From dfe9776bc4d91e37f46ff8714a64548b5c1a05ec Mon Sep 17 00:00:00 2001
From: Daniel Bauer <daniel.j.bauer@fau.de>
Date: Wed, 2 Mar 2022 18:24:02 +0100
Subject: [PATCH] Fix FreeSlip boundary condition

---
 lbmpy/boundaries/boundaryconditions.py |  24 ++--
 lbmpy_tests/test_boundary_handling.py  | 162 ++++++++++++++++++++++++-
 lbmpy_tests/test_free_slip.ipynb       |  23 ++--
 3 files changed, 192 insertions(+), 17 deletions(-)

diff --git a/lbmpy/boundaries/boundaryconditions.py b/lbmpy/boundaries/boundaryconditions.py
index 29e20bc4..b21208cf 100644
--- a/lbmpy/boundaries/boundaryconditions.py
+++ b/lbmpy/boundaries/boundaryconditions.py
@@ -120,9 +120,10 @@ class FreeSlip(LbBoundary):
 
     Args:
         stencil: LBM stencil which is used for the simulation
-        normal_direction: optional normal direction. If the Free slip boundary is applied to a certain side in the
-                          domain it is not necessary to calculate the normal direction since it can be stated for all
-                          boundary cells. This reduces the memory space for the index array significantly.
+        normal_direction: optional normal direction pointing from wall to fluid.
+                          If the Free slip boundary is applied to a certain side in the domain it is not necessary
+                          to calculate the normal direction since it can be stated for all boundary cells.
+                          This reduces the memory space for the index array significantly.
         name: optional name of the boundary.
     """
 
@@ -182,7 +183,12 @@ class FreeSlip(LbBoundary):
                     normal_direction[i] = direction[i]
                     ref_direction = MirroredStencilDirections.mirror_stencil(ref_direction, i)
 
-            ref_direction = inverse_direction(ref_direction)
+            # convex corner special case:
+            if all(n == 0 for n in normal_direction):
+                normal_direction = direction
+            else:
+                ref_direction = inverse_direction(ref_direction)
+
             for i, cell_name in zip(range(dim), self.additional_data):
                 cell[cell_name[0]] = -normal_direction[i]
             cell['ref_dir'] = self.stencil.index(ref_direction)
@@ -208,13 +214,14 @@ class FreeSlip(LbBoundary):
 
     def get_additional_code_nodes(self, lb_method):
         if self.normal_direction:
-            return [MirroredStencilDirections(self.stencil, self.mirror_axis)]
+            return [MirroredStencilDirections(self.stencil, self.mirror_axis), NeighbourOffsetArrays(lb_method.stencil)]
         else:
-            return []
+            return [NeighbourOffsetArrays(lb_method.stencil)]
 
     def __call__(self, f_out, f_in, dir_symbol, inv_dir, lb_method, index_field):
+        neighbor_offset = NeighbourOffsetArrays.neighbour_offset(dir_symbol, lb_method.stencil)
         if self.normal_direction:
-            normal_direction = self.normal_direction
+            tangential_offset = tuple(offset + normal for offset, normal in zip(neighbor_offset, self.normal_direction))
             mirrored_stencil_symbol = MirroredStencilDirections._mirrored_symbol(self.mirror_axis)
             mirrored_direction = inv_dir[sp.IndexedBase(mirrored_stencil_symbol, shape=(1,))[dir_symbol]]
         else:
@@ -222,10 +229,11 @@ class FreeSlip(LbBoundary):
             for i, cell_name in zip(range(self.dim), self.additional_data):
                 normal_direction.append(index_field[0](cell_name[0]))
             normal_direction = tuple(normal_direction)
+            tangential_offset = tuple(offset + normal for offset, normal in zip(neighbor_offset, normal_direction))
 
             mirrored_direction = index_field[0]('ref_dir')
 
-        return Assignment(f_in(inv_dir[dir_symbol]), f_in[normal_direction](mirrored_direction))
+        return Assignment(f_in.center(inv_dir[dir_symbol]), f_out[tangential_offset](mirrored_direction))
 
 
 # end class FreeSlip
diff --git a/lbmpy_tests/test_boundary_handling.py b/lbmpy_tests/test_boundary_handling.py
index a60c90fb..d94cfa63 100644
--- a/lbmpy_tests/test_boundary_handling.py
+++ b/lbmpy_tests/test_boundary_handling.py
@@ -112,6 +112,122 @@ def test_simple(target):
     assert (all(dh.cpu_arrays['pdfs'][0, 2:4, 8] == 5))
 
 
+@pytest.mark.parametrize("given_normal", [True, False])
+def test_free_slip(given_normal):
+    # check if Free slip BC is applied correctly
+
+    stencil = LBStencil(Stencil.D2Q9)
+    dh = create_data_handling(domain_size=(4, 4),)
+    src1 = dh.add_array('src1', values_per_cell=stencil.Q)
+    dh.fill('src1', 0.0, ghost_layers=True)
+
+    shape = dh.gather_array('src1', ghost_layers=True).shape
+
+    num = 0
+    for x in range(shape[0]):
+        for y in range(shape[1]):
+            for direction in range(shape[2]):
+                dh.cpu_arrays[src1.name][x, y, direction] = num
+                num += 1
+
+    method = create_lb_method(lbm_config=LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=1.8))
+
+    bh = LatticeBoltzmannBoundaryHandling(method, dh, 'src1', name="bh1")
+    if given_normal:
+        free_slipN = FreeSlip(stencil=stencil, normal_direction=(0, -1))
+        free_slipS = FreeSlip(stencil=stencil, normal_direction=(0, 1))
+        free_slipE = FreeSlip(stencil=stencil, normal_direction=(-1, 0))
+        free_slipW = FreeSlip(stencil=stencil, normal_direction=(1, 0))
+
+        bh.set_boundary(free_slipN, slice_from_direction('N', dh.dim))
+        bh.set_boundary(free_slipS, slice_from_direction('S', dh.dim))
+        bh.set_boundary(free_slipE, slice_from_direction('E', dh.dim))
+        bh.set_boundary(free_slipW, slice_from_direction('W', dh.dim))
+    else:
+        free_slip = FreeSlip(stencil=stencil)
+
+        bh.set_boundary(free_slip, slice_from_direction('N', dh.dim))
+        bh.set_boundary(free_slip, slice_from_direction('S', dh.dim))
+        bh.set_boundary(free_slip, slice_from_direction('E', dh.dim))
+        bh.set_boundary(free_slip, slice_from_direction('W', dh.dim))
+
+    bh()
+
+    mirrored_dirN = {6: 8, 1: 2, 5: 7}
+    mirrored_dirS = {7: 5, 2: 1, 8: 6}
+    mirrored_dirE = {6: 5, 4: 3, 8: 7}
+    mirrored_dirW = {5: 6, 3: 4, 7: 8}
+
+    # check North
+    assert dh.cpu_arrays[src1.name][1, -1, mirrored_dirN[6]] == dh.cpu_arrays[src1.name][1, -2, 6]
+    assert dh.cpu_arrays[src1.name][1, -1, mirrored_dirN[1]] == dh.cpu_arrays[src1.name][1, -2, 1]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][i, -1, mirrored_dirN[6]] == dh.cpu_arrays[src1.name][i, -2, 6]
+        assert dh.cpu_arrays[src1.name][i, -1, mirrored_dirN[1]] == dh.cpu_arrays[src1.name][i, -2, 1]
+        assert dh.cpu_arrays[src1.name][i, -1, mirrored_dirN[5]] == dh.cpu_arrays[src1.name][i, -2, 5]
+
+    assert dh.cpu_arrays[src1.name][4, -1, mirrored_dirN[1]] == dh.cpu_arrays[src1.name][4, -2, 1]
+    assert dh.cpu_arrays[src1.name][4, -1, mirrored_dirN[5]] == dh.cpu_arrays[src1.name][4, -2, 5]
+
+    # check East
+    assert dh.cpu_arrays[src1.name][-1, 1, mirrored_dirE[6]] == dh.cpu_arrays[src1.name][-2, 1, 6]
+    assert dh.cpu_arrays[src1.name][-1, 1, mirrored_dirE[4]] == dh.cpu_arrays[src1.name][-2, 1, 4]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][-1, i, mirrored_dirE[6]] == dh.cpu_arrays[src1.name][-2, i, 6]
+        assert dh.cpu_arrays[src1.name][-1, i, mirrored_dirE[4]] == dh.cpu_arrays[src1.name][-2, i, 4]
+        assert dh.cpu_arrays[src1.name][-1, i, mirrored_dirE[8]] == dh.cpu_arrays[src1.name][-2, i, 8]
+
+    assert dh.cpu_arrays[src1.name][-1, 4, mirrored_dirE[4]] == dh.cpu_arrays[src1.name][-2, 4, 4]
+    assert dh.cpu_arrays[src1.name][-1, 4, mirrored_dirE[8]] == dh.cpu_arrays[src1.name][-2, 4, 8]
+
+    # check South
+    assert dh.cpu_arrays[src1.name][1, 0, mirrored_dirS[8]] == dh.cpu_arrays[src1.name][1, 1, 8]
+    assert dh.cpu_arrays[src1.name][1, 0, mirrored_dirS[2]] == dh.cpu_arrays[src1.name][1, 1, 2]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][i, 0, mirrored_dirS[7]] == dh.cpu_arrays[src1.name][i, 1, 7]
+        assert dh.cpu_arrays[src1.name][i, 0, mirrored_dirS[2]] == dh.cpu_arrays[src1.name][i, 1, 2]
+        assert dh.cpu_arrays[src1.name][i, 0, mirrored_dirS[8]] == dh.cpu_arrays[src1.name][i, 1, 8]
+
+    assert dh.cpu_arrays[src1.name][4, 0, mirrored_dirS[2]] == dh.cpu_arrays[src1.name][4, 1, 2]
+    assert dh.cpu_arrays[src1.name][4, 0, mirrored_dirS[7]] == dh.cpu_arrays[src1.name][4, 1, 7]
+
+    # check West
+    assert dh.cpu_arrays[src1.name][0, 1, mirrored_dirW[5]] == dh.cpu_arrays[src1.name][1, 1, 5]
+    assert dh.cpu_arrays[src1.name][0, 1, mirrored_dirW[3]] == dh.cpu_arrays[src1.name][1, 1, 3]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][0, i, mirrored_dirW[5]] == dh.cpu_arrays[src1.name][1, i, 5]
+        assert dh.cpu_arrays[src1.name][0, i, mirrored_dirW[3]] == dh.cpu_arrays[src1.name][1, i, 3]
+        assert dh.cpu_arrays[src1.name][0, i, mirrored_dirW[7]] == dh.cpu_arrays[src1.name][1, i, 7]
+
+    assert dh.cpu_arrays[src1.name][0, 4, mirrored_dirW[3]] == dh.cpu_arrays[src1.name][1, 4, 3]
+    assert dh.cpu_arrays[src1.name][0, 4, mirrored_dirW[7]] == dh.cpu_arrays[src1.name][1, 4, 7]
+
+    if given_normal:
+        # check corners --> determined by the last boundary applied there.
+        # SouthWest --> West
+        assert dh.cpu_arrays[src1.name][0, 0, mirrored_dirW[5]] == dh.cpu_arrays[src1.name][1, 0, 5]
+        # NorthWest --> West
+        assert dh.cpu_arrays[src1.name][0, -1, mirrored_dirW[7]] == dh.cpu_arrays[src1.name][1, -1, 7]
+        # NorthEast --> East
+        assert dh.cpu_arrays[src1.name][-1, -1, mirrored_dirE[8]] == dh.cpu_arrays[src1.name][-2, -1, 8]
+        # SouthEast --> East
+        assert dh.cpu_arrays[src1.name][-1, 0, mirrored_dirE[6]] == dh.cpu_arrays[src1.name][-2, 0, 6]
+    else:
+        # check corners --> this time the normals are calculated correctly in the corners
+        # SouthWest --> Normal = (1, 1); dir 7 --> 6
+        assert dh.cpu_arrays[src1.name][0, 0, 6] == dh.cpu_arrays[src1.name][1, 1, 7]
+        # NorthWest --> Normal = (1, -1); dir 8 --> 5
+        assert dh.cpu_arrays[src1.name][0, -1, 8] == dh.cpu_arrays[src1.name][1, -2, 5]
+        # NorthEast --> Normal = (-1, -1); dir 7 --> 6
+        assert dh.cpu_arrays[src1.name][-1, -1, 7] == dh.cpu_arrays[src1.name][-2, -2, 6]
+        # SouthEast --> Normal = (-1, 1); dir 5 --> 8
+        assert dh.cpu_arrays[src1.name][-1, 0, 5] == dh.cpu_arrays[src1.name][-2, 1, 8]
+
+
 def test_free_slip_index_list():
     stencil = LBStencil(Stencil.D2Q9)
     dh = create_data_handling(domain_size=(4, 4), periodicity=(False, False))
@@ -181,6 +297,50 @@ def test_free_slip_index_list():
             assert normal == normal_north_east
 
 
+def test_free_slip_index_list_convex_corner():
+    stencil = LBStencil(Stencil.D2Q9)
+    dh = create_data_handling(domain_size=(4, 4))
+    src = dh.add_array('src', values_per_cell=len(stencil))
+    dh.fill('src', 0.0, ghost_layers=True)
+
+    lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=1.8)
+    method = create_lb_method(lbm_config=lbm_config)
+
+    def bh_callback(x, y):
+        radius = 2
+        x_mid = 2
+        y_mid = 2
+        return (x - x_mid) ** 2 + (y - y_mid) ** 2 > radius ** 2
+
+    bh = LatticeBoltzmannBoundaryHandling(method, dh, 'src', name="bh")
+
+    free_slip = FreeSlip(stencil=stencil)
+    bh.set_boundary(free_slip, mask_callback=bh_callback)
+
+    bh.prepare()
+    for b in dh.iterate():
+        for b_obj, idx_arr in b[bh._index_array_name].boundary_object_to_index_list.items():
+            index_array = idx_arr
+
+    # correct index array for this case with convex corners
+    test = [(2, 1, 2, 0, 1, 2), (2, 1, 3, 1, 0, 3), (2, 1, 7, 1, 1, 7),
+            (2, 1, 8, 0, 1, 7), (3, 1, 2, 0, 1, 2), (3, 1, 4, -1, 0, 4),
+            (3, 1, 7, 0, 1, 8), (3, 1, 8, -1, 1, 8), (1, 2, 2, 0, 1, 2),
+            (1, 2, 3, 1, 0, 3), (1, 2, 5, 1, 0, 7), (1, 2, 7, 1, 1, 7),
+            (2, 2, 7, 1, 1, 7), (3, 2, 8, -1, 1, 8), (4, 2, 2, 0, 1, 2),
+            (4, 2, 4, -1, 0, 4), (4, 2, 6, -1, 0, 8), (4, 2, 8, -1, 1, 8),
+            (1, 3, 1, 0, -1, 1), (1, 3, 3, 1, 0, 3), (1, 3, 5, 1, -1, 5),
+            (1, 3, 7, 1, 0, 5), (2, 3, 5, 1, -1, 5), (3, 3, 6, -1, -1, 6),
+            (4, 3, 1, 0, -1, 1), (4, 3, 4, -1, 0, 4), (4, 3, 6, -1, -1, 6),
+            (4, 3, 8, -1, 0, 6), (2, 4, 1, 0, -1, 1), (2, 4, 3, 1, 0, 3),
+            (2, 4, 5, 1, -1, 5), (2, 4, 6, 0, -1, 5), (3, 4, 1, 0, -1, 1),
+            (3, 4, 4, -1, 0, 4), (3, 4, 5, 0, -1, 6), (3, 4, 6, -1, -1, 6)]
+
+    for i, cell in enumerate(index_array):
+        for j in range(len(cell)):
+            assert cell[j] == test[i][j]
+
+
 def test_free_slip_equivalence():
     # check if Free slip BC does the same if the normal direction is specified or not
 
@@ -214,7 +374,7 @@ def test_free_slip_equivalence():
     bh1()
     bh2()
 
-    assert np.array_equal(dh.cpu_arrays['src1'], dh.cpu_arrays['src2'])
+    assert np.array_equal(dh.gather_array('src1'), dh.gather_array('src2'))
 
 
 def test_exotic_boundaries():
diff --git a/lbmpy_tests/test_free_slip.ipynb b/lbmpy_tests/test_free_slip.ipynb
index 179b8f36..08069b40 100644
--- a/lbmpy_tests/test_free_slip.ipynb
+++ b/lbmpy_tests/test_free_slip.ipynb
@@ -161,7 +161,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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7oQ8AAAAAoIt6OuD8VkRMi4hpOec5KaUJEfFcB65viIg/RcQ3c85PNHP++pTStRHx14ioiIifppS2zTnnLo57Uc75sS62AQAAAAD0sB4NOHPO/9nF6++OiLvbqHN1SumqiDgqIraOiF0j4sGu9AsAAAAAFMOGsov6LWXlrXttFAAAAADAOrWhBJz9ysr1vTYKAAAAAGCd2lACzv3Kyk92Q3v7ppQeTiktTSktTyk9l1K6IqV0REopdUP7AAAAAEA3WJe7qPeIlNIuEfG+0uGjOefuCDi3XOt4Qun1oYi4K6X04ZzzrI42mlLavI0qozvaJgAAAABszAodcKaU+kXEr6NxB/WIiK90scnVEXFNRFwfEY9FxOKIGBYRe0bEpyNifES8MyJuSCntmXNe3MH2X+ri+AAAAACAMoUOOCPiZxGxe6n8u5zz37vY3pSc86JmPr81pfSziPhLRBwSEdtHxH9GxOld7A8AAAAA6ILCBpwppX+PiBNLh9Mi4jNdbbOFcLPp3NKU0oci4tmI2CQiTkopnZ1zXt2BLsa3cX50NH4XAAAAAKAdChlwppROjoj/Lh3OiIj35pyX9XS/OefFKaXLI+LUiBgYjbNH7+7A9S+3dt7+RQAAAADQMYXbRT2l9JGIuKB0+EJEHJxznr8Oh/BEWXncOuwXAAAAAFhLoQLOlNL7I+LSaBz3KxFxYFuzIntAXsf9AQAAAAAtKEzAmVI6MCL+FI2P1S+Ixpmbz/TCUHYoK8/uhf4BAAAAgJJCBJwppb0i4uqI6BcRiyPi0Jzz470wjqERcXTpcHlEPLCuxwAAAAAAvG69DzhTSm+LiP+Nxk19lkXE+3LO0zvRzq0ppVx6TWjm/LtTSv1buX5QNM4gHVH66Dc551UdHQcAAAAA0H16dBf1lNLeEbFN2Ucjy8rbpJSOK6+fc75kreu3jojrImJY6aOvRsTilNJbW+l2bs55bieGe3ZEXJZSuioi7oyIZyKiJiKGRsReEXFKRLylVHdmRHyjE30AAAAAAN2oRwPOiDgxIo5t4dw7S69yl6x1vE9EbFp2/ON29PnN6Hz4uEk0jvnEVurcFhHH5JwXdrIPAAAAAKCb9HTAWSRfiogDI2LPiJgYjbNNh0XjWpuzI+K+iPifiLg+52wndQAAAABYD/RowJlzPi4ijuvC9ZfEm2d1drat/ds4/0DYNAgAAAAACmW932QIAAAAAKAlAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYfVowJlS2jSldFhK6VsppWtTSvNTSrn0uqQT7b0npfTXlNLLKaVVpfe/ppTe083jHpBS+nJKaVpKaWFKaVlKaUZK6ZyU0hbd2RcAAAAA0HmVPdz+nO5oJKXUJyIuiogT1jo1rvQ6IqX064g4Oefc0MW+tomI/4uIbdc6NbH0OjGldEzOeWpX+gEAAAAAum5dPqL+YkRc38lrvxOvh5sPRcRHImJK6f2h0ucnRsS3uzLAlNLgiPjfeD3c/FVEHBgRe0XEVyKiJiKGRMQVKaW3daUvAAAAAKDrenoG57ciYlpETMs5z0kpTYiI5zrSQEppu4j4UunwgYjYN+e8onQ8LaV0TUTcFhG7R8SZKaXf5pyf7uR4z4yI7UrlL+ecf1h27p6U0q2lvgZExE8iYv9O9gMAAAAAdIMencGZc/7PnPPUnHNXHlX/QrwexH6uLNxs6mN5RHyudFgZEV/sTCcppaqIOK10+GREnLN2nZzz3RHxm9LhfimlPTrTFwAAAADQPdbrXdRTSikiPlA6nJFzvre5eqXPZ5YOP1C6rqMOiIihpfLvWlnL85Ky8v/rRD8AAAAAQDdZrwPOiNgyIsaWyre1Ubfp/LiImNCJvvZupq3mPBARy0vld3aiHwAAAACgm/T0GpxdtUNZeUYbdcvPbx8dXOuzvX3lnOtSSk9HxM6lftotpbR5G1VGd6Q9AAAAANjYre8BZ3kg+HIbdV8qK4/vQl/Lcs6L2tHXzhExKqXUL+e8qp19vNR2FQAAAACgvdb3R9QHl5Vr2qi7rKw8qAt9tdVPd/QFAAAAAHSD9X0GZ3VZeXUbdctnUfbvQl9t9dOVvtqaWTo6IqZ1oD0AAAAA2Kit7wHnyrJy3zbq9isrr+hCX2310+m+cs6tPmbfuc3fAQAAAGDjtb4/or60rNzWo+ADy8rtecy8pb7a88h5V/sCAAAAALrB+h5wls94bGsH8vLHvzuzmU9TXwNTSsPa2de8DmwwBAAAAAB0s/U94HyirDypjbrl55/sqb5SSpURsXUX+gEAAAAAusn6HnA+FxGzS+X92qi7b+l9VkQ834m+7iwrt9bX7vH6I+p3daIfAAAAAKCbrNcBZ845R8TVpcNJKaV3NFev9HnTrMurS9d11K0RsbhUPja1vOPPcWXlv3aiHwAAAACgm6zXAWfJTyKivlT+aUqpf/nJ0vFPS4d1pfpvklK6JKWUS6/91z6fc14dEeeXDrePiC8108aeEXFC6fC2nPO0jnwRAAAAAKB7VfZk4ymlvSNim7KPRpaVt0kpHVdeP+d8ydpt5JyfSin9MCLOjsbHw+9KKX0/Ip6JxrUwz4qIXUvVf5hz/lcXhvzDiPhwRGwXET9IKW0TEZdHxIqIOCAi/iMa/8xWRMQXutAPAAAAANANejTgjIgTI+LYFs69s/Qqd0kLdb8SEZtGxCejMcy8vJk6v4mIr3Z8iK/LOS9NKb0vIv4vIraNiJNKr3JLIuKYnPPDXekLAAAAAOi6IjyiHjnnhpzzCRHxvmhck3N2RKwuvV8dEe/NOZ+Yc27ohr6ejsYQ9ayIeCAiFkXE8oiYGRE/joidc85Tu9oPAAAAANB1PTqDM+d8XLxxU56utvd/0Ti7skfHknNeFhE/KL0AAAAAgPVUIWZwAgAAAAA0R8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsCp7ewAAAADAhmf69OnjI2KviHhHRIyPiAG9OyKgF9VExGMRcWNE3D958uS67mxcwAkAAAB0m+nTp6eIODEiTo6I1KdPn/59+vSpDk+Rwsasvr6+flLO+f0R8fD06dNPmzx58vLualzACQAAAHSn/4yIwyorK0dWVlaOiIg+ffr0aejTp09DROReHhuwjuWcU0NDQ0VlZWVqaGhYtnr16j4Rcf706dNP6a6ZnAJOAAAAoFtMnz59m4g4rKqqanRFRcXwTTbZZMHQoUMX9+/ff1VKqbeHB/SShoaGtHjx4sFz5swZHRGbr169uiEi9oiIe7qjfdPDAQAAgO5yZERUVFRUDBs1atTcMWPGzB0wYIBwEzZyffr0ycOHD1+y2WabvdqnT5+BKaW+EXFQt7XfXQ0BAAAAG71dKyoqBkdE2mSTTV7r7cEA65ehQ4cuTSnlioqKgRGxU3e1u94HnCmlW1NKuYOv/TvRzzd6sn0AAADYCAxJKVVWVFTUVVZWNvT2YID1S58+fXKfPn3qI6IiIgZ1W7vd1dB6pCEi/tXbgwAAAICNUIqISCnZTAhoVk/cH4qwydDxETGwjTo7RMQVpfJNOedZXeyzrSmyz3WxfQAAAACgG6z3AWfOuc0wMaX08bLDS7uhz8e62gYAAAAA0PMK/4h6SqlPRBxTOqyJiKt6cTgAAAAAwDpU+IAzIg6MiHGl8l9yzst7czAAAAAAwLqzIQScnygrd/nxdAAAAIANyfnnnz8ipTQ5pTR55syZfXt7PL1typQpE1NKk6dMmTKxt8dC91jv1+BsTUppUET8v9LhCxFxaze1e31EvC0ihkXEooh4IiL+ERG/zDm/1oV2N2+jyujOtg0AAABFcvC5t22/cPnqqt4eR0/bZEDf2htO3+/J3h4HbMgKHXBGxFHx+g7rf8g5d9c28weXlUdFxH6l11kppeNyzld3st2XujwyAAAA2AAsXL66akHNhh9wAj2v6AFndz+e/mhE/C0i7o+I2RFRFRETo3ETo0OicUbnlSmlw3PO13ZDfwAAAACsQ/fff//M3h4D3auwAWfpce/9S4f35pyf6mKTP8k5f6OZz++LiEtTSidHxIURURERv04pbZ1zXtnBPsa3cX50REzrYJsAAAAAsNEqbMAZER+L1zdJ+l1XG8s5L2rj/C9TSntExAkRMTYaH4+/rIN9vNza+ZRSR5oDAAAAgI1ekXdR/3jpfVVEXLGO+vxlWXm/ddQnAAAAQIvmzZtXceqpp47bcsstd6yurt5tk0022WWvvfba7re//e3w9rYxc+bMvieccML4bbbZZseBAwfu2r9//1232GKLt370ox/d4v777+/f2rVNO7SffvrpYyMi/v73vw8+6KCDtt500013rq6u3m2rrbba8cwzzxyzZMmSN+RQV1xxxdD99ttvm6Z6W2+99Y7//u//PnrlypUtzgBbuXJl+uMf/zj0E5/4xFve+ta3bj9kyJC3VVZW7jZs2LC37bzzzpNOP/30sa+88kqrE/pa20V95syZfZu+z/nnnz8iIuKvf/3rkHe9613bjBw5cpe+ffvuNm7cuJ2OOeaYtzzzzDPWkF1PFHIGZ0pp94jYoXQ4tSs7m3fQE2XlceuoTwAAAIBmPfjgg9Xvfve7t5s3b96asG3VqlWV99xzz+B77rln8LXXXrtgn332WdpaGz/72c9GnHHGGVusXr36DcHiiy++2O/FF1/s96c//WnkmWeeOeu73/3uq22N5z/+4z9Gf+973xtXvg/0c889V/2jH/1o7E033TT01ltvfWrQoEENJ5xwwvhLLrlk0/Jrn3322ervfe974+66667BN998878qK98cWx1zzDFbXHXVVSPW/nzx4sUVjz766MBHH3104MUXXzzqiiuuePqQQw5Z1tZ42/KZz3xm3AUXXDC6/LPZs2f3/eMf/zjq2muvHX7jjTfO3G233Tq6hCHdrJABZ7xxc6EuP57eAd21SzsAAABAlyxcuLDP+973vm2bws33ve99r33iE5+YP2bMmLonnnii+qc//elmf/nLX0bMmDGjxRmYl19++dDTTjttQs45BgwY0HDyySfPOfTQQ5dUVlbmO+64Y9B55503etGiRZXf+973xg0bNqz+rLPOmtdSWzfeeOOQRx99dODb3va2ZaeccsrcHXbYYeXcuXMrzz///E1vv/32oQ899NDAr371q6M32WST+ksuuWTTfffdd/EnP/nJ+VtvvfXqF154oeqHP/zhmEceeWTgHXfcMeTcc88d9eUvf/lNfdXV1aXNN9981Xve855FU6ZMWbbllluurqqqys8++2zfG264Ycif//znkYsWLao8+uijt3n00UcfHzduXF1n/3wvueSSUQ899NDAPfbYo+aEE06Yt8MOO6xcuHBhxe9+97sRf/3rX0e89tprlZ/85CcnPPzwwzM62wfdI5Un6kWQUqqKiFkRMSoi5kXE2Jxzp3+sHex793h9E6Bf55w/1c3tbx4RL0VE3HRUxOiB3dl66947Zuq66wwAAIAWDd7+7HXWV+3C2ph5+poNpce3tXdEW6ZPn/5/lZWVO1RXVw+bOHHi063VnfztG3ZeULN6g3/Ed8SgvrXTv3rwP3ui7ZNPPnnziy66aLOIiLPPPvtNMyxXrVqVDjzwwG3uuuuuIU2fzZgx49GJEyeubjo/fvz4nebNm1c1YMCAhhtuuGHGXnvttaK8jaeeeqrv3nvvPWnevHlV1dXVDc8+++yjY8aMeUMOk1Ka3FQ+9NBDX5s6deqz5bMv6+rqYvfdd5/0yCOPDBw4cGBDXV1dOuaYY+b95je/eam8naVLl/aZNGnSjrNnz+673XbbrZg5c2b5k7QREfH444/323777Vf16dP8qov3339//wMOOGDS8uXL+5x22mmvnHfeebPXrjNlypSJ06ZNG7THHnvUrL2j+syZM/tOmjRpp6bjo48+ev5ll132wtr9HX300VtcccUVIyMi7rzzzife+c53vuHPjZbNnDlzm5UrVy6qq6t7YvLkye/tjjaLuAbne6Ix3IyI+OO6CjdLTi4r37YO+wUAAABYY+XKlenyyy8fGRGx3XbbrfjOd77zpsfH+/Xrl3/3u989X1lZ2ezstt///vfDmmZ/fuELX3hl7XCz1Pbqb37zmy+X+uxzwQUXvOnx8CbV1dUNv/vd715Y+9HyysrKOP744+dFRCxbtqzP8OHDay+44II3hemDBw9u+NCHPrQgIuKpp57qv2DBgoq16+y4444thpsREVOmTFlx9NFHz4+IuPbaa4e1WLEdRo0aVfvb3/72xeb6+/d///c1f9633HLL4K70Q9cVMeAsfzz90vZckFI6LqWUS69vNHN+p5TSNm20cVJEnFg6fDUi/trO8QIAAAB0qzvvvHPAkiVLKiIiPvKRjyxoKfTbeuuta/fee+8lzZ276aabhkREpJTiM5/5zPyW+jr++ONfGzRoUH1ExC233DKkpXrvfOc7l2y22Wb1zZ2bPHnymvD0Pe95z6J+/fo1G7q+7W1vW95UnjlzZt+W+moyb968iscff7zfAw88UD1t2rTqadOmVQ8bNqwuIuKZZ57pv2rVqhY3LGrLe9/73tf69+/f7Dh32WWXVQMGDGiIiHj22Wf7dbYPukeh1uBMKQ2PiMNKh4/lnB/spqYnR8SvU0q3RMS1EfFoRCyIxj+fSRFxTEQcUqpbHxEn5Zy7vFAtAAAAQGc8/PDDa9bVfMc73tFqRjF58uRlt95669C1P29am3PcuHGrxo4d2+ITstXV1XmHHXZYfv/99w9+6qmnWlzPc5tttlnV0rlNNtlkTfvbbbddi5vyDB8+fE1A2hTgru3+++/v/8Mf/nCzW2+9dcj8+fNbXOagoaEh5s+fX9HZdTgnTZrU6uZBQ4YMqVu+fHnfmpqaIk4g3KAUKuCMiA9HRFMq3q7Zmx1QEREHlV4tWRARJ+Sc/97NfQMAAAC028KFC9dkOmPGjKltre5mm23W7PlFixZVRESMGDGizQBw0003rY2IWLJkSYtZUtOMxuZUVFS0q175TNS6uro3zb788Y9/PPLMM898S319fbtmZi5btqzT4WNr44x4faztHQs9p2gJ88dL7/URcVk3tvt/EXFCRPw6IqZHxMsRsSIiVkbE7Gic1fn5iNgq53x1N/YLAAAA0CUpdS1f6+r168pDDz1U3RRubrLJJnVf+9rXXr7jjjuefPXVVx9euXLlgznn6Tnn6T/+8Y+fb7qmaJtr0zmFmsGZc35nJ6+7JCIuaeX83Ij4bekFAAAAsF4bPnz4mlmXs2fPrtp5551bfDx8zpw5zT7GPWzYsPqIiPnz57eZD82dO7cqovGx7I6Ptnv86le/GlFfX58qKirixhtvnLnrrrs2+wh5+exWNg5Fm8EJAAAAsNF729vetmbTnnvvvXdga3UffPDBZs9PmjRpRUTErFmz+s2ePbvFUHDVqlXpiSeeGBDRuGN750bcdU1rhk6cOHF5S+FmRMvflw2XgBMAAACgYPbee+/lQ4YMqY+IuOKKK0Y0NDS/XORzzz1Xdeeddza78/mBBx64JKLxMe4LLrhgREt9XXLJJcNramoqIiIOOOCAZndkXxea1uRcvnx5i3nWCy+8UHXTTTe9aUMlNmwCTgAAAICC6d+/f/7Qhz40P6JxZuPXv/71zdauU1tbG8cdd9wWtbW1zS6y+bGPfWzRqFGjaiMizjvvvDH333//m3ZIf/rpp6u+9rWvbR4RUV1d3XDqqacu6N5v0n5bbbXVyoiIF198sfqGG2540yzNpUuX9vnQhz605cqVK+VdGxn/wAEAAAAK6Hvf+94rTTukf+c739n88MMP3/Ivf/nLkDvvvHPARRddNHy33Xbb/vbbbx+64447Lm/u+urq6nz++ee/kFKKmpqaine9612TzjzzzDE33HDDwJtvvnngN7/5zU3f/va37zBv3ryqiIhvfOMbL48ZM6bX1uA8/vjjF0RENDQ0xFFHHbXt2WefPfraa68ddMsttwz4/ve/P2qnnXba4f777x+822671fTWGOkdFl0FAAAAKKARI0bUT5069an3vOc9282fP79q6tSpm0ydOnWT8jpHHXXUgn333Xfp5z//+QnNtXH00Ucvnjdv3vNf+tKXtli2bFmfH/3oR2N/9KMfjS2vU1FREWeeeeass846a14Pfp027bfffsvPOOOM2eecc87YpUuXVnz/+98f9/3vf/8NdT71qU/Neetb37riwQcfHNRLw6QXmMEJAAAAUFC77777yscee+zxU0455dUttthiVd++ffOwYcPq3v72ty+98MILn/vLX/7yfFttfO5zn1vwz3/+87Hjjz9+7lZbbbWyf//+DdXV1Q3jx49fdfTRR8+/++67n/jud7/76jr4Om360Y9+9Mrll1/+9Dvf+c4lQ4YMqa+qqsqbbbZZ7SGHHLLoqquu+tdFF130cm+PkXUv5Zx7ewyUpJQ2j4iXIiJuOipi9Drc8+u9Y6auu84AAABo0eDtz15nfdUurI2Zp89sOhyfc+5SODR9+vT/q6ys3KG6unrYxIkTn26t7uRv37DzgprVVV3prwhGDOpbO/2rB/+zt8cB64uZM2dus3LlykV1dXVPTJ48+b3d0aZH1AEAAIB1bpMBfWt7ewzrwsbyPaE3CTgBAACAde6G0/d7srfHAGwYrMEJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACisyt4eAAAAALDxeeY/3rp93dJ5Vb09jp5WOXhU7db//diTPdH2UUcdNeGqq64aERExY8aMRydOnLi6rWvGjRu30+zZs/uOHTt29axZsx4tP5dSmtzSdf369cvDhg2r22GHHZYfeeSRr5188skLqqpa/sfX1E9z56qqqvLQoUPrtt122xWHH374os9+9rMLBg8e3NDW2KElAk4AAABgnatbOq+qfsncDT7g3FCsWrUqzZkzp2rOnDlDb7nllqG/+MUvNv3HP/7xr/Hjx9d1tK3a2to0f/78qvnz51fdc889Qy644ILNpk6d+q9ddtllVU+MnQ2fgBMAAACANXbcccflF1988XPlny1ZsqTikUce6X/RRRdtOnPmzP5PPPHEgCOOOGLr6dOnz2ytrVGjRtX+7//+71Plny1fvrzP448/Xn3xxRePeuCBBwa9/PLL/Q4//PBtZ86c+Xj//v1zT3wnNmwCTgAAAADWGDBgQMMee+yxcu3PDzzwwGUnnXTSgp133nmH5557rvrBBx8cdOONNw486KCDlrXUVlVVVW6urf3222/5ySefvHCvvfba7v777x/80ksv9bv00kuHn3zyyQu7+/uw4bPJEAAAAADtMmjQoHziiSfObTq+9957B3a2rYqKivjCF74wp+l42rRpA7o6PjZOAk4AAAAA2m2rrbZas5nRqlWrUlfa2nbbbdesu7lq1So5FZ3ihwMAAABAuz3//PNrdkd/y1ve0ubO7a155pln+nVXW2y8BJwAAAAAtEtNTU369a9/vWlERP/+/RsOP/zwJZ1tq6GhIc4777zNIiJSSnHkkUcu6qZhspGxyRAAAAAAayxfvrzPtGnTqss/q6mp6fPwww8P+NWvfjXqmWeeqU4pxde//vWXR48eXd9aW7W1tWnttlasWNHniSeeqL7kkktG3nfffYMjIk455ZRXJ0+e/KbNiKA9BJwAAAAArPH4448PmDJlyo4tnX/nO9+55Kyzznr18MMPX9pWW/Pmzatqra2ddtpp2Ze+9KVXP/GJTyzq5HDBI+oAAAAAtN999903+Kc//emmTz/9dFVX23r88ccHXnjhhaPuv//+/t0xNjZOAk4AAAAA1thjjz1qcs7Ty18rV658cMaMGY9+97vffXHQoEH1N9xww7C99tpr+4ceeqi6tbbGjh27eu22Vq9ePf3ZZ5/9589+9rPnRo8evfqee+4ZcuCBB0687rrrBq2r78iGRcAJAAAAUEAppTXlnHO7rmlvvbX169cvT5w4cfXZZ58974YbbphZWVmZ582bV3XiiSdu0dG2qqqqYsstt6z9zGc+s/Duu++eMXTo0PqampqKT37yk1vW1tZ2anxs3AScAAAAAAVUXV3d0FRetmxZuzKeFStW9ImIGDBgQENbdVuy++67r9xvv/0WR0Q8+OCDg/75z3/262xbW2yxRe2RRx65ICJi9uzZff/+978P6WxbbLwEnAAAAAAFtMkmm9Q1lWfNmtXmepgrVqxIS5curYyIGDp0aF1b9Vuz3Xbbrdnx/MEHH+zS+pmTJk1a09YjjzxiLU46TMAJAAAAUEA777zziqbytGnTBrRV/9577+1fX18fERE77LDDijaqt6qurm7N8/G1tbWptbrtaKvZdqG9BJwAAAAABXTooYcuraioyBERV1111SYNDa0/df673/1uRFP5oIMOWtKVvh9++OGBTeUJEyas7kpb06dPX9PW+PHju9QWGycBJwAAAEABjR8/vu4973nPaxERTzzxxICvfOUro1uqe8011wy+7LLLRkU07mz+0Y9+dFFn+7388suHTps2bVBExLBhw+r233//ZZ1t68477xwwderUTSIiqqqq8uGHH96l4JWNU2VvDwAAAACAzvnZz3728t133z1k4cKFld/73vfG3XHHHYM/8pGPLNh+++1XVVVV5RdeeKHv3//+96FXXnnliPr6+tSnT5+48MILn6+sbDkSWr58eZ9p06ZVl3+2evXq9OKLL/adOnXq0CuuuGJk0+df+9rXZlVVtbz8Z21tbVq7rbq6uvTKK69UXX/99UN+97vfjVq9enWKiDjllFNeHTduXJfWBmXjJOAEAAAAKKgtttii9uabb55x5JFHbvPss89W33XXXUPuuuuuZnciHzx4cP1FF1303OGHH760tTYff/zxAVOmTNmxtTqVlZX5rLPOmnX66afPb63evHnzqtpqK6UUxx133Nyf/OQns1urBy0RcAIAAAAU2C677LJqxowZj//617/e5Jprrhn2z3/+c+DChQsr6+vr09ChQ+u23XbblQcddNDiz3/+8/NHjBhR35k+KioqYtCgQfVvectbVu69995LTz311PlvfetbV3WmrT59+sSAAQPqx48fv2qPPfaoOfnkk+fvtddeXdr0iI2bgBMAAACg4KqqquLTn/70wk9/+tMLO9tGznl6d41n1qxZj3ZXW9AWAScAAACwzlUOHlXb22NYFzaW7wm9qRABZ0opt7PqbTnn/buhv49ExPERsXNEDIuIORFxR0T8POd8T1fbBwAAgI3d1v/92JO9PQZgw9CntwewPkkp9U8p/W9E/DEiDo6IzSKiX0S8JSKOiYg7U0r/2YtDBAAAAADKFGIGZ5lfRMQFrZxf1sX2fxsR7y2Vb4mI8yJidkTsFBH/ERFbR8Q3Ukqv5Jwv6mJfAAAAAEAXFS3gnJtzfqwnGk4pvSsiji4d/j0i/l/OuWlnsWkppWsiYno0zub8fkrpzznn13piLAAAAABA+3hE/XVfKr3XRcSpZeFmRETknOdHxFmlw2ERceK6GxoAAAAA0BwBZ0SklAZHxIGlwxtzzi+3UPWqiFhSKv+/Hh8YAAAAANAqAWejPSKib6l8W0uVcs6rI+LepmtSSlU9PTAAAAAAoGVFCzj/LaX0REppeUppaUrpXyml36WUDuhiuzuUlWe0UbfpfGVEbNvFfgEAAACALijaJkM7rHW8Ten1iZTS3yLiuJzz4k60u3lZuaXH05u8VFYeHxFPtLeTlNLmbVQZ3d62AAAAAIDiBJzLI+KaiLgpGmdQ1kTEqIjYLyJOiYgREXFERFydUjo451zbwfYHl5Vr2qi7rKw8qIP9vNR2FQAAAACgvYoScI7LOS9q5vMbUko/jYhrI2LXaAw8Px0R53ew/eqy8uo26q4qK/fvYD8AAAAAQDcqRMDZQrjZdG5OSumD0TizsyoiPhcdDzhXlpX7tlirUb+y8ooO9jO+jfOjI2JaB9sEAAAAgI1WIQLOtuScn00p3RAR742IbVJKY3POszvQxNKycluPnQ8sK7f1OPsb5JxbXd8zpdSR5gAAAABgo1e0XdRbU77Zz7gOXlsePLa1EVD5LExragIAAABAL9qQAs7chWvLw9FJbdRtOl8XEf/qQp8AAAAAQBdtSAHnDmXljjyeHtG47mXT5kL7tVQppdQ3It7RdE0ndmsHAAAAALrRBhFwppS2jIiDS4fP5JxndeT6nPPSiLipdHhQSqmlx9SPjIghpfJfOzxQAAAAAKBbrfcBZ0rp8JRSi5shpZQ2i4gr4/Xdzy9ops5xKaVcen2jhaZ+VHqvjIifp5Qq1mpjZER8v3S4KCJ+3e4vAQAAAAD0iPU+4IyIn0bECyml81NKH0kp7ZlSeltK6aCU0rcj4rGI2LVU986I+HlnOsk53xwRl5cO3x8RN6SU3p9S2j2ldHxE3BsRbymdPyvn/FqnvxEAAAAAb1BTU5POOOOMMRMnTtyhf//+u6aUJqeUJn/yk58cHxFx/vnnj2j6bObMmX3bao+NR4szI9czYyPic6VXS66MiBNzzqu60M8no/ER9PdGxAGlV7mGiPivnPNFXegDAAAANnpHXH3E9q+tfK2qt8fR04ZXD6/92wf+9uS66GvBggUVF1100Sb/+Mc/hj799NP9Fy5cWFlZWZlHjBhRt/POOy87/PDDFx1//PGvVVauf3HQqlWr0r777jvxoYceGtjbY1lfzZw5s++kSZN26sg1Bx544KIbb7zxmZ4a0/pi/ftFv9mx0bjxz54RsVVEjIzGELImIl6KiLsj4nc553u62lHOeUVEvC+l9NGIOC4idomIYRExJyLuiIifdUc/AAAAsLF7beVrVQtXLtzgA8515Zxzzhn57W9/e9yiRYvelPXU1NRUvPDCC/3+/ve/b/L9739/5c9//vMXDj300JqeHlNKaXJExBe/+MVXzj333FY3hL744ouHN4WbRx111ILjjjtuwWabbVYbEbHZZpvV9fRYKbb1PuDMOd8WEbd1sY1LIuKSDtT/Y0T8sSt9AgAAAKwLJ5100ua/+tWvNouIqKioyIcddthr73//+xdtueWWq1avXt3niSeeqP7Tn/60yb333jv4mWeeqX7/+9+/3YUXXvjc8ccfv94sv3fTTTcNiYgYOXJk7eWXX/78+jjLdH1y4IEHLvrud7/b5ibbw4YNa1gX4+ltfi0AAAAABfW9731vVFO4udlmm9VeddVV/9prr71WlNc59NBDa774xS/Ov/DCCzc57bTTJqxevTqdcsopW06cOHHl2nV7yyuvvFIVETF+/PjVws22DR06tH6PPfZY2dvjWF8UYZMhAAAAANby1FNP9f3GN74xPiKif//+Dddff/3M1gLLU045ZeF55533fETE6tWr03HHHbdlQ8P6McFv9erVfSIiKisrc2+PheIRcAIAAAAU0Pe///3NVq1alSIiTj/99Nk777xzmxsvf/rTn164zz77LImI+Ne//tX/8ssvH7p2nXHjxu2UUpp81FFHTWitraOOOmpCSmnyuHHj3rDxTdP1Tcc//vGPxzTtft70OuqooybMnDmzb9PxtGnTBkVETJs2bVB5vbXbbo/Zs2dXnnbaaWO33377HQYPHvy2fv367TZu3LidjjjiiC2vu+66QS1d09TnD37wg1Gtfd/ynd3X9h//8R+jU0qTKysrd1u8eLHcbR3xBw0AAABQMA0NDXHllVeOiIiorq5u+OIXvzi/vdd+/vOfn9NUvuSSS0b2xPh6y1VXXTVk4sSJb/3pT386ZsaMGf1ramoqVq9enWbPnt336quv3uTd7373xE984hNvqa+vf8N1Y8eOrdt6661XRkTcfvvtg5tr+957713z+d13391snTvvvHNwRMSOO+64fOjQoWumxzaFvuXBL93HogYAAAAABTN9+vTqxYsXV0RETJ48uWbEiBH1bV3T5AMf+MCS6urqhpUrV/Z54IEHmp3R2BXXXnvtU6tWrUpTpkzZMSLimGOOmff5z39+bnmdkSNH1o8dO7bu/vvvfzwi4vjjj9/y8ccfH7Djjjsuv/jii59rqtevX792P7J+99139z/66KO3qa2tTZWVlfnYY4+de8QRRywePHhw/bRp0wb85Cc/GTNr1qy+v//970cNHDiw/he/+MUbNunZc889lz7zzDPV991335v+TJ566qm+s2fP7lt23H/OnDkVm2222Zo/99ra2njwwQcHRUTstddeS9s7brpOwAkAAABQMA888MCApvIuu+yyvCPXVlZWxsSJE1c88sgjA1977bXK559/vmrChAm13TW2tR+V33TTTeta2hCn6fMBAwY0NL13dvOck08+eUJtbW2qqKiIK6644ukjjzxySdO5/fbbb/mxxx772p577jnpmWeeqb7oootGn3DCCQt23333lWV1lv7hD38YNX/+/KqHHnqoetddd11z7vrrrx8cEbHNNtusXLFiRZ9Zs2b1vf766wd//OMfX9RU58477xywbNmyPhERBxxwQI8GnIsXL66YNm1adVv1Jk6cuHrIkCHrx0KrPUjACQAAAFAw8+fPX5PpjB49usPh5MiRI9dcM3fu3MruDDh7wy233DLgscceGxAR8eEPf3heebjZZNSoUfUXXHDB84ceeuikhoaGOO+88zb9/e9//2LT+UMPPXRNKHnDDTcMLg84b7vttsERjbM8V6xY0ecvf/nLiFtuueUNAedNN900OCKioqIiDj744Joe+aKv9zVsypQpw9qq9/e///2pww47bIOfTWoNTgAAAICCWbp0aUVTedCgQR2eoTdw4MA11yxatKjw+dB11103pKl88sknt7ge6SGHHLJsq622WhkRcccddwwpPzd+/Pi6Lbfcstl1OO+9995BEY0zM/fbb7+lEW9eh7Np/c3tt99++fDhw9/wz2TWrFmP5pyn55ynd+b70TozOAEAAAAKZvDgwWvWfqypqelwQNn0KHVExLBhwwr/CPPjjz/ePyKiqqoq77nnnq0+sr/rrrsue/bZZ6tffPHFfitXrkzV1dVr1vncc889lz733HNvWIfz6aefrnr55Zf7pZTi0EMPXdr0Z1e+DmddXV1Mnz59na2/eeSRRy648sorn+/pfoqi8Ak9AAAAwMZm5MiRdU3lV199taqj18+fP3/NNZtuumlda3WLYNGiRZUREUOHDq2rqmr9j2OzzTarjYjIOce8efMqys81zc5sWocz4vX1N7feeuuVY8eOrdt2221Xjxs3bnXOec25u+++e0BNTU1FRM+vv8mbCTgBAAAACmby5Mkrmsr//Oc/B7RWd211dXUxc+bM/hERw4cPryv6+pvlUkpduv7d7373G9bhjHj9cfU999xzzbl3vOMdSyMibrnllsER63b9Td5MwAkAAABQMLvvvvuKoUOH1kdEPPDAA4MWLFhQ0dY1Ta6++uohK1eu7BMRsccee7wpjGsKCRsaWn9yffny5etNrjRs2LC6iMaZnLW1ree1c+bMqYpo/J6jRo2qLz/3lre8pW6LLbZYFfF6sHnPPfcMjnjjzMy11+G84447BkdETJo0afmIESPe0CY9b735IQIAAADQPn369ImjjjpqQUTEypUr+5x33nkj23vtz372s02byscee+ybNuQZOHBgfUTE4sWLW9275dlnn61u/4h71o477rgiIqK2tjbdc889rc5offjhhwdGRLzlLW9ZVb7+ZpOmNTTvu+++Qc8991zViy++uGb9zaY6TeWnnnqq/yuvvFL5wAMPDIp44yxP1h0BJwAAAEABffnLX57Tt2/fHBFxzjnnjH3sscf6tXXNRRddNPzWW28dGhGx7bbbrjj66KMXr11n/PjxqyIiHnvssQEtzeJ84IEHqp966qn+rfXVr1+/HBGxatWqrj033g6HHnrokqbyRRdd1GLYe+ONNw585plnqiMi9tlnnyXN1dl3333XrMN53nnnjYp4ff3Npjrbbbfd6rFjx67OOcf3v//9TZt2tbf+Zu8QcAIAAAAU0MSJE1d//etffzmi8XHxQw45ZLt77rmnxdDx17/+9fDPfvazW0Y07jZ+ySWXPNenz5ujoX322WdpRMS8efOqLrrook3WPv/aa6/1OeGEEya0Nb6RI0fWRkQ899xzbQavXXXAAQcs33HHHZdHRFx++eUjr7766sFr11mwYEHFqaeeukVE4wzY0047bW5zbZWvw/mb3/xm04jmZ2Y2rcPZVKdPnz5xyCGHNLv+5rhx43ZKKU1OKU3u+LejLa1ONQYAAABg/fWVr3xl7jPPPNPv4osv3vSVV17pu88++2x/+OGHL3z/+9+/eKuttlq1evXq9Pjjj1f/6U9/GtG0lmTfvn3zhRde+Nxee+21ork2P/WpTy380Y9+NLampqbiC1/4woSnn36633vf+94lKaV8//33D7zgggs2mzNnTt/tt99++ZNPPtni4+CTJ0+umTVr1iY33XTTsB/+8Icj999//5r+/fvniIjhw4fXjxs3rlt3b7/oooue33///bevra1N//Zv/7btcccdN/cDH/jAosGDBzdMmzZtwE9+8pPRL7/8cr+IiJNOOunVPfbYY2Vz7UyYMKH2LW95y6oXX3yxX2s7o++3335Lr7rqqhFNdSZOnLh85MiR62T9zcWLF1dMmzatzSUCKioqYrfddmv2e25IBJwAAAAABfbb3/72pUmTJq387//+73GLFy+u+Nvf/jbib3/724jm6m611VYrf/7zn7/w7ne/u8WdvseOHVt33nnnvXDSSSdttWrVqnTOOeeMPeecc8Y2na+urm74xS9+8dzUqVOHthZwnnXWWa/+4x//GL569er05S9/eYvyc0ceeeSCK6+88vlOfN0W7bXXXisuv/zyp4899titampqKn71q19t9qtf/Wqztet9/OMfn/ezn/1sVmtt7bnnnktffPHFfhGNmxGVr7/ZZO3P9txzz3W2e/pNN900bMqUKcPaqjdo0KD6pUuXPtzzI+pdHlEHAAAAKLgvf/nL855++ulHv/Od77y4zz77LBk9evTqfv365QEDBjSMHz9+1WGHHbbwl7/85bMzZ858vLVws8knP/nJ12644YYnDz744EXDhw+vq6qqyqNHj1595JFHLrjjjjuePP74419rq4299tprxc033/zkYYcdtnDMmDGrm9YL7UlHHnnkkpkzZz722c9+9tVJkyatGDRoUH3fvn3zmDFjVr///e9f+I9//GPmpZde+mJFReubzjetwxnx5vU3m0ycOHH12LFjVzcdW3+z96Sce/y3RTullDaPiJciIm46KmL0wHXX93vHTF13nQEAANCiwdufvc76ql1YGzNPn9l0OD7n/HJX2ps+ffr/VVZW7lBdXT1s4sSJT7dW94irj9j+tZWvVXWlvyIYXj289m8f+NuTvT0OWF/MnDlzm5UrVy6qq6t7YvLkye/tjjY9og4AAACsc0I/oLt4RB0AAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAAC61VFHHTUhpTR53LhxOzV3fty4cTullCYfddRRE9bx0NgAVfb2AAAAAICNzzOHHb59/WuvVfX2OHpaxfDhtVtP/fuTvT0O2JAJOAEAAIB1rv6116rqFyzY4ANOoOd5RB0AAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAABXPAAQdsk1KavMsuu0xq7vzUqVMHp5Qmp5QmDx069G319fVvqvPiiy9WNtX5wQ9+MKrp8/r6+rjmmmsGn3TSSZvvtttuk4YPH75LZWXlboMHD37bpEmTdjjppJM2/9e//tW3B78edIiAEwAAAKBg9t5776UREY8//viAxYsXvynfueWWWwY1lZcsWVJx33339V+7znXXXTe4qXzwwQcvbSqfeeaZYz/wgQ9s96tf/Wqzhx56aOCiRYsq6+vrU01NTcXMmTP7/+pXv9ps55133vHSSy8d1u1fDDpBwAkAAABQMAcddNDSiIj6+vp0ww03DFr7/J133jm4/PjGG28cvHadW2+9dXBExIgRI+p23XXXlU2f19XVxahRo2o/9rGPzfv5z3/+3PXXXz/jjjvuePL3v//9M6eccsqrAwYMaFi5cmWfk046aasHH3ywuvu/HXSMgBMAAACgYN75zncuHzhwYENExM033/yG8HLFihXpkUceGRQRccABByyOiLj99tvfFHDee++9gyMi3v72ty8t//wzn/nM/JdeeunR3//+9y+eeuqpCw8++OBle++99/KPfexji37xi1/M+uc///nYpptuWrtq1ar0rW99a0xPfUdoLwEnAAAAQMFUVlbG5MmTl0ZE3HXXXW8IL2+99daBq1atSoMGDar/4he/OCciYtq0aYPL1+GcNWtW5bPPPlsdEbHPPvu8IeCcOHHi6n79+uWW+t56661rP/vZz74aEXHTTTcNbWho6LbvBZ0h4AQAAAAooL333rsm4s3rcDbN6Nx9991rDjrooJrq6uqGtdfhbGn9zeYsXLiwz4wZM/o+8MAD1dOmTaueNm1a9YABAxoiImpqaipmzJhhwyF6lYATAAAAoIAOPPDAZtfhbFp/c5999lnav3//vMsuuyyLeOM6nLfccsvgiIjhw4fXTZ48eWWs5amnnup77LHHjh83btxOI0aM2HX77bffaY899thxypQpO06ZMmXHL33pS1s01Z0zZ05lz31LaFshAs6U0u4ppa+nlK5PKb2cUlqVUqpJKT2VUro4pbR3N/XzjZRSbudr/+7oEwAAAKAz9tlnn2VNMymbZm2uXLkyPfzwwwMjXt+IqGnH9fJ1OFtafzMi4k9/+tOQXXbZZcdLL71009mzZ7c5O3P58uWFyJfYcK33P8CU0u0RMS0ivhkRB0fEuIjoGxEDI2LbiDguIu5IKf0upWRKNAAAALBRqKqqit12260m4vV1OG+//fYBK1eu7DNo0KD6vfbaa3lExAEHHLA04vV1OF955ZXKZ555ptn1N1955ZXKE088cauVK1f2GTBgQMPpp58++8Ybb5wxa9asR1asWPFgznl6znn61Vdf/VTTNTnntK6+MzSnCFOIx5beZ0fEnyPijoh4MSIqImLPiDgjGkPPT0REVUR8tJv63amN8891Uz8AAAAAnbL33nsvvfPOO4c0rcN50003rVl/s7KyMfZ517vetaxfv365aR3Op556ql/OjXsIrb3+5u9///vhS5curYiIuOyyy54+4ogjml2fc8GCBUXIlNhIrPczOCNiRkR8OCLeknP+Qs75ypzztJzzvTnnH0fE2yKi6W8NPpJS2rc7Os05P9bGa1l39AMAAADQWWuvw1m+/mZTndI6nDURjetw3nrrrYMjIoYNG/am9Tcff/zx6oiIoUOH1rcUbkZETJs2bUD3fxvonPU+4Mw5H5Zz/lPOub6F8/OjcRZnkw+um5EBAAAA9K599tlnef/+/RsiIq677rohDz300KCI19ffbFK+Dufdd989OCJiypQpNX36vDEaqqurSxERq1atSvX1zUYxsXTp0j5/+ctfRnT3d4HOWu8Dzna6pay8da+NAgAAAGAd6tevX951111rIiIuv/zykStWrHjD+ptNDjjggJqIiHvvvXfI008/3T/izetvRkRsu+22qyIiVq5c2ec3v/nN8LXP19XVxUc/+tEt5s2bV9UT3wc6Y0MJOPuVlZv/6wUAAACADVDT7MyampqKiDeuv9nkXe96V03fvn3zsmXL+rS0/mZExMc//vGFffv2zRERp5122pannnrquKuvvnrw7bffPuCnP/3piLe97W3bT506dZOmzY1gfbChBJz7lZWf7I4GU0rXp5TmppRWl95vTSmdnVJ6099edKDNzVt7RcTo7hg7AAAAsPFoWoezSXMzMwcMGJB32WWXNfuJDB06tH6PPfZYsXa9rbfeuvYHP/jBC3369IlVq1alX/ziF6OPOOKI7fbbb7/tTzvttAmPP/74gPe9732vff3rX5/dM98GOq7wAWdKqU9EnF320Z+6qemDI2JUNO7MPioaQ9TvRsSzKaUPdLLNl9p4TevimAEAAICNzL777ru8urq6oel47fU3mzTN9IyI2GOPPZauvf5mk89//vML/vGPf8w46KCDFg0fPryusrIyjxo1qnafffZZ8qtf/erZqVOnPltRUdHt3wM6q7LtKuu9L0bElFL5qpzz9C6292hE/C0i7o+I2dEYcE6MiGMi4pCIGBYRV6aUDs85X9vFvgAAAGCjVDF8eG1vj2FdWBffs7q6Oq9YseKhtuqde+65s88999x2zbw8+OCDlx188MHPtHT+sMMOW9paBnPllVc+HxHPt3R+1qxZj7ZnHNAehQ44U0r7RcT3SodzI+LTXWzyJznnbzTz+X0RcWlK6eSIuDAiKiLi1ymlrXPOKzvQ/vg2zo8OszgBAADYCGw99e/dssQcQGEDzpTSjhHx12j8Disj4t9yznO70mbOeVEb53+ZUtojIk6IiLERcVREXNaB9l9u7XxKqb1NAQAAAABR0DU4U0pbRsT1ETE8GndNPzrnfPs66v6XZeX9WqwFAAAAAPS4wgWcKaWxEXFjNM6gzBHxyZzz1etwCE+Ulcetw34BAAAAgLUUKuBMKY2MiBsiYqvSR5/LOV+6joeR13F/AAAAAEALChNwppSGRsR1EbFD6aOzc84/74Wh7FBWbtfOYwAAAABAzyhEwJlSGhAR/xsRu5U++k7O+fu9NJyTy8q39dIYAAAAAIAoQMCZUuobjbulv7P00Xk55692op3jUkq59PpGM+d3Silt00YbJ0XEiaXDV0vjAgAAAAB6SWVvD6Ad/iciDimVb46I36SU3tpK/dU556c60c/kiPh1SumWiLg2Ih6NiAXR+Gc0KSKOKRtHfUSclHNe1ol+AAAAAIBuUoSA88iy8rsi4p9t1H8hIiZ0sq+KiDio9GrJgog4Ief89072AQAAAAB0kyIEnOvK/0XECRGxZ0TsGhGbRcSIiEgRsTAiHomIf0TEJTnnJb01SAAAAADgdet9wJlzTt3UziURcUkr5+dGxG9LLwAAAACgANb7TYYAAAAAAFoi4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAoCSlNDmlNPn0008fu/a5qVOnDm46P3Xq1MG9MT7erLK3BwAAAABsfP7nm/dtv6JmdVVvj6On9R/Ut/Yj//n2J3ui7alTpw4+/PDDt2tv/fPOO+/50047bUFPjGV98swzz1T9/Oc/H3XbbbcNfvbZZ6uXLl1aUVlZGUOHDq0bP378qp122mn5vvvuW/P+979/yYgRI+p7e7y97aijjppw1VVXjWg6vuKKK/71oQ99aElr16SUJkdEHHnkkQuuvPLK53t4iG0ScAIAAADr3Iqa1VUrltZu8AEn69Y555wz8qtf/er4lStXvuGp5fr6+pg7d27V3Llzq6ZPnz7okksu2fR973vfa1OnTn22t8a6vvqv//qvcW0FnOsbAScAAABAwR1zzDHzPv/5z89trc6WW25Zu67G0xt++ctfbvKlL31pi4iIfv365Q9+8IPz3/3udy/ZYostVuec46WXXqqaNm3awOuvv37ok08+OaAzfRx22GFLc87Tu3fk65fHHntswGWXXTb0mGOOWdzbY2kvAScAAABAwW266aZ1e+yxx8reHkdvqauri69+9aubR0QMHDiw4aabbprx9re/fcXa9Uqh3ewHH3yw+sEHH+y/zge6nhs2bFjdokWLKr/97W+P+8hHPrK4T59ibN9TjFECAAAAQAtuueWWgfPnz6+KaJzN2ly4WW633XZbeeKJJ762bkZXHJ/97GdfjYiYMWNG/0svvXRYLw+n3QScAAAAABuR008/fWzTTuAREQsWLKg488wzx2y//fY7DB48+G0ppcnnn3/+iLWv+/3vfz/sPe95z1ZjxozZqV+/frsNHjz4bW9961u3P+OMM8bMmzevoj19d0cbzXnuuef6NpW32WabHpvJ2tYu6kcdddSElNLkcePG7VQaV9WJJ564+YQJE97av3//XYcPH77L/vvvv81f/vKXIT01xq748pe/PG/EiBF1ERHf+c53xtXXF2MPJgEnAAAAwEbq0Ucf7bfzzjvv8KMf/WjsjBkz+tfU1LwpZJw3b17Fnnvuud0nPvGJrf/xj38Mf/XVV/uuXr061dTUVDz++OMDzj333LGTJk1660033TSwpX66o43W9OvXLzeVn3zyyfXi0fPbb799wO67777Db37zm81eeOGFfitXruyzaNGiyttuu23ov/3bv237qU99avOutH/++eePaApbTz/99LHdMebBgwc3nHbaaa9ERDz99NPVv/71rzfpjnZ7moATAAAAYCP1wQ9+cOu5c+dWHXvssXP/+te/PnX77bc/+ctf/vLZHXbYYWVExIoVK9L++++/3b333ju4oqIijjjiiAW//OUvn73ppptm/OMf/5h51llnzRo2bFjdwoULK4888shtn3rqqb5r99EdbbTl7W9/+/Km8h//+MdR11xzzZtmV65LK1as6PORj3xk65qamopTTz311WuvvXbmzTff/OS3v/3tl0aNGlUbEfHrX/96s//6r//atDfH2ZwvfelL85rG+L3vfW9MXV1dbw+pTTYZAgAAACi4uXPnVk6bNq26pfNjx46tGzdu3JuSqqeffrr/n//8538deeSRS5o+22effdaEhV/+8pfHPvHEEwMGDx5c/7//+79PlZ+LiDj00ENrPvnJTy7ce++9J82bN6/qS1/60rhrrrnmufI63dFGWyZNmrT6gAMOWHzLLbcMXbVqVfrABz6w3Vvf+tblBx100OI999yzZp999lk+ZsyYdZbUvfbaa5VLly7N11xzzVPvec97apo+P+CAA5Yfc8wxr73jHe/Yfs6cOVXf/e53x51wwgkLx44du96kiAMGDMinn376K//+7//+lueff776wgsvHPHZz352QW+PqzVmcAIAAAAU3GWXXTZqypQpO7b0Ouecc5qdKXjUUUfNLw83yy1evLjPJZdcMioi4uyzz561djDZZLvttlt9xhlnvBIRce211w5fsmRJn+5so70uu+yy59/61reuaf+xxx4b8JOf/GTMhz/84W3Hjh27y4QJE9567LHHjr/zzjsHdLTtzvjoRz86rzzcbDJhwoTab3/72y9FNM70vPDCC9+03mlv+8IXvjB/zJgxqyMifvCDH4ypra3t7SG1SsAJAAAAsJH62Mc+trClc9dee+3gpjU5P/axj7W64/iBBx64NCKirq4u3XXXXWsCxO5oo73GjBlT98ADD8z40Y9+9MIOO+zwpiD1hRde6HfppZduus8++2x/xBFHbNmZELUjPvWpT7U46/HjH//4osGDB9dHRNxyyy2d2nDotNNOW5Bznp5znn7uuefO7uw4m1NdXZ2bAueXXnqp309/+tOR3dl+d/OIOgAAAEDBffGLX3ylMyHXHnvssaKlc9OmTVsTMm6xxRa7tLfNWbNmVXVnGx3Rr1+/fMYZZ8w/44wz5j///PNVN9xww6AHHnhg4PTp0wc+8sgjA+vq6lJExNVXX73JoYceWnXHHXc8VVnZ/fFYVVVVfsc73tHsbNWmce6www7L77vvvsEzZ85cLzZFWttpp502/yc/+cnol19+ud8555wz5tRTT11QXV2d275y3TODEwAAAGAjNWrUqBbXfpw7d26nQsbly5evyZu6o43OmjBhQu2nPvWp1375y1++/MADD8x8+eWXH/nMZz7zap8+jU3fe++9gy+66KIe2SV86NChdW0Fp00b+SxZsmS9nIBYVVUVX/7yl1+JiJg9e3bf8847b72dxble/gECAAAA0PNaC+Hq6+vXlO+8884n+vbt267Ze1tuueWaBRu7o43ustlmm9X/7Gc/m5VzjgsuuGB0RMSVV145/NRTT23xMf3OSil1d5O94tOf/vSCc845Z8wLL7zQ79xzzx1z2mmnze/fv/96N4tTwAkAAADAm4wYMWJNOjl69Oi6rbfeusOhY3e00d0+85nPzGsKOF944YUWd57vikWLFlXW1dW1GiDPmzevKiJiyJAh680O6murrKyMs88+e/anP/3pLefOnVv1ox/9aNTXvva1ub09rrV5RB0AAACAN9ltt93WrCF58803D+qtNrrbhAkT1oSsPTXTsra2Nt17770tbpRUW1sbTz755ICIiO22267FdVDXB5/61KcWbr311isjIs4///zRNTU16930VAEnAAAAAG9y+OGHL6murm6IiPjFL36xWUNDQ6+00R4dafeOO+4Y2FQeP378qh4ZUET85je/GdHSud///vfDlyxZUhERccABByzpqTF0h4qKivjKV74yOyJi/vz5VT/4wQ827e0xrU3ACQAAAMCbjBw5sv64446bGxHx0EMPDTzxxBPHl6+pubaXXnqp8txzz33DRjTd0UZ7/PnPfx763ve+d6u77rqr1R3J58yZU/HFL35xfNPx4YcfvqijfbXXH/7wh1HXXXfdm2atvvjii5Vf/epXN4+IqK6ubjjllFMWdKb9888/f0RKaXJKafLpp58+tqvjbc2xxx772sSJE1dERPz0pz8d3ZN9dYY1OAEAAABo1rnnnjv77rvvHvzPf/5z4MUXX7zp3XffPfjYY4+dN3ny5OWDBg1qWLBgQeWjjz5affPNNw+5/fbbh2633XYrTj/99Pnd3UZbGhoa4tprrx1+7bXXDp84ceKKgw8+ePGUKVOWjRs3rrZfv34Nr776atXtt98+6A9/+MOohQsXVkZE7Ljjjss/+9nPdqif9ho+fHhd//79Gz7wgQ9sd+KJJ845/PDDF1dXVzfcfffdA3/84x+PaVp/86yzzpo9bty49XYNziZ9+vSJr371q7M//vGPb71o0aL1Lk9c7wYEAAAAwPqhf//++dZbb33q6KOP3vL6668fNnPmzP7/8R//8ZaW6g8aNOhN0zO7o422jBgxoq5///4NK1as6DNz5sz+M2fObHUm51577bXkyiuvfLaqqqqjXbVL//79G/7nf/7nmQ984APb/vznPx/985///E2zHo877ri53/jGN+b0yAB6wMc+9rFF3/ve95Y//vjjLa4t2lsEnAAAAAC0aPjw4Q3XXXfdM9ddd92giy++eMR99903aN68eX1XrlyZBg0a1DB+/PhVu+6667LDDjts0ZFHHtnsepLd0UZrDjnkkGVz5859+Jprrhlyyy23DH7kkUcGPv/88/0WL15cWV9fH4MGDWoYN27cql122WXZRz/60dcOO+ywpV3/k2ndvvvuu3zatGlPfOc73xl90003DZ07d27f/v371++0007LP/vZz8750Ic+tF6vvdmcr3/967M+/OEPb9vb41hbyjn39hgoSSltHhEvRUTcdFTE6IFtXNCN3jtm6rrrDAAAgBYN3v7sddZX7cLamHn6zKbD8Tnnl7vS3vTp0/+vsrJyh+rq6mETJ058urW6//PN+7ZfUbO6Z6bPrUf6D+pb+5H/fPuTvT0O1o2jjjpqwlVXXTVi7Nixq2fNmvVob49nfTRz5sxtVq5cuaiuru6JyZMnv7c72jSDEwAAAFjnhH5Ad7GLOgAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwrLJEAAAAAB0gyuvvPL5iHi+l4ex0TGDEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAgO6SIyJyzqm3BwKsn3ri/iDgBAAAALrLkpxzXX19fWVdXZ3MAXiDhoaG1NDQUBER9RFR013tutkAAAAA3eWh+vr6pRGRFy5cOLy3BwOsXxYvXjw455zq6+uXRcSj3dWugBMAAADoLldFRH19ff2iefPmbfrKK69sunz58n45594eF9CLGhoa0muvvTZkzpw5oxsaGpblnFdHxI3d1X5ldzUEAAAAbNwmT5789PTp06fW1tYelnOuW7hw4YiFCxeO6NOnT0NKqSGlJOmEjUzOOTU0NFSU3petXr365Yh4OCKmdVcfAk4AAACgO30zIl6uq6s7pa6ubkGfPn369+nTpzo8RQobs/r6+vqmmZsPR8RpkydPruuuxpNp4uuPlNLmEfFSRMTNW20do6uqenlEAAAAbMhera2Ndz37TNPh+Jzzy93V9vTp08dHxJ6l1+YRMbC72gYKpyYa19y8MSKmdWe4GWEGJwAAANADJk+e/FI0TuL5U2+PBdiwmR4OAAAAABSWgBMAAAAAKCwBJwAAAABQWIULOFNKW6SUzkkpzUgpLUspLUwpTUspnZlSGtCN/bwnpfTXlNLLKaVVpfe/ppTe0119AAAAAABdU6hNhlJKh0fEHyJiSNnHAyJi99LrxJTS+3LOT3ehjz4RcVFEnLDWqXGl1xEppV9HxMk554bO9gMAAAAAdF1hZnCmlHaNiCuiMdysiYivRMReEXFgRPyqVG27iPjflNLgLnT1nXg93HwoIj4SEVNK7w+VPj8xIr7dhT4AAAAAgG5QpBmc50VE/4ioi4hDcs73lJ27OaX0r4j4QTSGnGdExDc62kFKabuI+FLp8IGI2DfnvKJ0PC2ldE1E3BaNs0XPTCn9tiuzRQEAAACArinEDM6U0pSI2Kd0+Ju1ws0m50TEk6Xy51NKVZ3o6gvxeuj7ubJwMyIics7LI+JzpcPKiPhiJ/oAAAAAALpJIQLOiDiirHxxcxVK62FeWjocFhEHdKSDlFKKiA+UDmfknO9toZ97I2Jm6fADpesAAAAAgF5QlIBz79L7soiY3kq928rK7+xgH1tGxNhm2mmtn3ERMaGD/QAAAAAA3aQoa3BuX3p/Oudc10q9Gc1c0147tNBOe/p5rj0dpJQ2b6PK6Pa0AwAAAAA0Wu8DzpRSdUSMLB2+3FrdnPNrKaVlETEwIsZ3sKvy8LHVfiLipbJyR/p5qe0qAAAAAEB7FeER9cFl5Zp21F9Weh/Ug/0sKyt3tB8AAAAAoJus9zM4I6K6rLy6HfVXld7792A/q8rKHemnrdmeoyNiWgfaAwAAAICNWhECzpVl5b7tqN+v9L6iB/vpV1Zudz8551YffbchOwAAAAB0TBEeUV9aVm7P4+ADS+/teZy9s/0MLCt3tB8AAAAAoJus9wFnznllRCwoHba6C3lKaXi8Hj52dEOf8tmVbe12Xv6ouY2DAAAAAKCXrPcBZ8kTpfdtUkqtPVY/qaz8ZCf7WLud7u4HAAAAAOgmRQk47yy9D4yIya3U26+sfFcH+3guImY3005z9i29z4qI5zvYDwAAAADQTYoScP6trHx8cxVSSn0i4hOlw0URcUtHOsg554i4unQ4KaX0jhb6eUe8PoPz6tJ1AAAAAEAvKETAmXO+PyLuKB2ekFLas5lqZ0TE9qXyeTnn2vKTKaX9U0q59Lqkha5+EhH1pfJPU0r912qjf0T8tHRYV6oPAAAAAPSSQgScJZ+PiBURURkR16eU/j2l9I6U0gEppV9GxA9K9Z6KiHM600HO+amI+GHpcPeIuCul9OGU0u4ppQ9H42Pvu5fO/zDn/K/OfhkAAAAAoOta27BnvZJzfqgUMv4hIoZExH83U+2piHhfznlpF7r6SkRsGhGfjIhdI+LyZur8JiK+2oU+AAAAAIBuUKQZnJFz/ntE7BwRP47GMHN5NK63+UBEnBURu+acn+5iHw055xMi4n3RuCbn7IhYXXq/OiLem3M+Mefc0JV+AAAAAICuS/bIWX+klDaPiJciIm7eausYXVXVyyMCAABgQ/ZqbW2869lnmg7H55xf7s3xAHRGoWZwAgAAAACUE3ACAAAAAIVVmE2GNjbb3nZrbL755r09DAAAADZgg19+OWL8+N4eBkCXmMEJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwKnt7ALxBRVPhlVde6c1xAAAAsBFY6/89K1qqB7A+Sznn3h4DJSml3SNiWm+PAwAAgI3SHjnnB3p7EAAd5RF1AAAAAKCwzOBcj6SU+kXETqXDeRFR34HLR8frsz/3iIhXu3FobBz8hugOfkd0ld8QXeU3RHfwO6KrivQbqoiIUaXyoznnVb05GIDOsAbneqT0L5JOPQ6QUio/fDXn/HK3DIqNht8Q3cHviK7yG6Kr/IboDn5HdFUBf0Mv9PYAALrCI+oAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYKefc22MAAAAAAOgUMzgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwbgBSSluklM5JKc1IKS1LKS1MKU1LKZ2ZUhrQ2+Nj/ZVSyu183drbY2XdSyltmlI6LKX0rZTStSml+WW/iUs60d57Ukp/TSm9nFJaVXr/a0rpPT0wfNYD3fEbSikd14F71XE9+43oDSml3VNKX08pXV92/6hJKT2VUro4pbR3B9tzL9rIdMdvyL1o45VSGpJSOrr0/1u3pZSeTiktTimtTinNTSndmlL6ckppRDvb2yul9IeU0gsppZUppVdTStellD7S098FYENW2dsDoGtSSodHxB8iYkjZxwMiYvfS68SU0vtyzk/3xviAQpvTHY2klPpExEURccJap8aVXkeklH4dESfnnBu6o0/WG93yG2LjlVK6PSL2aeZU34jYtvQ6LqV0aUR8Kue8upW23Is2Qt35G2KjNSUi/qeFc6MiYr/S68yU0sdyzte11FBK6RsR8bV440SjzSLikIg4JKV0TER8MOe8sjsGDrAxEXAWWEpp14i4IiL6R0RNRHw3Im4pHR8dEZ+KiO0i4n9TSrvnnJf21lhZ7/0iIi5o5fyydTUQ1lsvRsSMaPwP8I76TrweKDwUET+IiGciYuuI+HJE7BoRJ0bEvIj4jy6PlPVVV35DTQ6NiNmtnH+5C22zfhpbep8dEX+OiDui8bdUERF7RsQZ0RhOfiIiqiLio6205V60cerO31AT96KNz0vR+P9Z00vlV6IxpNw8Ij4YEUdGxMiIuCalNCXn/MjaDaSUTo6I/ywdPhMR/x0Rj0bjb/TzEXFARLwvIn4b7fsdAlAm5Zx7ewx0UtnfSNdFxL4553vWOn9mNP7He0TEN3PO31i3I2R9l1JqugH4ffAmKaVvRsS0iJiWc56TUpoQEc+VTv8u53xcO9rYLiIej8a/UHsgGu9VK8rOD4iI26JxxnldRGxvxvmGo5t+Q8dFxMWlwy1zzs93/0hZX6WUpkbEpRFxZc65vpnzIyPirmj8C92IiP1yzrc3U8+9aCPVjb+h48K9aKOUUqpo7rezVp0jIuKvpcO/5pyPXOv8JhHxbEQMjcaAfXLOeX55H6XrDy99dEDO+dZu+QIAGwlrcBZUSmlKvP64zW/WDjdLzomIJ0vlz6eUqtbJ4IANQs75P3POU3POXXnM+Avx+tMCnysPFEp9LI+Iz5UOKyPii13oi/VMN/2G2IjlnA/LOf+ppXChFBCcUfbRB1to6gvhXrRR6sbfEBuptsLNUp2/RcTM0mFzSyKcGI3hZkTEWeXhZlkfp0ZEU19ndmqwABsxAWdxHVFWvri5CqX1oy4tHQ6LxsceANaJlFKKiA+UDmfknO9trl7p86b/KfhA6TqA9rqlrLz12ifdi2iHVn9D0E5Ny4FVN3PuiNL7koi4qrmLc84vR8SNpcMDU0qDu3V0ABs4AWdxNe32uCwa14JpyW1l5Xf23HAA3mTLeH3ts9taq1h2flxETOipAQEbpH5l5eZmWrkX0Za2fkPQqpTSxIh4W+lwxlrn+kbjRkUREfe0sZFV0z2oXzQumQFAOwk4i2v70vvTOee6VuqV/wt2+xZrsbH7t5TSEyml5SmlpSmlf6WUfpdSMuuXrtihrDyjxVpvPu9eRUsuTinNTimtTinNTyndm1L6dkppXG8PjF61X1n5yWbOuxfRlrZ+Q2tzLyJSSgNSStumlE6PxmCyaRmMn6xVdbto3NQqwj0IoMcIOAsopVQdjbv0RbSxS2PO+bV4fQfs8T05Lgpth2j8j6j+ETEoIraJxt1Eb04p/TWlNLS1i6EFm5eV29pR9qWysnsVLdk/IsZE407HIyLi7RHxlYh4urQ7LRuZlFKfiDi77KM/NVPNvYgWtfM3tLb9w71oo5RSOi6llEsbdS6LiKeicd+DzUpVvhcRf1zrMvcggHWgsu0qrIfK12OpaUf9ZRExMBqDKyi3PCKuiYibovFvjGsiYlQ0zmQ4JRr/o/2IiLg6pXRwzrm2l8ZJMXXkXrWsrOxexdqejcY1y+6J1//nb6uIOCoaNwSpjogLU0o553xR7wyRXvLFeP3Rz6tyzs0t2+NeRGva8xtq4l5ESx6OiJNyztOaOeceBLAOCDiLqXzh6tbWcGmyqvTevwfGQrGNyzkvaubzG1JKP42IayNi12gMPD8dEeevw7FRfB25V60qK7tXUe6vEfG7nHNe6/NpEXFFSumwaAwcqiLixymla3LOr67rQbLupZT2i8bZUhERc6Px31PNcS+iWR34DUW4F9HobxHxQKncPxo3pfpQRPy/iPiflNIXcs5T17rGPQhgHfCIejGtLCv3bUf9poXTV/TAWCiwFsLNpnNzonE2QtOszc+tizGxQenIvap8gwf3KtbIOS9uJlAoPz81Ir5VOhwQESesk4HRq1JKO0Zj4FQZjfeaf8s5z22hunsRb9LB35B7ERHR+N/OOefHSq9pOefLc85HRuPSTltF41NPx611mXsQwDog4CympWXl9jy6MLD03p7H2WGNnPOzEXFD6XCblNLY1urDWjpyrxpYVnavoqMuioim4GG/1ipSfCmlLSPi+ogYHo07Xh+dc769lUvci3iDTvyG2su9aCOVc/59RPw5Gv//+mcppU3KTrsHAawDAs4CyjmvjIgFpcPNW6ubUhoer/+L8qXW6kILnigr2x2UjihfSL/Ve1W8cSF99yo6pDTrqunfi+5TG7DSX7TdGBFjozFI+mTO+eo2LnMvYo1O/obaxb1oo9f0OxoYEe8u+9w9CGAdEHAWV1PotE1KqbW1VCeVlZ/swfGw4WrxcSxoQ3k4PqnFWm8+715FZ7hXbeBSSiOj8amCrUoffS7nfGk7LnUvIiK69BvqCPeijde8svIWZeWnonGmcIR7EECPEXAW152l94ERMbmVeuWPx9zVc8NhA7ZDWXl2r42CInouXv/NtPWo3r6l91kR8XxPDYgNU0ppVESMLB26T22AUkpDI+K6eP3fSWfnnH/ezsvdi+jqb6i9fbgXbdzKZ+2uebw857w6Iu4vHe6ZUmptHc6me9SqeH0zIwDaQcBZXH8rKx/fXIWUUp9oXPA6ImJRRNzSs0NiQ1Nao+rg0uEzOedZvTkeiqW0GUPT41qTUkrvaK5e6fOmGQtXt7aJA7TgpIhIpfJtvTkQul9KaUBE/G9E7Fb66Ds55++393r3Irr6G+oA96KN27+VlR9d69zfSu9DIuLI5i5OKW0e/7+9+wmxqorjAP49WJCLVgpSBEoF0aJFYAuzyMCVZptaRC0Ki1y4KGlRRAUtyqKNYkR/DA0CoZAWRS3KNmlCLapFG1EqaiVBRqJl0a/FvTaDjI6jz5m58z4fGJhz332H8+Bw3rnfe859ydq+uK+q/pjqPACmJuAcqKr6KskXffHh1tqqKU57IsmN/f/bq+rvKc5hTLXWNpzr8QattWVJ9mbi1x5fm5WGsdBsy8S2rB2ttcWTX+zLO/riP/35kCRpra1ord08zTl3JXmuL55MsuuSN4xZ0690+iDJ6v7Q9qp65gKq2hZj0VgaRR8yFo231tpDrbUrpjlnS5J1ffGHTFynnbYzye/9/y+11pac8f5F6ebai/pDr1xUowHGUHNzerj6idaBJIvTbYN4Md0qzcVJ7kt3Fznpnvuy0l1AJmut/Zjk8nQh5sF0W/FOpttatSbJpkxss9qfZG1V/TXb7WTutNZuS3L9pENLMzHhPpBusv6/qtp9lnq2JnmqL36T5OUkR5Jcl+TJJKcvGrdW1dOjaDvzw8X2odbamnTfaweTfJjkuyRH+5evTXJv/3d6xdTmqnIzZgFpre3NxGqnz5M8nnM/4/BUVR06S13GojE0ij5kLBpv/Zz5ynRz5v3pxo3j/bGbkjyQiQD9VJL1VfXZFPVsSvJ6XzyS5IV0Kz2vTtcv7+xf21NV91+CjwKwoAk4B661tiHJu+m2O0zlULov2cOz1yqGoJ+sLZ/uvHSTuUeq6tglbRDzTmttd5IHz/f8qmpTHe8fl/FWko3nePvbSR6tqn9n0kbmt4vtQ5NChemcSLKlqt6cQfMYgNbaTCeqP1XVirPUZSwaQ6PoQ8ai8TaDOfMvSTZW1afnqOv5JM9mIgw/08dJ7qmqP2faToBxJ+BcAFpry5M8lmR9kmvS3Tk8nOT9JK9W1Yk5bB7zVGvtjnQPMl+VbvXB0nRB+fEkPyf5Msk7VXVwzhrJnBpVwDmpvnXpVpbfkq6//Zrk6yRvVNUnF95S5qsRBJxXJrk73Ti1MslV6frOZUl+S/J9kn1JdlbV0bDgjDLgnFSnsWiMjCjgNBaNsdbaDemus1an25WwLMmSdDufjib5NslHSd47n+uu1tqtSTYnub2v61i6VcG7qmrP6D8BwHgQcAIAAAAAg+VHhgAAAACAwRJwAgAAAACDJeAEAAAAAAZLwAkAAAAADJaAEwAAAAAYLAEnAAAAADBYAk4AAAAAYLAEnAAAAADAYAk4AQAAAIDBEnACAAAAAIMl4AQAAAAABkvACQAAAAAMloATAAAAABgsAScAAAAAMFgCTgAAAABgsAScAAAAAMBgCTgBAAAAgMEScAIAAAAAgyXgBAAAAAAGS8AJAAAAAAyWgBMAAAAAGCwBJwAAAAAwWAJOAAAAAGCwBJwAAAAAwGAJOAEAAACAwfoP5zIh3PcBOzMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -210,7 +210,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "timeloop(500)"
+    "timeloop(5000)"
    ]
   },
   {
@@ -230,7 +230,7 @@
     {
      "data": {
       "text/plain": [
-       "0.07442458175252653"
+       "0.07369491639715217"
       ]
      },
      "execution_count": 13,
@@ -250,7 +250,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12c65b790>]"
+       "[<matplotlib.lines.Line2D at 0x7f35955717f0>]"
       ]
      },
      "execution_count": 14,
@@ -259,7 +259,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -287,13 +287,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.testing.assert_almost_equal(np.gradient(vel_profile)[-1], 0)"
+    "np.testing.assert_almost_equal(np.gradient(vel_profile)[-1], 0, decimal=3)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -307,7 +314,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
-- 
GitLab