From 7e1201ec08515ec4b4bca3382af516197d1f90bc Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 6 Oct 2023 14:19:07 +0200
Subject: [PATCH 01/43] In progress

---
 snudda/place/region_mesh_redux.py | 115 ++++++++++++++++++++----------
 1 file changed, 77 insertions(+), 38 deletions(-)

diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index 854def168..f00a7035d 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -6,7 +6,9 @@
 
 class RegionMeshRedux:
 
-    def __init__(self):
+    def __init__(self, mesh_path):
+
+        self.mesh_path = mesh_path
 
         # Set by load_mesh
         self.mesh_vertices = None
@@ -26,6 +28,10 @@ def __init__(self):
         self.mesh_denom = None
         self.mesh_nrm = None
 
+        if self.mesh_path is not None:
+            self.load_mesh(file_path=self.mesh_path)
+            self.pre_compute()
+
     def load_mesh(self, file_path):
 
         """ Reads wavefront obj files. Updates mesh_vertices, mesh_faces, mesh_normals,
@@ -42,10 +48,14 @@ def load_mesh(self, file_path):
                     continue  # Skip empty lines
 
                 if parts[0][0] == 'v':
-                    if len(parts[0]) > 1 and parts[0][1] == 'n':
-                        # Parse normal vectors
-                        nx, ny, nz = map(float, parts[1:4])
-                        normals.append((nx, ny, nz))
+                    if len(parts[0]) > 1:
+                        if parts[0][1] == 'n':
+                            # Parse normal vectors
+                            nx, ny, nz = map(float, parts[1:4])
+                            normals.append((nx, ny, nz))
+                        else:
+                            # Skip "vt" lines and others
+                            continue
                     else:
                         # Parse vertex coordinates in micrometers and convert to meters
                         x, y, z = [float(coord) * 1e-6 for coord in parts[1:4]]
@@ -54,13 +64,21 @@ def load_mesh(self, file_path):
                     # Parse face definitions with vertex and normal indices
                     face_indices = []
                     for vertex in parts[1:]:
-                        indices = [int(idx) if idx else 0 for idx in vertex.split('/')]
-                        face_indices.append(tuple(indices))
+                        try:
+                            # Only take first value of each triplet 1/?/? 2/?/? 3/?/?
+                            indices = int(vertex.split('/')[0]) - 1
+                        except:
+                            import traceback
+                            print(traceback.format_exc())
+                            import pdb
+                            pdb.set_trace()
+                        # indices = [int(idx) - 1 if idx else 0 for idx in vertex.split('/')]
+                        face_indices.append(indices)
                     faces.append(face_indices)
 
         self.mesh_vertices = np.array(vertices)
-        self.mesh_faces = np.array(normals)
-        self.mesh_normals = np.array(faces)
+        self.mesh_faces = np.array(faces)
+        self.mesh_normals = np.array(normals)
 
         self.min_coord = np.min(self.mesh_vertices, axis=0)
         self.max_coord = np.max(self.mesh_vertices, axis=0)
@@ -76,10 +94,10 @@ def pre_compute(self):
         i1 = self.mesh_faces[:, 1]
         i2 = self.mesh_faces[:, 2]
 
-        self.mesh_u = self.mesh_vec[i1, :] - self.mesh_vec[i0, :]
-        self.mesh_v = self.mesh_vec[i2, :] - self.mesh_vec[i0, :]
+        self.mesh_u = self.mesh_vertices[i1, :] - self.mesh_vertices[i0, :]
+        self.mesh_v = self.mesh_vertices[i2, :] - self.mesh_vertices[i0, :]
 
-        self.mesh_v0 = self.mesh_vec[i0, :]
+        self.mesh_v0 = self.mesh_vertices[i0, :]
 
         self.mesh_uv = np.sum(np.multiply(self.mesh_u, self.mesh_v), axis=1)
         self.mesh_vv = np.sum(np.multiply(self.mesh_v, self.mesh_v), axis=1)
@@ -98,24 +116,24 @@ def ray_casting(self, point):
 
         """ Ray-casting, to determine if a point is inside or outside of mesh. """
 
-        return RegionMeshRedux.ray_casting_helper(point=point,
-                                                  self_mesh_faces=self.mesh_faces,
-                                                  self_mesh_nrm=self.mesh_nrm,
-                                                  self_mesh_v0=self.mesh_v0,
-                                                  self_point_out=self.point_out,
-                                                  self_mesh_denom=self.mesh_denom,
-                                                  self_mesh_uv=self.mesh_uv,
-                                                  self_mesh_uu=self.mesh_uu,
-                                                  self_mesh_vv=self.mesh_vv,
-                                                  self_mesh_u=self.mesh_u,
-                                                  self_mesh_v=self.mesh_v)
+        return RegionMeshRedux._ray_casting_helper(point=point,
+                                                   self_mesh_faces=self.mesh_faces,
+                                                   self_mesh_nrm=self.mesh_nrm,
+                                                   self_mesh_v0=self.mesh_v0,
+                                                   self_point_out=self.point_outside,
+                                                   self_mesh_denom=self.mesh_denom,
+                                                   self_mesh_uv=self.mesh_uv,
+                                                   self_mesh_uu=self.mesh_uu,
+                                                   self_mesh_vv=self.mesh_vv,
+                                                   self_mesh_u=self.mesh_u,
+                                                   self_mesh_v=self.mesh_v)
 
     @staticmethod
     @jit(nopython=True)
-    def ray_casting_helper(point,
-                           self_mesh_faces, self_mesh_nrm, self_point_out,
-                           self_mesh_v0, self_mesh_denom,
-                           self_mesh_uv, self_mesh_vv, self_mesh_uu, self_mesh_u, self_mesh_v):
+    def _ray_casting_helper(point,
+                            self_mesh_faces, self_mesh_nrm, self_point_out,
+                            self_mesh_v0, self_mesh_denom,
+                            self_mesh_uv, self_mesh_vv, self_mesh_uu, self_mesh_u, self_mesh_v):
 
         """
         Helper function for ray-casting, to determine if a point is inside the 3D-mesh.
@@ -158,10 +176,15 @@ def ray_casting_helper(point,
 
         return np.mod(intersect_count, 2) == 1
 
+    def point_inside(self, points):
+        # Let's see how fast it is if we do ray casting directly
 
-    def point_inside(self, point):
+        inside_flag = np.zeros(shape=(points.shape[0],), dtype=bool)
 
-        pass
+        for ctr, p in enumerate(points):
+            inside_flag[ctr] = self.ray_casting(p)
+
+        return inside_flag
 
     def distance_to_border(self, point):
 
@@ -180,13 +203,10 @@ def __init__(self, region_mesh: RegionMeshRedux, d_min: float, seed=None):
 
         self.rng = np.random.default_rng(seed)
 
-        # Temp values, should be read from the 3d mesh + padding
-        self.cube_side = 1000e-6
-        self.cube_offset = np.array([1, 2, 5000])
-
-        # Use kdtree to avoid d_min overlaps
-
-        pass
+        # We generate a cube of points, obs that we pad it with d_min on all sides to avoid
+        # edge effects (which would give higher densities at borders)
+        self.cube_side = self.region_mesh.max_coord-self.region_mesh.min_coord + self.d_min*2
+        self.cube_offset = self.region_mesh.min_coord - self.d_min
 
     def get_point_cloud(self, n):
         points = self.rng.uniform(size=(n, 3), low=0, high=self.cube_side) + self.cube_offset
@@ -236,6 +256,14 @@ def _remove_close_neurons_helper(self, points, remove_fraction=0.05):
 
         return points, False
 
+    def remove_outside(self, points):
+
+        print(f"Filtering {points.shape[0]} points..")
+        keep_flag = self.region_mesh.point_inside(points=points)
+
+        print(f"Filtering, keeping inside points: {np.sum(keep_flag)} / {len(keep_flag)}")
+
+        return points[keep_flag, :]
 
     def set_neuron_density(self, neuron_type, neuron_density):
 
@@ -267,9 +295,20 @@ def __init__(self):
 
 if __name__ == "__main__":
 
-    nep = NeuronPlacer(region_mesh=None, d_min=10e-6)
-    points = nep.get_point_cloud(n=1000000)
+    rmr = RegionMeshRedux(mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj")
+
+    nep = NeuronPlacer(region_mesh=rmr, d_min=10e-6)
+    points = nep.get_point_cloud(n=100000)
     new_points = nep.remove_close_neurons(points)
+    new_inside_points = nep.remove_outside(new_points)
+
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure()
+    ax = fig.add_subplot(projection='3d')
+    x,y,z = new_inside_points.T
+    ax.scatter(x, y, z, marker='.')
+    plt.show()
 
     import pdb
     pdb.set_trace()
\ No newline at end of file

From f764909aafed7ad092196039bdd3b2c61d9c24e0 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 6 Oct 2023 15:04:28 +0200
Subject: [PATCH 02/43] Switched to open3d, faster

---
 requirements.txt                  |   1 +
 snudda/place/region_mesh_redux.py | 200 ++++--------------------------
 2 files changed, 28 insertions(+), 173 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index c5360991b..fa06518d1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,6 +16,7 @@ setuptools
 psutil >= 5.8.0
 cython  # Needed for compiling NEURON
 wheel  # setup.py seems to need this now
+open3d
 
 # igraph
 # snudda
diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index f00a7035d..f22f3d69b 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -1,6 +1,7 @@
 
 import numpy as np
 from scipy.spatial import cKDTree
+import open3d as o3d
 from numba import jit
 
 
@@ -9,186 +10,35 @@ class RegionMeshRedux:
     def __init__(self, mesh_path):
 
         self.mesh_path = mesh_path
+        self.mesh = o3d.io.read_triangle_mesh(mesh_path)
 
-        # Set by load_mesh
-        self.mesh_vertices = None
-        self.mesh_faces = None
-        self.mesh_normals = None
-        self.min_coord = None
-        self.max_coord = None
-        self.point_outside = None
-
-        # Set by pre_compute
-        self.mesh_u = None
-        self.mesh_v = None
-        self.mesh_v0 = None
-        self.mesh_uv = None
-        self.mesh_vv = None
-        self.mesh_uu = None
-        self.mesh_denom = None
-        self.mesh_nrm = None
-
-        if self.mesh_path is not None:
-            self.load_mesh(file_path=self.mesh_path)
-            self.pre_compute()
-
-    def load_mesh(self, file_path):
-
-        """ Reads wavefront obj files. Updates mesh_vertices, mesh_faces, mesh_normals,
-            min_coord, max_coord, point_outside """
-
-        vertices = []
-        normals = []
-        faces = []
-
-        with open(file_path, 'r') as obj_file:
-            for line in obj_file:
-                parts = line.strip().split()
-                if not parts:
-                    continue  # Skip empty lines
-
-                if parts[0][0] == 'v':
-                    if len(parts[0]) > 1:
-                        if parts[0][1] == 'n':
-                            # Parse normal vectors
-                            nx, ny, nz = map(float, parts[1:4])
-                            normals.append((nx, ny, nz))
-                        else:
-                            # Skip "vt" lines and others
-                            continue
-                    else:
-                        # Parse vertex coordinates in micrometers and convert to meters
-                        x, y, z = [float(coord) * 1e-6 for coord in parts[1:4]]
-                        vertices.append((x, y, z))
-                elif parts[0] == 'f':
-                    # Parse face definitions with vertex and normal indices
-                    face_indices = []
-                    for vertex in parts[1:]:
-                        try:
-                            # Only take first value of each triplet 1/?/? 2/?/? 3/?/?
-                            indices = int(vertex.split('/')[0]) - 1
-                        except:
-                            import traceback
-                            print(traceback.format_exc())
-                            import pdb
-                            pdb.set_trace()
-                        # indices = [int(idx) - 1 if idx else 0 for idx in vertex.split('/')]
-                        face_indices.append(indices)
-                    faces.append(face_indices)
-
-        self.mesh_vertices = np.array(vertices)
-        self.mesh_faces = np.array(faces)
-        self.mesh_normals = np.array(normals)
-
-        self.min_coord = np.min(self.mesh_vertices, axis=0)
-        self.max_coord = np.max(self.mesh_vertices, axis=0)
-
-        # Used by ray casting when checking if another point is interior
-        self.point_outside = self.max_coord + np.array([1e-1, 1e-2, 1e-3])
-
-    def pre_compute(self):
-
-        """ Helper function, precomputes values for raytracing. """
-
-        i0 = self.mesh_faces[:, 0]
-        i1 = self.mesh_faces[:, 1]
-        i2 = self.mesh_faces[:, 2]
-
-        self.mesh_u = self.mesh_vertices[i1, :] - self.mesh_vertices[i0, :]
-        self.mesh_v = self.mesh_vertices[i2, :] - self.mesh_vertices[i0, :]
-
-        self.mesh_v0 = self.mesh_vertices[i0, :]
-
-        self.mesh_uv = np.sum(np.multiply(self.mesh_u, self.mesh_v), axis=1)
-        self.mesh_vv = np.sum(np.multiply(self.mesh_v, self.mesh_v), axis=1)
-        self.mesh_uu = np.sum(np.multiply(self.mesh_u, self.mesh_u), axis=1)
-
-        self.mesh_denom = np.multiply(self.mesh_uv, self.mesh_uv) - np.multiply(self.mesh_uu, self.mesh_vv)
-
-        # Normal of triangle
-        self.mesh_nrm = np.cross(self.mesh_u, self.mesh_v)
-
-        # We need to normalise it
-        nl = np.repeat(np.reshape(self.mesh_uv, [self.mesh_uv.shape[0], 1]), 3, axis=1)
-        self.mesh_nrm = np.divide(self.mesh_nrm, nl)
-
-    def ray_casting(self, point):
-
-        """ Ray-casting, to determine if a point is inside or outside of mesh. """
-
-        return RegionMeshRedux._ray_casting_helper(point=point,
-                                                   self_mesh_faces=self.mesh_faces,
-                                                   self_mesh_nrm=self.mesh_nrm,
-                                                   self_mesh_v0=self.mesh_v0,
-                                                   self_point_out=self.point_outside,
-                                                   self_mesh_denom=self.mesh_denom,
-                                                   self_mesh_uv=self.mesh_uv,
-                                                   self_mesh_uu=self.mesh_uu,
-                                                   self_mesh_vv=self.mesh_vv,
-                                                   self_mesh_u=self.mesh_u,
-                                                   self_mesh_v=self.mesh_v)
+        # Convert from micrometers to meters to get SI units
+        scale_factor = 1e-6
+        self.mesh.scale(scale_factor, center=self.mesh.get_center()*scale_factor)
 
-    @staticmethod
-    @jit(nopython=True)
-    def _ray_casting_helper(point,
-                            self_mesh_faces, self_mesh_nrm, self_point_out,
-                            self_mesh_v0, self_mesh_denom,
-                            self_mesh_uv, self_mesh_vv, self_mesh_uu, self_mesh_u, self_mesh_v):
-
-        """
-        Helper function for ray-casting, to determine if a point is inside the 3D-mesh.
-        It draws a line from the point given, and a second point defined as outside.
-        If that line intersects the surface of the 3D mesh an odd number of times, then the first point is inside.
-
-        Uses values pre-computed by pre_compute function.
-        """
-
-        # print(f"Processing {point}")
-
-        n_tri = self_mesh_faces.shape[0]
+        self.min_coord = self.mesh.get_min_bound()
+        self.max_coord = self.mesh.get_max_bound()
 
-        p = self_point_out - point
-        # rn = nominator, rd = denominator
-        rn = np.sum(np.multiply(self_mesh_nrm, self_mesh_v0 - point), axis=1)
-        rd = np.dot(self_mesh_nrm, p)
+        self.scene = o3d.t.geometry.RaycastingScene()
+        legacy_mesh = o3d.t.geometry.TriangleMesh.from_legacy(self.mesh)
+        self.scene.add_triangles(legacy_mesh)
 
-        # If rd == 0 and rn != 0 --> r = -1, parallel to plane, but outside, mark -1 to avoid counting
-        # If rd == 0 and rn == 0 --> r = 0, parallel and lies in plane
-        idx0 = (rd == 0)
-        idx1 = np.logical_and(idx0, rn != 0)
+    def check_inside(self, points):
 
-        rn[idx1] = -1
-        rd[idx0] = 1
-        r = np.divide(rn, rd)
+        # http://www.open3d.org/docs/latest/tutorial/geometry/distance_queries.html
+        query_point = o3d.core.Tensor(points, dtype=o3d.core.Dtype.Float32)
+        is_inside = self.scene.compute_occupancy(query_point)
+        return is_inside.numpy().astype(bool)
 
-        w = point + r.reshape(len(r), 1) * p.reshape(1, 3) - self_mesh_v0
-        n_points = len(r)
+    def distance_to_border(self, points):
 
-        s = np.divide(np.multiply(self_mesh_uv, np.sum(np.multiply(w, self_mesh_v), axis=1).reshape(n_points, ))
-                      - np.multiply(self_mesh_vv, np.sum(np.multiply(w, self_mesh_u), axis=1).reshape(n_points, )),
-                      self_mesh_denom)
+        """ Positive values are distance to mesh (outside), and negative (inside)"""
 
-        t = np.divide(np.multiply(self_mesh_uv, np.sum(np.multiply(w, self_mesh_u), axis=1).reshape(n_points, ))
-                      - np.multiply(self_mesh_uu, np.sum(np.multiply(w, self_mesh_v), axis=1).reshape(n_points, )),
-                      self_mesh_denom)
+        # http://www.open3d.org/docs/latest/tutorial/geometry/distance_queries.html
+        query_point = o3d.core.Tensor(points, dtype=o3d.core.Dtype.Float32)
+        signed_distance = self.scene.compute_signed_distance(query_point).numpy()
 
-        intersect_count = np.sum((0 <= r) * (r <= 1) * (0 <= s) * (s <= 1) * (0 <= t) * (s + t <= 1))
-
-        return np.mod(intersect_count, 2) == 1
-
-    def point_inside(self, points):
-        # Let's see how fast it is if we do ray casting directly
-
-        inside_flag = np.zeros(shape=(points.shape[0],), dtype=bool)
-
-        for ctr, p in enumerate(points):
-            inside_flag[ctr] = self.ray_casting(p)
-
-        return inside_flag
-
-    def distance_to_border(self, point):
-
-        pass
+        return signed_distance
 
 
 class NeuronPlacer:
@@ -259,7 +109,8 @@ def _remove_close_neurons_helper(self, points, remove_fraction=0.05):
     def remove_outside(self, points):
 
         print(f"Filtering {points.shape[0]} points..")
-        keep_flag = self.region_mesh.point_inside(points=points)
+        # keep_flag = self.region_mesh.point_inside(points=points)
+        keep_flag = self.region_mesh.check_inside(points=points)
 
         print(f"Filtering, keeping inside points: {np.sum(keep_flag)} / {len(keep_flag)}")
 
@@ -298,7 +149,7 @@ def __init__(self):
     rmr = RegionMeshRedux(mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj")
 
     nep = NeuronPlacer(region_mesh=rmr, d_min=10e-6)
-    points = nep.get_point_cloud(n=100000)
+    points = nep.get_point_cloud(n=1000000)
     new_points = nep.remove_close_neurons(points)
     new_inside_points = nep.remove_outside(new_points)
 
@@ -310,5 +161,8 @@ def __init__(self):
     ax.scatter(x, y, z, marker='.')
     plt.show()
 
+    # Lets calculate the distance to border
+    distance = nep.region_mesh.distance_to_border(new_inside_points)
+
     import pdb
     pdb.set_trace()
\ No newline at end of file

From 0c3562b3a47be63501aee997df477aac866d2f11 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 6 Oct 2023 15:43:47 +0200
Subject: [PATCH 03/43] Scaling fixed

---
 snudda/__init__.py                |  2 +-
 snudda/place/region_mesh_redux.py | 60 +++++++++++++++++--------------
 2 files changed, 35 insertions(+), 27 deletions(-)

diff --git a/snudda/__init__.py b/snudda/__init__.py
index e4e1fe4a9..957d75429 100644
--- a/snudda/__init__.py
+++ b/snudda/__init__.py
@@ -1,6 +1,6 @@
 from .core import Snudda
 
-__version__ = "1.4.73"
+__version__ = "1.5.0"
 
 from .init import SnuddaInit
 from .place import SnuddaPlace
diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index f22f3d69b..d54144ac7 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -14,7 +14,7 @@ def __init__(self, mesh_path):
 
         # Convert from micrometers to meters to get SI units
         scale_factor = 1e-6
-        self.mesh.scale(scale_factor, center=self.mesh.get_center()*scale_factor)
+        self.mesh.scale(scale_factor, center=(0, 0, 0))
 
         self.min_coord = self.mesh.get_min_bound()
         self.max_coord = self.mesh.get_max_bound()
@@ -23,6 +23,8 @@ def __init__(self, mesh_path):
         legacy_mesh = o3d.t.geometry.TriangleMesh.from_legacy(self.mesh)
         self.scene.add_triangles(legacy_mesh)
 
+
+
     def check_inside(self, points):
 
         # http://www.open3d.org/docs/latest/tutorial/geometry/distance_queries.html
@@ -43,21 +45,40 @@ def distance_to_border(self, points):
 
 class NeuronPlacer:
 
-    # Improvement suggestion. Randomize only points within a mesh-voxel, then randomise
-    # within which of the mesh voxels the points should be placed.
+    def __init__(self, mesh_path: str, d_min: float, seed=None, rng=None, n_putative_points=1000000):
 
-    def __init__(self, region_mesh: RegionMeshRedux, d_min: float, seed=None):
-
-        self.region_mesh = region_mesh
+        self.region_mesh = RegionMeshRedux(mesh_path=mesh_path)
         self.d_min = d_min
 
-        self.rng = np.random.default_rng(seed)
+        if rng:
+            if seed:
+                raise ValueError("If rng is set, then seed should not be set.")
+            self.rng = rng
+        else:
+            self.rng = np.random.default_rng(seed)
 
         # We generate a cube of points, obs that we pad it with d_min on all sides to avoid
         # edge effects (which would give higher densities at borders)
         self.cube_side = self.region_mesh.max_coord-self.region_mesh.min_coord + self.d_min*2
         self.cube_offset = self.region_mesh.min_coord - self.d_min
 
+        putative_points = self.get_point_cloud(n=n_putative_points)
+        putative_points = self.remove_close_neurons(putative_points)
+        putative_points = self.remove_outside(putative_points)
+
+        self.putative_points = putative_points
+        self.allocated_points = None
+
+    def plot_putative_points(self):
+
+        import matplotlib.pyplot as plt
+
+        fig = plt.figure()
+        ax = fig.add_subplot(projection='3d')
+        x, y, z = self.putative_points.T
+        ax.scatter(x, y, z, marker='.')
+        plt.show()
+
     def get_point_cloud(self, n):
         points = self.rng.uniform(size=(n, 3), low=0, high=self.cube_side) + self.cube_offset
 
@@ -88,8 +109,7 @@ def _remove_close_neurons_helper(self, points, remove_fraction=0.05):
                                        self.rng.integers(low=0, high=2, size=close_neurons.shape[0])]
 
         else:
-
-            # First remove worst offenders, with the most neighbours
+            # First remove the worst offenders, with the most neighbours
             # (but none with count 1, we do those separately
             sort_idx = np.argsort(-counts)
             sorted_offenders = unique[sort_idx]
@@ -97,7 +117,6 @@ def _remove_close_neurons_helper(self, points, remove_fraction=0.05):
 
             first_pair = np.argmax(sorted_counts == 1)
             remove_fraction_idx = int(np.ceil(remove_fraction*len(sorted_offenders)))
-
             remove_idx = sorted_offenders[:min(first_pair, remove_fraction_idx)]
 
         points = np.delete(points, remove_idx, axis=0)
@@ -122,6 +141,8 @@ def set_neuron_density(self, neuron_type, neuron_density):
 
     def place_neurons(self, neuron_type, number_of_neurons):
 
+        # använd np.choice ?
+
         pass
 
 
@@ -146,23 +167,10 @@ def __init__(self):
 
 if __name__ == "__main__":
 
-    rmr = RegionMeshRedux(mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj")
-
-    nep = NeuronPlacer(region_mesh=rmr, d_min=10e-6)
-    points = nep.get_point_cloud(n=1000000)
-    new_points = nep.remove_close_neurons(points)
-    new_inside_points = nep.remove_outside(new_points)
-
-    import matplotlib.pyplot as plt
-
-    fig = plt.figure()
-    ax = fig.add_subplot(projection='3d')
-    x,y,z = new_inside_points.T
-    ax.scatter(x, y, z, marker='.')
-    plt.show()
+    mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj"
 
-    # Lets calculate the distance to border
-    distance = nep.region_mesh.distance_to_border(new_inside_points)
+    nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6)
+    nep.plot_putative_points()
 
     import pdb
     pdb.set_trace()
\ No newline at end of file

From 806bbefad8c6f41ef18ead09ade33ff4b3d88b2a Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 6 Oct 2023 17:46:59 +0200
Subject: [PATCH 04/43] Updated

---
 snudda/place/region_mesh_redux.py | 85 ++++++++++++++++++++++++++-----
 1 file changed, 73 insertions(+), 12 deletions(-)

diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index d54144ac7..14ecb1129 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -1,4 +1,4 @@
-
+import numexpr
 import numpy as np
 from scipy.spatial import cKDTree
 import open3d as o3d
@@ -16,16 +16,20 @@ def __init__(self, mesh_path):
         scale_factor = 1e-6
         self.mesh.scale(scale_factor, center=(0, 0, 0))
 
+        self.mesh.remove_non_manifold_edges()
+
         self.min_coord = self.mesh.get_min_bound()
         self.max_coord = self.mesh.get_max_bound()
 
         self.scene = o3d.t.geometry.RaycastingScene()
         legacy_mesh = o3d.t.geometry.TriangleMesh.from_legacy(self.mesh)
-        self.scene.add_triangles(legacy_mesh)
 
+        # SHALL WE PICk NUMBER OF PUTATIVE POINTS BASED ON VOLUME???
 
+        self.scene.add_triangles(legacy_mesh)
 
     def check_inside(self, points):
+        """ Check if points are inside, returns bool array."""
 
         # http://www.open3d.org/docs/latest/tutorial/geometry/distance_queries.html
         query_point = o3d.core.Tensor(points, dtype=o3d.core.Dtype.Float32)
@@ -67,16 +71,23 @@ def __init__(self, mesh_path: str, d_min: float, seed=None, rng=None, n_putative
         putative_points = self.remove_outside(putative_points)
 
         self.putative_points = putative_points
-        self.allocated_points = None
+        self.allocated_points = np.zeros(shape=(putative_points.shape[0],), dtype=bool)
 
     def plot_putative_points(self):
 
+        self.plot_points(points=self.putative_points)
+
+    def plot_points(self, points, colour=None):
         import matplotlib.pyplot as plt
 
         fig = plt.figure()
         ax = fig.add_subplot(projection='3d')
-        x, y, z = self.putative_points.T
-        ax.scatter(x, y, z, marker='.')
+        x, y, z = points.T
+        ax.scatter(x, y, z, marker='.', color=colour)
+        ax.set_xlabel('X')
+        ax.set_ylabel('Y')
+        ax.set_zlabel('Z')
+        plt.ion()
         plt.show()
 
     def get_point_cloud(self, n):
@@ -135,15 +146,44 @@ def remove_outside(self, points):
 
         return points[keep_flag, :]
 
-    def set_neuron_density(self, neuron_type, neuron_density):
+    def get_neuron_positions(self, n_positions, neuron_density=None):
 
-        pass
+        """ neuron_density either None (even), or a str representing a function f(x,y,z)
+            where x,y,z are the coordinates in meters """
 
-    def place_neurons(self, neuron_type, number_of_neurons):
+        # We have the putative_points, pick positions from them, based on neuron density
+        # then update the allocated points
 
-        # använd np.choice ?
+        # 1. Calculate the distance to the closest free (non-allocated) neighbour for all points
+        # This is so that if we allocated neurons, we can correct for that, and get flat gradients
+
+        free_positions = self.putative_points[~self.allocated_points]
+
+        # k=2, since we don't want distance to point itself, but closest neighbour
+        # closest_distance, _ = cKDTree(data=free_positions).query(x=free_positions, k=2)
+        closest_distance, _ = cKDTree(data=free_positions).query(x=free_positions, k=2)
+
+        # Volume is proportional to distance**3, so scale probabilities to pick position by that
+        free_volume = np.power(np.mean(closest_distance[:, 1:2], axis=1), 3)
+        x, y, z = free_positions.T
+
+        if neuron_density:
+            # TODO: Temp disabled volume... still does not seem to work
+            P_neuron = np.multiply(numexpr.evaluate(neuron_density), free_volume)
+            # P_neuron = numexpr.evaluate(neuron_density)
+        else:
+            P_neuron = free_volume
+
+        P_neuron /= np.sum(P_neuron)
+
+        idx = self.rng.choice(len(free_positions), n_positions, p=P_neuron, replace=False)
+
+        neuron_positions = free_positions[idx, :]
+        used_idx = np.where(~self.allocated_points)[0][idx]
+        self.allocated_points[used_idx] = True
+
+        return neuron_positions
 
-        pass
 
 
 class NeuronBender:
@@ -169,8 +209,29 @@ def __init__(self):
 
     mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj"
 
-    nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6)
-    nep.plot_putative_points()
+    nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6, n_putative_points=10000000)
+    # nep.plot_putative_points()
+
+    points_flat = nep.get_neuron_positions(5000)
+    nep.plot_points(points_flat, colour="black")
+
+    points = nep.get_neuron_positions(200000, neuron_density="exp((y-0.0025)*2000)")
+    nep.plot_points(points, colour="red")
+
+    points_flat2 = nep.get_neuron_positions(5000)
+    nep.plot_points(points_flat, colour="blue")
+
+    import matplotlib.pyplot as plt
+    plt.figure()
+    plt.hist(points_flat[:,1], color="black")
+    plt.hist(points[:,1], color="red", alpha=0.5)
+    plt.show()
+
+    plt.figure()
+    plt.hist(points_flat[:,1], color="black")
+    plt.hist(points_flat2[:,1], color="blue", alpha=0.5)
+    plt.show()
+
 
     import pdb
     pdb.set_trace()
\ No newline at end of file

From 480fce027a84981e5c45c3e641b3fe3b6e58121c Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 9 Oct 2023 14:27:11 +0200
Subject: [PATCH 05/43] Switched to faster RegionMeshRedux and NeuronPlacer

---
 snudda/init/init.py                           | 50 ++++++++++----
 snudda/place/place.py                         | 29 +++++---
 snudda/place/region_mesh_redux.py             | 69 ++++++++++++++++---
 .../network_density/network-config.json       |  3 +-
 .../network_testing_place/network-config.json |  2 +-
 5 files changed, 120 insertions(+), 33 deletions(-)

diff --git a/snudda/init/init.py b/snudda/init/init.py
index 9f01e5901..96ba1f645 100644
--- a/snudda/init/init.py
+++ b/snudda/init/init.py
@@ -127,7 +127,8 @@ def define_structure(self,
                          struct_centre=None,
                          side_len=None,
                          slice_depth=None,
-                         mesh_bin_width=None):
+                         mesh_bin_width=None,
+                         n_putative_points=None):
         """
         Sets up definition for a brain structure (e.g. Cortex, Striatum, ...).
 
@@ -139,6 +140,8 @@ def define_structure(self,
             side_len (float, optional): side of cube, or slice
             slice_depth (float, optional): depth of slice
             mesh_bin_width (float): discretisation of 3D mesh during cell placement
+            n_putative_points (int): Number of putative locations (Before d_min filtering),
+                                     upper limit on number of neuron positions in volume (real number will be lower)
         """
 
         if d_min is None:
@@ -195,12 +198,13 @@ def define_structure(self,
         self.network_data["Volume"][struct_name] = \
             self.define_volume(d_min=d_min,
                                mesh_file=struct_mesh,
-                               mesh_bin_width=mesh_bin_width)
+                               mesh_bin_width=mesh_bin_width,
+                               n_putative_points=n_putative_points)
 
     ############################################################################
 
     @staticmethod
-    def define_volume(mesh_file=None, d_min=15e-6, mesh_bin_width=1e-4):
+    def define_volume(mesh_file=None, d_min=15e-6, mesh_bin_width=1e-4, n_putative_points=None):
 
         vol = dict([])
         vol["type"] = "mesh"
@@ -208,6 +212,10 @@ def define_volume(mesh_file=None, d_min=15e-6, mesh_bin_width=1e-4):
         vol["meshFile"] = mesh_file
         vol["meshBinWidth"] = mesh_bin_width
 
+        if n_putative_points:
+            # This is used for neuron placement, putative points are points picked before d_min filtering
+            vol["n_putative_points"] = n_putative_points
+
         return vol
 
     ############################################################################
@@ -879,8 +887,9 @@ def define_striatum(self,
             self.num_ChIN = np.round(f_ChIN * num_neurons / f_tot)
             self.num_LTS = np.round(f_LTS * num_neurons / f_tot)
 
-            self.num_neurons_total += self.num_FS + self.num_dSPN + self.num_iSPN + self.num_ChIN + self.num_LTS
+            n_neurons = int(self.num_FS + self.num_dSPN + self.num_iSPN + self.num_ChIN + self.num_LTS)
 
+            self.num_neurons_total += n_neurons
             if abs(num_neurons - self.num_neurons_total) > 5:
                 print("Striatum should have " + str(num_neurons) + " but " + str(self.num_neurons_total) \
                       + " are being requested, check fractions set for defineStriatum.")
@@ -894,7 +903,8 @@ def define_striatum(self,
             self.define_structure(struct_name="Striatum",
                                   struct_mesh=mesh_file,
                                   mesh_bin_width=mesh_bin_width,
-                                  d_min=d_min)
+                                  d_min=d_min,
+                                  n_putative_points=num_neurons*3)
 
         elif volume_type == "mouseStriatum":
             if mesh_bin_width is None:
@@ -903,7 +913,8 @@ def define_striatum(self,
             self.define_structure(struct_name="Striatum",
                                   struct_mesh=os.path.join("$SNUDDA_DATA", "mesh", "Striatum-d.obj"),
                                   mesh_bin_width=mesh_bin_width,
-                                  d_min=d_min)
+                                  d_min=d_min,
+                                  n_putative_points=num_neurons*3)
 
             density_file = os.path.join("$SNUDDA_DATA", "density", "dorsal_striatum_density.json")
 
@@ -918,7 +929,8 @@ def define_striatum(self,
                                   struct_mesh="slice",
                                   side_len=side_len,
                                   slice_depth=slice_depth,
-                                  d_min=d_min)
+                                  d_min=d_min,
+                                  n_putative_points=num_neurons*3)
 
         elif num_neurons <= 1e6:  # 1e6
             print("Using cube for striatum")
@@ -942,14 +954,16 @@ def define_striatum(self,
                                   struct_centre=striatum_centre,
                                   side_len=striatum_side_len,
                                   mesh_bin_width=mesh_bin_width,
-                                  d_min=d_min)
+                                  d_min=d_min,
+                                  n_putative_points=num_neurons*3)
 
         else:
             # Default, full size striatum
             self.define_structure(struct_name="Striatum",
                                   struct_mesh=os.path.join("$SNUDDA_DATA", "mesh", "Striatum-d.obj"),
                                   mesh_bin_width=1e-4,
-                                  d_min=d_min)
+                                  d_min=d_min,
+                                  nn_putative_points=num_neurons*3)
 
             density_file = os.path.join("$SNUDDA_DATA", "density", "dorsal_striatum_density.json")
 
@@ -1459,7 +1473,8 @@ def define_GPe(self, num_neurons, d_min=None, neurons_dir=None):
 
         self.define_structure(struct_name="GPe",
                               struct_mesh="mesh/GPe-mesh.obj",
-                              d_min=d_min)
+                              d_min=d_min,
+                              n_putative_points=num_neurons*3)
 
         # !!! Need to add targets for neurons in GPe
 
@@ -1476,7 +1491,8 @@ def define_GPi(self, num_neurons, d_min=None, neurons_dir=None):
 
         self.define_structure(struct_name="GPi",
                               struct_mesh="mesh/GPi-mesh.obj",
-                              d_min=d_min)
+                              d_min=d_min,
+                              n_putative_points=num_neurons*3)
 
         # !!! Need to add targets for neurons in GPi
 
@@ -1493,7 +1509,8 @@ def define_STN(self, num_neurons, d_min=None, neurons_dir=None):
 
         self.define_structure(struct_name="STN",
                               struct_mesh="mesh/STN-mesh.obj",
-                              d_min=d_min)
+                              d_min=d_min,
+                              n_putative_points=num_neurons*3)
 
         # !!! Need to add targets for neurons in STN
 
@@ -1511,7 +1528,8 @@ def define_SNr(self, num_neurons, d_min=None, neurons_dir=None):
         self.define_structure(struct_name="SNr",
                               struct_mesh="mesh/SNr-mesh.obj",
                               mesh_bin_width=1e-4,
-                              d_min=d_min)
+                              d_min=d_min,
+                              n_putative_points=num_neurons*3)
 
         # !!! Need to add targets for neurons in SNr
 
@@ -1537,7 +1555,8 @@ def define_cortex(self, num_neurons, d_min=None, neurons_dir=None):
                               struct_centre=np.array([7067e-6, 3007e-6, 2570e-6]),
                               side_len=200e-6,
                               mesh_bin_width=5e-5,
-                              d_min=d_min)
+                              d_min=d_min,
+                              n_putative_points=num_neurons*3)
 
         cortex_dir = os.path.join("$SNUDDA_DATA", "InputAxons", "Cortex", "Reg10")
 
@@ -1614,7 +1633,8 @@ def define_thalamus(self, num_neurons, d_min=None, neurons_dir=None):
                               struct_centre=np.array([4997e-6, 4260e-6, 7019e-6]),
                               side_len=200e-6,
                               mesh_bin_width=5e-5,
-                              d_min=d_min)
+                              d_min=d_min,
+                              n_putative_points=num_neurons*3)
 
         # Define neurons
 
diff --git a/snudda/place/place.py b/snudda/place/place.py
index a2567a47e..de8b71f9e 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -26,7 +26,9 @@
 
 from snudda.utils.snudda_path import get_snudda_data
 from snudda.neurons.neuron_prototype import NeuronPrototype
-from snudda.place.region_mesh import RegionMesh
+# from snudda.place.region_mesh import RegionMesh
+from snudda.place.region_mesh_redux import NeuronPlacer
+
 from snudda.place.rotation import SnuddaRotate
 from snudda.utils.snudda_path import snudda_parse_path, snudda_path_exists, snudda_simplify_path
 
@@ -394,14 +396,25 @@ def parse_config(self, config_file=None, resort_neurons=True):
                     self.write_log(f"Unable to find mesh file {vol_def['meshFile']}")
                     sys.exit(-1)
 
+                # self.volume[volume_id]["mesh"] \
+                #     = RegionMesh(mesh_file,
+                #                  d_view=d_view,
+                #                  raytrace_borders=self.raytrace_borders,
+                #                  d_min=vol_def["dMin"],
+                #                  bin_width=mesh_bin_width,
+                #                  log_file=mesh_logfile,
+                #                  random_seed=vol_seed[volume_id])
+
+                if "n_putative_points" in self.volume[volume_id]:
+                    n_putative_points = int(self.volume[volume_id]["n_putative_points"])
+                else:
+                    n_putative_points = None  # Autodetect
+
                 self.volume[volume_id]["mesh"] \
-                    = RegionMesh(mesh_file,
-                                 d_view=d_view,
-                                 raytrace_borders=self.raytrace_borders,
-                                 d_min=vol_def["dMin"],
-                                 bin_width=mesh_bin_width,
-                                 log_file=mesh_logfile,
-                                 random_seed=vol_seed[volume_id])
+                    = NeuronPlacer(mesh_path=mesh_file,
+                                   d_min=vol_def["dMin"],
+                                   random_seed=vol_seed[volume_id],
+                                   n_putative_points=n_putative_points)
 
                 if "density" in self.volume[volume_id]:
                     # We need to set up the neuron density functions also
diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index 14ecb1129..92fb11c6b 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -25,8 +25,11 @@ def __init__(self, mesh_path):
         legacy_mesh = o3d.t.geometry.TriangleMesh.from_legacy(self.mesh)
 
         # SHALL WE PICk NUMBER OF PUTATIVE POINTS BASED ON VOLUME???
+        self.volume = self.mesh.compute_convex_hull()[0].get_volume()
 
         self.scene.add_triangles(legacy_mesh)
+        # filled_mesh = legacy_mesh.fill_holes()
+        # self.scene.add_triangles(filled_mesh)
 
     def check_inside(self, points):
         """ Check if points are inside, returns bool array."""
@@ -49,23 +52,46 @@ def distance_to_border(self, points):
 
 class NeuronPlacer:
 
-    def __init__(self, mesh_path: str, d_min: float, seed=None, rng=None, n_putative_points=1000000):
+    def __init__(self, mesh_path: str, d_min: float, random_seed=None, rng=None,
+                 n_putative_points=None, putative_density=None):
+
+        """ Args:
+            mesh_path (str): Path to wavefront obj file
+            d_min (float): Minimum distance between neurons
+            random_seed (int): Random seed
+            rng: Numpy rng object, either rng or random_seed is given
+            n_putative_points (int): Number of putative positions to place within volume (before d_min filtering)"""
 
         self.region_mesh = RegionMeshRedux(mesh_path=mesh_path)
         self.d_min = d_min
+        self.density_functions = dict()
 
         if rng:
-            if seed:
+            if random_seed:
                 raise ValueError("If rng is set, then seed should not be set.")
             self.rng = rng
         else:
-            self.rng = np.random.default_rng(seed)
+            self.rng = np.random.default_rng(random_seed)
 
         # We generate a cube of points, obs that we pad it with d_min on all sides to avoid
         # edge effects (which would give higher densities at borders)
         self.cube_side = self.region_mesh.max_coord-self.region_mesh.min_coord + self.d_min*2
         self.cube_offset = self.region_mesh.min_coord - self.d_min
 
+        if n_putative_points is None:
+            # The volume of the cube multiplied by a density estimated by d_min
+
+            if putative_density:
+                n_putative_points = int(np.ceil(np.prod(self.cube_side)*putative_density*1e9))
+            else:
+                n_putative_points = int(np.ceil(np.prod(self.cube_side) * (1/self.d_min) ** 3))
+        else:
+            # We need to compenate n_putative_points for fact that we sample points outside volume also
+            n_putative_points *= np.prod(self.cube_side) / self.region_mesh.volume
+            n_putative_points = int(np.ceil(n_putative_points))
+
+        print(f"Generating {n_putative_points} points for {mesh_path}")
+
         putative_points = self.get_point_cloud(n=n_putative_points)
         putative_points = self.remove_close_neurons(putative_points)
         putative_points = self.remove_outside(putative_points)
@@ -73,10 +99,26 @@ def __init__(self, mesh_path: str, d_min: float, seed=None, rng=None, n_putative
         self.putative_points = putative_points
         self.allocated_points = np.zeros(shape=(putative_points.shape[0],), dtype=bool)
 
+    def define_density(self, neuron_type, density_function):
+        self.density_functions[neuron_type] = density_function
+
+    def place_neurons(self, num_neurons, neuron_type=None):
+
+        if neuron_type is None or neuron_type not in self.density_functions:
+            density_function = None
+        else:
+            density_function = self.density_functions[neuron_type]
+
+        return self.get_neuron_positions(n_positions=num_neurons, neuron_density=density_function)
+
     def plot_putative_points(self):
 
         self.plot_points(points=self.putative_points)
 
+    def plot_placed_points(self):
+
+        self.plot_points(points=self.putative_points[self.allocated_points, :])
+
     def plot_points(self, points, colour=None):
         import matplotlib.pyplot as plt
 
@@ -169,14 +211,26 @@ def get_neuron_positions(self, n_positions, neuron_density=None):
 
         if neuron_density:
             # TODO: Temp disabled volume... still does not seem to work
-            P_neuron = np.multiply(numexpr.evaluate(neuron_density), free_volume)
+            if type(neuron_density) == str:
+                P_neuron = np.multiply(numexpr.evaluate(neuron_density), free_volume)
+            else:
+                P_neuron = np.multiply(neuron_density(x=x, y=y, z=z), free_volume)
             # P_neuron = numexpr.evaluate(neuron_density)
         else:
             P_neuron = free_volume
 
         P_neuron /= np.sum(P_neuron)
 
-        idx = self.rng.choice(len(free_positions), n_positions, p=P_neuron, replace=False)
+        try:
+            idx = self.rng.choice(len(free_positions), n_positions, p=P_neuron, replace=False)
+        except ValueError as ve:
+            print("Error: Increase n_putative_points or putative_density, too few putative points set.")
+            print(f"      Mesh: {self.region_mesh.mesh_path}")
+
+            import pdb
+            pdb.set_trace()
+
+            raise ve
 
         neuron_positions = free_positions[idx, :]
         used_idx = np.where(~self.allocated_points)[0][idx]
@@ -184,8 +238,6 @@ def get_neuron_positions(self, n_positions, neuron_density=None):
 
         return neuron_positions
 
-
-
 class NeuronBender:
 
     def __init__(self):
@@ -209,7 +261,8 @@ def __init__(self):
 
     mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj"
 
-    nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6, n_putative_points=10000000)
+    # nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6, n_putative_points=10000000)
+    nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6, n_putative_points=None, putative_density=100e3)
     # nep.plot_putative_points()
 
     points_flat = nep.get_neuron_positions(5000)
diff --git a/tests/networks/network_density/network-config.json b/tests/networks/network_density/network-config.json
index a18feb34d..7e0573c2e 100644
--- a/tests/networks/network_density/network-config.json
+++ b/tests/networks/network_density/network-config.json
@@ -15,6 +15,7 @@
             "dMin": 1.5e-05,
             "meshFile": "networks/network_density/mesh/slice.obj",
             "meshBinWidth": 0.0005,
+            "n_putative_points": 6000,
             "density": {
                 "FS": {
                     "densityFunction": "abs(x)"
@@ -485,4 +486,4 @@
             "volumeID": "Striatum"
         }
     }
-}
+}
\ No newline at end of file
diff --git a/tests/networks/network_testing_place/network-config.json b/tests/networks/network_testing_place/network-config.json
index 5502328ae..fcf81ce8c 100644
--- a/tests/networks/network_testing_place/network-config.json
+++ b/tests/networks/network_testing_place/network-config.json
@@ -491,4 +491,4 @@
             "volumeID": "Striatum"
         }
     }
-}
+}
\ No newline at end of file

From c745d8b02cd1e77c3ab4f872a852477a98123f49 Mon Sep 17 00:00:00 2001
From: wthun <wilhelm.thunberg@gmail.com>
Date: Mon, 9 Oct 2023 18:01:55 +0200
Subject: [PATCH 06/43] Add watertight single-hemisphere versions of nuclei
 meshes

---
 snudda/data/mesh/GPe-left.obj         |  3116 +++++
 snudda/data/mesh/GPe-right.obj        |  3264 +++++
 snudda/data/mesh/GPi-left.obj         |  1032 ++
 snudda/data/mesh/GPi-right.obj        |  1096 ++
 snudda/data/mesh/SNc-left.obj         |  1180 ++
 snudda/data/mesh/SNc-right.obj        |  1212 ++
 snudda/data/mesh/SNr-left.obj         |  2748 ++++
 snudda/data/mesh/SNr-right.obj        |  2760 ++++
 snudda/data/mesh/STN-left.obj         |   772 ++
 snudda/data/mesh/STN-right.obj        |   780 ++
 snudda/data/mesh/Striatum-d-left.obj  | 16380 ++++++++++++++++++++++
 snudda/data/mesh/Striatum-d-right.obj | 17196 ++++++++++++++++++++++++
 snudda/data/mesh/Striatum-v-left.obj  | 11848 ++++++++++++++++
 snudda/data/mesh/Striatum-v-right.obj | 12196 +++++++++++++++++
 snudda/data/mesh/cut_mesh.py          |    24 +
 15 files changed, 75604 insertions(+)
 create mode 100644 snudda/data/mesh/GPe-left.obj
 create mode 100644 snudda/data/mesh/GPe-right.obj
 create mode 100644 snudda/data/mesh/GPi-left.obj
 create mode 100644 snudda/data/mesh/GPi-right.obj
 create mode 100644 snudda/data/mesh/SNc-left.obj
 create mode 100644 snudda/data/mesh/SNc-right.obj
 create mode 100644 snudda/data/mesh/SNr-left.obj
 create mode 100644 snudda/data/mesh/SNr-right.obj
 create mode 100644 snudda/data/mesh/STN-left.obj
 create mode 100644 snudda/data/mesh/STN-right.obj
 create mode 100644 snudda/data/mesh/Striatum-d-left.obj
 create mode 100644 snudda/data/mesh/Striatum-d-right.obj
 create mode 100644 snudda/data/mesh/Striatum-v-left.obj
 create mode 100644 snudda/data/mesh/Striatum-v-right.obj
 create mode 100644 snudda/data/mesh/cut_mesh.py

diff --git a/snudda/data/mesh/GPe-left.obj b/snudda/data/mesh/GPe-left.obj
new file mode 100644
index 000000000..c7a17a344
--- /dev/null
+++ b/snudda/data/mesh/GPe-left.obj
@@ -0,0 +1,3116 @@
+# Created by Open3D 
+# object name: GPe-left
+# number of vertices: 779
+# number of triangles: 1554
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diff --git a/snudda/data/mesh/GPe-right.obj b/snudda/data/mesh/GPe-right.obj
new file mode 100644
index 000000000..c1a85d361
--- /dev/null
+++ b/snudda/data/mesh/GPe-right.obj
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diff --git a/snudda/data/mesh/GPi-left.obj b/snudda/data/mesh/GPi-left.obj
new file mode 100644
index 000000000..b4ff846b3
--- /dev/null
+++ b/snudda/data/mesh/GPi-left.obj
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diff --git a/snudda/data/mesh/GPi-right.obj b/snudda/data/mesh/GPi-right.obj
new file mode 100644
index 000000000..4ea3b5de2
--- /dev/null
+++ b/snudda/data/mesh/GPi-right.obj
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diff --git a/snudda/data/mesh/SNc-left.obj b/snudda/data/mesh/SNc-left.obj
new file mode 100644
index 000000000..998f37091
--- /dev/null
+++ b/snudda/data/mesh/SNc-left.obj
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diff --git a/snudda/data/mesh/SNc-right.obj b/snudda/data/mesh/SNc-right.obj
new file mode 100644
index 000000000..57d685a9e
--- /dev/null
+++ b/snudda/data/mesh/SNc-right.obj
@@ -0,0 +1,1212 @@
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index 000000000..d866b3820
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diff --git a/snudda/data/mesh/SNr-right.obj b/snudda/data/mesh/SNr-right.obj
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index 000000000..87c405150
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diff --git a/snudda/data/mesh/STN-left.obj b/snudda/data/mesh/STN-left.obj
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diff --git a/snudda/data/mesh/STN-right.obj b/snudda/data/mesh/STN-right.obj
new file mode 100644
index 000000000..a6cd103c2
--- /dev/null
+++ b/snudda/data/mesh/STN-right.obj
@@ -0,0 +1,780 @@
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diff --git a/snudda/data/mesh/Striatum-d-left.obj b/snudda/data/mesh/Striatum-d-left.obj
new file mode 100644
index 000000000..c93871374
--- /dev/null
+++ b/snudda/data/mesh/Striatum-d-left.obj
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diff --git a/snudda/data/mesh/Striatum-d-right.obj b/snudda/data/mesh/Striatum-d-right.obj
new file mode 100644
index 000000000..6ab84e743
--- /dev/null
+++ b/snudda/data/mesh/Striatum-d-right.obj
@@ -0,0 +1,17196 @@
+# Created by Open3D 
+# object name: Striatum-d-right
+# number of vertices: 4299
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diff --git a/snudda/data/mesh/Striatum-v-left.obj b/snudda/data/mesh/Striatum-v-left.obj
new file mode 100644
index 000000000..50758e609
--- /dev/null
+++ b/snudda/data/mesh/Striatum-v-left.obj
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diff --git a/snudda/data/mesh/Striatum-v-right.obj b/snudda/data/mesh/Striatum-v-right.obj
new file mode 100644
index 000000000..e1bf9de15
--- /dev/null
+++ b/snudda/data/mesh/Striatum-v-right.obj
@@ -0,0 +1,12196 @@
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diff --git a/snudda/data/mesh/cut_mesh.py b/snudda/data/mesh/cut_mesh.py
new file mode 100644
index 000000000..df1ef9695
--- /dev/null
+++ b/snudda/data/mesh/cut_mesh.py
@@ -0,0 +1,24 @@
+import open3d as o3d
+import numpy as np
+
+
+def cut_mesh(path, save=False):
+    mesh = o3d.io.read_triangle_mesh(path)
+    x, y, z = np.array(mesh.vertices).T
+    left = z > np.mean(z)  # apparent mediolateral axis
+    left_idxs = np.where(left)[0]
+    left_mesh = mesh.select_by_index(left_idxs)
+    right_idxs = np.where(~left)[0]
+    right_mesh = mesh.select_by_index(right_idxs)
+
+    if save:
+        base = path.strip('.obj')
+        o3d.io.write_triangle_mesh(filename=base+'-left.obj', mesh=left_mesh)
+        o3d.io.write_triangle_mesh(filename=base+'-right.obj', mesh=right_mesh)
+
+    return mesh, right_mesh, left_mesh
+
+if __name__ == '__main__':
+    meshes = ['Striatum-v.obj', 'Striatum-d.obj', 'GPe.obj', 'GPi.obj', 'STN.obj', 'SNr.obj', 'SNc.obj']
+    for mesh_name in meshes:
+        cut_mesh(mesh_name, save=True)

From bca990b55e7a59843f43af1b740bf455ce4476f4 Mon Sep 17 00:00:00 2001
From: wthun <wilhelm.thunberg@gmail.com>
Date: Mon, 9 Oct 2023 20:16:11 +0200
Subject: [PATCH 07/43] changed path to local

---
 snudda/place/region_mesh_redux.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index 92fb11c6b..bfcaf3eb0 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -259,7 +259,7 @@ def __init__(self):
 
 if __name__ == "__main__":
 
-    mesh_path="/home/hjorth/HBP/Snudda/snudda/data/mesh/Striatum-dorsal-right-hemisphere.obj"
+    mesh_path="../data/mesh/Striatum-d-right.obj"
 
     # nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6, n_putative_points=10000000)
     nep = NeuronPlacer(mesh_path=mesh_path, d_min=10e-6, n_putative_points=None, putative_density=100e3)

From 63e054fec8d523e83c86f9272ed9fc21d0a6db5a Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Tue, 10 Oct 2023 08:51:07 +0200
Subject: [PATCH 08/43] Unit tests

---
 snudda/init/init.py                           |  2 +-
 .../network_testing_input/network-config.json |  5 +-
 .../network-config.json                       |  2 +-
 tests/test_degeneration.py                    |  2 +-
 tests/test_input.py                           | 68 ++++++-------------
 5 files changed, 26 insertions(+), 53 deletions(-)

diff --git a/snudda/init/init.py b/snudda/init/init.py
index 96ba1f645..6b02ca5e3 100644
--- a/snudda/init/init.py
+++ b/snudda/init/init.py
@@ -963,7 +963,7 @@ def define_striatum(self,
                                   struct_mesh=os.path.join("$SNUDDA_DATA", "mesh", "Striatum-d.obj"),
                                   mesh_bin_width=1e-4,
                                   d_min=d_min,
-                                  nn_putative_points=num_neurons*3)
+                                  n_putative_points=num_neurons*3)
 
             density_file = os.path.join("$SNUDDA_DATA", "density", "dorsal_striatum_density.json")
 
diff --git a/tests/networks/network_testing_input/network-config.json b/tests/networks/network_testing_input/network-config.json
index 94c9dda0e..5615704c5 100644
--- a/tests/networks/network_testing_input/network-config.json
+++ b/tests/networks/network_testing_input/network-config.json
@@ -14,7 +14,8 @@
             "type": "mesh",
             "dMin": 1.5e-05,
             "meshFile": "networks/network_testing_input/mesh/Striatum-cube-mesh-5e-05.obj",
-            "meshBinWidth": 5e-05
+            "meshBinWidth": 5e-05,
+            "n_putative_points": 30
         }
     },
     "Connectivity": {
@@ -519,4 +520,4 @@
             "structure": "Striatum"
         }
     }
-}
+}
\ No newline at end of file
diff --git a/tests/networks/network_testing_project/network-config.json b/tests/networks/network_testing_project/network-config.json
index 186bc7b13..77b689133 100644
--- a/tests/networks/network_testing_project/network-config.json
+++ b/tests/networks/network_testing_project/network-config.json
@@ -210,4 +210,4 @@
             "volumeID": "VolumeB"
         }
     }
-}
+}
\ No newline at end of file
diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index 7004d5dc4..34c04275a 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -152,7 +152,7 @@ def test_something(self):
         orig_load = SnuddaLoad(self.network_A)
         degen_load = SnuddaLoad(self.network_C)
 
-        self.assertEqual(orig_load.data["nSynapses"], 159)
+        self.assertEqual(orig_load.data["nSynapses"], 156)
 
         # Verify that it should be 99 synapses -- now it is just a regression test
         # Old version gave 99, new gives 165 --- CHECK WHY!
diff --git a/tests/test_input.py b/tests/test_input.py
index 68bfcdfb5..aefd51997 100644
--- a/tests/test_input.py
+++ b/tests/test_input.py
@@ -190,18 +190,12 @@ def test_input_1(self):
                     f_gen = len(t_idx) / (n_traces * (et - st))
                     print(f"ID {neuron_id_str} {neuron_name} {input_type} f={f}, f_gen={f_gen}")
 
-                    try:
-                        if np.max(input_info["spikes"].attrs["correlation"]) == 0:
-                            self.assertTrue(f_gen > f - 5*np.sqrt(f)/np.sqrt(n_traces))
-                            self.assertTrue(f_gen < f + 5*np.sqrt(f)/np.sqrt(n_traces))
-                        else:
-                            # For high correlations and short durations we have huge fluctuations, so skip those
-                            pass
-                    except:
-                        import pdb
-                        import traceback
-                        print(traceback.format_exc())
-                        pdb.set_trace()
+                    if np.max(input_info["spikes"].attrs["correlation"]) == 0:
+                        self.assertTrue(f_gen > f - 5*np.sqrt(f)/np.sqrt(n_traces))
+                        self.assertTrue(f_gen < f + 5*np.sqrt(f)/np.sqrt(n_traces))
+                    else:
+                        # For high correlations and short durations we have huge fluctuations, so skip those
+                        pass
 
                 if "populationUnitCorrelation" in config_data[neuron_type][input_type]:
                     correlation = config_data[neuron_type][input_type]["populationUnitCorrelation"]
@@ -227,16 +221,9 @@ def test_input_1(self):
                     readout = np.zeros((spikes.size, ))
                     ctr = 0
                     for t_idx in (spikes.flatten() / bin_size).astype(int):
-                        try:
-                            if t_idx > 0:
-                                readout[ctr] = binned_data[t_idx]
-                                ctr += 1
-                        except:
-                            import traceback
-                            t_str = traceback.format_exc()
-                            print(t_str)
-                            import pdb
-                            pdb.set_trace()
+                        if t_idx > 0:
+                            readout[ctr] = binned_data[t_idx]
+                            ctr += 1
 
                     readout = readout[:ctr]
 
@@ -272,19 +259,9 @@ def test_input_1(self):
 
                     print(f"Simultaneous spikes: {np.mean(readout):.2f} (expected {expected_mean:.2f}) "
                           f"- correlation {correlation}")
-                    try:
-                        if jitter <= 0.001:
-                            # Only do check for non-jittered input
-                            self.assertTrue(expected_mean * 0.75 < np.mean(readout) < expected_mean * 1.25)
-
-                    except:
-                        import traceback
-
-                        t_str = traceback.format_exc()
-                        print(t_str)
-                        import pdb
-
-                        pdb.set_trace()
+                    if jitter <= 0.001:
+                        # Only do check for non-jittered input
+                        self.assertTrue(expected_mean * 0.75 < np.mean(readout) < expected_mean * 1.25)
 
     def test_input_2(self):
 
@@ -305,23 +282,17 @@ def test_input_2(self):
 
         # OBS, population unit 0 does not get any of the extra mother spikes specified
         # So we need to check FS neuron that belongs to population unit 1 or 2.
-        some_spikes = input_data["input/1/Cortical/spikes"][()].flatten()
+        some_spikes = input_data["input/3/Cortical/spikes"][()].flatten()
         some_spikes = some_spikes[some_spikes >= 0]
-        n_trains = input_data["input/1/Cortical/spikes"][()].shape[0]
+        n_trains = input_data["input/3/Cortical/spikes"][()].shape[0]
 
         for extra_spike in [0.2, 0.3, 0.45]:
 
-            try:
-                self.assertTrue(np.sum(np.abs(some_spikes - extra_spike) < 1e-4)
-                                >= n_trains)
-                self.assertTrue(np.sum(np.abs(some_spikes - extra_spike + 0.05) < 1e-3) < 50)
-            except:
-                import traceback
-                print(traceback.format_exc())
-                import pdb
-                pdb.set_trace()
-
-        some_spikes2 = input_data["input/1/Thalamic/spikes"][()].flatten()
+            self.assertTrue(np.sum(np.abs(some_spikes - extra_spike) < 1e-4)
+                            >= n_trains)
+            self.assertTrue(np.sum(np.abs(some_spikes - extra_spike + 0.05) < 1e-3) < 50)
+
+        some_spikes2 = input_data["input/3/Thalamic/spikes"][()].flatten()
         some_spikes2 = some_spikes2[some_spikes2 >= 0]
 
         for spike in [0.1, 0.2, 0.3]:
@@ -332,6 +303,7 @@ def test_input_2(self):
         # Check input generated, this focuses on the frequency function generation
         # and also checks input correlation
 
+        # TODO: New cell numbering, so need to pick other cell numbers
         some_spikes_c3 = input_data["input/3/CorticalSignal/spikes"][()]
         some_spikes_c8 = input_data["input/8/CorticalSignal/spikes"][()]
 

From 9897134e461d8e1b43afd781c90379d0b7b5b0b2 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Tue, 10 Oct 2023 10:29:14 +0200
Subject: [PATCH 09/43] Tests

---
 .../network_testing_projection_detection/network-config.json    | 2 +-
 tests/test_degeneration.py                                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/networks/network_testing_projection_detection/network-config.json b/tests/networks/network_testing_projection_detection/network-config.json
index 51bc8dbaa..06493fccb 100644
--- a/tests/networks/network_testing_projection_detection/network-config.json
+++ b/tests/networks/network_testing_projection_detection/network-config.json
@@ -199,4 +199,4 @@
             "volumeID": "VolumeB"
         }
     }
-}
+}
\ No newline at end of file
diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index 34c04275a..01027e74f 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -158,7 +158,7 @@ def test_something(self):
         # Old version gave 99, new gives 165 --- CHECK WHY!
 
         # TODO: CHECK WHY NOT 99 SYNAPSES NOW
-        self.assertEqual(degen_load.data["nSynapses"], 163)  # -- Ilaria, we need to check what the true value should be?
+        self.assertEqual(degen_load.data["nSynapses"], 173)  # -- Ilaria, we need to check what the true value should be?
 
 
 if __name__ == '__main__':

From 175ed016228ff40f4e5572cc1eb78ae1c76f33b5 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Tue, 10 Oct 2023 10:47:27 +0200
Subject: [PATCH 10/43] Tests

---
 tests/test_degeneration.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index 01027e74f..79a1746bd 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -152,13 +152,13 @@ def test_something(self):
         orig_load = SnuddaLoad(self.network_A)
         degen_load = SnuddaLoad(self.network_C)
 
-        self.assertEqual(orig_load.data["nSynapses"], 156)
+        self.assertEqual(orig_load.data["nSynapses"], 165)
 
         # Verify that it should be 99 synapses -- now it is just a regression test
         # Old version gave 99, new gives 165 --- CHECK WHY!
 
         # TODO: CHECK WHY NOT 99 SYNAPSES NOW
-        self.assertEqual(degen_load.data["nSynapses"], 173)  # -- Ilaria, we need to check what the true value should be?
+        self.assertEqual(degen_load.data["nSynapses"], 155)  # -- Ilaria, we need to check what the true value should be?
 
 
 if __name__ == '__main__':

From 2641e39ab7b9bdfb7e3fdfc89984407e9ee34e09 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Tue, 10 Oct 2023 13:09:38 +0200
Subject: [PATCH 11/43] Printing morphology keys

---
 tests/test_degeneration.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index 79a1746bd..2c5e756a2 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -152,6 +152,12 @@ def test_something(self):
         orig_load = SnuddaLoad(self.network_A)
         degen_load = SnuddaLoad(self.network_C)
 
+        tmp = [(x["neuronID"], x["morphologyKey"]) for x in orig_load.data["neurons"]]
+        print(f"Morphologies: {tmp}")
+
+        #import pdb
+        #pdb.set_trace()
+
         self.assertEqual(orig_load.data["nSynapses"], 165)
 
         # Verify that it should be 99 synapses -- now it is just a regression test

From 75849031d68e7012f809ead3a1eb73320d23b413 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Wed, 11 Oct 2023 11:01:07 +0200
Subject: [PATCH 12/43] In progress

---
 snudda/place/bend_morphologies.py | 30 ++++++++++++++++++++++++++++++
 snudda/place/region_mesh_redux.py |  3 ++-
 2 files changed, 32 insertions(+), 1 deletion(-)
 create mode 100644 snudda/place/bend_morphologies.py

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
new file mode 100644
index 000000000..83b097fe8
--- /dev/null
+++ b/snudda/place/bend_morphologies.py
@@ -0,0 +1,30 @@
+import numpy as np
+
+from snudda.neurons import NeuronMorphologyExtended
+from snudda.place.region_mesh_redux import RegionMeshRedux
+
+
+class BendMorphologies:
+
+    def __init__(self, region_mesh: RegionMeshRedux):
+
+        self.region_mesh = region_mesh
+
+        pass
+
+    def check_if_inside(self, morphology: NeuronMorphologyExtended):
+
+        coords = morphology.morphology_data["neuron"].geometry
+        inside_flag = self.region_mesh.check_inside(points=coords[:, :3])
+
+        return inside_flag
+
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, inside_flag=None):
+
+        if inside_flag is None:
+            inside_flag = self.check_if_inside(morphology=morphology)
+
+        # Iterate over all parts of neuron
+        for section in morphology.section_iterator():
+            # We can use the inside_flag to determine if we need to rotate the branch anything
+            pass
\ No newline at end of file
diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index bfcaf3eb0..51931311f 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -67,9 +67,10 @@ def __init__(self, mesh_path: str, d_min: float, random_seed=None, rng=None,
         self.density_functions = dict()
 
         if rng:
+            self.rng = rng
+
             if random_seed:
                 raise ValueError("If rng is set, then seed should not be set.")
-            self.rng = rng
         else:
             self.rng = np.random.default_rng(random_seed)
 

From 01b186e13a184512d1455069d088257bd4ad6fd1 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Wed, 11 Oct 2023 12:47:21 +0200
Subject: [PATCH 13/43] Initial

---
 snudda/place/bend_morphologies.py | 22 +++++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 83b097fe8..011200b1e 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -19,12 +19,24 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
         return inside_flag
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended, inside_flag=None):
-
-        if inside_flag is None:
-            inside_flag = self.check_if_inside(morphology=morphology)
+    def bend_morphology(self, morphology: NeuronMorphologyExtended):
 
         # Iterate over all parts of neuron
         for section in morphology.section_iterator():
-            # We can use the inside_flag to determine if we need to rotate the branch anything
+            # We need to track the rotation of each point, in particular save rotations
+            # for each branch point, so that all children can start with that rotation
+
+            # Loop over all points in section
+
+            # Gradient is calculated based on distance to mesh
+            # Calculate delta_gradient = gradient_self - gradient_parent
+            # Amount of angle to bend f(delta_gradient)
+            # Randomize the rotation matrix
+
+            # After section is done, store the last rotation matrix, so its children can get their parent rotation
+            # store in dictionary?
+
+
+
+
             pass
\ No newline at end of file

From 446f380dfdd11ecbfc885a30641928864ced84b3 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Thu, 12 Oct 2023 09:00:38 +0200
Subject: [PATCH 14/43] In progrss

---
 snudda/place/bend_morphologies.py | 32 +++++++++++++++++++++++++++----
 1 file changed, 28 insertions(+), 4 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 011200b1e..6c8542603 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -1,4 +1,5 @@
 import numpy as np
+from scipy.spatial.transform import Rotation
 
 from snudda.neurons import NeuronMorphologyExtended
 from snudda.place.region_mesh_redux import RegionMeshRedux
@@ -6,11 +7,10 @@
 
 class BendMorphologies:
 
-    def __init__(self, region_mesh: RegionMeshRedux):
+    def __init__(self, region_mesh: RegionMeshRedux, rng):
 
         self.region_mesh = region_mesh
-
-        pass
+        self.rng = rng
 
     def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
@@ -19,14 +19,37 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
         return inside_flag
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended):
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
+
+        # k -- decay constant
+
+        parent_rotation_matrices = dict()  # indexed by parent_point_idx in SectionMetaData
 
         # Iterate over all parts of neuron
         for section in morphology.section_iterator():
+
             # We need to track the rotation of each point, in particular save rotations
             # for each branch point, so that all children can start with that rotation
 
+            if section.parent_section_idx in parent_rotation_matrices:
+                rotation_matrix = parent_rotation_matrices[section.parent_section_idx]
+            else:
+                rotation_matrix = np.eye(3)
+
             # Loop over all points in section
+            for point_idx in section.point_idx:
+                coords = section.morphology_data.geometry[point_idx, :]
+                dist = self.region_mesh.distance_to_border(points=coords)
+
+                P = 1 / (1 + np.exp(-k * dist))
+
+                if self.rng.uniform(1) < P:
+                    # We need to randomize new rotation matrix
+                    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
+                    Rotation
+                    pass
+
+
 
             # Gradient is calculated based on distance to mesh
             # Calculate delta_gradient = gradient_self - gradient_parent
@@ -36,6 +59,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended):
             # After section is done, store the last rotation matrix, so its children can get their parent rotation
             # store in dictionary?
 
+            parent_rotation_matrices[point_idx] = rotation_matrix
 
 
 

From a91f28f06f808b7ebf96766e235c7844a6854b52 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Thu, 12 Oct 2023 11:49:33 +0200
Subject: [PATCH 15/43] Work in progress

---
 snudda/place/bend_morphologies.py | 35 ++++++++++++++++++++++++++++---
 1 file changed, 32 insertions(+), 3 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 6c8542603..0f25fba24 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -22,6 +22,8 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
     def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
         # k -- decay constant
+        n_random = 10
+        candidate_pos = np.zeros((n_random, 3))
 
         parent_rotation_matrices = dict()  # indexed by parent_point_idx in SectionMetaData
 
@@ -36,6 +38,8 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
             else:
                 rotation_matrix = np.eye(3)
 
+            parent_coords = section.morphology_data.position[section.parent_section_idx, :]
+
             # Loop over all points in section
             for point_idx in section.point_idx:
                 coords = section.morphology_data.geometry[point_idx, :]
@@ -46,10 +50,33 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
                 if self.rng.uniform(1) < P:
                     # We need to randomize new rotation matrix
                     # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
-                    Rotation
-                    pass
+                    angles = self.rng.uniform(size=(n_random, 3), low=-0.2, high=0.2)  # Angles in radians
+                    rots = Rotation.from_euler(seq="XYZ", angles=angles)
+
+                    for idx, rot in enumerate(rots):
+                        candidate_pos[idx, :] = parent_coords[idx, :] + rots.apply(vectors=coords-parent_coords)
+
+                    candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
+
+                    P_candidate = np.divide(1, 1 + np.exp(-k * candidate_dist))
+                    picked_idx = self.rng.choice(n_random, p=P_candidate)
+
+                    new_coords = candidate_pos[picked_idx, :]
+
+                    picked_rotation = rots[picked_idx]
+                    rotation_point = parent_coords
+
+                    # TODO: We also need to apply this rotation to ALL the other points in this section,
+                    #       and to all child sections of this branch.
+
+            else:
+                # Keep the old coords
+                new_coords = coords
+
 
 
+                # Old point is next points parent
+                parent_coords = new_coords
 
             # Gradient is calculated based on distance to mesh
             # Calculate delta_gradient = gradient_self - gradient_parent
@@ -63,4 +90,6 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
 
 
-            pass
\ No newline at end of file
+    def rotate_subtree(self, section, from_idx, rotation, rotation_point):
+        
+        pass
\ No newline at end of file

From cec4a2301762f2edfd0e0c8be2f4813aad968750 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 13 Oct 2023 17:09:03 +0200
Subject: [PATCH 16/43] Work in progress

---
 snudda/place/bend_morphologies.py | 117 +++++++++++++++++++++++++++++-
 1 file changed, 114 insertions(+), 3 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 0f25fba24..fb9c18759 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -2,6 +2,7 @@
 from scipy.spatial.transform import Rotation
 
 from snudda.neurons import NeuronMorphologyExtended
+from snudda.neurons.morphology_data import MorphologyData, SectionMetaData
 from snudda.place.region_mesh_redux import RegionMeshRedux
 
 
@@ -21,6 +22,9 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
     def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
+        # TODO: Parent point idx is included if parent section is of same type as section!
+        #       So we should not rotate the first point!
+
         # k -- decay constant
         n_random = 10
         candidate_pos = np.zeros((n_random, 3))
@@ -88,8 +92,115 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
             parent_rotation_matrices[point_idx] = rotation_matrix
 
+    def get_full_rotation_representation(self, morphology: MorphologyData):
+
+        rotation_representation = dict()
+        parent_direction = dict()
+
+        for section in morphology.section_iterator():
+
+            if (section.section_id, section.section_type) in parent_direction:
+                parent_dir = parent_direction[section.section_id, section.section_type]
+            else:
+                parent_dir = np.array([[1, 0, 0]])
+
+            rot_and_len, last_direction = self.rotation_representation(section=section, parent_direction=parent_dir)
+
+            rotation_representation[section.section_id, section.section_type] = rot_and_len
+
+            for child_id, child_type in section.child_section_id:
+                parent_direction[child_id, child_type] = last_direction
+
+        return rotation_representation
+
+    def apply_rotation(self, morphology: MorphologyData, rotation_representation):
+
+        parent_direction = dict()
+
+        new_coords = np.array(morphology.geometry.shape)
+
+        for section in morphology.section_iterator():
+            if (section.section_id, section.section_type) in parent_direction:
+                parent_dir, parent_pos = parent_direction[section.section_id, section.section_type]
+            else:
+                parent_dir = np.matmul(morphology.rotation, np.array([[1, 0, 0]]))
+                parent_pos = morphology.position
+
+            rot_rep, last_dir = rotation_representation[section.section_id, section.section_type]
+            coords = self.coordinate_representation(rotation_representation=rot_rep,
+                                                    parent_direction=parent_dir,
+                                                    parent_point=parent_pos)
+
+            new_coords[section.point_idx, :3] = coords
+
+            for child_id, child_type in section.child_section_id:
+                parent_direction[child_id, child_type] = (last_dir, coords[-1, :3])
+
+        # TODO: This just returns the coords for now, add option to update coords in morphology?
+        #       OBS! Then rotation should also be reset, since it is now included in the coordinates
+
+        return new_coords
+
+
+    def rotation_representation(self, section: SectionMetaData, parent_direction=None):
+
+        """ Represent each section as a series of length of segment, and rotations relative the parent segment."""
+
+        if parent_direction is None:
+            parent_direction = np.array([[1, 0, 0]])
+
+        rotations_and_length = []
+        parent_direction = parent_direction / np.linalg.norm(parent_direction)
+
+        coords = section.morphology_data.geometry[section.point_idx, :3]
+        delta = np.diff(coords, axis=0)
+        delta_length = np.linalg.norm(delta, axis=1)
+        delta_direction = delta / delta_length.reshape((delta.shape[0], 1))
+
+        for segment_direction, segment_length in zip(delta_direction, delta_length):
+            segment_direction = segment_direction.reshape((1, 3))
+            rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
+
+            import pdb
+            pdb.set_trace()
+
+            rotations_and_length.append((rotation, segment_length))
+            parent_direction = segment_direction
+
+        return rotations_and_length, segment_direction
+
+    def coordinate_representation(self, rotation_representation,
+                                  parent_direction=None,
+                                  parent_point=None):
+
+        if parent_direction is None:
+            parent_direction = np.array([1, 0, 0])
+
+        if parent_point is None:
+            parent_point = np.zeros(3)
+
+        parent_direction = parent_direction / np.linalg.norm(parent_direction)
+
+        coords = np.zeros((len(rotation_representation), 3))
+
+        for idx, (rotation, length) in enumerate(rotation_representation):
+            segment_direction = rotation.apply(parent_direction)
+            parent_point = coords[idx, :] = segment_direction * length + parent_point
+            parent_direction = segment_direction
+
+        return coords
+
+
+if __name__ == "__main__":
+
+    file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
+
+    md = MorphologyData(swc_file=file_path)
 
+    bm = BendMorphologies(None, rng=np.random.default_rng())
+    sec = md.sections[3][0]
+    rot, _ = bm.rotation_representation(sec)
+    coords = bm.coordinate_representation(rotation_representation=rot, parent_point=sec.position[0, :])
 
-    def rotate_subtree(self, section, from_idx, rotation, rotation_point):
-        
-        pass
\ No newline at end of file
+    import pdb
+    pdb.set_trace()
\ No newline at end of file

From dc82c423e0f199efcdbb3ebb6638dd8c693cec56 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 16 Oct 2023 11:08:01 +0200
Subject: [PATCH 17/43] Work in progress

---
 snudda/neurons/morphology_data.py |  2 +-
 snudda/place/bend_morphologies.py | 55 +++++++++++++++++++------------
 2 files changed, 35 insertions(+), 22 deletions(-)

diff --git a/snudda/neurons/morphology_data.py b/snudda/neurons/morphology_data.py
index 787c19146..ebd37d7df 100644
--- a/snudda/neurons/morphology_data.py
+++ b/snudda/neurons/morphology_data.py
@@ -738,7 +738,7 @@ def section_iterator(self, section_type=None):
 
         if section_type is None:
             for section_dict in self.sections.values():
-                for section in section_dict:
+                for section in section_dict.values():
                     yield section
 
         elif section_type in self.sections:
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index fb9c18759..05750188d 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -108,7 +108,7 @@ def get_full_rotation_representation(self, morphology: MorphologyData):
 
             rotation_representation[section.section_id, section.section_type] = rot_and_len
 
-            for child_id, child_type in section.child_section_id:
+            for child_id, child_type in section.child_section_id.T:
                 parent_direction[child_id, child_type] = last_direction
 
         return rotation_representation
@@ -117,23 +117,31 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation):
 
         parent_direction = dict()
 
-        new_coords = np.array(morphology.geometry.shape)
+        # new_coords = np.zeros((morphology.geometry.shape[0], 3))
+        new_coords = np.full((morphology.geometry.shape[0], 3), np.nan)  # Use nan as fill value, to see problems
 
         for section in morphology.section_iterator():
             if (section.section_id, section.section_type) in parent_direction:
                 parent_dir, parent_pos = parent_direction[section.section_id, section.section_type]
             else:
-                parent_dir = np.matmul(morphology.rotation, np.array([[1, 0, 0]]))
-                parent_pos = morphology.position
-
-            rot_rep, last_dir = rotation_representation[section.section_id, section.section_type]
-            coords = self.coordinate_representation(rotation_representation=rot_rep,
-                                                    parent_direction=parent_dir,
-                                                    parent_point=parent_pos)
-
+                if morphology.rotation:
+                    parent_dir = np.matmul(morphology.rotation, np.array([[1, 0, 0]]))
+                else:
+                    parent_dir = np.array([[1, 0, 0]])
+
+                if morphology.position:
+                    parent_pos = morphology.position
+                else:
+                    parent_pos = np.zeros((3, ))
+
+            rot_rep = rotation_representation[section.section_id, section.section_type]
+            coords, last_dir = self.coordinate_representation(rotation_representation=rot_rep,
+                                                              parent_direction=parent_dir,
+                                                              parent_point=parent_pos,
+                                                              return_last_direction=True)
             new_coords[section.point_idx, :3] = coords
 
-            for child_id, child_type in section.child_section_id:
+            for child_id, child_type in section.child_section_id.T:
                 parent_direction[child_id, child_type] = (last_dir, coords[-1, :3])
 
         # TODO: This just returns the coords for now, add option to update coords in morphology?
@@ -141,7 +149,6 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation):
 
         return new_coords
 
-
     def rotation_representation(self, section: SectionMetaData, parent_direction=None):
 
         """ Represent each section as a series of length of segment, and rotations relative the parent segment."""
@@ -151,6 +158,7 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
 
         rotations_and_length = []
         parent_direction = parent_direction / np.linalg.norm(parent_direction)
+        segment_direction = parent_direction  # To handle soma...
 
         coords = section.morphology_data.geometry[section.point_idx, :3]
         delta = np.diff(coords, axis=0)
@@ -161,9 +169,6 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
             segment_direction = segment_direction.reshape((1, 3))
             rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
 
-            import pdb
-            pdb.set_trace()
-
             rotations_and_length.append((rotation, segment_length))
             parent_direction = segment_direction
 
@@ -171,7 +176,8 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
 
     def coordinate_representation(self, rotation_representation,
                                   parent_direction=None,
-                                  parent_point=None):
+                                  parent_point=None,
+                                  return_last_direction=False):
 
         if parent_direction is None:
             parent_direction = np.array([1, 0, 0])
@@ -181,13 +187,17 @@ def coordinate_representation(self, rotation_representation,
 
         parent_direction = parent_direction / np.linalg.norm(parent_direction)
 
-        coords = np.zeros((len(rotation_representation), 3))
+        coords = np.zeros((len(rotation_representation)+1, 3))
+        coords[0, :] = parent_point
 
         for idx, (rotation, length) in enumerate(rotation_representation):
             segment_direction = rotation.apply(parent_direction)
-            parent_point = coords[idx, :] = segment_direction * length + parent_point
+            parent_point = coords[idx+1, :] = segment_direction * length + parent_point
             parent_direction = segment_direction
 
+        if return_last_direction:
+            return coords, parent_direction
+
         return coords
 
 
@@ -198,9 +208,12 @@ def coordinate_representation(self, rotation_representation,
     md = MorphologyData(swc_file=file_path)
 
     bm = BendMorphologies(None, rng=np.random.default_rng())
-    sec = md.sections[3][0]
-    rot, _ = bm.rotation_representation(sec)
-    coords = bm.coordinate_representation(rotation_representation=rot, parent_point=sec.position[0, :])
+    # sec = md.sections[3][0]
+    # rot, _ = bm.rotation_representation(sec)
+    # coords = bm.coordinate_representation(rotation_representation=rot, parent_point=sec.position[0, :])
+
+    rot_rep = bm.get_full_rotation_representation(morphology=md)
+    coords = bm.apply_rotation(morphology=md, rotation_representation=rot_rep)
 
     import pdb
     pdb.set_trace()
\ No newline at end of file

From 8c8c6eb82df533debe1703c6bcb9da4126e8cded Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 16 Oct 2023 11:36:51 +0200
Subject: [PATCH 18/43] Check works

---
 snudda/place/bend_morphologies.py | 64 ++++++++++++++++++++++++++-----
 1 file changed, 55 insertions(+), 9 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 05750188d..40b7afa2a 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -104,7 +104,13 @@ def get_full_rotation_representation(self, morphology: MorphologyData):
             else:
                 parent_dir = np.array([[1, 0, 0]])
 
-            rot_and_len, last_direction = self.rotation_representation(section=section, parent_direction=parent_dir)
+            try:
+                rot_and_len, last_direction = self.rotation_representation(section=section, parent_direction=parent_dir)
+            except:
+                import traceback
+                print(traceback.format_exc())
+                import pdb
+                pdb.set_trace()
 
             rotation_representation[section.section_id, section.section_type] = rot_and_len
 
@@ -134,12 +140,33 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation):
                 else:
                     parent_pos = np.zeros((3, ))
 
+            if section.section_type == section.parent_section_type or section.section_type == 1:
+                # The section includes the parent point, if both sections are of the same type
+                # or if the section is the soma (since then it has no rotations, so position gets passed as parent point)
+                include_parent_point = True
+            else:
+                include_parent_point = False
+
             rot_rep = rotation_representation[section.section_id, section.section_type]
             coords, last_dir = self.coordinate_representation(rotation_representation=rot_rep,
                                                               parent_direction=parent_dir,
                                                               parent_point=parent_pos,
-                                                              return_last_direction=True)
-            new_coords[section.point_idx, :3] = coords
+                                                              return_last_direction=True,
+                                                              include_parent_point=include_parent_point)
+
+            #if include_parent_point:
+            try:
+                new_coords[section.point_idx, :3] = coords
+            except:
+                import traceback
+                print(traceback.format_exc())
+                import pdb
+                pdb.set_trace()
+            #else:
+            #    new_coords
+
+            #import pdb
+            #pdb.set_trace()
 
             for child_id, child_type in section.child_section_id.T:
                 parent_direction[child_id, child_type] = (last_dir, coords[-1, :3])
@@ -160,14 +187,30 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
         parent_direction = parent_direction / np.linalg.norm(parent_direction)
         segment_direction = parent_direction  # To handle soma...
 
-        coords = section.morphology_data.geometry[section.point_idx, :3]
+        # If parent compartment is of a different type (e.g. soma parent, for the dendrite) then we need
+        # to make sure that the first point is also included. So we need to artificially add the soma.
+
+        if section.section_type != section.parent_section_type and section.parent_section_type != -1:
+            coords = np.zeros((len(section.point_idx) + 1, 3))
+            coords[0, :3] = section.morphology_data.geometry[section.parent_point_idx, :3]
+            coords[1:, :3] = section.morphology_data.geometry[section.point_idx, :3]
+        else:
+            coords = section.morphology_data.geometry[section.point_idx, :3]
+
         delta = np.diff(coords, axis=0)
         delta_length = np.linalg.norm(delta, axis=1)
         delta_direction = delta / delta_length.reshape((delta.shape[0], 1))
 
         for segment_direction, segment_length in zip(delta_direction, delta_length):
             segment_direction = segment_direction.reshape((1, 3))
-            rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
+            try:
+                rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
+            except:
+                import traceback
+                print(traceback.format_exc())
+                import pdb
+                pdb.set_trace()
+
 
             rotations_and_length.append((rotation, segment_length))
             parent_direction = segment_direction
@@ -177,7 +220,8 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
     def coordinate_representation(self, rotation_representation,
                                   parent_direction=None,
                                   parent_point=None,
-                                  return_last_direction=False):
+                                  return_last_direction=False,
+                                  include_parent_point=True):
 
         if parent_direction is None:
             parent_direction = np.array([1, 0, 0])
@@ -187,12 +231,14 @@ def coordinate_representation(self, rotation_representation,
 
         parent_direction = parent_direction / np.linalg.norm(parent_direction)
 
-        coords = np.zeros((len(rotation_representation)+1, 3))
-        coords[0, :] = parent_point
+        coords = np.zeros((len(rotation_representation)+include_parent_point, 3))
+
+        if include_parent_point:
+            coords[0, :] = parent_point
 
         for idx, (rotation, length) in enumerate(rotation_representation):
             segment_direction = rotation.apply(parent_direction)
-            parent_point = coords[idx+1, :] = segment_direction * length + parent_point
+            parent_point = coords[idx+include_parent_point, :] = segment_direction * length + parent_point
             parent_direction = segment_direction
 
         if return_last_direction:

From cd935527bed131603c8bf1f5afb4afc44c13dd28 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 16 Oct 2023 13:35:34 +0200
Subject: [PATCH 19/43] Rot representation can appears to be working

---
 snudda/place/bend_morphologies.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 40b7afa2a..4b4a69a02 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -154,19 +154,7 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation):
                                                               return_last_direction=True,
                                                               include_parent_point=include_parent_point)
 
-            #if include_parent_point:
-            try:
-                new_coords[section.point_idx, :3] = coords
-            except:
-                import traceback
-                print(traceback.format_exc())
-                import pdb
-                pdb.set_trace()
-            #else:
-            #    new_coords
-
-            #import pdb
-            #pdb.set_trace()
+            new_coords[section.point_idx, :3] = coords
 
             for child_id, child_type in section.child_section_id.T:
                 parent_direction[child_id, child_type] = (last_dir, coords[-1, :3])
@@ -247,7 +235,7 @@ def coordinate_representation(self, rotation_representation,
         return coords
 
 
-if __name__ == "__main__":
+def test_rotation_representation():
 
     file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
 
@@ -261,5 +249,17 @@ def coordinate_representation(self, rotation_representation,
     rot_rep = bm.get_full_rotation_representation(morphology=md)
     coords = bm.apply_rotation(morphology=md, rotation_representation=rot_rep)
 
+    if not (np.abs(md.geometry[:, :3] - coords) < 1e-10).all():
+        print(f"Geometry mismatch, problem with representation.")
+        import pdb
+        pdb.set_trace()
+    else:
+        print(f"Geometry matches")
+
+
+if __name__ == "__main__":
+
+    test_rotation_representation()
+
     import pdb
     pdb.set_trace()
\ No newline at end of file

From 61a7b79a21bc3a44d7a9c46bb18682e2c94135c4 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 16 Oct 2023 14:48:50 +0200
Subject: [PATCH 20/43] In progress

---
 snudda/place/bend_morphologies.py | 113 ++++++++++++++++++++++++++++--
 1 file changed, 106 insertions(+), 7 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 4b4a69a02..c81144458 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -10,6 +10,9 @@ class BendMorphologies:
 
     def __init__(self, region_mesh: RegionMeshRedux, rng):
 
+        if type(region_mesh) == str:
+            region_mesh = RegionMeshRedux(mesh_path=region_mesh)
+
         self.region_mesh = region_mesh
         self.rng = rng
 
@@ -20,7 +23,7 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
         return inside_flag
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
+    def bend_morphology_OLD(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
         # TODO: Parent point idx is included if parent section is of same type as section!
         #       So we should not rotate the first point!
@@ -49,7 +52,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
                 coords = section.morphology_data.geometry[point_idx, :]
                 dist = self.region_mesh.distance_to_border(points=coords)
 
-                P = 1 / (1 + np.exp(-k * dist))
+                P = 1 / (1 + np.exp(-dist/k))
 
                 if self.rng.uniform(1) < P:
                     # We need to randomize new rotation matrix
@@ -62,7 +65,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
                     candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
 
-                    P_candidate = np.divide(1, 1 + np.exp(-k * candidate_dist))
+                    P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
                     picked_idx = self.rng.choice(n_random, p=P_candidate)
 
                     new_coords = candidate_pos[picked_idx, :]
@@ -92,6 +95,81 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
             parent_rotation_matrices[point_idx] = rotation_matrix
 
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=20e-6, n_random=10):
+
+        # k -- how early will the neuron start bending when it approaches the border
+
+        candidate_pos = np.zeros((n_random, 3))
+
+        parent_direction = dict()
+
+        old_rotation_representation = self.get_full_rotation_representation(morphology=morphology)
+        new_rotation_representation = dict()
+
+        for section in morphology.section_iterator():
+            if (section.section_id, section.section_type) in parent_direction:
+                parent_dir, parent_point = parent_direction[section.section_id, section.section_type]
+            else:
+                if morphology.rotation is not None:
+                    parent_dir = np.matmul(morphology.rotation, np.array([[1], [0], [0]])).T
+                else:
+                    parent_dir = np.array([[1, 0, 0]])
+
+                if morphology.position is not None:
+                    parent_point = morphology.position
+                else:
+                    parent_point = np.zeros((3, ))
+
+            rot_rep = old_rotation_representation[section.section_id, section.section_type]
+            new_rot_rep = []
+
+            # Loop over all points in section
+            for idx, (rotation, length) in enumerate(rot_rep):
+
+                try:
+                    segment_direction = rotation.apply(parent_dir)
+                    putative_point = segment_direction * length + parent_point
+                except:
+                    import traceback
+                    print(traceback.format_exc())
+                    import pdb
+                    pdb.set_trace()
+
+                # Check if point is too close to edge
+                dist = self.region_mesh.distance_to_border(points=putative_point)
+                P_keep = 1 / (1 + np.exp(-dist/k))
+
+                if self.rng.uniform(1) < P_keep:
+                    # We need to randomize new rotation matrix
+                    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
+                    angles = self.rng.uniform(size=(n_random, 3), low=-0.2, high=0.2)  # Angles in radians
+                    avoidance_rotation = Rotation.from_euler(seq="XYZ", angles=angles)
+
+                    for idx, av_rot in enumerate(avoidance_rotation):
+
+                        candidate_pos[idx, :] = parent_point + length * (av_rot*rotation).apply(vectors=parent_direction)
+
+                    candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
+
+                    P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
+                    picked_idx = self.rng.choice(n_random, p=P_candidate)
+
+                    new_rot = avoidance_rotation[picked_idx] * rotation
+                    new_rot_rep.append((new_rot, length))
+                    segment_direction = new_rot.apply(parent_direction)
+                else:
+                    new_rot_rep.append((rotation, length))
+
+                parent_point = segment_direction * length + parent_point
+                parent_dir = segment_direction
+
+            for child_id, child_type in section.child_section_id.T:
+                parent_direction[child_id, child_type] = (parent_dir, parent_point)
+
+            new_rotation_representation[section.section_id, section.section_type] = new_rot_rep
+
+        return new_rotation_representation
+
     def get_full_rotation_representation(self, morphology: MorphologyData):
 
         rotation_representation = dict()
@@ -130,12 +208,12 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation):
             if (section.section_id, section.section_type) in parent_direction:
                 parent_dir, parent_pos = parent_direction[section.section_id, section.section_type]
             else:
-                if morphology.rotation:
-                    parent_dir = np.matmul(morphology.rotation, np.array([[1, 0, 0]]))
+                if morphology.rotation is not None:
+                    parent_dir = np.matmul(morphology.rotation, np.array([[1, 0, 0]]).T).T
                 else:
                     parent_dir = np.array([[1, 0, 0]])
 
-                if morphology.position:
+                if morphology.position is not None:
                     parent_pos = morphology.position
                 else:
                     parent_pos = np.zeros((3, ))
@@ -257,9 +335,30 @@ def test_rotation_representation():
         print(f"Geometry matches")
 
 
+def test_bending():
+
+    file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
+    mesh_path = "../data/mesh/Striatum-d-right.obj"
+
+    md = MorphologyData(swc_file=file_path)
+    bm = BendMorphologies(mesh_path, rng=np.random.default_rng())
+
+    before = md.clone(position=np.array([7300, 4000, -0.8])*1e-6, rotation=np.eye(3))
+    after = md.clone(position=np.array([7300, 4000, -0.8])*1e-6, rotation=np.eye(3))
+
+    new_rot_rep = bm.bend_morphology(after)
+    new_coord = bm.apply_rotation(after, new_rot_rep)
+    after.geometry[:, :3] = new_coord
+
+    before.plot()
+    after.plot()
+
+
 if __name__ == "__main__":
 
-    test_rotation_representation()
+    # test_rotation_representation()
+
+    test_bending()
 
     import pdb
     pdb.set_trace()
\ No newline at end of file

From 1d14f156c1dd622ecb36cfd7898979a4df1f9f59 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Tue, 17 Oct 2023 15:39:06 +0200
Subject: [PATCH 21/43] In progress

---
 snudda/neurons/neuron_morphology_extended.py |  3 ++
 snudda/place/bend_morphologies.py            | 25 +++++++---
 snudda/place/region_mesh_redux.py            | 51 +++++++++++++++++++-
 3 files changed, 70 insertions(+), 9 deletions(-)

diff --git a/snudda/neurons/neuron_morphology_extended.py b/snudda/neurons/neuron_morphology_extended.py
index 0cd8c8fe9..9cf85cfdc 100644
--- a/snudda/neurons/neuron_morphology_extended.py
+++ b/snudda/neurons/neuron_morphology_extended.py
@@ -139,6 +139,9 @@ def section_iterator(self, section_type=None, subtree=None):
                 for section in subtree.section_iterator(section_type=section_type):
                     yield section
 
+    def get_morphology(self):
+        return self.morphology_data["neuron"]
+
     def section_iterator_selective(self, section_type, section_id, subtree="neuron"):
         return self.morphology_data[subtree].section_iterator_selective(section_type=section_type,
                                                                         section_id=section_id)
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index c81144458..8f3c5dd20 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -340,18 +340,27 @@ def test_bending():
     file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
     mesh_path = "../data/mesh/Striatum-d-right.obj"
 
-    md = MorphologyData(swc_file=file_path)
+    nm = NeuronMorphologyExtended(swc_filename=file_path)
+
+    # md = MorphologyData(swc_file=file_path)
     bm = BendMorphologies(mesh_path, rng=np.random.default_rng())
 
-    before = md.clone(position=np.array([7300, 4000, -0.8])*1e-6, rotation=np.eye(3))
-    after = md.clone(position=np.array([7300, 4000, -0.8])*1e-6, rotation=np.eye(3))
+    pos = np.array([7300, 4000, -0.8])*1e-6
+    pos = np.array([0.006, 0.004, 0.0021])
+    before = nm.clone(position=pos, rotation=np.eye(3))
+    after = nm.clone(position=pos, rotation=np.eye(3))
+
+    new_rot_rep = bm.bend_morphology(after.get_morphology())
+    new_coord = bm.apply_rotation(after.get_morphology(), new_rot_rep)
+    after.get_morphology().geometry[:, :3] = new_coord
 
-    new_rot_rep = bm.bend_morphology(after)
-    new_coord = bm.apply_rotation(after, new_rot_rep)
-    after.geometry[:, :3] = new_coord
+    before.plot_neuron()
+    after.plot_neuron()
 
-    before.plot()
-    after.plot()
+    bm.region_mesh.plot(neurons=[after], show_axis=True, show_faces=False)
+
+    import pdb
+    pdb.set_trace()
 
 
 if __name__ == "__main__":
diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index 51931311f..89fba2937 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -49,6 +49,56 @@ def distance_to_border(self, points):
 
         return signed_distance
 
+    def plot(self, line_set=None, neurons=None, show_axis=False, show_faces=True):
+
+        # Press w to see wireframe...
+
+        if show_faces:
+            plot_list = [self.mesh]
+        else:
+            # Just plot wireframe
+            plot_list = [o3d.geometry.LineSet.create_from_triangle_mesh(self.mesh)]
+
+        if line_set:
+            plot_list += line_set
+
+        if neurons:
+            plot_list += self.get_line_set(neurons)
+
+        if show_axis:
+            # The x, y, z axis will be rendered as red, green, and blue arrows respectively.
+            axis = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1000e-6, origin=[0, 0, 0])
+            plot_list += [axis]
+
+        o3d.visualization.draw_geometries(plot_list)
+
+    def get_line_set(self, neurons):
+
+        if type(neurons) != list:
+            return self.get_line_set(neurons=[neurons])
+
+        line_sets = []
+
+        for neuron in neurons:
+
+            morph_data = neuron.morphology_data["neuron"]
+
+            lines = []
+            for section in morph_data.section_iterator():
+                if len(section.point_idx) > 1:
+                    for start_point, end_point in zip(section.point_idx[:-1], section.point_idx[1:]):
+                        lines.append([start_point, end_point])
+
+            if len(lines) > 0:
+                #import pdb
+                #pdb.set_trace()
+                line_set = o3d.geometry.LineSet()
+                line_set.points = o3d.utility.Vector3dVector(morph_data.geometry[:, :3])
+                line_set.lines = o3d.utility.Vector2iVector(lines)
+
+                line_sets.append(line_set)
+
+        return line_sets
 
 class NeuronPlacer:
 
@@ -257,7 +307,6 @@ def __init__(self):
 # Remove the worst offenders -- how?
 
 
-
 if __name__ == "__main__":
 
     mesh_path="../data/mesh/Striatum-d-right.obj"

From 76f771baba7e23a12fa6f28ed82df7c2db4d2bd0 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Wed, 18 Oct 2023 10:31:28 +0200
Subject: [PATCH 22/43] Dendrite now bends, does axon also bend?

---
 snudda/place/bend_morphologies.py | 32 +++++++++++++++++++++----------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 8f3c5dd20..d49db49ba 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -95,7 +95,7 @@ def bend_morphology_OLD(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
             parent_rotation_matrices[point_idx] = rotation_matrix
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=20e-6, n_random=10):
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=10e-6, n_random=5):
 
         # k -- how early will the neuron start bending when it approaches the border
 
@@ -136,27 +136,34 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=20e-6, n_rando
                     pdb.set_trace()
 
                 # Check if point is too close to edge
-                dist = self.region_mesh.distance_to_border(points=putative_point)
-                P_keep = 1 / (1 + np.exp(-dist/k))
+                dist = self.region_mesh.distance_to_border(points=putative_point)[0]
+                P_move = 1 / (1 + np.exp(-dist/k))
+
+                if self.rng.uniform() < P_move:
 
-                if self.rng.uniform(1) < P_keep:
                     # We need to randomize new rotation matrix
                     # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
                     angles = self.rng.uniform(size=(n_random, 3), low=-0.2, high=0.2)  # Angles in radians
                     avoidance_rotation = Rotation.from_euler(seq="XYZ", angles=angles)
 
                     for idx, av_rot in enumerate(avoidance_rotation):
-
-                        candidate_pos[idx, :] = parent_point + length * (av_rot*rotation).apply(vectors=parent_direction)
+                        candidate_pos[idx, :] = parent_point + length * (av_rot*rotation).apply(vectors=parent_dir)
 
                     candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
 
-                    P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
-                    picked_idx = self.rng.choice(n_random, p=P_candidate)
+                    if False:
+                        P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
+                        P_candidate = P_candidate / np.sum(P_candidate)
+                        picked_idx = self.rng.choice(n_random, p=P_candidate)
+                    else:
+                        picked_idx = np.argsort(candidate_dist)[0]
 
                     new_rot = avoidance_rotation[picked_idx] * rotation
                     new_rot_rep.append((new_rot, length))
-                    segment_direction = new_rot.apply(parent_direction)
+                    segment_direction = new_rot.apply(parent_dir)
+
+                    #import pdb
+                    #pdb.set_trace()
                 else:
                     new_rot_rep.append((rotation, length))
 
@@ -346,7 +353,7 @@ def test_bending():
     bm = BendMorphologies(mesh_path, rng=np.random.default_rng())
 
     pos = np.array([7300, 4000, -0.8])*1e-6
-    pos = np.array([0.006, 0.004, 0.0021])
+    pos = np.array([0.006, 0.004, 0.00205])
     before = nm.clone(position=pos, rotation=np.eye(3))
     after = nm.clone(position=pos, rotation=np.eye(3))
 
@@ -354,9 +361,14 @@ def test_bending():
     new_coord = bm.apply_rotation(after.get_morphology(), new_rot_rep)
     after.get_morphology().geometry[:, :3] = new_coord
 
+    change = np.sum(np.abs(before.get_morphology().geometry[:, :3] - after.get_morphology().geometry[:, :3]))
+    print(f"Change = {change}")
+
     before.plot_neuron()
     after.plot_neuron()
 
+    bm.region_mesh.plot(neurons=[before], show_axis=True, show_faces=False)
+
     bm.region_mesh.plot(neurons=[after], show_axis=True, show_faces=False)
 
     import pdb

From b876b51afa177cb7bb456b37371f2728cbddc67d Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Wed, 18 Oct 2023 13:35:46 +0200
Subject: [PATCH 23/43] Bendy

---
 snudda/neurons/morphology_data.py |  2 +-
 snudda/place/bend_morphologies.py | 48 ++++++++++++++++++++++++++++++-
 2 files changed, 48 insertions(+), 2 deletions(-)

diff --git a/snudda/neurons/morphology_data.py b/snudda/neurons/morphology_data.py
index ebd37d7df..85838b599 100644
--- a/snudda/neurons/morphology_data.py
+++ b/snudda/neurons/morphology_data.py
@@ -179,7 +179,7 @@ class MorphologyData:
 
         Args:
             swc_file (str): Path to SWC file
-            parent_tree_info (tuple, optional): Specify subtree attachment point
+                parent_tree_info (tuple, optional): Specify subtree attachment point
                                                 (MorphologyData, parent_label, parent_point_idx, arc_factor)
 
     """
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index d49db49ba..81ca7d9d1 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -143,7 +143,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=10e-6, n_rando
 
                     # We need to randomize new rotation matrix
                     # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
-                    angles = self.rng.uniform(size=(n_random, 3), low=-0.2, high=0.2)  # Angles in radians
+                    angles = self.rng.uniform(size=(n_random, 3), low=-0.1, high=0.1)  # Angles in radians
                     avoidance_rotation = Rotation.from_euler(seq="XYZ", angles=angles)
 
                     for idx, av_rot in enumerate(avoidance_rotation):
@@ -319,6 +319,38 @@ def coordinate_representation(self, rotation_representation,
 
         return coords
 
+    def write_neuron(self, neuron: NeuronMorphologyExtended, output_file):
+
+        morphology = neuron.morphology_data["neuron"]
+        comment = f"{neuron.name} located at {neuron.position}"
+
+        self.write_swc(morphology=morphology, output_file=output_file, comment=comment)
+
+    def write_swc(self, morphology: MorphologyData, output_file, comment=None):
+        # We need to write file with micrometer units
+        # 0: compartment number (start from 1)
+        # 1: compartment type (1-soma, 2-axon, 3-dendrite)
+        # 2,3,4: x,y,z
+        # 5: r
+        # 6: parent compartment
+
+        swc_data = np.zeros((morphology.section_data.shape[0], 7))
+        swc_data[:, 0] = np.arange(1, swc_data.shape[0]+1)   # id, start from 1
+        swc_data[:, 1] = morphology.section_data[:, 2]       # type
+        swc_data[:, 2:5] = (morphology.geometry[:, :3] - morphology.geometry[0, :3]) * 1e6  # x,y,z,
+        swc_data[:, 5] = morphology.geometry[:, 3] * 1e6     # radie in micrometer
+        swc_data[:, 6] = morphology.section_data[:, 3] + 1   # parent compartment
+        swc_data[0, 6] = -1
+
+        with open(output_file, "wt") as f:
+            if comment:
+                f.write(f"#{comment}\n")
+
+            for row in swc_data:
+                f.write(f"{row[0]:.0f} {row[1]:.0f} {row[2]:.5f} {row[3]:.5f} {row[4]:.5f} {row[5]:.5f} {row[6]:.0f}\n")
+
+        print(f"Wrote {output_file}")
+
 
 def test_rotation_representation():
 
@@ -341,6 +373,16 @@ def test_rotation_representation():
     else:
         print(f"Geometry matches")
 
+def test_write():
+
+    file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
+    md = MorphologyData(swc_file=file_path)
+    bm = BendMorphologies(None, rng=np.random.default_rng())
+    bm.write_swc(morphology=md, output_file="test-write.swc")
+
+    import pdb
+    pdb.set_trace()
+
 
 def test_bending():
 
@@ -364,6 +406,9 @@ def test_bending():
     change = np.sum(np.abs(before.get_morphology().geometry[:, :3] - after.get_morphology().geometry[:, :3]))
     print(f"Change = {change}")
 
+    bm.write_neuron(neuron=before, output_file="before-neuron.swc")
+    bm.write_neuron(neuron=after, output_file="after-neuron.swc")
+
     before.plot_neuron()
     after.plot_neuron()
 
@@ -377,6 +422,7 @@ def test_bending():
 
 if __name__ == "__main__":
 
+    # test_write()
     # test_rotation_representation()
 
     test_bending()

From b9e6de1298d2d7074ea0cef17a8d7fa10e081be8 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Wed, 18 Oct 2023 17:12:22 +0200
Subject: [PATCH 24/43] Integrating code in workflow, added stayInsideMesh
 option to neurons (it might get renamed)

---
 snudda/init/init.py               |  6 +++++-
 snudda/place/bend_morphologies.py | 32 +++++++++++++++++++++------
 snudda/place/place.py             | 36 +++++++++++++++++++++++++++++++
 3 files changed, 67 insertions(+), 7 deletions(-)

diff --git a/snudda/init/init.py b/snudda/init/init.py
index 6b02ca5e3..7083e3419 100644
--- a/snudda/init/init.py
+++ b/snudda/init/init.py
@@ -409,7 +409,8 @@ def add_neurons(self, name,
                     axon_config=None,
                     model_type="neuron",
                     volume_id=None,
-                    rotation_mode="random"):
+                    rotation_mode="random",
+                    stay_inside=False):
 
         if num_neurons <= 0:
             return
@@ -563,6 +564,9 @@ def add_neurons(self, name,
             if axon_config is not None:
                 cell_data["axonConfig"] = axon_config
 
+            if stay_inside:
+                cell_data["stayInsideMesh"] = True
+
             self.network_data["Neurons"][unique_name] = cell_data
 
     def get_morphologies(self, neuron_dir):
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 81ca7d9d1..6fc5bae97 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -95,7 +95,7 @@ def bend_morphology_OLD(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
             parent_rotation_matrices[point_idx] = rotation_matrix
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=10e-6, n_random=5):
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_random=20):
 
         # k -- how early will the neuron start bending when it approaches the border
 
@@ -105,6 +105,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=10e-6, n_rando
 
         old_rotation_representation = self.get_full_rotation_representation(morphology=morphology)
         new_rotation_representation = dict()
+        morphology_changed = False
 
         for section in morphology.section_iterator():
             if (section.section_id, section.section_type) in parent_direction:
@@ -141,24 +142,30 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=10e-6, n_rando
 
                 if self.rng.uniform() < P_move:
 
+                    morphology_changed = True
+
                     # We need to randomize new rotation matrix
                     # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
                     angles = self.rng.uniform(size=(n_random, 3), low=-0.1, high=0.1)  # Angles in radians
-                    avoidance_rotation = Rotation.from_euler(seq="XYZ", angles=angles)
+                    avoidance_rotations = Rotation.from_euler(seq="XYZ", angles=angles)
 
-                    for idx, av_rot in enumerate(avoidance_rotation):
+                    for idx, av_rot in enumerate(avoidance_rotations):
                         candidate_pos[idx, :] = parent_point + length * (av_rot*rotation).apply(vectors=parent_dir)
 
                     candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
 
+                    #import pdb
+                    #pdb.set_trace()
+
                     if False:
                         P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
                         P_candidate = P_candidate / np.sum(P_candidate)
                         picked_idx = self.rng.choice(n_random, p=P_candidate)
                     else:
+                        # We want the smallest (or most negative) distance
                         picked_idx = np.argsort(candidate_dist)[0]
 
-                    new_rot = avoidance_rotation[picked_idx] * rotation
+                    new_rot = avoidance_rotations[picked_idx] * rotation
                     new_rot_rep.append((new_rot, length))
                     segment_direction = new_rot.apply(parent_dir)
 
@@ -175,7 +182,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=10e-6, n_rando
 
             new_rotation_representation[section.section_id, section.section_type] = new_rot_rep
 
-        return new_rotation_representation
+        return new_rotation_representation, morphology_changed
 
     def get_full_rotation_representation(self, morphology: MorphologyData):
 
@@ -351,6 +358,19 @@ def write_swc(self, morphology: MorphologyData, output_file, comment=None):
 
         print(f"Wrote {output_file}")
 
+    def edge_avoiding_morphology(self, swc_file, new_file):
+
+        md = MorphologyData(swc_file=swc_file)
+        rot_rep, morphology_changed = self.bend_morphology(md)
+
+        if morphology_changed:
+            new_coord = self.apply_rotation(md, rot_rep)
+            md.geometry[:, :3] = new_coord
+            self.write_swc(morphology=md, swc_file=new_file)
+            return new_file
+
+        # Returns None if morphology was not changed
+        return None
 
 def test_rotation_representation():
 
@@ -399,7 +419,7 @@ def test_bending():
     before = nm.clone(position=pos, rotation=np.eye(3))
     after = nm.clone(position=pos, rotation=np.eye(3))
 
-    new_rot_rep = bm.bend_morphology(after.get_morphology())
+    new_rot_rep, _ = bm.bend_morphology(after.get_morphology())
     new_coord = bm.apply_rotation(after.get_morphology(), new_rot_rep)
     after.get_morphology().geometry[:, :3] = new_coord
 
diff --git a/snudda/place/place.py b/snudda/place/place.py
index de8b71f9e..ee78775c3 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -91,6 +91,8 @@ def __init__(self,
 
         self.network_path = network_path
         self.config_file = config_file
+        self.config = None
+
         self.axon_config_cache = None
 
         self.snudda_data = get_snudda_data(snudda_data=snudda_data,
@@ -152,6 +154,7 @@ def place(self):
         """ Place neurons in 3D space. """
 
         self.parse_config()
+        self.avoid_edges()
         self.write_data()
 
     ############################################################################
@@ -544,6 +547,7 @@ def parse_config(self, config_file=None, resort_neurons=True):
                              config=config)
 
         self.config_file = config_file
+        self.config = config
 
         # We reorder neurons, sorting their IDs after position
         # -- UPDATE: Now we spatial cluster neurons depending on number of workers
@@ -560,6 +564,38 @@ def parse_config(self, config_file=None, resort_neurons=True):
 
     ############################################################################
 
+    def avoid_edges(self):
+
+        from snudda.place.bend_morphologies import BendMorphologies
+
+        bend_morph = dict()
+        bend_morph_path = os.path.join(self.network_path, "modified_morphologies")
+
+        if not os.path.isdir(bend_morph_path):
+            os.mkdir(bend_morph_path)
+
+        for neuron in self.neurons:
+            config = self.config["Neurons"][neuron.name]
+
+            if "stayInsideMesh" in config and config["stayInsideMesh"]:
+                volume_id = config["volumeID"]
+
+                if volume_id not in bend_morph:
+                    mesh_file = self.config["Volume"][volume_id]["meshFile"]
+                    bend_morph[volume_id] = BendMorphologies(region_mesh=mesh_file)
+
+                # Returns None if unchanged
+                new_morph_name = os.path.join(bend_morph_path, f"{neuron.name}-{neuron.neuron_id}")
+                new_morphology = bend_morph[volume_id].edge_avoiding_morphology(swc_file=neuron.swc_filename,
+                                                                                new_file=new_morph_name)
+
+                if new_morphology:
+                    # Replace the original morphology with the warped morphology, morphology includes rotation
+                    neuron.swc_filename = new_morphology
+                    neuron.rotation = np.eye(3)
+
+    ############################################################################
+
     def generate_extra_axon_info(self, source_neuron, position, config, rng):
 
         axon_info = []

From 47a02a5389bc1dfd90e93726134e4f836b4a0148 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Thu, 19 Oct 2023 14:21:37 +0200
Subject: [PATCH 25/43] Fixed rotation representation

---
 snudda/place/bend_morphologies.py | 52 ++++++++++++++++++++++---------
 1 file changed, 38 insertions(+), 14 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 6fc5bae97..7ca1b099a 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -95,7 +95,7 @@ def bend_morphology_OLD(self, morphology: NeuronMorphologyExtended, k=50e-6):
 
             parent_rotation_matrices[point_idx] = rotation_matrix
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_random=20):
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_random=5):
 
         # k -- how early will the neuron start bending when it approaches the border
 
@@ -136,10 +136,13 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                     import pdb
                     pdb.set_trace()
 
+                # TODO: This check can be cached (assuming no parent segment have been rotated, track that)
+
                 # Check if point is too close to edge
                 dist = self.region_mesh.distance_to_border(points=putative_point)[0]
                 P_move = 1 / (1 + np.exp(-dist/k))
 
+                # Cache the random numbers for segments in the section...
                 if self.rng.uniform() < P_move:
 
                     morphology_changed = True
@@ -149,14 +152,11 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                     angles = self.rng.uniform(size=(n_random, 3), low=-0.1, high=0.1)  # Angles in radians
                     avoidance_rotations = Rotation.from_euler(seq="XYZ", angles=angles)
 
-                    for idx, av_rot in enumerate(avoidance_rotations):
-                        candidate_pos[idx, :] = parent_point + length * (av_rot*rotation).apply(vectors=parent_dir)
+                    for idx2, av_rot in enumerate(avoidance_rotations):
+                        candidate_pos[idx2, :] = parent_point + length * (av_rot*rotation).apply(vectors=parent_dir)
 
                     candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
 
-                    #import pdb
-                    #pdb.set_trace()
-
                     if False:
                         P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
                         P_candidate = P_candidate / np.sum(P_candidate)
@@ -165,10 +165,23 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                         # We want the smallest (or most negative) distance
                         picked_idx = np.argsort(candidate_dist)[0]
 
-                    new_rot = avoidance_rotations[picked_idx] * rotation
+                    #if avoidance_rotations[picked_idx].magnitude() > 0.3:
+                    #    print(f"Magnitude of rotation is: {avoidance_rotations[picked_idx].magnitude()}")
+                    #    import pdb
+                    #    pdb.set_trace()
+
+                    new_rot = avoidance_rotations[picked_idx]*rotation
                     new_rot_rep.append((new_rot, length))
                     segment_direction = new_rot.apply(parent_dir)
 
+                    print(f"Avoidance rotation, magnitude {avoidance_rotations[picked_idx].magnitude()*180/np.pi} -- orig {rotation.magnitude()*180/np.pi}  -- total {new_rot.magnitude()*180/np.pi} ID: {section.section_id} ({idx})")
+
+
+                    if False and new_rot.magnitude() > 0.5 and not (parent_dir[0,0] == 1 and parent_dir[0,1] == 0 and parent_dir[0,2] == 0):
+                        print(f"Magnitude of final rotation is: {new_rot.magnitude()}")
+                        import pdb
+                        pdb.set_trace()
+
                     #import pdb
                     #pdb.set_trace()
                 else:
@@ -191,7 +204,7 @@ def get_full_rotation_representation(self, morphology: MorphologyData):
 
         for section in morphology.section_iterator():
 
-            if (section.section_id, section.section_type) in parent_direction:
+            if (section.section_id, section.section_type) in parent_direction.keys():
                 parent_dir = parent_direction[section.section_id, section.section_type]
             else:
                 parent_dir = np.array([[1, 0, 0]])
@@ -261,7 +274,8 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
         """ Represent each section as a series of length of segment, and rotations relative the parent segment."""
 
         if parent_direction is None:
-            parent_direction = np.array([[1, 0, 0]])
+            # parent_direction = np.array([[1, 0, 0]])
+            parent_direction = np.array([1, 0, 0])
 
         rotations_and_length = []
         parent_direction = parent_direction / np.linalg.norm(parent_direction)
@@ -282,16 +296,24 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
         delta_direction = delta / delta_length.reshape((delta.shape[0], 1))
 
         for segment_direction, segment_length in zip(delta_direction, delta_length):
-            segment_direction = segment_direction.reshape((1, 3))
+            # segment_direction = segment_direction.reshape((1, 3))
             try:
-                rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
+                # Calculate axis and angle of rotation
+                axis = np.cross(parent_direction, segment_direction)
+                angle = np.arccos(np.dot(parent_direction, segment_direction))
+                rotation = Rotation.from_rotvec(angle * axis)
+                # rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
+
+                # if rotation.magnitude() > np.pi/2 and (parent_direction != np.array([1,0,0])).all():
+                #     print(f"Strange rotation, magnitude: {rotation.magnitude()}")
+                #     import pdb
+                #     pdb.set_trace()
             except:
                 import traceback
                 print(traceback.format_exc())
                 import pdb
                 pdb.set_trace()
 
-
             rotations_and_length.append((rotation, segment_length))
             parent_direction = segment_direction
 
@@ -372,6 +394,7 @@ def edge_avoiding_morphology(self, swc_file, new_file):
         # Returns None if morphology was not changed
         return None
 
+
 def test_rotation_representation():
 
     file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
@@ -407,15 +430,16 @@ def test_write():
 def test_bending():
 
     file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
+    # file_path = "delme3.swc"
     mesh_path = "../data/mesh/Striatum-d-right.obj"
 
     nm = NeuronMorphologyExtended(swc_filename=file_path)
 
     # md = MorphologyData(swc_file=file_path)
-    bm = BendMorphologies(mesh_path, rng=np.random.default_rng())
+    bm = BendMorphologies(mesh_path, rng=np.random.default_rng(1))
 
-    pos = np.array([7300, 4000, -0.8])*1e-6
     pos = np.array([0.006, 0.004, 0.00205])
+
     before = nm.clone(position=pos, rotation=np.eye(3))
     after = nm.clone(position=pos, rotation=np.eye(3))
 

From adca95f5b515fdae05cbbf6b86738c1ea81c4e69 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Thu, 19 Oct 2023 14:58:08 +0200
Subject: [PATCH 26/43] Profiling, trying to speed up

---
 snudda/place/bend_morphologies.py | 38 ++++++++++++++++++++-----------
 1 file changed, 25 insertions(+), 13 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 7ca1b099a..b14804a4d 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -109,7 +109,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
 
         for section in morphology.section_iterator():
             if (section.section_id, section.section_type) in parent_direction:
-                parent_dir, parent_point = parent_direction[section.section_id, section.section_type]
+                parent_dir, parent_point, parent_dist, parent_moved = parent_direction[section.section_id, section.section_type]
             else:
                 if morphology.rotation is not None:
                     parent_dir = np.matmul(morphology.rotation, np.array([[1], [0], [0]])).T
@@ -121,6 +121,9 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                 else:
                     parent_point = np.zeros((3, ))
 
+                parent_dist = self.region_mesh.distance_to_border(points=parent_point.reshape((1,3)))[0]
+                parent_moved = False
+
             rot_rep = old_rotation_representation[section.section_id, section.section_type]
             new_rot_rep = []
 
@@ -143,9 +146,10 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                 P_move = 1 / (1 + np.exp(-dist/k))
 
                 # Cache the random numbers for segments in the section...
-                if self.rng.uniform() < P_move:
+                if dist > parent_dist and self.rng.uniform() < P_move:
 
                     morphology_changed = True
+                    parent_moved = True
 
                     # We need to randomize new rotation matrix
                     # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
@@ -164,6 +168,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                     else:
                         # We want the smallest (or most negative) distance
                         picked_idx = np.argsort(candidate_dist)[0]
+                        dist = candidate_dist[picked_idx]
 
                     #if avoidance_rotations[picked_idx].magnitude() > 0.3:
                     #    print(f"Magnitude of rotation is: {avoidance_rotations[picked_idx].magnitude()}")
@@ -176,7 +181,6 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
 
                     print(f"Avoidance rotation, magnitude {avoidance_rotations[picked_idx].magnitude()*180/np.pi} -- orig {rotation.magnitude()*180/np.pi}  -- total {new_rot.magnitude()*180/np.pi} ID: {section.section_id} ({idx})")
 
-
                     if False and new_rot.magnitude() > 0.5 and not (parent_dir[0,0] == 1 and parent_dir[0,1] == 0 and parent_dir[0,2] == 0):
                         print(f"Magnitude of final rotation is: {new_rot.magnitude()}")
                         import pdb
@@ -189,9 +193,10 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
 
                 parent_point = segment_direction * length + parent_point
                 parent_dir = segment_direction
+                parent_dist = dist
 
             for child_id, child_type in section.child_section_id.T:
-                parent_direction[child_id, child_type] = (parent_dir, parent_point)
+                parent_direction[child_id, child_type] = (parent_dir, parent_point, parent_dist, parent_moved)
 
             new_rotation_representation[section.section_id, section.section_type] = new_rot_rep
 
@@ -453,23 +458,30 @@ def test_bending():
     bm.write_neuron(neuron=before, output_file="before-neuron.swc")
     bm.write_neuron(neuron=after, output_file="after-neuron.swc")
 
-    before.plot_neuron()
-    after.plot_neuron()
-
-    bm.region_mesh.plot(neurons=[before], show_axis=True, show_faces=False)
+    # before.plot_neuron()
+    # after.plot_neuron()
 
-    bm.region_mesh.plot(neurons=[after], show_axis=True, show_faces=False)
+    # bm.region_mesh.plot(neurons=[before], show_axis=True, show_faces=False)
+    # bm.region_mesh.plot(neurons=[after], show_axis=True, show_faces=False)
 
-    import pdb
-    pdb.set_trace()
+    # import pdb
+    # pdb.set_trace()
 
 
 if __name__ == "__main__":
 
+    import cProfile
+
     # test_write()
     # test_rotation_representation()
 
+    profiler = cProfile.Profile()
+    profiler.enable()
+
     test_bending()
 
-    import pdb
-    pdb.set_trace()
\ No newline at end of file
+    profiler.disable()
+    profiler.print_stats(sort='cumulative')
+
+    # import pdb
+    # pdb.set_trace()
\ No newline at end of file

From 9033221a05542abc63f2e6237a7561d9ed8839fd Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Thu, 19 Oct 2023 15:22:36 +0200
Subject: [PATCH 27/43] Cleanup

---
 snudda/place/bend_morphologies.py | 90 ++++++-------------------------
 1 file changed, 16 insertions(+), 74 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index b14804a4d..aa11d5362 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -23,82 +23,17 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
         return inside_flag
 
-    def bend_morphology_OLD(self, morphology: NeuronMorphologyExtended, k=50e-6):
-
-        # TODO: Parent point idx is included if parent section is of same type as section!
-        #       So we should not rotate the first point!
-
-        # k -- decay constant
-        n_random = 10
-        candidate_pos = np.zeros((n_random, 3))
-
-        parent_rotation_matrices = dict()  # indexed by parent_point_idx in SectionMetaData
-
-        # Iterate over all parts of neuron
-        for section in morphology.section_iterator():
-
-            # We need to track the rotation of each point, in particular save rotations
-            # for each branch point, so that all children can start with that rotation
-
-            if section.parent_section_idx in parent_rotation_matrices:
-                rotation_matrix = parent_rotation_matrices[section.parent_section_idx]
-            else:
-                rotation_matrix = np.eye(3)
-
-            parent_coords = section.morphology_data.position[section.parent_section_idx, :]
-
-            # Loop over all points in section
-            for point_idx in section.point_idx:
-                coords = section.morphology_data.geometry[point_idx, :]
-                dist = self.region_mesh.distance_to_border(points=coords)
-
-                P = 1 / (1 + np.exp(-dist/k))
-
-                if self.rng.uniform(1) < P:
-                    # We need to randomize new rotation matrix
-                    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
-                    angles = self.rng.uniform(size=(n_random, 3), low=-0.2, high=0.2)  # Angles in radians
-                    rots = Rotation.from_euler(seq="XYZ", angles=angles)
-
-                    for idx, rot in enumerate(rots):
-                        candidate_pos[idx, :] = parent_coords[idx, :] + rots.apply(vectors=coords-parent_coords)
-
-                    candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
-
-                    P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
-                    picked_idx = self.rng.choice(n_random, p=P_candidate)
-
-                    new_coords = candidate_pos[picked_idx, :]
-
-                    picked_rotation = rots[picked_idx]
-                    rotation_point = parent_coords
-
-                    # TODO: We also need to apply this rotation to ALL the other points in this section,
-                    #       and to all child sections of this branch.
-
-            else:
-                # Keep the old coords
-                new_coords = coords
-
-
-
-                # Old point is next points parent
-                parent_coords = new_coords
-
-            # Gradient is calculated based on distance to mesh
-            # Calculate delta_gradient = gradient_self - gradient_parent
-            # Amount of angle to bend f(delta_gradient)
-            # Randomize the rotation matrix
-
-            # After section is done, store the last rotation matrix, so its children can get their parent rotation
-            # store in dictionary?
-
-            parent_rotation_matrices[point_idx] = rotation_matrix
-
     def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_random=5):
 
         # k -- how early will the neuron start bending when it approaches the border
 
+        # Check distance to border for all points, negative distance means inside
+        all_original_dist = self.region_mesh.distance_to_border(morphology.geometry[:, :3])
+        if (all_original_dist < 0).all():
+            # Morphology entirely inside mesh, nothing to do
+            return None, False
+
+        # We randomize n_random points, and store candidates here, and pick "best" one
         candidate_pos = np.zeros((n_random, 3))
 
         parent_direction = dict()
@@ -121,12 +56,14 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                 else:
                     parent_point = np.zeros((3, ))
 
-                parent_dist = self.region_mesh.distance_to_border(points=parent_point.reshape((1,3)))[0]
+                parent_dist = self.region_mesh.distance_to_border(points=parent_point.reshape((1, 3)))[0]
                 parent_moved = False
 
             rot_rep = old_rotation_representation[section.section_id, section.section_type]
             new_rot_rep = []
 
+            section_dist = all_original_dist[section.point_idx]
+
             # Loop over all points in section
             for idx, (rotation, length) in enumerate(rot_rep):
 
@@ -142,7 +79,12 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                 # TODO: This check can be cached (assuming no parent segment have been rotated, track that)
 
                 # Check if point is too close to edge
-                dist = self.region_mesh.distance_to_border(points=putative_point)[0]
+                if parent_moved:
+                    dist = self.region_mesh.distance_to_border(points=putative_point)[0]
+                else:
+                    # Parent has not moved, so use stored original distance
+                    dist = section_dist[idx]
+
                 P_move = 1 / (1 + np.exp(-dist/k))
 
                 # Cache the random numbers for segments in the section...

From 9ae9c507f3fb273dced67fb208f066ff3d2c3661 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Thu, 19 Oct 2023 16:30:48 +0200
Subject: [PATCH 28/43] Work in progress, testing on cube, starting to work on
 parallell  bending

---
 snudda/place/bend_morphologies.py | 16 +---------
 snudda/place/place.py             | 53 +++++++++++++++++++++++++++----
 2 files changed, 48 insertions(+), 21 deletions(-)

diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index aa11d5362..b065bb2ca 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -112,24 +112,10 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                         picked_idx = np.argsort(candidate_dist)[0]
                         dist = candidate_dist[picked_idx]
 
-                    #if avoidance_rotations[picked_idx].magnitude() > 0.3:
-                    #    print(f"Magnitude of rotation is: {avoidance_rotations[picked_idx].magnitude()}")
-                    #    import pdb
-                    #    pdb.set_trace()
-
                     new_rot = avoidance_rotations[picked_idx]*rotation
                     new_rot_rep.append((new_rot, length))
                     segment_direction = new_rot.apply(parent_dir)
 
-                    print(f"Avoidance rotation, magnitude {avoidance_rotations[picked_idx].magnitude()*180/np.pi} -- orig {rotation.magnitude()*180/np.pi}  -- total {new_rot.magnitude()*180/np.pi} ID: {section.section_id} ({idx})")
-
-                    if False and new_rot.magnitude() > 0.5 and not (parent_dir[0,0] == 1 and parent_dir[0,1] == 0 and parent_dir[0,2] == 0):
-                        print(f"Magnitude of final rotation is: {new_rot.magnitude()}")
-                        import pdb
-                        pdb.set_trace()
-
-                    #import pdb
-                    #pdb.set_trace()
                 else:
                     new_rot_rep.append((rotation, length))
 
@@ -335,7 +321,7 @@ def edge_avoiding_morphology(self, swc_file, new_file):
         if morphology_changed:
             new_coord = self.apply_rotation(md, rot_rep)
             md.geometry[:, :3] = new_coord
-            self.write_swc(morphology=md, swc_file=new_file)
+            self.write_swc(morphology=md, output_file=new_file)
             return new_file
 
         # Returns None if morphology was not changed
diff --git a/snudda/place/place.py b/snudda/place/place.py
index ee78775c3..247a7a24f 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -52,7 +52,7 @@ def __init__(self,
                  h5libver=None,
                  raytrace_borders=False,
                  random_seed=None,
-                 griddata_interpolation=False):  # Setting this to true is 5x slower
+                 griddata_interpolation=False):  # Setting this to true is 5x slower -- obsolete?
 
         """
         Constructor.
@@ -154,7 +154,8 @@ def place(self):
         """ Place neurons in 3D space. """
 
         self.parse_config()
-        self.avoid_edges()
+        self.avoid_edges_parallel()
+        # self.avoid_edges()
         self.write_data()
 
     ############################################################################
@@ -564,7 +565,36 @@ def parse_config(self, config_file=None, resort_neurons=True):
 
     ############################################################################
 
-    def avoid_edges(self):
+    def avoid_edges_parallel(self):
+
+        if self.d_view is None:
+            return self.avoid_edges()
+
+        # Make random permutation of neurons, to spread out the edge neurons
+        unsorted_neuron_id = self.random_generator.permutation(len(self.neurons))
+        ss = self.random_generator.SeedSequence()
+
+        worker_random_seed = ss.generate_state(len(self.d_view))
+
+        self.d_view.scatter("worker_neuron_id", unsorted_neuron_id, block=True)
+        self.d_view.scatter("worker_random_seed", worker_random_seed, block=True)
+        self.dview.push({"config": self.config,
+                         "neurons": self.neurons})
+
+        cmd_str = f"sp = SnuddaPlace(config_file={self.config_file},network_path={self.network_path},snudda_data={self.snudda_data}, random_seed=worker_random_seed[0])"
+        self.d_view.execute(cmd_str)
+        cmd_str2 = f"sp.config = config; sp.neurons = neurons"
+        self.d_view.execute(cmd_str2)
+        cmd_str3 = f"modified_neurons = sp.avoid_edges(neuron_id=unsorted_neuron_id)"
+        self.d_view.execute(cmd_str3)
+        modified_neurons = self.d_view.gather("modified_neurons", block=True)
+
+        for neuron_id, new_morphology in modified_neurons:
+            # Replace the original morphology with the warped morphology, morphology includes rotation
+            self.neurons[neuron_id].swc_filename = new_morphology
+            self.neurons[neuron_id].rotation = np.eye(3)
+
+    def avoid_edges(self, neuron_id=None):
 
         from snudda.place.bend_morphologies import BendMorphologies
 
@@ -574,7 +604,14 @@ def avoid_edges(self):
         if not os.path.isdir(bend_morph_path):
             os.mkdir(bend_morph_path)
 
-        for neuron in self.neurons:
+        if neuron_id is None:
+            neurons = self.neurons
+        else:
+            neurons = self.neurons[neuron_id]
+
+        modified_morphologies = []
+
+        for neuron in neurons:
             config = self.config["Neurons"][neuron.name]
 
             if "stayInsideMesh" in config and config["stayInsideMesh"]:
@@ -582,10 +619,10 @@ def avoid_edges(self):
 
                 if volume_id not in bend_morph:
                     mesh_file = self.config["Volume"][volume_id]["meshFile"]
-                    bend_morph[volume_id] = BendMorphologies(region_mesh=mesh_file)
+                    bend_morph[volume_id] = BendMorphologies(region_mesh=mesh_file, rng=self.random_generator)
 
                 # Returns None if unchanged
-                new_morph_name = os.path.join(bend_morph_path, f"{neuron.name}-{neuron.neuron_id}")
+                new_morph_name = os.path.join(bend_morph_path, f"{neuron.name}-{neuron.neuron_id}.swc")
                 new_morphology = bend_morph[volume_id].edge_avoiding_morphology(swc_file=neuron.swc_filename,
                                                                                 new_file=new_morph_name)
 
@@ -594,6 +631,10 @@ def avoid_edges(self):
                     neuron.swc_filename = new_morphology
                     neuron.rotation = np.eye(3)
 
+                    modified_morphologies.append((neuron.neuron_id, new_morphology))
+
+        return modified_morphologies
+
     ############################################################################
 
     def generate_extra_axon_info(self, source_neuron, position, config, rng):

From 612fde5e3911218eaf159ed4828eaa0a36bcf172 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 20 Oct 2023 13:39:37 +0200
Subject: [PATCH 29/43] Integrating into place, serial

---
 snudda/detect/detect.py            | 20 +++++++++++++--
 snudda/neurons/neuron_prototype.py | 24 +++++++++++-------
 snudda/place/bend_morphologies.py  | 39 ++++++++++++++++++++++--------
 snudda/plotting/plot_network.py    | 12 ++++++---
 4 files changed, 71 insertions(+), 24 deletions(-)

diff --git a/snudda/detect/detect.py b/snudda/detect/detect.py
index 53462680f..682967ee9 100644
--- a/snudda/detect/detect.py
+++ b/snudda/detect/detect.py
@@ -27,9 +27,10 @@
 from numba import jit
 
 import snudda.utils.memory
+from snudda.neurons import NeuronMorphologyExtended
 from snudda.neurons.morphology_data import MorphologyData
 from snudda.utils import NumpyEncoder
-from snudda.utils.snudda_path import get_snudda_data
+from snudda.utils.snudda_path import get_snudda_data, snudda_parse_path
 from snudda.detect.projection_detection import ProjectionDetection
 from snudda.neurons.neuron_prototype import NeuronPrototype
 from snudda.utils.load import SnuddaLoad
@@ -2303,12 +2304,18 @@ def load_neuron(self, neuron_info, use_cache=True):
         if use_cache and neuron_id in self.neuron_cache:
             return self.neuron_cache[neuron_id]
 
+        if os.path.isfile(snudda_parse_path(neuron_info["morphology"], self.snudda_data)):
+            morphology_path = neuron_info["morphology"]
+        else:
+            morphology_path = None  # Get morpholog automatically from morphology_key
+
         # Clone prototype neuron (it is centred, and not rotated)
         neuron = self.prototype_neurons[neuron_info["name"]].clone(parameter_key=neuron_info["parameterKey"],
                                                                    morphology_key=neuron_info["morphologyKey"],
                                                                    modulation_key=neuron_info["modulationKey"],
                                                                    rotation=neuron_info["rotation"],
-                                                                   position=neuron_info["position"])
+                                                                   position=neuron_info["position"],
+                                                                   morphology_path=morphology_path)
 
         if "extraAxons" in neuron_info:
             for axon_name, axon_info in neuron_info["extraAxons"].items():
@@ -2705,6 +2712,11 @@ def find_min_max_coord(self, volume_id=None, neuron_idx=None):
                     try:
                         max_coord = np.maximum(max_coord, np.max(subtree.geometry[:, :3], axis=0))
                         min_coord = np.minimum(min_coord, np.min(subtree.geometry[:, :3], axis=0))
+
+                        if np.isnan(min_coord).any() or np.isnan(max_coord).any():
+                            print(f"Problem with morphology: {neuron}")
+                            import pdb
+                            pdb.set_trace()
                     except:
                         import traceback
                         print(traceback.format_exc())
@@ -2916,8 +2928,12 @@ def fill_voxels_dend_helper(voxel_space, voxel_space_ctr,
         # Remove this check later... should be done in morphology_data
         if (num_steps <= 0).any(): 
             print(f"Found zero length dendrite segment in neuron_id {neuron_id}")
+            # import pdb
+            # pdb.set_trace()
+
             raise ValueError(f"Found zero length dendrite segment (please check morphologies).")
 
+
         # Loop through all point-pairs of the section
         for idx in range(0, len(scaled_soma_dist)-1):
 
diff --git a/snudda/neurons/neuron_prototype.py b/snudda/neurons/neuron_prototype.py
index 2da277e81..08749268b 100644
--- a/snudda/neurons/neuron_prototype.py
+++ b/snudda/neurons/neuron_prototype.py
@@ -423,7 +423,8 @@ def all_have_dend(self):
 
     def clone(self, parameter_id=None, morphology_id=None, modulation_id=None,
               parameter_key=None, morphology_key=None, modulation_key=None,
-              position=None, rotation=None, get_cache_original=False):
+              position=None, rotation=None, get_cache_original=False,
+              morphology_path=None):
         """
         Creates a clone of the neuron prototype, with given position and rotation.
 
@@ -442,17 +443,22 @@ def clone(self, parameter_id=None, morphology_id=None, modulation_id=None,
             rotation : rotation (3x3 numpy matrix)
             get_cache_original (bool) : return the original rather than a clone
 
+            morphology_path : If morphology path is given it can override morphology key
         """
 
-        morph_path, morph_key = self.get_morphology(parameter_key=parameter_key, morphology_key=morphology_key,
-                                                    parameter_id=parameter_id, morphology_id=morphology_id)
-        morph_tag = os.path.basename(morph_path)
-
-        if morphology_key is None:
-            morphology_key = morph_key
+        if morphology_path is not None:
+            morph_path = morphology_path
         else:
-            assert morphology_key == morph_key, \
-                f"Internal mismatch requested morphology_key {morphology_key}, got {morph_key}"
+            morph_path, morph_key = self.get_morphology(parameter_key=parameter_key, morphology_key=morphology_key,
+                                                        parameter_id=parameter_id, morphology_id=morphology_id)
+
+            if morphology_key is None:
+                morphology_key = morph_key
+            else:
+                assert morphology_key == morph_key, \
+                    f"Internal mismatch requested morphology_key {morphology_key}, got {morph_key}"
+
+        morph_tag = os.path.basename(morph_path)
 
         if morph_tag not in self.morphology_cache:
 
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index b065bb2ca..e7cff50ac 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -1,4 +1,5 @@
 import numpy as np
+import warnings
 from scipy.spatial.transform import Rotation
 
 from snudda.neurons import NeuronMorphologyExtended
@@ -194,12 +195,22 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation):
 
             new_coords[section.point_idx, :3] = coords
 
+            if np.isnan(coords).any():
+                raise ValueError(f"NaN coordinates calculated.")
+
             for child_id, child_type in section.child_section_id.T:
                 parent_direction[child_id, child_type] = (last_dir, coords[-1, :3])
 
         # TODO: This just returns the coords for now, add option to update coords in morphology?
         #       OBS! Then rotation should also be reset, since it is now included in the coordinates
 
+        if np.isnan(new_coords).any():
+            print(f"Some coordinates were not assigned!")
+            bad_idx = np.where(np.sum(np.isnan(new_coords), axis=1))[0]
+            print(f"missing_idx = {bad_idx}")
+            import pdb
+            pdb.set_trace()
+
         return new_coords
 
     def rotation_representation(self, section: SectionMetaData, parent_direction=None):
@@ -231,16 +242,24 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
         for segment_direction, segment_length in zip(delta_direction, delta_length):
             # segment_direction = segment_direction.reshape((1, 3))
             try:
-                # Calculate axis and angle of rotation
-                axis = np.cross(parent_direction, segment_direction)
-                angle = np.arccos(np.dot(parent_direction, segment_direction))
-                rotation = Rotation.from_rotvec(angle * axis)
-                # rotation, _ = Rotation.align_vectors(segment_direction, parent_direction)
-
-                # if rotation.magnitude() > np.pi/2 and (parent_direction != np.array([1,0,0])).all():
-                #     print(f"Strange rotation, magnitude: {rotation.magnitude()}")
-                #     import pdb
-                #     pdb.set_trace()
+                if 1.00001 > np.dot(parent_direction, segment_direction) > 1:
+                    # Sometimes numerical precision gives us values larger than 1
+                    rotation = Rotation.from_euler("xyz", [0, 0, 0])
+                else:
+
+                    with warnings.catch_warnings(record=True) as w:
+
+                        # Calculate axis and angle of rotation
+                        axis = np.cross(parent_direction, segment_direction)
+                        angle = np.arccos(np.dot(parent_direction, segment_direction))
+                        rotation = Rotation.from_rotvec(angle * axis)
+
+                    if w:
+                        for wm in w:
+                            print(wm.message)
+                        import pdb
+                        pdb.set_trace()
+
             except:
                 import traceback
                 print(traceback.format_exc())
diff --git a/snudda/plotting/plot_network.py b/snudda/plotting/plot_network.py
index 886283b76..700ee27ab 100644
--- a/snudda/plotting/plot_network.py
+++ b/snudda/plotting/plot_network.py
@@ -3,7 +3,7 @@
 
 from snudda.neurons.morphology_data import MorphologyData
 from snudda.plotting.plot_spike_raster_v2 import SnuddaPlotSpikeRaster2
-from snudda.utils.snudda_path import get_snudda_data
+from snudda.utils.snudda_path import get_snudda_data, snudda_parse_path
 from snudda.neurons.neuron_prototype import NeuronPrototype
 from snudda.utils.load import SnuddaLoad
 import matplotlib.pyplot as plt
@@ -184,11 +184,17 @@ def load_neuron(self, neuron_info=None, neuron_id=None):
                                                                   load_morphology=True,
                                                                   virtual_neuron=False)
 
+        if os.path.isfile(snudda_parse_path(neuron_info["morphology"], self.snudda_data)):
+            morphology_path = neuron_info["morphology"]
+        else:
+            morphology_path = None  # Get morpholog automatically from morphology_key
+
         neuron = self.prototype_neurons[neuron_name].clone(position=neuron_info["position"],
                                                            rotation=neuron_info["rotation"],
                                                            parameter_key=neuron_info["parameterKey"],
                                                            morphology_key=neuron_info["morphologyKey"],
-                                                           modulation_key=neuron_info["modulationKey"])
+                                                           modulation_key=neuron_info["modulationKey"],
+                                                           morphology_path=morphology_path)
 
         if "extraAxons" in neuron_info:
             for axon_name, axon_info in neuron_info["extraAxons"].items():
@@ -269,7 +275,7 @@ def snudda_plot_network_cli():
     from argparse import ArgumentParser
     parser = ArgumentParser(description="Plot snudda network from file (hdf5)")
     parser.add_argument("networkFile", help="Network file (hdf5)", type=str)
-    parser.add_argument("--neuronID", help="List of Neuron ID to show", type=int, default=None, nargs="+",
+    parser.add_argument("--neuronID", "--nid", help="List of Neuron ID to show", type=int, default=None, nargs="+",
                         dest="neuron_id_list")
     parser.add_argument("--showAxons", help="Show Axons of neurons", action="store_true")
     parser.add_argument("--showDendrites", help="Show dendrites of neurons", action="store_true")

From 305f2ab014994511c4e69b2f9f2af3e9703278a6 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 20 Oct 2023 15:13:38 +0200
Subject: [PATCH 30/43] Fixed representation, still need to check bending

---
 examples/notebooks/bend_morphologies.ipynb | 173 +++++++++++++++++++++
 snudda/place/bend_morphologies.py          |  17 +-
 2 files changed, 186 insertions(+), 4 deletions(-)
 create mode 100644 examples/notebooks/bend_morphologies.ipynb

diff --git a/examples/notebooks/bend_morphologies.ipynb b/examples/notebooks/bend_morphologies.ipynb
new file mode 100644
index 000000000..3b758a7e5
--- /dev/null
+++ b/examples/notebooks/bend_morphologies.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "412ae267",
+   "metadata": {},
+   "source": [
+    "# Bend Morphologies to stay inside Mesh\n",
+    "\n",
+    "This jupyter notebook explains how to setup a network where the axons and dendrites bend to stay within the mesh boundaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e15ebec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from snudda.place import create_cube_mesh\n",
+    "\n",
+    "network_path = os.path.join(\"networks\", \"bending_example\")\n",
+    "mesh_file = os.path.join(network_path, \"mesh\", \"cube_volume.obj\")\n",
+    "\n",
+    "create_cube_mesh(mesh_file, [500e-6,500e-6, 500e-6], 900e-6, \"Cube for bending example\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb06a470",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding neurons: dSPN from dir /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn\n",
+      "Writing networks/bending_example/network-config.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "from snudda import SnuddaInit\n",
+    "\n",
+    "si = SnuddaInit(network_path=network_path, random_seed=123)\n",
+    "\n",
+    "si.define_structure(struct_name=\"Striatum\", struct_mesh=mesh_file, d_min=15e-6, mesh_bin_width=50e-6)\n",
+    "\n",
+    "neuron_dir = os.path.join(\"$DATA\",\"neurons\",\"striatum\",\"dspn\")  # Use this instead for real morphologies\n",
+    "\n",
+    "# stay_inside=True means the axons and dendrites should be inside volume\n",
+    "si.add_neurons(name=\"dSPN\", num_neurons=10, volume_id=\"Striatum\",\n",
+    "               neuron_dir=neuron_dir, stay_inside=True)\n",
+    "\n",
+    "si.write_json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b2e162f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading SNUDDA_DATA=None from networks/bending_example/network-config.json\n",
+      "Generating 238329 points for networks/bending_example/mesh/cube_volume.obj\n",
+      "n_points = 226626, previous close_pairs = 490050\n",
+      "n_points = 215522, previous close_pairs = 404107\n",
+      "n_points = 204999, previous close_pairs = 336168\n",
+      "n_points = 195043, previous close_pairs = 281235\n",
+      "n_points = 185647, previous close_pairs = 235442\n",
+      "n_points = 176804, previous close_pairs = 198563\n",
+      "n_points = 168516, previous close_pairs = 165814\n",
+      "n_points = 160792, previous close_pairs = 139833\n",
+      "n_points = 153624, previous close_pairs = 118001\n",
+      "n_points = 147032, previous close_pairs = 98084\n",
+      "n_points = 141019, previous close_pairs = 82062\n",
+      "n_points = 135551, previous close_pairs = 70538\n",
+      "n_points = 130611, previous close_pairs = 60126\n",
+      "n_points = 126182, previous close_pairs = 50831\n",
+      "n_points = 122262, previous close_pairs = 42573\n",
+      "n_points = 120153, previous close_pairs = 35321\n",
+      "n_points = 88855, previous close_pairs = 31298\n",
+      "Filtering 88855 points..\n",
+      "Filtering, keeping inside points: 79548 / 88855\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-0.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_1-1.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-2.swc\n",
+      "Failed to load cache from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc-cache.pickle\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_0-3.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_0-4.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_1-5.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_1-6.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-7.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-8.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-9.swc\n"
+     ]
+    }
+   ],
+   "source": [
+    "from snudda import SnuddaPlace\n",
+    "sp = SnuddaPlace(network_path=network_path, verbose=False)\n",
+    "sp.place()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "29026e41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading SNUDDA_DATA=None from networks/bending_example/network-config.json\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x650 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from snudda.plotting import PlotNetwork\n",
+    "network_file = os.path.join(network_path, \"network-neuron-positions.hdf5\")\n",
+    "pn = PlotNetwork(network_file)\n",
+    "pn.plot(plot_axon=False, plot_dendrite=True, plot_synapses=False)\n",
+    "pn.close() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15d2b122",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index e7cff50ac..9343b2238 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -227,6 +227,7 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
 
         # If parent compartment is of a different type (e.g. soma parent, for the dendrite) then we need
         # to make sure that the first point is also included. So we need to artificially add the soma.
+        # However, the soma itself has no parent, so in that case we should not do it.
 
         if section.section_type != section.parent_section_type and section.parent_section_type != -1:
             coords = np.zeros((len(section.point_idx) + 1, 3))
@@ -242,7 +243,7 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
         for segment_direction, segment_length in zip(delta_direction, delta_length):
             # segment_direction = segment_direction.reshape((1, 3))
             try:
-                if 1.00001 > np.dot(parent_direction, segment_direction) > 1:
+                if np.allclose(parent_direction, segment_direction) or 1.00001 > np.dot(parent_direction, segment_direction) > 1:
                     # Sometimes numerical precision gives us values larger than 1
                     rotation = Rotation.from_euler("xyz", [0, 0, 0])
                 else:
@@ -251,9 +252,16 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non
 
                         # Calculate axis and angle of rotation
                         axis = np.cross(parent_direction, segment_direction)
-                        angle = np.arccos(np.dot(parent_direction, segment_direction))
+                        axis /= np.linalg.norm(axis)
+                        angle = np.arccos(np.dot(parent_direction.flatten(), segment_direction))
                         rotation = Rotation.from_rotvec(angle * axis)
 
+                        # Verify that we got same point back if we apply rotation
+                        # if not np.allclose(segment_direction, rotation.apply(parent_direction), atol=1e-3):
+                        #     print(f"sd: {segment_direction},  rotated parent: {rotation.apply(parent_direction)}")
+                        #     import pdb
+                        #     pdb.set_trace()
+
                     if w:
                         for wm in w:
                             print(wm.message)
@@ -361,13 +369,14 @@ def test_rotation_representation():
     rot_rep = bm.get_full_rotation_representation(morphology=md)
     coords = bm.apply_rotation(morphology=md, rotation_representation=rot_rep)
 
-    if not (np.abs(md.geometry[:, :3] - coords) < 1e-10).all():
+    if not (np.abs(md.geometry[:, :3] - coords) < 1e-6).all():
         print(f"Geometry mismatch, problem with representation.")
         import pdb
         pdb.set_trace()
     else:
         print(f"Geometry matches")
 
+
 def test_write():
 
     file_path = "../data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc"
@@ -420,7 +429,7 @@ def test_bending():
     import cProfile
 
     # test_write()
-    # test_rotation_representation()
+    test_rotation_representation()
 
     profiler = cProfile.Profile()
     profiler.enable()

From 236f47301480154de5325868d3cf15839b65861a Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 20 Oct 2023 15:30:12 +0200
Subject: [PATCH 31/43] Working in serial

---
 examples/notebooks/bend_morphologies.ipynb | 42 +++++++++++++---------
 snudda/place/bend_morphologies.py          |  4 ++-
 snudda/place/place.py                      |  4 ++-
 3 files changed, 32 insertions(+), 18 deletions(-)

diff --git a/examples/notebooks/bend_morphologies.ipynb b/examples/notebooks/bend_morphologies.ipynb
index 3b758a7e5..53805f18d 100644
--- a/examples/notebooks/bend_morphologies.ipynb
+++ b/examples/notebooks/bend_morphologies.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "412ae267",
+   "id": "86b06c63",
    "metadata": {},
    "source": [
     "# Bend Morphologies to stay inside Mesh\n",
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7e15ebec",
+   "id": "240433fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fb06a470",
+   "id": "389368d3",
    "metadata": {},
    "outputs": [
     {
@@ -51,7 +51,7 @@
     "neuron_dir = os.path.join(\"$DATA\",\"neurons\",\"striatum\",\"dspn\")  # Use this instead for real morphologies\n",
     "\n",
     "# stay_inside=True means the axons and dendrites should be inside volume\n",
-    "si.add_neurons(name=\"dSPN\", num_neurons=10, volume_id=\"Striatum\",\n",
+    "si.add_neurons(name=\"dSPN\", num_neurons=20, volume_id=\"Striatum\",\n",
     "               neuron_dir=neuron_dir, stay_inside=True)\n",
     "\n",
     "si.write_json()"
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b2e162f5",
+   "id": "6c2be32a",
    "metadata": {},
    "outputs": [
     {
@@ -88,17 +88,25 @@
       "n_points = 88855, previous close_pairs = 31298\n",
       "Filtering 88855 points..\n",
       "Filtering, keeping inside points: 79548 / 88855\n",
-      "Wrote networks/bending_example/modified_morphologies/dSPN_3-0.swc\n",
-      "Wrote networks/bending_example/modified_morphologies/dSPN_1-1.swc\n",
-      "Wrote networks/bending_example/modified_morphologies/dSPN_2-2.swc\n",
-      "Failed to load cache from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc-cache.pickle\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_1-0.swc\n",
+      "Failed to load cache from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/21-6-DE-cor-rep-ax.swc-cache.pickle\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-1.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-2.swc\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_0-3.swc\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_0-4.swc\n",
-      "Wrote networks/bending_example/modified_morphologies/dSPN_1-5.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_0-5.swc\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_1-6.swc\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_2-7.swc\n",
-      "Wrote networks/bending_example/modified_morphologies/dSPN_3-8.swc\n",
-      "Wrote networks/bending_example/modified_morphologies/dSPN_2-9.swc\n"
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-9.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-10.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_1-12.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-13.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-14.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_1-15.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_2-16.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_0-17.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-18.swc\n",
+      "Wrote networks/bending_example/modified_morphologies/dSPN_3-19.swc\n"
      ]
     }
    ],
@@ -111,19 +119,21 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "29026e41",
+   "id": "d4f90195",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reading SNUDDA_DATA=None from networks/bending_example/network-config.json\n"
+      "Reading SNUDDA_DATA=None from networks/bending_example/network-config.json\n",
+      "Failed to load cache from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/WT-0728MSN01-cor-rep-ax.swc-cache.pickle\n",
+      "Failed to load cache from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/WT-P270-20-15ak-cor.swc-cache.pickle\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x650 with 1 Axes>"
       ]
@@ -143,7 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "15d2b122",
+   "id": "b2c629fc",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 9343b2238..66aa6f324 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -90,6 +90,7 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
 
                 # Cache the random numbers for segments in the section...
                 if dist > parent_dist and self.rng.uniform() < P_move:
+                #  if self.rng.uniform() < P_move:
 
                     morphology_changed = True
                     parent_moved = True
@@ -340,9 +341,10 @@ def write_swc(self, morphology: MorphologyData, output_file, comment=None):
 
         print(f"Wrote {output_file}")
 
-    def edge_avoiding_morphology(self, swc_file, new_file):
+    def edge_avoiding_morphology(self, swc_file, new_file, original_position, original_rotation):
 
         md = MorphologyData(swc_file=swc_file)
+        md.place(rotation=original_rotation, position=original_position)
         rot_rep, morphology_changed = self.bend_morphology(md)
 
         if morphology_changed:
diff --git a/snudda/place/place.py b/snudda/place/place.py
index 247a7a24f..08791b2d5 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -624,7 +624,9 @@ def avoid_edges(self, neuron_id=None):
                 # Returns None if unchanged
                 new_morph_name = os.path.join(bend_morph_path, f"{neuron.name}-{neuron.neuron_id}.swc")
                 new_morphology = bend_morph[volume_id].edge_avoiding_morphology(swc_file=neuron.swc_filename,
-                                                                                new_file=new_morph_name)
+                                                                                new_file=new_morph_name,
+                                                                                original_position=neuron.position,
+                                                                                original_rotation=neuron.rotation)
 
                 if new_morphology:
                     # Replace the original morphology with the warped morphology, morphology includes rotation

From 62e9490f97d388e240fc3b01f8752f8d3ec8f9ff Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 20 Oct 2023 16:19:34 +0200
Subject: [PATCH 32/43] Fixed seeds

---
 examples/notebooks/README.md               |  1 +
 examples/notebooks/bend_morphologies.ipynb | 12 ++++-----
 snudda/place/bend_morphologies.py          | 29 +++++++++++-----------
 snudda/place/place.py                      | 29 ++++++++++++++--------
 4 files changed, 41 insertions(+), 30 deletions(-)

diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md
index 4074e606b..4e6f82ed6 100644
--- a/examples/notebooks/README.md
+++ b/examples/notebooks/README.md
@@ -10,6 +10,7 @@ Here is a collection of Jupyter Notebooks, some of the workflows are split over
 * [population_unit_network](population_unit_network.ipynb) how to define population units.
 * [example_of_density_function](example_of_density_function.ipynb) how to specify density variations using a function of (x,y,z) in a volume.
 * [example_of_neuron_rotations](example_of_neuron_rotations.ipynb) shows how to rotate neurons based on position.
+* [bend_morphologies](bend_morphologies.ipynb) shows how to make the neurons bend the axons and dendrites at the edge of the mesh, to keep them constrained to the volume.
 * [connect_structures_example](connect_structures_example.ipynb) shows how to create neuron projections between volumes when no-axon data is available ([parallel version](connect_structures_example_parallel.ipynb)). There is also an [alternative version](connect_structures_example_projection_detection.ipynb) that places axon voxels randomly within the projection zone and then applies touch detection.
 * [virtual_neurons](VirtualNeurons/VirtualNeurons.ipynb) shows how to only simulate the core of a volume of neurons, by turning the outer neurons to virtual neurons that spike at predetermined times. This is useful to avoid edge effects in your simulations.
 
diff --git a/examples/notebooks/bend_morphologies.ipynb b/examples/notebooks/bend_morphologies.ipynb
index 53805f18d..2c37bacc6 100644
--- a/examples/notebooks/bend_morphologies.ipynb
+++ b/examples/notebooks/bend_morphologies.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "86b06c63",
+   "id": "858c7f9d",
    "metadata": {},
    "source": [
     "# Bend Morphologies to stay inside Mesh\n",
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "240433fe",
+   "id": "c18817cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "389368d3",
+   "id": "8f1a7c56",
    "metadata": {},
    "outputs": [
     {
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6c2be32a",
+   "id": "89a08a73",
    "metadata": {},
    "outputs": [
     {
@@ -119,7 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d4f90195",
+   "id": "2e073869",
    "metadata": {},
    "outputs": [
     {
@@ -153,7 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2c629fc",
+   "id": "f854ad33",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py
index 66aa6f324..17df4dc24 100644
--- a/snudda/place/bend_morphologies.py
+++ b/snudda/place/bend_morphologies.py
@@ -24,10 +24,17 @@ def check_if_inside(self, morphology: NeuronMorphologyExtended):
 
         return inside_flag
 
-    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_random=5):
+    def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_random=5, random_seed=None):
 
         # k -- how early will the neuron start bending when it approaches the border
 
+        # print(f"random_seed = {random_seed}")
+
+        if random_seed is not None:
+            rng = np.random.default_rng(random_seed)
+        else:
+            rng = self.rng
+
         # Check distance to border for all points, negative distance means inside
         all_original_dist = self.region_mesh.distance_to_border(morphology.geometry[:, :3])
         if (all_original_dist < 0).all():
@@ -89,15 +96,14 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
                 P_move = 1 / (1 + np.exp(-dist/k))
 
                 # Cache the random numbers for segments in the section...
-                if dist > parent_dist and self.rng.uniform() < P_move:
-                #  if self.rng.uniform() < P_move:
+                if dist > parent_dist and rng.uniform() < P_move:
 
                     morphology_changed = True
                     parent_moved = True
 
                     # We need to randomize new rotation matrix
                     # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.html
-                    angles = self.rng.uniform(size=(n_random, 3), low=-0.1, high=0.1)  # Angles in radians
+                    angles = rng.uniform(size=(n_random, 3), low=-0.1, high=0.1)  # Angles in radians
                     avoidance_rotations = Rotation.from_euler(seq="XYZ", angles=angles)
 
                     for idx2, av_rot in enumerate(avoidance_rotations):
@@ -105,14 +111,9 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, k=30e-6, n_rando
 
                     candidate_dist = self.region_mesh.distance_to_border(points=candidate_pos)
 
-                    if False:
-                        P_candidate = np.divide(1, 1 + np.exp(-candidate_dist/k))
-                        P_candidate = P_candidate / np.sum(P_candidate)
-                        picked_idx = self.rng.choice(n_random, p=P_candidate)
-                    else:
-                        # We want the smallest (or most negative) distance
-                        picked_idx = np.argsort(candidate_dist)[0]
-                        dist = candidate_dist[picked_idx]
+                    # We want the smallest (or most negative) distance
+                    picked_idx = np.argsort(candidate_dist)[0]
+                    dist = candidate_dist[picked_idx]
 
                     new_rot = avoidance_rotations[picked_idx]*rotation
                     new_rot_rep.append((new_rot, length))
@@ -341,11 +342,11 @@ def write_swc(self, morphology: MorphologyData, output_file, comment=None):
 
         print(f"Wrote {output_file}")
 
-    def edge_avoiding_morphology(self, swc_file, new_file, original_position, original_rotation):
+    def edge_avoiding_morphology(self, swc_file, new_file, original_position, original_rotation, random_seed=None):
 
         md = MorphologyData(swc_file=swc_file)
         md.place(rotation=original_rotation, position=original_position)
-        rot_rep, morphology_changed = self.bend_morphology(md)
+        rot_rep, morphology_changed = self.bend_morphology(md, random_seed=random_seed)
 
         if morphology_changed:
             new_coord = self.apply_rotation(md, rot_rep)
diff --git a/snudda/place/place.py b/snudda/place/place.py
index 08791b2d5..ef32dab14 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -567,25 +567,26 @@ def parse_config(self, config_file=None, resort_neurons=True):
 
     def avoid_edges_parallel(self):
 
+        ss = np.random.SeedSequence(self.random_seed + 100)
+        neuron_random_seed = ss.generate_state(len(self.neurons))
+
         if self.d_view is None:
-            return self.avoid_edges()
+            # Make sure we use the same random seeds if we run in serial, as would have been used in parallel
+            return self.avoid_edges(neuron_random_seeds=neuron_random_seed)
 
         # Make random permutation of neurons, to spread out the edge neurons
         unsorted_neuron_id = self.random_generator.permutation(len(self.neurons))
-        ss = self.random_generator.SeedSequence()
-
-        worker_random_seed = ss.generate_state(len(self.d_view))
 
         self.d_view.scatter("worker_neuron_id", unsorted_neuron_id, block=True)
-        self.d_view.scatter("worker_random_seed", worker_random_seed, block=True)
         self.dview.push({"config": self.config,
-                         "neurons": self.neurons})
+                         "neurons": self.neurons,
+                         "neuron_random_seeds": neuron_random_seed})
 
         cmd_str = f"sp = SnuddaPlace(config_file={self.config_file},network_path={self.network_path},snudda_data={self.snudda_data}, random_seed=worker_random_seed[0])"
         self.d_view.execute(cmd_str)
         cmd_str2 = f"sp.config = config; sp.neurons = neurons"
         self.d_view.execute(cmd_str2)
-        cmd_str3 = f"modified_neurons = sp.avoid_edges(neuron_id=unsorted_neuron_id)"
+        cmd_str3 = f"modified_neurons = sp.avoid_edges(neuron_id=unsorted_neuron_id, random_seed=neuron_random_seeds[unsorted_neuron_id])"
         self.d_view.execute(cmd_str3)
         modified_neurons = self.d_view.gather("modified_neurons", block=True)
 
@@ -594,7 +595,7 @@ def avoid_edges_parallel(self):
             self.neurons[neuron_id].swc_filename = new_morphology
             self.neurons[neuron_id].rotation = np.eye(3)
 
-    def avoid_edges(self, neuron_id=None):
+    def avoid_edges(self, neuron_id=None, neuron_random_seeds=None):
 
         from snudda.place.bend_morphologies import BendMorphologies
 
@@ -609,9 +610,16 @@ def avoid_edges(self, neuron_id=None):
         else:
             neurons = self.neurons[neuron_id]
 
+        if neuron_random_seeds is None:
+            neuron_random_seeds = [None for n in neurons]
+        else:
+            neuron_random_seeds = neuron_random_seeds[neuron_id]
+
         modified_morphologies = []
 
-        for neuron in neurons:
+        for neuron, random_seed in zip(neurons, neuron_random_seeds.flatten()):
+
+            # print(f"neuron.name = {neuron.name}, random_seed = {random_seed}")
             config = self.config["Neurons"][neuron.name]
 
             if "stayInsideMesh" in config and config["stayInsideMesh"]:
@@ -626,7 +634,8 @@ def avoid_edges(self, neuron_id=None):
                 new_morphology = bend_morph[volume_id].edge_avoiding_morphology(swc_file=neuron.swc_filename,
                                                                                 new_file=new_morph_name,
                                                                                 original_position=neuron.position,
-                                                                                original_rotation=neuron.rotation)
+                                                                                original_rotation=neuron.rotation,
+                                                                                random_seed=random_seed)
 
                 if new_morphology:
                     # Replace the original morphology with the warped morphology, morphology includes rotation

From 98a8311e7807239bf416edd5c0d57c298bf75835 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Fri, 20 Oct 2023 16:26:28 +0200
Subject: [PATCH 33/43] In progress

---
 snudda/place/place.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/snudda/place/place.py b/snudda/place/place.py
index ef32dab14..23247476a 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -578,9 +578,12 @@ def avoid_edges_parallel(self):
         unsorted_neuron_id = self.random_generator.permutation(len(self.neurons))
 
         self.d_view.scatter("worker_neuron_id", unsorted_neuron_id, block=True)
-        self.dview.push({"config": self.config,
+        # TODO: We cant pickle self.neurons it seems
+        import pdb
+        pdb.set_trace()
+        self.d_view.push({"config": self.config,
                          "neurons": self.neurons,
-                         "neuron_random_seeds": neuron_random_seed})
+                         "neuron_random_seeds": neuron_random_seed}, block=True)
 
         cmd_str = f"sp = SnuddaPlace(config_file={self.config_file},network_path={self.network_path},snudda_data={self.snudda_data}, random_seed=worker_random_seed[0])"
         self.d_view.execute(cmd_str)
@@ -597,6 +600,9 @@ def avoid_edges_parallel(self):
 
     def avoid_edges(self, neuron_id=None, neuron_random_seeds=None):
 
+        # TODO: We need name, swc_file, position, rotation
+        # This needs to be passed, since self.neurons is not pickleable...
+
         from snudda.place.bend_morphologies import BendMorphologies
 
         bend_morph = dict()

From 6aadd90069876dff01b40c869b56828c12ff4e57 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 13:28:56 +0200
Subject: [PATCH 34/43] Work in progress

---
 snudda/place/place.py | 104 ++++++++++++++++++++----------------------
 1 file changed, 50 insertions(+), 54 deletions(-)

diff --git a/snudda/place/place.py b/snudda/place/place.py
index 23247476a..3f1843a3e 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -570,28 +570,38 @@ def avoid_edges_parallel(self):
         ss = np.random.SeedSequence(self.random_seed + 100)
         neuron_random_seed = ss.generate_state(len(self.neurons))
 
+        bend_neuron_info = []
+
+        for neuron in self.neurons:
+            config = self.config["Neurons"][neuron.name]
+
+            if "stayInsideMesh" in config and config["stayInsideMesh"]:
+                volume_id = config["volumeID"]
+                mesh_file = self.config["Volume"][volume_id]["meshFile"]
+                bend_neuron_info.append((neuron.neuron_id, neuron.name, neuron.swc_filename,
+                                         neuron.position, neuron.rotation,
+                                         neuron_random_seed[neuron.neuron_id],
+                                         volume_id, mesh_file))
+
         if self.d_view is None:
             # Make sure we use the same random seeds if we run in serial, as would have been used in parallel
-            return self.avoid_edges(neuron_random_seeds=neuron_random_seed)
-
-        # Make random permutation of neurons, to spread out the edge neurons
-        unsorted_neuron_id = self.random_generator.permutation(len(self.neurons))
-
-        self.d_view.scatter("worker_neuron_id", unsorted_neuron_id, block=True)
-        # TODO: We cant pickle self.neurons it seems
-        import pdb
-        pdb.set_trace()
-        self.d_view.push({"config": self.config,
-                         "neurons": self.neurons,
-                         "neuron_random_seeds": neuron_random_seed}, block=True)
-
-        cmd_str = f"sp = SnuddaPlace(config_file={self.config_file},network_path={self.network_path},snudda_data={self.snudda_data}, random_seed=worker_random_seed[0])"
-        self.d_view.execute(cmd_str)
-        cmd_str2 = f"sp.config = config; sp.neurons = neurons"
-        self.d_view.execute(cmd_str2)
-        cmd_str3 = f"modified_neurons = sp.avoid_edges(neuron_id=unsorted_neuron_id, random_seed=neuron_random_seeds[unsorted_neuron_id])"
-        self.d_view.execute(cmd_str3)
-        modified_neurons = self.d_view.gather("modified_neurons", block=True)
+
+            modified_neurons = SnuddaPlace.avoid_edges_helper(bend_neuron_info=bend_neuron_info, network_path=self.network_path)
+
+        else:
+
+            # Make random permutation of neurons, to spread out the edge neurons
+            unsorted_neuron_id = self.random_generator.permutation(len(bend_neuron_info))
+            bend_neuron_info = [bend_neuron_info[idx] for idx in unsorted_neuron_id]
+
+            self.d_view.scatter("bend_neuron_info", bend_neuron_info, block=True)
+
+            cmd_str = f"sp = SnuddaPlace(config_file={self.config_file},network_path={self.network_path},snudda_data={self.snudda_data})"
+            self.d_view.execute(cmd_str)
+            cmd_str3 = f"modified_neurons = sp.avoid_edges_helper(bend_neuron_info=bend_neuron_info)"
+            self.d_view.execute(cmd_str3)
+
+            modified_neurons = self.d_view.gather("modified_neurons", block=True)
 
         for neuron_id, new_morphology in modified_neurons:
             # Replace the original morphology with the warped morphology, morphology includes rotation
@@ -600,55 +610,41 @@ def avoid_edges_parallel(self):
 
     def avoid_edges(self, neuron_id=None, neuron_random_seeds=None):
 
+        neuron_name = [n.name for n in self.neurons]
+
+        SnuddaPlace.avoid_edges_helper(neuron_id, )
+
+    def avoid_edges_helper(bend_neuron_info, network_path):
+
         # TODO: We need name, swc_file, position, rotation
         # This needs to be passed, since self.neurons is not pickleable...
 
         from snudda.place.bend_morphologies import BendMorphologies
 
         bend_morph = dict()
-        bend_morph_path = os.path.join(self.network_path, "modified_morphologies")
+        bend_morph_path = os.path.join(network_path, "modified_morphologies")
 
         if not os.path.isdir(bend_morph_path):
             os.mkdir(bend_morph_path)
 
-        if neuron_id is None:
-            neurons = self.neurons
-        else:
-            neurons = self.neurons[neuron_id]
-
-        if neuron_random_seeds is None:
-            neuron_random_seeds = [None for n in neurons]
-        else:
-            neuron_random_seeds = neuron_random_seeds[neuron_id]
-
         modified_morphologies = []
 
-        for neuron, random_seed in zip(neurons, neuron_random_seeds.flatten()):
-
-            # print(f"neuron.name = {neuron.name}, random_seed = {random_seed}")
-            config = self.config["Neurons"][neuron.name]
-
-            if "stayInsideMesh" in config and config["stayInsideMesh"]:
-                volume_id = config["volumeID"]
-
-                if volume_id not in bend_morph:
-                    mesh_file = self.config["Volume"][volume_id]["meshFile"]
-                    bend_morph[volume_id] = BendMorphologies(region_mesh=mesh_file, rng=self.random_generator)
+        for neuron_id, neuron_name, swc_filename, position, rotation, random_seed, volume_id, mesh_file \
+                in bend_neuron_info:
 
-                # Returns None if unchanged
-                new_morph_name = os.path.join(bend_morph_path, f"{neuron.name}-{neuron.neuron_id}.swc")
-                new_morphology = bend_morph[volume_id].edge_avoiding_morphology(swc_file=neuron.swc_filename,
-                                                                                new_file=new_morph_name,
-                                                                                original_position=neuron.position,
-                                                                                original_rotation=neuron.rotation,
-                                                                                random_seed=random_seed)
+            if volume_id not in bend_morph:
+                bend_morph[volume_id] = BendMorphologies(region_mesh=mesh_file, rng=None)
 
-                if new_morphology:
-                    # Replace the original morphology with the warped morphology, morphology includes rotation
-                    neuron.swc_filename = new_morphology
-                    neuron.rotation = np.eye(3)
+            # Returns None if unchanged
+            new_morph_name = os.path.join(bend_morph_path, f"{neuron_name}-{neuron_id}.swc")
+            new_morphology = bend_morph[volume_id].edge_avoiding_morphology(swc_file=swc_filename,
+                                                                            new_file=new_morph_name,
+                                                                            original_position=position,
+                                                                            original_rotation=rotation,
+                                                                            random_seed=random_seed)
 
-                    modified_morphologies.append((neuron.neuron_id, new_morphology))
+            if new_morphology:
+                modified_morphologies.append((neuron_id, new_morphology))
 
         return modified_morphologies
 

From a8822456b5fa4f63bcd8485bc8984b70f3944333 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 14:03:47 +0200
Subject: [PATCH 35/43] Parallel execution

---
 snudda/place/place.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/snudda/place/place.py b/snudda/place/place.py
index 3f1843a3e..0101b5d79 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -594,12 +594,23 @@ def avoid_edges_parallel(self):
             unsorted_neuron_id = self.random_generator.permutation(len(bend_neuron_info))
             bend_neuron_info = [bend_neuron_info[idx] for idx in unsorted_neuron_id]
 
+            with self.d_view.sync_imports():
+                from snudda.place import SnuddaPlace
+
             self.d_view.scatter("bend_neuron_info", bend_neuron_info, block=True)
+            self.d_view.push({"config_file": self.config_file,
+                              "network_path": self.network_path,
+                              "snudda_data": self.snudda_data},
+                             block=True)
+
+            cmd_str = f"sp = SnuddaPlace(config_file=config_file,network_path=network_path,snudda_data=snudda_data)"
+            self.d_view.execute(cmd_str, block=True)
+
+            #import pdb
+            #pdb.set_trace()
 
-            cmd_str = f"sp = SnuddaPlace(config_file={self.config_file},network_path={self.network_path},snudda_data={self.snudda_data})"
-            self.d_view.execute(cmd_str)
-            cmd_str3 = f"modified_neurons = sp.avoid_edges_helper(bend_neuron_info=bend_neuron_info)"
-            self.d_view.execute(cmd_str3)
+            cmd_str3 = f"modified_neurons = SnuddaPlace.avoid_edges_helper(bend_neuron_info=bend_neuron_info, network_path=network_path)"
+            self.d_view.execute(cmd_str3, block=True)
 
             modified_neurons = self.d_view.gather("modified_neurons", block=True)
 
@@ -608,12 +619,7 @@ def avoid_edges_parallel(self):
             self.neurons[neuron_id].swc_filename = new_morphology
             self.neurons[neuron_id].rotation = np.eye(3)
 
-    def avoid_edges(self, neuron_id=None, neuron_random_seeds=None):
-
-        neuron_name = [n.name for n in self.neurons]
-
-        SnuddaPlace.avoid_edges_helper(neuron_id, )
-
+    @staticmethod
     def avoid_edges_helper(bend_neuron_info, network_path):
 
         # TODO: We need name, swc_file, position, rotation

From 005fd5883d8d1c61ac1074ffe363288496ecc87b Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 14:07:47 +0200
Subject: [PATCH 36/43] Updated notebook

---
 examples/notebooks/bend_morphologies.ipynb | 23 ++++++++++++++--------
 snudda/place/place.py                      |  5 +----
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/examples/notebooks/bend_morphologies.ipynb b/examples/notebooks/bend_morphologies.ipynb
index 2c37bacc6..696ff985c 100644
--- a/examples/notebooks/bend_morphologies.ipynb
+++ b/examples/notebooks/bend_morphologies.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "858c7f9d",
+   "id": "5c081ba0",
    "metadata": {},
    "source": [
     "# Bend Morphologies to stay inside Mesh\n",
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c18817cc",
+   "id": "a97d2f74",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,10 +26,18 @@
     "create_cube_mesh(mesh_file, [500e-6,500e-6, 500e-6], 900e-6, \"Cube for bending example\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2b0065b9",
+   "metadata": {},
+   "source": [
+    "When adding the neurons with ```add_neurons```, we pass ```stay_inside=True``` for the neurons to be inside the mesh."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8f1a7c56",
+   "id": "0807cc6a",
    "metadata": {},
    "outputs": [
     {
@@ -60,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "89a08a73",
+   "id": "ead3f895",
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +97,6 @@
       "Filtering 88855 points..\n",
       "Filtering, keeping inside points: 79548 / 88855\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_1-0.swc\n",
-      "Failed to load cache from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/21-6-DE-cor-rep-ax.swc-cache.pickle\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_2-1.swc\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_3-2.swc\n",
       "Wrote networks/bending_example/modified_morphologies/dSPN_0-3.swc\n",
@@ -119,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "2e073869",
+   "id": "4e9d6a16",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +140,7 @@
     },
     {
      "data": {
-      "image/png": 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JBG0jRJmgx6PT6dBoNCaPSjJFGFVVVREdHY1OpyM8PBxnZ2eLbGtPFGXiZmo+rY2EysjIkEdCSZMG2jsSqisiZW1hKNKkc1uKWNfW1gqRJhCYgRBlgh6L4RO6dHMytYi7tUhZbm4u8fHx+Pr6MmLECIsaivZEUQYiUtZR2jMSytQ0uTWJG8OZnSBEmkBgLkKUCXokhqOSoOl8wNZoqaZMq9WSmJjIxYsXCQ4Olp3gLUlPFGW96WZpDZElMG0klKurqyzQWhoJZS370xKtibTa2tpWLTiseb8Egs5CiDJBj8PQe8ywld9UmouUVVRUEBUVhUqlIjw8HEdHR0tustF79zRRBiJS1tk0NxJKEmlJSUnU1dU1OxKqp30uhiJN2n7pX21tLVVVVVy4cIGgoCDs7Oxk+w1LjEMTCHoCQpQJegyteY+Zg2GkTK/Xk52dTWJiIoGBgQwdOrRTTTJ7oigTN8Oux8HBgQEDBjBgwAD0er2RSDMcCaXX66moqMDBwaFHmrsaRrhtbGzQaDRkZWUxZMgQampq5GWUSiW2trZGczvFeSnojQhRJugRtOU9Zg5SpEyj0RAfH09RUREhISFyvU9n0hNFGYhIWXeiUChaHAklRdL0er3Vj4QyBSkdK9XTGUbSDEVac8PVe+L+CgSNEaJMYPWY4j1mDkqlkurqao4cOYKjoyMRERHY29tbaGtbp73TBAQCCWkklJOTEykpKVx22WVoNBqrHwllCpKdjUTjSJok0HQ6nRBpgl6JEGUCq8Uc7zFz1llVVcXFixcZNmwYgwcP7tKLd0+8UfTEbb4UkKKXSqWyxZFQRUVFVjMSyhR0Ol2bdjYtibTa2lpqampQKpVNGgeESBP0FIQoE1gllkxXStTV1REbG0tlZSW+vr4MGTLEEptqFj01Utab0pe95ebc0mfS0kio4uLiFkdCdVZji7mY203auOtaEmlarRatVtuiBYcQaQJrRYgygdUhRccsla4EKC4uJjo6Gg8PD3x8fLosXdmYnlhT1ptuXj3t2LeGtC9tfT6GI6GAZkdC2dvbG0XSOmMklCk0Tl+aS3PD1ZsTaY3TneZY6ggEnYkQZQKrobH3mKXSlWlpaaSnpzNixAgCAgJITEzstpuzuXM3W6K6upq8vDw8PDxwcXHp9BtKbxIzvQ1zP/vmRkJJRrZSJ7Kjo6PFR0KZQlvpS3NpSaRpNBrq6+vlvwuRJrAWhCgTWAWS95iU2rPERbGmpoaYmBhqa2sJDQ3Fzc1NXnd3phA7KnDy8/OJiYnBycnJqF7Iy8tLTkVZ+sYmsD5MjZS1hY2NDX369KFPnz5A6yOhJDPb9oyEMoWORsrawhyRZjhcvSfajQh6JkKUCboVw1FJlkxXFhQUEBMTg7e3NxMnTjQaWaNUKuVata6mI5EynU5HamoqFy5cYPTo0fJNVKoXunjxIikpKdjZ2ckCzdPT0yKpWhEpsz466zNpbiSU1Nl57tw5Kisr2z0Sqi0sHSlrC1NFWuNpA0KkCToLIcoE3UZnFPPrdDpSUlLIzMxk9OjR+Pn5NVmmOyNl7a0pq6mpISoqCo1GQ1hYGE5OTvJNQ6oXGjx4sFEqKjMzk4SEBKMoh6enp9k3UBEps04sFSlrC1tbW/r160e/fv0AqK2tlc+xxiOhPD09cXd3b/e82O4eG9WSSKuvr291JJQQaQJLIUSZoFswHJVkqehYVVUV0dHR6HQ6wsPDcXZ2bna57iy2b897S1G/fv36MXr0aGxsbNDpdM3elBunogyjHGlpaVRXV1vsBtpT6W0is6v3x97evsWRUImJiU1GQrm7u5ssWqRoubXQlkgTkTSBpRGiTNClWGpUUmNyc3OJj4/H19eXESNGtCo0LFVs3x7MEWU6nY6zZ8+SkZHRYtSvLRpHOQxvoAkJCfK4Hk9PT7y8vFp0gu8t6cvesh+AUf1ld9J4JFR1dbUcSTMcCWU4baAl0dLdkbK2aE6kSQ+Y9fX18jKGIk2lUln1PgmsCyHKBF2G9IR56tQpRo4caZGCdK1WS2JiIhcvXiQ4OFh+em+NnpC+rKmpITo6mrq6OqZOnYqrq6tF3r/xDVQa11NcXMyFCxcAjJoGrNVkVNCAtX02CoUCJycnnJycmh0JlZmZiU6nk+03PDw8jB4ErC1S1hZSU4CEoUiTImlKpbJJ44C1fW4C60GIMkGXYOg9VlxcjFar7fCFqaKigqioKFQqFeHh4SYbYFp7pKyoqIjo6Gj69u3LpEmTLFZE3dy2ODs74+zsjL+/v+wE39hk1MnJibq6OmpqarrNv0rQFGuPKkHz51hFRYUcSUtPT0ehUMgiTYo29VRaEmlpaWnU1tYybNgwIdIErSJEmaBTMfQek9rdlUplhyJVer1e9lMKDAxk6NChZj1dW0OkrLkbqqGn2siRI/H39+/yTjTJCX7QoEFotVpKS0vJycmhrKyMyMjIJv5VnWWNIGibnpiKVSgUuLq64urqSkBAADqdjoqKCnkklFqtBiAuLk6OpPXkaK0k0qTvu1QP2jiS1rgmrafur6DjCFEm6DR0Oh0ajaZJd2VHRJlGoyEuLo7i4mJCQkLktn1z6O5IWXPU1tYSExNDdXW1SenKrrhol5WVsWvXLsaMGYO9vT1TpkyR01Dp6enExcUZNQ14eHhcck0D3UlPiJS1hVKpNBoJlZGRQVFREc7Ozly8eJHU1FSrHQllDjqdThZb0ndEugbpdDrq6upaHAklRNqlhRBlAotjWFch3TgMLyrtFWWlpaVER0fj6OhIREREu/23ujtSBsY3VGkElKenJyEhISanKzv7Qn348GEeeeQRAPr27cucOXN48MEHGTNmDNAgJCWRlpSURF1dXZOmAWusD+pNN7jetC8SdnZ2DB48WLZ4aW4klKFI666Raeag0+mafK8NB6uDsUirra1t1YKjN37uggaEKBNYlMbeY80585sryvR6PRkZGaSmphIUFMTgwYM7dFGyhkiZlMI8d+4c586dk0dAWdPF1tbWltDQUE6fPk1hYSEbNmxgw4YNXHbZZTz00ENcfvnl9O/fn/79+8tdd1LTQFZWllzQLTUNODs7d/v+9cSUX0v0hkhZYxoX+jceCaXRaCgtLTXy4XNycjKa29lVI6HMwZQGBkORJqU8pX+NRZpUj6ZSqcRw9V6GEGUCi2Gq95g5oqyuro7Y2FjKy8uZPHmyfHHuCNYQKautrSUhIYHKykouu+wyeVi0NTF79mxmz55NdnY227Zt48iRI+zdu5fjx49z/PhxAHx9fXn88cd56KGH5K47Pz8/uaBbqhVKS0uTb7CG46AE7ac3CUyJtoSmSqWympFQ5tCerlLDB9rGIq2mpkZeRhJpUiRNiLSejRBlgg5jrveYqaJISut5eHgQERFhsYurNUTKjh07hoeHB+Hh4d1603jnnXeYPXu2nJJsDicnJyZNmsTSpUu5ePEin376KT///DOpqank5OTw7LPPkp+fzyuvvCK/xrCge+DAgeh0OjnCYZiGMhwHZY0RDmumN0bKzJ192XgkVF1dnSzS0tLSqKqqwtXV1ciCo7O6mVvDElYfQqRdGghRJugQ7RmV1JYoMuxC7Iy0XndFyvR6vewFNnDgQIYMGdKtF8uffvqJV155hX//+9+sWrWKe+65p8UblvR5+fj48OKLL/Liiy9SUVHBsmXL2LJlC++88w733nsvAQEBzb5eqVQ2SUM1jnB01jzF3kxvu9l2dPalnZ1dk5FQarWakpISi4+EMofO8F8zVaRJaU4h0noG4qonaDdS15C5zvytpS9ramqIiYmhtraW0NBQ3NzcLLnJ8vt3daSsvr6e2NhYysrKAPDz8+v2C+PMmTOZOXMmBw4c4KmnnuL777/n008/ZejQoUbLtbSdLi4ufPLJJyQkJBAXF8dNN93EX3/9hYuLS5vv3VyEQ2oaSElJoba2tt2jetqiu4+7peitkTJL7pO9vb1c9wjNj4Ryd3eXI2mWPM8M6QpT3JZEmk6nk0WaZEkkRJr1IkSZwGykdKXUXWnul7olUSbNePT29mbixImdapralZGykpISoqKicHV1JSwsjD///NMq6oH69OnD1q1b+fTTT3njjTc4efIkV1xxBdu3b2fSpElGy7a0vTY2NgwaNIi4uDhSU1P5+uuv5Y5Nc7CzszOapyg1DajVarKzs9FqtUZNAy4uLpf8jcQaziFLo9PpOjWd39xIKOk8M3cklDl0x6SClkSaVqtFq9VSU1MjRJoVIkSZwCxa8h4zh8aiTKfTkZKSQmZmZrtnPJr7/l1xQzPsGh06dCiDBg2SL5TWckNVqVQ88sgj3Hjjjdx9990cP36cG2+8kY8++oi5c+cCrUeW1q9fz86dO+Wfn332WWxsbHjggQc6dGF3dHTE0dFRHtVTWVkpd3amp6ejVCqNRJqpI7us5bhbAhEp6xiGI6Gk5hTDkVAXLlxAr9cbdXa292HAGsZHtTRcXRJpkk+alO40nNvZ284za0aIMoFJtOU9Zg6Goqyqqoro6Gh0Oh3h4eE4OztbcrObpStEUX19PXFxcZSUlDTpGrUmUSbh7+/Ptm3buPnmmzl69CiLFy/m4Ycf5s033wSaFzMHDhzg+eefb/L7FStWsGLFCubMmYNWq2XUqFE89thjcsecuSgUClxcXHBxcSEgIACtVsvu3bt59dVXKSsro66ujgEDBqBUKqmrq8PT05Pp06ezYMECvL292/WePYHeKMq6U7y0NBLK0DDZcCSUOTYv1iDKGtOSSNNoNNTX18t/b1yTJkRa5yJEmaBNDEclQfPeY+YgibLc3Fzi4+Px9fVlxIgRXeYG39npy9LSUqKionB2diYiIqJJV6E1ijIAV1dXfv31V1577TXeffdd/vOf/2Bvb89TTz1FfX09BQUFaLVafHx8yM/P57HHHgNg7ty5HDx4UB6RI/Hrr78CsHv3bn766Sc++OADZs6c2aFt3L17Ny+++CIpKSlGv09LS2uy3GuvvcbcuXNZuHBhh9/XWultN0drEprNdRC3ZPNiOG2gue2XbIKsGXNEmuHcTmvfr56GEGWCVjH0HjP8wnaUixcvUlNTQ3BwsFxH1FV0VvpSr9eTmZlJcnIyQ4YMabG7sjtFWW1tLXZ2di3e+Ozs7Fi1ahVDhw7l0UcfZd26dXh6erJr1y6OHTsGNDQpVFZWUlJSwqBBg3jvvfc4duwYCxYsMFpXUFAQ1113Hdu3bycjI4Mbb7yRuLg4Bg4caNY2q9VqPv/8c3788UcSEhKAhgLuuXPncvXVV6NSqUhPT5cLtjMyMti4cSNpaWls2rSJTZs2ccMNN/Dggw9ia2vbbR51lsaaBIylMNcSoytpPBJKp9PJ0wbaGglljZGytjBVpGk0GpycnGSh1tP209oQokzQLOZ6j5lKeXk5+fn5KJVKwsPDu8VAtDMiZdJMTrVazcSJE1tN1VlKlLVnPatWrWL79u08//zz/N///V+Lyy1evJi8vDxWr17NmjVr5MJ/hUJBdnY2AN7e3vz44494eXkxfPhw+bVBQUGkpaWRlpbGBx98gL+/PzY2Nmi1WpYtW8bWrVubfU+9Xs/Zs2dRq9W4ubkxcOBAvvrqK9555x0uXrwov/+iRYtYvXp1q8f4iSee4Oeff2bfvn1s3LiRHTt2sG/fPtauXUtNTQ25ublG46B6orixxmhrR+moJUZXItU1enh4yCOhSktLKSkpaTISyjDT0FNpSaRFRkYSEhKCk5NTsyOhhEgzDyHKBE1oj/eYKevMysoiKSkJFxcX3Nzcus3R3dKRsvLycs6cOYOjoyPh4eFtzuLrjkhZXl4emzZtYvfu3WRlZZGamsqGDRsYMmQIY8eOxcvLq8lrVqxYwc8//0xMTAxHjx4FjIVAaGioLMY8PDzk3x87doz9+/ezbt06YmJiyMrKkv/2+++/c/jwYSIiIuTfpaen8/PPP/PFF19w/vz5VvdDr9dz4MABPvjgA4YOHYqdnR1z5szBycnJaDkbGxvmz5/P/PnzueWWW3jmmWdISUnh/fff56uvvsLT05Pi4mLZN86wacDJyalHCIPeGinrqftkY2ODl5eX/F0yHAml0+mIjo7GycnJyIuvJxsmS5+TTqfD3t4elUol3zvq6upkESdEmnkIUSYwor3eY60hRZGKi4sJCQmhuLhYnuPWHUiiqKM3AL1eT3Z2NomJiQwePJigoCCT1tfVoqyuro477riDkydPyr979913jZbx8PBg6NChaDQabG1tUSqVnD9/Xo5QGS5XUlICNNSM1dbWEh0dTUFBgbzMwYMHGTt2LHv27KGmpoaNGzeyevVquebswQcf5OjRo1RUVPDRRx/xwQcfGM1Kbe7Y+Pj4MHz4cJKTk8nKyuKdd96R/xYYGMjatWuZNm0arq6uTV47a9YsNm3axNVXX83Zs2d58cUX2bZtm1zMXV5eTnFxMQUFBZw9exaVSmU0acDBwcHMI9519FQB0xI9Mc3XEtJIKC8vLy5cuMCUKVOoqamhpKSE8+fPU1FRYZUjocxByjgYNgAYRtKk8pf6+nqAJiJN6u4U/A8hygRAx73HWkIqendyciIiIgJ7e3tKSkq6ta7H8KLR3n3UaDQkJCRQWFhISEiIbIJqCl0tymxtbbnllltITU2ltLQUOzs7hgwZgre3N5GRkbK7vqFoawlJkEkMGjSIyspKo9/Nnz8faBjP5Ovryw033MCuXbtY/OQ6Rv7fdwA8sB0q8pM5sG6dvI3SuWeIg4MDNTU1XLx4URaIUr0eQFxcHBkZGSxYsAAHBwfeffddFi1a1GS7hw8fzq+//srll1/OH3/8wYkTJ7jssstQKBRyndCgQYPkFJTkj5aUlISjo6NRnZC13Dh7UqrPVHpypKwlpHPa3t4eV1dXuSO4pZFQhobJ1j7VwjCb0hipKUDCUKRJkTSlUtlsd+elTO94JBF0CCnkLD3NWCpdef78eY4fP05AQACTJ0+W03rmDCTvDAzD7u2hoqKCo0ePUl1dTXh4uFmCTHr/rhRlCoWCRx55RE4ZPv300xw/fpyNGzc2qXNRqVRmTVGQBJkkbqRCZ5VKRVVVFWfPnuXdd9/lrrvuYtTi7+SnaYVCgUu/EQyY0iCg6uvrGTBgAPfdd5+RjYXkRG64L+fOnWP//v34+/uzY8cOHnzwQQYMGEBNTQ0PP/wwV1xxBX///XeTbQ0ODmby5MkA3HvvvfJQdUOkFFRQUBCTJ09m+vTpcgQ0PT2dQ4cOceLECc6ePUtRUZF8UxJYBmsu9G8v0nWm8X5JI6FGjBjB1KlTiYiIICAggPr6epKTkzl06BAnT54kLS2N4uJiqzzXWhNljTG015CaAqR7T1VVFeXl5ZSVlVFRUUFtbS0ajaZX1k22hXXLcEGnI0XHLJmurKurIzY2lvLy8iYeXdC9A8Gl94f2FUpnZ2eTkJBAYGAgQ4cObdcNpKtFWUFBAa6uruzbtw+AK6+8EmgYk/Tbb7+RlpZGZGQk+fn5PPHEE4wdOxZ/f38ALrvsMm699VbWr19PRkZGi+9x0003MXr0aGxtbdm1axcKhUJevrCwkJSUFIbrwfD0UigUDJq5lMrkHVRUVJCbm8t///tfoOEzeuONN/Dy8uK9994jLi4OgMcff5y8vDw2b97Mhg0b+PHHH1m1ahV///0369evZ926dZw6dYrrrruOOXPmsHLlSkaPHi2/5/33309mZiYZGRnMnz+fPXv2MHbs2Bb3S6VS4e3tLQtFaZaiWq0mKSlJHtNj2DTQVaKiN0aVemP0ryVR1pjGI6Gqq6vlSJrhSCgpkubm5tbtAlan07U7umVqJO306dMUFRVxyy23WHLTrRYhyi5RGnuPWUqQFRcXEx0djYeHBxEREc2menpipEyr1ZKYmMjFixeZMGFCh0xJu3LM0xdffMGyZcsICgqivr4eR0dHQkJCgAbBERYWRlhYmFEn5tmzZ+X/f/TRRwkICGgzjbJt2za2bdvW6jJ6nQ5Fo4tw/HePyvNADdHpdDzzzDN4eXnJVhoADzzwAAEBASxZsoQXX3yREydO8OSTT/LMM8/w+uuvk5SUxKpVq/j+++/59ddf2blzJ88//zxPPfWUPBJq165dPPjggxw7doz58+ezc+dOhg0b1vbBxPjG2XhMT1ZWFjqdzqhpwFRz0fbQG0VZb46UmftZSVMtmhsJlZWVhVar7ZSRUOag1Wot5i9pKNKkh1adTsfvv/9Oenq6EGWC3ov0NGJ4sbBEujItLY309HRGjBhBQEBAi+vsblFmbqSssrKSqKgobGxsLGLj0ZFjrdFoTK4zycjI4IUXXgCQi+zDw8PbvIiuXbtW/v/Ro0dz3XXXUVBQgJubG9OnT2fXrl3tivTtWe7G7LfUKFUNQj1p56uUZZ0GGurPHB0dGTlyJG5ubtTV1REVFUVRUREbNmwAwNPTk4CAAHk/9u7dy0cffcTGjRtJSEjg6aefJjIyktdff5377ruPF154gcjISF577TWOHTvGG2+8gVarxc3NjS1btnDdddcRHx/PNddcw6+//sqYMWPM2p/mxvS0ZC5qOA7KUvRGUdZbI2UdfejtypFQ5tBZpriGMzurqqpwcXGx+HtYK0KUXUIYhoctma6sqakhJiaG2tpaQkND26xJ6m5RZk6kLDc3VzY8HTZsmEUuQB1JX953330cOnSI1157TS6ob47du3dz7733UllZyejRoxkwYAD79+9nwoQJRsvl5+fTr18/Tp48ya5du9i/fz9nzpyR/75lyxYKCgrw9vbm66+/5vrrr29z221sbNDpdE2W09ZVsetx+ybLShfeqqoqDh8+DDQU+P/8889kZWWxYsUKiouLUavVbN26Vd5vGxsbHnvsMZYuXcq///1v1qxZw7Zt2zh58iS7d+9mz549/Otf/2Lt2rX8/vvv/P7774wfP55t27bh6enJL7/8wi233IJCoZAbBzpCcw7wZWVlFBcXG/lWGXZ2dtQSobcJmN4qNC0tXEwdCSV1dXZW1NaSkbKWqKioaLarurciRNklglRQGRMTQ1BQkMkDnNuioKCAmJgY+vXrx8SJE02K4nS3KJO2oTVxodVqSUpKIi8vj/Hjx9OvXz+LvXdH0pdRUVFkZ2c38eUy5Pvvv+eRRx5Bo9EwZcoUnnvuOebPny8br0qUl5czY8YMPDw8iI+Pb7Ied3d3vvzySwBuueUWbrnlFpO2u7mCZMk8tjF+fn4cPHiQ22+/nT59+lBWVsbBgwepqanhscceY+/evXJ0rr6+nhdffJHrrrvOyKZCoVDw9NNPc/XVV3Pvvfdy9uxZZs6cyd69e3n22WeZMmUKb731FmfOnCEwMFB+nbe3N7t376aysrJTPPMMzUUBuctVrVaTkZFBfHw8Li4uRpYI5nTbCQHTM+iKfWppJFRxcbHZI6HMQaop60yqqqoYMGBAp76HNSFE2SWA4aik/Px8AgMDLfJlTElJITMzkzFjxuDr62vya61BlLUmjKqqqoiKikKhUBAWFtaqAGrve5sTKauqqmLDhg2MHTuWpKQkAMLCwgDjG3NiYiL//ve/+fHHHwG47bbb+Pjjj1m5ciUAN954I0OHDpXXu3z5crKzs2WH/iuuuEI2Z5Xmkf75558MGDCATz75pEOfWUudYxcuXOCzzz4jMjISGxsbYmJisLGxISIigpSUFKZOnUp8fDxVVVWEhoaSmZnJPffcw7JlywgJCTGqWQwJCWHbtm0sWLCAhIQElixZwi+//MKsWbOYNWsWWq2Wv/76y+j9pZRQV6BSqejbt6/crVtXVydHNlJSUqitrcXNzc3IEqG1m3lv7EwTQtMyGI6EkrbBcCRUSkoKdnZ2RpG09jyYdMVMz8rKyi77jloDQpT1YpoblaRUKjvcWl1VVUV0dDQ6nY7w8HCcnZ3Nen1XFrq3REuRsry8POLi4vDz82PEiBGdcsExp/v0woULzJ07l8TEREaMGAEg+6JJFiYA+/bt47bbbpM/26VLl7JmzRqUSiXp6enA/4QcNKRlt2zZIv/s6urK5s2befrppwEICAjgzz//lJdtjJubG4sXL2bEiBE8/vjj5ux+Ez788EMA7rjjDrlm7I033uCBBx4gLy+PkJAQtm/fzlNPPcXy5cv59ddf+fXXX3F0dCQoKIi77rqLWbNmMXToUAIDA/npp58IDw8nOjqa6667jr179+Lq6oqNjY3Z52pnYmdnh4+Pjzz71bCQOzs7G61WK98wvby8mtQI9UYB01sL/bt7n1oaCaVWq5uMhJL+tTWZBLomfVlZWSlqygQ9n5ZGJUn1Pu0lNzeX+Ph4fH19GTlyZLsuNt1tiQFNo1U6nY7k5GSys7MZO3as3JbeWZiy/9XV1cyZM4eUlBT69etHSkoKAO+//36TZdevX49Wq+WKK67g1VdfZfz48UDDRTMyMhJosLeQeOSRR4y2Yc2aNWi1WvlpWRo+3hyvvPIKy5Yto7KyUvb9aouBAwcyc+ZMvv/++ybTHCRD2htvvFH+3cKFCykpKeGtt97iwoUL/OMf/2Dz5s3s3LmTTz75hEOHDqFWq4mLi2PFihUAREREcP/993PDDTewc+dO5s2bR3x8PO+//77c8ADWW4clddv5+vqi1+uprKxErVZTXFzM+fPn5RurVJPWG0VZby70tyZaGwmVmZlJQkKCSSOhuiJ9KUSZoMfTmvdYe1OHhpYQwcHB8tN9e7CG9KXhNkiRP71eT3h4eKeHyk0RpZWVlVx99dWkpKTg5OTEbbfdxvr165k2bRpTpkwxen1OTg4nTpwAYPXq1YwbN07+24kTJygpKcHDw0O2wqipqZGXB5g+fTr19fWMHDmS0tLSVrf7vffe46qrrmLNmjV89tlnrS4vsX79eu688070ej0nT54kISGhyTIKhYIDBw4wdepU3N3dAXjooYeYN28eN954I4mJiSxZsoTDhw/z7bffotPpSE9PZ/fu3WzdupXTp09z+PBhDh8+TP/+/bn33nt59dVXefDBB3nnnXeYOnUqs2bNanNbrQWFQoGLiwsuLi4EBASg0+nkcVBS+kky4szLyzM5smHt9FahaW2irDHSSKg+ffoADWbOUv1jayOhuiJSVlVVZVUR7s5GiLJehKH3WEujktqTviwvLyc6OhqVSmURSwhrEGVSCjU/P5/Y2FgGDBjAiBEjOv0CI713W6Lstdde4/Tp0/Tp04dNmzbx+eefAzB16lSj5Xbt2sVjjz1GRUUFI0aMaGKEKnmHXXvttVRXV7N06VJ27Nghpz4dHByIjIzk0KFDQEO0prq62mgdAQEBZGZm8uyzz2Jvb8+UKVOoqKhodfudnJyoqqriySef5M477wTg0KFDJCQkNPv56/V6PvzwQzZt2sRzzz3HQw89BDTMvPz5558ZOXIk6enpjBo1isWLF7Nw4UJGjhzJ0qVLWbp0KTk5OXz55Zds2LCBvLw8XnvtNbljNjU1lXnz5vHQQw/h7+9Pfn4+Y8eOZfDgwa3ugzWhVCpxd3fH3d1dTj8lJydTVlYmRzYMb5rSVIWehDSP1toFjLl0Rd2VpbG1tTUyTW5pJBQ0pOHNseoxByliLCJlgh6HTqdDo9E0SVc2xpz0pV6vJysri6SkpA452DfGGkQZNNRrFRYWMnbs2C7t7mlLlKnVatb9/5mQ//3vf7n88st5+eWXAeR0AzRs/913301dXR0jR47kk08+4eDBg+Tl5VFVVYW3tzfbt28HGi6cQUFBRoJLoVDIY4zc3d2pra1tIsh27NjBXXfdJW9Lfn5+k+21tbXl8ssvZ//+/fLvqqqqAFi2bBnQcH6uWrUKgLvuuouvv/7a6OHg8ccfZ/fu3aSmpsp1bZIw69+/P//5z3/417/+xblz51i/fj3r16/H0dGRiRMn8n//938sWrSIF154gRUrVvDTTz+xcuVKLly4YLSdH3/8sdHPISEh3HzzzUycOBEnJyfq6uqora0lODjY6DhbIzY2NvLD0ejRo6mvr5fr0dLS0qiurm4yR7ErHjg6gvSdEJEy60MaCSV1oUuTLc6fP09paSmHDh3qtPNNWGIIehSG3mNS6L+1i5qpgkij0RAXF0dxcbHZA7fbortFWXV1NbW1tZSVlbWrUaGjtCXKCgsL5eNz5MgRYmJiOHr0KA4ODtx0003ycnv37qWuro4JEyZw/fXXs2DBAvLy8pqsT5ozJwmuPn36UFRUhF6vZ8iQIcyYMUO2vjDE0dGRffv2UVxcDNBEkHl5eVFcXMyqVasYM2aMkSgDmDhxopyK/Oqrrzhx4gQuLi4888wzjB8/XhZsAHv27CEyMpKXX36ZDz/8kGeffZbAwED+8Y9/AHD77bezYMEC9u3bx2effcbRo0cpKyvj8OHDTJs2TV6PnZ0dt99+O3PnzuW7775j7969nDt3jrS0NDQaDW5ubnh7e5ORkcGZM2eMPNkktm3b1iNSnYapPltbW6ObZk1NjSzSEhIS0Gg0TcZBWZv4kb4TPV3ANKY3iLLGSJMtiouLcXR0pH///qjVakpKSiw+EkpEygQ9hsbF/KY485uSviwtLSUqKgonJyciIiIsXqsiibLuqB+RfNVsbGwYPnx4t9QqtCXKhg0bJvt6/fvf/5Z///DDD8smpzU1NfznP/8BGuZO7t27VxZknp6eTJ06lQMHDlBdXU19fb2R6CoqKpL/f/jw4c0KMmgQr4ZNBSqVSh7L9eabb3LFFVdw9OhRVCqV3IFp6EeWnp5OdnY2CoWCl156CYCVK1fi6+vbpCYxJSWFjIwMXn/9dSoqKvjqq6946KGHiI2Nldv6FQoFs2fPZvbs2ej1ehISEoiMjGTmzJlNtt3JyYl7772Xe++9F2ioa3v++edlIb5v3z5++OEHDh48SFxcHBqNBjs7O+zt7XtUbVZL3x8HBwcGDBggj+hp7P4OGDUNODk5dbtIa+84ImunN4oyCanQv3GTiqVGQknnrqgpE1g9ht5j5jjzt5a+1Ov1ZGRkkJqaSlBQEIMHD+6UC6ThmKOuugDrdDrOnj1LRkYGo0eP5sKFC9128TelpiwmJobnn3+eM2fOyDfRBx98UP77NddcQ2pqKi4uLlx99dUEBwdTWVlJdHQ0c+bMYf369YwaNYrq6mpmzJjRxJ8LIDg4mJycHPnn5iKYUm0YNERPx48fz2uvvcbll18OwO+//y53Nkq1Z9BwnqnVap544gn69etHaWkpkyZN4oEHHgDghx9+aLI9aWlp+Pj4cOedd/Lbb7+Rl5fHihUrsLGx4Y8//iAnJ4cxY8YwduxYtFot9vb2eHt7c+rUKQYOHNiqO/7SpUvx9PTk4YcfZs+ePSQkJPDwww/z8MMPt/o5WDOmfn+ac3+XmgYKCgo4e/YsKpXKaByUoTlvVyEiZT2P5urlmhsJJXUSl5SUmDUSqqamBq1We0mlL3vnmdKLkYr56+rqzBZk0HLqsK6ujtOnT3P+/HkmT57MkCFDOk20SF/irkphSt2G+fn5hIWF4efn161eaaaIsqCgIDZv3swdd9wh/07yCztx4oTcPXnllVei1WqxtbUlOjoagDFjxvDrr7+Sk5ODk5MTmzdvbrY5IzY2lpiYGPln6XgMGTJEFjhVVVWyQauvry9//PEHl19+OXq9nvfee08WZPfffz9PPPGEvK477rgDpVLJnj17+P777wF4/fXX5WLg5tKGt912G/7+/syaNUuO+n333Xd8++23sniMj49n8+bN/Pjjj2zcuJF169Zx//33c//997d6PAEWLVrEVVddBTRYbpw/f77N11gz7bWVUSgUuLm5MWjQIEJCQpg+fTpjxozBwcGB7OxsIiMjOXr0KMnJyeTn5xv54XUmvTlSZu31fO3FlO5LqZM4ICCA4OBgpk+fzsSJE/H09EStVnP69GkOHTpEbGwsWVlZVFRUyOd2ZWUlQLPpyw8//JBBgwbh4OBAaGgox48fb3U7fvjhB0aOHImDgwPBwcHs2rXL6O96vZ6XXnqJAQMG4OjoyFVXXUVqaqrRMq+99prcoS9N6mjMhQsXuP7663FycqJfv36sWLFCzjCYgoiU9SBa8h4zh+bSl8XFxURHR+Ph4UFERISRS3pnYM7syY5SWFhITEwMffv2ZdKkSbIo6E6vNHMc/SUPL2iwvkhKSpLTlpMnT+bzzz/H3t6eoqIibG1tqa+vx9/fnxdffBGAm2++mTVr1jQp4G+Nc+fOyf/v6+vLvffey2uvvUZOTg533nknjz/+OCtWrJAF3SOPPMLChQuN6t1GjBjB6NGjiYuLo66ujtGjRxt1jrZ2IZdc75OSkrCzs+PRRx9lxowZDB06lKNHj5KVlYVGo6GgoAC1Ws2ePXuMxke1xiOPPEJqaioZGRnMnTuXP//80+qL+lvCUpFmw/E70BARNZyhGBcXZ1TE7eHh0Skiw5Sa2J5Ib46UtUdwNjcSqry8HLVaLUdu33zzTZycnAgJCWk2crt582aWL1/Oxx9/TGhoKOvWrWP27NkkJyc3OxLvyJEj3H777bzxxhvMmTOHTZs2cdNNN3H69Gm5Y33t2rW8//77fPXVVwwePJgXX3yR2bNnk5CQIL9/XV0dt956K2FhYXJHvCFarZbrr7+e/v37c+TIEXJzc7nzzjuxtbXl9ddfN+n4CFHWQ9DpdNTV1XV4kLhh+lKv15OWlkZ6ejojRowgICCgSy6IXREp0+v1nD17lvPnzzNq1Cg5OiZh7ZEyCcOLUVFREYWFhXIqctGiRbKA3rp1K/X19YwZM4bw8HBZWF1xxRVyXVVL8ycbM2zYMGbNmsXHH39MXl4eq1evlv+2c+dOdu7cKf/s4uKCSqXi6quvpra2FmhoEFi0aBFXX30106ZNo76+nsrKSqPjf+2117J+/XoAWUwqlUqOHz/O8OHDqa2tJTAwkKqqKq6//nrZ+HbgwIFNtreiosLkQmBnZ2fee+897rzzTtLT07nvvvvYsmVLj7OPgM5L/6tUKiM7BKnTTq1Wk5SUZFTELTUNWEJ09EbjWGjYr554fpmCJew+DO1eBg0aJDsJ7Nu3jz179lBSUsLgwYO54ooruPLKK7niiit45513uP/++1myZAnQ0Fm9c+dOvvjiC5599tkm7/Hee+9x7bXXykbTq1evZt++faxfv56PP/4YvV7PunXrWLlyJXPnzgXg66+/xsfHh+3bt7Nw4UIAuYN8w4YNze7L3r17SUhI4Pfff8fHx4cJEyawevVqnnnmGV555ZVWSyzk42H2ERR0KVK6sra2tsOCDP6XvqypqeH48ePk5uYSGhrKwIEDu+yC2NmirLa2lpMnT5Kbm8vUqVPx9/dv1q+tJ0TKDKNL7u7uTJ06VRZWo0aNkv8mpeIuv/xy2QbDzc2NZcuWye/VkiBzc3NjzZo17Nq1i8zMTE6dOiXbYOh0Ouzt7Rk6dCjjx483sg5xdnamoqKC999/n9raWvkY29jY0KdPH1xdXeXUV2FhodF7Gm573759GTlyJDqdjs2bNwMN3V3XX389QJvhf3M7syZMmMAHH3wANNTESf/fE+mK76zUaTdq1CjCw8MJDQ3Fx8eHyspKYmJiOHToENHR0WRmZhqlnsylNxrHQu+OlHWGeaxSqeSqq67iX//6F++88w79+vXjyy+/ZODAgXz66acEBQVx/PhxBg0a1OQ10vSSxkRGRsqlCxKzZ8+Wl09PTycvL89oGXd3d0JDQ1tcZ0vv09hcffbs2ZSVlREfH2/SOnqnfO8lmOo9Zg5KpVK2EujXr59RSq8r6SxbDCkV6+XlRUhISIv71t2RMlPeW6/XM3bsWFnEvf/++9xyyy1yvVVMTIws2qSQfU5ODl988QUAZWVlJm2Po6MjLi4ufP311+Tk5FBVVWV0ATl58iRpaWmMHz+eKVOmAA1TAE6dOtVke6FB9ADs3r0baKhRW758udGy0jLQkBKYMWMGSUlJRmnWNWvWsG/fPs6cOcOWLVuM6us6yrx588jLy+OZZ55h9erVzJ8/n8DAQIutvyvojoeK5oq4KyoqUKvVFBUVkZaWJqdDpaYBU82me6NxLAhR1hEkO4xZs2bJgiklJYURI0YQHBxstKyPjw9JSUnNricvL69Jx7ePj498LZX+29oyptDS+xi+R1v0zjOlhyMNEpeK+RUKhUUEmU6no7i4mOLiYkaNGkVwcHC3hdUtHamSUrGnTp0iKCiIcePGtbpv3R0pawuNRkNUVBRZWVkMHz4cgFOnTsm1YgBbtmyR90GKYJ08eVJOI5rCyy+/TFVVFcuWLeP777/n4MGDnDx50sjTTFr3P//5TwoLC7G1teXQoUNUVVUxceJEeYi4hFT8L0Xs7rnnHtnVXyItLU3+f8kpHJB9zaR9Wrp0KdBQ8G8ppOP/0EMPMWnSJDQaDe+9957F1t9VWENkSaoPGjhwIBMmTODyyy8nODgYJycncnNzOXr0KEeOHCEpKYmLFy82mXtqSG9OX/ZWUdbZTQyVlZVN7FqkyLhkldPb6J1nSg/GsLvSkoWvVVVVHDt2jKqqKry8vPD19bXA1rYfS0bK6urqOHXqFNnZ2SanYq05UlZeXs6RI0fQaDSEh4fzww8/yALznXfekZe7cOGC3JEZFBQEQHZ2NtAQOWt80WrcwDF69GhWrVpFeXl5i9v5xBNPcOTIEWpra/n111+BhvRk3759+eCDD3BwcJBtMACWLFlCREQEO3fulEc3SWlIQwwHhGu1Wq655hp27NjBLbfcYrTcggULADh48KC8bx3BUIgrFAp5esA333xjZA/SE7AGUdYYaWj6kCFDmDRpEtOnT2f48OHY2NiQkZHB33//zfHjx0lNTaWwsNAoLW2N+2MJeqsok4IHnblvzRnH9u3bFxsbGy5evGj0+4sXL9K/f/9m19O/f/9Wl5f+a846zXkfw/doi953pvRgpGJ+qQ7HEtExaLBSOHLkCB4eHp3mPWYulhJlarWaw4cPY2NjQ1hYWJtPT5s3b2bx4sXk5+dbZU1ZTk4OR48eZcCAAUyePBk7OzuGDx/ON9980+Til5+fL6ctJVNZiYCAAMrKynBzc5PD52FhYUY1ao0HgwcHB8s+YgCvvPIKjz32GJMmTUKr1XL33XdzzTXX8MgjjxAREcEzzzzDkSNH5O2aOHEi77zzDhqNRi6ovffee2XBaEhWVpbRzzt27GDGjBlNlg0MDCQ8PBy9Xt+st1lHufbaawkJCaG2ttbIqLcn0BNEjEqlom/fvgwbNozLLruMadOmERgYiFarJTU1lUOHDnHq1CnOnTtHWVmZ1e9Pe+iJsy9NQbp+d2akTBqEboidnR2TJk0ymiCi0+nYv38/YWFhza4nLCysycSRffv2ycsPHjyY/v37Gy1TVlbGsWPHWlxnS+8TGxtrNP1k3759uLm5MXr0aJPW0fvOlB5Ic95jlvgSa7Va4uLiSEhIIDg4mFGjRmFra2sVcyc7Ksr0ej3p6emcPHmSIUOGMGHChDatPLRaLS+//DI//PADu3fvtqruS51OR0JCAomJiUyYMIFhw4YZ3aDmzZvHjh07ZNsCidtvv5309PQm3UCnTp3C0dGRH3/8URZil112WbOeU3PmzOHQoUMsXLhQdvdXqVTMnj2b77//HmdnZ+zs7Lj++uuxtbXlnXfe4eeff6aqqgpPT0+5GWDatGm8++67PPPMM2RlZeHq6ip3KzXe18b7v3///haL+W+99Vbgf8PVLYlCoeDVV18F4Msvv5S93noKPU3E2NnZ4ePjw8iRI+WHhAEDBlBdXc358+epqakhKiqKjIwMysvLu+3ByZL01khZV4iyqqqqZht5li9fzmeffcZXX31FYmIiDz/8MJWVlXI35p133slzzz0nL//Pf/6TPXv28Pbbb5OUlMQrr7zCyZMn5fIIhULBsmXLWLNmDb/88guxsbHceeed+Pr6Gln9XLhwgaioKC5cuIBWqyUqKoqoqCgqKiqABlPv0aNHs3jxYqKjo/ntt99YuXIljz76qMmTQkShfzdjCe+x5igvLyc6OhqVSkV4eLhcbGvKmKWuoCOirK6ujtjYWMrLy5kyZUqLJn6N2bJlC+fPn8fV1ZVFixZ12wW/cT1bdXU1UVFR6PV6wsLCcHJyavKa9PR08vPzefTRR3n//fflIv59+/Yxfvz4Zt/nscceIyQkhLFjx/Lzzz/z4YcfNutXdvz4cZ577jn+/vtv+Xd2dnaymPvwww9JTk42qlUbPHgwHh4esglsbW2t0UgmaDgHhw8fjqurK5MnT+btt9+WDWgbU1FRQXR0NJMmTWryN6mmTrrwWZoZM2Ywf/58tm7dyuLFi9mzZ0+3p/dNoTcIFsPxPJJHVZ8+feRh11I61LBpoKcJ0d4qygzvWZ1FZWVlsyOWFixYQEFBAS+99BJ5eXlMmDCBPXv2yFmBCxcuGG1XeHg4mzZtYuXKlTz//PMMGzaM7du3yx5lAE8//TSVlZU88MADlJSUMG3aNPbs2WNkS/TSSy/x1VdfyT+HhIQA8OeffzJz5kxsbGz49ddfefjhhwkLC8PZ2Zm77rpLfvAzBYW+N3yzeyiW8h4zRK/Xk5WVRVJSEoGBgQwdOtTo5MzPzyc1NZWIiIgOv1dHOHLkCEFBQU06VdqipKSEqKgo3NzcCA4ONtno9sKFC4SFhVFUVMRjjz3GPffcAxhbM3QVZ8+epbq6muDgYAoLC4mOjsbHx4dRo0Y1+9QZGhra7giOk5MTR44cYcqUKU2iZOZYc7SFs7MzgwcPJjAwkGPHjlFYWGg0KxMaZnJu3bqVXbt2NZsqXLt2LQ899FCT3+/cuZPbb7+dcePGGQnH9nDkyBFGjRrVJOKYl5fHNddcw/nz5wkJCeHPP/+0+hupZOra07pGWyI/P5+MjAy5w1cyFS0uLkatVlNaWoqdnZ1RZ2dPmFN69OhRhg0bRp8+fbp7UyxKZWUlJ06caHb2rKV44YUXqKur4+OPP+6097A2RKSsG5AKJOvr6+U2cEsIMo1GQ1xcHMXFxYSEhNC3b98my3SWFYW5mFtobziXc+jQoQwaNMjkY6bT6bjvvvsoKioiJCSEVatWkZmZ2W0RQ2nf09LSOHfuHKNGjcLf37/ZZc+ePdusIJNqdS6//HK2bNkCQJ+RVxP66K8olSpqywv4/cXBVFVV8c9//rPJ6yUj2b59+1JcXGz0WUimoQUFBSbvU2VlJXFxccTFxcm/a5yOVKvVXHHFFS2u4++//25WlEnnceMC2vbS3HnTv39/duzYwW233cabb75p9YIMekZNmTk0tsQwNBUdPHgwWq2W0tJSiouLyczMJCEhAWdnZ3nSgKenp1WatPbmSFlnj4+qrKxs8gDV27G+M7iX01npytLSUqKionByciIiIqLFJ8iemL6sr68nLi6O0tJSJk+ebPaXdNOmTRw8eBAnJye+/fZbucW6s8VpRUUFL7/8MsuXL8fPz0/+vWRNUlpaSmhoaKvNCY3tJiZPnsySJUu4ePEiOTk5XLhwAQ8PDxwGXcXkB7bI55KDuw/Xv1fJr48omx1GLp0DjU1doXkxZmdnx+DBg3F2dkapVFJZWUlSUhJ6vZ4+ffrg6elJUVGRbG1hb2/PyJEjqayspKKigrKyMnmweUvs3r2bixcvNomeWtJsuLXIYGBgIEeOHOlRcwp7kyhryxLDxsYGLy8veSxWfX29PGkgLS2N6upqo3FQ7u7uVvFZ9lZR1hUzPaurq1t8YO2tCFHWhUjRMUunK8+fP8/Zs2cJCgpqs7vScMxSd2KqT5gkNp2dnQkPDzdpTIUhRUVFcsHn888/L3f3NZe60+v1FBQU4OnpaZH5n+vXr+fDDz/k22+/5amnnsLZ2Zn+/fvLEYCwsDCj99Hr9eTl5XH69GmOHj0qWwcYCtiTJ09y8uTJJu/1jyVfN/ncFQoFI256neTtz7e4jb6+vvTv3x93d3fc3Ny4ePEiJ0+eRKPREBgYyLx587jmmmuoqqpi+vTpRkagd999N1u3bmXGjBls2LCB9957T/ZR+/TTT5k3b57Re2k0Gv744w/Z9sLV1VW243B2dqayspKPP/6Yu+++Gy8vL1xcXFAoFLJIKy0t7fQbnDXcxE2lN0bKzNkfW1tb+vXrJ3cg19TUyCItISEBjUbTZBxUdxyv3irKuiJS1lz3ZW9HiLIuQOqulNI5lhJkhgXvpkaQrCV92dZ26PV6MjMzSU5ONklsGvLXX3+xdOlSgoKCGDVqFAUFBYwaNYrHH3/c6P0bi7Lg4GDOnj3L0aNHjRzn28uVV17JTz/9RGxsrJHpq1KpZOPGjeh0Og4dOsQff/zBwYMHiY+PNxpAbohU8Ozt7c2QIUPw8fHB29sbPz8/Bg4cyFcX7WjuaLale6uqqsjNzcXBwYEdO3bIn8nll1/Od999h6urK9BQyNqYiIgItm7dKtuSvPzyywCsXLnSqGNJQqVSGTlujxkzhuPHj6PT6eQmgqSkJEpLS+Uib09PT/nCX1dXR3V19SV3kW6J3lYO3FHx4uDgwIABAxgwYAB6vZ6qqipZpF24cAHAqGmgsSlpZ9GbRVln71dzPmW9HSHKOhmdTkd9fT2RkZGMHTtWfvrvKNI4IQ8PDyIiIkyO7PSE9KVUG6dWq5k0aZKcrjAVhUJBamoqer2eI0eOAPD6668bRdkM3z8rK4vt27dz9uxZoGF8kSVE2WWXXcbhw4d5//33Wblypfx7nU7Ha6+9xpIlS6ipqTF6jVKpZPjw4YSGhhIaGsq4cePw8/OjX79+TZ5KpcirUqlk3UNrcJv2ktG5pdfrSfnlBVqjpKSEkpIS2YS2f//+hIaGcvnll7Nx40bc3NwICgoiMTERDw8PBg4ciJ2dHa6urrLXmJOTE/fccw86nY6FCxeyYsWKZs9xvV7Pf//7X/lnyf/n6NGj8gPL7t27eeaZZ5g+fbpc5C2Z0Pbp04esrCy8vLzw8PCwSDSzJ3OpR8paQ6FQ4OzsjLOzM/7+/uj1evl8kro8VSqVUdOAYZedpdDr9b1alHVFTZkQZQKLIH0ZpXRldXW1PDKpo+tNS0sjPT2dESNGEBAQYNY6pQhRd1/QWxJlZWVlREVF4ejoSHh4uFndVXl5eaxbt06+8RcXF1NWVkZAQACzZ8+mvr6eTZs28fPPP5OQkEBRURG2trYUFxfL6/Dx8eHKK680Wm9BQQE7duxg0aJFZnd7aTQanJ2dsbW1pb6+Xk6bSgXxPj4+XH755Vx55ZVMnjyZYcOGtevmcGTLGjxjT3HZAz+isLGlvrKEfS8EgF7PunXreOWVV1qMwhmSl5fHzz//zM8//9ziMiqVCicnJ1lQ7t27l/r6ekaMGMG7777b4nmVlJQkD06HBk+fjIwM+ed58+axbds2VqxYwb59++Qib2kg8PDhw1EoFEb1Q1KNkZubm8k3vt4iZLr7O2xpOnP2pUKhwM3NDTc3NwYNGoRWq6WsrIzi4mKys7NJSkrC0dHRqGnAEqJfimb2pLS4qXRFTVlVVdUlFxkXoqwTaK6YX+p26wg1NTVER0dTV1fXZoF4S0hfoq74QrVGY1FmaOUxePBggoKC2rzh6PV6Tp48yXvvvcepU6dIT083+rsktqZNm8a2bdt48cUXOXfuXJP1KBQKQkNDWbBgAYsWLcLFxYX//ve/LFiwgE8++YTXX3+dqqoq/P39ueaaa0zex/z8fLZv385zzz0n21FIhfE33ngjS5cuZfTo0e2+sUriGmDFihW88cYb/PaEMxqNBj8/P3R1lUBD6jE+Pp5XX32Vr776qkl0rjlsbGzw8fFh4MCBZGVlUVNTQ1VVFVVVVWg0GqNh59K+TZ48udV9SU5OBv5Xz+fv7280ieDZZ59l586dHDt2jJSUFNmfTPKhk3zP4H/1Q8XFxcTGxqLT6eTUlJeXV5elprqb3rSPXTn7UhqaLpV8aDQaOdWZnp4u241Iy3h4eLTreild43prpKwz90uv11NZWSmXUFwqCFFmYaTomHTCSheZjhbY5+fnExsbS79+/Zg0aVK7W7+lL1FXhJ7b2g7peGg0GhISEigsLGTixIkm+fmUl5dz8803c/DgQaPf29nZNRl6vGXLFnmgtbe3N0uXLmXIkCFotVpGjBhBUFCQLHArKyu56aab2Lt3L88//7wsPsaNG2dyDY9OpyMlJYV//etfRoO0hwwZwuOPP86sWbMoKSlhzJgxJq3PFFasWMFnn30md1MGBwfLsyKPHTvGzTffzKJFi4iMjOSpp55qMnKkMVqtlpycHHJycvD19WXRokUsX74cR0dHioqKqKqqorKykpkzZ8qf48aNG6moqOCbb75pdp2S0ay9vT01NTXExMTI8zShYdDw1KlTOXjwIEeOHJEFmGSJYTijs3H9UEVFBWq1mqKiItLS0lCpVLJAM/Sz6k11WL1pX6B7I38qlQpvb2/ZDqa2tlYWaUlJSdTV1TVpGjBFkPR2USbSl5ZHiDILIXmPaTSaZrsr2xspk27wmZmZjBkzpsNO45a0F+jodkjmkFFRUdjb2xMeHm5S6i4lJYUHH3yQyMhI7OzsmDt3LhMmTJCNBhsjHfchQ4bwyiuvsG3bNj788EN5PpmDgwN2dnbY2dlRVFQk3+zKyspQqVQMGDCA/Px8brnlFurr61GpVLi5ueHt7S3Xori7u+Pk5IS9vT15eXmcOnXKyFritdde44knnkCpVJKdnS1bR1iK2tpabrvtNj766COgoU5LIi8vj7y8PKPfmUNOTg7vvfceGzduZOXKlVx11VUMHTqUDz/8EJ1Ox6BBg3jrrbd46qmnjEabNGbnzp1Aw2ioWbNmMW7cOKPC/wsXLjB+/HgOHjxo5Hdm+CDRHAqFAldXV1xdXRk4cKDsZ6VWq438rLy8vNDpdFZRU2kJelv60ppqr+zt7enfvz/9+/dHr9dTXV0ti7SsrCyjyKynpyfOzs7NfhbSdbY3fU4SXZFtEaJM0C5M8R5rjyirqqoiKioKaBgTYYncurRt3S3KFAoFZWVlZGZmMmjQIIKCgky6IK9du5ZXXnlFviA88sgjQMP4C2i9gaC8vJw777yzye9rampaTOlpNBoyMzOb/K64uNioFq05lEolTk5OXH/99Tg6OvLzzz8zYMCAJk73luDRRx9l69at8s9S/dgdd9zBpk2bmiw/evRoNmzYwKpVq2Sx1BaFhYUsW7YMaIhqSR2Tjz/+ONdccw2nT59uMYKbmppKSkoKAE8++SQDBw5ErVbzf//3f+zZs4fCwkJyc3MpLS0FMDI+ltKXpgpZQz+roKAg2c+quLgYjUZDdHQ07u7u8jLdZZXQUXqbKLPW/VEoFDg5OeHk5ISfn59RZLa4uJi0tDQ5HWo4Dgpo9gG9t9DZkTIpfSlqygRmYar3mLmiLDc3l/j4ePz8/BgxYoRFnyC7uwNTq9VSVFREZWUlISEhcsqgLZ5//nneeecdo/WsW7fOaJnWxKapDvX29vbY2Ng0a3bq6uqKh4eHLMTt7OywsbGhurqa2tpa6uvrqayslLeloqKCzZs3s3nzZqP12NraEhQUxLBhw8jMzMTBwYFrr72WK664AkdHR6qqqmRR5+7ujo+PT6sebYaCzHBbP/74Y9566y2OHTvGrbfeKovBhIQE3n//fb788ksmTZpEYWFhs3MxDXFwcJDFq+EcyhdeeIFffvmFoKAgampqGDZsGMOGDWPAgAFMnjwZgLfeeguA2bNnM3DgQKBh7NJHH33EkiVL+Omnn8jLy5O3z1DcSedHZWUl1dXVRl5ppmDoZ1VYWMjw4cOpr6+nuLhYtkowvKE2N3vUGrFWEdNeOrPQ35I0jszqdDq5aSA3N5fk5GTs7e271HajO9BqtZ06QaGqqgq9Xi9qygSmYeg9ZsqoJFOFkFarJTExkYsXLxIcHGz2bEhT6E6vsoqKCqKiotBoNEY1HG3xySefyILM1dWVsLAwvL29SUtL4/z58+Tl5bV7mxQKBUOHDmXevHlcf/31hIaGAg0iIDU1lYsXL+Lk5ERQUBADBgww+pwN7TsmTJiAp6cn5eXlpKenc/78edLT08nMzCQ7O5vc3Fy5Tqu+vp6kpCSj9N3Ro0d55ZVXmt1GlUrFuHHjmDRpEiNHjmTo0KEEBwfLhcpBQUGkpaXx5JNP8vbbbwMNXmPQENWaNWsWn332GUuWLJHXuWnTJt58800OHTqEnZ0dERERTZolDJEEmY+PDzU1NXJUq7q6mgMHDnDgwAGj5e+44w4mT56MXq+Xfc4MveIkpHOxoqKCw4cPAzBixAj571Ka2c7OziI3bQcHB/r16ydHPSSrhIsXL5KSkoK9vb1RPZo1W2/0pht+dzcftRfJQ1CK6Go0GnkcVE5ODlqtluPHjxs1DVjjOChz6exImfRwK9KXgjbR6XRoNBqzRiWZUuhfXl5OdHQ0KpWK8PBwsyMCptJdrv45OTnEx8czcOBAVCqVUeF2WyxevJjly5ej1WpZu3atkbgYP358s6KscZpw6NCh/PjjjwwfPpycnBz+/PNPtmzZwr59+0hNTeXXX3/lrrvukpd3dnZu1a+soqKCM2fOyPVwUjG5q6sr48aNY9y4cc2+Ljc3l8jISFxdXUlNTeWNN95ArVaj1WqNirednJyws7OjsrKS+vp6Tp8+zenTp43WNWTIEPz8/ORz8ccff5T/NnfuXKNlb775ZrZu3cqOHTuA/zWlSI0V//73v2W3/dYwnEGpUqlwdHSktra2ST3f5s2bOXv2LCNGjCAvLw8bGxsmTZrUZH2STUZiYqJskWE4/snT05N//etfFBQUWHwAdXNWCSUlJRQXFxt14Ukizd3d3WqiOb2x0N9ajm1HUKlU9OnThz59+tC3b18SEhIIDAxErVaTmppKTU0Nbm5uRuOgeuJ+d7aIrqysxMbGpkcMnbckQpSZgaH3mJQ6MPVJtbX0paEdRGBgIEOHDu3UL2lXR8q0Wi1JSUnk5eUxfvx4+vXrx/nz5826qTg5OXHXXXfxxRdfcPToUZYsWUJpaSnz5s2TrRYaYyjIpk+fzvbt2+X6BAcHBwYNGsQvv/zCn3/+yX333UdCQgKhoaFs2LCBOXPmtLo9ubm5xMXFMXDgQIYNG2bW56VSqejXrx/Tpk3j6quvluviSkpK+Omnn/jiiy84deqUnD79+eefGTJkCCdOnCA6OprU1FSSkpJISUnh3LlzRjYfhr5fO3fupLa2lsmTJ+Pr64uHhwdr164lMjKSwsJCHn74YaMpELNmzWL8+PFER0czadIkHB0d+fvvv5ts/5NPPklCQgKHDx+mrKxMFte33HILpaWlZGZmUlBQQFFREcePH+f48eNAw3kQHh7ODTfcwOrVq+X1SU/C27Ztk39nmErt27cvDz/8sMnHtzXaOudsbGzkGyr8rwuvuLiY+Ph4NBqNkfVGSwXeXUFvS192pSVGVyEJFx8fHznrYdg0kJOTI59TUgrdUgbjnU1nW2JI9WQ9UbB2BIW+tz1udRKNi/nNEWSAPLx51KhRRr+vr68nPj4etVrNuHHjTLKD6Ch///03w4cPl2fGdSaVlZVERUWhVCqZMGGCHP27cOECBQUFzUZOWuKvv/5i9uzZKBQKXn/9dV599dU266AMsbGxYcaMGdx6660MHTqUgoIC5s+fDzS4+t91110cPnwYhULBnXfeyZtvvtlkdJVOpyM5OZns7Ox2p5cLCgpISkpi+vTpLS4TFxfHk08+yV9//YWLiwvbt29n2rRpRsvk5eURExNDQUEBCQkJfPTRR60ej61bt3LVVVexbds27rrrLpydnUlNTTVKD0RHRzNz5ky0Wi1ffPEFPj4+vPjii3KEbsyYMRw5ckT2GktMTOTTTz/liy++AGDixIm8/fbbTJw4kfT0dCIjI3n66aeNoqLz589nw4YNQMP3aurUqSQmJgKwdOlS7rzzTkaOHGneQTWRQ4cOMX78+HZ5/EmFx5JIKykpQalUygLNy8urS5/qjx49yrBhw7rkmtEVxMfH4+zszKBBg7p7UyxGQUEB6enpXHbZZc3+3fCckv4pFIomTQPWKNJOnjxJQEBAp5TYAERGRrJkyRKysrKscv87CxEpM4GWvMfMwcbGRu5Wk5CGbTs5OZntXt8Ruip9mZeXR1xcXLPNCo2jdb///jt5eXl89dVXnDp1isGDB5Oeno6rqyu+vr64uroyceJEpk6dytGjR42sF9rqZrSzs8PJyYmSkhL++OMP/vjjD6DBQV4SZf7+/uzZs4cnn3ySTz/9lK+++opDhw7x008/yUK6pqaGqKgotFotYWFh7e4KMmUY+9ixY/nll1+YM2cOhw4d4o477mDnzp0EBwfLy3h7ezN9+nRsbGzYtGmTkSBzdHTk0Ucf5fDhwyQmJlJSUkL//v0BuOmmm+QatJtvvpnffvtNft348eN54okneOutt3jwwQf5/PPP2b17N7fddht//fUX1dXVlJaW4uHhgUKhYPTo0axbt44JEyawcuVKTp8+zZVXXsmtt97KhAkT5JotaGguSEhIwN3dXX6/Z555hsTERGxsbPjjjz8ICQlp1zHtChQKBS4uLri4uBAQEGBU4G3oCi8JtK6oHepNNytrssSwFG3tU3PnVHl5OWq1Wq5xtLOzMxJp1pLO6+z05aXo5g9ClLVKW95j5mAoQvR6PefPn+fs2bNmD9u2BJ2dvtTpdCQlJZGTk9NiNEnahry8PK644oomBebx8fFAwxdTqmFqbBQrodFomLrsAH2HXy7/riI/lQOvNBSL19XVGdU72djYYGtr2+QY2Nra8v7777Nw4UKWLFnCuXPnmDFjBu+//z5XXXUVMTEx9O3blzFjxnToYiRFmdrC3t6e7du3M2PGDOLi4pgxYwabN2/m6quvltcjIUUgBw8ezPnz56muruaBBx6QrUKqq6vl7k2FQsG8efN46623iIyM5MMPP+TRRx+V1/Xcc89x7tw5tm7dyt1338369et55plnmD9/PnPmzJELmg25++67+cc//sFLL73Ed999x5YtW9iyZYvRMgkJCcyaNcvo2AUHB6NQKHjzzTetWpA1h2GB95AhQ6ivr5fr0QxrhwytNywpOnpb+rK37Q+YLzSVSqU8XkyqcZSaBgw99wzHQXVX00BXFPr35u7VlhDpyxboaLqyMefPn0etVjNmzBhiY2MpLy9nwoQJzd7gOpsTJ07Qv39/AgICLL7uqqoqoqOj0ev1TJgwoUV7gdzcXNatW8dnn33WxHrC2dmZhx56iDvvvJPKykpyc3MpLCzk9OnTFBcXU1NTw86dO2VRNfXx3/EeNavJe9jUF+Jw5DF2794tdwoaIqUJLr/8csaNG8eIESMYOHAg/v7+2NrasmDBArkbMCwsjFdffZVp06Z1+CKhVqvlNKEpFBcXs3jxYvbv34+trS0vvvgi9913Hx4eHtTW1qJUKpkxYwZRUVH84x//4Pjx4xQVFXH06FFGjx7d7DrT09MZP3480OADdvLkSaN0tlarZdmyZXz11VcAPPTQQ7zwwgtGUa6WiIyM5OOPP0an05GWlmZkBDtkyBA++eQTucNVr9cTHx/P2LFjTToWHeXQoUNMmDChS9rspdqh4uJi1Go1Op2uifVGR86lI0eOMGrUqCYp9p5KdHQ0ffv2xc/Pr7s3xWJkZ2dTUFDQasOQOUiee9I/aQasYdNAV3Ww/v333wQHB5t0TWgPmzZt4ptvvmm2rrU3I0RZM+h0Ourq6ixq/JeZmSnPEPTw8GDs2LHd1mp/+vRp+vTpQ2BgoEXXe/HiRWJjY/H19WXkyJEtPiHq9XqWLl3K559/3uRvTz31FK+++mqrT5fHjx+X7R4A5nzUUoGwnk3/V4lSqeSLL75gxYoVJs19hP911Lq6uspGrCNGjODOO+/k9ttv79BkhZKSEs6cOcMVV1xh8mvq6uq455575O5KZ2dnHn30UZYtW8b333/P8uXLATh16hQ33HADOTk5/PXXX61Gn+bMmSNHH0NCQti7d69RakSv1/PCCy+wfv16oKEW7L333jPrIqzX6xk+fDgXL17E2dlZLt79888/O61urDW6UpQZIhmOSqbDpaWl2NraygLNy8urVR+65jh8+DBjxozplge7zuDMmTP4+Ph0eGqJNZGZmSnXC3cG0gxY6V99fX2TcVCdFWn666+/mDRpUqdZVnz22Wf89ttvRuUVlwIifWmAlK6UuistJcj0ej0FBQWUlpYyevRoAgICujUka+n0pTQKKisrizFjxjBgwIBWl3///febCDI7Ozu++OKLNi0Z4uPjuf32203aLr0efH19ufXWW7nnnns4fPgwjz/+uBz9cnV1bdGWQzo+kiCDhoHaL7zwAocPHyYwMBCtVouTkxNDhw4lMDAQFxcXlEol9fX1aDQa6uvrKSoqorCwEIVCIXvVlZSUyGlbU7Gzs+Orr77immuu4YMPPiA2Npa1a9fy008/MWzYMKBhPuewYcNwc3MjJyfHaGh4c0gpXWdnZ86cOcPdd9/Nhg0bZGGmUCh47bXX8PT0ZM2aNWzdupWqqqomacnWUCgU9OnTh4sXL/L555/zySef0K9fP3mbu5ruegY1NByVzh0pLXXhwgUSEhJwcXGRRZopA7B7W7qvt+0PdH6dXOMZsFVVVbJAk4yRDcdBWTId2BWWGKKm7BKmPd5jplBTU0N0dLRctCi5mXcnlnT0r66uJjo62uTi9yNHjvDss88a/c7X15d9+/YRFBTU4utyc3N54okn2L59OwAjR47k7bffZunSpZRkRuE5sGlEqK68gPLycr744gu++OILZs6cyYIFC5g1axZvv/025eXljBkzBmdnZ86ePdvs2KTmmgh27drV6j6agpubmxyBMhUbGxvuvPNOFi9ezC+//MITTzxBWloaaWlpAHInpre3N0lJSUZ+Ys0h3SweffRR3nvvPXbu3MnChQv58ccf5YutQqFgxYoVuLm58eyzz7Jnzx4++eQTbrjhBpMiGnq9nqKiIqDBdHbz5s3Y2tr2SJNQS2I4CgoaBLKU6kxKSpIjHtLNtKWIR28SMZdiob8lUSgUODs74+zsjL+/v9xko1arKSgo4OzZs6hUKqMUuimzhptDp9Oh1+s7XZRdasaxIERZh7zH2iI/P5/Y2Fj69etHYGAgZ8+etch6O4qlui8LCgqIiYnBx8eHUaNGtfkFzcvLY86cOUbRipCQEHbu3CnfnJojOTmZ6667juzsbABuvPFGPvjgA3x8fDh27Bj9+/dn+so4XPs3pMMUCnCx07P5MVcOTfiNDRs28MMPPxi5zkvbKjUUSPTp0wdHR0f0er3sIdSYq666ismTJ6NQKKioqCA5OZmcnByqqqrQ6XSoVCpsbW2xtbXF3d1d7nqUCmOVSqXJadTmUCgUzJ07l4iICJ544gl++OEHoKFRwc7OTq4VNPQsaw7JFsLPz48ffviB22+/nf379/P7778ze/Zso2XHjRvH7Nmz2bVrFytWrOCll15izZo13Hfffa1+X06fPs3Fixext7dn8ODB7b4J9Hbs7OxkLytpALaU6jx//jxKpbKJTUJviyz1tv2B7p1SYGiMLEVnG3cLOzg4yOeUOdMrDIMXnYUQZZcghqOSoOPF/BJSOi8zM5MxY8bg6+tLcXFxt86bNKSjkTKdTkdqaioXLlyQ968tiouLmTBhglzU36dPH2xtbfnll19kQabVaomMjOTcuXPk5eXh6+uLp6cnixcvpqqqiuHDh/PNN9/IBerQICwcHBz469XRDBw4kOPHjxvU2DQUwM+YMYNXXnmFTZs28ccff3D8+HEjexKFQsEdd9xBREQEI0eOJCQkBEdHR0pKSjh8+DCRkZGcPHmS1NRU1Go11113nWz42h4qKiqIjIxs9+slvLy8eP311/n111+prq5mzpw5cucWIEeoWsLf3x9ocNJfsmQJ7777Lu7u7k0EmcRrr72Gn58fBw4cIDU1lSeffJK9e/eycePGFuuhJCE9cuTIVoW34H8YDsD29/eXbRIMZys6ODig0WhQq9U4ODhY9SgoU+ktjv6GWFP0TxqaLjWGaDSaZqdXGI6DaklQSg/1nR0p6y0efOZwyYoyKTomnVyW+uJUVVURFRUFQHh4uJzOM3cgeWeiVCpb9fVqDSkdW19fT1hYmMlPMn///bdcn/XWW29x4403sn//fgoLCzl+/DgpKSl8++23TSJXEg4ODoSHh7N37142b95MZmYmWq2WmpoaWejl5OS0WPQcGBjIc889x3PPPUdtbS0XLlwgPj6ebdu2sXnzZr7//ntCQ0OZOnWqfC54eHhw/fXXc/311xutq6N1SaZaYpjCF198QXV1NV5eXvJw9sGDBwOwY8cOXn755RbHdc2YMYP//ve/7Nmzh7Vr17Jw4cJWt9nT05O3334bnU7Hxx9/zMsvv0zfvn1bLVCXavas5cYEPS/lZ2iTMHjwYPlmGhsbS1ZWFmlpafIoqJ4+tqenfTZtIUXOrRGVSkXfvn3p27cv8L/pFWq1muTkZGpra5s0DUjnVUc8O02lsrLS4s1oPQHrPFs6EUt6jzUmNzeX+Pj4Zs1SrUmU2djYNJlTaAqFhYXExMTg7e3NqFGjTL7YZGRkcO+99wINflZLly4lNzeXb775psn4HHd3dyZPnkz//v2JjY0lJiYGaBCDkgt8S5j6OUqptLS0NFasWEFubi4HDx7khRdeYPDgwbIPWEffp7XXW0qUSespLi4mISGBiIgIbr31Vt566y0yMzPZunUrixYtava1UnTF1Bu49F5KpZJHHnmEmTNntmmrEhERATTYHUj1boKOId1MlUol48ePx8bGRq5HkwZgGxZ3d+coKHMQkbLuxd7env79+9O/f385hS6JtKysLHQ6nTwOyt7evtPPKVHofwnQ2HvMUoJMq9WSmJjIxYsXWzVLlQZOd/cF0tz0pV6v5+zZs5w/f55Ro0bJaS9TX/vYY49RXl7O1KlTWb9+PUVFRdxwww3ExcWhVCoZN24cgYGBTJ8+nUWLFuHp6YlGo5GbIvr27cs777zD8ePHZTd5f39/FAoFb7/9tlzQPnHiRJO3S4oUFhUV8d1337FgwQL+/vtvbrjhBrZu3cp1111n8rrMRRJlljgX/vnPf3L+/HnOnz/P1KlTgYbU8KOPPsrLL7/MG2+8wcKFC+U0w9mzZ0lLS2PPnj1yB2xLXmaNt7kxprxu0KBBBAQEkJmZycmTJ/nHP/5hzu4JWkE6fxp34FVWVlJcXExRURFpaWmoVCoj6w1rcYRvTG+NlPUUUWaIYQrdz89PtnQxtN/Q6XTExcUZ1TlaElFT1svpDO8xaEjPREdHo1KpCA8Pb/HElG6K1iLKTC30r62tJTo6mtraWqZOnWq2v9Nvv/3G3r17sbOz47PPPqO6uloWZB4eHmzbto2wsDCj18TGxrJ8+XK5G3LChAncdttt3HbbbfIy6enp3HbbbUYdho3TjC1RWlrKmTNnAGSfnX/9619yVGfx4sWy31pnYCmblYyMDPLz83n++edxcnIyuvg/9NBDrFu3jgsXLvDXX39x5ZVXAvDee+/JhrASL774Yoe3p61tNfxvd2IN22ApmtsXw7E9AwcORKfTydYbWVlZJCYm4uzsbFTcbS2dsCJSZr0YWroMHDiQwsJCkpOTcXJykusc7e3tjZpRzPXda4wYs9RL6UzvsaysLJKSkhg0aBBBQUGtfvmkC5+Ui+9OTO2+LCoqIiYmBi8vLyZOnGh2bUR9fT0rVqwAGmwXnJ2dmTt3LqdPn8bJyYmVK1cyZcoUo9ds2rSJBx54QI5kDRgwgK1bt8p/1+l0fPvttzz11FNGPlwBAQH83//9HydOnAAahFzj4me9Xk9mZibJyckMHTqU5ORklEol27dvN0rxVVZWMnPmTP78889OcReXzr/2CnSNRkNcXBwlJSX4+flRWlpKRkYGycnJRsOx58+fz+eff87XX38tizIPDw+GDh1KWFgY1157LVdffbXJHZHtETQHDhwgKysLaHDzF1gOU84fqWvT09OToKAg2RG+uLiYlJQUuW5IupG6ubl120OjiJT1HPR6PXZ2dvJ3WqPRyOI/IyOD+Ph4XFxcjJoGzL1/iEhZL6Sz0pX19fXEx8ejVquZOHGiSR0ihqKsuzul2kpf6vV6zp07x7lz5xg5cqScKjSXDRs2kJqaire3N5dddhnh4eHk5eXh5OTEjh07KC8vl8VhYWEhq1at4rPPPjNax6effoqdnZ1cU7Zu3TrOnz8PNNRA1NbWolAoqKysNLrpOzk5MWPGDB588EFmz56NTqcjPj6ewsJCJk2ahJeXF6mpqfz44488/PDD8vEIDAzExsaGc+fO8eSTT/L999+bvd9tIR3L9lywKysrOXPmDHZ2doSGhqJQKAgMDGwyHDsxMZEpU6bw+eefs337ds6dO8eQIUNYvXo1q1evbvc2m8vOnTsBWLRoUbc4+PdWJIFs7udia2tLv3795JFahtYbktloY+uNrhJK1pBFsDTW8BDeGTTeL5VKRZ8+feR7oeS7p1arjebAGo6Dau24SGn4rp68YQ30WlGm1WrJy8vDzc0NW1tbi33ZS0tLiYqKwtnZmfDwcJPrMwwd3bub1iJldXV1REdHU11dTWhoqOxlZS7JycmySey4ceNYtGgROp2O0aNH8/333zN06FD27t2LTqdj165d3H333U0c6B944AGuuuoqtm3bxtNPP01mZibQEO1ZtmwZr776KtDwBZZSnb6+vnKB6u7du9m9ezfjx49n/vz5TJ06lfDwcDky9Ntvv/HRRx/JN7hJkyaxZ88euT5r+/btbNmyxShtagmki5G5kaf8/HxiYmLw9/dn+PDhwP9c+Zsbjq1Wq5k6dSpHjx6VO0+lKFp7ir/bEymT0suNI6LdSW+48bdXlDXG0dERPz8/uW5Ist64ePEiKSkp2Nvby+eMOT5W7UGkL3sObQ0jN/TdA4yaBiT/R6lpwMvLCxcXlybn8qUaKet1Z4sUHaurq+P06dPU1dVZLF2Znp7O8ePHCQgIYNKkSWYXzFpLB2ZLNWVqtZrDhw+jUqkICwtrtyCTZltWVlZiZ2fH/v370el0LFmyhCNHjjB8+HCUSiV6vZ533nmH+fPnGwmy0NBQ/vjjDxYuXEhoaCi33347mZmZ+Pn5sW7dOs6dO0dISIh8Y5o3bx779u2juLiYc+fOkZ2dzcmTJ1m2bBlOTk5ER0fz8ssvM3fuXJ588kliYmJ46aWX+PDDD9Hr9XKKcv78+bi6uhIcHMw///lPAO666y65/sxSGKYvTUFqtIiOjmbMmDGtzhWVkCIiUr3Ynj17SEtLQ61Wc/LkSQ4fPkxCQgJ5eXkmdeK29zv01VdfkZGRwa233tqu1wtax5ICUzIbHTRoEBMnTuTyyy9nxIgR2NjYkJ6ezqFDhzhx4gRpaWkUFxdbdFQbiPRlT8JcU1xHR0d8fX0ZM2YMERERTJ48mT59+lBaWsrp06c5dOgQsbGx/P7778TFxaHT6VqsKfvwww8ZNGgQDg4OhIaGcvz48Vbf+4cffmDkyJE4ODgQHBzcZCKLXq/npZdeYsCAATg6OnLVVVeRmppqtExxcTGLFi3Czc0NDw8P7r33XioqKoyW+e233+S6a29vb26++WY5q2MOvSpS1th7zFLO9XV1dcTGxlJRUcGUKVPaPQDY0jMn20vjiJ0kONPS0hg+fDgDBw5s98UxJyeHRx99lEOHDgENx87Pz49///vfzJ8/3+g9v/vuO6M5iv7+/qxfv57Zs2fz559/csstt1BVVYW9vT3Lly9nxYoVODk5sXv3bhYsWIBer+fuu+/m448/brJ/o0ePZsmSJUyePJm//vqLPXv2kJmZyeeff240d9PHx0eOsh07dozNmzdz5ZVX8vLLL6NWqyktLWXChAntOhYtYY4oq6+vJzY2Vu5eNTecP336dO655x5qamq47rrrcHR0lIu/i4qK5LmLks+Vl5eXRX2uJH8zgWXpioYFGxsbo5SU5GMl2a9I0Q4pitZctMNUDO1WehO9VZR1JC1r2IwSEBAgmyOr1WrWr1/PDz/8IKc4f//9d/r27SvPU968eTPLly/n448/JjQ0lHXr1jF79mySk5PllLwhR44c4fbbb+eNN95gzpw5bNq0iZtuuonTp08zduxYANauXcv777/PV199xeDBg3nxxReZPXs2CQkJclZl0aJF5Obmsm/fPurr61myZAkPPPAAmzZtAhoaz+bOncvy5cvZuHEjpaWlPPHEE8yfP5/Tp0+bd3z0vaAdyXBUkmF35YEDBxg3blyHnMSlYncPDw/Gjh3bofD9wYMHGTNmTLe7FKvVaqKjo5k5c6aR4JwwYYLsBm8u5eXlrFmzhv/85z9y5MXb25vXXnuNhQsXNunE+eGHH1i8eLH8c//+/Tl58iR9+/blxx9/5O6770aj0XDllVfyzTff0KdPH/R6Pf/973954okn0Gg0zJ07l2+++abJuqWO0bq6OiZMmICLiwt6vZ4dO3bw0EMPNTvj0hCFQkFoaCgPP/ww8+fPN/kzT0tLY//+/Xh6erYaGdLr9fz222/MnDmz1SL78vJyzpw5g5OTE+PHj2+yHVJHcVsXR41Gg42NTYs3zLq6OrmuSJo8Id1spbFTp06dYuDAgc1e+HoSBw4cYMqUKT2+q0uj0XDw4EGmT5/eLTWq0vBr6ZwpKSlBqVQaWW+YM1JLq9Xy119/MW3atA537VkTR44cYdSoUb3uwSQtLY36+vpOqRMtLy9n165dPP300wQGBhITE8PIkSOZNWsWe/bsYebMmXz66adAwzUwICCAxx57rMlMZYAFCxZQWVnJr7/+Kv9u6tSpTJgwgY8//hi9Xo+vry9PPvkkTz31FNBQouTj48OGDRtYuHAhiYmJjB49mhMnTjB58mSgIfNw3XXXkZWVha+vLz/++CO33347tbW18vV4x44dzJ07l9raWrO+oz0+UtZaMb9KpWp3utDQm2vEiBEEBAR0OLRubenLkpISoqKicHNzIzw8vN0X98LCQiZOnEh+fr78u4CAAGJjY5u9MOt0uiY2DO+++y4KhYLFixfLsxxvueUW/vvf/+Lg4EB6ejpPP/00O3bsAOC2227j888/b7LNarWaqKioJh2jycnJPPXUUxQXF+Pi4sL7779PWVkZrq6uPPDAA2i1Wm6++WaSkpKIj4/n6NGjHD16FDc3N5O9tU6dOsXjjz/O1KlTTUrXtfY8lJeXR2xsLIMGDWLo0KEdOvfa6nqys7MzMo2UfK4KCwtJS0vDzs4OrVZLaWlpp9cVCUzDUjVl7cVw+LUU7ZAaTXJyckhOTsbR0dHIeqO187C796ez6K2Rss6c6enq6sqMGTNQq9VkZ2dTU1PDgQMH+O2330hNTeWmm26Sl1UqlVx11VUtjq2LjIxk+fLlRr+bPXs227dvBxoiXHl5eVx11VXy393d3QkNDSUyMpKFCxcSGRmJh4eHLMigYf6xUqnk2LFjzJs3j0mTJqFUKvnyyy+5++67qaio4JtvvuGqq64y+3rZo0WZFB1raeRDe0WQNEqorq6uXSmjlrAWUaZQKKivr+fEiRMMGzaMwMDADl0M+/bty9SpUzlz5gzZ2dnodDq++eabFp+Uo6Oj5SHLOp2OefPm4e/vz9SpU8nMzMTGxobHHnuMNWvWoFKpOHDgADfddBM1NTXY2try8ssv8+STTxpts+TblZqaapSC1Wg0rF+/nlWrVlFdXc3QoUP58ccfGTlyJIcOHWLUqFFce+217Ny5k71791JVVcWtt97KiBEjOH78ONdee63Jx0E6T6SxTy0hzVhtTpTp9Xp5buq4ceOaNSLuTBr7XGm1WkpKSkhMTCQ/P5+srCw51dmnTx+j0SuCrsdaREzjRhNpLmdxcTFpaWlUV1fL3XdeXl64ubkZnTcifdmz0Gq1nRrRrKysxNbWFnt7exwdHZk3bx6hoaF88sknRqIMGkpQkpKSml1PXl5ek2uoj48PeXl58t+l37W2TOMMgWTILC0zePBg9u7dy2233caDDz6IVqslLCysSf2aKfRIUWbqqCQbGxuzZzzm5+cTGxtLv379mDRpkkXnllmDKKuvryc5ORm9Xt+h+rjGrF+/nnvuuYfMzExuuukm2V2+OSZMmMCrr77KSy+9hJ2dHc888wy33XYbmZmZDBo0iI0bNzJp0iSgIfJ17733UlNTQ1hYGOvXr2fMmDFG65N8u9RqNZMnT5ZTBVFRUTzyyCNyTv+KK67gm2++kWe9ScJo5cqVHDx4UJ7TWFBQwDfffGN2i77UrGCKiG9OlEmdrzU1NUydOtUqOo+kuiJHR0f8/f3x8PCQ3eKzs7PR6/XyjdbLy8virt6dgbUImY5g7ZEllUqFt7e3PFarpqZGTnVKD25SBM3Ly0uOuljr/rSX3izKOnO/KioqWuwQt8ZzJC8vj/vvv5+77rqL22+/nfLycl566SVuueUW9u3bZ9Y29zhRZo73mDnpS51OJ0coxowZ0ylu7t1d6C/ZeUg3zvbWjzUmNzeX++67j/3792Nra9uqD1ZZWRkvvvii7Ee2dOlSueg8MDCQI0eOyDWANTU13HTTTWRnZzN48GB27tyJk5OT0foqKio4c+YMDg4OREREYGdnR0ZGBmvWrGHjxo3yvLY333yTu+66y+hckT6PkJAQkpOTSUhIwM/PTx7obe6XXxq4bsrFqrEok6YMuLu7ExYWZpVDjPV6Pfb29kYjfRpbKDg4OMhRtPYYRgpMo6eVAjs4OODr64uvr688sscwRS6dJ/n5+Xh5efWKujKp1rm3irLOnAQhiTJD+vbti42NjdEUF2iw3enfv3+z6+nfv3+ry0v/vXjxotxMIP0sNXj179/fqDQHGgIBxcXF8us//PBD3N3dWbt2rbzMt99+S0BAAMeOHWs1SNGYHnXFlJz5TR2VZGpkqqqqiqioKADCw8M7rQi4uyJler2eCxcukJKSQlBQEH5+fvz5559otVqL3DTfe+899u/fDzSYvQ4bNqzZ5RITE1mwYAEpKSkAXH311SxduhRfX19OnDhBcXGxUVPGmjVrOHbsGJ6enmzZsqWJIMvNzSUuLo7AwECGDRuGQqHgyy+/5Mknn5RTiLfeeitr1641+sJJGAojLy8vpk2b1qHj8N133wFwzTXXtLms4XtnZ2eTkJBAUFAQgwcPtsonwZaeWN3c3GQbBY1GQ0lJCcXFxbJhpOQW7+Xlhaurq1XuW0/E2iNlrWE4sicwMBCtVkt+fj6JiYlkZmaSkJCAi4uLXI/m4eFhNaOgzEH6jHritrdFZ4syyQ7D8Py2s7Nj0qRJ7N+/X05h6nQ69u/fz9KlS5tdT1hYGPv372fZsmXy7/bt2yeP9hs8eDD9+/dn//79sggrKyvj2LFjPPzww/I6SkpKOHXqlJzB+eOPP9DpdISGhsrb21h8S8fH3EBMjxBler0ejUaDRqMxa1SSKSIoNzeX+Ph4/Pz8GDFiRKc+1XSHKGsutSdtg6Widrt37wbghRde4Pbbb292mZ9++omHHnqI8vJy/Pz8+Pzzz7G3t5c/xyFDhjQZwzNlyhT8/Px49913CQ4Oln+v0+lITk4mOzub8ePH069fP0pLS+V2ZIBp06bxxhtvtGpaasnIZUFBAceOHQNg4cKFbS6vUCjQarUkJCSQm5tLSEiInFa1VtqKzqhUKvr27Svvh+QWf/bsWc6ePWs0G69Pnz7dMhi7p0WYWqIni7LG2NjY4Obmho2NDVOmTJHd4IuLi0lKSqK+vh53d3f53Okp4l66tvTGSFlnRwArKyubDY4sX76cu+66i8mTJ3PZZZexbt06KisrWbJkCQB33nknfn5+vPHGGwD885//ZMaMGbz99ttcf/31fP/995w8eVLu3lQoFCxbtow1a9YwbNgw2RLD19dXFn5S3fH999/Pxx9/TH19PUuXLmXhwoVyRu3666/n3Xff5dVXX5XTl88//zyBgYGEhISYte89QpS1d1RSazVlWq2WxMRELl68yLhx47qk1b+rRVlZWZmcrjScPiB9mSwhSDQaDTNmzEChULT4tPLWW2+xcuVKoME3a+PGjfTr149jx461ug1z587lmmuuMapTqqmpISoqCq1WS3h4OE5OTpw6dYpFixbJzQMvvvgizzzzTJsXjZaK7dvDsmXL0Ov1jB07loCAAJNeEx8fDzQ8iTWOArZFfX19qzYX1oDkFr969Wp++eUXVq1axaxZs+TuPCcnJzmK1lOjId2JNX/25mJoHGvoBq/X641GQWVkZKBQKIysN6y1jrE3i7KuSF82V1O7YMECCgoKeOmll8jLy2PChAns2bNHLtS/cOGC0fEODw9n06ZNrFy5kueff55hw4axfft22aMM4Omnn6ayspIHHniAkpISpk2bxp49e4wa1TZu3MjSpUuZNWsWSqWSm2++mffff1/++5VXXsmmTZtYu3Yta9euxcnJibCwMPbs2WP2+dkjRBn8r2PNHFQqFbW1tU1+X15eTnR0NLa2toSHh3fZl7qrxiwZDkuXIlCGx046lpbYFpVKZXRyNt6OF154gXfeeQdoeMpZtWqV3CJsSqTK8LMpKioiOjoab29vRo8ejUajYdWqVfzrX/9Cp9MxaNAgvvzySzk03RYKhcIiwvTvv//mp59+QqlUsn79+jaXV6vV8tN/SEhIuy5uy5cvJzo6mpdeeonp06d3+g26IwL2yJEjVFRUMHToUPl8NByMnZycTF1dHe7u7vTp06fdY6AuJXrbnMiW9kehUODk5ISTkxP+/v6y0WhxcTF5eXlGdYxS44C1WLZI15be9DlJdKYlBtCimz801CG3FAA4cOBAk9/deuutrVoUKRQKXn31VXlsX3N4eXnJRrEtsXDhQpOyJG3RI0SZNJLHXBpHpgzFyqBBgwgKCurSp5j2dIOai0ajIT4+nqKiolaHpVtq2kFr/POf/5TDxC+99BLPPfdck0J7Uz5XwwHpo0aNwt/fn9LSUubMmcOJEyeAhi/eunXrzDLmbe951ZiDBw8CDb5qrRV06vV6MjMzSU5ORqVSMWTIkHZf2H7//XfS09P54osvCA0N7ZZUoKlI+2hoFWI4GNswGlJUVER6ejo2NjZyFM3Shd+94SbZW9KwEqbOvVQqlbi7u+Pu7s7gwYON6hjT09OJj4/H1dVVjqJZcjqFuZha+9wT6Y5C/0uFHiHK2ouhKKuvryc+Ph61Wt2qWOmq7ekMysvLiYqKwt7enoiIiFZv1F3RCRoWFsbnn3/OO++8w4MPPtiubTAcM3TZZZfh7u5OcXExN910EydOnMDLy4sPPviAm2++2ezts9QxqK6uBmh1coRUP1ZQUMCkSZOIjY3t0I116tSppKens3XrVtLT0/nzzz879ebTkRvL+PHjSUtLY+vWrVx//fXNrrtxNKS0tJTi4uImhd9SqrM3poTMobdFyto797JxHWNtba2c6oyPj5enU0girSsjsL218xI63xLjUh1GDj1ElLX3SyRFpiQrCGdnZ6Paqq6mM0VZVlYWiYmJJjvAd0Uq9fbbb+eyyy4jKCioxW1oTRSVlZVx5swZXFxcjCYOrFy5kuPHj+Pl5cXu3bsZP358u7bPUjVlki9Z49ZrierqaqPuXgcHhw7fGD755BOmTJnC8uXLOXPmDMXFxZ3eKNDeY6VWq4GGuaimoFQq5VRUUFCQ0RgoaeaioTeak5NTrxIoptKb9tnUSFlbNLZskaZTqNVqzp07J5t+SudPZ94Lerso68xIWUuF/pcCPUKUtRcbGxuqqqo4fvw4Q4cOZdCgQd16IesMUWYYgTGng68r0pdAi4IMWhdlkshsribujTfeIC8vj9WrVzcxkjUHS9WU/f777wCMGDGiyd+kOrh+/foxevRo+SLd0SidnZ0dixYtkkeIdHZdZEe+N9deey1//vknhw8fZuHChdx1110mj66ClsdAFRUVkZaWhq2trdzR2VZNUW9J+4lIWds0nk5hGIHNzs4mMTERZ2dnI+sNS/rqdXY0qbvQ6XTo9fpOrymTjIcvNXqtKKurqyM9PZ3a2lpCQ0Mt5lzfESwdnaqoqCAqKkpuWDBnAHB3G9m2tA2GXbEtiUx3d3e2bt1qkffv6E06IyODgwcPolAouPfee+Xf6/V6zp8/z9mzZxk5cmSzHZkdfe/Kykr5/83t3mwP7d3ehx56iD///JM9e/awa9cudu3aJU8/MJfmxkBJ5sPp6enExcXh5uYmR9Eaj/PpLfQWcSnRFSLTMAILGDWbGPrqGVpvdOTc6a2RMuma3dk1ZY0tki4VeoQoM/fLWlRURExMDE5OTtjZ2VmFIAPLRqdycnKIj49n4MCBDBs2zOwvf1d1gra1DYbHQzLxVSgUXdIVa4lI2W+//QZARESELLwkb7iSkpIWR1lZQhDW1dUBdIn7eUdumAqFolMmZABGDQFgXFMUGxsrj/ORImm9hd4WKbNU+tIcDJtNACPrjczMTACjUVCOjo5mHfPeKsoM7ak6i9a6L3s7PUKUmYper+fs2bOcP3+eESNG4O7uzsmTJ7t7s2Qskb40jCRNmDCh3SHerkpftoahMCwoKCAmJoYBAwYwcuTILrmYWUIYxcbGAshdl1VVVZw+fRpbW1vCwsJarFmxRD2bj48PKpWKuro6kpKSGDVqVIfW1xatbW9KSgofffQRffr04cUXX2zy9+uuu44vvvgCaPCf6ywa1xRVVFRQVFREfn4+qampAJw7d45+/frh6enZo8dA9SZR1hnpS3ORfPX8/PyMRohJ546h+bGXl1eb1hu9WZQpFIpOn30pCv17ODU1NURHR1NXV8fUqVNxdXWlsrKy26NBhnRUlFVWVhIVFYVSqexwJMka0peSV1pqairnz5/vtJmjrb1/R4+BFKEpKiqioKCA6Ohok6ZDWEKUOTg4cNVVV7Fnzx5++eWXThVlbd0wU1NT+eKLL2RH7MZcuHDBaNnq6uouiYRK43ykMVBSqjktLY3q6mo51dmnT58e4xQP1iFiLEl3RMpao/EIMa1WK1tvZGRkNGu90Tid11tFWWd7lEHDw60QZVZMWxef/Px8YmNj6devH5MmTZKffqVokLV8OToiyvLy8oiLi8Pf35/hw4d3eH+sIX2p1+spLCzExsZGFtJdiSWOgVT3kJqaSlRUlMnC0lKdnzfeeCN79uxhx44dPPPMMx1eX3sJDQ1FoVCQnp5OYmKiLBD1ej0ffPABL730EgDOzs4kJCTwyCOP8Pnnn3fp91K6LgwZMgQHB4dm01WG3mjm1GgKOoa1i0wbGxv69Okjp8ANO4ITExOpr683st5wcXGxmvuOpemKBgbRfdlDkWYgZmVlNXszlC7C1tIF057olE6nIykpiZycHIKDg+VxEt2xLZakpKSEzMxMbGxsCA8P75Y0kiUjZTk5OYSGhuLm5mbye1tClEkDcdPS0jq8rrZobXv79u3LDTfcwC+//MLdd9/NgQMHKC0tZd26dXz00UcALFmyhHnz5jF//nx++ukn/Pz8WLNmTadvt0Tj7W+criorK6O4uJjc3FySk5NxdHSUo2jWNgZK1JR1L407gquqqmTrDWncm1S6UFNT06sEfmfbYUgd1l39kG4t9FhRJhWFQ4P3U3OqWjpxtFqtVYzekCJlpl5QG++jJTvsuqumzNDVXnrq7K66no7WlFVUVFBRUQE0iDJz0nGWEmUDBgxAoVBQWVlJYWFhp3mVmXK+vvrqqxw5coTExERWrVrFl19+KRvrvvrqq/zzn/9EoVDw0Ucf8cADD/D+++9z1VVXMXPmzE7ZZnNQKBRGTvH19fWUlJRQVFREcnIytbW1eHh4yFE0FxeXbhVFvU2UWXukrDUUCgXOzs44OzsTEBCATqejrKyM9PR0KioqiIyMxNHRUY6i9fRaxs4WZXBppy97xKNJ4y9rbm4uR44cwdPTk6lTp7YY5pSKEbs7TSchncimiKGLFy9y5MgRPDw8mDp1qsUtD7rjuGg0GmJjY0lLS2PSpEn07du3W1v7OyKM8vLyiIyMZOLEiTg6OlJTU0NGRkaXvLchzs7O+Pn5AQ0zJjvC+vXr+f3336msrJS7Sg1pa3uHDBnCddddB8BHH31EdXU1KpWKf//73yxbtkz+Hi9cuJD7778fgNdff71D29weTLn529ra4u3tzciRIwkLCyM0NBRvb29KS0s5ffo0hw8fJj4+ntzc3Gbn63Y2vU2U9ab9USqVeHh44OHhQZ8+fZg+fTpBQUFyLeOhQ4c4deoU586do6SkpNtre82lK2rKRPqyh2DYeThu3Di5lbk1Onu0kTkYRu5aOql1Oh0pKSlkZWUxduxY+vfv3ynb0tXpy8rKSs6cOSN7qtnb21NVVdWtF6T2HAO9Xk9KSgqZmZmMGzcOHx8fRowYQVRUFIcPH2bo0KEmrcdSogzgtttu45133mHt2rVcd9117XoKV6vVrFy5Ep1Ox7Rp0/j777/ZsGED8+fPl7fXFAztORYsWMAHH3zQbOpm2bJlfPbZZxw7dozc3FwGDBhg9jZ3Fa2NgZJMjg3HQDVX9N1Z29Vb6I31V5J4UalUeHt7y53yNTU1cqrT0LZFiqRZ+4SKzi4H0mq1VFdXi0iZtSOFgSsrKwkPDzdJkEHXDAE3FelEbkkkVldXc+zYMYqKiggLC+s0QQZdm76Uokre3t5MmTJFrrXobGF4/Phxzp492+LfzRVGdXV1nDx5kvz8fKZOnSrX9910000A/Pe//zV5XZYUZY8++igeHh7ExMTwn//8p13riI+PR6fT4erqysSJEwF48sknKS8vl5dpa3t1Oh1//fUX0GBN8fbbb7dYSxMQEEBoaCh6vZ5169a1a5u7C8mENCgoiClTpjBt2jQCAwOpr68nMTGRQ4cOERUVRWZmJpWVlZ0SDRbmsdZPS0LTwcEBX19fxowZw7Rp05g4cSLu7u4UFhZy4sQJjhw5QkJCAnl5ebIXoTXRFcPIgUu2pqxHiDKtVsvJkyfx8fFhypQpZtXuqFQqq4mUtZZOzc/P58iRI7i5ubWakrUUXZG+lJoU4uLiGDt2bBObiM4UZdXV1dx1111MmjSJ7777rtllzHn/srIyIiMjUalUhIWFGT3F3XPPPSgUCk6cONHi/MvGWFKU9e3bl9WrVwMN6cD2FP1Lo6LKy8txd3dnyJAhFBUV8cknnxgtV1xcTF5eXrPr+Prrr2UvsNraWj799NNW3/Paa68FYPfu3WZvb3to7/EuLy/njz/+aPHvdnZ2+Pj4MGrUKMLDw5kyZQpeXl4UFRXJN1kpwl9fX9/ezTeit4mYnlbobwqmRP8k25bAwEBCQkKYPn06o0aNws7OjszMTP7++2+OHz9OamoqRUVFVnEv62xRVlVVBXDJRsp6RPrSxsaGadOmtSstY03pS2gaodLpdKSmpnLhwoUu9enq7EiZ5BtXX19PWFhYsyKzM0VZWVkZQ4YMIT09nYcffpjZs2fLnZJSCtLJycmkG7U0PaG5OZwA/fr1w9XVlbKyMkpKSkzqkLWkKANYvHgxW7Zs4dChQyxbtowdO3aY9fpJkybJ/7969Wp5FM27777Ltddei1KpJCcnhwULFgANQko6nhIffPABANdccw179+7l9OnTrb6nZENhzRQXFzNq1ChqampISEiQ6/dawrDo23AMVGN/K8NUZ3vESG8TZT250L8l2pOSbTyhoq6uTh4FJTWcuLu7y8t0h7deZ6eaKysrsbe379HNEB2hx+y1nZ1du27g1pS+BGOR2Fi4dOWTQWcKouLiYqKjo+nTp4+Rb1xjLDUQvDl8fHzYsWMHAQEB8lxE6UK3fft27rjjDm677TauueYaQkJCmn3ykyxXcnJyWp2ecPHiRcrKylAoFPj7+5u0fZYSZdJ6lEolH330ERMmTOCvv/4iNTWVYcOGmbyeWbNm4ebmRllZGR4eHqjVaqAhSnT99dezYMECNm3aRFlZGQMGDKCoqKiJKJPGSQ0aNAigxYhaeno6q1ev5scffwTg9ttvN3Ovuw4vLy9CQkI4cuQIW7du5bHHHjPr9a2NgYqLizMaA2XOKJ/eJsp62/6AZcSLFIX18fFBr9fL3npqtVo2ZG58/nQ2XZG+dHZ27nXng6n0rnhxM1hT+hL+J8oKCws5fPgwTk5OXS7IoHPSl3q9nvT0dE6dOkVQUBDBwcGtPu10dk2ZUqlk8ODBAPz888/y70+cOIFer2fz5s3ce++9BAcHN0mh1dbWcuLECYqLiwkLC2t1nJWULuzfv7/JaWdLR8oAAgMDCQ8PB+CBBx6Q7ShMwcnJiX/84x8A+Pr68sknn8h1m2q1mo8//piysjJCQkLYt29fs4JPOkZSzWBRUVGTZbRaLQsWLJAFmb+/P3PmzDFjLzuOuRd76ZgaTiVoL9IYqMb1RAUFBRw7dozIyEiSk5MpKCho82GyN920LtX0pTlIDSf+/v4EBwczbdo0xo8fj4uLCxcvXuTo0aNERkaSlJREfn6+xVLljelsUXYpd17CJSDKrC19qVQqyczM5MyZM4wcOZLg4OBuMaW0dPpSo9EQFRVFRkYGU6ZMYeDAgW3eNLqiA1SKbKxdu5atW7cCDXVXv//+OzNmzMDR0ZFz584xb9482ci0pKSEI0eO4ODgYJIdSWBgINAQGSosLDRpuzorSvj6669jb2/PqVOnzK7Vmj17NgAJCQn84x//YOnSpUY3lVmzZvH7778zcODAZl8vNVVIKfjmvnexsbEkJSVhY2PD7t27+emnn0hLS+Ott97ihx9+IDk5mS1btvDmm29azdxa6XOytGhoXE90+eWXM3z48CbWCenp6ZSWlhqJ+N5W6C/Sl+ajVCplX72JEycyffp0hg0bho2NDenp6Rw6dIiTJ0+SlpaGWq222PWmsy0xJFHW284HU+kx6cv2fkDWJMpqa2uprq42ms/ZXVhSEJWXl3PmzBkcHR0JDw83skVoaxs6++ayYMECTpw4wfr167n77rtxdXXl6quvZtq0aXz99dfExsaye/duPvzwQ9asWSM75A8bNozAwECTzjs/Pz9GjRpFYmIif/31FzfffHObr+mMSBnA+PHjueKKK9izZw+xsbGypYUp3Hjjjfj7+5OVlcXo0aPlLijpO3T8+PEWL8YJCQmkpKSgVCoJCQkBMOrclPjmm2+Ahsjc/fffT1ZWVovb8+abb/Lwww8zatQoXFxcCA8P79SO5JaoqakB6HRXdhsbG/r27SsbAEvWCYZjoKRUlUaj6VU3rd4YKevqSTIqlcro/JFS5Wq1mvj4eDQajZH1RnuFj4iUdS6961vQDNZSU1ZUVMThw4dRKpUMGTKk29t9LZW+zMnJ4ejRowwYMIDJkyebLMikbegKW44333yTefPmUVdXx6233sq+ffuABmHk4ODA22+/zdy5cwFYt24dEydOZODAgbz33nsmG8JKqT8pLdcWnSlIIyIigIZ9kSwq2iIjI4OHHnpIFkkVFRU4ODiwevVqvvrqK6BBZG3ZsqXZ10tdmjfccIOc9mz8vXv55Zf57LPP5HVlZWVhb2/PyJEjm/0+6HQ6PvzwQ5YuXcrdd9/Nrl27TNqXlmjv8ZbMYbu68FiyThg7dizTp09nwoQJuLi4kJeXR1paGqWlpaSkpFBYWGgV17iOICJllkdKlY8ePZqIiAgmT56Ml5cXarWaU6dOtdsAubPFZkVFxSXbeQmXgCjr7poyvV7P2bNnOX36NMOGDcPd3b3btsWQjqYvdTodCQkJJCYmMmHCBIYNG2b2RbWrRJlKpeKrr75izpw51NTUMH/+fLZs2SILo+rqambNmgXA/v37ueaaa1i8eDHPPvssTzzxhEnvIYmyEydOAA3u+q3VdJgTKTPXMf7xxx9nwYIFaLVali1b1urrU1JSuPvuuwkJCZEFpfQ51tTU8N5775Gfny93lEoO/OXl5ezatYuNGzeSmJjIpk2bAHjooYeMZs5CQ8rykUce4d1335Xf19nZmddee42RI0eSlJTE9OnT5bFbElJtmoeHB5MmTWLq1KlmHQdLIRnbRkdHd8v7Q8Nn4ubmxuDBg5k0aRJDhw7F2dkZvV5Pamoqhw4d4vTp05w/f57y8vIel97sjZGy7hZlhigUClxcXAgICGD8+PFMnz6dMWPG4ODgQHZ2NkeOHOHYsWMmifyusMSw9ASbnoR1nDGdSHemLyWzUWlYdUBAQLeLRImOCCLJ5LakpKTNInhTtqErbiB2dnZs2rSJm2++mfr6eu666y6+/fZb6uvriYyMJCwsjFWrVuHq6kp8fDzbtm0DGvy7TCmYHzduHABZWVkkJCQwa9YsRo4cyYEDB5pd3lRRVlFRQXh4OGvWrGn286qoqGgi/hQKBW+99RY+Pj6kpaWxfv36Ztf9ww8/EBYWxtatW9FoNMycOZNDhw5x2223AeDm5kZhYSHLly+X3+P8+fNcf/31DBo0iIULF/L0009z+eWXU1tby6xZswgPD5fFVVVVFTfccAMRERF8++238vu+9NJLnDx5kqCgIGJiYgDYtWsXRUVFuLm5sWTJEo4dO0Z0dDS2traUlJTwxhtvMHr06DaPV2vU19dTWlrKqVOn+Ouvv0hISCAtLY2//vqL48ePk5ycTHp6epOCfimK+ueffzabkk1KSuL/sXeeUVFdfxd+ZugiHQRsoIKIHRuCvZdoNPZoNJZYE2tii5rYEntiLLGb2Gti7yV2QBFQUUSkqQiI9N5m3g+89/wZOgi2ZK/lUmfu3Dlzyzn7/sreAwYMKFDLrLQhGV7b2dkJG6gKFSoQFxeHh4cHN27ceKc2UMXFf5Gyt4vsAshNmjShZcuWVKtWTYXkZ69nzCnjVNbpy39zpOxfUVP2LkL7kiyEkZERDg4OInrwvtS4lTR9+fr1a+7duyfEMt/k5pQmrLfVDq+pqcnOnTsxMzNj48aNzJo1i2HDhjF37lyqVq1KvXr1GDt2LKNHj+bEiRNoa2uTkpLC7t27hVdjfjAwMMDQ0JCYmBhu375NhQoVCAkJ4dNPP2XXrl1iYZdQlEJ/pVLJmDFjePjwIVFRUYwbN07Ui0hyHRKB0NfXx8TERGgXGRgY8MMPP/D111+zbNkyxowZo5IifPnyJRMmTCA9PV0QyiZNmlCvXj0h3tikSRMCAwMJDAwkKipKfPb69etAVi1daGgoCoUCW1tbtm7dikwmQ0tLCzMzMyIiIrh69arogvX390dHRwcHBweGDBmiomPWpUsXhg8fTqdOnVRS4IMGDWLXrl3s3LmzyJGygIAAdu7cKRxA0tPTCQsLEzIfhUEmk3Hjxg3q1asHgJ2dHdbW1gQFBeHq6kqnTp3Eti9evKBZs2ZAlnxG+/bti/Qdb4qchD6nDVRcXBxRUVGEhITg4+ODrq4uxsbGmJiYvDUbqOLgP0mMdwsNDQ0qVKggSg+yS29I9YyGhoYYGxuTnp5epufqv/TlR463TYKUSiUBAQFCFqJBgwYqtSjvi0F6cdOXSqUSf39/PD09sbOzo27dum88sUsT1tv0v1RTU2PlypV88cUXQJYK/ffff09cXByQNfGsWLEC+F+B9+rVqwVRKQhSiu/58+f06dOH5s2bk5aWxsCBA2nbti1//PGH+K1FiZTt3LmTv//+Gw0NDXbv3i0IWVpaGnfv3iUyMhJHR0eaN2+OhYWFaLiQoiQdO3akatWqpKSkcPLkSZV9y+Vycf7u37/P/fv3Wb16NVWqVBHCs5cvXyYwMDDXuGQyGVu2bEFfXx+FQkGHDh24c+cOJiYmJCYmMmjQICIiIoAszbLNmzcL2ZC6devy2Wef4eHhoZKi+OOPP/jkk09y1SQOHjwYyKrVe/LkSYHHKy0tjenTp9OoUSN++eUXXFxcuH//Pj4+PiqErLAFRalU5qpxk5oXso/hn3/+EXIZgIgwvg0URGIkQ+zq1avTpEkTWrVqRbVq1cjIyFCxgXr27BkJCQnvRarzQyIwRcXbMO4uK+jo6FCpUiWVekZ9fX0iIiJISUnh8ePHwqWitK2gkpKS/iv0/5jxNtOFaWlpeHh48Pz5c5o1a5anLMTb9JwsCMVJX6anp+Ph4cGLFy9wdHQsskBqUcYAb5eUJSUl4ebmxujRo1m0aBFqamocPnwYJycnvLy8gCwi0bBhQyDL6sPf35+xY8cWunhJmmhXr15lw4YNtGrVigkTJqCmpoarqyvjx49n2LBhZGZmFkrKfHx8mDNnDpBVIC8V78fHx+Pi4oKamhqOjo6UK1cOLS0tKlWqRL169WjVqhX16tVDW1tbnC+AqVOnsn37dnGsDQwMhMelhIyMDOLj48U1W79+fX755Rc8PDzE98P/Ing+Pj6Ym5uzadMm5HI5jx49on379ly8eFHUg2loaPDq1SsgK5on1dw1b96c+/fvi+0kEpcTzs7OtGjRgpSUFFq1asW2bdvyPWajR49m06ZNgiiuWLEiT6KnVCqF3MnYsWNZvXo1Bw4c4Ny5c3h5efHgwYNcsh/ScZMW2T179tCnTx9iYmJwcHDAy8srzyhZWloa9erVo0mTJnmmPt8ERY1WSFGQ7DZQJiYmREdH4+7uzs2bN9+51+J/kbL3F1I9o7W1tcj6WFtbo6GhQXBwMDdu3ODOnTs8ffqUqKioN15v/+3dl/+K9OXbIGXR0dHcu3cPAwMDnJ2d0dDQyHc874PJrFTkXtjEERsbi5eXl5AkyO93lQTSOX1bpCwiIoL79+9TsWJF7OzsqF27Ntra2qxZswZ/f3/atm3L2rVrGTp0KJMmTWLkyJFkZmairq4u6q8mTJiQ7/6bNWvG2bNnhWhqdHQ069atY/r06ezevVuo2FtbWzN8+PB8SZmfnx9du3bl9evXODg48PXXXwNZOmgPHjygWrVq1KhRAyBXPZkUJTE0NKRGjRpUrlyZixcvEh0dzZQpU1iyZAlOTk6cPXtWRAIlDBkyhAkTJrB27Vr2799Pt27d+Oqrr4CsAv6bN2+KbaWxt2vXjrS0NGbNmsXmzZvJyMjA3Nycn3/+mVGjRhERESFkLKRoJICrqyuJiYmYmJjw8uVLXr16JUhtdshkMnbs2MGIESO4fv06U6dOxcTERJjAZx9Pdl0zAwMDFixYIGQ96tSpw2effYZMJqNHjx7UqFGjWJ3CISEhQFbKdtOmTUyfPh2A/v37s379+nzlMl69ekVwcLCoATt48CBXr15lzZo1bxRFKWkNVnYbqCpVqqBQKIiJiSEqKopnz57x6NGjUrGBKi7+K/T/cKBQKDA2NhaR7rS0NJHq9PHxIT09XcUKqnz58sW6Vv/tNWUf3xWTA2VdU6ZUKgkKCsLd3V1EWAoiLu9LTZm0IBREiF68eMHt27epUqUKjRo1KlVCBm8vUialXr28vLC3t8fe3h65XI5cLsfOzg43Nze6du1KSkoKo0ePZtSoUfTu3ZsqVaqQnJws6slmzZolIj0FQSIhu3fv5sSJE1haWjJ9+nS+++47AA4ePJhvpCwoKIhu3boRHh5O/fr1OXXqFNra2vj5+fHgwQPq16+PjY1NkSa5yMhIxo0bp5K6Cw8P5+jRoyqETEq79unTBwsLC44ePQqo+mHmV2S/f/9+6tSpw++//05GRgaffPIJN2/eFB6RMpmMkSNH5vnZRYsWCQP3gqKvFSpU4OTJk4Kczpkzh8TERJVtpOYGqVTg77//JiEhAQcHB44cOcKtW7eYNm2a6FzMi5AVFLmUauqMjY2ZMWMGAMOGDWPLli0F6pc9fPgQgFq1anHmzBnGjh3Lrl272LdvX76feZuQy+UYGxtjY2NDs2bNaNmyJVWqVCE1NZWHDx9y/fp17t27x4sXL0hKSiqzVOfHVuhflAfeDxF5/S5NTU0sLCxUIrFmZmbExsaKphNvb29evnyZ60EwL/xHyj5ylGX6Mj09HU9PT4KCgmjatCnW1taFTizvCykriBBlZmby4MEDnjx5QqNGjfI04S7NcZQlKZOcBqRUXnbDd4kYGRsb8/fffzNz5kwgKzVVp04d4eWYmJhIx44dSUtLY9CgQVy7do2hQ4cyYMAAtmzZIqJVsbGxADRs2JDu3buTkpJC//79WbFiBUqlEk9PTyDrmpTJZBw+fJiBAwdy/vx5IIswffLJJ7x48QI7OztOnjyJvr4+np6ehIaG0rx58yKZnYeFhbF9+3aaN2/OP//8U+j2EmmbMmUKCxYsICUlhbp16wqFf6BAc3FpoTY0NKR79+4YGxvzyy+/qOxbQpMmTTh27BhGRkaYmpqSmZmJsbGxynnJCzKZjHnz5lG1alWeP3/OqlWrgCyytGvXLvr06cPgwYPFA5i6ujpbtmzhypUrdOjQodDr96+//qJ58+Y8evQo13sKhUKQxz/++EOkP1euXFnoohsQEABkXUNS6vrzzz8XtXIlRVml+6QFVtK2aty4MYaGhkRERHD79u0ys/H52CJl0j3xMf0m+J/MTX5RXikSW7lyZerXry/KKXR1dQkNDcXFxQVXV9cCrcTyImXr16/H2toabW1tHB0duX37doHjPHToELVq1UJbW5t69erl0jhUKpX88MMPWFpaoqOjQ8eOHfHz81PZJioqiiFDhqCvr4+hoSGjRo0Skffs+1m5ciU1a9YUZSQ//fRTgWMrDP+lL0uInGm94qjYvy81ZZDbCicpKQlPT0/U1NRwdnYucxXzshRRTUhIwNPTE21tbZycnHKdI+m7pQWhX79+7Nixg7CwMEJDQwkNDQWyCu6l7UNCQujcubPYx/Hjxzl27BgbN24U+09MTOTAgQPMmDGDDRs2MG/ePJKSkkTUZN68efj6+rJ8+XIgK4rStm1bBg0ahL+/P1ZWVpw5cwZdXV1cXFxE/VNh11hSUpJIleY8pvb29kKwFbJ01aT6Ounp9fnz5+K3hoaG8sUXX2Bra0twcDB//fVXocc7JiaGr7/+mm+//TbPJ+JatWpx/Phxypcvj4+Pj/AjrVu3bpHu73LlyvHtt98yefJkVq5cyd69e3n16pXKxN6oUSN69uzJ2LFj833azuu7jhw5go+PD8OHD+fMmTMqmmmvXr0iMTERmUwmxHN/+eWXAu8NhULBuXPnWLRoEYAQIR49ejTLli1748VaOr+bN29m48aNREZGMmHCBPFgURqQtK3Kly+PlZUVmZmZItUZGBjIw4cPRarTxMQEPT29Ev+uj62mrKysud41ctZWFobs5RRSs0l0dDTR0dH4+/uTnJyMnp4e8fHxpKWl0aZNm1yk7MCBA0ybNo2NGzfi6OjI6tWr6dKlC76+vqJbNDtu3brF559/zpIlS+jRowd79+6ld+/eeHh4ULduXSDLdm/NmjXs2LGDatWqMW/ePLp06cKjR4/EfT1kyBBCQ0O5cOEC6enpjBgxgjFjxghNRoDJkydz/vx5Vq5cSb169YQDx5vggyFlJYVUWF9aoWSlUsmzZ8948uQJNjY2RYqO5RzP+xApk8lkuQjiq1evuH//PpUqVcLOzu6tTChlRVLDw8NFwXZ+wrbSa9KCUK9ePaHr9eOPP+YiFtnHWb16dT7//HNWrlzJxYsXsbW1FdISx48fZ8WKFfz66694e3tz/fp1lixZIiaaAwcOqGhHffrpp8yaNQsXFxf09fU5ceIEGhoauLi4ULlyZWrWrFnguVAqlZw9e5YJEyaoeG+qq6tTq1YtKlasyKVLl8jMzERfX59169aJmqzk5GRu3rxJv379VH5fZGSk6MIsKsqVK0dSUlK+KYrHjx/j7OzM119/zeDBgwXJLCziolQqefjwIStWrOD48ePi9ZcvXwJZZK9v37706dMnT6P0omD58uW4u7vz+PFj+vbty+nTp9HS0mLt2rWi81JTU5PU1FSmTp0qunel8YWFheHh4cGjR494/Pgxd+7cISgoSGzTsmVLpk6dqiKn8SZQKpXs3LmT33//Xbz2NmygTExMBGHNbgMlkX3JwsfY2BgdHZ0i7/tjS19+rKRMalIq6blSV1fHzMxMaFtK19D+/ftZvXo1qampWFpacunSJerXr4+9vT2//PILo0ePZsSIEQBs3LiRU6dOsX37dmbNmpXrO3777Te6du0q6j4XLVrEhQsXWLduHRs3bkSpVLJ69Wrmzp0rpIp27tyJubk5R48eZdCgQfj4+HD27Fnu3LlDkyZNAFi7di3du3dn5cqVVKxYER8fHzZs2IC3tzd2dnYAedbFFvsYvfEe3nNkVxd/0xskPT2dhw8fEh0dTZMmTTAyMir2Pt4XUgaq4q1+fn4EBwdTt25doWD+NsdQWsj+W+rVq1egV2L2FK70bzU1NSZPnszIkSNp1aoVjx8/VvmMRDwCAgLQ19fnzJkz/Pjjj6L2BrLShzt37mT06NHs3bsXW1tbUlJSRB1URkaGSCv27NmT58+fi8X1jz/+QENDAy8vL+rUqVNoWi8qKor27duLNFl2ZGRk4O3tjbe3NwC9e/dm2bJlKudXqilq27YtV65ceaNzURTZkKCgIKZPn86yZcv49NNPAfDy8iItLU2QtLS0NK5du8bt27e5ceMGHh4eufatqakpGmaePXtG9erVS0zIIMtE/ejRo3Tt2hUPDw9GjhyJsbExu3fvFl2qqampqKmp8dlnn3HixAkuX76Mu7s7gYGBKk0MEqRre9KkScLsvrRw48YNcc18//33dOvWrcQiziWFZANVsWJFlEol8fHxREVFER4ezpMnT9DW1hbaeYaGhgXaVH1s6cvs0jcfE6R1tLR+l3QNTZs2jcmTJ3Pnzh3Gjx+Pl5cXjRs3xsjIiLCwMJycnHj16hUVKlRALpfTsWNHXFxc8tyni4sL06ZNU3mtS5cuolY2MDCQsLAwOnbsKN43MDDA0dERFxcXBg0ahIuLC4aGhoKQAXTs2BG5XI6bm5uYA6pXr87Jkyfp2rUrSqWSjh07snz5coyNjUt8TD4YUvYm6UvIupjepFA9Li4OLy8vypUrR4sWLYrVuZVzPO8LKVNTUxOaMykpKTg5Ob31AsvSJGVpaWncv3+fpKSkIv2W7JGynNDT08PDw4OePXty6dIl8Xp2cjBz5kzatm3LmjVrRI2CpAc2bdo0ypcvz+eff878+fOZNWuW+J6LFy+KfYwaNYovv/wSyJKtqFy5MsHBwTRr1qxQSy6lUkn9+vVVCEHVqlVxdnbGzMwMXV1dUlJSUFdXp1OnTjg5OQFZkbzFixfz4sWLXDUSecHW1pbevXvTunVrYmNjGTFiRInriaQJ/fXr12zfvh3IitY5Ojoik8kIDg4mMzMzz2tCTU2Nrl27smrVKkxMTARpTkpKYtSoUZw7d44VK1bk+7BUWJq8Vq1a7Nmzh969e3P69GlBxrLX0xkYGNCuXbtc97BcLsfe3p769etja2uLpaUl48ePR11dPc+n+eyQ7KzCwsJUrKjyw7Nnz0TdyldffZXn/k+fPk3dunVzSXuUFSTZBEk6QUpTRUVF4efnR0pKikpHnp6ensqc/jFGykqTvLwvKEvtNUnmJzo6mr1791K/fn2OHz/OwIEDOXv2LFWrVhVky9zcPNcDs4SwsLBctbfm5uaEhYWJ96XXCtomZ2pUXV0dY2NjsU1AQADBwcEcOnSInTt3kpmZydSpU+nXr98buXt8MKSspJDSdCUlQkqlkufPn+Pr60v16tXfuOj9fRGPlXD//n1MTExUXAfeJkqLlMXFxeHp6Ymenh5OTk5FIuCFdX/K5XJOnTrF06dPOXz4MIsWLVI5dzKZjCtXrtCsWTMmTZpE9erVAUSNxMiRI9HW1mbixIn89ddfuTo3J06cyNKlS4mNjaVp06Z069aN5ORknJychH5XQVi1apUgZHK5XAgW5welUsmaNWuYN29enu9LT605o25+fn48fvwEz7jqKNMT36jAO79jLYnL5oVmzZoxePBgevXqpVLrtXfvXjp37ixStgcPHuTmzZucP3+eKlWqlGh8LVq0YP/+/bkcByRI9SI2Nja0b9+eVq1aYWdnh5WVlUq6ThKZlWqy8kNgYCCDBw/m4cOHyGQyxo0bJ1IheSE4OJju3bsTFhZG5cqVmTt3bp5jHDRoEJBVXyPV0bxN5ExTSQrxUVFRQiJESnWamJh8lJGyj+n3SChrM3LIqsnV09NDW1ubli1bAlkZhHflfZsfFAoFqamp7Ny5k5o1awKwbds2GjdujK+vb4H3cUH4oEhZcUycs6OkshgZGRnC4qZRo0a5DJNLgvdBPFaqi0tNTaVy5crUqVPnnT3RlQYpe/nyJQ8fPiw2aS4oUpYdNjY2zJo1i2rVqomoVvbPpaens2rVKjFZTZ06ldDQULZs2cLIkSM5efIkU6dOVem4Gzx4MKmpqbi6uqKnp8f48eMxMTERch0FQalUMn/+fJYtWyZeO3DgQC5CplAouH37Nq6urkRERHDt2jUVU20NDQ3atm2LXC5ny5Ytotv0xYsX9OzZk9evXxMbG0uNLrNRdvsJnf8/Xj2aTMPlty5E+l4ocJyQRfTatm3LuXPnUCqVVKhQAQsLC5o1a0adOnU4ffo0Fy4UvJ/w8HDc3Nzw9fUlPDycjIwMLC0tOXHiRK4aOisrKyHHUVJ06NCBrVu3MmTIEJVrQ09Pj6+//prPP/+catWqERsbm29xu0Sq86uvS0hIYNeuXSxZsoSYmBgqVKjA0qVLC0zBhoSEMHDgQJ4/f07VqlXZuHFjnmmS7Od4w4YNrF+/vsi/vawgKcRXqlQJhUJBfHw8kZGRvHz5UkQ8nj17hrm5OYaGhh+sEr6Ej5mUleW5ycjIIDU1VTzImJqaoqamRnh4uMq8Hh4enm9pioWFheiWzmt76e/w8HCVUo7w8HAhGm5hYSFEr7OPLSoqSnze0tISdXV1Qcggq6EKsq7lfwUpKylKkjKMj4/Hy8sLLS0tnJ2dixS5KKuxlCayE81y5cphZmb2TkPsRfGAzA+S/2NISAgNGzYsdk1NccVrBw4cSOvWrbl16xY//fRTLvkEaT+//PILZ86c4dmzZ5w7d47PPvtMpUAdwMrKSsg6jBw5kpYtWxaaagoMDMTc3JxJkyapGHzL5XKVjlDIIgM9e/bEzc0t137kcjmLFi1i8ODBeT5oVK5cWch3BAUFMedGXch2jchkMppPOsuprwufnFNSUnB3d+e7775j1apVvHr1CiMjI4YNG4adnR0NGzYslJQFBweL7sWcMDExoUuXLuzdu5eMjAzu3LnDkiVLGDt2rLClyonCrvdLly4xevToXGQ9Pj6ejRs3Ur16dWJjY1myZAmurq78+uuv9OnTR2VbSVgzJSVFZYFOS0tjx44dLFu2TEz6TZo0Yffu3QXWD/r6+tKjRw/Cw8MxNTVlzZo1+S5KUpoasrTy3gdSlh1yuRwDAwMMDAyoXr066enpXL9+HYVCwePHj4X4qFSPpqur+8GlAd9GROldoKxJmVROIdmcaWpq0rhxYy5duiSakxQKBZcuXeKbb77Jcx9OTk5cunSJKVOmiNcuXLgg7otq1aphYWHBpUuXBAmLi4vDzc2N8ePHi33ExMRw9+5dodd4+fJlFAqFcElp0aIFGRkZ+Pv7iwdiKUJuZWVV4mPw8V01eaC4WmUvXrzA1dUVCwsLmjZtWmqEDP4XKXsXfnMJCQm4urqSmpoqZDzeddSupJGy1NRU7ty5Q2RkpKihKun3F+dcWFpa0rdvXzw8PDhw4IB4MsqOhIQE2rRpg76+Pk2aNCEuLo6ePXuqbLNz507S0tJo2rQpM2bMKFLtT//+/alQoQK7d+9GTU1NTCg563MgyyvSzc2NcuXK8dlnnzFx4kQ6dOgAQK9evZg4cWKehMzLy4uJEyeK+8Xa2hryWA/lMhmfffYZenp62NjY5Nscoq+vz+vXr3FxceHQoUMYGxvj6+vL559/zpEjR0hJScmza7BRo0Yq3pgS6tWrx88//8zkyZP55ptvuHjxIhs3buSvv/6ifv36pKens2zZMmxtbenXr5+KC0FRsHLlSj777DPRlCHVnchkMurWrUtMTAxjxoyhdevWnDlzhujoaHR0dEhOTlbZj5TiVVNTExH+o0eP0qxZM7799ltevXqFtbU1v/76K+fPny+QkN25c4cBAwYQHh5OrVq1+Oeff6hUqVK+REVbW1tId0iSL28LiYmJ7Nixo1jfKZVN2NjYCPFRU1PT98oGqrj4WCNlZe3nKd132W2Wpk2bxpYtW9ixYwc+Pj6MHz+exMRE0Y05bNgwZs+eLbafPHkyZ8+eZdWqVTx+/Jj58+fj7u4uSJxMJmPKlCksXryY48eP8+DBA4YNG0bFihUF8bO3t6dr166MHj2a27dvc/PmTb755hsGDRok7tWOHTvSqFEjRo4ciaenJ3fv3mXs2LF06tRJJXpWXHxQV01Za5VlF011cHDIV0rhTZC98eBtIiwsDBcXF8zMzGjSpAlaWlrvhWZaScYQExPDrVu30NLSonnz5nku3kXFm0TqevXqxa1bt4RkQnYoFAoOHTrEvXv3sLa2zuV7GBISgoGBAXv27ClSWjw+Ph5vb28yMjJQV1dn3759ws7J2to61/aff/4527dvZ+PGjezYsYMePXpw/fp1gFwEMft39OrVix07drB06dICx5OZnkJKSgrx8fE8ffqUtWvX5llgL9W83bhxA2dnZy5fvkzVqlUJCQlhzpw5JCcni4L67OTMy8uLw4cPq0hPADx48IB9+/Yxfvx4fv75Z/GE2qlTJ65fv86GDRuwt7cnMzOT8+fP061bNwYPHpwrnZETMTExTJs2jYULF6q8Ln1OqVSycOFCZs2alesBYODAgVhZWan4cmafN/r06UOVKlUYNmwYAQEBGBkZMWbMGIYPH46BgQFHjhwRkcOcv/f8+fN07dqVwMBAzMzM+Ouvv7CysiqU9GSvv/ntt98K3LY0MXfuXCZOnCgiDkWB9FskqQXJAqpBgwa0bt2aOnXqoKWlxfPnz7lx4wa3b9/m6dOnREdHv/P5Kz98rKSsrCNliYmJ6OjoqHzHwIEDWblyJT/88AMNGzbEy8uLs2fPigemZ8+eCU1JyPLL3bt3L5s3b6ZBgwYcPnyYo0ePqtRWzpgxg4kTJzJmzBiaNm1KQkICZ8+eVZmD9uzZQ61atejQoQPdu3enZcuWbN68Wbwvl8s5ceIEpqamtG7dmk8++QR7e3v279//RsdApnwXIZsSIj09vUQ34e3bt6lYsWKBVi4JCQl4eXmhoaFBgwYNykzzR6FQcP78edq1a1eqEbiCvk9K8dWrV0+l48Td3Z0KFSq8tQ6tvHD37l1MTU2LHO59/vw5jx8/xtbWFisrqzcmzZcuXaJp06bo6+uXeB+ZmZlCGkMSiJXJZNjY2AiVaENDQ2JiYlQ+98MPP/D9998Xuv/k5GRq1qwpTLu3b99O+fLlGTBgAABffPGFil5VdiiVSm7fvs2AAQOIjo7m008/ZefOnfkuGPv372fMmDHIZDKOHj1Ku3btiIyHScf/V6yemZHO6ck6mJmYEBkZiUKhoFy5chgYGKhMjhIk4r1161YGDBhAWFgYAwcOFI0ZQ4YMYePGjbRu3ZpmzZqxcuVKICsFuHbtWoyNjfn+++/x8fER+7S1teXSpUuiDi4n/Pz8WL9+PTt27BD6bIcPHyYpKYk2bdqISV+SI/nzzz/FU/qoUaNUCJadnR2+vr589dVXzJs3j5EjR6p05GbH7t276dGjBz/99BMrV64sdpTqk08+Yd++faSlpfHzzz/z66+/ilb7LVu2YGJiQkpKCleuXOHOnTtMmjQp3y5dAwMD8f1eXl6iEaUsUb16dV6/fs3UqVNZsGBBkT6TmZnJ1atXadWqVaENOpLPovQnIyNDRRutXLly70WqMzw8nOfPn6tIKnwMCA4OJj4+vsyaR+7evUv//v159erVe3Ee3wU+PiqfBwpLX758+RIXFxcqVKhA06ZNy1SEUbrQ3kakLCUlhdu3bxMVFYWTk1OuFuD3oemgqOlDhUKBt7c3fn5+NGrUqNiivfnhTSJlEtTU1OjRowd3797lxo0b9OjRQ+ilSchJyPr06UObNm0K3XdGRgZWVlaCkM2ZM4fatWur+Elm96jMjps3b9K0aVM6depEdHQ0TZs2ZfPmzQU+wQ8aNIgRI0agVCqZPHlylmm4HvzUwpvvG7qxZ0gCKzv4Y29nR0REhFhEk5KSVAiZhoaGSEFIx3f06NFYW1vz6aefYmpqioWFBfHx8WzatAnIekj49ttvRUo4KSmJr7/+mqdPn7JgwQK6d+8u9u/n54e9vT3Lli0TEcPssLW1ZfXq1Vy/fh07Ozvi4uJYsGCBuNb8/Pz44osvqFevHuvXrycxMZHatWtz+PBhrl69qrIvqTZl79691K1bVxCyOXPmEBkZKczKIYsgm5qaCmstCY0aNWLKlCn07t0bMzMzrKys6NmzJ46OjtSuXZsvvvhCOAA8fPiQ9u3b88svv6BUKhk+fDjr16/n1KlT9O/fn6pVqzJgwABWrFjBqVOn8j2Xf//9t/i3FCUta0g1QcXplCuOpldOGyhJLzIyMpI7d+5w69YtfHx8St0Gqrj4L1JWMiQmJr5R5uNjwAdV6F/a6cvMzEx8fHxE18XbEF+UyWRvhQxFRUXh5eWFqakpderUyfNG+lDSlykpKXh6eqJUKnFyciqWUnhRvr80g8VNmjTh0KFDbN++nZkzZ6rogEnCs5C10BdWH6NUKtmwYYNIATZu3Jjw8HDatGlDamqqqFXKyyz82LFjjBw5kvT0dDQ1NenRowdr1qwp0oS3ePFizp8/T1BQEOPGjeOPP/5QufcqV67MuXPnGDJkCNevX8+zKzo9PZ1+/fpx7Ngx8ZuVSqWIcGTXGJI+m5SUxJEjR+jbt68QW01JSWH+/PksX75cFNFKSExMFBGp2bNnM378+FzXRt26dTl48CANGzbk5s2b3Lx5k2rVqhEZGSmOa+vWrZk8eTIdO3Zk6dKlPH36FMiKNMXGxpKZmYmRkVEuH09jY2MSExM5ePAgtWvXFo0fOTu9ZTIZO3bsKDQarFQq2bRpE3PnziUtLQ0DAwNWrFiBt7c3TZs2VdGjU1NTo27duvlGCgGRdjl9+rRKl2pZQrpOstcEFYaS+kRmt4GqWrVqLhsob29v9PX1RRRNX1//rRGlsq69eld4GzVl5cuX/9dGyeADI2UlRV6kLDExES8vL+HxWJoLfUnGU1pQKpUEBQXx9OlTatWqReXKlfO9wN8HzbTCSJlELs3MzKhdu3apTwilESnLa5+jRo2iU6dONG7cWNSTZReezYvIpKSk8OTJE+rXr49SqaRq1aoiQibpkN29exf4Xzq0Y8eOohtIwvnz5xk+fDiZmZn06tWLdevWFSpEmx16enps3bqVTz/9lGPHjjFp0iRmzpypMl5DQ0P+/vtvxo8fz+HDh/Pcz7lz5zh27BidO3cWn+3cuTPR0dH4+fmpRA8lwrp7925OnDjB06dPRW1GYmIiX3/9NfA/JX8LCwsh4piamsr8+fNZs2YNY8eOpXfv3ioNGNWqVWPmzJmsXLmSjIwMAgMDxXszZ85kzpw5QJbG0JIlS8R7tra2uLu7s3nz5lyEDODbb79l7ty5oshfU1OTDh06UK5cORWvUKVSiYODA3369KFPnz5069Yt1z0ZGxvLhAkThLWVmZkZUVFRTJkyRVw3tWrVon///nTv3h2lUkn58uXzrCfMDomEl1TsujgICAgQx6IgspgT2WvK3gQ5baBSU1PFQ8CDBw9QKBQYGRmJrs6ynPP/i5SVDImJicUi9B8jPr6rJg/k1CmT3OpNTExo1qzZWyVk0njKggylp6fj6ekpFOGrVKlS4ET3vqQv8xqDRC7v3r2LjY0NdevWLZPJoCwN0Y2MjPI1iG7Tpg2zZs2iZcuWtG7dGisrKwwNDXF0dMTNzY0uXboIQgZZk3yFChX48ssvWbt2LTExMWhqaqpolQF4e3vz5ZdfkpmZyYABA/jzzz+LRcgktGjRgl27diGXy9m9e3eulB5kaXFt3bqVqVOnqrwuXXNRUVG4urqqpFqNjIxo3LixIGS1atXC1NRUEI9bt26xbds21q9fn2fjwFdffYW6urogZJB1HWtoaBAVFcWSJUtwdHRkwIABPHjwQHiMfv/99/j5+bFu3TqVuqVly5YRGhrKuHHjxO+QIuZSxOz+/fsqY7CxsRH/Tk5Oxs7OjiVLluDr68sff/wh6gqzy5RkZGRw8OBBBg0aRL9+/Xjx4gU3b95k7969rF+/nsaNG3PixAkhdh0REUFmZiZJSUlUr16dPXv24OLiwvTp06lTp47KcS4I0tyWszu0LCClda2srKhXr554PTMzkyNHjtCmTRuVlL4ESc2/tKMjWlpaWFpaUqdOHVq2bImDgwP6+vqEh4fj6uqKi4sLvr6+RERElEjHsiD8R8pKhpxm5P9GfFBXzZuYoErWLY8ePeLhw4fUr1+fWrVqvZMbpyxIWXx8PC4uLigUCpydnYu0EL+v6UupCzYwMJAmTZpQtWrVMgtnl0WkDLKI/8mTJwWxytlIoFAouHv3Lu7u7ty+fVuly69NmzZcu3Yt1z4nTJgg6q8gKzWXvfVaqVQybdo0EhMTadu2Lb///vsbTaDdu3dn4sSJQJapb17RIrlczoIFC0RxvjQOCfPmzcPFxUU0tRw4cICNGzcC8N133+Hi4sLVq1eFLhFkdUbVr19feGNmx+bNm0W9klT76ejoSHBwMGvWrKF169YAnD17lpYtW9KyZUsWLlzI48ePyczMxNPTk/T0dJXjsmXLFvbu3QtkkZhWrVoB/6sDzKnK//TpU+RyOZ988glnzpzBzc2Nr7/+GhMTE+bOncvjx48xNzdnw4YNom1fOlZyuZwLFy7QoEEDunXrxrhx45g9e7bQLFMqleJ6rFOnDsePHxd2X9nHrFQqi3RPPH/+HOCNBXWLAqlRonHjxshkMtLS0ti9ezdNmzblyy+/xNPTM0/NtKL+ljeBZANlbW1No0aNaNWqleiu9/f35/r169y9e1f4mL7pg9rHSsreRvryv5qyfwHU1NSEerpMJsPZ2fmdnvjSJkMhISE8evSIatWqUaNGjSJPcHK5/J3r/uRMoSYlJeHp6Ym6unqpivYW9P1vMgGnpqZy7do1Lly4QEBAgDBjzjmx52VYXdTxSdfKrVu3ePr0qSA1n332mcq2f/31F66urujo6LBx48ZSSVnNnj2bkydP4u/vz6JFizh79myei83o0aPZvXs3Xl5eud7LKbKrq6vLTz/9JCJoVapU4fDhw3Tp0kVsExISwo4dO1TSvFLK8saNGyraYBUqVKB8+fIMHz6c4cOH4+Liwvfff8/du3fx9fXF19eX/fv3s3z5cs6cOSN+18WLF3F1deWPP/5Q2X/2Ankglz/ounXr6Ny5cy7x1rt37wo/z02bNmFmZsZvv/1Gr169+P7771WOQ3p6er4OJdWrV2fu3Ln07du3wHu5KPe5RPbz05ErTbx8+RLIOh8bN25kzZo1vHjxAshKZ44bN46xY8fm+ty78L1UV1fH1NRUCAxnt4GSiKxUi2ZsbFzs5q+PlZT9Fykre/wrSFliYiKvX7+matWq2NnZvfObpbQiZQqFAh8fH8LCwkrUqPA+pC+zR6pev37NvXv3sLS0fGtRzJJad506dYpvv/2WoKCgYn+2y8ooNMoZ/v//lNz6rRNRvlkGtjmlM7Kfn/Pnz9OsWTOSkpIwNjZm1KhRYjHz9fUV6bfJkyerRJ7eBOXKlWPXrl20a9cOV1dXNm7cyIQJE3JtJ5PJmDRpkkqqMj8kJiYyffp0bG1tRVTKyckJk/+X2ICsiJWpqalYICHr+ujcuTNXr15VScdduXKFv/76iz59+iCTyXBycuKff/4hNDSUUaNGcePGDV68eMG2bdsEcXB0dBTeltJ3Zk+JFoTu3bvncguIi4vjq6++QqFQ0LdvX9q3by/ea9++Pa6urjx+/JibN2/y119/cevWrVz3XsOGDZk3bx4dO3YslKRkjy4VFGmS0nJF8YJ9Ezx+/JgHDx4AsGPHDnF+zM3NmThxIiNGjMj3mnwffC/zsoGKiooiNDQUX19fdHR0RC1aUWygPmZSVpa/6z9S9oGRsuI+TUkaXaGhoejq6uapvv4uUBqkLDk5WUQlStqo8L6kLzMzM/H39ycgIIDatWu/lVRL9u8v6BjcuXOHnTt38uzZMxISElBTUyMkJETFQFtLS4u0tDRB7iwtLWnevDm6urokJiZy5MgRIOv6bb8oCE3d7LVSMpwnX+TkhKyJLj+vRAnZOxkvXLhA3759uXXrFkOGDCE2NpbmzZtTu3ZtGjRowKJFixgyZEhJDosK6taty4wZM1i0aBFLly5l9OjReS7yffr0Yf/+/Zw/f77Qfaanp7N06VJBygBR/wVZ13d2QgZZBCOvfcfExDBixAj27t3LihUrhB6XpaWlyn0m1Tw1bNiQNm3aEBcXx+nTp/Mdo4aGhmgwqFq1KjNmzCAjI4MRI0bQtGlT7O3t6dGjBzo6OgwZMgR/f38qV64s7LNy4uXLl+zduzeXMX3z5s2ZPn16kciYBEmp/9ixY6xYsYL9+/fnqcMolTGUNFKbF8LCwvD29iYtLY3IyEiuXbvGoUOHxH2UnJyMlZUVU6ZMYciQIYVGmd5FpKwgZLeBqlatGunp6URHRxMVFYWvry+pqakYGhqKKFpe3YIKhUI4FXxM+K/Qv+zx8V01/w+JtCiVSuzs7MQT8vuANyVlERER3L9/HwsLiyIZWOeH96H7ErIKwqOjo2nWrFmJitLfBAXVlE2bNi1fUdbskMhE586dWbVqlYqpdGhoKMeOHRPWWjrGVfIcg37lRsS98CAlJUV0GErIizhGR0fz5ZdfMmfOHEJDQ8nMzKRJkyb88ccfdOvWjdevX6sQxzfFgAED+O2334iJieHKlSt06tQp1zZyuZxDhw7x888/52pAyAvXr1/Hzc1NdI/Wr1+fW7duAdC1a1fOnz+f63dLE7ZUv5QdFy5cwMHBAScnJ3r06IFMJsPFxSXXdoMGDeLHH38s9NxWrVpV+IMaGRnxww8/kJCQwNWrV0Xjg6GhIQYGBsKbc9SoUaJBQVrM4+LiuHz5Mt99953K/qWOUyMjI1q2bFksQnbnzh22bt0qUqKLFy8Wae3skMby888/c+rUKWJiYggJCSE1NZU2bdowZcqUIqc27969y+7du9mzZ0+BDw/W1tbcvXu3yNG5t1FT9ibQ0NCgQoUKVKhQAaVSqZLqDAoKQk1NTQjYmpiYCPu6jzFS9jZqyoyNjcts/x8CPkpS9urVKx48eICFhQW1atUiKirqvSAfEkpKhpRKJf7+/gQGBpZKROldpy8TEhJ49uwZkNXtV9waqJiYGDZv3oyWlhaTJ08u0RjyS19mZmZStWpV5HI5FSpUIDk5WdSJyeVy7O3tcXJyEoSpb9++tGvXTmUfSqWSMWPGiGNsbm6OIjMdNXXNXNslvMrS7cqewpOgUCioW7cu/fr14/nz5/j4+HD79m0yMjJEzQ5kEZYBAwYQFBSEhYUF06ZNK9ExyQtqamrUqVMHFxcXxowZw8GDB2natCmQJedw7949goKChMp6jRo1ikQKJ02ahIWFBfv27eP777+nR48e4jhs376d4cOHq2yfmJjIuHHjVAhI9pSvUqnk1q1bgtzlhVmzZhXpN/v7+xMREUFMTAzJycl5XicxMTEq6eYFCxYUScleR0cHBwcHbt26xZkzZ7C1tRUkRktLi3LlyqGrq4uWlhYmJiZYWlqio6NDWFgYt2/fVoki2tvbs2LFijy/R3LrePDggUgvSnj06BFHjx5l4cKF6Ovrk5SUREJCAkqlEhMTE3R0dHj48CEeHh48fPhQRSdO8vSU0qPZ7yMbGxs2bdpEy5YthT9rQfiQCIxMJqNcuXKUK1eOypUro1AoiI2NJSoqihcvXuDj40P58uXFPV/WkaW3jf/Sl2WPj4qUKRQK/Pz8ePbsGXXq1BHGoTklMd41SkKG0tLSuH//PklJSTRv3rxUaobeZfoyPDycBw8eYGBggEwmKzYhy8jIoF+/fty4cQN7e/sSk7Kchf4XL15k3759nD59WnQbZq81cnBwYM2aNTRq1IiHDx8SGRlJo0aN8ozwHT9+nAsXLojj/OrVK26uakWrGa4q9UBGsiiW/bSA6dOn56lO//PPPzN16lTxGUnpft++fSrbZZetWLBgQalNbn5+fpw+fZrq1avj4+NDZGSkMDd/06ivj48PPj4+tGrVirt37wpLo9u3b6ukM7Pjzz//BLLIiI+PDzExMQwfPpyRI0eK7ss3Qfb74unTp4wYMYJ9+/blGZ0rKZKTk1XM0t8kvTh8+HD09PRQKpWkp6ejrq4uFs7hw4cLy6j69evj7OyMl5cXHh4epKWlERoayujRo4v9nTnPec576OLFi0BWNLRBgwYF7ut9j5QVBLlcjpGREUZGRtSoUYO0tDSio6Px9/fn1atXhIeHv5c2UCXF20hfllY97IeKD4qUFXQxp6Sk4OXlRUZGBk5OTioLUmE2S28bxV3IYmNj8fT0RF9fHycnp1Ir2n0X6UulUsnTp08JCgqiXr16ZGRk5OmXWBDS09MZOXIkN27cQFNTk0GDBpV4PNkX4F9//ZXZs2eL9/T19WnZsiVdunShcePGVKlSBXNzc5KTk3Fzc0Mul+fbIapUKoVGmbW1NQEBASiVSmKDbnNygpxafVeiZ1GLMU4KhvbrjL9/d6ZPnw5kKcxnl8TIWWs0YcKEPE1vJRuthg0bMnDgwBIfk5y4efMm8+bNy/M96fqpVKkS9vb2mJmZYWBgwPnz5wkICMDY2JioqCixvZaWVp5ky8/PD4VCQePGjfH19cXPzy/fDumUlBTKly+v8v5XX31F/fr1uXDhAleuXOH169ds3bo13+u7oAaPbdu28fTpU3766SeuXbvG8OHDixR1LI2HHMmQOzMzk+TkZGQyGYaGhqSnpwtZn9TUVNTU1DAzM6NVq1ZoaWnRoUMHfH19BbmTy+VUr14dBwcHsW8bGxuVCKOBgQFJSUlFtiNydHSka9eu7Nq1i4CAAHR0dKhRowa+vr4q+6hevTpWVlYkJydTv379Qvf7PhT6lxY0NTUxNzcnLCwMExMTDA0NiYqKIjIyEn9/fzQ0NFS6Osu6AaO0Udbpy6SkpP8kMd71AEoDUo2Vubk59vb2uS6aslTQLwnU1NSKNBEqlUpevHjB48ePsbGxKTW/x+zjKM4iEhcXR0REBNWrVy/RONLT07l37x5JSUmCOL98+bLYC9nevXs5dOgQ6urq7N27V6S8SgKZTEZycjILFy7k559/BmDo0KEMGzYMJyenXMW60dHReHp6UqFCBWrXrp3vYiIRT7lcTkBAAJAlSirplj3+6zsMDAwYuiqcjIwM0Tmpra3NvHnzVGq2shvGHzx4kP3796Ompsbff//NoEGDxOLdrFkzfv/9d/T19Qs9P/PmzaNcuXKMHDkylydqTtjY2NC2bVu0tLSoV68e/v7+nDhxQiX6HBISQsuWLVmyZAn6+vpi8f/++++ZPXu2uN5TU1OpWbNmLsskyEpZZleov3fvnsr7ku0RZKW+PT09gSwyWqtWLSCLOEg1avXq1eObb77J8zflR8hat27NZ599JqQzjh49yokTJ4okHVMaUWelUqkiwaFUKomOjkZHRwddXV1SUlJITU0lMzOTsLAwDh06xKFDh/Icy9OnT4UALvzPC7NJkyYYGBjg4+MjjmdBqFmzJkOGDMHU1JSLFy8SEBCApqYm6enpeHt7A1myJi1btsTKyoqJEyeip6dHRkaGqNn09/dXqbXMOdYPOXqUF6SIUk4bKCnVGRwczMOHD9HT0xO1aG/TBqokUCqV/0XK3gI+aFKWPepSUI2VRD7el9oFNTW1QrvsMjMzefjwIa9fv6ZRo0bCOqQ0Udwn+4sXLzJ48GBq1qzJqlWr8iz2zg/x8fF4eHhQvnx5lWhfSaILrq6uAHz55ZdvRMgArl27xm+//SZMpSdOnJhvfc7z5895/PgxdnZ2hbol7NixA/hfvYxUdyaRFScnJxo3boxSqWTPnj1cvHiRcuXKcfz4cZydnVWK/SVCExcXJ8jbrFmz6NKlCxs3buTgwYOsX7++yAWyERERbNiwgbS0NFauXEnfvn2ZOHGiigo7ZBmEL168mBUrVrBx40bCw8NF5CUqKorly5ezZcsWMb4DBw5w7NgxqlT5XzNDeno6nTp1Uuly1NHREanH7JC6cHOiSpUqPH/+PBeBkI7t+fPnc6W/X79+LVLaampqjBs3Lk/h0py4du0aNWvWzGrKMKuOaa3OhLjtKPRzZY3k5ORiqfLr6OjQsGFDYmNjc+nEubu7q/xfLpfz+eefC2uuAwcOUK5cOTp16oSDgwMPHjzIdZ9J16aBgQFbtmyhS5cuKvfDq1evuHLlCqdOneL27dskJCTw+PHjPDvrlEolaWlpXLt2jVu3bnH79m3U1dU5ePBgkX/v+4a81ho1NTURIYP8baCypzrfJ0jzdFmuoQkJCf91X77rARQH2W/6lJQU7t+/T2pqaq50ZU5I0Y6yLlIsKgpLG+b05SyucGFpjSMnwsLC0NDQ4MmTJwwYMIBHjx4VqXMrNDQUb2/vPMVti0vKXr16JWqprKysaNq0KQkJCdSpUwcrKys0NTVp164dbdu2LbRO7fjx48yYMQPISr8tXbqUfv365dpO0oMLDw+ncePGBZKfjIwM5s6dy+rVq4GsRatBgwZcu3ZN5Vi5uLgQGhrKwIEDOXr0KAqFggkTJtCyZUsgq2hfWvh+++03Fi9ezIEDB4iMjKRmzZqiWL1v37707NmzWO33hoaGbNy4kY0bN3L79m3279/P/v376dKlC8OHD6dFixYYGBgwdepU7t27R+PGjalZsyZpaWlUqFCBQYMGMXDgQJYuXcrLly85evSo2HdKSoqKlc7s2bOpUaOG+L9cLufevXv89NNPODs752qOyAs5pTGyQ6FQ0L9/fw4fPqwSZWvTpo24trW1tYtEyCS8ev2a7r+lIFdTRyaT0eDz30mJDePi7IpF3kd+KF++PBYWFioRrDeFVBeZPfqXnJycq/O0fPnyKlE4ExMT+vfvz/Dhw1VM7Z2dnYEssrRy5UphDp8d+vr6TJs2je7duxMaGspff/1FREQE9+7d4+7duzx58kRlPAYGBjx8+JBmzZoBWdpwktXR1atX8fb2VpmLyuIh9G2iKAEAyQbK0tJSREijoqKIiIjAz89PNHkYGxtjZGT0ziU2pPNTVpEypVJJUlLSvz5SJlOWlfFfGSE1NZXIyEju3buHqakptWvXLvRiVSqVnDt3jrZt25YZwSkOnj17RkREBI0bN871nlQAX6lSpTIXuo2Li+POnTuiaLsoiImJoW3btjx+/Jiff/5Z1NpER0dz/vx5ateuLSIuCoWCJ0+e8OLFC+rXr0+FChVy7S8iIoLHjx+r6FUVhPnz57N06VKaNm3KrFmz6Nu3b57bmZmZ4eDgQFxcHMnJyaIAWltbGy0tLaG8D9CjRw927NiR5xNaamoqXl5eZGZm4uDgUKAe3KNHj5gwYYKI5GloaHD16lVmz57N1atX+f3334Xwqp6eHvHx8ZQvXx5HR0cCAwOZM2cOQ4YMIT4+XkUIuHLlyvj5+QnV/Llz5zJ37lwgiwTmtA0qDu7evcvatWsFMYSsBXfcuHE0atSILVu2cPny5VzpPl1dXerVq4eTkxOBgYGcOnWqyLVJErp3746hoaGwOHoTaGtr06nXEGi1HqVcE0VmGq6/dSbq6f9q89TV1VVSrg0aNMiVIgVoO98XPfOaKq8plUp8jn2P/7ml4rWC6tIcHBzYsmULMTExHD58mEuXLgmyamNjQ9OmTcXDha6urrASe/z4cYEPKUV5iMlvXM7Ozty6dQt9fX3U1NRUbLM++eQTJk6ciJOTE7GxsRw8eJAVK1YIR4Ds+1ZXV8fe3p4nT54UGPFv0KAB7du3p127dlStWhVPT0+uXLmCi4tLnunrihUr4uTkhLOzMzY2NkUi7O8rXF1dsbW1LTG5zMzMFNpoUVFRJCcno6+vL6JoRSlRKG1IRL9du3Zl8t1KpRIrKysuXLhAkyZNSn3/Hwo+KFKmVCp59OgRgYGB2NvbU6lSpSJfHOfPn6dFixbvRWg0JCSEkJAQ8dQIqp2j9erVy2XhUhZISEjAxcWlWGlIgLVr1zJ9+nQsLCzw8vLC0NCQy5cv0717d2xsbISwpJeXF2lpaTg4OOR73CMjI/H29qZNmzaFfm9KSgq1atUiLCyMvXv30rp1azw8PEhKShJq6l26dOHWrVu5FpP8YG5uzr59+0R0IDukBgtDQ0Pq1auXL/HJyMhg8eLFrFy5UmXRX758OZMmTWLu3LmsXLmSevXq8eLFC6Kjozlx4gTffvstT548oVWrVpw+fVqkdJ89e0bNmjVViuIDAwP5/PPPcXV1Zc+ePYKMvikpk/D06VPWrFnDxYsXVWQ2JC2tglAQOXlbHb5yLV26/xqvMh8olUqur3AmNtC1WOP6ZG0acvXcBdjxr/y58qNNHp/IDS0tLezs7NDT06NGjRrUq1ePAwcOiNShq6srTk5OJbb4qlChgvDLzIlvv/2Wli1bcuLECWH7lB3nzp2jUaNGnD59ml27dolOSch6WEhOTi5yt7qGhgYWFhbo6+ujq6uLhYUFlStXJiEhgfDwcIKCgnj27FmeqVc7OzucnZ2xs7OjevXquVKgHzJu3bqFvb290Il7U6SkpAiCJjXOGBkZiUja2wg2JCYm4u7uXqS5uiRQKpWYmpry4MED7OzsyuQ7PgR8UOlLSbivJJIQ75MsRs7Gg9TUVO7du0daWlqhqdjShJS+LG5L+siRI1m9ejUhISF8++23bNu2TWgg6enpcfLkSTZv3sz8+fNp1KhRgZHM4izamzdvJiwsjMqVK9OjRw80NTXp3LkzgwcPJiEhgbp167J9+3bU1dW5fPkyoaGhGBgYoKurK6IkkZGRqKurY2xsjI2NDfHx8XkSmpcvX/Lw4UNq1KhBtWrV8j0+R44cYcaMGSLFpq2tTUpKCvXr1+fLL78EYMqUKeIYSZFPGxsbpk6dyrRp07h+/Trr169nypQpAGLS1dfXJyIiAnV1dczMzER0Ib+C6ZIgNTUVTU1NbGxsWLNmDQqFguPHj7Nhwwbc3NwEIZNSwVJKVSaTqVw/+eFtSa40+epgrnMkk8loNu4YF2bmbmSQxiWTyYQsixQ5in/li0HFuirbK5VKnrv8kWv/lpaWqKurk5qaio6ODkqlksjISBISErh//z6AivSFhPbt26Ourl7s6KIEiZBZW1tjb28vGhMAbt++zbfffot1gw50HbuW0Dt/MvmbLGeCcuXKiYLz/fv34+3tjb6+vujajI+PL9Y40tPTC0wvS5DL5dSsWZNOnTrRsmVLmjVrJqJIz58/Jzo6+qMhZFD6XYra2tpUrFiRihUrolQqiYuLy2UDJUXRDA0NyyTVWdblP2lpaaSnp//r05cfFCmTy+XUr1+/RE+X71MHZvaxREdH4+XlhZGRUaEEpizGAcXXCdLV1WXr1q1069aNPXv2MGrUKLFoJyQk8MUXX5CSkkKjRo1o1KhRgfuSSFlcXBzHjx/Hw8ODmJgYvL29iYyMRKFQoK+vT/ny5UWn15w5c8T3Xbt2TdQ0bdq0SaQXs5tbFwRfX1+V60KpVOLr68uLFy8K9BN1cXFh/vz5Ksru7dq1E5ZKFy9eRF9fHwBTU1OGDBnCunXrUCgUVKpUiSpVqmBpacmXX37Jxo0bmTVrFjExMfzwww9iYZQKfXV0dEQqA7II3Ztg9+7dzJ8/H8ha3A0NDTE0NMTOzo4GDRrQs2dPzp49y7Nnzxg+fDgPHjxQ6T6U7KPel/sJICMt72ieTPa/RURTU5M2bdrQokUL0tPTOXfuHO7u7irir61bt8ZteRM6rIhBrq4looDJsS/xP/uTqCeMiopCqVQKp5DmzZszZMgQPvvsM3R1dbl69SpDhgzJV9ussOhjUREUFJRL2+769euM/tsMuZoaIAP5eBxGGuO5/XOSkpLo3r17sb5DQ0MDQ0NDtLS0ePXqFWlpaUL6QUNDQ/zR0tJCS0uLjIwMGjVqhJmZGWFhYdja2tK9e3eVLuLs+JgkMSSUZVOZ9CAh2UBlZGSI+eHJkyekpqZiYGAgujrzsoEqCd5G5yXwXmSz3iU+KFIGJTeQfp+0yuRyORkZGQQFBeHn50fNmjVFTcnbHgeUbALJXu8xZ84cYcBsZGTEhAkTmDp1KkuXLsXKyooRI0Zw/fp14uPjcXBwEAXv4eHhXLt2jdOnT9OvX798u8uy65jJZDLhYXr37l369u2LQqFg0KBBedboFYbsNkuSZEdycjJOTk55Tg4BAQHMmDGDkydPAlkL/eTJk6lUqZKIdEHWBCORMlCNGLVv314c78GDB3Pq1CmeP3/O0qVLef78OX369AGyiF5ERATx8fH8+OOPQFYn4pt0ZZ09e5avv/5a5R6SVOmDgoI4d+4cy5cvp3bt2nz22WfMmzcPa2trrl27xqlTp7hy5YqYPNXV1UWbfFFQlFRocaGpqUm3bt04/sdgKjr0zZW+vLWmgyiET0tL48KFC1y4cEFso6GhQc+ePWnbti3u7u4cOnSI1NRUrs42Zd2W3VSp25FRnzYkOMhfuC20b9+epUuXcvnyZc6fP88///yDq6srrq6uzJo1izFjxjBz5kzGjx/P6dOnSU1Nxd/fX8xdpf2AmDO61XzqFeRq2aZ2mYxKTQbiuf3zQveloaGBgYEBdnZ2NG3alJEjR4oGioyMDIKDgwkJCcHX11fUl2VmZnLlyhXu379PYmIiMpmMwMBAIf8CMGPGDHR0dKhduzYODg5UqVIFZ2dn6tWrl6ckhlKpJDExUeznzJkzLF269INRx3+bnf5SJF16gExKShJpzuDgYORyuYo2Wl66ikVBWWuUSU0o/5Gyfwnep0gZZIVqAwMDadKkSanVHRQX0g2WmZlZogjd2LFj2bRpE+np6QQGBgJQo0YNFQX88ePHs3DhQlJSUoiOjmb16tWMGzcOyCJVQ4cOFdtWr16dTz75hBMnThAUFMTcuXPp0aMHsbGxrFq1ivPnz6NUKlm4cCEbNmzg008/JT4+ntatW7Nhw4YSHQNpwU5ISMDDwwNdXd189clWrVrF2rVrhXjnsGHDmD17NlWrVuXzz/+34NnZ2eXqSs2ewpKspSBLZf3Jkyfs2rWL8ePHs2fPHqKjo9m8eTN16tTh9OnT/PTTT6I2KKftUEZGBikpKUWeyFq0aEHr1q0xNzdnxIgRVK1albi4OBGdvHnzJmfOnOHRo0cqUgp169alcuXKfP/99/j5+XHixIkiaVxlR05Cpquri46ODq9fvy7WfrJD6ghVl8Hpybq0X+SPlp45menJXFvajKSwR/l+dubMmcKrctCgQcKwXEtLiyNHjtC8eXNASUXLCgQH+fP111+zcuVKLl++zMGDB/nhhx+YMGECL1++5ODBg+zatQs/Pz9+/fVXGjRowLx585gxYwazZ8/G39+fWrVq8fjxYzIzM3OJ6BoZGZGZmYlMJiv0uErkbsKECdjY2DBt2jRkMhlNmzbl9u3bGFauW+DnIYt85ZU6rVy5Mt26daN+/fqYmZlx69Yttm/fzuPHj3F1dVWJKhYXycnJ3L17l7t376q8bmZmRs2aNXF0dKRq1aokJyezadMmgoKCVLZr165dsaN87wJKpfKdyi/ltIGSUp2SDZSurq6oRTMwMCgy0XobFku6urofXdS0uPhXkbL3oaYsISEBb29vlEplvmrwbwvS02lJ634kknvv3j1haXXlypVcHVlSpEtNTU3FBsfU1JTGjRuTkZHBxIkTGTJkCCkpKYKANG/enIYNGxIaGoqHh4f43OXLlxk5cqSwOPrrr78K7IosCDKZjKSkJFxdXbGyssLGxibXU/vt27cZNGiQSFW1a9eO1atXqxSj7t27l0mTJrFly5ZcEbuwsDDR5Sd1ZB46dAgDAwOROh42bBh6enp88cUXnD59mtOnT9OuXTvRgatQKKhataqKb2NsbCweHh6kpqZSrlw5TExMhIp4fhObnp4ehw8fRkNDI9c2LVq0YOzYsURHR3Ps2DEuX76Mt7c3/v7+eHt74+3tzdmzZ4GsWqaBAweipaVFaGgo169fF80VRY1mJyYmlop10ZYtW/7/X+lcnPU/2Yp+/fphYtKKI0eOqBTFS/WFd+7coXbt2vz000+iXq98+fIcP35cpfvL3t4eFxcXEhMT+f333xk+fDirV69m8ODB2NjYULFiRaZMmcLkyZM5efIk169f57PPPkMmk6GtrS0ippIum46OjogKy2QyrK2tiYqKIiEhQeVe1NDQoGnTpqSnpxMbG8unn36Kg4MDmzZt4tq1a2hra4tazu7du4vuTvOIv4my+gr433UskykJCAigevXqQNa99dtvv7F161bOnz+Pv78/SqWSwMDAAo3a1dXVMTAwoGHDhujq6pKRkYFCoaB27dq0a9cOExMTFAoFr1+/5ubNm3zzzTeoq6tz5swZLly4gJubGzExMcTFxYnfGhERQURERJ61dwYGBujp6dGmTRsx9vcd0rX/PpALuVwuyhOqV69Oenq6iKL5+PiQnp6OoaGhiKLp6urmm7F5G+nLgr7/34IPqvsSsiIDJYl4eXh4YGJigpWVVRmMqmiQ9LoqVqzI8+fP6dq16zsbi4Tz58/j7Oxc7OYCpVIpVLvzgo6ODnZ2dnh5eQEwaNAgJk+erGL7AlmF5v/88w+dO3dGLpdz//590ZUqGWtLCus2NjYq+k7ly5fn9u3bJZ6slUol7u7uREVF0aBBgzw7Xq9cuUKfPn1ISkrC1taWn3/+mR49euQ5cfz555+MGzeOLl26cOzYMfH6zp07GTNmDA0bNuTTTz9l4cKF2Nrasnz5ctq0aaOSjrxx4wabN2/m2LFjueyIjIyM8PPzo3z58uJaql69OhUqVCAuLo7IyEgiIyPJyMhQ6cwqKWGVEB4ezqlTp7hx4wZRUVG4uLgUKmRqbW2Nvr4+T58+LXHKUkNDA01NzSITN3UdQ+QaWqTFZZFDIyMjFdmHwlClShWOHz+uoqsG8PvvvzNr1ix69erFrl276Nu3LxcuXODzzz9n06ZN+e4vPT2dr776StQZFgfDhw9n1qxZ4mEnO/bs2cP48ePR1NSkcuXKBAQEsGrVKv755x9OnjzJrFmzMHOayJWXliiVkJmeit7diSxfOJdFixYJL8xTp04JKZqEhASWLl3Kpk2bSE1NRV1dnYoVK1K1alXs7e2pVasWdevWpVmzZqWyMCcmJuLh4cGdO3c4cuQIz549UzlXGhoajBs3junTp2NoaPjG3/c2kZGRwbVr12jduvU71xYrCJIumETSoqOjRROU9Ce71uOLFy+IjIws1Mu0pLh69SoTJ04kICDgX03M3t8rppTxLtOXCoUCX19fQkJCaNCgAQYGBjx//vy9cBgoiTm65DYwbtw41q1bl+t9R0dHpk6dSu/evbGzsyM4OJj9+/djbm7OtWvXSE1NJSIiAm1tbWxtbSlfvrw4FtnTcNbW1vzyyy+cOXMGDQ0NvvnmG5W6rU2bNpWYkGVkZPDgwQPi4uIwMjLKRcgUCgXz589nxYoVKJVKOnbsyL59+wrsDJIKmSVbJQnSotyjRw8mTZrEb7/9hp+fH3fu3MlloN2yZUtsbGy4cOGCIGXGxsbo6ekRHBzM6tWrGThwIMHBwTRo0ABTU1NSU1NFTYlUixMZGSm02MqVKyeKfguKouXEo0ePGDhwINra2lStWhVNTU2MjIzo3r07z58/5/bt27k+I5fLKV++fK7UU0lgY2Mjoks5Ncayw6JRf5p8dUDF5D3w6u88PKBqsaSvr49CoVARUJXQunVrDhw4kGcaWCK1UgRYIgkHDhxg/fr1eS68bm5uDB06VCWVXxC0tbWpUqUKfn5+GBsb89tvv+W7MA0ePJgtW7bg4eFBQEAAWlpafPrpp+KBJSQkhO/b6TGaBFatWsWCBQtwdHTEx8eHXr168c8//xAQEMDhw4dp2bIlMpmM8uXLs3jxYoYNG8bo0aPx9PTk2bNnlC9fHmdnZ3r27FmoGHNxoKurS6tWrWjVqhU9e/YEss7xqVOn2Lt3L97e3qxdu5YjR44wd+5c+vfv/8H4RL4N5fvSgOSzqqurS5UqVVRsoJ49e8ajR4+EDZSxsXGZR8okNf9/MyEDeL+vmlLEuyJlKSkp3L59m6ioKJydnalQoYJKgf27RnF1pJKSkoRUwuLFi7lx40YujRw3NzcGDRokiv0l/Pbbb8ycOZMffviBtWvXsmLFCsaMGcPIkSOZNWsWqamp1KhRQ1gUAUKc1s7OTkVZ/JdffslXOLYov8HV1ZX09HSqVauWa6J59uwZ3bt3Z/ny5SiVSoYOHcrBgwcLbdU2MDAQ+5fw+vVrUVjev39/ypcvz5gxYwDYv39/nsf+xx9/JDo6GlNTU8zNzYmKihIE7ZdffuHRo0c4OjoKMd7sk5i0wFpZWdGoUSNatWolOrQePXrE9evXuX//PiEhIYVafaWkpBAcHIyvry8XLlzg1KlT/PXXX/z111+5CJl0DUg1LNkhRXQKisaWt7Cn8ZgjVHYeJV7LbsOUb+mBXE2FkEnHoFqbCZSvVI8pU6bw9OlT/vrrLwYOHJhv5OLatWv88ccfeaZdpa7fatWq4evrK7wmc3YNKhQKjhw5QocOHejUqVOhhEwulzNw4EDOnTtHWFiY6HBt0qRJgQuTTCbj66+/Ft9drVo1KlSoIPw/JU/QqKgozp07B2Tdk15eXtSvX585c+YwaNAgqlatiouLC76+vkRERJCRkUHNmjU5c+YMc+bMwcDAgEePHjFixAhq167NypUrc53b0oBU6F+tWjW++eYbbt68yc6dO7G2tubFixeMGzeOOnXqCCu09x3SOvOhkQvJBsrGxoZmzZrRokULqlSpQmpqKg8fPiQgIIDY2FhevHhBUlJSiTX28kNiYuJbk4N6n/HBRcpKeqG/i5oyyXnAzMyM2rVri8X/TQvsSxPFsVp6/fo19+7dw9LSklq1aiGXy2nSpAmHDh2ib9++pKWlIZfLcXJy4ubNm2zYsAF7e3vatm3LlStXsLe3p2LFiqKgeujQody+fRtfX1/WrVtHQEAAf//9NwMHDkShUDBixAjx3QEBAYLszJ49WyjjFxfSb6hYsSJ2dnaEhISoTC5Hjx5l7NixxMbGoqOjw9q1a/niiy+KtG/p+sq+UB87doyMjAwcHBxEDdqUKVP4/fffCQgI4MiRIwwbNkxlP1LEY9WqVTg7O9O+fXueP3+OpqYmSUlJ3Lt3j169ehVpTOrq6lSoUIEKFSqIhobIyEjCwsJEFE2qRTMwMFAZe82aNbl48aKQbfH29qZZs2ZcuXIFX19flYaFggheWlqaiiBtTnRaGoqWvjkymYyKDr1pOGQz52aYkp5YeOqxasuxec4JMpmMBoM3c/XiBGF5lROWlpb8+uuvzJ49m8DAQL7//nuOHj0q7IhatGiBo6OjINXm5ua0aNFCfN7Kykocr3/++YcJEyYUiThIRfZbt24Vtl5KpVIU3hfl2jY1NUVDQ4PU1FQeP37M+vXrRX2qmZkZqampdO/eXaVZY/bs2WzatIn79+/Tv39/FdX4p0+fkpKSgoGBASYmJnz99deMHj2ajRs38ueffxIWFsbChQtZs2YNU6dOZfz48aUmWJqT3MpkMnr37k2nTp3YtGkTv//+O4aGhnmmct9HSFH/D42U5YSWlhYWFhZYWFhkOVr4+JCUlKRiAyVF0YyMjN44kpmYmPje+X2+C/xrImVvUxJDqcwqqvXw8KBmzZq51OClG/Z96AYtSvpS+j2enp7UqlWL2rVrq0yinTp14syZM0JewsvLS8g6LFy4kN69eyOTyfDx8WHIkCFUq1YNgAsXLvDbb78xcOBAIKu+TRpLzrSkRMjmzp0r5CGKA6VSSVBQkPgN9vb24jwoFApSU1P59ttvGTRoELGxsTg6OnLnzp0iEzJAGHGHhISIiIJUGP/pp5+K7UxMTEQEcMGCBSq1NEqlUhSlSymtAwcOYGZmJiIpEqktLmQyGXp6elhbW9O4cWNatmxJtWrVSE9P5+HDh5w6dYpp06axbt06rly5gpaWFs2aNaNLly6MGjVKNGNMnTqVxYsX88knnxT6nerq6pibm+eKMkoyC+YN+wtCJsYpl9Nuvh8FQS6Xo6GhQUL443y3SYoMFlEjKyurXA9AoaGh/P7771y+fJnvv/8edXV1bt++zeXLl7l9+za//vorgwYNIigoCLlczoIFC1T02nr06IFSqWTp0qX07t27UEL2ySefcPz4cdq0aYO9vb2KbldAQICK5llhkHx/pXTrnDlzRK1YkyZN6NevH48ePcLMzIwffvhBfC57pEtNTQ1TU1Nq1qyJk5OTiL7GxsZy9+5dHj16xGeffcbly5fZsGEDNWvWJCYmhh9//JHmzZtz8eLFUomW5CWJAVkpzmnTpvHo0SP279//wZCc96EspbQhyZ8YGhri4OBA69atsbOzQ01NjcDAQG7cuIG7u7uIppUkC/RfpCwLH1ykrKR4W+nL9PR0Hjx4QHx8PM2aNRMprXc1nsJQWPpSqr2KjY0t8Pe0aNGC/v37c/DgQRITE7l06RLW1tYEBQUxbdo02rVrx+XLl/nuu+84ePAgI0aMEM0OPXr0AKBVq1ZiMpMicdnH9s033wjPx+JAqoGLjIykadOmGBoaolAoRIt3ZGQkXbp0EZ6V06ZNY8GCBcV+8pOib76+vixZsoRFixYJcdmcVlaSyn9ISAijR4/m0KFDyGQy3Nzc8PPzo1y5crRu3ZqQkBAiIyNFVCU9Pb3IdUqFQUNDQyWK9s8//7B161bxvrq6uvAYLVeuHF988UWB9lUymUxF9w2yrp+8PiPVnFVpPiTPxVaznGGBY1coFCgUCqJ8L5OZkYqaumoXs1KpxPOP/8mUBAcH5zlePz8/IiMjmTVrFh07dmTBggVUrlyZunXrcufOHf7++2/xfRK0tLRwd3cXTUMvXrxAqVSKSFXOBo2KFSuybds2EWVr27ZtrrFk78K+e/duoV6w0jGtX78++vr6nDt3jkePHiGTydi0aRNxcXHI5XLWrFmjEtGMiorixo0bwvg+O3JKKcTExBAVFUVISAgVK1Zk/fr1uLq6iqh2nz596NGjBxs3blTR5CsuChOv1tTU/GA6L+HjJGWgmtlRU1MTEXZQtYGSouJGRkYiklaUZqP/SFkWPrgr531OX8bFxeHi4oJSqcTJySlfAiON530hZfmNIzExEVdXVzIyMnB2di7w90BWLZT05B4bG0tYWBhVqlRBoVBw+fJltLS0iIqKYvHixbi5uYmIwOXLl4EsaQ1p8cxukA1Zemc//PCDSqSiKEhJScHNzY3ExEScnJwwNDTk/v379O7dm++++47z588zfPhwXF1dMTIy4siRI/z8888lDsUvWbIEgDVr1rBv3z7i4uIwMTHJ1XWqq6vL7Nmz0dTU5OTJk/z666/A/1StjY2NCQ8P5/Hjxzg4ONClSxc2b94MZBGM48ePl2h8+UEmk2FhYcHQoUNF11hGRgYuLi706tWLSZMmFeonKukzFQc+x+bkGW1JiPAv8j5OT9QmLtQHpVKJUqkkNSmGM1MNoJAojlKpJDQ0lCZNmmBlZcXs2bPx8/MjKCgIFxeXPCUaIIt0XblyBcg6F1KEKzU1VRCyGjVq0KxZMzp06MDNmzdV0p55oXLlyiIlLV0LBUG6DxQKhUr6W7LgKVeuHEeOHOGTTz7hr7/+Uvls9+7dC+1olQRHbWxscHR0xNnZmSpVqtChQwfWrl1Lr169UFdX5+TJk7Rt2zZPg/Gi4mNT9P+YSVl+hf6SDVTdunVp1aoVDRs2RE9Pj7CwMCGs/OTJE16/fp3vOvwfKcvCx3fl5IOyJkEvXrzAzc2NSpUq0ahRo0I7lYpTy1WWyC99+erVK1xcXDAzM6Nx48ZF6ryqUaMG169fFwXoKSkpPH/+XNzI0oJ15coVRowYwfbt2zE2NhapyaioKO7cuUNAQABjx44V+7W3t2fVqlUMGTKErl275mvEnBPR0dHcunULfX19HB0defToEXPnzqV58+acP3+ezZs3M27cOGJjY6lbty5Xr16lW7duRdp3fujevTsdOnQgMzOTOXPmADBw4MA8JzNbW1sWLVoEwLx583BxcaF27dpA1vW0ePFimjdvjqmpKQCff/4533yT1VE4duzYAuu0SoLatWuzfv16qlatSkZGhgoxzV50L6FChQpivCVFYuhDnt/eI4iZUqkkLTmOK/OLZ0h8dWFtbs01Jf5AO1zmVyUzNW8PRx0dHVq0aMHw4cNZtmyZON/R0dG4ubnx8uVLbt68yfHjxwUJzavuc+3atcybNw9HR0cuXryImpoa3bp1Y8iQIZiZmfH3339z+vRpjhw5IqIJhUHSRivKg6d03N3d3VUaYwAGDBiAj4+PcN2Q7i+pTABQSWkWBVpaWmLR7dy5M6tXr2bmzJno6ury9OlTBg4cyNOnT4mOji42Mc8vffmh4t9IyrJDJpOhr68vyiRatWpFjRo1UCqV+Pn5cf36dTw8PAgKCiImJkasg5JOWU6sX78ea2trtLW1cXR0zLPrOzsOHTpErVq10NbWpl69epw+fVrlfaVSyQ8//IClpSU6Ojp07NhRaPxJiIqKYsiQIejr62NoaMioUaPy7NqGrBpgPT29UpNu+fiunHxQVjVlmZmZeHt74+vri4ODAzVq1CjSBFMSKYqyQM4UoXTj3Lt3jzp16gjx0qJCWtgh65hraGiI4579hj537hx16tTJdU4sLS3Zu3evymtDhw7l8uXLXLhwAQ8PDxXbpfzw/Plz3N3dqVGjBnXq1GHGjBk4OzuzcuVK8XuVSiUaGhoMGjSIW7duUbNmzSL/zoLw2WefAVnE1sTEhJkzZ+a5nVwuZ8iQIaLo+ssvv2TevHni/XPnzuWapH7++WcaN25MdHQ0X3zxRbEjh0XB3Llzsba2LtQs+9WrVyqF5EVFToJ/78+hnJwg5+TX6pycIOfcNANRn1cYDAwMaNKkCZ9++il37tzhxIkTDB48OM9tx44dy+7duzlz5gxr1qxh/PjxHDhwAH9/f+bNm6eiYZidiElP9tlTdE+ePOG3334jKSkJKysr+vXrR926dbG1tWXPnj1Uq1at2BIS0sJQFMP5atWqoaOjQ2ZmJufPn1d5r2XLlsIlxNXVVXSPZteXepOsgZubG+3bt+enn34SEbdOnTqRlpaGt7e3SodvYXp28F+k7ENBSW2WJBsoOzs7nJycaN68Oebm5sTHx7Nv3z6srKzo27cvoaGhudbEAwcOMG3aNH788Uc8PDxo0KABXbp0yffB/NatW3z++eeMGjUKT09PevfuTe/evcU9ALB8+XLWrFnDxo0bcXNzQ1dXly5duqg0Kw0ZMoSHDx9y4cIFTp48ybVr10TXfHakp6fz+eefF1puUBx8cDVlb5K+LG1SlpSUhJeXFzKZDGdn52KJdL6P6cv09HThX9e8efNCJSDyQ48ePejRo4fwh5QsZTIzM5k+fTq7d+8mNDQUpVKZy1ImS4X9f5GFoUOHMmnSJFHc369fvwLFCxUKBY8fPyY0NJRGjRoRFhbG+PHj+fPPP1W209XVZdSoUXzxxRfExsYWawFVKpWcPn2amzdv8vPPP+d639zcHMjqXlyyZIn4f05I1/Lvv//OnTt3CAoKYt++feL9+Ph4XFxccHJyEq9pamqyc+dOWrRogaurK3PmzOGnn34q8tiLgooVK3L79m0uXrwo9PTu3r1LSEgIqampeHp6vlGBd3YiaWhoyNSpU7Gzs2PQoEHi9efPnxe4D+lhIjY2Fnd3dyCrFqtNmzYcPnwYgEqVKqkU33fo0EHlPCsUCr799lv27duXS+A2O2nR0tIiMzMzXzmI4OBglZq1+fPnF6lYPyek78xpMJ4XlEoltWvXzmVZBFlR18jISG7evMnFixfF67/88guQdcyliGtx4OHhwZ49e9i2bRsKhQIjIyNatWpFnTp16Nq1K7Vr11bp8JV08nR0dISQsaGhYa6F/b9I2YeB0rJZ0tHRoVKlSlSqVAk7OztsbGw4d+4c586d49SpU1y+fJnOnTvTpUsXVq5cyejRo0Un/saNGzl16hTbt29XcTeR8Ntvv9G1a1emT58OwKJFi7hw4QLr1q1j48aNKJVKVq9ezdy5c0W5wM6dOzE3N+fo0aMMGjQIHx8fzp49y507d0T0eu3atXTv3p2VK1eqdAHPnTuXWrVq0aFDB27duvXGxwY+QFJWUpR2TVlERAT3799XkYco7njeB1ImRezi4+Px9PQU3o9v0t4sk8nYsWMHP/30Ezt37lTxNrxy5Qr9+vVj7dq1GBgYqJAyuVxOdHS06EaUyWQsXrwYdXV1UbdTkIRIamoqXl5eZGRk4OTkxP79+/nmm29Unr7Kly8vWvqNjY2JjIwslup7aGgogwcPxsXFBYBevXrh6Oioso00Vmtr6wI7FCU7Ij09PZYvX86AAQPIzMzE0tISBwcHTp8+zYkTJ1RIGWSlibdt20bfvn1Zv3499evXV/HeLA1oa2uLBgzICuf/9ddfLFmyBKVSSbly5XBwcCAxMREfHx8UCkWhkbXsUFdXx9LSkoyMDH788Uchr5CXSGx2WyIJCoUCBwcHOnfujLe3N2fOnCEkJEREWdXU1GjcuLHK0/fcuXPp1KkTN27cICQkhAsXLuRZJyfZI1laWhIQECDS7jKZjAYNGhAaGkp4eDgVK1YkKSmJxo0bU79+fRISEoiNjVWxaCoOpIegwqzXJEKcV7RAQ0OD2NhYFixYkOu9169fY21tzeHDh4sUjZPw4MEDfv75Z06dOiVe+/zzz1m5cmWuBzepw1fq8s3IyCA6OprIyEgeP34sbH0kklauXLlCC/0/NHzMpKy0xWM1NTXp1KkTnTp1IjAwkPHjx1OjRg3OnTvHqFGjePHihUoEWy6X07FjRzH/5oSLi4vobJfQpUsXjh49CkBgYCBhYWF07NhRvG9gYICjoyMuLi4MGjQIFxcXDA0NVe7jjh07IpfLcXNzE5mQy5cvc+jQIby8vERDUGngX0PKSit9qVQqefr0KUFBQdSpU6fE2jnvCymTy+XExcUREBCAtbV1nt6PJYGuri4///wz8+fPF6KjJ0+e5M6dO9y5cwdAELIWLVpw8+ZNFAoFgwcP5ubNmwQHB6NUKrGysmL9+vVC4+vx47wlEOLi4vDw8MDAwIDg4GDmzJkjuiklDBkyhGXLlokaLen3FzXqc+3aNcaNG0dAQADa2tp88803eS5u0hjr169f4P6kTsVHjx6hq6tL+/btuXz5MjExMYwaNYoxY8Zgb2+f52c/+eQTvvvuO1auXMnEiROpX78+derUKdLvKAni4+P56aefiIqKwtbWlq1bt+Lg4EBEREQuW6LCYGJiQmRkpEo0TEod5PXglF8KzNPTk/T0dA4fPsy6detwd3dn/PjxvH79mszMzFzNEE+ePMmzIN3U1JQJEybg5OSEiYkJVatW5ebNmwwdOhTIOk/9+/cXad1ffvmF+fPnk5KSgqura6npZ0mduoU1BUjXa1RUlMrrampqBRLjcePGMW/evGJFwI8ePcrw4cNRKBSoq6vz6aefMnjwYDp37lykz0upK8ltQrL1iYyMxN/fH01NTTIzM0lISCAjI+Od6zaWBkqa5nvf8Ta8LytUqEC/fv3o168fISEhVK5cWeXhELIyEfmtA2FhYbkyE+bm5qJjXfq7sG2kumgJkv2UtE1kZCTDhw9n9+7db9R5nBc+PjqfD0qDBKWlpeHu7k5oaCjNmzd/o8n4fSj0l9I/r169okGDBtja2pb6E6umpia9e/emdu3a+dY/PXjwQNzse/fuZeTIkYwcOVK8P3XqVJE6ykuwMjQ0FDc3NzQ1NRk1ahTDhg1TIWRmZmb89ddfbNu2TYWQQeHm2QkJCaxfv54ePXrQuXNnAgICsLCw4ObNmyxevBhjY+Ncn5EkCCpXrpzvfiX4+fkRFRWFk5MTJ06coFWrViQnJ3PkyBG6du1aoFfrwoUL6dq1K2lpaYwfP75I9TslxfLly4mKihLNHFI3qeRRmLMGzMjISBS+Dh06VGjTQdaEpqWlRd++fVmzZg03b95USV0WhJo1a6pco97e3jg5OXHx4kW6dOlCw4YNAZg8eTJDhw4tUvHt69evWbRoEXv37kVTU5O5c+fSt29fkdL8448/2Lp1q9BWk85rVFQU48aNK9K4iwLpOixo4cvIyMDU1JTevXvnEuzNbz6Ry+Vs2bKF5cuXF4uQnTt3ThAymUzGpUuX+PPPP4tMyHJCsvWpUqUKDRs2pFWrVuJhKzw8XKUAPD4+vtQV498WPtZIWVmTzZzdl9J9Ll0j7xNGjx7N4MGDc9nklQY+uMeSN6kpk7SNSnLDxMTE4OXlhYGBAc7Ozm/8RPeuC/3T0tLwwHKAiAABAABJREFU8vIiJSUFCwuLXE8GpQmZTEZISIj4vWpqamhpaYlFL2etzo8//oitrS0DBw7kwoULREVFcfDgQfFZCUqlEhcXF44ePUpqaqqKOXS5cuVISkqiVatW7N+/X9SpKRQKwsLCCAgI4OnTp/j7+5OSkkKtWrWwtLQUn4+JiWHp0qW51OAbNmxIx44dC0yFS2ml7PvLiYSEBDEJNWvWTFxPW7ZsYcuWLUXSY5PL5axbt45mzZrh5eXF8OHD2bVrV6l6FAI8fPiQ3bt3A1k1G9lVt3V1dbl7966Ieunr65OQkEBMTIxYVA8fPiwIY8OGDRk7dixdunRBXV1dmKjnVUOWsyYMsjqrfHx8GDFiBMnJyWhoaBAdHc3o0aMZO3YsjRs3Ft/Tt29fkpOTRY2ZtM/w8HBx/iQ/UH9/f3bt2sWuXbtUvq9atWoiXSFBimhBVjq+f//+wnrpTSARyILSl76+vmLsRX2oW79+vUrnZVHw4sULxowZI+5ZPT09zMzMirWPwiBpXWlpaVG9enV0dXVFFC04OFjY/kipzg/J+/JjJGWlVVOWFyTP3uykzNTUFDU1tVwlBuHh4bm8iiVYWFgUuL30d3h4uMr8HB4eLh7oLCwscpUGZGRkEBUVJT5/+fJljh8/zsqVK8X4pWjy5s2bVYIKxcUHR8qg8OhGXpAWveJeWEqlkufPn+Pr64uNjQ3W1talEk16l+nL2NhYPD09MTQ0pHLlysWqBSopli9fTps2bYRquLa2NvPnz2fZsmV5bu/n54efn5+4SaWwsYeHB87Ozujq6pKeni6IpQQjIyPU1dWJiIjA3t6eo0ePoqury7179/j666+5f/9+nhG7devW0aZNG5ycnNDW1mb58uV5ajl5eXnh5eWFjY1Nvg0HlSpV4s6dO0LjKyciIiK4d+8eGhoaWFtbqxB8a2vrYhXuV6xYkd27d9OnTx/OnDnDhAkT2LRpU6k90SoUCubMydISa9myZa4nw9DQUCIiIihfvjwLFy5kxowZuR42kpOT0dbWJiUlhUmTJtGvXz88PDy4desWY8aMoUaNGrmifE2bNhVp7uxo0KABHTt2ZOPGjcyYMUNlApYaEiCLTEndmNnx+vVrHjx4QKNGjUhOTiYyMpK+ffuSmpqap8SIkZERZ8+eRUdHB4VCQXJysiCohoaGxMTEcO7cOby9valbt24xjmxuxMdnyXgUlA4pLuE2NDREU1OTV69eFevBa/HixaLWUkdHB3d393wXwjeFFInLXgAuRfElgvbw4UP09fUFSdPX139v69A+RlImkY63GSnT1NSkcePGXLp0id69ewNZx/bSpUv5Nqo4OTlx6dIlpkyZIl67cOGCqMutVq0aFhYWXLp0SZCwuLg43NzcGD9+vNhHTEwMd+/eFQ95ly9fRqFQiPphFxcXlfX72LFjLFu2jFu3blGpUqU3Og4fJCkrCbL7TRb1iUsycY6MjKRx48Z5pqreZDzvgpS9ePECHx8fQTADAgIKNaYuDejq6jJgwACV1xYsWICmpqbQ6soLkjZMRkYGyGQojetw76EXman/04zR19enVq1a3LlzRywklpaW9O/fn5UrV3L58mXc3NwKHJ9SqeTKlStCFDQ7KlWqRKtWrahatSqRkZHI5fIC5TOGDRvG0aNH+fPPP0WjgvQdwcHB+Pn5UadOnUI7DIsKZ2dndu/ezaBBgzh48CDR0dH8+eefJe6elcbq6ekpjp+GhoaKF6mEqlWrcuPGDXx8fHB0dKRixYpCKDK7PpB0jTk7OwPw008/ceHCBf755x8OHjzIsmXLVFwP8iJkEi5evEjDhg25fv0648ePV7GdUigUmJqaUrt2bTp06EBqaiqtWrXi+vXrQFYzyOnTp7l27RpNmzYFEKK8FSpUoGfPnrx+/ZqTJ0+SmZmJh4dHvlGmmJgY8e+5c+eyb9++YnVgZ0d6ejr+/lmCuQWlvS0tLenbt6+KIKyZmRkREREq25ibm+Pl5SXqEyGL0DZp0oTx48cXeP3evn1bpQv4iy++wMLCgvv377Nt2zYcHR3zlR0pCfKSxJDL5RgZGQlpj9TUVBFFk8izpBYvRdveF3yspEypVJY5KcspATRt2jS+/PJLmjRpQrNmzVi9ejWJiYliLho2bBiVKlUSot2TJ0+mTZs2rFq1ik8++YT9+/fj7u4u7nGZTMaUKVNYvHgxtra2VKtWjXnz5lGxYkVB/Ozt7enatavwfk1PT+ebb75h0KBBomQpZ62vu7s7crn8jR/M4F9EymQyWbHquBITE/H09ERDQ0NET0oTampquexYyhLZpSIcHBxEbdW7TqPOmTMHW1tbRo0aVWDErtFXB6nYqJ+IkmakJnJxuhE6OjqMGjWKjIwMFVHB0NBQFi5cqLIPuVwuGhmeP3+OsbEx+vr6ZGZmEhYWlkueQ0JISAj79+8Hsha2Tp06ER8fn29hsuRpGBsbS3JyMnp6eqKgPyIiQlg9SfY8pYHOnTuzbds2xo4dy4ULF5g4cWIuGZD8EBkZSVpaGhYWFshkMs6cOcPSpUuFb6S6ujqrVq3KNx0rifNClnhufHy8StpXMvhu3749FStWRKlUiqaGCxcusHnzZtatW1fk36qurk7Hjh2RyWQcPHiQo0ePMnnyZKpUqYK/vz8zZsxg27ZtPHjwAENDQ6ysrAQpg6wo0LVr16hZs6ZK4f+PP/4oivtXr17NDz/8IKK6cXFxpKamqkT0mjRpwrhx4/j666+5fPkyw4YN4+DBgyWK4GR3A8jvOKenp/Pnn39y5MgRldf19PQEKdPU1BSdqJ988gnly5fHysqKhw8fcu/ePe7du8e2bdto1aoVEyZMoHv37irjVSqVIjIKWTWD3bt3Z9CgQYJke3l5lSopK4okhpaWFpaWllhaWgrXgqioKF6+fMnjx48pX768IGgGBgbvlBR9jKRMWjfLOn2Z80Fy4MCBRERE8MMPPxAWFkbDhg05e/asKNR/9uyZypicnZ3Zu3cvc+fO5fvvv8fW1pajR4+qkKUZM2aQmJjImDFjiImJoWXLlpw9e1Zljd+zZw/ffPMNHTp0QC6Xi/rXtwGZ8gOspkxPTy8Rkbh06RJNmjQp1C4oPDycBw8eULlyZWrWrFkmF6K/vz+JiYmFduiVBlJSUvDy8kKhUNCwYUOVmqBnz54REREhwrTvAv379+fEiRNoaGggl8vzJKs9fs89cfscm8vTs6qpPk1NTSG1IZfLRYRm6NCh/P7772hoaIhwtaurK6dPnxbkA7IaCWbPnk29evXw8PDAx8eHwMBAgoKCcnW7VatWjQ4dOlCvXj3q1q1L06ZNCQ0NpWvXrgQGBtKzZ08OHTpEWloanp6eZGZm0qhRI3Hz37lzB0tLyyI1BOQHhUJBWlqauEbd3Nzo3Lkz7dq14+DBgwWmu5RKJdOmTWP79u2i09XMzEzofmlpadGzZ08mTpxIjRo1uH//fp6eiRLS0tKYO3cuGzduBLJkE16/fs2FCxeALFHd7Orz0kIvpTZzQiLgampq/Pzzz8ycORM9PT3u3btHTEwMkZGRJCUloa+vT3JyMjVq1CAzM1NIoiQmJrJ69WpkMhnz589XkT6RZDZMTU3R1dUlJCSE2bNnC32jo0ePMmzYMJycnDh79iyDBg3izJkzlC9fnq1bt+Lo6CjqFK9du8Znn31Geno6x44dE0r6+R3zhISEXIuPUqmkadOm6Ovrs27dOvT09FSaJ9zd3RkxYkQuD89atWrl6kST7LnatGmDiYkJAQEBhIaGcvnyZbZv3y7OL2R1ndna2qKurs6mTZu4ffs2Q4cOFcfe0tKSsLAwEc3q3bs3o0aNomXLlqWWPrxx44bw8CwJ0tPTRRQtKiqKzMxM4btoYmJS4uhlSeHr64u6unqxu5LfZ6SkpHDr1i3atWtXJmnj5ORkzM3NCQ0NLbM0+YeCf02kDApPGSoUCvz8/Hj+/Dl169Yt04vjbaUvo6Oj8fLywsTEhDp16uQKPxdmSP6mUCqVnDt3jlq1aonutexYv369qPuxs7NTUV6WIJPnfZma1uooSJm6ujq2trakpKQQGBgotjExMWHMmDHY2tqyZMkSrly5gpubm8qxl8lk2NraMnnyZEaOHCkmne7du6t8X3h4OBcuXBA2PIGBgSoG3levXmXw4MGEhIRgbW3N2rVriY+P5+7duxgaGlKvXj2V41+S2sjC4OjoiLu7e5F0qA4fPsy2bdvE/7OLoI4aNYq5c+cK4pGfcKqEu3fvMnnyZO7fvw9keZX269dPHMP+/furRC6VSqXokM0vfd65c2fOnTtHx44d+eqrr5gzZw7x8fFER0dja2uLjY0NKSkpolngyZMnqKmpceXKFRITE6lRowbDhw9HLpfz6aefUr9+fVG3JUW89uzZg729PfHx8SokyMnJiYMHD1KxYkU2bdrEmTNn0NLS4sSJE7keYFq3bs3IkSPZtGkTU6dO5dKlS3laK509e5YZM2bg5OSk0pQCWdeCu7s7K1asoHnz5owaNUp4YB45coQxY8aoPKzUrl2bR48eqcgDSCLNZ86coWvXrsjlciIjIwkLC6NixYp88cUXfPHFF7x48YKtW7eyYcMGwsPDRV1ecnKyKCWQrkvJPaNbt24sWrSo1FwvsuNNdco0NDQwNzfH3NxcRFwiIyN59eoVfn5+6OjoiFSnkZFRmctVZGZmlnqzzbuGVItdVnV8Uv1uXjZL/zb8q0hZQVplkvBoeno6Tk5OZX5xlDUpy96gULNmTapWrZrnDVXW44iKihK5eoDGjRvToUMHxowZQ0JCAjNmzACyFpS8CBmAUpEBSgXIVDsvPXcMFf/PyMgQ/oy6uro0btwYHR0dHjx4IOoNsqNatWo0b96c5s2bY2BgQL9+/QrtqDU3NxcL2/Llyzl79iz379/H29ubkJAQBgwYQHh4OPb29pw8eRKZTIarqyvVqlXL036rLEgZFM2mBxBioBMmTGDq1KkcPHiQzMxMbG1t8xS9zT7W1NRUDh06xK1bt3B1deXp06dAVp3P77//LsjYjh070NTUVBFrhCy/uOypQF1dXSpWrKjiQSfVDklixtbW1jx9+pSzZ8+K36itra1SHB4TE8O1a9eArBox6YHExMQEHx8f9u3bJ5TAMzMzuXv3rjCqzw5zc3O6dOnC8uXLhWvD7Nmz840oz5o1i1OnThEQEEDPnj05cuRILi0kIyMjgoKCCAsLY/78+XmmKaValevXr6NUKvHx8eHLL78E/vcANXv2bFFHd+PGDfFZZ2dn/vnnHy5evEifPn2wtbXF19cXLy8vle+qXLky8+fPZ/LkyXh6egoHgQsXLuTScevUqROzZs0S9XdlgdJU9JfJZJQvX16kbSXx2qioKJ48eUJaWhoGBgbimihXrlypE42PMX1Z1kX+CQkJyGQylSzOvxX/KlKWHwGJiori3r17GBsb07hx47ciYFiWZCgzM5NHjx7x+vVrmjRpIopl80JZR8ri4uKwsrISEZi7d+9y9+5dli9frrKdFAXQ09NDU1OTyMhIFXX309MM6LI0HDWtcqBU4raxL6nReRtyJyUliYUZshSba9SoQc2aNWnZsiUdOnQQulkZGRlcvHix2ORIT0+P/v37079/fyAr2jN48GCeP3/O6dOnhSBvvXr18o24Fke4tiywbds2Bg0aRP369TE3N2fixIn5bpt94UpOTqZ27doqdkCampr07duXhQsXqpCRnNHGqKgotm3bxvLly0lNTaVcuXLMnj2bR48esW/fPtTU1JDJZKLJBv7Xxj5x4kQmT54sjORzNuzI5XKRsoKslnpzc3MiIyMJDAxETU2NmjVrsmnTJo4cOcKpU6fyjdKlpaUxbdo0du7cKV5bunQpV65cwcnJifr162Nra0vVqlXR0NDA2NiYQ4cO0atXL7y9vfniiy84c+aMylzSrFkzmjVrxu3bt1m9enWencfNmzdHU1OTJ0+e0KtXL9F4IqXk69evT4cOHcT9k30OkaKZEkFu0KABvr6+XLhwQRivZ4eRkRHt27cH4OTJk+IBCaBv374sWrTojVLrRUVZel/mFK+VOm6joqIICAhAQ0NDSG4YGxuXytz/MZKyspTDgP91Xr6vHbVvEx8kKXsTrbLs+lJKpZKgoCCePn2KnZ0dVapUeWsXRVmJxyYnJ+Pp6YlcLi9Sg0JZk7Jq1arh6+uLt7c3sbGxLF68mH/++Sff7aX0EqiquytSEzkztXxeH8mF7ETHzMyMNm3aMHPmTOrVq5drW2miedNjoK2tzYEDB4iOjubly5dER0fj6OhYaJ1MfqRMqVTyzz//EB0dTd++ffPdRqlUiuMkl8vFn6JATU2Nrl27Fmnb7GPV0dGhdu3a+Pn5MXjwYJo1a4azs3OBQq0hISGsW7eOP/74Q+jTdejQgdWrV7Ny5UpByP7880/c3NxYt24dpqamRERECOLUr18/Fi5cSHBwMDt37hRdhTkhST+EhISoRNFevnzJ06dP0dbWFpEmW1tbEhIS0NXVFfd+ZGQkQ4cO5caNG8jlcmxtbYmIiCAqKipXh670mXLlypGSkiKiPm5ubvTu3ZuxY8cKg3CZTMbMmTPp27cvW7ZsoW/fvjRr1kxl7KampjRq1AhXV1fxPRYWFqIebvr06YwaNUrMHRoaGqSnp1OzZk0hByKdU4m0FlZD++zZM4YOHSrOr4ODA9u2bSv1RTguLo7Hjx9z7949PDw8CAwM5PXr1yxbtuytzLtSJKZcuXJUqVKFzMxMYmJiiIqKIjAwUMhuSCRNT0+vROP6WElZWXdelkXU8kPEB0nKSors6cuMjAwePHhAbGys6IZ7myiLrsfIyEi8vLywsLDA3t6+SBPD26ptq1GjBhcuXFDxwZRgbGzMzJkzqVGjBhkZGbx8+ZKUlBR++eUXXr9+jaamJhkZGQUer8GDBwvfQ8hayJKTk4mIiODw4cMcPnyYX375hfHjx6vc+NK/SyNilZmZib+/PzKZDCcnp0Lb9AtKX4aEhIgok6mpKW3atFF5X6lUkpmZKQrhcxI0qdtY+vtNkXOy3L59uxB3LAgKhYKtW7fyww8/CDJWp04dJk2aRI8ePfj66685evQocrmczZs306tXLyEYKz1QSJ/T09Nj/vz5+Pv7069fv3y/UyLfjx49EgukXC7HwMAAhULB6tWrSUlJoV69elhZWeHu7i4iJjExMYwdO5bAwED09PTYtm0bXbt2RalU4uvry7Vr13B3d+fhw4cEBgYKyZa8NO2uXbvGtWvXkMlkNGnShDZt2uDg4EDXrl05e/Ysffv2ZevWrXTp0kXlc+3atcPV1RW5XI6joyP16tVj8+bNNG7cmGvXrqkU+3fp0gV7e3vhvde+fXumTJlCbGys8PsrqInn+fPnODk5iTnA3NycS5cuqVwzSqWS6OhoQkJCePHiBc+fP+f169eMGTNGxSFDKpnQ0dHB29sbT09PgoODefLkCf7+/kJrMCfi4+PfCYmRxGulyKpUnxgVFUVwcLBK5NXY2LjIdWIfIyl7Gxpl/9WTZeFfRcokAhIfH4+Xlxfa2to4Ozu/k6LM0iRD2SN+9vb2xUo5lHWkTIKbm5uKTtnIkSMZPXo0GRkZ6OrqkpGRQc2aNfH09MTc3BwHBwfRxXfy5Ens7e1p1aoVQUFBee4/KSmJ0aNHs2XLFiBLbFZTU5O6deuKWrVp06Zx/vx5vvnmG9q2bYu6ujoymUx4UL4JYmNj8fDwwMTEhLp16xZpUi7o2Gc/h7/88osKKZOEHKXJX7p+FQqFIGrSv7N/V3GiaHkhO4HMWS+VH86cOcN3330HZAnCzp49mw4dOpCZmUm3bt1wc3NDQ0ODLVu20KdPHwAhICkdm+xSJVJ9VUGQfqN0LLLj2LFjuLu7Y2hoyJ49e7C2thYRk6tXrzJx4kTi4+OxtLTk999/p3nz5qIQvVatWtSqVYsxY8aI4xEZGUlGRgbx8fFiUXny5Anjx48X5FKpVKp4vkJWujc2Npb+/ftTv3599u7dK6RUunfvzpIlS9DS0uLPP//E19cXd3d3xo0bp2LrVLNmTZYsWYK5ubmQq9DW1kYul3Pr1i0SExOxsrLKM3UJWYTM0dFREEt9fX127NjB3r17CQoKEnNKUFCQiiabhNDQUKKionj27BkvX74UnY8FwdzcnLp169K4cWNq1aqFhYUFiYmJnDx5kmfPnvH111+/syL5nPWJcXFxwm3i0aNH6OnpCYKmr6+f7730MZKysk5f5oxW/5vxQZKyN0lfxsTE4O/vX6rm2yUdS2mQsoyMDLy9vYmJiaFZs2aFpipy4m2RstjYWKytrdHU1GTIkCFMnDhRFHUGBATw+vVrXFxchPBn9qey8+fP07p1aw4fPsy3336rYnMj4ejRo5iamtKtWzfOnDkDZNUFyWQyxo0bJwje2bNnOXv2LLVq1eLixYuYmpq+ccF9aGgo3t7exXZ8KOx7vby8aNiwIefOnePx48fUqlVLRMikiT/7d2UnXQqFQiWaJpE46XuLG0Ur6X3SvXt3evbsSevWrRk9erSoo1uxYgVubm7o6+tz8OBBISoL/yOkUoTMxcWlWN957949gFzdrtevXxfR1CVLlohuYDU1NdTU1JgyZQrx8fE0atSINWvWCDKlqakpIirZu/dkMpmIFGWvG6xYsaLo0pTOb5MmTahVqxbu7u74+vqquErcv3+fVq1asWHDBjp37kzdunWxr10XDYcpTDmkpKmJDiNGjBCyKpCVdrWzs6NevXpYW1uzfft2hg4dKpTMpfRshw4dVI5BSEgIt2/f5ty5cxw8eFBEVsuXL09cXFyB6eyc12t2eZOcsLa2plGjRtja2lKjRg1sbGywsbHJlZHIyMjg5MmTzJ49m7CwMLS1tYWy+ruEXC7H0NAQQ0NDatSoQVpamoiiPXjwAKVSiZGRkSBp2ctEPlZSVpaRsqSkJBU1/38zPkhSVhJIth2JiYk0bNiwTL0ei4LSIGWSwK2mpmaJI35vyxi9V69eeVoOAUII0t7eXnSJKpVK0Y6/a9cuFi9eTIUKFYSmWF6E5vXr15w5c0b4XkJWlGLJkiVUqlSJxYsXi4aCx48f07p1a7788kuCg4Np0KBBscPnSqWSp0+fis8X95oqjJTVqlWLXr16cezYMebMmcPBgwcFwSqsPV1aFKSJNL8omhQpfNMoWn6QyWTs3r1bjFXqHtywYQOQVTyfnZBlR/ny5YmOjubWrVukp6cX2Ynj8uXLAMJGBbJsuz7//HOUSiVdunTJJX5qaGjIihUr2Lt3L3v27BELhBRFi4yMxM/Pj5SUFAwNDTE1NcXY2DjfOhgbGxsmTZrEb7/9BmTpjLm7u2NsbEz16tUxMDCgXr16/PPPPzx79ozo6GgGDRpEpUqV6DtlCzYT7wNZ+/XEjkzacHqjNjKZjIoVKxIbGyukZCpUqEC5cuX46aefePToEe7u7kLx39/fn969exMcHExQUFC+97oULSsIOa9VTU1NjIyMsLCwoHr16jRt2pR27dpRo0aNIottJyUlsWjRIsLCwqhevXqerhHvAzQ1NVXEa+Pj44mMjCQ0NBRfX1/KlSsnUp1lHVV6FyhrUpaQkPBf5+X/419BypKTk/Hy8iI1NZUKFSq8c0IG/yNDJdXoefXqFffv339jgVuptu1NtYJKAsllICIiAgMDA6ysrMR7MpmMhg0bcufOHcLDw7l58yYHDx4kLi6OKlWq0LBhQ06cOEHfvn05evSoymIjETIAb29vLCwsGD9+PJs2beLq1avs2bOHtLQ0AgIC+PHHH4GsFOnIkSP56quvipT+lWoS4+LicHR0LJGlUVEidPPmzePMmTOcOnWKjRs3MmbMmBLpBeUXRZMiaFIUTdp3XlG0kkYTs9ftjR8/Xlj4LFy4kC+++CLX9sePHweyUl3Pnz/P1zkhL5w5c0ZEcLI/BNja2tK3b1/i4uLYsWNHnsdv4MCBDBgwQOW9nHVHSUlJQhfN398/3yia9PsUCgVr164Vr0VFRQkRYg8PD6pWrcqSJUt48uQJhw8fJiQkhADdrshQHZ+augZt5z7gysI6uUzab9++TfPmzfM8HnlFlYsLAwMD7O3tqVGjBnXq1MHJyQk7O7s3jmzExcUxbNgw/Pz8MDIy4uDBg6XunFIWkMlk6Ovro6+vT7Vq1UhPTyc6OprIyEh8fHxITU0lMDCQ5ORkQdw/dLxt38t/Mz5IUlacBen169fcu3cPc3NzzMzM8izIfReQLvDikiGlUom/vz+BgYHUrVs3X0uWokJaeN82KUtLSxO6cDVq1FCRV5DQokULUYfTuXNnQby++OIL0f5fqVIlHj58yGeffSZ0yiRIkhqpqakqtj/StSAVgkMWyV26dCnLly/H3t6etm3bMmDAAOrUqZNrskhOTsbDwwN1dXWcnJxKXANTFFJWp04dFi1axMyZM/n+++9xdnZ+YxeI/KJoElnLK81ZGteGTCbDxsYGNTU1NmzYwKBBg3Jt4+fnJ2QosivPF+X7lUol8+bNA2DMmDG5lPVXrVrFw4cPC1wkC/uenN170mIsaWAZGhqqkLiePXty7do1kVKV9mFqakpISAjPnj1j9uzZ//t+uXq+Y9A1tyv4AJQAkv6boaEhwcHBaGpqoq+vj7q6OgqFglevXvHTTz/l6/9ZUiiVSr755hsuX76MpqYmO3bsKBNh2rcBDQ0N8bCvVCq5fv06+vr6RERE4Ofnh7a2toiiGRoavhXJpdLG2+i+/I+UZeHDuzqKCKVSSUBAAAEBAaL4PTg4WEVm4V0iu0F6UaNc6enp3L9/n8TERJo3b/5GhtNvMo43RVxcHB4eHhgaGtKoUSNevXqVZ11b9skreyTsu+++Y/fu3UBWvdGyZcvw9PRk0KBBouMMyHWuDQ0NSUhIECrm5ubmNGzYkHv37onOMIVCwcOHD3n48CHr169HXV0dBwcH6tSpw8iRI7GxscHLywtzc/Mid7jmh8JImUSQxo0bx5UrVzhz5gzDhw/n+vXrpdqpVFAUTTru6enpKqnTkmL69Ol8+umn2NnlTTB0dXXp27cvQUFBwkR+6tSpRdr3kydPePLkCdra2vzwww+53i/tRUVNTQ1TU1NMTU1RKpXExsbyzTffcO3aNSwtLfHz88tzvklKSuLZs2d57lOpyMj3ASkzLUl09KampmJnZ8ePP/6IgYEB/v7+bN++HS8vr0LHLRVUq6mpERsbi5+fH3K5nJEjRzJ//vxcMi5loaW3f/9+jh49irq6OgsXLqRt27al/h3vAtJ5q1ixIuXLlycjIyPP9LdUi/ahFLe/DZ2y/7ovs/BxJb7/H+np6Xh4ePDixQscHR1FOuptyT8UBdnJUFEQHx8vCp6dnJxKhZBB6el0FRWhoaG4ublRpUoVGjRogLq6ep7NBmfPnlWxMMoOExMTunXrhrq6Onfu3BFimfv27WPmzJn5NjvExMSgra1Nt27dMDMzIzw8nHPnzvHq1SuaNm0qut+yIyMjgzt37vDnn3/SunVrWrZsKdJObzpJFUTKJGIkPaFu3LgRS0tLfH19mTx5cqELpbu7O7Nnz1YhqUWBXC5HTU0NTU1NNDU10dLSQiaTERwcjI6ODhkZGaSlpZGeni6IW3GRHyGDrMVsy5YtjB07FsgSU12wYEGR9hsQECD2X1IfxZIgOTmZUaNGUaNGDY4fP05MTAw+Pj4qhExLSwtdXV3U1dVzkUNtbW2Vhfml59+5zq9SqeT6kiYqxuVSg0KLFi0YPnw4165dY9euXUCWPuD+/ftZu3YtEyZMoGrVquKYJCYmoq2tzfTp0zl9+jS9evUS0iUtWrTgwoULKue1tEnDpUuXmDRpEpBFuOvUqVOq+3/XyP7goq6ujqmpKXZ2djg5OeHo6IiZmRnR0dG4u7tz69YtfHx8ePXqFenp6e945Pnjv0jZ28MHScoKmiRiY2O5desWkGU7kn1yLshm6W1DSg0VZTxhYWG4urpiaWlJo0aNilzwXNRxQNHJYUmhVCp58uQJDx8+pEGDBiq2Q9lJmVKpZOXKlfTu3Vu04ecl+urt7S2iBosXLxa/ZcGCBXh5eanUKunq6oqur4SEBM6cOUNaWhqVKlUS2lV37twR0Yucx1dLSwsrKyvU1dUJCAhg7ty5VKxYka5du+ZawIqD/EhZ9nSilD40NTXlzz//RC6Xs2/fPv78888C93358mXWrFnDgQMHSjQ2yDovUnQ2IyODxo0bo6GhofJAIZG0wnTkiguJeBSnxkh6+AoICHgr93l6ejrr16+nTp06HD58WCyq2dPDAwYM4Ny5cwQEBODm5salS5fYv38/69evp1u3bujq6pKSkqJyHXhs6cftzX1IT4lHocgkKTKYc98Zk/jqCXK5XDh0PHr0iIEDB+Lo6MjevXtJSUkRXZ0WFhZ0796dL7/8kiVLlnD58mWCg4PZuHEjVatW5fXr18ydO5c+ffqQlJTEV199hbm5OcHBwfTt25e6devy5ZdfsmzZMpYtW8aJEyfemDQoFApWrlxJ3759SU1N5ZNPPmHSpEkfRKSoqCgsmlyuXDkqV65MgwYNaN26Nfb29mhoaBAYGMiNGze4e/cugYGBxMXFvVO3j5wo647S/7ov/4ePKn354sULfHx8qF69OtWrV891s79PkTIonkF6Sbr7igIpjVGWkbKcadecN192nbADBw4wd+5cIEtw1t/fn5YtW/LgwQMALC0tCQ0NFZpWAOfOnVNJ+VhaWrJ161bq16/PjBkzRB1hx44duX//Pq9evSI2NlZF/yrneLMjNTVV1Nt06NABf39/goKChLq7paUlHTt2xM7ODmNjY7S0tLC3t6d+/foF1o/kJGWSAGxmZiY7d+6kU6dOVKpUSbzfsmVLfvzxR3788Ue+/fZbbGxsaNWqVZ77bt++PQsWLODK/7F33mFRXd/X/8zQQbqAYkMUsIEIImDvGks0JrHEGk3UJMYkJhqjiS3VWKLGHktiEmOJGo29xN5QAWkCiiBVeu8w8/7Be893hg6CJb+s5/FRZ+7ce+fOveess/bea1+4UKPKRVVkZWXh4+ODiYlJGZsS1fwz6d+VGddKpLwylUwVZ8+eBUoqUKuLdu3a8cUXX+Du7l7u+9nZ2dy+fRsHBweioqKq/H0qQnFxMWfOnGHx4sUij9HU1BQrKysePHhAUVERpqamHDp0CBcXF/E5AwMDFi9ezMGDB9HU1Cxzn0mO89nZ2ST4/sVJX6MyKrJCoaB79+688sor+Pr68ssvvxASEiI8zKQQUG5uLnv37uX333/nzp07ZGZm0qJFC95++21u3LjB/v372bp1K4GBgZw5c6bMd4yOjiY6OppDhw4BJWNV9+7dsbGxoV27dowcObJG+aw5OTlMnDhRHGv8+PH88MMPwrbm3wLpea6uT6HU3ql169bk5eWRkpIivNFkMpl439zc/Jk2OX8a1Zf/hS9LIFM+T3S8mlAqlWo+P1Kvx8TERDp27CiSbEsjJSUFPz+/5yZ/4fz58zg7O5fbm7KgoIC7d++Sl5dHp06d6nUVce7cOdzc3Ool5JOVlYW3tzf6+vp07NixXHKQnJwsvNYmTJhAYWEh7u7uIqdo+fLlGBgY4O/vz2+//aZWrCFNWl999ZUwKZWgVCp577332LFjh3htxowZHDlyRNhtQMlkOXz4cBwdHcnIyMDf318YYbq6ulJYWMi9e/eEAiuTyXBzc8PAwIDbt2+rtYZShYmJCf369eP111/H1NSU7OxsBgwYIK7B/fv3yc/Pp0OHDmqeYjdv3mTAgAEYGRmxc+dONcd3hULB2LFjOXbsGLq6uuzZs4cBAwaUOXZxcTG2trYkJSVx5MgR+vXrV+VvpYqkpCT8/f1p3rx5uQuc0pDUvcuXL7Nt2zahRBoaGhIfH8+FCxeIjY3F399frcq2IjRp0oTMzExOnz5dYWVhTXHw4EGmTJmCgYGByGHp3r07rq6udO3alY4dO6qFvvPy8nj8+DERERE8evSI9PR0Hjx4wLFjx0hMTARKyFivXr0IDAwUzdRfeuklvvnmG1q1aiX29fDhQ15++eUKc8lsbGxYunQp9+/f56uvvsLY2Bg9PT0eP35Mq1at6NSpE3/++afYvlWrVhw+fBgTExN++umnaod4oWTR0rVrV/G8nDx5kuvXr+Pl5VWuSWx50NDQoF27dlhZWWFjY0Pz5s2xsbGhQ4cONGvWTK2bhUKhYPLkyRw+fBg9PT1WrlzJhAkTkMlkpKenExAQQLdu3ap9/s8zioqKuHTpEj179nyihH7JvFYiaZmZmRgaGgqSZmxs/FRtNyRTb2tr63rZ/yuvvMIrr7zCe++9Vy/7f5HwQpIy+F94IycnB19fX+RyOc7OzpWGOzIyMrh161aNJ6j6wqVLl2jfvn0ZEpmeno6Pj4/wMqrvap3KyGF1cerUKdatW0dCQgITJ06kX79+NGzYEH9/f5o1a4a9vX25E3tWVhb37t0jICBAmEY2a9aM2NjYKlXNgwcPEhQUJJS1TZs2letztG7dOrVmyw0aNGDo0KHk5ORw48YNMcE6ODgwcuRIXn31VZo0aYKvry/W1tY4ODggk8nYv38/mzZtKmNmamdnR4cOHUQYAkoIV0UT3G+//cZrr73GgwcPyM3NpUOHDmrhyuDgYGbOnCmqD8eOHcv8+fOxs7MDSsjC+PHjOXnyJNra2uzatYvhw4eXOc7s2bPZvn07kydPZuPGjZVeS1VERUURGhpKu3btalzde/DgQSZOnFjue7q6uuzcuZPBgwdXaVzr4uLCgwcPcHFx4bPPPivTiqg22LVrF0uWLCm31ZcE1ZB6ZfefsbEx7du35/bt22KBaGhoyNdff83YsWM5dOgQXl5ePHjwgIcPHxIVFSU+q62tLQonAN5++22++eYbdHR0SE5OpnXr1hQXF7Nt2zbCw8Oxtramb9++bNu2jVWrVon92NjYcPHiRUxNTbl16xYvv/yy2oJFU1OTgQMHMm/ePFq2bMmRI0f4+uuvRVGLjo4OX375pcjf27NnDw0aNGDPnj1cv35d7ToZGRkJ/7E7d+6oLYrLg46OjvD2ys7OFjYeR44cUVsUp6amcu/evQq96l40FBQUcOXKFXr37l2npKmgoEDYqSQnJ6NQKNRUtPq2Erlz5w5NmzatdiePmmLgwIHMnDmzWh07/u14oUmZ5NVlbW1NmzZtqnwIsrOzuXr1KgMHDnxKZ1k5rl69ip2dnVpYMiYmhqCgIFq1akXLli2firRfETmsLuLi4rCzsytTaWZlZYWHhwd2dnY0atQITU1NYmJiiI2NJS4ujqioKKEuVARtbW1atmxJkyZNMDIyYvTo0Xz99deiefDBgwc5fPgwP/74I3K5nJ9//lmtnZOEvXv3Mn36dLUk6ZkzZ/LJJ58we/ZsYcQpoX///qxfv164vqsiIiKC/fv3c/DgQXx9fcXk2rRpU8aMGUO/fv1wd3cnICCA3bt389dff5GYmCjCUDo6Ovz666+0b9+e7Oxs2rVrV8YQtqCggNmzZ4vEbW1tbZYtW8Y777yDpqYmBQUFTJ06lUOHDqGhocGBAwfKKGYXL15kyJAhGBsbExoaWqXaKoUY4+Li6NixY61IelJSEjt37kRTU1OQZShx9t+yZQv6+vrielXWWcDd3V2EBlu2bKlmKVFbpKenc/fuXXJycti/fz+HDx+uklzI5XKhUBgaGmJhYUFCQgIRERFCIXVxcWH8+PG4ublx9epVtm7dKsi5KjQ1NRk/fjy//fabCAetX7+e8ePHq203ePBgrl27xurVqxk7dixJSUmkpKSwd+9eYbqrra3N+++/z6JFi9TGiHXr1qlddyhpx7Rq1Sp69epFbm4uV69eZcuWLZw6dQqA0aNH06BBA3bs2MHLL7/Mb7/9hkKh4PTp02zevFmY8arC0NCQhg0bkpGRQVZWFoWFhZWmQBgbG/P5558LAighJSWF0NDQOlNDnzVyc3O5fv06ffr0qbexW6lUkpWVJfzyMjIy0NPTExWdJiYmdR5q9PLywtbWVq3XaV2iW7duLF68mFdffbVe9v8i4YUkZUqlksDAQCIiImjfvn21JdW8vDwuXLjAwIEDnwvH5evXr2NjY0Pjxo2Fkao0IdbXzV8erly5goODAxYWFrX6/K1bt0Ru0/Lly9mzZw9BQUGCAFUFyU8MSiZgPT09goKC8PDw4MyZM2VCnnFxcbzxxhtcv34dHR0dlixZQlBQkCAwe/bsYeTIkWWOExUVxYABA9T6Z1paWvLRRx/RuXNnrl69yoULF7h48SIjR44UJqeVIT4+nm3btrFlyxYSEhLE60ZGRqxdu5Zx48aJ1xITExk0aBBBQUHY2tpy4MAB0tPTadeuXYXO8D4+PixatEhMjB06dOCrr75iwIABFBUV8fbbb7Nv3z4sLS25e/euWghaoVDQvn17IiMj2bZtm9q5lIbUris7O5tOnTo9seGl1HBbgtR0urTlhurwo2rNoepdN336dFauXPlE5wOUCZUVFRURExODgYEB169f5+bNmwQEBBASElLGoLUqqHaRgJJE+2HDhrF3714yMzPR19dn8+bNvPXWWxQUFKClpcWJEyfo0qVLmX2tWrWKpUuX0rdvX1E9+88//zBu3Dhyc3OBkmu1a9cuWrZsWUYtuXPnDsnJybz33nvEx8eL/RoZGTFu3Dg+//xzEhISxH1bmkylp6er3YtnzpzhrbfeIjU1Vbzm6OjIsmXL6NmzJ1paWqK7xenTpwkNDSU5ORk9PT2aN29O165d6dq1K3p6emW+a1JSEmFhYRXmAb5oyM7O5tatW081RaaoqEhNRSssLMTU1FTcF3p6ek9MEK9fvy5yZusaSqUSFxcXNm7c+NwIJs8SLyQpKy4u5s6dO9ja2tbIGqKoqIizZ8/Sr1+/Oq1grC28vLywtrbGwsICX19fioqK6mRCrCmuXbuGra2tWv++miAuLo6WLVsil8s5ceIEurq62Nvbc/PmTfz8/Hj8+DEJCQkUFRXRuHFjrK2txd+2tra4uLiQk5PD5s2bmTJlCmfOnGH48OHY29vj5+dX7jFzcnKYNGkSR48eBUoSwm1sbDh58iQGBgZqDa5VERwczPjx4wkMDFR73cnJiS+++AJTU1MKCgpwdnau0QCUl5fH4cOHOX36NP/884/IWdu5c6caGSooKGDp0qUMHz4cOzs77t+/T3p6OgYGBlhYWNCwYUOMjY3VBlGFQsHPP//MokWLxMTo7u7OkiVLcHd3x83NjbCwMD755JMyuUVjxozh6NGjfP/99xXma+Tl5eHr64umpmaFeX/VQX5+PgUFBeTm5jJu3Dhu3LgBlJCBoKCgcpW30sa10nD066+/MmfOHJRKJf3792f//v1PvPqvSf5SZmYmt27d4tSpUwQFBRESEiLCfhK0tbXVlDYtLS08PDx4+eWX8fT05M033+T+/fu0a9eO8ePHs2zZMvLz85HJZJw5c6ZcQgYlrZE6deqEhoYGYWFh3Lhxg4kTJ1JYWEibNm0IDg4G4OOPP2by5MlCLdHX1xfGtVLO0datW/nmm29EF4GqIJfLSUpKKjdlIiUlhZ9++okNGzaI0LypqSnOzs5MmjSpVipHYmIi4eHhFV6LFw2ZmZn4+PjQs2fPZ3J8pVKp1nUiPT1ddJ0wMzPD1NS0VukwV65cwdHRsca9lat7zvb29hw4cOBfk1v4JHghSRmUTG41PXWlUsmpU6fo3bv3c9HO486dOxgYGBAXF4e5uTnt27ev1wqXiiD5htU2iTMnJ0cQmBUrVvDee+9VW4lcsmQJ3333HQYGBkRGRmJgYCCUN2NjY7WVfmkolUp+/vlnvvjiC5H/0qRJE6FyvPfee2o5OKqfk8KZUvWXUqlEX1+f7du388orr9T0EqhBoVDw0UcfsWXLFiZMmKDmt1Zec/CioiKSkpJISkoS6pBkSmpubi5IUnJyMt988w1btmwR1aZ///03qampTJw4UfiYqd5DUl5ZeYQNSvIsfX19MTc3fyIzXIVCwcsvv4yvr68IZ0mwtLTEysqKI0eOVFpBrNrySaFQEBQURP/+/dHV1eX06dM4ODg8kcKdlpZGYGBghQO/VO18/vx5jhw5wvXr18vklTk7OzNr1ixeffVVNDQ0yMvLIy0tjZycHCwtLUVjb09PT6KiorC0tGT48OFs375d7GP8+PEiDFkRunbtSkBAADNmzGDnzp0UFBTwyiuvMHfuXJF/JZFdIyMjCgsLhVIiFalISomZmRm7d+9mzpw5ZVSxBg0aYGlpSXR0tHgW5s+fz5w5c9SS9VWRkJDA8uXLOXjwoLhfFy1aVKbQpjpISEggMjKSzp071/izzyPS09Px9/ene/fuz/pUAPXerSkpKeTm5mJsbCzuiwYNGlRLRbt06RIuLi71UnCmVCpp0qQJV65coWPHjnW+/xcNLywpqyqHoSKcPn2abt26PfPyW6VSyfXr18nMzKRNmzaiEfezwK1bt2jcuHG1ej6WhlKpJDIykqlTp3L16lXeeust1q9fX+XniouL2b9/P1OmTAFg6tSprF+/Hrlczrlz5xg6dCitWrUqo2iVh7S0ND7//PNyzWbfeecd3nzzzXJbE928eZNRo0aRnJwswk9yuZzu3bszderUctsAVRdSv1VPT0/xmmrYrqJEd8kZPjExkaSkJLKzszE2NhYqWn5+PnFxcUyePJng4GAMDAwYO3Ys+/btIzMzkz179qgl/f/444/Mnz+ftm3bqrUtgpIJMSAgAFtbW1q0aFHr+y82NpbevXuXCfk1aNBAuMZbWVlx9uxZbG1tq7VPSTk7cuQItra2ODg4VCsXrTKUR8oSEhI4evQop06d4vr162WKM2xsbHB2dsbd3Z3hw4eXazCsisjISN544w38/Pxo1qwZHTp04MSJE+J9Y2Njbt++XWXC9PLly/n666/F/0eOHMmOHTvQ1NSkX79+ov3Y559/rlbEAqg1zJYq9wwMDDA3N+fu3busX79e7V7Q1NRk8uTJpKWliUbmdnZ2/PDDD5UqPvn5+fj4+ODr60vfvn1r1Sbp8ePHxMTE4OrqWuPPPo943gsXcnNzBUFLTU1FQ0NDjbxXpJKfP38ed3f3eoniKBQKTE1NuX//vlrF8v9V/Kt8yqoDDQ2NZ95qSbLwyMrKwtrauloWAfWJ6prYlobUkigpKYk//viDffv2lUnkLY2ff/6Zv/76i3Pnzgk1ZcKECQwfPlwku0t+PFlZWdUyLTQxMWH9+vX079+fzz77TC3JetOmTWzatAlPT0+GDBmCo6OjIKCGhobMmjWL5cuXC/PCrKwsLl26pEamagM9Pb0KCVllDcUl5ayoqAhPT09yc3OFivbnn3+yfPly3NzcCA4ORi6Xk52dzfbt20VI4oMPPsDT01PkJEoT5b1794S6JhHpsLAw2rdvX+uKqvj4eL777ju2bdsmFkjdunWjqKiImzdvkpOTg0KhwNXVld9++61KQqMK6TeXVMvSKprq/SrlolWHoEnXPS0tjfHjx3P16lW1xZ2enh6dO3fmpZdeYtiwYeUWelSE69evM3HiRBISEjA1NcXe3p4TJ06IfpJJSUksWrSoyutdVFSkphC/8cYbrF+/XvzGgwYNEqRs/fr1vPPOO2ppHOU1zJYmYgsLC5YsWUJubi4//vgjly5doqioiO3bt6Ojo0PPnj0JCAjg/v37DBs2DAcHB3r06MHIkSPp0aOH2n2ro6ODh4fHEyXpK5XK5yK/t65Q3yarTwo9PT2aNm1K06ZNUSgUpKenk5yczKNHj0TxlETQjIyMhIekUqmstyhObm4uSqXyP/PY/4//k6TsWRrI5ubm4uPjg0wmo1GjRs/UEFBCbcxj8/Ly8PHxAUraPunq6jJr1qwqP+fl5cXJkyfF/1977TVWrlzJjRs3xDm4ubmhpaVFfHw8Dx48qPYKfOTIkQwdOrTcPMPr16+XsbKAkpyYkSNHcvr0aVJSUmjdujWvvvpqnTZglvI8tLS0kMvlIsHcwsIChUKBrq6u2gp1165dfPHFFzg7O/PSSy/RrVs3unXrxk8//UROTg4XL15UMxWV9qmhoUF8fDxTp05l37596Orq0qNHD5H7FBoaip2dHSEhISQkJODq6lqrHJH09HTWrVvHmjVryMvLE69/9tlnDB8+XKgECoWCkSNHsm3btnKTvGuCivpzSnlolRnXlgdjY2Oio6NRKBR06tSJl19+mT59+uDo6FirnLqoqChGjx5Neno69vb2DB8+nFWrVqGpqcnevXtxdnZm69at5Vq2qCIjI4OJEydy/vx58VpCQoJaHpCqepWWlsZPP/3EnDlzKtynlpYWjRo1olGjRmoq2pIlSwgJCWHDhg0EBASQn5/PpUuXgJLQaFZWFiEhIYSEhHDt2jWRI1iXkFTjfwued1KmCqk7hJTrmZ+fr2ZeC4hqTqj73rESJBuX/0hZCV5YUlbbB/lZtlpKTk7G19eXRo0a0bZtW0JDQ5+LDgPl9Z6sDGlpafj4+NQqD27s2LG0bduWVq1a4e7ujrm5uQhLSeegq6uLgYEBaWlpNVY1VSfUNWvWEBYWxvbt29Uq4/T09EQVW2pqKnv27EFbWxtLS0sePHjA6tWrefjwIQsXLqyRo3xpSIRBoVDQo0cPkpOTMTIyIjo6mtzcXDQ1NbGysiI5ORl7e3scHR3p168fgYGByOVyfH19RYNpCwsLXnvtNXr37s2FCxfEtdLS0hKKo3QvnTt3jtdff519+/ahr69P9+7d+eeffzh8+DD9+/cnPz+fLl261Jgo5eXlsXXrVlasWFEmcXzVqlU4OTmp5dIYGxvzww8/PDEhKw3VNkbwv2IB6VpXpKKpZmrIZDI2bdpE06ZNa6TglQeFQsHs2bNJT0+nc+fOfP7550Lh+/7774Uv4meffVblvmbNmsX58+cxMDBAR0eHlJQU/vnnH7y8vHj06BFt2rShWbNmNGvWTEyca9as4c0336yWhUlpFc3JyYl+/fpx8+ZNli1bJixqMjIygBIjXHNzc9q1a1era1MV/lPKnh/o6OjQuHFjGjdujFKpJCMjg+TkZGJjY4GSHOjShSR1gaysLDQ0NJ6LPO/nAS9sTllRUVGtCM3169dp2bJlrSsNawOlUklERAQPHjygbdu2IndL1dH9WSIgIABtbe1qKVJSKys7O7snykMqjZMnT9KzZ0+Rs2Bvb09kZCTHjx+nb9++NdpXZGSkmgqZmprK1q1b2bFjB48ePWLPnj3069cPhULBn3/+KXJ1duzYwccffyyUPGNjY/z9/WvV3ko1oV+hUGBlZaWmKlVFhHV1dWnZsiUKhYLExMRqV8+polWrVuzfv58TJ06wcOFCHB0d2bhxY43bC+Xk5LB9+3bWrFlTpgJx7NixfPHFF6xfv57NmzerEZ+BAwfy66+/PtUVcGWWGxkZGcITqy4nzt9++413330XPT09Dhw4wLRp04iLi2Ps2LFs2bKlRs9IeHg4EyZMYMOGDQQHBzN9+nRAnXwbGRmRm5tLYWGhCLl/+OGHLFu27Im+h1Kp5OLFi6xatYpLly6Jazdjxgw+//xzEc6qS0RFRZGWllZuf9sXEXFxccTFxam113rRIZlst2/fXq2QxNTUVIQ6n2ThFRAQwODBg0lLS/tXqaa1xQurlNUWTzunTPJ/SktLw83NTUjB0rk8D0pZdcKXCoWCkJAQYmNjcXFxqbXRbEUoTVLMzMyIjIws14SzKpRWPkxNTZkyZQouLi6YmZnh7OwsVJZp06bh6emJhoYGrVq14q+//sLHx4fJkycTGhrKm2++ycKFC/H09Kz2gKEaWgNEI/MHDx6QlpaGtbU1rVu3JiYmhosXL3L48GEuXbok1DsoUaUk81QJUkhOX18fY2NjiouL1dpFlUZYWBguLi4iedbS0pLWrVtXW9nMzs5m8+bNrFu3TlS3Sr+TiYkJa9asQUtLi5deekmtfZBcLufLL7/kgw8+eOqDbEUqWnFxMYmJieL5r22xQGmkpaWxaNEiAObNm8eSJUuIi4vDwcGB1atX1/j7t2zZksuXL4sOJYsXLyYuLo7CwkJ0dHSEgiFBuse2bNnCe++990SO6zKZjN69e9O7d28iIyPZsGEDv//+O3379uXu3buiF6M0EddF6sW/MXz5LCro6xPFxcVC1beyslIzr42Pjyc0NBQ9PT1xb9TUvPa/vpfq+D9Hyp5m+DInJwdvb2+0tbXx9PQsU2L+vJCyqhL9CwoK8PX1paCgAE9Pz3qpwCkdXho1ahS+vr6sXbuWiRMn1noCUCqVPHz4kIcPH+Lo6FiuQqraEB2gU6dOrFy5kldeeYVz585x7tw53n33XVauXFnlBK5KyGQymZhwjI2Ny1SY2djYYGNjI1qLJCQkcObMGQ4cOMDNmzdJT08v07C8uLiYzMzMCvttloewsDCgZMK4desW2trawnLDzMyszACqVCo5efIkc+fOFaRYUmQUCgWOjo68+uqrfPnll2LfEqytrdmxY0eFjdKfNqT76t69e2RkZNCxY0exCCkvzCn9u7pYunQpSUlJtG7dmnPnzuHl5YWJiQm///57rRVC1eOfPHlS2ATk5+eX+a3GjRuHr68vt2/fZsWKFXVisgslC5vly5ezdOlSdHV1USgUIhctKiqKe/fuYWhoKMJZhoaGtSJX/4Uvn3+UJpoymQxDQ0MMDQ2xsbGhqKiI1NRUkpOTCQkJoaCgABMTE0HSKjLGliD1of0PJXhh757arq6eFhFKTEzk2rVrmJub07lz53I9f14EUpaZmcn169eFMWZ9GduWVspmzJiBpaUloaGhbNiwoVb7LC4uxs/Pj6ioKNzd3SsMWZcmhFASert+/Tqvv/46ABs3bqRv376cOXOmwuNVRMiqC0tLS8aPH8/BgweJjo4WeTza2trY2NjQtGlTTExMat0L9fz58/z888/iXgwJCeHChQv4+PgQFRVFbm4uUVFRvPrqq7z22muEh4djaGiItrY2WVlZQMlkHRwczJIlS9QImYGBAbNnz+bmzZvPDSGDEuscb29vcnNz6dKli/g+urq66OjooKmpKX5/qeq1sLBQhD8rw/r164X/WHFxMVeuXMHQ0JD9+/fXyh6iPLRs2VLNBFl6Tt3d3QkKCmL16tUsWbIEKDEqrmkngqog5fnI5XKMjY2xtbXFzc2Nrl270qRJE7Kzs/H19eXKlSsEBQXx+PFjNY+6qvBvVMr+baRMaglWETQ1NbGwsKBNmzZ4enri5uaGubk5KSkp3Lp1i2vXrhEcHExiYmK5USqJlP2b7oMnwb/r7qkG6psISe1GfH19adeuXaWGnLW1oqhrVBS+fPz4MTdu3KBp06Y4OzvXa2P00qTM2NiYL7/8EoBvvvlGrYVRdZCXl4eXlxe5ubl4enqqtR8qjdJKmQQnJyd+/fVXNm3ahL6+Pjdu3GD48OG4uLiwfft28dupJphLk0xtVYPU1FSOHz/Orl27MDMzQ1dXl4KCAiIiIoiOjlYrftDR0aFBgwY1mgQOHjxInz596Ny5M8v+MeGn6GEs9+/K0otN+GLxYlxcXDh16hRyuRxNTU0yMzPVXOsjIyPLTLpdu3YlPDycb7/9tl7asNQWubm53Lp1Cy0tLTp37lxGbZXL5WhpaYnm2VpaWmLyKS4upqioiIKCAoqKisrcH1euXGHBggXi/+Hh4VhaWnLy5Mk6bxlUXmXlzZs3GTNmDPfu3aNnz5706NGDwsJC1q1bV6fHrghSUniHDh3o3r07jo6O6OjoEBkZyZUrV7h9+zbh4eFkZGRUavL9n1L2/KO4uLja30kmk2FgYECzZs1wdnamR48etGnTRnSnuHz5Mt7e3kRERODr60txcXGFStmGDRuwsbFBV1cXd3d3vLy8Kj32/v37adOmDbq6ujg6OnL8+HG195VKJYsWLaJx48bo6enRv3//Mr2XU1JSGD9+PEZGRpiYmDBt2jSxIAW4cOECI0aMoHHjxhgYGODs7Mzvv/9erWtTXfy77p5qoD5zygoLC/Hx8SEmJgYPD48qHfJrY0VRHyhNiKTG1AEBAXTs2JFWrVrV+yqmvMT3iRMn4uzsTGZmJjt27Kj2vtLT07l+/ToGBgZ06dKlQmdy1WNXNnG8+eab+Pj4MGnSJACCgoJ47733aNasGStXriQjI0MoZJV5kJWHjIwMDh06xLRp02jevDlNmzbl9ddf59133+Xy5ctqxQGlkZ+fL0KKNUW/ryMxs+kCMhkgI0vRgPBW34oqVYVCIZ6T0gNyw4YNefvtt3n77beBkjZd8+bNq7K599NERkYGXl5emJqaipBlZZBIqLa2tvhTWkUrKCgQKtpbb72l9vkOHTpw+vTpeklYd3Jyon///sD/bAM0NTXx9/enV69ebN26VRC3Xbt2qfWofBqQy+WYmJjQqlUrunTpQteuXbG2tiYrKwsfHx+uXr1KUFAQ8fHxZQj9f0rZ84+qlLLKoKGhgbm5OXZ2dsLTzsrKisTERIYMGYKtrS2//vorBQUFIm8VYO/evcyZM4fFixfj7e1Nx44dGTRoUIWL82vXrjFu3DimTZuGj48PI0eOZOTIkQQEBIhtvv/+e9atW8fmzZu5efMmBgYGDBo0SG2MlVrwnTlzhqNHj3Lp0iVRbCMdx8nJiQMHDuDn58ebb76p1u6vLvDCVl8qFIoayeQS6qviMSsrC29vb/T19XFycqpWDlRSUhL37t175uGeyMhIEhIS6Ny5M4WFhfj5+ZGdnV1vbTXKQ0VN0b/99luWLl3KsGHD+PPPP6vcT1xcHAEBAbRu3RobG5tqDfhhYWFkZWVVq8VHVFQUe/bs4fvvvxd5XU2bNmXnzp3VdvF+8OABJ06c4OTJk1y9erXMfWxvb0/jxo1p3rw51tbWZGdnc/z4cR4+fFit/VcHwzaWnQyVSiVH3/3fhCIREqkF1UcffcRrr71G48aNRXuWESNGcPbsWaBEMdu2bdszN0NOSkrCz8/viTsVSChtuREfHy9Cyx06dMDOzo6NGzfWa17MjRs3GDhwIJqamiJM7uLigre3NwDDhw/nwYMH3Lt3j2+++aZanoFPA6oGpSkpKWRnZwuDUnNzc2JjY5HL5djZ2T3rU60TSMrLv+X7QEnHjoSEBJydnet0v/n5+Zw/f56dO3dy/fp1MjIy6Ny5M4MGDeLAgQP06tWLjRs3AiX3UbNmzXj//feZP39+mX2NGTOG7OxsNXLk4eGBs7OzqAq3trbm448/Fu3ApE4jP//8M2PHjuXevXu0a9eOW7duibZfJ0+eZMiQIURHR1cosgwdOhQrK6saCQeV4d9F6auB+ghfPn78mOvXr9OoUSNcXV2rnZT+vOSUSYpdVlYWN27cQKlU4unp+VStDCqyiBg2bBgAJ06cICQkpMLPK5VK7t+/T2BgIB07dqRly5bVnoyrUspU0axZM+bOnUtMTAybN2+madOmREdHM2DAAGbOnKlWGaeKzMxMfvzxR5ydnenYsSPz58/nwoULFBYW0rp1a2bPns2pU6eIjo7Gx8eH48ePs2bNGvT09Ni5cycPHz5ER0eH7777joyMDE6ePEnv3r0xMTGhefPmjBw5ksWLF3Px4kWGDx9eJ6t1yVKia9eu/P3330yaNImcnBy8vLy4cuUK9+7dIygoCCjJKbt27Rpdu3ZVW/E+bcTExHD37l3atm1bbVJeFaQwp6SgqYYrPvjgA7Zu3SosK6qTi1YbeHh40KlTJ4YPH87LL78MlIRali1bhpaWFn///bdQEfbt21fnx68tJIPS1q1b06VLFzw9PWncuDGZmZl4e3sTFxdHSkoKCQkJz7zTSl3g36qU1cd30tHRYfDgwTg5OTF8+HBiYmJ47733CAkJISgoiNOnT4tt5XI5/fv3L9cAHEqsriQ1WcKgQYPE9uHh4Tx+/FhtG2NjY9zd3cU2169fx8TERK0Pa//+/ZHL5dy8ebPC75Genl6naRv/56ov6zJ8KYX5oqKicHJyqnE5+vNCyuRyOfn5+dy4cYNmzZphb2//1EMKFeV1OTo60qVLF7y8vBg9ejTXrl0ro0gUFRXh7+9PRkYG7u7u5Tr6V3XsmgjGUpjyjTfeYNCgQXz++ef88ccf/Prrr5w4cYJ58+YxZcoU0Wx+w4YN7Nixg/T0dKDEc6pbt2689NJLDB48mNatW5fZ/6FDh1i4cKGwmnBzc2PdunWih2ePHj2EwhobG8u9e/dwcHCgadOm7Nmzh+LiYmbOnMnu3bvVvqd0/ycEn8OyTT/xOyuVSjIT/pdfoaOjQ35+Pk2aNOHkyZMifNG0aVOKi4tJTU0lKSlJkNCFCxeyYMEC0tLSnkkYU6qyjYyMpFOnTvWW2yaXy7l69ar4f1RUFFpaWmWMa6W8wuq2f6oOLly4gEwmIyAggKtXrxIREUFYWBjHjx9n2rRp4l7x9fXl7t27z2VzZ11dXaytrbG2tkahUODn50dRURHh4eEEBgZibGyMmZkZDRs2fCGTv4uLi5+LLi11iScJX1YHOTk5GBgY0KhRIyZNmkT//v3Zt28fK1asUNvOysqK4ODgcvfx+PHjMvOvlZWV8FWU/q5qm9KelJqampiZmZXxZ5Swb98+bt26xZYtW6r5bavGC0vpn3X1ZUFBAbdv3yYhIUHEyWuKmjrp1weUSiWJiYlkZ2fTrl07HBwcnslAWNm1+PXXX7G2tiYkJISPPvpI7b3c3Fxu3rxJYWEhnp6eNSZkVR27NFQT+uVyOZaWlmzdupUjR45gb29PUlIS8+bNo2XLlri6utKuXTt++OEH0tPTcXBwYP369URGRnLs2DFmzZqlRsjS09M5cOAAgwYNYuLEiURGRmJtbc327dv5559/yjRVl4pKQkJCcHZ2Vmsor6GhwdatW9m0aRM9e/akffv2on/d/PnzubtlGP57ZlNUmEdxUSH3z67m4hIH8fkjR46wdetWvv766zIDsoaGBg0bNsTBwUF4q0lFICYmJjx8+JD79++Tmpr6VO5vhUJBUFAQsbGxuLm51WuxQXh4uGhFBHDgwIEyuWiqHmmqxQJPqqJJz6WBgQGLFy9GJpPxyy+/cP/+fa5du8b48ePFtsOHDxf9MZ9XSH1uGzZsiLu7O56enlhZWZGRkcGdO3e4du0a9+7de6FUtH+rUlafpCwrK6vcqMzTNHivDc6fP8+bb77JTz/9RPv27etsv//nlLK68CnLyMjAx8cHIyMjPD09a12VKBFEyULhaaO4uBh/f39SUlLECvZZoTJi1KJFC37++WdGjBiBk5OTuF6pqan4+PhgZWVVaZVrVahIpVOFqkO/dL6qv1mfPn3w8vJi165d/PDDD4SHh4tVnUwmY+bMmcyYMYO4uDiuXLlCfn4+2dnZ5OTkkJaWxoULF7h69aqYfPT09Pjoo4/48MMPy81VKi4uJjAwkPT0dNzc3Mod1GQyGZMmTWLSpEkoFAqGDx/OhQsX+O677wB4dGk9jy6tL/f7Xr9+nblz51Z6TZKSkiguLkYmkwmVZujQodjZ2ZGYmMjdu3dRKpXCE83c3LzOVYSioiLu3r1LYWEhbm5u9d6qZceOHSgUCnr27MnNmzcJDAwkICAAR0fHCo1rpTCwdO88qXGtUqmkc+fOLFy4kK+++oq5c+fi7u7Opk2bsLa2ZsWKFaSlpTFgwADmzp3Lp59+Wq+V008C1UR/XV1dmjRpQpMmTVAoFKSlpZGcnMzDhw+Fiibloj2vKtq/kZTVtyFudna2Wh5qw4YNRR9fVcTHx1dI1Bo1alTp9tLf8fHxNG7cWG0bKVeuUaNGZQoJioqKSElJKXNcKU3khx9+EAVgdYV/191TDTypUhYbG8vNmzfrxCZCdfB+2sjNzeXGjRvk5+fTvn37Zz7AVaVW9ezZk5CQEGbNmoVMJiM6Oprbt2/TqlUr2rVr90QDYVU5Zapte6Tty7teWlpaTJs2DT8/PyZPnoyenh7NmjVDW1ubTZs2iQbjr7/+OhMmTGDGjBl89NFHLF26lIsXL1JUVISDgwMffvghvr6+LFy4sFxCVlBQwJ07d8jLy8Pd3b1auX9yuZwtW7aUUdsqwpIlSwSpfPjwIVOnTqVFixZYW1vj5uZGdHS06L1obm6Ov78/UBJWtbKyokOHDvTq1QsXFxf09PR49OgRly5dwsvLi/DwcDIzM2sUMi4PeXl53Lp1C5lMRufOneudkGVmZvLbb78BMH36dJGfUlFJfGnLDVUVrSrLjcogLUo++eQTevbsSXZ2NrNnz0ahUPDZZ5/RpEkToGRcWb58OUOHDq2088OzREWWGHK5HDMzM7WqPUtLS9LS0rh9+3aV3lfPCv9GUlZfOWUSpPClBG1tbVxdXTl37px4TaFQcO7cOTw9Pcvdh6enp9r2AGfOnBHbS60VVbfJyMjg5s2bYhtPT0/S0tK4c+eO2Oaff/5BoVCo2dxcuHCBoUOHsnz5crXKzLrC87l8qgaeJHxZm4dYoVAQHBxMXFwczs7OZaoEa3suUP/ycGmkpKTg4+MjGqNLlg7PEtUJIVpaWqJUKgkJCSEmJoZOnTrRsGHDJz52ZTllqgpZde0u5HI5q1atYtmyZZibm3PgwAGmTJmCnp4e1tbWGBsbo62tjYGBAQ0aNMDAwABHR0eGDBkiWiJVhKysLHx9fTEyMqpRM3iFQsHhw4fVPHeqwq5du7h69Sp37txRuz5SKFaCTCbDz88PQO25kMlkGBsbY2xsTOvWrcnLyyMpKYmkpCTCw8PR0tLC3NwcCwuLcjsLVAbJbsHMzOyJVNKa4MKFCyQkJNC0aVOGDBmCtrY2x44d49ChQ3z77beV3htVqWjSmFRdFU16f+PGjbi5uXH16lV+/fVXJk+ezIwZM1i0aBFWVlZkZ2dz/fp1+vbty5EjR567qsDqWmLo6enRtGlTkdOYlpZGSkoKYWFh5ObmYmJiIlS0qhzk6xP/VlJWnwue8nzK5syZw+TJk+ncuTNdunRhzZo1ZGdn8+abbwIwadIkmjRpwrfffguUFNz06tWLVatWMXToUPbs2cPt27fZunUrUPK8fPjhh3z11VfY2dnRsmVLvvjiC6ytrRk5ciQAbdu2ZfDgwbz99tts3ryZwsJCZs2axdixY0UU6fz58wwbNowPPviAV199VeSaaWtr11naxAtLymqL2oQv8/Pz8fX1paioqE7bDEkP79MiREqlksjISEJDQ2nTpg3NmjUDng+/tOpUQEqhqpycHDw8POrMgqAiQqiqkNXUf0xPT0806X3ttdd49dVXn3iiSE5Oxs/Pj2bNmtXIO+7UqVN8/PHHomWS1DtTUk+0tbXLTc5fu3at+PeAAQOYNm0ahw4dYu/evWrbJSYmin9rampy5swZBgwYQE5ODpGRkSQmJopJVEtLS3xGV1cXOzs7UlJSyM/Px9TUFAsLCxo2bFhpg+OUlBTu3r1L8+bNsbW1fWoT8MWLFwHo3bs3Ojo69O3bF11dXaKjo0UIs7pQLQBQbVxfUfsn1YleNd2hefPmvPfee6xcuZIFCxYwYsQI3nrrLX744Qfi4+P59ttv2bFjB/fv32fEiBHs27evzu2AngS1Sd2QvK8k/6vc3FySk5NFqFNbW1u8b2pq+lQXvP9GUvY0wpel1f4xY8aQmJjIokWLePz4Mc7Ozpw8eVLkbkdGRqpd565du7J7924+//xzFixYgJ2dHX/99ZfavT5v3jyys7OZPn06aWlpdO/enZMnT6oRzt9//51Zs2bRr18/5HI5r776qpoh8y+//EJOTg7ffvutIIQAvXr14sKFC3VyPV5YnzIoCePU9PSzs7O5evUqAwcOrNb2aWlpYkXeoUOHOr85T58+TdeuXevdfkJKhk5ISKBTp06YmpqK92p6TeoD/v7+6OnplalElJCTk8OdO3fQ09OjY8eOYnKvC8THxxMWFqbmM6Y6SdaUkNUHoqOjCQkJoW3btjXK/cvPz6djx45ERUVhZGREt27d8Pb2Jj4+ngYNGrB582asra3p379/jYm5rq4ueXl5WFpaoqmpSX5+PsnJyUBJE/jqmJiuXr2a6dOnk5OTQ2JiIklJSaSlpaGvry8ImrGxsRiA4+LiCAoKok2bNiJM9zSQmJhI+/btyc7O5q+//mLAgAFACeE+ceIEixcvZt68eU98HNX8M+nf0hgnqWNSeyuplVOnTp3Ea99++y3vvfce33zzDd999x2dO3dm7969vPTSS4SGhmJkZMQvv/xCv379nvhc6wLe3t40btxYLc/nSSCpaBJJy8vLe6oqmpeXFy1btqyTSMrzAl9fXywsLOrtefP09OTLL7/klVdeqZf9v2j4P6eUSapQVSsapVJJVFQUISEh2NnZ1YkJZWXnU5/Iy8vD19cXhUJB165dy0jRklL0rAoOVM+hPCQnJ+Pr64u1tTUODg51vhJVPbY0CUpqxbMmZJL/WmxsLC4uLmpkujrQ0dHhhx9+4IsvvuDRo0ecOHECgDZt2rB7924Rhvzll1+YOHFijfYtOWGX57ItETIjIyOsrKwwMTHB1NSUvLw8NDQ00NXVJTExUSRsGxgYYGBggI2NDYWFhaSkpAgTWIVCgbm5OVBCjjp27FgnYeua4Pjx42RnZ9OhQwc1r6NBgwZx4sQJLl++XCekrKIwp6rlRkFBAZqamhQVFSGXy4XKqaGhIUr633rrLVavXi3aHZ06dYrRo0dz69YtRo0axccff8ynn35aZbeL+kZdjzmqKhqULOaepor2b1TK6jO9RqlUkpOT81Q9MZ93/J8jZVJifmXJi8XFxaIU29XVtV5L7Ovbq0xS+szNzSvMQVKdAJ6m1K+KikhZZGSkUIhULR/qElJOmWpDcen1Z0nIpOrY7Oxs3NzcahSuLSoq4ujRo2zZskXNwqFdu3a88847jBs3Ti1EOGrUKKKiotT6OUJJGHbu3LkEBQVhamrKkSNH1KqcWrduTfPmzbGyssLOzo7o6Gh27NiBvr4+N2/erJGJrwQtLS2srKywsrJCqVSSlpZGSEgIWVlZwo8sIyMDCwsL0VmgviH5vZUOQ0ukVqo+rWuohi6Li4uJiIggOTkZR0dHsbiUij50dHQEybK0tOS1115j9+7dbN68mR07dnD06FHmz5/Pzp07WblyJUeOHGHNmjV07969Ts71xo0bXL16le7du1e7/2d9977U19dHX1+fZs2aCX+95ORkQkNDKSgoKKOiPSn+jaTsWYQv/y/jhSZlNTX9BPXk+vJCYLm5ufj6+gKUqyrVNeqTlMXExBAUFFSl0qea2/IsSZnqdVAtrKhvYixZYjxP4UpJ3dTU1KRLly7VCtdKfQZv3brF5cuXBXnS0NDg5ZdfZsaMGXTv3r3C7zZ79myys7P5+uuvxWu5ubl8/fXXWFtbEx8fL1SZ3r1789lnn9G9e3fi4uL45ptv2Llzp6jIzMnJISYmBltb2ye6DgqFgoiICBQKBd26dUMul4tigYiICDQ1NdUsN+rj/vXx8eHKlSvCMFgVUmFGeHg4BQUF9WYcKnnSPX78GDc3NwwNDdWKBQwMDETBgBTmnD59Ort37+bw4cMkJiZiYWHB2rVr6du3Lx9//DGhoaEsWLCACxcu1AmR2L17Nz///DOffPJJtUnZ0+x9KfnrNWzYUCg0KSkpJCcn8+DBA3R1dQVBMzExqdW99CzH0PpCfRei/UfK1PFCk7LaQBqwyiNCycnJ3L17F0tLyye2Wagu6oOUKRQKQkJCiI2NrVaFoupKvC5ztWoCuVwuekAWFBRw9+5d8vPz8fT0RC6Xk5CQgI6ODsbGxnV+bJlMJkJmJiYmz5yQZWZmijzGqu5DhULBgQMH2LFjh5oiBiV+P2+++SZvvfVWlSrjkiVLhJ+an58ff//9t3ivuLhYkC1ra2tWr15N165d2bFjB59++qlYxEjQ1tbm9ddff2IFpqCgAB8fHzQ0NHBzcxP3pqqXldRZIDQ0VBQLNGzYEAsLi0qLBWqCNWvWAPD666+XuY7W1tYify4gIAAXF5c6OaYqFAqF8KTr0qWL+F6liwVUC1OKi4vp0KEDTk5O+Pn5cebMGcaOHYtcLmfEiBH06tWLpUuXMn78+Dob59q2bQsgWm9VB/WtlFUE1ZB5aRUtJCSEgoICTE1NMTc3x8zMrNoq2r9RKatPSwyFQvFf+LIU/s+RMihri6FUKnn06BH3799Xq0p8GqiIINYWpQlNdQYTSRl6lhWY0vGlxu4NGjTAw8MDTU1NZs2axbZt2/j888/5/PPP6/S4CoUCPT09zM3NuXv3LjKZTEzq5ubmT910MzExEX9/f1q2bFlp78bCwkKOHj3K6tWrRVNqmUzGgAED8PDwwN3dHQ8Pj2opvcnJyaxfv57c3Fz69OnD/Pnz1UiZBHNzc2xtbVm8eHGZPqQtW7Zk2bJldO3aFUtLyycexLOzs4VBc4cOHSr0spKUDQcHB7Kzs0lKSiIxMZHQ0FD09fWFMmJiYlKrcyouLiYnJweADz/8sMz7MpkMZ2dnzp8/j7+/f52TsqKiIvz8/CgoKKBLly4VKnEV5aJ17NgRPz8/IiIi1FQ0IyMjfvjhhzo9V1dXVwAuXbpEXl5ete69Z5nHqoryVLTk5GQSExO5f/8+enp6mJmZVami/VtJWX0pZTk5OSiVyv9ImQpeaFJWF62WioqKCAwMJCUlBTc3N0xMTOrwDKt3LnVFhqQmv0ZGRnTq1KlGhOJZt3ySy+XC0LZ58+bY2dmptZUBKmz2XRuo5pBpamri6OiIUqkkPT2dxMREwsLC8Pf3FzYNdam8VITIyEgePHhAu3btynWulixN9u3bx9atW4mNjQXA0NCQ999/n/Hjx2NjY1Pj4168eJHc3Fzatm1Ljx49yMrKwsTEhLS0NNF3FErI25UrV8TnbG1teeeddxg4cCC2trZ1NhmlpaXh6+tLkyZNaN26dbWfc0n5aNGiBUVFRSQnJ5OUlIS/v78oFpAm3uqGGTU0NNi/fz+PHj1Scx1XhVQNW9eN2CWlUFNTk86dO9f4eZbagEHJNZXGmqosN2oLNzc3mjZtSnR0NKdPnxaN0yvD0wxfVheqKlrz5s0pKioSKlpwcDCFhYVCRTM3NxfjglSM8W8jZfUZks3Ozgb4j5Sp4IUmZbWF5FWWk5MjBr2uXbs+k0qkugpfPn78WCgsNfGwklDXil1NICVzp6Wl4ejoWMbyQSr3r2nlYWXHKy+hXyaTYWpqiqmpKfb29sKmQVV5kQiasbFxnU0mCoWC0NBQHj9+jIuLi9rCQKlUcvnyZY4fP87Ro0eF1xiUGLVOnTqVGTNm1Kr3qoS7d+8C4OHhgUwmw9DQkJkzZ/Ldd99x//59EQJzdnZm5MiRtGnTBicnJ5o3b17nE2pCQgIBAQHY2dk9kWKtqampViyQkZFBUlISUVFRBAUFYWRkJBTR6hQLVETIAIYNG4aNjQ3dunWr9fmWRm5uLt7e3hgaGlaoFFYH0r2UkZGBtra2WHg9iXFtRZDJZLz22musWbOGP/74o1qk7FmFL2sCTU1N8dwrlUqys7NJTk4mISFBqGhSNSfw3H+fmkAimvVJyqSuF/+hBP8nSZmGhgapqan4+fnVm81CTc7lSciQlAAcERGBk5NTrSfnZ2UgK+XLpKSkYGxsXK4Hl2oLnyeFat5NVZOPvr4+LVq0oEWLFhQWFgrlRcqhqoswpxSeklompaamcunSJZGAfOTIEW7fvi2219TUxMXFhbfeeovXXnutTgYzyY2/Y8eO4rU5c+Zw7tw5bt26RVpaGlDiV/TNN9/Qq1evJz5meZCUwg4dOgiFpy6g2lmgVatW5OfnV1gsYGZmVuPf8uWXX64WAakuJMXbysoKBweHJyK+UggxPz8f+B9hqK1xbVUYM2YMa9as4eTJk4SGhgovtYrwPCpllUEmk9GgQQMaNGggFFlVFQ3g3r174l6qb3W9vqFqDVQfyM7OfqYdGJ5HvNCkrDY/pFKppKCggIiICDp06PBMm3DDk5EyaULPysrCw8MDQ0PDWp/HswhfSp0SiouLadWqVbnhH6VSKXywntRg8kkc+rW0tGjUqBGNGjUSyl5SUlKZMGfDhg2rnRQsVfpqa2vj5ubGlClT+Ouvv8psZ2BgwIgRIxg2bBj9+vWrU6m/sLBQhCelxrzSMY8ePcqcOXPUejt+//33dU7KlEoloaGhotK2Poo5VKGjo1NuscD9+/fJzc3FzMxMkLS66t5RXaSmpuLr60uLFi1qZSdSGlOnTmXChAkV5ndVVCwghfdrqqK1b9+e/v37c/bsWX766SdWrFhR6fm9CEpZZVBV0fLz87l69SrGxsbEx8cLdV0qFqhtXuOzhDQn1JdSlpWVVWedWf4teKFJWU0hkZj8/HzRXPlZo7Zhw+zsbLy9vdHV1cXDw+OJS/GfdvgyMzOTO3fuYGJigqOjI/Hx8eWSwqysLHJzcwGqrZ4UFRWVUTuehJCVhmqY087OjpycnDIJ5hJBq6iaMz09XThlt2nTBrlcTsuWLZHL5XTo0IEmTZrQoEEDOnbsyLhx48rNMasL/Pzzz6SlpWFiYqJGyqAkz2Pr1q1MnDiRV199lezsbC5cuMBff/0l+sU9KYqLiwkMDCQjI4MuXbo8dRL0NIoFqgspdGtvb19nnny6urrVtvWpjnGt6rYVqWjTp0/n7NmzbN++nQULFlSadvCiKWWVQRq/WrRogY2NDUVFRULxDgoKori4WC0Xrb7tluoCxcXFtQ5nVweSHca/5R6oC/yfIWVSA2NdXV0sLCyemfVDadRGKUtMTOTu3bs0bdoUe3v7Onlgnmb4Mj4+Hj8/P2xtbUXvwoqUugYNGqCvr09OTg4JCQlVqoGpqal4enry1ltvMXv2bLS0tOrdoV9fX5/mzZuLpGCpakvK1VL10dLS0iI+Pp7AwEBatWqllpf14Ycf8umnnz6R4llTSDYaM2bMqPCZ6N69O6dOnWLQoEFkZ2fz5ZdfMmLEiCe+joWFhfj6+qJUKiutLHyaqI9igeogJiaGkJCQOg/dPgkqs9yQ/kjbSTmZcrmcwYMHY2NjQ0REBOfPn2fUqFEVHuN5qb6sC5Re8GlqamJpaYmlpSVKpZKsrCySk5N5/Pixmopmbm6u1krseUJ92mFASfXlf0qZOl5oUlbdhzk+Ph5/f39R1RcYGPjMktpLQ0NDo9xm0OVBqVQSERHBgwcPaN++fZ0qfU8jfCm5sT98+BBHR0c19aei48tkMqytrXnw4AFBQUHCrLMirFixgoiICHbv3s0HH3ygNnk8DYf+0gnmUjXnw4cPCQgIEP0iHRwcyiSyP+3WQRkZGZw7dw6gUk+xzz//nOjoaA4cOMDQoUMJDg5my5YtzJw5s9bHzs3NxcfHB319fRwdHZ9Lw83qFgs0bNgQQ0PDWqdTREREEBERUaYn7fOEilQ01V6d8L8wZ6dOnYiIiODy5csVkjIpRPo8kpHaoLLKS6mAxtDQUK2VWHJyspiPJMsNc3Pz5ybxvb6NY/8LX5bFC03KqoLUN/DRo0dqSfClfcqeJaqrlBUXFxMQEEBqaipdunSp87yb+g5fqp6/u7s7RkZGau9X5pPWpk0b4WZeGQoKCvjjjz8AWLx4MTKZrN7l98ogk8kwMTHBxMSEVq1aERAQQHJyMoaGhoSEhBAVFSWKBZ7FSvn8+fOkp6fTsGFDtWbsqli9erUwT33jjTf49ttvmTdvHp999hkeHh5lQp7VQUZGBj4+PnWSyP60UB/FAkqlkpCQEOLj4+ncufNTVUifFFUZ13p4eHDo0CGuXbumpqipQrX6+d+AmthhlG4lJqlocXFxhISEYGBgIAiakZHRMyOuT8PN/z9Spo5/LSkrKCjAz8+P3NxcPD091ZKjNTU1RTXSs0Z1yJCkKmhoaODp6Vkvq6j6DF/m5eXh4+ODTCar8PwrU+qksFp0dHSlxzl37hxxcXFYWlrSr1+/56ZlUmFhIX5+fhQWFuLp6Ymurm65YU5zc3NRzakaSqwv76PQ0FAA+vTpU25+yy+//MIXX3wBwNdff82gQYMYOHAgFy5c4Pjx44wYMYKDBw8K09DqQDLHtbW1rbT11/OO0sUCaWlpwmg0Nze3ysIPqepYyqV7Ear0kpKSMDMzK3MvlqeiSSa6oaGhpKSkYGRkVKZYQCJl/xeUsspQmYrm7++PUqnEzMxMKGlPU0Wr77ZRWVlZ/3mUlcK/kpRJK3FDQ0M8PT3LrFrruwl4TVAVGUpJScHX1xcrKyvatm1bbwNYfYUv09PT8fb2xtzcvFK/pcqOP3ToUA4dOsSaNWsYNWqUmnWDKo4dOwaUWBRoa2s/F4QsJycHX19f9PT01AxAKwpzhoeHExAQgImJCRYWFly6dIlff/2VP/74A3Nz8zo9t7179wIlFXOl8ffffzNr1iwAPvroI+FmL5PJ2LJlC8OGDePu3bsMHjyYX375hSFDhlR5vOjoaEJCQmjfvn29FS48C8jlcjFpOjg4lCn80NPTE4qoiYkJCoWCu3fvUlhYiJub23ORS1cVCgsLGTFiBMbGxmzbtq3S1Am5XC5yBAsKCggNDcXd3b1MsYBqyPPfgLpaPJVW0TIzM0lOTiY2Nvapq2hPI6fsP1KmjhealJX3MMfGxhIYGKiWRF4aL0L4UqlUEhUVRUhICA4ODjRv3rxez6M+wpdxcXEEBATQunXrSlsGSceviJSNHz+eQ4cOcezYMcaPH8+5c+fK+LEplUouXLgAQL9+/Z4LQiY50zdu3Bh7e/sKz0c1zGlnZ0dubi6JiYk8evSIefPmkZGRgbu7OwcOHMDR0bFOBsm4uDju3bsHlCSZq04oly5dYvLkySgUCiZNmsSXX36p9lkzMzNOnTrFxIkTOXPmDKNHj2b+/Pl89tln5a6qlUolYWFhREVF4eLi8tzmTdUVShd+pKSkCIVQCqfr6Ojg7Oz8QhAygJUrV+Lr64uZmVm1nitNTU3c3d25fPkyN27coEePHmr5ZwqFgpSUFGQyGUVFRSK37FmlGtQF6kPRlslkGBkZYWRkRMuWLSkoKChXRZNIWl3fT08jfPm0K66fd7yYd385UCgU3Lt3j3v37uHs7Fypq/3zppSVPhcptPHgwQM6d+5c74RMOo+6UsqkXL7AwEA6duxYLb+lykiZTCZj69atNGnShAcPHjBw4EDS09PVjhcTE8ODBw+Qy+X06tXrmROyuLg47ty5Q6tWrWqcN6Wnp0fz5s3p0aMHx44dw9zcnLi4OHr37s1nn32Gv78/cXFxooF7bbB27Vrx78zMTHF+vr6+jB49mvz8fIYPH86PP/5Y7rkbGhqyf/9+3nrrLZRKJd9++y2ffvppme2kezkuLg43N7d/PSErDakCr3379qKpuo6ODgUFBVy7do2bN28SFhZGRkaGCOc9b4iOjmblypVASY5hdf0Chw8fDsCBAweAkmdcQ0MDLS0t0tLSCA4Opk2bNiJUX1xcTFFREQUFBRQVFT3Ttm+1wdNosaStrU2jRo1o37493bt3p2PHjujr6xMdHc2VK1e4desWDx8+JD09vU7up/oOX0qWGP/hf/hXkLL8/Hxu3bpFcnIynp6eWFhYVLq91GbpeUBpUpafn4+XlxcZGRl4eno+tUmsrpSyoqIifH19iY2NxcPDo9rl/VWFT83NzTl16hRNmjQhJCSESZMmkZ+fLxKMJQPUNm3a1Lv5aGWQVKHg4GA6duz4xM3tXVxcOHXqFD169KCgoID169ezceNGQkNDuXjxIrdv3yYiIkL0kKsObt68yebNmwFo0qQJGzZsQCaT8eDBA0aOHElmZiY9e/bk559/rjRhXUtLi7Vr14p9bdq0iaNHj4r3CwsL8fHxISsriy5dujx3g+/XX3/NkCFDRG5dfSIzM5Nbt25hYWGBjo4O06dP5/HjxzRr1oysrCzu3LnDpUuXCAwMJCEh4blR8gGWL19OXl4e3bp147XXXqv25yTyVtpqJSYmhsDAQJycnGjatCna2trij4aGBl9++SVTp07Fy8uLgoICCgsLRRHB84yn3fdSKj6xtbXFzc2N7t2706xZM3Jzc7l79y5XrlwhMDCQx48fV7vCvzTqO3yZlZX1QhW4PA280KRMJpORlpbGtWvXhIlqdaTQ50kpUyVD6enpXLt2DT09Pdzd3Z9q8m9d5JTl5uZy8+ZNkdBek0lYSvytbHXXunVr9u/fj66uLqdOnWLevHlisPb29gbKz496WlAoFAQEBBAbG4ubm1udWVy0bduWEydOiKT7Xbt2MXPmTOLj47G0tCQlJYXr169z9epVQkJCSElJqfC3vHnzJiNGjBAq24wZM9DV1SU7O5sRI0aQmJhIx44d2bt3b7XNLSdOnMgHH3wAwMyZM4mOjiYvL4/bt28jk8no3LmzWnKyv78/33333TNXhv755x8uXrzI8ePH6/U4qamp3L59m2bNmuHg4MDx48eJiYlhxowZrFu3DkdHR3r16oWjoyNaWlrcv3+fCxcucOfOHR49ekROTk69nl9lCAkJ4dChQwAsWLCgRopvy5YtgZLeqlL/2sjISEJCQnB2dlZbPEsqmoaGBnv27OGvv/4STdThxVDR6ltVqgqqKlqPHj1wcnJCT0+PqKgorly5wu3bt3n48GGNVNn6Dl/m5ub+F74shRealBUWFnLnzh1atmyJk5NTtXvWPW85ZQqFgpiYGLy8vLCxscHJyempP9xPGr5MTU3l+vXrmJiY0Llz5xrnNkirsaoGCxcXF3755RegxI3+4cOHyOVyTp06BcDgwYNrcfZPjoKCAu7cuUNOTk69qEIymYz58+ezc+dOmjRpQmRkJNOnT2fDhg106tSJ3r17Y2dnR1FREf7+/ly8eBE/Pz+1MGd8fDyjRo0iMzNTqBetW7cG4NSpU0RERGBqasqhQ4fKWJZUhSVLluDq6kpqaioTJ07kypUrGBkZ4ezsrPZcZmdn069fP7788ktu3rxZR1endhgzZgwA27dvrzeCmJCQgI+PD3Z2diLHdeHChUJx+vHHH3n//feBklw9e3t7unXrRteuXbGwsCA5OZlr164Jwp2cnPzUCImfnx8eHh6kpqZiY2NT496znTp1YsCAARQXF7Nr1y7CwsJ4+PAhrq6umJmZlfuZM2fOEB8fj4mJCQMGDCijoknWOaoE7XlR0Z62UlYZylPRmjRpIgqPVFW0ytIgnoZP2fOmoD9rPB93UC2hpaVFjx49qkwiL43nKXwpl8spLCwUuXB10e+utudR22sSHR3N7du3ad26Ne3bt6/VwKTqeVQZlEolw4YNo2fPnuTn5/PWW2+Rn59PZmYm8L/V+dNEdnY2Xl5eaGtrl1GFVCG1i3oSjB49Gl9fXz766CMA1q9fz4oVK9Ryl3r27ImLiwv6+vo8evSIixcvcuvWLebNm0daWhpmZmYUFhbStm1bDA0NiYiIECrc66+/Xqum9tra2vzyyy80aNAALy8vFi1aVK59goGBAZ6enkDJBPwsMW7cOHR1dXn48CE+Pj51vv/o6Gj8/Pzo0KGDWtskuVzOL7/8wtatW5HL5fz888/s2LFD7bNSsYCLi4sg3JLX34ULF7h79y4xMTH1au2zatUqCgoKRMVtbSbnLl26AHD27Fmio6Pp3LkzxsbGZGRksHXrVn755RfR2xZKVGAoUV+lhZ2komlra6Orq4uOjg6amppCXZdUtGcd5nyeSFlpaGtr07hxYzp06ED37t1xdHREV1eXyMhIoaKFh4eXUdGeRk7Zf+FLdTyfd1ANUBvPluclfFlQUEBgYCAAHh4eVebC1SdqE75UKpUEBwcTEhJCp06dnqggoTqkTLVya8OGDZiYmODl5cWcOXPIy8sDyuavAKKUXCJudYmUlBS8vLywsrKqVOG8e/cuDg4O/Pbbb098TH19fb766iu++uorANatW6f2vrRKbt26NR4eHnTv3p3Y2Fj+/PNPcc4Anp6evP766/To0YOIiAgsLS1ZvHhxrc9LV1eXefPmYWRkxN27d+nevXu5alhsbCzAUylgqQyGhoYMGzYMgJ9++qnO9qtUKgkPD2fs2LGMHDmSdu3a4eHhwRdffMGBAweESj9+/HiWLFkCwPz580UIvjQkwt2uXTt69uwpjGZjYmK4fPmyKBaoq+Ru+F9uqPR9St9j1YVUkCOR0wYNGpCWlkaLFi346KOPePfdd/nmm2+AElXm4sWLALz66qsV7lMul4uCCVUVTdrHswpzPs+kTBVyuVwYWnfp0oWuXbtibW1NVlaWUNGCgoKIj4+nsLDwvzZLTxnP/x1UD5BCdc9S8s7MzOT69etiMHnWzWlrGr4sLCzE29ubxMREPDw8njh/Snrw/f39mTt3Lj/++KPa+6qO4QC2trYiwXzXrl3C7b+8xPrRo0fj4uLC1atXn+gcSyMmJgYfHx/s7e2xs7OrVOHctm0bycnJzJgxg3fffZe4uLgnPr4UTkpNTSUmJqbC7dLS0pg9e7baa2ZmZuzYsYO8vDzS0tKAEmIgGdnWBBIJCQ4OZsqUKVy8eJG2bdvy+PFjBg0apBYeVCqVQhnp0KEDQL2Q5erinXfeAeCPP/7g4cOHT7w/yaU/MjISa2trlEolubm5+Pv7s3r1aiZNmsTEiRPF9rNnz2bgwIHk5uYyceLEKu8LySLB1taWLl260LNnT5o1a0Z2djbe3t6iWCA+Pv6JUjR8fX158OABUEIKjx07xt9//13utqXVuszMTLy9vVEoFELtUigUnDx5ksLCQtavX4+9vb3YPjo6miVLlnD//n3S0tLQ09OjU6dO1TrPqlQ0iaA9DRXtRSFlpaGjo4O1tTWOjo5CRdPR0eHRo0ckJCQQFxdHREQEmZmZdRrmVyqV/1VfloMX7w4qhdqE+qQcl2ellsXHx3Pjxg2sra3F4POslbuahC9zcnK4ceMGSqUSDw+POlnpSL/jw4cP+fHHH9m/f794T1Uhk3JKIiMjCQsLQ0dHRwwUenp65aqNJiYmQNUdAaoLyfIjNDSUTp060aRJkyo/s2bNGubNmweUOOV37tyZPXv2PNEg165dOzG5jRgxogy5ycjIYNq0aTg4OJCUlASUEKH3339fqGVQMpnY2dlx9uxZhgwZwoIFC8jKyqrWuUlWNJGRkTRu3JghQ4YwaNAg/vnnH0aOHElhYSGzZ89m2rRppKSkEBMTQ1JSEpqampiZmTF58mT69ev3RPYeTwIPDw/69+9PYWGhUK1qC4VCgb+/P8nJyXTp0oVVq1bx4MED/Pz82LJli9hOtbBAS0uL7du307x5cyIiInj55ZfVfpuqoK2tjbW1NU5OTmrFAmFhYWrFAjWpzoWSFlwANjY2wjj4448/JisrS227q1ev4uzszD///CNe27x5Mz169GDw4MGkpqaK1w8ePMj777/Pt99+S3h4uHj9xIkTIucMSnrAVjc/uDRKq2haWlpPTUV7UUmZKkqraCYmJhgbGwuiffXqVYKCgkhISKiTZ/Y/UlYWL/YdVEuoPqRPE9Jk7ufnh5OTE3Z2duIhftakrLpKWXJyMtevX6dhw4a4uLiUGy6sDry8vIR5qQS5XC4sQO7du0d6ejpZWVkUFRVRVFTEnj17eOmll2jSpAlt27Zl4cKF5OfniwE8NzdXbXKQ0KZNG4A6sT4oLi7Gz8+P+Ph4unTpUmHCcmloaGiwePFizp49S6dOnUhLS2PatGlMmDCBxMTEWp2Lvr4+hw8fxsrKinv37qmpizk5OXTr1o09e/YIxaR3795cuXKlTG6bsbExVlZWgiyYmJhw8+bNKpPLi4uLuXv3LmlpaXTp0oWWLVsSHBxMQkICGRkZ/PbbbyxduhS5XM7evXtxdXVl48aNAFhaWtK1a1f+/PNP7t27V+cqZk0ghYEPHDiAv79/rfZRVFSEj48POTk5uLm5oaenR7NmzWjcuDGtWrViwoQJguiUDnGHhoaSk5ODkZERQUFBopK1MuTn55dRqKTOAvb29nTt2lWtWEC1OreqYoFHjx6JkOL06dP59NNPsbGxISYmRlwrCfv37ycyMpJp06YJtTo+Ph65XM7Vq1fZuXOn2PbUqVMMHz4cbW1tNZKooaFBfHy8yKuTQspPCrlcjqamplqxQH2qaP8GUlYezMzMcHR0pEePHrRv3x5tbW3Cw8O5cuUKd+7ceSIV7T9SVhb/vjuoGpBco58mEZIGbcm/S0qmlslk9dp3srqoTk5ZZGQk3t7eODg4PFHLp+DgYJGMrhpikcvldO7cmbZt25KZmcnixYt56aWX+PDDD9m8eTPTp0/n8uXLaqt1Y2Nj/vjjD3EuI0eO5Nq1a2rHk0KrTxImUyqV+Pv7c+nSJQoKCujSpUutFEJPT08uXLjAF198gaamJn/99RceHh5CJagpmjdvzsKFCwH45ptvxGR66dIltXCchoYGK1asQEtLq0yIt6CggCtXrqCjo8PWrVuZN28evXv3xsHBQSSXS9WcsbGxFBQUkJ+fz+3btykuLhYkREtLS4ThJUPaTz75hHPnztGmTRsSEhKEaW1sbCwZGRlYWVnx/vvvP5W2SxXd346OjgwdOhQo8eSqKaTKW6DSymNJWSh931y7do2kpCRBVA4ePFiG/JTG0aNHadasGXPnzgVKiFRp5aKiYoHAwMBKiwW2bNlCQUEBLi4uzJo1C319fVavXg3Axo0bRYg7PT2db7/9lnbt2pGQkMCUKVPIycnhtdde49dff2X06NFqkQzJZPjll19WO57kK3ju3Dngf1WxdQlV41pJRdPU1KxTFa2+Pb2eBVSrL6VFc+vWrXF3d8fT05NGjRqRkZEhVLR79+5V22evuLiY3Nzc/3LKSuGFv4NqW6n4NG0xsrOzuXHjBsXFxXh6epapNnkeCg8qI6kKhYKgoCDu379P586d1SrJaoOMjAygZJC2tbVVa0wsk8lEGOnXX3/l1q1bbN++XbT6kVQvCenp6cyePZtPPvkEKHnQBwwYwKuvvsr9+/eB/5lYRkdHk56ejq+vr1rFV3XQr18/PDw8CAoKwtXV9YnamWhqajJ//nwuXLiAg4MDjx8/ZuTIkWqhnppg8uTJTJkyBaVSyddff83Zs2eFiavkdffJJ5/Qrl074H+Tn4Tc3Fw6derElStXGD9+PFByT1pYWKCvr09KSgqurq4YGBgQGRnJxYsXuXLlCgqFAnt7e6FUJiQkCMJsZ2cn9t+mTRuGDx9ebkgqPj6etWvXihZZ9YXLly/Tq1evCnPvpGtz6NAhXF1dWbx4MV5eXlVOzrm5udy6dUvkQVUWduvYsSNyuZyUlBQ1Ev7RRx/x6quvUlxcLFSDb7/9ttLq1AsXLpCdnS0mzOHDh2Nvb1/h91MtFujRowdubm5qxQI3btwgLCyMjRs3CuI8Y8YMNDQ0KCwspF27drz00ksUFxczc+ZMbGxssLe3R0dHh99//50GDRpw+fJlZs+ejb6+Pi+//DI7d+4Ups4SwsLCyoT7pfu+sLAQGxsbOnfuXOH3ritIYc7SlhvS4rQ2lhv/RqWsMksMXV1dmjRpgpOTEz169KBdu3ZoamoSHh7O5cuX8fb25tGjRxWmQkiLkP+qL9Xx77qDaoCnRYQSExNFuK+iyfxpq3bloSK1TlIBUlJS6qzDQMeOHcXqOCEhgZdeeonvvvuOoKAgioqKGDx4MA4ODmRnZzN27Fh0dHTEZC+F3hYuXEhsbCxNmzYlLi4Of39/Hjx4wKRJkwA4efKkIBgtWrQAStSZKVOm0K1btxoZhiYlJQnfrtTU1DobeDt16sTx48dp3rw5Dx8+ZNasWbUKAWhqarJ+/XqR+D9ixAgRNsrNzaVZs2Yin23OnDlcunRJfFZPT4/ly5dz9uxZQUwkPH78mPbt2zNhwgQKCwtp1aoVbdq0QVNTE1NTU3R1dbl16xZXr14lODhYrfVVTk4O+fn5bNy4EUdHR1asWCEWQY0aNeKbb74R6ljTpk3Fb1Qf+OGHH3j77bfx9vamU6dOZUgplFg3SCpfcHAwK1eupE+fPtja2vLOO+8IJUwVmZmZeHl5YW5uXq2epA0aNMDJyQmAl19+WdzTMpmMTZs20aFDB7KyssSzMXHixAq93G7dugWUKK9ZWVmEhYWRkJDAK6+8UuX1kMlkGBoaqhULNG/enJSUFKHQTZgwgf79+1NUVMT9+/dp06YNN27cwNDQED8/P9LS0sjKyuLChQvY29sLJe2PP/4gOTlZXIvS1zojI0PYXkhQveeHDBny1C2BVIsFdHR0am258W8kZdW1xJBC53Z2dri7u4toUHp6Ordv3+batWsEBweTmJgoFF2JlJUOX27YsAEbGxt0dXVxd3cvQ+xLY//+/bRp0wZdXV0cHR3LjO1KpZJFixbRuHFj9PT06N+/v1iwS0hJSWH8+PEYGRlhYmLCtGnTyuRP+vn50aNHD3R1dWnWrBnff/99ldelNvh33UE1QH17lUkVab6+vrRt25Y2bdpU+MA+L0pZ6cEmKyuLGzduoKGhUe1uCdWBjo4OcXFxIox24cIFlixZwieffIK7u7uauaaXl5cIrUGJ2rVs2TIWLFiAsbExu3btQltbmxMnTuDj48OmTZu4cuUKAIGBgXh5eYlE/5iYGFq1agVQof1AaURFRXH37l1Byuo6zNyoUSN2794tQplS+52aQiaTsWLFCrXXJJIxZcoUdHV1uXTpklrCuZWVFbdv32bWrFnlVv+q2nds376dgQMHsmXLFmxtbXFxcRGmtQ4ODigUCnH8Fi1a8Pvvv9O+fXvmzp1LUlISNjY2YnDfunUrQ4YMIT4+HijxsHrppZdq/J2rgzt37rBo0SKioqIAROeC1atXq/2WQ4YMEcS1RYsWvPrqqxgZGZGYmMiuXbvo2bMnjo6ObNiwgcTEROHS37x58xr1Nl2zZg0AERERanl0BgYG/Pnnn1hYWJCeno6trS2ZmZnlEvXs7GwCAgIAhFHx119/DZTc85GRkZWeg2QfI0EqFrhw4QLp6emYm5vz/vvv8/DhQy5cuMD69evFdosWLQL+R6SOHTtGVlYWTZs2ZdSoUQB88MEHFBYWolQqWbVqlTiO5CEoKWPljYd9+vSp9NyfBmpiuaFK0v6NpKy25rF6enpCRevZsydt27ZFQ0ODsLAwhgwZQu/evVmzZg2WlpZq6vLevXuZM2cOixcvxtvbm44dOzJo0KAKIxvXrl1j3LhxTJs2DR8fH0aOHMnIkSPF8wHw/fffs27dOjZv3szNmzcxMDBg0KBBas/B+PHjCQwM5MyZMxw9epRLly4xffp08X5GRgYDBw6kRYsW3LlzhxUrVrBkyRK2bt1a42tTFV74O+hJwpf1RYSkZPCIiAi6dOlSZXXe85hTlpiYyI0bN2jUqFGVYZnaHk+aBJs1a8aoUaPQ0tIiOjqa9evX8/XXXyOTyXj48CFbtmyhbdu2QMm1XblypTD7dHd358033wRKcqqUSiWdOnUSeSl//fUXDg4OGBgYkJmZKfLLylNLVCFZG4SFhWFrayv8k2JiYmodZqwInTp14ocffgBg9+7d9O7du1aWGY6OjmrmuXl5echkMiZMmEBYWBiTJ08W75mZmeHj44ONjU2F+xs0aJD4944dO7h69SrLli3jvffeIzg4GPhfmLNdu3ZC1SkuLuaTTz4hLi4ODQ0NPvjgA3r27ElxcTF9+vShX79+4rd66aWXnrg/aGVo166dUE8lKJVKvvjiC2bPnq1GeKZNm4aenh6PHj2iZ8+ePHr0iL///psxY8agoaHBw4cPmTdvHm3atOGNN97g/v37mJub12gMUvVVLN3Yu1mzZnz++edASShPT0+PoKCgMvfq/fv3USqVmJmZiX1IqjCgVtlYHkaOHMmoUaOE5QWUKMtSPuKyZcvo0KEDXbt2xczMjF9//RUoScBv3749nTp1EqrnwYMH8fLyomnTpmzYsAEjIyPCwsJYsGABV65cITExURCV0tepvDGvus3OnxZqalz7LIy/6xN1kSenqqJ5eHiwbt06hgwZwp07d0hKShJq9JEjR1i5ciVvv/02b775Ju3atWPz5s3o6+uXMVeWsHbtWgYPHszcuXNp27YtX375JS4uLmIhoVQqWbNmDZ9//jkjRozAycmJXbt2ERsby19//QWUFJWdPHmSbdu24e7uTvfu3fnxxx/Zs2eP8FT8/fffKSgoYMeOHbRv356xY8cye/ZsoRDXJV54UlZb1FdOmdT/MTc3l65du1arOfbzoJRJ56BUKomIiMDX11dYLtTXQOPi4gKU5IVt27aN4OBg1qxZQ/fu3cWgByUeX9IEYmdnR0ZGBr169WL58uUoFAo+/fRTtLW18fHxEQ+vh4cHUDKB7d+/X5BKSYWqzCJAMs5MTk6mbdu2jB07lqSkJAwNDfntt9947bXXat3gtyJMnTqV06dP06pVK6Kiohg+fLhQkqoLpVJJcnKy2mtNmjTh4cOHYrUpKWKS0lgZHB0dRWghLy+Pt99+G01NTc6fP4+rqysLFiwQViVHjx4Vx46Ojhbkw9bWlhEjRrB3717x/wMHDgjLE6lIob6gp6fH+vXrOXDgQJligp07d+Lm5sbevXvJysqiYcOGQgn69NNPiYuLo2/fvuzYsYOYmBh++OEH2rdvT15eHpcuXeKjjz6iTZs2rFmzptr3g9QqCEoUpdLPvWSaGhUVJUKRM2bMUCPpUujFwcEBgBs3btC7d2/xvrSAKQ+XL1/m8uXL/PPPP0L59vPz44033qCgoAA7OzvCw8NFNd2qVatQKBS0atWKKVOmEBkZiaenp1oe4ePHj2natClGRkYi33TTpk2cPXsW+J+r/8OHDzE3N6/0+lhaWlb6/rNGRSpabm4uOTk5yOXy57o/Z00gWRHVtaN/27ZtmTdvHkuXLqVx48Zs2bIFbW1tPv74Y27fvs3ly5dFUZZcLqd///5cv3693H1dv36d/v37q702aNAgsX14eDiPHz9W28bY2Bh3d3exjWp7QAn9+/dHLpeLheb169fp2bOnWvrRoEGDCAkJqfNF+v9ZUlYf4Uup/6ORkRFdunSpdreB54GUSSQoICCA8PBw3NzcsLa2rtdjtmnTBnNzczIyMjh+/DhWVla8/fbbnDp1Cl9fX958800xIEjx/bFjxzJs2DCKi4tZtmwZ7777Lg0bNhRqy/bt2wHEBOzn58c777wj8p2OHTsGlG8yC4hG2gqFAjc3NzZt2kRAQAANGzZk69atmJiYcOPGDTZs2FDn16Nbt278/fffNG7cmHv37tG/f38WL15c4YBUGvn5+aKIQoKuri4vvfQScXFx6OvrC8m+On0Mi4uLxSSZmJiInp4ep0+fZsiQIUDJKnXmzJl069aNMWPGiGtsY2Mjws/x8fGEhoaKCr/t27fzxRdfCEXTysqqzgluachkMgYPHsydO3f48ssv1cLw9+7dY+rUqbRs2ZJPPvkEDw8POnbsSF5enmjEDSV5LwMGDOC7777jyJEjzJkzh1atWpGens7ChQtxcXHh7NmzVeYEtm/fXuSieHl5lXnuTU1NsbW1BUryxuzs7Hj8+DEjRowQVcVSkUDr1q1RKpV89tlnREREADB06FCuXbtWrhFubm6uCMlMmDABa2trsrOzmTJlCvn5+Tg7OxMREcHKlSu5du0aY8aMEWpCZGQkPXr0YObMmWzcuFFtQXvgwAEuX77MH3/8IWxulEqlUPgaNWokrHPGjRtX6fX5+eefn/lYWF1IKlphYSH+/v40atQICwuLZ2JcWx+Qzre+2ixJLZYGDx7M2rVrRTRi6NChanlmVlZWFaZ0PH78uExbONXtpb+r2qb0YkDyUVTdprx9qB6jrvDCk7LnJXwZGRlZ6/6PzwMpkwbZjIwMPD09RR5WfUGpVKKjo8PYsWOBkjCjakl/q1atWL9+Pd7e3mohjS+//BIfHx/Gjh2LhoYGv/76K3369BET1d27d/Hz8xOeUJJh7MyZM9HU1BRytLa2dpkJNCMjg5s3b2JoaEinTp3Q0tISRp4DBgzg5ZdfFlWgGzZsqJdBtkWLFpw6dUooXCtXrqR///589913VX5WR0enTHm5pDAaGxuTk5ODhoYG06dPp3379mIbpVJJTEwMN27c4Oeff2bixIl07doVBwcHtcqodevWYWtry759++jbty9QIuv7+flhZGTE8OHDgZLrKFlMZGRksGDBArEPKTwoPbfTpk3j6NGjeHl5qSk0tUV6ejqTJ08u44EHJf5rc+bMwc/Pr0yLp5ycHDZt2kSfPn2Eivrbb7+hVCrVXPrd3NxEQ3UfHx82btyIlZUV4eHhjBgxgj59+pRbFKAKaUWuq6tbblqA9Fvfv3+fFi1a0KBBAwIDAxk4cCArVqwQSpmXlxfff/+9WiXnsWPHGD9+PI6Ojri7u/Pll1/y+++/4+vry8SJE4mMjKRRo0biPl64cCEhISHo6uri6+tLYWEhLi4uzJgxQyxgoCScamJigouLC3379mXUqFGiwvb8+fN4enqyc+dOtd9OyusJDQ0V37OycDmUpCC0b9+eFStWqOUFPa/Iysri9u3bNG7cGAcHB1Es8LSNa+sD0pxUX3lyOTk55eYpDx8+/F8XBq4JZMq67JvwDKBQKGrlLBwQEICOjo5a6X5tj3/v3j3i4+NxdnautpmoKu7evUuDBg1EEvrTRmZmJnfu3CEvL49evXoJG4X6gmrLJG9vbxF6+eyzz0ROjSrS09Pp2LFjGZNVJycnAgICygxwxsbGapWAgPAkUvVk+u2330SIKCEhgYCAAGxtbWnRooUYFM6ePcuIESPQ0dHB39+fBg0aYG9vT1ZWFocPHy4jndcVEhMTOXz4MMePH+fUqVMAvPHGG2zatKnC/L7iYnjnsxX8sfU7FIUlipiJiQnFxcVkZmZiaWnJzp07adeuHY8ePeLatWvCvLWyZumurq6CaEyfPl30xpPg4uKCmZkZoaGhIsm8vN+gIkimvomJiSQnJ6OtrY2FhQUNGzbE1NSUx48fV6trwr179xg3bhz379+nQ4cOXL9+vcIJ5eHDh0yfPr2MCimTyVAqlSLH8syZMzRo0IDMzExcXFzKfTYyMjL48ssvRdsqTU1NXn/9dT788EPat29fZoJ54403OHz4MA0bNiQiIqLcCejixYsMGTIEDQ0Nxo4dy++//y7ea9iwoejQoK2tLZTGuXPnkpWVJfyiKhoXJdf28ipRGzRooFZ1ZmhoyGuvvca0adNwdnYmJiaG0NBQnJycSEtLo2PHjkBJuHfDhg1kZWXh5OTE48ePRXK2mZmZWNzY2NgIVa8q9OjRgxMnTjy3E7Q0bjZr1gxbW9sKz1OhUIgxTyL50rQreWZKfz9PyM3N5fr16/Tp06defoNff/2VvXv3CoWsoKAAfX19/vzzT0aOHCm2mzx5MmlpaRw+fLjMPpo3b86cOXNE1wmAxYsX89dff3H37l0ePnxIq1at8PHxwdnZWWzTq1cvnJ2dWbt2LTt27ODjjz9WC0MWFRWhq6vL/v37eeWVV5g0aRIZGRlCOYaSxUjfvn1JSUmpE1cCCc/XXfAUURc5Zfn5+dy6dYv09HQ8PT1rRcikc3lWKyep5ZOUC1LfA6AqIZPMYiVi/MMPP5Sb62VsbExERATLli1Ty4Py8/PD2dm5TFeB8shAUVFRGZNMMzMzkUMXEBBA+/btsbGxUbsG/fr1o2vXruTn5zN+/HgKCwtFEcHChQvrzevOwsKCt956iz///JMPP/wQTU1Ndu/ezdSpU8nJySmz/ew/dRj/ux4ZHRYzZG0OQ9bmgFxOWlqayM9ISEhg6NChtGzZkt69e7NgwQK8vb3Jzc1FJpNhZmZGu3bt6NevH66urmLfUk4QlFROqhIyKKlkPXv2rFrVX2WEzNTUlHfeeYdVq1axatUqLCwsaNKkCc7OzvTu3Zu2bduiVCoJDAxk0qRJuLi4cOHChTK/nyoOHz5M7969uX//Pk2bNmXTpk2VTnK2trai0mrYsGFiW2mylJ7Hn3/+mdzcXGGQWx6MjIxYsWIFQUFBDB06lKKiIv744w/c3d1p2bJlGb8xifQkJSUxffr0MqX3UDJpSMUR8fHxaqkEEiGD/7UQGzx4MEuWLGHlypVcv36d+/fv8+OPPzJ58mS6du2qZgCqUChISUkpt9hFOhcrKyt27NhBbGws69evp1OnTjx69Ij79+/TqVMnGjZsSOvWrUWbuPXr14vPvvLKK2oGsKpto6pLyKAk/23+/PnV3v5pIiMjgzt37tC8eXNatWpV6bhZ2nKjPoxr6wNSPll9zQnZ2dlqyr62tjaurq5q96VCoeDcuXN4enqWuw9PT88y9/GZM2fE9i1btqRRo0Zq20gREWkbT09P0tLS1BTuf/75B4VCgbu7u9jm0qVLagudM2fO4ODgUKeEDP4FpKy2N8yT5pSlp6dz/fp14aXyJOrSswhfKpVKwsLC8PPzw9HRkdatW4u+kvUFhUKhRshkMhkymYxffvkFKMnncnd3rzDH6OOPP8bf358RI0aI18qztpBInpQnoKmpyYEDB0RIE0oaQffo0YN79+7x6NEjXF1dy+QMQMn99eOPP2JoaMitW7cYOHAgr732GkZGRgQEBLBv377aX5BqQC6X8/XXX/P777+L79G7d2+1llH7fTVIyJEDMnHOGtp69FkcXOF+TU1NadSokQhPKpVKUlJSRLWf6gC1adOmMp+XEl5lMhmjR49m1apVwg3f0tJS5C6VDqc6OTkREhLCypUrmTlzJjNnzlRLntXQ0KBhw4Y4ODiwbds2/vrrL7Kysjhz5gyXL1/m5s2bPHz4UIQ5i4uLWbx4MW+88QZZWVn07NmTK1euiCKSyiCTyejTpw979+4lPj5ebXUu4fz587i4uFTLLNjKyop9+/Zx6dIlEcpNTEwUZfqSt9i0adPEZ3bv3s2nn34q/h8bGyuSiyU7ifPnz9OtWzcAtbAzINSot99+W+11CwsLpk6dysaNG3nvvfcoLi7GxMSE+/fvM2fOnAq/g1wuZ/HixYSFhTFmzBiRH/XgwQMiIiJwdXVVm4SWLVsGqBfOXL16lW3btqntV1o4lR6vS/+/dDJ16QXA84D09HTu3LmDjY2NyP+rCSoyrpXG39oY19YH6rtDQXktlubMmcNPP/3EL7/8wr1793jnnXfIzs4WFfaTJk3is88+E9t/8MEHnDx5klWrVhEcHMySJUuEzQ+U3F8ffvghX331FUeOHMHf359JkyZhbW0tnve2bdsyePBg3n77bby8vLh69SqzZs1i7NixYjH0xhtvoK2tzbRp0wgMDGTv3r2sXbu20meptnjhSVlt8SREKDY2Fi8vL1q0aIGTk9MTJ0I+bVImWXZERUXh7u4ukuJrcx5hYWF88cUXDB48mIEDBzJ+/HiWLl3KyZMnhaIjVfFI8r1EyCR07NhROPKHh4fTu3fvCs/D3NycX375hXfeeUe8prp60dbWFhNi586d6d27N0VFRSLZXyq+6NevHz4+PqSnp9OlS5dKKxHbtGnD+fPnsba2JiQkhKFDhwoyUxvritpg2LBhHDlyBEtLSwIDA/Hw8OCjjz7i+++/57i/EomQqcKgYSvWrl2Lj48PkZGRLFy4UHRESE1N5fHjx9VuPaU6OOvp6QnivGbNGnbu3MnMmTNF8USrVq2Eglha+ezevXu1FjA//vgjBw8eBEqKA77++mt69OhB06ZNyczM5NatW5w6dYqXXnqJlStXAvD+++/z999/l9uUviro6+vz+++/8+eff6o9z7GxsRw5cqRG+3J1dWXPnj3s379fXLczZ87Qu3dvJk+ejIeHBzNnzhTb//LLL/j4+BAXF8egQYMYPnw4J06coF27djg5OVFcXCzucdUEflUFs1+/fhWej1SwMHnyZB48eFBpGX/Tpk3VDFylfLrY2Fg6d+6Mnp4e8fHxZGVlce3aNWHlooqzZ8+qhcMtLS1FlwClUql23qrZM6NHj2bRokVqhNrR0bHCc30WSEtLw9vbm1atWlWZH1cd1NRy42kStNp6lFUXpZUyKGmxtXLlShYtWoSzszO+vr6cPHlSLJgjIyPVxtyuXbuye/dutm7dSseOHfnzzz/566+/6NChg9hm3rx5vP/++0yfPh03NzeysrI4efKkmjfj77//Tps2bejXrx9Dhgyhe/fuah5kxsbGnD59mvDwcFxdXfn4449ZtGiRmpdZXeGFzylTKpW1qt569OgRSUlJamGa6hwrJCSE6OhoOnbsWKvBvzyEhYWRnZ0t3L7rE3l5efj4+CCTyejUqZNaheg///yDq6trtWw8IiIihIJT0UChp6fHgAED6NmzJ82aNaNBgwbo6OhgZGSEmZkZDRs2VAs9fvDBB2KFPXDgQLXqt/Jw8OBBZs6cWWbiNzAwIDs7mxUrVjBu3Dh69epFWFgYjo6OhIeHk5WVxffff4+zszNOTk7VbvORkJDAJ598woEDB8Rre/furbMGytVBXFwc06dPV2u87vLWnzRxfbXsxsX5zGl/nWvXrvHdd99Vi4Dp6OiohQmbNm3K9OnTmTRpEjNnzuTkyZPivbZt23L79m3x/+3btzN79mz69u1LYGBghZYerVq1wt7enuXLl5ebR3n37l26du0KlKycpaR0VURERDBq1ChCQkLQ1tbm/fffZ9SoUTRs2BALC4tqVz6XRmZmJjdv3mTBggUEBgYCJavtefPmMW/ePHR1dVEqldVW6AcPHszly5dxc3PD29ub4uJidHV1GTNmDJmZmYJ4yuVyVqxYwbFjx/jnn3/Q0NBgzZo1xMXF8c0339C1a1fu3bsn8l60tbVZu3Yt77zzDvb29sK3rzyMGzeOI0eO8M4773D8+HFRaKE69NvZ2ZGSkkJycjKNGjXi6tWrWFpaEhQURFpaGmZmZsKnTjUcWREMDQ3F/fbpp5+io6MjVDXVHDNVzJ49m9mzZ7N06VLhjfbxxx/z7rvv4ufnJ4oVnpWXWUpKCr6+vtjb2z9xq7nqQDUXTfq3BLlcLgh/falZSUlJhIWFiRBeXePTTz9FLpcLT7H/UIK6dQR9gVBTVaiwsBBfX1/y8vLw9PSs0yaqT6vNUnp6Ot7e3jRs2LDcCtGqmpIrFArOnz/P9u3bOXTokBgkBg4cyMiRI/ntt9/o0KEDeXl5nD9/nqioKI4cOVKh0iCTyTA3N8fKygpjY2OysrLQ1dUlLy+P06dP07FjR44cOaLWgqewsJC4uDgUCgWjRo2ia9euvPXWW2qhSYmknThxgnfffZf9+/fTt29f/P39gRLiIYWkbt68ib6+PhYWFlhYWGBsbFzhhGtpacmuXbvQ19cXk0br1q2ruux1isaNG3PkyBGOHz/OuHHjKC4uxnvbazTqmI+G5v/CPkqlkrNf2PF3alSZfchkMpo2bUrbtm3JyckhLS2NyMhIMjIyyM/PRyaT0a9fP6ZNm8aQIUNEYcH8+fPVSNnDhw/VCIpEYrS1tSskZHp6eoSFhREWFsa6devKvJ+eni6MUE1NTUUfVFV4eXkxevRoEhMTRcjQwcGBpKQkYmNjCQ4OxtDQUBA0Q0PDSkmUaoKvr68vrVq14saNGzRu3Fj07Vu+fDkbNmxAU1OT5cuXM2HChAr3pwpphe/i4sKcOXOYPn06mZmZImTfokULIiMjUSgUfPzxx/Ts2ZNevXpx8eJF3n//fRYvXoxcLufatWsMGTJEtJAZM2aM6DJRlbGz1A9z27ZtQnErvRY3MDDg/v37WFtbExsby6hRo7C2tiY+Ph6FQoGvr2+5++7SpQs+Pj5ligr69u3L+fPnycjIoEWLFsyePVu8VxGpy8nJoXHjxmoqR9++fQkPD+err77izp07xMTE8PXXX2NgYPBUCwCSk5O5e/cuDg4O1So6qQtI47OkVqmmf0iWG1B/xQL14VGmiuzs7OfOLPh5wAtPyp5GTllmZiY+Pj4YGBioGSfWFZ5Gon9cXBwBAQG0bt26TDK76nmUd02USiV79+5l6dKlam7hko+Wm5sbO3fu5Nq1awQGBhISEoKRkRG+vr4cO3YMHx8f4uPjycnJIS8vj8zMTFJSUlAoFCQlJaklLqviwYMHTJs2jVOnTonBISwsDFdXV8zMzIiKikJHR4ejR49y/Phxxo8fT0FBAZqamhQVFXH9+nUyMjJwcHBg0aJFIv7v6uoqkjyLiopITk4mMTERHx8f5HK5mMzNzc3LHZQkCwKZTIa9vX0Nf4knh0wmY8iQIejq6goCevx9HZp1nY5Nn3dICb1M4P7ZZT7TunVrFi5ciK6uLvv27ePo0aPlqsx//PGHCAGronPnzrz//vv8+OOPQEmhy5IlS1i6dCmAuBaVmd62bt0af39/NDQ0yngD5eXlMXr0aHGPHTp0SO36p6amcuDAAebNm0d+fj5OTk7s379fqBaGhoa0bNmSgoICkpKSSExM5NGjR2hqaorf1MzMTG2ff/zxB5988gmvvPIKo0aNUlNBLly4wJgxY4iMjKSwsFAksq9atYpBgwaJxPfRo0dXOHn169ePP//8k127djFq1CgxaXbt2pVbt27x6NEjte2lvqT29vaEhoaydOlSnJyc8PPzU+vpl5eXJ1pmVRbi8/f3F/mBEnEqrZLJ5XJ8fX3R1dVlxowZrFixgrt373L37t1y99mtWzf69OnD5s2by/QllBZ2UqWcXC7nq6++oqioSO245Y01UjhQleB16dKF5ORkkTvav39/bt68iY6ODg0bNhQVuvVJHhITE/H396dt27bPlESoqmOSabP0R/VaSts9KUGr75yynJycMjll/+FfQMqg7CBTHVRXKYuPj8fPzw8bGxuRDF/XqM+cMilJ99GjRzg7O1caci1PKbt27RpffPGF6NNnYmLCqFGjeOedd8RkoFAoxEQ9b948jI2NKS4upkOHDjg5OZV7zYqLi0lKSuLx48ckJiaSkZGBoaGhaP/zwQcfACVOyo0bN+bvv//G3d1dhBozMjL48ccfWbVqFbt372bIkCHs27ePUaNGUVRUhI6ODrm5uWzdupWPP/4Yd3d3QdZUB1ZNTU2srKywsrJCoVCQlpZGYmKiMDw1MzPD0tKShg0bipBY//79uXbtGnp6ek/kqfUkmDVrVpmwbdS1rURdK78Xm9SL9c0331Q7Z1NTUzp06MCtW7fIy8vjgw8+KJeQQclz9t133xEREcHff/8NwMqVK+ncuTPDhw8XqlB5JLt58+ZERkaKxO3i4mIuX74seh3GxsYyceJEbty4gZGREcePHxeVfQA//fQT8+bNEySyUaNGuLu7c/bsWaZMmaJ2LKmXo7W1NQqFgtTUVBITEwkODqagoAAzMzNhuWFhYUFaWhq7d+/m3XffVQtLyeVyDA0NmTRpEpGRkaKKMjQ0FHt7exo2bEhsbCwLFizgvffeY8aMGWVC4WPGjGHNmjWEhIQwd+5cYWvi5eVFUVERrq6u+Pr6iudfS0uLwsJCQkND0dPTIzc3Fz8/PywsLNQsYQ4cOCCUDFUV6tGjRyQnJyOTyYiIiOCtt95SOx8HBwfi4+NJS0sT46ZCocDGxoY//viDtm3bYmFhwYcffiiutdSUfseOHURFRXH16lUxHlhaWqr1JVQoFHz00Uds3LiR/Px8FAqF8AZUKpW0adOG4ODgchehkimx6nurVq3i8OHDKJVKevbsyfDhwykuLiYlJYWkpCTu3btHQUEB5ubmgqSV18e1tkhISMDf35/27duX6QjxLFGaoEm/Y12qaM8ip+w//EtIWW1QlSWGVJ0YHh6Oo6NjvT6Q9UXKioqK8Pf3JzMzEw8PjypXJeWRsk2bNnH16lV0dXWZP38+s2fPLmP4t3HjRoKCgjAyMuLNN9+sMKFfFRoaGoIMlYarqyt5eXksXbqUnJwcsrOz6du3L02aNKFNmzbCA2v9+vUkJiYycOBApk6dytdff82mTZuYNWuWyItavXo19vb2mJiYYGJiQlJSEseOHSs3L0jq0WZmZoa9vT3Z2dkkJiYSExPDvXv3MDIywsLCgilTprB27VrS09M5fvx4hSRGFQqFgvDwcG7evMnx48dxdXXlo48+qvJzEu7cucPOnTsxNDQkLy+Pn3/+We398ePHq3lZlQfVgbpdu3YsX74cFxcXevfuTV5eHj179mTZsmViu4oUYYmcSvft1KlTefTokVBkyvM82759OwMGDKC4uFjk/B06dIg+ffpw+vRppk2bRkpKCsbGxuzbt08QMh8fH2bMmCFCo/r6+uTk5PD48WN++uknZDKZqIwqD3K5HHNzc8zNzXFwcBC/qRTm1NLSomXLloSHh7N7926++uor8dlLly7h6+srQnft2rUTpLKgoIDY2FjkcjkJCQksXryY9evXY2dnx7hx45g6daq4Vj/88ANDhgzBz89PtEaSrvGdO3d46623OHXqFFFRUUIlkgiZhNIefQqFAhMTE9LS0li6dCljxozhyJEj5Xo5Sfjhhx84deoUISEhwP9CmG3btuXMmTMYGBhw584dGjRooKagBgYGioq30s9MeRY2t2/f5tSpU2qtn4yNjdm7dy/dunVj3LhxHD16tMznpDFQVX1bvXo1BQUFyOVyoXRL/VYtLCxQKpVkZWWRlJREXFwcwcHBNGjQQBC0ytIRqkJ8fDwBAQE4Ojo+1+2fygtz1oWKVt/hy6ysrP+UsnLwwif6Q8kAWdOvkZGRwa1bt8qtWpLITEZGBi4uLtVOBK8tEhMTCQkJoXv37nW2z9zcXLy9vdHS0sLZ2blaJf03b96kadOmajkT9+/fZ82aNXz22WflJrcmJyfj4OBAVlYWa9asEeX+kt2FKmJiYli8eDErV66sdscAf39/1q1bx759+6r0BGvTpg1btmwhNzeXSZMmqa3gP5ozhwvnz4sih8ePH9doQMjPzxchseTkZObNm0dwcDCrV6/m7bffFoNccXExwcHBXLt2DS8vL2JjY3n48CExMTFqg2OLFi0ICAgQn8vLy2PNmjVMnTpVbQLIzc1l3rx5FTbklbBs2TLRt7G60NDQQEtLi7y8PLS0tHBxceHx48dER0djamrK5cuXyzjfAzg7O3P//n1cXFxEWKl79+7Y2dmxc+fOco8ltVRRKBQMGjSIU6dO0a5dO1566SVh/dCpUyd++eUXWrVqRWpqKm+//TYnTpwAyqrhLVu2xNzcHH19fXbu3FnjRZNSqSQoKIj4+Hi8vb1F7tbWrVvp378/ZmZmJCcnc+HCBY4fP87Ro0fJzc1VM2utDMOGDRP9PpVKJWPGjFFzyK8LmJiYkJGRUWYhZW5uLvqQamhoYGBgQEZGBnPmzClTedmwYUO8vb3R19fnn3/+QVtbm9DQUObNm1fhcbt27UqrVq1ExwNQNwuWyWRcvHiRnj17is80atRIhP1jYmJwcnISLb8krF27ljFjxtC8eXO1a9yqVSt27txZraKsgoICkpOTSUpKEtdAImjm5uZlPA0rQlxcHEFBQTg5OdVZQdezwJMY14aHh5Obm0u7du3q5dx69+7N3LlzRVeX/1CCf4VSVpfhy5ycHLy9vdHR0cHT07NaZOZJUdeJ/qmpqfj4+GBlZUXbtm2rLVuXl9tmZ2dXaZ9HVRdvyUumouN9/vnn7Nu3j7y8PH777bdKz+XIkSP8/fff9O7dm59++only5dz//59fHx8uHLlCseOHSszOQYHB9OrVy+MjIxo27YtWlpayFoOx/mNjdwHmrSGJkoFqSfeY+bMmTg7O/POO+9US0LX0dGhSZMmNGnShOLiYnHs/Px8Ll68SHFxMefPn+fPP/8UrZ1KQ1tbm06dOuHh4cEbb7yBXC4nPT2dnTt3snLlSlJTU/Hy8uKNN97g9OnT3L59mwcPHlR5b8hkshoTMighkNK+CwsLhT8WlIQgly1bVsZvCkpCVvfv38fb25vRo0ezb98+rly5Umlj3tjYWHFvSRWXQUFBQnmysrISpCA4OJg33nhDKDqqLvOTJk3i7bffrpYPWUVQKBQEBASQmZmJp6cnffv2JTg4WOSXLViwgDZt2mBmZkbXrl15+eWXCQ8P56WXXiqjWFWEo0ePYm5ujqOjI7GxsdWyTqmpYl5cXIyzs7OaV5+2trYgI8OGDaNPnz58/PHHGBoalvscf/fdd3z00UecO3eOtLS0Co8ljbF6enqYmJioFfoAamRH8rxThWqv2SZNmjBu3LgyBF5TU5OzZ8+qPdetW7fmwoUL1Tbo1NbWpnHjxjRu3BiFQkF6ejpJSUmEh4cTEBCAsbGxCF1XVCwgqajOzs5VNlB/3lFRsYCqTZHqtqoqWn2HLytqs/R/Hf8KUlYbSAREMjKFkono7t27WFtb4+Dg8NTaXtRlon90dDT37t3DwcGhXJWjMtSGHErVeO+++26V7s9SWKJ0nkt58Pf3Z/fu3WhpaTF+/HjMzMxwd3fH3d2dmTNnkpaWxuLFi8slDZJjs56FLf3e2Kh+TjINTIds4tAsTQ4dOkSHDh0YPHhwjb5zRkaGSEZPSEjgiy++ULMk0NPTo02bNvTp0wcHBweMjIwwMDBAX18fb29vAgMD+f333wkLC+P06dNqic2nTp0SbZWqi/IWJLa2tuU2pS4PHTp0oG/fvtjY2ODo6EhcXByTJk3ijz/+ICwsTJhlStdxzJgxIqdo1qxZHD9+nKysLBFiVIUUhpN6ZQLs2rVLbRtTU1NOnjwpujZIVihSqDIrKwtra2u1sGZtUVRUxN27dykqKsLNzU0sun744QdCQkLw9vZm/vz5fPTRR0yYMEEtJHb48GH++ecfHjx4QEhICIGBgeTk5FSo4Obl5QnD2OqgomevokVnZmamIGSvvPIKd+7cEV0VGjVqhKenpwg7KhSKcjsiVPUsvvnmm0yZMoXCwkKmTJlCdHS0WsEBlKhkSUlJaudZum9l6YpFVZImISMjgxUrVqi9Nm/ePExNTcnPzycnJweZTEZOTg6WlpZVFlzJ5XJMTU0xNTXFzs6O3NxcUVgUFhZWpp2XhoYG0dHRhIaG1rpl3vOO8nLRpIpO6Y+0XVFRUb2RMqVSKdTz/6CO/7OkTHqgi4uLRVLsgwcPaNeu3VMreZZQFzllkodaTEwMLi4utVrhVWWJUd4xpebInTt3rjJ3QwpvVCfUJCkLFYUOTExMWLt2LWvWrOHQoUPMmTOnjIrhMuX3cs9JJpNh0+t9Is6vVSMLVUEawFQbL6uaZzZt2hQbGxsKCgqIiYmp1KSzutDQ0GDChAnMmDGDoUOHqqlRw4cPFwn3pSERMk1NTXR0dMrN/ZHsRwICAsQk2qJFCzU/M4lI9+rVi169eqGlpSWUGCgJvS9atKjccJeVlRU7d+5k2LBhREX9z5pDta1Q3759GT16NJ988olaGxNtbW0xCQ8ZMoTVq1c/sTdUQUEBPj4+aGpq4urqqjapGxoacvLkST7++GN+/fVXVq1axbFjx/jqq6/o27evCIk5Ozvj4uIiJnOpQnf37t3Mnj1b5IGVfpakZ1wul6Otra0WuvP09BQ9OMsjYNWJArRv316QWU1NTR4/fszChQvF++X9/qVhYWHB/Pnz8fT0pEePHhQXF+Pl5cW5c+fUWmipolOnTvj4+JTpmXngwAG1MGpp5/vy7tuvvvpK7TzbtWsnQlvXrl1T8wMcPXp0haHyiqCnp0ezZs1o1qxZucUCenp65OTk4Ojo+K8kZKVRmeVGcXExWVlZmJqaUlhYWC+WG/8l+peP/7OkTLoRCwoKuHfvHsnJybi5uVU716muz+VJSFlhYSF+fn7k5OTg4eFR6xu9uoqdlJegUCjE5FLZiqe4uJiffvpJEIrqDHgSMagqn0MmkzFq1CgGDRpE9+7d1doPFeeX7REpQUORj729PS+//DLt27dn5MiR9OjRo8LtAwMDmTJlCvfu3atwkoyOjq4wdFkR5HI5Li4u9OnTh44dO/LTTz+JBr3Ozs78/fffmJmZ8frrr5cJD1bHI62oqIjp06fj4ODAhx9+qHbuUi7ZyJEjCQkJwd/fv4xFg4Q7d+6otV6SsGLFigqVRimUWxnZt7W1VXO3l1BQUECTJk3YtWsXHh4eVX7PqiDlWBoZGZXr0QclXl2bN2+mb9++zJ07l+DgYF577TXc3NxYt24dTk5Oom/k8uXL6dmzJ/r6+piZmdGjRw+Cg4MZOnQoAQEBZb6v9HwrFAomT55MYGAgV65cAeDWrVuCIKv+PosWLcLQ0JC5c+dW+f2kAoVu3brRoEGDKnuFloZcLic1NZX58+dTVFQkzkNV/bSxsSEtLU0tzCkpxDNmzMDX11f0GPTx8aFnz57C4kNVGYuOji7X7FaVkEmdO6RxurTxcUXN1quL0sUCoaGhREdHY2BggL+/Pw8fPhTE+0mKBV4kSCqaQqEgODiY4uJiIVDUh+VGTk7Of0pZOfhXkLLaPDBSIrq0cvb09KzTUuqaQGqpoRpKrS6ys7Px9vZGT08PDw+PaieyVnQeVZFD1dLrlJQUMTiWR2aLioo4duwYK1asEIPwtGnTqqxkiomJ4dq1a0D5lXylUVxczObNm9UIGcCd7aMZtCKxzP2hVCgIu7gZKLE3uHz5Mvb29oKUFRcXEx0dzdWrV7lx4wa3bt3Cz8+vwuO7urpia2uLiYkJ5ubmtGzZEqVSSVRUFI8ePSIqKorU1FSMjIzQ19dHU1MTCwsLnJ2d6dOnDy1atEBLS4sPPviAixcvoqury+rVq3njjTfQ0tLi5MmTImSkmlBdHkmCkiT4MWPGcOnSJa5du8a1a9dYsWIFgwYN4o033lDLQSosLOTIkSMcOHAAJycnOnfurFYgURVu3LghctFU1RJNTU1u3rwp2mepwsjIiIyMDIByw89WVlbMnTuXSZMm1clKWgrzWVlZ4eDgUOV4MXr0aAYOHMigQYMICAjg1q1beHp6Ymtry4gRI8jJyWHLli389NNP9OrViz59+gjCtmzZMpYvX66Wn1caBQUFnDhxgq5du+Lv709RUVG5IdBly5YJpdPAwEDkaUlh3fIghZVrCtXQlQSZTEajRo0oLCwkKSmpwmbi2traWFlZcfDgQaZPny4KHC5fviy2USVypUObpWFubs7BgwfVEsxVc3vnz5/P559/Xt2vViUiIiKIjY3Fzc0NIyMj8X2TkpJE5a25ubnwLnySMfZ5h5RvmZOTQ+fOncV1r2vj2qKiIvLy8v5TysrBv6L6UmrcWhOkpqZy8+ZNLC0tcXZ2fmr5Y+WhsLCQc+fO0b9//xoZ0yYnJ+Pr60uTJk2qNdlUhXv37gElJfLlQbWKB0oc80ePHk3r1q3VjCajoqLYtm0bf/zxh3ATNzIyYtGiRcycObPK8xwzZgxHjx7FzMyMs2fPChuB8lBQUICvry8jR44UZAVKQhVFRUUY23bD/f0zyOQlK+7ionzOLWhOQVYiBgYGODk5kZ2djZWVFdnZ2WRmZhIREVFmZa6hoYGLiwvJycl4eHhgY2ODs7Mzrq6utar8y8rKIjExkcTERDIzM9m9e7eYzHbu3Mno0aNLzre4GCsrq2qRUwnNmjVj//79rF+/nt9++4133nlH9IdUKpW8+uqr5eatDR06lK1bt6JQKDh06BALFiwgKyuLpk2bMmrUKH7//Xe10KUqpHYp7777LlA5cagII0aMYP78+Tg6OtaZMpGamoqvry82NjYVmiZXhHXr1rFnzx78/PzKqKOlw4xSSNTOzg5NTU3h51W6wlD67LfffsuECRPYuXMnX3zxRbXOx9DQEC8vLxo3bsyWLVvUGpk/CTQ0NGjWrBkNGzYkNzeX6OhotWdJFebm5vTv359+/frRtm1bFi5cKNSwvn37smHDBhYtWsT+/fvVPufi4iJI2t69e5k6dWqZll5QoqJfvHixTLhTmrw7deokFMYnhVKp5OHDh0RFReHq6lquaqNUKklPTycxMZGkpCSys7MxNjYWHndPu7NAfUKpVIoCGFVCVhqlLTfKa/9U1XyamppKixYtSElJqXYRx/8V/J8kZVFRUQQHB4v+j8+6wkahUHD69Gn69OlT7Z59kZGRhISE0LZt2zrrwxYaGkphYSHt27cv855qQqi0Mnrvvff4+eefmTFjxv9j77yjojq7Lr6H3ntHVFCQ3hGwF+wNW9QklpjyxiRGjSaWaDTmNbaY2KOJJsYUC4q9i2KLhd57lTrUAQam3+8Pvvu8MzAgnQHmt1ZWlsOdmTvt3n3Pc87epH9q7969xMEbqB9HX7ZsGT755BOpnmQNEQgEMDMzQ11dHR48eNBs7lpNTQ2ioqKgq6uLLVu24P79++RvI0aMwM6dOzF58uQW9dM0RFlZGY6Ojhg3bhycnJwwYcIEGBkZtfpxWkJoaCimTZsGAPjoo48wb948GBsbQ0FBAW5ublL3TU9Pr8lJQBUVFZSXl8Pf3x9xcXHw8/PDrVu3yEG2oqICPj4+UicCnZ2dERwcDEtLS2RlZWHcuHEksUFTUxOVlZXw8/OT6BGjGTdunEQm55uwsLCAvr4+EhISYGVlheTkZKnb3b9/H2PHjm110zGTyUR8fHy7swqrqqpw4sQJnDhxosnl3eZoqlFfU1MT7u7upLp14MAB7Nq1q8lJTTMzM2zfvh3e3t6wsbHBqFGjmq3gdhSamppYunQpPvvsM4nIM6D+uPDrr79i06ZNqKurg5qaGr744gv88ccf5IIMqL9IYjKZUFBQwP79+yX63cRZu3YtycikYbFYsLCwAFBfZbO2tm73a6JNtQsKCuDl5dViexwOh0OsccrLy6GiokIsNxqmRfQkKIpCQkICqqqq4OXl1eLzkDTj2pZYbuTl5cHR0RE8Hq9XVx7bQveVhzqQll6piEQiJCQkIDU1FZ6enlBTU+v0eKOWQH9xWyIsRSIREhMTkZ6eDm9v7w4Nxm1q+VKaIAPqxQRQn32ZlZWF7du3Y9u2bRAIBBg1ahT+/PNPEhXTEkFGJwPU1dVBXV0dPj4+TW5bVlaGsLAwWFhYwMXFBZcuXUJ6ejo+/fRTqKur4+nTpxg3bhwRZPQ+m5mZITAwsNHjqaqqwsLCAm5ubpgwYQI2bdqEv//+G1u3boWFhUWnCbKysjLSUzV58mTs3bsXNjY2uHDhAoYOHSqx7fDhw7Fy5UrEx8dLVGAangh4PB64XC65/cWLFxLh7vr6+vjll3rnfyUlJYnfT3x8PIYOHYrLly/D2toaISEhOHPmDKlU6OnpYdeuXVJfS0sFmaamJg4cOIDU1FSyRCXtqpz2Kps1axaZImwpeXl5iI+Ph7Ozc7t/Izo6Ovjiiy+QmJiIkJAQBAYGNqpoMxgMKCoqkvdT/HfS1HUvm80mgmzRokX44IMPmrWfKSoqwkcffQRPT0+YmJgQQdZ/xMeYuLcM/uueAYqNK+0KCgowNzfHhAkTsHv3bqnDLUpKSnBwcJA4GaupqeGTTz5BWloa9u7d20iQ0a/7o48+wtOnTzFy5EhwOBx8//33jV5zXV0dmdSWNgBDXxhnZ2dLVIWrq6uJOfPAgQNbPVEuDbqHrLCwEN7e3q3yK1RTU0O/fv3g4eGBMWPGwMHBAQwGA8nJyQgNDUVUVBRev34ttUIqq7RVkAH13y3a71BVVRUqKipQUlIixx6hUAiBQAAejweBQEDOt7W1tVBTU+uxIrYz6RWVMvqDbw4ul4vo6GgIBAJ4enpCXV0dz58/h7W1tUzEZ9y/f18iRkga9FIdj8eDp6dnh3u8ZGVlgcViwd3dndwmXqYWP9EkJSXB29sbioqKcHFxkQgs/vjjj4khaGtYtWoV6TFaunQpjh49KnW7vLw8UiWkr6DFefLkidTmc3Nzc1y+fBmDBg2CjY0N6WtqCe+99x4OHz7c4u2bgqIolJaWorKyEqmpqdiwYQMyMzOhq6uLly9fwtLSEvPnz5cI/jY2NsZff/1FYp+MjY0llnTpCTjgf0Mj9+7dQ0hICBFQK1eulBBTFEXBx8cHSUlJsLOzQ21tbaMhhQ0bNmDv3r1EcOjr65NUhcuXL7e6Atm/f38EBgZi9erVMDU1xenTp7FixQoAwG+//YYFCxYAqK+WnjlzBlu3biU5muvXr8eWLVuavACjKApxcXG4ePEi3n77bRQUFMDd3b3Tlkbq6urw6tUrPHz4EMeOHWu03N1a1NXV8d1330FRUVFq0oO0apuysjIm78wGpWEuIQBvrNIExW/5crc0DA0NoaSkhNu3b7c435WiKAQHB2Pjxo0SVTIaVVVVhIWFYeLEiSgqKiK3MxgMLFu2TGKact68eRgxYgTu379PPN+CgoIkKud0pq6Xl1eLDV7pKfWSkhJ4eXl12DGUtnigq2gsFguamppkmVNWhwVoE+XKykp4e3u3SpC9iYbGteIFkJiYGMybN49Egsn5H72i0f9NsFgsREVFQU9PjwgJ4M1RS13JmyYwa2pqEBkZCS0tLXh6enZ4KDpQf9WTmZmJvXv34vjx41BXV5fwrRH/8Vy9ehVA/Q8vOjoaDAYDY8eOxfz58/HOO++0+rmrqqrIQfnTTz/Fjh07Gm0jfoXr6enZ6ISbmpqKjRs3SggaoL4ys3r1anz22WfQ0dEBUO+r1lTFRxq///47xo0bhzlz5rT2pRF27dqF33//vZH40dDQwIULF2BlZYWoqKhG+798+XKMGDGCRAQdO3ZM4u/inwv9HZoxY4bE1XpDAUpbTSQlJSE1NRW7d+9GdXU1du3aRX4T4u+PQCBAUVERioqKpA4YWFlZSV3SpNm9ezc+++wzidt+/fVXAPWCxNLSEs+ePcM///yDx48fE0sPOzs7HD9+vFHVkKawsBA3b97EoUOHiD2LhoYGPvnkk06d7FJXVyc2ISUlJRKxV+bm5nBzc4O1tTUcHBzAYrFI3xiDwYC7uztiY2Mlfu91dXVShyKA+kqdlZVVIx84Pp8vIcjox/dfFQK1yM1YvXo1bt26hZiYGCgpKYHJZBJhz+FwUFlZKVU8ASC9g2vXrsWePXtgb2//xpMng8HA3LlzMWbMGGzbtg2nT5+uDyJXUAQlEoLL5WLhwoU4cuQI5s6dS+43Z86cRoNCFy5cwIULF8i/HRwcEB4eToyhL126hHfffRcAEBwcjEmTJjW7b0D98SMpKQnl5eXw9vaGurr6G+/TUhgMBrS0tKClpYWBAweCz+cTGxXxYQF6qVMWluzEBVlrK2QtoTnj2t9++w3V1dUQCoWdci7ryfSKSplIJGpyRLqgoAAJCQkYNGgQrK2tJQ4skZGRMDQ0lFqW72oePXoEZ2dnqf1tJSUliImJQf/+/WFra9shVxbS3JqfPn2KuXPngsVi4YMPPiAeXA37AQQCAQYOHEgsGmbOnIkff/xRIui7tURGRmLkyJEwMTEhxqwN9zcuLg5sNhseHh4SV7h1dXX473//iyNHjjT6HigqKsLNzQ2rVq3CvHnzJO4zefJkhIeHN3ouLS0tKCgooKqqCj/88AM5WSoqKuLAgQMkuaC1rFu3Dj///DOA+hOtvr4+ZsyYgdWrV0NZWRk7duzAiRMniBCmqyMXL17E5MmTQVEUPv/88zdGLtGoq6ujX79+MDc3x7x58/D+++/j1atXJK5m5MiRZECDwWDgs88+w4gRI/DJJ59INPSPHTsWO3bsgEgkQkZGBs6dO9fIQLQpDAwMUF5ejoEDB+LVq1fQ1NREaWkpduzYQZZQpWFoaIg1a9ZgxYoVZCq6qqoKUVFR+Pfff1FWVobk5GQ8ffpUwtvMx8cH33zzTasjyxISEnDs2DHY2tpi+PDhsLOza5WoE4lEZHiDTiwQ5/Dhw/jll19I1FBTWFpagslkSj2eMRgMqKioSDTHTz8qanQ8qMyNwpOdnjAwMMC4ceOwYMECVFVVoX///rh58yYePHiA8vJyCRFNf05NQQ97rFq1qsUrC79eeIL77Ink3yI+FzdXSQohDQ0N/PLLL/jmm2+IED969CjS0tJw8OBBqReq4vuqrKyMZ8+eSe2DFYdeomOxWPDy8urSSXt6WICuookPCxgZGUFLS6vLq0UNBWpXvR8URWHfvn04cOAAbt++3WyLSl+l14oyuqry+vVruLm5SS1vx8TEQFtbu9GkT3fw9OlTDBkyRGI/KYpCTk4O0tLS4OTkJHWprrVwOBx89NFHMDIykujtqKqqwrBhw5Ceng43NzdcunQJJiYmjQ4WWVlZWLVqFfEj2rJlC7766qt2T6/ev38fs2bNgrOzcyM7AQ6Hg+joaCgpKcHNzU3iKvPRo0dYuXIlOdmJeyOJs337dqxdu1biti+++ALHjx9v5KOlrq6O/Px8lJaWory8HH5+fkQgMRgMnDt3jjTmt4bTp0/j5s2bGD9+PAwNDVFdXY2amhqcOnWKxA0B9Q3zdJM6UG8noKCggJUrV+KPP/4Ag8HA8uXL8dtvv0ksaYlbUhgZGeHOnTsYNGgQeb/Cw8MREBCA6dOnQ1lZGefPn4eCggLGjRtHhiTU1NSwb98+HD58GCkpKVBUVASfz8fIkSPh6emJAwcOSLymCXuYUNX6X79d7ovTiD29DEpKSvjpp58we/ZsMlSwcOFCVFdX4969eyRKR0lJqVG1eseOHZgxYwYx9qSd9p8/fy71JO3s7Axvb2/Mnj0bo0aNalM02pkzZyTc7RkMBpydneHu7g4/Pz8MHz4c1tbW7b6qj42NxV9//YW//vpL6oSjtrY21NTUWhTnZGdnB8ePQyBUtwQgtny5WgsU782Tr1paWsSaYPTo0cQfD6h//YMGDYKCggKys7PJ56WlpYVDhw6R6eCm4PP5WHxGGw3blssyX+Dfvf7N3pe+KAwKCoK9vT1+/PFHhIeH4/79++S3raSkhOXLl+PTTz99o18f3UtcXV3dKRWh1kIPC9D5nF09LNCdguzgwYPYu3cv7t2716Is075IrxRlfD4fMTExqKurg6enZ5NeKPHx8VBVVYWtrW1X7WqTNOxvow8kpaWl8PDw6BBTW5FIhHfffRfBwcFQVlZGfHw8BgwYAKFQiNmzZ+Pu3bswNDRESEgIBg8e3EiQ1dXVwcHBgZwwbG1tJXrJmoJ2wb969Sqqqqqgq6tLruIXL14MAGQ5gl6moKmurkZUVBQMDQ0lcjyFQiG++uorspSnra0NDw8PxMbGSs3w27Ztm4QJ54kTJ7Bq1SoA9RmB169fl9j+zp07GDFiBH766Sds3rwZY8eOhY2NDU6ePAl9fX2EhYW1ujJIh7E3haurK7744gusXr0alZWVYDAYOHDgAN5//338/vvvZPlvzZo1mD9/PtasWdNIwFpaWiI/Px9jx47F119/DTabDX19fRgbGyMsLAxLliyR2P7bb7/FunXrcPXqVXz77bdITk7G5MmTUVtbCx6Ph5ycnCanAUd/kwgdc0n7FIqi8PzARGz9dCZmzJiBuLg4RERE4Ntvv210/xEjRkAoFEJLSwuVlZUoLCyUyMiUhpWVFfz8/GBlZYXBgwfDyckJfD4fKioqcHNza7NoiomJwdWrVxEVFYXIyEipokhFRQUDBw6EoaEhGf4wNTWFlZUVnJyc4ODg0GJByGazcf36dfz3v/9FZmYmFFU0oaZnAXZJBkC1bPjovffeg4mJCUpUnFBmOgc6ShwsGRSP2bOno6amBuPGjYOKigqeP3/eSABqa2vj559/xs2bN/HPP/+Q25sy+u3Xrx9Zwpa2XEhRFJKTk8n0aGxsLEbvZjU6hgj4HNz6vPllQ/GkiqCgIEydOpX87d9//0VBQQH8/f1blLwiEokQFxeH2tpaeHp6drsga4hQKERFRQURafTyMi3SOnKJFfjf51RWVtblguzYsWP473//i9u3bzc7Vd/X6RWLueI/fLr3SlNT841mqrLaU0YPJQiFwg4ztaUoClu3bkVwcDBUVFRw+fJlsmx769Yt3L17FxoaGti1axdycnJQUlICExMTGBsbQ0dHBwwGAwkJCRInK7ox+0189NFHOHv2bKPb9fT0iCgLDg4GAImrp5KSEsTFxcHa2rqRx1ROTg7+/PNP8u/q6mpyFa2iogIejyfxnm7fvh0lJSX4+OOPYWNjgz179pD7+vj44Pr167CwsEBBQQEA4NSpUxIJAf7+/vjyyy8RGRmJqKgoLFmyBFevXm3VQXPkyJFgs9lITk4Gj8eDlpYW+Hw+tLW1MWnSJMyaNQujRo1CZWUlnJ2d8fvvv5PpRHrZk36P6AD4hqLMwMAA+fn5GDJkCPz9/VFXV0f80HR1deHr6ytxn9WrVwOoX4KeMmUKLl68iE8//bTR9NiQIUPAZrMl+uG0zRr7xzEYDPgs+xXAtTdaFzTnN0VPDNrZ2cHBwQEODg4YO3asxGO2xKW/pbi5uUnYj+Tn5yMyMhKRkZF48uQJIiIiwOPxGhkUi6OiooJx48Zh+fLlGD58eJMXUhRFoaCgAAoKCliw+APEGK+FgoIyqcbWVRYgZNObJ0YlY4bqxfafYrc0Nw1bXV1NerJoxo4di4cPH2LAgAG4dOkSbt26hdu3b+Pff/+V+NyXL18OHx8fVFRUgMfjke9Xw2OpkF8HJZX/tRlQFIWCiPMS22hoaGD9+vXYunUruY1eGl+7dq2EIAOAYcOGNfmaGiISiRAbGwsOhwMvL682VVA7G0VFRSLAxIcFiouLkZKSQoYF6GSB9nzH6SGH0tLSLhdkv/32G7Zv346bN2/KBdkb6BWVMoqiwOPxwGQyERsb2+Leq7S0NHC5XDg7O3fRnjZNREQEjI2Noa+vj4iICOjp6cHFxaXDStk//PADccH+5ZdfSMWEoijs3r0b27Ztw6JFi/Drr79CIBCgrKwMTCYTZWVlJJIkOTlZ4kB+7969Nx4kHz9+jClTpoDBYGD79u2wtbUFi8VCWVkZXFxcSK6gjY0NBAIBnj9/DhcXF7x+/Rrp6elwcnJqZKeRn58PHx8fcvUvbQmM5s6dOzhw4IBED9T7778PBoOB33//HUKhEIGBgbh8+TJ8fX0RFRVFlmpCQkIwfvx4AMDx48fx7rvvIjk5GePGjQOLxcL8+fPx+++/d1g/yIYNG3Do0CGYm5vj9u3bEssyzs7OpNfOwsICaWlpoCgKTk5OyMnJIRWyo0ePYubMmdDU1Gx0EuLz+bhx4wYZxKC9p4yNjcmySUFBAakcq6io4MCBA/Dz8yO3bdiwAUFBQSguLsa0Q1woKDU+0TFKXuLqN2+ORtq0aRMRe/r6+jAzM0O/fv1AURRMTU2brXq11qW/vYhEIuTm5iIjI4NU9crKylBYWIisrCzExcU1qkYZGRmhX79+GDZsGGpra5GdnQ02m4309HTSjzn1IAeKyo2rN6XpT/B836g37petrS2sra1RWlqKrKwsVFRUQEVFBQwG440xS035p61btw7ffvstamtrkZWVhejoaPzxxx94/vx5iy2EnJ2d8eTJc7x/XgN8IQMUgIrMl4g8Mq5JI2R9fX3yvmhpaaGgoKDNxz+hUIiYmBjw+Xx4enrKRGN9axEfFigtLQVFUUSgGRoatkpkik+ddvSQw5ue988//8RXX32Fq1evYsyYMV3yvD2ZXlEpo40As7Ky4OLi0uJG1I4IAu8oFBQUUFlZiZSUFNjY2MDGxqbDTjRXr14lguz777+XEGS1tbV48eIFgPoqC4PBgLKyMszMzGBmZgaRSISKigrk5+dLHMBNTU3h7e0t9fkoisKdO3cQHByMixcvAqivxHzxxRdSt79x4wYEAgGcnZ3h7OyM5ORkMJlMeHl5QVdXt9FjT506VeIEKBAImjzBREVF4cyZM7h9+zZ27tyJ6OhonDx5EocPH8Z3332HTZs24eHDhwCAKVOmYMqUKdi2bRsAkP+rqqrC37++D8be3h7nz5/H1KlTERQUhLFjx2Lp0qVNv/kthMfj4fjx4wDqm8LFBdnr16+xZs0afP755wDq++aA+pPq559/jrVr15KTWXJycpP7o6ysjJkzZ+Kzzz6DSCTCF198gaqqKqSkpIDL5cLQ0FDCz+yff/7BlClTyL+fPn2K3377DbW1tdDQ0MAE4yjcLx8q8T0VCYW4IUWQ5eTkNBqomT17tkSUTktpj0t/W1FQUMDAgQNx48YN/PLLL5g7dy5Wr15Nvp8lJSV4+vQprl+/jlu3bpHGbvHpO6mPK0XUAoChzfAW7VdaWhry8vJgamoKNzc3TJ8+HSkpKWAymUhMTERGRgYRUlpaWjh79iwKCwtJeDqDwYCqqqpEZfTo0aPYv39/m1YR6Cnc+Ph4VFaW4s93TciSnDS0tLSgpKQENpuNb7/9lnzHXV1d2yXI6JWGnirIAEgch8WHBXJycpCQkNDiYQG6v7o7BNnZs2fx5Zdf4vLly3JB1kJ6RaWMFhYuLi7E8qAl5OTkoLS0tNsbDimKwr///ouamhq4u7u3yGi1pWRnZ8Pb2xs1NTX4+OOPsX//fvKcIpGIVGeAenEk/sMRiUQ4cuQIgoKCEBcXh/T0dLi6uqKyshKenp7YunUrDAwMyDKnqqoqnj9/jm+++YZkVwLAhAkT8NdffzVp0rh48WLibzR16lRwOBx4eHiQg0dxcTFpTN25cydpiqdtMUJDQ0lv0pdffglVVVVs2bKF9Bk+evQI3t7eEAgEuH//PoKCgnDo0CFUVVXBzs6OCPMXL17A0tKShCfTMTA//PAD8dOi2bt3L7Zt2wYNDQ3cvXsXHh4e7fqc8vLyMGTIECgpKZF+MpFIhCtXruDVq1d49eoVEc/Xrl0jBqB1dXVwcnIifl729vZNZmI2Bb1sUlJSgry8PMyYMQN8Ph9+fn7YsWMHhg4dil9//RWbN29GbW0trKysMGjQIISGhkJN1xx+XzyBup45ytKe4NXhen+4hjFLSUlJmD9/vkTuobThizdBD0AMGTKkRT1FHc2UKVMkBkn69etHqvQNUVNTA4PBaFQZGjBgAOzs7ODm5oaUQXtAN+mLIxIJcONTZYnhjY5CX18f8+bNw7lz51rl1dcQBoOByZMnw8DAAOnp6YiIiJAQcgwGA7q6uqiqqpJaYRs8eDDxjhQKhQgNDSVWGW117hcIBIiOjgZFUfDw8Oi1dgviwwLl5eVQVlaWOixAC7Li4mJ4e3t3uLdlc1y8eBErVqxAUFCQxMWdnObpFaIMqP+StvaKOS8vDwUFBU16IHUFQqEQ8fHxYDKZMDMzg4uLS4c+/pkzZ/Dee+9BWVmZ/HhpQz8+nw97e3sUFxdj06ZNjaJPHjx4QNy0gfoGc9om4+OPPyZ9WiUlJSgrK8M///xDKmNqampYunQp5s6di2HDhjX72Xh5eSE5ORlr166Fo6MjBg0ahMzMTDx8+BDR0dGIi4t74+ucMWMGfvjhB+LeTlEUfv75ZyQlJRHRKQ1/f3/ijP706VN4eHggICAAz58/BwAsWbIE3377baMQdXo4IiQkBNra2li3bh1WrlzZ5kbi169fw97eHqqqqvjmm28QFRWFZ8+eNWqy9/f3x40bNySe58GDB5g3bx64XC7U1NSQkZHRrsEQ8cGY2bNng8vltsgCw9TUFPv370f//v3h6Ogo4SPHZDLx999/Sxij/vHHHxI2JW8iLy8PqampcHZ2fmOofWdRU1OD4OBgbN68uVEOKF318fT0xNy5cxEdHY3ffvsNQqEQgwcPxv79+2Fvby8xILL2siryqyQrQhRFIfXWDqRea1kmZnfRXNtAU5Vrcb755huS3xkVFYWJEyeitrYWo0aNwq1bt1q9P3w+H1FRUVBUVIS7u3ufcYuXNiygr68PIyMj1NTUkB6yrhRkV69exQcffIB//vkHM2fO7LLn7Q30mssIBQWFNx4EGqKkpNSty5ccDgdRUVFgMBgwNzeXOIjQ9gut5cMPP4ShoSFWrVoFc3NzCcGppKQk4dB/7do1FBcXQ0VFBR9++GGjxxI/6SQkJJCmcAAYNGgQNDU1oampiYEDB+KDDz4ggmzChAlYtmwZnJycpFqRCIVChIWF4fbt23jy5AnJPGxJCgCDwYCpqSlGjhwJJSUlWFtbY+rUqY0qVQwGg4RjN4ebmxsRZfT7PXHiRCLK9u7dK7XCp6ioiD///BNz5szBixcvsHXrVgQFBWH79u3w9/dvUcWWz+eTmBJtbW2sWrUKysrKuHPnjkQ1ZujQoXBxccF7770Hd3f3Rt+LcePG4cCBA/j444/B4XDg4+ODQ4cOSU01eBP0MigNm83G3bt333i/adOm4fx5ySbu58+f4/fff8eQIUOgqamJt99+G5s3byZJABUVFcjIyHjjUj1FUcjKykJOTg48PDy6NcBYS0sLS5YswZw5c/D69WskJibC1NSUJGwIhUL8/fff2L59OxHUCxYswP79+6V+J/YFcvHfuyqIL/rfb3+mYx2GOI3C9trxxHpGT08PSkpKqKqqIj2PHQl9DFq4cCFGjRqF3377DdeuXWv2mNrc8iZFUdDT0yMGodK4fPkyVq5ciYiICPznP/9BbW0tbGxsWuzDJw6fz0dkZCSUlZXh5ubWZwQZ0HhYoLa2FiUlJcjJyQGHw4GGhgby8/M7ZFigJdy8eRPvv/8+Tp8+LRdkbaDXVMp4PF6rRVlJSQlSUlJabTTZEbBYLERGRsLIyAhOTk5IT08Hj8cjQwfbt29HdHQ0vvrqK/j5vblpmsbc3BwVFRV48OABhg0bhoyMDDg5OUFdXR2lpaUSxqSbNm3CwYMHsWzZMqmZe+Hh4Rg9ejQMDQ2Rm5uLMWPGICwsDACQnp5OrvhDQkIwc+ZMKCoq4tSpU/Dw8EBOTg7Ky8tRU1ODyspKJCYmoqioCPn5+cjJyZEa0dOwx09NTQ1qampQVVWFp6cn5s+fj2nTphGRxOfzoaio2K6DzN9//42PPvoIQP0021tvvYVvv/2WTGfSS59NIRQK8ddff2HLli1ExKqpqZGTdUNqa2vx7NkzPH36FGfOnMG+ffskqpEcDgc//PADqqur4eHhgeHDh5Pl1Oaglz9pGAwGLly40CphRveAiPt1iWNiYgImk0mWdWmUlZWRl5fXogzBly9fYvbs2RI9gQ4ODvjhhx+k9pzQDcrFxcXw9PTsVJf+9kBRFO7fv4/NmzeTJVobGxv88MMPLXKbb4rmwrsbLhFLQzyDkIZuC2iq4V4cepK5IQoKChg9ejRMTEwwbNgwWFlZQVtbG2vWrEF8fDw++ugjuLu7S1wYGRsbN7IaEa+2qaqq4uXLl622KOLxeIiMjISqqirc3Nw6XXTIOuJh6+7u7uBwOCgpKSHDAoaGhjA2Nm71sEBLuH//Pt5++238+uuvWLRoUYc+dl+h11TK2kJ3WWIUFhYiPj4egwcPJo3K4oIkJSUF+/fvR21tbaOx9TdhbW2NiooK4nhNn8Q4HA5YLBa0tbXBYDDAYDDIkpT4yVwc2ruLzsLctGkTZs+eDaC+IhcQEAArKyscPHgQQP3E1WeffSbVFLMl0K/f3Nwcq1atwsqVK6Vux+PxcObMGezduxffffcd2ae2sHDhQnz55ZdgsVhYtWoV/P398erVK/L3K1euNCvKFBUVkZOTI/GaORwOiZyilwIrKysRGxuLFy9eSJwM//rrLyLKaE+2OXPmwN7evlUnF3EBOHv2bFy6dAnLly/H48ePydDA3r17YWFhgYEDB5L8Vz6fj/j4eJw9exbnzp2T6s/Vr18/qKqqEoNecUHGYDBw8eJFvHjxAlpaWjA2NoaxsTH5njXE19cXmZmZOHjwIK5cuYKkpCQkJSVh+vTp+OmnnyQqtiKRCPHx8aiursbQoUO7rEG5LRw+fBgbNmwAAOjq6uKrr77Cxx9/3G7bgdWrV8Pe3h5nzpxBVFSURCLAmwQZAKlVKvr7p6enh9mzZyM1NRXR0dGoq6tr1PvVUJDRPW4ikQhGRkY4cuSIxOfi7++P+Ph4KCgoSHj/qaioICkpCf369ZMYKqCPv4sWLcLKlSvbJMgiIiKgoaEBFxcXuSCjKGRkZKCgoADe3t7Q1NSErq4uTE1NQVEUqqqqSBUtISEBOjo6MDY27pBkgUePHuGdd97B0aNHsXDhwg58VX2LXlMp4/P5LR7XpqmqqkJYWBixPehs6CuYnJycRikD2dnZqKiogKmpKQICApCZmYmxY8fixo0brTrQBAQE4OnTp/jrr78wb948UBQFExMTVFdXk2EIGkNDQ3A4HGKUKg6Hw8HgwYNRUVGBM2fOYObMmfj666/JoEBzKCkpQVtbG1wuF3V1daSCqaSkhEGDBoHH46GwsLCRF5Y4ZmZm2LhxI1RVVaGoqEj+y8jIwKlTp5CbmwsAbe4/EWfz5s2kV87GxgZ5eXnkZGRjY4PY2NhmD1YffvihhAHnm/pp+vXrh1GjRmH8+PGYPXs2VFVVUVZWhtjYWAwYMKBRHFhLePXqFcaOHQsGg4HS0lJMnToVL1++xKJFi3DixAlUVFSQfjtxpPUFubq6kvBoS0tLCAQCMkjQkNGjR+PmzZvg8/kkRqa0tBRKSkpEoBkYGDT5Ha6srMRXX32Fv//+GwwGA2FhYXBwcIBAIEBMTAwEAgE8PDxk0mNKnKKiIvj4+ODtt9/GV199JTUurb0UFBQgKioKGzdufGNcU0PGjRsHNTU13Llzp80tGxMmTABQb+BKV7r19fWxcuVKrFmzBioqKvjuu++wa9cu6OnpNTJxXrNmDQ4fPkwGcOjKq6amJhITE2FkZITWwOVyERERAS0tLTg7O/d5QQYAGRkZyMvLg5eX1xsr1w2HBZSUlEiAemuTBZ49e4a5c+fixx9/JJZDctpGnxZlbDYbz549w8SJE9+8cTsRCASIi4tDdXU1PD09G/1gXr9+jeLiYuzatQvXr1/HwIEDERoa2mJ7DxoHBwdkZWXh2rVrCAgIQHp6Ojw9PcHn8xEXF0cipUpLS4lFAX1gFOf27duYO3cuLCwsSM+XsbExqZK89dZb5DHT09OhrKyMUaNGwdHREZmZmbh58yYRJpqamvD19YWioiJevHiB6urq1r+BDTAxMcGqVavw4YcfNpnY0FIyMjLg6uoqcZuZmRmYTCZEIhGePXtGqoXSoJ3OBQIBLCwsUFFRgQsXLiAtLY2cgDQ0NIihq6Ojo8RBq6CgAElJSXBwcGhzlFZaWhrZx0uXLkFfX58sBz59+hQWFhb49ttvERERITEBCdRXTLy8vDBx4kR88MEHiIqKQkBAAIB6X7Jdu3aRE2hDNm7cSOxWaGgbFXoIhM/nkyUTIyOjRgKLoijMnj0b9+7dw/r167FhwwZERkZCRUUFrq6uPWaCjrYK6Qxyc3ORnp5OlqN8fX1RXl6OgIAAcDgcPHv27I3tG/7+/pg7dy4yMjJw4cKFFkU5NYf4xceYMWNgYmKCGzduSG1NaLg9UH8MCQ8PR2ZmptRBo+bgcDiIiIiArq4unJyc5CIArRNkDRH/zTYcFjA2Nm62Sv3y5UsEBgbi+++/xyeffCL/LNpJnxZlHA4HoaGhmDhxYqdeZdHO48rKynB3d5d61V9QUICHDx+Sq4zw8PA3huxKw9jYmCyD6enpwc3NjYy9i4uyhIQEDB06FKqqqigtLW30+o8cOYKvvvoKc+bMIc75Y8eOJUt7a9euhY6ODvbt24eqqiqp/S0BAQFYvHgxbt68iXPnzpHbG1Zn1NXVsWfPHigrK6OkpAS5ubkoLCxEdXU1aYQXCATg8/kwNjZGQEAAFi5c2KHLWdOnT8fDhw/JMrKZmRn8/f1x6dIlTJ48mQwxdCTiDeyurq7trq689dZbuHHjBjZs2IAtW7aQ1/TTTz+RvjmgXnCHhYXBz8+PCEHxz5++35IlS4j5I9242zBjNiUlRWoFTvw11tTUgMlkoqSkBDU1NdDT0yNVNFrE0NmTAwYMwOHDh2FsbNxul/7egPh3xNPTk/ii3bx5E/PnzwdQP8Xq5eWF3bt3k9+qlpYWxo0bh8LCQtIHKo03eTXSQsrIyAjjxo2DkZERRowYAX9/f7x48QJr164lKRjS7kfTMEkCAD7//HPY2Nhg9erVcHJykmgbaI66ujpERETAwMAADg4OchEAIDMzE7m5ufD29m61IGsIPSxAV74rKyuhoaEBIyMj6OvrQ0dHh0x/R0REYObMmdi2bRs+//xz+WfRAfSMS9AW0JYvA30FLhQKO+3gX1FRgaioKJiamqK0tBTvv/8+Dh482GiCTEFBgcSizJo1q02CDKivlL169QpPnjzBhAkTJHyIlixZgu+++04irobL5aKmpqbRZBj9o6OFVkZGBrGmYDAYjSYla2troaioCA8PD4waNQrvvvsuVFVVMWnSJOTl5YHBYGDcuHHQ19fHhQsXyGseP348tmzZIuEVJxQKUV5eTk7kAMhJ3NDQsFMmq1auXImHDx8SYV9UVISkpCQoKCjg9u3buHr1aodOEolEIiQnJ5Nx9Y5oYLe3t8eNGzdIf9u4ceOgoaHRSDRNnjy5yQGAqKgoPHz4EAoKCvj444/JdrNnz4aurq5EiD2AN+43g8GAtrY2tLW1MWjQINJ0XFJSgrS0NGhoaMDY2BgjRoyAjo4OcnJy8O233+Ly5ctyQUZRSEtLQ2FhYaPvyNSpU7F27Vrs27cP//nPf3DlyhUcO3YMs2fPxnvvvQcWi4V79+5h5syZzYqy5gSZuEdaaWkpma49evRoi/adxtDQEGPGjGkkyuiqH4BGJtFNUVtbi4iICBgZGcHe3l4uAgBkZWUhNze3TRUyaTAYDDJZP2DAAPD5fJSXl5PvwLfffgs/Pz/4+Pjg559/xtdffy0XZB1Inz7qSZtM6kjy8vIQHh6OwYMH4/Tp05g0aRKCgoIkMhvF94UO4p42bVqbno+iKLIUe+nSJVhZWUkEQUdFRWH69OlYunQpTp06RW4PDQ1t9Fh0RS0tLQ1CoRDDhw8nDcIURUFZWZk0MS9YsAAvX75EQUEBHj16hO+++w6DBw/Ge++9h7y8PKipqWHPnj3w8vIigkxLSwtRUVG4fPmyhCDjcrlIS0vD1q1bkZ+fj9GjR8Pd3R3KyspITU1FaGgooqOjkZ+f36HWABMnTsTQoUPJyURHRwfJycmkqrl8+XL8+uuvHfJctMEli8XC0KFDO2yikF7qfvLkCXHsp9MHmiMsLIzEB9GO6vR9nJ2doa+vj82bNzc6qQL136nWoKamBisrK3h6emLMmDEYNGgQ+cy//PJLqKurIzw8HBs3bmzV4/Y2KIpCUlISiouL4ePjI/U7snXrVmK2PHfuXISHh2PSpEn4999/4e3tjbq6OokKdWsRr9zq6OhIXUbW09ODra0tvLy8YGlpKfXEXFZWht27dze6/dChQ/jmm28A1OfCvgk2m43w8HCYmJjIBdn/Q1dRvby8Om0yWVlZGaampnBycsLHH3+M8+fPw8LCAqdOnUJ1dTWCg4OxY8cOYtorp330muVL2hW6tdy9exfDhw9vd1+SOPQYf35+Ptzd3VFVVQUHBwcA9bmLn332Gfk3TUFBAQYPHgyRSITU1FT079+/1c8pFAqRnp5O3Ot//fVXvP322/j++++xY8eOZu+/YMECbN68GWZmZggKCsKLFy9w+vRpWFpawsDAgFTJFBUVoaGhQfrCzMzMEBMT0+gKbd26dfj555+hrq6O7du3IykpifgP6ejo4I8//kBaWhoePXqE4uJiaGhooLS0FElJSeSH7ejoiJcvX5KKibjzPJPJRHV1NXR1dUmiQHv7ecLCwkgf1oEDB3Dy5EniYUZDLw22FS6Xi6ioKCgrK8PV1bVDI2DoZn+gfhrwvffee+N9OBxOm5ZN6UbuefPm4Y8//mj1/cVhMpmIi4uDlZUVwsPDyQTmsWPHMHbsWJIW0Vegp05ramrg6enZ7ARnXV0d5syZg8ePH0NLSwu//fYbpk2bhuDgYCxevBhAfXuAkpISFBUVSfO9lpYW9PT0SC/km3B2dsaLFy9QW1sLPp9PrGoaCiOhUIh///1XohI7fPhwPHv2rMnHZjAYePnyZbOrAzU1NYiIiICFhQUGDx4sF2SoHw7Lzs7uVEEmjeTkZEyZMgX/+c9/8PHHH+PWrVu4ceMG7t27B11dXXz77bd4//33u2x/eht9XpSFhITA29u7xeXzN8Hn8xEbG4va2lp4enpCU1MTISEhmDZtGhQUFJocY6eXGw0MDJCfn9+qgw4tyEQiERQUFPDdd99h9+7dYDAY+OuvvxAYGEimogDAx8cHAoFAapVjwIAByMnJafFzGxsb4+nTp+jXrx8EAgFKSkrw/Plz0o80ePBg5OTkSPQiGRgYEMsOaaiqqmLEiBHYtGlTsx5t4kth5eXl0NTUhLGxMUxMTJq0ZHgT7u7uSEtLw6lTpzB9+nRs3boVx44dk/huTZgwAZcvX271Y9fU1CAqKgr6+vpwdHTs8OW5hhmD9vb2CA8Pb/Z9YDKZGD16NMrKypps0HZ2dsaCBQugpqaG/fv3Iz8/H2ZmZigqKsLChQtx8uTJNu+zNJf+t99+G1euXIGZmRmOHDlCpnnpz1ZTU7PXnpSFQiFiY2PB5XLh6enZoqnTqqoqLFq0CKGhoWAwGPjxxx/xwQcfYO3atXj77bfh4+MDoP77sX37dvzxxx/EJHjo0KGYPn06NDQ0yKSzjo4OhEIhVq5ciaqqKnz88cdYvHhxs8Mu4vB4PCxZsgTXrl2Dvb09Nm3aRPJ2aUaMGIF///0XIpEIy5cvx9q1a8mFVcPPtrq6GhEREbCysurQTOCeTHZ2NrKysuDl5dWqaMH2kpaWhilTpmDx4sXYuXOnxDGMy+XiyZMn0NPTa9ZGSE7z9BpRJhQK2+Q5FhoaCldX1yYDc1sDm81GZGQk1NXV4ebmRqogubm5sLOzA4PBQG1trdSDys2bNzFnzhyYm5uTJv2W0FCQMRgMCIVCvPfee7h48SK8vb3x6NEjAPW9IF9++SUAwNvbG0eOHEFYWBjWrVvXyJ7C1tYWaWlpErdZWVmhrq4OpaWlErerqqpCU1MTFRUVTZavGzb+0hOZo0aNItM96urqGDp0KExNTVt94OXz+SgrKwOTySSWDHQFTV9fv8UCaNmyZQgKCsLOnTvJUl5mZiZ++OEHXLhwAWw2GwwGA4GBgViwYAF8fX1bFPlDh2jTuZGddWKJj4/H9u3bcevWLQwfPhy3b99u0f0iIyNJSkJUVBQMDAygrKzcqJL2008/YfPmzVBTUwOHw8GhQ4ewfPnyVu+neAO7u7u7RI9lXl4eJk2ahCFDhuDixYvg8/kScV6qqqqkx1BPT6/X9J6J5zbSS/Ythc/n48svvyRL7L/88gveeecdqdvW1tbihx9+wMGDB0lLws8//0yEU11dHUaMGIHk5GTY2tri6dOnre5Vqq2txZMnT8Dn85GSkkKWKRsSEBCA/fv3o6KiAhUVFY0+25qaGkRGRhKrGDn1mc2ZmZldLsiysrIwefJkYn3RW353skafF2VPnz7FkCFDpMYBtYaysjJER0fD0tISQ4YMkTjpVldXw9TUlEQb0X4/4pSUlMDV1RUVFRUYMGAATp8+DV9f3yafj6IoEiouLshoioqKYGtrC5FIJDF1efXqVaxYsQKVlZVksrKyshIDBw4k1Sx/f39UVlYiKSmpXe/JoEGDMHjwYDx+/Jgc/J2cnEjweGctSYlEIpSXl5NlTtro0sTEBIaGhs1aLBw9ehSPHj3CokWLEBgYKPG36upqzJkzRyJsHaivPErry6MpKipCQkIChgwZ0uykYkdCZ94NHDiwRdt/+eWXOHr0KObOnYvTp083ud2xY8ckQsT/85//NGr+fxP08j6TyYSHh4fUpZfq6mpwOJxGv0t6CIQWafRnSw+BdORycFfC4/HIsnZbY4IoisJXX32Fo0ePQkVFBQ8ePGgUPyZOYWEhNm3ahPPnz4PBYBDz3kWLFuHq1aswMjLCixcvJLI62wKdDNKQ999/H3v37iXHAaFQiLKyMmLLQF9s0j1ksu5V1xXk5uYiIyNDYhK3q5538uTJmDp1Kg4fPiwXZJ1Inxdlz58/h7W1dav9wMTJzc1FSkoKHBwcmjzpzp8/H9euXQMAxMbGws7OTuLvXC4XJ0+exMGDB5GdnQ1lZWWcO3dOapO2uBgDQBz6GzJz5kyEhITg448/lpiWDA8Px7hx4yAUCnHz5k2MHj0aSUlJzZac58+fj7KyMkRERIDFYsHGxgZTp05FXV0d/vnnn0aRLdOmTcPnn3+O5cuXIz8/HwDwySef4K233kJ+fj4yMzNRVlaG4uJilJeXo6ysDLW1tdDR0cHNmzc7TLDRLtb0JGdtbS0MDAxIFa0tzxMdHY2zZ8/i2rVryM7OxowZM3D27Fmpz00fRF1cXNot/DuTLVu24Mcff8RHH31EjHSlcfLkSXz++eewtLQky+xv6gcSR9yln04VaCviDuUlJSVgs9nQ19cnlRZZTgAQh8PhIDIyEpqamu12pacoCgsXLsT169dhZmaGp0+fNiuq6IEQusL2zjvv4O+//4aioiKuXr0qNfqqNZSWlsLd3b1Rpur48eNx9erVJu9XXl6O6Oho6OnpgcfjoaamBrq6usTrrjcvYTdFdwmygoICTJo0CePGjcPx48flgqyT6TWiTCQSNfJQagmvXr2ChYVFmyoYtK1BUVHRG4OSKysrMW7cOCQmJsLMzAyPHz+WaOYXCAS4f/8+fHx8MHfuXLx48QJeXl6NGmTp5Ur6Y2vuB/LgwQPMmDEDampqyMnJkViCWLNmDX755Re4ubnh6dOnUFBQICKORklJCQMGDCDmqu+99x7WrFkDALh16xZGjRoFoN5v6E19RdKc46VB52x2FuKDAlVVVSRmhO5Vai1VVVVgsViN8inFMxvd3d279CDaFnbv3o3t27dL+NJJgxZlY8eORU1NDcLCwlo8VNDZLv11dXVEoFVUVJAeQ2NjY+jo6MjkSZz23NLX14eDg0OHnPAqKysREBCApKQkeHh44MqVK80Oc1AUhfXr10vk327duhVfffVVu/ajtrYWgwYNIrY8U6ZMgYqKCq5cuYLFixfj2LFjUu9HCzI7OztyXBbPbywvLyfLnLR3Vm8XCq9fvyZG4F15LCkqKsKUKVPg5+eH3377rU8FvXcXfV6URUZGwtDQkLjbtxQej4fo6Gjw+fwWX/GXlJRgypQpiI+Px9ChQ3Hu3DlyFUtRFO7cuQN3d3dMmDABaWlpUFVVRVBQELG5kNY/1hwURcHNzQ0ZGRk4ceKEREBsSUkJXFxcUF1djWXLlmHfvn2kAgbUi72YmBjo6OjA0dERbDabBAq7ubmRJTwmkwlHR0fU1dVh9erVGDlyJP744w/cvHlTqggzMTEh/SEmJiYwNTWFoaEhDA0NoaGhAYqiuiz2isvlSgwKqKmpkQqarq5um0/iQqGQTM95eHh0mst7R5GRkYGAgAAwmcxGRrMNiY+Px7Vr19C/f3/s3r0bGRkZ+OeffzBr1qxmn4MOje4ql366x5A+kSsoKEjEPsnCyYXulzI1NSU9px1FRkYGxowZg/Lycri7uyMoKKjZtIh79+5h3rx55DdrZGQEMzMzODs7g8fjITc3Fz4+Pli2bFmLHPRzc3OJeS0AfPrpp9i9ezfmz5+PW7duYf369VL7zMrKyhATE4MhQ4bA0tJS6mPTy5y0ualIJIKhoSGMjIykJkb0dGhB5uHh0eJe446gpKQEU6dOhZubG06fPt1jkjV6On1elMXExEBbW5uIkZZAH0y1tLRafYLJyMjAsGHDwGKxoKenhxUrVmDixIkwMjLCb7/9hr///rtRnE12djZMTU3JAVP8gHjgwAG8++67TebGrV+/HocPH260hAkAp0+fxqeffgqRSIQBAwbAzc2NLCmIHzTPnj2LDz74gFTnDAwM4O7uDh0dHTx9+hSlpaVwcXHB8+fPERkZicmTJ6O2thZGRkZkKOCvv/7C2LFju/Sg0hoEAgE5iZeUlJCTuImJSbPZjQ2hxTqDwYCbm5tMnSCys7NhZWVFBAmbzcaFCxfw5Zdfgs1mY9CgQQgLC2vRki5FUdDR0YFIJEJ0dHSzQdJ0NYiOxOnqqkbD2Ccej0f60LrrJF5VVYXIyMhOnShMSkrC5MmTUVpaCi0tLWzfvh3vv/++1OPVhg0bcOjQIRKT1lwurYGBAdzc3ODu7g53d3c4OzvD3NwcaWlpyMzMxMWLFyXCyOkl8aqqKgwYMAA8Hg+vXr1qtORdUlKCuLg4ODg4tLiPjV7CpgUavcxJf749fZmTnk729PTs0mNnWVkZpk2bBjs7O5w5c6bH9mr2RHqNKKMoqk1movHx8VBVVW32pCJOSUkJYmJi0L9/f9ja2rbpB5+UlITly5c3abxpZ2eHr7/+GkuXLgVQH4szYsQI0kP2zTff4OTJkxAKhaitrYWNjQ1OnDghdTDg0KFD2LBhA2xtbREVFdVof2/fvo3PPvuMXNFCUQmjNoRDt58LjDQo7JzOhbZavaP73bt3pe6vhYUFLl26hIiICHz11VfEXykyMpLsQ1sm9LoLkUiEyspK0ofG5/MlTuJNHaBqa2sRFRVFApJloRpDw+fz4ebmBg6HA11dXeLQTePu7o4zZ860yh/P1NQUNTU1WLFiBX744Qep21RXVyMyMhJmZmYdXg1qC3TsEy3QaK87uorWkX6FTUFP4trY2LS6Qt9a0tPT8eGHH5IIo2HDhuHQoUOwt7eX2E4kEuHUqVN49913wefzkZiYiO3bt+Pff/9tVqC1BBaLBSUlJRw+fBjr16+HnZ0dIiMjJb4LtFeds7MzTE1N2/xcdMg2Xf1WVVUlv92etsxJC7I3tcZ0NJWVlZg+fTqsrKwQFBQkUxeWfYE+L8roCcOGZq7SHj8nJwdpaWlwcnJqc3A0jVAoxIULF3D+/HnExMSAxWJBJBJhxYoV2LJlC1RVVbFkyRKcP38eo0ePxuXLl6GsrIyrV6/i7bffBvA/p+W8vDwoKipiy5YtWLt2rcSBp7CwEM7OzuBwOPj333/h5ubWaF9qamoQFBSELd/twPCtDT3SKBydW4fAKaMQERGBH3/8EXZ2drh//z4UFBTg5+cHT09PfP7557h58yaAenfu169fIzs7G0uWLMHPP//crveqO6EoCtXV1aQPjW4mp5c5aWNPFouFqKgomJuby4T4aEhsbCymTJlCzENpBg4ciEWLFmHVqlWtNqAUt1i5fv06Ma6lKS8vR0xMDAYOHIiBAwfK3HsCSF/CpgWarq5uh5/E6WqQeL9UZyMSiXDs2DFs374d1dXVUFZWxo8//tiiCyWRSIQHDx4gODgY58+fJwM9CgoKGDZsGDgcDpKSksBms2FgYIDq6mqJFYtdu3Zh5cqVyM3NhZ+fH1gsVqMl8uLiYsTHx8PFxaVF9jItRXxSt7S0FAKBAIaGht1aIW0p+fn5SElJ6XJBVlVVhZkzZ8LQ0BCXL1/uU6bNskKfF2VpaWngcrlwdnZuchuRSISEhASUlpZ2aqPl48eP4eTkBENDQxKzMmLECNTW1mLbtm348ssvERQUhGXLlgGor3I5OzuTOB2gPjT8wIEDGDRoEHncUaPqBdXZs2cxY8aMRs9bW1uLCRMmQGvSL9Af6NPo70bqAtze2B+FhYW4ePEiVFRUMHDgQFhbWyM9PR0LFixASkoKVFRUsGXLFvTv3x9Lly6FsbExYmJiOvz9oigKCQkJzX5mnUVdXR2poFVWVkJLSwuamppgMpkYNGhQiy0ouoOqqiqEhYVBRUUFOjo6sLCwgJGRUbvEEm22+/bbb0vEUDGZTMTHxzfbGyRriFsy0Jmr4nYb7e2poa1RnJyc2jXt3VbS09OxatUqYt/yzTff4Msvv2yx8GSxWLh9+zZOnTqFx48fIzIyEkOGDEFubi7++usv7Nu3T6Kq9sEHH+DHH3+EQCDAhAkTEBERAW9vbzx48IBUkQsLC5GUlNTp08niF1elpaWorq4mQz6ytsxZUFCA5ORkuLu7d4h/ZkupqalBYGAgNDQ0cO3atR4zvdzb6Dm13DfQ1h+UoqJis0kAXC4Xr169QnV1Nfz9/Tt18oXeF9ryYvDgwdi/fz8AYM+ePcjKysKcOXOIGDlx4gR0dXXx+++/4+jRo1BXV8fDhw/h4+OD3bt3k/QA+uozOztb6vMGBQUhOjoaarrS+ziquRQKCwuhoqICkUgER0dH9OvXD19//TW8vb2RkpICS0tLPH78GF988QXu3bsHoH68vqPfrydPnmDatGnw8/NrFIHUFairq2PAgAHw9vbGqFGjoKWlhaKiIlAUhby8PKSkpDRrotud6OjoYPz48Rg5ciTc3NxgbGzc7hMRHX7u4uJCbsvLy0N8fDycnZ17jCAD6n9/JiYmcHJyIpmrqqqqyMjIQGhoKCIjI/H69es2Lefl5+cjMTERrq6u3SLIAGDw4MG4fv061q9fDwDYvn07Fi5cCC6X26L76+rqYsGCBTh//jwOHz6Mn3/+GT4+PnBwcMCOHTsk3pePPvoIBw4cQE1NDRYsWICIiAgYGBjg9OnTRJDl5+cjKSmJfBc7EwaDAR0dHQwaNAi+vr4YMWIELCwswGKx8PLlSzx9+hTJyckoKysjbSLdQXcJMjabjXnz5kFZWRlXrlyRC7JupNdUygC0+OAiTk5ODkpLSyVCsWnoZlw9PT24uLh0eo/Q8+fPMWDAAJiYmJAJSwCYPHkynj59Cjs7O9y8eRPZ2dmYOHEiRCKRREN+eno61qxZgwcPHgCoF2PLli0Di8XC8ePHYWdnhwcPHjQqhy9YsADXr1/H8vWHUDLwUwCSy5e2NZfx45dz4Orqilu3bgEAFi1ahMePHwOoD/M+cuQILCwsJPIj//zzT8yZM6dD3pvs7GysXr2aCD5lZWXs37+fVA27GoqikJ6eTvJNtbW1UV5eTqpoAMhVuKGhoUz1l3UUPB6PfJcSEhIwYMCAJl36ezq0lUpJSQlYLBa0tLTIIIiWllaz4pZ2YJeV94SiKJw8eRLr168Hh8OBu7s79uzZg2HDhjX7OoRCIQoKChAaGoqPP/64ye22bNmC9evXIycnB/PmzUNSUhLU1dVx7tw5MllN90t1tfiQBr3MSfeiddcyJ101dHNza1MebVupq6vDW2+9BS6Xi1u3bnVpjqacxvQqUcbj8VpdocjLy0NBQQGGDh0qcXtxcTFiY2NhY2PTZXlrr169gpGREfr16ydheZGXl4fx48cjLy8PRkZGOHz4MKqqqvDRRx9BUVERd+/eJRmRFEUhKCgIW7dulfD7omOOLC0tcePGDYnBhokTJ+LZs2ewsbGBhssSDJy0BQwwQAHIuLUdVZG/Ij8/Hzt37sTUqVOxcOFCJCUlQVtbGydPnsS0adMAAFFRURgxYgSAer+xmJiYdp+EKIrC5cuX8emnn4LFYkFZWRlLly7FF1980elN0k1BL2ezWCx4eHg0ag6nKAqVlZWkD43L5cLQ0BAmJiYy38vSGsQ/74qKCmRmZjbr0t9b4PF45AReVlYGZWVlIsDFm8kpikJmZiZev34NDw8PmfOqe/jwId59913SZ+jg4IC33noL7777bqOe2ZqaGowfPx7x8fFSH0tPTw/W1tb49NNPsWjRIsTGxmLGjBkoLS2Fubk5goKCSLoAbYIqKyJVHHqZk/58xZc5jYyM3ijA20p3CTIul4tFixahoqICd+/elbnvaF+kz4uyoqIiZGVlwd/fH8D/DqSZmZlwdXVt1yRQS6Ejk+jnpf2yTExMiOllVlYWFixYgISEBAD1odjl5eXEePLy5csSjvx8Ph+XLl3CjRs38ODBg0YB4Hp6enB1dcX8+fPx999/48WLFySM3MrKCq9fv5bY3tjYGB999BF++OEHcLlcmJub4+LFixKDA+KGtKdOnWqUWtBSampqcOPGDdy7dw9Pnz4l++Ln54fjx49j8ODBbXrcjoDP5yMmJgZCoZAsbzUHRVFgs9mkglZdXQ09PT1yEpd1D7PmSElJgaenJwBg8+bNGDVqVLtd+nsa4pFedJWF9suihbmXl1ersyO7iuLiYvz3v//FP//8Q5YfNTU1sW/fPixevJhs9+TJE8ydOxdsNptcXJiamsLb2xtTp06Ft7c3EaO5ubkYM2YMMU4W90ijg7S72gS1rXC5XAkBrqKiImFa2xEV8KKiIiQmJna5IOPxeFi8eDEKCgpw7969bq9Yyqmnz4uykpISpKSkYMSIEcT0s6KiAp6enl0S9trQoV8kEkkEa9N9LiYmJtDQ0MB3332HgwcPkr4HdXV11NXVQVFREUOGDIGvry+WLFkCLy8vcsAQCAR4/PgxTpw4gevXrzfZQ6enp4fKykoMGjQIGRkZEn9zdHREYmIiAEBbWxtqampQU1PDnDlzMGzYMNTU1GDVqlWoqanBW2+9RaZZWSwW2Gw2uFwu/Pz8iM2HtPchNjYWv//+O86cOYOamhryNzU1NXz88cfYunVrt1aZ6urqEBUVBXV1dbi6urbpgEw7kzOZTAnXeRMTE2hra8tMs3FLoCgKrq6uyMzMxIYNG7B+/fpeUwVsC3SVhclkIi8vD3w+Hzo6OjAzM5N5Ac5isXD58mUcPXoU8fHxOH/+PKmA0xQWFqKgoEBqqwdNRUUFAgICkJycDEdHR9y7d4/4a9FL2111bO1ohEIh8bsrLS0Fn8+XMK1ty6RicXExEhIS4Orq2qTXZGfA5/Px3nvvISMjAyEhIV363HKap1eJMj6f3+omzfLycsTGxsLPzw+RkZFQUFCAh4dHl4wC0w39QqFQqkM/fRVOV1koioKxsTFYLBYePHiAEydOEKPThq/bwMAAU6dOxerVqyXsPp48eYJPP/0UKioqyMrKarJpmV7ubIiysnKbTHoBwNvbGxs3boSJiQn09fXxxx9/QCgUori4GC9fvkR6ejrZdvDgwZg1axZGjRoFf3//LvGPao7q6mpERUXByMgI9vb2HWKVwOfzyVV4aWlpk8tgsgqPx8OTJ0/w77//4tNPP5VfaaP+NxsXFwc2mw1HR0cy8VdeXg4NDQ0Juw1ZFOBCoRB37tzB5MmTW/394/F4mDt3Lh48eABLS0s8fPgQlpaWEsu4Xl5evWJpW9zvrrS0FCwWS2KasyXLnLQg6+pcXIFAgI8++ghxcXF4+PBhh9qQyGk/fV6UVVVV4eXLl1BSUoKRkVGXOY63JTJJ3NCUx+OBwWBg+/btxBgS+N8UqvjH6uPjg9raWuTn5zfyqWov0gRhW1FRUcG0adPwwQcfYPTo0TJz0iorK0NsbGyn+m01FOAikQhGRkYwMTHpEDuGjqa7XfplEaFQiJiYGPD5/EbZntISI8TtNnr6IEhYWBg++eQTJCYmQkNDAyEhIXB1dSUDMXSFTVaXcdsLvcxZWlpK+gzFTWsbfr60N5urq2uXCjKhUIhPPvkEr169QmhoaIuTE+R0HX1elGVlZSElJQVDhgzpMoNLkUhE/muJIGsIfZXGZDJRWFiIx48fg8fjQU9PD7GxsTh37lyn7DcdPTRlyhQEBgbC09MThoaGSEtLQ6ypwKMAAHnASURBVFhYGOLj42FlZYXAwEDExMQgPj4eubm50NHRAZ/PR1ZWFvLy8lBaWgomkym14mZkZISJEydi2rRpGD16dLc3AhcUFCApKQmOjo5ddgCjo2NogVZXVwcDAwNyFd7dho6y5tIvC/D5fERHRwMAPDw8mhXRdGIELdC4XC4MDAxIr1Z3f76tQSgU4qeffsK3335LLiR+/fVXTJw4ERRFITU1FcXFxfDy8ur2andXQS9z0lVwHo8nMc3JYrEQFxfX5YJMJBLh888/x+PHj/Hw4UNYWVm16v5HjhzB3r17UVRUBDc3Nxw6dKjRgJw4QUFB2LJlC7Kzs2Fra4vdu3dj6tSp5O8URWHr1q349ddfUVlZieHDh+Pnn3+WGELbsWMHbty4gejoaKioqEgtKuTm5mLFihV4+PAhtLS0sHTpUuzcuVPmLmRbSq8SZQKBoFnPMXHoK7js7GwIhUJMnDix06/26YZ+eh8ZDEazJzQul9uiA3RtbS2YTCaYTCYeP36MrVu3AgCGDx+OzMxMVFRUkGVKFRUVaGlpQVlZGSwWi9xuZ2eHUaNGYdu2bVi/fj1u3bqFt956C76+vkhOTsbu3bthYGCAuLi4DslgoygKHA4H0dHRSE1Nxa1bt/Dw4UOJXjIFBQU4Ozujf//+8PPzg5WVFUxMTKCrqwszM7NOHcKgl1xyc3Ph5ubWrUtztB0Dk8lEVVUVdHR0SKJAV5/oeoJLf1dDh62rqqq2uteQHgShBRr9+cqiqak4FRUVuHr1Kk6cOEHi1ObPn48ffvgBRkZGoCgKKSkpZNBBlvvpOhP6ApoWaLSvn5mZGQYMGNBlfaQikQjr1q3DnTt38PDhw1abXJ87dw5LlizBsWPH4Ovri/379yMoKAgpKSlSlz///fdfjBo1Cjt37sT06dPxzz//YPfu3YiMjCQ+m7t378bOnTvxxx9/wNraGlu2bEFcXBwSExNJUsrWrVuhp6eHvLw8nDx5spEooweuzMzMsHfvXhQWFmLJkiX48MMP8f3337fpvepu+qQoEwgEiIuLQ3V1NVxdXfHixQuMHz++U0NXGzb0v0mQ3b59G1988QV+/PFHTJ48ucXPw2az4eDggLKyMmzbtg0jR46EsbEx9u/fD2trayxZsgS3bt3Cf//7X+Tl5QGo90HbvHkzXFxcpF5dCAQCIs7Wrl2L7du3t/LVtwwej4eXL1/i2rVrCAkJQXJycpPb7t27F5988kmn7IdIJEJSUhLKyspkzt5BPBaorKyM9CmJT+p2FnQPTE9y6e9sOBwOIiMjSd5pey/sGk77qaqqEoGmp6fXbcvE9CDOP//8g0ePHiE5OZlUunV0dLBz504sXbqU9KImJSWhvLwcXl5efWoatzno3OR+/fqBy+WirKwMSkpKpIJmYGDQKcvYIpEIGzduxOXLlxEaGiqR9tJSfH194ePjg8OHD5PHtLKywsqVK7Fhw4ZG2y9YsABsNlsimN7Pzw/u7u44duwYKIqChYUF1q5di3Xr1gGoHzYxNTXFqVOnsHDhQonHO3XqFFavXt1IlN26dQvTp09HQUEBuUg/duwY1q9fj5KSkh45eNQz63vtoK6uDpGRkVBWVoafnx8RYkKhsNNEWWv7x4D6yKWcnBzs3bu3VaJMU1MT+/fvh7m5OTw9PUmfUmBgIFRVVREUFER+BGZmZli4cCHefvttODo6NrlfSkpK2L59O9566y0cOXIE//nPfzrlpKyiooKRI0di5MiRAOr92WJiYpCSkoLo6GgUFRWByWSCxWJ12slJIBAgNjYWXC4XQ4cOJVdssoKqqir69euHfv36kT4lJpNJhlRogWZgYNCh7xFt9uns7CxvDP5/amtrERERAUNDQzg4OHSIIFZVVYWlpSUsLS0lshvj4uLI8iB9Eu/s5ZmamhoynXf27Flix0Pj6OiI+fPnY/HixWRpn45AY7FY8Pb2lrnfT3dBf4YuLi5EPIhEIjLNmZycTJY56c+4I5axRSIRtm3bhosXL7ZZkPF4PERERGDjxo3kNgUFBQQEBOD58+dS7/P8+XN88cUXErdNmjQJly9fBlDfNlRUVISAgADyd11dXfj6+uL58+eNRFlTPH/+XOI9pZ9nxYoVSEhIIN54PYleJcredFCsqKhAVFQUTE1N4eDgQE5aCgoKLV72bC1tEWQA8Omnn+LQoUN48eIFnj59Skw6W4K4i765uTnMzc1Jrt/nn38OoD4jk87gKygoQG1tLTQ1NUlfS0OmTp0KT09PREZG4s8//5R6ddTR0OKj4Wh+Z8HlchEVFQVlZWV4e3t3auW0I1BSUoKpqSlMTU0lDvBJSUng8/kSJ/C2vhaKooiVQVeHI8sydF+dubk5bG1tO6VCqaioSKpkdJ9hSUkJsrKyEB8fD319ffL3jqxGlZeXY9GiRXj69KnE7aqqqpg+fTrmzJkDFxeXRid42lS5uroa3t7ePao3rjMpLS1FbGwsnJ2dJcSDgoICDA0NYWhoiCFDhpBlbDpqiU6NMDY2btMyJ0VR2LlzJ/788088fPiwzb6RpaWlEAqFjdpFTE1Nm1zNKCoqkrp9UVER+Tt9W1PbtISmnkf8OXoavUqUNUdeXh6SkpIwZMgQ9O/fX+JvioqKEAgEHf6c7Wnot7S0xPLly3HixAns3LkTN27caPN+CIVCJCQkIDQ0FCkpKQDq3bwfPnwodXsHBweMGTMG06ZNg4uLC8LCwsDhcMiB/+DBg7h79y7s7OxgZ2cHPz8/DB06VOLKXSQS4cqVK7h69Spyc3MhEAhgb2+PqVOnYtasWW1+LZ1FTU0NoqKioK+vD0dHxx43TdjwAE/7ZWVnZyMhIQH6+vqkD62l1Qu6L4jJZMLHx6fXTs61FhaLhaioKPTv3x/W1tZd0hPEYDCgq6sLXV1dDB48GLW1taTPMDU1td0ncHEWL17cSJCNGjUKf/75Z5N+VrQVSG1tLby9vXvkslFnQAsyJyenZntgGQwGtLS0oKWlBWtra4nUiJycHOIOYGxs3KJlToqi8MMPP+D48eN48OABHB0dO/qlyekker0oo08s+fn5ZFqwIW8KJW/Lc4o39LdlwhKor5adOHECoaGhYLPZbWrqFolE8PPzI8avDfH39weLxUJFRQXYbDaqqqqQlJSEpKQk/Pzzz1LvQ4f4vnz5ktymr69PSsUcDgcFBQWNAtDDw8ORnp4uc6KsoqIC0dHRsLKywqBBg2Sysbo10OHLOjo6Eifw4uJipKSkQFtbmyxzNtVILhKJEB8fj+rqavj4+Mj7gv6f8vJyREdHY/DgwY0u7roSDQ0NDBgwAAMGDJDwu8vNzZWosLXFdd7Pzw+hoaESt8XHxzcp5kUiEWJiYsDlcuHl5SUXZP8PbaXj4ODQ6hB6FRUVWFhYwMLCQqIKnpKSQqZ16Sp4w8+FoigcPHgQBw4cwL179+Dq6tqu12FkZARFRUUUFxdL3F5cXNzk6zIzM2t2e/r/xcXFElPtdApESzEzM5OwhKIfQ/w5ehq9WpTRkTh1dXXw8/NrUtQoKSl1mCijDWFpa443NfQ3x+DBg2FkZITS0lLs2bMH27ZtI4/F4/EQFxcHgUAAPp+P2tpaGBgYQElJCU5OTlBWVkZqaio2b97cSJC5uLhAWVkZX375JWbOnCmx77m5ubh16xYePHiAe/fugcfjAaiPWTI1NSXZd++++y4GDhyIhIQEPHr0COXl5SQInUZDQwMrVqyAp6cnGAwGXrx4AScnpza9F51FUVERaV7v169fd+9OpyB+AqevwJlMJrKysqCqqkoqaHp6emAwGBAIBIiJiYFAIICPj4/8JPv/0H1B9vb2jbIhuxNlZWXSpiBtGbu14dpbtmzBli1bUF1djaSkJISEhJAqTkPEvdm8vLxkfsm/qygrK0NMTAwcHBzabaUjXgUXn9YtLCxEcnIy1NXVcfXqVUybNg3Dhg3Dr7/+ij179uD27dvNpi+0FBUVFXh5eSEkJASBgYEA6oV4SEgIPvvsM6n38ff3R0hICFavXk1uu3fvHokztLa2hpmZGUJCQogIoz1DV6xY0eJ98/f3x44dO8BkMkmv671796Cjo9Njq4O9avpSJBKRiSA2m43IyEioq6vDzc2t2YPF8+fPyZekPYj3jzEYjA5ZAjt79izef/99APXVKCcnJ/B4PCQkJIDNZku9T79+/TBw4ECJJYiJEyfiyZMnWLNmDb7++usWPTeLxcK5c+ewZs0aKCkpwcXFBVFRUQAAd3d3DBgwAMeOHYOGhgaioqKQnJwMRUVFqKurQ1dXF+7u7jLr8k5RFHJycpCZmdnljtqyAt1nSE9zAvVB8iwWi/xueqrXT0dTWFiIxMTERn1Bsoy46zydu6qrqytht9EehEIhoqOjIRQK4enpKf+u/D90NbUrxDuPx0Nqaio2btyI58+fQ01NDVwuF9988w1WrlzZYVYk586dw9KlS3H8+HEMHToU+/fvx/nz55GcnAxTU1MsWbIElpaW2LlzJ4B6S4zRo0dj165dmDZtGs6ePYvvv/++kSXGrl27JCwxYmNjJSwxcnNzUV5ejqtXr2Lv3r148uQJgPqChZaWFrHEsLCwwJ49e1BUVITFixfjgw8+kFtiyAK0KCsrK0N0dDQsLS0xZMiQN1aqXr16BQsLi3ZVStra0N+Sx123bh1+++03UrWiMTQ0hI6ODhQUFKCmpoaqqiqJIHEGgwEXFxf4+/tjxYoVGDx4cJv2a9y4cRJLleLY29vjwYMHPSJcmIZe0i4uLoaHh0ePzOHraEQiEYqLi5GcnEyW38UHBfpytYyePO3qwOiOhsPhkGXO8vJyqKmpkWXs1sY+CQQCREVFgcFgwN3dXS7I/p+uFGTiUBSF33//HYcOHYKzszOio6NRUFCA8ePHY8aMGZg2bVq7VwIOHz5MzGPd3d1x8OBB+Pr6AgDGjBmDgQMH4tSpU2T7oKAgbN68mZjH7tmzR6p57C+//ILKykqMGDECR48elRhIWLZsGf74449G+/Lw4UOMGTMGAJCTk4MVK1YgNDQUmpqaWLp0KXbt2tVjv5O9TpRlZGQgJSUFDg4OLf4SRkZGwtDQEAMGDGjT83aWIBOHro6lpaVBRUUFdnZ2UjMYmUwmDh48CAaDgcmTJ4PD4bR7aS41NRWTJk0Ck8mU+veVK1fivffeg4mJCVkCk1WEQiHJJvT09JT3Sv0/4i79tra2Eoa1NTU10NPTIyfwvvSeZWdnIysrCx4eHh1imiwriMc+lZaWAgCpoNFtEE3B5/MRFRUFRUVFuLu79/iIqI6Cnu7vah8/iqJw9uxZrF69GpcvX8b48eNBURSSk5Nx/fp1XLt2DUpKSo3aS+TIJr1KlJWXl+Ply5etHt2PiYmBtrY2bGxsWvV8dEVBJBKBoqh29Y91JHRaQV5eHlxdXTvk6j4+Pp5cFWlpaUk477u7u+P48eMoLS0Fg8EgB3dDQ0OZmmLk8XiIjo4mV/fy/pd63uTSz+FwiECrqKgg1ikdMeknq9C/IXpAqDdXU+lcXXqZk8PhNNlIzufzic+jm5ubXJD9P90lyADgwoUL+OSTTxAUFIQpU6ZI3UYgEPTYylFfo1eJMroJsrUn2/j4eFJ9as1zdVRDf0dC219UVVXBw8Ojw2J4Nm3ahAMHDgAA1q5di0uXLiEzM5P83d3dHVu2bIGNjQ1EIhFKSkqIVxbtfdadB4Xa2lpERkZCR0cHTk5O8pPJ/9Nal37xSb/S0lIoKyuTClp3Os53JOJWIH0ps5FGPPaJxWKRaV09PT2kpKRAXV0drq6uveKz7ggqKysRGRkJOzu7Lh8Wunr1Kt5//32cOXNGYmhLTs+l14myhn1XLSEpKQlAvT9XS59HPDJJVg5OXC4XMTExAOpFUkf2Aa1duxbHjh0DUL/UVVRUhIULFyIiIqLRtv369YONjQ1xJre0tISOjg6srKxgZWUFBQUF1NTUID4+Hjk5OaRnh6IoaGtrw93dHV9++WWHiTjaV8rc3FweoC0G/b63ddCBDl6mg9NFIpFElbQnXpmLRCIkJiaisrJSHhEEkGnd4uJilJaWQkFBARYWFjAxMYG+vr7MHPu6i8rKSkRFRcHW1rbLBdnNmzexdOlSnD59GnPnzu3S55bTefQqUQbUC5PWkpaWBi6XS6ZCmkNckMlKdQz4n/mpnp4eHB0dO7wSRE9yiedAzps3D7du3YKTk1OjCBZp0O8XXV1sjq+//hqbNm1q1z4D9T128fHx3e4rJUuIu/S7u7t3iEs/RVFgsVhkmVN8CayjImM6G7rfsK6uDp6enj1in7sCLpeLiIgIaGlpwczMDKWlpcTlXdxuo6+1A9CCbPDgwbCysurS57537x7eeecdnDhxosWRRHJ6BnJRBiAzMxPV1dVwc3NrdruuaOhvC6WlpYiLi0P//v1hY2PTJftVVVWF/v37g8/nIywsDC9fvsTatWvB5XLRr18/vP322+DxeEhPT8ejR4/AZrMlxJi6ujpsbGzI0pebmxv09PSQmpqK+/fv4/fff2+3p9nr16+Rlpb2RjftvoT40pynp2enufSz2WxSQauqqoKOjg7pQ5PF5UDam00oFMLDw6PPCYym4HA4iIiIgK6uLpycnMixRTz2qaSkBGw2u9Nin2QRFouFyMhIDBo0qMsv9kJDQ/HWW2/h6NGjWLx4scych+R0DL1OlPF4PLT2JeXk5KC0tLRJoz3xhn5ZE2SvX79GamoqHB0d221S2BquXr2KRYsWwdbWFtHR0QCA9PR0TJ48GYWFhRLbHjx4EO+//z7y8vKgoKAAExMTKCoqgsFgSJiZlpWVQU1NDfr6+mTJsy3vs3iTtru7e6+ammsP4i79XTl5yuVySQWtvLwcGhoaRIy39TPuSMSnCeXebP+jrq4OERERMDAweGPgel1dHRFo9DAILdBk4TPuSLpTkD19+hRz587F/v37sXz58l71vsqpR370QfMxSw0b+mVFkFEUhdTUVBQWFsLT07PLg6Lz8vIAQGJidfDgwQgJCcHWrVsRFBREbre3twcAqT0X4nEi9Jg+k8lEZGQkFBUVYWJi0qomclp4VFVVwcfHRyarMt1Bd7r0q6qqknD5hp8xLdJpK4au7lHicrmIjIyEmpoaXF1d5QMg/09tbS0iIiJgbGzcIq9HdXV19O/fn1TPabsN+jMWt9voye9xVVUVIiMjYWNj0+WC7OXLl5g/fz52794tF2S9GHmlDPVRO1lZWSQCgkZWG/oFAgHi4+PBZrPh4eHRYa7NrSEqKgojRozA4MGDER4e3mi5Jy0tDQkJCbC2tn7jsrA0RCIRysvLyRIYRVESTeTSDux0rBa9BNWXDU/F4XK5iIqKgoqKClxdXWWmEiQeCcRkMiEQCMi0rqGhYacvIdbV1UlM5MrK77u7YbPZiIiIgKmpabsHY0QikYTdBpfLJX1oxsbGPeo3WlVVhYiICNjY2LTZ07KtREREYObMmdi2bRs+//xzuSDrxfQ6Ucbn81vUSC4OHfQ6YsQIcpus9o9xOBxER0dDSUnpjfFRnU1oaChGjhzZ6Ve+dBN5cXExSkpKwOPxYGhoSKw2lJWVUVdXh6ioKDKu35OvxjsS2gqE7gmSVeFBURSqq6uJCGez2RKDAk2FYbcVOobNyMgI9vb2MvP77m5qamoQEREBCwuLNieANIV4bqN4r6F47JOsfg7V1dWIiIggXn5dSUxMDKZNm4aNGzdi3bp1MvseyekY5KIM9eaZsbGxJLZBVgVZVVUVoqOjYWhoCAcHB5k9wXYm9BQok8kEk8kEm82Gjo4OampqYGJiAkdHxz75vkhD3KW/p1mB1NbWkgoa7ZUlPijQntdCvy+dITx6MrTwsLKy6pKBIbrXkI59UlVVJQJNljzv6PdlwIABsLa27tLnTkhIwJQpU7BmzRps2rRJ/l3tA8hFGerFTlhYGMaPH0/6x2RNkNHWDtbW1lJd1/sq+fn5SEpKgqqqKrhcLnR1dUkfWm+fAGuON7n09yR4PB45eZeVlUFVVZUItNbGetE2BgMHDuzyE6wsQ/dKdYfwAOrtSMRjn2TF8647BVlycjKmTJmC//znP/j222979G9YTsvpdaJMIBA02bTfFGw2G8+ePSOiDJAdh36KopCbm4uMjAy5tUMD8vPzkZycTCZPuVwuWf4qLy8ncUAmJibQ0tKSic+zK2itS39PQvzkXVJSAgBkkvNNTeRlZWWIiYmBra1tl/tKyTK0UO2OXilpiHvelZSUoLa2tlOXspuipqYG4eHhxGqoK0lLS8OUKVOwePFi7Ny5U2aqhnI6H7koQ33D76NHjzBixAioqqrKzA9AJBIRTyl3d3fo6up29y7JBBRFITMzE7m5uXBzc4OBgUGjbeg4ICaTidLSUlJdMTExga6ubq8VaO116e9JSGsiNzIyImam4k3kTCYTcXFxXW4dI+tUVFQgOjq6W+wdWgq9lF1SUoLKykpoaWkRgdZZ2at0b12/fv0waNCgDn/85sjKysLkyZMxb9487Nu3T2bOR3K6hj4vyiiKgkAgQFRUFCoqKqCrqwtTU1OYmJh02RWZNPh8PuLi4sDlcuHu7t6nl+LEEYlESEpKQnl5OTw8PFpkfkpXV+gqGm3D0JuiYsSFqoeHR5/zZqN7Dek+tJqaGujp6cHExAQikQgZGRlwcXGBiYlJd++qzFBeXo7o6OhuyWxsKzweT2KZU0lJScJuoyN+y90pyHJzczFp0iRMnz4dhw4d6hXHJjmto9eJMqFQCIFA0KJtGzb0042nxcXFqKyshLa2NhFoXWk7QU8S0t5JsmJh0N0IBALExsaCy+XCw8OjTaKZtmGgBZpQKCQHdSMjox45tdlVLv09CdrM9PXr16itrYW6ujrMzc373FJ2U9BLufb29rCwsOju3WkT4pYqJSUl4PP5ErFPbbHbYLPZCA8Ph6WlJQYNGtSl35OCggJMnDgRAQEBOHbsmFyQ9VH6rCh7U0M/j8cjE350fxIt0DrzpMdisRAdHQ0TExMMGTJE/sP8f2grEGVl5Q5zXaejYujPmcPhEKsNY2PjHhG1010u/bIORVHIzs5GdnY2XFxcwOfzSWqEsrIy6UOTpSm/rqKkpARxcXFwcHDoNUu54lPZJSUlpFJKX3C15KKaFmTdMZVbVFSEyZMnY9iwYTh58mSPvDiU0zH0OVFGRybRS5wtaein+5OKi4tRVlYGdXV1svzVkT0NRUVFSExMJAG3ff1qnoYOW9fX1+80ywvaQ4kWaDU1NdDX1ycCrTuXsptC3KVfbpb7P+iYrYKCAnh6ekJbW5v8TSgUory8nFRXKIqS6EPr7SdDurfO2dm5Vw8NcTgcCbsNOtrL2NhYak8pbZhrbm7e5YKMyWRi6tSpcHd3x+nTp1t9wXnkyBHs3bsXRUVFcHNzw6FDhzB06NAmtw8KCsKWLVuQnZ0NW1tb7N69G1OnTiV/pygKW7duxa+//orKykoMHz4cP//8M2xtbck25eXlWLlyJa5duwYFBQXMnTsXBw4ckChY3LlzB1u3bkVCQgLU1NQwatQo7Nu3r8t93noavU6UiUQi8Pl8qX9r6NDflglLOiamuLgYpaWlUFFRaXcDOX1Vn5WV1ScatFsDbe3QlWHrQP3yFy3QWCyWzAVqy6pLf3dDURSSkpJQVlYGT0/PZj8r8Sk/ulJqYGBAPufeJnKLioqQkJDQ53rr6GM23YcGQMJug8vlIjw8HGZmZrC1te1SQVZWVoZp06bBzs4OZ86caXV1/ty5c1iyZAmOHTsGX19f7N+/H0FBQUhJSZH6Gf/7778YNWoUdu7cienTp+Off/7B7t27ERkZCWdnZwDA7t27sXPnTvzxxx+wtrbGli1bEBcXh8TERHJxOmXKFBQWFuL48ePg8/l477334OPjg3/++QdA/bCCg4MDvvjiC7z//vtgsVhYs2YN8QiU0zR9RpTRGZZCobDD/McaNpCLZzXq6+u36DnoxvWysjJ4eHhIXNX3deiTiL29fbdaO9BL2bRPloaGRqdUSltKT3Hp72pEIhESEhJQVVXVpqVculJKu83r6uqSZc7uiDLrSAoLC5GUlARXV1cYGRl19+50GyKRSEKIc7lcAICenh6cnJy6tCJeUVGBGTNmwMrKCkFBQW26CPD19YWPjw8OHz4MoP71WVlZYeXKldiwYUOj7RcsWAA2m43r16+T2/z8/ODu7o5jx46BoihYWFhg7dq1WLduHYD6lhpTU1OcOnUKCxcuRFJSEhwdHREWFgZvb28AwO3btzF16lTk5eXBwsICFy5cwKJFi8Dlcsnx6dq1a5g1axa4XG6PaA3pLvrE5XVnOfSLizDxrMbY2FgAIH9raiqIzmoUCAQYOnSoTC6RdQcURSEnJweZmZlwc3Pr9pOIioqKRKA2bbVBZ36Kh6Z3tkDryS79nYlQKCRDIG0NXNfU1IS1tTWsra3B4XDI55yenk6EuLGxMXR0dHrU+56fn4+UlBS4ubnB0NCwu3enW1FQUIC+vj709fXRr18/hIWFQUNDA0KhEE+fPoW2tjYR4p0Z+1RVVYXZs2fDzMwM58+fb9P3lcfjISIiAhs3biS3KSgoICAgAM+fP5d6n+fPn+OLL76QuG3SpEm4fPkygPoKV1FREQICAsjfdXV14evri+fPn2PhwoV4/vw59PT0iCADgICAACgoKODly5eYPXs2vLy8oKCggN9//x3Lli1DTU0N/vzzTwQEBMgF2Rvo9aKsqxz6FRQUYGRkBCMjIzg4OJAJv8TERDLhRwctKyoqgs1mIzo6GpqamvDw8Oj1vSwthaIoJCcng8lkwtvbGzo6Ot29SxIoKSnBzMwMZmZmpD+JyWQiJiYGAMiJ29DQsMMrWL3Jpb8jEQgEiI6OhkgkgpeXV4cc9NXU1BoJ8ZKSEkRGRkJRUZH8nmXdUoX2rXN3d5fq59dXqaurI6HrQ4YMAYPBkEiOyMrK6rTYp5qaGsyZMwc6Ojq4ePEiVFVV2/Q4paWlEAqFjXoDTU1NkZycLPU+RUVFUrcvKioif6dva26bhkujSkpKMDAwINtYW1vj7t27eOutt/Cf//wHQqEQ/v7+uHnzZptea1+i14ky+kTVsKG/KyOTGAwGDAwMYGBggCFDhoDFYoHJZCI1NRU8Hg86OjqoqqqChYUFOSDIqa92xMXFoba2FkOHDpX5SUL65GxsbCxhZJqcnAw+nw8jIyMSmt7enq/e7NLfHng8HqKioqCkpAQvL69OubgRF+LilioJCQkQCoVkYrcjPueOhE4C8fT07HO+dc1RV1eH8PBwGBsbSxx/VVRUYGlpCUtLS4mBkLi4OIhEIjIQYmho2Gbhz2azMW/ePKioqODKlSsyf4xrK0VFRfjwww+xdOlSLFq0CNXV1fjmm28wb9483Lt3T37OawbZOYJ0IB3R0N9RMBgM6OnpQU9PD7a2tsjMzERmZiZUVFSQn58PDofToywYOgsej4fo6GgwGAz4+Pj0uPdCQUGBCHE7OztUV1eDyWQiMzMTCQkJ7Wog70su/a2By+UiMjISGhoacHFx6ZKKlYKCAgwNDWFoaAh7e3tUVVWhpKQEmZmZiI+P75Y4IGnQg0Oenp7yJBAxOBwOIiIiYGRk1OwFsfgFF22dQ1fQ4uPjoa+vT/7eUmFVV1eHhQsXQiQS4caNG+0eGKKnhYuLiyVuLy4uhpmZmdT7mJmZNbs9/f/i4mIJu5Ti4mK4u7uTbZhMpsRjCAQClJeXk/sfOXIEurq62LNnD9nmr7/+gpWVFV6+fAk/P782vOK+Qa8TZfR0jZqaGhgMhswsLVAUhYyMDLx+/Rqenp4wNDQkvjo5OTlITEwkJ24TE5NeN/nVHGw2G1FRUdDR0YGTk1OPX8plMBjQ0dGBjo4OBg8eTBrI8/LykJSURJzm33RAF3fpl1c7JKGXn/T09DrNJuVNMBgM6OrqQldXF4MHD0ZtbS2YTCaKioqQkpICbW1tiYndrrowpL8zXl5eMrf8351wOByEh4fDwMAA9vb2Lf48Gn7OtDFxSUkJUlNToampSQRaU/2GXC4X77zzDthsNu7cudMhA10qKirw8vJCSEgIAgMDAdS364SEhOCzzz6Teh9/f3+EhIRg9erV5LZ79+7B398fQP2yo5mZGUJCQogIq6qqwsuXL7FixQryGJWVlYiIiICXlxcA4MGDBxCJRPD19QVQP4zU8DdJH9fpfGk50ul105fffPMNfvrpJ0ycOBGBgYGYNGlStzucC4VCJCQkgMViNRkNRB/Qi4uLUV1dDT09PZiamnb7FXdnU1lZiejoaFhYWHT5OHp3wOFwyIRfRUUFtLS0yOcs/r2ge+tKSkrkLv0NqKmpQWRkJDFYlsXvjHh/UllZGdTU1EgfWmdlr9Ii/vXr1/Dy8pJPcoshLsgcHBw67P3n8/kSdhsKCgowNjZGXV0d7O3toampCR6Ph8WLF6OgoAD379+Hvr5+hzw3UG+JsXTpUhw/fhxDhw7F/v37cf78eSQnJ8PU1BRLliyBpaUldu7cCaDeEmP06NHYtWsXpk2bhrNnz+L7779vZImxa9cuCUuM2NjYRpYYxcXFOHbsGLHE8Pb2JpYYDx48QEBAALZt20aWLzdt2oTk5GQkJSX12mXbjqDXiTKRSISIiAhcuHABly5dQl5eHgICAhAYGIgpU6Z0eSmfXpYDADc3txY1ddIn7uLiYuKRRacJ9KYvM5PJRHx8PAYPHiyzYcidCY/HIxN+9Imbrqzk5OSgpqZG7tLfgKqqKkRGRpJcQlkUZA0Rt84pLS0Fg8GQyGvsiMqwuGGul5eXXMSLQS9Z0lXVzvrOiMc+rVy5EhEREfD19YWCggKYTCYePXrUKZPkhw8fJuax7u7uOHjwIKlYjRkzBgMHDsSpU6fI9kFBQdi8eTMxj92zZ49U89hffvkFlZWVGDFiBI4ePQo7OzuyTXl5OT777DMJ89iDBw9KfO/Onj2LPXv2IDU1FRoaGvD398fu3bthb2/f4e9Bb6LXiTJxRCIRYmNjcfHiRQQHByM9PR3jx4/HrFmzMG3atBZ7ibWVmpoaREdHt2tZTjyPU7yyQo9s91Ryc3ORnp4OZ2fnPmVk2RTipsR0v4a5uTnMzc37ZBSQNCoqKhAdHQ1ra+se6wouPhDCZDIl8hrb2ldKURRSU1NRXFwMLy+vHn1c6GhoY9jOFmQNEYlEePnyJalCVVdXw8/PDzNnzsTMmTNltsIrp/vp1aJMHNrp+8KFCwgODkZiYiJGjx6NWbNmYcaMGTAyMurQH0lZWRliY2NhZWXVYVf0fD6fCDTaxFQ8j7Mn/MgpikJaWhoKCgrg7u4u75MSg3bpV1ZWhpWVFbFhoChKwoG8p/fctYXS0lLExsbCzs4O/fr16+7d6RCk5TW2toGcXuYuLS2Fl5dXjze57Ui4XC4iIiLIRXFXHh+FQiFWrFiBsLAwhIaGgqIoXL9+HVevXsX9+/fRv39/XLp0CU5OTl22T3J6Bn1GlIlDl/ppgRYdHY3hw4dj1qxZmDlzJszMzNr1A87Ly0NKSgocHBxgYWHRgXv+PwQCAbnaLi0thaqqKhFosmpuSffWVVVVwcPDQ35FL0ZTLv10FBAd+cTj8SQsGHralGpbKC4uRnx8PJycnJqcKusNiDeQ01Vxug9N2kUXfaFZXl4OLy8v+TK3GN0pyEQiET7//HM8fvwYDx8+hJWVlcTf2Ww27t27hwkTJsiPgXIa0SdFmTh07uTFixdx6dIlvHz5Er6+vpg1axZmzZqFfv36tfgHLV4FcnV17TKzRqFQSHqTSktLoaSk1KUu8y2Bz+cTg095eLYkLXXpF6+sMJlMsNlsCauNtppQyjK0G31fswPh8/kS/YZ0cgRtZMpgMMjwkJeXV68eBmotPB4P4eHh0NbWhrOzc5cLsnXr1uHOnTt4+PBhj11ml9N99HlRJg5FUcjLy0NwcDCCg4Px7NkzeHp6IjAwELNmzWrWRZ02PmWz2XB3d++2KyCRSCSRx8lgMCTyOLujN6murg5RUVHET6ovLr81Be3Sb21tjQEDBrTqBEJP7DKZTJLVSH/WvaFqkpOTg4yMjD7vRi9uZFpSUgKRSAQlJSVQFAVvb2/5kqUYdPSQpqYmnJ2du/R4JxKJsHHjRly5cgUPHz7EoEGDuuy55fQe5KKsCSiKQlFRES5duoTg4GA8evQIzs7ORKCJ2zdkZ2fj5MmTmDlzJtzc3GSmCiTuPs5kMklvEh331BUHrKqqKkRFRcHExKRV3kB9gY506edyuUSIl5eXQ1NTkwi0ntJvSCNu7eDh4SE3PxVDKBQiKioKNTU1UFJSApfLbZcxcW+iuwXZ1q1bcebMGYSGhkpMKsqR0xrkoqwFUBSFsrIyXLlyBRcuXMCDBw9gZ2eHWbNmwc7ODmvXroWvry/OnDkjs1UgiqJQWVlJBJpAIJCIAeqM/aabs21sbFpdBertvH79GmlpaZ2yLCe+9EX3G9ICrbM8sjoKepKwqKhIbu3QAJFIhJiYGHC5XHh6ekJZWRlsNpv0llZXV0NXV5dcePWlClp3CjKKorBjxw6cPHkSDx8+hKOjY5c9t5zeh1yUtRJa3Fy9ehVHjx4lrsajRo3CnDlzuizupT3QsSG0FxqXyyUCzdjYuEPy+/Lz85GcnAxHR0eJuI6+jrhLv4eHR6dPn4p7ZJWUlEBBQaHbl7ObgqIoJCYmksb1viQq3oRQKERMTAz4fD4RZA3hcDhkibO8vBwaGhrkNy2rwz8dAZ/PR0REBNTV1bv8+EtRFPbu3YvDhw/jwYMHcHV17bLnltM7kYuyNnLo0CFs3LgRhw8fhoqKCi5evIjbt2/DzMwMM2fOxOzZs+Hp6SlTJz1piDePFxcXo7a2FoaGhsRlvrXTfeKiw83NrU/3AjWku136xc0tmUwmhEIhsV/orGppa/YtPj6eGObKG9f/h1AolBiSaclFE+00Tw8K0FmOsijG20N3C7KDBw9i7969uHfvHokckiOnPchFWSsRCARYs2YNzp8/jytXrkgEq9bU1ODWrVsIDg7GjRs3oK+vj5kzZyIwMBBDhw6V2aVNceicxuLiYuKbRAu0N033iUQiMqLfVJxUX0UkEiEuLk5mXPrFq6VMJhMcDodYbbTVxLSt0FUgHo8HT0/PPt0X1RCBQICoqCgwGAy4u7u3qYot3ltaUlICoVAIIyMjIsY7ojLeHdCCTE1NDa6url0uyH7++Wfs2LEDd+7cwdChQ7vsueX0buSirJWkpqZi6dKlOHPmTLPjznV1dbhz5w6Cg4Nx7do1aGhoYMaMGQgMDMSwYcN6xIGwrq6OCDR6uo/2QmtYyRAIBGR5xd3dXV7pEEMgEJBKh7u7u8yJDoqiiBhnMplEjNMCrTM/S9oqBQDc3d37hO9aS+Hz+YiKioKioiLc3d07LI6pqqqKVEtra2t7pK0Kn89HZGQkVFRU4Obm1uWC7OTJk9iyZQtu3ryJ4cOHd9lzy+n9yEVZG6AoqlX9GRwOByEhIQgODsaVK1egoKBABNqoUaN6xImI7lcpLi5GZWUltLW1iUBTUFBAVFQUVFVV4erq2iMEZ1dBu/TTJ4+eUC2lxTiTySTZq/TSV0davfB4PIkTa094b7oKcdHh6uraae8NPShQUlLSqZ91R0KLVWVl5W4RZKdPn8b69etx7do1jB49usueW07fQC7Kuhg+n49Hjx7hwoULuHz5Mvh8PqZPn45Zs2Zh7NixPeJKlcfjkZN2eXk5AEBLSwuOjo7Q0dHp5r2THWiXfjp3ryf28dCfdUlJCYn2ogcFtLW129w8zuFwEBkZCS0trS6flpN1aLHa1ctyXC6XTO2Wl5dDTU2NVNBkZWpXIBAgMjISSkpKcHd373JBdubMGaxZswaXL1/G+PHju+y55fQd5KKsGxEIBHj69CkRaDU1NZgyZQoCAwMREBDQ7X1Hb6K8vBzR0dGkmb+srAzq6uodctLu6dD+bG9y6e9JCAQCCasN2mW+tckRtbW1iIiIgIGBQZeGRPcEuFwuIiMju8XaQRyBQICysjJSRVNQUCBDIQYGBt1S1RQXZN1RWb1w4QI++eQTXLhwAZMnT+7S55bTd5CLMhlBKBTixYsXJO6ptLQUkyZNQmBgICZNmiRzSwmFhYVITEyEvb09MT5teNJWUVHpMf5YHUl7XPp7CrTLPF1FA0CqKs0ZE9fU1CAiIqJXidWOgq4eamtrS+SfdjcikYh4HJaUlIDP53d5/io98KCgoNBh/XWt4cqVK/jggw9w5swZzJw5s0ufW07fQi7KZBCRSITw8HBcvHgRwcHBKCgoQEBAAAIDAzFlypRuXSKks0KzsrLg6uoKIyMjqds19MdSVFSU8MfqrSdj2qXf3t6+08LoZQ36pE03j/P5fAljYrrHkMViISoqClZWVrCxsem134G2wOFwEB4eDn19fZmuHopb6JSUlJChELqK1hnV/e4WZDdu3MCyZctw+vRpzJ07t0ufW07fQy7KZBzaxZsWaJmZmRg/fjxmzZqFadOmdWnguEgkQkpKCphMJjw8PFosDkUiEamqMJlMACACzcDAQGYqAu2lM136ewoURaG6upp81nV1dTAwMICmpiby8vIwaNAgDBgwoLt3U6aoq6sjy7kODg4yK8ikUVdXR8R4ZWUltLS0yKBAR8R70bFStCVIVwuye/fu4Z133sGJEyewcOHCLn1uOX0TuSjrQdCO5xcuXEBwcDCSkpIwZswYzJo1C9OnT4eRkVGnHdCFQiFiY2NRV1cHDw+PNl8RUxQlkcdJG5jSeZw9cQKvq136exJsNhtZWVkoLCwEAAmrDVnvmewK6P46Y2NjDBkypEcJsobweDyUlpaipKSExHvRFTQ9Pb1WX3zRggwAPDw8uvzYEBoairfeegtHjx7F4sWLe/RnI6fnIBdlPRSKopCWlkYEWkxMDEaMGIFZs2Zh5syZMDU17bCDCI/HI35Jbm5uHdZDQlEUWCwW8UKjl71MTU1haGjYI6w1utulX9YpKipCQkICnJ2doaurS5a9KioqoKWlJRGa3tdgs9mkv87W1rZXnfSl9RzSAq0lF1/iKQaenp5dLsiePHmCefPmYf/+/Vi+fHmv+mzkyDa9Y92oD8JgMGBnZ4dNmzYhLCwMKSkpmDp1Ks6fPw87OztMmjQJR44cQV5eHtqju9lsNl69egV1dfUmM/fa8xr09PRgZ2eHESNGwNvbGxoaGsjIyMCjR48QHR2NwsJC8Pn8DnvOjkQkEiE2Nhbl5eXw8fHpk8KiOfLy8pCYmAg3NzeYmppCTU0N/fv3J1mx/fv3R1VVFV6+fIlnz54hLS0NLBarXd/XnkJNTQ3Cw8NhYWHR6wQZABLr5OTkhNGjR5OLudTUVISGhiI6Ohr5+fng8XiN7tswVqqrBdmLFy/w1ltvYffu3W0SZEeOHMHAgQOhpqYGX19fvHr1qtntg4KCYG9vDzU1Nbi4uODmzZsSf6coCt988w3Mzc2hrq6OgIAApKWlSWxTXl6Od955Bzo6OtDT08P777+PmpqaRo/zww8/wM7ODqqqqrC0tMSOHTta9drkdD7ySlkvg6Io5OXlITg4GMHBwXj27Bm8vLwQGBiIWbNmtWoasLKyEtHR0bC0tMTgwYO79MQhnsfJZrNhYGBA4p5kwRFf1l36uxt6GMTd3R36+vrNbisUCiWmdsWHQtqy7CXrVFdXIyIiok8OPNDpEXQfWnV1NXR1dSUSBWJiYiAUCluc89mRhIeHY+bMmfj222/x+eeft/qzOXfuHJYsWYJjx47B19cX+/fvR1BQEFJSUmBiYtJo+3///RejRo3Czp07MX36dPzzzz/YvXs3IiMj4ezsDADYvXs3du7ciT/++APW1tbYsmUL4uLikJiYSNI2pkyZgsLCQhw/fhx8Ph/vvfcefHx88M8//5Dn+vzzz3H37l3s2bMHLi4uKC8vR3l5OSZMmNCOd0xORyMXZb0YiqJQVFSES5cu4eLFi3j8+DFcXFyIQGtOaDGZTMTHx8PW1hZWVlZdvOeS1NbWEoFWXV0NPT09ItC6I86pJ7r0dxUURSEjIwN5eXnw9PRs9aSw+FBISUkJKIpq1bKXrMNisRAZGYmBAwfC2tq6u3en26GTQkpKSlBeXg4FBQUoKSnBxcWlS4eYACA6OhrTpk3Dpk2bsG7dujY9t6+vL3x8fHD48GEA9d9nKysrrFy5Ehs2bGi0/YIFC8Bms3H9+nVym5+fH9zd3XHs2DFQFAULCwusXbsW69atA1D/HTI1NcWpU6ewcOFCJCUlwdHREWFhYfD29gYA3L59G1OnTkVeXh4sLCyQlJQEV1dXxMfHY8iQIW15e+R0Eb3rElSOBAwGA+bm5vjkk09w//59FBQUYMWKFfj333/h4+MDf39/7Ny5E4mJiRJLRt9//z1+++03ODs7d7sgAwANDQ0MHDgQvr6+GDFiBExMTFBUVISnT5/i1atXyMnJQV1dXZfsS21tLcLCwqClpdUt02CyDEVRSElJQUFBAby9vdtk3aKgoAAjIyM4Ojpi1KhRJA8zNTUVjx49QkxMjEwvaTdHZWUlIiMjYWNjIxdk/4+amhqsrKxIRVVFRQW6urqIiorCkydPkJycjLKyMohEok7dj/j4eMycORPr1q1rsyDj8XiIiIhAQEAAuU1BQQEBAQF4/vy51Ps8f/5cYnsAmDRpEtk+KysLRUVFEtvo6urC19eXbPP8+XPo6ekRQQYAAQEBUFBQwMuXLwEA165dg42NDa5fvw5ra2sMHDgQH3zwAUlkkSM7yH4ntZwOgcFgwNjYGB9++CE++OADVFRU4OrVq7h48SL27t0La2trzJgxA5mZmbh//z7+/vtvqeX27obuS+rfvz+4XC7J40xLS4OWlhbJ4+wMs93e6NLfUYhEIiQmJqKyshI+Pj4dMllJ9xzq6enB1taWLGlnZ2cjISGhRwVpV1RUICoqSiYqz7IGbfsjEAjg6+sLZWVlUjEtKSlBfHw8RCIR8b7r6CGgpKQkzJgxA5988gk2bdrU5t91aWkphEIhTE1NJW43NTVFcnKy1PsUFRVJ3b6oqIj8nb6tuW0aHquVlJRgYGBAtsnMzEROTg6CgoJw+vRpCIVCrFmzBvPmzcODBw/a9HrldA5yUdYHYTAYMDAwwLJly7Bs2TKwWCwEBwdjy5YtYLFYsLGxQUhICHR0dODh4SGzPT2qqqro168f+vXrBz6fTwRaRkYGNDU1JSb72iugxF36Bw4c2DEvoJcgEokQFxcHNpsNHx+fThFIDAYD2tra0NbWxqBBg8iSdkFBAZKTk6Grq0usVTQ0NDr8+dsDHUdmZ2eHfv36dffuyBS0IOPxeBKDRHTF1MjICPb29qiqqgKTyURGRgbi4uI6TJCnpaVh+vTpeO+997Bt27Zee6ElEonA5XJx+vRp2NnZAQBOnjwJLy8vpKSkyJc0ZQi5KJMDoVCI3377DVZWVnjy5AnCwsIQHByMadOmwcDAADNmzMDs2bPh4+Mjs8t1ysrKsLCwgIWFBQQCAWkkzs7OJsHKJiYm0NHRafWBty+69LcUelJOIBDA29u7ywYe6CXtgQMHkoopk8lEenp6hwvy9lBaWorY2Fj5d0cK9PQyl8uFl5dXk5PdDAYDurq60NXVha2tLRkUoAW5jo4OEeStqZBnZmZi+vTpWLhwIb7//vt2X3waGRlBUVERxcXFErcXFxfDzMxM6n3MzMya3Z7+f3FxMczNzSW2cXd3J9vQptw0AoEA5eXl5P7m5uZQUlIiggwAHBwcAAC5ublyUSZDyEVZHyc7OxtTpkyBvb09/v77b2hoaMDa2hpvvfUWamtrcffuXVy8eBFz586FhoYGZs6cicDAQPj7+8usj5iSkhLMzc1hbm4uMdkXERHR6hBtuUt/0/D5fBJ/4+Xl1W3fh4YVU/rzzs7Ohqqqarflr5aUlCA2NhaOjo4SJ1Q5/6uucjicZgWZNDQ1NaGpqSkhyEtKSpCZmUkuwIyNjZv9vHNycjBt2jTMnDkT+/bt65DVABUVFXh5eSEkJASBgYHkdYaEhOCzzz6Teh9/f3+EhIRg9erV5LZ79+7B398fAGBtbQ0zMzOEhIQQEUbbyKxYsYI8RmVlJSIiIuDl5QUAePDgAUQiEXx9fQEAw4cPh0AgQEZGBgYNGgQASE1NBQB5woaMIZ++7OOcO3cOz549w08//dRsFYzD4eD+/fsIDg7GlStXoKSkhBkzZiAwMBAjR47sklDi9iISiSTyOBkMhkQep/iBWe7S3zx0U7OamhpcXV1lsoLaMH9VQUGhyc+7o2EymYiLi4Ozs3OjfqC+Di3I6urqWi3ImkMgEKCsrIyINAUFBRgbG8PIyAhaWlpkWTs/Px+TJk1CQEAAjh071qHfg3PnzmHp0qU4fvw4hg4div379+P8+fNITk6GqakplixZAktLS+zcuRNAvSXG6NGjsWvXLkybNg1nz57F999/38gSY9euXRKWGLGxsY0sMYqLi3Hs2DFiieHt7U0sMUQiEfFS3L9/P0QiET799FPo6Ojg7t27Hfb65bQfuSiT02r4fD5CQ0Nx4cIFXL58GQKBADNmzMCsWbMwZswYmW+6BuoPUuJxT7T1Ap3HmZqaKnfpbwIOh4OIiAhoa2vD2dlZZnsOxaE/b3qZk473ok/aHSkq6RQDFxcXmRyW6U5EIhHi4+PBZrPh5eXVacvdIpEIlZWVYDKZePbsGdavXw9/f3+MGzcOf/zxB4YNG4aTJ092ysXE4cOHsXfvXhQVFcHd3R0HDx4kFasxY8Zg4MCBOHXqFNk+KCgImzdvRnZ2NmxtbbFnzx5MnTqV/J2iKGzduhW//PILKisrMWLECBw9elRiKbK8vByfffYZrl27BgUFBcydOxcHDx6UOHYVFBRg5cqVuHv3LjQ1NTFlyhTs27cPBgYGHf4eyGk7clEmp10IBAI8ffoUQUFBuHz5MthsNqZOnYrAwECMHz++R+QbUhRFDuDFxcXg8XhQUFCAra0tLCwsZLIK1F2w2WxERkbC0NCwx4Vn01AURRrHmUwmOBwODA0NybJXeyo3hYWFxBPKyMioA/e659NVgkza8z5//hznzp3DrVu3UFhYiHHjxmH27NmYOXOmfBpWjkwh+5e4LUBWYy1iY2MxcuRI4sWzZ8+ejnnBMoSSkhLGjBmDI0eOIDc3F9evX4epqSm++uorWFtbY+nSpbh06RLYbHZ372qTMBgM6OvrY9CgQVBXV4eGhgYsLCyQk5OD0NBQ4o0lEAi6e1e7lerqaoSHh8PU1LTHCjLgf43jtra2GDZsGHx9faGjo4Pc3Fw8evQIEREReP36NTgcTqseNz8/H0lJSXBzc5MLsgaIRCIkJCR0uSAD6ic57e3t8erVKwwbNgzJycmYMWMGLly4AGtra3h7e2Pfvn1dtj9y5DRHj6+UyWqsRVVVFezs7BAQEICNGzciLi4Oy5cvx/79+/HRRx913RvUTYhEIoSFheHixYu4dOkSCgoKMGHCBAQGBmLy5MltMhbtTKS59FMUJRH3VFtbC0NDQ5Im0BP66DqKyspKREVFkYnHnirI3kRdXR2xVmGxWC2e7KMHQtzd3eXLQQ2gKArx8fGorq7u0gldmoqKCsyYMQNWVlYICgqSeP7S0lLcuHEDRUVFWL9+fZfulxw50ujxokxWYy1+/vlnfP311ygqKiIHgQ0bNuDy5ctNGgn2VmgvogsXLiA4OBhZWVkICAjArFmzMG3atC6fimtIbW0tIiMjoaenB0dHxyZ7pNhsNhFoNTU10NfXJwKtJ/TRtZWysjLExMRg8ODB6N+/f3fvTpfB4/FID1pZWRk0NDTIoIC2tjb5zubm5iIjI0M+ECIFiqKQkJCAqqoqeHl5dfnvhMViYebMmTA2NsalS5d69e9UTu+gRy9fynKsxfPnzzFq1CiJq7JJkyYhJSUFFRUV7XzlPQsFBQV4eHhgx44dSExMRHh4OLy9vXHo0CEMHDgQc+bMwalTp1BaWoquvkaoqqpCWFgYjI2N4eTk1GzTuqamJqytreHn54fhw4fDyMgIBQUFxNstNze31Utesg6TyUR0dDTs7e37lCAD6i0OLC0t4eHhgTFjxsDGxga1tbUIDw/H06dPkZKSgqSkJKSnp8PT01MuyBrQ3YKsuroac+fOhZ6eHi5evCgXZHJ6BLJpNNVCZDnWoqioqFG+Hf2YRUVF0NfXb/Hr7E0wGAw4OzvD2dkZW7duRWpqKi5evIjffvsNq1atwsiRIzFr1izMmDEDpqamnVpBa49Lv7q6OgYMGIABAwaQUOXi4mKkpqZCR0eHVFRkzV2+NRQWFiIxMVE+RYj637eZmRnMzMwgFApRXl6OjIwMVFdXQ0lJCfn5+eDxeDA0NOwR06idDUVRSExMBIvFgre3d5cLIjabjfnz50NFRQWXL1/uEQNHcuQAPVyUyenZMBgMDBkyBJs2bcLGjRuRmZmJixcv4uzZs1i7di38/f0xa9YszJo1CxYWFh0q0IqLixEfHw8HB4d2O63TgxxWVlbg8Xhkqo92l6fzOHuStYZ4j5ShoWF3745MoaCgABaLBQ6HA19fX5IgkZycDD6fTzIajYyMZNZguTOhBVllZWW3VMjq6uqwYMECiEQi3Lhxo1NycOXI6Sx69BFDlmMtmnoe8eeQ8z8YDAYGDRqEr776Cl9++SVev36N4OBgBAcHY8OGDfD29sasWbMQGBiI/v37t0ug0YLD1dW1w136VVRUGuVxMplMZGVlQV1dXWpPkqyRlZWF7Oxs+ZKcFCiKQnp6OgoKCuDt7U2EtoGBAezs7FBdXQ0mk4nMzMxGoeld3eDeHVAUhaSkJFRUVMDb25sMRnUVHA4Hb7/9Nmpra3Hnzh1oa2t36fPLkdNeenSdXTzWgoaOtaBjKhpCx1qI01SsBQ0da0FvIx5rQdMw1sLf3x+PHz8Gn8+XeJ4hQ4b02aXLlsJgMNC/f3+sXr0ajx49Qm5uLhYvXox79+7B1dUVo0aNwr59+5Cent6qHjSKopCRkUF6gDo7NonO43R3d8fo0aMlepKePXuG1NRUVFZWdnkfXVNQFIW0tDTk5ubC29tbLsgaQFEUUlNTUVhYKCHIaBgMBnR0dDB48GAMGzYMfn5+0NPTQ15eHh4/fozw8HDk5uairq6um15B50JRFJKTk1FeXt4tgozH42HJkiUoLS3FrVu3oKur26XPL0dOR9Djpy9lNdaCxWJhyJAhmDhxItavX4/4+HgsX74cP/30U5+wxOgMKIpCaWkpLl26hIsXL+Lhw4ewt7dHYGAgZs2aBXt7+yarT/QJQxZc+hvG/ygqKkrE/3RHBY1+f0pLS+Hp6Slf8mmA+Pvj5eXV6l5Buu+QyWSioqICWlpaEqHpPR36/SkrK4OXl1eX93Dx+XwsW7YMmZmZePDggXzJXU6PpceLMkB2Yy1iY2Px6aefIiwsDEZGRli5cqXcC6eDoCgKFRUVuHLlCi5evIj79+/DxsYGM2fOxOzZsyUmKdlsNrZv347p06fD29tbppp+RSIRysvLSR8ag8GQiHvqiqZx2tizqqoKnp6eMvX+yAJ0j1RFRUWHCA4ej0dC08vKykiItomJCXR0dGR2WbspKIpCSkoKSkpKuuX3JRAI8MEHHyAhIQEPHz7s80Mpcno2vUKUyZHDYrFw7do1BAcH4/bt27CwsMCsWbMwfvx4fP311+Byubh161anL1m2B/G8PvF8RhMTExgaGnZK3JNQKCTh0J6ennLbgAaIRCIyRejl5dXhS3JCoZAItNLSUomqqZ6ensxPcna3IBMKhVixYgXCw8MRGhoq79eV0+ORizI5vY7q6mrcvHkTf//9N+7evQsrKytMnDgRc+fOhY+PT4/IsqQoCiwWi5jV0lN9pqamMDQ07JCpPoFAgOjoaIhEInh4ePSphIKWQGc11tTUdMkUoXjVtKSkBBRFkdD0zhLl7YHusWMymd0iyEQiEVauXImnT5/i4cOH6NevX5c+vxw5nYFclMnplWRkZGDixIkYOnQo5syZgytXruD69evQ0tLCzJkzERgYCH9/f5k70UmDoigy1VdcXEwCtE1NTWFkZNQmMcXn8xEVFQVFRUW4ubn1SeuG5hCJRIiLi0NtbW2XZzUCkqKcyWQSDzTaaqO7BTQ9FFJUVARvb+8u9+MTiURYu3Yt7t69i9DQUAwYMKBLn1+OnM5CLsrk9DqioqIwefJkLF68GHv27CFLQBwOB/fv38fFixdx9epVKCsrY8aMGQgMDMSIESO6/UTXUsTzONlsNgwMDEjcU0vEA5fLRWRkJNTV1eHi4tIjhGlXIhQKERsbCy6XC09Pz263shDPYGUymeQzp602unrJWRYE2caNG3HlyhU8fPgQgwYN6tLnlyOnM5GLMjm9Coqi4Ofnh/nz55PsUmnw+Xw8fPgQFy5cwJUrVyAUCjF9+nQEBgZizJgx3X4ibim1tbVEoFVXV0NPT48INGn9T3V1dYiIiHhjzmdfRSgUIiYmBgKBQGaXdOnPnMlkoqqqCrq6uqT3sLMFEu3TVlhYCC8vry6f0hWJRNi6dSvOnDmD0NBQieErOXJ6A3JRJqfXUVdX16r+FoFAgCdPniAoKAhXrlxBbW0tpk6dilmzZiEgIKDL/ZbaCofDIQKNxWJBR0eHpAmoq6uDzWYjIiICxsbGzdqH9FWEQiGioqJAUf/X3t3HxZzv/+N/zEiFVFJmEikUIkUXU9iyjA2lBrsby9a2jt09HyyLL46zm/Nx7Cac3dayshdO6xyXXcjKila5WCVdoWupFGomynQlqpnX7w+/3p8d5bqaSc/77ebWzXue875Kesz7/Xo/Xwzjxo3rErd0Hz58yLXaqKqqQp8+fVRabbTn97ilz9/t27fh6OjY6YGMMYYvv/wSP//8MxISEmBjY9Op2yekM1AoI+RPFAoFEhMTERkZiSNHjuDevXuYPn06JBIJpk2b1mX6dz18+JC7mnLv3j307t0bDQ0NMDU1xahRoyiQPaa5uRkZGRng8Xiwt7fvEoHscU1NTSpPcuro6HABzcDA4JW+55oQyLZu3YqdO3ciPj4etra2nbp9QjoL3bvQQDt37oSFhQV0dXUhEolw6dKlp9aHh4dj5MiR0NXVha2tLX777TeV1xljCAwMhKmpKXr16gWxWIyCggKVmqqqKixYsAD6+vowNDTEokWLUFdXx71+5swZ+Pj4wNTUFH369IG9vT327dvXfgetIXr06IE33ngDISEhKC4uxqlTpzBkyBAEBgbCwsICCxYsQHh4OGpra9W9q0+lo6ODwYMHw8HBAXZ2dmhoaICuri7Kyspw8eJFbjJt+kz2KMykp6eDz+d3mStkbenZsydMTU1hZ2eHyZMnw9raGo2Njbh8+TLOnTuH3NxcVFZWQqlUvvC6i4qKcPv2bbXcsmSM4dtvv8X27dtx8uRJCmTktUahTMMcOnQIK1euxIYNG5Ceng47Ozt4eHi0mmuzRWJiIubPn49FixYhIyMDEokEEokEWVlZXM2WLVuwfft2hIaGIjk5GX369IGHhwcePHjA1SxYsADZ2dmIi4tDTEwMzp07pzLzQGJiIsaOHYvIyEhcvXoVAQEB8PPzQ0xMTMedDDXj8/lwcXHB1q1bce3aNZw/fx6jRo1CUFAQLCws4Ovri/3792vUVEmPu3v3LjIzM2FtbY2JEydyzZTr6upw6dIlJCYmoqCgANXV1Rp7DB2pJZD17NkT9vb2r81DDy39zsaMGQM3NzeMGTMGPB4P2dnZOHv2LLKysiCTyaBQKJ65rsLCQty6dQsODg6dPvsAYwzff/89tm7ditjYWIwfP/6F16GJH3L/7Pr16+jbty9Na0YA0O1LjSMSieDk5IQdO3YAeDSwdfDgwVi2bBnWrVvXqt7X1xf19fUq4cjFxQX29vYIDQ0FYwwDBw7EqlWruIHv1dXVEAgECAsLw7x585CbmwsbGxukpKTA0dERABAbG4uZM2fi1q1bGDhwYJv76unpCYFAgD179rT3adBojDFkZWUhIiICUVFRuHbtGt588034+PjAy8sLRkZGGnF7UCaTISsrCzY2NjA1NW31+p8bl965cwc9e/ZUaVyqCcfQkRobG5Geng5dXV2MHTu2Wzz0wBhDTU0Nd2u7pb1Ky5Ocjz/YUFRUxM2Fqo5A9tNPPyEwMBAnTpzAhAkTXngdhw4dgp+fH0JDQyESiRASEoLw8HDk5+e32fk/MTERbm5uCAoKgpeXF/bv34/g4OBW0/AFBQWpTMOXmZnZahq+8vJy7N69m5uGz8nJiZuGr0VTUxMmTJgAExMTJCYmQi6Xv/iJIq8VCmUapLGxEb1790ZERAQkEgm33N/fH3K5HEePHm31HnNzc6xcuRIrVqzglm3YsAHR0dG4cuUKioqKMGzYMGRkZMDe3p6rcXd3h729Pb799lvs2bMHq1atwr1797jXm5uboauri/DwcMyePbvN/Z00aRJcXFywbdu2Vz72rqqlo3lkZCSioqJw9epVvPHGG5BIJJg1axYGDBiglnBTVlaGvLw82NraPtcsBkqlUmU+Th6PpzIf5+sWWB4+fIi0tDTo6elhzJgxr93xPQ/GGOrr67mAVldXh379+nEBrby8HCUlJXBwcEDfvn07fd/27t2LtWvX4tixY3B3d3+p9Wj6h9y1a9eirKwMU6dOxYoVKyiUEbp9qUnu3r0LhUIBgUCgslwgEEAqlbb5HqlU+tT6lq/Pqnn8U6OWlhaMjIyeuN3Dhw8jJSUFAQEBz3l0rycej4eRI0fi73//O1JTU5GXlwcPDw/s378fVlZWmDFjBnbt2oXbt2932u3B0tJS5OXlwd7e/rmnleLz+TAxMcHo0aO5210AkJWVhXPnziE7Oxt37tx5qfFImubBgwdIS0uDvr5+tw1kwKN/u3p6ehg6dChcXFwwceJEmJiYQCaT4fz58ygsLIRQKOz088MYw/79+7FmzRpER0e/dCBrbGxEWloaxGIxt4zP50MsFiMpKanN9yQlJanUA4CHhwdXX1xcDKlUqlJjYGAAkUjE1SQlJcHQ0JALZAAgFovB5/ORnJzMLYuPj0d4eDh27tz5UsdHXk/d838j8koSEhIQEBCAH3/8EaNHj1b37mgMHo+HYcOGYe3atUhKSkJhYSFmz56N6OhojBo1CmKxGNu3b0dJSUmHBDTGGIqKilBYWAgHBwcYGRm91Hr4fD769++PUaNGwc3Njev4n5eXh7NnzyIzM5Obm7OrefDgAVJTU2FgYKAyaT0BevXqBXNzcxgbG0NLSwuWlpZoaGhAUlISEhMTcf36ddTU1HT4h4uIiAh89tlnCA8Px5QpU156PZr8IbeyshIffPABwsLCoK+v/5JHSF5HXfMxo9eUsbExevToAZlMprJcJpM9caJdoVD41PqWrzKZTGVckUwm425nCoXCVg8SNDc3o6qqqtV2z549i1mzZuGbb76Bn5/fix9kN8Hj8TBkyBB89tlnWLFiBcrKynDkyBFERkbiiy++gJ2dHSQSCXx8fDB06NBXvsXZ0mW9vLwcjo6O7Xa7icfjoV+/fujXrx+sra258UjXrl3Dw4cPufk4W36Ra7KGhgakpqZygfN1HzP3MkpKSlBcXAwHBwcuLDQ3N3NjD1NTUzt07GF0dDSWLFmCAwcOYPr06e22Xk2zePFivPfee3Bzc1P3rhANQx8TNYi2tjYcHBxw+vRpbplSqcTp06fh6ura5ntcXV1V6gEgLi6Oq7e0tIRQKFSpqampQXJyMlfj6uoKuVyOtLQ0riY+Ph5KpRIikYhbdubMGXh6eiI4OFjlyUzydDweD2ZmZli6dCni4+Nx69YtLF68GOfOnYODgwMmTJiA4OBg5OXlvdRVCMYYcnNzIZPJ4OTk1GHjf3g8HgwMDGBlZYWJEyfC2dkZenp6KCoqwtmzZ5GRkYGysjI0NTV1yPZfxf3795GamgoTExMKZE9QUlKCoqIilUAGPLrKIxQKMXbsWEyePBkjR45Ec3Mzrly5grNnzyInJ6ddbm3HxMRg8eLF2Lt3L2bNmvWqh9PhH3KfVvOsD7nx8fHYtm0btLS0oKWlhUWLFqG6uhpaWlrd7sEpoooG+muYQ4cOwd/fH7t374azszNCQkJw+PBh5OXlQSAQwM/PD2ZmZggKCgLw6Gkhd3d3bN68GZ6enjh48CC++uqrVk8Lbd68WeVpoatXr7Z6WkgmkyE0NJR7WsjR0ZF7WighIQFeXl5Yvnw5Pv30U25/tbW1X/o2WXfHGENVVRWOHj2KyMhInD59GsOGDYO3tzdmz579XNMgKZVKZGVloa6uDuPHj1fb7AMtA8ZlMhk3YLxluqfOnpuxrX1LTU2FqakprKysKJC1obS0FIWFhRg/fjwMDAye6z2MMcjlcu5BgaamJhgbG3OTpr/IldNTp05h4cKF+Pnnn+Hr6/uyh9GKSCSCs7MzvvvuOwCPfl7Mzc2xdOnSJw70v3//Po4dO8YtmzBhAsaOHasy0H/16tVYtWoVgEcfcgcMGNBqoH9qaiocHBy445s+fTo30D83N1fl9v/Ro0cRHByMxMREmJmZoV+/fu12DkjXQqFMA+3YsQNbt26FVCqFvb09tm/fzl2xaukzFRYWxtWHh4fj888/x40bN2BlZYUtW7Zg5syZ3OuMMWzYsAE//PAD5HI5Jk2ahO+//15l3riqqiosXboUx44dA5/Px9y5c7F9+3buMfgPPvgAv/zyS6t9dXd3x5kzZzrmRHQzcrkcx44dQ1RUFE6ePAkzMzOu75ydnV2rgFZXV4fs7GwwxjRi4uwWDQ0NXEBrmZuxZbqnzg6NdXV1SEtLg5mZGYYNG0aBrA03b97E9evXXyiQPY4xhtraWi6gNTQ0qEya/rR/mwkJCfD19cWuXbuwcOHCdv0eaeqH3MeFhYXR05cEAIUyQjRSbW0tjh8/jqioKJw4cQLGxsbw9vaGRCKBk5MT5HI5vLy8uLYmmjhxNvBoYP2dO3cgk8kgl8uhr6/PjUfq6Mmza2trkZaWBnNzcwwdOrRDt9VVtQSycePGtWvz0j+32qitrYWhoSEGDBiA/v37q8wIcP78ebz99tsICQnBhx9+2CGhWRM/5D6OQhlpQaGMEA13//59xMbGIjIyEsePH0fv3r2hra2N/v3749ixY12mE3hjYyP3i7qqqgp6enoqk2e3p+rqaqSnp8PCwgKWlpbtuu7Xxa1bt1BQUNDugexxLcG8oqIC33zzDbKzs+Hh4QE7Ozt89tlnCA4OxieffEJXMQkBhTJCupSioiJMnjwZ2tra3MDgWbNmQSKRYOLEiRp7xexxTU1N3C/qyspK9OrViwtoffv2faVf0HK5HBkZGRg6dCiGDBnSjnv9+rh16xauXbuG8ePHd2qol0qliIiIwJEjR5CSkoL+/fsjICAAc+bMgZOTEwUz0u1RKCOkiygqKoJYLMbkyZPxww8/QKlUIiEhARERETh69CgYY/D09MTs2bPh7u6uMWPMnuXPLRfu3r0LbW1tLqAZGBi80C/qe/fuISMjA1ZWVhg8eHAH7nXXdfv2beTn52PcuHFqGVB++fJleHp64v/9v/8Ha2trREdHIyYmBn379oVEIoGfnx+cnJw6fb8I0QQUygjpAvLz8zFlyhS88847+Prrr1sN+m9ubsa5c+cQHh6Oo0ePoqGhAZ6enpBIJJgyZYransp8UQqFQmW6p5aJtVume3paQKusrMSVK1dgbW2NQYMGdeJedx3qDmRZWVmYMWMGVq5cifXr13Pfz8bGRiQkJODIkSMYN24cPv74407fN0I0AYUyQrqAiooKHDx4EMuWLXvmlSOFQoELFy4gMjISR44cQXV1NaZPnw6JRIJp06Z1+AD79qJUKlFVVcWNQ+PxeDAxMYFAIGg1H+fdu3dx9epVjBw5UmVuQfJ/WuZDtbe3V0sbm9zcXMyYMQN//etf8Y9//INuVRLSBgplhLzGlEolkpOTuYAmk8nw1ltvwcfHB9OnT+/0iaZfllKpVOmJpVAouIDW0qtt9OjRT2wK2t2Vl5cjNzdXbYHs2rVrmDFjBvz9/REUFESBjJAnoFBGSDehVCqRkZGBiIgIREVFobS0FGKxGD4+Ppg5c+YLj99SF8YYqqurUVFRgfLycjQ2NsLAwABDhgzhuriT/9MSyOzs7NC/f/9O335RURGmT5+Od999F9u2baP5Rgl5CvrpIB1u586dsLCwgK6uLkQiES5duvTU+vDwcIwcORK6urqwtbXFb7/9pvI6YwyBgYEwNTVFr169IBaLUVBQoFJTVVWFBQsWQF9fH4aGhli0aBHq6ura3N7169fRt2/fLtNa4mXx+Xw4ODggKCgIeXl5uHTpEsaNG4dvv/0WlpaWePvtt7F3715UVlZ2+KTTr4LH48HQ0BD6+vpobm6GtbU1jIyMcP36dZw5cwaXL19GeXm5Rk731NmkUqlaA1lJSQk3tpECGSHPRlfKSIc6dOgQ/Pz8EBoaCpFIhJCQEISHhyM/Px8DBgxoVZ+YmAg3NzcEBQXBy8sL+/fvR3BwcKuO2kFBQSodtTMzM1t11C4vL8fu3bu5jtpOTk6tOmo3NTVhwoQJMDExQWJiYrds3sgYQ15eHiIjIxEVFYWsrCy88cYbkEgkmDVrFkxMTDTuClrL+KixY8fC2NiYW15XV8fNJlBfXw8jIyNuuqeu8jRqe5FKpcjJyWl1jjrL7du34eHhgWnTpmHXrl0UyAh5DhTKSIcSiURwcnLCjh07ADy6hTZ48GAsW7bsiXPP1dfXIyYmhlvm4uICe3t7lbnnVq1ahdWrVwN41ChUIBC0mnsuJSUFjo6OAIDY2FjMnDmTm3uuxdq1a1FWVoapU6dSR208CmiFhYVcL6n09HS4urpCIpHA29sbpqamag9oLU8QPuvqz/3797mAVltbi379+nFPcqp7Ps6OJpPJkJWVBTs7O7UEMqlUiunTp2PixIn46aef6JYyIc+JPrqQDtPY2Ii0tDSIxWJuGZ/Ph1gsRlJSUpvvSUpKUqkHAA8PD66+uLgYUqlUpcbAwAAikYirSUpKgqGhIRfIAEAsFoPP5yM5OZlbFh8fj/DwcOzcufPVD/Y1wePxMHz4cKxbtw4XL15EQUEBJBIJoqKiMHLkSEybNg3fffcdSktL1XKL8+bNm1xLh2fdjuvduzcsLCwgEokwadIkmJiYQCqV4vz580hJSUFJSQkaGho6ac87j0wmQ3Z2ttqukFVUVMDT0xNOTk748ccfKZAR8gIolJEOc/fuXSgUCggEApXlAoEAUqm0zfdIpdKn1rd8fVbN47dGtbS0YGRkxNVUVlbigw8+QFhYGPT19V/yCF9vPB4PFhYWWLlyJc6fP4+SkhLMnz8fJ06cgK2tLSZPnoyvv/4ahYWFnRLQSktLuYmzX7THlq6uLszNzeHk5IQ33ngDQqEQd+/exYULF3Dx4kUUFxejvr6+g/a881RUVCArKwu2trYwMTHp9O3fvXsXs2bNwpgxYxAWFgYtLa1O3wdCujL6iSHd0uLFi/Hee+/Bzc1N3bvSJfB4PJiZmWHZsmVYunQpZDIZoqOjERkZiY0bN8LGxgY+Pj6QSCSwtrZu91ucN27cQHFxMcaPHw8DA4NXWpeOjg4GDx6MwYMHo7GxkZvuqbCwEH369FGZj1Pdt2pfREVFBTIzM9UWyO7duwcfHx8MGzYM+/bt6zJTfhGiSehKGekwLe0JZDKZynKZTPbEflJCofCp9S1fn1VTUVGh8npzczOqqqq4mvj4eGzbtg1aWlrQ0tLCokWLuLkk9+zZ85JH3D3weDwIhUJ88sknOHXqFMrLy7Fs2TKkpqbCxcUFIpEImzZtQnZ2NpRK5Stvr6ioCDdu3ICDg8MrB7LHaWtrw8zMDOPGjcPkyZNhYWGBuro6XLp0CYmJiSgoKEB1dbVGP40KAHfu3OECWVsP0HS06upqSCQSDBw4EIcOHep2D1UQ0l4olJEOo62tDQcHB5w+fZpbplQqcfr0abi6urb5HldXV5V6AIiLi+PqLS0tIRQKVWpqamqQnJzM1bi6ukIulyMtLY2riY+Ph1KphEgkAvBo3Nnly5e5Pxs3bkTfvn1x+fJlzJ49u31OQDfA4/HQv39/LFq0CDExMZDJZFizZg1ycnLg5uaG8ePHY8OGDbh8+fILBzTGGK5fv46bN2/CwcGhw28za2lpwdTUFHZ2dpg8eTKGDx+OBw8eIC0tDX/88Qfy8/Mhl8s1LqDduXMHV69exZgxY9QSyGprazFnzhwYGhoiMjLytX+IgpCORE9fkg516NAh+Pv7Y/fu3XB2dkZISAgOHz6MvLw8CAQC+Pn5wczMDEFBQQAetcRwd3fH5s2b4enpiYMHD+Krr75q1RJj8+bNKi0xrl692qolhkwmQ2hoKNcSw9HRsVVLjBZhYWH09GU7q6mpwfHjxxEVFYUTJ07AxMSEu8Xp6Oj41BYJjDEUFBSgvLwcDg4O0NPT68Q9V6VUKlXm4+TxeBgwYAAEAgEMDQ3V2uqhZXqp0aNHtxpn2Rnq6+sxd+5c8Pl8HD9+HH369On0fSDkdUJjykiH8vX1xZ07dxAYGAipVAp7e3vExsZyv0BKS0tVfqlNmDAB+/fvx+eff47169fDysoK0dHRXCADgDVr1qC+vh4fffQR5HI5Jk2ahNjYWJVJt/ft24elS5di6tSp4PP5mDt3LrZv3955B06gr6+P+fPnY/78+aivr0dsbCwiIyPh4+MDAwMDeHt7w8fHBy4uLipP6CmVShw+fBgDBw6Eo6Oj2n/R8/l8mJiYwMTEBEqlEvfu3ePGbzHGYGJiggEDBqB///6dGtBaApmNjY1aAllDQwN8fX2hVCopkBHSTuhKGSGkUzU0NCAuLg6RkZE4duwYdHR0MGvWLMyePRsuLi5YvHgxLly4gEuXLqmlC/3zYoypzMfZ3NwMY2NjCAQC9O/fv0NbQVRWVuLKlSuwsbFRy3yfDx48wPz581FdXY2TJ0+2+1g/QrorCmWEELVpbGxEfHw8IiIiEB0dzQWZL7/8Em+//XaXGTDOGENNTQ3XrPbhw4dcQDM2Nm7X1hAtgWzUqFEwNTVtt/U+r8bGRixcuBBSqRRxcXEv3J6EEPJkFMoIIWrX3NwMf39/nD9/Hm+++SZOnTqFhw8fcvMmvvnmmyq3pzUZY0xluqeGhgaV6Z5epVVEVVUVLl++rLZA1tTUBH9/fxQXFyM+Pl6jr2QS0hVRKCOEqFVTUxMWLlyI7Oxs/P777xAKhVAoFPjjjz8QGRmJI0eOoKamBjNmzIBEIoFYLEbv3r3VvdvPrb6+ngtodXV1MDIywoABA2BiYvJCTyq2BLKRI0eqTBXWWZqbm/GXv/wFOTk5iI+PV8uTnoS87qglBiFErT744APk5+cjISGBGx/Vo0cPuLu7Y/v27SgpKcGJEydgZmaG9evXw8LCAu+//z4iIiJQV1en5r1/tj59+sDS0hIuLi6YOHEijIyMUFZWxk33VFpaigcPHjx1HeoOZAqFAv/zP/+DzMxM/P777y8dyHbu3AkLCwvo6upCJBLh0qVLT60PDw/HyJEjoaurC1tbW/z2228qrzPGEBgYCFNTU/Tq1QtisRgFBQUqNVVVVViwYAH09fVhaGiIRYsWqfy7OXPmDHx8fGBqaoo+ffrA3t4e+/bte6njI+RV0ZUyQohanT9/HqNHj4aRkdEza5VKJdLT0xEREYGoqCjcunULYrEYPj4+mDlzJvT19btMF/4HDx5wDwnI5XLo6+tzswn8+UrgvXv3kJGRgREjRsDMzKzT91OhUODTTz/FH3/8gYSEBAwaNOil1nPo0CH4+fkhNDQUIpEIISEhCA8PR35+fpshLzExEW5ubggKCoKXlxf279+P4ODgVu1xgoKCVNrjZGZmtmqPU15ejt27d3PtcZycnLj2OF999RUaGhowY8YMCAQCxMTEYOXKlTh69Ci8vLxe8qwR8nIolBFCuiSlUonMzExERETgyJEjKCgowJQpU+Dj4wMvLy/069evywS0xsZGLqBVVVVBT08PAwYMgK6uLvLy8mBtbf3SYehVKJVKrFy5EnFxcThz5gyGDBny0usSiURwcnLCjh07uHUPHjwYy5Ytw7p161rV+/r6or6+HjExMdwyFxcX2NvbIzQ0FIwxDBw4EKtWrcLq1asBPJpZQCAQICwsDPPmzUNubi5sbGyQkpICR0dHAEBsbCxmzpyJW7duPfGqo6enJwQCAc3uQTod3b4khHRJfD4fdnZ2+Oc//4nMzExkZGTA1dUVu3fvxtChQ+Hj44M9e/bgzp07GteF/3Ha2toYNGgQxo8fD3d3d5ibm6OyshLZ2dno0aMHHjx4gJqamk49DqVSiXXr1iE2Nha///77KwWyxsZGpKWlQSwWc8v4fD7EYjGSkpLafE9SUpJKPQB4eHhw9cXFxZBKpSo1BgYGEIlEXE1SUhIMDQ25QAYAYrEYfD4fycnJT9zf6urq57pyS0h7o1BGyEvSxPExLevZtm0brK2toaOjAzMzM3z55Zftc9AaisfjwcbGBoGBgUhPT0d2djbEYjH27t2L4cOHY+bMmdi9ezfKy8s1PqD17NkTvXv3Rl1dHaytrTFixAjcv38fqampuHDhAq5du9bh0z0plUoEBgbiyJEj+P333zFs2LBXWt/du3ehUChaNbkVCASQSqVtvkcqlT61vuXrs2oevzWqpaUFIyOjJ2738OHDSElJQUBAwHMeHSHth0IZIS/h0KFDWLlyJTZs2ID09HTY2dnBw8Oj1UToLRITEzF//nwsWrQIGRkZkEgkkEgkyMrK4mq2bNmC7du3IzQ0FMnJyejTpw88PDxUBoEvWLAA2dnZiIuLQ0xMDM6dO4ePPvpIZVvLly/HTz/9hG3btiEvLw+//vornJ2dO+ZEaCAejwcrKyusW7cOycnJKCgogLe3NyIiIjBixAi89dZb2LFjB27evKmRAa26uhoZGRkYPnw4hgwZAqFQiLFjx8Ld3R3W1tZobGxERkYGzp8/j7y8PFRVVbXrcTDG8OWXX2L//v2Ii4uDtbV1u61b0yUkJCAgIAA//vgjRo8ere7dId0RI4S8MGdnZ7ZkyRLu7wqFgg0cOJAFBQW1Wf/uu+8yT09PlWUikYh9/PHHjDHGlEolEwqFbOvWrdzrcrmc6ejosAMHDjDGGMvJyWEAWEpKCldz4sQJxuPx2O3bt7kaLS0tlpeX1z4H+hpRKpXs5s2b7Ntvv2Vubm5MS0uLOTk5sU2bNrHMzExWV1fH6uvr1fqnrKyMxcTEsNzc3KfW1dbWspKSEpaSksKOHz/OfvvtN5aSksJKS0tZbW3tS2+/rq6OBQYGMmNjY3b16tV2O/cPHz5kPXr0YEeOHFFZ7ufnx7y9vdt8z+DBg9k333yjsiwwMJCNHTuWMcZYYWEhA8AyMjJUatzc3Ninn37KGGPs559/ZoaGhiqvNzU1sR49erCoqCiV5WfOnGF9+vRhu3fvfsGjI6T90JUyQl6QJo+POXbsGIYOHYqYmBhYWlrCwsICf/nLX1BVVdU+B9+F8Xg8DBo0CJ9++inOnDmDmzdvIiAgAAkJCRg3bhwmTZqELVu2ID8/Xy1X0Kqrq5Geno5hw4bB3Nz8qbV8Ph/GxsawsbGBm5sbbG1twefzkZ2djbNnzyIrKwt37tyBQqF47u0zxhASEoLvvvsOJ0+ehK2t7aseEkdbWxsODg44ffo0t0ypVOL06dNwdXVt8z2urq4q9QAQFxfH1VtaWkIoFKrU1NTUIDk5matxdXWFXC5HWloaVxMfHw+lUgmRSMQtO3PmDDw9PREcHNzqyjMhnYkmJCfkBT1tfExeXl6b7+ms8TFFRUUoKSlBeHg49u7dC4VCgc8++wxvv/024uPjX/KIXz88Hg9CoRB//etf8cknn6CyshJHjx5FZGQkgoKCYGVlBR8fH8yePRujRo3q8Kc4a2pqkJ6ejqFDhz4zkD2Oz+fDyMgIRkZGGDFiBKqrq1FRUYG8vDw0NTWpTPf0pPk4GWPYuXMntm3bhpMnT2L8+PHtcVgqVq5cCX9/fzg6OsLZ2RkhISGor6/nxm75+fnBzMwMQUFBAB7dhnd3d8e//vUveHp64uDBg0hNTcUPP/wA4NH3cMWKFdi0aROsrKy4lhgDBw6ERCIBAIwaNQrTp0/H4sWLERoaiqamJixduhTz5s3jnrxMSEiAl5cXli9fjrlz53I/S9ra2jTYn3Q6CmWEvEaUSiUePnyIvXv3cmOBfv75Zzg4OCA/Px8jRoxQ8x5qHh6PB2NjYyxatAgffvgh5HI5fv31V0RFReHrr7+Gubk5F9Barki1p5ZAZmlp+UpPOAKPjsXQ0BCGhoawsrJCbW0tKioqcP36dWRlZaF///4QCAQwMjLiZhNgjOGnn37Cl19+iRMnTnTY+ENfX1/cuXMHgYGBkEqlsLe3R2xsLPdBpLS0VOXcTpgwAfv378fnn3+O9evXw8rKCtHR0VyPMgBYs2YN6uvr8dFHH0Eul2PSpEmIjY1VmZJr3759WLp0KaZOnQo+n4+5c+di+/bt3Ou//PIL7t+/j6CgIC4QAoC7uzvOnDnTIeeCkCehUEbIC2q54iCTyVSWy2QyriP944RC4VPrW77KZDKVOQ1lMhns7e25mscfJGhubkZVVRX3flNTU2hpaakMzh41ahSAR7/0KJQ9HY/HQ79+/eDv7w9/f3/U1NQgJiYGUVFRmDZtGgYMGAAfHx9IJBI4ODi8ckCrra1Feno6LCwsYGFh0T4H8f/j8XjQ19eHvr4+hg0bxk33dOPGDXh6eqJfv36YNWsWtLW1sXHjRsTExGDChAntug+PW7p0KZYuXdrma20FoHfeeQfvvPPOE9fH4/GwceNGbNy48Yk1RkZGXKPYtoSFhSEsLOyJrxPSmWhMGSEvSJPHx0ycOBHNzc0oLCzkaq5duwYAr3wVpjvS19fHe++9h4iICMhkMmzZsgVSqRSzZs3C6NGjsXbtWiQmJr7Q2K0WtbW1SEtLw5AhQ9o9kD2Ox+NBT08PQ4cOhaurK/bu3YsJEybg3//+N9asWQMrKytkZ2c/sU0EIaRzUEd/Ql7CoUOH4O/vj927d3PjYw4fPoy8vDwIBIJW42MSExPh7u6OzZs3c+Njvvrqq1ZTxmzevFllypirV6+2mjJGJpNx42MCAgLg6OjIXQlQKpVwcnKCnp4eQkJCoFQqsWTJEujr6+PUqVPqOVmvoYaGBpw6dQqRkZGIiYmBrq4uZs2ahdmzZ2PChAnQ0nr6TYi6ujqkpqbC3NwcQ4cO7aS9/j+MMURERGDJkiX4/vvvce/ePURGRiIpKQkuLi6YM2cO5s2bp3LVlhDSCdT45CchXdp3333HzM3Nmba2NnN2dmYXL17kXnN3d2f+/v4q9YcPH2bW1tZMW1ubjR49mh0/flzldaVSyb744gsmEAiYjo4Omzp1KsvPz1epqaysZPPnz2d6enpMX1+fBQQEsNraWpWa27dvszlz5jA9PT0mEAjYBx98wCorK9v34Ann4cOH7Pjx4+zDDz9kxsbGzMTEhAUEBLBff/2VyeXyVm0nkpOTWXR0NMvMzFRb6419+/ax3r17s19//VXlWMrLy9muXbuYWCxmx44dU9MZJaT7oitlhBDSTpqamnD27FlEREQgOjoaTU1N8PT0hEQiwZtvvomsrCx4e3tj7dq1+PTTT9WyjzExMQgICMB//vMfzJkzRy37QAhpG4UyQgjpAAqFAn/88Qc3YXp1dTX4fD7eeust7Nq1C7179+70fTp58iQWLlyIPXv2wNfXt9O3Twh5OgplhBDSwXJycuDm5gZra2uUlZWhsrISHh4e8PHxgYeHB/T09Dp8HxISEuDr64tdu3Zh4cKFHd57jRDy4ujpS0II6UDXrl2DWCzG4sWLceHCBRQVFSE+Ph7Dhg3DP//5T1hYWGD+/Pk4ePAgqqurO2Qfzp8/j3nz5mH79u0UyAjRYHSljBBCOkhhYSHc3NywcOFCbN68uVUYUiqVuHr1KneLs7CwEFOmTIGPjw/XS+xVA1RSUhJmz56N4OBgfPLJJxTICNFgFMoIIaSD3L17F//973+xfPnyZ4Yhxhhyc3MRERGBqKgo5OTkwN3dHRKJBF5eXjA2Nn7hQJWamgpvb29s3LgRy5Yto0BGiIajUEYIIRqGMYbr169zAe3y5cuYOHEiJBIJvL29IRAInhmwLl++DE9PT/z973/HqlWrKJAR0gVQKCOEEA3GGMONGzcQGRmJqKgoXLp0CS4uLvDx8YGPjw/MzMxaBa6srCzMmDEDq1atwt/+9jcKZIR0ETTQnxCiYufOnbCwsICuri5EIhEuXbr01Prw8HCMHDkSurq6sLW1xW+//abyOmMMgYGBMDU1Ra9evSAWi1FQUKBSU1VVhQULFkBfXx+GhoZYtGgR6urqVGpOnjwJFxcX9O3bFyYmJpg7dy5u3LjRLsesyXg8HiwtLbF69WpcuHABxcXFeOeddxATEwMbGxtMmTIFISEhKC4u5m6Benl5YenSpRTICOlqOrlZLSFEgx08eJBpa2uzPXv2sOzsbLZ48WJmaGjIZDJZm/UXLlxgPXr0YFu2bGE5OTns888/Zz179mSZmZlczebNm5mBgQGLjo5mV65cYd7e3szS0pI1NDRwNdOnT2d2dnbs4sWL7Pz582z48OFs/vz53OtFRUVMR0eH/e1vf2PXr19naWlpzM3NjY0bN67jToaGUyqVrKysjO3cuZNNmTKFaWlpsVGjRrG+ffuydevWMaVSqe5dJIS8IAplhBCOs7MzW7JkCfd3hULBBg4cyIKCgtqsf/fdd5mnp6fKMpFIxD7++GPG2KPgIBQK2datW7nX5XI509HRYQcOHGCMMZaTk8MAsJSUFK7mxIkTjMfjsdu3bzPGGAsPD2daWlpMoVBwNb/++ivj8XissbHxFY+661MqlayiooJt2LCBTZs2TeU8EUK6Drp9SQgBADQ2NiItLQ1isZhbxufzIRaLkZSU1OZ7kpKSVOoBwMPDg6svLi6GVCpVqTEwMIBIJOJqkpKSYGhoCEdHR65GLBaDz+cjOTkZAODg4AA+n49///vfUCgUqK6uxn/+8x+IxWL07NmzfU5AF8bj8WBiYoJ//OMfOHXqFPh8+q+dkK6IfnIJIQAetW9QKBQQCAQqywUCAaRSaZvvkUqlT61v+fqsmgEDBqi8rqWlBSMjI67G0tISp06dwvr166GjowNDQ0PcunULhw8ffsmjJYQQzUOhjBCi8aRSKRYvXgx/f3+kpKTg7Nmz0NbWxttvvw1GD5ATQl4TWureAUKIZjA2NkaPHj0gk8lUlstkMgiFwjbfIxQKn1rf8lUmk8HU1FSlxt7enqupqKhQWUdzczOqqqq49+/cuRMGBgbYsmULV/Pf//4XgwcPRnJyMlxcXF7iiAkhRLPQlTJCCABAW1sbDg4OOH36NLdMqVTi9OnTcHV1bfM9rq6uKvUAEBcXx9VbWlpCKBSq1NTU1CA5OZmrcXV1hVwuR1paGlcTHx8PpVIJkUgEALh//36rcVI9evTg9pEQQl4L6n7SgBCiOQ4ePMh0dHRYWFgYy8nJYR999BEzNDRkUqmUMcbY+++/z9atW8fVX7hwgWlpabFt27ax3NxctmHDhjZbYhgaGrKjR4+yq1evMh8fnzZbYowbN44lJyezP/74g1lZWam0xDh9+jTj8Xjsf//3f9m1a9dYWloa8/DwYEOGDGH379/vhDNDCCEdj0IZIUTFd999x8zNzZm2tjZzdnZmFy9e5F5zd3dn/v7+KvWHDx9m1tbWTFtbm40ePZodP35c5XWlUsm++OILJhAImI6ODps6dSrLz89XqamsrGTz589nenp6TF9fnwUEBLDa2lqVmgMHDrBx48axPn36MBMTE+bt7c1yc3Pb9+C7iR07drAhQ4YwHR0d5uzszJKTk59af/jwYTZixAimo6PDxowZ88TvsVAoZLq6umzq1Kns2rVrKjWVlZXsvffeY3379mUGBgbsww8/bPU9vnLlCps0aRLT0dFhgwYNYsHBwe1zwIR0ERTKCCGkG9HUBsHV1dVMIBCwBQsWsKysLHbgwAHWq1cvtnv37o47GYRoGAplhBDSjWhqg+Dvv/+e9evXjz18+JCrWbt2LRsxYsQrHjEhXQcN9CeEkG5CkxsEJyUlwc3NDdra2irbyc/Px717917xyAnpGiiUEUJIN6HJDYKftJ0/b4OQ1x2FMkIIIYQQDUChjBBCuomObhD8tJpnNQh+0nb+vA1CXncUygghpJvQ5AbBrq6uOHfuHJqamlS2M2LECPTr1+8Vj5yQLkLdTxoQQgjpPJraIFgulzOBQMDef/99lpWVxQ4ePMh69+5NLTFIt0KhjBDSraijceqmTZuYq6sr69WrFzMwMGhzOyUlJWzmzJmsV69ezMTEhK1evZo1NTW90rE+iaY2CP5z81gzMzO2efPm9j1wQjQcjzHG1H21jhBCOsOhQ4fg5+eH0NBQiEQihISEIDw8HPn5+a2eDgSAxMREuLm5ISgoCF5eXti/fz+Cg4ORnp6OMWPGAACCg4MRFBSEX375BZaWlvjiiy+QmZmJnJwc6OrqAgA2bNgAQ0ND3Lp1Cz///DPkcrnKdhQKBezt7SEUCrF161aUl5fDz88PixcvxldffdXh54UQohkolBFCug2RSAQnJyfs2LEDwKPxVIMHD8ayZcuwbt26VvW+vr6or69HTEwMt8zFxQX29vYIDQ0FYwwDBw7EqlWrsHr1agBAdXU1BAIBwsLCMG/ePJX1hYWFYcWKFa1C2YkTJ+Dl5YWysjKuDURoaCjWrl2LO3fuqPTuIoS8vmigPyGkW1BX49TnkZSUBFtbW5U+XR4eHqipqUF2dvZzr4cQ0rVRKCOEdAvqapz6PKhxKiEEoFBGCCGEEKIRKJQRQroFdTVOfR7UOJUQAlAoI4R0E+pqnPo8XF1dkZmZqdL1Pi4uDvr6+rCxsXnu9RBCujYtde8AIYR0lpUrV8Lf3x+Ojo5wdnZGSEgI6uvrERAQAADw8/ODmZkZgoKCAADLly+Hu7s7/vWvf8HT0xMHDx5EamoqfvjhBwAAj8fDihUrsGnTJlhZWXEtMQYOHAiJRMJtt7S0FFVVVSgtLYVCocDly5cBAMOHD4eenh7eeust2NjY4P3338eWLVsglUrx+eefY8mSJdDR0enUc0QIUSO1dkkjhJBOpo7Gqf7+/gxAqz8JCQlczY0bN9iMGTNYr169mLGxMVu1alWHNY8lhGgm6lNGCCGEEKIBaEwZIYQQQogGoFBGCCGEEKIBKJQRQgghhGgACmWEEEIIIRqAQhkhhBBCiAagUEYIIYQQogEolBFCCCGEaAAKZYQQQgghGoBCGSGEEEKIBqBQRgghhBCiASiUEUIIIYRoAAplhBBCCCEagEIZIYQQQogGoFBGCCGEEKIBKJQRQgghhGgACmWEEEIIIRqAQhkhhBBCiAagUEYIIYQQogEolBFCCCGEaID/D/uBbTC5w0xQAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 600x650 with 1 Axes>"
       ]
@@ -153,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f854ad33",
+   "id": "abad015e",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/snudda/place/place.py b/snudda/place/place.py
index 0101b5d79..352c6e24a 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -586,7 +586,7 @@ def avoid_edges_parallel(self):
         if self.d_view is None:
             # Make sure we use the same random seeds if we run in serial, as would have been used in parallel
 
-            modified_neurons = SnuddaPlace.avoid_edges_helper(bend_neuron_info=bend_neuron_info, network_path=self.network_path)
+            modified_neurons = self.avoid_edges_helper(bend_neuron_info=bend_neuron_info, network_path=self.network_path)
 
         else:
 
@@ -606,9 +606,6 @@ def avoid_edges_parallel(self):
             cmd_str = f"sp = SnuddaPlace(config_file=config_file,network_path=network_path,snudda_data=snudda_data)"
             self.d_view.execute(cmd_str, block=True)
 
-            #import pdb
-            #pdb.set_trace()
-
             cmd_str3 = f"modified_neurons = SnuddaPlace.avoid_edges_helper(bend_neuron_info=bend_neuron_info, network_path=network_path)"
             self.d_view.execute(cmd_str3, block=True)
 

From bab251ad8a5db33e53a6decbdcf7fda7384db43e Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 14:27:37 +0200
Subject: [PATCH 37/43] adding debug info

---
 snudda/neurons/neuron_morphology_extended.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/snudda/neurons/neuron_morphology_extended.py b/snudda/neurons/neuron_morphology_extended.py
index 9cf85cfdc..e21084285 100644
--- a/snudda/neurons/neuron_morphology_extended.py
+++ b/snudda/neurons/neuron_morphology_extended.py
@@ -424,6 +424,9 @@ def dendrite_input_locations(self, synapse_density_str, rng, num_locations,
                 syn_idx = rng.choice(a=dend_idx, size=num_locations, replace=True,
                                      p=expected_synapses[dend_idx] / expected_sum)
             except:
+                print(f"dend_idx={dend_idx}\n"
+                      f"num_locations={num_locations}\n"
+                      f"p={expected_synapses[dend_idx] / expected_sum}")
                 import traceback
                 self.write_log(traceback.format_exc(), is_error=True)
                 import pdb

From 65c7c6cb6cb33b6e8bb84b2b3eacf1c9816cef26 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 14:54:54 +0200
Subject: [PATCH 38/43] Update unit test

---
 snudda/detect/detect.py         | 7 +++++--
 snudda/plotting/plot_network.py | 5 +++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/snudda/detect/detect.py b/snudda/detect/detect.py
index 682967ee9..45d452a41 100644
--- a/snudda/detect/detect.py
+++ b/snudda/detect/detect.py
@@ -2304,11 +2304,14 @@ def load_neuron(self, neuron_info, use_cache=True):
         if use_cache and neuron_id in self.neuron_cache:
             return self.neuron_cache[neuron_id]
 
-        if os.path.isfile(snudda_parse_path(neuron_info["morphology"], self.snudda_data)):
-            morphology_path = neuron_info["morphology"]
+        morph_path = snudda_parse_path(neuron_info["morphology"], self.snudda_data)
+        if os.path.isfile(morph_path):
+            morphology_path = morph_path
         else:
             morphology_path = None  # Get morpholog automatically from morphology_key
 
+        print(f"morphology_path = {morphology_path}")
+
         # Clone prototype neuron (it is centred, and not rotated)
         neuron = self.prototype_neurons[neuron_info["name"]].clone(parameter_key=neuron_info["parameterKey"],
                                                                    morphology_key=neuron_info["morphologyKey"],
diff --git a/snudda/plotting/plot_network.py b/snudda/plotting/plot_network.py
index 700ee27ab..f7da4ccb5 100644
--- a/snudda/plotting/plot_network.py
+++ b/snudda/plotting/plot_network.py
@@ -184,8 +184,9 @@ def load_neuron(self, neuron_info=None, neuron_id=None):
                                                                   load_morphology=True,
                                                                   virtual_neuron=False)
 
-        if os.path.isfile(snudda_parse_path(neuron_info["morphology"], self.snudda_data)):
-            morphology_path = neuron_info["morphology"]
+        morph_path = snudda_parse_path(neuron_info["morphology"], self.snudda_data)
+        if os.path.isfile(morph_path):
+            morphology_path = morph_path
         else:
             morphology_path = None  # Get morpholog automatically from morphology_key
 

From d86903146542244b65fcba2d3c12311e82be77ce Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 15:16:34 +0200
Subject: [PATCH 39/43] Fixing unit test

---
 snudda/detect/project.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/snudda/detect/project.py b/snudda/detect/project.py
index f8ab577bd..580d5d284 100644
--- a/snudda/detect/project.py
+++ b/snudda/detect/project.py
@@ -249,9 +249,10 @@ def connect_projection_helper(self, pre_neuron_type, post_neuron_type, connectio
                 target_name = [post_name_list[x] for x in d_idx]
                 axon_dist = d[d_idx]
 
-                n_synapses = self.rng.normal(number_of_synapses[0],
-                                             number_of_synapses[1],
-                                             len(target_id)).astype(int)
+                n_synapses = np.maximum(0, self.rng.normal(number_of_synapses[0],
+                                                           number_of_synapses[1],
+                                                           len(target_id))).astype(int)
+
 
                 for t_id, t_name, n_syn, ax_dist in zip(target_id, target_name, n_synapses, axon_dist):
 
@@ -272,8 +273,8 @@ def connect_projection_helper(self, pre_neuron_type, post_neuron_type, connectio
 
                     # We are not guaranteed to get n_syn positions, so use len(sec_x) to get how many after
                     # TODO: Fix so dendrite_input_locations always returns  n_syn synapses
-                    xyz, sec_id, sec_x, dist_to_soma = morph.dendrite_input_locations(dendrite_synapse_density,
-                                                                                      self.rng,
+                    xyz, sec_id, sec_x, dist_to_soma = morph.dendrite_input_locations(synapse_density_str=dendrite_synapse_density,
+                                                                                      rng=self.rng,
                                                                                       num_locations=n_syn)
 
                     # We need to convert xyz into voxel coordinates to match data format of synapse matrix

From 62c9c548e60deb19bebac02432dce0d658f52a70 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 15:52:30 +0200
Subject: [PATCH 40/43] Set random seed

---
 tests/test_degeneration.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index 2c5e756a2..e34d6b423 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -65,7 +65,8 @@ def setUp(self):
                                                      updated_network_file=network_B_file,
                                                      output_network_file=network_C_file,
                                                      original_snudda_data_dir=original_data_dir,
-                                                     updated_snudda_data_dir=updated_data_dir)
+                                                     updated_snudda_data_dir=updated_data_dir,
+                                                     random_seed=11)
 
         swap.write_new_network_file()
 

From c3d36f3c5b7912e3e055c9bf9a5e218f5ae3d2a8 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 16:15:26 +0200
Subject: [PATCH 41/43] Test

---
 tests/test_degeneration.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index e34d6b423..3484a1735 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -33,23 +33,23 @@ def setUp(self):
         network_A_pos_file = os.path.join(network_A, "network-neuron-positions.hdf5")
         self.set_neuron_positions(network_file=network_A_pos_file)
 
-        sd = SnuddaDetect(network_path=network_A, random_seed=9)
+        sd = SnuddaDetect(network_path=network_A, random_seed=11)
         sd.detect()
 
-        spr = SnuddaPrune(network_path=network_A, random_seed=9)
+        spr = SnuddaPrune(network_path=network_A, random_seed=13)
         spr.prune()
 
         # Setup degenerated network
-        sp2 = SnuddaPlace(network_path=network_B, random_seed=9)
+        sp2 = SnuddaPlace(network_path=network_B, random_seed=9)  # same as original
         sp2.place()
 
         network_B_pos_file = os.path.join(network_B, "network-neuron-positions.hdf5")
         self.set_neuron_positions(network_file=network_B_pos_file)
 
-        sd2 = SnuddaDetect(network_path=network_B, random_seed=9)
+        sd2 = SnuddaDetect(network_path=network_B, random_seed=11)
         sd2.detect()
 
-        spr2 = SnuddaPrune(network_path=network_B, random_seed=9)
+        spr2 = SnuddaPrune(network_path=network_B, random_seed=13)
         spr2.prune()
 
         network_A_file = os.path.join(network_A, "network-synapses.hdf5")
@@ -66,7 +66,7 @@ def setUp(self):
                                                      output_network_file=network_C_file,
                                                      original_snudda_data_dir=original_data_dir,
                                                      updated_snudda_data_dir=updated_data_dir,
-                                                     random_seed=11)
+                                                     random_seed=15)
 
         swap.write_new_network_file()
 
@@ -159,13 +159,13 @@ def test_something(self):
         #import pdb
         #pdb.set_trace()
 
-        self.assertEqual(orig_load.data["nSynapses"], 165)
+        # self.assertEqual(orig_load.data["nSynapses"], 165)
 
         # Verify that it should be 99 synapses -- now it is just a regression test
         # Old version gave 99, new gives 165 --- CHECK WHY!
 
         # TODO: CHECK WHY NOT 99 SYNAPSES NOW
-        self.assertEqual(degen_load.data["nSynapses"], 155)  # -- Ilaria, we need to check what the true value should be?
+        # self.assertEqual(degen_load.data["nSynapses"], 155)  # -- Ilaria, we need to check what the true value should be?
 
 
 if __name__ == '__main__':

From cc638a8fbb6f45db8ef655643cbdde62068829c3 Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 16:43:26 +0200
Subject: [PATCH 42/43] Sorting list of keys, to make sure order is the same

---
 snudda/neurons/neuron_prototype.py | 4 ++--
 snudda/place/region_mesh_redux.py  | 1 +
 tests/test_degeneration.py         | 4 ++--
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/snudda/neurons/neuron_prototype.py b/snudda/neurons/neuron_prototype.py
index 08749268b..9a6c32a94 100644
--- a/snudda/neurons/neuron_prototype.py
+++ b/snudda/neurons/neuron_prototype.py
@@ -152,7 +152,7 @@ def get_parameter_key(self, parameter_id):
         #                                   f"\nNeuron path: {self.neuron_path}")
 
         if self.parameter_info:
-            par_key_list = list(self.parameter_info.keys())
+            par_key_list = sorted(list(self.parameter_info.keys()))
             par_key = par_key_list[parameter_id % len(par_key_list)]
         else:
             par_key = None
@@ -171,7 +171,7 @@ def get_morph_key(self,  morphology_id, parameter_id=None, parameter_key=None):
 
         if self.meta_info:
             assert par_key in self.meta_info, f"Parameter key {par_key} missing in {self.meta_path}"
-            morph_key_list = list(self.meta_info[par_key].keys())
+            morph_key_list = sorted(list(self.meta_info[par_key].keys()))
             assert len(morph_key_list) > 0, f"No morphologies available for parameter key {par_key} in {self.meta_path}"
             morph_key = morph_key_list[morphology_id % len(morph_key_list)]
         else:
diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py
index 89fba2937..080bed482 100644
--- a/snudda/place/region_mesh_redux.py
+++ b/snudda/place/region_mesh_redux.py
@@ -100,6 +100,7 @@ def get_line_set(self, neurons):
 
         return line_sets
 
+
 class NeuronPlacer:
 
     def __init__(self, mesh_path: str, d_min: float, random_seed=None, rng=None,
diff --git a/tests/test_degeneration.py b/tests/test_degeneration.py
index 3484a1735..33f2a7531 100644
--- a/tests/test_degeneration.py
+++ b/tests/test_degeneration.py
@@ -159,13 +159,13 @@ def test_something(self):
         #import pdb
         #pdb.set_trace()
 
-        # self.assertEqual(orig_load.data["nSynapses"], 165)
+        self.assertEqual(orig_load.data["nSynapses"], 165)
 
         # Verify that it should be 99 synapses -- now it is just a regression test
         # Old version gave 99, new gives 165 --- CHECK WHY!
 
         # TODO: CHECK WHY NOT 99 SYNAPSES NOW
-        # self.assertEqual(degen_load.data["nSynapses"], 155)  # -- Ilaria, we need to check what the true value should be?
+        self.assertEqual(degen_load.data["nSynapses"], 155)  # -- Ilaria, we need to check what the true value should be?
 
 
 if __name__ == '__main__':

From bdaf814d8f0422a8b1eecd6b77963a7c0994505a Mon Sep 17 00:00:00 2001
From: Johannes Hjorth <hjorth@kth.se>
Date: Mon, 23 Oct 2023 17:44:17 +0200
Subject: [PATCH 43/43] Fixed mkdir

---
 snudda/place/place.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/snudda/place/place.py b/snudda/place/place.py
index 352c6e24a..9de74bff9 100644
--- a/snudda/place/place.py
+++ b/snudda/place/place.py
@@ -583,6 +583,11 @@ def avoid_edges_parallel(self):
                                          neuron_random_seed[neuron.neuron_id],
                                          volume_id, mesh_file))
 
+        bend_morph_path = os.path.join(self.network_path, "modified_morphologies")
+
+        if not os.path.isdir(bend_morph_path):
+            os.mkdir(bend_morph_path)
+
         if self.d_view is None:
             # Make sure we use the same random seeds if we run in serial, as would have been used in parallel
 
@@ -627,9 +632,6 @@ def avoid_edges_helper(bend_neuron_info, network_path):
         bend_morph = dict()
         bend_morph_path = os.path.join(network_path, "modified_morphologies")
 
-        if not os.path.isdir(bend_morph_path):
-            os.mkdir(bend_morph_path)
-
         modified_morphologies = []
 
         for neuron_id, neuron_name, swc_filename, position, rotation, random_seed, volume_id, mesh_file \