+       "       [6366000.61469342, 6430562.70619669, 6422554.20946466,\n",
+       "        6374007.80423591, 6366000.61469342, 6366000.61469342,\n",
+       "        6366000.61469342, 6366000.61469342],\n",
+       "       [6367054.1590734 , 6415306.77345248, 6439839.77998843,\n",
+       "        6388708.08064202, 6367054.1590734 , 6367054.1590734 ,\n",
+       "        6367054.1590734 , 6367054.1590734 ],\n",
+       "       [6368111.0852034 , 6381331.01984392, 6460237.55579163,\n",
+       "        6422497.68651059, 6370110.75840601, 6368111.0852034 ,\n",
+       "        6368111.0852034 , 6368111.0852034 ],\n",
+       "       [6369161.06645938, 6369161.06645938, 6474799.95534827,\n",
+       "        6437411.06645938, 6377228.38672081, 6369161.06645938,\n",
+       "        6369161.06645938, 6369161.06645938],\n",
+       "       [6370193.86944091, 6370193.86944091, 6498325.24198993,\n",
+       "        6418198.44460431, 6390202.69297032, 6386096.15702261,\n",
+       "        6375928.1831664 , 6370193.86944091],\n",
+       "       [6371199.45281694, 6371199.45281694, 6521874.28941824,\n",
+       "        6442603.04758818, 6397833.11294765, 6380069.71425484,\n",
+       "        6374047.49203262, 6375109.58353589],\n",
+       "       [6372168.06335902, 6372168.06335902, 6540534.07643091,\n",
+       "        6452020.02414333, 6430540.93917601, 6379914.14179039,\n",
+       "        6377417.40976424, 6387798.45551588],\n",
+       "       [6373090.32830943, 6373090.32830943, 6506234.11915911,\n",
+       "        6542877.58321139, 6408450.132231  , 6380659.93615257,\n",
+       "        6378951.11262316, 6387729.87079309],\n",
+       "       [6373957.34329949, 6373957.34329949, 6373957.34329949,\n",
+       "        6757730.87271126, 6452873.35637139, 6388514.53284197,\n",
+       "        6381659.3040838 , 6395065.84003152],\n",
+       "       [6374760.7551041 , 6374760.7551041 , 6374760.7551041 ,\n",
+       "        6758330.03614986, 6532219.25183613, 6393315.00347012,\n",
+       "        6385083.6309211 , 6404308.79431979],\n",
+       "       [6375492.83859063, 6375492.83859063, 6375492.83859063,\n",
+       "        6772482.3810743 , 6600308.52486514, 6404797.74055142,\n",
+       "        6387290.22421155, 6408284.34185861]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bottom</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.965e+06 6.16e+06 ... 6.375e+06</div><input id='attrs-28bd0726-51ed-4c5c-9b06-db3812bbb920' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28bd0726-51ed-4c5c-9b06-db3812bbb920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b849466-c819-4755-9ba8-6fa445960739' class='xr-var-data-in' type='checkbox'><label for='data-0b849466-c819-4755-9ba8-6fa445960739' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5964765.42665541, 6159855.29593645, 6349420.98221096,\n",
+       "        6363944.83842011, 6241039.60966194, 6169241.57044626,\n",
+       "        6173198.75998874, 6106141.57044626],\n",
+       "       [6095044.10450219, 6364960.77116885, 6364960.77116885,\n",
+       "        6358332.3397963 , 6348416.65352179, 6344437.24175709,\n",
+       "        6345729.72541722, 6230052.60123421],\n",
+       "       [6146248.32711172, 6366000.61469342, 6366000.61469342,\n",
+       "        6366000.61469342, 6354950.28789604, 6346090.15717708,\n",
+       "        6285944.07874571, 6154929.69966074],\n",
+       "       [6118654.48587078, 6367054.1590734 , 6367054.1590734 ,\n",
+       "        6367054.1590734 , 6358520.82574006, 6352329.32247209,\n",
+       "        6126209.38783157, 5828951.54469431],\n",
+       "       [6051518.27474588, 6368111.0852034 , 6368111.0852034 ,\n",
+       "        6368111.0852034 , 6368111.0852034 , 6358925.46428837,\n",
+       "        6214995.0721315 , 5877809.77801386],\n",
+       "       [5982794.39979271, 6329069.23639402, 6369161.06645938,\n",
+       "        6369161.06645938, 6369161.06645938, 6366051.58933519,\n",
+       "        6358036.88345284, 6151222.83116526],\n",
+       "       [5959519.0328396 , 6244423.608003  , 6370193.86944091,\n",
+       "        6370193.86944091, 6370193.86944091, 6370193.86944091,\n",
+       "        6370193.86944091, 6343772.62761084],\n",
+       "       [5968716.1194836 , 6067992.26327445, 6371199.45281694,\n",
+       "        6371199.45281694, 6371199.45281694, 6371199.45281694,\n",
+       "        6371199.45281694, 6371199.45281694],\n",
+       "       [5954199.43590804, 5956178.84767274, 6372168.06335902,\n",
+       "        6372168.06335902, 6372168.06335902, 6372168.06335902,\n",
+       "        6372168.06335902, 6372168.06335902],\n",
+       "       [5959558.95576041, 5928229.87079309, 6373090.32830943,\n",
+       "        6373090.32830943, 6373090.32830943, 6373090.32830943,\n",
+       "        6373090.32830943, 6373090.32830943],\n",
+       "       [5994506.68970472, 5976884.14068511, 6303705.38251518,\n",
+       "        6373957.34329949, 6373957.34329949, 6373957.34329949,\n",
+       "        6373957.34329949, 6373957.34329949],\n",
+       "       [5923431.99693417, 5977466.96425443, 6212188.2060845 ,\n",
+       "        6374760.7551041 , 6374760.7551041 , 6374760.7551041 ,\n",
+       "        6374760.7551041 , 6374760.7551041 ],\n",
+       "       [5904019.63597626, 5913395.12617233, 6195528.13270828,\n",
+       "        6375492.83859063, 6375492.83859063, 6375492.83859063,\n",
+       "        6375492.83859063, 6375492.83859063]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-267f754a-7598-4957-8f4d-4fded4698f5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-267f754a-7598-4957-8f4d-4fded4698f5c' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.63e+03 -1.63e+03 ... 2.67e+03</div><input id='attrs-55b73b94-6fce-4bb1-97a9-7267609f0b04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55b73b94-6fce-4bb1-97a9-7267609f0b04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7642286-6a4b-4743-80b2-94f96cea6b0c' class='xr-var-data-in' type='checkbox'><label for='data-e7642286-6a4b-4743-80b2-94f96cea6b0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1630., -1630., -1630.,  2670., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670., -1630., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670.,  2670., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670.,  2670., -1630., -1630., -1630., -1630.],\n",
+       "       [-1630.,  2670.,  2670.,  2670.,  2670., -1630., -1630., -1630.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670., -1630., -1630., -1630.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670., -1630.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.],\n",
+       "       [-1630., -1630., -1630.,  2670.,  2670.,  2670.,  2670.,  2670.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thickness</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.992e+05 2.041e+05 ... 3.279e+04</div><input id='attrs-3649a68e-1cb1-4e91-89b1-1de10cb69722' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3649a68e-1cb1-4e91-89b1-1de10cb69722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-306ad0b5-d679-4ae2-beda-f3b9ea1fb511' class='xr-var-data-in' type='checkbox'><label for='data-306ad0b5-d679-4ae2-beda-f3b9ea1fb511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[399179.41176471, 204089.54248366,  14523.85620915,\n",
+       "          8765.35947712, 122905.22875817, 194703.26797386,\n",
+       "        190746.07843137, 257803.26797386],\n",
+       "       [269916.66666667,  25546.73202614,  22023.52941176,\n",
+       "          6628.43137255,  16544.11764706,  20523.52941176,\n",
+       "         19231.04575163, 134908.16993464],\n",
+       "       [219752.2875817 ,  64562.09150327,  56553.59477124,\n",
+       "          8007.18954248,  11050.32679739,  19910.45751634,\n",
+       "         80056.53594771, 211070.91503268],\n",
+       "       [248399.67320261,  48252.61437908,  72785.62091503,\n",
+       "         21653.92156863,   8533.33333333,  14724.83660131,\n",
+       "        240844.77124183, 538102.61437908],\n",
+       "       [316592.81045752,  13219.93464052,  92126.47058824,\n",
+       "         54386.60130719,   1999.67320261,   9185.62091503,\n",
+       "        153116.0130719 , 490301.30718954],\n",
+       "       [386366.66666667,  40091.83006536, 105638.88888889,\n",
+       "         68250.        ,   8067.32026144,   3109.47712418,\n",
+       "         11124.18300654, 217938.23529412],\n",
+       "       [410674.83660131, 125770.26143791, 128131.37254902,\n",
+       "         48004.5751634 ,  20008.82352941,  15902.2875817 ,\n",
+       "          5734.31372549,  26421.24183006],\n",
+       "       [402483.33333333, 303207.18954248, 150674.83660131,\n",
+       "         71403.59477124,  26633.66013072,   8870.26143791,\n",
+       "          2848.03921569,   3910.13071895],\n",
+       "       [417968.62745098, 415989.21568627, 168366.0130719 ,\n",
+       "         79851.96078431,  58372.87581699,   7746.07843137,\n",
+       "          5249.34640523,  15630.39215686],\n",
+       "       [413531.37254902, 444860.45751634, 133143.79084967,\n",
+       "        169787.25490196,  35359.80392157,   7569.60784314,\n",
+       "          5860.78431373,  14639.54248366],\n",
+       "       [379450.65359477, 397073.20261438,  70251.96078431,\n",
+       "        383773.52941176,  78916.0130719 ,  14557.18954248,\n",
+       "          7701.96078431,  21108.49673203],\n",
+       "       [451328.75816993, 397293.79084967, 162572.54901961,\n",
+       "        383569.28104575, 157458.49673203,  18554.24836601,\n",
+       "         10322.87581699,  29548.03921569],\n",
+       "       [471473.20261438, 462097.7124183 , 179964.70588235,\n",
+       "        396989.54248366, 224815.68627451,  29304.90196078,\n",
+       "         11797.38562092,  32791.50326797]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-805a6208-7bbb-428d-a034-527cd574643b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-805a6208-7bbb-428d-a034-527cd574643b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>longitude_units :</span></dt><dd>degrees</dd><dt><span>latitude_units :</span></dt><dd>degrees</dd><dt><span>radius_units :</span></dt><dd>meters</dd><dt><span>properties_units :</span></dt><dd>SI</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3kB\n",
+       "Dimensions:    (latitude: 13, longitude: 8)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 104B -54.67 -51.83 -49.0 ... -23.5 -20.67\n",
+       "  * longitude  (longitude) float64 64B -76.58 -73.58 -70.58 ... -58.58 -55.58\n",
+       "    top        (latitude, longitude) float64 832B 6.364e+06 ... 6.408e+06\n",
+       "    bottom     (latitude, longitude) float64 832B 5.965e+06 ... 6.375e+06\n",
+       "Data variables:\n",
+       "    density    (latitude, longitude) float64 832B -1.63e+03 ... 2.67e+03\n",
+       "    thickness  (latitude, longitude) float64 832B 3.992e+05 ... 3.279e+04\n",
+       "Attributes:\n",
+       "    longitude_units:   degrees\n",
+       "    latitude_units:    degrees\n",
+       "    radius_units:      meters\n",
+       "    properties_units:  SI"
+      ]
+     },
+     "execution_count": 385,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tesseroid_layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c691ab4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_tesseroid_layer(tesseroid_layer, add_sphere=True):\n",
+    "    \"\"\"Plot a tesseroid using PyVista. This plots 1 large tesseroid, then adds some\n",
+    "    grid lines to denote each separate tesseroid.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # extract tesseroids from the layer\n",
+    "    tesseroids = tesseroid_layer.tesseroid_layer._to_tesseroids()\n",
+    "\n",
+    "    # discard thin tesseroids\n",
+    "    density = tesseroid_layer.tesseroid_layer._obj.density.values\n",
+    "    mask = tesseroid_layer.tesseroid_layer._get_nonans_mask(property_name=\"density\")\n",
+    "    density = density[mask]\n",
+    "    tesseroids = _discard_thin_tesseroids(\n",
+    "        tesseroids,\n",
+    "        density,\n",
+    "        1,\n",
+    "    )[0]\n",
+    "\n",
+    "    # get arrays of boundaries\n",
+    "    lon_mins = [i[0] for i in tesseroids]\n",
+    "    lon_maxs = [i[1] for i in tesseroids]\n",
+    "    lat_mins = [i[2] for i in tesseroids]\n",
+    "    lat_maxs = [i[3] for i in tesseroids]\n",
+    "    r_inners = [i[4] for i in tesseroids]\n",
+    "    r_outers = [i[5] for i in tesseroids]\n",
+    "\n",
+    "    # # transform latitudes to be centered at north pole\n",
+    "    # mean_lat = np.mean(np.array([lat_mins, lat_maxs]))\n",
+    "    # print(mean_lat)\n",
+    "    # lat_mins = [90-np.abs(x-mean_lat) for x in lat_mins]\n",
+    "    # lat_maxs = [90-np.abs(x-mean_lat) for x in lat_maxs]\n",
+    "    # print(min(lat_mins), max(lat_maxs))\n",
+    "\n",
+    "    # phi (latitude) is defined as 0 at north pole and 180 at south pole in PyVista\n",
+    "    min_phis = [90 - x for x in lat_maxs]\n",
+    "    max_phis = [90 - x for x in lat_mins]\n",
+    "\n",
+    "    # transform latitudes to be centered at north pole\n",
+    "    # mean_phi = np.mean(np.array([min_phis, max_phis]))\n",
+    "    # min_phis = [np.abs(x-mean_phi) for x in min_phis]\n",
+    "    # max_phis = [np.abs(x-mean_phi) for x in max_phis]\n",
+    "    # print(min(min_phis), max(max_phis))\n",
+    "\n",
+    "    # # transform longitudes to be centered at 0deg\n",
+    "    # mean_lon = np.mean(np.array([lon_mins, lon_maxs]))\n",
+    "    # lon_mins = [x-(mean_lon) for x in lon_mins]\n",
+    "    # lon_maxs = [x-(mean_lon) for x in lon_maxs]\n",
+    "\n",
+    "    mean_theta = np.mean(np.array([lon_mins, lon_maxs]))\n",
+    "    mean_phi = np.mean(np.array([min_phis, max_phis]))\n",
+    "\n",
+    "    plotter = pv.Plotter()\n",
+    "\n",
+    "    if add_sphere:\n",
+    "        # Create and plot the semi-transparent shell of a sphere\n",
+    "        sphere_mesh = pv.Sphere(radius=np.mean([r_outers, r_inners]))\n",
+    "        plotter.add_mesh(sphere_mesh, color=\"white\", style=\"wireframe\", opacity=0.1)\n",
+    "\n",
+    "    # loop through each tesseroid, generate its mesh, and append to a list\n",
+    "    meshes = []\n",
+    "    edges = []\n",
+    "    for i, _j in enumerate(tesseroids):\n",
+    "        # Create a solid sphere sector (tesseroid)\n",
+    "        tesseroid_mesh = pv.SolidSphere(\n",
+    "            outer_radius=r_outers[i],\n",
+    "            inner_radius=r_inners[i],\n",
+    "            start_theta=lon_mins[i],  # longitude\n",
+    "            end_theta=lon_maxs[i],  # longitude\n",
+    "            start_phi=min_phis[i],  # latitude\n",
+    "            end_phi=max_phis[i],  # latitude\n",
+    "            phi_resolution=5,  # resolutions of 2 will be flat faces\n",
+    "            theta_resolution=5,\n",
+    "            radius_resolution=5,\n",
+    "        )\n",
+    "        plotter.add_mesh(\n",
+    "            tesseroid_mesh,\n",
+    "            # color=\"lightblue\",\n",
+    "            scalars=[density[i]] * tesseroid_mesh.n_cells,\n",
+    "            cmap=\"viridis\",\n",
+    "            show_edges=False,\n",
+    "            opacity=1,\n",
+    "        )\n",
+    "\n",
+    "        meshes.append(tesseroid_mesh)\n",
+    "        tesseroid_edge = pv.SolidSphere(\n",
+    "            outer_radius=r_outers[i],\n",
+    "            inner_radius=r_inners[i],\n",
+    "            start_theta=lon_mins[i],  # longitude\n",
+    "            end_theta=lon_maxs[i],  # longitude\n",
+    "            start_phi=min_phis[i],  # latitude\n",
+    "            end_phi=max_phis[i],  # latitude\n",
+    "            phi_resolution=2,\n",
+    "            theta_resolution=2,\n",
+    "            radius_resolution=2,\n",
+    "        )\n",
+    "        edge = tesseroid_edge.extract_feature_edges()\n",
+    "        edges.append(edge)\n",
+    "\n",
+    "    transform = pv.Transform()\n",
+    "    transform.rotate_x(-mean_phi)  # rotate so latitude is at north pole\n",
+    "    # transform.rotate_z(180-mean_theta) # rotate so longitude is at prime meridian (180)\n",
+    "\n",
+    "    # # plot merged mesh of all tesseroids\n",
+    "    # mesh = pv.merge(meshes)\n",
+    "    # # mesh = mesh.transform(transform, inplace=False)\n",
+    "    # print(mesh)\n",
+    "    # plotter.add_mesh(\n",
+    "    #     mesh,\n",
+    "    #     color=\"lightblue\",\n",
+    "    #     cmap=\"viridis\",\n",
+    "    #     show_edges=False,\n",
+    "    #     opacity=1,\n",
+    "    # )\n",
+    "\n",
+    "    # plot merged edges of all tesseroids\n",
+    "    edges = pv.merge(edges)\n",
+    "    # edges = edges.transform(transform, inplace=False)\n",
+    "    plotter.add_mesh(edges, line_width=2, color=\"black\")\n",
+    "\n",
+    "    # r = r_outer_max*10\n",
+    "    # phi = 90\n",
+    "    # theta = np.mean(np.array([lon_min, lon_max]))\n",
+    "    # print(r, phi, theta)\n",
+    "    # print(pv.spherical_to_cartesian(r, phi, theta))\n",
+    "\n",
+    "    # set camera to look at equator from prime meridian\n",
+    "\n",
+    "    # cam_pos = pv.grid_from_sph_coords(\n",
+    "    #     phi = 0, # latitude\n",
+    "    #     theta = 90, # longitude\n",
+    "    #     # phi = [mean_phi], # latitude\n",
+    "    #     r = [np.max(r_outers)*3], # distance from the origin\n",
+    "    # )\n",
+    "    # plotter.set_position(cam_pos.points)\n",
+    "\n",
+    "    # plotter.camera_position = [\n",
+    "    #     (0.0, 0.0, 1.0),\n",
+    "    #     (0.0, 0.0, 0.0),\n",
+    "    #     (0.0, 1.0, 0.0)\n",
+    "    # ]\n",
+    "    # plotter.camera_position = 'yz' # looking directly at prime meridian and equator\n",
+    "    mean_radius = np.mean([r_outers, r_inners])\n",
+    "\n",
+    "    plotter.camera_position = \"yz\"  # looking directly at prime meridian and equator\n",
+    "    # plotter.camera.distance=12e6\n",
+    "    plotter.camera.distance = mean_radius * 1.5\n",
+    "    plotter.camera.clipping_range = (mean_radius * 0.5, mean_radius * 3)\n",
+    "    plotter.camera.azimuth = mean_theta - 90\n",
+    "    plotter.camera.roll = mean_phi + 90\n",
+    "    plotter.camera.elevation = 10\n",
+    "\n",
+    "    plotter.add_axes()\n",
+    "    # plotter.show_grid()\n",
+    "    # plotter.show()\n",
+    "\n",
+    "    return plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "id": "4f348cb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_theta: -66.08333333333334, mean_phi: 127.66666666666667\n",
+      "6334682.131887221\n",
+      "Camera (0x7f3e42e3ae00)\n",
+      "  Position:            (-4883641.116362658, -9353545.545712134, -4625712.260412406)\n",
+      "  Focal Point:         (3141031.037858445, -4365912.591440106, -3617446.8276798343)\n",
+      "  Parallel Projection: False\n",
+      "  Distance:            9502023.197830832\n",
+      "  Thickness:           15836705.329718053\n",
+      "  Parallel Scale:      2724055.8834156445\n",
+      "  Clipping Range:      (3167341.0659436104, 19004046.395661663)\n",
+      "  View Angle:          30.0\n",
+      "  Roll:                -138.98348238006494\n"
+     ]
+    }
+   ],
+   "source": [
+    "plotter = plot_tesseroid_layer(\n",
+    "    tesseroid_layer,\n",
+    "    add_sphere=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "id": "c24863aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/pyvista/jupyter/notebook.py:56: UserWarning: Failed to use notebook backend: \n",
+      "\n",
+      "cannot import name 'vtk' from 'trame.widgets' (/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/trame/widgets/__init__.py)\n",
+      "\n",
+      "Falling back to a static output.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
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gAAEIQAACEGgRARSAFoEkGQhAAAIQgAAEIAABCCSBAApAElqJMkIAAhCAAAQgAAEIQKBFBFAAWgSSZCAAAQhAAAIQgAAEIJAEAv8/pzRbp3Whw/oAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=1024x768>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f65f2348",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;topography&#x27; (northing: 13, easting: 8)&gt; Size: 832B\n",
+       "array([[-5027.35353599, -5022.56887127, -5017.22847872, -5011.47847872,\n",
+       "        -5005.9586783 , -5001.015152  , -4999.64600582, -5000.08195612],\n",
+       "       [-5011.00613722, -5006.02286526, -5000.66862188, -4996.68830449,\n",
+       "        -4994.02163782, -4991.9033141 , -4992.70257763, -4995.03591097],\n",
+       "       [-5000.4698694 , -4995.13653607, -4990.08241531, -4987.74908197,\n",
+       "        -4986.19926558, -4985.64944918, -4987.54120765, -4991.27060383],\n",
+       "       [-4995.50226139, -4990.77764148, -4986.90027788, -4984.15027788,\n",
+       "        -4982.89847961, -4983.23181295, -4986.11590647, -4990.11590647],\n",
+       "       [-4997.3376984 , -4992.31713788, -4988.70701264, -4986.83988093,\n",
+       "        -4985.96324403, -4985.6299107 , -4987.7532738 , -4991.48162202],\n",
+       "       [-5001.43401706, -4998.21633131, -4995.52041414, -4993.52041414,\n",
+       "        -4992.08571986, -4991.21633131, -4992.34694276, -4995.47755421],\n",
+       "       [-5007.74898964, -5005.76113601, -5004.69231023, -5002.16329512,\n",
+       "        -5000.16329512, -4998.89878757, -4999.03643912, -5000.36977245],\n",
+       "       [-5012.37390502, -5012.51459374, -5011.56085753, -5009.56085753,\n",
+       "        -5007.46643259, -5005.13309926, -5005.13309926, -5005.13309926],\n",
+       "       [-5013.84889281, -5013.9244464 , -5013.77333921, -5012.28889868,\n",
+       "        -5011.28889868, -5010.24446403, -5009.39557122, -5007.84889281],\n",
+       "       [-5013.1575228 , -5012.85044327, -5012.63097456, -5012.38097456,\n",
+       "        -5011.8424772 , -5011.8424772 , -5010.8424772 , -5008.33333333],\n",
+       "       [-5013.27875656, -5012.38791011, -5012.06139887, -5011.74853183,\n",
+       "        -5011.4151985 , -5011.16373032, -5009.32746065, -5006.32746065],\n",
+       "       [-5013.0565766 , -5012.        , -5011.16972979, -5010.16972979,\n",
+       "        -5009.34558584, -5007.67891917, -5005.67891917, -5002.67891917],\n",
+       "       [-5010.80139   , -5009.31024458, -5007.70208499, -5006.45651229,\n",
+       "        -5004.87760624, -5002.87760624, -5000.05312749, -4996.56198207]])\n",
+       "Coordinates:\n",
+       "  * northing  (northing) float64 104B -3.48e+06 -3.478e+06 ... -3.458e+06\n",
+       "  * easting   (easting) float64 64B 1.01e+06 1.011e+06 ... 1.019e+06 1.02e+06\n",
+       "Attributes:\n",
+       "    metadata:  Generated by Chain(steps=[(&#x27;mean&#x27;,\\n              BlockReduce(...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;topography&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>northing</span>: 13</li><li><span class='xr-has-index'>easting</span>: 8</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ab824f71-0df1-4fdd-97d4-9fbd4e63d415' class='xr-array-in' type='checkbox' checked><label for='section-ab824f71-0df1-4fdd-97d4-9fbd4e63d415' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-5.027e+03 -5.023e+03 -5.017e+03 ... -5.003e+03 -5e+03 -4.997e+03</span></div><div class='xr-array-data'><pre>array([[-5027.35353599, -5022.56887127, -5017.22847872, -5011.47847872,\n",
+       "        -5005.9586783 , -5001.015152  , -4999.64600582, -5000.08195612],\n",
+       "       [-5011.00613722, -5006.02286526, -5000.66862188, -4996.68830449,\n",
+       "        -4994.02163782, -4991.9033141 , -4992.70257763, -4995.03591097],\n",
+       "       [-5000.4698694 , -4995.13653607, -4990.08241531, -4987.74908197,\n",
+       "        -4986.19926558, -4985.64944918, -4987.54120765, -4991.27060383],\n",
+       "       [-4995.50226139, -4990.77764148, -4986.90027788, -4984.15027788,\n",
+       "        -4982.89847961, -4983.23181295, -4986.11590647, -4990.11590647],\n",
+       "       [-4997.3376984 , -4992.31713788, -4988.70701264, -4986.83988093,\n",
+       "        -4985.96324403, -4985.6299107 , -4987.7532738 , -4991.48162202],\n",
+       "       [-5001.43401706, -4998.21633131, -4995.52041414, -4993.52041414,\n",
+       "        -4992.08571986, -4991.21633131, -4992.34694276, -4995.47755421],\n",
+       "       [-5007.74898964, -5005.76113601, -5004.69231023, -5002.16329512,\n",
+       "        -5000.16329512, -4998.89878757, -4999.03643912, -5000.36977245],\n",
+       "       [-5012.37390502, -5012.51459374, -5011.56085753, -5009.56085753,\n",
+       "        -5007.46643259, -5005.13309926, -5005.13309926, -5005.13309926],\n",
+       "       [-5013.84889281, -5013.9244464 , -5013.77333921, -5012.28889868,\n",
+       "        -5011.28889868, -5010.24446403, -5009.39557122, -5007.84889281],\n",
+       "       [-5013.1575228 , -5012.85044327, -5012.63097456, -5012.38097456,\n",
+       "        -5011.8424772 , -5011.8424772 , -5010.8424772 , -5008.33333333],\n",
+       "       [-5013.27875656, -5012.38791011, -5012.06139887, -5011.74853183,\n",
+       "        -5011.4151985 , -5011.16373032, -5009.32746065, -5006.32746065],\n",
+       "       [-5013.0565766 , -5012.        , -5011.16972979, -5010.16972979,\n",
+       "        -5009.34558584, -5007.67891917, -5005.67891917, -5002.67891917],\n",
+       "       [-5010.80139   , -5009.31024458, -5007.70208499, -5006.45651229,\n",
+       "        -5004.87760624, -5002.87760624, -5000.05312749, -4996.56198207]])</pre></div></div></li><li class='xr-section-item'><input id='section-8aa13f44-4df5-41be-bf2f-13980b6f8004' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8aa13f44-4df5-41be-bf2f-13980b6f8004' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>northing</span></div><div class='xr-var-dims'>(northing)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.48e+06 -3.478e+06 ... -3.458e+06</div><input id='attrs-1593da9c-6ce2-4192-9984-4aa24cf36cdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1593da9c-6ce2-4192-9984-4aa24cf36cdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1893e78-e774-4122-aea9-aa4360b06cf9' class='xr-var-data-in' type='checkbox'><label for='data-e1893e78-e774-4122-aea9-aa4360b06cf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3480178.674068, -3478329.482779, -3476480.291491, -3474631.100202,\n",
+       "       -3472781.908913, -3470932.717625, -3469083.526336, -3467234.335047,\n",
+       "       -3465385.143758, -3463535.95247 , -3461686.761181, -3459837.569892,\n",
+       "       -3457988.378604])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>easting</span></div><div class='xr-var-dims'>(easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.01e+06 1.011e+06 ... 1.02e+06</div><input id='attrs-e89579e5-0dab-4496-907c-bf8ff2505d6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e89579e5-0dab-4496-907c-bf8ff2505d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e118012-a6a8-4ec4-b0ad-44ae356a21fa' class='xr-var-data-in' type='checkbox'><label for='data-7e118012-a6a8-4ec4-b0ad-44ae356a21fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1009618.37932 , 1011136.602447, 1012654.825574, 1014173.0487  ,\n",
+       "       1015691.271827, 1017209.494954, 1018727.718081, 1020245.941208])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c76ec64-0dc7-41c0-a0d2-be5dbe668c0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c76ec64-0dc7-41c0-a0d2-be5dbe668c0e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>metadata :</span></dt><dd>Generated by Chain(steps=[(&#x27;mean&#x27;,\n",
+       "              BlockReduce(reduction=&lt;function mean at 0x7fe0447ce730&gt;,\n",
+       "                          region=(np.float64(994436.148052042),\n",
+       "                                  np.float64(1144740.2376049461),\n",
+       "                                  np.float64(-3498670.5869551436),\n",
+       "                                  np.float64(-3315600.6493731057)),\n",
+       "                          spacing=(np.float64(1849.1912887074534),\n",
+       "                                   np.float64(1518.2231267970114)))),\n",
+       "             (&#x27;interpolator&#x27;, Linear())])</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'topography' (northing: 13, easting: 8)> Size: 832B\n",
+       "array([[-5027.35353599, -5022.56887127, -5017.22847872, -5011.47847872,\n",
+       "        -5005.9586783 , -5001.015152  , -4999.64600582, -5000.08195612],\n",
+       "       [-5011.00613722, -5006.02286526, -5000.66862188, -4996.68830449,\n",
+       "        -4994.02163782, -4991.9033141 , -4992.70257763, -4995.03591097],\n",
+       "       [-5000.4698694 , -4995.13653607, -4990.08241531, -4987.74908197,\n",
+       "        -4986.19926558, -4985.64944918, -4987.54120765, -4991.27060383],\n",
+       "       [-4995.50226139, -4990.77764148, -4986.90027788, -4984.15027788,\n",
+       "        -4982.89847961, -4983.23181295, -4986.11590647, -4990.11590647],\n",
+       "       [-4997.3376984 , -4992.31713788, -4988.70701264, -4986.83988093,\n",
+       "        -4985.96324403, -4985.6299107 , -4987.7532738 , -4991.48162202],\n",
+       "       [-5001.43401706, -4998.21633131, -4995.52041414, -4993.52041414,\n",
+       "        -4992.08571986, -4991.21633131, -4992.34694276, -4995.47755421],\n",
+       "       [-5007.74898964, -5005.76113601, -5004.69231023, -5002.16329512,\n",
+       "        -5000.16329512, -4998.89878757, -4999.03643912, -5000.36977245],\n",
+       "       [-5012.37390502, -5012.51459374, -5011.56085753, -5009.56085753,\n",
+       "        -5007.46643259, -5005.13309926, -5005.13309926, -5005.13309926],\n",
+       "       [-5013.84889281, -5013.9244464 , -5013.77333921, -5012.28889868,\n",
+       "        -5011.28889868, -5010.24446403, -5009.39557122, -5007.84889281],\n",
+       "       [-5013.1575228 , -5012.85044327, -5012.63097456, -5012.38097456,\n",
+       "        -5011.8424772 , -5011.8424772 , -5010.8424772 , -5008.33333333],\n",
+       "       [-5013.27875656, -5012.38791011, -5012.06139887, -5011.74853183,\n",
+       "        -5011.4151985 , -5011.16373032, -5009.32746065, -5006.32746065],\n",
+       "       [-5013.0565766 , -5012.        , -5011.16972979, -5010.16972979,\n",
+       "        -5009.34558584, -5007.67891917, -5005.67891917, -5002.67891917],\n",
+       "       [-5010.80139   , -5009.31024458, -5007.70208499, -5006.45651229,\n",
+       "        -5004.87760624, -5002.87760624, -5000.05312749, -4996.56198207]])\n",
+       "Coordinates:\n",
+       "  * northing  (northing) float64 104B -3.48e+06 -3.478e+06 ... -3.458e+06\n",
+       "  * easting   (easting) float64 64B 1.01e+06 1.011e+06 ... 1.019e+06 1.02e+06\n",
+       "Attributes:\n",
+       "    metadata:  Generated by Chain(steps=[('mean',\\n              BlockReduce(..."
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Project the grid\n",
+    "# import pyproj\n",
+    "\n",
+    "# projection = pyproj.Proj(proj=\"merc\", lat_ts=da.latitude.values.mean())\n",
+    "# da = vd.project_grid(da, projection=projection)\n",
+    "# da = da[10:23, 10:18]\n",
+    "# da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf34419",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
+       ".xr-header {\n",
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+       "\n",
+       ".xr-header > div,\n",
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+       "  margin-top: 0;\n",
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+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3kB\n",
+       "Dimensions:   (northing: 13, easting: 8)\n",
+       "Coordinates:\n",
+       "  * northing  (northing) float64 104B -3.48e+06 -3.478e+06 ... -3.458e+06\n",
+       "  * easting   (easting) float64 64B 1.01e+06 1.011e+06 ... 1.019e+06 1.02e+06\n",
+       "    top       (northing, easting) float64 832B 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
+       "    bottom    (northing, easting) float64 832B -5.027e+03 ... -4.997e+03\n",
+       "Data variables:\n",
+       "    density   (northing, easting) float64 832B -1.9e+03 -1.9e+03 ... -1.9e+03\n",
+       "Attributes:\n",
+       "    coords_units:      meters\n",
+       "    properties_units:  SI</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1d293b11-e70d-4f34-9495-69d1b20afb11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d293b11-e70d-4f34-9495-69d1b20afb11' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>northing</span>: 13</li><li><span class='xr-has-index'>easting</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-20af9b6e-8345-4cc2-aa83-63754cf38690' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20af9b6e-8345-4cc2-aa83-63754cf38690' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>northing</span></div><div class='xr-var-dims'>(northing)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.48e+06 -3.478e+06 ... -3.458e+06</div><input id='attrs-5c9c8142-3d44-4713-9762-02f15888d49b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c9c8142-3d44-4713-9762-02f15888d49b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11d26af0-11c9-40c0-b682-42a3447e8d70' class='xr-var-data-in' type='checkbox'><label for='data-11d26af0-11c9-40c0-b682-42a3447e8d70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3480178.674068, -3478329.482779, -3476480.291491, -3474631.100202,\n",
+       "       -3472781.908913, -3470932.717625, -3469083.526336, -3467234.335047,\n",
+       "       -3465385.143758, -3463535.95247 , -3461686.761181, -3459837.569892,\n",
+       "       -3457988.378604])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>easting</span></div><div class='xr-var-dims'>(easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.01e+06 1.011e+06 ... 1.02e+06</div><input id='attrs-08bde335-8563-4284-b9ce-722d30db6fab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08bde335-8563-4284-b9ce-722d30db6fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9317695-1c94-4db1-ae6a-2214ac2ed0b3' class='xr-var-data-in' type='checkbox'><label for='data-f9317695-1c94-4db1-ae6a-2214ac2ed0b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1009618.37932 , 1011136.602447, 1012654.825574, 1014173.0487  ,\n",
+       "       1015691.271827, 1017209.494954, 1018727.718081, 1020245.941208])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>top</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-8f777cdd-6dfa-4e0a-aa6e-71ec65b85c85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f777cdd-6dfa-4e0a-aa6e-71ec65b85c85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fb2a390-6b46-4c72-98f8-5c9aed59c2c1' class='xr-var-data-in' type='checkbox'><label for='data-1fb2a390-6b46-4c72-98f8-5c9aed59c2c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bottom</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.027e+03 ... -4.997e+03</div><input id='attrs-5467db78-b04e-456f-bba1-4f35eef7d719' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5467db78-b04e-456f-bba1-4f35eef7d719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab8ad7d6-b22e-4bee-bbb6-f61a026c6a04' class='xr-var-data-in' type='checkbox'><label for='data-ab8ad7d6-b22e-4bee-bbb6-f61a026c6a04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-5027.35353599, -5022.56887127, -5017.22847872, -5011.47847872,\n",
+       "        -5005.9586783 , -5001.015152  , -4999.64600582, -5000.08195612],\n",
+       "       [-5011.00613722, -5006.02286526, -5000.66862188, -4996.68830449,\n",
+       "        -4994.02163782, -4991.9033141 , -4992.70257763, -4995.03591097],\n",
+       "       [-5000.4698694 , -4995.13653607, -4990.08241531, -4987.74908197,\n",
+       "        -4986.19926558, -4985.64944918, -4987.54120765, -4991.27060383],\n",
+       "       [-4995.50226139, -4990.77764148, -4986.90027788, -4984.15027788,\n",
+       "        -4982.89847961, -4983.23181295, -4986.11590647, -4990.11590647],\n",
+       "       [-4997.3376984 , -4992.31713788, -4988.70701264, -4986.83988093,\n",
+       "        -4985.96324403, -4985.6299107 , -4987.7532738 , -4991.48162202],\n",
+       "       [-5001.43401706, -4998.21633131, -4995.52041414, -4993.52041414,\n",
+       "        -4992.08571986, -4991.21633131, -4992.34694276, -4995.47755421],\n",
+       "       [-5007.74898964, -5005.76113601, -5004.69231023, -5002.16329512,\n",
+       "        -5000.16329512, -4998.89878757, -4999.03643912, -5000.36977245],\n",
+       "       [-5012.37390502, -5012.51459374, -5011.56085753, -5009.56085753,\n",
+       "        -5007.46643259, -5005.13309926, -5005.13309926, -5005.13309926],\n",
+       "       [-5013.84889281, -5013.9244464 , -5013.77333921, -5012.28889868,\n",
+       "        -5011.28889868, -5010.24446403, -5009.39557122, -5007.84889281],\n",
+       "       [-5013.1575228 , -5012.85044327, -5012.63097456, -5012.38097456,\n",
+       "        -5011.8424772 , -5011.8424772 , -5010.8424772 , -5008.33333333],\n",
+       "       [-5013.27875656, -5012.38791011, -5012.06139887, -5011.74853183,\n",
+       "        -5011.4151985 , -5011.16373032, -5009.32746065, -5006.32746065],\n",
+       "       [-5013.0565766 , -5012.        , -5011.16972979, -5010.16972979,\n",
+       "        -5009.34558584, -5007.67891917, -5005.67891917, -5002.67891917],\n",
+       "       [-5010.80139   , -5009.31024458, -5007.70208499, -5006.45651229,\n",
+       "        -5004.87760624, -5002.87760624, -5000.05312749, -4996.56198207]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d4a2fea8-47cb-468c-95d4-81213f25ec93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d4a2fea8-47cb-468c-95d4-81213f25ec93' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.9e+03 -1.9e+03 ... -1.9e+03</div><input id='attrs-918950b1-6f02-4a4e-b8be-b8ba392acc51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-918950b1-6f02-4a4e-b8be-b8ba392acc51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7e5351a-f1bf-4286-bfb6-e1a0910a997b' class='xr-var-data-in' type='checkbox'><label for='data-a7e5351a-f1bf-4286-bfb6-e1a0910a997b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.],\n",
+       "       [-1900., -1900., -1900., -1900., -1900., -1900., -1900., -1900.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a4365ed-9207-44ba-8c1f-a6a319b52474' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a4365ed-9207-44ba-8c1f-a6a319b52474' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coords_units :</span></dt><dd>meters</dd><dt><span>properties_units :</span></dt><dd>SI</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3kB\n",
+       "Dimensions:   (northing: 13, easting: 8)\n",
+       "Coordinates:\n",
+       "  * northing  (northing) float64 104B -3.48e+06 -3.478e+06 ... -3.458e+06\n",
+       "  * easting   (easting) float64 64B 1.01e+06 1.011e+06 ... 1.019e+06 1.02e+06\n",
+       "    top       (northing, easting) float64 832B 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
+       "    bottom    (northing, easting) float64 832B -5.027e+03 ... -4.997e+03\n",
+       "Data variables:\n",
+       "    density   (northing, easting) float64 832B -1.9e+03 -1.9e+03 ... -1.9e+03\n",
+       "Attributes:\n",
+       "    coords_units:      meters\n",
+       "    properties_units:  SI"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Create a 2d array with the density of the prisms Points above the geoid will\n",
+    "# # have a density of 2670 kg/m^3 Points below the geoid will have a density\n",
+    "# # contrast equal to the difference between the density of the ocean and the\n",
+    "# # density of the upper crust: # 1000 kg/m^3 - 2900 kg/m^3\n",
+    "# density = da.copy()  # copy topography to a new xr.DataArray\n",
+    "# density.values[:] = 2670.0  # replace every value for the density of the topography\n",
+    "# # Change density values of ocean points\n",
+    "# density = density.where(da >= 0, 1000 - 2900)\n",
+    "\n",
+    "# # Create layer of prisms\n",
+    "# prisms = hm.prism_layer(\n",
+    "#     (da.easting, da.northing),\n",
+    "#     surface=da,\n",
+    "#     reference=0,\n",
+    "#     properties={\"density\": density},\n",
+    "# )\n",
+    "# prisms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "074ff569",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style='width: 100%;'><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
+       "<table style='width: 100%;'>\n",
+       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
+       "<tr><td>N Cells</td><td>104</td></tr>\n",
+       "<tr><td>N Points</td><td>832</td></tr>\n",
+       "<tr><td>X Bounds</td><td>1.009e+06, 1.021e+06</td></tr>\n",
+       "<tr><td>Y Bounds</td><td>-3.481e+06, -3.457e+06</td></tr>\n",
+       "<tr><td>Z Bounds</td><td>-5.027e+03, 0.000e+00</td></tr>\n",
+       "<tr><td>N Arrays</td><td>1</td></tr>\n",
+       "</table>\n",
+       "\n",
+       "</td><td>\n",
+       "<table style='width: 100%;'>\n",
+       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
+       "<tr><td><b>density</b></td><td>Cells</td><td>float64</td><td>1</td><td>-1.900e+03</td><td>-1.900e+03</td></tr>\n",
+       "</table>\n",
+       "\n",
+       "</td></tr> </table>"
+      ],
+      "text/plain": [
+       "UnstructuredGrid (0x7fdf1c625ae0)\n",
+       "  N Cells:    104\n",
+       "  N Points:   832\n",
+       "  X Bounds:   1.009e+06, 1.021e+06\n",
+       "  Y Bounds:   -3.481e+06, -3.457e+06\n",
+       "  Z Bounds:   -5.027e+03, 0.000e+00\n",
+       "  N Arrays:   1"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pv_grid = prisms.prism_layer.to_pyvista()\n",
+    "# pv_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a01de4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/pyvista/jupyter/notebook.py:56: UserWarning: Failed to use notebook backend: \n",
+      "\n",
+      "cannot import name 'vtk' from 'trame.widgets' (/home/sungw937/miniforge3/envs/harmonica/lib/python3.12/site-packages/trame/widgets/__init__.py)\n",
+      "\n",
+      "Falling back to a static output.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=1024x768>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # Plot with pyvista\n",
+    "# plotter = pv.Plotter()\n",
+    "# plotter.add_mesh(pv_grid, scalars=\"density\")\n",
+    "# plotter.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9192259e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "harmonica",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/harmonica/__init__.py b/harmonica/__init__.py
index 8c1a406b1..92ce8375e 100644
--- a/harmonica/__init__.py
+++ b/harmonica/__init__.py
@@ -27,7 +27,7 @@
 from ._forward.prism_gravity import prism_gravity
 from ._forward.prism_layer import DatasetAccessorPrismLayer, prism_layer
 from ._forward.prism_magnetic import prism_magnetic
-from ._forward.tesseroid import tesseroid_gravity
+from ._forward.tesseroid_gravity import tesseroid_gravity
 from ._forward.tesseroid_layer import DatasetAccessorTesseroidLayer, tesseroid_layer
 from ._gravity_corrections import bouguer_correction
 from ._io.icgem_gdf import load_icgem_gdf
diff --git a/harmonica/_forward/tesseroid_layer.py b/harmonica/_forward/tesseroid_layer.py
index 99f3e61b5..be8d6b6a4 100644
--- a/harmonica/_forward/tesseroid_layer.py
+++ b/harmonica/_forward/tesseroid_layer.py
@@ -14,7 +14,7 @@
 import verde as vd
 import xarray as xr
 
-from .tesseroid import tesseroid_gravity
+from .tesseroid_gravity import tesseroid_gravity
 
 
 def tesseroid_layer(coordinates, surface, reference, properties=None):
@@ -320,7 +320,13 @@ def update_top_bottom(self, surface, reference):
         self._obj.coords["bottom"] = (self.dims, bottom)
 
     def gravity(
-        self, coordinates, field, progressbar=False, density_name="density", **kwargs
+        self,
+        coordinates,
+        field,
+        progressbar=False,
+        density_name="density",
+        thickness_threshold=None,
+        **kwargs,
     ):
         """
         Compute the gravity generated by the layer of tesseroids.
@@ -331,6 +337,9 @@ def gravity(
         through the ``density_name`` argument.
         Ignores the tesseroids which ``top`` or ``bottom`` boundaries are
         ``np.nan``s.
+        Tesseroids thinner than a given threshold can be optionally ignored through
+        the ``thickness_threshold`` argument.
+        All ``kwargs`` will be passed to :func:`harmonica.tesseroid_gravity`.
 
         Parameters
         ----------
@@ -352,6 +361,10 @@ def gravity(
             Name of the property layer (or ``data_var`` of the
             :class:`xarray.Dataset`) that will be used for the density of each
             tesseroid in the layer. Default to ``"density"``
+        thickness_threshold : float or None
+            Tesseroids thinner than this threshold will be ignored in the
+            forward gravity calculation. If None, every tesseroid with non-zero
+            volume will be considered. Default to None.
 
         Returns
         -------
@@ -373,6 +386,13 @@ def gravity(
         # Select only the boundaries and density elements for masked tesseroid
         boundaries = boundaries[mask.ravel()]
         density = density[mask]
+        # Discard thin tesseroids and their densities
+        if thickness_threshold is not None:
+            boundaries, density = _discard_thin_tesseroids(
+                boundaries,
+                density,
+                thickness_threshold,
+            )
         # Return gravity field of tesseroids
         return tesseroid_gravity(
             coordinates,
@@ -489,15 +509,53 @@ def get_tesseroid(self, indices):
         center_longitude = self._obj.longitude.values[indices[1]]
         center_latitude = self._obj.latitude.values[indices[0]]
         # Calculate the boundaries of the tesseroid
-        # (
-        #     longitude_w,
-        #     Longitude_e,
-        #     latitude_s,
-        #     latitude_n,
-        boundaries = self._get_tesseroid_horizontal_boundaries(
-            center_longitude, center_latitude
-        )
+        (
+            longitude_w,
+            longitude_e,
+            latitude_s,
+            latitude_n,
+        ) = self._get_tesseroid_horizontal_boundaries(center_longitude, center_latitude)
         bottom = self._obj.bottom.values[indices]
         top = self._obj.top.values[indices]
         # return longitude_w, longitude_e, latitude_s, latitude_n, bottom, top
-        return boundaries[0], boundaries[1], boundaries[2], boundaries[3], bottom, top
+        return longitude_w, longitude_e, latitude_s, latitude_n, bottom, top
+
+
+def _discard_thin_tesseroids(
+    tesseroids,
+    density,
+    thickness_threshold,
+):
+    """
+    Discard tesseroids with a thickness below a threshold.
+
+    Parameters
+    ----------
+    tesseroids : 2d-array
+        Array containing the boundaries of the tesseroids in the following order:
+        ``longitude_w``, ``longitude_e``, ``latitude_s``, ``latitude_n``,
+           ``bottom``, ``top``.
+        The array must have the following shape: (``n_tesseroids``, 6), where
+        ``n_tesseroids`` is the total number of tesseroids.
+    density : 1d-array
+        Array containing the density of each tesseroid in kg/m^3. Must have the
+        same size as the number of tesseroids.
+    thickness_threshold : float
+        Tesseroids thinner than this threshold will be discarded.
+
+    Returns
+    -------
+    tesseroids : 2d-array
+        A copy of the ``tesseroids`` array that doesn't include the thin tesseroids.
+    density : 1d-array
+        A copy of the ``density`` array that doesn't include the density values
+        for thin tesseroids.
+    """
+    bottom, top = tesseroids[:, -2], tesseroids[:, -1]
+    # Mark tesseroids with thickness < threshold  as null tesseroids
+    thickness = top - bottom
+    null_tesseroids = thickness < thickness_threshold
+    # Keep only thick tesseroids and their densities
+    tesseroids = tesseroids[np.logical_not(null_tesseroids), :]
+    density = density[np.logical_not(null_tesseroids)]
+    return tesseroids, density
diff --git a/harmonica/tests/test_tesseroid.py b/harmonica/tests/test_tesseroid.py
index 6385aa0e3..682ba2ea0 100644
--- a/harmonica/tests/test_tesseroid.py
+++ b/harmonica/tests/test_tesseroid.py
@@ -25,7 +25,7 @@
     _split_tesseroid,
     _tesseroid_dimensions,
 )
-from .._forward.tesseroid import (
+from .._forward.tesseroid_gravity import (
     MAX_DISCRETIZATIONS,
     STACK_SIZE,
     _adaptive_discretization,
diff --git a/harmonica/tests/test_tesseroid_layer.py b/harmonica/tests/test_tesseroid_layer.py
index cb6d7f173..7bd6a4895 100644
--- a/harmonica/tests/test_tesseroid_layer.py
+++ b/harmonica/tests/test_tesseroid_layer.py
@@ -9,6 +9,7 @@
 """
 
 import warnings
+from unittest.mock import patch
 
 import boule
 import numpy as np
@@ -18,6 +19,12 @@
 import xarray as xr
 
 from .. import tesseroid_gravity, tesseroid_layer
+from .._forward.tesseroid_layer_layer import _discard_thin_tesseroids
+
+try:
+    from numba_progress import ProgressBar
+except ImportError:
+    ProgressBar = None
 
 
 @pytest.fixture
@@ -37,6 +44,7 @@ def dummy_layer(mean_earth_radius, request):
     longitude = np.linspace(-10, 10, 5)
     shape = (latitude.size, longitude.size)
     surface = mean_earth_radius * np.ones(shape) + 1e3
+    surface = np.arange(20, dtype=float).reshape(*shape)
     reference = mean_earth_radius
     if request.param == "reference-as-array":
         reference *= np.ones(shape)
@@ -216,7 +224,7 @@ def test_tesseroid_layer_no_regular_grid(
         )
 
 
-def test_tesseroi_layer_attibutes():
+def test_tesseroid_layer_attributes():
     """
     Check attributes of the DatasetAccessorTesseroidLayer class.
     """
@@ -461,3 +469,119 @@ def test_tesseroid_layer_gravity_density_nans(
         result,
         tesseroid_gravity(grid_coords, tesseroids, rho, field=field),
     )
+
+
+@pytest.mark.skipif(ProgressBar is None, reason="requires numba_progress")
+@pytest.mark.use_numba
+def test_progress_bar(dummy_layer):
+    """
+    Check if forward gravity results with and without progress bar match.
+    """
+    (longitude, latitude), surface, reference, density = dummy_layer
+    coordinates = vd.grid_coordinates(
+        (-10, 10, -10, 10), spacing=7, extra_coords=(surface[0] + 10e3)
+    )
+    layer = tesseroid_layer(
+        (longitude, latitude), surface, reference, properties={"density": density}
+    )
+    result_progress_true = layer.tesseroid_layer.gravity(
+        coordinates, field="g_z", progressbar=True
+    )
+
+    result_progress_false = layer.tesseroid_layer.gravity(
+        coordinates, field="g_z", progressbar=False
+    )
+    npt.assert_allclose(result_progress_true, result_progress_false)
+
+
+@patch("harmonica._forward.utils.ProgressBar", None)
+def test_numba_progress_missing_error(dummy_layer):
+    """
+    Check if error is raised when progressbar=True and numba_progress package
+    is not installed.
+    """
+    (longitude, latitude), surface, reference, density = dummy_layer
+    coordinates = vd.grid_coordinates(
+        (-10, 10, -10, 10), spacing=7, extra_coords=(surface[0] + 10e3)
+    )
+    layer = tesseroid_layer(
+        (longitude, latitude), surface, reference, properties={"density": density}
+    )
+    # Check if error is raised
+    with pytest.raises(ImportError):
+        layer.tesseroid_layer.gravity(coordinates, field="g_z", progressbar=True)
+
+
+def test_gravity_discarded_thin_tesseroids(dummy_layer):
+    """
+    Check if gravity of tesseroid layer after discarding thin tesseroids is correct.
+    """
+    (longitude, latitude), surface, reference, density = dummy_layer
+    coordinates = vd.grid_coordinates(
+        (-10, 10, -10, 10), spacing=7, extra_coords=(surface[0] + 10e3)
+    )
+    layer = tesseroid_layer(
+        (longitude, latitude), surface, reference, properties={"density": density}
+    )
+    # Check that result with no threshold is the same as with a threshold of 0
+    gravity_tesseroids_nothres = layer.tesseroid_layer.gravity(coordinates, field="g_z")
+    gravity_tesseroids_0thres = layer.tesseroid_layer.gravity(
+        coordinates, field="g_z", thickness_threshold=0
+    )
+    npt.assert_allclose(gravity_tesseroids_nothres, gravity_tesseroids_0thres)
+    # Check that gravity from manually removed tesseroids is the same as using a
+    # threshold
+    manually_removed_tesseroids = []
+    for _, j in enumerate(layer.tesseroid_layer._to_tesseroids()):
+        if abs(j[5] - j[4]) >= 5.0:
+            manually_removed_tesseroids.append(j)
+    gravity_manually_removed = tesseroid_gravity(
+        coordinates,
+        tesseroids=manually_removed_tesseroids,
+        density=[2670] * len(manually_removed_tesseroids),
+        field="g_z",
+    )
+    gravity_threshold_removed = layer.tesseroid_layer.gravity(
+        coordinates, field="g_z", thickness_threshold=5
+    )
+    npt.assert_allclose(gravity_manually_removed, gravity_threshold_removed)
+
+
+def test_discard_thin_tesseroids():
+    """
+    Check if thin tesseroids are properly discarded.
+    """
+    # create set of 4 tesseroids
+    # (longitude_w, longitude_e, latitude_s, latitude_n,, bottom, top)
+    tesseroid_boundaries = np.array(
+        [
+            [-5000.0, 5000.0, -5000.0, 5000.0, 0.0, 55.1],
+            [5000.0, 15000.0, -5000.0, 5000.0, 0.0, 55.01],
+            [-5000.0, 5000.0, 5000.0, 15000.0, 0.0, 35.0],
+            [5000.0, 15000.0, 5000.0, 15000.0, 0.0, 84.0],
+        ]
+    )
+
+    # assign densities to each tesseroid
+    densities = np.array([2306, 2122, 2190, 2069])
+
+    # drop tesseroids and respective densities thinner than 55.05
+    # (2nd and 3rd tesseroids)
+    thick_tesseroids, thick_densities = _discard_thin_tesseroids(
+        tesseroid_boundaries,
+        densities,
+        thickness_threshold=55.05,
+    )
+
+    # manually remove tesseroids and densities of thin tesseroids
+    expected_tesseroids = np.array(
+        [
+            [-5000.0, 5000.0, -5000.0, 5000.0, 0.0, 55.1],
+            [5000.0, 15000.0, 5000.0, 15000.0, 0.0, 84.0],
+        ]
+    )
+    expected_densities = np.array([2306, 2069])
+
+    # check the correct tesseroids and densities were discarded
+    npt.assert_allclose(expected_tesseroids, thick_tesseroids)
+    npt.assert_allclose(expected_densities, thick_densities)