From b4c2f291fbb8508a75a2959e60d1a80b67d2a1c1 Mon Sep 17 00:00:00 2001
From: Jonas Lindemann <jonas.lindemann@gmail.com>
Date: Thu, 8 Feb 2024 17:22:28 +0100
Subject: [PATCH 1/5] Reduced extensive logging in gmsh. Work om PDM packaging.

---
 .gitignore                    |   1 +
 .pdm-python                   |   1 +
 calfem/core.py                |   5 +-
 calfem/geometry.py            |   4 +-
 calfem/mesh.py                |   9 +-
 example_output.py             | 478 +++++++++++++++++++++
 examples/exs_beam1.ipynb      | 352 +--------------
 examples/exs_beam2.ipynb      | 515 +++++++++++-----------
 pyproject.toml                |  44 ++
 setup.py                      |   3 +-
 src/calfem_python/__init__.py |   0
 test_calfem.py                |  97 ++++-
 test_examples.log             | 784 ++++++++++++++++++++++++++++++++++
 test_examples_output.log      |   4 +
 test_examples_warnings.log    |   4 +
 test_output.log               |   2 +
 tests/__init__.py             |   0
 17 files changed, 1697 insertions(+), 606 deletions(-)
 create mode 100644 .pdm-python
 create mode 100644 example_output.py
 create mode 100644 pyproject.toml
 create mode 100644 src/calfem_python/__init__.py
 create mode 100644 test_examples.log
 create mode 100644 test_examples_output.log
 create mode 100644 test_examples_warnings.log
 create mode 100644 test_output.log
 create mode 100644 tests/__init__.py

diff --git a/.gitignore b/.gitignore
index fc9fc50..2d9fa14 100644
--- a/.gitignore
+++ b/.gitignore
@@ -41,3 +41,4 @@ exm6.vtk
 # new notebook examples
 docs/source/examples/.ipynb_checkpoints/
 .DS_Store
+*.lock
diff --git a/.pdm-python b/.pdm-python
new file mode 100644
index 0000000..34eb7c6
--- /dev/null
+++ b/.pdm-python
@@ -0,0 +1 @@
+C:/Users/Miniconda3/envs/calfem-clean/python.EXE
\ No newline at end of file
diff --git a/calfem/core.py b/calfem/core.py
index c491d73..fc157ae 100644
--- a/calfem/core.py
+++ b/calfem/core.py
@@ -5279,7 +5279,7 @@ def assem(edof, K, Ke, f=None, fe=None):
         return K, f
 
 
-def solveq(K, f, bcPrescr, bcVal=None):
+def solveq(K, f, bcPrescr=None, bcVal=None):
     """
     Solve static FE-equations considering boundary conditions.
     
@@ -5306,6 +5306,9 @@ def solveq(K, f, bcPrescr, bcVal=None):
     if bcVal is None:
         bcVal = np.zeros([nPdofs], 'd')
 
+    if bcPrescr is None:
+        return np.asmatrix(np.linalg.solve(K, f))
+
     bc = np.ones(nDofs, 'bool')
     bcDofs = np.arange(nDofs)
 
diff --git a/calfem/geometry.py b/calfem/geometry.py
index 16cda0f..74a90c4 100644
--- a/calfem/geometry.py
+++ b/calfem/geometry.py
@@ -454,7 +454,7 @@ def addRuledSurface(self, outer_loop, ID=None, marker=0):
         if len(outer_loop) not in [3, 4]:
             raise IndexError(
                 "Ruled Surface: outer_loop must be a list of 3 or 4 positive integers denoting curve indices")
-        self._addSurf("Ruled Surface", outer_loop, [],
+        self._addSurf("Surface", outer_loop, [],
                       ID, marker, is_structured=False)
 
     def addStructuredSurface(self, outer_loop, ID=None, marker=0):
@@ -472,7 +472,7 @@ def addStructuredSurface(self, outer_loop, ID=None, marker=0):
         marker    - Integer. Marker applied to this surface. Default 0.
         '''
         self._checkIfProperStructuredQuadBoundary(outer_loop, ID)
-        self._addSurf("Ruled Surface", outer_loop, [],
+        self._addSurf("Surface", outer_loop, [],
                       ID, marker, is_structured=True)
 
     def _addSurf(self, name, outer_loop, holes, ID, marker, is_structured):
diff --git a/calfem/mesh.py b/calfem/mesh.py
index 1930413..0e43bec 100644
--- a/calfem/mesh.py
+++ b/calfem/mesh.py
@@ -166,6 +166,7 @@ def __init__(self, geometry, el_type=2, el_size_factor=1, dofs_per_node=1,
         self.mesh_dir = mesh_dir
         self.return_boundary_elements = return_boundary_elements
         self.gmsh_options = {}
+        self.gmsh_verbosity = 0
 
         # gmsh elements that have rectangle faces
         self._ElementsWithQuadFaces = [3, 5, 10, 12, 16, 17, 92, 93]
@@ -270,9 +271,9 @@ def create(self, is3D=False, dim=3):
                 raise IOError(
                     "Error: Could not find GMSH. Please make sure that the \GMSH executable is available on the search path (PATH).")
             else:
-                print("Info    : GMSH -> %s" % gmshExe)
+                cflog.info(" GMSH -> %s" % gmshExe)
         else:
-            print("Info    : GMSH -> Python-module")
+            cflog.info(" GMSH -> Python-module")
 
         # Create a temporary directory for GMSH
 
@@ -353,6 +354,8 @@ def create(self, is3D=False, dim=3):
             if self.initialize_gmsh:
                 gmsh.initialize(sys.argv)
 
+            gmsh.option.setNumber("General.Verbosity", self.gmsh_verbosity)
+
             # This is a hack to enable the use of gmsh in
             # a separate thread.
 
@@ -411,7 +414,7 @@ def create(self, is3D=False, dim=3):
 
         with open(mshFileName, 'r') as mshFile:
 
-            info("Mesh file  : "+mshFileName)
+            info(" Mesh file  : "+mshFileName)
 
             # print("Reading msh file...")
 
diff --git a/example_output.py b/example_output.py
new file mode 100644
index 0000000..e38166a
--- /dev/null
+++ b/example_output.py
@@ -0,0 +1,478 @@
+# -*- coding: utf-8 -*-
+"""
+Module containing example output for comparisons.
+"""
+
+examples = {
+    "exs_beam1.py": 
+"""+-------------+
+|           a |
+|-------------|
+|  0.0000e+00 |
+| -9.4859e-03 |
+| -2.2766e-02 |
+| -3.7943e-03 |
+|  0.0000e+00 |
+|  7.5887e-03 |
++-------------+
++-------------+
+|           r |
+|-------------|
+|  6.6667e+03 |
+|  0.0000e+00 |
+|  3.6380e-12 |
+| -9.0949e-12 |
+|  3.3333e+03 |
+|  3.6380e-12 |
++-------------+
+
+## es1
+
++-------------+------------+
+|          V1 |         M1 |
+|-------------+------------|
+| -6.6667e+03 | 0.0000e+00 |
+| -6.6667e+03 | 6.6667e+03 |
+| -6.6667e+03 | 1.3333e+04 |
+| -6.6667e+03 | 2.0000e+04 |
++-------------+------------+
+
+## ed1
+
++-------------+
+|          v1 |
+|-------------|
+|  0.0000e+00 |
+| -9.2751e-03 |
+| -1.7285e-02 |
+| -2.2766e-02 |
++-------------+
+
+## ec1
+
++------------+
+|         x1 |
+|------------|
+| 0.0000e+00 |
+| 1.0000e+00 |
+| 2.0000e+00 |
+| 3.0000e+00 |
++------------+
+
+## es2
+
++------------+------------+
+|         V2 |         M2 |
+|------------+------------|
+| 3.3333e+03 | 2.0000e+04 |
+| 3.3333e+03 | 1.6667e+04 |
+| 3.3333e+03 | 1.3333e+04 |
+| 3.3333e+03 | 1.0000e+04 |
+| 3.3333e+03 | 6.6667e+03 |
+| 3.3333e+03 | 3.3333e+03 |
+| 3.3333e+03 | 0.0000e+00 |
++------------+------------+
+
+## ed2
+
++-------------+
+|          v2 |
+|-------------|
+| -2.2766e-02 |
+| -2.4769e-02 |
+| -2.3609e-02 |
+| -1.9920e-02 |
+| -1.4334e-02 |
+| -7.4833e-03 |
+|  6.9389e-18 |
++-------------+
+
+## ec2
+
++------------+
+|         x2 |
+|------------|
+| 0.0000e+00 |
+| 1.0000e+00 |
+| 2.0000e+00 |
+| 3.0000e+00 |
+| 4.0000e+00 |
+| 5.0000e+00 |
+| 6.0000e+00 |
++------------+
+""",
+    "exs_beam2.py": 
+"""+-------------+
+|           a |
+|-------------|
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  7.5357e-03 |
+| -2.8741e-04 |
+| -5.3735e-03 |
+|  7.5161e-03 |
+| -3.1259e-04 |
+|  4.6656e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -5.1513e-03 |
++-------------+
++-------------+
+|           r |
+|-------------|
+|  1.9268e+03 |
+|  2.8741e+04 |
+|  4.4527e+02 |
+|  0.0000e+00 |
+| -7.2760e-12 |
+|  3.6380e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  7.2760e-12 |
+| -3.9268e+03 |
+|  3.1259e+04 |
+|  0.0000e+00 |
++-------------+
+
+## es1
+
++-------------+------------+------------+
+|           N |         Vy |         Mz |
+|-------------+------------+------------|
+| -2.8741e+04 | 1.9268e+03 | 8.1523e+03 |
+| -2.8741e+04 | 1.9268e+03 | 7.7670e+03 |
+| -2.8741e+04 | 1.9268e+03 | 7.3816e+03 |
+| -2.8741e+04 | 1.9268e+03 | 6.9963e+03 |
+| -2.8741e+04 | 1.9268e+03 | 6.6109e+03 |
+| -2.8741e+04 | 1.9268e+03 | 6.2256e+03 |
+| -2.8741e+04 | 1.9268e+03 | 5.8402e+03 |
+| -2.8741e+04 | 1.9268e+03 | 5.4548e+03 |
+| -2.8741e+04 | 1.9268e+03 | 5.0695e+03 |
+| -2.8741e+04 | 1.9268e+03 | 4.6841e+03 |
+| -2.8741e+04 | 1.9268e+03 | 4.2988e+03 |
+| -2.8741e+04 | 1.9268e+03 | 3.9134e+03 |
+| -2.8741e+04 | 1.9268e+03 | 3.5281e+03 |
+| -2.8741e+04 | 1.9268e+03 | 3.1427e+03 |
+| -2.8741e+04 | 1.9268e+03 | 2.7574e+03 |
+| -2.8741e+04 | 1.9268e+03 | 2.3720e+03 |
+| -2.8741e+04 | 1.9268e+03 | 1.9867e+03 |
+| -2.8741e+04 | 1.9268e+03 | 1.6013e+03 |
+| -2.8741e+04 | 1.9268e+03 | 1.2160e+03 |
+| -2.8741e+04 | 1.9268e+03 | 8.3062e+02 |
+| -2.8741e+04 | 1.9268e+03 | 4.4527e+02 |
++-------------+------------+------------+
+
+## edi1
+
++------------+------------+
+|         u1 |         v1 |
+|------------+------------|
+| 2.8741e-04 | 7.5357e-03 |
+| 2.7304e-04 | 6.5112e-03 |
+| 2.5867e-04 | 5.5837e-03 |
+| 2.4430e-04 | 4.7485e-03 |
+| 2.2993e-04 | 4.0008e-03 |
+| 2.1556e-04 | 3.3357e-03 |
+| 2.0119e-04 | 2.7484e-03 |
+| 1.8682e-04 | 2.2341e-03 |
+| 1.7245e-04 | 1.7880e-03 |
+| 1.5807e-04 | 1.4053e-03 |
+| 1.4370e-04 | 1.0811e-03 |
+| 1.2933e-04 | 8.1067e-04 |
+| 1.1496e-04 | 5.8915e-04 |
+| 1.0059e-04 | 4.1173e-04 |
+| 8.6223e-05 | 2.7359e-04 |
+| 7.1852e-05 | 1.6993e-04 |
+| 5.7482e-05 | 9.5907e-05 |
+| 4.3111e-05 | 4.6722e-05 |
+| 2.8741e-05 | 1.7554e-05 |
+| 1.4370e-05 | 3.5858e-06 |
+| 0.0000e+00 | 0.0000e+00 |
++------------+------------+
+
+## es2
+
++-------------+-------------+-------------+
+|           N |          Vy |          Mz |
+|-------------+-------------+-------------|
+| -3.1259e+04 | -3.9268e+03 | -1.5707e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.4922e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.4136e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.3351e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.2566e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.1780e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.0995e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.0210e+04 |
+| -3.1259e+04 | -3.9268e+03 | -9.4242e+03 |
+| -3.1259e+04 | -3.9268e+03 | -8.6389e+03 |
+| -3.1259e+04 | -3.9268e+03 | -7.8535e+03 |
+| -3.1259e+04 | -3.9268e+03 | -7.0682e+03 |
+| -3.1259e+04 | -3.9268e+03 | -6.2828e+03 |
+| -3.1259e+04 | -3.9268e+03 | -5.4975e+03 |
+| -3.1259e+04 | -3.9268e+03 | -4.7121e+03 |
+| -3.1259e+04 | -3.9268e+03 | -3.9268e+03 |
+| -3.1259e+04 | -3.9268e+03 | -3.1414e+03 |
+| -3.1259e+04 | -3.9268e+03 | -2.3561e+03 |
+| -3.1259e+04 | -3.9268e+03 | -1.5707e+03 |
+| -3.1259e+04 | -3.9268e+03 | -7.8535e+02 |
+| -3.1259e+04 | -3.9268e+03 |  2.7756e-12 |
++-------------+-------------+-------------+
+
+## edi2
+
++------------+------------+
+|         u1 |         v1 |
+|------------+------------|
+| 3.1259e-04 | 7.5161e-03 |
+| 2.9696e-04 | 8.3527e-03 |
+| 2.8133e-04 | 9.0027e-03 |
+| 2.6570e-04 | 9.4761e-03 |
+| 2.5007e-04 | 9.7825e-03 |
+| 2.3444e-04 | 9.9319e-03 |
+| 2.1881e-04 | 9.9341e-03 |
+| 2.0318e-04 | 9.7988e-03 |
+| 1.8755e-04 | 9.5359e-03 |
+| 1.7193e-04 | 9.1552e-03 |
+| 1.5630e-04 | 8.6665e-03 |
+| 1.4067e-04 | 8.0796e-03 |
+| 1.2504e-04 | 7.4044e-03 |
+| 1.0941e-04 | 6.6506e-03 |
+| 9.3777e-05 | 5.8282e-03 |
+| 7.8148e-05 | 4.9468e-03 |
+| 6.2518e-05 | 4.0163e-03 |
+| 4.6889e-05 | 3.0466e-03 |
+| 3.1259e-05 | 2.0474e-03 |
+| 1.5630e-05 | 1.0286e-03 |
+| 0.0000e+00 | 0.0000e+00 |
++------------+------------+
+
+## es3
+
++-------------+-------------+-------------+
+|           N |          Vy |          Mz |
+|-------------+-------------+-------------|
+| -3.9268e+03 | -2.8741e+04 | -8.1523e+03 |
+| -3.9268e+03 | -2.5741e+04 |  1.9953e+01 |
+| -3.9268e+03 | -2.2741e+04 |  7.2922e+03 |
+| -3.9268e+03 | -1.9741e+04 |  1.3664e+04 |
+| -3.9268e+03 | -1.6741e+04 |  1.9137e+04 |
+| -3.9268e+03 | -1.3741e+04 |  2.3709e+04 |
+| -3.9268e+03 | -1.0741e+04 |  2.7381e+04 |
+| -3.9268e+03 | -7.7409e+03 |  3.0154e+04 |
+| -3.9268e+03 | -4.7409e+03 |  3.2026e+04 |
+| -3.9268e+03 | -1.7409e+03 |  3.2998e+04 |
+| -3.9268e+03 |  1.2591e+03 |  3.3070e+04 |
+| -3.9268e+03 |  4.2591e+03 |  3.2243e+04 |
+| -3.9268e+03 |  7.2591e+03 |  3.0515e+04 |
+| -3.9268e+03 |  1.0259e+04 |  2.7887e+04 |
+| -3.9268e+03 |  1.3259e+04 |  2.4359e+04 |
+| -3.9268e+03 |  1.6259e+04 |  1.9932e+04 |
+| -3.9268e+03 |  1.9259e+04 |  1.4604e+04 |
+| -3.9268e+03 |  2.2259e+04 |  8.3762e+03 |
+| -3.9268e+03 |  2.5259e+04 |  1.2484e+03 |
+| -3.9268e+03 |  2.8259e+04 | -6.7793e+03 |
+| -3.9268e+03 |  3.1259e+04 | -1.5707e+04 |
++-------------+-------------+-------------+
+
+## edi3
+
++------------+-------------+
+|         u1 |          v1 |
+|------------+-------------|
+| 7.5357e-03 | -2.8741e-04 |
+| 7.5347e-03 | -1.9218e-03 |
+| 7.5337e-03 | -3.5566e-03 |
+| 7.5328e-03 | -5.1312e-03 |
+| 7.5318e-03 | -6.5927e-03 |
+| 7.5308e-03 | -7.8952e-03 |
+| 7.5298e-03 | -9.0009e-03 |
+| 7.5288e-03 | -9.8789e-03 |
+| 7.5279e-03 | -1.0506e-02 |
+| 7.5269e-03 | -1.0868e-02 |
+| 7.5259e-03 | -1.0954e-02 |
+| 7.5249e-03 | -1.0766e-02 |
+| 7.5239e-03 | -1.0310e-02 |
+| 7.5229e-03 | -9.6000e-03 |
+| 7.5220e-03 | -8.6584e-03 |
+| 7.5210e-03 | -7.5143e-03 |
+| 7.5200e-03 | -6.2048e-03 |
+| 7.5190e-03 | -4.7743e-03 |
+| 7.5180e-03 | -3.2745e-03 |
+| 7.5171e-03 | -1.7650e-03 |
+| 7.5161e-03 | -3.1259e-04 |
++------------+-------------+
+sfac=
+54.77300198398879
+sfac=
+3.628630851048567e-05
+""",
+"exs_bar2.py":"""
+# Analysis of a plane truss.
+
+
+## Stiffness matrix K:
+
++-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
+|  7.5000e+07 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -7.5000e+07 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  6.4000e+07 | -4.8000e+07 | -6.4000e+07 |  4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 | -4.8000e+07 |  3.6000e+07 |  4.8000e+07 | -3.6000e+07 | 0.0000e+00 |  0.0000e+00 |
+| -7.5000e+07 | 0.0000e+00 | -6.4000e+07 |  4.8000e+07 |  1.3900e+08 | -4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  4.8000e+07 | -3.6000e+07 | -4.8000e+07 |  8.6000e+07 | 0.0000e+00 | -5.0000e+07 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -5.0000e+07 | 0.0000e+00 |  5.0000e+07 |
++-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -3.9793e-04 |
+| -1.1523e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+|  2.9845e+04 |
+|  0.0000e+00 |
+| -2.9845e+04 |
+|  2.2383e+04 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  5.7617e+04 |
++-------------+
+
+## Element forces r:
+
+N1 = 
+[[-29844.55958549]
+ [-29844.55958549]]
+N2 = 
+[[57616.58031088]
+ [57616.58031088]]
+N3 = 
+[[37305.69948187]
+ [37305.69948187]]
+sfac=
+138.8489208633094
+""",
+"exs_beambar2.py":"""
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.0175e-04 |
+| -5.5551e-04 |
+| -9.6319e-04 |
+|  3.7224e-04 |
+| -4.5567e-03 |
+| -3.2909e-03 |
+|  3.7224e-04 |
+| -1.2990e-02 |
+| -4.5254e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.0702e+04 |
+| -6.6044e+03 |
+| -1.4032e+03 |
+|  0.0000e+00 |
+| -1.4552e-11 |
+| -2.2737e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -7.2760e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  3.8654e-11 |
+|  8.0702e+04 |
+|  4.6604e+04 |
++-------------+
+
+## es1 = 
+
++------------+------------+-------------+
+|          N |          Q |           M |
+|------------+------------+-------------|
+| 8.0702e+04 | 6.6044e+03 |  1.4032e+03 |
+| 8.0702e+04 | 6.6044e+03 |  8.2292e+01 |
+| 8.0702e+04 | 6.6044e+03 | -1.2386e+03 |
+| 8.0702e+04 | 6.6044e+03 | -2.5595e+03 |
+| 8.0702e+04 | 6.6044e+03 | -3.8803e+03 |
+| 8.0702e+04 | 6.6044e+03 | -5.2012e+03 |
+| 8.0702e+04 | 6.6044e+03 | -6.5221e+03 |
+| 8.0702e+04 | 6.6044e+03 | -7.8430e+03 |
+| 8.0702e+04 | 6.6044e+03 | -9.1639e+03 |
+| 8.0702e+04 | 6.6044e+03 | -1.0485e+04 |
+| 8.0702e+04 | 6.6044e+03 | -1.1806e+04 |
++------------+------------+-------------+
+
+## es2 = 
+
++------------+-------------+-------------+
+|          N |           Q |           M |
+|------------+-------------+-------------|
+| 6.8194e+04 | -5.9028e+03 | -1.1806e+04 |
+| 6.8194e+04 | -3.9028e+03 | -1.0825e+04 |
+| 6.8194e+04 | -1.9028e+03 | -1.0245e+04 |
+| 6.8194e+04 |  9.7186e+01 | -1.0064e+04 |
+| 6.8194e+04 |  2.0972e+03 | -1.0283e+04 |
+| 6.8194e+04 |  4.0972e+03 | -1.0903e+04 |
+| 6.8194e+04 |  6.0972e+03 | -1.1922e+04 |
+| 6.8194e+04 |  8.0972e+03 | -1.3342e+04 |
+| 6.8194e+04 |  1.0097e+04 | -1.5161e+04 |
+| 6.8194e+04 |  1.2097e+04 | -1.7381e+04 |
+| 6.8194e+04 |  1.4097e+04 | -2.0000e+04 |
++------------+-------------+-------------+
+
+## es3 = 
+
++------------+-------------+-------------+
+|          N |           Q |           M |
+|------------+-------------+-------------|
+| 0.0000e+00 | -2.0000e+04 | -2.0000e+04 |
+| 0.0000e+00 | -1.8000e+04 | -1.6200e+04 |
+| 0.0000e+00 | -1.6000e+04 | -1.2800e+04 |
+| 0.0000e+00 | -1.4000e+04 | -9.8000e+03 |
+| 0.0000e+00 | -1.2000e+04 | -7.2000e+03 |
+| 0.0000e+00 | -1.0000e+04 | -5.0000e+03 |
+| 0.0000e+00 | -8.0000e+03 | -3.2000e+03 |
+| 0.0000e+00 | -6.0000e+03 | -1.8000e+03 |
+| 0.0000e+00 | -4.0000e+03 | -8.0000e+02 |
+| 0.0000e+00 | -2.0000e+03 | -2.0000e+02 |
+| 0.0000e+00 | -4.6838e-12 |  4.8594e-11 |
++------------+-------------+-------------+
+
+## es4 = 
+
++-------------+
+|           N |
+|-------------|
+| -1.7688e+04 |
+| -1.7688e+04 |
++-------------+
+
+## es5 = 
+
++-------------+
+|           N |
+|-------------|
+| -7.6244e+04 |
+| -7.6244e+04 |
++-------------+
+"""
+}
\ No newline at end of file
diff --git a/examples/exs_beam1.ipynb b/examples/exs_beam1.ipynb
index 5a203de..4a704b1 100644
--- a/examples/exs_beam1.ipynb
+++ b/examples/exs_beam1.ipynb
@@ -5,7 +5,7 @@
    "id": "21747083-42dc-453e-8d50-f582c0f8f220",
    "metadata": {},
    "source": [
-    "# Analysis of a plane frame"
+    "# Analysis of a simply supported beam"
    ]
   },
   {
@@ -37,31 +37,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "fa33f6e7-8616-43c1-ab6d-c033d33d5a4a",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'list' object has no attribute 'prepend'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m find_spec\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mprepend(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mJonas Lindemann\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mDevelopment\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mcalfem-python\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m find_spec(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalfem.core\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m      8\u001b[0m     get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install calfem-python\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'prepend'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from importlib.util import find_spec\n",
     "\n",
     "import sys\n",
     "\n",
-    "sys.path.prepend(r\"C:\\Users\\Jonas Lindemann\\Development\\calfem-python\")\n",
-    "\n",
     "if find_spec(\"calfem.core\") is None:\n",
     "    !pip install calfem-python\n",
     "else:\n",
@@ -78,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3d33663a-d8e9-46cf-b797-4f280930aac9",
    "metadata": {},
    "outputs": [],
@@ -100,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "ba1a0696-f45f-4798-ba51-f08be6dad7df",
    "metadata": {},
    "outputs": [],
@@ -121,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "f0a9449b-b9f7-42bb-b9c4-dabaa01c5875",
    "metadata": {},
    "outputs": [],
@@ -141,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9a39be9c-bb81-4caa-9de9-5fafdcb29013",
    "metadata": {},
    "outputs": [],
@@ -169,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "894a19bd-0eb9-42e8-bfff-5f6b92fc8c9a",
    "metadata": {},
    "outputs": [],
@@ -188,59 +174,10 @@
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   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "cc77fe68-7abe-48e2-a5b3-ef891e781d4b",
    "metadata": {},
-   "outputs": [
-    {
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-       "<tr><td style=\"text-align: right;\">-1.0914e-11</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 3.3333e+03</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">          r</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 6.6667e+03</td></tr>\\n<tr><td style=\"text-align: right;\">-3.6380e-12</td></tr>\\n<tr><td style=\"text-align: right;\"> 7.2760e-12</td></tr>\\n<tr><td style=\"text-align: right;\">-1.0914e-11</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.3333e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n</tbody>\\n</table>'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "bc = np.array([1, 5])\n",
     "a, r = cfc.solveq(K, f, bc)\n",
@@ -259,224 +196,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "cee82b10-4feb-49d3-ac3c-b4c45a29aa12",
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-       "<tr><td style=\"text-align: right;\">0.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">1.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">2.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">3.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">4.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">5.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">6.0000e+00</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">        x2</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">1.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">2.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">3.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">4.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">5.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">6.0000e+00</td></tr>\\n</tbody>\\n</table>'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ed = cfc.extract_ed(edof, a)\n",
     "\n",
@@ -507,23 +230,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "04d68478-5ac0-45cf-8903-5ac389f8faa8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x383.76 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cfv.figure(1)\n",
     "plt.plot([0, 9], [0, 0], color=(0.8, 0.8, 0.8))\n",
@@ -545,23 +255,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "9e420575-f743-4a5d-80d1-0591e73d1f45",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x383.76 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cfv.figure(2)\n",
     "plt.plot([0, 9], [0, 0], color=(0.8, 0.8, 0.8))\n",
@@ -583,23 +280,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "4734b313-1233-4f7f-bd97-cbbe411ced44",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x383.76 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cfv.figure(3)\n",
     "plt.plot([0, 9], [0, 0], color=(0.8, 0.8, 0.8))\n",
diff --git a/examples/exs_beam2.ipynb b/examples/exs_beam2.ipynb
index a105e86..cf67065 100644
--- a/examples/exs_beam2.ipynb
+++ b/examples/exs_beam2.ipynb
@@ -4,13 +4,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Analysis of a simply supported beam"
+    "# Analysis of a plane frame"
    ]
   },
   {
+   "attachments": {
+    "0be27f37-a622-4faf-83d4-0d6613593f22.png": {
+     "image/png": 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MyBcAAABAU1mcL4hermhnw4x5dg7e/40ezT6QLwDozcrlK8jfiocPHtIaADIT8gUAAADQVNbmC6L361J3prTtuOljdtqZkoF8AUBvoqKiWjZv/uvXL1oDQGZCvgAAAACaytJ8QfRmXXt76eAFS/tGc+5lr60jpKZPn+7h4bEbAPRi/vz59P8BQCYzMDBAvgAAAAAaycp8IeXevOZ8ZnKEZcWgPQn0cHayb98+Pz+/dgAAADkLObtxeS3V//3vf/37Bj998oTWAAAAwAFZli+Iv91a7O9sw0yOsLC0qz8+4kv2mx8BAAAA+idIFpCLh3+uXKE1AAAAcIDe8wXRm0NT+/fq2KJOJT6TLKTd7JxqtfDvMX7n02w4TwIAAAD0R1m+kJSU5O3tffXqVVoDAACAHmXt+o4AAAAAGlOYLyQkJNSrV69Nmzb/+9//6CEAAADQI+QL7J05cyYQAAAgFzh06BA9+XHDn/nCjx8/PD09O3ToIBRiHCQAAEDWQL7A3sKFCw0AAABygdDQUHry44Z0+UJ8fHz16tW7du0qEomYIwAAAKB/yBcogUBA/5/a0vKFxQAAADmUiYkJx/OFuLg4Nze3wMBAsRgLRQMAAGQl5AvUubNn+wT1OnXipPrbcTH5Qq9evWgNAACQ4wwfPpyc7KZOnUprbkjLF758+eLq6tq7d+9fv37RNgAAAMgiyBd+GzJoELlYcXNxDZ085eGDhxleqSBfAACAHI/JF6ZNm0ZrbmDyhaNHjzo5OQ0YMADhAgAAABcgX/jt69evHlXdyPUKc2vetNn6tetiv3yhzX9AvgDAHVMmhTx/9pwWAKA7w4YNy5J8ITk5+cXzF8pujyIfWZQvX65cuR49eqRr+vP26dMn+qAAAACQmZAvyDl14mRavsDc7Kz5yuZNIF8A4IjIh5FuLq5JSUm0BgDdGTp0KDnZTZ8+ndb6cu/u3bqenspu1dzcTIyNy5Qqne64wtuYUaPogwIAAEBm4kq+8OvXLyE3DB4wMF3EwNxI72XypEl379xNG4SJfAGAIwYPHLhowUJaAIBOMfnCjBkzaM0Bb9++tbayLlK4sOz+lAAAAJDluJIvrF29Jl1/nrO3Qf37JyYmkueMfAGAC0hPw6WSU1xcHK0BQKeGDBnCqXzh1atXlpaWkyZOImdk5AsAAACcwqHxCyJuGDRggGyakHZzd60ydcqUB/fv02esRr7g5uZmCwCZrEiRIkWLFqUFALDi6OhIT11/GDx4MDnZzZw5k9ZZ6vnz5xUqVCBPRnZ/SgAAAOAIrL8g5+TxE7KZArnZWfODe/c+ferUn+svLFiwQHW+UKhQIXIHAAAAjjMyMqKnrj8w+cKsWbNonXWePHlSrly5+fPnk/+PfAEAAICDkC/8lm7/iBbe3hvXr4+NjaXNf2Dyhd69e9P6DwULFiR3OHbs2G0AyBzTp05r3cqXFgCguYiICHKqMjQ0pKeuPwwaNIgL+UJkZGSZMmWWLFnClMgXAAAAOAj5wm+DB0pWdnR3rTItNDTyYSQ9qlyG+UKBAgXIHaKiomgNALrm36bt4YOHaAEAmvv+/bvqfGHgwIHkDrNnz6Z1Vrh3716pUqVWrlxJa+QLAAAAnIR8gTp75oyyeRDKIF8AyFof3r+vXLHSz58/aQ0Amvvx4wc5VfF4PFr/gckX5syZQ2u9u337dokSJdatW0drKeQLAAAAHIR8gRIIBPT/qS3DfCF//vzkDp8/f6Y1AOjUimXLhw8ZSgsAYCUhIYGcqgha/2HAgAGkNavyBXIOLV68+ObNm2mdCvkCAAAAByFfYC/DfCFfvnzkDtHR0bQGAJ1q1qhxxIUIWgAAKxnmC/379yetc+fOpbXePXz4kP4/GcgXAAAAOAj5AnsZ5gt58+Yld4iJiaE1AOjOo8hIj6puQqGQ1gDASmJioiRd4HC+oBDyBQAAAA5CvsBehvlCnjx5yB2+fPlCawDQndkzZ02ZFEILAGArKSlJki4YGPz69YsektevXz/SOm/ePFpzA/IFAAAADkK+wB7yBYCsQjpCtWvUvP3vbVoDAFsZ5gvBwcGkdf78+bTmBuQLAAAAHIR8gb0M8wUzMzNyh9jYWFoDgI5cu3rVq05dWgCAFn7+/ClJFwwMxGIxPSQP+QIAAACoCfkCe2FhYeSSq0+fPrT+g6mpKbnD169faQ0AOjJ+zNgF88NoAQBaSE5OlqQLBgYikYgekte3b1/SSk55tOYG5AsAAAAchHyBvQzzBRMTE3KHuLg4WgOALqSkpFSt7PLi+QtaA4AWBAKBJF1Qni+Q0xxpXbBgAa25AfkCAAAAByFfYA/5AkCWuHnjRiOvBrQAAO2k5QvKdmNBvgAAAABqQr7AXob5grGxMbnDt2/faA0AurBqxYqxo0bTAgC0k5KSIkkXDAz+97//0UPyevfuTVoXLlxIa25AvgAAAMBByBfYyzBfMDIyIneIj4+nNQDoQu+eQXt27aYFAGjnf//7nyRdMDBISUmhh+T16tWLtC5atIjW3IB8AQAAgIOQL7CXYb5gaGhI7vD9+3daA4DWfv365ebi+urlS1oDgHaQLwAAAICuIF9gD/kCgP69eP7Co0pVWgCA1oRCoSRdMDAQCAT0kLygoCDSunjxYlpzA/IFAAAADkK+wF6G+QKPxyN3+PHjB60BQGu7du7s26s3LQBAayKRSJIuGBgkJyfTQ/J69uxJWpcsWUJrbkC+AAAAwEHIF9jLMF+QXrAZJCQk0BoAtDZ65Mg1q1fTAgC0hnwBAAAAdAX5Antq5guJiYm0BgCtNazv9e+tf2kBAFoTi8XM2ernz5/0kLzAwEDSunTpUlpzA/IFAAAADkK+wN78+fPJJVffvn1pLe/Xr1/SCzaDpKQkeggAtBMbG+toZ6dsFToAYCHDs1WPHj1I67Jly2jNDcgXAAAAOAj5AnvIFwD07PSpU+3btqMFAOgC8gUAAADQFeQL7KnOFzIccQoAmpo1Y+acWbNpAQA6wpytlM3m6969O2ldvnw5rbkB+QIAAAAHIV9gT3W+kOGKWQCgKX+/NmfPnKEFAOgIc7ZSthoxky+sWLGC1tyAfAEAAICDkC+wpzpfyHBHcQDQlEslp48fP9ICAHSEOVspyxcCAgJIK/IFAAAAyBDyBfaQLwDo048fP2ytrEUiEa0BQEd4PB45W5FfMVrL69atG2lduXIlrbkB+QIAAAAHIV9gT3W+8L///U+SLhgYYK17AJ14+vRp7Ro1aQEAumNoaEjOVt+/f6e1PCZfWLVqFa25AfkCAAAAByFfYE91vpCSkiJJFwwM/ve//9FDAKCFiAsR/n5taAEAuqM6X+jatStpRb4AAAAAGUK+wJ7qfEEgEEjSBQMDoVBIDwGAFsJ37Bg8YCAtAEB3jIyMyNkqPj6e1vK6dOlCWlevXk1rbkC+AAAAwEHIF9ibN28eueRCvgCgHwvmh82aPoMWAKA7xsbG5Gz17ds3Wstj8oU1a9bQmhuQLwAAAHAQ8gX2mHwhODiY1vKSk5Ml6YKBAZajA9CJkcNHbNqwgRYAoDuq84XOnTuTVuQLAAAAkCHkC+ypzhd+/vwpSRcMDMRiMT0EAFro0rHTqRMnaQEAumNiYkLOVnFxcbSW16lTJ9K6du1aWnMD8gUAAAAOQr7Anup8ISkpSZIuGBj8+vWLHgIALTSoV//B/fu0AADdMTU1JWerr1+/0loeky+sW7eO1tyAfAEAAICDkC+wp2a+QGsA0E5FO/vYL19oAQC6ozpf6NixI2lFvgAAAAAZQu+XPdX5QmJioiRdQL4AoAuk5+Noa4fRQACZwczMjJytYmNjaS2vQ4cOpHX9+vW05gbkCwAAAByE3i97qvOFhIQESbqAfAFAFx4+eOhVpy4tAECn8uTJQ85WX5SMD2LyhQ0cW10V+QIAAAAHoffLnup84cePH6SVx+PRGgC0cO7s2U7tO9ACAHRKdb7Qvn175AsAAACgDuQL7KnOF75//05aDQ0NaQ0AWjhy+HBQj0BaAIBO5c2bl5ywYmJiaC3P39+ftG7cuJHW3IB8AQAAgIOQL7CHfAFAbw7s3x/cuzctAECn8uXLR05Y0dHRtJbH5AubNm2iNTcgXwAAAOAg5AvszZ07V0W+EB8fT1qNjIxoDQBa2LNr98B+/WkBADqlOl9o164d8gUAAABQB/IF9ph8oV+/frSW9+3bN9JqbGxMawDQQvj27UMHD6YFAOhU/vz5yQnr8+fPtJbXtm1b0rp582ZacwPyBQAAAA5CvsCe6nwhLi6OtJqYmNAaALSwZfPmkcOG0wIAdKpAgQLkhBUVFUVreUy+sGXLFlpzA/IFAAAADkK+wB7yBQC92bBu/dhRo2kBADpVsGBBFflCmzZtkC8AAACAOpAvsKc6X/j69StpNTU1pTUAaGHdmrUTxo6jBQDoFJMvfPr0idby/Pz8SOvWrVtpzQ3IFwAAADgI+QJ7qvOF2NhY0mpmZkZrANACxi8AZJ5ChQqRE9bHjx9pLY/JF7Zt20ZrbkC+AAAAwEHIF9hTnS98+fKFtObJk4fWAKCFTRs3jh45khYAoFOFCxdWkS+0bt0a+QIAAACoA/kCe+rkC3nz5qU1AGhh25atI4YOowUA6BSTL3z48IHW8nx9fUnr9u3bac0NyBcAAAA4CPkCe6rzhZiYGOQLALpyYP/+4N69aQEAOmVubk5OWO/fv6e1PCZf2LFjB625AfkCAAAAByFfYG/OnDkq8oXo6GjSmi9fPloDgBbOnjnTuUMHWgCAThUpUkRFvtCqVSvkCwAAAKAO5AvsMflC//79aS3v8+fPpDV//vy0BgAtXL92rWXz5rQAAJ1i8oV3797RWl7Lli1Ja3h4OK25AfkCAAAAByFfYE91vhAVFUVaCxQoQGsA0MLjR4/q165DCwDQqaJFi5IT1tu3b2ktj8kXdu7cSWtuQL4AAADAQcgX2EO+AKA3H96/r1rZhRYAoFPFihVTkS+0aNEC+QIAAACoA/kCe6rzhU+fPpHWggUL0hoAtPA9/rudNZ8WAKBTTL7w5s0bWsvz8fEhrbt27aI1NyBfAAAA4CDkC+ypzhc+fvxIWgsVKkRrANDCr1+/7Pk28fHxtAYA3SlevDg5Yb1+/ZrW8ph8Yffu3bTmBuQLAAAAHIR8gT3V+cKHDx9Ia+HChWkNANqp51n7UWQkLQBAd0qUKEFOWK9evaK1vObNmyNfAAAAAHUgX2AP+QKAPnVq3+HM6dO0AADdUZ0veHt7k9Y9e/bQmhuQLwAAAHAQ8gX2VOcL79+/J63m5ua0BgDtjBw2fNPGjbQAAN0pWbIkOWG9fPmS1vKYfGHv3r205gbkCwAAAByEfIE91fnCu3fvSGuRIkVoDQDaWTA/bNb0GbQAAN0pVaoUOWG9ePGC1vKaNWuGfAEAAADUgXyBvdmzZ6vIF96+fUtaixYtSmsA0M7O8PCB/RT/ugGANlTnC02bNiWt+/btozU3IF8AAADgIOQL7DH5woABA2gtj8kXihUrRmsA0M7lS5dat2xFCwDQndKlS5MT1vPnz2ktj8kX9u/fT2tuQL4AAADAQcgX2FOdL7x58wb5AoAOffr0ydmx4q9fv2gNADpSpkwZcsJ69uwZreU1adIE+QIAAACoA/kCe6rzhdevX5PW4sWL0xoAtFa1ssubN29oAQA6ojpfaNy4MWk9cOAArbkB+QIAAAAHIV9gT3W+8OrVK9JaokQJWgOA1jp36HDy+AlaAICOlC1blpywnj59Smt5TL5w8OBBWnMD8gUAAAAOQr7Anjr5QsmSJWkNAFoLnTxl0YKFtAAAHSlXrhw5YT158oTW8ho1aoR8AQAAANSBfIE91fnCy5cvkS8A6NaunTuD+/ShBQDoiOp8oWHDhqT10KFDtOYG5AsAAAAchHyBPdX5wosXL0hrqVKlaA0AWrt/755Xnbq0AAAdKV++PDlhPX78mNbykC8AAACAmpAvsKc6X3j+/DlpLV26NK0BQGsCgaCinX1sbCytAUAXKlSoQE5Yjx49orW8Bg0akNbDhw/TmhuQLwAAAHAQ8gX2Zs2apSJfePbsGWktU6YMrQFAFzq173D82DFaAIAuqM4XvLy8SOuRI0dozQ3IFwAAADgI+QJ7yBcA9G/xwoUhEyfSAgB04a+//iInrMjISFrLQ74AAAAAakK+wB6TLwwcOJDW8p4+fUpay5YtS2sA0IVrV682bdiIFgCgCxYWFuSE9fDhQ1rLq1+/Pmk9evQorbkB+QIAAAAHIV9gT3W+8OTJE9Jarlw5WgOALtAlGL58oTUAaE11vlCvXj3Seoxj85KQLwAAAHAQ8gX2VOcLjx8/Jq3ly5enNQDoSJeOHY9ybKg2QLZmaWlJTlgPHjygtTzkCwAAAKAm5AvsqZMvVKhQgdYAoCNLFi2aOH48LQBAa1ZWVuSEdf/+fVrLq1u3Lmk9fvw4rbkB+QIAAAAHIV9gT3W+8OjRI+QLAJnhxvXrjRs0oAUAaM3a2lpFvlCnTh3SeuLECVpzA/IFAAAADkK+wJ7qfCEyMpK0/vXXX7QGAB1JSUmpZO8QEx1DawDQDpMv3Lt3j9bykC8AAACAmpAvsKc6X3j48CFptbCwoDUA6E5gQMDO8HBaAIB2+Hw+OWHdvXuX1vJq165NWk+ePElrbkC+AAAAwEHIF9hTJ1+wtLSkNQDozoH9+zu086cFAGjHxsZGRb7g6elJWk+dOkVrbkC+AAAAwEHIF9ibOXOminzhwYMHyBcAMsnPnz9dKjm9e/eO1gCgBSZfuHPnDq3lIV8AAAAANSFfYI/JFwYNGkRreffv3yetVlZWtAYAnRo1YsTihQtpAQBasLW1JSes27dv01perVq1SOvp06dpzQ3IFwAAADgI+QJ7qvOFe/fukVZra2taA4BOXf3nav3adWgBAFqws7NTkS/UrFmTtJ45c4bW3IB8AQAAgIOQL7CnOl+4e/cuaeXz+bQGAJ369etXXU/Pmzdu0BoA2GLyhX///ZfW8pAvAAAAgJqQL7CnTr5gY2NDawDQtbB588eNHkMLAGDL3t6enLBu3bpFa3k1atQgrWfPnqU1NyBfAAAA4CDkC+ypzhfu3LmDfAEgU71+9crFyZl0M2gNAKw4ODioyBeqV69OWs+dO0drbkC+AAAAwEHIF9hTnS/cvn2btNra2tIaADKBv1+bI4cO0wIAWGHyhZs3b9JaHvIFAAAAUBPyBfZU5wv//vsvabWzs6M1AGSCHdu2BQYE0AIAWHF0dCQnrBtKVjOpVq0aaT1//jytuQH5AgAAAAchX2BPnXzB3t6e1gCQCb7Hf3dycIyOjqY1AGiuYsWKKvIFDw8P0nrhwgVacwPyBQAAAA5CvsDejBkzVOQLt27dQr4AoAeD+g9Ys2oVLQBAc0y+cP36dVrLQ74AAAAAakK+wJ7qfOHmzZuk1cHBgdYAkDmuXb3qWb1GSkoKrQFAQ5UqVSInrGvXrtFanru7O2mNiIigNTcgXwAAAOAg5AvsMfnC4MGDaS3vxo0bpNXR0ZHWAJBp2rdtF759Oy0AQENOTk4q8gU3NzfS+vfff9OaG5AvAAAAcBDyBfbUyRcqVqxIawDINBEXIup51hYKhbQGAE0w+cLVq1dpLQ/5AgAAAKgJ+QJ7qvOF69evI18A0JvWLVvt27uXFgCgCWdnZ3LC+ueff2gtr2rVqqT14sWLtOYG5AsAAAAchHyBPdX5wrVr10hrpUqVaA0Amen0qVONvBqIxWJaA4DaKleurCJfqFKlCmm9dOkSrbkB+QIAAAAHIV9gT3W+cPXqVdLq5OREawDITL9+/Qrfvh2rPAKwwOQLV5T01ZEvAAAAgJqQL7CnOl/4559/SKuzszOtAQAAOMnFxYWcsC5fvkxrea6uripaswryBQAAAA5CvsCeOvlC5cqVaQ0AAMBJTIKgbIQCkz4oG92QVZAvAAAAcBDyBfZU5wvkUgz5AgAAcB/yBQAAANAJ5AvsTZ8+XUW+cPnyZdJKLstoDQAAwEnMCgvKdohQvfpjVkG+AAAAwEHIF9hTnS9cunSJtLq6utIaAACAk5gdKP/++29ay2N2r7x69SqtuQH5AgAAAAchX2CPyReGDBlCa3lMvlClShVaA2R7wmcH5oVOnDRZ01vIvEPPhPQxAIB7kC8AAACATiBfYE91vnDx4kXkC5DD/Pz2+f3z26eW9aptbUmu7CU3yyrNOnYP7M7cAnp07dzF38+3UW13O2t6B3Kz67Y9WkwfQm+E396//673fxUgW3JzcyMnrIiICFrLc3JyIq3Xrl2jNTcgXwAAAOAg5Avsqc4X/v77b9JatWpVWgPkGMJ78xvbMNmBbY3xlwX0sCxxwstzS/o0sJPcx77VshcielxfBPcXta49+ISip5a5ft7ePnPSHyM4Um+hqxXPbwfIWu7u7uSEdeHCBVrLq1SpEmm9fv06rbkB+QIAAAAHIV9gT3W+EBERQVrd3NxoDZBjpFybWpcvzRes3AYdS6BH/yR6u72nuyW/0fR/U+gRPUm4MbuFHb/B1Bt6/ncJUeKXj2+f3jq2sEc1mREcls6+o9afvfvi47ef9H4AXIJ8AQAAAHQC+QJ76uQL5KKN1gA5hfjz5q7SgQl8C8ee4TGq5iAkHRviWn3sOf0OI4i/OKWBLd/Cpu1yvQ+b+E14d3ZjO5ovWNcddToWUzWAwzw8PMgJ6/z587SWV7FiRdJ648YNWnMD8gUAAAAOQr7Anup84cKFC8gXIEdKODKwqqW052zTds0blV14QcSEhv0OxNNKH8RfTo+qYy/p1dv12vWdHswCKden1KP5Ar/l0idY3hI4rVq1auSEde7cOVrLc3R0JK03b96kNTcgXwAAAOAg5Avsqc4Xzp8/T1o9PDxoDZBDCCJGV7eV9pztm857oLrjLLi1aclpPa7tKI46NNDTStqrt3QffVb/yy+kEkdt7mgneRrkXfJZ/BTxAnBb9erVkS8AAOQCyR9vHFg9d8qYwb27tWvRyKuht2/7wCFTluy8+CpB0eWa4N7y3hOOxNEKQB3IF9ibNm2ainyBXKiR1mrVqtEaIGdIuT6tHrP4gnXdiVeyrgevgOjdrr5uVkyvnm/daM5t/S+/kCr+0ABn5pnwm866k3XPA0AtTL5w9uxZWstzcHAgrbdu3aI1NyBfyPlErw9PHxDUs2egLvTs2WvwYsU7sALkBkkvTywe3qEO39yYR/6iG/CMC5R1cK/ToHGjejWq2JUpYMQzNrdrNmTt1c+y41KFj8LqFzatMeupwsGqKdfDOjZroommzXz82ncLGjBi8uIdZx98TqYPBDkM8gX21MkXyEUbrQFyBOHDMB8+M0CgyrDjifQoFwhfbOpSxb2+lwczfsGm/bp3Wbbmwc+zo2oyU0isPEO4FcIAKFCjRg1ywjpz5gyt5dnb25PWf//9l9bcgHwh5xOcH1+7koOdrfVfZczNjKR9IopnlLf4XzZ29g4K2JMfsChXopD8T0iY1JlPHxlytpSY+0dXTurbqXWTutXc3Gt5NfXtFDxpya6/X8RrflmQ+PLcmpAB3dt616/m5lbTq1W3EQv23Y5m8bWBzh6IhfgH4WOb2+SX/EbwjM3tmwbP233lRZzc0Mqkj7cOLuhb/6+8JmXqjjrwinleolermxczNDCpPvOJ4nzh2rx2Db3q163lUZlfIo+h3O+osblllVr1vBrI8qrnWd2tomWJfDTiMCpk27jfwqNPftDHg5wC+QJ7qvOFs2fPklZy0UZrgJxA9GqFn4Pka3lLvmPP3V/+OFOLnq3rO+54Fqx7IHi0wt/Vo094+MgaTMfeoc/BLEs/Uq5NrsssvmDjPuJMEj0KwFk1a9YkJyzkC8BdP84NsDMmn0MJw1J+m95muHyvKPHTg/PbZwXVKm3CdHt45h330DbIsZKeHghpaZPf0NCsiIWzZ7PWvs3qufGLMB8BnnFRJ7+x2+6quSiU4OXBSX7OxUxMSrp3nbh06+HzESd2r5sZ3MAyn2GecnUGhT9R98t3nT0QG6KPp0MalzdjooUS1fttuvtNVciS9GTnQI8ipuWaL7yVIPq4zb+MEflBpfmCLNHn4/0c6O8a+RG3yfeUZycpsY/OrJ/o71JUmjPw8vJbTT/zATNJcxDkC+wx+cLQoUNpLY9cqJFW5AuQo4ijtna2l/TeLSwd/de+S3+6EX85OsSr4/oPeh838PP2At9KNYcc/hy/O6ii9OnZVh/3d1aNGxDeD2tiI32XrKr0O5SFi0wCqInJF06fPk1reXZ2dqT19u3btOYG5Au5S/KRwNKG5HNIGJbucUiD+Fj06VBfxzykG2PsOPoaPQY5kjjmQkid4iYlqvdZdv6NbJ89+cPlNf1rlaIpQwGHTstvZpAxiL/+PblecSOemXW7JdfTbQCV9HL/UA9zQ6Ni1QfvfZnR+AOdPRArifdW+Fky2YJRSa9JJ96q9a8kPljhW860bNMePhWYcQZq5QvkbT7QrTj9JeXl8d2sfPfyVClvDkneAOkPmJTzWXgr4x+B7AH5Anuq8wVyoUZayUUbrQFygLi9wU7S0QEWNi3CItNlzYkPN/R0t/NdpPfNEr5fndmkYr0Rp76IJcMrpJtHWNp7L0j//PRF9GJFW2YKiUXFDPbvBOCGWrVqkRPWqVOnaC3P1taWtN65c4fW3IB8IVdJuTbakQ5f4BXy3fSVHlbT192dShkamDZc9okegBxIcGd2HfP8Dj3DlXTVU55u7sQ3ZSIGo9LNlz5QPmogJXJxo2KGBoZFvMIeKPymQvxxd1cLYx4vr/MQldtP6+yB2Ei+t8S7DBMQGJp7Tr78jR5XR/yVkBqFaVagdr4gONmnvGS4A8HL57dNrcGb4g87OpZjfohnXN5/q+pNySC7QL7Anup8gVyokVZy0UZrgOwv8fgQD2b2gaVb8IarN2/cILfrV86f2L0+bFSXBhWt+NZe029kSgivlPhbxASvSvXGXYgjZ+aUm1O9mIkJDp0263HfClniqM2dmTEUfLsOG98jXoBswNPTk5ywkC8AV4lezvNkeoYGPNM6819p2geJXeeTz7Bkj0NYTS7HSr42qWr+Yo0XP1J1CZJ4eayLZCiL5HNkVnnsP4p7wMIHsz0L8Mg93KYoX55Z/G5Nc3ND0ifm9z2hrOOuswdiQfxxX09bmqbkcRp4QuMrImHkvLoFmfdK7XzhdF+N8wXyy/10fp289B8yLN5y3RtcNuUAyBfYUydfIBdttAbI9gR/j63J7Eyp7GbjMer8T3pvvRDHHB9R175h6GVmFoLg5ODKNtJn4jLgqF6fyG9x+3o5Me+Gfatlz5HEQ3bA5AsnT56ktTwbGxvSevfuXVpzA/KF3CR2Y6u0ro7TuBsaD04THO9VztQ15A5meOdQ4o8bfYsVqDU7MqNz7rcDAWVTu8CF6i9Q0GsWR4d3KGVImvN6LVbZ1U3Y17Wk5H5mbqF3FX2wdPZALIherWsl+bclL9Os8pjLrJajSr46zkU6tyJz8wVJelibpocGBsZ2wy5hUezsD/kCe6rzBXKhRlqRL0DOkXJzRn1mZ0or1757o5OkEr7HRb95cG7dYC/JugyufQ/qc7kB8Yf9A6o5NJ9xnU7ZE3/Z3sVW+gyt64dczZozVOKJYVWYnSltvOfew8UsZAu1a9cmJ6wTJ07QWh6fzyet9+7dozU3IF/IRZKP9CzDdJYMjMr1Oq75KATB+QFW+Zuv03BaBWQXoldhdfMUbbddjf/AgvMDrWkfWLoiR/pxBeK3yxpJ91kwcQt9oPoUHrfFr5DknkbWA8792ZfW2QNpTvxuY+sSab8wPQ7G0eOaEkdtaGUufQmZmi/8JzjX3yL1v4mBkdXACwgYsj3kC+ypzhfIhRppJRdttAbI5oSPFraiywpU6rUz/dYRgpvTG1vbd9v8WX8D20Rvwnu4OfuE3U4bqCB8tKg5s7Cije+SZ1kydEBwcXxtZoNM63pT/7hwAeCmOnXqkBMW8gXgJtnFFwr7bWXRW0o+Elj2rz4n0W3JmcTRa5vnNyxYrpK7V7vROyJVf1mfsLtjUdr3NjBxn/pQ/lJB9HSup3QGhWGpHoczCLKEd0OqmEgexqh87xPpO9M6eyDN/TjRxzK1m2/qFnpfiy864nZ3kgQVmZwvCB+Euklfv5Rp3bDXtAGyLeQL7KnOF44fP05ayUUbrQGyN9Gb1e3ozpR2nbd8/CNGSNof7NR6uf7mA6Q8W9upSiW/ZfdlLhgFlybWtJbmC3aBO9gG9lpJuTW9AbMAhG210ReyaIYGgKaYfIGctmgtz9ramrTev3+f1tyAfCHXkF18wazeQjbTs39s9i1UbcajLEmdIdOlXB9bKXXzUp6Z66SbqnrUpDPvmtqZ5Zl32iPX9xe/XVSfTgnwnPcyo89LQrh/YemdjawHpfvOXWcPpDHRq6WNUicT8fLUnqfdZVnC4cCyRpmdL4iezKz+O18wqRLCrSwbWEC+wN7UqVPJ74HqfKFu3bq0BsjWxNHbuzlIu+6WDm1X/7nArzhma6D3LOUrGOmY4P7Sdk7OnVY/kfsHkw72c5QmIJZVRmTJF1XCx4t96M6U7oOOYaMlyC7IqYqcsI4dO0ZreVZWVqT1wYMHtOYG5Au5huziC5Un3FLed/y2v1f1fkcU9GpEz+d4luq0B3+TcyjB+YFWaePrDcyarIqhDYqI3yyoa0rvamDms15uTcW0wQ28/H5bM/y8pPw9xIbJNUzcp8kv/aCzB9KU8N5kN2YfTvJvm3jOfaHVo/33X9y2NoV5mT1+IXX8hgTPrMHSj7QBsi3kC+ypzhfIhRppRb4AOUT8/n7O0q67hY3CHSgFV0I7T7qopz590q35zR3dumx8If88xO/WdbSRPknrBjNvZcHcBNG7tR1smXfJqe+eLBlAAcBGvXr1yAkL+QJwkeziC+VVzXGI29GuZBXFazgKnpw9+eAHLSCnEX/e0qYY/Yzw8rhOUrkAqOjRdI+08QuF/HfK9oJTroy0Z/r5RuX7ns7wikb0cKo781C8wu3CZVMEnT2QpoS3J7mkddWN7Ude0fZSSPRkVg2TzF5/4XhQ6qKbBgYmzuNVjj+BbAH5AntMvjBs2DBayzt69ChpJRdttAbIzpJODqc7U/KbzFNrgeOfT06feqT22UUT3/8JbeRoYVXJtYZndfmbuzOdwcFvo2CIBSX6+S06OjYxE05f4pgd3Z2YJ2DXZUuUsjG8mfcEAFhi8gVy2qK1PEtLS9L68OFDWnMD8oVcQnbxBfO225Xv4Pd1e7vif6nRl4McSBwTMS+ggVvV2m3H7HqserUDwZngCqmdWWPboRdlO+CJO9rSoTLGDqOuZtg1F39c2oAOhTBxm/pQ5qyu3QPRoyzIbcXAM2+/U/sxO5K9V8zUGwfBMl8Q3ptcNS3xyVtzlpIBHIIPl9ZP7NHcw6a0eV4TY5MCRcs51GwVNHFNxFvNF3yFzIZ8gT3V+cKRI0dIa/369WkNkI0JLk+oxexMaVc39IYacXhK5GL/lpmxfYI49uzYBs6tJqzbEb4j/W3ruJaOtHvfa5+CfSwSn+4PDajvbC+5j7VzzbZjNt+O1+VylN8PBzszu2M6+K18reAEmdlPAIAlcqoiJyxy2qK1PAsLC9IaGRlJa25AvpA7yHSYeGZei98qDW5fr2hqbt5mm/L8AYCQfh8v/TwZGBgW77JPtgMu+bDRNhOXSWrsZhqzqokZc38jy/7nfkdbWj4QPcjCj+1tpDtRSJlUYrGV659SPt2/9uSrOtcq7PIFwfVxTnRGB8/Uceh5ReOMvt1Y3qVSkdLuncavOHLjRdSP5B/v7p7bMb1jpUKGBry8fzUef/i1tgM1QKeQL7CnTr7g5eVFa4DsK+X2rAbMzpTWtcdGZPztkCBylb+r9xxdbeT8mzj66HBP98DN6SZGMMRRGzoy4xdsPEb/sUKS4PG6rh727t7tu/cO8PNyli4DaenabcMznT3Jn2dGu9OdKVuGRf7xsJn/BADYIqcqcsJCvgDc83WTb9riC66KJz8QP26FNSllZFo37JUa37JCLiZ6GVZXuq0DYVQh6Gg8PS6Vcnm4HR0rY+IxXY3lQOPWNaexAK9gu/Df+1Zo+UD0oOaEd36vXWlgYNpkZTRt0A82+ULSnVl1CzMrVRiVahx2S8EPJd8Ja1jmr2bTz33849c/8fG2Hg5mPAOecelGs67K/ceELIV8gT3V+cLhw4dJa4MGDWgNkG0JHy/0ZXamtHQbcUL1zk+SEQaXQ70rKpxGkRz1MOLUqUuPY9l1qUXv9/WpXnvgoT83r5AS3p/XVDo0wNK+8dx0GzIJHy3v4T/5+Bu6o4Mw6lyotyO5p63HKJ3sNk0I/plcn+5M6fXn6g96eAIArDH5Ajlt0VreX3/9RVofPXpEa25AvpArJB9NW3zBsGS7VTfv/Xb39o2rl84e3LpkUpCXdX7SZTSuOOY6vsQEVYT3Qt1Sh8PkrznrgfyVgszUCZMas9RYcSBhUysaCxiYNlrxmR7V+oHoQc0lH+5Riv66SF5g2x36vbzQNF8QRV+a26KCdOwCL4912+V3FV1fCh/OqV208ogIZeFB8s0QN0lixDP+q+vuTxgSyhHIF9hTnS8cOnSItCJfgOxP9HxZ69SdKQPCv6j86y36HDHTz9WS9PDnpJscIf54bEKjStLpA9auPlNOK12eQBnhq80BNRtOjIhT9oOCC6Oq0ekJHdbLZxDCZ7tXH/8gd4pPihhfx8qSz/dbleHOUWoR3p/bhEk3bGuMv5R+NqAengAAe+RURU5Y5LRFa3kVKlQgrY8fP6Y1NyBfyA1Sro2pSL8HzpBhmZ5Hct9EbNHjDUENq1dxdnbSPefKbp4txx/93W3O9uIO9qAdYF5+95Br6TvAyUd6lqbdc5Oas59mfGpO2uaXlw6GMK2/6C09qvUD0YOaS9rRtgB9GMnsj24H9PsLoWa+IEqKfnJ575JRbV2LS7IFnmnpmsHrbyu5thMye4/yTMzt/RbdVLSchDhma5siknebZ1JpLBJGjkC+wJ7qfOHgwYOktWHDhrQGyKZS7oU1ZfZctLRxH3Ve6dlK+OXu3pkdq0v72DbN0k+OED5e4MN0v6U3K/feezRKGBJvLWjj6j3ruoq1ir7v7WnPPH7l4MN0nIAKsdu621raVB5wJN1KDewGWYieLmtBd6Z0G3wsozEeUsqeAIDekVMVOWEhXwCOkV18waTqwG2Hj8g4tH9P+OZV8yf2amxTgPQueAVabFC1LWEOlXJlonsxM6O0TqUu8XjG+cq1XPGc/lPZXvK18a5m0nfKsETTJQ//7Ikm7+vCbCopjQWeqxELhLdL7c9LtoKkR7V+IHpQc4nb/PKlfRQMS/Y4lHX5gmG+4uTMkV75cqWKFcoj/bjyjAuUq9yg89g1F16rGujwbWPL1OSFZ95hl6KrK/HnNd7My1ZvsQvQA+QL7KnOFw4cOEBaGzVqRGuA7Ej87dbiDlWZnSPIzcqty/QdJ89dvHzpkvQWceH00f3b1iycPLSHt4dj2t0UTI4QPl3UQiZfsOTbdVa+w0J6oveHh3na1R8foWolZOGTJT5MD9+69vhLGW8H9WJ5G75Nw5Arsmcr1oMshE8X0ykkFnYBW6LV+TGFTwAgSzD5wsGDB2ktr3z58qT1yZMntOYG5Au5gMziCyomP4g/rGpWkKfeOPQcSiwSCXVPlKPez+SbU6pJptEY8Aq4Dj8do+g0nbSnozn9vKm3I2PS9jap/Xny+XtKj2r9QPSg5pL3d03dp5O8znSbb2Y+mXzBwKRS0KrdexTYu/fA0TMXb9x/9VWtoQZJBwNK0JdkWDpQ8Qglwdl+f0n/XV7h9rv0+5JBCeQL7IWGhpIPs+p8oXHjxrQGyE7EMafDBvcK8K/vKhcKqHf7c3IEIf58enqb6k52rk0CRo9sW9VG0SIFColjL8/ycbKxqj7+jzUbZYm/7OnjxDwBq+ojz2aULwifLGnt7jNbfjkg1oMsfl6b4mXH/JRV7SlXMl4BU8kTAMgajRo1Iicsctqitbxy5cqR1qdPf189cwHyhZxPZvEFo7JBx5R+F5tyaZitqd2wSxgaDcrEnR/hIvkenJfXIWjvWyUd/uTffVkT92lKtkmUlbjFN3WxSJPa81/Ro1o/ED2ouZQrI+1/zycya75Ov9upyOULdcJeq/sNjUopkRu6u5UzNy9XLWjLE8WDE4Q3xzsxy1rq/SWDEsgX2FOdL+zfv5+0Il8A+IP4/Zr2DnzvBbKbRSsmir2zY2xzZtNHxw4rnyrtuAs/nh7bkC4SYWFp49p17UMVYx2EH89M8a3WYfWjdI/HbpCF4MOpkBYOaT/l4Df/tqLtlWQpewIAWYOcqsgJC/kCcIrM4gu8Qq03f6WH/yQ42adCqa77MRYMFBO93OhXzognDRd2vVZ+5SE4FlSW9o9Nqk5Ot0y0Ij82tkhbltFr8Tt6VOsHogc1J45e1TQ1p1D3n2aIP17ZtXnr9vCdu/fs3buPdGHI6eC3/fv37d2ze2f49q1bDtyKVXpVlCn5ghqEtyY4M/mC3rfMACWQL7CnOl/Yt28faW3SpAmtAYD6eX5UdftqYyJUrZEgendgUtfmni7Waf12S761c4N2Q7fKpRKij8dmD+kT4OfpZJt2N+ZmV82nQ+CAaYfeyH5xIP4SeXLr3L7Nqkof1s7VN/T4O9mH02SQhXSIR5/unZpWq5Q2MYTebKrU9unYbdC6W3/EBxk9AYAsweQL5JqS1vLKli1LWp89e0ZrbkC+kNPJLr5gWkfFzpPiNwvrmTddpSQMFr3YExp2RnmfCHK6+EsTPAryDHgFKvc78E7lUALBuQGWqbGAWjP549Z6p24rmdd38+9vFrR8IHqQhd/f5BOGJbqrvQCD4Gw/i9RoQBUTl4m3lb4cPeUL4sSP987v27h42rghfXt09GvVvHEN64LMcBHTxis+41edC5AvsMfkC8OHD6e1vL1795LWpk2b0hoApMRRu/u6OviG3c+Coayi139vW7ds+rAu9ZxpHmHffP4thTmH+oMsNKHBEwDQnyZNmpAT1r59+2gtr0yZMqQV+QLol8ziCyZO424o/1OcfDSI77XwheKeY+LpYOuS7XfG0RJyGcGjVa3KGvMMzauPOZ1hz1P4INSN9s5Vf+RSRa9sYsrc37C07O4lWj4QPchG8sk+aV18AxO3KWoPYBAmxn2NjYn+HPXh1eO7l49tCm3nIF2tQopn6hiw8vTtp28/fY5LVB7RZHK+kPDi7OpxXRs4ljDj8YwKVHBr1nnApPlrdhw6E7G1lx0z0gn5AlcgX2BPnXyhWbNmtAYAIvnp1h4eVdsuvZe1S/AIY66v7FlbuiKja9BuRXtuqjPIQgsZPwEA/WnatCk5YanOF54/59Yy8sgXcrjko0FlUxdfKBd0XOX3sGKxkr+h4qiNvsWt+p7G1IlcSfxxf5CdGc+4dMOZV5RubS3r61pvZoMJA2P+4IgMvwQRvZznSWMEY8fRV2Xur90D0aOsJJ3sUyG1j8/Lp3RYT8YSdrY3p7+AaoYkmZcviKKvLA2qUcaUZ8AzLOzgO3ZDxMsfMg8uMz8C+QJHIF9gT3W+sGfPHtKKfAEgTcLTwzPbezYIWnXrGxf+/idcnlDPTrLp5ugLf1y56mWQhaonAKBPTL6wd+9eWssrXbo0aX3x4vfua1yAfCFnk1t8wW8Lu/EHwoczqhesEoIt63KlbxcnVi9saGrVZtV9db/QSD4aRFcUVWtuAenXVmb6tTyzpqtkd47S7oHoUXYEl0fYG9ORB7zCrTaw7W8Lzg+wSgsLqs94rHJmCSNz8oX46wta/iWZKcUzKd9s2pkPf16WIV/gHuQL7KnOF3bv3k1avb29aQ2QqwkfrQrw9hu48OiTjNY+1B/h44WSTSUrDTiabpCCvgZZKH0CAPrVrFkzcsLas2cPreWVKlWKtCJfAD0SvZz/e/GFugvY9VS+HQmyKNx4+Zuc3OEQfzo9f1Bg5/bt/XWvfYeuvUavv8mdk7YGBA+X+ZQxzmPXfccLpV8TJL+8fOTqW9nwSfw6rC4zT4GXt+XGDLd3EqRNRjC2HXpR9t/R2QOxIHq5uEHqzCJewaYr37H7/KdEDLL+nS+os8lmZuQLKY+WeZc0krwaw1LNl0UqTmqQL3AP8gX2VOcLu3btIq3NmzenNUDuJkhI5NxXSMknh1W2sqs/TXYNR70OslD0BAD0z9vbm5ywVOcLL1++pDU3IF/I0eQWXxh/k83ZQ/hgRo38pTvvydlLLwhO9JbsjJBZeGYe0+7Tfyr7EL3bE2hrltc+cKeKzSIkQ2ScKgQdk1uHOeXKCDqT36RKyJ8bbacTu6YZs1sDz7zdDvkQQWcPxEbipZGVTJgPBc/Eedw1VjtVcSFfED1f0rCwdByIobn3KqVBoeJ8IfHjkycfMTMqiyBfYE+dfMHHx4fWAMA18Xv6OFjXHpu2xoLeB1mkfwIAWYPJF3bv3k1reSVLliStr1793t2dC5Av5GRyiy/0PsFiBpno1dqWJUxsh/6d42efpcS9ibx9/fo13bt+487jjwnZ7etgcVzEOI9C+Rx6qtqJUiJ2nY95rTnP5fvNySd6M11kw+LdDmTw2Um5Tufw8Ap4r06/zoHOHogN8dfj/exowmBYzHvVSzXCgfQE5wfKzI/Iknwh5dpoRyakMSzeea/y67KUv4fYMPeTyRfIQTvbQRmvfQGZAvkCe6rzhZ07d5LWFi1a0BoAuEX8YXuPqs6dNspcXeh3kIWCJwCQJZo3b05OWLt27aK1vBIlSpBW5AugN7KLLxT226r5CATh85XNixuaVZ8RiT+vuUvy/cXNSheoGLRHbm9qRQQn+1Qo2XV/+q5/8tn+zNaSJs4Tbqm8IBB/WNZQOuqAV9hn7cc/+tI6eyB24v8e7ZqPJgwlvJc/1rifnbS3U5G09R2zJF/4PcnEQPUcqYSd/oWZl4p8gSuQL7A3ZcoU8llWli+Eh4eTVuQLAFlP9Hbv4MZ16rcduvDY03jm1CP4cHZmu2oNxxx5r4+rzyx/AgAq+fj4kBPWzp07aS2PyRdev35Na25AvpBzyS6+YFZvoabrJ4g/HRlQOT/P0LzFOl111iBbEL3d2c2mSOXg/W8zPq8KLg2zy+M+9eEf9xTeneImXU3QtPZ8ld/7f93eVtoBNyrX4+A3ekyWzh6IJeHzjW0rMIMYeGZ2gbvVeFNkCC4PpxM8iKzJF4R3Q1yZaQ8GZs3WxNKjf0o+2Td1ywzThstTx3+kRAyytRvyN/KFrIF8gT3V+cKOHTtIa8uWLWkNAFlF9GJzV1dbC8lmkHaVajRr1bZt43qN2o/deitWT9eeWf4EAFRq0aIFOWEpyxeKFy9OWt+8eUNrbkC+kGOJo9e3TFt8IaPvftMRx15b3rVSQdJbMyrX83C2XJkQ2BF/PTfKraBphYZ9xoxVbsyo4UMG9g3s0NyjfF7Dwu13KpifL47e103STeaZuExU/uETv1neWPIpNSzstVBJ31tnD8Ra0oPVbS2ZqI5XwKX/gTfq9rbFH7a0KWWUN19e5vdQzXwh+WhPumsG+ZGas59q+2q+bvGj4xKM7YZdUvLcxR82t3V0c7OShiEmHtNShywlH+pezkNBfgR6gXyBPdX5wvbt20lrq1ataA0AWSgl5tH5Qzs3b9i0adueIxF33yXo+5yT5U8AQDkmXwgPD6e1vGLFipFW5AugH6I3K5ulbryv1uZ+DMHnO/vn961X3ozpTZlUGnsdX13mHkl3FzQukdq3VZNJ5Ym3FXb7xe/CO/9lwjMwLO6zWvHIA3Hs4SArY3KPoo0WRSr/nOnsgdhLeXN4VO0S0g0recbFPfpuvJvh+pHizydHepibWAbsurqqhfRNVS9fEL9d7MUsU0kY/dX7eAJtYC3hdH8+MwLDsHSbLYo2whA8XuNXsdGC20f7SIcw8Mxbb4qR3k3wz6hKVn1O5vjlV7gK+QJ7TL4wYsQIWstj8gVfX19aAwAAcFLLli3JCWvHjh20lsfkC2/fvqU1NyBfyDnE0de2L547a8b0KROH925bo3xaH8WAl8/Jf+zUmbMUmjljWmjIuOEDenbwqVu5QiG6nJ0UL1+9MKxsk2uI3+3qZi37318tvPytNyvta/+4Nt+7ginP0LzGuHNMh1VGwp0lPmWNeYaFqgw9ltF2iDp7IC0I3p4N6+5WXPoO8fKW9+wWsuWfDwo3lUh8E7F6YP1yefJat1v1IIm8sx/3B9mZ8pTmC6IXx5bOnjljeuikUf061rPKL/s7aFK8covAweNCps2YOWvulqtxrF5fwo15TctKn7hh4ar9djySGW8iirm2OtjT2qXX3nfi/0SRC7wkqSTPxMpv1t4zx9b0cy9fZ/Z9RIxZBfkCe6rzhW3btpFW5AsAAMBxrVq1IicsZflC0aJFSeu7d+9ozQ3IF3KOb9vametwj0WeYV77/sdz9raUIEtwvFe51Hn/6jN2GPmPqv6n6NOZKU3Km/JMy9QMWnD80VdyX9GP1xfXD29inZ9nWKhix0X/fFWrz6yzB9KKKOb6uqE+TiVSB/gUtqjS0L/PqCmzFyxesnDOlDH9uzSvbm1ubJjvr/r9Vl///YRSnm8LquG/RvFSJslHg8rnzVegUGHzIkWLFStevHiJNKQoVqxoEXPzQgXz58nvyX62hOjz5RWDvCsWNeEZ8MxKVqzXqlu/oYN7t/dyKFOuWo8lV1JDG/GnE5MaW+Q1JC+PZ1zEqd2MM5+QMGYd5Avsqc4Xtm7dSlpbt25NawAAAE5i8oXt27fTWl6RIkVIK/IFAMhlxAkvz6+fFNDQlV/WPH9eM7M8BUvaeHgHTtp46Z3C7/+V0tkDaUsYG3lyXWjfNg1rVXG0KmOe15hnZFqwaBlLe7dGnYfO2njyfjRHv/NPiX188eDmZXOnhUycNGXmoo1Hbn5QMPkhOfrx9Su3X8cjWshiyBfYU50vbNmyhbT6+fnRGgAAgJN8fX3JCWvbtm20lsfkC+/fv6c1NyBfAAB9Eol0NMpAZw+kA2IRuuKge8gX2FMnX2jTpg2tAQAAOKl169Yq8gVzc3PS+uHDB1pzA/IFAAAADkK+wJ7qfGHz5s3IFwAAgPuYfGHr1q20lle4cGHkCwAAAKAO5Avsqc4XNm3aRFrbtm1LawAAAE7y8/MjJ6wtW7bQWh6TL3z8+JHW3IB8AQAAgIOQL7A3efJkFfnCxo0bSWu7du1oDQAAwElt2rRRkS8UKlSItH769InW3IB8AQAAgIOQL7CnTr7g7+9PawAAAE5i8oXNmzfTWl7BggWRLwAAAIA6kC+wpzpf2LBhA2lt3749rQEAADipbdu25IS1adMmWstj8oWoqChacwPyBQAAAA5CvsAeky+MHDmS1vLWr1+PfAEAALivXbt2KvKFAgUKkNbPnz/TmhuQLwAAAHAQ8gX2VOcL69atI60dOnSgNQAAACcx+cLGjRtpLS9//vzIFwAAAEAdyBfYUydf6NixI60BAAA4yd/fn5ywNmzYQGt5TL4QHR1Na25AvgAAAMBByBfYU50vrF27lrR26tSJ1gAAAJzUvn17FflCvnz5SGtMTAytuQH5AgAAAAchX2BPdb6wZs0a5AsAAMB9TL6wfv16WsvLmzcv8gUAAABQB/IF9lTnC6tXryatnTt3pjUAAAAndejQgZyw1q1bR2t5TL7w5csXWnMD8gUAAAAOQr7AXkhIiIp8YdWqVaS1S5cutAYAAOCkjh07qsgX8uTJQ1pjY2NpzQ3IFwAAADgI+QJ76uQLXbt2pTUAAAAnMfnC2rVraS3PzMwM+QIAAACoA/kCe6rzhZUrVyJfAAAA7uvUqRM5Ya1Zs4bW8ph84evXr7TmBuQLAAAAHIR8gT0mXxg1ahSt5a1YsYK0duvWjdYAAACc1LlzZxX5gqmpKWmNi4ujNTcgXwAAAOAg5Avsqc4Xli9fTloDAgJoDQAAwElMvrB69WpayzMxMUG+AAAAAOpAvsCeOvlC9+7daQ0AAMBJXbp0ISesVatW0Voeky98+/aN1tyAfAEAANIRfb1/cNHoHq3quVWys3Vw9Wzi17V/yKoTT+Jpe04g/vZg//xhnZvUdHWwsXFwqebl12vc0sOR38S0PeshX2BPdb6wbNky0tqjRw9aAwAAcFLXrl1V5AvGxsakNT6eW5dnyBcAAEBGyss9g2tZ2zXqO3fX5cefE0X/pXx9eGxRkFsRQ55puXoj979OoXfMxoRvj4yuV66EfdO+09fsOnrm9OHti0e3cylqxOMZF3ULWnOLGyED8gX2VOcLS5cuRb4AAADcx+QLK1eupLU8IyMj5AsAAMBl384OcSrXaO6tH7ROJf60o0MZchbjmTn0Px7Lne/4WRBHHx/gVKpK353PBfQIQ/B8axcbU56BgWHROlP/4cC5GvkCe6rzhSVLlpDWwMBAWgMAAHBSt27dyAlrxYoVtJbH5Avfv3+nNTcgXwAAAEr8YVXTAjyeaQnXnltfiuhBKulwj9KG5DTGM6s561G6tuzkx+l+tuX8Nr1VlJH8ODfQzphHXqNppZEXE+nBLIN8gT118oWePXvSGgAAgJMCAgJU5AuGhpILsx8/0n8plLWQLwAAAJVyebidZCqfgYGJ57x0AYPw9sTKkmWEDHiFO+xOogezn6/b2xXNX6ldyPrLUQpCEtGjGdVMeORFGhZrH57VyzEjX2Bv0qRJ5L+isnxh8eLFpDUoKIjWAAAAnMTkC8uXL6e1PB5PcsmSm/MFcfz7p48iHz/7rOWFqSgx5u3LN9EJQlqDbgm/Prn54FMOmGENAJoSxx7oaUH617x8LuOvJNODlODCQCvJMDwDo3J9TsrPLMhG0hIUXoGGS1//mTCkRAzmS9uNymf5q0S+wJ7qfGHRokWkFfkCAABwXPfu3ckJa9myZbSWx+QLCQkJtOYG/eULotfbu1exsbC0rT35KstrNlHsrW2hAQ2r2liS58y3tKvh02/egUhuBTbZnTjmaD97M8OCflu4tdEJAOiJKDby73NXX/7xl1X8YXmjfNJv9ou338mtjZY1kXJlpD0doVEl5O6fMbXo4VR3ZpRGXr+tWTxDAvkCe6rzhYULF5LWXr160RoAAICTevToQU5YyvIFyfWKgUFiYpbP6JSjr3xB+HxDlyqWklyAZb4gjr+9uocn39LOvf20nf88ffPiwfmtIX5utpaVmg3fk26RLmDtx/nB9pKxwSZuoQ8wPgQA0oheLW5QgPxx4OWtMvFaupEN2Yro7f7hDSo51mo//fRnBUswpI1vMCzZ/VAWv07kC+wx+cLo0aNpLW/BggWktXfv3rQGAADgpMDAQHLCWrp0Ka3lSa5Xcmu+kPJkdSdnK8mgA5b5ws+HqzpWtbTkV/RbcldmdkXC9dnednwLu4Yjj33MxquNcYbgZohbXsn3kwZGfwWfQWiT26XE3D+6clLfTq2b1K3m5l7Lq6lvp+BJS3b9/SI+8zYPSHx5bk3IgO5tvetXc3Or6dWq24gF+25HZ5PZOvHXw9rX6bTqCbs/Rtx+6eKPu7v8ZWTAMyrddJHsH+EcR/xmkZeZ5K8gr0CzVR+zeJsM5AvsqZMv9OnTh9YAAACcxOQLS5YsobU80kQkJXHrykwf+YIgcrlfJSZcYJcvJPwztalkToRNs5m30/3ojzOjPK0s+ZaVu218ge/btSN6stCrsGQRUoKX328rt2bygF4lPT0Q0tImv6GhWRELZ89mrX2b1XPjF5Eue2fAMy7q5Dd2210db98neHlwkp9zMROTku5dJy7devh8xInd62YGN7DMZ5inXJ1B4U84/ZW5KPqf5d1dyO+PkeWA85pGc5x+6aLEqMcXd4T62eUzzGfRYOiORzk5XCAv99HsWtKQ1ahct31f6cEsg3yBPdX5QlhYGGlFvgAAABzXs2dPcsJSmC/8+vWLNBE/f/6kh7gh8/MFwb0FrZysPIL6tqvELl9IubewuZ0km7D1X/v2jy8GU27OqM8nrTYuvfdEZest2bOY+N0mv5I0XSD+XDsecgtxzIWQOsVNSlTvs+z8G9mebfKHy2v61ypFU4YCDp2W39RRxiD++vfkesWNeGbW7ZZcj5X/PU56uX+oh7mhUbHqg/e+5NxAhpSvT/4OnzewRUVzI+nbIskXzmnyB467L130eqVvuUJ5jKUrBxnwTMo3Ddn34GvO/qsgjt7XrYKRJEOr0H7bm6x/rcgX2FOdL8yfP5+09u3bl9YAAACcFBQURE5YixcvprUMsVgsuUTLfflC0q25rez4noMPvz0Q7MwmXxDHHh5YTbpwg73vshcKrvcEVybUspU8sk2Tmbex6QFL4thDgRb5rfjlpKvDE8Z2wy/j3cyNBHdm1zHP79AzXEmHNuXp5k58U6bLaVS6+dIH2n+3nhK5uFExQwPDIl5hDxT+bRB/3N3VgvRz8zoPOZ2uC54VRM+3DGjVtGG9ak5WJfKTp2VU0LJWfeciTDqnWb7A9ZcuEgmFQkFCzPMrO6d1qFTI0Mi8YrtZ53LqbDRx1P4eVsY8A8PC1UMu63h4DjvIF9hTnS/MmzePtAYHB9MaAACAk5h8YdGiRbSWkZYvJCdza5Bv5uYLCddmNbGz9xx1+ov450FW+YL4w5bOjuSn+BbWdSZeUfhzPw4EuzCPXG30hZw9dDfTJPw9zDGf47Bjq1tLl4cnDIt03sfp8eiQKZKvTaqav1jjxY9UhUuJl8e65GE+KDyzymP/0e63TvhgtmcBHnkktyl3lP6r4ndrmpsbGvCM+X1PZPnOJsLIpe1r1KjTwKddz2FTl++88DRenHJlBLMmoEb5QvZ66eKYkwMqmkqerk2nLc9z3nS0lEfLm5eSZD01x52N5shYOOQL7KmTL/Tr14/WAAAAnNSrVy9ywlKYL4hEIsmlp4GBQKB+31ofMjNf+H5lckMHvtfE89/ItRq7fEH8YVMXe8lP8S3sum+NUXjNJ3y4oAVfeh+rGuMvcOvtzR5Sbod6FLAIPBSbfCb4r9QBDKYNl2O+SW4j/rjRt1iBWrMjM/p++tuBgLL0k8IrVH8BywUNJcTR4R1Ip86Al9dr8RtVH7iEfV0l83dIXzxUwZ6CWY1NvqCjl/51d6B1YTMTE2MWTEzzl244jz6QGr7u7FhCMkyDV8hz1r0clTCIow4HV8zDMynnPfefOO784UO+wN7EiRPJR1VZvjB37lzS2r9/f1oDAABwUu/evckJa+HChbSWIRQKSRORa/IF8bfz4xvy7ZqGXmF2UWeXL/w42M9V+lN8q+rjlGUHCfuCaQZh4z03Z1306oPo+ZJGRUr5bXon/k/4INRNuvE7YeIy6Tbey9xF9Cqsbp6i7barsaqd4PxA67S5NI6jr7GdSyN+u6xRfslQiIx3RI3b4ldIck8j6wHnODdOiUW+oKuXLnxxZP74EcOGsjFs+JipW27QB1JH/Pa2haUjVwxLBxxg/rTnAOKvEROqmxsX8Rhy4DXbT3LmQL7Anup8Yc6cOaQV+QIAAHAcky8sWLCA1jLS8oWUFG5dvmRSviCOPT22tk1F79m3UrfjZJUvpNwIrWfH5Av8FkueKLkGF0SMr2YtzRcsK/bYpdYI4pSYe4eWjevSuE6H5amPmvjyzOqQ3m2b1a7hWbtZpwGz992X24RPGH17/8JRPf2aNahVu0mLbqOXnHiWI/ZXEH/c2raMuddC5hvor2u9pTuzEUZlg45zKwuDTCaOXts8v2HBcpXcvdqN3hGpeifdhN0di0q+yZYwcZ/6kN0IBtHTuZ7SmRaGpXoczmA+jvBuSBVp+mVUvvcJrgUMmucLXH3pos93ju7aG/Fc8Z834Y1xTkwESV7neXowm4u/OqN+cbO/fJfc4l5ggnyBPdX5wuzZs0nrgAEDaA0AAMBJffr0ISessLAwWsv43//+J7kkMzAg/4ce4oZMyRfEMSeGedo7tFp05/dilqzyhe97e9ozwQHfNiA8lh5NT3h3XiMb5m72jeaoHMAg/vEiYuv0YF8P6YYU5P5N5z8U/ieOv72hb31me4vfN/vGIWeYGRkp706EtnWV7FIhc7NyaTX/xnfpo2ZjX48EWRVwC7lJ/4skH+1ZmnYaeXlbbszyie6gTynXx1Zi+siSsfiuk26q+lUiXV7X1LEuPPNOe1gt1iF+u6g+E2ips19JQrg/8925kfUgrk2E0jhf4OhL/3a8L1+yP4hh4brzyJ/GP4geTnVn/rMbFum8lx7MzhJuzmtcupBT0M70y5mKP6xp6zrweBYvQoN8gT118oWBAwfSGgAAgJP69u1LTlgK84WUlBTJJVmuyBfEUYcG1rJ2ar88UvY6mE2+IHy82IcGB3yngceU7bwhfLyoOb2bjdvIs8ovCIWRawZ0DOof2KI6s16DNF+4H3N1QRtXF0//wZPmLlk8P6RXkypWtNW25ujz3wUvdw2ob2vn0az7yEkz50wfG9zczZ55Sha23rNvc6ybo5nESyMr5bcffD7tazvhrQmVUzuNJtVnajGtHrIfwfmBVqlzHgwMzJqsiqENiojfLKhrSu9qYOaznlUWlTYIgpffb2uG44FS/h5iw/ThTdynZbhEhH5pnC9w86WnXBvtyPw7vDwtNij4b5pyaRh9ncaVxl6nB7OtpHtLfcqV9Bx/5vOfKy4kHwv6yyOjiSuZDvkCe0y+MGbMGFrLmzVrFmkdNGgQrQEAADiJyRfmz59Paxlp+YJQmMXXK+noPF8Qvd/bz51fpeP6Z/IvlE2+kHJjen06asC22pgIZT8lerHcNzWGcAw+nPEGoN+PDapqI72/Xe2AgR3bDFl9Ner3l1eCJ2s6V5XuiMm3dO4yamBTz45zjr/4PShZ+G5f3+rSHTHJsxqr9FlxX8q9GTUKVgjYJ7NspvjTsoapnUYj60ER3JrMA5lL/HlLm2Kpw1fyuE66oepvlejRdI+08QuF/HeyGbWfcmWkPe2Ul+97OsPfpN/fnfMKtwvn1vQkTfMFjr508dtFXpJRFTyTMr5rFYyqEH9Y1lA6qYNnbD/iSvb+85AcucaPb91qseJZEYJLw+yKddilepJQ5kO+wJ7qfGHmzJmkFfkCAABwXHBwMDlhzZunYDlugUBAmgiRiFtfu+k4XxC93dnTzc61+/bX6V8mm3xBcGliTbqwgq3nxH+U/ZTo2bIWqfmCXe99GXd0RM+WtGLGINg4t1l4Q7K9hZzkiAk16L/r1GT8iXfpL6NTroc2sJb+c/zWKzMc2sxRopcrmhYt6bv+rdyrT97XJXVWPa9Qe1adRsi+xDER8wIauFWt3XbMrseqR4YLzgRXSFvg0XboRTZ9zcQdbQtKR/0bGDuMuprhI4g/Lm1A0y8Tt6kP6VFu0DRf0O6lZ15ILX63vmUx87pTLsco+rsmejzHU7qDrVHZ9ts//vmdv0aS31w7tm/HxtVL5k0PGTusf1BABz+fNjN+f5BEsQ+OrpzUr2ub5g0bNPXt0CN41KyN51/+2eEXxz86sSZ0YDe/5g29vBo3b9tj2NTVJ55mkMGkPN3c0crUpLRLgybpNWrgVae6K7+YqUnVyVm+XDDyBfZU5wszZswgrYMHD6Y1AAAAJ/Xr14+csHJxviB6tTnAzdo9ePf7P18kq3zhYlo/X9VPCSMXps6P4FfsfyTj8Qvijxs6Okjvz6y/kJ44ZmsnW+kD8htOu6ng2j92ew9b6T9n7TVDUTv3iaN2dChrXm/+o3Sv/vfXqgYGpvUXvdOyCwE5lejJrBqpwxcMi3fZx+YrddHLebXpY5i4TLqTcVcuZlUTM+b+Rpb9z9GD3KBhvqDlS1fzDygrojfh3e1KuXZfEvFB/k+b6POFSbXNDQ14hkVqTfpb6z0cZd6CNIZFu+6Xxlrxt1YFulnYNQicuHjz3sOH926cO7gpPx/PgJenfKNJpz+lnV8SHmwOrvGXVa1Oo+Zv2HXoyP6ti8e0qVSYPEeTUjWH7H+r5GQrfBUeYJu6kK1SnBgkg3yBPdX5wvTp00nrkCFDaA0AAMBJTL4wd+5cWstITk6WXLEYGIjF3Oqx6TBfED5b38XZptbAw1GKXiKr+RE3Z/yeHzHub6X5wu/1HW2qjjiT8YJc4i9buqrKF8iT7cOsK8n3mqxo572fB/sxO2JaeYZcycxr/czy7URffoEqE6//8V6JX4elzaqXTLDOluEJZDrRy7C60nHyhFGFoKPx9LhGUi4Pp31yAxOP6Y8yTl7j1jWnnWxewXbh9CA3aJgvaPnSM3nUvjjq7wUBbqWKWNZo3WfC3OXrt25ZM29891plTHiG+a0aDQ9/pJNxTclvb50/fezA9hUhnSoXZgZNSfMFcdyFCbVs6405/FLuz5P486EgG2bpyVrTb0maflyb6WXt1mfrQ/kJDvGXxlaVDLLg5bHvezhawclIcGFQ2t6qKqgX/GQy5AvsqZMvDB06lNYAAACc1L9/f3LCmjNnDq1l/Pz5U3LFYmDw69cveogbdJYvpDxe7e9s7znilMxsflls8gXRixWtU4ODKsqDA8HlSanTKOwazrqjRpc4dluAo/T+yvKFQ30dpQ+oLF841N9R8uN8q1qTLme/fCHpn7GV89sNOKOoU5gY3o6O2zYwLNn9UMZhDeRCwnuhbqbMx4SXv+YslovgyUyxMKkxS43FRBM2taKdbAPTRivoQW7QMF/Q8qV/pkczVfK7q3uWTx3eq4u/n69f+269h09bc/iOzEI1upO2cYlh0a57X2zr4FBn6nUFIwfEb5c1yi/51BkWabnuXfSR3pWqDD6p6HwTt7tTSUlgwTNxmXgryyMCrSBfYE91vjBt2jTSinwBAAA4bsCAAeSElTvzhfgDfV1Jd72iu2f1Gopvrg7Mkop8fqVq9GDdofvj6M8rlng42Enaz7fk2/dWOgQ7+fhQZyvmbo6dtijJN+Tk6nxB+GC2Z6HyXfYo+maPfCB+f7fHM2u2RtmeoDmN6PGGoIbVqzg7O+mec2U3z5bjj+qlT6gfcQd7lGc+JLz87iHX2H6bnXwkbTdUk5qzn2bcyU7a5peXhl+m9RfRg9ygYb6g5Ut/S4/mEOLolY2lo6YMi/r27+roFfZEcSggejyzunRKBS9/o/5BLm5jryr56MWsay5dJsLAxHPui4zfXA5DvsDehAkTyEdAWb4wdepU0jps2DBaAwAAcBKTL8yePZvWMpKSkiRXOwacu1rQUb4gjt3ajZlxoMnNtvvWLyrTAOGThS3oZpD81iuVXCiKY7YG2DAPyG8wRVEc8IdcnC+IXq1qXry4z5o/FuCkRE/oJTxh4pbl27PpS8qVie7FzIxoD063eDzjfOVarnhO/6lsL/naeFdm8rphiaZLHrL/QltmMVHSyX6uRic7vF0B+p+IdBzpQW7QNF/Q7qW/oEdzisTNzOgMnkm+gja9jinc0EEiKdy/kPRd4BkaFWu98ZOys4fgXH8LaQBmWLrnkWw9Bgv5Anuq84XQ0FDSOnz4cFoDAABw0sCBA8kJa9asWbSWkZiYKLnaybH5wn9xe/rXqlLVQ8XNyY6OX7B2cKUHqw/co3r8wn+CS+NrWzE9eU9lPfmU27ObMrs5WDgPOPidHlUp1+YL4s+7O5crXHuOohdNxW9qlfpFqdFf/c5kr/RES2KRSKh7HFvSVUvJN6dUkw5S5xVwHX5andFCSiXt6Wie2meuPlONSQJJ29swX0uTH6ih4M9sFtIwX9DypT+lR3OK1HzBwMCwTM8jysfDJB8JLMXEMryCLTfE0KN/Srk2pqL0vwYvf5vt2XoTHOQL7KmTL4wYMYLWAAAAnDRo0CBywsqd+UKG2Ky/QCSeHuXOzH2w7bj2vcLOTMLBfi7SR+Y79dqrejxEqtyaL8Sf7m+b32XcVVXf6AlO9C6XOkEiv99W9RdQF8dcnNXatdro87kqkshl4s6PcJHET7y8DkF7lS3Pr67kgwElUr/Ed58WmfGjJW7xTV1U0qT2fHpQAymvDo5rVcWiTAVn7xG7n+j0e21Nxy9o99Jf0aM5RVq+wDPzVjUnS3AsqCzztpl4TFfxrglvjndiRmGZea/NIMPmNuQL7DH5wtixY2ktb8qUKaR15MiRtAYAAOAkJl+YOXMmrWUkJCSQJh6PR2vO4Hq+8N/PyxM8bSU/aOUx5KSib6JSbk1vYCe9g1vwwVj1vk7NnflC8rUJVfKV77LtaZQqHy6MpJfmkp7MvJcZd31EP17/Ez6tW7XSpjzS7/Tbmq2/LwTlRC83+pUzkvxHdgja9Vr7mTOS3mLqIodVJ9/P+AF/bGxBv+Y2MPVaTA+qTbLwSOrapeQ1uE2+xX5qxx80Xd9Ru5f+jh7NKdLyhQzWlE3LF3hmzdepyA1k8oWmq5QPc8gGkC+wpzpfmDx5MmlFvgAAABw3ePBgcsKaMWMGrWX8+PGDNCFfUJ4viH+8u/3P1cjPP2mdRvhkWRs76Q9WHxvx56Wn6M2atraSrr6117Rr6nb1c2O+IIycVzetd6UmY7sRV5T2wYQPlrTzcHGwLFnAtKBFrbY+LpL54cgXcqz4SxM8yAeIV6ByvwPvtBy5wBCcG2CZ2slWay/AuLXetJPNy+u7mR5Ul/jdovqpW69KqLdvg7o0zRe0e+lKFyjIptLyBSOrgarGP/3OFwq136Xi74xMvtAE+UJupTpfCAkJIa2jRo2iNeR8wm/v3n/XZkofAEBWGDJkCDlhqcgXDA0Nac0Z3MgXRK939qtrS1qtXJqFXkg/COF7xPi6khEKVrXGn0+/8bvo+Qp/SfpgVT14/2d1TxzimM1dmNUo7RvPVfTd4c+DfRyk+YJ1/RBFYcjPg/0cJD/Ot6oxISJb5AviN+talihad9iqjRlaN9qrCB26bVi0yz6l3yaKY+8e33vgeMStl3Ep//2XfKiHZEs45As5k+DRqlZljXmG5tXHnFb79ywjwgehbnSojInTuBsZd7KjVzahCYFk3T56UG2JR3qmzvwhDEt03vONtuiAhvmCli9dp1M7OEA2X7igVr5QWM18wbTJymh6MFtCvsCeOvnC6NGjaQ05neDeorY1hpzU/x/Pn7fDp4dMnjhJ8W3ymkvqTesFgFyLyRemT59Oaxnfv38nTUZGRrTmDE7kC8KHYc1spP15cnPoGZ5+Aq74y5lxdSV3cOm8SW7Ot/jLqeG1bC0s7ar3P/Be/e8if29LYVtj/CUFl7Ox4d3spE/GymP4aQXt38IDJWkIuTn1P5g+8uAgccy+bhUK1Zx5X50B4b97SuTqvNHyKPVOfcgXci7xx/1BdmY849INZ16J0+WV0Ne13sxGFAbG/MERGX44RS/nedJOubHj6Kv0qPrE7/YGVy5sSP5FwwIO3bepMfVHfRrmC1q+dHV+j7MT5AvKIF9gT3W+MGnSJNKqbPVH7X2Jibl39+6J48d3bNu2euXK+XPnks7k8CFDewX27NS+g3+btm1bt/Zr5Uv+t0M7/y4dO/XoFhDUIzC4d+8hgwbNnD594/r1J4+fII8QEx3DtV3NZQmfHQwL+aPPnPFtctiBZ/Qh9CPhxhxve7t6U2/q/4+nKDH2w9unt44t6lZdOteXuVm5+ozaeObuyw/f/hi0CwBZIC4u7sH9+2dOn967e8+GdesXL1w4LTR01IgRwX36kD/OgQEBAV26kj/U5G84OTKo/4Bhg4eQ1imTQshf+COHD/9769+oqCixOFPiwqFDh5IT1rRp02gtIz4+njQhXyD/loJ8QfRmrT/Tn7fkW1YZelzBmoKJd1Z0cbXmWzq3nX05mvm2L/nN6Rlt3Swt7dy7rvpXrW0jGOLYy1ObpP5z1rVHHvso388Qf7+9qIMTs6ikpW3Ngfteyz9dSbu/i6X0xy2sPLpveML1DvWPc4Ps8lcec0W95yl+HVaHdmXUHLstgXwhp/p2cWL1woamVm1W3df1f9nko0FlpN1FA8MSKufdM4S3JlRmPpk8s6ar2HUbBTGPr5y/9PCzrr/E0jRf0O6l57Svu5AvKIN8gT3V+cLEiRNJq07yhZjomIt//71m9eqxo0Z369ylQb36jnZ2jrZ25P907dR58MCBE8aNnzNr9oply7du3nLowMGzZ85EnL/wd0TE5UuXyA+eP3fu1MlTx48dO3zw0L69e3ds27Zgfhi5bCU/27C+V0U7ewcb23qetTv6tyeXs3NnzyYPQh7h5Qtu7FL789vndy/unFzepyafXlRZWLk37hTYvTu9devWtWP7dj6N67nay/Su7btv1ecvZvylSQ0cLCztW6/QaaysGeGd2c3oVmeWtp6jzqi5XBiAGoTf3n+IxydKXYmJibdu3tyyeXNoyORegT2bNW5SuWKlSvYOTRo27N612+AB9I/2yuUrtm/dduTQ4ZPHT5w+derc2bNnTp8+cfw4ObJ/377dO3eR1jWrVk2ZFNK7Z5BPM+8qzpWZP9ed2ncYMXRY2Lz54Tt2kD/1b968of8wW8OGDSMnLBX5grGxMa05gyPrLyTe3Ti4WVXnSjXbTzzyRkmHNvHRzjHNK9ta8KvUbxcY2MW3RkUbS8f63eedfpPx1TwVf2Jyq0aeTmljJaQ3K8fq9Zu1nXIq4T/h0439fL2qVU49BdCbTeW6jXzHH4wRC59u7t+ySTUn6XKSv28Ozp7Ne65+oF4/XP8EN0PcCtgEn1B7MHjSzvaF6UINRuV6HVfv3c3m+YL40+n5gwI7t2/vr3vtO3TtNXr9zWw5Z17wcJlPGeM8dt13vFD6tU/yy8tHrr5l8/EXvw6ry4z65+VtuTGeHlVKcLJPeWaCg7Ht0Ivc+g5f03whB710HUC+oAzyBfbGjx9PPgHK8gXV6YNqUVFRBw8cmD51atdOnT2qulW0s/f1aTF65Mg1q1cfP3bs7p07X2J0OeggLi4u8mHk2TNntm7eMnf27GGDh3T0b+/uWsXNxZVc1JIr4BvXr5MrOXrvrCG8P7cJMyiUb1VzoqJRof+JE16dXRpc115yH77v8ud66+iLY0+Nrs+XPDfHwN0afBWlaynXQhvQi0sb38VPuHrFCFS2GZtDJN9b0rrmMDWv1nXp553t06ekfwfSblPWXvxC75jVBAIB6eguX7psYL/+kgjY1q5502Yjhw1fvXLl0SNHyB/t2C+6eaoJCQlPnz6NOH9hx7Zt8+bMIX+uO7TzJ6eJam7u/fsGb9qw4eGDhyzGODD5wtSpU2kt49u3b6TJxMSE1pyht3xBN0Rxj09vWRQ6duSwUZPnrT96OzrHXWnrmujJQi/zcp12aTBpPuXSMFs6QULS91Evl8jm+YJ0V04aqmQCnpnHtPv0n8o+RO/2BNqa5bUP3Klqs4iUa2OcKgQdY3Vi+90rN6kSci+j663YNc2YPRp55u12ZNgl1y9N84Uc9NJ1APmCMsgX2FMnXxg3bhytMxIVFXVg//6xo0Z71anr5OAY0KVr2Lx55ML0xfMXIlHWfCf+8sWLPbt2k6fUuEEDcrncxrf1jGnTTh4/8SVG/2uaplyfUo/54sXGZZCiIaiU6O32Xq5Wdl4z/tXXtZs46vDg6tbSXr1VtZFn9d8DSyWO2tSJWVecz2+x9CniBc7LHmNziISbs7wrWteffkP/HSJR4hfJ5J/jCwJqWaW9A5Y2zi3GrD9z70UWT/4h3fi7d+6sWLa8a6fO5I+2r0+L0MlTyN9M0sNPSdH3e/X61atdO3eOHD6inmdtFydn8vlZuXzFrZs31Xwmw4cPJyes0NBQWsuIi4sjTcgXQL/E7zb6lSrhs/q1Jtc/olfzazMX5+QjW32meovsZ/v5ESlxbyJvX79+Tfeu37jz+GNCdhu4Jo6LGOdRKJ9Dz4x2ooxd52Neaw7LL6OST/Rmvpc3LN7tQAazBFKuj6ko7ZHzCnivVnNZEL3ROF/IOS9dB5AvKIN8gT3V+QLTSv6X1opER0cfPHBg3OgxDerWYzKF5UuX3bp163//+x+9B2eQS8xzZ8/OnT27o3/7inb29WvXGT5kKLmS/h6vly/sxZ+3dKKTTp16hKtcrzDp+KAqtUap9TdSB0Tvdvdyt6HdHn7TWbez7iup74eDnZlnYtdw9t2sex6gIS6PzZGIvxjS2Ib8u61XvsiaoFNCeGduo9QUxspzzOmsnPzz/NnzzZs2Bffu7ers3KBe/UkTJpw4fvzbNx2u562tqKioI4cOkyfWrHGTSvYOndp3WDA/7O6dO7RZkREjRpATlop8wdTUlNacgXwhBxN/3tvtr8K15yjaglOVxO1tJHtNSqi1+JwE1l/IUZLvL25WukDFoD1vMjphCU72qVCy63626xkkn+3PbNRo4jzhlsrPqfjDsobS7/B5hX3WfuRaH1vzfCHHvHQdQL6gDPIF9lTnC+PGjSOtEyZMoLWMhISE3Tt3kWs+JwfHbp27cDZTUIY81Tu3b69fu657127k4rVvr95HjxxJTtb1mjOyEo4MrsysWWXrv0r1SUMQMbbRgP36GYQlfLGxq7urV8OqzPgF245r3mfZn8+fZ8d4MG+Rdd2JV/QUr4AOcHdsDiH+cmpsbemUb7uee7JwaGPKtal1ab5g32JxlozO+RITs3rlysYNGlR3dx82eMje3Xs+fvxI2zjs27dvZ06fnjl9el1Pz0ZeDcjpRuHTZvKFKVOm0FrG169fSZOZmRmtOQP5Qo4l/hDesbyp3ZAIjTe4kNmcn1eo/U618gLkCzmH6O3ObjZFKgfvl9usRTHBpWF2edynPlR4T9Hni4v7+zVs0LJP2PlPSh5LeHeKmynpO/NMa89XufTW1+1tpfumGpXrcZBDQTTFIl/IKS9dB5AvKIN8gT0mX1A2A2Ls2LGkVTZfEIlEERcihgwa5OxYsUvHTvv27ElMzAa7Q6lGrrk3bdjQxre1SyWnkcOG/x0RkRmzOQTnx9DR0fxmYYq2/JYhuLVx6Rm9LFEriFzRwbla8I7w0e5Mx94x+ECW/QdNuTrFi1l8wdJ91GlcJGUfnB2bQ4ijjgyswUzNsHEbeS4zI0TVxFGbutBN9Wx89RsvCIVC0j/vE9SrcsVKwwYPIb1ZLm+4owJ52teuXh0zapSLk3OXjh3TnYBGjhxJTliTJ0+mtYzY2FjShHwB9CX++tzGJQ2NHUax2MtOeG9y1dQJEqb1F75V51IA+UIOIf56bpRbQdMKDfuMIVfgyowZNXzIwL6BHZp7lM9rWLj9TkVXbaLnSxuZS3uDBgaGheuHPVJ8WSuO3tdNMlGAZ+IyUfnX+OI3yxsXJH1xw8JeC9WbsaNfgvMDrZhMTv01UXPIS9eB+A0taL5g0V/VBOnkI4GlmHyhYLtwFX9nUq6MsGfSHuQLuZfqfGHMmDGkdeLEieT/P370aMa0adXd3Zs0bLhy+Yps8a2Xpt6+fbtsyRLyAj2quk2ZFPLvrX9pgw6k3Aitz3zBa+s56R/99a5U+nl7YQun2oMOf47f09tO2vOxqjEhIquenPDBvKbMGHsb5/6Hc94SOjkYR8fmEKJ3O3tXo1vZSSbd3MnCyT8H+7kyz8S60Rx9PY+XL17MnjmL/N329WmxbctWPc0Fy3zJycmHDhzs0S3ApZLTiKHDLl+6JBaLR40aRU5YKvKFPHny0JozkC/kPKKvd7cPriH9wtOwdOfdsfSw2sRR4R3K0PEL5II/+Iw6oSjyhRwh6e6CxiVoJqAuk8oTbyvqHMeta24mGdTPMG20PIo2pCd+F975LxOegWFxn9WKv8cXxx4OsjIm9yjaaFFk1p1DlRO9XOiVn3mxvDyec9ReGjwHvHQdEN4NqcIEmryCLdYr/2ozbZ4I+dDVmKUia4kP96fJljqLZ3IZ8gX21MkXfHx8mjdt5u5aJTRk8v1792hbjvbwwcNZ02d4Vq9Rv3adsHnzPn36RBtYE0YuaMZsymXlPvgEN4Z8fL82vamz54hTMWLRyxXtpJtH8PneCzWdK6orohcrW9N9y5wz+A4cuIWbY3MI4fONAa5VGtZ3Y8YvOPivzcLJP+dGVWPWFrH1nJjZCWNSUtKeXbv927R1c3ENnTzl8aNHtCHH+fz585pVq5o1akz+XNerW5ecsEJCQmibjC9fvpCmvHnz0pozkC/kFOK4u3sXTR4W5O/lUIR0RyieobmjT+9xs3fcjFP9p0f45sKGhbNCxw0K8KlaWjJoOxUvr2X9gKETQ2fMmrN47z2lmywiX8j+xO92dbMmnV3N8PK33qwwsE+5OtYp9ZPEM7EbdlHFWefHtfneFUzJp7XGuHMx6T+pCXeW+JQ15hkWqjL0mAb7oGQi0ZvTK+bMImZMDRk7rG/nJs4lZN43nknxSo079Rk2NmTqDMmd5iw/oWKB1Wz20nXn2/Vtc2eSN3Bkr2Z2BVMzLZ5xiartBoybPG3GrEWHn0rfNeHDvfNmzZg2eWw/P5eiaZu8GBYmf9hGSd7hebvuSv/kiF4eXzZ75vQp4wd3qlE27T8HL7+1V8DQCaHTZ87ZdEXjrDXLIV9gT0W+cOf2bZfKlUmre1W3M6dPZ6O1FXSFGYs7euRIZ8eKI4ePePaM/Y56kg68jbQDZlGx184/+86iZ2v6Tjimz68Wxd/OT2hoX3/COclVT8qNqQ2ZiQk2nbfoq++Xjjhqc1dmDIWFXef1WdcNBI1xcmwOIYhc1c65Zp8dO0d6MB17pz5ZOfkntA6z+IJV9RGZOPknKipq8sRJLk7OPboFHD1yRCDgzn+PzPXgwQPPWrXICatmjRqvX72iR1PFxMSQJuQLkGmSj/UuZ2JobGKWr0Bh86LFixcvVsS8UIH8ec1MjI1MijRcpHpkteh5WL38RiameQsULlK8JFWiRPFixYoWMSePk8/MxMikaPMVr+n9/4B8IfsTHO9VLnXcivqMHUb+o+R79YSby7u4lS1UuGzVDguvZhBw/Sf6dGZKk/KmPNMyNYMWHH/0lTym6Mfri+uHN7HOTzrYFTsu+ucrV67LBOcG8vPmyVegYGFz8yJFixUjv27FS8iQ/PpJf3MKFyyQL08ey+DTKs+D2eml64zo5fx6BfIWKCR5A2XePvLOkTeucMF8eUsFMKuGJm31K5gnP3mrpW80vZvkfsWKkvvlz1Og+RrpdnyCE30s8uUj95M+IL1b6gMWKpA3f/VpDyX3y1aQL7CnMF/458o/XTt19qjq1rhRI9Kq8OugXCU6Onr2zFmuzs69ewbdunmTHtUA6Tt3qSjt4fBt2697l/46QxxzdFidThv12KcWR58Y6enQZPJlJtIQHB9chRm57TTwWBbtlhe3p7cL8xbxfZc/U3kpBpzCxbE5xM87YS1dagw68jl+b0/pphUW1p5js27yz/153swQIcvKAw9mSpL4+fPnKZNCXJycQydP+fjhAz2amzAD7ho1bFjFufKQQYOePn1KG1LzhXz58tGaM5AvgE4gXwDtiRNenl8/KaChK7+sef68ZmZ5Cpa08fAOnLTx0rucHlTn4pcOyiFfYC9dvvB3REQ7Pz/P6jU2bdiQnJysYrmsXOjHjx+rV66sX7uO5ru4xe3r7Szt4Vjaey+ITDd+PDFyUw8PR59F+lvwTfzhQHD1Sk1n3qDr/Itjt3Z1kD4929qTr2bN39LEk4OrMl8y2zeZm8EQe+AS7o3Nkfhxdbq3Y+1RJyWTf1b5MfGHTYuw9L98+iJ6ubw13b/TscfOPwZhaikmOiZ08hQXJ+cpk0I+f/5Mj+Y+Y6ULEk+cOPF7/PclixbVqeWZlET7WtHR0aQpf/78TMkdyBdAJ5AvgI6JRDnuO3t15eKXDnKQL7A3TroDZVq+sGrFip3h4SkpdLAVky8o3O4r12Kz7nriiWFVmNXvrDz6bLh648ZNcrt25cLxPRvmje5Wt5KNBb/h1Bv6WjlG9GZ7oIdjiwX/pg1UED5e4MN0fuxbLMmaoQPJFyfUYDbI5DeYck1fbwVoj3NjcwjxtwuT6js0GHuemfwzvT4zMcG26+Ysm/yzpRMzhsLSscP6D7p+EpcuXgyZOFEHy8Rkc0y+kLbhkew2QJ8/f0a+ADkY8gUAAN1CvsBeunwhHWY78dDQUFoDK4KIcbWZ1e+U3SyrjTmnp2kJKc/WdXF2arfkvswwBcHl8bWYBfAcA3bE0YN6lXJreiNmAQir6mP19VaALnBsbA4hjjk53LNSwylXmDWvkk8MdWbSPefBR7Ns8k/fStKPt4WN39LnmPyTSZjTmeyGymmYfKFAgQK05gzkC6ATyBcAAHQL+QJ7qvOF4cOHI1/QWsqtaV7M6nc2zn33RSdJJXyPi35z/9z6gQ0k3/06Bx/Sz4Z9yfeX+Tm7tl/9VG6EQNKhvhWlnR8r96GnsmJ6hPDJwhZ0Z0rXQcfprA3IDrg1NocQf9zfr5Z9s1nXUyf/fNkWwEzfsKoTmkWLTyYeH+LBrG/Cbzo/W2/WxG3MdD/yv7SWERUVRZoKFixIa85AvgA6kby3s2RHTJ5Ziw3Y2xkAQAeQL7CnOl8YNmwYaZ06dSqtgQXho0XN6baLlQN3xaYbGp18c2YDfsXOW/Sy803Srbk+Ti5dNz2X7+GI36/3t5U+Q36j6beyYG6C6N26dnbMW+TSa0+WDKAAdjg1NocQvdkeVLWi7/zbvyf/PFrYiu692nJZ1kz+EVwaV5MZH2RXN/Q6Jv9kGhXbIX369An5AuQwyd8+R0V9+vj2+f0rB2a1qiDdesCoVKNJuy7effrmw6eoqM9xyVkzIwwAIPtDvsAeky8o/MKHYPKFadOm0Ro0J3qzuj1d/c6+6+aPf5zskw70cW6z9IUe+j3fr4Q2tbW0sa/iWb2G/M3DlT5DG/+Vb/TfAxNH7wh0oG9Rt81RuB7KPrg0NodIeba+Y2UXvyUPpLsqMQSXxtMExDYgPIsm/8z0YhaAsK416jwm/2SeCRMmkBOWinyhUKFCtOYM5AvAVtLODkXypO0Hl35DONmd4wAAQGPIF9hTnS8MHTqUtE6fPp3WoDFx9NaAStIOGN+m3Zo/O+/imG0B3rNvZ/p3muLYs+PrVW49dn34jh3pb5vH+9pKn6GFfZ89GazzL/z+7YeuE4jvB/u5MqPHbdqsfp3Bo2fGEwCWODQ2hxA8WOLn4txhzRP5yT8H+jpLn6GN67BTMrmD3ggfL/SlO1NWGXIMk38y0cSJE8kJa+zYsbSW8fHjR9JUuHBhWnMG8gUAAAAOQr7Anup8YciQIaR1xowZtAaNxZPeDdMBs1e4yp3gn6kdQi5ldrdHHH1siGf1gC0vFE39Fkdt6MKMX7CsNvac0gnqKZ9vhocGNKjcYe0fewRo5+fZkR50Z8rmCx4pn5yeaU8AWOLO2Bwi6dbcVg6uARvTfcbF79e2Z/ZetfPKosk/a/wdpU+AX6n3Pkz+yUxMvjBmzBhay/jw4QPyBQAAAFAT8gX2VOcLgwcPRr6glaRTI6syq9/ZNJtzV3nf+befj0+fjtTt+s+iD3v61Ko58PCfHUAp4b153nSCehOFi8+lfL4RPiWggaP0hfD9ddy9F1wNrU13pmyopAOYuU8AWOLK2ByJ+CvTGtrxLR3c08/9qVHDmVlbxNLeT+nkH9HPbzHRsYnq/H5qShwT3t2ReQIVOyuf/EOeQXQmPYNcZNKkSarzBXNzc1pzBvIFAAAADkK+wJ7qfGHQoEGkdebMmbQGDQkuTaBzv63rTVVnYbeUyKV+rearFUSoS/hyS6BHw5ALccq6NoJzo2vS6QkdNirYml8cG3nt7sfvb8N7S9bA13X3XnhvbjMm3bCqOeGiwtETmfsEgC1ujM0hxLHnxtSv0nLchnQTfyS3LRNa0KVDKwbt+UF/II049saGsf51XKSfQFv7aq36zDvxXKcrJMQfGkB3x7T1X6lg8o/4y41No9rXd5JOM7FyqNm8T9jxF1ijgZ2QkBBywho9ejStZbx//540FSlShNacgXwBAACAg5AvsDd27NgM84VZs2bRGjSTcntGA2b1O9sa4/7OuKMleLSivVuTObrcwC7x34W+VXxmpG7Wp8iP3UGSdfjIrWK/I8p7NqK3q/1JN0xp914Q9SDi1IlLT75o9OxFT5e0pDtTugw+nkiPKpTREwD94sTYHEIcfXRoXfeArel2RWGIozZ1ZMYvWNUcdV4+vhLHngtpZk969XauHtU9HOlCErauHVfcUflB1MTPMyNr0J0pmy+MTP8MxV/OT2nswLewdnSuVqtqReYXgW9Vpcsy3T2D3ITJF0aNGkVrGe/evUO+AAAAAGpCvsCe6nxh4MCBpHX27Nm0Bo0IHy/2YTotVh5DT2bUYRDHXgr1sVPYVWPXdSc98vdHBtd2rD3+b1WLygmfLmrBdGxsa4y/rHT5BdKN29LVRkn3Xvzx+NjGlaX9KFvnFqGn1N4DQvh0aQvar3PssjVa5Y+pegKgd1wYm0OI3u8JrlZr8EGlk3/CmjAfMJtmc+WDu8RLIfWq+o7eeiNK+uSTP1xZ0auOdDkJx2bz7+lmVofgakgdujNl/T8n/yRenljfo8WYbdeZZyD4eHlFn1rSNMTWO+yubp5BrjJ58mTV+ULRokVpzRnIFwAAADgI+QJ7qvOFAQMGIF9gTfRseZvU1e96bP+isu8s+nxhVjtnKz6/ybx0HTDWXXdx7JUZLVwsrWuNiVAeGpC7fdkbREe527iPPKd8kIX4y7buSrr3wkcLWzFzHJjHce29V72n+fNqaENr5qes6036R9XzVPkEQO84MDaHEL7cGlCtycQI5ZN/zo/1oNMTOq+Xm/zz4+jw9pMivsn94M/7YS0kY3ms609TJzHJkPDe/MZMumFde9wfk3++Hx3ZNuTvdM/g3gJfO3J/foNQnTyD3GXKlCnkhDVy5Ehay3j79i1pKlasGK05A/kCAAAAByFfYE91vtC/f3/SOmfOHFqDBlLuz2tGBzxbuo9Rvi+DMObuvukda0n75/aN03fA2HXdRV/uhI9q7iJNJZzarXyq/B//eGpcExqCSPbPC1gdqWysg6p84ckiugMfvdl13RxF25QTvD81xZsufUdulVqF3Va5OSbyBQ7R2dic/wRRD/8+efLyY00H5xCJ/4a1dm8284aK4Tnf9/SWdNfJrdKAQ7KTf4SRG5cc+WPAjDhmR6ADubNDvwNyM4XYPUnR0yWpO1O6Dj2W/k0SPty47HDMn8+AWQ+yUt+DWIVBU0y+MGLECFrLePPmDfIFAAAAUBPyBfbUyRfmzp1La1CX+NutxZ0rM1+ckpu1R8cZ4cfPX7p06bL09ve508f2bVsbNmV4N+8adml3U9AB07TrLnp3cHInnzpOfJkf4bvW8x++WW7yt+jDsbmD+vRo5enCDHH/fbOv2axDUPC0w38sta+qey/+fGZq21oO9m4NA8YOb+dhKdkJgjb9QRx9esHAPoHtm9W0T3vh9GbvXKeVf8CQNf8qzEOQL3CHbsbm/Cf+eGxcs4rSj4GVs+9k9QfnEKIPh4bUs60zMULl5J+0Xx+rmhMvqR4fI8XsZmJVfdzvtRpYP0nh08V0bRG+Xddtqif//MZMqbCuNfqCGk8X5ISGhqrOF4oXL05rzkC+AAAAwEHIF9hj8oUJEybQWl6/fv1I67x582gNGZF0ngf07uHn5SYXCqh3U9ABI4+oQdc9U6nZvRe/W9vRxsZnfiStdQf5AmfoZmzOf8JHi5rT1TekD1UleLeanXdx7OWZvpWsbKuNVT35J3Z3L5fU5zn6rBod9uRzY9yt+E6998WmPhHWT/Ln1an1acxn6xmSweSf3wTnR3nYWDgH70l7BqCuqVOnkhPW8OHDaS3j9evXpKlEiRK05gzkCwAAAByEfIE91flCcHAw8gXuyLyuuzrU7N7/PDe6Fr/6uPO6H9yNfIEbdDY25z/hkyV0kgW9OXbanHHfXfTl7o7RLZ2kj+zgv1rl5J8JDZnNI8jNyr3LGqWTf1IJIxe04FvXHnXm90wddk9S8P70JG+ntJ+y913wr8rJP2mEkQu9bWxrjDobTw+A+ph8YdiwYbSW8erVK+QLAAAAoCbkC+ypzhf69u1LWufPn09ryFKZ13VXh1rde3HUnl5VKvkseJAJa9MhX8hauh+b85/48+mp/tUcK7o0DBw1on0VK0WbLMgQvTs0sXOrWs7MupLMzc65fochW9JN/jk+e1BwN9+6lazT7sbcKnp4d+7Rb8bBPyb/UIJ/ZzS0d+686aVsuyZPUhxzZv7A4O4dmns42Mj/03x+5frN2/cYtFbx5J9Uglszm/Cdu26QewagpmnTppET1tChQ2ktg8kXSpYsSWvOQL4AAADASHz78PFnrixvjXyBPXXyhbCwMFpDFsrMrrs61OjeC55uDqxRud2yu0n0gE4hX8jJxO/X+dvaNwuTSwr0S/xhZ2/Xyp1WP1H6G5bZT1L8YVfPKlXar37KlXNrNjN9+nRl+cLLly9JU6lSpWjNGcgXAADgN/GbtV3qtB40Z8vpux8SZC53hd9eXju8ZnJQ08bjc+r6TOK3W/3LmRTtsi/jTcn0AvkCe6rzhT59+pDWBQsW0BqyTOZ23dWRQfc+4emR6R3q1uu15qb8hnu6g3whJ/t5bmw1m1qjL2TZpgniqEP9a9TqtVPZ2AaJzH2S4qgDAzyr9d6l6hmAKky+MGTIEFrLePHiBfIFAADgOuHtiS4m5IRF8IzyFin7F9/O1vqv0oVNDXnkiEm5Fssf5Mx4QfR6vW8pQwNe4fa7sqyrIw/5Antjxowhn2Bl+ULv3r1J68KFC2kNWSLzu+7qUNG9F0au6tHEb3DY0afqzTFnB/lCziWO2tXb3b7FwvtZ9cW98MWWwHo+s/6JV/EblrlPUvh8a5Bni9lXVD0DUG3GjBnkhDV48GBay2DyhdKlS9OaM5AvAADAbzL5gjxeHovG4w6+yqEDHEXPV3oXN5S8TOQLOYHqfKFXr16kFflCFtJP110dqrr3yQmJmT+sHflCTiV4uiXI3aX9kqwanCOOuzy9deOhh1V+rjL1SZJnMLNlk+EH3+OTrY2ZM2eSE5bCfOH58+ekqUyZMrTmDOQLAADwmyRfKGjn5evlZleueKH8BcxLVnCo0SJwwupzrxLpXXIe4eMljStUqFDUEPlCDqE6XwgKCiKtixYtojXon1667uoQR2/uKunet12TReO3s/wJQCZIeHp4eifP+n1WZdngnKQHa7o37rHpsYrxhpn8JJMeru3apOcGVc8A1MHkC4MGDaK1jGfPniFfAAAArhPenuhautsBjixBoBcpD+d7WTact31kJWPkCzmEOvnC4sWLaQ25mPBhmA/fkm/tNf1G1gzOyvInADomjFzTrWm7gQuOPcmywTmCZ9uDG7dfeCP9ExB/e/rkvfRjltlPMvlZeJ8mHcMUPYPH7+n/B/XMmjWLnLAGDhxIaxlMvlC2bFlacwbyBQAA+C3X5QuCuzPrWDZb9jT5+ljkCzkGky9MnDiR1vJ69uxJWpcsWUJryJWETw7MGze4S92KzJZ7lTw7DB47e/9j/Q2ryPInAJlDkLWDcwTPwgc0bj3jQozcoASx4MvDo3O6BK54ygyTycwnmfxsZ3CTNtMivqR7BjGRx2Z1DVr2lB4A9cyePVtZvvD06VPSVK5cOVpzBvKFHE8cczK0Y0uflq3b+HfsEtAj8A/du3b0b+Pb0qdNyImv9GcAINfKZflC8s3QGlYt17wU/SdEvpCDqM4XyJkP+QIA5DxJj7b0rWHDt7arZO8gc7N35FuR/l5F/7WZPgsn6dHWXjXtLfiOck/AoaKtjSRHs2u/DvOANMTkCwMGDKC1jCdPniBfgCwherV9oE9DL696tatXsSuVz4hHPompeEb5StpU9qhVt75XQ+/O86/k3NnVoLX462Ht63Ra9UQv5wVh9J19C8cGd2vTrF41Nw9PL59OA0PXnXvFkV6fMtq9RYkvz60JGdC9rXf9am5uNb1adRuxYN/t6CwYLpur8oWkqxPdrfw2Si93kC/kJKrzhR49epDWpUuX0hoAIPsTxxwe4sEn/Tolt4rdt3zM3OUgxDFHBlWzS//v/r45d92ayc8gB5ozZw45YfXv35/WMph8oXz58rTmDOQLuUvy5RH25AJaytC8/rRrcfg1hwyJov9Z3t2lsKGBkeWA85m8UI/w47k5XTzK5jE0Kebg1WlwyNzFy5bMmzY6qKVbubx5LVvMvhjLyY+sVm+R4OXBSX7OxUxMSrp3nbh06+HzESd2r5sZ3MAyn2GecnUGhT/Rb1c/NV8Qxz04uCx0RO+OPl51vLzb9Zqw4tjjeHqfHCLh0ugq1u23f2A+U8gXchJ18oVly5bRGgAAgJPmzp1LTlgK84XHjx+TpgoVKtCaM5Av5C7JR3uWkezARhgW77Qnh3UWQMdSvj75O3zewBYVzemwF9J5PpeJ+UJi5PaBNUsa80wrNB4bfi/9csYJkTuCqxQq6b2Szh3kBK3fIvHXvyfXK27EM7Nut+R6uuwk6eX+oR7mhkbFqg/e+1J/Axkk+UIxr6A+Nf6yaxQ8Y8OhiOu3b13cHxbkVsw4z1+NJ534lFOGNsafH1bZpsvuT6nvOvKFnER1vtC9e3fSunz5cloDAABw0rx588gJq1+/frSW8ejRI9KEfAGylvDGOCe6sT2vQIv1MfQwQBrR8y0DWjVtWK+ak1WJ/MY8nlFBy1r1nYswqVRm5guJd5e2KGfMMyxYue/uVwo604mPDswd7udUyLhCnxNZ2/vT4VuUErm4UTFDA8MiXmEPFP6U+OPurhbk38jrPOS0vkZuCO9McjExtfZffT+BHmEkXA2pVoDHy+c06Dg3B5FoJu7kgIp2gQeif78U5As5yejRo8nvo7J8ISAggLQiXwAAAI5j8oXg4GBay2Dyhb/++ovWnIF8ITcRv15Q15Suv2BSczaXvgYGrhBGLm1fo0adBj7teg6bunznhafx4pQrI+yYWTWZli8IHq5oWc6YZ2BYovmq54o+l1/3dC1DRwgYO46+lqWbeOnsLRI+mO1ZgGfAM3ObckfpKxK/W9Pc3NCAZ8zve+IbPZa5xDFHQgatvqdgUkbi6WBLI/JU/up5KJuvBSuOPdrb3r7PUbmgBPlCTqI6X+jWrRtpXbFiBa0BAAA4af78+eSEpTBfiIyMJE0WFha05gzkC7lJ3Ba/QjReMLYfeSVL+2iQfWR6vvDj0tgqeckn06i03ybF6woL709xoyNvDIysB13IlJBDC2zeInF0eIdShqQ7m9dr8RtV4wES9nUtKbmfmVvo3Szetyz5WFA5I8lT9pzzOBvHk+LPBwLtKg44HUdrCvlCTqJOvrBy5UpaAwAAcFJYWBg5YfXt25fWMh4+fEiakC9AVko+3kvSN5AwLNXjEEeuoIHzMjlfSL463tWMR7p1Js7jbigLvZKvjHORRBDkXta9j+rne3wNsHiLxG+XNcoveUUmbqEPVMcGqcGgkfWAc3/82n7dHWhd2MzExJgFE9P8pRvOow+kDtGLuZ6SnIdnWmf+y+waMIg/7e5iU3nY+T+Wn0G+kJOozhe6du1KWletWkVrAAAATmLyhT59+tBaBpMvWFpa0pozkC/kHvKLL2zA4gugpkzNF0QvFjcoKA0O8tVf+EJFl1Uce+fAmqUrd179mMXf4Sui+VskejrXM4/kdRuW6nE4g/0hhHdDqkh/dY3K9/5j7QnhiyPzx48YNpSNYcPHTN1ygz6QWmJWNzWTPBPDEgEHs+cGluJ32/ytq4y+JL+2hBTyhZxEdb7QpUsX0rp69WpaAwAAcNKCBQvICUthvvDgwQPSZGVlRWvOQL6Qa4hehaUuvsAzqTVH4SR3AAUyM19IvjDYRvrYvAI+6z5n21UDNX6LxG8X1ZcM2jAwMPGcl+FIgIRw/8LSO2f63JCk24vbOZctZdds8jmFe9cmbvaV5gu8vH5bE+mxbCXpaM+y+Su1HUn6nX8Y37tOScnsDzMn/3HMkZCVETLrP+of8gX2mHxh0qRJtJbXuXNn0op8AQAAOG7hwoXkhNW7d29ay7h//z5pQr4AWUd28QWHUVex+AKoKxPzhfgDAcyGqZIB96+yb+al8VuUsLtjUeaF5/fbquCrdHkpfw9hUhgDE/dpkZn4NgkuDLSSzqEyLNZ1v4LxCeLolU1MJe1G5fqczNSgI7MkXwzr2U2JdtXLSF48z8TaqwtzJDDk4EfkC9mTOvnCmjVraA0AAMBJTL7Qq1cvWstg8gVra2tacwbyhdwi+bh0YTYJw9KBR7D4Aqgt8/KFH3s7F6fbOloPisjGmZemb1HKlZH29P7l+57O8P6ih1PdmblNvMLtwjOMI9hLnSFgVKzpckVDnGi7Ac+89eYcN8NK/G6xlyQ8wfyIHEF1vtCpUyfSunbtWloDAABw0qJFi8gJS2G+cO/ePdLE5/NpzRnIF3IJ4Y1xlVIXXyjYamMsPQyQsUzLF5JP9KahF8+s6apoejQ70vQtStzRVrrohJqDicQflzaQDhuQrAY59WEmLkCReKRn+fLtVt6JUvgaRM/nekomWRmVC9iv7QaVyW+uHdu3Y+PqJfOmh4wd1j8ooIOfT5sZF9PeC1Hsg6MrJ/Xr2qZ5wwZNfTv0CB41a+P5l3/OyRDHPzqxJnRgN7/mDb28Gjdv22PY1NUnnrLJYJAv5Ciq84WOHTuS1nXr1tEaAACAkxYvXkxOWEFBQbSWcffuXdKEfAGyiOhVWJ3UxRdMPeeqWkYPIJ3MyheE9ydXpaGXYemeR5nh+Cnvzi0d2ramXVnzfKZmBUtUcKjZuv/MbVc/cntwg4ZvkejlvNr0lZu4TLqTcV4Qs6qJdNkDyeP312XE84eUWyHuZRsvfqTo/f5xdoCtMY9nVLr1eq3nssi8BWkMi9JJGfG3VgW6Wdg1CJy4ePPew4f3bpw7uCk/H8+Al6d8o0mnP6X92wkPNgfX+MuqVqdR8zfsOnRk/9bFY9pUKmxowDMpVXPI/rcaPkfRq/l1JE+Jl7/NduQL2Z7qfKFDhw6kFfkCAABwHJMv9OzZk9YymHzBxsaG1pyBfCF3+Lq5ddriC46jr2Xjgeigf5mVL/zY5ke/xDcwcRp3Q/if6MOR4TUsK/qOXnvm/ttvKeKUby//2TWzi2sRIyPzyp3nR0Rxdv1HDd+ilMvD6d0NTDymP8q4Hxy3rjnNF3gF24Vn7sKKSbfneJW19V96LUbuef24u7KNhQnPqJhnSITCpR81lPz21vnTxw5sXxHSqXJhZpKMNF8Qx12YUMu23pjDL+XWfxB/PhRkY0I+LoaFa02/JWn6cW2ml7Vbn60PfzD3oOIvja2aj9yPl8e+72F1FmcUJX79/Onjmyc3Dk7yKiZ9IkYWbRedufP87cdPn78pWIRCj5AvsDdq1Cjy31JZvtC+fXvSun79eloDAABw0pIlS8gJS2G+cOfOHdJka2tLa85AvpArJB/7vfhCmZ5HsvaSGbKbTMoXRJHT6KIC0l525LfLE6rZes+/9o22p0l6sKJVOSMeL699t82Pufnh1fAtEpwJrkB/IU1qzHqScb6QsKkVzRcMTBut+EyPZpofd1b3cC1RzNGn76S5KzdtXrNgYk8vy/yG+a0bj9j5WOdf7afQRR0k+cLeF9s6ONSZel3B/Abx22WN8kvyKMMiLde9iz7Su1KVwSdjFAQIcbs7lZTkBDwTl4m3MhwbIno41SOPaZ78hcyLFCtevIRU8WJFzQsXzJfHJH+rTfLphZ4hX2BPnXxhw4YNtAYAAOCkpUuXkhNWYGAgrWXcvn2bNCFfgCwht/iC7yZtJ07nQKLHG4IaVq/i7Oyke86V3Txbjj+a6X3CzJNJ+YLgbD8L2sk2MKk9Y89YN6e+x2IVfuEsjj3Y4y9yX56JXZ+jirqUWU3Dtyj5SM/SzFf2BiY1Zz/NOF9I2uaXlw71MK2/6C09mqlEsfcOrwgZGNjRz69dl6AhU1YevPVJZ//t5YijVzaWri5hWNS3f1dHr7AnikMB0eOZ1aV/x3j5G/UPcnEbe1VJ1BGzrrlkBAN5c7P7XDDkC+ypzhf8/f1J68aNG2kNAADASUy+0KNHD1rLYPIFOzs7WnMG8oVcQDKtOG3xhdoZb7afC6VcmehezMyI9uB0i8czzleu5Yrn9J/KhjIpX0g+1L0E7WQbmDjX8bRtu0X5BAjRy4X1paPejcp12JGlWwYqpGm+sK8LszulNF9QtFFDOknh7QrQT6ekz0yP5hSJm5nRGTyTfAVteh1TOmIgKdyfmebFMzQq1nrjJ2UfA8G5/kxyZVg6m4/WQr7Anup8oV27dqQV+QIAAHDcsmXLyAlLYb7w77//kiZ7e3tacwbyhVxAdvGFimOuY/EFZcQikVD3RNk+z8mkfCFpT0fz1ESHxzO2CD6tauS9+PXC+nkkd+eZVBz1T+Z8kc6ehm+RzEs3qT5TjfkRSdvbMN/Ikx+oMespPZpTpOYLzPQt5Z+C5COBpZhYhlew5Qbl+2OmXBtTUfpfg0MrNbKDfIE91flC27ZtSeumTZtoDQAAwEnLly8nJ6zu3bvTWsatW7dIE/IFyALJx4LK0mHoRmWDjmHxBdBQZo1fOBhQPHX8AulYBh5W3RP8vSaiYTH/HWx2WE15dXBcqyoWZSo4e4/Y/USnvwiajl84GJA6dMPEfVpkxvlC4hZfabgi+YHa81/RozlFWr7AM/Neo+K/rID8KWPeNsl6HcrfNeHN8U7MhDAz77Vx9GC2hHyBPSZfCAkJobU8Jl/YvHkzrQEAADiJyRcCAgJoLYPJFxwcHGjNGcgXcjxh6uJp5Oq9UOvNmiy+II65OKu1a7XR57n2bTHoVSblC4LjvVJXHSUdy4bLlA53Z4g/LWsonaVvYGBo7h8eTw+rTfhgtmfqdhUGvLxuk2/pcCSPhm+RpKNMX7pJ1cn3M96f8sfGFvQbfgNTr8Xv6NGcIi1fMCzZ/ZCK3CctX+CZNV+nIjeQyRearlI+zCEbQL7Anup8oU2bNqR1y5YttAYAAOCkFStWkBOWwnzh5s2bpAn5Auid3OILdcLU27Ze9OP1P+HTulUrTX6Sl9dva7YeYgzayqR8IeXqKEeafBkYlet1PKMHTtrulzpHwMh60AV6VF3id4vq03hCSr19G9Slab5wboBlar7gMulOxvlC3Fpvmi/w8vpuztItDTJBWr5gZDVQVZj5O18o1H6Xij9KMvlCE+QLuZXqfMHPz4+0bt26ldYAAACctHLlSnLC6tatG61l3LhxgzQ5OjrSmjOQL+R0MosvmDiNu6GyJyN8sKSdh4uDZckCpgUtarX1cZEsKYd8IdfLpHxB9Gp+bbqtiYGx3fDLGQ0nkFkUkZen+Tp6VG2JR3qmDZcwMDAs0XnPHxthsqfhWyR8EOpGX3qGv5VS0SubpI7dyO5LFiogmy9cUCtfKKxmvmDaZGU0PZgtIV9gT3W+0Lp1a9KKfAEAADiOyRe6du1KaxlMvlCxYkVacwbyhRxOdvGFcr2Oq+6YiGPvHt974HjErZdxpK+XfKiHZBd55Au5XiblC/8l7u6Qusqhse3QixnmCwe6pa3XYOI5jx5Vn/jd3uDKhQ3Jv2hYwKH7Np3uo6LpW/R1rbcZ89KN+YMjMpyoIXo5z5PmEcaOo6/qcGIHJyBfUAb5Anvq5Avbtm2jNQAAACetWrWKnLAU5gvXr18nTcgXQM9SZBZfKOy3RaOlzpAvACOz8gXh3ZAqtNdsVKHv6YweOPlAt2Jp+UKdMHpUM4KYx1fOX3r4WdcjADR9i5KPBpVhXothCZVLDjCEtyZUZt4pnlnTVdGc255TS8gXlEG+wN7IkSPJJ0BZvuDr60tat2/fTmsAAABOWr16NTlhdenShdYyrl27RpoqVapEa85AvpCjiV7KLL5Qb8FrjfoluSlfEH86PX9QYOf27f11r32Hrr1Gr7+ZjefMZ1a+8F/SgQDJR4zgmXfak9GnLHlv5yI0X+Dl9+XWuu+avkXi12F1mQkPvLwtN2a4VqXgZJ/yzDAkdQZ6ZDvIF5RBvsCe6nyhVatWpHXHjh20BgAA4CQmX+jcuTOtZTD5gpOTE605A/lCjia3+ML4mxlP85aVm/IFwYne5YzoO5UJeGYe0+7TfyobyrR8Qfx5dTO6ZKNkV4QM4q+ELb556X8jY8fR1+hRbtD4Lfr9AyZVQu5l9JsZu6YZsz0lz7zdDo13zuA85AvKIF9gT3W+0LJlS9KKfAEAADhuzZo15ISlMF+4evUqaUK+AHolt/hC7xMajgnPXfMjUuLeRN6+fv2a7l2/cefxx4TsPKQ90/KF/8RvlngxmUHGyxCI3yygX/kbGJXteZRbn0nN36LkE72ZIQmGxbsdyOBXM+X6mIrSR+cV8F4dldNmRyBfUA75Anuq84UWLVqQ1vDwcFoDAABw0tq1a8kJq1OnTrSW8c8//5AmZ2dnWnMG8oUcLK1XIrke99uq0eILBNZfAEbm5Qv/id+tbCodYsMz81ryQWXPWTrGhD6L4NOJ9ChHsHiLks/2Z/aoNHGecEvlAAbxh2UNpcMXeIV91n7MefEC8gWlkC+wx+QLkydPprU8Jl/YuXMnrQEAADiJyRc6duxIaxlMvlC5cmVacwbyhZxL9HJ+7dTFF8zqLXyjab8E+QIw2OYLos8XF/f3a9igZZ+w85+U7dYQf6RnBUk3m2fut/UrPaZIyrXU7/Dz153/RJd7P+gCm7dIeHeKm+QXlGdae77KzSy+bm8rXXjCqFyPgzrcU5M7kC8og3yBPdX5go+PD2ndtWsXrQEAADhp3bp15ISlMF+4cuUKaUK+APojjl7foiATL7BYfIFAvgAMwfmBVnTkQLlex9XNF0TPlzYyl/YGDQwMC9cPe6SkCy1+t6VNackHraDXwqdKu9nfjvWWftvPy+M8MoJ7a2WyeovE0fu6SeZI8ExcJiofwSB+s7yx5BfZsLDXQs7lKroRv6EFzRcs+p9V8eYlHwksxeQLBduFq/ijlHJlhD2T9iBfyL1U5wvNmzcnrcgXAACA49avX09OWB06dKC1jMuXL5MmFxcXWnMG8oWcSvR6RdPU3h0vn99WzceTI18AKdHLhV756UCYPJ5znqiZVMWta25G8y3CtNHyKNrwB9Hr9b6Sj5ph8WbLHitchCHuZH87E0kf27zu7Du63ltSB1i+Rf+J34V3/ou8MMPiPqsVD2EQxx4OsjIm9yjaaFFkjts4gvF7n1JewRbrle++mTZPxMDApMYsFVlLfLg//dunzuKZXIZ8gT3V+YK3tzdp3b17N60BAAA4ickX2rdvT2sZTL7g6upKa85AvpBz/HhwYPm82bNmTJs8fmiQn0c5mc6dYWEH76Bh4ydPmzFz9rwVx1+o1U9BvpA7id6cXjFnFjFjasjYYX07N3EuIenaUzyT4pUad+ozbGzI1BmSO81ZfuK14o5eytWxTnR+Dvkpu2EXVX2r/+3KdK8SRjxeHrtOa+/JD08QfTwxro6kzaikV2hELBeWH9DVWyTx49p87wqmPEPzGuPOxaR/cQl3lviUNeYZFqoy9NjnHLbwwrfr2+bOJG/gyF7N7ArSJNSAZ1yiarsB48hfqlmLDjOjWYQP986T/FUb28/PpWjaJi+GhR19eo+SvMPzdt2V/n0SvTy+bPbM6VPGD+5Uo2zafw5efmuvgKETQqfPnLPpSqzkftkK8gX21MkX9uzZQ2sAAABO2rBhAzlh+fv701rGpUuXSBPyBcg8grMDrEyNTMzyFTQvUrzkH4oXNS+UP4+psXGeCoH71NrhDvlC7iQ4N5CfN0++AgULm5sXKVqsWHGihAxSFitWtIi5eeGCBfLlyWMZfFpZcJBwc3kXt7KFCpet2mHh1biMusc/bq0J8iD9dF7ev+p0Gzt/9eZtm1aGTerVxN7cmMfLZ+09ds9jrnwOdfcWSYk+nZnSpLwpz7RMzaAFxx99TSGHfry+uH54E+v8PMNCFTsu+udrDgsXJHHA/HoF8hYoJHkDZd4+8s6RN65wwXx5SwXsl45USdrqVzBPfvJWS99oejfJ/YoVJffLn6dA8zUxkvsJTvSxyJeP3E/6gPRuqQ9YqEDe/NWnPZTcL1tBvsDeiBEjVOQLzf7f3n2ANZE0YABmE0JHEOxY6FZsgKjYsIMVe2/Ye+9i73r23iv23s7e+2+vp6hwouIBcpQDLsn5T7IDBsymEWQJ3/vw3HMzOyRsDGTn2ymNG5OjBw4coGUAAABe2rJlC1e+cPXqVXKoUqVKtMwbyBeAC/IF+NUkkXd2zOjXooprQRtToUBkYefg5hXQe+rGC6E82y9C76TxoRc3TelWr6JLEVtLc1NTM+sCrt7+PadsuRau4WoOYICQL+hOdb7QqFEjcvTgwYO0DAAAwEtbt24lH1ht2rShZQVsvlC5cmVa5g3kC8AF+QJkJ6nU4O7Za0oiybWnDukgX9Cd6nyhYcOGyBcAAID/2HyhdevWtKzgypUryBcgZ0G+AACQjZAv6E6TfOHQoUO0DAAAwEvbtm3jyhcuX75MDnl6etIybyBfAC7IFwAAshHyBd2pzhcaNGhAjh4+fJiWAQAAeGn79u3kA6tVq1a0rIDNF7y8vGiZN5AvABfkCwAA2Qj5gu7YfGHatGm0nF79+vXJ0SNHjtAyAAAAL7H5QmBgIC0ruHTpEvIFyFmSDnTKK8sXTJtu1mi/CQAA0CPkC7pTnS/Uq1cP+QIAAPDfjh07uPKFixcvkkPe3t60zBvIF0BR0rcvnz9/igh78+TG4bnNiwnJu9ZIWLD+lL1XH73+8PHT589fYpKw9BwAwC+AfEF3qvOFunXrkqNHjx6lZQAAAF7auXMn+cBq2bIlLStg84UqVarQMm8gXwAFiXva5zVL20I+4x7yipvNAwBA1kK+oDtN8oVjx47RMgAAAC+x+UKLFi1oWcGFCxeQLwAAAICGkC/oTnW+4OfnR44eP36clgEAAHhp165dXPnC+fPnySEfHx9a5g3kCwAAADyEfEF3I0eOVJEv1KlTB/kCAADw3+7du8kHVvPmzWlZAZsvVK1alZZ5A/kCAAAADyFf0J3qfKF27drk6IkTJ2gZAACAl9h8oVmzZrSs4Ny5c8gXAAAAQEPIF3SnSb5w8uRJWgYAAOClkJAQrnzh7Nmz5FC1atVomTeQLwAAAPAQ8gXdqc4XatWqRY6eOnWKlgEAAHhpz5495AOradOmtKyAzReqV69Oy7yBfAEAAICHkC/oTnW+ULNmTXL09OnTtAwAAMBLbL7QpEkTWlbw+++/I18AAAAADSFf0B2bL0yfPp2W06tRowbyBQAA4L+9e/dy5Qtnzpwhh3x9fWmZN5AvAAAA8BDyBd1pki+QKzNaBgAA4KV9+/aRD6yAgABaVsDmC+QTjZZ5A/kCAAAADyFf0J3qfMHX15cc/f3332kZAACAl9h8wd/fn5YVnD59GvkCAAAAaAj5gu5U5wvVq1cnR8+ePUvLAAAAvLR//36ufOHUqVPkUM2aNWmZN5AvAAAA8BDyBd2NGDFCRb5QrVo15AsAAMB/Bw4cIB9YjRs3pmUFbL5Qq1YtWuYN5AsAAAA8hHxBd5rkC+fOnaNlAAAAXmLzhUaNGtGygpMnTyJfAACAHEES/eTI0rE9mtf2LOvuVqqib8PALgOD155+FUuPGwLpt6eHFo3o1LBaxVKurqUqVPEL7D1hxbHn36T0ePZDvqA71flC1apVydHz58/TMgAAAC8dPHiQK184ceIEOVS7dm1a5g3kCwAAoCAldP/Q6s7u9fst2Hv95ZcEyfeU6GcnlwZ55hUwJg61Rx96n0Ib5mDisONjazvkL9mo36z1e0+cO3ts17KxbSrYCRnG2M4zaP19foQMyBd0pzpf8PHxIUcvXLhAywAAALx06NAh8oHVsGFDWlbA5gt16tShZd5AvgAAAGm+nR9WzqH+gvtxtJxK+ml3+8JCIyPGtNTAU1H8ucevA2nkqUHlClbqt+dNMq1hJb/Z0dnVhDEyEtjVnHGTB0M1kC/oTnW+UKVKFeQLAADAf2y+0KBBA1pWcPz4ceQLAADAa9KPaxtZMYxJ/oq9doRKaCWVeKxHIQH5JGNMq819keFYThJ3doCbQ+DWMGUZSdyFwe7GDDlHk7KjrybQymyDfEF3qvMFb29vcvTixYu0DAAAwEuHDx/myheOHTtGDvn5+dEybyBfgIzE0a/uPf1kACOgAUBbKddHuhuTTysjI5HvwgwBg/jB5PIi2SHGpv2+RFqZ80TvamNnWbZN8Kbrn5WEJJIXs6uIGHKSAvt2ITG0MrsgX9Admy/MmDGDltNj84VLly7RMgAAAC8dOXKEfGDVr1+flhWw+ULdunVpmTeQL0A60q8nBpQ0FVgHbv9GawAg95BGHe5VgvSvGYsKE28k0Uoq+dJgJyH5JDMSOvQ9k35mQQ6SlqAwVvVWvP85YUi5PNRFflxYNNvPEvmC7lTnC15eXuTo5cuXaRkAAICX2HyhXr16tKzg6NGj5BDyBeC5uItDS8ru3Yk8pz8V0zoAyE0kUc+vXLgVmnH9he/Sj6vqW8jv7Odrtye77+zrLuXG6JJ0hEal4Ec//5mTPJvhxY7SMA/ckc0zJJAv6E51vuDp6UmOXrlyhZYBAAB4iQ0RlOYLKqKH7IV8AX5IvhfsaS7rPxgJi/c/l2PvT4LeiSMfHlwyvn/XVo1rV/H09vVr0nHw9I0X3mXBEPmE0Avrgwd1b+1fp4qnZzW/5l1H/XbwQWQOma0Te2dxu5od177SbWkCvp+65N2yulaylQnMK02+nWFkQ44iCTs0sm7Z0tXbzTr7RckSDGnjGwQFuh/N5vNEvqA71flC5cqVyVHkCwAAwHMqJkGomDqRvZAvQCrJqyV+NrLV2wjGMnBHPK2H3EwccWF+Z+8iZgKRfSm/jkODFyxbuXzhzLFBzTwdzM0dm867qreNBJJDj0wJ9LAXiQp4dZm8Ysexi5dP79s4p39dRwuBmUPNISGveN2llUTeXNW9Avn9EToOuqhtNJcTTl0asa9zcaERIyzUaOmjnLv2gnrSD0v9TGUpK2PVeG1ENm+TgXxBd8OHDyf/iqrzhatXr9IyAAAAL6lYxJFd+hH5AvCWNHxrYAGaLhA/r+0GuU7C812DqxUwZkyKNRgf8vhbhq5W/PPd/SvlKeC/5nXm3yjS6CtTa+cTMqbObZbfyZBYJIYeGu5tKxDa+ww9EMq7gQwp0a+uhCwc3LSMrVA+8keWL1zQJl/g/alLEj6/vLp7eqC7hcCiRN3hu18YcrhATvfFvOryQVxCh64Ho2lltkG+oDvV+UKlSpXI0WvXrtEyAAAAL7GbUCrNF1RsXZm9kC+AjDTqaM8Slk4uDvLV2whj95HXsYVEbpbwaEVTB2NGYF2+3753St4KCS8OLxgZWC6PcbG+pzPZ40x5vqy+vcBIkNdv8VOlXXNpxL4uJYwZxtxj2Fm9jZfQneTN9kHNG9WrXaWcU35L8mMJrR2r1/HIy6Zz2uULfD51yfs1LRzymJEnl50XIyraKPjg02jDTh2lkQe7FiN/BBnjYu12fsj+c0W+oDvV+ULFihXJ0evXr9MyAAAAL504cYJ8YNWpU4eWFbD5QsOGDWmZN5AvABF/ZURpi9IjTq5rKV++jRDk7XQwJ0+xhkxJfra6mYMxYyTIH7D2jbJeVvT+LoXpDXvj0mNvZyaKEj+d52vFGDGmntMecj6ONHx9gK2AdPtc+p3O9p1NxM9XtKtatWbdJm16jZixas+l17HSlBuj6K6O2uQL/D91iUQsFifHf31zY8/M9mXzCIS2ZdrMvRBhoCGD9POhHk6yt72NT/D1WFqZrZAv6E51vlChQgVyFPkCAADw3MmTJ8kHVu3atWlZwcGDB8kh5AvARykPpntbleh5NCrpXP/iqQMYTOqt+pz9d4ohO8RdG19JNkRcWChwq/J7uOIn0zzlS+wTQuchl7SZEJCeNDKkfUGBbNFAv2UfVL3h4g92kc3fIX3x6UrW/M9uuuQLOe3UpV/PDCpjIotDXDtuf2N428ukvFgVQP49BHmrTTgfyZO/fcgXdKdJvnADlz4AAMBvKvKFAwcOkEONGjWiZd5AvpDrSd4sr5+3YODWcOl38dPpab1GUYUpD7BDZW6UdGtiRdn6dozIY8JdrrvqSTcmVJDPUmdEzn1O6H5bXRq2sr6l7HHU74gasz0wj6yl0HnQBd4tAaBDvqCvU4/e19PZxlQkMtaByMSyUL2F9IE0EL2nQ37ZNBAmj+/cxwb150H6+Vj/MmaMyMF/wc0Y/gSryBd0x+YLM2fOpOX0ypcvT47evHmTlgEAAHjp1KlT5AOrVq1atKyAzRcaN25My7yBfCGXk0bsaF3Y1m8Ju6Ve9AZ/+crphLBI0Cnd70pDTiV5u6yutew9wFjUWfJWxTh4adTDw+tXrNlzKyIT/UzJ6wW+ZrJnExTscUzNfBzxo+BK8vRLWLRPZld80Dvt8wW9nbr47fFFE0eNIL0p7Y0YOW7G9rv0gTQRu6u1jfwvhKBQt8NxtDLHk0ZfnuRja5zXe9jh95mZ6qN/yBd0R97f5I3KlS94eHiQo7du3aJlAAAAXjp9+jT5wKpZsyYtK9i/fz85hHwBeCb6eJCTlWfwPdohSjrRq5Ds9iTBmDfbku0T3eFXS7o01FXeT2asmmz8ktX3caVhS+uwgZYm+5XEh7Rl+7aZm5GRJbTOF3h76pIvD0/sPXD5jfLtacV3J5RjhziR87xIK3O42Fuz6+QzLd5i+X3+BSbIF3SnOl8oV64cOXr79m1aBgAA4CUV+cK+ffvIIX9/f1rmDeQLuVnCtdFlLUsOvZh2WS2+P6l82gQJnznsoAbIPWIPdyssD5gYk5qL3mX5P3/8vg527NNZBu5Q3qFVkHJlGJt9GIm8Zj7n13tT63yBp6f+7VQ/FxFjZCSwqbXwmZKBKZJnM7zYvxCCvJ0O0MqcLP7ewgaF8pQL2pNx/0/px/WtKw4+lc2L3CJf0J3qfKFs2bLkKPIFAADguTNnzpAPrBo1atCyAjZfCAgIoGXeQL6Qe6U8nl3Vuli3g19/3KWWflpZz4S8U2WEzkMu82usMGS1uAOd8rHjV37Jv37KjdElaae8aL+zajvlP/q2jE2bELV98l9K23yBp6eecntsafbHYsyablYyfinl2gh6nsZlx9+hlTlW4uMVTRwK+E48p2SkTtLJoOLe6hbGyHLIF3Q3bNgw8j5VnS/cuZPj38QAAGDYfv/9d/KB5evrS8sK9u7dSw4hXwDekISubmRXoMWmsHRX1kkHO7N3VUkHI0+7PbxbRg+yUtLpPg7s/iGMaaO1kbQ26yTsbi1f6oF0VkuNuaU2zpBGrKhL0y+R54xntJYftM0XMnfqWdbplYYt9WPX9izcYoOSWRvSjyvryReNYIxLjrqRuQAq6cPtkwd3b1m3fOGs4PEjBgZ1ax/YpNXsq2kPKol6emLNlAFdWgXUq9uoRfse/cfM3XIxNIEe/UEa++L0+umDuwYG1PPzaxDQuseIGetOv1afwSQ9Xx/o4tx8mfJZEcnXRrjbt9/789P9WsgXdKc6XyhTpgw5eveuNquPAAAA/HJsvlC9enVaVrBnzx5yqEmTJrTMG8gXcifp593ti9jWXvQiQ0/lx21VIyOTOkvD+bOQOmQ58ZOplensGEGhXifYkeEp4RdWDG9dzb2IrYWJqXX+YqWqtRw4Z+etCD0MbpCELqxBn09UYcpD9Z3mr2sbmrLthY4DL9BKftAyX8jkqavPL3QlDd/UzN621rTrX5VNwpC8nO9rIYsXhEXa7YrI3B8HhZcgjcCuyyH52y72/tqeniXc6/acvGzbgWPHDmxZMLSRC3lmxqxo/SlnP6X9bPFPt/WvWtypescxizbvPXr80I5l41qVtREYMaKC1YYdCuOeSJLyelsHJxNRoQp1G2ZUv65fTZ+KLvYmospTs32PDOQLulOdL5QuXZocvXfvHi0DAADw0tmzZ8kHlop8oWnTprTMG8gXcqVvp/u5WFWafOenycXS94trpU6QkA2A1kMvEnKKuJ2B9J66kajchLvi75KPx0dWdSzTYuyGc0/CvqVIU76F3tw7p3PFvEKhbflOiy5/zlwPM+X6SNonNxJ5z3qhflWBmI0BtJPNWLcJoZX8oGW+kMlTz8q76pIPId3dC1bsvvzyx/S//ZIvl6bUsCV9d0He6lOuZH4Px6Sw+xfPnjy8a3Vwx/I27KApeb4gjbk0qbpb7XHHQtP9eZJ+ORrkyi4NUX3WfdmhuNtz/Jw9++54ln4AQuy18ZVlIQhjVrLfsUhlP6b4XUg3t9SNcjjxYhIO8gXdqc4XSpUqRY4iXwAAAJ47d+4c+cCqVq0aLSsICQkhh5AvAB8k3hxf3tJ90LlYWlaUENImtY8pKND96E8BBBgsyfOZdI6/vNP7/Nv1SVXc/Bfd/mkWfuLT1c0dhAxjXrLrtpeZeIckn+tfjJ2OYSSqOleDxUTjtzannWwjk/qraSU/aJkvZPLUv9DaLCL9fOW3bp4F8zpWbdl30oJVm3ZsX79wYvfqhUWMwNKp/siQF3qeN5VyZ3xZ+YsnsOty4O3O9qVqzrijpGcvDVtZ31L2x0mQt9nG8MjjfcpWGnpGYfWYNDH7OhaQBRaMqMLk+z+PQEi+NMSZvviqaDawJIshX9CdJvnC/fv3aRkAAICX2HyhatWqtKxg9+7d5FCzZs1omTeQL+Q64qfzfPMU7bxf6Z09xWtvxrTx+ihabegkLzcH1fOp5OFRTv88ynv6Npt4Iov7hJmXfH5AidRul6jG7P3jPcv1Oxml9F0ijTrSozhpy4jc+55Q1sPTSNLxtN1QRdXmvVbfyU7cGWhOwy+TOktpJT9omS9k8tTDaG2WSgq/tX/VjJG9O7cNbBHYrmufkTPXH3v4OStGNEkj1zSQj5oS2LUY2KW03+JXyvv1kpdzfOQJGGNZf2BQBc/xtziCjq8bA+TTOIxEvgveqn9xeQz5gu5U5wslS5YkR//3v//RMgAAAC+dP3+efGCpyBeaN29Oy7yBfCGXkbxbG5AvX5P17zmuuiWv6CU8IfLM9uXTf5WUG5O97E2FtAenXwxjbOHQbPUb+lS8lXS0e37a5zUSedT0dWu9nXsChCR0SR35IHShQ/vdOs7EV1hMlHSy32jQyQ5pY0X/iUjHkVbyg7b5QuZO/S2tNRQJ29jRGYzIwtq190mlCy7KJIa0zSN/FRiB0L7llk9c77vkCwPZqExQqNfxHD0GC/mC7th8YdasWbScnru7Ozn64MEDWgYAAOClCxcukA8sHx8fWlawa9cucgj5AmQv6Zd9nRxsasxXsQB97NbmqTdKhcUHnMu6leR4SCqRiPVPkkNuoCbu72BL/+VlmUiJ/mdVDYOXvl9Sh91JQFRmzE2d3iYKTyjymaPBJIHEXa3Y29LkG6rOpZX8oGW+kMlTf01rDUVqvmBkJCjc6zj32y7peM+CbCzDWDfb/JXW/izl9rgy8n8NxrLVrhy9CQ7yBd2pzhfc3NzIUeQLAADAc2y+UKVKFVpWsHPnTnKoRYsWtMwbyBdyk9izA90sK0y4peqOXnLaHoXk4jxwh7oFzqTfnh5aNKJTw2oVS7m6lqpQxS+w94QVx55/03XMPGSbpCPd8qWOXyD9vJ7HVHfMfixRKLBvu1uXiTTkCVMHTIi8Zj5X38lO2N5CHmnIvqHGIlqphZR3RyY0r1SicDEP/1H7Xun1vra24xcyd+rvaK2hSMsXGFN/VXOykk8GFWFfNtkCIdyvmvjexHLsKCxT/w0xtDJHQr6gO03yhYcPH9IyAAAAL128eJF8YKnIF1q2bEnLvIF8IfdIuj2pkkXRzjtff1bl46XR9NJc1pNZqGQP/DTisONjazvkL9mo36z1e0+cO3ts17KxbSrYCRnG2M4zaP19hAw5SvKp3qnJEunn1VvJOfqcJf20sh7dakRg2zZE2WKhash6i/QJRZWnPlE/FSduS1N6m9vIxG8ZrdSYbOGR1LVLjRhzz6n39biWgLbrO2bu1MNpraFIyxfUrCmbli8wpgEbVeQGCvlCo7XcwxxyAOQLulOdL7i6upKjjx49omUAAABeunTpEvnA8vb2pmUFO3bsIIeQL0C2ET9fWCutd6UhY/dRN7j6YNLIU4PKFazUb8+b9J2p5Dc7OruakCcS2NWccVOHXidkk5RbY0qzXWTSSXbofUpdJzlxV2DqkH2h85BLavvUP0m+MMgxtZOt0Vr9MRv8aSebMW+xjVZqShq+tE7q1qsymu3boClt84XMnTrnAgU5VFq+IHQafFHFi/cjX8jTbq+K4TUK+UJD5Au51dChQ8k7gCtfcHFxIUcfP35MywAAALzE5gteXl60rGD79u3kUGBgIC3zBvKF3EH6YWOz/Ha1RqzdotbGsX556dBtgV3ngxx3E+PODnBzCNwapuwed9yFwe7GpOfJmJQdfTUrt+oHfZK8W1QjdeSKsfvI6+ru7iusUciYBWyMptWaEz+d7kmfUFRuwl31nezINQ1TR0wU6nWcVmos4XivtPEZ5BHyd9r/086butMyX8jkqefoJQuVUMwXVAVVP/IFGw3zBZOGayJpZY6EfEF3qvMFZ2dncvTJkye0DAAAwEuXL18mH1jIF4BvpF8Pdi2Wp9qcJ5oMCP/RUyJX5/VXKd9CIHpXGzvLsm2CN13/rOQesOTF7Coi2a1tgX27kBw9/TlXSdjXPnXRQWO34VfV5guHu6at1yDyVTmThkP0Bn9T9gmNXYZeVvvmlIQu9KWdcuPSY2/RWs1Jww/0L28jIM8osCrVfacOPzA3LfOFTJ66Jr/HOQnyBS7IF3SnOl9wcnIiR5EvAAAAz125coV8YHl6etKygm3btpFDrVq1omXeQL6QC8RdGOJuWX7cDc0WUpe+X1yTdmU4x26nre3HWNVboWSry5TLQ13kx4VF+55R39kCXhA/Cq5E/+WFxfqdVffvlnS4q31avlBz8XvtV9tIOhFUmH0EQX6V8+5Z4vuTyrM/H2PaaK1u3cbkry9vXLz27Iu+RwBomy9k7tQNbWET5AtckC/oTpN84enTp7QMAADAS2y+ULlyZVpWsHXrVnKodevWtMwbyBcMXvK9YE8r1/6nNR4MnrinnQ29jc01DT/lxuiSbG9KVCn40c8JhOTZDC/59T1jHrgjJ8yQkH46u2hIz07t2rXVv3btu/Qeu+leDpgzn3i4WwG208vYdtyvLo5KOtApdSINY9limw5LbUjfL67FjvpnzJttUfsAyWf6FmUnOGgyvOLX0jZfMKBT1wPkC1yQL+hOdb7g6OhIjj579oyWAQAAeOnq1avkAwv5AvCI5NUSP1uHjnu/aH7LM+XaCDe2qyTv+yjNJSRhh0bWLVu6ertZZ5U9ctr4BjXrwfOGfFdOGqpkAcbUe2YOGIcr/bKuMV2yUbZJgZq3TPz2Fub0JTMuPfa2Lp3eH71yUaXgx+pWIYha35jdo5GxbbObZyuHapsvGNCp6wHyBS7IF3SnOl8oUaIEOfr8+XNaBgAA4KVr166RD6xKlSrRsoItW7aQQ23atKFl3kC+YNCk4VsCC+Zvsk7JFAZuiuv8iXzm6LDIvvTDUj/57HLGqvHaiJwxmDsl5sPzB3fu3Na/O3cfvoyIzxGvgvTDcj82M1C/KoD0w2/0DryRsEivE5pNvsko6XQf9r68IF/Xw2qCqJQ748rIe+SMlf865cuCZB+t8wXDOXU9QL7ABfmC7th8Yfbs2bScXvHixclR5AsAAMBzbL5QsWJFWlawefNmcqht27a0zBvIFwyY9MuBrsVtasx/pn55+nQSdrWySr0xrcnicxlJXsyrLu+lCh26HtR+WwHIPtLwNY3yyJMhU7/lH1V2ZOVDPuRvEqFj/7O6ToJJOj+Q3ahR5DHpvsr3qfTjynrye/iMTZMNvAuttM8XDObU9QD5AhfkC7rTJF948eIFLQMAAPDS9evXyQcW8gXgBenHkA5FTdyHXda666ewOT+Tp90eLe9MSyMPdi1Gvp0xLtZu5wd9rtEPv0Ds8V6yfz0jxjZwh6psKOV26i11y1qLOMa4SL5cXTYwsF7dZn0XX/zE8U4QP5rmaUL6zoxJjUUqd3SI3tVavtyD0KHHET1uLKknOuQLhnLqeoB8gQvyBd2pzheKFStGjr58+ZKWAQAAeOnGjRvkA6tChQq0rGDTpk3kULt27WiZN5AvGKjYOwsaFBAYlxqjw1524sdTK6dOkDCpsyRMmxum0s+HejgZM0YCG5/g64Y3UTwXkIZvb1WIdOIYa78lrzl7vd9O9pFnUIyZx+jLypeulLxZUd9W3hs0Im+HOotfKH8wWR4lmyjAiCpM5r6NL/2wqoE16YsLbPyW6DBjJ8slXxzsxGZyXGuiKmEYp64HsZub0nyhxMDzKl68pOM9C7L5gnWbEBX5QsqNUXT9WeQLudeQIUPIO4ArXyhatCg5+urVK1oGAADgJTZfKF++PC0r2LhxIznUvn17WuYN5AuGRxL9aNfQqvIbnoJCnfZF0WqNST+HtC/M9pVkF/z9z2m+QGPKi1UBpAMgyFttwnmD20Qv15C839RCto+EIF/jlS+VxlMxZwa6i2RdXtta8x5yvT1iNgbIF+FgmdRf9ZkeyEgaHtKpOHk4Qb4m65Tfx5dGHQuSp1Z29Zc+12UhyawmCV3iZ8meLGPmO/+VpjOSDODU9eDHxqiMddNN3H840uaJGBmJqs5VkbXEhrSlyZYmi2fyGfIF3anOFxwcHMhR5AsAAMBzN2/eJB9YyBcgO0hjHh1YOnVEUFu/UnlJd4RiBLalm/SZMG/3vRjV3X3xh0ubl8ydPmFItyaVC8kGbadizB3rdBs+efrsufOXHXisapNF6edj/cuYMSIH/wU31Twb8Ny3G7P88gsZxsy944YM/+iSiNMTasqOCQv4Tb8cxf0PnXJrfLnUdxIjch9xVcWN6bjbi/yLmZB3a9UJF75mfMj4h8ubFDFmBHkqDT+pxT4oWUjy4ezq+XOJ2TOCx4/o16mhR35Z3EIxonxlG3TsO2J88IzZskbzV51WscBqDjt1/fl2Z+eCOeQFHN27sbs1HedixBjnr9xm0ISpM2fPXXqMHT4jfnZg4dzZM6eOHxBYwS5tkxeBDfnDNkb2Ci/c+0g+lEESemrlvDmzpk0c2rFqkbR/DsbS2a/b8EnTZ82Zv/WG1llrtkO+oDtN8oXXr1/TMgAAAC/dunWLfGB5eHjQsoINGzaQQx06dKBl3kC+YCiSTvZxEAmMRaYWVja2dvny5bPPa5vHytLcVGQsFOWtt1T1yGrJm8W1LYUiE3Mrm7z5ClD58+ezt7fLa0sex8JUJBTZBax+T9v/RBp9eZKPrXFe72GH3xvoXdZcJu7++iBv0m1mzIvX7Dp+0bptO7euWTyld8OStsYMY+HsP37/S3Urc8TfW9XZs0gemyKV2y+5pS5yknw6N61hURPGpHC1oN9OvYgm7yJJ3Purm0Y2dLYkHewyHZbejOZLDzv5wmAXczMLK2sbW9u8dvb25NctX34Fsl8/+W+OjbWVhZmZY/+zKmdM5KRT1xtJ6KLaVuZWeWQvoMLLR1458sLZWFuYF+x2SD40JnFHoLWZJXmp5S80bSZrZ29H2lmaWQWs/yprl3y6bwkLC9JO/oC0WeoD5rEyt/SZ+UzWLkdBvqA71flCkSJFyNE//viDlgEAAHiJzRfKlStHywrWr19PDnXs2JGWeQP5AuhB7K3ZdfKZFm+x/L6qAQ6Q00gi7+yY0a9FFdeCNqZCgcjCzsHNK6D31I0XQnXdL0I1aXzoxU1TutWr6FLE1tLc1NTMuoCrt3/PKVuuhWu4pEGOlYtPHbghX9Cd6nyhcOHC5OibN29oGQAAgJdu376NfAFynfh7CxsUylMuaE9ohoEL0o/rW1ccfErz5RuAx6TSX3wLXSIxuHv2msrFpw7pIF/Qnep8oVChQuTo27dvaRkAAICX7ty5Qz6wypYtS8sK1q1bRw516tSJlnkD+QJkSuLjFU0cCvhOPKdkdnjSyaDi3tOf5uTl1QAAsg3yBd0hXwAAAANw9+5d8oFVpkwZWlawdu1acqhz5860zBvIF0B3Sc/XB7o4N1+mfFZE8rUR7vbt92bNSHoAAEOHfEF3bL4wZ84cWk6vYMGC5GhoaCgtAwAA8BLyBchFUl5v6+BkIipUoW7DjOrX9avpU9HF3kRUeWqO3h0OACD7IF/Qnep8oUCBAuTou3fvaBkAAICX7t27Rz6wSpcuTcsK1qxZQw516dKFlnkD+QLoQvwupJub6Y8t+ZRjbNqExNPvAAAArSBf0J3qfCF//vzk6Pv3nFsiAQAA8MH9+/fJB1apUqVoWcHq1avJoa5du9IybyBfAB0kXxriLCTvaDVEFaY8xPAFAACdIF/Q3eDBg8mHEFe+kC9fPnIU+QIAAPAc8gUAAADQC+QLutMkX/jw4QMtAwAA8NL//vc/8oFVsmRJWlawatUqcqhbt260zBvIFwAAAHgI+YLuVOcL9vb25GhYWBgtAwAA8NKDBw/IB5a7uzstK1i5ciU51L17d1rmDeQLAAAAPIR8QXeq8wU7OztyNDw8nJYBAAB4CfkCAAAA6AXyBd2pzhfy5s2LfAEAAPjv4cOH5APLzc2NlhWsWLGCHOrRowct8wbyBQAAAB5CvqA7TfKFP//8k5YBAAB46dGjR+QDy9XVlZYVLF++nBzq2bMnLfMG8gUAAAAeQr6gOzZfmDt3Li2nZ2trS45+/PiRlgEAAHgJ+QIAAADoBfIF3anOF2xsbMjRiIgIWgYAAOClx48fkw8sFxcXWlawbNkycqhXr160zBvIFwAAAHgI+YLuVOcLefLkIUc/ffpEywAAALz05MkT8oHl7OxMywrYfCEoKIiWeQP5AgAAAA8hX9DdoEGDkC8AAEBOpyJfWLp0KfIFAAAA0BDyBd2pzhesra3J0c+fP9MyAAAALz19+pR8YDk5OdGygiVLlpBDvXv3pmXeQL4AAADAQ8gXdKc6X7CysiJHv3z5QssAAAC89OzZM/KB5ejoSMsK2HyhT58+tMwbyBcAAAB4CPmC7lTnC5aWluRoZGQkLQMAAPASmy+UKFGClhX89ttvyBcAAAD4LCHs2csvKbSQ3ZAv6E51vmBhYYF8AQAA+O/58+dc+cLixYvJob59+9IybyBfAACAH6QfNnSu2XLI/O1nH32Ml9BKQvwt9Pax9VODGjWYeCmZ1hkYadiOtg4iu84Hk2hFNkO+oDtN8oWvX7/SMgAAAC+9ePGCfGAVL16clhWw+UK/fv1omTeQLwAAwA/iB5MriMgHFsEIzfMWKe7i7uZcvJCNiYAhNSKHpqueGma8IHm/qUVBgRFj025vIq3KZsgXdKc6XzA3NydH//rrL1oGAADgJTZfKFasGC0rWLRoETmEfAEAAHhNIV9IjzEr0WDCkXd8mTygZ5I3a/zzCWSniXzBELD5wrx582g5PTMzM3I0KiqKlgEAAHjp5cuX5ANLab6wcOFCcqh///60zBvIFwAA4AdZvmDt7tfCz9PdIV8eSyvbAsVKVW3ac9K6C+8SaBPDI365vAH58LYTIF8wEKrzBVNTU+QLAADAf69evSIfWEWLFqVlBWy+MGDAAFrmDeQLAADwg/jB5IqFuh7myRIEv0TKs0V+jvUW7hpd1hj5goHQJF+Ijo6mZQAAAF5i8wUHBwdaVrBgwQLkCwAAwHe5Ll9IfjSnpmPjla+T7oxHvmAwBg4cqCJfMDExIUdjYmJoGQAAgJdev37NlS/Mnz+fHCKfd7TMG8gXDJ7065npHZo1adayVdsOnbv16PmT7l06tG3VolmTVsGncS8H+Cnh7dVTdyLEtARZKpflC0n3pld1arY+VPJdjHzBgKjOF0Qi2RIj3759o2UAAABe+uOPP8gHVpEiRWhZAZsvDBo0iJZ5A/mCwZO82zW4ST0/v9o1fCq5F7QQMuSdmIoRWhRwLe9dvVYdv3r+nRbdMNzZ1aC55Msz2nabsuni2zgprVFCmvD+6o5ZfVoP2R2uopV+SD/t7VzMWOQ5/SnPA4bYO4vb1ey49pXCno5aSAi9sD54UPfW/nWqeHpW82veddRvBx9EZsNiirkqX0i8NdnLKXDLB9m/GfIFQ6I6XzA2Jv/SRrGxsbQMAADAS2y+ULhwYVpWQD7jyCHkC5DNkq6PKim7rJIR2NaZeTsmyzuHkNPEbWkqm5rMCCyKlK/Xrv/4Ocs3hRw6fvrsubOnjuzZvHL+5IEdGniWyGPMMMLCzTe80a0zrTlp5MFuxYWMkahi8EP+5guSyJurulewERgJHQdd1Hb3xuTQI1MCPexFogJeXSav2HHs4uXT+zbO6V/X0UJg5lBzSMirX9vVT80XpDFPj6ycPqpPhyZ+Nf382/SetPrkSwPrjsVfG1vJud2uj+yfQeQLhgT5AgAAGIA3b96QDyyl+cLcuXPJocGDB9MybyBfyF2STvQqLNuBjRDk67gf11bws9jN8nxBDcakRPNl9+Lp92QZadSxICdj2aAbfuYLKdGvroQsHNy0jC0dGSR0HHRBm3xBGn1lau18QsbUuc3yO1Hp477E0EPDvW0FQnufoQdCf91ABlm+YO8X1Ldqcff6/WdvPnr5zoP7Vw8tDvK0NzYr3mDK6U9ZHSn9KrEXR5R37bzvU+qrjnzBkKjOF4RCITn6999/0zIAAAAvvX37lnxgFSpUiJYVsPnCkCFDaJk3kC/kKuK7E8rRje0Zq6abvtJqAAVq8wVGkKd023kXIn5BNzPmdH83Edtx50++IHmzfVDzRvVqVynnlN9SNozD2rF6HY+8bHCnXb6Q8nxZfXuBkSCv3+KnSr9LGrGvSwnyHOYew85mSB+yjPjhlAoiE+e2656kj4/ibwVXsWIYi3JDTv2qHyUrxZwZVMa95+HIH6eCfMGQqM4XBALZ72tcXBwtAwAA8BKbLxQsWJCWFcyZM4ccQr4A2Ur6/rdaJnT9BVG1ea8N5TYk6JUsXzC2K1bclnbsf2CEVg5ercauvxym7QwA3cReGFrK0oR3+YL4+Yp2VavWrNukTa8RM1btufQ6VppyY5Q7O/FIm3xB/HSerxVjxJh6TnvIOTxBGr4+wFZgxBi79Dv9axajk349Hjxk3WMlkzISzvZ3FJIfpXivozl8LVhp1Ik+JUv2PZEuKEG+YEhU5wsMI/uzEh+f5SOwAAAAMiM0NJR8YCnNF2bPnk0ODR06lJZ5A/lCbhKzPTAP7TIalxx9IxtWjoMcIHZzUzOrNiGJ4ui3d87u37Jy8dw58xav2rjz0MUnn3/lQgDxV0d7FK4/sq8nO+SG1+sv6JIvSCND2hcUkO6sud+yD6rGA8Qf7FJA1s7Uc/qjbH4Fkk4GOQhlP7Lv/Jc5OJ6Ufjnc073MoLMZNidEvmBI2Hxh/vz5tJye/JcV+QIAAPDdu3fvyAdWgQIFaFkBmy8MGzaMlnkD+UIuknSqt6xvICMo2OMoT66ggW9S8wVazCaJNydUKlR3ybMnM70NM1+Qhq2sbymL+9RvjJEaDAqdB1346Z8lel9PZxtTkchYByITy0L1FtIH0oTk7QJf2T8HY1JzUWhODRikn/Z1di0/4uJPy88gXzAkmuQLCQnYMwkAAHjt/fv35AMrf/78tKxg1qxZ5BDyBchG6Rdf2IzFF0A5PuQLSXeDvQrWWvBULHluoPmC5PUCXzNZaCAo2OOYmmEh4kfBleQvgrBon9MZ/13Eb48vmjhqxHBdjBg5bsb2u/SBNPJ1XSP54hyC/N2O5MwNLKXhO9s6Vxp7TcmNa+QLhmTAgAHkjao6X0hM5Mk/NAAAgHIq8oWZM2eSQ+R6jpZ5A/lCriF5tzh18QVGVH1+lm8rCDlV9ucLyQ9nVStYfdZD0k831HxBGra0jqn811Hku1DtSID4kLY28sZC5yGXNJt9oaPEB8vaeBQp6N546gWle9cmbGshzxcY88AdOfLWb+KJXkUsy7YePVmJiX1qFpDN/jAt13YCWxO85rLC+o+/HvIF3anIF/777z/Zm9jI6J9//qFVAAAAvPThwwfygZUvXz5aVsDmCyNGjKBl3kC+kGsoLr5QaswtLL4AHLI7XxA/XVCroPfUe/Lb44aaL8Tv62An33CCsQzcoXYOeMqVYa7sw4u8Zj7Pwmgw+dJgJ/kcKoF9l0NKxidII9c0NJEdFzr0PZOlQUdWSbq6uFdXDm18CstOnhE5+3Vma3oGH4lAvpAzqcgXpFKp7E2MfAEAAHgvLCyMfGDZ29vTsoIZM2aQQ8gXINsknZIvzCYjKNTzOEaFApfszRfEL5fULVR54i369AaaL6TcGF2Sti/a76za9pJnM7zYV4GxaROShUvSpc4QENo3WqVsiBM9bsTYttxmcDOspOHL/GThCeZHGARN8oWkpJw5yQcAAHINFfnC9OnTyaGRI0fSMm8gX8glxHcnlGU7KEaMdfMtUbQa4CfZmS9I3qxqVLj8mGtpQ+8NNF9I2N3amh1NpNFgImnEirryYQPkZfCc8SwLX4aE472KFm2z5uFnpecgebPAVzbJSujQ7VBmN6hM+nD75MHdW9YtXzgrePyIgUHd2gc2aTX7atprIYl6emLNlAFdWgXUq9uoRfse/cfM3XIx9Oc5GdLYF6fXTx/cNTCgnp9fg4DWPUbMWHf6tS4ZDPIFg6IiX5BIJPJfJqPk5Bw5CAcAAHKP8PBw8oFlZ2dHywrYfGHUqFG0zBvIF3IHybvFNVMXXzDxXfAWiy8Ap/T5QsKHG3uWBo8e2KtT2/Y9BowKXrrn1sesuucnebe+aZEywy7G0TJhmPmCJHRhDRr3iSpM0eC0vq5tKF/2QPb4AzWZf6GzlPvBXkUaLHuhLPOIOz/IzZhhhIVabnqX2T8hCi9BGoEdnZQRe39tT88S7nV7Tl627cCxYwe2LBjayMWCMWLMitafcvZT2nPHP93Wv2pxp+odxyzavPfo8UM7lo1rVdZGYMSIClYbdihMy59R8m5RTdmPxFi22oV8IcdTkS+IxWL5G84oJQUTBQEAgNf+/PNP8oGVN29eWlYwbdo0cgj5AmST6G0t0xZfKD32Nq6pgBvNFxISXh+eGlg6r61zzQ5Dpsxdtn7zhmXT+jdytxaI8nl2W3rti75DKmnY1kCHkgPOfKNlOcPMF1Kuj6TNjUTes16ofyVjNgbQfIGxJv80tDZrJD6Y71fEre2K21/T/Vxxj9a0KiFihPa+wZeVLv2opaSw+xfPnjy8a3Vwx/I28qUo2HxBGnNpUnW32uOOhaaLsaRfjga5isgfMYFN9Vn3ZYfibs/xc/bsu+OZQhxFxF4bX9mCtGPMSvY7psnijJKE6C+fIj68untkip+9/AcRlmi99NzDN2ERn758y97x88gXdKciX/j3339l/85GRuR/aBUAAAAvqcgXpk6dSg6NHj2alnkD+UKukHTyx+ILhXsdz95LZuA5Wb5gUafvEJ8ChWsM2/nkW/ouWuLzLR1dTRhG5NBkyX09rgQgjdjdrphr7+PR6Z/OMPOF5HP9i9FfSFHVua/U5wvxW5vTfMHIpP7qL7Q2y8Q9XNejYn770k36TVmwZuu29b9N7uXnaCmwdG4was9Lvd/aT6GLOsjyhQNvd7YvVXPGHSXvLGnYyvqWspRUkLfZxvDI433KVhp65quSACFmX8cCspyAEVWYfF/tW0bybIa3mYmZZR7bvPb58uWXy2dvZ2tjbWEmsmy+NX168YshX9Admy8sWLCAlhUgXwAAgJzi48eP5APL1taWlhWw+cKYMWNomTeQL+QG6RZfaLE1sxOnDZDk5eagej6VPDzK6Z9HeU/fZhNPZHmfUG9iNzc1NWJM3Hrs/aC8c5Z0f7qPFWPECAv5r3ymn7Ew0i/7u5Rw6n7op/vNhpkvJB3vVYi9ZW8kqjbvtfp8IXFnoDkdgGRSZ2kYrc1SkqjHx1YHD+7ZITCwTeegYdPWHLn/KWsmZkgj1zSQry4hsGsxsEtpv8WvlP87S17O8ZG/FxjL+gODKniOT10ENKOvGwNkIxjIi5vT54IhX9CdinwhJSVF9vYwMhKLefsnBQAAQCYiIoJ8YNnY2NCyguDgYHII+QJkB9m04rTFF2qo32w/F0q5MdnL3lRIe3D6xTDGFg7NVr+hT8V/iXva2RYOWPlMRWcy4fxAZ1l3msnjO+dR5jud0q9HezmX6LT38883ow00XzjYmd2dUp4vKNuoIYPEkDZW9N0p6zPTWkORsI0dncGILKxde5/kHDGQGNKWnebFCIT2Lbd8UjJ2QS75wsAS7C6bhXL4aC3kC7pTkS8kJyfL3h5GRhIJPg0BAIDXVOQLU6ZMIYfGjh1Ly7yBfCEXUFx8ocy4O1h8gYtUIhHrX867gk2J+hylpoucfHmICzuk3TZgXThXP09DMaf6uhVtu/OjsocxzHwhcX8H29S4wGeOBvMjEne1Yu/Ik2+oOvc1rTUUqfkCeTsV7qVi79yk4z0LsrEMY91sM/f+mCm3x5WR/2vwaKVG3SBf0F3//v3JW0BpvpCUlCR7exgZSaWZ/NsFAACQtT59+kQ+sPLkyUPLCth8Ydy4cbTMG8gXDF/SyaAidK63sEjQSbW386Tfnh5aNKJTw2oVS7m6lqpQxS+w94QVx55nmIYPuVvigU7sHXhGVHGK+knuKsSeG1zKoeXmD8o72frMF1LeHZnQvFKJwsU8/Efte6XX+9rajl840i1/6vgFr5nP1ecLCdtbmKXmCzUWvaO1hiItX2BM/der2Ds3mfwpY182kfcsFa+a+N7EcuybxtR/QwytzJGQL+gO+QIAABiAz58/kw8sa2trWlYwefJkcgj5Avx64tTF08jVe56W21QvviAOOz62tkP+ko36zVq/98S5s8d2LRvbpoKdkGGM7TyD1t9HyAAs8dPpnmwXzkhUfpLuAUP85ZHlijRZxzlpR3/5gvjpPF9r2kc3Ysw9p97X40gebdd3/JH5iSpPfaL+tOK2NKV3+I1M/JaF01pDkZYvCAp0P6oi90nLFxjTgI0qcgOFfKHRWu5hDjkA8gXdqcgX/vnnH/nbw+i///6jVQAAALykIl+YNGkSOTR+/Hha5g3kC4Yu3eILNRer2rZeGnlqULmClfrteZO+f5T8ZkdnV9ljCOxqzrgZS2shd/u6tiHt8TImtVS+r1RIuD6uYuEGK1QscKi3fEEavrSOfBFBSrN9GzSlbb5wYZBjar5QYYoGpxWzwT/11TZvsS1btzTIAmn5gtBp8EUVL96PfCFPu70q5j0o5AsNkS/kViryhcTERPnbAy8vAADw3ZcvX8gHlpWVFS0rQL4A2URh8QVRuQl3VfRk4s4OcHMI3BqmbIhC3IXB7sbkYRiTsqOvZu3u+5AzJO5qJd8skBAU7HlMl9kGSXemeBbyW/xCVfdaf+MXEo73St2klRDk77T/Gz2iB1rmC4qjP1T/VlKRaxrScCTHL1mohGK+cEmjfMFGw3zBpOGaSFqZI6EDrDsV+UJCQoL87YGXFwAA+C4yMpJ8YFlaWtKygokTJ5JDEyZMoGXeQL5g4BQXX3DofUpFxyR6Vxs7y7Jtgjdd/6zktq7kxewqIll3UmDfLiRHz2gGVcSRT84fP3XjbazaiTCJe9vZ0HzByLTpFu1vqSc/mFG1UM15T1TPUtDj+gvS8AP9y9sIyA8tsCrVfade91HRMl/4Hr3B35R99Yxdhl5WO1FDErrQl+YRxqXH3jK0JVqRL3BBB1h3KvKF+Ph4+dsDLy8AAPCdinxhwoQJ5NDEiRNpmTeQLxi2FIXFF2wCt6sIBlKuj2Q7SIxVvRXvf+56pVweym4YICza94z6/hPkQCnPltS3l23SyZi6dN8boTpiSNzTNm01A9Omm7WeNpN0oFNeURHPgOZq1CuTl13ST2Bbyq8ZrSQCR4R8oA+lheSvL29cvPbsi75HAGibLySdCCpMTyu/yiUHWOL7k8qzHWbGtNHaSENbBQX5Ahd0gHWnNl9gGIaWAQAA+Orr16/kM8vCwoKWFSBfgOwgCVVYfKH2b+9V9EtSbowuyXaQRJWCH/18n1jybIaX/JKdMQ/cYXAzJKSfzi4a0rNTu3Zt9a9d+y69x266x/858yk3x5SiYRTpxtZf9UllNzZ+Wwtzmi8I8nc7onWHPfnMuNpVfdTycqXxgpHAslgFWinj231jKH0oHtA2X5C+X1yLnfDAmDfbojacST7Ttyg7DMnYbfhVg9thFvkCF+QLumPzhYULF9Kygri4OHJIIBDQMgAAAF/99ddf5DPL3NyclhWMHz+eHJo0aRIt8wbyBYOWbvGFifdUji6XhB0aWbds6ertZp39oqRjmTa+Qc0S7zlT8uk+DrIb91mEMfWe+YQ+FX+lXBnmmpYv2ATuUDkNRrZuKNuFI+8tvS6VmN63TU3Yrqce9qfMQtrmCwrfIKoU/FjdeUWtb8xuT8nYttlteCusIl/ggnxBdyryhb///pscEgqFtAwAAMBXKvKFcePGkUOTJ0+mZd5AvmDI0i2+0Od0ZlIB6YelfvIJ44xV47VqRs7nSCkxH54/uHPntv7dufvwZUR8DnjJJG8X+JqIzMxEJvblu258rrqTnDZfRrYiwJisWxHAUPOF70mn+7BDEgT5uh5W86uZcmdcGfmjM1b+6z4b3m8f8gUuyBd0pyJfiI2NJYeMjY1pGQAAgK+ioqLIZ5aZmRktK0C+AL9eWq9Edj2u5na0GpIX86rLR8MLHboejKaVYGBSbo4ubdd88xcNxiLIswj2jrqo4pT7WdfvN9h84XvS+YHsHpUij0mqX0Dpx5X15MMXGJsmGwwx3EO+wAX5gu769etH3gHIFwAAIEeLjo4mn1mmpqa0rGDs2LHk0JQpU2iZN5AvGC5J6KIaqYsvmNZe8kH3fok08mDXYqQrxBgXa7fzQ1aNhIdsl3J7fDnbmgtfqP0nlrxeUIMdsC/IH7iNq8sr+XJ12cDAenWb9V188ZOObxvDzRe+ix9N85T9gjImNRap3Mwieldr+SIUQoceR/S4pyZ/IF/ggnxBdyryhW/fvpFDIpGIlgEAAPhKRb4wZswYcig4OJiWeQP5gsGSRm5qmrrAv9rFF1SRfj7Uw8mYMRLY+ARfN7y536BA+nFzi/yFmq5/q/rdEnOqr7O8Ny0o0GTta46useTNivq28t4gaWdTZ7H61EKZr2sbpuYLZSfc5W2+kHxxsBM7FUm2Daxm+YI8t5PNkWBEFSZzj2CQfljVQPaLLLDxW5Jly1xkr9jNTWm+UGLgeRUvXtLxngXZfMG6TYiKfCHlxii6WC3yhdxLRb4QExMje3OYmNAyAAAAX6n4zEK+AL+Y5P3qRqm9O8ZC9y0fUl6sCiDX9IK81SacN7h98eAnknebWxa2qTTk+EeurqwkbHfH4say++6mpQac4H5PxGwMkK/YwTKpv+ozPaCN5NvjytJlJAX52oXwdWqOJHSJnyV7soyZ7/xXmuYg0vCQTsVFDDm3JuuUD2GQRh0Lkqd7dvWXPje4jSNY4kfBldh/ZMa66Sbud1TaPBEjNUuKxoa0pX/7NFk8k8+QL+hORb6g4l4QAAAAr6gYczd69GhyaOrUqbTMG8gXDEfc08OrFs6bO3vm1InDgwK9HRQ6dwKbUv5BIyZOnTl7zryFq0+91bSfIv18rH8ZM0bk4L/gZgzChdxB+vXSlBr5LBwbTzn8Op7WpZJE3fwt0MWcMWIEeX1GHgtT1XVLuTW+HJ2fY8SI3Edc1fCufuLDPQvnzpk9c9qkkX0CvYv8eBszQttSDbsNGRc8Y9acufM3Xla9f2ZWknw4u3r+XGL2jODxI/p1auiRX/Tjt40R5SvboGPfEeODZ8yWNZq/6vR77r5w3O1F/sVMGIFt1QkXvmY8o/iHy5sUMWYEeSoNP6lsV5ec7NudnQvmkBdwdO/G7tY0CTVijPNXbjNoAvlLNXfpMXZkjPjZgYWyv2rjBwRWsEvb5EVgU7pJnzGyV3jh3kfyoQyS0FMr582ZNW3i0I5Vi6T9czCWzn7dhk+aPmvO/K03omTtchTkC7pDvgAAAAaAzReUrhk0atQocmjatGm0zBvIFwxG8vlBTiZCkamFtW3efAV+ks/ONo+lmYmxsVmxngc1muUgjb48ycfWOK/3sMPvDfTGKXCIfbCxT5X8IlG+cvXb9x09fenGbZtXzJsQ1MDdhnTwGCvXJuP3PMuYPfws/t6qzp5F8tgUqdx+yS2N86nItf5WZpbWeWxt89rZ2+fLlz9Nvnz57O3t8traWFuaW1SYeCfbbkwnXxjsYm5mYWVtk/pDKv6YP35Q8pNaWZiZOfY/qzJbkXw6N61hURPGpHC1oN9OvYgmv22SuPdXN41s6GzJCPKU6bD0ZrTBpXuS0EW1rcyt8sheQIWXj7xy8n9hC/OC3Q7Jt9VI3BFoTd4PNrbp3g3yV1j2TjCzClj/VdYu+XTfEhYWpJ38AWmz1AfMY2Vu6TPzmaxdjoJ8QXcq8gUVa3EDAADwioo1iZEvQA4Te2t2nXymxVssvx9HayB3kUTe2zN/cGC1kkXtrUQCoalNgRJlarQaPHf7tbDM7HQKSknjQy9umtKtXkWXIraW5qamZtYFXL39e07Zci1cw3EfYICQL+hORb6gYi9xAAAAXvn777/JZ5ZQKKRlBSNHjiSHpk+fTsu8gXwBlIi/t7BBoTzlgvaEZhi4IP24vnXFwafQv8xlpFKDu3/OZxIJXm6QQb6gOzZfWLRoES0r+Pr1KzlkYWFBywAAAHzF5gsCgYCWFYwYMYIcmjFjBi3zBvIFyCjx8YomDgV8J55TMuE76WRQce/pT3PyimkAADkD8gXdqcgXIiMjkS8AAECOEBcXZ0j5wj///EP/D3KPpOfrA12cmy9TPisi+doId/v2e3XdiwIAADSGfEF3ffv2VZ0vWFpa0jIAAABfxcfHk88shmFoWcHw4cPJoZkzZ9Iyb3DlC/PmzWvdujUtQC6R8npbBycTUaEKdRtmVL+uX02fii72JqLKU3P0hm8AADkE8gXdqcgXvnz5Qg5ZWVnRMgAAAF8lJCSQzyyClhUMGzaM1M+aNYuWeUNpvrBs2TI3N7dPnz7RMuQG4nch3dwU9rRUjrFpE6J+4wAAAMgs5Au6U5EvfP78mRyytramZQAAAL4yjHxh/fr1jo6OYWFhtAy5Q/KlIc5C9v2riqjClIcYvgAAkPWQL+hORb7w6dMncihPnjy0DAAAwFeJiYnyLpiSS4KhQ4eS+tmzZ9Myb2TIF3bs2FGsWLHQ0FC2CAAAANkC+YLukC8AAIAB+Oeff2TpgpHRf//9R6tSDRkyhNTPmTOHlnlDMV/Yv39/kSJFXr58yR4CAACA7IJ8QXcq8oWIiAhyyMbGhpYBAAD4Ki1fuHv37rlz5zZv2rRm9epNmzYdOXKkaNGipJ7P+cLx48cLFSr05MkTegAAAACyjyHkC2KxOCk79OrVi1x1zZ07l5YVvH37lhyysbGhZT75999/6QsHAADw/Tv5aJClC9x4my8sXbKkQIEC9+/fp7UAAACQrQwhX2gT2IpcZPz6rzxWVuyFl5AlEJAvFltP/ifDt/Dha9qUYPrCAQBArieRSGZOnyEylhEZi0xEIisrK5s8NlaWlmamZuZmZhZm5hU9yt+6eYt+wy+0fOnSJYt/U/q1cP6CIgULmZubDx08JMOhn79WrVhJHxEAAACyUo7PFw4eOJCh//zLvtLyBS78zBcmjB1HXzsAAMjdJBLJ8KFD2U+HtoGtfj/ze2JiIj32/fuf4X+SzrlnhYrkaBn3ktevXaMHfpXFCxctnD9f6deAfv2EAkHrwMAM9Uq/li1ZQh8RAAAAslLOzhf++vq1cvkK5Lpn4/oN/6qUlJTUPKAJaUkupGgVt7GjR5OWjRs0JJdZtEqZ8PDwWTNmFCviUKZkqRs3brx8+fL169d//PHHmzdvQkND3717FxYWRpv+++/NGzfIY7o6Ot25fZtWcYiIiKjkUZ403rxxI63i8M8//zTzDyAtRwwdRqu4jR45krT0b9goOTmZvnwAAJCLpYULpd3cfz/zO639CfkoHNR/AGmWLRGDUg8ePChQoEDhAgUV96cEAACAbJez84WB/fqTK562ga2kUimt4rB29WrS0ruyZ0xMDK3iQC6eSEs3J+cnjx/TKg7h4eHlSpUmjQ8eOECrOCQnJder40daTg+eSqu4DejXT3ZSrVr/vI53BmtWyU6qiqfXt2/faBWHq1eusCf1FCtgAQCA3NxZs8lHQ2k398uXLtMqDmKxeMjAQaRxGfeSoW/f0tps8uzZs0KFCu3ft5/8PMgXAAAAeCUH5wunT51iL4zevlFzrfMuNLS0uztpfPLECVrFITExsZavL2k5d7b6tay6dupMWvbs3p2Wuc2bM5e0JI+sOO5UqVMnT8pOyt1d7QUcacCeFPkWWsUhISGhZnXZSc2fO49WAQBA7hYdHV22ZCny0XD2d86RC4rEYnH3Ll1J+/FjxtKq7PD69WsHB4c9e/Yo7k8JAAAAPJFT84XY2FgfLy9ybaF20ab//vuvXes2pGX/vn1pFbfpU6eRlnVr10lKSqJVHPbu2UNali9TNiIiglZxePL4sZuTM2l87epVWsXh27dvVTxlJ7V65SpaxUEqlbZt1Zq0HNCvH63iNm1KMGlZr44fuRqjVQAAkLst/W0J+Wggn4+0rIFnT5+Rbynt5h4ZGUmrfq13794VL15869at5P+RLwAAAPBQTs0XRo8cRS4smgUEiMViWsVh29atpGVFD4+vkV9pFYf79++7OjqRxnfv3KFVHMilFXlA0nLn9h20isO///4b0KgxaTluzBhaxW30CNkSCZqc1NYtW0jLSh7l1Z/UvXukJfm6d/curQIAgFyvtm8N8tFw7uxZWtZMl46dyHdt3riJln8hiURSunTpNWvWsEXyQTl29Gi1AxgBAADgV8qR+cLlS5fJ9Y27s8uzp89oFYePf/7pUboMabx/7z5axSE5OblB3bqkZfDkybSKW7/efUjLju3aq10iYfnSpaRlVS/vv2P/plUcLl+8xJ7U82fPaRWHP8P/ZNd9OLBvP63ikJyUXN9PdlLYkxIAgLeSkpLe/PHmV37dv3ef/cRRO1gvgw3r1pNvDOrZK8MD/pqvq1evkv/+9VVNsA4AAADZJeflC/Hx8TWqVSfXN4sWLKBV3Lp17kJadu/SlZa5LZw/n7Qkj0wen1ZxOH7sGGlZxr3k+/fvaRWH169fl3J1I43Vzm6VnVTVaqTl4oULaRU39qR6dO1Gy9wWzJtHWtas7puQkECrAACAZzZv3Ej+Vv/6rwZ169KfQGPXrl7N8CC//kuT2wAAAACQLXJevkAuLMjlRcN69dTus7h/7z7S0qN0mY9//kmrODx7+szd2YU0vnJZzRra0dHR3pUqk5br162jVRwkEklg8xak5dBBg2kVtymTJml4Uvv27NXwpJ4+fcqelNp1HwAAILv89fVrhbLlXB2dyIdLNe8qKr6qenmXdHElLT0rVMxw6OevsiVLkZaVy1fIUM9+kUcgnw5N/f3pD6Gxe3fvkm8kHy4ZHjDDl4+nF/lRNfk5yUmVcS+pSUvymOSMyFPPmDqN/jQAAADAMzksX7hz+za5siFXGP+7/z9axSFtiYRt8oWgVBCLxeQai7QcPXIUreLGbhXesllziURCqziwg0i9KlaKioqiVRxu37rFntSD/z2gVRzSTmr7tm20isO///7bpLHspMaOHk2rAACAf+hGyxrsSbxy+XLSUi8T7l48f84+FC1rjN25qX2btrTMYVD/AbKTCmxFy9xWr1zF/iRqVyDWfEdqAAAAyC45KV9ISkqqW7sOubyYMW06reLWv49siYR2rdtocMW2grT08fKKjY2lVRwunD9PWpZydXv96hWt4vD+/fsy7iVJ46OHj9AqDrKTqlWbtJw5Xf1Jses+kAs7PV6GAgBAdqEbLWuwJ/Eff/zBTrj7/fQZWsVBkwl3KSkppd1kOxx//vyZVmlm/lzZtLvpKkcQnDl1WnZSGuweTRqwPwb5FlrFQfMdqQEAACAb5aR8Ye7sOeTyorZvjcTERFrF4eSJE7ILEXf3d6GhtIrDmz/eaHhxE/f339Wr+JCWy5YsoVUcSOe/U/v2pGXvnr1oFbe5s2aTlnVq1Pznn39oFQd23QdNTurHZegZjXY1BwCAXy9tT+I1q1bTKg5SqbR1y5ak5eABA2kVNw0n3HVsJ/ucWrl8BS1r4N9//2U/B0lvn1b9RPPdo2UbLQe2Ii0H9R9AqzikvVBqHxMAAACyV47JF548fuzm5EwuL25cv06rOMTExHhX9iQt165Wf8XWJjBQk4sbYuK48aSlf8NG5AKLVnHYvXMnaVmhnIfa+0KPHz1iT0rtDt5p6z6sS92ai0vaZeiQgYNoFQAA8A+7J3HzgCZq9yTetEG2AKRnhYpRf/1FqzhoPuFu7549pCXpt6udmJDm4P795Ftq+fqq+IE13z166+bNpGXl8hXU7geh+ebNAAC5gCQx+tO7Fw9v334VjeliwD85I18gXXrSsSeXF+PHjKVV3EYOG05atmjSVO0SCVs2bdLw4ob0/0lLNyfnx48e0SoOnz59qlC2HGkcsns3reKQkpLSuEFD0nLiuPG0ihu77kOLps3UnpTml6EAAJBd2CUSSrq4vniuZk/iDx8+lC1ZijQ+fOgQreKg5YS73qQl+SIfmpqsaPDmjzdszL1182Za9RPNd48ODw9nN1o+dPAgreKg+WOCoZF+PDKhTUCjRg011qhRQIs2nXv2GxG8bOfZhxFqBrsC5CAp12c3rVq5rFvxQnZWJkLGSEZYtN9ZTfPh9JKvzApU8pvVqJF/4NBtz9VvWyyNJL+crdt37NylW/cePXqm6dG9W9fOHdu3aT1s5zs1HRYwYDkjX1i2ZAm5vKjmXSXubzWrCVy6cJG0JFdsL1+8oFUcNL+4+eeff+rUqElazp01m1Zx69WjB2nZuUNHWua29DfZSVWv4hMXF0erOFy8cEHDk0q7DD1y+DCtAgAAnlFYImERreLw33//de7QgbQkHy60ipvmE+5OHD8u+1hxdWUn040eMVJ1xJAWLrRu2ZLrwbXaPbprp86kZc/u3WmZg1aPCYZG/GJj78Z1/erUrFa5ZBEr2qOSYQRWDuWr1fbzq6vIr06Nqp5lnfJbGrMtGZMClVqP3/lIzdpaYHhSvj45sWZKv44tG9aq4ulV3a9Ri479pyzfe+VtbNbd7E8IvbA+eFD31v51qnh6VvNr3nXUbwcfRKbQo3qQcn12i9q+3h7O9qaC1N+FTOQLl6c39SO/W9V9Kpd1Lpj6OyPHmFWacFPdtvbSyP1D/XwquhVK960EIzDP5+zhXTto81vkC7lXDsgXXr96xV4AnTt7llZxIB11X5+qpOVvixbTKm5dOnbS5OKGmDl9OmlZt3adpCQ1gd7hQ4dIS9LDDwsLo1UcXr18WdLFlTS+cP48reKQdlJLFv9GqzikXYYG9ehJqwAAgH/SlkhISVFz+RmyaxdpWaFsuU+fPtEqDppPuFOcRXjxwgV2EaI+vYJev35NWyj4999/Dx44wO5n2bplSxWBuOa7R7NTM8qXKRsREUGrOGj+mGDgxI+nVhbRHoyxS7/T32i9Eilfn51ZO8rf1VLe72Es3dusfBBPj4GhS3x9OLiZq6VAYJq3hIdv45YtGtf2dMkrYt8LxnblAvWfOCWHHpkS6GEvEhXw6jJ5xY5jFy+f3rdxTv+6jhYCM4eaQ0JeqR8NoJWUsD1dSghlJ5SJfCG9+N1tbITCtNiCMa88+baGo3+Swi4t71rOSvatjJlbq3mn36qLJiAX4Hu+IJFIApu3IJcXQwcPplXcJk2YSFo2rt9A7RXbnpAQDS9u/nf/f66OTqTxndu3aRWHqL/+Yq/ANm/cRKs4kJNq2aw5aTl86FBaxW3S+AkanpTml6EAAJBdNF8iIW3C3e6dO2kVB60m3I0YOoy0TJtFmBYxkK+e3btv3bz51s1bDx88+P3M74sWLPCRL6xIvlSHC5rvHv3ly5cK5WQbLe/aoeakNH9MMHwpV4a5GrOdH2OXoZfV3xaWRl+fWtNWIPsGxtghcOMb3Ew1eNKvl4Jr5hPl9+m78uIHxU590sfr6wdWL0hTBqtSHVfd01PGII2+MrV2PiFj6txm+Z2o9KMjEkMPDfe2FQjtfYYeCNXjQAZyPgc65ZW/tfWVLyTu62BXvMPoLs7sS0ReJAvP4Dsa5yKS17/VtmRMKgc/0OtpQs7F93xhw7r15PLCq2KlqKgoWsWBXA+xFyLkqohWcfj8+TN7cbNz+w5axSE5OblhvXqk5ZRJk2gVt8EDBpKWbQID1U5kXb9uHWnpXalydHQ0reJw88ZN0tLNyfnRw4e0isOPdR927aJVAADAM1otkRDUoydp2al9e7V7Emdywt3rV69GDhvu7uxCDv385Vmh4uaNm1TMudBq9+i+QbJ1Hzq2U3NSWj0mGDzJq7lV6fAFQf4uhzS7Qyp5Oa+6GdtfEuRvsSks68bGAw8kP5xX09ayVK8Qjr58yuttHV1M2IhBWChgxdPMDytIeb6svr3ASJDXb/FTpd18acS+LiWMGcbcY9jZDOlDZiRfGOQoH8Cgz3zBccC5zyf7uqYlDJbeU+9p+hJJns3wMnMfeR3xArB4nS+8f/++jHtJcnlx7MhRWsWBXPf41axFWs6eOZNWcevTK0iTixti8cKFpGWNatXj49UMrfv99BnSsrSb+5s/3tAqDu/fvWNP6vixY7SKQ9q6D3NmzaJV3NjL0M4dOqg9KQAAyC6aL5Fw5PBh0rJsyVIfPnygVRx0mHCndBZhxMeP69asGdR/QECjxg3r1WMz66Ya7Nqg+e7Rx4/KNlomH4Lk851WcdD8MSEXkEZubMJOdzBiLBqv+6JhT03yZn51mkowIu9ZzzGEwXAl3Z5S2dK+wbIXqrq4CdfHV6CJE2NafvzNzP1tET+d52vFkEfynPaQ81ml4esDbAVGjJpJPdpJuTzEOQvyhfPJ0qjjQc6pCyowlj4zHmj26NKIFXUtPac/xQY/wOJvvkD6yezu3H16BdEqbrNnziQt/WrWUrtEwrEjRzW8uHn+7Dl7M+fypcu0ikPadt9qNxInJ9WhbTvSsl/vPrSK26wZM0jLurVqqz2ptMtQtes+AABAdtF8iYSoqCivipVIy43rN9AqDhKJpEXTZqSlnifc7d5NWmoy4U7z3aOjo6PZk1q/bh2t4qD5Y0LukHCkWwH5eHAjI5H3rBca5wQJu1rRWIL0Auut/IQRDAZKGrGlhb1V9XlqI6Rvh7sVYZcuMGLy1Pntle6RkzQypH1B8qZkzP2WfVD1xoo/2EX25mVMPac/0lP/O8vyBXJaX4/0ckxLGKyqznqoyeNLP6+qZ+k9EwkeUPzNF3bt2Cm7uCnn8eXLF1rF4eGDB+wSCbdu3qJVHNKu2NRe3IjF4mYBAaTl6BEjaRW3MaNk23039fdXe5Nn5/YdpGVFD4/IyEhaxeHB/+hJ3b6l7qRS133YtGEjrQIAAJ7RaomEoYMGk5atWrRUP+Fu7VrSUvMJd5rOItRs3Ye03aMnjB1Hq7gNGzKEtGzZrLnqjZa1ekzIFVKuj3RPXXzBddgVjQdhSyPXNDBhv8/ISFR+0n3cXjVMkneLa5nZtdml5m+gTPLFwWzXnDAuPfa2riP6pWEr68uzK5Ha2/Yx2wPzyFoKnQdd0M9orCzMF2S/NYe6l0hLGKx95zxW/xohX4D0eJovRERElC9Tllxe7AkJoVUcyBVbo3r1SctJEybSKm5DB8uu2NRe3BCrV64iLat4en37pmY405XLmm7NHfHxI3tS+/bspVUc0k5q8kT1JzVk4CDSsnVL9ZehAACQXTRfIuHs77+TlqVc3f744w9axUGHCXeazCLs3bMXaanJug+a7x59/tw59qRev3pFqziwjyl7odQ9JuQSkldz0hZfsO90QPO9IFJuji5JcwnSD6wy+yW6PwZJGrkhwFJg7VDWy6/N2N3PVa/OEU+60nQsjJHIa8Yz3d4TktcLfOUzLQQFexxTM8hY/Ci4kvz9Kyza57ReAoYszRfI6/nlQNdiqfvBMnlqzHuiLmFAvgDp8TRfYBc+6NKxEy1z271TNszB16eq2iu2G9eva3hxQ67Y2MW0T586Ras4JCQk1KzuS1ounD+fVnHr2a0badmtcxda5rZ44SLSskbVaurXfTij6WUoAABkF82XSPg79u+qXt6k5Yply2gVB9L5b9+mLWmp+YQ7TWYRHj18hLTUZN0H8mHKnpT63aP//ruadxXSctmSJbSKQ9pjnj93jlZBbifvPbKdHca80ZrPGt9LkX5YWseU9pKMBIWDTmi6Wh3kLCl3xpdNzZEY04pT7qkaT0B6+xVTdzplbDvu1+lNIQ1LfWuJfBeGqutVx4e0tZE3FjoPuaSPOCCL87YEOywAADlJSURBVAVZXrC3k0PqMA9BnloLn6keooF8AdLjab5ALptCdu0KDw+nZW5SqXTrli1ql0ggJBLJ+rVr16xaTcvcxGLxqhUrNZnLmpKS8tuixf4NG6ndmps85rw5cyuU8/gz/E9axW3u7Dmujk6XL16iZW7k2cl12Mrly2kZAAB4RqslEsaNGUNaBjRq/O+//9IqDju2bScttZpwp/ksQk3WfWA3WtZk9+gJY8eRluSzUvVJafWYkGskHP6x+II295uln9b7W6fGC0KHoBOaD3yAHCX54mCn1M6wkZFpw7Vf6QFlpB9+q5U2aca0ySadFl1MGwTBWAbuUPu++rG5qshLL33wLM8XyMsUEdL+R8JgU2fxc1UJA/IFSI+/6y/kIGqvAtPExmq64a7a5SfT/O/+/zT/AQAA4BfTfImEa1evkpZuTs5PnzyhVRx0mHCn0SxCzdd9kG+0rMnu0ezgQXJSTx4/plUcNH9MyEVSro1IXXxB6Dzkssbz5SVvl/ilLe5oWW0u1rY3WNIv21vZ0wiKMas45a6qf2rJi1neaeMX8rTdo8uEhZQbqRNvNOrgy3ZvZJ+SsWkTooeY6xfkC+RV/bizbepamEYCW78lL7hfVuQLkB7yBQAAgKyi+RIJiYmJtXxlE+7mz51Hq7j16KrdhDtNZhHqsO6D2t2jyUnV9q1BWs6dPYdWcdB8R2rIVSQv5/ikLr5g13Gfxp2zuAuD3ekidYx1lWl3MDfCkEm/Xl7Yra5n5Rqtx+19qfqfOvlc/2KpvWZjt+FXdVngMWF3azoyxrjUmFtqH0G2eyMdMiHynKFmsTZNqM4XEt/fvXTl2o1bd+7eu3efde/e3Tu3rl+59ixSeXCsLF8gP3f49taFfiQM9ZdzbreBfAHSQ74AAACQJbRaImF68FTSsl4dv+QkNTekDu7fT1qWK1Va7YS7F8/pRsuXLlykVRy0WveB3WhZk92jZ0ybTlrWrV1H9boP5DE135EachPpl/X+aYsvNFit6Q6T366MqSBffs+IETm23652hjzkFpJXc1MXCzUS5Ot8UJfRBJLQhTXoY4gqTHmofmDM17UNTdn2QseBF2hlJqjMFyRvF/imnmF6goI9jyv/O6w8XyC/fmFbAmWbcLLfnrfBitfKf5OQL0B6yBcAAACyxA6Nl0i4f+8eaUm+7t29S6s4fI38WsmjPGm5dcsWWsVBLBY3D2hCWo4cNpxWcdN83Qd2o2VNdo++f/8+u+7D3Tt3aBUHzXekhlwm4XDX/KmLL6jdCJBKeLyymYN87AJjWbr79pdq8jrIRSShi2uxwRPpnBcLOqHprOV0FPZLFXnPeqG+Ux2zMYDmC4x1GzX74mlC9fiFlIjb24dUsUmNBRiBZXHfjmMWbT1y8e67WG3GL8hI3m9qnrr8iZHArvHqt8pOF/kCpId8AQAAQP/SlkjYu2cPreKQnJRc368uaTltSjCt4jagXz/Ssm2r1mqXSFizajVp6V3ZMyYmhlZx0GLdB413j05OTm5QV3ZSwZMn0yoOmj8m5Dop10a4pS6+4DTootqkQPzp2spelWwFjBFjnM+7z/p70RoOeIBcQfx4uqcJHQ6j+5ocClMsRFXnck4a+CF+a3OaLxiZ1Fe/zLxaatdfoDMyGKFtxe5LLoWr/b3hzhe+f5e8W980X1rCYB+wVsloIOQLkB7yBQAAAP1j9yTu2qkzLXNbMG8eaVmzum9Cguqd27+fOnmStCzt5v72zVtaxSH07dvS7rKNlk+eOEGrOGi17kPP7t1JS012j160YAFpWaNadbUbLWv+mJDbSF7OrpK2+EL7vVwriIjjIp5d2b9iUrc6ztYChhFYOfv1XXLxo26dx5xF8nJzUD2fSh4e5fTPo7ynb7OJJwxoSFHMkR5F2WCAsfQKvq3L0o4yScd7FUodVFNtHseUAUWJOwPN6aAJkzpLaWUmqMsXJO/W+tsZ2/kMO/BWs3VHVOUL5OFC1/qnrp9pJMjXdP27jKeMfAHSQ74AAACgZwcPHCB9Zk2WSHj69Cm7RMK1q1dpFYdv375V8fQiLVevXEWrOEil0ratWpOW/fv2pVXcNF/34dDBg+xJqd09+tnTZ+xJqd09WvPHhNxH+mVdYwvaL2NENoWL/cyhcH5bKxMBacQYWzlUbNBp+IJ9D77mhmSBlXJjspe9qZC+SPrFMMYWDs1Wv6FPleMl3Z5Y0VT+SgnyN1r+TJeFHVlJBzuzu1PK84U3GuQLIW2s6D+RyHcBrcwElfmC+MPBgZXyu7Zb/1RNXK1Adb7w/bvkzapGqadsJCjQfNP79CeNfAHSQ74AAACgZ/+7/78Gdetu3byZlrndv3+/tm+NsaNH0zK3p0+f1qharVlAgFispvv04cMHHy+vSh7lv0aq2gieiPv7bzazULvug1QqrVurNmm5ZdMmWsWtc4cOpOXoESNpmdvunTvLuJfU5DEh90k41CV18QVhsVYL9u5X5sDBY2eu3H78KjxG9w5jTieVSMhfBX2TGFRvMenetCrylUIZq4ojz37NzLyZxP0dbFPjAp85GsyPSNzVKjUnE1WdSyszgTtfiHuwItApv9ewo+Fa/eOpyxe+f5e8XtEgb1rCULDllg+KT4B8AdJDvgAAAKB/ycnJapdIYCUmJqrdPJIVHx+v4X3+2NhYtasqsqKjow8eOEALKv319evihQs1OanIyMixo0d/+/aNllX6M/xPDV8oyF1Srg53pYsvCOw77tdlpX8AuZiLoyrIpigw5qWCDoRlshucdKRb2qKjXpp0qhO2t0hdVFJUYxGtzATl+YL085nx1Qs4NJh7I0bbv6fq84Xv38Uvl9a1TU0YhIVabQv78SzIFyA95AsAAAAAwC+SF7NSF19gzBuu0XRrSoAMJKFbAh2EjDxc2Ps+85Nnkk8GFWGXcTASVZ76RP0Dxm1pmra+o5+aDYA1oSRfSHqxtUtJc0HewK1fdPhF0SRf+P5d/Hyx34+EoXCbHR9Tnwr5AqSHfAEAfoGUuMiwt69evAr/ptvHjyTha1joh8j43DOrFgAgN1NcfEHkNeMZui6gk9hrk7ytGSPGqvyAw9rNGuCSfGGQY2q+UGHKQ/XXJTEb/FP3pzRvsY1WZkKGfEEafW1WvULsjqwmpYee12jcWDqa5Qvfv4ufLaydtvGlsEi7XRFswoB8AdJDvgAAWUUS8+Ls5rkjOzep6eHm7l65bpP2fZdc03KIqyTq/s7p3epVdnV0KeHo4uhetcmAhYefazSUHAAAcqqEg11Sd8Uzdhl6OfcurgCZkPxibfMixozA1mfcWV1u7Cslfjrdk46sEZWbcFd9vhC5pqEJ215QqNdxWpkJivnCyVf7+1fMW7RuO7/C8ipG5D7gtLbbsmqaL5CnfjKvRh4a+xkJHTrskY8rQr4A6SFfAIAskPDm1IJe9co6u5QoVadz8KbTjz7/Q49oQRr7YF0PXxdHd692M/fcfP3h7dOLO4IDPd0cyzYeuf+NBh+EAACQI6VcGfZj8YVOWHwBtCeNOBTkbsoYF6o3R/slCVSJ3uDPbkShWfQlCV3oS/MI49Jjb9HaTEjLFwTWLu5F85YN2vUmWfp5f9fi7BgGkXPQMe0WsNQ8X/j+PfnRnOrWaQlD0U57P0uRL0AGyBcAQM/inuwcUa+MSwlHd8+2s4/9ofkOSen982xth8qOji5lApc/UtilOv7OPH93lxLu9UafjMBHGQCAIZI8n+WdtvhCozWkCwOglW9XJ/vYCEycWq19onAJoRdJJ4IKs2NrBPm7H02itZzE9yeVZ9/MjGmjtZG0NhPS8gVGmL928MVI9rdDGnk0yFkkTxiMS3Q/qM1wDW3yhe/fkx/MrJq63yYjLNb1wBcJ8gVIB/kCAOiRJOLs9BYeTi4lHEvXHrLvDx0GLVDxN2c0ks2JcG0850GGj7y4c2N8nRxdHMt33fIW6zEAABgc6ee1jX4svoB+Cyfpp7OLhvTs1K5dW/1r175L77Gb7uXI+YjJz1Y2KWxs5t5991vO4QVJodeP3wrT5SpC+n5xLXbCA2PebEssreWUfKZvUXkcYGTsNvyqHmb6pOULomrzXiv8bkijTw9wZxMGYdFOe+jiCBrQLl8gZ3R/OrvZp/ypinc/8HQF8gVQgHwBAPRFEnFqbAN32UIJpWqOOP5nJj5oUh4vCXCXLbjg1nbDzztJpdybXceFHHWt0Gc/7moBABia+AOdFRZfuILFF7gkn+4j2xkhqzCm3jOf0KfKOSTh+3u6mZqX7LlH1WYRKbfHlSsWdFKnqZYpN0a5s7N3RJWCH6uLKKLWN2a3p2Rs2+xWm0Zo4Ee+4LvgbfpLpNgLw8qYyJ9MWKTNjnANr5C0zRe+f0+6G+yVmgAyxiWaB3paIF+ANMgXAEA//r4xt1lpWbjgWjZwheKMBq1Jo44NruIoe6iSLVZm+OyUS74xqbpbCdnohoZzHuDCEwDAoKRcGeqSuvhCvs4HsPiCCikxH54/uHPntv7dufvwZUR8TsvwpTGXJ3jnsSjVS91OlFEbm9hWn/9Gty5x0uk+7JAEQb6uh9VMkEi5M66M/N3MWPmv08s9ERX5wvfv8VfHVmDjDEGhwC3vNTo/7fMF8j23J3uaK0RbIuQLkAb5AgDogTTi2LBqsjEFLiXKdVj7KlN9funH7Z1Kyx/KuebkG0o/7+IO968ga+DoVmXsJX3PrAQAgGwkeT7TK3XxBYtGazFMDTSV9GRZ40JWZYL2f1DX1U0+07dYgS6H1C6ewCHp/EB2j0qRx6T7KoMM6ceV9eT9fcamyQbNpyyoojJfID3/W5M92bEFgvxN1oVq0OlP3Nc+bwkt8wXyXTcnVmKTDBnkC/AD8gUAyDRp5PHBPvIBBY4lG8x7oOvnNUv6cWvnkrKHcinh3n2H8jWQxc9+a+oib+NUdeIlnYY3AgAAH0k/r2mUemMUnRbQmCRsT1fXvOX7H/p5WuVPkq+NcDfzmvFMaUvJl6vLBgbWq9us7+KLnzgeS/xomqdsIgJjUmORyi589K7WeWVzfYQOPY58o3WZpCZf+P496f4MugKjwK7hSsUlGpSL29rcokAP9StVZpRwfTwdK4FfVVCEfAEAMivh6qQ6zmwiUK7Hjo+ZjOfjjgyoKI8qXJx8JnBlB/EH+9MMwtV/gdrJjwAAkFPE7m1vn7r4guswLL4AmpBGXxjjaW1SrF7fceO5jRszctjgfj3bB3gXNRfYtNujbIMryZsV9W3pG1BgU2fxC+W9Zmnkwa6yORKMqMJk7hEM0g+rGsh2cxTY+C15pa/ud/LZfsXYfCH9+o4Kkm6MLs1OMRLY1pmvZspq8rWR7sa65QPxV8d4sHt1Il+AH5AvAEDmSMK2digr7+o7ulUZde5vWq2rlLvTa7uz+YJL0+WvOD60ky9PrOLMPmmZHns1uiWQ8vXx0ZUTOjeo2X5V6qMmhJ5bF9yndeMaVX1rNO44aN7BJ7GK4Yg48sGhJWN6BTauW71Gw6Zdxy4//QemAQMAZCXpp+2t8tPenZFJg9XabLMHuVXio98apL1rNCQqP/mBskuMmI0BbI9ZzqT+qs/0QEbS8JBOxUWMkSAf1ywEadSxICdj0sKu/tLn+gvKvm4IYOc/CEv0O8sRHSTsaWdDz4IROXfY9Jzr8iU57MRIrzzktRPk85t++YvW92viLo8sKxvIgXwBfkC+AACZIn40vzE7VaGEc/UxF/8hH1ZfHp/dtWLO+FFD+w8aNX722kN3/tRiiYS/D/QqyQYHLm7dQqJobUbiRwvru7LNStafr3IAgzTu7eUds/q38JZvSEHaN1r0TPxdGvtgc786ZdknSvsq2SD4HDsjIyX89PTWFdkVJdK+nCo0X3Q3swEKAAAokMY83L98wdw5s6ZPGd2/XU3H1GXpCYFtmaZ9Rk2eNmv23PlL9z74hqwBfiYN39vVWb4tozYYy5bblG7mkHJrfDl2CwZZ19x9xFUVczDjbi/yL2bCCGyrTrjw03TO+IfLmxQxZgR5Kg0/mfmYTPL6+LK5s2dOnTC4rWcB49Sfz7RYra7DJ02fNWf+hkupizuIn+2bNriJe+oGknKMeTGfFl36DJuyi71gEr84tGDqhKE9W1YrYSlIa8gIbVxrte49YtL0pSc1WbeBFXtxeGkTBvkC/IB8AQAyI+XB7Lp0uIGj1/Cdv68b0bK6fItKxa9ytXqvvvmXRp+u4pfLmtDgwKXc4JP/0OqMxC+XBtBmrp6jz3NPGhQ/Xz+oQ9DAnk19aAgiyxeefL31W6uKFXzbDp2yYPmyRcG9G1Zyokfdqo29+Hdy6N5BddzcvRt3Hz1lzvxZ4/sHeJZkf6QSbv7zHmC9BwAAvUk6HlREZCwys7S2tctXIKN8eW2tLczI8bz19DfAHAxJ8qneDvLpAloxLjX6JseQgvh7qzp7FsljU6Ry+yW3YtRcu0g+nZvWsKgJY1K4WtBvp15Ek8eUxL2/umlkQ2dLRpCnTIelN6MzHS4QSYe7FzK3sLaxtbPPly9ffipfPnu7vLZ5rMwtyo27RU8ncVdrG3PLPLZ5ZS1pu/ykHflFMrVquU0+JyRxXwd7c0v5oyk2kj+ajbWFRYm+p7W41Im9PM6zoN9vSteCgNwI+QIAZIL49Yq0OKCEe9lS5Rv1mbv7wpPwv6Ii/rh9ZHHf+qXYQ46uZfxnXtQgYki5O6sOHTXgVmXcZa7PN8nbVS1Sn7d0/2NcMcQPf58cUtlV3t69RrfBHVoNW3fr848ri+RX6ztVlu+I6eLo0XnM4Ea+Heafevtj1IU4/GA/uoClW5XxnD8VAAAA5DLS+NCLm6Z0q1fRpYitpbmpqZl1AVdv/55TtlwLx/UC5ELIFwAgE2L3901NENyq9Vp3Q6HPLiP9emFSI7fUBl4Dj6gdIph8bXI1urCCm+/km5z5wh8rm6bmC+59DqqffyH5Y3lzdgyCq0erJXd/GmWbdHlSVfq85RpOPB2e8a5Gyp3pddk1LF1artF82CAAAADkHhKJPoYrAORgyBcAQHcKiy84eQ3/XVkaEH8z2I9OoCjh2nD2/zJ23DNIvprWz3erMfUWV74gfr4kdX6ES5mBx9WPX5BGbO5QSt6eXX8hI+nXHR3d5A/oUm/mPSU/ZNSuHmxQ4uw3W9lxAAAAAIBcDvkCAOgu+cKYquy0ghIujeY+VNrtlnzY2InuJenoXnPabdV985R7s3/Mj5hwhTNf+LG+o2vlUefUb9os/Wt7F1X5wvd/jvRl15V08Zuq7Gf858gA9iycfINvYMAjAAAAAEBGyBf05vjRY6OGj6AFgNwh+dSQijRfcG23Llz5tAHpn1s70L0bXFxar/+gcnKB5O3qlqnBQSXu4CD5+pTUaRTu9TiCjfSidnYrLW/PlS8c7Vda/oBc+cLRgaXlp+BUfcp15Atg4Jo09k9OwvscAAAAtIN8QW8OHTw4eMBAWgDIHZLPjfKm+YJ7952cm0k+X+RP919wrjVdzQCGhGP9y8kf0NGlZJ+DXBs2J50a7uHENivdcftPu0IpgXwBQHOujk7//KN+3hEAAACAIuQLeoN8AXKhlPuzG7CrHpZw67qNs5sff7hfeXnfXoPJBeJXS5rSMMKl5RqO3Y6kX3d0c2Wf16WuuikXLOQLAJpDvgAAAAA6QL6gN8gXIDeKCulF11Zwbbb4hZJuu1zyxXHVneTNXPyXKOvcK0q+NrEG29jJl6snn/JgXiOaa3gMOvI3rVUJ+QKA5pAvAAAAgA6QL+gN8gXIjcSvljehyyVUHHCMazZD8o3JNeWRgavH4BMJtJJTwtkxXuzcB7cOG/5UOiYi/siACvIndSnX+8BfGm0FhXwBQHPIFwAAAEAHyBf0BvkC5Eril7/R6QxuvsE3OfrdyaeHe8qXaajQc68GayX8c32Sr5vsMZ28h51JpJWKUu7Pqivf89LJs/+RKI3iBeQLAFpAvgAAAAA6QL6gN8gXIHeS/rmzZzl5x9vZb9ZdpSshSEJXtpRlEE7eI099o3Uy0rjwBzdvPf/yUy9G/GplK3fZY7r5jL/88xYSkg/rW7uxzzjztqZdfeQLAJpDvgAAAAA6QL6gN8gXILdKfrgwQN51d64+4kwsrVQgCdvUvqRLCacK7Te8+bFco+T9ngG13Mh3OVVoPP1SxkEIf1+eWEs2QsGp+sSLGSdUSN6sbitLH5x8+h/6otngBdmKkNs6l2LzhQYLnijLF470LSXPF5zrBN9Skh/8c2RAKdm3uzhVnXQZ+QIYOOQLAAAAoAPkC3qDfAFyr8SHy1uWcyV9b7fGc+9kiAOkMefG1nF2dK3YZesfCsMCxM8WN6YLN7iUKNUrJOPeltK/zk2oJWtQodPWMMVNJKR//T6yulsJR3efgYf/VL67hDI/tqVwqzrxmpJ8ICqkq7v8h3HyHnlWyfFvIT1laQj5KjfwiNo1JAByNuQLAAAAoAPkC3qDfAFyM0nE6Qn1ZPf/S1YfsOePtG6JNOrO8s6VnV09ms+9nH4ZRsmHDW3Z/ryji2Ol4aeULA2Z8HB154rOLo4ereddj2RHHCR9ODu7taejo7tXl7X/02jbCJY06vqMhqlP51xj9MmI9MmE9O8HS9uXYxeVdHSrNvjg+/QJg+x42wryJSRkAUT3za+ULQsBYDCQLwAAAIAOkC/oDfIFyOWkf91a07cu6YS7lq7VaejUBfNnTezTpIqbe/XWkw88VxYfPNoytHFlj7LV2k0+/kHJhAWZhBd7xgWUdyvhUqlOm549O7eoWsbVsXSd7gvPftB4hkLs6anN6/uWSxsrIf9yKu1Tp3Hrab/Hfxe/3jKghV+V8nS3y9Qv1/K16reYeOSrVPx628BmDauUky8n+eOrlIdvQK91Tzl+bICcDvkCAAAA6AD5gt4gXwD4/j3l69Pfdy6dNXnMyNGjJ85esvXk/YjM9lEkMS/Pbl86ffzoEWOmLtx04kGk0jUkAUCPkC8AAACADpAv6A3yBQAAMAzIFwAAAEAHyBf0BvkCAAAYBuQLAAAAoAPkC3qDfAEAAAwD8gUAAADQAfIFvUG+AAAAhgH5AgAAAOgA+YLeIF8AAADDgHwBAAAAdIB8QW+QLwAAgGFAvgAAAAA6QL6gNyryBVylAQBADoJ8AQAAcrrkm5Mr2zv33BcppRXwKyBf0Bul+cJ///03cODA3r170zIAAADvKc0XpkyZcujQIVoAAADgtbgj3QsLBHZtd8fQCvglkC/ozc/5wn///denT59KlSpFRUXRKgAAAN77OV8YO3asu7t7REQELQMAQI4mSYz+9O7Fw9u3X0Ub5u39b/s65hcICnTa/41WwK+BfEFvMuQLUqm0R48e3t7eMTHIzAAAICfJkC8MHz68dOnSnz9/pmUAAMiBUq7Pblq1clm34oXsrEyEjJGMsGi/s8n0uHaSr8wKbNTwJ40a+QcO3fY8ibbiJo08MqFN6/YdO3fp1r1Hj55penTv1rVzx/ZtWg/bSVvqJGpnazuBsEiPo/G0An4R5At6o5gvSCSSzp07V6tW7e+//2ZrAAAAcoq0fIGd5efh4REZGckeAgDIQWLvLG5Xs+PaVxJa1k5C6IX1wYO6t/avU8XTs5pf866jfjv4IDKFHtW/rH6+lOuzW9T29fZwtjcVsOlCpvKFy9Ob+vnVqVndp3JZ54KWxqmPSDBmlSbcTKDtuEgj9w/186noVijdtxKMwDyfs4d37aDNtKUOpF82N7dhhMX6nE6kNfCrIF/Qm7R8QSwWt2vXrmbNmnFxcewhAACAHITNFzDLDwByMEnkzVXdK9gIjISOgy5q24NODj0yJdDDXiQq4NVl8oodxy5ePr1v45z+dR0tBGYONYeEvFJ/e14rv/j5UsL2dCkhlHfmdc8X0ovf3cZGKEyLLRjzypNva9i1Twq7tLxrOSvZtzJmbq3mnX6rLppQSxqxtrE1I3QadEHP/1CgHvIFvWHzhX///TcwMNDPzy8hIdO/GQAAANnB1dGJfIr17NnTy8sLs/wAIEdJiX51JWTh4KZlbFNnADgOuqBND1oafWVq7XxCxtS5zfI7UenXJkgMPTTc21YgtPcZeiBUTwMLfvXzySUd6JRXIH919JQvJO7rYFe8w+guziIaMTAWnsF3NO7dS17/VtuSMakc/EAfZyn9sLyuBWPsPuJa1g02AS7IF/Tm0MGDA/r2a9q0aYMGDX5edhsAACCncCnh2L59+6pVq8bGxtIqAAD+krzZPqh5o3q1q5Rzym9pzDBCa8fqdTzYDrSW+ULK82X17QVGgrx+i58q/S5pxL4uJchzmHsMO5shDdDFr34+KvnCIEf5AAZ95guOA859PtnXNS1hsPSeek/ThEHybIaXmfvI6/oIBCRvF9U0Y4zLjLuNeCEbIF/Q0YXz548ePqL4NX7suIIFClSuXHn/3n0ZDuELX/jCF77wlVO+Dh04aGlhUapUqZDdIRkOYd4fAPCS+PmKdlWr1qzbpE2vETNW7bn0OlaacmOUu7G8m6tNviB+Os/XijFiTD2nPeTsm0rD1wfYCowYY5d+pzO5N8Gvfr40KZeHOGdBvnA+WRp1PMg5dUEFxtJnxgPNHl0asaKupef0p2JazgTJyzlVTRhRxSkP9fBgoDXkCzqaOG58z+7d0766du5sb2dna2PbrUsXxXp84Qtf+MIXvnLQV/euXUsUL25matapQ4cMh8gX9qcEgJxCl3xBGhnSvqBAtniA37IPqoYKxB/sUkDWztRz+qNM9GF/9fMpyLJ8gZzV1yO9HNMSBquqsx5q8vjSz6vqWXrPfK7bSpyKxE+meYoYkdcMfWQVoD3kC3qQkJBQu3ZtHx+fAX370SoAAICcJiUlpVmzZvXr13cuXgIT/QAgR9MhX5CGraxvKesXi9TeR4/ZHphH1lLoPOiCzhsU/OrnU5SF+YIsNznUvURawmDtO+ex+nkKessXxPcneYgY0+rzdNw0BDIL+UJmxcXF1ahRo3379vv37UvbnxIAACBnSUpK8vf3b9y4MfmftP0pAQByKO3zBcnrBb5msk6xoGCPY2qWDRA/Cq4kkj94UZ13QPzVz5dOluYL379LvxzoWoyur2nE5Kkx74m6hEFf+ULKzTGljBnz2r+FIl7IJsgXMiU2NrZq1apdunSRSCRp+1MCAADkLP/880/9+vWbNWuWnCy7OES+AAA5ndb5gjRsaR1TeY9Y5LtQbec0PqStjbyx0HnIJZ3657/6+dLL4nxBlhfs7eQgfwZCkKfWwmeqR2joKV9IvjLU1ZixrL8yTG9LYYKWkC/oLiYmxsvLq2fPnlKp7A2MfAEAAHKihISEOnXqtGrV6t9//2VrkC8AQE6ndb4QTzrI8g0nGMvAHfG0klPKlWGu7MOLvHTrFP/q50svy/OF79+lESHtfyQMNnUWP1eVMOgnX0g817+EkMkTsP4T4oVsg3xBR1FRURUrVuzbt+9///3H1iBfAACAHIed5deuXTux+MeVH/IFAMjptM0XUm6MLknba9Ljlm2nKJ+wYMTYtAlRGw/87Fc/Xwa/IF/4/l36cWfbImkJg63fkhfcCYNe8oWEE0EOQoFty62RiBeyD/IFXURGRnp4eAwaNCgtXCCQLwAAQM4SGxtbrVq1zp07SyTpLumQLwAPiCMf7F8yeWivtgENA9oFjVt5IUyh5yKNvr506LwLme9lgcHSNl9I2N3aml0vwLjUmFtq1yOUbadoIm9uJPKcoWbsvzKZez5aqzvV+ULi+7uXrly7cevO3Xv37rPu3bt759b1K9eecXTdleUL5McO39660I+Eof5yzkUX9ZEvxB3qWkggsG+7O5pW/CTpw+2TB3dvWbd84azg8SMGBnVrH9ik1eyraS+/JOrpiTVTBnRpFVCvbqMW7Xv0HzN3y8XQBHr0B2nsi9Prpw/uGhhQz8+vQUDrHiNmrDv9Gn+Q5JAvaO3z58+lS5cePnw4LadCvgAAADlITEyMt7d3jx492Fl+ipAvQLaSfru/cVA9F2uhhUvD/tNX7jq4f8vCIQ1dCnmOPC3v2kijzo3xtDEu3v+c+k4j5Fpa5guS0IU12PEBRqIKUx6qzwu+rm1oyrYXOg7UZP5Fepl8PlqpO5X5guTtAl/6w2UgKNjzuPKlKJXnC+TXNWxLoGwPTvbb8zZY8Vp5gqCHfCFmT/t8AkGBzgdiacVPFF71NAK7LofkpxR7f21PzxLudXtOXrbtwLFjB7YsGNrIxYIxYsyK1p9y9lPaDxb/dFv/qsWdqnccs2jz3qPHD+1YNq5VWRuBESMqWG3YobDMBCSGAfmC1lq0aDF27FhaUIB8AQAAcop///23cuXKffr0URyIlwb5AmQbafStpe3cLRmBdcU+O57G0VpC+vnkQA+HJuveJb9Y0Sg/uZa3bb3rGz0G8DMt84WU6yNpcyOR96wX6juJMRsDaH+fsW4T8vMdbjUy+Xy0Uneqxy+kRNzePqQK6TOzGIFlcV/Snd565OLdd7HajF+Qkbzf1LxAWsJg13j1W2Vnm/l84ev2QFuBsEjPYyqGESSF3b949uThXauDO5anpyfPF6QxlyZVd6s97lhouvRE+uVokKuIIY1sqs+6LzsUd3uOn7Nn3x3PFP44EbHXxle2IO0Ys5L9juX2yRnIF7QWF5f+/ZQK+QIAAOQgly5dUhouEMgXIHtIoy5O8LEVyK7Rgw6G/9TPiDs7wM0poJ0vaWHEmPotxQLxoIKW+ULyuf7F6Dh+UdW5nKP4f4jf2pz2941M6q/+Qms1lsnno5W6U7v+Ap2QwQhtK3Zfcilc7QvInS98/y55t75pvrSEwT5grZLtMjKbL0g/b2xqwwiL9dVw+86UO+PLyt8fArsuB97ubF+q5ow7SoIJadjK+payeSyCvM02hkce71O20tAzX5X86YnZ11EeojCiCpPvaz9dxpAgX9Ab5AsAAGAYkC9Adoi7Nbu2vSw6sKo+57HS6ejiuxPKpQ4pL5/br+EJycvNQfV8Knl4lNM/j/Kevs0mntC628wfWuYLScd7FaI9YFG1eRxj+BUl7gw0Z9dPMDKpszSM1mosk89HK3WnLl+QvFvrb2ds5zPswFvl8yEyUpUvyCYmrPWX/XbLCfI1Xf8u4xlnMl+Qflzd0IoROg++qPafmiWNXNNAvqCFwK7FwC6l/Ra/Uv4HRfJyjo/8rw5jWX9gUAXP8bc48ouvGwNkIxjIv6fvAqUjNHIN5At6g3wBAAAMA/IF+PW+nR9SykQ2vti0/PibXDcgEw90yivvowgd+pzWrNdjyFJuTPayNxXSTqd+MYyxhUOz1W/oU+VA2uYLBzuzu0XK+/tvNOjvh7Sxoi+9rENJazWWyeejlbpTmS+IPxwcWCm/a7v1TzWf96E6XyD99DerGqWesZGgQPNN79Ofc+byBcn7pX7mjLH7yOtqV8pMlbCNHRDCiCysXXufVD4+nUgMaZtH/sIzAqF9yy2cG18mXxhYQv6KCgr14lijIpdAvqA3yBcAAMAwIF+AX0wafbq/u2yWs5GwWK+jnMsq/LiTaBO4I4ZW5npSiUSsf+n3lMmJtMwXEvd3sE3tvvvM0WC+QuKuVuztavINVee+prUay+Tz0UrdcecLcQ9WBDrl9xp29OcpSqqoyxfI7+/rFQ3YfJAQFGy55YPiE2QqX5C8WVDDlDEuM+6OxvFCWr5AfpbCvY5zT6pIOt6Trk/JWDfb/JXW/izl9rgy8jccY9lql2ZzNAwU8gW9Qb4AAACGAfkC/FrJt8d7yNMFI+NSo29ydhCkX9b7y2dCMya1Fv80vBogHW3HLxzplj91PIGXJr3chO0tzFL7+zUWvaO1Gsvk89FK3SnPF6Sfz4yvXsChwdwbMdqubqI+X/j+XfxyaV3Z8ilywkKttimsoZKZfEHyfFYVE0ZUMViDfTjSpOULjKn/+ihaqUTyyaAi7M8s8p6l4scT35tIp2+Z+m/I1fEn8gW9Qb4AAACGAfkC/ErSL9sD6bhp49Jjb3Pff0w43JXtkRmXHX/n535E4rtzqyf0aFLDs6y7W+nKvgHdJ6059ZJjrXswfNqu70h6kfL+NulGVp76RH0/NW5LU3r728jEb1k4rdVYJp+PVupOSb6Q9GJrl5LmgryBW7/o8GujSb5AOuHPF/v9SBgKt9nxMfWpMpEviB8FVxIxIu+Zz7T55rR8QVCg+1EV8xnS8gXGNGCjitxAIV9otJZ7mEMugHxBb5AvAACAYUC+AL+QJPS32nTdOqHzkEvc3ZOUayPc5P1FQeGgExm6A3H3V7VztzQuUK3PooN33n2JDH96Zu2oRk7mlm6tF19Xttg7GDxt84ULgxxT+/sVpmhwHzxmgz/t7zPmLbZxzt7nksnno5W6y5AvSKOvzapXyJgdHlR66Hnt937VLF8g3fBnC2unbXwpLNJuVwT7+6l7viBf95UxrT5fg1UyFaTlC0InlYtC/sgX8rTbq2Leg0K+0BD5AugF8gUAADAMyBfg15GELqppysYLqmdBS17OposvWDffkm40s/j1+maFhcLCAcsepttfTvx+b3d3E0GeKsE3tO79QY6nZb4gfjrdk+0dGonKTbirvr8fuaahfPsB8sbVZT2/TD4frdSdYr5w8tX+/hXzFq3bzq+wvIoRuQ84Ha1lLKdpvkCe+sm8GuyKiYTQocMe+ZKJOucLKdfJPzRjXmeJlnOmFPMFFbmmQr5go2G+YNJwTSStzJWQL+gN8gUAADAMyBfg14na3Iyuis/YtNrFfddUYfGF9Lu/SUJXNbQVMFZ1lyvpXkQf6FJYyJhVnHxHg24PGBQt84Xv0Rv8adBl7DL0stplAiWhC31pPmBceuwtzZcVTJW556O1ukvLFwTWLu5F85YN2vUmWfp5f9fi7BgGkXPQMe0G/mieL5Au+6M51a3Zk5cFHJ32fpbqnC8kXyInwlg2WBWuZSCCfCGrIF/QG+QLAABgGJAvwC+TfGmwU+oo8WrzVIxv/rH4QqkxiktAJl0a4mpsxJjXX5U2kVtR/KGuBQRGgoKd9mO/iVxG23wh6URQYXbYviC/yvn4LPH9SeXZ3iRj2mhtpJZ9WyJzz0drdZeWLzDC/LWDL9ITkEYeDXKWr7XKGJfoflCbdRi0yRfIL/6DmVVTt9tkhMW6Hvgi0S1fSDzTt7iQyROwgXPfSC7IF7IK8gW9Qb4AAACGAfkC/DKxW5qlLr6gshP4Y/GFgj2OKlzjJ54IciC9JM597yVv51cn1/yMddNNOnQBeU766eyiIT07tWvXVv/ate/Se+ymezl4Yom2+YL0/eJa7AQExrzZllhayyn5TN+ibDJm7Db8qvbDF37582WQli9kyPWk0acHsHvFMsKinfbQxRE0oF2+QE7o/vQq8hFJ8qcq3v3A0xU65Avxx3o6CAW2Lbdp/+uNfCGrIF/QG+QLAABgGJAvwK8i/bjcL7WPFbAxmtb+7MfiC5ZNNir0JFJujSkl60Oa+C3lGB2dGNJGNhBbkK/zwXSLMxiC5NN9HIS0h5YFGFPvmU/oU+VA2uYLCt8gqhT8WN2CCFHrG7PbRTK2bXarTQeU+dXPl96PfCH9fCMi9sKwMiby5xIWabND02kH2uYL378n3Q32sqDvX8a4RPNATwtt84XYA50LCAT2bXdz/+3ghHwhqyBf0BvkCwAAYBiQL8AvE7MxgJ2ELnQaxL2Ge9riC0aiqnNe/eiASCPXNZYNf2DMW27nSA+ST/eWDXAwEpWffF/9Gno5TUrMh+cP7ty5rX937j58GRGv9T1hHtE6X/iedLoPO0RAkK/rYTUTFlLujCsjf3TGyn/dZ91ep1/9fOmoyBe+f4+/OrYCm2YICgVuea9Rl1/7fIF8z+3JnnT8kpxIy3whOqStvUBQoPMBXQIX5AtZBfmC3iBfAAAAw4B8AX6ZpOM9C8qv3UUVg7n36Iva2dpO3srYfcQ1haHhKbfGlpZ1ulRc+CdfHCRf4EFgr7YLBwZF+3zhe9L5geyekSKPSarTKOnHlfXkHXDGpskGzecQZPCrn0+RynyB9PxvTfZkxxYI8jdZF6pBpz9xX/u8JbTMF8h33ZxYiU0yZLTLF6SR21raCoRFeh7TaWgS8oWsgnxBb5AvAACAYUC+AL+M9POahvIbmCr26Eu6N61Kaleny6EEWiuTdLxXIdmVvyBvpwMc4UHKlWGu8l6mqf96HcZQQ46lQ77wXfxomqdsZgBjUmORyj519K7WeWXvPKFDjyMcm55IvlxdNjCwXt1mfRdf/MTxWPp8Pi2pyRfIL9f9GXQFRoFdw5Uqll6l4rY2tyjQQ/1ClRklXB9Px0pomS9IP20IyMMIi/U9o6LTrwLyhayCfEFvoqKiQt++pQUAAIAc69iRoxKJxhd5AJkhDV/bWLYXvsC2/V5ldyGlX04MKEO7H4xF43XpVrRP3NNWvo++IF+3I1z5Qmov08RvuV7u+0IOkXwxdWsSoUPvUxreVpdGHuwqm7PAiCqomE8j/bCqgXxVDxu/JQrTdRRJ3qyobyvvlcqa1Vn8QnkzvT2f1pLP9ivG5guc+7Yk3RgtHx4k++2sM/+R6l588rWR7sbazm9gxV8d48HOktLm+6XhqxpYMkLnwdzzqlSL3dyU5gslBqoadpE2xoqxbhOi4kUgf2pKsi8X8gUAAAAAgGwSd2GIbL16oUOPoxlnUYvfHxzk6eDuLl9BQUnvI3FXK/myDIICnBv8pdxkV4A0EtVY9E5PXTPIASShS/zo9gSMme/8VyqnHyiQhod0Kk7ej4J8XNMCpFHHgpyMSQu7+kufc23kkLawiJxJ/VWf6YGM9PR8Wvu6IYAdFCQs0e8sR685YU87G3oSjMi5w6bnXPMQksNOjPTKQzrhgnx+0y9/0fS1ThN3eWRZ2TgOLfIF8u9bx5zh3DhGPfGj4ErscAPVu8ukTU0hP17VuSrindiQtjRR0mS9TkOGfAEAAAAAslHs1Sk+tgLGvOLI81Gpl/nSmEc7Rvk5OTWccXpHT3Z4srHL0Mvp+xKJe9vL+z+C/KrGL7A3FU3qLA3D+AWDJflwdvX8ucTsGcHjR/Tr1NAjv3yTRRYjyle2Qce+I8YHz5gtazR/1WkVixbG3V7kX8yEEdhWnXDha8b3TPzD5U2KGDOCPJWGn0w3mCa9lFvjy7F7MMie3X3EVRU3yPXxfJqRvD6+bO7smVMnDG7rWcA49cczLVar6/BJ02fNmb/hUuoYH/GzfdMGN3FP3UBSjjEv5tOiS59hU3axvWfxi0MLpk4Y2rNltRKWgrSGjNDGtVbr3iMmTV96UpN1G1ixF4eXNmE0zxckr+ZXN2WMy46/o11H/tudnQvmkPfI6N6N3a3p+BIjxjh/5TaDJkydOXvu0mPsYA7xswMLZa/U+AGBFezSNmkR2JRu0meM7E20cC87nkMSemrlvDmzpk0c2rFqkbR3HGPp7NeNfUm33kj7m5ZrIF8AAAAAgGwljb65rFN5W2PzYr7t+g8d0KWpb8kixat2X3QhQpK6hqORwL7j/gz3T1NHLjO2HferXX+h8fooWgeGJ/nCYBdzMwsraxtb27x29vb5iPwKSNHe3i6vra2NtZWFmZlj/7MqR9VLPp2b1rCoCWNSuFrQb6deRKeQqrj3VzeNbOhsSfr6ZTosvRmtptsYf29VZ88ieWyKVG6/5FaMmsZ6eD5NJB3uXsjcgrxGdrIXiL405LWRvTJ5rMwtyo27RRO8xF2tbcwt88heS4WG9na21hamVi23yVdBSdzXwd7cUv5oio3kj2ZjbWFRou9pLaYuxF4e51nQ7zela0H8RPx0hpeIUbkorFKS0EW1rcyt6Hn9/DObF+x2SP6XJHFHoLWZsnMjL4GNtaWZVcD6r7J2yaf7lrCQvaLpXqgfL6mlz8xnGocshgL5AgAAAABkP8m31xd2rZw/ffq81TtP3H7PLuQoeTO/uvy2IGPeYPWnDF0s8d0J8hXVGIvAXRxDvFOnmWP/CNCSND704qYp3epVdClia2luampmXcDV27/nlC3XwnWc8a/ar36+HEz8cEpFkWy0Q+7ru+cEyBcAAAAAgKe+bmrKLmIv8pz2JOO9Sunn1Q3ks9xNGqzmGDuetL+jrayF9rc6AX6QSPQwfEALv/r5cpSUO+PKGDOm1eer39UCsgHyBQAAAADgp8Sj3dm124WOA5XsMZhyc7R8dQVl4YOcNHJNQxPSQLZCg+r17wEgR0i5NsLdmDGvswQLtvIT8gUAAAAA4KWU6yPZ3SUFedvtybi7hEzSmb6y6Q+C/BwbSNDlHZk8zTZlenE8AMh+SRcGOQkZywarwvEbzU/IFwAAAACAjyQv5/jIt5BjzOquUN6bSJbfyzQyLjPutpJ96iTvFtc0ZYwEBTvui6ZVAJCDJZ7qU1TI5GmyIeNqLMAXyBcAAAAAgIekkRsC2A3yVKyeIPmwqXl+gZHQuf/ZOFqVRvxomqcpw1jXnP8May8AGID4oz0KCwS2gdt+2skT+AL5AgAAAADwUOz+TgXYxReK9z/HvYS+9Mvx/qXNGJFju21vFWIEaeTp4RWtGOPCzda8UDK0AQBynG/7O+UXCOzbhmA8En8hXwAAAAAAvkkOOz3Kix29YCTyDH6kcnPJlA8npzZzsTQt5N05eP2Bk6ePbp8/oL6zlalD3QlH32FzPwDDELWrjZ1AULDLQWWLsQBPIF8AAAAAAF6Qftrbz9vdqbhDQVszIZstsBhj87yFizk6u9cKvsY1GCEx/Na+JRP6d28f2LxFu56j5u+8/DYOY6gBDIX0y5bmtozQodfxeFoDfIR8AQAAAAB4Qfr14m/DBw4ZPnr85OCp6QRPGjdq2OCBIxaf/4zMACAXkkas87dmhMX7nsFWs7yGfAEAAAAAAAD4S/pheV0LxthlyCXMeOI35AsAAAAAAADAX0mHutgJROUm3MFqrTyHfAEAAAAAAAB4TBxx+9i5Fz/tQgt8g3wBAAAAAAAAADIL+QIAAAAAAAAAZBbyBQAAAAAAAADILOQLAAAAAAAAAJBZyBcAAAAAAAAAILOQLwAAAAAAAABAZiFfAAAAAAAAAIDMQr4AAAAAAAAAAJmFfAEAAAAAAAAAMgv5AgAAAAAAAABkFvIFAAAAAAAAAMgs5AsAAAAAAAAAkFnIFwAAAAAAAAAgs5AvAAAAAAAAAEBmIV8AAAAAAAAAgMxCvgAAAAAAAAAAmYV8AQAAAAAAAAAyC/kCAAAAAAAAAGQW8gUAAAAAAAAAyCzkCwAAAAAAAACQWcgXAAAAAAAAACCzkC8AAAAAAAAAQGYhXwAAAAAAAACAzEK+AAAAAAAAAACZhXwBAAAAAAAAADIL+QIAAAAAAAAAZBbyBQAAAAAAAADILOQLAAAAAAAAAJBZyBcAAAAAAAAAILOQLwAAAAAAAABAZiFfAAAAAAAAAIDMQr4AAAAAAAAAAJmFfAEAAAAAAAAAMgv5AgAAAAAAAABkFvIFAAAAAAAAAMgs5AsAAAAAAAAAkFnIFwAAAAAAAAAgs5AvAAAAAAAAAEBmIV8AAAAAAAAAgMxCvgAAAAAAAAAAmYV8AQAAAAAAAAAyC/kCAAAAAAAAAGTO9+//B8O53eMLcDtTAAAAAElFTkSuQmCC"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "A frame consists of one horizontal and two vertical beams according to the figure.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:fef2b753-ea08-42d2-bb98-d90fa6b9b100.png\" width=\"800\"/>\n",
+    "</div>\n",
+    "\n",
+    "The corresponding finite element model consists of three beam elements and twelve\n",
+    "degrees of freedom.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:0be27f37-a622-4faf-83d4-0d6613593f22.png\" width=\"500\"/>\n",
+    "</div>"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -23,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 65,
    "metadata": {
     "tags": []
    },
@@ -54,13 +75,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 66,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import sys\n",
     "import numpy as np\n",
-    "sys.path.insert(0, r'D:\\Users\\Jonas\\Development\\calfem-python')\n",
     "import calfem.core as cfc\n",
     "import calfem.utils as cfu\n",
     "import calfem.vis_mpl as cfv"
@@ -75,8 +97,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 67,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "edof = np.array([\n",
@@ -95,13 +119,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 68,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "K = np.array(np.zeros((12, 12)))\n",
     "f = np.array(np.zeros((12, 1)))\n",
-    "f[3] = 2.0e3"
+    "f[3] = 20.0e3"
    ]
   },
   {
@@ -113,8 +139,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 69,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "E = 200.0e9\n",
@@ -149,8 +177,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 70,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "K = cfc.assem(edof[0, :], K, Ke1)\n",
@@ -167,34 +197,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 71,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table>\n",
        "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  </th><th style=\"text-align: right;\">      a (m)</th></tr>\n",
+       "<tr><th style=\"text-align: right;\">      a (m)</th></tr>\n",
        "</thead>\n",
        "<tbody>\n",
-       "<tr><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> 7.5357e-03</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\">-2.8741e-04</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">-5.3735e-03</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 6</td><td style=\"text-align: right;\"> 7.5161e-03</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 7</td><td style=\"text-align: right;\">-3.1259e-04</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 8</td><td style=\"text-align: right;\"> 4.6656e-03</td></tr>\n",
-       "<tr><td style=\"text-align: right;\"> 9</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">10</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">11</td><td style=\"text-align: right;\">-5.1513e-03</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 3.7225e-02</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">-2.1821e-04</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">-8.5898e-03</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 3.7185e-02</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">-3.8179e-04</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 3.9914e-03</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">-1.5940e-02</td></tr>\n",
        "</tbody>\n",
        "</table>"
       ],
       "text/plain": [
-       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  </th><th style=\"text-align: right;\">      a (m)</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> 7.5357e-03</td></tr>\\n<tr><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\">-2.8741e-04</td></tr>\\n<tr><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">-5.3735e-03</td></tr>\\n<tr><td style=\"text-align: right;\"> 6</td><td style=\"text-align: right;\"> 7.5161e-03</td></tr>\\n<tr><td style=\"text-align: right;\"> 7</td><td style=\"text-align: right;\">-3.1259e-04</td></tr>\\n<tr><td style=\"text-align: right;\"> 8</td><td style=\"text-align: right;\"> 4.6656e-03</td></tr>\\n<tr><td style=\"text-align: right;\"> 9</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">10</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">11</td><td style=\"text-align: right;\">-5.1513e-03</td></tr>\\n</tbody>\\n</table>'"
+       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">      a (m)</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.7225e-02</td></tr>\\n<tr><td style=\"text-align: right;\">-2.1821e-04</td></tr>\\n<tr><td style=\"text-align: right;\">-8.5898e-03</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.7185e-02</td></tr>\\n<tr><td style=\"text-align: right;\">-3.8179e-04</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.9914e-03</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">-1.5940e-02</td></tr>\\n</tbody>\\n</table>'"
       ]
      },
      "metadata": {},
@@ -205,26 +237,26 @@
       "text/html": [
        "<table>\n",
        "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  </th><th style=\"text-align: right;\">      r (N)</th></tr>\n",
+       "<tr><th style=\"text-align: right;\">      r (N)</th></tr>\n",
        "</thead>\n",
        "<tbody>\n",
-       "<tr><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 1.9268e+03</td></tr>\n",
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-       "<tr><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
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-       "<tr><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
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-       "<tr><td style=\"text-align: right;\">11</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
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+       "<tr><td style=\"text-align: right;\"> 2.1821e+04</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 3.0927e+04</td></tr>\n",
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+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
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+       "<tr><td style=\"text-align: right;\"> 3.8179e+04</td></tr>\n",
+       "<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\n",
        "</tbody>\n",
        "</table>"
       ],
       "text/plain": [
-       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  </th><th style=\"text-align: right;\">      r (N)</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 1.9268e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 2.8741e+04</td></tr>\\n<tr><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\"> 4.4527e+02</td></tr>\\n<tr><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 6</td><td style=\"text-align: right;\">-2.3283e-10</td></tr>\\n<tr><td style=\"text-align: right;\"> 7</td><td style=\"text-align: right;\"> 3.6380e-12</td></tr>\\n<tr><td style=\"text-align: right;\"> 8</td><td style=\"text-align: right;\"> 3.6380e-12</td></tr>\\n<tr><td style=\"text-align: right;\"> 9</td><td style=\"text-align: right;\">-3.9268e+03</td></tr>\\n<tr><td style=\"text-align: right;\">10</td><td style=\"text-align: right;\"> 3.1259e+04</td></tr>\\n<tr><td style=\"text-align: right;\">11</td><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n</tbody>\\n</table>'"
+       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">      r (N)</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">-1.2027e+04</td></tr>\\n<tr><td style=\"text-align: right;\"> 2.1821e+04</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.0927e+04</td></tr>\\n<tr><td style=\"text-align: right;\">-4.1473e-10</td></tr>\\n<tr><td style=\"text-align: right;\">-3.6380e-12</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\">-6.8394e-10</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.6380e-12</td></tr>\\n<tr><td style=\"text-align: right;\">-7.9726e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 3.8179e+04</td></tr>\\n<tr><td style=\"text-align: right;\"> 0.0000e+00</td></tr>\\n</tbody>\\n</table>'"
       ]
      },
      "metadata": {},
@@ -248,8 +280,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 72,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -268,35 +302,35 @@
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+       "<tr><td style=\"text-align: right;\">-2.1821e+04</td><td style=\"text-align: right;\">-1.2027e+04</td><td style=\"text-align: right;\"> 2.1305e+04</td></tr>\n",
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+       "<tr><td style=\"text-align: right;\">-2.1821e+04</td><td style=\"text-align: right;\">-1.2027e+04</td><td style=\"text-align: right;\"> 2.8521e+04</td></tr>\n",
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        "</tbody>\n",
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-       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  </th><th style=\"text-align: right;\">          N</th><th style=\"text-align: right;\">        Vy</th><th style=\"text-align: right;\">        Mz</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">8.1523e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">7.7670e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">7.3816e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">6.9963e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">6.6109e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 5</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">6.2256e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 6</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">5.8402e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 7</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">5.4548e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 8</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">5.0695e+03</td></tr>\\n<tr><td style=\"text-align: right;\"> 9</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">4.6841e+03</td></tr>\\n<tr><td style=\"text-align: right;\">10</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">4.2988e+03</td></tr>\\n<tr><td style=\"text-align: right;\">11</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">3.9134e+03</td></tr>\\n<tr><td style=\"text-align: right;\">12</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">3.5281e+03</td></tr>\\n<tr><td style=\"text-align: right;\">13</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">3.1427e+03</td></tr>\\n<tr><td style=\"text-align: right;\">14</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">2.7574e+03</td></tr>\\n<tr><td style=\"text-align: right;\">15</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">2.3720e+03</td></tr>\\n<tr><td style=\"text-align: right;\">16</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">1.9867e+03</td></tr>\\n<tr><td style=\"text-align: right;\">17</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">1.6013e+03</td></tr>\\n<tr><td style=\"text-align: right;\">18</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">1.2160e+03</td></tr>\\n<tr><td style=\"text-align: right;\">19</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">8.3062e+02</td></tr>\\n<tr><td style=\"text-align: right;\">20</td><td style=\"text-align: right;\">-2.8741e+04</td><td style=\"text-align: right;\">1.9268e+03</td><td style=\"text-align: right;\">4.4527e+02</td></tr>\\n</tbody>\\n</table>'"
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+       "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">        u1</th><th style=\"text-align: right;\">         v1</th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">3.7225e-02</td><td style=\"text-align: right;\">-2.1821e-04</td></tr>\\n<tr><td style=\"text-align: right;\">3.7223e-02</td><td style=\"text-align: right;\">-2.7148e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7221e-02</td><td style=\"text-align: right;\">-5.0180e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7219e-02</td><td style=\"text-align: right;\">-7.0845e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7217e-02</td><td style=\"text-align: right;\">-8.8785e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7215e-02</td><td style=\"text-align: right;\">-1.0372e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7213e-02</td><td style=\"text-align: right;\">-1.1543e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7211e-02</td><td style=\"text-align: right;\">-1.2380e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7209e-02</td><td style=\"text-align: right;\">-1.2876e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7207e-02</td><td style=\"text-align: right;\">-1.3034e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7205e-02</td><td style=\"text-align: right;\">-1.2861e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7203e-02</td><td style=\"text-align: right;\">-1.2375e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7201e-02</td><td style=\"text-align: right;\">-1.1601e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7199e-02</td><td style=\"text-align: right;\">-1.0569e-02</td></tr>\\n<tr><td style=\"text-align: right;\">3.7197e-02</td><td style=\"text-align: right;\">-9.3188e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7195e-02</td><td style=\"text-align: right;\">-7.8977e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7193e-02</td><td style=\"text-align: right;\">-6.3594e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7191e-02</td><td style=\"text-align: right;\">-4.7658e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7189e-02</td><td style=\"text-align: right;\">-3.1859e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7187e-02</td><td style=\"text-align: right;\">-1.6965e-03</td></tr>\\n<tr><td style=\"text-align: right;\">3.7185e-02</td><td style=\"text-align: right;\">-3.8179e-04</td></tr>\\n</tbody>\\n</table>'"
       ]
      },
      "metadata": {},
@@ -588,13 +622,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 73,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "sfac=54.77300198398877"
+       "sfac=16.11813230924568"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -605,14 +641,12 @@
     },
     {
      "data": {
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",
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mjVauXNlg/9mzZ2vOnDkNtpeVlSklJcWpMgEAAAB4XHl5uVJTU1uUDRwdycnIyNDgwYMDtg0aNEglJSWN7j9z5kyVlZXV33bu3OlkeQAAAAAM5OjCA+PHj9emTZsCtm3evFm9evVqdP/4+HjFx8c7WRIAAAAAwzk6knPPPfdo1apV+s1vfqOtW7fqL3/5i5555hndfvvtTh4WAAAAQARzNOScf/75euutt/Tqq69q6NCh+vWvf6158+bpu9/9rpOHBQAAABDBHF14oL1aM7kIAAAAgLk8s/AAAAAAAIQaIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEdDzuzZs+Xz+QJuPXr0cPKQAAAAACJcjNMHGDJkiD766KP6+9HR0U4fEgAAAEAEczzkxMTEMHoDAAAAIGQcn5OzZcsWZWZmqk+fPrrxxhtVVFTU5L7V1dUqLy8PuAEAAABAazgacsaMGaOXX35ZCxcu1LPPPqvS0lKNGzdOBw8ebHT/uXPnKjU1tf6WlZXlZHkAAAAADOSzLMsK1cEqKyvVr18/3X///ZoxY0aDx6urq1VdXV1/v7y8XFlZWSorK1NKSkqoygQAAADgMeXl5UpNTW1RNnB8Ts6pOnbsqGHDhmnLli2NPh4fH6/4+PhQlgQAAADAMCG9Tk51dbU2btyojIyMUB4WAAAAQARxNOTcd999WrZsmbZv365///vf+uY3v6ny8nJNmTLFycMCAAAAiGCOnq62a9cuffvb39aBAwfUrVs3XXDBBVq1apV69erl5GEBAAAARDBHQ85rr73m5MsDAAAAQAMhnZMDAAAAAE4j5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwSozbBQCR4oMPPtDLL7+sEydO6LrrrtO3v/1tt0sCAAAu2Lhxo1avXq3MzExdeumliopi3CHYQvaOzp07Vz6fT9OnTw/VIQFP2LJli7p3764rr7xSr732mv72t7/pO9/5jlJSUvTpp5+6XR4AAAiR5cuXq2fPnho8eLBuvvlmTZo0SUlJSfrFL37hdmnGCUnIWbNmjZ555hkNHz48FIcDPKO8vFwjR47U/v37NW3aNJWVlenYsWN68MEHdezYMU2cOFHbt293u0wAAOCwTz75RBdffLFKS0v1zW9+U3/5y1/00EMPKTExUQ8//LB+/OMfu12iURwPOUeOHNF3v/tdPfvss+rcubPThwM85YEHHtDRo0f14osv6sknn1RKSooSEhI0d+5cLVmyRCdOnNBtt93mdpkAAMBh3/3ud+Xz+ZSfn6833nhD3/72t/XII49o//796tu3r5599lmVlpa6XaYxHJ+Tc/vtt+uqq67SZZddpocffrjZfaurq1VdXV1/v7y83OnyEAZyc3OVm5vbYHtycrImTpyo7t27N3js0KFDWrp0qQ4ePNjgsa5du2rixImNhu69e/dq6dKlqqioaPBYRkaGLrroIiUlJTV4bNeuXVq6dKmqqqoCtr/yyitKSkrSjTfeKEl69VXp6FHpyiul8eMvVM+ePfXRRx/pueeea/Ca/fv318SJExtsl6TCwkKtWLGi0cdGjBih888/v9HH1qxZo/z8/EYfGzdunAYPHtzoY0uXLtXWrVsbbI+KitLEiRPVt2/fBo/V1NRo6dKlKikpafBYQkKCJk6cqJ49ezZ47MiRI1q2bJn27NnT4LFw+JlLUnZ2tiZOnKi4uLgGjxUVFWnp0qWqq6tr8Bg/c37mp+Jnzs/8VPzM7Z95nz59dOmllzb6/XjZtm3bVFJSomuuuUZDhgxRXZ20b5+UmCilpkbplVde0bhx4/TQQw/phRdecLtcM1gOevXVV62hQ4dax44dsyzLsi666CLr7rvvbnL/WbNmWZIa3MrKypwsEx72xRdfNPo74b898cQTjT7vr3/9a7PP++tf/9ro85544olmn7dixYpGn3fPPfc0+7ydO3dalmVZPXtalmTfEhMtKzq60JLetqT/saQ7LGmyJQ20pHiruf89Bw8e3OzxmtLccwYPHtym533zm99s9Dk7d+5s9nn33HNPo89bsWKFUT/z033zm99s08+On3lD/Mwbx8+8IX7mrX+el3/my5Yta/J7CrW6Oss6fNiyioosa+1ay/rwQ8t67TXL+vOfLevhhy1rxgzLuvlmyxo9erclLbcyMr60une3rNhYuy9w220nXys6Otq68MIL3ftmwkBZWZkltSwbODaSs3PnTt1999368MMPlZCQ0KLnzJw5UzNmzKi/X15erqysLKdKRBjwf+Lz85//XL169Qp4zP/JT2Muu+wyvfnmm81+2teY7373uzrrrLOa/LRv2LBhjT5vxowZGjVqVINP+37605/q2LFjSktLkyR17ixdcIH0ve9JRUXSzJmfyrJ6Kj39BzpwIFknTkRLknw+S2lp1broIqlvX6lfP7v1f/3Xv76hlSub/rSvKatXr272076mLFmypNlP+xqTnp6uhQsXNvtpX2OGDRumf/zjH81+2tcYr/zMJfsT3vT09Eaf9+ijj2ry5MlNfsLblDfeeKPZT3ibws+8IX7mjeNn3hA/84bc/JkXFxfr4YcfbnTUrb0sS6qslA4ckPbvl778Ujp40G7PdKutbfh60dFSly4nb/HxiZKK1L17ra67bqI6d5buuks66yx7/9LSUtXW1qpTp05B/94ilc+yLMuJF3777bf1jW98Q9HR0fXbamtr5fP5FBUVperq6oDHGlNeXq7U1FSVlZUpJSXFiTLhcbm5uTrvvPOUk5OjUaNGuV1Oq82ZM0ezZ8/W/fffr9/+9lElJ0tz5kj33is999xz+tGPfqSbbrpJL7/8smprpS++sMPPtm12e+rXBw6cfN2kpIbBx/91r15SI2dPAAAQ1lrTJzhxwg4g+/efDC7NtQcOSI1kWcXGSl27BgaWU29NPZacLPl8ga/l78seOHBAcXFx6tRJ+vnPpfvuk6699lq98847WrFihcaOHRukd8w8rckGjo3kXHrppVq/fn3Ath/84Ac655xz9MADD5wx4ACS6ufi5ObmhmXI+dnPfqann35ajz32mD78cIMqK9/X4cNrdOmlD2rx4sVKTk7WH/7wB0n2pz5ZWfbtoosavlZ5ubR9e8MA9M470o4d9h90SYqKsl+jTx+pd2879PTuffLrnj3tP9oAAISLo0elDz/cLGm0XnppnzZsaD60HDpkj86cKipKSkuzb9262W2/foH3/W1amh1WOnRoGFba6uc//7keeOAB9e3bV88//7ySki7X7t1l+q//+q7ef/99DR06lIATRI6FnOTkZA0dOjRgW8eOHdW1a9cG2wFTxcTEaPPmzbrsssv0739XSpIefvgmSZs0aNAgLV68uMVD0ykp0ogR9u10J05Iu3YFhp8dO6TPP5cWLpROPTsgKsoOOqeHH//XWVmMBAEAnFVTYweSfftOtv5bY/crKyXpRkk36j+fDSopqWFgGTOmYWDxt5062f8GuuX+++/X/v379cQTT+iKK66QtFHz5r0v6X0NGzZMq1evdq84Azm+uhoQ6ZKSkrRq1So98US5fvrTOj322G264YZrGswxao+YmJMh5ZJLGj5eVSWVlEjFxXb48d+KiqTFi+3T5PyfePl8UmZm4wGoVy8pO1tq4TQ7AECEqK2157C0NLQcPtzwNTp2lNLTT96GDj35dbduUk7OB5o37yE9+uhPdeed31FiYsi/zXZ7/PHH9bOf/Uy//OUv9eKLPqWlDdNf/7pGo0ePdrs044Q05CxdujSUhwM8pbQ0RX36SPfdd1fIj52QIJ19tn1rTE2NtHPnyfBzahhavlzavVs6df5sRsbJ8OM/xa5nz5Nterq7n5YBANrHsuzTpE8NJ40FFv/t4MHAfyck+6yAU0NLv3724junbuvW7WTboUPzNVVV7ZaUpy5djoZlwPHr1KmT/vCHP2j9eikzc6DIN85gJAeedtZ/lh3xt+Fs8+amQ4bb4uLsf3z69Wv88ePH7dPhTg9AO3ZIa9bYj51yiSvFxtorxpwafPyt/2uCEACEVlVV4wGlqeBSUxP4/Kiok6EkPV3q0UMaNuxkSOnePTC4NDb5vj1M6hNI9vvTyIJ/CBJCDjzNf2Gwxi4QFm62bJEuu8ztKtomNtZeyKBPn8Yftyx7oufOnXbg8bf+r1evblkQOj0UEYQAoGlVVXZAOXVuS2M3f2hprEPdqVPgyMpXvhIYZE69deni7t9kk/oEkj2nqLTU7SrMRciBp0VFRSk5OVlRYd7Tra21FwO47Ta3K3GGz2f/o9itm9TUInjtDUIZGfatR4+GX6elEYYAhL/KyqaDSmNB5siRhq/hn9fi/5s8cKD01a82Hly6dQuvhWZM6RP4JSUxkuMkQg48beTIkSovL3e7jHYrKbGH/b16ulootDcI7dkjFRTYn3p9+WXg86Kj7dMkmgtC/q/j453/XgHg+HF7nsrBg/bftdNb/3LHp96OHm34OsnJgaFl6NCTX5+63X8L57kqZ2JKn8AvObnxoIrgIOQAIbB5s90OGOBuHV7XkiAk2aM9paX2bc8e+3bq13l50gcfSHv3nrx+kF/nzk0HoR49Th6/a1euJwTAVlMTGE6aCi6n7tNYX9znO3kBSf/SxiNHNh1a0tJYzdJkSUmEHCcRcoAQ2LLFPiUgO9vtSswQH2+v7namVbjr6uxOx+lByN+WlNinye3Z0/g/NJ06NX6thabaYE+yBRA8liUdO2ZfJNJ/+/LLwPv+baePwDR2SlFUlB1W/IGla1d7Er7/vn/bqW2nTvbIMyCx8IDTCDlACGzebK9cxj9uoeVfCahbN2n48Ob3PXLEDj/NXUG7oODk1419ShsX17JA1LWr3dnp3Nk+f55gBLRcVdWZQ0pT205fLcyvY0f7/0f/rWtX+0Op00PKqV+npjIXEO3jH8mxLP4dcAIhB55WXFysX//61/rFL34R1ItnhtqWLZE9HyccJCVJ/fvbt5aorrY/4W0qEO3fb4emDRtObqutbfg60dF24Gnq1rlz84936MA/jggPJ07Yn1qXl9u3srKTXzd2v7Fthw7ZIacxiYmBQaVLF/sU4VO3+befvi2cJt9HMlP6BH7Jyfa/C1VVZs+lcgshB5528OBBPf/887rtttvC+g/a5s3S9de7XQWCKT5eysy0by1hWfYVvv3n65eV2fcPH7Y7bv6v/bedOwMfO3688deNjW06AKWk2OGtY8fG29O3dezIJ9Ow1dXZk+ArK0+2zX195MiZw0pjk+r9fD779zUlxR4h8X/dtau9dH1qqt0hPD2cnHpj7or5TOkT+CUl2e2RI4QcJxBy4Gk1/zm3oKapcwzCQE2NfdFMFh2IbD7fyc5Ya38X/HMJTg9CjYUj//aiIrtzeWontLGRpNMlJrYuGCUl2Z3L+PiTt7i4wPvNbecUzuZZlv03pLr6ZNuar6uqzhxQGvu6qdGS08XH26OJSUmB4SQtTerb9+T908PL6ds4bRMtYUKf4FSnhpxu3dytxUSEHHjahg0b6tsLLrjA5WraZts2+1NRQg7ayuezO5IdOrR85Oh0/s7ykSMnQ09r2y++aHx7e/obUVGtC0RRUfbt1K9PvbVme1P71tXZt9rawDaY22prA0NJU0GlqRG8loqPPzlK16FDYNuxox1GTn+8qa9P39ahgxRDLwIhZEKf4FTJyXbL4gPO4M8T4LCNG+120CB360Bk8/lOhoWuXYP72nV1gR3z0zvqzW1rzb6nBoUTJ06Gh9PDRHu219U1HYQa+/pMj5/6dWxs4LbTw9zpwa6px1q6X2wsoyOAl/mv23bqRbARPIQcwGEbN9oTXdPT3a4EcEZUlH3KGnMiAKDl/Ndia++ILRrHFFPAYRs32qM4fKIKAAD8/Kd7nn7RagQHIQee1qdPn4A2HBUWcqoaAADtZUKf4FSM5DiLkANP69y5c0AbburqpM8/lwYPdrsSAADCW7j3CU5HyHEWIQeelpCQoMGDByshTE/2Ly62l/5lJAcAgPYJ9z7B6ThdzVksPABPGzx4sAoKCtwuo838K6sxkgMAQPuEe5/gdIzkOIuRHMBBGzfa15TIynK7EgAA4CX+kRxCjjMIOYCD/IsOsLIaAAA4lX8kh9PVnEHIARzkXz4aAADgVJyu5ixCDjxty5Ytuvzyy7Vlyxa3S2k1y2L5aAAAgiWc+wSNYeEBZxFy4GkVFRX68MMPVVFR4XYprVZaKpWVsegAAADBEM59gsb4fFJ0NCM5TiHkwNOOHDkS0IYT/8pqjOQAANB+4dwnaEpMDCHHKYQceNrmzZsD2nBSWCjFxUl9+7pdCQAA4S+c+wRNiY3ldDWnEHIAh2zcKJ199slzbgEAAE4VG8tIjlMIOYBDCguZjwMAAJrG6WrOIeQADmH5aAAA0BxOV3MOIQeedvbZZwe04eLAAWnvXmnIELcrAQDADOHaJ2iOzyfV1bldhZkIOfC0pKSkgDZcbNhgt8OGuVsHAACmCNc+QXNqa+1lpBF8hBx4WnJysiZNmqTk5GS3S2mV9eul+Hipf3+3KwEAwAzh2idoDiHHOaz7BE8bMGCAFi5c6HYZrbZ+vT0fh5XVAAAIjnDtEzSHkOMcRnIAB2zYIA0d6nYVAADAy+rqCDlOIeQAQWZZdshhPg4AAGhOba0URW/cEbytQJCVlEgVFYzkAACA5nG6mnMIOfC0wsJCDRkyRIWFhW6X0mLr19stIzkAAARPOPYJzoSQ4xxCDjytqqpKhYWFqqqqcruUFlu/XkpNlXr2dLsSAADMEY59gjMh5DiHkANPO3ToUEAbDvyLDvh8blcCAIA5wrFPcCYsPOAcQg48bfv27QFtOFi/nlPVAAAItnDsEzTHsuyQw8IDzuBtBYLo+HHp889ZdAAAADSvrs5uGclxBiEHCKLNm+2gw0gOAABoTm2t3RJynEHIAYLIv7IaIzkAAKA5hBxnEXLgaUP/kxaGhklq2LBBysyUunRxuxIAAMwSbn2CM/GfrsacHGfwtsLT4uLiAlqvW7+eURwAAJwQbn2CM2Ekx1mEHHha165ddcstt6hr165ul9IiGzYwHwcAACeEW5/gTAg5zopxuwCgOb169dJzzz3ndhktUl4uFRURcgAAcEI49Qla4vhxu42NdbcOUzGSAwSJf9GBESPcrQMAAHjfsWN226GDu3WYipADBEl+vv1pzODBblcCAAC87uhRuyXkOIOQAwRJfr40aJBkyHxIAADgIH/ISUx0tw5TEXLgaXl5eUpJSVFeXp7bpZxRfj6nqgEA4JRw6hO0BCM5ziLkwNPq6upUUVGhOv9i8h5VWyt99hkhBwAAp4RLn6ClmJPjLEIOPG3v3r0BrVdt3Wr/sSLkAADgjHDpE7QUIznOIuTA03bv3h3QelV+vt0ScgAAcEa49AlaipDjLEIOEAT5+VJmptStm9uVAACAcOAPOQkJ7tZhKkIOEAQsOgAAAFrj2DE74ETRG3cEbysQBHl5hBwAANByR49yqpqTCDnwtFGjRgW0XnTwoLR7NyEHAAAnhUOfoDUIOc4i5ADtxKIDAACgtQg5ziLkwNMyMjI0a9YsZWRkuF1Kk/Lz7XNqBwxwuxIAAMwVDn2C1iDkOMtnWZbldhFNKS8vV2pqqsrKypSSkuJ2OUCjpkyRNm6UVq92uxIAABAufvhD6fPPpRUr3K4kfLQmGzCSA7QTK6sBAIDWYiTHWYQcoB1qaqTCQkIOAABoHUKOswg58LTa2lqVl5ertrbW7VIatXGjdPw4IQcAAKd5vU/QWpWVhBwnEXLgafn5+UpNTVW+fwkzj1m3zm5HjnS1DAAAjOf1PkFrHT4sderkdhXmIuQA7bBunb2qWnKy25UAAIBwcviw1Lmz21WYi5ADT9u1a1dA6zXr1knnnut2FQAAmM/rfYLWOnSIkRwnEXLgafv27QtovaSuTsrLI+QAABAKXu4TtFZdnVRWRshxEiEHaKNt26SKCkIOAABonSNH7KDD6WrOIeQAbeRfdICQAwAAWuPQIbtlJMc5hBx4WmxsbEDrJevWSZmZUnq625UAAGA+L/cJWuvwYbtlJMc5hBx42rBhwwJaL1m3Tho1yu0qAACIDF7uE7SWP+QwkuMcQg7QBpbFymoAAKBtOF3NeYQceFp2draeffZZZWdnu11KgD17pH37CDkAAISKV/sEbcFIjvNi3C4AaE5aWpqmTp3qdhkNsOgAAACh5dU+QVscPix17CgZML3IsxjJAdpg3Tp7smCvXm5XAgAAws2hQyw64DRHQ86TTz6p4cOHKyUlRSkpKRo7dqw++OADJw8JhMS6ddLIkZLP53YlAAAg3Bw+zKlqTnM05PTs2VO//e1vtXbtWq1du1aXXHKJrrnmGhUUFDh5WBjk2LFjKigo0LFjx9wuJUBuLiurAQAQSl7tE7TFoUOEHKc5GnKuvvpqXXnllTr77LN19tln65FHHlFSUpJWrVrl5GFhkI0bN2ro0KHauHGj26XUO3RI2rGD+TgAAISSF/sEbXX4MKerOS1kCw/U1tbqjTfeUGVlpcaOHdvoPtXV1aqurq6/X15eHqrygBbLy7NbQg4AAGiLw4el3r3drsJsji88sH79eiUlJSk+Pl7Tpk3TW2+9pcGDBze679y5c5Wamlp/y8rKcro8eFxRUVFA6wXr1kmJidLAgW5XAgBA5PBin6CtOF3NeY6HnIEDByovL0+rVq3ST37yE02ZMkWFhYWN7jtz5kyVlZXV33bu3Ol0efC4w/9ZSN7fekFurr3oQHS025UAABA5vNgnaKt9+6T0dLerMJvjp6vFxcWpf//+kqTRo0drzZo1+v3vf6+nn366wb7x8fGKj493uiSgXXJypEsvdbsKAAAQjmprpQMHCDlOC/l1cizLCph3A4STI0ekTZtYWQ0AALTNwYNSXZ3UvbvblZjN0ZGchx56SJMnT1ZWVpYqKir02muvaenSpVqwYIGTh4VBOnbsGNC6LS9PsizpvPPcrgQAgMjitT5BW+3da7eEHGc5GnL27t2rm266SXv27FFqaqqGDx+uBQsW6Gtf+5qTh4VBBv5ndv9Aj8zyz82V4uOlJtbOAAAADvFan6Ct9u2zW05Xc5ajIef555938uWBkMvJkYYPl2Jj3a4EAACEI0ZyQiPkc3KA1ujfv78WLFhQv3iF23JzmY8DAIAbvNYnaKt9+6QOHaQwP+vO8wg58LSUlBRdfvnlSklJcbsUHT0qFRYyHwcAADd4qU/QHnv3MooTCoQcoIU++8xeDYWRHAAA0FZ79zIfJxQIOUAL5eTYc3GGDnW7EgAAEK727WMkJxQIOfC08vJyLVy4UOXl5W6XotxcO+BwvVoAAELPS32C9mAkJzQIOfC0rVu36oorrtDWrVvdLoVFBwAAcJGX+gTtwUhOaBBygBaoqpI2bGDRAQAA0HaWxUhOqBBy4GmbNm0KaN2yYYN04gQjOQAAuMUrfYL2qKiQqqsZyQkFQg48rbKyMqB1S06OFB1tXwgUAACEnlf6BO3BhUBDh5ADtEBurjR4sJSY6HYlAAAgXPlDDqerOY+QA7RATg6nqgEAgPYh5IQOIQee1qlTp4DWDTU10vr1LDoAAICbvNAnaK8vvpDi4qSuXd2uxHyEHHha3759A1o3bNhgBx1CDgAA7vFCn6C9du2SevaUfD63KzEfIQc4g5wcKSpKGjnS7UoAAEA427lTyspyu4rIEON2AUBzBg0apA0bNrj6qU1Ojr3oQIcOrpUAAEDE80KfoL127ZKys92uIjIwkgNPS0xM1JAhQ5To4rJma9dyqhoAAG7zQp+gvfynq8F5hBygGSw6AAAAgqGuzg45nK4WGoQcoBksOgAAAIJh/37p+HFGckKFkANPO3DggJ577jkdOHDAleOz6AAAAN7gdp+gvXbtsltCTmgQcuBpJSUl+tGPfqSSkhJXjp+TIw0axKIDAAC4ze0+QXvt3Gm3nK4WGoQcoBk5OdLo0W5XAQAAwt2uXfaFQNPS3K4kMhBy4Gnr168PaEOppkb67DPm4wAA4AVu9gmCYedO6ayz7NPg4TzeZnja8ePHA9pQYtEBAAC8w80+QTCwslpoEXKAJrDoAAAACBaukRNahBygCSw6AAAAgmXnTkJOKBFy4Gnp6ekBbSjl5HCqGgAAXuFmn6C96uqk3bs5XS2UCDnwtJ7/+cijZ4g/+mDRAQAAvMWtPkEwHDhg9y3CsPSwRcgBGlFQwKIDAAAgOPzXyCHkhE6M2wUAzRkxYoTKysrUsWPHkB537VoWHQAAwEvc6hMEQ3Gx3WZnu1tHJCHkwNOio6OVkpIS8uP6Fx0Iw7+jAAAYya0+QTAUFUlJSVK3bm5XEjk4XQ1oBIsOAACAYNm+XerTR/L53K4kchBygNP4Fx0YPdrtSgAAgAmKiqS+fd2uIrIQcuBpe/bs0ezZs7Vnz56QHXPDBhYdAADAa9zoEwQLISf0CDnwtD179mjOnDkh/YOWk8OiAwAAeI0bfYJgqKuTduywT1dD6BBygNP4Fx3o0MHtSgAAQLj74gv7DBFGckKLkANPy83NDWhDISeH+TgAAHiNG32CYNi+3W4ZyQktQg5wCv+iA8zHAQAAwVBUZLe9e7taRsQh5ACnYNEBAAAQTEVFUkYGp8GHGiEHOAWLDgAAgGDyXyMHoUXIgaedddZZAa3TcnKkwYP5tAUAAK8JdZ8gWFg+2h2EHHha9+7dA1qnrV3LqWoAAHhRqPsEwULIcQchB54WFRWl5ORkRUU5/6taUyOtX0/IAQDAi0LZJwiWY8ekPXs4Xc0NMW4XADRn5MiRKi8vD8mxWHQAAADvCmWfIFh27LBbRnJCL3yiMOAwFh0AAADB5F8+mpATeoQc4D/WrmXRAQAAEDzbt0txcVJmptuVRB5CDvAfOTmcqgYAAIJn61b7IqBhNI3IGLzl8LTi4mJNnTpVxcXFjh6HRQcAAPC2UPUJgmnTJmngQLeriEyEHHjawYMH9fzzz+vgwYOOHqegwA46o0Y5ehgAANBGoeoTBNPmzYQctxBy4Gk1NTUBrVNYdAAAAG8LVZ8gWKqr7dXVCDnuIOTA0zZs2BDQOiU3VzrnHKljR0cPAwAA2ihUfYJg2bZNqquTzj7b7UoiEyEHEIsOAACA4Nq0yW4ZyXEHIQcR7/hxKT+f+TgAACB4Nm2SUlOl9HS3K4lMhBxEvMJC+7xZRnIAAECwbN5sn6rm87ldSWQi5MDT+vTpE9A6ITfX/gN07rmOHQIAALRTKPoEwcTy0e4i5MDTOnfuHNA6ISfH/iOUlOTYIQAAQDuFok8QTIQcdxFy4GkJCQkaPHiwEhISHDtGTg7zcQAA8LpQ9AmC5eBB+8bKau6JcbsAoDmDBw9WQUGBY69/4oS96MANNzh2CAAAEARO9wmCafNmu2Ukxz2M5CCiff65dOwYiw4AAIDg8S8f3b+/u3VEMkIOIlpOjt2y6AAAAAiWzZulrCwuMu4mQg4iWk6ONGCAlJLidiUAAMAULDrgPkIOPG3Lli26/PLLtWXLFkdePzeXU9UAAAgHTvcJgmnTJhYdcBshB55WUVGhDz/8UBUVFUF/7dpaad06Qg4AAOHAyT5BMNXWSlu3MpLjNkIOPO3IkSMBbTBt3iwdPcry0QAAhAMn+wTBVFwsVVcTctxGyIGnbf7PGoz+Nphyc+2WRQcAAPA+J/sEweRf5XrIEHfriHSEHESsdeukPn2kMLlwMgAACAMFBfaCRmed5XYlkY2Qg4iVm8soDgAACK6CAnsUx+dzu5LIRshBRLIseySH+TgAACCYNmzgVDUvIOTA087+z/qLZwd5HcYdO6TDhwk5AACEC6f6BMFUWyt9/jkhxwsIOfC0pKSkgDZYWHQAAIDw4lSfIJiKiqSqKkKOFxBy4GnJycmaNGmSkpOTg/q669ZJGRlSjx5BfVkAAOAQp/oEwcTKat4R43YBQHMGDBighQsXBv11WXQAAIDw4lSfIJgKCuxVWzMy3K4EjOQgIrHoAAAACDZWVvMOQg4izp49UmkpIQcAAAQXK6t5ByEHEYdFBwAAQLCdOCFt2kTI8QpCDjytsLBQQ4YMUWFhYdBec906+3zZXr2C9pIAAMBhTvQJgmnrVqmmhpDjFY6GnLlz5+r8889XcnKy0tPTde2112rTpk1OHhKGqaqqUmFhoaqqqoL2mv5FBzhfFgCA8OFEnyCYWFnNWxwNOcuWLdPtt9+uVatWadGiRTpx4oQmTZqkyspKJw8Lgxw6dCigDQYWHQAAIPw40ScIpoICqWtXKT3d7UogObyE9IIFCwLuz58/X+np6crJydFXv/pVJw8NQ2zfvj2gba8vv5R27CDkAAAQboLdJwg2/6IDnCniDSG9Tk5ZWZkkqUuXLo0+Xl1drerq6vr75eXlIakLkSMvz25ZdAAAAARTQYE0caLbVcAvZAsPWJalGTNm6MILL9TQoUMb3Wfu3LlKTU2tv2VlZYWqPESIdeukDh2kAQPcrgQAAJiipkbavJn5OF4SspBzxx136LPPPtOrr77a5D4zZ85UWVlZ/W3nzp2hKg8RIi9PGjFCio52uxIAAGCKjRvtJaSHD3e7EviF5HS1O++8U++++66WL1+unj17NrlffHy84uPjQ1ESwoR/1K+p0b/WysuTJkwIyksBAIAQCnafIJjy8+2WkOMdjo7kWJalO+64Q2+++aYWL16sPn36OHk4GCguLi6gbY+qKvuTlhEj2v1SAAAgxILZJwi2vDypb18pJcXtSuDn6EjO7bffrr/85S965513lJycrNLSUklSamqqEhMTnTw0DNG1a1fdcsst6tq1a7tfq6BAqq2VRo5sf10AACC0gtknCLb8fD5E9RqfZVmWYy/exBp68+fP180333zG55eXlys1NVVlZWVKIRqjnV54QfrRj6SKCnvxAQAAgPayLKlbN+nOO6VZs9yuxmytyQaOjuQ4mJ+AVsvLk84+m4ADAACC54svpIMHOVPEa0K2uhrgNv/KagAAAMHivwYffQxvIeQgIliWfb4sn7IAAIBgys+XUlOlXr3crgSnIuTA0/Ly8pSSkqI8/8ckbbRjh1ReTsgBACBcBatPEGz+RQeamIoOlxBy4Gl1dXWqqKhQXV1du17H//eQkAMAQHgKVp8g2FhZzZsIOfC0vXv3BrRtlZcnde8u9egRhKIAAEDIBatPEEyVldLmzYQcLyLkwNN2794d0LYViw4AABDegtUnCKYNG+x5v/QxvIeQg4jAogMAACDY8vOl6GhpyBC3K8HpCDkw3qFDUnExIQcAAARXfr40cKCUmOh2JTgdIQfGy8+3W0IOAAAIJk6H9y5CDjxt1KhRAW1b5OVJCQnSgAFBKgoAAIRcMPoEwVRXJ332GR+iehUhB8bLz5eGDpViYtyuBAAAmGL7dunIEUZyvIqQA0/LyMjQrFmzlJGR0ebX+Owz/gABABDugtEnCKacHLs991x360DjfJZlWW4X0ZTy8nKlpqaqrKxMKSkpbpeDMHTihJSUJD32mHTXXW5XAwAATPHAA9Jf/iLt3Ol2JZGjNdmAkRwYbetWqbpaGjbM7UoAAIBJcnKk885zuwo0hZADo61fb7eEHAAAECyWJeXmEnK8jJADT6utrVV5eblqa2vb9PzPPpMyMqS0tCAXBgAAQqq9fYJg2rHDvg4fIce7CDnwtPz8fKWmpirff7GbVlq/nlEcAABM0N4+QTD5Fx0g5HgXIQdG++wzQg4AAAiunBzprLOk7t3drgRNIeTA03bt2hXQtkZFhb2G/fDhwa4KAACEWnv6BMHGogPeR8iBp+3bty+gbY0NG+yWkRwAAMJfe/oEwWRZhJxwQMiBsdavl6KjpUGD3K4EAACYorhY+vJLQo7XEXJgrPXrpbPPlhIS3K4EAACYgkUHwgMhB54WGxsb0LYGiw4AAGCO9vQJgiknR8rMlHr0cLUMnAEhB5427D8pZVgr04plsXw0AAAmaWufINiYjxMeCDkw0u7d9kW6WFkNAAAEC4sOhA9CDjwtOztbzz77rLKzs1v1vPXr7ZaRHAAAzNDWPkEwlZRIBw8ScsJBjNsFAM1JS0vT1KlTW/28zz6TkpKkXr0cKAoAAIRcW/sEwcSiA+GDkRwYyT8fJ4rfcAAAECQ5OVJGhn2Dt9EFhJEKCqQhQ9yuAgAAmGTNGmn0aLerQEsQcuBpx44dU0FBgY4dO9bi59TWSp9/TsgBAMAkbekTBFNdnR1yxoxx5fBoJUIOPG3jxo0aOnSoNm7c2OLnbN8uVVURcgAAMElb+gTBtHWrdPiw9JWvuHJ4tBIhB8YpLLTbwYPdrQMAAJjj3/+22/PPd7cOtAwhB55WVFQU0LZEQYGUkmJfjRgAAJihLX2CYFq9Who4UOrUyZXDo5UIOfC0w4cPB7QtUVhon6rm8zlTEwAACL229AmCafVqTlULJ4QcGKeggFPVAABA8FRXS3l5hJxwQsiBUWprpY0bWXQAAAAET36+VFPDymrhhJADT+vYsWNAeybFxfbKaozkAABgltb2CYJp9WopLk4aPjzkh0YbEXLgaQMHDgxoz6SgwG4JOQAAmKW1fYJgWr1aGjlSio8P+aHRRoQcGKWwUEpOlnr2dLsSAABgin//m/k44YaQA0/r37+/FixYoP79+7dof/+iA6ysBgCAWVrbJwiWQ4ekzZuZjxNuYtwuAGhOSkqKLr/88hbvX1jI+bIAAJiotX2CYFm71m4ZyQkvjOTAGHV1rKwGAACC69//ti8AGuIBJLQTIQfGKC6Wjh5l0QEAABA8/ouARtFrDiv8uOBp5eXlWrhwocrLy8+4b2Gh3TKSAwCAeVrTJwgWyzoZchBeCDnwtK1bt+qKK67Q1q1bz7hvQYGUlCRlZYWgMAAAEFKt6RMEy86d0t69hJxwRMiBMT7/XDrnHFZWAwAAwbFqld0ScsIPIQeetmnTpoC2+X0lF64PBgAAQqA1fYJgWblS6ttX6t49ZIdEkBBy4GmVlZUBbXMIOQAAmKs1fYJgWbFCGjs2ZIdDEBFyYISDB+0bIQcAAARDVZW0bh0hJ1wRcmCEzZvt9uyz3a0DAACYISdHOn6ckBOuCDnwtE6dOgW0TfGfnjtggLP1AAAAd7S0TxAsK1dKHTpIw4eH5HAIMkIOPK1v374BbVM2bbKXju7YMRRVAQCAUGtpnyBYVq60V1WLiQnJ4RBkhBwYgUUHAABAsFgWiw6EO0IOPG3QoEHasGGDBg0a1Ox+mzczHwcAAJO1tE8QDMXFUmkpISecEXLgaYmJiRoyZIgSExOb3Ke2Vtq6lZEcAABM1pI+QbCsXGm3F1zg+KHgEEIOwl5xsVRdTcgBAADBsXKl1L+/1K2b25WgrQg5CHv+5aMJOQAAIBhWruRUtXBHyIGnHThwQM8995wOHDjQ5D6bNknx8VJ2dggLAwAAIdWSPkEwHD0q5eURcsIdIQeeVlJSoh/96EcqKSlpcp9Nm+zr40Tx2wwAgLFa0icIhrVrpRMnCDnhjm4hwh7LRwMAgGBZuVJKSpKGDnW7ErQHIQeetn79+oC2MZs3E3IAADBdS/oEwcBFQM1AyIGnHT9+PKA9XWWltGsXIQcAANOdqU8QDJbFogOmIOQgrG3ZYrdcCBQAALRXUZG0bx8hxwSEHIS1oiK77dfP3ToAAED4++QTux03zt060H6EHHhaenp6QHu6oiJ7cmBaWiirAgAAoXamPkEwfPqpveBA586OHQIhQsiBp/Xs2TOgPd22bfYojs8XyqoAAEConalPEAyffCKNH+/YyyOECDkIa0VFUt++blcBAADC3cGD0saN0oUXul0JgoHF8eBpI0aMUFlZmTp27Njo49u2Sd/4RoiLAgAAIXemPkF7rVhht4zkmIGQA0+Ljo5WSkpKo4+dOCEVFzOSAwBAJGiuTxAMn3wiZWZKvXs7dgiEEKerIWzt3GkHHVZWAwAA7fXpp/apaszzNQMhB2HLv3w0IzkAAKA9qqqkNWs4Vc0khBx42p49ezR79mzt2bOnwWNFRVJUlNSrlwuFAQCAkGquT9Bea9dKNTUsOmASQg48bc+ePZozZ06jf9C2bZOys6XYWBcKAwAAIdVcn6C9Pv3Uvu7e8OFBf2m4hJCDsFVUxHwcAADQfp98Il1wgRTDklzGIOTA03JzcwPaU23bxnwcAAAiRXN9gvaoq7OXj+ZUNbM4GnKWL1+uq6++WpmZmfL5fHr77bedPBwiDCM5AACgvT7/XPryS0KOaRwNOZWVlRoxYoT++Mc/OnkYRKAvv5QOH2YkBwAAtM8nn0jR0dKYMW5XgmBy9MzDyZMna/LkyU4eAhHKv3w0IzkAAKA9Pv1UGjnSXngA5vDU9Krq6mpVV1fX3y8vL3exGnjBWWedFdD6bdtmt4zkAAAQGZrqE7TXJ59I//VfQX1JeICnFh6YO3euUlNT629ZWVlulwSXde/ePaD1275d6txZ6tTJhaIAAEDINdUnaI89e+yzQ7gIqHk8FXJmzpypsrKy+tvOnTvdLgkui4qKUnJysqKiAn9VS0q4CCgAAJGkqT5Be3z8sd1+9atBe0l4hKdOV4uPj1d8fLzbZcBDRo4c2ehpiyUlEgN9AABEjqb6BO2xfLk0YIDUo0dQXxYe4KmRHKCldu6UsrPdrgIAAISz5csZxTGVoyM5R44c0datW+vvb9++XXl5eerSpYuy6aGiHUpKCDkAAKDtvvxS2rBBuvdetyuBExwNOWvXrtXFF19cf3/GjBmSpClTpujFF1908tAwWEWFfY0cTlcDAABt9emnkmUxkmMqR09XmzhxoizLanAj4KCliouLNXXqVBUXF9dv869HwUgOAACRo7E+QXssXy717Cn17h2Ul4PHMCcHnnbw4EE9//zzOnjwYP22khK7ZSQHAIDI0VifoD0+/liaMEHy+YLycvAYQg48raamJqCV7JATFSVlZrpVFQAACLXG+gRtdeSIlJPDqWomI+TA0zZs2BDQSvbpamedJcV4agF0AADgpMb6BG21apV04gQhx2SEHIQdrpEDAADa4+OPpbQ0adAgtyuBUwg5CDtcIwcAALTH8uXShRcyH8dkhByEHUZyAABAW1VX26ercaqa2Qg58LQ+ffoEtHV1jOQAABCJTu8TtFVOjlRVRcgxHSEHnta5c+eAdv9+qaaGkAMAQKQ5vU/QVsuXS0lJ0ogRwagKXkXIgaclJCRo8ODBSkhIkMQ1cgAAiFSn9wnaavlyafx4Vmk1HT9eeNrgwYNVUFBQf3/nTrtlJAcAgMhyep+gLWprpU8/lR54IEhFwbMYyUFYKSmREhOlLl3crgQAAISbzz6TysulCRPcrgROI+QgrOzZI2VmsuQjAABovWXLpIQE6fzz3a4ETiPkIKyUlko9erhdBQAACEdLl0oXXGAHHZiNkANP27Jliy6//HJt2bJFEiEHAIBIdXqfoLXq6qSPP5YuuijIhcGTCDnwtIqKCn344YeqqKiQZIecjAyXiwIAACF3ep+gtTZskL78Upo4Mbh1wZsIOfC0I0eOBLR79jCSAwBAJDq9T9BaS5dKcXHSmDFBLAqeRciBp23evLm+PX5cOnCAkAMAQCQ6tU/QFsuW2QEnMTGYVcGrCDkIG/v3S5ZFyAEAAK1TV2dfBJRT1SIHIQdho7TUbgk5AACgNQoL7bNBWHQgchByEDYIOQAAoC2WLZNiY6WxY92uBKFCyIGnnX322fWtP+Skp7tYEAAAcMWpfYLWWrZM+spXpA4dgl0VvIqQA09LSkqqb0tLpbQ0+5MYAAAQWU7tE7SGZdkhh1PVIgshB56WnJysSZMmKTk5meWjAQCIYKf2CVrj88+lffsIOZEmxu0CgOYMGDBACxculGTPySHkAAAQmU7tE7TGsmVSTIw0bpwDRcGzGMlB2CDkAACA1lq6VBo9WmrlWW4Ic4QchA1CDgAAaA3m40QuQg7CRmmplJHhdhUAACBcbNli9x+4CGjkIeTA0woLCzVkyBCtWbNRR45I3bu7XREAAHCDv09QWFjY4ucsXSpFR0vjxztXF7yJkANPq6qqUmFhofburZXENXIAAIhU/j5BVVVVi5+zdKk0apTUygXZYABCDjzt0KFDkqRdu45Kkrp0cbMaAADgFn+fwN+eiWXZIefiix0sCp5FyIGnbd++XZK0efMBSYQcAAAilb9P4G/PZPNmac8eQk6kIuQgLBw9Gi+JkAMAAFpmyRLm40QyQg7CQmVlvKKjpZQUtysBAADhYOlS6fzzmY8TqQg5CAuVlfHq3Fny+dyuBAAAeJ1/Pg5LR0cuQg48bejQoZKkxMSz1Lmzy8UAAADX+PsE/rY5n38u7d3LfJxIRsiBp8XFxUmSKivjmI8DAEAE8/cJ/G1zliyRYmKkceOcrgpeRciBp3Xt2lW33HKLjh3rQMgBACCC+fsEXbt2PeO+S5dKX/mKlJTkfF3wJkIOPK1Xr1567rnnVFVFyAEAIJL5+wS9evVqdj+ujwOJkIMw8eWXLB8NAADOrKBA2r+fRQciHSEHYYGQAwAAWmLpUik2lvk4kY6Qg7BAyAEAAC2xZIk0ZozUoYPblcBNhBx4Wl5enpKT01VVRcgBACCS5eXlKSUlRXl5eU3uU1cnLVvGfBwQcuBxdXV1OnIkVhIhBwCASFZXV6eKigrV1dU1uc+GDdLBg8zHASEHHrd3715Jdroh5AAAELnsPsHJtjFLl0pxcdLYsSEqCp5FyIGn7d69W4QcAABg9wlOto1ZskS64AIpMTFUVcGrCDkIA6n2f1NdLgMAAHgW83FwKkIOwoB9uWKuWgwAAJqSny8dOkTIgY2QgzDQURJDzwAAoGmLF0sJCfbpagAhB542atQoSR2VmFirKH5bAQCIWHaf4GR7uiVLpPHjpfj4UFYFr6LbiDDQUYmJTS8XCQAAItuJE9Ly5dIll7hdCbyCkANPy8jI0PjxVygpyed2KQAAwEUZGRmaNWuWMjIyGjyWkyNVVDAfByfFuF0A0JyMjAyNGpWhsjK3KwEAAG7KyMjQ7NmzG31syRKpY0dp9OjQ1gTvYiQHnldZycpqAACgaYsXS1/9qhQb63Yl8ApCDjyvstL+dAYAAOB0NTXSp59yqhoCEXLgabW1tTp8+Lg6dLDcLgUAALiotrZW5eXlqq2tDdi+erV09CiLDiAQIQeelp+fr4ULP9Hx44fcLgUAALgoPz9fqampys/PD9i+eLHUqZM0cqQrZcGjCDkIAywhDQAAGrdkiXTRRVJ0tNuVwEsIOfC0Xbt2SeqoEydYXg0AgEhm9wlOtpJ07Ji0ciXzcdAQIQeetm/fPklJqq0td7sUAADgIrtPcLKV7IBTXU3IQUOEHISBjkpIOO52EQAAwGOWLJHS0qShQ92uBF5DyEEY6Ki4uBNuFwEAADxm8WJp4kQpih4tTsOvBDwtOjpOUiJLSAMAEOFi/3OlT3975Ii9fDRLR6MxhBx42oABwyVJ/ftnulwJAABw07BhwwLaTz+VTpxgPg4aR8iBp9XW+iRJMTGM5AAAgJMWL5YyMqSBA92uBF5EyIGn9ejR8z9tmsuVAAAAN2VnZ+vZZ59Vdna2JDvkXHKJ5PO5XBg8iZADT0tN7SpJ6tIlxeVKAACAm9LS0jR16lSlpaXp8GEpN5f5OGgaIQeeduI/i6pxFWMAAOC3fLlUV0fIQdMIOfA0f8iJiXG3DgAA4B2LF0u9e9s3oDGEHHjakSNVkqTa2mqXKwEAAG46duyYCgoKdOzYsfr5OEBTCDnwtE2btkmSdu3a4W4hAADAVRs3btTQoUP16adbtH49IQfNI+TA0+rqIncJ6T//+c/q06ePEhISdN555+njjz9udv9ly5bpvPPOU0JCgvr27aunnnoq4PGCggJdf/316t27t3w+n+bNm+dg9QAAOGPt2iRJXB8HzSPkwNOKi3dLkkpLd7tcSWi9/vrrmj59un72s59p3bp1mjBhgiZPnqySkpJG99++fbuuvPJKTZgwQevWrdNDDz2ku+66S3//+9/r9zl69Kj69u2r3/72t+rRo0eovhUAAIKiqKhIkrR4saVzzpEyuU44mkHIgaeVlR2RJFVWlrtcSWj9z//8j2655RZNnTpVgwYN0rx585SVlaUnn3yy0f2feuopZWdna968eRo0aJCmTp2qH/7wh/rd735Xv8/555+vxx9/XDfeeKPi4+ND9a0AABAUhw8fliR99llXRnFwRoQceFptrf0rGhVV53IloVNTU6OcnBxNmjQpYPukSZO0YsWKRp+zcuXKBvtffvnlWrt2rY4fP+5YrQAAhNZZ2ru3E/NxcEaEHHhWXV2ddu7cI0k6eHCvy9WEzoEDB1RbW6vu3bsHbO/evbtKS0sbfU5paWmj+584cUIHDhxwrFYAAEKhrKxMhYWFkuwhnIkTXS0HYSAkIae1E6iB1157TQMGDNArr7wqSfrjH/9XY8eO1Zo1a1yuLHR8Pl/AfcuyGmw70/6NbQcAIFxUV1dr+vTpOuuss/S///u/ki6RlK+f/vQHKisrc7s8eJjjIae1E6iB+fPn69vf/rZGjBihe+99QJI0c+b9On78uCZOnKi1a9e6XKGz0tLSFB0d3WDUZt++fQ1Ga/x69OjR6P4xMTHq2rWrY7UCAOCUuro6XX/99Xr66ad133336e9/f1PSJTr33EN6++239bWvfU3Hjh1zu0x4lOMhp7UTqBHZjh49qnvvvVdTpkzR3//+d/XuPUCSdOGFF2j58uUaMGCAHnjgAZerdFZcXJzOO+88LVq0KGD7okWLNG7cuEafM3bs2Ab7f/jhhxo9erRiY2MdqxUAAKe8//77ev/99/W3v/1Ns2fPVkzMAEm99P3vZ+mjjz5STk6OXnrpJbfLhEc5GnLaMoEake2dd97RoUOH9Mtf/lI+n0/du58lSerfv7c6dOig+++/X4sXL9b27dtdrrSho0el3Fy7be/zZsyYoeeee04vvPCCNm7cqHvuuUclJSWaNm2aJGnmzJn6/ve/X7//tGnTVFxcrBkzZmjjxo164YUX9Pzzz+u+++6r36empkZ5eXnKy8tTTU2Ndu/erby8PG3durVd3zcAAE544YUXNHr0aF111VWSpOLifoqKsnT99d103nnn6eqrr9YLL7zgcpXwqhgnX7y1E6irq6tVXV1df7+8PLKWDYa0a9cupaamqm/fvpKkuLgOkqROnZIlSeeee279fn369HGnyCasWyddeKH0yivSoEEtf97GjdL3vid98ok0fry97b//+7918OBB/epXv9KePXs0dOhQ/fOf/1SvXr0kSXv27Ak45bNPnz765z//qXvuuUd/+tOflJmZqT/84Q+6/vrr6/f54osv6t8/Sfrd736n3/3ud7rooou0dOnSdn3vAAAE265duzRy5Mj6+ytXJmr0aCkrK0WS3Sf497//7VJ18DpHQ45fSydQz507V3PmzAlFSfCobt26qaKiQl988YUyMzM1fry0ZInUqZP9+KZNmyRJ6enp7hXZhB077PZ732v78/0hR5Juu+023XbbbY3u++KLLzbYdtFFFyk3N7fJ1+/du3f9YgQAAHhdt27dtHnz5vr7v/mNtH//ycc3bdrkyf4AvMFnOdjrqampUYcOHfTGG2/oG9/4Rv32u+++W3l5eVq2bFnA/o2N5GRlZamsrEwpKSlOlQkPKSsrU2ZmpqZNm6Ynnngi4LETJ07o4osvVk1NjSc/uTlwQFq4UOrdW0pMbPnzjh2zA87ll0tpaU5VBwBAeHn11Vf1ne98RytWrNDYsWMDHtu6dauGDBmiRx55JODUbJitvLxcqampLcoGjo7knDqB+tSQs2jRIl1zzTUN9o+Pj+dK7BEuNTVVv/jFLzRz5kzV1NRoxowZ6t27t9auXatZs2Zp5cqVWrhwodtlNiotTfrud9v23FNHcAAAgHT99ddrzJgxuuqqq/Too4/qO9/5jmJiYvTmm2/q/vvvV69evTR16lS3y4RHOTqSI9lLSN9000166qmnNHbsWD3zzDN69tlnVVBQUD+/oCmtSWswh2VZeuKJJ/Twww+rrKxMPp9PlmUpOztbf/7zn+snIAIAALMdPnxYU6dO1ZtvvhlwyvWll16ql156SWeddZaL1SHUWpMNHA85kn0x0Mcee6x+AvX//u//6qtf/eoZn0fIiWyVlZX64IMPdPDgQfXp00eXXnqpoqOj3S4LAACE2Pbt27V06VLV1tZq3LhxGjx4sNslwQWeCzltRcgBAAAAILUuGzh+MVAAAAAACCVCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGMXRkPPII49o3Lhx6tChgzp16uTkoQAAAABAksMhp6amRjfccIN+8pOfOHkYAAAAAKgX4+SLz5kzR5L04osvOnkYAAAAAKjHnBwAAAAARnF0JKe1qqurVV1dXX+/vLzcxWoAAAAAhKNWj+TMnj1bPp+v2dvatWvbVMzcuXOVmppaf8vKymrT6wAAAACIXD7LsqzWPOHAgQM6cOBAs/v07t1bCQkJ9fdffPFFTZ8+XYcPH272eY2N5GRlZamsrEwpKSmtKRMAAACAQcrLy5WamtqibNDq09XS0tKUlpbW5uKaEx8fr/j4eEdeGwAAAEBkcHROTklJib788kuVlJSotrZWeXl5kqT+/fsrKSnJyUMDAAAAiFCOhpxf/vKXeumll+rvn3vuuZKkJUuWaOLEiU4eGgAAAECEavWcnFBqzXl3AAAAAMzVmmzAdXIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZADAAAAwCiEHAAAAABGIeQAAAAAMAohBwAAAIBRCDkAAAAAjELIAQAAAGAUQg4AAAAAoxByAAAAABiFkAMAAADAKIQcAAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADBKjNsFNMeyLElSeXm5y5UAAAAAcJM/E/gzQnM8HXIqKiokSVlZWS5XAgAAAMALKioqlJqa2uw+PqslUcgldXV1+uKLL5ScnCyfz+d2Oc0qLy9XVlaWdu7cqZSUFLfLMQbvq3N4b53B++oM3ldn8L46g/fVGbyvzgin99WyLFVUVCgzM1NRUc3PuvH0SE5UVJR69uzpdhmtkpKS4vlfkHDE++oc3ltn8L46g/fVGbyvzuB9dQbvqzPC5X090wiOHwsPAAAAADAKIQcAAACAUQg5QRIfH69Zs2YpPj7e7VKMwvvqHN5bZ/C+OoP31Rm8r87gfXUG76szTH1fPb3wAAAAAAC0FiM5AAAAAIxCyAEAAABgFEIOAAAAAKMQcgAAAAAYhZDjkEceeUTjxo1Thw4d1KlTJ7fLCVt//vOf1adPHyUkJOi8887Txx9/7HZJYW/58uW6+uqrlZmZKZ/Pp7ffftvtksLe3Llzdf755ys5OVnp6em69tprtWnTJrfLCntPPvmkhg8fXn+BurFjx+qDDz5wuyzjzJ07Vz6fT9OnT3e7lLA3e/Zs+Xy+gFuPHj3cLssIu3fv1ve+9z117dpVHTp00MiRI5WTk+N2WWGtd+/eDX5ffT6fbr/9drdLCwpCjkNqamp0ww036Cc/+YnbpYSt119/XdOnT9fPfvYzrVu3ThMmTNDkyZNVUlLidmlhrbKyUiNGjNAf//hHt0sxxrJly3T77bdr1apVWrRokU6cOKFJkyapsrLS7dLCWs+ePfXb3/5Wa9eu1dq1a3XJJZfommuuUUFBgdulGWPNmjV65plnNHz4cLdLMcaQIUO0Z8+e+tv69evdLinsHTp0SOPHj1dsbKw++OADFRYW6oknnuBD5HZas2ZNwO/qokWLJEk33HCDy5UFB0tIO+zFF1/U9OnTdfjwYbdLCTtjxozRqFGj9OSTT9ZvGzRokK699lrNnTvXxcrM4fP59NZbb+naa691uxSj7N+/X+np6Vq2bJm++tWvul2OUbp06aLHH39ct9xyi9ulhL0jR45o1KhR+vOf/6yHH35YI0eO1Lx589wuK6zNnj1bb7/9tvLy8twuxSgPPvigPv30U87mcNj06dP1j3/8Q1u2bJHP53O7nHZjJAeeVFNTo5ycHE2aNClg+6RJk7RixQqXqgJapqysTJLdIUdw1NbW6rXXXlNlZaXGjh3rdjlGuP3223XVVVfpsssuc7sUo2zZskWZmZnq06ePbrzxRhUVFbldUth79913NXr0aN1www1KT0/Xueeeq2effdbtsoxSU1OjV155RT/84Q+NCDgSIQcedeDAAdXW1qp79+4B27t3767S0lKXqgLOzLIszZgxQxdeeKGGDh3qdjlhb/369UpKSlJ8fLymTZumt956S4MHD3a7rLD32muvKTc3l1HxIBszZoxefvllLVy4UM8++6xKS0s1btw4HTx40O3SwlpRUZGefPJJDRgwQAsXLtS0adN011136eWXX3a7NGO8/fbbOnz4sG6++Wa3SwkaQk4rNDah8PTb2rVr3S7TKKd/mmBZljGfMMBMd9xxhz777DO9+uqrbpdihIEDByovL0+rVq3ST37yE02ZMkWFhYVulxXWdu7cqbvvvluvvPKKEhIS3C7HKJMnT9b111+vYcOG6bLLLtP7778vSXrppZdcriy81dXVadSoUfrNb36jc889V7feeqt+9KMfBZzOjvZ5/vnnNXnyZGVmZrpdStDEuF1AOLnjjjt04403NrtP7969Q1OM4dLS0hQdHd1g1Gbfvn0NRncAr7jzzjv17rvvavny5erZs6fb5RghLi5O/fv3lySNHj1aa9as0e9//3s9/fTTLlcWvnJycrRv3z6dd9559dtqa2u1fPly/fGPf1R1dbWio6NdrNAcHTt21LBhw7Rlyxa3SwlrGRkZDUZwBw0apL///e8uVWSW4uJiffTRR3rzzTfdLiWoCDmtkJaWprS0NLfLiAhxcXE677zztGjRIn3jG9+o375o0SJdc801LlYGNGRZlu6880699dZbWrp0qfr06eN2ScayLEvV1dVulxHWLr300gYrfv3gBz/QOeecowceeICAE0TV1dXauHGjJkyY4HYpYW38+PENluXfvHmzevXq5VJFZpk/f77S09N11VVXuV1KUBFyHFJSUqIvv/xSJSUlqq2trV9ppX///kpKSnK3uDAxY8YM3XTTTRo9erTGjh2rZ555RiUlJZo2bZrbpYW1I0eOaOvWrfX3t2/frry8PHXp0kXZ2dkuVha+br/9dv3lL3/RO++8o+Tk5PoRyNTUVCUmJrpcXfh66KGHNHnyZGVlZamiokKvvfaali5dqgULFrhdWlhLTk5uMF+sY8eO6tq1K/PI2um+++7T1VdfrezsbO3bt08PP/ywysvLNWXKFLdLC2v33HOPxo0bp9/85jf61re+pdWrV+uZZ57RM88843ZpYa+urk7z58/XlClTFBNjWCyw4IgpU6ZYkhrclixZ4nZpYeVPf/qT1atXLysuLs4aNWqUtWzZMrdLCntLlixp9HdzypQpbpcWthp7PyVZ8+fPd7u0sPbDH/6w/v//bt26WZdeeqn14Ycful2WkS666CLr7rvvdruMsPff//3fVkZGhhUbG2tlZmZa1113nVVQUOB2WUZ47733rKFDh1rx8fHWOeecYz3zzDNul2SEhQsXWpKsTZs2uV1K0HGdHAAAAABGYXU1AAAAAEYh5AAAAAAwCiEHAAAAgFEIOQAAAACMQsgBAAAAYBRCDgAAAACjEHIAAAAAGIWQAwAAAMAohBwAAAAARiHkAAAAADAKIQcAAACAUQg5AAAAAIzy/wGl4kgzzq56SwAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -640,37 +674,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Draw normal force diagram"
+    "Draw Normal forces diagram"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 74,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plotpar = [2, 1]\n",
-    "sfac = cfv.scalfact2(ex3, ey3, es3[:, 1], 0.2)\n",
-    "cfv.figure(3, fig_size=(10, 10))\n",
-    "cfv.secforce2(ex1, ey1, es1[:, 1], plotpar, sfac)\n",
-    "cfv.secforce2(ex2, ey2, es2[:, 1], plotpar, sfac)\n",
-    "cfv.secforce2(ex3, ey3, es3[:, 1], plotpar, sfac)\n",
+    "sfac = cfv.scalfact2(ex1, ey1, es1[:, 0], 0.2)\n",
+    "cfv.figure(2, fig_size=(10, 10))\n",
+    "cfv.secforce2(ex1, ey1, es1[:, 0], plotpar, sfac)\n",
+    "cfv.secforce2(ex2, ey2, es2[:, 0], plotpar, sfac)\n",
+    "cfv.secforce2(ex3, ey3, es3[:, 0], plotpar, sfac)\n",
     "cfv.axis([-1.5, 7.5, -0.5, 5.5])\n",
-    "cfv.scalgraph2(sfac, [3e4, 0.5, 0], plotpar1)\n",
-    "cfv.title(\"Shear force\")"
+    "cfv.scalgraph2(sfac, [3e4, 1.5, 0], plotpar1)\n",
+    "cfv.title(\"Normal force\")"
    ]
   },
   {
@@ -682,64 +716,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 75,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "sfac=3.628630851048567e-05"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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SON3W0AUX+IJ07rmhkwCA9/nn/ioBt9wi9e8fOk0yMdW2EiUpoKSXpPqNJm+8kY0mAYS3ZIlfqH3aadLuu4dOk1yUpJVYuB1QZaX05z/7I6nqN5rcf3/ppZekgQNDJwKQRc75bUoGDJB++tPQaVqPdVTxQEkKKJfzi5/vvLN19xP6h6nhRpMvv8waAACld8010quvSq+8Ev45sRBC7kV3wQVsGlyP6baAkj7d1tAJJ/hTbY87jo0mAZTWSy/5X9Tuv98/r6J1mG5biZIUUJpKkpn/Te6NN/xbACiFqirp0EOl669nur9QKEkrMd0WUFL3SWpMRYU/5XbECGmrrUKnAZB2y5ZJBx8sjR8v7b136DTpQUlaiZGkgJK8T1JjBg1audEkABTTaadJ3bpJEyaETpIulKSVKEkBpWm6raEDDvAXlJSkRYuCRgGQUtdfLz31lHTTTVIbXskKxjmpupqSVI/ptoDSWpIk6fzzpc8+kw4/XLrvPr+nEgAUys9+Jj33nLTOOqGTpMu8eVKHDlLHjqGTxAP9O6CKCn+aZW1t6CSFZyZde61fM3DCCZzxBqAw5szxb6+5Rvre98JmSSOm2lZFSQqorMw39rTuR9G2rXT33dI//iFdeGHoNACSbsmSlesdx4wJmyWtKEmrYhIksPopt7Tu7VFZKT3yiLTDDtIGG0g//nHoRACSqKZGOuIIqVev0EnSjZK0KkaSAkvzuqR6vXpJjz4q/eIX0pQpodMASBrnpOOPlxYs8Au1mb4vHkrSqihJgaVtr6TGbLyx30Pp6KOlqVNDpwGQJL/6lX/euO8+qX370GnSjZK0KkpSYGncK6kxO+zg91Dabz/p449DpwGQBFddJd12m5+2r6wMnSb9KEmrYk1SYFmYbmvogAOk//xH2nNPf72lbt1CJwIQV3fdJV10kfT881KPHqHTZAMlaVWMJAWWtZIk+bUFBx0k7btves/sA9A6Tz/ttw/529+kAQNCp8kOStKqKEmBZWVN0uouushfwuSHP/RnrQBAvTfe8BvR3nWXtMUWodNkCyVpVZSkwLK0Jqmh+s0mFy2STjqJs1UAeDNmSPvsI119tbTzzqHTZMs33/hfWjt3Dp0kPihJgeVy0imn+NLQ0iOp2rWT7rlHevll6eKLQ6cBEFp1tbTHHv5stqxvFtma14SWHpWV0uLFyX5dKTRKUmC5nN8/yLmWH0nWubNfc/DXv/r9TwBk04IF/oSOH/1IGj8+dJrwWvOa0NLj73+XRowI/cjjhbPbAquslGbPDp0irPXX96f37rqr33jyBz8InQhAKS1Z4s983XFHacKE0Gmyi/VIa2IkKbCsrkla3fe+56fejjzSL9oEkA01Nf7nvnt36U9/YqonJErSmihJgWVxC4DGjBjhF2vuu6/0ySeh0wAoNuekE0+U5s2Tbr7ZX/Qb4VCS1sR0W2CUpFWNGbNys8kXX5TWWy90IgDFcv750muvSc88w+VG4qCqyo/qYyVKUmBZ3SdpbU48Ufr8c3/5kiefDJ0GQDFcfbUfPXrxRU45jwtGktbEdFtgrEmKdvHFfpfdH/4wdBIAhXb33dKvfy09/rjUs2foNKhXVeVPnsFKlKTAmG6L1qaNdN11Kwtk0rc6AOA984y/NNHf/iZ95zuh06AhRpLWREkKjJLUuHbtpHvv9e+feKJUWxs2D4DWefNN6bDDpDvvlIYODZ0GDS1d6n8p5aLjq6IkBcaapLXr3NlvMvfPf0rHHst13oAk23tv6S9/kXbZJXQSrK66WurRw4/iYyX+OgLr1En69ltGSdamc2dpyhS/LcC4cdKKFaETAWiO6mr/9pxzpIMOCpsF0Zhqi0ZJCqxNG1+UFi0KnSTeKiqkhx+WvvxSGjtWWrYsdCIA+fj0U2mnnfz7//u/YbOgcZSkaJSkGGBdUn46dpTuv19avlw6+GB/KQMA8fX++74gnXACJ1/EXXU1JSkKJSkGWJeUv/btpbvukjp0kPbf309VAoifadP89Rh/9SvplFNCp0FTGEmKRkmKAfZKap62baXbbvP7q+y9NwUTiJuXXpL22EP685+lH/84dBrkg5IUjZIUA0y3NV95uXTDDdLAgf7JeMGC0IkASH6X/P33l2680U+LIxkoSdEoSTFASWqZNm2ka66RttpK2n136auvQicCsu2BB/wu+ffe66+/iOSgJEWjJMVAZaWfNjJr2ZFlbdr4If1ddpFGjpTmzg2dCMimW27xZ689+qj0/e+HTpN8LX09aOnxxhvS8OGhH3X8UJJiIJeTrr3Wn/3RkiPrzKTf/MZfEHeXXfxvRABK56qrpF/8QnrqKWnrrUOnSYeWvh605Fixwi9hYGuVNZWHDgCm2wrBTDr/fH/22847+yfrvn1DpwLS75JLpL/+VXruOX9RaiTP3LnSuuv6k2KwKkpSDFCSCuess/x+SvVFiSdtoDic86NHDz0kPf+8tP76oROhpViP1DhKUgxUVkoLF4ZOkR6nn+73UdplF3+mzaBBoRMB6VJb6y86/Y9/SH//OxdFTTpKUuMoSTGQy0mzZoVOkS7HH++n3nbdVXr8cWnw4NCJgHRYscLvffTZZ360tnPn0InQWpSkxlGSYoDptuI45hhflHbbzV8gd4stQicCkm3JEunww/0C30cf9dedRPJVVUm9eoVOEU+UpBigJBXPkUf6qbc99vAXyAXQMt98Ix1wgNS1q3TnnVK7dqEToVCqqqSNNw6dIp4oSTHAtduK6+CD/YjSXnuFTgIk07x5fi+3wYP9Bq5lZaEToZCqqvwaTqyJfZJigGu3Fd+++0q33urfnzgxbBYgaXbdVdpuO3+qPwUpfViT1DhKUgww3VYao0ZJ778v/fGP0nHHsXEa0JTPPvNvDzxQ+v3v2eE/rShJjaMkxQAlqXQ22kh65RV/NuFuu0mzZ4dOBMTT66+vvLzIuedSkNLKOam6mpLUGEpSDLAmqbQ6d5buv99PIWyzjX8xALDSpEl+Dd9ll4VOgmKbN8+v2eRMxWgs3I4B1iSVXps2/jImQ4dKo0f7KbgjjgidCghryRLppJOkF1/0lxnZZBOuD5l2jCKtHSUpBjp29E9ONTUsiiy1MWP8FNwBB/irYF96qb/QI5A1n37qzwQdMEB69VU/wo30Yz3S2jHdFgNt2kgVFdKiRaGTZNOmm0qvvSa9/bafYvjqq9CJgNJ64glp2239RpF33EFByhJK0tpRkmKCdUlhde0qPfKItPnmfp3S9OmhEwHFV1srXXSRNG6cNHmydMYZLNDOGkrS2jGxEBOsSwqvvFz63e/8OqVdd/Wb5o0ZEzoVUBwLFvhyNGeOv1DtBhuEToQQqqqk9dcPnSK+GEmKCUpSfBx5pL/W22mnSeec43/bBtJk+nQ/Ytqnj/TssxSkLGMkae0oSTGRy/knLbPmHyi8rbf265SeecZvpLdwYehEQGHcfrsfKT37bOmKK7gGW1y15LWgJcfkyf4XQ0SjJMVEZaW/AKtzzT9QHD17Sk895X/L3m476YMPQicCWm75cunUU6UJE6Qnn5SOOip0IqxNS14LWnJstJH07ruhH218UZJigum2eGrXTrrqKj/19v3vS48+GjoR0HxVVdLIkdKMGX7z1C22CJ0IccF029pRkmKCS5PE27HHSvfeK/3kJ9IllzCCh+R44QVp2DB/7cIHH5TWXTd0IsTFokV+hHGddUIniS/ObosJSlL8jRjhN9kbM0aaNi10GmDtnJMuv1y68ELphhv8zvJAQ/WjSKxtbRwjSTHBPknJ0KePv1xD167+44ceCpsHaMwRR0jXXy+9/DIFCdGYamsaJSkmWJOUHB06+HVKzz4rnX66NHasNHdu6FSA9/77/m27dtJLL0nf+U7YPIgvSlLTKEkxwXRb8uy8s/TWW1LfvtJmm0m33spaJYSzdKn061/7aWHJjyJ17Bg2E+KNktQ0SlJMUJKSqVMn6Te/8ds3XHqptO++0uefh06FrPn73/0Za1On+gs1O8c6EzSNktQ0SlJMsCYp2YYN86dWb7edtNVW0tVXs1M3iu+LL6Qf/9jveXTJJdL990v9+oVOhaSgJDWNkhQTrElKvnbt/EZ9f/+7dOONfldjNqBEMTjn/49tuqnUpYv0zjvSAQeEToWkoSQ1jZIUE0y3pcfgwX5vmjFjpB128NNxK1aEToW0eP99vzHk5ZdLjzwi/eEPfiQaaC5KUtMoSTFBSUqXsjLplFP81dWfeELadlu/yBtoqSVLpF/9StpxR1/AX33VT+0CLUVJaholKSZYk5ROAwZIjz8unXii3/F4wgT/Ygc0x9NPS5tvLr39tvTmm9JJJ/kiDrTUsmX+wt3duoVOEm+UpJhgTVJ6mfnFtW+95S8kueWWfv8aoClz50pHH+3///z+99I99/gNTYHWqq6WevSQ2tAC1qrJvx4z62Bmr5nZW2b2jpmdV4pgWcN0W/r17u2v/3bBBdLBB0snn8y/OaLV1kqTJvmF2T16+IXZ++4bOhXShKm2/ORz7balkkY6574xs7aSXjCzR51zrxQ5W6Z07OiHP1eskMq5ol6qHXSQP/Pt9NP9JpQS+9pgpXfflf73f/3mkI89Jg0dGjoR0oiSlJ8mR5KcV//7btu6g32FC8yM0aQs6drVX3T06qv9x8OH+yu0s2N3tk2Y4HdyP+wwPyVLQUKxUJLyk9eYhZmVSZoqaaCkK51zr0Z8zXhJ4yWpH7uZtUguJ627bugUKKU99pBqaqT77pPOOUc691z/QnnggawVyIrly6Xbb/fvf/CBX7u2/vphMyG8Uo0sX3NNaf6cpMrradg5V+OcGyqpj6ThZrZpxNdMdM4Nc84N6969e4FjZkMuJ733nh9NaM6BZGvTxk/BvfmmdN55fufkzTeXJk/2BQrp9M03fo+j737XbwwpSXfeSUGC19zXgeYeP/nJypFsNK5Zv6s65+ZLelbSnsUIk3VMt2WbmbTfftJrr0m//a305z9LQ4ZIN9/MZpRpMmeOdPbZfnuIl1/2i/mfeopfeFBaTLflJ5+z27qbWZe69ztK2l3Sv4qcK5PYKwmSL0ujR0svvihdeaV07bXSJptI113np2aQTB9/LJ1wgv+3nDvXF6Q77/TX/QNKjZKUn3xGknpLesbM/inpH5KecM49XNxY2cRIEhoyk3bbzV8LbtIk6bbbpEGD/BD50qWh0yFfb74pjR3rF+evs44/e+3qq6WBA0MnQ5ZRkvKTz9lt/3TObemc29w5t6lz7vxSBMsiNpREY3beWXrySV+UHnjAv8Befrm0eHHoZIjinJ9C22MPv7/R1lv7kaSLLpJ69QqdDllXU+NHM3v0CJ0k/jh/JkYYSUJTdthBevRRv47liSf8ot/LLpMWLQqdDJJ/8bnrLmmbbfylaA47TProI+nMM6XOnUOnA7y5c6UuXaR27UIniT9KUoywJgn52mYbv6/SI4/4/XS++13p0ktDp8quJUv8qdSbbOLPWDv7bL9L9v/7f1L79qHTAatiqi1/lKQYYSQJzTV0qHT33X4q7u23/W2jRkkTJ/rfFlFc8+f7twMGSA895BfXv/iitP/+7HOF+KIk5Y8f4xihJKGlNt1UuuUW//9n/Hi/HmbgQGn33f0i4TlzQidMj5kzpauukvbcU+rb19/2xBPSww9L3/8+l5dB/FGS8kdJihEWbqO1KiqkQw6R7rjDPxEed5w/O26jjaSRI/2Le3V16JTJ4tzKjT633lraYgt/+v4xx0izZvnPb7rG9rpAfFGS8selVGOENUkopE6d/E7eBx3kz4KbMsVPzf3yl36a7pBDpDFjeLKMsnSpL5cPPuiPdu38FNpll0kjRnARaiRbVZW08cahUyQDP+oxwnQbiqVjR389uAMP9IuMH3vMF6YJE6TNNltZmDbYIHTScL76yi+Ef/BB6fHHpcGD/Q7ojz3mF2QzjYa0qKqSdtkldIpkoCTFCCUJpdChgx8V2X9/P2Ly+OO+MJ17ri8Ghxziv8659BeDjz7ypeiBB6Q33vBTkvvt5/eg6tkzdDqgOKqrGUHOFyUpRihJKLX27f1mh/vu6wvTU0/5fX4kab31/FqbhseQIf72JFq61JeiDz7wHw8ZIn35pX/sZ5zhdzfv1ClsRqAUWJOUP0pSjFRWsnAb4bRvL+21lz+uv96fETd9uj+mTfNnz02f7sv86uVp8GB/e2grVkiffCJ9+KEvQw3fVlVJG27oL+0i+dP1t9mGU/WRLc4xktQclKQYYSQJcdKjh59+Gjly5W3OSZ9/vrI8PfusdMUV0r/+5S+3sXp5Ksbi0Npafxp+wwJU//6nn/on/0GD/Bl9gwb50rfRRlL//iy4BubP9yciMGqaH54yYoQtABB3ZlK/fv7Ya6+Vt9fU+GuT1ZenBx6QLrzQ3yb54lJW5ktKWdnKo+HHjb1f/7Fz/r5yOX9JhfoStNFG0k47+bff+Y5fcwUgGlNtzUNJipFczq+RSPtiWaRPWZkvLIMG+TPo6i1d6kvL1Km+SNXU+CmxqPeb+pxzfsPG2bP91DSQZrwOxAMlKUY6dPAvNkuWNG9agB8mxFX79itHgAqhkPcFxFmx/q/fcovf6gL5YclijJixLgkAUDxMtzUPJSlmKEkAgGKhJDUPJSlmKEkAgGKhJDUPJSlmuH4bAKBYKEnNQ0mKGbYBAAAUS1WV39MM+aEkxQzTbQCAYmEkqXkoSTFDSQIAFMOiRdKyZX4zVuSHkhQzrEkCABRD/SgSe+vlj5IUM6xJAgAUA1NtzUdJihmm2wAAxUBJaj5KUsxQkgAAxUBJaj5KUsxUVjLdBgAovOpqSlJzUZJihpEkAEAxMJLUfJSkmKEkAQCKgZLUfJSkmKEkAQCKgZLUfJSkmGFNEgCgGChJzVceOgBWlctJr77KZl8AkGXFeg3o1Utyrjj3nUaMJMVMLidtuKH/T5zvAQBIl+a8BuRzfPaZtP76vGY0FyUpZliTBAAoNKbaWoaSFDOsSQIAFBolqWUoSTHTrp1UW+uv1AwAQCFQklqGkhQzZn7KbdGi0EkAAGlBSWoZSlIMsS4JAFBIlKSWoSTFEOuSAACFRElqGUpSDDGSBAAoJEpSy1CSYoiSBAAoJEpSy1CSYoiSBAAolJoaae5cqWfP0EmSh5IUQ6xJAgAUyhdfSOus47eYQfNQkmKIkSQAQKFUVzPV1lKUpBiiJAEACoX1SC1HSYohShIAoFAoSS1HSYoh1iQBAAqFktRylKQYYiQJAFAolKSWoyTFECUJAFAolKSWKw8dAGvK5aRbb/UHACB7zAp7f/fcIzlX2PvMAkaSYqiyUho50v+HzucAAKRLvs//+RwDBkgzZoR+RMlESYohptsAAIXgHNNtrUFJiiFKEgCgEBYs8Dttd+oUOkkyUZJiiJIEACgERpFah5IUQ+yTBAAohKoqqVev0CmSi5IUQ4wkAQAKgZGk1qEkxVD9lZqXLQubAwCQbJSk1qEkxVRlJaNJAIDWoSS1DiUppnI51iUBAFqHktQ6lKSYYl0SAKC1KEmtQ0mKKUoSAKC1KEmtQ0mKKdYkAQBai5LUOpSkmGJNEgCgNb791p8l3aVL6CTJRUmKKabbAACtUb+RpFnoJMlFSYopShIAoDWYams9SlJMsSYJANAalKTWoyTFFGuSAACtQUlqvfLQARAtl5POPFM6//zQSQAApVbIdURXXFG4+8oaRpJiKpeTxo+XnGv6AACkSz7P/U0d48ZJkyaFfiTJRkmKKdYkAQBag+m21qMkxRRrkgAArUFJaj1KUkyxBQAAoDUoSa1HSYopShIAoKWWLZPmz5e6dQudJNkoSTHFmiQAQEvNni117y6VlYVOkmyUpJhiTRIAoKWYaisMSlJMMd0GAGgpSlJhUJJiqr4ksQ8SAKC5KEmFQUmKqbZt/VzysmWhkwAAkoaSVBiUpBhjXRIAoCUoSYVBSYox1iUBAFqCklQYlKQYoyQBAFqiupqSVAiUpBhjryQAQEswklQYTZYkM+trZs+Y2Xtm9o6ZnVKKYGBNEgCg+WprpTlzpF69QidJvvI8vmaFpDOcc2+YWaWkqWb2hHPu3SJnyzym2wAAzfXFF1LnzlK7dqGTJF+TI0nOuSrn3Bt1738t6T1JGxQ7GChJAIDmY6qtcPIZSfovM+svaUtJr0Z8bryk8ZLUr1+/QmTLvMpK6eij/QEAyA6z0AkgNWPhtpnlJN0j6VTn3MLVP++cm+icG+acG9a9e/dCZsysXE669FK/6/baDgBAujT1vL+247rr+OW6UPIqSWbWVr4g3eqcu7e4kVCP6TYAQHMx3VY4+ZzdZpImSXrPOXdZ8SOhHiUJANBclKTCyWckaYSkoySNNLNpdcdeRc4FUZIAAM1HSSqcJhduO+dekMQSsgAqK9knCQDQPJSkwmHH7RhjJAkA0FyUpMKhJMUYJQkA0BzOUZIKiZIUY5QkAEBzLFggtW0rVVSETpIOlKQY49ptAIDmYBSpsChJMVZZyUgSACB/lKTCoiTFGNNtAIDmoCQVFiUpxioqfEni0iMAgHxQkgqLkhRjbdtK5eXSkiWhkwAAkqC6mpJUSJSkmGPKDQCQL0aSCouSFHOUJABAvihJhUVJijnOcAMA5IuSVFiUpJhjryQAQL4oSYXV5AVuEVYuJ40YEToFAKCUrBWXle/SpWAxMo+RpJjL5aR77vHbADR2AADSZW3P+Y0dH30k9e/fuoKFVVGSYo41SQCAfDDVVniUpJhjTRIAIB+UpMKjJMUcWwAAAPJBSSo8SlLMUZIAAPmgJBUeJSnmWJMEAMhHVZXUq1foFOlCSYo51iQBAPLBSFLhUZJijuk2AEA+KEmFR0mKOUoSACAflKTCoyTFHGuSAABNWb5cmjdP6t49dJJ0oSTFHGuSAABNmT3bF6SystBJ0oWSFHNMtwEAmsJUW3FQkmKOkgQAaAolqTgoSTHHmiQAQFMoScVBSYq5igpfkpwLnQQAEFeUpOKgJMVcebnUrp20eHHoJACAuKIkFQclKQFYlwQAWBtKUnFQkhKgspJtAAAAjaMkFUd56ABoWi4nDRwYOgUAoFTMmv89223H+tVCYyQpAXI56YUX/H/+qAMAkC6NPd9HHTU1Utu20tKloVOnDyUpAViTBABozBdfSJ07+5N8UFiUpARgryQAQGNYj1Q8lKQE4PptAIDGUJKKh5KUAEy3AQAaU1Ul9eoVOkU6UZISgJIEAGgMI0nFQ0lKANYkAQAaQ0kqHkpSArAmCQDQGEpS8VCSEoDpNgBAYyhJxUNJSgBKEgCgMZSk4qEkJQBrkgAAUZyTqqspScVCSUoA1iQBAKIsXCiVlfnXCRQeJSkBmG4DAERhqq24KEkJQEkCAEShJBUXJSkBWJMEAIhCSSouSlICsCYJABCFklRclKQEqKiQFi2SamtDJwEAxAklqbjKQwdA08rKpA4d/FsAQPqZNe/rf/az4uTIOkaSEqKy0u+F4dyaBwAgXaKe66OOXXeVnngidNr0oiQlBOuSAACrY7qtuChJCcE2AACA1VGSiouSlBCUJABAQ4sXS0uWSOuuGzpJelGSEoK9kgAADVVVSb16NX+RN/JHSUoI1iQBABpiqq34KEkJwXQbAKAhSlLxUZISgpIEAGiIklR8lKSEYE0SAKAhSlLxUZISgjVJAICGKEnFR0lKCKbbAAANUZKKj5KUEJQkAEBD1dWUpGKjJCUEa5IAAA0xklR8lKSEYE0SAKDeihXSvHlS9+6hk6QbJSkhmG4DANSbPVvq1k0qKwudJN0oSQlBSQIA1GOqrTQoSQlRWcl0GwDAoySVRnnoAMhPLid99BEXMgSALOC5Ph4oSQmRy0kVFdFTbvwwAUC6OLf2z593nlRTU5osWcZ0W0JUVEjffivV1oZOAgAIjem20qAkJUSbNlKnTr4oAQCyjZJUGpSkBGGvJACAREkqFUpSgrANAABAoiSVCiUpQShJAIDaWr+ZZM+eoZOkHyUpQbh+GwDgyy/960H79qGTpB8lKUFYkwQAYKqtdChJCcJ0GwCAklQ6lKQEoSQBAChJpUNJShCu3wYAoCSVDiUpQRhJAgBQkkqHkpQglCQAACWpdChJCUJJAgBQkkqnyZJkZteZ2Rwzm16KQGgca5IAAJSk0slnJOkGSXsWOQfywEgSAGSbc5SkUmqyJDnnnpP0VQmyoAlsJgkA2bZwoVRW5l8PUHzlhbojMxsvabwk9evXr1B3iwZyOenxxyWz0EkAAMXE83w8FGzhtnNuonNumHNuWPfu3Qt1t2igslLafHM/3NrwAACky+rP8/XH009LO+0UOl12cHZbgrAmCQCyjfVIpUVJShBKEgBkGyWptPLZAuB2SS9L2tjMZprZMcWPhSiUJADINkpSaTW5cNs5N7YUQdC0Tp2kJUukmhp/dgMAIFuqqvzaVJQG020J0qaNL0qLFoVOAgAIgZGk0qIkJQxTbgCQXZSk0qIkJQwlCQCyi5JUWpSkhOH6bQCQTYsX+6Nr19BJsoOSlDCMJAFANlVVSb16sRt3KVGSEoaSBADZxFRb6VGSEoaSBADZVF1NSSo1SlLCsCYJALKJkaTSoyQlDCNJAJBNlKTSoyQlDCUJALKJklR6lKSEoSQBQDZRkkqPkpQwrEkCgGyiJJUeJSlhcjnpqqv8Phn1BwAgXRo+x9cfb70lbb116GTZQklKmFxOGjNGcm7lAQBIl4bP8c5Jy5dL5eXSihWhk2ULJSlhWJMEANkze7bUrZtUVhY6SbZQkhKGNUkAkD2sRwqDkpQwjCQBQPZQksKgJCUMJQkAsqf+4rYoLUpSwlCSACB7GEkKg5KUMKxJAoDsoSSFQUlKmI4dpWXLOA0UALKEkhQGJSlhzKSKCmnRotBJAAClQkkKg5KUQKxLAoBsoSSFQUlKINYlAUB21Nb6zSQ5u630KEkJxEgSAGTHl1/65/0OHUInyR5KUgJRkgAgO6qrmWoLhZKUQJQkAMgO1iOFQ0lKINYkAUB2UJLCoSQlECNJAJAdlKRwKEkJREkCgOygJIVTHjoAmi+Xk04/3R8AgPQxW/O2004rfY6sYyQpgSorpZ/+VHLOHwCAdKl/fndO2nFH6dlnQyfKJkpSAjHdBgDZwXRbOJSkBKIkAUA2OEdJComSlECUJADIhq+/9uuTKitDJ8kmSlICsU8SAGQDo0hhUZISiJEkAMgGSlJYlKQEoiQBQDZQksKiJCUQJQkAsoGSFBYlKYFYkwQA2UBJCouSlECMJAFANlCSwqIkJVCHDtKKFdLy5aGTAACKiZIUFiUpgcz8aNKiRaGTAACKiZIUFiUpoXI51iUBQNpRksKiJCUU65IAIN0WL5a+/Vbq2jV0kuyiJCUUJQkA0q26WurVyy+xQBiUpISqrKQkAUCaMdUWHiUpoViTBADpRkkKrzx0ALRMLiftv3/oFACAYmCKLR4oSQmVy0nXXCONH88PEwCkjXPSWWf5ffEQDtNtCcWaJABIN6bbwqMkJRRrkgAg3ShJ4VGSEootAAAg3ShJ4VGSEoqSBADpRkkKj5KUUKxJAoD0WrFC+uorqXv30EmyjZKUUKxJAoD0mj1bWm89qZxz0IOiJCUU020AkF5MtcUDJSmhKEkAkF6UpHigJCUUa5IAIL0oSfFASUoo1iQBQHpRkuKBkpRQTLcBQHpRkuKBkpRQlCQASK/qakpSHFCSEqp9e6m2Vlq2LHQSAEChMZIUD5SkhDJjNAkA0oqSFA+UpASjJAFAOs2eLfXqFToFKEkJRkkCgHSqqJA6dAidApSkBKuslIYMCZ0CAFBo8+aFTgCJkpRouZz05JOhUwAACm233UIngERJSjSm2wAgnVi0HQ+UpASjJAFAOlGS4oGSlGBcvw0A0omSFA+UpATj+m0AkE6UpHigJCUY020AkE6UpHigJCUYJQkA0omSFA+UpARjTRIApBMlKR4oSQnGmiQASKfKytAJIFGSEo3pNgAAioeSlGCUJAAAioeSlGCsSQIAoHgoSQnGmiQAAIqHkpRgcZluMwudID74uwCA9MirJJnZnmb2vpnNMLOfFzsU8hOXkgQAQBo1WZLMrEzSlZJGSxosaayZDS52MDSNkpQ8S5Ys0fDhw7XFFltoyJAhOvfccyVJX331lUaNGqVBgwZp1KhRmjdv3n+/5+KLL9bAgQO18cYb67HHHvvv7VOnTtVmm22mgQMH6uSTT5ZzTpK0dOlSHXbYYRo4cKC23XZbffLJJ5FZGvt+AICXz0jScEkznHMfO+eWSZosaf/ixkI+2rcPnQDN1b59ez399NN66623NG3aNE2ZMkWvvPKKLrnkEu2222768MMPtdtuu+mSSy6RJL377ruaPHmy3nnnHU2ZMkXHH3+8ampqJEnHHXecJk6cqA8//FAffvihpkyZIkmaNGmS1l13Xc2YMUOnnXaa/u///i8yS2PfDwDw8ilJG0j6vMHHM+tuQwyw4ViymJlyuZwkafny5Vq+fLnMTA888IDGjRsnSRo3bpzuv/9+SdIDDzygww8/XO3bt9eAAQM0cOBAvfbaa6qqqtLChQu1/fbby8x09NFHr/I99fd18MEH66mnnlpjlGht3w8A8PIpSVFLUdcYlzez8Wb2upm9Pnfu3NYnQ17i8ldtxpHvou2amhoNHTpUPXr00KhRo7Tttttq9uzZ6l13HYLevXtrzpw5kqRZs2apb9++//3ePn36aNasWZo1a5b69Omzxu2rf095ebnWWWcdffnll6tkWNv3AwC88jy+Zqakvg0+7iPpP6t/kXNuoqSJkjRs2DAWN5RImzaKxVqSGESIhXyKUllZmaZNm6b58+frwAMP1PTp0xv92qh/WzNr9Pa1fU8+9wsgrDg8n2OlfEaS/iFpkJkNMLN2kg6X9GBxYwHp16VLF+2yyy6aMmWKevbsqaqqKkl+KqxHjx6S/AjP55+vnO2eOXOm1l9/ffXp00czZ85c4/bVv2fFihVasGCBunbtusqfvbbvBwB4TZYk59wKSSdKekzSe5LudM69U+xgQBrNnTtX8+fPlyQtXrxYTz75pDbZZBPtt99+uvHGGyVJN954o/bf358bsd9++2ny5MlaunSp/v3vf+vDDz/U8OHD1bt3b1VWVuqVV16Rc0433XTTKt9Tf1933323Ro4cucYo0dq+HwDg5TPdJufcI5IeKXIWIPWqqqo0btw41dTUqLa2Voceeqj22Wcfbb/99jr00EM1adIk9evXT3fddZckaciQITr00EM1ePBglZeX68orr1RZWZkk6S9/+Yt+9KMfafHixRo9erRGjx4tSTrmmGN01FFHaeDAgeratasmT5783z9/6NChmjZt2lq/HwDgWTHmP4cNG+Zef/31gt8vAABAoZnZVOfcsNVv57IkAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEShJAAAAEcw5V/g7NZsr6dOC33F+ukn6ItCfHRKPO1t43NnC484WHnfpbeic6776jUUpSSGZ2evOuWGhc5QajztbeNzZwuPOFh53fDDdBgAAEIGSBAAAECGNJWli6ACB8LizhcedLTzubOFxx0Tq1iQBAAAUQhpHkgAAAFqNkgQAABAhdSXJzA4xs3fMrNbMYnUqYTGY2Z5m9r6ZzTCzn4fOUypmdp2ZzTGz6aGzlIqZ9TWzZ8zsvbr/46eEzlQKZtbBzF4zs7fqHvd5oTOVkpmVmdmbZvZw6CylYmafmNnbZjbNzF4PnadUzKyLmd1tZv+q+znfPnSmYjOzjev+neuPhWZ2auhc9VK3JsnMviepVtI1ks50zqX2B8zMyiR9IGmUpJmS/iFprHPu3aDBSsDMdpL0jaSbnHObhs5TCmbWW1Jv59wbZlYpaaqkA9L+721mJqnCOfeNmbWV9IKkU5xzrwSOVhJmdrqkYZI6O+f2CZ2nFMzsE0nDnHOZ2lDRzG6U9Lxz7lozayepk3NufuBYJVP3mjZL0rbOuVAbUq8idSNJzrn3nHPvh85RIsMlzXDOfeycWyZpsqT9A2cqCefcc5K+Cp2jlJxzVc65N+re/1rSe5I2CJuq+Jz3Td2HbeuOdP121wgz6yNpb0nXhs6C4jKzzpJ2kjRJkpxzy7JUkOrsJumjuBQkKYUlKWM2kPR5g49nKgMvmpDMrL+kLSW9GjhKSdRNOU2TNEfSE865TDxuSX+U9DP50fEscZIeN7OpZjY+dJgS+Y6kuZKur5tevdbMKkKHKrHDJd0eOkRDiSxJZvakmU2PODIxitKARdyWid+ws8zMcpLukXSqc25h6Dyl4Jyrcc4NldRH0nAzS/0Uq5ntI2mOc25q6CwBjHDObSVptKQT6qbX065c0laS/uKc21LSIklZWmfaTtJ+ku4KnaWh8tABWsI5t3voDDExU1LfBh/3kfSfQFlQAnVrcu6RdKtz7t7QeUrNOTffzJ6VtKektC/aHyFpPzPbS1IHSZ3N7Bbn3JGBcxWdc+4/dW/nmNl98ksLngubquhmSprZYJT0bmWoJMkX4jecc7NDB2kokSNJ+K9/SBpkZgPqWvjhkh4MnAlFUreAeZKk95xzl4XOUypm1t3MutS931HS7pL+FTRUCTjnfuGc6+Oc6y//s/10FgqSmVXUnZiguummHyj9hVjOuWpJn5vZxnU37SYp1SdlrGasYjbVJqWwJJnZgWY2U9L2kv5mZo+FzlQszrkVkk6U9Jj8It47nXPvhE1VGmZ2u6SXJW1sZjPN7JjQmUpghKSjJI1scLrsXqFDlUBvSc+Y2T/lfzF4wjmXmdPhM6inpBfM7C1Jr0n6m3NuSuBMpXKSpFvr/q8PlXRR2DilYWad5M/Sjt3oeOq2AAAAACiE1I0kAQAAFAIlCQAAIAIlCQAAIAIlCQAAIAIlCQAAIAIlCQAAIAIlCQAAIML/B+QmAT7HPkHuAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
     "plotpar = [2, 1]\n",
-    "sfac = cfv.scalfact2(ex3, ey3, es3[:, 2], 0.2)\n",
-    "cfu.disp(f\"sfac={sfac}\")\n",
-    "\n",
-    "cfv.figure(4, fig_size=(10, 10))\n",
-    "cfv.secforce2(ex1, ey1, es1[:, 2], plotpar, sfac)\n",
-    "cfv.secforce2(ex2, ey2, es2[:, 2], plotpar, sfac)\n",
-    "cfv.secforce2(ex3, ey3, es3[:, 2], plotpar, sfac)\n",
+    "sfac = cfv.scalfact2(ex3, ey3, es3[:, 1], 0.2)\n",
+    "cfv.figure(3, fig_size=(10, 10))\n",
+    "cfv.secforce2(ex1, ey1, es1[:, 1], plotpar, sfac)\n",
+    "cfv.secforce2(ex2, ey2, es2[:, 1], plotpar, sfac)\n",
+    "cfv.secforce2(ex3, ey3, es3[:, 1], plotpar, sfac)\n",
     "cfv.axis([-1.5, 7.5, -0.5, 5.5])\n",
     "cfv.scalgraph2(sfac, [3e4, 0.5, 0], plotpar1)\n",
-    "cfv.title(\"Moment\")"
+    "cfv.title(\"Shear force\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Draw moment diagram"
+    "Draw shear force diagram"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 76,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "sfac=3.628630851048567e-05"
+       "sfac=2.9298895465410403e-05"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -750,14 +772,12 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -774,6 +794,13 @@
     "cfv.scalgraph2(sfac, [3e4, 0.5, 0], plotpar1)\n",
     "cfv.title(\"Moment\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -792,7 +819,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..c938c12
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,44 @@
+[project]
+name = "calfem-python"
+version = "3.6.5"
+description = "CALFEM for Python"
+authors = [
+    {name = "Jonas Lindemann", email = "jonas.lindemann@gmail.com"},
+    {name = "Jonas Lindemann, et al", email = "jonas.lindemann@lunarc.lu.se"},
+]
+dependencies = [
+    "gmsh",
+    "matplotlib",
+    "numpy",
+    "scipy",
+    "tabulate",
+    "visvis",
+    "vedo",
+    "pyvtk",
+    "qtpy",
+    "tabulate"
+]
+requires-python = ">=3.8"
+readme = "README.md"
+license = {text = "MIT"}
+keywords = ["finite element", "math", "numerics"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Topic :: Software Development :: Build Tools",
+]
+
+[project.urls]
+Homepage = "https://github.com/CALFEM/calfem-python"
+[build-system]
+requires = ["pdm-backend"]
+build-backend = "pdm.backend"
+
diff --git a/setup.py b/setup.py
index 6eb68ec..7d60969 100644
--- a/setup.py
+++ b/setup.py
@@ -32,7 +32,7 @@ def gen_data_files(*dirs):
     # the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
 
-    version='3.6.4',
+    version='3.6.5',
 
     description='CALFEM for Python',
     long_description='The computer program CALFEM is written for the software MATLAB and is an interactive tool for  learning the finite element method. CALFEM is an abbreviation of "Computer Aided Learning of the Finite Element  Method" and been developed by the Division of Structural Mechanics at Lund University since the late 70s.',
@@ -70,6 +70,7 @@ def gen_data_files(*dirs):
         'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',
         'Programming Language :: Python :: 3.11',
+        'Programming Language :: Python :: 3.12',
     ],
 
     # What does your project relate to?
diff --git a/src/calfem_python/__init__.py b/src/calfem_python/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test_calfem.py b/test_calfem.py
index 9fe84a0..41af951 100644
--- a/test_calfem.py
+++ b/test_calfem.py
@@ -6,6 +6,7 @@
 """
 
 import os
+import example_output as eo
 
 
 def test_examples():
@@ -13,26 +14,26 @@ def test_examples():
     examples_dir = "./examples"
 
     examples = [
-        "exd_beam2_b.py",
-        "exd_beam2_m.py",
-        "exd_beam2_t.py",
-        "exd_beam2_tr.py",
-        "exm_circle_bsplines.py",
-        "exm_flow_model.py",
-        "exm_geometry.py",
-        "exm_stress_2d.py",
-        "exm_stress_2d_export.py",
-        "exm_stress_2d_materials.py",
-        "exm_stress_2d_pyvtk.py",
-        "exm_structured_mesh.py",
-        "exm_temp_2d_markers.py",
-        "exm_temp_2d_splines_arcs.py",
-        "exm_tutorial_1.py",
-        "exm_tutorial_2.py",
-        "exn_bar2g.py",
-        "exn_bar2m.py",
-        "exn_beam2.py",
-        "exn_beam2_b.py",
+        # "exd_beam2_b.py",
+        # "exd_beam2_m.py",
+        # "exd_beam2_t.py",
+        # "exd_beam2_tr.py",
+        # "exm_circle_bsplines.py",
+        # "exm_flow_model.py",
+        # "exm_geometry.py",
+        # "exm_stress_2d.py",
+        # "exm_stress_2d_export.py",
+        # "exm_stress_2d_materials.py",
+        # "exm_stress_2d_pyvtk.py",
+        # "exm_structured_mesh.py",
+        # "exm_temp_2d_markers.py",
+        # "exm_temp_2d_splines_arcs.py",
+        # "exm_tutorial_1.py",
+        # "exm_tutorial_2.py",
+        # "exn_bar2g.py",
+        # "exn_bar2m.py",
+        # "exn_beam2.py",
+        # "exn_beam2_b.py",
         "exs_bar2.py",
         "exs_bar2_la.py",
         "exs_bar2_lb.py",
@@ -45,17 +46,29 @@ def test_examples():
         "exs_spring.py"
     ]
 
+    # Remove old log files
+
     if os.path.exists("test_examples.log"):
         os.remove("test_examples.log")
 
+    if os.path.exists("test_examples_warnings.log"):
+        os.remove("test_examples_warnings.log")
+
+    if os.path.exists("test_examples_output.log"):
+        os.remove("test_examples_output.log") 
+
+    # Set environment variable to avoid blocking of plots
+
     os.environ["CFV_NO_BLOCK"] = "YES"
 
+    # Assume 0 return codes 
+
     return_codes = 0
 
     for example in examples:
         print(f"Running: {example}", end="")
 
-        echo_string = f"echo ------ {example} "
+        echo_string = f"echo ## EXAMPLE: {example} "
         os.system(echo_string + "-"*(40-len(example)) +
                   " >> test_examples.log 2>&1")
 
@@ -68,4 +81,46 @@ def test_examples():
 
         print(f" return_code = {return_code}")
 
+    # Parse for warnings and errors
+
+    example_output = {}
+
+    with open("test_examples.log", "r") as f:
+
+        with open("test_examples_warnings.log", "w") as w:
+
+            log = f.readlines()
+
+            current_example = ""
+            for line in log:
+                if "## EXAMPLE:" in line:
+                    current_example = line.split("## EXAMPLE: ")[1].split()[0].strip()
+                    example_output[current_example] = ""
+                    continue
+
+                if "Warning:" in line:
+                    line_items = line.split(":")
+                    if len(line_items)>2:
+                        w.write(f"{current_example}: {line_items[-2]}: {line_items[-1]} at line: {line_items[-3]}\n")
+
+                if current_example!="":
+                    example_output[current_example] += line
+
+    # Compare output
+
+    with open("test_examples_output.log", "w") as f:            
+        for example, output in example_output.items():
+            if example in eo.examples.keys():
+                f.write(f"Comparing output of {example}")
+                if output.strip()!=eo.examples[example].strip():
+                    f.write(f"Example {example} failed!\n")
+                    f.write(f"Example output:\n{output}\n")
+                    f.write(f"Expected output:\n{eo.examples[example]}\n")
+                    return_codes += 1
+                else:
+                    f.write(f" --- PASSED!\n")
+    
     assert return_codes == 0
+
+if __name__ == "__main__":
+    test_examples()
\ No newline at end of file
diff --git a/test_examples.log b/test_examples.log
new file mode 100644
index 0000000..4d1360f
--- /dev/null
+++ b/test_examples.log
@@ -0,0 +1,784 @@
+## EXAMPLE: exs_bar2.py ----------------------------- 
+
+# Analysis of a plane truss.
+
+
+## Stiffness matrix K:
+
++-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
+|  7.5000e+07 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -7.5000e+07 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  6.4000e+07 | -4.8000e+07 | -6.4000e+07 |  4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 | -4.8000e+07 |  3.6000e+07 |  4.8000e+07 | -3.6000e+07 | 0.0000e+00 |  0.0000e+00 |
+| -7.5000e+07 | 0.0000e+00 | -6.4000e+07 |  4.8000e+07 |  1.3900e+08 | -4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  4.8000e+07 | -3.6000e+07 | -4.8000e+07 |  8.6000e+07 | 0.0000e+00 | -5.0000e+07 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -5.0000e+07 | 0.0000e+00 |  5.0000e+07 |
++-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -3.9793e-04 |
+| -1.1523e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+|  2.9845e+04 |
+|  0.0000e+00 |
+| -2.9845e+04 |
+|  2.2383e+04 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  5.7617e+04 |
++-------------+
+
+## Element forces r:
+
+N1 = 
+[[-29844.55958549]
+ [-29844.55958549]]
+N2 = 
+[[57616.58031088]
+ [57616.58031088]]
+N3 = 
+[[37305.69948187]
+ [37305.69948187]]
+sfac=
+138.8489208633094
+## EXAMPLE: exs_bar2_la.py -------------------------- 
+C:\Users\Jonas Lindemann\Development\calfem-python\examples\exs_bar2_la.py:109: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  N[i] = es[0]
+
+# Analysis of a plane truss using loops
+
+
+## Stiffness matrix K:
+
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|  3.5531e+08 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  3.5531e+08 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  9.2808e+07 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  7.1062e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |
+|  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  4.4812e+08 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |
+| -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  7.1062e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |
+|  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  4.4812e+08 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  3.5531e+08 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  9.2808e+07 |  3.5531e+08 |  0.0000e+00 | -2.6250e+08 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  3.5531e+08 | -9.2808e+07 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 | -9.2808e+07 |  3.5531e+08 |
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.3845e-03 |
+| -4.4633e-03 |
+| -1.6118e-03 |
+| -4.1987e-03 |
+|  3.0346e-03 |
+| -1.0684e-02 |
+| -1.6589e-03 |
+| -1.1334e-02 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.6603e+05 |
+|  2.4009e+05 |
+|  6.1603e+05 |
+|  1.9293e+05 |
+|  0.0000e+00 |
+|  8.7311e-11 |
+|  0.0000e+00 |
+| -5.2387e-10 |
+|  0.0000e+00 |
+|  6.9849e-10 |
+|  2.9104e-11 |
+|  2.3283e-10 |
++-------------+
+
+## Element forces:
+
+N1 = 625938
+N2 = -423100
+N3 = 170640
+N4 = -12372.8
+N5 = -69447
+N6 = 170640
+N7 = -272838
+N8 = -241321
+N9 = 339534
+N10 = 371051
+## EXAMPLE: exs_bar2_lb.py -------------------------- 
+C:\Users\Jonas Lindemann\Development\calfem-python\examples\exs_bar2_lb.py:107: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  N[i] = es[0]
+
+# Analysis of a plane truss using loops and extraction of element coordinates from a global coordinate matrix.
+
+
+## Stiffness matrix K:
+
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|  3.5531e+08 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  3.5531e+08 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  9.2808e+07 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  7.1062e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |
+|  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  4.4812e+08 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |
+| -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  7.1062e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |
+|  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  4.4812e+08 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  3.5531e+08 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  9.2808e+07 |  3.5531e+08 |  0.0000e+00 | -2.6250e+08 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  3.5531e+08 | -9.2808e+07 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 | -9.2808e+07 |  3.5531e+08 |
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.3845e-03 |
+| -4.4633e-03 |
+| -1.6118e-03 |
+| -4.1987e-03 |
+|  3.0346e-03 |
+| -1.0684e-02 |
+| -1.6589e-03 |
+| -1.1334e-02 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.6603e+05 |
+|  2.4009e+05 |
+|  6.1603e+05 |
+|  1.9293e+05 |
+|  0.0000e+00 |
+|  8.7311e-11 |
+|  0.0000e+00 |
+| -5.2387e-10 |
+|  0.0000e+00 |
+|  6.9849e-10 |
+|  2.9104e-11 |
+|  2.3283e-10 |
++-------------+
+
+## Element forces:
+
+N1 = 625938
+N2 = -423100
+N3 = 170640
+N4 = -12372.8
+N5 = -69447
+N6 = 170640
+N7 = -272838
+N8 = -241321
+N9 = 339534
+N10 = 371051
+## EXAMPLE: exs_beam1.py ---------------------------- 
++-------------+
+|           a |
+|-------------|
+|  0.0000e+00 |
+| -9.4859e-03 |
+| -2.2766e-02 |
+| -3.7943e-03 |
+|  0.0000e+00 |
+|  7.5887e-03 |
++-------------+
++-------------+
+|           r |
+|-------------|
+|  6.6667e+03 |
+|  0.0000e+00 |
+|  3.6380e-12 |
+| -9.0949e-12 |
+|  3.3333e+03 |
+|  3.6380e-12 |
++-------------+
+
+## es1
+
++-------------+------------+
+|          V1 |         M1 |
+|-------------+------------|
+| -6.6667e+03 | 0.0000e+00 |
+| -6.6667e+03 | 6.6667e+03 |
+| -6.6667e+03 | 1.3333e+04 |
+| -6.6667e+03 | 2.0000e+04 |
++-------------+------------+
+
+## ed1
+
++-------------+
+|          v1 |
+|-------------|
+|  0.0000e+00 |
+| -9.2751e-03 |
+| -1.7285e-02 |
+| -2.2766e-02 |
++-------------+
+
+## ec1
+
++------------+
+|         x1 |
+|------------|
+| 0.0000e+00 |
+| 1.0000e+00 |
+| 2.0000e+00 |
+| 3.0000e+00 |
++------------+
+
+## es2
+
++------------+------------+
+|         V2 |         M2 |
+|------------+------------|
+| 3.3333e+03 | 2.0000e+04 |
+| 3.3333e+03 | 1.6667e+04 |
+| 3.3333e+03 | 1.3333e+04 |
+| 3.3333e+03 | 1.0000e+04 |
+| 3.3333e+03 | 6.6667e+03 |
+| 3.3333e+03 | 3.3333e+03 |
+| 3.3333e+03 | 0.0000e+00 |
++------------+------------+
+
+## ed2
+
++-------------+
+|          v2 |
+|-------------|
+| -2.2766e-02 |
+| -2.4769e-02 |
+| -2.3609e-02 |
+| -1.9920e-02 |
+| -1.4334e-02 |
+| -7.4833e-03 |
+|  6.9389e-18 |
++-------------+
+
+## ec2
+
++------------+
+|         x2 |
+|------------|
+| 0.0000e+00 |
+| 1.0000e+00 |
+| 2.0000e+00 |
+| 3.0000e+00 |
+| 4.0000e+00 |
+| 5.0000e+00 |
+| 6.0000e+00 |
++------------+
+## EXAMPLE: exs_beam2.py ---------------------------- 
++-------------+
+|           a |
+|-------------|
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  7.5357e-03 |
+| -2.8741e-04 |
+| -5.3735e-03 |
+|  7.5161e-03 |
+| -3.1259e-04 |
+|  4.6656e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -5.1513e-03 |
++-------------+
++-------------+
+|           r |
+|-------------|
+|  1.9268e+03 |
+|  2.8741e+04 |
+|  4.4527e+02 |
+|  0.0000e+00 |
+| -7.2760e-12 |
+|  3.6380e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  7.2760e-12 |
+| -3.9268e+03 |
+|  3.1259e+04 |
+|  0.0000e+00 |
++-------------+
+
+## es1
+
++-------------+------------+------------+
+|           N |         Vy |         Mz |
+|-------------+------------+------------|
+| -2.8741e+04 | 1.9268e+03 | 8.1523e+03 |
+| -2.8741e+04 | 1.9268e+03 | 7.7670e+03 |
+| -2.8741e+04 | 1.9268e+03 | 7.3816e+03 |
+| -2.8741e+04 | 1.9268e+03 | 6.9963e+03 |
+| -2.8741e+04 | 1.9268e+03 | 6.6109e+03 |
+| -2.8741e+04 | 1.9268e+03 | 6.2256e+03 |
+| -2.8741e+04 | 1.9268e+03 | 5.8402e+03 |
+| -2.8741e+04 | 1.9268e+03 | 5.4548e+03 |
+| -2.8741e+04 | 1.9268e+03 | 5.0695e+03 |
+| -2.8741e+04 | 1.9268e+03 | 4.6841e+03 |
+| -2.8741e+04 | 1.9268e+03 | 4.2988e+03 |
+| -2.8741e+04 | 1.9268e+03 | 3.9134e+03 |
+| -2.8741e+04 | 1.9268e+03 | 3.5281e+03 |
+| -2.8741e+04 | 1.9268e+03 | 3.1427e+03 |
+| -2.8741e+04 | 1.9268e+03 | 2.7574e+03 |
+| -2.8741e+04 | 1.9268e+03 | 2.3720e+03 |
+| -2.8741e+04 | 1.9268e+03 | 1.9867e+03 |
+| -2.8741e+04 | 1.9268e+03 | 1.6013e+03 |
+| -2.8741e+04 | 1.9268e+03 | 1.2160e+03 |
+| -2.8741e+04 | 1.9268e+03 | 8.3062e+02 |
+| -2.8741e+04 | 1.9268e+03 | 4.4527e+02 |
++-------------+------------+------------+
+
+## edi1
+
++------------+------------+
+|         u1 |         v1 |
+|------------+------------|
+| 2.8741e-04 | 7.5357e-03 |
+| 2.7304e-04 | 6.5112e-03 |
+| 2.5867e-04 | 5.5837e-03 |
+| 2.4430e-04 | 4.7485e-03 |
+| 2.2993e-04 | 4.0008e-03 |
+| 2.1556e-04 | 3.3357e-03 |
+| 2.0119e-04 | 2.7484e-03 |
+| 1.8682e-04 | 2.2341e-03 |
+| 1.7245e-04 | 1.7880e-03 |
+| 1.5807e-04 | 1.4053e-03 |
+| 1.4370e-04 | 1.0811e-03 |
+| 1.2933e-04 | 8.1067e-04 |
+| 1.1496e-04 | 5.8915e-04 |
+| 1.0059e-04 | 4.1173e-04 |
+| 8.6223e-05 | 2.7359e-04 |
+| 7.1852e-05 | 1.6993e-04 |
+| 5.7482e-05 | 9.5907e-05 |
+| 4.3111e-05 | 4.6722e-05 |
+| 2.8741e-05 | 1.7554e-05 |
+| 1.4370e-05 | 3.5858e-06 |
+| 0.0000e+00 | 0.0000e+00 |
++------------+------------+
+
+## es2
+
++-------------+-------------+-------------+
+|           N |          Vy |          Mz |
+|-------------+-------------+-------------|
+| -3.1259e+04 | -3.9268e+03 | -1.5707e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.4922e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.4136e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.3351e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.2566e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.1780e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.0995e+04 |
+| -3.1259e+04 | -3.9268e+03 | -1.0210e+04 |
+| -3.1259e+04 | -3.9268e+03 | -9.4242e+03 |
+| -3.1259e+04 | -3.9268e+03 | -8.6389e+03 |
+| -3.1259e+04 | -3.9268e+03 | -7.8535e+03 |
+| -3.1259e+04 | -3.9268e+03 | -7.0682e+03 |
+| -3.1259e+04 | -3.9268e+03 | -6.2828e+03 |
+| -3.1259e+04 | -3.9268e+03 | -5.4975e+03 |
+| -3.1259e+04 | -3.9268e+03 | -4.7121e+03 |
+| -3.1259e+04 | -3.9268e+03 | -3.9268e+03 |
+| -3.1259e+04 | -3.9268e+03 | -3.1414e+03 |
+| -3.1259e+04 | -3.9268e+03 | -2.3561e+03 |
+| -3.1259e+04 | -3.9268e+03 | -1.5707e+03 |
+| -3.1259e+04 | -3.9268e+03 | -7.8535e+02 |
+| -3.1259e+04 | -3.9268e+03 |  2.7756e-12 |
++-------------+-------------+-------------+
+
+## edi2
+
++------------+------------+
+|         u1 |         v1 |
+|------------+------------|
+| 3.1259e-04 | 7.5161e-03 |
+| 2.9696e-04 | 8.3527e-03 |
+| 2.8133e-04 | 9.0027e-03 |
+| 2.6570e-04 | 9.4761e-03 |
+| 2.5007e-04 | 9.7825e-03 |
+| 2.3444e-04 | 9.9319e-03 |
+| 2.1881e-04 | 9.9341e-03 |
+| 2.0318e-04 | 9.7988e-03 |
+| 1.8755e-04 | 9.5359e-03 |
+| 1.7193e-04 | 9.1552e-03 |
+| 1.5630e-04 | 8.6665e-03 |
+| 1.4067e-04 | 8.0796e-03 |
+| 1.2504e-04 | 7.4044e-03 |
+| 1.0941e-04 | 6.6506e-03 |
+| 9.3777e-05 | 5.8282e-03 |
+| 7.8148e-05 | 4.9468e-03 |
+| 6.2518e-05 | 4.0163e-03 |
+| 4.6889e-05 | 3.0466e-03 |
+| 3.1259e-05 | 2.0474e-03 |
+| 1.5630e-05 | 1.0286e-03 |
+| 0.0000e+00 | 0.0000e+00 |
++------------+------------+
+
+## es3
+
++-------------+-------------+-------------+
+|           N |          Vy |          Mz |
+|-------------+-------------+-------------|
+| -3.9268e+03 | -2.8741e+04 | -8.1523e+03 |
+| -3.9268e+03 | -2.5741e+04 |  1.9953e+01 |
+| -3.9268e+03 | -2.2741e+04 |  7.2922e+03 |
+| -3.9268e+03 | -1.9741e+04 |  1.3664e+04 |
+| -3.9268e+03 | -1.6741e+04 |  1.9137e+04 |
+| -3.9268e+03 | -1.3741e+04 |  2.3709e+04 |
+| -3.9268e+03 | -1.0741e+04 |  2.7381e+04 |
+| -3.9268e+03 | -7.7409e+03 |  3.0154e+04 |
+| -3.9268e+03 | -4.7409e+03 |  3.2026e+04 |
+| -3.9268e+03 | -1.7409e+03 |  3.2998e+04 |
+| -3.9268e+03 |  1.2591e+03 |  3.3070e+04 |
+| -3.9268e+03 |  4.2591e+03 |  3.2243e+04 |
+| -3.9268e+03 |  7.2591e+03 |  3.0515e+04 |
+| -3.9268e+03 |  1.0259e+04 |  2.7887e+04 |
+| -3.9268e+03 |  1.3259e+04 |  2.4359e+04 |
+| -3.9268e+03 |  1.6259e+04 |  1.9932e+04 |
+| -3.9268e+03 |  1.9259e+04 |  1.4604e+04 |
+| -3.9268e+03 |  2.2259e+04 |  8.3762e+03 |
+| -3.9268e+03 |  2.5259e+04 |  1.2484e+03 |
+| -3.9268e+03 |  2.8259e+04 | -6.7793e+03 |
+| -3.9268e+03 |  3.1259e+04 | -1.5707e+04 |
++-------------+-------------+-------------+
+
+## edi3
+
++------------+-------------+
+|         u1 |          v1 |
+|------------+-------------|
+| 7.5357e-03 | -2.8741e-04 |
+| 7.5347e-03 | -1.9218e-03 |
+| 7.5337e-03 | -3.5566e-03 |
+| 7.5328e-03 | -5.1312e-03 |
+| 7.5318e-03 | -6.5927e-03 |
+| 7.5308e-03 | -7.8952e-03 |
+| 7.5298e-03 | -9.0009e-03 |
+| 7.5288e-03 | -9.8789e-03 |
+| 7.5279e-03 | -1.0506e-02 |
+| 7.5269e-03 | -1.0868e-02 |
+| 7.5259e-03 | -1.0954e-02 |
+| 7.5249e-03 | -1.0766e-02 |
+| 7.5239e-03 | -1.0310e-02 |
+| 7.5229e-03 | -9.6000e-03 |
+| 7.5220e-03 | -8.6584e-03 |
+| 7.5210e-03 | -7.5143e-03 |
+| 7.5200e-03 | -6.2048e-03 |
+| 7.5190e-03 | -4.7743e-03 |
+| 7.5180e-03 | -3.2745e-03 |
+| 7.5171e-03 | -1.7650e-03 |
+| 7.5161e-03 | -3.1259e-04 |
++------------+-------------+
+sfac=
+54.77300198398879
+sfac=
+3.628630851048567e-05
+## EXAMPLE: exs_beambar2.py ------------------------- 
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.0175e-04 |
+| -5.5551e-04 |
+| -9.6319e-04 |
+|  3.7224e-04 |
+| -4.5567e-03 |
+| -3.2909e-03 |
+|  3.7224e-04 |
+| -1.2990e-02 |
+| -4.5254e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.0702e+04 |
+| -6.6044e+03 |
+| -1.4032e+03 |
+|  0.0000e+00 |
+| -1.4552e-11 |
+| -2.2737e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -7.2760e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  3.8654e-11 |
+|  8.0702e+04 |
+|  4.6604e+04 |
++-------------+
+
+## es1 = 
+
++------------+------------+-------------+
+|          N |          Q |           M |
+|------------+------------+-------------|
+| 8.0702e+04 | 6.6044e+03 |  1.4032e+03 |
+| 8.0702e+04 | 6.6044e+03 |  8.2292e+01 |
+| 8.0702e+04 | 6.6044e+03 | -1.2386e+03 |
+| 8.0702e+04 | 6.6044e+03 | -2.5595e+03 |
+| 8.0702e+04 | 6.6044e+03 | -3.8803e+03 |
+| 8.0702e+04 | 6.6044e+03 | -5.2012e+03 |
+| 8.0702e+04 | 6.6044e+03 | -6.5221e+03 |
+| 8.0702e+04 | 6.6044e+03 | -7.8430e+03 |
+| 8.0702e+04 | 6.6044e+03 | -9.1639e+03 |
+| 8.0702e+04 | 6.6044e+03 | -1.0485e+04 |
+| 8.0702e+04 | 6.6044e+03 | -1.1806e+04 |
++------------+------------+-------------+
+
+## es2 = 
+
++------------+-------------+-------------+
+|          N |           Q |           M |
+|------------+-------------+-------------|
+| 6.8194e+04 | -5.9028e+03 | -1.1806e+04 |
+| 6.8194e+04 | -3.9028e+03 | -1.0825e+04 |
+| 6.8194e+04 | -1.9028e+03 | -1.0245e+04 |
+| 6.8194e+04 |  9.7186e+01 | -1.0064e+04 |
+| 6.8194e+04 |  2.0972e+03 | -1.0283e+04 |
+| 6.8194e+04 |  4.0972e+03 | -1.0903e+04 |
+| 6.8194e+04 |  6.0972e+03 | -1.1922e+04 |
+| 6.8194e+04 |  8.0972e+03 | -1.3342e+04 |
+| 6.8194e+04 |  1.0097e+04 | -1.5161e+04 |
+| 6.8194e+04 |  1.2097e+04 | -1.7381e+04 |
+| 6.8194e+04 |  1.4097e+04 | -2.0000e+04 |
++------------+-------------+-------------+
+
+## es3 = 
+
++------------+-------------+-------------+
+|          N |           Q |           M |
+|------------+-------------+-------------|
+| 0.0000e+00 | -2.0000e+04 | -2.0000e+04 |
+| 0.0000e+00 | -1.8000e+04 | -1.6200e+04 |
+| 0.0000e+00 | -1.6000e+04 | -1.2800e+04 |
+| 0.0000e+00 | -1.4000e+04 | -9.8000e+03 |
+| 0.0000e+00 | -1.2000e+04 | -7.2000e+03 |
+| 0.0000e+00 | -1.0000e+04 | -5.0000e+03 |
+| 0.0000e+00 | -8.0000e+03 | -3.2000e+03 |
+| 0.0000e+00 | -6.0000e+03 | -1.8000e+03 |
+| 0.0000e+00 | -4.0000e+03 | -8.0000e+02 |
+| 0.0000e+00 | -2.0000e+03 | -2.0000e+02 |
+| 0.0000e+00 | -4.6838e-12 |  4.8594e-11 |
++------------+-------------+-------------+
+
+## es4 = 
+
++-------------+
+|           N |
+|-------------|
+| -1.7688e+04 |
+| -1.7688e+04 |
++-------------+
+
+## es5 = 
+
++-------------+
+|           N |
+|-------------|
+| -7.6244e+04 |
+| -7.6244e+04 |
++-------------+
+## EXAMPLE: exs_flw_diff2.py ------------------------ 
+
+## Ex
+
++------------+------------+------------+------------+
+|         x0 |         x1 |         x2 |         x3 |
+|------------+------------+------------+------------|
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
++------------+------------+------------+------------+
+
+## Ey
+
++------------+------------+------------+------------+
+|         x0 |         x1 |         x2 |         x3 |
+|------------+------------+------------+------------|
+| 0.0000e+00 | 0.0000e+00 | 2.5000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 0.0000e+00 | 2.5000e-02 | 2.5000e-02 |
+| 2.5000e-02 | 2.5000e-02 | 5.0000e-02 | 5.0000e-02 |
+| 2.5000e-02 | 2.5000e-02 | 5.0000e-02 | 5.0000e-02 |
+| 5.0000e-02 | 5.0000e-02 | 7.5000e-02 | 7.5000e-02 |
+| 5.0000e-02 | 5.0000e-02 | 7.5000e-02 | 7.5000e-02 |
+| 7.5000e-02 | 7.5000e-02 | 1.0000e-01 | 1.0000e-01 |
+| 7.5000e-02 | 7.5000e-02 | 1.0000e-01 | 1.0000e-01 |
++------------+------------+------------+------------+
+
+## a
+
++------------+
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 6.6176e-05 |
+| 9.3487e-05 |
+| 0.0000e+00 |
+| 1.7857e-04 |
+| 2.5000e-04 |
+| 0.0000e+00 |
+| 4.3382e-04 |
+| 5.4937e-04 |
+| 5.0000e-04 |
+| 1.0000e-03 |
+| 1.0000e-03 |
++------------+
+
+## Ed
+
++------------+------------+------------+------------+
+|        ed0 |        ed1 |        ed2 |        ed3 |
+|------------+------------+------------+------------|
+| 0.0000e+00 | 0.0000e+00 | 6.6176e-05 | 0.0000e+00 |
+| 0.0000e+00 | 0.0000e+00 | 9.3487e-05 | 6.6176e-05 |
+| 0.0000e+00 | 6.6176e-05 | 1.7857e-04 | 0.0000e+00 |
+| 6.6176e-05 | 9.3487e-05 | 2.5000e-04 | 1.7857e-04 |
+| 0.0000e+00 | 1.7857e-04 | 4.3382e-04 | 0.0000e+00 |
+| 1.7857e-04 | 2.5000e-04 | 5.4937e-04 | 4.3382e-04 |
+| 0.0000e+00 | 4.3382e-04 | 1.0000e-03 | 5.0000e-04 |
+| 4.3382e-04 | 5.4937e-04 | 1.0000e-03 | 1.0000e-03 |
++------------+------------+------------+------------+
+## EXAMPLE: exs_flw_temp1.py ------------------------ 
+
+## Temperatures a:
+
++-------------+
+| -1.7000e+01 |
+| -1.6438e+01 |
+| -1.5861e+01 |
+|  1.9238e+01 |
+|  1.9475e+01 |
+|  2.0000e+01 |
++-------------+
+
+## Reaction flows r:
+
++-------------+
+| -1.4039e+01 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  5.6843e-14 |
+|  4.0394e+00 |
++-------------+
+
+## Element flows:
+
+q1 = 
+14.039386189223357
+q2 = 
+14.039386189223451
+q3 = 
+14.039386189223485
+q4 = 
+4.039386189223492
+q5 = 
+4.03938618922342
+## EXAMPLE: exs_flw_temp2.py ------------------------ 
+
+## Stiffness matrix K:
+
++-------------+-------------+-------------+-------------+-------------+-------------+
+|  2.5000e+01 | -2.5000e+01 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -2.5000e+01 |  4.9300e+01 | -2.4300e+01 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | -2.4300e+01 |  2.4700e+01 | -4.0000e-01 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 | -4.0000e-01 |  1.7400e+01 | -1.7000e+01 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -1.7000e+01 |  2.4700e+01 | -7.7000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -7.7000e+00 |  7.7000e+00 |
++-------------+-------------+-------------+-------------+-------------+-------------+
+
+## Temperatures a:
+
++-------------+
+| -1.7000e+01 |
+| -1.6438e+01 |
+| -1.5861e+01 |
+|  1.9238e+01 |
+|  1.9475e+01 |
+|  2.0000e+01 |
++-------------+
+
+## Reaction flows r:
+
++-------------+
+| -1.4039e+01 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  5.6843e-14 |
+|  4.0394e+00 |
++-------------+
+
+## Element flows r:
+
+q1 = 14.039386189223357
+q2 = 14.039386189223451
+q3 = 14.039386189223485
+q4 = 4.039386189223492
+q5 = 4.03938618922342
+## EXAMPLE: exs_spring.py --------------------------- 
+
+## Stiffness matrix K:
+
++-------------+-------------+-------------+
+|  3.0000e+03 | -3.0000e+03 |  0.0000e+00 |
+| -3.0000e+03 |  7.5000e+03 | -4.5000e+03 |
+|  0.0000e+00 | -4.5000e+03 |  4.5000e+03 |
++-------------+-------------+-------------+
+
+## Displacements a:
+
++------------+
+| 0.0000e+00 |
+| 1.3333e-02 |
+| 0.0000e+00 |
++------------+
+
+## Reaction forces r:
+
++-------------+
+| -4.0000e+01 |
+|  0.0000e+00 |
+| -6.0000e+01 |
++-------------+
+
+## Element forces N:
+
+N1 = 40.0
+N2 = -20.0
+N3 = -40.0
diff --git a/test_examples_output.log b/test_examples_output.log
new file mode 100644
index 0000000..18f2ee1
--- /dev/null
+++ b/test_examples_output.log
@@ -0,0 +1,4 @@
+Comparing output of exs_bar2.py --- PASSED!
+Comparing output of exs_beam1.py --- PASSED!
+Comparing output of exs_beam2.py --- PASSED!
+Comparing output of exs_beambar2.py --- PASSED!
diff --git a/test_examples_warnings.log b/test_examples_warnings.log
new file mode 100644
index 0000000..2bf2c7f
--- /dev/null
+++ b/test_examples_warnings.log
@@ -0,0 +1,4 @@
+exs_bar2_la.py:  DeprecationWarning:  Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+ at line: 109
+exs_bar2_lb.py:  DeprecationWarning:  Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+ at line: 107
diff --git a/test_output.log b/test_output.log
new file mode 100644
index 0000000..e7388d1
--- /dev/null
+++ b/test_output.log
@@ -0,0 +1,2 @@
+Comparing output of exs_beam1.py--- PASSED!
+Comparing output of exs_beam2.py--- PASSED!
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29

From eb4e8b94d38484afbd999eae1f10b41588225eb3 Mon Sep 17 00:00:00 2001
From: Jonas Lindemann <jonas.lindemann@gmail.com>
Date: Fri, 9 Feb 2024 17:22:34 +0100
Subject: [PATCH 2/5] Updating test-suite.

---
 .gitignore                 |   3 +
 example_output.py          | 668 +++++++++++++++++++++++++++----------
 examples/exs_bar2_la.py    |   2 +-
 examples/exs_bar2_lb.py    |   2 +-
 test_calfem.py             |  64 ++--
 test_examples.log          | 166 +++++----
 test_examples_output.log   |   7 +-
 test_examples_warnings.log |   4 -
 8 files changed, 610 insertions(+), 306 deletions(-)

diff --git a/.gitignore b/.gitignore
index 2d9fa14..3b03993 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,3 +42,6 @@ exm6.vtk
 docs/source/examples/.ipynb_checkpoints/
 .DS_Store
 *.lock
+*.log
+*.log
+*.log
diff --git a/example_output.py b/example_output.py
index e38166a..6b642ce 100644
--- a/example_output.py
+++ b/example_output.py
@@ -15,16 +15,16 @@
 |  0.0000e+00 |
 |  7.5887e-03 |
 +-------------+
-+-------------+
-|           r |
-|-------------|
-|  6.6667e+03 |
-|  0.0000e+00 |
-|  3.6380e-12 |
-| -9.0949e-12 |
-|  3.3333e+03 |
-|  3.6380e-12 |
-+-------------+
++------------+
+|          r |
+|------------|
+| 6.6667e+03 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 3.3333e+03 |
+| 3.6380e-12 |
++------------+
 
 ## es1
 
@@ -101,8 +101,309 @@
 | 6.0000e+00 |
 +------------+
 """,
-    "exs_beam2.py": 
-"""+-------------+
+"exs_bar2.py":"""
+# Analysis of a plane truss.
+
+
+## Stiffness matrix K:
+
++-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
+|  7.5000e+07 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -7.5000e+07 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  6.4000e+07 | -4.8000e+07 | -6.4000e+07 |  4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 | -4.8000e+07 |  3.6000e+07 |  4.8000e+07 | -3.6000e+07 | 0.0000e+00 |  0.0000e+00 |
+| -7.5000e+07 | 0.0000e+00 | -6.4000e+07 |  4.8000e+07 |  1.3900e+08 | -4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  4.8000e+07 | -3.6000e+07 | -4.8000e+07 |  8.6000e+07 | 0.0000e+00 | -5.0000e+07 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -5.0000e+07 | 0.0000e+00 |  5.0000e+07 |
++-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -3.9793e-04 |
+| -1.1523e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+|  2.9845e+04 |
+|  0.0000e+00 |
+| -2.9845e+04 |
+|  2.2383e+04 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  5.7617e+04 |
++-------------+
+
+## Element forces r:
+
+N1 =
+[[-29844.55958549]
+ [-29844.55958549]]
+N2 =
+[[57616.58031088]
+ [57616.58031088]]
+N3 =
+[[37305.69948187]
+ [37305.69948187]]
+sfac=
+138.8489208633094
+""",
+"exs_beam2.py":"""
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.0175e-04 |
+| -5.5551e-04 |
+| -9.6319e-04 |
+|  3.7224e-04 |
+| -4.5567e-03 |
+| -3.2909e-03 |
+|  3.7224e-04 |
+| -1.2990e-02 |
+| -4.5254e-03 |
+|  0.0000e+00 |
+|  0.0000e+00 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.0702e+04 |
+| -6.6044e+03 |
+| -1.4032e+03 |
+|  0.0000e+00 |
+| -1.4552e-11 |
+| -2.2737e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+| -7.2760e-12 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  3.8654e-11 |
+|  8.0702e+04 |
+|  4.6604e+04 |
++-------------+
+
+## es1 = 
+
++------------+------------+-------------+
+|          N |          Q |           M |
+|------------+------------+-------------|
+| 8.0702e+04 | 6.6044e+03 |  1.4032e+03 |
+| 8.0702e+04 | 6.6044e+03 |  8.2292e+01 |
+| 8.0702e+04 | 6.6044e+03 | -1.2386e+03 |
+| 8.0702e+04 | 6.6044e+03 | -2.5595e+03 |
+| 8.0702e+04 | 6.6044e+03 | -3.8803e+03 |
+| 8.0702e+04 | 6.6044e+03 | -5.2012e+03 |
+| 8.0702e+04 | 6.6044e+03 | -6.5221e+03 |
+| 8.0702e+04 | 6.6044e+03 | -7.8430e+03 |
+| 8.0702e+04 | 6.6044e+03 | -9.1639e+03 |
+| 8.0702e+04 | 6.6044e+03 | -1.0485e+04 |
+| 8.0702e+04 | 6.6044e+03 | -1.1806e+04 |
++------------+------------+-------------+
+
+## es2 = 
+
++------------+-------------+-------------+
+|          N |           Q |           M |
+|------------+-------------+-------------|
+| 6.8194e+04 | -5.9028e+03 | -1.1806e+04 |
+| 6.8194e+04 | -3.9028e+03 | -1.0825e+04 |
+| 6.8194e+04 | -1.9028e+03 | -1.0245e+04 |
+| 6.8194e+04 |  9.7186e+01 | -1.0064e+04 |
+| 6.8194e+04 |  2.0972e+03 | -1.0283e+04 |
+| 6.8194e+04 |  4.0972e+03 | -1.0903e+04 |
+| 6.8194e+04 |  6.0972e+03 | -1.1922e+04 |
+| 6.8194e+04 |  8.0972e+03 | -1.3342e+04 |
+| 6.8194e+04 |  1.0097e+04 | -1.5161e+04 |
+| 6.8194e+04 |  1.2097e+04 | -1.7381e+04 |
+| 6.8194e+04 |  1.4097e+04 | -2.0000e+04 |
++------------+-------------+-------------+
+
+## es3 = 
+
++------------+-------------+-------------+
+|          N |           Q |           M |
+|------------+-------------+-------------|
+| 0.0000e+00 | -2.0000e+04 | -2.0000e+04 |
+| 0.0000e+00 | -1.8000e+04 | -1.6200e+04 |
+| 0.0000e+00 | -1.6000e+04 | -1.2800e+04 |
+| 0.0000e+00 | -1.4000e+04 | -9.8000e+03 |
+| 0.0000e+00 | -1.2000e+04 | -7.2000e+03 |
+| 0.0000e+00 | -1.0000e+04 | -5.0000e+03 |
+| 0.0000e+00 | -8.0000e+03 | -3.2000e+03 |
+| 0.0000e+00 | -6.0000e+03 | -1.8000e+03 |
+| 0.0000e+00 | -4.0000e+03 | -8.0000e+02 |
+| 0.0000e+00 | -2.0000e+03 | -2.0000e+02 |
+| 0.0000e+00 | -4.6838e-12 |  4.8594e-11 |
++------------+-------------+-------------+
+
+## es4 = 
+
++-------------+
+|           N |
+|-------------|
+| -1.7688e+04 |
+| -1.7688e+04 |
++-------------+
+
+## es5 = 
+
++-------------+
+|           N |
+|-------------|
+| -7.6244e+04 |
+| -7.6244e+04 |
++-------------+
+""",
+"exs_bar2_la.py":"""
+# Analysis of a plane truss using loops
+
+
+## Stiffness matrix K:
+
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+  
+|  3.5531e+08 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  
+| -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  
+|  0.0000e+00 |  0.0000e+00 |  3.5531e+08 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  
+|  0.0000e+00 |  0.0000e+00 |  9.2808e+07 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  
+| -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  7.1062e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |  
+|  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  4.4812e+08 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  
+| -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  7.1062e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  
+|  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  4.4812e+08 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  3.5531e+08 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  9.2808e+07 |  3.5531e+08 |  0.0000e+00 | -2.6250e+08 |  
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  3.5531e+08 | -9.2808e+07 |  
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 | -9.2808e+07 |  3.5531e+08 |  
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+  
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.3845e-03 |
+| -4.4633e-03 |
+| -1.6118e-03 |
+| -4.1987e-03 |
+|  3.0346e-03 |
+| -1.0684e-02 |
+| -1.6589e-03 |
+| -1.1334e-02 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.6603e+05 |
+|  2.4009e+05 |
+|  6.1603e+05 |
+|  1.9293e+05 |
+| -1.1642e-10 |
+|  2.3283e-10 |
+| -1.1642e-10 |
+|  0.0000e+00 |
+| -2.3283e-10 |
+|  9.3132e-10 |
+|  1.4552e-10 |
+|  3.4925e-10 |
++-------------+
+
+## Element forces:
+
+N1 = 625938
+N2 = -423100
+N3 = 170640
+N4 = -12372.8
+N5 = -69447
+N6 = 170640
+N7 = -272838
+N8 = -241321
+N9 = 339534
+N10 = 371051""",
+"exs_bar2_lb.py":"""
+# Analysis of a plane truss using loops and extraction of element coordinates from a global coordinate matrix.
+
+
+## Stiffness matrix K:
+
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|  3.5531e+08 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -9.2808e+07 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  3.5531e+08 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  9.2808e+07 |  9.2808e+07 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  7.1062e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 |
+|  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  4.4812e+08 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |
+| -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  7.1062e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 | -2.6250e+08 |  0.0000e+00 |
+|  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 |  4.4812e+08 | -9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  3.5531e+08 |  9.2808e+07 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 | -9.2808e+07 |  9.2808e+07 |  3.5531e+08 |  0.0000e+00 | -2.6250e+08 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -9.2808e+07 |  9.2808e+07 | -2.6250e+08 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  3.5531e+08 | -9.2808e+07 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  9.2808e+07 | -9.2808e+07 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -2.6250e+08 | -9.2808e+07 |  3.5531e+08 |
++-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+
+
+## Displacements a:
+
++-------------+
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  2.3845e-03 |
+| -4.4633e-03 |
+| -1.6118e-03 |
+| -4.1987e-03 |
+|  3.0346e-03 |
+| -1.0684e-02 |
+| -1.6589e-03 |
+| -1.1334e-02 |
++-------------+
+
+## Reaction forces r:
+
++-------------+
+| -8.6603e+05 |
+|  2.4009e+05 |
+|  6.1603e+05 |
+|  1.9293e+05 |
+| -1.1642e-10 |
+|  2.3283e-10 |
+| -1.1642e-10 |
+|  0.0000e+00 |
+| -2.3283e-10 |
+|  9.3132e-10 |
+|  1.4552e-10 |
+|  3.4925e-10 |
++-------------+
+
+## Element forces:
+
+N1 = 625938
+N2 = -423100
+N3 = 170640
+N4 = -12372.8
+N5 = -69447
+N6 = 170640
+N7 = -272838
+N8 = -241321
+N9 = 339534
+N10 = 371051""",
+"exs_beam2.py":"""+-------------+
 |           a |
 |-------------|
 |  0.0000e+00 |
@@ -124,15 +425,15 @@
 |  1.9268e+03 |
 |  2.8741e+04 |
 |  4.4527e+02 |
-|  0.0000e+00 |
-| -7.2760e-12 |
-|  3.6380e-12 |
-|  0.0000e+00 |
+|  8.2764e-11 |
 |  0.0000e+00 |
 |  7.2760e-12 |
+|  5.9117e-11 |
+|  0.0000e+00 |
+|  3.6380e-12 |
 | -3.9268e+03 |
 |  3.1259e+04 |
-|  0.0000e+00 |
+| -9.0949e-13 |
 +-------------+
 
 ## es1
@@ -188,7 +489,7 @@
 | 4.3111e-05 | 4.6722e-05 |
 | 2.8741e-05 | 1.7554e-05 |
 | 1.4370e-05 | 3.5858e-06 |
-| 0.0000e+00 | 0.0000e+00 |
+| 0.0000e+00 | 1.7347e-18 |
 +------------+------------+
 
 ## es2
@@ -216,36 +517,36 @@
 | -3.1259e+04 | -3.9268e+03 | -2.3561e+03 |
 | -3.1259e+04 | -3.9268e+03 | -1.5707e+03 |
 | -3.1259e+04 | -3.9268e+03 | -7.8535e+02 |
-| -3.1259e+04 | -3.9268e+03 |  2.7756e-12 |
+| -3.1259e+04 | -3.9268e+03 | -5.5511e-12 |
 +-------------+-------------+-------------+
 
 ## edi2
 
-+------------+------------+
-|         u1 |         v1 |
-|------------+------------|
-| 3.1259e-04 | 7.5161e-03 |
-| 2.9696e-04 | 8.3527e-03 |
-| 2.8133e-04 | 9.0027e-03 |
-| 2.6570e-04 | 9.4761e-03 |
-| 2.5007e-04 | 9.7825e-03 |
-| 2.3444e-04 | 9.9319e-03 |
-| 2.1881e-04 | 9.9341e-03 |
-| 2.0318e-04 | 9.7988e-03 |
-| 1.8755e-04 | 9.5359e-03 |
-| 1.7193e-04 | 9.1552e-03 |
-| 1.5630e-04 | 8.6665e-03 |
-| 1.4067e-04 | 8.0796e-03 |
-| 1.2504e-04 | 7.4044e-03 |
-| 1.0941e-04 | 6.6506e-03 |
-| 9.3777e-05 | 5.8282e-03 |
-| 7.8148e-05 | 4.9468e-03 |
-| 6.2518e-05 | 4.0163e-03 |
-| 4.6889e-05 | 3.0466e-03 |
-| 3.1259e-05 | 2.0474e-03 |
-| 1.5630e-05 | 1.0286e-03 |
-| 0.0000e+00 | 0.0000e+00 |
-+------------+------------+
++------------+-------------+
+|         u1 |          v1 |
+|------------+-------------|
+| 3.1259e-04 |  7.5161e-03 |
+| 2.9696e-04 |  8.3527e-03 |
+| 2.8133e-04 |  9.0027e-03 |
+| 2.6570e-04 |  9.4761e-03 |
+| 2.5007e-04 |  9.7825e-03 |
+| 2.3444e-04 |  9.9319e-03 |
+| 2.1881e-04 |  9.9341e-03 |
+| 2.0318e-04 |  9.7988e-03 |
+| 1.8755e-04 |  9.5359e-03 |
+| 1.7193e-04 |  9.1552e-03 |
+| 1.5630e-04 |  8.6665e-03 |
+| 1.4067e-04 |  8.0796e-03 |
+| 1.2504e-04 |  7.4044e-03 |
+| 1.0941e-04 |  6.6506e-03 |
+| 9.3777e-05 |  5.8282e-03 |
+| 7.8148e-05 |  4.9468e-03 |
+| 6.2518e-05 |  4.0163e-03 |
+| 4.6889e-05 |  3.0466e-03 |
+| 3.1259e-05 |  2.0474e-03 |
+| 1.5630e-05 |  1.0286e-03 |
+| 0.0000e+00 | -6.9389e-18 |
++------------+-------------+
 
 ## es3
 
@@ -303,176 +604,179 @@
 | 7.5161e-03 | -3.1259e-04 |
 +------------+-------------+
 sfac=
-54.77300198398879
+54.773001983988756
 sfac=
-3.628630851048567e-05
-""",
-"exs_bar2.py":"""
-# Analysis of a plane truss.
-
-
-## Stiffness matrix K:
+3.6286308510485656e-05""",
+"exs_flw_diff2.py":"""
+## Ex
+
++------------+------------+------------+------------+
+|         x0 |         x1 |         x2 |         x3 |
+|------------+------------+------------+------------|
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 2.5000e-02 | 2.5000e-02 | 0.0000e+00 |
+| 2.5000e-02 | 5.0000e-02 | 5.0000e-02 | 2.5000e-02 |
++------------+------------+------------+------------+
+
+## Ey
+
++------------+------------+------------+------------+
+|         x0 |         x1 |         x2 |         x3 |
+|------------+------------+------------+------------|
+| 0.0000e+00 | 0.0000e+00 | 2.5000e-02 | 2.5000e-02 |
+| 0.0000e+00 | 0.0000e+00 | 2.5000e-02 | 2.5000e-02 |
+| 2.5000e-02 | 2.5000e-02 | 5.0000e-02 | 5.0000e-02 |
+| 2.5000e-02 | 2.5000e-02 | 5.0000e-02 | 5.0000e-02 |
+| 5.0000e-02 | 5.0000e-02 | 7.5000e-02 | 7.5000e-02 |
+| 5.0000e-02 | 5.0000e-02 | 7.5000e-02 | 7.5000e-02 |
+| 7.5000e-02 | 7.5000e-02 | 1.0000e-01 | 1.0000e-01 |
+| 7.5000e-02 | 7.5000e-02 | 1.0000e-01 | 1.0000e-01 |
++------------+------------+------------+------------+
+
+## a
 
-+-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
-|  7.5000e+07 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -7.5000e+07 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
-|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
-|  0.0000e+00 | 0.0000e+00 |  6.4000e+07 | -4.8000e+07 | -6.4000e+07 |  4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
-|  0.0000e+00 | 0.0000e+00 | -4.8000e+07 |  3.6000e+07 |  4.8000e+07 | -3.6000e+07 | 0.0000e+00 |  0.0000e+00 |
-| -7.5000e+07 | 0.0000e+00 | -6.4000e+07 |  4.8000e+07 |  1.3900e+08 | -4.8000e+07 | 0.0000e+00 |  0.0000e+00 |
-|  0.0000e+00 | 0.0000e+00 |  4.8000e+07 | -3.6000e+07 | -4.8000e+07 |  8.6000e+07 | 0.0000e+00 | -5.0000e+07 |
-|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |
-|  0.0000e+00 | 0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -5.0000e+07 | 0.0000e+00 |  5.0000e+07 |
-+-------------+------------+-------------+-------------+-------------+-------------+------------+-------------+
++------------+
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 6.6176e-05 |
+| 9.3487e-05 |
+| 0.0000e+00 |
+| 1.7857e-04 |
+| 2.5000e-04 |
+| 0.0000e+00 |
+| 4.3382e-04 |
+| 5.4937e-04 |
+| 5.0000e-04 |
+| 1.0000e-03 |
+| 1.0000e-03 |
++------------+
 
-## Displacements a:
+## Ed
+
++------------+------------+------------+------------+
+|        ed0 |        ed1 |        ed2 |        ed3 |
+|------------+------------+------------+------------|
+| 0.0000e+00 | 0.0000e+00 | 6.6176e-05 | 0.0000e+00 |
+| 0.0000e+00 | 0.0000e+00 | 9.3487e-05 | 6.6176e-05 |
+| 0.0000e+00 | 6.6176e-05 | 1.7857e-04 | 0.0000e+00 |
+| 6.6176e-05 | 9.3487e-05 | 2.5000e-04 | 1.7857e-04 |
+| 0.0000e+00 | 1.7857e-04 | 4.3382e-04 | 0.0000e+00 |
+| 1.7857e-04 | 2.5000e-04 | 5.4937e-04 | 4.3382e-04 |
+| 0.0000e+00 | 4.3382e-04 | 1.0000e-03 | 5.0000e-04 |
+| 4.3382e-04 | 5.4937e-04 | 1.0000e-03 | 1.0000e-03 |
++------------+------------+------------+------------+""",
+"exs_flw_temp1.py":"""
+## Temperatures a:
 
 +-------------+
-|  0.0000e+00 |
-|  0.0000e+00 |
-|  0.0000e+00 |
-|  0.0000e+00 |
-| -3.9793e-04 |
-| -1.1523e-03 |
-|  0.0000e+00 |
-|  0.0000e+00 |
+| -1.7000e+01 |
+| -1.6438e+01 |
+| -1.5861e+01 |
+|  1.9238e+01 |
+|  1.9475e+01 |
+|  2.0000e+01 |
 +-------------+
 
-## Reaction forces r:
+## Reaction flows r:
 
 +-------------+
-|  2.9845e+04 |
-|  0.0000e+00 |
-| -2.9845e+04 |
-|  2.2383e+04 |
+| -1.4039e+01 |
+| -5.6843e-14 |
 |  0.0000e+00 |
 |  0.0000e+00 |
 |  0.0000e+00 |
-|  5.7617e+04 |
+|  4.0394e+00 |
 +-------------+
 
-## Element forces r:
+## Element flows:
+
+q1 = 
+14.039386189223357
+q2 = 
+14.039386189223451
+q3 = 
+14.039386189223482
+q4 = 
+4.039386189223492
+q5 = 
+4.039386189223447""",
+"exs_spring.py":"""
+## Stiffness matrix K:
+
++-------------+-------------+-------------+
+|  3.0000e+03 | -3.0000e+03 |  0.0000e+00 |
+| -3.0000e+03 |  7.5000e+03 | -4.5000e+03 |
+|  0.0000e+00 | -4.5000e+03 |  4.5000e+03 |
++-------------+-------------+-------------+
 
-N1 = 
-[[-29844.55958549]
- [-29844.55958549]]
-N2 = 
-[[57616.58031088]
- [57616.58031088]]
-N3 = 
-[[37305.69948187]
- [37305.69948187]]
-sfac=
-138.8489208633094
-""",
-"exs_beambar2.py":"""
 ## Displacements a:
 
-+-------------+
-|  0.0000e+00 |
-|  0.0000e+00 |
-|  0.0000e+00 |
-|  2.0175e-04 |
-| -5.5551e-04 |
-| -9.6319e-04 |
-|  3.7224e-04 |
-| -4.5567e-03 |
-| -3.2909e-03 |
-|  3.7224e-04 |
-| -1.2990e-02 |
-| -4.5254e-03 |
-|  0.0000e+00 |
-|  0.0000e+00 |
-+-------------+
++------------+
+| 0.0000e+00 |
+| 1.3333e-02 |
+| 0.0000e+00 |
++------------+
 
 ## Reaction forces r:
 
 +-------------+
-| -8.0702e+04 |
-| -6.6044e+03 |
-| -1.4032e+03 |
-|  0.0000e+00 |
-| -1.4552e-11 |
-| -2.2737e-12 |
-|  0.0000e+00 |
-|  0.0000e+00 |
-| -7.2760e-12 |
-|  0.0000e+00 |
+| -4.0000e+01 |
 |  0.0000e+00 |
-|  3.8654e-11 |
-|  8.0702e+04 |
-|  4.6604e+04 |
+| -6.0000e+01 |
 +-------------+
 
-## es1 = 
-
-+------------+------------+-------------+
-|          N |          Q |           M |
-|------------+------------+-------------|
-| 8.0702e+04 | 6.6044e+03 |  1.4032e+03 |
-| 8.0702e+04 | 6.6044e+03 |  8.2292e+01 |
-| 8.0702e+04 | 6.6044e+03 | -1.2386e+03 |
-| 8.0702e+04 | 6.6044e+03 | -2.5595e+03 |
-| 8.0702e+04 | 6.6044e+03 | -3.8803e+03 |
-| 8.0702e+04 | 6.6044e+03 | -5.2012e+03 |
-| 8.0702e+04 | 6.6044e+03 | -6.5221e+03 |
-| 8.0702e+04 | 6.6044e+03 | -7.8430e+03 |
-| 8.0702e+04 | 6.6044e+03 | -9.1639e+03 |
-| 8.0702e+04 | 6.6044e+03 | -1.0485e+04 |
-| 8.0702e+04 | 6.6044e+03 | -1.1806e+04 |
-+------------+------------+-------------+
-
-## es2 = 
-
-+------------+-------------+-------------+
-|          N |           Q |           M |
-|------------+-------------+-------------|
-| 6.8194e+04 | -5.9028e+03 | -1.1806e+04 |
-| 6.8194e+04 | -3.9028e+03 | -1.0825e+04 |
-| 6.8194e+04 | -1.9028e+03 | -1.0245e+04 |
-| 6.8194e+04 |  9.7186e+01 | -1.0064e+04 |
-| 6.8194e+04 |  2.0972e+03 | -1.0283e+04 |
-| 6.8194e+04 |  4.0972e+03 | -1.0903e+04 |
-| 6.8194e+04 |  6.0972e+03 | -1.1922e+04 |
-| 6.8194e+04 |  8.0972e+03 | -1.3342e+04 |
-| 6.8194e+04 |  1.0097e+04 | -1.5161e+04 |
-| 6.8194e+04 |  1.2097e+04 | -1.7381e+04 |
-| 6.8194e+04 |  1.4097e+04 | -2.0000e+04 |
-+------------+-------------+-------------+
+## Element forces N:
 
-## es3 = 
+N1 = 40.0
+N2 = -20.0
+N3 = -40.0""",
+"exs_flw_temp2.py":"""
+## Stiffness matrix K:
 
-+------------+-------------+-------------+
-|          N |           Q |           M |
-|------------+-------------+-------------|
-| 0.0000e+00 | -2.0000e+04 | -2.0000e+04 |
-| 0.0000e+00 | -1.8000e+04 | -1.6200e+04 |
-| 0.0000e+00 | -1.6000e+04 | -1.2800e+04 |
-| 0.0000e+00 | -1.4000e+04 | -9.8000e+03 |
-| 0.0000e+00 | -1.2000e+04 | -7.2000e+03 |
-| 0.0000e+00 | -1.0000e+04 | -5.0000e+03 |
-| 0.0000e+00 | -8.0000e+03 | -3.2000e+03 |
-| 0.0000e+00 | -6.0000e+03 | -1.8000e+03 |
-| 0.0000e+00 | -4.0000e+03 | -8.0000e+02 |
-| 0.0000e+00 | -2.0000e+03 | -2.0000e+02 |
-| 0.0000e+00 | -4.6838e-12 |  4.8594e-11 |
-+------------+-------------+-------------+
++-------------+-------------+-------------+-------------+-------------+-------------+
+|  2.5000e+01 | -2.5000e+01 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+| -2.5000e+01 |  4.9300e+01 | -2.4300e+01 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 | -2.4300e+01 |  2.4700e+01 | -4.0000e-01 |  0.0000e+00 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 | -4.0000e-01 |  1.7400e+01 | -1.7000e+01 |  0.0000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -1.7000e+01 |  2.4700e+01 | -7.7000e+00 |
+|  0.0000e+00 |  0.0000e+00 |  0.0000e+00 |  0.0000e+00 | -7.7000e+00 |  7.7000e+00 |
++-------------+-------------+-------------+-------------+-------------+-------------+
 
-## es4 = 
+## Temperatures a:
 
 +-------------+
-|           N |
-|-------------|
-| -1.7688e+04 |
-| -1.7688e+04 |
+| -1.7000e+01 |
+| -1.6438e+01 |
+| -1.5861e+01 |
+|  1.9238e+01 |
+|  1.9475e+01 |
+|  2.0000e+01 |
 +-------------+
 
-## es5 = 
+## Reaction flows r:
 
 +-------------+
-|           N |
-|-------------|
-| -7.6244e+04 |
-| -7.6244e+04 |
+| -1.4039e+01 |
+| -5.6843e-14 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  0.0000e+00 |
+|  4.0394e+00 |
 +-------------+
-"""
+
+## Element flows r:
+
+q1 = 14.039386189223357
+q2 = 14.039386189223451
+q3 = 14.039386189223482
+q4 = 4.039386189223492
+q5 = 4.039386189223447"""
+
 }
\ No newline at end of file
diff --git a/examples/exs_bar2_la.py b/examples/exs_bar2_la.py
index 929f362..65e450b 100644
--- a/examples/exs_bar2_la.py
+++ b/examples/exs_bar2_la.py
@@ -106,6 +106,6 @@
 i = 0
 for elx, ely, eld in zip(ex, ey, ed):
     es = cfc.bar2s(elx, ely, ep, eld)
-    N[i] = es[0]
+    N[i] = es[0][0]
     print("N%d = %g" % (i + 1, N[i]))
     i += 1
diff --git a/examples/exs_bar2_lb.py b/examples/exs_bar2_lb.py
index e96bd1d..2210999 100644
--- a/examples/exs_bar2_lb.py
+++ b/examples/exs_bar2_lb.py
@@ -104,6 +104,6 @@
 i = 0
 for elx, ely, eld in zip(ex, ey, ed):
     es = cfc.bar2s(elx, ely, ep, eld)
-    N[i] = es[0]
+    N[i] = es[0][0]
     print("N%d = %g" % (i + 1, N[i]))
     i += 1
diff --git a/test_calfem.py b/test_calfem.py
index 41af951..bf44d27 100644
--- a/test_calfem.py
+++ b/test_calfem.py
@@ -5,35 +5,16 @@
 Make sure all examples run without errors.
 """
 
-import os
+import os, sys
 import example_output as eo
-
+import difflib
+from pprint import pprint
 
 def test_examples():
 
-    examples_dir = "./examples"
+    examples_dir = "examples"
 
     examples = [
-        # "exd_beam2_b.py",
-        # "exd_beam2_m.py",
-        # "exd_beam2_t.py",
-        # "exd_beam2_tr.py",
-        # "exm_circle_bsplines.py",
-        # "exm_flow_model.py",
-        # "exm_geometry.py",
-        # "exm_stress_2d.py",
-        # "exm_stress_2d_export.py",
-        # "exm_stress_2d_materials.py",
-        # "exm_stress_2d_pyvtk.py",
-        # "exm_structured_mesh.py",
-        # "exm_temp_2d_markers.py",
-        # "exm_temp_2d_splines_arcs.py",
-        # "exm_tutorial_1.py",
-        # "exm_tutorial_2.py",
-        # "exn_bar2g.py",
-        # "exn_bar2m.py",
-        # "exn_beam2.py",
-        # "exn_beam2_b.py",
         "exs_bar2.py",
         "exs_bar2_la.py",
         "exs_bar2_lb.py",
@@ -73,14 +54,18 @@ def test_examples():
                   " >> test_examples.log 2>&1")
 
         example_path = os.path.join(examples_dir, example)
-
+        python_executable = sys.executable
+        
         return_code = os.system(
-            f"python {example_path} >> test_examples.log 2>&1")
+            f'"{python_executable}" {example_path} >> test_examples.log 2>&1')
+        
+        if return_code == 0:
+            print(" --- PASSED!")
+        else:
+            print(" --- FAILED!")
 
         return_codes += return_code
 
-        print(f" return_code = {return_code}")
-
     # Parse for warnings and errors
 
     example_output = {}
@@ -104,21 +89,36 @@ def test_examples():
                         w.write(f"{current_example}: {line_items[-2]}: {line_items[-1]} at line: {line_items[-3]}\n")
 
                 if current_example!="":
-                    example_output[current_example] += line
+                    example_output[current_example] += line.rstrip() + "\n"
 
     # Compare output
 
     with open("test_examples_output.log", "w") as f:            
         for example, output in example_output.items():
             if example in eo.examples.keys():
+                print(f"Comparing output of {example}", end="")
+
                 f.write(f"Comparing output of {example}")
-                if output.strip()!=eo.examples[example].strip():
-                    f.write(f"Example {example} failed!\n")
-                    f.write(f"Example output:\n{output}\n")
-                    f.write(f"Expected output:\n{eo.examples[example]}\n")
+                d = difflib.Differ()
+
+                lines = eo.examples[example].splitlines()
+                stripped_lines = [line.rstrip() for line in lines]
+
+                diff = d.compare(output.splitlines(), stripped_lines)
+                diff_list = list(diff)
+
+                is_identical = all(line.startswith('  ') for line in diff_list)
+
+                if not is_identical:
+
+                    f.write(f"\n---------- {example} -----------\n")
+                    f.write("\n".join(diff_list))
+                    f.write(f"\n---------- {example} -----------\n")
                     return_codes += 1
+                    print(" --- FAILED!")
                 else:
                     f.write(f" --- PASSED!\n")
+                    print(" --- PASSED!")
     
     assert return_codes == 0
 
diff --git a/test_examples.log b/test_examples.log
index 4d1360f..d01c4d6 100644
--- a/test_examples.log
+++ b/test_examples.log
@@ -56,8 +56,6 @@ N3 =
 sfac=
 138.8489208633094
 ## EXAMPLE: exs_bar2_la.py -------------------------- 
-C:\Users\Jonas Lindemann\Development\calfem-python\examples\exs_bar2_la.py:109: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  N[i] = es[0]
 
 # Analysis of a plane truss using loops
 
@@ -103,14 +101,14 @@ C:\Users\Jonas Lindemann\Development\calfem-python\examples\exs_bar2_la.py:109:
 |  2.4009e+05 |
 |  6.1603e+05 |
 |  1.9293e+05 |
-|  0.0000e+00 |
-|  8.7311e-11 |
-|  0.0000e+00 |
-| -5.2387e-10 |
-|  0.0000e+00 |
-|  6.9849e-10 |
-|  2.9104e-11 |
+| -1.1642e-10 |
 |  2.3283e-10 |
+| -1.1642e-10 |
+|  0.0000e+00 |
+| -2.3283e-10 |
+|  9.3132e-10 |
+|  1.4552e-10 |
+|  3.4925e-10 |
 +-------------+
 
 ## Element forces:
@@ -126,8 +124,6 @@ N8 = -241321
 N9 = 339534
 N10 = 371051
 ## EXAMPLE: exs_bar2_lb.py -------------------------- 
-C:\Users\Jonas Lindemann\Development\calfem-python\examples\exs_bar2_lb.py:107: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  N[i] = es[0]
 
 # Analysis of a plane truss using loops and extraction of element coordinates from a global coordinate matrix.
 
@@ -173,14 +169,14 @@ C:\Users\Jonas Lindemann\Development\calfem-python\examples\exs_bar2_lb.py:107:
 |  2.4009e+05 |
 |  6.1603e+05 |
 |  1.9293e+05 |
-|  0.0000e+00 |
-|  8.7311e-11 |
-|  0.0000e+00 |
-| -5.2387e-10 |
-|  0.0000e+00 |
-|  6.9849e-10 |
-|  2.9104e-11 |
+| -1.1642e-10 |
 |  2.3283e-10 |
+| -1.1642e-10 |
+|  0.0000e+00 |
+| -2.3283e-10 |
+|  9.3132e-10 |
+|  1.4552e-10 |
+|  3.4925e-10 |
 +-------------+
 
 ## Element forces:
@@ -206,16 +202,16 @@ N10 = 371051
 |  0.0000e+00 |
 |  7.5887e-03 |
 +-------------+
-+-------------+
-|           r |
-|-------------|
-|  6.6667e+03 |
-|  0.0000e+00 |
-|  3.6380e-12 |
-| -9.0949e-12 |
-|  3.3333e+03 |
-|  3.6380e-12 |
-+-------------+
++------------+
+|          r |
+|------------|
+| 6.6667e+03 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 0.0000e+00 |
+| 3.3333e+03 |
+| 3.6380e-12 |
++------------+
 
 ## es1
 
@@ -314,15 +310,15 @@ N10 = 371051
 |  1.9268e+03 |
 |  2.8741e+04 |
 |  4.4527e+02 |
-|  0.0000e+00 |
-| -7.2760e-12 |
-|  3.6380e-12 |
-|  0.0000e+00 |
+|  8.2764e-11 |
 |  0.0000e+00 |
 |  7.2760e-12 |
+|  5.9117e-11 |
+|  0.0000e+00 |
+|  3.6380e-12 |
 | -3.9268e+03 |
 |  3.1259e+04 |
-|  0.0000e+00 |
+| -9.0949e-13 |
 +-------------+
 
 ## es1
@@ -378,7 +374,7 @@ N10 = 371051
 | 4.3111e-05 | 4.6722e-05 |
 | 2.8741e-05 | 1.7554e-05 |
 | 1.4370e-05 | 3.5858e-06 |
-| 0.0000e+00 | 0.0000e+00 |
+| 0.0000e+00 | 1.7347e-18 |
 +------------+------------+
 
 ## es2
@@ -406,36 +402,36 @@ N10 = 371051
 | -3.1259e+04 | -3.9268e+03 | -2.3561e+03 |
 | -3.1259e+04 | -3.9268e+03 | -1.5707e+03 |
 | -3.1259e+04 | -3.9268e+03 | -7.8535e+02 |
-| -3.1259e+04 | -3.9268e+03 |  2.7756e-12 |
+| -3.1259e+04 | -3.9268e+03 | -5.5511e-12 |
 +-------------+-------------+-------------+
 
 ## edi2
 
-+------------+------------+
-|         u1 |         v1 |
-|------------+------------|
-| 3.1259e-04 | 7.5161e-03 |
-| 2.9696e-04 | 8.3527e-03 |
-| 2.8133e-04 | 9.0027e-03 |
-| 2.6570e-04 | 9.4761e-03 |
-| 2.5007e-04 | 9.7825e-03 |
-| 2.3444e-04 | 9.9319e-03 |
-| 2.1881e-04 | 9.9341e-03 |
-| 2.0318e-04 | 9.7988e-03 |
-| 1.8755e-04 | 9.5359e-03 |
-| 1.7193e-04 | 9.1552e-03 |
-| 1.5630e-04 | 8.6665e-03 |
-| 1.4067e-04 | 8.0796e-03 |
-| 1.2504e-04 | 7.4044e-03 |
-| 1.0941e-04 | 6.6506e-03 |
-| 9.3777e-05 | 5.8282e-03 |
-| 7.8148e-05 | 4.9468e-03 |
-| 6.2518e-05 | 4.0163e-03 |
-| 4.6889e-05 | 3.0466e-03 |
-| 3.1259e-05 | 2.0474e-03 |
-| 1.5630e-05 | 1.0286e-03 |
-| 0.0000e+00 | 0.0000e+00 |
-+------------+------------+
++------------+-------------+
+|         u1 |          v1 |
+|------------+-------------|
+| 3.1259e-04 |  7.5161e-03 |
+| 2.9696e-04 |  8.3527e-03 |
+| 2.8133e-04 |  9.0027e-03 |
+| 2.6570e-04 |  9.4761e-03 |
+| 2.5007e-04 |  9.7825e-03 |
+| 2.3444e-04 |  9.9319e-03 |
+| 2.1881e-04 |  9.9341e-03 |
+| 2.0318e-04 |  9.7988e-03 |
+| 1.8755e-04 |  9.5359e-03 |
+| 1.7193e-04 |  9.1552e-03 |
+| 1.5630e-04 |  8.6665e-03 |
+| 1.4067e-04 |  8.0796e-03 |
+| 1.2504e-04 |  7.4044e-03 |
+| 1.0941e-04 |  6.6506e-03 |
+| 9.3777e-05 |  5.8282e-03 |
+| 7.8148e-05 |  4.9468e-03 |
+| 6.2518e-05 |  4.0163e-03 |
+| 4.6889e-05 |  3.0466e-03 |
+| 3.1259e-05 |  2.0474e-03 |
+| 1.5630e-05 |  1.0286e-03 |
+| 0.0000e+00 | -6.9389e-18 |
++------------+-------------+
 
 ## es3
 
@@ -493,9 +489,9 @@ N10 = 371051
 | 7.5161e-03 | -3.1259e-04 |
 +------------+-------------+
 sfac=
-54.77300198398879
+54.773001983988756
 sfac=
-3.628630851048567e-05
+3.6286308510485656e-05
 ## EXAMPLE: exs_beambar2.py ------------------------- 
 
 ## Displacements a:
@@ -523,15 +519,15 @@ sfac=
 | -8.0702e+04 |
 | -6.6044e+03 |
 | -1.4032e+03 |
+|  7.2760e-12 |
 |  0.0000e+00 |
-| -1.4552e-11 |
-| -2.2737e-12 |
-|  0.0000e+00 |
+|  5.0022e-12 |
+|  2.9104e-11 |
+|  1.0914e-11 |
 |  0.0000e+00 |
+|  2.9104e-11 |
 | -7.2760e-12 |
-|  0.0000e+00 |
-|  0.0000e+00 |
-|  3.8654e-11 |
+|  1.6826e-11 |
 |  8.0702e+04 |
 |  4.6604e+04 |
 +-------------+
@@ -577,17 +573,17 @@ sfac=
 +------------+-------------+-------------+
 |          N |           Q |           M |
 |------------+-------------+-------------|
-| 0.0000e+00 | -2.0000e+04 | -2.0000e+04 |
-| 0.0000e+00 | -1.8000e+04 | -1.6200e+04 |
-| 0.0000e+00 | -1.6000e+04 | -1.2800e+04 |
-| 0.0000e+00 | -1.4000e+04 | -9.8000e+03 |
-| 0.0000e+00 | -1.2000e+04 | -7.2000e+03 |
-| 0.0000e+00 | -1.0000e+04 | -5.0000e+03 |
-| 0.0000e+00 | -8.0000e+03 | -3.2000e+03 |
-| 0.0000e+00 | -6.0000e+03 | -1.8000e+03 |
-| 0.0000e+00 | -4.0000e+03 | -8.0000e+02 |
-| 0.0000e+00 | -2.0000e+03 | -2.0000e+02 |
-| 0.0000e+00 | -4.6838e-12 |  4.8594e-11 |
+| 2.1684e-11 | -2.0000e+04 | -2.0000e+04 |
+| 2.1684e-11 | -1.8000e+04 | -1.6200e+04 |
+| 2.1684e-11 | -1.6000e+04 | -1.2800e+04 |
+| 2.1684e-11 | -1.4000e+04 | -9.8000e+03 |
+| 2.1684e-11 | -1.2000e+04 | -7.2000e+03 |
+| 2.1684e-11 | -1.0000e+04 | -5.0000e+03 |
+| 2.1684e-11 | -8.0000e+03 | -3.2000e+03 |
+| 2.1684e-11 | -6.0000e+03 | -1.8000e+03 |
+| 2.1684e-11 | -4.0000e+03 | -8.0000e+02 |
+| 2.1684e-11 | -2.0000e+03 | -2.0000e+02 |
+| 2.1684e-11 | -3.2786e-11 |  2.9859e-11 |
 +------------+-------------+-------------+
 
 ## es4 = 
@@ -690,10 +686,10 @@ sfac=
 
 +-------------+
 | -1.4039e+01 |
+| -5.6843e-14 |
 |  0.0000e+00 |
 |  0.0000e+00 |
 |  0.0000e+00 |
-|  5.6843e-14 |
 |  4.0394e+00 |
 +-------------+
 
@@ -704,11 +700,11 @@ q1 =
 q2 = 
 14.039386189223451
 q3 = 
-14.039386189223485
+14.039386189223482
 q4 = 
 4.039386189223492
 q5 = 
-4.03938618922342
+4.039386189223447
 ## EXAMPLE: exs_flw_temp2.py ------------------------ 
 
 ## Stiffness matrix K:
@@ -737,10 +733,10 @@ q5 =
 
 +-------------+
 | -1.4039e+01 |
+| -5.6843e-14 |
 |  0.0000e+00 |
 |  0.0000e+00 |
 |  0.0000e+00 |
-|  5.6843e-14 |
 |  4.0394e+00 |
 +-------------+
 
@@ -748,9 +744,9 @@ q5 =
 
 q1 = 14.039386189223357
 q2 = 14.039386189223451
-q3 = 14.039386189223485
+q3 = 14.039386189223482
 q4 = 4.039386189223492
-q5 = 4.03938618922342
+q5 = 4.039386189223447
 ## EXAMPLE: exs_spring.py --------------------------- 
 
 ## Stiffness matrix K:
diff --git a/test_examples_output.log b/test_examples_output.log
index 18f2ee1..a6cf4a7 100644
--- a/test_examples_output.log
+++ b/test_examples_output.log
@@ -1,4 +1,9 @@
 Comparing output of exs_bar2.py --- PASSED!
+Comparing output of exs_bar2_la.py --- PASSED!
+Comparing output of exs_bar2_lb.py --- PASSED!
 Comparing output of exs_beam1.py --- PASSED!
 Comparing output of exs_beam2.py --- PASSED!
-Comparing output of exs_beambar2.py --- PASSED!
+Comparing output of exs_flw_diff2.py --- PASSED!
+Comparing output of exs_flw_temp1.py --- PASSED!
+Comparing output of exs_flw_temp2.py --- PASSED!
+Comparing output of exs_spring.py --- PASSED!
diff --git a/test_examples_warnings.log b/test_examples_warnings.log
index 2bf2c7f..e69de29 100644
--- a/test_examples_warnings.log
+++ b/test_examples_warnings.log
@@ -1,4 +0,0 @@
-exs_bar2_la.py:  DeprecationWarning:  Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
- at line: 109
-exs_bar2_lb.py:  DeprecationWarning:  Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
- at line: 107

From 9be7cb6456f9279a8dd24b49073926fbf08eaa68 Mon Sep 17 00:00:00 2001
From: Jonas Lindemann <jonas.lindemann@lunarc.lu.se>
Date: Thu, 15 Feb 2024 09:33:06 +0100
Subject: [PATCH 3/5] Small fix for Qt integration

---
 calfem/mesh.py    | 2 +-
 calfem/vis_mpl.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/calfem/mesh.py b/calfem/mesh.py
index 0e43bec..0fbe6cb 100644
--- a/calfem/mesh.py
+++ b/calfem/mesh.py
@@ -352,7 +352,7 @@ def create(self, is3D=False, dim=3):
             # Meshing using gmsh extension module
 
             if self.initialize_gmsh:
-                gmsh.initialize(sys.argv)
+                gmsh.initialize(sys.argv, interruptible=False)
 
             gmsh.option.setNumber("General.Verbosity", self.gmsh_verbosity)
 
diff --git a/calfem/vis_mpl.py b/calfem/vis_mpl.py
index 812b980..cd95431 100644
--- a/calfem/vis_mpl.py
+++ b/calfem/vis_mpl.py
@@ -113,7 +113,7 @@ def figure(figure=None, show=True, fig_size=(6, 5.33)):
 
 def figure_widget(fig, parent=None):
     widget = FigureCanvas(fig)
-    widget.axes = fig.add_subplot(111)
+    #widget.axes = fig.add_subplot(111)
     if parent != None:
         widget.setParent(parent)
     toolbar = NavigationToolbar(widget, widget)

From 09fb3d2a4af100a6ae4c93863d1fa56f2f94d1ad Mon Sep 17 00:00:00 2001
From: Jonas Lindemann <jonas.lindemann@gmail.com>
Date: Thu, 15 Feb 2024 17:09:50 +0100
Subject: [PATCH 4/5] Update vis_mpl.py

---
 calfem/vis_mpl.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/calfem/vis_mpl.py b/calfem/vis_mpl.py
index cd95431..bbb1e66 100644
--- a/calfem/vis_mpl.py
+++ b/calfem/vis_mpl.py
@@ -127,11 +127,17 @@ def close_all():
 
 closeAll = close_all
 
-
 def clf():
     """Clear visvis figure"""
     plt.clf()
 
+def close(fig=None):
+    """Close visvis figure"""
+    if fig == None:
+        plt.close()
+    else:
+        plt.close(fig)
+
 
 def gca():
     """Get current axis of the current visvis figure."""

From a4c9942bd37544faf6905cdaec70cb2de4f5429f Mon Sep 17 00:00:00 2001
From: Jonas Lindemann <jonas.lindemann@gmail.com>
Date: Thu, 15 Feb 2024 17:25:48 +0100
Subject: [PATCH 5/5] update version number

---
 build-package.py | 2 +-
 setup.py         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/build-package.py b/build-package.py
index 9c27f0e..139f6c8 100644
--- a/build-package.py
+++ b/build-package.py
@@ -14,7 +14,7 @@ def build_package():
 
 if __name__ == "__main__":
 
-    package_version = "3.6.5"
+    package_version = "3.6.6"
 
     update_setup("calfem-python", package_version, "'numpy', 'visvis', 'pyvtk', 'matplotlib', 'scipy', 'gmsh', 'qtpy', 'vedo', 'tabulate'")
 
diff --git a/setup.py b/setup.py
index 7d60969..74922e4 100644
--- a/setup.py
+++ b/setup.py
@@ -32,7 +32,7 @@ def gen_data_files(*dirs):
     # the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
 
-    version='3.6.5',
+    version='3.6.6',
 
     description='CALFEM for Python',
     long_description='The computer program CALFEM is written for the software MATLAB and is an interactive tool for  learning the finite element method. CALFEM is an abbreviation of "Computer Aided Learning of the Finite Element  Method" and been developed by the Division of Structural Mechanics at Lund University since the late 70s.',