From 86a73fd3add4f771fc5347d7bcc4921b2a410ad3 Mon Sep 17 00:00:00 2001
From: jngan <justinngan92@gmail.com>
Date: Thu, 24 Nov 2022 18:32:04 +1100
Subject: [PATCH 1/3] docs: example.ipynb * modify content of example.ipynb *
 add ospgrillage example to example.ipynb * add example to tests

---
 calabru/calibrator.py               |   2 +-
 docs/source/images/osp_example.png  | Bin 0 -> 56827 bytes
 docs/source/notebooks/example.ipynb | 249 ++++++++++++++++++++++++----
 tests/fixtures.py                   | 144 ++++++++++++++++
 tests/test_updating.py              |  28 ++++
 5 files changed, 387 insertions(+), 36 deletions(-)
 create mode 100644 docs/source/images/osp_example.png

diff --git a/calabru/calibrator.py b/calabru/calibrator.py
index 083afb1..9a999de 100644
--- a/calabru/calibrator.py
+++ b/calabru/calibrator.py
@@ -176,7 +176,7 @@ def update_model(self, **kwargs):
             # response error
             e_response = np.sqrt(
                 sum([resp**2 for resp in self.global_resp_diff])
-            ) / np.sqrt(sum([target**2 for target in self.target_response_list[0]]))
+            )
             # parameter estimation error
             error = np.sqrt(
                 sum([estimates**2 for estimates in param_increments])
diff --git a/docs/source/images/osp_example.png b/docs/source/images/osp_example.png
new file mode 100644
index 0000000000000000000000000000000000000000..325631db9b8591915ace8fc1c9db4b4fd0b80cf4
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diff --git a/docs/source/notebooks/example.ipynb b/docs/source/notebooks/example.ipynb
index eb22181..7b01a2b 100644
--- a/docs/source/notebooks/example.ipynb
+++ b/docs/source/notebooks/example.ipynb
@@ -5,7 +5,7 @@
    "id": "2ebd6341-6ab2-4dbe-8fd2-fe235520af5f",
    "metadata": {},
    "source": [
-    "# Examples"
+    "# Example and features"
    ]
   },
   {
@@ -13,7 +13,24 @@
    "id": "66ec8f0a-120e-4de7-85dc-69e7c1f84412",
    "metadata": {},
    "source": [
-    "Here are a few more examples of `calabru`"
+    "We will consider a bridge grillage model created using the `ospgrillage` module\n",
+    "as our running example to demonstrate the features of `calabru`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a20ddb77-e42a-498c-a5a4-7386f561bf82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import calabru as clb # calabru package\n",
+    "import numpy as np # for numerical arrays\n",
+    "import matplotlib.pyplot as plt # for plotting\n",
+    "from IPython import display # For images in notebook\n",
+    "import pycba as cba # for creating beam model\n",
+    "import ospgrillage as og\n",
+    "%matplotlib inline \n"
    ]
   },
   {
@@ -21,61 +38,147 @@
    "id": "4f64cddc-6ae4-41d1-b2ba-da14dc589fcc",
    "metadata": {},
    "source": [
-    "## Example 1 - Exploring input options"
+    "## Exploring parameter options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a8fc08-682c-4b26-b00d-3463293703ea",
+   "metadata": {},
+   "source": [
+    "### Inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0a9bc72-15a7-4774-9f73-4c6512920628",
+   "metadata": {},
+   "source": [
+    "`calabru` takes multiple inputs for the updating parameters as a list."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "12c1c381-3c9d-434c-b923-bc3dbfb26285",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "start = [\n",
+    "         0.2, \n",
+    "         0.4,\n",
+    "         0.6,\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac1d3f25-5e83-4dc3-b49c-af3995eabac0",
+   "metadata": {},
+   "source": [
+    "We can introduce bounds which the parameters will not exceed during the calibration process as follows:"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "eb813bc2-d3d1-4ce1-b27b-1199bf646a1e",
+   "execution_count": 3,
+   "id": "0889938c-3c0d-48f8-8003-cfbd05ea30e5",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "bounds = [\n",
+    "          [0.1,0.5], # lower and upper bounds for first param \n",
+    "          [0.7], # upper bound of second param, lower default to zero \n",
+    "          [], # no bounds for third param\n",
+    "         ]"
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "c22aa21d-0366-4a04-90ab-50b7603c91ff",
+   "id": "52e86ec1-c980-413d-8787-c8330575bb3f",
+   "metadata": {},
+   "source": [
+    "where the `bounds` list must be the same size as the `start` list.\n",
+    "Each element should be a  list of a lower and upper bound (second entry)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0ecc314-4e02-41f1-b757-9aa7cdded647",
+   "metadata": {},
+   "source": [
+    "### Targets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81cfcfa9-ca1a-4798-9f51-beaae1f624ab",
    "metadata": {},
    "source": [
-    "## Example 2 - Grillage model problem in ospgrillage\n",
+    "Each target responses can be a list/array.\n",
     "\n",
-    "We have a single-span bridge we'd like to use to determine the bridge load effects due to an imposed load. In turn, a grillage model of a single-span bridge created using the `ospgrillage` package. \n"
+    "For example, we have can have the deflected shape list and the force magnitude of Element 5 as the first and second element the list of `target` respectively."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "371f2a7d-87e4-4e89-b8f0-ec966ac57639",
+   "execution_count": 4,
+   "id": "693bc990-4fb3-4c1d-9c34-d3cbd66db625",
    "metadata": {},
    "outputs": [],
    "source": [
-    "display.Image(\"./images/intro_ex_1.png\",width=800)"
+    "target = [\n",
+    "    5948.52,  # element forces\n",
+    "    [\n",
+    "        0.0,\n",
+    "        5.244e-05,\n",
+    "        9.984e-05,\n",
+    "        0.000136,\n",
+    "        0.0001558,\n",
+    "        0.0001558,\n",
+    "        0.000136,\n",
+    "        9.984e-05,\n",
+    "        5.243e-05,\n",
+    "        0.0,\n",
+    "    ], # node displacements\n",
+    "    \n",
+    "] "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7b01d2b4-86bc-4e3c-835a-e4bdb76ebc82",
+   "id": "c22aa21d-0366-4a04-90ab-50b7603c91ff",
    "metadata": {},
    "source": [
-    "Next, we want to update the model using experimental measurements of the bridge deflections. The measurement is defined as follows:\n"
+    "## Example 1 - Grillage model problem in ospgrillage\n",
+    "\n",
+    "We have a full-fledged single-span bridge model made using `ospgrillage` module that we'd like to use to determine the bridge load effects due to an imposed load.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "f33e36ab-5091-4a91-b434-21fb771e9d37",
+   "execution_count": 11,
+   "id": "371f2a7d-87e4-4e89-b8f0-ec966ac57639",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "image/png": {
+       "width": 800
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "target_deflections = [[0.0, 5.24e-05, 9.98e-05, 0.000136, 0.000155,0.0001558, 0.000136, 9.98e-05, 5.24e-05,0.0]]"
+    "display.Image(\"../images/osp_example.png\",width=800)"
    ]
   },
   {
@@ -83,27 +186,57 @@
    "id": "d787bca0-bd20-49cc-bb8d-66c7cb26d1dc",
    "metadata": {},
    "source": [
-    "We want to update the stiffness of the girders (`I`) in the model to update the model:"
+    "We want to update the moment of inertia of the longitudinal girders (`I`) of the bridge model. In addition, we are uncertain of the moment of inertia of the two edge members as well. Therefore, we introduce two updating paramters to the model as:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "50e8038b-93f7-43e3-8e7b-fa5b91f1f9ab",
    "metadata": {},
    "outputs": [],
    "source": [
-    "I_start = [0.2] # m4"
+    "start = [0.2, # longitudinal girders, I\n",
+    "           0.1, # edge member, I\n",
+    "          ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b01d2b4-86bc-4e3c-835a-e4bdb76ebc82",
+   "metadata": {},
+   "source": [
+    "We update the model using some \"known\" measurements of the bridge deflections. For this,the measurements is taken as the\n",
+    "responses which corresponds to a parameter list = [0.5,0.1]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7ba40eb6-316e-4d56-881b-3c87c93a234a",
+   "execution_count": 7,
+   "id": "d67b5834-8f84-4997-8106-18a2157f8811",
    "metadata": {},
    "outputs": [],
    "source": [
-    "First we need to structure the model's generation and analysis script into a function handler (denoted as `main()`) with the following properties:\n",
+    "target = [[1047.057906932444,\n",
+    "  [0.0,\n",
+    "   3.847756717425916e-05,\n",
+    "   7.335795722107879e-05,\n",
+    "   0.0001001430188545662,\n",
+    "   0.00011472208165921942,\n",
+    "   0.00011472208165922035,\n",
+    "   0.00010014301885456844,\n",
+    "   7.335795722108123e-05,\n",
+    "   3.847756717426085e-05,\n",
+    "   0.0]]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05734b9f-1e03-42a7-94fe-895889115884",
+   "metadata": {},
+   "source": [
+    "Lets create the function handler (denote as `main()`) that creates and analyse the model with the following properties that is compatible with`calabru` to allow for updating procedures:\n",
     "\n",
     "1. `main()` takes in `I_start` as an argument.\n",
     "2. `main()` returns a list of the corresponding measurables of `target_deflections` from the model."
@@ -111,16 +244,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "989b4a2c-4fd5-4111-b9a7-b9dce37ec2b6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "def beam_with_patch_load_including_deflected_shape_output(p_matrix: list):\n",
+    "def main(p_matrix: list):\n",
     "    # sort parameter variable\n",
-    "    #P = p_matrix[0]\n",
+    "\n",
     "    P = 1000\n",
-    "    Iz = p_matrix[0]\n",
+    "    Iz1 = p_matrix[0]\n",
+    "    Iz2 = p_matrix[1]\n",
     "\n",
     "    # Adopted units: N and m\n",
     "    kilo = 1e3\n",
@@ -156,7 +292,7 @@
     "    edge_longitudinal_section = og.create_section(\n",
     "        A=0.934 * m2,\n",
     "        J=0.1857 * m3,\n",
-    "        Iz=0.3478 * m4,\n",
+    "        Iz=Iz2 * m4,\n",
     "        Iy=0.213602 * m4,\n",
     "        Az=0.444795 * m2,\n",
     "        Ay=0.258704 * m2,\n",
@@ -165,7 +301,7 @@
     "    longitudinal_section = og.create_section(\n",
     "        A=1.025 * m2,\n",
     "        J=0.1878 * m3,\n",
-    "        Iz=Iz * m4,\n",
+    "        Iz=Iz1 * m4,\n",
     "        Iy=0.113887e-3 * m4,\n",
     "        Az=0.0371929 * m2,\n",
     "        Ay=0.0371902 * m2,\n",
@@ -256,10 +392,53 @@
     "    results = simple_bridge.get_results()\n",
     "\n",
     "    # arbitrary force components\n",
-    "    r_mat = [[og.ops.nodeDisp(n)[1] for n in [2,9,16,23,30,37,44,51,58,65]]]\n",
-    "    #og.ops.eleForce(25)[1],\n",
+    "    r_mat = [[og.ops.eleForce(25)[1],[og.ops.nodeDisp(n)[1] for n in [2,9,16,23,30,37,44,51,58,65]]]]\n",
+    "\n",
     "    return r_mat"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "50e8e494-9924-413c-867e-0b7383e861a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_beam_updating = clb.ModelUpdating(\n",
+    "    function_handle=main,\n",
+    "    param_list=start,\n",
+    "    target_list=target,\n",
+    "    max_error=0.1,\n",
+    "    write_output_txt=False\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a1f0bdc8-307c-4fad-8be8-e7a65c339363",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.2, 0.1], [0.3365125562453617, 0.10119151305405859], [0.45716219684186105, 0.1008159604001884], [0.5002470885555945, 0.10009323201609568]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "simple_beam_updating.update_model()\n",
+    "print(simple_beam_updating.param_update_history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf76bc8-59ac-463a-bb05-c7d8ae89c612",
+   "metadata": {},
+   "source": [
+    "As can be seen the values converged to [0.5,0.1] "
+   ]
   }
  ],
  "metadata": {
diff --git a/tests/fixtures.py b/tests/fixtures.py
index 18e24e1..b6a6620 100644
--- a/tests/fixtures.py
+++ b/tests/fixtures.py
@@ -159,6 +159,150 @@ def beam_with_patch_load(p_matrix: list):
 
     return r_mat
 
+def main(p_matrix: list):
+    # sort parameter variable
+
+    P = 1000
+    Iz1 = p_matrix[0]
+    Iz2 = p_matrix[1]
+
+    # Adopted units: N and m
+    kilo = 1e3
+    milli = 1e-3
+    N = 1
+    m = 1
+    mm = milli * m
+    m2 = m ** 2
+    m3 = m ** 3
+    m4 = m ** 4
+    kN = kilo * N
+    MPa = N / ((mm) ** 2)
+    GPa = kilo * MPa
+
+    # parameters of bridge grillage
+    L = 10 * m  # span
+    w = 5 * m  # width
+    n_l = 7  # number of longitudinal members
+    n_t = 10  # number of transverse members
+    edge_dist = 1 * m  # distance between edge beam and first exterior beam
+    ext_to_int_dist = (
+        2.2775 * m
+    )  # distance between first exterior beam and first interior beam
+    angle = 0  # skew angle
+    mesh_type = "Oblique"
+
+    # define material
+    concrete = og.create_material(
+        material="concrete", code="AS5100-2017", grade="65MPa"
+    )
+
+    # define sections (parameters from LUSAS model)
+    edge_longitudinal_section = og.create_section(
+        A=0.934 * m2,
+        J=0.1857 * m3,
+        Iz=Iz2 * m4,
+        Iy=0.213602 * m4,
+        Az=0.444795 * m2,
+        Ay=0.258704 * m2,
+    )
+
+    longitudinal_section = og.create_section(
+        A=1.025 * m2,
+        J=0.1878 * m3,
+        Iz=Iz1 * m4,
+        Iy=0.113887e-3 * m4,
+        Az=0.0371929 * m2,
+        Ay=0.0371902 * m2,
+    )
+
+    transverse_section = og.create_section(
+        A=0.504 * m2,
+        J=5.22303e-3 * m3,
+        Iy=0.32928 * m4,
+        Iz=1.3608e-3 * m4,
+        Ay=0.42 * m2,
+        Az=0.42 * m2,
+    )
+
+    end_transverse_section = og.create_section(
+        A=0.504 / 2 * m2,
+        J=2.5012e-3 * m3,
+        Iy=0.04116 * m4,
+        Iz=0.6804e-3 * m4,
+        Ay=0.21 * m2,
+        Az=0.21 * m2,
+    )
+
+    # define grillage members
+    longitudinal_beam = og.create_member(
+        section=longitudinal_section, material=concrete
+    )
+    edge_longitudinal_beam = og.create_member(
+        section=edge_longitudinal_section, material=concrete
+    )
+    transverse_slab = og.create_member(section=transverse_section, material=concrete)
+    end_transverse_slab = og.create_member(
+        section=end_transverse_section, material=concrete
+    )
+
+    # create grillage
+    simple_bridge = og.create_grillage(
+        bridge_name="simple_bridge",
+        long_dim=L,
+        width=w,
+        skew=angle,
+        num_long_grid=n_l,
+        num_trans_grid=n_t,
+        edge_beam_dist=edge_dist,
+        mesh_type=mesh_type,
+    )
+
+    simple_bridge.set_member(longitudinal_beam, member="interior_main_beam")
+    simple_bridge.set_member(longitudinal_beam, member="exterior_main_beam_1")
+    simple_bridge.set_member(longitudinal_beam, member="exterior_main_beam_2")
+    simple_bridge.set_member(edge_longitudinal_beam, member="edge_beam")
+    simple_bridge.set_member(transverse_slab, member="transverse_slab")
+    simple_bridge.set_member(end_transverse_slab, member="start_edge")
+    simple_bridge.set_member(end_transverse_slab, member="end_edge")
+    simple_bridge.create_osp_model(pyfile=False)
+    og.opsplt.plot_model()
+    # add load case
+    # Patch load over entire bridge deck (P is kN/m2)
+    P = P * kN / m2  # magnitude of patch vertex
+    patch_point_1 = og.create_load_vertex(x=0, z=0, p=P)
+    patch_point_2 = og.create_load_vertex(x=L, z=0, p=P)
+    patch_point_3 = og.create_load_vertex(x=L, z=w, p=P)
+    patch_point_4 = og.create_load_vertex(x=0, z=w, p=P)
+    test_patch_load = og.create_load(
+        loadtype="patch",
+        name="Test Load",
+        point1=patch_point_1,
+        point2=patch_point_2,
+        point3=patch_point_3,
+        point4=patch_point_4,
+    )
+
+    test_point_load = og.create_load(
+        loadtype="point",
+        name="Test Load",
+        point1=og.create_load_vertex(x=L / 2, z=w / 2, p=P),
+    )
+
+    # Create load case, add loads, and assign
+    patch_case = og.create_load_case(name="test patch load case")
+    patch_case.add_load(test_patch_load)
+    point_case = og.create_load_case(name="test point load case")
+    point_case.add_load(test_point_load)
+    # sn8474.add_load_case(patch_case)
+    simple_bridge.add_load_case(point_case)
+
+    simple_bridge.analyze()
+    results = simple_bridge.get_results()
+
+    # arbitrary force components
+    r_mat = [[og.ops.eleForce(25)[1],[og.ops.nodeDisp(n)[1] for n in [2,9,16,23,30,37,44,51,58,65]]]]
+
+    return r_mat
 
 def beam_with_patch_load_including_deflected_shape_output(p_matrix: list):
     """
diff --git a/tests/test_updating.py b/tests/test_updating.py
index 3ce5efb..1c67a4e 100644
--- a/tests/test_updating.py
+++ b/tests/test_updating.py
@@ -34,6 +34,34 @@ def test_basic_update():
     )
 
 
+def test_main():
+    start = [0.2,  # load, P
+             0.1,  # moment of inertia, I
+             ]
+    target = [[1047.057906932444,
+  [0.0,
+   3.847756717425916e-05,
+   7.335795722107879e-05,
+   0.0001001430188545662,
+   0.00011472208165921942,
+   0.00011472208165922035,
+   0.00010014301885456844,
+   7.335795722108123e-05,
+   3.847756717426085e-05,
+   0.0]]]
+    # node displacements
+
+    simple_beam_updating = ModelUpdating(
+        function_handle=fixtures.main,
+        param_list=start,
+        target_list=target,
+        max_error=0.1,
+        write_output_txt=False
+    )
+    simple_beam_updating.update_model()
+    print(simple_beam_updating.param_update_history)
+
+
 def test_target_resp_as_list():
     """
     Checks updating using a list of measurements as an element entry of the target measurement. The updating should

From a165375243f31003fef8fbb8ba4f9b801f7047a6 Mon Sep 17 00:00:00 2001
From: jngan <justinngan92@gmail.com>
Date: Wed, 15 May 2024 15:31:36 +1000
Subject: [PATCH 2/3] docs: modify sphinx settings

---
 calabru/__init__.py                           |   3 +
 calabru/calibrator.py                         | 246 ++++-----
 calabru/helpers.py                            | 135 +++++
 docs/requirement.txt                          |   4 +-
 docs/source/api.rst                           |  10 +-
 docs/source/conf.py                           |  16 +-
 docs/source/images/logo.png                   |   0
 docs/source/index.rst                         |  26 +-
 docs/source/installation.md                   |   4 +-
 .../notebooks/ospgrillage_example.ipynb       | 465 ++++++++++++++++++
 pyproject.toml                                |   4 +-
 11 files changed, 736 insertions(+), 177 deletions(-)
 create mode 100644 calabru/helpers.py
 create mode 100644 docs/source/images/logo.png
 create mode 100644 docs/source/notebooks/ospgrillage_example.ipynb

diff --git a/calabru/__init__.py b/calabru/__init__.py
index eed70c9..50ec9eb 100644
--- a/calabru/__init__.py
+++ b/calabru/__init__.py
@@ -1 +1,4 @@
+__version__ = "0.1.0"
+
 from .calibrator import *
+from .helpers import *
diff --git a/calabru/calibrator.py b/calabru/calibrator.py
index 9a999de..e41e440 100644
--- a/calabru/calibrator.py
+++ b/calabru/calibrator.py
@@ -2,36 +2,47 @@
 """
 Main calibration module
 """
-import numpy as np
 
 from datetime import datetime
 import copy
-from scipy.interpolate import interp1d
-import matplotlib.pyplot as plt
+from helpers import *
 
 
 class ModelUpdating:
     """
-    Main updating/calibrating class.
+    Main class to handle updating.
     """
 
     def __init__(self, function_handle, **kwargs):
         """
 
-        :param function_handle: Function handler to create and analyze models in Python environment. Note the function
-         handler must be able to pass in updating parameters and return desirable responses.
-        :param sensitivity_type: Method to obtain the sensitivity matrix. Default "FDM"
-        :type sensitivity_type: str
-        :param param_list: A list of float or int of starting values of updating parameters.
-        :type param_list: list
-        :param target_list: A list of float or int of target/objectives of the updating function handler.
-        :type target_list: list
-        :param target_resp_list: Multi dimension list of k considered models/cases. If a single dimension list is provided
-         the class parses the list into a mutli dimension 1x(k=1) list.
-
-        :param max_increm_steps: Maximum updating increments. Default 50
-        :param param_increm_rate: Increment rate of parameters between each step. Default 10%
-        :param max_error: Maximum allowable parameter error.
+        Parameters
+        ----------
+        function_handle:
+            Python function handler to calibrate.
+        **kwargs: dict, optional
+            Extra arguments for class and function_handle. Refer below for the list of all possible arguments.
+
+        Other Parameters
+        ----------------
+        iterative_method: str, default="FDM"
+            Method to calculate sensitivity matrix.
+        param_list: list
+            A list of starting values for updating parameters
+        target_list: list
+            A list of target/objective values for the function handle outputs
+        target_resp_list: list
+            Multi dimension list of k considered models/cases. If a single dimension list is provided
+            the class parses the list into a mutli dimension 1x(k=1) list.
+        max_increm_steps: int, default=50
+            Maximum updating increments
+        param_increm_rate: float, default=1.1
+            Increment rate of parameter between updating iterations
+        max_error: float
+            maximum allowable parameter error
+        write_output_txt: bool
+            If true, writes
+
         """
 
         # get model and load case inputs
@@ -48,8 +59,14 @@ def __init__(self, function_handle, **kwargs):
             "param_bounds_list", [[] for item in self.param_list]
         )  # bounds for params
         # empty list means no bounds.
+        # weighting for param bounds
+        self.param_sen_weighting = kwargs.get("param_sen_weighting", [1 for item in self.param_list])
+        if not self.param_sen_weighting:
+            self.param_sen_weighting = [1 for item in self.param_list]
+
+
 
-        # check validity of param bounds
+            # check validity of param bounds
         if len(self.param_list) != len(self.param_bounds_list):
             raise Exception(
                 "Param bound list must match the number of parameters: Hint specify empty list [] to params that are not bounded"
@@ -102,17 +119,34 @@ def __init__(self, function_handle, **kwargs):
     def set_targets(self, target_list: list):
         """
         Set or overwrite updating parameters.
-        :param target_list: list of target response value. Note: list order must correspond to the outputs returned
+
+        Parameters
+        ----------
+        target_list: list
+            ist of target response value
+
+        Notes
+        -----
+         list order must correspond to the outputs returned
          by main() function. Refer to template main() function for information on setting up the updating procedure.
-        :type target_list: list
+
         """
         self.target_response_list = target_list
 
     def set_param(self, param_list: list, variance_range=None):
         """
         Set or overwrite updating parameters.
-        :param param_list: list of starting parameter values. Note: list order must correspond to the input of
+
+        Parameters
+        ----------
+        param_list: list
+            list of starting parameter values
+
+        Notes
+        -----
+        Note: list order must correspond to the input of
          main() updating function. Refer to template main() function for information on setting up the updating procedure.
+
         """
         self.param_list = param_list
 
@@ -120,7 +154,10 @@ def update_model(self, **kwargs):
         """
         Calibrate the model.
 
-        Keyword arguments passed under this function are passed to function handle of the updating object.
+        Parameters
+        ----------
+        **kwargs: dict, optional
+            Keyword arguments accepted by the function handler of the updating
 
         Example:
             If the function handler takes two keyword arguments -
@@ -146,7 +183,7 @@ def update_model(self, **kwargs):
                 self.global_sensitivity_matrix += self.sensitivity_matrix
                 self.global_resp_diff += self.resp_diff
 
-            esti = self._get_pseudo_inv_estimation(
+            esti = get_pseudo_inv_estimation(
                 sens_mat=self.global_sensitivity_matrix, resp_diff=self.global_resp_diff
             )
             # compute residue and check threshold
@@ -206,15 +243,24 @@ def update_model(self, **kwargs):
 
     def _compute_fdm_sensitivity(self, param_list, target_resp=None, model_index=0):
         """
-        Function to compute sensitivity matrix based on finite difference sensitivity method. The function runs the
+        Returns the sensitivity matrix based on finite difference sensitivity method. The function runs the
         function handle defined during construction of class with the initial parameters as inputs. Then increments
         the parameter values to obtain an incremental responses. Subsequently, assembles the sensitivity matrix.
 
-        :param param_list: Parameters at current updating step.
-        :type param_list:list
-        :param model_index: Index of output from function_handle which matches the current evaluating target response.
+        Parameters
+        ----------
+        param_list: list
+            list of updating parameter values
+        target_resp: list, optional
+            list of target responses
+        model_index: int, default=0
+            Index of output from function_handle which matches the current evaluating target response
+
+        Returns
+        -------
+        Array like
+            Array of sensitivity matrix - also stored as `sensitivity_matrix` in class
 
-        :returns array of sensitivity matrix stored in sensitivity_matrix var
         """
 
         # get base response
@@ -244,11 +290,11 @@ def _compute_fdm_sensitivity(self, param_list, target_resp=None, model_index=0):
                     model_time = list(response[model_index][k].keys())
 
                     if len(ref_resp) > len(response):
-                        interp_ref_resp = self._interpolate_measurements(
+                        interp_ref_resp = interpolate_measurements(
                             data_x=time, data_y=val, model_x=model_time
                         )
                     else:
-                        interp_ref_resp = self._interpolate_measurements(
+                        interp_ref_resp = interpolate_measurements(
                             data_x=model_time, data_y=val, model_x=time
                         )
                 else:
@@ -256,13 +302,13 @@ def _compute_fdm_sensitivity(self, param_list, target_resp=None, model_index=0):
                     model_abs_axis = np.linspace(0, 1, len(kth_model_response))
                     abs_time_measure = np.linspace(0, 1, len(ref_resp))
 
-                    interp_ref_resp = self._interpolate_measurements(
+                    interp_ref_resp = interpolate_measurements(
                         data_x=abs_time_measure, data_y=ref_resp, model_x=model_abs_axis
                     )
                     # plt.plot(model_abs_axis, interp_ref_resp)
                     # plt.plot(model_abs_axis, kth_model_response)
                     # plt.show()
-                rmse = self._calculate_rmse(
+                rmse = calculate_rmse(
                     ref_response_list=interp_ref_resp,
                     current_response_list=kth_model_response,
                 )
@@ -288,25 +334,33 @@ def _compute_fdm_sensitivity(self, param_list, target_resp=None, model_index=0):
                 # checks if the current response is a list
                 if hasattr(target_resp[i], "__len__"):
                     # calculate rmse
-                    rmse = self._calculate_rmse(
+                    rmse = calculate_rmse(
                         ref_response_list=inc_response[model_index][i],
                         current_response_list=response[model_index][i],
                     )
                     self.sensitivity_matrix[i].append(
-                        rmse / (param * abs(self.increment_rate - 1))
+                        rmse / (param * abs(self.increment_rate - 1)*self.param_sen_weighting[j])
                     )
                 else:
                     self.sensitivity_matrix[i].append(
-                        (inc_response[model_index][i] - response[model_index][i])
+                        (inc_response[model_index][i] - response[model_index][i]) * self.param_sen_weighting[j]
                         / (param * abs(self.increment_rate - 1))
                     )
 
     def get_updated_response(self, param_list):
+        """Returns the responses from a given list of parameters
 
+        Parameters
+        ----------
+        param_list: list
+            List of parameter values
+
+        """
         # get base response
         return self.function_handle(param_list, **self.kwarg)
 
     def print_to(self, current_step, error):
+        """Prints the status of each step"""
         # function to print updating status to
         print("Curent update step {}".format(current_step))
         print(self.param_update_history[-1])
@@ -314,6 +368,7 @@ def print_to(self, current_step, error):
         print(error)
 
     def _write_output_txt(self):
+        """Returns a report .txt file of the calibration process"""
         name = self.name + ".txt"
         with open(name, "w") as file_handle:
             # create py file or overwrite existing
@@ -335,122 +390,3 @@ def _write_output_txt(self):
 
             file_handle.write("Target response\n")
             file_handle.write(str(self.target_response_list) + "\n")
-
-    @staticmethod
-    def _get_pseudo_inv_estimation(sens_mat, resp_diff):
-        """
-        Function to estimate parameters for model updating using Pseudo Inverse Method
-        :param sens_mat: sensitivity matrix at current updating step
-        :type sens_mat: list of size r by p where r and p are the number of responses and parameters respectively
-        """
-        sens_mat_transpose = np.transpose(sens_mat)
-        resp_num = np.array(sens_mat).shape[0]
-        param_num = np.array(sens_mat).shape[1]
-        # init var
-        estimate = None
-        if resp_num == param_num:
-            estimate = np.dot(np.linalg.inv(sens_mat), resp_diff)
-        elif resp_num > param_num:
-            pseudo_inv = np.dot(
-                np.linalg.inv(np.dot(sens_mat_transpose, sens_mat)), sens_mat_transpose
-            )
-            estimate = np.dot(pseudo_inv, resp_diff)
-        elif resp_num < param_num:
-            pseudo_inv = np.dot(
-                sens_mat_transpose,
-                np.linalg.inv(
-                    np.dot(
-                        sens_mat,
-                        sens_mat_transpose,
-                    )
-                ),
-            )
-            estimate = np.dot(pseudo_inv, resp_diff)
-
-        return estimate
-
-    @staticmethod
-    def _calculate_rmse(ref_response_list, current_response_list):
-        N = len(ref_response_list)
-
-        return np.sqrt(
-            sum(
-                [
-                    (ref - current) ** 2 / N
-                    for (ref, current) in zip(ref_response_list, current_response_list)
-                ]
-            )
-        )
-
-    @staticmethod
-    def _get_bayesian_param_estimation(sens_mat, resp_diff, current_param, cp, cr):
-        """
-        Function to perform parameter estimation using Bayesian approach. Here, a gain matrix G is calculated
-        from the provided confidence weighing for parameter and responses.
-        :param current_param:
-        :param cp:
-        :parm cr:
-        """
-        sens_mat_transpose = np.transpose(sens_mat)
-        resp_num = np.array(sens_mat).shape[0]
-        param_num = np.array(sens_mat).shape[1]
-
-        # init var
-        gain_mat = []
-        estimate = None
-        if resp_num >= param_num:
-            gain_mat = np.dot(
-                np.dot(
-                    np.linalg.inv(
-                        cp + np.dot(np.dot(sens_mat_transpose, cr), sens_mat)
-                    ),
-                    sens_mat_transpose,
-                ),
-                cr,
-            )
-
-        elif resp_num < param_num:
-            cp_inv = np.linalg.inv(cp)
-            cr_inv = np.linalg.inv(cr)
-            gain_mat = np.dot(
-                np.dot(cp_inv, sens_mat_transpose),
-                np.linalg.inv(
-                    cr_inv + np.dot(np.dot(sens_mat, cp_inv), sens_mat_transpose)
-                ),
-            )
-
-        else:
-            raise Exception("Bayesian parameters error")
-        estimate = [
-            p + gr for (p, gr) in zip(current_param, np.dot(gain_mat, -resp_diff))
-        ]
-        return estimate
-
-    def get_updating_outputs(self):
-        """
-        Extracts information of the updating procedure
-
-        :return:
-        """
-        if self.update:
-            pass
-        else:
-            pass
-
-    @staticmethod
-    def _interpolate_measurements(data_x, data_y, model_x):
-        """
-        Function to interpolate measurement points on numerical/system model given vectors of measurement (1) time seires
-        , and (2) values.
-
-        This function is used when length missmatch between measurement vector and model output vector.
-
-        """
-        # takes in a vector of common x axis e.g. time ratio of positional times over total movement time window (0 to 1)
-
-        # create interp function using measurment data - linear interp
-        f1 = interp1d(data_x, data_y)
-
-        # apply interp function to ospgrillage data to get respctive positional strain values
-        return f1(model_x)
-        # return the decimated curve value
diff --git a/calabru/helpers.py b/calabru/helpers.py
new file mode 100644
index 0000000..0a83b2c
--- /dev/null
+++ b/calabru/helpers.py
@@ -0,0 +1,135 @@
+import numpy as np
+from scipy.interpolate import interp1d
+def get_pseudo_inv_estimation(sens_mat, resp_diff):
+    """
+    Estimate parameters for model updating using Pseudo Inverse Method
+
+    Parameters
+    ----------
+    sens_mat: array_like
+        Sensitivity matrix
+    resp_diff: list
+        list of response differences value
+
+    Notes
+    -----
+    The array sizes much match. list of size r by p where r and p are the number of responses and parameters respectively
+    """
+    sens_mat_transpose = np.transpose(sens_mat)
+    resp_num = np.array(sens_mat).shape[0]
+    param_num = np.array(sens_mat).shape[1]
+    # init var
+    estimate = None
+    if resp_num == param_num:
+        estimate = np.dot(np.linalg.inv(sens_mat), resp_diff)
+    elif resp_num > param_num:
+        pseudo_inv = np.dot(
+            np.linalg.inv(np.dot(sens_mat_transpose, sens_mat)), sens_mat_transpose
+        )
+        estimate = np.dot(pseudo_inv, resp_diff)
+    elif resp_num < param_num:
+        pseudo_inv = np.dot(
+            sens_mat_transpose,
+            np.linalg.inv(
+                np.dot(
+                    sens_mat,
+                    sens_mat_transpose,
+                )
+            ),
+        )
+        estimate = np.dot(pseudo_inv, resp_diff)
+
+    return estimate
+
+def calculate_rmse(ref_response_list, current_response_list):
+    """Get the root mean square error (RMSE) between the reference and current response values
+    Parameters
+    ----------
+    ref_response_list: list
+        list of reference response values
+    current_response_list: list
+        list of current response values
+
+    Returns
+    -------
+    float
+        Root mean square error
+
+    """
+    N = len(ref_response_list)
+
+    return np.sqrt(
+        sum(
+            [
+                (ref - current) ** 2 / N
+                for (ref, current) in zip(ref_response_list, current_response_list)
+            ]
+        )
+    )
+
+def get_bayesian_param_estimation(sens_mat, resp_diff, current_param, cp, cr):
+    """
+    Function to perform parameter estimation using Bayesian approach. Here, a gain matrix G is calculated
+    from the provided confidence weighing for parameter and responses.
+    :param current_param:
+    :param cp:
+    :parm cr:
+    """
+    sens_mat_transpose = np.transpose(sens_mat)
+    resp_num = np.array(sens_mat).shape[0]
+    param_num = np.array(sens_mat).shape[1]
+
+    # init var
+    gain_mat = []
+    estimate = None
+    if resp_num >= param_num:
+        gain_mat = np.dot(
+            np.dot(
+                np.linalg.inv(
+                    cp + np.dot(np.dot(sens_mat_transpose, cr), sens_mat)
+                ),
+                sens_mat_transpose,
+            ),
+            cr,
+        )
+
+    elif resp_num < param_num:
+        cp_inv = np.linalg.inv(cp)
+        cr_inv = np.linalg.inv(cr)
+        gain_mat = np.dot(
+            np.dot(cp_inv, sens_mat_transpose),
+            np.linalg.inv(
+                cr_inv + np.dot(np.dot(sens_mat, cp_inv), sens_mat_transpose)
+            ),
+        )
+
+    else:
+        raise Exception("Bayesian parameters error")
+    estimate = [
+        p + gr for (p, gr) in zip(current_param, np.dot(gain_mat, -resp_diff))
+    ]
+    return estimate
+
+
+def interpolate_measurements(data_x, data_y, model_x):
+    """
+    Returns the interpolation for set of data for a given value, x.
+
+    Parameters
+    ----------
+
+
+    Function to interpolate measurement points on numerical/system model given vectors of measurement (1) time seires
+    , and (2) values.
+
+    This function is used when length missmatch between measurement vector and model output vector.
+
+    """
+    # takes in a vector of common x axis e.g. time ratio of positional times over total movement time window (0 to 1)
+
+    # create interp function using measurment data - linear interp
+    f1 = interp1d(data_x, data_y)
+
+    # apply interp function to ospgrillage data to get respctive positional strain values
+    return f1(model_x)
+    # return the decimated curve value
diff --git a/docs/requirement.txt b/docs/requirement.txt
index 179d091..431240c 100644
--- a/docs/requirement.txt
+++ b/docs/requirement.txt
@@ -4,11 +4,11 @@ pytest>=6.1.1
 datetime
 scipy
 pydata-sphinx-theme
-Sphinx>=4.0
+Sphinx>=5.0
 nbsphinx
 sphinx-autodoc-typehints
 pandoc
 markupsafe==2.0.1
 pygments >= 2.7
-PyCBA
+pycba
 ospgrillage
\ No newline at end of file
diff --git a/docs/source/api.rst b/docs/source/api.rst
index cdb149f..395856e 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -1,7 +1,13 @@
+API Reference
+=============
+
+Package Modules
+---------------
+
 .. autosummary::
     :toctree: gen
-    :template: custom-module-template.rst
     :recursive:
 
-    calabru.ModelUpdating
+    calabru.calibrator
+    calabru.helpers
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 3a50cb8..0fa483c 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,8 +13,9 @@
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath("../../"))
-
+sys.path.insert(0, os.path.abspath("../../calabru/"))
+from calabru import __version__ as ver
+#
 
 # -- Project information -----------------------------------------------------
 
@@ -23,7 +24,12 @@
 author = "Colin Caprani, Jun Wei Ngan"
 
 # The full version, including alpha/beta/rc tags
-release = "0.1"
+# The short Major.Minor.Build version
+_v = ver.split(".")
+_build = "".join([c for c in _v[2] if c.isdigit()])
+version = _v[0] + "." + _v[1] + "." + _build
+release = ver
+
 
 
 # -- General configuration ---------------------------------------------------
@@ -81,7 +87,7 @@
     "icon_links": [
         {
             "name": "GitHub",
-            "url": "https://github.com/ccaprani/pycba",
+            "url": "https://github.com/MonashSmartStructures/calabru",
             "icon": "fab fa-github-square",
         },
         {
@@ -101,7 +107,7 @@
 
 # The name of an image file (relative to this directory) to place at the top
 # of the sidebar.
-# html_logo = "./images/logo.png"
+html_logo = "./images/logo.png"
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
diff --git a/docs/source/images/logo.png b/docs/source/images/logo.png
new file mode 100644
index 0000000..e69de29
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 9a82c60..f72c43b 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,25 +1,33 @@
-Calabru
-=======
-`calabru` [1]_ is a calibration framework for model/analysis/data systems in the Python
-environment.
+Welcome to calabru's documentation!
+===================================
+`calabru` [1]_ is a calibration framework in the Python environment.`calabru` is provides fast calibration of general model/functions/systems in Python environment.
 
-The calibration framework includes several state-of-the-art model updating methods such as:
+
+`calabru` uses several state-of-the-art model updating methods such as:
 
 - Sensitivity-based analysis
-- Bayesian-based approach (in progress)
+- Bayesian-based approach
 
 .. [1] Fun fact, "Calabru" means Calibration in the Irish language.
 
 
-Documentation
-=============
-
 .. toctree::
    :maxdepth: 2
+   :caption: Contents:
 
+   installation
+   api
    notebooks/intro
    notebooks/example
 
+
+
+Related Packages
+================
+- `pycba <https://github.com/ccaprani/pycba>`_ is an analysis package for continuous beam analysis.
+- `sectionproperties <https://github.com/robbievanleeuwen/section-properties>`_ is a package for the analysis of cross-sectional geometric properties and stress distributions.
+- `ospgrillage <https://github.com/ccaprani/ospgrillage>`_ is a bridge deck grillage analysis package which is a pre-processor for `OpenSeesPy <https://github.com/zhuminjie/OpenSeesPy>`_, a python wrapper for the general finite element analysis framework `OpenSees <https://github.com/OpenSees/OpenSees>`_.
+
 Indices and tables
 ==================
 
diff --git a/docs/source/installation.md b/docs/source/installation.md
index 7baaf2f..546b312 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -5,8 +5,6 @@ Required Dependencies
 ---------------------
 - Python 3.8 or later
 - numpy
-- scipy
-- matplotlib
 
 Instructions
 ------------
@@ -24,7 +22,7 @@ For contributions, first fork the repo and clone from your fork. `Here <https://
 
 Tests
 -----
-`PyCBA` comes with ``pytest`` functions to verify the correct functioning of the package. 
+`calabru` comes with ``pytest`` functions to verify the correct functioning of the package. 
 Users can test this using: ::
 
     python -m pytest
diff --git a/docs/source/notebooks/ospgrillage_example.ipynb b/docs/source/notebooks/ospgrillage_example.ipynb
new file mode 100644
index 0000000..7b01a2b
--- /dev/null
+++ b/docs/source/notebooks/ospgrillage_example.ipynb
@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2ebd6341-6ab2-4dbe-8fd2-fe235520af5f",
+   "metadata": {},
+   "source": [
+    "# Example and features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66ec8f0a-120e-4de7-85dc-69e7c1f84412",
+   "metadata": {},
+   "source": [
+    "We will consider a bridge grillage model created using the `ospgrillage` module\n",
+    "as our running example to demonstrate the features of `calabru`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a20ddb77-e42a-498c-a5a4-7386f561bf82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import calabru as clb # calabru package\n",
+    "import numpy as np # for numerical arrays\n",
+    "import matplotlib.pyplot as plt # for plotting\n",
+    "from IPython import display # For images in notebook\n",
+    "import pycba as cba # for creating beam model\n",
+    "import ospgrillage as og\n",
+    "%matplotlib inline \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f64cddc-6ae4-41d1-b2ba-da14dc589fcc",
+   "metadata": {},
+   "source": [
+    "## Exploring parameter options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a8fc08-682c-4b26-b00d-3463293703ea",
+   "metadata": {},
+   "source": [
+    "### Inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0a9bc72-15a7-4774-9f73-4c6512920628",
+   "metadata": {},
+   "source": [
+    "`calabru` takes multiple inputs for the updating parameters as a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "12c1c381-3c9d-434c-b923-bc3dbfb26285",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = [\n",
+    "         0.2, \n",
+    "         0.4,\n",
+    "         0.6,\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac1d3f25-5e83-4dc3-b49c-af3995eabac0",
+   "metadata": {},
+   "source": [
+    "We can introduce bounds which the parameters will not exceed during the calibration process as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0889938c-3c0d-48f8-8003-cfbd05ea30e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bounds = [\n",
+    "          [0.1,0.5], # lower and upper bounds for first param \n",
+    "          [0.7], # upper bound of second param, lower default to zero \n",
+    "          [], # no bounds for third param\n",
+    "         ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52e86ec1-c980-413d-8787-c8330575bb3f",
+   "metadata": {},
+   "source": [
+    "where the `bounds` list must be the same size as the `start` list.\n",
+    "Each element should be a  list of a lower and upper bound (second entry)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0ecc314-4e02-41f1-b757-9aa7cdded647",
+   "metadata": {},
+   "source": [
+    "### Targets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81cfcfa9-ca1a-4798-9f51-beaae1f624ab",
+   "metadata": {},
+   "source": [
+    "Each target responses can be a list/array.\n",
+    "\n",
+    "For example, we have can have the deflected shape list and the force magnitude of Element 5 as the first and second element the list of `target` respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "693bc990-4fb3-4c1d-9c34-d3cbd66db625",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = [\n",
+    "    5948.52,  # element forces\n",
+    "    [\n",
+    "        0.0,\n",
+    "        5.244e-05,\n",
+    "        9.984e-05,\n",
+    "        0.000136,\n",
+    "        0.0001558,\n",
+    "        0.0001558,\n",
+    "        0.000136,\n",
+    "        9.984e-05,\n",
+    "        5.243e-05,\n",
+    "        0.0,\n",
+    "    ], # node displacements\n",
+    "    \n",
+    "] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c22aa21d-0366-4a04-90ab-50b7603c91ff",
+   "metadata": {},
+   "source": [
+    "## Example 1 - Grillage model problem in ospgrillage\n",
+    "\n",
+    "We have a full-fledged single-span bridge model made using `ospgrillage` module that we'd like to use to determine the bridge load effects due to an imposed load.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "371f2a7d-87e4-4e89-b8f0-ec966ac57639",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ePFugwjgNTCaIHNwH0/XpLVj4EzHabDa0tbWhq6sL8fHxU/YihtJ+ztfruNtQacTonmDfF4VCAYVCMaErD8MwTvZ1KpWKnJlTYfQe1z3HYrGEZIC5kKDCGEI8HQEFhF4YfSm+cTgc6OzsRFtbG6kyjYqK8uhaoYoYKcHH31SqN0zkysMJZWdnJwBAo9HAYrFgbGxMcJGjUFO8HK4Ro8VioalUSuDxRhA5+MIYCrxJbzIMg97eXrS0tCA8PByLFy8mhRSeEKqeSV8FmBtzNTo6itjYWEG4uLgitPUA07MmvitPeno6WJbF8PAwTCYTBgcH0dXVhb6+PqeK1+l+P0+HiJG/PqvVisjIyGlcUeihwhhEvJ2JyIf7O6F2o5ns6ZplWfT390Ov1wMA5s2bh8TERK83GSEX33AFH2azGREREWhra4NEIoHdbsfAwADCw8MRHh4epBWfnoQyYpwKkUgEtVoNtVqNgYEBpKSkICIiAiaTCceOHYNerz/FlSfU0ZBQU7wcDofDKUtlNptDYsggJKgwBgFfZyLy4aLKUEaMwMQFCwMDA9DpdBgbG8OcOXOQmprq81NvKH1ZPb3O6OgodDodBgYGkJ2dTazOgJMjsL7//nscO3YMHR0dpOcuJibG7wG/viC0s06hrYeDS1lGRUUhKioKWVlZcDgcGBoacuvKw/0n2A8+p1vESNs1KH4RqBFQHNMljHwm6kX0ByGdMY6Pj6O1tRXd3d1OlbRcC41YLEZ0dDQkEgnmzZtHog/+gF+uQjImJmbKuYUzEaGd4XG4O8vj3k+uytJutxNXnu7ubjQ2NiIiIsIp9RpoV57TQRhdi2+oMFK8JtCCyDEdwsh9KUZGRqDX6zEwMIDMzEwsXrw4IG4kgDBSqfzCoejoaJx55plQqVST/j6WZSGVSp167vgVkj/88AMYhiEzC6OjoxEZGSlI0Qg0QnyNngi2RCJBbGwsYmNjAZyssOZaQzo6OjAyMgKlUukklP5mCIQsjPyJPcDJe2i1WqkwUjwnWILIEeqJF8DJjV6r1aKvrw+pqakT9iL6e63pEkaWZXHkyBHodDrIZDIsWbKEbIpT/S53cK0EKSkppELSaDTCaDSitbWVnGdxQjkTz2qEGjH6cpYnlUoRHx+P+Ph4AM6uPC0tLae48mg0Gq8zKEIXRgCnnDHS4hvKlARrJqIroRRGu90OAPjss8+QkJCA0tLSoH0ZpquP0WAwQKvVYnx8HPn5+UhOTp70PXP92VRr5ldIZmRkOE2Z4Jtn888nAxWFTydCPmP09zvpjSsPZzYwlVAKuV2DO1d3PWOciQ90k0GF0QtCNQKKIxTCaLfb0dnZSYbNLlq0CImJiUG9ZqjbNUZGRqDVamEymQJ2TuoJ/CkTOTk5sNvtZFPlPEH555NRUVEef5aEFqEJbT1AcATInSsP95729fXBbrefYjbgugahR4xc4R+HxWKhESPlVEItiBz8SfeBhmEY9PT0oLW1FXK5HEuWLMHXX389qWNNoAhVVarD4SBFFWlpaViwYIHPhRSBiHIlEolTms5qtcJkMsFoNOLw4cOw2+1O55NKpfK0cOCZSalUb3FNpZvNZiKU3d3d5MyZE0qlUiloYXRt7mcYhp4xUk6FS5nq9XpERUUhLi4uZB/qYESM/F5EkUjk1Iso9MZ7T7Hb7ejo6EBPTw/kcnlQ08L+IJfLkZycTKzOOAcXo9GI9vZ2EnFyQilU9xGhCTVHqAVbJBIhMjISkZGRSEtLO8WVp729HSKRCBKJBAqFAiMjI4IrznJXkQpAkN+fYEKFcQJchwSbTCbIZLKQfogDKYwsy2JgYIAMCp4zZw5SUlKcRP50NPfmw7Isenp60NLSQkRHLBYH5EsditmRfAcXhmEwNDQEo9FI+u3kcjliYmIwPj4uuIhDSJs7x3Sf5bl7T0dGRtDc3Ayr1YqvvvrKqX2Ee/iZznvpzg4OgGAfyoIFFUYX+III/Fhlyo2BCiWBuubg4CB0Oh2Gh4eRk5ODjIwMt2dsoSr2CcY4qIGBAWi1WjgcDhIFt7S0YGxsLKDXCRVhYWHQaDTQaDQAfhzuazQaMTg4CJvNBrPZTKJJT0cxBQOhplKFtq6wsDCo1WpERERApVIhPT2dmA0cPXoUer0eUqnUSShDXfTiGjGazWYAVBhnLe5mIvJbL8RicdDO+ybCX6Fy7UVcsmTJpFWQQugv9JahoSFotVoMDQ0hNzcXGRkZ5Il3Jo2d4g/3lUgkMJvNiIuLg9FoRGNjIyn6iImJQUxMzITnk8FAqKlUoVqvcWeM/Ief7OxsOBwOUsXc19eH5uZmyOVypzPKULvycM39QryPwWTWC6MnI6CA0LZOcPh65mexWNDS0oIjR44gLS0Nq1ev9ugLdTpFjBaLBXq9Hv39/ZMaEAh10/YXiURCqiP5RR9GoxEdHR1OFbExMTFBfeIXWmTGIeR1uUvxisVi8mADTO3KEx0dHfB2n4kmawjxPgaTWSuMngoix3REjN6mUsfHx8lcxMTERKxatcqrarJQFt/4M/uxvb0dHR0dSExMRFlZ2YSbfqC/zEIVWdeiD4ZhMDw8DKPRiKNHj0Kn0yE8PJykXaOjowNucya0jZNv2i80PK1KncyVp729HYcOHQq4Kw81ED/JrBNGX0ZAAdMTMXp6TX4vokajwcqVK6FWq326nlBTqQzDoLu7Gy0tLVCpVCguLp5y9uNMSqXymeo18Y2zs7OzSeRhNBrR2dmJw4cPQ6lUEqH0xb3FdT1Cuj+AsCZ+uOJritedK4+rby/flceXc2dqIH6SWSOMvgoih1gshs1mC/IqnZlKGDmxaG1thUKhwNKlS0kaJhjXCxRTTfLgw81G1Gq1EIlEWLBgAeLj4z0e3SXUKC+UuEYe4+PjMBqNMJlMaG5uxvj4uNP5pEql8mrjFuI95ls0Co1A9THKZDIkJiYSQw6uL9ZkMqGpqYm8r9wZpScGEq4RI02lzlD8mYnIZ7oiRnfpW87vU6/XQywW44wzzkBCQoLfH95QplKBqYVxcHAQWq0Wo6OjmDNnDtLS0qY1NSZEAfAFmUzmdD5psViIUHZ1dQHAKeeTU322hLZxCj1iDMbn2LUvli+Uvrry0IhxBsNFev4YfE9XVSo/SuXaEnQ6HWw2G5mLGEjT8lCOg5roWmazGTqdDsePH0dWVhaKiop8OjuZqalUIHDrEYlEiIiIQEREBGlK584nucG+MpmMpF1jYmJOOZ8Ucir1dD5j9AeRSDSpK09XVxdYlnXryuN6xjjbWjWAWSCMrr5/vjIdfYz8KNVkMkGn02FkZAS5ublIT08PeN/adESMfPizEZOTk/2e7BGMfsmZjkgkglqthlqtJoN9uYKPrq4uNDY2koIPzghdiPdlNkaMk+HOlWdkZGRCV57R0VEoFAqSSp1tzHhhBAKzQQbTt3Sya46NjeGbb76BwWDwK3ry9HqhjBg5EXY4HOjq6kJrays0Go1HsxE9vc5MjRhDhVgsPuV80nW6hEKhIAU+7tJz04GQzxin25EHOHlfVCoVVCoVmQTDme1bLBbU1NTgzjvvRH5+PgCgpaUFubm5Qbufvb29uPvuu1FTUwOz2Yw5c+bgtddew7Jly4JyvamYFcIYCEIdMVosFvT39+PEiRPIyMjwuBfRH0JdfMMwDPr6+qDX6yGRSLB48WLExcUF/foU33Et+LBYLOjs7MSxY8fw3XffgWVZJ3/X6WoOF4L4TIQQTcQ5Vx6FQoGEhAQsXboUKpUKr776Kr755hsUFhYiKSkJlZWVePnllwO6fpPJhNLSUqxduxY1NTWIj4+HXq9HdHR0wK7hLVQYPSRUESM/nciVXhcWFgb9uoBzhOVwOHDgwAEsX7484BM3uI3y66+/JrMRU1JSAr6BztRUqlDWAYDMlxwZGUFRURGGh4dhMplw/PhxtLS0EIszTiiD/XDHIcRzTw6hOvIAP1rCyeVyVFdX48CBA8jMzMTjjz+OTz75BI2NjQEX9d/97ndIT0/Ha6+9Rv4sOzs7oNfwllkhjIHYIIMdMXITIdrb2xETE4MzzzwTQ0ND6OnpCdo1XeFHjB0dHdi4cSOioqKwadMmVFVVoaKigvRQ+QqXrgGA2NhY5OXlBc3jk6ZSQwPfQpE7n8zMzHSyOOOcWyIjI53OJ4N1LCBU8eGGnAstYuRw53yjUqkQERGByspKVFZWBvyab7/9Nqqrq3HhhRfiww8/RGpqKm688UZce+21Ab+Wp8wKYQwEwYoY+b2IERERKCoqIr2IIyMjIU3f8otvuDOl0tJS/PDDD3j99dchEomwZMkSVFVVoaqqCsXFxR5bUo2NjaGlpQW9vb1IS0sDgAnNzAPJTIwYhcZE0Rnf4iw3Nxc2m+2UhnS1Wk2iSbVaHTDBEGrEKORqWcB9u0ZCQkJQr9nW1oY//elPuOOOO3Dffffhyy+/xC9+8QvIZDJcccUVQb32RFBh9JBAR4yuvYjz588/pXE9VMUw7q7HjWo6//zzsXXrVhw5cgQNDQ2ora3FK6+8gt/97ndQq9UoLy8nQpmVlXXK73Q4HOjo6EBbWxtiY2NRUlICpVKJnp6eoL82IW6MgeJ0fG1SqRQJCQlko7VYLMTftaenhwz15YTSn1mFQj1j5PYQIa4NcD+PMdh9jAzDYNmyZdi1axcAYMmSJTh06BCef/55KoxCJ1ARI8uyOH78OHQ6Hex2O/Ly8iY8Xwt1JSw/lcqluMbHxwEAycnJ2LJlC7Zs2QKGYfD//t//Q21tLerq6nDbbbfB4XAgLy8PVVVVWL9+PVatWoXBwUHo9XrI5XKnSBgITWsITaWGBl+jM9c+O659wGAwoLW1FRKJhKRdY2JivDqfFHIqFRCuMLqmUq1Wa9CFMTk5+ZQ6innz5uGtt94K6nUnY1YIYyC+IFzE6E+KxrUXkT8iyR2hdtvhi5VIJIJUKoXdbne7rqKiIhQVFeHee+/FiRMnsH//ftTW1uLdd9/Fc889B6lUijPOOAMbNmzABRdccEqFWSii4ZlafCM0ApG2dNc+wPm79vb2oqmpCREREU5G6JOdTwo1lXq6RYyhaPAvLS0ldQccOp0OmZmZQb3uZMwKYQwE3IfF9YPjCcPDw9DpdDAajcjOzva4FzHUwugaoUqlUhIxTkZUVBTOPfdcrFu3DldccQUaGxvR2dmJr7/+Gk8//TQeeeQRJCcnw2az4bzzzsP27dtD5mM6EyNGIQp0oO8Pf2wW8ONkCaPRiNbWVlIUwh/UzBcbIQujPw5cwcZd8U2wI8bbb78dJSUl2LVrF372s5/hiy++wIsvvogXX3wxqNedDCqMHsJ9WFxNdifDbDajpaUF/f39SE9Px/z5871KB4W6d9I1vSmVSj0yTrdardDr9Thy5AgyMjJw6aWXEtswq9WKgwcP4r333sNzzz2Hl19+Ga+88gry8/Nx1lln4ZxzzsHy5cuDUp3oz+bDsixsNpvT+yVEQRICobgvrpMlOB9Qo9FIfED5/q5CrfwU6ro4puOMcfny5fj3v/+Ne++9Fzt27EB2djaeeuopXHbZZUG97mRQYfQQfsQ4FWNjY2hra0N3dzeSkpK8novIMR0RI3+Tk8lkk0aMdrsdbW1t6OzsRHx8vNvXKZfLSZn3yy+/jPvuuw/Jycl4/fXX8dprr+HJJ5+ERqPB2rVrSRFPenp6QF6Pr1GpwWBAU1MTRkZGyHgmm80WckvA04XpiM5cDbNHR0eJETpnbwYAfX19iImJEcxMQaGefQI/DlyYDhPxTZs2YdOmTUG/jqfMCmEMxAeRS39MVgxjt9vJEN3Y2Fi/rc2mQxg9iRgZhkFPTw9aWloQGRmJ5cuXQ6PRTPn7JRIJlEolrrjiCmRlZaGwsBDt7e2oq6tDXV0dbr75ZjAMg4KCAqciHl/POLwVxtHRUWi1WhgMBuTk5CAuLg5DQ0MwGo0YHR2FXq/HwMAAYmNjERMTM22uLkJkOu+DSCSCUqmEUqkk55MdHR3o6+vDkSNHoNVqIZfLSRGPRqPxuM0o0Ag5YuS+K1wQwE1eodM1KJMyUWqTYRji9alUKrFs2bKA2BlxEVyonshdhcT1jJGrqNVqtWBZ1utxV/xiHs7cfcWKFVga/xRYAAAgAElEQVSxYgV+9atfwWQy4YMPPkBtbS3+9a9/4dlnn4VcLseqVauwfv16VFVVoaCgIOD3wmazkcg3JSUFq1evJq+dsz+zWq2IioqCVColVZNSqZRstjExMSHdbIUkyEJLMYeFhSEiIgIKhQJFRUWw2+3kfLKtrQ2jo6NQq9Uk7erJnMJAIWRh5B766dgpKoxe4VqcwrKsk9enN0N0Pb0e4FvBj6/Xc40YOSE7ceIEmpub/ZqNKJFInITR9SEjOjoaP/nJT/CTn/wELMuiubkZdXV1qK2txf33349t27YhNTWVRJPl5eWTDmaeKmJkWRY9PT3Q6/VQKpVYuXIl1Go1gFNT5mFhYQgPD0dqairS09OJq4vRaERnZycOHz5MikFiY2MD2qwudIRY6MLvY5RIJIiLiyM+vGNjYyTtevjwYdjtdjJ+KSYmBkqlMmivR8jC6K5ilgojZUr4LRtcL6LD4UB+fj6Sk5ODUpkHhE4YXcVKJpNhdHQU3333HY4ePer3dA9XYZxMtEQiEebNm4d58+bhF7/4BSwWCw4ePEh6J3fv3g0ACA8Px7Zt21BVVYWioiKn+zTZNQwGA5qbm2G3230a9Mx3dQF+3GyNRiN++OEHMAzj1IM3kzcXIQrjZGd54eHhTueTZrOZCGVHR4dTRSw3qDlQCNV4ADgZMfLH9HH3ZiZ/didiVghjICO4wcFB6HQ6mM1mMhcxWB90vjCGAn7xjc1mg8PhQHd3N0QiEcrKyvzeICQSCTmz9LaPUaFQkOIcAOjq6sL69ethMBjwzDPP4OGHH0ZMTAwqKipQWVmJqqoqyGSyU67BP0ecM2cOMjMzPX7/Jluv62Y7MjICo9GI48ePQ6/XIzw8nJxNTtWD58k6hCZEQluPp/eIP6cwPT0dDMOQQc39/f3Q6XQIDw8nDzjR0dF+pcyFHjHyHyytVisAUGGkTMzw8DDx+8zJyUFWVlbQDJA5XOcWBhsuVdzR0YHW1laEhYUhJiYGCxcuDMjv9yZinIqMjAwsX74cAwMDeOedd/DFF1+grq4O9fX1uP7668GyLAoKCjB//nzYbDasWLECfX19TueI3rTOeLPx85vVOTNtrrWA68HjPEJjYmKgVqsFJyzeILQzRsD3yCwsLAxRUVGIiopCdnY2OZ/kql0PHTpEpt5w55PeZHOEXJXqzicVoMJIcYPZbIZer8fRo0chlUqRn5+PjIyMkFybS2uEQhhZlsXg4CCGhobgcDiwcOFCjypNvUEikZAz2kANj7bb7ZBIJCgpKUFJSQkefPBBDAwM4P3338e7776L+vp6/POf/0R4eDgWL16MzZs3Iysri/RZeoOv6xWLxU5nXFarlaRdu7u7AcCpiEcorQWeIsQINlBrcnc+yRmhNzU1wWazISoqigilSqWa9LpCjhhdWzXMZjMAKowUHmNjY2htbUVPTw+Sk5NRVlaGQ4cOhXwDCIUwmkwmNDc3w2w2QyaTobS0FCKRCD/88AMaGxvxj3/8A5WVlZMWungCP5UaqFFg7tpn4uLi8LOf/QwrVqzApZdeiv7+fnR2duKzzz7Djh078Otf/xoZGRlORTxRUVF+rcUb5HI5UlJSiEco1xLCtRYoFAqn1F0ozpf9RWjCGKzILDw8HElJSUhKSiJncFw2oKurCwCc5k8qFAqndQhZGN0198vlcsGuN5jMCmH05gtis9nQ0dHhthcx1H2Fwb7m6OgodDodBgYGkJ2dDaVSCb1eT+5XeHg4RkdHcfnll5PWiqqqKlRXV2Pp0qUBr0r1FolE4vZ38M8RxWIxWT/3swMHDpDeyVdeeQVisRjFxcWoqKiASqXC5ZdfTqpTOYK18YtEolNSd9xGq9PpMDY2RiZOcBWTwVyPLwg1lRrse8Q/n0xLSyPnkyaTCUePHiXnk3yhFLIwumvul8vlgvqshYpZIYye4HA40NXVhba2tgl7ESeKUIJJMIRxfHwcLS0t6OnpQWpqKjlvMxgMTptccXExwsLC8PTTT5NqUK7QJS4uDpWVlaS/0JMBxq59jIGOGF37ERctWoTDhw87fdkjIyOxceNGbNy4EQDQ3t6O+vp61NXV4fHHH4fZbMaDDz6Ic845B+Xl5aisrERiYiKA0AiARCIh1mdcgzWXdu3o6IBYLIZEIoFCocDY2JhX56TBQqip1FALEP98MisrCw6Hg5xPci09MpkMEokEBoMBGo1GUNkAdwbiszGNClBhBMMw6OvrQ0tLC6RSKRYuXIi4uLgJx0BNR8QYKDHmCmva29sRHR1NZiPyr+XarmE2m5GamoqrrroKV111FWw2G7744gvs27cPtbW1eOONNyASibB06VKsX78e69evx4oVK9x+4fkRYyCma3DCOFE/4okTJ6a8RnZ2Nq699lpce+216OzsxNy5c1FRUQGtVos9e/YAABYsWIBFixZh3bp1SEpK8ul80hdEIhEiIiIQERFBIpITJ06gpaUFw8PD+Pjjj4llnS+FIIFeq5AQQpGLWCxGbGwsGfo9Pj4OrVaLkZERaLVajI2NISoqikSTKpVqWqNJdxFjsCdrCJVZK4wsy+LYsWPQ6XRgGMajXsTTNWLkGxHIZDIsWbKEfFn5uHO+cbWEk0qlKC0tRWlpKXbs2IH+/n7ShP/CCy/gt7/9LaKjo1FRUUGiyeTkZADBOWMcGxvDJ598AofDcUo/orfX4B4Stm7dis2bN6Ovrw8ffPAB6uvr8d///hd//etfERkZibKyMlRWVqKiogK5ubl+vQZv4Prr1Go1xGIxMjIySNqVKwRx7Z0MhTgINWIU2ppkMhkiIiIgk8mQn5/vNKiZO5/kD2oOteXgRCOnhHYfQ8GsEEbXN9ZgMECn08FisXjl4hLqaReA/8JoMBig1WoxPj4+pfh76pXKJykpCVu3bsXWrVvhcDjw1VdfYd++fairq8N1110HlmWxaNEiVFVVwWg0Ynh4GID/wjg6OgqDwYDR0VGkpKR41Y84EfzGZgCIj4/HRRddhIsuugjff/89+vr60NjYiPr6etxzzz2w2+3IysoiJullZWV+eeN6i0wmI5Z1fCPtUFvWCVGEhLgmwHnsFJcNSE1NBcuypH/y+PHjJIPFiWRMTEzQMxXTMaRYqMwKYeQYGhqCTqfD4OAgsrOzkZmZ6VUvYlhYmEfzCQOJr2I8PDwMrVaLwcFBMhR5qjSbr2On+GstLi5GcXExHnjgARw/fhwNDQ3Yt28f/vznPxO3oIsvvhgFBQWorq5GVlaWV6/LZrOhtbUVXV1dkEqlkMvlyM7OnvD1eCO+roYK3L8ViUQQi8WYN28eNmzYgDvuuAPDw8P46KOPSO/kyy+/DKlUiuLiYhJNLliwIGSpMVcjbXeWda69k4Fcm9BEiGVZQZ3fcUzkYiUSiaBWq6FWq8n5JPf+dXd3o7GxEZGRkUQoNRpNwPuoXQuDQjGkWKjMCmFkGIbYmmVkZGDhwoU+PX2dDhGj1WpFS0sL+vr6kJ6e7tVrdTd2yhthdCU+Ph4XX3wxLr74YjAMg8LCQoyPj6O/vx9vv/02HnnkEZxxxhnkbLKkpGTCYhJ354jvv//+pKltf4XR3Ro4VCoVzjrrLJx11lkAgNbWVtTX16OhoQGPPvooHnroIcTHx6O4uBijo6PYuXMnFixY4PFa/CWUlnVCjM6EcMboDoZhPIrcXd8/m81G0q56vR5Wq9XpQScQ55Ous2bpGeMMhzubyc/P9+uNDmQhjDfX9EQY+SOv4uLiUFpaisjISL+u5Tpdwx/CwsJQUFAAmUyGf/zjHzh48CC+/fZbfPfdd/j73/+OJ598EpGRkVi7di0RSi6a5HxNXc8RPTnzDaQwTkZubi5yc3Nx3XXXYWxsDJ9//jnq6+uxZ88eHDlyBKtWrcLixYtRUVGBiooKrFixwq/UprebvjvLOoPBgGPHjkGv1zuNZfLWsm62tmv4gq/tGlKpFAkJCUhISAAAUq1sMpnQ09NDHnS4hx1fzidpVeqPzAphBIDMzMyAFHsILWJkGAa9vb1oaWmBQqHwa+QV90XiNhV/I0ZX+O0aGo0G1dXVuPnmm8GyLL7//nvU1tZi3759uP322+FwODBnzhwUFRWhsLAQ5557LvLz8502lakeVLzdGCaz4PMm+gwPD8fq1auxevVqbNy4EdXV1XjggQfQ2NiI1157DY899hhUKhXWrFlDhNLblLI/8C3rsrKynMYyuVrWxcbGTunmIkQREuKagMA1+CsUCqSmppLzSc6blztflkgkTueTnrT1OBwOp+zSbJ2sAcwiYQyUBZlQIkb+hA+GYTBv3jwkJib6tRm4TvOQSCQBPVOVSCTEmJj/fohEIixatAiLFi3CXXfdhYGBAezZswf79u3D/v37sWfPHjzyyCNYs2YN1q9fj+rqauTm5k4ZMQY6leoL3Ptx9tln48477wTDMPj222/R0NCAhoYG/PKXvyQPAZwB+qpVqyaN9gMdobnannHVkgaDwWPLOqGJkFCnWARjXe68eTk3pd7eXjQ1NSEyMpKI5ETnk66ibbVaaSqVMjVCadc4ceIEtFothoeHSWFNIL5srsLY29sLk8lE/Ej9ZSoTcf454vLly7F161aoVCo0NTVh37592LdvH+6++27ccccdyMnJgUgkwsDAAEZGRpz6MTkCKYy+bvyu/y4sLAxLly7F0qVLcdddd+HEiRM4cOAAGhoaUFNTgxdeeAEymQwlJSWkiKewsDCkwqNQKKBQKKa0rIuNjYVGoxFkKlXIZ4zBFmyxWEzSqsDJ80kuI8A/n+SEkivEoqnUH6HC6AXTnUq1WCzQ6/Xo7+9HZmYmlixZEtASfH4qFQA+/vhjDA4OIi0tDRUVFaiursb69euRlJTk0+8Xi8UTCuNE54gAUFhYiMLCQtx+++0YGRnB/v37UVtbi1deeQUOhwOpqalYtWoVWd/cuXOdNkVP02reFN94y0T/NioqCps3b8bmzZvBsixaWlpIEc9vfvMb/PrXv0ZycjLWrVuHyspKlJeX+7wGX3C1rOM2Wa4NaGxsDFKpFEqlEsPDw0Ed8usNQk6lhnpdUqmUuCkBP2YETCYTKcTSaDQwm81QqVTk3lksFr/9kU9XqDB6wXSlUu12O7RaLTo7O5GUlBSQ2YgTXQv4URi2bt2Kp556CjfddBNqa2tJX+KSJUuwfv16bNiwYUKXG3fw2z84YeR8TY1GI3Jzc6fsR1Qqldi0aRM2bdqEhIQEPPPMM7j//vtRW1uL+++/H9u2bUNGRgbWr1+PtWvXevV07trHGEg8+Z0ikQh5eXnIy8vDDTfcAKvVik8//RQNDQ2or6/H66+/DpFIhLlz52JkZAS33norrr766qCPP+PD32Q5y7pDhw7BarXim2++IdEKN3syVC5Brgg1lSoEr1TXjADX/3rixAl0dHTggw8+wHvvvQexWEyKfULBI488gnvvvRe33nornnrqqZBd1x3C++QEiUA8pYU6YuRMiXt6enDixAkUFxdj4cKFQcv7uxafcHMC77//fnz00Ufo6urCq6++iry8PLz00ktYu3Yt0tLSsHXrVvz1r3/FsWPHJv39/LFTLMvCaDTi448/hkwmQ1lZGbKzs73aNBQKBcRiMW655Ra88847OHLkCP7zn/9g06ZN2L9/Py677DJs3boVGzZswBNPPIFDhw5NKlBTFd/4gj+fO7lcjrVr12Lnzp347LPP0NzcjD/+8Y9QqVTo6enBXXfdhezsbGzduhW7d+8m54Ghgt+knpKSgrKyMpxxxhmQy+Xo7u7GwYMH8cUXX6ClpQVGozHk3x2hRozTLYx8uP7XjIwMyOVyFBYWoqioCFlZWWhsbMSTTz6JgoIC3HTTTdi3b1/Q1vHll1/ihRdeCNjsV3+hEaMXhCpiZFmWuPNzNl/Lli0LyRed38sok8mcim/i4+Nx6aWX4tJLL4XD4cCXX35JPFOvueYaAEBRURGqq6tRXV2NZcuWOUWT3Bljd3c3uru7IZVKia+pL7ie+UZERJBrAyBC0t3djYcffhj33XcfUlNTyYSQdevWOY2b4guju3sdjFSqN6SkpGDLli1IT0/H5s2b8fDDD8NsNqOhoQG33XYbGIYhXq8VFRVYtWpVSIonuNfGtUVFR0cjNzcX4+PjpPeusbERdrs9ZJZ1Qk6lCkkY+XA9llwhXEtLC2655Rbk5eWhvr4e+/fvJ9+tQDIyMoLLLrsML730Enbu3Bnw3+8LVBi9IBQRo8lkglarhdlsRl5eHsbGxmA2m0P2Jee733Depu42GbFYjJUrV2LlypV48MEHcfToUdTV1WHfvn3405/+hF27diE2NhaVlZWorq5GVVUVaQtob29HQkICwsLCfBZFbg2TPajk5OTg7LPPRlVVFWw2Gw4ePIja2lrU1tZi9+7d5DVwla6LFi2asArY34gxkOlZ7ncVFhaiqqoK9957L0wmE/bv34+Ghgb85z//wXPPPQe5XE6KeCorK085ew3ketz93um0rKOpVO9xZyIeGxuLc845B+ecc07QrnvTTTfh7LPPRmVlJRXG0xEuYgzG0yh/NmJWVhaWLVsGiUSC9vb2kKagXCNGlmXhcDimPMdKTEzEli1bsGXLFtjtdjKBY9++fdizZw/pi3Q4HAgPD0dkZCQsFotfa/WkXQM4uUnK5XIiEL///e/R0dFBzM8fffRRPPjgg0hKSgLDMPjb3/6Gn/zkJ6dUuvoibsF8oOH/7ujoaJx//vk4//zzwbIstFot6uvrUV9fj+3bt5NomWsJWbNmjc/9rlOtZaKfT2RZ19HREXDLOqFGjEIVbMB9g7+3JiHe8sYbb+Cbb77Bl19+GdTreMusEcZAnTECgf3SjY+Po7W1Fd3d3eScht8nFuqCH37ExD3B22w2rwo8JBIJSkpKsHz5clxyySX49ttv0d7ejkcffRRjY2NYs2YNYmJisGLFClxyySWorKx0O+1jKrwRRleysrLIuKnx8XF88skneO+99/DMM8/g008/RXp6OpYtW4bKykpUVVUhIiLCL+/NULY0iEQiFBQUoKCgADfffDMsFgs+/vhjUu36l7/8BWFhYeT1VVRUYOnSpT6/Pl9eG9/ybM6cOZNa1sXGxnqdEqZnjN7jro/RXc9qoOju7satt96Kurq6oF7HF2aNMAYC7kPjmnLwBW4wcmtrKzQaDc4880y3kxlCPQOS30bBVRTabDavNiaWZdHd3Y2WlhYolUqcddZZUKvVMJvN2LNnD1599VXs2bMH+/fvxxVXXIGwsDCsWLGCVLouXrzYo/vrqfPNVBu3TCZDeXk5ysvL8eKLL2Lbtm1ITk5GTU0N/vCHP2DXrl3kPTr//PNRWVlJmuGnIlipS29+t0KhINEycHJD4gwG/vjHP2LXrl3kbDAjIwPPP/88UlJSvFqPv68z0JZ1Qo0YhSrYXGaIX5ltsViCGjF+/fXXOHbsGJYuXUr+zOFw4MCBA/jDH/6AsbGxaTOCp8LoBdyb5I9QsSyLI0eOQKfTQSqVTjgbkSPUwuguYvTG/cZgMKCpqQkMw5zSj8hVpa5atQppaWn4n//5HyQkJBAruCeffBI7duxAQkICOferrKycMOXHnfkGchPkprBfeeWV+OlPfwrg5Bd4z549+OSTT/Dzn/8cIpEIixcvRlVVFaqqqk4pMnKHkJrg09PTceWVV+LKK6+E3W7H119/jbq6Ojz55JNoa2tDQUEBCgsLSRFPSUnJpE/0gRahQFjWCVkYhRgxcp9PVxPxYDb4V1RU4IcffnD6s6uuugoFBQW4++67p3U6ChVGL+C+aL6mNvmzEfPy8pCSkjLllzfULSJ8YeRHjFPhST+iVCol944r8klNTcVVV12Fq666CjabDZ9++ik5m/zrX/+KsLAwFBcXY8OGDaRAhrtn/AeViUb5AN4biVutVnz99dcYGBiAXC5HbGwsrr76atx4443QaDTk7O6ll17C73//e2g0Gqxbtw5VVVWorKxEYmKiX2uYCm8jxsmQSCRkVNh3330Hh8OBSy+9FA0NDXjzzTfx7LPPQqFQYNWqVeR8Mi8vL6Si486yjku7TmRZJ0QBYllWsGeM3PfStfgmmMKoUqkwf/58pz+LjIxEbGzsKX8eamaNMAbii8xNdPBWqEZGRqDVamEymZCTk4PMzEyPn4amM5XKrXGyiJE/HzE1NRVlZWUTGhZPZQknlUqJ+fZvfvMbdHd3k2iSXyDDRZNjY2MATh2Xw38tgOeixK2ttbUVGzZswJlnnomRkRGYTCYMDg7CbrfDbDajrKwM5513HsLDw/HNN9+QmYw33ngjWJbFwoULiUgGo8E9WJ8HhmGgUCjw05/+FD/96U/BsiwOHz5M0q4PPPAA7rnnHmRkZJBocs2aNSGPzvgG2lyvr6tlnc1mw9DQEFQqlWDmMnLvmxCFkVsbv46CWsJRPMabYpixsTG0tLSgt7cXaWlpWLBggdcbpevw4GDDF+KPPvoIAHDLLbfgyiuvREVFBen7cz1HnOiMlA/X/sFdZyrBSk9Px9VXX42rr74a4+Pj+Pjjj1FbW4u9e/fiL3/5C/l7999/Py677DIsWLDAp/5DLr2t1WrJdbnZkXK5HPHx8RCJRBgfH4darYbRaERbWxukUiliY2Px85//HHfffTcGBwfx/vvvo66uDn/+85/x+OOPkxTkM888gx07diA1NXXStXhDoMXINcoSiUSYP38+5s+fj1tvvRWjo6P46KOPiFC+9tprEIvFJO163nnneXw+HCi41Dffss5kMqGxsdHpDJ+LJqfTso7f7yk0HA4HRCIRuTc2mw0Mw4RcGPfv3x/S600EFUYv8cRI3G63o6OjA+3t7T7PRuRfL9QRI3e9tLQ0AIBWq8Wll15Kqk1Xr16NrKwspKenn3KOOBn8sVPeGnzLZDKsXbsWa9euxW9/+1t0dnbiqquuwieffIKXXnoJTz/9NGne37BhA9atW+dRj+Tw8DAaGxthNpsxd+5cSKVStw8vIpEIUqkU6enpSE9Ph8PhIJ6h3LlXVFQUiouLcdZZZ0GhUOC7777Db37zG9TV1eGf//wn3nzzTRQWFpJK15UrV3o0Dmgigi2MrkRGRmLDhg3YsGEDAKCjowMNDQ1466238NJLL+Gpp55CbGws1q5dS6pd+WnlUMDNLWxubsaiRYsgFotJ2rWjo8OpGjbUlnVCjxj5kfXo6CgA0IiR4hmTpTZZlkVvby+povNnNqIn1wsG/EiuqKgIAPDmm29CrVbjnXfewb///W/SdpGRkYENGzZg48aNWLNmzZRfoqlSqd6QmZmJm2++GZ988gmamppw6NAhEk3u3r2biHh2djbi4uJQVFTkJCQ2mw16vR49PT3IzMxEUVERJBLJlO8vh1gsRmxsLCmcslgsMBgMMBgMaG9vJ83rN998M+rq6vDOO+9gYGAAdXV12LNnD5555hlERkZizZo1RCgzMzN9vh+BwNuUaFZWFq6++mosWrQISUlJ6OzsJNHkP//5TwDAggULMDIygksuuQR33HFHyISIO8vjLOvS0tLAMAzpnezu7kZjYyOUSiURSY1GE1TR4j5XQiwKctfcD1BhnPEE6sPoLmJkWRYDAwPQarVwOBwoKChAUlJSQK45HWeMrsU3FosFMpkMc+fOxbPPPovU1FR8/vnn2Lt3L2pqavDiiy8iPDwca9asIYN5c3JyTvnd/FRqIFLE3BOuRCIhZ16/+93v0N7eTs4m//73v+PPf/4zUlNTiV1cfn4++vr6EBUVhZKSEqdGfl/vt0KhQFpaGtLS0kjzusFgQG9vLwCgp6cHJSUlqKqqQmRkJA4dOoT6+nrU1dVh27ZtsNvtyMvLI2eTq1atmrASNJDFN3x8LVhhWRYymQylpaUoLS3FAw88gOPHj+P999/H3r178dZbb2HXrl14+umnUVZWRqLJ3NzcgK6fj7u2CHeWdVw0GQrLOm5NQhRG14iR+86H0qBeSMzOV+0Hrhvn0NAQtFothoaGAjobcaLrBRt+xMh9Kb788kusWLHC6RyRE5knnngCOp2OiOS2bdtw++23Y+7cuSTtVlpaCplMRtLC3Abhb6Um90V2fVDJzs7Gddddh+uuuw7vvvsuWJbFhx9+iJqaGrz66quQSCRYsWIFNm3ahMjISBQUFJDNKhCWcPx0HffkHRsbi6GhIXR2dpJo87LLLsMtt9wCs9mMDz/8EPX19cTOjasE5YRyzpw55PcHq/XDn4pJ1/sTHx+Piy66CBdccAHeeust3H333YiIiEB9fT3uvfde2Gw2ZGVloaKiAlVVVSgrK5vyjNobPIl+ZTIZkpKSkJSUNKllXWxsLKKjo/22rBNipSyHu4hRLpcLUsT9Zf/+/Vi7du2EPy8vL6fC6C1cxGi1WqHT6dDf34+MjAwsXrw4oF6PHNPVx2gwGPD9998DOFmMsnz5crdfEm4M0ty5c3HrrbdiaGgIH3zwAWpqavCPf/wDTz/9NJRKJSoqKsh5mt1u96j4ZiomEkY+4eHhKCoqQmZmJqqrqyEWi9HU1IS6ujrs2LED9913H9LT00k7CMuyAZ3HyN2zuLg4LFy4EAzDkJ68jo4ONDY2Qq1WY+HChSgvL8dTTz0FrVZLKl1/9atfYdu2bcjOziYiyY+6A4k/EeNEcO9NTk4OLrnkEtx+++0YHh7GwYMHUVdXh4aGBrzyyiuQSqUoLi4mLSELFizwWUR8KXKZyLKOS40fOnTIb8s6IQvjbBpSXFJSgiNHjpzy52+//Tauv/563HjjjVQYvUUkEqG/vx9NTU1ISEjAqlWrgvoB4gQkVCXxDocDPT09sFqtpPgmMjLS42ur1Wqce+65OPfcc8GyLL777jvs3bsXe/fuxWeffQYAKC4uJlHQypUrfS6nn0oYueb/r7/+GnFxcWTaxLp163DTTTfBYrHgo48+Iut76aWXAACPPfYYlKFlPJ0AACAASURBVEol1q1bR3r2/L33/M2ab4VmtVphMBhgNBpJNBkTE4OLLroIN9xwA8bGxvDRRx+R1PCLL75IXvcTTzyBxMRE5OfnB+Sz4Y8wTnR97kyZ/x6rVCps3LgRGzduBHCyPYY7m3zsscewfft2xMfHY926dSRFzg3Z9fR1AP49OPCjfgATWtZxcyc9cYYSsjC6ixhDMZllOuAyBXyamprwy1/+Evfddx8uvPDC2SOM/m4cDMOgp6cHRqMRcrkcK1ascBpZFCz4w4OD2Y/F9SMODAxArVajrKwMBoMBwI+bm7dwDjGLFy/GPffcg507d2Lnzp0oKCjA//7v/2JwcBDbt29HVVUVNm7ciKqqKq88U7l74259AwMDxIGnsLAQGRkZp/wdhUKB9evXY/369SQlvHTpUkREROCBBx7A3XffTYYeL1y40KdZcVN97uRyuVNPHheldHZ2EmPtgoIClJaW4vHHH0drayvuv/9+vPfee/jwww+xfPlyZGRkkAKe1atX+5yS9MeubKJ/xz20TPbZzc3NRW5uLn7+859jfHwcn3/+OfF13bNnDwBg8eLFRCSLi4snzc4E4wx2Iss6bjycJ5Z1QhZGdxGjQqGYkalUVwYHB3HuueeivLwcDz/8MAB6xjglLMvi2LFj0Gq1EIlE5HA+FKIIBF8YOcHX6/VQqVRISEiASqVCeHg4Kb7xxhJuMjg3iz/+8Y+w2+3417/+hSNHjpAJHJxnKnc2yXe5cYe7iNFisaC5uRkGgwFz5sxBS0sLNBqNR+vLz89HSkoKLr30Utx1111oaGhAXV0d6urq8PLLL0Mmk2HVqlVYv349qqqqMGfOHI+mSgCepWH5xSHAySiFq3Tt7u6GSCRCTEwMLr74Yrz33nt48803wTAMSbu++uqrkEqlOPPMM4lQFhYWery5BSNi9EQY+XBDq8vKyrB9+3YcPXqURJO7d+/G448/DpVKhdWrV2N4eBjXXnstzj333FPWAwSvLcJTyzoumuQs64TqegO4NxCfqREjH4ZhSCva66+//qN95TSvK6R4W/AxODgIrVaL0dFRzJkzB2lpaWhsbAz5mR8QHLcTvq/p/PnzkZCQgMbGRnKP+NM1AgH3FG232yGRSDBv3jxcf/312L59O/r6+ogV3OOPP46HHnoIycnJqK6uxsaNG7Fu3bpTIiG+JZzD4UB7ezva29uRlJREHHja2tq8toTjGpv5Q4/379+PDz74AN999x1xgOHO/bjikUCn1MPDw5GSkoKUlBQwDIOhoSEYDAZS0NPR0YF169ahuLgYv//979He3k4qXX/729/igQceQEpKCjEQLy8vn/QhIRhnjPzZnr6QmJhIhmMzDINvv/0WDQ0N2Lt3L7788kscOHAAubm5pNK1rKyMfG5DFe1MZlnX1dVFHqiDUYMQKFxTqTP5jJHPfffdh08//RRffPGF0/4yq4TRU8xmM3Q6HY4fP46srCzS4wZ41uAfSPhT5QPFZL6m/pqITwZ3D202G6RSqdOGmpKSQjxTOZebffv2oaamBrt374ZUKkVpaSk2btyIDRs2ID8/nwjj0aNH0dfXB6lUiuXLlztt/t4+DLkrdhKJRMjKysLFF1+MHTt2YHR0FAcOHCB9ky+++CLkcrlTNMm1IgTKKzUsLAwajQYajQY6nQ4AkJCQALPZjJ6eHgAn/UI3b96MK6+8EgzD4JNPPiFC+Ze//AVisZic71ZVVZ1S4BKqM0ZfCQsLw9KlS7F06VLcfPPNSExMxDXXXAOGYVBTU4MXXngBMpkMxcXFyMnJQXx8PObPnx/ydOBElnX9/f2wWCz4/PPPnXonhWBZ565dY6ZHjG+88QYee+wx/Pe//0VeXp7Tz6gw8hgfH0dbWxu6urqQnJx8ymxE4OSX09czN18QiUQBm8noia8pv7+QE7J///vfqK6uJoUIvsJ98ex2+6SCxXe5eeSRR9DW1oba2lrU1NTgwQcfxN13343s7Gzk5+cDAD777DNcdNFFSEtL88kSjo8nQhoZGUmKR1iWhU6nQ21tLerq6kgVaW5uLqqqqjBv3jyv1zAV3O+Ki4vD/PnzyeZrMBjQ09ODpqYmqFQqZGZmYtu2bdi5cyd6enqISD7xxBNkigkXTa5btw6A7+nHqVKpgU4hclmM0tJSXHDBBWBZFi0tLeQ1cv2rSUlJ5Gxy7dq1Ps399Ae+ZZ1cLkdvby8yMjJgNBrJQIGoqCjSFuJNoVsgmW0R47fffourr74ajzzyCMkK8aHCiJNPS52dnWhra4NarcbKlSsntBMTi8XEvDpU+Nva4HqOOJmvKV+EzWYzgJNlzO+++y7OPPNMIgjenF1xcBEo167haRSck5OD66+/Htdffz3MZjM++OAD7NmzB++88w4AYPv27fj444+JuQDfQcbbNU50rycSTH67yi233ILh4WEcOHAAdXV1ePfdd/H8888DAK699lrcfPPNqKqqQnZ2tldrcsX1vvE335ycHNK4bjAY8MMPP4BlWcTExGDDhg247LLLIBKJ8NlnnxER+dvf/kZed1dXFy677DIsXbrUYzHzpF0j0FER93DKT5vm5eUhLy8PV1xxBQ4ePAipVIqGhgbU19eT86OlS5eSlpBly5aFtIGdYRhIJBIkJCQgISGBzDwUgmUdtzaOmRwxDgwM4LzzzkN5eTm2bNmC/v5+p5+LxeLZJYyumxvLsujv74dOp4NEIsGiRYumHEAbau9Sf6/p7hxxMrHgR4wajQYymQz33Xcf4uLisHfvXuzatQu//vWvkZGRQURyzZo1Hn2JuC8eZ1jsrdizLIvBwUFERETgpptuwuWXX45Nmzbh6quvRlNTE+644w7Y7XbMmzePiKTD4fA5lerLGlUqFc4++2ycffbZYFkWL7/8Mu68807I5XLcc889+OUvf0kcbtavX4/S0lKf/VIneh9dG9e5aLKvrw/Nzc1QKpVITU3F7bffjoceegj9/f2or6/HLbfcAqPRiHXr1iE2NtZplNZk34tAFt94CieME1V/ck5Ma9euxc6dO9HX14f333/faVxYVFQUysvLSUSZnp4e0DW6W5erSbunlnWxsbGIiooKWvGOw+FwEmGr1TpjI8b//ve/6OzsRGdnJ5KTk0/5eWZm5uwSRj5cKsNqtSIvLw+pqakeRReBSmt6g0gk8vqansxHdIdrxCSTyRAZGYlrrrkG11xzDaxWKw4cOICamhpyrqNQKLB27Vpy/jfRBsM/Y/RWdIaGhtDY2Air1Uos97ghp1u2bMGyZctw4sQJvP/++6ipqcHrr7+OJ554AhEREVi7di3OOeccVFdXn9K/5O71++t8w/83q1evBgA8++yzmDdvHj788EPU1tbi//7v//Dcc88hIiICa9asIUU83vilerImkUgEtVoNtVqN7OxsJxu0Q4cOgWEYxMTEoLKyEgUFBVi1ahUuuOACUun65ptvejSYeaozxkBHZpP9XndCnZKSgi1btmDLli1wOBz45ptvSLXrbbfdBoZhkJ+fj8rKShQVFaGkpCSgk1C4dU32HZzMsu7w4cNBtaybqF1jJnLFFVfgiiuumPTvzDphHBkZgU6ng8FgQE5ODrKysrx6mp2OiNGbtKM38xE9uZZUKnUqvpHL5U69f83NzUQkb7vtNjgcDixYsICYixcXF5P7y0+leiqM4+Pj0Ov16O3tRVZWFnJycpwKoYAfo5KoqCicf/75OP/880kF4/PPP49Dhw7h+uuvB8uyWLp0KWkHKSoqOuW999RE3FtYloVarcbmzZuxefNmMuuwrq4OtbW1uOuuu+BwODB37lxSwFNSUhLwVNpE0eSRI0cwOjoKg8GAhIQE/OIXv8D999+PY8eOkbaVF1988ZTBzBUVFZNGjK5z/gIFd8boqTDyEYvFWL58OZYvX4577rkHJpMJH374IRoaGvD222/jueeeQ3JyMhlDFii8LW4KpWWduwb/UE9GERKzShj1ej1aW1uRmpqK1atX+5TCmo6I0RMx9uYccTJcBUsmk03YriESiTBv3jzMmzcPd9xxBwYHB1FXV4eamhq89tprePTRRxEbG0sa+Lm0hSfCyM171Ov10Gg0bkd3TeZ8w1Uwbt26FfPmzQPLsqirq8PevXvxpz/9Cbt27UJcXBzWr1+PDRs2oKqqCtHR0QGNGPn/zvW18mcd3n777RgcHMT+/ftRV1eHN998E88++yyUSiXWrFlDhJJzIgpUA7trNCmXy6HRaDA+Po7Dhw/D4XAgOjoa5eXluOCCCyCVSvHVV1+Rs0luMHNOTg7OOussnH322VixYoXTBh2qM0Y+3vYLRkdH47zzzsN5550HlmVRXl7uldOOp/hroOBqWcf1TgbCsm42WcJ5wqwSxpiYGCQmJjpNU/AWIUaM3p4jenMtiUTicbuGRqPBhRdeiAsvvBAOhwNffvklampqsHfvXrzxxhtkTQ899BC2b99OLNtc18oNmuWiz4SEBLfX88QrlRPg+Ph40g9nt9vxxRdfoKamBvv27SPFJytXrkR7ezsiIyMDVkXq6fug0WicNucffviBRJN33HEHHA4HCgsLyXSOYMCyLJRKJQoLC53cXbhz+IiICMTExOCGG27AvffeC6PRiIaGBvztb3/D3//+d/zhD3+AWq1GeXk5OZsMVlXqVGeM/giQRCIJSrQUSOcb17Fn7izr+EU8U6VFXSPG2dLgPxGzShjj4uL8jvamI2KcSBh9PUecDNdxUJNFjJMhFouxcuVKrFy5Etu3b0dPTw927tyJ3bt3Y//+/SgpKUF8fDzOP/98nH322Vi9ejVEIhG0Wi2OHTuGnJwcZGdnT3kmA0xuWecuMuVmNZaUlODhhx9GT08PMRcYGRnBZ599hry8PBKplZeXA/A/leopIpGIWNDdeeedMJlM+OCDD0gbwrFjxwAAN9xwA2699VZUVVUhJSXF57Vx8DduV3cXm80Gk8kEg8FAHlqio6NRWlqKxMREFBUVQa/Xk7PJW2+9FQzDkCj33XffxcKFC/0azMzH2zNGb7DZbEGpBg2mJZyrZR3XO+mpZZ27PkYaMVI8RggRo7/niFNdi7+JS6XSgDjfpKWl4a677sLu3bvx1ltvwWb7/+x9d3xUZfr9mZm0SUgy6b0XkkASUoA0QgjpQXERXHGVtStLEUFcygIKAqIoKKuCDdRVVHR1/ZEOroqIsBaQlEmvkDaTXqf+/uD7vntnMpPMTGaSuMn5fPigEO69c+fe93mf5znPOWK88847VBjbzMwMoaGhWLJkCe677z6NvPo0yRg1vbaHHnoIDz30EKKiouhgeH5+Pk6ePAkTExNER0cjOjoaZmZmI4aBDQ0bGxusWLECK1asgEwmw65du3Ds2DEAoAFo7ty5NJArlzM1xWgLt7GxscKYAelHtrW1QSaT4fr167C3t8fDDz+Mp556Ct3d3fjmm29w5MgRNDU14ciRI3jzzTf1Zsw8Vo9xPAFIJBIZRKVGJpNNiPoNs0SuTrKOzE4SyTrl734mMM5AK0xmxsjsI1pZWencR9TkXAT6CozAfxcxNpuNtLQ0cDgchIeH49y5c7hy5QqKi4vx0ksv4YUXXqAEnqysLCxYsEBlj4opCacO2rJfuVwufH198eKLL+LAgQOoqqpCUVERvvrqKxw/fhyvvvoqfH19kZaWhvT0dMTHx6s1FGZCX6VZNpuN+fPnAwBOnDgBX19f/Pvf/0ZhYSHef/99vPzyy7C2tsaSJUuQlpaGlJSUMZm4zGvUlOlK+l1eXl74+uuv4enpib6+PvD5fIjFYtjY2GD+/Pk4ePAgsrKy8N5776G2thZFRUWUaERYoKmpqRrfR4KxeozjyRgNGRgnQytVE8k6qVSKzs5OmJmZwdzcHIODgwYr2f8eMK0Coz6ozZOVMfb29uKHH36ATCZDaGgoHBwcDKKQoYp8oy9JOCYrleh9lpSUIDExEX/605/AZrPR2dmJc+fOqSXwpKWlUZFtbXqMmkJ5Y+Dn5wd/f38sW7YMN2/eREdHBwoLC+nwPhm3SE9PR2pq6ohRFX1JwqmDnZ0dVq5ciZUrV0Imk+HXX3+lvcl169ZBLpcjPDyczk2ONtSua2+OxWLBzs4O7u7uCuxJgUBAPT3Nzc2xZs0aPPHEE+jr68O3336LoqIiBWPmRYsW0UA5VsXAUD1G4PdZStUGqiTrfv31VwgEArzyyiv45ptvYGlpibKyMoMN+h88eBD//Oc/wefzweVyERcXh0OHDmH27Nl6P5cumFaBUR+Y6Iyxv78fXV1dEIvFCAgI0EsfcTQoB4bOzk7U1NTo5dhkEWtoaKCfISYmRoEMZWNjMyqBh/QuySgIMHYpVdv+njpWKpfLVZCCKysrQ0FBAQoLC7FlyxZKkElPT0daWhoWLFhgkM2LOlYqm81GVFQUoqKisG3bNgiFQpw/fx6FhYU4efIkDh8+rDBqkZqaqkBs0oeIuDJ7sre3l/55RUUFRCIReDweIiIisHTpUnC5XPD5fMp0VTZmVifQPp5xjbFgqIxxKrprENUk4Jb7ze7duxEVFYWXX34Zx48fx/PPP4/ExERs2LABt912m97O++2332LdunWYP38+JBIJduzYgbS0NJSWlk6JTHUmMGoJDoczIcbBzD4il8uFq6vruKXENIFyYGhoaEBdXR3CwsKQnZ2NzMxMxMbGar1wyOVydHR0ALgVbFetWoXLly+POvitisBDTIUPHDhAJeu2b98OY2NjJCYmjtjdavsdjZZhKgeAkJAQhISE0HGLr7/+GoWFhfjHP/6BI0eOwNraGpGRkQBuZcYJCQlaXYs6aDquYWdnh7vuugt33XUXHWon2SQZtYiIiKABSJmZqM9r8ff3h4+PDwYGBiAUCiEQCFBVVQVTU1PY2dnhnnvuoebRRFKP9J9NTU0RHx+PlJQUpKWlISAgYEzyzXgCkCEzxqnobyiXyyn5xt7eHmvWrMFzzz2Hzz//HA4ODigoKND7/cjPz1f4/1OnTsHR0RE///wzFcWYTEyrwKiPh5K8cFKp1CA6i6r6iKQPMBFQJt9ER0cDAEJCQnD69GkcOXIEPB4PqampyM7ORlpa2pji4r29vSgrK6NsSl9fX+qAoU025+7urqDA8/7772Pjxo1obW3F8uXLFRR4MjMzqaj4eN01NAGPx1MgyPz66690FAQAtm7ditOnT9PeZERExIRmD8yh9h07dkAgEODcuXMoLCykEmkA8Prrr8PHxwcpKSkazfKNFRiZc4wsFgsWFhawsLCgs3iE6VpRUYHh4WHweDzMnTsXixcvpsbMRKD92WefxY4dO+Dl5UVVaYgDDhMzpVTtQJ53ZRFxCwsLBAYGUrF+Q6K7uxsAxm1UoC9Mq8CoD2hC+NAVzHlEZh9R18VaFyify9raGhYWFjh+/Dhd8HNzc5Gbm4v7778fbDYbsbGxdMB79uzZdFESi8WoqqpCY2MjPDw8EBQUBOC/A/66aJESmJmZ4e6778bGjRvx97//HaGhocjLy0Nubi42bdqEDRs2IDQ0FPPmzcNtt92G7OxsjYbM9THgzyxprlq1ClFRUVi/fj1u3ryJN954A88//zzs7e2RmpqK9PR0JCcna2ymDOhnwN/e3h5333037r77bkilUly5coWWiB977DEqA0eYrqpUgpjQxXaKZCj29vZUUJsYM9fU1MDExAS2tra466678Nhjj0EkEuHChQs4d+4cPvjgAwBAeno64uLiaNZLxBymYil1qgfGybKdkslk2LRpE+Lj46mZ+WRjJjBqCWbGqC+MNY84kX3N0ZRvmAv+rl27cOPGDeTn5yMvLw/PPfccdu7cCR8fH2RmZmLBggVU1zEuLg6zZs2ixyGL5XgCI6C4SWEq8BACT25uLr766it88MEHsLOzQ1paGjIzM6nKjSqQwCgWi1FfXw8ul0t3sbpcK6kqpKWlISkpCWKxGFeuXEFhYSEKCwtx+vRpWjIm2WRwcPCoC7u+iTwcDgexsbGws7PD2rVrsWbNGhQVFaGoqAgnTpzAoUOHqJ4qGdwng+VjXYumyjdMQW0PDw+q7CIUClFVVYWhoSFYW1sjKCgI8fHxiI2Nxf3334+dO3fixx9/xIEDB7Br1y64ubkhNjYW8+bNg4+PD+2faQORSDStM0axWAyJRDJh4xrr1q1DcXExvv/++wk5nyaYCYxawpD+iImJiSpfSDabrbeRibGgalyjv79f5c+6ubnR+b/BwUF8++23+PLLL/HZZ5/h9ddfh4WFBVJTU5GVlYWMjAxamtNXYGS6dTDBJPD8+OOPaGtrw08//YT8/HwaiJgWWsxAxGaz0d/fj++//x5cLpfKo5mbm0Mul2stlaUc4Ijhcnx8PJ599lk0NjbSftqhQ4ewZ88euLu70yCZmJioloyg7/I6WbgdHR3xpz/9CX/6058gkUjw008/0XLmp59+ChaLhaioKKSlpSE5OXnU0qWyt6emUFZ2GRgYoDqhtbW1VMf0jjvuwBNPPAGJRIKLFy9SRvNnn32GPXv2YOHChTSYh4WFjXnPpFLpCKcJfWGqBkbSWyb3hvTuJ4IEs379epw9exbfffcdFYOYCphWgVFfC8l4Rza0nUecyFKqMvlG0zlGDocDd3d3rFixAps3b0Z/fz8KCgqQl5eHxx57DACwYMECAMDXX3+N+++/X28Z42ibFCMjI0REROD222/H3r170djYSAk8JMv18vKi9lnd3d00AyWZ4uDgIGpqatDR0YHLly+Dy+XSRZvH42m02Kn7nB4eHnjwwQfx4IMPYmhoCBcvXkRhYSEKCgrw7rvvwtTUFAkJCVS43c/PT29aqcpQtXAbGRlRAtTu3bupRVVhYSFee+01HDhwANbW1sjIyKCBkmkGrC9JOKY9k1QqRW1tLQCgsbERDQ0NsLa2hp+fHxYsWIDHHnsMlZWVaGhowLlz5/DSSy/h2WefhZOTE5YuXYrU1FQsWbJEZT+LPOvThZUKjPzeySiVITNGuVyODRs24IsvvsA333wzIcRCbTCtAqO+MJ6MUV0f0VDn0xaqlG9Gm2OUyWRU7NvW1hYJCQn0hYqIiMC2bdvQ1taG/Px8aix85swZXLp0CWFhYejp6UFmZqZO/Qxd5hg9PDzwyCOP4JFHHqEMyJycHDrAD9wKBmfPnkVKSgpcXFyoRqhYLEZoaCjNXIg0GnE4sLe3H5FpaBO8zMzMqDfgoUOHUFVVRcdBdu3ahb/+9a/w9/dXIJ7oE5os3M7OztS+SSwW44cffsCpU6dQXFyMTz75BGw2G9HR0TSQk2dHnyLiHA6H3uf4+HhIJBL6ndTX1wO4le3cdtttWLNmDeRyOX788UcqV0e0caOjo2l5mJChyPUaqsc4FVmpqpw1mPfYEFi3bh0++ugj/Otf/4KlpSU1C7a2tp4SGq0zgVEH6JIxjkfXdDLJNyYmJmq1SDs6OlBaWgqZTIbw8HC1LEZHR0esWbMGa9asAY/Hw+rVq2Fubo7PP/8cubm54HK5SE5ORmZmJrKysjTW/SQEHl03DWZmZggPD4epqSlWr14NY2NjZGZmQiQSUQutOXPmIDMzE1FRUXBychrhwK5sAmxpaUmDJLMKoEtm7O/vD39/f6xbt44OxRcWFuLjjz8GAKxevZoGoNTU1HH7B2q7cBsbGyM2NhZSqRSLFy+m2WRRURFeeeUVPPfcc1TN5qOPPsLq1avV9na1BfnOjYyMYGxsrDCwzufz0dPTg9raWpSUlMDKygqenp54+umnsXfvXty8eZNaaf3973/HgQMHYGdnh6VLlyImJoZ+Nn1DWY90qmAydFLfeOMNAKA6xAQnT57E/fffb9Bza4KZwKgDtMngNO0jjoaJVNthKrWwWCyVGePQ0BD4fD7a29vh7++vVZA3MzNDYGAgnnzySSxbtgxcLhcXL15Ebm4uNm7ciPXr1yMiIoIGycjIyFGPPda9UVeuHRgYQGlpKbq7uzF79mxqVB0fHw9TU1O88cYbtOT67rvv4vDhw7CyskJmZibS09OxdOlS2NjYjDABJqzKX3/9FRwOB8PDwwBGFzrXBLNmzUJ2djays7MRHR2NdevW4d5770VZWRk12g0NDaW9ydEUbtRhvK4Ubm5u1ARWJBLh8uXL2LRpEyorK7Ft2zbs3LkTCxYsoCo8oaGhOp9PLBbTERAm2Gw2TE1NwePxEBQUhKGhIQiFQnR0dKC+vh4cDge2trZIT0/H6tWrAQD/+c9/aDb56aefAgC++OIL3H777Tpdmzr8XjJGYlJsyGs1lBKUvjCtAuNE9hj1qWs60Rkj8N9dJLPHKJPJUFtbi5qaGjg5OWHRokVa6VsCt3b4JEiw2WwEBgYiJiYGW7ZsoXJrOTk5eP3113HgwAE4OztTzdTk5OQRlmEcDkerUirpT9XW1sLV1RVhYWEKGxVyr21sbLBy5UqsWLECcrkc+fn5yMnJwbVr1/DJJ59QJml6ejrS09MRFBQEExMT6nAgk8nQ3d2N4uJiAEBxcTEdTbCzsxuX+zoRjH/wwQcRFhaGzs5OfP311ygoKMCpU6fw0ksvgcfjYenSpUhPT0dKSgrVyRwNupBD1C1wJiYmWLRoEbZs2YLHH38cV69epRnvyy+/jL1798LZ2ZmOWSxZskQrBqlYLFYb+JnjGmZmZgrZZHd3Ny25lpaWwtLSEi4uLnjyySexe/duXLx4EVlZWXoTY2BiqpJvVGWMU6GcOZmYVoFRXxgrY9SljzjW+SYrMBKt1La2NvD5fHA4HERHR+tcEjMyMqKBVjlo2dra0tk6sViMS5cu0dnEU6dOwdTUFIsXL0ZWVhYyMzPh5eWlVWBsb29HWVkZjIyMMH/+fJWzg6ok4TgcDqKiouDs7IyoqCg0NTVRgszBgwexe/dueHp60iCZmJgIMzMz2NjY0OHzoKAgODo6QiAQ0Bk9EiR5PJ5WJTZyfeSZPh/lmQAAIABJREFUsrGxwZ133ok777yTKtyQcZBHH30ULBZLoe8XHh6ucoEeT2Aci5Xq5eWFBx54AA888ABEIhEuXbpEma4ffPAB3WiQbHLOnDmjvjMSiURtuVPdHCObzYaNjQ19doeHh2mG39jYCBaLRRnYvr6+mt8EDTFVA+NkZIxTHdMuMI6XCQmoz1IM4Y8ITDwrFfjvgkdEoa9fv46AgAB4eHiM64XhcDgajWsQibfExEQcPHgQ1dXVVFjgqaeewqZNmzBnzhwMDg6ioKAAa9euVRtcxGIxfv31VwiFQgQEBMDT01PtZ2Cz2WOWPd3d3SmTdHBwEBcuXKAkmbfeegtcLhdJSUlIT09HWFgYgFsZlLu7O2VVdnZ2QiAQUDcKJoFnLAux0Z5fpsLNzp070drainPnzqGgoADHjh3D/v374eTkRAMQM1MzRGBUxUo1MTHB4sWLsXjxYuzfvx8NDQ1Uqu6FF17AM888A1dXV5pNJiUlwcrKSuG4Eolk1IxRk89hamoKV1dXuLq6QiaToaenB1euXAEAlJWVwdLSkn4vVlZW43ruVanLTBXMZIwjMe0Coz6gXErVRx9xNExGxigSiVBXV4fW1lZIpVIsWrRIL5/J2NhYpzlGPz8/bNiwARs2bEB3dzfOnTuHs2fPoqSkBEVFRTRjy8rKQmpqKqytrSGTydDf34/W1la4uLho5FupzMolULcocrlcmonJ5XLw+XxqekyExQFg3759sLS0RHR09AjFl/7+fggEArS0tKCiogIWFhY0SI62IGuyUDs5OdGZRLFYjMuXL9Mg/o9//ANGRkaIjY1FWloaJBKJTvOyY2V2zBk5VfD09KTzsMPDw7h48SIVGHjvvffoNZJgHhwcPGZg1DaIsdls8Hg8atEVHR0NNzc3CIVCNDU1gcViUf9COzs7rd8F8kxNxcCoipU6nb0YgZnAqBNIKXUi/BHJ+Sba6urKlSswNzeHt7c35HK53gK9kZERDRa6Zu/W1ta0dFhQUIDly5fDwcEBeXl5+Oijj2BkZISFCxciNDQUERERiIyMRGhoqEbHZt5rmUymsACPda0sFosq8GzatAmdnZ04duwYDh8+DD6fj9TUVNjZ2SElJQUZGRlYunQpeDwedaPw9vaGWCym5b1r167RBdne3h62trYwNjbWeY7R2NgYCQkJSEhIwL59+9DQ0EBLrvv374dMJsOePXtQXV2NtLQ0JCYmjrlAaqJ8ow0JyNTUFMnJyUhOTsbBgwdRV1dHs0lStnZ3dweHw0FPTw+6u7tH9CbHQ3IhRDMLCwuFfjFhHzc1NdFs0s7ODra2trCyshoz4P2eMkZSSp3OmAmMOoDD4aCvr29C/BGBiQuMvb29KC0tBQD4+PjA29sbX331FQYGBjA0NKQ10UYVlHuM4/1cxsbG8PT0xPbt27F3715UVlbigw8+wPnz5/Huu+9CJBLB29sbd9xxBzIzMxEXFzcqFZ9ck1QqpYu+RCKBSCSCXC6HVCql6kdjwcbGBg8//DAOHz6MU6dOwdbWlmaTTAJPRkYG0tPTMXv2bBgbG8PZ2RnOzs60vMcki1hZWVGm63jh6elJRdl7enrg7u6O4OBgnDt3Dm+//TbMzMywaNEiynRVFusGxs7OpFLpuEYUvL296dzp0NAQvv/+exQVFeHNN9+EVCqFr68v4uLi6MjK7Nmzx6WVSp5NZUKWtbU1rK2t4evrS9nHHR0duH79OuRyuUI2qaoqodwXnkqYyRhHYtoFxvH2GPv7+yEUCjE0NITAwECD+yMChg+MYrEYlZWVaGpqgqenJ7q7u2Fvbw8WiwU+n4++vj64uroiJSWFEl+cnJx0OheTlarPfq9cLkdjYyPq6+vxhz/8Adu3b4dMJsPJkydx6dIlfPzxxzh69Cisra2RlpaGrKwspKWlKai0kAWVHM/Y2BhsNht1dXWor6+nGp4EbDab/hoLbDYbCxcuxMKFC7F7924FAg/R+fTy8qIEHsL45fF44PF48PPzo6MHv/zyCwDg6tWrMDIygp2dHWxsbMYVgEil4+GHH8aaNWtQWVlJs8kdO3Zg69atCAwMpF6TsbGxNHgYMjAyYWZmhpSUFKSkpMDU1BQff/wxNm/ejKKiIuzbtw87d+6Ep6cnoqKikJycDEdHR61lzTQZ8Geyj+VyOd28kFnWWbNm0WzS2tpa4f39PWSMMz3GaRgYdQWzj2hhYTFh/oiA4QKjXC5HU1MTKioqYGVlRcW+b9y4QQPWhx9+iOLiYnz77bc4e/Ys1q5dC+CWvBuZqwsJCdF4J8zsMarr52kDNpuNgYEB/PjjjxCJRAgLC1Mw3yUkGD8/P1y9epUSeB544AGw2WxqepyZmUmdQQgJpaOjA3w+n2qDWllZ0axRJpPRX+Q6iOAAc/FTd1/UEXjy8/Px5ptvwtzcHIsXL6ZSa25ubnT0gCxavr6+CgbANjY2VKpO24WN2QNjsVjUbmj9+vXo6emhoxafffYZjh07BktLSyxZsgSJiYmjjoLoMzAyIZFIYGlpiccffxyPP/44vYdFRUU4e/YsvvjiC2zZsgXx8fG0NxkQEDDmc0oCo6atAxaLNSKbJCo8xcXFkMlksLW1xaxZs6ZktgjcCozMcvdMxjgTGMeEqj5iS0sLhoaGJuwaDBEYu7q6UFpaCrFYjLlz58LR0VFBSJucj8vlUpbjU089ReXdcnNzcejQIezevRve3t40SCYkJIy6qOgzYxSLxVSSzt7eHr6+viMWYXIONpuNyMhIREZG4m9/+xtu3rxJP8fBgwexa9cueHt7o7+/HywWC7/88gu6urrg5+cHDw8PhWDHdPUgQZKYvZJzKhNORvucTAIP6UeSILl582ZIpVKEhobSbJKcx8bGBoGBgQgICMDAwAAEAgHa2tpQWVkJc3NzGiRJ1jIaRstorKyscNttt+G2226DXC7H9evXKYHn6aefhkwmw5EjR6iNFiEYASPLdPqC8rgG8x7ed9996OnpwW+//abg4+jt7U3NjtX1T1WVUrWBiYkJLYUzlZHa2togl8tx+fJlrb6XiYCyaPpMxjgTGEcFodMr9xHb29snlAxDMqvx+swBt2a3Kioq0NLSAh8fH/j4+KgNJqrAlHcbGhqiWqNffvklXnvtNVhZWVF2aHp6+gihZg6Ho3aOUVPI5XI0NzfTbM7FxQUBAQGj/rwyXFxcsGbNGtx3330YHBzE999/j7y8PLz99tuQy+VUEHvZsmVIT09XWTpmllFJcCSBkpRcmeIImkAVgef8+fMoKCjAO++8g8OHD9NF65133sGOHTtgY2NDDYC9vLwgFosVshbSAyMLsqpFX9MeGIvFQlhYGMLCwrB161bU19fj/fffR319PVUIsrGxQUpKCtLT09Hb22sQQ++xBvx9fHyQkJCAv/zlLxgYGMB3331Hma5vv/02FWcn2aS/vz+A/2aM+rhmFotFlZHs7Oxw7do1eHt7QygUoqSkBFKpVCHL10cPXxeoIt9oIgjxv4xpFxg1CSz9/f3g8/no7OyEv78/PD09J80fkZwPGJ/WokwmQ0NDA6qqqmBnZ4eEhAS1u0JNM1QzMzO6Sz969Ch+++03nD17lpYqORwO4uLikJ2djWXLlsHf33/EuIa2G4y+vj6UlJRgYGAAISEhMDc318pAl2wwSBmUzWbDwsICcXFxsLGxQWVlJZqbm7F69Wrk5eXhL3/5C+RyOaKjo2nJVZV9EfmOyCgPOQczcxKJRArZpKYEnpUrV2LlypWQSqX46aefsGXLFvz22284ceIE3n77barAk5GRQQk8Tk5OcHJyUuiBNTU1jdBzJSU+XXtgNjY2SEpKwqJFi+j1FRQUoKioCGfOnKE/98ILLyAjI2NcMnBMjMZ2VWalmpubIyMjAxkZGZDL5VScvaioCLt378a2bdvg4+OD1NRUSpzRt1YqKSkzv5e+vj4IhUI6pkPE6rVxbdHXtTHPNTQ0NFNKnewLmErQdB5xIrVLgfEHRqLEI5fLMW/evDF3g7qUblksFsLDwxEeHo6dO3fi5s2byM3NRU5ODp555hls27YNgYGB6OzsRH9/PyQSiVYZo0QiQXV1Nerr6ynBwsjISEEwQN11McUKmGxTssGpqalBY2MjPD094ebmhqGhIWzduhVbt25Fe3s7CgsLkZ+fj6NHj2Lfvn1wdXWlQTIpKWnEBoMZJMnzw2az6XPDHFfRJkhyOBwsXLgQjz32GNatW4f/9//+H13kySiDl5cX0tLSkJGRQQk8zB4YU+2loaGB+h4SkoouA/4kCJHrIwSj5uZmLFu2DFVVVVRU3MXFhZZck5KSdB5v0lQSThksFgsBAQEICAjA+vXr0dfXhwsXLtDvuKGhAcAthrY+/QiVRQdYLBYsLS1haWlJx3Q6OzsVXFuI0bcuPWNtMEO+GYmZwAjd/BEnI2OUSqVa7WQHBwfB5/MhFApVZr7qoA+2qKurKx0F6O/vx7///W+cPXsW7733Htrb2+Hp6YkFCxYgIyMD99xzzwhlEwK5XI62tjaUlZXBzMxsxHczliQcOQazF0iCUVtbG8rLy2FmZoaFCxdi1qxZIzY9Dg4OdEBeJBLh4sWLyMvLQ15eHt555x2qcpOZmYmMjIwRDhfMoGFiYqK25ApoznIl1+fi4oLFixdTo+jvvvuOjoO89dZbKgk8ymovXV1dEAqFqKqqAgA0NDSgsbGR6rlqAnVByMXFBcuXL8eZM2fw888/Uxm4goICvP/++9S0mVQdNCHHEOgiCacKs2bNopscuVyOp59+GidOnFAgcOkDY81WGhsbK7i2EPY76RkTD1BbW1utJQTHgqpxjYkwKZ7KmPaBUV0fcTRMZsaoCZhC2UTseyzFF+Xz6fPzWVhYYNmyZVi2bBmOHDmC77//Ht9//z0+//xzbNq0CVu3bkViYiKWLVuGrKwseHl5AbjV6ygrK0NXV5eCAwYTowVGEtz7+/sxODgIU1NTsNlsDA0Noby8HF1dXQgICICrq6sC8Wg0YewlS5ZgyZIlOHToECoqKpCXl4f8/Hw8+eST2LhxI8LCwuhIS2Rk5KglV2AkgYdZZh4rm2Qem8vlUnKOXC5HWVkZDZKqCDyEIEPm70gP1draGgKBAFVVVRoZMmsyx8hms0fIwNXW1qKoqAgFBQXYu3cvJceQmcnRSv3A2BmjLmVIkk2amJjovYypjdwei8Wiog9eXl6QSCQ0myQSgqQ3aWtrO+6y58yA/0hMu8BIXuKx+oijYaIzRkCzYEyyKz6fD2NjY53FvvUxeK8OXC6XamCuWLECPT09KC4uxtmzZ/H000/jySefxNy5cxEXF4fZs2dj0aJFo8rRqQuMJCuztbVFZ2cnLl68CCsrKxgZGaGzsxNOTk6Ii4tTaSysyWdnsViYPXs2Zs+ejU2bNqGjowPnzp1DXl4eTpw4geeffx6Ojo5ITEwEAGqgqwxlAg/zl7psUhMFnpCQEISEhODJJ59ER0fHCAKPnZ0dLWkuXbqU/lsHBwdERETQxVggEIwwZGYOsWuifKMqu/Hx8cGjjz6KRx99lJJjSDnzzTffBJfLRWJiIp2b9PT0HHFcTXuM2kAkEhnEoHc8AuJGRkZwcHCAg4MDzSY7OjrQ3t6OyspKmJmZKWxgtM0mZwb8R2LaBUaxWIzy8vLfjT8iwVhZXF9fH/h8Prq7uxEYGAh3d3edFwd9zBdqAhaLBVdXVyxatAhr165FT08P/vnPf+Kzzz7Dxx9/jJ6eHjg7OyMzMxPZ2dlITk4e8cIqB0ZCeiG/E5IJ2TCQjKyrqws1NTVwcHCAjY0NXRh0zZZtbW1x11134a677oJEIsGPP/6IvLw8fP755wCAv/71rygoKKBlO5IVM6GO5cokC5HPyPxdk2tbtWoVVq1aBalUiv/85z80m/z444/B4XAQGRkJALh06RKWL18+YjHu6+uDQCAYYcg8VmlfkzlGZXJMeXk5Lbk+/fTT2Lx5M4KCgmiQjImJ0bnHOBbEYrHBTIr1kYUys0lPT0+6geno6EB5eTlEIhF4PJ7CPOtY92KmxzgS0y4wNjc3o7e3d9z+iBOdMapbsJmkFHd3d4SHh4/7xTZkxqh8HrK4Dw0NoaamBu7u7njttdfg6uqKy5cvIycnBzk5OTh58iTMzMyQnJyMrKwsZGVlwdXVVUH5hhlASAlSIpFQpqmvry+8vLwgl8vpjrukpAQSiQR2dnY0CIz3sxsZGVFN0g0bNsDPzw+rVq1Ce3s7tm3bhi1btiA4OJiWXBcsWDAieKgquYrFYjQ2NlJrJKlUCrFYrDWBJyYmBjExMdizZw8aGxtRWFiI06dPA7jlrJ6Xl0cJPAkJCTAzM6NEEWVDZoFAAJlMhtLSUqrnygxY2g74s1gsBAUFISgoCBs3bkR3dze++eYbFBQU4PTp03jllVdgZWUFkUiEWbNmobW1dcQozXgC41TMGEeD8gZmYGCAfi9VVVUwNTWlQVKdOhIzY5TL5RgaGprpMU72BUw0PDw84OLiMm7rpInOGJWDFZnlKy8vh7m5uV4FzCcyYyT90KqqKjg6Oir0Q0lwOXjwICorK5GTk4OzZ89i48aNWL9+PaKiotDQ0KAwAkKuH7i1CWKq+jB3wczFpLe3F+3t7WhsbERLSwv6+vpQW1sLBwcHWFhYjOtZIUHiD3/4A2677Tb09vbi/PnzyMvLw3vvvYeXXnqJljUzMzORkpKi0ieS6NjK5XI6c0cc7HWVqQNuvQ8PPfQQli9fDl9fX2zZsgXd3d0KCjxJSUmUwOPq6qogiSYQCFBRUQETExPU1taipKQE1tbW1GtSWxFxZVhbW2P58uVYvnw5ZDIZrl27hsLCQhw4cABDQ0MICAhAREQEJfBERUWNKwgZKjDq2vfUBiwWi86zenp6UnszoVCIiooKDA8PK2STxCxbVY9xppQ6zTCWBY6mx5jMHmNPTw9KS0sxODiIoKAgODs761VuaqJEy8ViMVpbW2FiYoLIyEgF3VJlBAQEYNOmTbSfV1BQgLNnz+Lnn39Ge3s7goODack1MjIStbW1GBgYQHBwsIKqjzKYQ9h+fn5wc3NDdXU1LbWamprC3t5+RMlVUyj7W1paWuKOO+7AHXfcAZlMhp9//hm5ubnIy8ujZc34+HhacvX19UVVVRWamprg7e0NHx8fXLx4EcAtMhBx2yCZsrYEHgLyfS9YsIAyNJkEnk2bNkEmkyEsLIwSeKKiogDcejb9/f3h7++PwcFBmk3W1NTgxo0b1DFkvGxKNpuNiIgIRERE4MKFC7CyssLtt9+OwsJCnDhxAocOHYK9vT3Cw8PpRkTbHrshS6kTLQmnbG+m/N2YmJjA1taWPjcEg4ODkyY2MFUw7QKjPsAc4p6oh53NZkMkEqGkpAQ3btyAt7c3fH19DaIqoo9xjdEgEolQXl6OtrY28Hg8zJ8/X6uAY2tri7vvvhurVq3CgQMH8N133+GXX35Bbm4uNQqOi4vDypUrERwcrNV3ZGxsDA6Hg4iICEilUoWSq1QqpT1Le3t7rTILVfeTzWZTub09e/agqamJytQ988wz2L59O9zc3BAbG4vVq1dTghjzWOpYriRYajozqTzgPxqB5+2338aLL74IOzs7JCYmYs6cOZg9ezZ4PB64XK6CIfOHH34INps9wpB5vEovRCv17rvvxt133w2JRELFBb744gusX78eGzduxMKFC2lvcs6cORpppf6eSqmagsViwdzcHObm5lQMv6urCwKBAMAtm7nLly9jeHiY/pwh8dprr+HFF19ES0sLwsPDcezYMSxYsMCg59QGM4FRBzAXIUMIJCtDLpdDLBajpKQEtra2iI+PN2gPYCJEy21sbODm5gYOh6PVgqGcHbm5ueGee+5BWloali9fjhs3bqCyshJff/01/vKXvwAAFQrPzs6mQuHqwAw8HA5HZcm1oaEBpaWltGQ4WslVm6Ds7u5O3S2uXbuG8+fPo7y8HBcuXMBnn30GKysrpKamwt3dnd4LVdc/lkwd8+eUR4HUXa86Ak9OTg6++OILHDx4ELGxsdRCKzAwEBwOB8bGxlRZSFdDZlVQJt8YGRnR3ilx1iBzky+++CKeeeYZuLm50ZLr4sWLMWvWLJXH/V8MjMogwg5WVla4ceMG5s+fj9raWnzyySdoa2tDamoqsrOzaXlfnz6zn3zyCTZv3ozjx49j4cKFOHr0KNLT01FeXq73+VFdMRMYdQBz4N7QgbGzsxNlZWUQiUTw9PREUFCQQc8HGCYwdnd3o7S0FCKRCKGhoXB0dER5ebnGJWl15Bqi/UpEDBISEsBisbBr1y60trbSDOz555/H7t274evrS4OkKm9GdZ9dueQ6NDQEgUCA9vZ2WnJ1cHCAvb29QslVuZQ61mdsaWlBeXk5eDweNm3aBDMzM8jlcly9ehX5+fkKTNcVK1ZgzZo1yMzMVOlwos3MJNF01ZbAs3btWly5cgWtra0oKCjA/v378be//Q3e3t5IT09HRUUFxGKxApuSKL2oMmQm2eRY5cyxBvzd3Nxw//334/7778fw8DB++OEHynQ9efIkTExMqLhAeno67dtOdVaqvkHePwsLCzz44INYuXIl3N3dcfToUfzwww/Yvn07WCwW7rjjDr2d8+WXX8YjjzyCBx54AABw/Phx5OTk4N1338W2bdv0dp7xYNoFRn2UPpkLjaEwNDSEiooKtLa20pKpqh2uIaDPUirT69HHx0fBAUPT86iScgOAxsZGVFdXw97eHnFxcSNEDJycnPDnP/8Zf/7znzE4OIhvv/0Wubm5+Oc//4nXXnsNPB4Pqamp1JuRx+NpzMg1MzNTKBmSkiuxGiIsV003ToODgygrK0Nvb++IviiLxaK9te3bt+PYsWPYtm0b3N3dcejQIezZswdeXl5UfYdIwSljtJlJEiTJf2tK4JHL5XB2dsayZctGKPDk5uaisbERALB69Wpa0nR1dVUwZCZ6rgKBAA0NDSgrK6PC2/b29iqzcYlEovbeKvfzTE1NqTDDwYMHUV1dTb0mScna19cXaWlpVAFI35io6pK2IAGb3K/BwUEAQFZWFlatWoUjR47ota0iEonw888/Y/v27fTP2Gw2UlJScOnSJb2dZ7yYdoFRHyC+e4Yg4MhkMtTX16OqqgoODg5UAaSrq2vCmLD6yBiZDhiWlpbU65GJsQKj8kwieYG7u7tRVlYGqVSKsLCwUUk7BFwul87KvfLKK9SbMScnB2fOnIGRkRHi4uIwPDyMvr4+rT6rcsmVLPL19fVoaWkBALS2tqKvr2/EIi+Xy9HQ0IDq6mo4OzsjNDR0zIxl7ty5AIDXX38dzs7O+O6775Cfn4+cnBwcP34cFhYWSE5ORmZmJtLT0+Hs7DziGMpBkizapH/O7Dmq8plkXv9oCjzx8fFobm5GR0eHWgIPh8Oheq5MQ2ahUIi6ujoYGxtTlisZORgtsxur9+/n54e1a9di7dq16O/vp+ICZ8+eRVNTk0ECmEwmM0gmOl4oD/cPDAyAxWIpbKz0yaMQCASQSqUjRmycnJzA5/P1dp7xYiYw6ghDjGy0t7dTKyVlluZEMUWB8WeMfX19KC0tRX9/P4KDg9WyZtVlZ6PNJFZXV1Pykbe3t06LGDMD27lzJ27cuIG8vDzk5OTgwoULkMvliIyMRHZ2NrKyslTOGY52bOYi39bWRu/J5cuXacmVZJNEdGDevHkjLLrUgXw3ZAEjfbOXXnoJpaWlVMt13bp1kMvliIqKotnkvHnzVJZcyZ+ZmprC1NR0TJ9JTTV3/f394eDggH/9619UHUiZwEPKmcnJyeDxeNSQ2c3NjZJEyMgBMWQeGhpS+UyRZ0fTsqWFhYWCVurSpUvR29ur0b/VBhMxrqELlDNZ4qwx0QzaqYaZwKgjNBGv1hQDAwPg8/no6OhQK083kYGRzWbTnpM2YIoNeHh4IDIyclTWrKp5SXVl09bWVpSXl2PWrFmIiYnRK/nIzc2NCp7/9ttvOHPmDNrb2/H+++/j5Zdfhr29PfWYXLp0qVZEBFLe9fHxQVJSEhWGvnr1KqRSKSwsLODt7a3VMdVtWlgsFubMmYM5c+bgqaeegkAgoDJrr776KnW3YDqDEPYhM0PUhsAz1hgCc45RWR2IqcBz+vRpcDgclQQe0nckhsxCoRDDw8NobW0dYfzLvBfagsVi0U2LvjGVe4zKGaMmajm6wt7eHhwOB62trQp/3traqrKyMVmYdoFRX1+4PmYZieVRbW0tlUZTJ/Y90RmjNudSdsCIiYlR65ahfB6mvJmyTyKLxaKbht7eXgQGBup9ZlMZxIQXAPUXJCXXDz/8ECYmJkhMTKTZJGGIjvYZyefjcDgwMjJCV1cXzM3N4eXlhYGBATQ2NqKsrAzW1tZ0YdZk1z7W39vb2+Oee+7BPffcA7FYrOAM8u6778LMzIw6g8yePRvASPINk8DDzCDJL5JdqVPgUdcLNDIyQmxsLGJjY/HMM8+gsbGRBsnnnnuOEniUFXjIADuHw6FG2wKBgBoyk7lFiUSilXA+gUgkMgjjezLmGDXBRAuIm5iYICoqCufPn6eEHplMhvPnz2P9+vUGO6+2mHaBUV8YTylVLpejtbUVfD4fpqamWLhwocJuVxUmUlRAG+UbTRww1IEERlVZolwupw4hrq6uGvXe9A2mv+Czzz6L2tpa5ObmIjc3F1u3bsWTTz5J3TSysrIQERExIrCQeyGVSlFSUoLW1lb4+fnB09OT/h3pq7W3t0MgEKC6ulqh5KrsbKFLmdvY2BhJSUlISkrCoUOHUFlZidzcXOTn51P3DQB45plncODAAURFRY0aJAcHB6lhNGFKq5qZ1NQqzcPDg2btRFScEHhUKfCQsQqmVVNvby8tXV++fFmBwEMMmcfCdGOlKl8X0Uk1ZBDfvHkz/vznPyM6OhoLFizA0aNH0d9dKDVNAAAgAElEQVTfT1mqUwHTMjDqg3Wpa6Dq6+uj7MPAwECNA8lEl1LHOpdMJkNtbS1qamrg4uIyqgPGaJBKpZBIJAo+iR0dHSgrKwOHw0F0dPSYm4aJgo+PD9atW4d169ahu7sbRUVFyM3NpW4aTMHzpP8zMCbPGZ/Ph7+/P2JjY1XuyM3MzODh4UGHr4VCIdrb23H9+nUFlivTZHo8i1dAQACeeOIJPPHEE+jq6sK+fftw/PhxXL16FUlJSXBwcEBGRgYyMzORnJxMS71kFrWyshLOzs6YN28ejIyM1JZcJRIJ9aHUNDAoi4qXlpaioKAA+fn5lMADAKdOncKSJUsogcfKygpcLhcNDQ2IiYmhvUmmITMZp1FX4v9fHfBXB1XOGoYWEP/jH/+I9vZ27N69Gy0tLZg3bx7y8/NHEHImE9MyMOoD2maMYrEYVVVVaGxshIeHB+bNm6fVznQiM8axNg7EiojD4WD+/PkqtT1HA8kSTU1NIRQKceXKFSq5duPGDbS3t8PPzw8eHh5TcjEBbml4rly5EitXroREIsGlS5doNnny5ElwuVwsXrwYwcHBAG6Z9kZERGgUzDgcjkIm1NPTg/b2dtTX16OkpATl5eUA/kutHy94PB4efPBBHD9+HF999RXkcjnNJj/44AMYGxtj0aJFSElJga+vL3g8HsLDw0eQw8i1A/+dmSSzvrrI1JGfJ33TzZs3QygUoqCgAI8//jhaW1uRkpKiQOBJSEgAcGujocqQubq6GoODg+DxeJTpylR5mS4D/gSqnDUmQid1/fr1U6p0qoyZwKgjNA1UcrkcN27cQEVFBWbNmqWz2DeRhJsIqMsYh4aGwOfzIRAIEBAQoFPgYpZN7e3tsWjRIggEAjQ2NqKmpgZsNhvOzs4TNrOpDxgZGVHfyIMHD6K8vBynT59Gbm4uCgoKAAD79+9HR0cHsrKyMHfuXK3KzYTl6u/vj6GhIVou/OWXX9De3k7Vd9SZCWsC8n2THlBcXByee+451NbWIi8vD19++SV2794NiUSCoKAgZGZmUsauquyL6R1JNF3H8pnUBHZ2drjnnnuwefNm7NixAwsWLEB+fj4l8BgZGSEoKAh//OMfkZGRgcDAQLDZbGrIzCTwqDJkFolE06qUqo58M90xLQOjPkqpmmSMRO1leHgYISEhcHJyGpdH4mSRb2QyGRoaGlBZWQlHR0dKhNAG6mYSRSIRFZoOCwsDm81WGJQni769vb1BdGH1jb6+PnR3dyMlJQUbNmxAX18fHnvsMbBYLBw5cgR79+6Fh4cHldsajXClCmZmZrScGhcXBzMzM4WSK9Fxtbe312qBV9ZKJXB0dMT8+fMRFhYGT09P/Prrr8jLy8M//vEPHDlyBLa2tgrOIMqi3SRjJN/daD6TY81MMiGRSCjRKyYmhhJ4zp49izNnzmD//v3YtWsXVeAh2aSZmZmCZqiyIXNXVxd6enpw8+ZNBUPm8WKqjmsoq3fNeDHewtRfaaYoRssYRSIRKioqcPPmTcqcG++iPtE9RrJx6OzsRGlpKWQy2ZgOGKqgbiaRMHIbGxvh6empoIjDHJQnkmvFxcWwtbWlhJSppv5Peq51dXXw8PCAn58f7Wvl5+cDuPVcXLhwgZZcjx8/DktLS6SkpCArKwvp6eka3V/y3RgZGaksudbV1aGkpISWC4mW61jXr3yO+vp6VFdXw93dHf7+/uBwOPD09KQWUES4PS8vD5988gl1BiG9ycDAwBEL71gydZrOTKoyKvbw8MCaNWsQHByMBQsWKBB4Tpw4AQsLCyQlJdFA6eLiMsLPkMPhwNzcfIQhs729PSwtLXXe2P5eWKkTVUqd6pgJjDpCVcYok8nQ2NiIyspK2NraIiEhQW8P2UR6QJJh+uvXr6OlpQW+vr7w8fEZV9mUHJfFYqGtrQ3l5eUwMzPDwoULVZZNlUuIAwMDaG9vV5hndHBwgKOjo8aMQ0Ohq6sLpaWl1C1D3aiKiYkJli5diqVLl+Lw4cMoLi6mwgKPPPII2Gw2YmJiKMs1MDBQ55Lr4OAg1XIl5UKSeasquTK/o/7+fpSUlEAsFiMyMlKldRObzUZ0dDSio6Oxe/du3Lhxg2q57t27Fzt27IC/vz9u3ryJzs5OtaQWXUTPyc+pyoiJ6o0mBJ7w8HA6DkJYuFKpFA4ODoiOjlYwZL569SpYLBYNksqGzGNhqpZSVbFSZwLjTGDUGcoZY0dHB82swsPD9T4kPFEZo1wuh1AoRG9vL8zMzBAfH6/1i6JuJnFwcBDl5eXo6upCQEAAXF1dNV74ydyfl5cXRCIRXfTr6+thbGxMd/26eCbqColEgsrKSty8eZOOYGh6bhaLhdDQUISGhuLpp59Gc3MzzW6IELe/vz9lucbGxtKFmKl8ow5cLpeyXCUSCdVyZZZcHRwcqGA3OWZzczMEAoFC1qsJ3Nzc8NBDD+Ghhx7CwMAAvv32W+Tl5eGdd95BeXk5vLy8kJKSQsctVL0fyjOTAFSKnpNeuyYSdeQ+KRN4zp8/j/z8fLz11lt48cUXYW9vj9TUVHR2dqKnpwcAFAyZZTIZuru7IRQKVRoyjzV3OlUDo/I4zUxgvIVpGRj1JSQuk8koIYUwKb29vQ3yAkxEYOzp6UFJSQk1Ko2MjNT6GOpmEuvr61FTUwMnJyfExcWNi/lnYmJCGYeqBLwnoi/Z1tYGPp8PCwsLxMbGjnsxcXFxoW4Qg4OD+Oabb5Cbm4szZ87g2LFjsLGxQVpaGrKysrR+ftWVXGtra1FcXAwej4f6+noAgFAoRHZ2ttZMYybMzc2pus6VK1fg7e2NefPmIS8vD48++ihYLBY1RM7MzFTpk0jeIWY2SZ4rEiCJQhMpt5Jnbaz3z87OTqUCT35+Pvr6+lBUVDTi37DZbNjY2MDGxkalIbOJiQkNkqoMmadqYFQ14D+e7/5/BdMyMOoDZN6uqakJjo6Oah0N9AVDBkamA4a3tzesra1RUVGh1TGUfRJJltjV1YWysjLI5XJERERo7ag+FlQJeBuyLzk8PEzl+2bPng0XFxe9l3G5XC4NGkTwPCcnB7m5ufjkk0/oAvvqq6/i0UcfhY+Pj8bHVi659vf30344+fu2tjbI5XJYW1uPezEnFlDbtm3Dtm3b0NLSQoMQ8Un08PCgnzcxMXFMZxCmrivwX6cQADRQagplBR5HR0csW7ZszH+nbMjc2dkJoVCo1pB5qgZGVXOMLi4uk3hFUwMzgVEHtLW1oampiQo0ayr+PB4YIjASBwzSsyMOGEKhUGPWrjpyDZnbbG5uhq+vL7y8vAy+MBiyL0nGbiorK2FnZ6fS5soQYLPZiIyMRGRkJHbt2oXGxkbs3bsXn3/+OY4fP45XX30VwcHBVKIuOjpa4/Jnb28vSkpKIJPJqHqN9/95JV67do2O1DBLrtpCmXzj7OxMrcCGh4dx4cIFKlNHFG6YziCqFmnyHpiZmdGgI5VKIRaL0dzcDCMjI4hEIq1nJsmxPTw8tPqMHA6HMoEDAwPR398PoVBIDZnNzc0hl8vR19dncFUZbaFORHy6YyYwaoH+/n7w+Xx0dXXRzGcigiKg/8DIdMAICgpS0CDV9FzqBL+bm5tRUVEBa2trxMXFTRr9W199yf7+fpSVlWFgYABz5841iMi0pvDw8MBbb72Ft956C729vfj666+Rk5ODU6dO4fDhw1SxJjs7G8nJySrZqEwGrZeXF3x9fakXnoODAwICAiCXy9Hd3Q2BQKBQcmVquWqC0cy8TU1NkZKSgpSUFBw+fBhlZWXIy8tDfn4+NmzYQJnQJJsMDw9XELgngZrNZqO7uxslJSUwNTVFaGgobXVoMzMpl8vHPeDPNGT28vKCWCyGQCBAWVkZ+Hw++Hw+zSR13WzoE6oyxpnAOE0Do7Y7NolEgpqaGtTV1cHNzQ2LFi1CS0sL2tvbDXSFI6GvwMj8LOocMHT1Sezr6wOfz8fAwMAIs93JhqZ9SeZiRbwxa2pq4ObmRuXPpgosLS2xfPlyLF++HFKpFFeuXKGjIB988AFMTU2RlJSErKwsZGZmws3NjfaRASgwaJXnGFksFng8Hng8Hu2pES3XyspKmJub03s2Wsl1tMDIBIvFQkhICEJCQrBlyxYIBAIUFRUhPz8ff//737F//34quTd//nwA/3W4qa6uRmNjI3WmIc+ctjOT5Of0qXxDNmBlZWWIjY3FwMCAVobMhsZEi4j/XjB13vIpCLlcjpaWFoXRAqLbOZESbeR84w2Mra2tGjlgqDuXurIpyUDq6+vh7u4+5QKIMsbqS9rY2MDS0hLt7e1gsViIioqa8oQEYtkUGxuLffv2obq6mgbJzZs344knnkBwcDDCw8Nxxx13ICsrS2FBVDfgT8DlcuHp6QlPT09IJBKq5Xrt2jUAoFquylmQpoFRGfb29li9ejVWr14NsViMH374gWaTJ0+eBACsW7cOd955J2JjY5GcnDxi7EfbmcmhoSEA0HsWx7y3YxkyM/VcDWGYrOraZjLGkZi6q9cko7e3F2VlZejv70dgYOCI0QJ9+jFqgvEERm0dMFTZTqmbSRQIBODz+TA2Nh51hm+qQrkv2dvbCz6fT1malpaWEAqF4HA4kz4vqQ38/PywYcMGbNiwAfX19fjggw/w448/Ijc3Fx9//DFcXV1pJpmUlDRmYGTCyMgITk5OcHJyoiVXJsvVxsaGZpO6BkYmjI2NsXjxYixevBjPP/883n//faxduxYsFguvvfYaXn31VcydO5d+HiIqroyxZiaJ9iwpw+qrJ67u3o5lyMzUczVUFjdTSlWNmcCoBCZD09PTU63Z7kQO3AO6BUZdHTCYyjfqZhKHhoZQUVEBoVAIf39/uLu7/26ChjqQXpCZmRni4uJgbGw8JeYldQVRF2poaMA999yDHTt2QCqV4ocffqAs17fffhsWFhZU7LyxsRFeXl4an4NZcg0ICKAl1/b2dlRWVmJwcBDd3d3o7OwEj8cb9zPS399Pn+HXXnsN0dHROHfuHPLy8vD222/jhRdeUDCWTk5OVrlZUzUzSX43NjbWWfRcFUh1ZbTPrs6Qua2tDZWVleByuTRI6oMtzLw2somQy+Uz5Jv/w7QMjKoeUGKnU1FRASsrK8rQVIepXkplOmBER0drNSbBVBdRRa5paGhAVVUVHBwcJoydaUiIRCKUl5dTcXRmRs3sS3Z2dqKtrW3UvuRUAVHjIZ6S5Flms9k0+zp06BD4fD71PASAjIwMzJ8/n6rvhISEaBXMlEuuwK13i5RcCXtT23sml8upqhRZuG1sbMDj8ajLCbPPmpeXhw8//BDGxsZISEigBB5fX98RxybPNXnOzc3NYWRkRDeE4xE9B7SfYWSxWNSQmdzHjo4OBUNm5jjIeHqiqkTEDWHU/HvDtAyMyiCzdiKRCHPnztWINDIZGSOT7KIOTAcMQkbQdXdJdudkx9zd3Y2ysjJIpdIRtkO/R5BxlYqKCtjY2CA2NlbtrCOTkq9qSN7GxgaOjo6TruPKJKOMNSbDYrEQHByM4OBgbN68GXw+Hz/99BPy8vLojKGXlxcNkgkJCVotwqTS4uLigsWLF9OSK7OXS4QYRstSBgcHUVpaioGBAURERKCurg4ARpRLlfusdXV1VKbub3/7G55++mkEBQVRLdeYmBiFahBhu5qamioQsJglV2UCjyZBcrwC4soCDb29vRAIBGhqahqh56ptuV+VVupU0yGeDEzrwDg8PIyKigq0tLRQsW9N+yGTkTEC6nef+nLAIELKPB4PFy9epKy5gYEBtLW1wdvbG97e3hNCDDAkyGLb19eH4OBgrUxSp6qOa1dXF0pKSmBsbKxWg1YdmEHyvvvuw9DQEL777jvk5eXhq6++whtvvAErKyvqpJGenq7RqBLpMSqXXAk7s729nc76kTK1tbU1ZUaTOVsnJyeEh4fDyMgIlZWVAMYmyXh7e+Pxxx/H448/jt7eXvz73/9GXl4ePvroIxw9ehQ2NjZUwD01NZVKzTGD/1gEHk1KrvrsV7JYLFhZWcHKygq+vr4YHh6mBB5tDJkBqNxoDw4OzmSMmKaBUSaToa6uDlVVVbCzs9NJD3QyMkZgpAsCMH4HDECRXMNisRAdHY3h4WHU1NSgtrYWcrmcDir39/ePy2lgMkE2EDU1NXB2dkZYWNi4y6CTreMqlUpRVVWFpqamESMLusLMzAxpaWlIS0vDyy+/jN9++42yXB9++GGanZFsMiAgQO21qdpEmZubq2S5Xr16FcCt+eDBwUEMDg6OmB0lwUib783S0hK33347br/9duoMQoQFzpw5Aw6Hg5CQEABAU1OT2uMoE3iYv0YTPTfUu2JqaqqTITO5fkAx6M/0GG9hWgbG/v5+NDU1Yd68edTbTluQjFGVaLEhoCowEnsroi6jqwOGqpnEgYEB8Pl89Pb2Ys6cObC3t6dkgJ9++gnGxsa0dDgeg9yJRE9PD0pLSyGVSg0iTweMnJc0dF+SiNebmpoiJibGILt9FouF8PBwhIeHY/v27WhubqauIMRJIyAggAZJZolSKpWOObqjzHKtq6tDTU0NZUc3NDRgcHAQDg4O4HK5tOSp60gQ0xlk165d1BnknXfeAQAqj6fJcVSxXEnlhQRwsVhMA6Qh3xM2W7Uhs1AoRFVVFfXyJHquJJCTayKs3JmMcZoGRisrK8THx48roDGZXBMVGMlCwSQK8Xg8vThgkBKQXC5HbW0tamtr4erqitDQULqAOzs7w9nZGTKZDB0dHWhra8P169chl8tpVmRnZzflyqxMdqaXl5dWJfPxQJO+JLlv2tLxJRIJqqqqcPPmTfj7+8PDw2PCMngXFxc8+OCDePDBB9Hf349vvvkGOTk5OH36NF555RXY2toiLS0N2dnZEIlEGt9rsViM8vJytLe3IyQkBM7Ozgr2WaTkSkZp9DUrS5xB5s6di+TkZKSmpmp9DHUl1+HhYdy8eZMyXfXBctUUqgyZhUIh3RySmWyxWAwOh0MD48yA/zQNjMD4HTbIC6DM6jIk2Gw2enp6cO3aNQwPDyM0NBSOjo5aH0fdTGJHRwfKysook5W8OKqug7ngd3d3o62tjc5f2dnZwdHRUWsXeUOAZFQmJiZYsGABLC0tJ+U61PUlyX0jfUkHB4cxy9RkceNyuYiJiZnU0peFhQWys7ORnZ0NmUyGn3/+Gbm5ucjJycHHH38MAHjhhRcgl8uRlZWldhREKBSipKQEs2bNUiBBMUuuYrEYHR0d+O233wAA//nPf9DT00M3ZOMNlKTHqA+WNZvNRmdnJ4qLi2FtbY05c+bQrHG8LFddoGzI3NfXh+bmZnR0dODSpUv49NNP6UZuIlnmdXV12LdvH77++mu0tLTA1dUV9957L3bu3KlXBSJtMW0D43jBlJGaiMVfLBZDLpfjt99+g7e3t1ZeeQTqZhKHh4dRWVmJtrY2+Pn5wcPDQ+MXVZlU0dfXh7a2NtTX16OkpGTS2JpisRgVFRVobW2d8IxKEzD7kmKxmM7+jdaXlEgklCwWEBAw5WZH2exbRs3z58/Hnj17UFFRgXvvvRcsFgvbt2/HU089hTlz5tCSa3R0NORyOfW0HOszGRsbw8nJCe7u7gCA8PBwyOVyVFdX4/r16+PKwIH/Bsbxvs8ymQyVlZW4ceMGAgMDFcZ/yDuoLYFHn2CxWHSD2NraioULF6KhoQGff/45urq64O7uTn1As7OzDVpa5fP5kMlkOHHiBPz9/VFcXIxHHnkE/f39OHz4sMHOOxZYck1tFP7HMDw8PO5jFBQUYNGiRQbdsTOZeSKRCGFhYTrZwihnieRFJY4Rtra2mD17tl6D1+DgINra2tDW1obu7m5YWVlRtqahXja5XE7ZoVZWVggKCvpdlYaYfcn29nbIZDKqfNLc3AwLCwuEhIT8rj4TcKu/e+7cOeTm5qKgoAAdHR1wcHBAZGQkEhIScN9992kszv7FF1/g3nvvRVNTE+0TkwxcIBCgs7MTFhYWClqummwg8vPzceedd6Kqqkpn66W+vj5cv36dGlGP9ZwzfSZJoCQwdDbZ3d2N4uJixMfHAwB+/fVX3HHHHfjyyy9p1v/VV19pZWumD7z44ot44403UFNTM6HnZWImYxwHDD2ywXTAmD17NioqKrQuc6jzSSSSd8PDwwZzjOByuQpsTVI6rKmpAZfLpUHSyspKL5nP4OAg+Hw+uru7ERQUBCcnpymVUWkC5b5kR0cHzXyBW6XL9vZ2nbOiyYKVlRVWrFiBFStWQCQS4csvv8RXX32Fq1evoqCgAPv371cQPHd1dVV7LFWsVOUMXCgUQiAQUJYrk/SkruSq7NqhDUjfv7KyEh4eHvDz89MooI0mU6dqZpL8tz6gzBgmAuKLFi1CYmIinn/+eb2cR1t0d3dPmGuROkzbwDiWg4QmMNTIBhnSJg4YERERMDY2RnV1tcbnU0euIUSUxsZGeHp6wtfXd0KIKCYmJlQXktDz29ra8Msvv4DD4dByqy4jDUQVpaqqCk5OToiLi5vU/oS+QCTqZs2ahXnz5kEul+vcl5wq6O3tRXFxMTw8PPDGG2/A0tISlZWVVK3mySefxMaNGxEREYHs7GxqN8X8bGOxUo2NjRWIYkRYgJRcbW1taaBkbi5UzTFqApFIhJKSEvT29mLevHk6L+r6mJnUBlNRQLyqqgrHjh2b1DIqMI0Doz5giIyxra1NgX7P1Hkcr09ie3s7+Hw+uFyu1gPg+gSTni+TyUaMNJBMUhOGK8mqRSLR/4QaD6DIzlQWsGdmRQKBgPZzp7qOK3MMg3hAkmsMCAjAE088gSeeeAIdHR0oLCxEXl4eXn31VTz33HNwc3OjfcnExEStMjs2mw0bGxvY2NggMDBwBOnJwsKCqu/oEhgFAgHtpcfGxuqVb6DLzKQ2UCUgri8j5W3btuHQoUOj/kxZWRk1yAZutXUyMjKwatUqPPLII+O+hvFg2vYYRSLRuDPGCxcuIDg4WOdZSCaYDhiBgYEqSQiXLl2Cj48PnJ2dVR5D3Uwis8QYGBgIFxeXKZldMJ0a2traMDQ0RBmuDg4OCouOTCZDTU0N6uvrJzTzNTTa2tqoT19wcLBGPV8yPkMIPFKplN63qaDjOjAwgJKSEohEIsyZM0djCy+RSISLFy8iJycHeXl5qKurg4WFBdzc3FBRUYHy8nJKxNEFpORKepPnzp3D0aNHqXrUaCxXqVRKSUNBQUET+k6pmplUZphrkk02NzejpaUFERERAIBPP/0Ub731Fn788cdxX2N7ezuEQuGoP+Pr60s3ITdv3kRSUhJiYmJw6tSpSd/YzWSM44A+MkZtHDC09UmUy+XUaPf3UGJUNsft7+9HW1sbGhoaUFpaSlmHpqamqK6upizI35vVlSowhcxnz56t1ULLHJ8JCgrS67zkeMDsu7m6uiIgIECrzYuJiQmWLFmCJUuW4MUXX0RZWRlycnJw7NgxAEBQUBAWLlxIGZRBQUFaBSflkuv169fB4XBQW1uL0tJS2Nra0mySed9IOZjD4UzKuIy2PpPqgqQqAXF9PR/kedMEN27cwJIlSxAVFYWTJ09OelAEpnFgnAo9RjKPRpQ4xlJiURUY1c0kEmF0uVxuMJUXQ4LFYmHWrFmYNWsWfH19MTQ0hObmZtTV1WF4eBimpqZwc3OjG4CpmAFritbWVvD5fFhbW4/brUSf85LjwdDQENWi1UeJm8ViISQkBCEhIdi6dSvq6uqoluuhQ4ewZ88e+Pj40JJrfHy8Vpkym82GkZERTExMEB8fj/7+fggEAspwJixXmUyG/9/emcc3VaZv/+q+0o026Qbdm7WgwoDgwBRlVVnS6ozOjKyvKzCMo6PgjI4zoAPIjCgi4qiAA0ibAnUDxoUBUWQRWdImXVJKgUKTNN2yr+f9g985k9QWuqTNSfp8Px/+MMXwJE3OdZ77ue/runLlCjIzM93Kwd7kZg08Nyu5dmUgPtgNXY2NjSgsLERGRgY2bNgAjUbD/Ky7ythgMGSF0RP0dcdoNptRXV0NtVqNvLy8HidguApjdzOJVqsVSqWSMUa/WbqCL9HR0YErV64gKioKo0ePZnaTly5dQnh4OFNu7WlrPhuwWq2oqqpCS0sLeDwekpOTPb72zt2aA30uSVEUmpqaUFVVhaSkJI+fu9HQZvbz58+H2WzG0aNHceDAAezfvx+bN29GbGwspk2bhnvvvRfTp0/v0Y2h1Wpl1krHPtHvW1NTEy5evAir1Yrg4GBYLBY0NzcjISHBYw48nqA3DTy0Ew+NN5pvvvzySyiVSiiVyp+Uxb15yjdkzxhtNlu/O0rPnDmDpKQkjBw5skd/n/Z9VCqVTNmrN3ODZ8+eRXx8PDIyMrqcSaQjlGJjY31ufq876JuI1tbWLs9HHQ4H0+Gq0WgQGBjIRPSwsQkF+N+sZVVVFeLj48Hn8wc907K7c0m6dNgXMXMVeqFQ2CdXpv5CURTOnTvHeLmeO3cOQUFBuOuuu5jdZE5OTpf/72uvvYa33nqLsZyjoRvihg8fjvz8fGY3qdFoYDab3eKz2Pydc52ZtFqtkMvliIqKYna+a9euxfXr1/Hhhx96e6lehwhjPzh79izi4uJ6NABLRwI5HA4IhcI+NeycP3+euYt1nUnU6/WoqqqC0WgEn89HUlKSz+yauoOiKMZ8IDExETwe75bno3SHK106dDgcSExMZJpQ2HBnb7FYUFVVhdbW1l7HXQ0Urj6uGo0GBoOh1+eSGo0GcrkcsbGxEAgErAmvbmxsxMGDB3HgwAEcOXIEFosFfD6fOZccN24cs7t69dVX8cEHH0CpVAK4cdNFOw3RDTadMRgMTPNOW1sb0+WalJTksflcT9PR0YELFy4gPDwcfD4fwcHBsNvtmDt3LoAbTX5DnSErjHa7vd+NMxcuXEBUVFS3d6CA5xIwKIpCTW465FoAACAASURBVE0NGhsbweFwwOVyERMTg4aGBjQ0NCA9PR05OTmsuPj3F4PBALlcDrPZ3OeuX/piT+8kTSYTEhISmJLrYDch0SXG6upqJCQkgM/ns7YRis5KVKvVaGtru+m5pN1uZ44Fets0NNgYDAYcPnyYmZnUaDRITExkDM9PnTqF8vJyyOVydHR0oKKiAiEhIRCLxT26OaBL1fQfuimKLeb69LxvbW0tsrOzkZmZiYCAADQ1NWHJkiWoqanB3/72NyxZssSr62QDRBj7QUVFBUJDQ5Gfn/+Tn9E7nurqasTFxUEgEPSpfu/aXEPnrWk0GjQ1NcFutyMkJATZ2dlIT09nZdmwN9A5mfX19R4XevpMUq1WQ6fTITY2lim5DnT5y2KxQKFQoL29HQKBwCslxr5CX+zpXZHruSRFUVAoFIiMjPQ5mzqn04kffvgBn3/+OQ4cOAC5XI6goCCEh4fj5MmTuHjxIrKyspCZmdmn75Xrd7W5udmt5DrYvsHAjd8jbUJQUFCAuLg4UBSF//73v1iyZAkKCwvx7rvvdhscMNQgwtgPFAoFAEAgELg9Tuf+mc3mPp+1dDeTSJ+5abVapKWlMW4odrudKRsmJiZ6/e60t7S3t0MulwMAhELhgH5BzWYzU25tbW1ldkQcDgfR0dEe2/G4+twmJSUhPz+ftbvEnuAaN9bU1ASHw4Fhw4YhIyODFUkq/eHSpUt45ZVXUFlZifXr10MsFvd43rIn0CVXjUaD9vZ2REdHM7vJgS65trW1QSaTYdiwYRCJRAgJCYHNZsOrr76KzZs3Y8OGDXjsscd8/sbakxBh7AfV1dWw2+0QiUTMc9bW1rq1c/d2x9PVTCI9WnL16lUolUrmIkuf47iWDenBeFokk5KSWF1epXMFGxsbkZ2dPehdtK6dms3NzQgLC2N2kv3pcDWbzVAoFOjo6IBQKBwQL1pv0N7ejsrKSgQGBiIrKws6na7P55JsQqVSQaFQICkpCTweb0C/M667cK1WO2AlV1e3oby8PCZhprGxEYsWLUJzczNKSkowevRoj/x7/sSQFUaHw8G0LvcVpVIJo9GIgoICpkWdTj/oi91aVwkYAQEBaG9vh0KhgMPhAJ/Pv+lMGEVRMBgMUKlUUKvVMBgMbu4xbNqxuFrUCQQCryeH0x2u9J19QEAAs5NMSEjokWBTFIVr166hpqYGHA4H+fn5Pr2ToqGNKC5dutRlidFkMjG78FudS7IJ1zNSgUAw6LNzriVXjUYDi8Xi5uXa15KrxWJBZWUlc32KjY0FRVE4ePAgHn/8cdx///146623vJZPynaIMPaD+vp6aLVaUBQFnU7XZ2uo7mYSbTYb6urq0NjYyMxt9fZusvPZGp2PyOFwvNY5SLf1a7Va5OXlueXVsQX6gkU379hsNrdSdVc7CpPJBIVCAb1e3+fOYzai1+uZjmqxWHxLp6GbnUuyaYSGjl0KCwuDWCwe9HO/zlAUxRgyuJZc6VGQnpZctVot43gkFAoRHBwMq9WKv/zlL/jggw/w5ptvYuHChaz7zrEJIoz9+P9//PFHaLVajBw5Enl5eX2Oq+lqJpF23IiOjgafz/fIbqpzPuJgNqAA/ztzq6mp8dr8Xl+gb3xokTQYDD/pcG1sbERNTQ2Sk5P7/FlgGxRFMXO3dJRSb2/MBmJesr+4lhhduzPZRlclV/p966rkSvsHX7582S0gub6+HosWLYLJZEJJSQmEQqGXXpHvMGSF0el0Mi79vYU2egZu+DlOmDCh18/RXU4ibSau1+vB4/EGLFPQYrEwIkk3oHC53AELEaZfl8FgAJ/P96nOzM7QjRT0DQa9e+TxeDfNEfQlTCYTKisrYTabIRKJPGIp6Il5yf5iMplQUVEBq9UKsVjsM12Y3ZVcaaEEAJlMBpvNhlGjRiE6OhoUReHjjz/G0qVL8ctf/hKvv/6612OlfAUijL2ALpXRLiyBgYG4cuUK7rzzzh4/x82aa+gznLS0NOTk5Aza3bTNZoNGo4FKpYJWq0VkZCQjkv3t0qTdfmiT9NzcXL/ZTdEzYTExMQgMDGSS4+lduCc7XAcL+oy0uroaycnJyM/PH7BGlM7nkvR7N1Dnkk1NTVAoFOByueDxeD7XuU1D9xHQu8m2tjYAQHR0NIYPH47c3FxYrVa88MIL+Oijj7BlyxY8/PDDPvdZ9CZEGHv4d+kEjOTkZMaFRaVSoa6uDhMnTuzR83TXXNPS0gKFQoGgoCAIBAKv3sXa7XbGPJnu0qRFsrdt5fTYitPphEAg8Dkj8+6gY5QsFguEQiETTNvV2RotknFxcay/MFksFma4fbA7aTu/d8HBwUzjU3/PJe12O6qqqtDc3Ow1q7qBwOl0ora2FlevXkVqaipsNhuWLFmCpqYmZtRk3759pOu0DxBhvAWuCRhCodDt4k53VU6aNOmmz9HdTKLFYkFtbS3UajVyc3NZN6Tf2Yc0KCiIcd252YXe4XCgrq6OGVvpi9sPG6HP3Orq6pCamorc3Nxud1MOh4OZ+aMTA1w7XNm2W6HHFRISEiAQCLy6q/fkuWRbWxsqKioQEREBkUjk9QYbT2E0GiGTyUBRFAoKChAVFQWKorB7926mZNrS0oKmpibcc8892Lx5c489nQlDWBhpI93u6EkCRktLCy5cuIDCwsJu/42uyqYAGB/QhIQE8Hg81n9hXYe71Wo1M8rA5XLd7ui1Wi0UCgVCQ0P7PLbCRgwGg1vYbm92vxRFMR2uarUaNpuNGaHx9mC8zWZjOoT5fL5Xo366oq/nkq7jJbm5uRg5ciTrd+w9hS4Jp6SkMEc6RqMRzz33HMrLy/Hee+9BIpEAuGFC8umnn2LZsmVeH4fyJYgwdqI3CRjt7e04c+YM7r777i6f3zVhm94l6nQ6KBQKWK1W8Hg8nxz8dh1loM26hw8fDqvVivb2duTn5yM9Pd0vLkR02HNdXR3S09ORm5vbr90eRVHQ6/XMe0d3uNK7ycHs0tVqtaisrER0dDSEQiHrb86A/51LajQatzNd13NJk8kEmUwGu92OgoICv5nVczgcqK6uhkqlgkgkYkrCcrkcCxYswLBhw/DRRx/1KNSAcHOIMLrQ2wQMnU6HEydOYNq0aW7P29VMot1uR11dHa5evYqMjAxkZWWxrpzWF1zvzIEb56a3mvfzFfR6PeRyOWw2G0QikUctwmhcg4Tb29sRExPDXOgH6g6fTo24fv26W1u/r9HVuWRUVBTa2tqQkpLi0w02ndHr9ZDJZAgKCkJBQQEiIiJAURT+/e9/449//COeeOIJvPLKK6wy8PBliDCi7wkYRqMRx44dw4wZM5jn7GomsbPDi7+UF00mE6qqqtDR0QEejwcOh+O2GzKZTG6uO77Sjep0OtHQ0ICLFy/2eX6vL1itVuZMku4Oppt3PNWlSZ+5hYWFQSQS+U37vsVigUwmQ3t7O4KCguB0Ohn3GG+Xq/uDa5cw/VkMDAyETqfD008/jS+//BLbtm3Dfffd55M3N2xlyAojcOMcsT8JGGazGUeOHGF2jJ1nEmnhoMuLbI7k6Q30qIJSqQSXy+3W9sxVJPV6PTMUz+FwWHtn6+ryIhKJvNYhTHcH0x6utHsM3eHa22Ymp9OJuro6XL58GTk5OcjIyPCLzyIAtLa2oqKiAlFRURCJRAgNDf2JIYMv+rja7XYoFAq0tLRALBYzVpAXLlzA/PnzkZKSgp07d2LEiBFeXqn/MWSF0Waz4fjx40zmH4fD6fWFwmq14vDhwygsLGTEkJ5JpGf3uFwu8vLyWCsEvUWn0zHlRddRhVthNBoZkezo6EBcXBwjkmw423KNvBo5ciSys7NZU4ZzbXzSaDSgKMotgPlW69TpdKioqEBAQADEYrHfVCxop5eGhgY3k+zO9ORckm3odDomTFgsFiMsLAxOpxPvv/8+XnjhBTz99NN4+eWXffqogs0MWWGkB+q5XG6fPlwURTHiSlEUuFwuuFwunE4nqqqqAMCvZvccDgfq6+vR0NDQb+Ewm82MSLa1tTHnahwOxyulPZ1Oh8rKSlAUNeCRV/2Foii0t7cz75/FYmFKhp3L1a4l4YyMDGRnZ/vF2Azwv3EFp9OJgoKCHov9QM5LegI6RaempgZZWVnIyspiggSWLVuG7777Dv/+978xdepUVgq6vzBkhRG4sePr7cvvPJMI3BjbuH79OtRqNSiKQlxcHHJychAfH+8XH17agCA4OBhCodCjXX70uZparUZLSwuio6PdnGMGEtfGIV8UDtoBxbVcTZvER0VFQalUwmaz+ZT12a1wPXNLS0vrV5dw53lJOtPUW+eSNpsNcrkc7e3tKCgoYG6qz5w5gwULFiA7Oxs7d+5k3UiNP0KEsYcv/2YzibQxdkxMDJKTk9Ha2gq1Wt3jgXi2YrPZUFtbi6amJuTk5GDEiBEDKhz03TxtTRcREeHx5hOajo4OVFZWAgDEYrFftPTTJvFXrlyByWRCaGgo0tPTkZyc7BczbLRwtLW1QSQSeTS9pCuj+ME8l6TDhKOjo5lzUqfTibfffht//etfsWrVKqxatYo15X1/hwhjD15+dzOJer0eCoUCJpMJfD4fSUlJzMWbvhtVqVRMth9dbvUFkVSpVKiqqsKwYcMgEAgGvWHBbrdDq9Uy1nS0vRqXy+1XgLDruZQ/ufIAN0rUdAYfj8eDzWZjduLh4eHMTcZAJ8YPBC0tLaioqGBS6Af6zL6rc0m65OrJmzTXOVlXIwKtVounnnoKZ8+exa5duzB58mSf+535MkNaGG02G1MO7YruZhIdDgcT7zJixAhkZ2ff9JzS6XSitbWVCQ+mRZI+12DTB95sNqOqqgptbW3g8XhITk72+vpc7dVcd+K97dB0TZ8XiUR+sUsEbnxO6aDsrsKR6ZsMusM1KCiIVedqN4Pupr1y5Qry8vK8YhzR3blkUlJSjwOsu8JqtaKiouInYcInTpzAokWLUFBQgO3bt/ukCYivQ4SxG2HszvCbnkkMCwuDQCDo9cW1K5Gkd0LeFEn60F+pVCIpKQn5+fms7KSl3z9aJCmKYi7yw4cP7/Ii5Xojk52djYyMDFaLQW+wWq1QKBRoa2tjuqtvRuf3z+l0Mhf5xMREVpXqDAYDZDIZALCmm9b1/evPuWRLSwtkMhni4+MZb1qHw4GNGzfi73//O/7617/imWee8ZvPqa9BhLGTMHaXk0h7p7a0tHgsdb7zRQqA25nkYH0p6JIwPbriK8nznT1I6YsUl8tlxhhoN6Pg4GCIRCJWXFw9BZ0LSs/g9vZGhvYhpd8/s9ns5uHqrRsjiqKY4Gfaho+NAkGfS9LORT05l6Qoiinlu7oOqdVqPPbYY6itrcWuXbt6nNhDGBiIMP6fMNJlU3qX6DqTSO+kOBwO8vLyBsTPkqIot50kRVFuO8mBuDC4dmWmp6cjJyfHZ+eiXC/yKpUKFosFYWFhMJvNTNs7Gy+ufcFutzMG97TxtycqDXq9nrnI63Q6ZtZ0MIfirVYrE30lEomYoXZf4FbnkhaLhQlJpj1cKYrCsWPHsHjxYtx555147733ejwbTBg4hrQw2u12Rgi7Kpu2t7dDoVDA4XBAIBAM2gd2MESyra0NcrkcAQEBrJ/d6y10owYABAUF/cSajo0l4p7S0tKCyspKREZGDmiMEj1rSl/kXcdooqKiBqTkr9VqUVFR0ecdMJvofC4ZGBgIh8OBuLg4FBQUIDQ0FHa7HevXr8frr7+OtWvXYunSpX5z8+brDGlhtNlssNlsP8lJtNlsUCqVuHbtGrKyspCZmem1DyxdLlSpVFCpVP0WSbvdDqVSicbGRr87b3M4HKitrcW1a9eQk5PDdPi5zvrpdDpm1m+w0yz6g8PhYH5vg92EYrPZmJ2kVqtFeHg4sxPqT4cwDR2429jYCB6Ph9TUVK83fHkK1zDhuLg4dHR04NFHHwWPx4NOp0NLSwtKS0sxduxYby+V4MKQFUaLxQKJRILp06dj7ty5TOfXN998g4CAAERHR/faO3WgcRVJ18YJLpfbo+44jUYDhUKByMhICAQCv5hto2lpaYFcLkdYWBiEQmG3r42e9aPTLGJjYxmRZKuHJt1NS5+TevP31jm8OjAw0C2Aubc3WXRqRGBgIMRisV99Jk0mEy5cuOAWJuxwOPCvf/0LO3fuRENDAzo6OjB58mTMmTMHjz32GGs/g0ONISuMBoMBW7duhVQqxQ8//IAxY8bAaDSiqakJR44cYb3JsmvjiUqlgsPhYHaSnS9QFosF1dXV0Gq1yM/P96s7crvdjtraWly/fh25ubnd+mV2hcViYUSytbUVw4YNcysXehvXmUs27u67yuV09XC92Xm1q/XZyJEjmdQIf0GlUkEulyMlJQV5eXkICgqCzWbD6tWr8c477+D111/HkiVLcOXKFXzyySf46quvUFZW5rMpIP7GkBVGGrPZjFWrVmHz5s3IysrC9evXMW7cOBQXF2P27NmsmzPsCto/ky630iLJ4XBgsVhQW1uL4cOHg8fj+UzpsCdotVrI5XJERERAKBT2a3dvtVrdyoW00TRtTTfYnwG9Xo+KigpQFOUTzjx08xP9HppMJiZNpfO5rsVigVwuh16vh0gk8qtmEzrrsqmpCUKhEFwuFwBw+fJlLF68GG1tbdizZw9GjRrl5ZXemrVr12LVqlVYsWIFNm7c6O3lDCpDXhglEgkaGhqwdetWjB07FhcvXoRUKkVZWRkuXLiAKVOmQCKRYPbs2T7hWEOLZGNjI5qamuB0OhEfH4+MjIxu5/x8Dbvdzlx8BuK8zW63Mxf45uZmhIWFMYYMA+0a4+qE4ss7KfpcV6PRoKOjgylZBwUFoa6uzm1+z18wGAy4cOHCT8KEDxw4gMcffxxz587Fpk2bfGJk6PTp0/jlL3+JmJgYTJkyhQjjUOPKlStISUn5SdmHoigolUpIpVJIpVLI5XLcc889kEgkuP/++1nbxemaqJCamgoOh8NYq9ntduZM0ldFsrm5GXK5HFFRURAKhQN+JtP5TC04ONjNdceTImkymVBRUQGLxQKxWIy4uDiPPbc3MZvNUKlUaGhogMViQXh4OPPZ9MZufCC4du0aqqqq3MKELRYLXnrpJezYsQObNm3C/PnzfeK16vV63HHHHXj77bexZs0a3HbbbUQYCT+FoijU1NSgpKQEZWVlqKmpwdSpUyGRSHDfffchJibG20sEcMMYWy6Xw+l0QigUul1Y6VIXXW6lRbKnmX7exmazoaamBmq12mMGC72lK/9b+kajP2M0rgPt9JmUr86TdoVOp4NMJkNwcDD4fD6zm9RqtQgNDXULYPYF4XDFbrejqqoKzc3NEIvFjDnGxYsXsXDhQthsNuzZswcCgcDLK+05CxYsQEJCAl5//XUUFhYSYSTcGoqioFAoUFpairKyMtTV1WHatGkoKirCvffe65UyCd3Kf/Xq1R4ZY3cehrdarW47SbaJJN1NGx0dDaFQyJpgY9fmp84dwj19D+nzNp1OB6FQ6DOuQz2BDuxWKpVdfi5dPXBdbzSSkpJ8oqJBhwmHhYVBLBYjPDwcFEWhvLwcS5cuxUMPPYTXX3/dpzpN9+zZg1deeQWnT59GeHg4EUZC76EoChUVFSgtLYVUKsXly5cxY8YMFBUVYebMmYPS2Ug3oNBjCr0VZtrWit5J0iJJ24J5UyRtNhuqq6uh0WjA4/GQkpLCyh2Fa3iwSqWCzWZzs6brbvdHG38PHz4cfD7fr87bLBYLKisrYTAYUFBQcMuyMH2jQZ/t0u8h/Tlk0w7ataM2MzMT2dnZCAgIgMlkwgsvvICSkhK88847+NWvfsXKz2t3XLlyBWPHjsWXX37JNAcRYST0C4qiIJPJUFJSAqlUisbGRsycORNFRUWYMWOGx+chrVYrampqoNFokJub65EGlK5Ekr7AD7ZI0j6gMTExEAgErNgl9gTXXD+6O9PVdSckJAQ2mw0KhQItLS0QCARM56K/oFarIZfLkZiYCB6P12vB7yob0bXD1Zud1a5hwmKxmOmorampwYIFCxASEoKPPvoIeXl5XltjXykvL4dEInH7njscDgQEBDBnpmyrJg0URBgHAKfTiXPnzjHlVpVKhVmzZqGoqAjTpk3rV2mFjhiqrq5GXFwc+Hz+gIgGRVHQ6/WMSJrNZqZUOJAiabVaUV1djebmZo/6gHoLvV7PiKRer0d0dDSMRiNiY2MhFov9anzG4XCguroaKpUKfD4fKSkpHnleo9HIiGR7eztiYmKYBqjBNOBob2/HhQsXEBUVBbFYjNDQUFAUhT179uDpp5/GokWLsH79ep/9nep0OjQ0NLg9tmjRIvD5fDz//PMQi8VeWtngQ4RxgHE6nfjxxx9RUlKCvXv3orm5Gffeey+KioowderUXomayWSCQqGATqcDn88Hh8MZFNHoSiTpnWRSUpLHRJIOR46NjYVAIPDZC0xX2O12yOVyqNVqhIeHw2w2MybdHA7HZ3bE3dHR0QGZTIbQ0FCIxeIBO1ezWCxMubWlpWXAAoRdcR2hycnJYcw/DAYDnn32WXz66ad4//33MW/ePJ++iesKUkolDDhOpxOnT59GaWkp9u7di7a2Ntx3330oKirC3Xff3a0Q0E0MdXV1SE5ORl5entfOo1xFki4VupZb+3IWRGcKtra2gs/ng8vl+tUFprW1FZWVlQgPD4dIJEJERARj0q1Wq9HW1ua1XVB/cRUNOsVksH53drsdzc3NzLxpSEgIU271VGyb1WpFZWUl9Ho9Ro0axYxpyeVyzJ8/H3Fxcdi9ezcyMzP7/W+xESKMhEHF6XTixIkTjEjq9XrMnj0bRUVFKCwsZJxCGhoa0NTUBLvdPqgJHz2BoigYDAZmJ0mLJH1xupVIUhTF7BITEhLA5/N9OlGhMw6Hg0mfz83NZUzNO2O1WhmRbGlpcUuyYPMwuNlsRmVlJUwmE5NA7y2cTie0Wi2zmwTg5uHal6oGndISGxsLoVCIkJAQOJ1O/Pvf/8Yf//hHLF26FGvWrPGrpinCDYgwsgCHw4Hjx4+jtLQU+/btg8lkwr333guz2YxDhw7hwIEDuP3221l/8O1abqWbTuhya2eRtFgsbsnz/taA0tHRgYqKCsYcu6cC1znJIiIighHJgSoV9gXaC5TD4YDH47Gua5QepdFoNLBarW4BzLcSMjpM+NKlS8jPz2ca23Q6HVasWIGvv/4aO3bswKxZs1jz+yB4FiKMLMPhcGDTpk14+eWXERQUhMTERNx1110oLi7GpEmTWHUBuhmu5Vaj0ciIZGJiIpqbm1FdXc34t/rTLtHpdOLSpUuor6/v0UzpzbDb7YxrUXNzM0JDQxmR9ETcU1/XRIcku3qBshW69E+LpF6vZ2LHkpKSfnK2azabGfehUaNGMR6158+fx/z585Gamordu3cjLS3NGy+HMEgQYWQZa9aswbp167BmzRo8+eST+O6771BSUoL9+/eDoijMnTsXxcXFmDhxos+IJF1ubWpqgsFgQEBAANLT05GTk+NXZSiDwYDKykrY7XaIRCKPlhbpYXjadScoKMjNmm4whuHb29shk8kQHh7ODLT7Gp1jx2JiYpiSK23Jl5iYCD6fj+DgYDidTrz33nv405/+hGeeeQYvvfSSz3zvCH2HCCPLOH/+POLi4pCRkeH2uN1ux9GjRxmRDAoKwty5c1FUVISJEyeyusxKURSuXbuGmpoaxMfHIzo6mplPcy23+qpIUhSFK1euQKlUIi0tDbm5uQP6+3A6nWhtbWVEkqKoXuVy9haKonDp0iVcvHjRrSvT16ETVVQqFbRaLQAwDWQikQgdHR1YunQpTpw4gZ07d+Luu+/2i9dNuDVEGH0Qm82G//73vygtLcX+/fsRFhaGefPmoaioCOPHj2eVSJrNZiZiSCAQMIHQwP8SGFQqFfR6vU+KpMlkglwuh9Fo9EqEkut5mlqt9rgHLr2LslqtKCgoYI0vsKcwmUyQyWSw2+1IS0tDW1sb5syZA4qiEB4eDi6Xi/LycowYMcLbSyUMIkQYfRyr1YqvvvoKUqkUH3/8MSIjIxmRHDdunNf8Jmlj7NraWnA4HOTn599U7IxGI9O4o9frkZCQwEQ9sVEkKYrC9evXUV1dzZoGlM4euBaLpVddwp1pamqCQqEAl8sFj8dj1Q2XJ1Cr1aisrERycjLy8/MRFBQEp9OJjRs3YteuXYiNjWUSQWbPno3XXnsNHA7H28smDAJEGP0Iq9WKL774AlKpFJ988gmGDRuGefPmobi4GGPGjBk0kaR3UQaDoU/G2LTTiUqlgk6nY0Syc+Ctt7BarYwtmFAodNsFswXXphO1Ws2UrbsKDu6Ma2KEUCj0OzFwOByora3F9evX3RqItFotnnjiCchkMuzcuROTJk0CRVE4efIkPvnkE7z88steN534+9//jn379qGqqgoRERGYOHEi1q1bBx6P59V1+RtEGP0Us9mM//znP5BKpfj000+RkJDAiORtt902ICJJmyvX1tYyd+H93UWZTCZmJ0mLJN104g2RpH1A6aBdNgh1T6DL1mq1GjqdjunM5HA4bhf7trY2VFRUICIiAiKRyCcbbG6GwWCATCZDQEAARo0axYQJHz9+HIsXL8bo0aOxfft21qaczJw5Ew899BB+9rOfwW6344UXXkBFRQWTUUrwDEQYhwAmkwmHDh1CaWkpPvvsMyQlJUEikaC4uBijRo3yiEgajUbI5XKYTCYIhUIMHz7cAyt3p7NIxsfHM+XWgRYo16QPX/dw7dyZGRsbi6SkJJjNZjQ2Nt7UjMCXuX79OhQKBdLT05Gbm4vAwEA4HA7885//xPr16/G3v/0NTz/9NOvjrlzRaDTgcDg4evQoJk+e7O3l+A1EGIcYBoMBBw8ehFQqxeeff46UlBRGJEUiUa8vCq4dmYMZsktf3FUqFTo6mZL0agAAHRNJREFUOgZUJLVaLSorK1mVB+kpLBYLrl69ioaGBjgcDkRFRSElJQUcDsdvdiAOhwNVVVXQaDQQiURM6VulUuHRRx/FxYsXsXv3btx5551eXmnvUSqVyMvLg0wmG1Im3wMNEcYhjF6vx+effw6pVIqDBw8iPT0dEokEDzzwAAQCwS13DAaDAXK5HBaLBUKh0Gt2dWazmdlJ0iLZVZmwt9BnUdeuXUNeXp5Hor3YhGsDUUpKCjIzM6HVahnXnaioKDdrOl987TqdDjKZDCEhISgoKGDChL/55hssXrwYEydOxHvvvYf4+HhvL7XXOJ1OzJkzB21tbfj222+9vRy/wm+FcfPmzXjttdfQ1NSE0aNHY9OmTRg3bpy3l8VadDodPvvsM0ilUhw6dAiZmZmQSCQoKioCn893uyi6mpqnpqYiLy+PNR2LtDm3SqVCe3s74uLimJ1kb0Syvb0dFRUVCAkJgVgs9ilj757gmgnpuouisdvtjDVdc3MzwsPDGZGMiYlhvUjSXdE1NTXIyMhgHIjsdjvWrl2LN954A+vWrcNTTz3lU6VTV5588kkcPHgQ3377LdLT0729HL/CL4WxpKQE8+fPxzvvvIPx48dj48aNkEqlTGs94eZ0dHTgk08+gVQqxX/+8x/k5eVBIpFAIpHAaDQyF5SCggJW32n3RSSdTicuXryIhoYGvxpmd6W1tRUVFRWIioqCSCS65Q2Dw+Fws6YLDg52c91h2/tDi35raysKCgqYSsa1a9ewePFiNDU14aOPPsKYMWO8vNK+s2zZMnz88cf45ptvkJWV5e3l+B1+KYzjx4/Hz372M7z11lsAblzsRowYgeXLl2PlypVeXp1v0dbWho8//hglJSX44osvEBQUhMmTJ2PNmjUQi8Wsuyh2R2eRjI2NZUSSPjPU6XSorKwERVEQi8WMT6a/4Cr6eXl5GDFiRK9/f3SKBe09GhAQwIhkfHy813dftG1dZGSkW5jwF198gcceewzTp0/Hli1bfNaogKIoLF++HPv378eRI0eQl5fn7SX5JX4njFarFZGRkSgrK8O8efOYxxcsWMBc5Am9o7KyEosWLUJbWxsefPBBnD17Fl999RVEIhGKioogkUgGNYevv1gsFkYk29raEBsbi5CQEDQ3NyMzMxPZ2dlev8B7GoPBgIqKCjidThQUFHgkzsrpdDKuOyqVirGm60/UU1+hy/tKpdJtp2+1WvG3v/0N7777LjZu3IglS5b4zOe0K5566ins3r0bH3/8sdvsYmxs7ICFQw9F/E4Yr127hrS0NBw/fhwTJkxgHn/uuedw9OhRnDx50our801WrlwJiqLw8ssvM3NfLS0tKC8vR2lpKf773/+ioKAAxcXFkEgkPtXqT8/tWSwWUBSF2NhYcDgccLlcv+g+pX1qq6urB9THlaIotLe3MyJps9mYAOvhw4cPaKeya5hwQUEB4uLiAACXL1/GwoULodPpsGfPHhQUFAzYGgaL7r5X27Ztw8KFCwd3MX4MEUbCLaEoqtsvJEVR0Gq12Lt3L6RSKY4ePYrbb7+dOZPsS7luMHBtzqAbiOx2OzPf19raipiYGHC5XJ8VSZvNBrlcjra2NohEokEbWqcoCjqdjhFJs9nsFjvmSYu/1tZWyGQytzBhiqLw+eef44knnoBEIsEbb7zB6sBnAvvwO2EkpVTvQVEUNBoNI5LHjh3DmDFjmHJramoqK0TS1dhcJBJ1aUZgtVqZC7urSHI4HJ8oWdHp8zExMRAKhV516KGt6VQqFQwGg0fciyiKQn19Perr693OSy0WC1588UV8+OGH2Lx5M37729+y4jNH8C38ThiBG80348aNw6ZNmwDcOAsZOXIkli1bRppvBgmKotDU1MSI5PHjxzFu3DhmJ+kt5xjaGDspKQk8Hq9Hu5fOIjls2DBmJ8k2kXQ6nVAqlbh69Sry8/ORlpbGKmGgfXDVajU6OjoQFxfHiGRPd+UWiwUVFRUwm81uiR91dXVYsGABnE4n9uzZAz6fP5AvheDH+KUwlpSUYMGCBdi6dSvGjRuHjRs3orS0FFVVVaxPHPdH6HMuWiRPnDiBCRMmQCKRYO7cueByuQN+8bZaraiqqkJLSwsEAkGfPwe0SKrVarS0tLBKJGkfUAAoKChgvXMN3SmsVqvR1taGmJgY5ny3u/dSq9WioqICCQkJEAgECA4OBkVR2LdvH5YvX45f//rX+Oc//+mTpW8Ce/BLYQSAt956ixnwv+222/Dmm29i/Pjx3l7WkIc2Gi8rK0NZWRlOnTqFiRMnori4GHPmzEFSUpLHRVKj0UAulyM2NhYCgcBjCQmuQbctLS2Ijo5mRHIwDQFcz0tdfUB9ic43HNHR0W6uO06nE3V1dbhy5Qp4PB5TljeZTHj++eexd+9ebN26FQ8++CCrdsgE38RvhZHAfugWe6lUirKyMpw5cwaTJk1CUVER5syZg+HDh/frIme321FTUwOVSgUej4eUlJQBu2jabDam3DqYIklHYHV0dHR7Xupr2Gw2xnVHq9UiLCwMTqcTgYGBGD16NDNfWlVVhYULFyIsLAx79uxBTk6Ol1dO8BeIMBJYAUVRuHTpEiOSZ8+exS9+8QsUFxdj9uzZiI+P75Wo0e4ukZGREAqFg1rmpC/sKpUKWq2WEUlPG3PTZcW4uDifisDqDdevX4dcLkdYWBisViu2bduG6OhopKen44033sCjjz6KtWvXej0nkeBfEGEcYEiwaO+hKAoXL15EaWkpysrKIJPJMGXKFEgkEsyePfumNmQOhwNKpZKJT/L2uEhnkYyKimJ2kn0VSdfX6FpW9CecTidqampw/fp1CAQCJCcnw+FwYM+ePfjXv/6Fc+fOISYmBg899BAeeOABTJo0iTV+vQTfhwjjAEOCRfsHRVFQKpUoLS2FVCqFQqHAPffcA4lEgvvvvx+xsbHM321paUFVVRWCgoIgFotZ9/52LhFGRkb2WiT1ej1kMhkCAwNZ+Ro9gdFoxIULFxAQEICCggKmFF1ZWYn58+cjISEB27dvR319Pfbu3Yvy8nK8+eab+NWvfuXllRP8BSKMgwwJFu07FEWhurqa2UnW1NRg2rRpmD17Ns6cOYNjx46hpKSESVJgM3R6Bb2TjIyMZDoyuxpGp3Mva2trkZGR4Ze2dcCN0mlVVRVjuhAYGAin04kdO3bg+eefx7Jly7B69Wq3MRuHwwGn0+lR44D+QtJ9fBsijIMMCRb1DBRFQS6XY/Pmzdi+fTuioqIwYcIEFBUV4d577/Upp5POIhkREcHsJKOjo2GxWBhDArFYzOpEk77icDhQXV0NtVrtFoPV0dGB3/3udzhy5Ah27NiBmTNnsr5sTNJ9fB8ijIMICRb1LJs2bcLKlSvx5JNP4qGHHkJ5eTmkUimuXLmCGTNmoKioCDNnzvSpcqPdbkdzczMT8RQSEgKbzYb4+HgmLcLf0Ov1uHDhgluYMACcPXsWCxYswIgRI7Br1y6kpqZ6eaU9g6T7+D7+V4thMUuXLkVFRQX27Nnj7aX4BQ6HA4cOHcKGDRswduxYrFmzBgqFAsePH4dAIMDq1auRmZmJRx55BOXl5TAajd5e8i0JDg5GcnIyxGIxUlJSYLVaER0djdbWVvzwww9QKpXQ6XTwh/tZeqb11KlT4HA4GDNmDMLDw+F0OrFlyxZMnz4dv/3tb/Hll1/6jCharVacOXMGU6dOZR4LDAzE1KlT8f3333txZYTeQHaMgwQJFh18nE4nzp07h5KSEuzduxcqlQqzZs1CUVERpk2b5nWnmu7Q6XSQyWQIDg6GWCxGZGQkHA4H07ij0WgQHh7uVm5le3mxM3a7HXK5HK2trRCLxcz8ZWtrK5566imcPn0au3btQmFhoU+9NhJi4B+QHeMAQ1EUli1bhv379+Pw4cNEFAeRwMBA3HHHHVi3bh1qampw+PBhZGRkYNWqVcjKysLixYvx2WefwWw2e3upAG58VhoaGnDq1ClwuVyMHTuW6cgMCgpCcnIyRo0ahcLCQuTm5sJoNOLUqVM4fvy4T+0kOzo6cOLECdhsNtx5552MKNIuSGazGWfPnsWUKVN8ShQJ/gPZMQ4wJFiUfTidTpw6dQqlpaXYu3cv2tvbcd9996GoqAh33323V4bFzWYzKisrYTQa3TIFb4XD4XA7kwwNDWV2ksOGDWOVsLh21mZnZyMzMxMBAQFwOp3YtGkTVq9ejRdffBHPPfecz84kknQf/4AI4wBDgkXZjdPpxPfff8+IpMFgwOzZs1FUVITCwsJBaXZRq9WQy+VITEzsceJHVzgcDmi1WqhUKmg0GlaJpM1mQ2VlJXQ6nZvwNzc34/HHH0dlZSV27dqFSZMmeW2NnoKk+/g+RBgJhP/D4XDg+PHjKC0txb59+2AymTB79mw88MADmDx5ssfn5OgRBZVKBT6fj5SUFI8+d1ciyeFwEBMTM6gi2dbWBplMhmHDhkEkEjFhwt999x0WL16MO+64A9u2bfMLn1eApPv4A0QYCYQucDgcOHbsGCOSdrsdc+bMQXFxMSZNmoTg4OB+PX9HRwdkMhlCQ0MhFosHtKxOiyTduBMcHMzsJAdSJGn/24sXL7qFCTscDvzjH//Aa6+9hjVr1mDFihV+Z1ZA0n18GyKMBMItsNvtjKvOvn37AABz585FcXExJk6c2CuRdBWLrKwsZGVlDeruzel0uu0kB0ok6TBhk8mEUaNGMWHCTU1N+H//7/+hoaEBu3fvJmJBYCVEGAlYu3YtVq1ahRUrVmDjxo3eXg6rsdvtOHLkCEpLS7F//34EBQVh7ty5KCoqwsSJE2/aNGI2m92S5119Xr1BVyJJ29LFxsb2WSTp1I/4+HgIhUImTPjIkSNYsmQJJk2ahH/96189bjAiEAYbIoxDnNOnT+OXv/wlYmJiMGXKFCKMvcBms+Hw4cMoLS1FeXk5wsLCMG/ePBQVFWH8+PFuIkl7gHI4HPB4vH6XYj0NLZJ0WHBQUBCzk+ypSDqdTly8eBGXL19Gfn4+0tLSEBAQALvdjldffRVvvfUW1q9fjyeeeMLvSqcE/4II4xBGr9fjjjvuwNtvv401a9bgtttuI8LYR6xWK7766itIpVJ8/PHHiIyMxLx58zBr1ixs3boVYWFhWL9+vU80XzidTrS0tEClUjEiSe8ku4v8MpvNkMlksNlsGDVqFONV29jYiMWLF0OtVmPPnj24/fbbB/vlEAi9hty2DWGWLl2K++67z82+itA3QkNDce+992Lbtm24du0atmzZgrq6OkgkElRWVmL48OG4fPkynE6nt5d6SwIDA5GYmAiRSIRf/OIXEAqFcDgcOH/+PI4dO4aqqiq0trYyZgIajQYnTpxAVFQUxo8fj+joaFAUhUOHDmHChAnIzMzE6dOniSj2EoqiMHXqVMyYMeMnP3v77bcRFxeHq1evemFl/g+76jmEQWPPnj348ccfcfr0aW8vxe8ICQnBhQsX8M033+Dll1+GQCBAWVkZ5syZg4SEBMybNw/FxcW47bbbWF9SpEUyMTERTqcTra2tUKlUOH/+PAICAhAaGgqj0QiBQMD4mVqtVvz1r3/Fe++9h40bN2Lx4sWsMhrwFQICArBt2zYUFBRg69atePzxxwEA9fX1eO6557Blyxakp6d7eZX+CRHGIciVK1ewYsUKfPnll0ySAcFzUBSFmpoafPPNNxgzZgwAQCKRwGg04tChQ5BKpZg1axaSkpIgkUjwwAMPoKCgwCdEcvjw4Rg+fDgyMjJw7tw5WCwWBAYGYufOnZDL5bjnnnvw/vvvw2g04sSJExCJRN5etk8zYsQIvPHGG1i2bBmmT5+OzMxMLFmyBNOnT8cjjzzi7eX5LeSMcQhSXl4OiUTi1hzicDgQEBCAwMBAWCwWn7Xk8hUMBgMOHDgAqVSKAwcOICUlBRKJBMXFxRCJRKwWyaamJigUCqSkpCA/Px8A8O233+Kf//wnvvvuO1AUhd/85jf49a9/jcmTJ5PPkgeYN28e2tvbUVRUhNWrV6OyspLJrCR4HiKMQxCdToeGhga3xxYtWgQ+n4/nn3+eBCgPMnq9Hp9//jlKS0tx8OBBjBgxgtlJCgQC1pQhXZ16RCIRE7prNpvx5z//Gbt27cKmTZuQlpaGsrIy7Nu3D5MnT4ZUKvXyym/OpUuXsHr1ahw+fBhNTU1ITU3Fb3/7W/zpT39iTf4lHeDc0tKCvXv3uvmwEjwPEUYCAKCwsJB0pbIAnU6HTz/9FFKpFIcOHUJWVhYkEgmKiorA5/O9JpJ6vR4ymQxBQUEoKChgnHqUSiXj+fvRRx+5GeXTUVnJycneWHKPOXToEEpKSvDwww8jNzcXFRUVePTRR/HII49gw4YN3l4ew5///GeUl5ejoqLC20vxe9hbryEQhiDDhg3Dr3/9a+zfvx8qlQqrVq2CTCbDXXfdhTvvvBNr165FTU3NoMVLURSFxsZGnDp1ComJiRg7diwiIiJAURSkUikmTZqECRMm4Pjx426iCPwvKovtzJw5E9u2bcP06dORnZ2NOXPm4Nlnn2VcjthCcHAw6+Zf/RXyLhMAAEeOHPH2EgidiImJwSOPPIJHHnmEiSySSqVYt24deDweioqKUFRUhJycnAHZSdrtdigUCrS0tGD06NGMybfRaMRzzz2H/fv34/3330dxcTFryr2eor29HQkJCd5eBsFLkB0jgeADxMXFYcGCBfj0009x/fp1/P73v8fJkycxduxY/PznP8c//vEPXLx40WM7SZ1Oh5MnT8JqtbqFCSsUChQWFqKyshJnzpzBAw884HeiqFQqsWnTJmY8gjD0IMJIIPgQAQEBSEhIwOLFi3HgwAFcv34dS5cuxbFjx3D77bdj8uTJ2LhxIxoaGvokkhRF4fLlyzh16hRSUlJwxx13ICwsDBRFYefOnSgsLMSMGTNw7NgxZGdnD8Ar9BwrV65EQEDATf9UVVW5/T+NjY2YOXMmHnzwQTz66KNeWjnB25DmGwKraGxsxPPPP4+DBw/CaDQiNzcX27Ztw9ixY729NFZDURSam5uxb98+SKVSHD16FLfffjskEgkkEgkT+XQzbDYb5HI52tvbUVBQgPj4eAA3Gm/+8Ic/4NChQ9i2bRvuv/9+n9glajQaaLXam/6d7OxspvP02rVrKCwsxJ133ont27ezemSGMLAQYSSwhtbWVtx+++2YMmUKnnzySSQlJaG2thY5OTnIycnx9vJ8BoqioFarsXfvXkilUnz77bcYO3YsI5Kpqak/ETY6TDg6OhoikYgRC5lMhvnz54PD4WDXrl0YOXKkN17SgNPY2IgpU6ZgzJgx2LlzJ5m9HOIQYSSwhpUrV+K7777DsWPHvL0Uv4GiKDQ1NTEi+d1332H8+PGMSCYlJeHVV18Fn8/H+PHjMXLkSAQEBMDpdGLbtm1MHNnLL7+MkJAQb7+cAaGxsRGFhYXIyMjAjh073ETRF7pqCZ6HCCOBNQiFQsyYMQNXr17F0aNHkZaWhqeeeoqc9XgIiqJw7do1lJWVoaysDN9//z1GjBgBo9GILVu2YMaMGQgICEB7ezuWL1+OY8eO4cMPP8T06dN9onTaV7Zv345FixZ1+TNyeRyaEGEksAbat/UPf/gDHnzwQZw+fRorVqzAO++8gwULFnh5df7F119/jYcffhhpaWmIiorCyZMnMXHiRIwbNw5SqRTZ2dnYtWsXUlJSvL1UAmHQIcJIYA2hoaEYO3Ysjh8/zjz2u9/9DqdPn8b333/vxZX5F3K5HOPGjcM//vEPPPbYYwCAy5cvQyqV4tVXX8W0adOwe/ducs5GGLIQYSSwhoyMDEybNg3vvfce89iWLVuwZs0aNDY2enFl/kdjYyPS0tJ+8rjD4UBgYKBfl04JhFtBnG8IrOGuu+5CdXW122M1NTXIyMjw0or8l65EEQDZJRIIIAP+BBbx9NNP48SJE3j11VehVCqxe/duvPvuu1i6dKm3l0YgEIYQpJRKYBWfffYZVq1ahdraWmRlZeEPf/gD6UolEAiDChFGAoFAIBBcIKVUAoFAIBBcIMJIgMPhwMSJE1FUVOT2eHt7O0aMGIE//elPXloZgUAgDD5EGAkICgrC9u3bcejQIezatYt5fPny5UhISMBf/vIXL66OHTgcDrz44ovIyspCREQEcnJysHr1auKMQiD4IWRcgwAAyM/Px9q1a7F8+XLcfffdOHXqFPbs2YPTp08zhtJDmXXr1mHLli3YsWMHRCIRfvjhByxatAixsbH43e9+5+3l+T0WiwXjx4/H+fPncfbsWdx2223eXhLBjyHNNwQGiqJw9913IygoCDKZDMuXL8ef//xnby+LFdx///3gcrl4//33mceKi4sRERGBnTt3enFlQ4MVK1agtrYWBw8eJMJIGHBIKZXAEBAQgC1btuDrr78Gl8vFypUrvb0k1jBx4kR8/fXXqKmpAQCcP38e3377LWbNmuXllfk/Bw8exBdffIENGzZ4eymEIQIppRLc+OCDDxAZGYn6+npcvXoVmZmZ3l4SK1i5ciU6OjrA5/MRFBQEh8OBV155Bb/5zW+8vTS/RqVS4dFHH0V5eTkiIyO9vRzCEIHsGAkMx48fx+uvv47PPvsM48aNw5IlS0hzyf9RWlqKXbt2Yffu3fjxxx+xY8cObNiwATt27PD20vwWiqKwcOFCPPHEExg7dqy3l0MYSlAEAkVRBoOBysvLo5YvX05RFEXV19dT0dHR1Ntvv+3llbGD9PR06q233nJ7bPXq1RSPx/PSinyX559/ngJw0z8KhYJ64403qLvuuouy2+0URd34TAKgzp496+VXQPB3SCmVAABYtWoVKIrC2rVrAQCZmZnYsGEDnn32WcyaNWvIl1SNRiMCA90LLEFBQXA6nV5ake/yzDPPYOHChTf9O9nZ2Th8+DC+//57hIWFuf1s7Nix+M1vfkN264QBg3SlEnD06FHcc889OHLkCH7+85+7/WzGjBmw2+346quvhnQU0cKFC/HVV19h69atEIlEOHv2LB577DEsXrwY69at8/by/JLLly+jo6OD+e9r165hxowZKCsrw/jx45Genu7F1RH8GSKMBEIP0Ol0ePHFF7F//36o1Wqkpqbi4YcfxksvvUTmPAeJS5cuISsri4xrEAYcIowEAsEnIMJIGCyIMBIIBAKB4AIZ1yAQCAQCwQUijAQCgUAguECEkUDwQb755hvMnj0bqampCAgIQHl5udvPKYrCSy+9hJSUFERERGDq1Kmora310moJBN+CCCOB4IMYDAaMHj0amzdv7vLn69evx5tvvol33nkHJ0+eRFRUFGbMmAGz2TzIKyUQfA/SfEMg+DgBAQHYv38/5s2bB+DGbjE1NRXPPPMMnn32WQA3Qqe5XC62b9+Ohx56yJvLJRBYD9kxEgh+Rn19PZqamjB16lTmsdjYWIwfPx7ff/+9F1dGIPgGRBgJBD+jqakJAMDlct0e53K5zM8IBEL3EGEkEAgEAsEFIowEgp+RnJwM4EaWoSsqlYr5GYFA6B4ijASCn5GVlYXk5GR8/fXXzGMdHR04efIkJkyY4MWVEQi+AYmdIhB8EL1eD6VSyfx3fX09zp07h4SEBIwcORK///3vsWbNGuTl5SErKwsvvvgiUlNTmc5VAoHQPWRcg0DwQY4cOYIpU6b85PEFCxZg+/btoCgKf/nLX/Duu++ira0NP//5z/H2228jPz/fC6slEHwLIowEAoFAILhAzhgJBAKBQHCBCCOBQCAQCC4QYSQQCAQCwQUijAQCgUAguECEkUAgEAgEF4gwEggEAoHgAhFGAoFAIBBcIMJIIBAIBIILRBgJBAKBQHCBCCOBQCAQCC4QYSQQCAQCwQUijAQCgUAguECEkUAgEAgEF4gwEggEAoHgAhFGAoFAIBBcIMJIIBAIBIIL/x/FtGxrKGcUMgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "image/png": {
+       "width": 800
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display.Image(\"../images/osp_example.png\",width=800)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d787bca0-bd20-49cc-bb8d-66c7cb26d1dc",
+   "metadata": {},
+   "source": [
+    "We want to update the moment of inertia of the longitudinal girders (`I`) of the bridge model. In addition, we are uncertain of the moment of inertia of the two edge members as well. Therefore, we introduce two updating paramters to the model as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "50e8038b-93f7-43e3-8e7b-fa5b91f1f9ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = [0.2, # longitudinal girders, I\n",
+    "           0.1, # edge member, I\n",
+    "          ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b01d2b4-86bc-4e3c-835a-e4bdb76ebc82",
+   "metadata": {},
+   "source": [
+    "We update the model using some \"known\" measurements of the bridge deflections. For this,the measurements is taken as the\n",
+    "responses which corresponds to a parameter list = [0.5,0.1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d67b5834-8f84-4997-8106-18a2157f8811",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = [[1047.057906932444,\n",
+    "  [0.0,\n",
+    "   3.847756717425916e-05,\n",
+    "   7.335795722107879e-05,\n",
+    "   0.0001001430188545662,\n",
+    "   0.00011472208165921942,\n",
+    "   0.00011472208165922035,\n",
+    "   0.00010014301885456844,\n",
+    "   7.335795722108123e-05,\n",
+    "   3.847756717426085e-05,\n",
+    "   0.0]]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05734b9f-1e03-42a7-94fe-895889115884",
+   "metadata": {},
+   "source": [
+    "Lets create the function handler (denote as `main()`) that creates and analyse the model with the following properties that is compatible with`calabru` to allow for updating procedures:\n",
+    "\n",
+    "1. `main()` takes in `I_start` as an argument.\n",
+    "2. `main()` returns a list of the corresponding measurables of `target_deflections` from the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "989b4a2c-4fd5-4111-b9a7-b9dce37ec2b6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def main(p_matrix: list):\n",
+    "    # sort parameter variable\n",
+    "\n",
+    "    P = 1000\n",
+    "    Iz1 = p_matrix[0]\n",
+    "    Iz2 = p_matrix[1]\n",
+    "\n",
+    "    # Adopted units: N and m\n",
+    "    kilo = 1e3\n",
+    "    milli = 1e-3\n",
+    "    N = 1\n",
+    "    m = 1\n",
+    "    mm = milli * m\n",
+    "    m2 = m ** 2\n",
+    "    m3 = m ** 3\n",
+    "    m4 = m ** 4\n",
+    "    kN = kilo * N\n",
+    "    MPa = N / ((mm) ** 2)\n",
+    "    GPa = kilo * MPa\n",
+    "\n",
+    "    # parameters of bridge grillage\n",
+    "    L = 10 * m  # span\n",
+    "    w = 5 * m  # width\n",
+    "    n_l = 7  # number of longitudinal members\n",
+    "    n_t = 10  # number of transverse members\n",
+    "    edge_dist = 1 * m  # distance between edge beam and first exterior beam\n",
+    "    ext_to_int_dist = (\n",
+    "        2.2775 * m\n",
+    "    )  # distance between first exterior beam and first interior beam\n",
+    "    angle = 0  # skew angle\n",
+    "    mesh_type = \"Oblique\"\n",
+    "\n",
+    "    # define material\n",
+    "    concrete = og.create_material(\n",
+    "        material=\"concrete\", code=\"AS5100-2017\", grade=\"65MPa\"\n",
+    "    )\n",
+    "\n",
+    "    # define sections (parameters from LUSAS model)\n",
+    "    edge_longitudinal_section = og.create_section(\n",
+    "        A=0.934 * m2,\n",
+    "        J=0.1857 * m3,\n",
+    "        Iz=Iz2 * m4,\n",
+    "        Iy=0.213602 * m4,\n",
+    "        Az=0.444795 * m2,\n",
+    "        Ay=0.258704 * m2,\n",
+    "    )\n",
+    "\n",
+    "    longitudinal_section = og.create_section(\n",
+    "        A=1.025 * m2,\n",
+    "        J=0.1878 * m3,\n",
+    "        Iz=Iz1 * m4,\n",
+    "        Iy=0.113887e-3 * m4,\n",
+    "        Az=0.0371929 * m2,\n",
+    "        Ay=0.0371902 * m2,\n",
+    "    )\n",
+    "\n",
+    "    transverse_section = og.create_section(\n",
+    "        A=0.504 * m2,\n",
+    "        J=5.22303e-3 * m3,\n",
+    "        Iy=0.32928 * m4,\n",
+    "        Iz=1.3608e-3 * m4,\n",
+    "        Ay=0.42 * m2,\n",
+    "        Az=0.42 * m2,\n",
+    "    )\n",
+    "\n",
+    "    end_transverse_section = og.create_section(\n",
+    "        A=0.504 / 2 * m2,\n",
+    "        J=2.5012e-3 * m3,\n",
+    "        Iy=0.04116 * m4,\n",
+    "        Iz=0.6804e-3 * m4,\n",
+    "        Ay=0.21 * m2,\n",
+    "        Az=0.21 * m2,\n",
+    "    )\n",
+    "\n",
+    "    # define grillage members\n",
+    "    longitudinal_beam = og.create_member(\n",
+    "        section=longitudinal_section, material=concrete\n",
+    "    )\n",
+    "    edge_longitudinal_beam = og.create_member(\n",
+    "        section=edge_longitudinal_section, material=concrete\n",
+    "    )\n",
+    "    transverse_slab = og.create_member(section=transverse_section, material=concrete)\n",
+    "    end_transverse_slab = og.create_member(\n",
+    "        section=end_transverse_section, material=concrete\n",
+    "    )\n",
+    "\n",
+    "    # create grillage\n",
+    "    simple_bridge = og.create_grillage(\n",
+    "        bridge_name=\"simple_bridge\",\n",
+    "        long_dim=L,\n",
+    "        width=w,\n",
+    "        skew=angle,\n",
+    "        num_long_grid=n_l,\n",
+    "        num_trans_grid=n_t,\n",
+    "        edge_beam_dist=edge_dist,\n",
+    "        mesh_type=mesh_type,\n",
+    "    )\n",
+    "\n",
+    "    simple_bridge.set_member(longitudinal_beam, member=\"interior_main_beam\")\n",
+    "    simple_bridge.set_member(longitudinal_beam, member=\"exterior_main_beam_1\")\n",
+    "    simple_bridge.set_member(longitudinal_beam, member=\"exterior_main_beam_2\")\n",
+    "    simple_bridge.set_member(edge_longitudinal_beam, member=\"edge_beam\")\n",
+    "    simple_bridge.set_member(transverse_slab, member=\"transverse_slab\")\n",
+    "    simple_bridge.set_member(end_transverse_slab, member=\"start_edge\")\n",
+    "    simple_bridge.set_member(end_transverse_slab, member=\"end_edge\")\n",
+    "    simple_bridge.create_osp_model(pyfile=False)\n",
+    "\n",
+    "    # add load case\n",
+    "    # Patch load over entire bridge deck (P is kN/m2)\n",
+    "    P = P * kN / m2  # magnitude of patch vertex\n",
+    "    patch_point_1 = og.create_load_vertex(x=0, z=0, p=P)\n",
+    "    patch_point_2 = og.create_load_vertex(x=L, z=0, p=P)\n",
+    "    patch_point_3 = og.create_load_vertex(x=L, z=w, p=P)\n",
+    "    patch_point_4 = og.create_load_vertex(x=0, z=w, p=P)\n",
+    "    test_patch_load = og.create_load(\n",
+    "        loadtype=\"patch\",\n",
+    "        name=\"Test Load\",\n",
+    "        point1=patch_point_1,\n",
+    "        point2=patch_point_2,\n",
+    "        point3=patch_point_3,\n",
+    "        point4=patch_point_4,\n",
+    "    )\n",
+    "\n",
+    "    test_point_load = og.create_load(\n",
+    "        loadtype=\"point\",\n",
+    "        name=\"Test Load\",\n",
+    "        point1=og.create_load_vertex(x=L / 2, z=w / 2, p=P),\n",
+    "    )\n",
+    "\n",
+    "    # Create load case, add loads, and assign\n",
+    "    patch_case = og.create_load_case(name=\"test patch load case\")\n",
+    "    patch_case.add_load(test_patch_load)\n",
+    "    point_case = og.create_load_case(name=\"test point load case\")\n",
+    "    point_case.add_load(test_point_load)\n",
+    "    # sn8474.add_load_case(patch_case)\n",
+    "    simple_bridge.add_load_case(point_case)\n",
+    "\n",
+    "    simple_bridge.analyze()\n",
+    "    results = simple_bridge.get_results()\n",
+    "\n",
+    "    # arbitrary force components\n",
+    "    r_mat = [[og.ops.eleForce(25)[1],[og.ops.nodeDisp(n)[1] for n in [2,9,16,23,30,37,44,51,58,65]]]]\n",
+    "\n",
+    "    return r_mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "50e8e494-9924-413c-867e-0b7383e861a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_beam_updating = clb.ModelUpdating(\n",
+    "    function_handle=main,\n",
+    "    param_list=start,\n",
+    "    target_list=target,\n",
+    "    max_error=0.1,\n",
+    "    write_output_txt=False\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a1f0bdc8-307c-4fad-8be8-e7a65c339363",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.2, 0.1], [0.3365125562453617, 0.10119151305405859], [0.45716219684186105, 0.1008159604001884], [0.5002470885555945, 0.10009323201609568]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "simple_beam_updating.update_model()\n",
+    "print(simple_beam_updating.param_update_history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf76bc8-59ac-463a-bb05-c7d8ae89c612",
+   "metadata": {},
+   "source": [
+    "As can be seen the values converged to [0.5,0.1] "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index 5538eb1..ef20fd3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,7 @@
 [project]
-requires-python = ">=3.8,<=3.9"
+requires-python = ">=3.8,>=3.9"
+name = "calabru"
+dynamic = ["version"]
 
 [build-system]
 requires = ["wheel >= 0.31.0",

From fa310bb1eab35992fbdcd9fe164c881756083648 Mon Sep 17 00:00:00 2001
From: jngan <justinngan92@gmail.com>
Date: Thu, 23 May 2024 18:17:28 +1000
Subject: [PATCH 3/3] refactor: improve structure and naming for docs

---
 calabru/__init__.py                           |  4 --
 docs/requirement.txt                          |  3 +-
 .../_templates/custom-class-template.rst      | 34 ++++++++++
 .../_templates/custom-module-template.rst     | 66 +++++++++++++++++++
 docs/source/api.rst                           |  4 +-
 docs/source/conf.py                           |  3 +-
 docs/source/index.rst                         |  2 -
 pyproject.toml                                |  6 ++
 src/calabru/__init__.py                       |  8 +++
 {calabru => src/calabru}/calibrator.py        |  6 +-
 calabru/helpers.py => src/calabru/utils.py    |  6 ++
 tests/test_updating.py                        | 20 +++---
 12 files changed, 140 insertions(+), 22 deletions(-)
 delete mode 100644 calabru/__init__.py
 create mode 100644 docs/source/_templates/custom-class-template.rst
 create mode 100644 docs/source/_templates/custom-module-template.rst
 create mode 100644 src/calabru/__init__.py
 rename {calabru => src/calabru}/calibrator.py (99%)
 rename calabru/helpers.py => src/calabru/utils.py (98%)

diff --git a/calabru/__init__.py b/calabru/__init__.py
deleted file mode 100644
index 50ec9eb..0000000
--- a/calabru/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-__version__ = "0.1.0"
-
-from .calibrator import *
-from .helpers import *
diff --git a/docs/requirement.txt b/docs/requirement.txt
index 431240c..d380880 100644
--- a/docs/requirement.txt
+++ b/docs/requirement.txt
@@ -11,4 +11,5 @@ pandoc
 markupsafe==2.0.1
 pygments >= 2.7
 pycba
-ospgrillage
\ No newline at end of file
+ospgrillage
+ipython
\ No newline at end of file
diff --git a/docs/source/_templates/custom-class-template.rst b/docs/source/_templates/custom-class-template.rst
new file mode 100644
index 0000000..f73eda5
--- /dev/null
+++ b/docs/source/_templates/custom-class-template.rst
@@ -0,0 +1,34 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :show-inheritance:
+   :inherited-members:
+   :special-members: __call__, __add__, __mul__
+
+   {% block methods %}
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+      :nosignatures:
+   {% for item in methods %}
+      {%- if not item.startswith('_') %}
+      ~{{ name }}.{{ item }}
+      {%- endif -%}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/docs/source/_templates/custom-module-template.rst b/docs/source/_templates/custom-module-template.rst
new file mode 100644
index 0000000..d066d0e
--- /dev/null
+++ b/docs/source/_templates/custom-module-template.rst
@@ -0,0 +1,66 @@
+{{ fullname | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Module attributes
+
+   .. autosummary::
+      :toctree:
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+      :toctree:
+      :nosignatures:
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+      :nosignatures:
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 395856e..1982144 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -6,8 +6,10 @@ Package Modules
 
 .. autosummary::
     :toctree: gen
+    :template: custom-module-template.rst
     :recursive:
 
     calabru.calibrator
-    calabru.helpers
+    calabru.utils
+
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0fa483c..b7dc920 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,7 +13,8 @@
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath("../../calabru/"))
+sys.path.insert(0, os.path.abspath("../../src/."))
+sys.path.insert(0, os.path.abspath("../../src/calabru/"))
 from calabru import __version__ as ver
 #
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index f72c43b..ce249c6 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -17,8 +17,6 @@ Welcome to calabru's documentation!
 
    installation
    api
-   notebooks/intro
-   notebooks/example
 
 
 
diff --git a/pyproject.toml b/pyproject.toml
index ef20fd3..42f0fa8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,12 @@ requires = ["wheel >= 0.31.0",
             ]
 build-backend = "setuptools.build_meta"
 
+[project.optional-dependencies]
+test = ["pytest >= 6.2.2"]
+
+[tool.setuptools.dynamic]
+version = {attr = "calabru.__version__"}
+
 [tool.pytest.ini_options]
 minversion = "6.0"
 testpaths = [
diff --git a/src/calabru/__init__.py b/src/calabru/__init__.py
new file mode 100644
index 0000000..3d11a85
--- /dev/null
+++ b/src/calabru/__init__.py
@@ -0,0 +1,8 @@
+"""
+calabru - Calibration of models in Python
+"""
+
+__version__ = "0.1.0"
+
+from .calibrator import *
+from .utils import *
\ No newline at end of file
diff --git a/calabru/calibrator.py b/src/calabru/calibrator.py
similarity index 99%
rename from calabru/calibrator.py
rename to src/calabru/calibrator.py
index e41e440..c676d5e 100644
--- a/calabru/calibrator.py
+++ b/src/calabru/calibrator.py
@@ -5,16 +5,18 @@
 
 from datetime import datetime
 import copy
-from helpers import *
+import numpy as np
+from utils import get_pseudo_inv_estimation,interpolate_measurements,calculate_rmse
 
 
-class ModelUpdating:
+class Calibrator:
     """
     Main class to handle updating.
     """
 
     def __init__(self, function_handle, **kwargs):
         """
+        Init the class
 
         Parameters
         ----------
diff --git a/calabru/helpers.py b/src/calabru/utils.py
similarity index 98%
rename from calabru/helpers.py
rename to src/calabru/utils.py
index 0a83b2c..4a054f3 100644
--- a/calabru/helpers.py
+++ b/src/calabru/utils.py
@@ -1,3 +1,8 @@
+# -*- coding: utf-8 -*-
+"""
+Utility function module
+"""
+
 import numpy as np
 from scipy.interpolate import interp1d
 def get_pseudo_inv_estimation(sens_mat, resp_diff):
@@ -43,6 +48,7 @@ def get_pseudo_inv_estimation(sens_mat, resp_diff):
 
 def calculate_rmse(ref_response_list, current_response_list):
     """Get the root mean square error (RMSE) between the reference and current response values
+
     Parameters
     ----------
     ref_response_list: list
diff --git a/tests/test_updating.py b/tests/test_updating.py
index 1c67a4e..7bdb106 100644
--- a/tests/test_updating.py
+++ b/tests/test_updating.py
@@ -1,12 +1,10 @@
 """
 Main module for model calibration
 """
-import pytest
 import sys, os
-import numpy as np
 
-sys.path.insert(0, os.path.abspath("../"))
-from calabru.calibrator import *
+sys.path.insert(0, os.path.abspath("../src/calabru"))
+from calabru import *
 import fixtures
 
 
@@ -18,7 +16,7 @@ def test_basic_update():
     """
     start = [2000, 0.2]
     target = [1558.6480741602825, 2956.470189168508]  # [-30785, -3801]
-    simple_beam_updating = ModelUpdating(
+    simple_beam_updating = Calibrator(
         function_handle=fixtures.beam_with_patch_load,
         param_list=start,
         target_list=target,
@@ -51,7 +49,7 @@ def test_main():
    0.0]]]
     # node displacements
 
-    simple_beam_updating = ModelUpdating(
+    simple_beam_updating = Calibrator(
         function_handle=fixtures.main,
         param_list=start,
         target_list=target,
@@ -83,7 +81,7 @@ def test_target_resp_as_list():
             0.0,
         ]
     ]  # [-30785, -3801]
-    simple_beam_updating = ModelUpdating(
+    simple_beam_updating = Calibrator(
         function_handle=fixtures.beam_with_patch_load_including_deflected_shape_output,
         param_list=start,
         target_list=target,
@@ -109,7 +107,7 @@ def test_param_bounds():
     # bounds set to first param
     bounds = [[1500, 3000], []]
     target = [1558.6480741602825, 2956.470189168508]  # [-30785, -3801]
-    simple_beam_updating = ModelUpdating(
+    simple_beam_updating = Calibrator(
         function_handle=fixtures.beam_with_patch_load,
         param_list=start,
         target_list=target,
@@ -145,7 +143,7 @@ def test_robust():
     """
     start = [10]  # P magnitude of UDL in span 1
     target = [65.42524]
-    pycba_updating = ModelUpdating(
+    pycba_updating = Calibrator(
         function_handle=fixtures.pycba_example, param_list=start, target_list=target
     )
     pycba_updating.update_model()
@@ -170,7 +168,7 @@ def test_static_truck_pycba():
         -149.1317283092082 + np.random.rand(1)[0],
         -96.10790029773669 + np.random.rand(1)[0],
     ]
-    pycba_updating = ModelUpdating(
+    pycba_updating = Calibrator(
         function_handle=fixtures.pycba_example_find_ei,
         param_list=start_ei,
         target_list=target_def,
@@ -306,7 +304,7 @@ def test_moving_truck_pycba():
     known_measurement_with_noise = [a + np.random.rand(1)[0] for a in known_measurement]
 
     known_target_ei = 100
-    pycba_updating = ModelUpdating(
+    pycba_updating = Calibrator(
         function_handle=fixtures.pycba_example_moving_veh_ei,
         param_list=start_ei,
         target_list=[known_measurement_with_noise],